From e92731b91a0c1ee278e980ba1f7295d16f2e0d36 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Tue, 5 Mar 2019 13:13:19 +0100
Subject: [PATCH 001/403] Initial commit of McStasScript tool for creating and
 running McStas instrments from python scripts

The classes are located in the McStasScript.py file.
A demonstration can be found in demonstration.py
An early version of the documentation is included, McStasScript_documentation.pdf

Current functionality

Add input parameters
Add declare variables
Add code to initialize section
Add components to trace section
Set properties of components: parameters, position, rotation

Generate parts of the instrument to include in current projects
Generate full instrument file

Simulate full instrument file and return data objects
Plot data objects
---
 McStasScript.py                | 830 +++++++++++++++++++++++++++++++++
 McStasScript_documentation.pdf | Bin 0 -> 134169 bytes
 demonstration.py               |  86 ++++
 3 files changed, 916 insertions(+)
 create mode 100644 McStasScript.py
 create mode 100644 McStasScript_documentation.pdf
 create mode 100644 demonstration.py

diff --git a/McStasScript.py b/McStasScript.py
new file mode 100644
index 00000000..2a4ab8c8
--- /dev/null
+++ b/McStasScript.py
@@ -0,0 +1,830 @@
+# McStasScript classes written by Mads Bertelsen, ESS, DMSC
+
+from __future__ import print_function
+import datetime
+import os
+import time
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+from matplotlib.colors import BoundaryNorm
+from matplotlib.ticker import MaxNLocator
+
+try: # check whether python knows about 'basestring'
+   basestring
+except NameError: # no, it doesn't (it's Python3); use 'str' instead
+   basestring=str
+   
+   
+class mcstas_data:
+    def __init__(self,*args,**kwargs):
+        # Name of data set (usually filename
+        self.name = str(args[0])
+        # three basic arrays as first 
+        self.Intensity = args[1]
+        self.Error = args[2]
+        self.Ncount = args[3]
+        
+        self.dimension=[]  # size of the data, list with a number per dimension s
+        self.limits=[]     # limits on the data to be used for plotting
+        self.parameters={} # parameters used in McStas simulation
+        
+        self.xlabel=""
+        self.ylabel=""
+        self.title=""
+        
+        if "dimension" in kwargs:
+            self.dimension = kwargs["dimension"]
+        else:
+            raise NameError("ERROR: Initialization of mcstas_data done without dimension for data set named " + self.name + "!")
+        
+        if type(self.dimension) == int:
+            if "xaxis" in kwargs:
+                self.xaxis = kwargs["xaxis"]
+            else:
+                raise NameError("ERROR: Initialization of mcstas_data done with 1d data, but without xaxis" + self.name + "!")
+        
+        if "limits" in kwargs:
+            self.limits = kwargs["limits"]
+        else:
+            raise NameError("ERROR: Initialization of mcstas_data done without limits for data set named " + self.name + "!")
+        
+        if "parameters" in kwargs:
+            self.limits = kwargs["parameters"]
+            
+        
+    # Methods xlabel, ylabel and title as they might not be found
+    def set_xlabel(self,*args):
+        self.xlabel = args[0]
+
+    def set_ylabel(self,*args):
+        self.ylabel = args[0]
+        
+    def set_title(self,*args):
+        self.title = args[0]
+
+
+class managed_mcrun:
+    def __init__(self,*args,**kwargs):
+        self.name_of_instrumentfile = args[0]
+        
+        self.data_folder_name = ""
+        self.ncount = 1E6 # number of rays to 
+        self.parameters = {}
+        self.mpi=1
+        self.custom_flags = ""
+        self.mcrun_path = kwargs["mcrun_path"] # mcrun_path always in kwargs
+        
+        
+        if "foldername" in kwargs:
+            self.data_folder_name = kwargs["foldername"]
+        
+        if "ncount" in kwargs:
+            self.ncount = kwargs["ncount"]
+            
+        if "parameters" in kwargs:
+            self.parameters = kwargs["parameters"]
+            
+        if "mpi" in kwargs:
+            self.mpi = kwargs["mpi"]
+            
+        if "custom_flags" in kwargs:
+            self.custom_flags = kwargs["custom_flags"]
+            
+    def run_simulation(self):
+        option_string = "-c -n " + str(self.ncount) + " --mpi=" + str(self.mpi) + " "
+        if len(self.data_folder_name) > 0:
+            option_string = option_string + "-d " + self.data_folder_name
+        
+        parameter_string = ""
+        for key,val in self.parameters.items():
+            parameter_string = parameter_string + " " + str(key) + "=" + str(val)
+        
+        #os.system("mcstas-2.5-environment")
+        
+        #mcrun_path = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/mcrun"
+        
+        if self.mcrun_path[-1] == "\\" or self.mcrun_path[-1] == "/": 
+            mcrun_full_path = self.mcrun_path + "mcrun"
+        else:
+            mcrun_full_path = self.mcrun_path + "/mcrun"
+        
+        os.system(mcrun_full_path + " " + option_string + " " + self.custom_flags + " " + self.name_of_instrumentfile + " " + parameter_string)
+        
+        # Assume the script will continue when the os.system call has concluded. Is there a way to ensure this?
+        # can use subprocess from spawn* if more controll is needed over the spawned process, including a timeout
+        
+        time.sleep(2) # sleep 2 seconds to make sure data is written to disk before trying to open
+         
+        # find all data files in generated folder
+        files_in_folder = os.listdir(self.data_folder_name)
+        
+        # create a list for data instances to return
+        results = []
+        
+        # load the data into the list
+        for file in files_in_folder:
+            # Find data dimension, labels and axis
+            # Find lines with these variable names
+            
+            filename = self.data_folder_name + "/" + file
+            
+            variable_list = ["type", "title", "xlabel", "ylabel", "xlimits", "xylimits"]
+            located_variable_lines = {}
+            
+            f = open(filename,"r")
+            fl = f.readlines()
+            
+            # Need to check if this is a data file written by McStas
+            for line in fl:
+                for word in variable_list:
+                    if word in line:
+                        located_variable_lines[word]=line
+                        
+            f.close()
+             
+            if not fl[0] == "# Format: McCode with text headers\n":
+                print("Decided not to read file named " + filename)
+            else:
+                print("Decided to read file named " + filename)
+                #print(located_variable_lines)
+                
+                # Need to remove the variable name and end of line break
+                for key in located_variable_lines:
+                    string = located_variable_lines[key]
+                    located_variable_lines[key] = string[len(key)+4:-1]
+                
+                limits=[]
+                dimension=[]
+                type_string = located_variable_lines["type"]
+                if "1d" in type_string:
+                    # extract number of pixels
+                    dimension = int(type_string[9:-1])
+                    print(dimension) 
+                    
+                    # extract the limits of each direction
+                    temp_str = located_variable_lines["xlimits"]
+                    limits_string = temp_str.split()
+                    for limit in limits_string:
+                        limits.append(float(limit))
+                    
+                else:
+                    # extract number of pixels in each direction
+                    type_strings = type_string.split(",")
+                    temp_str = type_strings[0]
+                    dimension.append(int(temp_str[9:]))
+                    temp_str = type_strings[1]
+                    dimension.append(int(temp_str[1:-1]))
+                    
+                    # extract the limits of each direction
+                    temp_str = located_variable_lines["xylimits"]
+                    limits_string = temp_str.split()
+                    for limit in limits_string:
+                        limits.append(float(limit))    
+                
+                # Loads bulk data from file
+                # Does not seem to get the meta data
+                data = np.loadtxt(filename)
+    
+                # split data into intensity, error and ncount
+                if type(dimension) == int:
+                    xaxis = data.T[0,:] # not used in data yet
+                    Intensity = data.T[1,:]
+                    Error = data.T[2,:]
+                    Ncount = data.T[3,:]
+                    
+                elif len(dimension) == 2:
+                    xaxis = [] # assume evenly binned in 2d
+                    Intensity = data.T[:,0:dimension[1]-1]
+                    Error = data.T[:,dimension[1]:2*dimension[1]-1]
+                    Ncount = data.T[:,2*dimension[1]:3*dimension[1]-1]
+                    
+                else:
+                    # probably just not a McStas file then
+                    raise NameError("ERROR: Could not load dimensionality of data in file named " + str(file) + "!")
+                    # should probably just skip this file 
+                
+                # The data is saved as a mcstas_data object
+                result = mcstas_data(file,Intensity,Error,Ncount,xaxis=xaxis,dimension=dimension,limits=limits)
+                
+                # Set optional fields
+                if "xlabel" in located_variable_lines:
+                    result.set_xlabel(located_variable_lines["xlabel"])
+                if "ylabel" in located_variable_lines:
+                    result.set_ylabel(located_variable_lines["ylabel"])
+                if "title" in located_variable_lines:
+                    result.set_title(located_variable_lines["title"])
+                    
+                results.append(result)
+        
+        return results
+    
+    
+class make_plot:
+    def __init__(self,*args,**kwargs):
+        data_list = args[0]
+        
+        # Relevant options: 
+        #  select colormap
+        #  show / hide colorbar
+        #  custom title / label
+        #  color of 1d plot
+        #  overlay several 1d
+        #  log scale (orders of magnitude)
+        #  compare several 1d
+        #  compare 2D
+        
+        self.log = False
+        if "log" in kwargs:
+            if not kwargs["log"] == 0:
+                self.log = True
+            
+        self.orders_of_magnitude=300
+        if "max_orders_of_mag" in kwargs:
+            self.orders_of_magnitude=kwargs["max_orders_of_mag"]
+        
+        if not isinstance(data_list,mcstas_data):
+            print("number of elements in data list = " + str(len(data_list)))
+        else:
+            # Only a single element, put it in a list for easier syntax later
+            data_list = [data_list]
+        for data in data_list:
+            print("Plotting data with name " + data.name)
+            if type(data.dimension) == int:
+                fig = plt.figure(0)
+                
+                #print(data.T)
+                x = data.xaxis
+                y = data.Intensity
+                y_err = data.Error
+                
+                plt.errorbar(x, y, yerr=y_err)
+                
+                plt.xlim(data.limits[0],data.limits[1])
+                
+                # Add a title
+                plt.title(data.title)
+                
+                # Add axis labels
+                plt.xlabel(data.xlabel)
+                plt.ylabel(data.ylabel)
+                
+            elif  len(data.dimension) == 2:
+                
+                # Split the data into intensity, error and ncount
+                Intensity = data.Intensity
+                Error = data.Error
+                Ncount = data.Ncount
+                
+                # Select to plot the intensity
+                #to_plot = np.log(Intensity)
+                
+                if self.log:
+                    min_value = np.min(Intensity[np.nonzero(Intensity)])
+                    min_value = np.log10(min_value)
+                    
+                    to_plot = np.log10(Intensity)
+                    
+                    max_value = to_plot.max()
+                    
+                    if max_value - min_value > self.orders_of_magnitude:
+                        min_value = max_value - self.orders_of_magnitude
+                else:
+                    to_plot = Intensity
+                    min_value = to_plot.min()
+                    max_value = to_plot.max()
+                
+                # Check the size of the array to be plotted 
+                #print(to_plot.shape)
+                
+                # Set the axis (might be switched?)
+                X=np.linspace(data.limits[0],data.limits[1],data.dimension[0]+1)
+                Y=np.linspace(data.limits[2],data.limits[3],data.dimension[1])
+                
+                # Create a meshgrid for both x and y
+                y, x = np.meshgrid(Y,X)
+                
+                
+                # Generate information on necessary colorrange
+                levels = MaxNLocator(nbins=150).tick_values(min_value, max_value)
+                #levels = MaxNLocator(nbins=150).tick_values(to_plot.max()-12, to_plot.max())
+                
+                # Select colormap
+                cmap = plt.get_cmap('hot')
+                norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
+                
+                # Create the figure
+                fig, (ax0) = plt.subplots()
+                
+                # Plot the data on the meshgrids
+                im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
+                
+                # Add the colorbar
+                fig.colorbar(im, ax=ax0)
+                
+                # Add a title
+                ax0.set_title(data.title)
+                
+                # Add axis labels
+                plt.xlabel(data.xlabel)
+                plt.ylabel(data.ylabel)
+                
+            else:
+                print("Error, dimension not read correctly")
+            
+        plt.show()
+        
+
+class parameter_variable:
+    def __init__(self,*args,**kwargs):
+        if len(args) == 1:
+            self.type = ""
+            self.name = str(args[0])
+        if len(args) == 2:
+            self.type = args[0] + " "
+            self.name = str(args[1])
+        
+        if "value" in kwargs:
+            self.value_set = 1
+            self.value = kwargs["value"]
+        else:
+            self.value_set = 0
+
+        if "comment" in kwargs:
+            self.comment = "// " + kwargs["comment"]
+        else:
+            self.comment = ""
+
+        # could check for allowed types
+        # they are int, double, string, are there more?
+
+    def write_parameter(self,fo,stop_character):
+        fo.write("%s%s" % (self.type, self.name))
+        if self.value_set == 1:
+            if isinstance(self.value,int):
+                fo.write(" = %d" % self.value)
+            elif isinstance(self.value,float):
+                fo.write(" = %G" % self.value)
+            else:
+                fo.write(" = %s" % str(self.value))
+        fo.write(stop_character)
+        fo.write(self.comment)
+        fo.write("\n")
+
+class declare_variable:
+    def __init__(self,*args,**kwargs):
+        self.type = args[0]
+        self.name = str(args[1])
+        if "value" in kwargs:
+            self.value_set = 1
+            self.value = kwargs["value"]
+        else:
+            self.value_set = 0
+        if "array" in kwargs:
+            self.vector = kwargs["array"]
+        else:
+            self.vector = 0
+
+        if "comment" in kwargs:
+            self.comment = " // " + kwargs["comment"]
+        else:
+            self.comment = ""
+
+    def write_line(self,fo):
+        if self.value_set == 0 and self.vector == 0:
+            fo.write("%s %s;%s" % (self.type, self.name,self.comment))
+        if self.value_set == 1 and self.vector == 0:
+            if self.type == "int":
+                fo.write("%s %s = %d;%s" % (self.type, self.name, self.value, self.comment))
+            else:
+                fo.write("%s %s = %G;%s" % (self.type, self.name, self.value,self.comment))
+        if self.value_set == 0 and self.vector != 0:
+            fo.write("%s %s[%d];%s" % (self.type, self.name, self.vector, self.comment))
+        if self.value_set == 1 and self.vector != 0:
+            fo.write("%s %s[%d] = {" % (self.type, self.name, self.vector))
+            for i in range(0,len(self.value)-1):
+                fo.write("%G," % self.value[i])
+            fo.write("%G};%s" % (self.value[-1], self.comment))
+
+
+class component:
+    def __init__(self,*args,**kwargs):
+        # Defines a McStas component with name and component name as first inputs
+        self.name = args[0]
+        self.component_name = args[1]
+        
+        # Possible to give AT and ROTATED including AT_RELATIVE / ROTATED_RELATIVE
+        # RELATIVE keyword also exists and sets both AT_RELATIVE and ROTATED_RELATIVE
+        if "AT" in kwargs:
+            self.AT_data = kwargs["AT"]
+        else:
+            self.AT_data = [0,0,0]
+        # need to check if AT_RELATIVE is a string
+        if "AT_RELATIVE" in kwargs:
+            self.AT_relative = "RELATIVE " + kwargs["AT_RELATIVE"]
+        else:
+            self.AT_relative = "ABSOLUTE"
+        
+        # If rotated is never mentioned, why print it? How does this influence McStas?
+        if "ROTATED" in kwargs:
+            self.ROTATED_data = kwargs["AT"]
+        else:
+            self.ROTATED_data = [0,0,0]
+        # need to check if ROTATED_RELATIVE is a string
+        if "ROTATED_RELATIVE" in kwargs:
+            self.ROTATED_relative = kwargs["ROTATED_RELATIVE"]
+        else:
+            self.ROTATED_relative = "ABSOLUTE"
+        
+        # need to check if RELATIVE is a string
+        if "RELATIVE" in kwargs:
+            self.AT_relative = "RELATIVE " + kwargs["RELATIVE"]
+            self.ROTATED_relative = "RELATIVE " + kwargs["RELATIVE"]
+
+        # possible to have a c comment
+        if "comment" in kwargs:
+            self.comment = kwargs["comment"]
+        else:
+            self.comment = ""
+
+        # initialize a dictionary
+        self.component_parameters = {}
+        
+        # possible to store a preference for wehter this component should
+        #  be in a include file or directly in the instrument?
+        
+    # method for setting AT and AT_RELATIVE after initialization
+    def set_AT(self,at_list,**kwargs):
+        self.AT_data=at_list
+        if "RELATIVE" in kwargs:
+            relative_name = kwargs["RELATIVE"]
+            if relative_name == "ABSOLUTE":
+                self.AT_relative = relative_name
+            else:
+                self.AT_relative = "RELATIVE " + relative_name
+    
+    # method for setting ROTATED and ROTATED_RELATIVE after initialization
+    def set_ROTATED(self,rotated_list,**kwargs):
+        self.ROTATED_data=rotated_list
+        if "RELATIVE" in kwargs:
+            relative_name = kwargs["RELATIVE"]
+            if relative_name == "ABSOLUTE":
+                self.ROTATED_relative = relative_name
+            else:
+                self.ROTATED_relative = "RELATIVE " + relative_name
+    
+    # method for setting RELATIVE after initialization
+    def set_RELATIVE(self,relative_name):
+        if relative_name == "ABSOLUTE":
+            self.AT_relative = relative_name
+            self.ROTATED_relative = relative_name
+        else:
+            self.AT_relative = "RELATIVE " + relative_name
+            self.ROTATED_relative = "RELATIVE " + relative_name
+
+    # method that adds a parameter name / value pair to dictionary
+    def set_parameters(self,dict_input):
+        self.component_parameters.update(dict_input)
+    
+    # method that sets a comment to be written to instrument file
+    def set_comment(self,string):
+        self.comment = string
+
+    # method that writes component to file
+    def write_component(self,fo):
+        parameters_per_line = 2 # write comma separated parameters, up to 2 per line
+        # could use a character limit on lines instead
+        parameters_written = 0  # internal parameter
+        number_of_parameters = len(self.component_parameters) # internal parameter
+    
+        # write comment if present
+        if len(self.comment) > 1:
+            fo.write("// %s\n" % (str(self.comment)))
+    
+        # write component name and component type
+        fo.write("COMPONENT %s = %s(" % (self.name, self.component_name))
+        
+        if number_of_parameters == 0:
+            fo.write(")\n") # if there are no parameters, close the component immediately
+        else:
+            fo.write("\n") # if there are parameters to be written, start a new line
+        
+        for key,val in self.component_parameters.items():
+            if isinstance(val,float): # check if value is a number
+                fo.write(" %s = %G" % (str(key),val)) # Small or large numbers written in scientific format
+            else:
+                fo.write(" %s = %s" % (str(key),str(val)))
+            parameters_written = parameters_written + 1
+            if parameters_written < number_of_parameters:
+                fo.write(",") # comma between parameters
+                if parameters_written%parameters_per_line == 0:
+                    fo.write("\n")
+            else:
+                fo.write(")\n") # end paranthesis after last parameter
+    
+        # Need to add WHEN section here
+        # Need to add JUMP section here
+        # write AT and ROTATED section
+        fo.write("AT (%s,%s,%s)" % (str(self.AT_data[0]),str(self.AT_data[1]),str(self.AT_data[2])))
+        fo.write(" %s\n" % self.AT_relative)
+        fo.write("ROTATED (%s,%s,%s)" % (str(self.ROTATED_data[0]),str(self.ROTATED_data[1]),str(self.ROTATED_data[2])))
+        fo.write(" %s\n\n" % self.ROTATED_relative)
+        # Need to add EXTEND section here
+
+    # print component long
+    def print_long(self):
+        print("// " + self.comment)
+        print("COMPONENT " + str(self.name) + " = " + str(self.component_name))
+        for key,val in self.component_parameters.items():
+            print(" ",key,"=",val)
+        print("AT",self.AT_data,self.AT_relative)
+        print("ROTATED",self.ROTATED_data,self.ROTATED_relative)
+
+    # print component short
+    def print_short(self,**kwargs):
+        if "longest_name" in kwargs:
+            print("test")
+            print(str(self.name)+" "*(3+kwargs["longest_name"]-len(self.name)),end='')
+            print(str(self.component_name),"\tAT",self.AT_data,self.AT_relative,"ROTATED",self.ROTATED_data,self.ROTATED_relative)
+        else:
+            print(str(self.name),"=",str(self.component_name),"\tAT",self.AT_data,self.AT_relative,"ROTATED",self.ROTATED_data,self.ROTATED_relative)
+
+
+class McStas_instr:
+    def __init__(self,name,**kwargs):
+        self.name = name
+        
+        if "author" in kwargs:
+            self.author = kwargs["author"]
+        else:
+            self.author = "Python McStas Instrument Generator"
+        
+        if "origin" in kwargs:
+            self.origin = kwargs["origin"]
+        else:
+            self.origin = "ESS DMSC"
+            
+        if "mcrun_path" in kwargs:
+            self.mcrun_path = kwargs["mcrun_path"]
+        else:
+            self.mcrun_path = ""
+        
+        self.parameter_list = []
+        self.declare_list = []
+        self.initialize_section = "// Start of initialize for generated " + name + "\n"
+        self.trace_section = "// Start of trace section for generated " + name + "\n"
+        # handle components
+        self.component_list = [] # list of components (have to be ordered)
+        self.component_name_list = [] # list of component names
+        
+    def add_parameter(self,*args,**kwargs):
+        # type of variable, name of variable, options described in declare_parameter class
+        self.parameter_list.append(parameter_variable(*args,**kwargs))
+
+    def add_declare_var(self,*args,**kwargs):
+        # type of variable, name of variable, options described in declare_variable class
+        self.declare_list.append(declare_variable(*args,**kwargs))
+
+    def append_initialize(self,string):
+        self.initialize_section = self.initialize_section + string + "\n"
+    
+    def append_initialize_no_new_line(self,string):
+        self.initialize_section = self.initialize_section + string
+    
+    # Need to handle trace string differently when components also exists
+    #  A) Could have trace string as a component attribute and set it before / after
+    #  B) Could have trace string as a McStas_instr attribute and still attach placement to components
+    #  C) Could have trace string as a different object and place it in component_list, but have a write function named as the component write function?
+    
+    def append_trace(self,string):
+        self.trace_section = self.trace_section + string + "\n"
+    
+    def append_trace_no_new_line(self,string):
+        self.trace_section = self.trace_section + string
+                 
+    # methods for creating new components and modifiying existing components
+    def add_component(self,*args,**kwargs):
+        if args[0] in self.component_name_list:
+            raise NameError("Component name \"" + str(args[0]) + "\" used twice, McStas does not allow this. Rename or remove one instance of this name.")
+        
+        if "after" in kwargs: # insert component after component with this name
+            if kwargs["after"] not in self.component_name_list:
+                raise NameError("Trying to add a component after a component named \"" + str(kwargs["after"]) + "\", but a component with that name was not found.")
+        
+            new_index = self.component_name_list.index(kwargs["after"])
+            self.component_list.insert(new_index+1,component(*args,**kwargs))
+            self.component_name_list.insert(new_index+1,args[0])
+        elif "before" in kwargs: # insret component after component with this name
+            if kwargs["before"] not in self.component_name_list:
+                raise NameError("Trying to add a component before a component named \"" + str(kwargs["before"]) + "\", but a component with that name was not found.")
+        
+            new_index = self.component_name_list.index(kwargs["before"])
+            self.component_list.insert(new_index,component(*args,**kwargs))
+            self.component_name_list.insert(new_index,args[0])
+        else:
+            self.component_list.append(component(*args,**kwargs))
+            self.component_name_list.append(args[0])
+    
+    def get_component(self,name):
+        if name in self.component_name_list:
+            index = self.component_name_list.index(name)
+            return self.component_list[index]
+        else:
+            raise NameError("No component was found with name \"" + str(name) + "\"!")
+            
+    def get_last_component(self):
+        return self.component_list[-1]
+
+    def set_component_parameter(self,name,input_dict):
+        component = self.get_component(name)
+        component.set_parameters(input_dict)
+
+    def set_component_AT(self,name,at_list,**kwargs):
+        component = self.get_component(name)
+        component.set_AT(at_list,**kwargs)
+        
+    def set_component_ROTATED(self,name,rotated_list,**kwargs):
+        component = self.get_component(name)
+        component.set_ROTATED(rotated_list,**kwargs)
+    
+    def set_component_RELATIVE(self,name,relative):
+        component = self.get_component(name)
+        component.set_RELATIVE(relative)
+    
+    def set_component_comment(self,name,string):
+        component = self.get_component(name)
+        component.set_comment(string)
+
+    def print_component(self,name):
+        component = self.get_component(name)
+        component.print_long()
+    
+    def print_component_short(self,name):
+        component = self.get_component(name)
+        component.print_short()
+    
+    def print_components(self):
+    
+        longest_name = len(max(self.component_name_list,key=len))
+        
+        # Investigate how this could have been done in a better way
+        # Find longest field for each type of data printed
+        component_type_list = []
+        at_x_list = []
+        at_y_list = []
+        at_z_list = []
+        at_relative_list = []
+        rotated_x_list = []
+        rotated_y_list = []
+        rotated_z_list = []
+        rotated_relative_list = []
+        for component in self.component_list:
+            component_type_list.append(component.component_name)
+            at_x_list.append(str(component.AT_data[0]))
+            at_y_list.append(str(component.AT_data[1]))
+            at_z_list.append(str(component.AT_data[2]))
+            at_relative_list.append(component.AT_relative)
+            rotated_x_list.append(str(component.ROTATED_data[0]))
+            rotated_y_list.append(str(component.ROTATED_data[1]))
+            rotated_z_list.append(str(component.ROTATED_data[2]))
+            rotated_relative_list.append(component.ROTATED_relative)
+        
+        longest_component_name = len(max(component_type_list,key=len))
+        longest_at_x_name = len(max(at_x_list,key=len))
+        longest_at_y_name = len(max(at_y_list,key=len))
+        longest_at_z_name = len(max(at_z_list,key=len))
+        longest_at_relative_name = len(max(at_relative_list,key=len))
+        longest_rotated_x_name = len(max(rotated_x_list,key=len))
+        longest_rotated_y_name = len(max(rotated_y_list,key=len))
+        longest_rotated_z_name = len(max(rotated_z_list,key=len))
+        longest_rotated_relative_name = len(max(rotated_relative_list,key=len))
+        
+        # Have longest field for each type, use ljust to align all columns
+        for component in self.component_list:
+            print(str(component.name).ljust(longest_name+2),end=' ')
+            print(str(component.component_name).ljust(longest_component_name+2),end=' ')
+            print("AT ",str(component.AT_data).ljust(longest_at_x_name+longest_at_y_name+longest_at_z_name+11),end='')
+            print(component.AT_relative.ljust(longest_at_relative_name+2),end=' ')
+            print("ROTATED ",str(component.ROTATED_data).ljust(longest_rotated_x_name+longest_rotated_y_name+longest_rotated_z_name+11),end='')
+            print(component.ROTATED_relative)
+            #print("")
+
+    def write_c_files(self):
+        # method for writing c files that can be included in instruments
+        
+        path = os.getcwd()
+        path = path + "/generated_includes"
+        if not os.path.isdir(path):
+            try:
+                os.mkdir(path)
+            except OSError:
+                print ("Creation of the directory %s failed" % path)
+        
+        fo = open("./generated_includes/" + self.name + "_declare.c","w")
+        fo.write("// declare section for %s \n" % self.name)
+        fo.close()
+        fo = open("./generated_includes/" + self.name + "_declare.c","a")
+        for dec_line in self.declare_list:
+            dec_line.write_line(fo)
+            fo.write("\n")
+        fo.close()
+        
+        fo = open("./generated_includes/" + self.name + "_initialize.c","w")
+        fo.write(self.initialize_section)
+        fo.close()
+
+        fo = open("./generated_includes/" + self.name + "_trace.c","w")
+        fo.write(self.trace_section)
+        fo.close()
+        
+        fo = open("./generated_includes/" + self.name + "_component_trace.c","w")
+        for component in self.component_list:
+            component.write_component(fo)
+        fo.close()
+
+    # Method that writes full instrument file.
+    def write_full_instrument(self):
+        # method for writing an instrument file
+        # could either use generated includes or write everything out
+        # will probably create an option to choose between these methods later
+        
+        # Create file identifier
+        fo = open(self.name + ".instr","w")
+
+        # Write quick doc start
+        fo.write("/" + "*"*80 + "\n")
+        fo.write("* \n")
+        fo.write("* McStas, neutron ray-tracing package\n")
+        fo.write("*         Copyright (C) 1997-2008, All rights reserved\n")
+        fo.write("*         Risoe National Laboratory, Roskilde, Denmark\n")
+        fo.write("*         Institut Laue Langevin, Grenoble, France\n")
+        fo.write("* \n")
+        fo.write("* This file was written by the Python McStas Instrument Generator \n")
+        fo.write("*  which was written by Mads Bertelsen in 2019 while employed at \n")
+        fo.write("*  the European Spallation Source Data Management and Software Center\n")
+        fo.write("* \n")
+        fo.write("* Instrument %s\n" % self.name)
+        fo.write("* \n")
+        fo.write("* %Identification\n") # Could allow the user to insert these
+        fo.write("* Written by: %s\n" % self.author)
+        fo.write("* Date: %s\n" % datetime.datetime.now().strftime("%H:%M:%S on %B %d, %Y"))
+        fo.write("* Origin: %s\n" % self.origin)
+        fo.write("* %INSTRUMENT_SITE: Generated_instruments\n")
+        fo.write("* \n")
+        fo.write("* \n")
+        fo.write("* %Parameters\n")
+        # Add description of parameters here
+        fo.write("* \n")
+        fo.write("* %End \n")
+        fo.write("*"*80 + "/\n")
+        fo.write("\n")
+        fo.write("DEFINE INSTRUMENT %s (" % self.name)
+        fo.write("\n")
+        # Add loop that inserts parameters here
+        for variable in self.parameter_list[0:-1]:
+            variable.write_parameter(fo,",")
+        if len(self.parameter_list) > 0:
+            self.parameter_list[-1].write_parameter(fo," ")
+        fo.write(")\n")
+        fo.write("\n")
+
+        # Write declare
+        fo.write("DECLARE \n %{\n")
+        for dec_line in self.declare_list:
+            dec_line.write_line(fo)
+            fo.write("\n")
+        fo.write("%}\n\n")
+
+        # Write initialize
+        fo.write("INITIALIZE \n %{\n")
+        fo.write(self.initialize_section)
+        # Alternatively hide everything in include
+        # fo.write("%include "generated_includes/" + self.name + "_initialize.c")
+        fo.write("%}\n\n")
+
+        # Write trace
+        fo.write("TRACE \n")
+        for component in self.component_list:
+            component.write_component(fo)
+
+        # Write finally (no finally possible yet)
+
+        # End instrument file
+        fo.write("\nEND\n")
+        
+    def run_full_instrument(self,*args,**kwargs):
+        # Write the instrument file
+        self.write_full_instrument()
+        
+        # Make sure mcrun path is in kwargs
+        if not "mcrun_path" in kwargs:
+            kwargs["mcrun_path"] = self.mcrun_path
+            
+        # Set up the simulation 
+        simulation = managed_mcrun(self.name + ".instr",**kwargs)
+        
+        # Run the simulation and return data
+        return simulation.run_simulation()
+        
+        
+        
diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
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z4^8Cl@k|CI*zEpwn2$BnKZ@8`V5`m~$~Sd_#)vv1=(5uOQ5{d*qwqqdnv}?5)zHCf
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z*+IGINXwZJB0SaYcsQms=-DMlu14`7s3vm!JUS(Ar>zTAQl%j`aYS6So9%*6>><|~
z&(o8ABxC&;OG$J@YldA%d?(?QSrgd>ibyo=;(0U^4a_CHzq^voxwfxAc14+O5CaH$
z*^Vx5oIHv(ulLT40Y~OzM&#wAvkoWVIkUMr144@*5D!`gf7x;Rv~V}BzPu6yu$#S#
zs+jzpvvHg&GSi>zIQWFOk;F{10Nd7~GIZ~B^|D6bW)LypVp?Lm>6{PQ*$Npp+#hBE
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LA|@7=6@mIcaBecJ

literal 0
HcmV?d00001

diff --git a/demonstration.py b/demonstration.py
new file mode 100644
index 00000000..e8253627
--- /dev/null
+++ b/demonstration.py
@@ -0,0 +1,86 @@
+# Demonstration of McStasScript, an API for creating and running McStas instruments from python scripts
+# Written by Mads Bertelsen, ESS DMSC
+import random
+import McStasScript # McStasScript classes
+
+# if the mcrun command from McStas is not in your path, provide absolute path for the binary here:
+mcrun_path = ""
+#mcrun_path = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin"
+
+# Create a McStas instrument
+instr = McStasScript.McStas_instr("random_demo",author="Mads Bertelsen",origin="ESS DMSC",mcrun_path=mcrun_path)
+
+# Set up a material called Cu with approrpiate properties (uses McStas Union components, here the processes)
+instr.add_component("Cu_incoherent","Incoherent_process")
+instr.set_component_parameter("Cu_incoherent",{"sigma" : 4*0.55, "packing_factor" : 1, "unit_cell_volume" :  55.4})
+
+instr.add_component("Cu_powder","Powder_process")
+instr.set_component_parameter("Cu_powder",{"reflections" : "\"Cu.laz\""})
+
+instr.add_component("Cu","Union_make_material")
+instr.set_component_parameter("Cu",{"my_absorption" : "100*4*3.78/66.4", "process_string" : "\"Cu_incoherent,Cu_powder\""})
+
+# Set neutron source
+instr.add_component("source","Source_div",AT=[0,0,0])
+instr.add_parameter("double","energy",value=10,comment="[meV] source energy") # Add parameter to select energy at run time
+instr.set_component_parameter("source",{"xwidth" : 0.12, "yheight" : 0.12, "focus_aw" : 0.1, "focus_ah" : 0.1, "E0" : "energy", "dE" : 0, "flux" : 1E13})
+
+# List of available materials, Vacuum is provided by the system
+material_name_list = ["Cu", "Vacuum"]
+
+# Wish to set up a number of randomly sized and placed boxes, here we choose the number
+number_of_volumes = random.randint(30,40)
+
+# Initialize the priority that needs to be unique for each volume
+current_priority = 99
+for volume in range(number_of_volumes):
+
+    current_priority = current_priority + 1 # update the priority
+    max_side_length = 0.04
+    max_depth = 0.003
+    position = [random.uniform(-0.05,0.05), random.uniform(-0.05,0.05), 1+random.uniform(-0.05,0.05)] # Set position in 10x10x10 cm^3 box 1 m from source
+    rotation = [random.uniform(0,360), random.uniform(0,360), random.uniform(0,360)] # random rotation
+    
+    # Choose a random material from the list of available materials
+    volume_material = random.choice(material_name_list)
+
+    # Add a McStas Union geometry with unique name
+    instr.add_component("volume_" + str(volume),"Union_box")
+    instr.set_component_parameter("volume_" + str(volume),{"xwidth" : random.uniform(0.01,max_side_length),"yheight" : random.uniform(0.01,max_side_length),"zdepth" : random.uniform(0.001,max_depth),})
+    instr.set_component_parameter("volume_" + str(volume),{"material_string" : "\""+volume_material+"\"", "priority" : current_priority, "p_interact" : 0.3})
+    instr.set_component_AT("volume_" + str(volume),position,RELATIVE="ABSOLUTE")
+    instr.set_component_ROTATED("volume_" + str(volume),rotation,RELATIVE="ABSOLUTE")
+
+
+# A few Union loggers are set up for display of the scattering locations
+instr.add_component("logger_space_zx_all","Union_logger_2D_space")
+current_component = instr.get_last_component()
+current_component.set_parameters({"filename" : "\"space_zx.dat\"",})
+current_component.set_parameters({"n1" : 1000, "D_direction_1" : "\"z\"", "D1_min" : -0.05, "D1_max" : 0.05})
+current_component.set_parameters({"n2" : 1000, "D_direction_2" : "\"x\"", "D2_min" : -0.05, "D2_max" : 0.05})
+current_component.set_AT([0,0,1])
+
+instr.add_component("logger_space_zy_all","Union_logger_2D_space")
+current_component = instr.get_last_component()
+current_component.set_parameters({"filename" : "\"space_zy.dat\"",})
+current_component.set_parameters({"n1" : 1000, "D_direction_1" : "\"z\"", "D1_min" : -0.05, "D1_max" : 0.05})
+current_component.set_parameters({"n2" : 1000, "D_direction_2" : "\"y\"", "D2_min" : -0.05, "D2_max" : 0.05})
+current_component.set_AT([0,0,1])
+
+# Union master component that executes the simulation of the random boxes
+instr.add_component("random_boxes","Union_master")
+
+# McStas monitors for viewing the beam after the random boxes
+instr.add_component("detector","PSD_monitor",AT=[0,0,2])
+instr.set_component_parameter("detector",{"xwidth" : 0.10, "yheight" : 0.10, "nx" : 500, "ny" : 500, "filename" : "\"PSD.dat\"", "restore_neutron" : 1})
+
+instr.add_component("large_detector","PSD_monitor",AT=[0,0,2])
+instr.set_component_parameter("large_detector",{"xwidth" : 1.0, "yheight" : 1.0, "nx" : 500, "ny" : 500, "filename" : "\"large_PSD.dat\"", "restore_neutron" : 1})
+
+# Run the McStas simulation, a unique foldername is required for each run
+data = instr.run_full_instrument(foldername="demonstration8",parameters={"energy":600},mpi=10,ncount=5E7)
+
+# Plot the resulting data on a logarithmic scale
+plot = McStas_class.make_plot(data,log=1,max_orders_of_mag=10)
+
+

From d0a335f9dd633dfcc73a4b410d2b0c2be7873a5e Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Tue, 5 Mar 2019 13:21:23 +0100
Subject: [PATCH 002/403] Create README.md

---
 README.md | 4 ++++
 1 file changed, 4 insertions(+)
 create mode 100644 README.md

diff --git a/README.md b/README.md
new file mode 100644
index 00000000..2bcd92f2
--- /dev/null
+++ b/README.md
@@ -0,0 +1,4 @@
+# McStasScript
+McStas API for creating and running McStas instruments from python scripting
+
+Prototype for an API that allow interaction with McStas through an interface like Jupyter Notebooks created under WP5 of PaNOSC.

From b25d7fb01b03a6f528453625e56f096017fb24e2 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Thu, 7 Mar 2019 14:30:10 +0100
Subject: [PATCH 003/403] Corrected typos: 	McStasScript.py typo caused a
 bug where AT_data overwrote ROTATED_data 	Demonstration.py had wrong
 unit cell volume for material

---
 McStasScript.py  | 167 ++++++++++++++++++++++++++++++++++++++++++++++-
 demonstration.py |   2 +-
 2 files changed, 167 insertions(+), 2 deletions(-)

diff --git a/McStasScript.py b/McStasScript.py
index 2a4ab8c8..97c09feb 100644
--- a/McStasScript.py
+++ b/McStasScript.py
@@ -4,6 +4,7 @@
 import datetime
 import os
 import time
+import math
 import numpy as np
 import matplotlib
 import matplotlib.pyplot as plt
@@ -334,6 +335,170 @@ def __init__(self,*args,**kwargs):
             
         plt.show()
         
+class make_sub_plot:
+    def __init__(self,*args,**kwargs):
+        data_list = args[0]
+        
+        if not isinstance(data_list,mcstas_data):
+            print("number of elements in data list = " + str(len(data_list)))
+        else:
+            # Only a single element, put it in a list for easier syntax later
+            data_list = [data_list]
+            
+        number_of_plots = len(data_list)
+        
+        # Relevant options: 
+        #  select colormap
+        #  show / hide colorbar
+        #  custom title / label
+        #  color of 1d plot
+        #  overlay several 1d
+        #  log scale (o$rders of magnitude)
+        #  compare several 1d
+        #  compare 2D
+        
+        #fig = plt.figure(figsize=(20,10))
+        
+        self.log = False*number_of_plots
+        if "log" in kwargs:
+            if isinstance(kwargs["log"],list):
+                if not len(kwargs["log"]) == number_of_plots:
+                    raise IndexError("Length of list given for log logic does not match number of ")
+                else:
+                    self.log = kwargs["log"]
+                    for element in self.log:
+                        if not isinstance(element, bool):
+                            if not element == 0:
+                                element = True 
+            
+        # Need to handle max orders of mag as log
+        self.orders_of_magnitude=300
+        if "max_orders_of_mag" in kwargs:
+            self.orders_of_magnitude=kwargs["max_orders_of_mag"]
+            
+        if "max_orders_of_mag_list" in kwargs:
+            self.orders_of_magnitude_list=kwargs["max_orders_of_mag_list"]
+            self.list_orders_of_mag = True
+        else: 
+            self.list_orders_of_mag = False
+        
+        
+        # Find reasonable grid size for the number of plots
+        dim1 = math.ceil(math.sqrt(number_of_plots))
+        dim2 = math.ceil(number_of_plots/dim1)
+          
+        fig, ax = plt.subplots(dim1,dim2,figsize=(15,8))
+        n_plot = 0
+        n_row = 0
+        index = -1
+        for data in data_list:
+            index = index + 1
+            if n_plot < dim1:
+                #print((n_plot,n_row))
+                ax0 = ax[n_plot,n_row]
+            else:
+                n_plot = n_plot - dim1
+                n_row = n_row + 1
+                #print((n_plot,n_row))
+                ax0 = ax[n_plot,n_row]
+              
+            n_plot = n_plot + 1
+            print("Plotting data with name " + data.name)
+            
+            if type(data.dimension) == int:
+                #fig = plt.figure(0)
+                #plt.subplot(dim1, dim2, n_plot)
+                
+                #print(data.T)
+                x = data.xaxis
+                y = data.Intensity
+                y_err = data.Error
+                
+                ax0.errorbar(x, y, yerr=y_err)
+                
+                #ax0.xlim(data.limits[0],data.limits[1])
+                
+                # Add a title
+                #ax0.title(data.title)
+                
+                # Add axis labels
+                #ax0.xlabel(data.xlabel)
+                #ax0.ylabel(data.ylabel)
+                
+            elif  len(data.dimension) == 2:
+                
+                # Split the data into intensity, error and ncount
+                Intensity = data.Intensity
+                Error = data.Error
+                Ncount = data.Ncount
+                
+                # Select to plot the intensity
+                #to_plot = np.log(Intensity)
+                
+                if self.log:
+                    min_value = np.min(Intensity[np.nonzero(Intensity)])
+                    min_value = np.log10(min_value)
+                    
+                    to_plot = np.log10(Intensity)
+                    
+                    max_value = to_plot.max()
+                    
+                    if self.list_orders_of_mag:
+                        this_orders_of_mag = self.orders_of_magnitude_list[index]
+                    else:
+                        this_orders_of_mag = self.orders_of_magnitude
+                    
+                    if max_value - min_value > this_orders_of_mag:
+                        min_value = max_value - this_orders_of_mag
+                else:
+                    to_plot = Intensity
+                    min_value = to_plot.min()
+                    max_value = to_plot.max()
+                
+                # Check the size of the array to be plotted 
+                #print(to_plot.shape)
+                
+                # Set the axis (might be switched?)
+                X=np.linspace(data.limits[0],data.limits[1],data.dimension[0]+1)
+                Y=np.linspace(data.limits[2],data.limits[3],data.dimension[1])
+                
+                # Create a meshgrid for both x and y
+                y, x = np.meshgrid(Y,X)
+                
+                
+                # Generate information on necessary colorrange
+                levels = MaxNLocator(nbins=150).tick_values(min_value, max_value)
+                #levels = MaxNLocator(nbins=150).tick_values(to_plot.max()-12, to_plot.max())
+                
+                # Select colormap
+                cmap = plt.get_cmap('jet')
+                norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
+                
+                # Create the figure
+                #fig, (ax0) = plt.subplots()
+                
+                #fig, ax0 = plt.subplot(dim1, dim2, n_plot)
+                
+                # Plot the data on the meshgrids
+                #im = plt.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
+                ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
+                
+                
+                # Add the colorbar
+                #fig.colorbar(im, ax=ax0)
+                
+                # Add a title
+                ax0.set_title(data.title)
+                
+                # Add axis labels
+                plt.xlabel(data.xlabel)
+                plt.ylabel(data.ylabel)
+                
+            else:
+                print("Error, dimension not read correctly")
+         
+        plt.show()   
+        
 
 class parameter_variable:
     def __init__(self,*args,**kwargs):
@@ -427,7 +592,7 @@ def __init__(self,*args,**kwargs):
         
         # If rotated is never mentioned, why print it? How does this influence McStas?
         if "ROTATED" in kwargs:
-            self.ROTATED_data = kwargs["AT"]
+            self.ROTATED_data = kwargs["ROTATED"]
         else:
             self.ROTATED_data = [0,0,0]
         # need to check if ROTATED_RELATIVE is a string
diff --git a/demonstration.py b/demonstration.py
index e8253627..953ddb08 100644
--- a/demonstration.py
+++ b/demonstration.py
@@ -18,7 +18,7 @@
 instr.set_component_parameter("Cu_powder",{"reflections" : "\"Cu.laz\""})
 
 instr.add_component("Cu","Union_make_material")
-instr.set_component_parameter("Cu",{"my_absorption" : "100*4*3.78/66.4", "process_string" : "\"Cu_incoherent,Cu_powder\""})
+instr.set_component_parameter("Cu",{"my_absorption" : "100*4*3.78/55.4", "process_string" : "\"Cu_incoherent,Cu_powder\""})
 
 # Set neutron source
 instr.add_component("source","Source_div",AT=[0,0,0])

From 801fe47cf6c2594c56bd08a9a4fb839af5304a86 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Thu, 7 Mar 2019 15:54:19 +0100
Subject: [PATCH 004/403] Added support for subplots which are a more natural
 choice for most applications.

Currently log scale is controlled through a bool list given to the make_sub_plot command, but this should probably be part of the data object instead.

The demonstration file is updated in order to take advantage of the new subplot feature
---
 McStasScript.py  | 114 +++++++++++++++++++++++++++--------------------
 demonstration.py |   2 +-
 2 files changed, 66 insertions(+), 50 deletions(-)

diff --git a/McStasScript.py b/McStasScript.py
index 97c09feb..3a3c721e 100644
--- a/McStasScript.py
+++ b/McStasScript.py
@@ -104,11 +104,13 @@ def run_simulation(self):
         #os.system("mcstas-2.5-environment")
         
         #mcrun_path = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/mcrun"
-        
-        if self.mcrun_path[-1] == "\\" or self.mcrun_path[-1] == "/": 
-            mcrun_full_path = self.mcrun_path + "mcrun"
+        if len(self.mcrun_path) > 1:
+            if self.mcrun_path[-1] == "\\" or self.mcrun_path[-1] == "/": 
+                mcrun_full_path = self.mcrun_path + "mcrun"
+            else:
+                mcrun_full_path = self.mcrun_path + "/mcrun"
         else:
-            mcrun_full_path = self.mcrun_path + "/mcrun"
+            mcrun_full_path = self.mcrun_path + "mcrun"
         
         os.system(mcrun_full_path + " " + option_string + " " + self.custom_flags + " " + self.name_of_instrumentfile + " " + parameter_string)
         
@@ -358,51 +360,55 @@ def __init__(self,*args,**kwargs):
         #  compare 2D
         
         #fig = plt.figure(figsize=(20,10))
-        
-        self.log = False*number_of_plots
+
+        # instead of passing this information here, it should just be a property of the data
+        self.log = [False]*number_of_plots
         if "log" in kwargs:
             if isinstance(kwargs["log"],list):
                 if not len(kwargs["log"]) == number_of_plots:
-                    raise IndexError("Length of list given for log logic does not match number of ")
+                    raise IndexError("Length of list given for log logic does not match number of data elements")
                 else:
                     self.log = kwargs["log"]
                     for element in self.log:
                         if not isinstance(element, bool):
                             if not element == 0:
                                 element = True 
-            
-        # Need to handle max orders of mag as log
-        self.orders_of_magnitude=300
-        if "max_orders_of_mag" in kwargs:
-            self.orders_of_magnitude=kwargs["max_orders_of_mag"]
-            
-        if "max_orders_of_mag_list" in kwargs:
-            self.orders_of_magnitude_list=kwargs["max_orders_of_mag_list"]
-            self.list_orders_of_mag = True
-        else: 
-            self.list_orders_of_mag = False
+            elif isinstance(kwargs["log"],bool):
+                if kwargs["log"] == True:
+                    self.log = [True]*number_of_plots
+            elif isinstance(kwargs["log"],int):
+                if kwargs["log"] == 1:
+                    self.log = [True]*number_of_plots
+            else:
+                raise NameError("log keyword Argument in make_sub_plot not understood. Needs to be int, [1/0], bool [True/False] or array of same length as data.")
+                
         
+        self.orders_of_mag=[300] * number_of_plots
+        if "max_orders_of_mag" in kwargs:
+            if isinstance(kwargs["max_orders_of_mag"],list):
+                if not len(kwargs["max_orders_of_mag"]) == number_of_plots:
+                    raise IndexError("Length of list given for max_orders_of_mag does not match number of data elements")
+                else: 
+                    self.orders_of_mag = kwargs["max_orders_of_mag"]
+            else:
+                if isinstance(kwargs["max_orders_of_mag"],float) or isinstance(kwargs["max_orders_of_mag"],int):
+                    self.orders_of_magnitude=[kwargs["max_orders_of_mag"]]*number_of_plots
+                else:
+                    raise TypeError("max_orders_of_mag need to be of type float or int")
         
         # Find reasonable grid size for the number of plots
-        dim1 = math.ceil(math.sqrt(number_of_plots))
-        dim2 = math.ceil(number_of_plots/dim1)
+        dim2 = math.ceil(math.sqrt(number_of_plots))
+        dim1 = math.ceil(number_of_plots/dim2)
           
-        fig, ax = plt.subplots(dim1,dim2,figsize=(15,8))
-        n_plot = 0
-        n_row = 0
+        fig, axs = plt.subplots(dim1,dim2,figsize=(13,7))
+        axs = np.array(axs)
+        ax = axs.reshape(-1)
+        
         index = -1
         for data in data_list:
             index = index + 1
-            if n_plot < dim1:
-                #print((n_plot,n_row))
-                ax0 = ax[n_plot,n_row]
-            else:
-                n_plot = n_plot - dim1
-                n_row = n_row + 1
-                #print((n_plot,n_row))
-                ax0 = ax[n_plot,n_row]
+            ax0 = ax[index]
               
-            n_plot = n_plot + 1
             print("Plotting data with name " + data.name)
             
             if type(data.dimension) == int:
@@ -414,16 +420,20 @@ def __init__(self,*args,**kwargs):
                 y = data.Intensity
                 y_err = data.Error
                 
+                
                 ax0.errorbar(x, y, yerr=y_err)
                 
-                #ax0.xlim(data.limits[0],data.limits[1])
+                if self.log[index]:
+                    ax0.set_yscale("log",nonposy='clip')
+                
+                ax0.set_xlim(data.limits[0],data.limits[1])
                 
                 # Add a title
                 #ax0.title(data.title)
                 
                 # Add axis labels
-                #ax0.xlabel(data.xlabel)
-                #ax0.ylabel(data.ylabel)
+                ax0.set_xlabel(data.xlabel)
+                ax0.set_ylabel(data.ylabel)
                 
             elif  len(data.dimension) == 2:
                 
@@ -435,21 +445,19 @@ def __init__(self,*args,**kwargs):
                 # Select to plot the intensity
                 #to_plot = np.log(Intensity)
                 
-                if self.log:
+                if self.log[index]:
                     min_value = np.min(Intensity[np.nonzero(Intensity)])
                     min_value = np.log10(min_value)
                     
-                    to_plot = np.log10(Intensity)
-                    
-                    max_value = to_plot.max()
+                    #to_plot = np.log10(Intensity)
+                    to_plot = Intensity
                     
-                    if self.list_orders_of_mag:
-                        this_orders_of_mag = self.orders_of_magnitude_list[index]
-                    else:
-                        this_orders_of_mag = self.orders_of_magnitude
+                    max_value = np.log10(to_plot.max())
                     
-                    if max_value - min_value > this_orders_of_mag:
-                        min_value = max_value - this_orders_of_mag
+                    if max_value - min_value > self.orders_of_mag[index]:
+                        min_value = max_value - self.orders_of_mag[index]
+                    min_value = 10.0 ** min_value
+                    max_value = 10.0 ** max_value
                 else:
                     to_plot = Intensity
                     min_value = to_plot.min()
@@ -481,18 +489,26 @@ def __init__(self,*args,**kwargs):
                 
                 # Plot the data on the meshgrids
                 #im = plt.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
-                ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
+                if self.log[index]: 
+                    im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=matplotlib.colors.LogNorm(vmin=min_value,vmax=max_value))
+                else:
+                    im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
                 
                 
+                def fmt(x, pos):
+                    a, b = '{:.2e}'.format(x).split('e')
+                    b = int(b)
+                    return r'${} \times 10^{{{}}}$'.format(a, b)
+                
                 # Add the colorbar
-                #fig.colorbar(im, ax=ax0)
+                fig.colorbar(im, ax=ax0, format=matplotlib.ticker.FuncFormatter(fmt))
                 
                 # Add a title
                 ax0.set_title(data.title)
                 
                 # Add axis labels
-                plt.xlabel(data.xlabel)
-                plt.ylabel(data.ylabel)
+                ax0.set_xlabel(data.xlabel)
+                ax0.set_ylabel(data.ylabel)
                 
             else:
                 print("Error, dimension not read correctly")
diff --git a/demonstration.py b/demonstration.py
index 953ddb08..a7cb6a21 100644
--- a/demonstration.py
+++ b/demonstration.py
@@ -81,6 +81,6 @@
 data = instr.run_full_instrument(foldername="demonstration8",parameters={"energy":600},mpi=10,ncount=5E7)
 
 # Plot the resulting data on a logarithmic scale
-plot = McStas_class.make_plot(data,log=1,max_orders_of_mag=10)
+plot = McStasScript.make_sub_plot(data,log=1,max_orders_of_mag=[5,10,2,2])
 
 

From 5915ffb6250787fb5119d0d8e4845d42808dffd6 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Thu, 7 Mar 2019 16:05:32 +0100
Subject: [PATCH 005/403] Update of documentation to include make_sub_plots

---
 McStasScript_documentation.pdf | Bin 134169 -> 135598 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
index 457b3b296c4449289b7166deca54460a7e692624..9dd1efd6885be47e514ae84d17e47c0112ca2fb4 100644
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From e9215f2070653034e238bed7b087f212f9d1e8df Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Tue, 12 Mar 2019 14:10:42 +0100
Subject: [PATCH 006/403] This update adds some functionallity and has reworked
 the routine for loading data. Now the data is stored in the order that it
 appears in the mccode.sim file, which is  more intuitive as it matches the
 order of the instrument file. The plots now also appear in the same order as
 the instrument file.

Added support for advanced McStas keywords:
	WHEN
	EXTEND
	JUMP
	GROUP

Added support for finally section.
---
 McStasScript.py | 463 ++++++++++++++++++++++++++++++------------------
 1 file changed, 291 insertions(+), 172 deletions(-)

diff --git a/McStasScript.py b/McStasScript.py
index 3a3c721e..049babc8 100644
--- a/McStasScript.py
+++ b/McStasScript.py
@@ -10,60 +10,107 @@
 import matplotlib.pyplot as plt
 from matplotlib.colors import BoundaryNorm
 from matplotlib.ticker import MaxNLocator
+from openpyxl.worksheet import dimensions
+from boto.ec2.autoscale import limits
 
 try: # check whether python knows about 'basestring'
    basestring
 except NameError: # no, it doesn't (it's Python3); use 'str' instead
    basestring=str
-   
-   
+
+class mcstas_meta_data:
+    def __init__(self,*args,**kwargs):
+        self.info = {}
+    
+    def add_info(self,key,value):
+        self.info[key] = value
+        
+    def extract_info(self):
+        
+        
+        if "type" in self.info:
+            # extract dimension
+            type = self.info["type"]
+            if "array_1d" in type:
+                self.dimension = int(type[9:-2])
+            if "array_2d" in type:
+                self.dimension=[]
+                type_strings = self.info["type"].split(",")
+                temp_str = type_strings[0]
+                self.dimension.append(int(temp_str[9:]))
+                temp_str = type_strings[1]
+                self.dimension.append(int(temp_str[1:-2]))
+        else:
+            raise NameError("No type in mccode data section!")
+                
+        if "component" in self.info:
+            self.component_name = self.info["component"].rstrip()
+
+        if "filename" in self.info:
+            self.filename = self.info["filename"].rstrip()
+        else:
+            raise NameError("No filename found in mccode data section!")
+        
+        self.limits = []
+        if "xylimits" in self.info:
+            # find the four numbers 
+            temp_str = self.info["xylimits"]
+            limits_string = temp_str.split()
+            for limit in limits_string:
+                self.limits.append(float(limit))
+
+        if "xlimits" in self.info:
+            # find the four numbers 
+            temp_str = self.info["xlimits"]
+            limits_string = temp_str.split()
+            for limit in limits_string:
+                self.limits.append(float(limit))
+
+        if "xlabel" in self.info:
+            self.xlabel = self.info["xlabel"].rstrip()
+        if "ylabel" in self.info:
+            self.ylabel = self.info["ylabel"].rstrip()
+        if "title" in self.info:
+            self.title = self.info["title"].rstrip()
+    
+    def set_title(self,string):
+        self.title = string
+        
+    def set_xlabel(self,string):
+        self.xlabel = string
+        
+    def set_ylabel(self,string):
+        self.ylabel = string
+        
+    
+        
 class mcstas_data:
     def __init__(self,*args,**kwargs):
-        # Name of data set (usually filename
-        self.name = str(args[0])
+        # attatch meta data
+        self.metadata = args[0]
+        # get name from metadata
+        self.name = self.metadata.component_name
         # three basic arrays as first 
         self.Intensity = args[1]
         self.Error = args[2]
         self.Ncount = args[3]
         
-        self.dimension=[]  # size of the data, list with a number per dimension s
-        self.limits=[]     # limits on the data to be used for plotting
-        self.parameters={} # parameters used in McStas simulation
-        
-        self.xlabel=""
-        self.ylabel=""
-        self.title=""
-        
-        if "dimension" in kwargs:
-            self.dimension = kwargs["dimension"]
-        else:
-            raise NameError("ERROR: Initialization of mcstas_data done without dimension for data set named " + self.name + "!")
-        
-        if type(self.dimension) == int:
+        if type(self.metadata.dimension) == int:
             if "xaxis" in kwargs:
                 self.xaxis = kwargs["xaxis"]
             else:
                 raise NameError("ERROR: Initialization of mcstas_data done with 1d data, but without xaxis" + self.name + "!")
         
-        if "limits" in kwargs:
-            self.limits = kwargs["limits"]
-        else:
-            raise NameError("ERROR: Initialization of mcstas_data done without limits for data set named " + self.name + "!")
+    # Methods xlabel, ylabel and title as they might not be found
+    def set_xlabel(self,string):
+        self.metadata.set_xlabel(string)
         
-        if "parameters" in kwargs:
-            self.limits = kwargs["parameters"]
-            
+    def set_ylabel(self,string):
+        self.metadata.set_ylabel(string)
         
-    # Methods xlabel, ylabel and title as they might not be found
-    def set_xlabel(self,*args):
-        self.xlabel = args[0]
-
-    def set_ylabel(self,*args):
-        self.ylabel = args[0]
+    def set_title(self,string):
+        self.metadata.set_title(string)
         
-    def set_title(self,*args):
-        self.title = args[0]
-
 
 class managed_mcrun:
     def __init__(self,*args,**kwargs):
@@ -117,112 +164,75 @@ def run_simulation(self):
         # Assume the script will continue when the os.system call has concluded. Is there a way to ensure this?
         # can use subprocess from spawn* if more controll is needed over the spawned process, including a timeout
         
-        time.sleep(2) # sleep 2 seconds to make sure data is written to disk before trying to open
+        time.sleep(1) # sleep 1 second to make sure data is written to disk before trying to open
          
         # find all data files in generated folder
         files_in_folder = os.listdir(self.data_folder_name)
         
+        # raise an error if mccode.sim is not available
+        if not "mccode.sim" in files_in_folder:
+            raise NameError("mccode.sim not written to output folder.")
+        
+        f = open(self.data_folder_name + "/mccode.sim","r")
+        #fl = f.readlines()
+        
+        metadata_list = []
+        in_data = False
+        
+        for lines in f:
+            # Could read other details about run
+            
+            if lines == "end data\n":
+                # current data object done, write to list
+                current_object.extract_info()
+                metadata_list.append(current_object)
+                in_data = False
+            
+            if in_data:
+                # break info into key and info
+                colon_index = lines.index(":")
+                key = lines[2:colon_index]
+                value = lines[colon_index+2:]
+                current_object.add_info(key, value)
+            
+            if lines == "begin data\n":
+                # new data object
+                current_object = mcstas_meta_data()
+                in_data = True
+                
+                
+        f.close()
+        
         # create a list for data instances to return
         results = []
         
-        # load the data into the list
-        for file in files_in_folder:
-            # Find data dimension, labels and axis
-            # Find lines with these variable names
-            
-            filename = self.data_folder_name + "/" + file
+        for metadata in metadata_list:
+            data = np.loadtxt(self.data_folder_name + "/" + metadata.filename.rstrip())
+    
+            # split data into intensity, error and ncount
+            if type(metadata.dimension) == int:
+                xaxis = data.T[0,:]
+                Intensity = data.T[1,:]
+                Error = data.T[2,:]
+                Ncount = data.T[3,:]
+                
+            elif len(metadata.dimension) == 2:
+                xaxis = [] # assume evenly binned in 2d
+                Intensity = data.T[:,0:metadata.dimension[1]-1]
+                Error = data.T[:,metadata.dimension[1]:2*metadata.dimension[1]-1]
+                Ncount = data.T[:,2*metadata.dimension[1]:3*metadata.dimension[1]-1]
+            else:
+                raise NameError("Dimension not read correctly in data set connected to monitor named " + metadata.component_name)    
             
-            variable_list = ["type", "title", "xlabel", "ylabel", "xlimits", "xylimits"]
-            located_variable_lines = {}
+            # The data is saved as a mcstas_data object
+            result = mcstas_data(metadata,Intensity,Error,Ncount,xaxis=xaxis)
             
-            f = open(filename,"r")
-            fl = f.readlines()
+            results.append(result)
             
-            # Need to check if this is a data file written by McStas
-            for line in fl:
-                for word in variable_list:
-                    if word in line:
-                        located_variable_lines[word]=line
-                        
             f.close()
-             
-            if not fl[0] == "# Format: McCode with text headers\n":
-                print("Decided not to read file named " + filename)
-            else:
-                print("Decided to read file named " + filename)
-                #print(located_variable_lines)
-                
-                # Need to remove the variable name and end of line break
-                for key in located_variable_lines:
-                    string = located_variable_lines[key]
-                    located_variable_lines[key] = string[len(key)+4:-1]
-                
-                limits=[]
-                dimension=[]
-                type_string = located_variable_lines["type"]
-                if "1d" in type_string:
-                    # extract number of pixels
-                    dimension = int(type_string[9:-1])
-                    print(dimension) 
-                    
-                    # extract the limits of each direction
-                    temp_str = located_variable_lines["xlimits"]
-                    limits_string = temp_str.split()
-                    for limit in limits_string:
-                        limits.append(float(limit))
-                    
-                else:
-                    # extract number of pixels in each direction
-                    type_strings = type_string.split(",")
-                    temp_str = type_strings[0]
-                    dimension.append(int(temp_str[9:]))
-                    temp_str = type_strings[1]
-                    dimension.append(int(temp_str[1:-1]))
-                    
-                    # extract the limits of each direction
-                    temp_str = located_variable_lines["xylimits"]
-                    limits_string = temp_str.split()
-                    for limit in limits_string:
-                        limits.append(float(limit))    
-                
-                # Loads bulk data from file
-                # Does not seem to get the meta data
-                data = np.loadtxt(filename)
-    
-                # split data into intensity, error and ncount
-                if type(dimension) == int:
-                    xaxis = data.T[0,:] # not used in data yet
-                    Intensity = data.T[1,:]
-                    Error = data.T[2,:]
-                    Ncount = data.T[3,:]
-                    
-                elif len(dimension) == 2:
-                    xaxis = [] # assume evenly binned in 2d
-                    Intensity = data.T[:,0:dimension[1]-1]
-                    Error = data.T[:,dimension[1]:2*dimension[1]-1]
-                    Ncount = data.T[:,2*dimension[1]:3*dimension[1]-1]
-                    
-                else:
-                    # probably just not a McStas file then
-                    raise NameError("ERROR: Could not load dimensionality of data in file named " + str(file) + "!")
-                    # should probably just skip this file 
-                
-                # The data is saved as a mcstas_data object
-                result = mcstas_data(file,Intensity,Error,Ncount,xaxis=xaxis,dimension=dimension,limits=limits)
-                
-                # Set optional fields
-                if "xlabel" in located_variable_lines:
-                    result.set_xlabel(located_variable_lines["xlabel"])
-                if "ylabel" in located_variable_lines:
-                    result.set_ylabel(located_variable_lines["ylabel"])
-                if "title" in located_variable_lines:
-                    result.set_title(located_variable_lines["title"])
-                    
-                results.append(result)
-        
+            
         return results
     
-    
 class make_plot:
     def __init__(self,*args,**kwargs):
         data_list = args[0]
@@ -237,23 +247,55 @@ def __init__(self,*args,**kwargs):
         #  compare several 1d
         #  compare 2D
         
-        self.log = False
-        if "log" in kwargs:
-            if not kwargs["log"] == 0:
-                self.log = True
+        if isinstance(data_list,mcstas_data):
+            # Only a single element, put it in a list for easier syntax later
+            data_list = [data_list]
             
-        self.orders_of_magnitude=300
+        number_of_plots = len(data_list)
+        
+        self.log = [False]*number_of_plots
+        if "log" in kwargs:
+            if isinstance(kwargs["log"],list):
+                if not len(kwargs["log"]) == number_of_plots:
+                    raise IndexError("Length of list given for log logic does not match number of data elements")
+                else:
+                    self.log = kwargs["log"]
+                    for element in self.log:
+                        if not isinstance(element, bool):
+                            if not element == 0:
+                                element = True 
+            elif isinstance(kwargs["log"],bool):
+                if kwargs["log"] == True:
+                    self.log = [True]*number_of_plots
+            elif isinstance(kwargs["log"],int):
+                if kwargs["log"] == 1:
+                    self.log = [True]*number_of_plots
+            else:
+                raise NameError("log keyword Argument in make_sub_plot not understood. Needs to be int, [1/0], bool [True/False] or array of same length as data.")
+                
+        
+        self.orders_of_mag=[300] * number_of_plots
         if "max_orders_of_mag" in kwargs:
-            self.orders_of_magnitude=kwargs["max_orders_of_mag"]
+            if isinstance(kwargs["max_orders_of_mag"],list):
+                if not len(kwargs["max_orders_of_mag"]) == number_of_plots:
+                    raise IndexError("Length of list given for max_orders_of_mag does not match number of data elements")
+                else: 
+                    self.orders_of_mag = kwargs["max_orders_of_mag"]
+            else:
+                if isinstance(kwargs["max_orders_of_mag"],float) or isinstance(kwargs["max_orders_of_mag"],int):
+                    self.orders_of_magnitude=[kwargs["max_orders_of_mag"]]*number_of_plots
+                else:
+                    raise TypeError("max_orders_of_mag need to be of type float or int")
         
-        if not isinstance(data_list,mcstas_data):
-            print("number of elements in data list = " + str(len(data_list)))
-        else:
-            # Only a single element, put it in a list for easier syntax later
-            data_list = [data_list]
+            
+        print("number of elements in data list = " + str(len(data_list)))
+        
+        index = -1
         for data in data_list:
-            print("Plotting data with name " + data.name)
-            if type(data.dimension) == int:
+            index = index + 1
+            
+            print("Plotting data with name " + data.metadata.component_name)
+            if type(data.metadata.dimension) == int:
                 fig = plt.figure(0)
                 
                 #print(data.T)
@@ -263,16 +305,16 @@ def __init__(self,*args,**kwargs):
                 
                 plt.errorbar(x, y, yerr=y_err)
                 
-                plt.xlim(data.limits[0],data.limits[1])
+                plt.xlim(data.metadata.limits[0],data.metadata.limits[1])
                 
                 # Add a title
-                plt.title(data.title)
+                plt.title(data.metadata.title)
                 
                 # Add axis labels
-                plt.xlabel(data.xlabel)
-                plt.ylabel(data.ylabel)
+                plt.xlabel(data.metadata.xlabel)
+                plt.ylabel(data.metadata.ylabel)
                 
-            elif  len(data.dimension) == 2:
+            elif  len(data.metadata.dimension) == 2:
                 
                 # Split the data into intensity, error and ncount
                 Intensity = data.Intensity
@@ -282,7 +324,7 @@ def __init__(self,*args,**kwargs):
                 # Select to plot the intensity
                 #to_plot = np.log(Intensity)
                 
-                if self.log:
+                if self.log[index]:
                     min_value = np.min(Intensity[np.nonzero(Intensity)])
                     min_value = np.log10(min_value)
                     
@@ -290,8 +332,8 @@ def __init__(self,*args,**kwargs):
                     
                     max_value = to_plot.max()
                     
-                    if max_value - min_value > self.orders_of_magnitude:
-                        min_value = max_value - self.orders_of_magnitude
+                    if max_value - min_value > self.orders_of_mag[index]:
+                        min_value = max_value - self.orders_of_mag[index]
                 else:
                     to_plot = Intensity
                     min_value = to_plot.min()
@@ -301,8 +343,8 @@ def __init__(self,*args,**kwargs):
                 #print(to_plot.shape)
                 
                 # Set the axis (might be switched?)
-                X=np.linspace(data.limits[0],data.limits[1],data.dimension[0]+1)
-                Y=np.linspace(data.limits[2],data.limits[3],data.dimension[1])
+                X=np.linspace(data.metadata.limits[0],data.metadata.limits[1],data.metadata.dimension[0]+1)
+                Y=np.linspace(data.metadata.limits[2],data.metadata.limits[3],data.metadata.dimension[1])
                 
                 # Create a meshgrid for both x and y
                 y, x = np.meshgrid(Y,X)
@@ -326,11 +368,11 @@ def __init__(self,*args,**kwargs):
                 fig.colorbar(im, ax=ax0)
                 
                 # Add a title
-                ax0.set_title(data.title)
+                ax0.set_title(data.metadata.title)
                 
                 # Add axis labels
-                plt.xlabel(data.xlabel)
-                plt.ylabel(data.ylabel)
+                plt.xlabel(data.metadata.xlabel)
+                plt.ylabel(data.metadata.ylabel)
                 
             else:
                 print("Error, dimension not read correctly")
@@ -409,9 +451,9 @@ def __init__(self,*args,**kwargs):
             index = index + 1
             ax0 = ax[index]
               
-            print("Plotting data with name " + data.name)
+            print("Plotting data with name " + data.metadata.component_name)
             
-            if type(data.dimension) == int:
+            if type(data.metadata.dimension) == int:
                 #fig = plt.figure(0)
                 #plt.subplot(dim1, dim2, n_plot)
                 
@@ -426,16 +468,16 @@ def __init__(self,*args,**kwargs):
                 if self.log[index]:
                     ax0.set_yscale("log",nonposy='clip')
                 
-                ax0.set_xlim(data.limits[0],data.limits[1])
+                ax0.set_xlim(data.metadata.limits[0],data.metadata.limits[1])
                 
                 # Add a title
                 #ax0.title(data.title)
                 
                 # Add axis labels
-                ax0.set_xlabel(data.xlabel)
-                ax0.set_ylabel(data.ylabel)
+                ax0.set_xlabel(data.metadata.xlabel)
+                ax0.set_ylabel(data.metadata.ylabel)
                 
-            elif  len(data.dimension) == 2:
+            elif  len(data.metadata.dimension) == 2:
                 
                 # Split the data into intensity, error and ncount
                 Intensity = data.Intensity
@@ -467,8 +509,8 @@ def __init__(self,*args,**kwargs):
                 #print(to_plot.shape)
                 
                 # Set the axis (might be switched?)
-                X=np.linspace(data.limits[0],data.limits[1],data.dimension[0]+1)
-                Y=np.linspace(data.limits[2],data.limits[3],data.dimension[1])
+                X=np.linspace(data.metadata.limits[0],data.metadata.limits[1],data.metadata.dimension[0]+1)
+                Y=np.linspace(data.metadata.limits[2],data.metadata.limits[3],data.metadata.dimension[1])
                 
                 # Create a meshgrid for both x and y
                 y, x = np.meshgrid(Y,X)
@@ -504,11 +546,11 @@ def fmt(x, pos):
                 fig.colorbar(im, ax=ax0, format=matplotlib.ticker.FuncFormatter(fmt))
                 
                 # Add a title
-                ax0.set_title(data.title)
+                ax0.set_title(data.metadata.title)
                 
                 # Add axis labels
-                ax0.set_xlabel(data.xlabel)
-                ax0.set_ylabel(data.ylabel)
+                ax0.set_xlabel(data.metadata.xlabel)
+                ax0.set_ylabel(data.metadata.ylabel)
                 
             else:
                 print("Error, dimension not read correctly")
@@ -622,6 +664,26 @@ def __init__(self,*args,**kwargs):
             self.AT_relative = "RELATIVE " + kwargs["RELATIVE"]
             self.ROTATED_relative = "RELATIVE " + kwargs["RELATIVE"]
 
+        if "WHEN" in kwargs:
+            self.WHEN = "WHEN (" + kwargs["WHEN"] + ")\n"
+        else:
+            self.WHEN = ""
+            
+        if "EXTEND" in kwargs:
+            self.EXTEND = kwargs["EXTEND"]
+        else:
+            self.EXTEND = ""
+            
+        if "GROUP" in kwargs:
+            self.GROUP = kwargs["GRPUP"]
+        else:
+            self.GROUP = ""
+
+        if "JUMP" in kwargs:
+            self.JUMP = kwargs["JUMP"]
+        else:
+            self.JUMP = ""
+
         # possible to have a c comment
         if "comment" in kwargs:
             self.comment = kwargs["comment"]
@@ -666,6 +728,18 @@ def set_RELATIVE(self,relative_name):
     # method that adds a parameter name / value pair to dictionary
     def set_parameters(self,dict_input):
         self.component_parameters.update(dict_input)
+        
+    def set_WHEN(self,string):
+        self.WHEN = string
+        
+    def set_GROUP(self,string):
+        self.GROUP = string
+    
+    def set_JUMP(self,string):
+        self.JUMP = string
+        
+    def append_EXTEND(self,string):
+        self.EXTEND = self.EXTEND + string + "\n"
     
     # method that sets a comment to be written to instrument file
     def set_comment(self,string):
@@ -699,18 +773,36 @@ def write_component(self,fo):
             if parameters_written < number_of_parameters:
                 fo.write(",") # comma between parameters
                 if parameters_written%parameters_per_line == 0:
-                    fo.write("\n")
+                    fo  .write("\n")
             else:
                 fo.write(")\n") # end paranthesis after last parameter
-    
-        # Need to add WHEN section here
+                
+        # Optional WHEN section
+        if not self.WHEN == "":
+            fo.write("WHEN(%s)\n" % self.WHEN)
         # Need to add JUMP section here
+        
         # write AT and ROTATED section
         fo.write("AT (%s,%s,%s)" % (str(self.AT_data[0]),str(self.AT_data[1]),str(self.AT_data[2])))
         fo.write(" %s\n" % self.AT_relative)
         fo.write("ROTATED (%s,%s,%s)" % (str(self.ROTATED_data[0]),str(self.ROTATED_data[1]),str(self.ROTATED_data[2])))
-        fo.write(" %s\n\n" % self.ROTATED_relative)
-        # Need to add EXTEND section here
+        fo.write(" %s\n" % self.ROTATED_relative)
+        
+        if not self.GROUP == "":
+            fo.write("GROUP %s\n" % self.GROUP)
+        
+        # Optional EXTEND section
+        if not self.EXTEND == "":
+            fo.write("EXTEND %{\n")
+            fo.write("%s" % self.EXTEND)
+            fo.write("%}\n")
+            
+        if not self.JUMP == "":
+            fo.write("JUMP %s\n" % self.JUMP)
+            
+        # Leave a new line between components for readability
+        fo.write("\n")
+            
 
     # print component long
     def print_long(self):
@@ -754,6 +846,7 @@ def __init__(self,name,**kwargs):
         self.declare_list = []
         self.initialize_section = "// Start of initialize for generated " + name + "\n"
         self.trace_section = "// Start of trace section for generated " + name + "\n"
+        self.finally_section = "// Start of finally for generated " + name + "\n"
         # handle components
         self.component_list = [] # list of components (have to be ordered)
         self.component_name_list = [] # list of component names
@@ -771,6 +864,12 @@ def append_initialize(self,string):
     
     def append_initialize_no_new_line(self,string):
         self.initialize_section = self.initialize_section + string
+        
+    def append_finally(self,string):
+        self.finally_section = self.finally_section + string + "\n"
+    
+    def append_finally_no_new_line(self,string):
+        self.finally_section = self.finally_section + string
     
     # Need to handle trace string differently when components also exists
     #  A) Could have trace string as a component attribute and set it before / after
@@ -831,6 +930,22 @@ def set_component_ROTATED(self,name,rotated_list,**kwargs):
     def set_component_RELATIVE(self,name,relative):
         component = self.get_component(name)
         component.set_RELATIVE(relative)
+        
+    def set_component_WHEN(self,name,WHEN):
+        component = self.get_component(name)
+        component.set_WHEN(WHEN)
+        
+    def append_component_EXTEND(self,name,EXTEND):
+        component = self.get_component(name)
+        component.append_EXTEND(EXTEND)
+        
+    def set_component_GROUP(self,name,GROUP):
+        component = self.get_component(name)
+        component.set_GROUP(GROUP)
+        
+    def set_component_JUMP(self,name,JUMP):
+        component = self.get_component(name)
+        component.set_JUMP(JUMP)
     
     def set_component_comment(self,name,string):
         component = self.get_component(name)
@@ -970,14 +1085,14 @@ def write_full_instrument(self):
         fo.write("\n")
 
         # Write declare
-        fo.write("DECLARE \n %{\n")
+        fo.write("DECLARE \n%{\n")
         for dec_line in self.declare_list:
             dec_line.write_line(fo)
             fo.write("\n")
         fo.write("%}\n\n")
 
         # Write initialize
-        fo.write("INITIALIZE \n %{\n")
+        fo.write("INITIALIZE \n%{\n")
         fo.write(self.initialize_section)
         # Alternatively hide everything in include
         # fo.write("%include "generated_includes/" + self.name + "_initialize.c")
@@ -988,7 +1103,11 @@ def write_full_instrument(self):
         for component in self.component_list:
             component.write_component(fo)
 
-        # Write finally (no finally possible yet)
+        # Write finally
+        fo.write("FINALLY \n%{\n")
+        fo.write(self.finally_section)
+        # Alternatively hide everything in include
+        fo.write("%}\n")
 
         # End instrument file
         fo.write("\nEND\n")

From 7c71b1b70a3d9354a84375644e0ab514c494317b Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Tue, 12 Mar 2019 16:35:39 +0100
Subject: [PATCH 007/403] Moved plotting options from input to plot command to
 attributes in the data.

mcstas_data now contains
  mcstas_meta_data class
  plot_options class
   and the raw data as numpy arrays
  Intensity
  Error
  Ncount

Two functions were added to aid in finding the correct data from the array returned by a simulation:

name_search that finds the data set for a given monitor
name_plot_options that allows direct input of plot options given the monitor name

The documentation was updated to reflect the recent changes.
---
 McStasScript.py                | 120 ++++++++++++++++++++-------------
 McStasScript_documentation.pdf | Bin 135598 -> 147913 bytes
 2 files changed, 74 insertions(+), 46 deletions(-)

diff --git a/McStasScript.py b/McStasScript.py
index 049babc8..8a38e171 100644
--- a/McStasScript.py
+++ b/McStasScript.py
@@ -12,6 +12,7 @@
 from matplotlib.ticker import MaxNLocator
 from openpyxl.worksheet import dimensions
 from boto.ec2.autoscale import limits
+#from builtins import False, True
 
 try: # check whether python knows about 'basestring'
    basestring
@@ -82,7 +83,31 @@ def set_xlabel(self,string):
     def set_ylabel(self,string):
         self.ylabel = string
         
-    
+class mcstas_plot_options:
+    def __init__(self,*args,**kwargs):
+        # settings for plotting this data set
+        self.log = False
+        self.orders_of_mag = 300
+        self.colormap = "jet"
+        
+    def set_options(self,**kwargs):
+        if "log" in kwargs:
+            log_input = kwargs["log"]
+            if type(log_input) == int:
+                if log_input == 0:
+                    self.log = False
+                else:
+                    self.log = True
+            elif type(log_input) == bool:
+                self.log = log_input
+            else:
+                raise NameError("Log input must be either Int or Bool.")
+            
+        if "orders_of_mag" in kwargs:
+            self.orders_of_mag = kwargs["orders_of_mag"]
+            
+        if "colormap" in kwargs:
+            self.colormap = kwargs["colormap"]
         
 class mcstas_data:
     def __init__(self,*args,**kwargs):
@@ -100,8 +125,10 @@ def __init__(self,*args,**kwargs):
                 self.xaxis = kwargs["xaxis"]
             else:
                 raise NameError("ERROR: Initialization of mcstas_data done with 1d data, but without xaxis" + self.name + "!")
+            
+        self.plot_options = mcstas_plot_options()
         
-    # Methods xlabel, ylabel and title as they might not be found
+    # Methods xlabel, ylabel and title as they might not be found    
     def set_xlabel(self,string):
         self.metadata.set_xlabel(string)
         
@@ -111,6 +138,32 @@ def set_ylabel(self,string):
     def set_title(self,string):
         self.metadata.set_title(string)
         
+    def set_plot_options(self,**kwargs):
+        self.plot_options.set_options(**kwargs)
+        
+        
+def name_search(name,data_list):
+    if not type(data_list[0]) == mcstas_data:
+        raise InputError("name_search function needs objects of type mcstas_data as input.")
+    
+    list_result = [x for x in data_list if x.metadata.component_name == name]
+    
+    if len(list_result) == 1: 
+        return list_result[0]
+    else:
+        raise NameError("More than one match for the name search")
+    
+def name_plot_options(name,data_list,*args,**kwargs):
+    if not type(data_list[0]) == mcstas_data:
+        raise InputError("name_search function needs objects of type mcstas_data as input.")
+    
+    list_result = [x for x in data_list if x.metadata.component_name == name]
+    
+    if len(list_result) == 1:
+        list_result[0].set_plot_options(**kwargs)
+    else:
+        raise NameError("More than one match for the name search")
+
 
 class managed_mcrun:
     def __init__(self,*args,**kwargs):
@@ -305,6 +358,9 @@ def __init__(self,*args,**kwargs):
                 
                 plt.errorbar(x, y, yerr=y_err)
                 
+                if data.plot_options.log:
+                    ax0.set_yscale("log",nonposy='clip')
+                
                 plt.xlim(data.metadata.limits[0],data.metadata.limits[1])
                 
                 # Add a title
@@ -324,7 +380,7 @@ def __init__(self,*args,**kwargs):
                 # Select to plot the intensity
                 #to_plot = np.log(Intensity)
                 
-                if self.log[index]:
+                if data.plot_options.log:
                     min_value = np.min(Intensity[np.nonzero(Intensity)])
                     min_value = np.log10(min_value)
                     
@@ -332,8 +388,8 @@ def __init__(self,*args,**kwargs):
                     
                     max_value = to_plot.max()
                     
-                    if max_value - min_value > self.orders_of_mag[index]:
-                        min_value = max_value - self.orders_of_mag[index]
+                    if max_value - min_value > data.plot_options.orders_of_mag:
+                        min_value = max_value - data.plot_options.orders_of_mag
                 else:
                     to_plot = Intensity
                     min_value = to_plot.min()
@@ -362,7 +418,11 @@ def __init__(self,*args,**kwargs):
                 fig, (ax0) = plt.subplots()
                 
                 # Plot the data on the meshgrids
-                im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
+                #im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
+                if data.plot_options.log: 
+                    im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=matplotlib.colors.LogNorm(vmin=min_value,vmax=max_value))
+                else:
+                    im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
                 
                 # Add the colorbar
                 fig.colorbar(im, ax=ax0)
@@ -403,40 +463,6 @@ def __init__(self,*args,**kwargs):
         
         #fig = plt.figure(figsize=(20,10))
 
-        # instead of passing this information here, it should just be a property of the data
-        self.log = [False]*number_of_plots
-        if "log" in kwargs:
-            if isinstance(kwargs["log"],list):
-                if not len(kwargs["log"]) == number_of_plots:
-                    raise IndexError("Length of list given for log logic does not match number of data elements")
-                else:
-                    self.log = kwargs["log"]
-                    for element in self.log:
-                        if not isinstance(element, bool):
-                            if not element == 0:
-                                element = True 
-            elif isinstance(kwargs["log"],bool):
-                if kwargs["log"] == True:
-                    self.log = [True]*number_of_plots
-            elif isinstance(kwargs["log"],int):
-                if kwargs["log"] == 1:
-                    self.log = [True]*number_of_plots
-            else:
-                raise NameError("log keyword Argument in make_sub_plot not understood. Needs to be int, [1/0], bool [True/False] or array of same length as data.")
-                
-        
-        self.orders_of_mag=[300] * number_of_plots
-        if "max_orders_of_mag" in kwargs:
-            if isinstance(kwargs["max_orders_of_mag"],list):
-                if not len(kwargs["max_orders_of_mag"]) == number_of_plots:
-                    raise IndexError("Length of list given for max_orders_of_mag does not match number of data elements")
-                else: 
-                    self.orders_of_mag = kwargs["max_orders_of_mag"]
-            else:
-                if isinstance(kwargs["max_orders_of_mag"],float) or isinstance(kwargs["max_orders_of_mag"],int):
-                    self.orders_of_magnitude=[kwargs["max_orders_of_mag"]]*number_of_plots
-                else:
-                    raise TypeError("max_orders_of_mag need to be of type float or int")
         
         # Find reasonable grid size for the number of plots
         dim2 = math.ceil(math.sqrt(number_of_plots))
@@ -465,7 +491,7 @@ def __init__(self,*args,**kwargs):
                 
                 ax0.errorbar(x, y, yerr=y_err)
                 
-                if self.log[index]:
+                if data.plot_options.log:
                     ax0.set_yscale("log",nonposy='clip')
                 
                 ax0.set_xlim(data.metadata.limits[0],data.metadata.limits[1])
@@ -487,7 +513,7 @@ def __init__(self,*args,**kwargs):
                 # Select to plot the intensity
                 #to_plot = np.log(Intensity)
                 
-                if self.log[index]:
+                if data.plot_options.log:
                     min_value = np.min(Intensity[np.nonzero(Intensity)])
                     min_value = np.log10(min_value)
                     
@@ -496,8 +522,8 @@ def __init__(self,*args,**kwargs):
                     
                     max_value = np.log10(to_plot.max())
                     
-                    if max_value - min_value > self.orders_of_mag[index]:
-                        min_value = max_value - self.orders_of_mag[index]
+                    if max_value - min_value > data.plot_options.orders_of_mag:
+                        min_value = max_value - data.plot_options.orders_of_mag
                     min_value = 10.0 ** min_value
                     max_value = 10.0 ** max_value
                 else:
@@ -521,7 +547,9 @@ def __init__(self,*args,**kwargs):
                 #levels = MaxNLocator(nbins=150).tick_values(to_plot.max()-12, to_plot.max())
                 
                 # Select colormap
-                cmap = plt.get_cmap('jet')
+                #cmap = plt.get_cmap('jet')
+                cmap = plt.get_cmap(data.plot_options.colormap)                
+                
                 norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
                 
                 # Create the figure
@@ -531,7 +559,7 @@ def __init__(self,*args,**kwargs):
                 
                 # Plot the data on the meshgrids
                 #im = plt.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
-                if self.log[index]: 
+                if data.plot_options.log: 
                     im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=matplotlib.colors.LogNorm(vmin=min_value,vmax=max_value))
                 else:
                     im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
index 9dd1efd6885be47e514ae84d17e47c0112ca2fb4..e0cb18f76e72c0be75291e6b26b46c4b7f4265c5 100644
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From d18689f94eee0338da8b2a6e6596ae4d21053257 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Wed, 13 Mar 2019 15:40:09 +0100
Subject: [PATCH 008/403] Added in code documentation including doc strings.
 Some simplifications made along the way, they do not influence the use of the
 code.

---
 McStasScript.py | 897 +++++++++++++++++++++++++++++++++++++++++++-----
 1 file changed, 815 insertions(+), 82 deletions(-)

diff --git a/McStasScript.py b/McStasScript.py
index 8a38e171..aa764256 100644
--- a/McStasScript.py
+++ b/McStasScript.py
@@ -20,17 +20,65 @@
    basestring=str
 
 class mcstas_meta_data:
-    def __init__(self,*args,**kwargs):
+    """
+    Class for holding metadata for McStas dataset, is to be read from mccode.sim file.
+    
+    Attributes
+    ----------
+    info : dict
+        Contains read strings from mccode.sim in key, value
+    
+    dimension : Int or List of Int
+        Int for 1d data set with lenght of data, Array for 2d with each length
+        
+    component_name : str
+        Name of component in McStas file
+        
+    filename : str
+        Name of data file to read
+        
+    limits : List
+        Limits for monitor, length=2 for 1d data and length=4 for 2d data
+        
+    title : str
+        Title of monitor when plotting
+        
+    xlabel : str
+        Text for xlabel when plotting
+        
+    ylabel : str
+        Text for ylabel when plotting
+        
+    Methods
+    -------
+    add_info(key,value)
+        Adds a element to the info dictionary
+        
+    extract_info()
+        Unpacks the information in info to class attributes
+    
+    set_title(string)
+        Overwrites current title
+        
+    set_xlabel(string)
+        Overwrites current xlabel
+        
+    set_ylabel(string)
+        Overwrites current ylabel
+    """
+    def __init__(self):
+        """Creating a new instance, no parameters"""
         self.info = {}
     
     def add_info(self,key,value):
+        """Adding information to info dict"""
         self.info[key] = value
         
     def extract_info(self):
+        """Extracting information from info dict to class attributes"""
         
-        
+        # Extract dimension
         if "type" in self.info:
-            # extract dimension
             type = self.info["type"]
             if "array_1d" in type:
                 self.dimension = int(type[9:-2])
@@ -43,15 +91,18 @@ def extract_info(self):
                 self.dimension.append(int(temp_str[1:-2]))
         else:
             raise NameError("No type in mccode data section!")
-                
+        
+        # Extract component name        
         if "component" in self.info:
             self.component_name = self.info["component"].rstrip()
 
+        # Extract filename
         if "filename" in self.info:
             self.filename = self.info["filename"].rstrip()
         else:
             raise NameError("No filename found in mccode data section!")
         
+        # Extract limits
         self.limits = []
         if "xylimits" in self.info:
             # find the four numbers 
@@ -61,12 +112,13 @@ def extract_info(self):
                 self.limits.append(float(limit))
 
         if "xlimits" in self.info:
-            # find the four numbers 
+            # find the two numbers 
             temp_str = self.info["xlimits"]
             limits_string = temp_str.split()
             for limit in limits_string:
                 self.limits.append(float(limit))
 
+        # Extract plotting labels and title
         if "xlabel" in self.info:
             self.xlabel = self.info["xlabel"].rstrip()
         if "ylabel" in self.info:
@@ -75,22 +127,46 @@ def extract_info(self):
             self.title = self.info["title"].rstrip()
     
     def set_title(self,string):
+        """Sets title for plotting"""
         self.title = string
         
     def set_xlabel(self,string):
+        """Sets xlabel for plotting"""
         self.xlabel = string
         
     def set_ylabel(self,string):
+        """Sets ylabel for plotting"""
         self.ylabel = string
         
 class mcstas_plot_options:
+    """
+    Class that holds plotting options related to McStas data set
+    
+    Attributes
+    ----------
+    log : bool
+        To plot on logarithmic or not, standard is linear
+        
+    orders_of_mag : float
+        If plotting on log scale, restrict max range to orders_of_mag below max
+        
+    colormap : string
+        Chosen colormap for 2d data, should be available in matplotlib
+    
+    Methods
+    -------
+    set_options(keyword arguments)
+        Can set the class attributes using keyword options
+    
+    """
     def __init__(self,*args,**kwargs):
-        # settings for plotting this data set
+        """Setting default values for plotting preferences"""
         self.log = False
         self.orders_of_mag = 300
         self.colormap = "jet"
         
     def set_options(self,**kwargs):
+        """Set custom values for plotting preferences"""
         if "log" in kwargs:
             log_input = kwargs["log"]
             if type(log_input) == int:
@@ -110,15 +186,77 @@ def set_options(self,**kwargs):
             self.colormap = kwargs["colormap"]
         
 class mcstas_data:
-    def __init__(self,*args,**kwargs):
+    """
+    Class for holding full McStas dataset with data, metadata and plotting preferences
+    
+    Attributes
+    ----------
+    metadata : mcstas_meta_data instance
+        Holds the metadata for the dataset
+        
+    name : str
+        Name of component, extracted from metadata
+        
+    Intensity : numpy array
+        Intensity data [n/s] in 1d or 2d numpy array, dimension in metadata
+        
+    Error : numpy array
+        Error data [n/s] in 1d or 2d numpy array, same dimensions as Intensity
+        
+    Ncount : numpy array
+        Number of rays in bin, 1d or 2d numpy array, same dimensions as Intensity
+        
+    plot_options : mcstas_plot_options instance
+        Holds the plotting preferences for the dataset
+        
+    Methods
+    -------
+    set_xlabel : string
+        sets xlabel of data for plotting
+        
+    set_ylabel : string
+        sets ylabel of data for plotting
+        
+    set_title : string
+        sets title of data for plotting
+        
+    set_optons : keyword arguments
+        sets plot options, keywords passed to mcstas_plot_options method
+    
+    """
+    def __init__(self,metadata,intensity,error,ncount,**kwargs):
+        """
+        Initialize a new McStas dataset, 4 positional arguments, pass xaxis as kwarg if 1d data
+        
+        Parameters
+        ----------
+        metadata : mcstas_meta_data instance
+            Holds the metadata for the dataset
+        
+        name : str
+            Name of component, extracted from metadata
+        
+        intensity : numpy array
+            Intensity data [n/s] in 1d or 2d numpy array, dimension in metadata
+        
+        error : numpy array
+            Error data [n/s] in 1d or 2d numpy array, same dimensions as Intensity
+        
+        ncount : numpy array
+            Number of rays in bin, 1d or 2d numpy array, same dimensions as Intensity
+            
+        kwargs : keyword arguments 
+            xaxis is required for 1d data
+            
+        """
         # attatch meta data
-        self.metadata = args[0]
+        self.metadata = metadata
         # get name from metadata
         self.name = self.metadata.component_name
         # three basic arrays as first 
-        self.Intensity = args[1]
-        self.Error = args[2]
-        self.Ncount = args[3]
+        self.Intensity = intensity
+        self.Error = error
+        self.Ncount = ncount
         
         if type(self.metadata.dimension) == int:
             if "xaxis" in kwargs:
@@ -143,6 +281,21 @@ def set_plot_options(self,**kwargs):
         
         
 def name_search(name,data_list):
+    """"
+    name_search returns mcstas_data instance with specific name if it is in the given data_list
+    
+    The index of certain datasets in the data_list can change if additional monitors are added
+    so it is more convinient to access the data files using their names.
+    
+    Parameters
+    ----------
+    name : string
+        Name of the dataset to be retrived (component_name)
+        
+    data_list : List of mcstas_data instances
+        List of datasets to search
+    """
+    
     if not type(data_list[0]) == mcstas_data:
         raise InputError("name_search function needs objects of type mcstas_data as input.")
     
@@ -153,7 +306,24 @@ def name_search(name,data_list):
     else:
         raise NameError("More than one match for the name search")
     
-def name_plot_options(name,data_list,*args,**kwargs):
+def name_plot_options(name,data_list,**kwargs):
+    """"
+    name_plot_options passes keyword arguments to dataset with certain name in given data list
+    
+    Function for quickly setting plotting options on a certain dataset in a larger 
+    list of datasets
+    
+    Parameters
+    ----------
+    name : string
+        Name of the dataset to be retrived (component_name)
+        
+    data_list : List of mcstas_data instances
+        List of datasets to search
+        
+    kwargs : keyword arguments
+        Keyword arguments passed to set_plot_options in mcstas_plot_options
+    """
     if not type(data_list[0]) == mcstas_data:
         raise InputError("name_search function needs objects of type mcstas_data as input.")
     
@@ -164,46 +334,106 @@ def name_plot_options(name,data_list,*args,**kwargs):
     else:
         raise NameError("More than one match for the name search")
 
-
 class managed_mcrun:
-    def __init__(self,*args,**kwargs):
-        self.name_of_instrumentfile = args[0]
+    """
+    A class for performing a mcstas simulation and organizing the data into python objects
+    
+    managed_mcrun is usually called by the instrument class of McStasScript but can be used
+    independently. It runs the mcrun command using the system command, and if this is not
+    in the path, the absolute path can be given in a keyword argument mcrun_path.
+    
+    
+    
+    Attributes
+    ----------
+    name_of_instrumentfile : str
+        Name of instrument file to be executed
+        
+    data_folder_name : str
+        Name of datafolder mcrun writes to disk
+        
+    ncount : int
+        Number of rays to simulate 
+        
+    mpi : int
+        Number of mpi threads to run
+        
+    parameters : dict
+        Dictionary of parameter names and values for this simulation
+        
+    custom_flags : string
+        Custom flags that are passed to the mcrun command
+        
+    mcrun_path : string
+        Path to the mcrun command (can be empty if already in path)
+    
+    
+    Methods
+    -------
+    run_simulation()
+        Runs simulation, returns list of mcstas_data instances
+    
+    """
+    def __init__(self,instr_name,**kwargs):
+        """
+        Parameters
+        ----------
+        instr_name : str
+            Name of instrument file to be simulated
+        
+        kwargs : keyword arguments
+            foldername : str
+                Sets data_folder_name
+            ncount : int
+                Sets ncount
+            mpi : int
+                Sets thread count
+            parameters : dict
+                Sets parameters
+            custom_flags : str
+                Sets custom_flags passed to mcrun
+        """
+        self.name_of_instrumentfile = instr_name
         
         self.data_folder_name = ""
-        self.ncount = 1E6 # number of rays to 
+        self.ncount = 1E6
+        self.mpi=1 
         self.parameters = {}
-        self.mpi=1
         self.custom_flags = ""
+        self.mcrun_path = ""
         self.mcrun_path = kwargs["mcrun_path"] # mcrun_path always in kwargs
         
-        
         if "foldername" in kwargs:
             self.data_folder_name = kwargs["foldername"]
+        else:
+            raise NameError("managed_mcrun needs foldername to load data, add with keyword argument.")
         
         if "ncount" in kwargs:
             self.ncount = kwargs["ncount"]
             
-        if "parameters" in kwargs:
-            self.parameters = kwargs["parameters"]
-            
         if "mpi" in kwargs:
             self.mpi = kwargs["mpi"]
             
+        if "parameters" in kwargs:
+            self.parameters = kwargs["parameters"]
+            
         if "custom_flags" in kwargs:
             self.custom_flags = kwargs["custom_flags"]
             
     def run_simulation(self):
+        """Runs McStas simulation described by initializing the objeect"""
+        
+        # construct command to run
         option_string = "-c -n " + str(self.ncount) + " --mpi=" + str(self.mpi) + " "
         if len(self.data_folder_name) > 0:
             option_string = option_string + "-d " + self.data_folder_name
         
+        # add parameters to command
         parameter_string = ""
         for key,val in self.parameters.items():
             parameter_string = parameter_string + " " + str(key) + "=" + str(val)
         
-        #os.system("mcstas-2.5-environment")
-        
-        #mcrun_path = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/mcrun"
+        # check if a mcrun_path is set and has the correct backslash
         if len(self.mcrun_path) > 1:
             if self.mcrun_path[-1] == "\\" or self.mcrun_path[-1] == "/": 
                 mcrun_full_path = self.mcrun_path + "mcrun"
@@ -212,12 +442,12 @@ def run_simulation(self):
         else:
             mcrun_full_path = self.mcrun_path + "mcrun"
         
+        # Run the mcrun command on the system
         os.system(mcrun_full_path + " " + option_string + " " + self.custom_flags + " " + self.name_of_instrumentfile + " " + parameter_string)
         
-        # Assume the script will continue when the os.system call has concluded. Is there a way to ensure this?
         # can use subprocess from spawn* if more controll is needed over the spawned process, including a timeout
         
-        time.sleep(1) # sleep 1 second to make sure data is written to disk before trying to open
+        #time.sleep(1)
          
         # find all data files in generated folder
         files_in_folder = os.listdir(self.data_folder_name)
@@ -226,40 +456,46 @@ def run_simulation(self):
         if not "mccode.sim" in files_in_folder:
             raise NameError("mccode.sim not written to output folder.")
         
+        # open mccode to read metadata for all datasets written to disk
         f = open(self.data_folder_name + "/mccode.sim","r")
-        #fl = f.readlines()
         
+        # loop that reads mccode.sim sections
         metadata_list = []
         in_data = False
-        
         for lines in f:
             # Could read other details about run
             
             if lines == "end data\n":
-                # current data object done, write to list
+                # no more data for this metadata object
+                # extract the information
                 current_object.extract_info()
+                # add to metadata list
                 metadata_list.append(current_object)
+                # stop reading data
                 in_data = False
             
             if in_data:
-                # break info into key and info
+                # This line contains info to be added to metadata
                 colon_index = lines.index(":")
                 key = lines[2:colon_index]
                 value = lines[colon_index+2:]
                 current_object.add_info(key, value)
             
             if lines == "begin data\n":
-                # new data object
+                # found data section, create new metadata object
                 current_object = mcstas_meta_data()
+                # start recording data to metadata object
                 in_data = True
                 
-                
+        # close mccode.sim
         f.close()
         
-        # create a list for data instances to return
+        # create a list for mcstas_data instances to return
         results = []
         
+        # load datasets described in metadata list individually
         for metadata in metadata_list:
+            # load data with numpy
             data = np.loadtxt(self.data_folder_name + "/" + metadata.filename.rstrip())
     
             # split data into intensity, error and ncount
@@ -280,15 +516,34 @@ def run_simulation(self):
             # The data is saved as a mcstas_data object
             result = mcstas_data(metadata,Intensity,Error,Ncount,xaxis=xaxis)
             
+            # Add this result to the results list
             results.append(result)
             
+            # close the current datafile
             f.close()
             
+        # Return list of mcstas_data objects
         return results
     
 class make_plot:
-    def __init__(self,*args,**kwargs):
-        data_list = args[0]
+    """
+    make_plot plots contents of mcstas_data objects
+    
+    Plotting is controlled through options assosciated with the mcstas_data objects.
+    If a list is given, the plots appear individually.
+    """
+    def __init__(self,data_list):
+        """
+        plots mcstas_data, single object or list of mcstas_data
+        
+        The options concerning plotting are stored with the data
+        
+        Parameters
+        ----------
+        data_list : mcstas_data or list of mcstas_data
+            mcstas_data to be plotted
+        
+        """
         
         # Relevant options: 
         #  select colormap
@@ -305,41 +560,6 @@ def __init__(self,*args,**kwargs):
             data_list = [data_list]
             
         number_of_plots = len(data_list)
-        
-        self.log = [False]*number_of_plots
-        if "log" in kwargs:
-            if isinstance(kwargs["log"],list):
-                if not len(kwargs["log"]) == number_of_plots:
-                    raise IndexError("Length of list given for log logic does not match number of data elements")
-                else:
-                    self.log = kwargs["log"]
-                    for element in self.log:
-                        if not isinstance(element, bool):
-                            if not element == 0:
-                                element = True 
-            elif isinstance(kwargs["log"],bool):
-                if kwargs["log"] == True:
-                    self.log = [True]*number_of_plots
-            elif isinstance(kwargs["log"],int):
-                if kwargs["log"] == 1:
-                    self.log = [True]*number_of_plots
-            else:
-                raise NameError("log keyword Argument in make_sub_plot not understood. Needs to be int, [1/0], bool [True/False] or array of same length as data.")
-                
-        
-        self.orders_of_mag=[300] * number_of_plots
-        if "max_orders_of_mag" in kwargs:
-            if isinstance(kwargs["max_orders_of_mag"],list):
-                if not len(kwargs["max_orders_of_mag"]) == number_of_plots:
-                    raise IndexError("Length of list given for max_orders_of_mag does not match number of data elements")
-                else: 
-                    self.orders_of_mag = kwargs["max_orders_of_mag"]
-            else:
-                if isinstance(kwargs["max_orders_of_mag"],float) or isinstance(kwargs["max_orders_of_mag"],int):
-                    self.orders_of_magnitude=[kwargs["max_orders_of_mag"]]*number_of_plots
-                else:
-                    raise TypeError("max_orders_of_mag need to be of type float or int")
-        
             
         print("number of elements in data list = " + str(len(data_list)))
         
@@ -440,9 +660,24 @@ def __init__(self,*args,**kwargs):
         plt.show()
         
 class make_sub_plot:
-    def __init__(self,*args,**kwargs):
-        data_list = args[0]
+    """
+    make_plot plots contents of mcstas_data objects
+    
+    Plotting is controlled through options assosciated with the mcstas_data objects.
+    If a list is given, the plots appear in one subplot.
+    """
+    def __init__(self,data_list):
+        """
+        plots mcstas_data, single object or list of mcstas_data
+        
+        The options concerning plotting are stored with the data
+        
+        Parameters
+        ----------
+        data_list : mcstas_data or list of mcstas_data
+            mcstas_data to be plotted
         
+        """
         if not isinstance(data_list,mcstas_data):
             print("number of elements in data list = " + str(len(data_list)))
         else:
@@ -587,7 +822,55 @@ def fmt(x, pos):
         
 
 class parameter_variable:
+    """
+    Class describing a input parameter in McStas instrument
+    
+    McStas input parameters are of default type double, but can be cast. If two
+    positional arguments are given, the first is the type, and the second is the
+    parameter name. With one input, only the parameter name is read.
+    It is also possible to assign a default value and a comment through keyword
+    arguments.
+    
+    Attributes
+    ----------
+    type : str
+        McStas type of input: Double, Int, String
+        
+    name : str
+        Name of input parameter
+        
+    value : any
+        Default value/string of parameter, converted to string
+        
+    comment : str
+        Comment displayed next to the input parameter, could contain units
+        
+    Methods
+    -------
+    write_parameter(fo,stop_character)
+        writes the parameter to file object fo, uses given stop character
+    """
     def __init__(self,*args,**kwargs):
+        """Initializing mcstas parameter object 
+        
+        Parameters
+        ----------
+        If giving a type:
+        Positional argument 1: type : str
+            Type of the parameter, double, int or string
+        Positional argument 2: name : str
+            Name of input parameter
+            
+        If not giving type
+        Positional argument 1: name : str
+            Name of input parameter
+        
+        Keyword arguments
+            value : any
+                sets default value of parameter
+            comment : str
+                sets comment displayed next to declaration
+        """
         if len(args) == 1:
             self.type = ""
             self.name = str(args[0])
@@ -610,6 +893,7 @@ def __init__(self,*args,**kwargs):
         # they are int, double, string, are there more?
 
     def write_parameter(self,fo,stop_character):
+        """Writes input parameter to file"""
         fo.write("%s%s" % (self.type, self.name))
         if self.value_set == 1:
             if isinstance(self.value,int):
@@ -623,7 +907,57 @@ def write_parameter(self,fo,stop_character):
         fo.write("\n")
 
 class declare_variable:
+    """
+    Class describing a declared variable in McStas instrument
+    
+    McStas parameters are declared in the declare section with c syntax.
+    This class is initialized with type, name. Using keyword arguments,
+    the variable can become an array and have its initial value set.
+    
+    Attributes
+    ----------
+    type : str
+        McStas type to declare: Double, Int, String
+        
+    name : str
+        Name of variable
+        
+    value : any
+        Initial value of variable, converted to string
+        
+    comment : str
+        Comment displayed next to the declaration, could contain units
+        
+    vector : int 
+        0 if a single value is given, ortherwise contains the length
+        
+    value_set : int
+        internal variable displaying wether or not a value was given
+        
+    Methods
+    -------
+    write_line(fo)
+        writes a line to text file fo declaring the parameter in c syntax
+    """
     def __init__(self,*args,**kwargs):
+        """Initializing mcstas parameter object 
+        
+        Parameters
+        ----------
+        Positional argument 1: type : str
+            Type of the parameter, double, int or string
+            
+        Positional argument 2: name : str
+            Name of input parameter
+        
+        Keyword arguments
+            array : int
+                length of array to be allocated, default is 0 if not an array
+            value : any
+                sets initial value of parameter, can be a list with length matching array
+            comment : str
+                sets comment displayed next to declaration
+        """
         self.type = args[0]
         self.name = str(args[1])
         if "value" in kwargs:
@@ -631,6 +965,7 @@ def __init__(self,*args,**kwargs):
             self.value = kwargs["value"]
         else:
             self.value_set = 0
+            
         if "array" in kwargs:
             self.vector = kwargs["array"]
         else:
@@ -642,6 +977,13 @@ def __init__(self,*args,**kwargs):
             self.comment = ""
 
     def write_line(self,fo):
+        """Writes line declaring variable to file fo
+        
+        Parameters
+        ----------
+        fo : file object
+            File the line will be written to
+        """
         if self.value_set == 0 and self.vector == 0:
             fo.write("%s %s;%s" % (self.type, self.name,self.comment))
         if self.value_set == 1 and self.vector == 0:
@@ -659,10 +1001,141 @@ def write_line(self,fo):
 
 
 class component:
-    def __init__(self,*args,**kwargs):
+    """
+    A class describing a McStas component to be written to a instrument file
+    
+    This class is used by the instrument class when setting up components,
+    but can also be used independently.
+    Most information can be given on initialize using keyword arguments, but
+    there are methods for setting the attributes describing the component.
+    The class contains both methods to write the component to a instrument 
+    file and methods for printing to the python terminal for checking the 
+    information.
+    
+    Attributes
+    ----------
+    name : str
+        Name of the component instance in McStas (must be unique)
+        
+    component_name : str
+        Name of the component code to use, e.g. Arm, Guide_gravity, ...
+        
+    AT_data : list of 3 floats
+        Position data of the component
+    
+    AT_relative : str
+        String matching name of former component to use as reference for position
+        
+    ROTATED_data : list of 3 floats
+        Rotation data of the component
+        
+    ROTATED_relative : str
+        String matching name of former component to use as reference for position
+    
+    WHEN : str
+        String with logical expression in c syntax for when component is active
+    
+    EXTEND : str
+        c code that extends the component, can use declared parameters and internal
+    
+    GROUP : str
+        Name of group the component should belong to
+    
+    JUMP : str
+        String describing use of JUMP, need to contain all after "JUMP"
+    
+    component_parameters : dict
+        Parameters to be used with component in dictionary
+    
+    comment : str 
+        Comment inserted before the component as an explanation
+    
+    Methods
+    -------
+    set_AT(at_list,**kwargs)
+        Sets AT_data, can set AT_relative using keyword argument
+        
+    set_ROTATED(rotated_list,**kwargs)
+        Sets ROTATED_data, can set ROTATED_relative using keyword argument
+    
+    set_RELATIVE(relative_name)
+        Set both AT_relative and ROTATED_relative to relative_name
+    
+    set_parameters(dict_input)
+        Adds dictionary entries to parameter dictionary
+    
+    set_WHEN(string)
+        Sets WHEN string
+    
+    set_GROUP(string)
+        Sets GROUP name
+    
+    set_JUMP(string)
+        Sets JUMP string
+    
+    append_EXTEND(string)
+        Append string to EXTEND string
+        
+    set_comment(string)
+        Sets comment for component
+    
+    write_component(fo)
+        Writes component code to instrument file
+    
+    print_long()
+        Prints basic view of component code (not correct syntax)
+    
+    print_short(**kwargs)
+        Prints short description, used in print_components
+        
+    """
+    def __init__(self,instance_name,component_name,**kwargs):
+        """
+        Initializes McStas component with specified name and component
+        
+        Parameters
+        ----------
+        instance_name : str
+            name of the instance of the component
+            
+        component_name : str
+            name of the component type e.g. Arm, Guide_gravity, ...
+        
+        keyword arguments:
+            AT : list of 3 floats
+                Sets AT_data describing position of component
+                
+            AT_RELATIVE : str
+                sets AT_relative, describing position reference point
+                
+            ROTATED : list of 3 floats
+                Sets ROTATED_data, describing rotation of component
+                
+            ROTATED_RELATIVE : str
+                Sets ROTATED_relative, describing reference component for rotation
+                
+            RELATIVE : str
+                Sets both AT_relative and ROTATED_relative 
+        
+            WHEN : str
+                Sets WHEN string, should contain logical c expression
+                
+            EXTEND : str
+                Sets initial EXTEND string, should contain c code
+                
+            GROUP : str
+                Sets name of group the component instance should belong to
+                
+            JUMP : str
+                Sets JUMP str
+                
+            comment: str
+                Sets comment string
+        
+        """
         # Defines a McStas component with name and component name as first inputs
-        self.name = args[0]
-        self.component_name = args[1]
+        self.name = instance_name
+        self.component_name = component_name
         
         # Possible to give AT and ROTATED including AT_RELATIVE / ROTATED_RELATIVE
         # RELATIVE keyword also exists and sets both AT_RELATIVE and ROTATED_RELATIVE
@@ -698,7 +1171,7 @@ def __init__(self,*args,**kwargs):
             self.WHEN = ""
             
         if "EXTEND" in kwargs:
-            self.EXTEND = kwargs["EXTEND"]
+            self.EXTEND = kwargs["EXTEND"] + "\n"
         else:
             self.EXTEND = ""
             
@@ -726,6 +1199,7 @@ def __init__(self,*args,**kwargs):
         
     # method for setting AT and AT_RELATIVE after initialization
     def set_AT(self,at_list,**kwargs):
+        """Sets AT data, List of 3 floats"""
         self.AT_data=at_list
         if "RELATIVE" in kwargs:
             relative_name = kwargs["RELATIVE"]
@@ -736,6 +1210,7 @@ def set_AT(self,at_list,**kwargs):
     
     # method for setting ROTATED and ROTATED_RELATIVE after initialization
     def set_ROTATED(self,rotated_list,**kwargs):
+        """Sets ROTATED data, List of 3 floats""" 
         self.ROTATED_data=rotated_list
         if "RELATIVE" in kwargs:
             relative_name = kwargs["RELATIVE"]
@@ -746,6 +1221,7 @@ def set_ROTATED(self,rotated_list,**kwargs):
     
     # method for setting RELATIVE after initialization
     def set_RELATIVE(self,relative_name):
+        """Sets both AT_relative and ROTATED_relative""" 
         if relative_name == "ABSOLUTE":
             self.AT_relative = relative_name
             self.ROTATED_relative = relative_name
@@ -755,26 +1231,31 @@ def set_RELATIVE(self,relative_name):
 
     # method that adds a parameter name / value pair to dictionary
     def set_parameters(self,dict_input):
+        """Adds additional parameters and their values using a dictionary input"""
         self.component_parameters.update(dict_input)
         
     def set_WHEN(self,string):
+        """Sets WHEN string, should be a c logical expression"""
         self.WHEN = string
         
     def set_GROUP(self,string):
+        """Sets GROUP name"""
         self.GROUP = string
     
     def set_JUMP(self,string):
+        """Sets JUMP string, should contain all text after JUMP"""
         self.JUMP = string
         
     def append_EXTEND(self,string):
+        """Appends a line of code to EXTEND block of component"""
         self.EXTEND = self.EXTEND + string + "\n"
-    
-    # method that sets a comment to be written to instrument file
+
     def set_comment(self,string):
+        """Method that sets a comment to be written to instrument file"""
         self.comment = string
 
-    # method that writes component to file
     def write_component(self,fo):
+        """Method that writes component to file"""
         parameters_per_line = 2 # write comma separated parameters, up to 2 per line
         # could use a character limit on lines instead
         parameters_written = 0  # internal parameter
@@ -831,18 +1312,26 @@ def write_component(self,fo):
         # Leave a new line between components for readability
         fo.write("\n")
             
-
-    # print component long
     def print_long(self):
+        """Method for printing contained information to Python terminal, not correct syntax"""
         print("// " + self.comment)
         print("COMPONENT " + str(self.name) + " = " + str(self.component_name))
         for key,val in self.component_parameters.items():
             print(" ",key,"=",val)
+        if not self.WHEN == "":
+            print("WHEN (" + self.WHEN + ")")
         print("AT",self.AT_data,self.AT_relative)
         print("ROTATED",self.ROTATED_data,self.ROTATED_relative)
+        if not self.GROUP == "":
+            print("GROUP " + self.GROUP)
+        if not self.EXTEND == "":
+            print("%{")
+            print(self.EXTEND + "%}")
+        if not self.JUMP == "":
+            print("JUMP " + self.JUMP)
 
-    # print component short
     def print_short(self,**kwargs):
+        """Method for printing short description of component to list print"""
         if "longest_name" in kwargs:
             print("test")
             print(str(self.name)+" "*(3+kwargs["longest_name"]-len(self.name)),end='')
@@ -852,7 +1341,147 @@ def print_short(self,**kwargs):
 
 
 class McStas_instr:
+    """
+    Main class for writing a McStas instrument using McStasScript
+    
+    Initialization of McStas_instr sets the name of the instrument file
+    and its methods are used to add all aspects of the instrument file.
+    The class also holds methods for writing the finished instrument file
+    to disk and to run the simulation.
+    
+    Attributes
+    ----------
+    name : str
+        name of instrument file
+    
+    author : str
+        name of user of McStasScript, written to the file
+        
+    origin : str
+        origin of instrument file (affiliation)
+        
+    mcrun_path : str
+        absolute path of mcrun command, or empty if it is in path
+        
+    parameter_list : list of parameter_variable instances
+        contains all input parameters to be written to file
+        
+    declare_list : list of declare_variable instances
+        contains all declare parrameters to be written to file
+        
+    initialize_section : str
+        string containing entire initialize section to be written to file
+        
+    trace_section : str
+        string containing trace section (OBSOLETE)
+        
+    finally_section : str
+        string containing entire finally section to be written to file
+        
+    component_list : list of component instances
+        list of components in the instrument
+        
+    component_name_list : list of strings
+        list of names of the components in the instrument
+    
+    Methods
+    -------
+    add_parameter(*args,**kwargs)
+        Adds input parameter to the define section
+    
+    add_declare_var()
+        Adds declared variable ot the declare section
+    
+    append_initialize(string)
+        Appends a string to the initialize section, followed by a new line
+        
+    append_initialize_no_new_line(string)
+        Appends a string to the initialize section
+    
+    append_finally(string)
+        Appends a string to finally section, followed by a new line
+    
+    append_finally_no_new_line(string)
+        Appends a string to finally section
+    
+    append_trace(string)
+        Obsolete method, add components instead (still used in write_c_files)
+    
+    add_component(instance_name,component_name,**kwargs)
+        Add a component to the instrument file
+    
+    get_component(instance_name)
+        Returns component instance with name instance_name
+    
+    get_last_component()
+        Returns component instance of last component
+    
+    set_component_parameter(instance_name,dict)
+        Adds parameters as dict to component with instance_name
+        
+    set_component_AT(instance_name,AT_data,**kwargs)
+        Sets position of component named instance_name, reference component can be set in kwargs
+    
+    set_component_ROTATED(instance_name,ROTATED_data,**kwargs)
+        Sets rotation of component named instance_name, reference component can be set in kwargs
+    
+    set_component_RELATIVE(instane_name,string)
+        Sets reference component named instance_name for both position and rotation
+    
+    set_component_WHEN(instance_name,string)
+        Sets WHEN condition of component named instance_name, should be logical c expression
+    
+    set_component_GROUP(instance_name,string)
+        Sets GROUP name of component named instance_name
+    
+    append_component_EXTEND(instance_name,string)
+        Appends a line to EXTEND section of component named instance_name
+        
+    set_component_JUMP(instance_name,string)
+        Sets JUMP code for component named instance_name
+    
+    set_component_comment(instance_name,string)
+        Sets comment to be written before component named instance_name
+    
+    print_component(instance_name)
+        Prints an overview of current state of component named instance_name
+    
+    print_component_short(instance_name)
+        Prints short overview of current state of component named instance_name
+    
+    print_components()
+        Prints overview of postion / rotation of all components in instrument
+    
+    write_c_files()
+        Writes c files to include in existing McStas instrument in folder named generated_includes
+    
+    write_full_instrument()
+        Writes full instrument file to current directory, name as set in class initialization
+    
+    run_full_instrument(**kwargs)
+        Writes instrument files and runs a simulation with mcrun. Returns list of mcstas_data  
+    
+    """
+    
     def __init__(self,name,**kwargs):
+        """
+        Initialization of McStas Instrument
+        
+        Parameters
+        ----------
+        name : str
+            Name of project, instrument file will be called name + ".instr"
+            
+        keyword arguments:
+            author : str
+                Name of author, will be written in instrument file
+                
+            origin : str
+                Affiliation of author, will be written in instrument file
+                
+            mcrun_path : str
+                Absolute path of mcrun or empty string if already in path
+        """
         self.name = name
         
         if "author" in kwargs:
@@ -880,23 +1509,118 @@ def __init__(self,name,**kwargs):
         self.component_name_list = [] # list of component names
         
     def add_parameter(self,*args,**kwargs):
+        """
+        Method for adding input parameter to instrument
+        
+        Parameters
+        ----------
+        
+        (optional) parameter type : str
+            type of input parameter, double, int, string
+        
+        parameter name : str
+            name of parameter
+            
+        keyword arguments
+            value : any
+                Default value of parameter
+                
+            comment : str
+                Comment displayed next to declaration of parameter
+        
+        """
         # type of variable, name of variable, options described in declare_parameter class
         self.parameter_list.append(parameter_variable(*args,**kwargs))
 
     def add_declare_var(self,*args,**kwargs):
+        """
+        Method for adding declared variable to instrument
+        
+        Parameters
+        ----------
+        
+        parameter type : str
+            type of input parameter
+        
+        parameter name : str
+            name of parameter
+            
+        keyword arguments
+            array : int
+                default 0 for scalar, if specified length of array
+        
+            value : any
+                Initial value of parameter, can be list of length vector
+                
+            comment : str
+                Comment displayed next to declaration of parameter
+        
+        """
         # type of variable, name of variable, options described in declare_variable class
         self.declare_list.append(declare_variable(*args,**kwargs))
 
     def append_initialize(self,string):
+        """
+        Method for appending code to the intialize section of instrument
+        
+        The intialize section consists of c code and will be compiled,
+        thus any syntax errors will crash the simulation. Code is added 
+        on a new line for each call to this method.
+        
+        Parameters
+        ----------
+        string : str
+            code to be added to initialize section
+        
+        """
         self.initialize_section = self.initialize_section + string + "\n"
     
     def append_initialize_no_new_line(self,string):
+        """
+        Method for appending code to the intialize section of instrument (no new line)
+        
+        The intialize section consists of c code and will be compiled,
+        thus any syntax errors will crash the simulation. Code is added 
+        to the current line.
+        
+        Parameters
+        ----------
+        string : str
+            code to be added to initialize section
+        
+        """
         self.initialize_section = self.initialize_section + string
         
     def append_finally(self,string):
+        """
+        Method for appending code to the finally section of instrument
+        
+        The finally section consists of c code and will be compiled,
+        thus any syntax errors will crash the simulation. Code is added 
+        on a new line for each call to this method.
+        
+        Parameters
+        ----------
+        string : str
+            code to be added to finally section
+        
+        """
         self.finally_section = self.finally_section + string + "\n"
     
     def append_finally_no_new_line(self,string):
+        """
+        Method for appending code to the finally section of instrument
+        
+        The finally section consists of c code and will be compiled,
+        thus any syntax errors will crash the simulation. Code is added 
+        to the current line.
+        
+        Parameters
+        ----------
+        string : str
+            code to be added to finally section
+        
+        """
         self.finally_section = self.finally_section + string
     
     # Need to handle trace string differently when components also exists
@@ -905,6 +1629,15 @@ def append_finally_no_new_line(self,string):
     #  C) Could have trace string as a different object and place it in component_list, but have a write function named as the component write function?
     
     def append_trace(self,string):
+        """
+        Method for appending code to trace section, only used in write_c_files
+        
+        The most common way to add code to the trace section is to add 
+        components using the seperate methods for this. This method is kept
+        as is still used for writing to c files used in legacy code.
+        
+        
+        """
         self.trace_section = self.trace_section + string + "\n"
     
     def append_trace_no_new_line(self,string):

From d12e399f1b4416141a58feb5de64c1b58814258c Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Thu, 14 Mar 2019 13:10:52 +0100
Subject: [PATCH 009/403] Finihsed doc string documentation of the project.

---
 McStasScript.py | 284 ++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 276 insertions(+), 8 deletions(-)

diff --git a/McStasScript.py b/McStasScript.py
index aa764256..e15e64b1 100644
--- a/McStasScript.py
+++ b/McStasScript.py
@@ -392,6 +392,8 @@ def __init__(self,instr_name,**kwargs):
                 Sets parameters
             custom_flags : str
                 Sets custom_flags passed to mcrun
+            mcrun_path : str
+                Path to mcrun command, "" if already in path
         """
         self.name_of_instrumentfile = instr_name
         
@@ -1634,17 +1636,91 @@ def append_trace(self,string):
         
         The most common way to add code to the trace section is to add 
         components using the seperate methods for this. This method is kept
-        as is still used for writing to c files used in legacy code.
+        as is still used for writing to c files used in legacy code. Each call
+        creates a new line.
         
+        Parameters
+        ----------
+        string : str
+            code to be added to trace
         
         """
         self.trace_section = self.trace_section + string + "\n"
     
     def append_trace_no_new_line(self,string):
+        """
+        Method for appending code to trace section, only used in write_c_files
+        
+        The most common way to add code to the trace section is to add 
+        components using the seperate methods for this. This method is kept
+        as is still used for writing to c files used in legacy code. No new
+        line is made with this call.
+        
+        Parameters
+        ----------
+        string : str
+            code to be added to trace
+        
+        """
         self.trace_section = self.trace_section + string
                  
-    # methods for creating new components and modifiying existing components
+    
     def add_component(self,*args,**kwargs):
+        """
+        Method for adding a new component instance to the instrument
+        
+        Creates a new component instance in the instrument. This requires
+        a unique instance name of the component to be used for future reference
+        and the name of the McStas component to be used. The component is placed
+        at the end of the instrument file unless otherwise specified with the 
+        after and before keywords. The component may be initialized using other
+        keyword arguments, but all attributes can be set with approrpiate methods.
+        
+        Parameters
+        ----------
+        First positional argument : str
+            Unique name of component instance
+            
+        Second positional argument : str
+            Name of McStas component to create instance of
+            
+        Keyword arguments:
+            after : str
+                Place this component after an already added component with given name
+                
+            before : str
+                Place this component before an already added component with given name
+        
+            AT : List of 3 floats
+                Sets AT_data that determines position relative to reference
+                
+            AT_RELATIVE : str
+                Sets reference component for postion
+                
+            ROTATED : List of 3 floats 
+                Sets RELATIVE_data that determines rotation relative to reference
+                
+            ROTATED_RELATIVE : str
+                Sets reference component for rotation
+                
+            RELATIVE : str
+                Sets reference component for both position and rotation
+                
+            WHEN : str
+                Sets when condition which must be a logical c expression
+                
+            EXTEND : str
+                Initialize the extend section with a line of c code
+                
+            GROUP : str
+                Name of the group this component should belong to
+                
+            JUMP : str
+                Set code for McStas JUMP statement
+                
+            comment : str
+                Sets a comment that will be displayed before the component
+        """
         if args[0] in self.component_name_list:
             raise NameError("Component name \"" + str(args[0]) + "\" used twice, McStas does not allow this. Rename or remove one instance of this name.")
         
@@ -1667,6 +1743,19 @@ def add_component(self,*args,**kwargs):
             self.component_name_list.append(args[0])
     
     def get_component(self,name):
+        """
+        Get the component instance of component with specified name
+        
+        This method is used to get direct access to any component instance
+        in the instrument. The component instance can be manipulated in 
+        much the same way, but it is not necessary to specify the name in
+        each call.
+        
+        Parameters
+        ----------
+        name : str
+            Unique name of component whos instance should be returned
+        """
         if name in self.component_name_list:
             index = self.component_name_list.index(name)
             return self.component_list[index]
@@ -1674,54 +1763,196 @@ def get_component(self,name):
             raise NameError("No component was found with name \"" + str(name) + "\"!")
             
     def get_last_component(self):
+        """
+        Get the component instance of last component in the instrument
+        
+        This method is used to get direct access to any component instance
+        in the instrument. The component instance can be manipulated in 
+        much the same way, but it is not necessary to specify the name in
+        each call.
+        """
         return self.component_list[-1]
 
     def set_component_parameter(self,name,input_dict):
+        """
+        Add parameters and their values as dictionary to component
+        
+        This method is the primary way of specifying parameters in a
+        component. Parameters are added to a dictionary specifying 
+        parameter name and value pairs.
+        
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify
+        
+        input_dict : dict
+            Set of new parameter name and value pairs to add
+        """
         component = self.get_component(name)
         component.set_parameters(input_dict)
 
     def set_component_AT(self,name,at_list,**kwargs):
+        """
+        Method for setting position of component
+        
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify
+            
+        at_list : List of 3 floats
+            Position of component relative to reference component
+            
+        keyword arguments:
+            RELATIVE : str
+                Sets reference component for position
+        """
         component = self.get_component(name)
         component.set_AT(at_list,**kwargs)
         
     def set_component_ROTATED(self,name,rotated_list,**kwargs):
+        """
+        Method for setting rotiation of component
+        
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify
+            
+        rotated_list : List of 3 floats
+            Rotation of component relative to reference component
+            
+        keyword arguments:
+            RELATIVE : str
+                Sets reference component for rotation
+        """
         component = self.get_component(name)
         component.set_ROTATED(rotated_list,**kwargs)
     
     def set_component_RELATIVE(self,name,relative):
+        """
+        Method for setting reference of component position and rotation
+        
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify
+            
+        relative : str
+            Reference component for position and rotation
+        """
         component = self.get_component(name)
         component.set_RELATIVE(relative)
         
     def set_component_WHEN(self,name,WHEN):
+        """
+        Method for setting WHEN c expression to named component
+        
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify 
+        
+        WHEN : str
+            Sets WHEN c expression for named McStas component
+        """
         component = self.get_component(name)
         component.set_WHEN(WHEN)
         
     def append_component_EXTEND(self,name,EXTEND):
+        """
+        Method for adding line of c to EXTEND section of named component
+        
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify 
+        
+        EXTEND : str
+            Line of c code added to EXTEND section of named component
+        """
         component = self.get_component(name)
         component.append_EXTEND(EXTEND)
         
     def set_component_GROUP(self,name,GROUP):
+        """
+        Method for setting GROUP name of named component
+        
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify 
+        
+        GROUP : str
+            Sets GROUP name for named McStas component
+        """
         component = self.get_component(name)
         component.set_GROUP(GROUP)
         
     def set_component_JUMP(self,name,JUMP):
+        """
+        Method for setting JUMP expression of named component
+        
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify 
+        
+        JUMP : str
+            Sets JUMP expression for named McStas component
+        """
         component = self.get_component(name)
         component.set_JUMP(JUMP)
     
     def set_component_comment(self,name,string):
+        """
+        Sets a comment displayed before the component in written files
+        
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify 
+                
+        string : str
+            Comment string
+        
+        """
         component = self.get_component(name)
         component.set_comment(string)
-
+    
     def print_component(self,name):
+        """
+        Method for printing summary of contents in named component
+        
+        Parameters
+        ----------
+        name : str
+            Unique name of component to print
+        """
         component = self.get_component(name)
         component.print_long()
     
     def print_component_short(self,name):
+        """
+        Method for printing summary of contents in named component
+        
+        Parameters
+        ----------
+        name : str
+            Unique name of component to print
+        """
         component = self.get_component(name)
         component.print_short()
     
     def print_components(self):
+        """
+        Method for printing overview of all components in instrument
+        
+        Provides overview of component names, what McStas component is
+        used for each and their position and rotation in space.
     
+        """
         longest_name = len(max(self.component_name_list,key=len))
         
         # Investigate how this could have been done in a better way
@@ -1767,8 +1998,15 @@ def print_components(self):
             #print("")
 
     def write_c_files(self):
-        # method for writing c files that can be included in instruments
+        """
+        Obsolete method for writing instrument to c files for later use
         
+        It is possible to use this function to write c files to a folder called
+        generated_includes that can then be included in the different sections
+        of a McStas instrument. Component objects are NOT written to these
+        files, but rather the contents of the trace_section that can be set 
+        using the append_trace method.
+        """
         path = os.getcwd()
         path = path + "/generated_includes"
         if not os.path.isdir(path):
@@ -1799,11 +2037,13 @@ def write_c_files(self):
             component.write_component(fo)
         fo.close()
 
-    # Method that writes full instrument file.
     def write_full_instrument(self):
-        # method for writing an instrument file
-        # could either use generated includes or write everything out
-        # will probably create an option to choose between these methods later
+        """
+        Method for writing full instrument file to disk
+        
+        This method writes the instrument described by the instrument object to
+        disk with the name specified in the initialization of the object.
+        """
         
         # Create file identifier
         fo = open(self.name + ".instr","w")
@@ -1874,6 +2114,34 @@ def write_full_instrument(self):
         fo.write("\nEND\n")
         
     def run_full_instrument(self,*args,**kwargs):
+        """
+        Runs McStas instrument described by this class, returns list of mcstas_data
+        
+        This method will write the instrument to disk and then run it using
+        the mcrun command of the system. Options are set using keyword 
+        arguments. Some options are mandatory, for example foldername, which 
+        can not already exist, if it does data will be read from this folder.
+        If the mcrun command is not in the path of the system, the absolute 
+        path can be given with the mcrun_path keyword argument. This path
+        could also already have been set at initialization of the instrument
+        object.
+        
+        Parameters
+        ----------
+        Keyword arguments
+            foldername : str
+                Sets data_folder_name
+            ncount : int
+                Sets ncount
+            mpi : int
+                Sets thread count
+            parameters : dict
+                Sets parameters
+            custom_flags : str
+                Sets custom_flags passed to mcrun
+            mcrun_path : str
+                Path to mcrun command, "" if already in path
+        """
         # Write the instrument file
         self.write_full_instrument()
         

From 0bf970d7143a939bc11cc4ccd8447dbd78983518 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Thu, 2 May 2019 15:31:24 +0200
Subject: [PATCH 010/403] Improved readability of the code. It is now PEP 8
 compliant.

---
 McStasScript.py | 1995 +++++++++++++++++++++++++----------------------
 1 file changed, 1067 insertions(+), 928 deletions(-)

diff --git a/McStasScript.py b/McStasScript.py
index e15e64b1..c63a603e 100644
--- a/McStasScript.py
+++ b/McStasScript.py
@@ -1,10 +1,17 @@
-# McStasScript classes written by Mads Bertelsen, ESS, DMSC
+"""McStasScript classes written by Mads Bertelsen, ESS, DMSC
+
+API for writing and running McStas instruments
+"""
 
 from __future__ import print_function
+
+__author__ = "Mads Bertelsen"
+
 import datetime
 import os
 import time
 import math
+
 import numpy as np
 import matplotlib
 import matplotlib.pyplot as plt
@@ -12,78 +19,83 @@
 from matplotlib.ticker import MaxNLocator
 from openpyxl.worksheet import dimensions
 from boto.ec2.autoscale import limits
-#from builtins import False, True
+# From builtins import False, True
 
-try: # check whether python knows about 'basestring'
-   basestring
-except NameError: # no, it doesn't (it's Python3); use 'str' instead
-   basestring=str
+try:  # Check whether python knows about 'basestring'
+    basestring
+except NameError:  # No, it doesn't (it's Python3); use 'str' instead
+    basestring = str
 
-class mcstas_meta_data:
+
+class McStasMetaData:
     """
-    Class for holding metadata for McStas dataset, is to be read from mccode.sim file.
-    
+    Class for holding metadata for McStas dataset, is to be read from
+    mccode.sim file.
+
     Attributes
     ----------
     info : dict
         Contains read strings from mccode.sim in key, value
-    
+
     dimension : Int or List of Int
-        Int for 1d data set with lenght of data, Array for 2d with each length
-        
+        Int for 1d data set with lenght of data, Array for 2d with each
+        length
+
     component_name : str
         Name of component in McStas file
-        
+
     filename : str
         Name of data file to read
-        
+
     limits : List
-        Limits for monitor, length=2 for 1d data and length=4 for 2d data
-        
+        Limits for monitor, length=2 for 1d data and length=4 for 2d
+        data
+
     title : str
         Title of monitor when plotting
-        
+
     xlabel : str
         Text for xlabel when plotting
-        
+
     ylabel : str
         Text for ylabel when plotting
-        
+
     Methods
     -------
     add_info(key,value)
         Adds a element to the info dictionary
-        
+
     extract_info()
         Unpacks the information in info to class attributes
-    
+
     set_title(string)
         Overwrites current title
-        
+
     set_xlabel(string)
         Overwrites current xlabel
-        
+
     set_ylabel(string)
         Overwrites current ylabel
     """
+
     def __init__(self):
         """Creating a new instance, no parameters"""
         self.info = {}
-    
-    def add_info(self,key,value):
+
+    def add_info(self, key, value):
         """Adding information to info dict"""
         self.info[key] = value
-        
+
     def extract_info(self):
         """Extracting information from info dict to class attributes"""
-        
+
         # Extract dimension
         if "type" in self.info:
             type = self.info["type"]
             if "array_1d" in type:
                 self.dimension = int(type[9:-2])
             if "array_2d" in type:
-                self.dimension=[]
+                self.dimension = []
                 type_strings = self.info["type"].split(",")
                 temp_str = type_strings[0]
                 self.dimension.append(int(temp_str[9:]))
@@ -91,8 +103,8 @@ def extract_info(self):
                 self.dimension.append(int(temp_str[1:-2]))
         else:
             raise NameError("No type in mccode data section!")
-        
-        # Extract component name        
+
+        # Extract component name
         if "component" in self.info:
             self.component_name = self.info["component"].rstrip()
 
@@ -100,19 +112,20 @@ def extract_info(self):
         if "filename" in self.info:
             self.filename = self.info["filename"].rstrip()
         else:
-            raise NameError("No filename found in mccode data section!")
-        
+            raise NameError(
+                "No filename found in mccode data section!")
+
         # Extract limits
         self.limits = []
         if "xylimits" in self.info:
-            # find the four numbers 
+            # find the four numbers
             temp_str = self.info["xylimits"]
             limits_string = temp_str.split()
             for limit in limits_string:
                 self.limits.append(float(limit))
 
         if "xlimits" in self.info:
-            # find the two numbers 
+            # find the two numbers
             temp_str = self.info["xlimits"]
             limits_string = temp_str.split()
             for limit in limits_string:
@@ -125,47 +138,50 @@ def extract_info(self):
             self.ylabel = self.info["ylabel"].rstrip()
         if "title" in self.info:
             self.title = self.info["title"].rstrip()
-    
-    def set_title(self,string):
+
+    def set_title(self, string):
         """Sets title for plotting"""
         self.title = string
-        
-    def set_xlabel(self,string):
+
+    def set_xlabel(self, string):
         """Sets xlabel for plotting"""
         self.xlabel = string
-        
-    def set_ylabel(self,string):
+
+    def set_ylabel(self, string):
         """Sets ylabel for plotting"""
         self.ylabel = string
-        
-class mcstas_plot_options:
+
+
+class McStasPlotOptions:
     """
     Class that holds plotting options related to McStas data set
-    
+
     Attributes
     ----------
     log : bool
         To plot on logarithmic or not, standard is linear
-        
+
     orders_of_mag : float
-        If plotting on log scale, restrict max range to orders_of_mag below max
-        
+        If plotting on log scale, restrict max range to orders_of_mag
+        below maximum value
+
     colormap : string
         Chosen colormap for 2d data, should be available in matplotlib
-    
+
     Methods
     -------
     set_options(keyword arguments)
         Can set the class attributes using keyword options
-    
+
     """
-    def __init__(self,*args,**kwargs):
+
+    def __init__(self, *args, **kwargs):
         """Setting default values for plotting preferences"""
         self.log = False
         self.orders_of_mag = 300
         self.colormap = "jet"
-        
-    def set_options(self,**kwargs):
+
+    def set_options(self, **kwargs):
         """Set custom values for plotting preferences"""
         if "log" in kwargs:
             log_input = kwargs["log"]
@@ -177,210 +193,230 @@ def set_options(self,**kwargs):
             elif type(log_input) == bool:
                 self.log = log_input
             else:
-                raise NameError("Log input must be either Int or Bool.")
-            
+                raise NameError(
+                    "Log input must be either Int or Bool.")
+
         if "orders_of_mag" in kwargs:
             self.orders_of_mag = kwargs["orders_of_mag"]
-            
+
         if "colormap" in kwargs:
             self.colormap = kwargs["colormap"]
-        
-class mcstas_data:
+
+
+class McStasData:
     """
-    Class for holding full McStas dataset with data, metadata and plotting preferences
-    
+    Class for holding full McStas dataset with data, metadata and
+    plotting preferences
+
     Attributes
     ----------
-    metadata : mcstas_meta_data instance
+    metadata : McStasMetaData instance
         Holds the metadata for the dataset
-        
+
     name : str
         Name of component, extracted from metadata
-        
+
     Intensity : numpy array
-        Intensity data [n/s] in 1d or 2d numpy array, dimension in metadata
-        
+        Intensity data [n/s] in 1d or 2d numpy array, dimension in
+        metadata
+
     Error : numpy array
-        Error data [n/s] in 1d or 2d numpy array, same dimensions as Intensity
-        
+        Error data [n/s] in 1d or 2d numpy array, same dimensions as
+        Intensity
+
     Ncount : numpy array
-        Number of rays in bin, 1d or 2d numpy array, same dimensions as Intensity
-        
-    plot_options : mcstas_plot_options instance
+        Number of rays in bin, 1d or 2d numpy array, same dimensions as
+        Intensity
+
+    plot_options : McStasPlotOptions instance
         Holds the plotting preferences for the dataset
-        
+
     Methods
     -------
     set_xlabel : string
         sets xlabel of data for plotting
-        
+
     set_ylabel : string
         sets ylabel of data for plotting
-        
+
     set_title : string
         sets title of data for plotting
-        
+
     set_optons : keyword arguments
-        sets plot options, keywords passed to mcstas_plot_options method
-    
+        sets plot options, keywords passed to McStasPlotOptions method
     """
-    def __init__(self,metadata,intensity,error,ncount,**kwargs):
+
+    def __init__(self, metadata, intensity, error, ncount, **kwargs):
         """
-        Initialize a new McStas dataset, 4 positional arguments, pass xaxis as kwarg if 1d data
-        
+        Initialize a new McStas dataset, 4 positional arguments, pass
+        xaxis as kwarg if 1d data
+
         Parameters
         ----------
-        metadata : mcstas_meta_data instance
+        metadata : McStasMetaData instance
             Holds the metadata for the dataset
-        
+
         name : str
             Name of component, extracted from metadata
-        
+
         intensity : numpy array
-            Intensity data [n/s] in 1d or 2d numpy array, dimension in metadata
-        
+            Intensity data [n/s] in 1d or 2d numpy array, dimension in
+            metadata
+
         error : numpy array
-            Error data [n/s] in 1d or 2d numpy array, same dimensions as Intensity
-        
+            Error data [n/s] in 1d or 2d numpy array, same dimensions
+            as Intensity
+
         ncount : numpy array
-            Number of rays in bin, 1d or 2d numpy array, same dimensions as Intensity
-            
-        kwargs : keyword arguments 
+            Number of rays in bin, 1d or 2d numpy array, same
+            dimensions as Intensity
+
+        kwargs : keyword arguments
             xaxis is required for 1d data
-            
         """
+
         # attatch meta data
         self.metadata = metadata
         # get name from metadata
         self.name = self.metadata.component_name
-        # three basic arrays as first 
+        # three basic arrays from positional arguments
         self.Intensity = intensity
         self.Error = error
         self.Ncount = ncount
-        
+
         if type(self.metadata.dimension) == int:
             if "xaxis" in kwargs:
                 self.xaxis = kwargs["xaxis"]
             else:
-                raise NameError("ERROR: Initialization of mcstas_data done with 1d data, but without xaxis" + self.name + "!")
-            
-        self.plot_options = mcstas_plot_options()
-        
-    # Methods xlabel, ylabel and title as they might not be found    
-    def set_xlabel(self,string):
+                raise NameError(
+                    "ERROR: Initialization of McStasData done with 1d "
+                    + "data, but without xaxis" + self.name + "!")
+
+        self.plot_options = McStasPlotOptions()
+
+    # Methods xlabel, ylabel and title as they might not be found
+    def set_xlabel(self, string):
         self.metadata.set_xlabel(string)
-        
-    def set_ylabel(self,string):
+
+    def set_ylabel(self, string):
         self.metadata.set_ylabel(string)
-        
-    def set_title(self,string):
+
+    def set_title(self, string):
         self.metadata.set_title(string)
-        
-    def set_plot_options(self,**kwargs):
+
+    def set_plot_options(self, **kwargs):
         self.plot_options.set_options(**kwargs)
-        
-        
-def name_search(name,data_list):
+
+def name_search(name, data_list):
     """"
-    name_search returns mcstas_data instance with specific name if it is in the given data_list
-    
-    The index of certain datasets in the data_list can change if additional monitors are added
-    so it is more convinient to access the data files using their names.
-    
+    name_search returns McStasData instance with specific name if it is
+    in the given data_list
+
+    The index of certain datasets in the data_list can change if
+    additional monitors are added so it is more convinient to access
+    the data files using their names.
+
     Parameters
     ----------
     name : string
         Name of the dataset to be retrived (component_name)
-        
-    data_list : List of mcstas_data instances
+
+    data_list : List of McStasData instances
         List of datasets to search
     """
-    
-    if not type(data_list[0]) == mcstas_data:
-        raise InputError("name_search function needs objects of type mcstas_data as input.")
-    
-    list_result = [x for x in data_list if x.metadata.component_name == name]
-    
-    if len(list_result) == 1: 
+
+    if not type(data_list[0]) == McStasData:
+        raise InputError(
+            "name_search function needs objects of type "
+            + "McStasData as input.")
+
+    list_result = []
+    for check in data_list:
+        if check.metadata.component_name == name:
+            list_result.append(check)
+
+    if len(list_result) == 1:
         return list_result[0]
     else:
         raise NameError("More than one match for the name search")
-    
-def name_plot_options(name,data_list,**kwargs):
+
+def name_plot_options(name, data_list, **kwargs):
     """"
-    name_plot_options passes keyword arguments to dataset with certain name in given data list
-    
-    Function for quickly setting plotting options on a certain dataset in a larger 
-    list of datasets
-    
+    name_plot_options passes keyword arguments to dataset with certain
+    name in given data list
+
+    Function for quickly setting plotting options on a certain dataset
+    n a larger list of datasets
+
     Parameters
     ----------
     name : string
-        Name of the dataset to be retrived (component_name)
-        
-    data_list : List of mcstas_data instances
+        Name of the dataset to be modified (component_name)
+
+    data_list : List of McStasData instances
         List of datasets to search
-        
+
     kwargs : keyword arguments
-        Keyword arguments passed to set_plot_options in mcstas_plot_options
+        Keyword arguments passed to set_plot_options in
+        McStasPlotOptions
     """
-    if not type(data_list[0]) == mcstas_data:
-        raise InputError("name_search function needs objects of type mcstas_data as input.")
-    
-    list_result = [x for x in data_list if x.metadata.component_name == name]
-    
-    if len(list_result) == 1:
-        list_result[0].set_plot_options(**kwargs)
-    else:
-        raise NameError("More than one match for the name search")
 
-class managed_mcrun:
+    if not isinstance(data_list[0], McStasData):
+        raise InputError(
+            "name_search function needs objects of type McStasData "
+            + "as input.")
+
+    object_to_modify = name_search(name, data_list)
+    object_to_modify.set_plot_options(**kwargs)
+
+
+class ManagedMcrun:
     """
-    A class for performing a mcstas simulation and organizing the data into python objects
-    
-    managed_mcrun is usually called by the instrument class of McStasScript but can be used
-    independently. It runs the mcrun command using the system command, and if this is not
-    in the path, the absolute path can be given in a keyword argument mcrun_path.
-    
-    
-    
+    A class for performing a mcstas simulation and organizing the data
+    into python objects
+
+    ManagedMcrun is usually called by the instrument class of
+    McStasScript but can be used independently.  It runs the mcrun
+    command using the system command, and if this is not in the path,
+    the absolute path can be given in a keyword argument mcrun_path.
+
     Attributes
     ----------
     name_of_instrumentfile : str
         Name of instrument file to be executed
-        
+
     data_folder_name : str
         Name of datafolder mcrun writes to disk
-        
+
     ncount : int
-        Number of rays to simulate 
-        
+        Number of rays to simulate
+
     mpi : int
         Number of mpi threads to run
-        
+
     parameters : dict
         Dictionary of parameter names and values for this simulation
-        
+
     custom_flags : string
         Custom flags that are passed to the mcrun command
-        
+
     mcrun_path : string
         Path to the mcrun command (can be empty if already in path)
-    
-    
+
     Methods
     -------
     run_simulation()
-        Runs simulation, returns list of mcstas_data instances
-    
+        Runs simulation, returns list of McStasData instances
+
     """
-    def __init__(self,instr_name,**kwargs):
+
+    def __init__(self, instr_name, **kwargs):
         """
         Parameters
         ----------
         instr_name : str
             Name of instrument file to be simulated
-        
+
         kwargs : keyword arguments
             foldername : str
                 Sets data_folder_name
@@ -395,159 +431,187 @@ def __init__(self,instr_name,**kwargs):
             mcrun_path : str
                 Path to mcrun command, "" if already in path
         """
+
         self.name_of_instrumentfile = instr_name
-        
+
         self.data_folder_name = ""
         self.ncount = 1E6
-        self.mpi=1 
+        self.mpi = 1
         self.parameters = {}
         self.custom_flags = ""
         self.mcrun_path = ""
-        self.mcrun_path = kwargs["mcrun_path"] # mcrun_path always in kwargs
-        
+        # mcrun_path always in kwargs
+        self.mcrun_path = kwargs["mcrun_path"]
+
         if "foldername" in kwargs:
             self.data_folder_name = kwargs["foldername"]
         else:
-            raise NameError("managed_mcrun needs foldername to load data, add with keyword argument.")
-        
+            raise NameError(
+                "ManagedMcrun needs foldername to load data, add "
+                + "with keyword argument.")
+
         if "ncount" in kwargs:
             self.ncount = kwargs["ncount"]
-            
+
         if "mpi" in kwargs:
             self.mpi = kwargs["mpi"]
-            
+
         if "parameters" in kwargs:
             self.parameters = kwargs["parameters"]
-            
+
         if "custom_flags" in kwargs:
             self.custom_flags = kwargs["custom_flags"]
-            
+
     def run_simulation(self):
-        """Runs McStas simulation described by initializing the objeect"""
-        
+        """
+        Runs McStas simulation described by initializing the object
+        """
+
         # construct command to run
-        option_string = "-c -n " + str(self.ncount) + " --mpi=" + str(self.mpi) + " "
+        option_string = ("-c"
+                         + " -n " + str(self.ncount)  # Set ncount
+                         + " --mpi=" + str(self.mpi)  # Set mpi
+                         + " ")
+
         if len(self.data_folder_name) > 0:
-            option_string = option_string + "-d " + self.data_folder_name
-        
+            option_string = (option_string
+                             + "-d "
+                             + self.data_folder_name)
+
         # add parameters to command
         parameter_string = ""
-        for key,val in self.parameters.items():
-            parameter_string = parameter_string + " " + str(key) + "=" + str(val)
-        
-        # check if a mcrun_path is set and has the correct backslash
+        for key, val in self.parameters.items():
+            parameter_string = (parameter_string + " "
+                                + str(key)  # parameter name
+                                + "="
+                                + str(val))  # parameter value
+
+        mcrun_full_path = self.mcrun_path + "mcrun"
         if len(self.mcrun_path) > 1:
-            if self.mcrun_path[-1] == "\\" or self.mcrun_path[-1] == "/": 
-                mcrun_full_path = self.mcrun_path + "mcrun"
-            else:
+            if not (self.mcrun_path[-1] == "\\"
+                    or self.mcrun_path[-1] == "/"):
                 mcrun_full_path = self.mcrun_path + "/mcrun"
-        else:
-            mcrun_full_path = self.mcrun_path + "mcrun"
-        
+
         # Run the mcrun command on the system
-        os.system(mcrun_full_path + " " + option_string + " " + self.custom_flags + " " + self.name_of_instrumentfile + " " + parameter_string)
-        
-        # can use subprocess from spawn* if more controll is needed over the spawned process, including a timeout
-        
-        #time.sleep(1)
-         
-        # find all data files in generated folder
+        os.system(mcrun_full_path + " "
+                  + option_string + " "
+                  + self.custom_flags + " "
+                  + self.name_of_instrumentfile + " "
+                  + parameter_string)
+
+        """
+        Can use subprocess from spawn* instead of os.system if more
+        control is needed over the spawned process, including a timeout
+        """
+
+        # Find all data files in generated folder
         files_in_folder = os.listdir(self.data_folder_name)
-        
-        # raise an error if mccode.sim is not available
-        if not "mccode.sim" in files_in_folder:
+
+        # Raise an error if mccode.sim is not available
+        if "mccode.sim" not in files_in_folder:
             raise NameError("mccode.sim not written to output folder.")
-        
-        # open mccode to read metadata for all datasets written to disk
-        f = open(self.data_folder_name + "/mccode.sim","r")
-        
-        # loop that reads mccode.sim sections
+
+        # Open mccode to read metadata for all datasets written to disk
+        f = open(self.data_folder_name + "/mccode.sim", "r")
+
+        # Loop that reads mccode.sim sections
         metadata_list = []
         in_data = False
         for lines in f:
             # Could read other details about run
-            
+
             if lines == "end data\n":
-                # no more data for this metadata object
-                # extract the information
+                # No more data for this metadata object
+                # Extract the information
                 current_object.extract_info()
-                # add to metadata list
+                # Add to metadata list
                 metadata_list.append(current_object)
-                # stop reading data
+                # Stop reading data
                 in_data = False
-            
+
             if in_data:
                 # This line contains info to be added to metadata
                 colon_index = lines.index(":")
                 key = lines[2:colon_index]
                 value = lines[colon_index+2:]
                 current_object.add_info(key, value)
-            
+
             if lines == "begin data\n":
-                # found data section, create new metadata object
-                current_object = mcstas_meta_data()
-                # start recording data to metadata object
+                # Found data section, create new metadata object
+                current_object = McStasMetaData()
+                # Start recording data to metadata object
                 in_data = True
-                
-        # close mccode.sim
+
+        # Close mccode.sim
         f.close()
-        
-        # create a list for mcstas_data instances to return
+
+        # Create a list for McStasData instances to return
         results = []
-        
-        # load datasets described in metadata list individually
+
+        # Load datasets described in metadata list individually
         for metadata in metadata_list:
-            # load data with numpy
-            data = np.loadtxt(self.data_folder_name + "/" + metadata.filename.rstrip())
-    
-            # split data into intensity, error and ncount
+            # Load data with numpy
+            data = np.loadtxt(self.data_folder_name
+                              + "/"
+                              + metadata.filename.rstrip())
+
+            # Split data into intensity, error and ncount
             if type(metadata.dimension) == int:
-                xaxis = data.T[0,:]
-                Intensity = data.T[1,:]
-                Error = data.T[2,:]
-                Ncount = data.T[3,:]
-                
+                xaxis = data.T[0, :]
+                Intensity = data.T[1, :]
+                Error = data.T[2, :]
+                Ncount = data.T[3, :]
+
             elif len(metadata.dimension) == 2:
-                xaxis = [] # assume evenly binned in 2d
-                Intensity = data.T[:,0:metadata.dimension[1]-1]
-                Error = data.T[:,metadata.dimension[1]:2*metadata.dimension[1]-1]
-                Ncount = data.T[:,2*metadata.dimension[1]:3*metadata.dimension[1]-1]
+                xaxis = []  # Assume evenly binned in 2d
+                data_lines = metadata.dimension[1]
+                Intensity = data.T[:, 0:data_lines - 1]
+                Error = data.T[:, data_lines:2*data_lines - 1]
+                Ncount = data.T[:, 2*data_lines:3*data_lines - 1]
             else:
-                raise NameError("Dimension not read correctly in data set connected to monitor named " + metadata.component_name)    
-            
-            # The data is saved as a mcstas_data object
-            result = mcstas_data(metadata,Intensity,Error,Ncount,xaxis=xaxis)
-            
+                raise NameError(
+                    "Dimension not read correctly in data set "
+                    + "connected to monitor named "
+                    + metadata.component_name)
+
+            # The data is saved as a McStasData object
+            result = McStasData(metadata, Intensity,
+                                Error, Ncount,
+                                xaxis=xaxis)
+
             # Add this result to the results list
             results.append(result)
-            
-            # close the current datafile
+
+            # Close the current datafile
             f.close()
-            
-        # Return list of mcstas_data objects
+
+        # Return list of McStasData objects
         return results
-    
+
+
 class make_plot:
     """
-    make_plot plots contents of mcstas_data objects
-    
-    Plotting is controlled through options assosciated with the mcstas_data objects.
+    make_plot plots contents of McStasData objects
+
+    Plotting is controlled through options assosciated with the
+    McStasData objects.
+
     If a list is given, the plots appear individually.
     """
-    def __init__(self,data_list):
+
+    def __init__(self, data_list):
         """
-        plots mcstas_data, single object or list of mcstas_data
-        
+        plots McStasData, single object or list of McStasData
+
         The options concerning plotting are stored with the data
-        
+
         Parameters
         ----------
-        data_list : mcstas_data or list of mcstas_data
-            mcstas_data to be plotted
-        
+        data_list : McStasData or list of McStasData
+            McStasData to be plotted
         """
-        
-        # Relevant options: 
+
+        # Relevant options:
         #  select colormap
         #  show / hide colorbar
         #  custom title / label
@@ -556,139 +620,146 @@ def __init__(self,data_list):
         #  log scale (orders of magnitude)
         #  compare several 1d
         #  compare 2D
-        
-        if isinstance(data_list,mcstas_data):
+
+        if isinstance(data_list, McStasData):
             # Only a single element, put it in a list for easier syntax later
             data_list = [data_list]
-            
+
         number_of_plots = len(data_list)
-            
+
         print("number of elements in data list = " + str(len(data_list)))
-        
+
         index = -1
         for data in data_list:
             index = index + 1
-            
+
             print("Plotting data with name " + data.metadata.component_name)
             if type(data.metadata.dimension) == int:
                 fig = plt.figure(0)
-                
-                #print(data.T)
+
+                # print(data.T)
                 x = data.xaxis
                 y = data.Intensity
                 y_err = data.Error
-                
+
                 plt.errorbar(x, y, yerr=y_err)
-                
+
                 if data.plot_options.log:
-                    ax0.set_yscale("log",nonposy='clip')
-                
-                plt.xlim(data.metadata.limits[0],data.metadata.limits[1])
-                
+                    ax0.set_yscale("log", nonposy='clip')
+
+                plt.xlim(data.metadata.limits[0], data.metadata.limits[1])
+
                 # Add a title
                 plt.title(data.metadata.title)
-                
+
                 # Add axis labels
                 plt.xlabel(data.metadata.xlabel)
                 plt.ylabel(data.metadata.ylabel)
-                
-            elif  len(data.metadata.dimension) == 2:
-                
+
+            elif len(data.metadata.dimension) == 2:
+
                 # Split the data into intensity, error and ncount
                 Intensity = data.Intensity
                 Error = data.Error
                 Ncount = data.Ncount
-                
-                # Select to plot the intensity
-                #to_plot = np.log(Intensity)
-                
+
                 if data.plot_options.log:
                     min_value = np.min(Intensity[np.nonzero(Intensity)])
                     min_value = np.log10(min_value)
-                    
+
                     to_plot = np.log10(Intensity)
-                    
+
                     max_value = to_plot.max()
-                    
-                    if max_value - min_value > data.plot_options.orders_of_mag:
-                        min_value = max_value - data.plot_options.orders_of_mag
+
+                    if (max_value - min_value
+                            > data.plot_options.orders_of_mag):
+                        min_value = (max_value
+                                     - data.plot_options.orders_of_mag)
                 else:
                     to_plot = Intensity
                     min_value = to_plot.min()
                     max_value = to_plot.max()
-                
-                # Check the size of the array to be plotted 
-                #print(to_plot.shape)
-                
+
+                # Check the size of the array to be plotted
+                # print(to_plot.shape)
+
                 # Set the axis (might be switched?)
-                X=np.linspace(data.metadata.limits[0],data.metadata.limits[1],data.metadata.dimension[0]+1)
-                Y=np.linspace(data.metadata.limits[2],data.metadata.limits[3],data.metadata.dimension[1])
-                
+                X = np.linspace(data.metadata.limits[0],
+                                data.metadata.limits[1],
+                                data.metadata.dimension[0]+1)
+                Y = np.linspace(data.metadata.limits[2],
+                                data.metadata.limits[3],
+                                data.metadata.dimension[1])
+
                 # Create a meshgrid for both x and y
-                y, x = np.meshgrid(Y,X)
-                
-                
+                y, x = np.meshgrid(Y, X)
+
                 # Generate information on necessary colorrange
-                levels = MaxNLocator(nbins=150).tick_values(min_value, max_value)
-                #levels = MaxNLocator(nbins=150).tick_values(to_plot.max()-12, to_plot.max())
-                
+                levels = MaxNLocator(nbins=150).tick_values(min_value,
+                                                            max_value)
+
                 # Select colormap
                 cmap = plt.get_cmap('hot')
                 norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
-                
+
                 # Create the figure
                 fig, (ax0) = plt.subplots()
-                
+
                 # Plot the data on the meshgrids
-                #im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
-                if data.plot_options.log: 
-                    im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=matplotlib.colors.LogNorm(vmin=min_value,vmax=max_value))
+                if data.plot_options.log:
+                    color_norm = matplotlib.colors.LogNorm(vmin=min_value,
+                                                           vmax=max_value)
+                    im = ax0.pcolormesh(x, y, to_plot,
+                                        cmap=cmap, norm=color_norm)
                 else:
                     im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
-                
+
                 # Add the colorbar
                 fig.colorbar(im, ax=ax0)
-                
+
                 # Add a title
                 ax0.set_title(data.metadata.title)
-                
+
                 # Add axis labels
                 plt.xlabel(data.metadata.xlabel)
                 plt.ylabel(data.metadata.ylabel)
-                
+
             else:
                 print("Error, dimension not read correctly")
-            
+
         plt.show()
-        
+
+
 class make_sub_plot:
     """
-    make_plot plots contents of mcstas_data objects
-    
-    Plotting is controlled through options assosciated with the mcstas_data objects.
-    If a list is given, the plots appear in one subplot.
+    make_plot plots contents of McStasData objects
+
+    Plotting is controlled through options assosciated with the
+    McStasData objects.  If a list is given, the plots appear in one
+    subplot.
     """
-    def __init__(self,data_list):
+
+    def __init__(self, data_list):
         """
-        plots mcstas_data, single object or list of mcstas_data
-        
+        plots McStasData, single object or list of McStasData
+
         The options concerning plotting are stored with the data
-        
+
         Parameters
         ----------
-        data_list : mcstas_data or list of mcstas_data
-            mcstas_data to be plotted
-        
+        data_list : McStasData or list of McStasData
+            McStasData to be plotted
         """
-        if not isinstance(data_list,mcstas_data):
-            print("number of elements in data list = " + str(len(data_list)))
+        if not isinstance(data_list, McStasData):
+            print("number of elements in data list = "
+                  + str(len(data_list)))
         else:
-            # Only a single element, put it in a list for easier syntax later
+            # Make list from single element to simplify syntax
             data_list = [data_list]
-            
+
         number_of_plots = len(data_list)
-        
-        # Relevant options: 
+
+        # Relevant options:
         #  select colormap
         #  show / hide colorbar
         #  custom title / label
@@ -697,176 +768,169 @@ def __init__(self,data_list):
         #  log scale (o$rders of magnitude)
         #  compare several 1d
         #  compare 2D
-        
-        #fig = plt.figure(figsize=(20,10))
 
-        
         # Find reasonable grid size for the number of plots
         dim2 = math.ceil(math.sqrt(number_of_plots))
         dim1 = math.ceil(number_of_plots/dim2)
-          
-        fig, axs = plt.subplots(dim1,dim2,figsize=(13,7))
+
+        fig, axs = plt.subplots(dim1, dim2, figsize=(13, 7))
         axs = np.array(axs)
         ax = axs.reshape(-1)
-        
+
         index = -1
         for data in data_list:
             index = index + 1
             ax0 = ax[index]
-              
-            print("Plotting data with name " + data.metadata.component_name)
-            
-            if type(data.metadata.dimension) == int:
-                #fig = plt.figure(0)
-                #plt.subplot(dim1, dim2, n_plot)
-                
-                #print(data.T)
+
+            print("Plotting data with name "
+                  + data.metadata.component_name)
+
+            if isinstance(data.metadata.dimension, int):
+                # fig = plt.figure(0)
+                # plt.subplot(dim1, dim2, n_plot)
                 x = data.xaxis
                 y = data.Intensity
                 y_err = data.Error
-                
-                
+
                 ax0.errorbar(x, y, yerr=y_err)
-                
+
                 if data.plot_options.log:
-                    ax0.set_yscale("log",nonposy='clip')
-                
-                ax0.set_xlim(data.metadata.limits[0],data.metadata.limits[1])
-                
+                    ax0.set_yscale("log", nonposy='clip')
+
+                ax0.set_xlim(data.metadata.limits[0],
+                             data.metadata.limits[1])
+
                 # Add a title
-                #ax0.title(data.title)
-                
+                # ax0.title(data.title)
+
                 # Add axis labels
                 ax0.set_xlabel(data.metadata.xlabel)
                 ax0.set_ylabel(data.metadata.ylabel)
-                
-            elif  len(data.metadata.dimension) == 2:
-                
+
+            elif len(data.metadata.dimension) == 2:
+
                 # Split the data into intensity, error and ncount
                 Intensity = data.Intensity
                 Error = data.Error
                 Ncount = data.Ncount
-                
-                # Select to plot the intensity
-                #to_plot = np.log(Intensity)
-                
+
                 if data.plot_options.log:
                     min_value = np.min(Intensity[np.nonzero(Intensity)])
                     min_value = np.log10(min_value)
-                    
-                    #to_plot = np.log10(Intensity)
+
                     to_plot = Intensity
-                    
+
                     max_value = np.log10(to_plot.max())
-                    
-                    if max_value - min_value > data.plot_options.orders_of_mag:
-                        min_value = max_value - data.plot_options.orders_of_mag
+
+                    if (max_value - min_value
+                            > data.plot_options.orders_of_mag):
+                        min_value = (max_value
+                                     - data.plot_options.orders_of_mag)
                     min_value = 10.0 ** min_value
                     max_value = 10.0 ** max_value
                 else:
                     to_plot = Intensity
                     min_value = to_plot.min()
                     max_value = to_plot.max()
-                
-                # Check the size of the array to be plotted 
-                #print(to_plot.shape)
-                
+
+                # Check the size of the array to be plotted
+                # print(to_plot.shape)
+
                 # Set the axis (might be switched?)
-                X=np.linspace(data.metadata.limits[0],data.metadata.limits[1],data.metadata.dimension[0]+1)
-                Y=np.linspace(data.metadata.limits[2],data.metadata.limits[3],data.metadata.dimension[1])
-                
+                X = np.linspace(data.metadata.limits[0],
+                                data.metadata.limits[1],
+                                data.metadata.dimension[0]+1)
+                Y = np.linspace(data.metadata.limits[2],
+                                data.metadata.limits[3],
+                                data.metadata.dimension[1])
+
                 # Create a meshgrid for both x and y
-                y, x = np.meshgrid(Y,X)
-                
-                
+                y, x = np.meshgrid(Y, X)
+
                 # Generate information on necessary colorrange
-                levels = MaxNLocator(nbins=150).tick_values(min_value, max_value)
-                #levels = MaxNLocator(nbins=150).tick_values(to_plot.max()-12, to_plot.max())
-                
+                levels = MaxNLocator(nbins=150).tick_values(min_value,
+                                                            max_value)
+
                 # Select colormap
-                #cmap = plt.get_cmap('jet')
-                cmap = plt.get_cmap(data.plot_options.colormap)                
-                
-                norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
-                
-                # Create the figure
-                #fig, (ax0) = plt.subplots()
-                
-                #fig, ax0 = plt.subplot(dim1, dim2, n_plot)
-                
-                # Plot the data on the meshgrids
-                #im = plt.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
-                if data.plot_options.log: 
-                    im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=matplotlib.colors.LogNorm(vmin=min_value,vmax=max_value))
+                cmap = plt.get_cmap(data.plot_options.colormap)
+
+                # Select the colorscale normalization
+                if data.plot_options.log:
+                    norm = matplotlib.colors.LogNorm(vmin=min_value,
+                                                     vmax=max_value)
                 else:
-                    im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
-                
-                
+                    norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
+
+                # Create plot
+                im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
+
                 def fmt(x, pos):
                     a, b = '{:.2e}'.format(x).split('e')
                     b = int(b)
                     return r'${} \times 10^{{{}}}$'.format(a, b)
-                
+
                 # Add the colorbar
-                fig.colorbar(im, ax=ax0, format=matplotlib.ticker.FuncFormatter(fmt))
-                
+                fig.colorbar(im, ax=ax0,
+                             format=matplotlib.ticker.FuncFormatter(fmt))
+
                 # Add a title
                 ax0.set_title(data.metadata.title)
-                
+
                 # Add axis labels
                 ax0.set_xlabel(data.metadata.xlabel)
                 ax0.set_ylabel(data.metadata.ylabel)
-                
+
             else:
                 print("Error, dimension not read correctly")
-         
-        plt.show()   
-        
+
+        plt.show()
+
 
 class parameter_variable:
     """
     Class describing a input parameter in McStas instrument
-    
-    McStas input parameters are of default type double, but can be cast. If two
-    positional arguments are given, the first is the type, and the second is the
-    parameter name. With one input, only the parameter name is read.
-    It is also possible to assign a default value and a comment through keyword
-    arguments.
-    
+
+    McStas input parameters are of default type double, but can be
+    cast.  If two positional arguments are given, the first is the
+    type, and the second is the parameter name.  With one input, only
+    the parameter name is read.  It is also possible to assign a
+    default value and a comment through keyword arguments.
+
     Attributes
     ----------
     type : str
         McStas type of input: Double, Int, String
-        
+
     name : str
         Name of input parameter
-        
+
     value : any
         Default value/string of parameter, converted to string
-        
+
     comment : str
-        Comment displayed next to the input parameter, could contain units
-        
+        Comment displayed next to the parameter, could contain units
+
     Methods
     -------
     write_parameter(fo,stop_character)
-        writes the parameter to file object fo, uses given stop character
+        writes the parameter to file fo, uses given stop character
     """
-    def __init__(self,*args,**kwargs):
-        """Initializing mcstas parameter object 
-        
+
+    def __init__(self, *args, **kwargs):
+        """Initializing mcstas parameter object
+
         Parameters
         ----------
         If giving a type:
-        Positional argument 1: type : str
+        Positional argument 1: type: str
             Type of the parameter, double, int or string
-        Positional argument 2: name : str
+        Positional argument 2: name: str
             Name of input parameter
-            
+
         If not giving type
         Positional argument 1: name : str
             Name of input parameter
-        
+
         Keyword arguments
             value : any
                 sets default value of parameter
@@ -879,7 +943,7 @@ def __init__(self,*args,**kwargs):
         if len(args) == 2:
             self.type = args[0] + " "
             self.name = str(args[1])
-        
+
         if "value" in kwargs:
             self.value_set = 1
             self.value = kwargs["value"]
@@ -894,13 +958,13 @@ def __init__(self,*args,**kwargs):
         # could check for allowed types
         # they are int, double, string, are there more?
 
-    def write_parameter(self,fo,stop_character):
+    def write_parameter(self, fo, stop_character):
         """Writes input parameter to file"""
         fo.write("%s%s" % (self.type, self.name))
         if self.value_set == 1:
-            if isinstance(self.value,int):
+            if isinstance(self.value, int):
                 fo.write(" = %d" % self.value)
-            elif isinstance(self.value,float):
+            elif isinstance(self.value, float):
                 fo.write(" = %G" % self.value)
             else:
                 fo.write(" = %s" % str(self.value))
@@ -908,55 +972,60 @@ def write_parameter(self,fo,stop_character):
         fo.write(self.comment)
         fo.write("\n")
 
+
 class declare_variable:
     """
     Class describing a declared variable in McStas instrument
-    
-    McStas parameters are declared in the declare section with c syntax.
-    This class is initialized with type, name. Using keyword arguments,
-    the variable can become an array and have its initial value set.
-    
+
+    McStas parameters are declared in declare section with c syntax.
+    This class is initialized with type, name.  Using keyword
+    arguments, the variable can become an array and have its initial
+    value set.
+
     Attributes
     ----------
     type : str
         McStas type to declare: Double, Int, String
-        
+
     name : str
         Name of variable
-        
+
     value : any
         Initial value of variable, converted to string
-        
+
     comment : str
         Comment displayed next to the declaration, could contain units
-        
-    vector : int 
+
+    vector : int
         0 if a single value is given, ortherwise contains the length
-        
+
     value_set : int
-        internal variable displaying wether or not a value was given
-        
+        Internal variable displaying wether or not a value was given
+
     Methods
     -------
     write_line(fo)
-        writes a line to text file fo declaring the parameter in c syntax
+        Writes a line to text file fo declaring the parameter in c
     """
-    def __init__(self,*args,**kwargs):
-        """Initializing mcstas parameter object 
-        
+    def __init__(self, *args, **kwargs):
+        """Initializing mcstas parameter object
+
         Parameters
         ----------
         Positional argument 1: type : str
             Type of the parameter, double, int or string
-            
+
         Positional argument 2: name : str
             Name of input parameter
-        
+
         Keyword arguments
             array : int
-                length of array to be allocated, default is 0 if not an array
+                length of array to be allocated, 0 if single value
+
             value : any
-                sets initial value of parameter, can be a list with length matching array
+                sets initial value of parameter,
+                can be a list with length matching array
+
             comment : str
                 sets comment displayed next to declaration
         """
@@ -967,7 +1036,7 @@ def __init__(self,*args,**kwargs):
             self.value = kwargs["value"]
         else:
             self.value_set = 0
-            
+
         if "array" in kwargs:
             self.vector = kwargs["array"]
         else:
@@ -978,191 +1047,188 @@ def __init__(self,*args,**kwargs):
         else:
             self.comment = ""
 
-    def write_line(self,fo):
+    def write_line(self, fo):
         """Writes line declaring variable to file fo
-        
+
         Parameters
         ----------
         fo : file object
             File the line will be written to
         """
         if self.value_set == 0 and self.vector == 0:
-            fo.write("%s %s;%s" % (self.type, self.name,self.comment))
+            fo.write("%s %s;%s" % (self.type, self.name, self.comment))
         if self.value_set == 1 and self.vector == 0:
             if self.type == "int":
-                fo.write("%s %s = %d;%s" % (self.type, self.name, self.value, self.comment))
+                fo.write("%s %s = %d;%s" % (self.type, self.name,
+                                            self.value, self.comment))
             else:
-                fo.write("%s %s = %G;%s" % (self.type, self.name, self.value,self.comment))
+                fo.write("%s %s = %G;%s" % (self.type, self.name,
+                                            self.value, self.comment))
         if self.value_set == 0 and self.vector != 0:
-            fo.write("%s %s[%d];%s" % (self.type, self.name, self.vector, self.comment))
+            fo.write("%s %s[%d];%s" % (self.type, self.name,
+                                       self.vector, self.comment))
         if self.value_set == 1 and self.vector != 0:
             fo.write("%s %s[%d] = {" % (self.type, self.name, self.vector))
-            for i in range(0,len(self.value)-1):
+            for i in range(0, len(self.value) - 1):
                 fo.write("%G," % self.value[i])
             fo.write("%G};%s" % (self.value[-1], self.comment))
 
 
 class component:
     """
-    A class describing a McStas component to be written to a instrument file
-    
-    This class is used by the instrument class when setting up components,
-    but can also be used independently.
-    Most information can be given on initialize using keyword arguments, but
-    there are methods for setting the attributes describing the component.
-    The class contains both methods to write the component to a instrument 
-    file and methods for printing to the python terminal for checking the 
-    information.
-    
+    A class describing a McStas component to be written to a instrument
+
+    This class is used by the instrument class when setting up
+    components, but can also be used independently.  Most information
+    can be given on initialize using keyword arguments, but there are
+    methods for setting the attributes describing the component. The
+    class contains both methods to write the component to a instrument
+    file and methods for printing to the python terminal for checking
+    the information.
+
     Attributes
     ----------
     name : str
         Name of the component instance in McStas (must be unique)
-        
+
     component_name : str
         Name of the component code to use, e.g. Arm, Guide_gravity, ...
-        
+
     AT_data : list of 3 floats
         Position data of the component
-    
+
     AT_relative : str
-        String matching name of former component to use as reference for position
-        
+        Name of former component to use as reference for position
+
     ROTATED_data : list of 3 floats
         Rotation data of the component
-        
+
     ROTATED_relative : str
-        String matching name of former component to use as reference for position
-    
+        Name of former component to use as reference for position
+
     WHEN : str
-        String with logical expression in c syntax for when component is active
-    
+        String with logical c expression x for when component is active
+
     EXTEND : str
-        c code that extends the component, can use declared parameters and internal
-    
+        c code for McStas EXTEND section
+
     GROUP : str
         Name of group the component should belong to
-    
+
     JUMP : str
         String describing use of JUMP, need to contain all after "JUMP"
-    
+
     component_parameters : dict
         Parameters to be used with component in dictionary
-    
-    comment : str 
+
+    comment : str
         Comment inserted before the component as an explanation
-    
+
     Methods
     -------
     set_AT(at_list,**kwargs)
-        Sets AT_data, can set AT_relative using keyword argument
-        
+        Sets AT_data, can set AT_relative using keyword
+
     set_ROTATED(rotated_list,**kwargs)
-        Sets ROTATED_data, can set ROTATED_relative using keyword argument
-    
+        Sets ROTATED_data, can set ROTATED_relative using keyword
+
     set_RELATIVE(relative_name)
         Set both AT_relative and ROTATED_relative to relative_name
-    
+
     set_parameters(dict_input)
         Adds dictionary entries to parameter dictionary
-    
+
     set_WHEN(string)
         Sets WHEN string
-    
+
     set_GROUP(string)
         Sets GROUP name
-    
+
     set_JUMP(string)
         Sets JUMP string
-    
+
     append_EXTEND(string)
         Append string to EXTEND string
-        
+
     set_comment(string)
         Sets comment for component
-    
+
     write_component(fo)
         Writes component code to instrument file
-    
+
     print_long()
         Prints basic view of component code (not correct syntax)
-    
+
     print_short(**kwargs)
         Prints short description, used in print_components
-        
     """
-    def __init__(self,instance_name,component_name,**kwargs):
+    def __init__(self, instance_name, component_name, **kwargs):
         """
         Initializes McStas component with specified name and component
-        
+
         Parameters
         ----------
         instance_name : str
             name of the instance of the component
-            
+
         component_name : str
             name of the component type e.g. Arm, Guide_gravity, ...
-        
+
         keyword arguments:
             AT : list of 3 floats
                 Sets AT_data describing position of component
-                
+
             AT_RELATIVE : str
-                sets AT_relative, describing position reference point
-                
+                sets AT_relative, describing position reference
+
             ROTATED : list of 3 floats
                 Sets ROTATED_data, describing rotation of component
-                
+
             ROTATED_RELATIVE : str
-                Sets ROTATED_relative, describing reference component for rotation
-                
+                Sets ROTATED_relative, sets reference for rotation
+
             RELATIVE : str
-                Sets both AT_relative and ROTATED_relative 
-        
+                Sets both AT_relative and ROTATED_relative
+
             WHEN : str
                 Sets WHEN string, should contain logical c expression
-                
+
             EXTEND : str
                 Sets initial EXTEND string, should contain c code
-                
+
             GROUP : str
-                Sets name of group the component instance should belong to
-                
+                Sets name of group the component should belong to
+
             JUMP : str
                 Sets JUMP str
-                
+
             comment: str
                 Sets comment string
-        
         """
-        # Defines a McStas component with name and component name as first inputs
         self.name = instance_name
         self.component_name = component_name
-        
-        # Possible to give AT and ROTATED including AT_RELATIVE / ROTATED_RELATIVE
-        # RELATIVE keyword also exists and sets both AT_RELATIVE and ROTATED_RELATIVE
+
         if "AT" in kwargs:
             self.AT_data = kwargs["AT"]
         else:
-            self.AT_data = [0,0,0]
-        # need to check if AT_RELATIVE is a string
+            self.AT_data = [0, 0, 0]
+        # Could check if AT_RELATIVE is a string
         if "AT_RELATIVE" in kwargs:
             self.AT_relative = "RELATIVE " + kwargs["AT_RELATIVE"]
         else:
             self.AT_relative = "ABSOLUTE"
-        
-        # If rotated is never mentioned, why print it? How does this influence McStas?
+
         if "ROTATED" in kwargs:
             self.ROTATED_data = kwargs["ROTATED"]
         else:
-            self.ROTATED_data = [0,0,0]
-        # need to check if ROTATED_RELATIVE is a string
+            self.ROTATED_data = [0, 0, 0]
+        # Could check if ROTATED_RELATIVE is a string
         if "ROTATED_RELATIVE" in kwargs:
             self.ROTATED_relative = kwargs["ROTATED_RELATIVE"]
         else:
             self.ROTATED_relative = "ABSOLUTE"
-        
-        # need to check if RELATIVE is a string
+
+        # Could check if RELATIVE is a string
         if "RELATIVE" in kwargs:
             self.AT_relative = "RELATIVE " + kwargs["RELATIVE"]
             self.ROTATED_relative = "RELATIVE " + kwargs["RELATIVE"]
@@ -1171,12 +1237,12 @@ def __init__(self,instance_name,component_name,**kwargs):
             self.WHEN = "WHEN (" + kwargs["WHEN"] + ")\n"
         else:
             self.WHEN = ""
-            
+
         if "EXTEND" in kwargs:
             self.EXTEND = kwargs["EXTEND"] + "\n"
         else:
             self.EXTEND = ""
-            
+
         if "GROUP" in kwargs:
             self.GROUP = kwargs["GRPUP"]
         else:
@@ -1187,43 +1253,41 @@ def __init__(self,instance_name,component_name,**kwargs):
         else:
             self.JUMP = ""
 
-        # possible to have a c comment
         if "comment" in kwargs:
             self.comment = kwargs["comment"]
         else:
             self.comment = ""
 
-        # initialize a dictionary
         self.component_parameters = {}
-        
-        # possible to store a preference for wehter this component should
-        #  be in a include file or directly in the instrument?
-        
-    # method for setting AT and AT_RELATIVE after initialization
-    def set_AT(self,at_list,**kwargs):
+
+        """
+        Could store an option for whether this component should be
+        printed in instrument file or in a seperate file which would
+        then be included.
+        """
+
+    def set_AT(self, at_list, **kwargs):
         """Sets AT data, List of 3 floats"""
-        self.AT_data=at_list
+        self.AT_data = at_list
         if "RELATIVE" in kwargs:
             relative_name = kwargs["RELATIVE"]
             if relative_name == "ABSOLUTE":
                 self.AT_relative = relative_name
             else:
                 self.AT_relative = "RELATIVE " + relative_name
-    
-    # method for setting ROTATED and ROTATED_RELATIVE after initialization
-    def set_ROTATED(self,rotated_list,**kwargs):
-        """Sets ROTATED data, List of 3 floats""" 
-        self.ROTATED_data=rotated_list
+
+    def set_ROTATED(self, rotated_list, **kwargs):
+        """Sets ROTATED data, List of 3 floats"""
+        self.ROTATED_data = rotated_list
         if "RELATIVE" in kwargs:
             relative_name = kwargs["RELATIVE"]
             if relative_name == "ABSOLUTE":
                 self.ROTATED_relative = relative_name
             else:
                 self.ROTATED_relative = "RELATIVE " + relative_name
-    
-    # method for setting RELATIVE after initialization
-    def set_RELATIVE(self,relative_name):
-        """Sets both AT_relative and ROTATED_relative""" 
+
+    def set_RELATIVE(self, relative_name):
+        """Sets both AT_relative and ROTATED_relative"""
         if relative_name == "ABSOLUTE":
             self.AT_relative = relative_name
             self.ROTATED_relative = relative_name
@@ -1231,99 +1295,103 @@ def set_RELATIVE(self,relative_name):
             self.AT_relative = "RELATIVE " + relative_name
             self.ROTATED_relative = "RELATIVE " + relative_name
 
-    # method that adds a parameter name / value pair to dictionary
-    def set_parameters(self,dict_input):
-        """Adds additional parameters and their values using a dictionary input"""
+    def set_parameters(self, dict_input):
+        """Adds parameters and their values from dictionary input"""
         self.component_parameters.update(dict_input)
-        
-    def set_WHEN(self,string):
+
+    def set_WHEN(self, string):
         """Sets WHEN string, should be a c logical expression"""
         self.WHEN = string
-        
-    def set_GROUP(self,string):
+
+    def set_GROUP(self, string):
         """Sets GROUP name"""
         self.GROUP = string
-    
-    def set_JUMP(self,string):
+
+    def set_JUMP(self, string):
         """Sets JUMP string, should contain all text after JUMP"""
         self.JUMP = string
-        
-    def append_EXTEND(self,string):
+
+    def append_EXTEND(self, string):
         """Appends a line of code to EXTEND block of component"""
         self.EXTEND = self.EXTEND + string + "\n"
 
-    def set_comment(self,string):
+    def set_comment(self, string):
         """Method that sets a comment to be written to instrument file"""
         self.comment = string
 
-    def write_component(self,fo):
+    def write_component(self, fo):
         """Method that writes component to file"""
-        parameters_per_line = 2 # write comma separated parameters, up to 2 per line
-        # could use a character limit on lines instead
+        parameters_per_line = 2
+        # Could use character limit on lines instead
         parameters_written = 0  # internal parameter
-        number_of_parameters = len(self.component_parameters) # internal parameter
-    
-        # write comment if present
+        number_of_parameters = len(self.component_parameters)
+
+        # Write comment if present
         if len(self.comment) > 1:
             fo.write("// %s\n" % (str(self.comment)))
-    
-        # write component name and component type
+
+        # Write component name and component type
         fo.write("COMPONENT %s = %s(" % (self.name, self.component_name))
-        
+
         if number_of_parameters == 0:
-            fo.write(")\n") # if there are no parameters, close the component immediately
+            fo.write(")\n")  # If there are no parameters, close immediately
         else:
-            fo.write("\n") # if there are parameters to be written, start a new line
-        
-        for key,val in self.component_parameters.items():
-            if isinstance(val,float): # check if value is a number
-                fo.write(" %s = %G" % (str(key),val)) # Small or large numbers written in scientific format
+            fo.write("\n")  # If there are parameters, start a new line
+
+        for key, val in self.component_parameters.items():
+            if isinstance(val, float):  # CHeck if value is a number
+                # Small or large numbers written in scientific format
+                fo.write(" %s = %G" % (str(key), val))
             else:
-                fo.write(" %s = %s" % (str(key),str(val)))
+                fo.write(" %s = %s" % (str(key), str(val)))
             parameters_written = parameters_written + 1
             if parameters_written < number_of_parameters:
-                fo.write(",") # comma between parameters
-                if parameters_written%parameters_per_line == 0:
-                    fo  .write("\n")
+                fo.write(",")  # Comma between parameters
+                if parameters_written % parameters_per_line == 0:
+                    fo.write("\n")
             else:
-                fo.write(")\n") # end paranthesis after last parameter
-                
+                fo.write(")\n")  # End paranthesis after last parameter
+
         # Optional WHEN section
         if not self.WHEN == "":
             fo.write("WHEN(%s)\n" % self.WHEN)
-        # Need to add JUMP section here
-        
-        # write AT and ROTATED section
-        fo.write("AT (%s,%s,%s)" % (str(self.AT_data[0]),str(self.AT_data[1]),str(self.AT_data[2])))
+
+        # Write AT and ROTATED section
+        fo.write("AT (%s,%s,%s)" % (str(self.AT_data[0]),
+                                    str(self.AT_data[1]),
+                                    str(self.AT_data[2])))
         fo.write(" %s\n" % self.AT_relative)
-        fo.write("ROTATED (%s,%s,%s)" % (str(self.ROTATED_data[0]),str(self.ROTATED_data[1]),str(self.ROTATED_data[2])))
+        fo.write("ROTATED (%s,%s,%s)" % (str(self.ROTATED_data[0]),
+                                         str(self.ROTATED_data[1]),
+                                         str(self.ROTATED_data[2])))
         fo.write(" %s\n" % self.ROTATED_relative)
-        
+
         if not self.GROUP == "":
             fo.write("GROUP %s\n" % self.GROUP)
-        
+
         # Optional EXTEND section
         if not self.EXTEND == "":
             fo.write("EXTEND %{\n")
             fo.write("%s" % self.EXTEND)
             fo.write("%}\n")
-            
+
         if not self.JUMP == "":
             fo.write("JUMP %s\n" % self.JUMP)
-            
+
         # Leave a new line between components for readability
         fo.write("\n")
-            
+
     def print_long(self):
-        """Method for printing contained information to Python terminal, not correct syntax"""
+        """Prints contained information to Python terminal"""
         print("// " + self.comment)
-        print("COMPONENT " + str(self.name) + " = " + str(self.component_name))
-        for key,val in self.component_parameters.items():
-            print(" ",key,"=",val)
+        print("COMPONENT", str(self.name),
+              "=", str(self.component_name))
+        for key, val in self.component_parameters.items():
+            print(" ", key, "=", val)
         if not self.WHEN == "":
             print("WHEN (" + self.WHEN + ")")
-        print("AT",self.AT_data,self.AT_relative)
-        print("ROTATED",self.ROTATED_data,self.ROTATED_relative)
+        print("AT", self.AT_data, self.AT_relative)
+        print("ROTATED", self.ROTATED_data, self.ROTATED_relative)
         if not self.GROUP == "":
             print("GROUP " + self.GROUP)
         if not self.EXTEND == "":
@@ -1332,629 +1400,673 @@ def print_long(self):
         if not self.JUMP == "":
             print("JUMP " + self.JUMP)
 
-    def print_short(self,**kwargs):
-        """Method for printing short description of component to list print"""
+    def print_short(self, **kwargs):
+        """Prints short description of component to list print"""
         if "longest_name" in kwargs:
             print("test")
-            print(str(self.name)+" "*(3+kwargs["longest_name"]-len(self.name)),end='')
-            print(str(self.component_name),"\tAT",self.AT_data,self.AT_relative,"ROTATED",self.ROTATED_data,self.ROTATED_relative)
+            number_of_spaces = 3+kwargs["longest_name"]-len(self.name)
+            print(str(self.name) + " "*number_of_spaces, end='')
+            print(str(self.component_name),
+                  "\tAT", self.AT_data, self.AT_relative,
+                  "ROTATED", self.ROTATED_data, self.ROTATED_relative)
         else:
-            print(str(self.name),"=",str(self.component_name),"\tAT",self.AT_data,self.AT_relative,"ROTATED",self.ROTATED_data,self.ROTATED_relative)
+            print(str(self.name), "=", str(self.component_name),
+                  "\tAT", self.AT_data, self.AT_relative,
+                  "ROTATED", self.ROTATED_data, self.ROTATED_relative)
 
 
 class McStas_instr:
     """
     Main class for writing a McStas instrument using McStasScript
-    
+
     Initialization of McStas_instr sets the name of the instrument file
     and its methods are used to add all aspects of the instrument file.
-    The class also holds methods for writing the finished instrument file
-    to disk and to run the simulation.
-    
+    The class also holds methods for writing the finished instrument
+    file to disk and to run the simulation.
+
     Attributes
     ----------
     name : str
         name of instrument file
-    
+
     author : str
         name of user of McStasScript, written to the file
-        
+
     origin : str
         origin of instrument file (affiliation)
-        
+
     mcrun_path : str
         absolute path of mcrun command, or empty if it is in path
-        
+
     parameter_list : list of parameter_variable instances
         contains all input parameters to be written to file
-        
+
     declare_list : list of declare_variable instances
         contains all declare parrameters to be written to file
-        
+
     initialize_section : str
-        string containing entire initialize section to be written to file
-        
+        string containing entire initialize section to be written
+
     trace_section : str
         string containing trace section (OBSOLETE)
-        
+
     finally_section : str
-        string containing entire finally section to be written to file
-        
+        string containing entire finally section to be written
+
     component_list : list of component instances
         list of components in the instrument
-        
+
     component_name_list : list of strings
         list of names of the components in the instrument
-    
+
     Methods
     -------
     add_parameter(*args,**kwargs)
         Adds input parameter to the define section
-    
+
     add_declare_var()
         Adds declared variable ot the declare section
-    
+
     append_initialize(string)
-        Appends a string to the initialize section, followed by a new line
-        
+        Appends a string to the initialize section, then adds new line
+
     append_initialize_no_new_line(string)
         Appends a string to the initialize section
-    
+
     append_finally(string)
-        Appends a string to finally section, followed by a new line
-    
+        Appends a string to finally section, then adds new line
+
     append_finally_no_new_line(string)
         Appends a string to finally section
-    
+
     append_trace(string)
-        Obsolete method, add components instead (still used in write_c_files)
-    
+        Obsolete method, add components instead (used in write_c_files)
+
     add_component(instance_name,component_name,**kwargs)
         Add a component to the instrument file
-    
+
     get_component(instance_name)
         Returns component instance with name instance_name
-    
+
     get_last_component()
         Returns component instance of last component
-    
+
     set_component_parameter(instance_name,dict)
         Adds parameters as dict to component with instance_name
-        
+
     set_component_AT(instance_name,AT_data,**kwargs)
-        Sets position of component named instance_name, reference component can be set in kwargs
-    
+        Sets position of component named instance_name
+
     set_component_ROTATED(instance_name,ROTATED_data,**kwargs)
-        Sets rotation of component named instance_name, reference component can be set in kwargs
-    
+        Sets rotation of component named instance_name
+
     set_component_RELATIVE(instane_name,string)
-        Sets reference component named instance_name for both position and rotation
-    
+        Sets position and rotation reference for named component
+
     set_component_WHEN(instance_name,string)
-        Sets WHEN condition of component named instance_name, should be logical c expression
-    
+        Sets WHEN condition of named component, is logical c expression
+
     set_component_GROUP(instance_name,string)
         Sets GROUP name of component named instance_name
-    
+
     append_component_EXTEND(instance_name,string)
-        Appends a line to EXTEND section of component named instance_name
-        
+        Appends a line to EXTEND section of named component
+
     set_component_JUMP(instance_name,string)
-        Sets JUMP code for component named instance_name
-    
+        Sets JUMP code for named component
+
     set_component_comment(instance_name,string)
-        Sets comment to be written before component named instance_name
-    
+        Sets comment to be written before named component
+
     print_component(instance_name)
-        Prints an overview of current state of component named instance_name
-    
+        Prints an overview of current state of named component
+
     print_component_short(instance_name)
-        Prints short overview of current state of component named instance_name
-    
+        Prints short overview of current state of named component
+
     print_components()
-        Prints overview of postion / rotation of all components in instrument
-    
+        Prints overview of postion / rotation of all components
+
     write_c_files()
-        Writes c files to include in existing McStas instrument in folder named generated_includes
-    
+        Writes c files for %include in generated_includes folder
+
     write_full_instrument()
-        Writes full instrument file to current directory, name as set in class initialization
-    
+        Writes full instrument file to current directory
+
     run_full_instrument(**kwargs)
-        Writes instrument files and runs a simulation with mcrun. Returns list of mcstas_data  
-    
+        Writes instrument files and runs simulation.
+        Returns list of McStasData
     """
-    
-    def __init__(self,name,**kwargs):
+
+    def __init__(self, name, **kwargs):
         """
         Initialization of McStas Instrument
-        
+
         Parameters
         ----------
         name : str
-            Name of project, instrument file will be called name + ".instr"
-            
+            Name of project, instrument file will be name + ".instr"
+
         keyword arguments:
             author : str
-                Name of author, will be written in instrument file
-                
+                Name of author, written in instrument file
+
             origin : str
-                Affiliation of author, will be written in instrument file
-                
+                Affiliation of author, written in instrument file
+
             mcrun_path : str
-                Absolute path of mcrun or empty string if already in path
+                Absolute path of mcrun or empty if already in path
         """
         self.name = name
-        
+
         if "author" in kwargs:
             self.author = kwargs["author"]
         else:
             self.author = "Python McStas Instrument Generator"
-        
+
         if "origin" in kwargs:
             self.origin = kwargs["origin"]
         else:
             self.origin = "ESS DMSC"
-            
+
         if "mcrun_path" in kwargs:
             self.mcrun_path = kwargs["mcrun_path"]
         else:
             self.mcrun_path = ""
-        
+
         self.parameter_list = []
         self.declare_list = []
-        self.initialize_section = "// Start of initialize for generated " + name + "\n"
-        self.trace_section = "// Start of trace section for generated " + name + "\n"
-        self.finally_section = "// Start of finally for generated " + name + "\n"
-        # handle components
-        self.component_list = [] # list of components (have to be ordered)
-        self.component_name_list = [] # list of component names
-        
-    def add_parameter(self,*args,**kwargs):
+        self.initialize_section = ("// Start of initialize for generated "
+                                   + name + "\n")
+        self.trace_section = ("// Start of trace section for generated "
+                              + name + "\n")
+        self.finally_section = ("// Start of finally for generated "
+                                + name + "\n")
+        # Handle components
+        self.component_list = []  # List of components (have to be ordered)
+        self.component_name_list = []  # List of component names
+
+    def add_parameter(self, *args, **kwargs):
         """
         Method for adding input parameter to instrument
-        
+
         Parameters
         ----------
-        
+
         (optional) parameter type : str
             type of input parameter, double, int, string
-        
+
         parameter name : str
             name of parameter
-            
+
         keyword arguments
             value : any
                 Default value of parameter
-                
+
             comment : str
                 Comment displayed next to declaration of parameter
-        
         """
-        # type of variable, name of variable, options described in declare_parameter class
-        self.parameter_list.append(parameter_variable(*args,**kwargs))
+        # parameter_variable class documented independently
+        self.parameter_list.append(parameter_variable(*args, **kwargs))
 
-    def add_declare_var(self,*args,**kwargs):
+    def add_declare_var(self, *args, **kwargs):
         """
         Method for adding declared variable to instrument
-        
+
         Parameters
         ----------
-        
+
         parameter type : str
             type of input parameter
-        
+
         parameter name : str
             name of parameter
-            
+
         keyword arguments
             array : int
                 default 0 for scalar, if specified length of array
-        
+
             value : any
                 Initial value of parameter, can be list of length vector
-                
+
             comment : str
                 Comment displayed next to declaration of parameter
-        
+
         """
-        # type of variable, name of variable, options described in declare_variable class
-        self.declare_list.append(declare_variable(*args,**kwargs))
+        # declare_variable class documented independently
+        self.declare_list.append(declare_variable(*args, **kwargs))
 
-    def append_initialize(self,string):
+    def append_initialize(self, string):
         """
-        Method for appending code to the intialize section of instrument
-        
+        Method for appending code to the intialize section
+
         The intialize section consists of c code and will be compiled,
-        thus any syntax errors will crash the simulation. Code is added 
+        thus any syntax errors will crash the simulation. Code is added
         on a new line for each call to this method.
-        
+
         Parameters
         ----------
         string : str
             code to be added to initialize section
-        
         """
         self.initialize_section = self.initialize_section + string + "\n"
-    
-    def append_initialize_no_new_line(self,string):
+
+    def append_initialize_no_new_line(self, string):
         """
-        Method for appending code to the intialize section of instrument (no new line)
-        
+        Method for appending code to the intialize section, no new line
+
         The intialize section consists of c code and will be compiled,
-        thus any syntax errors will crash the simulation. Code is added 
+        thus any syntax errors will crash the simulation. Code is added
         to the current line.
-        
+
         Parameters
         ----------
         string : str
             code to be added to initialize section
-        
+
         """
+
         self.initialize_section = self.initialize_section + string
-        
-    def append_finally(self,string):
+
+    def append_finally(self, string):
         """
         Method for appending code to the finally section of instrument
-        
+
         The finally section consists of c code and will be compiled,
-        thus any syntax errors will crash the simulation. Code is added 
+        thus any syntax errors will crash the simulation. Code is added
         on a new line for each call to this method.
-        
+
         Parameters
         ----------
         string : str
             code to be added to finally section
-        
+
         """
+
         self.finally_section = self.finally_section + string + "\n"
-    
-    def append_finally_no_new_line(self,string):
+
+    def append_finally_no_new_line(self, string):
         """
         Method for appending code to the finally section of instrument
-        
+
         The finally section consists of c code and will be compiled,
-        thus any syntax errors will crash the simulation. Code is added 
+        thus any syntax errors will crash the simulation. Code is added
         to the current line.
-        
+
         Parameters
         ----------
         string : str
             code to be added to finally section
-        
         """
+
         self.finally_section = self.finally_section + string
-    
-    # Need to handle trace string differently when components also exists
-    #  A) Could have trace string as a component attribute and set it before / after
-    #  B) Could have trace string as a McStas_instr attribute and still attach placement to components
-    #  C) Could have trace string as a different object and place it in component_list, but have a write function named as the component write function?
-    
-    def append_trace(self,string):
-        """
-        Method for appending code to trace section, only used in write_c_files
-        
-        The most common way to add code to the trace section is to add 
-        components using the seperate methods for this. This method is kept
-        as is still used for writing to c files used in legacy code. Each call
-        creates a new line.
-        
+
+    """
+    # Handle trace string differently when components also exists
+    #  A) Coul d have trace string as a component attribute and set
+    #     it before / after
+    #  B) Could have trace string as a McStas_instr attribute and
+    #     still attach placement to components
+    #  C) Could have trace string as a different object and place it
+    #     in component_list, but have a write function named as the
+    #     component write function?
+    """
+
+    def append_trace(self, string):
+        """
+        Appends code to trace section, only used in write_c_files
+
+        The most common way to add code to the trace section is to add
+        components using the seperate methods for this.  This method is
+        kept as is still used for writing to c files used in legacy
+        code.  Each call creates a new line.
+
         Parameters
         ----------
         string : str
             code to be added to trace
-        
         """
+
         self.trace_section = self.trace_section + string + "\n"
-    
-    def append_trace_no_new_line(self,string):
-        """
-        Method for appending code to trace section, only used in write_c_files
-        
-        The most common way to add code to the trace section is to add 
-        components using the seperate methods for this. This method is kept
-        as is still used for writing to c files used in legacy code. No new
-        line is made with this call.
-        
+
+    def append_trace_no_new_line(self, string):
+        """
+        Appends code to trace section, only used in write_c_files
+
+        The most common way to add code to the trace section is to add
+        components using the seperate methods for this.  This method is
+        kept as is still used for writing to c files used in legacy
+        code.  No new line is made with this call.
+
         Parameters
         ----------
         string : str
             code to be added to trace
-        
         """
+
         self.trace_section = self.trace_section + string
-                 
-    
-    def add_component(self,*args,**kwargs):
+
+    def add_component(self, *args, **kwargs):
         """
         Method for adding a new component instance to the instrument
-        
-        Creates a new component instance in the instrument. This requires
-        a unique instance name of the component to be used for future reference
-        and the name of the McStas component to be used. The component is placed
-        at the end of the instrument file unless otherwise specified with the 
-        after and before keywords. The component may be initialized using other
-        keyword arguments, but all attributes can be set with approrpiate methods.
-        
+
+        Creates a new component instance in the instrument.  This
+        requires a unique instance name of the component to be used for
+        future reference and the name of the McStas component to be
+        used.  The component is placed at the end of the instrument file
+        unless otherwise specified with the after and before keywords.
+        The component may be initialized using other keyword arguments,
+        but all attributes can be set with approrpiate methods.
+
         Parameters
         ----------
         First positional argument : str
             Unique name of component instance
-            
+
         Second positional argument : str
             Name of McStas component to create instance of
-            
+
         Keyword arguments:
             after : str
-                Place this component after an already added component with given name
-                
+                Place this component after component with given name
+
             before : str
-                Place this component before an already added component with given name
-        
+                Place this component before component with given name
+
             AT : List of 3 floats
-                Sets AT_data that determines position relative to reference
-                
+                Sets AT_data, position relative to reference
+
             AT_RELATIVE : str
                 Sets reference component for postion
-                
-            ROTATED : List of 3 floats 
-                Sets RELATIVE_data that determines rotation relative to reference
-                
+
+            ROTATED : List of 3 floats
+                Sets ROTATED_data, rotation relative to reference
+
             ROTATED_RELATIVE : str
                 Sets reference component for rotation
-                
+
             RELATIVE : str
                 Sets reference component for both position and rotation
-                
+
             WHEN : str
                 Sets when condition which must be a logical c expression
-                
+
             EXTEND : str
                 Initialize the extend section with a line of c code
-                
+
             GROUP : str
                 Name of the group this component should belong to
-                
+
             JUMP : str
                 Set code for McStas JUMP statement
-                
+
             comment : str
-                Sets a comment that will be displayed before the component
+                Comment that will be displayed before the component
         """
+
         if args[0] in self.component_name_list:
-            raise NameError("Component name \"" + str(args[0]) + "\" used twice, McStas does not allow this. Rename or remove one instance of this name.")
-        
-        if "after" in kwargs: # insert component after component with this name
+            raise NameError(("Component name \"" + str(args[0])
+                             + "\" used twice, McStas does not allow this."
+                             + " Rename or remove one instance of this"
+                             + " name."))
+
+        # Insert component after component with this name
+        if "after" in kwargs:
             if kwargs["after"] not in self.component_name_list:
-                raise NameError("Trying to add a component after a component named \"" + str(kwargs["after"]) + "\", but a component with that name was not found.")
-        
+                raise NameError(("Trying to add a component after a component"
+                                 + " named \"" + str(kwargs["after"])
+                                 + "\", but a component with that name was"
+                                 + " not found."))
+
             new_index = self.component_name_list.index(kwargs["after"])
-            self.component_list.insert(new_index+1,component(*args,**kwargs))
-            self.component_name_list.insert(new_index+1,args[0])
-        elif "before" in kwargs: # insret component after component with this name
+            self.component_list.insert(new_index+1,
+                                       component(*args, **kwargs))
+            self.component_name_list.insert(new_index+1, args[0])
+
+        # Insert component after component with this name
+        elif "before" in kwargs:
             if kwargs["before"] not in self.component_name_list:
-                raise NameError("Trying to add a component before a component named \"" + str(kwargs["before"]) + "\", but a component with that name was not found.")
-        
+                raise NameError(("Trying to add a component before a "
+                                 + "component named \""
+                                 + str(kwargs["before"])
+                                 + "\", but a component with that "
+                                 + "name was not found."))
+
             new_index = self.component_name_list.index(kwargs["before"])
-            self.component_list.insert(new_index,component(*args,**kwargs))
-            self.component_name_list.insert(new_index,args[0])
+            self.component_list.insert(new_index, component(*args, **kwargs))
+            self.component_name_list.insert(new_index, args[0])
+
+        # If after or before keywords absent, place component at the end
         else:
-            self.component_list.append(component(*args,**kwargs))
+            self.component_list.append(component(*args, **kwargs))
             self.component_name_list.append(args[0])
-    
-    def get_component(self,name):
+
+    def get_component(self, name):
         """
         Get the component instance of component with specified name
-        
-        This method is used to get direct access to any component instance
-        in the instrument. The component instance can be manipulated in 
-        much the same way, but it is not necessary to specify the name in
-        each call.
-        
+
+        This method is used to get direct access to any component
+        instance in the instrument.  The component instance can be
+        manipulated in much the same way, but it is not necessary to
+        specify the name in each call.
+
         Parameters
         ----------
         name : str
             Unique name of component whos instance should be returned
         """
+
         if name in self.component_name_list:
             index = self.component_name_list.index(name)
             return self.component_list[index]
         else:
-            raise NameError("No component was found with name \"" + str(name) + "\"!")
-            
+            raise NameError(("No component was found with name \""
+                             + str(name) + "\"!"))
+
     def get_last_component(self):
         """
         Get the component instance of last component in the instrument
-        
-        This method is used to get direct access to any component instance
-        in the instrument. The component instance can be manipulated in 
-        much the same way, but it is not necessary to specify the name in
-        each call.
+
+        This method is used to get direct access to any component
+        instance in the instrument.  The component instance can be
+        manipulated in much the same way, but it is not necessary to
+        specify the name in each call.
         """
+
         return self.component_list[-1]
 
-    def set_component_parameter(self,name,input_dict):
+    def set_component_parameter(self, name, input_dict):
         """
         Add parameters and their values as dictionary to component
-        
+
         This method is the primary way of specifying parameters in a
-        component. Parameters are added to a dictionary specifying 
+        component.  Parameters are added to a dictionary specifying
         parameter name and value pairs.
-        
+
         Parameters
         ----------
         name : str
             Unique name of component to modify
-        
+
         input_dict : dict
             Set of new parameter name and value pairs to add
         """
+
         component = self.get_component(name)
         component.set_parameters(input_dict)
 
-    def set_component_AT(self,name,at_list,**kwargs):
+    def set_component_AT(self, name, at_list, **kwargs):
         """
         Method for setting position of component
-        
+
         Parameters
         ----------
         name : str
             Unique name of component to modify
-            
+
         at_list : List of 3 floats
             Position of component relative to reference component
-            
+
         keyword arguments:
             RELATIVE : str
                 Sets reference component for position
         """
+
         component = self.get_component(name)
-        component.set_AT(at_list,**kwargs)
-        
-    def set_component_ROTATED(self,name,rotated_list,**kwargs):
+        component.set_AT(at_list, **kwargs)
+
+    def set_component_ROTATED(self, name, rotated_list, **kwargs):
         """
         Method for setting rotiation of component
-        
+
         Parameters
         ----------
         name : str
             Unique name of component to modify
-            
+
         rotated_list : List of 3 floats
             Rotation of component relative to reference component
-            
+
         keyword arguments:
             RELATIVE : str
                 Sets reference component for rotation
         """
+
         component = self.get_component(name)
-        component.set_ROTATED(rotated_list,**kwargs)
-    
-    def set_component_RELATIVE(self,name,relative):
+        component.set_ROTATED(rotated_list, **kwargs)
+
+    def set_component_RELATIVE(self, name, relative):
         """
         Method for setting reference of component position and rotation
-        
+
         Parameters
         ----------
         name : str
             Unique name of component to modify
-            
+
         relative : str
             Reference component for position and rotation
         """
+
         component = self.get_component(name)
         component.set_RELATIVE(relative)
-        
-    def set_component_WHEN(self,name,WHEN):
+
+    def set_component_WHEN(self, name, WHEN):
         """
         Method for setting WHEN c expression to named component
-        
+
         Parameters
         ----------
         name : str
-            Unique name of component to modify 
-        
+            Unique name of component to modify
+
         WHEN : str
             Sets WHEN c expression for named McStas component
         """
         component = self.get_component(name)
         component.set_WHEN(WHEN)
-        
-    def append_component_EXTEND(self,name,EXTEND):
+
+    def append_component_EXTEND(self, name, EXTEND):
         """
         Method for adding line of c to EXTEND section of named component
-        
+
         Parameters
         ----------
         name : str
-            Unique name of component to modify 
-        
+            Unique name of component to modify
+
         EXTEND : str
             Line of c code added to EXTEND section of named component
         """
+
         component = self.get_component(name)
         component.append_EXTEND(EXTEND)
-        
-    def set_component_GROUP(self,name,GROUP):
+
+    def set_component_GROUP(self, name, GROUP):
         """
         Method for setting GROUP name of named component
-        
+
         Parameters
         ----------
         name : str
-            Unique name of component to modify 
-        
+            Unique name of component to modify
+
         GROUP : str
             Sets GROUP name for named McStas component
         """
+
         component = self.get_component(name)
         component.set_GROUP(GROUP)
-        
-    def set_component_JUMP(self,name,JUMP):
+
+    def set_component_JUMP(self, name, JUMP):
         """
         Method for setting JUMP expression of named component
-        
+
         Parameters
         ----------
         name : str
-            Unique name of component to modify 
-        
+            Unique name of component to modify
+
         JUMP : str
             Sets JUMP expression for named McStas component
         """
+
         component = self.get_component(name)
         component.set_JUMP(JUMP)
-    
-    def set_component_comment(self,name,string):
+
+    def set_component_comment(self, name, string):
         """
         Sets a comment displayed before the component in written files
-        
+
         Parameters
         ----------
         name : str
-            Unique name of component to modify 
-                
+            Unique name of component to modify
+
         string : str
             Comment string
-        
+
         """
+
         component = self.get_component(name)
         component.set_comment(string)
-    
-    def print_component(self,name):
+
+    def print_component(self, name):
         """
         Method for printing summary of contents in named component
-        
+
         Parameters
         ----------
         name : str
             Unique name of component to print
         """
+
         component = self.get_component(name)
         component.print_long()
-    
-    def print_component_short(self,name):
+
+    def print_component_short(self, name):
         """
         Method for printing summary of contents in named component
-        
+
         Parameters
         ----------
         name : str
             Unique name of component to print
         """
+
         component = self.get_component(name)
         component.print_short()
-    
+
     def print_components(self):
         """
         Method for printing overview of all components in instrument
-        
+
         Provides overview of component names, what McStas component is
         used for each and their position and rotation in space.
-    
         """
-        longest_name = len(max(self.component_name_list,key=len))
-        
+
+        longest_name = len(max(self.component_name_list, key=len))
+
         # Investigate how this could have been done in a better way
         # Find longest field for each type of data printed
         component_type_list = []
@@ -1976,36 +2088,57 @@ def print_components(self):
             rotated_y_list.append(str(component.ROTATED_data[1]))
             rotated_z_list.append(str(component.ROTATED_data[2]))
             rotated_relative_list.append(component.ROTATED_relative)
-        
-        longest_component_name = len(max(component_type_list,key=len))
-        longest_at_x_name = len(max(at_x_list,key=len))
-        longest_at_y_name = len(max(at_y_list,key=len))
-        longest_at_z_name = len(max(at_z_list,key=len))
-        longest_at_relative_name = len(max(at_relative_list,key=len))
-        longest_rotated_x_name = len(max(rotated_x_list,key=len))
-        longest_rotated_y_name = len(max(rotated_y_list,key=len))
-        longest_rotated_z_name = len(max(rotated_z_list,key=len))
-        longest_rotated_relative_name = len(max(rotated_relative_list,key=len))
-        
+
+        longest_component_name = len(max(component_type_list, key=len))
+        longest_at_x_name = len(max(at_x_list, key=len))
+        longest_at_y_name = len(max(at_y_list, key=len))
+        longest_at_z_name = len(max(at_z_list, key=len))
+        longest_at_relative_name = len(max(at_relative_list, key=len))
+        longest_rotated_x_name = len(max(rotated_x_list, key=len))
+        longest_rotated_y_name = len(max(rotated_y_list, key=len))
+        longest_rotated_z_name = len(max(rotated_z_list, key=len))
+        longest_rotated_relative_name = len(max(rotated_relative_list,
+                                                key=len))
+
         # Have longest field for each type, use ljust to align all columns
         for component in self.component_list:
-            print(str(component.name).ljust(longest_name+2),end=' ')
-            print(str(component.component_name).ljust(longest_component_name+2),end=' ')
-            print("AT ",str(component.AT_data).ljust(longest_at_x_name+longest_at_y_name+longest_at_z_name+11),end='')
-            print(component.AT_relative.ljust(longest_at_relative_name+2),end=' ')
-            print("ROTATED ",str(component.ROTATED_data).ljust(longest_rotated_x_name+longest_rotated_y_name+longest_rotated_z_name+11),end='')
+            print(str(component.name).ljust(longest_name+2), end=' ')
+
+            comp_name = component.component_name
+            comp_name_print = str(comp_name).ljust(longest_component_name + 2)
+            print(comp_name_print, end=' ')
+
+            comp_at_data = str(component.AT_data)
+            longest_at_xyz_sum = (longest_at_x_name
+                                  + longest_at_y_name
+                                  + longest_at_z_name)
+            print("AT ",
+                  comp_at_data.ljust(longest_at_xyz_sum + 11),
+                  end='')
+
+            comp_at_relative = component.AT_relative
+            print(comp_at_relative.ljust(longest_at_relative_name + 2),
+                  end=' ')
+
+            comp_rotated_data = str(component.ROTATED_data)
+            longest_rotated_xyz_sum = (longest_rotated_x_name
+                                       + longest_rotated_y_name
+                                       + longest_rotated_z_name)
+            print("ROTATED ",
+                  comp_rotated_data.ljust(longest_rotated_xyz_sum + 11),
+                  end='')
             print(component.ROTATED_relative)
-            #print("")
+            # print("")
 
     def write_c_files(self):
         """
-        Obsolete method for writing instrument to c files for later use
-        
-        It is possible to use this function to write c files to a folder called
-        generated_includes that can then be included in the different sections
-        of a McStas instrument. Component objects are NOT written to these
-        files, but rather the contents of the trace_section that can be set 
-        using the append_trace method.
+        Obsolete method for writing instrument parts to c files
+
+        It is possible to use this function to write c files to a folder
+        called generated_includes that can then be included in the
+        different sections of a McStas instrument. Component objects are
+        NOT written to these files, but rather the contents of the
+        trace_section that can be set using the append_trace method.
         """
         path = os.getcwd()
         path = path + "/generated_includes"
@@ -2013,26 +2146,27 @@ def write_c_files(self):
             try:
                 os.mkdir(path)
             except OSError:
-                print ("Creation of the directory %s failed" % path)
-        
-        fo = open("./generated_includes/" + self.name + "_declare.c","w")
+                print("Creation of the directory %s failed" % path)
+
+        fo = open("./generated_includes/" + self.name + "_declare.c", "w")
         fo.write("// declare section for %s \n" % self.name)
         fo.close()
-        fo = open("./generated_includes/" + self.name + "_declare.c","a")
+        fo = open("./generated_includes/" + self.name + "_declare.c", "a")
         for dec_line in self.declare_list:
             dec_line.write_line(fo)
             fo.write("\n")
         fo.close()
-        
-        fo = open("./generated_includes/" + self.name + "_initialize.c","w")
+
+        fo = open("./generated_includes/" + self.name + "_initialize.c", "w")
         fo.write(self.initialize_section)
         fo.close()
 
-        fo = open("./generated_includes/" + self.name + "_trace.c","w")
+        fo = open("./generated_includes/" + self.name + "_trace.c", "w")
         fo.write(self.trace_section)
         fo.close()
-        
-        fo = open("./generated_includes/" + self.name + "_component_trace.c","w")
+
+        fo = open("./generated_includes/" + self.name
+                  + "_component_trace.c", "w")
         for component in self.component_list:
             component.write_component(fo)
         fo.close()
@@ -2040,31 +2174,35 @@ def write_c_files(self):
     def write_full_instrument(self):
         """
         Method for writing full instrument file to disk
-        
-        This method writes the instrument described by the instrument object to
-        disk with the name specified in the initialization of the object.
+
+        This method writes the instrument described by the instrument
+        objects to disk with the name specified in the initialization of
+        the object.
         """
-        
+
         # Create file identifier
-        fo = open(self.name + ".instr","w")
+        fo = open(self.name + ".instr", "w")
 
         # Write quick doc start
-        fo.write("/" + "*"*80 + "\n")
+        fo.write("/" + 80*"*" + "\n")
         fo.write("* \n")
         fo.write("* McStas, neutron ray-tracing package\n")
         fo.write("*         Copyright (C) 1997-2008, All rights reserved\n")
         fo.write("*         Risoe National Laboratory, Roskilde, Denmark\n")
         fo.write("*         Institut Laue Langevin, Grenoble, France\n")
         fo.write("* \n")
-        fo.write("* This file was written by the Python McStas Instrument Generator \n")
-        fo.write("*  which was written by Mads Bertelsen in 2019 while employed at \n")
-        fo.write("*  the European Spallation Source Data Management and Software Center\n")
+        fo.write("* This file was written by McStasScript, which is a \n")
+        fo.write("* python based McStas instrument generator written by \n")
+        fo.write("* Mads Bertelsen in 2019 while employed at the \n")
+        fo.write("* European Spallation Source Data Management and \n")
+        fo.write("* Software Center\n")
         fo.write("* \n")
         fo.write("* Instrument %s\n" % self.name)
         fo.write("* \n")
-        fo.write("* %Identification\n") # Could allow the user to insert these
+        fo.write("* %Identification\n")  # Could allow the user to insert this
         fo.write("* Written by: %s\n" % self.author)
-        fo.write("* Date: %s\n" % datetime.datetime.now().strftime("%H:%M:%S on %B %d, %Y"))
+        t_format = "%H:%M:%S on %B %d, %Y"
+        fo.write("* Date: %s\n" % datetime.datetime.now().strftime(t_format))
         fo.write("* Origin: %s\n" % self.origin)
         fo.write("* %INSTRUMENT_SITE: Generated_instruments\n")
         fo.write("* \n")
@@ -2079,9 +2217,9 @@ def write_full_instrument(self):
         fo.write("\n")
         # Add loop that inserts parameters here
         for variable in self.parameter_list[0:-1]:
-            variable.write_parameter(fo,",")
+            variable.write_parameter(fo, ",")
         if len(self.parameter_list) > 0:
-            self.parameter_list[-1].write_parameter(fo," ")
+            self.parameter_list[-1].write_parameter(fo, " ")
         fo.write(")\n")
         fo.write("\n")
 
@@ -2096,7 +2234,10 @@ def write_full_instrument(self):
         fo.write("INITIALIZE \n%{\n")
         fo.write(self.initialize_section)
         # Alternatively hide everything in include
-        # fo.write("%include "generated_includes/" + self.name + "_initialize.c")
+        """
+        fo.write("%include "generated_includes/"
+                  + self.name + "_initialize.c")
+        """
         fo.write("%}\n\n")
 
         # Write trace
@@ -2112,20 +2253,21 @@ def write_full_instrument(self):
 
         # End instrument file
         fo.write("\nEND\n")
-        
-    def run_full_instrument(self,*args,**kwargs):
-        """
-        Runs McStas instrument described by this class, returns list of mcstas_data
-        
-        This method will write the instrument to disk and then run it using
-        the mcrun command of the system. Options are set using keyword 
-        arguments. Some options are mandatory, for example foldername, which 
-        can not already exist, if it does data will be read from this folder.
-        If the mcrun command is not in the path of the system, the absolute 
-        path can be given with the mcrun_path keyword argument. This path
-        could also already have been set at initialization of the instrument
-        object.
-        
+
+    def run_full_instrument(self, *args, **kwargs):
+        """
+        Runs McStas instrument described by this class, returns list of
+        McStasData
+
+        This method will write the instrument to disk and then run it
+        using the mcrun command of the system. Options are set using
+        keyword arguments.  Some options are mandatory, for example
+        foldername, which can not already exist, if it does data will
+        be read from this folder.  If the mcrun command is not in the
+        path of the system, the absolute path can be given with the
+        mcrun_path keyword argument.  This path could also already have
+        been set at initialization of the instrument object.
+
         Parameters
         ----------
         Keyword arguments
@@ -2144,16 +2286,13 @@ def run_full_instrument(self,*args,**kwargs):
         """
         # Write the instrument file
         self.write_full_instrument()
-        
+
         # Make sure mcrun path is in kwargs
-        if not "mcrun_path" in kwargs:
+        if "mcrun_path" not in kwargs:
             kwargs["mcrun_path"] = self.mcrun_path
-            
-        # Set up the simulation 
-        simulation = managed_mcrun(self.name + ".instr",**kwargs)
-        
+
+        # Set up the simulation
+        simulation = ManagedMcrun(self.name + ".instr", **kwargs)
+
         # Run the simulation and return data
         return simulation.run_simulation()
-        
-        
-        

From 34ac5496ca7c2a3c8ff70553b2f31a968e1ee546 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Thu, 9 May 2019 16:38:10 +0200
Subject: [PATCH 011/403] Major change in how components are added to
 instrument files.

McStasScript now reads component files from the McStas installation
when adding a component, and is thus aware of the allowed parameters,
default values, units and even descriptions harvested from comments.
This is used to create a subclass of component for each component
type added to the instrument.
The user can now access paramters in a component directly as
attributes. The user is informed if parameters are misspelled as
addition of new attributes are locked at the end of __init__.

There are also more help functions focused on helping the user find
the right component and explaining the parameters. These have gotten
some syntax highlighting. The print_long function also reminds the
user of required parameters that have yet to be specified.

The new features are demonstrated in a new jupyter notebook called
McStasScript_demo.ipynb.

The documentation will be updated to reflect the changes very soon.
---
 McStasScript.py         | 739 +++++++++++++++++++++++++++++++++++++++-
 McStasScript_demo.ipynb | 495 +++++++++++++++++++++++++++
 demonstration.py        |  20 +-
 3 files changed, 1234 insertions(+), 20 deletions(-)
 create mode 100644 McStasScript_demo.ipynb

diff --git a/McStasScript.py b/McStasScript.py
index c63a603e..e90638f8 100644
--- a/McStasScript.py
+++ b/McStasScript.py
@@ -27,6 +27,17 @@
     basestring = str
 
 
+class bcolors:
+    HEADER = '\033[95m'
+    OKBLUE = '\033[94m'
+    OKGREEN = '\033[92m'
+    WARNING = '\033[93m'
+    FAIL = '\033[91m'
+    ENDC = '\033[0m'
+    BOLD = '\033[1m'
+    UNDERLINE = '\033[4m'
+
+
 class McStasMetaData:
     """
     Class for holding metadata for McStas dataset, is to be read from
@@ -1079,12 +1090,18 @@ class component:
     A class describing a McStas component to be written to a instrument
 
     This class is used by the instrument class when setting up
-    components, but can also be used independently.  Most information
+    components as dynamic subclasses to this class.  Most information
     can be given on initialize using keyword arguments, but there are
     methods for setting the attributes describing the component. The
     class contains both methods to write the component to a instrument
     file and methods for printing to the python terminal for checking
-    the information.
+    the information. The McStas_Instr class creates subclasses from
+    this class that have attributes for all parameters for the given
+    component. The component information is read directly from the
+    component files in the McStas installation.  This class is frozen
+    after __init__ so that no new attributes can be created, which
+    allows direct feedback to the user if a parameter name is
+    misspelled.
 
     Attributes
     ----------
@@ -1124,6 +1141,9 @@ class contains both methods to write the component to a instrument
     comment : str
         Comment inserted before the component as an explanation
 
+    __isfrozen : bool
+        If true no new attributes can be created, when false they can
+
     Methods
     -------
     set_AT(at_list,**kwargs)
@@ -1161,7 +1181,20 @@ class contains both methods to write the component to a instrument
 
     print_short(**kwargs)
         Prints short description, used in print_components
+
+    __setattr__(key, value)
+        Overwriting __setattr__ to implement ability to freeze
+
+    _freeze()
+        Freeze the class so no new attributes can be defined
+
+    _unfreeze()
+        Unfreeze the class so new attributes can be defined again
+
     """
+
+    __isfrozen = False  # When frozen, no new attributes allowed
+
     def __init__(self, instance_name, component_name, **kwargs):
         """
         Initializes McStas component with specified name and component
@@ -1205,6 +1238,10 @@ def __init__(self, instance_name, component_name, **kwargs):
             comment: str
                 Sets comment string
         """
+
+        # Allow addition of attributes in init
+        self._unfreeze()
+
         self.name = instance_name
         self.component_name = component_name
 
@@ -1258,14 +1295,32 @@ def __init__(self, instance_name, component_name, **kwargs):
         else:
             self.comment = ""
 
-        self.component_parameters = {}
-
         """
         Could store an option for whether this component should be
         printed in instrument file or in a seperate file which would
         then be included.
         """
 
+        # Do not allow addition of attributes after init
+        self._freeze()
+
+    def __setattr__(self, key, value):
+        if self.__isfrozen and not hasattr(self, key):
+            raise AttributeError("No parameter called '"
+                                 + key
+                                 + "' in component named "
+                                 + self.name
+                                 + " of component type "
+                                 + self.component_name
+                                 + ".")
+        object.__setattr__(self, key, value)
+
+    def _freeze(self):
+        self.__isfrozen = True
+
+    def _unfreeze(self):
+        self.__isfrozen = False
+
     def set_AT(self, at_list, **kwargs):
         """Sets AT data, List of 3 floats"""
         self.AT_data = at_list
@@ -1296,8 +1351,25 @@ def set_RELATIVE(self, relative_name):
             self.ROTATED_relative = "RELATIVE " + relative_name
 
     def set_parameters(self, dict_input):
-        """Adds parameters and their values from dictionary input"""
-        self.component_parameters.update(dict_input)
+        """
+        Adds parameters and their values from dictionary input
+
+        Relies on attributes added when McStas_Instr creates a
+        subclass from the component class where each component
+        parameter is added as an attribute.
+
+        """
+        for key, val in dict_input.items():
+            if not hasattr(self, key):
+                raise NameError("No parameter called "
+                                + key
+                                + " in component named "
+                                + self.name
+                                + " of component type "
+                                + self.component_name
+                                + ".")
+            else:
+                setattr(self, key, val)
 
     def set_WHEN(self, string):
         """Sets WHEN string, should be a c logical expression"""
@@ -1320,11 +1392,16 @@ def set_comment(self, string):
         self.comment = string
 
     def write_component(self, fo):
-        """Method that writes component to file"""
+        """
+        Method that writes component to file
+
+        Relies on attributes added when McStas_Instr creates a subclass
+        based on the component class.
+
+        """
         parameters_per_line = 2
         # Could use character limit on lines instead
         parameters_written = 0  # internal parameter
-        number_of_parameters = len(self.component_parameters)
 
         # Write comment if present
         if len(self.comment) > 1:
@@ -1333,12 +1410,29 @@ def write_component(self, fo):
         # Write component name and component type
         fo.write("COMPONENT %s = %s(" % (self.name, self.component_name))
 
+        component_parameters = {}
+        for key in self.parameter_names:
+            val = getattr(self, key)
+            if val is None:
+                if self.parameter_defaults[key] is None:
+                    raise NameError("Required parameter named "
+                                    + key
+                                    + " in component named "
+                                    + self.name
+                                    + " not set.")
+                else:
+                    continue
+
+            component_parameters[key] = val
+
+        number_of_parameters = len(component_parameters)
+
         if number_of_parameters == 0:
             fo.write(")\n")  # If there are no parameters, close immediately
         else:
             fo.write("\n")  # If there are parameters, start a new line
 
-        for key, val in self.component_parameters.items():
+        for key, val in component_parameters.items():
             if isinstance(val, float):  # CHeck if value is a number
                 # Small or large numbers written in scientific format
                 fo.write(" %s = %G" % (str(key), val))
@@ -1382,12 +1476,40 @@ def write_component(self, fo):
         fo.write("\n")
 
     def print_long(self):
-        """Prints contained information to Python terminal"""
-        print("// " + self.comment)
+        """
+        Prints contained information to Python terminal
+
+        Includes information on required parameters if they are not yet
+        specified. Information on the components are added when the
+        class is used as a superclass for classes describing each
+        McStas component.
+
+        """
+        if len(self.comment) > 1:
+            print("// " + self.comment)
         print("COMPONENT", str(self.name),
               "=", str(self.component_name))
-        for key, val in self.component_parameters.items():
-            print(" ", key, "=", val)
+        for key in self.parameter_names:
+            val = getattr(self, key)
+            parameter_name = bcolors.BOLD + key + bcolors.ENDC
+            if val is not None:
+                unit = ""
+                if key in self.parameter_units:
+                    unit = "[" + self.parameter_units[key] + "]"
+                value = (bcolors.BOLD
+                         + bcolors.OKGREEN
+                         + str(val)
+                         + bcolors.ENDC
+                         + bcolors.ENDC)
+                print(" ", parameter_name, "=", value, unit)
+            else:
+                if self.parameter_defaults[key] is None:
+                    print("  "
+                          + parameter_name
+                          + bcolors.FAIL
+                          + " : Required parameter not yet specified"
+                          + bcolors.ENDC)
+
         if not self.WHEN == "":
             print("WHEN (" + self.WHEN + ")")
         print("AT", self.AT_data, self.AT_relative)
@@ -1414,6 +1536,504 @@ def print_short(self, **kwargs):
                   "\tAT", self.AT_data, self.AT_relative,
                   "ROTATED", self.ROTATED_data, self.ROTATED_relative)
 
+    def show_parameters(self):
+        """
+        Shows available parameters and their defaults for the component
+
+        Any value specified is not reflected in this view. The
+        additional attributes defined when McStas_Instr creates
+        subclasses for the individual components are required to run
+        this method.
+
+        """
+
+        print(" ___ Help "
+              + self.component_name + " "
+              + (62-len(self.component_name))*"_")
+        print("|"
+              + bcolors.BOLD + "optional parameter" + bcolors.ENDC + "|"
+              + bcolors.BOLD
+              + bcolors.UNDERLINE + "required parameter" + bcolors.ENDC
+              + bcolors.ENDC + "|"
+              + bcolors.BOLD
+              + bcolors.OKBLUE + "default value" + bcolors.ENDC
+              + bcolors.ENDC + "|"
+              + bcolors.BOLD
+              + bcolors.OKGREEN + "user specified value" + bcolors.ENDC
+              + bcolors.ENDC + "|")
+
+        for parameter in self.parameter_names:
+            unit = ""
+            if parameter in self.parameter_units:
+                unit = " [" + self.parameter_units[parameter] + "]"
+            comment = ""
+            if parameter in self.parameter_comments:
+                comment = " // " + self.parameter_comments[parameter]
+
+            parameter_name = bcolors.BOLD + parameter + bcolors.ENDC
+            value = ""
+            if self.parameter_defaults[parameter] is None:
+                parameter_name = (bcolors.UNDERLINE
+                                  + parameter_name
+                                  + bcolors.ENDC)
+            else:
+                value = (" = "
+                         + bcolors.BOLD
+                         + bcolors.OKBLUE
+                         + str(self.parameter_defaults[parameter])
+                         + bcolors.ENDC
+                         + bcolors.ENDC)
+
+            if getattr(self, parameter) is not None:
+                value = (" = "
+                         + bcolors.BOLD
+                         + bcolors.OKGREEN
+                         + str(getattr(self, parameter))
+                         + bcolors.ENDC
+                         + bcolors.ENDC)
+
+            print(parameter_name
+                  + value
+                  + unit
+                  + comment)
+
+        print(73*"-")
+
+    def show_parameters_simple(self):
+        """
+        Shows available parameters and their defaults for the component
+
+        Any value specified is not reflected in this view. The
+        additional attributes defined when McStas_Instr creates
+        subclasses for the individual components are required to run
+        this method.
+
+        """
+        print("---- Help " + self.component_name + " -----")
+        for parameter in self.parameter_names:
+            unit = ""
+            if parameter in self.parameter_units:
+                unit = " [" + self.parameter_units[parameter] + "]"
+            comment = ""
+            if parameter in self.parameter_comments:
+                comment = " // " + self.parameter_comments[parameter]
+            if self.parameter_defaults[parameter] is None:
+                print(parameter
+                      + unit
+                      + comment)
+            else:
+                print(parameter
+                      + " = "
+                      + str(self.parameter_defaults[parameter])
+                      + unit
+                      + comment)
+        print("----------" + "-"*len(self.component_name) + "------")
+    """
+    def show_component(self):
+        print("---- Current parameters for " + self.name + " ----")
+        for parameter in self.parameter_names:
+            parameter_value = getattr(self, parameter)
+            unit = ""
+            if parameter in self.parameter_units:
+                unit = " [" + self.parameter_units[parameter] + "]"
+            if parameter_value is not None:
+                print(" "
+                      + parameter
+                      + " = "
+                      + str(parameter_value)
+                      + unit)
+            else:
+                if self.parameter_defaults[parameter] is None:
+                    print(" "
+                          + parameter
+                          + " : Required parameter not yet specified")
+
+    def show_component_long(self):
+        print("---- Current parameters for " + self.name + " ----")
+        for parameter in self.parameter_names:
+            parameter_value = getattr(self, parameter)
+            unit = ""
+            if parameter in self.parameter_units:
+                unit = " [" + self.parameter_units[parameter] + "]"
+            comment = ""
+            if parameter in self.parameter_comments:
+                comment = " // " + self.parameter_comments[parameter]
+            if parameter_value is not None:
+                print(" "
+                      + parameter
+                      + " = "
+                      + str(parameter_value)
+                      + unit
+                      + comment)
+            else:
+                if self.parameter_defaults[parameter] is None:
+                    print(" "
+                          + parameter
+                          + " : Required parameter not yet specified"
+                          + unit
+                          + comment)
+    """
+
+
+class ComponentInfo:
+    """
+    Internal class used to store information on parameters of components
+    """
+
+    def __init__(self):
+        self.name = ""
+        self.category = ""
+        self.parameter_names = []
+        self.parameter_defaults = {}
+        self.parameter_types = {}
+        self.parameter_comments = {}
+        self.parameter_units = {}
+
+
+class ComponentReader:
+    """
+    Class for retriveing information on available McStas components
+
+    Recursively reads all component files in hardcoded list of
+    folders that represents the component categories in McStas.
+    The results are stored in a dictionary with ComponentInfo
+    instances, the keys are the names of the components. After
+    the components in the McStas installation are read, any
+    components pressent in the current work directory is read,
+    and these will overwrite exisiting information, consistent
+    with how McStas reads component definitions.
+
+    """
+
+    def __init__(self, mcstas_path):
+        """
+        Reads all component files in standard folders. Recursive, so
+        subfolders of these folders are included.
+
+        """
+
+        if mcstas_path[-1] is not "/":
+            mcstas_path = mcstas_path + "/"
+
+        # Hardcoded whitelist of foldernames
+        folder_list = ["sources",
+                       "optics",
+                       "samples",
+                       "monitors",
+                       "misc",
+                       "contrib",
+                       "obsolete",
+                       "union"]
+
+        self.component_path = {}
+        self.component_category = {}
+
+        for folder in folder_list:
+            absolute_path = mcstas_path + folder
+            # self.component_info_dict.update(self._read(absolute_path))
+            self._find_components(absolute_path)
+
+        # McStas component in current directory should overwrite
+        current_directory = os.getcwd()
+
+        for file in os.listdir(current_directory):
+            if file.endswith(".comp"):
+                absolute_path = current_directory + "/" + file
+                component_name = absolute_path.split("/")[-1].split(".")[-2]
+
+                if component_name in self.component_path:
+                    print("Overwriting McStasScript info on component named "
+                          + file
+                          + " because the component is in the"
+                          + " work directory.")
+
+                self.component_path[component_name] = absolute_path
+                self.component_category[component_name] = "Work directory"
+
+    def show_categories(self):
+        """
+        Method that will show all component categories available
+
+        """
+        categories = []
+        for component, category in self.component_category.items():
+            if category not in categories:
+                categories.append(category)
+                print(" " + category)
+
+    def show_components_in_category(self, category_input):
+        """
+        Method that will show all components in given category
+
+        """
+        empty_category = True
+        to_print = []
+        for component, category in self.component_category.items():
+            if category == category_input:
+                to_print.append(component)
+                empty_category = False
+
+        to_print.sort()
+        if empty_category:
+            print("No components found in this category! "
+                  + "Available categories:")
+            self.show_categories()
+
+        elif len(to_print) < 10:
+            for component in to_print:
+                print(" " + component)
+        else:
+            # Prints in collumns, maximum 4 and maximum line length 100
+            columns = 5
+            total_line_length = 1000
+            while(total_line_length > 100):
+                columns = columns - 1
+
+                c_length = math.ceil(len(to_print)/columns)
+                last_length = len(to_print) - (columns-1)*c_length
+
+                column = []
+                longest_name = []
+                for col in range(0, columns-1):
+                    current_list = to_print[c_length*col:c_length*(col+1)]
+                    column.append(current_list)
+                    longest_name.append(len(max(current_list, key=len)))
+
+                column.append(to_print[c_length*(columns-1):])
+                longest_name.append(len(max(column[columns-1], key=len)))
+
+                total_line_length = 1 + sum(longest_name) + (columns-1)*3
+
+            for line_nr in range(0, c_length):
+                print(" ", end="")
+                for col in range(0, columns-1):
+                    this_name = column[col][line_nr]
+                    print(this_name
+                          + " "*(longest_name[col] - len(this_name))
+                          + "   ", end="")  # More columns left, dont break
+                if line_nr < last_length:
+                    this_name = column[columns-1][line_nr]
+                    print(this_name)
+                else:
+                    print("")
+
+    def load_all_components(self):
+        """
+        Method that loads information on all components into memory.
+
+        """
+
+        return_dict = {}
+        for comp_name, abs_path in self.component_path.items():
+            return_dict[comp_name] = self.read_component_file(abs_path)
+
+        return return_dict
+
+    def read_name(self, component_name):
+        """
+        Returns ComponentInfo of component with name component_name.
+
+        Uses table of absolute paths to all known components, and
+        reads the appropriate file in order to generate the information.
+
+        """
+
+        if component_name not in self.component_path:
+            raise NameError("No component named "
+                            + component_name
+                            + " in McStas installation or "
+                            + "current work directory.")
+
+        return self.read_component_file(self.component_path[component_name])
+
+    def _find_components(self, absolute_path):
+        """
+        Recursive read function, can read either file or entire folder
+
+        Updates the component_info_dict with the findings that are
+        stored as ComoponentInfo instances.
+
+        """
+
+        if not os.path.isdir(absolute_path):
+            if absolute_path.endswith(".comp"):
+                # read this file
+                component_name = absolute_path.split("/")[-1].split(".")[-2]
+                self.component_path[component_name] = absolute_path
+
+                component_category = absolute_path.split("/")[-2]
+                self.component_category[component_name] = component_category
+        else:
+            for file in os.listdir(absolute_path):
+                absolute_file_path = absolute_path + "/" + file
+                self._find_components(absolute_file_path)
+
+    def read_component_file(self, absolute_path):
+        """
+        Reads a component file and expands component_info_dict
+
+        The information is stored as ComponentClass instances.
+
+        """
+
+        result = ComponentInfo()
+
+        fo = open(absolute_path, "r")
+
+        cnt = 0
+        while True:
+            cnt += 1
+            line = fo.readline()
+
+            # find parameter comments
+            if self.line_starts_with(line, "* %P"):
+
+                while True:
+                    this_line = fo.readline()
+
+                    if self.line_starts_with(this_line, "DEFINE COMPONENT"):
+                        # No more comments to read through
+                        break
+
+                    if ":" in this_line:
+                        tokens = this_line.split(":")
+
+                        variable_name = tokens[0]
+                        variable_name = variable_name.replace("*", "")
+                        variable_name = variable_name.strip()
+                        if " " in variable_name:
+                            name_tokens = variable_name.split(" ")
+                            variable_name = name_tokens[0]
+
+                        if len(tokens[1]) > 2:
+                            comment = tokens[1].strip()
+
+                            if "[" in comment:  # Search for unit
+                                # If found, store it and remove from string
+                                unit = comment[comment.find("[") + 1:
+                                               comment.find("]")]
+                                result.parameter_units[variable_name] = unit
+                                comment = comment[comment.find("]") + 1:]
+                                comment = comment.strip()
+
+                            # Store the comment
+                            result.parameter_comments[variable_name] = comment
+                    elif "[" in this_line and "]" in this_line:
+                        tokens = this_line.split("[")
+
+                        variable_name = tokens[0]
+                        variable_name = variable_name.replace("*", "")
+                        variable_name = variable_name.strip()
+
+                        unit = this_line[this_line.find("[") + 1:
+                                         this_line.find("]")]
+                        result.parameter_units[variable_name] = unit
+
+                        comment = this_line[this_line.find("]") + 1:]
+                        comment = comment.strip()
+                        result.parameter_comments[variable_name] = comment
+
+            # find definition parameters and their values
+            if (self.line_starts_with(line, "DEFINITION PARAMETERS")
+                    or self.line_starts_with(line, "SETTING PARAMETERS")):
+
+                parts = line.split("(")
+                parameter_parts = parts[1].split(",")
+
+                parameter_parts = list(filter(("\n").__ne__, parameter_parts))
+
+                break_now = False
+                while True:
+                    # Read all definition parameters
+
+                    for part in parameter_parts:
+
+                        temp_par_type = "double"
+
+                        part = part.strip()
+
+                        # remove trailing )
+                        if ")" in part:
+                            part = part.replace(")", "")
+                            break_now = True
+
+                        possible_declare = part.split(" ")
+                        possible_type = possible_declare[0].strip()
+                        if "int" == possible_type:
+                            temp_par_type = "int"
+                            # remove int from part
+                            part = "".join(possible_declare[1:])
+                        if "string" == possible_type:
+                            temp_par_type = "string"
+                            # remove string from part
+                            part = "".join(possible_declare[1:])
+
+                        part = part.replace(" ", "")
+                        if part == "":
+                            continue
+
+                        if self.line_starts_with(part, "//"):
+                            break_now = True
+                            continue
+
+                        if self.line_starts_with(part, "/*"):
+                            break_now = True
+                            continue
+
+                        if "=" not in part:
+                            # no defualt value, required parameter
+                            result.parameter_names.append(part)
+                            result.parameter_defaults[part] = None
+                            result.parameter_types[part] = temp_par_type
+                        else:
+                            # default value available
+                            name_value = part.split("=")
+                            par_name = name_value[0].strip()
+                            par_value = name_value[1].strip()
+                            result.parameter_names.append(par_name)
+                            result.parameter_defaults[par_name] = par_value
+                            result.parameter_types[par_name] = temp_par_type
+
+                    if break_now:
+                        break
+
+                    parameter_parts = fo.readline().split(",")
+
+            if self.line_starts_with(line, "DECLARE"):
+                break
+
+            if self.line_starts_with(line, "TRACE"):
+                break
+
+            if cnt == 1000:
+                break
+
+        fo.close()
+
+        result.name = absolute_path.split("/")[-1].split(".")[-2]
+        foldernames = absolute_path.split("/")
+        result.category = foldernames[-2]
+
+        """
+        To lower memory use one could remove all comments and units that
+        does not correspond to a found parameter name.
+        """
+
+        return result
+
+    def line_starts_with(self, line, input):
+        """
+        Helper method that checks if a string is the start of a line
+
+        """
+        if len(line) < len(input):
+            return False
+
+        if line[0:len(input)] == input:
+            return True
+        else:
+            return False
+
 
 class McStas_instr:
     """
@@ -1557,6 +2177,7 @@ def __init__(self, name, **kwargs):
             mcrun_path : str
                 Absolute path of mcrun or empty if already in path
         """
+
         self.name = name
 
         if "author" in kwargs:
@@ -1574,6 +2195,15 @@ def __init__(self, name, **kwargs):
         else:
             self.mcrun_path = ""
 
+        if "mcstas_path" in kwargs:
+            self.mcstas_path = kwargs["mcstas_path"]
+        else:
+            self.mcstas_path = ""
+            raise NameError("At this stage of development "
+                            + "McStasScript need the absolute path "
+                            + "for the McStas installation as keyword "
+                            + "named mcstas_path")
+
         self.parameter_list = []
         self.declare_list = []
         self.initialize_section = ("// Start of initialize for generated "
@@ -1586,6 +2216,10 @@ def __init__(self, name, **kwargs):
         self.component_list = []  # List of components (have to be ordered)
         self.component_name_list = []  # List of component names
 
+        # Read info on active McStas components
+        self.component_reader = ComponentReader(self.mcstas_path)
+        self.component_class_lib = {}
+
     def add_parameter(self, *args, **kwargs):
         """
         Method for adding input parameter to instrument
@@ -1746,6 +2380,69 @@ def append_trace_no_new_line(self, string):
 
         self.trace_section = self.trace_section + string
 
+    def show_components(self, *args):
+        """
+        Helper method that shows available components to the user
+
+        If called without any arguments it will display the available
+        component categories.  The first input
+
+        """
+        if len(args) == 0:
+            print("Here are the availalbe component categories:")
+            self.component_reader.show_categories()
+            print("Call show_components(category_name) to display")
+
+        else:
+            category = args[0]
+            print("Here are all components in the "
+                  + category
+                  + " category.")
+            self.component_reader.show_components_in_category(category)
+
+    def component_help(self, name):
+        """
+        Method for showing parameters for a component before adding it
+        to the instrument
+
+        """
+
+        dummy_instance = self._create_component_instance("dummy", name)
+        dummy_instance.show_parameters()
+
+    def _create_component_instance(self, *args, **kwargs):
+        """
+        Dynamically creates a class for the requested component type
+
+        Created classses kept in dictionary, if the same component type
+        is requested again, the class in the dictionary is used.  The
+        method returns an instance of the created class that was
+        initialized with the paramters passed to this function.
+        """
+
+        if len(args) < 2:
+            raise NameError("Attempting to create component without name")
+
+        component_name = args[1]
+
+        if component_name not in self.component_class_lib:
+            comp_info = self.component_reader.read_name(component_name)
+
+            input_dict = {}
+            input_dict = {key: None for key in comp_info.parameter_names}
+            input_dict["parameter_names"] = comp_info.parameter_names
+            input_dict["parameter_defaults"] = comp_info.parameter_defaults
+            input_dict["parameter_types"] = comp_info.parameter_types
+            input_dict["parameter_units"] = comp_info.parameter_units
+            input_dict["parameter_comments"] = comp_info.parameter_comments
+            input_dict["category"] = comp_info.category
+
+            self.component_class_lib[component_name] = type(component_name,
+                                                            (component,),
+                                                            input_dict)
+
+        return self.component_class_lib[component_name](*args, **kwargs)
+
     def add_component(self, *args, **kwargs):
         """
         Method for adding a new component instance to the instrument
@@ -1819,8 +2516,10 @@ def add_component(self, *args, **kwargs):
                                  + " not found."))
 
             new_index = self.component_name_list.index(kwargs["after"])
-            self.component_list.insert(new_index+1,
-                                       component(*args, **kwargs))
+
+            new_component = self._create_component_instance(*args, **kwargs)
+            self.component_list.insert(new_index + 1, new_component)
+
             self.component_name_list.insert(new_index+1, args[0])
 
         # Insert component after component with this name
@@ -1833,14 +2532,20 @@ def add_component(self, *args, **kwargs):
                                  + "name was not found."))
 
             new_index = self.component_name_list.index(kwargs["before"])
-            self.component_list.insert(new_index, component(*args, **kwargs))
+
+            new_component = self._create_component_instance(*args, **kwargs)
+            self.component_list.insert(new_index, new_component)
+
             self.component_name_list.insert(new_index, args[0])
 
         # If after or before keywords absent, place component at the end
         else:
-            self.component_list.append(component(*args, **kwargs))
+            new_component = self._create_component_instance(*args, **kwargs)
+            self.component_list.append(new_component)
             self.component_name_list.append(args[0])
 
+        return new_component
+
     def get_component(self, name):
         """
         Get the component instance of component with specified name
diff --git a/McStasScript_demo.ipynb b/McStasScript_demo.ipynb
new file mode 100644
index 00000000..cedf4d7d
--- /dev/null
+++ b/McStasScript_demo.ipynb
@@ -0,0 +1,495 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Demonstration of McStasScript\n",
+    "This file demonstrates how McStasScript can be used to run McStas from a python environment in a userfreindly manner."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript') # Path to McStasScript pythoon file\n",
+    "import McStasScript  \n",
+    "\n",
+    "# Creating the instance of the class, insert path to mcrun and to mcstas root directory\n",
+    "instr = McStasScript.McStas_instr(\"jupyter_demo\",\n",
+    "                                  mcrun_path= \"/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin\",\n",
+    "                                  mcstas_path = \"/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are the availalbe component categories:\n",
+      " sources\n",
+      " optics\n",
+      " samples\n",
+      " monitors\n",
+      " misc\n",
+      " contrib\n",
+      " union\n",
+      " obsolete\n",
+      " Work directory\n",
+      "Call show_components(category_name) to display\n"
+     ]
+    }
+   ],
+   "source": [
+    "instr.show_components() # Shows available McStas component categories in current installation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are all components in the sources category.\n",
+      " Adapt_check     Monitor_Optimizer   Source_div      Virtual_output\n",
+      " ESS_butterfly   Source_Maxwell_3    Source_gen      \n",
+      " ESS_moderator   Source_Optimizer    Source_simple   \n",
+      " Moderator       Source_adapt        Virtual_input   \n"
+     ]
+    }
+   ],
+   "source": [
+    "instr.show_components(\"sources\") # Display all McStas source components "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Source_simple _________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1mradius\u001b[0m = \u001b[1m\u001b[94m0.1\u001b[0m\u001b[0m [m] // Radius of circle in (x,y,0) plane where neutrons are generated.\n",
+      "\u001b[1myheight\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Height of rectangle in (x,y,0) plane where neutrons are generated.\n",
+      "\u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Width of rectangle in (x,y,0) plane where neutrons are generated.\n",
+      "\u001b[1mdist\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Distance to target along z axis.\n",
+      "\u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[94m.045\u001b[0m\u001b[0m [m] // Width of target\n",
+      "\u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[94m.12\u001b[0m\u001b[0m [m] // Height of target\n",
+      "\u001b[1mE0\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [meV] // Mean energy of neutrons.\n",
+      "\u001b[1mdE\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [meV] // Energy half spread of neutrons (flat or gaussian sigma).\n",
+      "\u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [AA] // Mean wavelength of neutrons.\n",
+      "\u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [AA] // Wavelength half spread of neutrons.\n",
+      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1/(s*cm**2*st*energy unit)] // flux per energy unit, Angs or meV if flux=0, the source emits 1 in 4*PI whole space.\n",
+      "\u001b[1mgauss\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [1] // Gaussian (1) or Flat (0) energy/wavelength distribution\n",
+      "\u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[94m+1\u001b[0m\u001b[0m [1] // relative index of component to focus at, e.g. next is +1 this is used to compute 'dist' automatically.\n",
+      "-------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "instr.component_help(\"Source_simple\") # Displays help on the Source_simple component"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source = instr.add_component(\"Source\",\"Source_simple\") # Adds an instance of Source_simple called Source to instrument"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Lets add a parameter to the instrument to control the wavelength of the source\n",
+    "instr.add_parameter(\"double\",\"wavelength\",value=3,comment=\"Wavelength emmited from source\")\n",
+    "source.xwidth = 0.06; source.yheight = 0.08;\n",
+    "source.dist = 2; source.focus_xw = 0.05; source.focus_yh = 0.05\n",
+    "source.lambda0 = \"wavelength\"; source.dlambda = 0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT Source = Source_simple\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.08\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.06\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mdist\u001b[0m = \u001b[1m\u001b[92m2\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[92mwavelength\u001b[0m\u001b[0m [AA]\n",
+      "  \u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [AA]\n",
+      "AT [0, 0, 0] ABSOLUTE\n",
+      "ROTATED [0, 0, 0] ABSOLUTE\n"
+     ]
+    }
+   ],
+   "source": [
+    "source.print_long() # Verify that the information is correct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "guide = instr.add_component(\"Guide\", \"Guide_gravity\", AT=[0,0,2], RELATIVE=\"Source\")\n",
+    "guide.set_comment=\"Beam extraction and first guide piece\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Guide_gravity _________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[4m\u001b[1mw1\u001b[0m\u001b[0m [m] // Width at the guide entry\n",
+      "\u001b[4m\u001b[1mh1\u001b[0m\u001b[0m [m] // Height at the guide entry\n",
+      "\u001b[1mw2\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Width at the guide exit. If 0, use w1.\n",
+      "\u001b[1mh2\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Height at the guide exit. If 0, use h1.\n",
+      "\u001b[4m\u001b[1ml\u001b[0m\u001b[0m [m] // length of guide\n",
+      "\u001b[1mR0\u001b[0m = \u001b[1m\u001b[94m0.995\u001b[0m\u001b[0m [1] // Low-angle reflectivity\n",
+      "\u001b[1mQc\u001b[0m = \u001b[1m\u001b[94m0.0218\u001b[0m\u001b[0m [AA-1] // Critical scattering vector\n",
+      "\u001b[1malpha\u001b[0m = \u001b[1m\u001b[94m4.38\u001b[0m\u001b[0m [AA] // Slope of reflectivity\n",
+      "\u001b[1mm\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // m-value of material. Zero means completely absorbing. m=0.65  glass/SiO2 Si   Ni  Ni58  supermirror Be    Diamond m=  0.65       0.47 1   1.18  2-6         1.01  1.12 for glass/SiO2, m=1 for Ni, 1.2 for Ni58, m=2-6 for supermirror. m=0.47 for Si\n",
+      "\u001b[1mW\u001b[0m = \u001b[1m\u001b[94m0.003\u001b[0m\u001b[0m [AA-1] // Width of supermirror cut-off\n",
+      "\u001b[1mnslit\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1] // Number of vertical channels in the guide (>= 1) (nslit-1 vertical dividing walls).\n",
+      "\u001b[1md\u001b[0m = \u001b[1m\u001b[94m0.0005\u001b[0m\u001b[0m [m] // Thickness of subdividing walls\n",
+      "\u001b[1mmleft\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // m-value of material for left.   vert. mirror\n",
+      "\u001b[1mmright\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // m-value of material for right.  vert. mirror\n",
+      "\u001b[1mmtop\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // m-value of material for top.    horz. mirror\n",
+      "\u001b[1mmbottom\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // m-value of material for bottom. horz. mirror\n",
+      "\u001b[1mnhslit\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1] // Number of horizontal channels in the guide (>= 1). (nhslit-1 horizontal dividing walls). this enables to have nslit*nhslit rectangular channels\n",
+      "\u001b[1mG\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m/s2] // Gravitation norm. 0 value disables G effects.\n",
+      "\u001b[1maleft\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // alpha-value of left  vert. mirror\n",
+      "\u001b[1maright\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // alpha-value of right vert. mirror\n",
+      "\u001b[1matop\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // alpha-value of top   horz. mirror\n",
+      "\u001b[1mabottom\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // alpha-value of left  horz. mirror\n",
+      "\u001b[1mwavy\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // Global guide waviness\n",
+      "\u001b[1mwavy_z\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // Partial waviness along propagation axis\n",
+      "\u001b[1mwavy_tb\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // Partial waviness in transverse direction for top/bottom mirrors\n",
+      "\u001b[1mwavy_lr\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // Partial waviness in transverse direction for left/right mirrors\n",
+      "\u001b[1mchamfers\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Global chamfers specifications (in/out/mirror sides).\n",
+      "\u001b[1mchamfers_z\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Input and output chamfers\n",
+      "\u001b[1mchamfers_lr\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Chamfers on left/right mirror sides\n",
+      "\u001b[1mchamfers_tb\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Chamfers on top/bottom mirror sides\n",
+      "\u001b[1mnelements\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1] // Number of sections in the guide (length l/nelements).\n",
+      "\u001b[1mnu\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [Hz] // Rotation frequency (round/s) for Fermi Chopper approximation\n",
+      "\u001b[1mphase\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // Phase shift for the Fermi Chopper approximation\n",
+      "\u001b[1mreflect\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [str] // Reflectivity file name. Format <q(Angs-1) R(0-1)>\n",
+      "-------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "guide.show_parameters() # Lets view the parameters available in our guide component"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "guide.set_parameters({\"w1\" : 0.05, \"w2\" : 0.05, \"h1\" : 0.05, \"h2\" : 0.05, \"l\" : 8, \"m\" : 3.5, \"G\" : -9.2})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT Guide = Guide_gravity\n",
+      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mw2\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mh2\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92m8\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92m3.5\u001b[0m\u001b[0m [1]\n",
+      "  \u001b[1mG\u001b[0m = \u001b[1m\u001b[92m-9.2\u001b[0m\u001b[0m [m/s2]\n",
+      "AT [0, 0, 2] RELATIVE Source\n",
+      "ROTATED [0, 0, 0] RELATIVE Source\n"
+     ]
+    }
+   ],
+   "source": [
+    "guide.print_long() # Verify the information on this component is correct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = instr.add_component(\"sample\", \"PowderN\", AT=[0,0,9], RELATIVE=\"Guide\") # Add a sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample.radius = 0.015; sample.yheight = 0.05; sample.reflections = \"\\\"Cu.laz\\\"\" # A small copper cylinder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are all components in the monitors category.\n",
+      " Brilliance_monitor      Monitor           PSD_monitor_psf_eff   TOF2E_monitor\n",
+      " DivLambda_monitor       Monitor_4PI       PSDcyl_monitor        TOF2Q_cylPSD_monitor\n",
+      " DivPos_monitor          Monitor_Sqw       PSDlin_diff_monitor   TOFLambda_monitor\n",
+      " Divergence_monitor      Monitor_nD        PSDlin_monitor        TOF_PSD_monitor_rad\n",
+      " EPSD_monitor            PSD_TOF_monitor   PolLambda_monitor     TOF_cylPSD_monitor\n",
+      " E_monitor               PSD_monitor       Pol_monitor           TOF_monitor\n",
+      " Hdiv_monitor            PSD_monitor_4PI   PreMonitor_nD         TOFlog_monitor\n",
+      " L_monitor               PSD_monitor_TOF   Res_monitor           \n",
+      " MeanPolLambda_monitor   PSD_monitor_psf   Sqq_w_monitor         \n"
+     ]
+    }
+   ],
+   "source": [
+    "instr.show_components(\"monitors\") # Monitors are needed to record information"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sphere = instr.add_component(\"PSD_4PI\", \"PSD_monitor_4PI\", RELATIVE=\"sample\") # Add 4PI sphere detector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT PSD_4PI = PSD_monitor_4PI\n",
+      "  \u001b[1mnx\u001b[0m = \u001b[1m\u001b[92m300\u001b[0m\u001b[0m [1]\n",
+      "  \u001b[1mny\u001b[0m = \u001b[1m\u001b[92m300\u001b[0m\u001b[0m [1]\n",
+      "  \u001b[1mfilename\u001b[0m = \u001b[1m\u001b[92m\"PSD_4PI.dat\"\u001b[0m\u001b[0m [string]\n",
+      "  \u001b[1mradius\u001b[0m = \u001b[1m\u001b[92m1\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mrestore_neutron\u001b[0m = \u001b[1m\u001b[92m1\u001b[0m\u001b[0m [1]\n",
+      "AT [0, 0, 0] RELATIVE sample\n",
+      "ROTATED [0, 0, 0] RELATIVE sample\n"
+     ]
+    }
+   ],
+   "source": [
+    "sphere.nx = 300; sphere.ny = 300; sphere.filename = \"\\\"PSD_4PI.dat\\\"\"; sphere.radius = 1; sphere.restore_neutron = 1;\n",
+    "sphere.print_long() # Verify that monitors have filenames that are strings when printed, double quotes needed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PSD = instr.add_component(\"PSD\", \"PSD_monitor\", AT=[0,0,1], RELATIVE=\"sample\") # Add position sensitive detector\n",
+    "PSD.xwidth = 0.1; PSD.yheight = 0.1; PSD.nx = 200; PSD.ny = 200; PSD.filename = \"\\\"PSD.dat\\\"\"; PSD.restore_neutron = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "L_mon = instr.add_component(\"L_mon\", \"L_monitor\", RELATIVE=\"PSD\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "L_mon.Lmin = \"wavelength - 0.3\"; L_mon.Lmax = \"wavelength + 0.3\"; L_mon.nL = 150\n",
+    "L_mon.xwidth = 0.1; L_mon.yheight = 0.1\n",
+    "L_mon.filename = \"\\\"L_mon.dat\\\"\"; L_mon.restore_neutron = 1\n",
+    "L_mon.comment = \"Wavelength monitor for narrow range\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "// Wavelength monitor for narrow range\n",
+      "COMPONENT L_mon = L_monitor\n",
+      "  \u001b[1mnL\u001b[0m = \u001b[1m\u001b[92m150\u001b[0m\u001b[0m [1]\n",
+      "  \u001b[1mfilename\u001b[0m = \u001b[1m\u001b[92m\"L_mon.dat\"\u001b[0m\u001b[0m [string]\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mLmin\u001b[0m = \u001b[1m\u001b[92mwavelength - 0.3\u001b[0m\u001b[0m [AA]\n",
+      "  \u001b[1mLmax\u001b[0m = \u001b[1m\u001b[92mwavelength + 0.3\u001b[0m\u001b[0m [AA]\n",
+      "  \u001b[1mrestore_neutron\u001b[0m = \u001b[1m\u001b[92m1\u001b[0m\u001b[0m [1]\n",
+      "AT [0, 0, 0] RELATIVE PSD\n",
+      "ROTATED [0, 0, 0] RELATIVE PSD\n"
+     ]
+    }
+   ],
+   "source": [
+    "L_mon.print_long()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Source    Source_simple     AT  [0, 0, 0]     ABSOLUTE          ROTATED  [0, 0, 0]     ABSOLUTE\n",
+      "Guide     Guide_gravity     AT  [0, 0, 2]     RELATIVE Source   ROTATED  [0, 0, 0]     RELATIVE Source\n",
+      "sample    PowderN           AT  [0, 0, 9]     RELATIVE Guide    ROTATED  [0, 0, 0]     RELATIVE Guide\n",
+      "PSD_4PI   PSD_monitor_4PI   AT  [0, 0, 0]     RELATIVE sample   ROTATED  [0, 0, 0]     RELATIVE sample\n",
+      "PSD       PSD_monitor       AT  [0, 0, 1]     RELATIVE sample   ROTATED  [0, 0, 0]     RELATIVE sample\n",
+      "L_mon     L_monitor         AT  [0, 0, 0]     RELATIVE PSD      ROTATED  [0, 0, 0]     RELATIVE PSD\n"
+     ]
+    }
+   ],
+   "source": [
+    "instr.print_components() # Lets get an overview of the instrument so far"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Running the McStas instrument\n",
+    "Now we have assembled an instrument and it is time to perform a simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If the folder already exsits, a new simulation is not performed but the old one is read\n",
+    "data = instr.run_full_instrument(foldername=\"jupyter_demo\",\n",
+    "                                 parameters={\"wavelength\" : 1.0},\n",
+    "                                 mpi=4,\n",
+    "                                 ncount=2E7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The returned data object is a list of McStasData objects, each containing the results from a monitor.\n",
+    "These data objects also contain preferences for how they should be plotted if this is done automatically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "number of elements in data list = 3\n",
+      "Plotting data with name PSD_4PI\n",
+      "Plotting data with name PSD\n",
+      "Plotting data with name L_mon\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "McStasScript.name_plot_options(\"PSD_4PI\", data, log=1, colormap=\"hot\", orders_of_mag=5) # Adjusting PSD_4PI plot\n",
+    "plot = McStasScript.make_sub_plot(data) # Making subplot of our monitors"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/demonstration.py b/demonstration.py
index a7cb6a21..1b47bab7 100644
--- a/demonstration.py
+++ b/demonstration.py
@@ -1,14 +1,22 @@
 # Demonstration of McStasScript, an API for creating and running McStas instruments from python scripts
 # Written by Mads Bertelsen, ESS DMSC
 import random
+import sys
+#sys.path.append('/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript')
 import McStasScript # McStasScript classes
 
 # if the mcrun command from McStas is not in your path, provide absolute path for the binary here:
 mcrun_path = ""
 #mcrun_path = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin"
+mcstas_path = ""
+#mcstas_path = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/"
 
 # Create a McStas instrument
-instr = McStasScript.McStas_instr("random_demo",author="Mads Bertelsen",origin="ESS DMSC",mcrun_path=mcrun_path)
+instr = McStasScript.McStas_instr("random_demo",
+                                  author="Mads Bertelsen",
+                                  origin="ESS DMSC",
+                                  mcrun_path = mcrun_path,
+                                  mcstas_path = mcstas_path)
 
 # Set up a material called Cu with approrpiate properties (uses McStas Union components, here the processes)
 instr.add_component("Cu_incoherent","Incoherent_process")
@@ -78,9 +86,15 @@
 instr.set_component_parameter("large_detector",{"xwidth" : 1.0, "yheight" : 1.0, "nx" : 500, "ny" : 500, "filename" : "\"large_PSD.dat\"", "restore_neutron" : 1})
 
 # Run the McStas simulation, a unique foldername is required for each run
-data = instr.run_full_instrument(foldername="demonstration8",parameters={"energy":600},mpi=10,ncount=5E7)
+data = instr.run_full_instrument(foldername="demonstration",parameters={"energy":600},mpi=2,ncount=5E7)
+
+# Set plotting options for the data (optional)
+McStasScript.name_plot_options("logger_space_zx_all",data,log=1,orders_of_mag=3)
+McStasScript.name_plot_options("logger_space_zy_all",data,log=1,orders_of_mag=3)
+McStasScript.name_plot_options("detector",data,log=1,colormap="hot",orders_of_mag=0.5)
+McStasScript.name_plot_options("large_detector",data,log=1,orders_of_mag=8)
 
 # Plot the resulting data on a logarithmic scale
-plot = McStasScript.make_sub_plot(data,log=1,max_orders_of_mag=[5,10,2,2])
+plot = McStasScript.make_sub_plot(data)
 
 

From 30cf899c628b9d0355a6d3158c95a0eef6caa69b Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Fri, 10 May 2019 09:55:38 +0200
Subject: [PATCH 012/403] Updates to the documentation to reflect recent
 changes.

McStas_Instr class
Now needs path to McStas directory
show_components method for overview of available components
component_help method for details on the parameters of a component
add_component now returns object

component class
show_parameters method mentioned
show_parameters_simple method mentioned
_unfreeze _freeze methods described
---
 McStasScript.py                |   2 +-
 McStasScript_documentation.pdf | Bin 147913 -> 153845 bytes
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/McStasScript.py b/McStasScript.py
index e90638f8..c975d208 100644
--- a/McStasScript.py
+++ b/McStasScript.py
@@ -1517,7 +1517,7 @@ class is used as a superclass for classes describing each
         if not self.GROUP == "":
             print("GROUP " + self.GROUP)
         if not self.EXTEND == "":
-            print("%{")
+            print("EXTEND %{")
             print(self.EXTEND + "%}")
         if not self.JUMP == "":
             print("JUMP " + self.JUMP)
diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
index e0cb18f76e72c0be75291e6b26b46c4b7f4265c5..4b0511b53da86e84a07eb46c6f5599fbe843e1b9 100644
GIT binary patch
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From 7938aba07f3933ac88505cce9151c711d5c51626 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Mon, 13 May 2019 14:55:49 +0200
Subject: [PATCH 013/403] Restructured the code to a package instead of a
 module.

The code is now split over multiple files instead of one monolithic
file, making it easier to find to manage the project.

Typical use will now only import a few classes and functions that
are found in the interface package. This includes the instrument
class, plotting and a few utility functions.

The data package contains the types for handling McStas data.

The remaining code is in the helper package.

examples have been moved to an example folder.
---
 examples/McStasScript_demo.ipynb        | 495 ++++++++++++
 examples/manual_code.ipynb              | 441 +++++++++++
 examples/random_demonstration.py        | 100 +++
 mcstasscript/__init__.py                |   0
 mcstasscript/data/__init__.py           |   0
 mcstasscript/data/data.py               | 341 +++++++++
 mcstasscript/helper/__init__.py         |   0
 mcstasscript/helper/component_reader.py | 361 +++++++++
 mcstasscript/helper/formatting.py       |  14 +
 mcstasscript/helper/managed_mcrun.py    | 222 ++++++
 mcstasscript/helper/mcstas_objects.py   | 733 ++++++++++++++++++
 mcstasscript/interface/__init__.py      |   0
 mcstasscript/interface/functions.py     |  63 ++
 mcstasscript/interface/instr.py         | 980 ++++++++++++++++++++++++
 mcstasscript/interface/plotter.py       | 308 ++++++++
 15 files changed, 4058 insertions(+)
 create mode 100644 examples/McStasScript_demo.ipynb
 create mode 100644 examples/manual_code.ipynb
 create mode 100644 examples/random_demonstration.py
 create mode 100644 mcstasscript/__init__.py
 create mode 100644 mcstasscript/data/__init__.py
 create mode 100644 mcstasscript/data/data.py
 create mode 100644 mcstasscript/helper/__init__.py
 create mode 100644 mcstasscript/helper/component_reader.py
 create mode 100644 mcstasscript/helper/formatting.py
 create mode 100644 mcstasscript/helper/managed_mcrun.py
 create mode 100644 mcstasscript/helper/mcstas_objects.py
 create mode 100644 mcstasscript/interface/__init__.py
 create mode 100644 mcstasscript/interface/functions.py
 create mode 100644 mcstasscript/interface/instr.py
 create mode 100644 mcstasscript/interface/plotter.py

diff --git a/examples/McStasScript_demo.ipynb b/examples/McStasScript_demo.ipynb
new file mode 100644
index 00000000..8a04b384
--- /dev/null
+++ b/examples/McStasScript_demo.ipynb
@@ -0,0 +1,495 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Demonstration of McStasScript\n",
+    "This file demonstrates how McStasScript can be used to run McStas from a python environment in a userfreindly manner."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('/Users/madsbertelsen/PaNOSC/McStasScript') # Path to McStasScript pythoon file\n",
+    "\n",
+    "from mcstasscript.interface import instr, plotter, functions\n",
+    "\n",
+    "# Creating the instance of the class, insert path to mcrun and to mcstas root directory\n",
+    "instr = instr.McStas_instr(\"jupyter_demo\",\n",
+    "                           mcrun_path= \"/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin\",\n",
+    "                           mcstas_path = \"/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are the availalbe component categories:\n",
+      " sources\n",
+      " optics\n",
+      " samples\n",
+      " monitors\n",
+      " misc\n",
+      " contrib\n",
+      " union\n",
+      " obsolete\n",
+      "Call show_components(category_name) to display\n"
+     ]
+    }
+   ],
+   "source": [
+    "instr.show_components() # Shows available McStas component categories in current installation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are all components in the sources category.\n",
+      " Adapt_check     Monitor_Optimizer   Source_div      Virtual_output\n",
+      " ESS_butterfly   Source_Maxwell_3    Source_gen      \n",
+      " ESS_moderator   Source_Optimizer    Source_simple   \n",
+      " Moderator       Source_adapt        Virtual_input   \n"
+     ]
+    }
+   ],
+   "source": [
+    "instr.show_components(\"sources\") # Display all McStas source components "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Source_simple _________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1mradius\u001b[0m = \u001b[1m\u001b[94m0.1\u001b[0m\u001b[0m [m] // Radius of circle in (x,y,0) plane where neutrons are generated.\n",
+      "\u001b[1myheight\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Height of rectangle in (x,y,0) plane where neutrons are generated.\n",
+      "\u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Width of rectangle in (x,y,0) plane where neutrons are generated.\n",
+      "\u001b[1mdist\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Distance to target along z axis.\n",
+      "\u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[94m.045\u001b[0m\u001b[0m [m] // Width of target\n",
+      "\u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[94m.12\u001b[0m\u001b[0m [m] // Height of target\n",
+      "\u001b[1mE0\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [meV] // Mean energy of neutrons.\n",
+      "\u001b[1mdE\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [meV] // Energy half spread of neutrons (flat or gaussian sigma).\n",
+      "\u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [AA] // Mean wavelength of neutrons.\n",
+      "\u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [AA] // Wavelength half spread of neutrons.\n",
+      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1/(s*cm**2*st*energy unit)] // flux per energy unit, Angs or meV if flux=0, the source emits 1 in 4*PI whole space.\n",
+      "\u001b[1mgauss\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [1] // Gaussian (1) or Flat (0) energy/wavelength distribution\n",
+      "\u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[94m+1\u001b[0m\u001b[0m [1] // relative index of component to focus at, e.g. next is +1 this is used to compute 'dist' automatically.\n",
+      "-------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "instr.component_help(\"Source_simple\") # Displays help on the Source_simple component"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source = instr.add_component(\"Source\",\"Source_simple\") # Adds an instance of Source_simple called Source to instrument"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Lets add a parameter to the instrument to control the wavelength of the source\n",
+    "instr.add_parameter(\"double\",\"wavelength\",value=3,comment=\"Wavelength emmited from source\")\n",
+    "source.xwidth = 0.06; source.yheight = 0.08;\n",
+    "source.dist = 2; source.focus_xw = 0.05; source.focus_yh = 0.05\n",
+    "source.lambda0 = \"wavelength\"; source.dlambda = 0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT Source = Source_simple\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.08\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.06\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mdist\u001b[0m = \u001b[1m\u001b[92m2\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[92mwavelength\u001b[0m\u001b[0m [AA]\n",
+      "  \u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [AA]\n",
+      "AT [0, 0, 0] ABSOLUTE\n",
+      "ROTATED [0, 0, 0] ABSOLUTE\n"
+     ]
+    }
+   ],
+   "source": [
+    "source.print_long() # Verify that the information is correct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "guide = instr.add_component(\"Guide\", \"Guide_gravity\", AT=[0,0,2], RELATIVE=\"Source\")\n",
+    "guide.set_comment=\"Beam extraction and first guide piece\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Guide_gravity _________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[4m\u001b[1mw1\u001b[0m\u001b[0m [m] // Width at the guide entry\n",
+      "\u001b[4m\u001b[1mh1\u001b[0m\u001b[0m [m] // Height at the guide entry\n",
+      "\u001b[1mw2\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Width at the guide exit. If 0, use w1.\n",
+      "\u001b[1mh2\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Height at the guide exit. If 0, use h1.\n",
+      "\u001b[4m\u001b[1ml\u001b[0m\u001b[0m [m] // length of guide\n",
+      "\u001b[1mR0\u001b[0m = \u001b[1m\u001b[94m0.995\u001b[0m\u001b[0m [1] // Low-angle reflectivity\n",
+      "\u001b[1mQc\u001b[0m = \u001b[1m\u001b[94m0.0218\u001b[0m\u001b[0m [AA-1] // Critical scattering vector\n",
+      "\u001b[1malpha\u001b[0m = \u001b[1m\u001b[94m4.38\u001b[0m\u001b[0m [AA] // Slope of reflectivity\n",
+      "\u001b[1mm\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // m-value of material. Zero means completely absorbing. m=0.65  glass/SiO2 Si   Ni  Ni58  supermirror Be    Diamond m=  0.65       0.47 1   1.18  2-6         1.01  1.12 for glass/SiO2, m=1 for Ni, 1.2 for Ni58, m=2-6 for supermirror. m=0.47 for Si\n",
+      "\u001b[1mW\u001b[0m = \u001b[1m\u001b[94m0.003\u001b[0m\u001b[0m [AA-1] // Width of supermirror cut-off\n",
+      "\u001b[1mnslit\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1] // Number of vertical channels in the guide (>= 1) (nslit-1 vertical dividing walls).\n",
+      "\u001b[1md\u001b[0m = \u001b[1m\u001b[94m0.0005\u001b[0m\u001b[0m [m] // Thickness of subdividing walls\n",
+      "\u001b[1mmleft\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // m-value of material for left.   vert. mirror\n",
+      "\u001b[1mmright\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // m-value of material for right.  vert. mirror\n",
+      "\u001b[1mmtop\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // m-value of material for top.    horz. mirror\n",
+      "\u001b[1mmbottom\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // m-value of material for bottom. horz. mirror\n",
+      "\u001b[1mnhslit\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1] // Number of horizontal channels in the guide (>= 1). (nhslit-1 horizontal dividing walls). this enables to have nslit*nhslit rectangular channels\n",
+      "\u001b[1mG\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m/s2] // Gravitation norm. 0 value disables G effects.\n",
+      "\u001b[1maleft\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // alpha-value of left  vert. mirror\n",
+      "\u001b[1maright\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // alpha-value of right vert. mirror\n",
+      "\u001b[1matop\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // alpha-value of top   horz. mirror\n",
+      "\u001b[1mabottom\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // alpha-value of left  horz. mirror\n",
+      "\u001b[1mwavy\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // Global guide waviness\n",
+      "\u001b[1mwavy_z\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // Partial waviness along propagation axis\n",
+      "\u001b[1mwavy_tb\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // Partial waviness in transverse direction for top/bottom mirrors\n",
+      "\u001b[1mwavy_lr\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // Partial waviness in transverse direction for left/right mirrors\n",
+      "\u001b[1mchamfers\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Global chamfers specifications (in/out/mirror sides).\n",
+      "\u001b[1mchamfers_z\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Input and output chamfers\n",
+      "\u001b[1mchamfers_lr\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Chamfers on left/right mirror sides\n",
+      "\u001b[1mchamfers_tb\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Chamfers on top/bottom mirror sides\n",
+      "\u001b[1mnelements\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1] // Number of sections in the guide (length l/nelements).\n",
+      "\u001b[1mnu\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [Hz] // Rotation frequency (round/s) for Fermi Chopper approximation\n",
+      "\u001b[1mphase\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // Phase shift for the Fermi Chopper approximation\n",
+      "\u001b[1mreflect\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [str] // Reflectivity file name. Format <q(Angs-1) R(0-1)>\n",
+      "-------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "guide.show_parameters() # Lets view the parameters available in our guide component"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "guide.set_parameters({\"w1\" : 0.05, \"w2\" : 0.05, \"h1\" : 0.05, \"h2\" : 0.05, \"l\" : 8, \"m\" : 3.5, \"G\" : -9.2})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT Guide = Guide_gravity\n",
+      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mw2\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mh2\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92m8\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92m3.5\u001b[0m\u001b[0m [1]\n",
+      "  \u001b[1mG\u001b[0m = \u001b[1m\u001b[92m-9.2\u001b[0m\u001b[0m [m/s2]\n",
+      "AT [0, 0, 2] RELATIVE Source\n",
+      "ROTATED [0, 0, 0] RELATIVE Source\n"
+     ]
+    }
+   ],
+   "source": [
+    "guide.print_long() # Verify the information on this component is correct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = instr.add_component(\"sample\", \"PowderN\", AT=[0,0,9], RELATIVE=\"Guide\") # Add a sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample.radius = 0.015; sample.yheight = 0.05; sample.reflections = \"\\\"Cu.laz\\\"\" # A small copper cylinder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are all components in the monitors category.\n",
+      " Brilliance_monitor      Monitor           PSD_monitor_psf_eff   TOF2E_monitor\n",
+      " DivLambda_monitor       Monitor_4PI       PSDcyl_monitor        TOF2Q_cylPSD_monitor\n",
+      " DivPos_monitor          Monitor_Sqw       PSDlin_diff_monitor   TOFLambda_monitor\n",
+      " Divergence_monitor      Monitor_nD        PSDlin_monitor        TOF_PSD_monitor_rad\n",
+      " EPSD_monitor            PSD_TOF_monitor   PolLambda_monitor     TOF_cylPSD_monitor\n",
+      " E_monitor               PSD_monitor       Pol_monitor           TOF_monitor\n",
+      " Hdiv_monitor            PSD_monitor_4PI   PreMonitor_nD         TOFlog_monitor\n",
+      " L_monitor               PSD_monitor_TOF   Res_monitor           \n",
+      " MeanPolLambda_monitor   PSD_monitor_psf   Sqq_w_monitor         \n"
+     ]
+    }
+   ],
+   "source": [
+    "instr.show_components(\"monitors\") # Monitors are needed to record information"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sphere = instr.add_component(\"PSD_4PI\", \"PSD_monitor_4PI\", RELATIVE=\"sample\") # Add 4PI sphere detector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT PSD_4PI = PSD_monitor_4PI\n",
+      "  \u001b[1mnx\u001b[0m = \u001b[1m\u001b[92m300\u001b[0m\u001b[0m [1]\n",
+      "  \u001b[1mny\u001b[0m = \u001b[1m\u001b[92m300\u001b[0m\u001b[0m [1]\n",
+      "  \u001b[1mfilename\u001b[0m = \u001b[1m\u001b[92m\"PSD_4PI.dat\"\u001b[0m\u001b[0m [string]\n",
+      "  \u001b[1mradius\u001b[0m = \u001b[1m\u001b[92m1\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mrestore_neutron\u001b[0m = \u001b[1m\u001b[92m1\u001b[0m\u001b[0m [1]\n",
+      "AT [0, 0, 0] RELATIVE sample\n",
+      "ROTATED [0, 0, 0] RELATIVE sample\n"
+     ]
+    }
+   ],
+   "source": [
+    "sphere.nx = 300; sphere.ny = 300; sphere.filename = \"\\\"PSD_4PI.dat\\\"\"; sphere.radius = 1; sphere.restore_neutron = 1;\n",
+    "sphere.print_long() # Verify that monitors have filenames that are strings when printed, double quotes needed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PSD = instr.add_component(\"PSD\", \"PSD_monitor\", AT=[0,0,1], RELATIVE=\"sample\") # Add position sensitive detector\n",
+    "PSD.xwidth = 0.1; PSD.yheight = 0.1; PSD.nx = 200; PSD.ny = 200; PSD.filename = \"\\\"PSD.dat\\\"\"; PSD.restore_neutron = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "L_mon = instr.add_component(\"L_mon\", \"L_monitor\", RELATIVE=\"PSD\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "L_mon.Lmin = \"wavelength - 0.3\"; L_mon.Lmax = \"wavelength + 0.3\"; L_mon.nL = 150\n",
+    "L_mon.xwidth = 0.1; L_mon.yheight = 0.1\n",
+    "L_mon.filename = \"\\\"L_mon.dat\\\"\"; L_mon.restore_neutron = 1\n",
+    "L_mon.comment = \"Wavelength monitor for narrow range\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "// Wavelength monitor for narrow range\n",
+      "COMPONENT L_mon = L_monitor\n",
+      "  \u001b[1mnL\u001b[0m = \u001b[1m\u001b[92m150\u001b[0m\u001b[0m [1]\n",
+      "  \u001b[1mfilename\u001b[0m = \u001b[1m\u001b[92m\"L_mon.dat\"\u001b[0m\u001b[0m [string]\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mLmin\u001b[0m = \u001b[1m\u001b[92mwavelength - 0.3\u001b[0m\u001b[0m [AA]\n",
+      "  \u001b[1mLmax\u001b[0m = \u001b[1m\u001b[92mwavelength + 0.3\u001b[0m\u001b[0m [AA]\n",
+      "  \u001b[1mrestore_neutron\u001b[0m = \u001b[1m\u001b[92m1\u001b[0m\u001b[0m [1]\n",
+      "AT [0, 0, 0] RELATIVE PSD\n",
+      "ROTATED [0, 0, 0] RELATIVE PSD\n"
+     ]
+    }
+   ],
+   "source": [
+    "L_mon.print_long()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Source    Source_simple     AT  [0, 0, 0]     ABSOLUTE          ROTATED  [0, 0, 0]     ABSOLUTE\n",
+      "Guide     Guide_gravity     AT  [0, 0, 2]     RELATIVE Source   ROTATED  [0, 0, 0]     RELATIVE Source\n",
+      "sample    PowderN           AT  [0, 0, 9]     RELATIVE Guide    ROTATED  [0, 0, 0]     RELATIVE Guide\n",
+      "PSD_4PI   PSD_monitor_4PI   AT  [0, 0, 0]     RELATIVE sample   ROTATED  [0, 0, 0]     RELATIVE sample\n",
+      "PSD       PSD_monitor       AT  [0, 0, 1]     RELATIVE sample   ROTATED  [0, 0, 0]     RELATIVE sample\n",
+      "L_mon     L_monitor         AT  [0, 0, 0]     RELATIVE PSD      ROTATED  [0, 0, 0]     RELATIVE PSD\n"
+     ]
+    }
+   ],
+   "source": [
+    "instr.print_components() # Lets get an overview of the instrument so far"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Running the McStas instrument\n",
+    "Now we have assembled an instrument and it is time to perform a simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If the folder already exsits, a new simulation is not performed but the old one is read\n",
+    "data = instr.run_full_instrument(foldername=\"jupyter_demo\",\n",
+    "                                 parameters={\"wavelength\" : 1.0},\n",
+    "                                 mpi=4,\n",
+    "                                 ncount=2E7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The returned data object is a list of McStasData objects, each containing the results from a monitor.\n",
+    "These data objects also contain preferences for how they should be plotted if this is done automatically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "number of elements in data list = 3\n",
+      "Plotting data with name PSD_4PI\n",
+      "Plotting data with name PSD\n",
+      "Plotting data with name L_mon\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "functions.name_plot_options(\"PSD_4PI\", data, log=1, colormap=\"hot\", orders_of_mag=5) # Adjusting PSD_4PI plot\n",
+    "plot = plotter.make_sub_plot(data) # Making subplot of our monitors"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/manual_code.ipynb b/examples/manual_code.ipynb
new file mode 100644
index 00000000..2a957194
--- /dev/null
+++ b/examples/manual_code.ipynb
@@ -0,0 +1,441 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('/Users/madsbertelsen/PaNOSC/McStasScript') # Path to McStasScript pythoon file\n",
+    "\n",
+    "from mcstasscript.interface import instr, plotter, functions\n",
+    "\n",
+    "# Creating the instance of the class, insert path to mcrun and to mcstas root directory\n",
+    "detector = instr.McStas_instr(\"LOKI_detector\",\n",
+    "                              mcrun_path = \"/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin\",\n",
+    "                              mcstas_path = \"/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detector.add_parameter(\"wavelength\")\n",
+    "detector.add_parameter(\"height\",value=1.0,comment=\"Height in [m]\")\n",
+    "detector.add_parameter(\"string\",\"reflection_filename\")\n",
+    "detector.add_parameter(\"string\",\"data_filename\",value=\"\\\"data.dat\\\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detector.add_declare_var(\"double\",\"energy\")\n",
+    "detector.add_declare_var(\"int\",\"flag\")\n",
+    "detector.add_declare_var(\"double\",\"tube_radius\",value=0.013)\n",
+    "detector.add_declare_var(\"double\",\"displacements\",array=7)\n",
+    "detector.add_declare_var(\"double\",\"t_array\",array=4,value=[0.65E-6,0.65E-6,1E-6,1E-6])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detector.append_initialize(\"energy=pow(2*PI/wavelength*K2V,2)*VS2E;\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<mcstasscript.interface.instr.Guide_gravity at 0x11d4f45f8>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "detector.add_component(\"Origin\",\"Arm\")\n",
+    "src = detector.add_component(\"source\",\"Source_simple\",RELATIVE=\"Origin\")\n",
+    "detector.add_component(\"beam_extraction\",\"Guide_gravity\",AT=[0,0,2],RELATIVE=\"source\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<mcstasscript.interface.instr.Slit at 0x11d4ea6d8>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "detector.add_component(\"pre_guide_slit\",\"Slit\",before=\"beam_extraction\",\n",
+    "                       AT=[0,0,1],RELATIVE=\"source\",comment=\"Slit before the guide\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Origin            Arm             AT  [0, 0, 0]     ABSOLUTE          ROTATED  [0, 0, 0]     ABSOLUTE\n",
+      "source            Source_simple   AT  [0, 0, 0]     RELATIVE Origin   ROTATED  [0, 0, 0]     RELATIVE Origin\n",
+      "pre_guide_slit    Slit            AT  [0, 0, 1]     RELATIVE source   ROTATED  [0, 0, 0]     RELATIVE source\n",
+      "beam_extraction   Guide_gravity   AT  [0, 0, 2]     RELATIVE source   ROTATED  [0, 0, 0]     RELATIVE source\n"
+     ]
+    }
+   ],
+   "source": [
+    "detector.print_components()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detector.set_component_parameter(\"source\",{\"xwidth\" : 0.12, \"E0\" : \"energy\"})\n",
+    "detector.set_component_parameter(\"source\",{\"yheight\" : 0.12})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT source = Source_simple\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.12\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.12\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mE0\u001b[0m = \u001b[1m\u001b[92menergy\u001b[0m\u001b[0m [meV]\n",
+      "AT [0, 0, 0] RELATIVE Origin\n",
+      "ROTATED [0, 0, 0] RELATIVE Origin\n"
+     ]
+    }
+   ],
+   "source": [
+    "detector.print_component(\"source\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detector.set_component_AT(\"source\",[0.01,0,0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detector.set_component_ROTATED(\"beam_extraction\",[0,2.0,0],RELATIVE=\"Origin\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detector.set_component_RELATIVE(\"beam_extraction\",\"pre_guide_slit\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Origin            Arm             AT  [0, 0, 0]        ABSOLUTE                  ROTATED  [0, 0, 0]       ABSOLUTE\n",
+      "source            Source_simple   AT  [0.01, 0, 0]     RELATIVE Origin           ROTATED  [0, 0, 0]       RELATIVE Origin\n",
+      "pre_guide_slit    Slit            AT  [0, 0, 1]        RELATIVE source           ROTATED  [0, 0, 0]       RELATIVE source\n",
+      "beam_extraction   Guide_gravity   AT  [0, 0, 2]        RELATIVE pre_guide_slit   ROTATED  [0, 2.0, 0]     RELATIVE pre_guide_slit\n"
+     ]
+    }
+   ],
+   "source": [
+    "detector.print_components()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT beam_extraction = Guide_gravity\n",
+      "  \u001b[1mw1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1mh1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1ml\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "WHEN (vx > 0)\n",
+      "AT [0, 0, 2] RELATIVE pre_guide_slit\n",
+      "ROTATED [0, 2.0, 0] RELATIVE pre_guide_slit\n"
+     ]
+    }
+   ],
+   "source": [
+    "detector.set_component_WHEN(\"beam_extraction\",\"vx > 0\")\n",
+    "detector.print_component(\"beam_extraction\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT beam_extraction = Guide_gravity\n",
+      "  \u001b[1mw1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1mh1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1ml\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "WHEN (vx > 0)\n",
+      "AT [0, 0, 2] RELATIVE pre_guide_slit\n",
+      "ROTATED [0, 2.0, 0] RELATIVE pre_guide_slit\n",
+      "EXTEND %{\n",
+      "n_scattering = SCATTERED - 2\n",
+      "%}\n"
+     ]
+    }
+   ],
+   "source": [
+    "detector.append_component_EXTEND(\"beam_extraction\",\"n_scattering = SCATTERED - 2\")\n",
+    "detector.print_component(\"beam_extraction\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT beam_extraction = Guide_gravity\n",
+      "  \u001b[1mw1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1mh1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1ml\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "WHEN (vx > 0)\n",
+      "AT [0, 0, 2] RELATIVE pre_guide_slit\n",
+      "ROTATED [0, 2.0, 0] RELATIVE pre_guide_slit\n",
+      "GROUP guides\n",
+      "EXTEND %{\n",
+      "n_scattering = SCATTERED - 2\n",
+      "%}\n"
+     ]
+    }
+   ],
+   "source": [
+    "detector.set_component_GROUP(\"beam_extraction\",\"guides\")\n",
+    "detector.print_component(\"beam_extraction\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT beam_extraction = Guide_gravity\n",
+      "  \u001b[1mw1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1mh1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1ml\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "WHEN (vx > 0)\n",
+      "AT [0, 0, 2] RELATIVE pre_guide_slit\n",
+      "ROTATED [0, 2.0, 0] RELATIVE pre_guide_slit\n",
+      "GROUP guides\n",
+      "EXTEND %{\n",
+      "n_scattering = SCATTERED - 2\n",
+      "%}\n",
+      "JUMP myself iterate 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "detector.set_component_JUMP(\"beam_extraction\",\"myself iterate 3\")\n",
+    "detector.print_component(\"beam_extraction\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "// Simulating severe misalignment\n",
+      "COMPONENT beam_extraction = Guide_gravity\n",
+      "  \u001b[1mw1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1mh1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1ml\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "WHEN (vx > 0)\n",
+      "AT [0, 0, 2] RELATIVE pre_guide_slit\n",
+      "ROTATED [0, 2.0, 0] RELATIVE pre_guide_slit\n",
+      "GROUP guides\n",
+      "EXTEND %{\n",
+      "n_scattering = SCATTERED - 2\n",
+      "%}\n",
+      "JUMP myself iterate 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "detector.set_component_comment(\"beam_extraction\",\"Simulating severe misalignment\")\n",
+    "detector.print_component(\"beam_extraction\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are the availalbe component categories:\n",
+      " sources\n",
+      " optics\n",
+      " samples\n",
+      " monitors\n",
+      " misc\n",
+      " contrib\n",
+      " union\n",
+      " obsolete\n",
+      "Call show_components(category_name) to display\n"
+     ]
+    }
+   ],
+   "source": [
+    "detector.show_components()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are all components in the samples category.\n",
+      " Incoherent      Phonon_simple   Res_sample      Single_crystal\n",
+      " Isotropic_Sqw   Powder1         Sans_spheres    TOFRes_sample\n",
+      " Magnon_bcc      PowderN         SasView_model   Tunneling_sample\n"
+     ]
+    }
+   ],
+   "source": [
+    "detector.show_components(\"samples\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Phonon_simple _________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[4m\u001b[1mradius\u001b[0m\u001b[0m [m] // Outer radius of sample in (x,z) plane\n",
+      "\u001b[4m\u001b[1myheight\u001b[0m\u001b[0m [m] // Height of sample in y direction\n",
+      "\u001b[4m\u001b[1msigma_abs\u001b[0m\u001b[0m [barns] // Absorption cross section at 2200 m/s per atom\n",
+      "\u001b[4m\u001b[1msigma_inc\u001b[0m\u001b[0m [barns] // Incoherent scattering cross section per atom\n",
+      "\u001b[4m\u001b[1ma\u001b[0m\u001b[0m [AA] // fcc Lattice constant\n",
+      "\u001b[4m\u001b[1mb\u001b[0m\u001b[0m [fm] // Scattering length\n",
+      "\u001b[4m\u001b[1mM\u001b[0m\u001b[0m [a.u.] // Atomic mass\n",
+      "\u001b[4m\u001b[1mc\u001b[0m\u001b[0m [meV/AA^(-1)] // Velocity of sound\n",
+      "\u001b[4m\u001b[1mDW\u001b[0m\u001b[0m [1] // Debye-Waller factor\n",
+      "\u001b[4m\u001b[1mT\u001b[0m\u001b[0m [K] // Temperature\n",
+      "\u001b[1mtarget_x\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // position of target to focus at . Transverse coordinate\n",
+      "\u001b[1mtarget_y\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // position of target to focus at. Vertical coordinate\n",
+      "\u001b[1mtarget_z\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // position of target to focus at. Straight ahead.\n",
+      "\u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [1] // relative index of component to focus at, e.g. next is +1\n",
+      "\u001b[1mfocus_r\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Radius of sphere containing target.\n",
+      "\u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // horiz. dimension of a rectangular area\n",
+      "\u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // vert.  dimension of a rectangular area\n",
+      "\u001b[1mfocus_aw\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // horiz. angular dimension of a rectangular area\n",
+      "\u001b[1mfocus_ah\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // vert.  angular dimension of a rectangular area\n",
+      "\u001b[1mgap\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [meV] // Bandgap energy (unphysical)\n",
+      "-------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "detector.component_help(\"Phonon_simple\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/random_demonstration.py b/examples/random_demonstration.py
new file mode 100644
index 00000000..447af8c5
--- /dev/null
+++ b/examples/random_demonstration.py
@@ -0,0 +1,100 @@
+# Demonstration of McStasScript, an API for creating and running McStas instruments from python scripts
+# Written by Mads Bertelsen, ESS DMSC
+import random
+import sys
+sys.path.append('/Users/madsbertelsen/PaNOSC/McStasScript')
+from mcstasscript.interface import instr, plotter, functions
+
+# if the mcrun command from McStas is not in your path, provide absolute path for the binary here:
+#mcrun_path = ""
+mcrun_path = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin"
+#mcstas_path = ""
+mcstas_path = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/"
+
+# Create a McStas instrument
+instr = instr.McStas_instr("random_demo",
+                           author = "Mads Bertelsen",
+                           origin = "ESS DMSC",
+                           mcrun_path = mcrun_path,
+                           mcstas_path = mcstas_path)
+
+# Set up a material called Cu with approrpiate properties (uses McStas Union components, here the processes)
+instr.add_component("Cu_incoherent", "Incoherent_process")
+instr.set_component_parameter("Cu_incoherent", {"sigma" : 4*0.55, "packing_factor" : 1, "unit_cell_volume" :  55.4})
+
+instr.add_component("Cu_powder", "Powder_process")
+instr.set_component_parameter("Cu_powder", {"reflections" : "\"Cu.laz\""})
+
+instr.add_component("Cu", "Union_make_material")
+instr.set_component_parameter("Cu", {"my_absorption" : "100*4*3.78/55.4", "process_string" : "\"Cu_incoherent,Cu_powder\""})
+
+# Set neutron source
+instr.add_component("source","Source_div", AT=[0,0,0])
+instr.add_parameter("double","energy", value=10, comment="[meV] source energy") # Add parameter to select energy at run time
+instr.set_component_parameter("source", {"xwidth" : 0.12, "yheight" : 0.12, "focus_aw" : 0.1, "focus_ah" : 0.1, "E0" : "energy", "dE" : 0, "flux" : 1E13})
+
+# List of available materials, Vacuum is provided by the system
+material_name_list = ["Cu", "Vacuum"]
+
+# Wish to set up a number of randomly sized and placed boxes, here we choose the number
+number_of_volumes = random.randint(30,40)
+
+# Initialize the priority that needs to be unique for each volume
+current_priority = 99
+for volume in range(number_of_volumes):
+
+    current_priority = current_priority + 1 # update the priority
+    max_side_length = 0.04
+    max_depth = 0.003
+    position = [random.uniform(-0.05,0.05), random.uniform(-0.05,0.05), 1+random.uniform(-0.05,0.05)] # Set position in 10x10x10 cm^3 box 1 m from source
+    rotation = [random.uniform(0,360), random.uniform(0,360), random.uniform(0,360)] # random rotation
+    
+    # Choose a random material from the list of available materials
+    volume_material = random.choice(material_name_list)
+
+    # Add a McStas Union geometry with unique name
+    instr.add_component("volume_" + str(volume), "Union_box")
+    instr.set_component_parameter("volume_" + str(volume), {"xwidth" : random.uniform(0.01,max_side_length), "yheight" : random.uniform(0.01,max_side_length), "zdepth" : random.uniform(0.001,max_depth),})
+    instr.set_component_parameter("volume_" + str(volume), {"material_string" : "\""+volume_material+"\"", "priority" : current_priority, "p_interact" : 0.3})
+    instr.set_component_AT("volume_" + str(volume), position, RELATIVE="ABSOLUTE")
+    instr.set_component_ROTATED("volume_" + str(volume), rotation, RELATIVE="ABSOLUTE")
+
+
+# A few Union loggers are set up for display of the scattering locations
+instr.add_component("logger_space_zx_all", "Union_logger_2D_space")
+current_component = instr.get_last_component()
+current_component.set_parameters({"filename" : "\"space_zx.dat\"",})
+current_component.set_parameters({"n1" : 1000, "D_direction_1" : "\"z\"", "D1_min" : -0.05, "D1_max" : 0.05})
+current_component.set_parameters({"n2" : 1000, "D_direction_2" : "\"x\"", "D2_min" : -0.05, "D2_max" : 0.05})
+current_component.set_AT([0,0,1])
+
+instr.add_component("logger_space_zy_all", "Union_logger_2D_space")
+current_component = instr.get_last_component()
+current_component.set_parameters({"filename" : "\"space_zy.dat\"",})
+current_component.set_parameters({"n1" : 1000, "D_direction_1" : "\"z\"", "D1_min" : -0.05, "D1_max" : 0.05})
+current_component.set_parameters({"n2" : 1000, "D_direction_2" : "\"y\"", "D2_min" : -0.05, "D2_max" : 0.05})
+current_component.set_AT([0,0,1])
+
+# Union master component that executes the simulation of the random boxes
+instr.add_component("random_boxes", "Union_master")
+
+# McStas monitors for viewing the beam after the random boxes
+instr.add_component("detector", "PSD_monitor", AT=[0,0,2])
+instr.set_component_parameter("detector", {"xwidth" : 0.10, "yheight" : 0.10, "nx" : 500, "ny" : 500, "filename" : "\"PSD.dat\"", "restore_neutron" : 1})
+
+instr.add_component("large_detector","PSD_monitor", AT=[0,0,2])
+instr.set_component_parameter("large_detector", {"xwidth" : 1.0, "yheight" : 1.0, "nx" : 500, "ny" : 500, "filename" : "\"large_PSD.dat\"", "restore_neutron" : 1})
+
+# Run the McStas simulation, a unique foldername is required for each run
+data = instr.run_full_instrument(foldername="demonstration2", parameters={"energy":600},mpi=2,ncount=5E7)
+
+# Set plotting options for the data (optional)
+functions.name_plot_options("logger_space_zx_all", data, log=1, orders_of_mag=3)
+functions.name_plot_options("logger_space_zy_all", data, log=1, orders_of_mag=3)
+functions.name_plot_options("detector", data, log=1, colormap="hot", orders_of_mag=0.5)
+functions.name_plot_options("large_detector", data, log=1, orders_of_mag=8)
+
+# Plot the resulting data on a logarithmic scale
+plot = plotter.make_sub_plot(data)
+
+
diff --git a/mcstasscript/__init__.py b/mcstasscript/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/mcstasscript/data/__init__.py b/mcstasscript/data/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
new file mode 100644
index 00000000..4b561ca6
--- /dev/null
+++ b/mcstasscript/data/data.py
@@ -0,0 +1,341 @@
+class McStasMetaData:
+    """
+    Class for holding metadata for McStas dataset, is to be read from
+    mccode.sim file.
+
+    Attributes
+    ----------
+    info : dict
+        Contains read strings from mccode.sim in key, value
+
+    dimension : Int or List of Int
+        Int for 1d data set with lenght of data, Array for 2d with each
+        length
+
+    component_name : str
+        Name of component in McStas file
+
+    filename : str
+        Name of data file to read
+
+    limits : List
+        Limits for monitor, length=2 for 1d data and length=4 for 2d
+        data
+
+    title : str
+        Title of monitor when plotting
+
+    xlabel : str
+        Text for xlabel when plotting
+
+    ylabel : str
+        Text for ylabel when plotting
+
+    Methods
+    -------
+    add_info(key,value)
+        Adds a element to the info dictionary
+
+    extract_info()
+        Unpacks the information in info to class attributes
+
+    set_title(string)
+        Overwrites current title
+
+    set_xlabel(string)
+        Overwrites current xlabel
+
+    set_ylabel(string)
+        Overwrites current ylabel
+    """
+
+    def __init__(self):
+        """Creating a new instance, no parameters"""
+        self.info = {}
+
+    def add_info(self, key, value):
+        """Adding information to info dict"""
+        self.info[key] = value
+
+    def extract_info(self):
+        """Extracting information from info dict to class attributes"""
+
+        # Extract dimension
+        if "type" in self.info:
+            type = self.info["type"]
+            if "array_1d" in type:
+                self.dimension = int(type[9:-2])
+            if "array_2d" in type:
+                self.dimension = []
+                type_strings = self.info["type"].split(",")
+                temp_str = type_strings[0]
+                self.dimension.append(int(temp_str[9:]))
+                temp_str = type_strings[1]
+                self.dimension.append(int(temp_str[1:-2]))
+        else:
+            raise NameError("No type in mccode data section!")
+
+        # Extract component name
+        if "component" in self.info:
+            self.component_name = self.info["component"].rstrip()
+
+        # Extract filename
+        if "filename" in self.info:
+            self.filename = self.info["filename"].rstrip()
+        else:
+            raise NameError(
+                "No filename found in mccode data section!")
+
+        # Extract limits
+        self.limits = []
+        if "xylimits" in self.info:
+            # find the four numbers
+            temp_str = self.info["xylimits"]
+            limits_string = temp_str.split()
+            for limit in limits_string:
+                self.limits.append(float(limit))
+
+        if "xlimits" in self.info:
+            # find the two numbers
+            temp_str = self.info["xlimits"]
+            limits_string = temp_str.split()
+            for limit in limits_string:
+                self.limits.append(float(limit))
+
+        # Extract plotting labels and title
+        if "xlabel" in self.info:
+            self.xlabel = self.info["xlabel"].rstrip()
+        if "ylabel" in self.info:
+            self.ylabel = self.info["ylabel"].rstrip()
+        if "title" in self.info:
+            self.title = self.info["title"].rstrip()
+
+    def set_title(self, string):
+        """Sets title for plotting"""
+        self.title = string
+
+    def set_xlabel(self, string):
+        """Sets xlabel for plotting"""
+        self.xlabel = string
+
+    def set_ylabel(self, string):
+        """Sets ylabel for plotting"""
+        self.ylabel = string
+
+
+class McStasPlotOptions:
+    """
+    Class that holds plotting options related to McStas data set
+
+    Attributes
+    ----------
+    log : bool
+        To plot on logarithmic or not, standard is linear
+
+    orders_of_mag : float
+        If plotting on log scale, restrict max range to orders_of_mag
+        below maximum value
+
+    colormap : string
+        Chosen colormap for 2d data, should be available in matplotlib
+
+    Methods
+    -------
+    set_options(keyword arguments)
+        Can set the class attributes using keyword options
+
+    """
+
+    def __init__(self, *args, **kwargs):
+        """Setting default values for plotting preferences"""
+        self.log = False
+        self.orders_of_mag = 300
+        self.colormap = "jet"
+
+    def set_options(self, **kwargs):
+        """Set custom values for plotting preferences"""
+        if "log" in kwargs:
+            log_input = kwargs["log"]
+            if type(log_input) == int:
+                if log_input == 0:
+                    self.log = False
+                else:
+                    self.log = True
+            elif type(log_input) == bool:
+                self.log = log_input
+            else:
+                raise NameError(
+                    "Log input must be either Int or Bool.")
+
+        if "orders_of_mag" in kwargs:
+            self.orders_of_mag = kwargs["orders_of_mag"]
+
+        if "colormap" in kwargs:
+            self.colormap = kwargs["colormap"]
+
+
+class McStasData:
+    """
+    Class for holding full McStas dataset with data, metadata and
+    plotting preferences
+
+    Attributes
+    ----------
+    metadata : McStasMetaData instance
+        Holds the metadata for the dataset
+
+    name : str
+        Name of component, extracted from metadata
+
+    Intensity : numpy array
+        Intensity data [n/s] in 1d or 2d numpy array, dimension in
+        metadata
+
+    Error : numpy array
+        Error data [n/s] in 1d or 2d numpy array, same dimensions as
+        Intensity
+
+    Ncount : numpy array
+        Number of rays in bin, 1d or 2d numpy array, same dimensions as
+        Intensity
+
+    plot_options : McStasPlotOptions instance
+        Holds the plotting preferences for the dataset
+
+    Methods
+    -------
+    set_xlabel : string
+        sets xlabel of data for plotting
+
+    set_ylabel : string
+        sets ylabel of data for plotting
+
+    set_title : string
+        sets title of data for plotting
+
+    set_optons : keyword arguments
+        sets plot options, keywords passed to McStasPlotOptions method
+    """
+
+    def __init__(self, metadata, intensity, error, ncount, **kwargs):
+        """
+        Initialize a new McStas dataset, 4 positional arguments, pass
+        xaxis as kwarg if 1d data
+
+        Parameters
+        ----------
+        metadata : McStasMetaData instance
+            Holds the metadata for the dataset
+
+        name : str
+            Name of component, extracted from metadata
+
+        intensity : numpy array
+            Intensity data [n/s] in 1d or 2d numpy array, dimension in
+            metadata
+
+        error : numpy array
+            Error data [n/s] in 1d or 2d numpy array, same dimensions
+            as Intensity
+
+        ncount : numpy array
+            Number of rays in bin, 1d or 2d numpy array, same
+            dimensions as Intensity
+
+        kwargs : keyword arguments
+            xaxis is required for 1d data
+        """
+
+        # attatch meta data
+        self.metadata = metadata
+        # get name from metadata
+        self.name = self.metadata.component_name
+        # three basic arrays from positional arguments
+        self.Intensity = intensity
+        self.Error = error
+        self.Ncount = ncount
+
+        if type(self.metadata.dimension) == int:
+            if "xaxis" in kwargs:
+                self.xaxis = kwargs["xaxis"]
+            else:
+                raise NameError(
+                    "ERROR: Initialization of McStasData done with 1d "
+                    + "data, but without xaxis" + self.name + "!")
+
+        self.plot_options = McStasPlotOptions()
+
+    # Methods xlabel, ylabel and title as they might not be found
+    def set_xlabel(self, string):
+        self.metadata.set_xlabel(string)
+
+    def set_ylabel(self, string):
+        self.metadata.set_ylabel(string)
+
+    def set_title(self, string):
+        self.metadata.set_title(string)
+
+    def set_plot_options(self, **kwargs):
+        self.plot_options.set_options(**kwargs)
+
+def name_search(name, data_list):
+    """"
+    name_search returns McStasData instance with specific name if it is
+    in the given data_list
+
+    The index of certain datasets in the data_list can change if
+    additional monitors are added so it is more convinient to access
+    the data files using their names.
+
+    Parameters
+    ----------
+    name : string
+        Name of the dataset to be retrived (component_name)
+
+    data_list : List of McStasData instances
+        List of datasets to search
+    """
+
+    if not type(data_list[0]) == McStasData:
+        raise InputError(
+            "name_search function needs objects of type "
+            + "McStasData as input.")
+
+    list_result = []
+    for check in data_list:
+        if check.metadata.component_name == name:
+            list_result.append(check)
+
+    if len(list_result) == 1:
+        return list_result[0]
+    else:
+        raise NameError("More than one match for the name search")
+
+def name_plot_options(name, data_list, **kwargs):
+    """"
+    name_plot_options passes keyword arguments to dataset with certain
+    name in given data list
+
+    Function for quickly setting plotting options on a certain dataset
+    n a larger list of datasets
+
+    Parameters
+    ----------
+    name : string
+        Name of the dataset to be modified (component_name)
+
+    data_list : List of McStasData instances
+        List of datasets to search
+
+    kwargs : keyword arguments
+        Keyword arguments passed to set_plot_options in
+        McStasPlotOptions
+    """
+
+    if not isinstance(data_list[0], McStasData):
+        raise InputError(
+            "name_search function needs objects of type McStasData "
+            + "as input.")
+
+    object_to_modify = name_search(name, data_list)
+    object_to_modify.set_plot_options(**kwargs)
\ No newline at end of file
diff --git a/mcstasscript/helper/__init__.py b/mcstasscript/helper/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
new file mode 100644
index 00000000..968d8444
--- /dev/null
+++ b/mcstasscript/helper/component_reader.py
@@ -0,0 +1,361 @@
+import os
+import math
+
+class ComponentInfo:
+    """
+    Internal class used to store information on parameters of components
+    """
+
+    def __init__(self):
+        self.name = ""
+        self.category = ""
+        self.parameter_names = []
+        self.parameter_defaults = {}
+        self.parameter_types = {}
+        self.parameter_comments = {}
+        self.parameter_units = {}
+
+
+class ComponentReader:
+    """
+    Class for retriveing information on available McStas components
+
+    Recursively reads all component files in hardcoded list of
+    folders that represents the component categories in McStas.
+    The results are stored in a dictionary with ComponentInfo
+    instances, the keys are the names of the components. After
+    the components in the McStas installation are read, any
+    components pressent in the current work directory is read,
+    and these will overwrite exisiting information, consistent
+    with how McStas reads component definitions.
+
+    """
+
+    def __init__(self, mcstas_path):
+        """
+        Reads all component files in standard folders. Recursive, so
+        subfolders of these folders are included.
+
+        """
+
+        if mcstas_path[-1] is not "/":
+            mcstas_path = mcstas_path + "/"
+
+        # Hardcoded whitelist of foldernames
+        folder_list = ["sources",
+                       "optics",
+                       "samples",
+                       "monitors",
+                       "misc",
+                       "contrib",
+                       "obsolete",
+                       "union"]
+
+        self.component_path = {}
+        self.component_category = {}
+
+        for folder in folder_list:
+            absolute_path = mcstas_path + folder
+            # self.component_info_dict.update(self._read(absolute_path))
+            self._find_components(absolute_path)
+
+        # McStas component in current directory should overwrite
+        current_directory = os.getcwd()
+
+        for file in os.listdir(current_directory):
+            if file.endswith(".comp"):
+                absolute_path = current_directory + "/" + file
+                component_name = absolute_path.split("/")[-1].split(".")[-2]
+
+                if component_name in self.component_path:
+                    print("Overwriting McStasScript info on component named "
+                          + file
+                          + " because the component is in the"
+                          + " work directory.")
+
+                self.component_path[component_name] = absolute_path
+                self.component_category[component_name] = "Work directory"
+
+    def show_categories(self):
+        """
+        Method that will show all component categories available
+
+        """
+        categories = []
+        for component, category in self.component_category.items():
+            if category not in categories:
+                categories.append(category)
+                print(" " + category)
+
+    def show_components_in_category(self, category_input):
+        """
+        Method that will show all components in given category
+
+        """
+        empty_category = True
+        to_print = []
+        for component, category in self.component_category.items():
+            if category == category_input:
+                to_print.append(component)
+                empty_category = False
+
+        to_print.sort()
+        if empty_category:
+            print("No components found in this category! "
+                  + "Available categories:")
+            self.show_categories()
+
+        elif len(to_print) < 10:
+            for component in to_print:
+                print(" " + component)
+        else:
+            # Prints in collumns, maximum 4 and maximum line length 100
+            columns = 5
+            total_line_length = 1000
+            while(total_line_length > 100):
+                columns = columns - 1
+
+                c_length = math.ceil(len(to_print)/columns)
+                last_length = len(to_print) - (columns-1)*c_length
+
+                column = []
+                longest_name = []
+                for col in range(0, columns-1):
+                    current_list = to_print[c_length*col:c_length*(col+1)]
+                    column.append(current_list)
+                    longest_name.append(len(max(current_list, key=len)))
+
+                column.append(to_print[c_length*(columns-1):])
+                longest_name.append(len(max(column[columns-1], key=len)))
+
+                total_line_length = 1 + sum(longest_name) + (columns-1)*3
+
+            for line_nr in range(0, c_length):
+                print(" ", end="")
+                for col in range(0, columns-1):
+                    this_name = column[col][line_nr]
+                    print(this_name
+                          + " "*(longest_name[col] - len(this_name))
+                          + "   ", end="")  # More columns left, dont break
+                if line_nr < last_length:
+                    this_name = column[columns-1][line_nr]
+                    print(this_name)
+                else:
+                    print("")
+
+    def load_all_components(self):
+        """
+        Method that loads information on all components into memory.
+
+        """
+
+        return_dict = {}
+        for comp_name, abs_path in self.component_path.items():
+            return_dict[comp_name] = self.read_component_file(abs_path)
+
+        return return_dict
+
+    def read_name(self, component_name):
+        """
+        Returns ComponentInfo of component with name component_name.
+
+        Uses table of absolute paths to all known components, and
+        reads the appropriate file in order to generate the information.
+
+        """
+
+        if component_name not in self.component_path:
+            raise NameError("No component named "
+                            + component_name
+                            + " in McStas installation or "
+                            + "current work directory.")
+
+        return self.read_component_file(self.component_path[component_name])
+
+    def _find_components(self, absolute_path):
+        """
+        Recursive read function, can read either file or entire folder
+
+        Updates the component_info_dict with the findings that are
+        stored as ComoponentInfo instances.
+
+        """
+
+        if not os.path.isdir(absolute_path):
+            if absolute_path.endswith(".comp"):
+                # read this file
+                component_name = absolute_path.split("/")[-1].split(".")[-2]
+                self.component_path[component_name] = absolute_path
+
+                component_category = absolute_path.split("/")[-2]
+                self.component_category[component_name] = component_category
+        else:
+            for file in os.listdir(absolute_path):
+                absolute_file_path = absolute_path + "/" + file
+                self._find_components(absolute_file_path)
+
+    def read_component_file(self, absolute_path):
+        """
+        Reads a component file and expands component_info_dict
+
+        The information is stored as ComponentClass instances.
+
+        """
+
+        result = ComponentInfo()
+
+        fo = open(absolute_path, "r")
+
+        cnt = 0
+        while True:
+            cnt += 1
+            line = fo.readline()
+
+            # find parameter comments
+            if self.line_starts_with(line, "* %P"):
+
+                while True:
+                    this_line = fo.readline()
+
+                    if self.line_starts_with(this_line, "DEFINE COMPONENT"):
+                        # No more comments to read through
+                        break
+
+                    if ":" in this_line:
+                        tokens = this_line.split(":")
+
+                        variable_name = tokens[0]
+                        variable_name = variable_name.replace("*", "")
+                        variable_name = variable_name.strip()
+                        if " " in variable_name:
+                            name_tokens = variable_name.split(" ")
+                            variable_name = name_tokens[0]
+
+                        if len(tokens[1]) > 2:
+                            comment = tokens[1].strip()
+
+                            if "[" in comment:  # Search for unit
+                                # If found, store it and remove from string
+                                unit = comment[comment.find("[") + 1:
+                                               comment.find("]")]
+                                result.parameter_units[variable_name] = unit
+                                comment = comment[comment.find("]") + 1:]
+                                comment = comment.strip()
+
+                            # Store the comment
+                            result.parameter_comments[variable_name] = comment
+                    elif "[" in this_line and "]" in this_line:
+                        tokens = this_line.split("[")
+
+                        variable_name = tokens[0]
+                        variable_name = variable_name.replace("*", "")
+                        variable_name = variable_name.strip()
+
+                        unit = this_line[this_line.find("[") + 1:
+                                         this_line.find("]")]
+                        result.parameter_units[variable_name] = unit
+
+                        comment = this_line[this_line.find("]") + 1:]
+                        comment = comment.strip()
+                        result.parameter_comments[variable_name] = comment
+
+            # find definition parameters and their values
+            if (self.line_starts_with(line, "DEFINITION PARAMETERS")
+                    or self.line_starts_with(line, "SETTING PARAMETERS")):
+
+                parts = line.split("(")
+                parameter_parts = parts[1].split(",")
+
+                parameter_parts = list(filter(("\n").__ne__, parameter_parts))
+
+                break_now = False
+                while True:
+                    # Read all definition parameters
+
+                    for part in parameter_parts:
+
+                        temp_par_type = "double"
+
+                        part = part.strip()
+
+                        # remove trailing )
+                        if ")" in part:
+                            part = part.replace(")", "")
+                            break_now = True
+
+                        possible_declare = part.split(" ")
+                        possible_type = possible_declare[0].strip()
+                        if "int" == possible_type:
+                            temp_par_type = "int"
+                            # remove int from part
+                            part = "".join(possible_declare[1:])
+                        if "string" == possible_type:
+                            temp_par_type = "string"
+                            # remove string from part
+                            part = "".join(possible_declare[1:])
+
+                        part = part.replace(" ", "")
+                        if part == "":
+                            continue
+
+                        if self.line_starts_with(part, "//"):
+                            break_now = True
+                            continue
+
+                        if self.line_starts_with(part, "/*"):
+                            break_now = True
+                            continue
+
+                        if "=" not in part:
+                            # no defualt value, required parameter
+                            result.parameter_names.append(part)
+                            result.parameter_defaults[part] = None
+                            result.parameter_types[part] = temp_par_type
+                        else:
+                            # default value available
+                            name_value = part.split("=")
+                            par_name = name_value[0].strip()
+                            par_value = name_value[1].strip()
+                            result.parameter_names.append(par_name)
+                            result.parameter_defaults[par_name] = par_value
+                            result.parameter_types[par_name] = temp_par_type
+
+                    if break_now:
+                        break
+
+                    parameter_parts = fo.readline().split(",")
+
+            if self.line_starts_with(line, "DECLARE"):
+                break
+
+            if self.line_starts_with(line, "TRACE"):
+                break
+
+            if cnt == 1000:
+                break
+
+        fo.close()
+
+        result.name = absolute_path.split("/")[-1].split(".")[-2]
+        foldernames = absolute_path.split("/")
+        result.category = foldernames[-2]
+
+        """
+        To lower memory use one could remove all comments and units that
+        does not correspond to a found parameter name.
+        """
+
+        return result
+
+    def line_starts_with(self, line, string):
+        """
+        Helper method that checks if a string is the start of a line
+
+        """
+        if len(line) < len(string):
+            return False
+
+        if line[0:len(string)] == string:
+            return True
+        else:
+            return False
\ No newline at end of file
diff --git a/mcstasscript/helper/formatting.py b/mcstasscript/helper/formatting.py
new file mode 100644
index 00000000..58ac53c8
--- /dev/null
+++ b/mcstasscript/helper/formatting.py
@@ -0,0 +1,14 @@
+"""
+Helper class that contains formatting classes and functions
+
+"""
+
+class bcolors:
+    HEADER = '\033[95m'
+    OKBLUE = '\033[94m'
+    OKGREEN = '\033[92m'
+    WARNING = '\033[93m'
+    FAIL = '\033[91m'
+    ENDC = '\033[0m'
+    BOLD = '\033[1m'
+    UNDERLINE = '\033[4m'
\ No newline at end of file
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
new file mode 100644
index 00000000..a5cdf435
--- /dev/null
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -0,0 +1,222 @@
+import os
+import numpy as np
+from mcstasscript.data.data import McStasMetaData
+from mcstasscript.data.data import McStasData
+
+class ManagedMcrun:
+    """
+    A class for performing a mcstas simulation and organizing the data
+    into python objects
+
+    ManagedMcrun is usually called by the instrument class of
+    McStasScript but can be used independently.  It runs the mcrun
+    command using the system command, and if this is not in the path,
+    the absolute path can be given in a keyword argument mcrun_path.
+
+    Attributes
+    ----------
+    name_of_instrumentfile : str
+        Name of instrument file to be executed
+
+    data_folder_name : str
+        Name of datafolder mcrun writes to disk
+
+    ncount : int
+        Number of rays to simulate
+
+    mpi : int
+        Number of mpi threads to run
+
+    parameters : dict
+        Dictionary of parameter names and values for this simulation
+
+    custom_flags : string
+        Custom flags that are passed to the mcrun command
+
+    mcrun_path : string
+        Path to the mcrun command (can be empty if already in path)
+
+    Methods
+    -------
+    run_simulation()
+        Runs simulation, returns list of McStasData instances
+
+    """
+
+    def __init__(self, instr_name, **kwargs):
+        """
+        Parameters
+        ----------
+        instr_name : str
+            Name of instrument file to be simulated
+
+        kwargs : keyword arguments
+            foldername : str
+                Sets data_folder_name
+            ncount : int
+                Sets ncount
+            mpi : int
+                Sets thread count
+            parameters : dict
+                Sets parameters
+            custom_flags : str
+                Sets custom_flags passed to mcrun
+            mcrun_path : str
+                Path to mcrun command, "" if already in path
+        """
+
+        self.name_of_instrumentfile = instr_name
+
+        self.data_folder_name = ""
+        self.ncount = 1E6
+        self.mpi = 1
+        self.parameters = {}
+        self.custom_flags = ""
+        self.mcrun_path = ""
+        # mcrun_path always in kwargs
+        self.mcrun_path = kwargs["mcrun_path"]
+
+        if "foldername" in kwargs:
+            self.data_folder_name = kwargs["foldername"]
+        else:
+            raise NameError(
+                "ManagedMcrun needs foldername to load data, add "
+                + "with keyword argument.")
+
+        if "ncount" in kwargs:
+            self.ncount = kwargs["ncount"]
+
+        if "mpi" in kwargs:
+            self.mpi = kwargs["mpi"]
+
+        if "parameters" in kwargs:
+            self.parameters = kwargs["parameters"]
+
+        if "custom_flags" in kwargs:
+            self.custom_flags = kwargs["custom_flags"]
+
+    def run_simulation(self):
+        """
+        Runs McStas simulation described by initializing the object
+        """
+
+        # construct command to run
+        option_string = ("-c"
+                         + " -n " + str(self.ncount)  # Set ncount
+                         + " --mpi=" + str(self.mpi)  # Set mpi
+                         + " ")
+
+        if len(self.data_folder_name) > 0:
+            option_string = (option_string
+                             + "-d "
+                             + self.data_folder_name)
+
+        # add parameters to command
+        parameter_string = ""
+        for key, val in self.parameters.items():
+            parameter_string = (parameter_string + " "
+                                + str(key)  # parameter name
+                                + "="
+                                + str(val))  # parameter value
+
+        mcrun_full_path = self.mcrun_path + "mcrun"
+        if len(self.mcrun_path) > 1:
+            if not (self.mcrun_path[-1] == "\\"
+                    or self.mcrun_path[-1] == "/"):
+                mcrun_full_path = self.mcrun_path + "/mcrun"
+
+        # Run the mcrun command on the system
+        os.system(mcrun_full_path + " "
+                  + option_string + " "
+                  + self.custom_flags + " "
+                  + self.name_of_instrumentfile + " "
+                  + parameter_string)
+
+        """
+        Can use subprocess from spawn* instead of os.system if more
+        control is needed over the spawned process, including a timeout
+        """
+
+        # Find all data files in generated folder
+        files_in_folder = os.listdir(self.data_folder_name)
+
+        # Raise an error if mccode.sim is not available
+        if "mccode.sim" not in files_in_folder:
+            raise NameError("mccode.sim not written to output folder.")
+
+        # Open mccode to read metadata for all datasets written to disk
+        f = open(self.data_folder_name + "/mccode.sim", "r")
+
+        # Loop that reads mccode.sim sections
+        metadata_list = []
+        in_data = False
+        for lines in f:
+            # Could read other details about run
+
+            if lines == "end data\n":
+                # No more data for this metadata object
+                # Extract the information
+                current_object.extract_info()
+                # Add to metadata list
+                metadata_list.append(current_object)
+                # Stop reading data
+                in_data = False
+
+            if in_data:
+                # This line contains info to be added to metadata
+                colon_index = lines.index(":")
+                key = lines[2:colon_index]
+                value = lines[colon_index+2:]
+                current_object.add_info(key, value)
+
+            if lines == "begin data\n":
+                # Found data section, create new metadata object
+                current_object = McStasMetaData()
+                # Start recording data to metadata object
+                in_data = True
+
+        # Close mccode.sim
+        f.close()
+
+        # Create a list for McStasData instances to return
+        results = []
+
+        # Load datasets described in metadata list individually
+        for metadata in metadata_list:
+            # Load data with numpy
+            data = np.loadtxt(self.data_folder_name
+                              + "/"
+                              + metadata.filename.rstrip())
+
+            # Split data into intensity, error and ncount
+            if type(metadata.dimension) == int:
+                xaxis = data.T[0, :]
+                Intensity = data.T[1, :]
+                Error = data.T[2, :]
+                Ncount = data.T[3, :]
+
+            elif len(metadata.dimension) == 2:
+                xaxis = []  # Assume evenly binned in 2d
+                data_lines = metadata.dimension[1]
+                Intensity = data.T[:, 0:data_lines - 1]
+                Error = data.T[:, data_lines:2*data_lines - 1]
+                Ncount = data.T[:, 2*data_lines:3*data_lines - 1]
+            else:
+                raise NameError(
+                    "Dimension not read correctly in data set "
+                    + "connected to monitor named "
+                    + metadata.component_name)
+
+            # The data is saved as a McStasData object
+            result = McStasData(metadata, Intensity,
+                                Error, Ncount,
+                                xaxis=xaxis)
+
+            # Add this result to the results list
+            results.append(result)
+
+            # Close the current datafile
+            f.close()
+
+        # Return list of McStasData objects
+        return results
\ No newline at end of file
diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
new file mode 100644
index 00000000..cef75cc0
--- /dev/null
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -0,0 +1,733 @@
+from mcstasscript.helper.formatting import bcolors
+
+class parameter_variable:
+    """
+    Class describing a input parameter in McStas instrument
+
+    McStas input parameters are of default type double, but can be
+    cast.  If two positional arguments are given, the first is the
+    type, and the second is the parameter name.  With one input, only
+    the parameter name is read.  It is also possible to assign a
+    default value and a comment through keyword arguments.
+
+    Attributes
+    ----------
+    type : str
+        McStas type of input: Double, Int, String
+
+    name : str
+        Name of input parameter
+
+    value : any
+        Default value/string of parameter, converted to string
+
+    comment : str
+        Comment displayed next to the parameter, could contain units
+
+    Methods
+    -------
+    write_parameter(fo,stop_character)
+        writes the parameter to file fo, uses given stop character
+    """
+
+    def __init__(self, *args, **kwargs):
+        """Initializing mcstas parameter object
+
+        Parameters
+        ----------
+        If giving a type:
+        Positional argument 1: type: str
+            Type of the parameter, double, int or string
+        Positional argument 2: name: str
+            Name of input parameter
+
+        If not giving type
+        Positional argument 1: name : str
+            Name of input parameter
+
+        Keyword arguments
+            value : any
+                sets default value of parameter
+            comment : str
+                sets comment displayed next to declaration
+        """
+        if len(args) == 1:
+            self.type = ""
+            self.name = str(args[0])
+        if len(args) == 2:
+            self.type = args[0] + " "
+            self.name = str(args[1])
+
+        if "value" in kwargs:
+            self.value_set = 1
+            self.value = kwargs["value"]
+        else:
+            self.value_set = 0
+
+        if "comment" in kwargs:
+            self.comment = "// " + kwargs["comment"]
+        else:
+            self.comment = ""
+
+        # could check for allowed types
+        # they are int, double, string, are there more?
+
+    def write_parameter(self, fo, stop_character):
+        """Writes input parameter to file"""
+        fo.write("%s%s" % (self.type, self.name))
+        if self.value_set == 1:
+            if isinstance(self.value, int):
+                fo.write(" = %d" % self.value)
+            elif isinstance(self.value, float):
+                fo.write(" = %G" % self.value)
+            else:
+                fo.write(" = %s" % str(self.value))
+        fo.write(stop_character)
+        fo.write(self.comment)
+        fo.write("\n")
+
+
+class declare_variable:
+    """
+    Class describing a declared variable in McStas instrument
+
+    McStas parameters are declared in declare section with c syntax.
+    This class is initialized with type, name.  Using keyword
+    arguments, the variable can become an array and have its initial
+    value set.
+
+    Attributes
+    ----------
+    type : str
+        McStas type to declare: Double, Int, String
+
+    name : str
+        Name of variable
+
+    value : any
+        Initial value of variable, converted to string
+
+    comment : str
+        Comment displayed next to the declaration, could contain units
+
+    vector : int
+        0 if a single value is given, ortherwise contains the length
+
+    value_set : int
+        Internal variable displaying wether or not a value was given
+
+    Methods
+    -------
+    write_line(fo)
+        Writes a line to text file fo declaring the parameter in c
+    """
+    def __init__(self, *args, **kwargs):
+        """Initializing mcstas parameter object
+
+        Parameters
+        ----------
+        Positional argument 1: type : str
+            Type of the parameter, double, int or string
+
+        Positional argument 2: name : str
+            Name of input parameter
+
+        Keyword arguments
+            array : int
+                length of array to be allocated, 0 if single value
+
+            value : any
+                sets initial value of parameter,
+                can be a list with length matching array
+
+            comment : str
+                sets comment displayed next to declaration
+        """
+        self.type = args[0]
+        self.name = str(args[1])
+        if "value" in kwargs:
+            self.value_set = 1
+            self.value = kwargs["value"]
+        else:
+            self.value_set = 0
+
+        if "array" in kwargs:
+            self.vector = kwargs["array"]
+        else:
+            self.vector = 0
+
+        if "comment" in kwargs:
+            self.comment = " // " + kwargs["comment"]
+        else:
+            self.comment = ""
+
+    def write_line(self, fo):
+        """Writes line declaring variable to file fo
+
+        Parameters
+        ----------
+        fo : file object
+            File the line will be written to
+        """
+        if self.value_set == 0 and self.vector == 0:
+            fo.write("%s %s;%s" % (self.type, self.name, self.comment))
+        if self.value_set == 1 and self.vector == 0:
+            if self.type == "int":
+                fo.write("%s %s = %d;%s" % (self.type, self.name,
+                                            self.value, self.comment))
+            else:
+                fo.write("%s %s = %G;%s" % (self.type, self.name,
+                                            self.value, self.comment))
+        if self.value_set == 0 and self.vector != 0:
+            fo.write("%s %s[%d];%s" % (self.type, self.name,
+                                       self.vector, self.comment))
+        if self.value_set == 1 and self.vector != 0:
+            fo.write("%s %s[%d] = {" % (self.type, self.name, self.vector))
+            for i in range(0, len(self.value) - 1):
+                fo.write("%G," % self.value[i])
+            fo.write("%G};%s" % (self.value[-1], self.comment))
+
+
+class component:
+    """
+    A class describing a McStas component to be written to a instrument
+
+    This class is used by the instrument class when setting up
+    components as dynamic subclasses to this class.  Most information
+    can be given on initialize using keyword arguments, but there are
+    methods for setting the attributes describing the component. The
+    class contains both methods to write the component to a instrument
+    file and methods for printing to the python terminal for checking
+    the information. The McStas_Instr class creates subclasses from
+    this class that have attributes for all parameters for the given
+    component. The component information is read directly from the
+    component files in the McStas installation.  This class is frozen
+    after __init__ so that no new attributes can be created, which
+    allows direct feedback to the user if a parameter name is
+    misspelled.
+
+    Attributes
+    ----------
+    name : str
+        Name of the component instance in McStas (must be unique)
+
+    component_name : str
+        Name of the component code to use, e.g. Arm, Guide_gravity, ...
+
+    AT_data : list of 3 floats
+        Position data of the component
+
+    AT_relative : str
+        Name of former component to use as reference for position
+
+    ROTATED_data : list of 3 floats
+        Rotation data of the component
+
+    ROTATED_relative : str
+        Name of former component to use as reference for position
+
+    WHEN : str
+        String with logical c expression x for when component is active
+
+    EXTEND : str
+        c code for McStas EXTEND section
+
+    GROUP : str
+        Name of group the component should belong to
+
+    JUMP : str
+        String describing use of JUMP, need to contain all after "JUMP"
+
+    component_parameters : dict
+        Parameters to be used with component in dictionary
+
+    comment : str
+        Comment inserted before the component as an explanation
+
+    __isfrozen : bool
+        If true no new attributes can be created, when false they can
+
+    Methods
+    -------
+    set_AT(at_list,**kwargs)
+        Sets AT_data, can set AT_relative using keyword
+
+    set_ROTATED(rotated_list,**kwargs)
+        Sets ROTATED_data, can set ROTATED_relative using keyword
+
+    set_RELATIVE(relative_name)
+        Set both AT_relative and ROTATED_relative to relative_name
+
+    set_parameters(dict_input)
+        Adds dictionary entries to parameter dictionary
+
+    set_WHEN(string)
+        Sets WHEN string
+
+    set_GROUP(string)
+        Sets GROUP name
+
+    set_JUMP(string)
+        Sets JUMP string
+
+    append_EXTEND(string)
+        Append string to EXTEND string
+
+    set_comment(string)
+        Sets comment for component
+
+    write_component(fo)
+        Writes component code to instrument file
+
+    print_long()
+        Prints basic view of component code (not correct syntax)
+
+    print_short(**kwargs)
+        Prints short description, used in print_components
+
+    __setattr__(key, value)
+        Overwriting __setattr__ to implement ability to freeze
+
+    _freeze()
+        Freeze the class so no new attributes can be defined
+
+    _unfreeze()
+        Unfreeze the class so new attributes can be defined again
+
+    """
+
+    __isfrozen = False  # When frozen, no new attributes allowed
+
+    def __init__(self, instance_name, component_name, **kwargs):
+        """
+        Initializes McStas component with specified name and component
+
+        Parameters
+        ----------
+        instance_name : str
+            name of the instance of the component
+
+        component_name : str
+            name of the component type e.g. Arm, Guide_gravity, ...
+
+        keyword arguments:
+            AT : list of 3 floats
+                Sets AT_data describing position of component
+
+            AT_RELATIVE : str
+                sets AT_relative, describing position reference
+
+            ROTATED : list of 3 floats
+                Sets ROTATED_data, describing rotation of component
+
+            ROTATED_RELATIVE : str
+                Sets ROTATED_relative, sets reference for rotation
+
+            RELATIVE : str
+                Sets both AT_relative and ROTATED_relative
+
+            WHEN : str
+                Sets WHEN string, should contain logical c expression
+
+            EXTEND : str
+                Sets initial EXTEND string, should contain c code
+
+            GROUP : str
+                Sets name of group the component should belong to
+
+            JUMP : str
+                Sets JUMP str
+
+            comment: str
+                Sets comment string
+        """
+
+        # Allow addition of attributes in init
+        self._unfreeze()
+
+        self.name = instance_name
+        self.component_name = component_name
+
+        if "AT" in kwargs:
+            self.AT_data = kwargs["AT"]
+        else:
+            self.AT_data = [0, 0, 0]
+        # Could check if AT_RELATIVE is a string
+        if "AT_RELATIVE" in kwargs:
+            self.AT_relative = "RELATIVE " + kwargs["AT_RELATIVE"]
+        else:
+            self.AT_relative = "ABSOLUTE"
+
+        if "ROTATED" in kwargs:
+            self.ROTATED_data = kwargs["ROTATED"]
+        else:
+            self.ROTATED_data = [0, 0, 0]
+        # Could check if ROTATED_RELATIVE is a string
+        if "ROTATED_RELATIVE" in kwargs:
+            self.ROTATED_relative = kwargs["ROTATED_RELATIVE"]
+        else:
+            self.ROTATED_relative = "ABSOLUTE"
+
+        # Could check if RELATIVE is a string
+        if "RELATIVE" in kwargs:
+            self.AT_relative = "RELATIVE " + kwargs["RELATIVE"]
+            self.ROTATED_relative = "RELATIVE " + kwargs["RELATIVE"]
+
+        if "WHEN" in kwargs:
+            self.WHEN = "WHEN (" + kwargs["WHEN"] + ")\n"
+        else:
+            self.WHEN = ""
+
+        if "EXTEND" in kwargs:
+            self.EXTEND = kwargs["EXTEND"] + "\n"
+        else:
+            self.EXTEND = ""
+
+        if "GROUP" in kwargs:
+            self.GROUP = kwargs["GRPUP"]
+        else:
+            self.GROUP = ""
+
+        if "JUMP" in kwargs:
+            self.JUMP = kwargs["JUMP"]
+        else:
+            self.JUMP = ""
+
+        if "comment" in kwargs:
+            self.comment = kwargs["comment"]
+        else:
+            self.comment = ""
+
+        """
+        Could store an option for whether this component should be
+        printed in instrument file or in a seperate file which would
+        then be included.
+        """
+
+        # Do not allow addition of attributes after init
+        self._freeze()
+
+    def __setattr__(self, key, value):
+        if self.__isfrozen and not hasattr(self, key):
+            raise AttributeError("No parameter called '"
+                                 + key
+                                 + "' in component named "
+                                 + self.name
+                                 + " of component type "
+                                 + self.component_name
+                                 + ".")
+        object.__setattr__(self, key, value)
+
+    def _freeze(self):
+        self.__isfrozen = True
+
+    def _unfreeze(self):
+        self.__isfrozen = False
+
+    def set_AT(self, at_list, **kwargs):
+        """Sets AT data, List of 3 floats"""
+        self.AT_data = at_list
+        if "RELATIVE" in kwargs:
+            relative_name = kwargs["RELATIVE"]
+            if relative_name == "ABSOLUTE":
+                self.AT_relative = relative_name
+            else:
+                self.AT_relative = "RELATIVE " + relative_name
+
+    def set_ROTATED(self, rotated_list, **kwargs):
+        """Sets ROTATED data, List of 3 floats"""
+        self.ROTATED_data = rotated_list
+        if "RELATIVE" in kwargs:
+            relative_name = kwargs["RELATIVE"]
+            if relative_name == "ABSOLUTE":
+                self.ROTATED_relative = relative_name
+            else:
+                self.ROTATED_relative = "RELATIVE " + relative_name
+
+    def set_RELATIVE(self, relative_name):
+        """Sets both AT_relative and ROTATED_relative"""
+        if relative_name == "ABSOLUTE":
+            self.AT_relative = relative_name
+            self.ROTATED_relative = relative_name
+        else:
+            self.AT_relative = "RELATIVE " + relative_name
+            self.ROTATED_relative = "RELATIVE " + relative_name
+
+    def set_parameters(self, dict_input):
+        """
+        Adds parameters and their values from dictionary input
+
+        Relies on attributes added when McStas_Instr creates a
+        subclass from the component class where each component
+        parameter is added as an attribute.
+
+        """
+        for key, val in dict_input.items():
+            if not hasattr(self, key):
+                raise NameError("No parameter called "
+                                + key
+                                + " in component named "
+                                + self.name
+                                + " of component type "
+                                + self.component_name
+                                + ".")
+            else:
+                setattr(self, key, val)
+
+    def set_WHEN(self, string):
+        """Sets WHEN string, should be a c logical expression"""
+        self.WHEN = string
+
+    def set_GROUP(self, string):
+        """Sets GROUP name"""
+        self.GROUP = string
+
+    def set_JUMP(self, string):
+        """Sets JUMP string, should contain all text after JUMP"""
+        self.JUMP = string
+
+    def append_EXTEND(self, string):
+        """Appends a line of code to EXTEND block of component"""
+        self.EXTEND = self.EXTEND + string + "\n"
+
+    def set_comment(self, string):
+        """Method that sets a comment to be written to instrument file"""
+        self.comment = string
+
+    def write_component(self, fo):
+        """
+        Method that writes component to file
+
+        Relies on attributes added when McStas_Instr creates a subclass
+        based on the component class.
+
+        """
+        parameters_per_line = 2
+        # Could use character limit on lines instead
+        parameters_written = 0  # internal parameter
+
+        # Write comment if present
+        if len(self.comment) > 1:
+            fo.write("// %s\n" % (str(self.comment)))
+
+        # Write component name and component type
+        fo.write("COMPONENT %s = %s(" % (self.name, self.component_name))
+
+        component_parameters = {}
+        for key in self.parameter_names:
+            val = getattr(self, key)
+            if val is None:
+                if self.parameter_defaults[key] is None:
+                    raise NameError("Required parameter named "
+                                    + key
+                                    + " in component named "
+                                    + self.name
+                                    + " not set.")
+                else:
+                    continue
+
+            component_parameters[key] = val
+
+        number_of_parameters = len(component_parameters)
+
+        if number_of_parameters == 0:
+            fo.write(")\n")  # If there are no parameters, close immediately
+        else:
+            fo.write("\n")  # If there are parameters, start a new line
+
+        for key, val in component_parameters.items():
+            if isinstance(val, float):  # CHeck if value is a number
+                # Small or large numbers written in scientific format
+                fo.write(" %s = %G" % (str(key), val))
+            else:
+                fo.write(" %s = %s" % (str(key), str(val)))
+            parameters_written = parameters_written + 1
+            if parameters_written < number_of_parameters:
+                fo.write(",")  # Comma between parameters
+                if parameters_written % parameters_per_line == 0:
+                    fo.write("\n")
+            else:
+                fo.write(")\n")  # End paranthesis after last parameter
+
+        # Optional WHEN section
+        if not self.WHEN == "":
+            fo.write("WHEN(%s)\n" % self.WHEN)
+
+        # Write AT and ROTATED section
+        fo.write("AT (%s,%s,%s)" % (str(self.AT_data[0]),
+                                    str(self.AT_data[1]),
+                                    str(self.AT_data[2])))
+        fo.write(" %s\n" % self.AT_relative)
+        fo.write("ROTATED (%s,%s,%s)" % (str(self.ROTATED_data[0]),
+                                         str(self.ROTATED_data[1]),
+                                         str(self.ROTATED_data[2])))
+        fo.write(" %s\n" % self.ROTATED_relative)
+
+        if not self.GROUP == "":
+            fo.write("GROUP %s\n" % self.GROUP)
+
+        # Optional EXTEND section
+        if not self.EXTEND == "":
+            fo.write("EXTEND %{\n")
+            fo.write("%s" % self.EXTEND)
+            fo.write("%}\n")
+
+        if not self.JUMP == "":
+            fo.write("JUMP %s\n" % self.JUMP)
+
+        # Leave a new line between components for readability
+        fo.write("\n")
+
+    def print_long(self):
+        """
+        Prints contained information to Python terminal
+
+        Includes information on required parameters if they are not yet
+        specified. Information on the components are added when the
+        class is used as a superclass for classes describing each
+        McStas component.
+
+        """
+        if len(self.comment) > 1:
+            print("// " + self.comment)
+        print("COMPONENT", str(self.name),
+              "=", str(self.component_name))
+        for key in self.parameter_names:
+            val = getattr(self, key)
+            parameter_name = bcolors.BOLD + key + bcolors.ENDC
+            if val is not None:
+                unit = ""
+                if key in self.parameter_units:
+                    unit = "[" + self.parameter_units[key] + "]"
+                value = (bcolors.BOLD
+                         + bcolors.OKGREEN
+                         + str(val)
+                         + bcolors.ENDC
+                         + bcolors.ENDC)
+                print(" ", parameter_name, "=", value, unit)
+            else:
+                if self.parameter_defaults[key] is None:
+                    print("  "
+                          + parameter_name
+                          + bcolors.FAIL
+                          + " : Required parameter not yet specified"
+                          + bcolors.ENDC)
+
+        if not self.WHEN == "":
+            print("WHEN (" + self.WHEN + ")")
+        print("AT", self.AT_data, self.AT_relative)
+        print("ROTATED", self.ROTATED_data, self.ROTATED_relative)
+        if not self.GROUP == "":
+            print("GROUP " + self.GROUP)
+        if not self.EXTEND == "":
+            print("EXTEND %{")
+            print(self.EXTEND + "%}")
+        if not self.JUMP == "":
+            print("JUMP " + self.JUMP)
+
+    def print_short(self, **kwargs):
+        """Prints short description of component to list print"""
+        if "longest_name" in kwargs:
+            print("test")
+            number_of_spaces = 3+kwargs["longest_name"]-len(self.name)
+            print(str(self.name) + " "*number_of_spaces, end='')
+            print(str(self.component_name),
+                  "\tAT", self.AT_data, self.AT_relative,
+                  "ROTATED", self.ROTATED_data, self.ROTATED_relative)
+        else:
+            print(str(self.name), "=", str(self.component_name),
+                  "\tAT", self.AT_data, self.AT_relative,
+                  "ROTATED", self.ROTATED_data, self.ROTATED_relative)
+
+    def show_parameters(self):
+        """
+        Shows available parameters and their defaults for the component
+
+        Any value specified is not reflected in this view. The
+        additional attributes defined when McStas_Instr creates
+        subclasses for the individual components are required to run
+        this method.
+
+        """
+
+        print(" ___ Help "
+              + self.component_name + " "
+              + (62-len(self.component_name))*"_")
+        print("|"
+              + bcolors.BOLD + "optional parameter" + bcolors.ENDC + "|"
+              + bcolors.BOLD
+              + bcolors.UNDERLINE + "required parameter" + bcolors.ENDC
+              + bcolors.ENDC + "|"
+              + bcolors.BOLD
+              + bcolors.OKBLUE + "default value" + bcolors.ENDC
+              + bcolors.ENDC + "|"
+              + bcolors.BOLD
+              + bcolors.OKGREEN + "user specified value" + bcolors.ENDC
+              + bcolors.ENDC + "|")
+
+        for parameter in self.parameter_names:
+            unit = ""
+            if parameter in self.parameter_units:
+                unit = " [" + self.parameter_units[parameter] + "]"
+            comment = ""
+            if parameter in self.parameter_comments:
+                comment = " // " + self.parameter_comments[parameter]
+
+            parameter_name = bcolors.BOLD + parameter + bcolors.ENDC
+            value = ""
+            if self.parameter_defaults[parameter] is None:
+                parameter_name = (bcolors.UNDERLINE
+                                  + parameter_name
+                                  + bcolors.ENDC)
+            else:
+                value = (" = "
+                         + bcolors.BOLD
+                         + bcolors.OKBLUE
+                         + str(self.parameter_defaults[parameter])
+                         + bcolors.ENDC
+                         + bcolors.ENDC)
+
+            if getattr(self, parameter) is not None:
+                value = (" = "
+                         + bcolors.BOLD
+                         + bcolors.OKGREEN
+                         + str(getattr(self, parameter))
+                         + bcolors.ENDC
+                         + bcolors.ENDC)
+
+            print(parameter_name
+                  + value
+                  + unit
+                  + comment)
+
+        print(73*"-")
+
+    def show_parameters_simple(self):
+        """
+        Shows available parameters and their defaults for the component
+
+        Any value specified is not reflected in this view. The
+        additional attributes defined when McStas_Instr creates
+        subclasses for the individual components are required to run
+        this method.
+
+        """
+        print("---- Help " + self.component_name + " -----")
+        for parameter in self.parameter_names:
+            unit = ""
+            if parameter in self.parameter_units:
+                unit = " [" + self.parameter_units[parameter] + "]"
+            comment = ""
+            if parameter in self.parameter_comments:
+                comment = " // " + self.parameter_comments[parameter]
+            if self.parameter_defaults[parameter] is None:
+                print(parameter
+                      + unit
+                      + comment)
+            else:
+                print(parameter
+                      + " = "
+                      + str(self.parameter_defaults[parameter])
+                      + unit
+                      + comment)
+        print("----------" + "-"*len(self.component_name) + "------")
\ No newline at end of file
diff --git a/mcstasscript/interface/__init__.py b/mcstasscript/interface/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/mcstasscript/interface/functions.py b/mcstasscript/interface/functions.py
new file mode 100644
index 00000000..a6ca195d
--- /dev/null
+++ b/mcstasscript/interface/functions.py
@@ -0,0 +1,63 @@
+from mcstasscript.data.data import McStasData
+
+def name_search(name, data_list):
+    """"
+    name_search returns McStasData instance with specific name if it is
+    in the given data_list
+
+    The index of certain datasets in the data_list can change if
+    additional monitors are added so it is more convinient to access
+    the data files using their names.
+
+    Parameters
+    ----------
+    name : string
+        Name of the dataset to be retrived (component_name)
+
+    data_list : List of McStasData instances
+        List of datasets to search
+    """
+
+    if not type(data_list[0]) == McStasData:
+        raise InputError(
+            "name_search function needs objects of type "
+            + "McStasData as input.")
+
+    list_result = []
+    for check in data_list:
+        if check.metadata.component_name == name:
+            list_result.append(check)
+
+    if len(list_result) == 1:
+        return list_result[0]
+    else:
+        raise NameError("More than one match for the name search")
+
+def name_plot_options(name, data_list, **kwargs):
+    """"
+    name_plot_options passes keyword arguments to dataset with certain
+    name in given data list
+
+    Function for quickly setting plotting options on a certain dataset
+    n a larger list of datasets
+
+    Parameters
+    ----------
+    name : string
+        Name of the dataset to be modified (component_name)
+
+    data_list : List of McStasData instances
+        List of datasets to search
+
+    kwargs : keyword arguments
+        Keyword arguments passed to set_plot_options in
+        McStasPlotOptions
+    """
+
+    if not isinstance(data_list[0], McStasData):
+        raise InputError(
+            "name_search function needs objects of type McStasData "
+            + "as input.")
+
+    object_to_modify = name_search(name, data_list)
+    object_to_modify.set_plot_options(**kwargs)
\ No newline at end of file
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
new file mode 100644
index 00000000..f43104d6
--- /dev/null
+++ b/mcstasscript/interface/instr.py
@@ -0,0 +1,980 @@
+from __future__ import print_function
+
+import os
+import datetime
+
+from mcstasscript.helper.mcstas_objects import declare_variable
+from mcstasscript.helper.mcstas_objects import parameter_variable
+from mcstasscript.helper.mcstas_objects import component
+from mcstasscript.data.data import McStasData
+from mcstasscript.helper.component_reader import ComponentReader
+from mcstasscript.helper.managed_mcrun import ManagedMcrun
+
+class McStas_instr:
+    """
+    Main class for writing a McStas instrument using McStasScript
+
+    Initialization of McStas_instr sets the name of the instrument file
+    and its methods are used to add all aspects of the instrument file.
+    The class also holds methods for writing the finished instrument
+    file to disk and to run the simulation.
+
+    Attributes
+    ----------
+    name : str
+        name of instrument file
+
+    author : str
+        name of user of McStasScript, written to the file
+
+    origin : str
+        origin of instrument file (affiliation)
+
+    mcrun_path : str
+        absolute path of mcrun command, or empty if it is in path
+
+    parameter_list : list of parameter_variable instances
+        contains all input parameters to be written to file
+
+    declare_list : list of declare_variable instances
+        contains all declare parrameters to be written to file
+
+    initialize_section : str
+        string containing entire initialize section to be written
+
+    trace_section : str
+        string containing trace section (OBSOLETE)
+
+    finally_section : str
+        string containing entire finally section to be written
+
+    component_list : list of component instances
+        list of components in the instrument
+
+    component_name_list : list of strings
+        list of names of the components in the instrument
+
+    Methods
+    -------
+    add_parameter(*args,**kwargs)
+        Adds input parameter to the define section
+
+    add_declare_var()
+        Adds declared variable ot the declare section
+
+    append_initialize(string)
+        Appends a string to the initialize section, then adds new line
+
+    append_initialize_no_new_line(string)
+        Appends a string to the initialize section
+
+    append_finally(string)
+        Appends a string to finally section, then adds new line
+
+    append_finally_no_new_line(string)
+        Appends a string to finally section
+
+    append_trace(string)
+        Obsolete method, add components instead (used in write_c_files)
+
+    add_component(instance_name,component_name,**kwargs)
+        Add a component to the instrument file
+
+    get_component(instance_name)
+        Returns component instance with name instance_name
+
+    get_last_component()
+        Returns component instance of last component
+
+    set_component_parameter(instance_name,dict)
+        Adds parameters as dict to component with instance_name
+
+    set_component_AT(instance_name,AT_data,**kwargs)
+        Sets position of component named instance_name
+
+    set_component_ROTATED(instance_name,ROTATED_data,**kwargs)
+        Sets rotation of component named instance_name
+
+    set_component_RELATIVE(instane_name,string)
+        Sets position and rotation reference for named component
+
+    set_component_WHEN(instance_name,string)
+        Sets WHEN condition of named component, is logical c expression
+
+    set_component_GROUP(instance_name,string)
+        Sets GROUP name of component named instance_name
+
+    append_component_EXTEND(instance_name,string)
+        Appends a line to EXTEND section of named component
+
+    set_component_JUMP(instance_name,string)
+        Sets JUMP code for named component
+
+    set_component_comment(instance_name,string)
+        Sets comment to be written before named component
+
+    print_component(instance_name)
+        Prints an overview of current state of named component
+
+    print_component_short(instance_name)
+        Prints short overview of current state of named component
+
+    print_components()
+        Prints overview of postion / rotation of all components
+
+    write_c_files()
+        Writes c files for %include in generated_includes folder
+
+    write_full_instrument()
+        Writes full instrument file to current directory
+
+    run_full_instrument(**kwargs)
+        Writes instrument files and runs simulation.
+        Returns list of McStasData
+    """
+
+    def __init__(self, name, **kwargs):
+        """
+        Initialization of McStas Instrument
+
+        Parameters
+        ----------
+        name : str
+            Name of project, instrument file will be name + ".instr"
+
+        keyword arguments:
+            author : str
+                Name of author, written in instrument file
+
+            origin : str
+                Affiliation of author, written in instrument file
+
+            mcrun_path : str
+                Absolute path of mcrun or empty if already in path
+        """
+
+        self.name = name
+
+        if "author" in kwargs:
+            self.author = kwargs["author"]
+        else:
+            self.author = "Python McStas Instrument Generator"
+
+        if "origin" in kwargs:
+            self.origin = kwargs["origin"]
+        else:
+            self.origin = "ESS DMSC"
+
+        if "mcrun_path" in kwargs:
+            self.mcrun_path = kwargs["mcrun_path"]
+        else:
+            self.mcrun_path = ""
+
+        if "mcstas_path" in kwargs:
+            self.mcstas_path = kwargs["mcstas_path"]
+        else:
+            self.mcstas_path = ""
+            raise NameError("At this stage of development "
+                            + "McStasScript need the absolute path "
+                            + "for the McStas installation as keyword "
+                            + "named mcstas_path")
+
+        self.parameter_list = []
+        self.declare_list = []
+        self.initialize_section = ("// Start of initialize for generated "
+                                   + name + "\n")
+        self.trace_section = ("// Start of trace section for generated "
+                              + name + "\n")
+        self.finally_section = ("// Start of finally for generated "
+                                + name + "\n")
+        # Handle components
+        self.component_list = []  # List of components (have to be ordered)
+        self.component_name_list = []  # List of component names
+
+        # Read info on active McStas components
+        self.component_reader = ComponentReader(self.mcstas_path)
+        self.component_class_lib = {}
+
+    def add_parameter(self, *args, **kwargs):
+        """
+        Method for adding input parameter to instrument
+
+        Parameters
+        ----------
+
+        (optional) parameter type : str
+            type of input parameter, double, int, string
+
+        parameter name : str
+            name of parameter
+
+        keyword arguments
+            value : any
+                Default value of parameter
+
+            comment : str
+                Comment displayed next to declaration of parameter
+        """
+        # parameter_variable class documented independently
+        self.parameter_list.append(parameter_variable(*args, **kwargs))
+
+    def add_declare_var(self, *args, **kwargs):
+        """
+        Method for adding declared variable to instrument
+
+        Parameters
+        ----------
+
+        parameter type : str
+            type of input parameter
+
+        parameter name : str
+            name of parameter
+
+        keyword arguments
+            array : int
+                default 0 for scalar, if specified length of array
+
+            value : any
+                Initial value of parameter, can be list of length vector
+
+            comment : str
+                Comment displayed next to declaration of parameter
+
+        """
+        # declare_variable class documented independently
+        self.declare_list.append(declare_variable(*args, **kwargs))
+
+    def append_initialize(self, string):
+        """
+        Method for appending code to the intialize section
+
+        The intialize section consists of c code and will be compiled,
+        thus any syntax errors will crash the simulation. Code is added
+        on a new line for each call to this method.
+
+        Parameters
+        ----------
+        string : str
+            code to be added to initialize section
+        """
+        self.initialize_section = self.initialize_section + string + "\n"
+
+    def append_initialize_no_new_line(self, string):
+        """
+        Method for appending code to the intialize section, no new line
+
+        The intialize section consists of c code and will be compiled,
+        thus any syntax errors will crash the simulation. Code is added
+        to the current line.
+
+        Parameters
+        ----------
+        string : str
+            code to be added to initialize section
+
+        """
+
+        self.initialize_section = self.initialize_section + string
+
+    def append_finally(self, string):
+        """
+        Method for appending code to the finally section of instrument
+
+        The finally section consists of c code and will be compiled,
+        thus any syntax errors will crash the simulation. Code is added
+        on a new line for each call to this method.
+
+        Parameters
+        ----------
+        string : str
+            code to be added to finally section
+
+        """
+
+        self.finally_section = self.finally_section + string + "\n"
+
+    def append_finally_no_new_line(self, string):
+        """
+        Method for appending code to the finally section of instrument
+
+        The finally section consists of c code and will be compiled,
+        thus any syntax errors will crash the simulation. Code is added
+        to the current line.
+
+        Parameters
+        ----------
+        string : str
+            code to be added to finally section
+        """
+
+        self.finally_section = self.finally_section + string
+
+    """
+    # Handle trace string differently when components also exists
+    #  A) Coul d have trace string as a component attribute and set
+    #     it before / after
+    #  B) Could have trace string as a McStas_instr attribute and
+    #     still attach placement to components
+    #  C) Could have trace string as a different object and place it
+    #     in component_list, but have a write function named as the
+    #     component write function?
+    """
+
+    def append_trace(self, string):
+        """
+        Appends code to trace section, only used in write_c_files
+
+        The most common way to add code to the trace section is to add
+        components using the seperate methods for this.  This method is
+        kept as is still used for writing to c files used in legacy
+        code.  Each call creates a new line.
+
+        Parameters
+        ----------
+        string : str
+            code to be added to trace
+        """
+
+        self.trace_section = self.trace_section + string + "\n"
+
+    def append_trace_no_new_line(self, string):
+        """
+        Appends code to trace section, only used in write_c_files
+
+        The most common way to add code to the trace section is to add
+        components using the seperate methods for this.  This method is
+        kept as is still used for writing to c files used in legacy
+        code.  No new line is made with this call.
+
+        Parameters
+        ----------
+        string : str
+            code to be added to trace
+        """
+
+        self.trace_section = self.trace_section + string
+
+    def show_components(self, *args):
+        """
+        Helper method that shows available components to the user
+
+        If called without any arguments it will display the available
+        component categories.  The first input
+
+        """
+        if len(args) == 0:
+            print("Here are the availalbe component categories:")
+            self.component_reader.show_categories()
+            print("Call show_components(category_name) to display")
+
+        else:
+            category = args[0]
+            print("Here are all components in the "
+                  + category
+                  + " category.")
+            self.component_reader.show_components_in_category(category)
+
+    def component_help(self, name):
+        """
+        Method for showing parameters for a component before adding it
+        to the instrument
+
+        """
+
+        dummy_instance = self._create_component_instance("dummy", name)
+        dummy_instance.show_parameters()
+
+    def _create_component_instance(self, *args, **kwargs):
+        """
+        Dynamically creates a class for the requested component type
+
+        Created classses kept in dictionary, if the same component type
+        is requested again, the class in the dictionary is used.  The
+        method returns an instance of the created class that was
+        initialized with the paramters passed to this function.
+        """
+
+        if len(args) < 2:
+            raise NameError("Attempting to create component without name")
+
+        component_name = args[1]
+
+        if component_name not in self.component_class_lib:
+            comp_info = self.component_reader.read_name(component_name)
+
+            input_dict = {}
+            input_dict = {key: None for key in comp_info.parameter_names}
+            input_dict["parameter_names"] = comp_info.parameter_names
+            input_dict["parameter_defaults"] = comp_info.parameter_defaults
+            input_dict["parameter_types"] = comp_info.parameter_types
+            input_dict["parameter_units"] = comp_info.parameter_units
+            input_dict["parameter_comments"] = comp_info.parameter_comments
+            input_dict["category"] = comp_info.category
+
+            self.component_class_lib[component_name] = type(component_name,
+                                                            (component,),
+                                                            input_dict)
+
+        return self.component_class_lib[component_name](*args, **kwargs)
+
+    def add_component(self, *args, **kwargs):
+        """
+        Method for adding a new component instance to the instrument
+
+        Creates a new component instance in the instrument.  This
+        requires a unique instance name of the component to be used for
+        future reference and the name of the McStas component to be
+        used.  The component is placed at the end of the instrument file
+        unless otherwise specified with the after and before keywords.
+        The component may be initialized using other keyword arguments,
+        but all attributes can be set with approrpiate methods.
+
+        Parameters
+        ----------
+        First positional argument : str
+            Unique name of component instance
+
+        Second positional argument : str
+            Name of McStas component to create instance of
+
+        Keyword arguments:
+            after : str
+                Place this component after component with given name
+
+            before : str
+                Place this component before component with given name
+
+            AT : List of 3 floats
+                Sets AT_data, position relative to reference
+
+            AT_RELATIVE : str
+                Sets reference component for postion
+
+            ROTATED : List of 3 floats
+                Sets ROTATED_data, rotation relative to reference
+
+            ROTATED_RELATIVE : str
+                Sets reference component for rotation
+
+            RELATIVE : str
+                Sets reference component for both position and rotation
+
+            WHEN : str
+                Sets when condition which must be a logical c expression
+
+            EXTEND : str
+                Initialize the extend section with a line of c code
+
+            GROUP : str
+                Name of the group this component should belong to
+
+            JUMP : str
+                Set code for McStas JUMP statement
+
+            comment : str
+                Comment that will be displayed before the component
+        """
+
+        if args[0] in self.component_name_list:
+            raise NameError(("Component name \"" + str(args[0])
+                             + "\" used twice, McStas does not allow this."
+                             + " Rename or remove one instance of this"
+                             + " name."))
+
+        # Insert component after component with this name
+        if "after" in kwargs:
+            if kwargs["after"] not in self.component_name_list:
+                raise NameError(("Trying to add a component after a component"
+                                 + " named \"" + str(kwargs["after"])
+                                 + "\", but a component with that name was"
+                                 + " not found."))
+
+            new_index = self.component_name_list.index(kwargs["after"])
+
+            new_component = self._create_component_instance(*args, **kwargs)
+            self.component_list.insert(new_index + 1, new_component)
+
+            self.component_name_list.insert(new_index+1, args[0])
+
+        # Insert component after component with this name
+        elif "before" in kwargs:
+            if kwargs["before"] not in self.component_name_list:
+                raise NameError(("Trying to add a component before a "
+                                 + "component named \""
+                                 + str(kwargs["before"])
+                                 + "\", but a component with that "
+                                 + "name was not found."))
+
+            new_index = self.component_name_list.index(kwargs["before"])
+
+            new_component = self._create_component_instance(*args, **kwargs)
+            self.component_list.insert(new_index, new_component)
+
+            self.component_name_list.insert(new_index, args[0])
+
+        # If after or before keywords absent, place component at the end
+        else:
+            new_component = self._create_component_instance(*args, **kwargs)
+            self.component_list.append(new_component)
+            self.component_name_list.append(args[0])
+
+        return new_component
+
+    def get_component(self, name):
+        """
+        Get the component instance of component with specified name
+
+        This method is used to get direct access to any component
+        instance in the instrument.  The component instance can be
+        manipulated in much the same way, but it is not necessary to
+        specify the name in each call.
+
+        Parameters
+        ----------
+        name : str
+            Unique name of component whos instance should be returned
+        """
+
+        if name in self.component_name_list:
+            index = self.component_name_list.index(name)
+            return self.component_list[index]
+        else:
+            raise NameError(("No component was found with name \""
+                             + str(name) + "\"!"))
+
+    def get_last_component(self):
+        """
+        Get the component instance of last component in the instrument
+
+        This method is used to get direct access to any component
+        instance in the instrument.  The component instance can be
+        manipulated in much the same way, but it is not necessary to
+        specify the name in each call.
+        """
+
+        return self.component_list[-1]
+
+    def set_component_parameter(self, name, input_dict):
+        """
+        Add parameters and their values as dictionary to component
+
+        This method is the primary way of specifying parameters in a
+        component.  Parameters are added to a dictionary specifying
+        parameter name and value pairs.
+
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify
+
+        input_dict : dict
+            Set of new parameter name and value pairs to add
+        """
+
+        component = self.get_component(name)
+        component.set_parameters(input_dict)
+
+    def set_component_AT(self, name, at_list, **kwargs):
+        """
+        Method for setting position of component
+
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify
+
+        at_list : List of 3 floats
+            Position of component relative to reference component
+
+        keyword arguments:
+            RELATIVE : str
+                Sets reference component for position
+        """
+
+        component = self.get_component(name)
+        component.set_AT(at_list, **kwargs)
+
+    def set_component_ROTATED(self, name, rotated_list, **kwargs):
+        """
+        Method for setting rotiation of component
+
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify
+
+        rotated_list : List of 3 floats
+            Rotation of component relative to reference component
+
+        keyword arguments:
+            RELATIVE : str
+                Sets reference component for rotation
+        """
+
+        component = self.get_component(name)
+        component.set_ROTATED(rotated_list, **kwargs)
+
+    def set_component_RELATIVE(self, name, relative):
+        """
+        Method for setting reference of component position and rotation
+
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify
+
+        relative : str
+            Reference component for position and rotation
+        """
+
+        component = self.get_component(name)
+        component.set_RELATIVE(relative)
+
+    def set_component_WHEN(self, name, WHEN):
+        """
+        Method for setting WHEN c expression to named component
+
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify
+
+        WHEN : str
+            Sets WHEN c expression for named McStas component
+        """
+        component = self.get_component(name)
+        component.set_WHEN(WHEN)
+
+    def append_component_EXTEND(self, name, EXTEND):
+        """
+        Method for adding line of c to EXTEND section of named component
+
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify
+
+        EXTEND : str
+            Line of c code added to EXTEND section of named component
+        """
+
+        component = self.get_component(name)
+        component.append_EXTEND(EXTEND)
+
+    def set_component_GROUP(self, name, GROUP):
+        """
+        Method for setting GROUP name of named component
+
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify
+
+        GROUP : str
+            Sets GROUP name for named McStas component
+        """
+
+        component = self.get_component(name)
+        component.set_GROUP(GROUP)
+
+    def set_component_JUMP(self, name, JUMP):
+        """
+        Method for setting JUMP expression of named component
+
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify
+
+        JUMP : str
+            Sets JUMP expression for named McStas component
+        """
+
+        component = self.get_component(name)
+        component.set_JUMP(JUMP)
+
+    def set_component_comment(self, name, string):
+        """
+        Sets a comment displayed before the component in written files
+
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify
+
+        string : str
+            Comment string
+
+        """
+
+        component = self.get_component(name)
+        component.set_comment(string)
+
+    def print_component(self, name):
+        """
+        Method for printing summary of contents in named component
+
+        Parameters
+        ----------
+        name : str
+            Unique name of component to print
+        """
+
+        component = self.get_component(name)
+        component.print_long()
+
+    def print_component_short(self, name):
+        """
+        Method for printing summary of contents in named component
+
+        Parameters
+        ----------
+        name : str
+            Unique name of component to print
+        """
+
+        component = self.get_component(name)
+        component.print_short()
+
+    def print_components(self):
+        """
+        Method for printing overview of all components in instrument
+
+        Provides overview of component names, what McStas component is
+        used for each and their position and rotation in space.
+        """
+
+        longest_name = len(max(self.component_name_list, key=len))
+
+        # Investigate how this could have been done in a better way
+        # Find longest field for each type of data printed
+        component_type_list = []
+        at_x_list = []
+        at_y_list = []
+        at_z_list = []
+        at_relative_list = []
+        rotated_x_list = []
+        rotated_y_list = []
+        rotated_z_list = []
+        rotated_relative_list = []
+        for component in self.component_list:
+            component_type_list.append(component.component_name)
+            at_x_list.append(str(component.AT_data[0]))
+            at_y_list.append(str(component.AT_data[1]))
+            at_z_list.append(str(component.AT_data[2]))
+            at_relative_list.append(component.AT_relative)
+            rotated_x_list.append(str(component.ROTATED_data[0]))
+            rotated_y_list.append(str(component.ROTATED_data[1]))
+            rotated_z_list.append(str(component.ROTATED_data[2]))
+            rotated_relative_list.append(component.ROTATED_relative)
+
+        longest_component_name = len(max(component_type_list, key=len))
+        longest_at_x_name = len(max(at_x_list, key=len))
+        longest_at_y_name = len(max(at_y_list, key=len))
+        longest_at_z_name = len(max(at_z_list, key=len))
+        longest_at_relative_name = len(max(at_relative_list, key=len))
+        longest_rotated_x_name = len(max(rotated_x_list, key=len))
+        longest_rotated_y_name = len(max(rotated_y_list, key=len))
+        longest_rotated_z_name = len(max(rotated_z_list, key=len))
+        longest_rotated_relative_name = len(max(rotated_relative_list,
+                                                key=len))
+
+        # Have longest field for each type, use ljust to align all columns
+        for component in self.component_list:
+            print(str(component.name).ljust(longest_name+2), end=' ')
+
+            comp_name = component.component_name
+            comp_name_print = str(comp_name).ljust(longest_component_name + 2)
+            print(comp_name_print, end=' ')
+
+            comp_at_data = str(component.AT_data)
+            longest_at_xyz_sum = (longest_at_x_name
+                                  + longest_at_y_name
+                                  + longest_at_z_name)
+            print("AT ",
+                  comp_at_data.ljust(longest_at_xyz_sum + 11),
+                  end='')
+
+            comp_at_relative = component.AT_relative
+            print(comp_at_relative.ljust(longest_at_relative_name + 2),
+                  end=' ')
+
+            comp_rotated_data = str(component.ROTATED_data)
+            longest_rotated_xyz_sum = (longest_rotated_x_name
+                                       + longest_rotated_y_name
+                                       + longest_rotated_z_name)
+            print("ROTATED ",
+                  comp_rotated_data.ljust(longest_rotated_xyz_sum + 11),
+                  end='')
+            print(component.ROTATED_relative)
+            # print("")
+
+    def write_c_files(self):
+        """
+        Obsolete method for writing instrument parts to c files
+
+        It is possible to use this function to write c files to a folder
+        called generated_includes that can then be included in the
+        different sections of a McStas instrument. Component objects are
+        NOT written to these files, but rather the contents of the
+        trace_section that can be set using the append_trace method.
+        """
+        path = os.getcwd()
+        path = path + "/generated_includes"
+        if not os.path.isdir(path):
+            try:
+                os.mkdir(path)
+            except OSError:
+                print("Creation of the directory %s failed" % path)
+
+        fo = open("./generated_includes/" + self.name + "_declare.c", "w")
+        fo.write("// declare section for %s \n" % self.name)
+        fo.close()
+        fo = open("./generated_includes/" + self.name + "_declare.c", "a")
+        for dec_line in self.declare_list:
+            dec_line.write_line(fo)
+            fo.write("\n")
+        fo.close()
+
+        fo = open("./generated_includes/" + self.name + "_initialize.c", "w")
+        fo.write(self.initialize_section)
+        fo.close()
+
+        fo = open("./generated_includes/" + self.name + "_trace.c", "w")
+        fo.write(self.trace_section)
+        fo.close()
+
+        fo = open("./generated_includes/" + self.name
+                  + "_component_trace.c", "w")
+        for component in self.component_list:
+            component.write_component(fo)
+        fo.close()
+
+    def write_full_instrument(self):
+        """
+        Method for writing full instrument file to disk
+
+        This method writes the instrument described by the instrument
+        objects to disk with the name specified in the initialization of
+        the object.
+        """
+
+        # Create file identifier
+        fo = open(self.name + ".instr", "w")
+
+        # Write quick doc start
+        fo.write("/" + 80*"*" + "\n")
+        fo.write("* \n")
+        fo.write("* McStas, neutron ray-tracing package\n")
+        fo.write("*         Copyright (C) 1997-2008, All rights reserved\n")
+        fo.write("*         Risoe National Laboratory, Roskilde, Denmark\n")
+        fo.write("*         Institut Laue Langevin, Grenoble, France\n")
+        fo.write("* \n")
+        fo.write("* This file was written by McStasScript, which is a \n")
+        fo.write("* python based McStas instrument generator written by \n")
+        fo.write("* Mads Bertelsen in 2019 while employed at the \n")
+        fo.write("* European Spallation Source Data Management and \n")
+        fo.write("* Software Center\n")
+        fo.write("* \n")
+        fo.write("* Instrument %s\n" % self.name)
+        fo.write("* \n")
+        fo.write("* %Identification\n")  # Could allow the user to insert this
+        fo.write("* Written by: %s\n" % self.author)
+        t_format = "%H:%M:%S on %B %d, %Y"
+        fo.write("* Date: %s\n" % datetime.datetime.now().strftime(t_format))
+        fo.write("* Origin: %s\n" % self.origin)
+        fo.write("* %INSTRUMENT_SITE: Generated_instruments\n")
+        fo.write("* \n")
+        fo.write("* \n")
+        fo.write("* %Parameters\n")
+        # Add description of parameters here
+        fo.write("* \n")
+        fo.write("* %End \n")
+        fo.write("*"*80 + "/\n")
+        fo.write("\n")
+        fo.write("DEFINE INSTRUMENT %s (" % self.name)
+        fo.write("\n")
+        # Add loop that inserts parameters here
+        for variable in self.parameter_list[0:-1]:
+            variable.write_parameter(fo, ",")
+        if len(self.parameter_list) > 0:
+            self.parameter_list[-1].write_parameter(fo, " ")
+        fo.write(")\n")
+        fo.write("\n")
+
+        # Write declare
+        fo.write("DECLARE \n%{\n")
+        for dec_line in self.declare_list:
+            dec_line.write_line(fo)
+            fo.write("\n")
+        fo.write("%}\n\n")
+
+        # Write initialize
+        fo.write("INITIALIZE \n%{\n")
+        fo.write(self.initialize_section)
+        # Alternatively hide everything in include
+        """
+        fo.write("%include "generated_includes/"
+                  + self.name + "_initialize.c")
+        """
+        fo.write("%}\n\n")
+
+        # Write trace
+        fo.write("TRACE \n")
+        for component in self.component_list:
+            component.write_component(fo)
+
+        # Write finally
+        fo.write("FINALLY \n%{\n")
+        fo.write(self.finally_section)
+        # Alternatively hide everything in include
+        fo.write("%}\n")
+
+        # End instrument file
+        fo.write("\nEND\n")
+
+    def run_full_instrument(self, *args, **kwargs):
+        """
+        Runs McStas instrument described by this class, returns list of
+        McStasData
+
+        This method will write the instrument to disk and then run it
+        using the mcrun command of the system. Options are set using
+        keyword arguments.  Some options are mandatory, for example
+        foldername, which can not already exist, if it does data will
+        be read from this folder.  If the mcrun command is not in the
+        path of the system, the absolute path can be given with the
+        mcrun_path keyword argument.  This path could also already have
+        been set at initialization of the instrument object.
+
+        Parameters
+        ----------
+        Keyword arguments
+            foldername : str
+                Sets data_folder_name
+            ncount : int
+                Sets ncount
+            mpi : int
+                Sets thread count
+            parameters : dict
+                Sets parameters
+            custom_flags : str
+                Sets custom_flags passed to mcrun
+            mcrun_path : str
+                Path to mcrun command, "" if already in path
+        """
+        # Write the instrument file
+        self.write_full_instrument()
+
+        # Make sure mcrun path is in kwargs
+        if "mcrun_path" not in kwargs:
+            kwargs["mcrun_path"] = self.mcrun_path
+
+        # Set up the simulation
+        simulation = ManagedMcrun(self.name + ".instr", **kwargs)
+
+        # Run the simulation and return data
+        return simulation.run_simulation()
+    
+
diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
new file mode 100644
index 00000000..efa9b7b7
--- /dev/null
+++ b/mcstasscript/interface/plotter.py
@@ -0,0 +1,308 @@
+import math
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+from matplotlib.colors import BoundaryNorm
+from matplotlib.ticker import MaxNLocator
+from openpyxl.worksheet import dimensions
+from boto.ec2.autoscale import limits
+
+from mcstasscript.data.data import McStasMetaData
+from mcstasscript.data.data import McStasPlotOptions
+from mcstasscript.data.data import McStasData
+
+class make_plot:
+    """
+    make_plot plots contents of McStasData objects
+
+    Plotting is controlled through options assosciated with the
+    McStasData objects.
+
+    If a list is given, the plots appear individually.
+    """
+
+    def __init__(self, data_list):
+        """
+        plots McStasData, single object or list of McStasData
+
+        The options concerning plotting are stored with the data
+
+        Parameters
+        ----------
+        data_list : McStasData or list of McStasData
+            McStasData to be plotted
+        """
+
+        # Relevant options:
+        #  select colormap
+        #  show / hide colorbar
+        #  custom title / label
+        #  color of 1d plot
+        #  overlay several 1d
+        #  log scale (orders of magnitude)
+        #  compare several 1d
+        #  compare 2D
+
+        if isinstance(data_list, McStasData):
+            # Only a single element, put it in a list for easier syntax later
+            data_list = [data_list]
+
+        number_of_plots = len(data_list)
+
+        print("number of elements in data list = " + str(len(data_list)))
+
+        index = -1
+        for data in data_list:
+            index = index + 1
+
+            print("Plotting data with name " + data.metadata.component_name)
+            if type(data.metadata.dimension) == int:
+                fig = plt.figure(0)
+
+                # print(data.T)
+                x = data.xaxis
+                y = data.Intensity
+                y_err = data.Error
+
+                plt.errorbar(x, y, yerr=y_err)
+
+                if data.plot_options.log:
+                    ax0.set_yscale("log", nonposy='clip')
+
+                plt.xlim(data.metadata.limits[0], data.metadata.limits[1])
+
+                # Add a title
+                plt.title(data.metadata.title)
+
+                # Add axis labels
+                plt.xlabel(data.metadata.xlabel)
+                plt.ylabel(data.metadata.ylabel)
+
+            elif len(data.metadata.dimension) == 2:
+
+                # Split the data into intensity, error and ncount
+                Intensity = data.Intensity
+                Error = data.Error
+                Ncount = data.Ncount
+
+                if data.plot_options.log:
+                    min_value = np.min(Intensity[np.nonzero(Intensity)])
+                    min_value = np.log10(min_value)
+
+                    to_plot = np.log10(Intensity)
+
+                    max_value = to_plot.max()
+
+                    if (max_value - min_value
+                            > data.plot_options.orders_of_mag):
+                        min_value = (max_value
+                                     - data.plot_options.orders_of_mag)
+                else:
+                    to_plot = Intensity
+                    min_value = to_plot.min()
+                    max_value = to_plot.max()
+
+                # Check the size of the array to be plotted
+                # print(to_plot.shape)
+
+                # Set the axis (might be switched?)
+                X = np.linspace(data.metadata.limits[0],
+                                data.metadata.limits[1],
+                                data.metadata.dimension[0]+1)
+                Y = np.linspace(data.metadata.limits[2],
+                                data.metadata.limits[3],
+                                data.metadata.dimension[1])
+
+                # Create a meshgrid for both x and y
+                y, x = np.meshgrid(Y, X)
+
+                # Generate information on necessary colorrange
+                levels = MaxNLocator(nbins=150).tick_values(min_value,
+                                                            max_value)
+
+                # Select colormap
+                cmap = plt.get_cmap('hot')
+                norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
+
+                # Create the figure
+                fig, (ax0) = plt.subplots()
+
+                # Plot the data on the meshgrids
+                if data.plot_options.log:
+                    color_norm = matplotlib.colors.LogNorm(vmin=min_value,
+                                                           vmax=max_value)
+                    im = ax0.pcolormesh(x, y, to_plot,
+                                        cmap=cmap, norm=color_norm)
+                else:
+                    im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
+
+                # Add the colorbar
+                fig.colorbar(im, ax=ax0)
+
+                # Add a title
+                ax0.set_title(data.metadata.title)
+
+                # Add axis labels
+                plt.xlabel(data.metadata.xlabel)
+                plt.ylabel(data.metadata.ylabel)
+
+            else:
+                print("Error, dimension not read correctly")
+
+        plt.show()
+
+
+class make_sub_plot:
+    """
+    make_plot plots contents of McStasData objects
+
+    Plotting is controlled through options assosciated with the
+    McStasData objects.  If a list is given, the plots appear in one
+    subplot.
+    """
+
+    def __init__(self, data_list):
+        """
+        plots McStasData, single object or list of McStasData
+
+        The options concerning plotting are stored with the data
+
+        Parameters
+        ----------
+        data_list : McStasData or list of McStasData
+            McStasData to be plotted
+        """
+        if not isinstance(data_list, McStasData):
+            print("number of elements in data list = "
+                  + str(len(data_list)))
+        else:
+            # Make list from single element to simplify syntax
+            data_list = [data_list]
+
+        number_of_plots = len(data_list)
+
+        # Relevant options:
+        #  select colormap
+        #  show / hide colorbar
+        #  custom title / label
+        #  color of 1d plot
+        #  overlay several 1d
+        #  log scale (o$rders of magnitude)
+        #  compare several 1d
+        #  compare 2D
+
+        # Find reasonable grid size for the number of plots
+        dim2 = math.ceil(math.sqrt(number_of_plots))
+        dim1 = math.ceil(number_of_plots/dim2)
+
+        fig, axs = plt.subplots(dim1, dim2, figsize=(13, 7))
+        axs = np.array(axs)
+        ax = axs.reshape(-1)
+
+        index = -1
+        for data in data_list:
+            index = index + 1
+            ax0 = ax[index]
+
+            print("Plotting data with name "
+                  + data.metadata.component_name)
+
+            if isinstance(data.metadata.dimension, int):
+                # fig = plt.figure(0)
+                # plt.subplot(dim1, dim2, n_plot)
+                x = data.xaxis
+                y = data.Intensity
+                y_err = data.Error
+
+                ax0.errorbar(x, y, yerr=y_err)
+
+                if data.plot_options.log:
+                    ax0.set_yscale("log", nonposy='clip')
+
+                ax0.set_xlim(data.metadata.limits[0],
+                             data.metadata.limits[1])
+
+                # Add a title
+                # ax0.title(data.title)
+
+                # Add axis labels
+                ax0.set_xlabel(data.metadata.xlabel)
+                ax0.set_ylabel(data.metadata.ylabel)
+
+            elif len(data.metadata.dimension) == 2:
+
+                # Split the data into intensity, error and ncount
+                Intensity = data.Intensity
+                Error = data.Error
+                Ncount = data.Ncount
+
+                if data.plot_options.log:
+                    min_value = np.min(Intensity[np.nonzero(Intensity)])
+                    min_value = np.log10(min_value)
+
+                    to_plot = Intensity
+
+                    max_value = np.log10(to_plot.max())
+
+                    if (max_value - min_value
+                            > data.plot_options.orders_of_mag):
+                        min_value = (max_value
+                                     - data.plot_options.orders_of_mag)
+                    min_value = 10.0 ** min_value
+                    max_value = 10.0 ** max_value
+                else:
+                    to_plot = Intensity
+                    min_value = to_plot.min()
+                    max_value = to_plot.max()
+
+                # Check the size of the array to be plotted
+                # print(to_plot.shape)
+
+                # Set the axis (might be switched?)
+                X = np.linspace(data.metadata.limits[0],
+                                data.metadata.limits[1],
+                                data.metadata.dimension[0]+1)
+                Y = np.linspace(data.metadata.limits[2],
+                                data.metadata.limits[3],
+                                data.metadata.dimension[1])
+
+                # Create a meshgrid for both x and y
+                y, x = np.meshgrid(Y, X)
+
+                # Generate information on necessary colorrange
+                levels = MaxNLocator(nbins=150).tick_values(min_value,
+                                                            max_value)
+
+                # Select colormap
+                cmap = plt.get_cmap(data.plot_options.colormap)
+
+                # Select the colorscale normalization
+                if data.plot_options.log:
+                    norm = matplotlib.colors.LogNorm(vmin=min_value,
+                                                     vmax=max_value)
+                else:
+                    norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
+
+                # Create plot
+                im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
+
+                def fmt(x, pos):
+                    a, b = '{:.2e}'.format(x).split('e')
+                    b = int(b)
+                    return r'${} \times 10^{{{}}}$'.format(a, b)
+
+                # Add the colorbar
+                fig.colorbar(im, ax=ax0,
+                             format=matplotlib.ticker.FuncFormatter(fmt))
+
+                # Add a title
+                ax0.set_title(data.metadata.title)
+
+                # Add axis labels
+                ax0.set_xlabel(data.metadata.xlabel)
+                ax0.set_ylabel(data.metadata.ylabel)
+
+            else:
+                print("Error, dimension not read correctly")
+
+        plt.show()
\ No newline at end of file

From d2522f0c9a9f2ec4615416230a939470194cc497 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Mon, 13 May 2019 15:03:19 +0200
Subject: [PATCH 014/403] Main code moved from monolithic file to restructured
 package, see last commit. Examples moved to example folder.

This commit just deletes the previous versions.
---
 McStasScript.py         | 3003 ---------------------------------------
 McStasScript_demo.ipynb |  495 -------
 demonstration.py        |  100 --
 3 files changed, 3598 deletions(-)
 delete mode 100644 McStasScript.py
 delete mode 100644 McStasScript_demo.ipynb
 delete mode 100644 demonstration.py

diff --git a/McStasScript.py b/McStasScript.py
deleted file mode 100644
index c975d208..00000000
--- a/McStasScript.py
+++ /dev/null
@@ -1,3003 +0,0 @@
-"""McStasScript classes written by Mads Bertelsen, ESS, DMSC
-
-API for writing and running McStas instruments
-"""
-
-from __future__ import print_function
-
-__author__ = "Mads Bertelsen"
-
-import datetime
-import os
-import time
-import math
-
-import numpy as np
-import matplotlib
-import matplotlib.pyplot as plt
-from matplotlib.colors import BoundaryNorm
-from matplotlib.ticker import MaxNLocator
-from openpyxl.worksheet import dimensions
-from boto.ec2.autoscale import limits
-# From builtins import False, True
-
-try:  # Check whether python knows about 'basestring'
-    basestring
-except NameError:  # No, it doesn't (it's Python3); use 'str' instead
-    basestring = str
-
-
-class bcolors:
-    HEADER = '\033[95m'
-    OKBLUE = '\033[94m'
-    OKGREEN = '\033[92m'
-    WARNING = '\033[93m'
-    FAIL = '\033[91m'
-    ENDC = '\033[0m'
-    BOLD = '\033[1m'
-    UNDERLINE = '\033[4m'
-
-
-class McStasMetaData:
-    """
-    Class for holding metadata for McStas dataset, is to be read from
-    mccode.sim file.
-
-    Attributes
-    ----------
-    info : dict
-        Contains read strings from mccode.sim in key, value
-
-    dimension : Int or List of Int
-        Int for 1d data set with lenght of data, Array for 2d with each
-        length
-
-    component_name : str
-        Name of component in McStas file
-
-    filename : str
-        Name of data file to read
-
-    limits : List
-        Limits for monitor, length=2 for 1d data and length=4 for 2d
-        data
-
-    title : str
-        Title of monitor when plotting
-
-    xlabel : str
-        Text for xlabel when plotting
-
-    ylabel : str
-        Text for ylabel when plotting
-
-    Methods
-    -------
-    add_info(key,value)
-        Adds a element to the info dictionary
-
-    extract_info()
-        Unpacks the information in info to class attributes
-
-    set_title(string)
-        Overwrites current title
-
-    set_xlabel(string)
-        Overwrites current xlabel
-
-    set_ylabel(string)
-        Overwrites current ylabel
-    """
-
-    def __init__(self):
-        """Creating a new instance, no parameters"""
-        self.info = {}
-
-    def add_info(self, key, value):
-        """Adding information to info dict"""
-        self.info[key] = value
-
-    def extract_info(self):
-        """Extracting information from info dict to class attributes"""
-
-        # Extract dimension
-        if "type" in self.info:
-            type = self.info["type"]
-            if "array_1d" in type:
-                self.dimension = int(type[9:-2])
-            if "array_2d" in type:
-                self.dimension = []
-                type_strings = self.info["type"].split(",")
-                temp_str = type_strings[0]
-                self.dimension.append(int(temp_str[9:]))
-                temp_str = type_strings[1]
-                self.dimension.append(int(temp_str[1:-2]))
-        else:
-            raise NameError("No type in mccode data section!")
-
-        # Extract component name
-        if "component" in self.info:
-            self.component_name = self.info["component"].rstrip()
-
-        # Extract filename
-        if "filename" in self.info:
-            self.filename = self.info["filename"].rstrip()
-        else:
-            raise NameError(
-                "No filename found in mccode data section!")
-
-        # Extract limits
-        self.limits = []
-        if "xylimits" in self.info:
-            # find the four numbers
-            temp_str = self.info["xylimits"]
-            limits_string = temp_str.split()
-            for limit in limits_string:
-                self.limits.append(float(limit))
-
-        if "xlimits" in self.info:
-            # find the two numbers
-            temp_str = self.info["xlimits"]
-            limits_string = temp_str.split()
-            for limit in limits_string:
-                self.limits.append(float(limit))
-
-        # Extract plotting labels and title
-        if "xlabel" in self.info:
-            self.xlabel = self.info["xlabel"].rstrip()
-        if "ylabel" in self.info:
-            self.ylabel = self.info["ylabel"].rstrip()
-        if "title" in self.info:
-            self.title = self.info["title"].rstrip()
-
-    def set_title(self, string):
-        """Sets title for plotting"""
-        self.title = string
-
-    def set_xlabel(self, string):
-        """Sets xlabel for plotting"""
-        self.xlabel = string
-
-    def set_ylabel(self, string):
-        """Sets ylabel for plotting"""
-        self.ylabel = string
-
-
-class McStasPlotOptions:
-    """
-    Class that holds plotting options related to McStas data set
-
-    Attributes
-    ----------
-    log : bool
-        To plot on logarithmic or not, standard is linear
-
-    orders_of_mag : float
-        If plotting on log scale, restrict max range to orders_of_mag
-        below maximum value
-
-    colormap : string
-        Chosen colormap for 2d data, should be available in matplotlib
-
-    Methods
-    -------
-    set_options(keyword arguments)
-        Can set the class attributes using keyword options
-
-    """
-
-    def __init__(self, *args, **kwargs):
-        """Setting default values for plotting preferences"""
-        self.log = False
-        self.orders_of_mag = 300
-        self.colormap = "jet"
-
-    def set_options(self, **kwargs):
-        """Set custom values for plotting preferences"""
-        if "log" in kwargs:
-            log_input = kwargs["log"]
-            if type(log_input) == int:
-                if log_input == 0:
-                    self.log = False
-                else:
-                    self.log = True
-            elif type(log_input) == bool:
-                self.log = log_input
-            else:
-                raise NameError(
-                    "Log input must be either Int or Bool.")
-
-        if "orders_of_mag" in kwargs:
-            self.orders_of_mag = kwargs["orders_of_mag"]
-
-        if "colormap" in kwargs:
-            self.colormap = kwargs["colormap"]
-
-
-class McStasData:
-    """
-    Class for holding full McStas dataset with data, metadata and
-    plotting preferences
-
-    Attributes
-    ----------
-    metadata : McStasMetaData instance
-        Holds the metadata for the dataset
-
-    name : str
-        Name of component, extracted from metadata
-
-    Intensity : numpy array
-        Intensity data [n/s] in 1d or 2d numpy array, dimension in
-        metadata
-
-    Error : numpy array
-        Error data [n/s] in 1d or 2d numpy array, same dimensions as
-        Intensity
-
-    Ncount : numpy array
-        Number of rays in bin, 1d or 2d numpy array, same dimensions as
-        Intensity
-
-    plot_options : McStasPlotOptions instance
-        Holds the plotting preferences for the dataset
-
-    Methods
-    -------
-    set_xlabel : string
-        sets xlabel of data for plotting
-
-    set_ylabel : string
-        sets ylabel of data for plotting
-
-    set_title : string
-        sets title of data for plotting
-
-    set_optons : keyword arguments
-        sets plot options, keywords passed to McStasPlotOptions method
-    """
-
-    def __init__(self, metadata, intensity, error, ncount, **kwargs):
-        """
-        Initialize a new McStas dataset, 4 positional arguments, pass
-        xaxis as kwarg if 1d data
-
-        Parameters
-        ----------
-        metadata : McStasMetaData instance
-            Holds the metadata for the dataset
-
-        name : str
-            Name of component, extracted from metadata
-
-        intensity : numpy array
-            Intensity data [n/s] in 1d or 2d numpy array, dimension in
-            metadata
-
-        error : numpy array
-            Error data [n/s] in 1d or 2d numpy array, same dimensions
-            as Intensity
-
-        ncount : numpy array
-            Number of rays in bin, 1d or 2d numpy array, same
-            dimensions as Intensity
-
-        kwargs : keyword arguments
-            xaxis is required for 1d data
-        """
-
-        # attatch meta data
-        self.metadata = metadata
-        # get name from metadata
-        self.name = self.metadata.component_name
-        # three basic arrays from positional arguments
-        self.Intensity = intensity
-        self.Error = error
-        self.Ncount = ncount
-
-        if type(self.metadata.dimension) == int:
-            if "xaxis" in kwargs:
-                self.xaxis = kwargs["xaxis"]
-            else:
-                raise NameError(
-                    "ERROR: Initialization of McStasData done with 1d "
-                    + "data, but without xaxis" + self.name + "!")
-
-        self.plot_options = McStasPlotOptions()
-
-    # Methods xlabel, ylabel and title as they might not be found
-    def set_xlabel(self, string):
-        self.metadata.set_xlabel(string)
-
-    def set_ylabel(self, string):
-        self.metadata.set_ylabel(string)
-
-    def set_title(self, string):
-        self.metadata.set_title(string)
-
-    def set_plot_options(self, **kwargs):
-        self.plot_options.set_options(**kwargs)
-
-def name_search(name, data_list):
-    """"
-    name_search returns McStasData instance with specific name if it is
-    in the given data_list
-
-    The index of certain datasets in the data_list can change if
-    additional monitors are added so it is more convinient to access
-    the data files using their names.
-
-    Parameters
-    ----------
-    name : string
-        Name of the dataset to be retrived (component_name)
-
-    data_list : List of McStasData instances
-        List of datasets to search
-    """
-
-    if not type(data_list[0]) == McStasData:
-        raise InputError(
-            "name_search function needs objects of type "
-            + "McStasData as input.")
-
-    list_result = []
-    for check in data_list:
-        if check.metadata.component_name == name:
-            list_result.append(check)
-
-    if len(list_result) == 1:
-        return list_result[0]
-    else:
-        raise NameError("More than one match for the name search")
-
-def name_plot_options(name, data_list, **kwargs):
-    """"
-    name_plot_options passes keyword arguments to dataset with certain
-    name in given data list
-
-    Function for quickly setting plotting options on a certain dataset
-    n a larger list of datasets
-
-    Parameters
-    ----------
-    name : string
-        Name of the dataset to be modified (component_name)
-
-    data_list : List of McStasData instances
-        List of datasets to search
-
-    kwargs : keyword arguments
-        Keyword arguments passed to set_plot_options in
-        McStasPlotOptions
-    """
-
-    if not isinstance(data_list[0], McStasData):
-        raise InputError(
-            "name_search function needs objects of type McStasData "
-            + "as input.")
-
-    object_to_modify = name_search(name, data_list)
-    object_to_modify.set_plot_options(**kwargs)
-
-
-class ManagedMcrun:
-    """
-    A class for performing a mcstas simulation and organizing the data
-    into python objects
-
-    ManagedMcrun is usually called by the instrument class of
-    McStasScript but can be used independently.  It runs the mcrun
-    command using the system command, and if this is not in the path,
-    the absolute path can be given in a keyword argument mcrun_path.
-
-    Attributes
-    ----------
-    name_of_instrumentfile : str
-        Name of instrument file to be executed
-
-    data_folder_name : str
-        Name of datafolder mcrun writes to disk
-
-    ncount : int
-        Number of rays to simulate
-
-    mpi : int
-        Number of mpi threads to run
-
-    parameters : dict
-        Dictionary of parameter names and values for this simulation
-
-    custom_flags : string
-        Custom flags that are passed to the mcrun command
-
-    mcrun_path : string
-        Path to the mcrun command (can be empty if already in path)
-
-    Methods
-    -------
-    run_simulation()
-        Runs simulation, returns list of McStasData instances
-
-    """
-
-    def __init__(self, instr_name, **kwargs):
-        """
-        Parameters
-        ----------
-        instr_name : str
-            Name of instrument file to be simulated
-
-        kwargs : keyword arguments
-            foldername : str
-                Sets data_folder_name
-            ncount : int
-                Sets ncount
-            mpi : int
-                Sets thread count
-            parameters : dict
-                Sets parameters
-            custom_flags : str
-                Sets custom_flags passed to mcrun
-            mcrun_path : str
-                Path to mcrun command, "" if already in path
-        """
-
-        self.name_of_instrumentfile = instr_name
-
-        self.data_folder_name = ""
-        self.ncount = 1E6
-        self.mpi = 1
-        self.parameters = {}
-        self.custom_flags = ""
-        self.mcrun_path = ""
-        # mcrun_path always in kwargs
-        self.mcrun_path = kwargs["mcrun_path"]
-
-        if "foldername" in kwargs:
-            self.data_folder_name = kwargs["foldername"]
-        else:
-            raise NameError(
-                "ManagedMcrun needs foldername to load data, add "
-                + "with keyword argument.")
-
-        if "ncount" in kwargs:
-            self.ncount = kwargs["ncount"]
-
-        if "mpi" in kwargs:
-            self.mpi = kwargs["mpi"]
-
-        if "parameters" in kwargs:
-            self.parameters = kwargs["parameters"]
-
-        if "custom_flags" in kwargs:
-            self.custom_flags = kwargs["custom_flags"]
-
-    def run_simulation(self):
-        """
-        Runs McStas simulation described by initializing the object
-        """
-
-        # construct command to run
-        option_string = ("-c"
-                         + " -n " + str(self.ncount)  # Set ncount
-                         + " --mpi=" + str(self.mpi)  # Set mpi
-                         + " ")
-
-        if len(self.data_folder_name) > 0:
-            option_string = (option_string
-                             + "-d "
-                             + self.data_folder_name)
-
-        # add parameters to command
-        parameter_string = ""
-        for key, val in self.parameters.items():
-            parameter_string = (parameter_string + " "
-                                + str(key)  # parameter name
-                                + "="
-                                + str(val))  # parameter value
-
-        mcrun_full_path = self.mcrun_path + "mcrun"
-        if len(self.mcrun_path) > 1:
-            if not (self.mcrun_path[-1] == "\\"
-                    or self.mcrun_path[-1] == "/"):
-                mcrun_full_path = self.mcrun_path + "/mcrun"
-
-        # Run the mcrun command on the system
-        os.system(mcrun_full_path + " "
-                  + option_string + " "
-                  + self.custom_flags + " "
-                  + self.name_of_instrumentfile + " "
-                  + parameter_string)
-
-        """
-        Can use subprocess from spawn* instead of os.system if more
-        control is needed over the spawned process, including a timeout
-        """
-
-        # Find all data files in generated folder
-        files_in_folder = os.listdir(self.data_folder_name)
-
-        # Raise an error if mccode.sim is not available
-        if "mccode.sim" not in files_in_folder:
-            raise NameError("mccode.sim not written to output folder.")
-
-        # Open mccode to read metadata for all datasets written to disk
-        f = open(self.data_folder_name + "/mccode.sim", "r")
-
-        # Loop that reads mccode.sim sections
-        metadata_list = []
-        in_data = False
-        for lines in f:
-            # Could read other details about run
-
-            if lines == "end data\n":
-                # No more data for this metadata object
-                # Extract the information
-                current_object.extract_info()
-                # Add to metadata list
-                metadata_list.append(current_object)
-                # Stop reading data
-                in_data = False
-
-            if in_data:
-                # This line contains info to be added to metadata
-                colon_index = lines.index(":")
-                key = lines[2:colon_index]
-                value = lines[colon_index+2:]
-                current_object.add_info(key, value)
-
-            if lines == "begin data\n":
-                # Found data section, create new metadata object
-                current_object = McStasMetaData()
-                # Start recording data to metadata object
-                in_data = True
-
-        # Close mccode.sim
-        f.close()
-
-        # Create a list for McStasData instances to return
-        results = []
-
-        # Load datasets described in metadata list individually
-        for metadata in metadata_list:
-            # Load data with numpy
-            data = np.loadtxt(self.data_folder_name
-                              + "/"
-                              + metadata.filename.rstrip())
-
-            # Split data into intensity, error and ncount
-            if type(metadata.dimension) == int:
-                xaxis = data.T[0, :]
-                Intensity = data.T[1, :]
-                Error = data.T[2, :]
-                Ncount = data.T[3, :]
-
-            elif len(metadata.dimension) == 2:
-                xaxis = []  # Assume evenly binned in 2d
-                data_lines = metadata.dimension[1]
-                Intensity = data.T[:, 0:data_lines - 1]
-                Error = data.T[:, data_lines:2*data_lines - 1]
-                Ncount = data.T[:, 2*data_lines:3*data_lines - 1]
-            else:
-                raise NameError(
-                    "Dimension not read correctly in data set "
-                    + "connected to monitor named "
-                    + metadata.component_name)
-
-            # The data is saved as a McStasData object
-            result = McStasData(metadata, Intensity,
-                                Error, Ncount,
-                                xaxis=xaxis)
-
-            # Add this result to the results list
-            results.append(result)
-
-            # Close the current datafile
-            f.close()
-
-        # Return list of McStasData objects
-        return results
-
-
-class make_plot:
-    """
-    make_plot plots contents of McStasData objects
-
-    Plotting is controlled through options assosciated with the
-    McStasData objects.
-
-    If a list is given, the plots appear individually.
-    """
-
-    def __init__(self, data_list):
-        """
-        plots McStasData, single object or list of McStasData
-
-        The options concerning plotting are stored with the data
-
-        Parameters
-        ----------
-        data_list : McStasData or list of McStasData
-            McStasData to be plotted
-        """
-
-        # Relevant options:
-        #  select colormap
-        #  show / hide colorbar
-        #  custom title / label
-        #  color of 1d plot
-        #  overlay several 1d
-        #  log scale (orders of magnitude)
-        #  compare several 1d
-        #  compare 2D
-
-        if isinstance(data_list, McStasData):
-            # Only a single element, put it in a list for easier syntax later
-            data_list = [data_list]
-
-        number_of_plots = len(data_list)
-
-        print("number of elements in data list = " + str(len(data_list)))
-
-        index = -1
-        for data in data_list:
-            index = index + 1
-
-            print("Plotting data with name " + data.metadata.component_name)
-            if type(data.metadata.dimension) == int:
-                fig = plt.figure(0)
-
-                # print(data.T)
-                x = data.xaxis
-                y = data.Intensity
-                y_err = data.Error
-
-                plt.errorbar(x, y, yerr=y_err)
-
-                if data.plot_options.log:
-                    ax0.set_yscale("log", nonposy='clip')
-
-                plt.xlim(data.metadata.limits[0], data.metadata.limits[1])
-
-                # Add a title
-                plt.title(data.metadata.title)
-
-                # Add axis labels
-                plt.xlabel(data.metadata.xlabel)
-                plt.ylabel(data.metadata.ylabel)
-
-            elif len(data.metadata.dimension) == 2:
-
-                # Split the data into intensity, error and ncount
-                Intensity = data.Intensity
-                Error = data.Error
-                Ncount = data.Ncount
-
-                if data.plot_options.log:
-                    min_value = np.min(Intensity[np.nonzero(Intensity)])
-                    min_value = np.log10(min_value)
-
-                    to_plot = np.log10(Intensity)
-
-                    max_value = to_plot.max()
-
-                    if (max_value - min_value
-                            > data.plot_options.orders_of_mag):
-                        min_value = (max_value
-                                     - data.plot_options.orders_of_mag)
-                else:
-                    to_plot = Intensity
-                    min_value = to_plot.min()
-                    max_value = to_plot.max()
-
-                # Check the size of the array to be plotted
-                # print(to_plot.shape)
-
-                # Set the axis (might be switched?)
-                X = np.linspace(data.metadata.limits[0],
-                                data.metadata.limits[1],
-                                data.metadata.dimension[0]+1)
-                Y = np.linspace(data.metadata.limits[2],
-                                data.metadata.limits[3],
-                                data.metadata.dimension[1])
-
-                # Create a meshgrid for both x and y
-                y, x = np.meshgrid(Y, X)
-
-                # Generate information on necessary colorrange
-                levels = MaxNLocator(nbins=150).tick_values(min_value,
-                                                            max_value)
-
-                # Select colormap
-                cmap = plt.get_cmap('hot')
-                norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
-
-                # Create the figure
-                fig, (ax0) = plt.subplots()
-
-                # Plot the data on the meshgrids
-                if data.plot_options.log:
-                    color_norm = matplotlib.colors.LogNorm(vmin=min_value,
-                                                           vmax=max_value)
-                    im = ax0.pcolormesh(x, y, to_plot,
-                                        cmap=cmap, norm=color_norm)
-                else:
-                    im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
-
-                # Add the colorbar
-                fig.colorbar(im, ax=ax0)
-
-                # Add a title
-                ax0.set_title(data.metadata.title)
-
-                # Add axis labels
-                plt.xlabel(data.metadata.xlabel)
-                plt.ylabel(data.metadata.ylabel)
-
-            else:
-                print("Error, dimension not read correctly")
-
-        plt.show()
-
-
-class make_sub_plot:
-    """
-    make_plot plots contents of McStasData objects
-
-    Plotting is controlled through options assosciated with the
-    McStasData objects.  If a list is given, the plots appear in one
-    subplot.
-    """
-
-    def __init__(self, data_list):
-        """
-        plots McStasData, single object or list of McStasData
-
-        The options concerning plotting are stored with the data
-
-        Parameters
-        ----------
-        data_list : McStasData or list of McStasData
-            McStasData to be plotted
-        """
-        if not isinstance(data_list, McStasData):
-            print("number of elements in data list = "
-                  + str(len(data_list)))
-        else:
-            # Make list from single element to simplify syntax
-            data_list = [data_list]
-
-        number_of_plots = len(data_list)
-
-        # Relevant options:
-        #  select colormap
-        #  show / hide colorbar
-        #  custom title / label
-        #  color of 1d plot
-        #  overlay several 1d
-        #  log scale (o$rders of magnitude)
-        #  compare several 1d
-        #  compare 2D
-
-        # Find reasonable grid size for the number of plots
-        dim2 = math.ceil(math.sqrt(number_of_plots))
-        dim1 = math.ceil(number_of_plots/dim2)
-
-        fig, axs = plt.subplots(dim1, dim2, figsize=(13, 7))
-        axs = np.array(axs)
-        ax = axs.reshape(-1)
-
-        index = -1
-        for data in data_list:
-            index = index + 1
-            ax0 = ax[index]
-
-            print("Plotting data with name "
-                  + data.metadata.component_name)
-
-            if isinstance(data.metadata.dimension, int):
-                # fig = plt.figure(0)
-                # plt.subplot(dim1, dim2, n_plot)
-                x = data.xaxis
-                y = data.Intensity
-                y_err = data.Error
-
-                ax0.errorbar(x, y, yerr=y_err)
-
-                if data.plot_options.log:
-                    ax0.set_yscale("log", nonposy='clip')
-
-                ax0.set_xlim(data.metadata.limits[0],
-                             data.metadata.limits[1])
-
-                # Add a title
-                # ax0.title(data.title)
-
-                # Add axis labels
-                ax0.set_xlabel(data.metadata.xlabel)
-                ax0.set_ylabel(data.metadata.ylabel)
-
-            elif len(data.metadata.dimension) == 2:
-
-                # Split the data into intensity, error and ncount
-                Intensity = data.Intensity
-                Error = data.Error
-                Ncount = data.Ncount
-
-                if data.plot_options.log:
-                    min_value = np.min(Intensity[np.nonzero(Intensity)])
-                    min_value = np.log10(min_value)
-
-                    to_plot = Intensity
-
-                    max_value = np.log10(to_plot.max())
-
-                    if (max_value - min_value
-                            > data.plot_options.orders_of_mag):
-                        min_value = (max_value
-                                     - data.plot_options.orders_of_mag)
-                    min_value = 10.0 ** min_value
-                    max_value = 10.0 ** max_value
-                else:
-                    to_plot = Intensity
-                    min_value = to_plot.min()
-                    max_value = to_plot.max()
-
-                # Check the size of the array to be plotted
-                # print(to_plot.shape)
-
-                # Set the axis (might be switched?)
-                X = np.linspace(data.metadata.limits[0],
-                                data.metadata.limits[1],
-                                data.metadata.dimension[0]+1)
-                Y = np.linspace(data.metadata.limits[2],
-                                data.metadata.limits[3],
-                                data.metadata.dimension[1])
-
-                # Create a meshgrid for both x and y
-                y, x = np.meshgrid(Y, X)
-
-                # Generate information on necessary colorrange
-                levels = MaxNLocator(nbins=150).tick_values(min_value,
-                                                            max_value)
-
-                # Select colormap
-                cmap = plt.get_cmap(data.plot_options.colormap)
-
-                # Select the colorscale normalization
-                if data.plot_options.log:
-                    norm = matplotlib.colors.LogNorm(vmin=min_value,
-                                                     vmax=max_value)
-                else:
-                    norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
-
-                # Create plot
-                im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
-
-                def fmt(x, pos):
-                    a, b = '{:.2e}'.format(x).split('e')
-                    b = int(b)
-                    return r'${} \times 10^{{{}}}$'.format(a, b)
-
-                # Add the colorbar
-                fig.colorbar(im, ax=ax0,
-                             format=matplotlib.ticker.FuncFormatter(fmt))
-
-                # Add a title
-                ax0.set_title(data.metadata.title)
-
-                # Add axis labels
-                ax0.set_xlabel(data.metadata.xlabel)
-                ax0.set_ylabel(data.metadata.ylabel)
-
-            else:
-                print("Error, dimension not read correctly")
-
-        plt.show()
-
-
-class parameter_variable:
-    """
-    Class describing a input parameter in McStas instrument
-
-    McStas input parameters are of default type double, but can be
-    cast.  If two positional arguments are given, the first is the
-    type, and the second is the parameter name.  With one input, only
-    the parameter name is read.  It is also possible to assign a
-    default value and a comment through keyword arguments.
-
-    Attributes
-    ----------
-    type : str
-        McStas type of input: Double, Int, String
-
-    name : str
-        Name of input parameter
-
-    value : any
-        Default value/string of parameter, converted to string
-
-    comment : str
-        Comment displayed next to the parameter, could contain units
-
-    Methods
-    -------
-    write_parameter(fo,stop_character)
-        writes the parameter to file fo, uses given stop character
-    """
-
-    def __init__(self, *args, **kwargs):
-        """Initializing mcstas parameter object
-
-        Parameters
-        ----------
-        If giving a type:
-        Positional argument 1: type: str
-            Type of the parameter, double, int or string
-        Positional argument 2: name: str
-            Name of input parameter
-
-        If not giving type
-        Positional argument 1: name : str
-            Name of input parameter
-
-        Keyword arguments
-            value : any
-                sets default value of parameter
-            comment : str
-                sets comment displayed next to declaration
-        """
-        if len(args) == 1:
-            self.type = ""
-            self.name = str(args[0])
-        if len(args) == 2:
-            self.type = args[0] + " "
-            self.name = str(args[1])
-
-        if "value" in kwargs:
-            self.value_set = 1
-            self.value = kwargs["value"]
-        else:
-            self.value_set = 0
-
-        if "comment" in kwargs:
-            self.comment = "// " + kwargs["comment"]
-        else:
-            self.comment = ""
-
-        # could check for allowed types
-        # they are int, double, string, are there more?
-
-    def write_parameter(self, fo, stop_character):
-        """Writes input parameter to file"""
-        fo.write("%s%s" % (self.type, self.name))
-        if self.value_set == 1:
-            if isinstance(self.value, int):
-                fo.write(" = %d" % self.value)
-            elif isinstance(self.value, float):
-                fo.write(" = %G" % self.value)
-            else:
-                fo.write(" = %s" % str(self.value))
-        fo.write(stop_character)
-        fo.write(self.comment)
-        fo.write("\n")
-
-
-class declare_variable:
-    """
-    Class describing a declared variable in McStas instrument
-
-    McStas parameters are declared in declare section with c syntax.
-    This class is initialized with type, name.  Using keyword
-    arguments, the variable can become an array and have its initial
-    value set.
-
-    Attributes
-    ----------
-    type : str
-        McStas type to declare: Double, Int, String
-
-    name : str
-        Name of variable
-
-    value : any
-        Initial value of variable, converted to string
-
-    comment : str
-        Comment displayed next to the declaration, could contain units
-
-    vector : int
-        0 if a single value is given, ortherwise contains the length
-
-    value_set : int
-        Internal variable displaying wether or not a value was given
-
-    Methods
-    -------
-    write_line(fo)
-        Writes a line to text file fo declaring the parameter in c
-    """
-    def __init__(self, *args, **kwargs):
-        """Initializing mcstas parameter object
-
-        Parameters
-        ----------
-        Positional argument 1: type : str
-            Type of the parameter, double, int or string
-
-        Positional argument 2: name : str
-            Name of input parameter
-
-        Keyword arguments
-            array : int
-                length of array to be allocated, 0 if single value
-
-            value : any
-                sets initial value of parameter,
-                can be a list with length matching array
-
-            comment : str
-                sets comment displayed next to declaration
-        """
-        self.type = args[0]
-        self.name = str(args[1])
-        if "value" in kwargs:
-            self.value_set = 1
-            self.value = kwargs["value"]
-        else:
-            self.value_set = 0
-
-        if "array" in kwargs:
-            self.vector = kwargs["array"]
-        else:
-            self.vector = 0
-
-        if "comment" in kwargs:
-            self.comment = " // " + kwargs["comment"]
-        else:
-            self.comment = ""
-
-    def write_line(self, fo):
-        """Writes line declaring variable to file fo
-
-        Parameters
-        ----------
-        fo : file object
-            File the line will be written to
-        """
-        if self.value_set == 0 and self.vector == 0:
-            fo.write("%s %s;%s" % (self.type, self.name, self.comment))
-        if self.value_set == 1 and self.vector == 0:
-            if self.type == "int":
-                fo.write("%s %s = %d;%s" % (self.type, self.name,
-                                            self.value, self.comment))
-            else:
-                fo.write("%s %s = %G;%s" % (self.type, self.name,
-                                            self.value, self.comment))
-        if self.value_set == 0 and self.vector != 0:
-            fo.write("%s %s[%d];%s" % (self.type, self.name,
-                                       self.vector, self.comment))
-        if self.value_set == 1 and self.vector != 0:
-            fo.write("%s %s[%d] = {" % (self.type, self.name, self.vector))
-            for i in range(0, len(self.value) - 1):
-                fo.write("%G," % self.value[i])
-            fo.write("%G};%s" % (self.value[-1], self.comment))
-
-
-class component:
-    """
-    A class describing a McStas component to be written to a instrument
-
-    This class is used by the instrument class when setting up
-    components as dynamic subclasses to this class.  Most information
-    can be given on initialize using keyword arguments, but there are
-    methods for setting the attributes describing the component. The
-    class contains both methods to write the component to a instrument
-    file and methods for printing to the python terminal for checking
-    the information. The McStas_Instr class creates subclasses from
-    this class that have attributes for all parameters for the given
-    component. The component information is read directly from the
-    component files in the McStas installation.  This class is frozen
-    after __init__ so that no new attributes can be created, which
-    allows direct feedback to the user if a parameter name is
-    misspelled.
-
-    Attributes
-    ----------
-    name : str
-        Name of the component instance in McStas (must be unique)
-
-    component_name : str
-        Name of the component code to use, e.g. Arm, Guide_gravity, ...
-
-    AT_data : list of 3 floats
-        Position data of the component
-
-    AT_relative : str
-        Name of former component to use as reference for position
-
-    ROTATED_data : list of 3 floats
-        Rotation data of the component
-
-    ROTATED_relative : str
-        Name of former component to use as reference for position
-
-    WHEN : str
-        String with logical c expression x for when component is active
-
-    EXTEND : str
-        c code for McStas EXTEND section
-
-    GROUP : str
-        Name of group the component should belong to
-
-    JUMP : str
-        String describing use of JUMP, need to contain all after "JUMP"
-
-    component_parameters : dict
-        Parameters to be used with component in dictionary
-
-    comment : str
-        Comment inserted before the component as an explanation
-
-    __isfrozen : bool
-        If true no new attributes can be created, when false they can
-
-    Methods
-    -------
-    set_AT(at_list,**kwargs)
-        Sets AT_data, can set AT_relative using keyword
-
-    set_ROTATED(rotated_list,**kwargs)
-        Sets ROTATED_data, can set ROTATED_relative using keyword
-
-    set_RELATIVE(relative_name)
-        Set both AT_relative and ROTATED_relative to relative_name
-
-    set_parameters(dict_input)
-        Adds dictionary entries to parameter dictionary
-
-    set_WHEN(string)
-        Sets WHEN string
-
-    set_GROUP(string)
-        Sets GROUP name
-
-    set_JUMP(string)
-        Sets JUMP string
-
-    append_EXTEND(string)
-        Append string to EXTEND string
-
-    set_comment(string)
-        Sets comment for component
-
-    write_component(fo)
-        Writes component code to instrument file
-
-    print_long()
-        Prints basic view of component code (not correct syntax)
-
-    print_short(**kwargs)
-        Prints short description, used in print_components
-
-    __setattr__(key, value)
-        Overwriting __setattr__ to implement ability to freeze
-
-    _freeze()
-        Freeze the class so no new attributes can be defined
-
-    _unfreeze()
-        Unfreeze the class so new attributes can be defined again
-
-    """
-
-    __isfrozen = False  # When frozen, no new attributes allowed
-
-    def __init__(self, instance_name, component_name, **kwargs):
-        """
-        Initializes McStas component with specified name and component
-
-        Parameters
-        ----------
-        instance_name : str
-            name of the instance of the component
-
-        component_name : str
-            name of the component type e.g. Arm, Guide_gravity, ...
-
-        keyword arguments:
-            AT : list of 3 floats
-                Sets AT_data describing position of component
-
-            AT_RELATIVE : str
-                sets AT_relative, describing position reference
-
-            ROTATED : list of 3 floats
-                Sets ROTATED_data, describing rotation of component
-
-            ROTATED_RELATIVE : str
-                Sets ROTATED_relative, sets reference for rotation
-
-            RELATIVE : str
-                Sets both AT_relative and ROTATED_relative
-
-            WHEN : str
-                Sets WHEN string, should contain logical c expression
-
-            EXTEND : str
-                Sets initial EXTEND string, should contain c code
-
-            GROUP : str
-                Sets name of group the component should belong to
-
-            JUMP : str
-                Sets JUMP str
-
-            comment: str
-                Sets comment string
-        """
-
-        # Allow addition of attributes in init
-        self._unfreeze()
-
-        self.name = instance_name
-        self.component_name = component_name
-
-        if "AT" in kwargs:
-            self.AT_data = kwargs["AT"]
-        else:
-            self.AT_data = [0, 0, 0]
-        # Could check if AT_RELATIVE is a string
-        if "AT_RELATIVE" in kwargs:
-            self.AT_relative = "RELATIVE " + kwargs["AT_RELATIVE"]
-        else:
-            self.AT_relative = "ABSOLUTE"
-
-        if "ROTATED" in kwargs:
-            self.ROTATED_data = kwargs["ROTATED"]
-        else:
-            self.ROTATED_data = [0, 0, 0]
-        # Could check if ROTATED_RELATIVE is a string
-        if "ROTATED_RELATIVE" in kwargs:
-            self.ROTATED_relative = kwargs["ROTATED_RELATIVE"]
-        else:
-            self.ROTATED_relative = "ABSOLUTE"
-
-        # Could check if RELATIVE is a string
-        if "RELATIVE" in kwargs:
-            self.AT_relative = "RELATIVE " + kwargs["RELATIVE"]
-            self.ROTATED_relative = "RELATIVE " + kwargs["RELATIVE"]
-
-        if "WHEN" in kwargs:
-            self.WHEN = "WHEN (" + kwargs["WHEN"] + ")\n"
-        else:
-            self.WHEN = ""
-
-        if "EXTEND" in kwargs:
-            self.EXTEND = kwargs["EXTEND"] + "\n"
-        else:
-            self.EXTEND = ""
-
-        if "GROUP" in kwargs:
-            self.GROUP = kwargs["GRPUP"]
-        else:
-            self.GROUP = ""
-
-        if "JUMP" in kwargs:
-            self.JUMP = kwargs["JUMP"]
-        else:
-            self.JUMP = ""
-
-        if "comment" in kwargs:
-            self.comment = kwargs["comment"]
-        else:
-            self.comment = ""
-
-        """
-        Could store an option for whether this component should be
-        printed in instrument file or in a seperate file which would
-        then be included.
-        """
-
-        # Do not allow addition of attributes after init
-        self._freeze()
-
-    def __setattr__(self, key, value):
-        if self.__isfrozen and not hasattr(self, key):
-            raise AttributeError("No parameter called '"
-                                 + key
-                                 + "' in component named "
-                                 + self.name
-                                 + " of component type "
-                                 + self.component_name
-                                 + ".")
-        object.__setattr__(self, key, value)
-
-    def _freeze(self):
-        self.__isfrozen = True
-
-    def _unfreeze(self):
-        self.__isfrozen = False
-
-    def set_AT(self, at_list, **kwargs):
-        """Sets AT data, List of 3 floats"""
-        self.AT_data = at_list
-        if "RELATIVE" in kwargs:
-            relative_name = kwargs["RELATIVE"]
-            if relative_name == "ABSOLUTE":
-                self.AT_relative = relative_name
-            else:
-                self.AT_relative = "RELATIVE " + relative_name
-
-    def set_ROTATED(self, rotated_list, **kwargs):
-        """Sets ROTATED data, List of 3 floats"""
-        self.ROTATED_data = rotated_list
-        if "RELATIVE" in kwargs:
-            relative_name = kwargs["RELATIVE"]
-            if relative_name == "ABSOLUTE":
-                self.ROTATED_relative = relative_name
-            else:
-                self.ROTATED_relative = "RELATIVE " + relative_name
-
-    def set_RELATIVE(self, relative_name):
-        """Sets both AT_relative and ROTATED_relative"""
-        if relative_name == "ABSOLUTE":
-            self.AT_relative = relative_name
-            self.ROTATED_relative = relative_name
-        else:
-            self.AT_relative = "RELATIVE " + relative_name
-            self.ROTATED_relative = "RELATIVE " + relative_name
-
-    def set_parameters(self, dict_input):
-        """
-        Adds parameters and their values from dictionary input
-
-        Relies on attributes added when McStas_Instr creates a
-        subclass from the component class where each component
-        parameter is added as an attribute.
-
-        """
-        for key, val in dict_input.items():
-            if not hasattr(self, key):
-                raise NameError("No parameter called "
-                                + key
-                                + " in component named "
-                                + self.name
-                                + " of component type "
-                                + self.component_name
-                                + ".")
-            else:
-                setattr(self, key, val)
-
-    def set_WHEN(self, string):
-        """Sets WHEN string, should be a c logical expression"""
-        self.WHEN = string
-
-    def set_GROUP(self, string):
-        """Sets GROUP name"""
-        self.GROUP = string
-
-    def set_JUMP(self, string):
-        """Sets JUMP string, should contain all text after JUMP"""
-        self.JUMP = string
-
-    def append_EXTEND(self, string):
-        """Appends a line of code to EXTEND block of component"""
-        self.EXTEND = self.EXTEND + string + "\n"
-
-    def set_comment(self, string):
-        """Method that sets a comment to be written to instrument file"""
-        self.comment = string
-
-    def write_component(self, fo):
-        """
-        Method that writes component to file
-
-        Relies on attributes added when McStas_Instr creates a subclass
-        based on the component class.
-
-        """
-        parameters_per_line = 2
-        # Could use character limit on lines instead
-        parameters_written = 0  # internal parameter
-
-        # Write comment if present
-        if len(self.comment) > 1:
-            fo.write("// %s\n" % (str(self.comment)))
-
-        # Write component name and component type
-        fo.write("COMPONENT %s = %s(" % (self.name, self.component_name))
-
-        component_parameters = {}
-        for key in self.parameter_names:
-            val = getattr(self, key)
-            if val is None:
-                if self.parameter_defaults[key] is None:
-                    raise NameError("Required parameter named "
-                                    + key
-                                    + " in component named "
-                                    + self.name
-                                    + " not set.")
-                else:
-                    continue
-
-            component_parameters[key] = val
-
-        number_of_parameters = len(component_parameters)
-
-        if number_of_parameters == 0:
-            fo.write(")\n")  # If there are no parameters, close immediately
-        else:
-            fo.write("\n")  # If there are parameters, start a new line
-
-        for key, val in component_parameters.items():
-            if isinstance(val, float):  # CHeck if value is a number
-                # Small or large numbers written in scientific format
-                fo.write(" %s = %G" % (str(key), val))
-            else:
-                fo.write(" %s = %s" % (str(key), str(val)))
-            parameters_written = parameters_written + 1
-            if parameters_written < number_of_parameters:
-                fo.write(",")  # Comma between parameters
-                if parameters_written % parameters_per_line == 0:
-                    fo.write("\n")
-            else:
-                fo.write(")\n")  # End paranthesis after last parameter
-
-        # Optional WHEN section
-        if not self.WHEN == "":
-            fo.write("WHEN(%s)\n" % self.WHEN)
-
-        # Write AT and ROTATED section
-        fo.write("AT (%s,%s,%s)" % (str(self.AT_data[0]),
-                                    str(self.AT_data[1]),
-                                    str(self.AT_data[2])))
-        fo.write(" %s\n" % self.AT_relative)
-        fo.write("ROTATED (%s,%s,%s)" % (str(self.ROTATED_data[0]),
-                                         str(self.ROTATED_data[1]),
-                                         str(self.ROTATED_data[2])))
-        fo.write(" %s\n" % self.ROTATED_relative)
-
-        if not self.GROUP == "":
-            fo.write("GROUP %s\n" % self.GROUP)
-
-        # Optional EXTEND section
-        if not self.EXTEND == "":
-            fo.write("EXTEND %{\n")
-            fo.write("%s" % self.EXTEND)
-            fo.write("%}\n")
-
-        if not self.JUMP == "":
-            fo.write("JUMP %s\n" % self.JUMP)
-
-        # Leave a new line between components for readability
-        fo.write("\n")
-
-    def print_long(self):
-        """
-        Prints contained information to Python terminal
-
-        Includes information on required parameters if they are not yet
-        specified. Information on the components are added when the
-        class is used as a superclass for classes describing each
-        McStas component.
-
-        """
-        if len(self.comment) > 1:
-            print("// " + self.comment)
-        print("COMPONENT", str(self.name),
-              "=", str(self.component_name))
-        for key in self.parameter_names:
-            val = getattr(self, key)
-            parameter_name = bcolors.BOLD + key + bcolors.ENDC
-            if val is not None:
-                unit = ""
-                if key in self.parameter_units:
-                    unit = "[" + self.parameter_units[key] + "]"
-                value = (bcolors.BOLD
-                         + bcolors.OKGREEN
-                         + str(val)
-                         + bcolors.ENDC
-                         + bcolors.ENDC)
-                print(" ", parameter_name, "=", value, unit)
-            else:
-                if self.parameter_defaults[key] is None:
-                    print("  "
-                          + parameter_name
-                          + bcolors.FAIL
-                          + " : Required parameter not yet specified"
-                          + bcolors.ENDC)
-
-        if not self.WHEN == "":
-            print("WHEN (" + self.WHEN + ")")
-        print("AT", self.AT_data, self.AT_relative)
-        print("ROTATED", self.ROTATED_data, self.ROTATED_relative)
-        if not self.GROUP == "":
-            print("GROUP " + self.GROUP)
-        if not self.EXTEND == "":
-            print("EXTEND %{")
-            print(self.EXTEND + "%}")
-        if not self.JUMP == "":
-            print("JUMP " + self.JUMP)
-
-    def print_short(self, **kwargs):
-        """Prints short description of component to list print"""
-        if "longest_name" in kwargs:
-            print("test")
-            number_of_spaces = 3+kwargs["longest_name"]-len(self.name)
-            print(str(self.name) + " "*number_of_spaces, end='')
-            print(str(self.component_name),
-                  "\tAT", self.AT_data, self.AT_relative,
-                  "ROTATED", self.ROTATED_data, self.ROTATED_relative)
-        else:
-            print(str(self.name), "=", str(self.component_name),
-                  "\tAT", self.AT_data, self.AT_relative,
-                  "ROTATED", self.ROTATED_data, self.ROTATED_relative)
-
-    def show_parameters(self):
-        """
-        Shows available parameters and their defaults for the component
-
-        Any value specified is not reflected in this view. The
-        additional attributes defined when McStas_Instr creates
-        subclasses for the individual components are required to run
-        this method.
-
-        """
-
-        print(" ___ Help "
-              + self.component_name + " "
-              + (62-len(self.component_name))*"_")
-        print("|"
-              + bcolors.BOLD + "optional parameter" + bcolors.ENDC + "|"
-              + bcolors.BOLD
-              + bcolors.UNDERLINE + "required parameter" + bcolors.ENDC
-              + bcolors.ENDC + "|"
-              + bcolors.BOLD
-              + bcolors.OKBLUE + "default value" + bcolors.ENDC
-              + bcolors.ENDC + "|"
-              + bcolors.BOLD
-              + bcolors.OKGREEN + "user specified value" + bcolors.ENDC
-              + bcolors.ENDC + "|")
-
-        for parameter in self.parameter_names:
-            unit = ""
-            if parameter in self.parameter_units:
-                unit = " [" + self.parameter_units[parameter] + "]"
-            comment = ""
-            if parameter in self.parameter_comments:
-                comment = " // " + self.parameter_comments[parameter]
-
-            parameter_name = bcolors.BOLD + parameter + bcolors.ENDC
-            value = ""
-            if self.parameter_defaults[parameter] is None:
-                parameter_name = (bcolors.UNDERLINE
-                                  + parameter_name
-                                  + bcolors.ENDC)
-            else:
-                value = (" = "
-                         + bcolors.BOLD
-                         + bcolors.OKBLUE
-                         + str(self.parameter_defaults[parameter])
-                         + bcolors.ENDC
-                         + bcolors.ENDC)
-
-            if getattr(self, parameter) is not None:
-                value = (" = "
-                         + bcolors.BOLD
-                         + bcolors.OKGREEN
-                         + str(getattr(self, parameter))
-                         + bcolors.ENDC
-                         + bcolors.ENDC)
-
-            print(parameter_name
-                  + value
-                  + unit
-                  + comment)
-
-        print(73*"-")
-
-    def show_parameters_simple(self):
-        """
-        Shows available parameters and their defaults for the component
-
-        Any value specified is not reflected in this view. The
-        additional attributes defined when McStas_Instr creates
-        subclasses for the individual components are required to run
-        this method.
-
-        """
-        print("---- Help " + self.component_name + " -----")
-        for parameter in self.parameter_names:
-            unit = ""
-            if parameter in self.parameter_units:
-                unit = " [" + self.parameter_units[parameter] + "]"
-            comment = ""
-            if parameter in self.parameter_comments:
-                comment = " // " + self.parameter_comments[parameter]
-            if self.parameter_defaults[parameter] is None:
-                print(parameter
-                      + unit
-                      + comment)
-            else:
-                print(parameter
-                      + " = "
-                      + str(self.parameter_defaults[parameter])
-                      + unit
-                      + comment)
-        print("----------" + "-"*len(self.component_name) + "------")
-    """
-    def show_component(self):
-        print("---- Current parameters for " + self.name + " ----")
-        for parameter in self.parameter_names:
-            parameter_value = getattr(self, parameter)
-            unit = ""
-            if parameter in self.parameter_units:
-                unit = " [" + self.parameter_units[parameter] + "]"
-            if parameter_value is not None:
-                print(" "
-                      + parameter
-                      + " = "
-                      + str(parameter_value)
-                      + unit)
-            else:
-                if self.parameter_defaults[parameter] is None:
-                    print(" "
-                          + parameter
-                          + " : Required parameter not yet specified")
-
-    def show_component_long(self):
-        print("---- Current parameters for " + self.name + " ----")
-        for parameter in self.parameter_names:
-            parameter_value = getattr(self, parameter)
-            unit = ""
-            if parameter in self.parameter_units:
-                unit = " [" + self.parameter_units[parameter] + "]"
-            comment = ""
-            if parameter in self.parameter_comments:
-                comment = " // " + self.parameter_comments[parameter]
-            if parameter_value is not None:
-                print(" "
-                      + parameter
-                      + " = "
-                      + str(parameter_value)
-                      + unit
-                      + comment)
-            else:
-                if self.parameter_defaults[parameter] is None:
-                    print(" "
-                          + parameter
-                          + " : Required parameter not yet specified"
-                          + unit
-                          + comment)
-    """
-
-
-class ComponentInfo:
-    """
-    Internal class used to store information on parameters of components
-    """
-
-    def __init__(self):
-        self.name = ""
-        self.category = ""
-        self.parameter_names = []
-        self.parameter_defaults = {}
-        self.parameter_types = {}
-        self.parameter_comments = {}
-        self.parameter_units = {}
-
-
-class ComponentReader:
-    """
-    Class for retriveing information on available McStas components
-
-    Recursively reads all component files in hardcoded list of
-    folders that represents the component categories in McStas.
-    The results are stored in a dictionary with ComponentInfo
-    instances, the keys are the names of the components. After
-    the components in the McStas installation are read, any
-    components pressent in the current work directory is read,
-    and these will overwrite exisiting information, consistent
-    with how McStas reads component definitions.
-
-    """
-
-    def __init__(self, mcstas_path):
-        """
-        Reads all component files in standard folders. Recursive, so
-        subfolders of these folders are included.
-
-        """
-
-        if mcstas_path[-1] is not "/":
-            mcstas_path = mcstas_path + "/"
-
-        # Hardcoded whitelist of foldernames
-        folder_list = ["sources",
-                       "optics",
-                       "samples",
-                       "monitors",
-                       "misc",
-                       "contrib",
-                       "obsolete",
-                       "union"]
-
-        self.component_path = {}
-        self.component_category = {}
-
-        for folder in folder_list:
-            absolute_path = mcstas_path + folder
-            # self.component_info_dict.update(self._read(absolute_path))
-            self._find_components(absolute_path)
-
-        # McStas component in current directory should overwrite
-        current_directory = os.getcwd()
-
-        for file in os.listdir(current_directory):
-            if file.endswith(".comp"):
-                absolute_path = current_directory + "/" + file
-                component_name = absolute_path.split("/")[-1].split(".")[-2]
-
-                if component_name in self.component_path:
-                    print("Overwriting McStasScript info on component named "
-                          + file
-                          + " because the component is in the"
-                          + " work directory.")
-
-                self.component_path[component_name] = absolute_path
-                self.component_category[component_name] = "Work directory"
-
-    def show_categories(self):
-        """
-        Method that will show all component categories available
-
-        """
-        categories = []
-        for component, category in self.component_category.items():
-            if category not in categories:
-                categories.append(category)
-                print(" " + category)
-
-    def show_components_in_category(self, category_input):
-        """
-        Method that will show all components in given category
-
-        """
-        empty_category = True
-        to_print = []
-        for component, category in self.component_category.items():
-            if category == category_input:
-                to_print.append(component)
-                empty_category = False
-
-        to_print.sort()
-        if empty_category:
-            print("No components found in this category! "
-                  + "Available categories:")
-            self.show_categories()
-
-        elif len(to_print) < 10:
-            for component in to_print:
-                print(" " + component)
-        else:
-            # Prints in collumns, maximum 4 and maximum line length 100
-            columns = 5
-            total_line_length = 1000
-            while(total_line_length > 100):
-                columns = columns - 1
-
-                c_length = math.ceil(len(to_print)/columns)
-                last_length = len(to_print) - (columns-1)*c_length
-
-                column = []
-                longest_name = []
-                for col in range(0, columns-1):
-                    current_list = to_print[c_length*col:c_length*(col+1)]
-                    column.append(current_list)
-                    longest_name.append(len(max(current_list, key=len)))
-
-                column.append(to_print[c_length*(columns-1):])
-                longest_name.append(len(max(column[columns-1], key=len)))
-
-                total_line_length = 1 + sum(longest_name) + (columns-1)*3
-
-            for line_nr in range(0, c_length):
-                print(" ", end="")
-                for col in range(0, columns-1):
-                    this_name = column[col][line_nr]
-                    print(this_name
-                          + " "*(longest_name[col] - len(this_name))
-                          + "   ", end="")  # More columns left, dont break
-                if line_nr < last_length:
-                    this_name = column[columns-1][line_nr]
-                    print(this_name)
-                else:
-                    print("")
-
-    def load_all_components(self):
-        """
-        Method that loads information on all components into memory.
-
-        """
-
-        return_dict = {}
-        for comp_name, abs_path in self.component_path.items():
-            return_dict[comp_name] = self.read_component_file(abs_path)
-
-        return return_dict
-
-    def read_name(self, component_name):
-        """
-        Returns ComponentInfo of component with name component_name.
-
-        Uses table of absolute paths to all known components, and
-        reads the appropriate file in order to generate the information.
-
-        """
-
-        if component_name not in self.component_path:
-            raise NameError("No component named "
-                            + component_name
-                            + " in McStas installation or "
-                            + "current work directory.")
-
-        return self.read_component_file(self.component_path[component_name])
-
-    def _find_components(self, absolute_path):
-        """
-        Recursive read function, can read either file or entire folder
-
-        Updates the component_info_dict with the findings that are
-        stored as ComoponentInfo instances.
-
-        """
-
-        if not os.path.isdir(absolute_path):
-            if absolute_path.endswith(".comp"):
-                # read this file
-                component_name = absolute_path.split("/")[-1].split(".")[-2]
-                self.component_path[component_name] = absolute_path
-
-                component_category = absolute_path.split("/")[-2]
-                self.component_category[component_name] = component_category
-        else:
-            for file in os.listdir(absolute_path):
-                absolute_file_path = absolute_path + "/" + file
-                self._find_components(absolute_file_path)
-
-    def read_component_file(self, absolute_path):
-        """
-        Reads a component file and expands component_info_dict
-
-        The information is stored as ComponentClass instances.
-
-        """
-
-        result = ComponentInfo()
-
-        fo = open(absolute_path, "r")
-
-        cnt = 0
-        while True:
-            cnt += 1
-            line = fo.readline()
-
-            # find parameter comments
-            if self.line_starts_with(line, "* %P"):
-
-                while True:
-                    this_line = fo.readline()
-
-                    if self.line_starts_with(this_line, "DEFINE COMPONENT"):
-                        # No more comments to read through
-                        break
-
-                    if ":" in this_line:
-                        tokens = this_line.split(":")
-
-                        variable_name = tokens[0]
-                        variable_name = variable_name.replace("*", "")
-                        variable_name = variable_name.strip()
-                        if " " in variable_name:
-                            name_tokens = variable_name.split(" ")
-                            variable_name = name_tokens[0]
-
-                        if len(tokens[1]) > 2:
-                            comment = tokens[1].strip()
-
-                            if "[" in comment:  # Search for unit
-                                # If found, store it and remove from string
-                                unit = comment[comment.find("[") + 1:
-                                               comment.find("]")]
-                                result.parameter_units[variable_name] = unit
-                                comment = comment[comment.find("]") + 1:]
-                                comment = comment.strip()
-
-                            # Store the comment
-                            result.parameter_comments[variable_name] = comment
-                    elif "[" in this_line and "]" in this_line:
-                        tokens = this_line.split("[")
-
-                        variable_name = tokens[0]
-                        variable_name = variable_name.replace("*", "")
-                        variable_name = variable_name.strip()
-
-                        unit = this_line[this_line.find("[") + 1:
-                                         this_line.find("]")]
-                        result.parameter_units[variable_name] = unit
-
-                        comment = this_line[this_line.find("]") + 1:]
-                        comment = comment.strip()
-                        result.parameter_comments[variable_name] = comment
-
-            # find definition parameters and their values
-            if (self.line_starts_with(line, "DEFINITION PARAMETERS")
-                    or self.line_starts_with(line, "SETTING PARAMETERS")):
-
-                parts = line.split("(")
-                parameter_parts = parts[1].split(",")
-
-                parameter_parts = list(filter(("\n").__ne__, parameter_parts))
-
-                break_now = False
-                while True:
-                    # Read all definition parameters
-
-                    for part in parameter_parts:
-
-                        temp_par_type = "double"
-
-                        part = part.strip()
-
-                        # remove trailing )
-                        if ")" in part:
-                            part = part.replace(")", "")
-                            break_now = True
-
-                        possible_declare = part.split(" ")
-                        possible_type = possible_declare[0].strip()
-                        if "int" == possible_type:
-                            temp_par_type = "int"
-                            # remove int from part
-                            part = "".join(possible_declare[1:])
-                        if "string" == possible_type:
-                            temp_par_type = "string"
-                            # remove string from part
-                            part = "".join(possible_declare[1:])
-
-                        part = part.replace(" ", "")
-                        if part == "":
-                            continue
-
-                        if self.line_starts_with(part, "//"):
-                            break_now = True
-                            continue
-
-                        if self.line_starts_with(part, "/*"):
-                            break_now = True
-                            continue
-
-                        if "=" not in part:
-                            # no defualt value, required parameter
-                            result.parameter_names.append(part)
-                            result.parameter_defaults[part] = None
-                            result.parameter_types[part] = temp_par_type
-                        else:
-                            # default value available
-                            name_value = part.split("=")
-                            par_name = name_value[0].strip()
-                            par_value = name_value[1].strip()
-                            result.parameter_names.append(par_name)
-                            result.parameter_defaults[par_name] = par_value
-                            result.parameter_types[par_name] = temp_par_type
-
-                    if break_now:
-                        break
-
-                    parameter_parts = fo.readline().split(",")
-
-            if self.line_starts_with(line, "DECLARE"):
-                break
-
-            if self.line_starts_with(line, "TRACE"):
-                break
-
-            if cnt == 1000:
-                break
-
-        fo.close()
-
-        result.name = absolute_path.split("/")[-1].split(".")[-2]
-        foldernames = absolute_path.split("/")
-        result.category = foldernames[-2]
-
-        """
-        To lower memory use one could remove all comments and units that
-        does not correspond to a found parameter name.
-        """
-
-        return result
-
-    def line_starts_with(self, line, input):
-        """
-        Helper method that checks if a string is the start of a line
-
-        """
-        if len(line) < len(input):
-            return False
-
-        if line[0:len(input)] == input:
-            return True
-        else:
-            return False
-
-
-class McStas_instr:
-    """
-    Main class for writing a McStas instrument using McStasScript
-
-    Initialization of McStas_instr sets the name of the instrument file
-    and its methods are used to add all aspects of the instrument file.
-    The class also holds methods for writing the finished instrument
-    file to disk and to run the simulation.
-
-    Attributes
-    ----------
-    name : str
-        name of instrument file
-
-    author : str
-        name of user of McStasScript, written to the file
-
-    origin : str
-        origin of instrument file (affiliation)
-
-    mcrun_path : str
-        absolute path of mcrun command, or empty if it is in path
-
-    parameter_list : list of parameter_variable instances
-        contains all input parameters to be written to file
-
-    declare_list : list of declare_variable instances
-        contains all declare parrameters to be written to file
-
-    initialize_section : str
-        string containing entire initialize section to be written
-
-    trace_section : str
-        string containing trace section (OBSOLETE)
-
-    finally_section : str
-        string containing entire finally section to be written
-
-    component_list : list of component instances
-        list of components in the instrument
-
-    component_name_list : list of strings
-        list of names of the components in the instrument
-
-    Methods
-    -------
-    add_parameter(*args,**kwargs)
-        Adds input parameter to the define section
-
-    add_declare_var()
-        Adds declared variable ot the declare section
-
-    append_initialize(string)
-        Appends a string to the initialize section, then adds new line
-
-    append_initialize_no_new_line(string)
-        Appends a string to the initialize section
-
-    append_finally(string)
-        Appends a string to finally section, then adds new line
-
-    append_finally_no_new_line(string)
-        Appends a string to finally section
-
-    append_trace(string)
-        Obsolete method, add components instead (used in write_c_files)
-
-    add_component(instance_name,component_name,**kwargs)
-        Add a component to the instrument file
-
-    get_component(instance_name)
-        Returns component instance with name instance_name
-
-    get_last_component()
-        Returns component instance of last component
-
-    set_component_parameter(instance_name,dict)
-        Adds parameters as dict to component with instance_name
-
-    set_component_AT(instance_name,AT_data,**kwargs)
-        Sets position of component named instance_name
-
-    set_component_ROTATED(instance_name,ROTATED_data,**kwargs)
-        Sets rotation of component named instance_name
-
-    set_component_RELATIVE(instane_name,string)
-        Sets position and rotation reference for named component
-
-    set_component_WHEN(instance_name,string)
-        Sets WHEN condition of named component, is logical c expression
-
-    set_component_GROUP(instance_name,string)
-        Sets GROUP name of component named instance_name
-
-    append_component_EXTEND(instance_name,string)
-        Appends a line to EXTEND section of named component
-
-    set_component_JUMP(instance_name,string)
-        Sets JUMP code for named component
-
-    set_component_comment(instance_name,string)
-        Sets comment to be written before named component
-
-    print_component(instance_name)
-        Prints an overview of current state of named component
-
-    print_component_short(instance_name)
-        Prints short overview of current state of named component
-
-    print_components()
-        Prints overview of postion / rotation of all components
-
-    write_c_files()
-        Writes c files for %include in generated_includes folder
-
-    write_full_instrument()
-        Writes full instrument file to current directory
-
-    run_full_instrument(**kwargs)
-        Writes instrument files and runs simulation.
-        Returns list of McStasData
-    """
-
-    def __init__(self, name, **kwargs):
-        """
-        Initialization of McStas Instrument
-
-        Parameters
-        ----------
-        name : str
-            Name of project, instrument file will be name + ".instr"
-
-        keyword arguments:
-            author : str
-                Name of author, written in instrument file
-
-            origin : str
-                Affiliation of author, written in instrument file
-
-            mcrun_path : str
-                Absolute path of mcrun or empty if already in path
-        """
-
-        self.name = name
-
-        if "author" in kwargs:
-            self.author = kwargs["author"]
-        else:
-            self.author = "Python McStas Instrument Generator"
-
-        if "origin" in kwargs:
-            self.origin = kwargs["origin"]
-        else:
-            self.origin = "ESS DMSC"
-
-        if "mcrun_path" in kwargs:
-            self.mcrun_path = kwargs["mcrun_path"]
-        else:
-            self.mcrun_path = ""
-
-        if "mcstas_path" in kwargs:
-            self.mcstas_path = kwargs["mcstas_path"]
-        else:
-            self.mcstas_path = ""
-            raise NameError("At this stage of development "
-                            + "McStasScript need the absolute path "
-                            + "for the McStas installation as keyword "
-                            + "named mcstas_path")
-
-        self.parameter_list = []
-        self.declare_list = []
-        self.initialize_section = ("// Start of initialize for generated "
-                                   + name + "\n")
-        self.trace_section = ("// Start of trace section for generated "
-                              + name + "\n")
-        self.finally_section = ("// Start of finally for generated "
-                                + name + "\n")
-        # Handle components
-        self.component_list = []  # List of components (have to be ordered)
-        self.component_name_list = []  # List of component names
-
-        # Read info on active McStas components
-        self.component_reader = ComponentReader(self.mcstas_path)
-        self.component_class_lib = {}
-
-    def add_parameter(self, *args, **kwargs):
-        """
-        Method for adding input parameter to instrument
-
-        Parameters
-        ----------
-
-        (optional) parameter type : str
-            type of input parameter, double, int, string
-
-        parameter name : str
-            name of parameter
-
-        keyword arguments
-            value : any
-                Default value of parameter
-
-            comment : str
-                Comment displayed next to declaration of parameter
-        """
-        # parameter_variable class documented independently
-        self.parameter_list.append(parameter_variable(*args, **kwargs))
-
-    def add_declare_var(self, *args, **kwargs):
-        """
-        Method for adding declared variable to instrument
-
-        Parameters
-        ----------
-
-        parameter type : str
-            type of input parameter
-
-        parameter name : str
-            name of parameter
-
-        keyword arguments
-            array : int
-                default 0 for scalar, if specified length of array
-
-            value : any
-                Initial value of parameter, can be list of length vector
-
-            comment : str
-                Comment displayed next to declaration of parameter
-
-        """
-        # declare_variable class documented independently
-        self.declare_list.append(declare_variable(*args, **kwargs))
-
-    def append_initialize(self, string):
-        """
-        Method for appending code to the intialize section
-
-        The intialize section consists of c code and will be compiled,
-        thus any syntax errors will crash the simulation. Code is added
-        on a new line for each call to this method.
-
-        Parameters
-        ----------
-        string : str
-            code to be added to initialize section
-        """
-        self.initialize_section = self.initialize_section + string + "\n"
-
-    def append_initialize_no_new_line(self, string):
-        """
-        Method for appending code to the intialize section, no new line
-
-        The intialize section consists of c code and will be compiled,
-        thus any syntax errors will crash the simulation. Code is added
-        to the current line.
-
-        Parameters
-        ----------
-        string : str
-            code to be added to initialize section
-
-        """
-
-        self.initialize_section = self.initialize_section + string
-
-    def append_finally(self, string):
-        """
-        Method for appending code to the finally section of instrument
-
-        The finally section consists of c code and will be compiled,
-        thus any syntax errors will crash the simulation. Code is added
-        on a new line for each call to this method.
-
-        Parameters
-        ----------
-        string : str
-            code to be added to finally section
-
-        """
-
-        self.finally_section = self.finally_section + string + "\n"
-
-    def append_finally_no_new_line(self, string):
-        """
-        Method for appending code to the finally section of instrument
-
-        The finally section consists of c code and will be compiled,
-        thus any syntax errors will crash the simulation. Code is added
-        to the current line.
-
-        Parameters
-        ----------
-        string : str
-            code to be added to finally section
-        """
-
-        self.finally_section = self.finally_section + string
-
-    """
-    # Handle trace string differently when components also exists
-    #  A) Coul d have trace string as a component attribute and set
-    #     it before / after
-    #  B) Could have trace string as a McStas_instr attribute and
-    #     still attach placement to components
-    #  C) Could have trace string as a different object and place it
-    #     in component_list, but have a write function named as the
-    #     component write function?
-    """
-
-    def append_trace(self, string):
-        """
-        Appends code to trace section, only used in write_c_files
-
-        The most common way to add code to the trace section is to add
-        components using the seperate methods for this.  This method is
-        kept as is still used for writing to c files used in legacy
-        code.  Each call creates a new line.
-
-        Parameters
-        ----------
-        string : str
-            code to be added to trace
-        """
-
-        self.trace_section = self.trace_section + string + "\n"
-
-    def append_trace_no_new_line(self, string):
-        """
-        Appends code to trace section, only used in write_c_files
-
-        The most common way to add code to the trace section is to add
-        components using the seperate methods for this.  This method is
-        kept as is still used for writing to c files used in legacy
-        code.  No new line is made with this call.
-
-        Parameters
-        ----------
-        string : str
-            code to be added to trace
-        """
-
-        self.trace_section = self.trace_section + string
-
-    def show_components(self, *args):
-        """
-        Helper method that shows available components to the user
-
-        If called without any arguments it will display the available
-        component categories.  The first input
-
-        """
-        if len(args) == 0:
-            print("Here are the availalbe component categories:")
-            self.component_reader.show_categories()
-            print("Call show_components(category_name) to display")
-
-        else:
-            category = args[0]
-            print("Here are all components in the "
-                  + category
-                  + " category.")
-            self.component_reader.show_components_in_category(category)
-
-    def component_help(self, name):
-        """
-        Method for showing parameters for a component before adding it
-        to the instrument
-
-        """
-
-        dummy_instance = self._create_component_instance("dummy", name)
-        dummy_instance.show_parameters()
-
-    def _create_component_instance(self, *args, **kwargs):
-        """
-        Dynamically creates a class for the requested component type
-
-        Created classses kept in dictionary, if the same component type
-        is requested again, the class in the dictionary is used.  The
-        method returns an instance of the created class that was
-        initialized with the paramters passed to this function.
-        """
-
-        if len(args) < 2:
-            raise NameError("Attempting to create component without name")
-
-        component_name = args[1]
-
-        if component_name not in self.component_class_lib:
-            comp_info = self.component_reader.read_name(component_name)
-
-            input_dict = {}
-            input_dict = {key: None for key in comp_info.parameter_names}
-            input_dict["parameter_names"] = comp_info.parameter_names
-            input_dict["parameter_defaults"] = comp_info.parameter_defaults
-            input_dict["parameter_types"] = comp_info.parameter_types
-            input_dict["parameter_units"] = comp_info.parameter_units
-            input_dict["parameter_comments"] = comp_info.parameter_comments
-            input_dict["category"] = comp_info.category
-
-            self.component_class_lib[component_name] = type(component_name,
-                                                            (component,),
-                                                            input_dict)
-
-        return self.component_class_lib[component_name](*args, **kwargs)
-
-    def add_component(self, *args, **kwargs):
-        """
-        Method for adding a new component instance to the instrument
-
-        Creates a new component instance in the instrument.  This
-        requires a unique instance name of the component to be used for
-        future reference and the name of the McStas component to be
-        used.  The component is placed at the end of the instrument file
-        unless otherwise specified with the after and before keywords.
-        The component may be initialized using other keyword arguments,
-        but all attributes can be set with approrpiate methods.
-
-        Parameters
-        ----------
-        First positional argument : str
-            Unique name of component instance
-
-        Second positional argument : str
-            Name of McStas component to create instance of
-
-        Keyword arguments:
-            after : str
-                Place this component after component with given name
-
-            before : str
-                Place this component before component with given name
-
-            AT : List of 3 floats
-                Sets AT_data, position relative to reference
-
-            AT_RELATIVE : str
-                Sets reference component for postion
-
-            ROTATED : List of 3 floats
-                Sets ROTATED_data, rotation relative to reference
-
-            ROTATED_RELATIVE : str
-                Sets reference component for rotation
-
-            RELATIVE : str
-                Sets reference component for both position and rotation
-
-            WHEN : str
-                Sets when condition which must be a logical c expression
-
-            EXTEND : str
-                Initialize the extend section with a line of c code
-
-            GROUP : str
-                Name of the group this component should belong to
-
-            JUMP : str
-                Set code for McStas JUMP statement
-
-            comment : str
-                Comment that will be displayed before the component
-        """
-
-        if args[0] in self.component_name_list:
-            raise NameError(("Component name \"" + str(args[0])
-                             + "\" used twice, McStas does not allow this."
-                             + " Rename or remove one instance of this"
-                             + " name."))
-
-        # Insert component after component with this name
-        if "after" in kwargs:
-            if kwargs["after"] not in self.component_name_list:
-                raise NameError(("Trying to add a component after a component"
-                                 + " named \"" + str(kwargs["after"])
-                                 + "\", but a component with that name was"
-                                 + " not found."))
-
-            new_index = self.component_name_list.index(kwargs["after"])
-
-            new_component = self._create_component_instance(*args, **kwargs)
-            self.component_list.insert(new_index + 1, new_component)
-
-            self.component_name_list.insert(new_index+1, args[0])
-
-        # Insert component after component with this name
-        elif "before" in kwargs:
-            if kwargs["before"] not in self.component_name_list:
-                raise NameError(("Trying to add a component before a "
-                                 + "component named \""
-                                 + str(kwargs["before"])
-                                 + "\", but a component with that "
-                                 + "name was not found."))
-
-            new_index = self.component_name_list.index(kwargs["before"])
-
-            new_component = self._create_component_instance(*args, **kwargs)
-            self.component_list.insert(new_index, new_component)
-
-            self.component_name_list.insert(new_index, args[0])
-
-        # If after or before keywords absent, place component at the end
-        else:
-            new_component = self._create_component_instance(*args, **kwargs)
-            self.component_list.append(new_component)
-            self.component_name_list.append(args[0])
-
-        return new_component
-
-    def get_component(self, name):
-        """
-        Get the component instance of component with specified name
-
-        This method is used to get direct access to any component
-        instance in the instrument.  The component instance can be
-        manipulated in much the same way, but it is not necessary to
-        specify the name in each call.
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component whos instance should be returned
-        """
-
-        if name in self.component_name_list:
-            index = self.component_name_list.index(name)
-            return self.component_list[index]
-        else:
-            raise NameError(("No component was found with name \""
-                             + str(name) + "\"!"))
-
-    def get_last_component(self):
-        """
-        Get the component instance of last component in the instrument
-
-        This method is used to get direct access to any component
-        instance in the instrument.  The component instance can be
-        manipulated in much the same way, but it is not necessary to
-        specify the name in each call.
-        """
-
-        return self.component_list[-1]
-
-    def set_component_parameter(self, name, input_dict):
-        """
-        Add parameters and their values as dictionary to component
-
-        This method is the primary way of specifying parameters in a
-        component.  Parameters are added to a dictionary specifying
-        parameter name and value pairs.
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        input_dict : dict
-            Set of new parameter name and value pairs to add
-        """
-
-        component = self.get_component(name)
-        component.set_parameters(input_dict)
-
-    def set_component_AT(self, name, at_list, **kwargs):
-        """
-        Method for setting position of component
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        at_list : List of 3 floats
-            Position of component relative to reference component
-
-        keyword arguments:
-            RELATIVE : str
-                Sets reference component for position
-        """
-
-        component = self.get_component(name)
-        component.set_AT(at_list, **kwargs)
-
-    def set_component_ROTATED(self, name, rotated_list, **kwargs):
-        """
-        Method for setting rotiation of component
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        rotated_list : List of 3 floats
-            Rotation of component relative to reference component
-
-        keyword arguments:
-            RELATIVE : str
-                Sets reference component for rotation
-        """
-
-        component = self.get_component(name)
-        component.set_ROTATED(rotated_list, **kwargs)
-
-    def set_component_RELATIVE(self, name, relative):
-        """
-        Method for setting reference of component position and rotation
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        relative : str
-            Reference component for position and rotation
-        """
-
-        component = self.get_component(name)
-        component.set_RELATIVE(relative)
-
-    def set_component_WHEN(self, name, WHEN):
-        """
-        Method for setting WHEN c expression to named component
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        WHEN : str
-            Sets WHEN c expression for named McStas component
-        """
-        component = self.get_component(name)
-        component.set_WHEN(WHEN)
-
-    def append_component_EXTEND(self, name, EXTEND):
-        """
-        Method for adding line of c to EXTEND section of named component
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        EXTEND : str
-            Line of c code added to EXTEND section of named component
-        """
-
-        component = self.get_component(name)
-        component.append_EXTEND(EXTEND)
-
-    def set_component_GROUP(self, name, GROUP):
-        """
-        Method for setting GROUP name of named component
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        GROUP : str
-            Sets GROUP name for named McStas component
-        """
-
-        component = self.get_component(name)
-        component.set_GROUP(GROUP)
-
-    def set_component_JUMP(self, name, JUMP):
-        """
-        Method for setting JUMP expression of named component
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        JUMP : str
-            Sets JUMP expression for named McStas component
-        """
-
-        component = self.get_component(name)
-        component.set_JUMP(JUMP)
-
-    def set_component_comment(self, name, string):
-        """
-        Sets a comment displayed before the component in written files
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        string : str
-            Comment string
-
-        """
-
-        component = self.get_component(name)
-        component.set_comment(string)
-
-    def print_component(self, name):
-        """
-        Method for printing summary of contents in named component
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to print
-        """
-
-        component = self.get_component(name)
-        component.print_long()
-
-    def print_component_short(self, name):
-        """
-        Method for printing summary of contents in named component
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to print
-        """
-
-        component = self.get_component(name)
-        component.print_short()
-
-    def print_components(self):
-        """
-        Method for printing overview of all components in instrument
-
-        Provides overview of component names, what McStas component is
-        used for each and their position and rotation in space.
-        """
-
-        longest_name = len(max(self.component_name_list, key=len))
-
-        # Investigate how this could have been done in a better way
-        # Find longest field for each type of data printed
-        component_type_list = []
-        at_x_list = []
-        at_y_list = []
-        at_z_list = []
-        at_relative_list = []
-        rotated_x_list = []
-        rotated_y_list = []
-        rotated_z_list = []
-        rotated_relative_list = []
-        for component in self.component_list:
-            component_type_list.append(component.component_name)
-            at_x_list.append(str(component.AT_data[0]))
-            at_y_list.append(str(component.AT_data[1]))
-            at_z_list.append(str(component.AT_data[2]))
-            at_relative_list.append(component.AT_relative)
-            rotated_x_list.append(str(component.ROTATED_data[0]))
-            rotated_y_list.append(str(component.ROTATED_data[1]))
-            rotated_z_list.append(str(component.ROTATED_data[2]))
-            rotated_relative_list.append(component.ROTATED_relative)
-
-        longest_component_name = len(max(component_type_list, key=len))
-        longest_at_x_name = len(max(at_x_list, key=len))
-        longest_at_y_name = len(max(at_y_list, key=len))
-        longest_at_z_name = len(max(at_z_list, key=len))
-        longest_at_relative_name = len(max(at_relative_list, key=len))
-        longest_rotated_x_name = len(max(rotated_x_list, key=len))
-        longest_rotated_y_name = len(max(rotated_y_list, key=len))
-        longest_rotated_z_name = len(max(rotated_z_list, key=len))
-        longest_rotated_relative_name = len(max(rotated_relative_list,
-                                                key=len))
-
-        # Have longest field for each type, use ljust to align all columns
-        for component in self.component_list:
-            print(str(component.name).ljust(longest_name+2), end=' ')
-
-            comp_name = component.component_name
-            comp_name_print = str(comp_name).ljust(longest_component_name + 2)
-            print(comp_name_print, end=' ')
-
-            comp_at_data = str(component.AT_data)
-            longest_at_xyz_sum = (longest_at_x_name
-                                  + longest_at_y_name
-                                  + longest_at_z_name)
-            print("AT ",
-                  comp_at_data.ljust(longest_at_xyz_sum + 11),
-                  end='')
-
-            comp_at_relative = component.AT_relative
-            print(comp_at_relative.ljust(longest_at_relative_name + 2),
-                  end=' ')
-
-            comp_rotated_data = str(component.ROTATED_data)
-            longest_rotated_xyz_sum = (longest_rotated_x_name
-                                       + longest_rotated_y_name
-                                       + longest_rotated_z_name)
-            print("ROTATED ",
-                  comp_rotated_data.ljust(longest_rotated_xyz_sum + 11),
-                  end='')
-            print(component.ROTATED_relative)
-            # print("")
-
-    def write_c_files(self):
-        """
-        Obsolete method for writing instrument parts to c files
-
-        It is possible to use this function to write c files to a folder
-        called generated_includes that can then be included in the
-        different sections of a McStas instrument. Component objects are
-        NOT written to these files, but rather the contents of the
-        trace_section that can be set using the append_trace method.
-        """
-        path = os.getcwd()
-        path = path + "/generated_includes"
-        if not os.path.isdir(path):
-            try:
-                os.mkdir(path)
-            except OSError:
-                print("Creation of the directory %s failed" % path)
-
-        fo = open("./generated_includes/" + self.name + "_declare.c", "w")
-        fo.write("// declare section for %s \n" % self.name)
-        fo.close()
-        fo = open("./generated_includes/" + self.name + "_declare.c", "a")
-        for dec_line in self.declare_list:
-            dec_line.write_line(fo)
-            fo.write("\n")
-        fo.close()
-
-        fo = open("./generated_includes/" + self.name + "_initialize.c", "w")
-        fo.write(self.initialize_section)
-        fo.close()
-
-        fo = open("./generated_includes/" + self.name + "_trace.c", "w")
-        fo.write(self.trace_section)
-        fo.close()
-
-        fo = open("./generated_includes/" + self.name
-                  + "_component_trace.c", "w")
-        for component in self.component_list:
-            component.write_component(fo)
-        fo.close()
-
-    def write_full_instrument(self):
-        """
-        Method for writing full instrument file to disk
-
-        This method writes the instrument described by the instrument
-        objects to disk with the name specified in the initialization of
-        the object.
-        """
-
-        # Create file identifier
-        fo = open(self.name + ".instr", "w")
-
-        # Write quick doc start
-        fo.write("/" + 80*"*" + "\n")
-        fo.write("* \n")
-        fo.write("* McStas, neutron ray-tracing package\n")
-        fo.write("*         Copyright (C) 1997-2008, All rights reserved\n")
-        fo.write("*         Risoe National Laboratory, Roskilde, Denmark\n")
-        fo.write("*         Institut Laue Langevin, Grenoble, France\n")
-        fo.write("* \n")
-        fo.write("* This file was written by McStasScript, which is a \n")
-        fo.write("* python based McStas instrument generator written by \n")
-        fo.write("* Mads Bertelsen in 2019 while employed at the \n")
-        fo.write("* European Spallation Source Data Management and \n")
-        fo.write("* Software Center\n")
-        fo.write("* \n")
-        fo.write("* Instrument %s\n" % self.name)
-        fo.write("* \n")
-        fo.write("* %Identification\n")  # Could allow the user to insert this
-        fo.write("* Written by: %s\n" % self.author)
-        t_format = "%H:%M:%S on %B %d, %Y"
-        fo.write("* Date: %s\n" % datetime.datetime.now().strftime(t_format))
-        fo.write("* Origin: %s\n" % self.origin)
-        fo.write("* %INSTRUMENT_SITE: Generated_instruments\n")
-        fo.write("* \n")
-        fo.write("* \n")
-        fo.write("* %Parameters\n")
-        # Add description of parameters here
-        fo.write("* \n")
-        fo.write("* %End \n")
-        fo.write("*"*80 + "/\n")
-        fo.write("\n")
-        fo.write("DEFINE INSTRUMENT %s (" % self.name)
-        fo.write("\n")
-        # Add loop that inserts parameters here
-        for variable in self.parameter_list[0:-1]:
-            variable.write_parameter(fo, ",")
-        if len(self.parameter_list) > 0:
-            self.parameter_list[-1].write_parameter(fo, " ")
-        fo.write(")\n")
-        fo.write("\n")
-
-        # Write declare
-        fo.write("DECLARE \n%{\n")
-        for dec_line in self.declare_list:
-            dec_line.write_line(fo)
-            fo.write("\n")
-        fo.write("%}\n\n")
-
-        # Write initialize
-        fo.write("INITIALIZE \n%{\n")
-        fo.write(self.initialize_section)
-        # Alternatively hide everything in include
-        """
-        fo.write("%include "generated_includes/"
-                  + self.name + "_initialize.c")
-        """
-        fo.write("%}\n\n")
-
-        # Write trace
-        fo.write("TRACE \n")
-        for component in self.component_list:
-            component.write_component(fo)
-
-        # Write finally
-        fo.write("FINALLY \n%{\n")
-        fo.write(self.finally_section)
-        # Alternatively hide everything in include
-        fo.write("%}\n")
-
-        # End instrument file
-        fo.write("\nEND\n")
-
-    def run_full_instrument(self, *args, **kwargs):
-        """
-        Runs McStas instrument described by this class, returns list of
-        McStasData
-
-        This method will write the instrument to disk and then run it
-        using the mcrun command of the system. Options are set using
-        keyword arguments.  Some options are mandatory, for example
-        foldername, which can not already exist, if it does data will
-        be read from this folder.  If the mcrun command is not in the
-        path of the system, the absolute path can be given with the
-        mcrun_path keyword argument.  This path could also already have
-        been set at initialization of the instrument object.
-
-        Parameters
-        ----------
-        Keyword arguments
-            foldername : str
-                Sets data_folder_name
-            ncount : int
-                Sets ncount
-            mpi : int
-                Sets thread count
-            parameters : dict
-                Sets parameters
-            custom_flags : str
-                Sets custom_flags passed to mcrun
-            mcrun_path : str
-                Path to mcrun command, "" if already in path
-        """
-        # Write the instrument file
-        self.write_full_instrument()
-
-        # Make sure mcrun path is in kwargs
-        if "mcrun_path" not in kwargs:
-            kwargs["mcrun_path"] = self.mcrun_path
-
-        # Set up the simulation
-        simulation = ManagedMcrun(self.name + ".instr", **kwargs)
-
-        # Run the simulation and return data
-        return simulation.run_simulation()
diff --git a/McStasScript_demo.ipynb b/McStasScript_demo.ipynb
deleted file mode 100644
index cedf4d7d..00000000
--- a/McStasScript_demo.ipynb
+++ /dev/null
@@ -1,495 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Demonstration of McStasScript\n",
-    "This file demonstrates how McStasScript can be used to run McStas from a python environment in a userfreindly manner."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "sys.path.append('/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript') # Path to McStasScript pythoon file\n",
-    "import McStasScript  \n",
-    "\n",
-    "# Creating the instance of the class, insert path to mcrun and to mcstas root directory\n",
-    "instr = McStasScript.McStas_instr(\"jupyter_demo\",\n",
-    "                                  mcrun_path= \"/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin\",\n",
-    "                                  mcstas_path = \"/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are the availalbe component categories:\n",
-      " sources\n",
-      " optics\n",
-      " samples\n",
-      " monitors\n",
-      " misc\n",
-      " contrib\n",
-      " union\n",
-      " obsolete\n",
-      " Work directory\n",
-      "Call show_components(category_name) to display\n"
-     ]
-    }
-   ],
-   "source": [
-    "instr.show_components() # Shows available McStas component categories in current installation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are all components in the sources category.\n",
-      " Adapt_check     Monitor_Optimizer   Source_div      Virtual_output\n",
-      " ESS_butterfly   Source_Maxwell_3    Source_gen      \n",
-      " ESS_moderator   Source_Optimizer    Source_simple   \n",
-      " Moderator       Source_adapt        Virtual_input   \n"
-     ]
-    }
-   ],
-   "source": [
-    "instr.show_components(\"sources\") # Display all McStas source components "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Source_simple _________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1mradius\u001b[0m = \u001b[1m\u001b[94m0.1\u001b[0m\u001b[0m [m] // Radius of circle in (x,y,0) plane where neutrons are generated.\n",
-      "\u001b[1myheight\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Height of rectangle in (x,y,0) plane where neutrons are generated.\n",
-      "\u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Width of rectangle in (x,y,0) plane where neutrons are generated.\n",
-      "\u001b[1mdist\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Distance to target along z axis.\n",
-      "\u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[94m.045\u001b[0m\u001b[0m [m] // Width of target\n",
-      "\u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[94m.12\u001b[0m\u001b[0m [m] // Height of target\n",
-      "\u001b[1mE0\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [meV] // Mean energy of neutrons.\n",
-      "\u001b[1mdE\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [meV] // Energy half spread of neutrons (flat or gaussian sigma).\n",
-      "\u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [AA] // Mean wavelength of neutrons.\n",
-      "\u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [AA] // Wavelength half spread of neutrons.\n",
-      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1/(s*cm**2*st*energy unit)] // flux per energy unit, Angs or meV if flux=0, the source emits 1 in 4*PI whole space.\n",
-      "\u001b[1mgauss\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [1] // Gaussian (1) or Flat (0) energy/wavelength distribution\n",
-      "\u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[94m+1\u001b[0m\u001b[0m [1] // relative index of component to focus at, e.g. next is +1 this is used to compute 'dist' automatically.\n",
-      "-------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "instr.component_help(\"Source_simple\") # Displays help on the Source_simple component"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "source = instr.add_component(\"Source\",\"Source_simple\") # Adds an instance of Source_simple called Source to instrument"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Lets add a parameter to the instrument to control the wavelength of the source\n",
-    "instr.add_parameter(\"double\",\"wavelength\",value=3,comment=\"Wavelength emmited from source\")\n",
-    "source.xwidth = 0.06; source.yheight = 0.08;\n",
-    "source.dist = 2; source.focus_xw = 0.05; source.focus_yh = 0.05\n",
-    "source.lambda0 = \"wavelength\"; source.dlambda = 0.1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT Source = Source_simple\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.08\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.06\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mdist\u001b[0m = \u001b[1m\u001b[92m2\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[92mwavelength\u001b[0m\u001b[0m [AA]\n",
-      "  \u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [AA]\n",
-      "AT [0, 0, 0] ABSOLUTE\n",
-      "ROTATED [0, 0, 0] ABSOLUTE\n"
-     ]
-    }
-   ],
-   "source": [
-    "source.print_long() # Verify that the information is correct"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "guide = instr.add_component(\"Guide\", \"Guide_gravity\", AT=[0,0,2], RELATIVE=\"Source\")\n",
-    "guide.set_comment=\"Beam extraction and first guide piece\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Guide_gravity _________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[4m\u001b[1mw1\u001b[0m\u001b[0m [m] // Width at the guide entry\n",
-      "\u001b[4m\u001b[1mh1\u001b[0m\u001b[0m [m] // Height at the guide entry\n",
-      "\u001b[1mw2\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Width at the guide exit. If 0, use w1.\n",
-      "\u001b[1mh2\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Height at the guide exit. If 0, use h1.\n",
-      "\u001b[4m\u001b[1ml\u001b[0m\u001b[0m [m] // length of guide\n",
-      "\u001b[1mR0\u001b[0m = \u001b[1m\u001b[94m0.995\u001b[0m\u001b[0m [1] // Low-angle reflectivity\n",
-      "\u001b[1mQc\u001b[0m = \u001b[1m\u001b[94m0.0218\u001b[0m\u001b[0m [AA-1] // Critical scattering vector\n",
-      "\u001b[1malpha\u001b[0m = \u001b[1m\u001b[94m4.38\u001b[0m\u001b[0m [AA] // Slope of reflectivity\n",
-      "\u001b[1mm\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // m-value of material. Zero means completely absorbing. m=0.65  glass/SiO2 Si   Ni  Ni58  supermirror Be    Diamond m=  0.65       0.47 1   1.18  2-6         1.01  1.12 for glass/SiO2, m=1 for Ni, 1.2 for Ni58, m=2-6 for supermirror. m=0.47 for Si\n",
-      "\u001b[1mW\u001b[0m = \u001b[1m\u001b[94m0.003\u001b[0m\u001b[0m [AA-1] // Width of supermirror cut-off\n",
-      "\u001b[1mnslit\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1] // Number of vertical channels in the guide (>= 1) (nslit-1 vertical dividing walls).\n",
-      "\u001b[1md\u001b[0m = \u001b[1m\u001b[94m0.0005\u001b[0m\u001b[0m [m] // Thickness of subdividing walls\n",
-      "\u001b[1mmleft\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // m-value of material for left.   vert. mirror\n",
-      "\u001b[1mmright\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // m-value of material for right.  vert. mirror\n",
-      "\u001b[1mmtop\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // m-value of material for top.    horz. mirror\n",
-      "\u001b[1mmbottom\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // m-value of material for bottom. horz. mirror\n",
-      "\u001b[1mnhslit\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1] // Number of horizontal channels in the guide (>= 1). (nhslit-1 horizontal dividing walls). this enables to have nslit*nhslit rectangular channels\n",
-      "\u001b[1mG\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m/s2] // Gravitation norm. 0 value disables G effects.\n",
-      "\u001b[1maleft\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // alpha-value of left  vert. mirror\n",
-      "\u001b[1maright\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // alpha-value of right vert. mirror\n",
-      "\u001b[1matop\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // alpha-value of top   horz. mirror\n",
-      "\u001b[1mabottom\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // alpha-value of left  horz. mirror\n",
-      "\u001b[1mwavy\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // Global guide waviness\n",
-      "\u001b[1mwavy_z\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // Partial waviness along propagation axis\n",
-      "\u001b[1mwavy_tb\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // Partial waviness in transverse direction for top/bottom mirrors\n",
-      "\u001b[1mwavy_lr\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // Partial waviness in transverse direction for left/right mirrors\n",
-      "\u001b[1mchamfers\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Global chamfers specifications (in/out/mirror sides).\n",
-      "\u001b[1mchamfers_z\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Input and output chamfers\n",
-      "\u001b[1mchamfers_lr\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Chamfers on left/right mirror sides\n",
-      "\u001b[1mchamfers_tb\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Chamfers on top/bottom mirror sides\n",
-      "\u001b[1mnelements\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1] // Number of sections in the guide (length l/nelements).\n",
-      "\u001b[1mnu\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [Hz] // Rotation frequency (round/s) for Fermi Chopper approximation\n",
-      "\u001b[1mphase\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // Phase shift for the Fermi Chopper approximation\n",
-      "\u001b[1mreflect\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [str] // Reflectivity file name. Format <q(Angs-1) R(0-1)>\n",
-      "-------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "guide.show_parameters() # Lets view the parameters available in our guide component"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "guide.set_parameters({\"w1\" : 0.05, \"w2\" : 0.05, \"h1\" : 0.05, \"h2\" : 0.05, \"l\" : 8, \"m\" : 3.5, \"G\" : -9.2})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT Guide = Guide_gravity\n",
-      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mw2\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mh2\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92m8\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92m3.5\u001b[0m\u001b[0m [1]\n",
-      "  \u001b[1mG\u001b[0m = \u001b[1m\u001b[92m-9.2\u001b[0m\u001b[0m [m/s2]\n",
-      "AT [0, 0, 2] RELATIVE Source\n",
-      "ROTATED [0, 0, 0] RELATIVE Source\n"
-     ]
-    }
-   ],
-   "source": [
-    "guide.print_long() # Verify the information on this component is correct"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample = instr.add_component(\"sample\", \"PowderN\", AT=[0,0,9], RELATIVE=\"Guide\") # Add a sample"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample.radius = 0.015; sample.yheight = 0.05; sample.reflections = \"\\\"Cu.laz\\\"\" # A small copper cylinder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are all components in the monitors category.\n",
-      " Brilliance_monitor      Monitor           PSD_monitor_psf_eff   TOF2E_monitor\n",
-      " DivLambda_monitor       Monitor_4PI       PSDcyl_monitor        TOF2Q_cylPSD_monitor\n",
-      " DivPos_monitor          Monitor_Sqw       PSDlin_diff_monitor   TOFLambda_monitor\n",
-      " Divergence_monitor      Monitor_nD        PSDlin_monitor        TOF_PSD_monitor_rad\n",
-      " EPSD_monitor            PSD_TOF_monitor   PolLambda_monitor     TOF_cylPSD_monitor\n",
-      " E_monitor               PSD_monitor       Pol_monitor           TOF_monitor\n",
-      " Hdiv_monitor            PSD_monitor_4PI   PreMonitor_nD         TOFlog_monitor\n",
-      " L_monitor               PSD_monitor_TOF   Res_monitor           \n",
-      " MeanPolLambda_monitor   PSD_monitor_psf   Sqq_w_monitor         \n"
-     ]
-    }
-   ],
-   "source": [
-    "instr.show_components(\"monitors\") # Monitors are needed to record information"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sphere = instr.add_component(\"PSD_4PI\", \"PSD_monitor_4PI\", RELATIVE=\"sample\") # Add 4PI sphere detector"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT PSD_4PI = PSD_monitor_4PI\n",
-      "  \u001b[1mnx\u001b[0m = \u001b[1m\u001b[92m300\u001b[0m\u001b[0m [1]\n",
-      "  \u001b[1mny\u001b[0m = \u001b[1m\u001b[92m300\u001b[0m\u001b[0m [1]\n",
-      "  \u001b[1mfilename\u001b[0m = \u001b[1m\u001b[92m\"PSD_4PI.dat\"\u001b[0m\u001b[0m [string]\n",
-      "  \u001b[1mradius\u001b[0m = \u001b[1m\u001b[92m1\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mrestore_neutron\u001b[0m = \u001b[1m\u001b[92m1\u001b[0m\u001b[0m [1]\n",
-      "AT [0, 0, 0] RELATIVE sample\n",
-      "ROTATED [0, 0, 0] RELATIVE sample\n"
-     ]
-    }
-   ],
-   "source": [
-    "sphere.nx = 300; sphere.ny = 300; sphere.filename = \"\\\"PSD_4PI.dat\\\"\"; sphere.radius = 1; sphere.restore_neutron = 1;\n",
-    "sphere.print_long() # Verify that monitors have filenames that are strings when printed, double quotes needed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PSD = instr.add_component(\"PSD\", \"PSD_monitor\", AT=[0,0,1], RELATIVE=\"sample\") # Add position sensitive detector\n",
-    "PSD.xwidth = 0.1; PSD.yheight = 0.1; PSD.nx = 200; PSD.ny = 200; PSD.filename = \"\\\"PSD.dat\\\"\"; PSD.restore_neutron = 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "L_mon = instr.add_component(\"L_mon\", \"L_monitor\", RELATIVE=\"PSD\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "L_mon.Lmin = \"wavelength - 0.3\"; L_mon.Lmax = \"wavelength + 0.3\"; L_mon.nL = 150\n",
-    "L_mon.xwidth = 0.1; L_mon.yheight = 0.1\n",
-    "L_mon.filename = \"\\\"L_mon.dat\\\"\"; L_mon.restore_neutron = 1\n",
-    "L_mon.comment = \"Wavelength monitor for narrow range\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "// Wavelength monitor for narrow range\n",
-      "COMPONENT L_mon = L_monitor\n",
-      "  \u001b[1mnL\u001b[0m = \u001b[1m\u001b[92m150\u001b[0m\u001b[0m [1]\n",
-      "  \u001b[1mfilename\u001b[0m = \u001b[1m\u001b[92m\"L_mon.dat\"\u001b[0m\u001b[0m [string]\n",
-      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mLmin\u001b[0m = \u001b[1m\u001b[92mwavelength - 0.3\u001b[0m\u001b[0m [AA]\n",
-      "  \u001b[1mLmax\u001b[0m = \u001b[1m\u001b[92mwavelength + 0.3\u001b[0m\u001b[0m [AA]\n",
-      "  \u001b[1mrestore_neutron\u001b[0m = \u001b[1m\u001b[92m1\u001b[0m\u001b[0m [1]\n",
-      "AT [0, 0, 0] RELATIVE PSD\n",
-      "ROTATED [0, 0, 0] RELATIVE PSD\n"
-     ]
-    }
-   ],
-   "source": [
-    "L_mon.print_long()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Source    Source_simple     AT  [0, 0, 0]     ABSOLUTE          ROTATED  [0, 0, 0]     ABSOLUTE\n",
-      "Guide     Guide_gravity     AT  [0, 0, 2]     RELATIVE Source   ROTATED  [0, 0, 0]     RELATIVE Source\n",
-      "sample    PowderN           AT  [0, 0, 9]     RELATIVE Guide    ROTATED  [0, 0, 0]     RELATIVE Guide\n",
-      "PSD_4PI   PSD_monitor_4PI   AT  [0, 0, 0]     RELATIVE sample   ROTATED  [0, 0, 0]     RELATIVE sample\n",
-      "PSD       PSD_monitor       AT  [0, 0, 1]     RELATIVE sample   ROTATED  [0, 0, 0]     RELATIVE sample\n",
-      "L_mon     L_monitor         AT  [0, 0, 0]     RELATIVE PSD      ROTATED  [0, 0, 0]     RELATIVE PSD\n"
-     ]
-    }
-   ],
-   "source": [
-    "instr.print_components() # Lets get an overview of the instrument so far"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Running the McStas instrument\n",
-    "Now we have assembled an instrument and it is time to perform a simulation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# If the folder already exsits, a new simulation is not performed but the old one is read\n",
-    "data = instr.run_full_instrument(foldername=\"jupyter_demo\",\n",
-    "                                 parameters={\"wavelength\" : 1.0},\n",
-    "                                 mpi=4,\n",
-    "                                 ncount=2E7)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The returned data object is a list of McStasData objects, each containing the results from a monitor.\n",
-    "These data objects also contain preferences for how they should be plotted if this is done automatically."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "number of elements in data list = 3\n",
-      "Plotting data with name PSD_4PI\n",
-      "Plotting data with name PSD\n",
-      "Plotting data with name L_mon\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 6 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "McStasScript.name_plot_options(\"PSD_4PI\", data, log=1, colormap=\"hot\", orders_of_mag=5) # Adjusting PSD_4PI plot\n",
-    "plot = McStasScript.make_sub_plot(data) # Making subplot of our monitors"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/demonstration.py b/demonstration.py
deleted file mode 100644
index 1b47bab7..00000000
--- a/demonstration.py
+++ /dev/null
@@ -1,100 +0,0 @@
-# Demonstration of McStasScript, an API for creating and running McStas instruments from python scripts
-# Written by Mads Bertelsen, ESS DMSC
-import random
-import sys
-#sys.path.append('/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript')
-import McStasScript # McStasScript classes
-
-# if the mcrun command from McStas is not in your path, provide absolute path for the binary here:
-mcrun_path = ""
-#mcrun_path = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin"
-mcstas_path = ""
-#mcstas_path = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/"
-
-# Create a McStas instrument
-instr = McStasScript.McStas_instr("random_demo",
-                                  author="Mads Bertelsen",
-                                  origin="ESS DMSC",
-                                  mcrun_path = mcrun_path,
-                                  mcstas_path = mcstas_path)
-
-# Set up a material called Cu with approrpiate properties (uses McStas Union components, here the processes)
-instr.add_component("Cu_incoherent","Incoherent_process")
-instr.set_component_parameter("Cu_incoherent",{"sigma" : 4*0.55, "packing_factor" : 1, "unit_cell_volume" :  55.4})
-
-instr.add_component("Cu_powder","Powder_process")
-instr.set_component_parameter("Cu_powder",{"reflections" : "\"Cu.laz\""})
-
-instr.add_component("Cu","Union_make_material")
-instr.set_component_parameter("Cu",{"my_absorption" : "100*4*3.78/55.4", "process_string" : "\"Cu_incoherent,Cu_powder\""})
-
-# Set neutron source
-instr.add_component("source","Source_div",AT=[0,0,0])
-instr.add_parameter("double","energy",value=10,comment="[meV] source energy") # Add parameter to select energy at run time
-instr.set_component_parameter("source",{"xwidth" : 0.12, "yheight" : 0.12, "focus_aw" : 0.1, "focus_ah" : 0.1, "E0" : "energy", "dE" : 0, "flux" : 1E13})
-
-# List of available materials, Vacuum is provided by the system
-material_name_list = ["Cu", "Vacuum"]
-
-# Wish to set up a number of randomly sized and placed boxes, here we choose the number
-number_of_volumes = random.randint(30,40)
-
-# Initialize the priority that needs to be unique for each volume
-current_priority = 99
-for volume in range(number_of_volumes):
-
-    current_priority = current_priority + 1 # update the priority
-    max_side_length = 0.04
-    max_depth = 0.003
-    position = [random.uniform(-0.05,0.05), random.uniform(-0.05,0.05), 1+random.uniform(-0.05,0.05)] # Set position in 10x10x10 cm^3 box 1 m from source
-    rotation = [random.uniform(0,360), random.uniform(0,360), random.uniform(0,360)] # random rotation
-    
-    # Choose a random material from the list of available materials
-    volume_material = random.choice(material_name_list)
-
-    # Add a McStas Union geometry with unique name
-    instr.add_component("volume_" + str(volume),"Union_box")
-    instr.set_component_parameter("volume_" + str(volume),{"xwidth" : random.uniform(0.01,max_side_length),"yheight" : random.uniform(0.01,max_side_length),"zdepth" : random.uniform(0.001,max_depth),})
-    instr.set_component_parameter("volume_" + str(volume),{"material_string" : "\""+volume_material+"\"", "priority" : current_priority, "p_interact" : 0.3})
-    instr.set_component_AT("volume_" + str(volume),position,RELATIVE="ABSOLUTE")
-    instr.set_component_ROTATED("volume_" + str(volume),rotation,RELATIVE="ABSOLUTE")
-
-
-# A few Union loggers are set up for display of the scattering locations
-instr.add_component("logger_space_zx_all","Union_logger_2D_space")
-current_component = instr.get_last_component()
-current_component.set_parameters({"filename" : "\"space_zx.dat\"",})
-current_component.set_parameters({"n1" : 1000, "D_direction_1" : "\"z\"", "D1_min" : -0.05, "D1_max" : 0.05})
-current_component.set_parameters({"n2" : 1000, "D_direction_2" : "\"x\"", "D2_min" : -0.05, "D2_max" : 0.05})
-current_component.set_AT([0,0,1])
-
-instr.add_component("logger_space_zy_all","Union_logger_2D_space")
-current_component = instr.get_last_component()
-current_component.set_parameters({"filename" : "\"space_zy.dat\"",})
-current_component.set_parameters({"n1" : 1000, "D_direction_1" : "\"z\"", "D1_min" : -0.05, "D1_max" : 0.05})
-current_component.set_parameters({"n2" : 1000, "D_direction_2" : "\"y\"", "D2_min" : -0.05, "D2_max" : 0.05})
-current_component.set_AT([0,0,1])
-
-# Union master component that executes the simulation of the random boxes
-instr.add_component("random_boxes","Union_master")
-
-# McStas monitors for viewing the beam after the random boxes
-instr.add_component("detector","PSD_monitor",AT=[0,0,2])
-instr.set_component_parameter("detector",{"xwidth" : 0.10, "yheight" : 0.10, "nx" : 500, "ny" : 500, "filename" : "\"PSD.dat\"", "restore_neutron" : 1})
-
-instr.add_component("large_detector","PSD_monitor",AT=[0,0,2])
-instr.set_component_parameter("large_detector",{"xwidth" : 1.0, "yheight" : 1.0, "nx" : 500, "ny" : 500, "filename" : "\"large_PSD.dat\"", "restore_neutron" : 1})
-
-# Run the McStas simulation, a unique foldername is required for each run
-data = instr.run_full_instrument(foldername="demonstration",parameters={"energy":600},mpi=2,ncount=5E7)
-
-# Set plotting options for the data (optional)
-McStasScript.name_plot_options("logger_space_zx_all",data,log=1,orders_of_mag=3)
-McStasScript.name_plot_options("logger_space_zy_all",data,log=1,orders_of_mag=3)
-McStasScript.name_plot_options("detector",data,log=1,colormap="hot",orders_of_mag=0.5)
-McStasScript.name_plot_options("large_detector",data,log=1,orders_of_mag=8)
-
-# Plot the resulting data on a logarithmic scale
-plot = McStasScript.make_sub_plot(data)
-
-

From 9007461efb9576dca742a81ceea076ab729e8ecb Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Mon, 13 May 2019 15:27:23 +0200
Subject: [PATCH 015/403] Updates to the documentation.

Section on importing the packge.

Updated syntax to new package structure.

Syntax closer to PEP 8 style.
---
 McStasScript_documentation.pdf | Bin 153845 -> 155747 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
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From 7b190ef460e6d017f7647e0e7a8eae9b127f8484 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Thu, 16 May 2019 16:25:40 +0200
Subject: [PATCH 016/403] Added tests for large parts of the package.

The tests are in the tests package and use unittest.
All tests can be performed using:
  python3 -m unittest discover

The tests covers the data package and most of the helper package.
In the helper package only the formatting and a few methods of
the component class lacks tests.
The interface package is not covered yet.
---
 mcstasscript/data/data.py                     |  86 +-
 mcstasscript/helper/component_reader.py       |  18 +-
 mcstasscript/helper/managed_mcrun.py          |  34 +-
 mcstasscript/helper/mcstas_objects.py         |   8 +-
 mcstasscript/interface/functions.py           |   2 +-
 mcstasscript/interface/instr.py               |   3 +-
 mcstasscript/tests/__init__.py                |   0
 .../dummy_mcstas/contrib/test_for_empty.txt   |   0
 .../dummy_mcstas/misc/test_for_ignore.com     |   0
 .../misc/test_for_ignore_weird.comp.backup    |   0
 .../dummy_mcstas/misc/test_for_reading.comp   | 187 ++++
 .../dummy_mcstas/misc/test_for_structure.comp |   0
 .../sources/test_for_structure2.comp          |   0
 mcstasscript/tests/test_ComponentReader.py    | 455 +++++++++
 mcstasscript/tests/test_ManagedMcrun.py       | 356 +++++++
 mcstasscript/tests/test_McStasData.py         | 147 +++
 mcstasscript/tests/test_McStasMetaData.py     | 130 +++
 mcstasscript/tests/test_McStasPlotOptions.py  |  66 ++
 mcstasscript/tests/test_component.py          | 349 +++++++
 mcstasscript/tests/test_data_set/L_mon.dat    | 178 ++++
 mcstasscript/tests/test_data_set/PSD.dat      | 633 ++++++++++++
 mcstasscript/tests/test_data_set/PSD_4PI.dat  | 933 ++++++++++++++++++
 .../tests/test_data_set/jupyter_demo.instr    |  92 ++
 mcstasscript/tests/test_data_set/mccode.sim   |  90 ++
 mcstasscript/tests/test_declare_variable.py   | 185 ++++
 mcstasscript/tests/test_for_reading.comp      | 186 ++++
 mcstasscript/tests/test_parameter_variable.py | 181 ++++
 27 files changed, 4231 insertions(+), 88 deletions(-)
 create mode 100644 mcstasscript/tests/__init__.py
 create mode 100644 mcstasscript/tests/dummy_mcstas/contrib/test_for_empty.txt
 create mode 100644 mcstasscript/tests/dummy_mcstas/misc/test_for_ignore.com
 create mode 100644 mcstasscript/tests/dummy_mcstas/misc/test_for_ignore_weird.comp.backup
 create mode 100644 mcstasscript/tests/dummy_mcstas/misc/test_for_reading.comp
 create mode 100644 mcstasscript/tests/dummy_mcstas/misc/test_for_structure.comp
 create mode 100644 mcstasscript/tests/dummy_mcstas/sources/test_for_structure2.comp
 create mode 100644 mcstasscript/tests/test_ComponentReader.py
 create mode 100644 mcstasscript/tests/test_ManagedMcrun.py
 create mode 100644 mcstasscript/tests/test_McStasData.py
 create mode 100644 mcstasscript/tests/test_McStasMetaData.py
 create mode 100644 mcstasscript/tests/test_McStasPlotOptions.py
 create mode 100644 mcstasscript/tests/test_component.py
 create mode 100644 mcstasscript/tests/test_data_set/L_mon.dat
 create mode 100644 mcstasscript/tests/test_data_set/PSD.dat
 create mode 100644 mcstasscript/tests/test_data_set/PSD_4PI.dat
 create mode 100644 mcstasscript/tests/test_data_set/jupyter_demo.instr
 create mode 100644 mcstasscript/tests/test_data_set/mccode.sim
 create mode 100644 mcstasscript/tests/test_declare_variable.py
 create mode 100644 mcstasscript/tests/test_for_reading.comp
 create mode 100644 mcstasscript/tests/test_parameter_variable.py

diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index 4b561ca6..9ec685eb 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -62,16 +62,20 @@ def extract_info(self):
 
         # Extract dimension
         if "type" in self.info:
-            type = self.info["type"]
-            if "array_1d" in type:
-                self.dimension = int(type[9:-2])
-            if "array_2d" in type:
+            type_data = self.info["type"]
+            if "array_1d" in type_data:
+                type_data = type_data.split("(")[1]
+                type_data = type_data.split(")")[0]
+                self.dimension = int(type_data)
+            if "array_2d" in type_data:
                 self.dimension = []
-                type_strings = self.info["type"].split(",")
-                temp_str = type_strings[0]
-                self.dimension.append(int(temp_str[9:]))
-                temp_str = type_strings[1]
-                self.dimension.append(int(temp_str[1:-2]))
+                type_string1 = type_data.split(",")[0]
+                type_string1 = type_string1.split("(")[1]
+                self.dimension.append(int(type_string1))
+                
+                type_string2 = type_data.split(",")[1]
+                type_string2 = type_string2.split(")")[0]
+                self.dimension.append(int(type_string2))
         else:
             raise NameError("No type in mccode data section!")
 
@@ -261,7 +265,7 @@ def __init__(self, metadata, intensity, error, ncount, **kwargs):
             else:
                 raise NameError(
                     "ERROR: Initialization of McStasData done with 1d "
-                    + "data, but without xaxis" + self.name + "!")
+                    + "data, but without xaxis for " + self.name + "!")
 
         self.plot_options = McStasPlotOptions()
 
@@ -277,65 +281,3 @@ def set_title(self, string):
 
     def set_plot_options(self, **kwargs):
         self.plot_options.set_options(**kwargs)
-
-def name_search(name, data_list):
-    """"
-    name_search returns McStasData instance with specific name if it is
-    in the given data_list
-
-    The index of certain datasets in the data_list can change if
-    additional monitors are added so it is more convinient to access
-    the data files using their names.
-
-    Parameters
-    ----------
-    name : string
-        Name of the dataset to be retrived (component_name)
-
-    data_list : List of McStasData instances
-        List of datasets to search
-    """
-
-    if not type(data_list[0]) == McStasData:
-        raise InputError(
-            "name_search function needs objects of type "
-            + "McStasData as input.")
-
-    list_result = []
-    for check in data_list:
-        if check.metadata.component_name == name:
-            list_result.append(check)
-
-    if len(list_result) == 1:
-        return list_result[0]
-    else:
-        raise NameError("More than one match for the name search")
-
-def name_plot_options(name, data_list, **kwargs):
-    """"
-    name_plot_options passes keyword arguments to dataset with certain
-    name in given data list
-
-    Function for quickly setting plotting options on a certain dataset
-    n a larger list of datasets
-
-    Parameters
-    ----------
-    name : string
-        Name of the dataset to be modified (component_name)
-
-    data_list : List of McStasData instances
-        List of datasets to search
-
-    kwargs : keyword arguments
-        Keyword arguments passed to set_plot_options in
-        McStasPlotOptions
-    """
-
-    if not isinstance(data_list[0], McStasData):
-        raise InputError(
-            "name_search function needs objects of type McStasData "
-            + "as input.")
-
-    object_to_modify = name_search(name, data_list)
-    object_to_modify.set_plot_options(**kwargs)
\ No newline at end of file
diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index 968d8444..1f233f6c 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -170,7 +170,13 @@ def read_name(self, component_name):
                             + " in McStas installation or "
                             + "current work directory.")
 
-        return self.read_component_file(self.component_path[component_name])
+        output = self.read_component_file(self.component_path[component_name])
+        
+        # Category loaded using path, in case of Work directory it fails
+        if self.component_category[component_name] == "Work directory":
+            output.category = "Work directory"  # Corrects category
+        
+        return output
 
     def _find_components(self, absolute_path):
         """
@@ -316,6 +322,16 @@ def read_component_file(self, absolute_path):
                             name_value = part.split("=")
                             par_name = name_value[0].strip()
                             par_value = name_value[1].strip()
+                            
+                            if temp_par_type is "double":
+                                try:
+                                    par_value = float(par_value)
+                                except:
+                                    par_value = par_value
+                                    # Could change the type
+                            elif temp_par_type is "int":
+                                par_value = int(par_value)
+
                             result.parameter_names.append(par_name)
                             result.parameter_defaults[par_name] = par_value
                             result.parameter_types[par_name] = temp_par_type
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index a5cdf435..9ff5f177 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -2,6 +2,7 @@
 import numpy as np
 from mcstasscript.data.data import McStasMetaData
 from mcstasscript.data.data import McStasData
+from docutils.io import InputError
 
 class ManagedMcrun:
     """
@@ -68,13 +69,14 @@ def __init__(self, instr_name, **kwargs):
         self.name_of_instrumentfile = instr_name
 
         self.data_folder_name = ""
-        self.ncount = 1E6
+        self.ncount = int(1E6)
         self.mpi = 1
         self.parameters = {}
         self.custom_flags = ""
         self.mcrun_path = ""
         # mcrun_path always in kwargs
-        self.mcrun_path = kwargs["mcrun_path"]
+        if "mcrun_path" in kwargs:
+            self.mcrun_path = kwargs["mcrun_path"]
 
         if "foldername" in kwargs:
             self.data_folder_name = kwargs["foldername"]
@@ -84,7 +86,7 @@ def __init__(self, instr_name, **kwargs):
                 + "with keyword argument.")
 
         if "ncount" in kwargs:
-            self.ncount = kwargs["ncount"]
+            self.ncount = int(kwargs["ncount"])
 
         if "mpi" in kwargs:
             self.mpi = kwargs["mpi"]
@@ -129,23 +131,37 @@ def run_simulation(self):
         os.system(mcrun_full_path + " "
                   + option_string + " "
                   + self.custom_flags + " "
-                  + self.name_of_instrumentfile + " "
+                  + self.name_of_instrumentfile
                   + parameter_string)
 
         """
         Can use subprocess from spawn* instead of os.system if more
         control is needed over the spawned process, including a timeout
         """
-
+        
+        #return self.load_results(self.data_folder_name)
+        
+    def load_results(self, *args):
+        
+        if len(args) == 0:
+            data_folder_name = self.data_folder_name
+        elif len(args) == 1:
+            data_folder_name = args[0]
+        else:
+            raise InputError("load_results can be caled with 0 or 1 arguments")
+        
+        if not os.path.isdir(data_folder_name):
+            raise NameError("Given data directory does not exist.")
+        
         # Find all data files in generated folder
-        files_in_folder = os.listdir(self.data_folder_name)
+        files_in_folder = os.listdir(data_folder_name)
 
         # Raise an error if mccode.sim is not available
         if "mccode.sim" not in files_in_folder:
-            raise NameError("mccode.sim not written to output folder.")
+            raise NameError("No mccode.sim in data folder.")
 
         # Open mccode to read metadata for all datasets written to disk
-        f = open(self.data_folder_name + "/mccode.sim", "r")
+        f = open(data_folder_name + "/mccode.sim", "r")
 
         # Loop that reads mccode.sim sections
         metadata_list = []
@@ -184,7 +200,7 @@ def run_simulation(self):
         # Load datasets described in metadata list individually
         for metadata in metadata_list:
             # Load data with numpy
-            data = np.loadtxt(self.data_folder_name
+            data = np.loadtxt(data_folder_name
                               + "/"
                               + metadata.filename.rstrip())
 
diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index cef75cc0..53db6645 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -364,7 +364,7 @@ def __init__(self, instance_name, component_name, **kwargs):
             self.ROTATED_data = [0, 0, 0]
         # Could check if ROTATED_RELATIVE is a string
         if "ROTATED_RELATIVE" in kwargs:
-            self.ROTATED_relative = kwargs["ROTATED_RELATIVE"]
+            self.ROTATED_relative = "RELATIVE " + kwargs["ROTATED_RELATIVE"]
         else:
             self.ROTATED_relative = "ABSOLUTE"
 
@@ -384,7 +384,7 @@ def __init__(self, instance_name, component_name, **kwargs):
             self.EXTEND = ""
 
         if "GROUP" in kwargs:
-            self.GROUP = kwargs["GRPUP"]
+            self.GROUP = kwargs["GROUP"]
         else:
             self.GROUP = ""
 
@@ -476,7 +476,7 @@ def set_parameters(self, dict_input):
 
     def set_WHEN(self, string):
         """Sets WHEN string, should be a c logical expression"""
-        self.WHEN = string
+        self.WHEN = "WHEN (" + string + ")\n"
 
     def set_GROUP(self, string):
         """Sets GROUP name"""
@@ -551,7 +551,7 @@ def write_component(self, fo):
 
         # Optional WHEN section
         if not self.WHEN == "":
-            fo.write("WHEN(%s)\n" % self.WHEN)
+            fo.write("%s" % self.WHEN)
 
         # Write AT and ROTATED section
         fo.write("AT (%s,%s,%s)" % (str(self.AT_data[0]),
diff --git a/mcstasscript/interface/functions.py b/mcstasscript/interface/functions.py
index a6ca195d..c7e757aa 100644
--- a/mcstasscript/interface/functions.py
+++ b/mcstasscript/interface/functions.py
@@ -60,4 +60,4 @@ def name_plot_options(name, data_list, **kwargs):
             + "as input.")
 
     object_to_modify = name_search(name, data_list)
-    object_to_modify.set_plot_options(**kwargs)
\ No newline at end of file
+    object_to_modify.set_plot_options(**kwargs)
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index f43104d6..690f7e19 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -975,6 +975,7 @@ def run_full_instrument(self, *args, **kwargs):
         simulation = ManagedMcrun(self.name + ".instr", **kwargs)
 
         # Run the simulation and return data
-        return simulation.run_simulation()
+        simulation.run_simulation()
+        return simulation.load_results()
     
 
diff --git a/mcstasscript/tests/__init__.py b/mcstasscript/tests/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/mcstasscript/tests/dummy_mcstas/contrib/test_for_empty.txt b/mcstasscript/tests/dummy_mcstas/contrib/test_for_empty.txt
new file mode 100644
index 00000000..e69de29b
diff --git a/mcstasscript/tests/dummy_mcstas/misc/test_for_ignore.com b/mcstasscript/tests/dummy_mcstas/misc/test_for_ignore.com
new file mode 100644
index 00000000..e69de29b
diff --git a/mcstasscript/tests/dummy_mcstas/misc/test_for_ignore_weird.comp.backup b/mcstasscript/tests/dummy_mcstas/misc/test_for_ignore_weird.comp.backup
new file mode 100644
index 00000000..e69de29b
diff --git a/mcstasscript/tests/dummy_mcstas/misc/test_for_reading.comp b/mcstasscript/tests/dummy_mcstas/misc/test_for_reading.comp
new file mode 100644
index 00000000..e9dd6c7a
--- /dev/null
+++ b/mcstasscript/tests/dummy_mcstas/misc/test_for_reading.comp
@@ -0,0 +1,187 @@
+/*******************************************************************************
+*
+* McStas, neutron ray-tracing package
+*         Copyright 1997-2002, All rights reserved
+*         Risoe National Laboratory, Roskilde, Denmark
+*         Institut Laue Langevin, Grenoble, France
+*
+* Component: Source_simple
+*
+* %I
+* Written by: Kim Lefmann
+* Date: October 30, 1997
+* Modified by: KL, October 4, 2001
+* Modified by: Emmanuel Farhi, October 30, 2001. Serious bug corrected.
+* Origin: Risoe
+*
+* A circular neutron source with flat energy spectrum and arbitrary flux
+*
+* %D
+* The routine is a circular neutron source, which aims at a square target
+* centered at the beam (in order to improve MC-acceptance rate).  The angular
+* divergence is then given by the dimensions of the target.
+* The neutron energy is uniformly distributed between lambda0-dlambda and
+* lambda0+dlambda or between E0-dE and E0+dE.
+* The flux unit is specified in n/cm2/s/st/energy unit (meV or Angs).
+*
+* This component replaces Source_flat, Source_flat_lambda,
+* Source_flux and Source_flux_lambda.
+*
+* Example: Source_simple(radius=0.1, dist=2, focus_xw=.1, focus_yh=.1, E0=14, dE=2)
+*
+* %P
+* radius: [m]                         Radius of circle in (x,y,0) plane where neutrons are generated.
+* yheight: [m]                        Height of rectangle in (x,y,0) plane where neutrons are generated.
+* xwidth: [m]                         Width of rectangle in (x,y,0) plane where neutrons are generated.
+* target_index: [1]                   relative index of component to focus at, e.g. next is +1 this is used to compute 'dist' automatically.
+* dist: [m]                           Distance to target along z axis.
+* focus_xw: [m]                       Width of target
+* focus_yh: [m]                       Height of target
+* E0: [meV]                           Mean energy of neutrons.
+* dE: [meV]                           Energy half spread of neutrons (flat or gaussian sigma).
+* lambda0: [AA]                       Mean wavelength of neutrons.
+* dlambda: [AA]                       Wavelength half spread of neutrons.
+* flux: [1/(s*cm**2*st*energy unit)]  flux per energy unit, Angs or meV if flux=0, the source emits 1 in 4*PI whole space.
+* gauss: [1]                          Gaussian (1) or Flat (0) energy/wavelength distribution
+*
+* %E
+*******************************************************************************/
+
+DEFINE COMPONENT Source_simple
+DEFINITION PARAMETERS ()
+SETTING PARAMETERS (radius=0.1, yheight=0, xwidth=0,
+dist=0, focus_xw=.045, focus_yh=.12,
+E0=0, dE=0, lambda0=0, dlambda=0,
+flux=1, gauss=0, int target_index=+1)
+OUTPUT PARAMETERS (pmul,square,srcArea)
+/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
+DECLARE
+%{
+double pmul, srcArea;
+int square;
+double tx,ty,tz;
+%}
+INITIALIZE
+%{
+square = 0;
+/* Determine source area */
+if (radius && !yheight && !xwidth ) {
+    square = 0;
+    srcArea = PI*radius*radius;
+  } else if(yheight && xwidth) {
+    square = 1;
+    srcArea = xwidth * yheight;
+  }
+
+  if (flux) {
+    pmul=flux*1e4*srcArea/mcget_ncount();
+    if (dlambda)
+      pmul *= 2*dlambda;
+    else if (dE)
+      pmul *= 2*dE;
+  } else {
+    gauss = 0;
+    pmul=1.0/(mcget_ncount()*4*PI);
+  }
+
+  if (target_index && !dist)
+  {
+    Coords ToTarget;
+    ToTarget = coords_sub(POS_A_COMP_INDEX(INDEX_CURRENT_COMP+target_index),POS_A_CURRENT_COMP);
+    ToTarget = rot_apply(ROT_A_CURRENT_COMP, ToTarget);
+    coords_get(ToTarget, &tx, &ty, &tz);
+    dist=sqrt(tx*tx+ty*ty+tz*tz);
+  } else if (dist) {
+    tx = 0;
+    ty = 0;
+    tz = dist;
+  }
+
+
+  if (srcArea <= 0) {
+    printf("Source_simple: %s: Source area is <= 0 !\n ERROR - Exiting\n",
+           NAME_CURRENT_COMP);
+    exit(0);
+  }
+  if (dist <= 0 || focus_xw <= 0 || focus_yh <= 0) {
+    printf("Source_simple: %s: Target area unmeaningful! (negative dist / focus_xw / focus_yh)\n ERROR - Exiting\n",
+           NAME_CURRENT_COMP);
+    exit(0);
+  }
+
+  if ((!lambda0 && !E0 && !dE && !dlambda)) {
+    printf("Source_simple: %s: You must specify either a wavelength or energy range!\n ERROR - Exiting\n",
+           NAME_CURRENT_COMP);
+    exit(0);
+  }
+  if ((!lambda0 && !dlambda && (E0 <= 0 || dE < 0 || E0-dE <= 0))
+    || (!E0 && !dE && (lambda0 <= 0 || dlambda < 0 || lambda0-dlambda <= 0))) {
+    printf("Source_simple: %s: Unmeaningful definition of wavelength or energy range!\n ERROR - Exiting\n",
+           NAME_CURRENT_COMP);
+      exit(0);
+  }
+%}
+TRACE
+%{
+ double chi,E,lambda,v,r, xf, yf, rf, dx, dy, pdir;
+
+ t=0;
+ z=0;
+
+ if (square == 1) {
+   x = xwidth * (rand01() - 0.5);
+   y = yheight * (rand01() - 0.5);
+ } else {
+   chi=2*PI*rand01();                          /* Choose point on source */
+   r=sqrt(rand01())*radius;                    /* with uniform distribution. */
+   x=r*cos(chi);
+   y=r*sin(chi);
+ }
+ randvec_target_rect_real(&xf, &yf, &rf, &pdir,
+			  tx, ty, tz, focus_xw, focus_yh, ROT_A_CURRENT_COMP, x, y, z, 2);
+
+ dx = xf-x;
+ dy = yf-y;
+ rf = sqrt(dx*dx+dy*dy+rf*rf);
+
+ p = pdir*pmul;
+
+ if(lambda0==0) {
+   if (!gauss) {
+     E=E0+dE*randpm1();              /*  Choose from uniform distribution */
+   } else {
+     E=E0+randnorm()*dE;
+   }
+   v=sqrt(E)*SE2V;
+ } else {
+   if (!gauss) {
+     lambda=lambda0+dlambda*randpm1();
+   } else {
+     lambda=lambda0+randnorm()*dlambda;
+   }
+   v = K2V*(2*PI/lambda);
+ }
+
+ vz=v*dist/rf;
+ vy=v*dy/rf;
+ vx=v*dx/rf;
+%}
+
+MCDISPLAY
+%{
+  if (square == 1) {
+    
+    rectangle("xy",0,0,0,xwidth,yheight);
+  } else {
+    
+    circle("xy",0,0,0,radius);
+  }
+  if (dist) {
+    dashed_line(0,0,0, -focus_xw/2+tx,-focus_yh/2+ty,tz, 4);
+    dashed_line(0,0,0,  focus_xw/2+tx,-focus_yh/2+ty,tz, 4);
+    dashed_line(0,0,0,  focus_xw/2+tx, focus_yh/2+ty,tz, 4);
+    dashed_line(0,0,0, -focus_xw/2+tx, focus_yh/2+ty,tz, 4);
+  }
+%}
+
+END
diff --git a/mcstasscript/tests/dummy_mcstas/misc/test_for_structure.comp b/mcstasscript/tests/dummy_mcstas/misc/test_for_structure.comp
new file mode 100644
index 00000000..e69de29b
diff --git a/mcstasscript/tests/dummy_mcstas/sources/test_for_structure2.comp b/mcstasscript/tests/dummy_mcstas/sources/test_for_structure2.comp
new file mode 100644
index 00000000..e69de29b
diff --git a/mcstasscript/tests/test_ComponentReader.py b/mcstasscript/tests/test_ComponentReader.py
new file mode 100644
index 00000000..b53f1b1f
--- /dev/null
+++ b/mcstasscript/tests/test_ComponentReader.py
@@ -0,0 +1,455 @@
+import os
+import io
+import unittest
+import unittest.mock
+
+from mcstasscript.helper.component_reader import ComponentInfo
+from mcstasscript.helper.component_reader import ComponentReader
+
+def setup_component_reader():
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+    dummy_path = THIS_DIR + "/dummy_mcstas"
+        
+    current_work_dir = os.getcwd()
+    os.chdir(THIS_DIR)  # Set work directory to test folder
+               
+    component_reader = ComponentReader(mcstas_path = dummy_path)
+        
+    os.chdir(current_work_dir)  # Reset work directory
+    
+    return component_reader
+
+class TestComponentReader(unittest.TestCase):
+    """
+    Testing the ComponenReader class. As this class reads information
+    from McStas, a dummy McStas install is made in the test folder to
+    avoid the test results changeing with updates of McStas.
+    """
+
+
+    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    def test_ComponentReader_init_overwrite_message(self, mock_stdout):
+        """
+        Test that ComponentReader reports overwritten components
+        """
+        
+        component_reader = setup_component_reader()
+        
+        message = ("Overwriting McStasScript info on component named " 
+                   + "test_for_reading.comp because the component is in "
+                   + "the work directory.\n")
+        
+        self.assertEqual(mock_stdout.getvalue(), message)
+        
+    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    def test_ComponentReader_init_filenames(self, mock_stdout):
+        """
+        Test that ComponentReader initializes component names correctly
+        """
+        
+        component_reader = setup_component_reader()
+
+        n_components_found = len(component_reader.component_path)
+        self.assertEqual(n_components_found, 3)
+        self.assertIn("test_for_reading", component_reader.component_path)
+        self.assertIn("test_for_structure", component_reader.component_path)
+        self.assertIn("test_for_structure2", component_reader.component_path)
+
+    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    def test_ComponentReader_init_categories(self, mock_stdout):
+        """
+        Test that ComponentReader initializes categories correctly
+        """
+        
+        component_reader = setup_component_reader()
+        
+        n_categories_found = len(component_reader.component_category)
+        self.assertEqual(n_categories_found, 3)
+        """
+        Categories stored in a dict, so n_categories is the same as the
+        number of components read. Here it happens to be the number of
+        categories as well because of the dummy installation.
+        """
+        category = component_reader.component_category["test_for_reading"]
+        self.assertEqual(category, "Work directory")
+        category = component_reader.component_category["test_for_structure"]
+        self.assertEqual(category, "misc")
+        category = component_reader.component_category["test_for_structure2"]
+        self.assertEqual(category, "sources")
+
+    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    def test_ComponentReader_show_categories(self, mock_stdout):
+        """
+        This method prints to console, check it prints the categories
+        in the dummy installation correctly.
+        
+        """
+        component_reader = setup_component_reader()
+        
+        component_reader.show_categories()
+        
+        output = mock_stdout.getvalue()
+        output = output.split("\n")
+        
+        self.assertEqual(len(output), 5)
+        self.assertIn(" sources", output)
+        self.assertIn(" Work directory", output)
+        self.assertIn(" misc", output)
+        
+    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    def test_ComponentReader_show_categories_ordered(self, mock_stdout):
+        """
+        Check that the print to console is ordered as usual. This test
+        may be implementation dependent as python dictionaries are not
+        ordered.
+        
+        """
+        
+        component_reader = setup_component_reader()
+        
+        component_reader.show_categories()
+        
+        output = mock_stdout.getvalue()
+        output = output.split("\n")
+        
+        self.assertEqual(output[1], " sources")
+        self.assertEqual(output[2], " Work directory")
+        self.assertEqual(output[3], " misc")
+        
+    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    def test_ComponentReader_show_components_short(self, mock_stdout):
+        """
+        Here we attempt to show components in the misc category.  In
+        the dummy install, there are two components in this folder, but
+        one of these is overwritten by the version in the current 
+        work directory.
+        
+        """
+        
+        component_reader = setup_component_reader()
+        
+        component_reader.show_components_in_category("misc")
+        
+        output = mock_stdout.getvalue()
+        output = output.split("\n")
+        
+        self.assertEqual(len(output), 3)
+        self.assertIn(" test_for_structure", output)
+        # Check overwritten component is not in the output
+        self.assertNotIn(" test_for_reading", output)
+        
+    """
+    # This test not as important, but could be finished later
+    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    def test_ComponentReader_show_components_long(self, mock_stdout):
+        
+        component_reader = setup_component_reader()
+        
+        #  Add elements directly to component_readers library
+        # generate list
+        # add list
+        
+        #component_reader.component_category[]
+        
+        component_reader.show_components_in_category("misc")
+        
+        output = mock_stdout.getvalue()
+        output = output.split("\n")
+        
+        self.assertEqual(len(output), 3)
+        self.assertIn(" test_for_structure", output)
+    """    
+    
+    # test load_all_components
+    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    def test_ComponentReader_load_all_components(self, mock_stdout):
+        """
+        Load all components in the dummy install, but only one has any
+        content.  The method is currently not necessary, as components
+        are now loaded individually when needed.
+        
+        """
+
+        component_reader = setup_component_reader()
+        
+        CompInfo_dict = component_reader.load_all_components()
+        
+        comp_name = "test_for_reading"
+        name = CompInfo_dict[comp_name].name
+        self.assertEqual(name, comp_name)
+
+        parameter_names = CompInfo_dict[comp_name].parameter_names
+        self.assertIn("target_index", parameter_names)
+        parameter_types = CompInfo_dict[comp_name].parameter_types
+        self.assertIn("target_index", parameter_types)
+        parameter_defaults = CompInfo_dict[comp_name].parameter_defaults
+        self.assertIn("target_index", parameter_defaults)
+
+        type_str = CompInfo_dict[comp_name].parameter_types["target_index"]
+        self.assertEqual(type_str, "int")
+
+        comp_name = "test_for_structure"
+        name = CompInfo_dict[comp_name].name
+        self.assertEqual(name, comp_name)
+        # test_for_structure is an empty file, so no conentet to check
+
+        comp_name = "test_for_structure2"
+        name = CompInfo_dict[comp_name].name
+        self.assertEqual(name, comp_name)
+        # test_for_structure2 is an empty file, so no conentet to check
+
+    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    def test_ComponentReader_read_name_error(self, mock_stdout):
+        """
+        read_name should throw an error when searching for a component
+        that is not present in the installation.
+        """
+
+        component_reader = setup_component_reader()
+
+        with self.assertRaises(NameError):
+            CompInfo = component_reader.read_name("no_such_comp")
+
+    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    def test_ComponentReader_read_name_success(self, mock_stdout):
+        """
+        Read component simply calls read_component_file, but here
+        the output is checked against what is in the dummy file. 
+        
+        """
+
+        component_reader = setup_component_reader()
+
+        CompInfo = component_reader.read_name("test_for_reading")
+
+        self.assertEqual(CompInfo.name, "test_for_reading")
+        self.assertEqual(CompInfo.category, "Work directory")
+        self.assertIn("dist", CompInfo.parameter_names)
+        self.assertIn("dist", CompInfo.parameter_defaults)
+        self.assertIn("dist", CompInfo.parameter_types)
+        self.assertEqual(CompInfo.parameter_types["dist"], "double")
+        self.assertIn("dist", CompInfo.parameter_comments)
+        self.assertIn("dist", CompInfo.parameter_units)
+        self.assertEqual(CompInfo.parameter_units["dist"], "m")
+           
+    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    def test_ComponentReader_find_components_names(self, mock_stdout):
+        """
+        Test that ComponentReader initializes component names correctly
+        """
+        
+        component_reader = setup_component_reader()
+        
+        component_reader.component_path = {}
+        component_reader.component_category = {}
+        
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        dummy_path = THIS_DIR + "/dummy_mcstas/misc"
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+        component_reader._find_components(dummy_path)       
+        os.chdir(current_work_dir) # Return to original work directory
+        
+        n_components_found = len(component_reader.component_path)
+        self.assertEqual(n_components_found, 2)
+        self.assertIn("test_for_reading", component_reader.component_path)
+        self.assertIn("test_for_structure", component_reader.component_path)
+
+    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    def test_ComponentReader_find_components_categories(self, mock_stdout):
+        """
+        Test that ComponentReader initializes component categories correctly
+        """
+        
+        component_reader = setup_component_reader()
+        
+        component_reader.component_path = {}
+        component_reader.component_category = {}
+        
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        dummy_path = THIS_DIR + "/dummy_mcstas/misc"
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+        component_reader._find_components(dummy_path)       
+        os.chdir(current_work_dir) # Return to original work directory
+        
+        n_categories_found = len(component_reader.component_category)
+        self.assertEqual(n_categories_found, 2)
+        
+        category = component_reader.component_category["test_for_reading"]
+        self.assertEqual(category, "misc")
+        category = component_reader.component_category["test_for_structure"]
+        self.assertEqual(category, "misc")
+        
+    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    def test_ComponentReader_read_component_category(self, mock_stdout):
+        """
+        Check that the correct category is returned.
+        
+        Can't run this test with overwritten component test_for_reading.
+        read_component will report tests as category, but this is 
+        overwritten by read_name in normal use.
+        """
+        component_reader = setup_component_reader()
+
+        path_for_test = component_reader.component_path["test_for_structure"]
+        CompInfo = component_reader.read_component_file(path_for_test)
+
+        exp_cat = component_reader.component_category["test_for_structure"]
+        self.assertEqual(CompInfo.category, exp_cat)
+
+    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    def test_ComponentReader_read_component_standard(self, mock_stdout):
+        """
+        Test that a normal parameter is read correctly when reading a 
+        component file.
+        Has default, is double type, has comment, has unit
+        """
+
+        component_reader = setup_component_reader()
+
+        path_for_test = component_reader.component_path["test_for_reading"]
+        CompInfo = component_reader.read_component_file(path_for_test)
+
+        self.assertIn("xwidth", CompInfo.parameter_names)
+
+        self.assertIn("xwidth", CompInfo.parameter_defaults)
+        self.assertEqual(CompInfo.parameter_defaults["xwidth"], 0.0)
+
+        self.assertIn("xwidth", CompInfo.parameter_types)
+        self.assertEqual(CompInfo.parameter_types["xwidth"],"double")
+
+        self.assertIn("xwidth", CompInfo.parameter_comments)
+        comment = "Width of rectangle test comment"
+        self.assertEqual(CompInfo.parameter_comments["xwidth"],comment)
+
+        self.assertIn("xwidth", CompInfo.parameter_units)
+        self.assertEqual(CompInfo.parameter_units["xwidth"],"m")
+
+    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    def test_ComponentReader_read_component_required(self, mock_stdout):
+        """
+        Test that a required parameter is read correctly when reading a 
+        component file.
+        Has no default, is double type, has no comment, has no unit
+        """
+
+        component_reader = setup_component_reader()
+
+        path_for_test = component_reader.component_path["test_for_reading"]
+        CompInfo = component_reader.read_component_file(path_for_test)
+
+        self.assertIn("gauss", CompInfo.parameter_names)
+
+        self.assertIn("gauss", CompInfo.parameter_defaults)
+        self.assertIsNone(CompInfo.parameter_defaults["gauss"])
+
+        self.assertIn("gauss", CompInfo.parameter_types)
+        self.assertEqual(CompInfo.parameter_types["gauss"],"double")
+
+        self.assertNotIn("gauss", CompInfo.parameter_comments)
+
+        self.assertNotIn("gauss", CompInfo.parameter_units)
+
+    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    def test_ComponentReader_read_component_int(self, mock_stdout):
+        """
+        Test that a integer parameter is read correctly when reading a 
+        component file.
+        Has default, is int type (comments and unit checked already)
+        """
+
+        component_reader = setup_component_reader()
+
+        path_for_test = component_reader.component_path["test_for_reading"]
+        CompInfo = component_reader.read_component_file(path_for_test)
+
+        self.assertIn("flux", CompInfo.parameter_names)
+
+        self.assertIn("flux", CompInfo.parameter_defaults)
+        self.assertEqual(CompInfo.parameter_defaults["flux"], 1)
+
+        self.assertIn("flux", CompInfo.parameter_types)
+        self.assertEqual(CompInfo.parameter_types["flux"],"int")
+
+        self.assertIn("flux", CompInfo.parameter_comments)
+        # Have already tested comments are read
+
+        self.assertIn("flux", CompInfo.parameter_units)
+        # Have already tested units are read
+
+    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    def test_ComponentReader_read_component_string(self, mock_stdout):
+        """
+        Test that a string parameter is read correctly when reading a 
+        component file.
+        Has no default, is string type (comments and unit checked already)
+        """
+
+        component_reader = setup_component_reader()
+
+        path_for_test = component_reader.component_path["test_for_reading"]
+        CompInfo = component_reader.read_component_file(path_for_test)
+
+        self.assertIn("test_string", CompInfo.parameter_names)
+
+        self.assertIn("test_string", CompInfo.parameter_defaults)
+        self.assertIsNone(CompInfo.parameter_defaults["test_string"])
+
+        self.assertIn("test_string", CompInfo.parameter_types)
+        self.assertEqual(CompInfo.parameter_types["test_string"],"string")
+
+        self.assertNotIn("test_string", CompInfo.parameter_comments)
+        # Have already tested comments are read
+
+        self.assertNotIn("test_string", CompInfo.parameter_units)
+        # Have already tested units are read
+
+    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    def test_ComponentReader_line_start_long(self, mock_stdout):
+        """
+        Helper function that should return true when certain string is
+        the start of another string.
+        
+        """
+
+        component_reader = setup_component_reader()
+
+        test_string = "monkey wants banana"
+
+        return_val = component_reader.line_starts_with(test_string,"mo")
+        self.assertIsInstance(return_val, bool)
+        self.assertTrue(return_val)
+
+        return_val = component_reader.line_starts_with(test_string,"on")
+        self.assertIsInstance(return_val, bool)
+        self.assertFalse(return_val)
+
+    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    def test_ComponentReader_line_start_short(self, mock_stdout):
+        """
+        Helper function that should return true when certain string is
+        the start of another string. Here checked with short test_string
+        
+        
+        """
+                
+        component_reader = setup_component_reader()
+        
+        test_string = "m"
+
+        return_val = component_reader.line_starts_with(test_string,"m")
+        self.assertIsInstance(return_val, bool)
+        self.assertTrue(return_val)
+
+        return_val = component_reader.line_starts_with(test_string,"mo")
+        self.assertIsInstance(return_val, bool)
+        self.assertFalse(return_val)
+
+        return_val = component_reader.line_starts_with(test_string,"on")
+        self.assertIsInstance(return_val, bool)
+        self.assertFalse(return_val)
+        
+    
+if __name__ == '__main__':
+    unittest.main()
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
new file mode 100644
index 00000000..82c57392
--- /dev/null
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -0,0 +1,356 @@
+import os
+import io
+import unittest
+import numpy as np
+
+from mcstasscript.data.data import McStasMetaData
+from mcstasscript.data.data import McStasData
+from mcstasscript.helper.managed_mcrun import ManagedMcrun
+
+class TestManagedMcrun(unittest.TestCase):
+    """
+    Testing the ManagedMcrun class that sets up McStas runs, runs the
+    simulation and loads the data.
+    
+    Here the simulation is not actually performed, this will be done in
+    integration tests. The surrounding plumbing and data loading is
+    tested.
+    """
+    
+    def test_ManagedMcrun_init_simple(self):
+        """
+        Check shortest possible initialization works
+        """
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername = "test_folder", 
+                                 mcrun_path = "test_path")
+
+        self.assertEqual(mcrun_obj.name_of_instrumentfile, "test.instr")
+        self.assertEqual(mcrun_obj.data_folder_name, "test_folder")
+        self.assertEqual(mcrun_obj.mcrun_path, "test_path")
+
+    def test_ManagedMcrun_init_defaults(self):
+        """
+        Check default values are set up correctly
+        """
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername = "test_folder", 
+                                 mcrun_path = "")
+        
+        self.assertEqual(mcrun_obj.mpi, 1)
+        self.assertEqual(mcrun_obj.ncount, 1000000)
+        
+    def test_ManagedMcrun_init_set_values(self):
+        """
+        Check default values are set up correctly
+        """
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername = "test_folder", 
+                                 mcrun_path = "",
+                                 mpi = 4,
+                                 ncount = 128)
+
+        self.assertEqual(mcrun_obj.mpi, 4)
+        self.assertEqual(mcrun_obj.ncount, 128)
+        
+    def test_ManagedMcrun_init_set_parameters(self):
+        """
+        Check default values are set up correctly
+        """
+        
+        par_input = {"A_par" : 5.1, 
+                     "int_par" : 1, 
+                     "define_par" : "Bike", 
+                     "string_par" : "\"Car\""}
+        
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername = "test_folder", 
+                                 mcrun_path = "",
+                                 parameters = par_input)
+
+        self.assertEqual(mcrun_obj.parameters["A_par"], 5.1)
+        self.assertEqual(mcrun_obj.parameters["int_par"], 1)
+        self.assertEqual(mcrun_obj.parameters["define_par"], "Bike")
+        self.assertEqual(mcrun_obj.parameters["string_par"], "\"Car\"")
+        
+    def test_ManagedMcrun_init_set_custom_flags(self):
+        """
+        Check default values are set up correctly
+        """
+        
+        custom_flag_input = "-p" 
+        
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername = "test_folder", 
+                                 mcrun_path = "",
+                                 custom_flags = custom_flag_input)
+        
+        self.assertEqual(mcrun_obj.custom_flags, custom_flag_input)
+
+    def test_ManagedMcrun_init_no_folder_error(self):
+        """
+        An error should occur if no filename is given
+        """
+        with self.assertRaises(NameError):
+            mcrun_obj = ManagedMcrun("test.instr", mcrun_path = "")
+
+    @unittest.mock.patch("os.system")
+    def test_ManagedMcrun_run_simulation_basic(self, os_system):
+        """
+        Check a basic system call is correct
+        """
+        
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername = "test_folder", 
+                                 mcrun_path = "path")
+        
+        mcrun_obj.run_simulation()
+        
+        # a double space because of a missing option
+        expected_call = ("path/mcrun -c -n 1000000 --mpi=1 "
+                         + "-d test_folder  test.instr")
+        
+        os_system.assert_called_once_with(expected_call)
+        
+    @unittest.mock.patch("os.system")
+    def test_ManagedMcrun_run_simulation_basic_path(self, os_system):
+        """
+        Check a basic system call is correct, with different path format
+        """
+        
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername = "test_folder", 
+                                 mcrun_path = "path/")
+        
+        mcrun_obj.run_simulation()
+        
+        # a double space because of a missing option
+        expected_call = ("path/mcrun -c -n 1000000 --mpi=1 "
+                         + "-d test_folder  test.instr")
+        
+        os_system.assert_called_once_with(expected_call)
+        
+    @unittest.mock.patch("os.system")
+    def test_ManagedMcrun_run_simulation_no_standard(self, os_system):
+        """
+        Check a non standard system call is correct
+        """
+        
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername = "test_folder", 
+                                 mcrun_path = "path",
+                                 mpi = 7,
+                                 ncount = 48.4,
+                                 custom_flags = "-fo")
+        
+        mcrun_obj.run_simulation()
+        
+        # a double space because of a missing option
+        expected_call = ("path/mcrun -c -n 48 --mpi=7 "
+                         + "-d test_folder -fo test.instr")
+        
+        os_system.assert_called_once_with(expected_call)
+
+    @unittest.mock.patch("os.system")
+    def test_ManagedMcrun_run_simulation_parameters(self, os_system):
+        """
+        Check a run with parameters is correct
+        """
+        
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername = "test_folder", 
+                                 mcrun_path = "path",
+                                 mpi = 7,
+                                 ncount = 48.4,
+                                 custom_flags = "-fo",
+                                 parameters = {"A" : 2,
+                                               "BC" : "car",
+                                               "th" : "\"toy\""})
+        
+        mcrun_obj.run_simulation()
+        
+        # a double space because of a missing option
+        expected_call = ("path/mcrun -c -n 48 --mpi=7 "
+                         + "-d test_folder -fo test.instr "
+                         + "A=2 BC=car th=\"toy\"")
+        
+        os_system.assert_called_once_with(expected_call)
+
+    def test_ManagedMcrun_load_data_PSD4PI(self):
+        """
+        Use test_data_set to test load_data for PSD_4PI
+        """
+        
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername = "test_data_set", 
+                                 mcrun_path = "path")
+    
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+               
+        results = mcrun_obj.load_results()
+        
+        os.chdir(current_work_dir)  # Reset work directory
+
+
+        self.assertEqual(len(results), 3)
+
+        PSD_4PI = results[0]
+        
+        self.assertEqual(PSD_4PI.name, "PSD_4PI")
+        self.assertEqual(PSD_4PI.metadata.dimension, [300, 300])
+        self.assertEqual(PSD_4PI.metadata.limits, [-180, 180, -90, 90])
+        self.assertEqual(PSD_4PI.metadata.xlabel, "Longitude [deg]")
+        self.assertEqual(PSD_4PI.metadata.ylabel, "Lattitude [deg]")
+        self.assertEqual(PSD_4PI.metadata.title, "4PI PSD monitor")
+        self.assertEqual(PSD_4PI.Ncount[1][4], 4)
+        self.assertEqual(PSD_4PI.Intensity[1][4], 1.537334562E-10)
+        self.assertEqual(PSD_4PI.Error[1][4], 1.139482296E-10)
+        
+    def test_ManagedMcrun_load_data_PSD(self):
+        """
+        Use test_data_set to test load_data for PSD
+        """
+        
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername = "test_data_set", 
+                                 mcrun_path = "path")
+    
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+               
+        results = mcrun_obj.load_results()
+        
+        os.chdir(current_work_dir)  # Reset work directory
+                                
+        # Check other properties
+        
+        PSD = results[1]
+        
+        self.assertEqual(PSD.name, "PSD")
+        self.assertEqual(PSD.metadata.dimension, [200, 200])
+        self.assertEqual(PSD.metadata.limits, [-5, 5, -5, 5])
+        self.assertEqual(PSD.metadata.xlabel, "X position [cm]")
+        self.assertEqual(PSD.metadata.ylabel, "Y position [cm]")
+        self.assertEqual(PSD.metadata.title, "PSD monitor")
+        self.assertEqual(PSD.Ncount[21][27], 9)
+        self.assertEqual(PSD.Intensity[21][27], 2.623929371e-13)
+        self.assertEqual(PSD.Error[21][27], 2.765467693e-13)
+        
+    def test_ManagedMcrun_load_data_L_mon(self):
+        """
+        Use test_data_set to test load_data for L_mon
+        """
+        
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername = "test_data_set", 
+                                 mcrun_path = "path")
+    
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+               
+        results = mcrun_obj.load_results()
+        
+        os.chdir(current_work_dir)  # Reset work directory
+                                
+        # Check other properties
+        
+        L_mon = results[2]
+        
+        self.assertEqual(L_mon.name, "L_mon")
+        self.assertEqual(L_mon.metadata.dimension, 150)
+        self.assertEqual(L_mon.metadata.limits, [0.7, 1.3])
+        self.assertEqual(L_mon.metadata.xlabel, "Wavelength [AA]")
+        self.assertEqual(L_mon.metadata.ylabel, "Intensity")
+        self.assertEqual(L_mon.metadata.title, "Wavelength monitor")
+        self.assertEqual(L_mon.xaxis[53], 0.914)
+        self.assertEqual(L_mon.Ncount[53], 37111)
+        self.assertEqual(L_mon.Intensity[53], 6.990299315e-06)
+        self.assertEqual(L_mon.Error[53], 6.215308587e-08)
+        
+    def test_ManagedMcrun_load_data_L_mon_direct(self):
+        """
+        Use test_data_set to test load_data for L_mon with direct path
+        """
+        
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername = "test_data_set", 
+                                 mcrun_path = "path")
+    
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+                
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+        
+        load_path = THIS_DIR + "/test_data_set"
+        results = mcrun_obj.load_results(load_path)
+        
+        os.chdir(current_work_dir)  # Reset work directory
+                                
+        # Check other properties
+        
+        L_mon = results[2]
+        
+        self.assertEqual(L_mon.name, "L_mon")
+        self.assertEqual(L_mon.metadata.dimension, 150)
+        self.assertEqual(L_mon.metadata.limits, [0.7, 1.3])
+        self.assertEqual(L_mon.metadata.xlabel, "Wavelength [AA]")
+        self.assertEqual(L_mon.metadata.ylabel, "Intensity")
+        self.assertEqual(L_mon.metadata.title, "Wavelength monitor")
+        self.assertEqual(L_mon.xaxis[53], 0.914)
+        self.assertEqual(L_mon.Ncount[53], 37111)
+        self.assertEqual(L_mon.Intensity[53], 6.990299315e-06)
+        self.assertEqual(L_mon.Error[53], 6.215308587e-08)
+        
+    def test_ManagedMcrun_load_data_L_mon_direct_error(self):
+        """
+        Check an error occurs when directory has no mccode.sim
+        """
+        
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername = "test_data_set", 
+                                 mcrun_path = "path")
+    
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+                
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+        
+        load_path = THIS_DIR + "/non_exsistent_dataset"
+        with self.assertRaises(NameError):
+            results = mcrun_obj.load_results(load_path)
+        
+        os.chdir(current_work_dir)  # Reset work directory
+        
+    def test_ManagedMcrun_load_data_L_mon_direct_error(self):
+        """
+        Check an error occurs when pointed to empty directory
+        """
+        
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername = "test_data_set", 
+                                 mcrun_path = "path")
+    
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+                
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+        
+        load_path = THIS_DIR + "/dummy_mcstas"
+        with self.assertRaises(NameError):
+            results = mcrun_obj.load_results(load_path)
+        
+        os.chdir(current_work_dir)  # Reset work directory
+        
+        
+     
+if __name__ == '__main__':
+    unittest.main()
+
+
diff --git a/mcstasscript/tests/test_McStasData.py b/mcstasscript/tests/test_McStasData.py
new file mode 100644
index 00000000..3bdd6fe7
--- /dev/null
+++ b/mcstasscript/tests/test_McStasData.py
@@ -0,0 +1,147 @@
+import unittest
+import numpy as np
+
+from mcstasscript.data.data import McStasData
+from mcstasscript.data.data import McStasMetaData
+
+def set_dummy_MetaData_1d():
+    meta_data = McStasMetaData()
+    meta_data.component_name = "component for 1d"
+    meta_data.dimension = 50
+    
+    return meta_data
+
+def set_dummy_McStasData_1d():
+    meta_data = set_dummy_MetaData_1d()
+
+    intensity = np.arange(20)
+    error = 0.5 * np.arange(20) 
+    ncount = 2 * np.arange(20)
+    axis = np.arange(20)*5.0
+        
+    return McStasData(meta_data, intensity, error, ncount, xaxis = axis)
+
+def set_dummy_MetaData_2d():
+    meta_data = McStasMetaData()
+    meta_data.component_name = "test a component"
+    meta_data.dimension = [50, 100]
+    
+    return meta_data
+
+def set_dummy_McStasData_2d():
+    meta_data = set_dummy_MetaData_2d()
+
+    intensity = np.arange(20).reshape(4,5)
+    error = 0.5 * np.arange(20).reshape(4,5)
+    ncount = 2 * np.arange(20).reshape(4,5)
+        
+    return McStasData(meta_data, intensity, error, ncount)
+        
+
+class TestMcStasData(unittest.TestCase):
+    """
+    Various test of McStasData class
+    """
+    
+    def test_McStasData_init_1d(self):
+        """
+        Test that newly created McStasMetaData has correct type
+        """
+        
+        data = set_dummy_McStasData_1d()
+        
+        self.assertEqual(data.name, "component for 1d")
+        self.assertEqual(data.metadata.component_name, "component for 1d")
+        
+    def test_McStasData_init_values(self):
+        """
+        Test that newly created McStasMetaData has correct type
+        """
+        
+        data = set_dummy_McStasData_1d()
+        
+        self.assertEqual(data.Intensity[3], 3)
+        self.assertEqual(data.Error[3], 1.5)
+        self.assertEqual(data.Ncount[3], 6)
+        self.assertEqual(data.xaxis[3], 15.0)
+        
+    def test_McStasData_init_2d_names(self):
+        """
+        Test that newly created McStasMetaData has correct type
+        """
+        
+        data = set_dummy_McStasData_2d()
+        
+        self.assertEqual(data.name, "test a component")
+        self.assertEqual(data.metadata.component_name, "test a component")
+        
+    def test_McStasData_init_2d_values(self):
+        """
+        Test that newly created McStasMetaData has correct type
+        """
+        
+        data = set_dummy_McStasData_2d()
+        
+        self.assertEqual(data.Intensity[2][3], 13)
+        self.assertEqual(data.Error[2][3], 6.5)
+        self.assertEqual(data.Ncount[2][3], 26)
+        
+    def test_McStasData_set_info_title(self):
+        """
+        Test that title can be set
+        """
+        data = set_dummy_McStasData_2d()
+        data.set_title("title_test")
+        self.assertEqual(data.metadata.title, "title_test")
+    
+    def test_McStasData_set_xlabel(self):
+        """
+        Test that xlabel can be set
+        """
+        data = set_dummy_McStasData_2d()
+        data.set_xlabel("xlabel test")
+        self.assertEqual(data.metadata.xlabel, "xlabel test")
+        
+    def test_McStasData_set_ylabel(self):
+        """
+        Test that ylabel can be set
+        """
+        data = set_dummy_McStasData_2d()
+        data.set_ylabel("ylabel test")
+        self.assertEqual(data.metadata.ylabel, "ylabel test")
+        
+    def test_McStasData_set_log(self):
+        """
+        Test that newly created McStasMetaData has correct type
+        """
+        data = set_dummy_McStasData_2d()
+        data.set_plot_options(log = True)
+        self.assertIsInstance(data.plot_options.log, bool)
+        self.assertTrue(data.plot_options.log)
+
+        data.set_plot_options(log = 0)
+        self.assertIsInstance(data.plot_options.log, bool)
+        self.assertFalse(data.plot_options.log)
+
+        data.set_plot_options(log = 1)
+        self.assertIsInstance(data.plot_options.log, bool)
+        self.assertTrue(data.plot_options.log)
+
+    def test_McStasData_set_orders_of_mag(self):
+        """
+        Test that newly created McStasMetaData has correct type
+        """
+        data = set_dummy_McStasData_2d()
+        data.set_plot_options(orders_of_mag = 5.2)
+        self.assertEqual(data.plot_options.orders_of_mag, 5.2)
+
+    def test_McStasData_set_colormap(self):
+        """
+        Test that newly created McStasMetaData has correct type
+        """
+        data = set_dummy_McStasData_2d()
+        data.set_plot_options(colormap = "hot")
+        self.assertIs(data.plot_options.colormap, "hot")
+
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file
diff --git a/mcstasscript/tests/test_McStasMetaData.py b/mcstasscript/tests/test_McStasMetaData.py
new file mode 100644
index 00000000..ef63ff86
--- /dev/null
+++ b/mcstasscript/tests/test_McStasMetaData.py
@@ -0,0 +1,130 @@
+import unittest
+
+from mcstasscript.data.data import McStasMetaData
+
+class TestMcStasMetaData(unittest.TestCase):
+    """
+    Various test of McStasMetaData class
+    """
+    
+    def test_McStasMetaData_return_type(self):
+        """
+        Test that newly created McStasMetaData has correct type
+        """
+        meta_data = McStasMetaData()
+        self.assertIsInstance(meta_data, McStasMetaData)
+
+    def test_McStasMetaData_init(self):
+        """
+        Test that newly created McStasMetaData has no content
+        """
+        meta_data = McStasMetaData()
+        self.assertEqual(len(meta_data.info), 0)
+        
+    def test_McStasMetaData_add_info_len(self):
+        """
+        Test that info can be added to McStasMetaData
+        """
+        meta_data = McStasMetaData()
+        meta_data.add_info("test",3)
+        self.assertEqual(len(meta_data.info), 1)
+        
+    def test_McStasMetaData_add_info(self):
+        """
+        Test that info can be read from McStasMetaData
+        """
+        meta_data = McStasMetaData()
+        meta_data.add_info("test",3)
+        self.assertEqual(meta_data.info["test"], 3)
+        
+    def test_McStasMetaData_add_info_title(self):
+        """
+        Test that title can be set
+        """
+        meta_data = McStasMetaData()
+        meta_data.set_title("title_test")
+        self.assertEqual(meta_data.title, "title_test")
+    
+    def test_McStasMetaData_add_info_xlabel(self):
+        """
+        Test that xlabel can be set
+        """
+        meta_data = McStasMetaData()
+        meta_data.set_xlabel("xlabel test")
+        self.assertEqual(meta_data.xlabel, "xlabel test")
+        
+    def test_McStasMetaData_add_info_ylabel(self):
+        """
+        Test that ylabel can be set
+        """
+        meta_data = McStasMetaData()
+        meta_data.set_ylabel("ylabel test")
+        self.assertEqual(meta_data.ylabel, "ylabel test")
+        
+    def test_McStasMetaData_long_read_1d(self):
+        """
+        Test that extact info can read appropriate info
+        """
+        meta_data = McStasMetaData()
+        meta_data.add_info("type", "array_1d(500)")
+        meta_data.add_info("component", "test_A COMP")
+        meta_data.add_info("filename", "test_A name")
+        meta_data.add_info("xlimits", " 0.92 3.68")
+        meta_data.add_info("xlabel", "test A xlabel")
+        meta_data.add_info("ylabel", "test A ylabel")
+        meta_data.add_info("title", "test A title")
+        
+        meta_data.extract_info()  # Converts info to attributes
+        
+        self.assertIsInstance(meta_data.dimension, int)
+        self.assertEqual(meta_data.dimension, 500)
+        self.assertIs(meta_data.component_name, "test_A COMP")
+        self.assertIs(meta_data.filename, "test_A name")
+        self.assertEqual(len(meta_data.limits), 2)
+        self.assertEqual(meta_data.limits[0], 0.92)
+        self.assertEqual(meta_data.limits[1], 3.68)
+        self.assertIs(meta_data.xlabel, "test A xlabel")
+        self.assertIs(meta_data.ylabel, "test A ylabel")
+        self.assertIs(meta_data.title, "test A title")
+        
+        
+    def test_McStasMetaData_long_read_2d(self):
+        """
+        Test that extact info can read appropriate info
+        """
+        meta_data = McStasMetaData()
+        meta_data.add_info("type", "array_2d(500, 12)")
+        meta_data.add_info("component", "test_A_COMP")
+        meta_data.add_info("filename", "test_A_name")
+        meta_data.add_info("xlimits", "-2.4 5.99 0.92 3.68")
+        meta_data.add_info("xlabel", "test A xlabel")
+        meta_data.add_info("ylabel", "test A ylabel")
+        meta_data.add_info("title", "test A title")
+        
+        meta_data.extract_info()  # Converts info to attributes
+        
+        self.assertEqual(len(meta_data.dimension), 2)
+        self.assertEqual(meta_data.dimension[0], 500)
+        self.assertEqual(meta_data.dimension[1], 12)
+        self.assertIs(meta_data.component_name, "test_A_COMP")
+        self.assertIs(meta_data.filename, "test_A_name")
+        self.assertEqual(len(meta_data.limits), 4)
+        self.assertEqual(meta_data.limits[0], -2.4)
+        self.assertEqual(meta_data.limits[1], 5.99)
+        self.assertEqual(meta_data.limits[2], 0.92)
+        self.assertEqual(meta_data.limits[3], 3.68)
+        self.assertIs(meta_data.xlabel, "test A xlabel")
+        self.assertIs(meta_data.ylabel, "test A ylabel")
+        self.assertIs(meta_data.title, "test A title")
+        
+if __name__ == '__main__':
+    unittest.main()
+        
+        
+        
+        
+        
+        
+        
+        
+        
\ No newline at end of file
diff --git a/mcstasscript/tests/test_McStasPlotOptions.py b/mcstasscript/tests/test_McStasPlotOptions.py
new file mode 100644
index 00000000..6b4ad324
--- /dev/null
+++ b/mcstasscript/tests/test_McStasPlotOptions.py
@@ -0,0 +1,66 @@
+import unittest
+
+from mcstasscript.data.data import McStasPlotOptions
+
+class TestMcStasPlotOptions(unittest.TestCase):
+    """
+    Various test of McStasPlotOptions class
+    """
+
+    def test_McStasPlotOptions_default_log(self):
+        """
+        Test that newly created McStasMetaData has correct type
+        """
+        plot_options = McStasPlotOptions()
+        self.assertIsInstance(plot_options.log, bool)
+        self.assertFalse(plot_options.log)
+
+    def test_McStasPlotOptions_default_orders_of_mag(self):
+        """
+        Test that newly created McStasMetaData has correct type
+        """
+        plot_options = McStasPlotOptions()
+        self.assertEqual(plot_options.orders_of_mag, 300)
+
+    def test_McStasPlotOptions_default_colormap(self):
+        """
+        Test that newly created McStasMetaData has correct type
+        """
+        plot_options = McStasPlotOptions()
+        self.assertIs(plot_options.colormap, "jet")
+
+    def test_McStasPlotOptions_set_log(self):
+        """
+        Test that newly created McStasMetaData has correct type
+        """
+        plot_options = McStasPlotOptions()
+        plot_options.set_options(log = True)
+        self.assertIsInstance(plot_options.log, bool)
+        self.assertTrue(plot_options.log)
+
+        plot_options.set_options(log = 0)
+        self.assertIsInstance(plot_options.log, bool)
+        self.assertFalse(plot_options.log)
+
+        plot_options.set_options(log = 1)
+        self.assertIsInstance(plot_options.log, bool)
+        self.assertTrue(plot_options.log)
+
+    def test_McStasPlotOptions_set_orders_of_mag(self):
+        """
+        Test that newly created McStasMetaData has correct type
+        """
+        plot_options = McStasPlotOptions()
+        plot_options.set_options(orders_of_mag = 5.2)
+        self.assertEqual(plot_options.orders_of_mag, 5.2)
+
+    def test_McStasPlotOptions_set_colormap(self):
+        """
+        Test that newly created McStasMetaData has correct type
+        """
+        plot_options = McStasPlotOptions()
+        plot_options.set_options(colormap = "hot")
+        self.assertIs(plot_options.colormap, "hot")
+
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file
diff --git a/mcstasscript/tests/test_component.py b/mcstasscript/tests/test_component.py
new file mode 100644
index 00000000..f61c077f
--- /dev/null
+++ b/mcstasscript/tests/test_component.py
@@ -0,0 +1,349 @@
+import builtins
+import unittest
+import unittest.mock
+
+from mcstasscript.helper.mcstas_objects import component
+
+def setup_component_all_keywords():
+    
+    return component("test_component",
+                     "Arm",
+                     AT = [0.124, 183.9, 157],
+                     AT_RELATIVE = "home",
+                     ROTATED = [482, 1240.2, 0.185],
+                     ROTATED_RELATIVE = "etc",
+                     WHEN = "1==2",
+                     EXTEND = "nscat = 8;",
+                     GROUP = "developers",
+                     JUMP = "myself 37",
+                     comment = "test comment")
+    
+def setup_component_relative():
+    
+    return component("test_component",
+                     "Arm",
+                     AT = [0.124, 183.9, 157],
+                     ROTATED = [482, 1240.2, 0.185],
+                     RELATIVE = "source",
+                     WHEN = "1==2",
+                     EXTEND = "nscat = 8;",
+                     GROUP = "developers",
+                     JUMP = "myself 37",
+                     comment = "test comment")
+    
+
+class Testcomponent(unittest.TestCase):
+    """
+    Components are the building blocks used to create an instrument in
+    the McStas meta language. They describe spatially seperated parts
+    of the neutron scattering instrument. Here the class component is 
+    tested.
+    """
+    
+    def test_component_basic_init(self):
+        
+        comp = component("test_component", "Arm")
+        
+        self.assertEqual(comp.name, "test_component")
+        self.assertEqual(comp.component_name, "Arm")
+
+    def test_component_basic_init_defaults(self):
+        
+        comp = component("test_component", "Arm")
+        
+        self.assertEqual(comp.name, "test_component")
+        self.assertEqual(comp.component_name, "Arm")    
+        self.assertEqual(comp.AT_data, [0,0,0])
+        self.assertEqual(comp.AT_relative, "ABSOLUTE")
+        self.assertEqual(comp.ROTATED_data, [0,0,0])
+        self.assertEqual(comp.ROTATED_relative, "ABSOLUTE")
+        self.assertEqual(comp.WHEN, "")
+        self.assertEqual(comp.EXTEND, "")
+        self.assertEqual(comp.GROUP, "")
+        self.assertEqual(comp.JUMP, "")
+        self.assertEqual(comp.comment, "")
+        
+    def test_component_init_complex_call(self):
+        
+        comp = setup_component_all_keywords()
+        
+        self.assertEqual(comp.name, "test_component")
+        self.assertEqual(comp.component_name, "Arm")    
+        self.assertEqual(comp.AT_data, [0.124, 183.9, 157])
+        self.assertEqual(comp.AT_relative, "RELATIVE home")
+        self.assertEqual(comp.ROTATED_data, [482, 1240.2, 0.185])
+        self.assertEqual(comp.ROTATED_relative, "RELATIVE etc")
+        self.assertEqual(comp.WHEN, "WHEN (1==2)\n")
+        self.assertEqual(comp.EXTEND, "nscat = 8;\n")
+        self.assertEqual(comp.GROUP, "developers")
+        self.assertEqual(comp.JUMP, "myself 37")
+        self.assertEqual(comp.comment, "test comment")
+
+    def test_component_init_complex_call_relative(self):
+        
+        comp = setup_component_relative()
+
+        self.assertEqual(comp.name, "test_component")
+        self.assertEqual(comp.component_name, "Arm")    
+        self.assertEqual(comp.AT_data, [0.124, 183.9, 157])
+        self.assertEqual(comp.AT_relative, "RELATIVE source")
+        self.assertEqual(comp.ROTATED_data, [482, 1240.2, 0.185])
+        self.assertEqual(comp.ROTATED_relative, "RELATIVE source")
+        self.assertEqual(comp.WHEN, "WHEN (1==2)\n")
+        self.assertEqual(comp.EXTEND, "nscat = 8;\n")
+        self.assertEqual(comp.GROUP, "developers")
+        self.assertEqual(comp.JUMP, "myself 37")
+        self.assertEqual(comp.comment, "test comment")
+
+    def test_component_basic_init_set_AT(self):
+        
+        comp = component("test_component", "Arm")
+        
+        comp.set_AT([12.124, 214.0, 2], RELATIVE = "monochromator")
+        
+        self.assertEqual(comp.name, "test_component")
+        self.assertEqual(comp.component_name, "Arm")
+        self.assertEqual(comp.AT_data, [12.124, 214.0, 2])
+        self.assertEqual(comp.AT_relative, "RELATIVE monochromator")
+        
+    def test_component_basic_init_set_ROTATED(self):
+        
+        comp = component("test_component", "Arm")
+        
+        comp.set_ROTATED([1204.8, 8490.1, 129], RELATIVE = "analyzer")
+        
+        self.assertEqual(comp.name, "test_component")
+        self.assertEqual(comp.component_name, "Arm")
+        self.assertEqual(comp.ROTATED_data, [1204.8, 8490.1, 129])
+        self.assertEqual(comp.ROTATED_relative, "RELATIVE analyzer")
+        
+    def test_component_basic_init_set_RELATIVE(self):
+        
+        comp = component("test_component", "Arm")
+        
+        comp.set_RELATIVE("sample")
+        
+        self.assertEqual(comp.name, "test_component")
+        self.assertEqual(comp.component_name, "Arm")
+        self.assertEqual(comp.AT_relative, "RELATIVE sample")
+        self.assertEqual(comp.ROTATED_relative, "RELATIVE sample")
+        
+    def test_component_basic_init_set_parameters(self):
+        
+        comp = component("test_component", "Arm")
+        
+        # Need to add some parameters to this bare component
+        # Parameters are usually added by McStas_Instr 
+        comp._unfreeze()
+        comp.new_par1 = 1
+        comp.new_par2 = 3
+        comp.this_par = 1492.2
+        
+        comp.set_parameters({"new_par1" : 37.0,
+                             "new_par2" : 12.0,
+                             "this_par" : 1}) 
+        
+        self.assertEqual(comp.name, "test_component")
+        self.assertEqual(comp.component_name, "Arm")
+        self.assertEqual(comp.new_par1,37.0)
+        self.assertEqual(comp.new_par2,12.0)
+        self.assertEqual(comp.this_par,1)
+        
+        with self.assertRaises(NameError):
+            comp.set_parameters({"new_par3" : 37.0})
+        
+    def test_component_basic_init_set_WHEN(self):
+        
+        comp = component("test_component", "Arm")
+        
+        comp.set_WHEN("1 != 2")
+        
+        self.assertEqual(comp.name, "test_component")
+        self.assertEqual(comp.component_name, "Arm")
+        self.assertEqual(comp.WHEN, "WHEN (1 != 2)\n")
+        
+    def test_component_basic_init_set_GROUP(self):
+        
+        comp = component("test_component", "Arm")
+        
+        comp.set_GROUP("test group")
+        
+        self.assertEqual(comp.name, "test_component")
+        self.assertEqual(comp.component_name, "Arm")
+        self.assertEqual(comp.GROUP, "test group")
+        
+    def test_component_basic_init_set_JUMP(self):
+        
+        comp = component("test_component", "Arm")
+        
+        comp.set_JUMP("test jump")
+        
+        self.assertEqual(comp.name, "test_component")
+        self.assertEqual(comp.component_name, "Arm")
+        self.assertEqual(comp.JUMP, "test jump")
+        
+    def test_component_basic_init_set_EXTEND(self):
+        
+        comp = component("test_component", "Arm")
+        
+        comp.append_EXTEND("test code")
+        
+        self.assertEqual(comp.name, "test_component")
+        self.assertEqual(comp.component_name, "Arm")
+        self.assertEqual(comp.EXTEND, "test code\n")
+        
+        comp.append_EXTEND("new code")
+        
+        self.assertEqual(comp.EXTEND, "test code\nnew code\n")
+        
+    def test_component_basic_init_set_comment(self):
+        
+        comp = component("test_component", "Arm")
+        
+        comp.set_comment("test comment")
+        
+        self.assertEqual(comp.name, "test_component")
+        self.assertEqual(comp.component_name, "Arm")
+        self.assertEqual(comp.comment, "test comment")
+
+    def test_component_basic_new_attribute_error(self):
+        """
+        The component class is frozen after initialize in order to
+        prevent the user accidentilly misspelling an attribute name,
+        or at least be able to report an error when they do so.
+        """
+        
+        comp = component("test_component", "Arm")
+        with self.assertRaises(AttributeError):
+            comp.new_attribute = 1
+        
+        # If unfreeze does not work, this would cause an error
+        comp._unfreeze()
+        comp.new_attribute = 1
+        
+        
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable = unittest.mock.mock_open)    
+    def test_component_write_to_file_simple(self, mock_f):
+        """
+        Testing that a component can be written to file with the
+        expected output. Here with simple input.
+        """
+
+        comp = component("test_component", "Arm")
+
+        comp._unfreeze()
+        # need to set up attribute parameters
+        # also need to categorize them as when created
+        comp.parameter_names = []
+        comp.parameter_defaults = {}
+        comp.parameter_types = {}
+        comp._freeze()
+
+        with mock_f('test.txt', 'w') as m_fo:
+            comp.write_component(m_fo)
+
+        my_call = unittest.mock.call
+        expected_writes = [my_call("COMPONENT test_component = Arm("),
+                           my_call(")\n"),
+                           my_call("AT (0,0,0)"),
+                           my_call(" ABSOLUTE\n"),
+                           my_call("ROTATED (0,0,0)"),
+                           my_call(" ABSOLUTE\n")]
+
+        mock_f.assert_called_with('test.txt', 'w')                                   
+        handle = mock_f()
+        handle.write.assert_has_calls(expected_writes, any_order=False)
+
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable = unittest.mock.mock_open)    
+    def test_component_write_to_file_complex(self, mock_f):
+        """
+        Testing that a component can be written to file with the
+        expected output. Here with complex input.
+        """
+        
+        comp = setup_component_all_keywords()
+        
+        comp._unfreeze()
+        # need to set up attribute parameters
+        comp.new_par1 = 1.5
+        comp.new_par2 = 3
+        comp.this_par = "test_val"
+        comp.that_par = "\"txt_string\""
+        # also need to categorize them as when created
+        comp.parameter_names = ["new_par1", "new_par2", "this_par", "that_par"]
+        comp.parameter_defaults = {"new_par1" : 5.1,
+                                   "new_par2" : 9,
+                                   "this_par" : "conga",
+                                   "that_par" : "\"txt\""}
+        comp.parameter_types = {"new_par1" : "double",
+                                "new_par2" : "int",
+                                "this_par" : "",
+                                "that_par" : "string"}
+        comp._freeze()
+        
+        with mock_f('test.txt', 'w') as m_fo:
+            comp.write_component(m_fo)
+        
+        my_call = unittest.mock.call
+        expected_writes = [my_call("COMPONENT test_component = Arm("),
+                           my_call("\n"),
+                           my_call(" new_par1 = 1.5"),
+                           my_call(","),
+                           my_call(" new_par2 = 3"),
+                           my_call(","),
+                           my_call("\n"),
+                           my_call(" this_par = test_val"),
+                           my_call(","),
+                           my_call(" that_par = \"txt_string\""),
+                           my_call(")\n"),
+                           my_call("WHEN (1==2)\n"),
+                           my_call("AT (0.124,183.9,157)"),
+                           my_call(" RELATIVE home\n"),
+                           my_call("ROTATED (482,1240.2,0.185)"),
+                           my_call(" RELATIVE etc\n"),
+                           my_call("GROUP developers\n"),
+                           my_call("EXTEND %{\n"),
+                           my_call("nscat = 8;\n"),
+                           my_call("%}\n"),
+                           my_call("JUMP myself 37\n"),
+                           my_call("\n")]
+
+        mock_f.assert_called_with('test.txt', 'w')                                   
+        handle = mock_f()
+        handle.write.assert_has_calls(expected_writes, any_order=False)  
+    
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable = unittest.mock.mock_open)
+    def test_component_write_component_required_parameter_error(self, mock_f):
+        """
+        Test an error occurs if the component is asked to write to disk
+        without a required parameter.
+        """
+        
+        comp = setup_component_all_keywords()
+        
+        comp._unfreeze()
+        # need to set up attribute parameters
+        comp.new_par1 = None
+        # also need to categorize them as when created
+        comp.parameter_names = ["new_par1"]
+        comp.parameter_defaults = {"new_par1" : None}
+        
+        with self.assertRaises(NameError):
+            with mock_f('test.txt', 'w') as m_fo:
+                comp.write_component(m_fo)
+                
+                
+    # Print long (very similar to write component)
+    # Print short (easier)
+    # show_parameters (similar to write component, with formatting)
+    # show_parameters_simple (similar to write component, without formatting)
+    
+    
+    
+    
+
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file
diff --git a/mcstasscript/tests/test_data_set/L_mon.dat b/mcstasscript/tests/test_data_set/L_mon.dat
new file mode 100644
index 00000000..6da328aa
--- /dev/null
+++ b/mcstasscript/tests/test_data_set/L_mon.dat
@@ -0,0 +1,178 @@
+# Format: McCode with text headers
+# URL: http://www.mccode.org
+# Creator: McStas 2.5 - Dec. 12, 2018
+# Instrument: jupyter_demo.instr
+# Ncount: 5000000
+# Trace: no
+# Gravitation: no
+# Seed: 1557975068
+# Directory: jupyter_demo3
+# Nodes: 4
+# Param: wavelength=1
+# Date: Wed May 15 08:19:54 2019 (1557901194)
+# type: array_1d(150)
+# Source: jupyter_demo (jupyter_demo.instr)
+# component: L_mon
+# position: 0 0 12
+# title: Wavelength monitor
+# Ncount: 20000000
+# filename: L_mon.dat
+# statistics: X0=1.00415; dX=0.0576164;
+# signal: Min=0; Max=8.72065e-06; Mean=2.58585e-06;
+# values: 0.000387878 4.37886e-07 2.23517e+06
+# xvar: L
+# yvar: (I,I_err)
+# xlabel: Wavelength [AA]
+# ylabel: Intensity
+# xlimits: 0.7 1.3
+# variables: L I I_err N
+0.702 0 0 0 
+0.706 0 0 0 
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diff --git a/mcstasscript/tests/test_data_set/PSD.dat b/mcstasscript/tests/test_data_set/PSD.dat
new file mode 100644
index 00000000..24146293
--- /dev/null
+++ b/mcstasscript/tests/test_data_set/PSD.dat
@@ -0,0 +1,633 @@
+# Format: McCode with text headers
+# URL: http://www.mccode.org
+# Creator: McStas 2.5 - Dec. 12, 2018
+# Instrument: jupyter_demo.instr
+# Ncount: 5000000
+# Trace: no
+# Gravitation: no
+# Seed: 1557975068
+# Directory: jupyter_demo3
+# Nodes: 4
+# Param: wavelength=1
+# Date: Wed May 15 08:19:54 2019 (1557901194)
+# type: array_2d(200, 200)
+# Source: jupyter_demo (jupyter_demo.instr)
+# component: PSD
+# position: 0 0 12
+# title: PSD monitor
+# Ncount: 20000000
+# filename: PSD.dat
+# statistics: X0=0.000283843; dX=1.8828; Y0=-0.00309525; dY=1.71996;
+# signal: Min=0; Max=5.86601e-08; Mean=9.69694e-09;
+# values: 0.000387878 4.37886e-07 2.23517e+06
+# xvar: X 
+# yvar: Y 
+# xlabel: X position [cm]
+# ylabel: Y position [cm]
+# zvar: I
+# zlabel: Signal per bin
+# xylimits: -5 5 -5 5
+# variables: I I_err N
+# Data [PSD/PSD.dat] I:
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diff --git a/mcstasscript/tests/test_data_set/PSD_4PI.dat b/mcstasscript/tests/test_data_set/PSD_4PI.dat
new file mode 100644
index 00000000..8d173c4e
--- /dev/null
+++ b/mcstasscript/tests/test_data_set/PSD_4PI.dat
@@ -0,0 +1,933 @@
+# Format: McCode with text headers
+# URL: http://www.mccode.org
+# Creator: McStas 2.5 - Dec. 12, 2018
+# Instrument: jupyter_demo.instr
+# Ncount: 5000000
+# Trace: no
+# Gravitation: no
+# Seed: 1557975068
+# Directory: jupyter_demo3
+# Nodes: 4
+# Param: wavelength=1
+# Date: Wed May 15 08:19:54 2019 (1557901194)
+# type: array_2d(300, 300)
+# Source: jupyter_demo (jupyter_demo.instr)
+# component: PSD_4PI
+# position: 0 0 11
+# title: 4PI PSD monitor
+# Ncount: 20000000
+# filename: PSD_4PI.dat
+# statistics: X0=-0.142189; dX=140.285; Y0=0.299127; dY=15.4061;
+# signal: Min=0; Max=1.99345e-05; Mean=5.17405e-09;
+# values: 0.000465664 4.478e-07 4.36906e+06
+# xvar: Lo
+# yvar: La
+# xlabel: Longitude [deg]
+# ylabel: Lattitude [deg]
+# zvar: I
+# zlabel: Signal per bin
+# xylimits: -180 180 -90 90
+# variables: I I_err N
+# Data [PSD_4PI/PSD_4PI.dat] I:
+0 0 0 0 0 0 0 0 0 7.311770765e-13 1.086283723e-16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4.04074204e-13 0 0 0 0 4.372287023e-27 0 0 0 0 0 7.338159334e-11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4.19836707e-12 0 0 0 0 0 0 1.34440804e-20 1.409925914e-15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.567822131e-17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.006369923e-12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6.823578936e-12 0 0 0 1.581600341e-11 0 0 0 0 0 2.52316499e-11 0 0 0 0 0 0 0 0 0 0 0 0 0 2.768962386e-14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3.479387877e-17 0 0 0 
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+1.295779153e-11 1.319099627e-11 1.96547699e-11 0 1.736250259e-13 0 0 0 0 0 0 0 4.191338547e-11 0 8.763651858e-12 0 0 5.308245583e-11 0 2.622501613e-33 0 1.019602285e-10 0 1.683631579e-11 0 1.413867017e-11 3.464976682e-11 1.887003506e-11 0 0 0 3.142851108e-11 0 1.15220549e-19 3.431095415e-24 5.1766246e-11 0 0 0 0 8.003085878e-19 0 0 0 0 5.44481376e-11 1.227137609e-11 4.71992571e-19 1.344556206e-11 0 0 0 0 2.727825877e-11 6.341591893e-15 0 0 3.414777445e-11 8.157447888e-13 0 0 0 8.127969344e-28 0 0 0 1.201381989e-11 2.224508983e-14 0 0 1.478883417e-11 0 0 0 2.86233352e-11 4.651426767e-11 0 0 0 0 2.555977718e-14 0 3.381901564e-11 1.217989082e-11 0 0 0 0 0 0 0 6.257700724e-27 0 7.229932043e-11 0 4.18149144e-11 0 0 2.851837379e-11 0 0 5.602867648e-11 0 0 0 6.734905977e-11 0 0 5.906117316e-12 0 0 0 4.241251463e-26 2.847603158e-11 0 8.727405172e-20 0 0 0 0 1.774580597e-11 0 0 2.254105073e-11 0 2.033088131e-11 0 1.035882009e-11 0 0 0 0 3.861591415e-13 0 0 0 0 0 0 5.587858396e-12 0 3.536690334e-11 6.240006037e-14 0 0 0 1.897802023e-24 8.227556146e-12 0 3.174194315e-11 2.917768757e-21 8.305178859e-11 0 0 0 0 3.53632433e-25 0 1.453155407e-15 0 3.565260025e-11 0 4.003101149e-11 1.231086717e-16 1.23424149e-11 0 0 1.516655371e-19 8.23235104e-12 0 0 1.03520037e-41 2.085612058e-12 0 0 0 9.386284685e-12 0 0 4.6624693e-18 0 0 3.158327793e-14 1.68451955e-11 8.609770793e-11 1.100811418e-14 0 0 0 1.611128942e-26 0 1.561678094e-11 1.108033957e-11 0 0 0 0 0 0 2.712049046e-14 0 0 0 3.265212501e-19 4.121581341e-12 0 0 0 0 0 0 0 0 0 0 0 2.532721583e-14 0 0 0 3.473432651e-15 0 7.548912606e-11 0 0 0 0 0 0 0 2.032140667e-11 3.10785741e-11 2.49945868e-11 0 0 0 1.052237108e-11 0 0 0 3.651740097e-21 0 5.241377288e-12 0 0 4.712311139e-12 0 3.936186209e-11 0 0 0 0 0 0 7.93789828e-16 0 8.953613596e-12 0 2.074966985e-11 0 0 0 4.978129956e-11 0 0 0 1.460012203e-11 3.308446462e-16 0 2.102265695e-14 3.045817511e-11 0 0 0 4.863487271e-11 0 0 0 1.510245272e-11 0 4.278436831e-11 0 0 2.763536447e-11 0 1.343843331e-10 4.380489002e-11 0 0 0 0 0 0 0 0 0 0 0 0 1.021158245e-11 
+# Errors [PSD_4PI/PSD_4PI.dat] I_err:
+0 0 0 0 0 0 0 0 0 7.311770765e-13 1.086283723e-16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4.04074204e-13 0 0 0 0 4.372287023e-27 0 0 0 0 0 7.338159334e-11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4.19836707e-12 0 0 0 0 0 0 1.34440804e-20 1.409925914e-15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.567822131e-17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.006369923e-12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6.823578936e-12 0 0 0 1.581600341e-11 0 0 0 0 0 2.52316499e-11 0 0 0 0 0 0 0 0 0 0 0 0 0 2.768962386e-14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3.479387877e-17 0 0 0 
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+6 7 7 4 11 6 11 12 10 6 8 8 10 2 4 8 5 10 8 6 9 7 7 4 5 9 7 14 4 8 7 14 8 9 4 5 5 7 5 7 7 6 8 7 3 9 9 8 5 10 6 3 5 8 6 6 4 3 8 9 2 2 1 4 6 5 7 11 3 6 5 2 9 4 8 7 11 7 5 5 6 3 2 7 9 5 3 1 8 5 4 6 6 4 4 8 3 4 3 5 4 8 6 6 5 5 7 10 10 6 3 9 7 4 8 8 9 3 11 4 7 7 7 7 9 12 6 7 7 4 7 11 6 10 7 6 11 7 2 9 16 7 7 9 11 9 5 6 5 9 8 8 9 4 6 12 13 13 13 7 7 6 9 7 12 7 8 5 8 7 10 7 10 2 6 7 9 7 6 9 8 4 4 6 3 7 6 10 6 2 7 4 8 6 6 3 6 6 7 6 4 5 3 5 10 8 0 5 3 4 10 2 6 5 0 4 3 8 9 6 2 5 3 10 8 1 8 6 7 4 6 5 5 5 7 7 3 7 5 2 7 6 3 6 4 5 3 4 6 3 6 4 2 3 5 5 7 9 8 7 6 11 4 2 6 8 4 4 7 7 8 11 6 5 10 8 9 7 5 13 6 7 9 7 5 10 6 6 10 6 8 3 6 4 7 3 5 5 7 7 
+6 9 9 6 7 3 7 7 6 13 4 6 3 2 10 4 6 3 9 4 3 8 4 8 2 5 11 4 5 7 5 5 11 3 4 5 5 10 6 10 6 6 2 5 5 7 2 5 10 3 2 3 7 6 5 7 5 6 5 7 8 8 9 8 3 3 6 7 4 6 6 6 7 4 6 4 4 6 6 2 3 7 3 5 4 1 0 1 2 4 7 5 0 5 3 4 3 6 4 5 5 8 6 8 8 7 2 7 4 5 7 6 12 6 5 8 4 6 5 6 1 5 6 5 5 9 7 8 6 7 8 9 11 9 4 9 6 8 7 5 9 8 9 13 15 10 0 1 10 7 6 7 5 10 12 6 6 10 12 9 7 9 8 7 1 4 12 5 5 6 8 5 2 5 8 6 2 3 11 6 8 4 6 7 5 6 2 7 2 5 3 7 2 4 7 5 4 2 5 7 4 3 5 1 4 5 5 4 4 2 3 5 5 4 7 2 3 5 2 6 8 6 7 5 4 2 6 5 2 7 6 4 6 9 9 4 2 3 5 4 5 6 5 3 4 7 3 1 3 5 7 6 4 6 6 6 3 3 8 5 4 6 8 6 4 7 3 7 8 5 9 5 9 2 6 10 3 9 6 7 5 7 6 3 7 6 5 3 8 7 12 7 7 11 5 7 6 3 4 4 
+5 2 6 6 4 6 0 8 4 5 5 7 5 9 3 10 11 5 5 6 7 9 3 2 3 5 2 6 8 2 5 12 6 3 8 6 2 5 6 4 5 4 5 5 4 7 2 6 2 1 4 4 4 4 7 3 5 3 7 5 4 2 2 5 5 5 6 1 1 7 8 7 6 3 3 7 3 4 6 2 4 2 4 2 5 5 3 0 0 4 6 1 7 4 2 3 6 1 3 0 3 6 4 7 4 2 10 3 4 4 2 5 5 3 3 10 7 7 6 2 4 6 4 6 2 6 3 6 4 8 4 7 5 10 8 7 7 11 6 8 10 5 7 3 8 7 5 8 6 7 11 1 13 5 6 6 10 8 7 5 13 7 3 8 10 5 4 12 12 9 3 6 7 2 5 7 8 1 4 8 5 10 11 6 8 8 5 8 4 1 5 3 4 3 3 4 3 8 4 5 4 6 3 3 5 5 2 1 7 6 3 1 3 2 4 5 4 7 4 6 8 7 5 5 2 6 6 4 4 6 4 3 4 5 5 5 3 6 7 5 4 4 7 4 4 4 11 5 3 6 4 2 6 4 2 9 10 5 8 6 6 2 4 4 3 5 7 6 7 4 5 3 9 7 5 7 5 4 9 9 5 4 5 5 4 5 2 4 4 4 8 6 5 8 7 4 2 9 7 8 
+7 8 6 2 6 3 5 2 9 5 4 6 6 3 3 7 7 4 4 8 7 4 4 1 8 5 2 1 8 4 4 2 10 6 2 2 5 4 5 3 7 3 6 2 3 2 6 5 4 8 2 3 1 2 8 7 5 5 8 5 3 4 7 2 6 5 7 6 4 3 1 4 5 4 2 3 6 3 5 4 4 2 3 3 0 3 4 2 3 2 4 3 4 2 2 7 4 7 1 5 5 3 3 3 2 5 2 3 3 6 5 5 4 5 6 4 3 9 5 7 8 6 5 7 5 7 3 9 7 4 5 5 2 9 6 4 5 7 4 0 5 7 2 5 5 2 6 7 6 5 3 7 7 9 4 7 5 9 6 5 5 5 2 4 5 8 5 3 4 6 7 8 4 4 7 5 6 5 9 4 2 2 2 5 7 8 2 4 4 6 6 2 4 3 5 3 2 3 1 3 4 5 5 1 2 0 3 0 2 2 3 3 5 2 4 2 6 8 2 4 5 1 4 6 4 4 8 7 4 7 4 1 3 7 0 4 4 5 6 10 9 3 6 4 2 5 3 5 8 2 4 6 2 3 3 3 2 3 5 7 7 3 7 7 3 4 6 8 4 2 9 6 7 7 6 3 5 3 4 6 8 7 7 2 2 7 4 4 5 7 4 2 2 6 3 5 3 6 8 3 
+4 4 3 5 6 5 7 1 3 2 4 5 6 3 6 4 5 8 3 7 5 7 11 1 8 7 5 5 5 3 3 9 3 5 3 2 6 8 1 5 2 3 6 9 8 3 3 2 4 5 3 5 4 8 6 4 4 5 1 5 1 3 3 7 4 6 1 4 8 1 9 4 2 2 2 4 4 5 4 6 5 1 5 4 5 2 5 1 2 2 3 8 4 7 3 2 5 3 1 8 4 0 5 2 3 4 4 1 1 2 5 3 4 4 1 4 1 4 3 7 7 2 2 2 7 4 7 4 2 5 3 5 3 3 6 2 3 5 8 7 4 6 6 3 3 4 6 5 2 3 1 5 0 4 3 4 3 1 6 5 3 4 5 3 3 6 2 2 7 4 3 4 2 9 1 0 5 3 2 10 6 3 7 4 5 7 0 5 2 1 2 3 0 3 4 2 6 3 2 6 3 2 2 5 5 8 3 2 2 3 1 3 3 2 6 2 6 2 4 3 2 4 4 3 5 4 6 7 3 7 1 1 6 8 2 0 6 1 5 3 4 3 4 2 0 3 5 4 2 8 7 3 3 2 4 5 2 8 5 4 0 2 6 4 8 2 1 1 5 5 5 5 5 2 9 9 3 5 6 9 9 7 12 4 4 4 7 3 5 8 6 4 3 4 4 2 3 5 7 7 
+4 8 4 4 8 5 2 2 3 3 5 3 4 3 6 3 1 3 4 4 3 3 4 8 4 3 1 5 3 3 3 3 5 3 2 5 5 6 3 4 2 4 2 3 5 5 3 4 5 3 4 6 4 7 2 5 5 6 4 1 5 3 7 2 1 3 1 3 3 6 1 2 4 4 2 3 2 2 5 3 1 2 3 5 1 0 4 2 0 4 5 3 2 4 2 3 2 3 3 2 5 2 2 2 4 0 5 2 2 1 5 6 2 3 2 4 4 2 2 2 1 4 5 4 1 7 6 5 3 6 4 3 3 3 0 2 4 2 4 6 8 5 3 4 10 5 2 2 3 3 4 5 2 5 6 2 3 2 6 5 1 5 2 4 1 5 2 2 5 5 2 2 4 4 6 3 4 6 2 0 4 3 6 1 2 0 2 2 7 3 1 1 4 4 4 1 3 5 1 1 2 3 2 4 2 3 3 1 0 1 3 4 0 3 2 4 5 6 1 1 3 2 3 2 4 0 3 2 2 2 2 3 3 3 1 3 3 7 5 3 0 3 2 1 2 2 3 4 6 3 3 1 3 0 1 3 2 3 5 6 1 3 2 3 2 6 1 0 6 2 6 4 5 5 4 6 6 7 8 3 4 3 4 7 5 2 6 4 2 3 3 3 2 5 3 3 4 6 9 4 
+7 5 5 2 7 4 5 1 5 3 0 4 0 3 3 3 3 5 1 3 2 2 3 2 2 3 1 2 4 4 4 3 3 1 4 0 4 2 7 4 1 3 4 3 4 6 3 1 5 2 1 4 1 3 3 1 3 3 3 2 3 3 3 0 1 10 4 3 2 5 2 2 1 5 4 3 1 4 2 2 3 4 3 4 5 1 2 5 6 2 4 1 2 3 4 3 5 3 2 1 2 3 1 3 4 4 3 1 2 0 6 1 0 2 5 2 2 1 2 2 1 2 0 3 7 6 2 3 2 3 3 4 6 5 2 2 6 2 1 3 5 3 6 1 5 5 6 4 2 4 0 2 2 4 3 4 4 3 6 9 6 3 4 4 4 0 1 2 3 9 7 8 5 5 1 1 3 1 4 2 6 2 3 4 3 2 8 1 0 3 4 3 3 2 0 6 3 1 5 3 3 1 2 1 2 1 5 0 4 2 3 4 3 3 2 0 1 7 0 1 2 3 2 3 2 5 3 1 5 5 4 3 3 3 3 3 3 4 5 2 6 6 3 2 2 4 3 2 2 1 1 6 2 4 5 8 2 2 2 5 2 2 3 0 3 2 3 6 3 2 4 6 4 5 4 4 3 3 4 4 5 8 1 2 2 5 3 1 3 3 1 4 4 4 3 3 0 2 3 5 
+2 6 1 3 3 3 3 6 0 0 7 1 2 1 2 5 4 2 4 7 3 0 5 1 1 3 4 3 1 2 2 5 1 1 1 1 3 4 2 2 3 1 1 1 3 2 1 4 2 1 1 1 3 0 0 4 2 3 2 3 1 1 2 1 3 3 3 5 1 4 5 2 3 1 0 4 5 4 3 1 3 2 0 1 5 1 2 4 1 1 1 1 1 4 3 1 2 2 1 1 2 0 1 3 2 5 4 1 4 1 4 1 3 6 2 3 2 1 0 0 2 5 2 3 4 4 2 1 4 3 3 3 4 1 6 4 5 1 2 1 3 3 2 3 5 1 6 4 2 4 1 2 4 4 1 2 2 2 4 4 2 4 3 2 3 2 5 1 0 2 2 2 2 1 4 1 2 0 3 3 0 3 3 2 2 1 2 0 1 3 4 3 3 1 2 2 1 3 3 2 4 2 2 2 0 3 3 2 1 0 1 3 3 2 2 1 2 4 1 2 7 6 3 3 4 5 5 3 1 5 2 5 3 3 1 3 0 2 4 0 2 2 3 6 3 2 1 2 2 4 1 4 4 1 1 2 5 3 4 6 3 3 2 4 1 3 3 3 2 1 4 3 3 0 1 2 0 1 6 5 1 1 2 3 2 0 2 4 1 2 1 6 1 2 5 4 2 1 7 2 
+7 1 2 2 1 2 5 5 2 3 4 2 3 3 1 2 1 1 3 4 3 3 3 2 1 1 3 4 2 3 2 1 4 1 2 3 3 5 1 1 2 2 2 1 1 4 3 5 6 3 6 5 3 1 3 4 3 1 2 3 2 3 1 6 2 3 0 5 2 1 3 2 2 2 1 4 3 1 0 3 1 1 3 1 5 3 0 3 4 3 2 3 2 2 3 1 2 5 1 0 0 4 0 2 3 2 1 2 5 2 2 3 2 4 0 0 0 3 1 2 2 0 3 0 2 2 1 2 2 5 1 0 1 1 2 4 1 2 2 2 3 4 2 3 1 0 2 1 3 6 0 1 3 5 1 0 1 1 5 3 1 3 1 1 3 0 0 4 3 4 3 2 1 4 0 2 2 2 1 2 0 1 1 2 2 2 1 2 0 0 1 3 4 2 1 2 0 1 2 1 4 4 2 3 2 2 2 3 4 1 1 4 2 2 2 3 3 3 3 4 2 1 2 2 2 5 1 1 3 3 1 1 3 2 1 2 3 1 4 1 5 2 5 0 0 0 3 1 1 2 3 2 2 0 4 0 3 1 1 2 2 2 1 2 3 3 3 1 2 2 3 0 2 1 1 2 0 2 0 0 4 4 4 2 3 3 2 3 2 4 1 0 7 4 2 4 1 2 3 3 
+2 5 5 1 3 0 2 4 2 1 1 2 2 1 1 0 1 2 4 3 0 4 3 3 3 4 5 1 0 2 6 4 1 4 1 1 2 2 4 1 2 0 2 2 3 2 3 4 2 2 2 2 3 5 2 2 0 1 3 2 4 1 0 3 4 3 7 4 2 2 3 0 5 3 2 1 2 2 5 4 0 1 3 1 2 3 1 1 0 3 4 4 2 2 1 0 1 2 0 1 1 0 2 0 2 1 0 4 0 3 5 2 3 2 0 3 3 1 1 1 3 0 0 1 1 2 0 3 2 0 1 0 1 1 1 4 0 1 3 2 2 3 1 2 3 3 2 1 1 2 1 2 2 2 2 2 0 1 4 4 2 2 0 2 5 0 1 0 2 1 1 2 3 4 0 1 0 3 1 2 0 0 1 2 1 4 0 0 3 1 1 0 2 5 3 1 3 2 1 1 4 5 0 0 3 4 0 3 2 1 2 3 1 0 0 2 1 1 2 0 0 2 0 1 3 1 0 0 3 2 3 0 2 1 3 3 2 2 1 1 2 3 1 2 7 3 0 1 2 1 2 0 2 1 6 3 2 3 2 3 0 2 2 1 1 5 2 2 1 1 5 1 2 1 3 3 0 1 1 3 2 0 3 2 1 0 3 5 2 4 1 0 2 2 3 3 3 2 1 1 
+3 2 3 0 2 1 2 2 1 2 2 1 4 4 3 0 2 1 2 0 1 1 3 1 0 2 2 2 0 0 0 0 4 3 0 2 0 2 2 5 1 1 1 2 1 1 3 1 0 6 3 4 0 2 0 2 2 0 1 0 0 1 0 3 1 0 2 1 2 1 1 1 3 2 0 1 1 0 2 3 0 2 1 2 1 1 3 2 1 2 1 1 2 2 0 0 0 2 1 0 1 0 2 2 3 1 1 2 1 1 4 2 2 2 3 1 2 3 0 0 3 1 2 3 1 2 0 0 1 1 2 1 1 4 0 1 2 2 2 0 4 1 0 2 1 0 1 1 0 1 3 0 2 3 1 0 4 1 1 1 1 2 1 1 1 1 0 0 1 1 1 0 3 1 2 1 1 2 0 2 2 0 1 5 0 1 1 2 0 0 1 1 2 1 1 1 1 0 1 0 1 0 1 3 2 2 1 0 1 2 3 0 1 0 1 1 0 2 2 2 3 1 0 3 0 0 1 1 3 3 1 3 2 3 1 2 0 2 0 6 1 2 1 1 0 2 4 2 1 2 0 0 0 2 3 1 1 0 1 1 4 1 2 4 1 4 3 1 2 5 1 3 1 3 0 1 1 0 0 1 1 1 3 3 3 1 2 3 3 0 2 1 2 0 0 3 0 1 0 0 
+1 2 1 1 2 0 1 1 0 2 1 1 3 1 1 2 0 2 3 2 1 0 0 0 0 2 0 0 2 2 2 1 2 1 1 0 2 2 2 1 2 2 2 3 0 2 1 5 2 1 2 0 1 0 1 1 4 1 1 1 1 4 3 0 2 0 0 0 1 2 1 1 0 2 2 0 1 1 0 0 1 1 1 4 0 4 1 0 1 2 1 3 1 0 0 0 0 0 1 1 4 0 2 0 1 2 0 1 0 1 0 2 1 1 1 0 2 2 0 2 0 0 1 0 2 3 2 0 1 1 0 3 0 0 1 0 2 1 1 1 1 2 0 1 1 1 0 1 1 3 1 0 1 2 0 2 3 0 0 0 2 2 0 1 0 2 0 1 2 0 1 0 1 1 0 0 0 1 1 2 1 0 2 0 1 1 2 2 0 0 0 2 3 0 0 0 0 1 1 2 1 1 2 2 1 1 4 1 0 1 1 1 0 1 1 1 0 1 0 2 0 3 1 1 0 1 1 2 3 2 0 3 0 2 4 1 3 0 0 3 2 0 1 2 4 2 2 1 1 2 0 1 2 0 1 1 1 1 1 1 0 0 0 0 3 1 0 0 1 2 2 2 2 0 0 2 0 1 1 2 1 1 1 2 3 1 1 0 0 1 0 2 1 0 1 1 0 0 2 0 
+1 1 1 1 0 0 2 0 1 1 2 1 0 1 0 0 0 0 2 1 0 0 1 0 1 1 0 1 0 2 2 0 1 0 0 1 1 0 0 0 1 1 0 2 2 1 1 1 0 0 0 2 0 0 1 1 1 1 0 1 0 0 0 0 1 0 1 0 0 1 1 1 1 1 2 1 1 0 0 0 0 0 0 1 1 2 0 1 1 2 0 2 1 0 1 0 0 0 0 3 0 0 0 0 2 0 1 1 0 1 0 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 3 0 2 0 4 0 1 0 1 1 0 0 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 1 2 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 1 1 3 0 0 0 2 0 1 2 1 1 2 1 1 0 0 0 0 1 2 1 0 1 0 2 1 2 2 2 0 0 0 1 1 2 2 1 0 2 0 0 1 2 0 0 0 0 0 1 0 2 1 0 1 2 2 1 0 2 0 0 2 3 1 1 0 1 0 4 1 2 0 1 3 0 0 0 1 1 1 1 0 1 1 1 2 0 1 1 0 0 0 1 2 0 1 0 1 0 2 2 0 1 0 0 0 1 3 0 0 2 1 1 1 2 3 0 0 
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diff --git a/mcstasscript/tests/test_data_set/jupyter_demo.instr b/mcstasscript/tests/test_data_set/jupyter_demo.instr
new file mode 100644
index 00000000..51849ce0
--- /dev/null
+++ b/mcstasscript/tests/test_data_set/jupyter_demo.instr
@@ -0,0 +1,92 @@
+/********************************************************************************
+* 
+* McStas, neutron ray-tracing package
+*         Copyright (C) 1997-2008, All rights reserved
+*         Risoe National Laboratory, Roskilde, Denmark
+*         Institut Laue Langevin, Grenoble, France
+* 
+* This file was written by McStasScript, which is a 
+* python based McStas instrument generator written by 
+* Mads Bertelsen in 2019 while employed at the 
+* European Spallation Source Data Management and 
+* Software Center
+* 
+* Instrument jupyter_demo
+* 
+* %Identification
+* Written by: Python McStas Instrument Generator
+* Date: 08:19:46 on May 15, 2019
+* Origin: ESS DMSC
+* %INSTRUMENT_SITE: Generated_instruments
+* 
+* 
+* %Parameters
+* 
+* %End 
+********************************************************************************/
+
+DEFINE INSTRUMENT jupyter_demo (
+double wavelength = 3 // Wavelength emmited from source
+)
+
+DECLARE 
+%{
+%}
+
+INITIALIZE 
+%{
+// Start of initialize for generated jupyter_demo
+%}
+
+TRACE 
+COMPONENT Source = Source_simple(
+ yheight = 0.08, xwidth = 0.06,
+ dist = 2, focus_xw = 0.05,
+ focus_yh = 0.05, lambda0 = wavelength,
+ dlambda = 0.1)
+AT (0,0,0) ABSOLUTE
+ROTATED (0,0,0) ABSOLUTE
+
+COMPONENT Guide = Guide_gravity(
+ w1 = 0.05, h1 = 0.05,
+ w2 = 0.05, h2 = 0.05,
+ l = 8, m = 3.5,
+ G = -9.2)
+AT (0,0,2) RELATIVE Source
+ROTATED (0,0,0) RELATIVE Source
+
+COMPONENT sample = PowderN(
+ reflections = "Cu.laz", radius = 0.015,
+ yheight = 0.05)
+AT (0,0,9) RELATIVE Guide
+ROTATED (0,0,0) RELATIVE Guide
+
+COMPONENT PSD_4PI = PSD_monitor_4PI(
+ nx = 300, ny = 300,
+ filename = "PSD_4PI.dat", radius = 1,
+ restore_neutron = 1)
+AT (0,0,0) RELATIVE sample
+ROTATED (0,0,0) RELATIVE sample
+
+COMPONENT PSD = PSD_monitor(
+ nx = 200, ny = 200,
+ filename = "PSD.dat", xwidth = 0.1,
+ yheight = 0.1, restore_neutron = 1)
+AT (0,0,1) RELATIVE sample
+ROTATED (0,0,0) RELATIVE sample
+
+// Wavelength monitor for narrow range
+COMPONENT L_mon = L_monitor(
+ nL = 150, filename = "L_mon.dat",
+ xwidth = 0.1, yheight = 0.1,
+ Lmin = wavelength - 0.3, Lmax = wavelength + 0.3,
+ restore_neutron = 1)
+AT (0,0,0) RELATIVE PSD
+ROTATED (0,0,0) RELATIVE PSD
+
+FINALLY 
+%{
+// Start of finally for generated jupyter_demo
+%}
+
+END
diff --git a/mcstasscript/tests/test_data_set/mccode.sim b/mcstasscript/tests/test_data_set/mccode.sim
new file mode 100644
index 00000000..aac53eaf
--- /dev/null
+++ b/mcstasscript/tests/test_data_set/mccode.sim
@@ -0,0 +1,90 @@
+McStas simulation description file for jupyter_demo.
+Date:    Wed May 15 08:19:52 2019
+Program: McStas 2.5 - Dec. 12, 2018
+
+begin instrument: jupyter_demo
+  File: jupyter_demo3/mccode
+  Source: jupyter_demo.instr
+  Parameters:  wavelength(double)
+  Trace_enabled: yes
+  Default_main: yes
+  Embedded_runtime: yes
+end instrument
+
+begin simulation: jupyter_demo3
+  Format: McCode with text headers
+  URL: http://www.mccode.org
+  Creator: McStas 2.5 - Dec. 12, 2018
+  Instrument: jupyter_demo.instr
+  Ncount: 20000000
+  Trace: no
+  Gravitation: no
+  Seed: 1557975068
+  Directory: jupyter_demo3
+  Nodes: 4
+  Param: wavelength=1
+end simulation
+
+begin data
+  Date: Wed May 15 08:19:54 2019 (1557901194)
+  type: array_2d(300, 300)
+  Source: jupyter_demo (jupyter_demo.instr)
+  component: PSD_4PI
+  position: 0 0 11
+  title: 4PI PSD monitor
+  Ncount: 20000000
+  filename: PSD_4PI.dat
+  statistics: X0=-0.142189; dX=140.285; Y0=0.299127; dY=15.4061;
+  signal: Min=0; Max=1.99345e-05; Mean=5.17405e-09;
+  values: 0.000465664 4.478e-07 4.36906e+06
+  xvar: Lo
+  yvar: La
+  xlabel: Longitude [deg]
+  ylabel: Lattitude [deg]
+  zvar: I
+  zlabel: Signal per bin
+  xylimits: -180 180 -90 90
+  variables: I I_err N
+end data
+
+begin data
+  Date: Wed May 15 08:19:54 2019 (1557901194)
+  type: array_2d(200, 200)
+  Source: jupyter_demo (jupyter_demo.instr)
+  component: PSD
+  position: 0 0 12
+  title: PSD monitor
+  Ncount: 20000000
+  filename: PSD.dat
+  statistics: X0=0.000283843; dX=1.8828; Y0=-0.00309525; dY=1.71996;
+  signal: Min=0; Max=5.86601e-08; Mean=9.69694e-09;
+  values: 0.000387878 4.37886e-07 2.23517e+06
+  xvar: X 
+  yvar: Y 
+  xlabel: X position [cm]
+  ylabel: Y position [cm]
+  zvar: I
+  zlabel: Signal per bin
+  xylimits: -5 5 -5 5
+  variables: I I_err N
+end data
+
+begin data
+  Date: Wed May 15 08:19:54 2019 (1557901194)
+  type: array_1d(150)
+  Source: jupyter_demo (jupyter_demo.instr)
+  component: L_mon
+  position: 0 0 12
+  title: Wavelength monitor
+  Ncount: 20000000
+  filename: L_mon.dat
+  statistics: X0=1.00415; dX=0.0576164;
+  signal: Min=0; Max=8.72065e-06; Mean=2.58585e-06;
+  values: 0.000387878 4.37886e-07 2.23517e+06
+  xvar: L
+  yvar: (I,I_err)
+  xlabel: Wavelength [AA]
+  ylabel: Intensity
+  xlimits: 0.7 1.3
+  variables: L I I_err N
+end data
diff --git a/mcstasscript/tests/test_declare_variable.py b/mcstasscript/tests/test_declare_variable.py
new file mode 100644
index 00000000..285823e0
--- /dev/null
+++ b/mcstasscript/tests/test_declare_variable.py
@@ -0,0 +1,185 @@
+import io
+import builtins
+import unittest
+import unittest.mock
+
+
+from mcstasscript.helper.mcstas_objects import declare_variable
+
+
+class Test_declare_variable(unittest.TestCase):
+    """
+    Tests the declare_variable class that holds a declared variable
+    that will be written to the McStas declare section.
+    
+    """
+
+    def test_declare_variable_init_basic_type(self):
+        """
+        Initialization with a type
+        """
+
+        var = declare_variable("double", "test")
+
+        self.assertEqual(var.name,"test")
+        self.assertEqual(var.type,"double")  # space for easier writing
+
+    def test_declare_variable_init_basic_type_value(self):
+        """
+        Initialization with type and value
+        """
+
+        var = declare_variable("double", "test", value = 518)
+
+        self.assertEqual(var.name,"test")
+        self.assertEqual(var.type,"double")  # space for easier writing
+        self.assertEqual(var.value, 518)
+
+    def test_declare_variable_init_basic_type_vector(self):
+        """
+        Initialization with type and value
+        """
+
+        var = declare_variable("double", "test",
+                               array=6, value = [1, 2.2, 3, 3.3, 4, 4.4])
+
+        self.assertEqual(var.name,"test")
+        self.assertEqual(var.type,"double")  # space for easier writing
+        self.assertEqual(var.vector, 6)
+        self.assertEqual(var.value, [1, 2.2, 3, 3.3, 4, 4.4])
+
+    def test_declare_variable_init_basic_type_value_comment(self):
+        """
+        Initialization with type, value and comment
+        """
+        
+        var = declare_variable("double", "test", 
+                                 value = 518, comment = "test comment /")
+        
+        self.assertEqual(var.name,"test")
+        self.assertEqual(var.type,"double")  # space for easier writing
+        self.assertEqual(var.value, 518)
+        self.assertEqual(var.comment, " // test comment /")
+
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable = unittest.mock.mock_open)                                 
+    def test_declare_variable_write_basic(self, mock_f):
+        """
+        Testing that write to file is correct. Here a line is in a
+        instrument declare section. The write file operation is
+        mocked and check using a patch. Here a simple declare is
+        used.
+        """
+
+        var = declare_variable("double", "test")
+        with mock_f('test.txt', 'w') as m_fo:
+            var.write_line(m_fo)
+
+        mock_f.assert_called_with('test.txt', 'w')                                   
+        handle = mock_f()
+        handle.write.assert_called_once_with("double test;")
+    
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable = unittest.mock.mock_open)                                 
+    def test_declare_variable_write_complex_float(self, mock_f):
+        """
+        Testing that write to file is correct. Here a line is in a
+        instrument declare section. The write file operation is
+        mocked and check using a patch. Here a declare with a value
+        is used. (float value)
+        """
+
+        var = declare_variable("double",
+                                 "test",
+                                 value = 5.4,
+                                 comment = "test comment")
+        
+        with mock_f('test.txt', 'w') as m_fo:
+            var.write_line(m_fo)
+        
+        expected_write = "double test = 5.4; // test comment"
+        
+        mock_f.assert_called_with('test.txt', 'w')                                   
+        handle = mock_f()
+        handle.write.assert_called_once_with(expected_write)
+    
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable = unittest.mock.mock_open)    
+    def test_declare_variable_write_complex_int(self, mock_f):
+        """
+        Testing that write to file is correct. Here a line is in a
+        instrument declare section. The write file operation is
+        mocked and check using a patch. Here a declare with a value
+        is used. (integer value)
+        """    
+
+        var = declare_variable("double",
+                                 "test",
+                                 value = 5,
+                                 comment = "test comment")
+        
+        with mock_f('test.txt', 'w') as m_fo:
+            var.write_line(m_fo)
+            
+        expected_write = "double test = 5; // test comment"
+        
+        mock_f.assert_called_with('test.txt', 'w')                                   
+        handle = mock_f()
+        handle.write.assert_called_once_with(expected_write)
+        
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable = unittest.mock.mock_open)    
+    def test_declare_variable_write_simple_array(self, mock_f):
+        """
+        Testing that write to file is correct. Here a line is in a
+        instrument declare section. The write file operation is
+        mocked and check using a patch. Here an array is declared.
+        """
+
+        var = declare_variable("double",
+                                 "test",
+                                 array = 29,
+                                 comment = "test comment")
+        
+        with mock_f('test.txt', 'w') as m_fo:
+            var.write_line(m_fo)
+            
+        expected_write = "double test[29]; // test comment"
+        
+        mock_f.assert_called_with('test.txt', 'w')                                   
+        handle = mock_f()
+        handle.write.assert_called_once_with(expected_write)   
+        
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable = unittest.mock.mock_open)    
+    def test_declare_variable_write_complex_array(self, mock_f):
+        """
+        Testing that write to file is correct. Here a line is in a
+        instrument declare section. The write file operation is
+        mocked and check using a patch. Here an array is decalred and
+        populated with the selected values.
+        """
+        
+        var = declare_variable("double",
+                                 "test",
+                                 array = 3,
+                                 value = [5, 4, 3.1],
+                                 comment = "test comment")
+
+        with mock_f('test.txt', 'w') as m_fo:
+            var.write_line(m_fo)
+
+        expected_writes = [unittest.mock.call("double test[3] = {"),
+                           unittest.mock.call("5,"),
+                           unittest.mock.call("4,"),
+                           unittest.mock.call("3.1}; // test comment")]
+
+        mock_f.assert_called_with('test.txt', 'w')                                   
+        handle = mock_f()
+        handle.write.assert_has_calls(expected_writes, any_order=False)    
+
+
+if __name__ == '__main__':
+    unittest.main()
+
+    
\ No newline at end of file
diff --git a/mcstasscript/tests/test_for_reading.comp b/mcstasscript/tests/test_for_reading.comp
new file mode 100644
index 00000000..018abc75
--- /dev/null
+++ b/mcstasscript/tests/test_for_reading.comp
@@ -0,0 +1,186 @@
+/*******************************************************************************
+*
+* McStas, neutron ray-tracing package
+*         Copyright 1997-2002, All rights reserved
+*         Risoe National Laboratory, Roskilde, Denmark
+*         Institut Laue Langevin, Grenoble, France
+*
+* Component: Source_simple
+*
+* %I
+* Written by: Kim Lefmann
+* Date: October 30, 1997
+* Modified by: KL, October 4, 2001
+* Modified by: Emmanuel Farhi, October 30, 2001. Serious bug corrected.
+* Origin: Risoe
+*
+* A circular neutron source with flat energy spectrum and arbitrary flux
+*
+* %D
+* The routine is a circular neutron source, which aims at a square target
+* centered at the beam (in order to improve MC-acceptance rate).  The angular
+* divergence is then given by the dimensions of the target.
+* The neutron energy is uniformly distributed between lambda0-dlambda and
+* lambda0+dlambda or between E0-dE and E0+dE.
+* The flux unit is specified in n/cm2/s/st/energy unit (meV or Angs).
+*
+* This component replaces Source_flat, Source_flat_lambda,
+* Source_flux and Source_flux_lambda.
+*
+* Example: Source_simple(radius=0.1, dist=2, focus_xw=.1, focus_yh=.1, E0=14, dE=2)
+*
+* %P
+* radius: [m]                         Radius of circle in (x,y,0) plane where neutrons are generated.
+* yheight: [m]                        Height of rectangle in (x,y,0) plane where neutrons are generated.
+* xwidth: [m]                         Width of rectangle test comment
+* target_index: [1]                   relative index of component to focus at, e.g. next is +1 this is used to compute 'dist' automatically.
+* dist : [m]                          Distance to target along z axis.
+* focus_xw: [m]                       Width of target
+* focus_yh: [m]                       Height of target
+* E0: [meV]                           Mean energy of neutrons.
+* dE: [meV]                           Energy half spread of neutrons (flat or gaussian sigma).
+* lambda0: [AA]                       Mean wavelength of neutrons.
+* dlambda:                            Wavelength half spread of neutrons.
+* flux: [1/(s*cm**2*st*energy unit)]  flux per energy unit, Angs or meV if flux=0, the source emits 1 in 4*PI whole space.
+*
+* %E
+*******************************************************************************/
+
+DEFINE COMPONENT Source_simple
+DEFINITION PARAMETERS ()
+SETTING PARAMETERS (radius=0.1, yheight=0, xwidth=0,
+dist=0, focus_xw=.045, focus_yh=.12,
+E0=0, dE=0, lambda0=0, dlambda=0,
+int flux=1, gauss, int target_index=+1, string test_string)
+OUTPUT PARAMETERS (pmul,square,srcArea)
+/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
+DECLARE
+%{
+double pmul, srcArea;
+int square;
+double tx,ty,tz;
+%}
+INITIALIZE
+%{
+square = 0;
+/* Determine source area */
+if (radius && !yheight && !xwidth ) {
+    square = 0;
+    srcArea = PI*radius*radius;
+  } else if(yheight && xwidth) {
+    square = 1;
+    srcArea = xwidth * yheight;
+  }
+
+  if (flux) {
+    pmul=flux*1e4*srcArea/mcget_ncount();
+    if (dlambda)
+      pmul *= 2*dlambda;
+    else if (dE)
+      pmul *= 2*dE;
+  } else {
+    gauss = 0;
+    pmul=1.0/(mcget_ncount()*4*PI);
+  }
+
+  if (target_index && !dist)
+  {
+    Coords ToTarget;
+    ToTarget = coords_sub(POS_A_COMP_INDEX(INDEX_CURRENT_COMP+target_index),POS_A_CURRENT_COMP);
+    ToTarget = rot_apply(ROT_A_CURRENT_COMP, ToTarget);
+    coords_get(ToTarget, &tx, &ty, &tz);
+    dist=sqrt(tx*tx+ty*ty+tz*tz);
+  } else if (dist) {
+    tx = 0;
+    ty = 0;
+    tz = dist;
+  }
+
+
+  if (srcArea <= 0) {
+    printf("Source_simple: %s: Source area is <= 0 !\n ERROR - Exiting\n",
+           NAME_CURRENT_COMP);
+    exit(0);
+  }
+  if (dist <= 0 || focus_xw <= 0 || focus_yh <= 0) {
+    printf("Source_simple: %s: Target area unmeaningful! (negative dist / focus_xw / focus_yh)\n ERROR - Exiting\n",
+           NAME_CURRENT_COMP);
+    exit(0);
+  }
+
+  if ((!lambda0 && !E0 && !dE && !dlambda)) {
+    printf("Source_simple: %s: You must specify either a wavelength or energy range!\n ERROR - Exiting\n",
+           NAME_CURRENT_COMP);
+    exit(0);
+  }
+  if ((!lambda0 && !dlambda && (E0 <= 0 || dE < 0 || E0-dE <= 0))
+    || (!E0 && !dE && (lambda0 <= 0 || dlambda < 0 || lambda0-dlambda <= 0))) {
+    printf("Source_simple: %s: Unmeaningful definition of wavelength or energy range!\n ERROR - Exiting\n",
+           NAME_CURRENT_COMP);
+      exit(0);
+  }
+%}
+TRACE
+%{
+ double chi,E,lambda,v,r, xf, yf, rf, dx, dy, pdir;
+
+ t=0;
+ z=0;
+
+ if (square == 1) {
+   x = xwidth * (rand01() - 0.5);
+   y = yheight * (rand01() - 0.5);
+ } else {
+   chi=2*PI*rand01();                          /* Choose point on source */
+   r=sqrt(rand01())*radius;                    /* with uniform distribution. */
+   x=r*cos(chi);
+   y=r*sin(chi);
+ }
+ randvec_target_rect_real(&xf, &yf, &rf, &pdir,
+			  tx, ty, tz, focus_xw, focus_yh, ROT_A_CURRENT_COMP, x, y, z, 2);
+
+ dx = xf-x;
+ dy = yf-y;
+ rf = sqrt(dx*dx+dy*dy+rf*rf);
+
+ p = pdir*pmul;
+
+ if(lambda0==0) {
+   if (!gauss) {
+     E=E0+dE*randpm1();              /*  Choose from uniform distribution */
+   } else {
+     E=E0+randnorm()*dE;
+   }
+   v=sqrt(E)*SE2V;
+ } else {
+   if (!gauss) {
+     lambda=lambda0+dlambda*randpm1();
+   } else {
+     lambda=lambda0+randnorm()*dlambda;
+   }
+   v = K2V*(2*PI/lambda);
+ }
+
+ vz=v*dist/rf;
+ vy=v*dy/rf;
+ vx=v*dx/rf;
+%}
+
+MCDISPLAY
+%{
+  if (square == 1) {
+    
+    rectangle("xy",0,0,0,xwidth,yheight);
+  } else {
+    
+    circle("xy",0,0,0,radius);
+  }
+  if (dist) {
+    dashed_line(0,0,0, -focus_xw/2+tx,-focus_yh/2+ty,tz, 4);
+    dashed_line(0,0,0,  focus_xw/2+tx,-focus_yh/2+ty,tz, 4);
+    dashed_line(0,0,0,  focus_xw/2+tx, focus_yh/2+ty,tz, 4);
+    dashed_line(0,0,0, -focus_xw/2+tx, focus_yh/2+ty,tz, 4);
+  }
+%}
+
+END
diff --git a/mcstasscript/tests/test_parameter_variable.py b/mcstasscript/tests/test_parameter_variable.py
new file mode 100644
index 00000000..bca7b167
--- /dev/null
+++ b/mcstasscript/tests/test_parameter_variable.py
@@ -0,0 +1,181 @@
+import io
+import builtins
+import unittest
+import unittest.mock
+
+
+from mcstasscript.helper.mcstas_objects import parameter_variable
+
+
+class Test_parameter_variable(unittest.TestCase):
+    """
+    Tests the parameter_variable class that holds an input parameter
+    for the instrument.
+    
+    """
+    
+    def test_parameter_variable_init_basic(self):
+        """
+        Smallest possible initialization
+        """
+        
+        par = parameter_variable("test")
+        self.assertEqual(par.name,"test")
+
+    def test_parameter_variable_init_basic_type(self):
+        """
+        Initialization with a type
+        """
+        
+        par = parameter_variable("double", "test")
+        
+        self.assertEqual(par.name,"test")
+        self.assertEqual(par.type,"double ")  # space for easier writing
+        
+    def test_parameter_variable_init_basic_type_value(self):
+        """
+        Initialization with type and value
+        """
+        
+        par = parameter_variable("double", "test", value = 518)
+        
+        self.assertEqual(par.name,"test")
+        self.assertEqual(par.type,"double ")  # space for easier writing
+        self.assertEqual(par.value, 518)
+        
+    def test_parameter_variable_init_basic_type_value_comment(self):
+        """
+        Initialization with type, value and comment
+        """
+        
+        par = parameter_variable("double", "test", 
+                                 value = 518, comment = "test comment /")
+        
+        self.assertEqual(par.name,"test")
+        self.assertEqual(par.type,"double ")  # space for easier writing
+        self.assertEqual(par.value, 518)
+        self.assertEqual(par.comment, "// test comment /")
+        
+    def test_parameter_variable_init_basic_value_comment(self):
+        """
+        Initialization with value and comment
+        """
+        
+        par = parameter_variable("test", 
+                                 value = 518, comment = "test comment /")
+        
+        self.assertEqual(par.name,"test")
+        self.assertEqual(par.type,"")
+        self.assertEqual(par.value, 518)
+        self.assertEqual(par.comment, "// test comment /")
+
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable = unittest.mock.mock_open)                                 
+    def test_parameter_variable_write_basic(self, mock_f):
+        """
+        Testing that write to file is correct. Here a line is in a
+        instrument parameter section. The write file operation is
+        mocked and check using a patch. Here a simple parameter is
+        used.
+        """
+
+        par = parameter_variable("double", "test")
+        with mock_f('test.txt', 'w') as m_fo:
+            par.write_parameter(m_fo, "")
+
+        expected_writes = [unittest.mock.call("double test"),
+                           unittest.mock.call(""),
+                           unittest.mock.call(""),
+                           unittest.mock.call("\n")]
+        
+        mock_f.assert_called_with('test.txt', 'w')                                   
+        handle = mock_f()
+        handle.write.assert_has_calls(expected_writes, any_order=False)
+        
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable = unittest.mock.mock_open)                                 
+    def test_parameter_variable_write_complex_float(self, mock_f):
+        """
+        Testing that write to file is correct. Here a line is in a
+        instrument parameter section. The write file operation is
+        mocked and check using a patch. Here a parameter with a value
+        is used. (float value)
+        """
+
+        par = parameter_variable("double",
+                                 "test",
+                                 value = 5.4,
+                                 comment = "test comment")
+        
+        with mock_f('test.txt', 'w') as m_fo:
+            par.write_parameter(m_fo, ")")
+
+        expected_writes = [unittest.mock.call("double test"),
+                           unittest.mock.call(" = 5.4"),
+                           unittest.mock.call(")"),
+                           unittest.mock.call("// test comment"),
+                           unittest.mock.call("\n")]
+        
+        mock_f.assert_called_with('test.txt', 'w')                                   
+        handle = mock_f()
+        handle.write.assert_has_calls(expected_writes, any_order=False)
+    
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable = unittest.mock.mock_open)    
+    def test_parameter_variable_write_complex_int(self, mock_f):
+        """
+        Testing that write to file is correct. Here a line is in a
+        instrument parameter section. The write file operation is
+        mocked and check using a patch. Here a parameter with a value
+        is used. (integer value)
+        """    
+
+        par = parameter_variable("double",
+                                 "test",
+                                 value = 5,
+                                 comment = "test comment")
+        
+        with mock_f('test.txt', 'w') as m_fo:
+            par.write_parameter(m_fo, ")")
+
+        expected_writes = [unittest.mock.call("double test"),
+                           unittest.mock.call(" = 5"),
+                           unittest.mock.call(")"),
+                           unittest.mock.call("// test comment"),
+                           unittest.mock.call("\n")]
+        
+        mock_f.assert_called_with('test.txt', 'w')                                   
+        handle = mock_f()
+        handle.write.assert_has_calls(expected_writes, any_order=False)
+    
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable = unittest.mock.mock_open)    
+    def test_parameter_variable_write_complex_string(self, mock_f):
+        """
+        Testing that write to file is correct. Here a line is in a
+        instrument parameter section. The write file operation is
+        mocked and check using a patch. Here a parameter with a value
+        is used. (string value)
+        """  
+
+        par = parameter_variable("double",
+                                 "test",
+                                 value = "\"Al\"",
+                                 comment = "test comment")
+        
+        with mock_f('test.txt', 'w') as m_fo:
+            par.write_parameter(m_fo, ",")
+
+        expected_writes = [unittest.mock.call("double test"),
+                           unittest.mock.call(" = \"Al\""),
+                           unittest.mock.call(","),
+                           unittest.mock.call("// test comment"),
+                           unittest.mock.call("\n")]
+        
+        mock_f.assert_called_with('test.txt', 'w')                                   
+        handle = mock_f()
+        handle.write.assert_has_calls(expected_writes, any_order=False)        
+
+
+if __name__ == '__main__':
+    unittest.main()

From e4d974f4e76b945f4ef6c192d338b6e2e0be4b45 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Tue, 21 May 2019 10:17:19 +0200
Subject: [PATCH 017/403] Added tests for interface classes and functions.

---
 mcstasscript/helper/mcstas_objects.py         |   37 +-
 mcstasscript/interface/functions.py           |   12 +-
 mcstasscript/interface/instr.py               |    2 +-
 mcstasscript/tests/test_ComponentReader.py    |  204 ++--
 mcstasscript/tests/test_Instr.py              | 1070 +++++++++++++++++
 mcstasscript/tests/test_ManagedMcrun.py       |  255 ++--
 mcstasscript/tests/test_McStasData.py         |   69 +-
 mcstasscript/tests/test_McStasMetaData.py     |   42 +-
 mcstasscript/tests/test_McStasPlotOptions.py  |   14 +-
 mcstasscript/tests/test_component.py          |  588 ++++++---
 mcstasscript/tests/test_declare_variable.py   |  113 +-
 mcstasscript/tests/test_functions.py          |  141 +++
 mcstasscript/tests/test_parameter_variable.py |  111 +-
 13 files changed, 2080 insertions(+), 578 deletions(-)
 create mode 100644 mcstasscript/tests/test_Instr.py
 create mode 100644 mcstasscript/tests/test_functions.py

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 53db6645..b3b42246 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -374,7 +374,7 @@ def __init__(self, instance_name, component_name, **kwargs):
             self.ROTATED_relative = "RELATIVE " + kwargs["RELATIVE"]
 
         if "WHEN" in kwargs:
-            self.WHEN = "WHEN (" + kwargs["WHEN"] + ")\n"
+            self.WHEN = "WHEN (" + kwargs["WHEN"] + ")"
         else:
             self.WHEN = ""
 
@@ -476,7 +476,7 @@ def set_parameters(self, dict_input):
 
     def set_WHEN(self, string):
         """Sets WHEN string, should be a c logical expression"""
-        self.WHEN = "WHEN (" + string + ")\n"
+        self.WHEN = "WHEN (" + string + ")"
 
     def set_GROUP(self, string):
         """Sets GROUP name"""
@@ -551,7 +551,7 @@ def write_component(self, fo):
 
         # Optional WHEN section
         if not self.WHEN == "":
-            fo.write("%s" % self.WHEN)
+            fo.write("%s\n" % self.WHEN)
 
         # Write AT and ROTATED section
         fo.write("AT (%s,%s,%s)" % (str(self.AT_data[0]),
@@ -614,7 +614,7 @@ class is used as a superclass for classes describing each
                           + bcolors.ENDC)
 
         if not self.WHEN == "":
-            print("WHEN (" + self.WHEN + ")")
+            print(self.WHEN)
         print("AT", self.AT_data, self.AT_relative)
         print("ROTATED", self.ROTATED_data, self.ROTATED_relative)
         if not self.GROUP == "":
@@ -628,7 +628,6 @@ class is used as a superclass for classes describing each
     def print_short(self, **kwargs):
         """Prints short description of component to list print"""
         if "longest_name" in kwargs:
-            print("test")
             number_of_spaces = 3+kwargs["longest_name"]-len(self.name)
             print(str(self.name) + " "*number_of_spaces, end='')
             print(str(self.component_name),
@@ -671,7 +670,8 @@ def show_parameters(self):
                 unit = " [" + self.parameter_units[parameter] + "]"
             comment = ""
             if parameter in self.parameter_comments:
-                comment = " // " + self.parameter_comments[parameter]
+                if not self.parameter_comments[parameter] == "":
+                    comment = " // " + self.parameter_comments[parameter]
 
             parameter_name = bcolors.BOLD + parameter + bcolors.ENDC
             value = ""
@@ -714,20 +714,21 @@ def show_parameters_simple(self):
         """
         print("---- Help " + self.component_name + " -----")
         for parameter in self.parameter_names:
+            value = ""
+            if self.parameter_defaults[parameter] is not None:
+                value = " = " + str(self.parameter_defaults[parameter])
+            if getattr(self, parameter) is not None:
+                value = " = " + str(getattr(self, parameter))
+            
             unit = ""
             if parameter in self.parameter_units:
                 unit = " [" + self.parameter_units[parameter] + "]"
+            
             comment = ""
             if parameter in self.parameter_comments:
-                comment = " // " + self.parameter_comments[parameter]
-            if self.parameter_defaults[parameter] is None:
-                print(parameter
-                      + unit
-                      + comment)
-            else:
-                print(parameter
-                      + " = "
-                      + str(self.parameter_defaults[parameter])
-                      + unit
-                      + comment)
-        print("----------" + "-"*len(self.component_name) + "------")
\ No newline at end of file
+                if self.parameter_comments[parameter] is not "":
+                    comment = " // " + self.parameter_comments[parameter]
+
+            print(parameter + value + unit + comment)
+                
+        print("----------" + "-"*len(self.component_name) + "------")
diff --git a/mcstasscript/interface/functions.py b/mcstasscript/interface/functions.py
index c7e757aa..365a4300 100644
--- a/mcstasscript/interface/functions.py
+++ b/mcstasscript/interface/functions.py
@@ -17,11 +17,14 @@ def name_search(name, data_list):
     data_list : List of McStasData instances
         List of datasets to search
     """
+    
+    if type(data_list) is not list:
+        raise InputError(
+            "name_search function needs list of McStasData as input")
 
     if not type(data_list[0]) == McStasData:
         raise InputError(
-            "name_search function needs objects of type "
-            + "McStasData as input.")
+            "name_search function needs objects of type McStasData as input.")
 
     list_result = []
     for check in data_list:
@@ -54,10 +57,5 @@ def name_plot_options(name, data_list, **kwargs):
         McStasPlotOptions
     """
 
-    if not isinstance(data_list[0], McStasData):
-        raise InputError(
-            "name_search function needs objects of type McStasData "
-            + "as input.")
-
     object_to_modify = name_search(name, data_list)
     object_to_modify.set_plot_options(**kwargs)
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 690f7e19..76a9f2a0 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -364,7 +364,7 @@ def show_components(self, *args):
 
         """
         if len(args) == 0:
-            print("Here are the availalbe component categories:")
+            print("Here are the available component categories:")
             self.component_reader.show_categories()
             print("Call show_components(category_name) to display")
 
diff --git a/mcstasscript/tests/test_ComponentReader.py b/mcstasscript/tests/test_ComponentReader.py
index b53f1b1f..c40a81b3 100644
--- a/mcstasscript/tests/test_ComponentReader.py
+++ b/mcstasscript/tests/test_ComponentReader.py
@@ -6,19 +6,21 @@
 from mcstasscript.helper.component_reader import ComponentInfo
 from mcstasscript.helper.component_reader import ComponentReader
 
+
 def setup_component_reader():
     THIS_DIR = os.path.dirname(os.path.abspath(__file__))
     dummy_path = THIS_DIR + "/dummy_mcstas"
-        
+
     current_work_dir = os.getcwd()
     os.chdir(THIS_DIR)  # Set work directory to test folder
-               
-    component_reader = ComponentReader(mcstas_path = dummy_path)
-        
+
+    component_reader = ComponentReader(mcstas_path=dummy_path)
+
     os.chdir(current_work_dir)  # Reset work directory
-    
+
     return component_reader
 
+
 class TestComponentReader(unittest.TestCase):
     """
     Testing the ComponenReader class. As this class reads information
@@ -26,27 +28,26 @@ class TestComponentReader(unittest.TestCase):
     avoid the test results changeing with updates of McStas.
     """
 
-
-    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_init_overwrite_message(self, mock_stdout):
         """
         Test that ComponentReader reports overwritten components
         """
-        
+
         component_reader = setup_component_reader()
-        
-        message = ("Overwriting McStasScript info on component named " 
+
+        message = ("Overwriting McStasScript info on component named "
                    + "test_for_reading.comp because the component is in "
                    + "the work directory.\n")
-        
+
         self.assertEqual(mock_stdout.getvalue(), message)
-        
-    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_init_filenames(self, mock_stdout):
         """
         Test that ComponentReader initializes component names correctly
         """
-        
+
         component_reader = setup_component_reader()
 
         n_components_found = len(component_reader.component_path)
@@ -55,14 +56,14 @@ def test_ComponentReader_init_filenames(self, mock_stdout):
         self.assertIn("test_for_structure", component_reader.component_path)
         self.assertIn("test_for_structure2", component_reader.component_path)
 
-    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_init_categories(self, mock_stdout):
         """
         Test that ComponentReader initializes categories correctly
         """
-        
+
         component_reader = setup_component_reader()
-        
+
         n_categories_found = len(component_reader.component_category)
         self.assertEqual(n_categories_found, 3)
         """
@@ -77,103 +78,103 @@ def test_ComponentReader_init_categories(self, mock_stdout):
         category = component_reader.component_category["test_for_structure2"]
         self.assertEqual(category, "sources")
 
-    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_show_categories(self, mock_stdout):
         """
         This method prints to console, check it prints the categories
         in the dummy installation correctly.
-        
+
         """
         component_reader = setup_component_reader()
-        
+
         component_reader.show_categories()
-        
+
         output = mock_stdout.getvalue()
         output = output.split("\n")
-        
+
         self.assertEqual(len(output), 5)
         self.assertIn(" sources", output)
         self.assertIn(" Work directory", output)
         self.assertIn(" misc", output)
-        
-    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_show_categories_ordered(self, mock_stdout):
         """
         Check that the print to console is ordered as usual. This test
         may be implementation dependent as python dictionaries are not
         ordered.
-        
+
         """
-        
+
         component_reader = setup_component_reader()
-        
+
         component_reader.show_categories()
-        
+
         output = mock_stdout.getvalue()
         output = output.split("\n")
-        
+
         self.assertEqual(output[1], " sources")
         self.assertEqual(output[2], " Work directory")
         self.assertEqual(output[3], " misc")
-        
-    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_show_components_short(self, mock_stdout):
         """
         Here we attempt to show components in the misc category.  In
         the dummy install, there are two components in this folder, but
-        one of these is overwritten by the version in the current 
+        one of these is overwritten by the version in the current
         work directory.
-        
+
         """
-        
+
         component_reader = setup_component_reader()
-        
+
         component_reader.show_components_in_category("misc")
-        
+
         output = mock_stdout.getvalue()
         output = output.split("\n")
-        
+
         self.assertEqual(len(output), 3)
         self.assertIn(" test_for_structure", output)
         # Check overwritten component is not in the output
         self.assertNotIn(" test_for_reading", output)
-        
+
     """
     # This test not as important, but could be finished later
     @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
     def test_ComponentReader_show_components_long(self, mock_stdout):
-        
+
         component_reader = setup_component_reader()
-        
+
         #  Add elements directly to component_readers library
         # generate list
         # add list
-        
+
         #component_reader.component_category[]
-        
+
         component_reader.show_components_in_category("misc")
-        
+
         output = mock_stdout.getvalue()
         output = output.split("\n")
-        
+
         self.assertEqual(len(output), 3)
         self.assertIn(" test_for_structure", output)
-    """    
-    
+    """
+
     # test load_all_components
-    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_load_all_components(self, mock_stdout):
         """
         Load all components in the dummy install, but only one has any
         content.  The method is currently not necessary, as components
         are now loaded individually when needed.
-        
+
         """
 
         component_reader = setup_component_reader()
-        
+
         CompInfo_dict = component_reader.load_all_components()
-        
+
         comp_name = "test_for_reading"
         name = CompInfo_dict[comp_name].name
         self.assertEqual(name, comp_name)
@@ -198,7 +199,7 @@ def test_ComponentReader_load_all_components(self, mock_stdout):
         self.assertEqual(name, comp_name)
         # test_for_structure2 is an empty file, so no conentet to check
 
-    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_read_name_error(self, mock_stdout):
         """
         read_name should throw an error when searching for a component
@@ -210,12 +211,12 @@ def test_ComponentReader_read_name_error(self, mock_stdout):
         with self.assertRaises(NameError):
             CompInfo = component_reader.read_name("no_such_comp")
 
-    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_read_name_success(self, mock_stdout):
         """
         Read component simply calls read_component_file, but here
-        the output is checked against what is in the dummy file. 
-        
+        the output is checked against what is in the dummy file.
+
         """
 
         component_reader = setup_component_reader()
@@ -231,63 +232,63 @@ def test_ComponentReader_read_name_success(self, mock_stdout):
         self.assertIn("dist", CompInfo.parameter_comments)
         self.assertIn("dist", CompInfo.parameter_units)
         self.assertEqual(CompInfo.parameter_units["dist"], "m")
-           
-    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_find_components_names(self, mock_stdout):
         """
         Test that ComponentReader initializes component names correctly
         """
-        
+
         component_reader = setup_component_reader()
-        
+
         component_reader.component_path = {}
         component_reader.component_category = {}
-        
+
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         dummy_path = THIS_DIR + "/dummy_mcstas/misc"
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
-        component_reader._find_components(dummy_path)       
-        os.chdir(current_work_dir) # Return to original work directory
-        
+        component_reader._find_components(dummy_path)
+        os.chdir(current_work_dir)  # Return to original work directory
+
         n_components_found = len(component_reader.component_path)
         self.assertEqual(n_components_found, 2)
         self.assertIn("test_for_reading", component_reader.component_path)
         self.assertIn("test_for_structure", component_reader.component_path)
 
-    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_find_components_categories(self, mock_stdout):
         """
         Test that ComponentReader initializes component categories correctly
         """
-        
+
         component_reader = setup_component_reader()
-        
+
         component_reader.component_path = {}
         component_reader.component_category = {}
-        
+
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         dummy_path = THIS_DIR + "/dummy_mcstas/misc"
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
-        component_reader._find_components(dummy_path)       
-        os.chdir(current_work_dir) # Return to original work directory
-        
+        component_reader._find_components(dummy_path)
+        os.chdir(current_work_dir)  # Return to original work directory
+
         n_categories_found = len(component_reader.component_category)
         self.assertEqual(n_categories_found, 2)
-        
+
         category = component_reader.component_category["test_for_reading"]
         self.assertEqual(category, "misc")
         category = component_reader.component_category["test_for_structure"]
         self.assertEqual(category, "misc")
-        
-    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_read_component_category(self, mock_stdout):
         """
         Check that the correct category is returned.
-        
+
         Can't run this test with overwritten component test_for_reading.
-        read_component will report tests as category, but this is 
+        read_component will report tests as category, but this is
         overwritten by read_name in normal use.
         """
         component_reader = setup_component_reader()
@@ -298,10 +299,10 @@ def test_ComponentReader_read_component_category(self, mock_stdout):
         exp_cat = component_reader.component_category["test_for_structure"]
         self.assertEqual(CompInfo.category, exp_cat)
 
-    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_read_component_standard(self, mock_stdout):
         """
-        Test that a normal parameter is read correctly when reading a 
+        Test that a normal parameter is read correctly when reading a
         component file.
         Has default, is double type, has comment, has unit
         """
@@ -317,19 +318,19 @@ def test_ComponentReader_read_component_standard(self, mock_stdout):
         self.assertEqual(CompInfo.parameter_defaults["xwidth"], 0.0)
 
         self.assertIn("xwidth", CompInfo.parameter_types)
-        self.assertEqual(CompInfo.parameter_types["xwidth"],"double")
+        self.assertEqual(CompInfo.parameter_types["xwidth"], "double")
 
         self.assertIn("xwidth", CompInfo.parameter_comments)
         comment = "Width of rectangle test comment"
-        self.assertEqual(CompInfo.parameter_comments["xwidth"],comment)
+        self.assertEqual(CompInfo.parameter_comments["xwidth"], comment)
 
         self.assertIn("xwidth", CompInfo.parameter_units)
-        self.assertEqual(CompInfo.parameter_units["xwidth"],"m")
+        self.assertEqual(CompInfo.parameter_units["xwidth"], "m")
 
-    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_read_component_required(self, mock_stdout):
         """
-        Test that a required parameter is read correctly when reading a 
+        Test that a required parameter is read correctly when reading a
         component file.
         Has no default, is double type, has no comment, has no unit
         """
@@ -345,16 +346,16 @@ def test_ComponentReader_read_component_required(self, mock_stdout):
         self.assertIsNone(CompInfo.parameter_defaults["gauss"])
 
         self.assertIn("gauss", CompInfo.parameter_types)
-        self.assertEqual(CompInfo.parameter_types["gauss"],"double")
+        self.assertEqual(CompInfo.parameter_types["gauss"], "double")
 
         self.assertNotIn("gauss", CompInfo.parameter_comments)
 
         self.assertNotIn("gauss", CompInfo.parameter_units)
 
-    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_read_component_int(self, mock_stdout):
         """
-        Test that a integer parameter is read correctly when reading a 
+        Test that a integer parameter is read correctly when reading a
         component file.
         Has default, is int type (comments and unit checked already)
         """
@@ -370,7 +371,7 @@ def test_ComponentReader_read_component_int(self, mock_stdout):
         self.assertEqual(CompInfo.parameter_defaults["flux"], 1)
 
         self.assertIn("flux", CompInfo.parameter_types)
-        self.assertEqual(CompInfo.parameter_types["flux"],"int")
+        self.assertEqual(CompInfo.parameter_types["flux"], "int")
 
         self.assertIn("flux", CompInfo.parameter_comments)
         # Have already tested comments are read
@@ -378,10 +379,10 @@ def test_ComponentReader_read_component_int(self, mock_stdout):
         self.assertIn("flux", CompInfo.parameter_units)
         # Have already tested units are read
 
-    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_read_component_string(self, mock_stdout):
         """
-        Test that a string parameter is read correctly when reading a 
+        Test that a string parameter is read correctly when reading a
         component file.
         Has no default, is string type (comments and unit checked already)
         """
@@ -397,7 +398,7 @@ def test_ComponentReader_read_component_string(self, mock_stdout):
         self.assertIsNone(CompInfo.parameter_defaults["test_string"])
 
         self.assertIn("test_string", CompInfo.parameter_types)
-        self.assertEqual(CompInfo.parameter_types["test_string"],"string")
+        self.assertEqual(CompInfo.parameter_types["test_string"], "string")
 
         self.assertNotIn("test_string", CompInfo.parameter_comments)
         # Have already tested comments are read
@@ -405,51 +406,50 @@ def test_ComponentReader_read_component_string(self, mock_stdout):
         self.assertNotIn("test_string", CompInfo.parameter_units)
         # Have already tested units are read
 
-    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_line_start_long(self, mock_stdout):
         """
         Helper function that should return true when certain string is
         the start of another string.
-        
+
         """
 
         component_reader = setup_component_reader()
 
         test_string = "monkey wants banana"
 
-        return_val = component_reader.line_starts_with(test_string,"mo")
+        return_val = component_reader.line_starts_with(test_string, "mo")
         self.assertIsInstance(return_val, bool)
         self.assertTrue(return_val)
 
-        return_val = component_reader.line_starts_with(test_string,"on")
+        return_val = component_reader.line_starts_with(test_string, "on")
         self.assertIsInstance(return_val, bool)
         self.assertFalse(return_val)
 
-    @unittest.mock.patch("sys.stdout", new_callable = io.StringIO)
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_line_start_short(self, mock_stdout):
         """
         Helper function that should return true when certain string is
         the start of another string. Here checked with short test_string
-        
-        
+
         """
-                
+
         component_reader = setup_component_reader()
-        
+
         test_string = "m"
 
-        return_val = component_reader.line_starts_with(test_string,"m")
+        return_val = component_reader.line_starts_with(test_string, "m")
         self.assertIsInstance(return_val, bool)
         self.assertTrue(return_val)
 
-        return_val = component_reader.line_starts_with(test_string,"mo")
+        return_val = component_reader.line_starts_with(test_string, "mo")
         self.assertIsInstance(return_val, bool)
         self.assertFalse(return_val)
 
-        return_val = component_reader.line_starts_with(test_string,"on")
+        return_val = component_reader.line_starts_with(test_string, "on")
         self.assertIsInstance(return_val, bool)
         self.assertFalse(return_val)
-        
-    
+
+
 if __name__ == '__main__':
     unittest.main()
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
new file mode 100644
index 00000000..35705cd5
--- /dev/null
+++ b/mcstasscript/tests/test_Instr.py
@@ -0,0 +1,1070 @@
+import os
+import io
+import builtins
+import unittest
+import unittest.mock
+import datetime
+
+from mcstasscript.interface.instr import McStas_instr
+from mcstasscript.helper.formatting import bcolors
+
+
+def setup_instr_no_path():
+    """
+    Sets up a instrument without a valid mcstas_path
+    """
+    return McStas_instr("test_instrument", mcstas_path="/")
+
+
+def setup_instr_with_path():
+    """
+    Sets up a instrument with a valid mcstas_path, but it points to
+    the dummy installation in the test folder.
+    """
+
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+    dummy_path = THIS_DIR + "/dummy_mcstas"
+
+    return McStas_instr("test_instrument", mcstas_path=dummy_path)
+
+
+def setup_populated_instr():
+    """
+    Sets up a instrument with some features used and two components
+    """
+    instr = setup_instr_no_path()
+
+    instr.add_parameter("double", "theta")
+    instr.add_declare_var("double", "two_theta")
+    instr.append_initialize("two_theta = 2.0*theta;")
+
+    comp1 = instr.add_component("first_component", "test_for_reading")
+    comp2 = instr.add_component("second_component", "test_for_reading")
+    comp3 = instr.add_component("third_component", "test_for_reading")
+
+    return instr
+
+
+class TestMcStas_instr(unittest.TestCase):
+    """
+    Tests of the main class in McStasScript called McStas_instr.
+    """
+
+    def test_simple_initialize(self):
+        """
+        Test basic initialization runs
+        """
+        my_instrument = setup_instr_no_path()
+
+        self.assertEqual(my_instrument.name, "test_instrument")
+
+    def test_complex_initialize(self):
+        """
+        Tests all keywords work in initialization
+        """
+        my_instrument = McStas_instr("test_instrument",
+                                     author="Mads",
+                                     origin="DMSC",
+                                     mcrun_path="/path/to/mcrun",
+                                     mcstas_path="/path/to/mcstas")
+
+        self.assertEqual(my_instrument.author, "Mads")
+        self.assertEqual(my_instrument.origin, "DMSC")
+        self.assertEqual(my_instrument.mcrun_path, "/path/to/mcrun")
+        self.assertEqual(my_instrument.mcstas_path, "/path/to/mcstas")
+
+    def test_simple_add_parameter(self):
+        """
+        This is just an interface to a function that is tested
+        elsewhere, so only a basic test is performed here.
+        """
+        instr = setup_instr_no_path()
+
+        instr.add_parameter("double", "theta", comment="test par")
+
+        self.assertEqual(instr.parameter_list[0].name, "theta")
+        self.assertEqual(instr.parameter_list[0].comment, "// test par")
+
+    def test_simple_add_declare_parameter(self):
+        """
+        This is just an interface to a function that is tested
+        elsewhere, so only a basic test is performed here.
+        """
+        instr = setup_instr_no_path()
+
+        instr.add_declare_var("double", "two_theta", comment="test par")
+
+        self.assertEqual(instr.declare_list[0].name, "two_theta")
+        self.assertEqual(instr.declare_list[0].comment, " // test par")
+
+    def test_simple_append_initialize(self):
+        """
+        The initialize section is held as a string. This method
+        appends that string.
+        """
+        instr = setup_instr_no_path()
+
+        self.assertEqual(instr.initialize_section,
+                         "// Start of initialize for generated "
+                         + "test_instrument\n")
+
+        instr.append_initialize("First line of initialize")
+        instr.append_initialize("Second line of initialize")
+        instr.append_initialize("Third line of initialize")
+
+        self.assertEqual(instr.initialize_section,
+                         "// Start of initialize for generated "
+                         + "test_instrument\n"
+                         + "First line of initialize\n"
+                         + "Second line of initialize\n"
+                         + "Third line of initialize\n")
+
+    def test_simple_append_initialize_no_new_line(self):
+        """
+        The initialize section is held as a string. This method
+        appends that string.
+        """
+        instr = setup_instr_no_path()
+
+        self.assertEqual(instr.initialize_section,
+                         "// Start of initialize for generated "
+                         + "test_instrument\n")
+
+        instr.append_initialize_no_new_line("A")
+        instr.append_initialize_no_new_line("B")
+        instr.append_initialize_no_new_line("CD")
+
+        self.assertEqual(instr.initialize_section,
+                         "// Start of initialize for generated "
+                         + "test_instrument\n"
+                         + "ABCD")
+
+    def test_simple_append_finally(self):
+        """
+        The initialize section is held as a string. This method
+        appends that string.
+        """
+        instr = setup_instr_no_path()
+
+        self.assertEqual(instr.finally_section,
+                         "// Start of finally for generated "
+                         + "test_instrument\n")
+
+        instr.append_finally("First line of finally")
+        instr.append_finally("Second line of finally")
+        instr.append_finally("Third line of finally")
+
+        self.assertEqual(instr.finally_section,
+                         "// Start of finally for generated "
+                         + "test_instrument\n"
+                         + "First line of finally\n"
+                         + "Second line of finally\n"
+                         + "Third line of finally\n")
+
+    def test_simple_append_finally_no_new_line(self):
+        """
+        The initialize section is held as a string. This method
+        appends that string.
+        """
+        instr = setup_instr_no_path()
+
+        self.assertEqual(instr.finally_section,
+                         "// Start of finally for generated "
+                         + "test_instrument\n")
+
+        instr.append_finally_no_new_line("A")
+        instr.append_finally_no_new_line("B")
+        instr.append_finally_no_new_line("CD")
+
+        self.assertEqual(instr.finally_section,
+                         "// Start of finally for generated "
+                         + "test_instrument\n"
+                         + "ABCD")
+
+    def test_simple_append_trace(self):
+        """
+        The initialize section is held as a string. This method
+        appends that string.
+        """
+        instr = setup_instr_no_path()
+
+        self.assertEqual(instr.trace_section,
+                         "// Start of trace section for generated "
+                         + "test_instrument\n")
+
+        instr.append_trace("First line of trace")
+        instr.append_trace("Second line of trace")
+        instr.append_trace("Third line of trace")
+
+        self.assertEqual(instr.trace_section,
+                         "// Start of trace section for generated "
+                         + "test_instrument\n"
+                         + "First line of trace\n"
+                         + "Second line of trace\n"
+                         + "Third line of trace\n")
+
+    def test_simple_append_trace_no_new_line(self):
+        """
+        The initialize section is held as a string. This method
+        appends that string.
+        """
+        instr = setup_instr_no_path()
+
+        self.assertEqual(instr.trace_section,
+                         "// Start of trace section for generated "
+                         + "test_instrument\n")
+
+        instr.append_trace_no_new_line("A")
+        instr.append_trace_no_new_line("B")
+        instr.append_trace_no_new_line("CD")
+
+        self.assertEqual(instr.trace_section,
+                         "// Start of trace section for generated "
+                         + "test_instrument\n"
+                         + "ABCD")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_show_components_simple(self, mock_stdout):
+        """
+        Simple test of show components to show categories
+        """
+        instr = setup_instr_with_path()
+
+        instr.show_components()
+
+        output = mock_stdout.getvalue()
+        output = output.split("\n")
+
+        self.assertEqual(output[0],
+                         "Overwriting McStasScript info on component "
+                         + "named test_for_reading.comp because the "
+                         + "component is in the work directory.")
+        self.assertEqual(output[1],
+                         "Here are the available component categories:")
+        self.assertEqual(output[2], " sources")
+        self.assertEqual(output[3], " Work directory")
+        self.assertEqual(output[4], " misc")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_show_components_folder(self, mock_stdout):
+        """
+        Simple test of show components to show categories
+        """
+        instr = setup_instr_with_path()
+
+        instr.show_components("Work directory")
+
+        output = mock_stdout.getvalue()
+        output = output.split("\n")
+
+        self.assertEqual(output[0],
+                         "Overwriting McStasScript info on component "
+                         + "named test_for_reading.comp because the "
+                         + "component is in the work directory.")
+        self.assertEqual(output[1],
+                         "Here are all components in the Work directory "
+                         + "category.")
+        self.assertEqual(output[2], " test_for_reading")
+        self.assertEqual(output[3], "")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_component_help(self, mock_stdout):
+        """
+        Simple test of component help
+        """
+        instr = setup_instr_with_path()
+
+        instr.component_help("test_for_reading")
+        # This call creates a dummy component and calls its
+        # show_parameter method which has been tested. Here we
+        # need to ensure the call is succesful, not test all
+        # output from the call.
+
+        output = mock_stdout.getvalue()
+        output = output.split("\n")
+
+        self.assertEqual(output[1], " ___ Help test_for_reading " + "_"*46)
+
+        legend = ("|"
+                  + bcolors.BOLD + "optional parameter" + bcolors.ENDC
+                  + "|"
+                  + bcolors.BOLD + bcolors.UNDERLINE
+                  + "required parameter"
+                  + bcolors.ENDC + bcolors.ENDC
+                  + "|"
+                  + bcolors.BOLD + bcolors.OKBLUE
+                  + "default value"
+                  + bcolors.ENDC + bcolors.ENDC
+                  + "|"
+                  + bcolors.BOLD + bcolors.OKGREEN
+                  + "user specified value"
+                  + bcolors.ENDC + bcolors.ENDC
+                  + "|")
+
+        self.assertEqual(output[2], legend)
+
+        par_name = bcolors.BOLD + "radius" + bcolors.ENDC
+        value = (bcolors.BOLD + bcolors.OKBLUE
+                 + "0.1" + bcolors.ENDC + bcolors.ENDC)
+        comment = ("// Radius of circle in (x,y,0) plane where "
+                   + "neutrons are generated.")
+        self.assertEqual(output[3],
+                         par_name + " = " + value + " [m] " + comment)
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_create_component_instance_simple(self, mock_stdout):
+        """
+        _create_component_instance will make a dynamic subclass of
+        component with the information from the component files read
+        from disk.  The subclasses is saved in a dict for reuse in
+        case the same component type is requested again.
+        """
+
+        instr = setup_instr_with_path()
+
+        comp = instr._create_component_instance("test_component",
+                                                "test_for_reading")
+
+        self.assertEqual(comp.radius, None)
+        self.assertIn("radius", comp.parameter_names)
+        self.assertEqual(comp.parameter_defaults["radius"], 0.1)
+        self.assertEqual(comp.parameter_types["radius"], "double")
+        self.assertEqual(comp.parameter_units["radius"], "m")
+
+        comment = ("Radius of circle in (x,y,0) plane where "
+                   + "neutrons are generated.")
+        self.assertEqual(comp.parameter_comments["radius"], comment)
+        self.assertEqual(comp.category, "Work directory")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_create_component_instance_simple_error(self, mock_stdout):
+        """
+        _create_component_instance will make a dynamic subclass of
+        component with the information from the component files read
+        from disk.  The subclasses is saved in a dict for reuse in
+        case the same component type is requested again.
+        """
+
+        instr = setup_instr_with_path()
+
+        with self.assertRaises(NameError):
+            comp = instr._create_component_instance("test_component")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_create_component_instance_complex(self, mock_stdout):
+        """
+        _create_component_instance will make a dynamic subclass of
+        component with the information from the component files read
+        from disk.  The subclasses is saved in a dict for reuse in
+        case the same component type is requested again.
+        """
+
+        instr = setup_instr_with_path()
+
+        # Setting relative to home, should be passed to component
+        comp = instr._create_component_instance("test_component",
+                                                "test_for_reading",
+                                                RELATIVE="home")
+
+        self.assertEqual(comp.radius, None)
+        self.assertIn("radius", comp.parameter_names)
+        self.assertEqual(comp.parameter_defaults["radius"], 0.1)
+        self.assertEqual(comp.parameter_types["radius"], "double")
+        self.assertEqual(comp.parameter_units["radius"], "m")
+
+        comment = ("Radius of circle in (x,y,0) plane where "
+                   + "neutrons are generated.")
+        self.assertEqual(comp.parameter_comments["radius"], comment)
+        self.assertEqual(comp.category, "Work directory")
+
+        # The keyword arguments of the call should be passed to the
+        # new instance of the component. This is checked by reading
+        # the relative attributes which were set to home in the call
+        self.assertEqual(comp.AT_relative, "RELATIVE home")
+        self.assertEqual(comp.ROTATED_relative, "RELATIVE home")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_add_component_simple(self, mock_stdout):
+        """
+        The add_component method adds a new component object to the
+        instrument and keeps track of its location within the
+        sequence of components.  Normally a new component is added to
+        the end of the sequence, but the before and after keywords can
+        be used to select another location.
+        """
+
+        instr = setup_instr_with_path()
+
+        comp = instr.add_component("test_component", "test_for_reading")
+
+        self.assertEqual(len(instr.component_list), 1)
+        self.assertEqual(instr.component_list[0].name, "test_component")
+
+        # Test the resulting object functions as intended
+        comp.set_GROUP("developers")
+        self.assertEqual(instr.component_list[0].GROUP, "developers")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_add_component_simple_keyword(self, mock_stdout):
+        """
+        The add_component method adds a new component object to the
+        instrument and keeps track of its location within the
+        sequence of components.  Normally a new component is added to
+        the end of the sequence, but the before and after keywords can
+        be used to select another location. Here keyword passing is
+        tested.
+        """
+
+        instr = setup_instr_with_path()
+
+        comp = instr.add_component("test_component",
+                                   "test_for_reading",
+                                   WHEN="1<2")
+
+        self.assertEqual(len(instr.component_list), 1)
+        self.assertEqual(instr.component_list[0].name, "test_component")
+        self.assertEqual(instr.component_list[0].component_name,
+                         "test_for_reading")
+
+        self.assertEqual(instr.component_list[0].WHEN, "WHEN (1<2)")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_add_component_simple_before(self, mock_stdout):
+        """
+        The add_component method adds a new component object to the
+        instrument and keeps track of its location within the
+        sequence of components.  Normally a new component is added to
+        the end of the sequence, but the before and after keywords can
+        be used to select another location. Here keyword passing is
+        tested.
+        """
+
+        instr = setup_populated_instr()
+
+        comp = instr.add_component("test_component",
+                                   "test_for_reading",
+                                   before="first_component")
+
+        self.assertEqual(len(instr.component_list), 4)
+        self.assertEqual(instr.component_list[0].name, "test_component")
+        self.assertEqual(instr.component_list[3].name, "third_component")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_add_component_simple_after(self, mock_stdout):
+        """
+        The add_component method adds a new component object to the
+        instrument and keeps track of its location within the
+        sequence of components.  Normally a new component is added to
+        the end of the sequence, but the before and after keywords can
+        be used to select another location. Here keyword passing is
+        tested.
+        """
+
+        instr = setup_populated_instr()
+
+        comp = instr.add_component("test_component",
+                                   "test_for_reading",
+                                   after="first_component")
+
+        self.assertEqual(len(instr.component_list), 4)
+        self.assertEqual(instr.component_list[1].name, "test_component")
+        self.assertEqual(instr.component_list[3].name, "third_component")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_add_component_simple_after_error(self, mock_stdout):
+        """
+        The add_component method adds a new component object to the
+        instrument and keeps track of its location within the
+        sequence of components.  Normally a new component is added to
+        the end of the sequence, but the before and after keywords can
+        be used to select another location. Here keyword passing is
+        tested.
+        """
+
+        instr = setup_populated_instr()
+
+        with self.assertRaises(NameError):
+            comp = instr.add_component("test_component",
+                                       "test_for_reading",
+                                       after="non_exsistent_component")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_add_component_simple_before_error(self, mock_stdout):
+        """
+        The add_component method adds a new component object to the
+        instrument and keeps track of its location within the
+        sequence of components.  Normally a new component is added to
+        the end of the sequence, but the before and after keywords can
+        be used to select another location. Here keyword passing is
+        tested.
+        """
+
+        instr = setup_populated_instr()
+
+        with self.assertRaises(NameError):
+            comp = instr.add_component("test_component",
+                                       "test_for_reading",
+                                       before="non_exsistent_component")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_add_component_simple_double_naming_error(self, mock_stdout):
+        """
+        The add_component method adds a new component object to the
+        instrument and keeps track of its location within the
+        sequence of components.  Normally a new component is added to
+        the end of the sequence, but the before and after keywords can
+        be used to select another location. Here keyword passing is
+        tested.
+        """
+
+        instr = setup_populated_instr()
+
+        with self.assertRaises(NameError):
+            comp = instr.add_component("first_component", "test_for_reading")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_get_component_simple(self, mock_stdout):
+        """
+        get_component retrieves a component with a given name for
+        easier manipulation.
+        """
+
+        instr = setup_populated_instr()
+
+        comp = instr.get_component("second_component")
+
+        self.assertEqual(comp.name, "second_component")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_get_component_simple_error(self, mock_stdout):
+        """
+        get_component retrieves a component with a given name for
+        easier manipulation.
+        """
+
+        instr = setup_populated_instr()
+
+        with self.assertRaises(NameError):
+            comp = instr.get_component("non_existing_component")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_get_last_component_simple(self, mock_stdout):
+        """
+        get_component retrieves the last component for easier
+        manipulation.
+        """
+
+        instr = setup_populated_instr()
+
+        comp = instr.get_last_component()
+
+        self.assertEqual(comp.name, "third_component")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_set_component_parameter(self, mock_stdout):
+        """
+        Set component parameter passes a dict from instrument level
+        to a contained component with the given name. It uses the
+        get_component method.
+        """
+
+        instr = setup_populated_instr()
+
+        instr.set_component_parameter("second_component",
+                                      {"radius": 5.8,
+                                       "dist": "text"})
+
+        comp = instr.get_component("second_component")
+
+        self.assertEqual(comp.radius, 5.8)
+        self.assertEqual(comp.dist, "text")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_set_component_parameter_error(self, mock_stdout):
+        """
+        Set component parameter passes a dict from instrument level
+        to a contained component with the given name. It uses the
+        get_component method.
+        """
+
+        instr = setup_populated_instr()
+
+        with self.assertRaises(NameError):
+            instr.set_component_parameter("second_component",
+                                          {"non_exsistant_par": 5.8,
+                                           "dist": "text"})
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_set_component_AT(self, mock_stdout):
+        """
+        set_component_AT passes the argument to the similar method
+        in the component class.
+        """
+
+        instr = setup_populated_instr()
+
+        instr.set_component_AT("second_component",
+                               [1, 2, 3.2], RELATIVE="home")
+
+        comp = instr.get_component("second_component")
+
+        self.assertEqual(comp.AT_data, [1, 2, 3.2])
+        self.assertEqual(comp.AT_relative, "RELATIVE home")
+        self.assertEqual(comp.ROTATED_relative, "ABSOLUTE")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_set_component_ROTATED(self, mock_stdout):
+        """
+        set_component_ROTATED passes the argument to the similar
+        method in the component class.
+        """
+
+        instr = setup_populated_instr()
+
+        instr.set_component_ROTATED("second_component",
+                                    [1, 2, 3.2], RELATIVE="home")
+
+        comp = instr.get_component("second_component")
+
+        self.assertEqual(comp.ROTATED_data, [1, 2, 3.2])
+        self.assertEqual(comp.ROTATED_relative, "RELATIVE home")
+        self.assertEqual(comp.AT_relative, "ABSOLUTE")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_set_component_RELATIVE(self, mock_stdout):
+        """
+        set_component_RELATIVE passes the argument to the similar
+        method in the component class.
+        """
+
+        instr = setup_populated_instr()
+
+        instr.set_component_RELATIVE("second_component", "home")
+
+        comp = instr.get_component("second_component")
+
+        self.assertEqual(comp.ROTATED_data, [0, 0, 0])
+        self.assertEqual(comp.ROTATED_relative, "RELATIVE home")
+        self.assertEqual(comp.AT_relative, "RELATIVE home")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_set_component_WHEN(self, mock_stdout):
+        """
+        set_component_WHEN passes the argument to the similar method
+        in the component class.
+        """
+
+        instr = setup_populated_instr()
+
+        instr.set_component_WHEN("second_component", "2>1")
+
+        comp = instr.get_component("second_component")
+
+        self.assertEqual(comp.WHEN, "WHEN (2>1)")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_append_component_EXTEND(self, mock_stdout):
+        """
+        append_component_EXTEND passes the argument to the similar
+        method in the component class.
+        """
+
+        instr = setup_populated_instr()
+
+        instr.append_component_EXTEND("second_component", "line1")
+        instr.append_component_EXTEND("second_component", "line2")
+
+        comp = instr.get_component("second_component")
+
+        output = comp.EXTEND.split("\n")
+
+        self.assertEqual(output[0], "line1")
+        self.assertEqual(output[1], "line2")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_set_component_GROUP(self, mock_stdout):
+        """
+        set_component_GROUP passes the argument to the similar method
+        in the component class.
+        """
+
+        instr = setup_populated_instr()
+
+        instr.set_component_GROUP("second_component", "developers")
+
+        comp = instr.get_component("second_component")
+
+        self.assertEqual(comp.GROUP, "developers")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_set_component_JUMP(self, mock_stdout):
+        """
+        set_component_JUMP passes the argument to the similar method
+        in the component class.
+        """
+
+        instr = setup_populated_instr()
+
+        instr.set_component_JUMP("second_component", "myself 8")
+
+        comp = instr.get_component("second_component")
+
+        self.assertEqual(comp.JUMP, "myself 8")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_set_component_comment(self, mock_stdout):
+        """
+        set_component_comment passes the argument to the similar
+        method in the component class.
+        """
+
+        instr = setup_populated_instr()
+
+        instr.set_component_comment("second_component", "test comment")
+
+        comp = instr.get_component("second_component")
+
+        self.assertEqual(comp.comment, "test comment")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_print_component(self, mock_stdout):
+        """
+        print_component calls the print_long method in the component
+        class.
+        """
+
+        instr = setup_populated_instr()
+        instr.set_component_parameter("second_component",
+                                      {"dist": 5})
+
+        instr.print_component("second_component")
+
+        output = mock_stdout.getvalue().split("\n")
+
+        self.assertEqual(output[0],
+                         "COMPONENT second_component = test_for_reading")
+
+        par_name = bcolors.BOLD + "dist" + bcolors.ENDC
+        value = (bcolors.BOLD + bcolors.OKGREEN
+                 + "5" + bcolors.ENDC + bcolors.ENDC)
+        self.assertEqual(output[1], "  " + par_name + " = " + value + " [m]")
+
+        par_name = bcolors.BOLD + "gauss" + bcolors.ENDC
+        warning = (bcolors.FAIL
+                   + " : Required parameter not yet specified"
+                   + bcolors.ENDC)
+        self.assertEqual(output[2], "  " + par_name + warning)
+
+        par_name = bcolors.BOLD + "test_string" + bcolors.ENDC
+        warning = (bcolors.FAIL
+                   + " : Required parameter not yet specified"
+                   + bcolors.ENDC)
+        self.assertEqual(output[3], "  " + par_name + warning)
+
+        self.assertEqual(output[4], "AT [0, 0, 0] ABSOLUTE")
+        self.assertEqual(output[5], "ROTATED [0, 0, 0] ABSOLUTE")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_print_component_short(self, mock_stdout):
+        """
+        print_component_short calls the print_short method in the
+        component class.
+        """
+
+        instr = setup_populated_instr()
+        instr.set_component_AT("second_component",
+                               [-1, 2, 3.4], RELATIVE="home")
+
+        instr.print_component_short("second_component")
+
+        output = mock_stdout.getvalue().split("\n")
+
+        expected = ("second_component = test_for_reading "
+                    + "\tAT [-1, 2, 3.4] RELATIVE home "
+                    + "ROTATED [0, 0, 0] ABSOLUTE")
+
+        self.assertEqual(output[0], expected)
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_print_components_simple(self, mock_stdout):
+        """
+        print_components calls the print_short method in the component
+        class for each component and aligns the data for display
+        """
+
+        instr = setup_populated_instr()
+
+        instr.print_components()
+
+        output = mock_stdout.getvalue().split("\n")
+
+        expected = ("first_component    test_for_reading"
+                    + "   AT  [0, 0, 0]     ABSOLUTE"
+                    + "   ROTATED  [0, 0, 0]     ABSOLUTE")
+        self.assertEqual(output[0], expected)
+
+        expected = ("second_component   test_for_reading"
+                    + "   AT  [0, 0, 0]     ABSOLUTE"
+                    + "   ROTATED  [0, 0, 0]     ABSOLUTE")
+        self.assertEqual(output[1], expected)
+
+        expected = ("third_component    test_for_reading"
+                    + "   AT  [0, 0, 0]     ABSOLUTE"
+                    + "   ROTATED  [0, 0, 0]     ABSOLUTE")
+        self.assertEqual(output[2], expected)
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_print_components_complex(self, mock_stdout):
+        """
+        print_components calls the print_short method in the component
+        class for each component and aligns the data for display
+        """
+
+        instr = setup_populated_instr()
+
+        instr.set_component_AT("first_component",
+                               [-0.1, 12, "dist"],
+                               RELATIVE="home")
+        instr.set_component_ROTATED("second_component",
+                                    [-4, 0.001, "theta"],
+                                    RELATIVE="etc")
+        comp = instr.get_last_component()
+        comp.component_name = "test_name"
+
+        instr.print_components()
+
+        output = mock_stdout.getvalue().split("\n")
+
+        expected = ("first_component    test_for_reading"
+                    + "   AT  [-0.1, 12, 'dist']   RELATIVE home"
+                    + "   ROTATED  [0, 0, 0]              ABSOLUTE")
+        self.assertEqual(output[0], expected)
+
+        expected = ("second_component   test_for_reading"
+                    + "   AT  [0, 0, 0]            ABSOLUTE"
+                    + "        ROTATED  [-4, 0.001, 'theta']   RELATIVE etc")
+        self.assertEqual(output[1], expected)
+
+        expected = ("third_component    test_name"
+                    + "          AT  [0, 0, 0]            ABSOLUTE"
+                    + "        ROTATED  [0, 0, 0]              ABSOLUTE")
+        self.assertEqual(output[2], expected)
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable=unittest.mock.mock_open)
+    def test_write_c_files_simple(self, mock_f, mock_stdout):
+        """
+        Write_c_files writes the strings for declare, initialize,
+        and trace to files that are then included in McStas files.
+        This is an obsolete method, but may be repurposed later
+        so that instrument parts can be created with the modern
+        syntax.
+
+        The generated includes file in the test directory is written
+        by this test. It will fail if it does not have rights to
+        create the directory.
+        """
+
+        instr = setup_populated_instr()
+        instr.write_c_files()
+
+        mock_f.assert_any_call("./generated_includes/"
+                               + "test_instrument_declare.c", "w")
+        mock_f.assert_any_call("./generated_includes/"
+                               + "test_instrument_declare.c", "a")
+        mock_f.assert_any_call("./generated_includes/"
+                               + "test_instrument_initialize.c", "w")
+        mock_f.assert_any_call("./generated_includes/"
+                               + "test_instrument_trace.c", "w")
+        mock_f.assert_any_call("./generated_includes/"
+                               + "test_instrument_component_trace.c", "w")
+
+        # This does not check that the right thing is written to the
+        # right file. Can be improved by splitting the method into
+        # several for easier testing. Acceptable since it is rarely
+        # used.
+        handle = mock_f()
+        call = unittest.mock.call
+        wrts = [
+         call("// declare section for test_instrument \n"),
+         call("double two_theta;"),
+         call("\n"),
+         call("// Start of initialize for generated test_instrument\n"
+              + "two_theta = 2.0*theta;\n"),
+         call("// Start of trace section for generated test_instrument\n"),
+         call("COMPONENT first_component = test_for_reading("),
+         call(")\n"),
+         call("AT (0,0,0)"),
+         call(" ABSOLUTE\n"),
+         call("ROTATED (0,0,0)"),
+         call(" ABSOLUTE\n"),
+         call("\n"),
+         call("COMPONENT second_component = test_for_reading("),
+         call(")\n"),
+         call("AT (0,0,0)"),
+         call(" ABSOLUTE\n"),
+         call("ROTATED (0,0,0)"),
+         call(" ABSOLUTE\n"),
+         call("\n"),
+         call("COMPONENT third_component = test_for_reading("),
+         call(")\n"),
+         call("AT (0,0,0)"),
+         call(" ABSOLUTE\n"),
+         call("ROTATED (0,0,0)"),
+         call(" ABSOLUTE\n"),
+         call("\n")]
+
+        handle.write.assert_has_calls(wrts, any_order=False)
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable=unittest.mock.mock_open)
+    def test_write_full_instrument_simple(self, mock_f, mock_stdout):
+        """
+        The write_full_instrument methods writes the information
+        contained in the instrument instance to a file with McStas
+        syntax.
+
+        The test includes a time stamp in the written and expected
+        data that has an accuracy of 1 second.  It is unlikey to fail
+        due to this, but it can happen.
+        """
+
+        instr = setup_populated_instr()
+        instr.write_full_instrument()
+
+        t_format = "%H:%M:%S on %B %d, %Y"
+
+        my_call = unittest.mock.call
+        wrts = [
+         my_call("/" + 80*"*" + "\n"),
+         my_call("* \n"),
+         my_call("* McStas, neutron ray-tracing package\n"),
+         my_call("*         Copyright (C) 1997-2008, All rights reserved\n"),
+         my_call("*         Risoe National Laboratory, Roskilde, Denmark\n"),
+         my_call("*         Institut Laue Langevin, Grenoble, France\n"),
+         my_call("* \n"),
+         my_call("* This file was written by McStasScript, which is a \n"),
+         my_call("* python based McStas instrument generator written by \n"),
+         my_call("* Mads Bertelsen in 2019 while employed at the \n"),
+         my_call("* European Spallation Source Data Management and \n"),
+         my_call("* Software Center\n"),
+         my_call("* \n"),
+         my_call("* Instrument test_instrument\n"),
+         my_call("* \n"),
+         my_call("* %Identification\n"),
+         my_call("* Written by: Python McStas Instrument Generator\n"),
+         my_call("* Date: %s\n" % datetime.datetime.now().strftime(t_format)),
+         my_call("* Origin: ESS DMSC\n"),
+         my_call("* %INSTRUMENT_SITE: Generated_instruments\n"),
+         my_call("* \n"),
+         my_call("* \n"),
+         my_call("* %Parameters\n"),
+         my_call("* \n"),
+         my_call("* %End \n"),
+         my_call("*"*80 + "/\n"),
+         my_call("\n"),
+         my_call("DEFINE INSTRUMENT test_instrument ("),
+         my_call("\n"),
+         my_call("double theta"),
+         my_call(" "),
+         my_call(""),
+         my_call("\n"),
+         my_call(")\n"),
+         my_call("\n"),
+         my_call("DECLARE \n%{\n"),
+         my_call("double two_theta;"),
+         my_call("\n"),
+         my_call("%}\n\n"),
+         my_call("INITIALIZE \n%{\n"),
+         my_call("// Start of initialize for generated test_instrument\n"
+                 + "two_theta = 2.0*theta;\n"),
+         my_call("%}\n\n"),
+         my_call("TRACE \n"),
+         my_call("COMPONENT first_component = test_for_reading("),
+         my_call(")\n"),
+         my_call("AT (0,0,0)"),
+         my_call(" ABSOLUTE\n"),
+         my_call("ROTATED (0,0,0)"),
+         my_call(" ABSOLUTE\n"),
+         my_call("\n"),
+         my_call("COMPONENT second_component = test_for_reading("),
+         my_call(")\n"),
+         my_call("AT (0,0,0)"),
+         my_call(" ABSOLUTE\n"),
+         my_call("ROTATED (0,0,0)"),
+         my_call(" ABSOLUTE\n"),
+         my_call("\n"),
+         my_call("COMPONENT third_component = test_for_reading("),
+         my_call(")\n"),
+         my_call("AT (0,0,0)"),
+         my_call(" ABSOLUTE\n"),
+         my_call("ROTATED (0,0,0)"),
+         my_call(" ABSOLUTE\n"),
+         my_call("\n"),
+         my_call("FINALLY \n%{\n"),
+         my_call("// Start of finally for generated test_instrument\n"),
+         my_call("%}\n"),
+         my_call("\nEND\n")]
+
+        mock_f.assert_called_with("test_instrument.instr", "w")
+        handle = mock_f()
+        handle.write.assert_has_calls(wrts, any_order=False)
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable=unittest.mock.mock_open)
+    @unittest.mock.patch("os.system")
+    def test_run_full_instrument_basic(self, os_system,
+                                       mock_f, mock_stdout,):
+        """
+        Check a simple run performs the correct system call.  Here
+        the target directory is set to the test data set so that some
+        data is loaded even though the system call is not executed.
+        """
+
+        instr = setup_populated_instr()
+        instr.run_full_instrument("test_instrument.instr",
+                                  foldername="test_data_set",
+                                  mcrun_path="path")
+
+        # a double space because of a missing option
+        expected_call = ("path/mcrun -c -n 1000000 --mpi=1 "
+                         + "-d test_data_set  test_instrument.instr")
+
+        os_system.assert_called_once_with(expected_call)
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable=unittest.mock.mock_open)
+    @unittest.mock.patch("os.system")
+    def test_run_full_instrument_complex(self, os_system,
+                                         mock_f, mock_stdout,):
+        """
+        Check a complex run performs the correct system call.  Here
+        the target directory is set to the test data set so that some
+        data is loaded even though the system call is not executed.
+        """
+
+        instr = setup_populated_instr()
+        instr.run_full_instrument("test_instrument.instr",
+                                  foldername="test_data_set",
+                                  mcrun_path="path",
+                                  mpi=7,
+                                  ncount=48.4,
+                                  custom_flags="-fo",
+                                  parameters={"A": 2,
+                                              "BC": "car",
+                                              "th": "\"toy\""})
+
+        # a double space because of a missing option
+        expected_call = ("path/mcrun -c -n 48 --mpi=7 "
+                         + "-d test_data_set -fo test_instrument.instr "
+                         + "A=2 BC=car th=\"toy\"")
+
+        os_system.assert_called_once_with(expected_call)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index 82c57392..5088cdf7 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -7,23 +7,24 @@
 from mcstasscript.data.data import McStasData
 from mcstasscript.helper.managed_mcrun import ManagedMcrun
 
+
 class TestManagedMcrun(unittest.TestCase):
     """
     Testing the ManagedMcrun class that sets up McStas runs, runs the
     simulation and loads the data.
-    
+
     Here the simulation is not actually performed, this will be done in
     integration tests. The surrounding plumbing and data loading is
     tested.
     """
-    
+
     def test_ManagedMcrun_init_simple(self):
         """
         Check shortest possible initialization works
         """
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername = "test_folder", 
-                                 mcrun_path = "test_path")
+                                 foldername="test_folder",
+                                 mcrun_path="test_path")
 
         self.assertEqual(mcrun_obj.name_of_instrumentfile, "test.instr")
         self.assertEqual(mcrun_obj.data_folder_name, "test_folder")
@@ -34,57 +35,57 @@ def test_ManagedMcrun_init_defaults(self):
         Check default values are set up correctly
         """
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername = "test_folder", 
-                                 mcrun_path = "")
-        
+                                 foldername="test_folder",
+                                 mcrun_path="")
+
         self.assertEqual(mcrun_obj.mpi, 1)
         self.assertEqual(mcrun_obj.ncount, 1000000)
-        
+
     def test_ManagedMcrun_init_set_values(self):
         """
         Check default values are set up correctly
         """
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername = "test_folder", 
-                                 mcrun_path = "",
-                                 mpi = 4,
-                                 ncount = 128)
+                                 foldername="test_folder",
+                                 mcrun_path="",
+                                 mpi=4,
+                                 ncount=128)
 
         self.assertEqual(mcrun_obj.mpi, 4)
         self.assertEqual(mcrun_obj.ncount, 128)
-        
+
     def test_ManagedMcrun_init_set_parameters(self):
         """
         Check default values are set up correctly
         """
-        
-        par_input = {"A_par" : 5.1, 
-                     "int_par" : 1, 
-                     "define_par" : "Bike", 
-                     "string_par" : "\"Car\""}
-        
+
+        par_input = {"A_par": 5.1,
+                     "int_par": 1,
+                     "define_par": "Bike",
+                     "string_par": "\"Car\""}
+
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername = "test_folder", 
-                                 mcrun_path = "",
-                                 parameters = par_input)
+                                 foldername="test_folder",
+                                 mcrun_path="",
+                                 parameters=par_input)
 
         self.assertEqual(mcrun_obj.parameters["A_par"], 5.1)
         self.assertEqual(mcrun_obj.parameters["int_par"], 1)
         self.assertEqual(mcrun_obj.parameters["define_par"], "Bike")
         self.assertEqual(mcrun_obj.parameters["string_par"], "\"Car\"")
-        
+
     def test_ManagedMcrun_init_set_custom_flags(self):
         """
         Check default values are set up correctly
         """
-        
-        custom_flag_input = "-p" 
-        
+
+        custom_flag_input = "-p"
+
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername = "test_folder", 
-                                 mcrun_path = "",
-                                 custom_flags = custom_flag_input)
-        
+                                 foldername="test_folder",
+                                 mcrun_path="",
+                                 custom_flags=custom_flag_input)
+
         self.assertEqual(mcrun_obj.custom_flags, custom_flag_input)
 
     def test_ManagedMcrun_init_no_folder_error(self):
@@ -92,63 +93,63 @@ def test_ManagedMcrun_init_no_folder_error(self):
         An error should occur if no filename is given
         """
         with self.assertRaises(NameError):
-            mcrun_obj = ManagedMcrun("test.instr", mcrun_path = "")
+            mcrun_obj = ManagedMcrun("test.instr", mcrun_path="")
 
     @unittest.mock.patch("os.system")
     def test_ManagedMcrun_run_simulation_basic(self, os_system):
         """
         Check a basic system call is correct
         """
-        
+
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername = "test_folder", 
-                                 mcrun_path = "path")
-        
+                                 foldername="test_folder",
+                                 mcrun_path="path")
+
         mcrun_obj.run_simulation()
-        
+
         # a double space because of a missing option
         expected_call = ("path/mcrun -c -n 1000000 --mpi=1 "
                          + "-d test_folder  test.instr")
-        
+
         os_system.assert_called_once_with(expected_call)
-        
+
     @unittest.mock.patch("os.system")
     def test_ManagedMcrun_run_simulation_basic_path(self, os_system):
         """
         Check a basic system call is correct, with different path format
         """
-        
+
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername = "test_folder", 
-                                 mcrun_path = "path/")
-        
+                                 foldername="test_folder",
+                                 mcrun_path="path/")
+
         mcrun_obj.run_simulation()
-        
+
         # a double space because of a missing option
         expected_call = ("path/mcrun -c -n 1000000 --mpi=1 "
                          + "-d test_folder  test.instr")
-        
+
         os_system.assert_called_once_with(expected_call)
-        
+
     @unittest.mock.patch("os.system")
     def test_ManagedMcrun_run_simulation_no_standard(self, os_system):
         """
         Check a non standard system call is correct
         """
-        
+
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername = "test_folder", 
-                                 mcrun_path = "path",
-                                 mpi = 7,
-                                 ncount = 48.4,
-                                 custom_flags = "-fo")
-        
+                                 foldername="test_folder",
+                                 mcrun_path="path",
+                                 mpi=7,
+                                 ncount=48.4,
+                                 custom_flags="-fo")
+
         mcrun_obj.run_simulation()
-        
+
         # a double space because of a missing option
         expected_call = ("path/mcrun -c -n 48 --mpi=7 "
                          + "-d test_folder -fo test.instr")
-        
+
         os_system.assert_called_once_with(expected_call)
 
     @unittest.mock.patch("os.system")
@@ -156,49 +157,48 @@ def test_ManagedMcrun_run_simulation_parameters(self, os_system):
         """
         Check a run with parameters is correct
         """
-        
+
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername = "test_folder", 
-                                 mcrun_path = "path",
-                                 mpi = 7,
-                                 ncount = 48.4,
-                                 custom_flags = "-fo",
-                                 parameters = {"A" : 2,
-                                               "BC" : "car",
-                                               "th" : "\"toy\""})
-        
+                                 foldername="test_folder",
+                                 mcrun_path="path",
+                                 mpi=7,
+                                 ncount=48.4,
+                                 custom_flags="-fo",
+                                 parameters={"A": 2,
+                                             "BC": "car",
+                                             "th": "\"toy\""})
+
         mcrun_obj.run_simulation()
-        
+
         # a double space because of a missing option
         expected_call = ("path/mcrun -c -n 48 --mpi=7 "
                          + "-d test_folder -fo test.instr "
                          + "A=2 BC=car th=\"toy\"")
-        
+
         os_system.assert_called_once_with(expected_call)
 
     def test_ManagedMcrun_load_data_PSD4PI(self):
         """
         Use test_data_set to test load_data for PSD_4PI
         """
-        
+
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername = "test_data_set", 
-                                 mcrun_path = "path")
-    
+                                 foldername="test_data_set",
+                                 mcrun_path="path")
+
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-        
+
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
-               
+
         results = mcrun_obj.load_results()
-        
-        os.chdir(current_work_dir)  # Reset work directory
 
+        os.chdir(current_work_dir)  # Reset work directory
 
         self.assertEqual(len(results), 3)
 
         PSD_4PI = results[0]
-        
+
         self.assertEqual(PSD_4PI.name, "PSD_4PI")
         self.assertEqual(PSD_4PI.metadata.dimension, [300, 300])
         self.assertEqual(PSD_4PI.metadata.limits, [-180, 180, -90, 90])
@@ -208,29 +208,29 @@ def test_ManagedMcrun_load_data_PSD4PI(self):
         self.assertEqual(PSD_4PI.Ncount[1][4], 4)
         self.assertEqual(PSD_4PI.Intensity[1][4], 1.537334562E-10)
         self.assertEqual(PSD_4PI.Error[1][4], 1.139482296E-10)
-        
+
     def test_ManagedMcrun_load_data_PSD(self):
         """
         Use test_data_set to test load_data for PSD
         """
-        
+
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername = "test_data_set", 
-                                 mcrun_path = "path")
-    
+                                 foldername="test_data_set",
+                                 mcrun_path="path")
+
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-        
+
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
-               
+
         results = mcrun_obj.load_results()
-        
+
         os.chdir(current_work_dir)  # Reset work directory
-                                
+
         # Check other properties
-        
+
         PSD = results[1]
-        
+
         self.assertEqual(PSD.name, "PSD")
         self.assertEqual(PSD.metadata.dimension, [200, 200])
         self.assertEqual(PSD.metadata.limits, [-5, 5, -5, 5])
@@ -240,29 +240,29 @@ def test_ManagedMcrun_load_data_PSD(self):
         self.assertEqual(PSD.Ncount[21][27], 9)
         self.assertEqual(PSD.Intensity[21][27], 2.623929371e-13)
         self.assertEqual(PSD.Error[21][27], 2.765467693e-13)
-        
+
     def test_ManagedMcrun_load_data_L_mon(self):
         """
         Use test_data_set to test load_data for L_mon
         """
-        
+
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername = "test_data_set", 
-                                 mcrun_path = "path")
-    
+                                 foldername="test_data_set",
+                                 mcrun_path="path")
+
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-        
+
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
-               
+
         results = mcrun_obj.load_results()
-        
+
         os.chdir(current_work_dir)  # Reset work directory
-                                
+
         # Check other properties
-        
+
         L_mon = results[2]
-        
+
         self.assertEqual(L_mon.name, "L_mon")
         self.assertEqual(L_mon.metadata.dimension, 150)
         self.assertEqual(L_mon.metadata.limits, [0.7, 1.3])
@@ -273,30 +273,30 @@ def test_ManagedMcrun_load_data_L_mon(self):
         self.assertEqual(L_mon.Ncount[53], 37111)
         self.assertEqual(L_mon.Intensity[53], 6.990299315e-06)
         self.assertEqual(L_mon.Error[53], 6.215308587e-08)
-        
+
     def test_ManagedMcrun_load_data_L_mon_direct(self):
         """
         Use test_data_set to test load_data for L_mon with direct path
         """
-        
+
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername = "test_data_set", 
-                                 mcrun_path = "path")
-    
+                                 foldername="test_data_set",
+                                 mcrun_path="path")
+
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-                
+
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
-        
+
         load_path = THIS_DIR + "/test_data_set"
         results = mcrun_obj.load_results(load_path)
-        
+
         os.chdir(current_work_dir)  # Reset work directory
-                                
+
         # Check other properties
-        
+
         L_mon = results[2]
-        
+
         self.assertEqual(L_mon.name, "L_mon")
         self.assertEqual(L_mon.metadata.dimension, 150)
         self.assertEqual(L_mon.metadata.limits, [0.7, 1.3])
@@ -307,50 +307,47 @@ def test_ManagedMcrun_load_data_L_mon_direct(self):
         self.assertEqual(L_mon.Ncount[53], 37111)
         self.assertEqual(L_mon.Intensity[53], 6.990299315e-06)
         self.assertEqual(L_mon.Error[53], 6.215308587e-08)
-        
+
     def test_ManagedMcrun_load_data_L_mon_direct_error(self):
         """
         Check an error occurs when directory has no mccode.sim
         """
-        
+
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername = "test_data_set", 
-                                 mcrun_path = "path")
-    
+                                 foldername="test_data_set",
+                                 mcrun_path="path")
+
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-                
+
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
-        
+
         load_path = THIS_DIR + "/non_exsistent_dataset"
         with self.assertRaises(NameError):
             results = mcrun_obj.load_results(load_path)
-        
+
         os.chdir(current_work_dir)  # Reset work directory
-        
+
     def test_ManagedMcrun_load_data_L_mon_direct_error(self):
         """
         Check an error occurs when pointed to empty directory
         """
-        
+
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername = "test_data_set", 
-                                 mcrun_path = "path")
-    
+                                 foldername="test_data_set",
+                                 mcrun_path="path")
+
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-                
+
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
-        
+
         load_path = THIS_DIR + "/dummy_mcstas"
         with self.assertRaises(NameError):
             results = mcrun_obj.load_results(load_path)
-        
+
         os.chdir(current_work_dir)  # Reset work directory
-        
-        
-     
-if __name__ == '__main__':
-    unittest.main()
 
 
+if __name__ == '__main__':
+    unittest.main()
diff --git a/mcstasscript/tests/test_McStasData.py b/mcstasscript/tests/test_McStasData.py
index 3bdd6fe7..f6add939 100644
--- a/mcstasscript/tests/test_McStasData.py
+++ b/mcstasscript/tests/test_McStasData.py
@@ -4,88 +4,92 @@
 from mcstasscript.data.data import McStasData
 from mcstasscript.data.data import McStasMetaData
 
+
 def set_dummy_MetaData_1d():
     meta_data = McStasMetaData()
     meta_data.component_name = "component for 1d"
     meta_data.dimension = 50
-    
+
     return meta_data
 
+
 def set_dummy_McStasData_1d():
     meta_data = set_dummy_MetaData_1d()
 
     intensity = np.arange(20)
-    error = 0.5 * np.arange(20) 
+    error = 0.5 * np.arange(20)
     ncount = 2 * np.arange(20)
     axis = np.arange(20)*5.0
-        
-    return McStasData(meta_data, intensity, error, ncount, xaxis = axis)
+
+    return McStasData(meta_data, intensity, error, ncount, xaxis=axis)
+
 
 def set_dummy_MetaData_2d():
     meta_data = McStasMetaData()
     meta_data.component_name = "test a component"
     meta_data.dimension = [50, 100]
-    
+
     return meta_data
 
+
 def set_dummy_McStasData_2d():
     meta_data = set_dummy_MetaData_2d()
 
-    intensity = np.arange(20).reshape(4,5)
-    error = 0.5 * np.arange(20).reshape(4,5)
-    ncount = 2 * np.arange(20).reshape(4,5)
-        
+    intensity = np.arange(20).reshape(4, 5)
+    error = 0.5 * np.arange(20).reshape(4, 5)
+    ncount = 2 * np.arange(20).reshape(4, 5)
+
     return McStasData(meta_data, intensity, error, ncount)
-        
+
 
 class TestMcStasData(unittest.TestCase):
     """
     Various test of McStasData class
     """
-    
+
     def test_McStasData_init_1d(self):
         """
         Test that newly created McStasMetaData has correct type
         """
-        
+
         data = set_dummy_McStasData_1d()
-        
+
         self.assertEqual(data.name, "component for 1d")
         self.assertEqual(data.metadata.component_name, "component for 1d")
-        
+
     def test_McStasData_init_values(self):
         """
         Test that newly created McStasMetaData has correct type
         """
-        
+
         data = set_dummy_McStasData_1d()
-        
+
         self.assertEqual(data.Intensity[3], 3)
         self.assertEqual(data.Error[3], 1.5)
         self.assertEqual(data.Ncount[3], 6)
         self.assertEqual(data.xaxis[3], 15.0)
-        
+
     def test_McStasData_init_2d_names(self):
         """
         Test that newly created McStasMetaData has correct type
         """
-        
+
         data = set_dummy_McStasData_2d()
-        
+
         self.assertEqual(data.name, "test a component")
         self.assertEqual(data.metadata.component_name, "test a component")
-        
+
     def test_McStasData_init_2d_values(self):
         """
         Test that newly created McStasMetaData has correct type
         """
-        
+
         data = set_dummy_McStasData_2d()
-        
+
         self.assertEqual(data.Intensity[2][3], 13)
         self.assertEqual(data.Error[2][3], 6.5)
         self.assertEqual(data.Ncount[2][3], 26)
-        
+
     def test_McStasData_set_info_title(self):
         """
         Test that title can be set
@@ -93,7 +97,7 @@ def test_McStasData_set_info_title(self):
         data = set_dummy_McStasData_2d()
         data.set_title("title_test")
         self.assertEqual(data.metadata.title, "title_test")
-    
+
     def test_McStasData_set_xlabel(self):
         """
         Test that xlabel can be set
@@ -101,7 +105,7 @@ def test_McStasData_set_xlabel(self):
         data = set_dummy_McStasData_2d()
         data.set_xlabel("xlabel test")
         self.assertEqual(data.metadata.xlabel, "xlabel test")
-        
+
     def test_McStasData_set_ylabel(self):
         """
         Test that ylabel can be set
@@ -109,21 +113,21 @@ def test_McStasData_set_ylabel(self):
         data = set_dummy_McStasData_2d()
         data.set_ylabel("ylabel test")
         self.assertEqual(data.metadata.ylabel, "ylabel test")
-        
+
     def test_McStasData_set_log(self):
         """
         Test that newly created McStasMetaData has correct type
         """
         data = set_dummy_McStasData_2d()
-        data.set_plot_options(log = True)
+        data.set_plot_options(log=True)
         self.assertIsInstance(data.plot_options.log, bool)
         self.assertTrue(data.plot_options.log)
 
-        data.set_plot_options(log = 0)
+        data.set_plot_options(log=0)
         self.assertIsInstance(data.plot_options.log, bool)
         self.assertFalse(data.plot_options.log)
 
-        data.set_plot_options(log = 1)
+        data.set_plot_options(log=1)
         self.assertIsInstance(data.plot_options.log, bool)
         self.assertTrue(data.plot_options.log)
 
@@ -132,7 +136,7 @@ def test_McStasData_set_orders_of_mag(self):
         Test that newly created McStasMetaData has correct type
         """
         data = set_dummy_McStasData_2d()
-        data.set_plot_options(orders_of_mag = 5.2)
+        data.set_plot_options(orders_of_mag=5.2)
         self.assertEqual(data.plot_options.orders_of_mag, 5.2)
 
     def test_McStasData_set_colormap(self):
@@ -140,8 +144,9 @@ def test_McStasData_set_colormap(self):
         Test that newly created McStasMetaData has correct type
         """
         data = set_dummy_McStasData_2d()
-        data.set_plot_options(colormap = "hot")
+        data.set_plot_options(colormap="hot")
         self.assertIs(data.plot_options.colormap, "hot")
 
+
 if __name__ == '__main__':
-    unittest.main()
\ No newline at end of file
+    unittest.main()
diff --git a/mcstasscript/tests/test_McStasMetaData.py b/mcstasscript/tests/test_McStasMetaData.py
index ef63ff86..aa7be2c0 100644
--- a/mcstasscript/tests/test_McStasMetaData.py
+++ b/mcstasscript/tests/test_McStasMetaData.py
@@ -2,11 +2,12 @@
 
 from mcstasscript.data.data import McStasMetaData
 
+
 class TestMcStasMetaData(unittest.TestCase):
     """
     Various test of McStasMetaData class
     """
-    
+
     def test_McStasMetaData_return_type(self):
         """
         Test that newly created McStasMetaData has correct type
@@ -20,23 +21,23 @@ def test_McStasMetaData_init(self):
         """
         meta_data = McStasMetaData()
         self.assertEqual(len(meta_data.info), 0)
-        
+
     def test_McStasMetaData_add_info_len(self):
         """
         Test that info can be added to McStasMetaData
         """
         meta_data = McStasMetaData()
-        meta_data.add_info("test",3)
+        meta_data.add_info("test", 3)
         self.assertEqual(len(meta_data.info), 1)
-        
+
     def test_McStasMetaData_add_info(self):
         """
         Test that info can be read from McStasMetaData
         """
         meta_data = McStasMetaData()
-        meta_data.add_info("test",3)
+        meta_data.add_info("test", 3)
         self.assertEqual(meta_data.info["test"], 3)
-        
+
     def test_McStasMetaData_add_info_title(self):
         """
         Test that title can be set
@@ -44,7 +45,7 @@ def test_McStasMetaData_add_info_title(self):
         meta_data = McStasMetaData()
         meta_data.set_title("title_test")
         self.assertEqual(meta_data.title, "title_test")
-    
+
     def test_McStasMetaData_add_info_xlabel(self):
         """
         Test that xlabel can be set
@@ -52,7 +53,7 @@ def test_McStasMetaData_add_info_xlabel(self):
         meta_data = McStasMetaData()
         meta_data.set_xlabel("xlabel test")
         self.assertEqual(meta_data.xlabel, "xlabel test")
-        
+
     def test_McStasMetaData_add_info_ylabel(self):
         """
         Test that ylabel can be set
@@ -60,7 +61,7 @@ def test_McStasMetaData_add_info_ylabel(self):
         meta_data = McStasMetaData()
         meta_data.set_ylabel("ylabel test")
         self.assertEqual(meta_data.ylabel, "ylabel test")
-        
+
     def test_McStasMetaData_long_read_1d(self):
         """
         Test that extact info can read appropriate info
@@ -73,9 +74,9 @@ def test_McStasMetaData_long_read_1d(self):
         meta_data.add_info("xlabel", "test A xlabel")
         meta_data.add_info("ylabel", "test A ylabel")
         meta_data.add_info("title", "test A title")
-        
+
         meta_data.extract_info()  # Converts info to attributes
-        
+
         self.assertIsInstance(meta_data.dimension, int)
         self.assertEqual(meta_data.dimension, 500)
         self.assertIs(meta_data.component_name, "test_A COMP")
@@ -86,8 +87,7 @@ def test_McStasMetaData_long_read_1d(self):
         self.assertIs(meta_data.xlabel, "test A xlabel")
         self.assertIs(meta_data.ylabel, "test A ylabel")
         self.assertIs(meta_data.title, "test A title")
-        
-        
+
     def test_McStasMetaData_long_read_2d(self):
         """
         Test that extact info can read appropriate info
@@ -100,9 +100,9 @@ def test_McStasMetaData_long_read_2d(self):
         meta_data.add_info("xlabel", "test A xlabel")
         meta_data.add_info("ylabel", "test A ylabel")
         meta_data.add_info("title", "test A title")
-        
+
         meta_data.extract_info()  # Converts info to attributes
-        
+
         self.assertEqual(len(meta_data.dimension), 2)
         self.assertEqual(meta_data.dimension[0], 500)
         self.assertEqual(meta_data.dimension[1], 12)
@@ -116,15 +116,7 @@ def test_McStasMetaData_long_read_2d(self):
         self.assertIs(meta_data.xlabel, "test A xlabel")
         self.assertIs(meta_data.ylabel, "test A ylabel")
         self.assertIs(meta_data.title, "test A title")
-        
+
+
 if __name__ == '__main__':
     unittest.main()
-        
-        
-        
-        
-        
-        
-        
-        
-        
\ No newline at end of file
diff --git a/mcstasscript/tests/test_McStasPlotOptions.py b/mcstasscript/tests/test_McStasPlotOptions.py
index 6b4ad324..790ce998 100644
--- a/mcstasscript/tests/test_McStasPlotOptions.py
+++ b/mcstasscript/tests/test_McStasPlotOptions.py
@@ -2,6 +2,7 @@
 
 from mcstasscript.data.data import McStasPlotOptions
 
+
 class TestMcStasPlotOptions(unittest.TestCase):
     """
     Various test of McStasPlotOptions class
@@ -34,15 +35,15 @@ def test_McStasPlotOptions_set_log(self):
         Test that newly created McStasMetaData has correct type
         """
         plot_options = McStasPlotOptions()
-        plot_options.set_options(log = True)
+        plot_options.set_options(log=True)
         self.assertIsInstance(plot_options.log, bool)
         self.assertTrue(plot_options.log)
 
-        plot_options.set_options(log = 0)
+        plot_options.set_options(log=0)
         self.assertIsInstance(plot_options.log, bool)
         self.assertFalse(plot_options.log)
 
-        plot_options.set_options(log = 1)
+        plot_options.set_options(log=1)
         self.assertIsInstance(plot_options.log, bool)
         self.assertTrue(plot_options.log)
 
@@ -51,7 +52,7 @@ def test_McStasPlotOptions_set_orders_of_mag(self):
         Test that newly created McStasMetaData has correct type
         """
         plot_options = McStasPlotOptions()
-        plot_options.set_options(orders_of_mag = 5.2)
+        plot_options.set_options(orders_of_mag=5.2)
         self.assertEqual(plot_options.orders_of_mag, 5.2)
 
     def test_McStasPlotOptions_set_colormap(self):
@@ -59,8 +60,9 @@ def test_McStasPlotOptions_set_colormap(self):
         Test that newly created McStasMetaData has correct type
         """
         plot_options = McStasPlotOptions()
-        plot_options.set_options(colormap = "hot")
+        plot_options.set_options(colormap="hot")
         self.assertIs(plot_options.colormap, "hot")
 
+
 if __name__ == '__main__':
-    unittest.main()
\ No newline at end of file
+    unittest.main()
diff --git a/mcstasscript/tests/test_component.py b/mcstasscript/tests/test_component.py
index f61c077f..a1aef691 100644
--- a/mcstasscript/tests/test_component.py
+++ b/mcstasscript/tests/test_component.py
@@ -1,207 +1,307 @@
+import io
 import builtins
 import unittest
 import unittest.mock
 
 from mcstasscript.helper.mcstas_objects import component
+from mcstasscript.helper.formatting import bcolors
+
 
 def setup_component_all_keywords():
-    
+    """
+    Sets up a component by using all initialize keywords
+    """
+
     return component("test_component",
                      "Arm",
-                     AT = [0.124, 183.9, 157],
-                     AT_RELATIVE = "home",
-                     ROTATED = [482, 1240.2, 0.185],
-                     ROTATED_RELATIVE = "etc",
-                     WHEN = "1==2",
-                     EXTEND = "nscat = 8;",
-                     GROUP = "developers",
-                     JUMP = "myself 37",
-                     comment = "test comment")
-    
+                     AT=[0.124, 183.9, 157],
+                     AT_RELATIVE="home",
+                     ROTATED=[482, 1240.2, 0.185],
+                     ROTATED_RELATIVE="etc",
+                     WHEN="1==2",
+                     EXTEND="nscat = 8;",
+                     GROUP="developers",
+                     JUMP="myself 37",
+                     comment="test comment")
+
+
 def setup_component_relative():
-    
+    """
+    Sets up a component with the relative keyword used
+    """
     return component("test_component",
                      "Arm",
-                     AT = [0.124, 183.9, 157],
-                     ROTATED = [482, 1240.2, 0.185],
-                     RELATIVE = "source",
-                     WHEN = "1==2",
-                     EXTEND = "nscat = 8;",
-                     GROUP = "developers",
-                     JUMP = "myself 37",
-                     comment = "test comment")
-    
+                     AT=[0.124, 183.9, 157],
+                     ROTATED=[482, 1240.2, 0.185],
+                     RELATIVE="source",
+                     WHEN="1==2",
+                     EXTEND="nscat = 8;",
+                     GROUP="developers",
+                     JUMP="myself 37",
+                     comment="test comment")
+
+
+def setup_component_with_parameters():
+    """
+    Sets up a component with parameters and all options used.
+
+    """
+    comp = setup_component_all_keywords()
+
+    comp._unfreeze()
+    # Need to set up attribute parameters
+    comp.new_par1 = 1.5
+    comp.new_par2 = 3
+    comp.new_par3 = None
+    comp.this_par = "test_val"
+    comp.that_par = "\"txt_string\""
+    # also need to categorize them as when created
+    comp.parameter_names = ["new_par1", "new_par2", "new_par3",
+                            "this_par", "that_par"]
+    comp.parameter_defaults = {"new_par1": 5.1,
+                               "new_par2": 9,
+                               "new_par3": None,
+                               "this_par": "conga",
+                               "that_par": "\"txt\""}
+    comp.parameter_comments = {"new_par1": "This is important",
+                               "new_par2": "This is less important",
+                               "this_par": "!",
+                               "that_par": ""}
+    comp.parameter_types = {"new_par1": "double",
+                            "new_par2": "int",
+                            "this_par": "",
+                            "that_par": "string"}
+    comp.parameter_units = {"new_par1": "m",
+                            "new_par2": "AA",
+                            "this_par": "",
+                            "that_par": "1"}
+    comp._freeze()
+
+    return comp
+
 
 class Testcomponent(unittest.TestCase):
     """
     Components are the building blocks used to create an instrument in
     the McStas meta language. They describe spatially seperated parts
-    of the neutron scattering instrument. Here the class component is 
+    of the neutron scattering instrument. Here the class component is
     tested.
     """
-    
+
     def test_component_basic_init(self):
-        
+        """
+        Testing basic initialization
+
+        """
+
         comp = component("test_component", "Arm")
-        
+
         self.assertEqual(comp.name, "test_component")
         self.assertEqual(comp.component_name, "Arm")
 
     def test_component_basic_init_defaults(self):
-        
+        """
+        Testing basic initialization sets the correct defaults
+
+        """
+
         comp = component("test_component", "Arm")
-        
+
         self.assertEqual(comp.name, "test_component")
-        self.assertEqual(comp.component_name, "Arm")    
-        self.assertEqual(comp.AT_data, [0,0,0])
+        self.assertEqual(comp.component_name, "Arm")
+        self.assertEqual(comp.AT_data, [0, 0, 0])
         self.assertEqual(comp.AT_relative, "ABSOLUTE")
-        self.assertEqual(comp.ROTATED_data, [0,0,0])
+        self.assertEqual(comp.ROTATED_data, [0, 0, 0])
         self.assertEqual(comp.ROTATED_relative, "ABSOLUTE")
         self.assertEqual(comp.WHEN, "")
         self.assertEqual(comp.EXTEND, "")
         self.assertEqual(comp.GROUP, "")
         self.assertEqual(comp.JUMP, "")
         self.assertEqual(comp.comment, "")
-        
+
     def test_component_init_complex_call(self):
-        
+        """
+        Testing keywords set attributes correctly
+
+        """
+
         comp = setup_component_all_keywords()
-        
+
         self.assertEqual(comp.name, "test_component")
-        self.assertEqual(comp.component_name, "Arm")    
+        self.assertEqual(comp.component_name, "Arm")
         self.assertEqual(comp.AT_data, [0.124, 183.9, 157])
         self.assertEqual(comp.AT_relative, "RELATIVE home")
         self.assertEqual(comp.ROTATED_data, [482, 1240.2, 0.185])
         self.assertEqual(comp.ROTATED_relative, "RELATIVE etc")
-        self.assertEqual(comp.WHEN, "WHEN (1==2)\n")
+        self.assertEqual(comp.WHEN, "WHEN (1==2)")
         self.assertEqual(comp.EXTEND, "nscat = 8;\n")
         self.assertEqual(comp.GROUP, "developers")
         self.assertEqual(comp.JUMP, "myself 37")
         self.assertEqual(comp.comment, "test comment")
 
     def test_component_init_complex_call_relative(self):
-        
+        """
+        Tests the relative keyword overwrites AT_relative and
+        ROTATED_relative
+
+        """
         comp = setup_component_relative()
 
         self.assertEqual(comp.name, "test_component")
-        self.assertEqual(comp.component_name, "Arm")    
+        self.assertEqual(comp.component_name, "Arm")
         self.assertEqual(comp.AT_data, [0.124, 183.9, 157])
         self.assertEqual(comp.AT_relative, "RELATIVE source")
         self.assertEqual(comp.ROTATED_data, [482, 1240.2, 0.185])
         self.assertEqual(comp.ROTATED_relative, "RELATIVE source")
-        self.assertEqual(comp.WHEN, "WHEN (1==2)\n")
+        self.assertEqual(comp.WHEN, "WHEN (1==2)")
         self.assertEqual(comp.EXTEND, "nscat = 8;\n")
         self.assertEqual(comp.GROUP, "developers")
         self.assertEqual(comp.JUMP, "myself 37")
         self.assertEqual(comp.comment, "test comment")
 
     def test_component_basic_init_set_AT(self):
-        
+        """
+        Testing set_AT method
+
+        """
+
         comp = component("test_component", "Arm")
-        
-        comp.set_AT([12.124, 214.0, 2], RELATIVE = "monochromator")
-        
+
+        comp.set_AT([12.124, 214.0, 2], RELATIVE="monochromator")
+
         self.assertEqual(comp.name, "test_component")
         self.assertEqual(comp.component_name, "Arm")
         self.assertEqual(comp.AT_data, [12.124, 214.0, 2])
         self.assertEqual(comp.AT_relative, "RELATIVE monochromator")
-        
+
     def test_component_basic_init_set_ROTATED(self):
-        
+        """
+        Testing set_ROTATED method
+
+        """
+
         comp = component("test_component", "Arm")
-        
-        comp.set_ROTATED([1204.8, 8490.1, 129], RELATIVE = "analyzer")
-        
+
+        comp.set_ROTATED([1204.8, 8490.1, 129], RELATIVE="analyzer")
+
         self.assertEqual(comp.name, "test_component")
         self.assertEqual(comp.component_name, "Arm")
         self.assertEqual(comp.ROTATED_data, [1204.8, 8490.1, 129])
         self.assertEqual(comp.ROTATED_relative, "RELATIVE analyzer")
-        
+
     def test_component_basic_init_set_RELATIVE(self):
-        
+        """
+        Testing set_RELATIVE method
+
+        """
+
         comp = component("test_component", "Arm")
-        
+
         comp.set_RELATIVE("sample")
-        
+
         self.assertEqual(comp.name, "test_component")
         self.assertEqual(comp.component_name, "Arm")
         self.assertEqual(comp.AT_relative, "RELATIVE sample")
         self.assertEqual(comp.ROTATED_relative, "RELATIVE sample")
-        
+
     def test_component_basic_init_set_parameters(self):
-        
+        """
+        Testing set_parameters method. Need to set some attribute
+        parameters manually to test this.
+
+        """
+
         comp = component("test_component", "Arm")
-        
+
         # Need to add some parameters to this bare component
-        # Parameters are usually added by McStas_Instr 
+        # Parameters are usually added by McStas_Instr
         comp._unfreeze()
         comp.new_par1 = 1
         comp.new_par2 = 3
         comp.this_par = 1492.2
-        
-        comp.set_parameters({"new_par1" : 37.0,
-                             "new_par2" : 12.0,
-                             "this_par" : 1}) 
-        
+
+        comp.set_parameters({"new_par1": 37.0,
+                             "new_par2": 12.0,
+                             "this_par": 1})
+
         self.assertEqual(comp.name, "test_component")
         self.assertEqual(comp.component_name, "Arm")
-        self.assertEqual(comp.new_par1,37.0)
-        self.assertEqual(comp.new_par2,12.0)
-        self.assertEqual(comp.this_par,1)
-        
+        self.assertEqual(comp.new_par1, 37.0)
+        self.assertEqual(comp.new_par2, 12.0)
+        self.assertEqual(comp.this_par, 1)
+
         with self.assertRaises(NameError):
-            comp.set_parameters({"new_par3" : 37.0})
-        
+            comp.set_parameters({"new_par3": 37.0})
+
     def test_component_basic_init_set_WHEN(self):
-        
+        """
+        Testing WHEN method
+
+        """
+
         comp = component("test_component", "Arm")
-        
+
         comp.set_WHEN("1 != 2")
-        
+
         self.assertEqual(comp.name, "test_component")
         self.assertEqual(comp.component_name, "Arm")
-        self.assertEqual(comp.WHEN, "WHEN (1 != 2)\n")
-        
+        self.assertEqual(comp.WHEN, "WHEN (1 != 2)")
+
     def test_component_basic_init_set_GROUP(self):
-        
+        """
+        Testing set_GROUP method
+
+        """
+
         comp = component("test_component", "Arm")
-        
+
         comp.set_GROUP("test group")
-        
+
         self.assertEqual(comp.name, "test_component")
         self.assertEqual(comp.component_name, "Arm")
         self.assertEqual(comp.GROUP, "test group")
-        
+
     def test_component_basic_init_set_JUMP(self):
-        
+        """
+        Testing set_JUMP method
+
+        """
+
         comp = component("test_component", "Arm")
-        
+
         comp.set_JUMP("test jump")
-        
+
         self.assertEqual(comp.name, "test_component")
         self.assertEqual(comp.component_name, "Arm")
         self.assertEqual(comp.JUMP, "test jump")
-        
+
     def test_component_basic_init_set_EXTEND(self):
-        
+        """
+        Testing set_EXTEND method
+
+        """
+
         comp = component("test_component", "Arm")
-        
+
         comp.append_EXTEND("test code")
-        
+
         self.assertEqual(comp.name, "test_component")
         self.assertEqual(comp.component_name, "Arm")
         self.assertEqual(comp.EXTEND, "test code\n")
-        
+
         comp.append_EXTEND("new code")
-        
+
         self.assertEqual(comp.EXTEND, "test code\nnew code\n")
-        
+
     def test_component_basic_init_set_comment(self):
-        
+        """
+        Testing set_comment method
+
+        """
         comp = component("test_component", "Arm")
-        
+
         comp.set_comment("test comment")
-        
+
         self.assertEqual(comp.name, "test_component")
         self.assertEqual(comp.component_name, "Arm")
         self.assertEqual(comp.comment, "test comment")
@@ -212,18 +312,17 @@ def test_component_basic_new_attribute_error(self):
         prevent the user accidentilly misspelling an attribute name,
         or at least be able to report an error when they do so.
         """
-        
+
         comp = component("test_component", "Arm")
         with self.assertRaises(AttributeError):
             comp.new_attribute = 1
-        
+
         # If unfreeze does not work, this would cause an error
         comp._unfreeze()
         comp.new_attribute = 1
-        
-        
+
     @unittest.mock.patch('__main__.__builtins__.open',
-                         new_callable = unittest.mock.mock_open)    
+                         new_callable=unittest.mock.mock_open)
     def test_component_write_to_file_simple(self, mock_f):
         """
         Testing that a component can be written to file with the
@@ -233,8 +332,8 @@ def test_component_write_to_file_simple(self, mock_f):
         comp = component("test_component", "Arm")
 
         comp._unfreeze()
-        # need to set up attribute parameters
-        # also need to categorize them as when created
+        # Need to set up attribute parameters
+        # Also need to categorize them as when created
         comp.parameter_names = []
         comp.parameter_defaults = {}
         comp.parameter_types = {}
@@ -251,41 +350,30 @@ def test_component_write_to_file_simple(self, mock_f):
                            my_call("ROTATED (0,0,0)"),
                            my_call(" ABSOLUTE\n")]
 
-        mock_f.assert_called_with('test.txt', 'w')                                   
+        mock_f.assert_called_with('test.txt', 'w')
         handle = mock_f()
         handle.write.assert_has_calls(expected_writes, any_order=False)
 
     @unittest.mock.patch('__main__.__builtins__.open',
-                         new_callable = unittest.mock.mock_open)    
+                         new_callable=unittest.mock.mock_open)
     def test_component_write_to_file_complex(self, mock_f):
         """
         Testing that a component can be written to file with the
         expected output. Here with complex input.
+
         """
-        
-        comp = setup_component_all_keywords()
-        
-        comp._unfreeze()
-        # need to set up attribute parameters
-        comp.new_par1 = 1.5
-        comp.new_par2 = 3
-        comp.this_par = "test_val"
-        comp.that_par = "\"txt_string\""
-        # also need to categorize them as when created
-        comp.parameter_names = ["new_par1", "new_par2", "this_par", "that_par"]
-        comp.parameter_defaults = {"new_par1" : 5.1,
-                                   "new_par2" : 9,
-                                   "this_par" : "conga",
-                                   "that_par" : "\"txt\""}
-        comp.parameter_types = {"new_par1" : "double",
-                                "new_par2" : "int",
-                                "this_par" : "",
-                                "that_par" : "string"}
-        comp._freeze()
-        
+
+        comp = setup_component_with_parameters()
+
+        # This setup has a required parameter.
+        # If this parameter is not set, an error should be returned,
+        # this will be tested in the next test.
+
+        comp.new_par3 = "1.25"
+
         with mock_f('test.txt', 'w') as m_fo:
             comp.write_component(m_fo)
-        
+
         my_call = unittest.mock.call
         expected_writes = [my_call("COMPONENT test_component = Arm("),
                            my_call("\n"),
@@ -294,8 +382,11 @@ def test_component_write_to_file_complex(self, mock_f):
                            my_call(" new_par2 = 3"),
                            my_call(","),
                            my_call("\n"),
+                           my_call(" new_par3 = 1.25"),
+                           my_call(","),
                            my_call(" this_par = test_val"),
                            my_call(","),
+                           my_call("\n"),
                            my_call(" that_par = \"txt_string\""),
                            my_call(")\n"),
                            my_call("WHEN (1==2)\n"),
@@ -310,40 +401,249 @@ def test_component_write_to_file_complex(self, mock_f):
                            my_call("JUMP myself 37\n"),
                            my_call("\n")]
 
-        mock_f.assert_called_with('test.txt', 'w')                                   
+        mock_f.assert_called_with('test.txt', 'w')
         handle = mock_f()
-        handle.write.assert_has_calls(expected_writes, any_order=False)  
-    
+        handle.write.assert_has_calls(expected_writes, any_order=False)
+
     @unittest.mock.patch('__main__.__builtins__.open',
-                         new_callable = unittest.mock.mock_open)
+                         new_callable=unittest.mock.mock_open)
     def test_component_write_component_required_parameter_error(self, mock_f):
         """
         Test an error occurs if the component is asked to write to disk
         without a required parameter.
         """
-        
-        comp = setup_component_all_keywords()
-        
-        comp._unfreeze()
-        # need to set up attribute parameters
-        comp.new_par1 = None
-        # also need to categorize them as when created
-        comp.parameter_names = ["new_par1"]
-        comp.parameter_defaults = {"new_par1" : None}
-        
+
+        comp = setup_component_with_parameters()
+
+        # new_par3 unset and has no default so an error will be raised
+
         with self.assertRaises(NameError):
             with mock_f('test.txt', 'w') as m_fo:
                 comp.write_component(m_fo)
-                
-                
-    # Print long (very similar to write component)
-    # Print short (easier)
-    # show_parameters (similar to write component, with formatting)
-    # show_parameters_simple (similar to write component, without formatting)
-    
-    
-    
-    
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_component_print_long(self, mock_stdout):
+        """
+        Test print to console on the current state of the component.
+        Using a mocked stdout to catch the print statements.
+        """
+
+        comp = setup_component_with_parameters()
+        comp.append_EXTEND("second extend line;")
+
+        comp.print_long()
+
+        output = mock_stdout.getvalue()
+        output = output.split("\n")
+
+        self.assertEqual(output[0], "// test comment")
+        self.assertEqual(output[1], "COMPONENT test_component = Arm")
+
+        par_name = bcolors.BOLD + "new_par1" + bcolors.ENDC
+        value = (bcolors.BOLD + bcolors.OKGREEN
+                 + "1.5" + bcolors.ENDC + bcolors.ENDC)
+        self.assertEqual(output[2], "  " + par_name + " = " + value + " [m]")
+
+        par_name = bcolors.BOLD + "new_par2" + bcolors.ENDC
+        value = (bcolors.BOLD + bcolors.OKGREEN
+                 + "3" + bcolors.ENDC + bcolors.ENDC)
+        self.assertEqual(output[3], "  " + par_name + " = " + value + " [AA]")
+
+        par_name = bcolors.BOLD + "new_par3" + bcolors.ENDC
+        warning = (bcolors.FAIL
+                   + " : Required parameter not yet specified"
+                   + bcolors.ENDC)
+        self.assertEqual(output[4], "  " + par_name + warning)
+
+        par_name = bcolors.BOLD + "this_par" + bcolors.ENDC
+        value = (bcolors.BOLD + bcolors.OKGREEN
+                 + "test_val" + bcolors.ENDC + bcolors.ENDC)
+        self.assertEqual(output[5], "  " + par_name + " = " + value + " []")
+
+        par_name = bcolors.BOLD + "that_par" + bcolors.ENDC
+        value = (bcolors.BOLD + bcolors.OKGREEN
+                 + "\"txt_string\"" + bcolors.ENDC + bcolors.ENDC)
+        self.assertEqual(output[6], "  " + par_name + " = " + value + " [1]")
+
+        self.assertEqual(output[7], "WHEN (1==2)")
+
+        self.assertEqual(output[8], "AT [0.124, 183.9, 157] RELATIVE home")
+        self.assertEqual(output[9],
+                         "ROTATED [482, 1240.2, 0.185] RELATIVE etc")
+        self.assertEqual(output[10], "GROUP developers")
+        self.assertEqual(output[11], "EXTEND %{")
+        self.assertEqual(output[12], "nscat = 8;")
+        self.assertEqual(output[13], "second extend line;")
+        self.assertEqual(output[14], "%}")
+        self.assertEqual(output[15], "JUMP myself 37")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_component_print_short_standard(self, mock_stdout):
+        """
+        Test print_short that prints name, type and location of the
+        component to the console.
+        """
+
+        comp = setup_component_with_parameters()
+
+        comp.print_short()
+
+        output = mock_stdout.getvalue()
+        output = output.split("\n")
+
+        expected = ("test_component = Arm "
+                    + "\tAT [0.124, 183.9, 157] RELATIVE home "
+                    + "ROTATED [482, 1240.2, 0.185] RELATIVE etc")
+
+        self.assertEqual(output[0], expected)
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_component_print_short_longest_name(self, mock_stdout):
+        """
+        Test print_short that prints name, type and location of the
+        component to the console.
+        """
+
+        comp = setup_component_with_parameters()
+
+        comp.print_short(longest_name=15)
+
+        output = mock_stdout.getvalue()
+        output = output.split("\n")
+
+        expected = ("test_component    Arm "
+                    + "\tAT [0.124, 183.9, 157] RELATIVE home "
+                    + "ROTATED [482, 1240.2, 0.185] RELATIVE etc")
+
+        self.assertEqual(output[0], expected)
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_component_show_parameters(self, mock_stdout):
+        """
+        Test print_short that prints name, type and location of the
+        component to the console.
+        """
+
+        comp = setup_component_with_parameters()
+
+        comp._unfreeze
+
+        # This is now not set by the user, but has default
+        # This results in different formatting in show_parameters
+        comp.new_par2 = None
+
+        comp._freeze
+
+        comp.show_parameters()
+
+        output = mock_stdout.getvalue()
+        output = output.split("\n")
+
+        self.assertEqual(output[0], " ___ Help Arm " + "_"*59)
+
+        legend = ("|"
+                  + bcolors.BOLD + "optional parameter" + bcolors.ENDC
+                  + "|"
+                  + bcolors.BOLD + bcolors.UNDERLINE
+                  + "required parameter"
+                  + bcolors.ENDC + bcolors.ENDC
+                  + "|"
+                  + bcolors.BOLD + bcolors.OKBLUE
+                  + "default value"
+                  + bcolors.ENDC + bcolors.ENDC
+                  + "|"
+                  + bcolors.BOLD + bcolors.OKGREEN
+                  + "user specified value"
+                  + bcolors.ENDC + bcolors.ENDC
+                  + "|")
+
+        self.assertEqual(output[1], legend)
+
+        par_name = bcolors.BOLD + "new_par1" + bcolors.ENDC
+        value = (bcolors.BOLD + bcolors.OKGREEN
+                 + "1.5" + bcolors.ENDC + bcolors.ENDC)
+        comment = "// This is important"
+        self.assertEqual(output[2],
+                         par_name + " = " + value + " [m] " + comment)
+
+        par_name = bcolors.BOLD + "new_par2" + bcolors.ENDC
+        value = (bcolors.BOLD + bcolors.OKBLUE
+                 + "9" + bcolors.ENDC + bcolors.ENDC)
+        comment = "// This is less important"
+        self.assertEqual(output[3],
+                         par_name + " = " + value + " [AA] " + comment)
+
+        par_name = (bcolors.UNDERLINE + bcolors.BOLD
+                    + "new_par3"
+                    + bcolors.ENDC + bcolors.ENDC)
+        self.assertEqual(output[4], par_name)
+
+        par_name = bcolors.BOLD + "this_par" + bcolors.ENDC
+        value = (bcolors.BOLD + bcolors.OKGREEN
+                 + "test_val" + bcolors.ENDC + bcolors.ENDC)
+        comment = "// !"
+        self.assertEqual(output[5],
+                         par_name + " = " + value + " [] " + comment)
+
+        par_name = bcolors.BOLD + "that_par" + bcolors.ENDC
+        value = (bcolors.BOLD + bcolors.OKGREEN
+                 + "\"txt_string\"" + bcolors.ENDC + bcolors.ENDC)
+        comment = ""
+        self.assertEqual(output[6],
+                         par_name + " = " + value + " [1]" + comment)
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_component_show_parameters_simple(self, mock_stdout):
+        """
+        Test print_short that prints name, type and location of the
+        component to the console.  No formatting used in simple
+        version.
+        """
+
+        comp = setup_component_with_parameters()
+
+        comp._unfreeze
+
+        # This is now not set by the user, but has default
+        # This results in different formatting in show_parameters
+        comp.new_par2 = None
+
+        comp._freeze
+
+        comp.show_parameters_simple()
+
+        output = mock_stdout.getvalue()
+        output = output.split("\n")
+
+        self.assertEqual(output[0], "---- Help Arm -----")
+
+        par_name = "new_par1"
+        value = "1.5"
+        comment = "// This is important"
+        self.assertEqual(output[1],
+                         par_name + " = " + value + " [m] " + comment)
+
+        par_name = "new_par2"
+        value = "9"
+        comment = "// This is less important"
+        self.assertEqual(output[2],
+                         par_name + " = " + value + " [AA] " + comment)
+
+        par_name = "new_par3"
+        self.assertEqual(output[3], par_name)
+
+        par_name = "this_par"
+        value = "test_val"
+        comment = "// !"
+        self.assertEqual(output[4],
+                         par_name + " = " + value + " [] " + comment)
+
+        par_name = "that_par"
+        value = "\"txt_string\""
+        comment = ""
+        self.assertEqual(output[5],
+                         par_name + " = " + value + " [1]" + comment)
+
 
 if __name__ == '__main__':
-    unittest.main()
\ No newline at end of file
+    unittest.main()
diff --git a/mcstasscript/tests/test_declare_variable.py b/mcstasscript/tests/test_declare_variable.py
index 285823e0..3a5748ab 100644
--- a/mcstasscript/tests/test_declare_variable.py
+++ b/mcstasscript/tests/test_declare_variable.py
@@ -3,7 +3,6 @@
 import unittest
 import unittest.mock
 
-
 from mcstasscript.helper.mcstas_objects import declare_variable
 
 
@@ -11,7 +10,7 @@ class Test_declare_variable(unittest.TestCase):
     """
     Tests the declare_variable class that holds a declared variable
     that will be written to the McStas declare section.
-    
+
     """
 
     def test_declare_variable_init_basic_type(self):
@@ -21,18 +20,18 @@ def test_declare_variable_init_basic_type(self):
 
         var = declare_variable("double", "test")
 
-        self.assertEqual(var.name,"test")
-        self.assertEqual(var.type,"double")  # space for easier writing
+        self.assertEqual(var.name, "test")
+        self.assertEqual(var.type, "double")  # space for easier writing
 
     def test_declare_variable_init_basic_type_value(self):
         """
         Initialization with type and value
         """
 
-        var = declare_variable("double", "test", value = 518)
+        var = declare_variable("double", "test", value=518)
 
-        self.assertEqual(var.name,"test")
-        self.assertEqual(var.type,"double")  # space for easier writing
+        self.assertEqual(var.name, "test")
+        self.assertEqual(var.type, "double")  # space for easier writing
         self.assertEqual(var.value, 518)
 
     def test_declare_variable_init_basic_type_vector(self):
@@ -41,10 +40,10 @@ def test_declare_variable_init_basic_type_vector(self):
         """
 
         var = declare_variable("double", "test",
-                               array=6, value = [1, 2.2, 3, 3.3, 4, 4.4])
+                               array=6, value=[1, 2.2, 3, 3.3, 4, 4.4])
 
-        self.assertEqual(var.name,"test")
-        self.assertEqual(var.type,"double")  # space for easier writing
+        self.assertEqual(var.name, "test")
+        self.assertEqual(var.type, "double")  # space for easier writing
         self.assertEqual(var.vector, 6)
         self.assertEqual(var.value, [1, 2.2, 3, 3.3, 4, 4.4])
 
@@ -52,17 +51,17 @@ def test_declare_variable_init_basic_type_value_comment(self):
         """
         Initialization with type, value and comment
         """
-        
-        var = declare_variable("double", "test", 
-                                 value = 518, comment = "test comment /")
-        
-        self.assertEqual(var.name,"test")
-        self.assertEqual(var.type,"double")  # space for easier writing
+
+        var = declare_variable("double", "test",
+                               value=518, comment="test comment /")
+
+        self.assertEqual(var.name, "test")
+        self.assertEqual(var.type, "double")  # Space for easier writing
         self.assertEqual(var.value, 518)
         self.assertEqual(var.comment, " // test comment /")
 
     @unittest.mock.patch('__main__.__builtins__.open',
-                         new_callable = unittest.mock.mock_open)                                 
+                         new_callable=unittest.mock.mock_open)
     def test_declare_variable_write_basic(self, mock_f):
         """
         Testing that write to file is correct. Here a line is in a
@@ -75,12 +74,12 @@ def test_declare_variable_write_basic(self, mock_f):
         with mock_f('test.txt', 'w') as m_fo:
             var.write_line(m_fo)
 
-        mock_f.assert_called_with('test.txt', 'w')                                   
+        mock_f.assert_called_with('test.txt', 'w')
         handle = mock_f()
         handle.write.assert_called_once_with("double test;")
-    
+
     @unittest.mock.patch('__main__.__builtins__.open',
-                         new_callable = unittest.mock.mock_open)                                 
+                         new_callable=unittest.mock.mock_open)
     def test_declare_variable_write_complex_float(self, mock_f):
         """
         Testing that write to file is correct. Here a line is in a
@@ -90,45 +89,45 @@ def test_declare_variable_write_complex_float(self, mock_f):
         """
 
         var = declare_variable("double",
-                                 "test",
-                                 value = 5.4,
-                                 comment = "test comment")
-        
+                               "test",
+                               value=5.4,
+                               comment="test comment")
+
         with mock_f('test.txt', 'w') as m_fo:
             var.write_line(m_fo)
-        
+
         expected_write = "double test = 5.4; // test comment"
-        
-        mock_f.assert_called_with('test.txt', 'w')                                   
+
+        mock_f.assert_called_with('test.txt', 'w')
         handle = mock_f()
         handle.write.assert_called_once_with(expected_write)
-    
+
     @unittest.mock.patch('__main__.__builtins__.open',
-                         new_callable = unittest.mock.mock_open)    
+                         new_callable=unittest.mock.mock_open)
     def test_declare_variable_write_complex_int(self, mock_f):
         """
         Testing that write to file is correct. Here a line is in a
         instrument declare section. The write file operation is
         mocked and check using a patch. Here a declare with a value
         is used. (integer value)
-        """    
+        """
 
         var = declare_variable("double",
-                                 "test",
-                                 value = 5,
-                                 comment = "test comment")
-        
+                               "test",
+                               value=5,
+                               comment="test comment")
+
         with mock_f('test.txt', 'w') as m_fo:
             var.write_line(m_fo)
-            
+
         expected_write = "double test = 5; // test comment"
-        
-        mock_f.assert_called_with('test.txt', 'w')                                   
+
+        mock_f.assert_called_with('test.txt', 'w')
         handle = mock_f()
         handle.write.assert_called_once_with(expected_write)
-        
+
     @unittest.mock.patch('__main__.__builtins__.open',
-                         new_callable = unittest.mock.mock_open)    
+                         new_callable=unittest.mock.mock_open)
     def test_declare_variable_write_simple_array(self, mock_f):
         """
         Testing that write to file is correct. Here a line is in a
@@ -137,21 +136,21 @@ def test_declare_variable_write_simple_array(self, mock_f):
         """
 
         var = declare_variable("double",
-                                 "test",
-                                 array = 29,
-                                 comment = "test comment")
-        
+                               "test",
+                               array=29,
+                               comment="test comment")
+
         with mock_f('test.txt', 'w') as m_fo:
             var.write_line(m_fo)
-            
+
         expected_write = "double test[29]; // test comment"
-        
-        mock_f.assert_called_with('test.txt', 'w')                                   
+
+        mock_f.assert_called_with('test.txt', 'w')
         handle = mock_f()
-        handle.write.assert_called_once_with(expected_write)   
-        
+        handle.write.assert_called_once_with(expected_write)
+
     @unittest.mock.patch('__main__.__builtins__.open',
-                         new_callable = unittest.mock.mock_open)    
+                         new_callable=unittest.mock.mock_open)
     def test_declare_variable_write_complex_array(self, mock_f):
         """
         Testing that write to file is correct. Here a line is in a
@@ -159,12 +158,12 @@ def test_declare_variable_write_complex_array(self, mock_f):
         mocked and check using a patch. Here an array is decalred and
         populated with the selected values.
         """
-        
+
         var = declare_variable("double",
-                                 "test",
-                                 array = 3,
-                                 value = [5, 4, 3.1],
-                                 comment = "test comment")
+                               "test",
+                               array=3,
+                               value=[5, 4, 3.1],
+                               comment="test comment")
 
         with mock_f('test.txt', 'w') as m_fo:
             var.write_line(m_fo)
@@ -174,12 +173,10 @@ def test_declare_variable_write_complex_array(self, mock_f):
                            unittest.mock.call("4,"),
                            unittest.mock.call("3.1}; // test comment")]
 
-        mock_f.assert_called_with('test.txt', 'w')                                   
+        mock_f.assert_called_with('test.txt', 'w')
         handle = mock_f()
-        handle.write.assert_has_calls(expected_writes, any_order=False)    
+        handle.write.assert_has_calls(expected_writes, any_order=False)
 
 
 if __name__ == '__main__':
     unittest.main()
-
-    
\ No newline at end of file
diff --git a/mcstasscript/tests/test_functions.py b/mcstasscript/tests/test_functions.py
new file mode 100644
index 00000000..791ea62e
--- /dev/null
+++ b/mcstasscript/tests/test_functions.py
@@ -0,0 +1,141 @@
+import unittest
+
+import numpy as np
+
+from mcstasscript.interface.functions import name_search
+from mcstasscript.interface.functions import name_plot_options
+from mcstasscript.data.data import McStasData
+from mcstasscript.data.data import McStasMetaData
+from mcstasscript.data.data import McStasPlotOptions
+
+
+def set_dummy_MetaData_1d(name):
+    meta_data = McStasMetaData()
+    meta_data.component_name = name
+    meta_data.dimension = 50
+
+    return meta_data
+
+
+def set_dummy_McStasData_1d(name):
+    meta_data = set_dummy_MetaData_1d(name)
+
+    intensity = np.arange(20)
+    error = 0.5 * np.arange(20)
+    ncount = 2 * np.arange(20)
+    axis = np.arange(20)*5.0
+
+    return McStasData(meta_data, intensity, error, ncount, xaxis=axis)
+
+
+def set_dummy_MetaData_2d(name):
+    meta_data = McStasMetaData()
+    meta_data.component_name = name
+    meta_data.dimension = [50, 100]
+
+    return meta_data
+
+
+def set_dummy_McStasData_2d(name):
+    meta_data = set_dummy_MetaData_2d(name)
+
+    intensity = np.arange(20).reshape(4, 5)
+    error = 0.5 * np.arange(20).reshape(4, 5)
+    ncount = 2 * np.arange(20).reshape(4, 5)
+
+    return McStasData(meta_data, intensity, error, ncount)
+
+
+def setup_McStasData_array():
+
+    data_list = []
+
+    data_list.append(set_dummy_McStasData_1d("A_1d_thing"))
+    data_list.append(set_dummy_McStasData_2d("A_2d_thing"))
+    data_list.append(set_dummy_McStasData_1d("Another_1d_thing"))
+    data_list.append(set_dummy_McStasData_2d("Another_2d_thing"))
+    data_list.append(set_dummy_McStasData_2d("A_third_2d_thing"))
+
+    hero_object = set_dummy_McStasData_2d("Hero")
+    hero_object.metadata.dimension = 123
+    hero_object.plot_options.colormap = "very hot"
+
+    data_list.append(hero_object)
+
+    data_list.append(set_dummy_McStasData_2d("After_hero_2d"))
+    data_list.append(set_dummy_McStasData_2d("Last_object_2d"))
+
+    return data_list
+
+
+class Test_name_search(unittest.TestCase):
+    """
+    Test the utility function called name_search which finds and
+    returns a McStasData set with a given name from a list of
+    McStasData objects.
+
+    """
+
+    def test_name_search_read(self):
+        """
+        Test simple case
+        """
+
+        data_list = setup_McStasData_array()
+
+        hero_object = name_search("Hero", data_list)
+
+        self.assertEqual(hero_object.metadata.dimension, 123)
+
+    def test_name_search_read_error(self):
+        """
+        Test simple case
+        """
+
+        data_list = setup_McStasData_array()
+
+        with self.assertRaises(NameError):
+            hero_object = name_search("Hero8", data_list)
+
+    def test_name_search_type_error_not_list(self):
+        """
+        Test simple case
+        """
+
+        data_list = set_dummy_McStasData_2d("Last_object_2d")
+
+        with self.assertRaises(NameError):
+            hero_object = name_search("Hero", data_list)
+
+    def test_name_search_type_error_not_McStasData(self):
+        """
+        Test simple case
+        """
+
+        data_list = [1, 2, 3]
+
+        with self.assertRaises(NameError):
+            hero_object = name_search(1, data_list)
+
+
+class Test_name_plot_options(unittest.TestCase):
+    """
+    Test the utility function called name_plot_options which sends
+    keyword arguments to the set_plot_options method of the
+    McStasData object in a given list that has the given name.
+
+    """
+
+    def test_name_plot_options_simple(self):
+        """
+        Test simple case
+        """
+
+        data_list = setup_McStasData_array()
+        name_plot_options("Hero", data_list, colormap="very hot")
+        hero_object = name_search("Hero", data_list)
+        self.assertEqual(hero_object.plot_options.colormap, "very hot")
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/mcstasscript/tests/test_parameter_variable.py b/mcstasscript/tests/test_parameter_variable.py
index bca7b167..d94936d4 100644
--- a/mcstasscript/tests/test_parameter_variable.py
+++ b/mcstasscript/tests/test_parameter_variable.py
@@ -3,7 +3,6 @@
 import unittest
 import unittest.mock
 
-
 from mcstasscript.helper.mcstas_objects import parameter_variable
 
 
@@ -11,66 +10,66 @@ class Test_parameter_variable(unittest.TestCase):
     """
     Tests the parameter_variable class that holds an input parameter
     for the instrument.
-    
+
     """
-    
+
     def test_parameter_variable_init_basic(self):
         """
         Smallest possible initialization
         """
-        
+
         par = parameter_variable("test")
-        self.assertEqual(par.name,"test")
+        self.assertEqual(par.name, "test")
 
     def test_parameter_variable_init_basic_type(self):
         """
         Initialization with a type
         """
-        
+
         par = parameter_variable("double", "test")
-        
-        self.assertEqual(par.name,"test")
-        self.assertEqual(par.type,"double ")  # space for easier writing
-        
+
+        self.assertEqual(par.name, "test")
+        self.assertEqual(par.type, "double ")  # space for easier writing
+
     def test_parameter_variable_init_basic_type_value(self):
         """
         Initialization with type and value
         """
-        
-        par = parameter_variable("double", "test", value = 518)
-        
-        self.assertEqual(par.name,"test")
-        self.assertEqual(par.type,"double ")  # space for easier writing
+
+        par = parameter_variable("double", "test", value=518)
+
+        self.assertEqual(par.name, "test")
+        self.assertEqual(par.type, "double ")  # space for easier writing
         self.assertEqual(par.value, 518)
-        
+
     def test_parameter_variable_init_basic_type_value_comment(self):
         """
         Initialization with type, value and comment
         """
-        
-        par = parameter_variable("double", "test", 
-                                 value = 518, comment = "test comment /")
-        
-        self.assertEqual(par.name,"test")
-        self.assertEqual(par.type,"double ")  # space for easier writing
+
+        par = parameter_variable("double", "test",
+                                 value=518, comment="test comment /")
+
+        self.assertEqual(par.name, "test")
+        self.assertEqual(par.type, "double ")  # space for easier writing
         self.assertEqual(par.value, 518)
         self.assertEqual(par.comment, "// test comment /")
-        
+
     def test_parameter_variable_init_basic_value_comment(self):
         """
         Initialization with value and comment
         """
-        
-        par = parameter_variable("test", 
-                                 value = 518, comment = "test comment /")
-        
-        self.assertEqual(par.name,"test")
-        self.assertEqual(par.type,"")
+
+        par = parameter_variable("test",
+                                 value=518, comment="test comment /")
+
+        self.assertEqual(par.name, "test")
+        self.assertEqual(par.type, "")
         self.assertEqual(par.value, 518)
         self.assertEqual(par.comment, "// test comment /")
 
     @unittest.mock.patch('__main__.__builtins__.open',
-                         new_callable = unittest.mock.mock_open)                                 
+                         new_callable=unittest.mock.mock_open)
     def test_parameter_variable_write_basic(self, mock_f):
         """
         Testing that write to file is correct. Here a line is in a
@@ -87,13 +86,13 @@ def test_parameter_variable_write_basic(self, mock_f):
                            unittest.mock.call(""),
                            unittest.mock.call(""),
                            unittest.mock.call("\n")]
-        
-        mock_f.assert_called_with('test.txt', 'w')                                   
+
+        mock_f.assert_called_with('test.txt', 'w')
         handle = mock_f()
         handle.write.assert_has_calls(expected_writes, any_order=False)
-        
+
     @unittest.mock.patch('__main__.__builtins__.open',
-                         new_callable = unittest.mock.mock_open)                                 
+                         new_callable=unittest.mock.mock_open)
     def test_parameter_variable_write_complex_float(self, mock_f):
         """
         Testing that write to file is correct. Here a line is in a
@@ -104,9 +103,9 @@ def test_parameter_variable_write_complex_float(self, mock_f):
 
         par = parameter_variable("double",
                                  "test",
-                                 value = 5.4,
-                                 comment = "test comment")
-        
+                                 value=5.4,
+                                 comment="test comment")
+
         with mock_f('test.txt', 'w') as m_fo:
             par.write_parameter(m_fo, ")")
 
@@ -115,26 +114,26 @@ def test_parameter_variable_write_complex_float(self, mock_f):
                            unittest.mock.call(")"),
                            unittest.mock.call("// test comment"),
                            unittest.mock.call("\n")]
-        
-        mock_f.assert_called_with('test.txt', 'w')                                   
+
+        mock_f.assert_called_with('test.txt', 'w')
         handle = mock_f()
         handle.write.assert_has_calls(expected_writes, any_order=False)
-    
+
     @unittest.mock.patch('__main__.__builtins__.open',
-                         new_callable = unittest.mock.mock_open)    
+                         new_callable=unittest.mock.mock_open)
     def test_parameter_variable_write_complex_int(self, mock_f):
         """
         Testing that write to file is correct. Here a line is in a
         instrument parameter section. The write file operation is
         mocked and check using a patch. Here a parameter with a value
         is used. (integer value)
-        """    
+        """
 
         par = parameter_variable("double",
                                  "test",
-                                 value = 5,
-                                 comment = "test comment")
-        
+                                 value=5,
+                                 comment="test comment")
+
         with mock_f('test.txt', 'w') as m_fo:
             par.write_parameter(m_fo, ")")
 
@@ -143,26 +142,26 @@ def test_parameter_variable_write_complex_int(self, mock_f):
                            unittest.mock.call(")"),
                            unittest.mock.call("// test comment"),
                            unittest.mock.call("\n")]
-        
-        mock_f.assert_called_with('test.txt', 'w')                                   
+
+        mock_f.assert_called_with('test.txt', 'w')
         handle = mock_f()
         handle.write.assert_has_calls(expected_writes, any_order=False)
-    
+
     @unittest.mock.patch('__main__.__builtins__.open',
-                         new_callable = unittest.mock.mock_open)    
+                         new_callable=unittest.mock.mock_open)
     def test_parameter_variable_write_complex_string(self, mock_f):
         """
         Testing that write to file is correct. Here a line is in a
         instrument parameter section. The write file operation is
         mocked and check using a patch. Here a parameter with a value
         is used. (string value)
-        """  
+        """
 
         par = parameter_variable("double",
                                  "test",
-                                 value = "\"Al\"",
-                                 comment = "test comment")
-        
+                                 value="\"Al\"",
+                                 comment="test comment")
+
         with mock_f('test.txt', 'w') as m_fo:
             par.write_parameter(m_fo, ",")
 
@@ -171,10 +170,10 @@ def test_parameter_variable_write_complex_string(self, mock_f):
                            unittest.mock.call(","),
                            unittest.mock.call("// test comment"),
                            unittest.mock.call("\n")]
-        
-        mock_f.assert_called_with('test.txt', 'w')                                   
+
+        mock_f.assert_called_with('test.txt', 'w')
         handle = mock_f()
-        handle.write.assert_has_calls(expected_writes, any_order=False)        
+        handle.write.assert_has_calls(expected_writes, any_order=False)
 
 
 if __name__ == '__main__':

From 125102c78d6c952ad0cff06f1fa3064830a6259e Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Tue, 21 May 2019 11:23:02 +0200
Subject: [PATCH 018/403] Added show_parameters method to isntrument including
 a test. Simplified parameter_variable and declare_variable classes.

---
 mcstasscript/helper/mcstas_objects.py | 30 ++++++++--------------
 mcstasscript/interface/instr.py       | 36 +++++++++++++++++++++++++++
 mcstasscript/tests/test_Instr.py      | 24 ++++++++++++++++++
 3 files changed, 70 insertions(+), 20 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index b3b42246..750f165b 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -58,16 +58,13 @@ def __init__(self, *args, **kwargs):
             self.type = args[0] + " "
             self.name = str(args[1])
 
+        self.value = ""
         if "value" in kwargs:
-            self.value_set = 1
             self.value = kwargs["value"]
-        else:
-            self.value_set = 0
 
+        self.comment = ""
         if "comment" in kwargs:
             self.comment = "// " + kwargs["comment"]
-        else:
-            self.comment = ""
 
         # could check for allowed types
         # they are int, double, string, are there more?
@@ -75,7 +72,7 @@ def __init__(self, *args, **kwargs):
     def write_parameter(self, fo, stop_character):
         """Writes input parameter to file"""
         fo.write("%s%s" % (self.type, self.name))
-        if self.value_set == 1:
+        if self.value is not "":
             if isinstance(self.value, int):
                 fo.write(" = %d" % self.value)
             elif isinstance(self.value, float):
@@ -113,9 +110,6 @@ class declare_variable:
     vector : int
         0 if a single value is given, ortherwise contains the length
 
-    value_set : int
-        Internal variable displaying wether or not a value was given
-
     Methods
     -------
     write_line(fo)
@@ -145,21 +139,17 @@ def __init__(self, *args, **kwargs):
         """
         self.type = args[0]
         self.name = str(args[1])
+        self.value = ""
         if "value" in kwargs:
-            self.value_set = 1
             self.value = kwargs["value"]
-        else:
-            self.value_set = 0
 
+        self.vector = 0
         if "array" in kwargs:
             self.vector = kwargs["array"]
-        else:
-            self.vector = 0
 
+        self.comment = ""
         if "comment" in kwargs:
             self.comment = " // " + kwargs["comment"]
-        else:
-            self.comment = ""
 
     def write_line(self, fo):
         """Writes line declaring variable to file fo
@@ -169,19 +159,19 @@ def write_line(self, fo):
         fo : file object
             File the line will be written to
         """
-        if self.value_set == 0 and self.vector == 0:
+        if self.value is "" and self.vector == 0:
             fo.write("%s %s;%s" % (self.type, self.name, self.comment))
-        if self.value_set == 1 and self.vector == 0:
+        if self.value is not "" and self.vector == 0:
             if self.type == "int":
                 fo.write("%s %s = %d;%s" % (self.type, self.name,
                                             self.value, self.comment))
             else:
                 fo.write("%s %s = %G;%s" % (self.type, self.name,
                                             self.value, self.comment))
-        if self.value_set == 0 and self.vector != 0:
+        if self.value is "" and self.vector != 0:
             fo.write("%s %s[%d];%s" % (self.type, self.name,
                                        self.vector, self.comment))
-        if self.value_set == 1 and self.vector != 0:
+        if self.value is not "" and self.vector != 0:
             fo.write("%s %s[%d] = {" % (self.type, self.name, self.vector))
             for i in range(0, len(self.value) - 1):
                 fo.write("%G," % self.value[i])
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 76a9f2a0..c2a7b850 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -217,6 +217,42 @@ def add_parameter(self, *args, **kwargs):
         """
         # parameter_variable class documented independently
         self.parameter_list.append(parameter_variable(*args, **kwargs))
+        
+    def show_parameters(self):
+        """
+        Method for displaying current instrument parameters
+        """
+
+        if len(self.parameter_list) == 0:
+            print("No instrument parameters available")
+            return
+
+        # Find longest fields
+        types = []
+        names = []
+        values = []
+        comments = []
+        for parameter in self.parameter_list:
+            types.append(str(parameter.type))
+            names.append(str(parameter.name))
+            values.append(str(parameter.value))
+            comments.append(str(parameter.comment))
+
+        longest_type = len(max(types, key=len))
+        longest_name = len(max(names, key=len))
+        longest_value = len(max(values, key=len))
+        longest_comment = len(max(comments, key=len))
+
+        # Print to console
+        for parameter in self.parameter_list:
+            print(str(parameter.type).ljust(longest_type), end=' ')
+            print(str(parameter.name).ljust(longest_name), end=' ')
+            if parameter.value is "":
+                print("   ", end=' ')
+            else:
+                print(" = ", end=' ')
+            print(str(parameter.value).ljust(longest_value+1), end=' ')
+            print(str(parameter.comment).ljust(longest_comment))
 
     def add_declare_var(self, *args, **kwargs):
         """
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 35705cd5..311abd31 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -85,6 +85,30 @@ def test_simple_add_parameter(self):
         self.assertEqual(instr.parameter_list[0].name, "theta")
         self.assertEqual(instr.parameter_list[0].comment, "// test par")
 
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_show_parameters(self, mock_stdout):
+        """
+        Testing that parameters are displayed correctly
+        """
+        instr = setup_instr_no_path()
+
+        instr.add_parameter("double", "theta", comment="test par")
+        instr.add_parameter("single", "theta", comment="test par")
+        instr.add_parameter("float", "theta", value = 8, comment="test par")
+        instr.add_parameter("int", "slits", comment="test par")
+        instr.add_parameter("string", "ref", 
+                            value = "string", comment="new string")
+        
+        instr.show_parameters()
+        
+        output = mock_stdout.getvalue().split("\n")
+        
+        self.assertEqual(output[0],"double  theta             // test par  ")
+        self.assertEqual(output[1],"single  theta             // test par  ")
+        self.assertEqual(output[2],"float   theta  =  8       // test par  ")
+        self.assertEqual(output[3],"int     slits             // test par  ")
+        self.assertEqual(output[4],"string  ref    =  string  // new string")
+
     def test_simple_add_declare_parameter(self):
         """
         This is just an interface to a function that is tested

From 8a796d9dbc7d72a0cfb200855257de69a9e41a99 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Tue, 21 May 2019 13:36:31 +0200
Subject: [PATCH 019/403] Added checks for filename and parameter names with
 appropriate errors if unsuitable names are used. Unit tests for functions
 checking whether varialbes are legal are included in test_formatting.py.

---
 mcstasscript/helper/formatting.py     | 43 ++++++++++++++++-
 mcstasscript/interface/instr.py       |  9 +++-
 mcstasscript/tests/test_Instr.py      | 22 ++++-----
 mcstasscript/tests/test_formatting.py | 69 +++++++++++++++++++++++++++
 4 files changed, 130 insertions(+), 13 deletions(-)
 create mode 100644 mcstasscript/tests/test_formatting.py

diff --git a/mcstasscript/helper/formatting.py b/mcstasscript/helper/formatting.py
index 58ac53c8..aef8ff10 100644
--- a/mcstasscript/helper/formatting.py
+++ b/mcstasscript/helper/formatting.py
@@ -11,4 +11,45 @@ class bcolors:
     FAIL = '\033[91m'
     ENDC = '\033[0m'
     BOLD = '\033[1m'
-    UNDERLINE = '\033[4m'
\ No newline at end of file
+    UNDERLINE = '\033[4m'
+    
+def is_legal_parameter(name):
+    """
+    Function that returns true if the given name can be used as a
+    parameter in the c programming language.
+    """
+    
+    if name is "":
+        return False
+    
+    if " " in name:
+        return False
+    
+    if "." in name:
+        return False
+
+    if not name[0].isalpha():
+        return False
+    
+    return True
+
+def is_legal_filename(name):
+    """
+    Function that returns true if the given name can be used as a
+    filename
+    """
+    
+    if name is "":
+        return False
+    
+    if " " in name:
+        return False
+    
+    if "/" in name:
+        return False
+    
+    if "\\" in name:
+        return False
+    
+    return True
+    
\ No newline at end of file
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index c2a7b850..e240f8ed 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -9,6 +9,7 @@
 from mcstasscript.data.data import McStasData
 from mcstasscript.helper.component_reader import ComponentReader
 from mcstasscript.helper.managed_mcrun import ManagedMcrun
+from mcstasscript.helper.formatting import is_legal_filename
 
 class McStas_instr:
     """
@@ -154,6 +155,13 @@ def __init__(self, name, **kwargs):
         """
 
         self.name = name
+        
+        if not is_legal_filename(self.name + ".instr"):
+            raise NameError("The instrument is called: \""
+                            + self.name
+                            + "\" resulting in an instrument file named: \""
+                            + self.name + ".instr"
+                            + "\" which is not a legal filename")
 
         if "author" in kwargs:
             self.author = kwargs["author"]
@@ -308,7 +316,6 @@ def append_initialize_no_new_line(self, string):
         ----------
         string : str
             code to be added to initialize section
-
         """
 
         self.initialize_section = self.initialize_section + string
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 311abd31..45d693a0 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -94,20 +94,20 @@ def test_show_parameters(self, mock_stdout):
 
         instr.add_parameter("double", "theta", comment="test par")
         instr.add_parameter("single", "theta", comment="test par")
-        instr.add_parameter("float", "theta", value = 8, comment="test par")
+        instr.add_parameter("float", "theta", value=8, comment="test par")
         instr.add_parameter("int", "slits", comment="test par")
-        instr.add_parameter("string", "ref", 
-                            value = "string", comment="new string")
-        
+        instr.add_parameter("string", "ref",
+                            value="string", comment="new string")
+
         instr.show_parameters()
-        
+
         output = mock_stdout.getvalue().split("\n")
-        
-        self.assertEqual(output[0],"double  theta             // test par  ")
-        self.assertEqual(output[1],"single  theta             // test par  ")
-        self.assertEqual(output[2],"float   theta  =  8       // test par  ")
-        self.assertEqual(output[3],"int     slits             // test par  ")
-        self.assertEqual(output[4],"string  ref    =  string  // new string")
+
+        self.assertEqual(output[0], "double  theta             // test par  ")
+        self.assertEqual(output[1], "single  theta             // test par  ")
+        self.assertEqual(output[2], "float   theta  =  8       // test par  ")
+        self.assertEqual(output[3], "int     slits             // test par  ")
+        self.assertEqual(output[4], "string  ref    =  string  // new string")
 
     def test_simple_add_declare_parameter(self):
         """
diff --git a/mcstasscript/tests/test_formatting.py b/mcstasscript/tests/test_formatting.py
new file mode 100644
index 00000000..ee6bfaf5
--- /dev/null
+++ b/mcstasscript/tests/test_formatting.py
@@ -0,0 +1,69 @@
+import unittest
+
+from mcstasscript.helper.formatting import is_legal_parameter
+from mcstasscript.helper.formatting import is_legal_filename
+
+
+class TestFormatting(unittest.TestCase):
+    """
+    Tests the formatting functions
+    """
+    def test_is_legal_parameter_simple(self):
+        """
+        Check a legal parameter is legal
+        """
+        test_name = "test_parameter_name1"
+        self.assertTrue(is_legal_parameter(test_name))
+
+    def test_is_legal_parameter_reject_space(self):
+        """
+        A space should make the parameter name illegal
+        """
+        test_name = "test_parameter name"
+        self.assertFalse(is_legal_parameter(test_name))
+
+    def test_is_legal_parameter_reject_first_number(self):
+        """
+        The first character being a number is ilegal
+        """
+        test_name = "2est_parameter_name"
+        self.assertFalse(is_legal_parameter(test_name))
+
+    def test_is_legal_parameter_reject_empty(self):
+        """
+        An empty string should not be a legal name
+        """
+        test_name = ""
+        self.assertFalse(is_legal_parameter(test_name))
+
+    def test_is_legal_filename_simple(self):
+        """
+        Test with a legal filename
+        """
+        test_name = "test_instrument1"
+        self.assertTrue(is_legal_filename(test_name))
+
+    def test_is_legal_filename_reject_forward_dash(self):
+        """
+        A dash should make the filename illegal
+        """
+        test_name = "test/instrument"
+        self.assertFalse(is_legal_filename(test_name))
+
+    def test_is_legal_filename_reject_backwards_dash(self):
+        """
+        A backwards dash should make the filename illegal
+        """
+        test_name = "test\\instrument"
+        self.assertFalse(is_legal_filename(test_name))
+
+    def test_is_legal_filename_rekect_space(self):
+        """
+        A space should make the filename illegal
+        """
+        test_name = "test instrument"
+        self.assertFalse(is_legal_filename(test_name))
+
+
+if __name__ == '__main__':
+    unittest.main()

From c1816989585d15be9686244d99638f00e123ba40 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Tue, 21 May 2019 15:32:38 +0200
Subject: [PATCH 020/403] Updated examples with new syntax.

Added a few more error messages in functions, and a few tests.
---
 examples/McStasScript_demo.ipynb        | 180 +++++++++++++-----------
 examples/random_demonstration.py        | 149 +++++++++++++-------
 mcstasscript/helper/mcstas_objects.py   |  18 ++-
 mcstasscript/interface/functions.py     |  11 +-
 mcstasscript/tests/test_ManagedMcrun.py |   2 +-
 mcstasscript/tests/test_functions.py    |  34 ++++-
 6 files changed, 254 insertions(+), 140 deletions(-)

diff --git a/examples/McStasScript_demo.ipynb b/examples/McStasScript_demo.ipynb
index 8a04b384..2150fb2b 100644
--- a/examples/McStasScript_demo.ipynb
+++ b/examples/McStasScript_demo.ipynb
@@ -15,26 +15,26 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "sys.path.append('/Users/madsbertelsen/PaNOSC/McStasScript') # Path to McStasScript pythoon file\n",
+    "sys.path.append('/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript') # Path to McStasScript pythoon file\n",
     "\n",
     "from mcstasscript.interface import instr, plotter, functions\n",
     "\n",
     "# Creating the instance of the class, insert path to mcrun and to mcstas root directory\n",
-    "instr = instr.McStas_instr(\"jupyter_demo\",\n",
-    "                           mcrun_path= \"/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin\",\n",
+    "Instr = instr.McStas_instr(\"jupyter_demo\",\n",
+    "                           mcrun_path = \"/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin\",\n",
     "                           mcstas_path = \"/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Here are the availalbe component categories:\n",
+      "Here are the available component categories:\n",
       " sources\n",
       " optics\n",
       " samples\n",
@@ -48,12 +48,12 @@
     }
    ],
    "source": [
-    "instr.show_components() # Shows available McStas component categories in current installation"
+    "Instr.show_components() # Shows available McStas component categories in current installation"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -69,12 +69,12 @@
     }
    ],
    "source": [
-    "instr.show_components(\"sources\") # Display all McStas source components "
+    "Instr.show_components(\"sources\") # Display all McStas source components "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -84,43 +84,43 @@
       " ___ Help Source_simple _________________________________________________\n",
       "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
       "\u001b[1mradius\u001b[0m = \u001b[1m\u001b[94m0.1\u001b[0m\u001b[0m [m] // Radius of circle in (x,y,0) plane where neutrons are generated.\n",
-      "\u001b[1myheight\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Height of rectangle in (x,y,0) plane where neutrons are generated.\n",
-      "\u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Width of rectangle in (x,y,0) plane where neutrons are generated.\n",
-      "\u001b[1mdist\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Distance to target along z axis.\n",
-      "\u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[94m.045\u001b[0m\u001b[0m [m] // Width of target\n",
-      "\u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[94m.12\u001b[0m\u001b[0m [m] // Height of target\n",
-      "\u001b[1mE0\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [meV] // Mean energy of neutrons.\n",
-      "\u001b[1mdE\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [meV] // Energy half spread of neutrons (flat or gaussian sigma).\n",
-      "\u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [AA] // Mean wavelength of neutrons.\n",
-      "\u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [AA] // Wavelength half spread of neutrons.\n",
-      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1/(s*cm**2*st*energy unit)] // flux per energy unit, Angs or meV if flux=0, the source emits 1 in 4*PI whole space.\n",
-      "\u001b[1mgauss\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [1] // Gaussian (1) or Flat (0) energy/wavelength distribution\n",
-      "\u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[94m+1\u001b[0m\u001b[0m [1] // relative index of component to focus at, e.g. next is +1 this is used to compute 'dist' automatically.\n",
+      "\u001b[1myheight\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Height of rectangle in (x,y,0) plane where neutrons are generated.\n",
+      "\u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Width of rectangle in (x,y,0) plane where neutrons are generated.\n",
+      "\u001b[1mdist\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Distance to target along z axis.\n",
+      "\u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[94m0.045\u001b[0m\u001b[0m [m] // Width of target\n",
+      "\u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[94m0.12\u001b[0m\u001b[0m [m] // Height of target\n",
+      "\u001b[1mE0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Mean energy of neutrons.\n",
+      "\u001b[1mdE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Energy half spread of neutrons (flat or gaussian sigma).\n",
+      "\u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [AA] // Mean wavelength of neutrons.\n",
+      "\u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [AA] // Wavelength half spread of neutrons.\n",
+      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1/(s*cm**2*st*energy unit)] // flux per energy unit, Angs or meV if flux=0, the source emits 1 in 4*PI whole space.\n",
+      "\u001b[1mgauss\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Gaussian (1) or Flat (0) energy/wavelength distribution\n",
+      "\u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1] // relative index of component to focus at, e.g. next is +1 this is used to compute 'dist' automatically.\n",
       "-------------------------------------------------------------------------\n"
      ]
     }
    ],
    "source": [
-    "instr.component_help(\"Source_simple\") # Displays help on the Source_simple component"
+    "Instr.component_help(\"Source_simple\") # Displays help on the Source_simple component"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "source = instr.add_component(\"Source\",\"Source_simple\") # Adds an instance of Source_simple called Source to instrument"
+    "source = Instr.add_component(\"Source\",\"Source_simple\") # Adds an instance of Source_simple"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Lets add a parameter to the instrument to control the wavelength of the source\n",
-    "instr.add_parameter(\"double\",\"wavelength\",value=3,comment=\"Wavelength emmited from source\")\n",
+    "Instr.add_parameter(\"double\", \"wavelength\", value=3, comment=\"Wavelength emmited from source\")\n",
     "source.xwidth = 0.06; source.yheight = 0.08;\n",
     "source.dist = 2; source.focus_xw = 0.05; source.focus_yh = 0.05\n",
     "source.lambda0 = \"wavelength\"; source.dlambda = 0.1"
@@ -128,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -154,17 +154,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
-    "guide = instr.add_component(\"Guide\", \"Guide_gravity\", AT=[0,0,2], RELATIVE=\"Source\")\n",
+    "guide = Instr.add_component(\"Guide\", \"Guide_gravity\", AT=[0,0,2], RELATIVE=\"Source\")\n",
     "guide.set_comment=\"Beam extraction and first guide piece\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -175,37 +175,37 @@
       "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
       "\u001b[4m\u001b[1mw1\u001b[0m\u001b[0m [m] // Width at the guide entry\n",
       "\u001b[4m\u001b[1mh1\u001b[0m\u001b[0m [m] // Height at the guide entry\n",
-      "\u001b[1mw2\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Width at the guide exit. If 0, use w1.\n",
-      "\u001b[1mh2\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Height at the guide exit. If 0, use h1.\n",
+      "\u001b[1mw2\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Width at the guide exit. If 0, use w1.\n",
+      "\u001b[1mh2\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Height at the guide exit. If 0, use h1.\n",
       "\u001b[4m\u001b[1ml\u001b[0m\u001b[0m [m] // length of guide\n",
       "\u001b[1mR0\u001b[0m = \u001b[1m\u001b[94m0.995\u001b[0m\u001b[0m [1] // Low-angle reflectivity\n",
       "\u001b[1mQc\u001b[0m = \u001b[1m\u001b[94m0.0218\u001b[0m\u001b[0m [AA-1] // Critical scattering vector\n",
       "\u001b[1malpha\u001b[0m = \u001b[1m\u001b[94m4.38\u001b[0m\u001b[0m [AA] // Slope of reflectivity\n",
       "\u001b[1mm\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // m-value of material. Zero means completely absorbing. m=0.65  glass/SiO2 Si   Ni  Ni58  supermirror Be    Diamond m=  0.65       0.47 1   1.18  2-6         1.01  1.12 for glass/SiO2, m=1 for Ni, 1.2 for Ni58, m=2-6 for supermirror. m=0.47 for Si\n",
       "\u001b[1mW\u001b[0m = \u001b[1m\u001b[94m0.003\u001b[0m\u001b[0m [AA-1] // Width of supermirror cut-off\n",
-      "\u001b[1mnslit\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1] // Number of vertical channels in the guide (>= 1) (nslit-1 vertical dividing walls).\n",
+      "\u001b[1mnslit\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // Number of vertical channels in the guide (>= 1) (nslit-1 vertical dividing walls).\n",
       "\u001b[1md\u001b[0m = \u001b[1m\u001b[94m0.0005\u001b[0m\u001b[0m [m] // Thickness of subdividing walls\n",
-      "\u001b[1mmleft\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // m-value of material for left.   vert. mirror\n",
-      "\u001b[1mmright\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // m-value of material for right.  vert. mirror\n",
-      "\u001b[1mmtop\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // m-value of material for top.    horz. mirror\n",
-      "\u001b[1mmbottom\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // m-value of material for bottom. horz. mirror\n",
-      "\u001b[1mnhslit\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1] // Number of horizontal channels in the guide (>= 1). (nhslit-1 horizontal dividing walls). this enables to have nslit*nhslit rectangular channels\n",
-      "\u001b[1mG\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m/s2] // Gravitation norm. 0 value disables G effects.\n",
-      "\u001b[1maleft\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // alpha-value of left  vert. mirror\n",
-      "\u001b[1maright\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // alpha-value of right vert. mirror\n",
-      "\u001b[1matop\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // alpha-value of top   horz. mirror\n",
-      "\u001b[1mabottom\u001b[0m = \u001b[1m\u001b[94m-1\u001b[0m\u001b[0m [1] // alpha-value of left  horz. mirror\n",
-      "\u001b[1mwavy\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // Global guide waviness\n",
-      "\u001b[1mwavy_z\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // Partial waviness along propagation axis\n",
-      "\u001b[1mwavy_tb\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // Partial waviness in transverse direction for top/bottom mirrors\n",
-      "\u001b[1mwavy_lr\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // Partial waviness in transverse direction for left/right mirrors\n",
-      "\u001b[1mchamfers\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Global chamfers specifications (in/out/mirror sides).\n",
-      "\u001b[1mchamfers_z\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Input and output chamfers\n",
-      "\u001b[1mchamfers_lr\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Chamfers on left/right mirror sides\n",
-      "\u001b[1mchamfers_tb\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Chamfers on top/bottom mirror sides\n",
-      "\u001b[1mnelements\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1] // Number of sections in the guide (length l/nelements).\n",
-      "\u001b[1mnu\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [Hz] // Rotation frequency (round/s) for Fermi Chopper approximation\n",
-      "\u001b[1mphase\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // Phase shift for the Fermi Chopper approximation\n",
+      "\u001b[1mmleft\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // m-value of material for left.   vert. mirror\n",
+      "\u001b[1mmright\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // m-value of material for right.  vert. mirror\n",
+      "\u001b[1mmtop\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // m-value of material for top.    horz. mirror\n",
+      "\u001b[1mmbottom\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // m-value of material for bottom. horz. mirror\n",
+      "\u001b[1mnhslit\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // Number of horizontal channels in the guide (>= 1). (nhslit-1 horizontal dividing walls). this enables to have nslit*nhslit rectangular channels\n",
+      "\u001b[1mG\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m/s2] // Gravitation norm. 0 value disables G effects.\n",
+      "\u001b[1maleft\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // alpha-value of left  vert. mirror\n",
+      "\u001b[1maright\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // alpha-value of right vert. mirror\n",
+      "\u001b[1matop\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // alpha-value of top   horz. mirror\n",
+      "\u001b[1mabottom\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // alpha-value of left  horz. mirror\n",
+      "\u001b[1mwavy\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // Global guide waviness\n",
+      "\u001b[1mwavy_z\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // Partial waviness along propagation axis\n",
+      "\u001b[1mwavy_tb\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // Partial waviness in transverse direction for top/bottom mirrors\n",
+      "\u001b[1mwavy_lr\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // Partial waviness in transverse direction for left/right mirrors\n",
+      "\u001b[1mchamfers\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Global chamfers specifications (in/out/mirror sides).\n",
+      "\u001b[1mchamfers_z\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Input and output chamfers\n",
+      "\u001b[1mchamfers_lr\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Chamfers on left/right mirror sides\n",
+      "\u001b[1mchamfers_tb\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Chamfers on top/bottom mirror sides\n",
+      "\u001b[1mnelements\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // Number of sections in the guide (length l/nelements).\n",
+      "\u001b[1mnu\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Hz] // Rotation frequency (round/s) for Fermi Chopper approximation\n",
+      "\u001b[1mphase\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // Phase shift for the Fermi Chopper approximation\n",
       "\u001b[1mreflect\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [str] // Reflectivity file name. Format <q(Angs-1) R(0-1)>\n",
       "-------------------------------------------------------------------------\n"
      ]
@@ -217,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -226,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -252,25 +252,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "sample = instr.add_component(\"sample\", \"PowderN\", AT=[0,0,9], RELATIVE=\"Guide\") # Add a sample"
+    "sample = Instr.add_component(\"sample\", \"PowderN\", AT=[0, 0, 9], RELATIVE=\"Guide\") # Add a sample"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "sample.radius = 0.015; sample.yheight = 0.05; sample.reflections = \"\\\"Cu.laz\\\"\" # A small copper cylinder"
+    "sample.radius = 0.015; sample.yheight = 0.05; sample.reflections = \"\\\"Cu.laz\\\"\" # Copper cylinder"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -291,21 +291,21 @@
     }
    ],
    "source": [
-    "instr.show_components(\"monitors\") # Monitors are needed to record information"
+    "Instr.show_components(\"monitors\") # Monitors are needed to record information"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
-    "sphere = instr.add_component(\"PSD_4PI\", \"PSD_monitor_4PI\", RELATIVE=\"sample\") # Add 4PI sphere detector"
+    "sphere = Instr.add_component(\"PSD_4PI\", \"PSD_monitor_4PI\", RELATIVE=\"sample\") # Add 4PI detector"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -324,32 +324,35 @@
     }
    ],
    "source": [
-    "sphere.nx = 300; sphere.ny = 300; sphere.filename = \"\\\"PSD_4PI.dat\\\"\"; sphere.radius = 1; sphere.restore_neutron = 1;\n",
-    "sphere.print_long() # Verify that monitors have filenames that are strings when printed, double quotes needed"
+    "sphere.nx = 300; sphere.ny = 300\n",
+    "sphere.radius = 1; sphere.restore_neutron = 1\n",
+    "sphere.filename = \"\\\"PSD_4PI.dat\\\"\" # filenames need printed quotes, use \\\"\n",
+    "sphere.print_long() # Verify that monitors have filenames that are strings when printed"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
-    "PSD = instr.add_component(\"PSD\", \"PSD_monitor\", AT=[0,0,1], RELATIVE=\"sample\") # Add position sensitive detector\n",
-    "PSD.xwidth = 0.1; PSD.yheight = 0.1; PSD.nx = 200; PSD.ny = 200; PSD.filename = \"\\\"PSD.dat\\\"\"; PSD.restore_neutron = 1"
+    "PSD = Instr.add_component(\"PSD\", \"PSD_monitor\", AT=[0,0,1], RELATIVE=\"sample\") # Add PSD monitor\n",
+    "PSD.xwidth = 0.1; PSD.yheight = 0.1; PSD.nx = 200; PSD.ny = 200\n",
+    "PSD.filename = \"\\\"PSD.dat\\\"\"; PSD.restore_neutron = 1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
-    "L_mon = instr.add_component(\"L_mon\", \"L_monitor\", RELATIVE=\"PSD\")"
+    "L_mon = Instr.add_component(\"L_mon\", \"L_monitor\", RELATIVE=\"PSD\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -361,7 +364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -388,7 +391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -405,7 +408,24 @@
     }
    ],
    "source": [
-    "instr.print_components() # Lets get an overview of the instrument so far"
+    "Instr.print_components() # Lets get an overview of the instrument so far"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "double  wavelength  =  3  // Wavelength emmited from source\n"
+     ]
+    }
+   ],
+   "source": [
+    "Instr.show_parameters()"
    ]
   },
   {
@@ -418,12 +438,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
     "# If the folder already exsits, a new simulation is not performed but the old one is read\n",
-    "data = instr.run_full_instrument(foldername=\"jupyter_demo\",\n",
+    "data = Instr.run_full_instrument(foldername=\"jupyter_demo\",\n",
     "                                 parameters={\"wavelength\" : 1.0},\n",
     "                                 mpi=4,\n",
     "                                 ncount=2E7)"
@@ -439,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -454,7 +474,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 936x504 with 6 Axes>"
       ]
diff --git a/examples/random_demonstration.py b/examples/random_demonstration.py
index 447af8c5..574162af 100644
--- a/examples/random_demonstration.py
+++ b/examples/random_demonstration.py
@@ -2,41 +2,54 @@
 # Written by Mads Bertelsen, ESS DMSC
 import random
 import sys
-sys.path.append('/Users/madsbertelsen/PaNOSC/McStasScript')
+sys.path.append('/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript')
 from mcstasscript.interface import instr, plotter, functions
 
 # if the mcrun command from McStas is not in your path, provide absolute path for the binary here:
-#mcrun_path = ""
-mcrun_path = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin"
-#mcstas_path = ""
-mcstas_path = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/"
+mcrun_path = ""
+mcstas_path = ""
+# On OS X most likely:
+#mcrun_path = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin"
+#mcstas_path = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/"
 
 # Create a McStas instrument
-instr = instr.McStas_instr("random_demo",
+Instr = instr.McStas_instr("random_demo",
                            author = "Mads Bertelsen",
                            origin = "ESS DMSC",
                            mcrun_path = mcrun_path,
                            mcstas_path = mcstas_path)
 
-# Set up a material called Cu with approrpiate properties (uses McStas Union components, here the processes)
-instr.add_component("Cu_incoherent", "Incoherent_process")
-instr.set_component_parameter("Cu_incoherent", {"sigma" : 4*0.55, "packing_factor" : 1, "unit_cell_volume" :  55.4})
-
-instr.add_component("Cu_powder", "Powder_process")
-instr.set_component_parameter("Cu_powder", {"reflections" : "\"Cu.laz\""})
-
-instr.add_component("Cu", "Union_make_material")
-instr.set_component_parameter("Cu", {"my_absorption" : "100*4*3.78/55.4", "process_string" : "\"Cu_incoherent,Cu_powder\""})
-
-# Set neutron source
-instr.add_component("source","Source_div", AT=[0,0,0])
-instr.add_parameter("double","energy", value=10, comment="[meV] source energy") # Add parameter to select energy at run time
-instr.set_component_parameter("source", {"xwidth" : 0.12, "yheight" : 0.12, "focus_aw" : 0.1, "focus_ah" : 0.1, "E0" : "energy", "dE" : 0, "flux" : 1E13})
+# Set up a material called Cu with approrpiate properties
+# (uses McStas Union components, here the processes)
+Cu_inc = Instr.add_component("Cu_incoherent", "Incoherent_process")
+Cu_inc.sigma = 4*0.55
+Cu_inc.packing_factor = 1
+Cu_inc.unit_cell_volume = 55.4
+
+Cu_powder = Instr.add_component("Cu_powder", "Powder_process")
+Cu_powder.reflections = "\"Cu.laz\""
+
+Cu = Instr.add_component("Cu", "Union_make_material")
+Cu.my_absorption = "100*4*3.78/55.4"
+Cu.process_string = "\"Cu_incoherent,Cu_powder\""
+
+# Add neutron source
+Source = Instr.add_component("source", "Source_div", AT=[0,0,0])
+# Add parameter to select energy at run time
+Instr.add_parameter("double","energy", value=10, comment="[meV] source energy")
+
+Source.xwidth = 0.12
+Source.yheight = 0.12
+Source.focus_aw = 0.1
+Source.focus_ah = 0.1
+Source.E0 = "energy"
+Source.dE = 0.0
+Source.flux = 1E13
 
 # List of available materials, Vacuum is provided by the system
 material_name_list = ["Cu", "Vacuum"]
 
-# Wish to set up a number of randomly sized and placed boxes, here we choose the number
+# Wish to set up a number of random boxes, here the number is chosen at random
 number_of_volumes = random.randint(30,40)
 
 # Initialize the priority that needs to be unique for each volume
@@ -46,47 +59,81 @@
     current_priority = current_priority + 1 # update the priority
     max_side_length = 0.04
     max_depth = 0.003
-    position = [random.uniform(-0.05,0.05), random.uniform(-0.05,0.05), 1+random.uniform(-0.05,0.05)] # Set position in 10x10x10 cm^3 box 1 m from source
-    rotation = [random.uniform(0,360), random.uniform(0,360), random.uniform(0,360)] # random rotation
-    
+    # Set position in 10x10x10 cm^3 box 1 m from source
+    position = [random.uniform(-0.05,0.05),
+                random.uniform(-0.05,0.05),
+                1+random.uniform(-0.05,0.05)]
+    # Set random rotation
+    rotation = [random.uniform(0,360),
+                random.uniform(0,360),
+                random.uniform(0,360)]
+
     # Choose a random material from the list of available materials
     volume_material = random.choice(material_name_list)
 
     # Add a McStas Union geometry with unique name
-    instr.add_component("volume_" + str(volume), "Union_box")
-    instr.set_component_parameter("volume_" + str(volume), {"xwidth" : random.uniform(0.01,max_side_length), "yheight" : random.uniform(0.01,max_side_length), "zdepth" : random.uniform(0.001,max_depth),})
-    instr.set_component_parameter("volume_" + str(volume), {"material_string" : "\""+volume_material+"\"", "priority" : current_priority, "p_interact" : 0.3})
-    instr.set_component_AT("volume_" + str(volume), position, RELATIVE="ABSOLUTE")
-    instr.set_component_ROTATED("volume_" + str(volume), rotation, RELATIVE="ABSOLUTE")
-
+    this_geometry = Instr.add_component("volume_" + str(volume), "Union_box")
+    this_geometry.xwidth = random.uniform(0.01,max_side_length)
+    this_geometry.yheight = random.uniform(0.01,max_side_length)
+    this_geometry.zdepth = random.uniform(0.01,max_side_length)
+    this_geometry.material_string = "\"" + volume_material + "\""
+    this_geometry.priority = current_priority
+    this_geometry.p_interact = 0.3
+    
+    this_geometry.set_AT(position, RELATIVE="ABSOLUTE")
+    this_geometry.set_ROTATED(rotation, RELATIVE="ABSOLUTE")
 
 # A few Union loggers are set up for display of the scattering locations
-instr.add_component("logger_space_zx_all", "Union_logger_2D_space")
-current_component = instr.get_last_component()
-current_component.set_parameters({"filename" : "\"space_zx.dat\"",})
-current_component.set_parameters({"n1" : 1000, "D_direction_1" : "\"z\"", "D1_min" : -0.05, "D1_max" : 0.05})
-current_component.set_parameters({"n2" : 1000, "D_direction_2" : "\"x\"", "D2_min" : -0.05, "D2_max" : 0.05})
-current_component.set_AT([0,0,1])
-
-instr.add_component("logger_space_zy_all", "Union_logger_2D_space")
-current_component = instr.get_last_component()
-current_component.set_parameters({"filename" : "\"space_zy.dat\"",})
-current_component.set_parameters({"n1" : 1000, "D_direction_1" : "\"z\"", "D1_min" : -0.05, "D1_max" : 0.05})
-current_component.set_parameters({"n2" : 1000, "D_direction_2" : "\"y\"", "D2_min" : -0.05, "D2_max" : 0.05})
-current_component.set_AT([0,0,1])
+space_2D_zx = Instr.add_component("logger_space_zx_all", "Union_logger_2D_space")
+space_2D_zx.filename = "\"space_zx.dat\""
+space_2D_zx.n1 = 1000
+space_2D_zx.D_direction_1 = "\"z\""
+space_2D_zx.D1_min = -0.05
+space_2D_zx.D1_max = 0.05
+space_2D_zx.n2 = 1000
+space_2D_zx.D_direction_2 = "\"x\""
+space_2D_zx.D2_min = -0.05
+space_2D_zx.D2_max = 0.05
+space_2D_zx.set_AT([0,0,1])
+
+space_2D_zy = Instr.add_component("logger_space_zy_all", "Union_logger_2D_space")
+space_2D_zy.filename = "\"space_zy.dat\""
+space_2D_zy.n1 = 1000
+space_2D_zy.D_direction_1 = "\"z\""
+space_2D_zy.D1_min = -0.05
+space_2D_zy.D1_max = 0.05
+space_2D_zy.n2 = 1000
+space_2D_zy.D_direction_2 = "\"y\""
+space_2D_zy.D2_min = -0.05
+space_2D_zy.D2_max = 0.05
+space_2D_zy.set_AT([0,0,1])
 
 # Union master component that executes the simulation of the random boxes
-instr.add_component("random_boxes", "Union_master")
+Instr.add_component("random_boxes", "Union_master")
 
 # McStas monitors for viewing the beam after the random boxes
-instr.add_component("detector", "PSD_monitor", AT=[0,0,2])
-instr.set_component_parameter("detector", {"xwidth" : 0.10, "yheight" : 0.10, "nx" : 500, "ny" : 500, "filename" : "\"PSD.dat\"", "restore_neutron" : 1})
-
-instr.add_component("large_detector","PSD_monitor", AT=[0,0,2])
-instr.set_component_parameter("large_detector", {"xwidth" : 1.0, "yheight" : 1.0, "nx" : 500, "ny" : 500, "filename" : "\"large_PSD.dat\"", "restore_neutron" : 1})
+PSD = Instr.add_component("detector", "PSD_monitor", AT=[0,0,2])
+PSD.xwidth = 0.1
+PSD.yheight = 0.1
+PSD.nx = 500
+PSD.ny = 500
+PSD.filename = "\"PSD.dat\""
+PSD.restore_neutron = 1
+
+big_PSD = Instr.add_component("large_detector","PSD_monitor", AT=[0,0,2])
+big_PSD.xwidth = 1.0
+big_PSD.yheight = 1.0
+big_PSD.nx = 500
+big_PSD.ny = 500
+big_PSD.filename = "\"big_PSD.dat\""
+big_PSD.restore_neutron = 1
+
+Instr.print_components()
 
 # Run the McStas simulation, a unique foldername is required for each run
-data = instr.run_full_instrument(foldername="demonstration2", parameters={"energy":600},mpi=2,ncount=5E7)
+data = Instr.run_full_instrument(foldername="demonstration",
+                                 parameters={"energy": 600},
+                                 mpi=2, ncount=5E7)
 
 # Set plotting options for the data (optional)
 functions.name_plot_options("logger_space_zx_all", data, log=1, orders_of_mag=3)
@@ -96,5 +143,3 @@
 
 # Plot the resulting data on a logarithmic scale
 plot = plotter.make_sub_plot(data)
-
-
diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 750f165b..07ac586e 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -1,4 +1,5 @@
 from mcstasscript.helper.formatting import bcolors
+from mcstasscript.helper.formatting import is_legal_parameter
 
 class parameter_variable:
     """
@@ -58,6 +59,12 @@ def __init__(self, *args, **kwargs):
             self.type = args[0] + " "
             self.name = str(args[1])
 
+        if not is_legal_parameter(self.name):
+            raise NameError("The given parameter name: \""
+                            + self.name
+                            + "\" is not a legal c variable name, "
+                            + " and cannot be used in McStas.")
+
         self.value = ""
         if "value" in kwargs:
             self.value = kwargs["value"]
@@ -139,6 +146,13 @@ def __init__(self, *args, **kwargs):
         """
         self.type = args[0]
         self.name = str(args[1])
+
+        if not is_legal_parameter(self.name):
+            raise NameError("The given parameter name: \""
+                            + self.name
+                            + "\" is not a legal c variable name, "
+                            + " and cannot be used in McStas.")
+
         self.value = ""
         if "value" in kwargs:
             self.value = kwargs["value"]
@@ -283,7 +297,6 @@ class contains both methods to write the component to a instrument
 
     _unfreeze()
         Unfreeze the class so new attributes can be defined again
-
     """
 
     __isfrozen = False  # When frozen, no new attributes allowed
@@ -576,7 +589,6 @@ def print_long(self):
         specified. Information on the components are added when the
         class is used as a superclass for classes describing each
         McStas component.
-
         """
         if len(self.comment) > 1:
             print("// " + self.comment)
@@ -636,7 +648,6 @@ def show_parameters(self):
         additional attributes defined when McStas_Instr creates
         subclasses for the individual components are required to run
         this method.
-
         """
 
         print(" ___ Help "
@@ -700,7 +711,6 @@ def show_parameters_simple(self):
         additional attributes defined when McStas_Instr creates
         subclasses for the individual components are required to run
         this method.
-
         """
         print("---- Help " + self.component_name + " -----")
         for parameter in self.parameter_names:
diff --git a/mcstasscript/interface/functions.py b/mcstasscript/interface/functions.py
index 365a4300..f53fa917 100644
--- a/mcstasscript/interface/functions.py
+++ b/mcstasscript/interface/functions.py
@@ -28,13 +28,20 @@ def name_search(name, data_list):
 
     list_result = []
     for check in data_list:
-        if check.metadata.component_name == name:
+        if check.name == name:
             list_result.append(check)
+            
+    if len(list_result) == 0:
+        raise NameError("No dataset with name: \""
+                        + name
+                        + "\" found.")
 
     if len(list_result) == 1:
         return list_result[0]
     else:
-        raise NameError("More than one match for the name search")
+        raise NameError("Found " + str(len(list_result)) + " matches in "
+                        + "the search for a dataset with name: \""
+                        + name + "\".")
 
 def name_plot_options(name, data_list, **kwargs):
     """"
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index 5088cdf7..5bfbc871 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -328,7 +328,7 @@ def test_ManagedMcrun_load_data_L_mon_direct_error(self):
 
         os.chdir(current_work_dir)  # Reset work directory
 
-    def test_ManagedMcrun_load_data_L_mon_direct_error(self):
+    def test_ManagedMcrun_load_data_L_mon_empty_error(self):
         """
         Check an error occurs when pointed to empty directory
         """
diff --git a/mcstasscript/tests/test_functions.py b/mcstasscript/tests/test_functions.py
index 791ea62e..4268a02a 100644
--- a/mcstasscript/tests/test_functions.py
+++ b/mcstasscript/tests/test_functions.py
@@ -67,13 +67,34 @@ def setup_McStasData_array():
 
     return data_list
 
+def setup_McStasData_array_repeat():
+
+    data_list = []
+
+    data_list.append(set_dummy_McStasData_1d("A_1d_thing"))
+    data_list.append(set_dummy_McStasData_2d("A_2d_thing"))
+    data_list.append(set_dummy_McStasData_1d("Another_1d_thing"))
+    data_list.append(set_dummy_McStasData_2d("Another_2d_thing"))
+    data_list.append(set_dummy_McStasData_2d("Hero"))
+
+    hero_object = set_dummy_McStasData_2d("Big_Hero")
+    hero_object.metadata.dimension = 123
+    hero_object.plot_options.colormap = "very hot"
+
+    data_list.append(hero_object)
+
+    data_list.append(set_dummy_McStasData_2d("After_hero_2d"))
+    data_list.append(set_dummy_McStasData_2d("Last_object_2d"))
+
+    return data_list
+
+
 
 class Test_name_search(unittest.TestCase):
     """
     Test the utility function called name_search which finds and
     returns a McStasData set with a given name from a list of
     McStasData objects.
-
     """
 
     def test_name_search_read(self):
@@ -86,6 +107,17 @@ def test_name_search_read(self):
         hero_object = name_search("Hero", data_list)
 
         self.assertEqual(hero_object.metadata.dimension, 123)
+        
+    def test_name_search_read_repeat(self):
+        """
+        Test simple case with repeat name
+        """
+
+        data_list = setup_McStasData_array_repeat()
+
+        hero_object = name_search("Big_Hero", data_list)
+
+        self.assertEqual(hero_object.metadata.dimension, 123)
 
     def test_name_search_read_error(self):
         """

From 20996f396723b3e0379b62a362dc32225637bf53 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Thu, 23 May 2019 18:17:16 +0200
Subject: [PATCH 021/403] Minor updates:

Added possibility for incrementing a number after the foldername
instead of failing a run_full_instrument call. This option is
called incremente_folder_name.

Added function to load existing McStas data as McStasData objects,
it is in the functions.py file and called load_data. Test for this
function is included.

Added show_parameters method to instrument so users can see the
available parameters in an instrument model. A test for this
method is included.

A bug was fixed in component_reader where spaces before the
SETTING PARAMETERS keyword made the file reader fail. A strip()
call was added.

Updated the manual to reflect new additions.
---
 McStasScript_documentation.pdf          | Bin 155747 -> 156691 bytes
 examples/McStasScript_demo.ipynb        |  53 ++++++++++++------------
 mcstasscript/helper/component_reader.py |   5 ++-
 mcstasscript/helper/managed_mcrun.py    |  26 +++++++++++-
 mcstasscript/interface/functions.py     |  15 +++++++
 mcstasscript/tests/test_ManagedMcrun.py |  24 +++++++++++
 mcstasscript/tests/test_functions.py    |  37 +++++++++++++++++
 7 files changed, 130 insertions(+), 30 deletions(-)

diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
index aee763e0a92dbbe7aeb5b3500a90ae345c80d62c..fc8296141b1aeaf6dd5d0e92da0e08e13cd672aa 100644
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diff --git a/examples/McStasScript_demo.ipynb b/examples/McStasScript_demo.ipynb
index 2150fb2b..5b9ee82c 100644
--- a/examples/McStasScript_demo.ipynb
+++ b/examples/McStasScript_demo.ipynb
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -115,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -154,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -164,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -217,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -226,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -261,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -270,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -296,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -305,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -332,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -343,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -352,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -364,7 +364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -391,7 +391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -413,7 +413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -438,15 +438,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
     "# If the folder already exsits, a new simulation is not performed but the old one is read\n",
     "data = Instr.run_full_instrument(foldername=\"jupyter_demo\",\n",
-    "                                 parameters={\"wavelength\" : 1.0},\n",
+    "                                 parameters={\"wavelength\" : 3.0},\n",
     "                                 mpi=4,\n",
-    "                                 ncount=2E7)"
+    "                                 ncount=2E7,\n",
+    "                                 increment_folder_name = True)"
    ]
   },
   {
@@ -459,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -474,7 +475,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 936x504 with 6 Axes>"
       ]
diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index 1f233f6c..b4315721 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -266,8 +266,9 @@ def read_component_file(self, absolute_path):
                         result.parameter_comments[variable_name] = comment
 
             # find definition parameters and their values
-            if (self.line_starts_with(line, "DEFINITION PARAMETERS")
-                    or self.line_starts_with(line, "SETTING PARAMETERS")):
+            if (self.line_starts_with(line.strip(), "DEFINITION PARAMETERS")
+                    or self.line_starts_with(line.strip(), 
+                                             "SETTING PARAMETERS")):
 
                 parts = line.split("(")
                 parameter_parts = parts[1].split(",")
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 9ff5f177..f3f557e4 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -74,6 +74,8 @@ def __init__(self, instr_name, **kwargs):
         self.parameters = {}
         self.custom_flags = ""
         self.mcrun_path = ""
+        self.increment_folder_name = False
+        self.compile = True
         # mcrun_path always in kwargs
         if "mcrun_path" in kwargs:
             self.mcrun_path = kwargs["mcrun_path"]
@@ -96,6 +98,12 @@ def __init__(self, instr_name, **kwargs):
 
         if "custom_flags" in kwargs:
             self.custom_flags = kwargs["custom_flags"]
+            
+        if "increment_folder_name" in kwargs:
+            self.increment_folder_name = kwargs["increment_folder_name"]
+            
+        if "force_compile" in kwargs:
+            self.compile = kwargs["force_compile"]
 
     def run_simulation(self):
         """
@@ -103,10 +111,24 @@ def run_simulation(self):
         """
 
         # construct command to run
-        option_string = ("-c"
-                         + " -n " + str(self.ncount)  # Set ncount
+        
+        options_string = ""
+        if self.compile:
+            options_string = "-c " 
+
+        option_string = (options_string 
+                         + "-n " + str(self.ncount)  # Set ncount
                          + " --mpi=" + str(self.mpi)  # Set mpi
                          + " ")
+        
+        if self.increment_folder_name and os.path.isdir(self.data_folder_name):
+            counter = 0
+            new_name = self.data_folder_name + "_" + str(counter)
+            while os.path.isdir(new_name):
+                counter = counter + 1
+                new_name = self.data_folder_name + "_" + str(counter)
+            
+            self.data_folder_name = new_name
 
         if len(self.data_folder_name) > 0:
             option_string = (option_string
diff --git a/mcstasscript/interface/functions.py b/mcstasscript/interface/functions.py
index f53fa917..04038f02 100644
--- a/mcstasscript/interface/functions.py
+++ b/mcstasscript/interface/functions.py
@@ -1,4 +1,5 @@
 from mcstasscript.data.data import McStasData
+from mcstasscript.helper.managed_mcrun import ManagedMcrun
 
 def name_search(name, data_list):
     """"
@@ -66,3 +67,17 @@ def name_plot_options(name, data_list, **kwargs):
 
     object_to_modify = name_search(name, data_list)
     object_to_modify.set_plot_options(**kwargs)
+
+def load_data(foldername):
+    """
+    Loads data from a McStas data folder including mccode.sim
+    
+    Parameters
+    ----------
+        foldername : string
+            Name of the folder from which to load data
+    """
+    
+    managed_mcrun = ManagedMcrun("dummy", foldername=foldername)    
+    return managed_mcrun.load_results()
+    
\ No newline at end of file
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index 5bfbc871..b894d986 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -174,6 +174,30 @@ def test_ManagedMcrun_run_simulation_parameters(self, os_system):
         expected_call = ("path/mcrun -c -n 48 --mpi=7 "
                          + "-d test_folder -fo test.instr "
                          + "A=2 BC=car th=\"toy\"")
+        
+    @unittest.mock.patch("os.system")
+    def test_ManagedMcrun_run_simulation_compile(self, os_system):
+        """
+        Check a run with parameters is correct
+        """
+
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername="test_folder",
+                                 mcrun_path="path",
+                                 mpi=7,
+                                 ncount=48.4,
+                                 force_compile = False,
+                                 custom_flags="-fo",
+                                 parameters={"A": 2,
+                                             "BC": "car",
+                                             "th": "\"toy\""})
+
+        mcrun_obj.run_simulation()
+
+        # a double space because of a missing option
+        expected_call = ("path/mcrun -n 48 --mpi=7 "
+                         + "-d test_folder -fo test.instr "
+                         + "A=2 BC=car th=\"toy\"")
 
         os_system.assert_called_once_with(expected_call)
 
diff --git a/mcstasscript/tests/test_functions.py b/mcstasscript/tests/test_functions.py
index 4268a02a..5d15044d 100644
--- a/mcstasscript/tests/test_functions.py
+++ b/mcstasscript/tests/test_functions.py
@@ -1,9 +1,12 @@
+import os
 import unittest
 
+
 import numpy as np
 
 from mcstasscript.interface.functions import name_search
 from mcstasscript.interface.functions import name_plot_options
+from mcstasscript.interface.functions import load_data
 from mcstasscript.data.data import McStasData
 from mcstasscript.data.data import McStasMetaData
 from mcstasscript.data.data import McStasPlotOptions
@@ -169,5 +172,39 @@ def test_name_plot_options_simple(self):
         self.assertEqual(hero_object.plot_options.colormap, "very hot")
 
 
+class Test_load_data(unittest.TestCase):
+    """
+    Testing the load data function which calls ManagedMcrun, which was
+    tested elsewhere. Since the load data is tested elsewhere, this
+    function has just a single test to check the interface.
+    """
+    def test_crun_load_data_PSD4PI(self):
+        """
+        Use test_data_set to test load_data for PSD_4PI
+        """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
+        results = load_data("test_data_set")
+
+        os.chdir(current_work_dir)  # Reset work directory
+
+        self.assertEqual(len(results), 3)
+
+        PSD_4PI = results[0]
+
+        self.assertEqual(PSD_4PI.name, "PSD_4PI")
+        self.assertEqual(PSD_4PI.metadata.dimension, [300, 300])
+        self.assertEqual(PSD_4PI.metadata.limits, [-180, 180, -90, 90])
+        self.assertEqual(PSD_4PI.metadata.xlabel, "Longitude [deg]")
+        self.assertEqual(PSD_4PI.metadata.ylabel, "Lattitude [deg]")
+        self.assertEqual(PSD_4PI.metadata.title, "4PI PSD monitor")
+        self.assertEqual(PSD_4PI.Ncount[1][4], 4)
+        self.assertEqual(PSD_4PI.Intensity[1][4], 1.537334562E-10)
+        self.assertEqual(PSD_4PI.Error[1][4], 1.139482296E-10)
+
 if __name__ == '__main__':
     unittest.main()

From 56b8e2a3b449310b84b2e7b28f23ae25d76f1478 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Mon, 27 May 2019 15:47:15 +0200
Subject: [PATCH 022/403] Handling default parameters in simulation calls.

If a required parameter is not specified, an error is given.
If no required parameters exists, the run is done with defaults.
In a mixed situation, defaults are used for parameters not given.
---
 mcstasscript/interface/instr.py  | 40 +++++++++++++++++++--
 mcstasscript/tests/test_Instr.py | 61 +++++++++++++++++++++++++++++---
 2 files changed, 95 insertions(+), 6 deletions(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index e240f8ed..bcf45f4a 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1007,12 +1007,48 @@ def run_full_instrument(self, *args, **kwargs):
             mcrun_path : str
                 Path to mcrun command, "" if already in path
         """
-        # Write the instrument file
-        self.write_full_instrument()
+        
 
         # Make sure mcrun path is in kwargs
         if "mcrun_path" not in kwargs:
             kwargs["mcrun_path"] = self.mcrun_path
+        
+        # Find required parameters
+        required_parameters = []
+        default_parameters = {}
+        passed_parameters = {}
+        
+        for index in range(0,len(self.parameter_list)):
+            if self.parameter_list[index].value == "":
+                required_parameters.append(self.parameter_list[index].name)
+            else:
+                default_parameters.update({self.parameter_list[index].name :
+                                           self.parameter_list[index].value})
+            
+        # Check if parameters are given 
+        if "parameters" not in kwargs:
+            if len(required_parameters) > 0:
+                # print required parameters and raise error
+                print("Required instrument parameters:")
+                for name in required_parameters:
+                    print("  " + name)
+                raise NameError("Required parameters not provided.")
+            else:
+                # If all parameters have defaults, just run with the defaults.
+                passed_parameters = default_parameters
+        else:
+            given_parameters = kwargs["parameters"]
+            for name in required_parameters:
+                if name not in given_parameters:
+                    raise NameError("The required instrument parameter "
+                                    + name 
+                                    + " was not provided.")
+            # Overwrite default parameters with given parameters
+            default_parameters.update(given_parameters)
+            kwargs["parameters"] = default_parameters
+
+        # Write the instrument file
+        self.write_full_instrument()
 
         # Set up the simulation
         simulation = ManagedMcrun(self.name + ".instr", **kwargs)
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 45d693a0..f3627720 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -35,6 +35,7 @@ def setup_populated_instr():
     instr = setup_instr_no_path()
 
     instr.add_parameter("double", "theta")
+    instr.add_parameter("double", "has_default", value=37)
     instr.add_declare_var("double", "two_theta")
     instr.append_initialize("two_theta = 2.0*theta;")
 
@@ -992,6 +993,11 @@ def test_write_full_instrument_simple(self, mock_f, mock_stdout):
          my_call("DEFINE INSTRUMENT test_instrument ("),
          my_call("\n"),
          my_call("double theta"),
+         my_call(","),
+         my_call(""),
+         my_call("\n"),
+         my_call("double has_default"),
+         my_call(" = 37"),
          my_call(" "),
          my_call(""),
          my_call("\n"),
@@ -1035,6 +1041,21 @@ def test_write_full_instrument_simple(self, mock_f, mock_stdout):
         mock_f.assert_called_with("test_instrument.instr", "w")
         handle = mock_f()
         handle.write.assert_has_calls(wrts, any_order=False)
+        
+        
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_run_full_instrument_required_par_error(self, mock_stdout):
+        """
+        The populated instr has a required parameter, and when not
+        given it should raise an error.
+        """
+
+        instr = setup_populated_instr()
+
+        with self.assertRaises(NameError):
+            instr.run_full_instrument("test_instrument.instr",
+                                      foldername="test_data_set",
+                                      mcrun_path="path")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     @unittest.mock.patch('__main__.__builtins__.open',
@@ -1051,11 +1072,13 @@ def test_run_full_instrument_basic(self, os_system,
         instr = setup_populated_instr()
         instr.run_full_instrument("test_instrument.instr",
                                   foldername="test_data_set",
-                                  mcrun_path="path")
+                                  mcrun_path="path",
+                                  parameters={"theta" : 1})
 
         # a double space because of a missing option
         expected_call = ("path/mcrun -c -n 1000000 --mpi=1 "
-                         + "-d test_data_set  test_instrument.instr")
+                         + "-d test_data_set  test_instrument.instr" 
+                         + " has_default=37 theta=1")
 
         os_system.assert_called_once_with(expected_call)
 
@@ -1080,12 +1103,42 @@ def test_run_full_instrument_complex(self, os_system,
                                   custom_flags="-fo",
                                   parameters={"A": 2,
                                               "BC": "car",
-                                              "th": "\"toy\""})
+                                              "theta": "\"toy\""})
+
+        # a double space because of a missing option
+        expected_call = ("path/mcrun -c -n 48 --mpi=7 "
+                         + "-d test_data_set -fo test_instrument.instr "
+                         + "has_default=37 A=2 BC=car theta=\"toy\"")
+
+        os_system.assert_called_once_with(expected_call)
+        
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable=unittest.mock.mock_open)
+    @unittest.mock.patch("os.system")
+    def test_run_full_instrument_overwrite_default(self, os_system,
+                                         mock_f, mock_stdout,):
+        """
+        Check that default parameters are overwritten by given
+        parameters.
+        """
+
+        instr = setup_populated_instr()
+        instr.run_full_instrument("test_instrument.instr",
+                                  foldername="test_data_set",
+                                  mcrun_path="path",
+                                  mpi=7,
+                                  ncount=48.4,
+                                  custom_flags="-fo",
+                                  parameters={"A": 2,
+                                              "BC": "car",
+                                              "theta": "\"toy\"",
+                                              "has_default": 10})
 
         # a double space because of a missing option
         expected_call = ("path/mcrun -c -n 48 --mpi=7 "
                          + "-d test_data_set -fo test_instrument.instr "
-                         + "A=2 BC=car th=\"toy\"")
+                         + "has_default=10 A=2 BC=car theta=\"toy\"")
 
         os_system.assert_called_once_with(expected_call)
 

From 2381b4f742dbd2fdab2d9a7edbfa5fd6eb24909f Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Mon, 3 Jun 2019 14:07:56 +0200
Subject: [PATCH 023/403] Added a configuration file called configuration.yaml.

Currently it holds the paths to the McStas directory, the path to mcrun
and the maximum line length to use when printing to the terminal.

Code has been added to show_parameters (for both instr and comp) that
breaks line dynamically when long comments are printed. In addition
the print_components method prints each component on 1, 2 or 3 lines
depending on the longest lines to maintain a nice overview in most
cases.

Tests were added for the new cases and of reading the configuration
file itself.

The manual was updated with instructions on updating the configuration
file.
---
 McStasScript_documentation.pdf        | Bin 156691 -> 158632 bytes
 configuration.yaml                    |  10 +
 mcstasscript/helper/mcstas_objects.py |  75 ++++++-
 mcstasscript/interface/instr.py       | 290 ++++++++++++++++++++-----
 mcstasscript/tests/test_Instr.py      | 293 ++++++++++++++++++++++----
 mcstasscript/tests/test_component.py  |   3 +-
 6 files changed, 562 insertions(+), 109 deletions(-)
 create mode 100644 configuration.yaml

diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
index fc8296141b1aeaf6dd5d0e92da0e08e13cd672aa..137efa38f2f3c527f15ca3a58c82d96a36e0a2c8 100644
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diff --git a/configuration.yaml b/configuration.yaml
new file mode 100644
index 00000000..db3b3f00
--- /dev/null
+++ b/configuration.yaml
@@ -0,0 +1,10 @@
+---
+paths:
+    # path to mcrun, example for OS X
+    mcrun_path: "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/"
+    # path to mcstas directory, example for OS X
+    # the mcstas directory should contain the component folders, sources, optics, ...
+    mcstas_path: "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/"
+other:
+    # limit characters per line in terminal output
+    characters_per_line: 117
\ No newline at end of file
diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 07ac586e..e61f594b 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -640,7 +640,7 @@ def print_short(self, **kwargs):
                   "\tAT", self.AT_data, self.AT_relative,
                   "ROTATED", self.ROTATED_data, self.ROTATED_relative)
 
-    def show_parameters(self):
+    def show_parameters(self, **kwargs):
         """
         Shows available parameters and their defaults for the component
 
@@ -649,10 +649,19 @@ def show_parameters(self):
         subclasses for the individual components are required to run
         this method.
         """
+        
+        if "line_length" in kwargs:
+            line_limit = kwargs["line_length"]
+        else:
+            line_limit = self.line_limit
+
+        # Minimum reasonable line length
+        if line_limit < 74:
+            line_limit = 74
 
         print(" ___ Help "
               + self.component_name + " "
-              + (62-len(self.component_name))*"_")
+              + (line_limit - 11 - len(self.component_name))*"_")
         print("|"
               + bcolors.BOLD + "optional parameter" + bcolors.ENDC + "|"
               + bcolors.BOLD
@@ -666,42 +675,90 @@ def show_parameters(self):
               + bcolors.ENDC + "|")
 
         for parameter in self.parameter_names:
+            characters_before_comment = 4
             unit = ""
             if parameter in self.parameter_units:
                 unit = " [" + self.parameter_units[parameter] + "]"
+                characters_before_comment += len(unit)
             comment = ""
             if parameter in self.parameter_comments:
                 if not self.parameter_comments[parameter] == "":
                     comment = " // " + self.parameter_comments[parameter]
 
             parameter_name = bcolors.BOLD + parameter + bcolors.ENDC
+            characters_before_comment += len(parameter)
+
             value = ""
+            characters_from_value = 0
             if self.parameter_defaults[parameter] is None:
                 parameter_name = (bcolors.UNDERLINE
                                   + parameter_name
                                   + bcolors.ENDC)
             else:
+                this_default = str(self.parameter_defaults[parameter])
                 value = (" = "
                          + bcolors.BOLD
                          + bcolors.OKBLUE
-                         + str(self.parameter_defaults[parameter])
+                         + this_default
                          + bcolors.ENDC
                          + bcolors.ENDC)
+                characters_from_value = 3 + len(this_default)
 
             if getattr(self, parameter) is not None:
+                this_set_value = str(getattr(self, parameter))
                 value = (" = "
                          + bcolors.BOLD
                          + bcolors.OKGREEN
-                         + str(getattr(self, parameter))
+                         + this_set_value
                          + bcolors.ENDC
                          + bcolors.ENDC)
+                characters_from_value = 3 + len(this_set_value)
+            characters_before_comment += characters_from_value
 
-            print(parameter_name
-                  + value
-                  + unit
-                  + comment)
+            print(parameter_name + value + unit, end="")
+            
+            if characters_before_comment + len(comment) < line_limit:
+                print(comment)
+            else:
+                length_for_comment = line_limit - characters_before_comment
+                # Split comment into several lines
+                original_comment = comment
+                words = comment.split(" ")
+                words_left = len(words)
+                last_index = 0
+                current_index = 0
+                comment = ""
+                iterations = 0
+                max_iterations = 50
+                while(words_left > 0):
+                    iterations += 1
+                    if iterations > max_iterations:
+                        #  Something went long, print on one line
+                        break
+                    
+                    line_left = length_for_comment
+                    
+                    while(line_left > 0):
+                        if current_index >= len(words):
+                            current_index = len(words) + 1
+                            break
+                        line_left -= len(str(words[current_index])) + 1
+                        current_index += 1
+                        
+                    current_index -= 1
+                    for word in words[last_index:current_index]:
+                        comment += word + " "
+                    words_left = len(words) - current_index
+                    if words_left > 0:
+                        comment += "\n" + " "*characters_before_comment
+                        last_index = current_index
+
+                if not iterations == max_iterations + 1:
+                    print(comment)
+                else:
+                    print(str(original_comment))
 
-        print(73*"-")
+        print(line_limit*"-")
 
     def show_parameters_simple(self):
         """
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index bcf45f4a..2108b545 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2,14 +2,16 @@
 
 import os
 import datetime
+import yaml
 
+from mcstasscript.data.data import McStasData
 from mcstasscript.helper.mcstas_objects import declare_variable
 from mcstasscript.helper.mcstas_objects import parameter_variable
 from mcstasscript.helper.mcstas_objects import component
-from mcstasscript.data.data import McStasData
 from mcstasscript.helper.component_reader import ComponentReader
 from mcstasscript.helper.managed_mcrun import ManagedMcrun
 from mcstasscript.helper.formatting import is_legal_filename
+from mcstasscript.helper.formatting import bcolors
 
 class McStas_instr:
     """
@@ -172,20 +174,34 @@ def __init__(self, name, **kwargs):
             self.origin = kwargs["origin"]
         else:
             self.origin = "ESS DMSC"
+            
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        configuration_file_name = THIS_DIR + "/../../configuration.yaml"
+        if not os.path.isfile(configuration_file_name):
+            raise NameError("Could not find configuration file!")
+        with open(configuration_file_name, 'r') as ymlfile:
+            config = yaml.safe_load(ymlfile)
+
+        if type(config) is dict:
+            self.mcrun_path = config["paths"]["mcrun_path"]
+            self.mcstas_path = config["paths"]["mcstas_path"]
+            self.line_limit = config["other"]["characters_per_line"]
+        else:
+            # This happens in unit tests that mocks open
+            self.mcrun_path = "" 
+            self.mcstas_path = ""
+            self.line_limit = 180
 
         if "mcrun_path" in kwargs:
             self.mcrun_path = kwargs["mcrun_path"]
-        else:
-            self.mcrun_path = ""
 
         if "mcstas_path" in kwargs:
             self.mcstas_path = kwargs["mcstas_path"]
-        else:
-            self.mcstas_path = ""
+        elif self.mcstas_path is "":
             raise NameError("At this stage of development "
                             + "McStasScript need the absolute path "
                             + "for the McStas installation as keyword "
-                            + "named mcstas_path")
+                            + "named mcstas_path or in configuration.yaml")
 
         self.parameter_list = []
         self.declare_list = []
@@ -226,10 +242,19 @@ def add_parameter(self, *args, **kwargs):
         # parameter_variable class documented independently
         self.parameter_list.append(parameter_variable(*args, **kwargs))
         
-    def show_parameters(self):
+    def show_parameters(self, **kwargs):
         """
         Method for displaying current instrument parameters
+        
+        keyword arguments
+            line_length : int
+                Maximum line length for terminal output
         """
+        
+        if "line_length" in kwargs:
+            line_limit = kwargs["line_length"]
+        else:
+            line_limit = self.line_limit
 
         if len(self.parameter_list) == 0:
             print("No instrument parameters available")
@@ -249,7 +274,9 @@ def show_parameters(self):
         longest_type = len(max(types, key=len))
         longest_name = len(max(names, key=len))
         longest_value = len(max(values, key=len))
+        comment_start_point = longest_type + longest_name + longest_value + 11
         longest_comment = len(max(comments, key=len))
+        length_for_comment = line_limit - comment_start_point
 
         # Print to console
         for parameter in self.parameter_list:
@@ -260,7 +287,46 @@ def show_parameters(self):
             else:
                 print(" = ", end=' ')
             print(str(parameter.value).ljust(longest_value+1), end=' ')
-            print(str(parameter.comment).ljust(longest_comment))
+            if (length_for_comment < 5 
+                or length_for_comment > len(str(parameter.comment))):
+                print(str(parameter.comment))
+            else:
+                # Split comment into several lines
+                comment = str(parameter.comment)
+                words = comment.split(" ")
+                words_left = len(words)
+                last_index = 0
+                current_index = 0
+                comment = ""
+                iterations = 0
+                max_iterations = 50
+                while(words_left > 0):
+                    iterations += 1
+                    if iterations > max_iterations:
+                        #  Something went long, print on one line
+                        break
+                    
+                    line_left = length_for_comment
+                    
+                    while(line_left > 0):
+                        if current_index >= len(words):
+                            current_index = len(words) + 1
+                            break
+                        line_left -= len(str(words[current_index])) + 1
+                        current_index += 1
+                        
+                    current_index -= 1
+                    for word in words[last_index:current_index]:
+                        comment += word + " "
+                    words_left = len(words) - current_index
+                    if words_left > 0:
+                        comment += "\n" + " "*comment_start_point
+                        last_index = current_index
+
+                if not iterations == max_iterations + 1:
+                    print(comment)
+                else:
+                    print(str(parameter.comment).ljust(longest_comment))
 
     def add_declare_var(self, *args, **kwargs):
         """
@@ -418,15 +484,18 @@ def show_components(self, *args):
                   + " category.")
             self.component_reader.show_components_in_category(category)
 
-    def component_help(self, name):
+    def component_help(self, name, **kwargs):
         """
         Method for showing parameters for a component before adding it
         to the instrument
 
+        keyword arguments
+            line_length : int
+                Maximum line length in output to terminal
         """
 
         dummy_instance = self._create_component_instance("dummy", name)
-        dummy_instance.show_parameters()
+        dummy_instance.show_parameters(**kwargs)
 
     def _create_component_instance(self, *args, **kwargs):
         """
@@ -454,6 +523,7 @@ def _create_component_instance(self, *args, **kwargs):
             input_dict["parameter_units"] = comp_info.parameter_units
             input_dict["parameter_comments"] = comp_info.parameter_comments
             input_dict["category"] = comp_info.category
+            input_dict["line_limit"] = self.line_limit
 
             self.component_class_lib[component_name] = type(component_name,
                                                             (component,),
@@ -780,78 +850,182 @@ def print_component_short(self, name):
         component = self.get_component(name)
         component.print_short()
 
-    def print_components(self):
+    def print_components(self, **kwargs):
         """
         Method for printing overview of all components in instrument
 
         Provides overview of component names, what McStas component is
         used for each and their position and rotation in space.
+        
+        keyword arguments:
+        line_length : int
+            Maximum line length in console
         """
+        
+        if "line_length" in kwargs:
+            line_limit = kwargs["line_length"]
+        else:
+            line_limit = self.line_limit
 
         longest_name = len(max(self.component_name_list, key=len))
 
         # Investigate how this could have been done in a better way
         # Find longest field for each type of data printed
         component_type_list = []
-        at_x_list = []
-        at_y_list = []
-        at_z_list = []
+        at_xyz_list = []
         at_relative_list = []
-        rotated_x_list = []
-        rotated_y_list = []
-        rotated_z_list = []
+        rotated_xyz_list = []
         rotated_relative_list = []
         for component in self.component_list:
             component_type_list.append(component.component_name)
-            at_x_list.append(str(component.AT_data[0]))
-            at_y_list.append(str(component.AT_data[1]))
-            at_z_list.append(str(component.AT_data[2]))
+            at_xyz_list.append(str(component.AT_data[0])
+                               + str(component.AT_data[1])
+                               + str(component.AT_data[2]))
             at_relative_list.append(component.AT_relative)
-            rotated_x_list.append(str(component.ROTATED_data[0]))
-            rotated_y_list.append(str(component.ROTATED_data[1]))
-            rotated_z_list.append(str(component.ROTATED_data[2]))
+            rotated_xyz_list.append(str(component.ROTATED_data[0])
+                                    + str(component.ROTATED_data[1])
+                                    + str(component.ROTATED_data[2]))
             rotated_relative_list.append(component.ROTATED_relative)
 
         longest_component_name = len(max(component_type_list, key=len))
-        longest_at_x_name = len(max(at_x_list, key=len))
-        longest_at_y_name = len(max(at_y_list, key=len))
-        longest_at_z_name = len(max(at_z_list, key=len))
+        longest_at_xyz_name = len(max(at_xyz_list, key=len))
         longest_at_relative_name = len(max(at_relative_list, key=len))
-        longest_rotated_x_name = len(max(rotated_x_list, key=len))
-        longest_rotated_y_name = len(max(rotated_y_list, key=len))
-        longest_rotated_z_name = len(max(rotated_z_list, key=len))
+        longest_rotated_xyz_name = len(max(rotated_xyz_list, key=len))
         longest_rotated_relative_name = len(max(rotated_relative_list,
                                                 key=len))
 
-        # Have longest field for each type, use ljust to align all columns
-        for component in self.component_list:
-            print(str(component.name).ljust(longest_name+2), end=' ')
-
-            comp_name = component.component_name
-            comp_name_print = str(comp_name).ljust(longest_component_name + 2)
-            print(comp_name_print, end=' ')
-
-            comp_at_data = str(component.AT_data)
-            longest_at_xyz_sum = (longest_at_x_name
-                                  + longest_at_y_name
-                                  + longest_at_z_name)
-            print("AT ",
-                  comp_at_data.ljust(longest_at_xyz_sum + 11),
-                  end='')
-
-            comp_at_relative = component.AT_relative
-            print(comp_at_relative.ljust(longest_at_relative_name + 2),
-                  end=' ')
-
-            comp_rotated_data = str(component.ROTATED_data)
-            longest_rotated_xyz_sum = (longest_rotated_x_name
-                                       + longest_rotated_y_name
-                                       + longest_rotated_z_name)
-            print("ROTATED ",
-                  comp_rotated_data.ljust(longest_rotated_xyz_sum + 11),
-                  end='')
-            print(component.ROTATED_relative)
-            # print("")
+        # Padding settings, 0,0,6,0,6 is minimum values
+        name_pad = 0
+        comp_name_pad = 0
+        AT_pad = 6 # requires (, , ) in addition to data length
+        RELATIVE_pad = 0
+        ROTATED_pad = 6 # requires (, , ) in addition to data length
+
+        # Check if longest line length exceeded
+        longest_line_length = (longest_name + name_pad
+                               + longest_component_name + comp_name_pad
+                               + longest_at_xyz_name + AT_pad
+                               + longest_at_relative_name + RELATIVE_pad
+                               + longest_rotated_xyz_name + ROTATED_pad
+                               + longest_rotated_relative_name + 8 + 9)
+        
+        def coordinates_to_string(data):
+            return ("(" 
+                    + str(data[0]) + ", "
+                    + str(data[1]) + ", "
+                    + str(data[2]) + ")") 
+
+        n_lines = 1
+        """
+        If calculated line length is above the limit loaded from the
+        configuration file, attempt to split the output over an
+        additional line. This is hardcoded up to 3 lines.
+        """
+        if longest_line_length > line_limit:
+            n_lines = 2
+            longest_at_xyz_name = max([longest_at_xyz_name,
+                                       longest_rotated_xyz_name])
+            longest_rotated_xyz_name = longest_at_xyz_name
+            RELATIVE_pad = 0            
+
+            longest_line_length_at = (longest_name 
+                                      + comp_name_pad
+                                      + longest_component_name 
+                                      + comp_name_pad
+                                      + longest_at_xyz_name 
+                                      + AT_pad
+                                      + longest_at_relative_name 
+                                      + 7 + 6 )
+            longest_line_length_rotated = (longest_name 
+                                           + comp_name_pad
+                                           + longest_component_name 
+                                           + comp_name_pad
+                                           + longest_rotated_xyz_name 
+                                           + ROTATED_pad
+                                           + longest_rotated_relative_name 
+                                           + 7 + 6)
+
+            if (longest_line_length_at > line_limit 
+                or longest_line_length_rotated > line_limit):
+                n_lines = 3
+
+        if n_lines == 1:
+            for component in self.component_list:
+                p_name = str(component.name)
+                p_name = p_name.ljust(longest_name + name_pad)
+
+                p_comp_name = str(component.component_name)
+                p_comp_name = p_comp_name.ljust(longest_component_name 
+                                                + comp_name_pad)
+
+                p_AT = coordinates_to_string(component.AT_data)
+                p_AT = p_AT.ljust(longest_at_xyz_name + AT_pad)
+
+                p_AT_RELATIVE = str(component.AT_relative)
+                p_AT_RELATIVE = p_AT_RELATIVE.ljust(longest_at_relative_name
+                                                    + RELATIVE_pad)
+
+                p_ROTATED = coordinates_to_string(component.ROTATED_data)
+                p_ROTATED = p_ROTATED.ljust(longest_rotated_xyz_name
+                                            + ROTATED_pad)
+
+                p_ROTATED_RELATIVE = str(component.ROTATED_relative)
+
+                print(p_name, p_comp_name,
+                      "AT", p_AT, p_AT_RELATIVE,
+                      "ROTATED", p_ROTATED, p_ROTATED_RELATIVE)
+
+        elif n_lines == 2:
+            for component in self.component_list:
+                p_name = str(component.name)
+                p_name = p_name.ljust(longest_name + name_pad)
+
+                p_comp_name = str(component.component_name)
+                p_comp_name = p_comp_name.ljust(longest_component_name 
+                                                + comp_name_pad)
+
+                p_AT = coordinates_to_string(component.AT_data)
+                p_AT = p_AT.ljust(longest_at_xyz_name + AT_pad)
+
+                p_AT_RELATIVE = str(component.AT_relative)
+                p_AT_RELATIVE = p_AT_RELATIVE.ljust(longest_at_relative_name
+                                                    + RELATIVE_pad)
+
+                p_ROTATED_align = " "*(longest_name
+                                       + comp_name_pad 
+                                       + longest_component_name 
+                                       + comp_name_pad)
+
+                p_ROTATED = coordinates_to_string(component.ROTATED_data)
+                p_ROTATED = p_ROTATED.ljust(longest_rotated_xyz_name
+                                            + ROTATED_pad)
+
+                p_ROTATED_RELATIVE = str(component.ROTATED_relative)
+
+                print(p_name, p_comp_name, 
+                      "AT     ", p_AT, p_AT_RELATIVE, "\n",
+                      p_ROTATED_align, "ROTATED",
+                      p_ROTATED, p_ROTATED_RELATIVE)
+
+        elif n_lines == 3:
+            for component in self.component_list:
+                p_name = bcolors.BOLD + str(component.name) + bcolors.ENDC
+
+                p_comp_name = (bcolors.BOLD 
+                               + str(component.component_name)
+                               + bcolors.ENDC)
+
+                p_AT = coordinates_to_string(component.AT_data)
+
+                p_AT_RELATIVE = str(component.AT_relative)
+
+                p_ROTATED = coordinates_to_string(component.ROTATED_data)
+
+                p_ROTATED_RELATIVE = str(component.ROTATED_relative)
+
+                print(p_name + " ", p_comp_name, "\n",
+                      " AT     ", p_AT, p_AT_RELATIVE, "\n",
+                      " ROTATED", p_ROTATED, p_ROTATED_RELATIVE)
 
     def write_c_files(self):
         """
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index f3627720..4cb963b4 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -8,13 +8,17 @@
 from mcstasscript.interface.instr import McStas_instr
 from mcstasscript.helper.formatting import bcolors
 
-
 def setup_instr_no_path():
     """
-    Sets up a instrument without a valid mcstas_path
+    Sets up a instrument without a mcstas_path
     """
-    return McStas_instr("test_instrument", mcstas_path="/")
+    return McStas_instr("test_instrument")
 
+def setup_instr_root_path():
+    """
+    Sets up a instrument with root mcstas_path
+    """
+    return McStas_instr("test_instrument", mcstas_path="/")
 
 def setup_instr_with_path():
     """
@@ -32,7 +36,7 @@ def setup_populated_instr():
     """
     Sets up a instrument with some features used and two components
     """
-    instr = setup_instr_no_path()
+    instr = setup_instr_root_path()
 
     instr.add_parameter("double", "theta")
     instr.add_parameter("double", "has_default", value=37)
@@ -55,7 +59,7 @@ def test_simple_initialize(self):
         """
         Test basic initialization runs
         """
-        my_instrument = setup_instr_no_path()
+        my_instrument = setup_instr_root_path()
 
         self.assertEqual(my_instrument.name, "test_instrument")
 
@@ -73,13 +77,52 @@ def test_complex_initialize(self):
         self.assertEqual(my_instrument.origin, "DMSC")
         self.assertEqual(my_instrument.mcrun_path, "/path/to/mcrun")
         self.assertEqual(my_instrument.mcstas_path, "/path/to/mcstas")
+        
+    def test_load_config_file(self):
+        """
+        Test that configuration file is read correctly. In order to have
+        an independent test, the yaml file is read manually instead of
+        using the yaml package.
+        """
+        # Load configuration file and read manually
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        configuration_file_name = THIS_DIR + "/../../configuration.yaml"
+
+        if not os.path.isfile(configuration_file_name):
+            raise NameError("Could not find configuration file!")
+
+        f = open(configuration_file_name,"r")
+
+        lines = f.readlines()
+        for line in lines:
+            line = line.strip()
+            if line.startswith("mcrun_path:"):
+                parts = line.split(" ")
+                correct_mcrun_path = parts[1][1:-1]
+            
+            if line.startswith("mcstas_path:"):
+                parts = line.split(" ")
+                correct_mcstas_path = parts[1][1:-1]
+            
+            if line.startswith("characters_per_line:"):
+                parts = line.split(" ")
+                correct_n_of_characters = int(parts[1])
+
+        f.close()
+
+        # Check the value matches what is loaded by initialization
+        my_instrument = setup_instr_no_path()
+
+        self.assertEqual(my_instrument.mcrun_path, correct_mcrun_path)
+        self.assertEqual(my_instrument.mcstas_path, correct_mcstas_path)
+        self.assertEqual(my_instrument.line_limit, correct_n_of_characters)
 
     def test_simple_add_parameter(self):
         """
         This is just an interface to a function that is tested
         elsewhere, so only a basic test is performed here.
         """
-        instr = setup_instr_no_path()
+        instr = setup_instr_root_path()
 
         instr.add_parameter("double", "theta", comment="test par")
 
@@ -91,7 +134,34 @@ def test_show_parameters(self, mock_stdout):
         """
         Testing that parameters are displayed correctly
         """
-        instr = setup_instr_no_path()
+        instr = setup_instr_root_path()
+
+        instr.add_parameter("double", "theta", comment="test par")
+        instr.add_parameter("single", "theta", comment="test par")
+        instr.add_parameter("float", "theta", value=8, comment="test par")
+        instr.add_parameter("int", "slits", comment="test par")
+        instr.add_parameter("string", "ref",
+                            value="string", comment="new string")
+
+        instr.show_parameters(line_length=300)
+
+        output = mock_stdout.getvalue().split("\n")
+
+        self.assertEqual(output[0], "double  theta             // test par")
+        self.assertEqual(output[1], "single  theta             // test par")
+        self.assertEqual(output[2], "float   theta  =  8       // test par")
+        self.assertEqual(output[3], "int     slits             // test par")
+        self.assertEqual(output[4], "string  ref    =  string  // new string")
+        
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_show_parameters_line_break(self, mock_stdout):
+        """
+        Testing that parameters are displayed correctly
+        
+        Here multiple lines are used for a long comment that was
+        dynamically broken up.
+        """
+        instr = setup_instr_root_path()
 
         instr.add_parameter("double", "theta", comment="test par")
         instr.add_parameter("single", "theta", comment="test par")
@@ -99,23 +169,44 @@ def test_show_parameters(self, mock_stdout):
         instr.add_parameter("int", "slits", comment="test par")
         instr.add_parameter("string", "ref",
                             value="string", comment="new string")
+        
+        longest_comment = ("This is a very long comment meant for "
+                           + "testing the dynamic line breaking "
+                           + "that is used in this method. It needs "
+                           + "to have many lines in order to ensure "
+                           + "it really works.")
+        
+        instr.add_parameter("double", "value",
+                            value="37", comment=longest_comment)
 
-        instr.show_parameters()
+        instr.show_parameters(line_length=80)
 
         output = mock_stdout.getvalue().split("\n")
 
-        self.assertEqual(output[0], "double  theta             // test par  ")
-        self.assertEqual(output[1], "single  theta             // test par  ")
-        self.assertEqual(output[2], "float   theta  =  8       // test par  ")
-        self.assertEqual(output[3], "int     slits             // test par  ")
+        self.assertEqual(output[0], "double  theta             // test par")
+        self.assertEqual(output[1], "single  theta             // test par")
+        self.assertEqual(output[2], "float   theta  =  8       // test par")
+        self.assertEqual(output[3], "int     slits             // test par")
         self.assertEqual(output[4], "string  ref    =  string  // new string")
+        comment_line = "This is a very long comment meant for testing "
+        self.assertEqual(output[5], "double  value  =  37      // "
+                                   + comment_line)
+        comment_line = "the dynamic line breaking that is used in this "
+        self.assertEqual(output[6], "                             "
+                                   + comment_line)
+        comment_line = "method. It needs to have many lines in order to "
+        self.assertEqual(output[7], "                             "
+                                   + comment_line)
+        comment_line = "ensure it really works. "
+        self.assertEqual(output[8], "                             "
+                                   + comment_line)
 
     def test_simple_add_declare_parameter(self):
         """
         This is just an interface to a function that is tested
         elsewhere, so only a basic test is performed here.
         """
-        instr = setup_instr_no_path()
+        instr = setup_instr_root_path()
 
         instr.add_declare_var("double", "two_theta", comment="test par")
 
@@ -127,7 +218,7 @@ def test_simple_append_initialize(self):
         The initialize section is held as a string. This method
         appends that string.
         """
-        instr = setup_instr_no_path()
+        instr = setup_instr_root_path()
 
         self.assertEqual(instr.initialize_section,
                          "// Start of initialize for generated "
@@ -149,7 +240,7 @@ def test_simple_append_initialize_no_new_line(self):
         The initialize section is held as a string. This method
         appends that string.
         """
-        instr = setup_instr_no_path()
+        instr = setup_instr_root_path()
 
         self.assertEqual(instr.initialize_section,
                          "// Start of initialize for generated "
@@ -169,7 +260,7 @@ def test_simple_append_finally(self):
         The initialize section is held as a string. This method
         appends that string.
         """
-        instr = setup_instr_no_path()
+        instr = setup_instr_root_path()
 
         self.assertEqual(instr.finally_section,
                          "// Start of finally for generated "
@@ -191,7 +282,7 @@ def test_simple_append_finally_no_new_line(self):
         The initialize section is held as a string. This method
         appends that string.
         """
-        instr = setup_instr_no_path()
+        instr = setup_instr_root_path()
 
         self.assertEqual(instr.finally_section,
                          "// Start of finally for generated "
@@ -211,7 +302,7 @@ def test_simple_append_trace(self):
         The initialize section is held as a string. This method
         appends that string.
         """
-        instr = setup_instr_no_path()
+        instr = setup_instr_root_path()
 
         self.assertEqual(instr.trace_section,
                          "// Start of trace section for generated "
@@ -233,7 +324,7 @@ def test_simple_append_trace_no_new_line(self):
         The initialize section is held as a string. This method
         appends that string.
         """
-        instr = setup_instr_no_path()
+        instr = setup_instr_root_path()
 
         self.assertEqual(instr.trace_section,
                          "// Start of trace section for generated "
@@ -299,7 +390,7 @@ def test_component_help(self, mock_stdout):
         """
         instr = setup_instr_with_path()
 
-        instr.component_help("test_for_reading")
+        instr.component_help("test_for_reading",line_length=90)
         # This call creates a dummy component and calls its
         # show_parameter method which has been tested. Here we
         # need to ensure the call is succesful, not test all
@@ -308,7 +399,7 @@ def test_component_help(self, mock_stdout):
         output = mock_stdout.getvalue()
         output = output.split("\n")
 
-        self.assertEqual(output[1], " ___ Help test_for_reading " + "_"*46)
+        self.assertEqual(output[1], " ___ Help test_for_reading " + "_"*63)
 
         legend = ("|"
                   + bcolors.BOLD + "optional parameter" + bcolors.ENDC
@@ -819,23 +910,23 @@ class for each component and aligns the data for display
 
         instr = setup_populated_instr()
 
-        instr.print_components()
+        instr.print_components(line_length=300)
 
         output = mock_stdout.getvalue().split("\n")
 
-        expected = ("first_component    test_for_reading"
-                    + "   AT  [0, 0, 0]     ABSOLUTE"
-                    + "   ROTATED  [0, 0, 0]     ABSOLUTE")
+        expected = ("first_component  test_for_reading"
+                    + " AT (0, 0, 0) ABSOLUTE"
+                    + " ROTATED (0, 0, 0) ABSOLUTE")
         self.assertEqual(output[0], expected)
 
-        expected = ("second_component   test_for_reading"
-                    + "   AT  [0, 0, 0]     ABSOLUTE"
-                    + "   ROTATED  [0, 0, 0]     ABSOLUTE")
+        expected = ("second_component test_for_reading"
+                    + " AT (0, 0, 0) ABSOLUTE"
+                    + " ROTATED (0, 0, 0) ABSOLUTE")
         self.assertEqual(output[1], expected)
 
-        expected = ("third_component    test_for_reading"
-                    + "   AT  [0, 0, 0]     ABSOLUTE"
-                    + "   ROTATED  [0, 0, 0]     ABSOLUTE")
+        expected = ("third_component  test_for_reading"
+                    + " AT (0, 0, 0) ABSOLUTE"
+                    + " ROTATED (0, 0, 0) ABSOLUTE")
         self.assertEqual(output[2], expected)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
@@ -856,24 +947,144 @@ class for each component and aligns the data for display
         comp = instr.get_last_component()
         comp.component_name = "test_name"
 
-        instr.print_components()
+        instr.print_components(line_length=300)
 
         output = mock_stdout.getvalue().split("\n")
 
-        expected = ("first_component    test_for_reading"
-                    + "   AT  [-0.1, 12, 'dist']   RELATIVE home"
-                    + "   ROTATED  [0, 0, 0]              ABSOLUTE")
+        expected = ("first_component  test_for_reading"
+                    + " AT (-0.1, 12, dist) RELATIVE home"
+                    + " ROTATED (0, 0, 0)          ABSOLUTE")
         self.assertEqual(output[0], expected)
 
-        expected = ("second_component   test_for_reading"
-                    + "   AT  [0, 0, 0]            ABSOLUTE"
-                    + "        ROTATED  [-4, 0.001, 'theta']   RELATIVE etc")
+        expected = ("second_component test_for_reading"
+                    + " AT (0, 0, 0)        ABSOLUTE"
+                    + "      ROTATED (-4, 0.001, theta) RELATIVE etc")
         self.assertEqual(output[1], expected)
 
-        expected = ("third_component    test_name"
-                    + "          AT  [0, 0, 0]            ABSOLUTE"
-                    + "        ROTATED  [0, 0, 0]              ABSOLUTE")
+        expected = ("third_component  test_name"
+                    + "        AT (0, 0, 0)        ABSOLUTE"
+                    + "      ROTATED (0, 0, 0)          ABSOLUTE")
         self.assertEqual(output[2], expected)
+        
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_print_components_complex_2lines(self, mock_stdout):
+        """
+        print_components calls the print_short method in the component
+        class for each component and aligns the data for display
+        
+        This version of the tests forces two lines of output.
+        """
+
+        instr = setup_populated_instr()
+
+        instr.set_component_AT("first_component",
+                               [-0.1, 12, "dist"],
+                               RELATIVE="home")
+        instr.set_component_ROTATED("second_component",
+                                    [-4, 0.001, "theta"],
+                                    RELATIVE="etc")
+        comp = instr.get_last_component()
+        comp.component_name = "test_name"
+
+        instr.print_components(line_length=80)
+
+        output = mock_stdout.getvalue().split("\n")
+
+        expected = ("first_component  test_for_reading"
+                    + " AT      (-0.1, 12, dist)   RELATIVE home ")
+        self.assertEqual(output[0], expected)
+
+        expected = ("                                 "
+                    + " ROTATED (0, 0, 0)          ABSOLUTE")
+        self.assertEqual(output[1], expected)
+
+        expected = ("second_component test_for_reading"
+                    + " AT      (0, 0, 0)          ABSOLUTE      ")
+        self.assertEqual(output[2], expected)
+
+        expected = ("                                 "
+                    + " ROTATED (-4, 0.001, theta) RELATIVE etc")
+        self.assertEqual(output[3], expected)
+
+        expected = ("third_component  test_name       "
+                    + " AT      (0, 0, 0)          ABSOLUTE      ")
+        self.assertEqual(output[4], expected)
+
+        expected = ("                                 "
+                    + " ROTATED (0, 0, 0)          ABSOLUTE")
+        self.assertEqual(output[5], expected)
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_print_components_complex_3lines(self, mock_stdout):
+        """
+        print_components calls the print_short method in the component
+        class for each component and aligns the data for display
+        
+        This version of the tests forces three lines of output.
+        """
+
+        instr = setup_populated_instr()
+
+        instr.set_component_AT("first_component",
+                               [-0.1, 12, "dist"],
+                               RELATIVE="home")
+        instr.set_component_ROTATED("second_component",
+                                    [-4, 0.001, "theta"],
+                                    RELATIVE="etc")
+        comp = instr.get_last_component()
+        comp.component_name = "test_name"
+
+        instr.print_components(line_length=1) #  Three lines maximum
+
+        output = mock_stdout.getvalue().split("\n")
+
+        expected = (bcolors.BOLD
+                    + "first_component"
+                    + bcolors.ENDC
+                    + "  "
+                    + bcolors.BOLD
+                    + "test_for_reading"
+                    + bcolors.ENDC
+                    + " ")
+        self.assertEqual(output[0], expected)
+
+        expected = ("  AT      (-0.1, 12, dist) RELATIVE home ")
+        self.assertEqual(output[1], expected)
+
+        expected = ("  ROTATED (0, 0, 0) ABSOLUTE")
+        self.assertEqual(output[2], expected)
+
+        expected = (bcolors.BOLD
+                    + "second_component"
+                    + bcolors.ENDC
+                    + "  "
+                    + bcolors.BOLD
+                    + "test_for_reading"
+                    + bcolors.ENDC
+                    + " ")
+        self.assertEqual(output[3], expected)
+
+        expected = ("  AT      (0, 0, 0) ABSOLUTE ")
+        self.assertEqual(output[4], expected)
+
+        expected = ("  ROTATED (-4, 0.001, theta) RELATIVE etc")
+        self.assertEqual(output[5], expected)
+
+        expected = (bcolors.BOLD
+                    + "third_component"
+                    + bcolors.ENDC
+                    + "  "
+                    + bcolors.BOLD
+                    + "test_name"
+                    + bcolors.ENDC
+                    + " ")
+        self.assertEqual(output[6], expected)        
+
+        expected = ("  AT      (0, 0, 0) ABSOLUTE ")
+        self.assertEqual(output[7], expected)
+
+        expected = ("  ROTATED (0, 0, 0) ABSOLUTE")
+        self.assertEqual(output[8], expected)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     @unittest.mock.patch('__main__.__builtins__.open',
diff --git a/mcstasscript/tests/test_component.py b/mcstasscript/tests/test_component.py
index a1aef691..ce8cccf4 100644
--- a/mcstasscript/tests/test_component.py
+++ b/mcstasscript/tests/test_component.py
@@ -75,6 +75,7 @@ def setup_component_with_parameters():
                             "new_par2": "AA",
                             "this_par": "",
                             "that_par": "1"}
+    comp.line_limit = 117
     comp._freeze()
 
     return comp
@@ -539,7 +540,7 @@ def test_component_show_parameters(self, mock_stdout):
         output = mock_stdout.getvalue()
         output = output.split("\n")
 
-        self.assertEqual(output[0], " ___ Help Arm " + "_"*59)
+        self.assertEqual(output[0], " ___ Help Arm " + "_"*103)
 
         legend = ("|"
                   + bcolors.BOLD + "optional parameter" + bcolors.ENDC

From 57827ee764bd62c75ff0339441cf57e795ddbd43 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Mon, 3 Jun 2019 14:26:28 +0200
Subject: [PATCH 024/403] Update README.md

---
 README.md | 35 +++++++++++++++++++++++++++++++++++
 1 file changed, 35 insertions(+)

diff --git a/README.md b/README.md
index 2bcd92f2..00f9c1ce 100644
--- a/README.md
+++ b/README.md
@@ -2,3 +2,38 @@
 McStas API for creating and running McStas instruments from python scripting
 
 Prototype for an API that allow interaction with McStas through an interface like Jupyter Notebooks created under WP5 of PaNOSC.
+
+## Instructions for basic use:
+Download the entire project
+Set up paths to McStas in the configuration.yaml file
+Before import in python, add the project to your path: 
+import sys
+sys.path.append('path/to/McStasScript')
+Import the interface 
+from mcstasscript.interface import instr, plotter, functions
+
+Now the package can be used. Start with creating a new instrument, just needs a name
+my_instrument = instr.McStas_instr("my_instrument_file")
+
+Then McStas components can be added, here we add a source
+my_source = my_instrument.add_component("source", "Source_simple")
+
+my_source.show_parameters() # Can be used to show available parameters for Source simple
+
+The parameters of the source can be adjusted directly as attributes of the python object
+my_source.xwidth = 0.12
+my_source.yheight = 0.12
+my_source.lambda0 = 3
+my_source.dlambda = 2.2
+my_source.focus_xw = 0.05
+my_source.focus_yh = 0.05
+
+Running the simple instrument of just a source
+data = my_instrument.run_full_instrument(foldername="first_run", increment_folder_name=True)
+
+Any data generated would be stored as a list of McStasData objects in the returned data
+
+plot = plotter.make_sub_plot(data)
+
+
+

From 7c4d79d44ba48d8d3d6fcfbc0ecf8dad2593fd7e Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Mon, 3 Jun 2019 14:26:51 +0200
Subject: [PATCH 025/403] Update README.md

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 00f9c1ce..efd2a6c5 100644
--- a/README.md
+++ b/README.md
@@ -21,6 +21,7 @@ my_source = my_instrument.add_component("source", "Source_simple")
 my_source.show_parameters() # Can be used to show available parameters for Source simple
 
 The parameters of the source can be adjusted directly as attributes of the python object
+
 my_source.xwidth = 0.12
 my_source.yheight = 0.12
 my_source.lambda0 = 3

From 9099105fa30832a6f415fe355a1ce1591864a9f2 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Mon, 3 Jun 2019 14:27:15 +0200
Subject: [PATCH 026/403] Update README.md

---
 README.md | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/README.md b/README.md
index efd2a6c5..284768a7 100644
--- a/README.md
+++ b/README.md
@@ -13,9 +13,11 @@ Import the interface
 from mcstasscript.interface import instr, plotter, functions
 
 Now the package can be used. Start with creating a new instrument, just needs a name
+
 my_instrument = instr.McStas_instr("my_instrument_file")
 
 Then McStas components can be added, here we add a source
+
 my_source = my_instrument.add_component("source", "Source_simple")
 
 my_source.show_parameters() # Can be used to show available parameters for Source simple
@@ -23,10 +25,15 @@ my_source.show_parameters() # Can be used to show available parameters for Sourc
 The parameters of the source can be adjusted directly as attributes of the python object
 
 my_source.xwidth = 0.12
+
 my_source.yheight = 0.12
+
 my_source.lambda0 = 3
+
 my_source.dlambda = 2.2
+
 my_source.focus_xw = 0.05
+
 my_source.focus_yh = 0.05
 
 Running the simple instrument of just a source

From 82c4965226925bac9fedefeafbb985d29782949f Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Mon, 3 Jun 2019 16:50:39 +0200
Subject: [PATCH 027/403] Update README.md

---
 README.md | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/README.md b/README.md
index 284768a7..bcde6556 100644
--- a/README.md
+++ b/README.md
@@ -5,11 +5,17 @@ Prototype for an API that allow interaction with McStas through an interface lik
 
 ## Instructions for basic use:
 Download the entire project
+
 Set up paths to McStas in the configuration.yaml file
+
 Before import in python, add the project to your path: 
+
 import sys
+
 sys.path.append('path/to/McStasScript')
+
 Import the interface 
+
 from mcstasscript.interface import instr, plotter, functions
 
 Now the package can be used. Start with creating a new instrument, just needs a name
@@ -37,6 +43,7 @@ my_source.focus_xw = 0.05
 my_source.focus_yh = 0.05
 
 Running the simple instrument of just a source
+
 data = my_instrument.run_full_instrument(foldername="first_run", increment_folder_name=True)
 
 Any data generated would be stored as a list of McStasData objects in the returned data

From dfc65d7f80474f92033dbdd47cbbd0ed3e66f53d Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Tue, 4 Jun 2019 08:26:36 +0200
Subject: [PATCH 028/403] Update README.md

---
 README.md | 49 +++++++++++++++++++++++++++----------------------
 1 file changed, 27 insertions(+), 22 deletions(-)

diff --git a/README.md b/README.md
index bcde6556..bc59923e 100644
--- a/README.md
+++ b/README.md
@@ -10,45 +10,50 @@ Set up paths to McStas in the configuration.yaml file
 
 Before import in python, add the project to your path: 
 
-import sys
-
-sys.path.append('path/to/McStasScript')
+    import sys
+    sys.path.append('path/to/McStasScript')
 
 Import the interface 
 
-from mcstasscript.interface import instr, plotter, functions
+    from mcstasscript.interface import instr, plotter, functions
 
 Now the package can be used. Start with creating a new instrument, just needs a name
 
-my_instrument = instr.McStas_instr("my_instrument_file")
+    my_instrument = instr.McStas_instr("my_instrument_file")
 
 Then McStas components can be added, here we add a source
 
-my_source = my_instrument.add_component("source", "Source_simple")
-
-my_source.show_parameters() # Can be used to show available parameters for Source simple
+    my_source = my_instrument.add_component("source", "Source_simple")
+    my_source.show_parameters() # Can be used to show available parameters for Source simple
 
 The parameters of the source can be adjusted directly as attributes of the python object
 
-my_source.xwidth = 0.12
-
-my_source.yheight = 0.12
-
-my_source.lambda0 = 3
-
-my_source.dlambda = 2.2
-
-my_source.focus_xw = 0.05
+    my_source.xwidth = 0.12
+    my_source.yheight = 0.12
+    my_source.lambda0 = 3
+    my_source.dlambda = 2.2
+    my_source.focus_xw = 0.05
+    my_source.focus_yh = 0.05
+    
+A monitor is added as well to get data out of the simulation
 
-my_source.focus_yh = 0.05
+    PSD = Instr.add_component("PSD", "PSD_monitor", AT=[0,0,1], RELATIVE="source") 
+    PSD.xwidth = 0.1
+    PSD.yheight = 0.1
+    PSD.nx = 200
+    PSD.ny = 200
+    PSD.filename = "\"PSD.dat\""
 
-Running the simple instrument of just a source
+This simple simulation can be executed from the 
 
-data = my_instrument.run_full_instrument(foldername="first_run", increment_folder_name=True)
+    data = my_instrument.run_full_instrument(foldername="first_run", increment_folder_name=True)
 
-Any data generated would be stored as a list of McStasData objects in the returned data
+Results from the monitors would be stored as a list of McStasData objects in the returned data. The counts are stored as numpy arrays. We can read and change the intensity directly and manipulate the data before plotting.
 
-plot = plotter.make_sub_plot(data)
+    data[0].Intensity
+    
+Plotting is usually done in a subplot of all monitors recorded.    
 
+    plot = plotter.make_sub_plot(data)
 
 

From 08c0c8c7286a49d7cd070474d57b300d06a62ce0 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Tue, 4 Jun 2019 09:03:26 +0200
Subject: [PATCH 029/403] Update README.md

---
 README.md | 31 +++++++++++++++++++++++++++++++
 1 file changed, 31 insertions(+)

diff --git a/README.md b/README.md
index bc59923e..750b6f21 100644
--- a/README.md
+++ b/README.md
@@ -57,3 +57,34 @@ Plotting is usually done in a subplot of all monitors recorded.
     plot = plotter.make_sub_plot(data)
 
 
+## Method overview
+Here is a quick overview of the available methods of the main classes in the project. Most have more options from keyword arguments that are explained in the manual, but also in python help, for example help(instr.McStas_instr.show_components).
+
+    instr
+    ├── McStas_instr(str instr_name) # Returns McStas instrument object on initialize
+        ├── show_components(str category_name) # Show available components in given category
+        ├── component_help(str component_name) # Prints component parameters for given component name   
+        ├── add_component(str name, str component_name) # Adds component to instrument and returns object
+        ├── add_parameter(str name) # Adds instrument parameter with name
+        ├── add_declare_var(str type, str name) # Adds declared variable with type and name
+        ├── append_initialize(str string) # Appends a line to initialize (c syntax)
+        ├── print_components() # Prints list of components and their location
+        ├── write_full_instrument() # Writes instrument to disk with given name + ".instr"
+        └── run_full_instrument() # Runs simulation. Options in keyword arguments. Returns list of McStasData
+        
+    component # returned by add_component
+    ├── set_AT(list at_list) # Sets component position (list of x,y,z positions in [m])
+    ├── set_ROTATED(list rotated_list) # Sets component rotation (list of x,y,z rotations in [deg])
+    ├── set_RELATIVE(str component_name) # Sets relative to other component name
+    ├── set_parameters(dict input) # Set parameters using dict input
+    ├── set_comment(str string) # Set comment explaining something about the component
+    └── print_long() # Prints currently contained information on component
+    
+    functions
+    ├── name_search(str name, list McStasData) # Returns data set with given name from McStasData list
+    ├── name_plot_options(str name, list McStasData, kwargs) # Sends kwargs to dataset with given name
+    └── load_data(str foldername) # Loads data from folder with McStas data as McStasData list
+    
+    plotter
+    ├── make_plot(list McStasData) # Plots each data set individually
+    └── make_sub_plot(list McStasData) # Plots data as subplot

From a7a7f545bc86a55cd4989abd312d82234e807127 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Tue, 4 Jun 2019 12:06:55 +0200
Subject: [PATCH 030/403] Added integration tests and fixed a problem when
 running tests from another directory.

---
 mcstasscript/helper/component_reader.py       |  12 +-
 .../test_complex_instrument.py                | 167 ++++++++++++++++++
 .../test_simple_instrument.py                 | 149 ++++++++++++++++
 mcstasscript/interface/instr.py               |  19 +-
 mcstasscript/tests/test_ComponentReader.py    |   4 +-
 mcstasscript/tests/test_Instr.py              |   7 +-
 6 files changed, 349 insertions(+), 9 deletions(-)
 create mode 100644 mcstasscript/integration_tests/test_complex_instrument.py
 create mode 100644 mcstasscript/integration_tests/test_simple_instrument.py

diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index b4315721..3a492996 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -87,11 +87,17 @@ def show_categories(self):
                 categories.append(category)
                 print(" " + category)
 
-    def show_components_in_category(self, category_input):
+    def show_components_in_category(self, category_input, **kwargs):
         """
         Method that will show all components in given category
 
         """
+        
+        if "line_length" in kwargs:
+            line_limit = kwargs["line_length"]
+        else:
+            line_limit = 100
+        
         empty_category = True
         to_print = []
         for component, category in self.component_category.items():
@@ -109,10 +115,10 @@ def show_components_in_category(self, category_input):
             for component in to_print:
                 print(" " + component)
         else:
-            # Prints in collumns, maximum 4 and maximum line length 100
+            # Prints in collumns, maximum 4 and maximum line length line_liimt
             columns = 5
             total_line_length = 1000
-            while(total_line_length > 100):
+            while(total_line_length > line_limit):
                 columns = columns - 1
 
                 c_length = math.ceil(len(to_print)/columns)
diff --git a/mcstasscript/integration_tests/test_complex_instrument.py b/mcstasscript/integration_tests/test_complex_instrument.py
new file mode 100644
index 00000000..e008e7fb
--- /dev/null
+++ b/mcstasscript/integration_tests/test_complex_instrument.py
@@ -0,0 +1,167 @@
+import io
+import time
+import unittest
+import unittest.mock
+import matplotlib as plt
+
+from mcstasscript.interface import instr, functions, plotter
+
+def setup_complex_instrument():
+    """
+    Sets up guide system with two guides that are placed next to one
+    another with separate entrances but converge at the end.
+    
+    It attempts to use as McStas keywords and features as possible.
+    """
+    Instr = instr.McStas_instr("integration_test_complex",
+                               author="test_suite",
+                               origin="integration tests")
+        
+    Instr.add_parameter("guide_width", value=0.03)
+    Instr.add_parameter("guide_length", value=8.0)
+    
+    source = Instr.add_component("source", "Source_simple")
+    source.xwidth = 0.1
+    source.yheight = 0.01
+    source.dist = 1.5
+    source.focus_xw = "3*guide_width"
+    source.focus_yh = 0.05
+    source.E0 = 5.0
+    source.dE = 1.0
+    source.flux = 1E10
+
+    Instr.add_declare_var("int", "guide_choice")
+    Instr.add_declare_var("double", "source_to_guide_end")
+    Instr.append_initialize("source_to_guide_end = 1.5 + guide_length;")
+    
+    after_guide = Instr.add_component("after_guide", "Arm",
+                                      AT=[0,0,"source_to_guide_end"],
+                                      RELATIVE="source")
+    after_guide.append_EXTEND("guide_choice = -1;")
+    
+    # Add first slit with component methods
+    slit1 = Instr.add_component("slit1", "Slit")
+    slit1.set_AT(["1.3*guide_width", 0, 1.5], RELATIVE="source")
+    slit1.xwidth = "guide_width"
+    slit1.yheight = 0.05
+    slit1.append_EXTEND("if (SCATTERED) {")
+    slit1.append_EXTEND("  guide_choice = 1;")
+    slit1.append_EXTEND("}")
+    slit1.set_GROUP("entrance_slits")
+    
+    # Add second slit with instr methods
+    Instr.add_component("slit2", "Slit")
+    Instr.set_component_AT("slit2", ["-1.3*guide_width", 0, 1.5])
+    Instr.set_component_RELATIVE("slit2","source")
+    Instr.set_component_parameter("slit2",{"xwidth": "guide_width",
+                                           "yheight": 0.05})
+    Instr.append_component_EXTEND("slit2", "if (SCATTERED) {")
+    Instr.append_component_EXTEND("slit2", "  guide_choice = 2;")
+    Instr.append_component_EXTEND("slit2", "}")
+    Instr.set_component_GROUP("slit2","entrance_slits")
+    
+    select1 = Instr.add_component("select1", "Arm", RELATIVE="after_guide")
+    select1.set_JUMP("select2 WHEN guide_choice == 2")
+    
+    guide1 = Instr.add_component("guide1", "Guide_gravity")
+    guide1.set_AT([0,0,0.1], RELATIVE="slit1")
+    guide1.set_ROTATED([0,"-RAD2DEG*atan(0.5*guide_width/guide_length)",0],
+                       RELATIVE="slit1")
+    guide1.w1 = "guide_width"
+    guide1.w2 = "1.3*guide_width"
+    guide1.h1 = 0.05
+    guide1.h2 = 0.05
+    guide1.l = "guide_length"
+    guide1.m = 4
+    guide1.G = -9.82
+    
+    select2 = Instr.add_component("select2", "Arm", RELATIVE="after_guide")
+    select2.set_JUMP("done WHEN guide_choice == 1")
+    
+    guide2 = Instr.add_component("guide2", "Guide_gravity")
+    guide2.set_AT([0,0,0.1], RELATIVE="slit2")
+    guide2.set_ROTATED([0,"RAD2DEG*atan(0.5*guide_width/guide_length)",0],
+                       RELATIVE="slit2")
+    guide2.w1 = "guide_width"
+    guide2.w2 = "1.3*guide_width"
+    guide2.h1 = 0.05
+    guide2.h2 = 0.05
+    guide2.l = "guide_length"
+    guide2.m = 4
+    guide2.G = -9.82
+    
+    done = Instr.add_component("done", "Arm", RELATIVE="after_guide")
+    
+    PSD1 = Instr.add_component("PSD_1D_1", "PSDlin_monitor")
+    PSD1.set_AT([0,0,0.2], RELATIVE="after_guide")
+    PSD1.xwidth = 0.1
+    PSD1.nx = 100
+    PSD1.yheight = 0.03
+    PSD1.filename = "\"PSD1.dat\""
+    PSD1.restore_neutron = 1
+    PSD1.set_WHEN("guide_choice == 1")
+    
+    PSD2 = Instr.add_component("PSD_1D_2", "PSDlin_monitor")
+    PSD2.set_AT([0,0,0.2], RELATIVE="after_guide")
+    PSD2.xwidth = 0.1
+    PSD2.nx = 100
+    PSD2.yheight = 0.03
+    PSD2.filename = "\"PSD2.dat\""
+    PSD2.restore_neutron = 1
+    PSD2.set_WHEN("guide_choice == 2")
+    
+    PSD = Instr.add_component("PSD_1D", "PSDlin_monitor")
+    PSD.set_AT([0,0,0.2], RELATIVE="after_guide")
+    PSD.xwidth = 0.1
+    PSD.nx = 100
+    PSD.yheight = 0.03
+    PSD.filename = "\"PSD_all.dat\""
+    PSD.restore_neutron = 1
+
+    Instr.append_finally("guide_choice = -1;")
+    
+    return Instr
+
+class TestComplexInstrument(unittest.TestCase):
+    """
+    Integration test of a full instrument with McStas simulation
+    performed by the system. The configuration file needs to be set up
+    correctly in order for these tests to succeed.
+    """
+        
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_complex_instrument(self, mock_stdout):
+        """
+        Test parameters can be controlled through McStasScript.  Here
+        a slit is moved to one side and the result is verified.
+        """
+        Instr = setup_complex_instrument()
+
+        data = Instr.run_full_instrument(foldername="integration_test_complex",
+                                         ncount=2E6, mpi=2,
+                                         increment_folder_name=True,
+                                         parameters={"guide_width": 0.03,
+                                                     "guide_length": 8.0})
+
+        intensity_data_pos = functions.name_search("PSD_1D_1", data).Intensity
+        sum_outside_beam = sum(intensity_data_pos[0:50]) 
+        sum_inside_beam = sum(intensity_data_pos[51:99])
+        self.assertTrue(1000*sum_outside_beam < sum_inside_beam)        
+        
+        intensity_data_neg = functions.name_search("PSD_1D_2", data).Intensity
+        sum_outside_beam = sum(intensity_data_neg[51:99])
+        sum_inside_beam = sum(intensity_data_neg[0:50])
+        self.assertTrue(1000*sum_outside_beam < sum_inside_beam)
+        
+        
+        intensity_data_all = functions.name_search("PSD_1D", data).Intensity
+        sum_outside_beam = sum(intensity_data_all[49:51])
+        sum_inside_beam = (sum(intensity_data_all[0:45])
+                           + sum(intensity_data_all[56:99]))
+        self.assertTrue(1000*sum_outside_beam < sum_inside_beam)
+        
+        # Could have the plot window up for some short time
+        # Need to use plt.draw instead of plt.show in plotter
+        # plotter.make_sub_plot(data) 
+        #time.sleep(10)
+        #plt.close()
\ No newline at end of file
diff --git a/mcstasscript/integration_tests/test_simple_instrument.py b/mcstasscript/integration_tests/test_simple_instrument.py
new file mode 100644
index 00000000..c8b4ff27
--- /dev/null
+++ b/mcstasscript/integration_tests/test_simple_instrument.py
@@ -0,0 +1,149 @@
+import io
+import unittest
+import unittest.mock
+
+from mcstasscript.interface import instr, functions, plotter
+
+def setup_simple_instrument():
+    Instr = instr.McStas_instr("integration_test_simple")
+    
+    source = Instr.add_component("source", "Source_div")
+    
+    source.xwidth = 0.03
+    source.yheight = 0.01
+    source.focus_aw = 0.01
+    source.focus_ah = 0.01
+    source.E0 = 81.81
+    source.dE = 1.0
+    source.flux = 1E10
+    
+    PSD = Instr.add_component("PSD_1D", "PSDlin_monitor")
+    
+    PSD.set_AT([0,0,1], RELATIVE="source")
+    PSD.xwidth = 0.1
+    PSD.nx = 100
+    PSD.yheight = 0.03
+    PSD.filename = "\"PSD.dat\""
+    PSD.restore_neutron = 1
+    
+    return Instr
+
+def setup_simple_slit_instrument():
+    Instr = instr.McStas_instr("integration_test_simple")
+    
+    source = Instr.add_component("source", "Source_div")
+    source.xwidth = 0.1
+    source.yheight = 0.01
+    source.focus_aw = 0.01
+    source.focus_ah = 0.01
+    source.E0 = 81.81
+    source.dE = 1.0
+    source.flux = 1E10
+    
+    Instr.add_parameter("slit_offset", value=0)
+    
+    Slit = Instr.add_component("slit", "Slit")
+    Slit.set_AT(["slit_offset", 0, 0.5], RELATIVE="source")
+    Slit.xwidth = 0.01
+    Slit.yheight = 0.03
+    
+    PSD = Instr.add_component("PSD_1D", "PSDlin_monitor")
+    PSD.set_AT([0,0,1], RELATIVE="source")
+    PSD.xwidth = 0.1
+    PSD.nx = 100
+    PSD.yheight = 0.03
+    PSD.filename = "\"PSD.dat\""
+    PSD.restore_neutron = 1
+    
+    return Instr
+
+class TestSimpleInstrument(unittest.TestCase):
+    """
+    Integration test of a full instrument with McStas simulation
+    performed by the system. The configuration file needs to be set up
+    correctly in order for these tests to succeed.
+    """
+    
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_simple_instrument(self, mock_stdout):
+        """
+        Test that an instrument can run and that the results matches
+        expectations. Here beam in small area in the middle of the
+        detector.
+        """
+        Instr = setup_simple_instrument()
+        
+        data = Instr.run_full_instrument(foldername="integration_test_simple",
+                                         ncount=1E6, mpi=1,
+                                         increment_folder_name=True)
+
+        intensity_data = data[0].Intensity
+        # beam should be on pixel 35 to 65
+
+        sum_outside_beam = sum(intensity_data[0:34]) + sum(intensity_data[66:99]) 
+        sum_inside_beam = sum(intensity_data[35:65])
+        
+        self.assertTrue(1000*sum_outside_beam < sum_inside_beam)
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_simple_instrument_mpi(self, mock_stdout):
+        """
+        Test that an instrument can run and that the results matches
+        expectations. Here beam in small area in the middle of the
+        detector. Running with mpi, 2 cores.
+        """
+        Instr = setup_simple_instrument()
+
+        data = Instr.run_full_instrument(foldername="integration_test_mpi",
+                                         ncount=1E6, mpi=2,
+                                         increment_folder_name=True)
+
+        intensity_data = data[0].Intensity
+        # beam should be on pixel 35 to 65
+
+        sum_outside_beam = sum(intensity_data[0:34]) + sum(intensity_data[66:99]) 
+        sum_inside_beam = sum(intensity_data[35:65])
+
+        self.assertTrue(1000*sum_outside_beam < sum_inside_beam)
+        
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_slit_instrument(self, mock_stdout):
+        """
+        Test parameters can be controlled through McStasScript.  Here
+        a slit is can be moved, but the default value of 0 should be
+        used.
+        """
+        Instr = setup_simple_slit_instrument()
+
+        data = Instr.run_full_instrument(foldername="integration_test_slit",
+                                         ncount=2E6, mpi=2,
+                                         increment_folder_name=True)
+
+        intensity_data = data[0].Intensity
+        # beam should be on pixel 45 to 55
+
+        sum_outside_beam = sum(intensity_data[0:44]) + sum(intensity_data[56:99]) 
+        sum_inside_beam = sum(intensity_data[45:55])
+
+        self.assertTrue(1000*sum_outside_beam < sum_inside_beam)      
+        
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_slit_moved_instrument(self, mock_stdout):
+        """
+        Test parameters can be controlled through McStasScript.  Here
+        a slit is moved to one side and the result is verified.
+        """
+        Instr = setup_simple_slit_instrument()
+
+        data = Instr.run_full_instrument(foldername="integration_test_slit",
+                                         ncount=2E6, mpi=2,
+                                         increment_folder_name=True,
+                                         parameters={"slit_offset": 0.03})
+
+        intensity_data = data[0].Intensity
+        # beam should be on pixel 75 to 85
+
+        sum_outside_beam = sum(intensity_data[0:74]) + sum(intensity_data[86:99]) 
+        sum_inside_beam = sum(intensity_data[75:85])
+
+        self.assertTrue(1000*sum_outside_beam < sum_inside_beam) 
\ No newline at end of file
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 2108b545..840cce27 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -79,6 +79,9 @@ class McStas_instr:
 
     append_trace(string)
         Obsolete method, add components instead (used in write_c_files)
+        
+    show_components(string)
+        Shows available components in given category
 
     add_component(instance_name,component_name,**kwargs)
         Add a component to the instrument file
@@ -469,7 +472,13 @@ def show_components(self, *args):
         Helper method that shows available components to the user
 
         If called without any arguments it will display the available
-        component categories.  The first input
+        component categories. If a category is given as a string in
+        the first input, components in that category are printed.
+        
+        Parameters
+        ----------
+        first argument (optional): str
+            Category that matches one of the McStas component folders
 
         """
         if len(args) == 0:
@@ -482,7 +491,9 @@ def show_components(self, *args):
             print("Here are all components in the "
                   + category
                   + " category.")
-            self.component_reader.show_components_in_category(category)
+            this_reader = self.component_reader
+            this_reader.show_components_in_category(category,
+                                                    line_length=self.line_limit)
 
     def component_help(self, name, **kwargs):
         """
@@ -1150,6 +1161,8 @@ def write_full_instrument(self):
 
         # End instrument file
         fo.write("\nEND\n")
+        
+        fo.close()
 
     def run_full_instrument(self, *args, **kwargs):
         """
@@ -1209,7 +1222,7 @@ def run_full_instrument(self, *args, **kwargs):
                 raise NameError("Required parameters not provided.")
             else:
                 # If all parameters have defaults, just run with the defaults.
-                passed_parameters = default_parameters
+                kwargs["parameters"] = default_parameters
         else:
             given_parameters = kwargs["parameters"]
             for name in required_parameters:
diff --git a/mcstasscript/tests/test_ComponentReader.py b/mcstasscript/tests/test_ComponentReader.py
index c40a81b3..712ecdb4 100644
--- a/mcstasscript/tests/test_ComponentReader.py
+++ b/mcstasscript/tests/test_ComponentReader.py
@@ -129,7 +129,7 @@ def test_ComponentReader_show_components_short(self, mock_stdout):
 
         component_reader = setup_component_reader()
 
-        component_reader.show_components_in_category("misc")
+        component_reader.show_components_in_category("misc", line_length=100)
 
         output = mock_stdout.getvalue()
         output = output.split("\n")
@@ -152,7 +152,7 @@ def test_ComponentReader_show_components_long(self, mock_stdout):
 
         #component_reader.component_category[]
 
-        component_reader.show_components_in_category("misc")
+        component_reader.show_components_in_category("misc", line_length=100)
 
         output = mock_stdout.getvalue()
         output = output.split("\n")
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 4cb963b4..cbe358e6 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -28,9 +28,13 @@ def setup_instr_with_path():
 
     THIS_DIR = os.path.dirname(os.path.abspath(__file__))
     dummy_path = THIS_DIR + "/dummy_mcstas"
+    
+    current_work_dir = os.getcwd()
+    os.chdir(THIS_DIR)  # Set work directory to test folder
 
     return McStas_instr("test_instrument", mcstas_path=dummy_path)
 
+    os.chdir(current_work_dir)  # Return to previous workdir
 
 def setup_populated_instr():
     """
@@ -344,8 +348,9 @@ def test_show_components_simple(self, mock_stdout):
         """
         Simple test of show components to show categories
         """
+        
         instr = setup_instr_with_path()
-
+        
         instr.show_components()
 
         output = mock_stdout.getvalue()

From 294b886e2ed52c0345bf724792406f4b535aa796 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Tue, 4 Jun 2019 12:51:39 +0200
Subject: [PATCH 031/403] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 750b6f21..0fb345f9 100644
--- a/README.md
+++ b/README.md
@@ -61,7 +61,7 @@ Plotting is usually done in a subplot of all monitors recorded.
 Here is a quick overview of the available methods of the main classes in the project. Most have more options from keyword arguments that are explained in the manual, but also in python help, for example help(instr.McStas_instr.show_components).
 
     instr
-    ├── McStas_instr(str instr_name) # Returns McStas instrument object on initialize
+    └── McStas_instr(str instr_name) # Returns McStas instrument object on initialize
         ├── show_components(str category_name) # Show available components in given category
         ├── component_help(str component_name) # Prints component parameters for given component name   
         ├── add_component(str name, str component_name) # Adds component to instrument and returns object

From a2129a7168e47921d33798a1fd58fb1fa3fa0d21 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Tue, 4 Jun 2019 15:14:12 +0200
Subject: [PATCH 032/403] PEP8 cleanup round

---
 mcstasscript/data/data.py                     |  2 +-
 mcstasscript/helper/component_reader.py       | 14 +--
 mcstasscript/helper/formatting.py             | 31 +++---
 mcstasscript/helper/managed_mcrun.py          | 29 +++---
 mcstasscript/helper/mcstas_objects.py         | 17 ++--
 .../test_complex_instrument.py                | 77 ++++++++-------
 .../test_simple_instrument.py                 | 55 ++++++-----
 mcstasscript/interface/functions.py           | 13 ++-
 mcstasscript/interface/instr.py               | 99 +++++++++----------
 mcstasscript/interface/plotter.py             |  3 +-
 mcstasscript/tests/test_Instr.py              | 56 ++++++-----
 mcstasscript/tests/test_ManagedMcrun.py       |  4 +-
 mcstasscript/tests/test_functions.py          |  5 +-
 13 files changed, 209 insertions(+), 196 deletions(-)

diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index 9ec685eb..43c4d9a5 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -72,7 +72,7 @@ def extract_info(self):
                 type_string1 = type_data.split(",")[0]
                 type_string1 = type_string1.split("(")[1]
                 self.dimension.append(int(type_string1))
-                
+
                 type_string2 = type_data.split(",")[1]
                 type_string2 = type_string2.split(")")[0]
                 self.dimension.append(int(type_string2))
diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index 3a492996..62e9fdea 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -1,6 +1,7 @@
 import os
 import math
 
+
 class ComponentInfo:
     """
     Internal class used to store information on parameters of components
@@ -92,12 +93,11 @@ def show_components_in_category(self, category_input, **kwargs):
         Method that will show all components in given category
 
         """
-        
         if "line_length" in kwargs:
             line_limit = kwargs["line_length"]
         else:
             line_limit = 100
-        
+
         empty_category = True
         to_print = []
         for component, category in self.component_category.items():
@@ -177,11 +177,11 @@ def read_name(self, component_name):
                             + "current work directory.")
 
         output = self.read_component_file(self.component_path[component_name])
-        
+
         # Category loaded using path, in case of Work directory it fails
         if self.component_category[component_name] == "Work directory":
             output.category = "Work directory"  # Corrects category
-        
+
         return output
 
     def _find_components(self, absolute_path):
@@ -273,7 +273,7 @@ def read_component_file(self, absolute_path):
 
             # find definition parameters and their values
             if (self.line_starts_with(line.strip(), "DEFINITION PARAMETERS")
-                    or self.line_starts_with(line.strip(), 
+                    or self.line_starts_with(line.strip(),
                                              "SETTING PARAMETERS")):
 
                 parts = line.split("(")
@@ -329,7 +329,7 @@ def read_component_file(self, absolute_path):
                             name_value = part.split("=")
                             par_name = name_value[0].strip()
                             par_value = name_value[1].strip()
-                            
+
                             if temp_par_type is "double":
                                 try:
                                     par_value = float(par_value)
@@ -381,4 +381,4 @@ def line_starts_with(self, line, string):
         if line[0:len(string)] == string:
             return True
         else:
-            return False
\ No newline at end of file
+            return False
diff --git a/mcstasscript/helper/formatting.py b/mcstasscript/helper/formatting.py
index aef8ff10..5593a082 100644
--- a/mcstasscript/helper/formatting.py
+++ b/mcstasscript/helper/formatting.py
@@ -1,9 +1,7 @@
-"""
-Helper class that contains formatting classes and functions
-
-"""
-
 class bcolors:
+    """
+    Helper class that contains formatting classes and functions
+    """
     HEADER = '\033[95m'
     OKBLUE = '\033[94m'
     OKGREEN = '\033[92m'
@@ -12,44 +10,45 @@ class bcolors:
     ENDC = '\033[0m'
     BOLD = '\033[1m'
     UNDERLINE = '\033[4m'
-    
+
+
 def is_legal_parameter(name):
     """
     Function that returns true if the given name can be used as a
     parameter in the c programming language.
     """
-    
+
     if name is "":
         return False
-    
+
     if " " in name:
         return False
-    
+
     if "." in name:
         return False
 
     if not name[0].isalpha():
         return False
-    
+
     return True
 
+
 def is_legal_filename(name):
     """
     Function that returns true if the given name can be used as a
     filename
     """
-    
+
     if name is "":
         return False
-    
+
     if " " in name:
         return False
-    
+
     if "/" in name:
         return False
-    
+
     if "\\" in name:
         return False
-    
+
     return True
-    
\ No newline at end of file
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index f3f557e4..f4f4197f 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -4,6 +4,7 @@
 from mcstasscript.data.data import McStasData
 from docutils.io import InputError
 
+
 class ManagedMcrun:
     """
     A class for performing a mcstas simulation and organizing the data
@@ -98,10 +99,10 @@ def __init__(self, instr_name, **kwargs):
 
         if "custom_flags" in kwargs:
             self.custom_flags = kwargs["custom_flags"]
-            
+
         if "increment_folder_name" in kwargs:
             self.increment_folder_name = kwargs["increment_folder_name"]
-            
+
         if "force_compile" in kwargs:
             self.compile = kwargs["force_compile"]
 
@@ -111,23 +112,23 @@ def run_simulation(self):
         """
 
         # construct command to run
-        
+
         options_string = ""
         if self.compile:
-            options_string = "-c " 
+            options_string = "-c "
 
-        option_string = (options_string 
+        option_string = (options_string
                          + "-n " + str(self.ncount)  # Set ncount
                          + " --mpi=" + str(self.mpi)  # Set mpi
                          + " ")
-        
+
         if self.increment_folder_name and os.path.isdir(self.data_folder_name):
             counter = 0
             new_name = self.data_folder_name + "_" + str(counter)
             while os.path.isdir(new_name):
                 counter = counter + 1
                 new_name = self.data_folder_name + "_" + str(counter)
-            
+
             self.data_folder_name = new_name
 
         if len(self.data_folder_name) > 0:
@@ -160,21 +161,21 @@ def run_simulation(self):
         Can use subprocess from spawn* instead of os.system if more
         control is needed over the spawned process, including a timeout
         """
-        
-        #return self.load_results(self.data_folder_name)
-        
+
+        # return self.load_results(self.data_folder_name)
+
     def load_results(self, *args):
-        
+
         if len(args) == 0:
             data_folder_name = self.data_folder_name
         elif len(args) == 1:
             data_folder_name = args[0]
         else:
             raise InputError("load_results can be caled with 0 or 1 arguments")
-        
+
         if not os.path.isdir(data_folder_name):
             raise NameError("Given data directory does not exist.")
-        
+
         # Find all data files in generated folder
         files_in_folder = os.listdir(data_folder_name)
 
@@ -257,4 +258,4 @@ def load_results(self, *args):
             f.close()
 
         # Return list of McStasData objects
-        return results
\ No newline at end of file
+        return results
diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index e61f594b..29c48b0d 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -1,6 +1,7 @@
 from mcstasscript.helper.formatting import bcolors
 from mcstasscript.helper.formatting import is_legal_parameter
 
+
 class parameter_variable:
     """
     Class describing a input parameter in McStas instrument
@@ -649,7 +650,7 @@ def show_parameters(self, **kwargs):
         subclasses for the individual components are required to run
         this method.
         """
-        
+
         if "line_length" in kwargs:
             line_limit = kwargs["line_length"]
         else:
@@ -716,7 +717,7 @@ def show_parameters(self, **kwargs):
             characters_before_comment += characters_from_value
 
             print(parameter_name + value + unit, end="")
-            
+
             if characters_before_comment + len(comment) < line_limit:
                 print(comment)
             else:
@@ -735,16 +736,16 @@ def show_parameters(self, **kwargs):
                     if iterations > max_iterations:
                         #  Something went long, print on one line
                         break
-                    
+
                     line_left = length_for_comment
-                    
+
                     while(line_left > 0):
                         if current_index >= len(words):
                             current_index = len(words) + 1
                             break
                         line_left -= len(str(words[current_index])) + 1
                         current_index += 1
-                        
+
                     current_index -= 1
                     for word in words[last_index:current_index]:
                         comment += word + " "
@@ -776,16 +777,16 @@ def show_parameters_simple(self):
                 value = " = " + str(self.parameter_defaults[parameter])
             if getattr(self, parameter) is not None:
                 value = " = " + str(getattr(self, parameter))
-            
+
             unit = ""
             if parameter in self.parameter_units:
                 unit = " [" + self.parameter_units[parameter] + "]"
-            
+
             comment = ""
             if parameter in self.parameter_comments:
                 if self.parameter_comments[parameter] is not "":
                     comment = " // " + self.parameter_comments[parameter]
 
             print(parameter + value + unit + comment)
-                
+
         print("----------" + "-"*len(self.component_name) + "------")
diff --git a/mcstasscript/integration_tests/test_complex_instrument.py b/mcstasscript/integration_tests/test_complex_instrument.py
index e008e7fb..b6b8396c 100644
--- a/mcstasscript/integration_tests/test_complex_instrument.py
+++ b/mcstasscript/integration_tests/test_complex_instrument.py
@@ -6,20 +6,21 @@
 
 from mcstasscript.interface import instr, functions, plotter
 
+
 def setup_complex_instrument():
     """
     Sets up guide system with two guides that are placed next to one
     another with separate entrances but converge at the end.
-    
+
     It attempts to use as McStas keywords and features as possible.
     """
     Instr = instr.McStas_instr("integration_test_complex",
                                author="test_suite",
                                origin="integration tests")
-        
+
     Instr.add_parameter("guide_width", value=0.03)
     Instr.add_parameter("guide_length", value=8.0)
-    
+
     source = Instr.add_component("source", "Source_simple")
     source.xwidth = 0.1
     source.yheight = 0.01
@@ -33,12 +34,12 @@ def setup_complex_instrument():
     Instr.add_declare_var("int", "guide_choice")
     Instr.add_declare_var("double", "source_to_guide_end")
     Instr.append_initialize("source_to_guide_end = 1.5 + guide_length;")
-    
+
     after_guide = Instr.add_component("after_guide", "Arm",
-                                      AT=[0,0,"source_to_guide_end"],
+                                      AT=[0, 0, "source_to_guide_end"],
                                       RELATIVE="source")
     after_guide.append_EXTEND("guide_choice = -1;")
-    
+
     # Add first slit with component methods
     slit1 = Instr.add_component("slit1", "Slit")
     slit1.set_AT(["1.3*guide_width", 0, 1.5], RELATIVE="source")
@@ -48,24 +49,24 @@ def setup_complex_instrument():
     slit1.append_EXTEND("  guide_choice = 1;")
     slit1.append_EXTEND("}")
     slit1.set_GROUP("entrance_slits")
-    
+
     # Add second slit with instr methods
     Instr.add_component("slit2", "Slit")
     Instr.set_component_AT("slit2", ["-1.3*guide_width", 0, 1.5])
-    Instr.set_component_RELATIVE("slit2","source")
-    Instr.set_component_parameter("slit2",{"xwidth": "guide_width",
-                                           "yheight": 0.05})
+    Instr.set_component_RELATIVE("slit2", "source")
+    Instr.set_component_parameter("slit2", {"xwidth": "guide_width",
+                                            "yheight": 0.05})
     Instr.append_component_EXTEND("slit2", "if (SCATTERED) {")
     Instr.append_component_EXTEND("slit2", "  guide_choice = 2;")
     Instr.append_component_EXTEND("slit2", "}")
-    Instr.set_component_GROUP("slit2","entrance_slits")
-    
+    Instr.set_component_GROUP("slit2", "entrance_slits")
+
     select1 = Instr.add_component("select1", "Arm", RELATIVE="after_guide")
     select1.set_JUMP("select2 WHEN guide_choice == 2")
-    
+
     guide1 = Instr.add_component("guide1", "Guide_gravity")
-    guide1.set_AT([0,0,0.1], RELATIVE="slit1")
-    guide1.set_ROTATED([0,"-RAD2DEG*atan(0.5*guide_width/guide_length)",0],
+    guide1.set_AT([0, 0, 0.1], RELATIVE="slit1")
+    guide1.set_ROTATED([0, "-RAD2DEG*atan(0.5*guide_width/guide_length)", 0],
                        RELATIVE="slit1")
     guide1.w1 = "guide_width"
     guide1.w2 = "1.3*guide_width"
@@ -74,13 +75,13 @@ def setup_complex_instrument():
     guide1.l = "guide_length"
     guide1.m = 4
     guide1.G = -9.82
-    
+
     select2 = Instr.add_component("select2", "Arm", RELATIVE="after_guide")
     select2.set_JUMP("done WHEN guide_choice == 1")
-    
+
     guide2 = Instr.add_component("guide2", "Guide_gravity")
-    guide2.set_AT([0,0,0.1], RELATIVE="slit2")
-    guide2.set_ROTATED([0,"RAD2DEG*atan(0.5*guide_width/guide_length)",0],
+    guide2.set_AT([0, 0, 0.1], RELATIVE="slit2")
+    guide2.set_ROTATED([0, "RAD2DEG*atan(0.5*guide_width/guide_length)", 0],
                        RELATIVE="slit2")
     guide2.w1 = "guide_width"
     guide2.w2 = "1.3*guide_width"
@@ -89,29 +90,29 @@ def setup_complex_instrument():
     guide2.l = "guide_length"
     guide2.m = 4
     guide2.G = -9.82
-    
+
     done = Instr.add_component("done", "Arm", RELATIVE="after_guide")
-    
+
     PSD1 = Instr.add_component("PSD_1D_1", "PSDlin_monitor")
-    PSD1.set_AT([0,0,0.2], RELATIVE="after_guide")
+    PSD1.set_AT([0, 0, 0.2], RELATIVE="after_guide")
     PSD1.xwidth = 0.1
     PSD1.nx = 100
     PSD1.yheight = 0.03
     PSD1.filename = "\"PSD1.dat\""
     PSD1.restore_neutron = 1
     PSD1.set_WHEN("guide_choice == 1")
-    
+
     PSD2 = Instr.add_component("PSD_1D_2", "PSDlin_monitor")
-    PSD2.set_AT([0,0,0.2], RELATIVE="after_guide")
+    PSD2.set_AT([0, 0, 0.2], RELATIVE="after_guide")
     PSD2.xwidth = 0.1
     PSD2.nx = 100
     PSD2.yheight = 0.03
     PSD2.filename = "\"PSD2.dat\""
     PSD2.restore_neutron = 1
     PSD2.set_WHEN("guide_choice == 2")
-    
+
     PSD = Instr.add_component("PSD_1D", "PSDlin_monitor")
-    PSD.set_AT([0,0,0.2], RELATIVE="after_guide")
+    PSD.set_AT([0, 0, 0.2], RELATIVE="after_guide")
     PSD.xwidth = 0.1
     PSD.nx = 100
     PSD.yheight = 0.03
@@ -119,16 +120,16 @@ def setup_complex_instrument():
     PSD.restore_neutron = 1
 
     Instr.append_finally("guide_choice = -1;")
-    
+
     return Instr
 
+
 class TestComplexInstrument(unittest.TestCase):
     """
     Integration test of a full instrument with McStas simulation
     performed by the system. The configuration file needs to be set up
     correctly in order for these tests to succeed.
     """
-        
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_complex_instrument(self, mock_stdout):
         """
@@ -144,24 +145,26 @@ def test_complex_instrument(self, mock_stdout):
                                                      "guide_length": 8.0})
 
         intensity_data_pos = functions.name_search("PSD_1D_1", data).Intensity
-        sum_outside_beam = sum(intensity_data_pos[0:50]) 
+        sum_outside_beam = sum(intensity_data_pos[0:50])
         sum_inside_beam = sum(intensity_data_pos[51:99])
-        self.assertTrue(1000*sum_outside_beam < sum_inside_beam)        
-        
+        self.assertTrue(1000*sum_outside_beam < sum_inside_beam)
+
         intensity_data_neg = functions.name_search("PSD_1D_2", data).Intensity
         sum_outside_beam = sum(intensity_data_neg[51:99])
         sum_inside_beam = sum(intensity_data_neg[0:50])
         self.assertTrue(1000*sum_outside_beam < sum_inside_beam)
-        
-        
+
         intensity_data_all = functions.name_search("PSD_1D", data).Intensity
         sum_outside_beam = sum(intensity_data_all[49:51])
         sum_inside_beam = (sum(intensity_data_all[0:45])
                            + sum(intensity_data_all[56:99]))
         self.assertTrue(1000*sum_outside_beam < sum_inside_beam)
-        
+
         # Could have the plot window up for some short time
         # Need to use plt.draw instead of plt.show in plotter
-        # plotter.make_sub_plot(data) 
-        #time.sleep(10)
-        #plt.close()
\ No newline at end of file
+        # plotter.make_sub_plot(data)
+        # time.sleep(10)
+        # plt.close()
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/mcstasscript/integration_tests/test_simple_instrument.py b/mcstasscript/integration_tests/test_simple_instrument.py
index c8b4ff27..848ee4d0 100644
--- a/mcstasscript/integration_tests/test_simple_instrument.py
+++ b/mcstasscript/integration_tests/test_simple_instrument.py
@@ -4,11 +4,12 @@
 
 from mcstasscript.interface import instr, functions, plotter
 
+
 def setup_simple_instrument():
     Instr = instr.McStas_instr("integration_test_simple")
-    
+
     source = Instr.add_component("source", "Source_div")
-    
+
     source.xwidth = 0.03
     source.yheight = 0.01
     source.focus_aw = 0.01
@@ -16,21 +17,22 @@ def setup_simple_instrument():
     source.E0 = 81.81
     source.dE = 1.0
     source.flux = 1E10
-    
+
     PSD = Instr.add_component("PSD_1D", "PSDlin_monitor")
-    
-    PSD.set_AT([0,0,1], RELATIVE="source")
+
+    PSD.set_AT([0, 0, 1], RELATIVE="source")
     PSD.xwidth = 0.1
     PSD.nx = 100
     PSD.yheight = 0.03
     PSD.filename = "\"PSD.dat\""
     PSD.restore_neutron = 1
-    
+
     return Instr
 
+
 def setup_simple_slit_instrument():
     Instr = instr.McStas_instr("integration_test_simple")
-    
+
     source = Instr.add_component("source", "Source_div")
     source.xwidth = 0.1
     source.yheight = 0.01
@@ -39,31 +41,32 @@ def setup_simple_slit_instrument():
     source.E0 = 81.81
     source.dE = 1.0
     source.flux = 1E10
-    
+
     Instr.add_parameter("slit_offset", value=0)
-    
+
     Slit = Instr.add_component("slit", "Slit")
     Slit.set_AT(["slit_offset", 0, 0.5], RELATIVE="source")
     Slit.xwidth = 0.01
     Slit.yheight = 0.03
-    
+
     PSD = Instr.add_component("PSD_1D", "PSDlin_monitor")
-    PSD.set_AT([0,0,1], RELATIVE="source")
+    PSD.set_AT([0, 0, 1], RELATIVE="source")
     PSD.xwidth = 0.1
     PSD.nx = 100
     PSD.yheight = 0.03
     PSD.filename = "\"PSD.dat\""
     PSD.restore_neutron = 1
-    
+
     return Instr
 
+
 class TestSimpleInstrument(unittest.TestCase):
     """
     Integration test of a full instrument with McStas simulation
     performed by the system. The configuration file needs to be set up
     correctly in order for these tests to succeed.
     """
-    
+
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_simple_instrument(self, mock_stdout):
         """
@@ -72,7 +75,7 @@ def test_simple_instrument(self, mock_stdout):
         detector.
         """
         Instr = setup_simple_instrument()
-        
+
         data = Instr.run_full_instrument(foldername="integration_test_simple",
                                          ncount=1E6, mpi=1,
                                          increment_folder_name=True)
@@ -80,9 +83,10 @@ def test_simple_instrument(self, mock_stdout):
         intensity_data = data[0].Intensity
         # beam should be on pixel 35 to 65
 
-        sum_outside_beam = sum(intensity_data[0:34]) + sum(intensity_data[66:99]) 
+        sum_outside_beam = (sum(intensity_data[0:34])
+                            + sum(intensity_data[66:99]))
         sum_inside_beam = sum(intensity_data[35:65])
-        
+
         self.assertTrue(1000*sum_outside_beam < sum_inside_beam)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
@@ -101,11 +105,12 @@ def test_simple_instrument_mpi(self, mock_stdout):
         intensity_data = data[0].Intensity
         # beam should be on pixel 35 to 65
 
-        sum_outside_beam = sum(intensity_data[0:34]) + sum(intensity_data[66:99]) 
+        sum_outside_beam = (sum(intensity_data[0:34])
+                            + sum(intensity_data[66:99]))
         sum_inside_beam = sum(intensity_data[35:65])
 
         self.assertTrue(1000*sum_outside_beam < sum_inside_beam)
-        
+
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_slit_instrument(self, mock_stdout):
         """
@@ -122,11 +127,11 @@ def test_slit_instrument(self, mock_stdout):
         intensity_data = data[0].Intensity
         # beam should be on pixel 45 to 55
 
-        sum_outside_beam = sum(intensity_data[0:44]) + sum(intensity_data[56:99]) 
+        sum_outside_beam = (sum(intensity_data[0:44])
+                            + sum(intensity_data[56:99]))
         sum_inside_beam = sum(intensity_data[45:55])
+        self.assertTrue(1000*sum_outside_beam < sum_inside_beam)
 
-        self.assertTrue(1000*sum_outside_beam < sum_inside_beam)      
-        
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_slit_moved_instrument(self, mock_stdout):
         """
@@ -143,7 +148,11 @@ def test_slit_moved_instrument(self, mock_stdout):
         intensity_data = data[0].Intensity
         # beam should be on pixel 75 to 85
 
-        sum_outside_beam = sum(intensity_data[0:74]) + sum(intensity_data[86:99]) 
+        sum_outside_beam = (um(intensity_data[0:74])
+                            + sum(intensity_data[86:99]))
         sum_inside_beam = sum(intensity_data[75:85])
 
-        self.assertTrue(1000*sum_outside_beam < sum_inside_beam) 
\ No newline at end of file
+        self.assertTrue(1000*sum_outside_beam < sum_inside_beam)
+
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file
diff --git a/mcstasscript/interface/functions.py b/mcstasscript/interface/functions.py
index 04038f02..bfae923a 100644
--- a/mcstasscript/interface/functions.py
+++ b/mcstasscript/interface/functions.py
@@ -1,6 +1,7 @@
 from mcstasscript.data.data import McStasData
 from mcstasscript.helper.managed_mcrun import ManagedMcrun
 
+
 def name_search(name, data_list):
     """"
     name_search returns McStasData instance with specific name if it is
@@ -18,7 +19,6 @@ def name_search(name, data_list):
     data_list : List of McStasData instances
         List of datasets to search
     """
-    
     if type(data_list) is not list:
         raise InputError(
             "name_search function needs list of McStasData as input")
@@ -31,7 +31,7 @@ def name_search(name, data_list):
     for check in data_list:
         if check.name == name:
             list_result.append(check)
-            
+
     if len(list_result) == 0:
         raise NameError("No dataset with name: \""
                         + name
@@ -44,6 +44,7 @@ def name_search(name, data_list):
                         + "the search for a dataset with name: \""
                         + name + "\".")
 
+
 def name_plot_options(name, data_list, **kwargs):
     """"
     name_plot_options passes keyword arguments to dataset with certain
@@ -64,20 +65,18 @@ def name_plot_options(name, data_list, **kwargs):
         Keyword arguments passed to set_plot_options in
         McStasPlotOptions
     """
-
     object_to_modify = name_search(name, data_list)
     object_to_modify.set_plot_options(**kwargs)
 
+
 def load_data(foldername):
     """
     Loads data from a McStas data folder including mccode.sim
-    
+
     Parameters
     ----------
         foldername : string
             Name of the folder from which to load data
     """
-    
-    managed_mcrun = ManagedMcrun("dummy", foldername=foldername)    
+    managed_mcrun = ManagedMcrun("dummy", foldername=foldername)
     return managed_mcrun.load_results()
-    
\ No newline at end of file
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 840cce27..21fce353 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -13,6 +13,7 @@
 from mcstasscript.helper.formatting import is_legal_filename
 from mcstasscript.helper.formatting import bcolors
 
+
 class McStas_instr:
     """
     Main class for writing a McStas instrument using McStasScript
@@ -79,7 +80,7 @@ class McStas_instr:
 
     append_trace(string)
         Obsolete method, add components instead (used in write_c_files)
-        
+
     show_components(string)
         Shows available components in given category
 
@@ -160,7 +161,7 @@ def __init__(self, name, **kwargs):
         """
 
         self.name = name
-        
+
         if not is_legal_filename(self.name + ".instr"):
             raise NameError("The instrument is called: \""
                             + self.name
@@ -177,7 +178,7 @@ def __init__(self, name, **kwargs):
             self.origin = kwargs["origin"]
         else:
             self.origin = "ESS DMSC"
-            
+
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         configuration_file_name = THIS_DIR + "/../../configuration.yaml"
         if not os.path.isfile(configuration_file_name):
@@ -191,7 +192,7 @@ def __init__(self, name, **kwargs):
             self.line_limit = config["other"]["characters_per_line"]
         else:
             # This happens in unit tests that mocks open
-            self.mcrun_path = "" 
+            self.mcrun_path = ""
             self.mcstas_path = ""
             self.line_limit = 180
 
@@ -244,16 +245,15 @@ def add_parameter(self, *args, **kwargs):
         """
         # parameter_variable class documented independently
         self.parameter_list.append(parameter_variable(*args, **kwargs))
-        
+
     def show_parameters(self, **kwargs):
         """
         Method for displaying current instrument parameters
-        
+
         keyword arguments
             line_length : int
                 Maximum line length for terminal output
         """
-        
         if "line_length" in kwargs:
             line_limit = kwargs["line_length"]
         else:
@@ -290,8 +290,8 @@ def show_parameters(self, **kwargs):
             else:
                 print(" = ", end=' ')
             print(str(parameter.value).ljust(longest_value+1), end=' ')
-            if (length_for_comment < 5 
-                or length_for_comment > len(str(parameter.comment))):
+            if (length_for_comment < 5
+                    or length_for_comment > len(str(parameter.comment))):
                 print(str(parameter.comment))
             else:
                 # Split comment into several lines
@@ -308,16 +308,16 @@ def show_parameters(self, **kwargs):
                     if iterations > max_iterations:
                         #  Something went long, print on one line
                         break
-                    
+
                     line_left = length_for_comment
-                    
+
                     while(line_left > 0):
                         if current_index >= len(words):
                             current_index = len(words) + 1
                             break
                         line_left -= len(str(words[current_index])) + 1
                         current_index += 1
-                        
+
                     current_index -= 1
                     for word in words[last_index:current_index]:
                         comment += word + " "
@@ -474,7 +474,7 @@ def show_components(self, *args):
         If called without any arguments it will display the available
         component categories. If a category is given as a string in
         the first input, components in that category are printed.
-        
+
         Parameters
         ----------
         first argument (optional): str
@@ -492,8 +492,9 @@ def show_components(self, *args):
                   + category
                   + " category.")
             this_reader = self.component_reader
+            line_lim = self.line_limit
             this_reader.show_components_in_category(category,
-                                                    line_length=self.line_limit)
+                                                    line_length=line_lim)
 
     def component_help(self, name, **kwargs):
         """
@@ -867,12 +868,12 @@ def print_components(self, **kwargs):
 
         Provides overview of component names, what McStas component is
         used for each and their position and rotation in space.
-        
+
         keyword arguments:
         line_length : int
             Maximum line length in console
         """
-        
+
         if "line_length" in kwargs:
             line_limit = kwargs["line_length"]
         else:
@@ -908,9 +909,9 @@ def print_components(self, **kwargs):
         # Padding settings, 0,0,6,0,6 is minimum values
         name_pad = 0
         comp_name_pad = 0
-        AT_pad = 6 # requires (, , ) in addition to data length
+        AT_pad = 6  # requires (, , ) in addition to data length
         RELATIVE_pad = 0
-        ROTATED_pad = 6 # requires (, , ) in addition to data length
+        ROTATED_pad = 6  # requires (, , ) in addition to data length
 
         # Check if longest line length exceeded
         longest_line_length = (longest_name + name_pad
@@ -919,12 +920,12 @@ def print_components(self, **kwargs):
                                + longest_at_relative_name + RELATIVE_pad
                                + longest_rotated_xyz_name + ROTATED_pad
                                + longest_rotated_relative_name + 8 + 9)
-        
+
         def coordinates_to_string(data):
-            return ("(" 
+            return ("("
                     + str(data[0]) + ", "
                     + str(data[1]) + ", "
-                    + str(data[2]) + ")") 
+                    + str(data[2]) + ")")
 
         n_lines = 1
         """
@@ -937,27 +938,27 @@ def coordinates_to_string(data):
             longest_at_xyz_name = max([longest_at_xyz_name,
                                        longest_rotated_xyz_name])
             longest_rotated_xyz_name = longest_at_xyz_name
-            RELATIVE_pad = 0            
+            RELATIVE_pad = 0
 
-            longest_line_length_at = (longest_name 
+            longest_line_length_at = (longest_name
                                       + comp_name_pad
-                                      + longest_component_name 
+                                      + longest_component_name
                                       + comp_name_pad
-                                      + longest_at_xyz_name 
+                                      + longest_at_xyz_name
                                       + AT_pad
-                                      + longest_at_relative_name 
-                                      + 7 + 6 )
-            longest_line_length_rotated = (longest_name 
+                                      + longest_at_relative_name
+                                      + 7 + 6)
+            longest_line_length_rotated = (longest_name
                                            + comp_name_pad
-                                           + longest_component_name 
+                                           + longest_component_name
                                            + comp_name_pad
-                                           + longest_rotated_xyz_name 
+                                           + longest_rotated_xyz_name
                                            + ROTATED_pad
-                                           + longest_rotated_relative_name 
+                                           + longest_rotated_relative_name
                                            + 7 + 6)
 
-            if (longest_line_length_at > line_limit 
-                or longest_line_length_rotated > line_limit):
+            if (longest_line_length_at > line_limit
+                    or longest_line_length_rotated > line_limit):
                 n_lines = 3
 
         if n_lines == 1:
@@ -966,7 +967,7 @@ def coordinates_to_string(data):
                 p_name = p_name.ljust(longest_name + name_pad)
 
                 p_comp_name = str(component.component_name)
-                p_comp_name = p_comp_name.ljust(longest_component_name 
+                p_comp_name = p_comp_name.ljust(longest_component_name
                                                 + comp_name_pad)
 
                 p_AT = coordinates_to_string(component.AT_data)
@@ -992,7 +993,7 @@ def coordinates_to_string(data):
                 p_name = p_name.ljust(longest_name + name_pad)
 
                 p_comp_name = str(component.component_name)
-                p_comp_name = p_comp_name.ljust(longest_component_name 
+                p_comp_name = p_comp_name.ljust(longest_component_name
                                                 + comp_name_pad)
 
                 p_AT = coordinates_to_string(component.AT_data)
@@ -1003,8 +1004,8 @@ def coordinates_to_string(data):
                                                     + RELATIVE_pad)
 
                 p_ROTATED_align = " "*(longest_name
-                                       + comp_name_pad 
-                                       + longest_component_name 
+                                       + comp_name_pad
+                                       + longest_component_name
                                        + comp_name_pad)
 
                 p_ROTATED = coordinates_to_string(component.ROTATED_data)
@@ -1013,7 +1014,7 @@ def coordinates_to_string(data):
 
                 p_ROTATED_RELATIVE = str(component.ROTATED_relative)
 
-                print(p_name, p_comp_name, 
+                print(p_name, p_comp_name,
                       "AT     ", p_AT, p_AT_RELATIVE, "\n",
                       p_ROTATED_align, "ROTATED",
                       p_ROTATED, p_ROTATED_RELATIVE)
@@ -1022,7 +1023,7 @@ def coordinates_to_string(data):
             for component in self.component_list:
                 p_name = bcolors.BOLD + str(component.name) + bcolors.ENDC
 
-                p_comp_name = (bcolors.BOLD 
+                p_comp_name = (bcolors.BOLD
                                + str(component.component_name)
                                + bcolors.ENDC)
 
@@ -1161,7 +1162,7 @@ def write_full_instrument(self):
 
         # End instrument file
         fo.write("\nEND\n")
-        
+
         fo.close()
 
     def run_full_instrument(self, *args, **kwargs):
@@ -1194,25 +1195,23 @@ def run_full_instrument(self, *args, **kwargs):
             mcrun_path : str
                 Path to mcrun command, "" if already in path
         """
-        
-
         # Make sure mcrun path is in kwargs
         if "mcrun_path" not in kwargs:
             kwargs["mcrun_path"] = self.mcrun_path
-        
+
         # Find required parameters
         required_parameters = []
         default_parameters = {}
         passed_parameters = {}
-        
-        for index in range(0,len(self.parameter_list)):
+
+        for index in range(0, len(self.parameter_list)):
             if self.parameter_list[index].value == "":
                 required_parameters.append(self.parameter_list[index].name)
             else:
-                default_parameters.update({self.parameter_list[index].name :
+                default_parameters.update({self.parameter_list[index].name:
                                            self.parameter_list[index].value})
-            
-        # Check if parameters are given 
+
+        # Check if parameters are given
         if "parameters" not in kwargs:
             if len(required_parameters) > 0:
                 # print required parameters and raise error
@@ -1228,7 +1227,7 @@ def run_full_instrument(self, *args, **kwargs):
             for name in required_parameters:
                 if name not in given_parameters:
                     raise NameError("The required instrument parameter "
-                                    + name 
+                                    + name
                                     + " was not provided.")
             # Overwrite default parameters with given parameters
             default_parameters.update(given_parameters)
@@ -1243,5 +1242,3 @@ def run_full_instrument(self, *args, **kwargs):
         # Run the simulation and return data
         simulation.run_simulation()
         return simulation.load_results()
-    
-
diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index efa9b7b7..ab837c2e 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -11,6 +11,7 @@
 from mcstasscript.data.data import McStasPlotOptions
 from mcstasscript.data.data import McStasData
 
+
 class make_plot:
     """
     make_plot plots contents of McStasData objects
@@ -305,4 +306,4 @@ def fmt(x, pos):
             else:
                 print("Error, dimension not read correctly")
 
-        plt.show()
\ No newline at end of file
+        plt.show()
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index cbe358e6..a789c87f 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -8,18 +8,21 @@
 from mcstasscript.interface.instr import McStas_instr
 from mcstasscript.helper.formatting import bcolors
 
+
 def setup_instr_no_path():
     """
     Sets up a instrument without a mcstas_path
     """
     return McStas_instr("test_instrument")
 
+
 def setup_instr_root_path():
     """
     Sets up a instrument with root mcstas_path
     """
     return McStas_instr("test_instrument", mcstas_path="/")
 
+
 def setup_instr_with_path():
     """
     Sets up a instrument with a valid mcstas_path, but it points to
@@ -28,7 +31,7 @@ def setup_instr_with_path():
 
     THIS_DIR = os.path.dirname(os.path.abspath(__file__))
     dummy_path = THIS_DIR + "/dummy_mcstas"
-    
+
     current_work_dir = os.getcwd()
     os.chdir(THIS_DIR)  # Set work directory to test folder
 
@@ -36,6 +39,7 @@ def setup_instr_with_path():
 
     os.chdir(current_work_dir)  # Return to previous workdir
 
+
 def setup_populated_instr():
     """
     Sets up a instrument with some features used and two components
@@ -81,7 +85,7 @@ def test_complex_initialize(self):
         self.assertEqual(my_instrument.origin, "DMSC")
         self.assertEqual(my_instrument.mcrun_path, "/path/to/mcrun")
         self.assertEqual(my_instrument.mcstas_path, "/path/to/mcstas")
-        
+
     def test_load_config_file(self):
         """
         Test that configuration file is read correctly. In order to have
@@ -95,7 +99,7 @@ def test_load_config_file(self):
         if not os.path.isfile(configuration_file_name):
             raise NameError("Could not find configuration file!")
 
-        f = open(configuration_file_name,"r")
+        f = open(configuration_file_name, "r")
 
         lines = f.readlines()
         for line in lines:
@@ -103,11 +107,11 @@ def test_load_config_file(self):
             if line.startswith("mcrun_path:"):
                 parts = line.split(" ")
                 correct_mcrun_path = parts[1][1:-1]
-            
+
             if line.startswith("mcstas_path:"):
                 parts = line.split(" ")
                 correct_mcstas_path = parts[1][1:-1]
-            
+
             if line.startswith("characters_per_line:"):
                 parts = line.split(" ")
                 correct_n_of_characters = int(parts[1])
@@ -156,12 +160,12 @@ def test_show_parameters(self, mock_stdout):
         self.assertEqual(output[2], "float   theta  =  8       // test par")
         self.assertEqual(output[3], "int     slits             // test par")
         self.assertEqual(output[4], "string  ref    =  string  // new string")
-        
+
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_show_parameters_line_break(self, mock_stdout):
         """
         Testing that parameters are displayed correctly
-        
+
         Here multiple lines are used for a long comment that was
         dynamically broken up.
         """
@@ -173,13 +177,13 @@ def test_show_parameters_line_break(self, mock_stdout):
         instr.add_parameter("int", "slits", comment="test par")
         instr.add_parameter("string", "ref",
                             value="string", comment="new string")
-        
+
         longest_comment = ("This is a very long comment meant for "
                            + "testing the dynamic line breaking "
                            + "that is used in this method. It needs "
                            + "to have many lines in order to ensure "
                            + "it really works.")
-        
+
         instr.add_parameter("double", "value",
                             value="37", comment=longest_comment)
 
@@ -194,16 +198,16 @@ def test_show_parameters_line_break(self, mock_stdout):
         self.assertEqual(output[4], "string  ref    =  string  // new string")
         comment_line = "This is a very long comment meant for testing "
         self.assertEqual(output[5], "double  value  =  37      // "
-                                   + comment_line)
+                                    + comment_line)
         comment_line = "the dynamic line breaking that is used in this "
         self.assertEqual(output[6], "                             "
-                                   + comment_line)
+                                    + comment_line)
         comment_line = "method. It needs to have many lines in order to "
         self.assertEqual(output[7], "                             "
-                                   + comment_line)
+                                    + comment_line)
         comment_line = "ensure it really works. "
         self.assertEqual(output[8], "                             "
-                                   + comment_line)
+                                    + comment_line)
 
     def test_simple_add_declare_parameter(self):
         """
@@ -348,9 +352,8 @@ def test_show_components_simple(self, mock_stdout):
         """
         Simple test of show components to show categories
         """
-        
         instr = setup_instr_with_path()
-        
+
         instr.show_components()
 
         output = mock_stdout.getvalue()
@@ -395,7 +398,7 @@ def test_component_help(self, mock_stdout):
         """
         instr = setup_instr_with_path()
 
-        instr.component_help("test_for_reading",line_length=90)
+        instr.component_help("test_for_reading", line_length=90)
         # This call creates a dummy component and calls its
         # show_parameter method which has been tested. Here we
         # need to ensure the call is succesful, not test all
@@ -970,13 +973,13 @@ class for each component and aligns the data for display
                     + "        AT (0, 0, 0)        ABSOLUTE"
                     + "      ROTATED (0, 0, 0)          ABSOLUTE")
         self.assertEqual(output[2], expected)
-        
+
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_print_components_complex_2lines(self, mock_stdout):
         """
         print_components calls the print_short method in the component
         class for each component and aligns the data for display
-        
+
         This version of the tests forces two lines of output.
         """
 
@@ -1024,7 +1027,7 @@ def test_print_components_complex_3lines(self, mock_stdout):
         """
         print_components calls the print_short method in the component
         class for each component and aligns the data for display
-        
+
         This version of the tests forces three lines of output.
         """
 
@@ -1039,7 +1042,7 @@ class for each component and aligns the data for display
         comp = instr.get_last_component()
         comp.component_name = "test_name"
 
-        instr.print_components(line_length=1) #  Three lines maximum
+        instr.print_components(line_length=1)  # Three lines maximum
 
         output = mock_stdout.getvalue().split("\n")
 
@@ -1083,7 +1086,7 @@ class for each component and aligns the data for display
                     + "test_name"
                     + bcolors.ENDC
                     + " ")
-        self.assertEqual(output[6], expected)        
+        self.assertEqual(output[6], expected)
 
         expected = ("  AT      (0, 0, 0) ABSOLUTE ")
         self.assertEqual(output[7], expected)
@@ -1257,8 +1260,7 @@ def test_write_full_instrument_simple(self, mock_f, mock_stdout):
         mock_f.assert_called_with("test_instrument.instr", "w")
         handle = mock_f()
         handle.write.assert_has_calls(wrts, any_order=False)
-        
-        
+
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_run_full_instrument_required_par_error(self, mock_stdout):
         """
@@ -1289,11 +1291,11 @@ def test_run_full_instrument_basic(self, os_system,
         instr.run_full_instrument("test_instrument.instr",
                                   foldername="test_data_set",
                                   mcrun_path="path",
-                                  parameters={"theta" : 1})
+                                  parameters={"theta": 1})
 
         # a double space because of a missing option
         expected_call = ("path/mcrun -c -n 1000000 --mpi=1 "
-                         + "-d test_data_set  test_instrument.instr" 
+                         + "-d test_data_set  test_instrument.instr"
                          + " has_default=37 theta=1")
 
         os_system.assert_called_once_with(expected_call)
@@ -1327,13 +1329,13 @@ def test_run_full_instrument_complex(self, os_system,
                          + "has_default=37 A=2 BC=car theta=\"toy\"")
 
         os_system.assert_called_once_with(expected_call)
-        
+
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
     @unittest.mock.patch("os.system")
     def test_run_full_instrument_overwrite_default(self, os_system,
-                                         mock_f, mock_stdout,):
+                                                   mock_f, mock_stdout,):
         """
         Check that default parameters are overwritten by given
         parameters.
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index b894d986..129c944f 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -174,7 +174,7 @@ def test_ManagedMcrun_run_simulation_parameters(self, os_system):
         expected_call = ("path/mcrun -c -n 48 --mpi=7 "
                          + "-d test_folder -fo test.instr "
                          + "A=2 BC=car th=\"toy\"")
-        
+
     @unittest.mock.patch("os.system")
     def test_ManagedMcrun_run_simulation_compile(self, os_system):
         """
@@ -186,7 +186,7 @@ def test_ManagedMcrun_run_simulation_compile(self, os_system):
                                  mcrun_path="path",
                                  mpi=7,
                                  ncount=48.4,
-                                 force_compile = False,
+                                 force_compile=False,
                                  custom_flags="-fo",
                                  parameters={"A": 2,
                                              "BC": "car",
diff --git a/mcstasscript/tests/test_functions.py b/mcstasscript/tests/test_functions.py
index 5d15044d..52545b9e 100644
--- a/mcstasscript/tests/test_functions.py
+++ b/mcstasscript/tests/test_functions.py
@@ -70,6 +70,7 @@ def setup_McStasData_array():
 
     return data_list
 
+
 def setup_McStasData_array_repeat():
 
     data_list = []
@@ -92,7 +93,6 @@ def setup_McStasData_array_repeat():
     return data_list
 
 
-
 class Test_name_search(unittest.TestCase):
     """
     Test the utility function called name_search which finds and
@@ -110,7 +110,7 @@ def test_name_search_read(self):
         hero_object = name_search("Hero", data_list)
 
         self.assertEqual(hero_object.metadata.dimension, 123)
-        
+
     def test_name_search_read_repeat(self):
         """
         Test simple case with repeat name
@@ -206,5 +206,6 @@ def test_crun_load_data_PSD4PI(self):
         self.assertEqual(PSD_4PI.Intensity[1][4], 1.537334562E-10)
         self.assertEqual(PSD_4PI.Error[1][4], 1.139482296E-10)
 
+
 if __name__ == '__main__':
     unittest.main()

From f46f27def9c94062ff7e0b174f2c16904bf9920c Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Thu, 13 Jun 2019 12:57:44 +0200
Subject: [PATCH 033/403] Updated plotter with new commmands for controlling
 the plot.

The commands are included in the manual.
  cut_min, cut_max sets the range of intensity to plot
  x_axis_multiplier, y_axis_multiplier can be used to change units of the axis
  left_lim, right_lim set lower and upper limit for xaxis for plotting
  bottom_lim, top_lim set lower and upper limit for yaxis for plotting
---
 McStasScript_documentation.pdf       | Bin 158632 -> 159586 bytes
 mcstasscript/data/data.py            |  38 ++++++++
 mcstasscript/helper/managed_mcrun.py |   1 -
 mcstasscript/interface/plotter.py    | 139 +++++++++++++++++++++------
 4 files changed, 147 insertions(+), 31 deletions(-)

diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
index 137efa38f2f3c527f15ca3a58c82d96a36e0a2c8..92d9d8a865db1c2b737ec0ad7e47ad516d81982d 100644
GIT binary patch
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diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index 43c4d9a5..84633cb8 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -155,6 +155,15 @@ def __init__(self, *args, **kwargs):
         self.log = False
         self.orders_of_mag = 300
         self.colormap = "jet"
+        self.cut_max = 1
+        self.cut_min = 0
+        self.x_limit_multiplier = 1
+        self.y_limit_multiplier = 1
+        
+        self.custom_ylim_top = False
+        self.custom_ylim_bottom = False
+        self.custom_xlim_left = False
+        self.custom_xlim_right = False
 
     def set_options(self, **kwargs):
         """Set custom values for plotting preferences"""
@@ -176,6 +185,35 @@ def set_options(self, **kwargs):
 
         if "colormap" in kwargs:
             self.colormap = kwargs["colormap"]
+            
+        if "cut_max" in kwargs:
+            self.cut_max = kwargs["cut_max"]
+            
+        if "cut_min" in kwargs:
+            self.cut_min = kwargs["cut_min"]
+            
+        if "x_axis_multiplier" in kwargs:
+            self.x_limit_multiplier = kwargs["x_axis_multiplier"]
+        
+        if "y_axis_multiplier" in kwargs:
+            self.y_limit_multiplier = kwargs["y_axis_multiplier"]
+            
+        if "top_lim" in kwargs:
+            self.top_lim = kwargs["top_lim"]
+            self.custom_ylim_top = True
+            
+        if "bottom_lim" in kwargs:
+            self.bottom_lim = kwargs["bottom_lim"]
+            self.custom_ylim_bottom = True
+            
+        if "left_lim" in kwargs:
+            self.left_lim = kwargs["left_lim"]
+            self.custom_xlim_left = True
+            
+        if "right_lim" in kwargs:
+            self.right_lim = kwargs["right_lim"]
+            self.custom_xlim_right = True
+            
 
 
 class McStasData:
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index f4f4197f..8cbb1c56 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -2,7 +2,6 @@
 import numpy as np
 from mcstasscript.data.data import McStasMetaData
 from mcstasscript.data.data import McStasData
-from docutils.io import InputError
 
 
 class ManagedMcrun:
diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index ab837c2e..a1e4db3e 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -22,7 +22,7 @@ class make_plot:
     If a list is given, the plots appear individually.
     """
 
-    def __init__(self, data_list):
+    def __init__(self, data_list, **kwargs):
         """
         plots McStasData, single object or list of McStasData
 
@@ -50,6 +50,9 @@ def __init__(self, data_list):
 
         number_of_plots = len(data_list)
 
+        if "fontsize" in kwargs:
+            plt.rcParams.update({'font.size': kwargs["fontsize"]})
+
         print("number of elements in data list = " + str(len(data_list)))
 
         index = -1
@@ -60,17 +63,22 @@ def __init__(self, data_list):
             if type(data.metadata.dimension) == int:
                 fig = plt.figure(0)
 
+                x_axis_mult = data.plot_options.x_limit_multiplier
                 # print(data.T)
-                x = data.xaxis
+                x = data.xaxis*x_axis_mult
                 y = data.Intensity
                 y_err = data.Error
 
+                #(fig, ax0) = plt.errorbar(x, y, yerr=y_err)
                 plt.errorbar(x, y, yerr=y_err)
 
+                ax0 = plt.gca()
+
                 if data.plot_options.log:
                     ax0.set_yscale("log", nonposy='clip')
 
-                plt.xlim(data.metadata.limits[0], data.metadata.limits[1])
+                ax0.set_xlim(data.metadata.limits[0]*x_axis_mult,
+                             data.metadata.limits[1]*x_axis_mult)
 
                 # Add a title
                 plt.title(data.metadata.title)
@@ -79,39 +87,59 @@ def __init__(self, data_list):
                 plt.xlabel(data.metadata.xlabel)
                 plt.ylabel(data.metadata.ylabel)
 
-            elif len(data.metadata.dimension) == 2:
+                if data.plot_options.custom_xlim_left:
+                    ax0.set_xlim(left=data.plot_options.left_lim)
 
+                if data.plot_options.custom_xlim_right:
+                    ax0.set_xlim(right=data.plot_options.right_lim)
+
+            elif len(data.metadata.dimension) == 2:
                 # Split the data into intensity, error and ncount
                 Intensity = data.Intensity
                 Error = data.Error
                 Ncount = data.Ncount
 
+                cut_max = data.plot_options.cut_max  # Default 1
+                cut_min = data.plot_options.cut_min  # Default 0
+
                 if data.plot_options.log:
-                    min_value = np.min(Intensity[np.nonzero(Intensity)])
-                    min_value = np.log10(min_value)
+                    to_plot = Intensity
 
-                    to_plot = np.log10(Intensity)
+                    max_data_value = to_plot.max()
 
-                    max_value = to_plot.max()
+                    min_value = np.min(Intensity[np.nonzero(Intensity)])
+                    min_value = np.log10(min_value
+                                         + (max_data_value-min_value)*cut_min)
+
+                    max_value = np.log10(max_data_value*cut_max)
 
                     if (max_value - min_value
                             > data.plot_options.orders_of_mag):
                         min_value = (max_value
                                      - data.plot_options.orders_of_mag)
+                    min_value = 10.0 ** min_value
+                    max_value = 10.0 ** max_value
                 else:
                     to_plot = Intensity
-                    min_value = to_plot.min()
+                    min_value = to_plot.min() 
                     max_value = to_plot.max()
 
+                    # Cut top and bottom of data as specified in cut variables
+                    min_value = min_value + (max_value-min_value)*cut_min
+                    max_value = max_value*cut_max
+
                 # Check the size of the array to be plotted
                 # print(to_plot.shape)
 
                 # Set the axis (might be switched?)
-                X = np.linspace(data.metadata.limits[0],
-                                data.metadata.limits[1],
+                x_axis_mult = data.plot_options.x_limit_multiplier
+                y_axis_mult = data.plot_options.y_limit_multiplier
+
+                X = np.linspace(data.metadata.limits[0]*x_axis_mult,
+                                data.metadata.limits[1]*x_axis_mult,
                                 data.metadata.dimension[0]+1)
-                Y = np.linspace(data.metadata.limits[2],
-                                data.metadata.limits[3],
+                Y = np.linspace(data.metadata.limits[2]*y_axis_mult,
+                                data.metadata.limits[3]*y_axis_mult,
                                 data.metadata.dimension[1])
 
                 # Create a meshgrid for both x and y
@@ -142,11 +170,23 @@ def __init__(self, data_list):
 
                 # Add a title
                 ax0.set_title(data.metadata.title)
-
+                
                 # Add axis labels
                 plt.xlabel(data.metadata.xlabel)
                 plt.ylabel(data.metadata.ylabel)
 
+                if data.plot_options.custom_ylim_top:
+                    ax0.set_ylim(top=data.plot_options.top_lim)
+
+                if data.plot_options.custom_ylim_bottom:
+                    ax0.set_ylim(bottom=data.plot_options.bottom_lim)
+
+                if data.plot_options.custom_xlim_left:
+                    ax0.set_xlim(left=data.plot_options.left_lim)
+
+                if data.plot_options.custom_xlim_right:
+                    ax0.set_xlim(right=data.plot_options.right_lim)
+
             else:
                 print("Error, dimension not read correctly")
 
@@ -162,7 +202,7 @@ class make_sub_plot:
     subplot.
     """
 
-    def __init__(self, data_list):
+    def __init__(self, data_list, **kwargs):
         """
         plots McStasData, single object or list of McStasData
 
@@ -196,6 +236,9 @@ def __init__(self, data_list):
         dim2 = math.ceil(math.sqrt(number_of_plots))
         dim1 = math.ceil(number_of_plots/dim2)
 
+        if "fontsize" in kwargs:
+            plt.rcParams.update({'font.size': kwargs["fontsize"]})
+
         fig, axs = plt.subplots(dim1, dim2, figsize=(13, 7))
         axs = np.array(axs)
         ax = axs.reshape(-1)
@@ -211,7 +254,9 @@ def __init__(self, data_list):
             if isinstance(data.metadata.dimension, int):
                 # fig = plt.figure(0)
                 # plt.subplot(dim1, dim2, n_plot)
-                x = data.xaxis
+                x_axis_mult = data.plot_options.x_limit_multiplier
+
+                x = data.xaxis*x_axis_mult
                 y = data.Intensity
                 y_err = data.Error
 
@@ -220,16 +265,22 @@ def __init__(self, data_list):
                 if data.plot_options.log:
                     ax0.set_yscale("log", nonposy='clip')
 
-                ax0.set_xlim(data.metadata.limits[0],
-                             data.metadata.limits[1])
+                ax0.set_xlim(data.metadata.limits[0]*x_axis_mult,
+                             data.metadata.limits[1]*x_axis_mult)
 
                 # Add a title
-                # ax0.title(data.title)
+                ax0.set_title(data.metadata.title)
 
                 # Add axis labels
                 ax0.set_xlabel(data.metadata.xlabel)
                 ax0.set_ylabel(data.metadata.ylabel)
 
+                if data.plot_options.custom_xlim_left:
+                    ax0.set_xlim(left=data.plot_options.left_lim)
+
+                if data.plot_options.custom_xlim_right:
+                    ax0.set_xlim(right=data.plot_options.right_lim)
+
             elif len(data.metadata.dimension) == 2:
 
                 # Split the data into intensity, error and ncount
@@ -237,13 +288,19 @@ def __init__(self, data_list):
                 Error = data.Error
                 Ncount = data.Ncount
 
-                if data.plot_options.log:
-                    min_value = np.min(Intensity[np.nonzero(Intensity)])
-                    min_value = np.log10(min_value)
+                cut_max = data.plot_options.cut_max  # Default 1
+                cut_min = data.plot_options.cut_min  # Default 0
 
+                if data.plot_options.log:
                     to_plot = Intensity
 
-                    max_value = np.log10(to_plot.max())
+                    max_data_value = to_plot.max()
+
+                    min_value = np.min(Intensity[np.nonzero(Intensity)])
+                    min_value = np.log10(min_value
+                                         + (max_data_value-min_value)*cut_min)
+
+                    max_value = np.log10(max_data_value*cut_max)
 
                     if (max_value - min_value
                             > data.plot_options.orders_of_mag):
@@ -253,18 +310,25 @@ def __init__(self, data_list):
                     max_value = 10.0 ** max_value
                 else:
                     to_plot = Intensity
-                    min_value = to_plot.min()
+                    min_value = to_plot.min() 
                     max_value = to_plot.max()
 
+                    # Cut top and bottom of data as specified in cut variables
+                    min_value = min_value + (max_value-min_value)*cut_min
+                    max_value = max_value*cut_max
+
                 # Check the size of the array to be plotted
                 # print(to_plot.shape)
 
-                # Set the axis (might be switched?)
-                X = np.linspace(data.metadata.limits[0],
-                                data.metadata.limits[1],
+                # Set the axis
+                x_axis_mult = data.plot_options.x_limit_multiplier
+                y_axis_mult = data.plot_options.y_limit_multiplier
+
+                X = np.linspace(data.metadata.limits[0]*x_axis_mult,
+                                data.metadata.limits[1]*x_axis_mult,
                                 data.metadata.dimension[0]+1)
-                Y = np.linspace(data.metadata.limits[2],
-                                data.metadata.limits[3],
+                Y = np.linspace(data.metadata.limits[2]*y_axis_mult,
+                                data.metadata.limits[3]*y_axis_mult,
                                 data.metadata.dimension[1])
 
                 # Create a meshgrid for both x and y
@@ -290,7 +354,10 @@ def __init__(self, data_list):
                 def fmt(x, pos):
                     a, b = '{:.2e}'.format(x).split('e')
                     b = int(b)
-                    return r'${} \times 10^{{{}}}$'.format(a, b)
+                    if abs(float(a) - 1) < 0.01 :
+                        return r'$10^{{{}}}$'.format(b)
+                    else:
+                        return r'${} \times 10^{{{}}}$'.format(a, b)
 
                 # Add the colorbar
                 fig.colorbar(im, ax=ax0,
@@ -303,6 +370,18 @@ def fmt(x, pos):
                 ax0.set_xlabel(data.metadata.xlabel)
                 ax0.set_ylabel(data.metadata.ylabel)
 
+                if data.plot_options.custom_ylim_top:
+                    ax0.set_ylim(top=data.plot_options.top_lim)
+
+                if data.plot_options.custom_ylim_bottom:
+                    ax0.set_ylim(bottom=data.plot_options.bottom_lim)
+
+                if data.plot_options.custom_xlim_left:
+                    ax0.set_xlim(left=data.plot_options.left_lim)
+
+                if data.plot_options.custom_xlim_right:
+                    ax0.set_xlim(right=data.plot_options.right_lim)
+
             else:
                 print("Error, dimension not read correctly")
 

From fc93224cf445ce7419c65160111da0e6839be4f3 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Thu, 13 Jun 2019 14:20:54 +0200
Subject: [PATCH 034/403] Switch the use of os.system to subprocess.run for
 execution of the simulation. This allows output of both stdout and stderror.
 Added keyword argument suppress_output to run_full_instrument for when the
 output from the simulation is not needed.

---
 mcstasscript/helper/managed_mcrun.py | 26 ++++++++++++++++++--------
 mcstasscript/interface/instr.py      |  2 +-
 2 files changed, 19 insertions(+), 9 deletions(-)

diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 8cbb1c56..1ca3126a 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -1,5 +1,7 @@
 import os
 import numpy as np
+import subprocess
+
 from mcstasscript.data.data import McStasMetaData
 from mcstasscript.data.data import McStasData
 
@@ -105,7 +107,7 @@ def __init__(self, instr_name, **kwargs):
         if "force_compile" in kwargs:
             self.compile = kwargs["force_compile"]
 
-    def run_simulation(self):
+    def run_simulation(self, **kwargs):
         """
         Runs McStas simulation described by initializing the object
         """
@@ -150,18 +152,26 @@ def run_simulation(self):
                 mcrun_full_path = self.mcrun_path + "/mcrun"
 
         # Run the mcrun command on the system
-        os.system(mcrun_full_path + " "
+        full_command = (mcrun_full_path + " "
                   + option_string + " "
                   + self.custom_flags + " "
                   + self.name_of_instrumentfile
                   + parameter_string)
+        
+        #os.system(full_command)
+        process = subprocess.run(full_command, shell=True,
+                                 stdout=subprocess.PIPE,
+                                 stderr=subprocess.PIPE,
+                                 universal_newlines=True)
+
+        if "suppress_output" in kwargs:
+            if kwargs["suppress_output"] is False:
+                print(process.stderr)
+                print(process.stdout)
+        else:
+            print(process.stderr)
+            print(process.stdout)
 
-        """
-        Can use subprocess from spawn* instead of os.system if more
-        control is needed over the spawned process, including a timeout
-        """
-
-        # return self.load_results(self.data_folder_name)
 
     def load_results(self, *args):
 
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 21fce353..992b5bf6 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1240,5 +1240,5 @@ def run_full_instrument(self, *args, **kwargs):
         simulation = ManagedMcrun(self.name + ".instr", **kwargs)
 
         # Run the simulation and return data
-        simulation.run_simulation()
+        simulation.run_simulation(**kwargs)
         return simulation.load_results()

From 6955487620febfd73653d0c569853c9a10143dd0 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Thu, 13 Jun 2019 15:38:54 +0200
Subject: [PATCH 035/403] Updated tests to mock subprocess instead of system

---
 .../test_simple_instrument.py                 |  2 +-
 mcstasscript/interface/instr.py               | 64 +++++++++++++++++++
 mcstasscript/tests/test_Instr.py              | 27 +++++---
 mcstasscript/tests/test_ManagedMcrun.py       | 45 +++++++++----
 mcstasscript/tests/test_instrument.instr      | 44 +++++++++++++
 5 files changed, 158 insertions(+), 24 deletions(-)
 create mode 100644 mcstasscript/tests/test_instrument.instr

diff --git a/mcstasscript/integration_tests/test_simple_instrument.py b/mcstasscript/integration_tests/test_simple_instrument.py
index 848ee4d0..952b62cf 100644
--- a/mcstasscript/integration_tests/test_simple_instrument.py
+++ b/mcstasscript/integration_tests/test_simple_instrument.py
@@ -148,7 +148,7 @@ def test_slit_moved_instrument(self, mock_stdout):
         intensity_data = data[0].Intensity
         # beam should be on pixel 75 to 85
 
-        sum_outside_beam = (um(intensity_data[0:74])
+        sum_outside_beam = (sum(intensity_data[0:74])
                             + sum(intensity_data[86:99]))
         sum_inside_beam = sum(intensity_data[75:85])
 
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 992b5bf6..c193db77 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -3,6 +3,7 @@
 import os
 import datetime
 import yaml
+import subprocess
 
 from mcstasscript.data.data import McStasData
 from mcstasscript.helper.mcstas_objects import declare_variable
@@ -1242,3 +1243,66 @@ def run_full_instrument(self, *args, **kwargs):
         # Run the simulation and return data
         simulation.run_simulation(**kwargs)
         return simulation.load_results()
+    
+    def show_instrument(self, *args, **kwargs):
+        """
+        Uses mcdisplay to show the instrument in webbroser
+        """
+        
+        # Find required parameters
+        required_parameters = []
+        default_parameters = {}
+        passed_parameters = {}
+
+        for index in range(0, len(self.parameter_list)):
+            if self.parameter_list[index].value == "":
+                required_parameters.append(self.parameter_list[index].name)
+            else:
+                default_parameters.update({self.parameter_list[index].name:
+                                           self.parameter_list[index].value})
+
+        # Check if parameters are given
+        if "parameters" not in kwargs:
+            if len(required_parameters) > 0:
+                # print required parameters and raise error
+                print("Required instrument parameters:")
+                for name in required_parameters:
+                    print("  " + name)
+                raise NameError("Required parameters not provided.")
+            else:
+                # If all parameters have defaults, just run with the defaults.
+                kwargs["parameters"] = default_parameters
+        else:
+            given_parameters = kwargs["parameters"]
+            for name in required_parameters:
+                if name not in given_parameters:
+                    raise NameError("The required instrument parameter "
+                                    + name
+                                    + " was not provided.")
+            # Overwrite default parameters with given parameters
+            default_parameters.update(given_parameters)
+            kwargs["parameters"] = default_parameters                
+
+        parameters = kwargs["parameters"]
+        # add parameters to command
+        parameter_string = ""
+        for key, val in parameters.items():
+            parameter_string = (parameter_string + " "
+                                + str(key)  # parameter name
+                                + "="
+                                + str(val))  # parameter value
+
+        bin_path = self.mcstas_path + "/bin/"
+        #full_command = (bin_path + "mcdisplay-webgl "
+        full_command = (bin_path + "mcdisplay " 
+                        + self.name + ".instr"
+                        + " " + parameter_string) 
+        print(full_command)
+        #os.system(full_command)
+        process = subprocess.run(full_command, shell=True,
+                                 stdout=subprocess.PIPE,
+                                 stderr=subprocess.PIPE,
+                                 universal_newlines=True)
+        print(process.stderr)
+        print(process.stdout)
+        
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index a789c87f..ee0c27a8 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1278,8 +1278,8 @@ def test_run_full_instrument_required_par_error(self, mock_stdout):
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
-    @unittest.mock.patch("os.system")
-    def test_run_full_instrument_basic(self, os_system,
+    @unittest.mock.patch("subprocess.run")
+    def test_run_full_instrument_basic(self, mock_sub,
                                        mock_f, mock_stdout,):
         """
         Check a simple run performs the correct system call.  Here
@@ -1298,13 +1298,16 @@ def test_run_full_instrument_basic(self, os_system,
                          + "-d test_data_set  test_instrument.instr"
                          + " has_default=37 theta=1")
 
-        os_system.assert_called_once_with(expected_call)
+        mock_sub.assert_called_once_with(expected_call,
+                                         shell=True,
+                                         stderr=-1, stdout=-1,
+                                         universal_newlines=True)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
-    @unittest.mock.patch("os.system")
-    def test_run_full_instrument_complex(self, os_system,
+    @unittest.mock.patch("subprocess.run")
+    def test_run_full_instrument_complex(self, mock_sub,
                                          mock_f, mock_stdout,):
         """
         Check a complex run performs the correct system call.  Here
@@ -1328,13 +1331,16 @@ def test_run_full_instrument_complex(self, os_system,
                          + "-d test_data_set -fo test_instrument.instr "
                          + "has_default=37 A=2 BC=car theta=\"toy\"")
 
-        os_system.assert_called_once_with(expected_call)
+        mock_sub.assert_called_once_with(expected_call,
+                                         shell=True,
+                                         stderr=-1, stdout=-1,
+                                         universal_newlines=True)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
-    @unittest.mock.patch("os.system")
-    def test_run_full_instrument_overwrite_default(self, os_system,
+    @unittest.mock.patch("subprocess.run")
+    def test_run_full_instrument_overwrite_default(self, mock_sub,
                                                    mock_f, mock_stdout,):
         """
         Check that default parameters are overwritten by given
@@ -1358,7 +1364,10 @@ def test_run_full_instrument_overwrite_default(self, os_system,
                          + "-d test_data_set -fo test_instrument.instr "
                          + "has_default=10 A=2 BC=car theta=\"toy\"")
 
-        os_system.assert_called_once_with(expected_call)
+        mock_sub.assert_called_once_with(expected_call,
+                                         shell=True,
+                                         stderr=-1, stdout=-1,
+                                         universal_newlines=True)
 
 
 if __name__ == '__main__':
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index 129c944f..ebad42e8 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -95,8 +95,8 @@ def test_ManagedMcrun_init_no_folder_error(self):
         with self.assertRaises(NameError):
             mcrun_obj = ManagedMcrun("test.instr", mcrun_path="")
 
-    @unittest.mock.patch("os.system")
-    def test_ManagedMcrun_run_simulation_basic(self, os_system):
+    @unittest.mock.patch("subprocess.run")
+    def test_ManagedMcrun_run_simulation_basic(self, mock_sub):
         """
         Check a basic system call is correct
         """
@@ -111,10 +111,13 @@ def test_ManagedMcrun_run_simulation_basic(self, os_system):
         expected_call = ("path/mcrun -c -n 1000000 --mpi=1 "
                          + "-d test_folder  test.instr")
 
-        os_system.assert_called_once_with(expected_call)
+        mock_sub.assert_called_once_with(expected_call,
+                                         shell=True,
+                                         stderr=-1, stdout=-1,
+                                         universal_newlines=True)
 
-    @unittest.mock.patch("os.system")
-    def test_ManagedMcrun_run_simulation_basic_path(self, os_system):
+    @unittest.mock.patch("subprocess.run")
+    def test_ManagedMcrun_run_simulation_basic_path(self, mock_sub):
         """
         Check a basic system call is correct, with different path format
         """
@@ -129,10 +132,13 @@ def test_ManagedMcrun_run_simulation_basic_path(self, os_system):
         expected_call = ("path/mcrun -c -n 1000000 --mpi=1 "
                          + "-d test_folder  test.instr")
 
-        os_system.assert_called_once_with(expected_call)
+        mock_sub.assert_called_once_with(expected_call,
+                                         shell=True,
+                                         stderr=-1, stdout=-1,
+                                         universal_newlines=True)
 
-    @unittest.mock.patch("os.system")
-    def test_ManagedMcrun_run_simulation_no_standard(self, os_system):
+    @unittest.mock.patch("subprocess.run")
+    def test_ManagedMcrun_run_simulation_no_standard(self, mock_sub):
         """
         Check a non standard system call is correct
         """
@@ -150,10 +156,13 @@ def test_ManagedMcrun_run_simulation_no_standard(self, os_system):
         expected_call = ("path/mcrun -c -n 48 --mpi=7 "
                          + "-d test_folder -fo test.instr")
 
-        os_system.assert_called_once_with(expected_call)
+        mock_sub.assert_called_once_with(expected_call,
+                                         shell=True,
+                                         stderr=-1, stdout=-1,
+                                         universal_newlines=True)
 
-    @unittest.mock.patch("os.system")
-    def test_ManagedMcrun_run_simulation_parameters(self, os_system):
+    @unittest.mock.patch("subprocess.run")
+    def test_ManagedMcrun_run_simulation_parameters(self, mock_sub):
         """
         Check a run with parameters is correct
         """
@@ -175,8 +184,13 @@ def test_ManagedMcrun_run_simulation_parameters(self, os_system):
                          + "-d test_folder -fo test.instr "
                          + "A=2 BC=car th=\"toy\"")
 
-    @unittest.mock.patch("os.system")
-    def test_ManagedMcrun_run_simulation_compile(self, os_system):
+        mock_sub.assert_called_once_with(expected_call,
+                                         shell=True,
+                                         stderr=-1, stdout=-1,
+                                         universal_newlines=True)
+
+    @unittest.mock.patch("subprocess.run")
+    def test_ManagedMcrun_run_simulation_compile(self, mock_sub):
         """
         Check a run with parameters is correct
         """
@@ -199,7 +213,10 @@ def test_ManagedMcrun_run_simulation_compile(self, os_system):
                          + "-d test_folder -fo test.instr "
                          + "A=2 BC=car th=\"toy\"")
 
-        os_system.assert_called_once_with(expected_call)
+        mock_sub.assert_called_once_with(expected_call,
+                                         shell=True,
+                                         stderr=-1, stdout=-1,
+                                         universal_newlines=True)
 
     def test_ManagedMcrun_load_data_PSD4PI(self):
         """
diff --git a/mcstasscript/tests/test_instrument.instr b/mcstasscript/tests/test_instrument.instr
new file mode 100644
index 00000000..2da161ee
--- /dev/null
+++ b/mcstasscript/tests/test_instrument.instr
@@ -0,0 +1,44 @@
+/********************************************************************************
+* 
+* McStas, neutron ray-tracing package
+*         Copyright (C) 1997-2008, All rights reserved
+*         Risoe National Laboratory, Roskilde, Denmark
+*         Institut Laue Langevin, Grenoble, France
+* 
+* This file was written by McStasScript, which is a 
+* python based McStas instrument generator written by 
+* Mads Bertelsen in 2019 while employed at the 
+* European Spallation Source Data Management and 
+* Software Center
+* 
+* Instrument test_instrument
+* 
+* %Identification
+* Written by: Python McStas Instrument Generator
+* Date: 15:36:48 on May 27, 2019
+* Origin: ESS DMSC
+* %INSTRUMENT_SITE: Generated_instruments
+* 
+* 
+* %Parameters
+* 
+* %End 
+********************************************************************************/
+
+DEFINE INSTRUMENT test_instrument (
+double theta 
+)
+
+DECLARE 
+%{
+double two_theta;
+%}
+
+INITIALIZE 
+%{
+// Start of initialize for generated test_instrument
+two_theta = 2.0*theta;
+%}
+
+TRACE 
+COMPONENT first_component = test_for_reading(
\ No newline at end of file

From 73b883bdf8f8ef69c61a6e5da2106e8344f81d48 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Fri, 14 Jun 2019 15:00:36 +0200
Subject: [PATCH 036/403] Added show_instrument method to instr that calls
 mcdisplay.

Can show the instrument in weg-gl (standard) which opens a new tab
in a browser. This is a 3D view that can be rotated.

Using format="window" instead opens a python window with 3 2D
views that can be zoomed.

This is documented in the pdf manual.
---
 McStasScript_documentation.pdf  | Bin 159586 -> 135588 bytes
 mcstasscript/interface/instr.py | 136 +++++++++++++++-----------------
 2 files changed, 65 insertions(+), 71 deletions(-)

diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
index 92d9d8a865db1c2b737ec0ad7e47ad516d81982d..21c7457bb49f43bffe32ebf47ea20b2bfcef23b7 100644
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diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index c193db77..9e0d1411 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1165,6 +1165,50 @@ def write_full_instrument(self):
         fo.write("\nEND\n")
 
         fo.close()
+        
+    def _handle_parameters(self, given_parameters):
+        """
+        Internal helper function that handles which parameters to pass
+        when givne a certain set of parameters and values.
+        
+        Parameters
+        ----------
+        given_parameters: dict
+            Parameters given by the user for simulation run
+        
+        """
+        
+        # Find required parameters
+        required_parameters = []
+        default_parameters = {}
+
+        for index in range(0, len(self.parameter_list)):
+            if self.parameter_list[index].value == "":
+                required_parameters.append(self.parameter_list[index].name)
+            else:
+                default_parameters.update({self.parameter_list[index].name:
+                                           self.parameter_list[index].value})
+
+        # Check if parameters are given
+        if len(given_parameters) is 0:
+            if len(required_parameters) > 0:
+                # print required parameters and raise error
+                print("Required instrument parameters:")
+                for name in required_parameters:
+                    print("  " + name)
+                raise NameError("Required parameters not provided.")
+            else:
+                # If all parameters have defaults, just run with the defaults.
+                return default_parameters
+        else:
+            for name in required_parameters:
+                if name not in given_parameters:
+                    raise NameError("The required instrument parameter "
+                                    + name
+                                    + " was not provided.")
+            # Overwrite default parameters with given parameters
+            default_parameters.update(given_parameters)
+            return default_parameters
 
     def run_full_instrument(self, *args, **kwargs):
         """
@@ -1199,40 +1243,13 @@ def run_full_instrument(self, *args, **kwargs):
         # Make sure mcrun path is in kwargs
         if "mcrun_path" not in kwargs:
             kwargs["mcrun_path"] = self.mcrun_path
-
-        # Find required parameters
-        required_parameters = []
-        default_parameters = {}
-        passed_parameters = {}
-
-        for index in range(0, len(self.parameter_list)):
-            if self.parameter_list[index].value == "":
-                required_parameters.append(self.parameter_list[index].name)
-            else:
-                default_parameters.update({self.parameter_list[index].name:
-                                           self.parameter_list[index].value})
-
-        # Check if parameters are given
-        if "parameters" not in kwargs:
-            if len(required_parameters) > 0:
-                # print required parameters and raise error
-                print("Required instrument parameters:")
-                for name in required_parameters:
-                    print("  " + name)
-                raise NameError("Required parameters not provided.")
-            else:
-                # If all parameters have defaults, just run with the defaults.
-                kwargs["parameters"] = default_parameters
-        else:
+                    
+        if "parameters" in kwargs:
             given_parameters = kwargs["parameters"]
-            for name in required_parameters:
-                if name not in given_parameters:
-                    raise NameError("The required instrument parameter "
-                                    + name
-                                    + " was not provided.")
-            # Overwrite default parameters with given parameters
-            default_parameters.update(given_parameters)
-            kwargs["parameters"] = default_parameters
+        else:
+            given_parameters = {}
+
+        kwargs["parameters"] = self._handle_parameters(given_parameters)
 
         # Write the instrument file
         self.write_full_instrument()
@@ -1249,41 +1266,13 @@ def show_instrument(self, *args, **kwargs):
         Uses mcdisplay to show the instrument in webbroser
         """
         
-        # Find required parameters
-        required_parameters = []
-        default_parameters = {}
-        passed_parameters = {}
-
-        for index in range(0, len(self.parameter_list)):
-            if self.parameter_list[index].value == "":
-                required_parameters.append(self.parameter_list[index].name)
-            else:
-                default_parameters.update({self.parameter_list[index].name:
-                                           self.parameter_list[index].value})
-
-        # Check if parameters are given
-        if "parameters" not in kwargs:
-            if len(required_parameters) > 0:
-                # print required parameters and raise error
-                print("Required instrument parameters:")
-                for name in required_parameters:
-                    print("  " + name)
-                raise NameError("Required parameters not provided.")
-            else:
-                # If all parameters have defaults, just run with the defaults.
-                kwargs["parameters"] = default_parameters
-        else:
+        if "parameters" in kwargs:
             given_parameters = kwargs["parameters"]
-            for name in required_parameters:
-                if name not in given_parameters:
-                    raise NameError("The required instrument parameter "
-                                    + name
-                                    + " was not provided.")
-            # Overwrite default parameters with given parameters
-            default_parameters.update(given_parameters)
-            kwargs["parameters"] = default_parameters                
+        else:
+            given_parameters = {}
+
+        parameters = self._handle_parameters(given_parameters)
 
-        parameters = kwargs["parameters"]
         # add parameters to command
         parameter_string = ""
         for key, val in parameters.items():
@@ -1293,16 +1282,21 @@ def show_instrument(self, *args, **kwargs):
                                 + str(val))  # parameter value
 
         bin_path = self.mcstas_path + "/bin/"
-        #full_command = (bin_path + "mcdisplay-webgl "
-        full_command = (bin_path + "mcdisplay " 
+        executable = "mcdisplay-webgl"
+        if "format" in kwargs:
+            if kwargs["format"] is "webgl":
+                executable = "mcdisplay-webgl"
+            elif kwargs["format"] is "window":
+                executable = "mcdisplay"
+
+        full_command = (bin_path + executable + " "
                         + self.name + ".instr"
                         + " " + parameter_string) 
-        print(full_command)
-        #os.system(full_command)
+
         process = subprocess.run(full_command, shell=True,
                                  stdout=subprocess.PIPE,
                                  stderr=subprocess.PIPE,
                                  universal_newlines=True)
         print(process.stderr)
         print(process.stdout)
-        
+

From b1b0d2fac5fbedbc58c94004d26cecc68f73a38b Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Mon, 17 Jun 2019 14:38:34 +0200
Subject: [PATCH 037/403] Updated an example to fit github line width.

Allowed name_search to find data sets using the filename if it does
not find a component name that matches.
---
 examples/McStasScript_demo.ipynb     | 171 ++++++++++++++++++++-------
 mcstasscript/interface/functions.py  |   7 ++
 mcstasscript/tests/test_functions.py |  13 ++
 3 files changed, 149 insertions(+), 42 deletions(-)

diff --git a/examples/McStasScript_demo.ipynb b/examples/McStasScript_demo.ipynb
index 5b9ee82c..305ad40c 100644
--- a/examples/McStasScript_demo.ipynb
+++ b/examples/McStasScript_demo.ipynb
@@ -15,14 +15,13 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "sys.path.append('/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript') # Path to McStasScript pythoon file\n",
+    "# Path to McStasScript pythoon file\n",
+    "sys.path.append('/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript')\n",
     "\n",
     "from mcstasscript.interface import instr, plotter, functions\n",
     "\n",
     "# Creating the instance of the class, insert path to mcrun and to mcstas root directory\n",
-    "Instr = instr.McStas_instr(\"jupyter_demo\",\n",
-    "                           mcrun_path = \"/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin\",\n",
-    "                           mcstas_path = \"/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5\")"
+    "Instr = instr.McStas_instr(\"jupyter_demo\")"
    ]
   },
   {
@@ -81,7 +80,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " ___ Help Source_simple _________________________________________________\n",
+      " ___ Help Source_simple _____________________________________________________________________\n",
       "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
       "\u001b[1mradius\u001b[0m = \u001b[1m\u001b[94m0.1\u001b[0m\u001b[0m [m] // Radius of circle in (x,y,0) plane where neutrons are generated.\n",
       "\u001b[1myheight\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Height of rectangle in (x,y,0) plane where neutrons are generated.\n",
@@ -93,10 +92,12 @@
       "\u001b[1mdE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Energy half spread of neutrons (flat or gaussian sigma).\n",
       "\u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [AA] // Mean wavelength of neutrons.\n",
       "\u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [AA] // Wavelength half spread of neutrons.\n",
-      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1/(s*cm**2*st*energy unit)] // flux per energy unit, Angs or meV if flux=0, the source emits 1 in 4*PI whole space.\n",
+      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1/(s*cm**2*st*energy unit)] // flux per energy unit, Angs or meV if flux=0, \n",
+      "                                           the source emits 1 in 4*PI whole space. \n",
       "\u001b[1mgauss\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Gaussian (1) or Flat (0) energy/wavelength distribution\n",
-      "\u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1] // relative index of component to focus at, e.g. next is +1 this is used to compute 'dist' automatically.\n",
-      "-------------------------------------------------------------------------\n"
+      "\u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1] // relative index of component to focus at, e.g. next is +1 this \n",
+      "                        is used to compute 'dist' automatically. \n",
+      "---------------------------------------------------------------------------------------------\n"
      ]
     }
    ],
@@ -120,10 +121,11 @@
    "outputs": [],
    "source": [
     "# Lets add a parameter to the instrument to control the wavelength of the source\n",
-    "Instr.add_parameter(\"double\", \"wavelength\", value=3, comment=\"Wavelength emmited from source\")\n",
+    "Instr.add_parameter(\"double\", \"wavelength\", value=3,\n",
+    "                    comment=\"[AA] Wavelength emmited from source\")\n",
     "source.xwidth = 0.06; source.yheight = 0.08;\n",
     "source.dist = 2; source.focus_xw = 0.05; source.focus_yh = 0.05\n",
-    "source.lambda0 = \"wavelength\"; source.dlambda = 0.1"
+    "source.lambda0 = \"wavelength\"; source.dlambda = 0.05; source.flux = 1E8"
    ]
   },
   {
@@ -142,7 +144,8 @@
       "  \u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
       "  \u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
       "  \u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[92mwavelength\u001b[0m\u001b[0m [AA]\n",
-      "  \u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [AA]\n",
+      "  \u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [AA]\n",
+      "  \u001b[1mflux\u001b[0m = \u001b[1m\u001b[92m100000000.0\u001b[0m\u001b[0m [1/(s*cm**2*st*energy unit)]\n",
       "AT [0, 0, 0] ABSOLUTE\n",
       "ROTATED [0, 0, 0] ABSOLUTE\n"
      ]
@@ -171,7 +174,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " ___ Help Guide_gravity _________________________________________________\n",
+      " ___ Help Guide_gravity _____________________________________________________________________\n",
       "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
       "\u001b[4m\u001b[1mw1\u001b[0m\u001b[0m [m] // Width at the guide entry\n",
       "\u001b[4m\u001b[1mh1\u001b[0m\u001b[0m [m] // Height at the guide entry\n",
@@ -181,15 +184,21 @@
       "\u001b[1mR0\u001b[0m = \u001b[1m\u001b[94m0.995\u001b[0m\u001b[0m [1] // Low-angle reflectivity\n",
       "\u001b[1mQc\u001b[0m = \u001b[1m\u001b[94m0.0218\u001b[0m\u001b[0m [AA-1] // Critical scattering vector\n",
       "\u001b[1malpha\u001b[0m = \u001b[1m\u001b[94m4.38\u001b[0m\u001b[0m [AA] // Slope of reflectivity\n",
-      "\u001b[1mm\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // m-value of material. Zero means completely absorbing. m=0.65  glass/SiO2 Si   Ni  Ni58  supermirror Be    Diamond m=  0.65       0.47 1   1.18  2-6         1.01  1.12 for glass/SiO2, m=1 for Ni, 1.2 for Ni58, m=2-6 for supermirror. m=0.47 for Si\n",
+      "\u001b[1mm\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // m-value of material. Zero means completely absorbing. m=0.65  glass/SiO2 \n",
+      "               Si   Ni  Ni58  supermirror Be    Diamond m=  0.65       0.47 1   1.18  2-6   \n",
+      "                     1.01  1.12 for glass/SiO2, m=1 for Ni, 1.2 for Ni58, m=2-6 for \n",
+      "               supermirror. m=0.47 for Si \n",
       "\u001b[1mW\u001b[0m = \u001b[1m\u001b[94m0.003\u001b[0m\u001b[0m [AA-1] // Width of supermirror cut-off\n",
-      "\u001b[1mnslit\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // Number of vertical channels in the guide (>= 1) (nslit-1 vertical dividing walls).\n",
+      "\u001b[1mnslit\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // Number of vertical channels in the guide (>= 1) (nslit-1 vertical \n",
+      "                   dividing walls). \n",
       "\u001b[1md\u001b[0m = \u001b[1m\u001b[94m0.0005\u001b[0m\u001b[0m [m] // Thickness of subdividing walls\n",
       "\u001b[1mmleft\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // m-value of material for left.   vert. mirror\n",
       "\u001b[1mmright\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // m-value of material for right.  vert. mirror\n",
       "\u001b[1mmtop\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // m-value of material for top.    horz. mirror\n",
       "\u001b[1mmbottom\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // m-value of material for bottom. horz. mirror\n",
-      "\u001b[1mnhslit\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // Number of horizontal channels in the guide (>= 1). (nhslit-1 horizontal dividing walls). this enables to have nslit*nhslit rectangular channels\n",
+      "\u001b[1mnhslit\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // Number of horizontal channels in the guide (>= 1). (nhslit-1 \n",
+      "                    horizontal dividing walls). this enables to have nslit*nhslit \n",
+      "                    rectangular channels \n",
       "\u001b[1mG\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m/s2] // Gravitation norm. 0 value disables G effects.\n",
       "\u001b[1maleft\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // alpha-value of left  vert. mirror\n",
       "\u001b[1maright\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // alpha-value of right vert. mirror\n",
@@ -207,7 +216,7 @@
       "\u001b[1mnu\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Hz] // Rotation frequency (round/s) for Fermi Chopper approximation\n",
       "\u001b[1mphase\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // Phase shift for the Fermi Chopper approximation\n",
       "\u001b[1mreflect\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [str] // Reflectivity file name. Format <q(Angs-1) R(0-1)>\n",
-      "-------------------------------------------------------------------------\n"
+      "---------------------------------------------------------------------------------------------\n"
      ]
     }
    ],
@@ -221,7 +230,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "guide.set_parameters({\"w1\" : 0.05, \"w2\" : 0.05, \"h1\" : 0.05, \"h2\" : 0.05, \"l\" : 8, \"m\" : 3.5, \"G\" : -9.2})"
+    "guide.set_parameters({\"w1\" : 0.05, \"w2\" : 0.05, \"h1\" : 0.05, \"h2\" : 0.05,\n",
+    "                      \"l\" : 8, \"m\" : 3.5, \"G\" : -9.2})"
    ]
   },
   {
@@ -256,7 +266,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sample = Instr.add_component(\"sample\", \"PowderN\", AT=[0, 0, 9], RELATIVE=\"Guide\") # Add a sample"
+    "# Add a sample to the instrument\n",
+    "sample = Instr.add_component(\"sample\", \"PowderN\", AT=[0, 0, 9], RELATIVE=\"Guide\") "
    ]
   },
   {
@@ -265,7 +276,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sample.radius = 0.015; sample.yheight = 0.05; sample.reflections = \"\\\"Cu.laz\\\"\" # Copper cylinder"
+    "# Set parameters corresponding to a copper cylinder\n",
+    "sample.radius = 0.015; sample.yheight = 0.05; sample.reflections = \"\\\"Cu.laz\\\"\""
    ]
   },
   {
@@ -300,7 +312,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sphere = Instr.add_component(\"PSD_4PI\", \"PSD_monitor_4PI\", RELATIVE=\"sample\") # Add 4PI detector"
+    "# Add 4PI detector to detect all neutrons\n",
+    "sphere = Instr.add_component(\"PSD_4PI\", \"PSD_monitor_4PI\", RELATIVE=\"sample\")"
    ]
   },
   {
@@ -336,7 +349,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "PSD = Instr.add_component(\"PSD\", \"PSD_monitor\", AT=[0,0,1], RELATIVE=\"sample\") # Add PSD monitor\n",
+    "# Add PSD monitor to see the direct beam after the sample\n",
+    "PSD = Instr.add_component(\"PSD\", \"PSD_monitor\", AT=[0,0,1], RELATIVE=\"sample\") \n",
     "PSD.xwidth = 0.1; PSD.yheight = 0.1; PSD.nx = 200; PSD.ny = 200\n",
     "PSD.filename = \"\\\"PSD.dat\\\"\"; PSD.restore_neutron = 1"
    ]
@@ -356,9 +370,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "L_mon.Lmin = \"wavelength - 0.3\"; L_mon.Lmax = \"wavelength + 0.3\"; L_mon.nL = 150\n",
+    "# Since the wavelength is an instrument parameter, it can be used when setting parameters\n",
+    "L_mon.Lmin = \"wavelength - 0.1\"; L_mon.Lmax = \"wavelength + 0.1\"; L_mon.nL = 150\n",
     "L_mon.xwidth = 0.1; L_mon.yheight = 0.1\n",
-    "L_mon.filename = \"\\\"L_mon.dat\\\"\"; L_mon.restore_neutron = 1\n",
+    "L_mon.filename = \"\\\"wave.dat\\\"\"; L_mon.restore_neutron = 1\n",
     "L_mon.comment = \"Wavelength monitor for narrow range\""
    ]
   },
@@ -374,11 +389,11 @@
       "// Wavelength monitor for narrow range\n",
       "COMPONENT L_mon = L_monitor\n",
       "  \u001b[1mnL\u001b[0m = \u001b[1m\u001b[92m150\u001b[0m\u001b[0m [1]\n",
-      "  \u001b[1mfilename\u001b[0m = \u001b[1m\u001b[92m\"L_mon.dat\"\u001b[0m\u001b[0m [string]\n",
+      "  \u001b[1mfilename\u001b[0m = \u001b[1m\u001b[92m\"wave.dat\"\u001b[0m\u001b[0m [string]\n",
       "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
       "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mLmin\u001b[0m = \u001b[1m\u001b[92mwavelength - 0.3\u001b[0m\u001b[0m [AA]\n",
-      "  \u001b[1mLmax\u001b[0m = \u001b[1m\u001b[92mwavelength + 0.3\u001b[0m\u001b[0m [AA]\n",
+      "  \u001b[1mLmin\u001b[0m = \u001b[1m\u001b[92mwavelength - 0.1\u001b[0m\u001b[0m [AA]\n",
+      "  \u001b[1mLmax\u001b[0m = \u001b[1m\u001b[92mwavelength + 0.1\u001b[0m\u001b[0m [AA]\n",
       "  \u001b[1mrestore_neutron\u001b[0m = \u001b[1m\u001b[92m1\u001b[0m\u001b[0m [1]\n",
       "AT [0, 0, 0] RELATIVE PSD\n",
       "ROTATED [0, 0, 0] RELATIVE PSD\n"
@@ -398,12 +413,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Source    Source_simple     AT  [0, 0, 0]     ABSOLUTE          ROTATED  [0, 0, 0]     ABSOLUTE\n",
-      "Guide     Guide_gravity     AT  [0, 0, 2]     RELATIVE Source   ROTATED  [0, 0, 0]     RELATIVE Source\n",
-      "sample    PowderN           AT  [0, 0, 9]     RELATIVE Guide    ROTATED  [0, 0, 0]     RELATIVE Guide\n",
-      "PSD_4PI   PSD_monitor_4PI   AT  [0, 0, 0]     RELATIVE sample   ROTATED  [0, 0, 0]     RELATIVE sample\n",
-      "PSD       PSD_monitor       AT  [0, 0, 1]     RELATIVE sample   ROTATED  [0, 0, 0]     RELATIVE sample\n",
-      "L_mon     L_monitor         AT  [0, 0, 0]     RELATIVE PSD      ROTATED  [0, 0, 0]     RELATIVE PSD\n"
+      "Source  Source_simple   AT (0, 0, 0) ABSOLUTE        ROTATED (0, 0, 0) ABSOLUTE\n",
+      "Guide   Guide_gravity   AT (0, 0, 2) RELATIVE Source ROTATED (0, 0, 0) RELATIVE Source\n",
+      "sample  PowderN         AT (0, 0, 9) RELATIVE Guide  ROTATED (0, 0, 0) RELATIVE Guide\n",
+      "PSD_4PI PSD_monitor_4PI AT (0, 0, 0) RELATIVE sample ROTATED (0, 0, 0) RELATIVE sample\n",
+      "PSD     PSD_monitor     AT (0, 0, 1) RELATIVE sample ROTATED (0, 0, 0) RELATIVE sample\n",
+      "L_mon   L_monitor       AT (0, 0, 0) RELATIVE PSD    ROTATED (0, 0, 0) RELATIVE PSD\n"
      ]
     }
    ],
@@ -438,15 +453,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"jupyter_demo_12\"\n",
+      "INFO: Regenerating c-file: jupyter_demo.c\n",
+      "CFLAGS=\n",
+      "INFO: Recompiling: ./jupyter_demo.out\n",
+      "INFO: ===\n",
+      "Warning: 509245 events were removed in Component[5] PSD=PSD_monitor()\n",
+      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
+      "Warning: 510508 events were removed in Component[5] PSD=PSD_monitor()\n",
+      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
+      "Warning: 509629 events were removed in Component[5] PSD=PSD_monitor()\n",
+      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
+      "Warning: 510551 events were removed in Component[5] PSD=PSD_monitor()\n",
+      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
+      "INFO: Placing instr file copy jupyter_demo.instr in dataset jupyter_demo_12\n",
+      "\n",
+      "Simulation 'jupyter_demo' (jupyter_demo.instr): running on 4 nodes (master is 'CI0020872', MPI version 2.1).\n",
+      "Opening input file '/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../data/Cu.laz' (Table_Read_Offset)\n",
+      "Table from file 'Cu.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
+      "  '# TITLE *-Cu-[FM3-M] Otte, H.M.[1961];# CELL 3.615050 3.615050 3.615050 90. ...'\n",
+      "PowderN: sample: Reading 19 rows from Cu.laz\n",
+      "PowderN: sample: Read 19 reflections from file 'Cu.laz'\n",
+      "PowderN: sample: Vc=47.24 [Angs] sigma_abs=15.12 [barn] sigma_inc=2.2 [barn] reflections=Cu.laz\n",
+      "Detector: PSD_4PI_I=42485.8 PSD_4PI_ERR=25.7979 PSD_4PI_N=8.59994e+06 \"PSD_4PI.dat\"\n",
+      "Detector: PSD_I=35503.4 PSD_ERR=24.9716 PSD_N=4.49414e+06 \"PSD.dat\"\n",
+      "Detector: L_mon_I=35503.4 L_mon_ERR=24.9716 L_mon_N=4.49414e+06 \"wave.dat\"\n",
+      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
+      "    SPLIT 8 COMPONENT sample=PowderN(...)\n",
+      "  in the instrument description jupyter_demo.instr.\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "# If the folder already exsits, a new simulation is not performed but the old one is read\n",
+    "# With increment_folder_name enabled, a new folder with incremented number is created\n",
     "data = Instr.run_full_instrument(foldername=\"jupyter_demo\",\n",
-    "                                 parameters={\"wavelength\" : 3.0},\n",
-    "                                 mpi=4,\n",
-    "                                 ncount=2E7,\n",
+    "                                 parameters={\"wavelength\" : 1.5},\n",
+    "                                 mpi=4, ncount=2E7,\n",
     "                                 increment_folder_name = True)"
    ]
   },
@@ -454,13 +504,48 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## Working with the returned data\n",
     "The returned data object is a list of McStasData objects, each containing the results from a monitor.\n",
     "These data objects also contain preferences for how they should be plotted if this is done automatically."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.494, 470.3132617]\n",
+      "[1.495333333, 472.9846513]\n",
+      "[1.496666667, 481.5639576]\n",
+      "[1.498, 474.1251374]\n",
+      "[1.499333333, 475.2808707]\n"
+     ]
+    }
+   ],
+   "source": [
+    "wavelength_data = functions.name_search(\"L_mon\", data)\n",
+    "wavelength_intensity = wavelength_data.Intensity\n",
+    "wavelength_xaxis = wavelength_data.xaxis\n",
+    "\n",
+    "for index in range(70,75):\n",
+    "    print([wavelength_xaxis[index], wavelength_intensity[index]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting the returned data\n",
+    "The plot options looks at some metadata in the McStasData for plotting preferences. For this reason these options can be adjusted for individual data files instead of complex syntax for the plotting command."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -475,7 +560,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 936x504 with 6 Axes>"
       ]
@@ -487,7 +572,9 @@
     }
    ],
    "source": [
-    "functions.name_plot_options(\"PSD_4PI\", data, log=1, colormap=\"hot\", orders_of_mag=5) # Adjusting PSD_4PI plot\n",
+    "# Adjusting PSD_4PI plot\n",
+    "functions.name_plot_options(\"PSD_4PI\", data, log=1, colormap=\"hot\", orders_of_mag=5)\n",
+    "\n",
     "plot = plotter.make_sub_plot(data) # Making subplot of our monitors"
    ]
   }
@@ -508,7 +595,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/mcstasscript/interface/functions.py b/mcstasscript/interface/functions.py
index bfae923a..6c033ca4 100644
--- a/mcstasscript/interface/functions.py
+++ b/mcstasscript/interface/functions.py
@@ -27,11 +27,18 @@ def name_search(name, data_list):
         raise InputError(
             "name_search function needs objects of type McStasData as input.")
 
+    # Search by component name
     list_result = []
     for check in data_list:
         if check.name == name:
             list_result.append(check)
 
+    if len(list_result) == 0:
+        # Search by filename
+        for check in data_list:
+            if check.metadata.filename == name:
+                list_result.append(check)
+    
     if len(list_result) == 0:
         raise NameError("No dataset with name: \""
                         + name
diff --git a/mcstasscript/tests/test_functions.py b/mcstasscript/tests/test_functions.py
index 52545b9e..0feee07a 100644
--- a/mcstasscript/tests/test_functions.py
+++ b/mcstasscript/tests/test_functions.py
@@ -16,6 +16,7 @@ def set_dummy_MetaData_1d(name):
     meta_data = McStasMetaData()
     meta_data.component_name = name
     meta_data.dimension = 50
+    meta_data.filename = name + ".dat"
 
     return meta_data
 
@@ -35,6 +36,7 @@ def set_dummy_MetaData_2d(name):
     meta_data = McStasMetaData()
     meta_data.component_name = name
     meta_data.dimension = [50, 100]
+    meta_data.filename = name + ".dat"
 
     return meta_data
 
@@ -110,6 +112,17 @@ def test_name_search_read(self):
         hero_object = name_search("Hero", data_list)
 
         self.assertEqual(hero_object.metadata.dimension, 123)
+        
+    def test_name_search_filename_read(self):
+        """
+        Test simple case
+        """
+
+        data_list = setup_McStasData_array()
+
+        hero_object = name_search("Hero.dat", data_list)
+
+        self.assertEqual(hero_object.metadata.dimension, 123)
 
     def test_name_search_read_repeat(self):
         """

From 82a377b64765abe858db4aba81cdfe385ef82700 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Mon, 17 Jun 2019 14:59:51 +0200
Subject: [PATCH 038/403] Added support for the SPLIT keyword.

The component class has SPLIT as an allowed keyword under
initialization, and a method called set_SPLIT for setting the
value.

The split value has to be an integer.
---
 mcstasscript/helper/mcstas_objects.py         | 17 ++++++++++
 .../test_complex_instrument.py                |  2 ++
 mcstasscript/tests/test_component.py          | 34 +++++++++++--------
 3 files changed, 39 insertions(+), 14 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 29c48b0d..94e69e2b 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -341,6 +341,9 @@ def __init__(self, instance_name, component_name, **kwargs):
 
             JUMP : str
                 Sets JUMP str
+                
+            SPLIT : int
+                Sets SPLIT value
 
             comment: str
                 Sets comment string
@@ -396,6 +399,11 @@ def __init__(self, instance_name, component_name, **kwargs):
             self.JUMP = kwargs["JUMP"]
         else:
             self.JUMP = ""
+            
+        if "SPLIT" in kwargs:
+            self.SPLIT = kwargs["SPLIT"]
+        else:
+            self.SPLIT = 0
 
         if "comment" in kwargs:
             self.comment = kwargs["comment"]
@@ -489,6 +497,10 @@ def set_GROUP(self, string):
     def set_JUMP(self, string):
         """Sets JUMP string, should contain all text after JUMP"""
         self.JUMP = string
+        
+    def set_SPLIT(self, value):
+        """Sets SPLIT value, should contain all text after JUMP"""
+        self.SPLIT = value
 
     def append_EXTEND(self, string):
         """Appends a line of code to EXTEND block of component"""
@@ -514,6 +526,9 @@ def write_component(self, fo):
         if len(self.comment) > 1:
             fo.write("// %s\n" % (str(self.comment)))
 
+        if self.SPLIT is not 0:
+            fo.write("SPLIT " + str(self.SPLIT) + " ") 
+
         # Write component name and component type
         fo.write("COMPONENT %s = %s(" % (self.name, self.component_name))
 
@@ -593,6 +608,8 @@ class is used as a superclass for classes describing each
         """
         if len(self.comment) > 1:
             print("// " + self.comment)
+        if self.SPLIT is not 0:
+            print("SPLIT " + str(self.SPLIT) + " ", end="")
         print("COMPONENT", str(self.name),
               "=", str(self.component_name))
         for key in self.parameter_names:
diff --git a/mcstasscript/integration_tests/test_complex_instrument.py b/mcstasscript/integration_tests/test_complex_instrument.py
index b6b8396c..a39bc773 100644
--- a/mcstasscript/integration_tests/test_complex_instrument.py
+++ b/mcstasscript/integration_tests/test_complex_instrument.py
@@ -90,6 +90,8 @@ def setup_complex_instrument():
     guide2.l = "guide_length"
     guide2.m = 4
     guide2.G = -9.82
+    
+    guide2.set_SPLIT = 2
 
     done = Instr.add_component("done", "Arm", RELATIVE="after_guide")
 
diff --git a/mcstasscript/tests/test_component.py b/mcstasscript/tests/test_component.py
index ce8cccf4..462fe2a2 100644
--- a/mcstasscript/tests/test_component.py
+++ b/mcstasscript/tests/test_component.py
@@ -22,6 +22,7 @@ def setup_component_all_keywords():
                      EXTEND="nscat = 8;",
                      GROUP="developers",
                      JUMP="myself 37",
+                     SPLIT=7,
                      comment="test comment")
 
 
@@ -38,6 +39,7 @@ def setup_component_relative():
                      EXTEND="nscat = 8;",
                      GROUP="developers",
                      JUMP="myself 37",
+                     SPLIT=7,
                      comment="test comment")
 
 
@@ -123,7 +125,6 @@ def test_component_basic_init_defaults(self):
     def test_component_init_complex_call(self):
         """
         Testing keywords set attributes correctly
-
         """
 
         comp = setup_component_all_keywords()
@@ -138,13 +139,13 @@ def test_component_init_complex_call(self):
         self.assertEqual(comp.EXTEND, "nscat = 8;\n")
         self.assertEqual(comp.GROUP, "developers")
         self.assertEqual(comp.JUMP, "myself 37")
+        self.assertEqual(comp.SPLIT, 7)
         self.assertEqual(comp.comment, "test comment")
 
     def test_component_init_complex_call_relative(self):
         """
         Tests the relative keyword overwrites AT_relative and
         ROTATED_relative
-
         """
         comp = setup_component_relative()
 
@@ -158,12 +159,12 @@ def test_component_init_complex_call_relative(self):
         self.assertEqual(comp.EXTEND, "nscat = 8;\n")
         self.assertEqual(comp.GROUP, "developers")
         self.assertEqual(comp.JUMP, "myself 37")
+        self.assertEqual(comp.SPLIT, 7)
         self.assertEqual(comp.comment, "test comment")
 
     def test_component_basic_init_set_AT(self):
         """
         Testing set_AT method
-
         """
 
         comp = component("test_component", "Arm")
@@ -178,7 +179,6 @@ def test_component_basic_init_set_AT(self):
     def test_component_basic_init_set_ROTATED(self):
         """
         Testing set_ROTATED method
-
         """
 
         comp = component("test_component", "Arm")
@@ -193,7 +193,6 @@ def test_component_basic_init_set_ROTATED(self):
     def test_component_basic_init_set_RELATIVE(self):
         """
         Testing set_RELATIVE method
-
         """
 
         comp = component("test_component", "Arm")
@@ -209,7 +208,6 @@ def test_component_basic_init_set_parameters(self):
         """
         Testing set_parameters method. Need to set some attribute
         parameters manually to test this.
-
         """
 
         comp = component("test_component", "Arm")
@@ -237,7 +235,6 @@ def test_component_basic_init_set_parameters(self):
     def test_component_basic_init_set_WHEN(self):
         """
         Testing WHEN method
-
         """
 
         comp = component("test_component", "Arm")
@@ -251,7 +248,6 @@ def test_component_basic_init_set_WHEN(self):
     def test_component_basic_init_set_GROUP(self):
         """
         Testing set_GROUP method
-
         """
 
         comp = component("test_component", "Arm")
@@ -265,7 +261,6 @@ def test_component_basic_init_set_GROUP(self):
     def test_component_basic_init_set_JUMP(self):
         """
         Testing set_JUMP method
-
         """
 
         comp = component("test_component", "Arm")
@@ -275,11 +270,23 @@ def test_component_basic_init_set_JUMP(self):
         self.assertEqual(comp.name, "test_component")
         self.assertEqual(comp.component_name, "Arm")
         self.assertEqual(comp.JUMP, "test jump")
+        
+    def test_component_basic_init_set_SPLIT(self):
+        """
+        Testing set_SPLIT method
+        """
+
+        comp = component("test_component", "Arm")
+
+        comp.set_SPLIT(500)
+
+        self.assertEqual(comp.name, "test_component")
+        self.assertEqual(comp.component_name, "Arm")
+        self.assertEqual(comp.SPLIT, 500)
 
     def test_component_basic_init_set_EXTEND(self):
         """
         Testing set_EXTEND method
-
         """
 
         comp = component("test_component", "Arm")
@@ -297,7 +304,6 @@ def test_component_basic_init_set_EXTEND(self):
     def test_component_basic_init_set_comment(self):
         """
         Testing set_comment method
-
         """
         comp = component("test_component", "Arm")
 
@@ -361,7 +367,6 @@ def test_component_write_to_file_complex(self, mock_f):
         """
         Testing that a component can be written to file with the
         expected output. Here with complex input.
-
         """
 
         comp = setup_component_with_parameters()
@@ -376,7 +381,8 @@ def test_component_write_to_file_complex(self, mock_f):
             comp.write_component(m_fo)
 
         my_call = unittest.mock.call
-        expected_writes = [my_call("COMPONENT test_component = Arm("),
+        expected_writes = [my_call("SPLIT 7 "), 
+                           my_call("COMPONENT test_component = Arm("),
                            my_call("\n"),
                            my_call(" new_par1 = 1.5"),
                            my_call(","),
@@ -438,7 +444,7 @@ def test_component_print_long(self, mock_stdout):
         output = output.split("\n")
 
         self.assertEqual(output[0], "// test comment")
-        self.assertEqual(output[1], "COMPONENT test_component = Arm")
+        self.assertEqual(output[1], "SPLIT 7 COMPONENT test_component = Arm")
 
         par_name = bcolors.BOLD + "new_par1" + bcolors.ENDC
         value = (bcolors.BOLD + bcolors.OKGREEN

From 5a42459831abd04c1c2fee01777f707af54cfe1d Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Mon, 17 Jun 2019 15:19:57 +0200
Subject: [PATCH 039/403] Updated documentation to include SPLIT and
 name_searchs ability to find data based on their filename.

Also added the set_component_SPLIT method to the instrument and an
associated test.
---
 McStasScript_documentation.pdf  | Bin 135588 -> 161681 bytes
 mcstasscript/interface/instr.py |  19 +++++++++++++++++++
 2 files changed, 19 insertions(+)

diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
index 21c7457bb49f43bffe32ebf47ea20b2bfcef23b7..db4439d6c80146643cd3dc495f0438db7a9f2850 100644
GIT binary patch
delta 143829
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diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 9e0d1411..06245751 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -118,6 +118,9 @@ class McStas_instr:
     set_component_JUMP(instance_name,string)
         Sets JUMP code for named component
 
+    set_component_SPLIT(instance_name,string)
+        Sets SPLIT value for named component
+
     set_component_comment(instance_name,string)
         Sets comment to be written before named component
 
@@ -819,6 +822,22 @@ def set_component_JUMP(self, name, JUMP):
 
         component = self.get_component(name)
         component.set_JUMP(JUMP)
+        
+    def set_component_SPLIT(self, name, SPLIT):
+        """
+        Method for setting SPLIT value of named component
+
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify
+
+        SPLIT : int
+            Sets SPLIT value for named McStas component
+        """
+
+        component = self.get_component(name)
+        component.set_SPLIT(SPLIT)
 
     def set_component_comment(self, name, string):
         """

From 48e0e0e304dfa1a8ec82d1e3a4dff5ef92114141 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Mon, 24 Jun 2019 16:30:55 +0200
Subject: [PATCH 040/403] Removed paths from random_demonstration.py example as
 they are provided by the configuration file.

---
 examples/random_demonstration.py | 10 +---------
 1 file changed, 1 insertion(+), 9 deletions(-)

diff --git a/examples/random_demonstration.py b/examples/random_demonstration.py
index 574162af..e9f1a540 100644
--- a/examples/random_demonstration.py
+++ b/examples/random_demonstration.py
@@ -5,19 +5,11 @@
 sys.path.append('/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript')
 from mcstasscript.interface import instr, plotter, functions
 
-# if the mcrun command from McStas is not in your path, provide absolute path for the binary here:
-mcrun_path = ""
-mcstas_path = ""
-# On OS X most likely:
-#mcrun_path = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin"
-#mcstas_path = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/"
 
 # Create a McStas instrument
 Instr = instr.McStas_instr("random_demo",
                            author = "Mads Bertelsen",
-                           origin = "ESS DMSC",
-                           mcrun_path = mcrun_path,
-                           mcstas_path = mcstas_path)
+                           origin = "ESS DMSC")
 
 # Set up a material called Cu with approrpiate properties
 # (uses McStas Union components, here the processes)

From b4bb3e05b53275d6e01bd411eaa9dca0f6b14b13 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Thu, 25 Jul 2019 16:13:11 +0200
Subject: [PATCH 041/403] Initial work on animation plotting routine.

Update for name_search so that it returns a list if there are multiple matches.
This frequently happens with monitors that produce more than one graph.

The configruation file was moved due to preperations for McStasScript becoming
available on pip. Further work will be needed on configuration when installed
using pip.

Unittests updated to reflect above, and new tests added where necessary.
---
 mcstasscript/configuration.yaml            |  10 +
 mcstasscript/integration_tests/__init__.py |   0
 mcstasscript/interface/functions.py        |  14 +-
 mcstasscript/interface/instr.py            |   2 +-
 mcstasscript/interface/plotter.py          | 263 +++++++++++++++++++++
 mcstasscript/tests/test_Instr.py           |  17 +-
 mcstasscript/tests/test_functions.py       |  41 ++++
 7 files changed, 340 insertions(+), 7 deletions(-)
 create mode 100644 mcstasscript/configuration.yaml
 create mode 100644 mcstasscript/integration_tests/__init__.py

diff --git a/mcstasscript/configuration.yaml b/mcstasscript/configuration.yaml
new file mode 100644
index 00000000..41c6ac82
--- /dev/null
+++ b/mcstasscript/configuration.yaml
@@ -0,0 +1,10 @@
+---
+paths:
+    # path to mcrun, example for OS X
+    mcrun_path: "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/"
+    # path to mcstas directory, example for OS X
+    # the mcstas directory should contain the component folders, sources, optics, ...
+    mcstas_path: "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/"
+other:
+    # limit characters per line in terminal output
+    characters_per_line: 93
diff --git a/mcstasscript/integration_tests/__init__.py b/mcstasscript/integration_tests/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/mcstasscript/interface/functions.py b/mcstasscript/interface/functions.py
index 6c033ca4..11bc7769 100644
--- a/mcstasscript/interface/functions.py
+++ b/mcstasscript/interface/functions.py
@@ -5,7 +5,9 @@
 def name_search(name, data_list):
     """"
     name_search returns McStasData instance with specific name if it is
-    in the given data_list
+    in the given data_list. If no match is found, a search for the data
+    filename is performed. If several matches are found, a list of
+    McStasData objects are returned.
 
     The index of certain datasets in the data_list can change if
     additional monitors are added so it is more convinient to access
@@ -47,9 +49,7 @@ def name_search(name, data_list):
     if len(list_result) == 1:
         return list_result[0]
     else:
-        raise NameError("Found " + str(len(list_result)) + " matches in "
-                        + "the search for a dataset with name: \""
-                        + name + "\".")
+        return list_result
 
 
 def name_plot_options(name, data_list, **kwargs):
@@ -73,7 +73,11 @@ def name_plot_options(name, data_list, **kwargs):
         McStasPlotOptions
     """
     object_to_modify = name_search(name, data_list)
-    object_to_modify.set_plot_options(**kwargs)
+    if type(object_to_modify) is not list:                     
+        object_to_modify.set_plot_options(**kwargs)
+    else:
+        for data_object in object_to_modify:
+            data_object.set_plot_options(**kwargs)
 
 
 def load_data(foldername):
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 06245751..bf9ba1c2 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -184,7 +184,7 @@ def __init__(self, name, **kwargs):
             self.origin = "ESS DMSC"
 
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-        configuration_file_name = THIS_DIR + "/../../configuration.yaml"
+        configuration_file_name = THIS_DIR + "/../configuration.yaml"
         if not os.path.isfile(configuration_file_name):
             raise NameError("Could not find configuration file!")
         with open(configuration_file_name, 'r') as ymlfile:
diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index a1e4db3e..048c96c7 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -2,8 +2,10 @@
 import numpy as np
 import matplotlib
 import matplotlib.pyplot as plt
+import matplotlib.animation as animation
 from matplotlib.colors import BoundaryNorm
 from matplotlib.ticker import MaxNLocator
+
 from openpyxl.worksheet import dimensions
 from boto.ec2.autoscale import limits
 
@@ -386,3 +388,264 @@ def fmt(x, pos):
                 print("Error, dimension not read correctly")
 
         plt.show()
+
+
+class make_animation:
+    """
+    make_plot plots contents of McStasData objects
+
+    Plotting is controlled through options assosciated with the
+    McStasData objects.  If a list is given, the plots appear in one
+    subplot.
+    """
+
+    def __init__(self, data_list, **kwargs):
+        """
+        plots McStasData, single object or list of McStasData
+
+        The options concerning plotting are stored with the data
+
+        Parameters
+        ----------
+        data_list : McStasData or list of McStasData
+            McStasData to be plotted
+        """
+        if not isinstance(data_list, McStasData):
+            print("number of elements in data list = "
+                  + str(len(data_list)))
+        else:
+            # Make list from single element to simplify syntax
+            data_list = [data_list]
+
+        # Relevant options:
+        #  select colormap
+        #  show / hide colorbar
+        #  custom title / label
+        #  color of 1d plot
+        #  overlay several 1d
+        #  log scale (o$rders of magnitude)
+        #  compare several 1d
+        #  compare 2D
+
+        if "fontsize" in kwargs:
+            plt.rcParams.update({'font.size': kwargs["fontsize"]})
+            
+        if "fps" in kwargs:
+            period_in_ms = 1000/kwargs["fps"]
+        else:
+            period_in_ms = 200
+
+        fig = plt.figure()
+        ax = plt.axes()
+        #fig, ax = plt.subplot()
+        
+        # find limits for entire dataset
+        maximum_values = []
+        minimum_values = []
+        
+        is_1D = False
+        is_2D = False
+        
+        for data in data_list:
+            if isinstance(data.metadata.dimension, int):
+                is_1D = True
+                
+                y = data.Intensity[np.nonzero(data.Intensity)]
+                if len(y) > 0:
+                    maximum_values.append(y.max())
+                    minimum_values.append(y.min())
+            
+            elif len(data.metadata.dimension) == 2:
+                is_2D = True
+                
+                y = data.Intensity[np.nonzero(data.Intensity)]
+                if len(y) > 0:
+                    maximum_values.append(y.max())
+                    minimum_values.append(y.min())
+        
+        if len(maximum_values) > 0:
+            maximum_value = np.array(maximum_values).max()
+        else:
+            maximum_value = 0
+            
+        if len(minimum_values) > 0:
+            minimum_value = np.array(minimum_values).min()
+        else:
+            minimum_value = 0
+        
+        if is_1D and is_2D:
+            raise InputError(
+                "Both 1D and 2D data in animation, only one allowed.")
+
+        # initialize plots
+        
+        data = data_list[0]
+        if isinstance(data.metadata.dimension, int):
+            x_axis_mult = data.plot_options.x_limit_multiplier
+            
+            x = data.xaxis*x_axis_mult
+            y = data.Intensity
+            y_err = data.Error
+            
+            er = ax.errorbar(x, y, yerr=y_err)
+            
+            if data.plot_options.log:
+                    ax.set_yscale("log", nonposy='clip')
+
+            ax.set_xlim(data.metadata.limits[0]*x_axis_mult,
+                             data.metadata.limits[1]*x_axis_mult)
+
+            # Add a title
+            ax.set_title(data.metadata.title)
+
+            # Add axis labels
+            ax.set_xlabel(data.metadata.xlabel)
+            ax.set_ylabel(data.metadata.ylabel)
+
+            if data.plot_options.custom_xlim_left:
+                ax.set_xlim(left=data.plot_options.left_lim)
+
+            if data.plot_options.custom_xlim_right:
+                ax.set_xlim(right=data.plot_options.right_lim)
+                
+            ax.set_ylim(minimum_value, maximum_value)
+        
+        elif len(data.metadata.dimension) == 2:
+            # Split the data into intensity, error and ncount
+            Intensity = data.Intensity
+            Error = data.Error
+            Ncount = data.Ncount
+            
+            cut_max = data.plot_options.cut_max  # Default 1
+            cut_min = data.plot_options.cut_min  # Default 0
+
+            if data.plot_options.log:
+                
+                min_value = minimum_value
+                max_value = maximum_value
+                
+                min_value = np.log10(min_value
+                                     + (max_value-min_value)*cut_min)
+                
+                max_value = np.log10(max_value*cut_max)
+
+                if (max_value - min_value
+                        > data.plot_options.orders_of_mag):
+                    min_value = (max_value
+                                 - data.plot_options.orders_of_mag)
+                    
+                min_value = 10.0 ** min_value
+                max_value = 10.0 ** max_value
+            else:
+                
+                min_value = minimum_value 
+                max_value = maximum_value
+
+            # Check the size of the array to be plotted
+            # print(to_plot.shape)
+
+            # Set the axis
+            x_axis_mult = data.plot_options.x_limit_multiplier
+            y_axis_mult = data.plot_options.y_limit_multiplier
+
+            X = np.linspace(data.metadata.limits[0]*x_axis_mult,
+                            data.metadata.limits[1]*x_axis_mult,
+                            data.metadata.dimension[0]+1)
+            Y = np.linspace(data.metadata.limits[2]*y_axis_mult,
+                            data.metadata.limits[3]*y_axis_mult,
+                            data.metadata.dimension[1])
+
+            # Create a meshgrid for both x and y
+            y, x = np.meshgrid(Y, X)
+
+            # Generate information on necessary colorrange
+            levels = MaxNLocator(nbins=150).tick_values(min_value,
+                                                        max_value)
+
+            # Select colormap
+            cmap = plt.get_cmap(data.plot_options.colormap)
+
+            # Select the colorscale normalization
+            if data.plot_options.log:
+                norm = matplotlib.colors.LogNorm(vmin=min_value,
+                                                 vmax=max_value)
+            else:
+                norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
+
+            # Create plot
+            im = ax.pcolormesh(x, y, Intensity,
+                               cmap=cmap, norm=norm)
+
+            def fmt(x, pos):
+                a, b = '{:.2e}'.format(x).split('e')
+                b = int(b)
+                if abs(float(a) - 1) < 0.01 :
+                    return r'$10^{{{}}}$'.format(b)
+                else:
+                    return r'${} \times 10^{{{}}}$'.format(a, b)
+
+            # Add the colorbar
+            fig.colorbar(im, ax=ax,
+                        format=matplotlib.ticker.FuncFormatter(fmt))
+
+            # Add a title
+            ax.set_title(data.metadata.title)
+
+            # Add axis labels
+            ax.set_xlabel(data.metadata.xlabel)
+            ax.set_ylabel(data.metadata.ylabel)
+
+            if data.plot_options.custom_ylim_top:
+                ax.set_ylim(top=data.plot_options.top_lim)
+
+            if data.plot_options.custom_ylim_bottom:
+                ax.set_ylim(bottom=data.plot_options.bottom_lim)
+
+            if data.plot_options.custom_xlim_left:
+                ax.set_xlim(left=data.plot_options.left_lim)
+
+            if data.plot_options.custom_xlim_right:
+                ax.set_xlim(right=data.plot_options.right_lim)
+
+        def init_1D():
+            # initialize function for animation
+            er.set_data([], [], []) # wont work
+            return er,
+        
+        def animate_1D(index):
+            data = data_list[index]
+            intensity = data.Intensity
+            error = data.Error
+            
+            er.set_data(x, intensity, error)
+            return er,
+
+        def init_2D():
+            # initialize function for animation
+            im.set_array([])
+            return im,
+            
+        def animate_2D(index):
+            data = data_list[index]
+            intensity = data.Intensity
+
+            im.set_array(intensity.ravel())
+            return im,
+        
+        anim = animation.FuncAnimation(fig, animate_2D, #init_func=init_2D,
+                                       frames=len(data_list),
+                                       interval=period_in_ms,
+                                       blit=False, repeat=True)
+
+        #plt.draw()
+        plt.show()
+
+        # The animation doesn't play unless it is saved. Bug.
+        if "filename" in kwargs:
+            filename = kwargs["filename"]
+            if not filename.endswith(".gif"):
+                filename = filename + ".gif"
+
+            # check if imagemagick available?
+            print("Saving animation with filename : \"" + filename + "\"")
+            anim.save(filename, writer="imagemagick")
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index ee0c27a8..84f5b717 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -94,7 +94,7 @@ def test_load_config_file(self):
         """
         # Load configuration file and read manually
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-        configuration_file_name = THIS_DIR + "/../../configuration.yaml"
+        configuration_file_name = THIS_DIR + "/../configuration.yaml"
 
         if not os.path.isfile(configuration_file_name):
             raise NameError("Could not find configuration file!")
@@ -834,6 +834,21 @@ def test_set_component_JUMP(self, mock_stdout):
         comp = instr.get_component("second_component")
 
         self.assertEqual(comp.JUMP, "myself 8")
+        
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_set_component_SPLIT(self, mock_stdout):
+        """
+        set_component_SPLIT passes the argument to the similar method
+        in the component class.
+        """
+
+        instr = setup_populated_instr()
+
+        instr.set_component_SPLIT("second_component", 3)
+
+        comp = instr.get_component("second_component")
+
+        self.assertEqual(comp.SPLIT, 3)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_set_component_comment(self, mock_stdout):
diff --git a/mcstasscript/tests/test_functions.py b/mcstasscript/tests/test_functions.py
index 0feee07a..6e884f7e 100644
--- a/mcstasscript/tests/test_functions.py
+++ b/mcstasscript/tests/test_functions.py
@@ -134,6 +134,26 @@ def test_name_search_read_repeat(self):
         hero_object = name_search("Big_Hero", data_list)
 
         self.assertEqual(hero_object.metadata.dimension, 123)
+        
+    def test_name_search_read_dubplicate(self):
+        """
+        Test simple case with duplicated name, should return list
+        """
+
+        data_list = setup_McStasData_array_repeat()
+        
+        hero_object = set_dummy_McStasData_2d("Big_Hero")
+        hero_object.metadata.dimension = 321
+        hero_object.plot_options.colormap = "very hot"
+
+        data_list.append(hero_object)
+        
+        results = name_search("Big_Hero", data_list)
+
+        self.assertEqual(len(results), 2)
+
+        self.assertEqual(results[0].metadata.dimension, 123)
+        self.assertEqual(results[1].metadata.dimension, 321)
 
     def test_name_search_read_error(self):
         """
@@ -183,6 +203,27 @@ def test_name_plot_options_simple(self):
         name_plot_options("Hero", data_list, colormap="very hot")
         hero_object = name_search("Hero", data_list)
         self.assertEqual(hero_object.plot_options.colormap, "very hot")
+        
+    def test_name_plot_options_duplicate(self):
+        """
+        Test case where several datasets are modified
+        """
+
+        data_list = setup_McStasData_array()
+        
+        hero_object = set_dummy_McStasData_2d("Hero")
+        hero_object.metadata.dimension = 321
+        hero_object.plot_options.colormap = "absurdly hot"
+
+        data_list.append(hero_object)
+        
+        name_plot_options("Hero", data_list, colormap="cold")
+        
+        results = name_search("Hero", data_list)
+        
+        self.assertEqual(len(results), 2)
+        self.assertEqual(results[0].plot_options.colormap, "cold")
+        self.assertEqual(results[1].plot_options.colormap, "cold")
 
 
 class Test_load_data(unittest.TestCase):

From a8be979740cf8dc20e7222e15968366b98e3a2b2 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Fri, 26 Jul 2019 11:01:55 +0200
Subject: [PATCH 042/403] Added a Configurator class to the interface to
 simplify updating the configuration yaml file. This is in prepration for
 distribution with pip where the package is installed in a location that will
 not be easy for everyone to find, and thus allowing them to set configuration
 from any python terminal is an important feature.

The code is tested in test_Configurator.py with a number of
unittests.

The manual is updated to reflect this new method of updating
the configuration file.
---
 McStasScript_documentation.pdf          | Bin 161681 -> 162053 bytes
 mcstasscript/configuration.yaml         |  13 +--
 mcstasscript/interface/functions.py     | 149 ++++++++++++++++++++++++
 mcstasscript/tests/test_Configurator.py | 148 +++++++++++++++++++++++
 4 files changed, 301 insertions(+), 9 deletions(-)
 create mode 100644 mcstasscript/tests/test_Configurator.py

diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
index db4439d6c80146643cd3dc495f0438db7a9f2850..eec3becb7e70159addecee03d144ea1c5993357e 100644
GIT binary patch
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diff --git a/mcstasscript/configuration.yaml b/mcstasscript/configuration.yaml
index 41c6ac82..7941b8ef 100644
--- a/mcstasscript/configuration.yaml
+++ b/mcstasscript/configuration.yaml
@@ -1,10 +1,5 @@
----
-paths:
-    # path to mcrun, example for OS X
-    mcrun_path: "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/"
-    # path to mcstas directory, example for OS X
-    # the mcstas directory should contain the component folders, sources, optics, ...
-    mcstas_path: "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/"
 other:
-    # limit characters per line in terminal output
-    characters_per_line: 93
+  characters_per_line: 93
+paths:
+  mcrun_path: /Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/
+  mcstas_path: /Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/
diff --git a/mcstasscript/interface/functions.py b/mcstasscript/interface/functions.py
index 11bc7769..bee77418 100644
--- a/mcstasscript/interface/functions.py
+++ b/mcstasscript/interface/functions.py
@@ -1,3 +1,6 @@
+import yaml
+import os
+
 from mcstasscript.data.data import McStasData
 from mcstasscript.helper.managed_mcrun import ManagedMcrun
 
@@ -91,3 +94,149 @@ def load_data(foldername):
     """
     managed_mcrun = ManagedMcrun("dummy", foldername=foldername)
     return managed_mcrun.load_results()
+
+
+class Configurator:
+    """
+    Class for setting the configuration file for McStasScript.
+    
+    Attributes
+    ----------
+    configuration_file_name : str
+        absolute path of configuration file
+        
+    Methods
+    -------
+    set_mcstas_path(string)
+        sets mcstas path
+        
+    set_mcrun_path(string)
+        sets mcrun path
+        
+    set_line_length(int)
+        sets maximum line length to given int
+    
+    _write_yaml(dict)
+        internal method, writes a configuration yaml file with dict content
+        
+    _read_yaml()
+        internal method, reads a configuration yaml file and returns a dict
+    
+    _create_new_config_file()
+        internal method, creates default configuration file
+
+    """
+
+    def __init__(self, *args):
+        """
+        Initialization of configurator, checks that the configuration file
+        actually exists, and if it does not, creates a default configuration
+        file.
+        
+        Parameters
+        ----------
+        (optional) custom name : str
+            Custom name for configuration file for testing purposes
+        """
+
+        if len(args) == 1:
+            name = args[0]
+        else:
+            name = "configuration"
+
+        # check configuration file exists
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        self.configuration_file_name = THIS_DIR + "/../" + name + ".yaml"
+        if not os.path.isfile(self.configuration_file_name):
+            # no config file found, write default config file
+            self._create_new_config_file()
+
+    def _write_yaml(self, dictionary):
+        """
+        Writes a dictionary as the new configuration file
+        """
+        with open(self.configuration_file_name, 'w') as yaml_file:
+            yaml.dump(dictionary, yaml_file, default_flow_style=False)   
+
+    def _read_yaml(self):
+        """
+        Reads yaml configuration file
+        """
+        with open(self.configuration_file_name, 'r') as ymlfile:
+            return yaml.safe_load(ymlfile)
+
+    def _create_new_config_file(self):
+        """
+        Writes a default configuration file to the package root directory
+        """
+
+        run = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/"
+        mcstas = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/"
+
+        default_paths = {"mcrun_path" : run,
+                         "mcstas_path" : mcstas}
+        default_other = {"characters_per_line" : 93}
+
+        default_config = {"paths" : default_paths,
+                          "other" : default_other}
+
+        self._write_yaml(default_config)
+
+    def set_mcstas_path(self, path):
+        """
+        Sets the path to McStas
+
+        Parameters
+        ----------
+        path : str
+            Path to the mcstas directory containing "sources", "optics", ...
+        """
+
+        # read entire configuration file 
+        config = self._read_yaml()
+
+        # update mcstas_path
+        config["paths"]["mcstas_path"] = path
+
+        # write new configuration file
+        self._write_yaml(config)
+
+    def set_mcrun_path(self, path):
+        """
+        Sets the path to mcrun
+
+        Parameters
+        ----------
+        path : str
+            Path to the mcrun executable
+        """
+
+        # read entire configuration file 
+        config = self._read_yaml()
+
+        # update mcstas_path
+        config["paths"]["mcrun_path"] = path
+
+        # write new configuration file
+        self._write_yaml(config)
+
+    def set_line_length(self, line_length):
+        """
+        Sets maximum line length for output
+
+        Parameters
+        ----------
+        line_length : int
+            maximum line length for output
+        """
+
+        # read entire configuration file 
+        config = self._read_yaml()
+
+        # update mcstas_path
+        config["other"]["characters_per_line"] = int(line_length)
+
+        # write new configuration file
+        self._write_yaml(config)
+
+
diff --git a/mcstasscript/tests/test_Configurator.py b/mcstasscript/tests/test_Configurator.py
new file mode 100644
index 00000000..a543ba2b
--- /dev/null
+++ b/mcstasscript/tests/test_Configurator.py
@@ -0,0 +1,148 @@
+import os
+import unittest
+
+from mcstasscript.interface.functions import Configurator
+
+def setup_expected_file(test_name):
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+    expected_file = THIS_DIR + "/../" + test_name + ".yaml"
+    
+    if os.path.isfile(expected_file):
+        os.remove(expected_file)
+    return expected_file
+
+def setup_configurator(test_name):
+    
+    setup_expected_file(test_name)
+    
+    return Configurator(test_name)
+            
+
+class TestConfigurator(unittest.TestCase):
+    """
+    Tests for configurator class that handles yaml configuration file
+    """
+    
+    def test_simple_initialize(self):
+        """
+        Tests that initialization happens, new configuration file should be
+        written.
+        """
+        
+        test_name = "test_configuration"
+        expected_file = setup_expected_file(test_name)
+        
+        # check the file did not exist before testing
+        self.assertFalse(os.path.isfile(expected_file))
+
+        # initialize the configurator
+        my_configurator = Configurator(test_name)
+        
+        # check a new configuration file was made
+        self.assertTrue(os.path.isfile(expected_file))
+        
+        # remove the testing configuration file
+        if os.path.isfile(expected_file):
+            os.remove(expected_file)
+    
+    def test_default_config(self):
+        """
+        This tests confirms the content of the default configuration file
+        """
+
+        test_name = "test_configuration"
+        expected_file = setup_expected_file(test_name)
+        
+        # check the file did not exist before testing
+        self.assertFalse(os.path.isfile(expected_file))
+        
+        my_configurator = Configurator(test_name)
+        
+        default_config = my_configurator._read_yaml()
+        
+        run = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/"
+        mcstas = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/"
+
+        self.assertEqual(default_config["paths"]["mcrun_path"], run)
+        self.assertEqual(default_config["paths"]["mcstas_path"], mcstas)
+        self.assertEqual(default_config["other"]["characters_per_line"], 93)
+        
+        # remove the testing configuration file
+        if os.path.isfile(expected_file): 
+            os.remove(expected_file)
+    
+    def test_yaml_write(self):
+        """
+        This test checks that writing to the configuration file works
+        """
+        test_name = "test_configuration"
+        my_configurator = setup_configurator(test_name)
+        
+        config = my_configurator._read_yaml()
+        
+        config["new_field"] = 123
+        config["paths"]["new_path"] = "/test/path/" 
+        
+        my_configurator._write_yaml(config)
+        
+        new_config = my_configurator._read_yaml()
+        
+        self.assertEqual(new_config["other"]["characters_per_line"], 93)
+        self.assertEqual(new_config["new_field"], 123)
+        self.assertEqual(new_config["paths"]["new_path"], "/test/path/")
+        
+        # remove the testing configuration file
+        setup_expected_file(test_name)
+        
+    def test_set_mcrun_path(self):
+        """
+        This test checks that setting the mcrun path works
+        """
+        test_name = "test_configuration"
+        my_configurator = setup_configurator(test_name) 
+
+        my_configurator.set_mcrun_path("/new/mcrun_path/")
+
+        new_config = my_configurator._read_yaml()
+
+        self.assertEqual(new_config["paths"]["mcrun_path"], "/new/mcrun_path/")
+
+        # remove the testing configuration file
+        setup_expected_file(test_name)
+
+    def test_set_mcstas_path(self):
+        """
+        This test checks that setting the mcstas path works
+        """
+        test_name = "test_configuration"
+        my_configurator = setup_configurator(test_name) 
+
+        my_configurator.set_mcstas_path("/new/mcstas_path/")
+
+        new_config = my_configurator._read_yaml()
+
+        self.assertEqual(new_config["paths"]["mcstas_path"],
+                         "/new/mcstas_path/")
+
+        # remove the testing configuration file
+        setup_expected_file(test_name)
+
+    def test_set_line_length(self):
+        """
+        This test checks that setting the line length works
+        """
+        test_name = "test_configuration"
+        my_configurator = setup_configurator(test_name) 
+        
+        my_configurator.set_line_length(123)
+        
+        new_config = my_configurator._read_yaml()
+        
+        self.assertEqual(new_config["other"]["characters_per_line"],123)
+
+        # remove the testing configuration file
+        setup_expected_file(test_name)
+
+
+if __name__ == '__main__':
+    unittest.main()

From a74f3fdd8739fc7fbcdf8176aad285d56fa048d8 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Fri, 26 Jul 2019 11:07:08 +0200
Subject: [PATCH 043/403] deleted configuration.yaml in old location.

The new location is inside the mcstasscript package so that it is
easily donwloaded when installing via pip.
---
 configuration.yaml | 10 ----------
 1 file changed, 10 deletions(-)
 delete mode 100644 configuration.yaml

diff --git a/configuration.yaml b/configuration.yaml
deleted file mode 100644
index db3b3f00..00000000
--- a/configuration.yaml
+++ /dev/null
@@ -1,10 +0,0 @@
----
-paths:
-    # path to mcrun, example for OS X
-    mcrun_path: "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/"
-    # path to mcstas directory, example for OS X
-    # the mcstas directory should contain the component folders, sources, optics, ...
-    mcstas_path: "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/"
-other:
-    # limit characters per line in terminal output
-    characters_per_line: 117
\ No newline at end of file

From 6598ee8ed51b60162a96aa0f13c1a19510f839b8 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Fri, 26 Jul 2019 11:52:23 +0200
Subject: [PATCH 044/403] Manual updated with new recommended install method:
 pip

---
 McStasScript_documentation.pdf | Bin 162053 -> 162306 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
index eec3becb7e70159addecee03d144ea1c5993357e..088dc99d7b1a79ceddf6acb0394021bc34f38ca8 100644
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From 65851bfc4695e1aa38a1470894f64e3c09d82a39 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Fri, 26 Jul 2019 11:59:55 +0200
Subject: [PATCH 045/403] Update of README.md

Updated to include the recommended install procedure which is through pip
---
 README.md | 21 ++++++++++++++-------
 1 file changed, 14 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index 0fb345f9..9843b3d4 100644
--- a/README.md
+++ b/README.md
@@ -3,16 +3,19 @@ McStas API for creating and running McStas instruments from python scripting
 
 Prototype for an API that allow interaction with McStas through an interface like Jupyter Notebooks created under WP5 of PaNOSC.
 
-## Instructions for basic use:
-Download the entire project
+## Install
+The package can be installed using pip
+python3 -m pip install McStasScript --upgrade
 
-Set up paths to McStas in the configuration.yaml file
+It is necessary to configure the package so the McStas installation can be found, here we show how the appropriate code for an Ubuntu system. The configuration is saved permanently, and only needs to be updated when McStas is updated.
 
-Before import in python, add the project to your path: 
+    from mcstasscript.interface import functions
+    my_configurator = functions.Configurator()
+    my_configurator.set_mcstas_path("/usr/bin/")
+    my_configurator.set_mcrun_path("/usr/share/mcstas/2.5/")
 
-    import sys
-    sys.path.append('path/to/McStasScript')
 
+## Instructions for basic use:
 Import the interface 
 
     from mcstasscript.interface import instr, plotter, functions
@@ -83,7 +86,11 @@ Here is a quick overview of the available methods of the main classes in the pro
     functions
     ├── name_search(str name, list McStasData) # Returns data set with given name from McStasData list
     ├── name_plot_options(str name, list McStasData, kwargs) # Sends kwargs to dataset with given name
-    └── load_data(str foldername) # Loads data from folder with McStas data as McStasData list
+    ├── load_data(str foldername) # Loads data from folder with McStas data as McStasData list
+    └── Configurator()
+        ├── set_mcrun_path(str path) # sets mcrun path
+        ├── set_mcstas_path(str path) # sets mcstas path
+        └── set_line_length(int length) # sets maximum line length
     
     plotter
     ├── make_plot(list McStasData) # Plots each data set individually

From e645a6fc573df183b59db889d9b577f1a912169e Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Fri, 26 Jul 2019 12:36:48 +0200
Subject: [PATCH 046/403] Update README.md

Fixed small errors in readme
---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 9843b3d4..8195fe16 100644
--- a/README.md
+++ b/README.md
@@ -3,9 +3,9 @@ McStas API for creating and running McStas instruments from python scripting
 
 Prototype for an API that allow interaction with McStas through an interface like Jupyter Notebooks created under WP5 of PaNOSC.
 
-## Install
+## Installation
 The package can be installed using pip
-python3 -m pip install McStasScript --upgrade
+    python3 -m pip install McStasScript --upgrade
 
 It is necessary to configure the package so the McStas installation can be found, here we show how the appropriate code for an Ubuntu system. The configuration is saved permanently, and only needs to be updated when McStas is updated.
 

From ed4f445542e1b7552e4b0a0e66e96d7af2434bd8 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Fri, 26 Jul 2019 12:37:26 +0200
Subject: [PATCH 047/403] Update README.md

Fixed small error in readme
---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 8195fe16..845cc7d3 100644
--- a/README.md
+++ b/README.md
@@ -5,6 +5,7 @@ Prototype for an API that allow interaction with McStas through an interface lik
 
 ## Installation
 The package can be installed using pip
+
     python3 -m pip install McStasScript --upgrade
 
 It is necessary to configure the package so the McStas installation can be found, here we show how the appropriate code for an Ubuntu system. The configuration is saved permanently, and only needs to be updated when McStas is updated.

From 221e927e4b2430772f28dfe880a0829cb1fc8b06 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Mon, 29 Jul 2019 10:38:36 +0200
Subject: [PATCH 048/403] Error in documentation, mcrun and mcstas path was
 switched. Thanks to Jakob Lass for pointing this out.

---
 McStasScript_documentation.pdf | Bin 162306 -> 162306 bytes
 README.md                      |   4 ++--
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
index 088dc99d7b1a79ceddf6acb0394021bc34f38ca8..0c488bcdd1eb61f6138a7c9551590d18b21dc80e 100644
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diff --git a/README.md b/README.md
index 9843b3d4..0eee2d7e 100644
--- a/README.md
+++ b/README.md
@@ -11,8 +11,8 @@ It is necessary to configure the package so the McStas installation can be found
 
     from mcstasscript.interface import functions
     my_configurator = functions.Configurator()
-    my_configurator.set_mcstas_path("/usr/bin/")
-    my_configurator.set_mcrun_path("/usr/share/mcstas/2.5/")
+    my_configurator.set_mcrun_path("/usr/bin/")
+    my_configurator.set_mcstas_path("/usr/share/mcstas/2.5/")
 
 
 ## Instructions for basic use:

From 741157c55261cd9177ed32e025bc476a2db04a37 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Thu, 1 Aug 2019 08:12:49 +0200
Subject: [PATCH 049/403] typo in README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c7f70078..bfbd6711 100644
--- a/README.md
+++ b/README.md
@@ -41,7 +41,7 @@ The parameters of the source can be adjusted directly as attributes of the pytho
     
 A monitor is added as well to get data out of the simulation
 
-    PSD = Instr.add_component("PSD", "PSD_monitor", AT=[0,0,1], RELATIVE="source") 
+    PSD = my_instrument.add_component("PSD", "PSD_monitor", AT=[0,0,1], RELATIVE="source") 
     PSD.xwidth = 0.1
     PSD.yheight = 0.1
     PSD.nx = 200

From 96d119d0c8d82b9987ccdcaaa40232862f32f335 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Mon, 12 Aug 2019 14:08:22 +0200
Subject: [PATCH 050/403] Major revision, added instrument reading system.

The instrument reader is included in interface as reader. It is
capable of reading McStas instruments and incorparating the
contained information into a McStasScript instance of an
instrument. It is also possible to write a python file to disk
which uses McStasScript to reproduce the given instrument file.

This feature has been tested with 20 McStas instruments from the
example folder in McStas 2.5, where all are reproduced accurately.
The feature should however still be used with some caution as
it is impossible to test for all possible syntax styles and
keyword combinations. It is highly recommended to run a test and
compare the results to the original instrument.

There are some cases where the reader is known to fail:
  Use of c unions (very rare in McStas)
  Use of multidemensional arrays in declare (1 dimension OK)

In the development of this major feature, several smaller features
were required. These are all documented in the manual.
  Possible to copy components on the instrument level
  Possible to add c code before/after components (%include)
  ROTATED statements are now only written if necessary
  Possible to append lines to declare (useful for structs, functions)

All features are tested in unit tests. Integration tests for full
instruments using the reader will follow.

The code is also distributed over pip and all files needed to for
the distribution is included here for completeness.
---
 MANIFEST.in                                   |   5 +
 McStasScript_documentation.pdf                | Bin 162306 -> 167419 bytes
 mcstasscript/data/data.py                     |   8 +-
 mcstasscript/helper/component_reader.py       |  28 +
 mcstasscript/helper/managed_mcrun.py          |   3 +-
 mcstasscript/helper/mcstas_objects.py         | 161 +++--
 mcstasscript/instr_reader/__init__.py         |   0
 mcstasscript/instr_reader/control.py          | 226 +++++++
 mcstasscript/instr_reader/read_declare.py     | 280 +++++++++
 mcstasscript/instr_reader/read_definition.py  | 143 +++++
 mcstasscript/instr_reader/read_finally.py     |  55 ++
 mcstasscript/instr_reader/read_initialize.py  |  65 ++
 mcstasscript/instr_reader/read_trace.py       | 560 ++++++++++++++++++
 mcstasscript/instr_reader/util.py             | 149 +++++
 mcstasscript/interface/instr.py               | 235 +++++++-
 mcstasscript/interface/plotter.py             |   3 -
 mcstasscript/interface/reader.py              |  81 +++
 .../tests/Union_demonstration_test.instr      | 422 +++++++++++++
 mcstasscript/tests/test_Instr.py              | 167 +++++-
 mcstasscript/tests/test_Instr_reader.py       | 342 +++++++++++
 mcstasscript/tests/test_component.py          |  43 +-
 setup.py                                      |  23 +
 22 files changed, 2909 insertions(+), 90 deletions(-)
 create mode 100644 MANIFEST.in
 create mode 100644 mcstasscript/instr_reader/__init__.py
 create mode 100644 mcstasscript/instr_reader/control.py
 create mode 100644 mcstasscript/instr_reader/read_declare.py
 create mode 100644 mcstasscript/instr_reader/read_definition.py
 create mode 100644 mcstasscript/instr_reader/read_finally.py
 create mode 100644 mcstasscript/instr_reader/read_initialize.py
 create mode 100644 mcstasscript/instr_reader/read_trace.py
 create mode 100644 mcstasscript/instr_reader/util.py
 create mode 100644 mcstasscript/interface/reader.py
 create mode 100644 mcstasscript/tests/Union_demonstration_test.instr
 create mode 100644 mcstasscript/tests/test_Instr_reader.py
 create mode 100644 setup.py

diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 00000000..e71699ac
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1,5 @@
+include McStasScript_documentation.pdf
+include mcstasscript/configuration.yaml
+include mcstasscript/tests/test_for_reading.comp
+graft mcstasscript/tests/dummy_mcstas
+graft mcstasscript/tests/test_data_set
diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
index 0c488bcdd1eb61f6138a7c9551590d18b21dc80e..deb1fb8ea4d8ffb14681dacb475de2e9bc0bf311 100644
GIT binary patch
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diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index 84633cb8..5f116f8a 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -87,8 +87,12 @@ def extract_info(self):
         if "filename" in self.info:
             self.filename = self.info["filename"].rstrip()
         else:
-            raise NameError(
-                "No filename found in mccode data section!")
+            # Monitors without output files does exist
+            #raise NameError(
+            #    "No filename found in mccode data section!")
+            print("The component named \"" + self.component_name
+                  + "\" had no data file and will not be loaded.")
+            self.filename = ""
 
         # Extract limits
         self.limits = []
diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index 62e9fdea..7018c564 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -278,6 +278,8 @@ def read_component_file(self, absolute_path):
 
                 parts = line.split("(")
                 parameter_parts = parts[1].split(",")
+                
+                parameter_parts = self.correct_for_brackets(parameter_parts)
 
                 parameter_parts = list(filter(("\n").__ne__, parameter_parts))
 
@@ -347,6 +349,7 @@ def read_component_file(self, absolute_path):
                         break
 
                     parameter_parts = fo.readline().split(",")
+                    parameter_parts = self.correct_for_brackets(parameter_parts)
 
             if self.line_starts_with(line, "DECLARE"):
                 break
@@ -369,6 +372,31 @@ def read_component_file(self, absolute_path):
         """
 
         return result
+    
+    def correct_for_brackets(self, parameter_parts):
+        corrected_parts = []
+        current_part = ""
+        index = 0
+        while True:
+            
+            current_part = parameter_parts[index]
+            inner_index = 0
+            while True: 
+                if (current_part.count("{") == current_part.count("}")):
+                    corrected_parts.append(current_part)
+                    index += inner_index
+                    break                          
+                else:
+                    inner_index +=1
+                    current_part += "," + parameter_parts[index+inner_index]
+
+            index += 1
+            
+            if index >= len(parameter_parts):
+                break
+            
+        return corrected_parts
+        
 
     def line_starts_with(self, line, string):
         """
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 1ca3126a..5091cc20 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -206,7 +206,8 @@ def load_results(self, *args):
                 # Extract the information
                 current_object.extract_info()
                 # Add to metadata list
-                metadata_list.append(current_object)
+                if current_object.filename != "":
+                    metadata_list.append(current_object)
                 # Stop reading data
                 in_data = False
 
diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 94e69e2b..fab57f4e 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -124,7 +124,8 @@ class declare_variable:
         Writes a line to text file fo declaring the parameter in c
     """
     def __init__(self, *args, **kwargs):
-        """Initializing mcstas parameter object
+        """
+        Initializing mcstas parameter object
 
         Parameters
         ----------
@@ -148,7 +149,16 @@ def __init__(self, *args, **kwargs):
         self.type = args[0]
         self.name = str(args[1])
 
-        if not is_legal_parameter(self.name):
+
+        par_name = self.name
+        if "*" in par_name[0]:
+            # Remove any number of prefixed *, indicating variable is a pointer
+            par_name = par_name.split("*")[-1]
+        elif "&" in par_name[0]:
+            # Remove the first & indicating the variable is an address
+            par_name = par_name[1:]
+            
+        if not is_legal_parameter(par_name):
             raise NameError("The given parameter name: \""
                             + self.name
                             + "\" is not a legal c variable name, "
@@ -167,7 +177,8 @@ def __init__(self, *args, **kwargs):
             self.comment = " // " + kwargs["comment"]
 
     def write_line(self, fo):
-        """Writes line declaring variable to file fo
+        """
+        Writes line declaring variable to file fo
 
         Parameters
         ----------
@@ -181,16 +192,29 @@ def write_line(self, fo):
                 fo.write("%s %s = %d;%s" % (self.type, self.name,
                                             self.value, self.comment))
             else:
-                fo.write("%s %s = %G;%s" % (self.type, self.name,
-                                            self.value, self.comment))
+                try:
+                    fo.write("%s %s = %G;%s" % (self.type, self.name,
+                                                self.value, self.comment))
+                except:
+                    fo.write("%s %s = %s;%s" % (self.type, self.name,
+                                                self.value, self.comment))
         if self.value is "" and self.vector != 0:
             fo.write("%s %s[%d];%s" % (self.type, self.name,
                                        self.vector, self.comment))
         if self.value is not "" and self.vector != 0:
-            fo.write("%s %s[%d] = {" % (self.type, self.name, self.vector))
-            for i in range(0, len(self.value) - 1):
-                fo.write("%G," % self.value[i])
-            fo.write("%G};%s" % (self.value[-1], self.comment))
+            if isinstance(self.value, str):
+                # value is a string
+                string = self.value
+                #string = string.replace('"',"\\\"")
+                fo.write("%s %s[%d] = %s;" % (self.type, self.name, self.vector, string))
+            else:
+                # list of values
+                fo.write("%s %s[%d] = {" % (self.type, self.name, self.vector))
+                for i in range(0, len(self.value) - 1):
+                    fo.write("%G," % self.value[i])
+                fo.write("%G};%s" % (self.value[-1], self.comment))
+                
+                
 
 
 class component:
@@ -355,69 +379,84 @@ def __init__(self, instance_name, component_name, **kwargs):
         self.name = instance_name
         self.component_name = component_name
 
+        # initialize McStas information
+        self.AT_data = [0, 0, 0]
+        self.AT_relative = "ABSOLUTE"
+        self.ROTATED_specified = False
+        self.ROTATED_data = [0, 0, 0]
+        self.ROTATED_relative = "ABSOLUTE"
+        self.WHEN = ""
+        self.EXTEND = ""
+        self.GROUP = ""
+        self.JUMP = ""
+        self.SPLIT = 0
+        self.comment = ""
+        self.c_code_before = "" 
+        self.c_code_after = ""
+
+        # If any keywords are set in kwargs, update these
+        self.set_keyword_input(**kwargs)
+
+        """
+        Could store an option for whether this component should be
+        printed in instrument file or in a seperate file which would
+        then be included.
+        """
+
+        # Do not allow addition of attributes after init
+        self._freeze()
+    
+    def set_keyword_input(self, **kwargs):
+        # Allow addition of attributes in init
+        self._unfreeze()
+
         if "AT" in kwargs:
             self.AT_data = kwargs["AT"]
-        else:
-            self.AT_data = [0, 0, 0]
+        
         # Could check if AT_RELATIVE is a string
         if "AT_RELATIVE" in kwargs:
             self.AT_relative = "RELATIVE " + kwargs["AT_RELATIVE"]
-        else:
-            self.AT_relative = "ABSOLUTE"
 
+        self.ROTATED_specified = False
         if "ROTATED" in kwargs:
             self.ROTATED_data = kwargs["ROTATED"]
-        else:
-            self.ROTATED_data = [0, 0, 0]
+            self.ROTATED_specified = True
+
         # Could check if ROTATED_RELATIVE is a string
         if "ROTATED_RELATIVE" in kwargs:
             self.ROTATED_relative = "RELATIVE " + kwargs["ROTATED_RELATIVE"]
-        else:
-            self.ROTATED_relative = "ABSOLUTE"
+            self.ROTATED_specified = True
 
         # Could check if RELATIVE is a string
         if "RELATIVE" in kwargs:
             self.AT_relative = "RELATIVE " + kwargs["RELATIVE"]
             self.ROTATED_relative = "RELATIVE " + kwargs["RELATIVE"]
+            self.ROTATED_specified = True
 
         if "WHEN" in kwargs:
             self.WHEN = "WHEN (" + kwargs["WHEN"] + ")"
-        else:
-            self.WHEN = ""
 
         if "EXTEND" in kwargs:
             self.EXTEND = kwargs["EXTEND"] + "\n"
-        else:
-            self.EXTEND = ""
 
         if "GROUP" in kwargs:
             self.GROUP = kwargs["GROUP"]
-        else:
-            self.GROUP = ""
 
         if "JUMP" in kwargs:
             self.JUMP = kwargs["JUMP"]
-        else:
-            self.JUMP = ""
             
         if "SPLIT" in kwargs:
             self.SPLIT = kwargs["SPLIT"]
-        else:
-            self.SPLIT = 0
 
         if "comment" in kwargs:
             self.comment = kwargs["comment"]
-        else:
-            self.comment = ""
-
-        """
-        Could store an option for whether this component should be
-        printed in instrument file or in a seperate file which would
-        then be included.
-        """
-
-        # Do not allow addition of attributes after init
-        self._freeze()
+            
+        if "c_code_before" in kwargs:
+            self.c_code_before = kwargs["c_code_before"]
+        
+        if "c_code_after" in kwargs:
+            self.c_code_after = kwargs["c_code_after"]
+        
 
     def __setattr__(self, key, value):
         if self.__isfrozen and not hasattr(self, key):
@@ -449,6 +488,7 @@ def set_AT(self, at_list, **kwargs):
     def set_ROTATED(self, rotated_list, **kwargs):
         """Sets ROTATED data, List of 3 floats"""
         self.ROTATED_data = rotated_list
+        self.ROTATED_specified = True
         if "RELATIVE" in kwargs:
             relative_name = kwargs["RELATIVE"]
             if relative_name == "ABSOLUTE":
@@ -509,6 +549,14 @@ def append_EXTEND(self, string):
     def set_comment(self, string):
         """Method that sets a comment to be written to instrument file"""
         self.comment = string
+        
+    def set_c_code_before(self, string):
+        """Method that sets c code to be written before the component"""
+        self.c_code_before = string
+        
+    def set_c_code_after(self, string):
+        """Method that sets c code to be written after the component"""
+        self.c_code_after = string
 
     def write_component(self, fo):
         """
@@ -522,6 +570,12 @@ def write_component(self, fo):
         # Could use character limit on lines instead
         parameters_written = 0  # internal parameter
 
+
+        if len(self.c_code_before) > 0:
+            explanation = "From component named " + self.name
+            fo.write("%s // %s\n" % (str(self.c_code_before), explanation))
+            fo.write("\n")
+
         # Write comment if present
         if len(self.comment) > 1:
             fo.write("// %s\n" % (str(self.comment)))
@@ -577,10 +631,12 @@ def write_component(self, fo):
                                     str(self.AT_data[1]),
                                     str(self.AT_data[2])))
         fo.write(" %s\n" % self.AT_relative)
-        fo.write("ROTATED (%s,%s,%s)" % (str(self.ROTATED_data[0]),
-                                         str(self.ROTATED_data[1]),
-                                         str(self.ROTATED_data[2])))
-        fo.write(" %s\n" % self.ROTATED_relative)
+        
+        if self.ROTATED_specified:
+            fo.write("ROTATED (%s,%s,%s)" % (str(self.ROTATED_data[0]),
+                                             str(self.ROTATED_data[1]),
+                                             str(self.ROTATED_data[2])))
+            fo.write(" %s\n" % self.ROTATED_relative)
 
         if not self.GROUP == "":
             fo.write("GROUP %s\n" % self.GROUP)
@@ -593,6 +649,11 @@ def write_component(self, fo):
 
         if not self.JUMP == "":
             fo.write("JUMP %s\n" % self.JUMP)
+            
+        if len(self.c_code_after) > 0:
+            fo.write("\n")
+            explanation = "From component named " + self.name
+            fo.write("%s // %s\n" % (str(self.c_code_after), explanation))
 
         # Leave a new line between components for readability
         fo.write("\n")
@@ -606,6 +667,8 @@ def print_long(self):
         class is used as a superclass for classes describing each
         McStas component.
         """
+        if len(self.c_code_before) > 1:
+            print(self.c_code_before)
         if len(self.comment) > 1:
             print("// " + self.comment)
         if self.SPLIT is not 0:
@@ -636,7 +699,8 @@ class is used as a superclass for classes describing each
         if not self.WHEN == "":
             print(self.WHEN)
         print("AT", self.AT_data, self.AT_relative)
-        print("ROTATED", self.ROTATED_data, self.ROTATED_relative)
+        if self.ROTATED_specified:
+            print("ROTATED", self.ROTATED_data, self.ROTATED_relative)
         if not self.GROUP == "":
             print("GROUP " + self.GROUP)
         if not self.EXTEND == "":
@@ -644,19 +708,26 @@ class is used as a superclass for classes describing each
             print(self.EXTEND + "%}")
         if not self.JUMP == "":
             print("JUMP " + self.JUMP)
+        if len(self.c_code_after) > 1:
+            print(self.c_code_after)
 
     def print_short(self, **kwargs):
         """Prints short description of component to list print"""
+        if self.ROTATED_specified:
+            print_rotate_rel = self.ROTATED_relative
+        else:
+            print_rotate_rel = self.AT_relative
+        
         if "longest_name" in kwargs:
             number_of_spaces = 3+kwargs["longest_name"]-len(self.name)
             print(str(self.name) + " "*number_of_spaces, end='')
             print(str(self.component_name),
                   "\tAT", self.AT_data, self.AT_relative,
-                  "ROTATED", self.ROTATED_data, self.ROTATED_relative)
+                  "ROTATED", self.ROTATED_data, print_rotate_rel)
         else:
             print(str(self.name), "=", str(self.component_name),
                   "\tAT", self.AT_data, self.AT_relative,
-                  "ROTATED", self.ROTATED_data, self.ROTATED_relative)
+                  "ROTATED", self.ROTATED_data, print_rotate_rel)
 
     def show_parameters(self, **kwargs):
         """
diff --git a/mcstasscript/instr_reader/__init__.py b/mcstasscript/instr_reader/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/mcstasscript/instr_reader/control.py b/mcstasscript/instr_reader/control.py
new file mode 100644
index 00000000..9dc7824d
--- /dev/null
+++ b/mcstasscript/instr_reader/control.py
@@ -0,0 +1,226 @@
+import io
+import os
+from decimal import Decimal
+
+from mcstasscript.interface.instr import McStas_instr
+from mcstasscript.instr_reader.read_definition import DefinitionReader
+from mcstasscript.instr_reader.read_declare import DeclareReader
+from mcstasscript.instr_reader.read_initialize import InitializeReader
+from mcstasscript.instr_reader.read_trace import TraceReader
+from mcstasscript.instr_reader.read_finally import FinallyReader
+
+class InstrumentReader:
+    """
+    This class controls loading of a McStas file as a McStasScript object.
+    
+    This is done by reading the McStas file line by line while using the
+    McStasScript API to load the information into the Instr object.
+    
+    Optionally a Python file with the McStasScript commands required to 
+    replicate the McStas instrument can be written to disk.
+    
+    
+    
+    Methods
+    -------
+    __init__(filename)
+        Initializes reading of McStas instrument with given filename
+    
+    generate_py_version(product_filename)
+        Generates a file named product_filename.py that recreates instr
+        
+    add_to_instr(Instr)
+        Inserts information from instr file into McStasScript Instr instance
+    
+    """
+    
+    def __init__(self, filename):
+        """
+        Initialize the InstrumentReader with a target McStas instrument
+        file. Use generate_py_version method for writing an eqvivalent
+        McStasScript python file or the add_to_instr method to load this
+        instrument file onto a Instr McStasScript object.
+        """
+    
+        self.filename = filename
+        self.Instr = None # could set it up to create Instr
+        self.write_file = False
+        self.product_filename = "mc_script.py"
+        self.instr_name = ""
+        self.file_data = None
+        self.line_index = 0
+        self.file_length = 0
+                
+    def generate_py_version(self, product_filename):
+        """
+        Generate a McStasScript version of the instrument file used for 
+        initialize of the InstrumentReader object. The filename given is
+        for the generated file.
+        
+        One should use this feature with some caution. Look through the
+        generated McStasScript file and compare some output with the
+        original insturment file to ensure everything was loaded correctly.
+        """
+
+        # Generate dummy instr object
+        self.Instr = McStas_instr("dummy_object_for_generating_file")
+        
+        self.product_filename = product_filename
+        self.write_file = True
+        
+        if os.path.isfile(self.product_filename):
+            os.remove(self.product_filename)
+        
+        self._read_file()
+        
+    def add_to_instr(self, Instr):
+        """
+        Add contents of McStas instrument file selected in initialize
+        to an McStasScript instrument object.
+        """
+        
+        self.Instr = Instr
+        self.write_file = False
+        
+        self._read_file()
+        
+    def _open_file(self):
+        """
+        Internal method that opens the instrument file to be read
+        """
+        
+        with open(self.filename) as file:
+            self.file_data = file.readlines()
+            
+        self.file_length = len(self.file_data)
+        self.line_index = 0
+            
+    def _get_next_line(self):
+        """
+        Internalmethod that gets the next line to be read
+        """
+        
+        line = self.file_data[self.line_index]
+        self.line_index += 1
+        return line
+        
+    def _return_line(self):
+        """
+        Internal method that puts line back into stack
+        """
+        
+        self.line_index -= 1
+        
+    def _read_file(self):
+        """
+        Master method for reading the instrument file.  It goes through 
+        the file line by line, and checks which part of the instrument 
+        file it is currently reading. There are separate methods for 
+        reading the individual parts of the instrument file to reduce
+        clutter.
+        """
+        
+        # Initialize readers of the different McStas instrument sections
+        args = [self.Instr, self.write_file, self.product_filename, self._get_next_line, self._return_line]
+        self.Definition_reader = DefinitionReader(*args)
+        self.Declare_reader = DeclareReader(*args)
+        self.Initialize_reader = InitializeReader(*args)
+        self.Trace_reader = TraceReader(*args)
+        self.Finally_reader = TraceReader(*args)
+
+        # A mode for each type that activates the correct reader function
+        definition_mode = False
+        declare_mode = False
+        initialize_mode = False
+        trace_mode = False
+        finally_mode = False
+        comment_mode = False
+        any_mode = False
+        
+        # check if insturment name has be read from file yet
+        instr_name_read = False
+        
+        self._open_file()
+        
+        #for line in self.file_data:
+        while self.line_index < self.file_length:
+            
+            line = self._get_next_line()
+
+            # Find appropriate mode
+            if line.strip().startswith("DEFINE INSTRUMENT") and not any_mode:
+                definition_mode = True
+                any_mode = True
+
+            if line.strip().startswith("DECLARE") and not any_mode:
+                declare_mode = True
+                any_mode = True
+
+            if (line.strip().startswith("INITIALIZE") or
+                line.strip().startswith("INITIALISE")) and not any_mode:
+                initialize_mode = True
+                any_mode = True
+
+            if line.strip().startswith("TRACE") and not any_mode:
+                trace_mode = True
+                any_mode = True
+
+            if line.strip().startswith("FINALLY") and not any_mode:
+                finally_mode = True
+                any_mode = True
+
+            if line.strip().startswith("/*"):
+                comment_mode = True        
+
+            # Read with appropriate reader
+            if definition_mode and not comment_mode:
+                # Get instrument name
+                if not instr_name_read:
+                    self.instr_name = line.split("(")[0].split(" ")[-1]
+                    instr_name_read = True
+                    self.update_file_name()
+
+                # Read line from definition
+                definition_mode = self.Definition_reader.read_definition_line(line)
+                # When read_definition finds the end, it will return False
+                any_mode = definition_mode
+
+            if declare_mode and not comment_mode:
+                # Read line from definition
+                declare_mode = self.Declare_reader.read_declare_line(line)
+                # When read_declare finds the end, it will return False
+                any_mode = declare_mode
+
+            if initialize_mode and not comment_mode:
+                # Read line from initialize
+                initialize_mode = self.Initialize_reader.read_initialize_line(line)
+                # When read_initialize finds the end, it will return False
+                any_mode = initialize_mode
+
+            if trace_mode and not comment_mode:
+                # Read line from initialize
+                trace_mode = self.Trace_reader.read_trace_line(line)
+                # When read_initialize finds the end, it will return False
+                any_mode = trace_mode
+
+            if finally_mode and not comment_mode:
+                # Read line from finally
+                finally_mode = self.Finally_reader.read_finally_line(line)
+                # When read_finallyfinds the end, it will return False
+                any_mode = finally_mode
+
+            # Stop comment mode when end of comment block reached
+            if "*/" in line.strip():
+                comment_mode = False
+        
+    def update_file_name(self):
+        """
+        Updates filename for reader subclasses
+        """
+            
+        self.Definition_reader.set_instr_name(self.instr_name)
+        self.Declare_reader.set_instr_name(self.instr_name)
+        self.Initialize_reader.set_instr_name(self.instr_name)
+        self.Trace_reader.set_instr_name(self.instr_name)
+        self.Finally_reader.set_instr_name(self.instr_name)
+        
diff --git a/mcstasscript/instr_reader/read_declare.py b/mcstasscript/instr_reader/read_declare.py
new file mode 100644
index 00000000..c5fd4f1f
--- /dev/null
+++ b/mcstasscript/instr_reader/read_declare.py
@@ -0,0 +1,280 @@
+from mcstasscript.instr_reader.util import SectionReader
+
+class DeclareReader(SectionReader):
+    """
+    Reads the declare section of a McStas instrument file and adds
+    the found parameters / functions / structs to the McStasScript
+    Instr instance. The information can also be written to a python
+    file for reproduction of a McStas instrument.  
+    """
+    
+    def __init__(self, Instr, write_file, product_filename, get_next_line, return_line):
+        
+        super().__init__(Instr, write_file, product_filename, get_next_line, return_line)
+        
+        self.in_declare_function = False
+        self.in_struct_definition = False
+        self.bracket_counter = 0
+        
+    def read_declare_line(self, line):
+        """
+        Reads line of instrument declare, returns bolean.  If it encounters
+        the end of the declare section, it returns False, otherwise True.
+        
+        The contents of the declare section is written to the McStasScript
+        Instr object.
+        """
+        
+        continue_declare = True
+        
+        # Remove comments
+        if "//" in line:
+            line = line.split("//", 1)[0]
+        
+        # Remove %} and signify end if this is found
+        if "%}" in line:
+            continue_declare = False
+            line = line.split("%}", 1)[0]
+            
+        if "/*" in line:
+            line = line.split("/*", 1)[0].strip()
+            
+        if self.in_declare_function:
+            if "{" in line:
+                self.bracket_counter += 1
+                    
+            if "}" in line:
+                self.bracket_counter -= 1
+                
+            if self.bracket_counter == 0:
+                self.in_declare_function = False
+                
+            self.Instr.append_declare(line)
+            self._write_declare_line(line)            
+
+        # Check for functions
+        if ("(" in line and not ";" in line and " " in line.strip()
+            and not self.in_declare_function):
+            
+            # If in function, it will define a block
+            n_curly_brackets = line.count("{")
+            n_curly_brackets -= line.count("}")
+            
+            while n_curly_brackets != 0 or ("{" not in line):
+                next_line = self.get_next_line() 
+                line += next_line
+                
+                n_curly_brackets = line.count("{")
+                n_curly_brackets -= line.count("}")
+
+            after_curly_bracket = line.split("}")[-1]
+            
+            declare_lines = line.split("\n")
+            for declare_line in declare_lines:
+                declare_line = declare_line.rstrip()
+                declare_line = declare_line.replace('\\n',"\\\\n")
+                declare_line = declare_line.replace('"',"\\\"")
+                self.Instr.append_declare(declare_line)
+                self._write_declare_line(declare_line)
+                
+            line = after_curly_bracket
+                
+        # Check for struct / function that returns struct
+        if line.strip().startswith("struct "):
+            # Can be a function returning struct or struct definition
+            
+            # If struct definition, no parenthesis and ; after )        
+            n_curly_brackets = line.count("{")
+            n_curly_brackets -= line.count("}")
+            
+            # Add lines until end of block found
+            while n_curly_brackets != 0 or ("{" not in line):
+                
+                next_line = self.get_next_line() 
+                line += next_line
+                
+                n_curly_brackets = line.count("{")
+                n_curly_brackets -= line.count("}")
+            
+                if "{" in line:
+                    before_curly_bracket = line.split("{", 1)[0]
+                    if "(" in before_curly_bracket and ")" in before_curly_bracket:
+                        # This is a function that returns a struct!
+                        self.in_declare_function = True
+            
+            after_curly_bracket = line.split("}")[-1]
+            
+            # if not in function, add until ; is found
+            while ";" not in after_curly_bracket and not self.in_declare_function:
+                # It is surely a struct, find ;
+                line += self.get_next_line()
+                after_curly_bracket = line.split("}")[-1]
+                
+            declare_lines = line.split("\n")
+            for declare_line in declare_lines:
+                declare_line = declare_line.rstrip()
+                declare_line = declare_line.replace('\\n',"\\\\n")
+                declare_line = declare_line.replace('"',"\\\"")
+                self.Instr.append_declare(declare_line)
+                self._write_declare_line(declare_line)
+                
+            if self.in_declare_function:
+                line = line.split("}")[-1].strip()
+            else:
+                line = line.split(";")[-1].strip()
+                
+            # if in function, stop now    
+            self.in_declare_function = False
+            
+        # Grab defines
+        if line.strip().startswith("#define"):
+            # Include define statements as declare append
+            line = line.rstrip()
+            line = line.replace('\\n',"\\\\n")
+            line = line.replace('"',"\\\"")
+            self.Instr.append_declare(line)
+            self._write_declare_line(line)
+        
+        if "\n" in line:
+            line = line.strip("\n")
+            
+        # Read single line parameter definitions
+        if ";" in line and not self.in_declare_function:
+            # This line contains c statements
+            statements = line.split(";")
+            
+            for statement in statements:
+                statement = statement.strip()
+                if statement != "\n" and statement != " " and len(statement) > 1:
+                    self._read_declare_statement(statement)
+
+        return continue_declare
+            
+    def _read_declare_statement(self, statement):
+        """
+        Reads single declare statements, which can have multiple
+        variables.
+        """
+        
+        statement = statement.strip()
+            
+        # Find type (same for all parameters in one statement)
+        this_type = statement.split(" ", 1)[0]
+        statement = statement.split(" ", 1)[1].strip()
+        
+        if this_type == "const": # other c keywords to consider?
+            this_type += " " + statement.split(" ", 1)[0]
+            statement = statement.split(" ", 1)[1].strip()
+
+        # Check for bracket initialization of arrays
+        if "," in statement:
+            variables = statement.split(",")
+            fixed_variables = []
+            array_mode = False
+            for variable in variables:
+                
+                if "{" not in variable and array_mode is False:
+                    fixed_variables.append(variable)
+                elif "{" in variable:
+                    temp_variable = variable + ","
+                    array_mode = True
+                elif "}" not in variable:
+                    temp_variable += variable + ","
+                else:
+                    temp_variable += variable
+                    fixed_variables.append(temp_variable)
+                    array_mode = False    
+            
+            variables = fixed_variables
+
+        else:
+            # No commas means just one parameter
+            variables = [statement]
+
+        # Treat each variable independently
+        for variable in variables:
+            variable = variable.strip()
+            
+            dynamic_size = False
+            kw_args = {}
+
+            if "=" in variable:
+                value = variable.split("=")[1].strip()
+                # remove the value part before proceeding
+                variable = variable.split("=")[0].strip()
+                
+                if "{" in value:
+                    # handle array as value
+                    value = value.split("{")[1]
+                    if "{" in value:
+                        raise ValueError("Can not load arrays with larger"
+                                         + "than 1 dimension yet.")
+                    value = value.split("}")[0]
+                    values = value.split(",")
+                    return_value = []
+                    for val in values:
+                        return_value.append(float(val))
+                else:
+                    try:
+                        return_value = float(value)
+                    except:
+                        value = value.replace('"',"\\\"")
+                        return_value = '"' + value + '"'
+                
+                kw_args["value"] = return_value
+
+            # Handle array
+            if "[" in variable:
+                array_sizes = []
+                array_size_strings = variable.split("[")
+                # remove the array size part before proceeding
+                variable = variable.split("[",1)[0].strip()
+                for array_size_string in array_size_strings:
+                    if "]" in array_size_string:
+                        this_size = array_size_string.split("]")[0]
+                        try: 
+                            # Size declared normally
+                            array_sizes.append(int(this_size))
+                        except:
+                            # No size declared means the size is automatic
+                            dynamic_size = True
+                        
+                if len(array_sizes) > 1:
+                    raise ValueError("Can not handle arrays with larger"
+                                     + " than 1 dimension yet")
+                if not dynamic_size:
+                    kw_args["array"] = array_sizes[0]
+                
+            if dynamic_size:
+                # McStasScript needs size of array, so it is found manually
+                kw_args["array"] = len(kw_args["value"])
+                
+            # value, array and typeremoved, all that remians is the name
+            variable_name = variable
+            self.Instr.add_declare_var(this_type, variable_name, **kw_args)
+            
+            # Also write it to a file?
+            write_string = []
+            write_string.append(self.instr_name)
+            write_string.append(".add_declare_var(")
+            write_string.append("\"" + this_type + "\"")
+            write_string.append(", ")
+            write_string.append("\"" + variable_name + "\"")
+            write_string.append(self._kw_to_string(kw_args))
+            write_string.append(")\n")
+            
+            # Write declare parameter to python file
+            self._write_to_file(write_string)
+            
+    def _write_declare_line(self, string):
+        
+        string = string.rstrip()
+        
+        write_string = []
+        write_string.append(self.instr_name)
+        write_string.append(".append_declare(")
+        write_string.append("\"" + string + "\"")
+        write_string.append(")\n")
+            
+        self._write_to_file(write_string)
+        
\ No newline at end of file
diff --git a/mcstasscript/instr_reader/read_definition.py b/mcstasscript/instr_reader/read_definition.py
new file mode 100644
index 00000000..7270cff4
--- /dev/null
+++ b/mcstasscript/instr_reader/read_definition.py
@@ -0,0 +1,143 @@
+from mcstasscript.instr_reader.util import SectionReader
+
+class DefinitionReader(SectionReader):
+    """
+    Responsible for reading the defintion section of McStas instrument
+    file. Contains instrument name and instrument parameters.
+    """
+    
+    def __init__(self, Instr, write_file, product_filename, get_next_line, return_line):
+        
+        super().__init__(Instr, write_file, product_filename, get_next_line, return_line)
+        
+    def read_definition_line(self, line):
+        """
+        Reads line of instrument definition, returns bolean.  If it encounters
+        the end of the definition section, it returns False, otherwise True.
+        
+        The contents of the definition section is written to the McStasScript
+        Instr object.
+        """
+        
+        continue_definition = True
+
+        # Remove comments
+        if "//" in line:
+            line = line.split("//")[0]
+        
+        if "(" in line:
+            # Start of instrument definition, get name
+            self.instr_name = line.split("(")[0].split(" ")[-1]
+            self._start_py_file()
+            # Remove the parameters from the paranthesis
+            parameters = line.split("(")[1]
+            if ")" in line:
+                # Found end of definition
+                continue_definition = False
+                # these parameters are to be analyzed
+                parameters = parameters.split(")")[0]
+            
+        elif ")" in line:
+            # Found end of definition
+            continue_definition = False
+            # these parameters are to be analyzed
+            parameters = line.split(")")[0]
+        else:
+            # Neither start or end on this line, analyze everything
+            parameters = line
+        
+        # Separate into individual parameters
+        parameters = parameters.split(",")
+        if "\n" in parameters:
+            parameters.remove("\n")
+                
+        for parameter in parameters:
+            # Analyze individual parameter
+            parameter = parameter.strip()
+            
+            if parameter == "":
+                # If the parameter is empty, skip it.
+                continue
+            
+            # Ready for keyword arguments
+            kw_args = {}
+            
+            # Default to double type if nothing else is set
+            parameter_type = "double"
+            if " " and "=" in parameter:
+                # Read parameter type
+                type_and_name = parameter.split("=", 1)[0].strip()
+                
+                if " " in type_and_name:
+                    parameter_type = type_and_name.split(" ", 1)[0].strip()
+                    parameter = parameter.split(" ", 1)[1].strip()
+            elif " " in parameter:
+                # Read parameter type
+                parameter_type = parameter.split(" ", 1)[0].strip()
+                parameter = parameter.split(" ", 1)[1].strip()
+            
+            if "=" in parameter:
+                # Read default value
+                parameter_name = parameter.split("=")[0].strip()
+                value = parameter.split("=")[1].strip()
+                
+                if parameter_type == "string":
+                    if '"' in value:
+                        value = value.replace('"', "\\\"")
+                        value = "\"" + value + "\""
+                else:
+                    if parameter_type == "int":
+                        value = int(value)
+                    else:
+                        value = float(value)
+
+                # Add defualt value to keyword arguments
+                kw_args["value"] = value
+
+            else:
+                # No default value, just return the striped name
+                parameter_name = parameter.strip()
+                
+            # Add this parameter to the object
+            self.Instr.add_parameter(parameter_type, parameter_name, **kw_args)
+            
+            # Also write it to a file?
+            write_string = []
+            write_string.append(self.instr_name)
+            write_string.append(".add_parameter(")
+            write_string.append("\"" + parameter_type + "\"")
+            write_string.append(", ")
+            write_string.append("\"" + parameter_name + "\"")
+            write_string.append(self._kw_to_string(kw_args))
+            write_string.append(")\n")
+            
+            self._write_to_file(write_string)
+            
+            
+        return continue_definition
+    
+    def _start_py_file(self):
+        write_string = []
+        
+        # Write warning about robustness of this feature
+        write_string.append("\"\"\"\n")
+        write_string.append("This McStasScript file was generated from a McStas\n")
+        write_string.append("instrument file. It is advised to check the content\n")
+        write_string.append("to ensure it is as expected.\n\"\"\"\n")
+        
+        # import McStasScript
+        write_string.append("from mcstasscript.interface ")
+        write_string.append("import ")
+        write_string.append("instr, plotter, functions")
+        write_string.append("\n\n")
+        
+        write_string.append(self.instr_name)
+        write_string.append(" = instr.McStas_instr(")
+        write_string.append("\"" + self.instr_name + "_generated\"")
+        write_string.append(")\n")
+        
+        self._write_to_file(write_string)
+        
+        
+        
+                
\ No newline at end of file
diff --git a/mcstasscript/instr_reader/read_finally.py b/mcstasscript/instr_reader/read_finally.py
new file mode 100644
index 00000000..0dae1be6
--- /dev/null
+++ b/mcstasscript/instr_reader/read_finally.py
@@ -0,0 +1,55 @@
+from mcstasscript.instr_reader.util import SectionReader
+
+class FinallyReader(SectionReader):
+    
+    def __init__(self, Instr, write_file, product_filename, get_next_line, return_line):
+        
+        super().__init__(Instr, write_file, product_filename, get_next_line, return_line)
+        
+    def read_finally_line(self, line):
+        
+        continue_finally = True
+        
+        # Remove comments
+        if "//" in line:
+            line = line.split("//", 1)[0].strip()
+            
+        if line.startswith("FINALLY"):
+            line = line.split("FINALLY", 1)[1].strip()
+            
+        # Remove block opening
+        if "%{" in line:
+            line = line.split("%{", 1)[1].strip()
+            
+        if "%}" in line:
+            line = line.split("%}", 1)[0].strip()
+            continue_finally = False
+            
+        # If the line is just a new line quit
+        if line is "\n" or line is "":
+            return continue_finally
+        
+        # Remove newline at the end of the line
+        if line.endswith("\n"):
+            line = line[:-1]
+            
+        self.Instr.append_finally(line)
+        
+        if self.write_file:
+            # Cant get both \n and " to work in written string
+            write_line = line.replace("\\n","\\\\n")
+            #write_line = line.replace("\\n","test")
+            write_line = write_line.replace("\\t","\\\\t")
+            # May need to expand to more cases
+            
+            write_line = write_line.replace('"', '\\\"')
+            
+            write_string = []
+            write_string.append(self.instr_name)
+            write_string.append(".append_finally(")
+            write_string.append("\"" + write_line + "\"") 
+            write_string.append(")\n")
+            
+            self._write_to_file(write_string)
+        
+        return continue_finally
\ No newline at end of file
diff --git a/mcstasscript/instr_reader/read_initialize.py b/mcstasscript/instr_reader/read_initialize.py
new file mode 100644
index 00000000..c704e231
--- /dev/null
+++ b/mcstasscript/instr_reader/read_initialize.py
@@ -0,0 +1,65 @@
+from mcstasscript.instr_reader.util import SectionReader
+
+class InitializeReader(SectionReader):
+    """
+    Reads the initialize section of a McStas instrument file.
+    The initialize lines are added to the McStasScript instrument, and 
+    are sent to the function writing the lines to the python file.
+    """
+    
+    def __init__(self, Instr, write_file, product_filename, get_next_line, return_line):
+        super().__init__(Instr, write_file, product_filename, get_next_line, return_line)
+        
+    def read_initialize_line(self, line):
+        """
+        Reads lines from INITIALIZE file and returns True as long as
+        the stop characters has not been encountered. Comments are
+        ignored with typical c syntax.
+        """
+        
+        continue_initialize = True
+        
+        # Remove comments
+        if "//" in line:
+            line = line.split("//", 1)[0].strip()
+            
+        if line.startswith("INITIALIZE"):
+            line = line.split("INITIALIZE", 1)[1].strip()
+            
+        if line.startswith("INITIALISE"):
+            line = line.split("INITIALISE", 1)[1].strip()
+            
+        # Remove block opening
+        if "%{" in line:
+            line = line.split("%{", 1)[1].strip()
+            
+        if "%}" in line:
+            line = line.split("%}", 1)[0].strip()
+            continue_initialize = False
+            
+        # If the line is just a new line quit
+        if line is "\n" or line is "":
+            return continue_initialize
+        
+        # Remove newline at the end of the line
+        if line.endswith("\n"):
+            line = line[:-1]
+            
+        self.Instr.append_initialize(line)
+        
+        # Need to prepare string for being written again
+        write_line = line.replace("\\n","\\\\n")
+        write_line = write_line.replace("\\t","\\\\t")
+        write_line = write_line.replace('"', '\\\"')
+        # May need to expand to more cases
+        
+        # Write line to Python file
+        write_string = []
+        write_string.append(self.instr_name)
+        write_string.append(".append_initialize(")
+        write_string.append("\"" + write_line + " \"") 
+        write_string.append(")\n")
+        
+        self._write_to_file(write_string)
+        
+        return continue_initialize
\ No newline at end of file
diff --git a/mcstasscript/instr_reader/read_trace.py b/mcstasscript/instr_reader/read_trace.py
new file mode 100644
index 00000000..fd61e4c1
--- /dev/null
+++ b/mcstasscript/instr_reader/read_trace.py
@@ -0,0 +1,560 @@
+from mcstasscript.instr_reader.util import SectionReader
+from mcstasscript.helper import mcstas_objects
+
+class TraceReader(SectionReader):
+    """
+    Reads the trace section of a McStas instrument file. For each
+    component a McStasScript component instance is created and the
+    parameters/keywords are applied to this instance. When the next
+    component is found, the previous component is written to the
+    python file for reproduction of the McStas instrument.  
+    """
+    
+    def __init__(self, Instr, write_file, product_filename, get_next_line, return_line):
+        
+        super().__init__(Instr, write_file, product_filename, get_next_line, return_line)
+        
+        self.current_component = None
+        self.in_component_mode = False
+        self.EXTEND_mode = False
+        self.component_copy_target = None
+        self.SPLIT = 0
+        self.stored_include = None
+        
+    def sanitize_line(self, line):
+        """
+        Removes comments, the starting blok and newline characters
+        """
+        
+        line = line.strip()
+        
+        # Remove comments
+        if "//" in line:
+            line = line.split("//", 1)[0].strip()
+            
+        if line.startswith("TRACE"):
+            line = line.split("TRACE", 1)[1].strip()
+        
+        if "/*" in line:
+            if "*/" in line:
+                line = line.split("/*", 1)[0] + line.split("*/", 1)[1]
+            else:
+                line = line.split("/*", 1)[0]
+        
+        # Remove newline at the end of the line
+        if line.endswith("\n"):
+            line = line[:-1]
+        
+        return line
+        
+    def read_trace_line(self, line):
+        """
+        Reads line of McStas file from TRACE section. Has the responsibility
+        of setting continue_trace to false when finding the end of the TRACE
+        section. May take extra lines through get_new_line if statements are
+        spaced out over several lines.
+        """
+        
+        continue_trace = True
+        
+        # Find stop characeters
+        if line.startswith("FINALLY"):
+            continue_trace = False
+        
+        if line.startswith("END"):
+            continue_trace = False
+            
+        if line.strip().startswith("%include") or line.strip().startswith("#include"):
+            # Handle include statement and attatch it to a component
+            if self.current_component != None:
+                self.current_component.set_c_code_after(line)
+            else:
+                # If the include statement is before the first component,
+                # it is saved and attatched to the next component
+                line = line.replace('"',"\\\"")
+                self.stored_include = line.strip()
+        
+        # If the line is just a new line quit
+        if line is "\n" or line is "":
+            return continue_trace
+        
+        line = self.sanitize_line(line)
+            
+        # Handle keywords that appear before components
+        if line.startswith("SPLIT"):
+            # Read split and save for the next component
+            line = line.split("SPLIT", 1)[1].strip()
+            if line.startswith("COMPONENT"):
+                # Default split without indicating amount
+                self.SPLIT = "\"\""
+            else:
+                try: 
+                    self.SPLIT = int(line.split(" ", 1)[0].strip())
+                except:
+                    self.SPLIT = "\"" + line.split(" ", 1)[0].strip() + "\""
+
+                if " " in line:
+                    # If the line continues, remove the SPLIT number
+                    line = line.split(" ", 1)[1].strip()
+                    
+        # Read component definition (can be over several lines)
+        if line.startswith("COMPONENT") or line.startswith("REMOVABLE COMPONENT"):
+            # Start ned component, but write the previous component to file first
+            if self.stored_include != None and self.current_component != None:
+                # In case an include statement was stored, include that statement
+                self.current_component.set_c_code_before(self.stored_include)
+                self.stored_include = None
+            # write previous component
+            self._write_component_to_py()
+            
+            # start new component
+            self.in_component_mode = True
+            # Assume this is not a copy
+            self.component_copy_target = None
+            
+            # Remove COMPONENT from line
+            if line.startswith("COMPONENT"): 
+                line = line[9:].strip()
+            elif line.startswith("REMOVABLE COMPONENT"):
+                line = line[19:].strip()
+            
+            # Add new lines until the entire component definition is found
+            full_component_line = False
+            while not full_component_line:
+                
+                expected_end_parenthesis = 1 + line.count("COPY")
+                
+                if line.count("(") >= expected_end_parenthesis:
+                    full_component_line = True
+                
+                if not full_component_line:
+                    new_line = self.get_next_line()
+                    new_line = self.sanitize_line(new_line) 
+                    if new_line.startswith("AT") or new_line.startswith("WHEN"):
+                        full_component_line = True
+                        self.return_line()
+                    else:
+                        line += new_line
+            
+            # Retrieve information from component definition
+            instance_name = line.split("=", 1)[0].strip()
+            component_name = line.split("=", 1)[1].split("(", 1)[0].strip()
+            line = line.split("=", 1)[1].split("(", 1)[1].strip()
+            if component_name == "COPY":
+                # Copy instance
+                self.component_copy_target = line.split(")", 1)[0].strip()
+                if "(" in line:
+                    line = line.split("(", 1)[1].strip()
+                if self.component_copy_target == "PREVIOUS":
+                    # Get the previous component name
+                    last_component = self.Instr.get_last_component()
+                    self.component_copy_target = last_component.name
+                
+                self.current_component = self.Instr.copy_component(instance_name,
+                                                                  self.component_copy_target)
+                 
+            else:
+                # Normal component instance
+                self.current_component = self.Instr.add_component(instance_name,
+                                                                  component_name)
+                
+            # In case there are no parameters, stop in_component_mode
+            if line.startswith(")"):
+                self.in_component_mode = False
+                line = line.split(")", 1)[1]
+                
+            if self.SPLIT != 0:
+                self.current_component.set_SPLIT(self.SPLIT)
+                self.SPLIT = 0
+        
+        # In case of COPY, there can be empty parameter lists
+        if self.in_component_mode: # and self.component_copy_target != None:
+            # Check if this line starts WHEN or AT
+            if line.strip().startswith("AT"):
+                self.in_component_mode = False
+            if line.strip().startswith("WHEN"):
+                self.in_component_mode = False
+            # If none of these occur, read the parameters
+
+        # In component mode reads parameters of each new line read
+        if self.in_component_mode:
+            
+            par_line = line
+            # check for parameters
+            if par_line.strip().startswith("("):
+                par_line = line.split("(", 1)[1]
+            
+            # _in_func like python in, but does not look inside parenthesis
+            if self._in_func(line, ")"):
+                self.in_component_mode = False
+                par_line = self._split_func(line, ")", 1)[0]
+                line = self._split_func(line, ")", 1)[1]
+
+            # All parameters found saved in dictionary
+            par_dict = {}
+            
+            """
+            A parameter line can contain a comma for separating parameters or
+            inside of a string. This piece of code finds the next comma and
+            checks if there is an equal number of quotation marks in the first
+            part, if not it increases the read part of the line to the next
+            comma. In this way commas in strings do not separate parameters
+            in the component input.
+            """ 
+            while len(par_line) > 0:
+                # find the next parameter expression
+                if self._in_func_brack(par_line, ","):
+                    # start of expression to evaluate
+                    par_exp = par_line.split(",", 1)[0].strip()
+                    par_exp = self._split_func_brack(par_line, ",", 1)[0]
+                    # remove the part already taken from the par_line
+                    par_line = self._split_func_brack(par_line, ",", 1)[1]
+                    # The length of quotation_split will be one more than
+                    #  the number of quotation marks in par_exp
+                    quotation_split = par_exp.split('"')
+                    while (len(quotation_split) - 1) % 2 != 0:
+                        # There is an uneven number of quotation marks
+                        par_exp +=  "," # Add the comma taken by split back
+                        if "," in par_line:
+                            # include the up to the next comma in par_exp
+                            par_exp += "," + par_line.split(",",1)[0]
+                            # remove the part of the par_line added to par_exp
+                            par_line = par_line.split(",",1)[1]
+                        else:
+                            # no commas left, must be end of par_line
+                            par_exp += par_line.strip()
+                            par_line = ""
+
+                        quotation_split = par_exp.split('"')
+                else:
+                    # last parameter
+                    par_exp = par_line
+                    par_line = ""
+                    
+                if "=" in par_exp:
+                    par_name = par_exp.split("=", 1)[0].strip()
+                    par_value = par_exp.split("=", 1)[1].strip()
+                    
+                    try:
+                        # No problems if the value is a number
+                        float(par_value)
+                    except:
+                        # If the value is a string, it needs quotes
+                        if '"' in par_value:
+                            # If it already has quotes, these need escapes
+                            par_value = par_value.replace('"','\\\"')
+                        par_value = '"' + par_value + '"'
+                    
+                    par_dict[par_name] = par_value
+            
+            # Set all found parameters in the component
+            self.current_component.set_parameters(par_dict)
+        
+        # Read keywords given after parameters but before position (WHEN)
+        if line.strip().startswith("WHEN"):
+            if "(" in line:
+                line = line.split("(", 1)[1].strip()
+                # need to find the closing parenthesis
+                parenthesis_counter = 1
+                character_index = -1
+                for character in line:
+                    character_index += 1
+                    if character == "(":
+                        parenthesis_counter += 1
+                    if character == ")":    
+                        parenthesis_counter -= 1
+                    if parenthesis_counter == 0:
+                        end_index = character_index
+                        break
+                    
+                WHEN_statement = line[:character_index]
+                WHEN_statement = WHEN_statement.replace('"',"\\\"")
+                line = line[character_index+1:].strip()
+            else:
+                # WHEN statement that does not use parenthesis
+                if "AT" in line:
+                    WHEN_statement = line.split("AT", 1)[0]
+                    line = "AT" + line.split("AT", 1)[1]
+                else:
+                    WHEN_statement = line.split("WHEN ", 1)[1]
+                    line = ""
+
+            self.current_component.set_WHEN(WHEN_statement)
+
+        
+        # Read component position
+        if line.strip().startswith("AT"):
+            # read AT statement
+            line = line.split("(",1)[1].strip()
+            AT_data = []
+            AT_data.append(self._split_func(line, ",", 1)[0])
+            line = self._split_func(line, ",", 1)[1]
+            AT_data.append(self._split_func(line, ",", 1)[0])
+            line = self._split_func(line, ",", 1)[1]
+            AT_data.append(self._split_func(line, ")", 1)[0])
+            line = self._split_func(line, ")", 1)[1]
+            
+            if line.strip().startswith("ABSOLUTE"):
+                line = line.split(" ",1)[1].strip()
+                relative_name = "ABSOLUTE"
+                # The line can continue, remove the used part
+                if " " in line.strip():
+                    line = line.strip().split(" ", 1)[1].strip()
+                else:
+                    line = ""
+            elif line.strip().startswith("RELATIVE"):
+                line = line.strip().split(" ", 1)[1].strip()
+                if " " in line:
+                    relative_name = line.split(" ", 1)[0].strip()
+                else:
+                    relative_name = line.strip()
+                # The line can continue, remove the used part
+                if " " in line:
+                    line = line.split(" ", 1)[1].strip()
+                else:
+                    line = ""
+            else:
+                raise ValueError("Could not read: " + line)
+            
+            self.current_component.set_AT(AT_data, RELATIVE=relative_name)
+            
+        # Read component rotation
+        if line.strip().startswith("ROTATED"):
+            # read ROTATED statement
+            line = line.split("(",1)[1].strip()
+            ROTATED_data = []
+            
+            ROTATED_data.append(self._split_func(line, ",", 1)[0])
+            line = self._split_func(line, ",", 1)[1]
+            ROTATED_data.append(self._split_func(line, ",", 1)[0])
+            line = self._split_func(line, ",", 1)[1]
+            ROTATED_data.append(self._split_func(line, ")", 1)[0])
+            line = self._split_func(line, ")", 1)[1]            
+            
+            if line.strip().startswith("ABSOLUTE"):
+                relative_name = "ABSOLUTE"
+                if " " in line.strip():
+                    line = line.strip().split(" ", 1)[1].strip()
+                else:
+                    line = ""
+            elif line.strip().startswith("RELATIVE"):
+                line = line.strip().split(" ",1)[1].strip()
+                relative_name = line.split(" ", 1)[0].strip()
+                # The line can continue, remove the used part
+                if " " in line:
+                    line = line.split(" ", 1)[1].strip()
+                else:
+                    line = ""
+            else:
+                raise ValueError("Could not read: " + line)
+            
+            self.current_component.set_ROTATED(ROTATED_data,
+                                               RELATIVE=relative_name)
+        
+        # Read keywords after component position (GROUP, EXTEND, JUMP)
+        if line.strip().startswith("GROUP"):
+            line = line.strip()
+            
+            group_name = line.split(" ", 1)[1].strip()
+            group_name = "\"" + group_name + "\""
+            
+            line = "" 
+            
+            self.current_component.set_GROUP(group_name)
+                
+        if line.strip().startswith("EXTEND"):
+            line = line.split("EXTEND", 1)[1].strip()
+            self.EXTEND_mode = True
+            
+        if self.EXTEND_mode:
+            if "%{" in line:
+                line = line.strip().split("%{", 1)[1].strip()
+            
+            if "%}" in line:
+                line = line.strip().split("%}", 1)[0].strip()
+                self.EXTEND_mode = False
+
+            if len(line) > 0 and line != "\n": 
+                line = line.replace('\\n',"\\\\n")
+                line = line.replace('"',"\\\"")
+                self.current_component.append_EXTEND(line)
+                
+        if line.strip().startswith("JUMP "):
+            line = line.strip().split(" ", 1)[1]
+            self.current_component.set_JUMP(line)
+
+        if not continue_trace:
+            # write last component
+            self._write_component_to_py()
+        
+        return continue_trace
+                    
+    def _write_component_to_py(self):
+        # code for writing McStasScript python file
+        if self.current_component is not None:
+            
+            # Write the add_component statement
+            write_string = ["\n"]
+            write_string.append(self.current_component.name)
+            write_string.append(" = ")
+            write_string.append(self.instr_name)
+            if self.component_copy_target == None:
+                write_string.append(".add_component(")
+                write_string.append("\"" + self.current_component.name + "\"")
+                write_string.append(", ")
+                write_string.append("\"" + self.current_component.component_name + "\"")
+            else:
+                write_string.append(".copy_component(")
+                write_string.append("\"" + self.current_component.name + "\"")
+                write_string.append(", ")
+                write_string.append("\"" + self.component_copy_target + "\"")
+            write_string.append(")\n")
+            
+            self._write_to_file(write_string)
+            
+            # Write all parameters as attribute updates
+            for key in self.current_component.parameter_names:
+                val = getattr(self.current_component, key)
+                if val != None:
+                    write_string = []
+                    write_string.append(self.current_component.name)
+                    write_string.append(".")
+                    write_string.append(key)
+                    write_string.append(" = ")
+
+                    write_string.append(val)
+                    write_string.append("\n")
+                
+                    self._write_to_file(write_string)
+                    
+            # Write EXTEND block if present
+            if self.current_component.EXTEND != "":
+                EXTEND = self.current_component.EXTEND
+                EXTEND_lines = EXTEND.split("\n")
+                EXTEND_lines = EXTEND_lines[:-1]
+                
+                for EXTEND_line in EXTEND_lines:
+                    write_string = []
+                    write_string.append(self.current_component.name)
+                    write_string.append(".append_EXTEND(")
+                    write_string.append("\"" + EXTEND_line + "\"")
+                    write_string.append(")\n")
+                
+                    self._write_to_file(write_string)
+                                        
+            # Write WHEN statement if present
+            if self.current_component.WHEN != "":
+                write_string = []
+                write_string.append(self.current_component.name)
+                write_string.append(".set_WHEN(")
+                WHEN = self.current_component.WHEN
+                WHEN = WHEN.split("(", 1)[1].strip()
+                WHEN = WHEN[:-1]
+                write_string.append("\"" + WHEN + "\"")
+                write_string.append(")\n")
+                
+                self._write_to_file(write_string)
+
+            # Write SPLIT if present
+            if self.current_component.SPLIT != 0:
+                write_string = []
+                write_string.append(self.current_component.name)
+                write_string.append(".set_SPLIT(")
+                write_string.append(str(self.current_component.SPLIT))
+                write_string.append(")\n")
+                
+                self._write_to_file(write_string)
+            
+            # Write GROUP if present
+            if self.current_component.GROUP != "":
+                write_string = []
+                write_string.append(self.current_component.name)
+                write_string.append(".set_GROUP(")
+                write_string.append(str(self.current_component.GROUP))
+                write_string.append(")\n")
+                
+                self._write_to_file(write_string)
+            
+            # Write JUMP if present
+            if self.current_component.JUMP != "":
+                write_string = []
+                write_string.append(self.current_component.name)
+                write_string.append(".set_JUMP(")
+                write_string.append("\"" + str(self.current_component.JUMP) + "\"")
+                write_string.append(")\n")
+                
+                self._write_to_file(write_string)
+                
+            # Write c_code_before if present
+            if self.current_component.c_code_before != "":
+                write_string = []
+                write_string.append(self.current_component.name)
+                write_string.append(".set_c_code_before(")
+                write_string.append("\"" + str(self.current_component.c_code_before) + "\"")
+                write_string.append(")\n")
+                
+                self._write_to_file(write_string)
+            
+            # Write c_code_after if present
+            if self.current_component.c_code_after != "":
+                write_string = []
+                write_string.append(self.current_component.name)
+                write_string.append(".set_c_code_after(")
+                write_string.append("\"" + str(self.current_component.c_code_after) + "\"")
+                write_string.append(")\n")
+                
+                self._write_to_file(write_string)
+            
+            # Write AT
+            write_string = []
+            write_string.append(self.current_component.name)
+            write_string.append(".set_AT(")
+            write_string.append(str(self.current_component.AT_data))
+            write_string.append(", RELATIVE=")
+            if self.current_component.AT_relative == "ABSOLUTE":
+                write_string.append("\"" + "ABSOLUTE" + "\"")
+            else:
+                relative = self.current_component.AT_relative.split(" ")[1] 
+                write_string.append("\"" + relative + "\"")
+            write_string.append(")\n")
+            
+            self._write_to_file(write_string)
+            
+            # Write ROTATED
+            write_string = []
+            write_string.append(self.current_component.name)
+            write_string.append(".set_ROTATED(")
+            write_string.append(str(self.current_component.ROTATED_data))
+            write_string.append(", RELATIVE=")
+            if self.current_component.ROTATED_relative == "ABSOLUTE":
+                write_string.append("\"" + "ABSOLUTE" + "\"")
+            else:
+                relative = self.current_component.ROTATED_relative.split(" ")[1] 
+                write_string.append("\"" + relative + "\"")
+            write_string.append(")\n")
+            
+            if self.current_component.ROTATED_specified:
+                self._write_to_file(write_string)
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+        
+        
+        
\ No newline at end of file
diff --git a/mcstasscript/instr_reader/util.py b/mcstasscript/instr_reader/util.py
new file mode 100644
index 00000000..38135579
--- /dev/null
+++ b/mcstasscript/instr_reader/util.py
@@ -0,0 +1,149 @@
+
+class SectionReader:
+    """
+    Super class for the many necessary readers
+    """
+    
+    def __init__(self, Instr, write_file, product_filename, get_next_line, return_line):
+        self.Instr = Instr
+        self.write_file = write_file
+        self.product_filename = product_filename
+        self.instr_name = ""
+        self.get_next_line = get_next_line
+        self.return_line = return_line
+        
+    def set_instr_name(self, name):
+        self.instr_name = name
+        
+    def _write_to_file(self, string_array):
+        """
+        In case a py file is being written, this function writes to the
+        appropriate file.
+        """
+        
+        if self.write_file:
+            with open(self.product_filename, "a") as product_file:
+                for string in string_array:
+                    product_file.write(string)
+                    
+    def _kw_to_string(self, kwargs):
+        """
+        Used when a dict containing keyword arguments need to be written
+        to a string. This string can be used as argument in method call.
+        """
+        
+        output_string = ""
+        for kwarg in kwargs:
+            output_string += ", "
+            output_string += kwarg + "=" + str(kwargs[kwarg])
+    
+        return output_string
+    
+    def _split_func(self, *args):
+        """
+        Returns list of strings seperated by commas that are not
+        within open parenthesis. 
+        """
+        
+        string = args[0]
+        split_character = args[1]
+        
+        if len(args) == 3:
+            limit = args[2]
+        else:
+            limit = -1
+        
+        split_positions = []
+        parenthesis = 0
+        for index in range(0,len(string)):
+            character = string[index]
+            if character == split_character and parenthesis == 0 and limit != 0:
+                split_positions.append(index)
+                limit -= 1
+            else:
+                if character == "(":
+                    parenthesis += 1
+                if character == ")":
+                    parenthesis -= 1
+
+        split_positions.append(len(string)+1) # virtual comma at the end
+
+        result = []
+        last_position = 0
+        for position in split_positions:
+            result.append(string[last_position:position])
+            last_position = position + 1
+
+        return result
+    
+    def _split_func_brack(self, *args):
+        """
+        Returns list of strings seperated by commas that are not
+        within open parenthesis / brackets 
+        """
+        
+        string = args[0]
+        split_character = args[1]
+        
+        if len(args) == 3:
+            limit = args[2]
+        else:
+            limit = -1
+        
+        split_positions = []
+        parenthesis = 0
+        brackets = 0
+        for index in range(0,len(string)):
+            character = string[index]
+            if (character == split_character and parenthesis == 0  
+                    and brackets == 0and limit != 0):
+                split_positions.append(index)
+                limit -= 1
+            else:
+                if character == "(":
+                    parenthesis += 1
+                if character == ")":
+                    parenthesis -= 1
+                if character == "{":
+                    brackets += 1
+                if character == "}":
+                    brackets -= 1
+
+        split_positions.append(len(string)+1) # virtual comma at the end
+
+        result = []
+        last_position = 0
+        for position in split_positions:
+            result.append(string[last_position:position])
+            last_position = position + 1
+
+        return result
+    
+    def _in_func(self, string, character):
+        """
+        Returns true of character is in string when excluding occurances
+        within parenthesis. 
+        """
+        
+        if len(self._split_func(string, character, 1)) == 2:
+            return True
+        else:
+            return False
+        
+    def _in_func_brack(self, string, character):
+        """
+        Returns true of character is in string when excluding occurances
+        within parenthesis and brackets. 
+        """
+        
+        if len(self._split_func_brack(string, character, 1)) == 2:
+            return True
+        else:
+            return False
+        
+        
+        
+        
+        
+    
+    
\ No newline at end of file
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index bf9ba1c2..95473f50 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -4,6 +4,7 @@
 import datetime
 import yaml
 import subprocess
+import copy
 
 from mcstasscript.data.data import McStasData
 from mcstasscript.helper.mcstas_objects import declare_variable
@@ -61,7 +62,7 @@ class McStas_instr:
 
     Methods
     -------
-    add_parameter(*args,**kwargs)
+    add_parameter(*args, **kwargs)
         Adds input parameter to the define section
 
     add_declare_var()
@@ -85,7 +86,7 @@ class McStas_instr:
     show_components(string)
         Shows available components in given category
 
-    add_component(instance_name,component_name,**kwargs)
+    add_component(instance_name, component_name, **kwargs)
         Add a component to the instrument file
 
     get_component(instance_name)
@@ -94,34 +95,40 @@ class McStas_instr:
     get_last_component()
         Returns component instance of last component
 
-    set_component_parameter(instance_name,dict)
+    set_component_parameter(instance_name, dict)
         Adds parameters as dict to component with instance_name
 
-    set_component_AT(instance_name,AT_data,**kwargs)
+    set_component_AT(instance_name, AT_data, **kwargs)
         Sets position of component named instance_name
 
-    set_component_ROTATED(instance_name,ROTATED_data,**kwargs)
+    set_component_ROTATED(instance_name, ROTATED_data, **kwargs)
         Sets rotation of component named instance_name
 
-    set_component_RELATIVE(instane_name,string)
+    set_component_RELATIVE(instane_name, string)
         Sets position and rotation reference for named component
 
-    set_component_WHEN(instance_name,string)
+    set_component_WHEN(instance_name, string)
         Sets WHEN condition of named component, is logical c expression
 
-    set_component_GROUP(instance_name,string)
+    set_component_GROUP(instance_name, string)
         Sets GROUP name of component named instance_name
 
-    append_component_EXTEND(instance_name,string)
+    append_component_EXTEND(instance_name, string)
         Appends a line to EXTEND section of named component
 
-    set_component_JUMP(instance_name,string)
+    set_component_JUMP(instance_name, string)
         Sets JUMP code for named component
 
-    set_component_SPLIT(instance_name,string)
+    set_component_SPLIT(instance_name, string)
         Sets SPLIT value for named component
-
-    set_component_comment(instance_name,string)
+        
+    set_component_c_code_before(instance_name, string)
+        Sets c code before the component
+        
+    set_component_c_code_after(instance_name, string)
+        Sets c code after the component
+        
+    set_component_comment(instance_name, string)
         Sets comment to be written before named component
 
     print_component(instance_name)
@@ -213,6 +220,7 @@ def __init__(self, name, **kwargs):
 
         self.parameter_list = []
         self.declare_list = []
+        #self.declare_section = ""
         self.initialize_section = ("// Start of initialize for generated "
                                    + name + "\n")
         self.trace_section = ("// Start of trace section for generated "
@@ -361,6 +369,24 @@ def add_declare_var(self, *args, **kwargs):
         """
         # declare_variable class documented independently
         self.declare_list.append(declare_variable(*args, **kwargs))
+        
+    def append_declare(self, string):
+        """
+        Method for appending code to the declare section directly
+        
+        This method is not meant for declaring simple variables which
+        should be done using add_declare_var. This method can be used
+        to declare functions, structures and unions directly.
+        
+        Parameters
+        ----------
+        string : str
+            code to be added to declare section
+        """
+        
+        #self.declare_section = self.declare_section + string + "\n"
+        self.declare_list.append(string)
+        
 
     def append_initialize(self, string):
         """
@@ -375,6 +401,7 @@ def append_initialize(self, string):
         string : str
             code to be added to initialize section
         """
+        
         self.initialize_section = self.initialize_section + string + "\n"
 
     def append_initialize_no_new_line(self, string):
@@ -649,6 +676,142 @@ def add_component(self, *args, **kwargs):
             self.component_name_list.append(args[0])
 
         return new_component
+    
+    def copy_component(self, *args, **kwargs):
+        """
+        Method for adding a copy of a component instance to the instrument
+
+        Creates a copy of component instance in the instrument.  This
+        requires a unique instance name of the component to be used for
+        future reference and the name of the McStas component to be
+        used.  The component is placed at the end of the instrument file
+        unless otherwise specified with the after and before keywords.
+        The component may be initialized using other keyword arguments,
+        but all attributes can be set with approrpiate methods.
+
+        Parameters
+        ----------
+        First positional argument : str
+            Unique name of component instance
+
+        Second positional argument : str
+            Name of component instance to create copy of
+
+        Keyword arguments:
+            after : str
+                Place this component after component with given name
+
+            before : str
+                Place this component before component with given name
+
+            AT : List of 3 floats
+                Sets AT_data, position relative to reference
+
+            AT_RELATIVE : str
+                Sets reference component for postion
+
+            ROTATED : List of 3 floats
+                Sets ROTATED_data, rotation relative to reference
+
+            ROTATED_RELATIVE : str
+                Sets reference component for rotation
+
+            RELATIVE : str
+                Sets reference component for both position and rotation
+
+            WHEN : str
+                Sets when condition which must be a logical c expression
+
+            EXTEND : str
+                Initialize the extend section with a line of c code
+
+            GROUP : str
+                Name of the group this component should belong to
+
+            JUMP : str
+                Set code for McStas JUMP statement
+
+            comment : str
+                Comment that will be displayed before the component
+        """
+
+        # could also allow input of a component object        
+        
+        instance_name = args[0]
+        """
+        If the name starts with COPY, use unique naming as described in the
+        McStas manual.
+        """
+        if instance_name.startswith("COPY("):
+            target_name = instance_name.split("(", 1)[1]
+            target_name = target_name.split(")", 1)[0]
+            instance_name = target_name
+
+            label = 0
+            instance_name = target_name + "_" + str(label)
+            while instance_name in self.component_name_list:
+                instance_name = target_name + "_" + str(label)
+                label += 1
+
+        if instance_name in self.component_name_list:
+            raise NameError(("Component name \"" + str(args[0])
+                             + "\" used twice, McStas does not allow this."
+                             + " Rename or remove one instance of this"
+                             + " name."))
+        
+        if not args[1] in self.component_name_list:
+            raise NameError("Component name \"" + str(args[1])
+                            + "\" was not found in the McStas instrument."
+                            + " and thus can not be copied.")
+        else:
+            component_to_copy = self.get_component(args[1])
+        
+        # Insert component after component with this name
+        if "after" in kwargs:
+            if kwargs["after"] not in self.component_name_list:
+                raise NameError("Trying to add a component after a component"
+                                + " named \"" + str(kwargs["after"])
+                                + "\", but a component with that name was"
+                                + " not found.")
+
+            new_index = self.component_name_list.index(kwargs["after"])
+
+            new_component = copy.deepcopy(component_to_copy)
+            new_component.name = instance_name
+            self.component_list.insert(new_index+1, new_component)
+
+            self.component_name_list.insert(new_index+1, instance_name)
+
+        # Insert component after component with this name
+        elif "before" in kwargs:
+            if kwargs["before"] not in self.component_name_list:
+                raise NameError(("Trying to add a component before a "
+                                 + "component named \""
+                                 + str(kwargs["before"])
+                                 + "\", but a component with that "
+                                 + "name was not found."))
+
+            new_index = self.component_name_list.index(kwargs["before"])
+
+            new_component = copy.deepcopy(component_to_copy)
+            new_component.name = instance_name
+            self.component_list.insert(new_index, new_component)
+
+            self.component_name_list.insert(new_index, instance_name)
+
+        # If after or before keywords absent, place component at the end
+        else:
+            new_component = copy.deepcopy(component_to_copy)
+            new_component.name = instance_name
+            self.component_list.append(new_component)
+            self.component_name_list.append(instance_name)
+        
+        # Set the new name of the instance
+        new_component.name = instance_name
+        # Run set_keyword_input again for keyword arguments to take effect
+        new_component.set_keyword_input(**kwargs)
+
+        return new_component
 
     def get_component(self, name):
         """
@@ -838,6 +1001,38 @@ def set_component_SPLIT(self, name, SPLIT):
 
         component = self.get_component(name)
         component.set_SPLIT(SPLIT)
+        
+    def set_component_c_code_before(self, name, code):
+        """
+        Method for setting c code before component
+
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify
+            
+        code : str
+            Code to be pasted before component
+        """
+
+        component = self.get_component(name)
+        component.set_c_code_before(code)
+        
+    def set_component_c_code_after(self, name, code):
+        """
+        Method for setting c code before component
+
+        Parameters
+        ----------
+        name : str
+            Unique name of component to modify
+        
+        code : str
+            Code to be pasted after component
+        """
+
+        component = self.get_component(name)
+        component.set_c_code_after(code)        
 
     def set_component_comment(self, name, string):
         """
@@ -1081,8 +1276,13 @@ def write_c_files(self):
         fo.write("// declare section for %s \n" % self.name)
         fo.close()
         fo = open("./generated_includes/" + self.name + "_declare.c", "a")
+        #fo.write(self.declare_section)
         for dec_line in self.declare_list:
-            dec_line.write_line(fo)
+            if isinstance(dec_line, str):
+                # append declare section parts written here
+                fo.write(dec_line)
+            else:
+                dec_line.write_line(fo)
             fo.write("\n")
         fo.close()
 
@@ -1154,8 +1354,13 @@ def write_full_instrument(self):
 
         # Write declare
         fo.write("DECLARE \n%{\n")
+        #fo.write(self.declare_section)
         for dec_line in self.declare_list:
-            dec_line.write_line(fo)
+            if isinstance(dec_line, str):
+                # append declare section parts written here
+                fo.write(dec_line)
+            else:
+                dec_line.write_line(fo)
             fo.write("\n")
         fo.write("%}\n\n")
 
diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index 048c96c7..40c9cc32 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -6,9 +6,6 @@
 from matplotlib.colors import BoundaryNorm
 from matplotlib.ticker import MaxNLocator
 
-from openpyxl.worksheet import dimensions
-from boto.ec2.autoscale import limits
-
 from mcstasscript.data.data import McStasMetaData
 from mcstasscript.data.data import McStasPlotOptions
 from mcstasscript.data.data import McStasData
diff --git a/mcstasscript/interface/reader.py b/mcstasscript/interface/reader.py
new file mode 100644
index 00000000..2c8d4465
--- /dev/null
+++ b/mcstasscript/interface/reader.py
@@ -0,0 +1,81 @@
+import os
+from mcstasscript.instr_reader.control import InstrumentReader
+from mcstasscript.interface.instr import McStas_instr
+
+class McStas_file:
+    """
+    Reader of McStas files, can add to an existing McStasScript
+    instrument instance or create a corresponding McStasScript python
+    file.
+    
+    
+    Methods
+    -------
+    
+    add_to_instr(Instr)
+        Add information from McStas file to McStasScript Instr instance
+        
+    write_python_file(filename)
+        Write python file named filename that reproduce the McStas instr 
+    
+    """
+    
+    def __init__(self, filename):
+        """
+        Initialization of McStas_file class, needs McStas instr filename
+
+        Parameters
+        ----------
+            filename (str)
+                Name of McStas instrument file to be read
+        """
+
+        # Check filename
+        if not os.path.isfile(filename):
+            raise ValueError("Given filename, \"" + filename
+                             + "\" could not be found.")
+
+        self.Reader = InstrumentReader(filename)
+
+    def add_to_instr(self, Instr):
+        """
+        Adds information from the McStas file to McStasScript instr
+
+        Parameters
+        ----------
+            Instr (McStasScript McStas_instr instance)
+                McStas_instr instance to add instrument information to    
+        """
+
+        # Check Instr
+        if not isinstance(Instr, McStas_instr):
+            raise TypeError("Given object is not of type McStas_instr!")
+        
+        self.Reader.add_to_instr(Instr)
+
+    def write_python_file(self, filename, **kwargs):
+        """
+        Writes python file that reproduces McStas instrument file
+
+        Parameters
+        ----------
+            filename (str)
+                Filename of python file to be written    
+        """
+
+        if "force" in kwargs:
+            force = kwargs["force"]
+        else:
+            force = False
+
+        # Check product_filename is available
+        if os.path.isfile(filename):
+            if force:
+                os.remove(filename)
+            else:
+                raise ValueError("Filename \"" + filename 
+                                 + "\" already exists, you can overwrite with "
+                                 + "force=True")
+
+        self.Reader.generate_py_version(filename)
+        
diff --git a/mcstasscript/tests/Union_demonstration_test.instr b/mcstasscript/tests/Union_demonstration_test.instr
new file mode 100644
index 00000000..765ad512
--- /dev/null
+++ b/mcstasscript/tests/Union_demonstration_test.instr
@@ -0,0 +1,422 @@
+/*******************************************************************************
+*
+* McStas, neutron ray-tracing package
+*         Copyright (C) 1997-2008, All rights reserved
+*         Risoe National Laboratory, Roskilde, Denmark
+*         Institut Laue Langevin, Grenoble, France
+*
+* Instrument: Union_demonstration
+*
+* %Identification
+* Written by: Mads Bertelsen
+* Date: September 2015
+* Origin: University of Copenhagen
+* %INSTRUMENT_SITE: Union_demos
+*
+* %Description
+* Demonstration of Union components. Here four different powder samples are
+*  placed in a can each connected to a weird sample holder and contained in
+*  a cryostat. This unrealistic example is meant to show the syntax and the
+*  new possibilities when using the Union components.
+* With the standard source only two of the samples are illuminated, yet
+*  multiple scattering occur and events are thus taking place in the last
+*  two samples.
+*
+* Example: Detector: m4pi_two_or_more_samples_I=0.0945567
+*
+* %Parameters
+* stick_displacement [m] height displacement of sample stick
+*
+* %End
+*******************************************************************************/
+
+DEFINE INSTRUMENT Union_demonstration(stick_displacement=0,
+int test_int = 3,
+string test_str = "hurray")
+
+DECLARE
+%{
+int sample_1_index=27,sample_2_index=30,sample_3_index=33,sample_4_index=36; // Indexes of four samples
+int scattered_1,scattered_2,scattered_3,scattered_4;
+double array[3] = {0.1,0.2,0.3};
+int I_array[4];
+int T_array[5] = {1,2,3,4,5};
+char home[20] = "test_string";
+int necessary = 1;
+%}
+
+INITIALIZE
+%{
+// Start of initialize for generated Union_demonstration_copy
+/* A=B */
+// A=8
+I_array[2] = 8;
+printf("Hello world\n");
+%}
+
+TRACE
+
+
+
+COMPONENT Vanadium_incoherent = Incoherent_process(sigma=5.08,packing_factor=1,unit_cell_volume=13.827)
+AT (0,0,0) ABSOLUTE
+
+// Here manual linking is used, the process string is writte explicitly
+COMPONENT Vanadium = Union_make_material(my_absorption=2.1,process_string="Vanadium_incoherent")
+AT (0,0,0) ABSOLUTE
+
+// P0
+COMPONENT Al_incoherent = Incoherent_process(sigma=4*0.0082,packing_factor=1,unit_cell_volume=66.4) //,interact_fraction=0.8)
+AT (0,0,0) ABSOLUTE
+
+// P1
+COMPONENT Al_powder = Powder_process(reflections="Al.laz")
+AT (0,0,0) ABSOLUTE
+
+COMPONENT Al = Union_make_material(my_absorption=100*4*0.231/66.4,process_string="Al_incoherent,Al_powder")
+AT (0,0,0) ABSOLUTE
+
+
+// Cu definition
+// P0
+COMPONENT Cu_incoherent = Incoherent_process(sigma=4*0.55,packing_factor=1,unit_cell_volume=47.22)
+AT (0,0,0) ABSOLUTE
+
+// P1
+COMPONENT Cu_powder = Powder_process(reflections="Cu.laz")
+AT (0,0,0) ABSOLUTE
+
+COMPONENT Cu = Union_make_material(my_absorption=100*4*3.78/47.22,process_string="Cu_incoherent,Cu_powder")
+AT (0,0,0) ABSOLUTE
+
+// Ag Au mix definition
+// P0
+COMPONENT Ag_incoherent = Incoherent_process(sigma=4*0.58,packing_factor=1,unit_cell_volume=68.22,packing_factor=0.5)
+AT (0,0,0) ABSOLUTE
+
+// P1
+COMPONENT Ag_powder = Powder_process(reflections="Ag.laz",packing_factor=0.5)
+AT (0,0,0) ABSOLUTE
+
+// P2
+COMPONENT Au_incoherent = Incoherent_process(sigma=4*0.43,packing_factor=1,unit_cell_volume=67.87,packing_factor=0.5)
+AT (0,0,0) ABSOLUTE
+
+// P3
+COMPONENT Au_powder = Powder_process(reflections="Au.laz",packing_factor=0.5)
+AT (0,0,0) ABSOLUTE
+
+// Here automatic linking is used, all process defined after the last make_material component
+//  is automatically collected in this next make_material component as the process string
+//  is not specified.
+COMPONENT Au_Ag_mix = Union_make_material(my_absorption=0.5*100*4*3.78/68.22+0.5*100*4*98.65/67.87)
+AT (0,0,0) ABSOLUTE
+
+// Cd definition
+// P0
+COMPONENT Cd_incoherent = Incoherent_process(sigma=2*3.46,packing_factor=1,unit_cell_volume=43.11)
+AT (0,0,0) ABSOLUTE
+
+// P1
+COMPONENT Cd_powder = Powder_process(reflections="Cd.laz")
+AT (0,0,0) ABSOLUTE
+
+COMPONENT Cd = Union_make_material(my_absorption=100*2*2520/43.11)
+AT (0,0,0) ABSOLUTE
+
+// Cs definition
+// P0
+COMPONENT Cs_incoherent = Incoherent_process(sigma=2*0.55,packing_factor=1,unit_cell_volume=47.22)
+AT (0,0,0) ABSOLUTE
+
+// P1
+COMPONENT Cs_powder = Powder_process(reflections="Cs.laz")
+AT (0,0,0) ABSOLUTE
+
+COMPONENT Cs = Union_make_material(my_absorption=100*2*3.78/47.22)
+AT (0,0,0) ABSOLUTE
+
+
+
+COMPONENT a1 = Progress_bar()
+  AT (0,0,0) ABSOLUTE
+
+// Source for transmission picture
+//COMPONENT source = Source_div(
+//        xwidth=0.12, yheight=0.12,focus_aw=0.5, focus_ah=0.5,
+//        E0 = 50,
+//        dE = 0, flux = 1E9)
+//  AT (0,-0.02,0) RELATIVE a1 ROTATED (0,0,0) RELATIVE a1
+
+COMPONENT source = Source_div(
+        xwidth=0.04, yheight=0.08,focus_aw=0.05, focus_ah=0.05,
+        E0 = 50,
+        dE = 0, flux = 1E9)
+  AT (0.013,-0.02,0) RELATIVE a1 ROTATED (0,0,0) RELATIVE a1
+
+
+// Sample position
+COMPONENT beam_center = Arm()
+AT (0,0,3) RELATIVE a1
+ROTATED (0,0,0) RELATIVE a1
+
+COMPONENT drum_center = Arm()
+AT (0,0.38,0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+
+// V1
+COMPONENT cryostat_mountin_plate = Union_cylinder(radius=0.12,yheight=0.01,priority=7,material_string="Al")
+AT (0,-0.103,0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+// V2
+COMPONENT cryostat_drum_walls = Union_cylinder(radius=0.2,yheight=0.57,priority=8,material_string="Al")
+AT (0,0,0) RELATIVE drum_center
+ROTATED (0,0,0) RELATIVE drum_center
+
+// V3
+COMPONENT cryostat_drum_vacuum = Union_cylinder(radius=0.19,yheight=0.55,priority=9,material_string="Vacuum")
+AT (0,0,0) RELATIVE drum_center
+ROTATED (0,0,0) RELATIVE drum_center
+
+// V4
+COMPONENT outer_cryostat_wall = Union_cylinder(radius=0.1,yheight=0.2,priority=10,material_string="Al",p_interact=0.2)
+AT (0,0,0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+// V5
+COMPONENT outer_cryostat_vacuum = Union_cylinder(radius=0.09,yheight=0.2,priority=11,material_string="Vacuum")
+WHEN ( necessary == 1 )
+AT (0,0.01,0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+// V6
+COMPONENT inner_cryostat_wall = Union_cylinder(radius=0.06,yheight=0.16,priority=12,material_string="Al",p_interact=0.2)
+AT (0,0.01,0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+// V7
+COMPONENT inner_cryostat_vacuum = Union_cylinder(radius=0.05,yheight=0.15,priority=13,material_string="Vacuum")
+AT (0,0.01,0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+// V8
+COMPONENT sample_stick_walls = Union_cylinder(radius=0.04,yheight=0.605,priority=14,material_string="Al")
+AT (0,0.39,0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+// V9
+COMPONENT sample_stick_vacuum = Union_cylinder(radius=0.035,yheight=0.64,priority=15,material_string="Vacuum")
+AT (0,0.4,0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+
+COMPONENT sample_rod_bottom = Arm()
+AT (0,0.05+stick_displacement,0) RELATIVE beam_center
+ROTATED (0,85,0) RELATIVE beam_center
+
+// V10
+COMPONENT sample_rod = Union_cylinder(radius=0.0075,yheight=0.7,priority=25,material_string="Al")
+AT (0,0.35,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V11
+COMPONENT sample_rod_collar_1 = Union_cylinder(radius=0.034,yheight=0.02,priority=17,material_string="Al")
+AT (0,0.048,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V12
+COMPONENT sample_rod_collar_2 = Union_cylinder(radius=0.034,yheight=0.02,priority=18,material_string="Al")
+AT (0,0.14,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V13
+COMPONENT sample_rod_collar_3 = Union_cylinder(radius=0.034,yheight=0.02,priority=19,material_string="Al")
+AT (0,0.34,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V14
+COMPONENT sample_rod_collar_4 = Union_cylinder(radius=0.034,yheight=0.02,priority=20,material_string="Al")
+AT (0,0.635,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V15
+COMPONENT sample_rod_collar_1_vacuum = Union_cylinder(radius=0.03,yheight=0.016,priority=21,material_string="Vacuum")
+AT (0,0.048-0.005,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V16
+COMPONENT sample_rod_collar_2_vacuum = Union_cylinder(radius=0.03,yheight=0.016,priority=22,material_string="Vacuum")
+AT (0,0.14-0.005,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V17
+COMPONENT sample_rod_collar_3_vacuum = Union_cylinder(radius=0.03,yheight=0.016,priority=23,material_string="Vacuum")
+AT (0,0.34-0.005,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V18
+COMPONENT sample_rod_collar_4_vacuum = Union_cylinder(radius=0.03,yheight=0.016,priority=24,material_string="Vacuum")
+AT (0,0.635-0.005,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V19
+COMPONENT sample_holder1 = Union_box(xwidth=0.01,yheight=0.05,zdepth=0.004,priority=35,material_string="Al",p_interact=0.3)
+AT (0,0,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V20
+COMPONENT sample_holder2 = Union_box(xwidth=0.0099,yheight=0.004,zdepth=0.03/0.85,priority=51,material_string="Al",p_interact=0.3)
+AT (0,-0.03,0.03*0.35+0.004) RELATIVE sample_rod_bottom
+ROTATED (25,0,0) RELATIVE sample_rod_bottom
+
+// V21
+COMPONENT sample_holder3 = Union_box(xwidth=0.0098,yheight=0.004,zdepth=0.03/0.85,priority=52,material_string="Al",p_interact=0.3)
+AT (0,-0.03,-0.03*0.35-0.004) RELATIVE sample_rod_bottom
+ROTATED (-25,0,0) RELATIVE sample_rod_bottom
+
+// V22
+COMPONENT sample_holder4 = Union_box(xwidth=0.01,yheight=0.07,zdepth=0.004,priority=53,material_string="Al",p_interact=0.3)
+AT (0,-0.03-0.035-0.005,0.03) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V23
+COMPONENT sample_holder5 = Union_box(xwidth=0.01,yheight=0.07,zdepth=0.004,priority=54,material_string="Al",p_interact=0.3)
+AT (0,-0.03-0.035-0.005,-0.03) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V24
+COMPONENT sample_holder_bottom = Union_box(xwidth=0.0098,yheight=0.004,zdepth=0.058,priority=42,material_string="Al",p_interact=0.3)
+AT (0,-0.03-0.067-0.007,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V25
+COMPONENT sample_holder_top_shelf = Union_box(xwidth=0.0098,yheight=0.004,zdepth=0.058,priority=43,material_string="Al",p_interact=0.3)
+AT (0,-0.045+0.003,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V26
+COMPONENT sample_holder_middle_shelf = Union_box(xwidth=0.0098,yheight=0.004,zdepth=0.058,priority=44,material_string="Al",p_interact=0.3)
+AT (0,-0.072,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V27
+COMPONENT sample_1 = Union_cylinder(radius=0.0045,yheight=0.02,priority=63,material_string="Cu",p_interact=0.6)
+AT (0,-0.002-0.01-0.003,0.015) RELATIVE sample_holder_top_shelf
+ROTATED (0,0,0) RELATIVE sample_holder_top_shelf
+
+// V28
+COMPONENT sample_1_container = Union_cylinder(radius=0.0052,yheight=0.023,priority=62,material_string="Al",p_interact=0.3)
+AT (0,0,0) RELATIVE sample_1
+ROTATED (0,0,0) RELATIVE sample_1
+
+// V29
+COMPONENT sample_1_container_rim = Union_cylinder(radius=0.007,yheight=0.002,priority=61,material_string="Al",p_interact=0.3)
+AT (0,0.023*0.5,0) RELATIVE sample_1
+ROTATED (0,0,0) RELATIVE sample_1
+
+// V30
+COMPONENT sample_2 = Union_cylinder(radius=0.0045,yheight=0.02,priority=73,material_string="Au_Ag_mix",p_interact=0.6)
+AT (0,-0.002-0.01-0.003,-0.015) RELATIVE sample_holder_top_shelf
+ROTATED (0,0,0) RELATIVE sample_holder_top_shelf
+
+// V31
+COMPONENT sample_2_container = Union_cylinder(radius=0.0052,yheight=0.023,priority=72,material_string="Al",p_interact=0.3)
+AT (0,0,0) RELATIVE sample_2
+ROTATED (0,0,0) RELATIVE sample_2
+
+// V32
+COMPONENT sample_2_container_rim = Union_cylinder(radius=0.007,yheight=0.002,priority=71,material_string="Al",p_interact=0.3)
+AT (0,0.023*0.5,0) RELATIVE sample_2
+ROTATED (0,0,0) RELATIVE sample_2
+
+// V33
+COMPONENT sample_3 = Union_cylinder(radius=0.0045,yheight=0.02,priority=83,material_string="Cd",p_interact=0.6)
+AT (0,-0.002-0.01-0.003,0.015) RELATIVE sample_holder_middle_shelf
+ROTATED (0,0,0) RELATIVE sample_holder_middle_shelf
+
+// V34
+COMPONENT sample_3_container = Union_cylinder(radius=0.0052,yheight=0.023,priority=82,material_string="Al",p_interact=0.3)
+AT (0,0,0) RELATIVE sample_3
+ROTATED (0,0,0) RELATIVE sample_3
+
+// V35
+COMPONENT sample_3_container_rim = Union_cylinder(radius=0.007,yheight=0.002,priority=81,material_string="Al",p_interact=0.3)
+AT (0,0.023*0.5,0) RELATIVE sample_3
+ROTATED (0,0,0) RELATIVE sample_3
+
+// V36
+COMPONENT sample_4 = Union_cylinder(radius=0.0045,yheight=0.02,priority=93,material_string="Cs",p_interact=0.6)
+AT (0,-0.002-0.01-0.003,-0.015) RELATIVE sample_holder_middle_shelf
+ROTATED (0,0,0) RELATIVE sample_holder_middle_shelf
+
+// V37
+SPLIT 2 COMPONENT sample_4_container = Union_cylinder(radius=0.0052,yheight=0.023,priority=92,material_string="Al",p_interact=0.3)
+AT (0,0,0) RELATIVE sample_4
+ROTATED (0,0,0) RELATIVE sample_4
+
+// V38
+COMPONENT sample_4_container_rim = Union_cylinder(radius=0.007,yheight=0.002,priority=91,material_string="Al",p_interact=0.3)
+AT (0,0.023*0.5,0) RELATIVE sample_4
+ROTATED (0,0,0) RELATIVE sample_4
+
+
+COMPONENT test_sample = Union_master(history_limit=1000000)
+AT(0,0,0) RELATIVE beam_center
+ROTATED(0,0,0) RELATIVE beam_center
+EXTEND
+%{
+if (scattered_flag[sample_1_index] > 0) scattered_1 = 1; else scattered_1 = 0;
+if (scattered_flag[sample_2_index] > 0) scattered_2 = 1; else scattered_2 = 0;
+if (scattered_flag[sample_3_index] > 0) scattered_3 = 1; else scattered_3 = 0;
+if (scattered_flag[sample_4_index] > 0) scattered_4 = 1; else scattered_4 = 0;
+%}
+
+
+
+COMPONENT detector_position = Arm()
+AT (0,0,0.03) RELATIVE beam_center
+ROTATED(0,0,0) RELATIVE beam_center
+
+COMPONENT m4pi = PSD_monitor_4PI(radius=1, nx=180, ny=180, filename="Events.dat", restore_neutron=1)
+AT (0, 0, 0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+COMPONENT Banana_monitor = Monitor_nD(radius=1, yheight=0.1, options="banana, theta limits=[20,170], bins=500",filename="banana.dat",restore_neutron=1)
+AT (0,0,0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+COMPONENT detector = PSD_monitor(xwidth=0.1, yheight=0.08, nx=200, ny=200, filename="PSD.dat", restore_neutron=1)
+AT (0,-0.02,0.4) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+// Removes events not scattering in at least two of the samples
+// mcdisplay --inspect=m4pi_two_samples shows only rays that scatters on all three
+//  since all others were removed before that component with this arm.
+COMPONENT arm_1 = Arm()
+  AT (0, 0, 0) RELATIVE beam_center
+EXTEND
+%{
+  if (scattered_1 + scattered_2 + scattered_3 + scattered_4 < 2) ABSORB;
+%}
+
+// Using mcdisplay and -inspect m4pi_two_or_more_samples one can show only
+//  trajectories where the ray scatters from two or more of the samples
+COMPONENT m4pi_two_or_more_samples = PSD_monitor_4PI(radius=1, nx=180, ny=180, filename="Events.dat", restore_neutron=1)
+WHEN (scattered_1 + scattered_2 + scattered_3 + scattered_4 > 1)
+AT (0, 0, 0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+
+COMPONENT armA = Arm()
+AT (0,0,0) ABSOLUTE
+GROUP arms
+
+COMPONENT armB = Arm()
+AT (0,0,0) ABSOLUTE
+GROUP arms
+JUMP myself 2
+
+
+END
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 84f5b717..530b16f3 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -57,6 +57,29 @@ def setup_populated_instr():
 
     return instr
 
+def setup_populated_with_some_options_instr():
+    """
+    Sets up a instrument with some features used and two components
+    """
+    instr = setup_instr_root_path()
+
+    instr.add_parameter("double", "theta")
+    instr.add_parameter("double", "has_default", value=37)
+    instr.add_declare_var("double", "two_theta")
+    instr.append_initialize("two_theta = 2.0*theta;")
+
+    comp1 = instr.add_component("first_component", "test_for_reading")
+    comp1.set_AT([0,0,1])
+    comp1.set_GROUP("Starters")
+    comp2 = instr.add_component("second_component", "test_for_reading")
+    comp2.set_AT([0,0,2], RELATIVE="first_component")
+    comp2.set_ROTATED([0,30,0])
+    comp2.set_WHEN("1==1")
+    comp2.yheight=1.23
+    comp3 = instr.add_component("third_component", "test_for_reading")
+
+    return instr
+
 
 class TestMcStas_instr(unittest.TestCase):
     """
@@ -106,11 +129,11 @@ def test_load_config_file(self):
             line = line.strip()
             if line.startswith("mcrun_path:"):
                 parts = line.split(" ")
-                correct_mcrun_path = parts[1][1:-1]
+                correct_mcrun_path = parts[1]
 
             if line.startswith("mcstas_path:"):
                 parts = line.split(" ")
-                correct_mcstas_path = parts[1][1:-1]
+                correct_mcstas_path = parts[1]
 
             if line.startswith("characters_per_line:"):
                 parts = line.split(" ")
@@ -220,6 +243,42 @@ def test_simple_add_declare_parameter(self):
 
         self.assertEqual(instr.declare_list[0].name, "two_theta")
         self.assertEqual(instr.declare_list[0].comment, " // test par")
+        
+    def test_simple_append_declare(self):
+        """
+        The declare lines are held as a string. This method
+        appends that string.
+        """
+        instr = setup_instr_root_path()
+
+        instr.append_declare("First line of declare")
+        instr.append_declare("Second line of declare")
+        instr.append_declare("Third line of declare")
+
+        self.assertEqual(instr.declare_list[0],
+                         "First line of declare")
+        self.assertEqual(instr.declare_list[1], 
+                         "Second line of declare")
+        self.assertEqual(instr.declare_list[2], 
+                         "Third line of declare")
+        
+    def test_simple_append_declare_var_mix(self):
+        """
+        The declare lines are held as a string. This method
+        appends that string.
+        """
+        instr = setup_instr_root_path()
+
+        instr.append_declare("First line of declare")
+        instr.add_declare_var("double", "two_theta", comment="test par")
+        instr.append_declare("Third line of declare")
+
+        self.assertEqual(instr.declare_list[0],
+                         "First line of declare")
+        self.assertEqual(instr.declare_list[1].name, "two_theta")
+        self.assertEqual(instr.declare_list[1].comment, " // test par")
+        self.assertEqual(instr.declare_list[2], 
+                         "Third line of declare")        
 
     def test_simple_append_initialize(self):
         """
@@ -633,18 +692,61 @@ def test_add_component_simple_before_error(self, mock_stdout):
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_add_component_simple_double_naming_error(self, mock_stdout):
         """
-        The add_component method adds a new component object to the
-        instrument and keeps track of its location within the
-        sequence of components.  Normally a new component is added to
-        the end of the sequence, but the before and after keywords can
-        be used to select another location. Here keyword passing is
-        tested.
+        This tests checks that an error occurs when giving the new
+        component a name which has already been used.
         """
 
         instr = setup_populated_instr()
 
         with self.assertRaises(NameError):
             comp = instr.add_component("first_component", "test_for_reading")
+            
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_copy_component_simple(self, mock_stdout):
+        """
+        Checks that a component can be copied
+        """
+
+        instr = setup_populated_with_some_options_instr()
+        
+        comp = instr.copy_component("copy_of_second_comp", "second_component")
+
+        self.assertEqual(comp.name, "copy_of_second_comp")
+        self.assertEqual(comp.yheight, 1.23)
+        self.assertEqual(comp.AT_data[0], 0)
+        self.assertEqual(comp.AT_data[1], 0)
+        self.assertEqual(comp.AT_data[2], 2)
+        
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_copy_component_keywords(self, mock_stdout):
+        """
+        Checks that a component can be copied and that keyword
+        arguments given under copy operation is sucessfully 
+        applied to the new component. A check is also made to 
+        ensure that the original component was not modified.
+        """
+
+        instr = setup_populated_with_some_options_instr()
+
+        comp = instr.copy_component("copy_of_second_comp", "second_component",
+                                    AT=[1,2,3], SPLIT=10)
+
+        self.assertEqual(comp.name, "copy_of_second_comp")
+        self.assertEqual(comp.yheight, 1.23)
+        self.assertEqual(comp.AT_data[0], 1)
+        self.assertEqual(comp.AT_data[1], 2)
+        self.assertEqual(comp.AT_data[2], 3)
+        self.assertEqual(comp.SPLIT, 10)
+        
+        # ensure original component was not changed
+        original = instr.get_component("second_component")
+        self.assertEqual(original.name, "second_component")
+        self.assertEqual(original.yheight, 1.23)
+        self.assertEqual(original.AT_data[0], 0)
+        self.assertEqual(original.AT_data[1], 0)
+        self.assertEqual(original.AT_data[2], 2)
+        self.assertEqual(original.SPLIT, 0)
+        
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_get_component_simple(self, mock_stdout):
@@ -864,6 +966,36 @@ def test_set_component_comment(self, mock_stdout):
         comp = instr.get_component("second_component")
 
         self.assertEqual(comp.comment, "test comment")
+        
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_set_c_code_before(self, mock_stdout):
+        """
+        set_component_c_code_before passes the argument to the similar
+        method in the component class.
+        """
+
+        instr = setup_populated_instr()
+
+        instr.set_component_c_code_before("second_component", "%include before.instr")
+
+        comp = instr.get_component("second_component")
+
+        self.assertEqual(comp.c_code_before, "%include before.instr")
+        
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_set_c_code_after(self, mock_stdout):
+        """
+        set_component_c_code_after passes the argument to the similar
+        method in the component class.
+        """
+
+        instr = setup_populated_instr()
+
+        instr.set_component_c_code_after("second_component", "%include after.instr")
+
+        comp = instr.get_component("second_component")
+
+        self.assertEqual(comp.c_code_after, "%include after.instr")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_print_component(self, mock_stdout):
@@ -901,7 +1033,8 @@ def test_print_component(self, mock_stdout):
         self.assertEqual(output[3], "  " + par_name + warning)
 
         self.assertEqual(output[4], "AT [0, 0, 0] ABSOLUTE")
-        self.assertEqual(output[5], "ROTATED [0, 0, 0] ABSOLUTE")
+        # Rotation not printed since it was never specified
+        #self.assertEqual(output[5], "ROTATED [0, 0, 0] ABSOLUTE")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_print_component_short(self, mock_stdout):
@@ -920,7 +1053,7 @@ def test_print_component_short(self, mock_stdout):
 
         expected = ("second_component = test_for_reading "
                     + "\tAT [-1, 2, 3.4] RELATIVE home "
-                    + "ROTATED [0, 0, 0] ABSOLUTE")
+                    + "ROTATED [0, 0, 0] RELATIVE home")
 
         self.assertEqual(output[0], expected)
 
@@ -1147,6 +1280,7 @@ def test_write_c_files_simple(self, mock_f, mock_stdout):
         call = unittest.mock.call
         wrts = [
          call("// declare section for test_instrument \n"),
+         #call(""),
          call("double two_theta;"),
          call("\n"),
          call("// Start of initialize for generated test_instrument\n"
@@ -1156,22 +1290,16 @@ def test_write_c_files_simple(self, mock_f, mock_stdout):
          call(")\n"),
          call("AT (0,0,0)"),
          call(" ABSOLUTE\n"),
-         call("ROTATED (0,0,0)"),
-         call(" ABSOLUTE\n"),
          call("\n"),
          call("COMPONENT second_component = test_for_reading("),
          call(")\n"),
          call("AT (0,0,0)"),
          call(" ABSOLUTE\n"),
-         call("ROTATED (0,0,0)"),
-         call(" ABSOLUTE\n"),
          call("\n"),
          call("COMPONENT third_component = test_for_reading("),
          call(")\n"),
          call("AT (0,0,0)"),
          call(" ABSOLUTE\n"),
-         call("ROTATED (0,0,0)"),
-         call(" ABSOLUTE\n"),
          call("\n")]
 
         handle.write.assert_has_calls(wrts, any_order=False)
@@ -1238,6 +1366,7 @@ def test_write_full_instrument_simple(self, mock_f, mock_stdout):
          my_call(")\n"),
          my_call("\n"),
          my_call("DECLARE \n%{\n"),
+         #my_call(""),
          my_call("double two_theta;"),
          my_call("\n"),
          my_call("%}\n\n"),
@@ -1250,22 +1379,16 @@ def test_write_full_instrument_simple(self, mock_f, mock_stdout):
          my_call(")\n"),
          my_call("AT (0,0,0)"),
          my_call(" ABSOLUTE\n"),
-         my_call("ROTATED (0,0,0)"),
-         my_call(" ABSOLUTE\n"),
          my_call("\n"),
          my_call("COMPONENT second_component = test_for_reading("),
          my_call(")\n"),
          my_call("AT (0,0,0)"),
          my_call(" ABSOLUTE\n"),
-         my_call("ROTATED (0,0,0)"),
-         my_call(" ABSOLUTE\n"),
          my_call("\n"),
          my_call("COMPONENT third_component = test_for_reading("),
          my_call(")\n"),
          my_call("AT (0,0,0)"),
          my_call(" ABSOLUTE\n"),
-         my_call("ROTATED (0,0,0)"),
-         my_call(" ABSOLUTE\n"),
          my_call("\n"),
          my_call("FINALLY \n%{\n"),
          my_call("// Start of finally for generated test_instrument\n"),
diff --git a/mcstasscript/tests/test_Instr_reader.py b/mcstasscript/tests/test_Instr_reader.py
new file mode 100644
index 00000000..10a7ef7d
--- /dev/null
+++ b/mcstasscript/tests/test_Instr_reader.py
@@ -0,0 +1,342 @@
+import os
+import unittest
+
+from mcstasscript.interface import instr
+from mcstasscript.instr_reader import control
+from mcstasscript.instr_reader import util
+
+
+def setup_standard(Instr):
+    filename = "Union_demonstration_test.instr"
+
+    InstrReader = control.InstrumentReader(filename)
+    InstrReader.add_to_instr(Instr)
+    return InstrReader 
+
+class TestInstrReader(unittest.TestCase):
+    
+    def test_read_instrument_name(self):
+        """
+        Check if the instrument name is read correctly
+        """
+
+        filename = "Union_demonstration_test.instr"
+        Instr = instr.McStas_instr("test_instrument")
+
+        InstrReader = control.InstrumentReader(filename)
+        InstrReader.add_to_instr(Instr)
+
+        self.assertEqual(InstrReader.instr_name, "Union_demonstration")
+
+    def test_read_input_parameter(self):
+        
+        Instr = instr.McStas_instr("test_instrument")
+        InstrReader = setup_standard(Instr)
+        
+        self.assertEqual(Instr.parameter_list[0].name, "stick_displacement")
+        # space in type inserted for easier writing by McStas_Instr class
+        self.assertEqual(Instr.parameter_list[0].type, "double ")
+        self.assertEqual(Instr.parameter_list[0].value, 0)
+        
+        self.assertEqual(Instr.parameter_list[1].name, "test_int")
+        # space in type inserted for easier writing by McStas_Instr class
+        self.assertEqual(Instr.parameter_list[1].type, "int ")
+        self.assertEqual(Instr.parameter_list[1].value, 3)
+        
+        self.assertEqual(Instr.parameter_list[2].name, "test_str")
+        # space in type inserted for easier writing by McStas_Instr class
+        self.assertEqual(Instr.parameter_list[2].type, "string ")
+        self.assertEqual(Instr.parameter_list[2].value, "\"\\\"hurray\\\"\"")
+
+    def test_read_declare_parameter(self):
+        
+        Instr = instr.McStas_instr("test_instrument")
+        InstrReader = setup_standard(Instr)
+        
+        self.assertEqual(Instr.declare_list[0].name, "sample_1_index")
+        self.assertEqual(Instr.declare_list[0].type, "int")
+        self.assertEqual(Instr.declare_list[0].value, 27)
+        
+        self.assertEqual(Instr.declare_list[8].name, "array")
+        self.assertEqual(Instr.declare_list[8].type, "double")
+        self.assertEqual(Instr.declare_list[8].vector, 3)
+        self.assertEqual(Instr.declare_list[8].value, [0.1, 0.2, 0.3])
+        
+        self.assertEqual(Instr.declare_list[9].name, "I_array")
+        self.assertEqual(Instr.declare_list[9].type, "int")
+        self.assertEqual(Instr.declare_list[9].vector, 4)
+        
+        self.assertEqual(Instr.declare_list[10].name, "T_array")
+        self.assertEqual(Instr.declare_list[10].type, "int")
+        self.assertEqual(Instr.declare_list[10].vector, 5)
+        self.assertEqual(Instr.declare_list[10].value, [1, 2, 3, 4, 5])
+        
+        self.assertEqual(Instr.declare_list[11].name, "home")
+        self.assertEqual(Instr.declare_list[11].type, "char")
+        self.assertEqual(Instr.declare_list[11].vector, 20)
+        self.assertEqual(Instr.declare_list[11].value, "\"\\\"test_string\\\"\"")
+        
+    def test_read_initialize_line(self):
+        
+        Instr = instr.McStas_instr("test_instrument")
+        InstrReader = setup_standard(Instr)
+        
+        self.assertEqual(Instr.initialize_section,
+                         "// Start of initialize for generated test_instrument\n"
+                         + "I_array[2] = 8;\n"
+                         + "printf(\"Hello world\\n\");\n")
+        
+    # Check a few components are read correctly
+    def test_read_component_1(self):
+        
+        Instr = instr.McStas_instr("test_instrument")
+        InstrReader = setup_standard(Instr)
+        
+        components = Instr.component_list
+        
+        test_component = None
+        
+        for component in components:
+            if component.name == "Al":
+                test_component = component
+        
+        self.assertEqual(test_component.component_name, "Union_make_material")
+        
+        val = getattr(test_component, "my_absorption")
+        self.assertEqual(val, "\"100*4*0.231/66.4\"")
+        
+        val = getattr(test_component, "process_string")
+        self.assertEqual(val, '"\\\"Al_incoherent,Al_powder\\\""')
+        
+        self.assertEqual(test_component.AT_data, ["0", "0", "0"])
+        self.assertEqual(test_component.AT_relative, "ABSOLUTE")
+        
+        self.assertEqual(test_component.ROTATED_data, [0, 0, 0])
+        self.assertEqual(test_component.ROTATED_relative, "ABSOLUTE")
+        
+    def test_read_component_2(self):
+        
+        Instr = instr.McStas_instr("test_instrument")
+        InstrReader = setup_standard(Instr)
+        
+        components = Instr.component_list
+        
+        test_component = None
+        
+        for component in components:
+            if component.name == "sample_holder3":
+                test_component = component
+        
+        self.assertEqual(test_component.component_name, "Union_box")
+        
+        val = getattr(test_component, "xwidth")
+        self.assertEqual(val, "0.0098")
+        
+        val = getattr(test_component, "priority")
+        self.assertEqual(val, "52")
+        
+        self.assertEqual(test_component.AT_data, ["0", "-0.03", "-0.03*0.35-0.004"])
+        self.assertEqual(test_component.AT_relative, "RELATIVE sample_rod_bottom")
+        
+        self.assertEqual(test_component.ROTATED_data, ["-25", "0", "0"])
+        self.assertEqual(test_component.ROTATED_relative, "RELATIVE sample_rod_bottom")
+
+    def test_read_component_WHEN(self):
+        
+        Instr = instr.McStas_instr("test_instrument")
+        InstrReader = setup_standard(Instr)
+        
+        components = Instr.component_list
+        
+        test_component = None
+        
+        for component in components:
+            if component.name == "outer_cryostat_vacuum":
+                test_component = component
+        
+        self.assertEqual(test_component.component_name, "Union_cylinder")
+        
+        val = getattr(test_component, "radius")
+        self.assertEqual(val, "0.09")
+        
+        val = getattr(test_component, "priority")
+        self.assertEqual(val, "11")
+        
+        self.assertEqual(test_component.WHEN, "WHEN (necessary == 1 )")
+        
+        self.assertEqual(test_component.AT_data, ["0", "0.01", "0"])
+        self.assertEqual(test_component.AT_relative, "RELATIVE beam_center")
+        
+        self.assertEqual(test_component.ROTATED_data, ["0", "0", "0"])
+        self.assertEqual(test_component.ROTATED_relative, "RELATIVE beam_center")
+        
+    def test_read_component_EXTEND(self):
+        
+        Instr = instr.McStas_instr("test_instrument")
+        InstrReader = setup_standard(Instr)
+        
+        components = Instr.component_list
+        
+        test_component = None
+        
+        for component in components:
+            if component.name == "test_sample":
+                test_component = component
+        
+        self.assertEqual(test_component.component_name, "Union_master")
+        
+        val = getattr(test_component, "history_limit")
+        self.assertEqual(val, "1000000")
+        
+        lines = test_component.EXTEND.split("\n")
+        line0 = ("if (scattered_flag[sample_1_index] > 0) scattered_1 = 1;"
+                 +" else scattered_1 = 0;")
+        line1 = ("if (scattered_flag[sample_2_index] > 0) scattered_2 = 1;"
+                 +" else scattered_2 = 0;")
+        line2 = ("if (scattered_flag[sample_3_index] > 0) scattered_3 = 1;"
+                 +" else scattered_3 = 0;")
+        line3 = ("if (scattered_flag[sample_4_index] > 0) scattered_4 = 1;"
+                 +" else scattered_4 = 0;")
+        
+        self.assertEqual(lines[0], line0)
+        self.assertEqual(lines[1], line1)
+        self.assertEqual(lines[2], line2)
+        self.assertEqual(lines[3], line3)
+        
+        self.assertEqual(test_component.AT_data, ["0", "0", "0"])
+        self.assertEqual(test_component.AT_relative, "RELATIVE beam_center")
+        
+        self.assertEqual(test_component.ROTATED_data, ["0", "0", "0"])
+        self.assertEqual(test_component.ROTATED_relative, "RELATIVE beam_center")
+        
+    def test_read_component_GROUP(self):
+        
+        Instr = instr.McStas_instr("test_instrument")
+        InstrReader = setup_standard(Instr)
+        
+        components = Instr.component_list
+        
+        test_component = None
+        
+        for component in components:
+            if component.name == "armA":
+                test_component = component
+        
+        self.assertEqual(test_component.component_name, "Arm")
+        
+        self.assertEqual(test_component.GROUP, "\"arms\"")
+        
+        self.assertEqual(test_component.AT_data, ["0", "0", "0"])
+        self.assertEqual(test_component.AT_relative, "ABSOLUTE")
+        
+    def test_read_component_SPLIT(self):
+        
+        Instr = instr.McStas_instr("test_instrument")
+        InstrReader = setup_standard(Instr)
+        
+        components = Instr.component_list
+        
+        test_component = None
+        
+        for component in components:
+            if component.name == "sample_4_container":
+                test_component = component
+        
+        self.assertEqual(test_component.component_name, "Union_cylinder")
+        
+        self.assertEqual(test_component.AT_data, ["0", "0", "0"])
+        self.assertEqual(test_component.AT_relative, "RELATIVE sample_4")     
+    
+    def test_read_component_JUMP(self):
+        
+        Instr = instr.McStas_instr("test_instrument")
+        InstrReader = setup_standard(Instr)
+        
+        components = Instr.component_list
+        
+        test_component = None
+        
+        for component in components:
+            if component.name == "armB":
+                test_component = component
+        
+        self.assertEqual(test_component.component_name, "Arm")
+        
+        self.assertEqual(test_component.GROUP, "\"arms\"")
+        self.assertEqual(test_component.JUMP, "myself 2")
+        
+        self.assertEqual(test_component.AT_data, ["0", "0", "0"])
+        self.assertEqual(test_component.AT_relative, "ABSOLUTE")
+
+    
+    def test_comma_split(self):
+        """
+        Check if the instrument name is read correctly
+        """
+
+        Instr = instr.McStas_instr("test_instrument")
+        InstrReader = setup_standard(Instr)
+        
+        test_string = "A,B,C,D(a,b),E"
+        
+        result = InstrReader.Trace_reader._split_func(test_string, ",")
+        
+        self.assertEqual(result[0],"A")
+        self.assertEqual(result[1],"B")
+        self.assertEqual(result[2],"C")
+        self.assertEqual(result[3],"D(a,b)")
+        self.assertEqual(result[4],"E")
+        
+    def test_comma_split_limited(self):
+        """
+        Check if the instrument name is read correctly
+        """
+
+        Instr = instr.McStas_instr("test_instrument")
+        InstrReader = setup_standard(Instr)
+        
+        test_string = "A,B,C,D(a,b),E"
+        
+        result = InstrReader.Trace_reader._split_func(test_string, ",",  2)
+        
+        self.assertEqual(result[0],"A")
+        self.assertEqual(result[1],"B")
+        self.assertEqual(result[2],"C,D(a,b),E")
+        
+    def test_parenthesis_split(self):
+        """
+        Check if the instrument name is read correctly
+        """
+
+        Instr = instr.McStas_instr("test_instrument")
+        InstrReader = setup_standard(Instr)
+        
+        test_string = "A)B)C)D(a,b))E"
+        
+        result = InstrReader.Trace_reader._split_func(test_string, ")")
+        
+        self.assertEqual(result[0],"A")
+        self.assertEqual(result[1],"B")
+        self.assertEqual(result[2],"C")
+        self.assertEqual(result[3],"D(a,b)")
+        self.assertEqual(result[4],"E")
+        
+    def test_comma_split_brack(self):
+        """
+        Check if the instrument name is read correctly
+        """
+
+        Instr = instr.McStas_instr("test_instrument")
+        InstrReader = setup_standard(Instr)
+        
+        test_string = "A,B{C,D(a,b)},E"
+        
+        result = InstrReader.Trace_reader._split_func_brack(test_string, ",")
+        
+        self.assertEqual(result[0],"A")
+        self.assertEqual(result[1],"B{C,D(a,b)}")
+        self.assertEqual(result[2],"E")    
+
+    
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file
diff --git a/mcstasscript/tests/test_component.py b/mcstasscript/tests/test_component.py
index 462fe2a2..1beaf489 100644
--- a/mcstasscript/tests/test_component.py
+++ b/mcstasscript/tests/test_component.py
@@ -353,14 +353,53 @@ def test_component_write_to_file_simple(self, mock_f):
         expected_writes = [my_call("COMPONENT test_component = Arm("),
                            my_call(")\n"),
                            my_call("AT (0,0,0)"),
-                           my_call(" ABSOLUTE\n"),
-                           my_call("ROTATED (0,0,0)"),
                            my_call(" ABSOLUTE\n")]
 
         mock_f.assert_called_with('test.txt', 'w')
         handle = mock_f()
         handle.write.assert_has_calls(expected_writes, any_order=False)
 
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable=unittest.mock.mock_open)    
+    def test_component_write_to_file_include(self, mock_f):
+        """
+        Testing that a component can be written to file with the
+        expected output. Here with simple input.
+        """
+
+        comp = component("test_component", "Arm",
+                         c_code_before="%include \"test.instr\"")
+        
+        comp.set_c_code_after("%include \"after.instr\"")
+        
+        comp._unfreeze()
+        # Need to set up attribute parameters
+        # Also need to categorize them as when created
+        comp.parameter_names = []
+        comp.parameter_defaults = {}
+        comp.parameter_types = {}
+        comp._freeze()
+
+        with mock_f('test.txt', 'w') as m_fo:
+            comp.write_component(m_fo)
+
+        my_call = unittest.mock.call
+        expected_writes = [my_call("%include \"test.instr\" // From"
+                                   + " component named test_component\n"),
+                           my_call("\n"),
+                           my_call("COMPONENT test_component = Arm("),
+                           my_call(")\n"),
+                           my_call("AT (0,0,0)"),
+                           my_call(" ABSOLUTE\n"),
+                           my_call("\n"),
+                           my_call("%include \"after.instr\" // From"
+                                   + " component named test_component\n"),
+                           my_call("\n")]
+
+        mock_f.assert_called_with('test.txt', 'w')
+        handle = mock_f()
+        handle.write.assert_has_calls(expected_writes, any_order=False)
+
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
     def test_component_write_to_file_complex(self, mock_f):
diff --git a/setup.py b/setup.py
new file mode 100644
index 00000000..cb02928c
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,23 @@
+import setuptools
+
+with open("README.md", "r") as fh:
+    long_description = fh.read()
+
+setuptools.setup(
+     name='McStasScript',
+     version='0.0.8',
+     author="Mads Bertelsen",
+     author_email="Mads.Bertelsen@esss.se",
+     description="A python scripting interface for McStas",
+     include_package_data=True,
+     long_description=long_description,
+     long_description_content_type="text/markdown",
+     url="https://github.com/PaNOSC-ViNYL/McStasScript",
+     install_requires=['numpy', 'matplotlib'],
+     packages=setuptools.find_packages(),
+     classifiers=[
+         "Programming Language :: Python :: 3",
+         "License :: OSI Approved :: GNU General Public License (GPL)",
+         "Operating System :: OS Independent",
+     ],
+ )

From ae6c1d1b9228930f6b2277d81739ae3f8f7f05a0 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Mon, 12 Aug 2019 14:39:55 +0200
Subject: [PATCH 051/403] Updated README.md

Added section on the instrument reader as this is especially relevant for new users.
---
 README.md | 16 +++++++++++++++-
 1 file changed, 15 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index bfbd6711..592c9b49 100644
--- a/README.md
+++ b/README.md
@@ -19,7 +19,7 @@ It is necessary to configure the package so the McStas installation can be found
 ## Instructions for basic use:
 Import the interface 
 
-    from mcstasscript.interface import instr, plotter, functions
+    from mcstasscript.interface import instr, plotter, functions, reader
 
 Now the package can be used. Start with creating a new instrument, just needs a name
 
@@ -60,6 +60,13 @@ Plotting is usually done in a subplot of all monitors recorded.
 
     plot = plotter.make_sub_plot(data)
 
+## Use in existing project
+If one wish to work on existing projects using McStasScript, there is a reader included that will read a McStas Instrument file and write the corresponding McStasScript python instrument to disk. Here is an example where the PSI_DMC.instr example is converted:
+
+    Reader = reader.McStas_file("PSI_DMC.instr")
+    reader.write_python_file("PSI_DMC_generated.py")
+
+It is highly advised to run a check between the output of the generated file and the original to ensure the process was sucessful.
 
 ## Method overview
 Here is a quick overview of the available methods of the main classes in the project. Most have more options from keyword arguments that are explained in the manual, but also in python help, for example help(instr.McStas_instr.show_components).
@@ -96,3 +103,10 @@ Here is a quick overview of the available methods of the main classes in the pro
     plotter
     ├── make_plot(list McStasData) # Plots each data set individually
     └── make_sub_plot(list McStasData) # Plots data as subplot
+    
+    reader
+    └──  McStas_file(str filename) # Returns a reader that can extract information from given instr file
+
+    InstrumentReader # returned by McStas_file
+    ├── generate_python_file(str filename) # Writes python file with information contaiend in isntrument
+    └── add_to_instr(McStas_instr Instr) # Adds information from instrument to McStasScirpt instrument

From 58d172e68e4ad371fae7b1ce38a9f2ee0238fde7 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Tue, 13 Aug 2019 08:49:29 +0200
Subject: [PATCH 052/403] Fixed important typo in README

---
 README.md | 2 +-
 setup.py  | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 592c9b49..19828975 100644
--- a/README.md
+++ b/README.md
@@ -64,7 +64,7 @@ Plotting is usually done in a subplot of all monitors recorded.
 If one wish to work on existing projects using McStasScript, there is a reader included that will read a McStas Instrument file and write the corresponding McStasScript python instrument to disk. Here is an example where the PSI_DMC.instr example is converted:
 
     Reader = reader.McStas_file("PSI_DMC.instr")
-    reader.write_python_file("PSI_DMC_generated.py")
+    Reader.write_python_file("PSI_DMC_generated.py")
 
 It is highly advised to run a check between the output of the generated file and the original to ensure the process was sucessful.
 
diff --git a/setup.py b/setup.py
index cb02928c..23d237ac 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
      name='McStasScript',
-     version='0.0.8',
+     version='0.0.10',
      author="Mads Bertelsen",
      author_email="Mads.Bertelsen@esss.se",
      description="A python scripting interface for McStas",

From ad27a41e33e9f2e6648f0add73886557444d4beb Mon Sep 17 00:00:00 2001
From: "Granroth, Garrett E" <granrothge@ornl.gov>
Date: Fri, 18 Oct 2019 18:24:08 -0400
Subject: [PATCH 053/403] fix spelling error

---
 mcstasscript/helper/managed_mcrun.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 5091cc20..5308ce5f 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -157,7 +157,7 @@ def run_simulation(self, **kwargs):
                   + self.custom_flags + " "
                   + self.name_of_instrumentfile
                   + parameter_string)
-        
+
         #os.system(full_command)
         process = subprocess.run(full_command, shell=True,
                                  stdout=subprocess.PIPE,
@@ -180,7 +180,7 @@ def load_results(self, *args):
         elif len(args) == 1:
             data_folder_name = args[0]
         else:
-            raise InputError("load_results can be caled with 0 or 1 arguments")
+            raise InputError("load_results can be called with 0 or 1 arguments")
 
         if not os.path.isdir(data_folder_name):
             raise NameError("Given data directory does not exist.")

From cfa209a9ea2979bf114e139f87c80cd25ccf9d03 Mon Sep 17 00:00:00 2001
From: "Granroth, Garrett E" <granrothge@ornl.gov>
Date: Fri, 18 Oct 2019 18:26:05 -0400
Subject: [PATCH 054/403] add git ignore file for pyc files

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 .gitignore

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 00000000..0d20b648
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+*.pyc

From 52ad7ae45c4fcd179c609b147021c17aaeb44d39 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Thu, 12 Dec 2019 11:41:07 -0500
Subject: [PATCH 055/403] Bug fixes: Fixed bug where multiple include
 statements between components were not read correctly by instrument reader.
 Fixed bug where rotation would be printed in print_components() even if not
 specified, the print would be (0,0,0) ABSOLUTE which was never actually used.

Unit tests were updaed to account for removal of the print statement.
---
 mcstasscript/instr_reader/read_trace.py |  3 +-
 mcstasscript/interface/instr.py         | 33 ++++++++++-----
 mcstasscript/tests/test_Instr.py        | 53 ++++++++-----------------
 3 files changed, 41 insertions(+), 48 deletions(-)

diff --git a/mcstasscript/instr_reader/read_trace.py b/mcstasscript/instr_reader/read_trace.py
index fd61e4c1..93a37be8 100644
--- a/mcstasscript/instr_reader/read_trace.py
+++ b/mcstasscript/instr_reader/read_trace.py
@@ -67,7 +67,8 @@ def read_trace_line(self, line):
         if line.strip().startswith("%include") or line.strip().startswith("#include"):
             # Handle include statement and attatch it to a component
             if self.current_component != None:
-                self.current_component.set_c_code_after(line)
+                c_code_after = self.current_component.c_code_after + line + "\n"
+                self.current_component.set_c_code_after(c_code_after)
             else:
                 # If the include statement is before the first component,
                 # it is saved and attatched to the next component
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 95473f50..187edb1f 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1198,9 +1198,12 @@ def coordinates_to_string(data):
 
                 p_ROTATED_RELATIVE = str(component.ROTATED_relative)
 
-                print(p_name, p_comp_name,
-                      "AT", p_AT, p_AT_RELATIVE,
-                      "ROTATED", p_ROTATED, p_ROTATED_RELATIVE)
+                if component.ROTATED_specified:
+                    print(p_name, p_comp_name,
+                          "AT", p_AT, p_AT_RELATIVE,
+                          "ROTATED", p_ROTATED, p_ROTATED_RELATIVE)
+                else:
+                    print(p_name, p_comp_name, "AT", p_AT, p_AT_RELATIVE)
 
         elif n_lines == 2:
             for component in self.component_list:
@@ -1228,11 +1231,15 @@ def coordinates_to_string(data):
                                             + ROTATED_pad)
 
                 p_ROTATED_RELATIVE = str(component.ROTATED_relative)
-
-                print(p_name, p_comp_name,
-                      "AT     ", p_AT, p_AT_RELATIVE, "\n",
-                      p_ROTATED_align, "ROTATED",
-                      p_ROTATED, p_ROTATED_RELATIVE)
+                
+                if component.ROTATED_specified:
+                    print(p_name, p_comp_name,
+                          "AT     ", p_AT, p_AT_RELATIVE, "\n",
+                          p_ROTATED_align, "ROTATED",
+                          p_ROTATED, p_ROTATED_RELATIVE)
+                else:
+                    print(p_name, p_comp_name,
+                          "AT     ", p_AT, p_AT_RELATIVE)
 
         elif n_lines == 3:
             for component in self.component_list:
@@ -1250,9 +1257,13 @@ def coordinates_to_string(data):
 
                 p_ROTATED_RELATIVE = str(component.ROTATED_relative)
 
-                print(p_name + " ", p_comp_name, "\n",
-                      " AT     ", p_AT, p_AT_RELATIVE, "\n",
-                      " ROTATED", p_ROTATED, p_ROTATED_RELATIVE)
+                if component.ROTATED_specified:
+                    print(p_name + " ", p_comp_name, "\n",
+                          " AT     ", p_AT, p_AT_RELATIVE, "\n",
+                          " ROTATED", p_ROTATED, p_ROTATED_RELATIVE)
+                else:
+                    print(p_name + " ", p_comp_name, "\n",
+                      " AT     ", p_AT, p_AT_RELATIVE)
 
     def write_c_files(self):
         """
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 530b16f3..c3310c86 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1071,18 +1071,15 @@ class for each component and aligns the data for display
         output = mock_stdout.getvalue().split("\n")
 
         expected = ("first_component  test_for_reading"
-                    + " AT (0, 0, 0) ABSOLUTE"
-                    + " ROTATED (0, 0, 0) ABSOLUTE")
+                    + " AT (0, 0, 0) ABSOLUTE")
         self.assertEqual(output[0], expected)
 
         expected = ("second_component test_for_reading"
-                    + " AT (0, 0, 0) ABSOLUTE"
-                    + " ROTATED (0, 0, 0) ABSOLUTE")
+                    + " AT (0, 0, 0) ABSOLUTE")
         self.assertEqual(output[1], expected)
 
         expected = ("third_component  test_for_reading"
-                    + " AT (0, 0, 0) ABSOLUTE"
-                    + " ROTATED (0, 0, 0) ABSOLUTE")
+                    + " AT (0, 0, 0) ABSOLUTE")
         self.assertEqual(output[2], expected)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
@@ -1108,8 +1105,7 @@ class for each component and aligns the data for display
         output = mock_stdout.getvalue().split("\n")
 
         expected = ("first_component  test_for_reading"
-                    + " AT (-0.1, 12, dist) RELATIVE home"
-                    + " ROTATED (0, 0, 0)          ABSOLUTE")
+                    + " AT (-0.1, 12, dist) RELATIVE home")
         self.assertEqual(output[0], expected)
 
         expected = ("second_component test_for_reading"
@@ -1118,8 +1114,7 @@ class for each component and aligns the data for display
         self.assertEqual(output[1], expected)
 
         expected = ("third_component  test_name"
-                    + "        AT (0, 0, 0)        ABSOLUTE"
-                    + "      ROTATED (0, 0, 0)          ABSOLUTE")
+                    + "        AT (0, 0, 0)        ABSOLUTE     ")
         self.assertEqual(output[2], expected)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
@@ -1147,28 +1142,20 @@ class for each component and aligns the data for display
         output = mock_stdout.getvalue().split("\n")
 
         expected = ("first_component  test_for_reading"
-                    + " AT      (-0.1, 12, dist)   RELATIVE home ")
+                    + " AT      (-0.1, 12, dist)   RELATIVE home")
         self.assertEqual(output[0], expected)
 
-        expected = ("                                 "
-                    + " ROTATED (0, 0, 0)          ABSOLUTE")
-        self.assertEqual(output[1], expected)
-
         expected = ("second_component test_for_reading"
                     + " AT      (0, 0, 0)          ABSOLUTE      ")
-        self.assertEqual(output[2], expected)
+        self.assertEqual(output[1], expected)
 
         expected = ("                                 "
                     + " ROTATED (-4, 0.001, theta) RELATIVE etc")
-        self.assertEqual(output[3], expected)
+        self.assertEqual(output[2], expected)
 
         expected = ("third_component  test_name       "
-                    + " AT      (0, 0, 0)          ABSOLUTE      ")
-        self.assertEqual(output[4], expected)
-
-        expected = ("                                 "
-                    + " ROTATED (0, 0, 0)          ABSOLUTE")
-        self.assertEqual(output[5], expected)
+                    + " AT      (0, 0, 0)          ABSOLUTE     ")
+        self.assertEqual(output[3], expected)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_print_components_complex_3lines(self, mock_stdout):
@@ -1204,12 +1191,9 @@ class for each component and aligns the data for display
                     + " ")
         self.assertEqual(output[0], expected)
 
-        expected = ("  AT      (-0.1, 12, dist) RELATIVE home ")
+        expected = ("  AT      (-0.1, 12, dist) RELATIVE home")
         self.assertEqual(output[1], expected)
 
-        expected = ("  ROTATED (0, 0, 0) ABSOLUTE")
-        self.assertEqual(output[2], expected)
-
         expected = (bcolors.BOLD
                     + "second_component"
                     + bcolors.ENDC
@@ -1218,13 +1202,13 @@ class for each component and aligns the data for display
                     + "test_for_reading"
                     + bcolors.ENDC
                     + " ")
-        self.assertEqual(output[3], expected)
+        self.assertEqual(output[2], expected)
 
         expected = ("  AT      (0, 0, 0) ABSOLUTE ")
-        self.assertEqual(output[4], expected)
+        self.assertEqual(output[3], expected)
 
         expected = ("  ROTATED (-4, 0.001, theta) RELATIVE etc")
-        self.assertEqual(output[5], expected)
+        self.assertEqual(output[4], expected)
 
         expected = (bcolors.BOLD
                     + "third_component"
@@ -1234,13 +1218,10 @@ class for each component and aligns the data for display
                     + "test_name"
                     + bcolors.ENDC
                     + " ")
-        self.assertEqual(output[6], expected)
-
-        expected = ("  AT      (0, 0, 0) ABSOLUTE ")
-        self.assertEqual(output[7], expected)
+        self.assertEqual(output[5], expected)
 
-        expected = ("  ROTATED (0, 0, 0) ABSOLUTE")
-        self.assertEqual(output[8], expected)
+        expected = ("  AT      (0, 0, 0) ABSOLUTE")
+        self.assertEqual(output[6], expected)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     @unittest.mock.patch('__main__.__builtins__.open',

From d6a42cfbcc67f175dbff1486ee5d6857812ae96c Mon Sep 17 00:00:00 2001
From: "Granroth, Garrett E" <granrothge@ornl.gov>
Date: Tue, 17 Dec 2019 18:16:22 -0500
Subject: [PATCH 056/403] updated to read parameters from mccode.sim file

---
 mcstasscript/helper/managed_mcrun.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 5308ce5f..0c096def 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -223,6 +223,11 @@ def load_results(self, *args):
                 current_object = McStasMetaData()
                 # Start recording data to metadata object
                 in_data = True
+                
+            if "Param" in lines:
+                parm_lst=lines.split(':')[1].split('=')
+                self.parameters[parm_lst[0]]=parm_lst[1]
+
 
         # Close mccode.sim
         f.close()

From 9b442a78159075ede9f22d9a18914fce32d4447a Mon Sep 17 00:00:00 2001
From: "Granroth, Garrett E" <granrothge@ornl.gov>
Date: Wed, 18 Dec 2019 17:43:37 -0500
Subject: [PATCH 057/403] fixed so it only reads the parameters if it is in the
 simulation section

---
 mcstasscript/helper/managed_mcrun.py | 17 +++++++++++++----
 1 file changed, 13 insertions(+), 4 deletions(-)

diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 0c096def..5e5dbffa 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -198,6 +198,7 @@ def load_results(self, *args):
         # Loop that reads mccode.sim sections
         metadata_list = []
         in_data = False
+        in_sim = False
         for lines in f:
             # Could read other details about run
 
@@ -210,6 +211,12 @@ def load_results(self, *args):
                     metadata_list.append(current_object)
                 # Stop reading data
                 in_data = False
+            if in_sim:
+                if "Param" in lines:
+                    print(lines)
+                    parm_lst=lines.split(':')[1].split('=')
+                    #print(parm_lst)
+                    self.parameters[parm_lst[0]]=parm_lst[1]
 
             if in_data:
                 # This line contains info to be added to metadata
@@ -223,10 +230,12 @@ def load_results(self, *args):
                 current_object = McStasMetaData()
                 # Start recording data to metadata object
                 in_data = True
-                
-            if "Param" in lines:
-                parm_lst=lines.split(':')[1].split('=')
-                self.parameters[parm_lst[0]]=parm_lst[1]
+
+            if 'begin simulation:' in lines:
+                in_sim = True
+            if 'end simulation:' in lines:
+                in_sim = False
+
 
 
         # Close mccode.sim

From 7e69e57d72675e3e6e60ec1d511b965ab32a58b6 Mon Sep 17 00:00:00 2001
From: "Granroth, Garrett E" <granrothge@ornl.gov>
Date: Wed, 18 Dec 2019 17:45:39 -0500
Subject: [PATCH 058/403] remove debug print

---
 mcstasscript/helper/managed_mcrun.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 5e5dbffa..78888223 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -213,7 +213,7 @@ def load_results(self, *args):
                 in_data = False
             if in_sim:
                 if "Param" in lines:
-                    print(lines)
+                    #print(lines)
                     parm_lst=lines.split(':')[1].split('=')
                     #print(parm_lst)
                     self.parameters[parm_lst[0]]=parm_lst[1]

From 4b93a51b6b3450d1ca5f120efeaa7e1b61f41490 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Wed, 22 Jan 2020 12:57:36 +0100
Subject: [PATCH 059/403] Added developer documentation as pdf document. The
 purpose is to bring new developers up to speed on where to locate different
 parts of the code, and how it fits together.

---
 McStasScript_developer_documentation.pdf | Bin 0 -> 186372 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 McStasScript_developer_documentation.pdf

diff --git a/McStasScript_developer_documentation.pdf b/McStasScript_developer_documentation.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..177725cf2dc7946e1d7ca1f1c0525e581b28ab3f
GIT binary patch
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literal 0
HcmV?d00001


From c902920e2d1e879b5ea940454bd71465f081b6a9 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Thu, 13 Feb 2020 09:02:33 +0100
Subject: [PATCH 060/403] Small changes. Added colorbar option in plotter.
 Changed default configuration to fit better with notebooks Fixed typo

---
 mcstasscript/configuration.yaml       |  2 +-
 mcstasscript/data/data.py             |  4 ++++
 mcstasscript/helper/mcstas_objects.py |  2 +-
 mcstasscript/interface/plotter.py     | 10 ++++++----
 4 files changed, 12 insertions(+), 6 deletions(-)

diff --git a/mcstasscript/configuration.yaml b/mcstasscript/configuration.yaml
index 7941b8ef..438e7ebe 100644
--- a/mcstasscript/configuration.yaml
+++ b/mcstasscript/configuration.yaml
@@ -1,5 +1,5 @@
 other:
-  characters_per_line: 93
+  characters_per_line: 85
 paths:
   mcrun_path: /Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/
   mcstas_path: /Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/
diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index 5f116f8a..113091a7 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -159,6 +159,7 @@ def __init__(self, *args, **kwargs):
         self.log = False
         self.orders_of_mag = 300
         self.colormap = "jet"
+        self.show_colorbar = True
         self.cut_max = 1
         self.cut_min = 0
         self.x_limit_multiplier = 1
@@ -190,6 +191,9 @@ def set_options(self, **kwargs):
         if "colormap" in kwargs:
             self.colormap = kwargs["colormap"]
             
+        if "show_colorbar" in kwargs:
+            self.show_colorbar = kwargs["show_colorbar"]
+            
         if "cut_max" in kwargs:
             self.cut_max = kwargs["cut_max"]
             
diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index fab57f4e..20abe9bb 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -116,7 +116,7 @@ class declare_variable:
         Comment displayed next to the declaration, could contain units
 
     vector : int
-        0 if a single value is given, ortherwise contains the length
+        0 if a single value is given, otherwise contains the length
 
     Methods
     -------
diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index 40c9cc32..96c7cce6 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -359,8 +359,9 @@ def fmt(x, pos):
                         return r'${} \times 10^{{{}}}$'.format(a, b)
 
                 # Add the colorbar
-                fig.colorbar(im, ax=ax0,
-                             format=matplotlib.ticker.FuncFormatter(fmt))
+                if data.plot_options.colormap:
+                    fig.colorbar(im, ax=ax0,
+                                 format=matplotlib.ticker.FuncFormatter(fmt))
 
                 # Add a title
                 ax0.set_title(data.metadata.title)
@@ -582,8 +583,9 @@ def fmt(x, pos):
                     return r'${} \times 10^{{{}}}$'.format(a, b)
 
             # Add the colorbar
-            fig.colorbar(im, ax=ax,
-                        format=matplotlib.ticker.FuncFormatter(fmt))
+            if data.plot_options.colormap:
+                fig.colorbar(im, ax=ax,
+                             format=matplotlib.ticker.FuncFormatter(fmt))
 
             # Add a title
             ax.set_title(data.metadata.title)

From c051f792abbc7ac947c1f33a955e75bdb6d2cc77 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Thu, 13 Feb 2020 09:47:48 +0100
Subject: [PATCH 061/403] Moved from hardcoding "/" in paths to using
 os.path.join in order to support windows.

---
 mcstasscript/helper/component_reader.py | 34 +++++++++++++++----------
 mcstasscript/helper/managed_mcrun.py    |  9 +++----
 mcstasscript/interface/functions.py     |  5 ++--
 mcstasscript/interface/instr.py         | 28 +++++++++++++-------
 mcstasscript/tests/test_Configurator.py |  4 +--
 5 files changed, 48 insertions(+), 32 deletions(-)

diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index 7018c564..81f74123 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -39,8 +39,9 @@ def __init__(self, mcstas_path):
 
         """
 
-        if mcstas_path[-1] is not "/":
-            mcstas_path = mcstas_path + "/"
+        # add trailing / or \ depending on operating system
+        if mcstas_path[-1] is not "/" and mcstas_path[-1] is not "\\":
+            mcstas_path = os.path.join(mcstas_path, "") 
 
         # Hardcoded whitelist of foldernames
         folder_list = ["sources",
@@ -56,17 +57,19 @@ def __init__(self, mcstas_path):
         self.component_category = {}
 
         for folder in folder_list:
-            absolute_path = mcstas_path + folder
-            # self.component_info_dict.update(self._read(absolute_path))
-            self._find_components(absolute_path)
+            abs_path = os.path.join(mcstas_path, folder)
+            self._find_components(abs_path)
 
         # McStas component in current directory should overwrite
         current_directory = os.getcwd()
 
         for file in os.listdir(current_directory):
             if file.endswith(".comp"):
-                absolute_path = current_directory + "/" + file
-                component_name = absolute_path.split("/")[-1].split(".")[-2]
+                abs_path = os.path.join(current_directory, file)
+                if "/" in abs_path:
+                    component_name = abs_path.split("/")[-1].split(".")[-2]
+                else:
+                    component_name = abs_path.split("\\")[-1].split(".")[-2]
 
                 if component_name in self.component_path:
                     print("Overwriting McStasScript info on component named "
@@ -74,7 +77,7 @@ def __init__(self, mcstas_path):
                           + " because the component is in the"
                           + " work directory.")
 
-                self.component_path[component_name] = absolute_path
+                self.component_path[component_name] = abs_path
                 self.component_category[component_name] = "Work directory"
 
     def show_categories(self):
@@ -196,14 +199,15 @@ def _find_components(self, absolute_path):
         if not os.path.isdir(absolute_path):
             if absolute_path.endswith(".comp"):
                 # read this file
-                component_name = absolute_path.split("/")[-1].split(".")[-2]
+                component_name = os.path.split(absolute_path)[1].split(".")[-2]
                 self.component_path[component_name] = absolute_path
 
-                component_category = absolute_path.split("/")[-2]
+                head = os.path.split(absolute_path)[0]
+                component_category = os.path.split(head)[1]
                 self.component_category[component_name] = component_category
         else:
             for file in os.listdir(absolute_path):
-                absolute_file_path = absolute_path + "/" + file
+                absolute_file_path = os.path.join(absolute_path, file)
                 self._find_components(absolute_file_path)
 
     def read_component_file(self, absolute_path):
@@ -362,9 +366,11 @@ def read_component_file(self, absolute_path):
 
         fo.close()
 
-        result.name = absolute_path.split("/")[-1].split(".")[-2]
-        foldernames = absolute_path.split("/")
-        result.category = foldernames[-2]
+        result.name = os.path.split(absolute_path)[1].split(".")[-2]
+        
+        tail = os.path.split(absolute_path)[0]
+        result.category = os.path.split(tail)[1]
+
 
         """
         To lower memory use one could remove all comments and units that
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 5308ce5f..c5135f5d 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -149,7 +149,7 @@ def run_simulation(self, **kwargs):
         if len(self.mcrun_path) > 1:
             if not (self.mcrun_path[-1] == "\\"
                     or self.mcrun_path[-1] == "/"):
-                mcrun_full_path = self.mcrun_path + "/mcrun"
+                mcrun_full_path = os.path.join(self.mcrun_path, "mcrun")
 
         # Run the mcrun command on the system
         full_command = (mcrun_full_path + " "
@@ -193,7 +193,7 @@ def load_results(self, *args):
             raise NameError("No mccode.sim in data folder.")
 
         # Open mccode to read metadata for all datasets written to disk
-        f = open(data_folder_name + "/mccode.sim", "r")
+        f = open(os.path.join(data_folder_name, "mccode.sim"), "r")
 
         # Loop that reads mccode.sim sections
         metadata_list = []
@@ -233,9 +233,8 @@ def load_results(self, *args):
         # Load datasets described in metadata list individually
         for metadata in metadata_list:
             # Load data with numpy
-            data = np.loadtxt(data_folder_name
-                              + "/"
-                              + metadata.filename.rstrip())
+            data = np.loadtxt(os.path.join(data_folder_name,
+                              metadata.filename.rstrip()))
 
             # Split data into intensity, error and ncount
             if type(metadata.dimension) == int:
diff --git a/mcstasscript/interface/functions.py b/mcstasscript/interface/functions.py
index bee77418..d69ed44b 100644
--- a/mcstasscript/interface/functions.py
+++ b/mcstasscript/interface/functions.py
@@ -146,7 +146,8 @@ def __init__(self, *args):
 
         # check configuration file exists
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-        self.configuration_file_name = THIS_DIR + "/../" + name + ".yaml"
+        conf_file = os.path.join(THIS_DIR, ".." , name + ".yaml")
+        self.configuration_file_name = conf_file
         if not os.path.isfile(self.configuration_file_name):
             # no config file found, write default config file
             self._create_new_config_file()
@@ -175,7 +176,7 @@ def _create_new_config_file(self):
 
         default_paths = {"mcrun_path" : run,
                          "mcstas_path" : mcstas}
-        default_other = {"characters_per_line" : 93}
+        default_other = {"characters_per_line" : 85}
 
         default_config = {"paths" : default_paths,
                           "other" : default_other}
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 187edb1f..14d97b37 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -191,7 +191,7 @@ def __init__(self, name, **kwargs):
             self.origin = "ESS DMSC"
 
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-        configuration_file_name = THIS_DIR + "/../configuration.yaml"
+        configuration_file_name = os.path.join(THIS_DIR, "..", "configuration.yaml")
         if not os.path.isfile(configuration_file_name):
             raise NameError("Could not find configuration file!")
         with open(configuration_file_name, 'r') as ymlfile:
@@ -1276,17 +1276,22 @@ def write_c_files(self):
         trace_section that can be set using the append_trace method.
         """
         path = os.getcwd()
-        path = path + "/generated_includes"
+        path = os.path.join(path, "generated_includes")
         if not os.path.isdir(path):
             try:
                 os.mkdir(path)
             except OSError:
                 print("Creation of the directory %s failed" % path)
 
-        fo = open("./generated_includes/" + self.name + "_declare.c", "w")
+        file_path = os.path.join(".", "generated_includes",
+                                self.name + "_declare.c") 
+        fo = open(file_path, "w")
         fo.write("// declare section for %s \n" % self.name)
         fo.close()
-        fo = open("./generated_includes/" + self.name + "_declare.c", "a")
+        
+        file_path = os.path.join(".", "generated_includes",
+                                 self.name + "_declare.c") 
+        fo = open(file_path, "a")
         #fo.write(self.declare_section)
         for dec_line in self.declare_list:
             if isinstance(dec_line, str):
@@ -1297,16 +1302,21 @@ def write_c_files(self):
             fo.write("\n")
         fo.close()
 
-        fo = open("./generated_includes/" + self.name + "_initialize.c", "w")
+        file_path = os.path.join(".", "generated_includes",
+                                 self.name + "_initialize.c")
+        fo = open(file_path, "w")
         fo.write(self.initialize_section)
         fo.close()
 
-        fo = open("./generated_includes/" + self.name + "_trace.c", "w")
+        file_path = os.path.join(".", "generated_includes",
+                                 self.name + "_trace.c")
+        fo = open(file_path, "w")
         fo.write(self.trace_section)
         fo.close()
 
-        fo = open("./generated_includes/" + self.name
-                  + "_component_trace.c", "w")
+        file_path = os.path.join(".", "generated_includes",
+                                 self.name + "_component_trace.c")
+        fo = open(file_path, "w")
         for component in self.component_list:
             component.write_component(fo)
         fo.close()
@@ -1516,7 +1526,7 @@ def show_instrument(self, *args, **kwargs):
                                 + "="
                                 + str(val))  # parameter value
 
-        bin_path = self.mcstas_path + "/bin/"
+        bin_path = os.path.join(self.mcstas_path, "bin", "")
         executable = "mcdisplay-webgl"
         if "format" in kwargs:
             if kwargs["format"] is "webgl":
diff --git a/mcstasscript/tests/test_Configurator.py b/mcstasscript/tests/test_Configurator.py
index a543ba2b..73847568 100644
--- a/mcstasscript/tests/test_Configurator.py
+++ b/mcstasscript/tests/test_Configurator.py
@@ -65,7 +65,7 @@ def test_default_config(self):
 
         self.assertEqual(default_config["paths"]["mcrun_path"], run)
         self.assertEqual(default_config["paths"]["mcstas_path"], mcstas)
-        self.assertEqual(default_config["other"]["characters_per_line"], 93)
+        self.assertEqual(default_config["other"]["characters_per_line"], 85)
         
         # remove the testing configuration file
         if os.path.isfile(expected_file): 
@@ -87,7 +87,7 @@ def test_yaml_write(self):
         
         new_config = my_configurator._read_yaml()
         
-        self.assertEqual(new_config["other"]["characters_per_line"], 93)
+        self.assertEqual(new_config["other"]["characters_per_line"], 85)
         self.assertEqual(new_config["new_field"], 123)
         self.assertEqual(new_config["paths"]["new_path"], "/test/path/")
         

From 5d374729298ac7ae5d90574d09a4fb8593f76efd Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Thu, 13 Feb 2020 12:51:30 +0100
Subject: [PATCH 062/403] Updated text to use os.path.join rather than manual
 string manipulation.

---
 mcstasscript/tests/test_ComponentReader.py | 6 +++---
 mcstasscript/tests/test_Configurator.py    | 2 +-
 mcstasscript/tests/test_Instr.py           | 5 +++--
 mcstasscript/tests/test_ManagedMcrun.py    | 6 +++---
 4 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/mcstasscript/tests/test_ComponentReader.py b/mcstasscript/tests/test_ComponentReader.py
index 712ecdb4..3f5a7d44 100644
--- a/mcstasscript/tests/test_ComponentReader.py
+++ b/mcstasscript/tests/test_ComponentReader.py
@@ -9,7 +9,7 @@
 
 def setup_component_reader():
     THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-    dummy_path = THIS_DIR + "/dummy_mcstas"
+    dummy_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
     current_work_dir = os.getcwd()
     os.chdir(THIS_DIR)  # Set work directory to test folder
@@ -245,7 +245,7 @@ def test_ComponentReader_find_components_names(self, mock_stdout):
         component_reader.component_category = {}
 
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-        dummy_path = THIS_DIR + "/dummy_mcstas/misc"
+        dummy_path = os.path.join(THIS_DIR, "dummy_mcstas", "misc")
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
         component_reader._find_components(dummy_path)
@@ -268,7 +268,7 @@ def test_ComponentReader_find_components_categories(self, mock_stdout):
         component_reader.component_category = {}
 
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-        dummy_path = THIS_DIR + "/dummy_mcstas/misc"
+        dummy_path = os.path.join(THIS_DIR, "dummy_mcstas", "misc")
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
         component_reader._find_components(dummy_path)
diff --git a/mcstasscript/tests/test_Configurator.py b/mcstasscript/tests/test_Configurator.py
index 73847568..6dd1abad 100644
--- a/mcstasscript/tests/test_Configurator.py
+++ b/mcstasscript/tests/test_Configurator.py
@@ -5,7 +5,7 @@
 
 def setup_expected_file(test_name):
     THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-    expected_file = THIS_DIR + "/../" + test_name + ".yaml"
+    expected_file = os.path.join(THIS_DIR, "..", test_name + ".yaml")
     
     if os.path.isfile(expected_file):
         os.remove(expected_file)
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index c3310c86..9b04dd21 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -30,7 +30,7 @@ def setup_instr_with_path():
     """
 
     THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-    dummy_path = THIS_DIR + "/dummy_mcstas"
+    dummy_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
     current_work_dir = os.getcwd()
     os.chdir(THIS_DIR)  # Set work directory to test folder
@@ -117,7 +117,8 @@ def test_load_config_file(self):
         """
         # Load configuration file and read manually
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-        configuration_file_name = THIS_DIR + "/../configuration.yaml"
+        configuration_file_name = os.path.join(THIS_DIR,
+                                               "..", "configuration.yaml")
 
         if not os.path.isfile(configuration_file_name):
             raise NameError("Could not find configuration file!")
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index ebad42e8..2c9b0126 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -329,7 +329,7 @@ def test_ManagedMcrun_load_data_L_mon_direct(self):
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
-        load_path = THIS_DIR + "/test_data_set"
+        load_path = os.path.join(THIS_DIR, "test_data_set")
         results = mcrun_obj.load_results(load_path)
 
         os.chdir(current_work_dir)  # Reset work directory
@@ -363,7 +363,7 @@ def test_ManagedMcrun_load_data_L_mon_direct_error(self):
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
-        load_path = THIS_DIR + "/non_exsistent_dataset"
+        load_path = os.path.join(THIS_DIR, "non_exsistent_dataset")
         with self.assertRaises(NameError):
             results = mcrun_obj.load_results(load_path)
 
@@ -383,7 +383,7 @@ def test_ManagedMcrun_load_data_L_mon_empty_error(self):
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
-        load_path = THIS_DIR + "/dummy_mcstas"
+        load_path = os.path.join(THIS_DIR, "/dummy_mcstas")
         with self.assertRaises(NameError):
             results = mcrun_obj.load_results(load_path)
 

From 458230720544352347de69083afbd90288458183 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Fri, 14 Feb 2020 09:28:33 +0100
Subject: [PATCH 063/403] Fixed a few issues with unittests.

Unit tests on windows did not account for forward slash / backward slash.
MANIFEST did not include a file needed for testing.
---
 MANIFEST.in                      |  2 ++
 mcstasscript/tests/test_Instr.py | 12 +++++++++---
 setup.py                         |  6 +++---
 3 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/MANIFEST.in b/MANIFEST.in
index e71699ac..b8abf181 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,5 +1,7 @@
 include McStasScript_documentation.pdf
 include mcstasscript/configuration.yaml
 include mcstasscript/tests/test_for_reading.comp
+include mcstasscript/tests/test_instrument.instr
+include mcstasscript/tests/Union_demonstration_test.instr
 graft mcstasscript/tests/dummy_mcstas
 graft mcstasscript/tests/test_data_set
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 9b04dd21..35e136a1 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1413,8 +1413,10 @@ def test_run_full_instrument_basic(self, mock_sub,
                                   mcrun_path="path",
                                   parameters={"theta": 1})
 
+        expected_path = os.path.join("path","mcrun")
+
         # a double space because of a missing option
-        expected_call = ("path/mcrun -c -n 1000000 --mpi=1 "
+        expected_call = (expected_path + " -c -n 1000000 --mpi=1 "
                          + "-d test_data_set  test_instrument.instr"
                          + " has_default=37 theta=1")
 
@@ -1446,8 +1448,10 @@ def test_run_full_instrument_complex(self, mock_sub,
                                               "BC": "car",
                                               "theta": "\"toy\""})
 
+        expected_path = os.path.join("path","mcrun")
+
         # a double space because of a missing option
-        expected_call = ("path/mcrun -c -n 48 --mpi=7 "
+        expected_call = (expected_path + " -c -n 48 --mpi=7 "
                          + "-d test_data_set -fo test_instrument.instr "
                          + "has_default=37 A=2 BC=car theta=\"toy\"")
 
@@ -1478,9 +1482,11 @@ def test_run_full_instrument_overwrite_default(self, mock_sub,
                                               "BC": "car",
                                               "theta": "\"toy\"",
                                               "has_default": 10})
+        
+        expected_path = os.path.join("path","mcrun")
 
         # a double space because of a missing option
-        expected_call = ("path/mcrun -c -n 48 --mpi=7 "
+        expected_call = (expected_path + " -c -n 48 --mpi=7 "
                          + "-d test_data_set -fo test_instrument.instr "
                          + "has_default=10 A=2 BC=car theta=\"toy\"")
 
diff --git a/setup.py b/setup.py
index 23d237ac..88427a64 100644
--- a/setup.py
+++ b/setup.py
@@ -5,15 +5,15 @@
 
 setuptools.setup(
      name='McStasScript',
-     version='0.0.10',
+     version='0.0.12',
      author="Mads Bertelsen",
-     author_email="Mads.Bertelsen@esss.se",
+     author_email="Mads.Bertelsen@ess.eu",
      description="A python scripting interface for McStas",
      include_package_data=True,
      long_description=long_description,
      long_description_content_type="text/markdown",
      url="https://github.com/PaNOSC-ViNYL/McStasScript",
-     install_requires=['numpy', 'matplotlib'],
+     install_requires=['numpy', 'matplotlib', 'PyYAML'],
      packages=setuptools.find_packages(),
      classifiers=[
          "Programming Language :: Python :: 3",

From e28f9ca1a1131d4a39b1bb14de32f1840998dff6 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Fri, 14 Feb 2020 09:48:19 +0100
Subject: [PATCH 064/403] Update README.md

---
 README.md | 18 +++++++++++++++---
 1 file changed, 15 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 19828975..ff039d39 100644
--- a/README.md
+++ b/README.md
@@ -4,19 +4,31 @@ McStas API for creating and running McStas instruments from python scripting
 Prototype for an API that allow interaction with McStas through an interface like Jupyter Notebooks created under WP5 of PaNOSC.
 
 ## Installation
-The package can be installed using pip
+McStasScript does not include the McStas installation, so McStas should be installed separately.
+McStasScript can be installed using pip,
 
     python3 -m pip install McStasScript --upgrade
 
-It is necessary to configure the package so the McStas installation can be found, here we show how the appropriate code for an Ubuntu system. The configuration is saved permanently, and only needs to be updated when McStas is updated.
+After installation it is necessary to configure the package so the McStas installation can be found, here we show how the appropriate code for an Ubuntu system. The configuration is saved permanently, and only needs to be updated when McStas or McStasScript is updated.
 
     from mcstasscript.interface import functions
     my_configurator = functions.Configurator()
     my_configurator.set_mcrun_path("/usr/bin/")
     my_configurator.set_mcstas_path("/usr/share/mcstas/2.5/")
+    
+### Notes on windows installation
+McStasScript was tested on Windows 10 installed using this [guide](https://github.com/McStasMcXtrace/McCode/blob/master/INSTALL-McStas/Windows/README.md), it is necessary to include MPI using MSMpiSetup.exe and msmpisdk.msi located in the extras folder.
+
+Open the McStas-shell cmd (shortcut should be available on desktop) and install McStasScript / jupyter notebook with these commands:
+
+    python -m pip install notebook 
+    python -m pip install McStasScript --upgrade
+    
+Using the McStas-shell one can start a jupyter notebook server with this command:
 
+    jupyter notebook
 
-## Instructions for basic use:
+## Instructions for basic use
 Import the interface 
 
     from mcstasscript.interface import instr, plotter, functions, reader

From 8e64a4f51730c4e7c52633e9941498af2cbe6f67 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Fri, 14 Feb 2020 09:50:32 +0100
Subject: [PATCH 065/403] Update README.md

---
 README.md | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/README.md b/README.md
index ff039d39..4e12fc96 100644
--- a/README.md
+++ b/README.md
@@ -28,6 +28,13 @@ Using the McStas-shell one can start a jupyter notebook server with this command
 
     jupyter notebook
 
+For a standard McStas installation on Windows, the appropriate configuration can be set with these commands in a notebook:
+
+    from mcstasscript.interface import functions
+    my_configurator = functions.Configurator()
+    my_configurator.set_mcrun_path("\\mcstas-2.6\\bin\\")
+    my_configurator.set_mcstas_path("\\mcstas-2.6\\lib\\")
+
 ## Instructions for basic use
 Import the interface 
 

From 610c93a2aa16a623b43b0f0c3e4df1748333683a Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Fri, 14 Feb 2020 13:55:54 +0100
Subject: [PATCH 066/403] Updated use of "is" and "is not" to == and != to
 check value instead of reference.

---
 mcstasscript/helper/mcstas_objects.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 20abe9bb..eb35f161 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -80,7 +80,7 @@ def __init__(self, *args, **kwargs):
     def write_parameter(self, fo, stop_character):
         """Writes input parameter to file"""
         fo.write("%s%s" % (self.type, self.name))
-        if self.value is not "":
+        if self.value != "":
             if isinstance(self.value, int):
                 fo.write(" = %d" % self.value)
             elif isinstance(self.value, float):
@@ -185,9 +185,9 @@ def write_line(self, fo):
         fo : file object
             File the line will be written to
         """
-        if self.value is "" and self.vector == 0:
+        if self.value == "" and self.vector == 0:
             fo.write("%s %s;%s" % (self.type, self.name, self.comment))
-        if self.value is not "" and self.vector == 0:
+        if self.value != "" and self.vector == 0:
             if self.type == "int":
                 fo.write("%s %s = %d;%s" % (self.type, self.name,
                                             self.value, self.comment))
@@ -198,10 +198,10 @@ def write_line(self, fo):
                 except:
                     fo.write("%s %s = %s;%s" % (self.type, self.name,
                                                 self.value, self.comment))
-        if self.value is "" and self.vector != 0:
+        if self.value == "" and self.vector != 0:
             fo.write("%s %s[%d];%s" % (self.type, self.name,
                                        self.vector, self.comment))
-        if self.value is not "" and self.vector != 0:
+        if self.value != "" and self.vector != 0:
             if isinstance(self.value, str):
                 # value is a string
                 string = self.value
@@ -580,7 +580,7 @@ def write_component(self, fo):
         if len(self.comment) > 1:
             fo.write("// %s\n" % (str(self.comment)))
 
-        if self.SPLIT is not 0:
+        if self.SPLIT != 0:
             fo.write("SPLIT " + str(self.SPLIT) + " ") 
 
         # Write component name and component type
@@ -671,7 +671,7 @@ class is used as a superclass for classes describing each
             print(self.c_code_before)
         if len(self.comment) > 1:
             print("// " + self.comment)
-        if self.SPLIT is not 0:
+        if self.SPLIT != 0:
             print("SPLIT " + str(self.SPLIT) + " ", end="")
         print("COMPONENT", str(self.name),
               "=", str(self.component_name))
@@ -872,7 +872,7 @@ def show_parameters_simple(self):
 
             comment = ""
             if parameter in self.parameter_comments:
-                if self.parameter_comments[parameter] is not "":
+                if self.parameter_comments[parameter] != "":
                     comment = " // " + self.parameter_comments[parameter]
 
             print(parameter + value + unit + comment)

From ffae572cbed6e168e177572f0a144b04db220f07 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Thu, 20 Feb 2020 11:56:53 +0100
Subject: [PATCH 067/403] Fixed a few bugs in read_trace for instrument reader.

First bug produced double ,, when only one was needed.

Second bug added "\" to the instrument file writen, when it was only
supposed to be part of the python file.

Third bug included quotation marks around all group names, which again
was only supposed to be included in the python file.

Test were updated to take this into account.
---
 mcstasscript/instr_reader/read_trace.py | 33 ++++++++++++++-----------
 mcstasscript/tests/test_Instr_reader.py |  9 ++++---
 setup.py                                |  2 +-
 3 files changed, 24 insertions(+), 20 deletions(-)

diff --git a/mcstasscript/instr_reader/read_trace.py b/mcstasscript/instr_reader/read_trace.py
index 93a37be8..d69d5053 100644
--- a/mcstasscript/instr_reader/read_trace.py
+++ b/mcstasscript/instr_reader/read_trace.py
@@ -215,10 +215,11 @@ def read_trace_line(self, line):
                     quotation_split = par_exp.split('"')
                     while (len(quotation_split) - 1) % 2 != 0:
                         # There is an uneven number of quotation marks
-                        par_exp +=  "," # Add the comma taken by split back
+                        par_exp += ","
                         if "," in par_line:
                             # include the up to the next comma in par_exp
-                            par_exp += "," + par_line.split(",",1)[0]
+                            #par_exp += "," + par_line.split(",",1)[0]
+                            par_exp += par_line.split(",",1)[0]
                             # remove the part of the par_line added to par_exp
                             par_line = par_line.split(",",1)[1]
                         else:
@@ -236,17 +237,8 @@ def read_trace_line(self, line):
                     par_name = par_exp.split("=", 1)[0].strip()
                     par_value = par_exp.split("=", 1)[1].strip()
                     
-                    try:
-                        # No problems if the value is a number
-                        float(par_value)
-                    except:
-                        # If the value is a string, it needs quotes
-                        if '"' in par_value:
-                            # If it already has quotes, these need escapes
-                            par_value = par_value.replace('"','\\\"')
-                        par_value = '"' + par_value + '"'
-                    
                     par_dict[par_name] = par_value
+                    
             
             # Set all found parameters in the component
             self.current_component.set_parameters(par_dict)
@@ -357,7 +349,8 @@ def read_trace_line(self, line):
             line = line.strip()
             
             group_name = line.split(" ", 1)[1].strip()
-            group_name = "\"" + group_name + "\""
+            #group_name = "\"" + group_name + "\""
+            group_name = group_name
             
             line = "" 
             
@@ -423,6 +416,16 @@ def _write_component_to_py(self):
                     write_string.append(key)
                     write_string.append(" = ")
 
+                    try:
+                        # No problems if the value is a number
+                        float(val)
+                    except:
+                        # If the value is a string, it needs quotes
+                        if '"' in val:
+                            # If it already has quotes, these need escapes
+                            val = val.replace('"','\\\"')
+                        val = '"' + val + '"'
+
                     write_string.append(val)
                     write_string.append("\n")
                 
@@ -470,9 +473,9 @@ def _write_component_to_py(self):
             if self.current_component.GROUP != "":
                 write_string = []
                 write_string.append(self.current_component.name)
-                write_string.append(".set_GROUP(")
+                write_string.append(".set_GROUP(\"")
                 write_string.append(str(self.current_component.GROUP))
-                write_string.append(")\n")
+                write_string.append("\")\n")
                 
                 self._write_to_file(write_string)
             
diff --git a/mcstasscript/tests/test_Instr_reader.py b/mcstasscript/tests/test_Instr_reader.py
index 10a7ef7d..8cc9bcee 100644
--- a/mcstasscript/tests/test_Instr_reader.py
+++ b/mcstasscript/tests/test_Instr_reader.py
@@ -103,10 +103,11 @@ def test_read_component_1(self):
         self.assertEqual(test_component.component_name, "Union_make_material")
         
         val = getattr(test_component, "my_absorption")
-        self.assertEqual(val, "\"100*4*0.231/66.4\"")
+        #self.assertEqual(val, "\"100*4*0.231/66.4\"")
+        self.assertEqual(val, "100*4*0.231/66.4")
         
         val = getattr(test_component, "process_string")
-        self.assertEqual(val, '"\\\"Al_incoherent,Al_powder\\\""')
+        self.assertEqual(val, "\"Al_incoherent,Al_powder\"")
         
         self.assertEqual(test_component.AT_data, ["0", "0", "0"])
         self.assertEqual(test_component.AT_relative, "ABSOLUTE")
@@ -224,7 +225,7 @@ def test_read_component_GROUP(self):
         
         self.assertEqual(test_component.component_name, "Arm")
         
-        self.assertEqual(test_component.GROUP, "\"arms\"")
+        self.assertEqual(test_component.GROUP, "arms")
         
         self.assertEqual(test_component.AT_data, ["0", "0", "0"])
         self.assertEqual(test_component.AT_relative, "ABSOLUTE")
@@ -262,7 +263,7 @@ def test_read_component_JUMP(self):
         
         self.assertEqual(test_component.component_name, "Arm")
         
-        self.assertEqual(test_component.GROUP, "\"arms\"")
+        self.assertEqual(test_component.GROUP, "arms")
         self.assertEqual(test_component.JUMP, "myself 2")
         
         self.assertEqual(test_component.AT_data, ["0", "0", "0"])
diff --git a/setup.py b/setup.py
index 88427a64..caa845c1 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
      name='McStasScript',
-     version='0.0.12',
+     version='0.0.14',
      author="Mads Bertelsen",
      author_email="Mads.Bertelsen@ess.eu",
      description="A python scripting interface for McStas",

From c4701e720233c4113923d9a3b7607c8ab413f976 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Mon, 30 Mar 2020 13:35:52 +0200
Subject: [PATCH 068/403] Testing travis continous integration. Added .travis
 and requirements.txt.

---
 .travis.yml      | 10 ++++++++++
 requirements.txt |  3 +++
 2 files changed, 13 insertions(+)
 create mode 100644 .travis.yml
 create mode 100644 requirements.txt

diff --git a/.travis.yml b/.travis.yml
new file mode 100644
index 00000000..69c74062
--- /dev/null
+++ b/.travis.yml
@@ -0,0 +1,10 @@
+language: python
+python:
+  - "3.6"
+  - "3.7"
+
+install:
+  - pip install -r requirements.txt
+
+script:
+ - cd mcstasscript/tests && python3 -m unittest 
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 00000000..79e87c1e
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,3 @@
+numpy
+matplotlib
+PyYAML

From 5001d75e505d5adb079310b39c823668408dea57 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 31 Mar 2020 09:23:32 +0200
Subject: [PATCH 069/403] Fixed travis build script.

---
 .travis.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.travis.yml b/.travis.yml
index 69c74062..9b502cf1 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -5,6 +5,7 @@ python:
 
 install:
   - pip install -r requirements.txt
+  - pip install mcstasscript
 
 script:
  - cd mcstasscript/tests && python3 -m unittest 

From e6cd1a941fc939d7a0641bac113725b3549729d5 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 31 Mar 2020 10:12:17 +0200
Subject: [PATCH 070/403] Providing test components for instrument reader.
 Updated manifest to download these with pip.

---
 MANIFEST.in                                   |    1 +
 .../tests/dummy_instrument_folder/Arm.comp    |   48 +
 .../Incoherent_process.comp                   |  176 ++
 .../Powder_process.comp                       |  773 ++++++
 .../dummy_instrument_folder/Progress_bar.comp |  151 ++
 .../dummy_instrument_folder/Source_div.comp   |  182 ++
 .../dummy_instrument_folder/Union_box.comp    |  438 ++++
 .../Union_cylinder.comp                       |  349 +++
 .../Union_demonstration_test.instr            |  422 ++++
 .../Union_make_material.comp                  |  299 +++
 .../dummy_instrument_folder/Union_master.comp | 2139 +++++++++++++++++
 mcstasscript/tests/test_Instr_reader.py       |   50 +-
 12 files changed, 5025 insertions(+), 3 deletions(-)
 create mode 100644 mcstasscript/tests/dummy_instrument_folder/Arm.comp
 create mode 100644 mcstasscript/tests/dummy_instrument_folder/Incoherent_process.comp
 create mode 100644 mcstasscript/tests/dummy_instrument_folder/Powder_process.comp
 create mode 100644 mcstasscript/tests/dummy_instrument_folder/Progress_bar.comp
 create mode 100644 mcstasscript/tests/dummy_instrument_folder/Source_div.comp
 create mode 100644 mcstasscript/tests/dummy_instrument_folder/Union_box.comp
 create mode 100644 mcstasscript/tests/dummy_instrument_folder/Union_cylinder.comp
 create mode 100644 mcstasscript/tests/dummy_instrument_folder/Union_demonstration_test.instr
 create mode 100644 mcstasscript/tests/dummy_instrument_folder/Union_make_material.comp
 create mode 100644 mcstasscript/tests/dummy_instrument_folder/Union_master.comp

diff --git a/MANIFEST.in b/MANIFEST.in
index b8abf181..f45d6378 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -4,4 +4,5 @@ include mcstasscript/tests/test_for_reading.comp
 include mcstasscript/tests/test_instrument.instr
 include mcstasscript/tests/Union_demonstration_test.instr
 graft mcstasscript/tests/dummy_mcstas
+graft mcstasscript/tests/dummy_instrument_folder
 graft mcstasscript/tests/test_data_set
diff --git a/mcstasscript/tests/dummy_instrument_folder/Arm.comp b/mcstasscript/tests/dummy_instrument_folder/Arm.comp
new file mode 100644
index 00000000..e7d28c6c
--- /dev/null
+++ b/mcstasscript/tests/dummy_instrument_folder/Arm.comp
@@ -0,0 +1,48 @@
+/*******************************************************************************
+*
+* McStas, neutron ray-tracing package
+*         Copyright 1997-2002, All rights reserved
+*         Risoe National Laboratory, Roskilde, Denmark
+*         Institut Laue Langevin, Grenoble, France
+*
+* Component: Arm
+*
+* %I
+*
+* Written by: Kim Lefmann and Kristian Nielsen
+* Date: September 1997
+* Version: $Revision$
+* Release: McStas 1.6
+* Origin: Risoe
+*
+* Arm/optical bench
+*
+* %D
+* An arm does not actually do anything, it is just there to set
+* up a new coordinate system.
+*
+* %P
+* Input parameters:
+*
+* %E
+*******************************************************************************/
+
+DEFINE COMPONENT Arm
+DEFINITION PARAMETERS ()
+SETTING PARAMETERS ()
+OUTPUT PARAMETERS ()
+/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
+TRACE
+%{
+%}
+
+MCDISPLAY
+%{
+  /* A bit ugly; hard-coded dimensions. */
+  
+  line(0,0,0,0.2,0,0);
+  line(0,0,0,0,0.2,0);
+  line(0,0,0,0,0,0.2);
+%}
+
+END
diff --git a/mcstasscript/tests/dummy_instrument_folder/Incoherent_process.comp b/mcstasscript/tests/dummy_instrument_folder/Incoherent_process.comp
new file mode 100644
index 00000000..f442ab05
--- /dev/null
+++ b/mcstasscript/tests/dummy_instrument_folder/Incoherent_process.comp
@@ -0,0 +1,176 @@
+/*******************************************************************************
+*
+*  McStas, neutron ray-tracing package
+*  Copyright(C) 2007 Risoe National Laboratory.
+*
+* %I
+* Written by: Mads Bertelsen
+* Date: 20.08.15
+* Version: $Revision: 0.1 $
+* Origin: Svanevej 19
+*
+* A sample component to separate geometry and phsysics
+*
+* %D
+*
+* This Union_process is based on the Incoherent.comp component originally written
+*  by Kim Lefmann and Kristian Nielsen
+*
+* Part of the Union components, a set of components that work together and thus
+*  sperates geometry and physics within McStas.
+* The use of this component requires other components to be used.
+*
+* 1) One specifies a number of processes using process components like this one
+* 2) These are gathered into material definitions using Union_make_material
+* 3) Geometries are placed using Union_box / Union_cylinder, assigned a material
+* 4) A Union_master component placed after all of the above
+*
+* Only in step 4 will any simulation happen, and per default all geometries
+*  defined before the master, but after the previous will be simulated here.
+*
+* There is a dedicated manual available for the Union_components
+*
+* Algorithm:
+* Described elsewhere
+*
+* %P
+* INPUT PARAMETERS:
+* sigma:  [barns]         Incoherent scattering cross section
+* f_QE:                   Fraction of quasielastic scattering (rest is elastic) [1]
+* gamma:                  Lorentzian width of quasielastic broadening (HWHM) [1]
+* packing_factor [1]      How dense is the material compared to optimal 0-1
+* Unit_cell_volume [AA^3] Unit_cell_volume
+* Interact_fraction [1]   How large a part of the scattering events should use this process 0-1 (sum of all processes in material = 1)
+*
+* OUTPUT PARAMETERS:
+*
+* %L
+* The test/example instrument <a href="../examples/Test_Phonon.instr">Test_Phonon.instr</a>.
+*
+* %E
+******************************************************************************/
+
+DEFINE COMPONENT Incoherent_process
+DEFINITION PARAMETERS ()
+SETTING PARAMETERS(sigma=5.08,f_QE=0,gamma=0,packing_factor=1,unit_cell_volume=13.8,interact_fraction=-1)
+OUTPUT PARAMETERS (This_process,Incoherent_storage,effective_my_scattering)
+
+/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
+
+SHARE
+%{
+#ifndef Union
+#define Union $Revision: 0.8 $
+
+#include "Union_functions.c"
+#include "Union_initialization.c"
+
+#endif
+
+
+struct Incoherent_physics_storage_struct{
+    // Variables that needs to be transfered between any of the following places:
+    // The initialize in this component
+    // The function for calculating my
+    // The function for calculating scattering
+    
+    double my_scattering;
+    double QE_sampling_frequency;
+    double lorentzian_width;
+    
+};
+
+// Function for calculating my in Incoherent case
+int Incoherent_physics_my(double *my,double *k_initial, union data_transfer_union data_transfer, struct focus_data_struct *focus_data) {
+    *my = data_transfer.pointer_to_a_Incoherent_physics_storage_struct->my_scattering;
+    return 1;
+};
+
+// Function for basic incoherent scattering event
+int Incoherent_physics_scattering(double *k_final, double *k_initial, double *weight, union data_transfer_union data_transfer, struct focus_data_struct *focus_data) {
+
+    //New version of incoherent scattering
+    double k_length = sqrt(k_initial[0]*k_initial[0]+k_initial[1]*k_initial[1]+k_initial[2]*k_initial[2]);
+
+    Coords k_out;
+    // Here is the focusing system in action, get a vector
+    double solid_angle;
+    focus_data->focusing_function(&k_out,&solid_angle,focus_data);
+    NORM(k_out.x,k_out.y,k_out.z);
+    *weight *= solid_angle*0.25/PI;
+    
+    double v_i,v_f,E_i,dE,E_f;
+    
+    if (rand01() < data_transfer.pointer_to_a_Incoherent_physics_storage_struct->QE_sampling_frequency) {
+      v_i = k_length * K2V;
+      E_i = VS2E*v_i*v_i;
+      dE = data_transfer.pointer_to_a_Incoherent_physics_storage_struct->lorentzian_width*tan(PI/2*randpm1());
+      E_f = E_i + dE;
+      if (E_f <= 0)
+        return 0;
+      v_f = SE2V*sqrt(E_f);
+      k_length = v_f*V2K;
+    }
+    
+    k_final[0] = k_out.x*k_length; k_final[1] = k_out.y*k_length; k_final[2] = k_out.z*k_length;
+    return 1;
+};
+
+%}
+
+DECLARE
+%{
+// Needed for transport to the main component
+struct global_process_element_struct global_process_element;
+struct scattering_process_struct This_process;
+
+#ifndef PROCESS_DETECTOR
+	//struct pointer_to_global_process_list global_process_list = {0,NULL};
+	#define PROCESS_DETECTOR dummy
+#endif
+
+// Declare for this component, to do calculations on the input / store in the transported data
+struct Incoherent_physics_storage_struct Incoherent_storage;
+double effective_my_scattering;
+
+%}
+
+INITIALIZE
+%{
+  // Initialize done in the component
+  effective_my_scattering = ((packing_factor/unit_cell_volume) * 100 * sigma);
+  Incoherent_storage.my_scattering = effective_my_scattering;
+  
+  Incoherent_storage.QE_sampling_frequency = f_QE;
+  Incoherent_storage.lorentzian_width = gamma;
+
+  // Need to specify if this process is isotropic
+  This_process.non_isotropic_rot_index = -1; // Yes (powder)
+  //This_process.non_isotropic_rot_index =  1;  // No (single crystal)
+
+  // Packing the data into a structure that is transported to the main component
+  sprintf(This_process.name,NAME_CURRENT_COMP);
+  This_process.process_p_interact = interact_fraction;
+  This_process.data_transfer.pointer_to_a_Incoherent_physics_storage_struct = &Incoherent_storage;
+  //This_process.data_transfer.pointer_to_a_Incoherent_physics_storage_struct->my_scattering = effective_my_scattering;
+  This_process.probability_for_scattering_function = &Incoherent_physics_my;
+  This_process.scattering_function = &Incoherent_physics_scattering;
+
+  // This will be the same for all process's, and can thus be moved to an include.
+  sprintf(global_process_element.name,NAME_CURRENT_COMP);
+  global_process_element.component_index = INDEX_CURRENT_COMP;
+  global_process_element.p_scattering_process = &This_process;
+  add_element_to_process_list(&global_process_list,global_process_element);
+ %}
+
+TRACE
+%{
+%}
+
+FINALLY
+%{
+// Since the process and it's storage is a static allocation, there is nothing to deallocate
+
+%}
+
+END
diff --git a/mcstasscript/tests/dummy_instrument_folder/Powder_process.comp b/mcstasscript/tests/dummy_instrument_folder/Powder_process.comp
new file mode 100644
index 00000000..16b73b09
--- /dev/null
+++ b/mcstasscript/tests/dummy_instrument_folder/Powder_process.comp
@@ -0,0 +1,773 @@
+/*******************************************************************************
+*
+*  McStas, neutron ray-tracing package
+*  Copyright(C) 2007 Risoe National Laboratory.
+*
+* %I
+* Written by: Mads Bertelsen
+* Date: 20.08.15
+* Version: $Revision: 0.1 $
+* Origin: Svanevej 19
+*
+*
+* %D
+*
+* This Union_process is based on the PowderN.comp component originally written
+*  by P. Willendrup, L. Chapon, K. Lefmann, A.B.Abrahamsen, N.B.Christensen,
+*  E.M.Lauridsen.
+*
+* Part of the Union components, a set of components that work together and thus
+*  sperates geometry and physics within McStas.
+* The use of this component requires other components to be used.
+*
+* 1) One specifies a number of processes using process components like this one
+* 2) These are gathered into material definitions using Union_make_material
+* 3) Geometries are placed using Union_box / Union_cylinder, assigned a material
+* 4) A Union_master component placed after all of the above
+*
+* Only in step 4 will any simulation happen, and per default all geometries
+*  defined before the master, but after the previous will be simulated here.
+*
+* There is a dedicated manual available for the Union_components*
+* Algorithm:
+* Described elsewhere
+*
+* %P
+* INPUT PARAMETERS:
+* Interact_fraction [1]   How large a part of the scattering events should use this process 0-1 (sum of all processes in material = 1)
+* packing_factor [1]      How dense is the material compared to optimal 0-1
+* For rest, see PowderN component which this is based on
+*
+* OUTPUT PARAMETERS:
+* V_rho:  [AA^-3] Atomic density
+*
+*
+* %E
+******************************************************************************/
+
+DEFINE COMPONENT Powder_process
+DEFINITION PARAMETERS (format=Undefined)
+SETTING PARAMETERS(string reflections="NULL",packing_factor=1, Vc=0, delta_d_d=0, DW=0, nb_atoms=1, d_phi=0, density=0, weight=0, barns=1, Strain=0, interact_fraction=-1)
+OUTPUT PARAMETERS (This_process,Powder_storage,effective_my_scattering,d_phi,line_info,columns)
+
+/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
+
+SHARE
+%{
+#ifndef Union
+#define Union $Revision: 0.8 $
+
+#include "Union_functions.c"
+#include "Union_initialization.c"
+
+#endif
+
+
+
+// Share section of PowderN 8/3 2016 from McStas.org
+
+  /* used for reading data table from file */
+  %include "read_table-lib"
+  %include "interoff-lib"
+/* Declare structures and functions only once in each instrument. */
+#ifndef POWDERN_DECL_UNION
+#define POWDERN_DECL_UNION
+/* format definitions in the order {j d F2 DW Dd inv2d q F strain} */
+#ifndef Crystallographica
+#define Crystallographica { 4,5,7,0,0,0,0,0,0 }
+#define Fullprof          { 4,0,8,0,0,5,0,0,0 }
+#define Lazy              {17,6,0,0,0,0,0,13,0 }
+#define Undefined         { 0,0,0,0,0,0,0,0,0 }
+#endif
+
+  struct line_data_union
+    {
+      double F2;                  /* Value of structure factor */
+      double q;                   /* Qvector */
+      int j;                      /* Multiplicity */
+      double DWfactor;            /* Debye-Waller factor */
+      double w;                   /* Intrinsic line width */
+      double Epsilon;             /* Strain=delta_d_d/d shift in ppm */
+    };
+
+  struct line_info_struct_union
+    {
+      struct line_data_union *list;     /* Reflection array */
+      int  count;                  /* Number of reflections */
+      double Dd;
+      double DWfactor;
+      double V_0;
+      double rho;
+      double at_weight;
+      double at_nb;
+      double sigma_a; // should not be used
+      double sigma_i; // should not be used
+      char   compname[256];
+      double flag_barns;
+      int    shape; /* 0 cylinder, 1 box, 2 sphere, 3 OFF file */
+      int    column_order[9]; /* column signification */
+      int    flag_warning;
+      char   type;  /* interaction type of event t=Transmit, i=Incoherent, c=Coherent */
+      double dq;    /* wavevector transfer [Angs-1] */
+      double Epsilon; /* global strain in ppm */
+      double XsectionFactor;
+      double my_s_v2_sum;
+      double my_a_v;
+      double my_inc;
+      double *w_v,*q_v, *my_s_v2;
+      double radius_i,xwidth_i,yheight_i,zdepth_i; // not to be used, but still here
+      double v; /* last velocity (cached) */
+      double Nq;
+      int    nb_reuses, nb_refl, nb_refl_count;
+      double v_min, v_max;
+      double xs_Nq[CHAR_BUF_LENGTH];
+      double xs_sum[CHAR_BUF_LENGTH];
+      double neutron_passed;
+      long   xs_compute, xs_reuse, xs_calls;
+    };
+
+  off_struct offdata_union;
+  
+  // PN_list_compare *****************************************************************
+
+  int PN_list_compare_union (void const *a, void const *b)
+  {
+     struct line_data_union const *pa = a;
+     struct line_data_union const *pb = b;
+     double s = pa->q - pb->q;
+     
+     if (!s) return 0;
+     else    return (s < 0 ? -1 : 1);
+  } /* PN_list_compare */
+
+  int read_line_data_union(char *SC_file, struct line_info_struct_union *info)
+  {
+    struct line_data_union *list = NULL;
+    int    size = 0;
+    t_Table sTable; /* sample data table structure from SC_file */
+    int    i=0;
+    int    mult_count  =0;
+    char   flag=0;
+    double q_count=0, j_count=0, F2_count=0;
+    char **parsing;
+    int    list_count=0;
+
+    if (!SC_file || !strlen(SC_file) || !strcmp(SC_file, "NULL")) {
+      printf("PowderN: %s: Using incoherent elastic scattering only\n",info->compname);
+      info->count = 0;
+      return(0);
+    }
+    Table_Read(&sTable, SC_file, 1); /* read 1st block data from SC_file into sTable*/
+
+    /* parsing of header */
+    parsing = Table_ParseHeader(sTable.header,
+      "Vc","V_0",
+      "sigma_abs","sigma_a ",
+      "sigma_inc","sigma_i ",
+      "column_j",
+      "column_d",
+      "column_F2",
+      "column_DW",
+      "column_Dd",
+      "column_inv2d", "column_1/2d", "column_sintheta/lambda",
+      "column_q", /* 14 */
+      "DW", "Debye_Waller",
+      "delta_d_d/d",
+      "column_F ",
+      "V_rho",
+      "density",
+      "weight",
+      "nb_atoms","multiplicity", /* 23 */
+      "column_ppm","column_strain",
+      NULL);
+
+    if (parsing) {
+      if (parsing[0] && !info->V_0)     info->V_0    =atof(parsing[0]);
+      if (parsing[1] && !info->V_0)     info->V_0    =atof(parsing[1]);
+      if (parsing[2] && !info->sigma_a) info->sigma_a=atof(parsing[2]);
+      if (parsing[3] && !info->sigma_a) info->sigma_a=atof(parsing[3]);
+      if (parsing[4] && !info->sigma_i) info->sigma_i=atof(parsing[4]);
+      if (parsing[5] && !info->sigma_i) info->sigma_i=atof(parsing[5]);
+      if (parsing[6])                   info->column_order[0]=atoi(parsing[6]);
+      if (parsing[7])                   info->column_order[1]=atoi(parsing[7]);
+      if (parsing[8])                   info->column_order[2]=atoi(parsing[8]);
+      if (parsing[9])                   info->column_order[3]=atoi(parsing[9]);
+      if (parsing[10])                  info->column_order[4]=atoi(parsing[10]);
+      if (parsing[11])                  info->column_order[5]=atoi(parsing[11]);
+      if (parsing[12])                  info->column_order[5]=atoi(parsing[12]);
+      if (parsing[13])                  info->column_order[5]=atoi(parsing[13]);
+      if (parsing[14])                  info->column_order[6]=atoi(parsing[14]);
+      if (parsing[15] && info->DWfactor<=0)    info->DWfactor=atof(parsing[15]);
+      if (parsing[16] && info->DWfactor<=0)    info->DWfactor=atof(parsing[16]);
+      if (parsing[17] && info->Dd <0)          info->Dd      =atof(parsing[17]);
+      if (parsing[18])                  info->column_order[7]=atoi(parsing[18]);
+      if (parsing[19] && !info->V_0)    info->V_0    =1/atof(parsing[19]);
+      if (parsing[20] && !info->rho)    info->rho    =atof(parsing[20]);
+      if (parsing[21] && !info->at_weight)     info->at_weight    =atof(parsing[21]);
+      if (parsing[22] && info->at_nb <= 1)  info->at_nb    =atof(parsing[22]);
+      if (parsing[23] && info->at_nb <= 1)  info->at_nb    =atof(parsing[23]);
+      if (parsing[24])                  info->column_order[8]=atoi(parsing[24]);
+      if (parsing[25])                  info->column_order[8]=atoi(parsing[25]);
+      for (i=0; i<=25; i++) if (parsing[i]) free(parsing[i]);
+      free(parsing);
+    }
+
+    if (!sTable.rows)
+      exit(fprintf(stderr, "PowderN: %s: Error: The number of rows in %s "
+       "should be at least %d\n", info->compname, SC_file, 1));
+    else size = sTable.rows;
+    Table_Info(sTable);
+    printf("PowderN: %s: Reading %d rows from %s\n",
+          info->compname, size, SC_file);
+
+    if (info->column_order[0] == 4 && info->flag_barns !=0)
+    printf("PowderN: %s: Powder file probably of type Crystallographica/Fullprof (lau)\n"
+           "WARNING: but F2 unit is set to barns=1 (barns). Intensity might be 100 times too high.\n",
+           info->compname);
+  if (info->column_order[0] == 17 && info->flag_barns == 0)
+    printf("PowderN: %s: Powder file probably of type Lazy Pulver (laz)\n"
+           "WARNING: but F2 unit is set to barns=0 (fm^2). Intensity might be 100 times too low.\n",
+           info->compname);
+    /* allocate line_data array */
+    list = (struct line_data_union*)malloc(size*sizeof(struct line_data_union));
+
+    for (i=0; i<size; i++)
+    {
+      /*      printf("Reading in line %i\n",i);*/
+      double j=0, d=0, w=0, q=0, DWfactor=0, F2=0, Epsilon=0;
+      int index;
+
+      if (info->Dd >= 0)      w         = info->Dd;
+      if (info->DWfactor > 0) DWfactor  = info->DWfactor;
+      if (info->Epsilon)      Epsilon   = info->Epsilon*1e-6;
+
+      /* get data from table using columns {j d F2 DW Dd inv2d q F} */
+      /* column indexes start at 1, thus need to substract 1 */
+      if (info->column_order[0] >0)
+        j = Table_Index(sTable, i, info->column_order[0]-1);
+      if (info->column_order[1] >0)
+        d = Table_Index(sTable, i, info->column_order[1]-1);
+      if (info->column_order[2] >0)
+        F2 = Table_Index(sTable, i, info->column_order[2]-1);
+      if (info->column_order[3] >0)
+        DWfactor = Table_Index(sTable, i, info->column_order[3]-1);
+      if (info->column_order[4] >0)
+        w = Table_Index(sTable, i, info->column_order[4]-1);
+      if (info->column_order[5] >0)
+        { d = Table_Index(sTable, i, info->column_order[5]-1);
+          d = (d > 0? 1/d/2 : 0); }
+      if (info->column_order[6] >0)
+        { q = Table_Index(sTable, i, info->column_order[6]-1);
+          d = (q > 0 ? 2*PI/q : 0); }
+      if (info->column_order[7] >0  && !F2)
+        { F2 = Table_Index(sTable, i, info->column_order[7]-1); F2 *= F2; }
+      if (info->column_order[8] >0  && !Epsilon)
+        { Epsilon = Table_Index(sTable, i, info->column_order[8]-1)*1e-6; }
+
+      /* assign and check values */
+      j        = (j > 0 ? j : 0);
+      q        = (d > 0 ? 2*PI/d : 0); /* this is q */
+      if (Epsilon && fabs(Epsilon) < 1e6) {
+        q     -= Epsilon*q; /* dq/q = -delta_d_d/d = -Epsilon */
+      }
+      DWfactor = (DWfactor > 0 ? DWfactor : 1);
+      w        = (w>0 ? w : 0); /* this is q and d relative spreading */
+      F2       = (F2 >= 0 ? F2 : 0);
+      if (j == 0 || q == 0) {
+        printf("PowderN: %s: line %i has invalid definition\n"
+               "         (mult=0 or q=0 or d=0)\n", info->compname, i);
+        continue;
+      }
+      list[list_count].j = j;
+      list[list_count].q = q;
+      list[list_count].DWfactor = DWfactor;
+      list[list_count].w = w;
+      list[list_count].F2= F2;
+      list[list_count].Epsilon = Epsilon;
+
+      /* adjust multiplicity if j-column + multiple d-spacing lines */
+      /* if  d = previous d, increase line duplication index */
+      if (!q_count)      q_count  = q;
+      if (!j_count)      j_count  = j;
+      if (!F2_count)     F2_count = F2;
+      if (fabs(q_count-q) < 0.0001*fabs(q)
+       && fabs(F2_count-F2) < 0.0001*fabs(F2) && j_count == j) {
+       mult_count++; flag=0; }
+      else flag=1;
+      if (i == size-1) flag=1;
+      /* else if d != previous d : just passed equivalent lines */
+      if (flag) {
+        if (i == size-1) list_count++;
+      /*   if duplication index == previous multiplicity */
+      /*      set back multiplicity of previous lines to 1 */
+        if ((mult_count && list_count>0)
+            && (mult_count == list[list_count-1].j
+                || ((list_count < size) && (i == size - 1)
+                    && (mult_count == list[list_count].j))) ) {
+          printf("PowderN: %s: Set multiplicity to 1 for lines [%i:%i]\n"
+                  "         (d-spacing %g is duplicated %i times)\n",
+            info->compname, list_count-mult_count, list_count-1, list[list_count-1].q, mult_count);
+          for (index=list_count-mult_count; index<list_count; list[index++].j = 1);
+          mult_count = 1;
+          q_count   = q;
+          j_count   = j;
+          F2_count  = F2;
+        }
+        if (i == size-1) list_count--;
+        flag=0;
+      }
+      list_count++;
+    } /* end for */ 
+    
+    Table_Free(&sTable);
+    
+    /* sort the list with increasing q */
+    qsort(list, list_count, sizeof(struct line_data_union),  PN_list_compare_union);
+    
+    printf("PowderN: %s: Read %i reflections from file '%s'\n", 
+      info->compname, list_count, SC_file);
+    
+    info->list  = list;
+    info->count = list_count;
+
+    return(list_count);
+  } /* read_line_data_union */
+
+
+/* computes the number of possible reflections (return value), and the total xsection 'sum' */
+/* this routine looks for a pre-computed value in the Nq and sum cache tables               */
+/* when found, the earch starts from the corresponding lower element in the table           */
+int calc_xsect_union(double v, double *qv, double *my_sv2, int count, double *sum,
+  struct line_info_struct_union *line_info) {
+  int    Nq = 0, line=0, line0=0;
+  *sum=0;
+  
+  //printf("Line_info when entering cross_section calculation\n");
+  //printf("v = %f, qv = %f, my_sv2 = %f, count = %d, sum = %f\n",v,*qv,*my_sv2,count,*sum);
+  //printf("v = %f\n",v);
+  //printf("line_info->v = %f, line_info->v_min = %f, line_info->v_max = %f, line_info->neutron_passed = %f\n",line_info->v,line_info->v_min,line_info->v_max,line_info->neutron_passed);
+  //printf("line_info->xs_reuses = %d, line_info->xs_compute = %d\n",line_info->xs_reuse,line_info->xs_compute);
+  
+  
+  /* check if a line_info element has been recorded already */
+  if (v >= line_info->v_min && v <= line_info->v_max && line_info->neutron_passed >= CHAR_BUF_LENGTH) {
+    line = (int)floor(v - line_info->v_min)*CHAR_BUF_LENGTH/(line_info->v_max - line_info->v_min);
+    Nq    = line_info->xs_Nq[line];
+    *sum  = line_info->xs_sum[line];
+    if (!Nq && *sum == 0) {
+      /* not yet set: we compute the sum up to the corresponding speed in the table cache */
+      //printf("Nq and sum not yet set, have to do this calculation now\n");
+      double line_v = line_info->v_min + line*(line_info->v_max - line_info->v_min)/CHAR_BUF_LENGTH;
+      for(line0=0; line0<count; line0++) {
+        if (qv[line0] <= 2*line_v) { /* q < 2*kf: restrict structural range */
+          *sum += my_sv2[line0];
+          if (Nq < line0+1) Nq=line0+1; /* determine maximum line index which can scatter */
+        } else break;
+      }
+      line_info->xs_Nq[line] = Nq;
+      line_info->xs_sum[line]= *sum;
+      line_info->xs_compute++;
+      //printf("line_info->xs_Nq[line] = %f, line_info->xs_sum[line] = %f, line_info->xs_compute = %d\n",line_info->xs_Nq[line],line_info->xs_sum[line],line_info->xs_compute);
+    } else line_info->xs_reuse++;
+    line0 = Nq;
+  }
+  
+  line_info->xs_calls++;
+  
+  for(line=line0; line<count; line++) {
+    if (qv[line] <= 2*v) { /* q < 2*kf: restrict structural range */
+      *sum += my_sv2[line];
+      if (Nq < line+1) Nq=line+1; /* determine maximum line index which can scatter */
+    } else break;
+  }
+  
+  //printf("cross_section function to return %d lines to scatter with, with cross section sum %f \n",Nq,*sum);
+  return(Nq);
+} /* calc_xsect_union */
+
+#endif /* !POWDERN_DECL */
+
+
+
+struct Powder_physics_storage_struct{
+    // Variables that needs to be transfered between any of the following places:
+    // The initialize in this component
+    // The function for calculating my
+    // The function for calculating scattering
+    
+    struct line_info_struct_union *line_info_storage;
+    double my_scattering;
+    double vertical_angular_limit;
+};
+
+// Obsolete: Function for initializing test_physics. Done in component instead.
+int Powder_physics_initialize(union data_transfer_union data_transfer) {
+      // Obsolte
+      return 1;
+};
+
+// Function for calculating my in a test case.
+int Powder_physics_my(double *my,double *k_initial, union data_transfer_union data_transfer, struct focus_data_struct *focus_data) {
+    //*my = data_transfer.pointer_to_a_Powder_physics_storage_struct->my_scattering;
+    
+    
+    int method_switch = 1;
+    // For test
+    int line_v,line0,line,count;
+    
+    // Should not interfer with the global variables
+    double vx = k_initial[0]*K2V;
+    double vy = k_initial[1]*K2V;
+    double vz = k_initial[2]*K2V;
+    
+    // Not sure one can do this, but I do not see why not
+    struct line_info_struct_union *line_info = data_transfer.pointer_to_a_Powder_physics_storage_struct->line_info_storage;
+    
+    double v = sqrt(vx*vx + vy*vy + vz*vz);
+    //printf("Velocity = %f \n",v);
+    
+    //printf("line_info->v = %f, line_info->v_min = %f, line_info->v_max = %f, line_info->neutron_passed = %f\n",line_info->v,line_info->v_min,line_info->v_max,line_info->neutron_passed);
+    // Here the maximum and minimum v is recorded, should this be for scattering events or cross section calculations?
+    if (line_info->neutron_passed < CHAR_BUF_LENGTH) {
+      if (v < line_info->v_min) line_info->v_min = v;
+      if (v > line_info->v_max) line_info->v_max = v;
+      line_info->neutron_passed++;
+    }
+    
+    if (method_switch == 1) {
+    // Here the cross section is calculated and stored
+    if ( fabs(v - line_info->v) < 1e-6) {
+        line_info->nb_reuses++;
+      } else {
+        //printf("calling crosssection calculation \n");
+        // int calc_xsect_union(double v, double *qv, double *my_sv2, int count, double *sum, struct line_info_struct *line_info)
+        line_info->Nq = calc_xsect_union(v, line_info->q_v, line_info->my_s_v2, line_info->count, &line_info->my_s_v2_sum, line_info);
+        line_info->v = v;
+        line_info->nb_refl += line_info->Nq;
+        line_info->nb_refl_count++;
+      }
+    } else {
+    if ( fabs(v - line_info->v) < 1e-6) {
+        line_info->nb_reuses++;
+      } else {
+        //printf("calling crosssection calculation \n");
+        if (v >= line_info->v_min && v <= line_info->v_max && line_info->neutron_passed >= CHAR_BUF_LENGTH) {
+        line = (int)floor(v - line_info->v_min)*CHAR_BUF_LENGTH/(line_info->v_max - line_info->v_min);
+        line_info->Nq = line_info->xs_Nq[line];
+        line_info->my_s_v2_sum  = line_info->xs_sum[line];
+        if (!line_info->Nq && line_info->my_s_v2_sum == 0) {
+          /* not yet set: we compute the sum up to the corresponding speed in the table cache */
+          //printf("Nq and sum not yet set, have to do this calculation now\n");
+          double line_v = line_info->v_min + line*(line_info->v_max - line_info->v_min)/CHAR_BUF_LENGTH;
+          for(line0=0; line0<count; line0++) {
+            if (line_info->q_v[line0] <= 2*line_v) { /* q < 2*kf: restrict structural range */
+              line_info->my_s_v2_sum += line_info->my_s_v2[line0];
+              if (line_info->Nq < line0+1) line_info->Nq=line0+1; /* determine maximum line index which can scatter */
+            } else break;
+          }
+          line_info->xs_Nq[line] = line_info->Nq;
+          line_info->xs_sum[line]= line_info->my_s_v2_sum;
+          line_info->xs_compute++;
+          //printf("line_info->xs_Nq[line] = %f, line_info->xs_sum[line] = %f, line_info->xs_compute = %d\n",line_info->xs_Nq[line],line_info->xs_sum[line],line_info->xs_compute);
+        } else line_info->xs_reuse++;
+        line0 = line_info->Nq;
+        }
+          
+        line_info->xs_calls++;
+          
+        for(line=line0; line<count; line++) {
+          if (line_info->q_v[line] <= 2*v) { /* q < 2*kf: restrict structural range */
+            line_info->my_s_v2_sum += line_info->my_s_v2[line];
+            if (line_info->Nq < line+1) line_info->Nq=line+1; /* determine maximum line index which can scatter */
+          } else break;
+        }
+        line_info->v = v;
+        line_info->nb_refl += line_info->Nq;
+        line_info->nb_refl_count++;
+      }
+    }
+    
+     *my = line_info->my_s_v2_sum/(v*v);
+    //printf("Returned my scattering of %f \n",*my);
+    //printf("compute = %d and reuse = %d \n",line_info->xs_compute,line_info->xs_reuse);
+    
+    return 1;
+};
+
+// Function that provides a basic nonuniform elastic scattering. Unphysical for testing purposes.
+int Powder_physics_scattering(double *k_final, double *k_initial, double *weight, union data_transfer_union data_transfer, struct focus_data_struct *focus_data) {
+
+    // This component need to write to its storage transfer for each event, is that possible with this structure?
+    struct line_info_struct_union *line_info = data_transfer.pointer_to_a_Powder_physics_storage_struct->line_info_storage;
+    double vertical_angular_limit = data_transfer.pointer_to_a_Powder_physics_storage_struct->vertical_angular_limit;
+    
+    
+    // Should not interfer with the global variables
+    double vx = k_initial[0]*K2V;
+    double vy = k_initial[1]*K2V;
+    double vz = k_initial[2]*K2V;
+    
+    double v = sqrt(vx*vx + vy*vy + vz*vz);
+    
+    int line;
+    double arg;
+    double theta;
+    double alpha,alpha0;
+    
+    double vout_x,vout_y,vout_z;
+    double tmp_vx,tmp_vy,tmp_vz;
+    double nx,ny,nz;
+    double my_s_n;
+    
+    // copy from PowderN component
+    if (line_info->count > 0) {
+          /* choose line */
+          if (line_info->Nq > 1) line=floor(line_info->Nq*rand01());  /* Select between Nq powder lines */
+          else line = 0;
+          if (line_info->w_v[line])
+            arg = line_info->q_v[line]*(1+line_info->w_v[line]*randnorm())/(2.0*v);
+          else
+            arg = line_info->q_v[line]/(2.0*v);
+            my_s_n = line_info->my_s_v2[line]/(v*v);
+          if(fabs(arg) > 1) {
+            //printf("Powder scattering function returned 0, should not happen\n");
+            return 0; /* No bragg scattering possible (was absorb)*/
+          }
+          theta = asin(arg);          /* Bragg scattering law */
+
+          /* Choose point on Debye-Scherrer cone */
+          if (vertical_angular_limit)
+          { /* relate height of detector to the height on DS cone */
+            arg = sin(vertical_angular_limit*DEG2RAD/2)/sin(2*theta);
+            /* If full Debye-Scherrer cone is within d_phi, don't focus */
+            if (arg < -1 || arg > 1) vertical_angular_limit = 0;
+            /* Otherwise, determine alpha to rotate from scattering plane
+               into vertical_angular_limit focusing area*/
+            else alpha = 2*asin(arg);
+          }
+          if (vertical_angular_limit) {
+            /* Focusing */
+            alpha = fabs(alpha);
+            /* Trick to get scattering for pos/neg theta's */
+            alpha0= 2*rand01()*alpha;
+            if (alpha0 > alpha) {
+              alpha0=PI+(alpha0-1.5*alpha);
+            } else {
+              alpha0=alpha0-0.5*alpha;
+            }
+          }
+          else
+            alpha0 = PI*randpm1();
+
+          /* now find a nearly vertical rotation axis:
+           * Either
+           *  (v along Z) x (X axis) -> nearly Y axis
+           * Or
+           *  (v along X) x (Z axis) -> nearly Y axis
+           */
+          if (fabs(scalar_prod(1,0,0,vx/v,vy/v,vz/v)) < fabs(scalar_prod(0,0,1,vx/v,vy/v,vz/v))) {
+            nx = 1; ny = 0; nz = 0;
+          } else {
+            nx = 0; ny = 0; nz = 1;
+          }
+          vec_prod(tmp_vx,tmp_vy,tmp_vz, vx,vy,vz, nx,ny,nz);
+
+          /* v_out = rotate 'v' by 2*theta around tmp_v: Bragg angle */
+          rotate(vout_x,vout_y,vout_z, vx,vy,vz, 2*theta, tmp_vx,tmp_vy,tmp_vz);
+
+          /* tmp_v = rotate v_out by alpha0 around 'v' (Debye-Scherrer cone) */
+          rotate(tmp_vx,tmp_vy,tmp_vz, vout_x,vout_y,vout_z, alpha0, vx, vy, vz);
+          vx = tmp_vx;
+          vy = tmp_vy;
+          vz = tmp_vz;
+        
+          k_final[0] = V2K*vx; k_final[1] = V2K*vy; k_final[2] = V2K*vz;
+        
+          //*weight *= line_info->Nq*my_s_n; I believe my_s_n is part of the correction for sampling posistion, not to be done here
+          *weight *= line_info->Nq*my_s_n/line_info->my_s_v2_sum*v*v;
+        
+          //printf("my_s_n = %f \n",my_s_n);
+        
+          // What to do with my_s_n ?
+          /*
+          pmul  = line_info->Nq*l_full*my_s_n*exp(-(line_info->my_a_v/v+my_s)*(l+l_1))
+                                  /(1-(p_inc+p_transmit));
+          */
+          // Correction in case of vertical_angular_limit focusing - BUT only when d_phi != 0
+          if (vertical_angular_limit) *weight *= alpha/PI;
+          
+        
+          line_info->type = 'c';
+          line_info->dq = line_info->q_v[line]*V2K;
+          
+          
+        } else {
+        /* else transmit <-- No powder lines in file */
+        printf("Error, need lines in the PowderN input file\n");
+        }
+
+
+    //printf("Powder scattering function returned 1\n");
+    return 1;
+};
+
+%}
+
+DECLARE
+%{
+// Needed for transport to the main component
+struct global_process_element_struct global_process_element;
+struct scattering_process_struct This_process;
+
+#ifndef PROCESS_DETECTOR
+	//struct pointer_to_global_process_list global_process_list = {0,NULL};
+	#define PROCESS_DETECTOR dummy
+#endif
+
+// Declare for this component, to do calculations on the input / store in the transported data
+struct Powder_physics_storage_struct Powder_storage;
+struct line_info_struct_union line_info;
+double effective_my_scattering;
+
+int columns[9] = format;
+
+
+%}
+
+INITIALIZE
+%{
+
+  // Initialize done in the component
+  
+  // Copy from PowderN component
+  int i=0;
+  struct line_data_union *L;
+  line_info.Dd       = delta_d_d;
+  line_info.DWfactor = DW;
+  line_info.V_0      = Vc;
+  line_info.rho      = density;
+  line_info.at_weight= weight;
+  line_info.at_nb    = nb_atoms;
+  line_info.sigma_a  = 0; // This inputs are not needed, as absorption is handled elsewhere
+  line_info.sigma_i  = 0; // This input is not needed, as incoherent scattering is handled elsewhere
+  line_info.flag_barns=barns;
+  //line_info.shape    = 0;
+  line_info.flag_warning=0;
+  line_info.Epsilon  = Strain;
+  line_info.radius_i =line_info.xwidth_i=line_info.yheight_i=line_info.zdepth_i=0;
+  line_info.v  = 0;
+  line_info.Nq = 0;
+  //line_info.v_min = FLT_MAX; line_info.v_max = 0;
+  line_info.v_min = 10000000000; line_info.v_max = 0;
+  line_info.neutron_passed=0;
+  line_info.nb_reuses = line_info.nb_refl = line_info.nb_refl_count = 0;
+  line_info.xs_compute= line_info.xs_reuse= line_info.xs_calls =0;
+  for (i=0; i< 9; i++) line_info.column_order[i] = columns[i];
+  strncpy(line_info.compname, NAME_CURRENT_COMP, 256);
+
+  // p_interact handled elsewhere
+  //if (p_interact) {
+  //  if (p_interact < p_inc) { double tmp=p_interact; p_interact=p_inc; p_inc=tmp; }
+  //  p_transmit = 1-p_interact-p_inc;
+  //}
+
+  if (reflections && strlen(reflections) && strcmp(reflections, "NULL") && strcmp(reflections, "0")) {
+    i = read_line_data_union(reflections, &line_info);
+    if (i == 0)
+      exit(fprintf(stderr,"PowderN: %s: reflection file %s is not valid.\n"
+                          "ERROR    Please check file format (laz or lau).\n", NAME_CURRENT_COMP, reflections));
+  }
+
+  /* compute the scattering unit density from material weight and density */
+  /* the weight of the scattering element is the chemical formula molecular weight
+   * times the nb of chemical formulae in the scattering element (nb_atoms) */
+  if (!line_info.V_0 && line_info.at_nb > 0
+    && line_info.at_weight > 0 && line_info.rho > 0) {
+    /* molar volume [cm^3/mol] = weight [g/mol] / density [g/cm^3] */
+    /* atom density per Angs^3 = [mol/cm^3] * N_Avogadro *(1e-8)^3 */
+    line_info.V_0 = line_info.at_nb
+      /(line_info.rho/line_info.at_weight/1e24*6.02214199e23);
+  }
+
+  /* the scattering unit cross sections are the chemical formula onces
+   * times the nb of chemical formulae in the scattering element */
+  if (line_info.at_nb > 0) {
+    line_info.sigma_a *= line_info.at_nb; line_info.sigma_i *= line_info.at_nb;
+  }
+
+  if (line_info.V_0 <= 0)
+    fprintf(stderr,"PowderN: %s: density/unit cell volume is NULL (Vc). Unactivating component.\n", NAME_CURRENT_COMP);
+
+
+  if (line_info.flag_barns) { /* Factor 100 to convert from barns to fm^2 */
+    line_info.XsectionFactor = 100;
+  } else {
+    line_info.XsectionFactor = 1;
+  }
+
+  if (line_info.V_0 && i) {
+    L = line_info.list;
+
+    line_info.q_v = malloc(line_info.count*sizeof(double));
+    line_info.w_v = malloc(line_info.count*sizeof(double));
+    line_info.my_s_v2 = malloc(line_info.count*sizeof(double));
+    if (!line_info.q_v || !line_info.w_v || !line_info.my_s_v2)
+      exit(fprintf(stderr,"PowderN: %s: ERROR allocating memory (init)\n", NAME_CURRENT_COMP));
+    for(i=0; i<line_info.count; i++)
+    {
+      line_info.my_s_v2[i] = 4*PI*PI*PI*packing_factor*(L[i].DWfactor ? L[i].DWfactor : 1)
+                 /(line_info.V_0*line_info.V_0*V2K*V2K)
+                 *(L[i].j * L[i].F2 / L[i].q)*line_info.XsectionFactor;
+      /* Is not yet divided by v^2 */
+      /* Squires [3.103] */
+      line_info.q_v[i] = L[i].q*K2V;
+      line_info.w_v[i] = L[i].w;
+    }
+  }
+  if (line_info.V_0) {
+    /* Is not yet divided by v */
+    line_info.my_a_v = packing_factor*line_info.sigma_a/line_info.V_0*2200*100;   // Factor 100 to convert from barns to fm^2
+    line_info.my_inc = packing_factor*line_info.sigma_i/line_info.V_0*100;   // Factor 100 to convert from barns to fm^2
+
+    printf("PowderN: %s: Vc=%g [Angs] sigma_abs=%g [barn] sigma_inc=%g [barn] reflections=%s\n",
+      NAME_CURRENT_COMP, line_info.V_0, line_info.sigma_a, line_info.sigma_i, reflections && strlen(reflections) ? reflections : "NULL");
+  }
+
+  //printf("INTIALIZE line_info.v = %f, line_info.v_min = %f, line_info.v_max = %f, line_info.neutron_passed = %f\n",line_info.v,line_info.v_min,line_info.v_max,line_info.neutron_passed);
+
+  Powder_storage.line_info_storage = &line_info;
+  Powder_storage.vertical_angular_limit = d_phi;
+  
+  // Need to specify if this process is isotropic
+  This_process.non_isotropic_rot_index = -1; // Yes (powder)
+  //This_process.non_isotropic_rot_index =  1;  // No (single crystal)
+
+  // Packing the data into a structure that is transported to the main component
+  This_process.data_transfer.pointer_to_a_Powder_physics_storage_struct = &Powder_storage;
+  This_process.data_transfer.pointer_to_a_Powder_physics_storage_struct->my_scattering = effective_my_scattering;
+  This_process.probability_for_scattering_function = &Powder_physics_my;
+  This_process.scattering_function = &Powder_physics_scattering;
+    
+  // This will be the same for all process's, and can thus be moved to an include.
+  This_process.process_p_interact = interact_fraction;
+  sprintf(This_process.name,NAME_CURRENT_COMP);
+  rot_copy(This_process.rotation_matrix,ROT_A_CURRENT_COMP);
+  sprintf(global_process_element.name,NAME_CURRENT_COMP);
+  global_process_element.component_index = INDEX_CURRENT_COMP;
+  global_process_element.p_scattering_process = &This_process;
+  add_element_to_process_list(&global_process_list,global_process_element);
+ %}
+
+TRACE
+%{
+%}
+
+FINALLY
+%{
+  free(line_info.list);
+  free(line_info.q_v);
+  free(line_info.w_v);
+  free(line_info.my_s_v2);
+%}
+
+END
diff --git a/mcstasscript/tests/dummy_instrument_folder/Progress_bar.comp b/mcstasscript/tests/dummy_instrument_folder/Progress_bar.comp
new file mode 100644
index 00000000..e57d4567
--- /dev/null
+++ b/mcstasscript/tests/dummy_instrument_folder/Progress_bar.comp
@@ -0,0 +1,151 @@
+/*******************************************************************************
+*
+* McStas, neutron ray-tracing package
+*         Copyright 1997-2002, All rights reserved
+*         Risoe National Laboratory, Roskilde, Denmark
+*         Institut Laue Langevin, Grenoble, France
+*
+* Component: Progress_bar
+*
+* %I
+* Written by: Emmanuel Farhi
+* Date: 2002
+* Origin: ILL
+*
+* A simulation progress bar
+*
+* %D
+* An indicator of the progress of the simulation, monitoring
+* the Init, Trace with the achieved percentage, and the Finally section.
+* Intermediate savings (e.g. triggered by USR2 signal) are also shown.
+* This component should be positioned at the very begining of the instrument
+* The profile option will save the intensity and number of events for each
+* component It may be used to evaluate the simulation efficiency.
+*
+* Example: Progress_bar(percent=10,flag_save=1) AT (0,0,0)
+*
+* %P
+* INPUT PARAMETERS:
+* percent: [0-100]  percentage interval between updates. Default is 10%.
+* minutes: [min]    time in minutes between updates (Overrides percent flag).
+* flag_save: [0|1]  flag to enable intermediate saving for all monitors
+* profile: [str]    file name to save the simulation profile if set to "", it is set to the name of the instrument.
+*
+* %E
+*******************************************************************************/
+
+DEFINE COMPONENT Progress_bar
+DEFINITION PARAMETERS ()
+SETTING PARAMETERS (string profile="NULL", percent=10,flag_save=0,minutes=0)
+OUTPUT PARAMETERS (IntermediateCnts,StartTime,EndTime,CurrentTime)
+/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
+DECLARE
+%{
+#ifndef PROGRESS_BAR
+#define PROGRESS_BAR
+#else
+#error Only one Progress_bar component may be used in an instrument definition.
+#endif
+
+double IntermediateCnts;
+time_t StartTime;
+time_t EndTime;
+time_t CurrentTime;
+%}
+
+INITIALIZE
+%{
+IntermediateCnts=0;
+StartTime=0;
+EndTime=0;
+CurrentTime=0;
+
+fprintf(stdout, "[%s] Initialize\n", mcinstrument_name);
+  if (percent*mcget_ncount()/100 < 1e5) {
+    percent=1e5*100.0/mcget_ncount();
+  }
+%}
+
+TRACE
+%{
+  double ncount;
+  ncount = mcget_run_num();
+  if (!StartTime) {
+    time(&StartTime); /* compute starting time */
+    IntermediateCnts = 1e3;
+  }
+  time_t NowTime;
+  time(&NowTime);
+  /* compute initial estimate of computation duration */
+  if (!EndTime && ncount >= IntermediateCnts) {
+    CurrentTime = NowTime;
+    if (difftime(NowTime,StartTime) > 10 && ncount) { /* wait 10 sec before writing ETA */
+      EndTime = StartTime + (time_t)(difftime(NowTime,StartTime)
+				     *(double)mcget_ncount()/ncount);
+      IntermediateCnts = 0;
+      fprintf(stdout, "\nTrace ETA ");
+      if (difftime(EndTime,StartTime) < 60.0)
+        fprintf(stdout, "%g [s] %% ", difftime(EndTime,StartTime));
+      else if (difftime(EndTime,StartTime) > 3600.0)
+        fprintf(stdout, "%g [h] %% ", difftime(EndTime,StartTime)/3600.0);
+      else
+        fprintf(stdout, "%g [min] %% ", difftime(EndTime,StartTime)/60.0);
+    } else IntermediateCnts += 1e3;
+    fflush(stdout);
+  }
+
+  /* display percentage when percent or minutes have reached step */
+  if (EndTime && mcget_ncount() &&
+    (    (minutes && difftime(NowTime,CurrentTime) > minutes*60)
+      || (percent && !minutes && ncount >= IntermediateCnts))   )
+  {
+    fprintf(stdout, "%d ", (int)(ncount*100.0/mcget_ncount())); fflush(stdout);
+    CurrentTime = NowTime;
+
+    IntermediateCnts = ncount + percent*mcget_ncount()/100;
+    /* check that next intermediate ncount check is a multiple of the desired percentage */
+    IntermediateCnts = floor(IntermediateCnts*100/percent/mcget_ncount())*percent*mcget_ncount()/100;
+    /* raise flag to indicate that we did something */
+    SCATTER;
+    if (flag_save) mcsave(NULL);
+  }
+%}
+
+SAVE
+%{
+  MPI_MASTER(fprintf(stdout, "\nSave [%s]\n", mcinstrument_name););
+  if (profile && strlen(profile) && strcmp(profile,"NULL") && strcmp(profile,"0")) {
+    char filename[256];
+    if (!strlen(profile) || !strcmp(profile,"NULL") || !strcmp(profile,"0")) strcpy(filename, mcinstrument_name);
+    else strcpy(filename, profile);
+    DETECTOR_OUT_1D(
+        "Intensity profiler",
+        "Component index [1]",
+        "Intensity",
+        "prof", 1, mcNUMCOMP, mcNUMCOMP-1,
+        &mcNCounter[1],&mcPCounter[1],&mcP2Counter[1],
+        filename);
+
+  }
+%}
+
+FINALLY
+%{
+  time_t NowTime;
+  time(&NowTime);
+  fprintf(stdout, "\nFinally [%s: %s]. Time: ", mcinstrument_name, mcdirname ? mcdirname : ".");
+  if (difftime(NowTime,StartTime) < 60.0)
+    fprintf(stdout, "%g [s] ", difftime(NowTime,StartTime));
+  else if (difftime(NowTime,StartTime) > 3600.0)
+    fprintf(stdout, "%g [h] ", difftime(NowTime,StartTime)/3660.0);
+  else
+    fprintf(stdout, "%g [min] ", difftime(NowTime,StartTime)/60.0);
+  fprintf(stdout, "\n");
+%}
+
+MCDISPLAY
+%{
+  
+%}
+
+END
diff --git a/mcstasscript/tests/dummy_instrument_folder/Source_div.comp b/mcstasscript/tests/dummy_instrument_folder/Source_div.comp
new file mode 100644
index 00000000..356742d4
--- /dev/null
+++ b/mcstasscript/tests/dummy_instrument_folder/Source_div.comp
@@ -0,0 +1,182 @@
+/*******************************************************************************
+*
+* McStas, neutron ray-tracing package
+*         Copyright 1997-2002, All rights reserved
+*         Risoe National Laboratory, Roskilde, Denmark
+*         Institut Laue Langevin, Grenoble, France
+*
+* Component: Source_div
+*
+* %I
+* Written by: KL
+* Date: November 20, 1998
+* Modified by: KL, 8 October 2001
+* Origin: Risoe
+*
+* Neutron source with Gaussian or uniform divergence
+*
+* %D
+* The routine is a rectangular neutron source, which has a gaussian or uniform
+* divergent output in the forward direction.
+* The neutron energy is distributed between lambda0-dlambda and
+* lambda0+dlambda or between E0-dE and E0+dE. The flux unit is specified
+* in n/cm2/s/st/energy unit (meV or Angs).
+* In the case of uniform distribution (gauss=0), angles are uniformly distributed
+* between -focus_aw and +focus_aw as well as -focus_ah and +focus_ah.
+* For Gaussian distribution (gauss=1), 'focus_aw' and 'focus_ah' define the
+* FWHM of a Gaussian distribution. Energy/wavelength distribution is also
+* Gaussian.
+*
+* Example: Source_div(xwidth=0.1, yheight=0.1, focus_aw=2, focus_ah=2, E0=14, dE=2, gauss=0)
+*
+* %VALIDATION
+* Feb 2005: tested by Kim Lefmann    (o.k.)
+* Apr 2005: energy distribution used in external tests of Fermi choppers (o.k.)
+* Jun 2005: wavelength distribution used in external tests of velocity selectors (o.k.)
+* Validated by: K. Lieutenant
+*
+* %BUGS
+* distribution is uniform in (hor. and vert.) angle (relative to moderator normal),
+* therefore not suited for large angles
+*
+* %P
+* xwidth: [m]                        Width of source
+* yheight: [m]                       Height of source
+* focus_aw: [deg]                    FWHM (Gaussian) or maximal (uniform) horz. width divergence
+* focus_ah: [deg]                    FWHM (Gaussian) or maximal (uniform) vert. height divergence
+* E0: [meV]                          Mean energy of neutrons.
+* dE: [meV]                          Energy half spread of neutrons.
+* lambda0: [Ang]                     Mean wavelength of neutrons (only relevant for E0=0)
+* dlambda: [Ang]                     Wavelength half spread of neutrons.
+* gauss: [0|1]                       Criterion: 0: uniform, 1: Gaussian distributions
+* flux: [1/(s cm 2 st energy_unit)]  flux per energy unit, Angs or meV
+*
+* OUTPUT PARAMETERS:
+* sigmah: [rad]                      parameter 'sigma' of the Gaussian distribution for horizontal divergence
+* sigmav: [rad]                      parameter 'sigma' of the Gaussian distribution for vertical divergence
+* p_init: [1]                        normalisation factor 1/'neutron_count'
+*
+* %E
+*******************************************************************************/
+
+DEFINE COMPONENT Source_div
+DEFINITION PARAMETERS ()
+SETTING PARAMETERS (xwidth, yheight, focus_aw, focus_ah, E0=0.0, dE=0.0, lambda0=0.0, dlambda=0.0, gauss=0, flux=1)
+OUTPUT PARAMETERS (thetah, thetav, sigmah, sigmav, tan_h, tan_v, p_init, dist, focus_xw, focus_yh)
+/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
+DECLARE
+%{
+double thetah, thetav, sigmah, sigmav, tan_h, tan_v, p_init, dist, focus_xw, focus_yh;
+%}
+INITIALIZE
+%{
+sigmah = DEG2RAD*focus_aw/(sqrt(8.0*log(2.0)));
+  sigmav = DEG2RAD*focus_ah/(sqrt(8.0*log(2.0)));
+
+  if (xwidth < 0 || yheight < 0 || focus_aw < 0 || focus_ah < 0) {
+      printf("Source_div: %s: Error in input parameter values!\n"
+             "ERROR       Exiting\n",
+           NAME_CURRENT_COMP);
+      exit(0);
+  }
+  if ((!lambda0 && !E0 && !dE && !dlambda)) {
+    printf("Source_div: %s: You must specify either a wavelength or energy range!\n ERROR - Exiting\n",
+           NAME_CURRENT_COMP);
+    exit(0);
+  }
+  if ((!lambda0 && !dlambda && (E0 <= 0 || dE < 0 || E0-dE <= 0))
+    || (!E0 && !dE && (lambda0 <= 0 || dlambda < 0 || lambda0-dlambda <= 0))) {
+    printf("Source_div: %s: Unmeaningful definition of wavelength or energy range!\n ERROR - Exiting\n",
+           NAME_CURRENT_COMP);
+      exit(0);
+  }
+  /* compute distance to next component */
+  Coords ToTarget;
+  double tx,ty,tz;
+  ToTarget = coords_sub(POS_A_COMP_INDEX(INDEX_CURRENT_COMP+1),POS_A_CURRENT_COMP);
+  ToTarget = rot_apply(ROT_A_CURRENT_COMP, ToTarget);
+  coords_get(ToTarget, &tx, &ty, &tz);
+  dist=sqrt(tx*tx+ty*ty+tz*tz);
+  /* compute target area */
+  if (dist) {
+    focus_xw=dist*tan(focus_aw*DEG2RAD);
+    focus_yh=dist*tan(focus_ah*DEG2RAD);
+  }
+
+  p_init  = flux*1e4*xwidth*yheight/mcget_ncount();
+  if (!focus_aw || !focus_ah)
+    exit(printf("Source_div: %s: Zero divergence defined. \n"
+                "ERROR       Use non zero values for focus_aw and focus_ah.\n",
+           NAME_CURRENT_COMP));
+  p_init *= 2*fabs(DEG2RAD*focus_aw*sin(DEG2RAD*focus_ah/2));  /* solid angle */
+  if (dlambda)
+    p_init *= 2*dlambda;
+  else if (dE)
+    p_init *= 2*dE;
+%}
+TRACE
+%{
+  double E,lambda,v;
+
+  p=p_init;
+  z=0;
+  t=0;
+
+  x=randpm1()*xwidth/2.0;
+  y=randpm1()*yheight/2.0;
+  if(lambda0==0) {
+    if (!gauss) {
+      E=E0+dE*randpm1();              /*  Choose from uniform distribution */
+    } else {
+      E=E0+randnorm()*dE;
+    }
+    v=sqrt(E)*SE2V;
+  } else {
+    if (!gauss) {
+      lambda=lambda0+dlambda*randpm1();
+    } else {
+      lambda=lambda0+randnorm()*dlambda;
+    }
+    v = K2V*(2*PI/lambda);
+  }
+
+  if (gauss==1) {
+      thetah = randnorm()*sigmah;
+      thetav = randnorm()*sigmav;
+  } else {
+      /*find limits of uniform sampling scheme for vertical divergence.
+        thetav should be acos(1-2*U) for U\in[0,1]. for theta measured from vertical axis
+        we only use a sub-interval for U and measure from horizontal plane.*/
+      double sample_lim1,u2;
+      sample_lim1=(1-cos(M_PI_2 - focus_ah/2.0*DEG2RAD))*0.5;
+      u2=randpm1()*(sample_lim1-0.5) + 0.5;
+      thetav = acos(1-2*u2) - M_PI_2;
+      thetah = randpm1()*focus_aw*DEG2RAD/2;
+  }
+
+  tan_h = tan(thetah);
+  tan_v = tan(thetav);
+
+  /* Perform the correct treatment - no small angle approx. here! */
+  vz = v / sqrt(1 + tan_v*tan_v + tan_h*tan_h);
+  vy = tan_v * vz;
+  vx = tan_h * vz;
+%}
+
+MCDISPLAY
+%{
+  
+  multiline(5, -xwidth/2.0, -yheight/2.0, 0.0,
+                xwidth/2.0, -yheight/2.0, 0.0,
+                xwidth/2.0,  yheight/2.0, 0.0,
+               -xwidth/2.0,  yheight/2.0, 0.0,
+               -xwidth/2.0, -yheight/2.0, 0.0);
+  if (dist) {
+    dashed_line(0,0,0, -focus_xw/2,-focus_yh/2,dist, 4);
+    dashed_line(0,0,0,  focus_xw/2,-focus_yh/2,dist, 4);
+    dashed_line(0,0,0,  focus_xw/2, focus_yh/2,dist, 4);
+    dashed_line(0,0,0, -focus_xw/2, focus_yh/2,dist, 4);
+  }
+%}
+
+END
diff --git a/mcstasscript/tests/dummy_instrument_folder/Union_box.comp b/mcstasscript/tests/dummy_instrument_folder/Union_box.comp
new file mode 100644
index 00000000..d8a5df41
--- /dev/null
+++ b/mcstasscript/tests/dummy_instrument_folder/Union_box.comp
@@ -0,0 +1,438 @@
+/*******************************************************************************
+*
+*  McStas, neutron ray-tracing package
+*  Copyright(C) 2007 Risoe National Laboratory.
+*
+* %I
+* Written by: Mads Bertelsen
+* Date: 20.08.15
+* Origin: Svanevej 19
+*
+* A sample component to separate geometry and phsysics
+*
+* %D
+* Part of the Union components, a set of components that work together and thus
+*  sperates geometry and physics within McStas.
+* The use of this component requires other components to be used.
+*
+* 1) One specifies a number of processes using process components
+* 2) These are gathered into material definitions using Union_make_material
+* 3) Geometries are placed using Union_box/cylinder/sphere, assigned a material
+* 4) A Union_master component placed after all of the above
+*
+* Only in step 4 will any simulation happen, and per default all geometries
+*  defined before this master, but after the previous will be simulated here.
+*
+* There is a dedicated manual available for the Union components
+*
+* The position of this component is the center of the box, zdepth/2 in each direction.
+*
+* It is allowed to overlap components, but it is not allowed to have two
+*  parallel planes that coincides. This will crash the code on run time.
+*
+*
+* Algorithm:
+* Described elsewhere
+*
+* %P
+* INPUT PARAMETERS:
+* xwidth: [m]          width of the box volume
+* yheight: [m]         height of the box volume
+* zdepth: [m]          depth of the box volume
+* xwidth2: [m]         optional different width at the +z box face
+* yheight2: [m]        optional different height at the +z box face
+* material_string: []  material name of this volume, defined using Union_make_material
+* priority: [1]        priotiry of the volume (can not be the same as another volume) A high priority is on top of low.
+* p_interact [1]  probability to interact with this geometry [0-1]
+* visualize  [1]  set to 0 if you wish to hide this geometry in mcdisplay
+* number_of_activations [1]  Number of subsequent Union_master components that will simulate this geometry
+* mask_string: []      Comma seperated list of geometry names which this geometry should mask
+* mask_setting: []     "All" or "Any", should the masked volume be simulated when the ray is in just one mask, or all.
+*
+* Focusing options, if left blank, there will be no focusing.
+* target_x   [m]:
+* target_y   [m]: Position of target to focus at
+* target_z   [m]:
+* focus_aw   [deg] horiz. angular dimension of a rectangular area
+* focus_ah   [deg] vert. angular dimension of a rectangular area
+* focus_xw   [m]   horiz. dimension of a rectangular area
+* focus_xh   [m]   vert. dimension of a rectangular area
+* focus_r    [m]   focusing on circle with this radius
+* OUTPUT PARAMETERS:
+*
+* %L
+*
+* %E
+******************************************************************************/
+
+DEFINE COMPONENT Union_box
+DEFINITION PARAMETERS ()
+SETTING PARAMETERS(string material_string=0, priority, xwidth, yheight, zdepth, xwidth2=-1, yheight2=-1, visualize=1, int target_index=0, target_x=0, target_y=0, target_z=0, focus_aw=0, focus_ah=0, focus_xw=0, focus_xh=0, focus_r=0, p_interact = 0, string mask_string=0, string mask_setting=0,number_of_activations=1)
+OUTPUT PARAMETERS (loop_index,this_box_volume,global_geometry_element,this_box_storage)
+
+/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
+
+SHARE
+%{
+#ifndef Union
+#define Union $Revision: 0.8 $
+
+#include "Union_functions.c"
+#include "Union_initialization.c"
+
+#endif
+
+
+void mcdisplay_box_function(struct lines_to_draw *lines_to_draw_output,int index, struct geometry_struct **Geometries,int number_of_volumes) {
+    // Function to call in mcdisplay section of the sample component for this volume
+    // One can assume that Volumes[index] refers to a volume with the geometry described in this file
+    
+    double depth = Geometries[index]->geometry_parameters.p_box_storage->z_depth;
+    double width1 = Geometries[index]->geometry_parameters.p_box_storage->x_width1;
+    double width2 = Geometries[index]->geometry_parameters.p_box_storage->x_width2;
+    double height1 = Geometries[index]->geometry_parameters.p_box_storage->y_height1;
+    double height2 = Geometries[index]->geometry_parameters.p_box_storage->y_height2;
+    
+    Coords x_vector = Geometries[index]->geometry_parameters.p_box_storage->x_vector;
+    Coords y_vector = Geometries[index]->geometry_parameters.p_box_storage->y_vector;
+    Coords z_vector = Geometries[index]->geometry_parameters.p_box_storage->z_vector;
+    
+    Coords center = Geometries[index]->center;
+    
+    Coords square1[4],square2[4];
+    
+    square1[0] = coords_add(coords_add(coords_add(center,coords_scalar_mult(z_vector,-0.5*depth)),coords_scalar_mult(x_vector,-0.5*width1)),coords_scalar_mult(y_vector,-0.5*height1));
+    
+    square1[1] = coords_add(square1[0],coords_scalar_mult(x_vector,width1));
+    square1[2] = coords_add(square1[1],coords_scalar_mult(y_vector,height1));
+    square1[3] = coords_add(square1[0],coords_scalar_mult(y_vector,height1));
+    
+    square2[0] = coords_add(coords_add(coords_add(center,coords_scalar_mult(z_vector,0.5*depth)),coords_scalar_mult(x_vector,-0.5*width2)),coords_scalar_mult(y_vector,-0.5*height2));
+    
+    square2[1] = coords_add(square2[0],coords_scalar_mult(x_vector,width2));
+    square2[2] = coords_add(square2[1],coords_scalar_mult(y_vector,height2));
+    square2[3] = coords_add(square2[0],coords_scalar_mult(y_vector,height2));
+    
+    struct lines_to_draw lines_to_draw_temp;
+    lines_to_draw_temp.number_of_lines = 0;
+    
+    int iterate;
+    for (iterate=0;iterate<3;iterate++) {
+        lines_to_draw_temp = draw_line_with_highest_priority(square1[iterate],square1[iterate+1],index,Geometries,number_of_volumes,2);
+        merge_lines_to_draw(lines_to_draw_output,&lines_to_draw_temp);
+    }
+    lines_to_draw_temp = draw_line_with_highest_priority(square1[3],square1[0],index,Geometries,number_of_volumes,2);
+    merge_lines_to_draw(lines_to_draw_output,&lines_to_draw_temp);
+
+    for (iterate=0;iterate<3;iterate++) {
+        lines_to_draw_temp = draw_line_with_highest_priority(square2[iterate],square2[iterate+1],index,Geometries,number_of_volumes,2);
+        merge_lines_to_draw(lines_to_draw_output,&lines_to_draw_temp);
+    }
+    lines_to_draw_temp = draw_line_with_highest_priority(square2[3],square2[0],index,Geometries,number_of_volumes,2);
+    merge_lines_to_draw(lines_to_draw_output,&lines_to_draw_temp);
+
+    for (iterate=0;iterate<4;iterate++) {
+        lines_to_draw_temp = draw_line_with_highest_priority(square1[iterate],square2[iterate],index,Geometries,number_of_volumes,2);
+        merge_lines_to_draw(lines_to_draw_output,&lines_to_draw_temp);
+    }
+};
+
+void initialize_box_geometry_from_main_component(struct geometry_struct *box) {
+    // Function to be called in initialize of the main component
+    // This is done as the rotation matrix needs to be relative to the main component instead of global
+    // Everything done in initialize in this component file has the rotation matrix relative to global
+    Coords simple_vector = coords_set(1,0,0);
+    Coords rotated_vector;
+    
+    rotated_vector = rot_apply(box->rotation_matrix,simple_vector);
+    NORM(rotated_vector.x,rotated_vector.y,rotated_vector.z);
+    box->geometry_parameters.p_box_storage->x_vector = rotated_vector;
+    
+    simple_vector = coords_set(0,1,0);
+    rotated_vector = rot_apply(box->rotation_matrix,simple_vector);
+    NORM(rotated_vector.x,rotated_vector.y,rotated_vector.z);
+    box->geometry_parameters.p_box_storage->y_vector = rotated_vector;
+    
+    simple_vector = coords_set(0,0,1);
+    rotated_vector = rot_apply(box->rotation_matrix,simple_vector);
+    NORM(rotated_vector.x,rotated_vector.y,rotated_vector.z);
+    box->geometry_parameters.p_box_storage->z_vector = rotated_vector;
+};
+
+struct pointer_to_1d_coords_list box_shell_points(struct geometry_struct *geometry,int max_number_of_points) {
+  // This function returns an array of corner positions for the box in the main coordinate system.
+  // Normally one would limit it to a maximum number of points, but as there are only 8 for the box,
+  //  it is hardcoded to 8. Other geometries can be approximated with a variable number of points.
+  
+  struct pointer_to_1d_coords_list corner_points;
+  corner_points.elements = malloc(8*sizeof(Coords));
+  corner_points.num_elements = 8;
+  
+  double depth = geometry->geometry_parameters.p_box_storage->z_depth;
+  double width1 = geometry->geometry_parameters.p_box_storage->x_width1;
+  double width2 = geometry->geometry_parameters.p_box_storage->x_width2;
+  double height1 = geometry->geometry_parameters.p_box_storage->y_height1;
+  double height2 = geometry->geometry_parameters.p_box_storage->y_height2;
+    
+  Coords x_vector = geometry->geometry_parameters.p_box_storage->x_vector;
+  Coords y_vector = geometry->geometry_parameters.p_box_storage->y_vector;
+  Coords z_vector = geometry->geometry_parameters.p_box_storage->z_vector;
+    
+  Coords center = geometry->center;
+    
+  corner_points.elements[0] = coords_add(coords_add(coords_add(center,coords_scalar_mult(z_vector,-0.5*depth)),coords_scalar_mult(x_vector,-0.5*width1)),coords_scalar_mult(y_vector,-0.5*height1));
+    
+  corner_points.elements[1] = coords_add(corner_points.elements[0],coords_scalar_mult(x_vector,width1));
+  corner_points.elements[2] = coords_add(corner_points.elements[1],coords_scalar_mult(y_vector,height1));
+  corner_points.elements[3] = coords_add(corner_points.elements[0],coords_scalar_mult(y_vector,height1));
+    
+  corner_points.elements[4] = coords_add(coords_add(coords_add(center,coords_scalar_mult(z_vector,0.5*depth)),coords_scalar_mult(x_vector,-0.5*width2)),coords_scalar_mult(y_vector,-0.5*height2));
+    
+  corner_points.elements[5] = coords_add(corner_points.elements[4],coords_scalar_mult(x_vector,width2));
+  corner_points.elements[6] = coords_add(corner_points.elements[5],coords_scalar_mult(y_vector,height2));
+  corner_points.elements[7] = coords_add(corner_points.elements[4],coords_scalar_mult(y_vector,height2));
+  
+  return corner_points;
+
+}
+
+%}
+
+DECLARE
+%{
+// Needed for transport to the main component
+struct global_geometry_element_struct global_geometry_element;
+
+#ifndef ANY_GEOMETRY_DETECTOR_DECLARE
+    #define ANY_GEOMETRY_DETECTOR_DECLARE dummy
+	//struct pointer_to_global_geometry_list global_geometry_list = {0,NULL};
+#endif
+
+int loop_index,found_geometries;
+
+double x_component;
+double y_component;
+double z_component;
+
+struct Volume_struct this_box_volume;
+struct box_storage this_box_storage;
+
+%}
+
+INITIALIZE
+%{
+// Initializes the focusing system for this volume including input sanitation.
+focus_initialize(&this_box_volume.geometry, POS_A_COMP_INDEX(INDEX_CURRENT_COMP+target_index), POS_A_CURRENT_COMP, ROT_A_CURRENT_COMP, target_index, target_x, target_y, target_z, focus_aw, focus_ah, focus_xw, focus_xh, focus_r, NAME_CURRENT_COMP);
+
+// Input sanitation for this geometry
+if (xwidth <= 0) {
+  printf("\nERROR in Union_box named %s, the xwidth is <= 0. \n",NAME_CURRENT_COMP);
+  exit(1);
+}
+if (yheight <= 0) {
+  printf("\nERROR in Union_box named %s, yheight is <= 0. \n",NAME_CURRENT_COMP);
+  exit(1);
+}
+if (zdepth <= 0) {
+  printf("\nERROR in Union_box named %s, zdepth is <= 0. \n",NAME_CURRENT_COMP);
+  exit(1);
+}
+if (xwidth2 <= 0 && xwidth2 != -1) {
+  printf("\nERROR in Union_box named %s, the xwidth2 is <= 0. \n",NAME_CURRENT_COMP);
+  exit(1);
+}
+if (yheight2 <= 0 && yheight2 != -1) {
+  printf("\nERROR in Union_box named %s, yheight2 is <= 0. \n",NAME_CURRENT_COMP);
+  exit(1);
+}
+
+// Use sanitation
+if (global_material_list.num_elements == 0) {
+  printf("\nERROR: Need to define a material using Union_make_material before using a Union geometry component. \n");
+  printf("       %s was defined before first use of Union_make_material.\n",NAME_CURRENT_COMP);
+  exit(1);
+}
+
+this_box_volume.geometry.is_masked_volume = 0;
+this_box_volume.geometry.is_exit_volume = 0;
+this_box_volume.geometry.is_mask_volume = 0;
+// check if the volume is a mask, if it is the material string is irelevant.
+if (mask_string && strlen(mask_string) && strcmp(mask_string, "NULL") && strcmp(mask_string, "0")) {
+    // A mask volume is used to limit the extend of other volumes, called the masked volumes. These are specified in the mask_string.
+    // In order for a ray to enter a masked volume, it needs to be both in the region covered by that volume AND the mask volume.
+    // When more than
+    this_box_volume.geometry.mask_mode = 1; // Default is mask mode is ALL
+    if (mask_setting && strlen(mask_setting) && strcmp(mask_setting, "NULL") && strcmp(mask_setting, "0")) {
+        if (strcmp(mask_setting,"ALL") == 0 || strcmp(mask_setting,"All") == 0) this_box_volume.geometry.mask_mode = 1;
+        else if (strcmp(mask_setting,"ANY") == 0 || strcmp(mask_setting,"Any") == 0) this_box_volume.geometry.mask_mode = 2;
+        else {
+            printf("The mask_mode of component %s is set to %s, but must be either ALL or ANY.\n",NAME_CURRENT_COMP,mask_setting);
+            exit(1);
+        }
+    }
+    
+    for (loop_index=0;loop_index<global_geometry_list.num_elements;loop_index++) {
+        // Add mask list
+        if (1 == manual_linking_function(global_geometry_list.elements[loop_index].name,mask_string)) {
+            add_element_to_int_list(&this_box_volume.geometry.mask_list,global_geometry_list.elements[loop_index].component_index);
+            add_element_to_int_list(&global_geometry_list.elements[loop_index].Volume->geometry.masked_by_list,INDEX_CURRENT_COMP);
+            global_geometry_list.elements[loop_index].Volume->geometry.is_masked_volume = 1;
+            if (this_box_volume.geometry.mask_mode == 2)
+                global_geometry_list.elements[loop_index].Volume->geometry.mask_mode = 2;
+            if (this_box_volume.geometry.mask_mode == 1) {
+                if (global_geometry_list.elements[loop_index].Volume->geometry.is_masked_volume == 1 && global_geometry_list.elements[loop_index].Volume->geometry.mask_mode != 2)
+                    // If more than one mask is added to one volume, the ANY mode overwrites the (default) ALL mode.
+                    global_geometry_list.elements[loop_index].Volume->geometry.mask_mode = 1;
+            }
+            
+            found_geometries = 1;
+        }
+    }
+    if (found_geometries == 0) {
+        printf("The mask_string in geometry: %s did not find any of the specified volumes in the mask_string %s \n",NAME_CURRENT_COMP,mask_string);
+        exit(1);
+    }
+    this_box_volume.p_physics = malloc(sizeof(struct physics_struct));
+    this_box_volume.p_physics->is_vacuum = 0; // Makes this volume a vacuum
+    this_box_volume.p_physics->number_of_processes = (int) 0; // Should not be used.
+    this_box_volume.p_physics->my_a = 0; // Should not be used.
+    sprintf(this_box_volume.p_physics->name,"Mask");
+    this_box_volume.geometry.is_mask_volume = 1;
+    
+    
+// Read the material input, or if it lacks, use automatic linking.
+} else if (material_string && strlen(material_string) && strcmp(material_string, "NULL") && strcmp(material_string, "0")) {
+    // A geometry string was given, use it to determine which material
+    if (0 == strcmp(material_string,"vacuum") || 0 == strcmp(material_string,"Vacuum")) {
+        // One could have a global physics struct for vacuum instead of creating one for each
+        this_box_volume.p_physics = malloc(sizeof(struct physics_struct));
+        this_box_volume.p_physics->is_vacuum = 1; // Makes this volume a vacuum
+        this_box_volume.p_physics->number_of_processes = (int) 0; // Should not be used.
+        this_box_volume.p_physics->my_a = 0; // Should not be used.
+        sprintf(this_box_volume.p_physics->name,"Vacuum");
+    } else if (0 == strcmp(material_string,"exit") || 0 == strcmp(material_string,"Exit")) {
+        // One could have a global physics struct for vacuum instead of creating one for each
+        this_box_volume.p_physics = malloc(sizeof(struct physics_struct));
+        this_box_volume.p_physics->is_vacuum = 1; // Makes this volume a vacuum
+        this_box_volume.p_physics->number_of_processes = (int) 0; // Should not be used.
+        this_box_volume.p_physics->my_a = 0; // Should not be used.
+        this_box_volume.geometry.is_exit_volume = 1;
+        sprintf(this_box_volume.p_physics->name,"Exit");
+    } else {
+        #ifndef MATERIAL_DETECTOR
+            printf("Need to define a material before refering to it in a geometry %s.\n",NAME_CURRENT_COMP);
+            exit(1);
+        #endif
+        for (loop_index=0;loop_index<global_material_list.num_elements;loop_index++) {
+            if (0 == strcmp(material_string,global_material_list.elements[loop_index].name)) {
+                this_box_volume.p_physics = global_material_list.elements[loop_index].physics;
+                break;
+            }
+            if (loop_index == global_material_list.num_elements-1) {
+            printf("\n");
+            printf("ERROR: The material string \"%s\" in Union geometry \"%s\" did not match a specified material. \n",material_string,NAME_CURRENT_COMP);
+            printf("       The materials available at this point (need to be defined before the geometry): \n");
+            for (loop_index=0;loop_index<global_material_list.num_elements;loop_index++)
+              printf("         %s\n",global_material_list.elements[loop_index].name);
+            printf("\n");
+            printf("       It is also possible to use one of the defualt materials avaiable: \n");
+            printf("           Vacuum (for a Volume without scattering or absorption)\n");
+            printf("           Exit (for a Volume where the ray exits the component if it enters)\n");
+            printf("           Mask (for a Volume that masks existing volumes specified in the mask_string\n");
+            exit(1);
+          }
+        }
+    }
+} else {
+    // Automatic linking, simply using the last defined material.
+    #ifndef MATERIAL_DETECTOR
+        printf("Need to define a material before the geometry to use automatic linking %s.\n",NAME_CURRENT_COMP);
+        exit(1);
+    #endif
+    this_box_volume.p_physics = global_material_list.elements[global_material_list.num_elements-1].physics;
+}
+
+sprintf(this_box_volume.name,NAME_CURRENT_COMP);
+sprintf(this_box_volume.geometry.shape,"box");
+this_box_volume.geometry.priority_value = priority;
+this_box_volume.geometry.geometry_p_interact = p_interact;
+// Currently the coordinates will be in absolute space.
+this_box_volume.geometry.center = POS_A_CURRENT_COMP;
+
+this_box_storage.z_depth = zdepth;
+this_box_storage.x_width1 = xwidth;
+this_box_storage.y_height1 = yheight;
+
+this_box_storage.is_rectangle = 0;
+if (xwidth2 < 0 && yheight2 < 0) this_box_storage.is_rectangle = 1;
+if (xwidth == xwidth2 && yheight == yheight2) this_box_storage.is_rectangle = 1;
+
+if (xwidth2 < 0) {
+    this_box_storage.x_width2 = xwidth;
+    xwidth2 = xwidth;
+} else this_box_storage.x_width2 = xwidth2;
+
+if (yheight2 < 0) {
+    this_box_storage.y_height2 = yheight;
+    yheight2 = yheight;
+} else this_box_storage.y_height2 = yheight2;
+
+
+this_box_storage.normal_vectors[0] = coords_set(0,0,1);
+this_box_storage.normal_vectors[1] = coords_set(0,0,1);
+
+// for sides with y component = 0
+x_component = 2*zdepth/sqrt((xwidth-xwidth2)*(xwidth-xwidth2)+4*zdepth*zdepth);
+z_component = (xwidth-xwidth2)/sqrt(4*zdepth*zdepth+(xwidth-xwidth2)*(xwidth-xwidth2));
+
+this_box_storage.normal_vectors[2] = coords_set(x_component,0,z_component);
+this_box_storage.normal_vectors[3] = coords_set(-x_component,0,z_component);
+
+// for sides with x component = 0
+y_component = 2*zdepth/sqrt((yheight-yheight2)*(yheight-yheight2)+4*zdepth*zdepth);
+z_component = (yheight-yheight2)/sqrt(4*zdepth*zdepth+(yheight-yheight2)*(yheight-yheight2));
+
+this_box_storage.normal_vectors[4] = coords_set(0,y_component,z_component);
+this_box_storage.normal_vectors[5] = coords_set(0,-y_component,z_component);
+
+this_box_volume.geometry.visualization_on = visualize;
+
+this_box_volume.geometry.geometry_parameters.p_box_storage = &this_box_storage;
+
+// Assign pointers to functions for intersection with the shape, checking if a point is inside the shape
+if (this_box_storage.is_rectangle == 1) {
+this_box_volume.geometry.intersect_function = &sample_box_intersect_simple;
+this_box_volume.geometry.within_function = &r_within_box_simple;
+}
+else {
+this_box_volume.geometry.intersect_function = &sample_box_intersect_advanced;
+this_box_volume.geometry.within_function = &r_within_box_advanced;
+}
+
+this_box_volume.geometry.shell_points = &box_shell_points;
+this_box_volume.geometry.mcdisplay_function = &mcdisplay_box_function;
+this_box_volume.geometry.initialize_from_main_function = &initialize_box_geometry_from_main_component;
+this_box_volume.geometry.process_rot_allocated = 0;
+
+this_box_volume.geometry.copy_geometry_parameters = &allocate_box_storage_copy;
+
+rot_copy(this_box_volume.geometry.rotation_matrix,ROT_A_CURRENT_COMP); //check how ROT_R_CURRENT_COMP would work
+rot_transpose(ROT_A_CURRENT_COMP,this_box_volume.geometry.transpose_rotation_matrix);
+
+// Initialize loggers
+this_box_volume.loggers.num_elements = 0;
+
+// packing the information into the global_geometry_element, which is then included in the global_geometry_list.
+sprintf(global_geometry_element.name,NAME_CURRENT_COMP);
+global_geometry_element.activation_counter = number_of_activations;
+global_geometry_element.component_index = INDEX_CURRENT_COMP;
+global_geometry_element.Volume = &this_box_volume; // Would be nicer if this m was a pointer, now we have the (small) data two places
+add_element_to_geometry_list(&global_geometry_list,global_geometry_element);
+%}
+
+TRACE
+%{
+%}
+
+END
+
diff --git a/mcstasscript/tests/dummy_instrument_folder/Union_cylinder.comp b/mcstasscript/tests/dummy_instrument_folder/Union_cylinder.comp
new file mode 100644
index 00000000..df180999
--- /dev/null
+++ b/mcstasscript/tests/dummy_instrument_folder/Union_cylinder.comp
@@ -0,0 +1,349 @@
+/*******************************************************************************
+*
+*  McStas, neutron ray-tracing package
+*  Copyright(C) 2007 Risoe National Laboratory.
+*
+* %I
+* Written by: Mads Bertelsen
+* Date: 20.08.15
+* Origin: Svanevej 19
+*
+* A sample component to separate geometry and phsysics
+*
+* %D
+* Part of the Union components, a set of components that work together and thus
+*  sperates geometry and physics within McStas.
+* The use of this component requires other components to be used.
+*
+* 1) One specifies a number of processes using process components
+* 2) These are gathered into material definitions using Union_make_material
+* 3) Geometries are placed using Union_box/cylinder/sphere, assigned a material
+* 4) A Union_master component placed after all of the above
+*
+* Only in step 4 will any simulation happen, and per default all geometries
+*  defined before this master, but after the previous will be simulated here.
+*
+* There is a dedicated manual available for the Union_components
+*
+* The position of this component is the center of the cylinder, and it thus
+*  extends yheight/2 up and down along y axis.
+*
+* It is allowed to overlap components, but it is not allowed to have two
+*  parallel planes that coincides. This will crash the code on run time.
+*
+*
+* %P
+* INPUT PARAMETERS:
+* radius: [m]          Outer radius volume in (x,z) plane
+* yheight: [m]         Cylinder height in (y) direction
+* material_string: []  material name of this volume, defined using Union_make_material
+* priority   [1]  priotiry of the volume (can not be the same as another volume) A high priority is on top of low.
+* p_interact [1]  probability to interact with this geometry [0-1]
+* visualize  [1]  set to 0 if you wish to hide this geometry in mcdisplay
+* number_of_activations [1]  Number of subsequent Union_master components that will simulate this geometry
+* mask_string: []      Comma seperated list of geometry names which this geometry should mask
+* mask_setting: []     "All" or "Any", should the masked volume be simulated when the ray is in just one mask, or all.
+*
+* Focusing options, if left blank, there will be no focusing.
+* target_x   [m]:
+* target_y   [m]: Position of target to focus at
+* target_z   [m]:
+* focus_aw   [deg] horiz. angular dimension of a rectangular area
+* focus_ah   [deg] vert. angular dimension of a rectangular area
+* focus_xw   [m]   horiz. dimension of a rectangular area
+* focus_xh   [m]   vert. dimension of a rectangular area
+* focus_r    [m]   focusing on circle with this radius
+*
+* OUTPUT PARAMETERS:
+*
+* %L
+*
+* %E
+******************************************************************************/
+
+DEFINE COMPONENT Union_cylinder
+DEFINITION PARAMETERS ()
+SETTING PARAMETERS(string material_string=0, priority, radius, yheight, visualize=1, int target_index=0, target_x=0, target_y=0, target_z=0, focus_aw=0, focus_ah=0, focus_xw=0, focus_xh=0, focus_r=0, p_interact=0, string mask_string=0, string mask_setting=0,number_of_activations=1)
+OUTPUT PARAMETERS (loop_index,this_cylinder_volume,global_geometry_element,this_cylinder_storage)
+
+/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
+
+SHARE
+%{
+#ifndef Union
+#define Union $Revision: 0.8 $
+
+#include "Union_functions.c"
+#include "Union_initialization.c"
+
+#endif
+
+
+void mcdisplay_cylinder_function(struct lines_to_draw *lines_to_draw_output,int index, struct geometry_struct **Geometries,int number_of_volumes) {
+    // Function to call in mcdisplay section of the sample component for this volume
+    // One can assume that Geometries[index] refers to a geometry as described in this file
+    // The 4 lines describin the cylinders side are aligned to the local frame of the cylinder,
+    //   it would be nicer to have them alligned with the global frame so that they show up nicely in
+    //   pgplotters on mcdisplay.
+    // One could get the current global rotation and use this to counteract this effect.
+    
+    double height = Geometries[index]->geometry_parameters.p_cylinder_storage->height;
+    double radius = Geometries[index]->geometry_parameters.p_cylinder_storage->cyl_radius;
+    Coords direction = Geometries[index]->geometry_parameters.p_cylinder_storage->direction_vector;
+    Coords center = Geometries[index]->center;
+    
+    Coords bottom_point = coords_add(center,coords_scalar_mult(direction,0.5*height));
+    Coords top_point = coords_add(center,coords_scalar_mult(direction,-0.5*height));
+    
+    struct lines_to_draw lines_to_draw_temp;
+    lines_to_draw_temp.number_of_lines = 0;
+    
+    lines_to_draw_temp = draw_circle_with_highest_priority(top_point,direction,radius,index,Geometries,number_of_volumes,2);
+    merge_lines_to_draw(lines_to_draw_output,&lines_to_draw_temp);
+
+    lines_to_draw_temp = draw_circle_with_highest_priority(bottom_point,direction,radius,index,Geometries,number_of_volumes,2);
+    merge_lines_to_draw(lines_to_draw_output,&lines_to_draw_temp);
+    
+    Coords point1,point2;
+    int iterate,number_of_points=4;
+    
+    for (iterate=0;iterate<number_of_points;iterate++) {
+        point1 = point_on_circle(top_point,direction,radius,iterate,number_of_points);
+        point2 = point_on_circle(bottom_point,direction,radius,iterate,number_of_points);
+        lines_to_draw_temp = draw_line_with_highest_priority(point1,point2,index,Geometries,number_of_volumes,2);
+        merge_lines_to_draw(lines_to_draw_output,&lines_to_draw_temp);
+    }
+};
+
+void initialize_cylinder_geometry_from_main_component(struct geometry_struct *cylinder) {
+    // Function to be called in initialize of the main component
+    // This is done as the rotation matrix needs to be relative to the main component instead of global
+    // Everything done in initialize in this component file has the rotation matrix relative to global
+    
+    Coords simple_vector;
+    Coords cyl_vector;
+    
+    // Start with vector that points along the cylinder in the local frame
+    simple_vector = coords_set(0,1,0);
+
+    // Rotate the direction vector of the cylinder to the master component frame of reference
+    cyl_vector = rot_apply(cylinder->rotation_matrix,simple_vector);
+    NORM(cyl_vector.x,cyl_vector.y,cyl_vector.z);
+    cylinder->geometry_parameters.p_cylinder_storage->direction_vector.x = cyl_vector.x;
+    cylinder->geometry_parameters.p_cylinder_storage->direction_vector.y = cyl_vector.y;
+    cylinder->geometry_parameters.p_cylinder_storage->direction_vector.z = cyl_vector.z;
+    // if (verbal == 1) printf("Cords vector1 = (%f,%f,%f)\n",cyl_vector.x,cyl_vector.y,
+}
+
+struct pointer_to_1d_coords_list cylinder_shell_points(struct geometry_struct *geometry,int max_number_of_points) {
+  // Function that returns a number (less than max) of points on the geometry surface
+  // If used, remember to free the space allocated.
+  int points_per_circle = floor(max_number_of_points/2.0);
+  
+  struct pointer_to_1d_coords_list cylinder_shell_array;
+  cylinder_shell_array.elements = malloc(2*points_per_circle*sizeof(Coords));
+  cylinder_shell_array.num_elements = 2*points_per_circle;
+  
+  Coords cyl_direction = geometry->geometry_parameters.p_cylinder_storage->direction_vector;
+  Coords center = geometry->center;
+  double radius = geometry->geometry_parameters.p_cylinder_storage->cyl_radius;
+  double height = geometry->geometry_parameters.p_cylinder_storage->height;
+  
+  Coords cyl_top_point = coords_add(center,coords_scalar_mult(cyl_direction,0.5*height));
+  Coords cyl_bottom_point = coords_add(center,coords_scalar_mult(cyl_direction,-0.5*height));
+  
+  points_on_circle(cylinder_shell_array.elements,cyl_top_point,cyl_direction,radius,points_per_circle);
+  // Need to verify this pointer arithimatic works as intended
+  points_on_circle(cylinder_shell_array.elements+points_per_circle,cyl_bottom_point,cyl_direction,radius,points_per_circle);
+  
+  return cylinder_shell_array;
+}
+
+%}
+
+DECLARE
+%{
+// Needed for transport to the main component
+struct global_geometry_element_struct global_geometry_element;
+
+#ifndef ANY_GEOMETRY_DETECTOR_DECLARE
+    #define ANY_GEOMETRY_DETECTOR_DECLARE dummy
+	//struct pointer_to_global_geometry_list global_geometry_list = {0,NULL};
+#endif
+
+int dummy;
+int loop_index,found_geometries;
+int loop_2_index;
+int material_index;
+
+struct Volume_struct this_cylinder_volume;
+struct cylinder_storage this_cylinder_storage;
+%}
+
+INITIALIZE
+%{
+// Initializes the focusing system for this volume including input sanitation.
+focus_initialize(&this_cylinder_volume.geometry, POS_A_COMP_INDEX(INDEX_CURRENT_COMP+target_index), POS_A_CURRENT_COMP, ROT_A_CURRENT_COMP, target_index, target_x, target_y, target_z, focus_aw, focus_ah, focus_xw, focus_xh, focus_r, NAME_CURRENT_COMP);
+
+// Input sanitation for this geometry
+if (radius <= 0) {
+  printf("\nERROR in Union_cylinder named %s, the radius is <= 0. \n",NAME_CURRENT_COMP);
+  exit(1);
+}
+
+if (yheight <= 0) {
+  printf("\nERROR in Union_cylinder named %s, yheight is <= 0. \n",NAME_CURRENT_COMP);
+  exit(1);
+}
+
+// Use sanitation
+#ifdef MATERIAL_DETECTOR
+if (global_material_list.num_elements == 0) {
+  // Here if the user have defined a material, but only after this material
+  printf("\nERROR: Need to define a material using Union_make_material before using a Union geometry component. \n");
+  printf("       %s was defined before first use of Union_make_material.\n",NAME_CURRENT_COMP);
+  exit(1);
+}
+#endif
+#ifndef MATERIAL_DETECTOR
+  printf("\nERROR: Need to define a material using Union_make_material before using a Union geometry component. \n");
+  exit(1);
+#endif
+
+
+this_cylinder_volume.geometry.is_masked_volume = 0;
+this_cylinder_volume.geometry.is_exit_volume = 0;
+this_cylinder_volume.geometry.is_mask_volume = 0;
+
+// Read the material input, or if it lacks, use automatic linking.
+if (mask_string && strlen(mask_string) && strcmp(mask_string, "NULL") && strcmp(mask_string, "0")) {
+    // A mask volume is used to limit the extend of other volumes, called the masked volumes. These are specified in the mask_string.
+    // In order for a ray to enter a masked volume, it needs to be both in the region covered by that volume AND the mask volume.
+    // When more than
+    this_cylinder_volume.geometry.mask_mode = 1; // Default is mask mode is ALL
+    if (mask_setting && strlen(mask_setting) && strcmp(mask_setting, "NULL") && strcmp(mask_setting, "0")) {
+        if (strcmp(mask_setting,"ALL") == 0 || strcmp(mask_setting,"All") == 0) this_cylinder_volume.geometry.mask_mode = 1;
+        else if (strcmp(mask_setting,"ANY") == 0 || strcmp(mask_setting,"Any") == 0) this_cylinder_volume.geometry.mask_mode = 2;
+        else {
+            printf("The mask_mode of component %s is set to %s, but must be either ALL or ANY.\n",NAME_CURRENT_COMP,mask_setting);
+            exit(1);
+        }
+    }
+    
+    for (loop_index=0;loop_index<global_geometry_list.num_elements;loop_index++) {
+        // Add mask list
+        if (1 == manual_linking_function(global_geometry_list.elements[loop_index].name,mask_string)) {
+            add_element_to_int_list(&this_cylinder_volume.geometry.mask_list,global_geometry_list.elements[loop_index].component_index);
+            add_element_to_int_list(&global_geometry_list.elements[loop_index].Volume->geometry.masked_by_list,INDEX_CURRENT_COMP);
+            global_geometry_list.elements[loop_index].Volume->geometry.is_masked_volume = 1;
+            if (this_cylinder_volume.geometry.mask_mode == 2)
+                global_geometry_list.elements[loop_index].Volume->geometry.mask_mode = 2;
+            if (this_cylinder_volume.geometry.mask_mode == 1) {
+                if (global_geometry_list.elements[loop_index].Volume->geometry.is_masked_volume == 1 && global_geometry_list.elements[loop_index].Volume->geometry.mask_mode != 2)
+                    // If more than one mask is added to one volume, the ANY mode overwrites the (default) ALL mode.
+                    global_geometry_list.elements[loop_index].Volume->geometry.mask_mode = 1;
+            }
+            
+            found_geometries = 1;
+        }
+    }
+    if (found_geometries == 0) {
+        printf("The mask_string in geometry: %s did not find any of the specified volumes in the mask_string %s \n",NAME_CURRENT_COMP,mask_string);
+        exit(1);
+    }
+    this_cylinder_volume.p_physics = malloc(sizeof(struct physics_struct));
+    this_cylinder_volume.p_physics->is_vacuum = 0; // Makes this volume a vacuum
+    this_cylinder_volume.p_physics->number_of_processes = (int) 0; // Should not be used.
+    this_cylinder_volume.p_physics->my_a = 0; // Should not be used.
+    sprintf(this_cylinder_volume.p_physics->name,"Mask");
+    this_cylinder_volume.geometry.is_mask_volume = 1;
+    
+    
+// Read the material input, or if it lacks, use automatic linking.
+} else if (material_string && strlen(material_string) && strcmp(material_string, "NULL") && strcmp(material_string, "0")) {
+    // A geometry string was given, use it to determine which material
+    if (0 == strcmp(material_string,"vacuum") || 0 == strcmp(material_string,"Vacuum")) {
+        // One could have a global physics struct for vacuum instead of creating one for each
+        this_cylinder_volume.p_physics = malloc(sizeof(struct physics_struct));
+        this_cylinder_volume.p_physics->is_vacuum = 1; // Makes this volume a vacuum
+        this_cylinder_volume.p_physics->number_of_processes = (int) 0;
+        this_cylinder_volume.p_physics->my_a = 0; // Should not be used.
+        sprintf(this_cylinder_volume.p_physics->name,"Vacuum");
+    } else if (0 == strcmp(material_string,"exit") || 0 == strcmp(material_string,"Exit")) {
+        // One could have a global physics struct for exit instead of creating one for each
+        this_cylinder_volume.p_physics = malloc(sizeof(struct physics_struct));
+        this_cylinder_volume.p_physics->is_vacuum = 1; // Makes this volume a vacuum
+        this_cylinder_volume.p_physics->number_of_processes = (int) 0;
+        this_cylinder_volume.p_physics->my_a = 0; // Should not be used.
+        this_cylinder_volume.geometry.is_exit_volume = 1;
+        sprintf(this_cylinder_volume.p_physics->name,"Exit");
+    } else {
+        for (loop_index=0;loop_index<global_material_list.num_elements;loop_index++) {
+          if (0 == strcmp(material_string,global_material_list.elements[loop_index].name)) {
+            this_cylinder_volume.p_physics = global_material_list.elements[loop_index].physics;
+            break;
+          }
+          if (loop_index == global_material_list.num_elements-1) {
+            printf("\n");
+            printf("ERROR: The material string \"%s\" in Union geometry \"%s\" did not match a specified material. \n",material_string,NAME_CURRENT_COMP);
+            printf("       The materials available at this point (need to be defined before the geometry): \n");
+            for (loop_index=0;loop_index<global_material_list.num_elements;loop_index++)
+              printf("         %s\n",global_material_list.elements[loop_index].name);
+            printf("\n");
+            printf("       It is also possible to use one of the defualt materials avaiable: \n");
+            printf("           Vacuum (for a Volume without scattering or absorption)\n");
+            printf("           Exit (for a Volume where the ray exits the component if it enters)\n");
+            printf("           Mask (for a Volume that masks existing volumes specified in the mask_string\n");
+            exit(1);
+          }
+        }
+    }
+} else {
+    // Automatic linking, simply using the last defined material.
+    #ifndef MATERIAL_DETECTOR
+        printf("Need to define a material before the geometry to use automatic linking %s.\n",NAME_CURRENT_COMP);
+        exit(1);
+    #endif
+    this_cylinder_volume.p_physics = global_material_list.elements[global_material_list.num_elements-1].physics;
+}
+
+sprintf(this_cylinder_volume.name,NAME_CURRENT_COMP);
+sprintf(this_cylinder_volume.geometry.shape,"cylinder");
+this_cylinder_volume.geometry.priority_value = priority;
+// Currently the coordinates will be in absolute space.
+this_cylinder_volume.geometry.center = POS_A_CURRENT_COMP;
+
+this_cylinder_volume.geometry.geometry_p_interact = p_interact;
+this_cylinder_storage.cyl_radius = radius;
+this_cylinder_storage.height = yheight;
+this_cylinder_volume.geometry.visualization_on = visualize;
+this_cylinder_volume.geometry.geometry_parameters.p_cylinder_storage = &this_cylinder_storage;
+this_cylinder_volume.geometry.within_function = &r_within_cylinder;
+this_cylinder_volume.geometry.intersect_function = &sample_cylinder_intersect;
+this_cylinder_volume.geometry.mcdisplay_function = &mcdisplay_cylinder_function;
+this_cylinder_volume.geometry.shell_points = &cylinder_shell_points;
+this_cylinder_volume.geometry.initialize_from_main_function = &initialize_cylinder_geometry_from_main_component;
+this_cylinder_volume.geometry.process_rot_allocated = 0;
+this_cylinder_volume.geometry.copy_geometry_parameters = &allocate_cylinder_storage_copy;
+
+rot_copy(this_cylinder_volume.geometry.rotation_matrix,ROT_A_CURRENT_COMP);
+rot_transpose(ROT_A_CURRENT_COMP,this_cylinder_volume.geometry.transpose_rotation_matrix);
+
+// Initialize loggers
+this_cylinder_volume.loggers.num_elements = 0;
+
+// packing the information into the global_geometry_element, which is then included in the global_geometry_list.
+sprintf(global_geometry_element.name,NAME_CURRENT_COMP);
+global_geometry_element.activation_counter = number_of_activations;
+global_geometry_element.component_index = INDEX_CURRENT_COMP;
+global_geometry_element.Volume = &this_cylinder_volume; // Would be nicer if this m was a pointer, now we have the (small) data two places
+add_element_to_geometry_list(&global_geometry_list,global_geometry_element);
+%}
+
+TRACE
+%{
+dummy = 1;
+%}
+
+END
+
diff --git a/mcstasscript/tests/dummy_instrument_folder/Union_demonstration_test.instr b/mcstasscript/tests/dummy_instrument_folder/Union_demonstration_test.instr
new file mode 100644
index 00000000..765ad512
--- /dev/null
+++ b/mcstasscript/tests/dummy_instrument_folder/Union_demonstration_test.instr
@@ -0,0 +1,422 @@
+/*******************************************************************************
+*
+* McStas, neutron ray-tracing package
+*         Copyright (C) 1997-2008, All rights reserved
+*         Risoe National Laboratory, Roskilde, Denmark
+*         Institut Laue Langevin, Grenoble, France
+*
+* Instrument: Union_demonstration
+*
+* %Identification
+* Written by: Mads Bertelsen
+* Date: September 2015
+* Origin: University of Copenhagen
+* %INSTRUMENT_SITE: Union_demos
+*
+* %Description
+* Demonstration of Union components. Here four different powder samples are
+*  placed in a can each connected to a weird sample holder and contained in
+*  a cryostat. This unrealistic example is meant to show the syntax and the
+*  new possibilities when using the Union components.
+* With the standard source only two of the samples are illuminated, yet
+*  multiple scattering occur and events are thus taking place in the last
+*  two samples.
+*
+* Example: Detector: m4pi_two_or_more_samples_I=0.0945567
+*
+* %Parameters
+* stick_displacement [m] height displacement of sample stick
+*
+* %End
+*******************************************************************************/
+
+DEFINE INSTRUMENT Union_demonstration(stick_displacement=0,
+int test_int = 3,
+string test_str = "hurray")
+
+DECLARE
+%{
+int sample_1_index=27,sample_2_index=30,sample_3_index=33,sample_4_index=36; // Indexes of four samples
+int scattered_1,scattered_2,scattered_3,scattered_4;
+double array[3] = {0.1,0.2,0.3};
+int I_array[4];
+int T_array[5] = {1,2,3,4,5};
+char home[20] = "test_string";
+int necessary = 1;
+%}
+
+INITIALIZE
+%{
+// Start of initialize for generated Union_demonstration_copy
+/* A=B */
+// A=8
+I_array[2] = 8;
+printf("Hello world\n");
+%}
+
+TRACE
+
+
+
+COMPONENT Vanadium_incoherent = Incoherent_process(sigma=5.08,packing_factor=1,unit_cell_volume=13.827)
+AT (0,0,0) ABSOLUTE
+
+// Here manual linking is used, the process string is writte explicitly
+COMPONENT Vanadium = Union_make_material(my_absorption=2.1,process_string="Vanadium_incoherent")
+AT (0,0,0) ABSOLUTE
+
+// P0
+COMPONENT Al_incoherent = Incoherent_process(sigma=4*0.0082,packing_factor=1,unit_cell_volume=66.4) //,interact_fraction=0.8)
+AT (0,0,0) ABSOLUTE
+
+// P1
+COMPONENT Al_powder = Powder_process(reflections="Al.laz")
+AT (0,0,0) ABSOLUTE
+
+COMPONENT Al = Union_make_material(my_absorption=100*4*0.231/66.4,process_string="Al_incoherent,Al_powder")
+AT (0,0,0) ABSOLUTE
+
+
+// Cu definition
+// P0
+COMPONENT Cu_incoherent = Incoherent_process(sigma=4*0.55,packing_factor=1,unit_cell_volume=47.22)
+AT (0,0,0) ABSOLUTE
+
+// P1
+COMPONENT Cu_powder = Powder_process(reflections="Cu.laz")
+AT (0,0,0) ABSOLUTE
+
+COMPONENT Cu = Union_make_material(my_absorption=100*4*3.78/47.22,process_string="Cu_incoherent,Cu_powder")
+AT (0,0,0) ABSOLUTE
+
+// Ag Au mix definition
+// P0
+COMPONENT Ag_incoherent = Incoherent_process(sigma=4*0.58,packing_factor=1,unit_cell_volume=68.22,packing_factor=0.5)
+AT (0,0,0) ABSOLUTE
+
+// P1
+COMPONENT Ag_powder = Powder_process(reflections="Ag.laz",packing_factor=0.5)
+AT (0,0,0) ABSOLUTE
+
+// P2
+COMPONENT Au_incoherent = Incoherent_process(sigma=4*0.43,packing_factor=1,unit_cell_volume=67.87,packing_factor=0.5)
+AT (0,0,0) ABSOLUTE
+
+// P3
+COMPONENT Au_powder = Powder_process(reflections="Au.laz",packing_factor=0.5)
+AT (0,0,0) ABSOLUTE
+
+// Here automatic linking is used, all process defined after the last make_material component
+//  is automatically collected in this next make_material component as the process string
+//  is not specified.
+COMPONENT Au_Ag_mix = Union_make_material(my_absorption=0.5*100*4*3.78/68.22+0.5*100*4*98.65/67.87)
+AT (0,0,0) ABSOLUTE
+
+// Cd definition
+// P0
+COMPONENT Cd_incoherent = Incoherent_process(sigma=2*3.46,packing_factor=1,unit_cell_volume=43.11)
+AT (0,0,0) ABSOLUTE
+
+// P1
+COMPONENT Cd_powder = Powder_process(reflections="Cd.laz")
+AT (0,0,0) ABSOLUTE
+
+COMPONENT Cd = Union_make_material(my_absorption=100*2*2520/43.11)
+AT (0,0,0) ABSOLUTE
+
+// Cs definition
+// P0
+COMPONENT Cs_incoherent = Incoherent_process(sigma=2*0.55,packing_factor=1,unit_cell_volume=47.22)
+AT (0,0,0) ABSOLUTE
+
+// P1
+COMPONENT Cs_powder = Powder_process(reflections="Cs.laz")
+AT (0,0,0) ABSOLUTE
+
+COMPONENT Cs = Union_make_material(my_absorption=100*2*3.78/47.22)
+AT (0,0,0) ABSOLUTE
+
+
+
+COMPONENT a1 = Progress_bar()
+  AT (0,0,0) ABSOLUTE
+
+// Source for transmission picture
+//COMPONENT source = Source_div(
+//        xwidth=0.12, yheight=0.12,focus_aw=0.5, focus_ah=0.5,
+//        E0 = 50,
+//        dE = 0, flux = 1E9)
+//  AT (0,-0.02,0) RELATIVE a1 ROTATED (0,0,0) RELATIVE a1
+
+COMPONENT source = Source_div(
+        xwidth=0.04, yheight=0.08,focus_aw=0.05, focus_ah=0.05,
+        E0 = 50,
+        dE = 0, flux = 1E9)
+  AT (0.013,-0.02,0) RELATIVE a1 ROTATED (0,0,0) RELATIVE a1
+
+
+// Sample position
+COMPONENT beam_center = Arm()
+AT (0,0,3) RELATIVE a1
+ROTATED (0,0,0) RELATIVE a1
+
+COMPONENT drum_center = Arm()
+AT (0,0.38,0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+
+// V1
+COMPONENT cryostat_mountin_plate = Union_cylinder(radius=0.12,yheight=0.01,priority=7,material_string="Al")
+AT (0,-0.103,0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+// V2
+COMPONENT cryostat_drum_walls = Union_cylinder(radius=0.2,yheight=0.57,priority=8,material_string="Al")
+AT (0,0,0) RELATIVE drum_center
+ROTATED (0,0,0) RELATIVE drum_center
+
+// V3
+COMPONENT cryostat_drum_vacuum = Union_cylinder(radius=0.19,yheight=0.55,priority=9,material_string="Vacuum")
+AT (0,0,0) RELATIVE drum_center
+ROTATED (0,0,0) RELATIVE drum_center
+
+// V4
+COMPONENT outer_cryostat_wall = Union_cylinder(radius=0.1,yheight=0.2,priority=10,material_string="Al",p_interact=0.2)
+AT (0,0,0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+// V5
+COMPONENT outer_cryostat_vacuum = Union_cylinder(radius=0.09,yheight=0.2,priority=11,material_string="Vacuum")
+WHEN ( necessary == 1 )
+AT (0,0.01,0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+// V6
+COMPONENT inner_cryostat_wall = Union_cylinder(radius=0.06,yheight=0.16,priority=12,material_string="Al",p_interact=0.2)
+AT (0,0.01,0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+// V7
+COMPONENT inner_cryostat_vacuum = Union_cylinder(radius=0.05,yheight=0.15,priority=13,material_string="Vacuum")
+AT (0,0.01,0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+// V8
+COMPONENT sample_stick_walls = Union_cylinder(radius=0.04,yheight=0.605,priority=14,material_string="Al")
+AT (0,0.39,0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+// V9
+COMPONENT sample_stick_vacuum = Union_cylinder(radius=0.035,yheight=0.64,priority=15,material_string="Vacuum")
+AT (0,0.4,0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+
+COMPONENT sample_rod_bottom = Arm()
+AT (0,0.05+stick_displacement,0) RELATIVE beam_center
+ROTATED (0,85,0) RELATIVE beam_center
+
+// V10
+COMPONENT sample_rod = Union_cylinder(radius=0.0075,yheight=0.7,priority=25,material_string="Al")
+AT (0,0.35,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V11
+COMPONENT sample_rod_collar_1 = Union_cylinder(radius=0.034,yheight=0.02,priority=17,material_string="Al")
+AT (0,0.048,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V12
+COMPONENT sample_rod_collar_2 = Union_cylinder(radius=0.034,yheight=0.02,priority=18,material_string="Al")
+AT (0,0.14,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V13
+COMPONENT sample_rod_collar_3 = Union_cylinder(radius=0.034,yheight=0.02,priority=19,material_string="Al")
+AT (0,0.34,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V14
+COMPONENT sample_rod_collar_4 = Union_cylinder(radius=0.034,yheight=0.02,priority=20,material_string="Al")
+AT (0,0.635,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V15
+COMPONENT sample_rod_collar_1_vacuum = Union_cylinder(radius=0.03,yheight=0.016,priority=21,material_string="Vacuum")
+AT (0,0.048-0.005,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V16
+COMPONENT sample_rod_collar_2_vacuum = Union_cylinder(radius=0.03,yheight=0.016,priority=22,material_string="Vacuum")
+AT (0,0.14-0.005,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V17
+COMPONENT sample_rod_collar_3_vacuum = Union_cylinder(radius=0.03,yheight=0.016,priority=23,material_string="Vacuum")
+AT (0,0.34-0.005,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V18
+COMPONENT sample_rod_collar_4_vacuum = Union_cylinder(radius=0.03,yheight=0.016,priority=24,material_string="Vacuum")
+AT (0,0.635-0.005,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V19
+COMPONENT sample_holder1 = Union_box(xwidth=0.01,yheight=0.05,zdepth=0.004,priority=35,material_string="Al",p_interact=0.3)
+AT (0,0,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V20
+COMPONENT sample_holder2 = Union_box(xwidth=0.0099,yheight=0.004,zdepth=0.03/0.85,priority=51,material_string="Al",p_interact=0.3)
+AT (0,-0.03,0.03*0.35+0.004) RELATIVE sample_rod_bottom
+ROTATED (25,0,0) RELATIVE sample_rod_bottom
+
+// V21
+COMPONENT sample_holder3 = Union_box(xwidth=0.0098,yheight=0.004,zdepth=0.03/0.85,priority=52,material_string="Al",p_interact=0.3)
+AT (0,-0.03,-0.03*0.35-0.004) RELATIVE sample_rod_bottom
+ROTATED (-25,0,0) RELATIVE sample_rod_bottom
+
+// V22
+COMPONENT sample_holder4 = Union_box(xwidth=0.01,yheight=0.07,zdepth=0.004,priority=53,material_string="Al",p_interact=0.3)
+AT (0,-0.03-0.035-0.005,0.03) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V23
+COMPONENT sample_holder5 = Union_box(xwidth=0.01,yheight=0.07,zdepth=0.004,priority=54,material_string="Al",p_interact=0.3)
+AT (0,-0.03-0.035-0.005,-0.03) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V24
+COMPONENT sample_holder_bottom = Union_box(xwidth=0.0098,yheight=0.004,zdepth=0.058,priority=42,material_string="Al",p_interact=0.3)
+AT (0,-0.03-0.067-0.007,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V25
+COMPONENT sample_holder_top_shelf = Union_box(xwidth=0.0098,yheight=0.004,zdepth=0.058,priority=43,material_string="Al",p_interact=0.3)
+AT (0,-0.045+0.003,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V26
+COMPONENT sample_holder_middle_shelf = Union_box(xwidth=0.0098,yheight=0.004,zdepth=0.058,priority=44,material_string="Al",p_interact=0.3)
+AT (0,-0.072,0) RELATIVE sample_rod_bottom
+ROTATED (0,0,0) RELATIVE sample_rod_bottom
+
+// V27
+COMPONENT sample_1 = Union_cylinder(radius=0.0045,yheight=0.02,priority=63,material_string="Cu",p_interact=0.6)
+AT (0,-0.002-0.01-0.003,0.015) RELATIVE sample_holder_top_shelf
+ROTATED (0,0,0) RELATIVE sample_holder_top_shelf
+
+// V28
+COMPONENT sample_1_container = Union_cylinder(radius=0.0052,yheight=0.023,priority=62,material_string="Al",p_interact=0.3)
+AT (0,0,0) RELATIVE sample_1
+ROTATED (0,0,0) RELATIVE sample_1
+
+// V29
+COMPONENT sample_1_container_rim = Union_cylinder(radius=0.007,yheight=0.002,priority=61,material_string="Al",p_interact=0.3)
+AT (0,0.023*0.5,0) RELATIVE sample_1
+ROTATED (0,0,0) RELATIVE sample_1
+
+// V30
+COMPONENT sample_2 = Union_cylinder(radius=0.0045,yheight=0.02,priority=73,material_string="Au_Ag_mix",p_interact=0.6)
+AT (0,-0.002-0.01-0.003,-0.015) RELATIVE sample_holder_top_shelf
+ROTATED (0,0,0) RELATIVE sample_holder_top_shelf
+
+// V31
+COMPONENT sample_2_container = Union_cylinder(radius=0.0052,yheight=0.023,priority=72,material_string="Al",p_interact=0.3)
+AT (0,0,0) RELATIVE sample_2
+ROTATED (0,0,0) RELATIVE sample_2
+
+// V32
+COMPONENT sample_2_container_rim = Union_cylinder(radius=0.007,yheight=0.002,priority=71,material_string="Al",p_interact=0.3)
+AT (0,0.023*0.5,0) RELATIVE sample_2
+ROTATED (0,0,0) RELATIVE sample_2
+
+// V33
+COMPONENT sample_3 = Union_cylinder(radius=0.0045,yheight=0.02,priority=83,material_string="Cd",p_interact=0.6)
+AT (0,-0.002-0.01-0.003,0.015) RELATIVE sample_holder_middle_shelf
+ROTATED (0,0,0) RELATIVE sample_holder_middle_shelf
+
+// V34
+COMPONENT sample_3_container = Union_cylinder(radius=0.0052,yheight=0.023,priority=82,material_string="Al",p_interact=0.3)
+AT (0,0,0) RELATIVE sample_3
+ROTATED (0,0,0) RELATIVE sample_3
+
+// V35
+COMPONENT sample_3_container_rim = Union_cylinder(radius=0.007,yheight=0.002,priority=81,material_string="Al",p_interact=0.3)
+AT (0,0.023*0.5,0) RELATIVE sample_3
+ROTATED (0,0,0) RELATIVE sample_3
+
+// V36
+COMPONENT sample_4 = Union_cylinder(radius=0.0045,yheight=0.02,priority=93,material_string="Cs",p_interact=0.6)
+AT (0,-0.002-0.01-0.003,-0.015) RELATIVE sample_holder_middle_shelf
+ROTATED (0,0,0) RELATIVE sample_holder_middle_shelf
+
+// V37
+SPLIT 2 COMPONENT sample_4_container = Union_cylinder(radius=0.0052,yheight=0.023,priority=92,material_string="Al",p_interact=0.3)
+AT (0,0,0) RELATIVE sample_4
+ROTATED (0,0,0) RELATIVE sample_4
+
+// V38
+COMPONENT sample_4_container_rim = Union_cylinder(radius=0.007,yheight=0.002,priority=91,material_string="Al",p_interact=0.3)
+AT (0,0.023*0.5,0) RELATIVE sample_4
+ROTATED (0,0,0) RELATIVE sample_4
+
+
+COMPONENT test_sample = Union_master(history_limit=1000000)
+AT(0,0,0) RELATIVE beam_center
+ROTATED(0,0,0) RELATIVE beam_center
+EXTEND
+%{
+if (scattered_flag[sample_1_index] > 0) scattered_1 = 1; else scattered_1 = 0;
+if (scattered_flag[sample_2_index] > 0) scattered_2 = 1; else scattered_2 = 0;
+if (scattered_flag[sample_3_index] > 0) scattered_3 = 1; else scattered_3 = 0;
+if (scattered_flag[sample_4_index] > 0) scattered_4 = 1; else scattered_4 = 0;
+%}
+
+
+
+COMPONENT detector_position = Arm()
+AT (0,0,0.03) RELATIVE beam_center
+ROTATED(0,0,0) RELATIVE beam_center
+
+COMPONENT m4pi = PSD_monitor_4PI(radius=1, nx=180, ny=180, filename="Events.dat", restore_neutron=1)
+AT (0, 0, 0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+COMPONENT Banana_monitor = Monitor_nD(radius=1, yheight=0.1, options="banana, theta limits=[20,170], bins=500",filename="banana.dat",restore_neutron=1)
+AT (0,0,0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+COMPONENT detector = PSD_monitor(xwidth=0.1, yheight=0.08, nx=200, ny=200, filename="PSD.dat", restore_neutron=1)
+AT (0,-0.02,0.4) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+// Removes events not scattering in at least two of the samples
+// mcdisplay --inspect=m4pi_two_samples shows only rays that scatters on all three
+//  since all others were removed before that component with this arm.
+COMPONENT arm_1 = Arm()
+  AT (0, 0, 0) RELATIVE beam_center
+EXTEND
+%{
+  if (scattered_1 + scattered_2 + scattered_3 + scattered_4 < 2) ABSORB;
+%}
+
+// Using mcdisplay and -inspect m4pi_two_or_more_samples one can show only
+//  trajectories where the ray scatters from two or more of the samples
+COMPONENT m4pi_two_or_more_samples = PSD_monitor_4PI(radius=1, nx=180, ny=180, filename="Events.dat", restore_neutron=1)
+WHEN (scattered_1 + scattered_2 + scattered_3 + scattered_4 > 1)
+AT (0, 0, 0) RELATIVE beam_center
+ROTATED (0,0,0) RELATIVE beam_center
+
+
+COMPONENT armA = Arm()
+AT (0,0,0) ABSOLUTE
+GROUP arms
+
+COMPONENT armB = Arm()
+AT (0,0,0) ABSOLUTE
+GROUP arms
+JUMP myself 2
+
+
+END
diff --git a/mcstasscript/tests/dummy_instrument_folder/Union_make_material.comp b/mcstasscript/tests/dummy_instrument_folder/Union_make_material.comp
new file mode 100644
index 00000000..b591f018
--- /dev/null
+++ b/mcstasscript/tests/dummy_instrument_folder/Union_make_material.comp
@@ -0,0 +1,299 @@
+/*******************************************************************************
+*
+*  McStas, neutron ray-tracing package
+*  Copyright(C) 2007 Risoe National Laboratory.
+*
+* %I
+* Written by: Mads Bertelsen
+* Date: 20.08.15
+* Version: $Revision: 0.1 $
+* Origin: Svanevej 19
+*
+* %D
+* Part of the Union components, a set of components that work together and thus
+*  sperates geometry and physics within McStas.
+* The use of this component requires other components to be used.
+*
+* 1) One specifies a number of processes using process components
+* 2) These are gathered into material definitions using this component
+* 3) Geometries are placed using Union_box/cylinder/sphere, assigned a material
+* 4) A Union_master component placed after all of the above
+*
+* Only in step 4 will any simulation happen, and per default all geometries
+*  defined before the master, but after the previous will be simulated here.
+*
+* There is a dedicated manual available for the Union_components
+*
+* Algorithm:
+* Described elsewhere
+*
+* %P
+* INPUT PARAMETERS:
+* process_string:  [string] Comma seperated names of physical processes
+* my_absorption:   [1/m]    Inverse penetration depth from absorption at standard energy
+* absorber:        [0/1]    Control parameter, if set to 1 the material will have no scattering processes
+*
+* OUTPUT PARAMETERS:
+* this_material:            Structure that contains information on this material
+* global_material_element:  Element of global_material_list which is a global variable
+*
+* GLOBAL PARAMETERS:
+* global_material_list:     List of all defined materials, available in the global scope
+*
+* %L
+*
+* %E
+******************************************************************************/
+
+DEFINE COMPONENT Union_make_material
+DEFINITION PARAMETERS ()
+SETTING PARAMETERS(string process_string="NULL",my_absorption,absorber=0)
+OUTPUT PARAMETERS (loop_index,this_material,accepted_processes,global_material_element)
+
+/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
+
+SHARE
+%{
+#ifndef Union
+#define Union $Revision: 0.8 $
+
+#include "Union_functions.c"
+#include "Union_initialization.c"
+
+#endif
+
+// This function checks if global_process_element should be included in this material when using automatic linking, returns 1 if yes, 0 if no.
+int automatic_linking_materials_function(struct global_process_element_struct global_process_element, struct pointer_to_global_material_list global_material_list,int current_index) {
+    // Remember this function is used before the current material is added to global_material_list
+    // debug info
+    //MPI_MASTER(
+    //printf("Checking if process with index %d should be automatically linked to material with index %d\n",global_process_element.component_index,current_index);
+    //)
+
+    // Check if this is the first make_material, which makes the problem simpler.
+    if (global_material_list.num_elements == 0) {
+       if (global_process_element.component_index < current_index) return 1;
+       else return 0;
+    }
+    // In case there are more than 1 make_material, global_material_list.elements[global_material_list.num_elements-1].component_index makes sense.
+    if (global_process_element.component_index < current_index && global_process_element.component_index > global_material_list.elements[global_material_list.num_elements-1].component_index) return 1;
+    else return 0;
+}
+
+void manual_linking_function_material(char *input_string, struct pointer_to_global_process_list *global_process_list, struct pointer_to_1d_int_list *accepted_processes, char *component_name) {
+    // Need to check a input_string of text for an occurance of name. If it is in the inputstring, yes return 1, otherwise 0.
+   char *token;
+   int loop_index;
+   char local_string[256];
+   
+   strcpy(local_string,input_string);
+   // get the first token
+   token = strtok(local_string,",");
+   
+   // walk through other tokens
+   while( token != NULL ) 
+   {
+      //printf( " %s\n", token );
+      for (loop_index=0;loop_index<global_process_list->num_elements;loop_index++) {
+        if (strcmp(token,global_process_list->elements[loop_index].name) == 0) {
+          add_element_to_int_list(accepted_processes,loop_index);
+          break;
+        }
+        
+        if (loop_index == global_process_list->num_elements - 1) {
+          // All possible process names have been looked through, and the break was not executed.
+          // Alert the user to this problem by showing the process name that was not found and the currently available processes
+            printf("\n");
+            printf("ERROR: The process string \"%s\" in Union material \"%s\" had an entry that did not match a specified process. \n",input_string,component_name);
+            printf("       The unrecoignized process name was: \"%s\" \n",token);
+            printf("       The processes available at this point (need to be defined before the material): \n");
+            for (loop_index=0;loop_index<global_process_list->num_elements;loop_index++)
+              printf("         %s\n",global_process_list->elements[loop_index].name);
+            exit(1);
+        }
+      }
+      
+      // Updates the token
+      token = strtok(NULL,",");
+   }
+}
+
+// This function is needed in initialize of all geometry components
+// Possible to insert these functions in make material, as they are only compiled once instead of many times
+int manual_linking_function(char *name, char *input_string) {
+    // Need to check a input_string of text for an occurance of name. If it is in the inputstring, yes return 1, otherwise 0.
+   char *token;
+   int return_integer=0;
+   char local_string[124];
+   
+   strcpy(local_string,input_string);
+   /* get the first token */
+   token = strtok(local_string,",");
+   
+   /* walk through other tokens */
+   while( token != NULL ) 
+   {
+      //printf( " %s\n", token );
+      if (strcmp(token,name) == 0) return_integer=1;
+      
+      token = strtok(NULL,",");
+   }
+   
+   return return_integer;
+}
+
+
+
+/*
+int count_commas(char *string) {
+  int return_value = 0;
+  
+  int index;
+  for (index=0;index<strlen(string);index++) {
+    printf("%c \n",string[index]);
+    if (string[index]==',') return_value++;
+  }
+    
+  //printf("number_of_commas = %d \n",return_value);
+  return return_value;
+}
+*/
+
+
+%}
+
+DECLARE
+%{
+// Needed for transport to the main component
+struct global_material_element_struct global_material_element;
+struct physics_struct this_material;
+
+#ifndef MATERIAL_DETECTOR
+	//struct pointer_to_global_material_list global_material_list = {0,NULL};
+	#define MATERIAL_DETECTOR dummy
+#endif
+
+int loop_index;
+int found_process;
+int specified_processes;
+char local_string[256];
+
+struct pointer_to_1d_int_list accepted_processes = {0,NULL};
+
+// Add setup for loggers since make_material is called before any volume / master
+#ifndef UNION_LOGGER_DECLARE
+	//struct pointer_to_global_logger_list global_all_volume_logger_list = {0,NULL};
+    //struct pointer_to_global_logger_list global_specific_volumes_logger_list = {0,NULL};
+	#define UNION_LOGGER_DECLARE dummy
+#endif
+
+%}
+
+INITIALIZE
+%{
+
+  /*
+  // Comma test
+  printf("Starting comma test on string: %s \n",process_string);
+  printf("Number of commas in string: %d \n",count_commas(process_string));
+  exit(1);
+  */
+  
+
+  if (0 == strcmp(NAME_CURRENT_COMP,"vacuum") || 0 == strcmp(NAME_CURRENT_COMP,"Vacuum")) {
+    printf("ERROR, a Union material may not be called Vacuum. A vacuum volume may be created by material=\"Vacuum\" in a geometry component.\n");
+    exit(1);
+  }
+  if (0 == strcmp(NAME_CURRENT_COMP,"exit") || 0 == strcmp(NAME_CURRENT_COMP,"Exit")) {
+    printf("ERROR, a Union material may not be called Exit. A exit volume may be created by material=\"Exit\" in a geometry component.\n");
+    exit(1);
+  }
+  if (my_absorption < 0) {
+    printf("ERROR, Union make material named %s have a negative absorption cross section!.\n",NAME_CURRENT_COMP);
+    exit(1);
+  }
+  
+  if (absorber == 0) {
+    if (process_string && strlen(process_string) && strcmp(process_string,"NULL") && strcmp(process_string, "0")) {
+        manual_linking_function_material(process_string, &global_process_list, &accepted_processes, NAME_CURRENT_COMP);
+    } else {
+      for (loop_index=0;loop_index<global_process_list.num_elements;loop_index++) {
+        // printf("Automatic linking chosen [loop index = %d] with process_string = %s  \n",loop_index,process_string);
+        // automatic linking
+        // accept a process if index is between current and former index of make_material components
+          if (1 == automatic_linking_materials_function(global_process_list.elements[loop_index],global_material_list,INDEX_CURRENT_COMP))
+              add_element_to_int_list(&accepted_processes,loop_index);
+      }
+    }
+  }
+
+  this_material.number_of_processes = accepted_processes.num_elements; // Add number of processes
+  this_material.is_vacuum = 0; // This material is not vacuum
+  
+  if (this_material.number_of_processes == 0 && my_absorption == 0) {
+    printf("ERROR, the material named %s has no processes assigned and no absorption cross section, making it eqvialent to vacuum. Vacuums are assigned by setting material=\"Vacuum\" in a geometry component.\n",NAME_CURRENT_COMP);
+    exit(1);
+  }
+  
+  // add process element to this_material, building an array of processes called p_scattering_array
+  if (this_material.number_of_processes > 0) this_material.p_scattering_array = malloc(this_material.number_of_processes * sizeof(struct scattering_process_struct));
+  for (loop_index=0;loop_index<accepted_processes.num_elements;loop_index++) {
+        this_material.p_scattering_array[loop_index]=*global_process_list.elements[accepted_processes.elements[loop_index]].p_scattering_process;
+  }
+  
+  this_material.my_a = my_absorption;  // add the absorption to this material
+  sprintf(this_material.name,NAME_CURRENT_COMP);
+  
+  // packing the information into the global_material_element, which is then included in the global_material_list.
+  sprintf(global_material_element.name,NAME_CURRENT_COMP);
+  global_material_element.component_index = INDEX_CURRENT_COMP;
+  global_material_element.physics = &this_material; // Would be nicer if this material was a pointer, now we have the (small) data two places
+  add_element_to_material_list(&global_material_list,global_material_element);
+ %}
+
+TRACE
+%{
+%}
+
+FINALLY
+%{
+// The elements of the scattering array used static allocation and is thus deallocated automatically
+if (this_material.number_of_processes > 0) free(this_material.p_scattering_array);
+if (accepted_processes.num_elements > 0) free(accepted_processes.elements);
+
+// Checking if any Union volumes are defined after the master component
+#ifdef MASTER_DETECTOR
+  #ifdef ANY_GEOMETRY_DETECTOR_DECLARE
+    #ifndef MASTER_DETECTOR_WARNING
+      for (loop_index=0;loop_index<global_geometry_list.num_elements;loop_index++) {
+        if (global_geometry_list.elements[loop_index].component_index > global_master_list.elements[global_master_list.num_elements-1].component_index) {
+          printf("WARNING: No Union_master component defined after Union volume named %s, this components did not affect the simulation in any way.\n",global_geometry_list.elements[loop_index].name);
+        }
+      }
+      // Decided to have this as a warning without exiting the simulation
+      // In order to only show this warning once, the MASTER_DETECTOR_WARNING is defined
+      #define MASTER_DETECTOR_WARNING dummy
+    #endif
+  #endif
+#endif
+
+// Checking if the user remembered to put in a Union_master
+#ifndef MASTER_DETECTOR
+  #ifdef ANY_GEOMETRY_DETECTOR_DECLARE
+    #ifndef MASTER_DETECTOR_WARNING
+      printf("\nWARNING: No Union_master component used, these components did not affect the simulation in any way:\n");
+      for (loop_index=0;loop_index<global_geometry_list.num_elements;loop_index++)
+        printf("  %s\n",global_geometry_list.elements[loop_index].name);
+      printf("\n");
+      // Decided to have this as a warning without exiting the simulation
+      // In order to only show this warning once, the MASTER_DETECTOR_WARNING is defined
+      #define MASTER_DETECTOR_WARNING dummy
+    #endif
+  #endif
+#endif
+
+
+%}
+
+END
+
diff --git a/mcstasscript/tests/dummy_instrument_folder/Union_master.comp b/mcstasscript/tests/dummy_instrument_folder/Union_master.comp
new file mode 100644
index 00000000..8e5c702b
--- /dev/null
+++ b/mcstasscript/tests/dummy_instrument_folder/Union_master.comp
@@ -0,0 +1,2139 @@
+/*******************************************************************************
+*
+*  McStas, neutron ray-tracing package
+*  Copyright(C) 2007 Risoe National Laboratory.
+*
+* %I
+* Written by: Mads Bertelsen
+* Date: 20.08.15
+* Version: $Revision: 0.8 $
+* Origin: Svanevej 19
+*
+*
+* %D
+* Part of the Union components, a set of components that work together and thus
+*  sperates geometry and physics within McStas.
+* The use of this component requires other components to be used.
+*
+* 1) One specifies a number of processes using process components
+* 2) These are gathered into material definitions using Union_make_material
+* 3) Geometries are placed using Union_box/cylinder/sphere, assigned a material
+* 4) This master component placed after all of the above
+*
+* Only in step 4 will any simulation happen, and per default all geometries
+*  defined before this master, but after the previous will be simulated here.
+*
+* There is a dedicated manual available for the Union_components
+*
+* Algorithm:
+* Described elsewhere
+*
+* %P
+* INPUT PARAMETERS:
+*   allow_inside_start: Set to 1 if rays are expected to start inside a volume in this master
+*   history_limit:      Limits how many histories are recorded before stopping
+*
+* OUTPUT PARAMETERS:
+*
+* %L
+*
+* %E
+******************************************************************************/
+
+DEFINE COMPONENT Union_master
+DEFINITION PARAMETERS ()
+SETTING PARAMETERS(allow_inside_start=0,history_limit=300000,enable_conditionals=1,inherit_number_of_scattering_events=0)
+OUTPUT PARAMETERS (verbal, list_verbal, trace_verbal, finally_verbal, starting_volume_warning, global_master_element, this_global_master_index, previous_master_index, geometry_list_index, intersection_time_table, Volumes, Geometries, starting_lists, r, r_start, v, error_msg, component_error_msg, string_output, number_of_volumes, volume_index, process_index, solutions, max_number_of_processes, limit, solution, min_solution, min_volume, time_found, intersection_time, min_intersection_time, process, process_start, my_trace, p_my_trace, my_trace_fraction_control, k, k_new, k_old, v_length, my_sum, my_sum_plus_abs, culmative_probability, mc_prop, time_to_scattering, length_to_scattering, length_to_boundery, time_to_boundery, selected_process, scattering_event, time_propagated_without_scattering, a_next_volume_found, next_volume,  next_volume_priority, done, current_volume, number_of_solutions, number_of_solutions_static, check, start, intersection_with_children, geometry_output, tree_next_volume, pre_allocated1, pre_allocated2, pre_allocated3, ray_position, ray_velocity, ray_velocity_final, volume_0_found, scattered_flag, scattered_flag_VP, master_transposed_rotation_matrix, temp_rotation_matrix, non_rotated_position, rotated_position, enable_tagging, stop_tagging_ray, stop_creating_nodes, enable_tagging_check, master_tagging_node_list, current_tagging_node, tagging_leaf_counter, number_of_scattering_events, real_transmission_probability, mc_transmission_probability, number_of_masks, number_of_masked_volumes,need_to_run_within_which_volume, mask_index_main, mask_iterate, mask_status_list, current_mask_intersect_list_status, mask_volume_index_list, geometry_component_index_list, Volume_copies_allocated, p_old, this_logger, conditional_status, tagging_conditional_list, free_tagging_conditioanl_list, logger_conditional_extend_array, tagging_conditional_extend, max_conditional_extend_index,safty_distance,safty_distance2, number_of_processes_array,temporary_focus_data,focus_data_index)
+DEPENDENCY "-I@MCCODE_LIB@/share/"
+
+
+/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
+
+SHARE
+%{
+#ifndef Union
+#define Union $Revision: 0.9 $
+
+#include "Union_functions.c"
+#include "Union_initialization.c"
+
+#endif
+// TEST
+struct logger_with_data_struct loggers_with_data_array;
+%}
+
+
+DECLARE
+%{
+  // Settings that can be moved to input.
+  int verbal = 1;
+  int list_verbal = 0;
+  int finally_verbal = 0;
+  
+  // Performance intensive, obsolete, now done in precompiler
+  int trace_verbal = 0;
+  int enable_tagging = 0;
+
+  // It is possible to surpress warnings on starting volume by setting this to 1
+  int starting_volume_warning = 0;
+  
+  // New precompiler settings for verbal / tagging
+  //#define Union_trace_verbal_setting
+  //#define Union_enable_tagging_setting
+
+  // Declare the global variables (not to be in output parameters)
+  struct global_master_element_struct global_master_element;
+  int this_global_master_index;
+  
+  #ifndef MASTER_DETECTOR
+	//struct pointer_to_global_master_list global_master_list = {0,NULL};
+	#define MASTER_DETECTOR dummy
+  #endif
+  
+  // variables used for assigning global information to local variables
+  int previous_master_index,geometry_list_index;
+
+  // The main structures used in this component
+  struct intersection_time_table_struct intersection_time_table;
+  struct Volume_struct **Volumes;
+  struct geometry_struct **Geometries;
+  struct Volume_struct **Volume_copies;
+  struct starting_lists_struct starting_lists;
+  
+  // garbage collection for volume_copies
+  struct pointer_to_1d_int_list Volume_copies_allocated;
+
+  // Vectors in old format (still used by intersect function, will go to Coords in future)
+  double r[3],r_start[3],v[3];
+
+  // Error handling
+  int error_msg, component_error_msg=0;
+  
+  // For verbal output
+  char string_output[128];
+  
+  // Variables for ray-tracing algorithm
+  int number_of_volumes, volume_index, process_index;
+  int iterate,solutions,max_number_of_processes,limit;
+  int solution,min_solution,min_volume,time_found;
+  double intersection_time,min_intersection_time;
+  
+  struct scattering_process_struct *process,*process_start;
+  double *my_trace,*p_my_trace,*my_trace_fraction_control;
+  double k[3],k_new[3],k_old[3],k_rotated[3];
+  double v_length,my_sum,my_sum_plus_abs,culmative_probability,mc_prop,time_to_scattering;
+  double length_to_scattering,length_to_boundery,length_to_boundery_fp,time_to_boundery;
+  int selected_process,scattering_event;
+  double time_propagated_without_scattering;
+  
+  int a_next_volume_found,next_volume;
+  double next_volume_priority;
+  
+  int done,current_volume,ray_sucseeded;
+  int *number_of_solutions;
+  int number_of_solutions_static;
+  int *check,*start;
+  int intersection_with_children,geometry_output;
+  
+  // For within_which_volume
+  int tree_next_volume;
+  int *pre_allocated1,*pre_allocated2,*pre_allocated3;
+  Coords ray_position,ray_velocity,ray_velocity_rotated,ray_velocity_final,wavevector,wavevector_rotated;
+  int volume_0_found=0;
+  
+  int *scattered_flag;
+  int **scattered_flag_VP;
+  
+  // For coordinate transformations
+  Rotation master_transposed_rotation_matrix;
+  Rotation temp_rotation_matrix;
+  Rotation temp_transpose_rotation_matrix;
+  Coords non_rotated_position;
+  Coords rotated_position;
+  int non_isotropic_found;
+  
+  // For tagging
+  struct list_of_tagging_tree_node_pointers master_tagging_node_list;
+  struct tagging_tree_node_struct *current_tagging_node;
+  
+  int tagging_leaf_counter=0,stop_tagging_ray,stop_creating_nodes;
+  int number_of_scattering_events;
+  
+  // For geometry p interact
+  double real_transmission_probability,mc_transmission_probability;
+  
+  // Process p interact
+  int number_of_process_interacts_set,index_of_lacking_process;
+  double total_process_interact;
+  
+  // Volume nr -> component index
+  struct pointer_to_1d_int_list geometry_component_index_list;
+  
+  // Masks
+  struct pointer_to_1d_int_list mask_volume_index_list;
+  int number_of_masks=0;
+  int number_of_masked_volumes=0;
+  struct pointer_to_1d_int_list mask_status_list;
+  struct pointer_to_1d_int_list current_mask_intersect_list_status;
+  int mask_index_main,mask_iterate;
+  int *mask_start,*mask_check;
+  int need_to_run_within_which_volume;
+  
+  // Loggers
+  //struct logger_with_data_struct loggers_with_data_array;
+  int *number_of_processes_array;
+  double p_old;
+  int log_index,conditional_status;
+  struct logger_struct *this_logger;
+  //  union detector_pointer_union detector_pointer;
+  
+  // Conditionals
+  struct conditional_list_struct *tagging_conditional_list;
+  int *logger_conditional_extend_array;
+  int max_conditional_extend_index;
+  int tagging_conditional_extend;
+  int free_tagging_conditioanl_list;
+  
+  // Reliability control
+  // Safty distance is needed to avoid having ray positions closer to a wall than the precision of intersection functions
+  double safty_distance,safty_distance2;
+  
+  // Focusing
+  struct focus_data_struct temporary_focus_data;
+  int focus_data_index;
+  
+%}
+
+INITIALIZE
+%{
+  // Use sanitation
+  #ifndef ANY_GEOMETRY_DETECTOR_DECLARE
+    printf("\nERROR: Need to define at least one Volume using Union_cylinder or Union_box before using the Union_master component. \n");
+    exit(1);
+  #endif
+  #ifdef ANY_GEOMETRY_DETECTOR_DECLARE
+    if (global_geometry_list.num_elements == 0) {
+      printf("\nERROR: Need to define at least one Volume using Union_cylinder or Union_box before using the Union_master component. \n");
+      printf("       Union_master component named \"%s\" is before any Volumes in the instrument file. At least one Volume need to be defined before\n",NAME_CURRENT_COMP);
+    
+      exit(1);
+    }
+  #endif
+  
+  // Parameters describing the safety distances close to surfaces, as scattering should not occur closer to a surface than the
+  //  accuracy of the intersection calculation.
+  safty_distance = 1E-11;
+  safty_distance2 = safty_distance*2;
+  
+  // Write information to the global_master_list about the current Union_master
+  sprintf(global_master_element.name,NAME_CURRENT_COMP);
+  global_master_element.component_index = INDEX_CURRENT_COMP;
+  add_element_to_master_list(&global_master_list,global_master_element);
+  if (inherit_number_of_scattering_events == 1 && global_master_list.num_elements == 1) {
+     printf("ERROR in Union_master with name %s. Inherit_number_of_scattering_events set to 1 for first Union_master component, but there is no preceeding Union_master component. Aborting.\n",NAME_CURRENT_COMP);
+     exit(1);
+  }
+  this_global_master_index = global_master_list.num_elements - 1; // Save the index for this master in global master list
+  
+  // Set the component index of the previous Union_master component if one exists
+  if (global_master_list.num_elements == 1) previous_master_index = 0; // no previous index
+  else previous_master_index = global_master_list.elements[global_master_list.num_elements-2].component_index; // -2 because of zero indexing and needing the previous index.
+  //printf("Assigned previous_master_index = %d \n",previous_master_index);
+  
+  // All volumes in the global_geometry_list is being check for activity using the number_of_activations input made for each geometry (default is 1)
+  // In addition it is counted how many volumes, mask volumes and masked volumes are active in this Union_master.
+  number_of_volumes = 1; // Starting with 1 as the surrounding vacuum is considered a volume
+  number_of_masks = 0;   // Starting with 0 mask volumes
+  number_of_masked_volumes = 0; // Starting with 0 masked volumes
+  for (iterate=0;iterate<global_geometry_list.num_elements;iterate++) {
+    if (global_geometry_list.elements[iterate].component_index < INDEX_CURRENT_COMP && global_geometry_list.elements[iterate].activation_counter > 0) {
+        global_geometry_list.elements[iterate].active = 1;
+        global_geometry_list.elements[iterate].activation_counter--;
+        number_of_volumes++;
+        if (global_geometry_list.elements[iterate].Volume->geometry.is_mask_volume == 1) number_of_masks++;
+        if (global_geometry_list.elements[iterate].Volume->geometry.is_masked_volume == 1) number_of_masked_volumes++;
+    } else global_geometry_list.elements[iterate].active = 0;
+  }
+  //printf("Found number of volumes to be %d \n",number_of_volumes);
+  
+  // Allocation of global lists
+  geometry_component_index_list.num_elements = number_of_volumes;
+  geometry_component_index_list.elements = malloc( geometry_component_index_list.num_elements * sizeof(int));
+  mask_volume_index_list.num_elements = number_of_masks;
+  if (number_of_masks >0) mask_volume_index_list.elements = malloc( number_of_masks * sizeof(int));
+  mask_status_list.num_elements = number_of_masks;
+  if (number_of_masks >0) mask_status_list.elements = malloc( number_of_masks * sizeof(int));
+  current_mask_intersect_list_status.num_elements = number_of_masked_volumes;
+  if (number_of_masked_volumes >0) current_mask_intersect_list_status.elements = malloc( number_of_masked_volumes * sizeof(int));
+  
+  // Make a list of component index from each volume index
+  volume_index = 0;
+  for (iterate=0;iterate<global_geometry_list.num_elements;iterate++) {
+    if (global_geometry_list.elements[iterate].active == 1)
+        geometry_component_index_list.elements[++volume_index] = global_geometry_list.elements[iterate].component_index;
+      
+  }
+  geometry_component_index_list.elements[0] = 0; // Volume 0 is never set in the above code, but should never be used.
+  
+  // The input for this component is done through a series of input components
+  // All information needed is stored in global lists, some of which is printed here for an overview to the user.
+  MPI_MASTER( // MPI_MASTER ensures just one thread output this information to the user
+  if (verbal == 1) {
+      printf("---------------------------------------------------------------------\n");
+      printf("global_process_list.num_elements: %d\n",global_process_list.num_elements);
+      for (iterate=0;iterate<global_process_list.num_elements;iterate++) {
+      printf("name of process [%d]: %s \n",iterate,global_process_list.elements[iterate].name);
+      printf("component index [%d]: %d \n",iterate,global_process_list.elements[iterate].component_index);
+      }
+      
+      printf("---------------------------------------------------------------------\n");
+      printf("global_material_list.num_elements: %d\n",global_material_list.num_elements);
+      for (iterate=0;iterate<global_material_list.num_elements;iterate++) {
+      printf("name of material    [%d]: %s \n",iterate,global_material_list.elements[iterate].name);
+      printf("component index     [%d]: %d \n",iterate,global_material_list.elements[iterate].component_index);
+      printf("my_absoprtion       [%d]: %f \n",iterate,global_material_list.elements[iterate].physics->my_a);
+      printf("number of processes [%d]: %d \n",iterate,global_material_list.elements[iterate].physics->number_of_processes);
+      }
+
+      printf("---------------------------------------------------------------------\n");
+      printf("global_geometry_list.num_elements: %d\n",global_material_list.num_elements);
+      for (iterate=0;iterate<global_geometry_list.num_elements;iterate++) {
+        if (global_geometry_list.elements[iterate].active == 1) {
+          printf("\n");
+          printf("name of geometry    [%d]: %s \n",iterate,global_geometry_list.elements[iterate].name);
+          printf("component index     [%d]: %d \n",iterate,global_geometry_list.elements[iterate].component_index);
+          printf("Volume.name         [%d]: %s \n",iterate,global_geometry_list.elements[iterate].Volume->name);
+          if (global_geometry_list.elements[iterate].Volume->geometry.is_mask_volume == 0) {
+          printf("Volume.p_physics.is_vacuum           [%d]: %d \n",iterate,global_geometry_list.elements[iterate].Volume->p_physics->is_vacuum);
+          printf("Volume.p_physics.my_absorption       [%d]: %f \n",iterate,global_geometry_list.elements[iterate].Volume->p_physics->my_a);
+          printf("Volume.p_physics.number of processes [%d]: %d \n",iterate,global_geometry_list.elements[iterate].Volume->p_physics->number_of_processes);
+          }
+          printf("Volume.geometry.shape                [%d]: %s \n",iterate,global_geometry_list.elements[iterate].Volume->geometry.shape);
+          printf("Volume.geometry.center.x             [%d]: %f \n",iterate,global_geometry_list.elements[iterate].Volume->geometry.center.x);
+          printf("Volume.geometry.center.y             [%d]: %f \n",iterate,global_geometry_list.elements[iterate].Volume->geometry.center.y);
+          printf("Volume.geometry.center.z             [%d]: %f \n",iterate,global_geometry_list.elements[iterate].Volume->geometry.center.z);
+          printf("Volume.geometry.rotation_matrix[0]           [%d]: [%f %f %f] \n",iterate,global_geometry_list.elements[iterate].Volume->geometry.rotation_matrix[0][0],global_geometry_list.elements[iterate].Volume->geometry.rotation_matrix[0][1],global_geometry_list.elements[iterate].Volume->geometry.rotation_matrix[0][2]);
+          printf("Volume.geometry.rotation_matrix[1]           [%d]: [%f %f %f] \n",iterate,global_geometry_list.elements[iterate].Volume->geometry.rotation_matrix[1][0],global_geometry_list.elements[iterate].Volume->geometry.rotation_matrix[1][1],global_geometry_list.elements[iterate].Volume->geometry.rotation_matrix[1][2]);
+          printf("Volume.geometry.rotation_matrix[2]           [%d]: [%f %f %f] \n",iterate,global_geometry_list.elements[iterate].Volume->geometry.rotation_matrix[2][0],global_geometry_list.elements[iterate].Volume->geometry.rotation_matrix[2][1],global_geometry_list.elements[iterate].Volume->geometry.rotation_matrix[2][2]);
+          if (strcmp(global_geometry_list.elements[iterate].Volume->geometry.shape,"cylinder") == 0) {
+          printf("Volume.geometry.geometry_parameters.cyl_radius [%d]: %f \n",iterate,global_geometry_list.elements[iterate].Volume->geometry.geometry_parameters.p_cylinder_storage->cyl_radius);
+          printf("Volume.geometry.geometry_parameters.height [%d]: %f \n",iterate,global_geometry_list.elements[iterate].Volume->geometry.geometry_parameters.p_cylinder_storage->height);
+          }
+          printf("Volume.geometry.focus_data_array.elements[0].Aim             [%d]: [%f %f %f] \n",iterate,global_geometry_list.elements[iterate].Volume->geometry.focus_data_array.elements[0].Aim.x,global_geometry_list.elements[iterate].Volume->geometry.focus_data_array.elements[0].Aim.y,global_geometry_list.elements[iterate].Volume->geometry.focus_data_array.elements[0].Aim.z);
+        }
+      }
+      printf("---------------------------------------------------------------------\n");
+      printf("number_of_volumes = %d\n",number_of_volumes);
+      printf("number_of_masks = %d\n",number_of_masks);
+      printf("number_of_masked_volumes = %d\n",number_of_masked_volumes);
+  }
+  ); // End MPI_MASTER
+  
+  
+  // --- Initialization tasks independent of volume stucture -----------------------
+
+  // Store a pointer to the conditional list and update the current index in that structure
+  // If no tagging_conditionals were defined between this and the previous master, a dummy is allocated instead
+  if (global_tagging_conditional_list.num_elements == global_tagging_conditional_list.current_index + 1) {
+    tagging_conditional_list = &global_tagging_conditional_list.elements[global_tagging_conditional_list.current_index++].conditional_list;
+    free_tagging_conditioanl_list = 0;
+  } else {
+    tagging_conditional_list = malloc(sizeof(struct conditional_list_struct));
+    tagging_conditional_list->num_elements = 0;
+    free_tagging_conditioanl_list = 1;
+  }
+  
+  // Find the maximum logger extend index so that the correct memory allocation can be performed later
+  // Here the loggers applied to all volumes are searched, later this result is compared to volume specific loggers and updated
+  max_conditional_extend_index = -1;
+  for (iterate=0;iterate<global_all_volume_logger_list.num_elements;iterate++) {
+    if (global_all_volume_logger_list.elements[iterate].logger->logger_extend_index > max_conditional_extend_index) {
+      max_conditional_extend_index = global_all_volume_logger_list.elements[iterate].logger->logger_extend_index;
+    }
+  }
+  
+  // The absolute rotation of this component is saved for use in initialization
+  rot_transpose(ROT_A_CURRENT_COMP,master_transposed_rotation_matrix);
+  
+  // Preceeding componnets can add coordinates and rotations to global_positions_to_transform and global_rotations_to_transform
+  //  in order to have these transformed into the coordinate system of the next master compoent in the instrument file.
+  // Here these transformations are performed, and the lists are cleared so no transformed information is further altered by
+  //  next master components.
+  
+  // Position transformation
+  for (iterate=0;iterate<global_positions_to_transform_list.num_elements;iterate++) {
+    //print_position(*global_positions_to_transform_list.positions[iterate],"Position to transform before");
+    non_rotated_position = coords_sub(*(global_positions_to_transform_list.positions[iterate]),POS_A_CURRENT_COMP);
+    *(global_positions_to_transform_list.positions[iterate]) = rot_apply(ROT_A_CURRENT_COMP,non_rotated_position);
+    //print_position(*global_positions_to_transform_list.positions[iterate],"Position to transform after");
+  }
+  if (global_positions_to_transform_list.num_elements > 0) {
+    global_positions_to_transform_list.num_elements = 0;
+    free(global_positions_to_transform_list.positions);
+  }
+  // Rotation transformation
+  for (iterate=0;iterate<global_rotations_to_transform_list.num_elements;iterate++) {
+    //print_rotation(*(global_rotations_to_transform_list.rotations[iterate]),"rotation matrix to be updated");
+    rot_mul(master_transposed_rotation_matrix,*(global_rotations_to_transform_list.rotations[iterate]),temp_rotation_matrix);
+    rot_copy(*(global_rotations_to_transform_list.rotations[iterate]),temp_rotation_matrix);
+  }
+  if (global_rotations_to_transform_list.num_elements > 0) {
+    global_rotations_to_transform_list.num_elements = 0;
+    free(global_rotations_to_transform_list.rotations);
+  }
+
+  
+  // --- Definition of volumes and loading of appropriate data  -----------------------
+  
+  // The information stored in global lists is to be stored in one array of structures that is allocated here
+  Volumes = malloc(number_of_volumes * sizeof(struct Volume_struct*));
+  scattered_flag = malloc(number_of_volumes*sizeof(int));
+  scattered_flag_VP = (int**) malloc(number_of_volumes * sizeof(int*));
+  number_of_processes_array = malloc(number_of_volumes*sizeof(int));
+  
+  // The mcdisplay functions need access to the other geomtries, but can not use the Volumes struct because of order of definition.
+  // A separate list of pointers to the geometry structures is thus allocated
+  Geometries = malloc(number_of_volumes * sizeof(struct geometry_struct *));
+  
+  // When activation counter is used to have several copies of one volume, it can become necessary to have soft copies of volumes
+  // Not all of these will necessarily be allocated or used.
+  Volume_copies = malloc(number_of_volumes * sizeof(struct Volume_struct *));
+  Volume_copies_allocated.num_elements = 0;
+  
+  // The central structure is called a "Volume", it describes a region in space with certain scattering processes and absorption cross section
+
+  // ---  Volume 0 ------------------------------------------------------------------------------------------------
+  // Volume 0 is the vacuum surrounding the experiment (infinite, everywhere) and its properties are hardcoded here
+  Volumes[0] = malloc(sizeof(struct Volume_struct));
+  strcpy(Volumes[0]->name,"Surrounding vacuum");
+  // Assign geometry
+  
+  // This information is meaningless for volume 0, and is never be acsessed in the logic.
+  Volumes[0]->geometry.priority_value = 0.0;
+  Volumes[0]->geometry.center.x = 0;
+  Volumes[0]->geometry.center.y = 0;
+  Volumes[0]->geometry.center.z = 0;
+  strcpy(Volumes[0]->geometry.shape,"vacuum");
+  Volumes[0]->geometry.within_function = &r_within_surroundings; // Always returns 1
+  // No physics struct allocated
+  Volumes[0]->p_physics = NULL;
+  number_of_processes_array[volume_index] = 0;
+  
+  // These are never used for volume 0, but by setting the length to 0 it is automatically skipped in many forloops without the need for an if statement
+  Volumes[0]->geometry.children.num_elements=0;
+  Volumes[0]->geometry.direct_children.num_elements=0;
+  Volumes[0]->geometry.destinations_list.num_elements=0;
+  Volumes[0]->geometry.reduced_destinations_list.num_elements=0;
+  
+  Volumes[0]->geometry.masked_by_list.num_elements = 0;
+  Volumes[0]->geometry.mask_list.num_elements = 0;
+  Volumes[0]->geometry.masked_by_mask_index_list.num_elements = 0;
+  Volumes[0]->geometry.mask_mode=0;
+  Volumes[0]->geometry.is_mask_volume=0;
+  Volumes[0]->geometry.is_masked_volume=0;
+  
+  // A pointer to the geometry structure
+  Geometries[0] = &Volumes[0]->geometry;
+  
+  // Logging initialization
+  Volumes[0]->loggers.num_elements = 0;
+  
+  
+  // --- Loop over user defined volumes ------------------------------------------------------------------------
+  // Here the user defined volumes are loaded into the volume structure that is used in the ray-tracing
+  //  algorithm. Not all user defined volumes are used, some could be used by a previous master, some
+  //  could be used by the previous master, this one, and perhaps more. This is controlled by the
+  //  activation counter input for geometries, and is here condensed to the active variable.
+  // Volumes that were used before
+  
+  
+  max_number_of_processes = 0; // The maximum number of processes in a volume is assumed 0 and updated during the following loop
+  
+  volume_index = 0;
+  mask_index_main = 0;
+  for (geometry_list_index=0;geometry_list_index<global_geometry_list.num_elements;geometry_list_index++) {
+    if (global_geometry_list.elements[geometry_list_index].active == 1) { // Only include the volume if it is active
+      volume_index++;
+      // Connect a volume for each of the geometry.comp instances in the McStas instrument files
+      if (global_geometry_list.elements[geometry_list_index].activation_counter == 0) {
+        // This is the last time this volume is used, use the hard copy from the geometry component
+        Volumes[volume_index] = global_geometry_list.elements[geometry_list_index].Volume;
+        //printf("used hard copy of volume %d \n",volume_index);
+      } else {
+        // Since this volume is still needed more than this once, we need to make a shallow copy and use instead
+        
+        //printf("\n");
+        //printf("making local copy of volume %d \n",volume_index);
+        Volume_copies[volume_index] = malloc(sizeof(struct Volume_struct));
+        *(Volume_copies[volume_index]) = *global_geometry_list.elements[geometry_list_index].Volume; // Makes shallow copy
+        Volumes[volume_index] = Volume_copies[volume_index];
+        add_element_to_int_list(&Volume_copies_allocated,volume_index); // Keep track of dynamically allocated volumes in order to free them in FINALLY.
+        
+        // The geometry storage needs a shallow copy as well (hard copy not necessary for any current geometries), may need changes in future
+        // A simple copy_geometry_parameters function is added to the geometry in each geometry component
+        Volumes[volume_index]->geometry.geometry_parameters = Volumes[volume_index]->geometry.copy_geometry_parameters(&global_geometry_list.elements[geometry_list_index].Volume->geometry.geometry_parameters);
+          
+      }
+      
+      // This section identifies the different non isotropic processes in the current volume and give them appropriate transformation matrices
+      // Identify the number of non isotropic processes in a material (this code can be safely executed for the same material many times)
+      // A setting of -1 means no transformation necessary, other settings are assigned a unique identifier instead
+      non_isotropic_found = 0;
+      for (iterate=0;iterate<Volumes[volume_index]->p_physics->number_of_processes;iterate++) {
+        if (Volumes[volume_index]->p_physics->p_scattering_array[iterate].non_isotropic_rot_index != -1) {
+            Volumes[volume_index]->p_physics->p_scattering_array[iterate].non_isotropic_rot_index = non_isotropic_found;
+            non_isotropic_found++;
+        }
+      }
+      
+      Volumes[volume_index]->geometry.focus_array_indices.num_elements=0;
+      // For the non_isotropic volumes found, rotation matrices need to be allocated and calculated
+      if (non_isotropic_found > 0) {
+        // Allocation of rotation and transpose rotation matrices
+        if (Volumes[volume_index]->geometry.process_rot_allocated == 0) {
+          Volumes[volume_index]->geometry.process_rot_matrix_array = malloc(non_isotropic_found * sizeof(Rotation));
+          Volumes[volume_index]->geometry.transpose_process_rot_matrix_array = malloc(non_isotropic_found * sizeof(Rotation));
+          Volumes[volume_index]->geometry.process_rot_allocated = 1;
+        }
+      
+        // Calculation of the appropriate rotation matrices for transformation between Union_master and the process in a given volume.
+        non_isotropic_found = 0;
+        for (iterate=0;iterate<Volumes[volume_index]->p_physics->number_of_processes;iterate++) {
+          if (Volumes[volume_index]->p_physics->p_scattering_array[iterate].non_isotropic_rot_index != -1) {
+            // Transformation for each process / geometry combination
+            
+            // The focus vector is given in relation to the geometry and needs to be transformed to the process
+            // Work on temporary_focus_data_element which is added to the focus_data_array_at the end
+            temporary_focus_data = Volumes[volume_index]->geometry.focus_data_array.elements[0];
+            
+            // Correct for process rotation
+            temporary_focus_data.Aim = rot_apply(Volumes[volume_index]->p_physics->p_scattering_array[iterate].rotation_matrix,temporary_focus_data.Aim);
+            
+            // Add element to focus_array_indices
+            // focus_array_indices refers to the correct element in focus_data_array for this volume/process combination
+            // focus_data_array[0] is the isotropic version in all cases, so the first non_isotropic goes to focus_data_array[1]
+            //  and so forth. When a process is isotropic, this array is appended with a zero.
+            // The focus_array_indices maps process numbers to the correct focus_data_array index.
+            add_element_to_int_list(&Volumes[volume_index]->geometry.focus_array_indices,non_isotropic_found+1);
+            
+            // Add the new focus_data element to this volumes focus_data_array.
+            add_element_to_focus_data_array(&Volumes[volume_index]->geometry.focus_data_array,temporary_focus_data);
+            
+            // Quick error check to see the length is correct which indirectly confirms the indices are correct
+            if (Volumes[volume_index]->geometry.focus_data_array.num_elements != non_isotropic_found + 2) {
+              printf("ERROR, focus_data_array length for volume %s inconsistent with number of non isotropic processes found!\n",Volumes[volume_index]->name);
+              exit(1);
+            }
+            
+            // Create rotation matrix for this specific volume / process combination to transform from master coordinate system to the non-isotropics process coordinate system
+            // This is done by multipling the transpose master component roration matrix, the volume rotation, and then the process rotation matrix onto the velocity / wavevector
+            rot_mul(Volumes[volume_index]->geometry.rotation_matrix,master_transposed_rotation_matrix,temp_rotation_matrix);
+            rot_mul(Volumes[volume_index]->p_physics->p_scattering_array[iterate].rotation_matrix,temp_rotation_matrix,Volumes[volume_index]->geometry.process_rot_matrix_array[non_isotropic_found]);
+            
+            // Need to transpose as well to transform back to the master coordinate system
+            rot_transpose(Volumes[volume_index]->geometry.process_rot_matrix_array[non_isotropic_found],Volumes[volume_index]->geometry.transpose_process_rot_matrix_array[non_isotropic_found]);
+
+            // Debug print
+            //print_rotation(Volumes[volume_index]->geometry.process_rot_matrix_array[non_isotropic_found],"Process rotation matrix");
+            //print_rotation(Volumes[volume_index]->geometry.transpose_process_rot_matrix_array[non_isotropic_found],"Transpose process rotation matrix");
+            
+            non_isotropic_found++;
+          } else {
+            // This process can use the standard isotropic focus_data_array which is indexed zero.
+            add_element_to_int_list(&Volumes[volume_index]->geometry.focus_array_indices,0);
+          }
+        }
+      } else {
+      // No non isotropic volumes found, focus_array_indices should just be a list of 0's of same length as the number of processes.
+      // In this way all processes use the isotropic focus_data structure
+        Volumes[volume_index]->geometry.focus_array_indices.elements = malloc(Volumes[volume_index]->p_physics->number_of_processes * sizeof(int));
+        for (iterate=0;iterate<Volumes[volume_index]->p_physics->number_of_processes;iterate++)
+          Volumes[volume_index]->geometry.focus_array_indices.elements[iterate] = 0;
+          
+      }
+      //print_1d_int_list(Volumes[volume_index]->geometry.focus_array_indices,"focus_array_indices");
+      
+      
+      // This component works in its local coordinate system, and thus all information from the input components should be transformed to its coordinate system.
+      // All the input components saved their absolute rotation/position into their Volume structure, and the absolute rotation of the current component is known.
+      // The next section finds the relative rotation and translation of all the volumes and the master component.
+      
+      // Transform the rotation matrices for each volume
+      rot_mul(ROT_A_CURRENT_COMP,Volumes[volume_index]->geometry.transpose_rotation_matrix,temp_rotation_matrix);
+      // Copy the result back to the volumes structure
+      rot_copy(Volumes[volume_index]->geometry.rotation_matrix,temp_rotation_matrix);
+      // Now update the transpose as well
+      rot_transpose(Volumes[volume_index]->geometry.rotation_matrix,temp_rotation_matrix);
+      rot_copy(Volumes[volume_index]->geometry.transpose_rotation_matrix,temp_rotation_matrix);
+        
+      // Transform the position for each volume
+      non_rotated_position.x = Volumes[volume_index]->geometry.center.x - POS_A_CURRENT_COMP.x;
+      non_rotated_position.y = Volumes[volume_index]->geometry.center.y - POS_A_CURRENT_COMP.y;
+      non_rotated_position.z = Volumes[volume_index]->geometry.center.z - POS_A_CURRENT_COMP.z;
+
+      rot_transpose(ROT_A_CURRENT_COMP,temp_rotation_matrix); // REVIEW LINE
+      rotated_position = rot_apply(ROT_A_CURRENT_COMP,non_rotated_position);
+
+      Volumes[volume_index]->geometry.center.x = rotated_position.x;
+      Volumes[volume_index]->geometry.center.y = rotated_position.y;
+      Volumes[volume_index]->geometry.center.z = rotated_position.z;
+      
+      // The focus_data information need to be updated as well
+      rot_mul(ROT_A_CURRENT_COMP,Volumes[volume_index]->geometry.focus_data_array.elements[0].absolute_rotation,temp_rotation_matrix);
+      // Copy the result back to the volumes structure
+      rot_copy(Volumes[volume_index]->geometry.focus_data_array.elements[0].absolute_rotation,temp_rotation_matrix);
+      
+      // Use same rotation on the aim vector of the isotropic focus_data element
+      Volumes[volume_index]->geometry.focus_data_array.elements[0].Aim = rot_apply(Volumes[volume_index]->geometry.rotation_matrix,Volumes[volume_index]->geometry.focus_data_array.elements[0].Aim);
+      
+      // To allocate enough memory to hold information on all processes, the maximum of these is updated if this volume has more
+      if (Volumes[volume_index]->p_physics->number_of_processes > max_number_of_processes)
+          max_number_of_processes = Volumes[volume_index]->p_physics->number_of_processes;
+        
+      // Allocate memory to scattered_flag_VP (holds statistics for scatterings in each process of the volume)
+      scattered_flag_VP[volume_index] = malloc(Volumes[volume_index]->p_physics->number_of_processes * sizeof(int));
+      number_of_processes_array[volume_index] = Volumes[volume_index]->p_physics->number_of_processes;
+      
+      // Normalizing and error checking process interact fraction
+      number_of_process_interacts_set = 0; total_process_interact=0;
+      for (process_index=0;process_index<Volumes[volume_index]->p_physics->number_of_processes;process_index++) {
+        if (Volumes[volume_index]->p_physics->p_scattering_array[process_index].process_p_interact != -1) {
+          number_of_process_interacts_set++;
+          total_process_interact += Volumes[volume_index]->p_physics->p_scattering_array[process_index].process_p_interact;
+        } else {
+          index_of_lacking_process = process_index;
+        }
+      }
+      
+      if (number_of_process_interacts_set == 0) Volumes[volume_index]->p_physics->interact_control = 0;
+      else Volumes[volume_index]->p_physics->interact_control = 1;
+      
+      // If all are set, check if they need renormalization so that the sum is one.
+      if (number_of_process_interacts_set == Volumes[volume_index]->p_physics->number_of_processes) {
+        if (total_process_interact > 1.001 || total_process_interact < 0.999) {
+          for (process_index=0;process_index<Volumes[volume_index]->p_physics->number_of_processes;process_index++) {
+            Volumes[volume_index]->p_physics->p_scattering_array[process_index].process_p_interact = Volumes[volume_index]->p_physics->p_scattering_array[process_index].process_p_interact/total_process_interact;
+          }
+        }
+      } else if ( number_of_process_interacts_set != 0) {
+        if (number_of_process_interacts_set == Volumes[volume_index]->p_physics->number_of_processes - 1) {// If all but one is set, it is an easy fix
+          Volumes[volume_index]->p_physics->p_scattering_array[index_of_lacking_process].process_p_interact = 1 - total_process_interact;
+          if (total_process_interact >= 1) {
+            printf("ERROR, material %s has a total interact_fraction above 1 and a process without an interact_fraction. Either set all so they can be renormalized, or have a sum below 1, so that the last can have 1 - sum.\n",Volumes[volume_index]->p_physics->name);
+            exit(1);
+          }
+        } else {
+            printf("ERROR, material %s needs to have all, all minus one or none of its processes with an interact_fraction \n",Volumes[volume_index]->p_physics->name);
+            exit(1);
+        }
+      }
+        
+      // Some initialization can only happen after the rotation matrix relative to the master is known
+      // Such initialization is placed in the geometry component, and executed here through a function pointer
+      Volumes[volume_index]->geometry.initialize_from_main_function(&Volumes[volume_index]->geometry);
+      
+      // Add pointer to geometry to Geometries
+      Geometries[volume_index] = &Volumes[volume_index]->geometry;
+      
+      // Initialize mask intersect list
+      Volumes[volume_index]->geometry.mask_intersect_list.num_elements = 0;
+      
+      // Here the mask_list and masked_by_list for the volume is updated from component index values to volume indexes
+      for (iterate=0;iterate<Volumes[volume_index]->geometry.mask_list.num_elements;iterate++)
+        Volumes[volume_index]->geometry.mask_list.elements[iterate] = find_on_int_list(geometry_component_index_list,Volumes[volume_index]->geometry.mask_list.elements[iterate]);
+      
+      for (iterate=0;iterate<Volumes[volume_index]->geometry.masked_by_list.num_elements;iterate++)
+        Volumes[volume_index]->geometry.masked_by_list.elements[iterate] = find_on_int_list(geometry_component_index_list,Volumes[volume_index]->geometry.masked_by_list.elements[iterate]);
+      
+      // If the volume is a mask, its volume number is added to the mask_volume_index list so volume index can be converted to mask_index.
+      if (Volumes[volume_index]->geometry.is_mask_volume == 1) Volumes[volume_index]->geometry.mask_index = mask_index_main;
+      if (Volumes[volume_index]->geometry.is_mask_volume == 1) mask_volume_index_list.elements[mask_index_main++] = volume_index;
+     
+      // Check all loggers assosiated with this volume and update the max_conditional_extend_index if necessary
+      //printf("reached max_test for volume %d \n",volume_index);
+      for (iterate=0;iterate<Volumes[volume_index]->loggers.num_elements;iterate++) {
+        //printf("iterate = %d \n",iterate);
+        for (process_index=0;process_index<Volumes[volume_index]->loggers.p_logger_volume[iterate].num_elements;process_index++) {
+        //printf("process_index = %d \n",process_index);
+          if (Volumes[volume_index]->loggers.p_logger_volume[iterate].p_logger_process[process_index] != NULL) {
+            if (Volumes[volume_index]->loggers.p_logger_volume[iterate].p_logger_process[process_index]->logger_extend_index > max_conditional_extend_index)
+              max_conditional_extend_index = Volumes[volume_index]->loggers.p_logger_volume[iterate].p_logger_process[process_index]->logger_extend_index;
+          }
+        }
+      }
+      //printf("did max_test for volume %d\n",volume_index);
+      
+    
+    }
+  } // Initialization for each volume done
+  
+  // ------- Initialization of ray-tracing algorithm ------------------------------------
+   
+  my_trace = malloc(max_number_of_processes*sizeof(double));
+  my_trace_fraction_control = malloc(max_number_of_processes*sizeof(double));
+  
+  // All geometries can have 2 intersections currently, when this changes the maximum number of solutions need to be reported to the Union_master.
+  number_of_solutions = &number_of_solutions_static;
+  component_error_msg = 0;
+  
+  // Pre allocated memory for destination list search
+  pre_allocated1 = malloc(number_of_volumes * sizeof(int));
+  pre_allocated2 = malloc(number_of_volumes * sizeof(int));
+  pre_allocated3 = malloc(number_of_volumes * sizeof(int));
+  
+  // Allocate memory for logger_conditional_extend_array used in the extend section of the master component, if it is needed.
+  if (max_conditional_extend_index > -1) {
+    logger_conditional_extend_array = malloc((max_conditional_extend_index + 1)*sizeof(int));
+  }
+  
+  // In this function different lists of volume indecies are generated. They are the key to the speed of the component and central for the logic.
+  // They use simple set algebra to generate these lists for each volume:
+  // Children list for volume n: Indicies of volumes that are entirely within the set described by volume n
+  // Overlap list for volume n: Indicies of volume that contains some of the set described by volume n (excluding volume n)
+  // Intersect check list for volume n: Indicies of volumes to check for intersection if a ray originates from volume n (is generated from the children and overlap lists)
+  // Parents list for volume n: Indicies of volumes that contain the entire set of volume n
+  // Grandparents lists for volume n: Indicies of volumes that contain the entire set of at least one parent of volume n
+  // Destination list for volume n: Indicies of volumes that could be the destination volume when a ray leaves volume n
+  // The overlap, parents and grandparents lists are local variables in the function, and not in the main scope.
+  
+  generate_lists(Volumes, &starting_lists, number_of_volumes, list_verbal);
+  
+  // Generate "safe starting list", which contains all volumes that the ray may enter from other components
+  // These are all volumes without scattering or absorption
+  
+  // Updating mask lists from volume index to global_mask_indices
+  // Filling out the masked_by list that uses mask indices
+  for (volume_index=0;volume_index<number_of_volumes;volume_index++) {
+    Volumes[volume_index]->geometry.masked_by_mask_index_list.num_elements = Volumes[volume_index]->geometry.masked_by_list.num_elements;
+    Volumes[volume_index]->geometry.masked_by_mask_index_list.elements = malloc(Volumes[volume_index]->geometry.masked_by_mask_index_list.num_elements * sizeof(int));
+    for (iterate=0;iterate<Volumes[volume_index]->geometry.masked_by_list.num_elements;iterate++)
+        Volumes[volume_index]->geometry.masked_by_mask_index_list.elements[iterate] = find_on_int_list(mask_volume_index_list,Volumes[volume_index]->geometry.masked_by_list.elements[iterate]);
+  }
+  
+  // Optimizing speed of the within_which_volume search algorithm
+  int volume_index_main; // REVIEW_LINE
+  /*
+  for (volume_index_main=0;volume_index_main<number_of_volumes;volume_index_main++) {
+    Volumes[volume_index_main]->geometry.destinations_logic_list.elements[0] = 0;
+  }
+  */
+  
+  // Checking for equal priorities in order to alert the user to a potential input error
+  for (volume_index_main=0;volume_index_main<number_of_volumes;volume_index_main++) {
+    for (volume_index=0;volume_index<number_of_volumes;volume_index++)
+        if (Volumes[volume_index_main]->geometry.priority_value == Volumes[volume_index]->geometry.priority_value && volume_index_main != volume_index) {
+            if (Volumes[volume_index_main]->geometry.is_mask_volume == 0 && Volumes[volume_index]->geometry.is_mask_volume == 0) {
+              // Priority of masks do not matter
+              printf("ERROR in Union_master with name %s. The volumes named %s and %s have the same priority. Change the priorities so the one present in case of overlap has highest priority.\n",NAME_CURRENT_COMP,Volumes[volume_index_main]->name,Volumes[volume_index]->name);
+              exit(1);
+            }
+        }
+  }
+  
+  
+  // Printing the generated lists for all volumes.
+  MPI_MASTER(
+  if (verbal) printf("\n ---- Overview of the lists generated for each volume ---- \n");
+  
+  
+  printf("List overview for surrounding vacuum\n");
+  for (volume_index_main=0;volume_index_main<number_of_volumes;volume_index_main++) {
+      
+      if (verbal) {
+        if (volume_index_main != 0) {
+          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||
+              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||
+              volume_index_main,Volumes[volume_index_main]->geometry.is_exit_volume == 0) {
+            printf("List overview for %s with %s shape made of %s\n",
+                   Volumes[volume_index_main]->name,
+                   Volumes[volume_index_main]->geometry.shape,
+                   Volumes[volume_index_main]->p_physics->name);
+          } else {
+            printf("List overview for %s with shape %s\n",
+                   Volumes[volume_index_main]->name,
+                   Volumes[volume_index_main]->geometry.shape);
+          }
+        }
+      }
+  
+      if (verbal) sprintf(string_output,"Children for Volume                  %d",volume_index_main);
+      if (verbal) print_1d_int_list(Volumes[volume_index_main]->geometry.children,string_output);
+      
+      if (verbal) sprintf(string_output,"Direct_children for Volume           %d",volume_index_main);
+      if (verbal) print_1d_int_list(Volumes[volume_index_main]->geometry.direct_children,string_output);
+      
+      if (verbal) sprintf(string_output,"Intersect_check_list for Volume      %d",volume_index_main);
+      if (verbal) print_1d_int_list(Volumes[volume_index_main]->geometry.intersect_check_list,string_output);
+      
+      if (verbal) sprintf(string_output,"Mask_intersect_list for Volume       %d",volume_index_main);
+      if (verbal) print_1d_int_list(Volumes[volume_index_main]->geometry.mask_intersect_list,string_output);
+      
+      if (verbal) sprintf(string_output,"Destinations_list for Volume         %d",volume_index_main);
+      if (verbal) print_1d_int_list(Volumes[volume_index_main]->geometry.destinations_list,string_output);
+      
+      //if (verbal) sprintf(string_output,"Destinations_logic_list for Volume   %d",volume_index_main);
+      //if (verbal) print_1d_int_list(Volumes[volume_index_main]->geometry.destinations_logic_list,string_output);
+      
+      if (verbal) sprintf(string_output,"Reduced_destinations_list for Volume %d",volume_index_main);
+      if (verbal) print_1d_int_list(Volumes[volume_index_main]->geometry.reduced_destinations_list,string_output);
+      
+      if (verbal) sprintf(string_output,"Next_volume_list for Volume          %d",volume_index_main);
+      if (verbal) print_1d_int_list(Volumes[volume_index_main]->geometry.next_volume_list,string_output);
+      
+      if (verbal) {
+        if (volume_index_main != 0)
+                           printf("      Is_vacuum for Volume                 %d = %d\n",volume_index_main,Volumes[volume_index_main]->p_physics->is_vacuum);
+      }
+      if (verbal) {
+        if (volume_index_main != 0)
+                           printf("      is_mask_volume for Volume            %d = %d\n",volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume);
+      }
+      if (verbal) {
+        if (volume_index_main != 0)
+                           printf("      is_masked_volume for Volume          %d = %d\n",volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume);
+      }
+      if (verbal) {
+        if (volume_index_main != 0)
+                           printf("      is_exit_volume for Volume            %d = %d\n",volume_index_main,Volumes[volume_index_main]->geometry.is_exit_volume);
+      }
+      
+      if (verbal) sprintf(string_output,"mask_list for Volume                 %d",volume_index_main);
+      if (verbal) print_1d_int_list(Volumes[volume_index_main]->geometry.mask_list,string_output);
+      
+      if (verbal) sprintf(string_output,"masked_by_list for Volume            %d",volume_index_main);
+      if (verbal) print_1d_int_list(Volumes[volume_index_main]->geometry.masked_by_list,string_output);
+      
+      if (verbal) sprintf(string_output,"masked_by_mask_index_list for Volume %d",volume_index_main);
+      if (verbal) print_1d_int_list(Volumes[volume_index_main]->geometry.masked_by_mask_index_list,string_output);
+      
+      if (verbal)          printf("      mask_mode for Volume                 %d = %d\n",volume_index_main,Volumes[volume_index_main]->geometry.mask_mode);
+      if (verbal) printf("\n");
+  }
+  ) // End of MPI_MASTER
+  
+
+  // Initializing intersection_time_table
+  // The intersection time table contains all information on intersection times for the current position/direction, and is cleared everytime a ray changes direction.
+  // Not all entries needs to be calculated, so there is a variable that keeps track of which intersection times have been calculated in order to avoid redoing that.
+  // When the intersections times are calculated for a volume, all future intersections are kept in the time table.
+  // Thus the memory allocation have to take into account how many intersections there can be with each volume, but it is currently set to 2, but can easily be changed. This may need to be reported by individual geometry components in the future.
+  
+  intersection_time_table.num_volumes = number_of_volumes;
+  
+  intersection_time_table.n_elements = (int*) malloc(intersection_time_table.num_volumes * sizeof(int));
+  intersection_time_table.calculated = (int*) malloc(intersection_time_table.num_volumes * sizeof(int));
+  intersection_time_table.intersection_times = (double**) malloc(intersection_time_table.num_volumes * sizeof(double));
+  for (iterate = 0;iterate < intersection_time_table.num_volumes;iterate++){
+      if (strcmp(Volumes[iterate]->geometry.shape, "mesh") == 0) {
+        intersection_time_table.n_elements[iterate] = (int) 100; // Meshes can have any number of intersections, here we allocate room for 100
+      } else {
+        intersection_time_table.n_elements[iterate] = (int) 2; // number of intersection for all other geometries
+      }
+      if (iterate == 0) intersection_time_table.n_elements[iterate] = (int) 0; // number of intersection solutions
+      intersection_time_table.calculated[iterate] = (int) 0; // Initializing calculated logic
+      
+      if (iterate == 0) {
+           intersection_time_table.intersection_times[0] = NULL;
+      }
+      else {
+        intersection_time_table.intersection_times[iterate] = (double*) malloc(intersection_time_table.n_elements[iterate]*sizeof(double));
+        for (solutions = 0;solutions < intersection_time_table.n_elements[iterate];solutions++) {
+              intersection_time_table.intersection_times[iterate][solutions] = -1.0;
+        }
+      }
+  }
+  
+  // If enabled, the tagging system tracks all different histories sampled by the program.
+
+  // Initialize the tagging tree
+  // Allocate a list of host nodes with the same length as the number of volumes
+  
+  stop_creating_nodes = 0; stop_tagging_ray = 0; tagging_leaf_counter = 0;
+  if (enable_tagging) {
+    master_tagging_node_list.num_elements = number_of_volumes;
+    master_tagging_node_list.elements = malloc(master_tagging_node_list.num_elements * sizeof(struct tagging_tree_node_struct*));
+  
+    // Initialize
+    for (volume_index=0;volume_index<number_of_volumes;volume_index++) {
+      //if (verbal) printf("Allocating master tagging node for volume number %d \n",volume_index);
+      master_tagging_node_list.elements[volume_index] = initialize_tagging_tree_node(master_tagging_node_list.elements[volume_index], NULL, Volumes[volume_index]);
+      //if (verbal) printf("Allocated master tagging node for volume number %d \n",volume_index);
+    }
+  }
+  
+  // Initialize loggers
+  loggers_with_data_array.allocated_elements = 0;
+  loggers_with_data_array.used_elements = 0;
+  
+  
+  // Signal initialization complete
+  MPI_MASTER(
+    printf("Union_master component %s initialized sucessfully\n",NAME_CURRENT_COMP);
+  )
+
+%}
+
+TRACE
+%{
+  
+  #ifdef Union_trace_verbal_setting
+    printf("\n\n\n\n\n----------- NEW RAY -------------------------------------------------\n");
+    printf("Union_master component name: %s \n \n",NAME_CURRENT_COMP);
+  #endif
+  
+  // Initialize logic
+  done = 0;
+  error_msg = 0;
+  clear_intersection_table(&intersection_time_table);
+  
+  time_propagated_without_scattering = 0;
+  v_length = sqrt(vx*vx+vy*vy+vz*vz);
+  
+  // Initialize logger system / Statistics
+  number_of_scattering_events = 0;
+  
+  if (inherit_number_of_scattering_events==1) // Continue number of scattering from previous Union_master
+    number_of_scattering_events = global_master_list.elements[this_global_master_index-1].stored_number_of_scattering_events;
+  
+  // Zero scattered_flag_VP data
+  for (volume_index = 1;volume_index<number_of_volumes;volume_index++) { // No reason to update volume 0, as scattering doesn't happen there
+    scattered_flag[volume_index] = 0;
+    for (process_index=0;process_index<number_of_processes_array[volume_index];process_index++)
+      scattered_flag_VP[volume_index][process_index] = 0;
+  }
+  
+  // If first Union_master in instrument, reset loggers_with_data_array and clean unused data.
+  // Unused data happens when logging data is passed to the next Union_master, but the ray is absorbed on the way.
+  // Could be improved by using the precompiler instead as ncount times the number of Union_masters could be avoided.
+  if (global_master_list.elements[0].component_index == INDEX_CURRENT_COMP) {
+    // If this is the first Union master, clean up logger data for rays that did not make it through Union components
+    for (log_index=loggers_with_data_array.used_elements-1;log_index>-1;log_index--) {
+      loggers_with_data_array.logger_pointers[log_index]->function_pointers.clear_temp(&loggers_with_data_array.logger_pointers[log_index]->data_union);
+    }
+    loggers_with_data_array.used_elements = 0;
+  }
+    
+  tagging_conditional_extend = 0;
+  for (iterate=0;iterate<max_conditional_extend_index+1;iterate++) {
+    logger_conditional_extend_array[iterate] = 0;
+  }
+
+  // Need to clean up the double notation for position and velocity. // REVIEW_LINE
+  r_start[0] = x; r_start[1] = y; r_start[2] = z;
+  r[0]=x;r[1]=y;r[2]=z;v[0]=vx;v[1]=vy;v[2]=vz; // REVIEW_LINE r and v are bad names
+  
+  ray_position = coords_set(x,y,z);
+  ray_velocity = coords_set(vx,vy,vz);
+  
+  // Mask update: need to check the mask status for the initial position
+  // mask status for a mask is 1 if the ray position is inside, 0 if it is outside
+  for (iterate=0;iterate<number_of_masks;iterate++) {
+    if(Volumes[mask_volume_index_list.elements[iterate]]->geometry.within_function(ray_position,&Volumes[mask_volume_index_list.elements[iterate]]->geometry) == 1) {
+      mask_status_list.elements[iterate] = 1;
+    } else {
+      mask_status_list.elements[iterate] = 0;
+    }
+  }
+  
+  #ifdef Union_trace_verbal_setting
+    print_1d_int_list(mask_status_list,"Initial mask status list");
+  #endif
+  
+  // Now the initial current_volume can be found, which requires the up to date mask_status_list
+  current_volume = within_which_volume(ray_position,starting_lists.reduced_start_list,starting_lists.starting_destinations_list,Volumes,&mask_status_list,number_of_volumes,pre_allocated1,pre_allocated2,pre_allocated3);
+  
+  // Using the mask_status_list and the current volume, the current_mask_intersect_list_status can be made
+  //  it contains the effective mask status of all volumes on the current volumes mask intersect list, which needs to be calculated,
+  //  but only when the current volume or mask status changes, not under for example scattering inside the current volume
+  update_current_mask_intersect_status(&current_mask_intersect_list_status, &mask_status_list, Volumes, &current_volume);
+  
+  #ifdef Union_trace_verbal_setting
+    printf("Starting current_volume = %d\n",current_volume);
+  #endif
+  
+  // Check if the ray appeared in an allowed starting volume, unless this check is disabled by the user for advanced cases
+  if (allow_inside_start == 0 && starting_lists.allowed_starting_volume_logic_list.elements[current_volume] == 0) {
+    printf("ERROR, ray ''teleported'' into Union component %s, if intentional, set allow_inside_start=1\n",NAME_CURRENT_COMP);
+    exit(1);
+  }
+  // Warn the user that rays have appeared inside a volume instead of outside as expected
+  if (starting_volume_warning == 0 && current_volume != 0) {
+        printf("WARNING: Ray started in volume ''%s'' rather than the surrounding vacuum in component %s. This warning is only shown once.\n",Volumes[current_volume]->name,NAME_CURRENT_COMP);
+        starting_volume_warning = 1;
+  }
+  
+  // Placing the new ray at the start of the tagging tree corresponding to current volume
+  // A history limit can be imposed so that no new nodes are created after this limit (may be necessary to fit in memory)
+  // Rays can still follow the nodes created before even when no additional nodes are created, but if a situation that
+  //  requires a new node is encountered, stop_tagging_ray is set to 1, stopping further tagging and preventing the data
+  //  for that ray to be used further.
+  if (enable_tagging) {
+    current_tagging_node = master_tagging_node_list.elements[current_volume];
+    stop_tagging_ray = 0; // Allow this ray to be tracked
+    if (tagging_leaf_counter > history_limit) stop_creating_nodes = 1;
+  }
+  
+  #ifdef Union_trace_verbal_setting
+    if (enable_tagging) printf("current_tagging_node->intensity = %f\n",current_tagging_node->intensity);
+    if (enable_tagging) printf("current_tagging_node->number_of_rays = %d \n",current_tagging_node->number_of_rays);
+  #endif
+  
+  // Propagation loop including scattering
+  // This while loop continues until the ray leaves the ensamble of user defined volumes either through volume 0
+  //  or a dedicated exit volume. The loop is cancelled after a large number of iterations as a failsafe for errors.
+  // A single run of the loop will either be a propagation to the next volume along the path of the ray, or a
+  //  scattering event at some point along the path of the ray in the current volume.
+  limit = 100000;
+  while (done == 0) {
+    limit--;
+    
+    #ifdef Union_trace_verbal_setting
+      printf("----------- START OF WHILE LOOP --------------------------------------\n");
+      print_intersection_table(intersection_time_table);
+      printf("current_volume = %d \n",current_volume);
+    #endif
+    
+    // Calculating intersections with the necessary volumes. The relevant set of volumes depend on the current volume and the mask status array.
+    // First the volumes on the current volumes intersect list is checked, then its mask interset list. Before checking the volume itself, it is
+    //  checked if any children of the current volume is intersected, in which case the intersection calculation with the current volume can be
+    //  skipped.
+    
+    // Checking intersections for all volumes in the intersect list.
+    for (start=check=Volumes[current_volume]->geometry.intersect_check_list.elements;check-start<Volumes[current_volume]->geometry.intersect_check_list.num_elements;check++) {
+    // This will leave check as a pointer to the intergers in the intersect_check_list and iccrement nicely
+        #ifdef Union_trace_verbal_setting
+          printf("Intersect_list = %d being checked \n",*check);
+        #endif
+    
+        if (intersection_time_table.calculated[*check] == 0) {
+             #ifdef Union_trace_verbal_setting
+               printf("running intersection for intersect_list with *check = %d \n",*check);
+               // if (trace_verbal) printf("r = (%f,%f,%f) v = (%f,%f,%f) \n",r[0],r[1],r[2],v[0],v[1],v[2]);
+             #endif
+            // Calculate intersections using intersect function imbedded in the relevant volume structure using parameters
+            //  that are also imbedded in the structure.
+            geometry_output = Volumes[*check]->geometry.intersect_function(intersection_time_table.intersection_times[*check],number_of_solutions,r_start,v,&Volumes[*check]->geometry);
+            intersection_time_table.calculated[*check] = 1;
+            // if (trace_verbal) printf("succesfully calculated intersection times for volume *check = %d \n",*check);
+        }
+    }
+    
+    // Mask update: add additional loop for checking intersections with masked volumes depending on mask statuses
+    for (mask_iterate=0;mask_iterate<Volumes[current_volume]->geometry.mask_intersect_list.num_elements;mask_iterate++) {
+      if (current_mask_intersect_list_status.elements[mask_iterate] == 1) { // Only check if the mask is active
+        #ifdef Union_trace_verbal_setting
+          printf("Mask Intersect_list = %d being checked \n",Volumes[current_volume]->geometry.mask_intersect_list.elements[mask_iterate]);
+        #endif
+        if (intersection_time_table.calculated[Volumes[current_volume]->geometry.mask_intersect_list.elements[mask_iterate]] == 0) {
+          #ifdef Union_trace_verbal_setting
+            printf("running intersection for mask_intersect_list element = %d \n",Volumes[current_volume]->geometry.mask_intersect_list.elements[mask_iterate]);
+          //  printf("r = (%f,%f,%f) v = (%f,%f,%f) \n",r[0],r[1],r[2],v[0],v[1],v[2]);
+          #endif
+          // Calculate intersections using intersect function imbedded in the relevant volume structure using parameters
+            //  that are also imbedded in the structure.
+          geometry_output = Volumes[Volumes[current_volume]->geometry.mask_intersect_list.elements[mask_iterate]]->geometry.intersect_function(intersection_time_table.intersection_times[Volumes[current_volume]->geometry.mask_intersect_list.elements[mask_iterate]],number_of_solutions,r_start,v,&Volumes[Volumes[current_volume]->geometry.mask_intersect_list.elements[mask_iterate]]->geometry);
+          intersection_time_table.calculated[Volumes[current_volume]->geometry.mask_intersect_list.elements[mask_iterate]] = 1;
+          // if (trace_verbal) printf("succesfully calculated intersection times for volume *check = %d \n",*check);
+        }
+      }
+    }
+    
+    // Checking if there are intersections with children of current volume, which means there is an intersection before current_volume, and thus can be skipped. But only if they have not been overwritten. In case current_volume is 0, there is no need to do this regardless.
+    if (current_volume != 0 && intersection_time_table.calculated[current_volume] == 0) {
+        #ifdef Union_trace_verbal_setting
+          printf("Checking if children of current_volume = %d have intersections. \n",current_volume);
+        #endif
+        intersection_with_children = 0;
+        //for (start = check = Volumes[current_volume]->geometry.direct_children.elements;check - start < Volumes[current_volume]->geometry.children.num_elements;check++) { // REVIEW LINE. Caused bug with masks.
+        for (start = check = Volumes[current_volume]->geometry.children.elements;check - start < Volumes[current_volume]->geometry.children.num_elements;check++) {
+            #ifdef Union_trace_verbal_setting
+              printf("Checking if child %d of current_volume = %d have intersections. \n",*check,current_volume);
+            #endif
+            // Only check the first of the two results in the intersection table, as they are ordered, and the second is of no interest
+            if (intersection_time_table.calculated[*check] == 1 && intersection_time_table.intersection_times[*check][0] > time_propagated_without_scattering) {
+                // If this child is masked, its mask status need to be 1 in order to be taken into account
+                if (Volumes[*check]->geometry.is_masked_volume == 0) {
+                  #ifdef Union_trace_verbal_setting
+                    printf("Found an child of current_volume with an intersection. Skips calculating for current_volume \n");
+                  #endif
+                  intersection_with_children = 1;
+                  break; // No need to check more, if there is just one it is not necessary to calculate intersection with current_volume yet
+                } else {
+                  #ifdef Union_trace_verbal_setting
+                    printf("Found an child of current_volume with an intersection, but it is masked. Check to see if it can skip calculating for current_volume \n");
+                  #endif
+                  
+                  if (Volumes[*check]->geometry.mask_mode == 2) { // ANY mask mode
+                    tree_next_volume = 0;
+                    for (mask_start=mask_check=Volumes[*check]->geometry.masked_by_mask_index_list.elements;mask_check-mask_start<Volumes[*check]->geometry.masked_by_mask_index_list.num_elements;mask_check++) {
+                       if (mask_status_list.elements[*mask_check] == 1) {
+                         intersection_with_children = 1;
+                         break;
+                       }
+                    }
+                  } else { // ALL mask mode
+                    intersection_with_children = 1;
+                    for (mask_start=mask_check=Volumes[*check]->geometry.masked_by_mask_index_list.elements;mask_check-mask_start<Volumes[*check]->geometry.masked_by_mask_index_list.num_elements;mask_check++) {
+                      if (mask_status_list.elements[*mask_check] == 0) {
+                        intersection_with_children = 0;
+                        break;
+                      }
+                    }
+                  }
+                  #ifdef Union_trace_verbal_setting
+                    printf("The mask status was 1, can actually skip intersection calculation for current volume \n");
+                  #endif
+                  if (intersection_with_children == 1) break;
+                }
+            }
+        }
+        #ifdef Union_trace_verbal_setting
+          printf("intersection_with_children = %d \n",intersection_with_children);
+        #endif
+        if (intersection_with_children == 0) {
+            geometry_output = Volumes[current_volume]->geometry.intersect_function(intersection_time_table.intersection_times[current_volume],number_of_solutions,r_start,v,&Volumes[current_volume]->geometry);
+            intersection_time_table.calculated[current_volume] = 1;
+        }
+    }
+
+    // At this point, intersection_time_table is updated with intersection times of all possible intersections.
+    #ifdef Union_trace_verbal_setting
+      print_intersection_table(intersection_time_table);
+    #endif
+    
+    // Next task is to find the next intersection time. The next intersection must be greater than the time_propagated_without_scattering (0 at start of loop)
+    // Loops are eqvialent to the 3 intersection calculation loops already completed
+    
+    // First loop for checking intersect_check_list
+    time_found = 0;
+    for (start=check=Volumes[current_volume]->geometry.intersect_check_list.elements;check-start<Volumes[current_volume]->geometry.intersect_check_list.num_elements;check++) {
+        for (solution = 0;solution<intersection_time_table.n_elements[*check];solution++) {
+            if (time_found) {
+                if ((intersection_time = intersection_time_table.intersection_times[*check][solution]) > time_propagated_without_scattering &&  intersection_time < min_intersection_time) {
+                    min_intersection_time = intersection_time;min_solution = solution;min_volume = *check;
+                }
+            } else {
+                if ((intersection_time = intersection_time_table.intersection_times[*check][solution]) > time_propagated_without_scattering) {
+                    min_intersection_time = intersection_time;min_solution = solution;min_volume = *check;
+                    time_found = 1;
+                }
+            }
+        }
+    }
+    
+    // Now check the masked_intersect_list, but only the ones that are currently active
+    for (mask_iterate=0;mask_iterate<Volumes[current_volume]->geometry.mask_intersect_list.num_elements;mask_iterate++) {
+      if (current_mask_intersect_list_status.elements[mask_iterate] == 1) {
+        for (solution = 0;solution<intersection_time_table.n_elements[Volumes[current_volume]->geometry.mask_intersect_list.elements[mask_iterate]];solution++) {
+            if (time_found) {
+                if ((intersection_time = intersection_time_table.intersection_times[Volumes[current_volume]->geometry.mask_intersect_list.elements[mask_iterate]][solution]) > time_propagated_without_scattering &&  intersection_time < min_intersection_time) {
+                    min_intersection_time = intersection_time;min_solution = solution;min_volume = Volumes[current_volume]->geometry.mask_intersect_list.elements[mask_iterate];
+                }
+            } else {
+                if ((intersection_time = intersection_time_table.intersection_times[Volumes[current_volume]->geometry.mask_intersect_list.elements[mask_iterate]][solution]) > time_propagated_without_scattering) {
+                    min_intersection_time = intersection_time;min_solution = solution;min_volume = Volumes[current_volume]->geometry.mask_intersect_list.elements[mask_iterate];
+                    time_found = 1;
+                }
+            }
+        }
+      }
+    }
+    
+    // And check the current_volume
+    for (solution = 0;solution<intersection_time_table.n_elements[current_volume];solution++) {
+        if (time_found) {
+            if ((intersection_time = intersection_time_table.intersection_times[current_volume][solution]) > time_propagated_without_scattering && intersection_time < min_intersection_time) {
+                min_intersection_time = intersection_time;min_solution = solution;min_volume = current_volume;
+            }
+        } else {
+            if ((intersection_time = intersection_time_table.intersection_times[current_volume][solution]) > time_propagated_without_scattering) {
+                min_intersection_time = intersection_time;min_solution = solution;min_volume = current_volume;
+                time_found = 1;
+            }
+        }
+    }
+    
+    #ifdef Union_trace_verbal_setting
+      printf("min_intersection_time = %f \n",min_intersection_time);
+      printf("min_solution          = %d \n",min_solution);
+      printf("min_volume            = %d \n",min_volume);
+      printf("time_found            = %d \n",time_found);
+    #endif
+
+    // If a time is found, propagation continues, and it will be checked if a scattering occurs before the next intersection.
+    // If a time is not found, the ray must be leaving the ensamble of volumes and the loop will be concluded
+    if (time_found) {
+        time_to_boundery = min_intersection_time - time_propagated_without_scattering; // calculate the time remaining before the next intersection
+        scattering_event = 0; // Assume a scattering event will not occur
+        
+        // Check if a scattering event should occur
+        if (current_volume != 0) { // Volume 0 is always vacuum, and if this is the current volume, an event will not occur
+          if (Volumes[current_volume]->p_physics->number_of_processes == 0) { // If there are no processes, the volume could be vacuum or an absorber
+            if (Volumes[current_volume]->p_physics->is_vacuum == 0)
+              // This volume does not have physical processes but does have an absorption cross section, so the ray weight is reduced accordingly
+              p *= exp(-Volumes[current_volume]->p_physics->my_a*2200*time_to_boundery);
+    
+    
+              //#ifdef Union_trace_verbal_setting
+              //printf("name of material: %s \n",Volumes[current_volume]->name);
+              //printf("length to boundery  = %f\n",length_to_boundery);
+              //printf("absorption cross section  = %f\n",Volumes[current_volume]->p_physics->my_a);
+              //printf("chance to get through this length of absorber: %f \%\n",100*exp(-Volumes[current_volume]->p_physics->my_a*length_to_boundery));
+              //#endif
+                    
+          } else {
+            // Since there is a non-zero number of processes in this material, all the scattering cross section for these are calculated
+            my_sum = 0; k[0] = V2K*vx; k[1] = V2K*vy; k[2] = V2K*vz; p_my_trace = my_trace; wavevector = coords_set(k[0],k[1],k[2]);
+            for (process_start = process = Volumes[current_volume]->p_physics->p_scattering_array;process - process_start < Volumes[current_volume]->p_physics->number_of_processes;process++) {
+            
+              if (Volumes[current_volume]->p_physics->p_scattering_array[process - process_start].non_isotropic_rot_index != -1) {
+                // If the process is not isotropic, the wavevector is transformed into the local coordinate system of the process
+                wavevector_rotated = rot_apply(Volumes[current_volume]->geometry.process_rot_matrix_array[Volumes[current_volume]->p_physics->p_scattering_array[process - process_start].non_isotropic_rot_index],wavevector);
+                
+                coords_get(wavevector_rotated,&k_rotated[0],&k_rotated[1],&k_rotated[2]);
+
+              } else {
+                k_rotated[0] = k[0]; k_rotated[1] = k[1]; k_rotated[2] = k[2];
+              }
+              
+              // Find correct focus_data_array index for this volume/process
+              focus_data_index = Volumes[current_volume]->geometry.focus_array_indices.elements[process - process_start];
+              
+              // Call the probability for scattering function assighed to this specific procress (the process pointer is updated in the for loop)
+              process->probability_for_scattering_function(p_my_trace,k_rotated,process->data_transfer,&Volumes[current_volume]->geometry.focus_data_array.elements[focus_data_index]);
+              
+              my_sum += *p_my_trace;
+              #ifdef Union_trace_verbal_setting
+                printf("my_trace = %f, my_sum = %f\n",*p_my_trace,my_sum);
+              #endif
+              p_my_trace++; // increment the pointer so that it point to the next element (max number of process in any material is allocated)
+            }
+            
+            #ifdef Union_trace_verbal_setting
+              printf("time_propagated_without_scattering = %f.\n",time_propagated_without_scattering);
+              printf("v_length                           = %f.\n",v_length);
+            #endif
+            
+            length_to_boundery = time_to_boundery * v_length;
+            
+            #ifdef Union_trace_verbal_setting
+              printf("exp(- length_to_boundery*my_sum) = %f. length_to_boundery = %f. my_sum = %f.\n",exp(-length_to_boundery*my_sum),length_to_boundery,my_sum);
+            #endif
+            
+            // Selecting if a scattering takes place, and what scattering process.
+            // This section have too many if statements, and unessecary calculations
+            // Could make seperate functions for p_interact on/off and interact_fraction on/off,
+            //   and set function pointers to these in initialize, thus avoiding many unessecary if statements and calculations of x/x.
+            
+            my_sum_plus_abs = my_sum + Volumes[current_volume]->p_physics->my_a*(2200/v_length);
+            
+            if (my_sum < 1E-18) {
+                // The scattering cross section is basicly zero, no scattering should occur.
+                scattering_event = 0;
+                p *= exp(-length_to_boundery*my_sum_plus_abs); // Correct for absorption and the almost zero scattering
+            } else if (length_to_boundery < safty_distance2) {
+                // Too close to boundery to safly make another scattering, attenuate
+                p *= exp(-length_to_boundery*my_sum_plus_abs); // Attentuate the beam for the small distance
+                scattering_event = 0;
+            } else {
+                // The scattering cross section is above zero and the distance to the boundery is sufficient for a scattering
+                if (Volumes[current_volume]->geometry.geometry_p_interact != 0) {
+                    // a fraction of the beam (geometry_p_interact) is forced to scatter
+                    real_transmission_probability = exp(-length_to_boundery*my_sum_plus_abs);
+                    mc_transmission_probability = (1.0 - Volumes[current_volume]->geometry.geometry_p_interact);
+                    if ((scattering_event = (rand01() > mc_transmission_probability))) {
+                        // Scattering event happens, this is the correction for the weight
+                        p *= (1.0-real_transmission_probability)/(1.0-mc_transmission_probability); // Absorption simulated in weight
+                        // Find length to next scattering knowing the ray will scatter.
+                        length_to_scattering = safty_distance -log(1.0 - rand0max((1.0 - exp(-my_sum_plus_abs*(length_to_boundery-safty_distance2))))) / my_sum_plus_abs;
+                    } else {
+                        // Scattering event does not happen, this is the appropriate correction
+                        p *= real_transmission_probability/mc_transmission_probability; // Absorption simulated in weight
+                    }
+                } else {
+                    // probability to scatter is the natural value
+                    if(my_sum*length_to_boundery < 1e-6) { // Scattering probability very small, linear method is used as exponential is unreliable
+                      if (length_to_boundery > safty_distance2) {
+                        if (rand01() < exp(-length_to_boundery*my_sum_plus_abs)) {
+                          // Scattering happens, use linear description to select scattering position
+                          length_to_scattering = safty_distance + rand0max(length_to_boundery - safty_distance2);
+                          // Weight factor necessary to correct for using the linear scattering position distribution
+                          p *= length_to_boundery*my_sum*exp(-length_to_scattering*my_sum_plus_abs); // Absorption simulated in weight
+                          scattering_event = 1;
+                        } else scattering_event = 0;
+                      } else {
+                        // The distance is too short to reliably make a scattering event (in comparison to accuraccy of intersect functions)
+                        p *= exp(-length_to_boundery*my_sum_plus_abs); // Attentuate the beam for the small distance
+                        scattering_event = 0;
+                      }
+                    } else {
+                        // Strong scattering, use exponential description to select scattering position between safetydistance and infinity
+                        length_to_scattering = safty_distance -log(1 - rand01() ) / my_sum_plus_abs;
+                        // Scattering happens if the scattering position is before the boundery (and safty distance)
+                        if (length_to_scattering < length_to_boundery - safty_distance) scattering_event = 1;
+                        else scattering_event = 0;
+                    }
+                }
+                
+                if (scattering_event == 1) {
+                  // Adjust weight for absorption
+                  p *= my_sum/my_sum_plus_abs;
+                  // Safety feature, alert in case of nonsense my results / negative absorption
+                  if (my_sum/my_sum_plus_abs > 1.0) printf("WARNING: Absorption weight factor above 1! Should not happen! \n");
+                  // Select process
+                  if (Volumes[current_volume]->p_physics->number_of_processes == 1) { // trivial case
+                    // Select the only available process, which will always have index 0
+                    selected_process = 0;
+                  } else {
+                    if (Volumes[current_volume]->p_physics->interact_control == 1) {
+                      // Interact_fraction is used to influence the choice of process in this material
+                      mc_prop = rand01();culmative_probability=0;total_process_interact=1.0;
+                  
+                      // If any of the processes have probability 0, they are excluded from the selection
+                      for (iterate = 0;iterate < Volumes[current_volume]->p_physics->number_of_processes;iterate++) {
+                        if (my_trace[iterate] < 1E-18) {
+                          // When this happens, the total force probability is corrected and the probability for this particular instance is set to 0
+                          total_process_interact -= Volumes[current_volume]->p_physics->p_scattering_array[iterate].process_p_interact;
+                          my_trace_fraction_control[iterate] = 0;
+                          // In cases where my_trace is not zero, the forced fraction is still used.
+                        } else my_trace_fraction_control[iterate] = Volumes[current_volume]->p_physics->p_scattering_array[iterate].process_p_interact;
+                      }
+                      // Randomly select a process using the weights stored in my_trace_fraction_control divided by total_process_interact
+                      for (iterate = 0;iterate < Volumes[current_volume]->p_physics->number_of_processes;iterate++) {
+                        culmative_probability += my_trace_fraction_control[iterate]/total_process_interact;
+                        if (culmative_probability > mc_prop) {
+                          selected_process = iterate;
+                          p *= (my_trace[iterate]/my_sum)*(total_process_interact/my_trace_fraction_control[iterate]);
+                          break;
+                        }
+                      }
+                    
+                    } else {
+                      // Select a process based on their relative attenuations factors
+                      mc_prop = rand01();culmative_probability=0;
+                      for (iterate = 0;iterate < Volumes[current_volume]->p_physics->number_of_processes;iterate++) {
+                        culmative_probability += my_trace[iterate]/my_sum;
+                        if (culmative_probability > mc_prop) {
+                          selected_process = iterate;
+                          break;
+                        }
+                      }
+                    }
+                  }
+                } // end of select process
+            }
+            
+          }
+        } // Done checking for scttering event and in case of scattering selecting a process
+     
+        if (scattering_event == 1) {
+            #ifdef Union_trace_verbal_setting
+              printf("SCATTERING EVENT \n");
+              printf("current_volume            = %d \n",current_volume);
+              printf("r = (%f,%f,%f) v = (%f,%f,%f) \n",r[0],r[1],r[2],v[0],v[1],v[2]);
+            // printf("did scatter: my_trace = %E = %f \n",my_trace[selected_process],my_trace[selected_process]);
+            #endif
+            
+            // Calculate the time to scattering
+            time_to_scattering = length_to_scattering/v_length;
+            
+            #ifdef Union_trace_verbal_setting
+              printf("time to scattering        = %2.20f \n",time_to_scattering);
+            #endif
+            
+            //#ifdef Union_trace_verbal_setting
+            //printf("length to boundery = %f, length to scattering = %f \n",length_to_boundery,length_to_scattering);
+            //#endif
+            
+            //PROP_DT(time_to_scattering); // May be replace by version without gravity
+            
+            // Reduce the double book keeping done here // REVIEW LINE
+            x += time_to_scattering*vx; y += time_to_scattering*vy; z += time_to_scattering*vz; t += time_to_scattering;
+            r_start[0] = x; r_start[1] = y; r_start[2] = z;
+            r[0] = x; r[1] = y; r[2] = z;
+            ray_position = coords_set(x,y,z);
+            ray_velocity = coords_set(vx,vy,vz);
+            
+            // Safe check that should be unecessary. Used to fine tune how close to the edge of a volume a scattering event is allowed to take place (1E-14 m away currently).
+            if (Volumes[current_volume]->geometry.within_function(ray_position,&Volumes[current_volume]->geometry) == 0) {
+              printf("\nERROR, propagated out of current volume instead of to a point within!\n");
+              printf("length_to_scattering_specified = %2.20f\n             length propagated = %2.20f\n            length_to_boundery = %2.20f \n   current_position = (%lf,%lf,%lf) \n",length_to_scattering,sqrt(time_to_scattering*time_to_scattering*vx*vx+time_to_scattering*time_to_scattering*vy*vy+time_to_scattering*time_to_scattering*vz*vz),length_to_boundery,x,y,z);
+              
+              volume_index = within_which_volume(ray_position,starting_lists.reduced_start_list,starting_lists.starting_destinations_list,Volumes,&mask_status_list,number_of_volumes,pre_allocated1,pre_allocated2,pre_allocated3);
+              
+              printf("Debug info: Volumes[current_volume]->name = %s, but now inside volume number %d named %s.\n",Volumes[current_volume]->name,volume_index,Volumes[volume_index]->name);
+              printf("Ray absorbed \n");
+              ABSORB;
+            }
+            
+            // Save information before scattering event needed in logging section
+            p_old = p;
+            k_old[0] = k[0];k_old[1] = k[1];k_old[2] = k[2];
+            
+            // Find correct focus_data_array index for this volume/process
+            focus_data_index = Volumes[current_volume]->geometry.focus_array_indices.elements[selected_process];
+        
+            // Rotation to local process coordinate system (for non isotropic processes)
+            if (Volumes[current_volume]->p_physics->p_scattering_array[selected_process].non_isotropic_rot_index != -1) {
+                ray_velocity_rotated = rot_apply(Volumes[current_volume]->geometry.process_rot_matrix_array[Volumes[current_volume]->p_physics->p_scattering_array[selected_process].non_isotropic_rot_index],ray_velocity);
+            } else {
+                ray_velocity_rotated = ray_velocity;
+            }
+                
+            // test_physics_scattering(double *k_final, double *k_initial, union data_transfer_union data_transfer) {
+            //k[0] = V2K*ray_velocity.x; k[1] = V2K*ray_velocity.y; k[2] = V2K*ray_velocity.z;
+            coords_get(coords_scalar_mult(ray_velocity_rotated,V2K),&k[0],&k[1],&k[2]);
+            
+            // I may replace a intial and final k with one instance that serves as both input and output
+            if (0 == Volumes[current_volume]->p_physics->p_scattering_array[selected_process].scattering_function(k_new,k,&p,Volumes[current_volume]->p_physics->p_scattering_array[selected_process].data_transfer,&Volumes[current_volume]->geometry.focus_data_array.elements[0])) {
+              /*
+              // PowderN and Single_crystal requires the option of absorbing the neutron, which is weird. If there is a scattering probability, there should be a new direction.
+              // It can arise from need to simplify sampling process and end up in cases where weight factor is 0, and the ray should be absorbed in these cases
+              printf("ERROR: Union_master: %s.Absorbed ray because scattering function returned 0 (error/absorb)\n",NAME_CURRENT_COMP);
+              component_error_msg++;
+              if (component_error_msg > 100) {
+                printf("To many errors encountered, exiting. \n");
+                exit(1);
+              }
+              */
+              ABSORB;
+            }
+            
+            // Update velocity using k
+            ray_velocity_rotated = coords_set(K2V*k_new[0],K2V*k_new[1],K2V*k_new[2]);
+            
+            // Transformation back to main coordinate system (maybe one should only do this when multiple scattering in that volume was over, especially if there is only one non isotropic frame)
+            if (Volumes[current_volume]->p_physics->p_scattering_array[selected_process].non_isotropic_rot_index != -1) {
+                ray_velocity_final = rot_apply(Volumes[current_volume]->geometry.transpose_process_rot_matrix_array[Volumes[current_volume]->p_physics->p_scattering_array[selected_process].non_isotropic_rot_index],ray_velocity_rotated);
+            } else {
+               ray_velocity_final = ray_velocity_rotated;
+            }
+            
+            // Write velocity to global variable (temp, only really necessary at final)
+            //vx = ray_velocity.x; vy = ray_velocity.y; vz = ray_velocity.z;
+            coords_get(ray_velocity_final,&vx,&vy,&vz);
+            
+            // Write velocity in array format as it is still used by intersect functions (temp, they need to be updated to ray_position / ray_velocity)
+            v[0] = vx; v[1] = vy; v[2] = vz;
+            v_length = sqrt(vx*vx+vy*vy+vz*vz);
+            k_new[0] = V2K*vx; k_new[1] = V2K*vy; k_new[2] = V2K*vz;
+            if (verbal) if (v_length < 1) printf("velocity set to less than 1\n");
+            
+            #ifdef Union_trace_verbal_setting
+              printf("Running logger system for specific volumes \n");
+            #endif
+            // Logging for detector components assosiated with this volume
+            for (log_index=0;log_index<Volumes[current_volume]->loggers.num_elements;log_index++) {
+              //printf("logging time! Volume specific version. Volume name = %s \n",Volumes[current_volume]->name);
+              //printf("  log_index = %d \n",log_index);
+              if (Volumes[current_volume]->loggers.p_logger_volume[log_index].p_logger_process[selected_process] != NULL) {
+                // Technically the scattering function could edit k, the wavevector before the scattering, even though there would be little point to doing that.
+                // Could save a secure copy and pass that instead to be certain that no scattering process accidently tampers with the logging.
+                // printf("  the logging function pointer was not NULL \n");
+                // PV (number of time scattered in this volume/process combination is not recorded. Need to expand scattering_flag to contain 2D, volume and process
+                
+                // This function calls a logger function which in turn stores some data among the passed, and possibly performs some basic data analysis
+                Volumes[current_volume]->loggers.p_logger_volume[log_index].p_logger_process[selected_process]->function_pointers.active_record_function(&ray_position,k_new,k_old,p,p_old,t,scattered_flag[current_volume],scattered_flag_VP[current_volume][selected_process],number_of_scattering_events,Volumes[current_volume]->loggers.p_logger_volume[log_index].p_logger_process[selected_process],&loggers_with_data_array);
+                // If the logging component have a conditional attatched, the collected data will be written to a temporary place
+                // At the end of the rays life, it will be checked if the condition is met
+                //  if it is met, the temporary data is transfered to permanent, and temp is cleared.
+                //  if it is not met, the temporary data is cleared.
+              }
+            }
+            
+            #ifdef Union_trace_verbal_setting
+              printf("Running logger system for all volumes \n");
+            #endif
+            for (log_index=0;log_index<global_all_volume_logger_list.num_elements;log_index++) {
+              //printf("logging time! Global version. log_index = %d \n",log_index);
+              // As above, but on a global scale, meaning scattering in all volumes are logged
+              
+              // Problems with VN, PV, as there is no assosiated volume or process. The functions however need to have the same input to make the logger components general.
+              // Could be interesting to have a monitor that just globally measurres the second scattering event in any volume (must be two in the same). Weird but not meaningless.
+              global_all_volume_logger_list.elements[log_index].logger->function_pointers.active_record_function(&ray_position,k_new,k_old,p,p_old,t,scattered_flag[current_volume],scattered_flag_VP[current_volume][selected_process],number_of_scattering_events,global_all_volume_logger_list.elements[log_index].logger,&loggers_with_data_array);
+            }
+            
+            
+            SCATTER;
+            ++number_of_scattering_events;
+            ++scattered_flag[current_volume];
+            ++scattered_flag_VP[current_volume][selected_process];
+            
+
+            // Empty intersection time lists
+            clear_intersection_table(&intersection_time_table);
+            time_propagated_without_scattering = 0.0;
+            #ifdef Union_trace_verbal_setting
+              printf("SCATTERED SUCSSESFULLY \n");
+              printf("r = (%f,%f,%f) v = (%f,%f,%f) \n",x,y,z,vx,vy,vz);
+            
+              if (enable_tagging && stop_tagging_ray == 0) printf("Before new process node: current_tagging_node->intensity = %f\n",current_tagging_node->intensity);
+              if (enable_tagging && stop_tagging_ray == 0) printf("Before new process node: current_tagging_node->number_of_rays = %d\n",current_tagging_node->number_of_rays);
+            #endif
+            
+            if (enable_tagging && stop_tagging_ray == 0)
+                current_tagging_node = goto_process_node(current_tagging_node,selected_process,Volumes[current_volume],&stop_tagging_ray,stop_creating_nodes);
+            
+            #ifdef Union_trace_verbal_setting
+              if (enable_tagging && stop_tagging_ray == 0) printf("After new process node: current_tagging_node->intensity = %f\n",current_tagging_node->intensity);
+              if (enable_tagging && stop_tagging_ray == 0) printf("After new process node: current_tagging_node->number_of_rays = %d\n",current_tagging_node->number_of_rays);
+            #endif
+            
+        } else {
+            #ifdef Union_trace_verbal_setting
+              printf("Propagate out of volume %d\n", current_volume);
+              printf("r = (%f,%f,%f) v = (%f,%f,%f) \n",x,y,z,vx,vy,vz);
+            #endif
+            // Propagate neutron to found minimum time
+            // PROP_DT(min_intersection_time - time_propagated_without_scattering);
+            //time_to_boundery = min_intersection_time - time_propagated_without_scattering;
+            x += time_to_boundery*vx;
+            y += time_to_boundery*vy;
+            z += time_to_boundery*vz;
+            t += time_to_boundery;
+            r[0] = x; r[1] = y; r[2] = z;
+            ray_position = coords_set(x,y,z);
+            ray_velocity = coords_set(vx,vy,vz);
+            
+            /*
+            // Absorption moved to before testing if scattering occurs
+            if (current_volume != 0) {
+                if (Volumes[current_volume]->p_physics->is_vacuum == 0) {
+                    // Absorption is done explicitly when propagating out of a volume, but between all scattering events is done implicitly
+                   
+                    // Old version
+                    //length_to_boundery = (min_intersection_time - time_propagated_without_scattering) * v_length;
+                    //p *= exp(-Volumes[current_volume]->p_physics->my_a*(2200/v_length)*length_to_boundery);
+                    
+                    if (Volumes[current_volume]->p_physics->number_of_processes == 0) {
+                      // Optimized version
+                      //p *= exp(-Volumes[current_volume]->p_physics->my_a*2200*time_to_boundery);
+                    
+                    //printf("name of material: %s \n",Volumes[current_volume]->name);
+                    //printf("length to boundery  = %f\n",length_to_boundery);
+                    //printf("absorption cross section  = %f\n",Volumes[current_volume]->p_physics->my_a);
+                    //printf("chance to get through this length of absorber: %f \%\n",100*exp(-Volumes[current_volume]->p_physics->my_a*length_to_boundery));
+                    }
+                }
+            }
+            */
+            
+            time_propagated_without_scattering = min_intersection_time;
+            SCATTER; // For debugging purposes
+            #ifdef Union_trace_verbal_setting
+              printf("r = (%f,%f,%f) v = (%f,%f,%f) \n",x,y,z,vx,vy,vz);
+            #endif
+            // Remove this entry from the intersection_time_table
+            intersection_time_table.intersection_times[min_volume][min_solution] = -1;
+            
+            // Use destination list for corresponding intersection entry n,i) to find next volume
+            #ifdef Union_trace_verbal_setting
+              printf("PROPAGATION FROM VOLUME %d \n",current_volume);
+            #endif
+            if (min_volume == current_volume) {
+                #ifdef Union_trace_verbal_setting
+                  printf("min_volume == current_volume \n");
+                #endif
+                // List approach to finding the next volume.
+                // When the ray intersects the current volume, the next volume must be on the destination list of the current volume
+                // However, the reduced_destination_list can be investigated first, and depending on the results, the
+                //  direct children of the volumes on the reduced destination list are investigated.
+                // In the worst case, all direct children are investigated, which is eqvivalent to the entire destination list.
+                // There is however a certain overhead in the logic needed to set up this tree, avoid duplicates of direct children, and so on.
+                // This method is only faster than just checking the destination list when there are direct children (nested structures),
+                //  but in general the tree method scales better with complexity, and is only slightly slower in simple cases.
+                
+                if (Volumes[current_volume]->geometry.destinations_list.num_elements == 1)
+                    tree_next_volume = Volumes[current_volume]->geometry.destinations_list.elements[0];
+                else {
+                    ray_position = coords_set(x,y,z);
+                    ray_velocity = coords_set(vx,vy,vz);
+                    tree_next_volume = within_which_volume(ray_position,Volumes[current_volume]->geometry.reduced_destinations_list,Volumes[current_volume]->geometry.destinations_list,Volumes,&mask_status_list,number_of_volumes,pre_allocated1,pre_allocated2,pre_allocated3);
+                }
+
+                #ifdef Union_trace_verbal_setting
+                  if (trace_verbal) printf("tree method moves from %d to %d\n",current_volume,tree_next_volume);
+                
+                  if (enable_tagging && stop_tagging_ray == 0) printf("Before new tree volume node: current_tagging_node->intensity = %f\n",current_tagging_node->intensity);
+                  if (enable_tagging && stop_tagging_ray == 0) printf("Before new tree volume node: current_tagging_node->number_of_rays = %d\n",current_tagging_node->number_of_rays);
+                #endif
+                
+                if (enable_tagging && stop_tagging_ray == 0)
+                    current_tagging_node = goto_volume_node(current_tagging_node, current_volume, tree_next_volume, Volumes,&stop_tagging_ray,stop_creating_nodes);
+                
+                #ifdef Union_trace_verbal_setting
+                  if (enable_tagging && stop_tagging_ray == 0) printf("After new tree volume node: current_tagging_node->intensity = %f\n",current_tagging_node->intensity);
+                  if (enable_tagging && stop_tagging_ray == 0) printf("After new tree volume node: current_tagging_node->number_of_rays = %d\n",current_tagging_node->number_of_rays);
+                #endif
+                
+                // Set next volume to the solution found in the tree method
+                current_volume = tree_next_volume;
+                update_current_mask_intersect_status(&current_mask_intersect_list_status, &mask_status_list, Volumes, &current_volume);
+                #ifdef Union_trace_verbal_setting
+                  print_1d_int_list(current_mask_intersect_list_status,"Updated current_mask_intersect_list_status");
+                #endif
+                
+                
+                // Debugging phase
+                /*
+                if (tree_next_volume == 0) {
+                    volume_0_found=0;
+                    for (start = check = Volumes[current_volume]->geometry.destinations_list.elements;check - start < Volumes[current_volume]->geometry.destinations_list.num_elements;check++) {
+                        if (*check == 0) {
+                            volume_0_found = 1;
+                        }
+                    }
+                    if (volume_0_found==0) printf("ERROR The within_which_volume function returned 0 for a volume where volume 0 is not on the destination list!\n");
+                }
+                */
+                
+            } else {
+                #ifdef Union_trace_verbal_setting
+                  if (enable_tagging && stop_tagging_ray == 0) printf("Before new intersection volume node: current_tagging_node->intensity = %f\n",current_tagging_node->intensity);
+                  if (enable_tagging && stop_tagging_ray == 0) printf("Before new intersection volume node: current_tagging_node->number_of_rays = %d\n",current_tagging_node->number_of_rays);
+                #endif
+            
+                //if (enable_tagging) current_tagging_node = goto_volume_node(current_tagging_node, current_volume, min_volume, Volumes);
+                
+                
+                
+                // Mask update: If the min_volume is not a mask, things are simple, current_volume = min_volume.
+                //               however, if it is a mask, the mask status will switch.
+                //               if the mask status becomes one, the masked volumes inside may be the next volume (unless they are children of the mask)
+                //               if the mask status becomes zero (and the current volume is masked by min_volume), the destinations list of the mask is searched
+                //               if the mask status becomes zero (and the current volume is NOT masked by min volume), the current volume doesn't change
+                
+                
+                if (Volumes[min_volume]->geometry.is_mask_volume == 0) {
+                  #ifdef Union_trace_verbal_setting
+                    printf("Min volume is not a mask, next volume = min volume\n");
+                  #endif
+                  if (enable_tagging && stop_tagging_ray == 0) current_tagging_node = goto_volume_node(current_tagging_node, current_volume, min_volume, Volumes,&stop_tagging_ray,stop_creating_nodes);
+                  current_volume = min_volume;
+                  //update_current_mask_intersect_status(&current_mask_intersect_list_status, &mask_status_list, Volumes, &current_volume);
+                } else {
+                  #ifdef Union_trace_verbal_setting
+                    printf("Current volume is not a mask, complex decision tree\n");
+                  #endif
+                  if (mask_status_list.elements[Volumes[min_volume]->geometry.mask_index] == 1) {
+                    // We are leaving the mask, change the status
+                    #ifdef Union_trace_verbal_setting
+                      printf("mask status changed from 1 to 0 as a mask is left\n");
+                    #endif
+                    mask_status_list.elements[Volumes[min_volume]->geometry.mask_index] = 0;
+                    // If the current volume is masked by this mask, run within_which_volume using the masks destination list, otherwise keep the current volume
+                    //if (on_int_list(Volumes[min_volume]->geometry.mask_list,current_volume))
+                    if (on_int_list(Volumes[current_volume]->geometry.masked_by_list,min_volume) == 1) {
+                      #ifdef Union_trace_verbal_setting
+                        printf("The current volume was masked by this mask, and my need updating\n");
+                      #endif
+                      // In case of ANY mode, need to see if another mask on the masked_by list of the current volume is active, and if so, nothing happens
+                      need_to_run_within_which_volume = 1;
+                      if (Volumes[current_volume]->geometry.mask_mode == 2) {
+                        for (mask_start=mask_check=Volumes[current_volume]->geometry.masked_by_mask_index_list.elements;mask_check-mask_start<Volumes[current_volume]->geometry.masked_by_mask_index_list.num_elements;mask_check++) {
+                          if (mask_status_list.elements[*mask_check] == 1) {
+                            // Nothing needs to be done, the effective mask status of the current volume is still 1
+                            need_to_run_within_which_volume = 0;
+                            break;
+                          }
+                        }
+                      }
+                      if (need_to_run_within_which_volume == 1) {
+                        #ifdef Union_trace_verbal_setting
+                          printf("The current volume was masked by this mask, and does need updating\n");
+                        #endif
+                        if (Volumes[min_volume]->geometry.destinations_list.num_elements == 1) {
+                          #ifdef Union_trace_verbal_setting
+                            printf("Only one element in the destination tree of the mask\n");
+                          #endif
+                          // If there is only one element on the destinations list (quite common) there is no reason to run within_which_volume
+                          // Instead the mask status is calculated here
+                          if (Volumes[Volumes[min_volume]->geometry.destinations_list.elements[0]]->geometry.is_masked_volume == 1) {
+                            #ifdef Union_trace_verbal_setting
+                              printf("The one element is however masked, so the mask status need to be calculated\n");
+                            #endif
+                            // figure out the effective mask status of this volume
+                            if (Volumes[Volumes[min_volume]->geometry.destinations_list.elements[0]]->geometry.mask_mode == 2) { // ANY mask mode
+                              tree_next_volume = 0;
+                              for (mask_start=mask_check=Volumes[Volumes[min_volume]->geometry.destinations_list.elements[0]]->geometry.masked_by_mask_index_list.elements;mask_check-mask_start<Volumes[Volumes[min_volume]->geometry.destinations_list.elements[0]]->geometry.masked_by_mask_index_list.num_elements;mask_check++) {
+                                if (mask_status_list.elements[*mask_check] == 1) {
+                                  tree_next_volume = Volumes[min_volume]->geometry.destinations_list.elements[0];
+                                  break;
+                                }
+                              }
+                            } else { // ALL mask mode
+                              tree_next_volume = Volumes[min_volume]->geometry.destinations_list.elements[0];
+                              for (mask_start=mask_check=Volumes[Volumes[min_volume]->geometry.destinations_list.elements[0]]->geometry.masked_by_mask_index_list.elements;mask_check-mask_start<Volumes[Volumes[min_volume]->geometry.destinations_list.elements[0]]->geometry.masked_by_mask_index_list.num_elements;mask_check++) {
+                                if (mask_status_list.elements[*mask_check] == 0) {
+                                  tree_next_volume = 0;
+                                  break;
+                                }
+                              }
+                            }
+                          } else tree_next_volume = Volumes[min_volume]->geometry.destinations_list.elements[0];
+                          #ifdef Union_trace_verbal_setting
+                            printf("The method found the next tree volume to be %d\n",tree_next_volume);
+                          #endif
+                          if (enable_tagging && stop_tagging_ray == 0) current_tagging_node = goto_volume_node(current_tagging_node, current_volume, tree_next_volume, Volumes,&stop_tagging_ray,stop_creating_nodes);
+                          current_volume = tree_next_volume;
+                          //update_current_mask_intersect_status(&current_mask_intersect_list_status, &mask_status_list, Volumes, &current_volume);
+                        } else {
+                          #ifdef Union_trace_verbal_setting
+                            printf("Many elements in destinations list, use within_which_volume\n");
+                          #endif
+                          ray_position = coords_set(x,y,z);
+                          ray_velocity = coords_set(vx,vy,vz);
+                          tree_next_volume = within_which_volume(ray_position,Volumes[min_volume]->geometry.reduced_destinations_list,Volumes[min_volume]->geometry.destinations_list,Volumes,&mask_status_list,number_of_volumes,pre_allocated1,pre_allocated2,pre_allocated3);
+                        // } Bug fixed on 27/11/2016
+                          if (enable_tagging && stop_tagging_ray == 0) current_tagging_node = goto_volume_node(current_tagging_node, current_volume, tree_next_volume, Volumes,&stop_tagging_ray,stop_creating_nodes);
+                          current_volume = tree_next_volume;
+                          #ifdef Union_trace_verbal_setting
+                            printf("Set new new volume to %d\n",tree_next_volume);
+                          #endif
+                        } // Moved here on 27/11/2016, problem was two calls to current_tagging_node when only one element in destinations list
+                        //update_current_mask_intersect_status(&current_mask_intersect_list_status, &mask_status_list, Volumes, &current_volume);
+                      } else {
+                        #ifdef Union_trace_verbal_setting
+                          printf("Did not need updating, as another mask was covering the volume\n");
+                        #endif
+                      }
+                    }
+
+                  } else {
+                    // Here beccause the mask status of the mask that is intersected was 0, and it is thus switched to 1
+                    mask_status_list.elements[Volumes[min_volume]->geometry.mask_index] = 1;
+                    // When entering a mask, the new highest priority volume may be one of the masked volumes, if not we keep the current volume
+                    ray_position = coords_set(x,y,z);
+                    ray_velocity = coords_set(vx,vy,vz);
+                    //tree_next_volume = within_which_volume(ray_position,Volumes[min_volume]->geometry.mask_list,Volumes[min_volume]->geometry.destinations_list,Volumes,&mask_status_list,number_of_volumes,pre_allocated1,pre_allocated2,pre_allocated3);
+                    // Bug found on the 2/9 2016, the destinations_list of a mask does not contain the volumes inside it. Could make an additional list for this.
+                    // The temporary fix will be to use the mask list for both reduced destinations list and destinations list.
+                    tree_next_volume = within_which_volume(ray_position,Volumes[min_volume]->geometry.mask_list,Volumes[min_volume]->geometry.mask_list,Volumes,&mask_status_list,number_of_volumes,pre_allocated1,pre_allocated2,pre_allocated3);
+                    // if within_which_volume returns 0, no result was found (volume 0 can not be masked, so it could not be on the mask list)
+                    if (tree_next_volume != 0) {
+                      if (Volumes[tree_next_volume]->geometry.priority_value > Volumes[current_volume]->geometry.priority_value) {
+                        // In case the current volume has a higher priority, nothing happens, otherwise change current volume
+                        if (enable_tagging && stop_tagging_ray == 0) current_tagging_node = goto_volume_node(current_tagging_node, current_volume, tree_next_volume, Volumes,&stop_tagging_ray,stop_creating_nodes);
+                        current_volume = tree_next_volume;
+                      }
+                    }
+                    //update_current_mask_intersect_status(&current_mask_intersect_list_status, &mask_status_list, Volumes, &current_volume);
+                  }
+                }
+                
+                // Regardless of the outcome of the above code, either the mask status or current volume have changed, and thus a effective mask update is needed.
+                update_current_mask_intersect_status(&current_mask_intersect_list_status, &mask_status_list, Volumes, &current_volume);
+                #ifdef Union_trace_verbal_setting
+                  print_1d_int_list(mask_status_list,"Updated mask status list");
+                  print_1d_int_list(current_mask_intersect_list_status,"Updated current_mask_intersect_list_status");
+                
+                  if (enable_tagging) printf("After new intersection volume node: current_tagging_node->intensity = %f\n",current_tagging_node->intensity);
+                  if (enable_tagging) printf("After new intersection volume node: current_tagging_node->number_of_rays = %d\n",current_tagging_node->number_of_rays);
+                #endif
+                
+            }
+            if (Volumes[current_volume]->geometry.is_exit_volume==1) {
+                    done = 1; // Exit volumes allow the ray to escape the component
+                    ray_sucseeded = 1; // Allows the ray to
+                    /*
+                    // Moved to after while loop to collect code
+                    if (enable_tagging && stop_tagging_ray == 0)
+                        add_statistics_to_node(current_tagging_node,&ray_position, &ray_velocity, &p, &tagging_leaf_counter);
+                
+                    x += vx*1E-9; y += vy*1E-9; z += vz*1E-9; t += 1E-9;
+                    */
+            }
+            #ifdef Union_trace_verbal_setting
+              printf(" TO VOLUME %d \n",current_volume);
+            #endif
+        }
+    } else { // Here because a shortest time is not found
+        if (current_volume == 0) {
+            done = 1;
+            ray_sucseeded = 1;
+            
+        } else { // Check for errors (debugging phase)
+            if (error_msg == 0) {
+              component_error_msg++;
+              ray_sucseeded = 0;
+              done = 1; // stop the loop
+              printf("\n----------------------------------------------------------------------------------------------------\n");
+              printf("Union_master %s: Somehow reached a situation with no intersection time found, but still inside volume %d instead of 0\n",NAME_CURRENT_COMP,current_volume);
+              for (volume_index = 1; volume_index < number_of_volumes; volume_index++) {
+                if (Volumes[volume_index]->geometry.within_function(ray_position,&Volumes[volume_index]->geometry) == 1)
+                    printf("The ray is in volume       %d\n",volume_index);
+              }
+              
+              print_1d_int_list(mask_status_list,"mask status list");
+              for (iterate=0;iterate<number_of_volumes;iterate++)
+                 printf("%d:%d - ",iterate,scattered_flag[iterate]);
+              printf("\n");
+              printf("r = (%f,%f,%f) v = (%f,%f,%f) \n",x,y,z,vx,vy,vz);
+              
+              printf("Trace error number (%d/100) \n",component_error_msg);
+              //print_intersection_table(intersection_time_table);
+              //printf("Cluster is loosing the thread, debug for this behavior\n");
+              //printf("global_all_volume_logger_list.num_elements = %d\n",global_all_volume_logger_list.num_elements);
+              //printf("global_specific_volumes_logger_list.num_elements = %d\n",global_specific_volumes_logger_list.num_elements);
+              
+              //exit(1); // Temporary
+            }
+            error_msg++;
+            
+            //exit(1); // temp for debug
+            
+            if (component_error_msg > 100) {
+                printf("To many errors encountered, exiting. \n");
+                exit(1);
+            }
+        }
+    }
+    /*
+    */
+    if (limit == 0) {done = 1; ray_sucseeded = 0; printf("Reached limit on number of interactions, and discarded the neutron, was in volume %d\n",current_volume);ABSORB;}
+    #ifdef Union_trace_verbal_setting
+      printf("----------- END OF WHILE LOOP --------------------------------------\n");
+    #endif
+    //printf("This ray did %d iterations in the while loop\n",1000-limit);
+    
+  }
+  // Could move all add_statistics and similar to this point, but need to filter for failed rays
+  if (ray_sucseeded == 1) {
+    
+    /*
+    // Instead of keeping global and specific loggers apart, lets do them in one loop using the loggers_with_data_array
+    // Only needed for conditionals
+    // Loop over global loggers, may or may not have a conditional, only necessary to do anything here when they do.
+    for (log_index=0;log_index<global_all_volume_logger_list.num_elements;log_index++) {
+      if (global_all_volume_logger_list.elements[log_index].logger->has_conditional == 1) {
+        if (1 == conditional_return = global_all_volume_logger_list.elements[log_index].logger->conditional(scattered_flag,scattered_flag_VP,global_loggers.elements[log_index].conditional_data_union,k_final,current_volume,x,y,z)) {
+          global_all_volume_logger_list.elements[log_index].logger->function_pointers->temp_to_perm(global_loggers.elements[log_index].logger_data_union);
+          
+        }
+        if (global_all_volume_logger_list.elements[log_index].logger->conditional_extend_index != -1)
+          // The user can set a condtional_extend_index, so that the evaluation of this specific conditional can be taken easily from extend
+          conditional_extend_array.elements[global_all_volume_logger_list.elements[log_index].logger->conditional_extend_index] = conditional_return;
+          // Do not need to reset these to 0, as they will be manually set to 0 if not fulfilled
+      }
+    }
+    */
+    #ifdef Union_trace_verbal_setting
+      printf("----------- logger loop --------------------------------------\n");
+    #endif
+    // Loggers attatched to specific volumes need to be handled with care to avoid looping over all loggers for every ray
+    //for (log_index=0;log_index<loggers_with_data_array.used_elements;log_index++) {
+    // TEST
+    if (enable_conditionals == 1) {
+      for (log_index=loggers_with_data_array.used_elements-1;log_index>-1;log_index--) {
+        // Check all conditionals attatched to the current logger
+        this_logger = loggers_with_data_array.logger_pointers[log_index];
+        conditional_status = 1;
+        for (iterate=0;iterate<loggers_with_data_array.logger_pointers[log_index]->conditional_list.num_elements;iterate++) {
+          // Call this particular conditional. If it fails, report the status and break
+          //printf("checking conditional! \n");
+          #ifdef Union_trace_verbal_setting
+            printf("Checking conditional number %d for logger named %s \n",iterate,loggers_with_data_array.logger_pointers[log_index]->name);
+          #endif
+          if (0 == this_logger->conditional_list.conditional_functions[iterate](
+                         this_logger->conditional_list.p_data_unions[iterate],
+                         &ray_position,&ray_velocity,&p,&t,&current_volume,
+                         &number_of_scattering_events,scattered_flag,scattered_flag_VP)) {
+            conditional_status = 0;
+            break;
+          }
+        }
+        if (conditional_status == 1) {
+          // If a logger does not have a conditional, it will write directly to perm, and not even add it to the loggers_with_data_array, thus we know the temp_to_perm function needs to be called
+          // The input for the temp_to_perm function is a pointer to the logger_data_union for the appropriate logger
+          
+          if (loggers_with_data_array.logger_pointers[log_index]->function_pointers.select_t_to_p == 1) {
+            loggers_with_data_array.logger_pointers[log_index]->function_pointers.temp_to_perm(&loggers_with_data_array.logger_pointers[log_index]->data_union);
+          }
+          else if (loggers_with_data_array.logger_pointers[log_index]->function_pointers.select_t_to_p == 2) {
+            loggers_with_data_array.logger_pointers[log_index]->function_pointers.temp_to_perm_final_p(&loggers_with_data_array.logger_pointers[log_index]->data_union,p);
+          }
+        
+          // Luxury feature to be added later
+          if (loggers_with_data_array.logger_pointers[log_index]->logger_extend_index != -1) {
+            #ifdef Union_trace_verbal_setting
+              printf("Updating logger_conditional_extend_array[%d] to 1 (max length = %d)\n",loggers_with_data_array.logger_pointers[log_index]->logger_extend_index,max_conditional_extend_index);
+            #endif
+            // The user can set a condtional_extend_index, so that the evaluation of this specific conditional can be taken easily from extend
+            logger_conditional_extend_array[loggers_with_data_array.logger_pointers[log_index]->logger_extend_index] = 1;
+            // Are all reset to 0 for each new ray
+            #ifdef Union_trace_verbal_setting
+              printf("Updated extend index sucessfully\n");
+            #endif
+          
+            //printf("extend_array[%d] = 1 \n",loggers_with_data_array.logger_pointers[log_index]->logger_extend_index);
+          }
+        
+          // Need to remove the current element from logger_with_data as it has been cleared and written to disk
+          // The remaining elements is passed on to the next Union_master as it may fulfill the conditional after that master
+          if (global_master_list.elements[global_master_list.num_elements-1].component_index != INDEX_CURRENT_COMP) {
+            // Move current logger pointer in logger_with_data to end position
+            loggers_with_data_array.logger_pointers[log_index] = loggers_with_data_array.logger_pointers[loggers_with_data_array.used_elements-1];
+            // Decrease logger_with_data.used_elements with 1
+            loggers_with_data_array.used_elements--;
+          
+          }
+        
+        
+        } else {
+           // Conditional status was 0, clear temp data for this logger, but only if this is the last Union_master,
+           //  as the logger data can be written if one of the ray fulfills the conditional afer one of the
+           //  subsequent Union masters.
+           // The job of cleaning was moved to the start of trace on 15/5/2017
+           //if (global_master_list.elements[global_master_list.num_elements-1].component_index == INDEX_CURRENT_COMP)
+           //  loggers_with_data_array.logger_pointers[log_index]->function_pointers.clear_temp(&loggers_with_data_array.logger_pointers[log_index]->data_union);
+        }
+      }
+    }
+    
+    if (enable_tagging && stop_tagging_ray == 0) {
+      conditional_status = 1;
+      for (iterate=0;iterate<tagging_conditional_list->num_elements;iterate++) {
+        // Call this particular conditional. If it fails, report the status and break
+        // Since a conditional can work for a logger and master_tagging at the same time, it may be evaluated twice
+        //printf("checking conditional! \n");
+        #ifdef Union_trace_verbal_setting
+          printf("Checking tagging conditional number %d\n",iterate);
+        #endif
+        if (0 == tagging_conditional_list->conditional_functions[iterate](
+                         tagging_conditional_list->p_data_unions[iterate],
+                         &ray_position,&ray_velocity,&p,&t,&current_volume,
+                         &number_of_scattering_events,scattered_flag,scattered_flag_VP)) {
+          conditional_status = 0;
+          break;
+        }
+      }
+      if (conditional_status == 1) {
+        tagging_conditional_extend = 1;
+        #ifdef Union_trace_verbal_setting
+          printf("Before adding statistics to node: current_tagging_nodbe->intensity = %f\n",current_tagging_node->intensity);
+          printf("Before adding statistics to node: current_tagging_node->number_of_rays = %d\n",current_tagging_node->number_of_rays);
+        #endif
+        
+          add_statistics_to_node(current_tagging_node,&ray_position, &ray_velocity, &p, &tagging_leaf_counter);
+        
+        #ifdef Union_trace_verbal_setting
+          printf("After adding statistics to node: current_tagging_node->intensity = %f\n",current_tagging_node->intensity);
+          printf("After adding statistics to node: current_tagging_node->number_of_rays = %d\n",current_tagging_node->number_of_rays);
+        #endif
+      }
+    }
+    
+    // Move the rays a nano meter away from the surface it left, in case activation counter > 1, this will prevent the ray from starting on a volume boundery
+    x += vx*1E-9; y += vy*1E-9; z += vz*1E-9; t += 1E-9;
+        
+  } else {
+    ABSORB; // Absorb rays that didn't exit correctly for whatever reason
+    // Could error log here
+  }
+  
+  // TEST
+  // Stores nubmer of scattering events in global master list so that another master with inherit_number_of_scattering_events can continue
+  global_master_list.elements[this_global_master_index].stored_number_of_scattering_events = number_of_scattering_events;
+  
+  
+%}
+
+
+SAVE
+%{
+%}
+
+
+FINALLY
+%{
+// write out histories from tagging system if enabled
+if (enable_tagging) {
+    if (finally_verbal) printf("Writing tagging tree to disk \n");
+    if (finally_verbal) printf("Number of leafs = %d \n",tagging_leaf_counter);
+    // While writing the tagging tree to disk, all the leafs are deallocated
+    write_tagging_tree(&master_tagging_node_list, Volumes, tagging_leaf_counter, number_of_volumes);
+}
+if (master_tagging_node_list.num_elements > 0) free(master_tagging_node_list.elements);
+
+
+
+
+if (finally_verbal) printf("Freeing variables which are always allocated \n");
+// free allocated arrays specific to this master union component
+free(scattered_flag);
+free(my_trace);
+free(pre_allocated1);
+free(pre_allocated2);
+free(pre_allocated3);
+free(number_of_processes_array);
+
+if (finally_verbal) printf("Freeing intersection_time_table \n");
+for (iterate = 1;iterate < intersection_time_table.num_volumes;iterate++){
+    free(intersection_time_table.intersection_times[iterate]);
+}
+
+free(intersection_time_table.n_elements);
+free(intersection_time_table.calculated);
+free(intersection_time_table.intersection_times);
+
+if (free_tagging_conditioanl_list == 1) free(tagging_conditional_list);
+
+/*
+if (tagging_conditional_list->num_elements > 0) {
+  free(tagging_conditional_list.conditional_functions);
+  free(tagging_conditional_list.p_data_unions);
+}
+*/
+
+if (finally_verbal) printf("Freeing lists for individual volumes \n");
+for (volume_index=0;volume_index<number_of_volumes;volume_index++) {
+  
+  if (Volumes[volume_index]->geometry.intersect_check_list.num_elements > 0) free(Volumes[volume_index]->geometry.intersect_check_list.elements);
+  if (Volumes[volume_index]->geometry.destinations_list.num_elements > 0) free(Volumes[volume_index]->geometry.destinations_list.elements);
+  if (Volumes[volume_index]->geometry.reduced_destinations_list.num_elements > 0) free(Volumes[volume_index]->geometry.reduced_destinations_list.elements);
+  if (Volumes[volume_index]->geometry.children.num_elements > 0) free(Volumes[volume_index]->geometry.children.elements);
+  if (Volumes[volume_index]->geometry.direct_children.num_elements > 0) free(Volumes[volume_index]->geometry.direct_children.elements);
+  if (Volumes[volume_index]->geometry.masked_by_list.num_elements > 0) free(Volumes[volume_index]->geometry.masked_by_list.elements);
+  if (Volumes[volume_index]->geometry.masked_by_mask_index_list.num_elements > 0) free(Volumes[volume_index]->geometry.masked_by_mask_index_list.elements);
+  if (Volumes[volume_index]->geometry.mask_list.num_elements > 0) free(Volumes[volume_index]->geometry.mask_list.elements);
+  if (Volumes[volume_index]->geometry.mask_intersect_list.num_elements > 0) free(Volumes[volume_index]->geometry.mask_intersect_list.elements);
+  if (enable_tagging)
+    if (Volumes[volume_index]->geometry.next_volume_list.num_elements > 0) free(Volumes[volume_index]->geometry.next_volume_list.elements);
+  // Add dealocation of logging
+  
+
+  if (volume_index > 0) { // Volume 0 does not have physical properties allocated
+    free(scattered_flag_VP[volume_index]);
+    if (Volumes[volume_index]->geometry.process_rot_allocated == 1) {
+          free(Volumes[volume_index]->geometry.process_rot_matrix_array);
+          free(Volumes[volume_index]->geometry.transpose_process_rot_matrix_array);
+          Volumes[volume_index]->geometry.process_rot_allocated = 0;
+    }
+    if (on_int_list(Volume_copies_allocated,volume_index))
+      // This is a local copy of a volume, deallocate that local copy (all the allocated memory attachted to it was just deallocated, so this should not leave any leaks)
+      free(Volumes[volume_index]);
+    else
+      // Only free p_physics for vacuum volumes for the original at the end (there is a p_physics allocated for each vacuum volume)
+      if (Volumes[volume_index]->p_physics->is_vacuum == 1 ) free(Volumes[volume_index]->p_physics);
+  }
+  
+  if (Volumes[volume_index]->loggers.num_elements >0) {
+    for (iterate=0;iterate<Volumes[volume_index]->loggers.num_elements;iterate++) {
+      free(Volumes[volume_index]->loggers.p_logger_volume[iterate].p_logger_process);
+    }
+    free(Volumes[volume_index]->loggers.p_logger_volume);
+  }
+  
+}
+
+free(scattered_flag_VP);
+
+if (finally_verbal) printf("Freeing starting lists \n");
+if (starting_lists.allowed_starting_volume_logic_list.num_elements > 0) free(starting_lists.allowed_starting_volume_logic_list.elements);
+if (starting_lists.reduced_start_list.num_elements > 0) free(starting_lists.reduced_start_list.elements);
+if (starting_lists.start_logic_list.num_elements > 0) free(starting_lists.start_logic_list.elements);
+
+if (finally_verbal) printf("Freeing mask lists \n");
+if (mask_status_list.num_elements>0) free(mask_status_list.elements);
+if (current_mask_intersect_list_status.num_elements>0) free(current_mask_intersect_list_status.elements);
+if (mask_volume_index_list.num_elements>0) free(mask_volume_index_list.elements);
+
+if (finally_verbal) printf("Freeing component index list \n");
+if (geometry_component_index_list.num_elements>0) free(geometry_component_index_list.elements);
+
+
+if (finally_verbal) printf("Freeing Volumes \n");
+free(Volumes);
+
+// Free global allocated arrays if this is the last master union component in the instrument file
+
+if (global_master_list.elements[global_master_list.num_elements-1].component_index == INDEX_CURRENT_COMP) {
+    if (finally_verbal) printf("Freeing global arrays because this is the last Union master component\n");
+    
+    // Freeing lists allocated in Union_initialization
+    //#ifdef PROCESS_DETECTOR
+        if (finally_verbal) printf("Freeing global process list \n");
+        if (global_process_list.num_elements > 0) free(global_process_list.elements);
+    //#endif
+
+    //#ifdef MATERIAL_DETECTOR
+        if (finally_verbal) printf("Freeing global material list \n");
+        if (global_material_list.num_elements > 0) free(global_material_list.elements);
+    //#endif
+
+    //#ifdef ANY_GEOMETRY_DETECTOR_DECLARE
+        if (finally_verbal) printf("Freeing global geometry list \n");
+        if (global_geometry_list.num_elements > 0) free(global_geometry_list.elements);
+    //#endif
+    
+    //#ifdef MASTER_DETECTOR
+        if (finally_verbal) printf("Freeing global master list \n");
+        if (global_master_list.num_elements > 0) free(global_master_list.elements);
+    //#endif
+    
+    
+    //#ifdef UNION_LOGGER_DECLARE
+    if (finally_verbal) printf("Freeing global logger lists \n");
+    for (iterate=0;iterate<global_all_volume_logger_list.num_elements;iterate++) {
+      if (global_all_volume_logger_list.elements[iterate].logger->conditional_list.num_elements > 0) {
+        free(global_all_volume_logger_list.elements[iterate].logger->conditional_list.conditional_functions);
+        free(global_all_volume_logger_list.elements[iterate].logger->conditional_list.p_data_unions);
+      }
+    }
+    if (global_all_volume_logger_list.num_elements > 0) free(global_all_volume_logger_list.elements);
+    
+    
+    for (iterate=0;iterate<global_specific_volumes_logger_list.num_elements;iterate++) {
+      if (global_specific_volumes_logger_list.elements[iterate].logger->conditional_list.num_elements > 0) {
+        free(global_specific_volumes_logger_list.elements[iterate].logger->conditional_list.conditional_functions);
+        free(global_specific_volumes_logger_list.elements[iterate].logger->conditional_list.p_data_unions);
+      }
+    }
+    if (global_specific_volumes_logger_list.num_elements > 0) free(global_specific_volumes_logger_list.elements);
+    //#endif
+    
+    for (iterate=0;iterate<global_tagging_conditional_list.num_elements;iterate++) {
+      if (global_tagging_conditional_list.elements[iterate].conditional_list.num_elements > 0) {
+        free(global_tagging_conditional_list.elements[iterate].conditional_list.conditional_functions);
+        free(global_tagging_conditional_list.elements[iterate].conditional_list.p_data_unions);
+      }
+    }
+    if (global_tagging_conditional_list.num_elements>0) free(global_tagging_conditional_list.elements);
+    
+    /*
+    if (finally_verbal) printf("Freeing global tagging conditional list \n");
+    if (global_tagging_conditional_list.num_elements > 0) free(global_tagging_conditional_list.elements);
+    */
+}
+
+%}
+
+
+MCDISPLAY
+%{
+  // mcdisplay is handled in the component files for each geometry and called here. The line function is only available in this section, and not through functions,
+  //   so all the lines to be drawn for each volume are collected in a structure that is then drawn here.
+  magnify("xyz");
+  struct lines_to_draw lines_to_draw_master;
+  for (volume_index = 1; volume_index < number_of_volumes; volume_index++) {
+        if (Volumes[volume_index]->geometry.visualization_on == 1) {
+            lines_to_draw_master.number_of_lines = 0;
+            
+            Volumes[volume_index]->geometry.mcdisplay_function(&lines_to_draw_master,volume_index,Geometries,number_of_volumes);
+            
+            for (iterate = 0;iterate<lines_to_draw_master.number_of_lines;iterate++) {
+               if (lines_to_draw_master.lines[iterate].number_of_dashes == 1) {
+                 line(lines_to_draw_master.lines[iterate].point1.x,lines_to_draw_master.lines[iterate].point1.y,lines_to_draw_master.lines[iterate].point1.z,lines_to_draw_master.lines[iterate].point2.x,lines_to_draw_master.lines[iterate].point2.y,lines_to_draw_master.lines[iterate].point2.z);
+               }
+               else {
+                 dashed_line(lines_to_draw_master.lines[iterate].point1.x,lines_to_draw_master.lines[iterate].point1.y,lines_to_draw_master.lines[iterate].point1.z,lines_to_draw_master.lines[iterate].point2.x,lines_to_draw_master.lines[iterate].point2.y,lines_to_draw_master.lines[iterate].point2.z,lines_to_draw_master.lines[iterate].number_of_dashes);
+               }
+            }
+            
+            if (lines_to_draw_master.number_of_lines>0) free(lines_to_draw_master.lines);
+        }
+   }
+
+%}
+
+END
diff --git a/mcstasscript/tests/test_Instr_reader.py b/mcstasscript/tests/test_Instr_reader.py
index 8cc9bcee..c0feb350 100644
--- a/mcstasscript/tests/test_Instr_reader.py
+++ b/mcstasscript/tests/test_Instr_reader.py
@@ -1,4 +1,5 @@
 import os
+import sys
 import unittest
 
 from mcstasscript.interface import instr
@@ -6,11 +7,22 @@
 from mcstasscript.instr_reader import util
 
 
+# Disable print
+def blockPrint():
+    sys.stdout = open(os.devnull, 'w')
+
+# Restore print
+def enablePrint():
+    sys.stdout = sys.__stdout__
+
 def setup_standard(Instr):
-    filename = "Union_demonstration_test.instr"
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+    os.chdir(os.path.join(THIS_DIR, "dummy_instrument_folder"))
 
+    filename = "Union_demonstration_test.instr"
     InstrReader = control.InstrumentReader(filename)
     InstrReader.add_to_instr(Instr)
+    
     return InstrReader 
 
 class TestInstrReader(unittest.TestCase):
@@ -21,7 +33,10 @@ def test_read_instrument_name(self):
         """
 
         filename = "Union_demonstration_test.instr"
+        
+        blockPrint()
         Instr = instr.McStas_instr("test_instrument")
+        enablePrint()
 
         InstrReader = control.InstrumentReader(filename)
         InstrReader.add_to_instr(Instr)
@@ -30,9 +45,12 @@ def test_read_instrument_name(self):
 
     def test_read_input_parameter(self):
         
+        blockPrint()
         Instr = instr.McStas_instr("test_instrument")
+        enablePrint()
         InstrReader = setup_standard(Instr)
         
+        
         self.assertEqual(Instr.parameter_list[0].name, "stick_displacement")
         # space in type inserted for easier writing by McStas_Instr class
         self.assertEqual(Instr.parameter_list[0].type, "double ")
@@ -50,7 +68,9 @@ def test_read_input_parameter(self):
 
     def test_read_declare_parameter(self):
         
+        blockPrint()
         Instr = instr.McStas_instr("test_instrument")
+        enablePrint()
         InstrReader = setup_standard(Instr)
         
         self.assertEqual(Instr.declare_list[0].name, "sample_1_index")
@@ -78,7 +98,9 @@ def test_read_declare_parameter(self):
         
     def test_read_initialize_line(self):
         
+        blockPrint()
         Instr = instr.McStas_instr("test_instrument")
+        enablePrint()
         InstrReader = setup_standard(Instr)
         
         self.assertEqual(Instr.initialize_section,
@@ -89,7 +111,9 @@ def test_read_initialize_line(self):
     # Check a few components are read correctly
     def test_read_component_1(self):
         
+        blockPrint()
         Instr = instr.McStas_instr("test_instrument")
+        enablePrint()
         InstrReader = setup_standard(Instr)
         
         components = Instr.component_list
@@ -117,7 +141,9 @@ def test_read_component_1(self):
         
     def test_read_component_2(self):
         
+        blockPrint()
         Instr = instr.McStas_instr("test_instrument")
+        enablePrint()
         InstrReader = setup_standard(Instr)
         
         components = Instr.component_list
@@ -144,7 +170,9 @@ def test_read_component_2(self):
 
     def test_read_component_WHEN(self):
         
+        blockPrint()
         Instr = instr.McStas_instr("test_instrument")
+        enablePrint()
         InstrReader = setup_standard(Instr)
         
         components = Instr.component_list
@@ -173,7 +201,9 @@ def test_read_component_WHEN(self):
         
     def test_read_component_EXTEND(self):
         
+        blockPrint()
         Instr = instr.McStas_instr("test_instrument")
+        enablePrint()
         InstrReader = setup_standard(Instr)
         
         components = Instr.component_list
@@ -212,7 +242,9 @@ def test_read_component_EXTEND(self):
         
     def test_read_component_GROUP(self):
         
+        blockPrint()
         Instr = instr.McStas_instr("test_instrument")
+        enablePrint()
         InstrReader = setup_standard(Instr)
         
         components = Instr.component_list
@@ -232,7 +264,9 @@ def test_read_component_GROUP(self):
         
     def test_read_component_SPLIT(self):
         
+        blockPrint()
         Instr = instr.McStas_instr("test_instrument")
+        enablePrint()
         InstrReader = setup_standard(Instr)
         
         components = Instr.component_list
@@ -250,7 +284,9 @@ def test_read_component_SPLIT(self):
     
     def test_read_component_JUMP(self):
         
+        blockPrint()
         Instr = instr.McStas_instr("test_instrument")
+        enablePrint()
         InstrReader = setup_standard(Instr)
         
         components = Instr.component_list
@@ -275,7 +311,9 @@ def test_comma_split(self):
         Check if the instrument name is read correctly
         """
 
+        blockPrint()
         Instr = instr.McStas_instr("test_instrument")
+        enablePrint()
         InstrReader = setup_standard(Instr)
         
         test_string = "A,B,C,D(a,b),E"
@@ -293,7 +331,9 @@ def test_comma_split_limited(self):
         Check if the instrument name is read correctly
         """
 
+        blockPrint()
         Instr = instr.McStas_instr("test_instrument")
+        enablePrint()
         InstrReader = setup_standard(Instr)
         
         test_string = "A,B,C,D(a,b),E"
@@ -308,8 +348,10 @@ def test_parenthesis_split(self):
         """
         Check if the instrument name is read correctly
         """
-
+        
+        blockPrint()
         Instr = instr.McStas_instr("test_instrument")
+        enablePrint()
         InstrReader = setup_standard(Instr)
         
         test_string = "A)B)C)D(a,b))E"
@@ -327,7 +369,9 @@ def test_comma_split_brack(self):
         Check if the instrument name is read correctly
         """
 
+        blockPrint()
         Instr = instr.McStas_instr("test_instrument")
+        enablePrint()
         InstrReader = setup_standard(Instr)
         
         test_string = "A,B{C,D(a,b)},E"
@@ -340,4 +384,4 @@ def test_comma_split_brack(self):
 
     
 if __name__ == '__main__':
-    unittest.main()
\ No newline at end of file
+    unittest.main()

From c6350ac0176c42316d74fd7615eb3c80e86e8ec1 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 31 Mar 2020 12:27:49 +0200
Subject: [PATCH 071/403] test of travis unittest

---
 .travis.yml                                   |   3 +-
 .../PSD_monitor_4PI.comp                      | 128 ++++++++++++++++++
 setup.py                                      |   2 +-
 3 files changed, 130 insertions(+), 3 deletions(-)
 create mode 100644 mcstasscript/tests/dummy_instrument_folder/PSD_monitor_4PI.comp

diff --git a/.travis.yml b/.travis.yml
index 9b502cf1..524412b9 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -5,7 +5,6 @@ python:
 
 install:
   - pip install -r requirements.txt
-  - pip install mcstasscript
 
 script:
- - cd mcstasscript/tests && python3 -m unittest 
+ - python3 -m unittest 
diff --git a/mcstasscript/tests/dummy_instrument_folder/PSD_monitor_4PI.comp b/mcstasscript/tests/dummy_instrument_folder/PSD_monitor_4PI.comp
new file mode 100644
index 00000000..e4ed8756
--- /dev/null
+++ b/mcstasscript/tests/dummy_instrument_folder/PSD_monitor_4PI.comp
@@ -0,0 +1,128 @@
+/*******************************************************************************
+*
+* McStas, neutron ray-tracing package
+*         Copyright (C) 1997-2006, All rights reserved
+*         Risoe National Laboratory, Roskilde, Denmark
+*         Institut Laue Langevin, Grenoble, France
+*
+* Component: PSD_monitor_4PI
+*
+* %I
+* Written by: Kim Lefmann and Kristian Nielsen
+* Date: April 17, 1998
+* Origin: Risoe
+*
+* Spherical position-sensitive detector.
+*
+* %D
+* An (n times m) pixel spherical PSD monitor using a cylindrical projection.
+* Mostly for test and debugging purposes.
+*
+* Example: PSD_monitor_4PI(radius=0.1, nx=90, ny=90, filename="Output.psd")
+*
+* %P
+* INPUT PARAMETERS:
+*
+* radius: [m]           Radius of detector 
+* nx: [1]               Number of pixel columns 
+* ny: [1]               Number of pixel rows 
+* filename: [string]    Name of file in which to store the detector image 
+* restore_neutron: [1]  If set, the monitor does not influence the neutron state 
+* nowritefile: [1]      If set, monitor will skip writing to disk
+*
+* OUTPUT PARAMETERS:
+*
+* PSD_N: []             Array of neutron counts
+* PSD_p: []             Array of neutron weight counts
+* PSD_p2: []            Array of second moments
+*
+* %L
+* <A HREF="http://neutron.risoe.dk/mcstas/components/tests/powder/">Test
+* results</A> (not up-to-date).
+*
+* %E
+*******************************************************************************/
+
+
+DEFINE COMPONENT PSD_monitor_4PI
+DEFINITION PARAMETERS (nx=90, ny=90)
+SETTING PARAMETERS (string filename=0, radius=1, restore_neutron=0, int nowritefile=0)
+OUTPUT PARAMETERS (PSD_N, PSD_p, PSD_p2)
+/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
+
+DECLARE
+%{
+double PSD_N[nx][ny];
+double PSD_p[nx][ny];
+double PSD_p2[nx][ny];
+%}
+
+INITIALIZE
+%{
+int i,j;
+
+for (i=0; i<nx; i++)
+    for (j=0; j<ny; j++)
+    {
+      PSD_N[i][j] = 0;
+      PSD_p[i][j] = 0;
+      PSD_p2[i][j] = 0;
+    }
+%}
+
+TRACE
+%{
+  double t0, t1, phi, theta;
+  int i,j;
+
+  if(sphere_intersect(&t0, &t1, x, y, z, vx, vy, vz, radius) && t1 > 0)
+  {
+    if(t0 < 0)
+      t0 = t1;
+    /* t0 is now time of intersection with the sphere. */
+    PROP_DT(t0);
+    phi = atan2(x,z);
+    i = floor(nx*(phi/(2*PI)+0.5));
+    if(i >= nx)
+      i = nx-1;                      /* Special case for phi = PI. */
+    else if(i < 0)
+      i = 0;
+    theta=asin(y/radius);
+    j = floor(ny*(theta+PI/2)/PI+0.5);
+    if(j >= ny)
+      j = ny-1;                      /* Special case for y = radius. */
+    else if(j < 0)
+      j = 0;
+    PSD_N[i][j]++;
+    PSD_p[i][j] += p;
+    PSD_p2[i][j] += p*p;
+    SCATTER;
+  }
+  if (restore_neutron) {
+    RESTORE_NEUTRON(INDEX_CURRENT_COMP, x, y, z, vx, vy, vz, t, sx, sy, sz, p);
+  }
+%}
+
+SAVE
+%{
+  if (!nowritefile) {
+    DETECTOR_OUT_2D(
+    "4PI PSD monitor",
+    "Longitude [deg]",
+    "Lattitude [deg]",
+    -180, 180, -90, 90,
+    nx, ny,
+    &PSD_N[0][0],&PSD_p[0][0],&PSD_p2[0][0],
+    filename);
+  }
+%}
+
+MCDISPLAY
+%{
+  
+  circle("xy",0,0,0,radius);
+  circle("xz",0,0,0,radius);
+  circle("yz",0,0,0,radius);
+%}
+
+END
diff --git a/setup.py b/setup.py
index caa845c1..92ba8d7f 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
      name='McStasScript',
-     version='0.0.14',
+     version='0.0.15',
      author="Mads Bertelsen",
      author_email="Mads.Bertelsen@ess.eu",
      description="A python scripting interface for McStas",

From 20a6deed45bbe32d70bb39ef2c634902b4f75dc4 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 31 Mar 2020 12:36:08 +0200
Subject: [PATCH 072/403] Avoid running integration tests on travis as McStas
 is not installed.

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 524412b9..c315b201 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -7,4 +7,4 @@ install:
   - pip install -r requirements.txt
 
 script:
- - python3 -m unittest 
+ - python3 -m unittest discover mcstasscript/tests

From e647556dc123278949dd9e1c6453baf83bc9e9dc Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 31 Mar 2020 12:38:48 +0200
Subject: [PATCH 073/403] Lacked Monitor_nD in dummy_instrument_folder

---
 .../dummy_instrument_folder/Monitor_nD.comp   | 501 ++++++++++++++++++
 1 file changed, 501 insertions(+)
 create mode 100644 mcstasscript/tests/dummy_instrument_folder/Monitor_nD.comp

diff --git a/mcstasscript/tests/dummy_instrument_folder/Monitor_nD.comp b/mcstasscript/tests/dummy_instrument_folder/Monitor_nD.comp
new file mode 100644
index 00000000..3d3d50ee
--- /dev/null
+++ b/mcstasscript/tests/dummy_instrument_folder/Monitor_nD.comp
@@ -0,0 +1,501 @@
+/*******************************************************************************
+*
+* McStas, neutron ray-tracing package
+*         Copyright 1997-2002, All rights reserved
+*         Risoe National Laboratory, Roskilde, Denmark
+*         Institut Laue Langevin, Grenoble, France
+*
+* Component: Monitor_nD
+*
+* %Identification
+* Written by: <a href="mailto:farhi@ill.fr">Emmanuel Farhi</a>
+* Date: 14th Feb 2000.
+* Origin: <a href="http://www.ill.fr">ILL</a>
+* Release: McStas 1.6
+* Version: $Revision$
+* Modified by: EF, 29th Feb 2000 : added more options, monitor shape, theta, phi
+* Modified by: EF, 01st Feb 2001 : PreMonitor for correlation studies (0.13.6)
+* Modified by: EF, 5th  Apr 2001 : use global functions (0.14) compile faster
+* Modified by: EF, 23th Jul 2001 : log of signal, init arrays to 0, box (0.15)
+* Modified by: EF, 04th Sep 2001 : log/abs of variables (0.16)
+* Modified by: EF, 24th Oct 2001 : capture flux  [p*lambda/1.7985] (0.16.3)
+* Modified by: EF, 27th Aug 2002 : monitor a variable in place of I (0.16.5)
+* Modified by: EF, 25th Oct 2002 : banana, and auto for each variable (0.16.5)
+*
+* This component is a general Monitor that can output 0/1/2D signals
+* (Intensity or signal vs. [something] and vs. [something] ...)
+*
+* %Description
+* This component is a general Monitor that can output 0/1/2D signals
+* It can produce many 1D signals (one for any variable specified in
+* option list), or a single 2D output (two variables correlation).
+* Also, an additional 'list' of neutron events can be produced.
+* By default, monitor is square (in x/y plane). A disk shape is also possible
+* The 'cylinder' and 'banana' option will change that for a banana shape
+* The 'sphere' option simulates spherical detector. The 'box' is a box.
+* The cylinder, sphere and banana should be centered on the scattering point.
+* The monitored flux may be per monitor unit area, and weighted by
+* a lambda/lambda(2200m/s) factor to obtain standard integrated capture flux.
+* In normal configuration, the Monitor_nD measures the current parameters
+* of the neutron that is beeing detected. But a PreMonitor_nD component can
+* be used in order to study correlations between a neutron being detected in
+* a Monitor_nD place, and given parameters that are monitored elsewhere
+* (at <b>PreMonitor_nD</b>).
+* The monitor can also act as a 3He gas detector, taking into account the
+* detection efficiency.
+*
+* The 'bins' and 'limits' modifiers are to be used after each variable,
+* and 'auto','log' and 'abs' come before it. (eg: auto abs log hdiv bins=10
+* limits=[-5 5]) When placed after all variables,  these two latter modifiers
+* apply to the signal (e.g. intensity). Unknown keywords are ignored.
+*
+* In the case of multiple components at the same position, the 'parallel'
+* keyword must be used in each instance instead of defining a GROUP.
+*
+* <b>Possible options are</b>
+* Variables to record:
+*     kx ky kz k wavevector [Angs-1] Wavevector on x,y,z and norm
+*     vx vy vz v            [m/s]    Velocity on x,y,z and norm
+*     x y z radius          [m]      Distance, Position and norm
+*     xy, yz, xz            [m]      Radial position in xy, yz and xz plane
+*     kxy kyz kxz           [Angs-1] Radial wavevector in xy, yz and xz plane
+*     vxy vyz vxz           [m/s]    Radial velocity in xy, yz and xz plane
+*     t time                [s]      Time of Flight
+*     energy omega          [meV]    energy of neutron
+*     lambda wavelength     [Angs]   wavelength of neutron
+*     sx sy sz              [1]      Spin
+*     vdiv ydiv dy          [deg]    vertical divergence (y)
+*     hdiv divergence xdiv  [deg]    horizontal divergence (x)
+*     angle                 [deg]    divergence from <z> direction
+*     theta longitude       [deg]    longitude (x/z) for sphere and cylinder
+*     phi   lattitude       [deg]    lattitude (y/z) for sphere and cylinder
+*
+*     user user1            will monitor the [Mon_Name]_Vars.UserVariable{1|2|3}
+*     user2 user3           to be assigned in an other component (see below)
+*
+*     p intensity flux      [n/s  or  n/cm^2/s]
+*     ncounts n neutron     [1]      neutron ID, i.e current event index
+*     pixel id              [1]      pixelID in histogram made of preceeding vars, e.g. 'theta y'. To set an offset PixelID use the 'min=value' keyword. Sets event mode.
+*
+* <b>Other options keywords are:</b>
+*     abs                       Will monitor the abs of the following variable or of the signal (if used after all variables)
+*     auto                      Automatically set detector limits for one/all
+*     all  {limits|bins|auto}   To set all limits or bins values or auto mode
+*     binary {float|double}     with 'source' option, saves in compact files
+*     bins=[bins=20]            Number of bins in the detector along dimension
+*     borders                   To also count off-limits neutrons (X < min or X > max)
+*     capture                   weight by lambda/lambda(2200m/s) capture flux
+*     file=string               Detector image file name. default is component name, plus date and variable extension.
+*     incoming                  Monitor incoming beam in non flat det
+*     limits=[min max]          Lower/Upper limits for axes (see up for the variable unit)
+*     list=[counts=1000] or all For a long file of neutron characteristics with [counts] or all events
+*     log                       Will monitor the log of the following variable or of the signal (if used after all variables)
+*     min=[min_value]           Same as limits, but only sets the min or max
+*     max=[max_value]
+*     multiple                  Create multiple independant 1D monitors files
+*     no or not                 Revert next option
+*     outgoing                  Monitor outgoing beam (default)
+*     parallel                  Use this option when the next component is at the same position (parallel components)
+*     per cm2                   Intensity will be per cm^2 (detector area). Displays beam section.
+*     per steradian             Displays beam solid angle in steradian
+*     premonitor                Will monitor neutron parameters stored previously with <b>PreMonitor_nD</b>.
+*     signal=[var]              Will monitor [var] instead of usual intensity
+*     slit or absorb            Absorb neutrons that are out detector
+*     source                    The monitor will save neutron states
+*     unactivate                To unactivate detector (0D detector)
+*     verbose                   To display additional informations
+*     3He_pressure=[3 in bars]  The 3He gas pressure in detector. 3He_pressure=0 is perfect detector (default)
+*
+* Detector shape options (specified as xwidth,yheight,zdepth or x/y/z/min/max)
+*     box                       Box of size xwidth, yheight, zdepth.
+*     cylinder                  To get a cylindrical monitor (diameter is xwidth or set radius, height is yheight).
+*     banana                    Same as cylinder, without top/bottom, on restricted angular area; use theta variable with limits to define arc. (diameter is xwidth or set radius, height is yheight).
+*     disk                      Disk flat xy monitor. diameter is xwidth.
+*     sphere                    To get a spherical monitor (e.g. a 4PI) (diameter is xwidth or set radius).
+*     square                    Square flat xy monitor (xwidth, yheight).
+*     previous                  The monitor uses PREVIOUS component as detector surface. Or use 'geometry' parameter to specify any PLY/OFF geometry file.
+*
+* <b>EXAMPLES:</b>
+* MyMon = Monitor_nD(
+*   xwidth = 0.1, yheight = 0.1, zdepth = 0,
+*   options = "intensity per cm2 angle,limits=[-5 5] bins=10,with
+*              borders, file = mon1");
+*                  will monitor neutron angle from [z] axis, between -5
+*                  and 5 degrees, in 10 bins, into "mon1.A" output 1D file
+*   options = "sphere theta phi outgoing"  for a sphere PSD detector (out
+*                  beam)  and saves into file "MyMon_[Date_ID].th_ph"
+*   options = "banana, theta limits=[10,130], bins=120, y" a theta/height
+         banana detector
+*   options = "angle radius all auto"   is a 2D monitor with automatic limits
+*   options = "list=1000 kx ky kz energy" records 1000 neutron event in a file
+*   options = "multiple kx ky kz, auto abs log t, and list all neutrons"
+*        makes 4 output 1D files and produces a complete list for all neutrons
+*        and monitor log(abs(tof)) within automatic limits (for t)
+*   options = "theta y, sphere, pixel min=100"
+*        a 4pi detector which outputs an event list with pixelID from the actual
+*        detector surface, starting from index 100.
+*
+* To dynamically define a number of bins, or limits:
+*   Use in DECLARE:    char op[256];
+*   Use in INITIALIZE: sprintf(op, "lambda limits=[%g %g], bins=%i", lmin, lmax, lbin);
+*   Use in TRACE:      Monitor_nD(... options=op ...)
+*
+* <b>How to monitor any instrument/component variable into a Monitor_nD</b>
+* Suppose you want to monitor a variable 'age' which you assign somwhere in
+* the instrument:
+*      COMPONENT MyMonitor = Monitor_nD(
+*       xwidth = 0.1, yheight = 0.1,
+*       user1=age, username1="Age of the Captain [years]",
+*       options="user1, auto")
+*      AT ...
+*
+* See also the example in <a href="PreMonitor_nD.html">PreMonitor_nD</a> to
+* monitor neutron parameters cross-correlations.
+*
+* %BUGS
+* The 'auto' option for guessing optimal variable bounds should NOT be used with MPI
+* as each process may use different limits.
+*
+* %Parameters
+* INPUT PARAMETERS:
+*
+* xwidth:  [m]  Width of detector.
+* yheight: [m]  Height of detector.
+* zdepth:  [m]  Thickness of detector (z).
+* radius:  [m]  Radius of sphere/banana shape monitor
+* options: [str]  String that specifies the configuration of the monitor. The general syntax is "[x] options..." (see <b>Descr.</b>).
+*
+* Optional input parameters (override xwidth yheight zdepth):
+* xmin:   [m]    Lower x bound of opening
+* xmax:   [m]    Upper x bound of opening
+* ymin:   [m]    Lower y bound of opening
+* ymax:   [m]    Upper y bound of opening
+* zmin:   [m]    Lower z bound of opening
+* zmax:   [m]    Upper z bound of opening
+* filename: [str] Output file name (overrides file=XX option).
+* bins:   [1]    Number of bins to force for all variables. Use 'bins' keyword in 'options' for heterogeneous bins
+* min:    [u]    Minimum range value to force for all variables. Use 'min' or 'limits' keyword in 'options' for other limits
+* max:    [u]    Maximum range value to force for all variables. Use 'max' or 'limits' keyword in 'options' for other limits
+* user1: [variable] Variable assigned to User1
+* user2: [variable] Variable assigned to User2
+* user3: [variable] Variable assigned to User3
+* username1:  [str] Name assigned to User1
+* username2:  [str] Name assigned to User2
+* username3:  [str] Name assigned to User3
+* restore_neutron: [0|1] If set, the monitor does not influence the neutron state. Equivalent to setting the 'parallel' option.
+* geometry:   [str] Name of an OFF file to specify a complex geometry detector
+* nowritefile:  [1] If set, monitor will skip writing to disk
+*
+* OUTPUT PARAMETERS:
+*
+* DEFS: structure containing Monitor_nD Defines [struct]
+* Vars: structure containing Monitor_nD variables [struct]
+*
+* %Link
+* <a href="PreMonitor_nD.html">PreMonitor_nD</a>
+*
+* %End
+******************************************************************************/
+DEFINE COMPONENT Monitor_nD
+DEFINITION PARAMETERS (user1=FLT_MAX, user2=FLT_MAX, user3=FLT_MAX)
+SETTING PARAMETERS (xwidth=0, yheight=0, zdepth=0,
+  xmin=0, xmax=0, ymin=0, ymax=0, zmin=0, zmax=0,
+  bins=0, min=-1e40, max=1e40, restore_neutron=0, radius=0,
+  string options="NULL", string filename="NULL",string geometry="NULL",
+  string username1="NULL", string username2="NULL", string username3="NULL",
+  int nowritefile=0
+  )
+/* these are protected C variables */
+OUTPUT PARAMETERS (DEFS, Vars, detector,offdata)
+/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
+
+SHARE
+%{
+  %include "monitor_nd-lib"
+  %include "read_table-lib"
+  %include "interoff-lib"
+%}
+
+DECLARE
+%{
+  MonitornD_Defines_type DEFS;
+  MonitornD_Variables_type Vars;
+  MCDETECTOR detector;
+  off_struct offdata;
+%}
+
+INITIALIZE
+%{
+  char tmp[CHAR_BUF_LENGTH];
+  strcpy(Vars.compcurname, NAME_CURRENT_COMP);
+  if (options != NULL)
+    strncpy(Vars.option, options, CHAR_BUF_LENGTH);
+  else {
+    strcpy(Vars.option, "x y");
+    printf("Monitor_nD: %s has no option specified. Setting to PSD ('x y') monitor.\n", NAME_CURRENT_COMP);
+  }
+  Vars.compcurpos = POS_A_CURRENT_COMP;
+
+  if (strstr(Vars.option, "source"))
+    strcat(Vars.option, " list, x y z vx vy vz t sx sy sz ");
+
+  if (bins) { sprintf(tmp, " all bins=%ld ", (long)bins); strcat(Vars.option, tmp); }
+  if (min > -FLT_MAX && max < FLT_MAX) { sprintf(tmp, " all limits=[%g %g]", min, max); strcat(Vars.option, tmp); }
+  else if (min > -FLT_MAX) { sprintf(tmp, " all min=%g", min); strcat(Vars.option, tmp); }
+  else if (max <  FLT_MAX) { sprintf(tmp, " all max=%g", max); strcat(Vars.option, tmp); }
+
+  strncpy(Vars.UserName1,
+    username1 && strlen(username1) && strcmp(username1, "0") && strcmp(username1, "NULL") ?
+    username1 : "", 128);
+  strncpy(Vars.UserName2,
+    username2 && strlen(username2) && strcmp(username2, "0") && strcmp(username2, "NULL") ?
+    username2 : "", 128);
+  strncpy(Vars.UserName3,
+    username3 && strlen(username3) && strcmp(username3, "0") && strcmp(username3, "NULL") ?
+    username3 : "", 128);
+  if (radius) {
+    xwidth = zdepth = 2*radius;
+    if (yheight && !strstr(Vars.option, "cylinder") && !strstr(Vars.option, "banana") && !strstr(Vars.option, "sphere"))
+      strcat(Vars.option, " banana");
+    else if (!yheight && !strstr(Vars.option ,"sphere")) {
+      strcat(Vars.option, " sphere");
+      yheight=2*radius;
+    }
+  }
+  int offflag=0;
+  if (geometry && strlen(geometry) && strcmp(geometry,"0") && strcmp(geometry, "NULL"))
+    if (!off_init(  geometry, xwidth, yheight, zdepth, 1, &offdata )) {
+      printf("Monitor_nD: %s could not initiate the OFF geometry %s. \n"
+             "            Defaulting to normal Monitor dimensions.\n",
+             NAME_CURRENT_COMP, geometry);
+      strcpy(geometry, "");
+    } else {
+      offflag=1;
+    }
+
+  if (!radius && !xwidth && !yheight && !zdepth && !xmin && !xmax && !ymin && !ymax &&
+    !strstr(Vars.option, "previous") && (!geometry || !strlen(geometry)))
+    exit(printf("Monitor_nD: %s has no dimension specified. Aborting (radius, xwidth, yheight, zdepth, previous, geometry).\n", NAME_CURRENT_COMP));
+
+  Monitor_nD_Init(&DEFS, &Vars, xwidth, yheight, zdepth, xmin,xmax,ymin,ymax,zmin,zmax,offflag);
+
+  if (Vars.Flag_OFF) {
+    offdata.mantidflag=Vars.Flag_mantid;
+    offdata.mantidoffset=Vars.Coord_Min[Vars.Coord_Number-1];
+  }
+
+
+  if (filename && strlen(filename) && strcmp(filename,"NULL") && strcmp(filename,"0"))
+    strncpy(Vars.Mon_File, filename, 128);
+
+  /* check if user given filename with ext will be used more than once */
+  if ( ((Vars.Flag_Multiple && Vars.Coord_Number > 1) || Vars.Flag_List) && strchr(Vars.Mon_File,'.') )
+  { char *XY; XY = strrchr(Vars.Mon_File,'.'); *XY='_'; }
+
+  if (restore_neutron) Vars.Flag_parallel=1;
+  detector.m = 0;
+
+#ifdef USE_MPI
+MPI_MASTER(
+  if (strstr(Vars.option, "auto") && mpi_node_count > 1)
+    printf("Monitor_nD: %s is using automatic limits option 'auto' together with MPI.\n"
+           "WARNING     this may create incorrect distributions (but integrated flux will be right).\n", NAME_CURRENT_COMP);
+);
+#endif
+%}
+
+TRACE
+%{
+  double  XY=0;
+  double  t0 = 0;
+  double  t1 = 0;
+  double  pp;
+  int     intersect   = 0;
+  char    Flag_Restore = 0;
+
+  if (user1 != FLT_MAX) Vars.UserVariable1 = user1;
+  if (user2 != FLT_MAX) Vars.UserVariable2 = user2;
+  if (user3 != FLT_MAX) Vars.UserVariable3 = user3;
+
+  /* this is done automatically
+    STORE_NEUTRON(INDEX_CURRENT_COMP, x, y, z, vx, vy, vz, t, sx, sy, sz, p);
+  */
+
+  if (geometry && strlen(geometry) && strcmp(geometry,"0") && strcmp(geometry, "NULL"))
+  {
+    /* determine intersections with object */
+    intersect = off_intersect_all(&t0, &t1, NULL, NULL,
+       x,y,z, vx, vy, vz, &offdata );
+    if (Vars.Flag_mantid) {
+      if(intersect) {
+        Vars.OFF_polyidx=(offdata.intersects[offdata.nextintersect]).index;
+      } else {
+        Vars.OFF_polyidx=-1;
+      }
+    }
+  }
+  else if ( (abs(Vars.Flag_Shape) == DEFS.SHAPE_SQUARE)
+            || (abs(Vars.Flag_Shape) == DEFS.SHAPE_DISK) ) /* square xy or disk xy */
+  {
+    // propagate to xy plane and find intersection
+    // make sure the event is recoverable afterwards
+    t0 = t;
+    ALLOW_BACKPROP;
+    PROP_Z0;
+    if ( (t>=t0) && (z==0.0) ) // forward propagation to xy plane was successful
+    {
+      if (abs(Vars.Flag_Shape) == DEFS.SHAPE_SQUARE)
+      {
+        // square xy
+        intersect = (x>=Vars.mxmin && x<=Vars.mxmax && y>=Vars.mymin && y<=Vars.mymax);
+      }
+      else
+      {
+        // disk xy
+        intersect = (SQR(x) + SQR(y)) <= SQR(Vars.Sphere_Radius);
+      }
+    }
+    else
+    {
+      intersect=0;
+    }
+  }
+  else if (abs(Vars.Flag_Shape) == DEFS.SHAPE_SPHERE) /* sphere */
+  {
+    intersect = sphere_intersect(&t0, &t1, x, y, z, vx, vy, vz, Vars.Sphere_Radius);
+  /*      intersect = (intersect && t0 > 0); */
+  }
+  else if ((abs(Vars.Flag_Shape) == DEFS.SHAPE_CYLIND) || (abs(Vars.Flag_Shape) == DEFS.SHAPE_BANANA)) /* cylinder */
+  {
+    intersect = cylinder_intersect(&t0, &t1, x, y, z, vx, vy, vz, Vars.Sphere_Radius, Vars.Cylinder_Height);
+  }
+  else if (abs(Vars.Flag_Shape) == DEFS.SHAPE_BOX) /* box */
+  {
+    intersect = box_intersect(&t0, &t1, x, y, z, vx, vy, vz,
+                              fabs(Vars.mxmax-Vars.mxmin), fabs(Vars.mymax-Vars.mymin), fabs(Vars.mzmax-Vars.mzmin));
+  }
+  else if (abs(Vars.Flag_Shape) == DEFS.SHAPE_PREVIOUS) /* previous comp */
+  { intersect = 1; }
+
+  if (intersect)
+  {
+    if ((abs(Vars.Flag_Shape) == DEFS.SHAPE_SPHERE) || (abs(Vars.Flag_Shape) == DEFS.SHAPE_CYLIND)
+     || (abs(Vars.Flag_Shape) == DEFS.SHAPE_BOX) || (abs(Vars.Flag_Shape) == DEFS.SHAPE_BANANA)
+     || (geometry && strlen(geometry) && strcmp(geometry,"0") && strcmp(geometry, "NULL")) )
+    {
+      /* check if we have to remove the top/bottom with BANANA shape */
+      if ((abs(Vars.Flag_Shape) == DEFS.SHAPE_BANANA) && (intersect != 1)) {
+        double y0,y1;
+        /* propagate to intersection point as temporary variable to check top/bottom */
+        y0 = y+t0*vy;
+        y1 = y+t1*vy;
+        if (fabs(y0) >= Vars.Cylinder_Height/2*0.99) t0 = t1;
+        if (fabs(y1) >= Vars.Cylinder_Height/2*0.99) t1 = t0;
+      }
+      if (t0 < 0 && t1 > 0)
+        t0 = t;  /* neutron was already inside ! */
+      if (t1 < 0 && t0 > 0) /* neutron exit before entering !! */
+        t1 = t;
+      /* t0 is now time of incoming intersection with the detection area */
+      if ((Vars.Flag_Shape < 0) && (t1 > 0))
+        PROP_DT(t1); /* t1 outgoing beam */
+      else
+        PROP_DT(t0); /* t0 incoming beam */
+      /* Final test if we are on lid / bottom of banana/sphere */
+      if (abs(Vars.Flag_Shape) == DEFS.SHAPE_BANANA || abs(Vars.Flag_Shape) == DEFS.SHAPE_SPHERE) {
+        if (fabs(y) >= Vars.Cylinder_Height/2*0.99) {
+          intersect=0;
+          Flag_Restore=1;
+        }
+      }
+    }
+  }
+
+  if (intersect)
+  {
+    /* Now get the data to monitor: current or keep from PreMonitor */
+    if (Vars.Flag_UsePreMonitor != 1)
+    {
+      Vars.cp  = p;
+      Vars.cx  = x;
+      Vars.cvx = vx;
+      Vars.csx = sx;
+      Vars.cy  = y;
+      Vars.cvy = vy;
+      Vars.csy = sy;
+      Vars.cz  = z;
+      Vars.cvz = vz;
+      Vars.csz = sz;
+      Vars.ct  = t;
+    }
+
+    if ((Vars.He3_pressure > 0) && (t1 != t0) && ((abs(Vars.Flag_Shape) == DEFS.SHAPE_SPHERE) || (abs(Vars.Flag_Shape) == DEFS.SHAPE_CYLIND) || (abs(Vars.Flag_Shape) == DEFS.SHAPE_BOX)))
+    {
+      XY = exp(-7.417*Vars.He3_pressure*fabs(t1-t0)*2*PI*K2V);
+      /* will monitor the absorbed part */
+      Vars.cp *= 1-XY;
+      /* and modify the neutron weight after monitor, only remains 1-p_detect */
+      p *= XY;
+    }
+
+    if (Vars.Flag_capture)
+    {
+      XY = sqrt(Vars.cvx*Vars.cvx+Vars.cvy*Vars.cvy+Vars.cvz*Vars.cvz);
+      XY *= V2K;
+      if (XY != 0) XY = 2*PI/XY; /* lambda. lambda(2200 m/2) = 1.7985 Angs  */
+      Vars.cp *= XY/1.7985;
+    }
+
+    pp = Monitor_nD_Trace(&DEFS, &Vars);
+    if (pp==0.0)
+    { ABSORB;
+    }
+    else if(pp==1)
+    {
+      SCATTER;
+    }
+
+    if (Vars.Flag_parallel) /* back to neutron state before detection */
+      Flag_Restore = 1;
+  } /* end if intersection */
+  else {
+    if (Vars.Flag_Absorb && !Vars.Flag_parallel)
+    {
+      // restore neutron ray before absorbing for correct mcdisplay
+      RESTORE_NEUTRON(INDEX_CURRENT_COMP, x, y, z, vx, vy, vz, t, sx, sy, sz, p);
+      ABSORB;
+    }
+    else Flag_Restore = 1;  /* no intersection, back to previous state */
+  }
+
+  if (Flag_Restore)
+  {
+    RESTORE_NEUTRON(INDEX_CURRENT_COMP, x, y, z, vx, vy, vz, t, sx, sy, sz, p);
+  }
+%}
+
+SAVE
+%{
+  /* save results, but do not free pointers */
+  detector = Monitor_nD_Save(&DEFS, &Vars);
+%}
+
+FINALLY
+%{
+  /* free pointers */
+  if (!nowritefile) {
+    Monitor_nD_Finally(&DEFS, &Vars);
+  }
+%}
+
+MCDISPLAY
+%{
+  if (geometry && strlen(geometry) && strcmp(geometry,"0") && strcmp(geometry, "NULL"))
+  {
+    off_display(offdata);
+  } else {
+    Monitor_nD_McDisplay(&DEFS, &Vars);
+  }
+%}
+
+END

From d668c7c273cab3c745aff557ece3459459f458c9 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 31 Mar 2020 12:41:46 +0200
Subject: [PATCH 074/403] Forgot PSD_monitor in dummy_instrument_folder

---
 .../dummy_instrument_folder/PSD_monitor.comp  | 124 ++++++++++++++++++
 1 file changed, 124 insertions(+)
 create mode 100644 mcstasscript/tests/dummy_instrument_folder/PSD_monitor.comp

diff --git a/mcstasscript/tests/dummy_instrument_folder/PSD_monitor.comp b/mcstasscript/tests/dummy_instrument_folder/PSD_monitor.comp
new file mode 100644
index 00000000..f1536e7b
--- /dev/null
+++ b/mcstasscript/tests/dummy_instrument_folder/PSD_monitor.comp
@@ -0,0 +1,124 @@
+/*******************************************************************************
+*
+* McStas, neutron ray-tracing package
+*         Copyright 1997-2002, All rights reserved
+*         Risoe National Laboratory, Roskilde, Denmark
+*         Institut Laue Langevin, Grenoble, France
+*
+* Component: PSD_monitor
+*
+* %I
+* Written by: Kim Lefmann
+* Date: Feb 3, 1998
+* Origin: Risoe
+*
+* Position-sensitive monitor.
+*
+* %D
+* An (n times m) pixel PSD monitor. This component may also be used as a beam
+* detector.
+*
+* Example: PSD_monitor(xmin=-0.1, xmax=0.1, ymin=-0.1, ymax=0.1, nx=90, ny=90, filename="Output.psd")
+*
+* %P
+* INPUT PARAMETERS:
+*
+* xmin: [m]             Lower x bound of detector opening 
+* xmax: [m]             Upper x bound of detector opening 
+* ymin: [m]             Lower y bound of detector opening 
+* ymax: [m]             Upper y bound of detector opening 
+* xwidth: [m]           Width of detector. Overrides xmin, xmax 
+* yheight: [m]          Height of detector. Overrides ymin, ymax 
+* nx: [1]               Number of pixel columns 
+* ny: [1]               Number of pixel rows 
+* filename: [string]    Name of file in which to store the detector image 
+* restore_neutron: [1]  If set, the monitor does not influence the neutron state 
+* nowritefile: [1]      If set, monitor will skip writing to disk
+*
+* OUTPUT PARAMETERS:
+*
+* PSD_N: []             Array of neutron counts
+* PSD_p: []             Array of neutron weight counts
+* PSD_p2: []            Array of second moments
+*
+* %E
+*******************************************************************************/
+
+
+DEFINE COMPONENT PSD_monitor
+DEFINITION PARAMETERS (nx=90, ny=90)
+SETTING PARAMETERS (string filename=0, xmin=-0.05, xmax=0.05, ymin=-0.05, ymax=0.05, xwidth=0, yheight=0, restore_neutron=0, int nowritefile=0)
+OUTPUT PARAMETERS (PSD_N, PSD_p, PSD_p2)
+/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
+
+DECLARE
+%{
+double PSD_N[nx][ny];
+double PSD_p[nx][ny];
+double PSD_p2[nx][ny];
+%}
+INITIALIZE
+%{
+int i,j;
+
+if (xwidth  > 0) { xmax = xwidth/2;  xmin = -xmax; }
+    if (yheight > 0) { ymax = yheight/2; ymin = -ymax; }
+
+    if ((xmin >= xmax) || (ymin >= ymax)) {
+            printf("PSD_monitor: %s: Null detection area !\n"
+                   "ERROR        (xwidth,yheight,xmin,xmax,ymin,ymax). Exiting",
+           NAME_CURRENT_COMP);
+      exit(0);
+    }
+
+    for (i=0; i<nx; i++)
+     for (j=0; j<ny; j++)
+     {
+      PSD_N[i][j] = 0;
+      PSD_p[i][j] = 0;
+      PSD_p2[i][j] = 0;
+     }
+  %}
+TRACE
+  %{
+    int i,j;
+
+    PROP_Z0;
+    if (x>xmin && x<xmax && y>ymin && y<ymax)
+    {
+      i = floor((x - xmin)*nx/(xmax - xmin));
+      j = floor((y - ymin)*ny/(ymax - ymin));
+      PSD_N[i][j]++;
+      PSD_p[i][j] += p;
+      PSD_p2[i][j] += p*p;
+      SCATTER;
+    }
+    if (restore_neutron) {
+      RESTORE_NEUTRON(INDEX_CURRENT_COMP, x, y, z, vx, vy, vz, t, sx, sy, sz, p);
+    }
+  %}
+SAVE
+  %{
+    if (!nowritefile) {
+      DETECTOR_OUT_2D(
+          "PSD monitor",
+          "X position [cm]",
+          "Y position [cm]",
+          xmin*100.0, xmax*100.0, ymin*100.0, ymax*100.0,
+          nx, ny,
+          &PSD_N[0][0],&PSD_p[0][0],&PSD_p2[0][0],
+          filename);
+    }
+  %}
+
+MCDISPLAY
+%{
+  
+  multiline(5, (double)xmin, (double)ymin, 0.0,
+               (double)xmax, (double)ymin, 0.0,
+               (double)xmax, (double)ymax, 0.0,
+               (double)xmin, (double)ymax, 0.0,
+               (double)xmin, (double)ymin, 0.0);
+%}
+
+END

From 565bc1caf996d8e6861c144b2e607bcffa91dad9 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 31 Mar 2020 13:16:43 +0200
Subject: [PATCH 075/403] Testing a run without troublesome test_comma_split
 test.

---
 mcstasscript/tests/test_Instr_reader.py | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/mcstasscript/tests/test_Instr_reader.py b/mcstasscript/tests/test_Instr_reader.py
index c0feb350..45f250cd 100644
--- a/mcstasscript/tests/test_Instr_reader.py
+++ b/mcstasscript/tests/test_Instr_reader.py
@@ -283,6 +283,9 @@ def test_read_component_SPLIT(self):
         self.assertEqual(test_component.AT_relative, "RELATIVE sample_4")     
     
     def test_read_component_JUMP(self):
+        """
+        Check a JUMP and GROUP statement is read correctly
+        """
         
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
@@ -308,7 +311,7 @@ def test_read_component_JUMP(self):
     
     def test_comma_split(self):
         """
-        Check if the instrument name is read correctly
+        Test the Tracer_reader._split_func
         """
 
         blockPrint()
@@ -325,10 +328,11 @@ def test_comma_split(self):
         self.assertEqual(result[2],"C")
         self.assertEqual(result[3],"D(a,b)")
         self.assertEqual(result[4],"E")
-        
+    
+    """
     def test_comma_split_limited(self):
         """
-        Check if the instrument name is read correctly
+        Test the Tracer_reader._split_func
         """
 
         blockPrint()
@@ -343,10 +347,11 @@ def test_comma_split_limited(self):
         self.assertEqual(result[0],"A")
         self.assertEqual(result[1],"B")
         self.assertEqual(result[2],"C,D(a,b),E")
+    """
         
     def test_parenthesis_split(self):
         """
-        Check if the instrument name is read correctly
+        Test the Tracer_reader._split_func
         """
         
         blockPrint()
@@ -366,7 +371,7 @@ def test_parenthesis_split(self):
         
     def test_comma_split_brack(self):
         """
-        Check if the instrument name is read correctly
+        Test the Tracer_reader._split_func
         """
 
         blockPrint()

From 095265748fc97ea08e765a8ea2e95a8cdc36df85 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 31 Mar 2020 13:18:52 +0200
Subject: [PATCH 076/403] New test of travis without troublesome
 test_comma_split test.

---
 mcstasscript/tests/test_Instr_reader.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/mcstasscript/tests/test_Instr_reader.py b/mcstasscript/tests/test_Instr_reader.py
index 45f250cd..1e0f76e3 100644
--- a/mcstasscript/tests/test_Instr_reader.py
+++ b/mcstasscript/tests/test_Instr_reader.py
@@ -331,9 +331,6 @@ def test_comma_split(self):
     
     """
     def test_comma_split_limited(self):
-        """
-        Test the Tracer_reader._split_func
-        """
 
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")

From ab748221c49ba6c241f3a33452d9b606652832b9 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 31 Mar 2020 13:28:06 +0200
Subject: [PATCH 077/403] Continued test of removing troublesome
 test_comma_split test.

---
 mcstasscript/tests/test_Instr_reader.py | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/mcstasscript/tests/test_Instr_reader.py b/mcstasscript/tests/test_Instr_reader.py
index 1e0f76e3..b85e163c 100644
--- a/mcstasscript/tests/test_Instr_reader.py
+++ b/mcstasscript/tests/test_Instr_reader.py
@@ -308,11 +308,11 @@ def test_read_component_JUMP(self):
         self.assertEqual(test_component.AT_data, ["0", "0", "0"])
         self.assertEqual(test_component.AT_relative, "ABSOLUTE")
 
-    
+    """
     def test_comma_split(self):
-        """
-        Test the Tracer_reader._split_func
-        """
+        
+        #Test the Tracer_reader._split_func
+        
 
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
@@ -328,10 +328,13 @@ def test_comma_split(self):
         self.assertEqual(result[2],"C")
         self.assertEqual(result[3],"D(a,b)")
         self.assertEqual(result[4],"E")
-    
     """
+    
     def test_comma_split_limited(self):
-
+        """
+        Test the Tracer_reader._split_func
+        """
+        
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
@@ -344,7 +347,7 @@ def test_comma_split_limited(self):
         self.assertEqual(result[0],"A")
         self.assertEqual(result[1],"B")
         self.assertEqual(result[2],"C,D(a,b),E")
-    """
+    
         
     def test_parenthesis_split(self):
         """

From f1a41dd933ed880fc6ebf8acc1bd23ecc55784b4 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 31 Mar 2020 13:42:07 +0200
Subject: [PATCH 078/403] Updated Instr_reader tests to set the correct
 dictionary.

---
 mcstasscript/tests/test_Instr_reader.py | 68 ++++++++++++++++---------
 1 file changed, 43 insertions(+), 25 deletions(-)

diff --git a/mcstasscript/tests/test_Instr_reader.py b/mcstasscript/tests/test_Instr_reader.py
index b85e163c..f429434c 100644
--- a/mcstasscript/tests/test_Instr_reader.py
+++ b/mcstasscript/tests/test_Instr_reader.py
@@ -15,15 +15,27 @@ def blockPrint():
 def enablePrint():
     sys.stdout = sys.__stdout__
 
-def setup_standard(Instr):
+
+def set_dummy_dir():
     THIS_DIR = os.path.dirname(os.path.abspath(__file__))
     os.chdir(os.path.join(THIS_DIR, "dummy_instrument_folder"))
 
+def setup_standard(Instr):
+    set_dummy_dir()
     filename = "Union_demonstration_test.instr"
     InstrReader = control.InstrumentReader(filename)
     InstrReader.add_to_instr(Instr)
     
-    return InstrReader 
+    return InstrReader
+
+def setup_standard_auto_instr():
+    set_dummy_dir()
+    
+    blockPrint()
+    Instr = instr.McStas_instr("test_instrument")
+    enablePrint()
+
+    return setup_standard(Instr)
 
 class TestInstrReader(unittest.TestCase):
     
@@ -32,25 +44,27 @@ def test_read_instrument_name(self):
         Check if the instrument name is read correctly
         """
 
-        filename = "Union_demonstration_test.instr"
+        set_dummy_dir()
         
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
 
+        filename = "Union_demonstration_test.instr"
         InstrReader = control.InstrumentReader(filename)
         InstrReader.add_to_instr(Instr)
 
         self.assertEqual(InstrReader.instr_name, "Union_demonstration")
 
     def test_read_input_parameter(self):
+    
+        set_dummy_dir()
         
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
         InstrReader = setup_standard(Instr)
         
-        
         self.assertEqual(Instr.parameter_list[0].name, "stick_displacement")
         # space in type inserted for easier writing by McStas_Instr class
         self.assertEqual(Instr.parameter_list[0].type, "double ")
@@ -68,6 +82,8 @@ def test_read_input_parameter(self):
 
     def test_read_declare_parameter(self):
         
+        set_dummy_dir()
+        
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
@@ -97,6 +113,8 @@ def test_read_declare_parameter(self):
         self.assertEqual(Instr.declare_list[11].value, "\"\\\"test_string\\\"\"")
         
     def test_read_initialize_line(self):
+    
+        set_dummy_dir()
         
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
@@ -111,6 +129,8 @@ def test_read_initialize_line(self):
     # Check a few components are read correctly
     def test_read_component_1(self):
         
+        set_dummy_dir()
+        
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
@@ -141,6 +161,8 @@ def test_read_component_1(self):
         
     def test_read_component_2(self):
         
+        set_dummy_dir()
+        
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
@@ -169,6 +191,8 @@ def test_read_component_2(self):
         self.assertEqual(test_component.ROTATED_relative, "RELATIVE sample_rod_bottom")
 
     def test_read_component_WHEN(self):
+    
+        set_dummy_dir()
         
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
@@ -201,6 +225,8 @@ def test_read_component_WHEN(self):
         
     def test_read_component_EXTEND(self):
         
+        set_dummy_dir()
+        
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
@@ -242,6 +268,8 @@ def test_read_component_EXTEND(self):
         
     def test_read_component_GROUP(self):
         
+        set_dummy_dir()
+        
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
@@ -264,6 +292,8 @@ def test_read_component_GROUP(self):
         
     def test_read_component_SPLIT(self):
         
+        set_dummy_dir()
+        
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
@@ -287,6 +317,8 @@ def test_read_component_JUMP(self):
         Check a JUMP and GROUP statement is read correctly
         """
         
+        set_dummy_dir()
+        
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
@@ -308,16 +340,12 @@ def test_read_component_JUMP(self):
         self.assertEqual(test_component.AT_data, ["0", "0", "0"])
         self.assertEqual(test_component.AT_relative, "ABSOLUTE")
 
-    """
     def test_comma_split(self):
+        """
+        Test the Tracer_reader._split_func
+        """
         
-        #Test the Tracer_reader._split_func
-        
-
-        blockPrint()
-        Instr = instr.McStas_instr("test_instrument")
-        enablePrint()
-        InstrReader = setup_standard(Instr)
+        InstrReader = setup_standard_auto_instr()
         
         test_string = "A,B,C,D(a,b),E"
         
@@ -328,17 +356,13 @@ def test_comma_split(self):
         self.assertEqual(result[2],"C")
         self.assertEqual(result[3],"D(a,b)")
         self.assertEqual(result[4],"E")
-    """
     
     def test_comma_split_limited(self):
         """
         Test the Tracer_reader._split_func
         """
         
-        blockPrint()
-        Instr = instr.McStas_instr("test_instrument")
-        enablePrint()
-        InstrReader = setup_standard(Instr)
+        InstrReader = setup_standard_auto_instr()
         
         test_string = "A,B,C,D(a,b),E"
         
@@ -354,10 +378,7 @@ def test_parenthesis_split(self):
         Test the Tracer_reader._split_func
         """
         
-        blockPrint()
-        Instr = instr.McStas_instr("test_instrument")
-        enablePrint()
-        InstrReader = setup_standard(Instr)
+        InstrReader = setup_standard_auto_instr()
         
         test_string = "A)B)C)D(a,b))E"
         
@@ -374,10 +395,7 @@ def test_comma_split_brack(self):
         Test the Tracer_reader._split_func
         """
 
-        blockPrint()
-        Instr = instr.McStas_instr("test_instrument")
-        enablePrint()
-        InstrReader = setup_standard(Instr)
+        InstrReader = setup_standard_auto_instr()
         
         test_string = "A,B{C,D(a,b)},E"
         

From 600ec725620e4c0c08c0672af137404854d9eaef Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 31 Mar 2020 14:43:26 +0200
Subject: [PATCH 079/403] Add codecov to project.

---
 .travis.yml | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index c315b201..69aa1f3a 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -5,6 +5,10 @@ python:
 
 install:
   - pip install -r requirements.txt
+  - pip install coverage
 
 script:
- - python3 -m unittest discover mcstasscript/tests
+  - coverage run -m unittest discover mcstasscript/tests
+
+after_success:
+  - bash <(curl -s https://codecov.io/bash)

From 1bbec43c8f57ef9032971dee0c9f6591d7a303f2 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 31 Mar 2020 15:02:29 +0200
Subject: [PATCH 080/403] Fix for extra commas when reading trace with
 InstrReader.

---
 mcstasscript/instr_reader/read_trace.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/mcstasscript/instr_reader/read_trace.py b/mcstasscript/instr_reader/read_trace.py
index d69d5053..62ca6733 100644
--- a/mcstasscript/instr_reader/read_trace.py
+++ b/mcstasscript/instr_reader/read_trace.py
@@ -217,8 +217,7 @@ def read_trace_line(self, line):
                         # There is an uneven number of quotation marks
                         par_exp += ","
                         if "," in par_line:
-                            # include the up to the next comma in par_exp
-                            #par_exp += "," + par_line.split(",",1)[0]
+                            # include up to the next comma in par_exp
                             par_exp += par_line.split(",",1)[0]
                             # remove the part of the par_line added to par_exp
                             par_line = par_line.split(",",1)[1]

From c227e55647aaa7c89c7e67be8648abd0212998b0 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Sun, 26 Apr 2020 11:48:14 +0200
Subject: [PATCH 081/403] Added options for selecting input_path for instrument
 where components are located and the mcrun command is executed. The folder
 has to exist.

The mcrun command now specifies the absolute path of the data folder,
as the command may be run from somewhere other than the current dir.

Unit tests modified to account for this, new tests for the feature
are not written yet.
---
 mcstasscript/helper/component_reader.py | 24 ++++++++++++--
 mcstasscript/helper/managed_mcrun.py    | 44 +++++++++++++++++++++----
 mcstasscript/interface/instr.py         | 11 ++++++-
 mcstasscript/tests/test_Instr.py        | 18 ++++++++--
 mcstasscript/tests/test_ManagedMcrun.py | 24 +++++++++++---
 5 files changed, 103 insertions(+), 18 deletions(-)

diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index 81f74123..11f36594 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -32,11 +32,20 @@ class ComponentReader:
 
     """
 
-    def __init__(self, mcstas_path):
+    def __init__(self, mcstas_path, input_path="."):
         """
         Reads all component files in standard folders. Recursive, so
         subfolders of these folders are included.
 
+        Parameters
+        ----------
+        mcstas_path : str
+            Path to McStas folder, used to find the installed components
+
+        keyword arguments:
+            input_path : str
+                Path to work directory, most often current directory
+
         """
 
         # add trailing / or \ depending on operating system
@@ -60,10 +69,19 @@ def __init__(self, mcstas_path):
             abs_path = os.path.join(mcstas_path, folder)
             self._find_components(abs_path)
 
-        # McStas component in current directory should overwrite
+        # Will overwrite McStas components with definitions in input_folder
         current_directory = os.getcwd()
 
-        for file in os.listdir(current_directory):
+        if os.path.isabs(input_path):
+            input_directory = input_path
+        else:
+            input_directory = os.path.join(current_directory, input_path)
+
+        if not os.path.isdir(input_directory):
+            raise ValueError("Can't find given input_path,"
+                             + " directory must exist.")
+
+        for file in os.listdir(input_directory):
             if file.endswith(".comp"):
                 abs_path = os.path.join(current_directory, file)
                 if "/" in abs_path:
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index c5135f5d..663aea50 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -66,6 +66,13 @@ def __init__(self, instr_name, **kwargs):
                 Sets custom_flags passed to mcrun
             mcrun_path : str
                 Path to mcrun command, "" if already in path
+            increment_folder_name : bool
+                If True, automaticaly appends foldername to make it unique
+            force_compile : bool
+                If True, forces compile, default is True
+            run_folder : str
+                Path to folder in which to run McStas
+
         """
 
         self.name_of_instrumentfile = instr_name
@@ -78,6 +85,7 @@ def __init__(self, instr_name, **kwargs):
         self.mcrun_path = ""
         self.increment_folder_name = False
         self.compile = True
+        self.run_path = "."
         # mcrun_path always in kwargs
         if "mcrun_path" in kwargs:
             self.mcrun_path = kwargs["mcrun_path"]
@@ -107,13 +115,28 @@ def __init__(self, instr_name, **kwargs):
         if "force_compile" in kwargs:
             self.compile = kwargs["force_compile"]
 
+        if "run_path" in kwargs:
+            self.run_path = kwargs["run_path"]
+
     def run_simulation(self, **kwargs):
         """
         Runs McStas simulation described by initializing the object
         """
 
-        # construct command to run
+        # get relevant paths
+        current_directory = os.getcwd()
+
+        if not os.path.isabs(self.data_folder_name):
+            self.data_folder_name = os.path.join(current_directory,
+                                                 self.data_folder_name)
+
+        if not os.path.isabs(self.run_path):
+            self.run_path = os.path.join(current_directory, self.run_path)
 
+        if not os.path.isdir(self.run_path):
+            raise ValueError("Given run_path for McStas not a directory!")
+
+        # construct command to run
         options_string = ""
         if self.compile:
             options_string = "-c "
@@ -158,11 +181,20 @@ def run_simulation(self, **kwargs):
                   + self.name_of_instrumentfile
                   + parameter_string)
 
-        #os.system(full_command)
-        process = subprocess.run(full_command, shell=True,
-                                 stdout=subprocess.PIPE,
-                                 stderr=subprocess.PIPE,
-                                 universal_newlines=True)
+        try:
+            os.chdir(self.run_path)
+
+            #os.system(full_command)
+            process = subprocess.run(full_command, shell=True,
+                                     stdout=subprocess.PIPE,
+                                     stderr=subprocess.PIPE,
+                                     universal_newlines=True)
+
+            os.chdir(current_directory)
+
+        except:
+            os.chdir(current_directory)
+            raise RuntimeError("Could not run McStas command")
 
         if "suppress_output" in kwargs:
             if kwargs["suppress_output"] is False:
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 14d97b37..a1ea7554 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -169,6 +169,9 @@ def __init__(self, name, **kwargs):
 
             mcrun_path : str
                 Absolute path of mcrun or empty if already in path
+
+            input_path : str
+                Work directory, will load components from this folder
         """
 
         self.name = name
@@ -190,6 +193,11 @@ def __init__(self, name, **kwargs):
         else:
             self.origin = "ESS DMSC"
 
+        if "input_path" in kwargs:
+            self.input_path = kwargs["input_path"]
+        else:
+            self.input_path = "."
+
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         configuration_file_name = os.path.join(THIS_DIR, "..", "configuration.yaml")
         if not os.path.isfile(configuration_file_name):
@@ -232,7 +240,8 @@ def __init__(self, name, **kwargs):
         self.component_name_list = []  # List of component names
 
         # Read info on active McStas components
-        self.component_reader = ComponentReader(self.mcstas_path)
+        self.component_reader = ComponentReader(self.mcstas_path,
+                                                input_path=self.input_path)
         self.component_class_lib = {}
 
     def add_parameter(self, *args, **kwargs):
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 35e136a1..10676bf6 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1415,9 +1415,13 @@ def test_run_full_instrument_basic(self, mock_sub,
 
         expected_path = os.path.join("path","mcrun")
 
+        current_directory = os.getcwd()
+        expected_folder_path = os.path.join(current_directory, "test_data_set")
+
         # a double space because of a missing option
         expected_call = (expected_path + " -c -n 1000000 --mpi=1 "
-                         + "-d test_data_set  test_instrument.instr"
+                         + "-d " + expected_folder_path
+                         + "  test_instrument.instr"
                          + " has_default=37 theta=1")
 
         mock_sub.assert_called_once_with(expected_call,
@@ -1450,9 +1454,13 @@ def test_run_full_instrument_complex(self, mock_sub,
 
         expected_path = os.path.join("path","mcrun")
 
+        current_directory = os.getcwd()
+        expected_folder_path = os.path.join(current_directory, "test_data_set")
+
         # a double space because of a missing option
         expected_call = (expected_path + " -c -n 48 --mpi=7 "
-                         + "-d test_data_set -fo test_instrument.instr "
+                         + "-d " + expected_folder_path
+                         + " -fo test_instrument.instr "
                          + "has_default=37 A=2 BC=car theta=\"toy\"")
 
         mock_sub.assert_called_once_with(expected_call,
@@ -1485,9 +1493,13 @@ def test_run_full_instrument_overwrite_default(self, mock_sub,
         
         expected_path = os.path.join("path","mcrun")
 
+        current_directory = os.getcwd()
+        expected_folder_path = os.path.join(current_directory, "test_data_set")
+
         # a double space because of a missing option
         expected_call = (expected_path + " -c -n 48 --mpi=7 "
-                         + "-d test_data_set -fo test_instrument.instr "
+                         + "-d " + expected_folder_path
+                         + " -fo test_instrument.instr "
                          + "has_default=10 A=2 BC=car theta=\"toy\"")
 
         mock_sub.assert_called_once_with(expected_call,
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index 2c9b0126..5fd493e9 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -107,9 +107,12 @@ def test_ManagedMcrun_run_simulation_basic(self, mock_sub):
 
         mcrun_obj.run_simulation()
 
+        current_directory = os.getcwd()
+        expected_folder_path = os.path.join(current_directory, "test_folder")
+
         # a double space because of a missing option
         expected_call = ("path/mcrun -c -n 1000000 --mpi=1 "
-                         + "-d test_folder  test.instr")
+                         + "-d " + expected_folder_path + "  test.instr")
 
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
@@ -128,9 +131,12 @@ def test_ManagedMcrun_run_simulation_basic_path(self, mock_sub):
 
         mcrun_obj.run_simulation()
 
+        current_directory = os.getcwd()
+        expected_folder_path = os.path.join(current_directory, "test_folder")
+
         # a double space because of a missing option
         expected_call = ("path/mcrun -c -n 1000000 --mpi=1 "
-                         + "-d test_folder  test.instr")
+                         + "-d " + expected_folder_path + "  test.instr")
 
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
@@ -152,9 +158,12 @@ def test_ManagedMcrun_run_simulation_no_standard(self, mock_sub):
 
         mcrun_obj.run_simulation()
 
+        current_directory = os.getcwd()
+        expected_folder_path = os.path.join(current_directory, "test_folder")
+
         # a double space because of a missing option
         expected_call = ("path/mcrun -c -n 48 --mpi=7 "
-                         + "-d test_folder -fo test.instr")
+                         + "-d " + expected_folder_path + " -fo test.instr")
 
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
@@ -179,9 +188,11 @@ def test_ManagedMcrun_run_simulation_parameters(self, mock_sub):
 
         mcrun_obj.run_simulation()
 
+        current_directory = os.getcwd()
+        expected_folder_path = os.path.join(current_directory, "test_folder")
         # a double space because of a missing option
         expected_call = ("path/mcrun -c -n 48 --mpi=7 "
-                         + "-d test_folder -fo test.instr "
+                         + "-d " + expected_folder_path + " -fo test.instr "
                          + "A=2 BC=car th=\"toy\"")
 
         mock_sub.assert_called_once_with(expected_call,
@@ -208,9 +219,12 @@ def test_ManagedMcrun_run_simulation_compile(self, mock_sub):
 
         mcrun_obj.run_simulation()
 
+        current_directory = os.getcwd()
+        expected_folder_path = os.path.join(current_directory, "test_folder")
+
         # a double space because of a missing option
         expected_call = ("path/mcrun -n 48 --mpi=7 "
-                         + "-d test_folder -fo test.instr "
+                         + "-d " + expected_folder_path + " -fo test.instr "
                          + "A=2 BC=car th=\"toy\"")
 
         mock_sub.assert_called_once_with(expected_call,

From ba1a833b1cf6e36c92977cb75a2059adec8b765a Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Sun, 26 Apr 2020 14:36:34 +0200
Subject: [PATCH 082/403] Updated output of overwritten components and updated
 unit tests accordingly.

---
 mcstasscript/helper/component_reader.py    | 15 ++++++---
 mcstasscript/tests/test_ComponentReader.py | 18 ++++++-----
 mcstasscript/tests/test_Instr.py           | 36 ++++++++++++----------
 3 files changed, 41 insertions(+), 28 deletions(-)

diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index 11f36594..633612a8 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -81,6 +81,7 @@ def __init__(self, mcstas_path, input_path="."):
             raise ValueError("Can't find given input_path,"
                              + " directory must exist.")
 
+        overwritten_components = []
         for file in os.listdir(input_directory):
             if file.endswith(".comp"):
                 abs_path = os.path.join(current_directory, file)
@@ -90,14 +91,20 @@ def __init__(self, mcstas_path, input_path="."):
                     component_name = abs_path.split("\\")[-1].split(".")[-2]
 
                 if component_name in self.component_path:
-                    print("Overwriting McStasScript info on component named "
-                          + file
-                          + " because the component is in the"
-                          + " work directory.")
+                    overwritten_components.append(file)
 
                 self.component_path[component_name] = abs_path
                 self.component_category[component_name] = "Work directory"
 
+        if len(overwritten_components) > 0:
+            print("The following components are found in the work_directory"
+                  + " / input_path:")
+            for name in overwritten_components:
+                print("    ", name)
+
+            print("These definitions will be used instead of the installed "
+                  + "versions.")
+
     def show_categories(self):
         """
         Method that will show all component categories available
diff --git a/mcstasscript/tests/test_ComponentReader.py b/mcstasscript/tests/test_ComponentReader.py
index 3f5a7d44..c8db32d1 100644
--- a/mcstasscript/tests/test_ComponentReader.py
+++ b/mcstasscript/tests/test_ComponentReader.py
@@ -36,9 +36,10 @@ def test_ComponentReader_init_overwrite_message(self, mock_stdout):
 
         component_reader = setup_component_reader()
 
-        message = ("Overwriting McStasScript info on component named "
-                   + "test_for_reading.comp because the component is in "
-                   + "the work directory.\n")
+        message = ("The following components are found in the work_directory "
+                   + "/ input_path:\n     test_for_reading.comp\n"
+                   + "These definitions will be used instead of the "
+                   + "installed versions.\n")
 
         self.assertEqual(mock_stdout.getvalue(), message)
 
@@ -92,7 +93,7 @@ def test_ComponentReader_show_categories(self, mock_stdout):
         output = mock_stdout.getvalue()
         output = output.split("\n")
 
-        self.assertEqual(len(output), 5)
+        self.assertEqual(len(output), 7)
         self.assertIn(" sources", output)
         self.assertIn(" Work directory", output)
         self.assertIn(" misc", output)
@@ -113,9 +114,10 @@ def test_ComponentReader_show_categories_ordered(self, mock_stdout):
         output = mock_stdout.getvalue()
         output = output.split("\n")
 
-        self.assertEqual(output[1], " sources")
-        self.assertEqual(output[2], " Work directory")
-        self.assertEqual(output[3], " misc")
+        # Ignoring message about overwritten components, starting from 3
+        self.assertEqual(output[3], " sources")
+        self.assertEqual(output[4], " Work directory")
+        self.assertEqual(output[5], " misc")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_show_components_short(self, mock_stdout):
@@ -134,7 +136,7 @@ def test_ComponentReader_show_components_short(self, mock_stdout):
         output = mock_stdout.getvalue()
         output = output.split("\n")
 
-        self.assertEqual(len(output), 3)
+        self.assertEqual(len(output), 5)
         self.assertIn(" test_for_structure", output)
         # Check overwritten component is not in the output
         self.assertNotIn(" test_for_reading", output)
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 10676bf6..b5d512ac 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -420,14 +420,16 @@ def test_show_components_simple(self, mock_stdout):
         output = output.split("\n")
 
         self.assertEqual(output[0],
-                         "Overwriting McStasScript info on component "
-                         + "named test_for_reading.comp because the "
-                         + "component is in the work directory.")
-        self.assertEqual(output[1],
+                         "The following components are found in the "
+                         + "work_directory / input_path:")
+        self.assertEqual(output[1], "     test_for_reading.comp")
+        self.assertEqual(output[2], "These definitions will be used "
+                                    +"instead of the installed versions.")
+        self.assertEqual(output[3],
                          "Here are the available component categories:")
-        self.assertEqual(output[2], " sources")
-        self.assertEqual(output[3], " Work directory")
-        self.assertEqual(output[4], " misc")
+        self.assertEqual(output[4], " sources")
+        self.assertEqual(output[5], " Work directory")
+        self.assertEqual(output[6], " misc")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_show_components_folder(self, mock_stdout):
@@ -442,14 +444,16 @@ def test_show_components_folder(self, mock_stdout):
         output = output.split("\n")
 
         self.assertEqual(output[0],
-                         "Overwriting McStasScript info on component "
-                         + "named test_for_reading.comp because the "
-                         + "component is in the work directory.")
-        self.assertEqual(output[1],
+                         "The following components are found in the "
+                         + "work_directory / input_path:")
+        self.assertEqual(output[1], "     test_for_reading.comp")
+        self.assertEqual(output[2], "These definitions will be used "
+                         + "instead of the installed versions.")
+        self.assertEqual(output[3],
                          "Here are all components in the Work directory "
                          + "category.")
-        self.assertEqual(output[2], " test_for_reading")
-        self.assertEqual(output[3], "")
+        self.assertEqual(output[4], " test_for_reading")
+        self.assertEqual(output[5], "")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_component_help(self, mock_stdout):
@@ -467,7 +471,7 @@ def test_component_help(self, mock_stdout):
         output = mock_stdout.getvalue()
         output = output.split("\n")
 
-        self.assertEqual(output[1], " ___ Help test_for_reading " + "_"*63)
+        self.assertEqual(output[3], " ___ Help test_for_reading " + "_"*63)
 
         legend = ("|"
                   + bcolors.BOLD + "optional parameter" + bcolors.ENDC
@@ -485,14 +489,14 @@ def test_component_help(self, mock_stdout):
                   + bcolors.ENDC + bcolors.ENDC
                   + "|")
 
-        self.assertEqual(output[2], legend)
+        self.assertEqual(output[4], legend)
 
         par_name = bcolors.BOLD + "radius" + bcolors.ENDC
         value = (bcolors.BOLD + bcolors.OKBLUE
                  + "0.1" + bcolors.ENDC + bcolors.ENDC)
         comment = ("// Radius of circle in (x,y,0) plane where "
                    + "neutrons are generated.")
-        self.assertEqual(output[3],
+        self.assertEqual(output[5],
                          par_name + " = " + value + " [m] " + comment)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)

From a9456d1c351ea0590e61ff2a1b3eb6747bce2af5 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Mon, 27 Apr 2020 11:18:21 +0200
Subject: [PATCH 083/403] Fix on input_path implementation, now McStas mcrun is
 actually executed from the input_path folder so it will load the components
 in that path.

Integration test that uses input_folder with modified component
runs successfully.
---
 MANIFEST.in                                   |   3 +
 mcstasscript/helper/component_reader.py       |   4 +-
 mcstasscript/helper/managed_mcrun.py          |   5 +-
 .../test_input_folder/PSDlin_monitor.comp     | 125 ++++++++++++++++++
 .../test_simple_instrument.py                 |  54 ++++++++
 mcstasscript/interface/instr.py               |   7 +-
 6 files changed, 193 insertions(+), 5 deletions(-)
 create mode 100644 mcstasscript/integration_tests/test_input_folder/PSDlin_monitor.comp

diff --git a/MANIFEST.in b/MANIFEST.in
index f45d6378..0288d07d 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -3,6 +3,9 @@ include mcstasscript/configuration.yaml
 include mcstasscript/tests/test_for_reading.comp
 include mcstasscript/tests/test_instrument.instr
 include mcstasscript/tests/Union_demonstration_test.instr
+include mcstasscript/integration_tests/test_input_folder
+include mcstasscript/integration_tests/test_input_folder/PSDlin_monitor.comp
 graft mcstasscript/tests/dummy_mcstas
 graft mcstasscript/tests/dummy_instrument_folder
 graft mcstasscript/tests/test_data_set
+
diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index 633612a8..c025d6d0 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -78,13 +78,14 @@ def __init__(self, mcstas_path, input_path="."):
             input_directory = os.path.join(current_directory, input_path)
 
         if not os.path.isdir(input_directory):
+            print("input_path: ", input_directory)
             raise ValueError("Can't find given input_path,"
                              + " directory must exist.")
 
         overwritten_components = []
         for file in os.listdir(input_directory):
             if file.endswith(".comp"):
-                abs_path = os.path.join(current_directory, file)
+                abs_path = os.path.join(input_directory, file)
                 if "/" in abs_path:
                     component_name = abs_path.split("/")[-1].split(".")[-2]
                 else:
@@ -105,6 +106,7 @@ def __init__(self, mcstas_path, input_path="."):
             print("These definitions will be used instead of the installed "
                   + "versions.")
 
+
     def show_categories(self):
         """
         Method that will show all component categories available
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 663aea50..270ac148 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -183,8 +183,7 @@ def run_simulation(self, **kwargs):
 
         try:
             os.chdir(self.run_path)
-
-            #os.system(full_command)
+            print("running mcrun from: ", os.getcwd())
             process = subprocess.run(full_command, shell=True,
                                      stdout=subprocess.PIPE,
                                      stderr=subprocess.PIPE,
@@ -194,7 +193,7 @@ def run_simulation(self, **kwargs):
 
         except:
             os.chdir(current_directory)
-            raise RuntimeError("Could not run McStas command")
+            raise RuntimeError("Could not run McStas command.")
 
         if "suppress_output" in kwargs:
             if kwargs["suppress_output"] is False:
diff --git a/mcstasscript/integration_tests/test_input_folder/PSDlin_monitor.comp b/mcstasscript/integration_tests/test_input_folder/PSDlin_monitor.comp
new file mode 100644
index 00000000..9ebc4a33
--- /dev/null
+++ b/mcstasscript/integration_tests/test_input_folder/PSDlin_monitor.comp
@@ -0,0 +1,125 @@
+/*******************************************************************************
+*
+* McStas, neutron ray-tracing package
+*         Copyright 1997-2002, All rights reserved
+*         Risoe National Laboratory, Roskilde, Denmark
+*         Institut Laue Langevin, Grenoble, France
+*
+* %I
+* Written by:  Kim Lefmann
+* Date: May 7, 2001
+* Version: $Revision$
+ Origin: Risoe
+* Release: McStas 1.6
+*
+* Rectangular 1D PSD, measuring intensity vs. vertical position, x
+*
+* %D
+*
+* Example: PSDlin_monitor(nx=20, filename="Output.x", xmin=-0.1, xmax=0.1, ymin=-0.1, ymax=0.1)
+*
+* %P
+* INPUT PARAMETERS:
+*
+* xmin:     Lower x bound of detector opening [m]
+* xmax:     Upper x bound of detector opening [m]
+* ymin:     Lower y bound of detector opening [m]
+* ymax:     Upper y bound of detector opening [m]
+* xwidth:   Width of detector. Overrides xmin, xmax [m]
+* yheight:  Height of detector. Overrides ymin, ymax [m]
+* nx:       Number of x bins [1]
+* filename: Name of file in which to store the detector image [string]
+* restore_neutron: If set, the monitor does not influence the neutron state [1]
+* nowritefile: [1]      If set, monitor will skip writing to disk
+*
+* OUTPUT PARAMETERS:
+*
+* PSDlin_N:    Array of neutron counts
+* PSDlin_p:    Array of neutron weight counts
+* PSDlin_p2:   Array of second moments
+*
+* %E
+******************************************************************************/
+
+DEFINE COMPONENT PSDlin_monitor
+DEFINITION PARAMETERS (nx=20)
+SETTING PARAMETERS (string filename=0, xmin=-0.05, xmax=0.05, ymin=-0.05, ymax=0.05,
+    xwidth=0, yheight=0, restore_neutron=0, int nowritefile=0)
+OUTPUT PARAMETERS (PSDlin_N, PSDlin_p, PSDlin_p2)
+/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
+
+DECLARE
+  %{
+    double PSDlin_N[nx];
+    double PSDlin_p[nx];
+    double PSDlin_p2[nx];
+  %}
+
+INITIALIZE
+  %{
+    int i;
+
+    if (xwidth  > 0) { xmax = xwidth/2;  xmin = -xmax; }
+    if (yheight > 0) { ymax = yheight/2; ymin = -ymax; }
+
+    if ((xmin >= xmax) || (ymin >= ymax)) {
+            printf("PSDlin_monitor: %s: Null detection area !\n"
+                   "ERROR           (xwidth,yheight,xmin,xmax,ymin,ymax). Exiting",
+           NAME_CURRENT_COMP);
+      exit(0);
+    }
+
+    for (i=0; i<nx; i++)
+    {
+      PSDlin_N[i] = 0;
+      PSDlin_p[i] = 0;
+      PSDlin_p2[i] = 0;
+    }
+  %}
+
+TRACE
+  %{
+    int i;
+
+    PROP_Z0;
+    if (x>xmin && x<xmax && y>ymin && y<ymax)
+    {
+      i = floor(nx*(x-xmin)/(xmax-xmin));              /* Bin number */
+      if((i >= nx) || (i<0))
+      {
+        printf("FATAL ERROR: wrong positioning in linear PSD. i= %i \n",i);
+        exit(1);
+      }
+      PSDlin_N[i]++;
+      PSDlin_p[i] += p;
+      PSDlin_p2[i] += p*p;
+    }
+    if (restore_neutron) {
+      RESTORE_NEUTRON(INDEX_CURRENT_COMP, x, y, z, vx, vy, vz, t, sx, sy, sz, p);
+    }
+  %}
+
+SAVE
+  %{
+    if (!nowritefile) {
+    DETECTOR_OUT_1D(
+        "Linear PSD monitor",
+        "Test",
+        "TEST I",
+        "x", xmin, xmax, nx,
+        &PSDlin_N[0],&PSDlin_p[0],&PSDlin_p2[0],
+        filename);
+    }
+  %}
+
+MCDISPLAY
+%{
+  
+  multiline(5, (double)xmin, (double)ymin, 0.0,
+               (double)xmax, (double)ymin, 0.0,
+               (double)xmax, (double)ymax, 0.0,
+               (double)xmin, (double)ymax, 0.0,
+               (double)xmin, (double)ymin, 0.0);
+%}
+
+END
diff --git a/mcstasscript/integration_tests/test_simple_instrument.py b/mcstasscript/integration_tests/test_simple_instrument.py
index 952b62cf..18215819 100644
--- a/mcstasscript/integration_tests/test_simple_instrument.py
+++ b/mcstasscript/integration_tests/test_simple_instrument.py
@@ -1,4 +1,5 @@
 import io
+import os
 import unittest
 import unittest.mock
 
@@ -29,6 +30,34 @@ def setup_simple_instrument():
 
     return Instr
 
+def setup_simple_instrument_input_path():
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+    input_path = os.path.join(THIS_DIR, "test_input_folder")
+
+    Instr = instr.McStas_instr("integration_test_simple_input",
+                               input_path=input_path)
+
+    source = Instr.add_component("source", "Source_div")
+
+    source.xwidth = 0.03
+    source.yheight = 0.01
+    source.focus_aw = 0.01
+    source.focus_ah = 0.01
+    source.E0 = 81.81
+    source.dE = 1.0
+    source.flux = 1E10
+
+    PSD = Instr.add_component("PSD_1D", "PSDlin_monitor")
+
+    PSD.set_AT([0, 0, 1], RELATIVE="source")
+    PSD.xwidth = 0.1
+    PSD.nx = 100
+    PSD.yheight = 0.03
+    PSD.filename = "\"PSD.dat\""
+    PSD.restore_neutron = 1
+
+    return Instr
+
 
 def setup_simple_slit_instrument():
     Instr = instr.McStas_instr("integration_test_simple")
@@ -89,6 +118,31 @@ def test_simple_instrument(self, mock_stdout):
 
         self.assertTrue(1000*sum_outside_beam < sum_inside_beam)
 
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_simple_instrument_input(self, mock_stdout):
+        """
+        Test that an instrument can run and that the results matches
+        expectations. Here beam in small area in the middle of the
+        detector.
+        """
+        Instr = setup_simple_instrument_input_path()
+
+        data = Instr.run_full_instrument(foldername="integration_test_simple_input",
+                                         ncount=1E6, mpi=1,
+                                         increment_folder_name=True)
+
+        intensity_data = data[0].Intensity
+        # beam should be on pixel 35 to 65
+
+        sum_outside_beam = (sum(intensity_data[0:34])
+                            + sum(intensity_data[66:99]))
+        sum_inside_beam = sum(intensity_data[35:65])
+
+        self.assertTrue(1000*sum_outside_beam < sum_inside_beam)
+
+        # Check component from input_folder read
+        self.assertEqual(data[0].metadata.xlabel, "Test")
+
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_simple_instrument_mpi(self, mock_stdout):
         """
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index a1ea7554..57e2d31d 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1340,7 +1340,7 @@ def write_full_instrument(self):
         """
 
         # Create file identifier
-        fo = open(self.name + ".instr", "w")
+        fo = open(os.path.join(self.input_path, self.name + ".instr"), "w")
 
         # Write quick doc start
         fo.write("/" + 80*"*" + "\n")
@@ -1497,6 +1497,11 @@ def run_full_instrument(self, *args, **kwargs):
         # Make sure mcrun path is in kwargs
         if "mcrun_path" not in kwargs:
             kwargs["mcrun_path"] = self.mcrun_path
+
+        if "run_path" not in kwargs:
+            # path where mcrun is executed, will load components there
+            # if not set, use input_folder given
+            kwargs["run_path"] = self.input_path
                     
         if "parameters" in kwargs:
             given_parameters = kwargs["parameters"]

From 6da13d1ed4ff48a2371db1ef2b6a997be88e71dc Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Mon, 27 Apr 2020 13:49:59 +0200
Subject: [PATCH 084/403] Updated documentation to mention that configuration
 with the configuration class happens from within a python file as a user
 mistakenly used the terminal instead.

---
 McStasScript_documentation.pdf | Bin 167419 -> 167921 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
index deb1fb8ea4d8ffb14681dacb475de2e9bc0bf311..a442cda3c984120a976b29b61f5dafd7580d916f 100644
GIT binary patch
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From 4ab178e91a41f55adeb5edd28b9005fb40f6518f Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Mon, 27 Apr 2020 19:18:25 +0200
Subject: [PATCH 085/403] Update to unittest for using default input_path.

---
 mcstasscript/tests/test_Instr.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index b5d512ac..236fa67d 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1381,7 +1381,7 @@ def test_write_full_instrument_simple(self, mock_f, mock_stdout):
          my_call("%}\n"),
          my_call("\nEND\n")]
 
-        mock_f.assert_called_with("test_instrument.instr", "w")
+        mock_f.assert_called_with("./test_instrument.instr", "w")
         handle = mock_f()
         handle.write.assert_has_calls(wrts, any_order=False)
 

From bd5375aca92f5e24cc296be313d73510048f6b80 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Mon, 27 Apr 2020 20:06:21 +0200
Subject: [PATCH 086/403] Work on unittests for input_path.

---
 mcstasscript/helper/managed_mcrun.py          |   2 +-
 mcstasscript/tests/test_Instr.py              |  43 ++++++
 .../test_input_folder/PSDlin_monitor.comp     | 125 ++++++++++++++++++
 .../test_input_folder/test_for_reading.comp   | 125 ++++++++++++++++++
 4 files changed, 294 insertions(+), 1 deletion(-)
 create mode 100644 mcstasscript/tests/test_input_folder/PSDlin_monitor.comp
 create mode 100644 mcstasscript/tests/test_input_folder/test_for_reading.comp

diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 270ac148..90cf4546 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -183,7 +183,7 @@ def run_simulation(self, **kwargs):
 
         try:
             os.chdir(self.run_path)
-            print("running mcrun from: ", os.getcwd())
+
             process = subprocess.run(full_command, shell=True,
                                      stdout=subprocess.PIPE,
                                      stderr=subprocess.PIPE,
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 236fa67d..b3202ea1 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -39,6 +39,25 @@ def setup_instr_with_path():
 
     os.chdir(current_work_dir)  # Return to previous workdir
 
+def setup_instr_with_input_path():
+    """
+    Sets up a instrument with a valid mcstas_path, but it points to
+    the dummy installation in the test folder.
+    """
+
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+    dummy_path = os.path.join(THIS_DIR, "dummy_mcstas")
+    input_path = os.path.join(THIS_DIR, "test_input_folder")
+
+    current_work_dir = os.getcwd()
+    os.chdir(THIS_DIR)  # Set work directory to test folder
+
+    return McStas_instr("test_instrument",
+                        mcstas_path=dummy_path,
+                        input_path=input_path)
+
+    os.chdir(current_work_dir)  # Return to previous workdir
+
 
 def setup_populated_instr():
     """
@@ -455,6 +474,30 @@ def test_show_components_folder(self, mock_stdout):
         self.assertEqual(output[4], " test_for_reading")
         self.assertEqual(output[5], "")
 
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_show_components_input_path_simple(self, mock_stdout):
+        """
+        Simple test of show components to show categories
+        """
+        instr = setup_instr_with_input_path()
+
+        instr.show_components()
+
+        output = mock_stdout.getvalue()
+        output = output.split("\n")
+
+        self.assertEqual(output[0],
+                         "The following components are found in the "
+                         + "work_directory / input_path:")
+        self.assertEqual(output[1], "     test_for_reading.comp")
+        self.assertEqual(output[2], "These definitions will be used "
+                                    +"instead of the installed versions.")
+        self.assertEqual(output[3],
+                         "Here are the available component categories:")
+        self.assertEqual(output[4], " sources")
+        self.assertEqual(output[5], " Work directory")
+        self.assertEqual(output[6], " misc")
+
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_component_help(self, mock_stdout):
         """
diff --git a/mcstasscript/tests/test_input_folder/PSDlin_monitor.comp b/mcstasscript/tests/test_input_folder/PSDlin_monitor.comp
new file mode 100644
index 00000000..9ebc4a33
--- /dev/null
+++ b/mcstasscript/tests/test_input_folder/PSDlin_monitor.comp
@@ -0,0 +1,125 @@
+/*******************************************************************************
+*
+* McStas, neutron ray-tracing package
+*         Copyright 1997-2002, All rights reserved
+*         Risoe National Laboratory, Roskilde, Denmark
+*         Institut Laue Langevin, Grenoble, France
+*
+* %I
+* Written by:  Kim Lefmann
+* Date: May 7, 2001
+* Version: $Revision$
+ Origin: Risoe
+* Release: McStas 1.6
+*
+* Rectangular 1D PSD, measuring intensity vs. vertical position, x
+*
+* %D
+*
+* Example: PSDlin_monitor(nx=20, filename="Output.x", xmin=-0.1, xmax=0.1, ymin=-0.1, ymax=0.1)
+*
+* %P
+* INPUT PARAMETERS:
+*
+* xmin:     Lower x bound of detector opening [m]
+* xmax:     Upper x bound of detector opening [m]
+* ymin:     Lower y bound of detector opening [m]
+* ymax:     Upper y bound of detector opening [m]
+* xwidth:   Width of detector. Overrides xmin, xmax [m]
+* yheight:  Height of detector. Overrides ymin, ymax [m]
+* nx:       Number of x bins [1]
+* filename: Name of file in which to store the detector image [string]
+* restore_neutron: If set, the monitor does not influence the neutron state [1]
+* nowritefile: [1]      If set, monitor will skip writing to disk
+*
+* OUTPUT PARAMETERS:
+*
+* PSDlin_N:    Array of neutron counts
+* PSDlin_p:    Array of neutron weight counts
+* PSDlin_p2:   Array of second moments
+*
+* %E
+******************************************************************************/
+
+DEFINE COMPONENT PSDlin_monitor
+DEFINITION PARAMETERS (nx=20)
+SETTING PARAMETERS (string filename=0, xmin=-0.05, xmax=0.05, ymin=-0.05, ymax=0.05,
+    xwidth=0, yheight=0, restore_neutron=0, int nowritefile=0)
+OUTPUT PARAMETERS (PSDlin_N, PSDlin_p, PSDlin_p2)
+/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
+
+DECLARE
+  %{
+    double PSDlin_N[nx];
+    double PSDlin_p[nx];
+    double PSDlin_p2[nx];
+  %}
+
+INITIALIZE
+  %{
+    int i;
+
+    if (xwidth  > 0) { xmax = xwidth/2;  xmin = -xmax; }
+    if (yheight > 0) { ymax = yheight/2; ymin = -ymax; }
+
+    if ((xmin >= xmax) || (ymin >= ymax)) {
+            printf("PSDlin_monitor: %s: Null detection area !\n"
+                   "ERROR           (xwidth,yheight,xmin,xmax,ymin,ymax). Exiting",
+           NAME_CURRENT_COMP);
+      exit(0);
+    }
+
+    for (i=0; i<nx; i++)
+    {
+      PSDlin_N[i] = 0;
+      PSDlin_p[i] = 0;
+      PSDlin_p2[i] = 0;
+    }
+  %}
+
+TRACE
+  %{
+    int i;
+
+    PROP_Z0;
+    if (x>xmin && x<xmax && y>ymin && y<ymax)
+    {
+      i = floor(nx*(x-xmin)/(xmax-xmin));              /* Bin number */
+      if((i >= nx) || (i<0))
+      {
+        printf("FATAL ERROR: wrong positioning in linear PSD. i= %i \n",i);
+        exit(1);
+      }
+      PSDlin_N[i]++;
+      PSDlin_p[i] += p;
+      PSDlin_p2[i] += p*p;
+    }
+    if (restore_neutron) {
+      RESTORE_NEUTRON(INDEX_CURRENT_COMP, x, y, z, vx, vy, vz, t, sx, sy, sz, p);
+    }
+  %}
+
+SAVE
+  %{
+    if (!nowritefile) {
+    DETECTOR_OUT_1D(
+        "Linear PSD monitor",
+        "Test",
+        "TEST I",
+        "x", xmin, xmax, nx,
+        &PSDlin_N[0],&PSDlin_p[0],&PSDlin_p2[0],
+        filename);
+    }
+  %}
+
+MCDISPLAY
+%{
+  
+  multiline(5, (double)xmin, (double)ymin, 0.0,
+               (double)xmax, (double)ymin, 0.0,
+               (double)xmax, (double)ymax, 0.0,
+               (double)xmin, (double)ymax, 0.0,
+               (double)xmin, (double)ymin, 0.0);
+%}
+
+END
diff --git a/mcstasscript/tests/test_input_folder/test_for_reading.comp b/mcstasscript/tests/test_input_folder/test_for_reading.comp
new file mode 100644
index 00000000..9ebc4a33
--- /dev/null
+++ b/mcstasscript/tests/test_input_folder/test_for_reading.comp
@@ -0,0 +1,125 @@
+/*******************************************************************************
+*
+* McStas, neutron ray-tracing package
+*         Copyright 1997-2002, All rights reserved
+*         Risoe National Laboratory, Roskilde, Denmark
+*         Institut Laue Langevin, Grenoble, France
+*
+* %I
+* Written by:  Kim Lefmann
+* Date: May 7, 2001
+* Version: $Revision$
+ Origin: Risoe
+* Release: McStas 1.6
+*
+* Rectangular 1D PSD, measuring intensity vs. vertical position, x
+*
+* %D
+*
+* Example: PSDlin_monitor(nx=20, filename="Output.x", xmin=-0.1, xmax=0.1, ymin=-0.1, ymax=0.1)
+*
+* %P
+* INPUT PARAMETERS:
+*
+* xmin:     Lower x bound of detector opening [m]
+* xmax:     Upper x bound of detector opening [m]
+* ymin:     Lower y bound of detector opening [m]
+* ymax:     Upper y bound of detector opening [m]
+* xwidth:   Width of detector. Overrides xmin, xmax [m]
+* yheight:  Height of detector. Overrides ymin, ymax [m]
+* nx:       Number of x bins [1]
+* filename: Name of file in which to store the detector image [string]
+* restore_neutron: If set, the monitor does not influence the neutron state [1]
+* nowritefile: [1]      If set, monitor will skip writing to disk
+*
+* OUTPUT PARAMETERS:
+*
+* PSDlin_N:    Array of neutron counts
+* PSDlin_p:    Array of neutron weight counts
+* PSDlin_p2:   Array of second moments
+*
+* %E
+******************************************************************************/
+
+DEFINE COMPONENT PSDlin_monitor
+DEFINITION PARAMETERS (nx=20)
+SETTING PARAMETERS (string filename=0, xmin=-0.05, xmax=0.05, ymin=-0.05, ymax=0.05,
+    xwidth=0, yheight=0, restore_neutron=0, int nowritefile=0)
+OUTPUT PARAMETERS (PSDlin_N, PSDlin_p, PSDlin_p2)
+/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
+
+DECLARE
+  %{
+    double PSDlin_N[nx];
+    double PSDlin_p[nx];
+    double PSDlin_p2[nx];
+  %}
+
+INITIALIZE
+  %{
+    int i;
+
+    if (xwidth  > 0) { xmax = xwidth/2;  xmin = -xmax; }
+    if (yheight > 0) { ymax = yheight/2; ymin = -ymax; }
+
+    if ((xmin >= xmax) || (ymin >= ymax)) {
+            printf("PSDlin_monitor: %s: Null detection area !\n"
+                   "ERROR           (xwidth,yheight,xmin,xmax,ymin,ymax). Exiting",
+           NAME_CURRENT_COMP);
+      exit(0);
+    }
+
+    for (i=0; i<nx; i++)
+    {
+      PSDlin_N[i] = 0;
+      PSDlin_p[i] = 0;
+      PSDlin_p2[i] = 0;
+    }
+  %}
+
+TRACE
+  %{
+    int i;
+
+    PROP_Z0;
+    if (x>xmin && x<xmax && y>ymin && y<ymax)
+    {
+      i = floor(nx*(x-xmin)/(xmax-xmin));              /* Bin number */
+      if((i >= nx) || (i<0))
+      {
+        printf("FATAL ERROR: wrong positioning in linear PSD. i= %i \n",i);
+        exit(1);
+      }
+      PSDlin_N[i]++;
+      PSDlin_p[i] += p;
+      PSDlin_p2[i] += p*p;
+    }
+    if (restore_neutron) {
+      RESTORE_NEUTRON(INDEX_CURRENT_COMP, x, y, z, vx, vy, vz, t, sx, sy, sz, p);
+    }
+  %}
+
+SAVE
+  %{
+    if (!nowritefile) {
+    DETECTOR_OUT_1D(
+        "Linear PSD monitor",
+        "Test",
+        "TEST I",
+        "x", xmin, xmax, nx,
+        &PSDlin_N[0],&PSDlin_p[0],&PSDlin_p2[0],
+        filename);
+    }
+  %}
+
+MCDISPLAY
+%{
+  
+  multiline(5, (double)xmin, (double)ymin, 0.0,
+               (double)xmax, (double)ymin, 0.0,
+               (double)xmax, (double)ymax, 0.0,
+               (double)xmin, (double)ymax, 0.0,
+               (double)xmin, (double)ymin, 0.0);
+%}
+
+END

From 665c8100dd5f897315d2f4387127687cc6c861c1 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 28 Apr 2020 09:21:48 +0200
Subject: [PATCH 087/403] Added unittest and changed component name in
 test_input_folder.

The test_input_folder did contain a component that was also in the
test folder, making it impossible to check which of these were used
for loading the component.
---
 mcstasscript/tests/test_ComponentReader.py    |  30 +++++
 mcstasscript/tests/test_Instr.py              |   9 +-
 .../test_input_folder/test_for_structure.comp | 125 ++++++++++++++++++
 3 files changed, 160 insertions(+), 4 deletions(-)
 create mode 100644 mcstasscript/tests/test_input_folder/test_for_structure.comp

diff --git a/mcstasscript/tests/test_ComponentReader.py b/mcstasscript/tests/test_ComponentReader.py
index c8db32d1..9b65a4b3 100644
--- a/mcstasscript/tests/test_ComponentReader.py
+++ b/mcstasscript/tests/test_ComponentReader.py
@@ -20,6 +20,21 @@ def setup_component_reader():
 
     return component_reader
 
+def setup_component_reader_input_path():
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+    dummy_path = os.path.join(THIS_DIR, "dummy_mcstas")
+    input_path = os.path.join(THIS_DIR, "test_input_folder")
+
+    current_work_dir = os.getcwd()
+    os.chdir(THIS_DIR)  # Set work directory to test folder
+
+    component_reader = ComponentReader(mcstas_path=dummy_path,
+                                       input_path=input_path)
+
+    os.chdir(current_work_dir)  # Reset work directory
+
+    return component_reader
+
 
 class TestComponentReader(unittest.TestCase):
     """
@@ -43,6 +58,21 @@ def test_ComponentReader_init_overwrite_message(self, mock_stdout):
 
         self.assertEqual(mock_stdout.getvalue(), message)
 
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_ComponentReader_init_overwrite_message_input(self, mock_stdout):
+        """
+        Test that ComponentReader reports overwritten components
+        """
+
+        component_reader = setup_component_reader_input_path()
+
+        message = ("The following components are found in the work_directory "
+                   + "/ input_path:\n     test_for_structure.comp\n"
+                   + "These definitions will be used instead of the "
+                   + "installed versions.\n")
+
+        self.assertEqual(mock_stdout.getvalue(), message)
+
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_init_filenames(self, mock_stdout):
         """
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index b3202ea1..b9cdf86b 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -477,7 +477,8 @@ def test_show_components_folder(self, mock_stdout):
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_show_components_input_path_simple(self, mock_stdout):
         """
-        Simple test of show components to show categories
+        Simple test of input_path being recoignized and passed
+        to component_reader so PSDlin_monitor is overwritten
         """
         instr = setup_instr_with_input_path()
 
@@ -489,14 +490,14 @@ def test_show_components_input_path_simple(self, mock_stdout):
         self.assertEqual(output[0],
                          "The following components are found in the "
                          + "work_directory / input_path:")
-        self.assertEqual(output[1], "     test_for_reading.comp")
+        self.assertEqual(output[1], "     test_for_structure.comp")
         self.assertEqual(output[2], "These definitions will be used "
                                     +"instead of the installed versions.")
         self.assertEqual(output[3],
                          "Here are the available component categories:")
         self.assertEqual(output[4], " sources")
-        self.assertEqual(output[5], " Work directory")
-        self.assertEqual(output[6], " misc")
+        self.assertEqual(output[5], " misc")
+        self.assertEqual(output[6], " Work directory")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_component_help(self, mock_stdout):
diff --git a/mcstasscript/tests/test_input_folder/test_for_structure.comp b/mcstasscript/tests/test_input_folder/test_for_structure.comp
new file mode 100644
index 00000000..9ebc4a33
--- /dev/null
+++ b/mcstasscript/tests/test_input_folder/test_for_structure.comp
@@ -0,0 +1,125 @@
+/*******************************************************************************
+*
+* McStas, neutron ray-tracing package
+*         Copyright 1997-2002, All rights reserved
+*         Risoe National Laboratory, Roskilde, Denmark
+*         Institut Laue Langevin, Grenoble, France
+*
+* %I
+* Written by:  Kim Lefmann
+* Date: May 7, 2001
+* Version: $Revision$
+ Origin: Risoe
+* Release: McStas 1.6
+*
+* Rectangular 1D PSD, measuring intensity vs. vertical position, x
+*
+* %D
+*
+* Example: PSDlin_monitor(nx=20, filename="Output.x", xmin=-0.1, xmax=0.1, ymin=-0.1, ymax=0.1)
+*
+* %P
+* INPUT PARAMETERS:
+*
+* xmin:     Lower x bound of detector opening [m]
+* xmax:     Upper x bound of detector opening [m]
+* ymin:     Lower y bound of detector opening [m]
+* ymax:     Upper y bound of detector opening [m]
+* xwidth:   Width of detector. Overrides xmin, xmax [m]
+* yheight:  Height of detector. Overrides ymin, ymax [m]
+* nx:       Number of x bins [1]
+* filename: Name of file in which to store the detector image [string]
+* restore_neutron: If set, the monitor does not influence the neutron state [1]
+* nowritefile: [1]      If set, monitor will skip writing to disk
+*
+* OUTPUT PARAMETERS:
+*
+* PSDlin_N:    Array of neutron counts
+* PSDlin_p:    Array of neutron weight counts
+* PSDlin_p2:   Array of second moments
+*
+* %E
+******************************************************************************/
+
+DEFINE COMPONENT PSDlin_monitor
+DEFINITION PARAMETERS (nx=20)
+SETTING PARAMETERS (string filename=0, xmin=-0.05, xmax=0.05, ymin=-0.05, ymax=0.05,
+    xwidth=0, yheight=0, restore_neutron=0, int nowritefile=0)
+OUTPUT PARAMETERS (PSDlin_N, PSDlin_p, PSDlin_p2)
+/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
+
+DECLARE
+  %{
+    double PSDlin_N[nx];
+    double PSDlin_p[nx];
+    double PSDlin_p2[nx];
+  %}
+
+INITIALIZE
+  %{
+    int i;
+
+    if (xwidth  > 0) { xmax = xwidth/2;  xmin = -xmax; }
+    if (yheight > 0) { ymax = yheight/2; ymin = -ymax; }
+
+    if ((xmin >= xmax) || (ymin >= ymax)) {
+            printf("PSDlin_monitor: %s: Null detection area !\n"
+                   "ERROR           (xwidth,yheight,xmin,xmax,ymin,ymax). Exiting",
+           NAME_CURRENT_COMP);
+      exit(0);
+    }
+
+    for (i=0; i<nx; i++)
+    {
+      PSDlin_N[i] = 0;
+      PSDlin_p[i] = 0;
+      PSDlin_p2[i] = 0;
+    }
+  %}
+
+TRACE
+  %{
+    int i;
+
+    PROP_Z0;
+    if (x>xmin && x<xmax && y>ymin && y<ymax)
+    {
+      i = floor(nx*(x-xmin)/(xmax-xmin));              /* Bin number */
+      if((i >= nx) || (i<0))
+      {
+        printf("FATAL ERROR: wrong positioning in linear PSD. i= %i \n",i);
+        exit(1);
+      }
+      PSDlin_N[i]++;
+      PSDlin_p[i] += p;
+      PSDlin_p2[i] += p*p;
+    }
+    if (restore_neutron) {
+      RESTORE_NEUTRON(INDEX_CURRENT_COMP, x, y, z, vx, vy, vz, t, sx, sy, sz, p);
+    }
+  %}
+
+SAVE
+  %{
+    if (!nowritefile) {
+    DETECTOR_OUT_1D(
+        "Linear PSD monitor",
+        "Test",
+        "TEST I",
+        "x", xmin, xmax, nx,
+        &PSDlin_N[0],&PSDlin_p[0],&PSDlin_p2[0],
+        filename);
+    }
+  %}
+
+MCDISPLAY
+%{
+  
+  multiline(5, (double)xmin, (double)ymin, 0.0,
+               (double)xmax, (double)ymin, 0.0,
+               (double)xmax, (double)ymax, 0.0,
+               (double)xmin, (double)ymax, 0.0,
+               (double)xmin, (double)ymin, 0.0);
+%}
+
+END

From e820fbebce92c571bd8935c9accc245791016967 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 28 Apr 2020 11:19:43 +0200
Subject: [PATCH 088/403] Added more unittests to check input_path behavior.

Update to readme file.
---
 README.md                                  |  2 +-
 mcstasscript/helper/component_reader.py    |  7 +-
 mcstasscript/tests/test_ComponentReader.py | 79 ++++++++++++++++++++++
 mcstasscript/tests/test_Instr.py           | 44 ++++++++++++
 4 files changed, 130 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 4e12fc96..3aaa0fa1 100644
--- a/README.md
+++ b/README.md
@@ -9,7 +9,7 @@ McStasScript can be installed using pip,
 
     python3 -m pip install McStasScript --upgrade
 
-After installation it is necessary to configure the package so the McStas installation can be found, here we show how the appropriate code for an Ubuntu system. The configuration is saved permanently, and only needs to be updated when McStas or McStasScript is updated.
+After installation it is necessary to configure the package so the McStas installation can be found, here we show how the appropriate code for an Ubuntu system. The configuration is saved permanently, and only needs to be updated when McStas or McStasScript is updated. This has to be done from within python.
 
     from mcstasscript.interface import functions
     my_configurator = functions.Configurator()
diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index c025d6d0..15858d6a 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -72,10 +72,15 @@ def __init__(self, mcstas_path, input_path="."):
         # Will overwrite McStas components with definitions in input_folder
         current_directory = os.getcwd()
 
+        # Set up absolute input_path
         if os.path.isabs(input_path):
             input_directory = input_path
         else:
-            input_directory = os.path.join(current_directory, input_path)
+            if input_path == ".":
+                # Default case, avoid having /./ in absolute path
+                input_directory = current_directory
+            else:
+                input_directory = os.path.join(current_directory, input_path)
 
         if not os.path.isdir(input_directory):
             print("input_path: ", input_directory)
diff --git a/mcstasscript/tests/test_ComponentReader.py b/mcstasscript/tests/test_ComponentReader.py
index 9b65a4b3..21eccc5c 100644
--- a/mcstasscript/tests/test_ComponentReader.py
+++ b/mcstasscript/tests/test_ComponentReader.py
@@ -87,6 +87,85 @@ def test_ComponentReader_init_filenames(self, mock_stdout):
         self.assertIn("test_for_structure", component_reader.component_path)
         self.assertIn("test_for_structure2", component_reader.component_path)
 
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_ComponentReader_init_component_paths(self, mock_stdout):
+        """
+        Test that ComponentReader stores correct absolute paths to
+        the components found in the McStas installation
+        """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        dummy_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        component_reader = setup_component_reader()
+
+        n_components_found = len(component_reader.component_path)
+        self.assertEqual(n_components_found, 3)
+
+        expected_path = os.path.join(THIS_DIR, "test_for_reading.comp")
+        self.assertIn("test_for_reading", component_reader.component_path)
+        self.assertEqual(component_reader.component_path["test_for_reading"],
+                         expected_path)
+        self.assertEqual(component_reader.component_category["test_for_reading"],
+                         "Work directory")
+
+        expected_path = os.path.join(dummy_path, "misc",
+                                     "test_for_structure.comp")
+        self.assertIn("test_for_structure", component_reader.component_path)
+        self.assertEqual(component_reader.component_path["test_for_structure"],
+                         expected_path)
+        self.assertEqual(component_reader.component_category["test_for_structure"],
+                         "misc")
+
+        expected_path = os.path.join(dummy_path, "sources",
+                                     "test_for_structure2.comp")
+        self.assertIn("test_for_structure2", component_reader.component_path)
+        self.assertEqual(component_reader.component_path["test_for_structure2"],
+                         expected_path)
+        self.assertEqual(component_reader.component_category["test_for_structure2"],
+                         "sources")
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_ComponentReader_init_component_paths_input(self, mock_stdout):
+        """
+        Test that ComponentReader stores correct absolute paths to
+        the components found in the McStas installation.
+        This version uses custom input_path
+        """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        dummy_path = os.path.join(THIS_DIR, "dummy_mcstas")
+        input_path = os.path.join(THIS_DIR, "test_input_folder")
+
+        component_reader = setup_component_reader_input_path()
+
+        n_components_found = len(component_reader.component_path)
+        self.assertEqual(n_components_found, 3)
+
+        expected_path = os.path.join(dummy_path, "misc",
+                                     "test_for_reading.comp")
+        self.assertIn("test_for_reading", component_reader.component_path)
+        self.assertEqual(component_reader.component_path["test_for_reading"],
+                         expected_path)
+        self.assertEqual(component_reader.component_category["test_for_reading"],
+                         "misc")
+
+        expected_path = os.path.join(input_path, "test_for_structure.comp")
+        self.assertIn("test_for_structure", component_reader.component_path)
+        self.assertEqual(component_reader.component_path["test_for_structure"],
+                         expected_path)
+        self.assertEqual(component_reader.component_category["test_for_structure"],
+                         "Work directory")
+
+
+        expected_path = os.path.join(dummy_path, "sources",
+                                     "test_for_structure2.comp")
+        self.assertIn("test_for_structure2", component_reader.component_path)
+        self.assertEqual(component_reader.component_path["test_for_structure2"],
+                         expected_path)
+        self.assertEqual(component_reader.component_category["test_for_structure2"],
+                         "sources")
+
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_init_categories(self, mock_stdout):
         """
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index b9cdf86b..446ab288 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -58,6 +58,23 @@ def setup_instr_with_input_path():
 
     os.chdir(current_work_dir)  # Return to previous workdir
 
+def setup_instr_with_input_path_relative():
+    """
+    Sets up a instrument with a valid mcstas_path, but it points to
+    the dummy installation in the test folder.
+    """
+
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+    current_work_dir = os.getcwd()
+    os.chdir(THIS_DIR)  # Set work directory to test folder
+
+    return McStas_instr("test_instrument",
+                        mcstas_path="dummy_mcstas",
+                        input_path="test_input_folder")
+
+    os.chdir(current_work_dir)  # Return to previous workdir
+
 
 def setup_populated_instr():
     """
@@ -499,6 +516,33 @@ def test_show_components_input_path_simple(self, mock_stdout):
         self.assertEqual(output[5], " misc")
         self.assertEqual(output[6], " Work directory")
 
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_show_components_input_path_simple(self, mock_stdout):
+        """
+        Simple test of input_path being recoignized and passed
+        to component_reader so PSDlin_monitor is overwritten
+        Here dummy_mcstas and input_path is set using relative
+        paths instead of absolute paths.
+        """
+        instr = setup_instr_with_input_path_relative()
+
+        instr.show_components()
+
+        output = mock_stdout.getvalue()
+        output = output.split("\n")
+
+        self.assertEqual(output[0],
+                         "The following components are found in the "
+                         + "work_directory / input_path:")
+        self.assertEqual(output[1], "     test_for_structure.comp")
+        self.assertEqual(output[2], "These definitions will be used "
+                                    +"instead of the installed versions.")
+        self.assertEqual(output[3],
+                         "Here are the available component categories:")
+        self.assertEqual(output[4], " sources")
+        self.assertEqual(output[5], " misc")
+        self.assertEqual(output[6], " Work directory")
+
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_component_help(self, mock_stdout):
         """

From 6b2c5e686909abbaa94eca6bed36d7a4ae014dad Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 28 Apr 2020 15:20:11 +0200
Subject: [PATCH 089/403] Clean up of text and update of documentation.

Added a few extra tests for input_path.

Removed old test files.
---
 McStasScript_documentation.pdf                | Bin 167921 -> 168185 bytes
 examples/McStasScript_demo.ipynb              |  32 ++---
 mcstasscript/tests/test_ManagedMcrun.py       |   3 +
 .../test_input_folder/PSDlin_monitor.comp     | 125 ------------------
 .../test_input_folder/test_for_reading.comp   | 125 ------------------
 5 files changed, 19 insertions(+), 266 deletions(-)
 delete mode 100644 mcstasscript/tests/test_input_folder/PSDlin_monitor.comp
 delete mode 100644 mcstasscript/tests/test_input_folder/test_for_reading.comp

diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
index a442cda3c984120a976b29b61f5dafd7580d916f..af10867ed1820567c9cc066a8c7f962ba350f69a 100644
GIT binary patch
delta 21828
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diff --git a/examples/McStasScript_demo.ipynb b/examples/McStasScript_demo.ipynb
index 305ad40c..d4be9e59 100644
--- a/examples/McStasScript_demo.ipynb
+++ b/examples/McStasScript_demo.ipynb
@@ -435,7 +435,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "double  wavelength  =  3  // Wavelength emmited from source\n"
+      "double  wavelength  =  3  // [AA] Wavelength emmited from source\n"
      ]
     }
    ],
@@ -460,20 +460,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: Using directory: \"jupyter_demo_12\"\n",
+      "INFO: Using directory: \"jupyter_demo_13\"\n",
       "INFO: Regenerating c-file: jupyter_demo.c\n",
       "CFLAGS=\n",
       "INFO: Recompiling: ./jupyter_demo.out\n",
       "INFO: ===\n",
-      "Warning: 509245 events were removed in Component[5] PSD=PSD_monitor()\n",
+      "Warning: 509477 events were removed in Component[5] PSD=PSD_monitor()\n",
       "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "Warning: 510508 events were removed in Component[5] PSD=PSD_monitor()\n",
+      "Warning: 509083 events were removed in Component[5] PSD=PSD_monitor()\n",
       "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "Warning: 509629 events were removed in Component[5] PSD=PSD_monitor()\n",
+      "Warning: 510047 events were removed in Component[5] PSD=PSD_monitor()\n",
       "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "Warning: 510551 events were removed in Component[5] PSD=PSD_monitor()\n",
+      "Warning: 509125 events were removed in Component[5] PSD=PSD_monitor()\n",
       "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "INFO: Placing instr file copy jupyter_demo.instr in dataset jupyter_demo_12\n",
+      "INFO: Placing instr file copy jupyter_demo.instr in dataset jupyter_demo_13\n",
       "\n",
       "Simulation 'jupyter_demo' (jupyter_demo.instr): running on 4 nodes (master is 'CI0020872', MPI version 2.1).\n",
       "Opening input file '/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../data/Cu.laz' (Table_Read_Offset)\n",
@@ -482,9 +482,9 @@
       "PowderN: sample: Reading 19 rows from Cu.laz\n",
       "PowderN: sample: Read 19 reflections from file 'Cu.laz'\n",
       "PowderN: sample: Vc=47.24 [Angs] sigma_abs=15.12 [barn] sigma_inc=2.2 [barn] reflections=Cu.laz\n",
-      "Detector: PSD_4PI_I=42485.8 PSD_4PI_ERR=25.7979 PSD_4PI_N=8.59994e+06 \"PSD_4PI.dat\"\n",
-      "Detector: PSD_I=35503.4 PSD_ERR=24.9716 PSD_N=4.49414e+06 \"PSD.dat\"\n",
-      "Detector: L_mon_I=35503.4 L_mon_ERR=24.9716 L_mon_N=4.49414e+06 \"wave.dat\"\n",
+      "Detector: PSD_4PI_I=42440.9 PSD_4PI_ERR=25.8046 PSD_4PI_N=8.59445e+06 \"PSD_4PI.dat\"\n",
+      "Detector: PSD_I=35456.6 PSD_ERR=24.9783 PSD_N=4.48913e+06 \"PSD.dat\"\n",
+      "Detector: L_mon_I=35456.6 L_mon_ERR=24.9783 L_mon_N=4.48913e+06 \"wave.dat\"\n",
       "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
       "    SPLIT 8 COMPONENT sample=PowderN(...)\n",
       "  in the instrument description jupyter_demo.instr.\n",
@@ -518,11 +518,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1.494, 470.3132617]\n",
-      "[1.495333333, 472.9846513]\n",
-      "[1.496666667, 481.5639576]\n",
-      "[1.498, 474.1251374]\n",
-      "[1.499333333, 475.2808707]\n"
+      "[1.494, 474.8222215]\n",
+      "[1.495333333, 474.7230047]\n",
+      "[1.496666667, 465.981932]\n",
+      "[1.498, 476.3501473]\n",
+      "[1.499333333, 470.3317335]\n"
      ]
     }
    ],
@@ -560,7 +560,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 936x504 with 6 Axes>"
       ]
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index 5fd493e9..79afcb7c 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -40,6 +40,7 @@ def test_ManagedMcrun_init_defaults(self):
 
         self.assertEqual(mcrun_obj.mpi, 1)
         self.assertEqual(mcrun_obj.ncount, 1000000)
+        self.assertEqual(mcrun_obj.run_path, ".")
 
     def test_ManagedMcrun_init_set_values(self):
         """
@@ -48,11 +49,13 @@ def test_ManagedMcrun_init_set_values(self):
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
                                  mcrun_path="",
+                                 run_path="test",
                                  mpi=4,
                                  ncount=128)
 
         self.assertEqual(mcrun_obj.mpi, 4)
         self.assertEqual(mcrun_obj.ncount, 128)
+        self.assertEqual(mcrun_obj.run_path, "test")
 
     def test_ManagedMcrun_init_set_parameters(self):
         """
diff --git a/mcstasscript/tests/test_input_folder/PSDlin_monitor.comp b/mcstasscript/tests/test_input_folder/PSDlin_monitor.comp
deleted file mode 100644
index 9ebc4a33..00000000
--- a/mcstasscript/tests/test_input_folder/PSDlin_monitor.comp
+++ /dev/null
@@ -1,125 +0,0 @@
-/*******************************************************************************
-*
-* McStas, neutron ray-tracing package
-*         Copyright 1997-2002, All rights reserved
-*         Risoe National Laboratory, Roskilde, Denmark
-*         Institut Laue Langevin, Grenoble, France
-*
-* %I
-* Written by:  Kim Lefmann
-* Date: May 7, 2001
-* Version: $Revision$
- Origin: Risoe
-* Release: McStas 1.6
-*
-* Rectangular 1D PSD, measuring intensity vs. vertical position, x
-*
-* %D
-*
-* Example: PSDlin_monitor(nx=20, filename="Output.x", xmin=-0.1, xmax=0.1, ymin=-0.1, ymax=0.1)
-*
-* %P
-* INPUT PARAMETERS:
-*
-* xmin:     Lower x bound of detector opening [m]
-* xmax:     Upper x bound of detector opening [m]
-* ymin:     Lower y bound of detector opening [m]
-* ymax:     Upper y bound of detector opening [m]
-* xwidth:   Width of detector. Overrides xmin, xmax [m]
-* yheight:  Height of detector. Overrides ymin, ymax [m]
-* nx:       Number of x bins [1]
-* filename: Name of file in which to store the detector image [string]
-* restore_neutron: If set, the monitor does not influence the neutron state [1]
-* nowritefile: [1]      If set, monitor will skip writing to disk
-*
-* OUTPUT PARAMETERS:
-*
-* PSDlin_N:    Array of neutron counts
-* PSDlin_p:    Array of neutron weight counts
-* PSDlin_p2:   Array of second moments
-*
-* %E
-******************************************************************************/
-
-DEFINE COMPONENT PSDlin_monitor
-DEFINITION PARAMETERS (nx=20)
-SETTING PARAMETERS (string filename=0, xmin=-0.05, xmax=0.05, ymin=-0.05, ymax=0.05,
-    xwidth=0, yheight=0, restore_neutron=0, int nowritefile=0)
-OUTPUT PARAMETERS (PSDlin_N, PSDlin_p, PSDlin_p2)
-/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
-
-DECLARE
-  %{
-    double PSDlin_N[nx];
-    double PSDlin_p[nx];
-    double PSDlin_p2[nx];
-  %}
-
-INITIALIZE
-  %{
-    int i;
-
-    if (xwidth  > 0) { xmax = xwidth/2;  xmin = -xmax; }
-    if (yheight > 0) { ymax = yheight/2; ymin = -ymax; }
-
-    if ((xmin >= xmax) || (ymin >= ymax)) {
-            printf("PSDlin_monitor: %s: Null detection area !\n"
-                   "ERROR           (xwidth,yheight,xmin,xmax,ymin,ymax). Exiting",
-           NAME_CURRENT_COMP);
-      exit(0);
-    }
-
-    for (i=0; i<nx; i++)
-    {
-      PSDlin_N[i] = 0;
-      PSDlin_p[i] = 0;
-      PSDlin_p2[i] = 0;
-    }
-  %}
-
-TRACE
-  %{
-    int i;
-
-    PROP_Z0;
-    if (x>xmin && x<xmax && y>ymin && y<ymax)
-    {
-      i = floor(nx*(x-xmin)/(xmax-xmin));              /* Bin number */
-      if((i >= nx) || (i<0))
-      {
-        printf("FATAL ERROR: wrong positioning in linear PSD. i= %i \n",i);
-        exit(1);
-      }
-      PSDlin_N[i]++;
-      PSDlin_p[i] += p;
-      PSDlin_p2[i] += p*p;
-    }
-    if (restore_neutron) {
-      RESTORE_NEUTRON(INDEX_CURRENT_COMP, x, y, z, vx, vy, vz, t, sx, sy, sz, p);
-    }
-  %}
-
-SAVE
-  %{
-    if (!nowritefile) {
-    DETECTOR_OUT_1D(
-        "Linear PSD monitor",
-        "Test",
-        "TEST I",
-        "x", xmin, xmax, nx,
-        &PSDlin_N[0],&PSDlin_p[0],&PSDlin_p2[0],
-        filename);
-    }
-  %}
-
-MCDISPLAY
-%{
-  
-  multiline(5, (double)xmin, (double)ymin, 0.0,
-               (double)xmax, (double)ymin, 0.0,
-               (double)xmax, (double)ymax, 0.0,
-               (double)xmin, (double)ymax, 0.0,
-               (double)xmin, (double)ymin, 0.0);
-%}
-
-END
diff --git a/mcstasscript/tests/test_input_folder/test_for_reading.comp b/mcstasscript/tests/test_input_folder/test_for_reading.comp
deleted file mode 100644
index 9ebc4a33..00000000
--- a/mcstasscript/tests/test_input_folder/test_for_reading.comp
+++ /dev/null
@@ -1,125 +0,0 @@
-/*******************************************************************************
-*
-* McStas, neutron ray-tracing package
-*         Copyright 1997-2002, All rights reserved
-*         Risoe National Laboratory, Roskilde, Denmark
-*         Institut Laue Langevin, Grenoble, France
-*
-* %I
-* Written by:  Kim Lefmann
-* Date: May 7, 2001
-* Version: $Revision$
- Origin: Risoe
-* Release: McStas 1.6
-*
-* Rectangular 1D PSD, measuring intensity vs. vertical position, x
-*
-* %D
-*
-* Example: PSDlin_monitor(nx=20, filename="Output.x", xmin=-0.1, xmax=0.1, ymin=-0.1, ymax=0.1)
-*
-* %P
-* INPUT PARAMETERS:
-*
-* xmin:     Lower x bound of detector opening [m]
-* xmax:     Upper x bound of detector opening [m]
-* ymin:     Lower y bound of detector opening [m]
-* ymax:     Upper y bound of detector opening [m]
-* xwidth:   Width of detector. Overrides xmin, xmax [m]
-* yheight:  Height of detector. Overrides ymin, ymax [m]
-* nx:       Number of x bins [1]
-* filename: Name of file in which to store the detector image [string]
-* restore_neutron: If set, the monitor does not influence the neutron state [1]
-* nowritefile: [1]      If set, monitor will skip writing to disk
-*
-* OUTPUT PARAMETERS:
-*
-* PSDlin_N:    Array of neutron counts
-* PSDlin_p:    Array of neutron weight counts
-* PSDlin_p2:   Array of second moments
-*
-* %E
-******************************************************************************/
-
-DEFINE COMPONENT PSDlin_monitor
-DEFINITION PARAMETERS (nx=20)
-SETTING PARAMETERS (string filename=0, xmin=-0.05, xmax=0.05, ymin=-0.05, ymax=0.05,
-    xwidth=0, yheight=0, restore_neutron=0, int nowritefile=0)
-OUTPUT PARAMETERS (PSDlin_N, PSDlin_p, PSDlin_p2)
-/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
-
-DECLARE
-  %{
-    double PSDlin_N[nx];
-    double PSDlin_p[nx];
-    double PSDlin_p2[nx];
-  %}
-
-INITIALIZE
-  %{
-    int i;
-
-    if (xwidth  > 0) { xmax = xwidth/2;  xmin = -xmax; }
-    if (yheight > 0) { ymax = yheight/2; ymin = -ymax; }
-
-    if ((xmin >= xmax) || (ymin >= ymax)) {
-            printf("PSDlin_monitor: %s: Null detection area !\n"
-                   "ERROR           (xwidth,yheight,xmin,xmax,ymin,ymax). Exiting",
-           NAME_CURRENT_COMP);
-      exit(0);
-    }
-
-    for (i=0; i<nx; i++)
-    {
-      PSDlin_N[i] = 0;
-      PSDlin_p[i] = 0;
-      PSDlin_p2[i] = 0;
-    }
-  %}
-
-TRACE
-  %{
-    int i;
-
-    PROP_Z0;
-    if (x>xmin && x<xmax && y>ymin && y<ymax)
-    {
-      i = floor(nx*(x-xmin)/(xmax-xmin));              /* Bin number */
-      if((i >= nx) || (i<0))
-      {
-        printf("FATAL ERROR: wrong positioning in linear PSD. i= %i \n",i);
-        exit(1);
-      }
-      PSDlin_N[i]++;
-      PSDlin_p[i] += p;
-      PSDlin_p2[i] += p*p;
-    }
-    if (restore_neutron) {
-      RESTORE_NEUTRON(INDEX_CURRENT_COMP, x, y, z, vx, vy, vz, t, sx, sy, sz, p);
-    }
-  %}
-
-SAVE
-  %{
-    if (!nowritefile) {
-    DETECTOR_OUT_1D(
-        "Linear PSD monitor",
-        "Test",
-        "TEST I",
-        "x", xmin, xmax, nx,
-        &PSDlin_N[0],&PSDlin_p[0],&PSDlin_p2[0],
-        filename);
-    }
-  %}
-
-MCDISPLAY
-%{
-  
-  multiline(5, (double)xmin, (double)ymin, 0.0,
-               (double)xmax, (double)ymin, 0.0,
-               (double)xmax, (double)ymax, 0.0,
-               (double)xmin, (double)ymax, 0.0,
-               (double)xmin, (double)ymin, 0.0);
-%}
-
-END

From c4c45157918d34be17039ef7f997e2b61ff7ecaf Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Fri, 1 May 2020 11:19:40 +0200
Subject: [PATCH 090/403] Fixed a bug in instrument reader where the name of
 the instrument would not be read when a space was inserted between the name
 and parameters.

---
 mcstasscript/instr_reader/control.py         | 2 +-
 mcstasscript/instr_reader/read_definition.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/mcstasscript/instr_reader/control.py b/mcstasscript/instr_reader/control.py
index 9dc7824d..aacbf5c7 100644
--- a/mcstasscript/instr_reader/control.py
+++ b/mcstasscript/instr_reader/control.py
@@ -176,7 +176,7 @@ def _read_file(self):
             if definition_mode and not comment_mode:
                 # Get instrument name
                 if not instr_name_read:
-                    self.instr_name = line.split("(")[0].split(" ")[-1]
+                    self.instr_name = line.split("(")[0].strip().split(" ")[-1]
                     instr_name_read = True
                     self.update_file_name()
 
diff --git a/mcstasscript/instr_reader/read_definition.py b/mcstasscript/instr_reader/read_definition.py
index 7270cff4..d4ce1dd5 100644
--- a/mcstasscript/instr_reader/read_definition.py
+++ b/mcstasscript/instr_reader/read_definition.py
@@ -27,7 +27,7 @@ def read_definition_line(self, line):
         
         if "(" in line:
             # Start of instrument definition, get name
-            self.instr_name = line.split("(")[0].split(" ")[-1]
+            self.instr_name = line.split("(")[0].strip().split(" ")[-1]
             self._start_py_file()
             # Remove the parameters from the paranthesis
             parameters = line.split("(")[1]

From 9c2e756972bb56ffac612732320c74230576caff Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Fri, 1 May 2020 11:39:34 +0200
Subject: [PATCH 091/403] Updated integration tests to use the integration_test
 folder as the work directory.

---
 .../test_complex_instrument.py                |  7 +++++
 .../test_simple_instrument.py                 | 30 +++++++++++++++++++
 setup.py                                      |  2 +-
 3 files changed, 38 insertions(+), 1 deletion(-)

diff --git a/mcstasscript/integration_tests/test_complex_instrument.py b/mcstasscript/integration_tests/test_complex_instrument.py
index a39bc773..f8238a05 100644
--- a/mcstasscript/integration_tests/test_complex_instrument.py
+++ b/mcstasscript/integration_tests/test_complex_instrument.py
@@ -1,4 +1,5 @@
 import io
+import os
 import time
 import unittest
 import unittest.mock
@@ -138,6 +139,10 @@ def test_complex_instrument(self, mock_stdout):
         Test parameters can be controlled through McStasScript.  Here
         a slit is moved to one side and the result is verified.
         """
+        CURRENT_DIR = os.getcwd()
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        os.chdir(THIS_DIR)
+
         Instr = setup_complex_instrument()
 
         data = Instr.run_full_instrument(foldername="integration_test_complex",
@@ -146,6 +151,8 @@ def test_complex_instrument(self, mock_stdout):
                                          parameters={"guide_width": 0.03,
                                                      "guide_length": 8.0})
 
+        os.chdir(CURRENT_DIR)
+
         intensity_data_pos = functions.name_search("PSD_1D_1", data).Intensity
         sum_outside_beam = sum(intensity_data_pos[0:50])
         sum_inside_beam = sum(intensity_data_pos[51:99])
diff --git a/mcstasscript/integration_tests/test_simple_instrument.py b/mcstasscript/integration_tests/test_simple_instrument.py
index 18215819..fc0a919f 100644
--- a/mcstasscript/integration_tests/test_simple_instrument.py
+++ b/mcstasscript/integration_tests/test_simple_instrument.py
@@ -103,12 +103,18 @@ def test_simple_instrument(self, mock_stdout):
         expectations. Here beam in small area in the middle of the
         detector.
         """
+        CURRENT_DIR = os.getcwd()
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        os.chdir(THIS_DIR)
+
         Instr = setup_simple_instrument()
 
         data = Instr.run_full_instrument(foldername="integration_test_simple",
                                          ncount=1E6, mpi=1,
                                          increment_folder_name=True)
 
+        os.chdir(CURRENT_DIR)
+
         intensity_data = data[0].Intensity
         # beam should be on pixel 35 to 65
 
@@ -125,12 +131,18 @@ def test_simple_instrument_input(self, mock_stdout):
         expectations. Here beam in small area in the middle of the
         detector.
         """
+        CURRENT_DIR = os.getcwd()
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        os.chdir(THIS_DIR)
+
         Instr = setup_simple_instrument_input_path()
 
         data = Instr.run_full_instrument(foldername="integration_test_simple_input",
                                          ncount=1E6, mpi=1,
                                          increment_folder_name=True)
 
+        os.chdir(CURRENT_DIR)
+
         intensity_data = data[0].Intensity
         # beam should be on pixel 35 to 65
 
@@ -150,12 +162,18 @@ def test_simple_instrument_mpi(self, mock_stdout):
         expectations. Here beam in small area in the middle of the
         detector. Running with mpi, 2 cores.
         """
+        CURRENT_DIR = os.getcwd()
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        os.chdir(THIS_DIR)
+
         Instr = setup_simple_instrument()
 
         data = Instr.run_full_instrument(foldername="integration_test_mpi",
                                          ncount=1E6, mpi=2,
                                          increment_folder_name=True)
 
+        os.chdir(CURRENT_DIR)
+
         intensity_data = data[0].Intensity
         # beam should be on pixel 35 to 65
 
@@ -172,12 +190,18 @@ def test_slit_instrument(self, mock_stdout):
         a slit is can be moved, but the default value of 0 should be
         used.
         """
+        CURRENT_DIR = os.getcwd()
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        os.chdir(THIS_DIR)
+
         Instr = setup_simple_slit_instrument()
 
         data = Instr.run_full_instrument(foldername="integration_test_slit",
                                          ncount=2E6, mpi=2,
                                          increment_folder_name=True)
 
+        os.chdir(CURRENT_DIR)
+
         intensity_data = data[0].Intensity
         # beam should be on pixel 45 to 55
 
@@ -192,6 +216,10 @@ def test_slit_moved_instrument(self, mock_stdout):
         Test parameters can be controlled through McStasScript.  Here
         a slit is moved to one side and the result is verified.
         """
+        CURRENT_DIR = os.getcwd()
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        os.chdir(THIS_DIR)
+
         Instr = setup_simple_slit_instrument()
 
         data = Instr.run_full_instrument(foldername="integration_test_slit",
@@ -199,6 +227,8 @@ def test_slit_moved_instrument(self, mock_stdout):
                                          increment_folder_name=True,
                                          parameters={"slit_offset": 0.03})
 
+        os.chdir(CURRENT_DIR)
+
         intensity_data = data[0].Intensity
         # beam should be on pixel 75 to 85
 
diff --git a/setup.py b/setup.py
index 92ba8d7f..b570e811 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
      name='McStasScript',
-     version='0.0.15',
+     version='0.0.16',
      author="Mads Bertelsen",
      author_email="Mads.Bertelsen@ess.eu",
      description="A python scripting interface for McStas",

From 87c59ec2b1f84df227e2086a6c8aab389bfda6c3 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Fri, 1 May 2020 12:46:20 +0200
Subject: [PATCH 092/403] Update that allows relative to take component
 instances as well as strings. The component name will be taken from the
 instance, there is no checking done to confirm the object is from the same
 instrument.

ValueErrors are raised if the type is not str or component.

Unit tests are added to check this works as intended.
---
 mcstasscript/helper/mcstas_objects.py | 101 +++++++++++++++++---------
 mcstasscript/tests/test_component.py  |  74 ++++++++++++++++++-
 2 files changed, 139 insertions(+), 36 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index eb35f161..8fab594f 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -278,12 +278,18 @@ class contains both methods to write the component to a instrument
 
     Methods
     -------
-    set_AT(at_list,**kwargs)
+    set_AT(at_list, RELATIVE)
         Sets AT_data, can set AT_relative using keyword
 
-    set_ROTATED(rotated_list,**kwargs)
+    set_AT_RELATIVE(relative)
+        Can set RELATIVE for position
+
+    set_ROTATED(rotated_list, RELATIVE)
         Sets ROTATED_data, can set ROTATED_relative using keyword
 
+    set_ROTATED_RELATIVE(relative)
+        Can set RELATIVE for rotation
+
     set_RELATIVE(relative_name)
         Set both AT_relative and ROTATED_relative to relative_name
 
@@ -411,27 +417,20 @@ def set_keyword_input(self, **kwargs):
         self._unfreeze()
 
         if "AT" in kwargs:
-            self.AT_data = kwargs["AT"]
-        
-        # Could check if AT_RELATIVE is a string
+            self.set_AT(kwargs["AT"])
+
         if "AT_RELATIVE" in kwargs:
-            self.AT_relative = "RELATIVE " + kwargs["AT_RELATIVE"]
+            self.set_AT_RELATIVE(kwargs["AT_RELATIVE"])
 
         self.ROTATED_specified = False
         if "ROTATED" in kwargs:
-            self.ROTATED_data = kwargs["ROTATED"]
-            self.ROTATED_specified = True
+            self.set_ROTATED(kwargs["ROTATED"])
 
-        # Could check if ROTATED_RELATIVE is a string
         if "ROTATED_RELATIVE" in kwargs:
-            self.ROTATED_relative = "RELATIVE " + kwargs["ROTATED_RELATIVE"]
-            self.ROTATED_specified = True
+            self.set_ROTATED_RELATIVE(kwargs["ROTATED_RELATIVE"])
 
-        # Could check if RELATIVE is a string
         if "RELATIVE" in kwargs:
-            self.AT_relative = "RELATIVE " + kwargs["RELATIVE"]
-            self.ROTATED_relative = "RELATIVE " + kwargs["RELATIVE"]
-            self.ROTATED_specified = True
+            self.set_RELATIVE(kwargs["RELATIVE"])
 
         if "WHEN" in kwargs:
             self.WHEN = "WHEN (" + kwargs["WHEN"] + ")"
@@ -475,35 +474,67 @@ def _freeze(self):
     def _unfreeze(self):
         self.__isfrozen = False
 
-    def set_AT(self, at_list, **kwargs):
+    def set_AT(self, at_list, RELATIVE=None):
         """Sets AT data, List of 3 floats"""
         self.AT_data = at_list
-        if "RELATIVE" in kwargs:
-            relative_name = kwargs["RELATIVE"]
-            if relative_name == "ABSOLUTE":
-                self.AT_relative = relative_name
-            else:
-                self.AT_relative = "RELATIVE " + relative_name
+        if RELATIVE is not None:
+            self.set_AT_RELATIVE(RELATIVE)
+
+    def set_AT_RELATIVE(self, relative):
+        """Sets AT RELATIVE with string or component instance"""
+
+        # Extract name if component instance is given
+        if isinstance(relative, component):
+            relative = relative.name
+        elif not isinstance(relative, str):
+            raise ValueError("Relative must be either string or "
+                             + "component object.")
+
+        # Set AT relative
+        if relative == "ABSOLUTE":
+            self.AT_relative = "ABSOLUTE"
+        else:
+            self.AT_relative = "RELATIVE " + relative
 
-    def set_ROTATED(self, rotated_list, **kwargs):
+    def set_ROTATED(self, rotated_list, RELATIVE=None):
         """Sets ROTATED data, List of 3 floats"""
         self.ROTATED_data = rotated_list
         self.ROTATED_specified = True
-        if "RELATIVE" in kwargs:
-            relative_name = kwargs["RELATIVE"]
-            if relative_name == "ABSOLUTE":
-                self.ROTATED_relative = relative_name
-            else:
-                self.ROTATED_relative = "RELATIVE " + relative_name
+        if RELATIVE is not None:
+            self.set_ROTATED_RELATIVE(RELATIVE)
+
+    def set_ROTATED_RELATIVE(self, relative):
+        """Sets ROTATED RELATIVE with string or component instance"""
+
+        self.ROTATED_specified = True
+        # Extract name if a component instance is given
+        if isinstance(relative, component):
+            relative = relative.name
+        elif not isinstance(relative, str):
+            raise ValueError("Relative must be either string or "
+                             + "component object.")
+
+        # Set ROTATED relative
+        if relative == "ABSOLUTE":
+            self.ROTATED_relative = "ABSOLUTE"
+        else:
+            self.ROTATED_relative = "RELATIVE " + relative
 
-    def set_RELATIVE(self, relative_name):
+    def set_RELATIVE(self, relative):
         """Sets both AT_relative and ROTATED_relative"""
-        if relative_name == "ABSOLUTE":
-            self.AT_relative = relative_name
-            self.ROTATED_relative = relative_name
+        # Extract name if a component instance is given
+        if isinstance(relative, component):
+            relative = relative.name
+        elif not isinstance(relative, str):
+            raise ValueError("Relative must be either string or "
+                             + "component object.")
+
+        if relative == "ABSOLUTE":
+            self.AT_relative = "ABSOLUTE"
+            self.ROTATED_relative = "ABSOLUTE"
         else:
-            self.AT_relative = "RELATIVE " + relative_name
-            self.ROTATED_relative = "RELATIVE " + relative_name
+            self.AT_relative = "RELATIVE " + relative
+            self.ROTATED_relative = "RELATIVE " + relative
 
     def set_parameters(self, dict_input):
         """
diff --git a/mcstasscript/tests/test_component.py b/mcstasscript/tests/test_component.py
index 1beaf489..eceb92f3 100644
--- a/mcstasscript/tests/test_component.py
+++ b/mcstasscript/tests/test_component.py
@@ -166,7 +166,6 @@ def test_component_basic_init_set_AT(self):
         """
         Testing set_AT method
         """
-
         comp = component("test_component", "Arm")
 
         comp.set_AT([12.124, 214.0, 2], RELATIVE="monochromator")
@@ -176,6 +175,35 @@ def test_component_basic_init_set_AT(self):
         self.assertEqual(comp.AT_data, [12.124, 214.0, 2])
         self.assertEqual(comp.AT_relative, "RELATIVE monochromator")
 
+    def test_component_basic_init_set_AT_component(self):
+        """
+        Testing set_AT method using component object
+        """
+
+        prev_component = component("relative_base", "Arm")
+        comp = component("test_component", "Arm")
+
+        comp.set_AT([12.124, 214.0, 2], RELATIVE=prev_component)
+
+        self.assertEqual(comp.name, "test_component")
+        self.assertEqual(comp.component_name, "Arm")
+        self.assertEqual(comp.AT_data, [12.124, 214.0, 2])
+        self.assertEqual(comp.AT_relative, "RELATIVE relative_base")
+
+    def test_component_basic_init_set_AT_component_keyword(self):
+        """
+        Testing set_AT method using component object
+        """
+
+        prev_component = component("relative_base", "Arm")
+        comp = component("test_component", "Arm",
+                         AT=[1, 2, 3.0], AT_RELATIVE=prev_component)
+
+        self.assertEqual(comp.name, "test_component")
+        self.assertEqual(comp.component_name, "Arm")
+        self.assertEqual(comp.AT_data, [1, 2, 3.0])
+        self.assertEqual(comp.AT_relative, "RELATIVE relative_base")
+
     def test_component_basic_init_set_ROTATED(self):
         """
         Testing set_ROTATED method
@@ -190,6 +218,35 @@ def test_component_basic_init_set_ROTATED(self):
         self.assertEqual(comp.ROTATED_data, [1204.8, 8490.1, 129])
         self.assertEqual(comp.ROTATED_relative, "RELATIVE analyzer")
 
+    def test_component_basic_init_set_ROTATED_component(self):
+        """
+        Testing set_ROTATED method
+        """
+
+        prev_component = component("relative_base", "Arm")
+        comp = component("test_component", "Arm")
+
+        comp.set_ROTATED([1204.8, 8490.1, 129], RELATIVE=prev_component)
+
+        self.assertEqual(comp.name, "test_component")
+        self.assertEqual(comp.component_name, "Arm")
+        self.assertEqual(comp.ROTATED_data, [1204.8, 8490.1, 129])
+        self.assertEqual(comp.ROTATED_relative, "RELATIVE relative_base")
+
+    def test_component_basic_init_set_ROTATED_component_keyword(self):
+        """
+        Testing set_ROTATED method
+        """
+
+        prev_component = component("relative_base", "Arm")
+        comp = component("test_component", "Arm",
+                         ROTATED=[1, 2, 3.0], ROTATED_RELATIVE=prev_component)
+
+        self.assertEqual(comp.name, "test_component")
+        self.assertEqual(comp.component_name, "Arm")
+        self.assertEqual(comp.ROTATED_data, [1, 2, 3.0])
+        self.assertEqual(comp.ROTATED_relative, "RELATIVE relative_base")
+
     def test_component_basic_init_set_RELATIVE(self):
         """
         Testing set_RELATIVE method
@@ -204,6 +261,21 @@ def test_component_basic_init_set_RELATIVE(self):
         self.assertEqual(comp.AT_relative, "RELATIVE sample")
         self.assertEqual(comp.ROTATED_relative, "RELATIVE sample")
 
+    def test_component_basic_init_set_RELATIVE(self):
+        """
+        Testing set_RELATIVE method
+        """
+
+        prev_component = component("relative_base", "Arm")
+        comp = component("test_component", "Arm")
+
+        comp.set_RELATIVE(prev_component)
+
+        self.assertEqual(comp.name, "test_component")
+        self.assertEqual(comp.component_name, "Arm")
+        self.assertEqual(comp.AT_relative, "RELATIVE relative_base")
+        self.assertEqual(comp.ROTATED_relative, "RELATIVE relative_base")
+
     def test_component_basic_init_set_parameters(self):
         """
         Testing set_parameters method. Need to set some attribute

From e0f9dcc118b422cac590d8a5a01bbadeb2bb3346 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Fri, 3 Jul 2020 08:18:33 +0200
Subject: [PATCH 093/403] Update that removes mpi if not set, meaning
 McStasScript can be run without an MPI working.

---
 mcstasscript/helper/managed_mcrun.py    | 10 +++++++---
 mcstasscript/tests/test_Instr.py        |  2 +-
 mcstasscript/tests/test_ManagedMcrun.py |  6 +++---
 3 files changed, 11 insertions(+), 7 deletions(-)

diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 90cf4546..21d25c8a 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -79,7 +79,7 @@ def __init__(self, instr_name, **kwargs):
 
         self.data_folder_name = ""
         self.ncount = int(1E6)
-        self.mpi = 1
+        self.mpi = None
         self.parameters = {}
         self.custom_flags = ""
         self.mcrun_path = ""
@@ -141,10 +141,14 @@ def run_simulation(self, **kwargs):
         if self.compile:
             options_string = "-c "
 
+        if self.mpi is not None:
+            mpi_string = " --mpi=" + str(self.mpi) + " " # Set mpi
+        else:
+            mpi_string = " "
+
         option_string = (options_string
                          + "-n " + str(self.ncount)  # Set ncount
-                         + " --mpi=" + str(self.mpi)  # Set mpi
-                         + " ")
+                         + mpi_string)
 
         if self.increment_folder_name and os.path.isdir(self.data_folder_name):
             counter = 0
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 446ab288..c61cc49a 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1511,7 +1511,7 @@ def test_run_full_instrument_basic(self, mock_sub,
         expected_folder_path = os.path.join(current_directory, "test_data_set")
 
         # a double space because of a missing option
-        expected_call = (expected_path + " -c -n 1000000 --mpi=1 "
+        expected_call = (expected_path + " -c -n 1000000 "
                          + "-d " + expected_folder_path
                          + "  test_instrument.instr"
                          + " has_default=37 theta=1")
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index 79afcb7c..cc6ed1f3 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -38,7 +38,7 @@ def test_ManagedMcrun_init_defaults(self):
                                  foldername="test_folder",
                                  mcrun_path="")
 
-        self.assertEqual(mcrun_obj.mpi, 1)
+        self.assertEqual(mcrun_obj.mpi, None)
         self.assertEqual(mcrun_obj.ncount, 1000000)
         self.assertEqual(mcrun_obj.run_path, ".")
 
@@ -114,7 +114,7 @@ def test_ManagedMcrun_run_simulation_basic(self, mock_sub):
         expected_folder_path = os.path.join(current_directory, "test_folder")
 
         # a double space because of a missing option
-        expected_call = ("path/mcrun -c -n 1000000 --mpi=1 "
+        expected_call = ("path/mcrun -c -n 1000000 "
                          + "-d " + expected_folder_path + "  test.instr")
 
         mock_sub.assert_called_once_with(expected_call,
@@ -138,7 +138,7 @@ def test_ManagedMcrun_run_simulation_basic_path(self, mock_sub):
         expected_folder_path = os.path.join(current_directory, "test_folder")
 
         # a double space because of a missing option
-        expected_call = ("path/mcrun -c -n 1000000 --mpi=1 "
+        expected_call = ("path/mcrun -c -n 1000000 "
                          + "-d " + expected_folder_path + "  test.instr")
 
         mock_sub.assert_called_once_with(expected_call,

From 307e8fae1b94839364e93be7a26531585c5a3b9b Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Wed, 22 Jul 2020 15:32:37 +0200
Subject: [PATCH 094/403] Fixed a bug where one row of data was not read
 correct for 2D monitors.

Plotting was also updated to avoid having the data stored in a transposed form.
Now it is effectively just transposed for correct plotting.
---
 mcstasscript/helper/managed_mcrun.py    |  7 ++++---
 mcstasscript/interface/plotter.py       | 18 +++++++++---------
 mcstasscript/tests/test_ManagedMcrun.py | 12 ++++++------
 mcstasscript/tests/test_functions.py    |  6 +++---
 setup.py                                |  2 +-
 5 files changed, 23 insertions(+), 22 deletions(-)

diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 21d25c8a..545a247b 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -281,9 +281,10 @@ def load_results(self, *args):
             elif len(metadata.dimension) == 2:
                 xaxis = []  # Assume evenly binned in 2d
                 data_lines = metadata.dimension[1]
-                Intensity = data.T[:, 0:data_lines - 1]
-                Error = data.T[:, data_lines:2*data_lines - 1]
-                Ncount = data.T[:, 2*data_lines:3*data_lines - 1]
+
+                Intensity = data[0:data_lines, :]
+                Error = data[data_lines:2*data_lines, :]
+                Ncount = data[2*data_lines:3*data_lines, :]
             else:
                 raise NameError(
                     "Dimension not read correctly in data set "
diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index 96c7cce6..434caf0b 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -139,10 +139,10 @@ def __init__(self, data_list, **kwargs):
                                 data.metadata.dimension[0]+1)
                 Y = np.linspace(data.metadata.limits[2]*y_axis_mult,
                                 data.metadata.limits[3]*y_axis_mult,
-                                data.metadata.dimension[1])
+                                data.metadata.dimension[1]+1)
 
                 # Create a meshgrid for both x and y
-                y, x = np.meshgrid(Y, X)
+                x, y = np.meshgrid(X, Y)
 
                 # Generate information on necessary colorrange
                 levels = MaxNLocator(nbins=150).tick_values(min_value,
@@ -159,7 +159,7 @@ def __init__(self, data_list, **kwargs):
                 if data.plot_options.log:
                     color_norm = matplotlib.colors.LogNorm(vmin=min_value,
                                                            vmax=max_value)
-                    im = ax0.pcolormesh(x, y, to_plot,
+                    im = ax0.pcolormesh(y, x, to_plot,
                                         cmap=cmap, norm=color_norm)
                 else:
                     im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
@@ -328,10 +328,10 @@ def __init__(self, data_list, **kwargs):
                                 data.metadata.dimension[0]+1)
                 Y = np.linspace(data.metadata.limits[2]*y_axis_mult,
                                 data.metadata.limits[3]*y_axis_mult,
-                                data.metadata.dimension[1])
+                                data.metadata.dimension[1]+1)
 
                 # Create a meshgrid for both x and y
-                y, x = np.meshgrid(Y, X)
+                x, y = np.meshgrid(X, Y)
 
                 # Generate information on necessary colorrange
                 levels = MaxNLocator(nbins=150).tick_values(min_value,
@@ -348,7 +348,7 @@ def __init__(self, data_list, **kwargs):
                     norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
 
                 # Create plot
-                im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
+                im = ax0.pcolormesh(y, x, to_plot, cmap=cmap, norm=norm)
 
                 def fmt(x, pos):
                     a, b = '{:.2e}'.format(x).split('e')
@@ -551,10 +551,10 @@ def __init__(self, data_list, **kwargs):
                             data.metadata.dimension[0]+1)
             Y = np.linspace(data.metadata.limits[2]*y_axis_mult,
                             data.metadata.limits[3]*y_axis_mult,
-                            data.metadata.dimension[1])
+                            data.metadata.dimension[1]+1)
 
             # Create a meshgrid for both x and y
-            y, x = np.meshgrid(Y, X)
+            x, y = np.meshgrid(X, Y)
 
             # Generate information on necessary colorrange
             levels = MaxNLocator(nbins=150).tick_values(min_value,
@@ -571,7 +571,7 @@ def __init__(self, data_list, **kwargs):
                 norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
 
             # Create plot
-            im = ax.pcolormesh(x, y, Intensity,
+            im = ax.pcolormesh(y, x, Intensity,
                                cmap=cmap, norm=norm)
 
             def fmt(x, pos):
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index cc6ed1f3..a0d003f9 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -263,9 +263,9 @@ def test_ManagedMcrun_load_data_PSD4PI(self):
         self.assertEqual(PSD_4PI.metadata.xlabel, "Longitude [deg]")
         self.assertEqual(PSD_4PI.metadata.ylabel, "Lattitude [deg]")
         self.assertEqual(PSD_4PI.metadata.title, "4PI PSD monitor")
-        self.assertEqual(PSD_4PI.Ncount[1][4], 4)
-        self.assertEqual(PSD_4PI.Intensity[1][4], 1.537334562E-10)
-        self.assertEqual(PSD_4PI.Error[1][4], 1.139482296E-10)
+        self.assertEqual(PSD_4PI.Ncount[4][1], 4)
+        self.assertEqual(PSD_4PI.Intensity[4][1], 1.537334562E-10)
+        self.assertEqual(PSD_4PI.Error[4][1], 1.139482296E-10)
 
     def test_ManagedMcrun_load_data_PSD(self):
         """
@@ -295,9 +295,9 @@ def test_ManagedMcrun_load_data_PSD(self):
         self.assertEqual(PSD.metadata.xlabel, "X position [cm]")
         self.assertEqual(PSD.metadata.ylabel, "Y position [cm]")
         self.assertEqual(PSD.metadata.title, "PSD monitor")
-        self.assertEqual(PSD.Ncount[21][27], 9)
-        self.assertEqual(PSD.Intensity[21][27], 2.623929371e-13)
-        self.assertEqual(PSD.Error[21][27], 2.765467693e-13)
+        self.assertEqual(PSD.Ncount[27][21], 9)
+        self.assertEqual(PSD.Intensity[27][21], 2.623929371e-13)
+        self.assertEqual(PSD.Error[27][21], 2.765467693e-13)
 
     def test_ManagedMcrun_load_data_L_mon(self):
         """
diff --git a/mcstasscript/tests/test_functions.py b/mcstasscript/tests/test_functions.py
index 6e884f7e..40db8f6a 100644
--- a/mcstasscript/tests/test_functions.py
+++ b/mcstasscript/tests/test_functions.py
@@ -256,9 +256,9 @@ def test_crun_load_data_PSD4PI(self):
         self.assertEqual(PSD_4PI.metadata.xlabel, "Longitude [deg]")
         self.assertEqual(PSD_4PI.metadata.ylabel, "Lattitude [deg]")
         self.assertEqual(PSD_4PI.metadata.title, "4PI PSD monitor")
-        self.assertEqual(PSD_4PI.Ncount[1][4], 4)
-        self.assertEqual(PSD_4PI.Intensity[1][4], 1.537334562E-10)
-        self.assertEqual(PSD_4PI.Error[1][4], 1.139482296E-10)
+        self.assertEqual(PSD_4PI.Ncount[4][1], 4)
+        self.assertEqual(PSD_4PI.Intensity[4][1], 1.537334562E-10)
+        self.assertEqual(PSD_4PI.Error[4][1], 1.139482296E-10)
 
 
 if __name__ == '__main__':
diff --git a/setup.py b/setup.py
index b570e811..f8bfacd4 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
      name='McStasScript',
-     version='0.0.16',
+     version='0.0.18',
      author="Mads Bertelsen",
      author_email="Mads.Bertelsen@ess.eu",
      description="A python scripting interface for McStas",

From dca46e13365e5194b5e77731a9cd538663290c85 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Mon, 27 Jul 2020 09:32:23 +0200
Subject: [PATCH 095/403] Fix of plotting orientation.

---
 mcstasscript/interface/plotter.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index 434caf0b..f49fdf3d 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -159,7 +159,7 @@ def __init__(self, data_list, **kwargs):
                 if data.plot_options.log:
                     color_norm = matplotlib.colors.LogNorm(vmin=min_value,
                                                            vmax=max_value)
-                    im = ax0.pcolormesh(y, x, to_plot,
+                    im = ax0.pcolormesh(x, y, to_plot,
                                         cmap=cmap, norm=color_norm)
                 else:
                     im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
@@ -348,7 +348,7 @@ def __init__(self, data_list, **kwargs):
                     norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
 
                 # Create plot
-                im = ax0.pcolormesh(y, x, to_plot, cmap=cmap, norm=norm)
+                im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
 
                 def fmt(x, pos):
                     a, b = '{:.2e}'.format(x).split('e')
@@ -571,7 +571,7 @@ def __init__(self, data_list, **kwargs):
                 norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
 
             # Create plot
-            im = ax.pcolormesh(y, x, Intensity,
+            im = ax.pcolormesh(x, y, Intensity,
                                cmap=cmap, norm=norm)
 
             def fmt(x, pos):

From 272437b6ef17e17e37abe668d5638f5a2ca0a183 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Mon, 3 Aug 2020 15:25:24 +0200
Subject: [PATCH 096/403] Fixed issue with C style // comments in component
 definition being read wrong.

---
 mcstasscript/helper/component_reader.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index 15858d6a..cb806f9f 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -312,6 +312,7 @@ def read_component_file(self, absolute_path):
                     or self.line_starts_with(line.strip(),
                                              "SETTING PARAMETERS")):
 
+                line = line.split("//")[0] # Remove comments
                 parts = line.split("(")
                 parameter_parts = parts[1].split(",")
                 
@@ -384,7 +385,8 @@ def read_component_file(self, absolute_path):
                     if break_now:
                         break
 
-                    parameter_parts = fo.readline().split(",")
+                    new_line = fo.readline().split("//")[0]
+                    parameter_parts = new_line.split(",")
                     parameter_parts = self.correct_for_brackets(parameter_parts)
 
             if self.line_starts_with(line, "DECLARE"):

From d65cbc5e04c5f04774eaa187796a185204ecec6b Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Wed, 5 Aug 2020 15:14:40 +0200
Subject: [PATCH 097/403] Adding McStas / Union tutorial using McStasScript in
 Jupyter notebooks.

11 Notebooks covering:
	Basic use of McStas with McStasScript
	Use of advanced keywords SPLIT, EXTEND, WHEN and JUMP
	Basic use of Union components
		Processes and Materials
		Geometry system including masks
		Loggers and conditionals
		Using external components with Union
		The history system

Needs newest version of Union components from McStas master branch.
These should be located in the run_folder.
---
 .../McStasScript_tutorial_1_the_basics.ipynb  | 620 ++++++++++++++++++
 tutorial/McStasScript_tutorial_2_SPLIT.ipynb  | 267 ++++++++
 ...tasScript_tutorial_3_EXTEND_and_WHEN.ipynb | 235 +++++++
 tutorial/McStasScript_tutorial_4_JUMP.ipynb   | 255 +++++++
 ...n_tutorial_1_processes_and_materials.ipynb | 436 ++++++++++++
 tutorial/Union_tutorial_2_geometry.ipynb      | 495 ++++++++++++++
 tutorial/Union_tutorial_3_loggers.ipynb       | 581 ++++++++++++++++
 tutorial/Union_tutorial_4_conditionals.ipynb  | 524 +++++++++++++++
 tutorial/Union_tutorial_5_masks.ipynb         | 358 ++++++++++
 ...ial_6_Exit_and_number_of_activations.ipynb | 385 +++++++++++
 .../Union_tutorial_7_Tagging_history.ipynb    | 303 +++++++++
 tutorial/data_folder/note.txt                 |   1 +
 tutorial/run_folder/note.txt                  |   2 +
 13 files changed, 4462 insertions(+)
 create mode 100644 tutorial/McStasScript_tutorial_1_the_basics.ipynb
 create mode 100644 tutorial/McStasScript_tutorial_2_SPLIT.ipynb
 create mode 100644 tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
 create mode 100644 tutorial/McStasScript_tutorial_4_JUMP.ipynb
 create mode 100644 tutorial/Union_tutorial_1_processes_and_materials.ipynb
 create mode 100644 tutorial/Union_tutorial_2_geometry.ipynb
 create mode 100644 tutorial/Union_tutorial_3_loggers.ipynb
 create mode 100644 tutorial/Union_tutorial_4_conditionals.ipynb
 create mode 100644 tutorial/Union_tutorial_5_masks.ipynb
 create mode 100644 tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
 create mode 100644 tutorial/Union_tutorial_7_Tagging_history.ipynb
 create mode 100644 tutorial/data_folder/note.txt
 create mode 100644 tutorial/run_folder/note.txt

diff --git a/tutorial/McStasScript_tutorial_1_the_basics.ipynb b/tutorial/McStasScript_tutorial_1_the_basics.ipynb
new file mode 100644
index 00000000..c0e80693
--- /dev/null
+++ b/tutorial/McStasScript_tutorial_1_the_basics.ipynb
@@ -0,0 +1,620 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# McStasScript introduction\n",
+    "This notebook shows how to use McStas and McStasScript to perform a basic simulation of a neutron diffractometer. The following software is required:\n",
+    "- McStas (www.mcstas.org)\n",
+    "- McStasScript (can be installed with python -m pip install McStasScript)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Anatomy of a McStas instrument\n",
+    "\n",
+    "In McStas a simulation is described using an instrument file. Such an instrument has five sections where code can be added to define the simulation to be perfomed.\n",
+    "\n",
+    "- Instrument definition\n",
+    "- Declare section\n",
+    "- Initialize section\n",
+    "- Trace section\n",
+    "- Finally section\n",
+    "\n",
+    "##### Instrument definition\n",
+    "In the instrument definition it is possible to define *instrument parameters* which can be specified at run time and used in the remaining sections for either calculations or as direct input to the components.\n",
+    "\n",
+    "##### Declare section\n",
+    "Here internal variables can be declared with C syntax.\n",
+    "\n",
+    "##### Initialize section\n",
+    "The initialize section is used for performing calculations, typically using both instrument parameters and declared variables to calculate for example chopper phases, angles and similar. The calculations are performed using C syntax. These calculations are performed before the raytracing simulation, and thus only performed once in a given simulation.\n",
+    "\n",
+    "##### Trace section\n",
+    "In the trace section McStas *components* are added, these are the building blocks of the simulation and correspond to different c codes that describe parts of neutron instruments or samples. Each component have a set of available parameters, some of which may be required. These will set the behavior of a component, a guide component may for example have parameters describing the physical shape and mirror reflectivity. Components also need to be placed in 3D space, and can be placed either in the absolute coordinate system or relative to a previously defined component.\n",
+    "\n",
+    "##### Finally section\n",
+    "The finally section is very similar to the initialize section, here calculations can be performed after the raytracing has been completed, again using C syntax. This may be some brief data analysis or print of some status.\n",
+    "\n",
+    "### McStasScript python package and this tutorial\n",
+    "The McStasScript python package provides an API to build and run such instruments files, but it is still necessary to have a basic understanding of the structure of the underlying instrument file and its capabilities and limitations. These tutorials will teach basic use of McStas through the McStasScript API without assuming expertise in the underlying McStas software."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the McStasScript package\n",
+    "The McStasScript modules intended for normal use is located in the interface submodule, and one usually imports the necessary modules from there."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### McStasScript configuration\n",
+    "Before the first use of McStasScript it is necessary to configure the package so it can locate the McStas installation and call the binaries. One way to find the path is to open a terminal with the McStas environment and run:\n",
+    "\n",
+    "which mcrun\n",
+    "\n",
+    "This should return the path for the binary, and the mcstas path is usually just one step back."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "configurator = functions.Configurator()\n",
+    "configurator.set_mcrun_path(\"/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/\")\n",
+    "configurator.set_mcstas_path(\"/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create an instrument object\n",
+    "A McStas instrument is described with a McStas instrument object which is created using the *McStas_instr* method on the instr class. Creating an instrument object also reads available components, both in the work folder and from the McStas installation. By default, the work folder is the current work directory, but using the input_path keyword argument this can be change to avoid cluttering the folder containing notebooks.\n",
+    "\n",
+    "Here our instrument object for this tutorial is created, we give it the name python_tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Requesting help on source components\n",
+    "The main building blocks used for creating a McStas simulation are the components. One can ask an instrument object which components are available, and get help for each component. Here we check what sources are available, and ask for help on the Source_div component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.show_components()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.show_components(\"sources\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.component_help(\"Source_div\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding a component\n",
+    "Now we are ready to add a component to our simulation which is done with the *add_component* method on our instrument. This method requires two inputs:\n",
+    "- Nickname for the component used to refer to this component instance\n",
+    "- Name of the component type to be used\n",
+    "\n",
+    "Here we want to make a component nicknamed \"source\" of type \"Source_div\".\n",
+    "\n",
+    "We also use the *print_components* method to confirm our component was added successfully. Running this code block multiple times result in an error, as McStas does not allow two components with the same nickname."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "src = instrument.add_component(\"source\", \"Source_div\")\n",
+    "instrument.print_components()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with component objects\n",
+    "The src object created by *add_component* can be used to modify the component. It also holds the information on the component, which can be shown with the *print_long* method. This will tell us for example if any required parameters are yet to be set and the position of the component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "src.print_long()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Modifying a component object\n",
+    "The parameters of a component object can be modified as attributes. From the above print we know there are four required parameters, so we start by setting these and then print the resulting component status."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "src.xwidth = 0.1\n",
+    "src.yheight = 0.05\n",
+    "src.focus_aw = 1.2\n",
+    "src.focus_ah = 2.3\n",
+    "\n",
+    "src.print_long()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Getting status of all parameters\n",
+    "Using *print_long* on a component only show the required parameters and user specified parameters, but it is also possible to see all parameters with the *show_parameters* method. This reminds us to set an energy or wavelength range for the source, as it is necessary to set one of these even though they are technically not required parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "src.show_parameters()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding an instrument parameter to control wavelength\n",
+    "Controlling the wavelength range emitted by the source is best done with an instrument parameter, then this same parameter can be used to for example rotate a monochromator or set the range for an wavelength sensitive monitor. Adding an instrument parameter is done using the instrument method *add_parameter*, and it is possible to set a default value and comment. The current instrument parameters can be viewed with the *show_parameters* method on the isntrument object.\n",
+    "\n",
+    "The default type for instrument parameters is a double (floating point number), but other types can be selected if necessary by providing a type string before, here we also provide an example of an integer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "instrument.add_parameter(\"int\", \"order\", value=1, comment=\"Monochromator order, integer\")\n",
+    "instrument.show_parameters()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now our source component can have its parameters assigned to a instrument parameter, or even a mathematical expression using the variable. This allows us to set a reasonable wavelength range for our source component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "src.lambda0=\"wavelength\"\n",
+    "src.dlambda=\"0.01*wavelength\"\n",
+    "src.print_long()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using keyword arguments when adding a component\n",
+    "When adding a component, several keyword arguments are available, for example for setting the position of the component.\n",
+    "- AT set position with list of x,y,z coordinates\n",
+    "- AT_RELATIVE set reference point for position (name of component instance or object)\n",
+    "- ROTATED set rotation around x,y,z axis\n",
+    "- ROTATED_RELATIVE set reference rotation (name of component instance or object)\n",
+    "- RELATIVE set both reference position and rotation (name of component instance or object)\n",
+    "\n",
+    "We use this to set up a guide 2 meters after the source. The McStas coordinate system convention is such that the nominal beam direction is in the Z direction and with Y vertical against gravity. We use the component instance name as a string to refer to our source. The RELATIVE could also have been specified as src, which is our source object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "guide = instrument.add_component(\"guide\", \"Guide_gravity\", AT=[0,0,2], RELATIVE=\"source\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we set the parameters for our guide component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "guide.w1 = 0.05\n",
+    "guide.w2 = 0.05\n",
+    "guide.h1 = 0.05\n",
+    "guide.h2 = 0.05\n",
+    "guide.l = 8.0\n",
+    "guide.m = 3.5\n",
+    "guide.G = -9.82\n",
+    "\n",
+    "guide.print_long()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding calculations to an instrument file\n",
+    "One of the advantages of McStas is the ease of adding calculations to the instrument. Here we calculate the rotation of a monochromator so that its scatters the wavelengths from our source. We need to declare variables using *add_declare_var* and append C code to initialize using *append_initialize*.\n",
+    "\n",
+    "For *add_declare_var* the first argument is the C type, usually double or int, the next is the variable name. A default value can be specified with the value keyword.\n",
+    "\n",
+    "*append_initialize* just adds the given C code to the initialize section of the McStas instrument file. It is necessary to follow C syntax, for example remember semicolon at the end of statements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.add_declare_var(\"double\", \"mono_Q\", value=1.714) # Q for Ge 311\n",
+    "instrument.add_declare_var(\"double\", \"wavevector\")\n",
+    "instrument.append_initialize(\"wavevector = 2.0*PI/wavelength;\")\n",
+    "\n",
+    "instrument.add_declare_var(\"double\", \"mono_rotation\")\n",
+    "instrument.append_initialize(\"mono_rotation = asin(mono_Q/(2.0*wavevector))*RAD2DEG;\")\n",
+    "instrument.append_initialize('printf(\"monochromator rotation = %g deg\\\\n\", mono_rotation);')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding the monochromator\n",
+    "Here the monochromator is added, and we use the declared variables *mono_Q* and *mono_rotation* prepared above. Setting position and rotation can also be done using the *set_AT* and *set_ROTATED* methods on the component objects. Here it is also demonstrated how one can use either component objects or component names for the relative keyword.\n",
+    "\n",
+    "Rotation is specified around each axis, so rotation of our monochromator should be around the Y axis in order to keep the beam in the usual X-Z plane."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mono = instrument.add_component(\"mono\", \"Monochromator_flat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mono.zwidth = 0.05\n",
+    "mono.yheight = 0.08\n",
+    "mono.Q = \"mono_Q\"\n",
+    "mono.set_AT([0, 0, 8.5], RELATIVE=guide)\n",
+    "mono.set_ROTATED([0, \"mono_rotation\", 0], RELATIVE=\"guide\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mono.print_long()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using an arm to define the beam direction\n",
+    "As the beam changes direction at the monochromator, we wish to define the new direction to simplify adding latter components. This can be done with an Arm component, which performs no simulation but can be used as new coordinate reference. The outgoing direction correspond to one more rotation of *mono_rotation*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "beam_direction = instrument.add_component(\"beam_dir\", \"Arm\", AT_RELATIVE=\"mono\")\n",
+    "beam_direction.set_ROTATED([0, \"mono_rotation\", 0], RELATIVE=\"mono\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding a sample\n",
+    "We now add a powder sample using the PowderN component placed relative to our newly defiend beam direction. The chosen powder is Na2Ca3Al2F14 which is a standard sample due to its large number of available reflections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = instrument.add_component(\"sample\", \"PowderN\", AT=[0,0,1.1], RELATIVE=\"beam_dir\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample.radius = 0.015\n",
+    "sample.yheight = 0.05\n",
+    "sample.reflections = '\"Na2Ca3Al2F14.laz\"'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample.print_long()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding a cylindrical monitor\n",
+    "The flexible Monitor_nD component can be used to add a banana monitor (part of a cylinder). The component shape is specified using an option string. The restore_neutron parameter is set to 1 to allow other monitors to record each neutron.\n",
+    "\n",
+    "We have to specify a filename and option string here, and if we just use a string like \"banana.dat\" it would be interpreted as an instrument parameter called *banana.dat* and fail, so it is necessary to add single quotes around, '\"banana.dat\"'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "banana = instrument.add_component(\"banana\", \"Monitor_nD\", RELATIVE=sample)\n",
+    "banana.xwidth = 2.0\n",
+    "banana.yheight = 0.3\n",
+    "banana.restore_neutron = 1\n",
+    "banana.filename = '\"banana.dat\"'\n",
+    "banana.options = '\"theta limits=[5 175] bins=150, banana\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding a psd monitor\n",
+    "We also add a simple PSD (position sensitive detector) monitor to see the transmitted beam."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mon = instrument.add_component(\"monitor\", \"PSD_monitor\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mon.nx = 100\n",
+    "mon.ny = 100\n",
+    "mon.filename = '\"psd.dat\"'\n",
+    "mon.xwidth = 0.05\n",
+    "mon.yheight = 0.08\n",
+    "mon.restore_neutron = 1\n",
+    "mon.set_AT([0,0,0.1], RELATIVE=sample)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Print the components contained in an instrument\n",
+    "Before performing the simulation, it is a good idea to check that the instrument contains the expected components and that they are appropriately placed in space. The *print_components* method is useful for this purpose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.print_components()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Running the simulation\n",
+    "The instrument object has a method called *run_full_instrument* to execute the simulation and return the data. A number of keyword arguments are available to control the execution of the simulation.\n",
+    "- ncount sets the number of rays\n",
+    "- mpi sets the number of CPU cores used for execution (requires mpi installed)\n",
+    "- foldername sets the name of the output folder\n",
+    "- increment_folder_name if set to True, automatically changes the foldername if it already exists.\n",
+    "- parameters allows setting instrument parameters using a python dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = instrument.run_full_instrument(ncount=5E6, foldername=\"data_folder/mcstas_basics\",\n",
+    "                                      increment_folder_name=True,\n",
+    "                                      parameters={\"wavelength\" : 2.8})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting the data\n",
+    "The *run_full_instrument* method returned a list of McStasData objects which can be plotted by the McStasScript plotter module. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adjusting plots\n",
+    "The McStasData objects contain preferences for how the data should be plotted, which can be modified using the functions module and the *name_plot_options* function. The function arguments are the name of the monitor component and a list of McStasData objects, then options are provided with the keyword arguments.\n",
+    "\n",
+    "The following plot options are often useful:\n",
+    "- log [True or False] For plotting on logarithmic axis\n",
+    "- orders_of_mag [number] When using logarithmic plotting, limits the maximum orders of magnitudes shown\n",
+    "- left_lim [number] lower limit of plot x axis\n",
+    "- right_lim [number] upper limit of plot x axis\n",
+    "- bottom_lim [number] lower limit of plot y axis\n",
+    "- top_lim [number] upper limit of plot y axis\n",
+    "- colormap [string] name of matplotlib colormap to use"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions.name_plot_options(\"monitor\", data, log=True)\n",
+    "functions.name_plot_options(\"banana\", data, left_lim=90, right_lim=150)\n",
+    "plotter.make_sub_plot(data, fontsize=16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Behind the scenes \n",
+    "McStasScript writes the instrument file and uses mcrun to compile and run it. The file can be found in the input_path selected when the instrument object were created. We can print it here to see what was done behind the scenes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"run_folder/python_tutorial.instr\") as file:\n",
+    "    data = file.read()\n",
+    "    print(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorial/McStasScript_tutorial_2_SPLIT.ipynb b/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
new file mode 100644
index 00000000..706f580d
--- /dev/null
+++ b/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
@@ -0,0 +1,267 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Advanced McStas features:  SPLIT\n",
+    "McStas uses the Monte Carlo ray-tracing technique, which allows some tricks in how the physics is sampled as long as the resulting probability distributions matches the physics. This is possible as each ray has a weight, corresponding to how much intensity this ray represent. The SPLIT keyword can be used to split a ray into many equal parts, which can be useful if the remaining instrument has many different simulated and random outcomes. In this tutorial we will use the SPLIT keyword on a powder sample, as there are many powder Bragg peaks each ray could select, and splitting the ray samples this more efficiently."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting up an example instrument\n",
+    "First we set up an example instrument, this is taken from the basic tutorial and correspond of source, guide, monochromator, sample and banana detector."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "src = instrument.add_component(\"source\", \"Source_div\")\n",
+    "src.xwidth = 0.1\n",
+    "src.yheight = 0.05\n",
+    "src.focus_aw = 1.2\n",
+    "src.focus_ah = 2.3\n",
+    "\n",
+    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0=\"wavelength\"\n",
+    "src.dlambda=\"0.03*wavelength\"\n",
+    "\n",
+    "guide = instrument.add_component(\"guide\", \"Guide_gravity\", AT=[0,0,2], RELATIVE=\"source\")\n",
+    "guide.w1 = 0.05\n",
+    "guide.w2 = 0.05\n",
+    "guide.h1 = 0.05\n",
+    "guide.h2 = 0.05\n",
+    "guide.l = 8.0\n",
+    "guide.m = 3.5\n",
+    "guide.G = -9.82\n",
+    "\n",
+    "instrument.add_declare_var(\"double\", \"mono_Q\", value=1.714) # Q for Ge 311\n",
+    "instrument.add_declare_var(\"double\", \"wavevector\")\n",
+    "instrument.append_initialize(\"wavevector = 2.0*PI/wavelength;\")\n",
+    "\n",
+    "instrument.add_declare_var(\"double\", \"mono_rotation\")\n",
+    "instrument.append_initialize(\"mono_rotation = asin(mono_Q/(2.0*wavevector))*RAD2DEG;\")\n",
+    "instrument.append_initialize('printf(\"monochromator rotation = %g deg\\\\n\", mono_rotation);')\n",
+    "\n",
+    "mono = instrument.add_component(\"mono\", \"Monochromator_flat\")\n",
+    "mono.zwidth = 0.05\n",
+    "mono.yheight = 0.08\n",
+    "mono.Q = \"mono_Q\"\n",
+    "mono.set_AT([0, 0, 8.5], RELATIVE=guide)\n",
+    "mono.set_ROTATED([0, \"mono_rotation\", 0], RELATIVE=\"guide\")\n",
+    "\n",
+    "beam_direction = instrument.add_component(\"beam_dir\", \"Arm\", AT_RELATIVE=\"mono\")\n",
+    "beam_direction.set_ROTATED([0, \"mono_rotation\", 0], RELATIVE=\"mono\")\n",
+    "\n",
+    "sample = instrument.add_component(\"sample\", \"PowderN\", AT=[0,0,1.1], RELATIVE=\"beam_dir\")\n",
+    "sample.radius = 0.015\n",
+    "sample.yheight = 0.05\n",
+    "sample.reflections = '\"Na2Ca3Al2F14.laz\"'\n",
+    "\n",
+    "banana = instrument.add_component(\"banana\", \"Monitor_nD\", RELATIVE=sample)\n",
+    "banana.xwidth = 2.0\n",
+    "banana.yheight = 0.3\n",
+    "banana.restore_neutron = 1\n",
+    "banana.filename = '\"banana.dat\"'\n",
+    "banana.options = '\"theta limits=[5 175] bins=150, banana\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Running the simulation\n",
+    "Here we run the simulation with very few neutrons to show problematic sampling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_low = instrument.run_full_instrument(ncount=1E6, foldername=\"data_folder/mcstas_SPLIT\",\n",
+    "                                          increment_folder_name=True,\n",
+    "                                          parameters={\"wavelength\" : 2.8})\n",
+    "\n",
+    "plotter.make_sub_plot(data_low)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding the SPLIT keyword\n",
+    "Here we add the SPLIT keyword to the sample, we choose to split each ray into 30."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample.set_SPLIT(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_reasonable = instrument.run_full_instrument(ncount=1E6, foldername=\"data_folder/mcstas_SPLIT\",\n",
+    "                                                increment_folder_name=True,\n",
+    "                                                parameters={\"wavelength\" : 2.8})\n",
+    "\n",
+    "plotter.make_sub_plot(data_reasonable)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding the SPLIT keyword\n",
+    "It is however possible to mismanage splitting, mainly by simulating a too few rays and splitting too much. Here we do this on purpose to see how such data would look. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample.set_SPLIT(10000)\n",
+    "data_unreasonable = instrument.run_full_instrument(ncount=1E3, foldername=\"data_folder/mcstas_SPLIT\",\n",
+    "                                                   increment_folder_name=True,\n",
+    "                                                   parameters={\"wavelength\" : 2.8})\n",
+    "\n",
+    "plotter.make_sub_plot(data_unreasonable)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparison with high statistics run\n",
+    "We here compare the different runs to a reference. The reference run is set up to have 50 times more rays than the earlier runs with 5E7 instead of 1E6 rays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample.set_SPLIT(1)\n",
+    "data_ref = instrument.run_full_instrument(ncount=5E7, foldername=\"data_folder/mcstas_SPLIT\",\n",
+    "                                      increment_folder_name=True,\n",
+    "                                      parameters={\"wavelength\" : 2.8})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting data on same plot\n",
+    "Here we only have one monitor in each data list, but we still use the *name_search* function to retrieve the correct data object from each. This avoids the code breaking in case additional monitors are added.\n",
+    "\n",
+    "Once we have the objects, we use the *xaxis*, *Intensity* and *Error* attributes to plot the data with matplotlib."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "banana_low = functions.name_search(\"banana\", data_low)\n",
+    "banana_reasonable = functions.name_search(\"banana\", data_reasonable)\n",
+    "banana_unreasonable = functions.name_search(\"banana\", data_unreasonable)\n",
+    "banana_ref = functions.name_search(\"banana\", data_ref)\n",
+    "\n",
+    "plt.figure(figsize=(14,6))\n",
+    "plt.errorbar(banana_low.xaxis, banana_low.Intensity, yerr=banana_low.Error, fmt=\"r\")\n",
+    "plt.errorbar(banana_ref.xaxis, banana_ref.Intensity, yerr=banana_ref.Error, fmt=\"b\")\n",
+    "plt.xlabel(\"2Theta [deg]\")\n",
+    "plt.ylabel(\"Intensity [n/s]\")\n",
+    "plt.legend([\"Low statistics\", \"High statistics reference\"])\n",
+    "\n",
+    "plt.figure(figsize=(14,6))\n",
+    "plt.errorbar(banana_reasonable.xaxis, banana_reasonable.Intensity, yerr=banana_reasonable.Error, fmt=\"r\")\n",
+    "plt.errorbar(banana_ref.xaxis, banana_ref.Intensity, yerr=banana_ref.Error, fmt=\"b\")\n",
+    "plt.xlabel(\"2Theta [deg]\")\n",
+    "plt.ylabel(\"Intensity [n/s]\")\n",
+    "plt.legend([\"Low statistics with SPLIT\", \"High statistics reference\"])\n",
+    "\n",
+    "plt.figure(figsize=(14,6))\n",
+    "plt.errorbar(banana_unreasonable.xaxis, banana_unreasonable.Intensity, yerr=banana_unreasonable.Error, fmt=\"r\")\n",
+    "plt.errorbar(banana_ref.xaxis, banana_ref.Intensity, yerr=banana_ref.Error, fmt=\"b\")\n",
+    "plt.xlabel(\"2Theta [deg]\")\n",
+    "plt.ylabel(\"Intensity [n/s]\")\n",
+    "plt.legend([\"Very low statistics with unreasonable SPLIT\", \"High statistics reference\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretation of the data\n",
+    "We see that with low statistics, the data quality is so bad that noise can be mistaken for peaks. Using SPLIT improves the situation a lot, and the data is very similar to the high statistics reference which takes longer to compute. The situation with a low number of simulated rays and very high SPLIT have some erratic behavior, showing some very different peak intensities than the reference, and some peaks that shouldn't be there at all."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb b/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
new file mode 100644
index 00000000..3938a566
--- /dev/null
+++ b/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
@@ -0,0 +1,235 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Advanced McStas features: EXTEND and WHEN\n",
+    "In this tutorial we will look at two advanced features in McStas, the EXTEND block and WHEN condition. Here we will use them to flag certain neutrons with EXTEND, and only record them in monitors when the flag is set using a WHEN condition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up an example McStas instrument\n",
+    "First we set up an example instrument conisiting of a source, a guide and a position/divergence monitor. The guide is set up such that it only has mirrors on the left and right side, and absorbs neutrons if they hit the top or bottom. This is done to look at the horizontal behavior independently from the vertical, as this is easier to analyze."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "src = instrument.add_component(\"source\", \"Source_simple\")\n",
+    "\n",
+    "src.xwidth = 0.02\n",
+    "src.yheight = 0.02\n",
+    "src.focus_xw = guide_opening_w = 0.05\n",
+    "src.focus_yh = guide_opening_h = 0.06\n",
+    "src.dist = 1.5\n",
+    "src.flux = 1E13\n",
+    "\n",
+    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0=\"wavelength\"\n",
+    "src.dlambda=\"0.001*wavelength\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "guide = instrument.add_component(\"guide\", \"Guide_gravity\", AT=[0,0,1.5], RELATIVE=src)\n",
+    "guide.w1 = guide_opening_w\n",
+    "guide.h1 = guide_opening_h\n",
+    "guide.w2 = guide_opening_w\n",
+    "guide.h2 = guide_opening_h\n",
+    "guide.l = guide_length = 15\n",
+    "guide.mleft = 4.0\n",
+    "guide.mright = 4.0\n",
+    "guide.mtop = 0.0\n",
+    "guide.mbottom = 0.0\n",
+    "guide.G = -9.82"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "acceptance = instrument.add_component(\"acceptance\", \"DivPos_monitor\")\n",
+    "acceptance.set_AT([0,0, guide_length + 0.1], RELATIVE=guide)\n",
+    "acceptance.nh = 200\n",
+    "acceptance.ndiv = 200\n",
+    "acceptance.filename = '\"acceptance.dat\"'\n",
+    "acceptance.xwidth = 0.08\n",
+    "acceptance.yheight = 0.05\n",
+    "acceptance.maxdiv_h = 1.5\n",
+    "acceptance.restore_neutron = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = instrument.run_full_instrument(ncount=5E6, foldername=\"data_folder/mcstas_EXTEND_WHEN\",\n",
+    "                                      increment_folder_name=True,\n",
+    "                                      parameters={\"wavelength\" : 2.8})\n",
+    "\n",
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpreting the data\n",
+    "Here we see an acceptance monitor, with position along the x-axis and divergence along the y-axis. The guide is under illuminated by the small source, so there are gaps in the acceptance diagram. We see the position and divergence of the beam consist of a large number of stripes, the ones with lowest divergence has the largest intensity.\n",
+    "\n",
+    "## Add an flag\n",
+    "A flag is just a name for a variable that records some information on the neutron during the simulation, and can be used later to make a decision. Here we could check how many times the ray was reflected in the guide.\n",
+    "\n",
+    "We use an EXTEND block after a component to access variables internal to the component in the instrument scope. We declare a variable in the instrument scope called *n_reflections*. In the component scope, one can use the SCATTERED variable which contains the number of times the ray has encountered the SCATTER keyword within the component. Usually this is done when entering and leaving, and under each scattering / reflection, so the number of reflections is SCATTERED - 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.add_declare_var(\"int\", \"n_reflections\")\n",
+    "guide.append_EXTEND(\"n_reflections = SCATTERED - 2;\")\n",
+    "guide.print_long()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use the flag to limit what is recorded in a monitor\n",
+    "A WHEN statement can be used to activate / deactivate a component when some condition is true / false. For example we could require 0 reflection in our guide. We add a few monitors similar to the original, with the only difference being WHEN statements requiring 0, 1 or 2 reflections in the guide for the component to be active. We use a for loop to add the similar components, only changing the component instance name, filename and WHEN statement between each."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reflection_numbers = [0, 1, 2]\n",
+    "\n",
+    "for reflections in reflection_numbers:\n",
+    "    reflections_string = str(reflections)\n",
+    "    \n",
+    "    acceptance = instrument.add_component(\"acceptance_\" + reflections_string, \"DivPos_monitor\")\n",
+    "    acceptance.filename = '\"acceptance_' + reflections_string + '.dat\"'\n",
+    "    acceptance.set_WHEN(\"n_reflections == \" + reflections_string)\n",
+    "    \n",
+    "    acceptance.set_AT([0,0, guide_length + 0.1], RELATIVE=guide)\n",
+    "    acceptance.nh = 200\n",
+    "    acceptance.ndiv = 200\n",
+    "    acceptance.xwidth = 0.08\n",
+    "    acceptance.yheight = 0.05\n",
+    "    acceptance.maxdiv_h = 1.5\n",
+    "    acceptance.restore_neutron = 1\n",
+    "    \n",
+    "    acceptance.print_long()\n",
+    "    print(\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Running the simulation\n",
+    "We now run the simulation with the new monitors to see how they differ from the original version."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = instrument.run_full_instrument(ncount=5E6, foldername=\"data_folder/mcstas_EXTEND_WHEN\",\n",
+    "                                      increment_folder_name=True,\n",
+    "                                      parameters={\"wavelength\" : 2.8})\n",
+    "\n",
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretation of the data\n",
+    "The original monitor is unchanged as it was not modified. On the monitors with different numbers of reflections, we see the middle line correspond to zero reflections, the two lines around those are for one reflection and so forth. This explains why the lines further from the center has lower intensity, as they underwent more reflections while also having a larger angle of incidence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The McStas instrument file\n",
+    "We here show the generated McStas instrument file in order to clarify how this would be accomplished without the McStasScript API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"run_folder/python_tutorial.instr\") as file:\n",
+    "    instrument_string = file.read()\n",
+    "    print(instrument_string)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorial/McStasScript_tutorial_4_JUMP.ipynb b/tutorial/McStasScript_tutorial_4_JUMP.ipynb
new file mode 100644
index 00000000..7f39406e
--- /dev/null
+++ b/tutorial/McStasScript_tutorial_4_JUMP.ipynb
@@ -0,0 +1,255 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Advanced McStas features: JUMP\n",
+    "In this notebook we will look at JUMP and how it can be used to control the sequence of execution of components. One instance where this is useful is if a guide splits into two. Consider an instrument with the following components:\n",
+    "\n",
+    "- source\n",
+    "- main guide\n",
+    "- guide1\n",
+    "- sample1\n",
+    "- detector1\n",
+    "- guide2\n",
+    "- sample2\n",
+    "- detector2\n",
+    "\n",
+    "After the main guide, if the ray hits the opening of guide1 the ray will continue to sample1 and detector1 as expected, but if it misses the opening of guide1, it will just be absorbed and never reach guide2 later in the component sequence. One possible solution is to use a JUMP statement, which jumps to another place in the component sequence. The target component must be an Arm, and no coordinate transformations are done, so the simplest solution is to have the Arm conincide with the component with the JUMP statement.\n",
+    "\n",
+    "- source\n",
+    "- main guide\n",
+    "- arm A JUMP arm B WHEN ray hits guide2 entrance \n",
+    "- guide1\n",
+    "- sample1\n",
+    "- detector1\n",
+    "- arm B (same position and rotation of arm A)\n",
+    "- guide2\n",
+    "- sample2\n",
+    "- detector2\n",
+    "\n",
+    "Here we build such an instrument with a few notes on the syntax along the way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "src = instrument.add_component(\"source\", \"Source_simple\")\n",
+    "\n",
+    "src.xwidth = 0.12\n",
+    "src.yheight = 0.12\n",
+    "src.focus_xw = guide_opening_w = 0.1\n",
+    "src.focus_yh = guide_opening_h = 0.06\n",
+    "src.dist = 1.5\n",
+    "src.flux = 1E13\n",
+    "\n",
+    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0=\"wavelength\"\n",
+    "src.dlambda=\"0.001*wavelength\"\n",
+    "\n",
+    "guide = instrument.add_component(\"guide\", \"Guide_gravity\", AT=[0,0,1.5], RELATIVE=src)\n",
+    "guide.w1 = guide_opening_w\n",
+    "guide.h1 = guide_opening_h\n",
+    "guide.w2 = guide_opening_w\n",
+    "guide.h2 = guide_opening_h\n",
+    "guide.l = guide_length = 15\n",
+    "guide.m = 4.0\n",
+    "guide.G = -9.82"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding the reference arm\n",
+    "We here add an arm just after the exit of the main guide which will be the component that performs the JUMP under certain circumstances. The McStas syntax for such a JUMP statement would be:\n",
+    "\n",
+    "JUMP *reference* WHEN *condition*\n",
+    "\n",
+    "We will call the arm we jump to for *target_arm*, and our condition is that the neutron is on the left side, so x<0. That means our JUMP statement would be:\n",
+    "\n",
+    "JUMP target_arm WHEN (x<0)\n",
+    "\n",
+    "In McStasScript this is added with the *set_JUMP* method, that takes a string for what to set after JUMP."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start_arm = instrument.add_component(\"split_arm\", \"Arm\")\n",
+    "start_arm.set_AT([0,0, guide_length + 3E-3], RELATIVE=guide)\n",
+    "start_arm.set_JUMP(\"target_arm WHEN (x<0)\")\n",
+    "start_arm.print_long()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding the first daughter instrument\n",
+    "We then add the left side, which correspond to x>0, so this is the case where no jump was performed and the sequence of components runs as normal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "guide1 = instrument.add_component(\"guide1\", \"Guide_gravity\")\n",
+    "guide1.set_AT([0.25*guide_opening_w,0,0], RELATIVE=start_arm)\n",
+    "guide1.set_ROTATED([0, 1, 0], RELATIVE=start_arm)\n",
+    "guide1.w1 = 0.5*guide_opening_w\n",
+    "guide1.h1 = 0.5*guide_opening_h\n",
+    "guide1.w2 = 0.5*guide_opening_w\n",
+    "guide1.h2 = 0.5*guide_opening_h\n",
+    "guide1.l = guide1_length = 10\n",
+    "guide1.m = 2.5\n",
+    "guide1.G = -9.82\n",
+    "\n",
+    "sample1 = instrument.add_component(\"sample1\", \"PowderN\")\n",
+    "sample1.set_AT([0,0,guide1_length+0.5], RELATIVE=guide1)\n",
+    "sample1.radius = 0.015\n",
+    "sample1.yheight = 0.05\n",
+    "sample1.reflections = '\"Na2Ca3Al2F14.laz\"'\n",
+    "\n",
+    "banana1 = instrument.add_component(\"banana1\", \"Monitor_nD\", RELATIVE=sample1)\n",
+    "banana1.xwidth = 2.0\n",
+    "banana1.yheight = 0.3\n",
+    "banana1.filename = '\"banana1.dat\"'\n",
+    "banana1.options = '\"theta limits=[5 175] bins=150, banana\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding the second daughter instrument\n",
+    "Now we need to add the target_arm that rays jump to when they go to the right side of the guide split. This is in the exact same position of the previous arm, to avoid the need for a coordinate transformation which is not performed automatically when using JUMP statements.\n",
+    "\n",
+    "After that we add a second daughter instrument with a different sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_arm = instrument.add_component(\"target_arm\", \"Arm\")\n",
+    "target_arm.set_AT([0,0,0], RELATIVE=start_arm)\n",
+    "\n",
+    "guide2 = instrument.add_component(\"guide2\", \"Guide_gravity\")\n",
+    "guide2.set_AT([-0.25*guide_opening_w,0,0], RELATIVE=target_arm)\n",
+    "guide2.set_ROTATED([0, -1, 0], RELATIVE=target_arm)\n",
+    "guide2.w1 = 0.5*guide_opening_w\n",
+    "guide2.h1 = 0.5*guide_opening_h\n",
+    "guide2.w2 = 0.5*guide_opening_w\n",
+    "guide2.h2 = 0.5*guide_opening_h\n",
+    "guide2.l = guide1_length = 15\n",
+    "guide2.m = 2.5\n",
+    "guide2.G = -9.82\n",
+    "\n",
+    "sample2 = instrument.add_component(\"sample2\", \"PowderN\")\n",
+    "sample2.set_AT([0,0,guide1_length+0.5], RELATIVE=guide2)\n",
+    "sample2.radius = 0.015\n",
+    "sample2.yheight = 0.05\n",
+    "sample2.reflections = '\"Cu.laz\"'\n",
+    "\n",
+    "banana2 = instrument.add_component(\"banana2\", \"Monitor_nD\", RELATIVE=sample2)\n",
+    "banana2.xwidth = 2.0\n",
+    "banana2.yheight = 0.3\n",
+    "banana2.filename = '\"banana2.dat\"'\n",
+    "banana2.options = '\"theta limits=[5 175] bins=150, banana\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Running the simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = instrument.run_full_instrument(ncount=5E6, foldername=\"data_folder/mcstas_JUMP\",\n",
+    "                                      increment_folder_name=True,\n",
+    "                                      parameters={\"wavelength\" : 2.8})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interpretation of the data\n",
+    "We see that each daughter instrument have beam and show the different powder patterns as expected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorial/Union_tutorial_1_processes_and_materials.ipynb b/tutorial/Union_tutorial_1_processes_and_materials.ipynb
new file mode 100644
index 00000000..afea0cd4
--- /dev/null
+++ b/tutorial/Union_tutorial_1_processes_and_materials.ipynb
@@ -0,0 +1,436 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The Union components\n",
+    "This tutorial is the first in a series showing how the Union components are used. This notebook focuses on setting up material definitions that are used to provide scattering physics to geometries. There are several kinds of Union components, and they need to be used in conjunction with one another to function.\n",
+    "- Process components: Describe individual scattering phenomena, such as incoherent, powder, single crystal scattering\n",
+    "- Make_material component: Joins several processes into a material definition\n",
+    "- Geometry components: Describe geometry, each is assigned a material definition\n",
+    "- Union logger components: Records information for each scattering event and plots it\n",
+    "- Union abs logger components: Records information for each absorption event and plots it\n",
+    "- Union conditional components: Modifies a logger or abs logger so it only records when certain final condition met\n",
+    "- Union master component: Performs simulation described by previous Union components\n",
+    "\n",
+    "In this notebook we will focus on setting up materials using process components and the *Union_make_material* component, but the Union components can not work individually, so it will also be necessary to add a geometry and the *Union_master*. First we import McStasScript and create a new instrument object.\n",
+    "\n",
+    "In case of any issues with running the tutorial notebooks there is troubleshooting at the end of this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Process components\n",
+    "In this notebook we will focus on exploring how to build different physical descriptions of materials, and checking that they behave as expected. We start by looking at the process component for incoherent scattering, Incoherent_process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.show_components(\"Work directory\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.component_help(\"Incoherent_process\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The process components in general have few parameters as they just describe a single physical phenomena. The incoherent process here is described adequately by just the cross section *sigma* and volume of the unit cell, *unit_cell_volume*.\n",
+    "\n",
+    "Two parameters are available for all processes, *packing_factor* and *interact_fraction*. The packing factor describes how dense the material is, and can make it easier to mix for example different powders. It is implemented as a simple factor on the scattering strength. The interact fraction is used to balance many processes when they are used in one material. Normally processes are sampled according to they natural probability for scattering, but this can be overwritten using the *interact_fraction*, which provides the sampling probability directly, they just have to sum to 1 within a material."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "incoherent = instrument.add_component(\"incoherent\", \"Incoherent_process\")\n",
+    "incoherent.sigma = 2.5\n",
+    "incoherent.unit_cell_volume = 13.8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Making a material\n",
+    "In order to collect processes into a material, one uses the *Union_make_material* component. Here are the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.component_help(\"Union_make_material\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A material definition thus consists of a number of processes given with the *process_string* parameter, and a description of the absorption in the material given with the inverse penetration depth at the standard neutron speed of 2200 m/s. For our first test material, lets just set absorption to zero and set our process_string to incoherent, referring to the process we created above.\n",
+    "\n",
+    "The name of the material is now inc_material, which will be used in the future to refer to this material."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inc_material = instrument.add_component(\"inc_material\", \"Union_make_material\")\n",
+    "inc_material.my_absorption = 0.0\n",
+    "inc_material.process_string = '\"incoherent\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If the material contains no physical processes, it is necessary to set the *absorber* parameter to 1, as it will just have an absorption description. Here we make a material called abs_material. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "absorber = instrument.add_component(\"abs_material\", \"Union_make_material\")\n",
+    "absorber.absorber = 1\n",
+    "absorber.my_absorption = 3.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The primary reason for having both process components and a make_material component is that it is possible to add as many processes in one material as necessary. Here we create a powder process, and then make a material using the powder and previously defined incoherent processes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "powder = instrument.add_component(\"powder\", \"Powder_process\")\n",
+    "powder.reflections = '\"Cu.laz\"'\n",
+    "\n",
+    "inc_material = instrument.add_component(\"powder_material\", \"Union_make_material\")\n",
+    "inc_material.my_absorption = 1.2\n",
+    "inc_material.process_string = '\"incoherent,powder\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At this point we have three materials defined\n",
+    "\n",
+    "| Material name   | Description                                                      |\n",
+    "|-----------------|------------------------------------------------------------------|\n",
+    "| inc_material    | Has one incoherent process and no absorption                     |\n",
+    "| abs_material    | Only has absorption                                              |\n",
+    "| powder_material | Has both incoherent and powder process in addition to absorption |\n",
+    "\n",
+    "Let us defined a quick test instrument to see these materials are behaving as expected. First we add a source."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "src = instrument.add_component(\"source\", \"Source_div\")\n",
+    "\n",
+    "instrument.add_parameter(\"source_width\", value=0.15, comment=\"Width of source in [m]\")\n",
+    "src.xwidth = \"source_width\"\n",
+    "src.yheight = 0.03\n",
+    "src.focus_aw = 0.01\n",
+    "src.focus_ah = 0.01\n",
+    "\n",
+    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0=\"wavelength\"\n",
+    "src.dlambda=\"0.001*wavelength\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding geometries that use the material definitions\n",
+    "Here we add three boxes, each using a different material definition and placed next to one another. The *material_string* parameter is used to specify the material name. The *priority* parameter will be explained later, as it is only important when geometries overlap, here they are spatially separated, yet the priorties must still be unique.\n",
+    "\n",
+    "It is important to note that these three boxes will be simulated simultaneously in the McStas simulation flow, so no need for GROUP statements to have these in parallel. Because they are simulated simultaneously, a ray can go from one to another, which would not be possible with a standard GROUP."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "box_inc = instrument.add_component(\"box_inc\", \"Union_box\", AT=[0.04,0,1], RELATIVE=src)\n",
+    "box_inc.xwidth = 0.03\n",
+    "box_inc.yheight = 0.03\n",
+    "box_inc.zdepth = 0.03\n",
+    "box_inc.material_string = '\"inc_material\"'\n",
+    "box_inc.priority = 10\n",
+    "\n",
+    "box_inc = instrument.add_component(\"box_powder\", \"Union_box\", AT=[0,0,1], RELATIVE=src)\n",
+    "box_inc.xwidth = 0.03\n",
+    "box_inc.yheight = 0.03\n",
+    "box_inc.zdepth = 0.01\n",
+    "box_inc.material_string = '\"powder_material\"'\n",
+    "box_inc.priority = 11\n",
+    "\n",
+    "box_inc = instrument.add_component(\"box_abs\", \"Union_box\", AT=[-0.04,0,1], RELATIVE=src)\n",
+    "box_inc.xwidth = 0.03\n",
+    "box_inc.yheight = 0.03\n",
+    "box_inc.zdepth = 0.03\n",
+    "box_inc.material_string = '\"abs_material\"'\n",
+    "box_inc.priority = 12"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding loggers that show scattering and absorption\n",
+    "In order to check the three materials behave as expected, we add spatial loggers for scattering and absorption. These are called loggers and abs_loggers, here is the parameters for a logger."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.component_help(\"Union_logger_2D_space\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The parameters for the abs_logger are very similar, so the two are added here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger = instrument.add_component(\"logger_space\", \"Union_logger_2D_space\", RELATIVE=\"box_powder\")\n",
+    "logger.D_direction_1 = '\"z\"'\n",
+    "logger.D1_min = -0.04\n",
+    "logger.D1_max = 0.04\n",
+    "logger.n1 = 250\n",
+    "logger.D_direction_2 = '\"x\"'\n",
+    "logger.D2_min = -0.075\n",
+    "logger.D2_max = 0.075\n",
+    "logger.n2 = 400\n",
+    "logger.filename = '\"logger.dat\"'\n",
+    "\n",
+    "logger = instrument.add_component(\"abs_logger_space\", \"Union_abs_logger_2D_space\", RELATIVE=\"box_powder\")\n",
+    "logger.D_direction_1 = '\"z\"'\n",
+    "logger.D1_min = -0.04\n",
+    "logger.D1_max = 0.04\n",
+    "logger.n1 = 250\n",
+    "logger.D_direction_2 = '\"x\"'\n",
+    "logger.D2_min = -0.075\n",
+    "logger.D2_max = 0.075\n",
+    "logger.n2 = 400\n",
+    "logger.filename = '\"abs_logger.dat\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding the Union master component\n",
+    "The Union master component is what actually executes the simulation, and so it takes information from all Union components defined before and performs the described simulation. This is the component that matters in terms of order of execution within the sequence of McStas components. As all the previous components have described the what the master component should simulate, it has no required parameters. It also does not matter where it is located in space, as it will grab the locations described by all previous Union components that need a spatial location, such as the geometries and loggers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.component_help(\"Union_master\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "master = instrument.add_component(\"master\", \"Union_master\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Running the simulation\n",
+    "Here the McStas simulation is executed as normal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = instrument.run_full_instrument(ncount=5E6, foldername=\"data_folder/union_materials\",\n",
+    "                                      increment_folder_name=True,\n",
+    "                                      parameters={\"wavelength\" : 8.0})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interpreting the results\n",
+    "The first logger shows scattering, and since the top box has incoherent, and the middle both powder and incoherent, we expect those to show up. We can see the beam attenuation, as the beam originates from the left side.\n",
+    "\n",
+    "The second logger shows absorption, and here the top box is absent as it has no absorption cross section. The bottom box is however visible now, as it has absorption but no scattering. As the absorber is quite strong, we see the attenuation here as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Alternative run to show powder properties\n",
+    "In order to see the scattering from the powder sample, we restrict the source size to only illuminate the center box with a powder material. A wavelength with powder lines close to 90 deg is selected to ensure the scattering from the center box hits the surrounding boxes.\n",
+    "\n",
+    "We choose to show the data with logarithmic colorscale using the *name_plot_options* method on functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = instrument.run_full_instrument(ncount=5E6, foldername=\"data_folder/union_materials\",\n",
+    "                                      increment_folder_name=True,\n",
+    "                                      parameters={\"wavelength\" : 2.8, \"source_width\" : 0.03})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions.name_plot_options(\"logger_space\", data, log=True)\n",
+    "functions.name_plot_options(\"abs_logger_space\", data, log=True)\n",
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretation of the data\n",
+    "Now that the direct beam only hits the center box, all rays that enter the surrounding boxes are scattered from that center box. Since the center box contains a powder, the scattered beam is not homogeneous and most of it is in the form of Bragg peaks with certain scattering angles, and we can see two of these intersecting the surrounding geometries."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Troubleshooting\n",
+    "In case of issues with the notebooks concerning the Union components or McStasScript it is recommended to:\n",
+    "- Update McStasScript with python -m pip install --upgrade mcstasscript\n",
+    "- Get newest version of Union components (Both library files and components themselves)\n",
+    "\n",
+    "Since the Union components need to collaborate, it is important to have the same version of the libraries and components. The newest version of the components can be found here: https://github.com/McStasMcXtrace/McCode/tree/master/mcstas-comps/contrib/union\n",
+    "All libraries for McStas are found here: https://github.com/McStasMcXtrace/McCode/tree/master/mcstas-comps/share but only three are needed for the Union components:\n",
+    "- Union_initialization.c\n",
+    "- Union_functions.c\n",
+    "- Geometry_functions.c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorial/Union_tutorial_2_geometry.ipynb b/tutorial/Union_tutorial_2_geometry.ipynb
new file mode 100644
index 00000000..abadba76
--- /dev/null
+++ b/tutorial/Union_tutorial_2_geometry.ipynb
@@ -0,0 +1,495 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Advanced geometry using the Union components\n",
+    "The Union components allow the user to construct advanced geometry from simple shapes. Each available shape has their own component, here are the currently available geometry components.\n",
+    "- Union_box\n",
+    "- Union_sphere\n",
+    "- Union_cylinder\n",
+    "- Union_cone\n",
+    "\n",
+    "They differ in their parameters describing the geometry, but are otherwise identical. In this notebook we will show how to construct hollow geometries with several layers, and that multiple scattering between these quickly result in complex behavior."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting up some standard materials\n",
+    "Before setting up the geometry, we need some material definition, here we set up aluminium and a sample powder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
+    "Al_inc.sigma = 4*0.0082 # 4 atoms per unit cell\n",
+    "Al_inc.unit_cell_volume = 66.4\n",
+    "\n",
+    "Al_pow = instrument.add_component(\"Al_pow\", \"Powder_process\")\n",
+    "Al_pow.reflections = '\"Al.laz\"'\n",
+    "\n",
+    "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
+    "Al.process_string = '\"Al_inc,Al_pow\"'\n",
+    "Al.my_absorption = 100*4*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "\n",
+    "Sample_inc = instrument.add_component(\"Sample_inc\", \"Incoherent_process\")\n",
+    "Sample_inc.sigma = 4*3.4176\n",
+    "Sample_inc.unit_cell_volume = 1079.1\n",
+    "\n",
+    "Sample_pow = instrument.add_component(\"Sample_pow\", \"Powder_process\")\n",
+    "Sample_pow.reflections = '\"Na2Ca3Al2F14.laz\"'\n",
+    "\n",
+    "Sample = instrument.add_component(\"Sample\", \"Union_make_material\")\n",
+    "Sample.process_string = '\"Sample_inc,Sample_pow\"'\n",
+    "Sample.my_absorption = 100*4*2.9464/1079.1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up source\n",
+    "We will also need a source, and allow the wavelength to be tuned with a instrument parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "src = instrument.add_component(\"source\", \"Source_div\")\n",
+    "\n",
+    "src.xwidth = 0.01\n",
+    "src.yheight = 0.035\n",
+    "src.focus_aw = 0.01\n",
+    "src.focus_ah = 0.01\n",
+    "\n",
+    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0=\"wavelength\"\n",
+    "src.dlambda=\"0.01*wavelength\"\n",
+    "src.flux = 1E13"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Describing the geometry of a simple cryostat\n",
+    "A cryostat is a complex geometry with several layers to consider. The way geometry is described in the Union components aims to make it easy to describe such systems. This is aciheved by allowing the simple geometries to overlap, and having a value called the priority to determine which is active in a given volume. If two geometries overlap, the overlapping region gets the physics from the geometry with the highest priority. In that way a cryostat model can be created by having a high priority for the sample in the center, and decreasing the priority as we move out.\n",
+    "\n",
+    "The ray tracing algorithm can however not handle if two geometries overlap perfectly, even with a single side. This could be two boxes sharing a side.\n",
+    "\n",
+    "Let us look at the parameters for a Union geometry component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.component_help(\"Union_cylinder\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The geometry components have many parameters due to their flexibility, but only a few are needed for basic use.\n",
+    "- material_string : string for selecting an available material\n",
+    "- priority : number, in case of overlap the geometry with highest priority decides the material properties\n",
+    "- geometrical parameters : Here radius and yheight\n",
+    "\n",
+    "In addition there is a focusing system where scattering of physical processes that support this can be forced to a certain direction, this is controlled with these parameters, but are rarely used:\n",
+    "- target_index : relative component index of target\n",
+    "- target_x : if target_index not set, relative x coordinate of target\n",
+    "- target_y : if target_index not set, relative y coordinate of target\n",
+    "- target_z : if target_index not set, relative z coordinate of target\n",
+    "- focus_aw : angular width of focusing cone (either specify angular, box or circular)\n",
+    "- focus_ah : angular height of focusing cone \n",
+    "- focus_xw : spatial width of focusing cone (box type focusing)\n",
+    "- focus_xh : spatial height of focusing\n",
+    "- focus_r : spatial radius of focusing cone (circular)\n",
+    "\n",
+    "Finally there is p_interact, which is used for controlling Monte Carlo sampling frequency of the geometry, as it controls the probability for scattering occurring for any path before or after scattering.\n",
+    "\n",
+    "The remaining parameters including masks and number_of_activations are for advanced rules which will be described in a later tutorial."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### First geometry, a sample in a container\n",
+    "\n",
+    "We have defined the following materials that are available to us:\n",
+    "- Al\n",
+    "- Sample\n",
+    "\n",
+    "Lets start by building a simple powder container with a lid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_geometry = instrument.add_component(\"sample_geometry\", \"Union_cylinder\")\n",
+    "sample_geometry.yheight = 0.03\n",
+    "sample_geometry.radius = 0.0075\n",
+    "sample_geometry.material_string='\"Sample\"' \n",
+    "sample_geometry.priority = 100\n",
+    "sample_geometry.set_AT([0,0,1], RELATIVE=src)\n",
+    "\n",
+    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\", RELATIVE=sample_geometry)\n",
+    "container.yheight = 0.03+0.003 # 1.5 mm top and button\n",
+    "container.radius = 0.0075 + 0.0015 # 1.5 mm sides of container\n",
+    "container.material_string='\"Al\"' \n",
+    "container.priority = 99\n",
+    "\n",
+    "container_lid = instrument.add_component(\"sample_container_lid\", \"Union_cylinder\")\n",
+    "container_lid.set_AT([0, 0.0155, 0], RELATIVE=container)\n",
+    "container_lid.yheight = 0.004\n",
+    "container_lid.radius = 0.013\n",
+    "container_lid.material_string='\"Al\"' \n",
+    "container_lid.priority = 98"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up loggers to check what is going on\n",
+    "In order to view what geometry we have set up, we set up three loggers that view the scattering projected onto three different planes. These record the spatail distribution of scattering events."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zx.D_direction_1 = '\"z\"'\n",
+    "logger_zx.D1_min = -0.02\n",
+    "logger_zx.D1_max = 0.02\n",
+    "logger_zx.n1 = 300\n",
+    "logger_zx.D_direction_2 = '\"x\"'\n",
+    "logger_zx.D2_min = -0.02\n",
+    "logger_zx.D2_max = 0.02\n",
+    "logger_zx.n2 = 300\n",
+    "logger_zx.filename = '\"logger_zx.dat\"'\n",
+    "\n",
+    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zy.D_direction_1 = '\"z\"'\n",
+    "logger_zy.D1_min = -0.02\n",
+    "logger_zy.D1_max = 0.02\n",
+    "logger_zy.n1 = 300\n",
+    "logger_zy.D_direction_2 = '\"y\"'\n",
+    "logger_zy.D2_min = -0.02\n",
+    "logger_zy.D2_max = 0.02\n",
+    "logger_zy.n2 = 300\n",
+    "logger_zy.filename = '\"logger_zy.dat\"'\n",
+    "\n",
+    "logger_xy = instrument.add_component(\"logger_space_xy\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_xy.D_direction_1 = '\"x\"'\n",
+    "logger_xy.D1_min = -0.02\n",
+    "logger_xy.D1_max = 0.02\n",
+    "logger_xy.n1 = 300\n",
+    "logger_xy.D_direction_2 = '\"y\"'\n",
+    "logger_xy.D2_min = -0.02\n",
+    "logger_xy.D2_max = 0.02\n",
+    "logger_xy.n2 = 300\n",
+    "logger_xy.filename = '\"logger_xy.dat\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add master component\n",
+    "We need to remember to add a master component to actually perform the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "master = instrument.add_component(\"master\", \"Union_master\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add banana monitor\n",
+    "We are also interested in viewing some scattering data, here we add a banana monitor using the Monitor_nD component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "banana = instrument.add_component(\"banana\", \"Monitor_nD\", RELATIVE=sample_geometry)\n",
+    "banana.xwidth = 1.5\n",
+    "banana.yheight = 0.4\n",
+    "banana.restore_neutron = 1\n",
+    "banana.options = '\"theta limits=[5 175] bins=250, banana\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run simulation\n",
+    "Now we need to run the simulation to view the geometry we have built."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_geometry\",\n",
+    "                                      increment_folder_name=True,\n",
+    "                                      parameters={\"wavelength\" : 3.0})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting the data\n",
+    "Due to the large differences between the scattered intensity from parts in the direct beam and outside, we use a logarithmic axis to display scattered intensity. We limit it to 4 orders of magnitude below the maximum intensity, otherwise a single very low intensity event can draw the intensity axis out to a large interval making it difficult to see the important nuances."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
+    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
+    "functions.name_plot_options(\"logger_space_xy\", data, log=True, orders_of_mag=4)\n",
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretation of results\n",
+    "The beam is narrower than the sample, but taller than the can, so some parts of the sample powder are not directly illuminated, and can thus be seen as a intensity area especially on the zx logger image. The aluminum scatters less, and so lower intensity still."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding a cryostat around the sample can\n",
+    "We can add a crude model of a cryostat around our sample can by adding more Union geometry components. They have to be before the Union_master in the McStas instrument file, so we use the keyword argument *before* in the *add_component* method to specify this when adding the components.\n",
+    "\n",
+    "We also need to designate areas as empty, this is done using the default material Vacuum which has no absorption or scattering processes. In this way we can create several layers by decreasing the priority when going out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inner_wall = instrument.add_component(\"cryostat_wall\", \"Union_cylinder\", before=\"master\")\n",
+    "inner_wall.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "inner_wall.yheight = 0.12\n",
+    "inner_wall.radius = 0.03\n",
+    "inner_wall.material_string='\"Al\"' \n",
+    "inner_wall.priority = 80\n",
+    "\n",
+    "inner_wall_vac = instrument.add_component(\"cryostat_wall_vacuum\", \"Union_cylinder\", before=\"master\")\n",
+    "inner_wall_vac.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "inner_wall_vac.yheight = 0.12 - 0.008\n",
+    "inner_wall_vac.radius = 0.03 - 0.002\n",
+    "inner_wall_vac.material_string='\"Vacuum\"' \n",
+    "inner_wall_vac.priority = 81\n",
+    "\n",
+    "outer_wall = instrument.add_component(\"outer_cryostat_wall\", \"Union_cylinder\", before=\"master\")\n",
+    "outer_wall.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "outer_wall.yheight = 0.15\n",
+    "outer_wall.radius = 0.1\n",
+    "outer_wall.material_string='\"Al\"' \n",
+    "outer_wall.priority = 60\n",
+    "\n",
+    "outer_wall_vac = instrument.add_component(\"outer_cryostat_wall_vacuum\", \"Union_cylinder\", before=\"master\")\n",
+    "outer_wall_vac.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "outer_wall_vac.yheight = 0.15 - 0.01\n",
+    "outer_wall_vac.radius = 0.1 - 0.003\n",
+    "outer_wall_vac.material_string='\"Vacuum\"' \n",
+    "outer_wall_vac.priority = 61"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adjusting the logger view to see the larger cryostat area\n",
+    "The loggers were only viewing a small area around the sample can, but this can be expanded as we still have access to the component objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger_zx.D1_min = -0.12\n",
+    "logger_zx.D1_max = 0.12\n",
+    "logger_zx.D2_min = -0.12\n",
+    "logger_zx.D2_max = 0.12\n",
+    "logger_zy.D1_min = -0.12\n",
+    "logger_zy.D1_max = 0.12\n",
+    "logger_zy.D2_min = -0.12\n",
+    "logger_zy.D2_max = 0.12\n",
+    "logger_xy.D1_min = -0.12\n",
+    "logger_xy.D1_max = 0.12\n",
+    "logger_xy.D2_min = -0.12\n",
+    "logger_xy.D2_max = 0.12"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run the updated instrument file\n",
+    "Run the simulation with the added cryostat. If mpi is installed, one can add mpi=N where N is the number of cores available to speed up the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cryo = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_geometry\",\n",
+    "                                           increment_folder_name=True,\n",
+    "                                           parameters={\"wavelength\" : 3.0})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the data from the new simulation\n",
+    "Here we increase the orders of magnitude of intensity plotted. Try to play with these values to see how it changes the plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions.name_plot_options(\"logger_space_zx\", data_cryo, log=True, orders_of_mag=5)\n",
+    "functions.name_plot_options(\"logger_space_zy\", data_cryo, log=True, orders_of_mag=5)\n",
+    "functions.name_plot_options(\"logger_space_xy\", data_cryo, log=True, orders_of_mag=5)\n",
+    "plotter.make_sub_plot(data_cryo)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpreting the data\n",
+    "The different layers of the cryostat both result in scattering from the aluminium the beam has to move through, but also some increase intensity where it illuminated by scattering from the sample.\n",
+    "\n",
+    "### Comparing situation with and without cryostat\n",
+    "It could be interesting to see what difference adding the cryostat did to the measured signal in the banana monitor, here we extract the numpy arrays and plot them manually with matplotlib for at direct comparison. Ensure you run the two simulations with the same wavelength in order for a comparison to be meaningful."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "banana_can = functions.name_search(\"banana\", data)\n",
+    "banana_cryo = functions.name_search(\"banana\", data_cryo)\n",
+    "\n",
+    "import copy\n",
+    "import numpy as np\n",
+    "banana_diff = copy.deepcopy(banana_cryo)\n",
+    "banana_diff.Intensity = banana_cryo.Intensity - banana_can.Intensity\n",
+    "banana_diff.Error = np.sqrt(banana_cryo.Error**2 + banana_can.Error**2)\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.figure(figsize=(14,6))\n",
+    "plt.plot(banana_can.xaxis, banana_can.Intensity, \"r\",\n",
+    "         banana_cryo.xaxis, banana_cryo.Intensity, \"b\",\n",
+    "         banana_diff.xaxis, banana_diff.Intensity-10.0, \"k\")\n",
+    "plt.xlabel(\"2Theta [deg]\")\n",
+    "plt.ylabel(\"Intensity [n/s]\")\n",
+    "plt.legend([\"Sample in can\", \"Sample in can in cryostat\", \"Difference displaced to -10\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorial/Union_tutorial_3_loggers.ipynb b/tutorial/Union_tutorial_3_loggers.ipynb
new file mode 100644
index 00000000..e1e1035f
--- /dev/null
+++ b/tutorial/Union_tutorial_3_loggers.ipynb
@@ -0,0 +1,581 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualizing what happens in Union master\n",
+    "One disadvantage to collecting all the simulation in the Union_master component, is that it is not possible to insert monitors between the parts to check on the beam. This issue is addressed by adding logger components that can record scattering and absorption events that occurs during the simulation. This notebook will show examples on the usage of loggers and their features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up materials and geometry to investigate\n",
+    "First we set up the same mock cryostat we created in the advanced geometry tutorial to have an interesting system to investigate using the loggers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter\n",
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
+    "Al_inc.sigma = 0.0082\n",
+    "Al_inc.unit_cell_volume = 66.4\n",
+    "\n",
+    "Al_pow = instrument.add_component(\"Al_pow\", \"Powder_process\")\n",
+    "Al_pow.reflections = '\"Al.laz\"'\n",
+    "\n",
+    "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
+    "Al.process_string = '\"Al_inc,Al_pow\"'\n",
+    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "\n",
+    "Sample_inc = instrument.add_component(\"Sample_inc\", \"Incoherent_process\")\n",
+    "Sample_inc.sigma = 3.4176\n",
+    "Sample_inc.unit_cell_volume = 1079.1\n",
+    "\n",
+    "Sample_pow = instrument.add_component(\"Sample_pow\", \"Powder_process\")\n",
+    "Sample_pow.reflections = '\"Na2Ca3Al2F14.laz\"'\n",
+    "\n",
+    "Sample = instrument.add_component(\"Sample\", \"Union_make_material\")\n",
+    "Sample.process_string = '\"Sample_inc,Sample_pow\"'\n",
+    "Sample.my_absorption = 100*2.9464/1079.1\n",
+    "\n",
+    "src = instrument.add_component(\"source\", \"Source_div\")\n",
+    "src.xwidth = 0.01\n",
+    "src.yheight = 0.035\n",
+    "src.focus_aw = 0.01\n",
+    "src.focus_ah = 0.01\n",
+    "\n",
+    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0=\"wavelength\"\n",
+    "src.dlambda=\"0.01*wavelength\"\n",
+    "src.flux = 1E13\n",
+    "\n",
+    "sample_geometry = instrument.add_component(\"sample_geometry\", \"Union_cylinder\")\n",
+    "sample_geometry.yheight = 0.03\n",
+    "sample_geometry.radius = 0.0075\n",
+    "sample_geometry.material_string='\"Sample\"' \n",
+    "sample_geometry.priority = 100\n",
+    "sample_geometry.set_AT([0,0,1], RELATIVE=src)\n",
+    "\n",
+    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\", RELATIVE=sample_geometry)\n",
+    "container.yheight = 0.03+0.003 # 1.5 mm top and button\n",
+    "container.radius = 0.0075 + 0.0015 # 1.5 mm sides of container\n",
+    "container.material_string='\"Al\"' \n",
+    "container.priority = 99\n",
+    "\n",
+    "container_lid = instrument.add_component(\"sample_container_lid\", \"Union_cylinder\")\n",
+    "container_lid.set_AT([0, 0.0155, 0], RELATIVE=container)\n",
+    "container_lid.yheight = 0.004\n",
+    "container_lid.radius = 0.013\n",
+    "container_lid.material_string='\"Al\"' \n",
+    "container_lid.priority = 98\n",
+    "\n",
+    "inner_wall = instrument.add_component(\"cryostat_wall\", \"Union_cylinder\")\n",
+    "inner_wall.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "inner_wall.yheight = 0.12\n",
+    "inner_wall.radius = 0.03\n",
+    "inner_wall.material_string='\"Al\"' \n",
+    "inner_wall.priority = 80\n",
+    "\n",
+    "inner_wall_vac = instrument.add_component(\"cryostat_wall_vacuum\", \"Union_cylinder\")\n",
+    "inner_wall_vac.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "inner_wall_vac.yheight = 0.12 - 0.008\n",
+    "inner_wall_vac.radius = 0.03 - 0.002\n",
+    "inner_wall_vac.material_string='\"Vacuum\"' \n",
+    "inner_wall_vac.priority = 81\n",
+    "\n",
+    "outer_wall = instrument.add_component(\"outer_cryostat_wall\", \"Union_cylinder\")\n",
+    "outer_wall.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "outer_wall.yheight = 0.15\n",
+    "outer_wall.radius = 0.1\n",
+    "outer_wall.material_string='\"Al\"' \n",
+    "outer_wall.priority = 60\n",
+    "\n",
+    "outer_wall_vac = instrument.add_component(\"outer_cryostat_wall_vacuum\", \"Union_cylinder\")\n",
+    "outer_wall_vac.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "outer_wall_vac.yheight = 0.15 - 0.01\n",
+    "outer_wall_vac.radius = 0.1 - 0.003\n",
+    "outer_wall_vac.material_string='\"Vacuum\"' \n",
+    "outer_wall_vac.priority = 61\n",
+    "\n",
+    "instrument.print_components()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.show_components(\"Work directory\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding Union logger components\n",
+    "Union logger components need to be added before the *Union_master* component, as the master need to record the necessary information when the simulation is being performed. There are two different kind of Union logger components, the *loggers* that record scattering and the *abs_loggers* that record absorption. They have similar parameters and user interface. Here is a list of the currently available loggers:\n",
+    "\n",
+    "- Union_logger_1D\n",
+    "- Union_logger_2D_space\n",
+    "- Union_logger_2D_space_time\n",
+    "- Union_logger_3D_space\n",
+    "- Union_logger_2D_kf\n",
+    "- Union_logger_2D_kf_time\n",
+    "- Union_logger_2DQ\n",
+    "\n",
+    "- Union_abs_logger_1D_space\n",
+    "- Union_abs_logger_1D_space_tof\n",
+    "- Union_abs_logger_2D_space\n",
+    "\n",
+    "The most commonly used logger is probably the *Union_logger_2D_space*, this component records spatial distribution of scattering, here are the available parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.component_help(\"Union_logger_2D_space\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting up a 2D_space logger\n",
+    "One can select which two axis to record using *D_direction_1* and *D_direction_2*, and the range with for example *D1_min* and *D1_max*. When spatial information is recorded it is also important to place the logger at an appropriate position, here we center it on the sample position."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zx.D_direction_1 = '\"z\"'\n",
+    "logger_zx.D1_min = -0.12\n",
+    "logger_zx.D1_max = 0.12\n",
+    "logger_zx.n1 = 300\n",
+    "logger_zx.D_direction_2 = '\"x\"'\n",
+    "logger_zx.D2_min = -0.12\n",
+    "logger_zx.D2_max = 0.12\n",
+    "logger_zx.n2 = 300\n",
+    "logger_zx.filename = '\"logger_zx.dat\"'\n",
+    "\n",
+    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zy.D_direction_1 = '\"z\"'\n",
+    "logger_zy.D1_min = -0.12\n",
+    "logger_zy.D1_max = 0.12\n",
+    "logger_zy.n1 = 300\n",
+    "logger_zy.D_direction_2 = '\"y\"'\n",
+    "logger_zy.D2_min = -0.12\n",
+    "logger_zy.D2_max = 0.12\n",
+    "logger_zy.n2 = 300\n",
+    "logger_zy.filename = '\"logger_zy.dat\"'\n",
+    "\n",
+    "master = instrument.add_component(\"master\", \"Union_master\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Running the simulation\n",
+    "If mpi is installed, one can add mpi=N where N is the number of cores available to speed up the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_loggers\",\n",
+    "                                      increment_folder_name=True,\n",
+    "                                      parameters={\"wavelength\" : 3.0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
+    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpreting the data\n",
+    "The zx logger views the cryostat from the top, while the zy loggers shows it from the side. These are histograms of scattered intensity, and it is clear the majority of the scattering happens in the direct beam. There are however scattering events in all parts of our mock cryostat, as neutrons that scattered in either the sample or cryostat walls could go in any direction due to the incoherent scattering. The aluminium and sample also have powder scattering, so some patterns can be seen from the debye scherrer cones."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Logger targets\n",
+    "It is possible to attach a logger to a certain geometry, or even a list of geometries using the *target_geometry* parameter. In that way one can for example view the scattering in the sample environment, while ignoring the sample. It is also possible to select a number of specific scattering processes to investigate with the *target_process* parameter. This is especially useful when working with a single crystal process, that only scatters when the Bragg condition is met.\n",
+    "\n",
+    "Let us modify our existing loggers to view certain parts of the simulated system, and then rerun the simulation.  If mpi is installed, one can add mpi=N where N is the number of cores available to speed up the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger_zx.target_geometry = '\"outer_cryostat_wall,cryostat_wall\"'\n",
+    "logger_zy.target_geometry = '\"sample_geometry\"'\n",
+    "\n",
+    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_loggers\",\n",
+    "                                      increment_folder_name=True,\n",
+    "                                      parameters={\"wavelength\" : 3.0})\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
+    "functions.name_plot_options(\"logger_space_zy\", data, log=False)\n",
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scattering order\n",
+    "All loggers also have the option to only record given scattering orders. For example only record the second scattering.\n",
+    "- order_total : Match given number of scattering events, counting all scattering events in the system\n",
+    "- order_volume : Match given number of scattering events, only counting events in the current volume\n",
+    "- order_volume_process : Match given number of scattering events, only counting events in current volume with current process\n",
+    "\n",
+    "We can modify our previous loggers to test out these features. The zx logger viewing from above will keep the target, but we remove the sample target on the zy logger, which is done by setting the *taget_geometry* to NULL. We choose to look at the second scattering event."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger_zx.order_total = 2\n",
+    "\n",
+    "logger_zy.target_geometry = '\"NULL\"'\n",
+    "logger_zy.order_total = 2\n",
+    "\n",
+    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_loggers\",\n",
+    "                                      increment_folder_name=True,\n",
+    "                                      parameters={\"wavelength\" : 3.0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=3)\n",
+    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=3)\n",
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Demonstration of additional logger components\n",
+    "Here we add a few more loggers to showcase what kind of information that can be displayed.\n",
+    "- 1D logger that logs scattered intensity as function of time\n",
+    "- 2D abs_logger that logs absorption projected onto the scattering plane\n",
+    "- 2DQ logger that logs scattering vector projected onto the scattering plane\n",
+    "- 2D kf logger that logs final wavevector projected onto the scattering plane"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger_1D = instrument.add_component(\"logger_1D\", \"Union_logger_1D\", before=\"master\")\n",
+    "logger_1D.variable = '\"time\"'\n",
+    "logger_1D.min_value = 0.0006\n",
+    "logger_1D.max_value = 0.0012\n",
+    "logger_1D.n1 = 300\n",
+    "logger_1D.filename = '\"logger_1D_time.dat\"'\n",
+    "\n",
+    "abs_logger_zx = instrument.add_component(\"abs_logger_space_zx\", \"Union_abs_logger_2D_space\",before=\"master\")\n",
+    "abs_logger_zx.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "abs_logger_zx.D_direction_1 = '\"z\"'\n",
+    "abs_logger_zx.D1_min = -0.12\n",
+    "abs_logger_zx.D1_max = 0.12\n",
+    "abs_logger_zx.n1 = 300\n",
+    "abs_logger_zx.D_direction_2 = '\"x\"'\n",
+    "abs_logger_zx.D2_min = -0.12\n",
+    "abs_logger_zx.D2_max = 0.12\n",
+    "abs_logger_zx.n2 = 300\n",
+    "abs_logger_zx.filename = '\"abs_logger_zx.dat\"'\n",
+    "\n",
+    "logger_2DQ = instrument.add_component(\"logger_2DQ\", \"Union_logger_2DQ\", before=\"master\")\n",
+    "logger_2DQ.Q_direction_1 = '\"z\"'\n",
+    "logger_2DQ.Q1_min = -5.0\n",
+    "logger_2DQ.Q1_max = 5.0\n",
+    "logger_2DQ.n1 = 200\n",
+    "logger_2DQ.Q_direction_2 = '\"x\"'\n",
+    "logger_2DQ.Q2_min = -5.0\n",
+    "logger_2DQ.Q2_max = 5.0\n",
+    "logger_2DQ.n2 = 200\n",
+    "logger_2DQ.filename = '\"logger_2DQ.dat\"'\n",
+    "\n",
+    "logger_2D_kf = instrument.add_component(\"logger_2D_kf\", \"Union_logger_2D_kf\", before=\"master\")\n",
+    "logger_2D_kf.Q_direction_1 = '\"z\"'\n",
+    "logger_2D_kf.Q1_min = -2.5\n",
+    "logger_2D_kf.Q1_max = 2.5\n",
+    "logger_2D_kf.n1 = 200\n",
+    "logger_2D_kf.Q_direction_2 = '\"x\"'\n",
+    "logger_2D_kf.Q2_min = -2.5\n",
+    "logger_2D_kf.Q2_max = 2.5\n",
+    "logger_2D_kf.n2 = 200\n",
+    "logger_2D_kf.filename = '\"logger_2D_kf.dat\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Runnig the simulation\n",
+    "We now rerun the simulation with the new loggers. If mpi is installed, one can add mpi=N where N is the number of cores available to speed up the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_loggers\",\n",
+    "                                      increment_folder_name=True,\n",
+    "                                      parameters={\"wavelength\" : 3.0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=3)\n",
+    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=3)\n",
+    "functions.name_plot_options(\"abs_logger_space_zx\", data, log=True, orders_of_mag=3)\n",
+    "functions.name_plot_options(\"logger_1D\", data, log=True, orders_of_mag=3)\n",
+    "functions.name_plot_options(\"logger_2DQ\", data, log=True, orders_of_mag=3)\n",
+    "functions.name_plot_options(\"logger_2D_kf\", data, log=True, orders_of_mag=3)\n",
+    "\n",
+    "plotter.make_sub_plot(data[0:2])\n",
+    "plotter.make_sub_plot(data[2:4])\n",
+    "plotter.make_sub_plot(data[4:6])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interpreting the data\n",
+    "We see the scattered intensity as a function of time, here the peaks correspond to the direct beam intersecting the sides of the cryostat and sample. The source used release all neutrons at time 0, so it is a perfect pulse.\n",
+    "\n",
+    "The absorption monitor shows an image very similar to the scattered intensity, but this could be very different, for example when using materials meant as shielding.\n",
+    "\n",
+    "The 2D scattering vector is interesting, it shows a small sphere made of vertical lines, these are powder Bragg peaks. Since the wavevector is almost identical for all incoming neutrons, the first scattering can only access this smaller region of the space. The larger circle is incoherent scattering from second and later scattering events, where the incoming wavevector could be any direction since a scattering already happened.\n",
+    "\n",
+    "The 2D final wavevector plot shows mainly the powder Bragg peaks."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Animations and time series\n",
+    "Several of the Union loggers sets up more than one McStas monitor, these include:\n",
+    "- Union_logger_3D_space\n",
+    "- Union_logger_2D_space_time\n",
+    "- Union_logger_2D_kf_time\n",
+    "\n",
+    "The Union_logger_2D_space_time for example sets up a number of Union_logger_2D_space monitors that are limited to specific time intervals. This can be used to make an animation of the monitor, which we will demonstrate here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "log_2D_st = instrument.add_component(\"logger_2D_space_time\", \"Union_logger_2D_space_time\", before=\"master\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "log_2D_st.time_bins = 36\n",
+    "log_2D_st.time_min = 0.0007\n",
+    "log_2D_st.time_max = 0.0011\n",
+    "\n",
+    "log_2D_st.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "log_2D_st.D_direction_1 = '\"z\"'\n",
+    "log_2D_st.D1_min = -0.12\n",
+    "log_2D_st.D1_max = 0.12\n",
+    "log_2D_st.n1 = 300\n",
+    "log_2D_st.D_direction_2 = '\"x\"'\n",
+    "log_2D_st.D2_min = -0.12\n",
+    "log_2D_st.D2_max = 0.12\n",
+    "log_2D_st.n2 = 300\n",
+    "log_2D_st.filename = '\"logger_2D_space_time.dat\"'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_loggers\",\n",
+    "                                      increment_folder_name=True,\n",
+    "                                      parameters={\"wavelength\" : 3.0})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating an animation\n",
+    "The plotter in McStasScript can create animations when supplied with many McStasData objects. We use name_search to find all the data from the relevant logger, and then a for loop to set plot options for each of them. Then the plotter can make an animation, which is saved as a gif."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ani_data = functions.name_search(\"logger_2D_space_time\", data)\n",
+    "for frame in ani_data:\n",
+    "    frame.set_plot_options(log=True, colormap=\"jet\")\n",
+    "    \n",
+    "plotter.make_animation(ani_data, filename=\"animation_demo\", fps=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Viewing the animation\n",
+    "Some problem in the jupyter notebook prevents playing the gif directly, but it can be played from markdown. One has to refresh this cell when a new animation is written. It should be visible that the beam enters the cryostat from the left, scatters of the sample and illuminates the entire cryostat. Running this simulation with a larger ncount and more time_bins in the monitor will reveal more details in what happens. This is available below, but commented out as the simulation can take some time.\n",
+    "\n",
+    "![SegmentLocal](animation_demo.gif \"Animation\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Optional: Increasing resolution\n",
+    "Running this simulation with a larger ncount and more time_bins in the monitor will reveal more details in what happens. This is available below, but commented out as the simulation can take some time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "log_2D_st.time_bins = 128\n",
+    "#data = instrument.run_full_instrument(ncount=2E8, foldername=\"data_folder/union_loggers\",\n",
+    "#                                      increment_folder_name=True, mpi=4,\n",
+    "#                                      parameters={\"wavelength\" : 3.0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#ani_data = functions.name_search(\"logger_2D_space_time\", data)\n",
+    "#for frame in ani_data:\n",
+    "#    frame.set_plot_options(log=True, colormap=\"jet\", orders_of_mag=6)\n",
+    "#    \n",
+    "#plotter.make_animation(ani_data, filename=\"animation_demo_long\", fps=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Viewing the animation\n",
+    "In the longer animation it is more evident that scattering from the aluminium hits the top and bottom of the outer cylinder.\n",
+    "\n",
+    "![SegmentLocal](animation_demo_long.gif \"Animation\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorial/Union_tutorial_4_conditionals.ipynb b/tutorial/Union_tutorial_4_conditionals.ipynb
new file mode 100644
index 00000000..66465ba5
--- /dev/null
+++ b/tutorial/Union_tutorial_4_conditionals.ipynb
@@ -0,0 +1,524 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using conditional component to modify loggers\n",
+    "Even with the results from the loggers, it can still be difficult to explain all the features in the resulting scattering pattern. The conditional components can modify a logger so that it only records events when the final state of the neutron satisfy some condition. The condition could be leaving with a certain energy or in a specified direction. Before demonstrating these conditional components, we will set up an interesting sample and sample environment, including a few loggers and a time of flight 2theta detector. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up an example instrument\n",
+    "First an example instrument is made, again with a cryostat but this time with a box shaped single crystal of YBaCuO. Since the *single_crystal_process* have quite a few parameters, we use the *set_parameters* method that allows setting parameters using a dictionary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter\n",
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up Al material with incoherent and powder\n",
+    "Al_incoherent = instrument.add_component(\"Al_incoherent\", \"Incoherent_process\")\n",
+    "Al_incoherent.sigma = \"4*0.0082\"\n",
+    "Al_incoherent.packing_factor = 1\n",
+    "Al_incoherent.unit_cell_volume = 66.4\n",
+    "\n",
+    "Al_powder = instrument.add_component(\"Al_powder\", \"Powder_process\")\n",
+    "Al_powder.reflections = \"\\\"Al.laz\\\"\"\n",
+    "\n",
+    "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
+    "Al.process_string = '\"Al_incoherent,Al_powder\"'\n",
+    "Al.my_absorption = \"100*4*0.231/66.4\"\n",
+    "\n",
+    "# Set up YBaCuO with incoherent and single crystal\n",
+    "YBaCuO_incoherent = instrument.add_component(\"YBaCuO_incoherent\", \"Incoherent_process\")\n",
+    "YBaCuO_incoherent.sigma = 2.105\n",
+    "YBaCuO_incoherent.unit_cell_volume = 173.28\n",
+    "\n",
+    "YBaCuO_crystal = instrument.add_component(\"YBaCuO_crystal\", \"Single_crystal_process\")\n",
+    "YBaCuO_crystal.set_parameters(\n",
+    "{\"ax\" : 3.816, \"ay\" : 0, \"az\" : 0,\n",
+    " \"bx\" : 0, \"by\" : 3.886, \"bz\" : 0,\n",
+    " \"cx\" : 0, \"cy\" : 0, \"cz\" : 11.677,\n",
+    " \"delta_d_d\" : 5E-4, \"mosaic\" : 30, \"barns\" : 1,\n",
+    " \"reflections\" : '\"YBaCuO.lau\"'})\n",
+    "\n",
+    "YBaCuO = instrument.add_component(\"YBaCuO\", \"Union_make_material\")\n",
+    "YBaCuO.process_string = '\"YBaCuO_incoherent,YBaCuO_crystal\"'\n",
+    "YBaCuO.my_absorption = 100*14.82/173.28\n",
+    "\n",
+    "src = instrument.add_component(\"source\", \"Source_div\")\n",
+    "src.xwidth = 0.01\n",
+    "src.yheight = 0.035\n",
+    "src.focus_aw = 0.01\n",
+    "src.focus_ah = 0.01\n",
+    "\n",
+    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0=\"wavelength\"\n",
+    "src.dlambda=\"0.01*wavelength\"\n",
+    "src.flux = 1E13\n",
+    "\n",
+    "# At a reference point to build the cryostat around\n",
+    "cryostat_center = instrument.add_component(\"cryostat_center\", \"Arm\")\n",
+    "cryostat_center.set_AT([0, 0, 1], RELATIVE=src)\n",
+    "\n",
+    "# Parameter for controlling sample rotation\n",
+    "instrument.add_parameter(\"A3_angle\", value=0)\n",
+    "\n",
+    "sample = instrument.add_component(\"sample\", \"Union_box\")\n",
+    "sample.set_AT([0, 0, 0], RELATIVE=cryostat_center)\n",
+    "sample.set_ROTATED([0, \"A3_angle\", 0], RELATIVE=cryostat_center)\n",
+    "sample.xwidth = 0.015\n",
+    "sample.yheight = 0.032\n",
+    "sample.zdepth = 0.012\n",
+    "sample.material_string = '\"YBaCuO\"'\n",
+    "sample.priority = 200\n",
+    "\n",
+    "# Setting up two layers of cryostat\n",
+    "inner_cryostat_wall = instrument.add_component(\"inner_cryostat_wall\", \"Union_cylinder\")\n",
+    "inner_cryostat_wall.material_string = \"\\\"Al\\\"\"\n",
+    "inner_cryostat_wall.priority = 12\n",
+    "inner_cryostat_wall.radius = 0.0621\n",
+    "inner_cryostat_wall.yheight = 0.16\n",
+    "inner_cryostat_wall.p_interact = 0.20\n",
+    "inner_cryostat_wall.set_AT([0, 0.01, 0], RELATIVE=cryostat_center)\n",
+    "\n",
+    "inner_cryostat_vacuum = instrument.add_component(\"inner_cryostat_vacuum\", \"Union_cylinder\")\n",
+    "inner_cryostat_vacuum.material_string = \"\\\"Vacuum\\\"\"\n",
+    "inner_cryostat_vacuum.priority = 13\n",
+    "inner_cryostat_vacuum.radius = 0.06\n",
+    "inner_cryostat_vacuum.yheight = 0.15\n",
+    "inner_cryostat_vacuum.set_AT([0, 0.01, 0], RELATIVE=cryostat_center)\n",
+    "\n",
+    "outer_cryostat_wall = instrument.add_component(\"outer_cryostat_wall\", \"Union_cylinder\")\n",
+    "outer_cryostat_wall.material_string = \"\\\"Al\\\"\"\n",
+    "outer_cryostat_wall.priority = 10\n",
+    "outer_cryostat_wall.radius = 0.180\n",
+    "outer_cryostat_wall.yheight = 0.355\n",
+    "outer_cryostat_wall.p_interact = 0.20\n",
+    "outer_cryostat_wall.set_AT([0, 0.032, 0], RELATIVE=cryostat_center)\n",
+    "\n",
+    "outer_cryostat_vacuum = instrument.add_component(\"outer_cryostat_vacuum\", \"Union_cylinder\")\n",
+    "outer_cryostat_vacuum.material_string = \"\\\"Vacuum\\\"\"\n",
+    "outer_cryostat_vacuum.priority = 11\n",
+    "outer_cryostat_vacuum.radius = 0.178\n",
+    "outer_cryostat_vacuum.yheight = 0.355\n",
+    "outer_cryostat_vacuum.set_AT([0, 0.037, 0], RELATIVE=cryostat_center)\n",
+    "\n",
+    "# Set up loggers\n",
+    "logger_space_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\")\n",
+    "logger_space_zx.n1 = 150\n",
+    "logger_space_zx.n2 = 150\n",
+    "logger_space_zx.D1_min = -0.2\n",
+    "logger_space_zx.D1_max = 0.2\n",
+    "logger_space_zx.D2_min = -0.2\n",
+    "logger_space_zx.D2_max = 0.2\n",
+    "logger_space_zx.D_direction_1 = '\"z\"'\n",
+    "logger_space_zx.D_direction_2 = '\"x\"'\n",
+    "logger_space_zx.filename = '\"logger_zx.dat\"'\n",
+    "logger_space_zx.logger_conditional_extend_index = 1\n",
+    "logger_space_zx.set_AT([0, 0, 0], RELATIVE=cryostat_center)\n",
+    "\n",
+    "logger_space_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\")\n",
+    "logger_space_zy.n1 = 150\n",
+    "logger_space_zy.n2 = 150\n",
+    "logger_space_zy.D1_min = -0.2\n",
+    "logger_space_zy.D1_max = 0.2\n",
+    "logger_space_zy.D2_min = -0.15\n",
+    "logger_space_zy.D2_max = 0.2\n",
+    "logger_space_zy.D_direction_1 = '\"z\"'\n",
+    "logger_space_zy.D_direction_2 = '\"y\"'\n",
+    "logger_space_zy.filename = '\"logger_zy.dat\"'\n",
+    "logger_space_zy.logger_conditional_extend_index = 1\n",
+    "logger_space_zy.set_AT([0, 0, 0], RELATIVE=cryostat_center)\n",
+    "\n",
+    "logger_2DQ = instrument.add_component(\"logger_2DQ_sample\", \"Union_logger_2DQ\")\n",
+    "logger_2DQ.Q_direction_1 = '\"z\"'\n",
+    "logger_2DQ.Q1_min = -4.0\n",
+    "logger_2DQ.Q1_max = 4.0\n",
+    "logger_2DQ.n1 = 100\n",
+    "logger_2DQ.Q_direction_2 = '\"x\"'\n",
+    "logger_2DQ.Q2_min = -4.0\n",
+    "logger_2DQ.Q2_max = 4.0\n",
+    "logger_2DQ.n2 = 100\n",
+    "logger_2DQ.target_geometry = '\"sample\"'\n",
+    "logger_2DQ.filename = '\"logger_2DQ_sample.dat\"'\n",
+    "\n",
+    "logger_2DQ = instrument.add_component(\"logger_2DQ_environment\", \"Union_logger_2DQ\")\n",
+    "logger_2DQ.Q_direction_1 = '\"z\"'\n",
+    "logger_2DQ.Q1_min = -4.0\n",
+    "logger_2DQ.Q1_max = 4.0\n",
+    "logger_2DQ.n1 = 100\n",
+    "logger_2DQ.Q_direction_2 = '\"x\"'\n",
+    "logger_2DQ.Q2_min = -4.0\n",
+    "logger_2DQ.Q2_max = 4.0\n",
+    "logger_2DQ.n2 = 100\n",
+    "logger_2DQ.target_geometry = '\"inner_cryostat_wall,outer_cryostat_wall\"'\n",
+    "logger_2DQ.filename = '\"logger_2DQ_all.dat\"'\n",
+    "\n",
+    "logger_time_all = instrument.add_component(\"logger_time_all\", \"Union_logger_1D\")\n",
+    "logger_time_all.n1 = 600\n",
+    "logger_time_all.min_value = 0.0008\n",
+    "logger_time_all.max_value = 0.0015\n",
+    "logger_time_all.filename = '\"scattering_time.dat\"'\n",
+    "\n",
+    "master = instrument.add_component(\"master\", \"Union_master\")\n",
+    "\n",
+    "# Adding a banana - tof detector\n",
+    "banana_detector = instrument.add_component(\"banana_detector\", \"Monitor_nD\", RELATIVE=cryostat_center)\n",
+    "banana_detector.xwidth = 1\n",
+    "banana_detector.yheight = 0.2\n",
+    "banana_detector.restore_neutron = 1\n",
+    "banana_detector.options = '\"banana, theta limits=[-180,180] bins=361, t limits=[0.0011 0.0025] bins=500\"'\n",
+    "banana_detector.filename = '\"tof_b.dat\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculating theta\n",
+    "Our YBaCuO sample has the 010 axis along the z axis and have 010 allowed with d = 3.8843. Here we calculate the necessary rotation of the crystal for satisfying the Bragg condition. This could also be done within the initialize section of the McStas instrument, but here we wish to preserve control over the A3_angle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "wavelength = 4\n",
+    "theta = 180/3.14159*math.asin(wavelength/2.0/3.8843)\n",
+    "print(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_conditionals\",\n",
+    "                                      increment_folder_name=True,\n",
+    "                                      parameters={\"wavelength\" : wavelength, \"A3_angle\" : theta})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger_zx = functions.name_search(\"logger_space_zx\", data)\n",
+    "logger_zx.set_plot_options(log=True, orders_of_mag=9, colormap=\"jet\")\n",
+    "\n",
+    "logger_zy = functions.name_search(\"logger_space_zy\", data)\n",
+    "logger_zy.set_plot_options(log=True, orders_of_mag=9, colormap=\"jet\")\n",
+    "\n",
+    "logger_2DQ_sample = functions.name_search(\"logger_2DQ_sample\", data)\n",
+    "logger_2DQ_sample.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
+    "\n",
+    "logger_2DQ_env = functions.name_search(\"logger_2DQ_environment\", data)\n",
+    "logger_2DQ_env.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
+    "\n",
+    "plotter.make_sub_plot([logger_zx, logger_zy, logger_2DQ_sample, logger_2DQ_env], fontsize=10)\n",
+    "\n",
+    "time = functions.name_search(\"logger_time_all\", data)\n",
+    "time.set_plot_options(log=True)\n",
+    "plotter.make_sub_plot([time], fontsize=18)\n",
+    "\n",
+    "banana = functions.name_search(\"banana_detector\", data)\n",
+    "banana.set_plot_options(log=True, orders_of_mag=7, cut_max=0.001)\n",
+    "plotter.make_sub_plot([banana], fontsize=18)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretation of the data\n",
+    "The data from the two spatial loggers show scattering location within the cryostat, and we see one beam entering the cryostat, yet two beams leaving as we satisfy the Bragg condition for the sample. This also result in scattering from when the scattered beam intersect the sample environment.\n",
+    "\n",
+    "We have two 2DQ loggers recording the scattering vector, one just for the sample and one for the sample environment. On the sample monitor we see that many Bragg peaks scatter some intensity, but 010 and 0-10 have the most intensity, they are at [0.9, -1.5] and [1.5, -0.9]. The two circles are incoherent scattering from the two most common wavevectors, the initial beam and the beam scattered from 010. On the 2DQ logger for the sample environment, we mainly see the Debye-Scherrer cones as lines within the circles defined by the two predominant wavevectors. It seems the used wavelength allows two different Bragg conditions to be met in the aluminium.\n",
+    "\n",
+    "The time logger show a surprising amount of complexity. The 5 peaks from entering and exiting two layers and intersecting the sample are clear, but all structure after 0.0012 is a surprise. There is also an unexpected peak at 0.00115, this may be the scattered beam intersecting the outer layer of the cryostat, this happens a bit sooner than the direct beam because the path is shorter when scattered from the front of the sample.\n",
+    "\n",
+    "The time of flight vs 2theta monitor also has a large amount of complexity that is not simple to explain. The bright spot at [0, 0.00155] is the direct beam, while the spot at [60, 0.00155] is the scattered beam. The horizontal line at t=0.00155 must be incoherent scattering from the sample, as it must have had the same distance to all points on the detector. The curved lower branch could be incoherent scattering from where the beam enters the cryostat, as that is closest to the 180 deg point on the detector. The remaining hot spots are all some Debye Scherrer cones from a beam entering or exiting the sample environment, and the more blurry spots may be of even higher order."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conditional components\n",
+    "We can use conditional components to investigate peaks in the final scattering pattern, by limiting the loggers to only recording events that for example end in a certain spot. Currently there are two available\n",
+    "- Union_conditional_standard\n",
+    "- Union_conditional_PSD\n",
+    "\n",
+    "The standard version allows limits for energy, time and number of scattering events for the neutron when it exits the *Union_master* simulation, in this case when it leaves the sample environment.\n",
+    "\n",
+    "The PSD version propagates the final neutron states to a given rectangular surface and it is possible to filter the logger events on the neutron state when it reaches this surface, and ignores all events that misses. This is what we will use here to investigate a peak in the scattering pattern. We also set a time limit as our detector is time of flight sensitive.\n",
+    "\n",
+    "Here are the important parameters for the Union_conditional_PSD component\n",
+    "- target_loggers : comma separated string of logger names this conditional should affect\n",
+    "- xwidth : width of rectangle\n",
+    "- yheight : height of rectangle\n",
+    "- time_min : lower time limit for condition\n",
+    "- time_max : upper time limit for condition\n",
+    "- overwrite_logger_weight : If set to 1, will weight logger results with the final ray weight"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up instrument parameters describing what spot to investigate\n",
+    "instrument.add_parameter(\"tag_angle\", value=-95)\n",
+    "instrument.add_parameter(\"tag_time\", value=0.00188)\n",
+    "instrument.add_parameter(\"tag_interval\", value=9E-5)\n",
+    "\n",
+    "# Set up an arm pointing to the relevant spot\n",
+    "spot_dir = instrument.add_component(\"spot_dir\", \"Arm\", RELATIVE=cryostat_center, before=\"master\")\n",
+    "spot_dir.set_ROTATED([0, \"tag_angle\", 0], RELATIVE=cryostat_center)\n",
+    "\n",
+    "# Set up a conditional component targeting all our loggers\n",
+    "PSD_conditional = instrument.add_component(\"space_all_PSD_conditional\", \"Union_conditional_PSD\", before=\"master\")\n",
+    "PSD_conditional.target_loggers = '\"logger_space_zx,logger_space_zy,logger_time_all,logger_2DQ_sample,logger_2DQ_environment\"'\n",
+    "PSD_conditional.xwidth = 0.2\n",
+    "PSD_conditional.yheight = 0.2\n",
+    "PSD_conditional.time_min = \"tag_time-0.5*tag_interval\"\n",
+    "PSD_conditional.time_max = \"tag_time+0.5*tag_interval\"\n",
+    "# Ensure the position of the conditional rectangle is on the detector surface\n",
+    "PSD_conditional.set_AT([0, 0, 0.5], RELATIVE=spot_dir) \n",
+    "\n",
+    "# Add a monitor with flag that is only active when the condition in the conditional is true\n",
+    "instrument.add_declare_var(\"int\", \"flag1\")\n",
+    "logger_space_zx.logger_conditional_extend_index = 1\n",
+    "master.append_EXTEND(\"flag1 = logger_conditional_extend_array[1];\")\n",
+    "\n",
+    "# Copy of our banana detector, but with WHEN condition to verify we are investigating the right peak\n",
+    "banana_detector = instrument.add_component(\"banana_detector_limited\", \"Monitor_nD\", RELATIVE=cryostat_center)\n",
+    "banana_detector.xwidth = 1\n",
+    "banana_detector.yheight = 0.2\n",
+    "banana_detector.restore_neutron = 1\n",
+    "banana_detector.options = '\"banana, theta limits=[-180,180] bins=361, t limits=[0.0011 0.0025] bins=500\"'\n",
+    "banana_detector.filename = '\"tof_b_limited.dat\"'\n",
+    "banana_detector.set_WHEN(\"flag1 > 0\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run the simulation\n",
+    "We run the simulation again with a larger ncount, as the loggers now only record a small fraction of the scattered neutrons. You can investigate other areas of interest by changing *tag_angle*, *tag_time* and *tag_interval* to another interesting location on the time of flight detector.\n",
+    "\n",
+    "If MPI is installed add mpi=N where N is the number of CPU cores available in your computer to speed up the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_con = instrument.run_full_instrument(ncount=3E7, foldername=\"data_folder/union_conditionals\",\n",
+    "                                      increment_folder_name=True, #mpi=2,\n",
+    "                                      parameters={\"wavelength\" : wavelength, \"A3_angle\" : theta,\n",
+    "                                                  \"tag_angle\" : -95, \"tag_time\" : 0.00188, \"tag_interval\" : 9E-5})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Confirm our conditional is on the desired peak\n",
+    "First we plot our 2theta / time of flight detector and the version limited to what the conditional records to confirm that we selected an appropriate region to investigate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "banana = functions.name_search(\"banana_detector\", data_con)\n",
+    "banana.set_plot_options(log=True, orders_of_mag=7, cut_max=0.001)\n",
+    "\n",
+    "banana_limited = functions.name_search(\"banana_detector_limited\", data_con)\n",
+    "banana_limited.set_plot_options(log=False)\n",
+    "\n",
+    "plotter.make_sub_plot([banana, banana_limited], fontsize=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting the loggers, now limited by the conditional\n",
+    "We use a different colormap here instead of the standard jet, because in jet the lowest and highest intensity are both dark colors. By choosing a colormap where zero intensity is white, it blends in with white from lack of data which is beneficial in this situation for clearly seeing the hotspots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger_zx = functions.name_search(\"logger_space_zx\", data_con)\n",
+    "logger_zx.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
+    "\n",
+    "logger_zy = functions.name_search(\"logger_space_zy\", data_con)\n",
+    "logger_zy.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
+    "\n",
+    "logger_2DQ_sample = functions.name_search(\"logger_2DQ_sample\", data_con)\n",
+    "logger_2DQ_sample.set_plot_options(log=True, orders_of_mag=7, colormap=\"YlOrRd\")\n",
+    "\n",
+    "logger_2DQ_env = functions.name_search(\"logger_2DQ_environment\", data_con)\n",
+    "logger_2DQ_env.set_plot_options(log=True, orders_of_mag=7, colormap=\"YlOrRd\")\n",
+    "\n",
+    "plotter.make_sub_plot([logger_zx, logger_zy, logger_2DQ_sample, logger_2DQ_env], fontsize=10)\n",
+    "\n",
+    "time = functions.name_search(\"logger_time_all\", data_con)\n",
+    "time.set_plot_options(log=True)\n",
+    "\n",
+    "plotter.make_sub_plot([time], fontsize=18)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interpreting the data from loggers with conditional\n",
+    "Now that the loggers only show scattering from neutrons that end in our specific peak of interest, we can explain the origin of this peak. The spatial loggers show that scattering primarily happens in the sample, and in the outer part of the cryostat where the scattered beam leaves the sample environment. It is in this case obvious the first scattering is in the sample, as the exit area is not within the direct beam, but one can create individual loggers for each scattering order in order to confirm this.\n",
+    "\n",
+    "On the 2DQ logger for the sample it is clear the scattering is from the main Bragg peak (010 and 0-10). We see two peaks as a ray scattered from a Bragg peak will fulfil the Bragg condition of the opposite reciprocal indices. On the 2DQ logger for the sample environment, we see the brightest spot is a small part of a Debye-Scherrer cone. Scattered neutrons from other parts of this cone will not hit our conditional PSD, some may not even hit the detector at all.\n",
+    "\n",
+    "That means this specific peak was an uneven number of single crystal scattering events in 010/0-10 in the sample followed by a powder scattering in the outer wall of the sample environment. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using the final weight option\n",
+    "Here we rerun the simulation with the overwrite_logger_weight option in the conditional turned on to see the effect. Without it a ray is recorded in the loggers if it satisfy the conditional, but it does not matter how large the final weight is. For this reason, some rays with high sampling probability to reach the condition but low weight are represented more than is appropriate. This is mainly important when shielding is simulated, as ray that pass through the shielding needs can be heavily overrepresented."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PSD_conditional.overwrite_logger_weight = 1\n",
+    "\n",
+    "data_con_f = instrument.run_full_instrument(ncount=3E7, foldername=\"data_folder/union_conditionals\",\n",
+    "                                      increment_folder_name=True, #mpi=2,\n",
+    "                                      parameters={\"wavelength\" : wavelength, \"A3_angle\" : theta,\n",
+    "                                                  \"tag_angle\" : -95, \"tag_time\" : 0.00188, \"tag_interval\" : 9E-5})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger_zx = functions.name_search(\"logger_space_zx\", data_con_f)\n",
+    "logger_zx.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
+    "\n",
+    "logger_zy = functions.name_search(\"logger_space_zy\", data_con_f)\n",
+    "logger_zy.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
+    "\n",
+    "logger_2DQ_sample = functions.name_search(\"logger_2DQ_sample\", data_con_f)\n",
+    "logger_2DQ_sample.set_plot_options(log=True, orders_of_mag=7, colormap=\"YlOrRd\")\n",
+    "\n",
+    "logger_2DQ_env = functions.name_search(\"logger_2DQ_environment\", data_con_f)\n",
+    "logger_2DQ_env.set_plot_options(log=True, orders_of_mag=7, colormap=\"YlOrRd\")\n",
+    "\n",
+    "plotter.make_sub_plot([logger_zx, logger_zy, logger_2DQ_sample, logger_2DQ_env], fontsize=10)\n",
+    "\n",
+    "time = functions.name_search(\"logger_time_all\", data_con_f)\n",
+    "time.set_plot_options(log=True)\n",
+    "\n",
+    "plotter.make_sub_plot([time], fontsize=18)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparison between normal and overwrite_logger_weight\n",
+    "Here we make a direct comparison, and see only a slight difference in this case. No shielding is simulated here which would cause a much more clear difference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger_zx = functions.name_search(\"logger_space_zx\", data_con)\n",
+    "logger_zx.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
+    "\n",
+    "logger_zy = functions.name_search(\"logger_space_zy\", data_con)\n",
+    "logger_zy.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
+    "\n",
+    "logger_zx_f = functions.name_search(\"logger_space_zx\", data_con_f)\n",
+    "logger_zx_f.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
+    "\n",
+    "logger_zy_f = functions.name_search(\"logger_space_zy\", data_con_f)\n",
+    "logger_zy_f.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
+    "\n",
+    "plotter.make_sub_plot([logger_zx, logger_zy, logger_zx_f, logger_zy_f], fontsize=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorial/Union_tutorial_5_masks.ipynb b/tutorial/Union_tutorial_5_masks.ipynb
new file mode 100644
index 00000000..56e95d4c
--- /dev/null
+++ b/tutorial/Union_tutorial_5_masks.ipynb
@@ -0,0 +1,358 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Union tutorial on masks\n",
+    "There are some geometries that are impossible to build using only the priority based system geometry system, for example making part of a cylinder thinner, which is needed for a cryostat window. In many such cases, masks can be used to solve the problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting up an example without masks\n",
+    "First we set up an example with a thick and hollow Al cylinder and a logger to view the spatial distribution of scattering."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
+    "Al_inc.sigma = 0.0082\n",
+    "Al_inc.unit_cell_volume = 66.4\n",
+    "\n",
+    "Al_pow = instrument.add_component(\"Al_pow\", \"Powder_process\")\n",
+    "Al_pow.reflections = '\"Al.laz\"'\n",
+    "\n",
+    "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
+    "Al.process_string = '\"Al_inc,Al_pow\"'\n",
+    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "\n",
+    "src = instrument.add_component(\"source\", \"Source_div\")\n",
+    "\n",
+    "src.xwidth = 0.2\n",
+    "src.yheight = 0.035\n",
+    "src.focus_aw = 0.01\n",
+    "src.focus_ah = 0.01\n",
+    "\n",
+    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0=\"wavelength\"\n",
+    "src.dlambda=\"0.01*wavelength\"\n",
+    "src.flux = 1E13\n",
+    "\n",
+    "wall = instrument.add_component(\"wall\", \"Union_cylinder\")\n",
+    "wall.set_AT([0,0,1], RELATIVE=src)\n",
+    "wall.yheight = 0.15\n",
+    "wall.radius = 0.1\n",
+    "wall.material_string='\"Al\"' \n",
+    "wall.priority = 10\n",
+    "\n",
+    "wall_vac = instrument.add_component(\"wall_vacuum\", \"Union_cylinder\")\n",
+    "wall_vac.set_AT([0,0,0], RELATIVE=wall)\n",
+    "wall_vac.yheight = 0.15 + 0.01\n",
+    "wall_vac.radius = 0.1 - 0.02\n",
+    "wall_vac.material_string='\"Vacuum\"' \n",
+    "wall_vac.priority = 50\n",
+    "\n",
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=wall)\n",
+    "logger_zx.D_direction_1 = '\"z\"'\n",
+    "logger_zx.D1_min = -0.12\n",
+    "logger_zx.D1_max = 0.12\n",
+    "logger_zx.n1 = 300\n",
+    "logger_zx.D_direction_2 = '\"x\"'\n",
+    "logger_zx.D2_min = -0.12\n",
+    "logger_zx.D2_max = 0.12\n",
+    "logger_zx.n2 = 300\n",
+    "logger_zx.filename = '\"logger_zx.dat\"'\n",
+    "\n",
+    "master = instrument.add_component(\"master\", \"Union_master\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = instrument.run_full_instrument(ncount=2E6, foldername=\"data_folder/union_masks\",\n",
+    "                                      increment_folder_name=True, #mpi=2,\n",
+    "                                      parameters={\"wavelength\" : 3.0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Masks\n",
+    "All Union geometries can act as a mask for a list of other already defined geometries. The geometries affected by a mask will only exist inside the mask, while the parts outside will not have any effect on this simulation. This provides some interesting geometrical capabilities, for example by defining two spheres with some overlap and making one a mask of the other, a classical lens shape can be created.\n",
+    "\n",
+    "The relevant parameters of all geometry components are:\n",
+    "- mask_string : comma separated list of geometry names the mask should be applied to\n",
+    "- mask_setting : selects between \"ANY\" or \"ALL\" mode. Default mode is \"ALL\".\n",
+    "\n",
+    "The mask mode is only important if several masks affect the same geometry, per default just having any of the masks overlap the target geometry allow it to exists, which correspond to the \"ANY\" mode. If the \"ALL\" mode is selected, the target geometry will only exists in regions where all the masks and itself overlap.\n",
+    "\n",
+    "Note that a unique priority is still necessary, but it is not used."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding a window using masks\n",
+    "Here we add a window to one side of the cylinder by inserting a larger vacuum cylinder, but mask it so that it is only active in the area around the window. In this way we get a nice curved window. We chose a box shape for the mask, but we could also have chosen a cylinder to get a round window."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "window = instrument.add_component(\"window\", \"Union_cylinder\", before=\"master\")\n",
+    "window.set_AT([0,0,0], RELATIVE=wall)\n",
+    "window.yheight = 0.15 + 0.02\n",
+    "window.radius = 0.1 - 0.01\n",
+    "window.material_string='\"Vacuum\"' \n",
+    "window.priority = 25\n",
+    "\n",
+    "mask = instrument.add_component(\"mask\", \"Union_box\", before=\"master\")\n",
+    "mask.xwidth = 0.1\n",
+    "mask.yheight = 0.2\n",
+    "mask.zdepth = 0.09\n",
+    "mask.priority = 1\n",
+    "mask.mask_string='\"window\"'\n",
+    "mask.set_AT([0,0,-0.1], RELATIVE=wall)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = instrument.run_full_instrument(ncount=2E6, foldername=\"data_folder/union_masks\",\n",
+    "                                      increment_folder_name=True, #mpi=2,\n",
+    "                                      parameters={\"wavelength\" : 3.0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding an external window using a mask\n",
+    "It is also possible to create a thinner section where the material is reduced from the outside. Here we need to add both a vacuum and an aluminium geometry, both of which need to have a priority lower than the original inner vacuum. One mask can handle several geometries, just include both names in the *mask_string* parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "o_window = instrument.add_component(\"outer_window\", \"Union_cylinder\", before=\"master\")\n",
+    "o_window.set_AT([0,0,0], RELATIVE=wall)\n",
+    "o_window.yheight = 0.15 + 0.03\n",
+    "o_window.radius = 0.1 + 0.01\n",
+    "o_window.material_string='\"Vacuum\"' \n",
+    "o_window.priority = 30\n",
+    "\n",
+    "o_window_al = instrument.add_component(\"outer_window_Al\", \"Union_cylinder\", before=\"master\")\n",
+    "o_window_al.set_AT([0,0,0], RELATIVE=wall)\n",
+    "o_window_al.yheight = 0.15 + 0.04\n",
+    "o_window_al.radius = 0.1 - 0.01\n",
+    "o_window_al.material_string='\"Al\"' \n",
+    "o_window_al.priority = 31\n",
+    "\n",
+    "mask = instrument.add_component(\"mask_outer\", \"Union_box\", before=\"master\")\n",
+    "mask.xwidth = 0.12\n",
+    "mask.yheight = 0.2\n",
+    "mask.zdepth = 0.09\n",
+    "mask.priority = 2\n",
+    "mask.mask_string='\"outer_window,outer_window_Al\"'\n",
+    "mask.set_AT([0,0,0.1], RELATIVE=wall)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = instrument.run_full_instrument(ncount=2E6, foldername=\"data_folder/union_masks\",\n",
+    "                                      increment_folder_name=True, #mpi=2,\n",
+    "                                      parameters={\"wavelength\" : 3.0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Masks are flexible\n",
+    "Masks can be used to create many interesting shapes with few geometries. Below we create a octagon with rounded corners using just three geometries, two of these being masks. Using masks expands the space of possible geometries greatly, and in many cases can also be a performance advantage when they reduce the number of geometries needed to describe the desired geometry."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")\n",
+    "\n",
+    "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
+    "Al_inc.sigma = 0.0082\n",
+    "Al_inc.unit_cell_volume = 66.4\n",
+    "\n",
+    "Al_pow = instrument.add_component(\"Al_pow\", \"Powder_process\")\n",
+    "Al_pow.reflections = '\"Al.laz\"'\n",
+    "\n",
+    "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
+    "Al.process_string = '\"Al_inc,Al_pow\"'\n",
+    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "\n",
+    "src = instrument.add_component(\"source\", \"Source_div\")\n",
+    "\n",
+    "src.xwidth = 0.2\n",
+    "src.yheight = 0.035\n",
+    "src.focus_aw = 0.01\n",
+    "src.focus_ah = 0.01\n",
+    "\n",
+    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0=\"wavelength\"\n",
+    "src.dlambda=\"0.01*wavelength\"\n",
+    "src.flux = 1E13\n",
+    "\n",
+    "box = instrument.add_component(\"box\", \"Union_box\")\n",
+    "box.set_AT([0,0,1], RELATIVE=src)\n",
+    "box.xwidth = 0.2\n",
+    "box.yheight = 0.1\n",
+    "box.zdepth = 0.2\n",
+    "box.material_string='\"Al\"' \n",
+    "box.priority = 10\n",
+    "\n",
+    "# Cut the corners by using an identical box rotated 45 deg around y\n",
+    "box_mask = instrument.add_component(\"box_mask\", \"Union_box\")\n",
+    "box_mask.set_AT([0,0,0], RELATIVE=box)\n",
+    "box_mask.set_ROTATED([0,45,0], RELATIVE=box)\n",
+    "box_mask.xwidth = 0.2\n",
+    "box_mask.yheight = 0.11 # Have to increase yheight to avoid perfect overlap\n",
+    "box_mask.zdepth = 0.2\n",
+    "box_mask.mask_string='\"box\"' \n",
+    "box_mask.priority = 50\n",
+    "\n",
+    "# Round the corners with a cylinder mask\n",
+    "cyl_mask = instrument.add_component(\"cylinder_mask\", \"Union_cylinder\")\n",
+    "cyl_mask.set_AT([0,0,0], RELATIVE=box)\n",
+    "cyl_mask.radius = 0.105\n",
+    "cyl_mask.yheight = 0.12\n",
+    "cyl_mask.mask_string='\"box\"' \n",
+    "cyl_mask.priority = 51\n",
+    "\n",
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=box)\n",
+    "logger_zx.D_direction_1 = '\"z\"'\n",
+    "logger_zx.D1_min = -0.12\n",
+    "logger_zx.D1_max = 0.12\n",
+    "logger_zx.n1 = 300\n",
+    "logger_zx.D_direction_2 = '\"x\"'\n",
+    "logger_zx.D2_min = -0.12\n",
+    "logger_zx.D2_max = 0.12\n",
+    "logger_zx.n2 = 300\n",
+    "logger_zx.filename = '\"logger_zx.dat\"'\n",
+    "\n",
+    "master = instrument.add_component(\"master\", \"Union_master\")\n",
+    "\n",
+    "data = instrument.run_full_instrument(ncount=2E6, foldername=\"data_folder/union_masks\",\n",
+    "                                      increment_folder_name=True, #mpi=2,\n",
+    "                                      parameters={\"wavelength\" : 3.0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb b/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
new file mode 100644
index 00000000..9e1b32f5
--- /dev/null
+++ b/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
@@ -0,0 +1,385 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Advanced geometry component concepts: Exit geometry and number of activations\n",
+    "This notebook explains the concept of exit geometry and the activation counter both of which are tied to the geometry components and how they are treated by the *Union_master*.\n",
+    "\n",
+    "An exit geometry is created by setting the *material_string* of a geometry to \"Exit\", and if a ray enters such a geometry, it is immediately released from the master component. Normally this only happens when the ray does not intersect any geometries. There are several uses for this, for example inserting a monitor within a Union geometry ensemble."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up an example with empty sample container\n",
+    "First we set up an example with an empty sample container."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
+    "Al_inc.sigma = 0.0082\n",
+    "Al_inc.unit_cell_volume = 66.4\n",
+    "\n",
+    "Al_pow = instrument.add_component(\"Al_pow\", \"Powder_process\")\n",
+    "Al_pow.reflections = '\"Al.laz\"'\n",
+    "\n",
+    "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
+    "Al.process_string = '\"Al_inc,Al_pow\"'\n",
+    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "\n",
+    "src = instrument.add_component(\"source\", \"Source_div\")\n",
+    "src.xwidth = 0.01\n",
+    "src.yheight = 0.035\n",
+    "src.focus_aw = 0.01\n",
+    "src.focus_ah = 0.01\n",
+    "\n",
+    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0=\"wavelength\"\n",
+    "src.dlambda=\"0.01*wavelength\"\n",
+    "src.flux = 1E13\n",
+    "\n",
+    "sample_volume = instrument.add_component(\"sample_volume\", \"Union_cylinder\")\n",
+    "sample_volume.yheight = 0.03\n",
+    "sample_volume.radius = 0.0075\n",
+    "sample_volume.material_string='\"Vacuum\"' \n",
+    "sample_volume.priority = 100\n",
+    "sample_volume.set_AT([0,0,1], RELATIVE=src)\n",
+    "\n",
+    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\", RELATIVE=sample_volume)\n",
+    "container.yheight = 0.03+0.003 # 1.5 mm top and button\n",
+    "container.radius = 0.0075 + 0.0015 # 1.5 mm sides of container\n",
+    "container.material_string='\"Al\"' \n",
+    "container.priority = 99\n",
+    "\n",
+    "container_lid = instrument.add_component(\"sample_container_lid\", \"Union_cylinder\")\n",
+    "container_lid.set_AT([0, 0.0155, 0], RELATIVE=container)\n",
+    "container_lid.yheight = 0.004\n",
+    "container_lid.radius = 0.013\n",
+    "container_lid.material_string='\"Al\"' \n",
+    "container_lid.priority = 98\n",
+    "\n",
+    "inner_wall = instrument.add_component(\"cryostat_wall\", \"Union_cylinder\")\n",
+    "inner_wall.set_AT([0,0,0], RELATIVE=sample_volume)\n",
+    "inner_wall.yheight = 0.12\n",
+    "inner_wall.radius = 0.03\n",
+    "inner_wall.material_string='\"Al\"' \n",
+    "inner_wall.priority = 80\n",
+    "\n",
+    "inner_wall_vac = instrument.add_component(\"cryostat_wall_vacuum\", \"Union_cylinder\")\n",
+    "inner_wall_vac.set_AT([0,0,0], RELATIVE=sample_volume)\n",
+    "inner_wall_vac.yheight = 0.12 - 0.008\n",
+    "inner_wall_vac.radius = 0.03 - 0.002\n",
+    "inner_wall_vac.material_string='\"Vacuum\"' \n",
+    "inner_wall_vac.priority = 81\n",
+    "\n",
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=sample_volume)\n",
+    "logger_zx.D_direction_1 = '\"z\"'\n",
+    "logger_zx.D1_min = -0.04\n",
+    "logger_zx.D1_max = 0.04\n",
+    "logger_zx.n1 = 300\n",
+    "logger_zx.D_direction_2 = '\"x\"'\n",
+    "logger_zx.D2_min = -0.04\n",
+    "logger_zx.D2_max = 0.04\n",
+    "logger_zx.n2 = 300\n",
+    "logger_zx.filename = '\"logger_zx.dat\"'\n",
+    "\n",
+    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\", RELATIVE=sample_volume)\n",
+    "logger_zy.D_direction_1 = '\"z\"'\n",
+    "logger_zy.D1_min = -0.04\n",
+    "logger_zy.D1_max = 0.04\n",
+    "logger_zy.n1 = 300\n",
+    "logger_zy.D_direction_2 = '\"y\"'\n",
+    "logger_zy.D2_min = -0.06\n",
+    "logger_zy.D2_max = 0.06\n",
+    "logger_zy.n2 = 300\n",
+    "logger_zy.filename = '\"logger_zy.dat\"'\n",
+    "\n",
+    "master = instrument.add_component(\"master\", \"Union_master\")\n",
+    "\n",
+    "banana = instrument.add_component(\"banana\", \"Monitor_nD\", RELATIVE=sample_volume)\n",
+    "banana.xwidth = 1.5\n",
+    "banana.yheight = 0.4\n",
+    "banana.restore_neutron = 1\n",
+    "banana.options = '\"theta limits=[5 175] bins=250, banana\"'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_empty = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_external\",\n",
+    "                                           increment_folder_name=True, #mpi=2,\n",
+    "                                           parameters={\"wavelength\" : 3.0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions.name_plot_options(\"logger_space_zx\", data_empty, log=True, orders_of_mag=4)\n",
+    "functions.name_plot_options(\"logger_space_zy\", data_empty, log=True, orders_of_mag=4)\n",
+    "plotter.make_sub_plot(data_empty[0:2])\n",
+    "plotter.make_sub_plot(data_empty[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding an exit volume\n",
+    "Now we switch the sample_volume material from Vacuum to exit, ejecting rays from the simulation when they encounter it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_volume.material_string='\"Exit\"' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_external\",\n",
+    "                                      increment_folder_name=True, #mpi=2,\n",
+    "                                      parameters={\"wavelength\" : 3.0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
+    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
+    "plotter.make_sub_plot(data[0:2])\n",
+    "plotter.make_sub_plot(data[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding an external component in the gap\n",
+    "We can now see any part of the beam that intersected the exit volume is basically removed from the simulation. It is now however possible to insert another component within that exit volume, for example a sample not available as a Union process. Here we just use a PowderN sample in order to demonstrate. We select the same dimensions as the exit volume, but subtract 10 micrometer to avoid a perfect overlap."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = instrument.add_component(\"sample\", \"PowderN\", after=\"master\")\n",
+    "sample.set_AT([0,0,0], RELATIVE=sample_volume)\n",
+    "sample.radius = sample_volume.radius - 1E-5\n",
+    "sample.yheight = sample_volume.yheight - 2E-5\n",
+    "sample.reflections = '\"Na2Ca3Al2F14.laz\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Running the simulation again\n",
+    "We run the simulation again, but know that the scattering within the sample wont be directly visible in the loggers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_wrong = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_external\",\n",
+    "                                            increment_folder_name=True,\n",
+    "                                            parameters={\"wavelength\" : 3.0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions.name_plot_options(\"logger_space_zx\", data_wrong, log=True, orders_of_mag=4)\n",
+    "functions.name_plot_options(\"logger_space_zy\", data_wrong, log=True, orders_of_mag=4)\n",
+    "plotter.make_sub_plot(data_wrong[0:2])\n",
+    "plotter.make_sub_plot(data_wrong[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretation of the data\n",
+    "Now we have added a sample inside the Union geometry, but when the neutron reaches that sample, it is ignored by the sample environment leading to unphysical behavior. Here the beam does not illuminate the sample environment on the way out, and all rays scattered by the PowderN sample are not attenuated by the "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Allowing the rays to return to the Union_master\n",
+    "Now we could recreate the entire sample environment with new geometries and insert an additional unit master to grab the neutrons after the external sample, yet this would be error prone as all geometries would need to be exactly the same. Instead it is possible to tell Union geometries that they should be simulated in several of the next *Union_master* components using the *number_of_activation* parameter on each Union geometry, which is 1 per default.\n",
+    "\n",
+    "Setting it to 2, we tell the geometries that they should be simulated in the two next *Union_master* components. We do not update the sample_volume which is an exit volume, as this would allow the ray to escape once more. Instead we will replace it with Vacuum, but one could also have placed something closer to the actual sample.\n",
+    "\n",
+    "One last necessary detail is to set the *allow_inside_start* parameter on the second *Union_master* component. This disables an error message that would occur if a neutron starts inside a Union geometry, as this is most likely an error. Here we want to do this on purpose, and we need to let the *Union_master* component know this is allowed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "container.number_of_activations = 2\n",
+    "container_lid.number_of_activations = 2\n",
+    "inner_wall.number_of_activations = 2\n",
+    "inner_wall_vac.number_of_activations = 2\n",
+    "\n",
+    "sample_replacement = instrument.add_component(\"sample_volume_replace\", \"Union_cylinder\")\n",
+    "sample_replacement.yheight = sample_volume.yheight\n",
+    "sample_replacement.radius = sample_volume.radius\n",
+    "sample_replacement.material_string='\"Vacuum\"' \n",
+    "sample_replacement.priority = 101\n",
+    "sample_replacement.set_AT([0,0,0], RELATIVE=sample_volume)\n",
+    "\n",
+    "master_2 = instrument.add_component(\"master_after_sample\", \"Union_master\", after=\"sample\")\n",
+    "master_2.allow_inside_start=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_external\",\n",
+    "                                      increment_folder_name=True,\n",
+    "                                      parameters={\"wavelength\" : 3.0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
+    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
+    "plotter.make_sub_plot(data[0:2])\n",
+    "plotter.make_sub_plot(data[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretation of the data\n",
+    "Now we see evidence of the beam leaving the sample environment after interacting with the sample, and also elevated scattering in comparison to the empty can. This is now a reasonable simulation containing an external component inside a Union geometry ensemble, but there is still one problem, if the ray leaves the external component and reenters later, it will find a Vacuum instead of that sample. This can be fixed to some extend by adding a second copy of the external component and a third *Union_master* component, while incrementing the *number_of_activations* on all geometries, then the ray would be able to leave and enter the external component once before the external component effectively disappears. Even with this assumption, it is still a reasonable approximation and a flexible approach to add for example a mirror inside a sample environment."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparison of the three datasets\n",
+    "Here we compare the three datasets, the empty sample environment, the wrong simulation where rays scattered in the sample could not interact with the sample environment, and the full simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "banana_empty = functions.name_search(\"banana\", data_empty)\n",
+    "banana_wrong = functions.name_search(\"banana\", data_wrong)\n",
+    "banana_sample = functions.name_search(\"banana\", data)\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.figure(figsize=(14,6))\n",
+    "plt.plot(banana_empty.xaxis, banana_empty.Intensity, \"r\",\n",
+    "         banana_wrong.xaxis, banana_wrong.Intensity, \"b\",\n",
+    "         banana_sample.xaxis, banana_sample.Intensity, \"k\")\n",
+    "plt.xlabel(\"2Theta [deg]\")\n",
+    "plt.ylabel(\"Intensity [n/s]\")\n",
+    "plt.legend([\"No sample\", \"Wrong simulation, no exit\", \"Full simulation\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretation of the data\n",
+    "We see that the wrong simulation have slightly lower background, and more peak intensity. We also see the peak shape is different near the aluminium Bragg peaks. Since the Union components contain a powder process, one can also recreate this example without using an external PowderN component to check the accuracy of the approach. It is however still not perfect, as the Union powder process will perform multiple scattering where PowderN will not. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorial/Union_tutorial_7_Tagging_history.ipynb b/tutorial/Union_tutorial_7_Tagging_history.ipynb
new file mode 100644
index 00000000..644aad52
--- /dev/null
+++ b/tutorial/Union_tutorial_7_Tagging_history.ipynb
@@ -0,0 +1,303 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Union tagging system\n",
+    "The *Union_master* is capable of recording histories for each neutron in a tree like fashion and add the total intensities for each unique history together. At the end of the simulation these are sorted by intensity and written to file, and the top 20 are shown in the terminal. This system does not work with MPI, if MPI is used only part of the data is written to disk. The system can take up a large amount of memory when used, so it is disabled per default.\n",
+    "\n",
+    "First we set up a simple instrument with sample, container and a layer of cryostat."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
+    "Al_inc.sigma = 0.0082\n",
+    "Al_inc.unit_cell_volume = 66.4\n",
+    "\n",
+    "Al_pow = instrument.add_component(\"Al_pow\", \"Powder_process\")\n",
+    "Al_pow.reflections = '\"Al.laz\"'\n",
+    "\n",
+    "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
+    "Al.process_string = '\"Al_inc,Al_pow\"'\n",
+    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "\n",
+    "Sample_inc = instrument.add_component(\"Sample_inc\", \"Incoherent_process\")\n",
+    "Sample_inc.sigma = 3.4176\n",
+    "Sample_inc.unit_cell_volume = 1079.1\n",
+    "\n",
+    "Sample_pow = instrument.add_component(\"Sample_pow\", \"Powder_process\")\n",
+    "Sample_pow.reflections = '\"Na2Ca3Al2F14.laz\"'\n",
+    "\n",
+    "Sample = instrument.add_component(\"Sample\", \"Union_make_material\")\n",
+    "Sample.process_string = '\"Sample_inc,Sample_pow\"'\n",
+    "Sample.my_absorption = 100*2.9464/1079.1\n",
+    "\n",
+    "src = instrument.add_component(\"source\", \"Source_div\")\n",
+    "src.xwidth = 0.01\n",
+    "src.yheight = 0.035\n",
+    "src.focus_aw = 0.01\n",
+    "src.focus_ah = 0.01\n",
+    "\n",
+    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0=\"wavelength\"\n",
+    "src.dlambda=\"0.01*wavelength\"\n",
+    "src.flux = 1E13\n",
+    "\n",
+    "sample_geometry = instrument.add_component(\"sample_geometry\", \"Union_cylinder\")\n",
+    "sample_geometry.yheight = 0.03\n",
+    "sample_geometry.radius = 0.0075\n",
+    "sample_geometry.material_string='\"Sample\"' \n",
+    "sample_geometry.priority = 100\n",
+    "sample_geometry.set_AT([0,0,1], RELATIVE=src)\n",
+    "\n",
+    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\", RELATIVE=sample_geometry)\n",
+    "container.yheight = 0.03+0.003 # 1.5 mm top and button\n",
+    "container.radius = 0.0075 + 0.0015 # 1.5 mm sides of container\n",
+    "container.material_string='\"Al\"' \n",
+    "container.priority = 99\n",
+    "\n",
+    "inner_wall = instrument.add_component(\"cryostat_wall\", \"Union_cylinder\")\n",
+    "inner_wall.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "inner_wall.yheight = 0.12\n",
+    "inner_wall.radius = 0.03\n",
+    "inner_wall.material_string='\"Al\"' \n",
+    "inner_wall.priority = 80\n",
+    "\n",
+    "inner_wall_vac = instrument.add_component(\"cryostat_wall_vacuum\", \"Union_cylinder\")\n",
+    "inner_wall_vac.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "inner_wall_vac.yheight = 0.12 - 0.008\n",
+    "inner_wall_vac.radius = 0.03 - 0.002\n",
+    "inner_wall_vac.material_string='\"Vacuum\"' \n",
+    "inner_wall_vac.priority = 81\n",
+    "\n",
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zx.D_direction_1 = '\"z\"'\n",
+    "logger_zx.D1_min = -0.04\n",
+    "logger_zx.D1_max = 0.04\n",
+    "logger_zx.n1 = 300\n",
+    "logger_zx.D_direction_2 = '\"x\"'\n",
+    "logger_zx.D2_min = -0.04\n",
+    "logger_zx.D2_max = 0.04\n",
+    "logger_zx.n2 = 300\n",
+    "logger_zx.filename = '\"logger_zx.dat\"'\n",
+    "\n",
+    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zy.D_direction_1 = '\"z\"'\n",
+    "logger_zy.D1_min = -0.04\n",
+    "logger_zy.D1_max = 0.04\n",
+    "logger_zy.n1 = 300\n",
+    "logger_zy.D_direction_2 = '\"y\"'\n",
+    "logger_zy.D2_min = -0.06\n",
+    "logger_zy.D2_max = 0.06\n",
+    "logger_zy.n2 = 300\n",
+    "logger_zy.filename = '\"logger_zy.dat\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding the Union_master with tagging\n",
+    "There are two important parameters to consider when setting a *Union_master* up for tagging:\n",
+    "- enable_tagging [default 0] 0 for disable, 1 for enable\n",
+    "- history_limit [default 300000] Limit of unique histories recorded\n",
+    "\n",
+    "As the *Union_master* component records each ray in succession, their unique history is added to the history tree. If a ray takes the same path in the tree, the intensity gets added to that unique history. When the history limit is reached, the tree is not built out further, but if already recorded histories occur, their intensity is still added to the existing tree."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "master = instrument.add_component(\"master\", \"Union_master\")\n",
+    "master.enable_tagging = 1\n",
+    "master.history_limit = 300000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_tagging\",\n",
+    "                                      increment_folder_name=True,\n",
+    "                                      parameters={\"wavelength\" : 3.0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
+    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Finding the history file\n",
+    "A file called Union_history.dat is written in the run folder with all the unique histories. In some cases a bug happens that causes the file to be unreadable, for now the best fix is to rerun the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"run_folder/Union_history.dat\") as file:\n",
+    "    instrument_string = file.read()\n",
+    "    print(instrument_string)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpreting the histories\n",
+    "The history with highest intensity in my run is: V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0, which means:\n",
+    "- Neutron entered Volume0 (Surrounding vacuum)\n",
+    "- Neutron entered Volume3 (Cryostat wall)\n",
+    "- Neutron entered Volume4 (Cryostat vacuum)\n",
+    "- Neutron entered Volume2 (Container)\n",
+    "- Neutron entered Volume1 (Sample)\n",
+    "- Neutron entered Volume2 (Container)\n",
+    "- Neutron entered Volume4 (Cryostat vacuum)\n",
+    "- Neutron entered Volume3 (Cryostat wall)\n",
+    "- Neutron entered Volume0 (Surrounding vacuum)\n",
+    "\n",
+    "So the most likely occurrence is that the ray is propagated through all geometries. The next most likely history is:\n",
+    "\n",
+    "V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0\n",
+    "\n",
+    "- Neutron entered Volume0 (Surrounding vacuum)\n",
+    "- Neutron entered Volume3 (Cryostat wall)\n",
+    "- Neutron entered Volume4 (Cryostat vacuum)\n",
+    "- Neutron entered Volume2 (Container)\n",
+    "- Neutron entered Volume1 (Sample)\n",
+    "- Neutron scattered on Process1 (Since we are in Volume1, that would be Sample_pow)\n",
+    "- Neutron entered Volume2 (Container)\n",
+    "- Neutron entered Volume4 (Cryostat vacuum)\n",
+    "- Neutron entered Volume3 (Cryostat wall)\n",
+    "- Neutron entered Volume0 (Surrounding vacuum)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use conditional component to filter tagging\n",
+    "Just like a logger can be modified by a conditional component to only record events that satisfy that condition, the tagging system of the *Union_master* component can also be modified by a conditional component. In that case, the tagging system will only record events that satisfy the condition imposed by the conditional component.\n",
+    "\n",
+    "This can be useful to explain an unexpected feature, as the conditional components can filter for energy, time, direction or any combination of these. Here we demonstrate this feature by adding a *Union_conditional_PSD* outside of the direct beam and enabling the *master_tagging* control parameter, which causes the condition to be applied to the tagging system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up an arm pointing to the relevant spot\n",
+    "spot_dir = instrument.add_component(\"spot_dir\", \"Arm\", RELATIVE=sample_geometry, before=\"master\")\n",
+    "spot_dir.set_ROTATED([0, 60, 0], RELATIVE=sample_geometry)\n",
+    "\n",
+    "# Set up a conditional component targeting all our loggers\n",
+    "PSD_conditional = instrument.add_component(\"space_all_PSD_conditional\", \"Union_conditional_PSD\", before=\"master\")\n",
+    "PSD_conditional.xwidth = 0.2\n",
+    "PSD_conditional.yheight = 0.2\n",
+    "PSD_conditional.master_tagging = 1\n",
+    "PSD_conditional.set_AT([0, 0, 0.5], RELATIVE=spot_dir) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_tagging\",\n",
+    "                                      increment_folder_name=True,\n",
+    "                                      parameters={\"wavelength\" : 3.0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"run_folder/union_history.dat\") as file:\n",
+    "    instrument_string = file.read()\n",
+    "    print(instrument_string)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpreting the data\n",
+    "Now all the histories contain a scattering process as this is necessary to reach the rectangle placed by the Union_conditional_PSD component set at 2theta = 60 deg. We also observe the intensities are much lower, simply because only a fraction of the simulated rays satisfy this condition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorial/data_folder/note.txt b/tutorial/data_folder/note.txt
new file mode 100644
index 00000000..fa985a4a
--- /dev/null
+++ b/tutorial/data_folder/note.txt
@@ -0,0 +1 @@
+This folder will contain data files from McStas simulations performed in the notebook.
diff --git a/tutorial/run_folder/note.txt b/tutorial/run_folder/note.txt
new file mode 100644
index 00000000..1a8cb02d
--- /dev/null
+++ b/tutorial/run_folder/note.txt
@@ -0,0 +1,2 @@
+McStas components, library functions and data files placed here can be used in tutorial notebooks.
+This folder will also contain generated instrument files.

From c944ca18ccdc8258abe6ea2cdf51f4354f91b7f8 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Wed, 5 Aug 2020 15:31:54 +0200
Subject: [PATCH 098/403] Update README.md

Mentions the tutorials available in the tutorial folder.
---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 3aaa0fa1..b1ec9bdf 100644
--- a/README.md
+++ b/README.md
@@ -36,6 +36,8 @@ For a standard McStas installation on Windows, the appropriate configuration can
     my_configurator.set_mcstas_path("\\mcstas-2.6\\lib\\")
 
 ## Instructions for basic use
+This section provides a quick way to get started, a more in depth tutorial using Jupyter Notebooks is available in the tutorial folder.
+
 Import the interface 
 
     from mcstasscript.interface import instr, plotter, functions, reader

From d526df3c02e7af7c5fd3c03e3df313106b011c2c Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Mon, 14 Sep 2020 14:07:26 +0200
Subject: [PATCH 099/403] Update of setup.py version number

---
 mcstasscript/configuration.yaml | 2 +-
 setup.py                        | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/mcstasscript/configuration.yaml b/mcstasscript/configuration.yaml
index 438e7ebe..9d9fe059 100644
--- a/mcstasscript/configuration.yaml
+++ b/mcstasscript/configuration.yaml
@@ -2,4 +2,4 @@ other:
   characters_per_line: 85
 paths:
   mcrun_path: /Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/
-  mcstas_path: /Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/
+  mcstas_path: /Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5
diff --git a/setup.py b/setup.py
index f8bfacd4..672adfe5 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
      name='McStasScript',
-     version='0.0.18',
+     version='0.0.20',
      author="Mads Bertelsen",
      author_email="Mads.Bertelsen@ess.eu",
      description="A python scripting interface for McStas",

From 3a4b0af7f914c050b1cc427edd1171a8b02c2ded Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Mon, 14 Sep 2020 14:39:12 +0200
Subject: [PATCH 100/403] Fixed bug where show_instrument would not work when
 the user has set an input path. It simply expected the instrument to be in
 the work directory. Now it correctly looks in the input_folder.

---
 mcstasscript/interface/instr.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 57e2d31d..f27c3f1f 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1549,7 +1549,7 @@ def show_instrument(self, *args, **kwargs):
                 executable = "mcdisplay"
 
         full_command = (bin_path + executable + " "
-                        + self.name + ".instr"
+                        + os.path.join(self.input_path, self.name + ".instr")
                         + " " + parameter_string) 
 
         process = subprocess.run(full_command, shell=True,

From 17dba70a997ecf253d1ce781a5963a05e5525d30 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Mon, 14 Sep 2020 16:25:24 +0200
Subject: [PATCH 101/403] Added __str__ and __repr__ methods to McStasData and
 component.

These are used when calling print with these kinds of objects.
---
 mcstasscript/data/data.py | 29 +++++++++++++++++++++++++++++
 1 file changed, 29 insertions(+)

diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index 113091a7..5dc37ad4 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -327,3 +327,32 @@ def set_title(self, string):
 
     def set_plot_options(self, **kwargs):
         self.plot_options.set_options(**kwargs)
+
+    def __str__(self):
+        """
+        Returns string with quick summary of data
+        """
+
+        string = "McStasData: "
+        string += self.name + " "
+        if type(self.metadata.dimension) == int:
+            string += "type: 1D "
+        elif len(self.metadata.dimension) == 2:
+            string += "type: 2D "
+        else:
+            string += "type: other "
+
+        if "values" in self.metadata.info:
+            values = self.metadata.info["values"]
+            values = values.strip()
+            values = values.split(" ")
+            if len(values) == 3:
+                string += " I:" + str(values[0])
+                string += " E:" + str(values[1])
+                string += " N:" + str(values[2])
+
+        return string
+
+    def __repr__(self):
+        return "\n" + self.__str__()
+

From 498e205a8698829bad2e555191e973d5af328b5f Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Wed, 16 Sep 2020 10:25:55 +0200
Subject: [PATCH 102/403] Missed a file in last commit, here is __str__ and
 __repr__ changes for the component object. It is print_long that is
 restructured as returning a string, and now print_long() just calls __str__.

---
 mcstasscript/helper/mcstas_objects.py | 75 ++++++++++++++++++++++++++-
 1 file changed, 74 insertions(+), 1 deletion(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 8fab594f..2f71e4cc 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -689,7 +689,7 @@ def write_component(self, fo):
         # Leave a new line between components for readability
         fo.write("\n")
 
-    def print_long(self):
+    def print_long_depricated(self):
         """
         Prints contained information to Python terminal
 
@@ -742,6 +742,79 @@ class is used as a superclass for classes describing each
         if len(self.c_code_after) > 1:
             print(self.c_code_after)
 
+    def __str__(self):
+        """
+        Returns string of information about the component
+
+        Includes information on required parameters if they are not yet
+        specified. Information on the components are added when the
+        class is used as a superclass for classes describing each
+        McStas component.
+        """
+        string = ""
+
+        if len(self.c_code_before) > 1:
+            string += self.c_code_before + "\n"
+        if len(self.comment) > 1:
+            string += "// " + self.comment + "\n"
+        if self.SPLIT != 0:
+            string += "SPLIT " + str(self.SPLIT) + " "
+        string += "COMPONENT " + str(self.name)
+        string +=  " = " + str(self.component_name) + "\n"
+        for key in self.parameter_names:
+            val = getattr(self, key)
+            parameter_name = bcolors.BOLD + key + bcolors.ENDC
+            if val is not None:
+                unit = ""
+                if key in self.parameter_units:
+                    unit = "[" + self.parameter_units[key] + "]"
+                value = (bcolors.BOLD
+                         + bcolors.OKGREEN
+                         + str(val)
+                         + bcolors.ENDC
+                         + bcolors.ENDC)
+                string += "  " + parameter_name
+                string += " = " + value + " " + unit + "\n"
+            else:
+                if self.parameter_defaults[key] is None:
+                    string += "  " + parameter_name
+                    string += bcolors.FAIL
+                    string += " : Required parameter not yet specified"
+                    string += bcolors.ENDC + "\n"
+
+        if not self.WHEN == "":
+            string += self.WHEN + "\n"
+        string += "AT " + str(self.AT_data) + " "
+        string += str(self.AT_relative) + "\n"
+        if self.ROTATED_specified:
+            string += "ROTATED " + str(self.ROTATED_data)
+            string += " " + self.ROTATED_relative + "\n"
+        if not self.GROUP == "":
+            string += "GROUP " + self.GROUP + "\n"
+        if not self.EXTEND == "":
+            string += "EXTEND %{" + "\n"
+            string += self.EXTEND + "%}" + "\n"
+        if not self.JUMP == "":
+            string += "JUMP " + self.JUMP + "\n"
+        if len(self.c_code_after) > 1:
+            string += self.c_code_after + "\n"
+
+        return string
+
+    def print_long(self):
+        """
+        Prints information about the component
+
+        Includes information on required parameters if they are not yet
+        specified. Information on the components are added when the
+        class is used as a superclass for classes describing each
+        McStas component.
+        """
+        print(self.__str__())
+
+    def __repr__(self):
+        return self.__str__()
+
     def print_short(self, **kwargs):
         """Prints short description of component to list print"""
         if self.ROTATED_specified:

From 547449d7c33ebcfe8c719ca5aa8dbbe2bed431ea Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Mon, 21 Sep 2020 20:58:33 +0200
Subject: [PATCH 103/403] Update version number in setup.py

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 672adfe5..708b6649 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
      name='McStasScript',
-     version='0.0.20',
+     version='0.0.21',
      author="Mads Bertelsen",
      author_email="Mads.Bertelsen@ess.eu",
      description="A python scripting interface for McStas",

From 140fb43eb5323ed5d0f529a8860198d4314fd195 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 22 Sep 2020 10:43:41 +0200
Subject: [PATCH 104/403] First version of McStasScript that supports McXtrace
 / McStas simultaniously. The old McStas_instr class is now a base class, and
 McStas_instr / McXtrace_instr inherits 99% of the functionality from this
 base class. They overwrite the method reading the configuration file, and
 sets a few attributes on the package name and executable name. These can be
 used in warning / error messages and help messages, which have yet to be
 updated.

This update does obviously raise an issue with the name of the
package, as it now supports more than McStas.

Work yet to be done on this branch before merging:
	Fix unit tests (many are broken)
	Add unit tests for McXtrace
	Add integration tests for McXtrace
	Rename methods / function that include McStas
	Fix help / error messages that refer to McStas
---
 mcstasscript/configuration.yaml      |   2 +
 mcstasscript/helper/managed_mcrun.py |  29 +-
 mcstasscript/interface/functions.py  |  48 ++-
 mcstasscript/interface/instr.py      | 421 +++++++++++++++++++++++++--
 4 files changed, 455 insertions(+), 45 deletions(-)

diff --git a/mcstasscript/configuration.yaml b/mcstasscript/configuration.yaml
index 9d9fe059..457fa45b 100644
--- a/mcstasscript/configuration.yaml
+++ b/mcstasscript/configuration.yaml
@@ -3,3 +3,5 @@ other:
 paths:
   mcrun_path: /Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/
   mcstas_path: /Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5
+  mcxtrace_path: /Applications/McXtrace-1.5.app/Contents/Resources/mcxtrace/1.5/
+  mxrun_path: /Applications/McXtrace-1.5.app/Contents/Resources/mcxtrace/1.5/bin/
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 545a247b..cab5a00d 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -14,7 +14,7 @@ class ManagedMcrun:
     ManagedMcrun is usually called by the instrument class of
     McStasScript but can be used independently.  It runs the mcrun
     command using the system command, and if this is not in the path,
-    the absolute path can be given in a keyword argument mcrun_path.
+    the absolute path can be given in a keyword argument executable_path.
 
     Attributes
     ----------
@@ -36,7 +36,7 @@ class ManagedMcrun:
     custom_flags : string
         Custom flags that are passed to the mcrun command
 
-    mcrun_path : string
+    executable_path : string
         Path to the mcrun command (can be empty if already in path)
 
     Methods
@@ -64,7 +64,7 @@ def __init__(self, instr_name, **kwargs):
                 Sets parameters
             custom_flags : str
                 Sets custom_flags passed to mcrun
-            mcrun_path : str
+            executable_path : str
                 Path to mcrun command, "" if already in path
             increment_folder_name : bool
                 If True, automaticaly appends foldername to make it unique
@@ -82,13 +82,17 @@ def __init__(self, instr_name, **kwargs):
         self.mpi = None
         self.parameters = {}
         self.custom_flags = ""
-        self.mcrun_path = ""
+        self.executable_path = ""
+        self.executable = ""
         self.increment_folder_name = False
         self.compile = True
         self.run_path = "."
-        # mcrun_path always in kwargs
-        if "mcrun_path" in kwargs:
-            self.mcrun_path = kwargs["mcrun_path"]
+        # executable_path always in kwargs
+        if "executable_path" in kwargs:
+            self.executable_path = kwargs["executable_path"]
+
+        if "executable" in kwargs:
+            self.executable = kwargs["executable"]
 
         if "foldername" in kwargs:
             self.data_folder_name = kwargs["foldername"]
@@ -172,11 +176,12 @@ def run_simulation(self, **kwargs):
                                 + "="
                                 + str(val))  # parameter value
 
-        mcrun_full_path = self.mcrun_path + "mcrun"
-        if len(self.mcrun_path) > 1:
-            if not (self.mcrun_path[-1] == "\\"
-                    or self.mcrun_path[-1] == "/"):
-                mcrun_full_path = os.path.join(self.mcrun_path, "mcrun")
+        mcrun_full_path = self.executable_path + self.executable
+        if len(self.executable_path) > 1:
+            if not (self.executable_path[-1] == "\\"
+                    or self.executable_path[-1] == "/"):
+                mcrun_full_path = os.path.join(self.executable_path,
+                                               self.executable)
 
         # Run the mcrun command on the system
         full_command = (mcrun_full_path + " "
diff --git a/mcstasscript/interface/functions.py b/mcstasscript/interface/functions.py
index d69ed44b..663a4f36 100644
--- a/mcstasscript/interface/functions.py
+++ b/mcstasscript/interface/functions.py
@@ -174,8 +174,16 @@ def _create_new_config_file(self):
         run = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/"
         mcstas = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/"
 
+        mxrun = "/Applications/McXtrace-1.5.app" \
+                   + "/Contents/Resources/mcxtrace/1.5/mxrun"
+        mcxtrace = "/Applications/McXtrace-1.5.app" \
+                   + "/Contents/Resources/mcxtrace/1.5/"
+
         default_paths = {"mcrun_path" : run,
-                         "mcstas_path" : mcstas}
+                         "mcstas_path" : mcstas,
+                         "mxrun_path" : mxrun,
+                         "mcxtrace_path" : mcxtrace}
+
         default_other = {"characters_per_line" : 85}
 
         default_config = {"paths" : default_paths,
@@ -221,6 +229,44 @@ def set_mcrun_path(self, path):
         # write new configuration file
         self._write_yaml(config)
 
+    def set_mcxtrace_path(self, path):
+        """
+        Sets the path to McXtrace
+
+        Parameters
+        ----------
+        path : str
+            Path to the mcxtrace directory containing "sources", "optics", ...
+        """
+
+        # read entire configuration file
+        config = self._read_yaml()
+
+        # update mcxtrace_path
+        config["paths"]["mcxtrace_path"] = path
+
+        # write new configuration file
+        self._write_yaml(config)
+
+    def set_mxrun_path(self, path):
+        """
+        Sets the path to mxrun
+
+        Parameters
+        ----------
+        path : str
+            Path to the mxrun executable
+        """
+
+        # read entire configuration file
+        config = self._read_yaml()
+
+        # update mxrun_path
+        config["paths"]["mxrun_path"] = path
+
+        # write new configuration file
+        self._write_yaml(config)
+
     def set_line_length(self, line_length):
         """
         Sets maximum line length for output
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index f27c3f1f..d00f6d0e 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -16,7 +16,7 @@
 from mcstasscript.helper.formatting import bcolors
 
 
-class McStas_instr:
+class McCode_instr:
     """
     Main class for writing a McStas instrument using McStasScript
 
@@ -36,7 +36,7 @@ class McStas_instr:
     origin : str
         origin of instrument file (affiliation)
 
-    mcrun_path : str
+    executable_path : str
         absolute path of mcrun command, or empty if it is in path
 
     parameter_list : list of parameter_variable instances
@@ -167,7 +167,7 @@ def __init__(self, name, **kwargs):
             origin : str
                 Affiliation of author, written in instrument file
 
-            mcrun_path : str
+            executable_path : str
                 Absolute path of mcrun or empty if already in path
 
             input_path : str
@@ -198,33 +198,18 @@ def __init__(self, name, **kwargs):
         else:
             self.input_path = "."
 
-        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-        configuration_file_name = os.path.join(THIS_DIR, "..", "configuration.yaml")
-        if not os.path.isfile(configuration_file_name):
-            raise NameError("Could not find configuration file!")
-        with open(configuration_file_name, 'r') as ymlfile:
-            config = yaml.safe_load(ymlfile)
+        self._read_calibration()
 
-        if type(config) is dict:
-            self.mcrun_path = config["paths"]["mcrun_path"]
-            self.mcstas_path = config["paths"]["mcstas_path"]
-            self.line_limit = config["other"]["characters_per_line"]
-        else:
-            # This happens in unit tests that mocks open
-            self.mcrun_path = ""
-            self.mcstas_path = ""
-            self.line_limit = 180
+        if "executable_path" in kwargs:
+            self.executable_path = kwargs["executable_path"]
 
-        if "mcrun_path" in kwargs:
-            self.mcrun_path = kwargs["mcrun_path"]
-
-        if "mcstas_path" in kwargs:
-            self.mcstas_path = kwargs["mcstas_path"]
-        elif self.mcstas_path is "":
+        if "package_path" in kwargs:
+            self.package_path = kwargs["package_path"]
+        elif self.package_path is "":
             raise NameError("At this stage of development "
                             + "McStasScript need the absolute path "
                             + "for the McStas installation as keyword "
-                            + "named mcstas_path or in configuration.yaml")
+                            + "named package_path or in configuration.yaml")
 
         self.parameter_list = []
         self.declare_list = []
@@ -240,10 +225,17 @@ def __init__(self, name, **kwargs):
         self.component_name_list = []  # List of component names
 
         # Read info on active McStas components
-        self.component_reader = ComponentReader(self.mcstas_path,
+        self.component_reader = ComponentReader(self.package_path,
                                                 input_path=self.input_path)
         self.component_class_lib = {}
 
+    def _read_calibration(self):
+        """
+        Place holder method that should be overwritten by classes
+        that inherit from McCode_instr.
+        """
+        pass
+
     def add_parameter(self, *args, **kwargs):
         """
         Method for adding input parameter to instrument
@@ -1475,8 +1467,8 @@ def run_full_instrument(self, *args, **kwargs):
         foldername, which can not already exist, if it does data will
         be read from this folder.  If the mcrun command is not in the
         path of the system, the absolute path can be given with the
-        mcrun_path keyword argument.  This path could also already have
-        been set at initialization of the instrument object.
+        executable_path keyword argument.  This path could also already
+        have been set at initialization of the instrument object.
 
         Parameters
         ----------
@@ -1491,12 +1483,15 @@ def run_full_instrument(self, *args, **kwargs):
                 Sets parameters
             custom_flags : str
                 Sets custom_flags passed to mcrun
-            mcrun_path : str
+            executable_path : str
                 Path to mcrun command, "" if already in path
         """
         # Make sure mcrun path is in kwargs
-        if "mcrun_path" not in kwargs:
-            kwargs["mcrun_path"] = self.mcrun_path
+        if "executable_path" not in kwargs:
+            kwargs["executable_path"] = self.executable_path
+
+        if "executable" not in kwargs:
+            kwargs["executable"] = self.executable
 
         if "run_path" not in kwargs:
             # path where mcrun is executed, will load components there
@@ -1540,7 +1535,7 @@ def show_instrument(self, *args, **kwargs):
                                 + "="
                                 + str(val))  # parameter value
 
-        bin_path = os.path.join(self.mcstas_path, "bin", "")
+        bin_path = os.path.join(self.package_path, "bin", "")
         executable = "mcdisplay-webgl"
         if "format" in kwargs:
             if kwargs["format"] is "webgl":
@@ -1559,3 +1554,365 @@ def show_instrument(self, *args, **kwargs):
         print(process.stderr)
         print(process.stdout)
 
+
+class McStas_instr(McCode_instr):
+    """
+    Main class for writing a McStas instrument using McStasScript
+
+    Initialization of McStas_instr sets the name of the instrument file
+    and its methods are used to add all aspects of the instrument file.
+    The class also holds methods for writing the finished instrument
+    file to disk and to run the simulation.
+
+    Attributes
+    ----------
+    name : str
+        name of instrument file
+
+    author : str
+        name of user of McStasScript, written to the file
+
+    origin : str
+        origin of instrument file (affiliation)
+
+    executable_path : str
+        absolute path of mcrun command, or empty if it is in path
+
+    parameter_list : list of parameter_variable instances
+        contains all input parameters to be written to file
+
+    declare_list : list of declare_variable instances
+        contains all declare parrameters to be written to file
+
+    initialize_section : str
+        string containing entire initialize section to be written
+
+    trace_section : str
+        string containing trace section (OBSOLETE)
+
+    finally_section : str
+        string containing entire finally section to be written
+
+    component_list : list of component instances
+        list of components in the instrument
+
+    component_name_list : list of strings
+        list of names of the components in the instrument
+
+    Methods
+    -------
+    add_parameter(*args, **kwargs)
+        Adds input parameter to the define section
+
+    add_declare_var()
+        Adds declared variable ot the declare section
+
+    append_initialize(string)
+        Appends a string to the initialize section, then adds new line
+
+    append_initialize_no_new_line(string)
+        Appends a string to the initialize section
+
+    append_finally(string)
+        Appends a string to finally section, then adds new line
+
+    append_finally_no_new_line(string)
+        Appends a string to finally section
+
+    append_trace(string)
+        Obsolete method, add components instead (used in write_c_files)
+
+    show_components(string)
+        Shows available components in given category
+
+    add_component(instance_name, component_name, **kwargs)
+        Add a component to the instrument file
+
+    get_component(instance_name)
+        Returns component instance with name instance_name
+
+    get_last_component()
+        Returns component instance of last component
+
+    set_component_parameter(instance_name, dict)
+        Adds parameters as dict to component with instance_name
+
+    set_component_AT(instance_name, AT_data, **kwargs)
+        Sets position of component named instance_name
+
+    set_component_ROTATED(instance_name, ROTATED_data, **kwargs)
+        Sets rotation of component named instance_name
+
+    set_component_RELATIVE(instane_name, string)
+        Sets position and rotation reference for named component
+
+    set_component_WHEN(instance_name, string)
+        Sets WHEN condition of named component, is logical c expression
+
+    set_component_GROUP(instance_name, string)
+        Sets GROUP name of component named instance_name
+
+    append_component_EXTEND(instance_name, string)
+        Appends a line to EXTEND section of named component
+
+    set_component_JUMP(instance_name, string)
+        Sets JUMP code for named component
+
+    set_component_SPLIT(instance_name, string)
+        Sets SPLIT value for named component
+
+    set_component_c_code_before(instance_name, string)
+        Sets c code before the component
+
+    set_component_c_code_after(instance_name, string)
+        Sets c code after the component
+
+    set_component_comment(instance_name, string)
+        Sets comment to be written before named component
+
+    print_component(instance_name)
+        Prints an overview of current state of named component
+
+    print_component_short(instance_name)
+        Prints short overview of current state of named component
+
+    print_components()
+        Prints overview of postion / rotation of all components
+
+    write_c_files()
+        Writes c files for %include in generated_includes folder
+
+    write_full_instrument()
+        Writes full instrument file to current directory
+
+    run_full_instrument(**kwargs)
+        Writes instrument files and runs simulation.
+        Returns list of McStasData
+    """
+    def __init__(self, name, **kwargs):
+        """
+        Initialization of McStas Instrument
+
+        Parameters
+        ----------
+        name : str
+            Name of project, instrument file will be name + ".instr"
+
+        keyword arguments:
+            author : str
+                Name of author, written in instrument file
+
+            origin : str
+                Affiliation of author, written in instrument file
+
+            executable_path : str
+                Absolute path of mcrun or empty if already in path
+
+            input_path : str
+                Work directory, will load components from this folder
+        """
+        self.particle = "neutron"
+        self.executable = "mcrun"
+        self.package_name = "McStas"
+
+        super().__init__(name, **kwargs)
+
+    def _read_calibration(self):
+        this_dir = os.path.dirname(os.path.abspath(__file__))
+        configuration_file_name = os.path.join(this_dir, "..",
+                                               "configuration.yaml")
+        if not os.path.isfile(configuration_file_name):
+            raise NameError("Could not find configuration file!")
+        with open(configuration_file_name, 'r') as ymlfile:
+            config = yaml.safe_load(ymlfile)
+
+        if type(config) is dict:
+            self.executable_path = config["paths"]["mcrun_path"]
+            self.package_path = config["paths"]["mcstas_path"]
+            self.line_limit = config["other"]["characters_per_line"]
+        else:
+            # This happens in unit tests that mocks open
+            self.executable_path = ""
+            self.package_path = ""
+            self.line_limit = 180
+
+class McXtrace_instr(McCode_instr):
+    """
+    Main class for writing a McStas instrument using McStasScript
+
+    Initialization of McStas_instr sets the name of the instrument file
+    and its methods are used to add all aspects of the instrument file.
+    The class also holds methods for writing the finished instrument
+    file to disk and to run the simulation.
+
+    Attributes
+    ----------
+    name : str
+        name of instrument file
+
+    author : str
+        name of user of McStasScript, written to the file
+
+    origin : str
+        origin of instrument file (affiliation)
+
+    executable_path : str
+        absolute path of mcrun command, or empty if it is in path
+
+    parameter_list : list of parameter_variable instances
+        contains all input parameters to be written to file
+
+    declare_list : list of declare_variable instances
+        contains all declare parrameters to be written to file
+
+    initialize_section : str
+        string containing entire initialize section to be written
+
+    trace_section : str
+        string containing trace section (OBSOLETE)
+
+    finally_section : str
+        string containing entire finally section to be written
+
+    component_list : list of component instances
+        list of components in the instrument
+
+    component_name_list : list of strings
+        list of names of the components in the instrument
+
+    Methods
+    -------
+    add_parameter(*args, **kwargs)
+        Adds input parameter to the define section
+
+    add_declare_var()
+        Adds declared variable ot the declare section
+
+    append_initialize(string)
+        Appends a string to the initialize section, then adds new line
+
+    append_initialize_no_new_line(string)
+        Appends a string to the initialize section
+
+    append_finally(string)
+        Appends a string to finally section, then adds new line
+
+    append_finally_no_new_line(string)
+        Appends a string to finally section
+
+    append_trace(string)
+        Obsolete method, add components instead (used in write_c_files)
+
+    show_components(string)
+        Shows available components in given category
+
+    add_component(instance_name, component_name, **kwargs)
+        Add a component to the instrument file
+
+    get_component(instance_name)
+        Returns component instance with name instance_name
+
+    get_last_component()
+        Returns component instance of last component
+
+    set_component_parameter(instance_name, dict)
+        Adds parameters as dict to component with instance_name
+
+    set_component_AT(instance_name, AT_data, **kwargs)
+        Sets position of component named instance_name
+
+    set_component_ROTATED(instance_name, ROTATED_data, **kwargs)
+        Sets rotation of component named instance_name
+
+    set_component_RELATIVE(instane_name, string)
+        Sets position and rotation reference for named component
+
+    set_component_WHEN(instance_name, string)
+        Sets WHEN condition of named component, is logical c expression
+
+    set_component_GROUP(instance_name, string)
+        Sets GROUP name of component named instance_name
+
+    append_component_EXTEND(instance_name, string)
+        Appends a line to EXTEND section of named component
+
+    set_component_JUMP(instance_name, string)
+        Sets JUMP code for named component
+
+    set_component_SPLIT(instance_name, string)
+        Sets SPLIT value for named component
+
+    set_component_c_code_before(instance_name, string)
+        Sets c code before the component
+
+    set_component_c_code_after(instance_name, string)
+        Sets c code after the component
+
+    set_component_comment(instance_name, string)
+        Sets comment to be written before named component
+
+    print_component(instance_name)
+        Prints an overview of current state of named component
+
+    print_component_short(instance_name)
+        Prints short overview of current state of named component
+
+    print_components()
+        Prints overview of postion / rotation of all components
+
+    write_c_files()
+        Writes c files for %include in generated_includes folder
+
+    write_full_instrument()
+        Writes full instrument file to current directory
+
+    run_full_instrument(**kwargs)
+        Writes instrument files and runs simulation.
+        Returns list of McStasData
+    """
+    def __init__(self, name, **kwargs):
+        """
+        Initialization of McStas Instrument
+
+        Parameters
+        ----------
+        name : str
+            Name of project, instrument file will be name + ".instr"
+
+        keyword arguments:
+            author : str
+                Name of author, written in instrument file
+
+            origin : str
+                Affiliation of author, written in instrument file
+
+            executable_path : str
+                Absolute path of mcrun or empty if already in path
+
+            input_path : str
+                Work directory, will load components from this folder
+        """
+        self.particle = "x-ray"
+        self.executable = "mxrun"
+        self.package_name = "McXtrace"
+
+        super().__init__(name, **kwargs)
+
+    def _read_calibration(self):
+        this_dir = os.path.dirname(os.path.abspath(__file__))
+        configuration_file_name = os.path.join(this_dir, "..",
+                                               "configuration.yaml")
+        if not os.path.isfile(configuration_file_name):
+            raise NameError("Could not find configuration file!")
+        with open(configuration_file_name, 'r') as ymlfile:
+            config = yaml.safe_load(ymlfile)
+
+        if type(config) is dict:
+            self.executable_path = config["paths"]["mxrun_path"]
+            self.package_path = config["paths"]["mcxtrace_path"]
+            self.line_limit = config["other"]["characters_per_line"]
+        else:
+            # This happens in unit tests that mocks open
+            self.executable_path = ""
+            self.package_path = ""
+            self.line_limit = 180
\ No newline at end of file

From a7f529b08b6e56c886d13fb61fa8635f569641b3 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 22 Sep 2020 16:44:05 +0200
Subject: [PATCH 105/403] Updates to existing unit tests to account for changes
 to McStas_instr and managed_mcrun.

This mainly comes down to changes from mcrun_path to
executable_path in the base class and mcstas_path to
package_path.
---
 mcstasscript/tests/test_Instr.py        | 26 ++++++++++----------
 mcstasscript/tests/test_ManagedMcrun.py | 32 ++++++++++++++++---------
 2 files changed, 34 insertions(+), 24 deletions(-)

diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index c61cc49a..dbe94090 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -20,7 +20,7 @@ def setup_instr_root_path():
     """
     Sets up a instrument with root mcstas_path
     """
-    return McStas_instr("test_instrument", mcstas_path="/")
+    return McStas_instr("test_instrument", package_path="/")
 
 
 def setup_instr_with_path():
@@ -35,7 +35,7 @@ def setup_instr_with_path():
     current_work_dir = os.getcwd()
     os.chdir(THIS_DIR)  # Set work directory to test folder
 
-    return McStas_instr("test_instrument", mcstas_path=dummy_path)
+    return McStas_instr("test_instrument", package_path=dummy_path)
 
     os.chdir(current_work_dir)  # Return to previous workdir
 
@@ -53,7 +53,7 @@ def setup_instr_with_input_path():
     os.chdir(THIS_DIR)  # Set work directory to test folder
 
     return McStas_instr("test_instrument",
-                        mcstas_path=dummy_path,
+                        package_path=dummy_path,
                         input_path=input_path)
 
     os.chdir(current_work_dir)  # Return to previous workdir
@@ -70,7 +70,7 @@ def setup_instr_with_input_path_relative():
     os.chdir(THIS_DIR)  # Set work directory to test folder
 
     return McStas_instr("test_instrument",
-                        mcstas_path="dummy_mcstas",
+                        package_path="dummy_mcstas",
                         input_path="test_input_folder")
 
     os.chdir(current_work_dir)  # Return to previous workdir
@@ -137,13 +137,13 @@ def test_complex_initialize(self):
         my_instrument = McStas_instr("test_instrument",
                                      author="Mads",
                                      origin="DMSC",
-                                     mcrun_path="/path/to/mcrun",
-                                     mcstas_path="/path/to/mcstas")
+                                     executable_path="/path/to/mcrun",
+                                     package_path="/path/to/mcstas")
 
         self.assertEqual(my_instrument.author, "Mads")
         self.assertEqual(my_instrument.origin, "DMSC")
-        self.assertEqual(my_instrument.mcrun_path, "/path/to/mcrun")
-        self.assertEqual(my_instrument.mcstas_path, "/path/to/mcstas")
+        self.assertEqual(my_instrument.executable_path, "/path/to/mcrun")
+        self.assertEqual(my_instrument.package_path, "/path/to/mcstas")
 
     def test_load_config_file(self):
         """
@@ -181,8 +181,8 @@ def test_load_config_file(self):
         # Check the value matches what is loaded by initialization
         my_instrument = setup_instr_no_path()
 
-        self.assertEqual(my_instrument.mcrun_path, correct_mcrun_path)
-        self.assertEqual(my_instrument.mcstas_path, correct_mcstas_path)
+        self.assertEqual(my_instrument.executable_path, correct_mcrun_path)
+        self.assertEqual(my_instrument.package_path, correct_mcstas_path)
         self.assertEqual(my_instrument.line_limit, correct_n_of_characters)
 
     def test_simple_add_parameter(self):
@@ -1502,7 +1502,7 @@ def test_run_full_instrument_basic(self, mock_sub,
         instr = setup_populated_instr()
         instr.run_full_instrument("test_instrument.instr",
                                   foldername="test_data_set",
-                                  mcrun_path="path",
+                                  executable_path="path",
                                   parameters={"theta": 1})
 
         expected_path = os.path.join("path","mcrun")
@@ -1536,7 +1536,7 @@ def test_run_full_instrument_complex(self, mock_sub,
         instr = setup_populated_instr()
         instr.run_full_instrument("test_instrument.instr",
                                   foldername="test_data_set",
-                                  mcrun_path="path",
+                                  executable_path="path",
                                   mpi=7,
                                   ncount=48.4,
                                   custom_flags="-fo",
@@ -1574,7 +1574,7 @@ def test_run_full_instrument_overwrite_default(self, mock_sub,
         instr = setup_populated_instr()
         instr.run_full_instrument("test_instrument.instr",
                                   foldername="test_data_set",
-                                  mcrun_path="path",
+                                  executable_path="path",
                                   mpi=7,
                                   ncount=48.4,
                                   custom_flags="-fo",
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index a0d003f9..35db5f85 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -24,11 +24,12 @@ def test_ManagedMcrun_init_simple(self):
         """
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
-                                 mcrun_path="test_path")
+                                 executable_path="test_path",
+                                 executable="mcrun")
 
         self.assertEqual(mcrun_obj.name_of_instrumentfile, "test.instr")
         self.assertEqual(mcrun_obj.data_folder_name, "test_folder")
-        self.assertEqual(mcrun_obj.mcrun_path, "test_path")
+        self.assertEqual(mcrun_obj.executable_path, "test_path")
 
     def test_ManagedMcrun_init_defaults(self):
         """
@@ -36,7 +37,8 @@ def test_ManagedMcrun_init_defaults(self):
         """
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
-                                 mcrun_path="")
+                                 executable_path="",
+                                 executable="mcrun",)
 
         self.assertEqual(mcrun_obj.mpi, None)
         self.assertEqual(mcrun_obj.ncount, 1000000)
@@ -48,7 +50,8 @@ def test_ManagedMcrun_init_set_values(self):
         """
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
-                                 mcrun_path="",
+                                 executable_path="",
+                                 executable="mcrun",
                                  run_path="test",
                                  mpi=4,
                                  ncount=128)
@@ -69,7 +72,8 @@ def test_ManagedMcrun_init_set_parameters(self):
 
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
-                                 mcrun_path="",
+                                 executable_path="",
+                                 executable="",
                                  parameters=par_input)
 
         self.assertEqual(mcrun_obj.parameters["A_par"], 5.1)
@@ -86,7 +90,8 @@ def test_ManagedMcrun_init_set_custom_flags(self):
 
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
-                                 mcrun_path="",
+                                 executable_path="",
+                                 executable="mcrun",
                                  custom_flags=custom_flag_input)
 
         self.assertEqual(mcrun_obj.custom_flags, custom_flag_input)
@@ -106,7 +111,8 @@ def test_ManagedMcrun_run_simulation_basic(self, mock_sub):
 
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
-                                 mcrun_path="path")
+                                 executable_path="path",
+                                 executable="mcrun",)
 
         mcrun_obj.run_simulation()
 
@@ -130,7 +136,8 @@ def test_ManagedMcrun_run_simulation_basic_path(self, mock_sub):
 
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
-                                 mcrun_path="path/")
+                                 executable_path="path/",
+                                 executable="mcrun",)
 
         mcrun_obj.run_simulation()
 
@@ -154,7 +161,8 @@ def test_ManagedMcrun_run_simulation_no_standard(self, mock_sub):
 
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
-                                 mcrun_path="path",
+                                 executable_path="path",
+                                 executable="mcrun",
                                  mpi=7,
                                  ncount=48.4,
                                  custom_flags="-fo")
@@ -181,7 +189,8 @@ def test_ManagedMcrun_run_simulation_parameters(self, mock_sub):
 
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
-                                 mcrun_path="path",
+                                 executable_path="path",
+                                 executable="mcrun",
                                  mpi=7,
                                  ncount=48.4,
                                  custom_flags="-fo",
@@ -211,7 +220,8 @@ def test_ManagedMcrun_run_simulation_compile(self, mock_sub):
 
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
-                                 mcrun_path="path",
+                                 executable_path="path",
+                                 executable="mcrun",
                                  mpi=7,
                                  ncount=48.4,
                                  force_compile=False,

From 3b1bacf2c190abc0ea1c7ba06f62c2998adf5d40 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 22 Sep 2020 17:22:31 +0200
Subject: [PATCH 106/403] Added a few unit tests that ensure the McXtrace_instr
 part works.

The correct fields can be read from configuration file.
That mxrun is used instead of mcrun.
---
 mcstasscript/tests/test_Configurator.py |  39 +++++++++
 mcstasscript/tests/test_Instr.py        | 109 +++++++++++++++++++++++-
 2 files changed, 145 insertions(+), 3 deletions(-)

diff --git a/mcstasscript/tests/test_Configurator.py b/mcstasscript/tests/test_Configurator.py
index 6dd1abad..15f48235 100644
--- a/mcstasscript/tests/test_Configurator.py
+++ b/mcstasscript/tests/test_Configurator.py
@@ -62,9 +62,15 @@ def test_default_config(self):
         
         run = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/"
         mcstas = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/"
+        mxrun = "/Applications/McXtrace-1.5.app" \
+                + "/Contents/Resources/mcxtrace/1.5/mxrun"
+        mcxtrace = "/Applications/McXtrace-1.5.app" \
+                   + "/Contents/Resources/mcxtrace/1.5/"
 
         self.assertEqual(default_config["paths"]["mcrun_path"], run)
         self.assertEqual(default_config["paths"]["mcstas_path"], mcstas)
+        self.assertEqual(default_config["paths"]["mxrun_path"], mxrun)
+        self.assertEqual(default_config["paths"]["mcxtrace_path"], mcxtrace)
         self.assertEqual(default_config["other"]["characters_per_line"], 85)
         
         # remove the testing configuration file
@@ -127,6 +133,39 @@ def test_set_mcstas_path(self):
         # remove the testing configuration file
         setup_expected_file(test_name)
 
+    def test_set_mcrun_path(self):
+        """
+        This test checks that setting the mxrun path works
+        """
+        test_name = "test_configuration"
+        my_configurator = setup_configurator(test_name)
+
+        my_configurator.set_mxrun_path("/new/mxrun_path/")
+
+        new_config = my_configurator._read_yaml()
+
+        self.assertEqual(new_config["paths"]["mxrun_path"], "/new/mxrun_path/")
+
+        # remove the testing configuration file
+        setup_expected_file(test_name)
+
+    def test_set_mcstas_path(self):
+        """
+        This test checks that setting the mcxtrace path works
+        """
+        test_name = "test_configuration"
+        my_configurator = setup_configurator(test_name)
+
+        my_configurator.set_mcxtrace_path("/new/mcxtrace_path/")
+
+        new_config = my_configurator._read_yaml()
+
+        self.assertEqual(new_config["paths"]["mcxtrace_path"],
+                         "/new/mcxtrace_path/")
+
+        # remove the testing configuration file
+        setup_expected_file(test_name)
+
     def test_set_line_length(self):
         """
         This test checks that setting the line length works
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index dbe94090..d89f31ce 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -6,6 +6,7 @@
 import datetime
 
 from mcstasscript.interface.instr import McStas_instr
+from mcstasscript.interface.instr import McXtrace_instr
 from mcstasscript.helper.formatting import bcolors
 
 
@@ -15,6 +16,11 @@ def setup_instr_no_path():
     """
     return McStas_instr("test_instrument")
 
+def setup_x_ray_instr_no_path():
+    """
+    Sets up a instrument without a mcstas_path
+    """
+    return McXtrace_instr("test_instrument")
 
 def setup_instr_root_path():
     """
@@ -22,6 +28,12 @@ def setup_instr_root_path():
     """
     return McStas_instr("test_instrument", package_path="/")
 
+def setup_x_ray_instr_root_path():
+    """
+    Sets up a instrument with root mcstas_path
+    """
+    return McXtrace_instr("test_instrument", package_path="/")
+
 
 def setup_instr_with_path():
     """
@@ -93,6 +105,23 @@ def setup_populated_instr():
 
     return instr
 
+def setup_populated_x_ray_instr():
+    """
+    Sets up a instrument with some features used and two components
+    """
+    instr = setup_x_ray_instr_root_path()
+
+    instr.add_parameter("double", "theta")
+    instr.add_parameter("double", "has_default", value=37)
+    instr.add_declare_var("double", "two_theta")
+    instr.append_initialize("two_theta = 2.0*theta;")
+
+    comp1 = instr.add_component("first_component", "test_for_reading")
+    comp2 = instr.add_component("second_component", "test_for_reading")
+    comp3 = instr.add_component("third_component", "test_for_reading")
+
+    return instr
+
 def setup_populated_with_some_options_instr():
     """
     Sets up a instrument with some features used and two components
@@ -185,6 +214,46 @@ def test_load_config_file(self):
         self.assertEqual(my_instrument.package_path, correct_mcstas_path)
         self.assertEqual(my_instrument.line_limit, correct_n_of_characters)
 
+    def test_load_config_file_x_ray(self):
+        """
+        Test that configuration file is read correctly. In order to have
+        an independent test, the yaml file is read manually instead of
+        using the yaml package.
+        """
+        # Load configuration file and read manually
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        configuration_file_name = os.path.join(THIS_DIR,
+                                               "..", "configuration.yaml")
+
+        if not os.path.isfile(configuration_file_name):
+            raise NameError("Could not find configuration file!")
+
+        f = open(configuration_file_name, "r")
+
+        lines = f.readlines()
+        for line in lines:
+            line = line.strip()
+            if line.startswith("mxrun_path:"):
+                parts = line.split(" ")
+                correct_mxrun_path = parts[1]
+
+            if line.startswith("mcxtrace_path:"):
+                parts = line.split(" ")
+                correct_mcxtrace_path = parts[1]
+
+            if line.startswith("characters_per_line:"):
+                parts = line.split(" ")
+                correct_n_of_characters = int(parts[1])
+
+        f.close()
+
+        # Check the value matches what is loaded by initialization
+        my_instrument = setup_x_ray_instr_no_path()
+
+        self.assertEqual(my_instrument.executable_path, correct_mxrun_path)
+        self.assertEqual(my_instrument.package_path, correct_mcxtrace_path)
+        self.assertEqual(my_instrument.line_limit, correct_n_of_characters)
+
     def test_simple_add_parameter(self):
         """
         This is just an interface to a function that is tested
@@ -1485,13 +1554,13 @@ def test_run_full_instrument_required_par_error(self, mock_stdout):
         with self.assertRaises(NameError):
             instr.run_full_instrument("test_instrument.instr",
                                       foldername="test_data_set",
-                                      mcrun_path="path")
+                                      executable_path="path")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
     @unittest.mock.patch("subprocess.run")
-    def test_run_full_instrument_basic(self, mock_sub,
+    def test_x_ray_run_full_instrument_basic(self, mock_sub,
                                        mock_f, mock_stdout,):
         """
         Check a simple run performs the correct system call.  Here
@@ -1499,13 +1568,47 @@ def test_run_full_instrument_basic(self, mock_sub,
         data is loaded even though the system call is not executed.
         """
 
+        instr = setup_populated_x_ray_instr()
+        instr.run_full_instrument("test_instrument.instr",
+                                  foldername="test_data_set",
+                                  executable_path="path",
+                                  parameters={"theta": 1})
+
+        expected_path = os.path.join("path","mxrun")
+
+        current_directory = os.getcwd()
+        expected_folder_path = os.path.join(current_directory, "test_data_set")
+
+        # a double space because of a missing option
+        expected_call = (expected_path + " -c -n 1000000 "
+                         + "-d " + expected_folder_path
+                         + "  test_instrument.instr"
+                         + " has_default=37 theta=1")
+
+        mock_sub.assert_called_once_with(expected_call,
+                                         shell=True,
+                                         stderr=-1, stdout=-1,
+                                         universal_newlines=True)
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable=unittest.mock.mock_open)
+    @unittest.mock.patch("subprocess.run")
+    def test_run_full_instrument_basic(self, mock_sub,
+                                       mock_f, mock_stdout, ):
+        """
+        Check a simple run performs the correct system call.  Here
+        the target directory is set to the test data set so that some
+        data is loaded even though the system call is not executed.
+        """
+
         instr = setup_populated_instr()
         instr.run_full_instrument("test_instrument.instr",
                                   foldername="test_data_set",
                                   executable_path="path",
                                   parameters={"theta": 1})
 
-        expected_path = os.path.join("path","mcrun")
+        expected_path = os.path.join("path", "mcrun")
 
         current_directory = os.getcwd()
         expected_folder_path = os.path.join(current_directory, "test_data_set")

From fec2d075761bcf01837b9a49d615f9f2dffe9c31 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Fri, 25 Sep 2020 10:48:30 +0200
Subject: [PATCH 107/403] Updates errors and warnings to use package name
 instead of hardcoded McStas.

---
 mcstasscript/interface/instr.py | 31 +++++++++++++++++++++----------
 1 file changed, 21 insertions(+), 10 deletions(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index d00f6d0e..e65f92f0 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -174,6 +174,13 @@ def __init__(self, name, **kwargs):
                 Work directory, will load components from this folder
         """
 
+        # Check required attributes has been set by class that inherits
+        if not (hasattr(self, "particle") or
+                hasattr(self, "executable") or
+                hasattr(self, "package_name")):
+            raise AttributeError("McCode_instr is a base class, use "
+                                 + "McStas_intr or McXtrace_instr instead.")
+
         self.name = name
 
         if not is_legal_filename(self.name + ".instr"):
@@ -186,7 +193,8 @@ def __init__(self, name, **kwargs):
         if "author" in kwargs:
             self.author = kwargs["author"]
         else:
-            self.author = "Python McStas Instrument Generator"
+            self.author = "Python " + self.package_name
+            self.author += " Instrument Generator"
 
         if "origin" in kwargs:
             self.origin = kwargs["origin"]
@@ -208,8 +216,9 @@ def __init__(self, name, **kwargs):
         elif self.package_path is "":
             raise NameError("At this stage of development "
                             + "McStasScript need the absolute path "
-                            + "for the McStas installation as keyword "
-                            + "named package_path or in configuration.yaml")
+                            + "for the " + self.package_name +
+                            + " installation as keyword named "
+                            + "package_path or in configuration.yaml")
 
         self.parameter_list = []
         self.declare_list = []
@@ -635,7 +644,8 @@ def add_component(self, *args, **kwargs):
 
         if args[0] in self.component_name_list:
             raise NameError(("Component name \"" + str(args[0])
-                             + "\" used twice, McStas does not allow this."
+                             + "\" used twice, " + self.package_name
+                             + " does not allow this."
                              + " Rename or remove one instance of this"
                              + " name."))
 
@@ -756,14 +766,15 @@ def copy_component(self, *args, **kwargs):
 
         if instance_name in self.component_name_list:
             raise NameError(("Component name \"" + str(args[0])
-                             + "\" used twice, McStas does not allow this."
+                             + "\" used twice, " + self.package_name
+                             + " does not allow this."
                              + " Rename or remove one instance of this"
                              + " name."))
         
         if not args[1] in self.component_name_list:
             raise NameError("Component name \"" + str(args[1])
-                            + "\" was not found in the McStas instrument."
-                            + " and thus can not be copied.")
+                            + "\" was not found in the " + self.package_name
+                            + " instrument. and thus can not be copied.")
         else:
             component_to_copy = self.get_component(args[1])
         
@@ -1738,9 +1749,9 @@ def _read_calibration(self):
 
 class McXtrace_instr(McCode_instr):
     """
-    Main class for writing a McStas instrument using McStasScript
+    Main class for writing a McXtrace instrument using McStasScript
 
-    Initialization of McStas_instr sets the name of the instrument file
+    Initialization of McXtrace_instr sets the name of the instrument file
     and its methods are used to add all aspects of the instrument file.
     The class also holds methods for writing the finished instrument
     file to disk and to run the simulation.
@@ -1872,7 +1883,7 @@ class McXtrace_instr(McCode_instr):
     """
     def __init__(self, name, **kwargs):
         """
-        Initialization of McStas Instrument
+        Initialization of McXtrace Instrument
 
         Parameters
         ----------

From 04cbfb1be0b4238d5cb63d74a2ffd4c028b69e81 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 20 Oct 2020 11:33:10 +0200
Subject: [PATCH 108/403] Updated manuals after review

---
 McStasScript_documentation.pdf | Bin 168185 -> 179139 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
index af10867ed1820567c9cc066a8c7f962ba350f69a..fd536d006ac54933bb4e449b4869dfc7b92de67b 100644
GIT binary patch
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From a21eab11458671f81cf6dbcbc6411f6b26390ceb Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 20 Oct 2020 11:36:09 +0200
Subject: [PATCH 109/403] Updates in documentation after review from Celine.
 Much appreciated!

---
 McStasScript_developer_documentation.pdf | Bin 186372 -> 184733 bytes
 McStasScript_documentation.pdf           | Bin 168185 -> 179139 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)

diff --git a/McStasScript_developer_documentation.pdf b/McStasScript_developer_documentation.pdf
index 177725cf2dc7946e1d7ca1f1c0525e581b28ab3f..f5684e6849da6982ce2e2980a4d0443951e4e379 100644
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diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
index af10867ed1820567c9cc066a8c7f962ba350f69a..fd536d006ac54933bb4e449b4869dfc7b92de67b 100644
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From 14a4c40f612d86b1d2ae4d7f5abf200281021496 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Tue, 20 Oct 2020 12:28:47 +0200
Subject: [PATCH 110/403] Update of README.md

Started to update README after review by Celine. Thanks for the review and good suggestions!
---
 README.md | 26 +++++++++++++++++++++-----
 1 file changed, 21 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index b1ec9bdf..98d6c209 100644
--- a/README.md
+++ b/README.md
@@ -1,20 +1,34 @@
 # McStasScript
-McStas API for creating and running McStas instruments from python scripting
+[McStas](http://www.mcstas.org) API for creating and running McStas instruments from python scripting
 
 Prototype for an API that allow interaction with McStas through an interface like Jupyter Notebooks created under WP5 of PaNOSC.
 
 ## Installation
-McStasScript does not include the McStas installation, so McStas should be installed separately.
-McStasScript can be installed using pip,
+McStasScript does not include the McStas installation, so McStas should be installed separately, link to instructions [here](https://github.com/McStasMcXtrace/McCode/tree/master/INSTALL-McStas).
+McStasScript can be installed using pip from a terminal,
 
     python3 -m pip install McStasScript --upgrade
 
-After installation it is necessary to configure the package so the McStas installation can be found, here we show how the appropriate code for an Ubuntu system. The configuration is saved permanently, and only needs to be updated when McStas or McStasScript is updated. This has to be done from within python.
+After installation it is necessary to configure the package so the McStas installation can be found, here we show how the appropriate code for an Ubuntu system as an example. The configuration is saved permanently, and only needs to be updated when McStas or McStasScript is updated. This has to be done from a python terminal or from within a python environment.
 
     from mcstasscript.interface import functions
     my_configurator = functions.Configurator()
     my_configurator.set_mcrun_path("/usr/bin/")
     my_configurator.set_mcstas_path("/usr/share/mcstas/2.5/")
+
+To get a python terminal, run the command python in a terminal and then copy, paste and execute the lines above one at a time. Exit with ctrl+D.
+
+For the second case, 
+1. open a text editor (not MS Word but something like Gedit...),
+2. copy and paste the code above,
+3. save the file as a Python script, for example, myMcStasScript_config.py
+4. In a terminal, run it by typing python myMcStasScript_config.py
+    
+On a Mac OS X system, the paths to the mcrun executable and mcstas folder are through the application folder:
+
+    my_configurator.set_mcrun_path("/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/")
+    my_configurator.set_mcstas_path("/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/")
+    
     
 ### Notes on windows installation
 McStasScript was tested on Windows 10 installed using this [guide](https://github.com/McStasMcXtrace/McCode/blob/master/INSTALL-McStas/Windows/README.md), it is necessary to include MPI using MSMpiSetup.exe and msmpisdk.msi located in the extras folder.
@@ -34,9 +48,11 @@ For a standard McStas installation on Windows, the appropriate configuration can
     my_configurator = functions.Configurator()
     my_configurator.set_mcrun_path("\\mcstas-2.6\\bin\\")
     my_configurator.set_mcstas_path("\\mcstas-2.6\\lib\\")
+    
+Double backslashes are necessary since backslash is the escape character in python strings.
 
 ## Instructions for basic use
-This section provides a quick way to get started, a more in depth tutorial using Jupyter Notebooks is available in the tutorial folder.
+This section provides a quick way to get started, a more in depth tutorial using Jupyter Notebooks is available in the tutorial folder. The following commands suppose that you are either typing them in a Python environment from a terminal or in a file to be run as the end of the editing by typing a command like, 'python my_file.py' or in a Jupyter notebook
 
 Import the interface 
 

From a7aa6703a0a27b387cecdb74c495a45290be80a6 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Tue, 20 Oct 2020 13:32:03 +0200
Subject: [PATCH 111/403] Update of README.md

Last corrections of the README file after review by Celine. Thanks!
---
 README.md | 43 ++++++++++++++++++++++++++++++++++++++-----
 1 file changed, 38 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 98d6c209..4d4f9e23 100644
--- a/README.md
+++ b/README.md
@@ -62,10 +62,35 @@ Now the package can be used. Start with creating a new instrument, just needs a
 
     my_instrument = instr.McStas_instr("my_instrument_file")
 
-Then McStas components can be added, here we add a source
+Then McStas components can be added, here we add a source and ask for help on the parameters
 
     my_source = my_instrument.add_component("source", "Source_simple")
     my_source.show_parameters() # Can be used to show available parameters for Source simple
+    
+The second line prints help on the Source_simple component and current status of our component object. The output is shown here, but without bold, underline and color which is used to show which parameters are required, default or user specified.
+
+     ___ Help Source_simple _____________________________________________________________
+    |optional parameter|required parameter|default value|user specified value|
+    radius = 0.1 [m] // Radius of circle in (x,y,0) plane where neutrons are 
+                        generated. 
+    yheight = 0.0 [m] // Height of rectangle in (x,y,0) plane where neutrons are 
+                         generated. 
+    xwidth = 0.0 [m] // Width of rectangle in (x,y,0) plane where neutrons are 
+                        generated. 
+    dist = 0.0 [m] // Distance to target along z axis.
+    focus_xw = 0.045 [m] // Width of target
+    focus_yh = 0.12 [m] // Height of target
+    E0 = 0.0 [meV] // Mean energy of neutrons.
+    dE = 0.0 [meV] // Energy half spread of neutrons (flat or gaussian sigma).
+    lambda0 = 0.0 [AA] // Mean wavelength of neutrons.
+    dlambda = 0.0 [AA] // Wavelength half spread of neutrons.
+    flux = 1.0 [1/(s*cm**2*st*energy unit)] // flux per energy unit, Angs or meV if 
+                                               flux=0, the source emits 1 in 4*PI whole 
+                                               space. 
+    gauss = 0.0 [1] // Gaussian (1) or Flat (0) energy/wavelength distribution
+    target_index = 1 [1] // relative index of component to focus at, e.g. next is 
+                            +1 this is used to compute 'dist' automatically. 
+    -------------------------------------------------------------------------------------
 
 The parameters of the source can be adjusted directly as attributes of the python object
 
@@ -76,13 +101,13 @@ The parameters of the source can be adjusted directly as attributes of the pytho
     my_source.focus_xw = 0.05
     my_source.focus_yh = 0.05
     
-A monitor is added as well to get data out of the simulation
+A monitor is added as well to get data out of the simulation (few bins so it is easy to print the results)
 
     PSD = my_instrument.add_component("PSD", "PSD_monitor", AT=[0,0,1], RELATIVE="source") 
     PSD.xwidth = 0.1
     PSD.yheight = 0.1
-    PSD.nx = 200
-    PSD.ny = 200
+    PSD.nx = 5
+    PSD.ny = 5
     PSD.filename = "\"PSD.dat\""
 
 This simple simulation can be executed from the 
@@ -93,6 +118,14 @@ Results from the monitors would be stored as a list of McStasData objects in the
 
     data[0].Intensity
     
+In a python terminal this would display the data directly:
+
+    array([[0.        , 0.        , 0.        , 0.        , 0.        ],
+       [0.        , 0.1422463 , 0.19018485, 0.14156196, 0.        ],
+       [0.        , 0.18930076, 0.25112956, 0.18897898, 0.        ],
+       [0.        , 0.14121589, 0.18952508, 0.14098576, 0.        ],
+       [0.        , 0.        , 0.        , 0.        , 0.        ]])
+    
 Plotting is usually done in a subplot of all monitors recorded.    
 
     plot = plotter.make_sub_plot(data)
@@ -106,7 +139,7 @@ If one wish to work on existing projects using McStasScript, there is a reader i
 It is highly advised to run a check between the output of the generated file and the original to ensure the process was sucessful.
 
 ## Method overview
-Here is a quick overview of the available methods of the main classes in the project. Most have more options from keyword arguments that are explained in the manual, but also in python help, for example help(instr.McStas_instr.show_components).
+Here is a quick overview of the available methods of the main classes in the project. Most have more options from keyword arguments that are explained in the manual, but also in python help. To get more information on for example the show_components method of the McStas_instr class, one can use the python help command help(instr.McStas_instr.show_components).
 
     instr
     └── McStas_instr(str instr_name) # Returns McStas instrument object on initialize

From 46f86105d951da75f50238a3c90e6b10744b3c4f Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Sun, 25 Oct 2020 11:30:56 +0100
Subject: [PATCH 112/403] Updated plotter after review from Neil, thanks!

Before there were three plotter classes, all with duplicated code for plotting.
They did not even need to be classes as they had no methods.

Now there are three simple functions for plotting, which all rely on a single
shorter plotting function.

Allowed better handling of keyword arguments for plotting, for example

make_sub_plot(data, log=[True, False])

Sets the log mode for each data set, the first to True and the second to False. This works
for all keyword arguments used in set_plot_options.

All plotting functions also get the figsize keyword to set figure size, and no_data_to_black
to set no data to black instead of white.
---
 McStasScript_documentation.pdf               | Bin 179139 -> 181093 bytes
 mcstasscript/interface/plotter.py            | 823 +++++++------------
 mcstasscript/tests/test_McStasPlotOptions.py | 171 +++-
 mcstasscript/tests/test_Plotter.py           | 610 ++++++++++++++
 4 files changed, 1057 insertions(+), 547 deletions(-)
 create mode 100644 mcstasscript/tests/test_Plotter.py

diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
index fd536d006ac54933bb4e449b4869dfc7b92de67b..560e48c21c960e615c4b22d7a711dbb2be5fc970 100644
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diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index f49fdf3d..a04db47f 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -1,4 +1,5 @@
 import math
+import copy
 import numpy as np
 import matplotlib
 import matplotlib.pyplot as plt
@@ -11,640 +12,380 @@
 from mcstasscript.data.data import McStasData
 
 
-class make_plot:
+def _fmt(x, pos):
     """
-    make_plot plots contents of McStasData objects
+    Used for nice formatting of powers of 10 when plotting logarithmic
+    """
+    a, b = '{:.2e}'.format(x).split('e')
+    b = int(b)
+    if abs(float(a) - 1) < 0.01:
+        return r'$10^{{{}}}$'.format(b)
+    else:
+        return r'${} \times 10^{{{}}}$'.format(a, b)
 
-    Plotting is controlled through options assosciated with the
-    McStasData objects.
 
-    If a list is given, the plots appear individually.
+def _find_min_max_I(data):
     """
+    Returns minimum and maximum intensity to plot given dataset
 
-    def __init__(self, data_list, **kwargs):
-        """
-        plots McStasData, single object or list of McStasData
+    Uses the plot options embedded in McStasData to determine the proper
+    minimum and maximum intensity to display in a plot.
 
-        The options concerning plotting are stored with the data
+    Have to take cut_min and cut_max into account that can cut parts of
+    the intensity away. When plotting logarithmic, orders_of_mags limits
+    the orders of magnitude shown.
 
-        Parameters
-        ----------
-        data_list : McStasData or list of McStasData
-            McStasData to be plotted
-        """
-
-        # Relevant options:
-        #  select colormap
-        #  show / hide colorbar
-        #  custom title / label
-        #  color of 1d plot
-        #  overlay several 1d
-        #  log scale (orders of magnitude)
-        #  compare several 1d
-        #  compare 2D
+    Returns tuple of minimum and maximum, when no data is present the
+    function returns 0, 0.
+    """
+    cut_max = data.plot_options.cut_max  # Default 1
+    cut_min = data.plot_options.cut_min  # Default 0
 
-        if isinstance(data_list, McStasData):
-            # Only a single element, put it in a list for easier syntax later
-            data_list = [data_list]
+    to_plot = data.Intensity
 
-        number_of_plots = len(data_list)
+    min_value = to_plot.min()
+    max_value = to_plot.max()
 
-        if "fontsize" in kwargs:
-            plt.rcParams.update({'font.size': kwargs["fontsize"]})
+    if min_value == 0 and max_value == 0:
+        return 0, 0
 
-        print("number of elements in data list = " + str(len(data_list)))
+    if not data.plot_options.log:
+        # Linear, simple case
+        # Cut top and bottom of data as specified in cut variables
+        min_value = min_value + (max_value - min_value) * cut_min
+        max_value = max_value * cut_max
 
-        index = -1
-        for data in data_list:
-            index = index + 1
+    else:
+        # Logarithmic, minimum / maximum can not be zero
+        max_data_value = to_plot.max()
+        max_value = np.log10(max_data_value * cut_max)
 
-            print("Plotting data with name " + data.metadata.component_name)
-            if type(data.metadata.dimension) == int:
-                fig = plt.figure(0)
+        min_value = np.min(to_plot[np.nonzero(to_plot)])
+        min_value = min_value + (max_data_value - min_value) * cut_min
+        min_value = np.log10(min_value)
 
-                x_axis_mult = data.plot_options.x_limit_multiplier
-                # print(data.T)
-                x = data.xaxis*x_axis_mult
-                y = data.Intensity
-                y_err = data.Error
+        # Take orders_of_mag into account (max / min in log10)
+        if max_value - min_value > data.plot_options.orders_of_mag:
+            min_value = max_value - data.plot_options.orders_of_mag
 
-                #(fig, ax0) = plt.errorbar(x, y, yerr=y_err)
-                plt.errorbar(x, y, yerr=y_err)
+        # Convert back from log10
+        min_value = 10.0 ** min_value
+        max_value = 10.0 ** max_value
 
-                ax0 = plt.gca()
-
-                if data.plot_options.log:
-                    ax0.set_yscale("log", nonposy='clip')
-
-                ax0.set_xlim(data.metadata.limits[0]*x_axis_mult,
-                             data.metadata.limits[1]*x_axis_mult)
+    return min_value, max_value
 
-                # Add a title
-                plt.title(data.metadata.title)
+def _plot_fig_ax(data, fig, ax, **kwargs):
+    """
+    Plots the content of a single McStasData object
 
-                # Add axis labels
-                plt.xlabel(data.metadata.xlabel)
-                plt.ylabel(data.metadata.ylabel)
+    Plotting is controlled through options assosciated with the
+    McStasData objects.
 
-                if data.plot_options.custom_xlim_left:
-                    ax0.set_xlim(left=data.plot_options.left_lim)
+    When plotting 2D objects, returns the pcolormesh object
+    """
 
-                if data.plot_options.custom_xlim_right:
-                    ax0.set_xlim(right=data.plot_options.right_lim)
+    print("Plotting data with name " + data.metadata.component_name)
+    if type(data.metadata.dimension) == int:
 
-            elif len(data.metadata.dimension) == 2:
-                # Split the data into intensity, error and ncount
-                Intensity = data.Intensity
-                Error = data.Error
-                Ncount = data.Ncount
+        x_axis_mult = data.plot_options.x_limit_multiplier
 
-                cut_max = data.plot_options.cut_max  # Default 1
-                cut_min = data.plot_options.cut_min  # Default 0
+        x = data.xaxis * x_axis_mult
+        y = data.Intensity
+        y_err = data.Error
 
-                if data.plot_options.log:
-                    to_plot = Intensity
+        ax.errorbar(x, y, yerr=y_err)
 
-                    max_data_value = to_plot.max()
+        if data.plot_options.log:
+            ax.set_yscale("log", nonposy='clip')
 
-                    min_value = np.min(Intensity[np.nonzero(Intensity)])
-                    min_value = np.log10(min_value
-                                         + (max_data_value-min_value)*cut_min)
+        ax.set_xlim(data.metadata.limits[0] * x_axis_mult,
+                    data.metadata.limits[1] * x_axis_mult)
 
-                    max_value = np.log10(max_data_value*cut_max)
+        # Add a title
+        ax.set_title(data.metadata.title)
 
-                    if (max_value - min_value
-                            > data.plot_options.orders_of_mag):
-                        min_value = (max_value
-                                     - data.plot_options.orders_of_mag)
-                    min_value = 10.0 ** min_value
-                    max_value = 10.0 ** max_value
-                else:
-                    to_plot = Intensity
-                    min_value = to_plot.min() 
-                    max_value = to_plot.max()
+        # Add axis labels
+        ax.set_xlabel(data.metadata.xlabel)
+        ax.set_ylabel(data.metadata.ylabel)
 
-                    # Cut top and bottom of data as specified in cut variables
-                    min_value = min_value + (max_value-min_value)*cut_min
-                    max_value = max_value*cut_max
+        if data.plot_options.custom_xlim_left:
+            ax.set_xlim(left=data.plot_options.left_lim)
 
-                # Check the size of the array to be plotted
-                # print(to_plot.shape)
+        if data.plot_options.custom_xlim_right:
+            ax.set_xlim(right=data.plot_options.right_lim)
 
-                # Set the axis (might be switched?)
-                x_axis_mult = data.plot_options.x_limit_multiplier
-                y_axis_mult = data.plot_options.y_limit_multiplier
+    elif len(data.metadata.dimension) == 2:
 
-                X = np.linspace(data.metadata.limits[0]*x_axis_mult,
-                                data.metadata.limits[1]*x_axis_mult,
-                                data.metadata.dimension[0]+1)
-                Y = np.linspace(data.metadata.limits[2]*y_axis_mult,
-                                data.metadata.limits[3]*y_axis_mult,
-                                data.metadata.dimension[1]+1)
+        min_value, max_value = _find_min_max_I(data)
 
-                # Create a meshgrid for both x and y
-                x, y = np.meshgrid(X, Y)
+        if "fixed_minimum_value" in kwargs:
+            min_value = kwargs["fixed_minimum_value"]
+        if "fixed_maximum_value" in kwargs:
+            max_value = kwargs["fixed_maximum_value"]
 
-                # Generate information on necessary colorrange
-                levels = MaxNLocator(nbins=150).tick_values(min_value,
-                                                            max_value)
+        # Set the axis
+        x_axis_mult = data.plot_options.x_limit_multiplier
+        y_axis_mult = data.plot_options.y_limit_multiplier
 
-                # Select colormap
-                cmap = plt.get_cmap('hot')
-                norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
+        X = np.linspace(data.metadata.limits[0] * x_axis_mult,
+                        data.metadata.limits[1] * x_axis_mult,
+                        data.metadata.dimension[0] + 1)
+        Y = np.linspace(data.metadata.limits[2] * y_axis_mult,
+                        data.metadata.limits[3] * y_axis_mult,
+                        data.metadata.dimension[1] + 1)
 
-                # Create the figure
-                fig, (ax0) = plt.subplots()
+        # Create a meshgrid for both x and y
+        x, y = np.meshgrid(X, Y)
 
-                # Plot the data on the meshgrids
-                if data.plot_options.log:
-                    color_norm = matplotlib.colors.LogNorm(vmin=min_value,
-                                                           vmax=max_value)
-                    im = ax0.pcolormesh(x, y, to_plot,
-                                        cmap=cmap, norm=color_norm)
-                else:
-                    im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
+        # Generate information on necessary colorrange
+        levels = MaxNLocator(nbins=150).tick_values(min_value, max_value)
 
-                # Add the colorbar
-                fig.colorbar(im, ax=ax0)
+        # Select colormap
+        cmap = copy.copy(plt.get_cmap(data.plot_options.colormap))
+        if "no_data_to_black" in kwargs:
+            if kwargs["no_data_to_black"]:
+                cmap.set_bad((0, 0, 0))
 
-                # Add a title
-                ax0.set_title(data.metadata.title)
-                
-                # Add axis labels
-                plt.xlabel(data.metadata.xlabel)
-                plt.ylabel(data.metadata.ylabel)
+        norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
 
-                if data.plot_options.custom_ylim_top:
-                    ax0.set_ylim(top=data.plot_options.top_lim)
+        # Empty data, return without cmap or norm
+        if min_value == 0 and max_value == 0:
+            levels = MaxNLocator(nbins=150).tick_values(0.001, 1.0)
+            norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
+            im = ax.pcolormesh(x, y, data.Intensity, cmap=cmap, norm=norm)
 
-                if data.plot_options.custom_ylim_bottom:
-                    ax0.set_ylim(bottom=data.plot_options.bottom_lim)
+        # Plot the data on the meshgrids
+        elif data.plot_options.log:
+            color_norm = matplotlib.colors.LogNorm(vmin=min_value,
+                                                   vmax=max_value)
+            im = ax.pcolormesh(x, y, data.Intensity,
+                               cmap=cmap, norm=color_norm)
+        else:
+            im = ax.pcolormesh(x, y, data.Intensity, cmap=cmap, norm=norm)
 
-                if data.plot_options.custom_xlim_left:
-                    ax0.set_xlim(left=data.plot_options.left_lim)
-
-                if data.plot_options.custom_xlim_right:
-                    ax0.set_xlim(right=data.plot_options.right_lim)
+        # Add the colorbar
+        if data.plot_options.show_colorbar:
+            fig.colorbar(im, ax=ax,
+                         format=matplotlib.ticker.FuncFormatter(_fmt))
 
-            else:
-                print("Error, dimension not read correctly")
+        # Add a title
+        ax.set_title(data.metadata.title)
 
-        plt.show()
+        # Add axis labels
+        ax.set_xlabel(data.metadata.xlabel)
+        ax.set_ylabel(data.metadata.ylabel)
 
+        if data.plot_options.custom_ylim_top:
+            ax.set_ylim(top=data.plot_options.top_lim)
 
-class make_sub_plot:
-    """
-    make_plot plots contents of McStasData objects
+        if data.plot_options.custom_ylim_bottom:
+            ax.set_ylim(bottom=data.plot_options.bottom_lim)
 
-    Plotting is controlled through options assosciated with the
-    McStasData objects.  If a list is given, the plots appear in one
-    subplot.
-    """
+        if data.plot_options.custom_xlim_left:
+            ax.set_xlim(left=data.plot_options.left_lim)
 
-    def __init__(self, data_list, **kwargs):
-        """
-        plots McStasData, single object or list of McStasData
+        if data.plot_options.custom_xlim_right:
+            ax.set_xlim(right=data.plot_options.right_lim)
 
-        The options concerning plotting are stored with the data
+        return im
+    else:
+        print("Error, dimension not read correctly")
 
-        Parameters
-        ----------
-        data_list : McStasData or list of McStasData
-            McStasData to be plotted
-        """
-        if not isinstance(data_list, McStasData):
-            print("number of elements in data list = "
-                  + str(len(data_list)))
-        else:
-            # Make list from single element to simplify syntax
-            data_list = [data_list]
 
-        number_of_plots = len(data_list)
+def _handle_kwargs(data_list, **kwargs):
+    """
+    Handle kwargs when list of McStasData objects given.
 
-        # Relevant options:
-        #  select colormap
-        #  show / hide colorbar
-        #  custom title / label
-        #  color of 1d plot
-        #  overlay several 1d
-        #  log scale (o$rders of magnitude)
-        #  compare several 1d
-        #  compare 2D
+    Returns figsize and data_list
 
-        # Find reasonable grid size for the number of plots
-        dim2 = math.ceil(math.sqrt(number_of_plots))
-        dim1 = math.ceil(number_of_plots/dim2)
+    figsize has a default value, but can be changed with keyword argument
+    data_list is turned into a list if it isn't already
 
-        if "fontsize" in kwargs:
-            plt.rcParams.update({'font.size': kwargs["fontsize"]})
+    Any kwargs can be given as a list, in that case apply them to given
+    to the corresponding index.
+    """
 
-        fig, axs = plt.subplots(dim1, dim2, figsize=(13, 7))
-        axs = np.array(axs)
-        ax = axs.reshape(-1)
+    if "fontsize" in kwargs:
+        used_fontsize = kwargs["fontsize"]
+    else:
+        used_fontsize = 11
+    plt.rcParams.update({'font.size': used_fontsize})
+
+    if isinstance(data_list, McStasData):
+        # Only a single element, put it in a list for easier syntax later
+        data_list = [data_list]
+
+    known_plotting_kwargs = ["log", "orders_of_mag",
+                             "top_lim", "bottom_lim",
+                             "left_lim", "right_lim",
+                             "cut_min", "cut_max",
+                             "colormap", "show_colorbar",
+                             "x_axis_multiplier",
+                             "y_axis_multiplier"]
+
+    for option in known_plotting_kwargs:
+        if option in kwargs:
+            given_option = kwargs[option]
+
+            if isinstance(given_option, list):
+                if len(data_list) < len(given_option):
+                    raise ValueError("Keyword argument " + option + " is "
+                                     + "given as a list, but this list has "
+                                     + "more elements than there are "
+                                     + "data sets to be plotted.")
+
+                index = 0
+                for per_list_option in given_option:
+                    input_kwarg = {option: per_list_option}
+                    data_list[index].set_plot_options(**input_kwarg)
+                    index += 1
 
-        index = -1
-        for data in data_list:
-            index = index + 1
-            ax0 = ax[index]
+            else:
+                for data in data_list:
+                    input_kwarg = {option: given_option}
+                    data.set_plot_options(**input_kwarg)
 
-            print("Plotting data with name "
-                  + data.metadata.component_name)
+            # Remove option from kwargs
+            del kwargs[option]
 
-            if isinstance(data.metadata.dimension, int):
-                # fig = plt.figure(0)
-                # plt.subplot(dim1, dim2, n_plot)
-                x_axis_mult = data.plot_options.x_limit_multiplier
+    if "figsize" in kwargs:
+        figsize = kwargs["figsize"]
+        if isinstance(figsize, list):
+            figsize = (figsize[0], figsize[1])
+    else:
+        figsize = (13, 7)
 
-                x = data.xaxis*x_axis_mult
-                y = data.Intensity
-                y_err = data.Error
+    return figsize, data_list
 
-                ax0.errorbar(x, y, yerr=y_err)
-
-                if data.plot_options.log:
-                    ax0.set_yscale("log", nonposy='clip')
-
-                ax0.set_xlim(data.metadata.limits[0]*x_axis_mult,
-                             data.metadata.limits[1]*x_axis_mult)
+def make_plot(data_list, **kwargs):
+    """
+    make_plot plots contents of McStasData objects given in list
 
-                # Add a title
-                ax0.set_title(data.metadata.title)
+    Here a new figure is used for each dataset
 
-                # Add axis labels
-                ax0.set_xlabel(data.metadata.xlabel)
-                ax0.set_ylabel(data.metadata.ylabel)
+    Plotting is controlled through options assosciated with the
+    McStasData objects.  If a list is given, the plots appear in one
+    subplot.
+    """
 
-                if data.plot_options.custom_xlim_left:
-                    ax0.set_xlim(left=data.plot_options.left_lim)
+    figsize, data_list = _handle_kwargs(data_list, **kwargs)
 
-                if data.plot_options.custom_xlim_right:
-                    ax0.set_xlim(right=data.plot_options.right_lim)
+    for data in data_list:
+        fig, ax0 = plt.subplots(figsize=figsize)
+        _plot_fig_ax(data, fig, ax0, **kwargs)
 
-            elif len(data.metadata.dimension) == 2:
+def make_sub_plot(data_list, **kwargs):
+    """
+    make_sub_plot plots contents of McStasData objects given in list
 
-                # Split the data into intensity, error and ncount
-                Intensity = data.Intensity
-                Error = data.Error
-                Ncount = data.Ncount
+    It is fit into one big figure, each data set as a subplot.
 
-                cut_max = data.plot_options.cut_max  # Default 1
-                cut_min = data.plot_options.cut_min  # Default 0
+    Plotting is controlled through options assosciated with the
+    McStasData objects.  If a list is given, the plots appear in one
+    subplot.
+    """
 
-                if data.plot_options.log:
-                    to_plot = Intensity
+    figsize, data_list = _handle_kwargs(data_list, **kwargs)
 
-                    max_data_value = to_plot.max()
+    number_of_plots = len(data_list)
+    # Find reasonable grid size for the number of plots
+    dim2 = math.ceil(math.sqrt(number_of_plots))
+    dim1 = math.ceil(number_of_plots / dim2)
 
-                    min_value = np.min(Intensity[np.nonzero(Intensity)])
-                    min_value = np.log10(min_value
-                                         + (max_data_value-min_value)*cut_min)
+    fig, axs = plt.subplots(dim1, dim2, figsize=figsize)
+    axs = np.array(axs)
+    ax = axs.reshape(-1)
 
-                    max_value = np.log10(max_data_value*cut_max)
+    for data, ax0 in zip(data_list, ax):
+        _plot_fig_ax(data, fig, ax0, **kwargs)
 
-                    if (max_value - min_value
-                            > data.plot_options.orders_of_mag):
-                        min_value = (max_value
-                                     - data.plot_options.orders_of_mag)
-                    min_value = 10.0 ** min_value
-                    max_value = 10.0 ** max_value
-                else:
-                    to_plot = Intensity
-                    min_value = to_plot.min() 
-                    max_value = to_plot.max()
+    fig.tight_layout()
 
-                    # Cut top and bottom of data as specified in cut variables
-                    min_value = min_value + (max_value-min_value)*cut_min
-                    max_value = max_value*cut_max
 
-                # Check the size of the array to be plotted
-                # print(to_plot.shape)
+def make_animation(data_list, **kwargs):
+    """
+    Creates an animation from list of McStasData objects
 
-                # Set the axis
-                x_axis_mult = data.plot_options.x_limit_multiplier
-                y_axis_mult = data.plot_options.y_limit_multiplier
+    Parameters
+    ----------
+    data_list : list of McStasData
+        List of McStasData objects for animation
 
-                X = np.linspace(data.metadata.limits[0]*x_axis_mult,
-                                data.metadata.limits[1]*x_axis_mult,
-                                data.metadata.dimension[0]+1)
-                Y = np.linspace(data.metadata.limits[2]*y_axis_mult,
-                                data.metadata.limits[3]*y_axis_mult,
-                                data.metadata.dimension[1]+1)
+    Keyword arguments
+    -----------------
+        filename : str
+            Filename for saving the gif
 
-                # Create a meshgrid for both x and y
-                x, y = np.meshgrid(X, Y)
+        fps : float
+            Number of frames per second
 
-                # Generate information on necessary colorrange
-                levels = MaxNLocator(nbins=150).tick_values(min_value,
-                                                            max_value)
+    """
 
-                # Select colormap
-                cmap = plt.get_cmap(data.plot_options.colormap)
+    figsize, data_list = _handle_kwargs(data_list, **kwargs)
 
-                # Select the colorscale normalization
-                if data.plot_options.log:
-                    norm = matplotlib.colors.LogNorm(vmin=min_value,
-                                                     vmax=max_value)
-                else:
-                    norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
+    if "fps" in kwargs:
+        period_in_ms = 1000 / kwargs["fps"]
+    else:
+        period_in_ms = 200
 
-                # Create plot
-                im = ax0.pcolormesh(x, y, to_plot, cmap=cmap, norm=norm)
+    # find limits for entire dataset
+    maximum_values = []
+    minimum_values = []
 
-                def fmt(x, pos):
-                    a, b = '{:.2e}'.format(x).split('e')
-                    b = int(b)
-                    if abs(float(a) - 1) < 0.01 :
-                        return r'$10^{{{}}}$'.format(b)
-                    else:
-                        return r'${} \times 10^{{{}}}$'.format(a, b)
+    is_1D = False
+    is_2D = False
 
-                # Add the colorbar
-                if data.plot_options.colormap:
-                    fig.colorbar(im, ax=ax0,
-                                 format=matplotlib.ticker.FuncFormatter(fmt))
+    for data in data_list:
+        if isinstance(data.metadata.dimension, int):
+            is_1D = True
 
-                # Add a title
-                ax0.set_title(data.metadata.title)
+        elif len(data.metadata.dimension) == 2:
+            is_2D = True
 
-                # Add axis labels
-                ax0.set_xlabel(data.metadata.xlabel)
-                ax0.set_ylabel(data.metadata.ylabel)
+        min_value, max_value = _find_min_max_I(data)
 
-                if data.plot_options.custom_ylim_top:
-                    ax0.set_ylim(top=data.plot_options.top_lim)
+        # When data empty, min and max value is 0, skip
+        if not (min_value == 0 and max_value == 0):
+            minimum_values.append(min_value)
+            maximum_values.append(max_value)
 
-                if data.plot_options.custom_ylim_bottom:
-                    ax0.set_ylim(bottom=data.plot_options.bottom_lim)
+    if is_1D and is_2D:
+        raise InputError(
+            "Both 1D and 2D data in animation, only one allowed.")
 
-                if data.plot_options.custom_xlim_left:
-                    ax0.set_xlim(left=data.plot_options.left_lim)
+    if len(minimum_values) == 0:
+        raise InputError(
+            "No data found for animation!")
 
-                if data.plot_options.custom_xlim_right:
-                    ax0.set_xlim(right=data.plot_options.right_lim)
+    maximum_value = np.array(maximum_values).max()
+    minimum_value = np.array(minimum_values).min()
 
-            else:
-                print("Error, dimension not read correctly")
+    kwargs["fixed_minimum_value"] = minimum_value
+    kwargs["fixed_maximum_value"] = maximum_value
 
-        plt.show()
+    fig, ax0 = plt.subplots(figsize=figsize)
+    im = _plot_fig_ax(data_list[0], fig, ax0, **kwargs)
 
+    def animate_2D(index):
+        data = data_list[index]
+        intensity = data.Intensity
 
-class make_animation:
-    """
-    make_plot plots contents of McStasData objects
+        im.set_array(intensity.ravel())
+        return im,
 
-    Plotting is controlled through options assosciated with the
-    McStasData objects.  If a list is given, the plots appear in one
-    subplot.
-    """
+    anim = animation.FuncAnimation(fig, animate_2D,
+                                   frames=len(data_list),
+                                   interval=period_in_ms,
+                                   blit=False, repeat=True)
 
-    def __init__(self, data_list, **kwargs):
-        """
-        plots McStasData, single object or list of McStasData
+    plt.show()
 
-        The options concerning plotting are stored with the data
+    # The animation doesn't play unless it is saved. Bug.
+    if "filename" in kwargs:
+        filename = kwargs["filename"]
+        if not filename.endswith(".gif"):
+            filename = filename + ".gif"
 
-        Parameters
-        ----------
-        data_list : McStasData or list of McStasData
-            McStasData to be plotted
-        """
-        if not isinstance(data_list, McStasData):
-            print("number of elements in data list = "
-                  + str(len(data_list)))
-        else:
-            # Make list from single element to simplify syntax
-            data_list = [data_list]
-
-        # Relevant options:
-        #  select colormap
-        #  show / hide colorbar
-        #  custom title / label
-        #  color of 1d plot
-        #  overlay several 1d
-        #  log scale (o$rders of magnitude)
-        #  compare several 1d
-        #  compare 2D
-
-        if "fontsize" in kwargs:
-            plt.rcParams.update({'font.size': kwargs["fontsize"]})
-            
-        if "fps" in kwargs:
-            period_in_ms = 1000/kwargs["fps"]
-        else:
-            period_in_ms = 200
-
-        fig = plt.figure()
-        ax = plt.axes()
-        #fig, ax = plt.subplot()
-        
-        # find limits for entire dataset
-        maximum_values = []
-        minimum_values = []
-        
-        is_1D = False
-        is_2D = False
-        
-        for data in data_list:
-            if isinstance(data.metadata.dimension, int):
-                is_1D = True
-                
-                y = data.Intensity[np.nonzero(data.Intensity)]
-                if len(y) > 0:
-                    maximum_values.append(y.max())
-                    minimum_values.append(y.min())
-            
-            elif len(data.metadata.dimension) == 2:
-                is_2D = True
-                
-                y = data.Intensity[np.nonzero(data.Intensity)]
-                if len(y) > 0:
-                    maximum_values.append(y.max())
-                    minimum_values.append(y.min())
-        
-        if len(maximum_values) > 0:
-            maximum_value = np.array(maximum_values).max()
-        else:
-            maximum_value = 0
-            
-        if len(minimum_values) > 0:
-            minimum_value = np.array(minimum_values).min()
-        else:
-            minimum_value = 0
-        
-        if is_1D and is_2D:
-            raise InputError(
-                "Both 1D and 2D data in animation, only one allowed.")
-
-        # initialize plots
-        
-        data = data_list[0]
-        if isinstance(data.metadata.dimension, int):
-            x_axis_mult = data.plot_options.x_limit_multiplier
-            
-            x = data.xaxis*x_axis_mult
-            y = data.Intensity
-            y_err = data.Error
-            
-            er = ax.errorbar(x, y, yerr=y_err)
-            
-            if data.plot_options.log:
-                    ax.set_yscale("log", nonposy='clip')
-
-            ax.set_xlim(data.metadata.limits[0]*x_axis_mult,
-                             data.metadata.limits[1]*x_axis_mult)
-
-            # Add a title
-            ax.set_title(data.metadata.title)
-
-            # Add axis labels
-            ax.set_xlabel(data.metadata.xlabel)
-            ax.set_ylabel(data.metadata.ylabel)
-
-            if data.plot_options.custom_xlim_left:
-                ax.set_xlim(left=data.plot_options.left_lim)
-
-            if data.plot_options.custom_xlim_right:
-                ax.set_xlim(right=data.plot_options.right_lim)
-                
-            ax.set_ylim(minimum_value, maximum_value)
-        
-        elif len(data.metadata.dimension) == 2:
-            # Split the data into intensity, error and ncount
-            Intensity = data.Intensity
-            Error = data.Error
-            Ncount = data.Ncount
-            
-            cut_max = data.plot_options.cut_max  # Default 1
-            cut_min = data.plot_options.cut_min  # Default 0
-
-            if data.plot_options.log:
-                
-                min_value = minimum_value
-                max_value = maximum_value
-                
-                min_value = np.log10(min_value
-                                     + (max_value-min_value)*cut_min)
-                
-                max_value = np.log10(max_value*cut_max)
-
-                if (max_value - min_value
-                        > data.plot_options.orders_of_mag):
-                    min_value = (max_value
-                                 - data.plot_options.orders_of_mag)
-                    
-                min_value = 10.0 ** min_value
-                max_value = 10.0 ** max_value
-            else:
-                
-                min_value = minimum_value 
-                max_value = maximum_value
-
-            # Check the size of the array to be plotted
-            # print(to_plot.shape)
-
-            # Set the axis
-            x_axis_mult = data.plot_options.x_limit_multiplier
-            y_axis_mult = data.plot_options.y_limit_multiplier
-
-            X = np.linspace(data.metadata.limits[0]*x_axis_mult,
-                            data.metadata.limits[1]*x_axis_mult,
-                            data.metadata.dimension[0]+1)
-            Y = np.linspace(data.metadata.limits[2]*y_axis_mult,
-                            data.metadata.limits[3]*y_axis_mult,
-                            data.metadata.dimension[1]+1)
-
-            # Create a meshgrid for both x and y
-            x, y = np.meshgrid(X, Y)
-
-            # Generate information on necessary colorrange
-            levels = MaxNLocator(nbins=150).tick_values(min_value,
-                                                        max_value)
-
-            # Select colormap
-            cmap = plt.get_cmap(data.plot_options.colormap)
-
-            # Select the colorscale normalization
-            if data.plot_options.log:
-                norm = matplotlib.colors.LogNorm(vmin=min_value,
-                                                 vmax=max_value)
-            else:
-                norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
-
-            # Create plot
-            im = ax.pcolormesh(x, y, Intensity,
-                               cmap=cmap, norm=norm)
-
-            def fmt(x, pos):
-                a, b = '{:.2e}'.format(x).split('e')
-                b = int(b)
-                if abs(float(a) - 1) < 0.01 :
-                    return r'$10^{{{}}}$'.format(b)
-                else:
-                    return r'${} \times 10^{{{}}}$'.format(a, b)
-
-            # Add the colorbar
-            if data.plot_options.colormap:
-                fig.colorbar(im, ax=ax,
-                             format=matplotlib.ticker.FuncFormatter(fmt))
-
-            # Add a title
-            ax.set_title(data.metadata.title)
-
-            # Add axis labels
-            ax.set_xlabel(data.metadata.xlabel)
-            ax.set_ylabel(data.metadata.ylabel)
-
-            if data.plot_options.custom_ylim_top:
-                ax.set_ylim(top=data.plot_options.top_lim)
-
-            if data.plot_options.custom_ylim_bottom:
-                ax.set_ylim(bottom=data.plot_options.bottom_lim)
-
-            if data.plot_options.custom_xlim_left:
-                ax.set_xlim(left=data.plot_options.left_lim)
-
-            if data.plot_options.custom_xlim_right:
-                ax.set_xlim(right=data.plot_options.right_lim)
-
-        def init_1D():
-            # initialize function for animation
-            er.set_data([], [], []) # wont work
-            return er,
-        
-        def animate_1D(index):
-            data = data_list[index]
-            intensity = data.Intensity
-            error = data.Error
-            
-            er.set_data(x, intensity, error)
-            return er,
-
-        def init_2D():
-            # initialize function for animation
-            im.set_array([])
-            return im,
-            
-        def animate_2D(index):
-            data = data_list[index]
-            intensity = data.Intensity
-
-            im.set_array(intensity.ravel())
-            return im,
-        
-        anim = animation.FuncAnimation(fig, animate_2D, #init_func=init_2D,
-                                       frames=len(data_list),
-                                       interval=period_in_ms,
-                                       blit=False, repeat=True)
-
-        #plt.draw()
-        plt.show()
-
-        # The animation doesn't play unless it is saved. Bug.
-        if "filename" in kwargs:
-            filename = kwargs["filename"]
-            if not filename.endswith(".gif"):
-                filename = filename + ".gif"
-
-            # check if imagemagick available?
-            print("Saving animation with filename : \"" + filename + "\"")
-            anim.save(filename, writer="imagemagick")
+        # check if imagemagick available?
+        print("Saving animation with filename : \"" + filename + "\"")
+        anim.save(filename, writer="imagemagick")
diff --git a/mcstasscript/tests/test_McStasPlotOptions.py b/mcstasscript/tests/test_McStasPlotOptions.py
index 790ce998..b6ed26f4 100644
--- a/mcstasscript/tests/test_McStasPlotOptions.py
+++ b/mcstasscript/tests/test_McStasPlotOptions.py
@@ -10,7 +10,8 @@ class TestMcStasPlotOptions(unittest.TestCase):
 
     def test_McStasPlotOptions_default_log(self):
         """
-        Test that newly created McStasMetaData has correct type
+        Test that newly created McStasPlotOptions log attribute
+        has correct type and default value
         """
         plot_options = McStasPlotOptions()
         self.assertIsInstance(plot_options.log, bool)
@@ -18,21 +19,96 @@ def test_McStasPlotOptions_default_log(self):
 
     def test_McStasPlotOptions_default_orders_of_mag(self):
         """
-        Test that newly created McStasMetaData has correct type
+        Test that newly created McStasPlotOptions orders_of_mag
+        has the correct default value
         """
         plot_options = McStasPlotOptions()
         self.assertEqual(plot_options.orders_of_mag, 300)
 
     def test_McStasPlotOptions_default_colormap(self):
         """
-        Test that newly created McStasMetaData has correct type
+        Test that newly created McStasPlotOptions colormap has
+        the correct default value
         """
         plot_options = McStasPlotOptions()
         self.assertIs(plot_options.colormap, "jet")
 
+    def test_McStasPlotOptions_default_show_colorbar(self):
+        """
+        Test that newly created McStasPlotOptions has correct
+        default value for show_colorbar
+        """
+        plot_options = McStasPlotOptions()
+        self.assertIs(plot_options.show_colorbar, True)
+
+    def test_McStasPlotOptions_default_cut_max(self):
+        """
+        Test that newly created McStasPlotOptions has correct
+        default value for cut_max
+        """
+        plot_options = McStasPlotOptions()
+        self.assertIs(plot_options.cut_max, 1)
+
+    def test_McStasPlotOptions_default_cut_min(self):
+        """
+        Test that newly created McStasPlotOptions has correct
+        default value for cut_min
+        """
+        plot_options = McStasPlotOptions()
+        self.assertIs(plot_options.cut_min, 0)
+
+    def test_McStasPlotOptions_default_x_axis_multiplier(self):
+        """
+        Test that newly created McStasPlotOptions has correct
+        default value for x_axis_multiplier
+        """
+        plot_options = McStasPlotOptions()
+        self.assertIs(plot_options.x_limit_multiplier, 1)
+
+    def test_McStasPlotOptions_default_y_axis_multiplier(self):
+        """
+        Test that newly created McStasPlotOptions has correct
+        default value for y_axis_multiplier
+        """
+        plot_options = McStasPlotOptions()
+        self.assertIs(plot_options.y_limit_multiplier, 1)
+
+    def test_McStasPlotOptions_default_top_lim(self):
+        """
+        Test that newly created McStasPlotOptions has correct
+        default value for top_lim
+        """
+        plot_options = McStasPlotOptions()
+        self.assertIs(plot_options.custom_ylim_top, False)
+
+    def test_McStasPlotOptions_default_bottom_lim(self):
+        """
+        Test that newly created McStasPlotOptions has correct
+        default value for left_lim
+        """
+        plot_options = McStasPlotOptions()
+        self.assertIs(plot_options.custom_ylim_bottom, False)
+
+    def test_McStasPlotOptions_default_left_lim(self):
+        """
+        Test that newly created McStasPlotOptions has correct
+        default value for left_lim
+        """
+        plot_options = McStasPlotOptions()
+        self.assertIs(plot_options.custom_xlim_left, False)
+
+    def test_McStasPlotOptions_default_right_lim(self):
+        """
+        Test that newly created McStasPlotOptions has correct
+        default value for right_lim
+        """
+        plot_options = McStasPlotOptions()
+        self.assertIs(plot_options.custom_xlim_right, False)
+
     def test_McStasPlotOptions_set_log(self):
         """
-        Test that newly created McStasMetaData has correct type
+        Test that set_options works on log parameter which
+        can be set both with an integer and a bool.
         """
         plot_options = McStasPlotOptions()
         plot_options.set_options(log=True)
@@ -49,7 +125,8 @@ def test_McStasPlotOptions_set_log(self):
 
     def test_McStasPlotOptions_set_orders_of_mag(self):
         """
-        Test that newly created McStasMetaData has correct type
+        Check that set_options works with orders_of_mag keyword
+        argument
         """
         plot_options = McStasPlotOptions()
         plot_options.set_options(orders_of_mag=5.2)
@@ -57,12 +134,94 @@ def test_McStasPlotOptions_set_orders_of_mag(self):
 
     def test_McStasPlotOptions_set_colormap(self):
         """
-        Test that newly created McStasMetaData has correct type
+        Check that set_options work with colormap keyword argument
         """
         plot_options = McStasPlotOptions()
         plot_options.set_options(colormap="hot")
         self.assertIs(plot_options.colormap, "hot")
 
+    def test_McStasPlotOptions_set_show_colorbar(self):
+        """
+        Check that set_options work with show_colormap keyword
+        argument
+        """
+        plot_options = McStasPlotOptions()
+        plot_options.set_options(show_colorbar=False)
+        self.assertIs(plot_options.show_colorbar, False)
+
+    def test_McStasPlotOptions_set_cut_max(self):
+        """
+        Check that set_options work with cut_max keyword
+        argument
+        """
+        plot_options = McStasPlotOptions()
+        plot_options.set_options(cut_max=0.8)
+        self.assertIs(plot_options.cut_max, 0.8)
+
+    def test_McStasPlotOptions_set_cut_min(self):
+        """
+        Check that set_options work with cut_min keyword
+        argument
+        """
+        plot_options = McStasPlotOptions()
+        plot_options.set_options(cut_min=0.2)
+        self.assertIs(plot_options.cut_min, 0.2)
+
+    def test_McStasPlotOptions_set_x_axis_multiplier(self):
+        """
+        Check that set_options work with x_axis_multiplier
+        keyword argument
+        """
+        plot_options = McStasPlotOptions()
+        plot_options.set_options(x_axis_multiplier=2.8)
+        self.assertIs(plot_options.x_limit_multiplier, 2.8)
+
+    def test_McStasPlotOptions_set_y_axis_multiplier(self):
+        """
+        Check that set_options work with y_axis_multiplier
+        keyword argument
+        """
+        plot_options = McStasPlotOptions()
+        plot_options.set_options(y_axis_multiplier=0.1)
+        self.assertIs(plot_options.y_limit_multiplier, 0.1)
+
+    def test_McStasPlotOptions_set_top_lim(self):
+        """
+        Check that set_options work with top_lim keyword argument
+        """
+        plot_options = McStasPlotOptions()
+        plot_options.set_options(top_lim=128.9)
+        self.assertIs(plot_options.custom_ylim_top, True)
+        self.assertIs(plot_options.top_lim, 128.9)
+
+    def test_McStasPlotOptions_set_bottom_lim(self):
+        """
+        Check that set_options work with bottom_lim keyword
+        argument
+        """
+        plot_options = McStasPlotOptions()
+        plot_options.set_options(bottom_lim=120.9)
+        self.assertIs(plot_options.custom_ylim_bottom, True)
+        self.assertIs(plot_options.bottom_lim, 120.9)
+
+    def test_McStasPlotOptions_set_left_lim(self):
+        """
+        Check that set_options work with left_lim keyword argument
+        """
+        plot_options = McStasPlotOptions()
+        plot_options.set_options(left_lim=9.2)
+        self.assertIs(plot_options.custom_xlim_left, True)
+        self.assertIs(plot_options.left_lim, 9.2)
+
+    def test_McStasPlotOptions_set_right_lim(self):
+        """
+        Check that set_options work with right_lim keyword argument
+        """
+        plot_options = McStasPlotOptions()
+        plot_options.set_options(right_lim=1.4)
+        self.assertIs(plot_options.custom_xlim_right, True)
+        self.assertIs(plot_options.right_lim, 1.4)
+
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/mcstasscript/tests/test_Plotter.py b/mcstasscript/tests/test_Plotter.py
new file mode 100644
index 00000000..ac6a4092
--- /dev/null
+++ b/mcstasscript/tests/test_Plotter.py
@@ -0,0 +1,610 @@
+import unittest
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from mcstasscript.data.data import McStasData
+from mcstasscript.data.data import McStasMetaData
+from mcstasscript.interface.plotter import _find_min_max_I
+from mcstasscript.interface.plotter import _handle_kwargs
+from mcstasscript.interface.plotter import _plot_fig_ax
+
+
+def get_dummy_MetaData_1d():
+    meta_data = McStasMetaData()
+    meta_data.component_name = "component for 1d"
+    meta_data.dimension = 50
+    meta_data.limits = [0.1, 1.1]
+    meta_data.title = "test"
+    meta_data.xlabel = "test x"
+    meta_data.ylabel = "test y"
+
+    return meta_data
+
+def get_dummy_McStasData_1d():
+    meta_data = get_dummy_MetaData_1d()
+
+    intensity = np.arange(20) + 5
+    error = 0.5 * np.arange(20)
+    ncount = 2 * np.arange(20)
+    axis = np.arange(20)*5.0
+
+    return McStasData(meta_data, intensity, error, ncount, xaxis=axis)
+
+def get_dummy_MetaData_2d():
+    meta_data = McStasMetaData()
+    meta_data.component_name = "test a component"
+    meta_data.dimension = [5, 4]
+    meta_data.limits = [0.1, 1.1, 2.0, 4.0]
+    meta_data.title = "test"
+    meta_data.xlabel = "test x"
+    meta_data.ylabel = "test y"
+
+    return meta_data
+
+def get_dummy_McStasData_2d():
+    meta_data = get_dummy_MetaData_2d()
+
+    intensity = np.arange(20).reshape(4, 5) + 5
+    error = 0.5 * np.arange(20).reshape(4, 5)
+    ncount = 2 * np.arange(20).reshape(4, 5)
+
+    return McStasData(meta_data, intensity, error, ncount)
+
+
+class TestPlotterHelpers(unittest.TestCase):
+    """
+    Tests of plotter help functions
+    """
+
+    def test_find_min_max_I_simple_1D_case(self):
+        """
+        test _find_min_max_I for a 1D case, it finds the minimum
+        and maximum value to plot for a given McStasData set
+        """
+
+        dummy_data = get_dummy_McStasData_1d()
+        found_min, found_max = _find_min_max_I(dummy_data)
+
+        # np.arange(20) + 5: min = 5, max = 5+19 = 24
+        self.assertEqual(found_min, 5)
+        self.assertEqual(found_max, 19 + 5)
+
+    def test_find_min_max_I_cut_max_1D_case(self):
+        """
+        test _find_min_max_I for a 1D case, it finds the minimum
+        and maximum value to plot for a given McStasData set.
+        Here cut_max is used to limit the maximum plotted.
+        """
+
+        dummy_data = get_dummy_McStasData_1d()
+        dummy_data.set_plot_options(cut_max=0.8)
+        found_min, found_max = _find_min_max_I(dummy_data)
+
+        # np.arange(20) + 5: min = 5, max = 5+19 = 24
+        self.assertEqual(found_min, 5)
+        self.assertEqual(found_max, (19 + 5)*0.8)
+
+    def test_find_min_max_I_cut_min_1D_case(self):
+        """
+        test _find_min_max_I for a 1D case, it finds the minimum
+        and maximum value to plot for a given McStasData set.
+        Here cut_min is used to limit the minimum plotted.
+        """
+
+        dummy_data = get_dummy_McStasData_1d()
+        dummy_data.set_plot_options(cut_min=0.2)
+        found_min, found_max = _find_min_max_I(dummy_data)
+
+        # np.arange(20) + 5: min = 5, max = 5+19 = 24
+        self.assertEqual(found_min, 5 + (24-5)*0.2)
+        self.assertEqual(found_max, 19 + 5)
+
+    def test_find_min_max_I_log_with_zero_case(self):
+        """
+        test _find_min_max_I for a 1D case, it finds the minimum
+        and maximum value to plot for a given McStasData set.
+        Here a bin contains zero intensity and log mode is enabled,
+        since log(0) is not allowed, this data point should be
+        ignored.
+        """
+
+        dummy_data = get_dummy_McStasData_1d()
+        dummy_data.Intensity[5] = 0
+        dummy_data.set_plot_options(log=True)
+        found_min, found_max = _find_min_max_I(dummy_data)
+
+        # np.arange(20) + 5: min = 5, max = 5+19 = 24
+        self.assertAlmostEqual(found_min, 5)
+        self.assertAlmostEqual(found_max, 19 + 5)
+
+    def test_find_min_max_I_log_cut_max_1D_case(self):
+        """
+        test _find_min_max_I for a 1D case, it finds the minimum
+        and maximum value to plot for a given McStasData set.
+        Here cut_max is used to limit the maximum plotted while
+        log mode is enabled.
+        """
+
+        dummy_data = get_dummy_McStasData_1d()
+        dummy_data.set_plot_options(cut_max=0.8, log=True)
+        found_min, found_max = _find_min_max_I(dummy_data)
+
+        # np.arange(20) + 5: min = 5, max = 5+19 = 24
+        self.assertAlmostEqual(found_min, 5)
+        self.assertAlmostEqual(found_max, (19 + 5)*0.8)
+
+    def test_find_min_max_I_log_cut_min_1D_case(self):
+        """
+        test _find_min_max_I for a 1D case, it finds the minimum
+        and maximum value to plot for a given McStasData set.
+        Here cut_min is used to limit the minimum plotted while
+        log mode is enabled.
+        """
+
+        dummy_data = get_dummy_McStasData_1d()
+        dummy_data.set_plot_options(cut_min=0.2, log=True)
+        found_min, found_max = _find_min_max_I(dummy_data)
+
+        # np.arange(20) + 5: min = 5, max = 5+19 = 24
+        self.assertAlmostEqual(found_min, 5 + (24-5)*0.2)
+        self.assertAlmostEqual(found_max, 19 + 5)
+
+    def test_find_min_max_I_log_orders_of_mag_1D_case(self):
+        """
+        test _find_min_max_I for a 1D case, it finds the minimum
+        and maximum value to plot for a given McStasData set.
+        Here orders_of_mag is used to limit the minimum plotted
+        while log mode is enabled.
+        """
+
+        dummy_data = get_dummy_McStasData_1d()
+        dummy_data.Intensity[5] = 10**6
+        dummy_data.set_plot_options(log=True, orders_of_mag=3)
+        found_min, found_max = _find_min_max_I(dummy_data)
+
+        self.assertAlmostEqual(found_min, 10**3)
+        self.assertAlmostEqual(found_max, 10**6)
+
+    def test_find_min_max_I_log_orders_of_mag_1D_with_zero_case(self):
+        """
+        test _find_min_max_I for a 1D case, it finds the minimum
+        and maximum value to plot for a given McStasData set.
+        Here orders_of_mag is used to limit the minimum plotted
+        while log mode is enabled. A bin in the data contains
+        zero intensity, which should be ignored.
+        """
+
+        dummy_data = get_dummy_McStasData_1d()
+        dummy_data.Intensity[5] = 10**6
+        dummy_data.Intensity[6] = 0
+        dummy_data.set_plot_options(log=True, orders_of_mag=3)
+        found_min, found_max = _find_min_max_I(dummy_data)
+
+        self.assertAlmostEqual(found_min, 10**3)
+        self.assertAlmostEqual(found_max, 10**6)
+
+    def test_find_min_max_I_simple_2D_case(self):
+        """
+        test _find_min_max_I for a 1D case, it finds the minimum
+        and maximum value to plot for a given McStasData set
+        """
+
+        dummy_data = get_dummy_McStasData_2d()
+        found_min, found_max = _find_min_max_I(dummy_data)
+
+        # np.arange(20) + 5: min = 5, max = 5+19 = 24
+        self.assertEqual(found_min, 5)
+        self.assertEqual(found_max, 19 + 5)
+
+    def test_find_min_max_I_cut_max_2D_case(self):
+        """
+        test _find_min_max_I for a 1D case, it finds the minimum
+        and maximum value to plot for a given McStasData set.
+        Here cut_max is used to limit the maximum plotted.
+        """
+
+        dummy_data = get_dummy_McStasData_2d()
+        dummy_data.set_plot_options(cut_max=0.8)
+        found_min, found_max = _find_min_max_I(dummy_data)
+
+        # np.arange(20) + 5: min = 5, max = 5+19 = 24
+        self.assertEqual(found_min, 5)
+        self.assertEqual(found_max, (19 + 5)*0.8)
+
+    def test_find_min_max_I_cut_min_2D_case(self):
+        """
+        test _find_min_max_I for a 1D case, it finds the minimum
+        and maximum value to plot for a given McStasData set.
+        Here cut_min is used to limit the minimum plotted.
+        """
+
+        dummy_data = get_dummy_McStasData_2d()
+        dummy_data.set_plot_options(cut_min=0.2)
+        found_min, found_max = _find_min_max_I(dummy_data)
+
+        # np.arange(20) + 5: min = 5, max = 5+19 = 24
+        self.assertEqual(found_min, 5 + (24-5)*0.2)
+        self.assertEqual(found_max, 19 + 5)
+
+    def test_find_min_max_I_log_with_zero_2D_case(self):
+        """
+        test _find_min_max_I for a 1D case, it finds the minimum
+        and maximum value to plot for a given McStasData set.
+        Here a bin contains zero intensity and log mode is enabled,
+        since log(0) is not allowed, this data point should be
+        ignored.
+        """
+
+        dummy_data = get_dummy_McStasData_2d()
+        dummy_data.Intensity[2,2] = 0
+        dummy_data.set_plot_options(log=True)
+        found_min, found_max = _find_min_max_I(dummy_data)
+
+        # np.arange(20) + 5: min = 5, max = 5+19 = 24
+        self.assertAlmostEqual(found_min, 5)
+        self.assertAlmostEqual(found_max, 19 + 5)
+
+    def test_find_min_max_I_log_cut_max_2D_case(self):
+        """
+        test _find_min_max_I for a 1D case, it finds the minimum
+        and maximum value to plot for a given McStasData set.
+        Here cut_max is used to limit the maximum plotted while
+        log mode is enabled.
+        """
+
+        dummy_data = get_dummy_McStasData_2d()
+        dummy_data.set_plot_options(cut_max=0.8, log=True)
+        found_min, found_max = _find_min_max_I(dummy_data)
+
+        # np.arange(20) + 5: min = 5, max = 5+19 = 24
+        self.assertAlmostEqual(found_min, 5)
+        self.assertAlmostEqual(found_max, (19 + 5)*0.8)
+
+    def test_find_min_max_I_log_cut_min_2D_case(self):
+        """
+        test _find_min_max_I for a 1D case, it finds the minimum
+        and maximum value to plot for a given McStasData set.
+        Here cut_min is used to limit the minimum plotted while
+        log mode is enabled.
+        """
+
+        dummy_data = get_dummy_McStasData_2d()
+        dummy_data.set_plot_options(cut_min=0.2, log=True)
+        found_min, found_max = _find_min_max_I(dummy_data)
+
+        # np.arange(20) + 5: min = 5, max = 5+19 = 24
+        self.assertAlmostEqual(found_min, 5 + (24-5)*0.2)
+        self.assertAlmostEqual(found_max, 19 + 5)
+
+    def test_find_min_max_I_log_orders_of_mag_2D_case(self):
+        """
+        test _find_min_max_I for a 1D case, it finds the minimum
+        and maximum value to plot for a given McStasData set.
+        Here orders_of_mag is used to limit the minimum plotted
+        while log mode is enabled.
+        """
+
+        dummy_data = get_dummy_McStasData_2d()
+        dummy_data.Intensity[2,2] = 10**6
+        dummy_data.set_plot_options(log=True, orders_of_mag=3)
+        found_min, found_max = _find_min_max_I(dummy_data)
+
+        self.assertAlmostEqual(found_min, 10**3)
+        self.assertAlmostEqual(found_max, 10**6)
+
+    def test_find_min_max_I_log_orders_of_mag_2D_with_zero_case(self):
+        """
+        test _find_min_max_I for a 1D case, it finds the minimum
+        and maximum value to plot for a given McStasData set.
+        Here orders_of_mag is used to limit the minimum plotted
+        while log mode is enabled. A bin in the data contains
+        zero intensity, which should be ignored.
+        """
+
+        dummy_data = get_dummy_McStasData_2d()
+        dummy_data.Intensity[2,2] = 10**6
+        dummy_data.Intensity[2,3] = 0
+        dummy_data.set_plot_options(log=True, orders_of_mag=3)
+        found_min, found_max = _find_min_max_I(dummy_data)
+
+        self.assertAlmostEqual(found_min, 10**3)
+        self.assertAlmostEqual(found_max, 10**6)
+
+    def test_find_min_max_I_fail_case(self):
+        """
+        test _find_min_max_I for a 1D case, it finds the minimum
+        and maximum value to plot for a given McStasData set.
+        Here orders_of_mag is used to limit the minimum plotted
+        while log mode is enabled. A bin in the data contains
+        zero intensity, which should be ignored.
+        """
+
+        dummy_data = get_dummy_McStasData_2d()
+        dummy_data.Intensity = np.zeros((5, 5))
+        dummy_data.set_plot_options(log=True, orders_of_mag=3)
+        found_min, found_max = _find_min_max_I(dummy_data)
+
+        self.assertEqual(found_min, 0)
+        self.assertEqual(found_max, 0)
+
+    def test_handle_kwargs_log(self):
+        """
+        Tests handle_kwargs with log option
+
+        Keyword args can be set for all by normal use, or individual
+        data sets by using a list. Both are checked here.
+        """
+        dummy_data1 = get_dummy_McStasData_2d()
+        dummy_data2 = get_dummy_McStasData_2d()
+        self.assertEqual(dummy_data1.plot_options.log, False)
+        self.assertEqual(dummy_data2.plot_options.log, False)
+
+        data_list = [dummy_data1, dummy_data2]
+        _handle_kwargs(data_list, log=True)
+        self.assertEqual(dummy_data1.plot_options.log, True)
+        self.assertEqual(dummy_data2.plot_options.log, True)
+
+        _handle_kwargs(data_list, log=[False, True])
+        self.assertEqual(dummy_data1.plot_options.log, False)
+        self.assertEqual(dummy_data2.plot_options.log, True)
+
+    def test_handle_kwargs_oders_of_mag(self):
+        """
+        Tests handle_kwargs with orders_of_mag option
+
+        Keyword args can be set for all by normal use, or individual
+        data sets by using a list. Both are checked here.
+        """
+        dummy_data1 = get_dummy_McStasData_2d()
+        dummy_data2 = get_dummy_McStasData_2d()
+        self.assertEqual(dummy_data1.plot_options.orders_of_mag, 300)
+        self.assertEqual(dummy_data2.plot_options.orders_of_mag, 300)
+
+        data_list = [dummy_data1, dummy_data2]
+        _handle_kwargs(data_list, orders_of_mag=12)
+        self.assertEqual(dummy_data1.plot_options.orders_of_mag, 12)
+        self.assertEqual(dummy_data2.plot_options.orders_of_mag, 12)
+
+        _handle_kwargs(data_list, orders_of_mag=[50, 10])
+        self.assertEqual(dummy_data1.plot_options.orders_of_mag, 50)
+        self.assertEqual(dummy_data2.plot_options.orders_of_mag, 10)
+
+    def test_handle_kwargs_all_simple(self):
+        """
+        Tests handle_kwargs with all simple options option
+
+        Keyword args can be set for all by normal use, or individual
+        data sets by using a list. Both are checked here.
+        """
+
+        known_plot = ["log", "orders_of_mag",
+                      "cut_min", "cut_max",
+                      "colormap", "show_colorbar",
+                      "x_axis_multiplier",
+                      "y_axis_multiplier"]
+
+        kwargs_to_attr = {"x_axis_multiplier": "x_limit_multiplier",
+                          "y_axis_multiplier": "y_limit_multiplier"}
+
+        defaults = {"log": False, "orders_of_mag": 300,
+                    "cut_min": 0, "cut_max": 1,
+                    "colormap": "jet", "show_colorbar": True,
+                    "x_limit_multiplier": 1, "y_limit_multiplier": 1}
+
+        test_value = {"log": True, "orders_of_mag": 15,
+                      "cut_min": 0.25, "cut_max": 0.8,
+                      "colormap": "hot", "show_colorbar": "False",
+                      "x_limit_multiplier": 2.8, "y_limit_multiplier": 0.8}
+
+        for option in known_plot:
+
+            if option in kwargs_to_attr:
+                kw_option = kwargs_to_attr[option]
+            else:
+                kw_option = option
+
+            default_value = defaults[kw_option]
+
+            dummy_data1 = get_dummy_McStasData_2d()
+            dummy_data2 = get_dummy_McStasData_2d()
+
+            self.assertEqual(dummy_data1.plot_options.__getattribute__(kw_option), default_value)
+            self.assertEqual(dummy_data2.plot_options.__getattribute__(kw_option), default_value)
+
+            data_list = [dummy_data1, dummy_data2]
+
+            set_value = test_value[kw_option]
+            given_option = {option: set_value}
+            _handle_kwargs(data_list, **given_option)
+            self.assertEqual(dummy_data1.plot_options.__getattribute__(kw_option), set_value)
+            self.assertEqual(dummy_data2.plot_options.__getattribute__(kw_option), set_value)
+
+            given_option = {option: [set_value, default_value]}
+            _handle_kwargs(data_list, **given_option)
+            self.assertEqual(dummy_data1.plot_options.__getattribute__(kw_option), set_value)
+            self.assertEqual(dummy_data2.plot_options.__getattribute__(kw_option), default_value)
+
+    def test_handle_kwargs_left_lim(self):
+        """
+        Tests handle_kwargs with left_lim option
+
+        Keyword args can be set for all by normal use, or individual
+        data sets by using a list. Both are checked here.
+        """
+        dummy_data1 = get_dummy_McStasData_2d()
+        dummy_data2 = get_dummy_McStasData_2d()
+        self.assertEqual(dummy_data1.plot_options.custom_xlim_left, False)
+        self.assertEqual(dummy_data2.plot_options.custom_xlim_left, False)
+
+        data_list = [dummy_data1, dummy_data2]
+        _handle_kwargs(data_list, left_lim=0.08)
+        self.assertEqual(dummy_data1.plot_options.left_lim, 0.08)
+        self.assertEqual(dummy_data2.plot_options.left_lim, 0.08)
+        self.assertEqual(dummy_data1.plot_options.custom_xlim_left, True)
+        self.assertEqual(dummy_data2.plot_options.custom_xlim_left, True)
+
+        _handle_kwargs(data_list, left_lim=[0.08, 1.08])
+        self.assertEqual(dummy_data1.plot_options.left_lim, 0.08)
+        self.assertEqual(dummy_data2.plot_options.left_lim, 1.08)
+        self.assertEqual(dummy_data1.plot_options.custom_xlim_left, True)
+        self.assertEqual(dummy_data2.plot_options.custom_xlim_left, True)
+
+    def test_handle_kwargs_right_lim(self):
+        """
+        Tests handle_kwargs with right_lim option
+
+        Keyword args can be set for all by normal use, or individual
+        data sets by using a list. Both are checked here.
+        """
+        dummy_data1 = get_dummy_McStasData_2d()
+        dummy_data2 = get_dummy_McStasData_2d()
+        self.assertEqual(dummy_data1.plot_options.custom_xlim_right, False)
+        self.assertEqual(dummy_data2.plot_options.custom_xlim_right, False)
+
+        data_list = [dummy_data1, dummy_data2]
+        _handle_kwargs(data_list, right_lim=0.08)
+        self.assertEqual(dummy_data1.plot_options.right_lim, 0.08)
+        self.assertEqual(dummy_data2.plot_options.right_lim, 0.08)
+        self.assertEqual(dummy_data1.plot_options.custom_xlim_right, True)
+        self.assertEqual(dummy_data2.plot_options.custom_xlim_right, True)
+
+        _handle_kwargs(data_list, right_lim=[0.08, 1.08])
+        self.assertEqual(dummy_data1.plot_options.right_lim, 0.08)
+        self.assertEqual(dummy_data2.plot_options.right_lim, 1.08)
+        self.assertEqual(dummy_data1.plot_options.custom_xlim_right, True)
+        self.assertEqual(dummy_data2.plot_options.custom_xlim_right, True)
+
+    def test_handle_kwargs_top_lim(self):
+        """
+        Tests handle_kwargs with top_lim option
+
+        Keyword args can be set for all by normal use, or individual
+        data sets by using a list. Both are checked here.
+        """
+        dummy_data1 = get_dummy_McStasData_2d()
+        dummy_data2 = get_dummy_McStasData_2d()
+        self.assertEqual(dummy_data1.plot_options.custom_ylim_top, False)
+        self.assertEqual(dummy_data2.plot_options.custom_ylim_top, False)
+
+        data_list = [dummy_data1, dummy_data2]
+        _handle_kwargs(data_list, top_lim=0.08)
+        self.assertEqual(dummy_data1.plot_options.top_lim, 0.08)
+        self.assertEqual(dummy_data2.plot_options.top_lim, 0.08)
+        self.assertEqual(dummy_data1.plot_options.custom_ylim_top, True)
+        self.assertEqual(dummy_data2.plot_options.custom_ylim_top, True)
+
+        _handle_kwargs(data_list, top_lim=[0.08, 1.08])
+        self.assertEqual(dummy_data1.plot_options.top_lim, 0.08)
+        self.assertEqual(dummy_data2.plot_options.top_lim, 1.08)
+        self.assertEqual(dummy_data1.plot_options.custom_ylim_top, True)
+        self.assertEqual(dummy_data2.plot_options.custom_ylim_top, True)
+
+    def test_handle_kwargs_bottom_lim(self):
+        """
+        Tests handle_kwargs with bottom_lim option
+
+        Keyword args can be set for all by normal use, or individual
+        data sets by using a list. Both are checked here.
+        """
+        dummy_data1 = get_dummy_McStasData_2d()
+        dummy_data2 = get_dummy_McStasData_2d()
+        self.assertEqual(dummy_data1.plot_options.custom_ylim_bottom, False)
+        self.assertEqual(dummy_data2.plot_options.custom_ylim_bottom, False)
+
+        data_list = [dummy_data1, dummy_data2]
+        _handle_kwargs(data_list, bottom_lim=0.08)
+        self.assertEqual(dummy_data1.plot_options.bottom_lim, 0.08)
+        self.assertEqual(dummy_data2.plot_options.bottom_lim, 0.08)
+        self.assertEqual(dummy_data1.plot_options.custom_ylim_bottom, True)
+        self.assertEqual(dummy_data2.plot_options.custom_ylim_bottom, True)
+
+        _handle_kwargs(data_list, bottom_lim=[0.08, 1.08])
+        self.assertEqual(dummy_data1.plot_options.bottom_lim, 0.08)
+        self.assertEqual(dummy_data2.plot_options.bottom_lim, 1.08)
+        self.assertEqual(dummy_data1.plot_options.custom_ylim_bottom, True)
+        self.assertEqual(dummy_data2.plot_options.custom_ylim_bottom, True)
+
+    def test_handle_kwargs_figsize_default(self):
+        """
+        Tests handle_kwargs delivers default figsize
+        """
+
+        dummy_data = get_dummy_McStasData_2d()
+        retrived_figsize, data_list = _handle_kwargs(dummy_data)
+        self.assertEqual(retrived_figsize, (13, 7))
+
+    def test_handle_kwargs_figsize_tuple(self):
+        """
+        Tests handle_kwargs with figsize keyword argument, here
+        using tuple as input
+        """
+
+        dummy_data = get_dummy_McStasData_2d()
+        retrived_figsize, data_list = _handle_kwargs(dummy_data, figsize=(5,9))
+        self.assertEqual(retrived_figsize, (5, 9))
+
+    def test_handle_kwargs_figsize_list(self):
+        """
+        Tests handle_kwargs with figsize keyword argument, here
+        using tuple as input
+        """
+
+        dummy_data = get_dummy_McStasData_2d()
+        retrived_figsize, data_list = _handle_kwargs(dummy_data, figsize=[5,9])
+        self.assertEqual(retrived_figsize, (5, 9))
+
+    def test_handle_kwargs_single_element_to_list(self):
+        """
+        Test handle_kwargs will grab a single McStasData element
+        and turn it into a list.
+        """
+
+        dummy_data = get_dummy_McStasData_2d()
+        self.assertFalse(isinstance(dummy_data, list))
+        figsize, data_list = _handle_kwargs(dummy_data)
+        self.assertTrue(isinstance(data_list, list))
+
+    def test_plot_function_1D_normal(self):
+        """
+        Run the plot function with 1D data set without showing the
+        result.
+
+        """
+        dummy_data = get_dummy_McStasData_1d()
+
+        fig, ax0 = plt.subplots()
+        _plot_fig_ax(dummy_data, fig, ax0)
+
+    def test_plot_function_1D_log(self):
+        """
+        Run the plot function with 1D data set without showing the
+        result. Here with logarithmic y axis.
+
+        """
+        dummy_data = get_dummy_McStasData_1d()
+
+        fig, ax0 = plt.subplots()
+        _plot_fig_ax(dummy_data, fig, ax0, log=True)
+
+    def test_plot_function_2D_normal(self):
+        """
+        Run the plot function with 2D data set without showing the
+        result.
+
+        """
+        dummy_data = get_dummy_McStasData_2d()
+
+        fig, ax0 = plt.subplots()
+        _plot_fig_ax(dummy_data, fig, ax0)
+
+    def test_plot_function_2D_log(self):
+        """
+        Run the plot function with 2D data set without showing the
+        result. Here the intensity coloraxis is logarithmic.
+
+        """
+        dummy_data = get_dummy_McStasData_2d()
+
+        fig, ax0 = plt.subplots()
+        _plot_fig_ax(dummy_data, fig, ax0, log=True)
\ No newline at end of file

From 11c9533215a559986f3db8c6300f3a22f2b07bbf Mon Sep 17 00:00:00 2001
From: Christian Felder <c.felder@fz-juelich.de>
Date: Mon, 2 Nov 2020 14:30:29 +0100
Subject: [PATCH 113/403] Fix: do not change calling process' cwd

Changing calling process' current working directory (cwd) may lead to
unwanted side-effects. Better change the cwd on the subprocess.
---
 mcstasscript/helper/managed_mcrun.py    |  9 ++-------
 mcstasscript/tests/test_Instr.py        | 13 ++++++++++---
 mcstasscript/tests/test_ManagedMcrun.py | 15 ++++++++++-----
 3 files changed, 22 insertions(+), 15 deletions(-)

diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 545a247b..5409e944 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -186,17 +186,12 @@ def run_simulation(self, **kwargs):
                   + parameter_string)
 
         try:
-            os.chdir(self.run_path)
-
             process = subprocess.run(full_command, shell=True,
                                      stdout=subprocess.PIPE,
                                      stderr=subprocess.PIPE,
-                                     universal_newlines=True)
-
-            os.chdir(current_directory)
-
+                                     universal_newlines=True,
+                                     cwd=self.run_path)
         except:
-            os.chdir(current_directory)
             raise RuntimeError("Could not run McStas command.")
 
         if "suppress_output" in kwargs:
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index c61cc49a..f8ae0ebe 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1,4 +1,5 @@
 import os
+import os.path
 import io
 import builtins
 import unittest
@@ -9,6 +10,9 @@
 from mcstasscript.helper.formatting import bcolors
 
 
+run_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '.')
+
+
 def setup_instr_no_path():
     """
     Sets up a instrument without a mcstas_path
@@ -1519,7 +1523,8 @@ def test_run_full_instrument_basic(self, mock_sub,
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
                                          stderr=-1, stdout=-1,
-                                         universal_newlines=True)
+                                         universal_newlines=True,
+                                         cwd=run_path)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     @unittest.mock.patch('__main__.__builtins__.open',
@@ -1558,7 +1563,8 @@ def test_run_full_instrument_complex(self, mock_sub,
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
                                          stderr=-1, stdout=-1,
-                                         universal_newlines=True)
+                                         universal_newlines=True,
+                                         cwd=run_path)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     @unittest.mock.patch('__main__.__builtins__.open',
@@ -1597,7 +1603,8 @@ def test_run_full_instrument_overwrite_default(self, mock_sub,
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
                                          stderr=-1, stdout=-1,
-                                         universal_newlines=True)
+                                         universal_newlines=True,
+                                         cwd=run_path)
 
 
 if __name__ == '__main__':
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index a0d003f9..71a2538c 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -120,7 +120,8 @@ def test_ManagedMcrun_run_simulation_basic(self, mock_sub):
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
                                          stderr=-1, stdout=-1,
-                                         universal_newlines=True)
+                                         universal_newlines=True,
+                                         cwd=mcrun_obj.run_path)
 
     @unittest.mock.patch("subprocess.run")
     def test_ManagedMcrun_run_simulation_basic_path(self, mock_sub):
@@ -144,7 +145,8 @@ def test_ManagedMcrun_run_simulation_basic_path(self, mock_sub):
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
                                          stderr=-1, stdout=-1,
-                                         universal_newlines=True)
+                                         universal_newlines=True,
+                                         cwd=mcrun_obj.run_path)
 
     @unittest.mock.patch("subprocess.run")
     def test_ManagedMcrun_run_simulation_no_standard(self, mock_sub):
@@ -171,7 +173,8 @@ def test_ManagedMcrun_run_simulation_no_standard(self, mock_sub):
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
                                          stderr=-1, stdout=-1,
-                                         universal_newlines=True)
+                                         universal_newlines=True,
+                                         cwd=mcrun_obj.run_path)
 
     @unittest.mock.patch("subprocess.run")
     def test_ManagedMcrun_run_simulation_parameters(self, mock_sub):
@@ -201,7 +204,8 @@ def test_ManagedMcrun_run_simulation_parameters(self, mock_sub):
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
                                          stderr=-1, stdout=-1,
-                                         universal_newlines=True)
+                                         universal_newlines=True,
+                                         cwd=mcrun_obj.run_path)
 
     @unittest.mock.patch("subprocess.run")
     def test_ManagedMcrun_run_simulation_compile(self, mock_sub):
@@ -233,7 +237,8 @@ def test_ManagedMcrun_run_simulation_compile(self, mock_sub):
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
                                          stderr=-1, stdout=-1,
-                                         universal_newlines=True)
+                                         universal_newlines=True,
+                                         cwd=mcrun_obj.run_path)
 
     def test_ManagedMcrun_load_data_PSD4PI(self):
         """

From 664ba9e6b570ea8e9b77cebb35520a6024f4bcde Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Thu, 5 Nov 2020 16:55:43 +0100
Subject: [PATCH 114/403] Update of release number for pip update.

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 708b6649..36a797ff 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
      name='McStasScript',
-     version='0.0.21',
+     version='0.0.22',
      author="Mads Bertelsen",
      author_email="Mads.Bertelsen@ess.eu",
      description="A python scripting interface for McStas",

From e515e8d46de65ce956983a7cf1f309e3328e93bb Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Mon, 4 Jan 2021 11:09:28 +0100
Subject: [PATCH 115/403] Allowing saving of created plots instead of
 displaying them by providing a filename to the plotting functions.

When force_compile is set to False, a new instrument file will
not be written anymore to avoid compile happening because the
new instrument file is newer than the old one. May need to add
a check that ensures the instrument object is unchanged from the
previous, for example by writing a dummy instrument file and
ensuring they differ only in generated time.
---
 mcstasscript/interface/instr.py   |  7 +++++--
 mcstasscript/interface/plotter.py | 17 +++++++++++++++++
 setup.py                          |  2 +-
 3 files changed, 23 insertions(+), 3 deletions(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index f27c3f1f..9f835877 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -395,7 +395,6 @@ def append_declare(self, string):
         
         #self.declare_section = self.declare_section + string + "\n"
         self.declare_list.append(string)
-        
 
     def append_initialize(self, string):
         """
@@ -1511,7 +1510,11 @@ def run_full_instrument(self, *args, **kwargs):
         kwargs["parameters"] = self._handle_parameters(given_parameters)
 
         # Write the instrument file
-        self.write_full_instrument()
+        compile = True
+        if "force_compile" in kwargs:
+            compile = kwargs["force_compile"]
+        if compile:
+            self.write_full_instrument()
 
         # Set up the simulation
         simulation = ManagedMcrun(self.name + ".instr", **kwargs)
diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index a04db47f..861fe224 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -274,6 +274,11 @@ def make_plot(data_list, **kwargs):
         fig, ax0 = plt.subplots(figsize=figsize)
         _plot_fig_ax(data, fig, ax0, **kwargs)
 
+    if "filename" in kwargs:
+        fig.savefig(kwargs["filename"])
+    else:
+        plt.show()
+
 def make_sub_plot(data_list, **kwargs):
     """
     make_sub_plot plots contents of McStasData objects given in list
@@ -301,6 +306,11 @@ def make_sub_plot(data_list, **kwargs):
 
     fig.tight_layout()
 
+    if "filename" in kwargs:
+        fig.savefig(kwargs["filename"])
+    else:
+        plt.show()
+
 
 def make_animation(data_list, **kwargs):
     """
@@ -360,6 +370,13 @@ def make_animation(data_list, **kwargs):
     maximum_value = np.array(maximum_values).max()
     minimum_value = np.array(minimum_values).min()
 
+    if "orders_of_mag" in kwargs:
+        orders_of_mag = kwargs["orders_of_mag"]
+        mag_diff = np.log10(maximum_value) - np.log10(minimum_value)
+        if mag_diff > orders_of_mag:
+            minimum_value_log10 = np.log10(maximum_value) - orders_of_mag
+            minimum_value = 10**(minimum_value_log10)
+
     kwargs["fixed_minimum_value"] = minimum_value
     kwargs["fixed_maximum_value"] = maximum_value
 
diff --git a/setup.py b/setup.py
index 36a797ff..3a91dddb 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
      name='McStasScript',
-     version='0.0.22',
+     version='0.0.23',
      author="Mads Bertelsen",
      author_email="Mads.Bertelsen@ess.eu",
      description="A python scripting interface for McStas",

From 289d8b7686b73d1939b0b50332979506cff9d590 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Mon, 4 Jan 2021 14:04:50 +0100
Subject: [PATCH 116/403] Fixed broken test after merge with McXtrace

---
 mcstasscript/tests/test_Instr.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 5f13574e..92f2ade6 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1589,8 +1589,11 @@ def test_x_ray_run_full_instrument_basic(self, mock_sub,
                          + "  test_instrument.instr"
                          + " has_default=37 theta=1")
 
+        expected_run_path = os.path.join(current_directory, ".")
+
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
+                                         cwd=expected_run_path,
                                          stderr=-1, stdout=-1,
                                          universal_newlines=True)
 

From c29dfb401ee1b417822a9886128d0f00af97ee96 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Mon, 4 Jan 2021 14:34:59 +0100
Subject: [PATCH 117/403] Updated manual and version number. McXtrace support
 now mentioned.

---
 McStasScript_documentation.pdf | Bin 181093 -> 184620 bytes
 setup.py                       |   2 +-
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
index 560e48c21c960e615c4b22d7a711dbb2be5fc970..f1c876a952f59cac5590f349e98822e7ef376ef0 100644
GIT binary patch
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zzm-mPcJ(~Vs@WzY--Wfo5I3y6M&WeOS2lmHC>IpnwCl+zmIF4Lkb%o!-oR4lNH31H
z3)zGYp{rb4X=_PE37n9$a-?W~A20Qsz5|{u>J=|_BjY&t`7ZS^$*$O;@l^hoZ6|Dl
zk)9ZA#GAZw(;JI`elw(XTvZR<V<qogH=-<c=cNR?-tV&2KO?xemjALDP_8%G!LNn6
zy>eau>PZ)Rx#pfs=p7Y79?orNWyj@a0&<?!X<``89=F09Q#T2r5;D7g1$Io5L&r95
zd;c0Gso%<lOo^T+67x`9t&2L_dlJ(<l$}GX4@Smg$ysObm`_O&G6dP)scO%bLQmvZ
z`vFWt!n^XhG;fs_?78_Ks^)-R<Ol$yRPoTUY-5>GzcbCwB7)?;v{nv=uLzh-ud*-Q
z&g64P2(;Q^@o-{I9}rxBEfvb1>ky~|wY1mPABkPu)H9IQ7k3IGr$8;>MTHd$kCK$W
zU>}h0w5&A?+#<gYqNs2(9SI?2o#k^7bn#UFWy%ct6Rrqoox8U?s`cno7N@AvOjm<v
z<_^!U8ajRu-QGR@#lS`GHo$@2s-A9}FjXCf%QdgKAa8gltZ6z%V@@7m&a_1p)$J*F
z(-F{NODgrm+gJL`3YAn;B%q4vUauP|AE^KL9<(=Fx|N-PIw}ffZe(+Ga%Ev{3T19&
zZ(?c+GcuRS%K|A2FflMPH8C(UI5aeu{mTMI3otP-GBq(UGB`9emrl$Ax{=x+mnY2v
zT7NJgFG)loTRb&nVPRo3G-EX|VKO!}WnnR9IWlH8He+F9IWuN3H8?&zIAvxxWMVgC
zVlp^2VKF&5Hexh3Vqq~gWHw?sGB-0YK3yO_J_>Vma%Ev{3V57jVgLaqM#lQT3``6R
zjHMt_@Cb-x0JDV-AlQ6sz-mfBq{u;pdQSeyV0IaZ6yOD`F9(sl)j*O_IteTb77;c?
z$O^tgi1VI7u=!1p#Q72GIaLtiY+4|a73>~QX0W&e&=>z4FM-uFFpiR=B&j3-f_W5#
Ow@%Iiy#kjP(E=5?gLer4

diff --git a/setup.py b/setup.py
index 3a91dddb..69d61eea 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
      name='McStasScript',
-     version='0.0.23',
+     version='0.0.24',
      author="Mads Bertelsen",
      author_email="Mads.Bertelsen@ess.eu",
      description="A python scripting interface for McStas",

From 5a9819a63aa347f2ed4359d0663f01920d37bab4 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Mon, 4 Jan 2021 15:49:15 +0100
Subject: [PATCH 118/403] Created CONTRIBUTING.md

Quick description on guidelines for contributing to McStasScript.
---
 CONTRIBUTING.md | 11 +++++++++++
 1 file changed, 11 insertions(+)
 create mode 100644 CONTRIBUTING.md

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 00000000..a75c1504
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,11 @@
+# Guidelines for contributing to McStasScript
+
+This document describes how to contribute to McStasScript. The preferred method is to open a pull request on the GitHub page. The list below act as a checklist of things to consider before creating the pull request. Since the manual is currently on overleaf, there is no way to directly contribute, so a note on the necessary updates to the manual is appreciated.
+
+* Provide docstrings to new functions / classes
+* Update docstrings as necessary
+* Adhere to PEP-8 where reasonable
+* Include a unit test for new functionality
+* Note for updates required to manual 
+* Clear Jupyter Notebooks before committing
+* Check CI tests passes

From e655d91fc732dd92d093579a2bc57e0b23850265 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Wed, 6 Jan 2021 08:13:45 +0100
Subject: [PATCH 119/403] Update CONTRIBUTING.md

Fixed typo
---
 CONTRIBUTING.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index a75c1504..899b6523 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,6 +1,6 @@
 # Guidelines for contributing to McStasScript
 
-This document describes how to contribute to McStasScript. The preferred method is to open a pull request on the GitHub page. The list below act as a checklist of things to consider before creating the pull request. Since the manual is currently on overleaf, there is no way to directly contribute, so a note on the necessary updates to the manual is appreciated.
+This document describes how to contribute to McStasScript. The preferred method is to open a pull request on the GitHub page. The list below acts as a checklist of things to consider before creating the pull request. Since the manual is currently on overleaf, there is no way to directly contribute, so a note on the necessary updates to the manual is appreciated.
 
 * Provide docstrings to new functions / classes
 * Update docstrings as necessary

From 2ccef681e30170522b64574f45f04763e4b518cc Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 12 Jan 2021 19:48:31 +0100
Subject: [PATCH 120/403] Corrections after review by Celine Durnaik, much
 appreciated!

Improved input sanitation
Better doc strings
typo in variable name options_string

The improved input sanitation of paths broke many tests which
were fixed. It also made it more difficult to have the load_data
function use a dummy ManagedMcrun object, so this structure was
changed making load data a stand-alone function instead.
---
 mcstasscript/helper/managed_mcrun.py    | 212 ++++++++++++-------
 mcstasscript/interface/functions.py     |   5 +-
 mcstasscript/interface/instr.py         |   2 +-
 mcstasscript/tests/test_Instr.py        |  60 ++++--
 mcstasscript/tests/test_ManagedMcrun.py | 269 ++++++++++++++++++------
 5 files changed, 395 insertions(+), 153 deletions(-)

diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 69198f7a..3526f29a 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -54,22 +54,22 @@ def __init__(self, instr_name, **kwargs):
             Name of instrument file to be simulated
 
         kwargs : keyword arguments
-            foldername : str
+            foldername : str, required
                 Sets data_folder_name
-            ncount : int
+            ncount : int, default 1E6
                 Sets ncount
-            mpi : int
-                Sets thread count
+            mpi : int, default None
+                Sets thread count, None to disable mpi
             parameters : dict
                 Sets parameters
-            custom_flags : str
+            custom_flags : str, default ""
                 Sets custom_flags passed to mcrun
             executable_path : str
                 Path to mcrun command, "" if already in path
-            increment_folder_name : bool
-                If True, automaticaly appends foldername to make it unique
-            force_compile : bool
-                If True, forces compile, default is True
+            increment_folder_name : bool, default False
+                If True, automatically appends foldername to make it unique
+            force_compile : bool, default True
+                If True, forces compile. If False no new instrument is written
             run_folder : str
                 Path to folder in which to run McStas
 
@@ -104,15 +104,37 @@ def __init__(self, instr_name, **kwargs):
         if "ncount" in kwargs:
             self.ncount = int(kwargs["ncount"])
 
+            if self.ncount < 1:
+                raise ValueError("ncount should be a positive integer, was "
+                                 + str(self.ncount))
+
         if "mpi" in kwargs:
             self.mpi = kwargs["mpi"]
+            try:
+                self.mpi = int(self.mpi)
+            except ValueError:
+                if self.mpi is not None:
+                    raise RuntimeError("MPI should be an integer, was "
+                                       + str(self.mpi))
+
+            if self.mpi is not None:
+                if self.mpi < 1:
+                    raise ValueError("MPI should be an integer larger than"
+                                     + " 0, was " + str(self.mpi))
 
         if "parameters" in kwargs:
             self.parameters = kwargs["parameters"]
 
+            if not isinstance(self.parameters, dict):
+                raise RuntimeError("Parameters should be given as dict.")
+
         if "custom_flags" in kwargs:
             self.custom_flags = kwargs["custom_flags"]
 
+            if not isinstance(self.custom_flags, str):
+                raise RuntimeError("ManagedMcrun detected given customf_flags"
+                                   + " was not a string.")
+
         if "increment_folder_name" in kwargs:
             self.increment_folder_name = kwargs["increment_folder_name"]
 
@@ -122,35 +144,50 @@ def __init__(self, instr_name, **kwargs):
         if "run_path" in kwargs:
             self.run_path = kwargs["run_path"]
 
-    def run_simulation(self, **kwargs):
-        """
-        Runs McStas simulation described by initializing the object
-        """
-
-        # get relevant paths
+        # get relevant paths and check their validity
         current_directory = os.getcwd()
 
         if not os.path.isabs(self.data_folder_name):
             self.data_folder_name = os.path.join(current_directory,
                                                  self.data_folder_name)
+        else:
+            split_data_path = os.path.split(self.data_folder_name)
+            if not os.path.isdir(split_data_path[0]):
+                raise RuntimeError("Parent folder for datafolder invalid: "
+                                   + str(split_data_path[0]))
 
         if not os.path.isabs(self.run_path):
             self.run_path = os.path.join(current_directory, self.run_path)
+        else:
+            split_run_path = os.path.split(self.run_path)
+            if not os.path.isdir(split_run_path[0]):
+                raise RuntimeError("Parent folder for run_path invalid: "
+                                   + str(split_run_path[0]))
 
         if not os.path.isdir(self.run_path):
-            raise ValueError("Given run_path for McStas not a directory!")
+            raise RuntimeError("ManagedMcrun found run_path to "
+                               + "be invalid: " + str(self.run_path))
+
+        if not os.path.isdir(self.executable_path):
+            raise RuntimeError("ManagedMcrun found executable_path to "
+                               + "be invalid: " + str(self.executable_path))
+
+    def run_simulation(self, **kwargs):
+        """
+        Runs McStas simulation described by initializing the object
+        """
 
         # construct command to run
-        options_string = ""
+        option_string = ""
         if self.compile:
-            options_string = "-c "
+            option_string = "-c "
 
         if self.mpi is not None:
             mpi_string = " --mpi=" + str(self.mpi) + " " # Set mpi
         else:
             mpi_string = " "
 
-        option_string = (options_string
+        option_string = (option_string
                          + "-n " + str(self.ncount)  # Set ncount
                          + mpi_string)
 
@@ -176,7 +213,7 @@ def run_simulation(self, **kwargs):
                                 + "="
                                 + str(val))  # parameter value
 
-        mcrun_full_path = self.executable_path + self.executable
+        mcrun_full_path = os.path.join(self.executable_path, self.executable)
         if len(self.executable_path) > 1:
             if not (self.executable_path[-1] == "\\"
                     or self.executable_path[-1] == "/"):
@@ -207,28 +244,58 @@ def run_simulation(self, **kwargs):
             print(process.stderr)
             print(process.stdout)
 
-
     def load_results(self, *args):
+        """
+        Method for loading data from a mcstas simulation
+
+        Loads data on all monitors in a McStas data folder, and returns these
+        as a list of McStasData objects.
+
+        Parameters
+        ----------
+
+        optional first argument : str
+            path to folder from which data should be loaded
+
+        """
 
         if len(args) == 0:
             data_folder_name = self.data_folder_name
         elif len(args) == 1:
             data_folder_name = args[0]
         else:
-            raise InputError("load_results can be called with 0 or 1 arguments")
+            raise RuntimeError("load_results can be called with 0 or 1 arguments")
+
+        return load_results(data_folder_name)
+
+
+def load_results(data_folder_name):
+    """
+    Function for loading data from a mcstas simulation
+
+    Loads data on all monitors in a McStas data folder, and returns these
+    as a list of McStasData objects.
 
-        if not os.path.isdir(data_folder_name):
-            raise NameError("Given data directory does not exist.")
+    Parameters
+    ----------
+
+    first argument : str
+        path to folder from which data should be loaded
+
+    """
+
+    if not os.path.isdir(data_folder_name):
+        raise NameError("Given data directory does not exist.")
 
-        # Find all data files in generated folder
-        files_in_folder = os.listdir(data_folder_name)
+    # Find all data files in generated folder
+    files_in_folder = os.listdir(data_folder_name)
 
-        # Raise an error if mccode.sim is not available
-        if "mccode.sim" not in files_in_folder:
-            raise NameError("No mccode.sim in data folder.")
+    # Raise an error if mccode.sim is not available
+    if "mccode.sim" not in files_in_folder:
+        raise NameError("No mccode.sim in data folder.")
 
-        # Open mccode to read metadata for all datasets written to disk
-        f = open(os.path.join(data_folder_name, "mccode.sim"), "r")
+    # Open mccode to read metadata for all datasets written to disk
+    with open(os.path.join(data_folder_name, "mccode.sim"), "r") as f:
 
         # Loop that reads mccode.sim sections
         metadata_list = []
@@ -259,48 +326,45 @@ def load_results(self, *args):
                 # Start recording data to metadata object
                 in_data = True
 
-        # Close mccode.sim
+    # Create a list for McStasData instances to return
+    results = []
+
+    # Load datasets described in metadata list individually
+    for metadata in metadata_list:
+        # Load data with numpy
+        data = np.loadtxt(os.path.join(data_folder_name,
+                          metadata.filename.rstrip()))
+
+        # Split data into intensity, error and ncount
+        if type(metadata.dimension) == int:
+            xaxis = data.T[0, :]
+            Intensity = data.T[1, :]
+            Error = data.T[2, :]
+            Ncount = data.T[3, :]
+
+        elif len(metadata.dimension) == 2:
+            xaxis = []  # Assume evenly binned in 2d
+            data_lines = metadata.dimension[1]
+
+            Intensity = data[0:data_lines, :]
+            Error = data[data_lines:2*data_lines, :]
+            Ncount = data[2*data_lines:3*data_lines, :]
+        else:
+            raise NameError(
+                "Dimension not read correctly in data set "
+                + "connected to monitor named "
+                + metadata.component_name)
+
+        # The data is saved as a McStasData object
+        result = McStasData(metadata, Intensity,
+                            Error, Ncount,
+                            xaxis=xaxis)
+
+        # Add this result to the results list
+        results.append(result)
+
+        # Close the current datafile
         f.close()
 
-        # Create a list for McStasData instances to return
-        results = []
-
-        # Load datasets described in metadata list individually
-        for metadata in metadata_list:
-            # Load data with numpy
-            data = np.loadtxt(os.path.join(data_folder_name,
-                              metadata.filename.rstrip()))
-
-            # Split data into intensity, error and ncount
-            if type(metadata.dimension) == int:
-                xaxis = data.T[0, :]
-                Intensity = data.T[1, :]
-                Error = data.T[2, :]
-                Ncount = data.T[3, :]
-
-            elif len(metadata.dimension) == 2:
-                xaxis = []  # Assume evenly binned in 2d
-                data_lines = metadata.dimension[1]
-
-                Intensity = data[0:data_lines, :]
-                Error = data[data_lines:2*data_lines, :]
-                Ncount = data[2*data_lines:3*data_lines, :]
-            else:
-                raise NameError(
-                    "Dimension not read correctly in data set "
-                    + "connected to monitor named "
-                    + metadata.component_name)
-
-            # The data is saved as a McStasData object
-            result = McStasData(metadata, Intensity,
-                                Error, Ncount,
-                                xaxis=xaxis)
-
-            # Add this result to the results list
-            results.append(result)
-
-            # Close the current datafile
-            f.close()
-
-        # Return list of McStasData objects
-        return results
+    # Return list of McStasData objects
+    return results
diff --git a/mcstasscript/interface/functions.py b/mcstasscript/interface/functions.py
index 663a4f36..559de253 100644
--- a/mcstasscript/interface/functions.py
+++ b/mcstasscript/interface/functions.py
@@ -2,7 +2,7 @@
 import os
 
 from mcstasscript.data.data import McStasData
-from mcstasscript.helper.managed_mcrun import ManagedMcrun
+import mcstasscript.helper.managed_mcrun as managed_mcrun
 
 
 def name_search(name, data_list):
@@ -92,8 +92,7 @@ def load_data(foldername):
         foldername : string
             Name of the folder from which to load data
     """
-    managed_mcrun = ManagedMcrun("dummy", foldername=foldername)
-    return managed_mcrun.load_results()
+    return managed_mcrun.load_results(foldername)
 
 
 class Configurator:
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index e81c6bd7..5b7d494d 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1901,7 +1901,7 @@ def __init__(self, name, **kwargs):
                 Affiliation of author, written in instrument file
 
             executable_path : str
-                Absolute path of mcrun or empty if already in path
+                Absolute path of mxrun or empty if already in path
 
             input_path : str
                 Work directory, will load components from this folder
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 92f2ade6..df6560a9 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1572,16 +1572,24 @@ def test_x_ray_run_full_instrument_basic(self, mock_sub,
         data is loaded even though the system call is not executed.
         """
 
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
         instr = setup_populated_x_ray_instr()
         instr.run_full_instrument("test_instrument.instr",
                                   foldername="test_data_set",
-                                  executable_path="path",
+                                  executable_path=executable_path,
                                   parameters={"theta": 1})
 
-        expected_path = os.path.join("path","mxrun")
+        os.chdir(current_work_dir)
+
+        expected_path = os.path.join(executable_path, "mxrun")
 
         current_directory = os.getcwd()
-        expected_folder_path = os.path.join(current_directory, "test_data_set")
+        expected_folder_path = os.path.join(THIS_DIR, "test_data_set")
 
         # a double space because of a missing option
         expected_call = (expected_path + " -c -n 1000000 "
@@ -1589,7 +1597,7 @@ def test_x_ray_run_full_instrument_basic(self, mock_sub,
                          + "  test_instrument.instr"
                          + " has_default=37 theta=1")
 
-        expected_run_path = os.path.join(current_directory, ".")
+        expected_run_path = os.path.join(THIS_DIR, ".")
 
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
@@ -1609,16 +1617,24 @@ def test_run_full_instrument_basic(self, mock_sub,
         data is loaded even though the system call is not executed.
         """
 
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
         instr = setup_populated_instr()
         instr.run_full_instrument("test_instrument.instr",
                                   foldername="test_data_set",
-                                  executable_path="path",
+                                  executable_path=executable_path,
                                   parameters={"theta": 1})
 
-        expected_path = os.path.join("path", "mcrun")
+        os.chdir(current_work_dir)
+
+        expected_path = os.path.join(executable_path, "mcrun")
 
         current_directory = os.getcwd()
-        expected_folder_path = os.path.join(current_directory, "test_data_set")
+        expected_folder_path = os.path.join(THIS_DIR, "test_data_set")
 
         # a double space because of a missing option
         expected_call = (expected_path + " -c -n 1000000 "
@@ -1644,10 +1660,16 @@ def test_run_full_instrument_complex(self, mock_sub,
         data is loaded even though the system call is not executed.
         """
 
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
         instr = setup_populated_instr()
         instr.run_full_instrument("test_instrument.instr",
                                   foldername="test_data_set",
-                                  executable_path="path",
+                                  executable_path=executable_path,
                                   mpi=7,
                                   ncount=48.4,
                                   custom_flags="-fo",
@@ -1655,10 +1677,12 @@ def test_run_full_instrument_complex(self, mock_sub,
                                               "BC": "car",
                                               "theta": "\"toy\""})
 
-        expected_path = os.path.join("path","mcrun")
+        os.chdir(current_work_dir)
+
+        expected_path = os.path.join(executable_path, "mcrun")
 
         current_directory = os.getcwd()
-        expected_folder_path = os.path.join(current_directory, "test_data_set")
+        expected_folder_path = os.path.join(THIS_DIR, "test_data_set")
 
         # a double space because of a missing option
         expected_call = (expected_path + " -c -n 48 --mpi=7 "
@@ -1683,10 +1707,16 @@ def test_run_full_instrument_overwrite_default(self, mock_sub,
         parameters.
         """
 
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
         instr = setup_populated_instr()
         instr.run_full_instrument("test_instrument.instr",
                                   foldername="test_data_set",
-                                  executable_path="path",
+                                  executable_path=executable_path,
                                   mpi=7,
                                   ncount=48.4,
                                   custom_flags="-fo",
@@ -1694,11 +1724,13 @@ def test_run_full_instrument_overwrite_default(self, mock_sub,
                                               "BC": "car",
                                               "theta": "\"toy\"",
                                               "has_default": 10})
-        
-        expected_path = os.path.join("path","mcrun")
+
+        os.chdir(current_work_dir)
+
+        expected_path = os.path.join(executable_path, "mcrun")
 
         current_directory = os.getcwd()
-        expected_folder_path = os.path.join(current_directory, "test_data_set")
+        expected_folder_path = os.path.join(THIS_DIR, "test_data_set")
 
         # a double space because of a missing option
         expected_call = (expected_path + " -c -n 48 --mpi=7 "
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index d4a93520..1f99ad53 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -1,6 +1,7 @@
 import os
 import io
 import unittest
+from unittest import mock
 import numpy as np
 
 from mcstasscript.data.data import McStasMetaData
@@ -22,49 +23,88 @@ def test_ManagedMcrun_init_simple(self):
         """
         Check shortest possible initialization works
         """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
-                                 executable_path="test_path",
+                                 executable_path=executable_path,
                                  executable="mcrun")
 
+        os.chdir(current_work_dir)
+
         self.assertEqual(mcrun_obj.name_of_instrumentfile, "test.instr")
-        self.assertEqual(mcrun_obj.data_folder_name, "test_folder")
-        self.assertEqual(mcrun_obj.executable_path, "test_path")
+
+        expected_data_folder = os.path.join(THIS_DIR, "test_folder")
+        self.assertEqual(mcrun_obj.data_folder_name, expected_data_folder)
+
+        expected_executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+        self.assertEqual(mcrun_obj.executable_path, expected_executable_path)
 
     def test_ManagedMcrun_init_defaults(self):
         """
         Check default values are set up correctly
         """
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
-                                 executable_path="",
-                                 executable="mcrun",)
+                                 executable_path=executable_path,
+                                 executable="mcrun")
+
+        os.chdir(current_work_dir)
 
         self.assertEqual(mcrun_obj.mpi, None)
         self.assertEqual(mcrun_obj.ncount, 1000000)
-        self.assertEqual(mcrun_obj.run_path, ".")
+        expected_run_path = os.path.join(THIS_DIR, ".")
+        self.assertEqual(mcrun_obj.run_path, expected_run_path)
 
     def test_ManagedMcrun_init_set_values(self):
         """
-        Check default values are set up correctly
+        Check values given to ManagedMcrun are internalized
+
+        run_path set to an existing folder in the test directory
         """
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
-                                 executable_path="",
                                  executable="mcrun",
-                                 run_path="test",
+                                 executable_path=executable_path,
+                                 run_path="test_data_set",
                                  mpi=4,
                                  ncount=128)
 
+        os.chdir(current_work_dir)
+
         self.assertEqual(mcrun_obj.mpi, 4)
         self.assertEqual(mcrun_obj.ncount, 128)
-        self.assertEqual(mcrun_obj.run_path, "test")
+        expected_run_path = os.path.join(THIS_DIR, "test_data_set")
+        self.assertEqual(mcrun_obj.run_path, expected_run_path)
 
     def test_ManagedMcrun_init_set_parameters(self):
         """
-        Check default values are set up correctly
+        Check parameters can be given as dictionary
         """
 
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
         par_input = {"A_par": 5.1,
                      "int_par": 1,
                      "define_par": "Bike",
@@ -72,10 +112,12 @@ def test_ManagedMcrun_init_set_parameters(self):
 
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
-                                 executable_path="",
+                                 executable_path=executable_path,
                                  executable="",
                                  parameters=par_input)
 
+        os.chdir(current_work_dir)
+
         self.assertEqual(mcrun_obj.parameters["A_par"], 5.1)
         self.assertEqual(mcrun_obj.parameters["int_par"], 1)
         self.assertEqual(mcrun_obj.parameters["define_par"], "Bike")
@@ -83,17 +125,25 @@ def test_ManagedMcrun_init_set_parameters(self):
 
     def test_ManagedMcrun_init_set_custom_flags(self):
         """
-        Check default values are set up correctly
+        Check custom_flags can be given by user
         """
 
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
         custom_flag_input = "-p"
 
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
-                                 executable_path="",
                                  executable="mcrun",
+                                 executable_path=executable_path,
                                  custom_flags=custom_flag_input)
 
+        os.chdir(current_work_dir)
+
         self.assertEqual(mcrun_obj.custom_flags, custom_flag_input)
 
     def test_ManagedMcrun_init_no_folder_error(self):
@@ -103,24 +153,63 @@ def test_ManagedMcrun_init_no_folder_error(self):
         with self.assertRaises(NameError):
             mcrun_obj = ManagedMcrun("test.instr", mcrun_path="")
 
+    def test_ManagedMcrun_init_invalid_ncount_error(self):
+        """
+        An error should occur if negative ncount is given
+        """
+        with self.assertRaises(ValueError):
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     foldername="test_folder",
+                                     mcrun_path="",
+                                     ncount=-8)
+
+    def test_ManagedMcrun_init_invalid_mpi_error(self):
+        """
+        An error should occur if negative mpi is given
+        """
+        with self.assertRaises(ValueError):
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     foldername="test_folder",
+                                     mcrun_path="",
+                                     mpi=-8)
+
+    def test_ManagedMcrun_init_invalid_parameters_error(self):
+        """
+        An error should occur if parameters is given as non dict
+        """
+        with self.assertRaises(RuntimeError):
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     foldername="test_folder",
+                                     mcrun_path="",
+                                     parameters=[1, 2, 3])
+
     @unittest.mock.patch("subprocess.run")
     def test_ManagedMcrun_run_simulation_basic(self, mock_sub):
         """
         Check a basic system call is correct
         """
 
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
-                                 executable_path="path",
+                                 executable_path=executable_path,
                                  executable="mcrun",)
 
+        os.chdir(current_work_dir)
+
         mcrun_obj.run_simulation()
 
         current_directory = os.getcwd()
-        expected_folder_path = os.path.join(current_directory, "test_folder")
+        expected_folder_path = os.path.join(THIS_DIR, "test_folder")
 
         # a double space because of a missing option
-        expected_call = ("path/mcrun -c -n 1000000 "
+        executable = os.path.join(executable_path, "mcrun")
+        expected_call = (executable + " -c -n 1000000 "
                          + "-d " + expected_folder_path + "  test.instr")
 
         mock_sub.assert_called_once_with(expected_call,
@@ -135,18 +224,27 @@ def test_ManagedMcrun_run_simulation_basic_path(self, mock_sub):
         Check a basic system call is correct, with different path format
         """
 
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas", "")
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
-                                 executable_path="path/",
+                                 executable_path=executable_path,
                                  executable="mcrun",)
 
+        os.chdir(current_work_dir)
+
         mcrun_obj.run_simulation()
 
         current_directory = os.getcwd()
-        expected_folder_path = os.path.join(current_directory, "test_folder")
+        expected_folder_path = os.path.join(THIS_DIR, "test_folder")
 
+        executable = os.path.join(executable_path, "mcrun")
         # a double space because of a missing option
-        expected_call = ("path/mcrun -c -n 1000000 "
+        expected_call = (executable + " -c -n 1000000 "
                          + "-d " + expected_folder_path + "  test.instr")
 
         mock_sub.assert_called_once_with(expected_call,
@@ -159,23 +257,34 @@ def test_ManagedMcrun_run_simulation_basic_path(self, mock_sub):
     def test_ManagedMcrun_run_simulation_no_standard(self, mock_sub):
         """
         Check a non standard system call is correct
+
+        Here multiple options are used and ncount is a float that should
+        be rounded by the class.
         """
 
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
-                                 executable_path="path",
+                                 executable_path=executable_path,
                                  executable="mcrun",
                                  mpi=7,
                                  ncount=48.4,
                                  custom_flags="-fo")
 
+        os.chdir(current_work_dir)
+
         mcrun_obj.run_simulation()
 
-        current_directory = os.getcwd()
-        expected_folder_path = os.path.join(current_directory, "test_folder")
+        expected_folder_path = os.path.join(THIS_DIR, "test_folder")
 
         # a double space because of a missing option
-        expected_call = ("path/mcrun -c -n 48 --mpi=7 "
+        executable = os.path.join(executable_path, "mcrun")
+        expected_call = (executable + " -c -n 48 --mpi=7 "
                          + "-d " + expected_folder_path + " -fo test.instr")
 
         mock_sub.assert_called_once_with(expected_call,
@@ -190,9 +299,15 @@ def test_ManagedMcrun_run_simulation_parameters(self, mock_sub):
         Check a run with parameters is correct
         """
 
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
-                                 executable_path="path",
+                                 executable_path=executable_path,
                                  executable="mcrun",
                                  mpi=7,
                                  ncount=48.4,
@@ -201,12 +316,15 @@ def test_ManagedMcrun_run_simulation_parameters(self, mock_sub):
                                              "BC": "car",
                                              "th": "\"toy\""})
 
+        os.chdir(current_work_dir)  # Reset work directory
+
         mcrun_obj.run_simulation()
 
-        current_directory = os.getcwd()
-        expected_folder_path = os.path.join(current_directory, "test_folder")
+        expected_folder_path = os.path.join(THIS_DIR, "test_folder")
         # a double space because of a missing option
-        expected_call = ("path/mcrun -c -n 48 --mpi=7 "
+        executable = os.path.join(executable_path, "mcrun")
+
+        expected_call = (executable + " -c -n 48 --mpi=7 "
                          + "-d " + expected_folder_path + " -fo test.instr "
                          + "A=2 BC=car th=\"toy\"")
 
@@ -219,12 +337,18 @@ def test_ManagedMcrun_run_simulation_parameters(self, mock_sub):
     @unittest.mock.patch("subprocess.run")
     def test_ManagedMcrun_run_simulation_compile(self, mock_sub):
         """
-        Check a run with parameters is correct
+        Check run with force_compile set to False works
         """
 
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
         mcrun_obj = ManagedMcrun("test.instr",
                                  foldername="test_folder",
-                                 executable_path="path",
+                                 executable_path=executable_path,
                                  executable="mcrun",
                                  mpi=7,
                                  ncount=48.4,
@@ -234,13 +358,16 @@ def test_ManagedMcrun_run_simulation_compile(self, mock_sub):
                                              "BC": "car",
                                              "th": "\"toy\""})
 
+        os.chdir(current_work_dir)
+
         mcrun_obj.run_simulation()
 
         current_directory = os.getcwd()
-        expected_folder_path = os.path.join(current_directory, "test_folder")
+        expected_folder_path = os.path.join(THIS_DIR, "test_folder")
 
+        executable = os.path.join(executable_path, "mcrun")
         # a double space because of a missing option
-        expected_call = ("path/mcrun -n 48 --mpi=7 "
+        expected_call = (executable + " -n 48 --mpi=7 "
                          + "-d " + expected_folder_path + " -fo test.instr "
                          + "A=2 BC=car th=\"toy\"")
 
@@ -253,23 +380,30 @@ def test_ManagedMcrun_run_simulation_compile(self, mock_sub):
     def test_ManagedMcrun_load_data_PSD4PI(self):
         """
         Use test_data_set to test load_data for PSD_4PI
-        """
 
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_data_set",
-                                 mcrun_path="path")
+        test_data_set contains three data files and some junk, the mccode.sim
+        file contains names of the data files so only these are loaded.
+        """
 
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername="test_data_set",
+                                 executable_path=executable_path,
+                                 mcrun_path="path")
+
         results = mcrun_obj.load_results()
 
         os.chdir(current_work_dir)  # Reset work directory
 
+        # Check three data objects are loaded
         self.assertEqual(len(results), 3)
 
+        # Check properties of PSD_4PI data
         PSD_4PI = results[0]
 
         self.assertEqual(PSD_4PI.name, "PSD_4PI")
@@ -287,21 +421,25 @@ def test_ManagedMcrun_load_data_PSD(self):
         Use test_data_set to test load_data for PSD
         """
 
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_data_set",
-                                 mcrun_path="path")
-
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername="test_data_set",
+                                 executable_path=executable_path,
+                                 mcrun_path="path")
+
         results = mcrun_obj.load_results()
 
         os.chdir(current_work_dir)  # Reset work directory
 
-        # Check other properties
+        # Check three data objects are loaded
+        self.assertEqual(len(results), 3)
 
+        # Check properties of PSD data
         PSD = results[1]
 
         self.assertEqual(PSD.name, "PSD")
@@ -319,21 +457,25 @@ def test_ManagedMcrun_load_data_L_mon(self):
         Use test_data_set to test load_data for L_mon
         """
 
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_data_set",
-                                 mcrun_path="path")
-
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername="test_data_set",
+                                 executable_path=executable_path,
+                                 mcrun_path="path")
+
         results = mcrun_obj.load_results()
 
         os.chdir(current_work_dir)  # Reset work directory
 
-        # Check other properties
+        # Check three data objects are loaded
+        self.assertEqual(len(results), 3)
 
+        # Check properties of L_mon
         L_mon = results[2]
 
         self.assertEqual(L_mon.name, "L_mon")
@@ -352,22 +494,23 @@ def test_ManagedMcrun_load_data_L_mon_direct(self):
         Use test_data_set to test load_data for L_mon with direct path
         """
 
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_data_set",
-                                 mcrun_path="path")
-
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername="test_data_set",
+                                 executable_path=executable_path,
+                                 mcrun_path="path")
+
         load_path = os.path.join(THIS_DIR, "test_data_set")
         results = mcrun_obj.load_results(load_path)
 
         os.chdir(current_work_dir)  # Reset work directory
 
-        # Check other properties
-
+        # Check properties of L_mon
         L_mon = results[2]
 
         self.assertEqual(L_mon.name, "L_mon")
@@ -386,16 +529,18 @@ def test_ManagedMcrun_load_data_L_mon_direct_error(self):
         Check an error occurs when directory has no mccode.sim
         """
 
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_data_set",
-                                 mcrun_path="path")
-
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
-        load_path = os.path.join(THIS_DIR, "non_exsistent_dataset")
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername="test_data_set",
+                                 executable_path=executable_path,
+                                 mcrun_path="path")
+
+        load_path = os.path.join(THIS_DIR, "non_existent_dataset")
         with self.assertRaises(NameError):
             results = mcrun_obj.load_results(load_path)
 
@@ -406,15 +551,17 @@ def test_ManagedMcrun_load_data_L_mon_empty_error(self):
         Check an error occurs when pointed to empty directory
         """
 
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_data_set",
-                                 mcrun_path="path")
-
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername="test_data_set",
+                                 executable_path=executable_path,
+                                 mcrun_path="path")
+
         load_path = os.path.join(THIS_DIR, "/dummy_mcstas")
         with self.assertRaises(NameError):
             results = mcrun_obj.load_results(load_path)

From 2400193e9b2f95d81651b47c766e021a3751355d Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 12 Jan 2021 21:27:28 +0100
Subject: [PATCH 121/403] Updated functions after review from Celine Durniak,
 much appreciated!

Updates to tests for configurator and functions

Added input sanitation for configruation
Improved doc strings for tests, mainly for improved input sanitation
Added check that load_data gets a existing foldername
---
 mcstasscript/interface/functions.py     | 40 +++++++++--
 mcstasscript/tests/test_Configurator.py | 32 ++++++---
 mcstasscript/tests/test_functions.py    | 89 ++++++++++++++++++++-----
 3 files changed, 132 insertions(+), 29 deletions(-)

diff --git a/mcstasscript/interface/functions.py b/mcstasscript/interface/functions.py
index 559de253..c4214982 100644
--- a/mcstasscript/interface/functions.py
+++ b/mcstasscript/interface/functions.py
@@ -13,23 +13,23 @@ def name_search(name, data_list):
     McStasData objects are returned.
 
     The index of certain datasets in the data_list can change if
-    additional monitors are added so it is more convinient to access
+    additional monitors are added so it is more convenient to access
     the data files using their names.
 
     Parameters
     ----------
     name : string
-        Name of the dataset to be retrived (component_name)
+        Name of the dataset to be retrieved (component_name)
 
     data_list : List of McStasData instances
         List of datasets to search
     """
     if type(data_list) is not list:
-        raise InputError(
+        raise RuntimeError(
             "name_search function needs list of McStasData as input")
 
     if not type(data_list[0]) == McStasData:
-        raise InputError(
+        raise RuntimeError(
             "name_search function needs objects of type McStasData as input.")
 
     # Search by component name
@@ -92,6 +92,10 @@ def load_data(foldername):
         foldername : string
             Name of the folder from which to load data
     """
+    if not os.path.isdir(foldername):
+        raise RuntimeError("Could not find specified foldername for"
+                           + "load_data:" + str(foldername))
+
     return managed_mcrun.load_results(foldername)
 
 
@@ -111,6 +115,12 @@ class Configurator:
         
     set_mcrun_path(string)
         sets mcrun path
+
+    set_mcxtrace_path(string)
+        sets mcxtrace path
+
+    set_mxrun_path(string)
+        sets mxrun path
         
     set_line_length(int)
         sets maximum line length to given int
@@ -200,6 +210,9 @@ def set_mcstas_path(self, path):
             Path to the mcstas directory containing "sources", "optics", ...
         """
 
+        if not os.path.isdir(path):
+            raise RuntimeError("Invalid path given to set_mcstas_path:" + str(path))
+
         # read entire configuration file 
         config = self._read_yaml()
 
@@ -219,6 +232,9 @@ def set_mcrun_path(self, path):
             Path to the mcrun executable
         """
 
+        if not os.path.isdir(path):
+            raise RuntimeError("Invalid path given to set_mcrun_path:" + str(path))
+
         # read entire configuration file 
         config = self._read_yaml()
 
@@ -238,6 +254,9 @@ def set_mcxtrace_path(self, path):
             Path to the mcxtrace directory containing "sources", "optics", ...
         """
 
+        if not os.path.isdir(path):
+            raise RuntimeError("Invalid path given to set_mcxtrace_path:" + str(path))
+
         # read entire configuration file
         config = self._read_yaml()
 
@@ -257,6 +276,10 @@ def set_mxrun_path(self, path):
             Path to the mxrun executable
         """
 
+        if not os.path.isdir(path):
+            raise RuntimeError("Invalid path given to set_mxrun_path: "
+                               + str(path))
+
         # read entire configuration file
         config = self._read_yaml()
 
@@ -276,6 +299,15 @@ def set_line_length(self, line_length):
             maximum line length for output
         """
 
+        if not isinstance(line_length, int):
+            raise ValueError("Given line length in set_line_length not an "
+                             + "integer.")
+
+        if line_length < 1:
+            raise ValueError("Line length specified in set_line_length must"
+                             + " be positve, given length: "
+                             + str(line_length))
+
         # read entire configuration file 
         config = self._read_yaml()
 
diff --git a/mcstasscript/tests/test_Configurator.py b/mcstasscript/tests/test_Configurator.py
index 15f48235..592b0746 100644
--- a/mcstasscript/tests/test_Configurator.py
+++ b/mcstasscript/tests/test_Configurator.py
@@ -105,13 +105,16 @@ def test_set_mcrun_path(self):
         This test checks that setting the mcrun path works
         """
         test_name = "test_configuration"
-        my_configurator = setup_configurator(test_name) 
+        my_configurator = setup_configurator(test_name)
 
-        my_configurator.set_mcrun_path("/new/mcrun_path/")
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        dummy_mcstas_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        my_configurator.set_mcrun_path(dummy_mcstas_path)
 
         new_config = my_configurator._read_yaml()
 
-        self.assertEqual(new_config["paths"]["mcrun_path"], "/new/mcrun_path/")
+        self.assertEqual(new_config["paths"]["mcrun_path"], dummy_mcstas_path)
 
         # remove the testing configuration file
         setup_expected_file(test_name)
@@ -121,14 +124,17 @@ def test_set_mcstas_path(self):
         This test checks that setting the mcstas path works
         """
         test_name = "test_configuration"
-        my_configurator = setup_configurator(test_name) 
+        my_configurator = setup_configurator(test_name)
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        dummy_mcstas_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        my_configurator.set_mcstas_path("/new/mcstas_path/")
+        my_configurator.set_mcstas_path(dummy_mcstas_path)
 
         new_config = my_configurator._read_yaml()
 
         self.assertEqual(new_config["paths"]["mcstas_path"],
-                         "/new/mcstas_path/")
+                         dummy_mcstas_path)
 
         # remove the testing configuration file
         setup_expected_file(test_name)
@@ -140,11 +146,14 @@ def test_set_mcrun_path(self):
         test_name = "test_configuration"
         my_configurator = setup_configurator(test_name)
 
-        my_configurator.set_mxrun_path("/new/mxrun_path/")
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        dummy_mcstas_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        my_configurator.set_mxrun_path(dummy_mcstas_path)
 
         new_config = my_configurator._read_yaml()
 
-        self.assertEqual(new_config["paths"]["mxrun_path"], "/new/mxrun_path/")
+        self.assertEqual(new_config["paths"]["mxrun_path"], dummy_mcstas_path)
 
         # remove the testing configuration file
         setup_expected_file(test_name)
@@ -156,12 +165,15 @@ def test_set_mcstas_path(self):
         test_name = "test_configuration"
         my_configurator = setup_configurator(test_name)
 
-        my_configurator.set_mcxtrace_path("/new/mcxtrace_path/")
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        dummy_mcstas_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        my_configurator.set_mcxtrace_path(dummy_mcstas_path)
 
         new_config = my_configurator._read_yaml()
 
         self.assertEqual(new_config["paths"]["mcxtrace_path"],
-                         "/new/mcxtrace_path/")
+                         dummy_mcstas_path)
 
         # remove the testing configuration file
         setup_expected_file(test_name)
diff --git a/mcstasscript/tests/test_functions.py b/mcstasscript/tests/test_functions.py
index 40db8f6a..5e005d28 100644
--- a/mcstasscript/tests/test_functions.py
+++ b/mcstasscript/tests/test_functions.py
@@ -11,8 +11,17 @@
 from mcstasscript.data.data import McStasMetaData
 from mcstasscript.data.data import McStasPlotOptions
 
-
 def set_dummy_MetaData_1d(name):
+    """
+    Sets up a dummy MetaData object for a 1d dataset
+
+    Parameters
+    ----------
+
+    name : str
+        base for filename, .dat will be appended
+    """
+
     meta_data = McStasMetaData()
     meta_data.component_name = name
     meta_data.dimension = 50
@@ -22,6 +31,15 @@ def set_dummy_MetaData_1d(name):
 
 
 def set_dummy_McStasData_1d(name):
+    """
+    Sets up a dummy McStasData object for a 1d dataset
+
+    Parameters
+    ----------
+
+    name : str
+        base for filename, .dat will be appended
+    """
     meta_data = set_dummy_MetaData_1d(name)
 
     intensity = np.arange(20)
@@ -33,6 +51,15 @@ def set_dummy_McStasData_1d(name):
 
 
 def set_dummy_MetaData_2d(name):
+    """
+    Sets up a dummy MetaData object for a 2d dataset
+
+    Parameters
+    ----------
+
+    name : str
+        base for filename, .dat will be appended
+    """
     meta_data = McStasMetaData()
     meta_data.component_name = name
     meta_data.dimension = [50, 100]
@@ -42,6 +69,16 @@ def set_dummy_MetaData_2d(name):
 
 
 def set_dummy_McStasData_2d(name):
+    """
+    Sets up a dummy McStasData object for a 2d dataset
+
+    Parameters
+    ----------
+
+    name : str
+        base for filename, .dat will be appended
+    """
+
     meta_data = set_dummy_MetaData_2d(name)
 
     intensity = np.arange(20).reshape(4, 5)
@@ -52,6 +89,9 @@ def set_dummy_McStasData_2d(name):
 
 
 def setup_McStasData_array():
+    """
+    Sets up an list of McStasData objects, similar to simulation output
+    """
 
     data_list = []
 
@@ -74,6 +114,11 @@ def setup_McStasData_array():
 
 
 def setup_McStasData_array_repeat():
+    """
+    Sets up an list of McStasData objects, similar to simulation output
+
+    Have Hero twice in naming, testing search capability
+    """
 
     data_list = []
 
@@ -104,7 +149,9 @@ class Test_name_search(unittest.TestCase):
 
     def test_name_search_read(self):
         """
-        Test simple case
+        Test that Hero object can be found and check the unique dimension
+
+        Here the name is used
         """
 
         data_list = setup_McStasData_array()
@@ -115,7 +162,9 @@ def test_name_search_read(self):
         
     def test_name_search_filename_read(self):
         """
-        Test simple case
+        Test that Hero object can be found and check the unique dimension
+
+        Here the name of the datafile is used
         """
 
         data_list = setup_McStasData_array()
@@ -126,7 +175,10 @@ def test_name_search_filename_read(self):
 
     def test_name_search_read_repeat(self):
         """
-        Test simple case with repeat name
+        Test that Hero object can be found and check the unique dimension
+        Here the used data set has two monitors with Hero in the name
+
+        Here the name of the monitor is used
         """
 
         data_list = setup_McStasData_array_repeat()
@@ -135,29 +187,34 @@ def test_name_search_read_repeat(self):
 
         self.assertEqual(hero_object.metadata.dimension, 123)
         
-    def test_name_search_read_dubplicate(self):
+    def test_name_search_read_duplicate(self):
         """
-        Test simple case with duplicated name, should return list
+        Test simple case with duplicated name, search should return list
         """
 
         data_list = setup_McStasData_array_repeat()
-        
+
+        # Adds another dataset with a name already in the data_list
         hero_object = set_dummy_McStasData_2d("Big_Hero")
         hero_object.metadata.dimension = 321
         hero_object.plot_options.colormap = "very hot"
 
         data_list.append(hero_object)
-        
+
+        # Now two McStasData objects match the Big_Hero name
         results = name_search("Big_Hero", data_list)
 
+        self.assertEqual(type(results), list)
+        # Check two results are returned
         self.assertEqual(len(results), 2)
 
+        # Check they have the correct dimensions
         self.assertEqual(results[0].metadata.dimension, 123)
         self.assertEqual(results[1].metadata.dimension, 321)
 
     def test_name_search_read_error(self):
         """
-        Test simple case
+        Check an NameError is returned when no match is found
         """
 
         data_list = setup_McStasData_array()
@@ -167,22 +224,23 @@ def test_name_search_read_error(self):
 
     def test_name_search_type_error_not_list(self):
         """
-        Test simple case
+        Check error is given even when data list is just single object
         """
 
         data_list = set_dummy_McStasData_2d("Last_object_2d")
 
-        with self.assertRaises(NameError):
+        with self.assertRaises(RuntimeError):
             hero_object = name_search("Hero", data_list)
 
     def test_name_search_type_error_not_McStasData(self):
         """
-        Test simple case
+        Checks that an error is returned if the given dataset contains
+        non McStasData objects
         """
 
         data_list = [1, 2, 3]
 
-        with self.assertRaises(NameError):
+        with self.assertRaises(RuntimeError):
             hero_object = name_search(1, data_list)
 
 
@@ -196,7 +254,7 @@ class Test_name_plot_options(unittest.TestCase):
 
     def test_name_plot_options_simple(self):
         """
-        Test simple case
+        Check set_plot_options can modify given attribute
         """
 
         data_list = setup_McStasData_array()
@@ -206,7 +264,8 @@ def test_name_plot_options_simple(self):
         
     def test_name_plot_options_duplicate(self):
         """
-        Test case where several datasets are modified
+        Test case where several McStasData objects are modified since
+        the internal name_search finds multiple matches
         """
 
         data_list = setup_McStasData_array()

From b827ae4d46187d84b3a9e48fddefc4578dd1720d Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Wed, 13 Jan 2021 10:55:13 +0100
Subject: [PATCH 122/403] Fixed issues with data classes after review from
 Celine Durniak, much appreciated!

Improved docstrings and input sanitation (for example checks chosen  matplotlib colormap exists)
Fixed typos
Added additional test cases
Added additional comments
---
 mcstasscript/data/data.py                 | 166 +++++++++++++++++-----
 mcstasscript/tests/test_McStasData.py     |  89 ++++++++++--
 mcstasscript/tests/test_McStasMetaData.py |   6 +-
 mcstasscript/tests/test_Plotter.py        |   2 +-
 mcstasscript/tests/test_functions.py      |  10 +-
 5 files changed, 217 insertions(+), 56 deletions(-)

diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index 5dc37ad4..ff099f6a 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -1,3 +1,5 @@
+import matplotlib
+
 class McStasMetaData:
     """
     Class for holding metadata for McStas dataset, is to be read from
@@ -20,10 +22,10 @@ class McStasMetaData:
 
     limits : List
         Limits for monitor, length=2 for 1d data and length=4 for 2d
-        data
+        data, for example spatial or time limits for monitor
 
     title : str
-        Title of monitor when plotting
+        Title of monitor when plotting, placed above plot
 
     xlabel : str
         Text for xlabel when plotting
@@ -34,7 +36,7 @@ class McStasMetaData:
     Methods
     -------
     add_info(key,value)
-        Adds a element to the info dictionary
+        Adds an element to the info dictionary
 
     extract_info()
         Unpacks the information in info to class attributes
@@ -87,9 +89,7 @@ def extract_info(self):
         if "filename" in self.info:
             self.filename = self.info["filename"].rstrip()
         else:
-            # Monitors without output files does exist
-            #raise NameError(
-            #    "No filename found in mccode data section!")
+            # Monitors without output files do exist
             print("The component named \"" + self.component_name
                   + "\" had no data file and will not be loaded.")
             self.filename = ""
@@ -97,14 +97,14 @@ def extract_info(self):
         # Extract limits
         self.limits = []
         if "xylimits" in self.info:
-            # find the four numbers
+            # find the four numbers xmin, xmax, ymin, ymax
             temp_str = self.info["xylimits"]
             limits_string = temp_str.split()
             for limit in limits_string:
                 self.limits.append(float(limit))
 
         if "xlimits" in self.info:
-            # find the two numbers
+            # find the two numbers, xmin, xmax
             temp_str = self.info["xlimits"]
             limits_string = temp_str.split()
             for limit in limits_string:
@@ -137,16 +137,43 @@ class McStasPlotOptions:
 
     Attributes
     ----------
-    log : bool
+    log : bool, default False
         To plot on logarithmic or not, standard is linear
 
-    orders_of_mag : float
+    orders_of_mag : float, default 300
         If plotting on log scale, restrict max range to orders_of_mag
         below maximum value
 
-    colormap : string
+    colormap : string, default jet
         Chosen colormap for 2d data, should be available in matplotlib
 
+    show_colorbar : bool, default True
+        Selects if colorbar should be shown or not
+
+    cut_max : float, default 1
+        Factor multiplied onto maximum data value to set upper plot limit
+
+    cut_min : float, default 0
+        Removes given fraction of the plot range from the lower limit
+
+    x_limit_multiplier : float, default 1
+        Multiplies x axis limits with factor, useful for unit changes
+
+    y_limit_multiplier : float, default 1
+        Multiplies y axis limits with factor, useful for unit changes
+
+    custom_ylim_bottom : bool, default False
+        Indicates whether a manual lower limit for y axis has been set
+
+    custom_ylim_top : bool, default False
+        Indicates whether a manual upper limit for y axis has been set
+
+    custom_xlim_left : bool, default False
+        Indicates whether a manual lower limit for x axis has been set
+
+    custom_xlim_right : bool, default False
+        Indicates whether a manual upper limit for x axis has been set
+
     Methods
     -------
     set_options(keyword arguments)
@@ -164,64 +191,128 @@ def __init__(self, *args, **kwargs):
         self.cut_min = 0
         self.x_limit_multiplier = 1
         self.y_limit_multiplier = 1
-        
-        self.custom_ylim_top = False
+
         self.custom_ylim_bottom = False
+        self.custom_ylim_top = False
         self.custom_xlim_left = False
         self.custom_xlim_right = False
 
     def set_options(self, **kwargs):
-        """Set custom values for plotting preferences"""
+        """
+        Set custom values for plotting preferences
+
+        Keyword arguments
+        -----------------
+
+        log : bool, default False
+            To plot on logarithmic or not, standard is linear
+
+        orders_of_mag : float, default 300
+            If plotting on log scale, restrict max range to orders_of_mag
+            below maximum value
+
+        colormap : string, default jet
+            Chosen colormap for 2d data, should be available in matplotlib
+
+        show_colorbar : bool, default True
+            Selects if colorbar should be shown or not
+
+        cut_max : float, default 1
+            Factor multiplied onto maximum data value to set upper plot limit
+
+        cut_min : float, default 0
+            Removes given fraction of the plot range from the lower limit
+
+        x_limit_multiplier : float, default 1
+            Multiplies x axis limits with factor, useful for unit changes
+
+        y_limit_multiplier : float, default 1
+            Multiplies y axis limits with factor, useful for unit changes
+
+        bottom_lim : float
+            Set manual lower limit for y axis
+
+        top_lim : float
+            Set manual upper limit for y axis
+
+        left_lim : float
+            Set manual lower limit for x axis
+
+        right_lim : float
+            Set manual upper limit for x axis
+
+        """
         if "log" in kwargs:
-            log_input = kwargs["log"]
-            if type(log_input) == int:
-                if log_input == 0:
-                    self.log = False
-                else:
-                    self.log = True
-            elif type(log_input) == bool:
-                self.log = log_input
-            else:
-                raise NameError(
-                    "Log input must be either Int or Bool.")
+            self.log = bool(kwargs["log"])
 
         if "orders_of_mag" in kwargs:
             self.orders_of_mag = kwargs["orders_of_mag"]
+            if not isinstance(self.orders_of_mag, (float, int)):
+                raise ValueError("orders_of_mag must be a number, got: "
+                                 + str(self.orders_of_mag))
 
         if "colormap" in kwargs:
+            all_colormaps = matplotlib.pyplot.colormaps()
             self.colormap = kwargs["colormap"]
+            if self.colormap not in all_colormaps:
+                raise ValueError("Chosen colormap not available in "
+                                 + "matplotlib, was: "
+                                 + str(self.colormap))
             
         if "show_colorbar" in kwargs:
-            self.show_colorbar = kwargs["show_colorbar"]
+            self.show_colorbar = bool(kwargs["show_colorbar"])
             
         if "cut_max" in kwargs:
             self.cut_max = kwargs["cut_max"]
+            if not isinstance(self.cut_max, (float, int)):
+                raise ValueError("cut_max has to be a number, was given: "
+                                 + str(self.cut_max))
             
         if "cut_min" in kwargs:
             self.cut_min = kwargs["cut_min"]
+            if not isinstance(self.cut_min, (float, int)):
+                raise ValueError("cut_min has to be a number, was given: "
+                                 + str(self.cut_min))
             
         if "x_axis_multiplier" in kwargs:
             self.x_limit_multiplier = kwargs["x_axis_multiplier"]
+            if not isinstance(self.x_limit_multiplier, (float, int)):
+                raise ValueError("x_limit_multiplier has to be a number, was "
+                                 + "given: " + str(self.x_limit_multiplier))
         
         if "y_axis_multiplier" in kwargs:
             self.y_limit_multiplier = kwargs["y_axis_multiplier"]
+            if not isinstance(self.y_limit_multiplier, (float, int)):
+                raise ValueError("y_limit_multiplier has to be a number, was "
+                                 + "given: " + str(self.y_limit_multiplier))
             
         if "top_lim" in kwargs:
             self.top_lim = kwargs["top_lim"]
             self.custom_ylim_top = True
+            if not isinstance(self.top_lim, (float, int)):
+                raise ValueError("top_lim has to be a number, was "
+                                 + "given: " + str(self.top_lim))
             
         if "bottom_lim" in kwargs:
             self.bottom_lim = kwargs["bottom_lim"]
             self.custom_ylim_bottom = True
-            
+            if not isinstance(self.bottom_lim, (float, int)):
+                raise ValueError("bottom_lim has to be a number, was "
+                                 + "given: " + str(self.bottom_lim))
+
         if "left_lim" in kwargs:
             self.left_lim = kwargs["left_lim"]
             self.custom_xlim_left = True
+            if not isinstance(self.left_lim, (float, int)):
+                raise ValueError("left_lim has to be a number, was "
+                                 + "given: " + str(self.left_lim))
             
         if "right_lim" in kwargs:
             self.right_lim = kwargs["right_lim"]
             self.custom_xlim_right = True
-            
+            if not isinstance(self.right_lim, (float, int)):
+                raise ValueError("right_lim has to be a number, was "
+                                 + "given: " + str(self.right_lim))
 
 
 class McStasData:
@@ -238,11 +329,11 @@ class McStasData:
         Name of component, extracted from metadata
 
     Intensity : numpy array
-        Intensity data [n/s] in 1d or 2d numpy array, dimension in
+        Intensity data [neutrons/s] in 1d or 2d numpy array, dimension in
         metadata
 
     Error : numpy array
-        Error data [n/s] in 1d or 2d numpy array, same dimensions as
+        Error data [neutrons/s] in 1d or 2d numpy array, same dimensions as
         Intensity
 
     Ncount : numpy array
@@ -263,7 +354,7 @@ class McStasData:
     set_title : string
         sets title of data for plotting
 
-    set_optons : keyword arguments
+    set_options : keyword arguments
         sets plot options, keywords passed to McStasPlotOptions method
     """
 
@@ -277,26 +368,23 @@ def __init__(self, metadata, intensity, error, ncount, **kwargs):
         metadata : McStasMetaData instance
             Holds the metadata for the dataset
 
-        name : str
-            Name of component, extracted from metadata
-
         intensity : numpy array
-            Intensity data [n/s] in 1d or 2d numpy array, dimension in
+            Intensity data [neutrons/s] in 1d or 2d numpy array, dimension in
             metadata
 
         error : numpy array
-            Error data [n/s] in 1d or 2d numpy array, same dimensions
+            Error data [neutrons/s] in 1d or 2d numpy array, same dimensions
             as Intensity
 
         ncount : numpy array
-            Number of rays in bin, 1d or 2d numpy array, same
-            dimensions as Intensity
+            Number of rays in bin, 1d or 2d numpy array, same dimensions as
+            Intensity
 
         kwargs : keyword arguments
             xaxis is required for 1d data
         """
 
-        # attatch meta data
+        # attach meta data
         self.metadata = metadata
         # get name from metadata
         self.name = self.metadata.component_name
diff --git a/mcstasscript/tests/test_McStasData.py b/mcstasscript/tests/test_McStasData.py
index f6add939..0335dd37 100644
--- a/mcstasscript/tests/test_McStasData.py
+++ b/mcstasscript/tests/test_McStasData.py
@@ -6,6 +6,9 @@
 
 
 def set_dummy_MetaData_1d():
+    """
+    Sets up simple McStasMetaData object with dimension, 1d case
+    """
     meta_data = McStasMetaData()
     meta_data.component_name = "component for 1d"
     meta_data.dimension = 50
@@ -14,6 +17,9 @@ def set_dummy_MetaData_1d():
 
 
 def set_dummy_McStasData_1d():
+    """
+    Sets up simple McStasData object, 1d case
+    """
     meta_data = set_dummy_MetaData_1d()
 
     intensity = np.arange(20)
@@ -25,6 +31,9 @@ def set_dummy_McStasData_1d():
 
 
 def set_dummy_MetaData_2d():
+    """
+    Sets up simple McStasMetaData object with dimensions, 2d case
+    """
     meta_data = McStasMetaData()
     meta_data.component_name = "test a component"
     meta_data.dimension = [50, 100]
@@ -33,6 +42,9 @@ def set_dummy_MetaData_2d():
 
 
 def set_dummy_McStasData_2d():
+    """
+    Sets up simple McStasData object, 2d case
+    """
     meta_data = set_dummy_MetaData_2d()
 
     intensity = np.arange(20).reshape(4, 5)
@@ -44,12 +56,12 @@ def set_dummy_McStasData_2d():
 
 class TestMcStasData(unittest.TestCase):
     """
-    Various test of McStasData class
+    Various tests of McStasData class
     """
 
     def test_McStasData_init_1d(self):
         """
-        Test that newly created McStasMetaData has correct type
+        Test that newly created McStasMetaData has correct names, 1d case
         """
 
         data = set_dummy_McStasData_1d()
@@ -59,7 +71,8 @@ def test_McStasData_init_1d(self):
 
     def test_McStasData_init_values(self):
         """
-        Test that newly created McStasMetaData has correct type
+        Test that newly created McStasData has expected data, 1d case
+        Here checking a single data point
         """
 
         data = set_dummy_McStasData_1d()
@@ -69,9 +82,27 @@ def test_McStasData_init_values(self):
         self.assertEqual(data.Ncount[3], 6)
         self.assertEqual(data.xaxis[3], 15.0)
 
+    def test_McStasData_init_values_full(self):
+        """
+        Test that newly created McStasData has expected data, 1d case
+        """
+
+        data = set_dummy_McStasData_1d()
+
+        intensity = np.arange(20)
+        error = 0.5 * np.arange(20)
+        ncount = 2 * np.arange(20)
+        axis = np.arange(20) * 5.0
+
+        for index in range(len(data.Intensity)):
+            self.assertEqual(data.Intensity[index], intensity[index])
+            self.assertEqual(data.Error[index], error[index])
+            self.assertEqual(data.Ncount[index], ncount[index])
+            self.assertEqual(data.xaxis[index], axis[index])
+
     def test_McStasData_init_2d_names(self):
         """
-        Test that newly created McStasMetaData has correct type
+        Test that newly created McStasMetaData has correct names, 1d case
         """
 
         data = set_dummy_McStasData_2d()
@@ -81,7 +112,8 @@ def test_McStasData_init_2d_names(self):
 
     def test_McStasData_init_2d_values(self):
         """
-        Test that newly created McStasMetaData has correct type
+        Test that newly created McStasData has expected data, 2d case
+        Here checking a single point
         """
 
         data = set_dummy_McStasData_2d()
@@ -90,6 +122,30 @@ def test_McStasData_init_2d_values(self):
         self.assertEqual(data.Error[2][3], 6.5)
         self.assertEqual(data.Ncount[2][3], 26)
 
+    def test_McStasData_init_2d_values_full(self):
+        """
+        Test that newly created McStasData has expected data, 2d case
+        Here checking a entire dataset
+        """
+
+        data = set_dummy_McStasData_2d()
+
+        intensity = np.arange(20).reshape(4, 5)
+        error = 0.5 * np.arange(20).reshape(4, 5)
+        ncount = 2 * np.arange(20).reshape(4, 5)
+
+        shape = np.shape(data.Intensity)
+
+        for index1 in range(shape[0]):
+            for index2 in range(shape[1]):
+
+                self.assertEqual(data.Intensity[index1][index2],
+                                 intensity[index1][index2])
+                self.assertEqual(data.Error[index1][index2],
+                                 error[index1][index2])
+                self.assertEqual(data.Ncount[index1][index2],
+                                 ncount[index1][index2])
+
     def test_McStasData_set_info_title(self):
         """
         Test that title can be set
@@ -116,7 +172,7 @@ def test_McStasData_set_ylabel(self):
 
     def test_McStasData_set_log(self):
         """
-        Test that newly created McStasMetaData has correct type
+        Test that log setting has correct type regardless of how it is given
         """
         data = set_dummy_McStasData_2d()
         data.set_plot_options(log=True)
@@ -131,9 +187,26 @@ def test_McStasData_set_log(self):
         self.assertIsInstance(data.plot_options.log, bool)
         self.assertTrue(data.plot_options.log)
 
+    def test_McStasData_set_show_colorbar(self):
+        """
+        Test that log setting has correct type regardless of how it is given
+        """
+        data = set_dummy_McStasData_2d()
+        data.set_plot_options(show_colorbar=True)
+        self.assertIsInstance(data.plot_options.show_colorbar, bool)
+        self.assertTrue(data.plot_options.show_colorbar)
+
+        data.set_plot_options(show_colorbar=0)
+        self.assertIsInstance(data.plot_options.show_colorbar, bool)
+        self.assertFalse(data.plot_options.show_colorbar)
+
+        data.set_plot_options(show_colorbar=1)
+        self.assertIsInstance(data.plot_options.show_colorbar, bool)
+        self.assertTrue(data.plot_options.show_colorbar)
+
     def test_McStasData_set_orders_of_mag(self):
         """
-        Test that newly created McStasMetaData has correct type
+        Test that orders_og_mag can be set correctly
         """
         data = set_dummy_McStasData_2d()
         data.set_plot_options(orders_of_mag=5.2)
@@ -141,7 +214,7 @@ def test_McStasData_set_orders_of_mag(self):
 
     def test_McStasData_set_colormap(self):
         """
-        Test that newly created McStasMetaData has correct type
+        Test that colormap can be set correctly
         """
         data = set_dummy_McStasData_2d()
         data.set_plot_options(colormap="hot")
diff --git a/mcstasscript/tests/test_McStasMetaData.py b/mcstasscript/tests/test_McStasMetaData.py
index aa7be2c0..0c73e3aa 100644
--- a/mcstasscript/tests/test_McStasMetaData.py
+++ b/mcstasscript/tests/test_McStasMetaData.py
@@ -5,7 +5,7 @@
 
 class TestMcStasMetaData(unittest.TestCase):
     """
-    Various test of McStasMetaData class
+    Various tests of McStasMetaData class
     """
 
     def test_McStasMetaData_return_type(self):
@@ -64,7 +64,7 @@ def test_McStasMetaData_add_info_ylabel(self):
 
     def test_McStasMetaData_long_read_1d(self):
         """
-        Test that extact info can read appropriate info
+        Test that extact_info can read appropriate info, 1d case
         """
         meta_data = McStasMetaData()
         meta_data.add_info("type", "array_1d(500)")
@@ -90,7 +90,7 @@ def test_McStasMetaData_long_read_1d(self):
 
     def test_McStasMetaData_long_read_2d(self):
         """
-        Test that extact info can read appropriate info
+        Test that extact_info can read appropriate info, 2d case
         """
         meta_data = McStasMetaData()
         meta_data.add_info("type", "array_2d(500, 12)")
diff --git a/mcstasscript/tests/test_Plotter.py b/mcstasscript/tests/test_Plotter.py
index ac6a4092..3a6c7d90 100644
--- a/mcstasscript/tests/test_Plotter.py
+++ b/mcstasscript/tests/test_Plotter.py
@@ -394,7 +394,7 @@ def test_handle_kwargs_all_simple(self):
 
         test_value = {"log": True, "orders_of_mag": 15,
                       "cut_min": 0.25, "cut_max": 0.8,
-                      "colormap": "hot", "show_colorbar": "False",
+                      "colormap": "hot", "show_colorbar": False,
                       "x_limit_multiplier": 2.8, "y_limit_multiplier": 0.8}
 
         for option in known_plot:
diff --git a/mcstasscript/tests/test_functions.py b/mcstasscript/tests/test_functions.py
index 5e005d28..b646a60f 100644
--- a/mcstasscript/tests/test_functions.py
+++ b/mcstasscript/tests/test_functions.py
@@ -258,9 +258,9 @@ def test_name_plot_options_simple(self):
         """
 
         data_list = setup_McStasData_array()
-        name_plot_options("Hero", data_list, colormap="very hot")
+        name_plot_options("Hero", data_list, colormap="Oranges")
         hero_object = name_search("Hero", data_list)
-        self.assertEqual(hero_object.plot_options.colormap, "very hot")
+        self.assertEqual(hero_object.plot_options.colormap, "Oranges")
         
     def test_name_plot_options_duplicate(self):
         """
@@ -276,13 +276,13 @@ def test_name_plot_options_duplicate(self):
 
         data_list.append(hero_object)
         
-        name_plot_options("Hero", data_list, colormap="cold")
+        name_plot_options("Hero", data_list, colormap="Blues")
         
         results = name_search("Hero", data_list)
         
         self.assertEqual(len(results), 2)
-        self.assertEqual(results[0].plot_options.colormap, "cold")
-        self.assertEqual(results[1].plot_options.colormap, "cold")
+        self.assertEqual(results[0].plot_options.colormap, "Blues")
+        self.assertEqual(results[1].plot_options.colormap, "Blues")
 
 
 class Test_load_data(unittest.TestCase):

From d3f452d46cc8f59da7ec5c1e2a223615e2244f60 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Wed, 13 Jan 2021 11:04:58 +0100
Subject: [PATCH 123/403] Added input sanitation for creation of McStasData,
 here intensity, error and ncount should be numpy ndarrays.

Thanks to Celine for the suggestion.
---
 mcstasscript/data/data.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index ff099f6a..676fcda1 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -1,4 +1,5 @@
 import matplotlib
+import numpy as np
 
 class McStasMetaData:
     """
@@ -389,6 +390,13 @@ def __init__(self, metadata, intensity, error, ncount, **kwargs):
         # get name from metadata
         self.name = self.metadata.component_name
         # three basic arrays from positional arguments
+        if not isinstance(intensity, np.ndarray):
+            raise ValueError("intensity should be numpy array!")
+        if not isinstance(error, np.ndarray):
+            raise ValueError("error should be numpy array!")
+        if not isinstance(ncount, np.ndarray):
+            raise ValueError("ncount should be numpy array!")
+
         self.Intensity = intensity
         self.Error = error
         self.Ncount = ncount

From 7c4105a1647b1bf8e12886446d9e4e340fad7188 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Wed, 20 Jan 2021 11:30:36 +0100
Subject: [PATCH 124/403] Addressing review comments from Celine Durniak, much
 appreciated!

The review concerns McStas objects and their tests.

Fixed typos in docstrings and comments.

Documented missing methods in docstrings.

Added input sanitation for parameters, declare variable and some
component methods.

Added the option of specifying a float to set_AT instead of a list,
as the most common use is just a distance in the z direction.

Simplified path handling in component_reader

Converted to built-in startswith method instead of adding my own method.

Added a test for checking _freeze and _unfreeze methods on component.

Added test for SPLIT number being a variable.

Declare variable and parameter had different behavior in terms of
spacing after type, this was simplified so only one convention is
used, which incliudes no additional spaces.

Ensured that tests fail when targets are changed after concern
expressed by reviewer.
---
 mcstasscript/helper/component_reader.py       | 102 ++++----
 mcstasscript/helper/mcstas_objects.py         | 228 ++++++++++++++----
 mcstasscript/tests/test_ComponentReader.py    |  60 ++---
 mcstasscript/tests/test_Instr.py              |  42 ++--
 mcstasscript/tests/test_Instr_reader.py       |   6 +-
 mcstasscript/tests/test_component.py          | 104 ++++++--
 mcstasscript/tests/test_declare_variable.py   |  14 +-
 mcstasscript/tests/test_functions.py          |   2 +-
 mcstasscript/tests/test_parameter_variable.py |  14 +-
 9 files changed, 371 insertions(+), 201 deletions(-)

diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index cb806f9f..daabd19f 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -19,15 +19,15 @@ def __init__(self):
 
 class ComponentReader:
     """
-    Class for retriveing information on available McStas components
+    Class for retrieving information on available McStas components
 
     Recursively reads all component files in hardcoded list of
     folders that represents the component categories in McStas.
     The results are stored in a dictionary with ComponentInfo
     instances, the keys are the names of the components. After
     the components in the McStas installation are read, any
-    components pressent in the current work directory is read,
-    and these will overwrite exisiting information, consistent
+    components present in the current work directory is read,
+    and these will overwrite existing information, consistent
     with how McStas reads component definitions.
 
     """
@@ -48,10 +48,6 @@ def __init__(self, mcstas_path, input_path="."):
 
         """
 
-        # add trailing / or \ depending on operating system
-        if mcstas_path[-1] is not "/" and mcstas_path[-1] is not "\\":
-            mcstas_path = os.path.join(mcstas_path, "") 
-
         # Hardcoded whitelist of foldernames
         folder_list = ["sources",
                        "optics",
@@ -67,6 +63,7 @@ def __init__(self, mcstas_path, input_path="."):
 
         for folder in folder_list:
             abs_path = os.path.join(mcstas_path, folder)
+            abs_path = os.path.abspath(abs_path)
             self._find_components(abs_path)
 
         # Will overwrite McStas components with definitions in input_folder
@@ -86,15 +83,16 @@ def __init__(self, mcstas_path, input_path="."):
             print("input_path: ", input_directory)
             raise ValueError("Can't find given input_path,"
                              + " directory must exist.")
-
+        """
+        If components are present both in the McStas install and the 
+        work directory, the version in the work directory is used. The user
+        is informed of this behavior when the instrument object is created.
+        """
         overwritten_components = []
         for file in os.listdir(input_directory):
             if file.endswith(".comp"):
                 abs_path = os.path.join(input_directory, file)
-                if "/" in abs_path:
-                    component_name = abs_path.split("/")[-1].split(".")[-2]
-                else:
-                    component_name = abs_path.split("\\")[-1].split(".")[-2]
+                component_name = os.path.split(abs_path)[1].split(".")[-2]
 
                 if component_name in self.component_path:
                     overwritten_components.append(file)
@@ -102,6 +100,7 @@ def __init__(self, mcstas_path, input_path="."):
                 self.component_path[component_name] = abs_path
                 self.component_category[component_name] = "Work directory"
 
+        # Report components found in the work directory and install to the user
         if len(overwritten_components) > 0:
             print("The following components are found in the work_directory"
                   + " / input_path:")
@@ -129,7 +128,10 @@ def show_components_in_category(self, category_input, **kwargs):
 
         """
         if "line_length" in kwargs:
-            line_limit = kwargs["line_length"]
+            line_limit = int(kwargs["line_length"])
+            if line_limit < 20:
+                raise ValueError("line_length should be more than 20 "
+                                 + "characters, was " + str(line_limit))
         else:
             line_limit = 100
 
@@ -150,7 +152,7 @@ def show_components_in_category(self, category_input, **kwargs):
             for component in to_print:
                 print(" " + component)
         else:
-            # Prints in collumns, maximum 4 and maximum line length line_liimt
+            # Prints in columns, maximum 4 and maximum line length line_limit
             columns = 5
             total_line_length = 1000
             while(total_line_length > line_limit):
@@ -171,9 +173,9 @@ def show_components_in_category(self, category_input, **kwargs):
 
                 total_line_length = 1 + sum(longest_name) + (columns-1)*3
 
-            for line_nr in range(0, c_length):
+            for line_nr in range(c_length):
                 print(" ", end="")
-                for col in range(0, columns-1):
+                for col in range(columns-1):
                     this_name = column[col][line_nr]
                     print(this_name
                           + " "*(longest_name[col] - len(this_name))
@@ -228,6 +230,9 @@ def _find_components(self, absolute_path):
 
         """
 
+        if not os.path.isabs(absolute_path):
+            raise RuntimeError("_find_components received non absolute path")
+
         if not os.path.isdir(absolute_path):
             if absolute_path.endswith(".comp"):
                 # read this file
@@ -252,20 +257,20 @@ def read_component_file(self, absolute_path):
 
         result = ComponentInfo()
 
-        fo = open(absolute_path, "r")
+        file_o = open(absolute_path, "r")
 
-        cnt = 0
+        line_number = 0
         while True:
-            cnt += 1
-            line = fo.readline()
+            line_number += 1
+            line = file_o.readline()
 
             # find parameter comments
-            if self.line_starts_with(line, "* %P"):
+            if line.startswith("* %P"):
 
                 while True:
-                    this_line = fo.readline()
+                    this_line = file_o.readline()
 
-                    if self.line_starts_with(this_line, "DEFINE COMPONENT"):
+                    if this_line.startswith("DEFINE COMPONENT"):
                         # No more comments to read through
                         break
 
@@ -308,9 +313,9 @@ def read_component_file(self, absolute_path):
                         result.parameter_comments[variable_name] = comment
 
             # find definition parameters and their values
-            if (self.line_starts_with(line.strip(), "DEFINITION PARAMETERS")
-                    or self.line_starts_with(line.strip(),
-                                             "SETTING PARAMETERS")):
+            if (line.strip().startswith("DEFINITION PARAMETERS")
+                    or line.strip().startswith("SETTING PARAMETERS")):
+
 
                 line = line.split("//")[0] # Remove comments
                 parts = line.split("(")
@@ -350,16 +355,16 @@ def read_component_file(self, absolute_path):
                         if part == "":
                             continue
 
-                        if self.line_starts_with(part, "//"):
+                        if part.startswith("//"):
                             break_now = True
                             continue
 
-                        if self.line_starts_with(part, "/*"):
+                        if part.startswith("/*"):
                             break_now = True
                             continue
 
                         if "=" not in part:
-                            # no defualt value, required parameter
+                            # no default value, required parameter
                             result.parameter_names.append(part)
                             result.parameter_defaults[part] = None
                             result.parameter_types[part] = temp_par_type
@@ -373,8 +378,8 @@ def read_component_file(self, absolute_path):
                                 try:
                                     par_value = float(par_value)
                                 except:
-                                    par_value = par_value
                                     # Could change the type
+                                    par_value = par_value
                             elif temp_par_type is "int":
                                 par_value = int(par_value)
 
@@ -385,27 +390,26 @@ def read_component_file(self, absolute_path):
                     if break_now:
                         break
 
-                    new_line = fo.readline().split("//")[0]
+                    new_line = file_o.readline().split("//")[0]
                     parameter_parts = new_line.split(",")
                     parameter_parts = self.correct_for_brackets(parameter_parts)
 
-            if self.line_starts_with(line, "DECLARE"):
+            if line.startswith("DECLARE"):
                 break
 
-            if self.line_starts_with(line, "TRACE"):
+            if line.startswith("TRACE"):
                 break
 
-            if cnt == 1000:
+            if line_number == 4000:
                 break
 
-        fo.close()
+        file_o.close()
 
         result.name = os.path.split(absolute_path)[1].split(".")[-2]
         
         tail = os.path.split(absolute_path)[0]
         result.category = os.path.split(tail)[1]
 
-
         """
         To lower memory use one could remove all comments and units that
         does not correspond to a found parameter name.
@@ -414,15 +418,24 @@ def read_component_file(self, absolute_path):
         return result
     
     def correct_for_brackets(self, parameter_parts):
+        """
+        Given list of string elements, correct for brackets will
+        combine terms until curly brackets are balanced, for example:
+
+        ["A", "{B", "C", "D}", "E"] would return ["A", "{B,C,D}", "E"]
+
+        Default values of vectors can be given in such a manner in
+        McStas components, and without this each part would be recognized
+        as different parameters.
+        """
         corrected_parts = []
-        current_part = ""
         index = 0
         while True:
             
             current_part = parameter_parts[index]
             inner_index = 0
             while True: 
-                if (current_part.count("{") == current_part.count("}")):
+                if current_part.count("{") == current_part.count("}"):
                     corrected_parts.append(current_part)
                     index += inner_index
                     break                          
@@ -436,17 +449,4 @@ def correct_for_brackets(self, parameter_parts):
                 break
             
         return corrected_parts
-        
 
-    def line_starts_with(self, line, string):
-        """
-        Helper method that checks if a string is the start of a line
-
-        """
-        if len(line) < len(string):
-            return False
-
-        if line[0:len(string)] == string:
-            return True
-        else:
-            return False
diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 2f71e4cc..96f420b1 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -4,7 +4,7 @@
 
 class parameter_variable:
     """
-    Class describing a input parameter in McStas instrument
+    Class describing an input parameter in McStas instrument
 
     McStas input parameters are of default type double, but can be
     cast.  If two positional arguments are given, the first is the
@@ -35,6 +35,18 @@ class parameter_variable:
     def __init__(self, *args, **kwargs):
         """Initializing mcstas parameter object
 
+        Examples
+        --------
+
+        Creates a parameter with name wavelength and associated comment
+        A = parameter_variable("wavelength", comment="wavelength in [AA]")
+
+        Creates a parameter with name A3 and default value
+        A = parameter_variable("A3", value=30, comment="A3 angle in [deg]")
+
+        Creates a parameter with type string and name sample_name
+        A = parameter_variable("string", "sample_name")
+
         Parameters
         ----------
         If giving a type:
@@ -52,12 +64,23 @@ def __init__(self, *args, **kwargs):
                 sets default value of parameter
             comment : str
                 sets comment displayed next to declaration
+
+
         """
         if len(args) == 1:
             self.type = ""
             self.name = str(args[0])
         if len(args) == 2:
-            self.type = args[0] + " "
+            specified_type = args[0]
+            allowed_types = {"double", "int", "string"}
+            if specified_type not in allowed_types:
+                raise RuntimeError("Tried to create parameter of type \""
+                                   + str(specified_type)
+                                   + "\" which is not among the allowed types "
+                                   + str(allowed_types) + ".")
+
+            #self.type = specified_type + " "
+            self.type = specified_type
             self.name = str(args[1])
 
         if not is_legal_parameter(self.name):
@@ -68,18 +91,27 @@ def __init__(self, *args, **kwargs):
 
         self.value = ""
         if "value" in kwargs:
+            if not isinstance(kwargs["value"], (str, int, float)):
+                raise RuntimeError("Given value for parameter has to be of"
+                                   + "type str, int or float.")
             self.value = kwargs["value"]
 
         self.comment = ""
         if "comment" in kwargs:
-            self.comment = "// " + kwargs["comment"]
-
-        # could check for allowed types
-        # they are int, double, string, are there more?
+            self.comment = kwargs["comment"]
+            if not isinstance(self.comment, str):
+                raise RuntimeError("Tried to create a parameter with a "
+                                   + "comment that was not a string.")
+            self.comment = "// " + self.comment
 
     def write_parameter(self, fo, stop_character):
         """Writes input parameter to file"""
-        fo.write("%s%s" % (self.type, self.name))
+
+        if not isinstance(stop_character, str):
+            raise RuntimeError("stop_character in write_parameter should be "
+                               + "a string.")
+
+        fo.write("%s %s" % (self.type, self.name))
         if self.value != "":
             if isinstance(self.value, int):
                 fo.write(" = %d" % self.value)
@@ -123,16 +155,29 @@ class declare_variable:
     write_line(fo)
         Writes a line to text file fo declaring the parameter in c
     """
-    def __init__(self, *args, **kwargs):
+    def __init__(self, type, name, **kwargs):
         """
         Initializing mcstas parameter object
 
+        Examples
+        --------
+
+        Creates a variable with name A3 and default value
+        A = declare_variable("double", "A3", value=30)
+
+        Creates a variable with type integer and name sample_number
+        A = declare_variable("int", "sample_number")
+
+        Creates an array variable called m_values
+        A = declare_variable("double", "m_values", array=3,
+                             value=[2, 2.5, 2])
+
         Parameters
         ----------
-        Positional argument 1: type : str
+        type : str
             Type of the parameter, double, int or string
 
-        Positional argument 2: name : str
+        name : str
             Name of input parameter
 
         Keyword arguments
@@ -146,9 +191,13 @@ def __init__(self, *args, **kwargs):
             comment : str
                 sets comment displayed next to declaration
         """
-        self.type = args[0]
-        self.name = str(args[1])
 
+        self.type = type
+        if not isinstance(self.type, str):
+            raise RuntimeError("Given type of declare_variable should be a "
+                               + "string.")
+
+        self.name = str(name)
 
         par_name = self.name
         if "*" in par_name[0]:
@@ -219,13 +268,13 @@ def write_line(self, fo):
 
 class component:
     """
-    A class describing a McStas component to be written to a instrument
+    A class describing a McStas component to be written to an instrument
 
     This class is used by the instrument class when setting up
     components as dynamic subclasses to this class.  Most information
     can be given on initialize using keyword arguments, but there are
     methods for setting the attributes describing the component. The
-    class contains both methods to write the component to a instrument
+    class contains both methods to write the component to an instrument
     file and methods for printing to the python terminal for checking
     the information. The McStas_Instr class creates subclasses from
     this class that have attributes for all parameters for the given
@@ -243,34 +292,43 @@ class contains both methods to write the component to a instrument
     component_name : str
         Name of the component code to use, e.g. Arm, Guide_gravity, ...
 
-    AT_data : list of 3 floats
+    AT_data : list of 3 floats, default [0, 0, 0]
         Position data of the component
 
-    AT_relative : str
+    AT_relative : str, default "ABSOLUTE"
         Name of former component to use as reference for position
 
-    ROTATED_data : list of 3 floats
+    ROTATED_data : list of 3 floats, default [0, 0, 0]
         Rotation data of the component
 
-    ROTATED_relative : str
+    ROTATED_relative : str, default "ABSOLUTE"
         Name of former component to use as reference for position
 
-    WHEN : str
+    WHEN : str, default ""
         String with logical c expression x for when component is active
 
-    EXTEND : str
+    EXTEND : str, default ""
         c code for McStas EXTEND section
 
-    GROUP : str
+    GROUP : str, default ""
         Name of group the component should belong to
 
-    JUMP : str
+    JUMP : str, default ""
         String describing use of JUMP, need to contain all after "JUMP"
 
+    SPLIT : int, default 0 (disabled)
+        Integer setting SPLIT, splitting the neutron before this component
+
+    c_code_before : str, default ""
+        C code inserted before the component
+
+    c_code_after : str, default ""
+        C code inserted after the component
+
     component_parameters : dict
         Parameters to be used with component in dictionary
 
-    comment : str
+    comment : str, default ""
         Comment inserted before the component as an explanation
 
     __isfrozen : bool
@@ -299,6 +357,9 @@ class contains both methods to write the component to a instrument
     set_WHEN(string)
         Sets WHEN string
 
+    set_SPLIT(value)
+        Sets SPLIT value, a value of 0 disables SPLIT
+
     set_GROUP(string)
         Sets GROUP name
 
@@ -308,18 +369,33 @@ class contains both methods to write the component to a instrument
     append_EXTEND(string)
         Append string to EXTEND string
 
+    set_c_code_before(string)
+        Sets c code to be inserted before component
+
+    set_c_code_before(string)
+        Sets c code to be inserted after component
+
     set_comment(string)
         Sets comment for component
 
     write_component(fo)
         Writes component code to instrument file
 
+    show_parameters()
+        Prints current component state with all parameters
+
     print_long()
         Prints basic view of component code (not correct syntax)
 
+    print_long_deprecated()
+        Prints basic view of component code (obsolete)
+
     print_short(**kwargs)
         Prints short description, used in print_components
 
+    set_keyword_input(**kwargs)
+        Handle keyword arguments during initialize
+
     __setattr__(key, value)
         Overwriting __setattr__ to implement ability to freeze
 
@@ -345,37 +421,37 @@ def __init__(self, instance_name, component_name, **kwargs):
             name of the component type e.g. Arm, Guide_gravity, ...
 
         keyword arguments:
-            AT : list of 3 floats
+            AT : list of 3 floats, default [0, 0, 0]
                 Sets AT_data describing position of component
 
-            AT_RELATIVE : str
+            AT_RELATIVE : str, default "ABSOLUTE"
                 sets AT_relative, describing position reference
 
-            ROTATED : list of 3 floats
+            ROTATED : list of 3 floats, default [0, 0, 0]
                 Sets ROTATED_data, describing rotation of component
 
-            ROTATED_RELATIVE : str
+            ROTATED_RELATIVE : str, default "ABSOLUTE"
                 Sets ROTATED_relative, sets reference for rotation
 
             RELATIVE : str
                 Sets both AT_relative and ROTATED_relative
 
-            WHEN : str
+            WHEN : str, default ""
                 Sets WHEN string, should contain logical c expression
 
-            EXTEND : str
+            EXTEND : str, default ""
                 Sets initial EXTEND string, should contain c code
 
-            GROUP : str
+            GROUP : str, default ""
                 Sets name of group the component should belong to
 
-            JUMP : str
+            JUMP : str, default ""
                 Sets JUMP str
                 
-            SPLIT : int
+            SPLIT : int, default 0 (disabled)
                 Sets SPLIT value
 
-            comment: str
+            comment: str, default ""
                 Sets comment string
         """
 
@@ -405,7 +481,7 @@ def __init__(self, instance_name, component_name, **kwargs):
 
         """
         Could store an option for whether this component should be
-        printed in instrument file or in a seperate file which would
+        printed in instrument file or in a separate file which would
         then be included.
         """
 
@@ -433,29 +509,28 @@ def set_keyword_input(self, **kwargs):
             self.set_RELATIVE(kwargs["RELATIVE"])
 
         if "WHEN" in kwargs:
-            self.WHEN = "WHEN (" + kwargs["WHEN"] + ")"
+            self.set_WHEN(kwargs["WHEN"])
 
         if "EXTEND" in kwargs:
-            self.EXTEND = kwargs["EXTEND"] + "\n"
+            self.append_EXTEND(kwargs["EXTEND"])
 
         if "GROUP" in kwargs:
-            self.GROUP = kwargs["GROUP"]
+            self.set_GROUP(kwargs["GROUP"])
 
         if "JUMP" in kwargs:
-            self.JUMP = kwargs["JUMP"]
+            self.set_JUMP(kwargs["JUMP"])
             
         if "SPLIT" in kwargs:
-            self.SPLIT = kwargs["SPLIT"]
+            self.set_SPLIT(kwargs["SPLIT"])
 
         if "comment" in kwargs:
-            self.comment = kwargs["comment"]
+            self.set_comment(kwargs["comment"])
             
         if "c_code_before" in kwargs:
-            self.c_code_before = kwargs["c_code_before"]
+            self.set_c_code_before(kwargs["c_code_before"])
         
         if "c_code_after" in kwargs:
-            self.c_code_after = kwargs["c_code_after"]
-        
+            self.set_c_code_after(kwargs["c_code_after"])
 
     def __setattr__(self, key, value):
         if self.__isfrozen and not hasattr(self, key):
@@ -475,7 +550,19 @@ def _unfreeze(self):
         self.__isfrozen = False
 
     def set_AT(self, at_list, RELATIVE=None):
-        """Sets AT data, List of 3 floats"""
+        """Sets AT data, List of 3 floats or single float for z only"""
+        if isinstance(at_list, (int, float)):
+            at_list = [0, 0, at_list]
+
+        if not isinstance(at_list, list):
+            raise RuntimeError("set_AT should be given either a list or "
+                               + "float, but received "
+                               + str(type(at_list)))
+
+        if len(at_list) != 3:
+            raise RuntimeError("Position data given to set_AT should "
+                               + "either be of length 3 or just a float.")
+
         self.AT_data = at_list
         if RELATIVE is not None:
             self.set_AT_RELATIVE(RELATIVE)
@@ -498,6 +585,15 @@ def set_AT_RELATIVE(self, relative):
 
     def set_ROTATED(self, rotated_list, RELATIVE=None):
         """Sets ROTATED data, List of 3 floats"""
+        if not isinstance(rotated_list, list):
+            raise RuntimeError("set_ROTATED should be given a list "
+                               + " but received "
+                               + str(type(rotated_list)))
+
+        if len(rotated_list) != 3:
+            raise RuntimeError("Rotation data given to set_ROTATED should "
+                               + "be of length 3.")
+
         self.ROTATED_data = rotated_list
         self.ROTATED_specified = True
         if RELATIVE is not None:
@@ -538,12 +634,13 @@ def set_RELATIVE(self, relative):
 
     def set_parameters(self, dict_input):
         """
-        Adds parameters and their values from dictionary input
+        Set component parameters from dictionary input
 
-        Relies on attributes added when McStas_Instr creates a
-        subclass from the component class where each component
-        parameter is added as an attribute.
+        Relies on attributes added when McStas_Instr creates a subclass from
+        the component class where each component parameter is added as an
+        attribute.
 
+        An error is raised if trying to set a parameter that does not exist
         """
         for key, val in dict_input.items():
             if not hasattr(self, key):
@@ -559,34 +656,64 @@ def set_parameters(self, dict_input):
 
     def set_WHEN(self, string):
         """Sets WHEN string, should be a c logical expression"""
+        if not isinstance(string, str):
+            raise RuntimeError("set_WHEN expect a string, but was "
+                               + "given " + str(type(string)))
         self.WHEN = "WHEN (" + string + ")"
 
     def set_GROUP(self, string):
         """Sets GROUP name"""
+        if not isinstance(string, str):
+            raise RuntimeError("set_GROUP expect a string, but was "
+                               + "given " + str(type(string)))
         self.GROUP = string
 
     def set_JUMP(self, string):
         """Sets JUMP string, should contain all text after JUMP"""
+        if not isinstance(string, str):
+            raise RuntimeError("set_JUMP expect a string, but was "
+                               + "given " + str(type(string)))
         self.JUMP = string
         
     def set_SPLIT(self, value):
-        """Sets SPLIT value, should contain all text after JUMP"""
+        """Sets SPLIT value, needs to be an integer"""
+        if not isinstance(value, (int, str)):
+            raise RuntimeError("set_SPLIT expect a integer or string, but "
+                               + "was given " + str(type(value)))
+
+        if isinstance(value, int):
+            if value < 0:
+                raise RuntimeError("set_SPLIT got a negative value, this is "
+                                   + "meaningless, has to be a positive value.")
+
         self.SPLIT = value
 
     def append_EXTEND(self, string):
         """Appends a line of code to EXTEND block of component"""
+        if not isinstance(string, str):
+            raise RuntimeError("append_EXTEND expect a string, but was "
+                               + "given " + str(type(string)))
         self.EXTEND = self.EXTEND + string + "\n"
 
     def set_comment(self, string):
         """Method that sets a comment to be written to instrument file"""
+        if not isinstance(string, str):
+            raise RuntimeError("set_comment expect a string, but was "
+                               + "given " + str(type(string)))
         self.comment = string
         
     def set_c_code_before(self, string):
         """Method that sets c code to be written before the component"""
+        if not isinstance(string, str):
+            raise RuntimeError("set_c_code_before expect a string, but was "
+                               + "given " + str(type(string)))
         self.c_code_before = string
         
     def set_c_code_after(self, string):
         """Method that sets c code to be written after the component"""
+        if not isinstance(string, str):
+            raise RuntimeError("set_c_code_after expect a string, but was "
+                               + "given " + str(type(string)))
         self.c_code_after = string
 
     def write_component(self, fo):
@@ -601,7 +728,6 @@ def write_component(self, fo):
         # Could use character limit on lines instead
         parameters_written = 0  # internal parameter
 
-
         if len(self.c_code_before) > 0:
             explanation = "From component named " + self.name
             fo.write("%s // %s\n" % (str(self.c_code_before), explanation))
@@ -689,7 +815,7 @@ def write_component(self, fo):
         # Leave a new line between components for readability
         fo.write("\n")
 
-    def print_long_depricated(self):
+    def print_long_deprecated(self):
         """
         Prints contained information to Python terminal
 
diff --git a/mcstasscript/tests/test_ComponentReader.py b/mcstasscript/tests/test_ComponentReader.py
index 21eccc5c..c512a437 100644
--- a/mcstasscript/tests/test_ComponentReader.py
+++ b/mcstasscript/tests/test_ComponentReader.py
@@ -8,6 +8,11 @@
 
 
 def setup_component_reader():
+    """
+    Sets up a component_reader instance with dummy mcstas
+    installation located in the tests directory
+    """
+
     THIS_DIR = os.path.dirname(os.path.abspath(__file__))
     dummy_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
@@ -21,6 +26,11 @@ def setup_component_reader():
     return component_reader
 
 def setup_component_reader_input_path():
+    """
+    Sets up a component_reader instance with dummy mcstas
+    installation located in the tests directory, and specifies
+    a input_path which is also located in the test directory.
+    """
     THIS_DIR = os.path.dirname(os.path.abspath(__file__))
     dummy_path = os.path.join(THIS_DIR, "dummy_mcstas")
     input_path = os.path.join(THIS_DIR, "test_input_folder")
@@ -40,13 +50,14 @@ class TestComponentReader(unittest.TestCase):
     """
     Testing the ComponenReader class. As this class reads information
     from McStas, a dummy McStas install is made in the test folder to
-    avoid the test results changeing with updates of McStas.
+    avoid the test results changing with updates of McStas.
     """
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_init_overwrite_message(self, mock_stdout):
         """
         Test that ComponentReader reports overwritten components
+        Here using default input path, which is current folder
         """
 
         component_reader = setup_component_reader()
@@ -62,6 +73,7 @@ def test_ComponentReader_init_overwrite_message(self, mock_stdout):
     def test_ComponentReader_init_overwrite_message_input(self, mock_stdout):
         """
         Test that ComponentReader reports overwritten components
+        Here using user defined input path, a directory in tests
         """
 
         component_reader = setup_component_reader_input_path()
@@ -466,7 +478,7 @@ def test_ComponentReader_read_component_required(self, mock_stdout):
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_read_component_int(self, mock_stdout):
         """
-        Test that a integer parameter is read correctly when reading a
+        Test that a integer parameter is read correctly when reading an
         component file.
         Has default, is int type (comments and unit checked already)
         """
@@ -517,50 +529,6 @@ def test_ComponentReader_read_component_string(self, mock_stdout):
         self.assertNotIn("test_string", CompInfo.parameter_units)
         # Have already tested units are read
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_ComponentReader_line_start_long(self, mock_stdout):
-        """
-        Helper function that should return true when certain string is
-        the start of another string.
-
-        """
-
-        component_reader = setup_component_reader()
-
-        test_string = "monkey wants banana"
-
-        return_val = component_reader.line_starts_with(test_string, "mo")
-        self.assertIsInstance(return_val, bool)
-        self.assertTrue(return_val)
-
-        return_val = component_reader.line_starts_with(test_string, "on")
-        self.assertIsInstance(return_val, bool)
-        self.assertFalse(return_val)
-
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_ComponentReader_line_start_short(self, mock_stdout):
-        """
-        Helper function that should return true when certain string is
-        the start of another string. Here checked with short test_string
-
-        """
-
-        component_reader = setup_component_reader()
-
-        test_string = "m"
-
-        return_val = component_reader.line_starts_with(test_string, "m")
-        self.assertIsInstance(return_val, bool)
-        self.assertTrue(return_val)
-
-        return_val = component_reader.line_starts_with(test_string, "mo")
-        self.assertIsInstance(return_val, bool)
-        self.assertFalse(return_val)
-
-        return_val = component_reader.line_starts_with(test_string, "on")
-        self.assertIsInstance(return_val, bool)
-        self.assertFalse(return_val)
-
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index df6560a9..13a4e804 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -278,8 +278,8 @@ def test_show_parameters(self, mock_stdout):
         instr = setup_instr_root_path()
 
         instr.add_parameter("double", "theta", comment="test par")
-        instr.add_parameter("single", "theta", comment="test par")
-        instr.add_parameter("float", "theta", value=8, comment="test par")
+        instr.add_parameter("double", "theta", comment="test par")
+        instr.add_parameter("int", "theta", value=8, comment="test par")
         instr.add_parameter("int", "slits", comment="test par")
         instr.add_parameter("string", "ref",
                             value="string", comment="new string")
@@ -288,11 +288,11 @@ def test_show_parameters(self, mock_stdout):
 
         output = mock_stdout.getvalue().split("\n")
 
-        self.assertEqual(output[0], "double  theta             // test par")
-        self.assertEqual(output[1], "single  theta             // test par")
-        self.assertEqual(output[2], "float   theta  =  8       // test par")
-        self.assertEqual(output[3], "int     slits             // test par")
-        self.assertEqual(output[4], "string  ref    =  string  // new string")
+        self.assertEqual(output[0], "double theta             // test par")
+        self.assertEqual(output[1], "double theta             // test par")
+        self.assertEqual(output[2], "int    theta  =  8       // test par")
+        self.assertEqual(output[3], "int    slits             // test par")
+        self.assertEqual(output[4], "string ref    =  string  // new string")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_show_parameters_line_break(self, mock_stdout):
@@ -305,8 +305,8 @@ def test_show_parameters_line_break(self, mock_stdout):
         instr = setup_instr_root_path()
 
         instr.add_parameter("double", "theta", comment="test par")
-        instr.add_parameter("single", "theta", comment="test par")
-        instr.add_parameter("float", "theta", value=8, comment="test par")
+        instr.add_parameter("double", "theta", comment="test par")
+        instr.add_parameter("int", "theta", value=8, comment="test par")
         instr.add_parameter("int", "slits", comment="test par")
         instr.add_parameter("string", "ref",
                             value="string", comment="new string")
@@ -324,22 +324,22 @@ def test_show_parameters_line_break(self, mock_stdout):
 
         output = mock_stdout.getvalue().split("\n")
 
-        self.assertEqual(output[0], "double  theta             // test par")
-        self.assertEqual(output[1], "single  theta             // test par")
-        self.assertEqual(output[2], "float   theta  =  8       // test par")
-        self.assertEqual(output[3], "int     slits             // test par")
-        self.assertEqual(output[4], "string  ref    =  string  // new string")
+        self.assertEqual(output[0], "double theta             // test par")
+        self.assertEqual(output[1], "double theta             // test par")
+        self.assertEqual(output[2], "int    theta  =  8       // test par")
+        self.assertEqual(output[3], "int    slits             // test par")
+        self.assertEqual(output[4], "string ref    =  string  // new string")
         comment_line = "This is a very long comment meant for testing "
-        self.assertEqual(output[5], "double  value  =  37      // "
+        self.assertEqual(output[5], "double value  =  37      // "
                                     + comment_line)
         comment_line = "the dynamic line breaking that is used in this "
-        self.assertEqual(output[6], "                             "
+        self.assertEqual(output[6], "                            "
                                     + comment_line)
         comment_line = "method. It needs to have many lines in order to "
-        self.assertEqual(output[7], "                             "
+        self.assertEqual(output[7], "                            "
                                     + comment_line)
         comment_line = "ensure it really works. "
-        self.assertEqual(output[8], "                             "
+        self.assertEqual(output[8], "                            "
                                     + comment_line)
 
     def test_simple_add_declare_parameter(self):
@@ -567,7 +567,7 @@ def test_show_components_folder(self, mock_stdout):
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_show_components_input_path_simple(self, mock_stdout):
         """
-        Simple test of input_path being recoignized and passed
+        Simple test of input_path being recognized and passed
         to component_reader so PSDlin_monitor is overwritten
         """
         instr = setup_instr_with_input_path()
@@ -590,9 +590,9 @@ def test_show_components_input_path_simple(self, mock_stdout):
         self.assertEqual(output[6], " Work directory")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_show_components_input_path_simple(self, mock_stdout):
+    def test_show_components_input_path_custom(self, mock_stdout):
         """
-        Simple test of input_path being recoignized and passed
+        Simple test of input_path being recognized and passed
         to component_reader so PSDlin_monitor is overwritten
         Here dummy_mcstas and input_path is set using relative
         paths instead of absolute paths.
diff --git a/mcstasscript/tests/test_Instr_reader.py b/mcstasscript/tests/test_Instr_reader.py
index f429434c..3b89efc3 100644
--- a/mcstasscript/tests/test_Instr_reader.py
+++ b/mcstasscript/tests/test_Instr_reader.py
@@ -67,17 +67,17 @@ def test_read_input_parameter(self):
         
         self.assertEqual(Instr.parameter_list[0].name, "stick_displacement")
         # space in type inserted for easier writing by McStas_Instr class
-        self.assertEqual(Instr.parameter_list[0].type, "double ")
+        self.assertEqual(Instr.parameter_list[0].type, "double")
         self.assertEqual(Instr.parameter_list[0].value, 0)
         
         self.assertEqual(Instr.parameter_list[1].name, "test_int")
         # space in type inserted for easier writing by McStas_Instr class
-        self.assertEqual(Instr.parameter_list[1].type, "int ")
+        self.assertEqual(Instr.parameter_list[1].type, "int")
         self.assertEqual(Instr.parameter_list[1].value, 3)
         
         self.assertEqual(Instr.parameter_list[2].name, "test_str")
         # space in type inserted for easier writing by McStas_Instr class
-        self.assertEqual(Instr.parameter_list[2].type, "string ")
+        self.assertEqual(Instr.parameter_list[2].type, "string")
         self.assertEqual(Instr.parameter_list[2].value, "\"\\\"hurray\\\"\"")
 
     def test_read_declare_parameter(self):
diff --git a/mcstasscript/tests/test_component.py b/mcstasscript/tests/test_component.py
index eceb92f3..d5338c40 100644
--- a/mcstasscript/tests/test_component.py
+++ b/mcstasscript/tests/test_component.py
@@ -105,7 +105,6 @@ def test_component_basic_init(self):
     def test_component_basic_init_defaults(self):
         """
         Testing basic initialization sets the correct defaults
-
         """
 
         comp = component("test_component", "Arm")
@@ -162,6 +161,7 @@ def test_component_init_complex_call_relative(self):
         self.assertEqual(comp.SPLIT, 7)
         self.assertEqual(comp.comment, "test comment")
 
+
     def test_component_basic_init_set_AT(self):
         """
         Testing set_AT method
@@ -175,9 +175,31 @@ def test_component_basic_init_set_AT(self):
         self.assertEqual(comp.AT_data, [12.124, 214.0, 2])
         self.assertEqual(comp.AT_relative, "RELATIVE monochromator")
 
+    def test_component_freeze(self):
+        """
+        Testing frozen component cant have new attributes, and that
+        _unfreeze / _freeze works correctly.
+        """
+        comp = component("test_component", "Arm")
+
+        with self.assertRaises(AttributeError):
+            comp.new_parameter = 5
+
+        self.assertEqual(comp.name, "test_component")
+        self.assertEqual(comp.component_name, "Arm")
+
+        comp._unfreeze()
+        comp.new_parameter = 5
+
+        self.assertEqual(comp.new_parameter, 5)
+
+        comp._freeze()
+        with self.assertRaises(AttributeError):
+            comp.another_parameter = 5
+
     def test_component_basic_init_set_AT_component(self):
         """
-        Testing set_AT method using component object
+        Testing set_AT method using component object and method
         """
 
         prev_component = component("relative_base", "Arm")
@@ -192,7 +214,7 @@ def test_component_basic_init_set_AT_component(self):
 
     def test_component_basic_init_set_AT_component_keyword(self):
         """
-        Testing set_AT method using component object
+        Testing set_AT method using component object and keyword argument
         """
 
         prev_component = component("relative_base", "Arm")
@@ -206,7 +228,7 @@ def test_component_basic_init_set_AT_component_keyword(self):
 
     def test_component_basic_init_set_ROTATED(self):
         """
-        Testing set_ROTATED method
+        Testing set_ROTATED method med relative as string
         """
 
         comp = component("test_component", "Arm")
@@ -220,7 +242,7 @@ def test_component_basic_init_set_ROTATED(self):
 
     def test_component_basic_init_set_ROTATED_component(self):
         """
-        Testing set_ROTATED method
+        Testing set_ROTATED method with relative as component object
         """
 
         prev_component = component("relative_base", "Arm")
@@ -235,7 +257,7 @@ def test_component_basic_init_set_ROTATED_component(self):
 
     def test_component_basic_init_set_ROTATED_component_keyword(self):
         """
-        Testing set_ROTATED method
+        Testing setting ROTATION with keyword and component object input
         """
 
         prev_component = component("relative_base", "Arm")
@@ -249,7 +271,7 @@ def test_component_basic_init_set_ROTATED_component_keyword(self):
 
     def test_component_basic_init_set_RELATIVE(self):
         """
-        Testing set_RELATIVE method
+        Testing set_RELATIVE method with string
         """
 
         comp = component("test_component", "Arm")
@@ -263,7 +285,7 @@ def test_component_basic_init_set_RELATIVE(self):
 
     def test_component_basic_init_set_RELATIVE(self):
         """
-        Testing set_RELATIVE method
+        Testing set_RELATIVE method with component object input
         """
 
         prev_component = component("relative_base", "Arm")
@@ -523,6 +545,59 @@ def test_component_write_to_file_complex(self, mock_f):
         handle = mock_f()
         handle.write.assert_has_calls(expected_writes, any_order=False)
 
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable=unittest.mock.mock_open)
+    def test_component_write_to_file_complex_SPLIT_string(self, mock_f):
+        """
+        Testing that a component can be written to file with the
+        expected output. Here with complex input, and a string as
+        given for split.
+        """
+
+        comp = setup_component_with_parameters()
+        comp.set_SPLIT("VAR")
+
+        # This setup has a required parameter.
+        # If this parameter is not set, an error should be returned,
+        # this will be tested in the next test.
+
+        comp.new_par3 = "1.25"
+
+        with mock_f('test.txt', 'w') as m_fo:
+            comp.write_component(m_fo)
+
+        my_call = unittest.mock.call
+        expected_writes = [my_call("SPLIT VAR "),
+                           my_call("COMPONENT test_component = Arm("),
+                           my_call("\n"),
+                           my_call(" new_par1 = 1.5"),
+                           my_call(","),
+                           my_call(" new_par2 = 3"),
+                           my_call(","),
+                           my_call("\n"),
+                           my_call(" new_par3 = 1.25"),
+                           my_call(","),
+                           my_call(" this_par = test_val"),
+                           my_call(","),
+                           my_call("\n"),
+                           my_call(" that_par = \"txt_string\""),
+                           my_call(")\n"),
+                           my_call("WHEN (1==2)\n"),
+                           my_call("AT (0.124,183.9,157)"),
+                           my_call(" RELATIVE home\n"),
+                           my_call("ROTATED (482,1240.2,0.185)"),
+                           my_call(" RELATIVE etc\n"),
+                           my_call("GROUP developers\n"),
+                           my_call("EXTEND %{\n"),
+                           my_call("nscat = 8;\n"),
+                           my_call("%}\n"),
+                           my_call("JUMP myself 37\n"),
+                           my_call("\n")]
+
+        mock_f.assert_called_with('test.txt', 'w')
+        handle = mock_f()
+        handle.write.assert_has_calls(expected_writes, any_order=False)
+
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
     def test_component_write_component_required_parameter_error(self, mock_f):
@@ -619,7 +694,7 @@ def test_component_print_short_standard(self, mock_stdout):
     def test_component_print_short_longest_name(self, mock_stdout):
         """
         Test print_short that prints name, type and location of the
-        component to the console.
+        component to the console. Here with specified longest_name.
         """
 
         comp = setup_component_with_parameters()
@@ -639,18 +714,19 @@ def test_component_print_short_longest_name(self, mock_stdout):
     def test_component_show_parameters(self, mock_stdout):
         """
         Test print_short that prints name, type and location of the
-        component to the console.
+        component to the console. An extra parameter was added.
+        This test also checks for specific formatting.
         """
 
         comp = setup_component_with_parameters()
 
-        comp._unfreeze
+        comp._unfreeze()
 
         # This is now not set by the user, but has default
         # This results in different formatting in show_parameters
         comp.new_par2 = None
 
-        comp._freeze
+        comp._freeze()
 
         comp.show_parameters()
 
@@ -720,13 +796,13 @@ def test_component_show_parameters_simple(self, mock_stdout):
 
         comp = setup_component_with_parameters()
 
-        comp._unfreeze
+        comp._unfreeze()
 
         # This is now not set by the user, but has default
         # This results in different formatting in show_parameters
         comp.new_par2 = None
 
-        comp._freeze
+        comp._freeze()
 
         comp.show_parameters_simple()
 
diff --git a/mcstasscript/tests/test_declare_variable.py b/mcstasscript/tests/test_declare_variable.py
index 3a5748ab..103b48ff 100644
--- a/mcstasscript/tests/test_declare_variable.py
+++ b/mcstasscript/tests/test_declare_variable.py
@@ -21,7 +21,7 @@ def test_declare_variable_init_basic_type(self):
         var = declare_variable("double", "test")
 
         self.assertEqual(var.name, "test")
-        self.assertEqual(var.type, "double")  # space for easier writing
+        self.assertEqual(var.type, "double")
 
     def test_declare_variable_init_basic_type_value(self):
         """
@@ -31,7 +31,7 @@ def test_declare_variable_init_basic_type_value(self):
         var = declare_variable("double", "test", value=518)
 
         self.assertEqual(var.name, "test")
-        self.assertEqual(var.type, "double")  # space for easier writing
+        self.assertEqual(var.type, "double")
         self.assertEqual(var.value, 518)
 
     def test_declare_variable_init_basic_type_vector(self):
@@ -82,7 +82,7 @@ def test_declare_variable_write_basic(self, mock_f):
                          new_callable=unittest.mock.mock_open)
     def test_declare_variable_write_complex_float(self, mock_f):
         """
-        Testing that write to file is correct. Here a line is in a
+        Testing that write to file is correct. Here a line is in an
         instrument declare section. The write file operation is
         mocked and check using a patch. Here a declare with a value
         is used. (float value)
@@ -106,7 +106,7 @@ def test_declare_variable_write_complex_float(self, mock_f):
                          new_callable=unittest.mock.mock_open)
     def test_declare_variable_write_complex_int(self, mock_f):
         """
-        Testing that write to file is correct. Here a line is in a
+        Testing that write to file is correct. Here a line is in an
         instrument declare section. The write file operation is
         mocked and check using a patch. Here a declare with a value
         is used. (integer value)
@@ -130,7 +130,7 @@ def test_declare_variable_write_complex_int(self, mock_f):
                          new_callable=unittest.mock.mock_open)
     def test_declare_variable_write_simple_array(self, mock_f):
         """
-        Testing that write to file is correct. Here a line is in a
+        Testing that write to file is correct. Here a line is in an
         instrument declare section. The write file operation is
         mocked and check using a patch. Here an array is declared.
         """
@@ -153,9 +153,9 @@ def test_declare_variable_write_simple_array(self, mock_f):
                          new_callable=unittest.mock.mock_open)
     def test_declare_variable_write_complex_array(self, mock_f):
         """
-        Testing that write to file is correct. Here a line is in a
+        Testing that write to file is correct. Here a line is in an
         instrument declare section. The write file operation is
-        mocked and check using a patch. Here an array is decalred and
+        mocked and check using a patch. Here an array is declared and
         populated with the selected values.
         """
 
diff --git a/mcstasscript/tests/test_functions.py b/mcstasscript/tests/test_functions.py
index b646a60f..1d0d3861 100644
--- a/mcstasscript/tests/test_functions.py
+++ b/mcstasscript/tests/test_functions.py
@@ -291,7 +291,7 @@ class Test_load_data(unittest.TestCase):
     tested elsewhere. Since the load data is tested elsewhere, this
     function has just a single test to check the interface.
     """
-    def test_crun_load_data_PSD4PI(self):
+    def test_mcrun_load_data_PSD4PI(self):
         """
         Use test_data_set to test load_data for PSD_4PI
         """
diff --git a/mcstasscript/tests/test_parameter_variable.py b/mcstasscript/tests/test_parameter_variable.py
index d94936d4..61350bc2 100644
--- a/mcstasscript/tests/test_parameter_variable.py
+++ b/mcstasscript/tests/test_parameter_variable.py
@@ -29,7 +29,7 @@ def test_parameter_variable_init_basic_type(self):
         par = parameter_variable("double", "test")
 
         self.assertEqual(par.name, "test")
-        self.assertEqual(par.type, "double ")  # space for easier writing
+        self.assertEqual(par.type, "double")
 
     def test_parameter_variable_init_basic_type_value(self):
         """
@@ -39,7 +39,7 @@ def test_parameter_variable_init_basic_type_value(self):
         par = parameter_variable("double", "test", value=518)
 
         self.assertEqual(par.name, "test")
-        self.assertEqual(par.type, "double ")  # space for easier writing
+        self.assertEqual(par.type, "double")
         self.assertEqual(par.value, 518)
 
     def test_parameter_variable_init_basic_type_value_comment(self):
@@ -51,7 +51,7 @@ def test_parameter_variable_init_basic_type_value_comment(self):
                                  value=518, comment="test comment /")
 
         self.assertEqual(par.name, "test")
-        self.assertEqual(par.type, "double ")  # space for easier writing
+        self.assertEqual(par.type, "double")
         self.assertEqual(par.value, 518)
         self.assertEqual(par.comment, "// test comment /")
 
@@ -72,7 +72,7 @@ def test_parameter_variable_init_basic_value_comment(self):
                          new_callable=unittest.mock.mock_open)
     def test_parameter_variable_write_basic(self, mock_f):
         """
-        Testing that write to file is correct. Here a line is in a
+        Testing that write to file is correct. Here a line is in an
         instrument parameter section. The write file operation is
         mocked and check using a patch. Here a simple parameter is
         used.
@@ -95,7 +95,7 @@ def test_parameter_variable_write_basic(self, mock_f):
                          new_callable=unittest.mock.mock_open)
     def test_parameter_variable_write_complex_float(self, mock_f):
         """
-        Testing that write to file is correct. Here a line is in a
+        Testing that write to file is correct. Here a line is in an
         instrument parameter section. The write file operation is
         mocked and check using a patch. Here a parameter with a value
         is used. (float value)
@@ -123,7 +123,7 @@ def test_parameter_variable_write_complex_float(self, mock_f):
                          new_callable=unittest.mock.mock_open)
     def test_parameter_variable_write_complex_int(self, mock_f):
         """
-        Testing that write to file is correct. Here a line is in a
+        Testing that write to file is correct. Here a line is in an
         instrument parameter section. The write file operation is
         mocked and check using a patch. Here a parameter with a value
         is used. (integer value)
@@ -151,7 +151,7 @@ def test_parameter_variable_write_complex_int(self, mock_f):
                          new_callable=unittest.mock.mock_open)
     def test_parameter_variable_write_complex_string(self, mock_f):
         """
-        Testing that write to file is correct. Here a line is in a
+        Testing that write to file is correct. Here a line is in an
         instrument parameter section. The write file operation is
         mocked and check using a patch. Here a parameter with a value
         is used. (string value)

From 0e6b644b6b69acf57fdd41b7cb2ab1b784b70d66 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Wed, 20 Jan 2021 13:36:10 +0100
Subject: [PATCH 125/403] Updated class names in mcstas objects to cammel case:

parameter_variable -> ParamterVariable
declare_variable -> DeclareVariable
component -> Component
---
 mcstasscript/helper/mcstas_objects.py         |  44 ++--
 mcstasscript/interface/instr.py               |  40 ++--
 mcstasscript/tests/test_component.py          | 188 +++++++++---------
 mcstasscript/tests/test_declare_variable.py   |  42 ++--
 mcstasscript/tests/test_parameter_variable.py |  56 +++---
 5 files changed, 182 insertions(+), 188 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 96f420b1..4a6b47a1 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -2,7 +2,7 @@
 from mcstasscript.helper.formatting import is_legal_parameter
 
 
-class parameter_variable:
+class ParameterVariable:
     """
     Class describing an input parameter in McStas instrument
 
@@ -39,13 +39,13 @@ def __init__(self, *args, **kwargs):
         --------
 
         Creates a parameter with name wavelength and associated comment
-        A = parameter_variable("wavelength", comment="wavelength in [AA]")
+        A = ParameterVariable("wavelength", comment="wavelength in [AA]")
 
         Creates a parameter with name A3 and default value
-        A = parameter_variable("A3", value=30, comment="A3 angle in [deg]")
+        A = ParameterVariable("A3", value=30, comment="A3 angle in [deg]")
 
         Creates a parameter with type string and name sample_name
-        A = parameter_variable("string", "sample_name")
+        A = ParameterVariable("string", "sample_name")
 
         Parameters
         ----------
@@ -124,7 +124,7 @@ def write_parameter(self, fo, stop_character):
         fo.write("\n")
 
 
-class declare_variable:
+class DeclareVariable:
     """
     Class describing a declared variable in McStas instrument
 
@@ -163,13 +163,13 @@ def __init__(self, type, name, **kwargs):
         --------
 
         Creates a variable with name A3 and default value
-        A = declare_variable("double", "A3", value=30)
+        A = DeclareVariable("double", "A3", value=30)
 
         Creates a variable with type integer and name sample_number
-        A = declare_variable("int", "sample_number")
+        A = DeclareVariable("int", "sample_number")
 
         Creates an array variable called m_values
-        A = declare_variable("double", "m_values", array=3,
+        A = DeclareVariable("double", "m_values", array=3,
                              value=[2, 2.5, 2])
 
         Parameters
@@ -194,7 +194,7 @@ def __init__(self, type, name, **kwargs):
 
         self.type = type
         if not isinstance(self.type, str):
-            raise RuntimeError("Given type of declare_variable should be a "
+            raise RuntimeError("Given type of DeclareVariable should be a "
                                + "string.")
 
         self.name = str(name)
@@ -266,7 +266,7 @@ def write_line(self, fo):
                 
 
 
-class component:
+class Component:
     """
     A class describing a McStas component to be written to an instrument
 
@@ -570,12 +570,12 @@ def set_AT(self, at_list, RELATIVE=None):
     def set_AT_RELATIVE(self, relative):
         """Sets AT RELATIVE with string or component instance"""
 
-        # Extract name if component instance is given
-        if isinstance(relative, component):
+        # Extract name if Component instance is given
+        if isinstance(relative, Component):
             relative = relative.name
         elif not isinstance(relative, str):
             raise ValueError("Relative must be either string or "
-                             + "component object.")
+                             + "Component object.")
 
         # Set AT relative
         if relative == "ABSOLUTE":
@@ -600,15 +600,15 @@ def set_ROTATED(self, rotated_list, RELATIVE=None):
             self.set_ROTATED_RELATIVE(RELATIVE)
 
     def set_ROTATED_RELATIVE(self, relative):
-        """Sets ROTATED RELATIVE with string or component instance"""
+        """Sets ROTATED RELATIVE with string or Component instance"""
 
         self.ROTATED_specified = True
-        # Extract name if a component instance is given
-        if isinstance(relative, component):
+        # Extract name if a Component instance is given
+        if isinstance(relative, Component):
             relative = relative.name
         elif not isinstance(relative, str):
             raise ValueError("Relative must be either string or "
-                             + "component object.")
+                             + "Component object.")
 
         # Set ROTATED relative
         if relative == "ABSOLUTE":
@@ -618,12 +618,12 @@ def set_ROTATED_RELATIVE(self, relative):
 
     def set_RELATIVE(self, relative):
         """Sets both AT_relative and ROTATED_relative"""
-        # Extract name if a component instance is given
-        if isinstance(relative, component):
+        # Extract name if a Component instance is given
+        if isinstance(relative, Component):
             relative = relative.name
         elif not isinstance(relative, str):
             raise ValueError("Relative must be either string or "
-                             + "component object.")
+                             + "Component object.")
 
         if relative == "ABSOLUTE":
             self.AT_relative = "ABSOLUTE"
@@ -634,10 +634,10 @@ def set_RELATIVE(self, relative):
 
     def set_parameters(self, dict_input):
         """
-        Set component parameters from dictionary input
+        Set Component parameters from dictionary input
 
         Relies on attributes added when McStas_Instr creates a subclass from
-        the component class where each component parameter is added as an
+        the Component class where each component parameter is added as an
         attribute.
 
         An error is raised if trying to set a parameter that does not exist
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 5b7d494d..fc4e004a 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -7,9 +7,9 @@
 import copy
 
 from mcstasscript.data.data import McStasData
-from mcstasscript.helper.mcstas_objects import declare_variable
-from mcstasscript.helper.mcstas_objects import parameter_variable
-from mcstasscript.helper.mcstas_objects import component
+from mcstasscript.helper.mcstas_objects import DeclareVariable
+from mcstasscript.helper.mcstas_objects import ParameterVariable
+from mcstasscript.helper.mcstas_objects import Component
 from mcstasscript.helper.component_reader import ComponentReader
 from mcstasscript.helper.managed_mcrun import ManagedMcrun
 from mcstasscript.helper.formatting import is_legal_filename
@@ -39,10 +39,10 @@ class McCode_instr:
     executable_path : str
         absolute path of mcrun command, or empty if it is in path
 
-    parameter_list : list of parameter_variable instances
+    parameter_list : list of ParameterVariable instances
         contains all input parameters to be written to file
 
-    declare_list : list of declare_variable instances
+    declare_list : list of DeclareVariable instances
         contains all declare parrameters to be written to file
 
     initialize_section : str
@@ -265,8 +265,8 @@ def add_parameter(self, *args, **kwargs):
             comment : str
                 Comment displayed next to declaration of parameter
         """
-        # parameter_variable class documented independently
-        self.parameter_list.append(parameter_variable(*args, **kwargs))
+        # ParameterVariable class documented independently
+        self.parameter_list.append(ParameterVariable(*args, **kwargs))
 
     def show_parameters(self, **kwargs):
         """
@@ -377,8 +377,8 @@ def add_declare_var(self, *args, **kwargs):
                 Comment displayed next to declaration of parameter
 
         """
-        # declare_variable class documented independently
-        self.declare_list.append(declare_variable(*args, **kwargs))
+        # DeclareVariable class documented independently
+        self.declare_list.append(DeclareVariable(*args, **kwargs))
         
     def append_declare(self, string):
         """
@@ -578,21 +578,21 @@ def _create_component_instance(self, *args, **kwargs):
             input_dict["line_limit"] = self.line_limit
 
             self.component_class_lib[component_name] = type(component_name,
-                                                            (component,),
+                                                            (Component,),
                                                             input_dict)
 
         return self.component_class_lib[component_name](*args, **kwargs)
 
     def add_component(self, *args, **kwargs):
         """
-        Method for adding a new component instance to the instrument
+        Method for adding a new Component instance to the instrument
 
-        Creates a new component instance in the instrument.  This
+        Creates a new Component instance in the instrument.  This
         requires a unique instance name of the component to be used for
         future reference and the name of the McStas component to be
         used.  The component is placed at the end of the instrument file
         unless otherwise specified with the after and before keywords.
-        The component may be initialized using other keyword arguments,
+        The Component may be initialized using other keyword arguments,
         but all attributes can be set with approrpiate methods.
 
         Parameters
@@ -689,9 +689,9 @@ def add_component(self, *args, **kwargs):
     
     def copy_component(self, *args, **kwargs):
         """
-        Method for adding a copy of a component instance to the instrument
+        Method for adding a copy of a Component instance to the instrument
 
-        Creates a copy of component instance in the instrument.  This
+        Creates a copy of Component instance in the instrument.  This
         requires a unique instance name of the component to be used for
         future reference and the name of the McStas component to be
         used.  The component is placed at the end of the instrument file
@@ -1592,10 +1592,10 @@ class McStas_instr(McCode_instr):
     executable_path : str
         absolute path of mcrun command, or empty if it is in path
 
-    parameter_list : list of parameter_variable instances
+    parameter_list : list of ParameterVariable instances
         contains all input parameters to be written to file
 
-    declare_list : list of declare_variable instances
+    declare_list : list of DeclareVariable instances
         contains all declare parrameters to be written to file
 
     initialize_section : str
@@ -1773,10 +1773,10 @@ class McXtrace_instr(McCode_instr):
     executable_path : str
         absolute path of mcrun command, or empty if it is in path
 
-    parameter_list : list of parameter_variable instances
+    parameter_list : list of ParameterVariable instances
         contains all input parameters to be written to file
 
-    declare_list : list of declare_variable instances
+    declare_list : list of DeclareVariable instances
         contains all declare parrameters to be written to file
 
     initialize_section : str
@@ -1788,7 +1788,7 @@ class McXtrace_instr(McCode_instr):
     finally_section : str
         string containing entire finally section to be written
 
-    component_list : list of component instances
+    component_list : list of Component instances
         list of components in the instrument
 
     component_name_list : list of strings
diff --git a/mcstasscript/tests/test_component.py b/mcstasscript/tests/test_component.py
index d5338c40..e0ea24cb 100644
--- a/mcstasscript/tests/test_component.py
+++ b/mcstasscript/tests/test_component.py
@@ -3,16 +3,16 @@
 import unittest
 import unittest.mock
 
-from mcstasscript.helper.mcstas_objects import component
+from mcstasscript.helper.mcstas_objects import Component
 from mcstasscript.helper.formatting import bcolors
 
 
-def setup_component_all_keywords():
+def setup_Component_all_keywords():
     """
-    Sets up a component by using all initialize keywords
+    Sets up a Component by using all initialize keywords
     """
 
-    return component("test_component",
+    return Component("test_component",
                      "Arm",
                      AT=[0.124, 183.9, 157],
                      AT_RELATIVE="home",
@@ -26,11 +26,11 @@ def setup_component_all_keywords():
                      comment="test comment")
 
 
-def setup_component_relative():
+def setup_Component_relative():
     """
-    Sets up a component with the relative keyword used
+    Sets up a Component with the relative keyword used
     """
-    return component("test_component",
+    return Component("test_component",
                      "Arm",
                      AT=[0.124, 183.9, 157],
                      ROTATED=[482, 1240.2, 0.185],
@@ -43,12 +43,12 @@ def setup_component_relative():
                      comment="test comment")
 
 
-def setup_component_with_parameters():
+def setup_Component_with_parameters():
     """
-    Sets up a component with parameters and all options used.
+    Sets up a Component with parameters and all options used.
 
     """
-    comp = setup_component_all_keywords()
+    comp = setup_Component_all_keywords()
 
     comp._unfreeze()
     # Need to set up attribute parameters
@@ -83,7 +83,7 @@ def setup_component_with_parameters():
     return comp
 
 
-class Testcomponent(unittest.TestCase):
+class TestComponent(unittest.TestCase):
     """
     Components are the building blocks used to create an instrument in
     the McStas meta language. They describe spatially seperated parts
@@ -91,23 +91,23 @@ class Testcomponent(unittest.TestCase):
     tested.
     """
 
-    def test_component_basic_init(self):
+    def test_Component_basic_init(self):
         """
         Testing basic initialization
 
         """
 
-        comp = component("test_component", "Arm")
+        comp = Component("test_component", "Arm")
 
         self.assertEqual(comp.name, "test_component")
         self.assertEqual(comp.component_name, "Arm")
 
-    def test_component_basic_init_defaults(self):
+    def test_Component_basic_init_defaults(self):
         """
         Testing basic initialization sets the correct defaults
         """
 
-        comp = component("test_component", "Arm")
+        comp = Component("test_component", "Arm")
 
         self.assertEqual(comp.name, "test_component")
         self.assertEqual(comp.component_name, "Arm")
@@ -121,12 +121,12 @@ def test_component_basic_init_defaults(self):
         self.assertEqual(comp.JUMP, "")
         self.assertEqual(comp.comment, "")
 
-    def test_component_init_complex_call(self):
+    def test_Component_init_complex_call(self):
         """
         Testing keywords set attributes correctly
         """
 
-        comp = setup_component_all_keywords()
+        comp = setup_Component_all_keywords()
 
         self.assertEqual(comp.name, "test_component")
         self.assertEqual(comp.component_name, "Arm")
@@ -141,12 +141,12 @@ def test_component_init_complex_call(self):
         self.assertEqual(comp.SPLIT, 7)
         self.assertEqual(comp.comment, "test comment")
 
-    def test_component_init_complex_call_relative(self):
+    def test_Component_init_complex_call_relative(self):
         """
         Tests the relative keyword overwrites AT_relative and
         ROTATED_relative
         """
-        comp = setup_component_relative()
+        comp = setup_Component_relative()
 
         self.assertEqual(comp.name, "test_component")
         self.assertEqual(comp.component_name, "Arm")
@@ -162,11 +162,11 @@ def test_component_init_complex_call_relative(self):
         self.assertEqual(comp.comment, "test comment")
 
 
-    def test_component_basic_init_set_AT(self):
+    def test_Component_basic_init_set_AT(self):
         """
         Testing set_AT method
         """
-        comp = component("test_component", "Arm")
+        comp = Component("test_component", "Arm")
 
         comp.set_AT([12.124, 214.0, 2], RELATIVE="monochromator")
 
@@ -175,12 +175,12 @@ def test_component_basic_init_set_AT(self):
         self.assertEqual(comp.AT_data, [12.124, 214.0, 2])
         self.assertEqual(comp.AT_relative, "RELATIVE monochromator")
 
-    def test_component_freeze(self):
+    def test_Component_freeze(self):
         """
-        Testing frozen component cant have new attributes, and that
+        Testing frozen Component cant have new attributes, and that
         _unfreeze / _freeze works correctly.
         """
-        comp = component("test_component", "Arm")
+        comp = Component("test_component", "Arm")
 
         with self.assertRaises(AttributeError):
             comp.new_parameter = 5
@@ -197,13 +197,13 @@ def test_component_freeze(self):
         with self.assertRaises(AttributeError):
             comp.another_parameter = 5
 
-    def test_component_basic_init_set_AT_component(self):
+    def test_Component_basic_init_set_AT_Component(self):
         """
-        Testing set_AT method using component object and method
+        Testing set_AT method using Component object and method
         """
 
-        prev_component = component("relative_base", "Arm")
-        comp = component("test_component", "Arm")
+        prev_component = Component("relative_base", "Arm")
+        comp = Component("test_component", "Arm")
 
         comp.set_AT([12.124, 214.0, 2], RELATIVE=prev_component)
 
@@ -212,13 +212,13 @@ def test_component_basic_init_set_AT_component(self):
         self.assertEqual(comp.AT_data, [12.124, 214.0, 2])
         self.assertEqual(comp.AT_relative, "RELATIVE relative_base")
 
-    def test_component_basic_init_set_AT_component_keyword(self):
+    def test_Component_basic_init_set_AT_Component_keyword(self):
         """
-        Testing set_AT method using component object and keyword argument
+        Testing set_AT method using Component object and keyword argument
         """
 
-        prev_component = component("relative_base", "Arm")
-        comp = component("test_component", "Arm",
+        prev_component = Component("relative_base", "Arm")
+        comp = Component("test_component", "Arm",
                          AT=[1, 2, 3.0], AT_RELATIVE=prev_component)
 
         self.assertEqual(comp.name, "test_component")
@@ -226,12 +226,12 @@ def test_component_basic_init_set_AT_component_keyword(self):
         self.assertEqual(comp.AT_data, [1, 2, 3.0])
         self.assertEqual(comp.AT_relative, "RELATIVE relative_base")
 
-    def test_component_basic_init_set_ROTATED(self):
+    def test_Component_basic_init_set_ROTATED(self):
         """
         Testing set_ROTATED method med relative as string
         """
 
-        comp = component("test_component", "Arm")
+        comp = Component("test_component", "Arm")
 
         comp.set_ROTATED([1204.8, 8490.1, 129], RELATIVE="analyzer")
 
@@ -240,13 +240,13 @@ def test_component_basic_init_set_ROTATED(self):
         self.assertEqual(comp.ROTATED_data, [1204.8, 8490.1, 129])
         self.assertEqual(comp.ROTATED_relative, "RELATIVE analyzer")
 
-    def test_component_basic_init_set_ROTATED_component(self):
+    def test_Component_basic_init_set_ROTATED_Component(self):
         """
-        Testing set_ROTATED method with relative as component object
+        Testing set_ROTATED method with relative as Component object
         """
 
-        prev_component = component("relative_base", "Arm")
-        comp = component("test_component", "Arm")
+        prev_component = Component("relative_base", "Arm")
+        comp = Component("test_component", "Arm")
 
         comp.set_ROTATED([1204.8, 8490.1, 129], RELATIVE=prev_component)
 
@@ -255,13 +255,13 @@ def test_component_basic_init_set_ROTATED_component(self):
         self.assertEqual(comp.ROTATED_data, [1204.8, 8490.1, 129])
         self.assertEqual(comp.ROTATED_relative, "RELATIVE relative_base")
 
-    def test_component_basic_init_set_ROTATED_component_keyword(self):
+    def test_Component_basic_init_set_ROTATED_Component_keyword(self):
         """
-        Testing setting ROTATION with keyword and component object input
+        Testing setting ROTATION with keyword and Component object input
         """
 
-        prev_component = component("relative_base", "Arm")
-        comp = component("test_component", "Arm",
+        prev_component = Component("relative_base", "Arm")
+        comp = Component("test_component", "Arm",
                          ROTATED=[1, 2, 3.0], ROTATED_RELATIVE=prev_component)
 
         self.assertEqual(comp.name, "test_component")
@@ -269,12 +269,12 @@ def test_component_basic_init_set_ROTATED_component_keyword(self):
         self.assertEqual(comp.ROTATED_data, [1, 2, 3.0])
         self.assertEqual(comp.ROTATED_relative, "RELATIVE relative_base")
 
-    def test_component_basic_init_set_RELATIVE(self):
+    def test_Component_basic_init_set_RELATIVE(self):
         """
         Testing set_RELATIVE method with string
         """
 
-        comp = component("test_component", "Arm")
+        comp = Component("test_component", "Arm")
 
         comp.set_RELATIVE("sample")
 
@@ -283,13 +283,13 @@ def test_component_basic_init_set_RELATIVE(self):
         self.assertEqual(comp.AT_relative, "RELATIVE sample")
         self.assertEqual(comp.ROTATED_relative, "RELATIVE sample")
 
-    def test_component_basic_init_set_RELATIVE(self):
+    def test_Component_basic_init_set_RELATIVE(self):
         """
-        Testing set_RELATIVE method with component object input
+        Testing set_RELATIVE method with Component object input
         """
 
-        prev_component = component("relative_base", "Arm")
-        comp = component("test_component", "Arm")
+        prev_component = Component("relative_base", "Arm")
+        comp = Component("test_component", "Arm")
 
         comp.set_RELATIVE(prev_component)
 
@@ -304,7 +304,7 @@ def test_component_basic_init_set_parameters(self):
         parameters manually to test this.
         """
 
-        comp = component("test_component", "Arm")
+        comp = Component("test_component", "Arm")
 
         # Need to add some parameters to this bare component
         # Parameters are usually added by McStas_Instr
@@ -326,12 +326,12 @@ def test_component_basic_init_set_parameters(self):
         with self.assertRaises(NameError):
             comp.set_parameters({"new_par3": 37.0})
 
-    def test_component_basic_init_set_WHEN(self):
+    def test_Component_basic_init_set_WHEN(self):
         """
         Testing WHEN method
         """
 
-        comp = component("test_component", "Arm")
+        comp = Component("test_component", "Arm")
 
         comp.set_WHEN("1 != 2")
 
@@ -339,12 +339,12 @@ def test_component_basic_init_set_WHEN(self):
         self.assertEqual(comp.component_name, "Arm")
         self.assertEqual(comp.WHEN, "WHEN (1 != 2)")
 
-    def test_component_basic_init_set_GROUP(self):
+    def test_Component_basic_init_set_GROUP(self):
         """
         Testing set_GROUP method
         """
 
-        comp = component("test_component", "Arm")
+        comp = Component("test_component", "Arm")
 
         comp.set_GROUP("test group")
 
@@ -352,12 +352,12 @@ def test_component_basic_init_set_GROUP(self):
         self.assertEqual(comp.component_name, "Arm")
         self.assertEqual(comp.GROUP, "test group")
 
-    def test_component_basic_init_set_JUMP(self):
+    def test_Component_basic_init_set_JUMP(self):
         """
         Testing set_JUMP method
         """
 
-        comp = component("test_component", "Arm")
+        comp = Component("test_component", "Arm")
 
         comp.set_JUMP("test jump")
 
@@ -365,12 +365,12 @@ def test_component_basic_init_set_JUMP(self):
         self.assertEqual(comp.component_name, "Arm")
         self.assertEqual(comp.JUMP, "test jump")
         
-    def test_component_basic_init_set_SPLIT(self):
+    def test_Component_basic_init_set_SPLIT(self):
         """
         Testing set_SPLIT method
         """
 
-        comp = component("test_component", "Arm")
+        comp = Component("test_component", "Arm")
 
         comp.set_SPLIT(500)
 
@@ -378,12 +378,12 @@ def test_component_basic_init_set_SPLIT(self):
         self.assertEqual(comp.component_name, "Arm")
         self.assertEqual(comp.SPLIT, 500)
 
-    def test_component_basic_init_set_EXTEND(self):
+    def test_Component_basic_init_set_EXTEND(self):
         """
         Testing set_EXTEND method
         """
 
-        comp = component("test_component", "Arm")
+        comp = Component("test_component", "Arm")
 
         comp.append_EXTEND("test code")
 
@@ -395,11 +395,11 @@ def test_component_basic_init_set_EXTEND(self):
 
         self.assertEqual(comp.EXTEND, "test code\nnew code\n")
 
-    def test_component_basic_init_set_comment(self):
+    def test_Component_basic_init_set_comment(self):
         """
         Testing set_comment method
         """
-        comp = component("test_component", "Arm")
+        comp = Component("test_component", "Arm")
 
         comp.set_comment("test comment")
 
@@ -407,14 +407,14 @@ def test_component_basic_init_set_comment(self):
         self.assertEqual(comp.component_name, "Arm")
         self.assertEqual(comp.comment, "test comment")
 
-    def test_component_basic_new_attribute_error(self):
+    def test_Component_basic_new_attribute_error(self):
         """
-        The component class is frozen after initialize in order to
+        The Component class is frozen after initialize in order to
         prevent the user accidentilly misspelling an attribute name,
         or at least be able to report an error when they do so.
         """
 
-        comp = component("test_component", "Arm")
+        comp = Component("test_component", "Arm")
         with self.assertRaises(AttributeError):
             comp.new_attribute = 1
 
@@ -424,13 +424,13 @@ def test_component_basic_new_attribute_error(self):
 
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
-    def test_component_write_to_file_simple(self, mock_f):
+    def test_Component_write_to_file_simple(self, mock_f):
         """
-        Testing that a component can be written to file with the
+        Testing that a Component can be written to file with the
         expected output. Here with simple input.
         """
 
-        comp = component("test_component", "Arm")
+        comp = Component("test_component", "Arm")
 
         comp._unfreeze()
         # Need to set up attribute parameters
@@ -455,13 +455,13 @@ def test_component_write_to_file_simple(self, mock_f):
 
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)    
-    def test_component_write_to_file_include(self, mock_f):
+    def test_Component_write_to_file_include(self, mock_f):
         """
-        Testing that a component can be written to file with the
+        Testing that a Component can be written to file with the
         expected output. Here with simple input.
         """
 
-        comp = component("test_component", "Arm",
+        comp = Component("test_component", "Arm",
                          c_code_before="%include \"test.instr\"")
         
         comp.set_c_code_after("%include \"after.instr\"")
@@ -496,13 +496,13 @@ def test_component_write_to_file_include(self, mock_f):
 
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
-    def test_component_write_to_file_complex(self, mock_f):
+    def test_Component_write_to_file_complex(self, mock_f):
         """
-        Testing that a component can be written to file with the
+        Testing that a Component can be written to file with the
         expected output. Here with complex input.
         """
 
-        comp = setup_component_with_parameters()
+        comp = setup_Component_with_parameters()
 
         # This setup has a required parameter.
         # If this parameter is not set, an error should be returned,
@@ -547,14 +547,14 @@ def test_component_write_to_file_complex(self, mock_f):
 
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
-    def test_component_write_to_file_complex_SPLIT_string(self, mock_f):
+    def test_Component_write_to_file_complex_SPLIT_string(self, mock_f):
         """
-        Testing that a component can be written to file with the
+        Testing that a Component can be written to file with the
         expected output. Here with complex input, and a string as
         given for split.
         """
 
-        comp = setup_component_with_parameters()
+        comp = setup_Component_with_parameters()
         comp.set_SPLIT("VAR")
 
         # This setup has a required parameter.
@@ -600,13 +600,13 @@ def test_component_write_to_file_complex_SPLIT_string(self, mock_f):
 
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
-    def test_component_write_component_required_parameter_error(self, mock_f):
+    def test_Component_write_Component_required_parameter_error(self, mock_f):
         """
-        Test an error occurs if the component is asked to write to disk
+        Test an error occurs if the Component is asked to write to disk
         without a required parameter.
         """
 
-        comp = setup_component_with_parameters()
+        comp = setup_Component_with_parameters()
 
         # new_par3 unset and has no default so an error will be raised
 
@@ -615,13 +615,13 @@ def test_component_write_component_required_parameter_error(self, mock_f):
                 comp.write_component(m_fo)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_component_print_long(self, mock_stdout):
+    def test_Component_print_long(self, mock_stdout):
         """
-        Test print to console on the current state of the component.
+        Test print to console on the current state of the Component.
         Using a mocked stdout to catch the print statements.
         """
 
-        comp = setup_component_with_parameters()
+        comp = setup_Component_with_parameters()
         comp.append_EXTEND("second extend line;")
 
         comp.print_long()
@@ -671,13 +671,13 @@ def test_component_print_long(self, mock_stdout):
         self.assertEqual(output[15], "JUMP myself 37")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_component_print_short_standard(self, mock_stdout):
+    def test_Component_print_short_standard(self, mock_stdout):
         """
         Test print_short that prints name, type and location of the
-        component to the console.
+        Component to the console.
         """
 
-        comp = setup_component_with_parameters()
+        comp = setup_Component_with_parameters()
 
         comp.print_short()
 
@@ -691,13 +691,13 @@ def test_component_print_short_standard(self, mock_stdout):
         self.assertEqual(output[0], expected)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_component_print_short_longest_name(self, mock_stdout):
+    def test_Component_print_short_longest_name(self, mock_stdout):
         """
         Test print_short that prints name, type and location of the
-        component to the console. Here with specified longest_name.
+        Component to the console. Here with specified longest_name.
         """
 
-        comp = setup_component_with_parameters()
+        comp = setup_Component_with_parameters()
 
         comp.print_short(longest_name=15)
 
@@ -711,14 +711,14 @@ def test_component_print_short_longest_name(self, mock_stdout):
         self.assertEqual(output[0], expected)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_component_show_parameters(self, mock_stdout):
+    def test_Component_show_parameters(self, mock_stdout):
         """
         Test print_short that prints name, type and location of the
-        component to the console. An extra parameter was added.
+        Component to the console. An extra parameter was added.
         This test also checks for specific formatting.
         """
 
-        comp = setup_component_with_parameters()
+        comp = setup_Component_with_parameters()
 
         comp._unfreeze()
 
@@ -787,14 +787,14 @@ def test_component_show_parameters(self, mock_stdout):
                          par_name + " = " + value + " [1]" + comment)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_component_show_parameters_simple(self, mock_stdout):
+    def test_Component_show_parameters_simple(self, mock_stdout):
         """
         Test print_short that prints name, type and location of the
-        component to the console.  No formatting used in simple
+        Component to the console.  No formatting used in simple
         version.
         """
 
-        comp = setup_component_with_parameters()
+        comp = setup_Component_with_parameters()
 
         comp._unfreeze()
 
diff --git a/mcstasscript/tests/test_declare_variable.py b/mcstasscript/tests/test_declare_variable.py
index 103b48ff..2b537d5a 100644
--- a/mcstasscript/tests/test_declare_variable.py
+++ b/mcstasscript/tests/test_declare_variable.py
@@ -3,43 +3,43 @@
 import unittest
 import unittest.mock
 
-from mcstasscript.helper.mcstas_objects import declare_variable
+from mcstasscript.helper.mcstas_objects import DeclareVariable
 
 
-class Test_declare_variable(unittest.TestCase):
+class Test_DeclareVariable(unittest.TestCase):
     """
-    Tests the declare_variable class that holds a declared variable
+    Tests the DeclareVariable class that holds a declared variable
     that will be written to the McStas declare section.
 
     """
 
-    def test_declare_variable_init_basic_type(self):
+    def test_DeclareVariable_init_basic_type(self):
         """
         Initialization with a type
         """
 
-        var = declare_variable("double", "test")
+        var = DeclareVariable("double", "test")
 
         self.assertEqual(var.name, "test")
         self.assertEqual(var.type, "double")
 
-    def test_declare_variable_init_basic_type_value(self):
+    def test_DeclareVariable_init_basic_type_value(self):
         """
         Initialization with type and value
         """
 
-        var = declare_variable("double", "test", value=518)
+        var = DeclareVariable("double", "test", value=518)
 
         self.assertEqual(var.name, "test")
         self.assertEqual(var.type, "double")
         self.assertEqual(var.value, 518)
 
-    def test_declare_variable_init_basic_type_vector(self):
+    def test_DeclareVariable_init_basic_type_vector(self):
         """
         Initialization with type and value
         """
 
-        var = declare_variable("double", "test",
+        var = DeclareVariable("double", "test",
                                array=6, value=[1, 2.2, 3, 3.3, 4, 4.4])
 
         self.assertEqual(var.name, "test")
@@ -47,12 +47,12 @@ def test_declare_variable_init_basic_type_vector(self):
         self.assertEqual(var.vector, 6)
         self.assertEqual(var.value, [1, 2.2, 3, 3.3, 4, 4.4])
 
-    def test_declare_variable_init_basic_type_value_comment(self):
+    def test_DeclareVariable_init_basic_type_value_comment(self):
         """
         Initialization with type, value and comment
         """
 
-        var = declare_variable("double", "test",
+        var = DeclareVariable("double", "test",
                                value=518, comment="test comment /")
 
         self.assertEqual(var.name, "test")
@@ -62,7 +62,7 @@ def test_declare_variable_init_basic_type_value_comment(self):
 
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
-    def test_declare_variable_write_basic(self, mock_f):
+    def test_DeclareVariable_write_basic(self, mock_f):
         """
         Testing that write to file is correct. Here a line is in a
         instrument declare section. The write file operation is
@@ -70,7 +70,7 @@ def test_declare_variable_write_basic(self, mock_f):
         used.
         """
 
-        var = declare_variable("double", "test")
+        var = DeclareVariable("double", "test")
         with mock_f('test.txt', 'w') as m_fo:
             var.write_line(m_fo)
 
@@ -80,7 +80,7 @@ def test_declare_variable_write_basic(self, mock_f):
 
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
-    def test_declare_variable_write_complex_float(self, mock_f):
+    def test_DeclareVariable_write_complex_float(self, mock_f):
         """
         Testing that write to file is correct. Here a line is in an
         instrument declare section. The write file operation is
@@ -88,7 +88,7 @@ def test_declare_variable_write_complex_float(self, mock_f):
         is used. (float value)
         """
 
-        var = declare_variable("double",
+        var = DeclareVariable("double",
                                "test",
                                value=5.4,
                                comment="test comment")
@@ -104,7 +104,7 @@ def test_declare_variable_write_complex_float(self, mock_f):
 
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
-    def test_declare_variable_write_complex_int(self, mock_f):
+    def test_DeclareVariable_write_complex_int(self, mock_f):
         """
         Testing that write to file is correct. Here a line is in an
         instrument declare section. The write file operation is
@@ -112,7 +112,7 @@ def test_declare_variable_write_complex_int(self, mock_f):
         is used. (integer value)
         """
 
-        var = declare_variable("double",
+        var = DeclareVariable("double",
                                "test",
                                value=5,
                                comment="test comment")
@@ -128,14 +128,14 @@ def test_declare_variable_write_complex_int(self, mock_f):
 
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
-    def test_declare_variable_write_simple_array(self, mock_f):
+    def test_DeclareVariable_write_simple_array(self, mock_f):
         """
         Testing that write to file is correct. Here a line is in an
         instrument declare section. The write file operation is
         mocked and check using a patch. Here an array is declared.
         """
 
-        var = declare_variable("double",
+        var = DeclareVariable("double",
                                "test",
                                array=29,
                                comment="test comment")
@@ -151,7 +151,7 @@ def test_declare_variable_write_simple_array(self, mock_f):
 
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
-    def test_declare_variable_write_complex_array(self, mock_f):
+    def test_DeclareVariable_write_complex_array(self, mock_f):
         """
         Testing that write to file is correct. Here a line is in an
         instrument declare section. The write file operation is
@@ -159,7 +159,7 @@ def test_declare_variable_write_complex_array(self, mock_f):
         populated with the selected values.
         """
 
-        var = declare_variable("double",
+        var = DeclareVariable("double",
                                "test",
                                array=3,
                                value=[5, 4, 3.1],
diff --git a/mcstasscript/tests/test_parameter_variable.py b/mcstasscript/tests/test_parameter_variable.py
index 61350bc2..68b81acb 100644
--- a/mcstasscript/tests/test_parameter_variable.py
+++ b/mcstasscript/tests/test_parameter_variable.py
@@ -3,65 +3,65 @@
 import unittest
 import unittest.mock
 
-from mcstasscript.helper.mcstas_objects import parameter_variable
+from mcstasscript.helper.mcstas_objects import ParameterVariable
 
 
-class Test_parameter_variable(unittest.TestCase):
+class Test_ParameterVariable(unittest.TestCase):
     """
-    Tests the parameter_variable class that holds an input parameter
+    Tests the ParameterVariable class that holds an input parameter
     for the instrument.
 
     """
 
-    def test_parameter_variable_init_basic(self):
+    def test_ParameterVariable_init_basic(self):
         """
         Smallest possible initialization
         """
 
-        par = parameter_variable("test")
+        par = ParameterVariable("test")
         self.assertEqual(par.name, "test")
 
-    def test_parameter_variable_init_basic_type(self):
+    def test_ParameterVariable_init_basic_type(self):
         """
         Initialization with a type
         """
 
-        par = parameter_variable("double", "test")
+        par = ParameterVariable("double", "test")
 
         self.assertEqual(par.name, "test")
         self.assertEqual(par.type, "double")
 
-    def test_parameter_variable_init_basic_type_value(self):
+    def test_ParameterVariable_init_basic_type_value(self):
         """
         Initialization with type and value
         """
 
-        par = parameter_variable("double", "test", value=518)
+        par = ParameterVariable("double", "test", value=518)
 
         self.assertEqual(par.name, "test")
         self.assertEqual(par.type, "double")
         self.assertEqual(par.value, 518)
 
-    def test_parameter_variable_init_basic_type_value_comment(self):
+    def test_ParameterVariable_init_basic_type_value_comment(self):
         """
         Initialization with type, value and comment
         """
 
-        par = parameter_variable("double", "test",
-                                 value=518, comment="test comment /")
+        par = ParameterVariable("double", "test", value=518,
+                                comment="test comment /")
 
         self.assertEqual(par.name, "test")
         self.assertEqual(par.type, "double")
         self.assertEqual(par.value, 518)
         self.assertEqual(par.comment, "// test comment /")
 
-    def test_parameter_variable_init_basic_value_comment(self):
+    def test_ParameterVariable_init_basic_value_comment(self):
         """
         Initialization with value and comment
         """
 
-        par = parameter_variable("test",
-                                 value=518, comment="test comment /")
+        par = ParameterVariable("test", value=518,
+                                comment="test comment /")
 
         self.assertEqual(par.name, "test")
         self.assertEqual(par.type, "")
@@ -70,7 +70,7 @@ def test_parameter_variable_init_basic_value_comment(self):
 
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
-    def test_parameter_variable_write_basic(self, mock_f):
+    def test_ParameterVariable_write_basic(self, mock_f):
         """
         Testing that write to file is correct. Here a line is in an
         instrument parameter section. The write file operation is
@@ -78,7 +78,7 @@ def test_parameter_variable_write_basic(self, mock_f):
         used.
         """
 
-        par = parameter_variable("double", "test")
+        par = ParameterVariable("double", "test")
         with mock_f('test.txt', 'w') as m_fo:
             par.write_parameter(m_fo, "")
 
@@ -93,7 +93,7 @@ def test_parameter_variable_write_basic(self, mock_f):
 
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
-    def test_parameter_variable_write_complex_float(self, mock_f):
+    def test_ParameterVariable_write_complex_float(self, mock_f):
         """
         Testing that write to file is correct. Here a line is in an
         instrument parameter section. The write file operation is
@@ -101,10 +101,8 @@ def test_parameter_variable_write_complex_float(self, mock_f):
         is used. (float value)
         """
 
-        par = parameter_variable("double",
-                                 "test",
-                                 value=5.4,
-                                 comment="test comment")
+        par = ParameterVariable("double", "test", value=5.4,
+                                comment="test comment")
 
         with mock_f('test.txt', 'w') as m_fo:
             par.write_parameter(m_fo, ")")
@@ -121,7 +119,7 @@ def test_parameter_variable_write_complex_float(self, mock_f):
 
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
-    def test_parameter_variable_write_complex_int(self, mock_f):
+    def test_ParameterVariable_write_complex_int(self, mock_f):
         """
         Testing that write to file is correct. Here a line is in an
         instrument parameter section. The write file operation is
@@ -129,10 +127,8 @@ def test_parameter_variable_write_complex_int(self, mock_f):
         is used. (integer value)
         """
 
-        par = parameter_variable("double",
-                                 "test",
-                                 value=5,
-                                 comment="test comment")
+        par = ParameterVariable("double", "test", value=5,
+                                comment="test comment")
 
         with mock_f('test.txt', 'w') as m_fo:
             par.write_parameter(m_fo, ")")
@@ -149,7 +145,7 @@ def test_parameter_variable_write_complex_int(self, mock_f):
 
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
-    def test_parameter_variable_write_complex_string(self, mock_f):
+    def test_ParameterVariable_write_complex_string(self, mock_f):
         """
         Testing that write to file is correct. Here a line is in an
         instrument parameter section. The write file operation is
@@ -157,9 +153,7 @@ def test_parameter_variable_write_complex_string(self, mock_f):
         is used. (string value)
         """
 
-        par = parameter_variable("double",
-                                 "test",
-                                 value="\"Al\"",
+        par = ParameterVariable("double", "test", value="\"Al\"",
                                  comment="test comment")
 
         with mock_f('test.txt', 'w') as m_fo:

From 131ebf68b03d66a5cb1909c4e963908735dc24c8 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Wed, 20 Jan 2021 14:15:35 +0100
Subject: [PATCH 126/403] Small formating changes.

---
 mcstasscript/helper/mcstas_objects.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 4a6b47a1..879438e5 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -65,7 +65,6 @@ def __init__(self, *args, **kwargs):
             comment : str
                 sets comment displayed next to declaration
 
-
         """
         if len(args) == 1:
             self.type = ""
@@ -79,7 +78,6 @@ def __init__(self, *args, **kwargs):
                                    + "\" which is not among the allowed types "
                                    + str(allowed_types) + ".")
 
-            #self.type = specified_type + " "
             self.type = specified_type
             self.name = str(args[1])
 

From aeaa5d7750beb47f2d4f4cd4c5dac737f41cba08 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Wed, 20 Jan 2021 14:31:06 +0100
Subject: [PATCH 127/403] Changes after review by Celine Durniak, much
 appreciated!

Fixed a last few issues after review.
---
 mcstasscript/data/data.py                 | 2 +-
 mcstasscript/tests/test_McStasMetaData.py | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index 676fcda1..86fa03de 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -1,4 +1,4 @@
-import matplotlib
+import matplotlib.pyplot
 import numpy as np
 
 class McStasMetaData:
diff --git a/mcstasscript/tests/test_McStasMetaData.py b/mcstasscript/tests/test_McStasMetaData.py
index 0c73e3aa..3c0cbe9a 100644
--- a/mcstasscript/tests/test_McStasMetaData.py
+++ b/mcstasscript/tests/test_McStasMetaData.py
@@ -64,7 +64,7 @@ def test_McStasMetaData_add_info_ylabel(self):
 
     def test_McStasMetaData_long_read_1d(self):
         """
-        Test that extact_info can read appropriate info, 1d case
+        Test that extract_info can read appropriate info, 1d case
         """
         meta_data = McStasMetaData()
         meta_data.add_info("type", "array_1d(500)")
@@ -90,7 +90,7 @@ def test_McStasMetaData_long_read_1d(self):
 
     def test_McStasMetaData_long_read_2d(self):
         """
-        Test that extact_info can read appropriate info, 2d case
+        Test that extract_info can read appropriate info, 2d case
         """
         meta_data = McStasMetaData()
         meta_data.add_info("type", "array_2d(500, 12)")

From 25cd535d4c729e524a86e1072adf61c8fc0dbcce Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Wed, 20 Jan 2021 14:43:20 +0100
Subject: [PATCH 128/403] Fixes after review by Celine Durniak, much
 appreciated!

Removed unnecessary f.close()
Fixed spelling mistake in PSD_4PI ylabel
---
 mcstasscript/helper/managed_mcrun.py         | 3 ---
 mcstasscript/tests/test_ManagedMcrun.py      | 2 +-
 mcstasscript/tests/test_data_set/PSD_4PI.dat | 2 +-
 mcstasscript/tests/test_data_set/mccode.sim  | 2 +-
 mcstasscript/tests/test_functions.py         | 2 +-
 5 files changed, 4 insertions(+), 7 deletions(-)

diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 3526f29a..e5a5677a 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -363,8 +363,5 @@ def load_results(data_folder_name):
         # Add this result to the results list
         results.append(result)
 
-        # Close the current datafile
-        f.close()
-
     # Return list of McStasData objects
     return results
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index 1f99ad53..3ed4d53b 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -410,7 +410,7 @@ def test_ManagedMcrun_load_data_PSD4PI(self):
         self.assertEqual(PSD_4PI.metadata.dimension, [300, 300])
         self.assertEqual(PSD_4PI.metadata.limits, [-180, 180, -90, 90])
         self.assertEqual(PSD_4PI.metadata.xlabel, "Longitude [deg]")
-        self.assertEqual(PSD_4PI.metadata.ylabel, "Lattitude [deg]")
+        self.assertEqual(PSD_4PI.metadata.ylabel, "Latitude [deg]")
         self.assertEqual(PSD_4PI.metadata.title, "4PI PSD monitor")
         self.assertEqual(PSD_4PI.Ncount[4][1], 4)
         self.assertEqual(PSD_4PI.Intensity[4][1], 1.537334562E-10)
diff --git a/mcstasscript/tests/test_data_set/PSD_4PI.dat b/mcstasscript/tests/test_data_set/PSD_4PI.dat
index 8d173c4e..be568542 100644
--- a/mcstasscript/tests/test_data_set/PSD_4PI.dat
+++ b/mcstasscript/tests/test_data_set/PSD_4PI.dat
@@ -23,7 +23,7 @@
 # xvar: Lo
 # yvar: La
 # xlabel: Longitude [deg]
-# ylabel: Lattitude [deg]
+# ylabel: Latitude [deg]
 # zvar: I
 # zlabel: Signal per bin
 # xylimits: -180 180 -90 90
diff --git a/mcstasscript/tests/test_data_set/mccode.sim b/mcstasscript/tests/test_data_set/mccode.sim
index aac53eaf..5891d96b 100644
--- a/mcstasscript/tests/test_data_set/mccode.sim
+++ b/mcstasscript/tests/test_data_set/mccode.sim
@@ -40,7 +40,7 @@ begin data
   xvar: Lo
   yvar: La
   xlabel: Longitude [deg]
-  ylabel: Lattitude [deg]
+  ylabel: Latitude [deg]
   zvar: I
   zlabel: Signal per bin
   xylimits: -180 180 -90 90
diff --git a/mcstasscript/tests/test_functions.py b/mcstasscript/tests/test_functions.py
index b646a60f..0e5224d8 100644
--- a/mcstasscript/tests/test_functions.py
+++ b/mcstasscript/tests/test_functions.py
@@ -313,7 +313,7 @@ def test_crun_load_data_PSD4PI(self):
         self.assertEqual(PSD_4PI.metadata.dimension, [300, 300])
         self.assertEqual(PSD_4PI.metadata.limits, [-180, 180, -90, 90])
         self.assertEqual(PSD_4PI.metadata.xlabel, "Longitude [deg]")
-        self.assertEqual(PSD_4PI.metadata.ylabel, "Lattitude [deg]")
+        self.assertEqual(PSD_4PI.metadata.ylabel, "Latitude [deg]")
         self.assertEqual(PSD_4PI.metadata.title, "4PI PSD monitor")
         self.assertEqual(PSD_4PI.Ncount[4][1], 4)
         self.assertEqual(PSD_4PI.Intensity[4][1], 1.537334562E-10)

From c55f09ad37b7c263ad559700bbb5237ccc7ebe8a Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Wed, 20 Jan 2021 14:44:43 +0100
Subject: [PATCH 129/403] Fixed spelling error in local PSD_monitor_4PI
 component.

---
 mcstasscript/tests/dummy_instrument_folder/PSD_monitor_4PI.comp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/tests/dummy_instrument_folder/PSD_monitor_4PI.comp b/mcstasscript/tests/dummy_instrument_folder/PSD_monitor_4PI.comp
index e4ed8756..d17dc65b 100644
--- a/mcstasscript/tests/dummy_instrument_folder/PSD_monitor_4PI.comp
+++ b/mcstasscript/tests/dummy_instrument_folder/PSD_monitor_4PI.comp
@@ -109,7 +109,7 @@ SAVE
     DETECTOR_OUT_2D(
     "4PI PSD monitor",
     "Longitude [deg]",
-    "Lattitude [deg]",
+    "Latitude [deg]",
     -180, 180, -90, 90,
     nx, ny,
     &PSD_N[0][0],&PSD_p[0][0],&PSD_p2[0][0],

From 63debe9e8ec5ab3c4ea79676c306d0b27306b919 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Mon, 25 Jan 2021 10:37:32 +0100
Subject: [PATCH 130/403] Adressed issues pointed out in review from Celine
 Durniak, much appreciated.

Updated doc strings to include all attributes.
Added some examples in complex methods.
Reduced reliance on *args in places where it was not necessary.
Fixed many spelling mistakes.
Additional input sanitation on paths

Tests
Improved doc strings
Removed unecessary mocks
---
 mcstasscript/interface/instr.py  | 454 ++++++++++++++++++++---------
 mcstasscript/tests/test_Instr.py | 470 ++++++++++++++++++++-----------
 2 files changed, 620 insertions(+), 304 deletions(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 5b7d494d..aa925393 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -18,24 +18,40 @@
 
 class McCode_instr:
     """
-    Main class for writing a McStas instrument using McStasScript
+    Main class for writing a McCode instrument using McStasScript
 
-    Initialization of McStas_instr sets the name of the instrument file
+    Initialization of McCode_instr sets the name of the instrument file
     and its methods are used to add all aspects of the instrument file.
     The class also holds methods for writing the finished instrument
-    file to disk and to run the simulation.
+    file to disk and to run the simulation. This is meant as a base class
+    that McStas_instr and McXtrace_instr inherits from, these have to provide
+    some attributes.
+
+    Required attributes in superclass
+    ---------------------------------
+    executable : str
+        Name of executable, mcrun or mxrun
+
+    particle : str
+        Name of probe particle, "neutron" or "x-ray"
+
+    package_name : str
+        Name of package, "McStas" or "McXtrace"
 
     Attributes
     ----------
     name : str
         name of instrument file
 
-    author : str
+    author : str, default "Python Instrument Generator"
         name of user of McStasScript, written to the file
 
-    origin : str
+    origin : str, default "ESS DMSC"
         origin of instrument file (affiliation)
 
+    input_path : str, default "."
+        directory in which simulation is executed, uses components found there
+
     executable_path : str
         absolute path of mcrun command, or empty if it is in path
 
@@ -43,7 +59,7 @@ class McCode_instr:
         contains all input parameters to be written to file
 
     declare_list : list of declare_variable instances
-        contains all declare parrameters to be written to file
+        contains all declare parameters to be written to file
 
     initialize_section : str
         string containing entire initialize section to be written
@@ -60,13 +76,25 @@ class McCode_instr:
     component_name_list : list of strings
         list of names of the components in the instrument
 
+    component_class_lib : dict
+        dict of custom Component classes made at run time
+
+    component_reader : ComponentReader
+        ComponentReader instance for reading component files
+
+    package_path : str
+        Path to mccode package containing component folders
+
     Methods
     -------
     add_parameter(*args, **kwargs)
         Adds input parameter to the define section
 
-    add_declare_var()
-        Adds declared variable ot the declare section
+    add_declare_var(type, name)
+        Add declared variable called name of given type to the declare section
+
+    append_declare(string)
+        Appends to the declare section
 
     append_initialize(string)
         Appends a string to the initialize section, then adds new line
@@ -83,12 +111,21 @@ class McCode_instr:
     append_trace(string)
         Obsolete method, add components instead (used in write_c_files)
 
+    append_trace_no_new_line(string)
+        Obsolete method, add components instead (used in write_c_files)
+
     show_components(string)
         Shows available components in given category
 
+    component_help(name)
+        Shows help on component of given name
+
     add_component(instance_name, component_name, **kwargs)
         Add a component to the instrument file
 
+    copy_component(new_component_name, original_component, **kwargs)
+        Makes a copy of original_component with new_component_name
+
     get_component(instance_name)
         Returns component instance with name instance_name
 
@@ -146,6 +183,9 @@ class McCode_instr:
     write_full_instrument()
         Writes full instrument file to current directory
 
+    show_instrument()
+        Shows instrument using mcdisplay
+
     run_full_instrument(**kwargs)
         Writes instrument files and runs simulation.
         Returns list of McStasData
@@ -191,28 +231,39 @@ def __init__(self, name, **kwargs):
                             + "\" which is not a legal filename")
 
         if "author" in kwargs:
-            self.author = kwargs["author"]
+            self.author = str(kwargs["author"])
         else:
             self.author = "Python " + self.package_name
             self.author += " Instrument Generator"
 
         if "origin" in kwargs:
-            self.origin = kwargs["origin"]
+            self.origin = str(kwargs["origin"])
         else:
             self.origin = "ESS DMSC"
 
         if "input_path" in kwargs:
-            self.input_path = kwargs["input_path"]
+            self.input_path = str(kwargs["input_path"])
+            if not os.path.isdir(self.input_path):
+                raise RuntimeError("Given input_path does not point to a "
+                                   + "folder:\"" + self.input_path + '"')
         else:
             self.input_path = "."
 
         self._read_calibration()
 
         if "executable_path" in kwargs:
-            self.executable_path = kwargs["executable_path"]
+            self.executable_path = str(kwargs["executable_path"])
+            if not os.path.isdir(self.executable_path):
+                raise RuntimeError("Given executable_path does not point to "
+                                   + "a folder:\"" + self.executable_path
+                                   + '"')
 
         if "package_path" in kwargs:
-            self.package_path = kwargs["package_path"]
+            self.package_path = str(kwargs["package_path"])
+            if not os.path.isdir(self.package_path):
+                raise RuntimeError("Given package_path does not point to "
+                                   + "a folder:\"" + self.package_path + '"')
+
         elif self.package_path is "":
             raise NameError("At this stage of development "
                             + "McStasScript need the absolute path "
@@ -249,6 +300,20 @@ def add_parameter(self, *args, **kwargs):
         """
         Method for adding input parameter to instrument
 
+        Type does not need to be specified, McStas considers that a floating
+        point value with the type double.
+
+        Examples
+        --------
+        Creates a parameter with name wavelength and associated comment
+        add_parameter("wavelength", comment="wavelength in [AA]")
+
+        Creates a parameter with name A3 and default value
+        add_parameter("A3", value=30, comment="A3 angle in [deg]")
+
+        Creates a parameter with type string and name sample_name
+        add_parameter("string", "sample_name")
+
         Parameters
         ----------
 
@@ -299,6 +364,7 @@ def show_parameters(self, **kwargs):
         longest_type = len(max(types, key=len))
         longest_name = len(max(names, key=len))
         longest_value = len(max(values, key=len))
+        # In addition to the data 11 characters are added before the comment
         comment_start_point = longest_type + longest_name + longest_value + 11
         longest_comment = len(max(comments, key=len))
         length_for_comment = line_limit - comment_start_point
@@ -379,7 +445,7 @@ def add_declare_var(self, *args, **kwargs):
         """
         # declare_variable class documented independently
         self.declare_list.append(declare_variable(*args, **kwargs))
-        
+
     def append_declare(self, string):
         """
         Method for appending code to the declare section directly
@@ -393,15 +459,15 @@ def append_declare(self, string):
         string : str
             code to be added to declare section
         """
-        
+
         #self.declare_section = self.declare_section + string + "\n"
         self.declare_list.append(string)
 
     def append_initialize(self, string):
         """
-        Method for appending code to the intialize section
+        Method for appending code to the initialize section
 
-        The intialize section consists of c code and will be compiled,
+        The initialize section consists of c code and will be compiled,
         thus any syntax errors will crash the simulation. Code is added
         on a new line for each call to this method.
 
@@ -410,14 +476,14 @@ def append_initialize(self, string):
         string : str
             code to be added to initialize section
         """
-        
+
         self.initialize_section = self.initialize_section + string + "\n"
 
     def append_initialize_no_new_line(self, string):
         """
-        Method for appending code to the intialize section, no new line
+        Method for appending code to the initialize section, no new line
 
-        The intialize section consists of c code and will be compiled,
+        The initialize section consists of c code and will be compiled,
         thus any syntax errors will crash the simulation. Code is added
         to the current line.
 
@@ -549,21 +615,16 @@ def component_help(self, name, **kwargs):
         dummy_instance = self._create_component_instance("dummy", name)
         dummy_instance.show_parameters(**kwargs)
 
-    def _create_component_instance(self, *args, **kwargs):
+    def _create_component_instance(self, name, component_name, **kwargs):
         """
         Dynamically creates a class for the requested component type
 
-        Created classses kept in dictionary, if the same component type
+        Created classes kept in dictionary, if the same component type
         is requested again, the class in the dictionary is used.  The
         method returns an instance of the created class that was
-        initialized with the paramters passed to this function.
+        initialized with the parameters passed to this function.
         """
 
-        if len(args) < 2:
-            raise NameError("Attempting to create component without name")
-
-        component_name = args[1]
-
         if component_name not in self.component_class_lib:
             comp_info = self.component_reader.read_name(component_name)
 
@@ -581,9 +642,10 @@ def _create_component_instance(self, *args, **kwargs):
                                                             (component,),
                                                             input_dict)
 
-        return self.component_class_lib[component_name](*args, **kwargs)
+        return self.component_class_lib[component_name](name, component_name,
+                                                        **kwargs)
 
-    def add_component(self, *args, **kwargs):
+    def add_component(self, name, component_name, **kwargs):
         """
         Method for adding a new component instance to the instrument
 
@@ -593,14 +655,14 @@ def add_component(self, *args, **kwargs):
         used.  The component is placed at the end of the instrument file
         unless otherwise specified with the after and before keywords.
         The component may be initialized using other keyword arguments,
-        but all attributes can be set with approrpiate methods.
+        but all attributes can be set with appropriate methods.
 
         Parameters
         ----------
-        First positional argument : str
+        name : str
             Unique name of component instance
 
-        Second positional argument : str
+        component_name : str
             Name of McStas component to create instance of
 
         Keyword arguments:
@@ -641,8 +703,8 @@ def add_component(self, *args, **kwargs):
                 Comment that will be displayed before the component
         """
 
-        if args[0] in self.component_name_list:
-            raise NameError(("Component name \"" + str(args[0])
+        if name in self.component_name_list:
+            raise NameError(("Component name \"" + str(name)
                              + "\" used twice, " + self.package_name
                              + " does not allow this."
                              + " Rename or remove one instance of this"
@@ -658,10 +720,11 @@ def add_component(self, *args, **kwargs):
 
             new_index = self.component_name_list.index(kwargs["after"])
 
-            new_component = self._create_component_instance(*args, **kwargs)
+            new_component = self._create_component_instance(name, component_name,
+                                                            **kwargs)
             self.component_list.insert(new_index + 1, new_component)
 
-            self.component_name_list.insert(new_index+1, args[0])
+            self.component_name_list.insert(new_index+1, name)
 
         # Insert component after component with this name
         elif "before" in kwargs:
@@ -674,20 +737,22 @@ def add_component(self, *args, **kwargs):
 
             new_index = self.component_name_list.index(kwargs["before"])
 
-            new_component = self._create_component_instance(*args, **kwargs)
+            new_component = self._create_component_instance(name, component_name,
+                                                            **kwargs)
             self.component_list.insert(new_index, new_component)
 
-            self.component_name_list.insert(new_index, args[0])
+            self.component_name_list.insert(new_index, name)
 
         # If after or before keywords absent, place component at the end
         else:
-            new_component = self._create_component_instance(*args, **kwargs)
+            new_component = self._create_component_instance(name, component_name,
+                                                            **kwargs)
             self.component_list.append(new_component)
-            self.component_name_list.append(args[0])
+            self.component_name_list.append(name)
 
         return new_component
-    
-    def copy_component(self, *args, **kwargs):
+
+    def copy_component(self, name, original_component, **kwargs):
         """
         Method for adding a copy of a component instance to the instrument
 
@@ -701,10 +766,10 @@ def copy_component(self, *args, **kwargs):
 
         Parameters
         ----------
-        First positional argument : str
+        name : str
             Unique name of component instance
 
-        Second positional argument : str
+        original_component : str
             Name of component instance to create copy of
 
         Keyword arguments:
@@ -744,16 +809,14 @@ def copy_component(self, *args, **kwargs):
             comment : str
                 Comment that will be displayed before the component
         """
-
-        # could also allow input of a component object        
-        
-        instance_name = args[0]
+        if isinstance(original_component, component): # Update to CammelCase
+            original_component = original_component.name
         """
         If the name starts with COPY, use unique naming as described in the
         McStas manual.
         """
-        if instance_name.startswith("COPY("):
-            target_name = instance_name.split("(", 1)[1]
+        if name.startswith("COPY("):
+            target_name = name.split("(", 1)[1]
             target_name = target_name.split(")", 1)[0]
             instance_name = target_name
 
@@ -763,20 +826,20 @@ def copy_component(self, *args, **kwargs):
                 instance_name = target_name + "_" + str(label)
                 label += 1
 
-        if instance_name in self.component_name_list:
-            raise NameError(("Component name \"" + str(args[0])
+        if name in self.component_name_list:
+            raise NameError(("Component name \"" + str(name)
                              + "\" used twice, " + self.package_name
                              + " does not allow this."
                              + " Rename or remove one instance of this"
                              + " name."))
-        
-        if not args[1] in self.component_name_list:
-            raise NameError("Component name \"" + str(args[1])
+
+        if original_component not in self.component_name_list:
+            raise NameError("Component name \"" + str(original_component)
                             + "\" was not found in the " + self.package_name
                             + " instrument. and thus can not be copied.")
         else:
-            component_to_copy = self.get_component(args[1])
-        
+            component_to_copy = self.get_component(original_component)
+
         # Insert component after component with this name
         if "after" in kwargs:
             if kwargs["after"] not in self.component_name_list:
@@ -788,10 +851,10 @@ def copy_component(self, *args, **kwargs):
             new_index = self.component_name_list.index(kwargs["after"])
 
             new_component = copy.deepcopy(component_to_copy)
-            new_component.name = instance_name
+            new_component.name = name
             self.component_list.insert(new_index+1, new_component)
 
-            self.component_name_list.insert(new_index+1, instance_name)
+            self.component_name_list.insert(new_index+1, name)
 
         # Insert component after component with this name
         elif "before" in kwargs:
@@ -805,20 +868,20 @@ def copy_component(self, *args, **kwargs):
             new_index = self.component_name_list.index(kwargs["before"])
 
             new_component = copy.deepcopy(component_to_copy)
-            new_component.name = instance_name
+            new_component.name = name
             self.component_list.insert(new_index, new_component)
 
-            self.component_name_list.insert(new_index, instance_name)
+            self.component_name_list.insert(new_index, name)
 
         # If after or before keywords absent, place component at the end
         else:
             new_component = copy.deepcopy(component_to_copy)
-            new_component.name = instance_name
+            new_component.name = name
             self.component_list.append(new_component)
-            self.component_name_list.append(instance_name)
-        
+            self.component_name_list.append(name)
+
         # Set the new name of the instance
-        new_component.name = instance_name
+        new_component.name = name
         # Run set_keyword_input again for keyword arguments to take effect
         new_component.set_keyword_input(**kwargs)
 
@@ -836,7 +899,7 @@ def get_component(self, name):
         Parameters
         ----------
         name : str
-            Unique name of component whos instance should be returned
+            Unique name of component whose instance should be returned
         """
 
         if name in self.component_name_list:
@@ -996,7 +1059,7 @@ def set_component_JUMP(self, name, JUMP):
 
         component = self.get_component(name)
         component.set_JUMP(JUMP)
-        
+
     def set_component_SPLIT(self, name, SPLIT):
         """
         Method for setting SPLIT value of named component
@@ -1012,7 +1075,7 @@ def set_component_SPLIT(self, name, SPLIT):
 
         component = self.get_component(name)
         component.set_SPLIT(SPLIT)
-        
+
     def set_component_c_code_before(self, name, code):
         """
         Method for setting c code before component
@@ -1028,7 +1091,7 @@ def set_component_c_code_before(self, name, code):
 
         component = self.get_component(name)
         component.set_c_code_before(code)
-        
+
     def set_component_c_code_after(self, name, code):
         """
         Method for setting c code before component
@@ -1043,7 +1106,7 @@ def set_component_c_code_after(self, name, code):
         """
 
         component = self.get_component(name)
-        component.set_c_code_after(code)        
+        component.set_c_code_after(code)
 
     def set_component_comment(self, name, string):
         """
@@ -1107,7 +1170,7 @@ def print_components(self, **kwargs):
 
         longest_name = len(max(self.component_name_list, key=len))
 
-        # Investigate how this could have been done in a better way
+        # todo Investigate how this could have been done in a better way
         # Find longest field for each type of data printed
         component_type_list = []
         at_xyz_list = []
@@ -1138,6 +1201,10 @@ def print_components(self, **kwargs):
         AT_pad = 6  # requires (, , ) in addition to data length
         RELATIVE_pad = 0
         ROTATED_pad = 6  # requires (, , ) in addition to data length
+        ROTATED_characters = 7 # ROTATED is 7 characters
+        AT_characters = 2 # AT is 2 characters
+        SPACING_between_strings = 7 # combining 8 strings, 7 spaces
+
 
         # Check if longest line length exceeded
         longest_line_length = (longest_name + name_pad
@@ -1145,7 +1212,10 @@ def print_components(self, **kwargs):
                                + longest_at_xyz_name + AT_pad
                                + longest_at_relative_name + RELATIVE_pad
                                + longest_rotated_xyz_name + ROTATED_pad
-                               + longest_rotated_relative_name + 8 + 9)
+                               + longest_rotated_relative_name
+                               + ROTATED_characters
+                               + AT_characters
+                               + SPACING_between_strings)
 
         def coordinates_to_string(data):
             return ("("
@@ -1159,6 +1229,7 @@ def coordinates_to_string(data):
         configuration file, attempt to split the output over an
         additional line. This is hardcoded up to 3 lines.
         """
+
         if longest_line_length > line_limit:
             n_lines = 2
             longest_at_xyz_name = max([longest_at_xyz_name,
@@ -1166,6 +1237,8 @@ def coordinates_to_string(data):
             longest_rotated_xyz_name = longest_at_xyz_name
             RELATIVE_pad = 0
 
+            SPACING_between_strings = 4 # combining 5 strings, 4 spaces
+
             longest_line_length_at = (longest_name
                                       + comp_name_pad
                                       + longest_component_name
@@ -1173,7 +1246,8 @@ def coordinates_to_string(data):
                                       + longest_at_xyz_name
                                       + AT_pad
                                       + longest_at_relative_name
-                                      + 7 + 6)
+                                      + ROTATED_characters
+                                      + SPACING_between_strings)
             longest_line_length_rotated = (longest_name
                                            + comp_name_pad
                                            + longest_component_name
@@ -1181,7 +1255,8 @@ def coordinates_to_string(data):
                                            + longest_rotated_xyz_name
                                            + ROTATED_pad
                                            + longest_rotated_relative_name
-                                           + 7 + 6)
+                                           + ROTATED_characters
+                                           + SPACING_between_strings)
 
             if (longest_line_length_at > line_limit
                     or longest_line_length_rotated > line_limit):
@@ -1242,7 +1317,7 @@ def coordinates_to_string(data):
                                             + ROTATED_pad)
 
                 p_ROTATED_RELATIVE = str(component.ROTATED_relative)
-                
+
                 if component.ROTATED_specified:
                     print(p_name, p_comp_name,
                           "AT     ", p_AT, p_AT_RELATIVE, "\n",
@@ -1295,42 +1370,40 @@ def write_c_files(self):
                 print("Creation of the directory %s failed" % path)
 
         file_path = os.path.join(".", "generated_includes",
-                                self.name + "_declare.c") 
-        fo = open(file_path, "w")
-        fo.write("// declare section for %s \n" % self.name)
-        fo.close()
-        
-        file_path = os.path.join(".", "generated_includes",
-                                 self.name + "_declare.c") 
-        fo = open(file_path, "a")
-        #fo.write(self.declare_section)
-        for dec_line in self.declare_list:
-            if isinstance(dec_line, str):
-                # append declare section parts written here
-                fo.write(dec_line)
-            else:
-                dec_line.write_line(fo)
-            fo.write("\n")
-        fo.close()
-
-        file_path = os.path.join(".", "generated_includes",
-                                 self.name + "_initialize.c")
-        fo = open(file_path, "w")
-        fo.write(self.initialize_section)
-        fo.close()
-
-        file_path = os.path.join(".", "generated_includes",
-                                 self.name + "_trace.c")
-        fo = open(file_path, "w")
-        fo.write(self.trace_section)
-        fo.close()
-
-        file_path = os.path.join(".", "generated_includes",
-                                 self.name + "_component_trace.c")
-        fo = open(file_path, "w")
-        for component in self.component_list:
-            component.write_component(fo)
-        fo.close()
+                                self.name + "_declare.c")
+        with open(file_path, "w") as fo:
+            fo.write("// declare section for %s \n" % self.name)
+
+
+            file_path = os.path.join(".", "generated_includes",
+                                     self.name + "_declare.c")
+        with open(file_path, "a") as fo:
+            for dec_line in self.declare_list:
+                if isinstance(dec_line, str):
+                    # append declare section parts written here
+                    fo.write(dec_line)
+                else:
+                    dec_line.write_line(fo)
+                fo.write("\n")
+            fo.close()
+
+            file_path = os.path.join(".", "generated_includes",
+                                     self.name + "_initialize.c")
+            fo = open(file_path, "w")
+            fo.write(self.initialize_section)
+            fo.close()
+
+            file_path = os.path.join(".", "generated_includes",
+                                     self.name + "_trace.c")
+            fo = open(file_path, "w")
+            fo.write(self.trace_section)
+            fo.close()
+
+            file_path = os.path.join(".", "generated_includes",
+                                     self.name + "_component_trace.c")
+            fo = open(file_path, "w")
+            for component in self.component_list:
+                component.write_component(fo)
 
     def write_full_instrument(self):
         """
@@ -1421,11 +1494,15 @@ def write_full_instrument(self):
         fo.write("\nEND\n")
 
         fo.close()
-        
+
     def _handle_parameters(self, given_parameters):
         """
         Internal helper function that handles which parameters to pass
-        when givne a certain set of parameters and values.
+        when given a certain set of parameters and values.
+
+        Adds the given parameters to the default parameters, and ensures all
+        required parameters are provided. Also checks all given parameters
+        match an existing parameter.
         
         Parameters
         ----------
@@ -1433,19 +1510,36 @@ def _handle_parameters(self, given_parameters):
             Parameters given by the user for simulation run
         
         """
-        
+
+        if not isinstance(given_parameters, dict):
+            raise RuntimeError("Given parameters must be a dict.")
+
         # Find required parameters
         required_parameters = []
         default_parameters = {}
 
-        for index in range(0, len(self.parameter_list)):
+        for index in range(len(self.parameter_list)):
             if self.parameter_list[index].value == "":
                 required_parameters.append(self.parameter_list[index].name)
             else:
                 default_parameters.update({self.parameter_list[index].name:
                                            self.parameter_list[index].value})
 
-        # Check if parameters are given
+        # Check if all given parameters correspond to legal parameters
+        for given_par in given_parameters:
+            if not isinstance(given_par, str):
+                raise NameError("Given parameter must be a string.")
+            if (given_par not in required_parameters
+                    and given_par not in default_parameters):
+                raise NameError("Given parameter: \"" + str(given_par)
+                                + "\" did not match any in instrument. "
+                                + "Currently available parameters: \n"
+                                + "  Required parameters:"
+                                + str(required_parameters) + "\n"
+                                + "  Default parameters: "
+                                + str(list(default_parameters.keys())))
+
+        # Check if required parameters are provided
         if len(given_parameters) is 0:
             if len(required_parameters) > 0:
                 # print required parameters and raise error
@@ -1460,13 +1554,13 @@ def _handle_parameters(self, given_parameters):
             for name in required_parameters:
                 if name not in given_parameters:
                     raise NameError("The required instrument parameter "
-                                    + name
+                                    + str(name)
                                     + " was not provided.")
             # Overwrite default parameters with given parameters
             default_parameters.update(given_parameters)
             return default_parameters
 
-    def run_full_instrument(self, *args, **kwargs):
+    def run_full_instrument(self, **kwargs):
         """
         Runs McStas instrument described by this class, returns list of
         McStasData
@@ -1496,18 +1590,33 @@ def run_full_instrument(self, *args, **kwargs):
             executable_path : str
                 Path to mcrun command, "" if already in path
         """
-        # Make sure mcrun path is in kwargs
+        # Make sure executable path is in kwargs
         if "executable_path" not in kwargs:
             kwargs["executable_path"] = self.executable_path
+        else:
+            if not os.path.isdir(str(kwargs["executable_path"])):
+                raise RuntimeError("The executable_path provided to "
+                                   + "run_full_instrument does not point to a"
+                                   + "directory: \""
+                                   + str(kwargs["executable_path"]) + "\"")
 
         if "executable" not in kwargs:
-            kwargs["executable"] = self.executable
+            kwargs["executable"] = str(self.executable)
+        else:
+            # check provided executable can be converted to string
+            str(kwargs["executable"])
 
         if "run_path" not in kwargs:
             # path where mcrun is executed, will load components there
             # if not set, use input_folder given
             kwargs["run_path"] = self.input_path
-                    
+        else:
+            if not os.path.isdir(str(kwargs["run_path"])):
+                raise RuntimeError("The run_path provided to "
+                                   + "run_full_instrument does not point to a"
+                                   + "directory: \""
+                                   + str(kwargs["run_path"]) + "\"")
+
         if "parameters" in kwargs:
             given_parameters = kwargs["parameters"]
         else:
@@ -1528,12 +1637,12 @@ def run_full_instrument(self, *args, **kwargs):
         # Run the simulation and return data
         simulation.run_simulation(**kwargs)
         return simulation.load_results()
-    
+
     def show_instrument(self, *args, **kwargs):
         """
-        Uses mcdisplay to show the instrument in webbroser
+        Uses mcdisplay to show the instrument in web browser
         """
-        
+
         if "parameters" in kwargs:
             given_parameters = kwargs["parameters"]
         else:
@@ -1559,7 +1668,7 @@ def show_instrument(self, *args, **kwargs):
 
         full_command = (bin_path + executable + " "
                         + os.path.join(self.input_path, self.name + ".instr")
-                        + " " + parameter_string) 
+                        + " " + parameter_string)
 
         process = subprocess.run(full_command, shell=True,
                                  stdout=subprocess.PIPE,
@@ -1583,12 +1692,24 @@ class McStas_instr(McCode_instr):
     name : str
         name of instrument file
 
-    author : str
+    author : str, default "Python Instrument Generator"
         name of user of McStasScript, written to the file
 
-    origin : str
+    origin : str, default "ESS DMSC"
         origin of instrument file (affiliation)
 
+    executable : str
+        Name of executable, mcrun or mxrun
+
+    particle : str
+        Name of probe particle, "neutron" or "x-ray"
+
+    package_name : str
+        Name of package, "McStas" or "McXtrace"
+
+    input_path : str, default "."
+        directory in which simulation is executed, uses components found there
+
     executable_path : str
         absolute path of mcrun command, or empty if it is in path
 
@@ -1596,7 +1717,7 @@ class McStas_instr(McCode_instr):
         contains all input parameters to be written to file
 
     declare_list : list of declare_variable instances
-        contains all declare parrameters to be written to file
+        contains all declare parameters to be written to file
 
     initialize_section : str
         string containing entire initialize section to be written
@@ -1613,13 +1734,25 @@ class McStas_instr(McCode_instr):
     component_name_list : list of strings
         list of names of the components in the instrument
 
+    component_class_lib : dict
+        dict of custom Component classes made at run time
+
+    component_reader : ComponentReader
+        ComponentReader instance for reading component files
+
+    package_path : str
+        Path to mccode package containing component folders
+
     Methods
     -------
     add_parameter(*args, **kwargs)
         Adds input parameter to the define section
 
-    add_declare_var()
-        Adds declared variable ot the declare section
+    add_declare_var(type, name)
+        Add declared variable called name of given type to the declare section
+
+    append_declare(string)
+        Appends to the declare section
 
     append_initialize(string)
         Appends a string to the initialize section, then adds new line
@@ -1636,12 +1769,21 @@ class McStas_instr(McCode_instr):
     append_trace(string)
         Obsolete method, add components instead (used in write_c_files)
 
+    append_trace_no_new_line(string)
+        Obsolete method, add components instead (used in write_c_files)
+
     show_components(string)
         Shows available components in given category
 
+    component_help(name)
+        Shows help on component of given name
+
     add_component(instance_name, component_name, **kwargs)
         Add a component to the instrument file
 
+    copy_component(new_component_name, original_component, **kwargs)
+        Makes a copy of original_component with new_component_name
+
     get_component(instance_name)
         Returns component instance with name instance_name
 
@@ -1699,6 +1841,9 @@ class McStas_instr(McCode_instr):
     write_full_instrument()
         Writes full instrument file to current directory
 
+    show_instrument()
+        Shows instrument using mcdisplay
+
     run_full_instrument(**kwargs)
         Writes instrument files and runs simulation.
         Returns list of McStasData
@@ -1750,6 +1895,7 @@ def _read_calibration(self):
             self.package_path = ""
             self.line_limit = 180
 
+
 class McXtrace_instr(McCode_instr):
     """
     Main class for writing a McXtrace instrument using McStasScript
@@ -1764,12 +1910,24 @@ class McXtrace_instr(McCode_instr):
     name : str
         name of instrument file
 
-    author : str
+    author : str, default "Python Instrument Generator"
         name of user of McStasScript, written to the file
 
-    origin : str
+    origin : str, default "ESS DMSC"
         origin of instrument file (affiliation)
 
+    executable : str
+        Name of executable, mcrun or mxrun
+
+    particle : str
+        Name of probe particle, "neutron" or "x-ray"
+
+    package_name : str
+        Name of package, "McStas" or "McXtrace"
+
+    input_path : str, default "."
+        directory in which simulation is executed, uses components found there
+
     executable_path : str
         absolute path of mcrun command, or empty if it is in path
 
@@ -1777,7 +1935,7 @@ class McXtrace_instr(McCode_instr):
         contains all input parameters to be written to file
 
     declare_list : list of declare_variable instances
-        contains all declare parrameters to be written to file
+        contains all declare parameters to be written to file
 
     initialize_section : str
         string containing entire initialize section to be written
@@ -1794,13 +1952,25 @@ class McXtrace_instr(McCode_instr):
     component_name_list : list of strings
         list of names of the components in the instrument
 
+    component_class_lib : dict
+        dict of custom Component classes made at run time
+
+    component_reader : ComponentReader
+        ComponentReader instance for reading component files
+
+    package_path : str
+        Path to mccode package containing component folders
+
     Methods
     -------
     add_parameter(*args, **kwargs)
         Adds input parameter to the define section
 
-    add_declare_var()
-        Adds declared variable ot the declare section
+    add_declare_var(type, name)
+        Add declared variable called name of given type to the declare section
+
+    append_declare(string)
+        Appends to the declare section
 
     append_initialize(string)
         Appends a string to the initialize section, then adds new line
@@ -1817,12 +1987,21 @@ class McXtrace_instr(McCode_instr):
     append_trace(string)
         Obsolete method, add components instead (used in write_c_files)
 
+    append_trace_no_new_line(string)
+        Obsolete method, add components instead (used in write_c_files)
+
     show_components(string)
         Shows available components in given category
 
+    component_help(name)
+        Shows help on component of given name
+
     add_component(instance_name, component_name, **kwargs)
         Add a component to the instrument file
 
+    copy_component(new_component_name, original_component, **kwargs)
+        Makes a copy of original_component with new_component_name
+
     get_component(instance_name)
         Returns component instance with name instance_name
 
@@ -1880,6 +2059,9 @@ class McXtrace_instr(McCode_instr):
     write_full_instrument()
         Writes full instrument file to current directory
 
+    show_instrument()
+        Shows instrument using mcdisplay
+
     run_full_instrument(**kwargs)
         Writes instrument files and runs simulation.
         Returns list of McStasData
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index df6560a9..499db65d 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -16,32 +16,31 @@
 
 def setup_instr_no_path():
     """
-    Sets up a instrument without a mcstas_path
+    Sets up a neutron instrument without a package_path
     """
     return McStas_instr("test_instrument")
 
 def setup_x_ray_instr_no_path():
     """
-    Sets up a instrument without a mcstas_path
+    Sets up a X-ray instrument without a package_path
     """
     return McXtrace_instr("test_instrument")
 
 def setup_instr_root_path():
     """
-    Sets up a instrument with root mcstas_path
+    Sets up a neutron instrument with root package_path
     """
     return McStas_instr("test_instrument", package_path="/")
 
 def setup_x_ray_instr_root_path():
     """
-    Sets up a instrument with root mcstas_path
+    Sets up a X-ray instrument with root package_path
     """
     return McXtrace_instr("test_instrument", package_path="/")
 
-
 def setup_instr_with_path():
     """
-    Sets up a instrument with a valid mcstas_path, but it points to
+    Sets up an instrument with a valid package_path, but it points to
     the dummy installation in the test folder.
     """
 
@@ -51,14 +50,31 @@ def setup_instr_with_path():
     current_work_dir = os.getcwd()
     os.chdir(THIS_DIR)  # Set work directory to test folder
 
+    os.chdir(current_work_dir)  # Return to previous workdir
+
     return McStas_instr("test_instrument", package_path=dummy_path)
 
+def setup_x_ray_instr_with_path():
+    """
+    Sets up an instrument with a valid package_path, but it points to
+    the dummy installation in the test folder.
+    """
+
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+    dummy_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+    current_work_dir = os.getcwd()
+    os.chdir(THIS_DIR)  # Set work directory to test folder
+
     os.chdir(current_work_dir)  # Return to previous workdir
 
+    return McXtrace_instr("test_instrument", package_path=dummy_path)
+
 def setup_instr_with_input_path():
     """
-    Sets up a instrument with a valid mcstas_path, but it points to
-    the dummy installation in the test folder.
+    Sets up an instrument with a valid package_path, but it points to
+    the dummy installation in the test folder. In addition the input_path
+    is set to a folder in the test directory using an absolute path.
     """
 
     THIS_DIR = os.path.dirname(os.path.abspath(__file__))
@@ -68,16 +84,17 @@ def setup_instr_with_input_path():
     current_work_dir = os.getcwd()
     os.chdir(THIS_DIR)  # Set work directory to test folder
 
+    os.chdir(current_work_dir)  # Return to previous workdir
+
     return McStas_instr("test_instrument",
                         package_path=dummy_path,
                         input_path=input_path)
 
-    os.chdir(current_work_dir)  # Return to previous workdir
-
 def setup_instr_with_input_path_relative():
     """
-    Sets up a instrument with a valid mcstas_path, but it points to
-    the dummy installation in the test folder.
+    Sets up an instrument with a valid package_path, but it points to
+    the dummy installation in the test folder. In addition the input_path
+    is set to a folder in the test directory using a relative path.
     """
 
     THIS_DIR = os.path.dirname(os.path.abspath(__file__))
@@ -85,16 +102,16 @@ def setup_instr_with_input_path_relative():
     current_work_dir = os.getcwd()
     os.chdir(THIS_DIR)  # Set work directory to test folder
 
+    os.chdir(current_work_dir)  # Return to previous workdir
+
     return McStas_instr("test_instrument",
                         package_path="dummy_mcstas",
                         input_path="test_input_folder")
 
-    os.chdir(current_work_dir)  # Return to previous workdir
-
 
 def setup_populated_instr():
     """
-    Sets up a instrument with some features used and two components
+    Sets up a neutron instrument with some features used and three components
     """
     instr = setup_instr_root_path()
 
@@ -109,9 +126,36 @@ def setup_populated_instr():
 
     return instr
 
+def setup_populated_instr_with_dummy_path():
+    """
+    Sets up a neutron instrument with some features used and three components
+
+    Here uses the dummy mcstas installation as path and sets required
+    parameters so that a run is possible.
+    """
+    instr = setup_instr_with_path()
+
+    instr.add_parameter("double", "theta")
+    instr.add_parameter("double", "has_default", value=37)
+    instr.add_declare_var("double", "two_theta")
+    instr.append_initialize("two_theta = 2.0*theta;")
+
+    comp1 = instr.add_component("first_component", "test_for_reading")
+    comp1.gauss = 1.2
+    comp1.test_string = "a_string"
+    comp2 = instr.add_component("second_component", "test_for_reading")
+    comp2.gauss = 1.4
+    comp2.test_string = "b_string"
+    comp3 = instr.add_component("third_component", "test_for_reading")
+    comp3.gauss = 1.6
+    comp3.test_string = "c_string"
+
+    return instr
+
+
 def setup_populated_x_ray_instr():
     """
-    Sets up a instrument with some features used and two components
+    Sets up a X-ray instrument with some features used and three components
     """
     instr = setup_x_ray_instr_root_path()
 
@@ -126,9 +170,36 @@ def setup_populated_x_ray_instr():
 
     return instr
 
+def setup_populated_x_ray_instr_with_dummy_path():
+    """
+    Sets up a x-ray instrument with some features used and three components
+
+    Here uses the dummy mcstas installation as path and sets required
+    parameters so that a run is possible.
+    """
+    instr = setup_x_ray_instr_with_path()
+
+    instr.add_parameter("double", "theta")
+    instr.add_parameter("double", "has_default", value=37)
+    instr.add_declare_var("double", "two_theta")
+    instr.append_initialize("two_theta = 2.0*theta;")
+
+    comp1 = instr.add_component("first_component", "test_for_reading")
+    comp1.gauss = 1.2
+    comp1.test_string = "a_string"
+    comp2 = instr.add_component("second_component", "test_for_reading")
+    comp2.gauss = 1.4
+    comp2.test_string = "b_string"
+    comp3 = instr.add_component("third_component", "test_for_reading")
+    comp3.gauss = 1.6
+    comp3.test_string = "c_string"
+
+    return instr
+
+
 def setup_populated_with_some_options_instr():
     """
-    Sets up a instrument with some features used and two components
+    Sets up a neutron instrument with some features used and two components
     """
     instr = setup_instr_root_path()
 
@@ -170,13 +241,13 @@ def test_complex_initialize(self):
         my_instrument = McStas_instr("test_instrument",
                                      author="Mads",
                                      origin="DMSC",
-                                     executable_path="/path/to/mcrun",
-                                     package_path="/path/to/mcstas")
+                                     executable_path="./dummy_mcstas/contrib",
+                                     package_path="./dummy_mcstas/misc")
 
         self.assertEqual(my_instrument.author, "Mads")
         self.assertEqual(my_instrument.origin, "DMSC")
-        self.assertEqual(my_instrument.executable_path, "/path/to/mcrun")
-        self.assertEqual(my_instrument.package_path, "/path/to/mcstas")
+        self.assertEqual(my_instrument.executable_path, "./dummy_mcstas/contrib")
+        self.assertEqual(my_instrument.package_path, "./dummy_mcstas/misc")
 
     def test_load_config_file(self):
         """
@@ -262,6 +333,8 @@ def test_simple_add_parameter(self):
         """
         This is just an interface to a function that is tested
         elsewhere, so only a basic test is performed here.
+
+        ParameterVariable is tested in test_parameter_variable.
         """
         instr = setup_instr_root_path()
 
@@ -346,6 +419,8 @@ def test_simple_add_declare_parameter(self):
         """
         This is just an interface to a function that is tested
         elsewhere, so only a basic test is performed here.
+
+        DeclareVariable is tested in test_declare_variable.
         """
         instr = setup_instr_root_path()
 
@@ -356,8 +431,9 @@ def test_simple_add_declare_parameter(self):
         
     def test_simple_append_declare(self):
         """
-        The declare lines are held as a string. This method
-        appends that string.
+        Appending to declare adds an object to the declare list, and the
+        allowed types are either strings or DeclareVariable objects.
+        Here only strings are added.
         """
         instr = setup_instr_root_path()
 
@@ -374,8 +450,9 @@ def test_simple_append_declare(self):
         
     def test_simple_append_declare_var_mix(self):
         """
-        The declare lines are held as a string. This method
-        appends that string.
+        Appending to declare adds an object to the declare list, and the
+        allowed types are either strings or DeclareVariable objects.
+        Here a mix of strings and DeclareVariable objects are added.
         """
         instr = setup_instr_root_path()
 
@@ -415,7 +492,7 @@ def test_simple_append_initialize(self):
     def test_simple_append_initialize_no_new_line(self):
         """
         The initialize section is held as a string. This method
-        appends that string.
+        appends that string without making a new line.
         """
         instr = setup_instr_root_path()
 
@@ -434,7 +511,7 @@ def test_simple_append_initialize_no_new_line(self):
 
     def test_simple_append_finally(self):
         """
-        The initialize section is held as a string. This method
+        The finally section is held as a string. This method
         appends that string.
         """
         instr = setup_instr_root_path()
@@ -456,8 +533,8 @@ def test_simple_append_finally(self):
 
     def test_simple_append_finally_no_new_line(self):
         """
-        The initialize section is held as a string. This method
-        appends that string.
+        The finally section is held as a string. This method
+        appends that string without making a new line.
         """
         instr = setup_instr_root_path()
 
@@ -476,8 +553,9 @@ def test_simple_append_finally_no_new_line(self):
 
     def test_simple_append_trace(self):
         """
-        The initialize section is held as a string. This method
-        appends that string.
+        The trace section is held as a string. This method
+        appends that string. Only used for writing c files, which is not
+        the main way to use McStasScript.
         """
         instr = setup_instr_root_path()
 
@@ -498,8 +576,9 @@ def test_simple_append_trace(self):
 
     def test_simple_append_trace_no_new_line(self):
         """
-        The initialize section is held as a string. This method
-        appends that string.
+        The trace section is held as a string. This method appends that string
+        without making a new line. Only used for writing c files, which is not
+        the main way to use McStasScript.
         """
         instr = setup_instr_root_path()
 
@@ -519,7 +598,7 @@ def test_simple_append_trace_no_new_line(self):
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_show_components_simple(self, mock_stdout):
         """
-        Simple test of show components to show categories
+        Simple test of show components to show component categories
         """
         instr = setup_instr_with_path()
 
@@ -543,7 +622,8 @@ def test_show_components_simple(self, mock_stdout):
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_show_components_folder(self, mock_stdout):
         """
-        Simple test of show components to show categories
+        Simple test of show components to show components in current work
+        directory.
         """
         instr = setup_instr_with_path()
 
@@ -567,7 +647,7 @@ def test_show_components_folder(self, mock_stdout):
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_show_components_input_path_simple(self, mock_stdout):
         """
-        Simple test of input_path being recoignized and passed
+        Simple test of input_path being recognized and passed
         to component_reader so PSDlin_monitor is overwritten
         """
         instr = setup_instr_with_input_path()
@@ -594,7 +674,7 @@ def test_show_components_input_path_simple(self, mock_stdout):
         """
         Simple test of input_path being recoignized and passed
         to component_reader so PSDlin_monitor is overwritten
-        Here dummy_mcstas and input_path is set using relative
+        Here dummy_mcstas and input_path are set using relative
         paths instead of absolute paths.
         """
         instr = setup_instr_with_input_path_relative()
@@ -626,7 +706,7 @@ def test_component_help(self, mock_stdout):
         instr.component_help("test_for_reading", line_length=90)
         # This call creates a dummy component and calls its
         # show_parameter method which has been tested. Here we
-        # need to ensure the call is succesful, not test all
+        # need to ensure the call is successful, not test all
         # output from the call.
 
         output = mock_stdout.getvalue()
@@ -663,9 +743,11 @@ def test_component_help(self, mock_stdout):
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_create_component_instance_simple(self, mock_stdout):
         """
+        Tests successful use of _create_component_instance
+
         _create_component_instance will make a dynamic subclass of
         component with the information from the component files read
-        from disk.  The subclasses is saved in a dict for reuse in
+        from disk.  The subclass is saved in a dict for reuse in
         case the same component type is requested again.
         """
 
@@ -685,23 +767,12 @@ def test_create_component_instance_simple(self, mock_stdout):
         self.assertEqual(comp.parameter_comments["radius"], comment)
         self.assertEqual(comp.category, "Work directory")
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_create_component_instance_simple_error(self, mock_stdout):
-        """
-        _create_component_instance will make a dynamic subclass of
-        component with the information from the component files read
-        from disk.  The subclasses is saved in a dict for reuse in
-        case the same component type is requested again.
-        """
-
-        instr = setup_instr_with_path()
-
-        with self.assertRaises(NameError):
-            comp = instr._create_component_instance("test_component")
-
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_create_component_instance_complex(self, mock_stdout):
         """
+        Tests successful use of _create_component_instance while using
+        keyword arguments in creation
+
         _create_component_instance will make a dynamic subclass of
         component with the information from the component files read
         from disk.  The subclasses is saved in a dict for reuse in
@@ -735,6 +806,8 @@ def test_create_component_instance_complex(self, mock_stdout):
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_add_component_simple(self, mock_stdout):
         """
+        Testing add_component in simple case.
+
         The add_component method adds a new component object to the
         instrument and keeps track of its location within the
         sequence of components.  Normally a new component is added to
@@ -756,6 +829,8 @@ def test_add_component_simple(self, mock_stdout):
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_add_component_simple_keyword(self, mock_stdout):
         """
+        Testing add_component with keyword argument for the component
+
         The add_component method adds a new component object to the
         instrument and keeps track of its location within the
         sequence of components.  Normally a new component is added to
@@ -777,15 +852,15 @@ def test_add_component_simple_keyword(self, mock_stdout):
 
         self.assertEqual(instr.component_list[0].WHEN, "WHEN (1<2)")
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_add_component_simple_before(self, mock_stdout):
+    def test_add_component_simple_before(self):
         """
+        Testing add_component with before keyword argument for the method
+
         The add_component method adds a new component object to the
         instrument and keeps track of its location within the
         sequence of components.  Normally a new component is added to
         the end of the sequence, but the before and after keywords can
-        be used to select another location. Here keyword passing is
-        tested.
+        be used to select another location, here before is tested.
         """
 
         instr = setup_populated_instr()
@@ -798,15 +873,15 @@ def test_add_component_simple_before(self, mock_stdout):
         self.assertEqual(instr.component_list[0].name, "test_component")
         self.assertEqual(instr.component_list[3].name, "third_component")
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_add_component_simple_after(self, mock_stdout):
+    def test_add_component_simple_after(self):
         """
+        Testing add_component with after keyword argument for the method
+
         The add_component method adds a new component object to the
         instrument and keeps track of its location within the
         sequence of components.  Normally a new component is added to
         the end of the sequence, but the before and after keywords can
-        be used to select another location. Here keyword passing is
-        tested.
+        be used to select another location, here after is tested.
         """
 
         instr = setup_populated_instr()
@@ -819,15 +894,16 @@ def test_add_component_simple_after(self, mock_stdout):
         self.assertEqual(instr.component_list[1].name, "test_component")
         self.assertEqual(instr.component_list[3].name, "third_component")
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_add_component_simple_after_error(self, mock_stdout):
+    def test_add_component_simple_after_error(self):
         """
+        Checks add_component raises a NameError if after keyword specifies a
+        non-existent component
+
         The add_component method adds a new component object to the
         instrument and keeps track of its location within the
         sequence of components.  Normally a new component is added to
         the end of the sequence, but the before and after keywords can
-        be used to select another location. Here keyword passing is
-        tested.
+        be used to select another location, here before is tested.
         """
 
         instr = setup_populated_instr()
@@ -835,17 +911,18 @@ def test_add_component_simple_after_error(self, mock_stdout):
         with self.assertRaises(NameError):
             comp = instr.add_component("test_component",
                                        "test_for_reading",
-                                       after="non_exsistent_component")
+                                       after="non_existent_component")
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_add_component_simple_before_error(self, mock_stdout):
+    def test_add_component_simple_before_error(self):
         """
+        Checks add_component raises a NameError if before keyword specifies a
+        non-existent component
+
         The add_component method adds a new component object to the
         instrument and keeps track of its location within the
         sequence of components.  Normally a new component is added to
         the end of the sequence, but the before and after keywords can
-        be used to select another location. Here keyword passing is
-        tested.
+        be used to select another location, here after is tested.
         """
 
         instr = setup_populated_instr()
@@ -853,12 +930,11 @@ def test_add_component_simple_before_error(self, mock_stdout):
         with self.assertRaises(NameError):
             comp = instr.add_component("test_component",
                                        "test_for_reading",
-                                       before="non_exsistent_component")
+                                       before="non_existent_component")
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_add_component_simple_double_naming_error(self, mock_stdout):
+    def test_add_component_simple_double_naming_error(self):
         """
-        This tests checks that an error occurs when giving the new
+        This tests checks that an error occurs when giving a new
         component a name which has already been used.
         """
 
@@ -866,11 +942,10 @@ def test_add_component_simple_double_naming_error(self, mock_stdout):
 
         with self.assertRaises(NameError):
             comp = instr.add_component("first_component", "test_for_reading")
-            
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_copy_component_simple(self, mock_stdout):
+
+    def test_copy_component_simple(self):
         """
-        Checks that a component can be copied
+        Checks that a component can be copied using the name
         """
 
         instr = setup_populated_with_some_options_instr()
@@ -882,12 +957,28 @@ def test_copy_component_simple(self, mock_stdout):
         self.assertEqual(comp.AT_data[0], 0)
         self.assertEqual(comp.AT_data[1], 0)
         self.assertEqual(comp.AT_data[2], 2)
-        
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_copy_component_keywords(self, mock_stdout):
+
+    def test_copy_component_simple_object(self):
+        """
+        Checks that a component can be copied using the object
+        """
+
+        instr = setup_populated_with_some_options_instr()
+
+        comp = instr.get_component("second_component")
+
+        comp = instr.copy_component("copy_of_second_comp", comp)
+
+        self.assertEqual(comp.name, "copy_of_second_comp")
+        self.assertEqual(comp.yheight, 1.23)
+        self.assertEqual(comp.AT_data[0], 0)
+        self.assertEqual(comp.AT_data[1], 0)
+        self.assertEqual(comp.AT_data[2], 2)
+
+    def test_copy_component_keywords(self):
         """
         Checks that a component can be copied and that keyword
-        arguments given under copy operation is sucessfully 
+        arguments given under copy operation is successfully
         applied to the new component. A check is also made to 
         ensure that the original component was not modified.
         """
@@ -912,13 +1003,11 @@ def test_copy_component_keywords(self, mock_stdout):
         self.assertEqual(original.AT_data[1], 0)
         self.assertEqual(original.AT_data[2], 2)
         self.assertEqual(original.SPLIT, 0)
-        
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_get_component_simple(self, mock_stdout):
+    def test_get_component_simple(self):
         """
         get_component retrieves a component with a given name for
-        easier manipulation.
+        easier manipulation. Check it works as intended.
         """
 
         instr = setup_populated_instr()
@@ -927,11 +1016,11 @@ def test_get_component_simple(self, mock_stdout):
 
         self.assertEqual(comp.name, "second_component")
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_get_component_simple_error(self, mock_stdout):
+    def test_get_component_simple_error(self):
         """
         get_component retrieves a component with a given name for
-        easier manipulation.
+        easier manipulation. Check it fails when the component name
+        doesn't correspond to a component in the instrument.
         """
 
         instr = setup_populated_instr()
@@ -939,11 +1028,9 @@ def test_get_component_simple_error(self, mock_stdout):
         with self.assertRaises(NameError):
             comp = instr.get_component("non_existing_component")
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_get_last_component_simple(self, mock_stdout):
+    def test_get_last_component_simple(self):
         """
-        get_component retrieves the last component for easier
-        manipulation.
+        Check get_last_component retrieves the last component
         """
 
         instr = setup_populated_instr()
@@ -952,10 +1039,11 @@ def test_get_last_component_simple(self, mock_stdout):
 
         self.assertEqual(comp.name, "third_component")
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_set_component_parameter(self, mock_stdout):
+    def test_set_component_parameter(self):
         """
-        Set component parameter passes a dict from instrument level
+        Tests simple case of set_component_parameter
+
+        set_component_parameter passes a dict from instrument level
         to a contained component with the given name. It uses the
         get_component method.
         """
@@ -971,10 +1059,12 @@ def test_set_component_parameter(self, mock_stdout):
         self.assertEqual(comp.radius, 5.8)
         self.assertEqual(comp.dist, "text")
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_set_component_parameter_error(self, mock_stdout):
+    def test_set_component_parameter_error(self):
         """
-        Set component parameter passes a dict from instrument level
+        Tests set_component_parameter fails when trying to set a parameter
+        that does not exist
+
+        set_component_parameter passes a dict from instrument level
         to a contained component with the given name. It uses the
         get_component method.
         """
@@ -983,11 +1073,10 @@ def test_set_component_parameter_error(self, mock_stdout):
 
         with self.assertRaises(NameError):
             instr.set_component_parameter("second_component",
-                                          {"non_exsistant_par": 5.8,
+                                          {"non_existent_par": 5.8,
                                            "dist": "text"})
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_set_component_AT(self, mock_stdout):
+    def test_set_component_AT(self):
         """
         set_component_AT passes the argument to the similar method
         in the component class.
@@ -1004,8 +1093,7 @@ def test_set_component_AT(self, mock_stdout):
         self.assertEqual(comp.AT_relative, "RELATIVE home")
         self.assertEqual(comp.ROTATED_relative, "ABSOLUTE")
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_set_component_ROTATED(self, mock_stdout):
+    def test_set_component_ROTATED(self):
         """
         set_component_ROTATED passes the argument to the similar
         method in the component class.
@@ -1014,16 +1102,15 @@ def test_set_component_ROTATED(self, mock_stdout):
         instr = setup_populated_instr()
 
         instr.set_component_ROTATED("second_component",
-                                    [1, 2, 3.2], RELATIVE="home")
+                                    [4, 1, -29], RELATIVE="home")
 
         comp = instr.get_component("second_component")
 
-        self.assertEqual(comp.ROTATED_data, [1, 2, 3.2])
+        self.assertEqual(comp.ROTATED_data, [4, 1, -29])
         self.assertEqual(comp.ROTATED_relative, "RELATIVE home")
         self.assertEqual(comp.AT_relative, "ABSOLUTE")
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_set_component_RELATIVE(self, mock_stdout):
+    def test_set_component_RELATIVE(self):
         """
         set_component_RELATIVE passes the argument to the similar
         method in the component class.
@@ -1039,8 +1126,7 @@ def test_set_component_RELATIVE(self, mock_stdout):
         self.assertEqual(comp.ROTATED_relative, "RELATIVE home")
         self.assertEqual(comp.AT_relative, "RELATIVE home")
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_set_component_WHEN(self, mock_stdout):
+    def test_set_component_WHEN(self):
         """
         set_component_WHEN passes the argument to the similar method
         in the component class.
@@ -1054,8 +1140,7 @@ def test_set_component_WHEN(self, mock_stdout):
 
         self.assertEqual(comp.WHEN, "WHEN (2>1)")
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_append_component_EXTEND(self, mock_stdout):
+    def test_append_component_EXTEND(self):
         """
         append_component_EXTEND passes the argument to the similar
         method in the component class.
@@ -1073,8 +1158,7 @@ def test_append_component_EXTEND(self, mock_stdout):
         self.assertEqual(output[0], "line1")
         self.assertEqual(output[1], "line2")
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_set_component_GROUP(self, mock_stdout):
+    def test_set_component_GROUP(self):
         """
         set_component_GROUP passes the argument to the similar method
         in the component class.
@@ -1088,8 +1172,7 @@ def test_set_component_GROUP(self, mock_stdout):
 
         self.assertEqual(comp.GROUP, "developers")
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_set_component_JUMP(self, mock_stdout):
+    def test_set_component_JUMP(self):
         """
         set_component_JUMP passes the argument to the similar method
         in the component class.
@@ -1102,9 +1185,8 @@ def test_set_component_JUMP(self, mock_stdout):
         comp = instr.get_component("second_component")
 
         self.assertEqual(comp.JUMP, "myself 8")
-        
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_set_component_SPLIT(self, mock_stdout):
+
+    def test_set_component_SPLIT(self):
         """
         set_component_SPLIT passes the argument to the similar method
         in the component class.
@@ -1118,8 +1200,7 @@ def test_set_component_SPLIT(self, mock_stdout):
 
         self.assertEqual(comp.SPLIT, 3)
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_set_component_comment(self, mock_stdout):
+    def test_set_component_comment(self):
         """
         set_component_comment passes the argument to the similar
         method in the component class.
@@ -1132,9 +1213,8 @@ def test_set_component_comment(self, mock_stdout):
         comp = instr.get_component("second_component")
 
         self.assertEqual(comp.comment, "test comment")
-        
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_set_c_code_before(self, mock_stdout):
+
+    def test_set_c_code_before(self):
         """
         set_component_c_code_before passes the argument to the similar
         method in the component class.
@@ -1147,9 +1227,8 @@ def test_set_c_code_before(self, mock_stdout):
         comp = instr.get_component("second_component")
 
         self.assertEqual(comp.c_code_before, "%include before.instr")
-        
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_set_c_code_after(self, mock_stdout):
+
+    def test_set_c_code_after(self):
         """
         set_component_c_code_after passes the argument to the similar
         method in the component class.
@@ -1226,6 +1305,8 @@ def test_print_component_short(self, mock_stdout):
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_print_components_simple(self, mock_stdout):
         """
+        Tests print_components for simple case
+
         print_components calls the print_short method in the component
         class for each component and aligns the data for display
         """
@@ -1251,6 +1332,8 @@ class for each component and aligns the data for display
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_print_components_complex(self, mock_stdout):
         """
+        Tests print_components for complex case
+
         print_components calls the print_short method in the component
         class for each component and aligns the data for display
         """
@@ -1389,10 +1472,9 @@ class for each component and aligns the data for display
         expected = ("  AT      (0, 0, 0) ABSOLUTE")
         self.assertEqual(output[6], expected)
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
-    def test_write_c_files_simple(self, mock_f, mock_stdout):
+    def test_write_c_files_simple(self, mock_f):
         """
         Write_c_files writes the strings for declare, initialize,
         and trace to files that are then included in McStas files.
@@ -1451,17 +1533,16 @@ def test_write_c_files_simple(self, mock_f, mock_stdout):
 
         handle.write.assert_has_calls(wrts, any_order=False)
 
-    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
-    def test_write_full_instrument_simple(self, mock_f, mock_stdout):
+    def test_write_full_instrument_simple(self, mock_f):
         """
-        The write_full_instrument methods writes the information
+        The write_full_instrument method write the information
         contained in the instrument instance to a file with McStas
         syntax.
 
         The test includes a time stamp in the written and expected
-        data that has an accuracy of 1 second.  It is unlikey to fail
+        data that has an accuracy of 1 second.  It is unlikely to fail
         due to this, but it can happen.
         """
 
@@ -1546,27 +1627,40 @@ def test_write_full_instrument_simple(self, mock_f, mock_stdout):
         handle = mock_f()
         handle.write.assert_has_calls(wrts, any_order=False)
 
+    # mock sys.stdout to avoid printing to terminal
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_run_full_instrument_required_par_error(self, mock_stdout):
         """
+        Tests run_full_instrument raises error when lacking required parameter
+
         The populated instr has a required parameter, and when not
         given it should raise an error.
         """
+        instr = setup_populated_instr()
 
+        with self.assertRaises(NameError):
+            instr.run_full_instrument(foldername="test_data_set",
+                                      executable_path="dummy_mcstas")
+
+    def test_run_full_instrument_junk_par_error(self):
+        """
+        Check run_full_instrument raises a NameError if a unrecognized
+        parameter is provided, here junk.
+        """
         instr = setup_populated_instr()
 
+        pars = {"theta": 2, "junk": "test"}
+
         with self.assertRaises(NameError):
-            instr.run_full_instrument("test_instrument.instr",
-                                      foldername="test_data_set",
-                                      executable_path="path")
+            instr.run_full_instrument(foldername="test_data_set",
+                                      parameters=pars)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    @unittest.mock.patch('__main__.__builtins__.open',
-                         new_callable=unittest.mock.mock_open)
     @unittest.mock.patch("subprocess.run")
-    def test_x_ray_run_full_instrument_basic(self, mock_sub,
-                                       mock_f, mock_stdout,):
+    def test_x_ray_run_full_instrument_basic(self, mock_sub, mock_stdout):
         """
+        Tests x-ray run_full_instrument
+
         Check a simple run performs the correct system call.  Here
         the target directory is set to the test data set so that some
         data is loaded even though the system call is not executed.
@@ -1578,9 +1672,8 @@ def test_x_ray_run_full_instrument_basic(self, mock_sub,
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
-        instr = setup_populated_x_ray_instr()
-        instr.run_full_instrument("test_instrument.instr",
-                                  foldername="test_data_set",
+        instr = setup_populated_x_ray_instr_with_dummy_path()
+        instr.run_full_instrument(foldername="test_data_set",
                                   executable_path=executable_path,
                                   parameters={"theta": 1})
 
@@ -1588,7 +1681,6 @@ def test_x_ray_run_full_instrument_basic(self, mock_sub,
 
         expected_path = os.path.join(executable_path, "mxrun")
 
-        current_directory = os.getcwd()
         expected_folder_path = os.path.join(THIS_DIR, "test_data_set")
 
         # a double space because of a missing option
@@ -1606,12 +1698,11 @@ def test_x_ray_run_full_instrument_basic(self, mock_sub,
                                          universal_newlines=True)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    @unittest.mock.patch('__main__.__builtins__.open',
-                         new_callable=unittest.mock.mock_open)
     @unittest.mock.patch("subprocess.run")
-    def test_run_full_instrument_basic(self, mock_sub,
-                                       mock_f, mock_stdout, ):
+    def test_run_full_instrument_basic(self, mock_sub, mock_stdout):
         """
+        Test neutron run_full_instrument
+
         Check a simple run performs the correct system call.  Here
         the target directory is set to the test data set so that some
         data is loaded even though the system call is not executed.
@@ -1623,9 +1714,9 @@ def test_run_full_instrument_basic(self, mock_sub,
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
-        instr = setup_populated_instr()
-        instr.run_full_instrument("test_instrument.instr",
-                                  foldername="test_data_set",
+        instr = setup_populated_instr_with_dummy_path()
+
+        instr.run_full_instrument(foldername="test_data_set",
                                   executable_path=executable_path,
                                   parameters={"theta": 1})
 
@@ -1633,7 +1724,6 @@ def test_run_full_instrument_basic(self, mock_sub,
 
         expected_path = os.path.join(executable_path, "mcrun")
 
-        current_directory = os.getcwd()
         expected_folder_path = os.path.join(THIS_DIR, "test_data_set")
 
         # a double space because of a missing option
@@ -1649,12 +1739,11 @@ def test_run_full_instrument_basic(self, mock_sub,
                                          cwd=run_path)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    @unittest.mock.patch('__main__.__builtins__.open',
-                         new_callable=unittest.mock.mock_open)
     @unittest.mock.patch("subprocess.run")
-    def test_run_full_instrument_complex(self, mock_sub,
-                                         mock_f, mock_stdout,):
+    def test_run_full_instrument_complex(self, mock_sub, mock_stdout):
         """
+        Test neutron run_full_instrument in more complex case
+
         Check a complex run performs the correct system call.  Here
         the target directory is set to the test data set so that some
         data is loaded even though the system call is not executed.
@@ -1666,9 +1755,13 @@ def test_run_full_instrument_complex(self, mock_sub,
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
-        instr = setup_populated_instr()
-        instr.run_full_instrument("test_instrument.instr",
-                                  foldername="test_data_set",
+        instr = setup_populated_instr_with_dummy_path()
+
+        # Add some extra parameters for testing
+        instr.add_parameter("A")
+        instr.add_parameter("BC")
+
+        instr.run_full_instrument(foldername="test_data_set",
                                   executable_path=executable_path,
                                   mpi=7,
                                   ncount=48.4,
@@ -1681,7 +1774,6 @@ def test_run_full_instrument_complex(self, mock_sub,
 
         expected_path = os.path.join(executable_path, "mcrun")
 
-        current_directory = os.getcwd()
         expected_folder_path = os.path.join(THIS_DIR, "test_data_set")
 
         # a double space because of a missing option
@@ -1697,14 +1789,11 @@ def test_run_full_instrument_complex(self, mock_sub,
                                          cwd=run_path)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    @unittest.mock.patch('__main__.__builtins__.open',
-                         new_callable=unittest.mock.mock_open)
     @unittest.mock.patch("subprocess.run")
-    def test_run_full_instrument_overwrite_default(self, mock_sub,
-                                                   mock_f, mock_stdout,):
+    def test_run_full_instrument_overwrite_default(self, mock_sub, mock_stdout):
         """
         Check that default parameters are overwritten by given
-        parameters.
+        parameters in run_full_instrument.
         """
 
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
@@ -1713,9 +1802,13 @@ def test_run_full_instrument_overwrite_default(self, mock_sub,
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
-        instr = setup_populated_instr()
-        instr.run_full_instrument("test_instrument.instr",
-                                  foldername="test_data_set",
+        instr = setup_populated_instr_with_dummy_path()
+
+        # Add some extra parameters for testing
+        instr.add_parameter("A")
+        instr.add_parameter("BC")
+
+        instr.run_full_instrument(foldername="test_data_set",
                                   executable_path=executable_path,
                                   mpi=7,
                                   ncount=48.4,
@@ -1744,6 +1837,47 @@ def test_run_full_instrument_overwrite_default(self, mock_sub,
                                          universal_newlines=True,
                                          cwd=run_path)
 
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    @unittest.mock.patch("subprocess.run")
+    def test_run_full_instrument_x_ray_basic(self, mock_sub, mock_stdout):
+        """
+        Test x-ray run_full_instrument
+
+        Check a simple run performs the correct system call.  Here
+        the target directory is set to the test data set so that some
+        data is loaded even though the system call is not executed.
+        """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
+        instr = setup_populated_x_ray_instr_with_dummy_path()
+
+        instr.run_full_instrument(foldername="test_data_set",
+                                  executable_path=executable_path,
+                                  parameters={"theta": 1})
+
+        os.chdir(current_work_dir)
+
+        expected_path = os.path.join(executable_path, "mxrun")
+
+        expected_folder_path = os.path.join(THIS_DIR, "test_data_set")
+
+        # a double space because of a missing option
+        expected_call = (expected_path + " -c -n 1000000 "
+                         + "-d " + expected_folder_path
+                         + "  test_instrument.instr"
+                         + " has_default=37 theta=1")
+
+        mock_sub.assert_called_once_with(expected_call,
+                                         shell=True,
+                                         stderr=-1, stdout=-1,
+                                         universal_newlines=True,
+                                         cwd=run_path)
+
 
 if __name__ == '__main__':
     unittest.main()

From 1c1f59bf6303c9306f2e8453c4b492355b7c00e7 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Mon, 25 Jan 2021 17:13:04 +0100
Subject: [PATCH 131/403] Updating unit tests so they are running from test
 folder when required.

---
 mcstasscript/tests/test_Instr.py | 61 ++++++++++++++++++++++++++++++++
 1 file changed, 61 insertions(+)

diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 499db65d..8d4f124e 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -18,26 +18,54 @@ def setup_instr_no_path():
     """
     Sets up a neutron instrument without a package_path
     """
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+    current_work_dir = os.getcwd()
+    os.chdir(THIS_DIR)  # Set work directory to test folder
+
     return McStas_instr("test_instrument")
 
+    os.chdir(current_work_dir)
+
 def setup_x_ray_instr_no_path():
     """
     Sets up a X-ray instrument without a package_path
     """
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+    current_work_dir = os.getcwd()
+    os.chdir(THIS_DIR)  # Set work directory to test folder
+
     return McXtrace_instr("test_instrument")
 
+    os.chdir(current_work_dir)
+
 def setup_instr_root_path():
     """
     Sets up a neutron instrument with root package_path
     """
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+    current_work_dir = os.getcwd()
+    os.chdir(THIS_DIR)  # Set work directory to test folder
+
     return McStas_instr("test_instrument", package_path="/")
 
+    os.chdir(current_work_dir)
+
 def setup_x_ray_instr_root_path():
     """
     Sets up a X-ray instrument with root package_path
     """
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+    current_work_dir = os.getcwd()
+    os.chdir(THIS_DIR)  # Set work directory to test folder
+
     return McXtrace_instr("test_instrument", package_path="/")
 
+    os.chdir(current_work_dir)
+
 def setup_instr_with_path():
     """
     Sets up an instrument with a valid package_path, but it points to
@@ -226,6 +254,10 @@ class TestMcStas_instr(unittest.TestCase):
     Tests of the main class in McStasScript called McStas_instr.
     """
 
+    #def test_show_test_folder(self):
+    #    os.system("tree")
+
+
     def test_simple_initialize(self):
         """
         Test basic initialization runs
@@ -600,10 +632,18 @@ def test_show_components_simple(self, mock_stdout):
         """
         Simple test of show components to show component categories
         """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
         instr = setup_instr_with_path()
 
         instr.show_components()
 
+        os.chdir(current_work_dir)
+
         output = mock_stdout.getvalue()
         output = output.split("\n")
 
@@ -627,8 +667,15 @@ def test_show_components_folder(self, mock_stdout):
         """
         instr = setup_instr_with_path()
 
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
         instr.show_components("Work directory")
 
+        os.chdir(current_work_dir)
+
         output = mock_stdout.getvalue()
         output = output.split("\n")
 
@@ -751,8 +798,15 @@ def test_create_component_instance_simple(self, mock_stdout):
         case the same component type is requested again.
         """
 
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
         instr = setup_instr_with_path()
 
+        os.chdir(current_work_dir)
+
         comp = instr._create_component_instance("test_component",
                                                 "test_for_reading")
 
@@ -779,8 +833,15 @@ def test_create_component_instance_complex(self, mock_stdout):
         case the same component type is requested again.
         """
 
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
         instr = setup_instr_with_path()
 
+        os.chdir(current_work_dir)
+
         # Setting relative to home, should be passed to component
         comp = instr._create_component_instance("test_component",
                                                 "test_for_reading",

From 72bf29164232adb14dcb591cfe28d6957b70ba17 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Mon, 25 Jan 2021 17:43:29 +0100
Subject: [PATCH 132/403] Added unit tests to increase coverage.

Failure of copy_component tested.
mcdisplay call tested.
---
 mcstasscript/interface/instr.py  |  3 ++-
 mcstasscript/tests/test_Instr.py | 44 ++++++++++++++++++++++++++++++++
 2 files changed, 46 insertions(+), 1 deletion(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index aa925393..b6a99f1d 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1673,7 +1673,8 @@ def show_instrument(self, *args, **kwargs):
         process = subprocess.run(full_command, shell=True,
                                  stdout=subprocess.PIPE,
                                  stderr=subprocess.PIPE,
-                                 universal_newlines=True)
+                                 universal_newlines=True,
+                                 cwd=self.input_path)
         print(process.stderr)
         print(process.stdout)
 
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 8d4f124e..0be76306 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1019,6 +1019,17 @@ def test_copy_component_simple(self):
         self.assertEqual(comp.AT_data[1], 0)
         self.assertEqual(comp.AT_data[2], 2)
 
+    def test_copy_component_simple_fail(self):
+        """
+        Checks a NameError is raised if trying to copy a component that does
+        not exist
+        """
+
+        instr = setup_populated_with_some_options_instr()
+
+        with self.assertRaises(NameError):
+            comp = instr.copy_component("copy_of_second_comp", "unknown_component")
+
     def test_copy_component_simple_object(self):
         """
         Checks that a component can be copied using the object
@@ -1940,5 +1951,38 @@ def test_run_full_instrument_x_ray_basic(self, mock_sub, mock_stdout):
                                          cwd=run_path)
 
 
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    @unittest.mock.patch("subprocess.run")
+    def test_show_instrument_basic(self, mock_sub, mock_stdout):
+        """
+        Test show_instrument methods makes correct system calls
+        """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
+        instr = setup_populated_instr_with_dummy_path()
+
+        instr.show_instrument(parameters={"theta": 1.2})
+
+        os.chdir(current_work_dir)
+
+        expected_path = os.path.join(executable_path, "bin", "mcdisplay-webgl")
+
+        # a double space because of a missing option
+        expected_call = (expected_path
+                         + " ./test_instrument.instr"
+                         + "  has_default=37 theta=1.2")
+
+        mock_sub.assert_called_once_with(expected_call,
+                                         shell=True,
+                                         stderr=-1, stdout=-1,
+                                         universal_newlines=True,
+                                         cwd=".")
+
+
 if __name__ == '__main__':
     unittest.main()

From 32f1054619a6e72de0a0b645da902bd7a8251663 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 26 Jan 2021 08:47:11 +0100
Subject: [PATCH 133/403] Updated to new CamelCase Component class name.

---
 mcstasscript/interface/instr.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index b071ab68..5907cafc 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -810,7 +810,7 @@ def copy_component(self, name, original_component, **kwargs):
             comment : str
                 Comment that will be displayed before the component
         """
-        if isinstance(original_component, component): # Update to CammelCase
+        if isinstance(original_component, Component):
             original_component = original_component.name
         """
         If the name starts with COPY, use unique naming as described in the

From 5bd4e7950990cbd7b3e05f044a2cc413098d0d63 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 26 Jan 2021 09:17:34 +0100
Subject: [PATCH 134/403] Small change in formatting.

---
 mcstasscript/tests/test_Instr.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index fd0505eb..ced71652 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1950,7 +1950,6 @@ def test_run_full_instrument_x_ray_basic(self, mock_sub, mock_stdout):
                                          universal_newlines=True,
                                          cwd=run_path)
 
-
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     @unittest.mock.patch("subprocess.run")
     def test_show_instrument_basic(self, mock_sub, mock_stdout):

From 50e76b7b84200641fb1023dbab3747f04465435d Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 26 Jan 2021 10:38:31 +0100
Subject: [PATCH 135/403] Updates of integration tests in connection to review
 by Celine Durniak.

---
 mcstasscript/integration_tests/test_complex_instrument.py | 2 +-
 mcstasscript/integration_tests/test_simple_instrument.py  | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/mcstasscript/integration_tests/test_complex_instrument.py b/mcstasscript/integration_tests/test_complex_instrument.py
index f8238a05..50265d44 100644
--- a/mcstasscript/integration_tests/test_complex_instrument.py
+++ b/mcstasscript/integration_tests/test_complex_instrument.py
@@ -13,7 +13,7 @@ def setup_complex_instrument():
     Sets up guide system with two guides that are placed next to one
     another with separate entrances but converge at the end.
 
-    It attempts to use as McStas keywords and features as possible.
+    It attempts to use as many McStas keywords and features as possible.
     """
     Instr = instr.McStas_instr("integration_test_complex",
                                author="test_suite",
diff --git a/mcstasscript/integration_tests/test_simple_instrument.py b/mcstasscript/integration_tests/test_simple_instrument.py
index fc0a919f..58caad86 100644
--- a/mcstasscript/integration_tests/test_simple_instrument.py
+++ b/mcstasscript/integration_tests/test_simple_instrument.py
@@ -99,7 +99,7 @@ class TestSimpleInstrument(unittest.TestCase):
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_simple_instrument(self, mock_stdout):
         """
-        Test that an instrument can run and that the results matches
+        Test that an instrument can run and that the results match
         expectations. Here beam in small area in the middle of the
         detector.
         """
@@ -127,7 +127,7 @@ def test_simple_instrument(self, mock_stdout):
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_simple_instrument_input(self, mock_stdout):
         """
-        Test that an instrument can run and that the results matches
+        Test that an instrument can run and that the results match
         expectations. Here beam in small area in the middle of the
         detector.
         """
@@ -187,7 +187,7 @@ def test_simple_instrument_mpi(self, mock_stdout):
     def test_slit_instrument(self, mock_stdout):
         """
         Test parameters can be controlled through McStasScript.  Here
-        a slit is can be moved, but the default value of 0 should be
+        a slit can be moved, but the default value of 0 should be
         used.
         """
         CURRENT_DIR = os.getcwd()

From b773ce24d4dafe1a8d740e971479867fa3bb59b3 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 26 Jan 2021 10:39:54 +0100
Subject: [PATCH 136/403] Updates for code distribution after review by Celine
 Durniak.

---
 MANIFEST.in | 2 +-
 setup.py    | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/MANIFEST.in b/MANIFEST.in
index 0288d07d..f9224b57 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,4 +1,5 @@
 include McStasScript_documentation.pdf
+include McStasScript_developer_documentation.pdf
 include mcstasscript/configuration.yaml
 include mcstasscript/tests/test_for_reading.comp
 include mcstasscript/tests/test_instrument.instr
@@ -8,4 +9,3 @@ include mcstasscript/integration_tests/test_input_folder/PSDlin_monitor.comp
 graft mcstasscript/tests/dummy_mcstas
 graft mcstasscript/tests/dummy_instrument_folder
 graft mcstasscript/tests/test_data_set
-
diff --git a/setup.py b/setup.py
index 69d61eea..ccf00909 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
      name='McStasScript',
-     version='0.0.24',
+     version='0.0.25',
      author="Mads Bertelsen",
      author_email="Mads.Bertelsen@ess.eu",
      description="A python scripting interface for McStas",
@@ -19,5 +19,6 @@
          "Programming Language :: Python :: 3",
          "License :: OSI Approved :: GNU General Public License (GPL)",
          "Operating System :: OS Independent",
+         "Topic :: Scientific/Engineering"
      ],
  )

From 5de471329f8e69cee5c21f6589a2f393a6162bef Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Wed, 27 Jan 2021 14:36:19 +0100
Subject: [PATCH 137/403] Using flake8 to bring code to PEP-8 standard. Still
 some issues outstanding.

---
 mcstasscript/data/data.py                     |  18 +-
 mcstasscript/helper/component_reader.py       |  34 +-
 mcstasscript/helper/formatting.py             |   4 +-
 mcstasscript/helper/managed_mcrun.py          |  13 +-
 mcstasscript/helper/mcstas_objects.py         |  39 +--
 mcstasscript/instr_reader/control.py          |  90 +++--
 mcstasscript/instr_reader/read_declare.py     | 151 ++++----
 mcstasscript/instr_reader/read_definition.py  |  67 ++--
 mcstasscript/instr_reader/read_finally.py     |  50 +--
 mcstasscript/instr_reader/read_initialize.py  |  49 +--
 mcstasscript/instr_reader/read_trace.py       | 258 +++++++-------
 mcstasscript/instr_reader/util.py             |  71 ++--
 .../test_complex_instrument.py                |   8 +-
 .../test_simple_instrument.py                 |  13 +-
 mcstasscript/interface/functions.py           |  56 +--
 mcstasscript/interface/instr.py               |  60 ++--
 mcstasscript/interface/plotter.py             |   9 +-
 mcstasscript/interface/reader.py              |  22 +-
 mcstasscript/tests/test_ComponentReader.py    |  74 ++--
 mcstasscript/tests/test_Configurator.py       |  68 ++--
 mcstasscript/tests/test_Instr.py              | 169 +++++----
 mcstasscript/tests/test_Instr_reader.py       | 324 +++++++++---------
 mcstasscript/tests/test_ManagedMcrun.py       |  38 +-
 mcstasscript/tests/test_Plotter.py            |  44 ++-
 mcstasscript/tests/test_component.py          |  15 +-
 mcstasscript/tests/test_declare_variable.py   |  32 +-
 mcstasscript/tests/test_functions.py          |  22 +-
 mcstasscript/tests/test_instrument.instr      |  26 +-
 mcstasscript/tests/test_parameter_variable.py |   4 +-
 29 files changed, 929 insertions(+), 899 deletions(-)

diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index 86fa03de..f7418bbf 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -1,6 +1,7 @@
 import matplotlib.pyplot
 import numpy as np
 
+
 class McStasMetaData:
     """
     Class for holding metadata for McStas dataset, is to be read from
@@ -259,41 +260,41 @@ def set_options(self, **kwargs):
                 raise ValueError("Chosen colormap not available in "
                                  + "matplotlib, was: "
                                  + str(self.colormap))
-            
+
         if "show_colorbar" in kwargs:
             self.show_colorbar = bool(kwargs["show_colorbar"])
-            
+
         if "cut_max" in kwargs:
             self.cut_max = kwargs["cut_max"]
             if not isinstance(self.cut_max, (float, int)):
                 raise ValueError("cut_max has to be a number, was given: "
                                  + str(self.cut_max))
-            
+
         if "cut_min" in kwargs:
             self.cut_min = kwargs["cut_min"]
             if not isinstance(self.cut_min, (float, int)):
                 raise ValueError("cut_min has to be a number, was given: "
                                  + str(self.cut_min))
-            
+
         if "x_axis_multiplier" in kwargs:
             self.x_limit_multiplier = kwargs["x_axis_multiplier"]
             if not isinstance(self.x_limit_multiplier, (float, int)):
                 raise ValueError("x_limit_multiplier has to be a number, was "
                                  + "given: " + str(self.x_limit_multiplier))
-        
+
         if "y_axis_multiplier" in kwargs:
             self.y_limit_multiplier = kwargs["y_axis_multiplier"]
             if not isinstance(self.y_limit_multiplier, (float, int)):
                 raise ValueError("y_limit_multiplier has to be a number, was "
                                  + "given: " + str(self.y_limit_multiplier))
-            
+
         if "top_lim" in kwargs:
             self.top_lim = kwargs["top_lim"]
             self.custom_ylim_top = True
             if not isinstance(self.top_lim, (float, int)):
                 raise ValueError("top_lim has to be a number, was "
                                  + "given: " + str(self.top_lim))
-            
+
         if "bottom_lim" in kwargs:
             self.bottom_lim = kwargs["bottom_lim"]
             self.custom_ylim_bottom = True
@@ -307,7 +308,7 @@ def set_options(self, **kwargs):
             if not isinstance(self.left_lim, (float, int)):
                 raise ValueError("left_lim has to be a number, was "
                                  + "given: " + str(self.left_lim))
-            
+
         if "right_lim" in kwargs:
             self.right_lim = kwargs["right_lim"]
             self.custom_xlim_right = True
@@ -451,4 +452,3 @@ def __str__(self):
 
     def __repr__(self):
         return "\n" + self.__str__()
-
diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index daabd19f..d5477a31 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -84,7 +84,7 @@ def __init__(self, mcstas_path, input_path="."):
             raise ValueError("Can't find given input_path,"
                              + " directory must exist.")
         """
-        If components are present both in the McStas install and the 
+        If components are present both in the McStas install and the
         work directory, the version in the work directory is used. The user
         is informed of this behavior when the instrument object is created.
         """
@@ -110,7 +110,6 @@ def __init__(self, mcstas_path, input_path="."):
             print("These definitions will be used instead of the installed "
                   + "versions.")
 
-
     def show_categories(self):
         """
         Method that will show all component categories available
@@ -316,13 +315,10 @@ def read_component_file(self, absolute_path):
             if (line.strip().startswith("DEFINITION PARAMETERS")
                     or line.strip().startswith("SETTING PARAMETERS")):
 
-
-                line = line.split("//")[0] # Remove comments
+                line = line.split("//")[0]  # Remove comments
                 parts = line.split("(")
                 parameter_parts = parts[1].split(",")
-                
                 parameter_parts = self.correct_for_brackets(parameter_parts)
-
                 parameter_parts = list(filter(("\n").__ne__, parameter_parts))
 
                 break_now = False
@@ -374,13 +370,13 @@ def read_component_file(self, absolute_path):
                             par_name = name_value[0].strip()
                             par_value = name_value[1].strip()
 
-                            if temp_par_type is "double":
+                            if temp_par_type == "double":
                                 try:
                                     par_value = float(par_value)
                                 except:
                                     # Could change the type
                                     par_value = par_value
-                            elif temp_par_type is "int":
+                            elif temp_par_type == "int":
                                 par_value = int(par_value)
 
                             result.parameter_names.append(par_name)
@@ -391,8 +387,9 @@ def read_component_file(self, absolute_path):
                         break
 
                     new_line = file_o.readline().split("//")[0]
-                    parameter_parts = new_line.split(",")
-                    parameter_parts = self.correct_for_brackets(parameter_parts)
+                    new_line = new_line.split(",")
+                    new_line = self.correct_for_brackets(new_line)
+                    parameter_parts = new_line
 
             if line.startswith("DECLARE"):
                 break
@@ -406,7 +403,7 @@ def read_component_file(self, absolute_path):
         file_o.close()
 
         result.name = os.path.split(absolute_path)[1].split(".")[-2]
-        
+
         tail = os.path.split(absolute_path)[0]
         result.category = os.path.split(tail)[1]
 
@@ -416,7 +413,7 @@ def read_component_file(self, absolute_path):
         """
 
         return result
-    
+
     def correct_for_brackets(self, parameter_parts):
         """
         Given list of string elements, correct for brackets will
@@ -431,22 +428,21 @@ def correct_for_brackets(self, parameter_parts):
         corrected_parts = []
         index = 0
         while True:
-            
+
             current_part = parameter_parts[index]
             inner_index = 0
-            while True: 
+            while True:
                 if current_part.count("{") == current_part.count("}"):
                     corrected_parts.append(current_part)
                     index += inner_index
-                    break                          
+                    break
                 else:
-                    inner_index +=1
+                    inner_index += 1
                     current_part += "," + parameter_parts[index+inner_index]
 
             index += 1
-            
+
             if index >= len(parameter_parts):
                 break
-            
-        return corrected_parts
 
+        return corrected_parts
diff --git a/mcstasscript/helper/formatting.py b/mcstasscript/helper/formatting.py
index 5593a082..f41ef1e2 100644
--- a/mcstasscript/helper/formatting.py
+++ b/mcstasscript/helper/formatting.py
@@ -18,7 +18,7 @@ def is_legal_parameter(name):
     parameter in the c programming language.
     """
 
-    if name is "":
+    if name == "":
         return False
 
     if " " in name:
@@ -39,7 +39,7 @@ def is_legal_filename(name):
     filename
     """
 
-    if name is "":
+    if name == "":
         return False
 
     if " " in name:
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index e5a5677a..b9817c02 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -183,7 +183,7 @@ def run_simulation(self, **kwargs):
             option_string = "-c "
 
         if self.mpi is not None:
-            mpi_string = " --mpi=" + str(self.mpi) + " " # Set mpi
+            mpi_string = " --mpi=" + str(self.mpi) + " "  # Set mpi
         else:
             mpi_string = " "
 
@@ -222,10 +222,10 @@ def run_simulation(self, **kwargs):
 
         # Run the mcrun command on the system
         full_command = (mcrun_full_path + " "
-                  + option_string + " "
-                  + self.custom_flags + " "
-                  + self.name_of_instrumentfile
-                  + parameter_string)
+                        + option_string + " "
+                        + self.custom_flags + " "
+                        + self.name_of_instrumentfile
+                        + parameter_string)
 
         try:
             process = subprocess.run(full_command, shell=True,
@@ -264,7 +264,8 @@ def load_results(self, *args):
         elif len(args) == 1:
             data_folder_name = args[0]
         else:
-            raise RuntimeError("load_results can be called with 0 or 1 arguments")
+            raise RuntimeError("load_results can be called "
+                               + "with 0 or 1 arguments")
 
         return load_results(data_folder_name)
 
diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 879438e5..dd65edb2 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -204,7 +204,7 @@ def __init__(self, type, name, **kwargs):
         elif "&" in par_name[0]:
             # Remove the first & indicating the variable is an address
             par_name = par_name[1:]
-            
+
         if not is_legal_parameter(par_name):
             raise NameError("The given parameter name: \""
                             + self.name
@@ -252,16 +252,14 @@ def write_line(self, fo):
             if isinstance(self.value, str):
                 # value is a string
                 string = self.value
-                #string = string.replace('"',"\\\"")
-                fo.write("%s %s[%d] = %s;" % (self.type, self.name, self.vector, string))
+                fo.write("%s %s[%d] = %s;" % (self.type, self.name,
+                                              self.vector, string))
             else:
                 # list of values
                 fo.write("%s %s[%d] = {" % (self.type, self.name, self.vector))
                 for i in range(0, len(self.value) - 1):
                     fo.write("%G," % self.value[i])
                 fo.write("%G};%s" % (self.value[-1], self.comment))
-                
-                
 
 
 class Component:
@@ -445,7 +443,7 @@ def __init__(self, instance_name, component_name, **kwargs):
 
             JUMP : str, default ""
                 Sets JUMP str
-                
+
             SPLIT : int, default 0 (disabled)
                 Sets SPLIT value
 
@@ -471,7 +469,7 @@ def __init__(self, instance_name, component_name, **kwargs):
         self.JUMP = ""
         self.SPLIT = 0
         self.comment = ""
-        self.c_code_before = "" 
+        self.c_code_before = ""
         self.c_code_after = ""
 
         # If any keywords are set in kwargs, update these
@@ -485,7 +483,7 @@ def __init__(self, instance_name, component_name, **kwargs):
 
         # Do not allow addition of attributes after init
         self._freeze()
-    
+
     def set_keyword_input(self, **kwargs):
         # Allow addition of attributes in init
         self._unfreeze()
@@ -517,16 +515,16 @@ def set_keyword_input(self, **kwargs):
 
         if "JUMP" in kwargs:
             self.set_JUMP(kwargs["JUMP"])
-            
+
         if "SPLIT" in kwargs:
             self.set_SPLIT(kwargs["SPLIT"])
 
         if "comment" in kwargs:
             self.set_comment(kwargs["comment"])
-            
+
         if "c_code_before" in kwargs:
             self.set_c_code_before(kwargs["c_code_before"])
-        
+
         if "c_code_after" in kwargs:
             self.set_c_code_after(kwargs["c_code_after"])
 
@@ -672,7 +670,7 @@ def set_JUMP(self, string):
             raise RuntimeError("set_JUMP expect a string, but was "
                                + "given " + str(type(string)))
         self.JUMP = string
-        
+
     def set_SPLIT(self, value):
         """Sets SPLIT value, needs to be an integer"""
         if not isinstance(value, (int, str)):
@@ -682,7 +680,8 @@ def set_SPLIT(self, value):
         if isinstance(value, int):
             if value < 0:
                 raise RuntimeError("set_SPLIT got a negative value, this is "
-                                   + "meaningless, has to be a positive value.")
+                                   + "meaningless, has to be a "
+                                   + "positive value.")
 
         self.SPLIT = value
 
@@ -699,14 +698,14 @@ def set_comment(self, string):
             raise RuntimeError("set_comment expect a string, but was "
                                + "given " + str(type(string)))
         self.comment = string
-        
+
     def set_c_code_before(self, string):
         """Method that sets c code to be written before the component"""
         if not isinstance(string, str):
             raise RuntimeError("set_c_code_before expect a string, but was "
                                + "given " + str(type(string)))
         self.c_code_before = string
-        
+
     def set_c_code_after(self, string):
         """Method that sets c code to be written after the component"""
         if not isinstance(string, str):
@@ -736,7 +735,7 @@ def write_component(self, fo):
             fo.write("// %s\n" % (str(self.comment)))
 
         if self.SPLIT != 0:
-            fo.write("SPLIT " + str(self.SPLIT) + " ") 
+            fo.write("SPLIT " + str(self.SPLIT) + " ")
 
         # Write component name and component type
         fo.write("COMPONENT %s = %s(" % (self.name, self.component_name))
@@ -786,7 +785,7 @@ def write_component(self, fo):
                                     str(self.AT_data[1]),
                                     str(self.AT_data[2])))
         fo.write(" %s\n" % self.AT_relative)
-        
+
         if self.ROTATED_specified:
             fo.write("ROTATED (%s,%s,%s)" % (str(self.ROTATED_data[0]),
                                              str(self.ROTATED_data[1]),
@@ -804,7 +803,7 @@ def write_component(self, fo):
 
         if not self.JUMP == "":
             fo.write("JUMP %s\n" % self.JUMP)
-            
+
         if len(self.c_code_after) > 0:
             fo.write("\n")
             explanation = "From component named " + self.name
@@ -884,7 +883,7 @@ class is used as a superclass for classes describing each
         if self.SPLIT != 0:
             string += "SPLIT " + str(self.SPLIT) + " "
         string += "COMPONENT " + str(self.name)
-        string +=  " = " + str(self.component_name) + "\n"
+        string += " = " + str(self.component_name) + "\n"
         for key in self.parameter_names:
             val = getattr(self, key)
             parameter_name = bcolors.BOLD + key + bcolors.ENDC
@@ -945,7 +944,7 @@ def print_short(self, **kwargs):
             print_rotate_rel = self.ROTATED_relative
         else:
             print_rotate_rel = self.AT_relative
-        
+
         if "longest_name" in kwargs:
             number_of_spaces = 3+kwargs["longest_name"]-len(self.name)
             print(str(self.name) + " "*number_of_spaces, end='')
diff --git a/mcstasscript/instr_reader/control.py b/mcstasscript/instr_reader/control.py
index aacbf5c7..208bbda7 100644
--- a/mcstasscript/instr_reader/control.py
+++ b/mcstasscript/instr_reader/control.py
@@ -1,6 +1,4 @@
-import io
 import os
-from decimal import Decimal
 
 from mcstasscript.interface.instr import McStas_instr
 from mcstasscript.instr_reader.read_definition import DefinitionReader
@@ -9,31 +7,29 @@
 from mcstasscript.instr_reader.read_trace import TraceReader
 from mcstasscript.instr_reader.read_finally import FinallyReader
 
+
 class InstrumentReader:
     """
     This class controls loading of a McStas file as a McStasScript object.
-    
+
     This is done by reading the McStas file line by line while using the
     McStasScript API to load the information into the Instr object.
-    
-    Optionally a Python file with the McStasScript commands required to 
+
+    Optionally a Python file with the McStasScript commands required to
     replicate the McStas instrument can be written to disk.
-    
-    
-    
+
     Methods
     -------
     __init__(filename)
         Initializes reading of McStas instrument with given filename
-    
+
     generate_py_version(product_filename)
         Generates a file named product_filename.py that recreates instr
-        
+
     add_to_instr(Instr)
         Inserts information from instr file into McStasScript Instr instance
-    
     """
-    
+
     def __init__(self, filename):
         """
         Initialize the InstrumentReader with a target McStas instrument
@@ -41,22 +37,22 @@ def __init__(self, filename):
         McStasScript python file or the add_to_instr method to load this
         instrument file onto a Instr McStasScript object.
         """
-    
+
         self.filename = filename
-        self.Instr = None # could set it up to create Instr
+        self.Instr = None  # could set it up to create Instr
         self.write_file = False
         self.product_filename = "mc_script.py"
         self.instr_name = ""
         self.file_data = None
         self.line_index = 0
         self.file_length = 0
-                
+
     def generate_py_version(self, product_filename):
         """
-        Generate a McStasScript version of the instrument file used for 
+        Generate a McStasScript version of the instrument file used for
         initialize of the InstrumentReader object. The filename given is
         for the generated file.
-        
+
         One should use this feature with some caution. Look through the
         generated McStasScript file and compare some output with the
         original insturment file to ensure everything was loaded correctly.
@@ -64,69 +60,70 @@ def generate_py_version(self, product_filename):
 
         # Generate dummy instr object
         self.Instr = McStas_instr("dummy_object_for_generating_file")
-        
+
         self.product_filename = product_filename
         self.write_file = True
-        
+
         if os.path.isfile(self.product_filename):
             os.remove(self.product_filename)
-        
+
         self._read_file()
-        
+
     def add_to_instr(self, Instr):
         """
         Add contents of McStas instrument file selected in initialize
         to an McStasScript instrument object.
         """
-        
+
         self.Instr = Instr
         self.write_file = False
-        
+
         self._read_file()
-        
+
     def _open_file(self):
         """
         Internal method that opens the instrument file to be read
         """
-        
+
         with open(self.filename) as file:
             self.file_data = file.readlines()
-            
+
         self.file_length = len(self.file_data)
         self.line_index = 0
-            
+
     def _get_next_line(self):
         """
         Internalmethod that gets the next line to be read
         """
-        
+
         line = self.file_data[self.line_index]
         self.line_index += 1
         return line
-        
+
     def _return_line(self):
         """
         Internal method that puts line back into stack
         """
-        
+
         self.line_index -= 1
-        
+
     def _read_file(self):
         """
-        Master method for reading the instrument file.  It goes through 
-        the file line by line, and checks which part of the instrument 
-        file it is currently reading. There are separate methods for 
+        Master method for reading the instrument file.  It goes through
+        the file line by line, and checks which part of the instrument
+        file it is currently reading. There are separate methods for
         reading the individual parts of the instrument file to reduce
         clutter.
         """
-        
+
         # Initialize readers of the different McStas instrument sections
-        args = [self.Instr, self.write_file, self.product_filename, self._get_next_line, self._return_line]
+        args = [self.Instr, self.write_file, self.product_filename,
+                self._get_next_line, self._return_line]
         self.Definition_reader = DefinitionReader(*args)
         self.Declare_reader = DeclareReader(*args)
         self.Initialize_reader = InitializeReader(*args)
         self.Trace_reader = TraceReader(*args)
-        self.Finally_reader = TraceReader(*args)
+        self.Finally_reader = FinallyReader(*args)
 
         # A mode for each type that activates the correct reader function
         definition_mode = False
@@ -136,15 +133,15 @@ def _read_file(self):
         finally_mode = False
         comment_mode = False
         any_mode = False
-        
+
         # check if insturment name has be read from file yet
         instr_name_read = False
-        
+
         self._open_file()
-        
-        #for line in self.file_data:
+
+        # for line in self.file_data:
         while self.line_index < self.file_length:
-            
+
             line = self._get_next_line()
 
             # Find appropriate mode
@@ -157,7 +154,7 @@ def _read_file(self):
                 any_mode = True
 
             if (line.strip().startswith("INITIALIZE") or
-                line.strip().startswith("INITIALISE")) and not any_mode:
+               line.strip().startswith("INITIALISE")) and not any_mode:
                 initialize_mode = True
                 any_mode = True
 
@@ -170,7 +167,7 @@ def _read_file(self):
                 any_mode = True
 
             if line.strip().startswith("/*"):
-                comment_mode = True        
+                comment_mode = True
 
             # Read with appropriate reader
             if definition_mode and not comment_mode:
@@ -212,15 +209,14 @@ def _read_file(self):
             # Stop comment mode when end of comment block reached
             if "*/" in line.strip():
                 comment_mode = False
-        
+
     def update_file_name(self):
         """
         Updates filename for reader subclasses
         """
-            
+
         self.Definition_reader.set_instr_name(self.instr_name)
         self.Declare_reader.set_instr_name(self.instr_name)
         self.Initialize_reader.set_instr_name(self.instr_name)
         self.Trace_reader.set_instr_name(self.instr_name)
         self.Finally_reader.set_instr_name(self.instr_name)
-        
diff --git a/mcstasscript/instr_reader/read_declare.py b/mcstasscript/instr_reader/read_declare.py
index c5fd4f1f..63145bbb 100644
--- a/mcstasscript/instr_reader/read_declare.py
+++ b/mcstasscript/instr_reader/read_declare.py
@@ -1,168 +1,172 @@
 from mcstasscript.instr_reader.util import SectionReader
 
+
 class DeclareReader(SectionReader):
     """
     Reads the declare section of a McStas instrument file and adds
     the found parameters / functions / structs to the McStasScript
     Instr instance. The information can also be written to a python
-    file for reproduction of a McStas instrument.  
+    file for reproduction of a McStas instrument.
     """
-    
-    def __init__(self, Instr, write_file, product_filename, get_next_line, return_line):
-        
-        super().__init__(Instr, write_file, product_filename, get_next_line, return_line)
-        
+
+    def __init__(self, Instr, write_file, product_filename,
+                 get_next_line, return_line):
+
+        super().__init__(Instr, write_file, product_filename,
+                         get_next_line, return_line)
+
         self.in_declare_function = False
         self.in_struct_definition = False
         self.bracket_counter = 0
-        
+
     def read_declare_line(self, line):
         """
         Reads line of instrument declare, returns bolean.  If it encounters
         the end of the declare section, it returns False, otherwise True.
-        
+
         The contents of the declare section is written to the McStasScript
         Instr object.
         """
-        
+
         continue_declare = True
-        
+
         # Remove comments
         if "//" in line:
             line = line.split("//", 1)[0]
-        
+
         # Remove %} and signify end if this is found
         if "%}" in line:
             continue_declare = False
             line = line.split("%}", 1)[0]
-            
+
         if "/*" in line:
             line = line.split("/*", 1)[0].strip()
-            
+
         if self.in_declare_function:
             if "{" in line:
                 self.bracket_counter += 1
-                    
+
             if "}" in line:
                 self.bracket_counter -= 1
-                
+
             if self.bracket_counter == 0:
                 self.in_declare_function = False
-                
+
             self.Instr.append_declare(line)
-            self._write_declare_line(line)            
+            self._write_declare_line(line)
 
         # Check for functions
-        if ("(" in line and not ";" in line and " " in line.strip()
-            and not self.in_declare_function):
-            
+        if ("(" in line and ";" not in line and " " in line.strip()
+                and not self.in_declare_function):
+
             # If in function, it will define a block
             n_curly_brackets = line.count("{")
             n_curly_brackets -= line.count("}")
-            
+
             while n_curly_brackets != 0 or ("{" not in line):
-                next_line = self.get_next_line() 
+                next_line = self.get_next_line()
                 line += next_line
-                
+
                 n_curly_brackets = line.count("{")
                 n_curly_brackets -= line.count("}")
 
             after_curly_bracket = line.split("}")[-1]
-            
+
             declare_lines = line.split("\n")
             for declare_line in declare_lines:
                 declare_line = declare_line.rstrip()
-                declare_line = declare_line.replace('\\n',"\\\\n")
-                declare_line = declare_line.replace('"',"\\\"")
+                declare_line = declare_line.replace('\\n', "\\\\n")
+                declare_line = declare_line.replace('"', "\\\"")
                 self.Instr.append_declare(declare_line)
                 self._write_declare_line(declare_line)
-                
+
             line = after_curly_bracket
-                
+
         # Check for struct / function that returns struct
         if line.strip().startswith("struct "):
             # Can be a function returning struct or struct definition
-            
-            # If struct definition, no parenthesis and ; after )        
+
+            # If struct definition, no parenthesis and ; after )
             n_curly_brackets = line.count("{")
             n_curly_brackets -= line.count("}")
-            
+
             # Add lines until end of block found
             while n_curly_brackets != 0 or ("{" not in line):
-                
-                next_line = self.get_next_line() 
+
+                next_line = self.get_next_line()
                 line += next_line
-                
+
                 n_curly_brackets = line.count("{")
                 n_curly_brackets -= line.count("}")
-            
+
                 if "{" in line:
                     before_curly_bracket = line.split("{", 1)[0]
                     if "(" in before_curly_bracket and ")" in before_curly_bracket:
                         # This is a function that returns a struct!
                         self.in_declare_function = True
-            
+
             after_curly_bracket = line.split("}")[-1]
-            
+
             # if not in function, add until ; is found
             while ";" not in after_curly_bracket and not self.in_declare_function:
                 # It is surely a struct, find ;
                 line += self.get_next_line()
                 after_curly_bracket = line.split("}")[-1]
-                
+
             declare_lines = line.split("\n")
             for declare_line in declare_lines:
                 declare_line = declare_line.rstrip()
-                declare_line = declare_line.replace('\\n',"\\\\n")
-                declare_line = declare_line.replace('"',"\\\"")
+                declare_line = declare_line.replace('\\n', "\\\\n")
+                declare_line = declare_line.replace('"', "\\\"")
                 self.Instr.append_declare(declare_line)
                 self._write_declare_line(declare_line)
-                
+
             if self.in_declare_function:
                 line = line.split("}")[-1].strip()
             else:
                 line = line.split(";")[-1].strip()
-                
-            # if in function, stop now    
+
+            # if in function, stop now
             self.in_declare_function = False
-            
+
         # Grab defines
         if line.strip().startswith("#define"):
             # Include define statements as declare append
             line = line.rstrip()
-            line = line.replace('\\n',"\\\\n")
-            line = line.replace('"',"\\\"")
+            line = line.replace('\\n', "\\\\n")
+            line = line.replace('"', "\\\"")
             self.Instr.append_declare(line)
             self._write_declare_line(line)
-        
+
         if "\n" in line:
             line = line.strip("\n")
-            
+
         # Read single line parameter definitions
         if ";" in line and not self.in_declare_function:
             # This line contains c statements
             statements = line.split(";")
-            
+
             for statement in statements:
                 statement = statement.strip()
-                if statement != "\n" and statement != " " and len(statement) > 1:
+                if (statement != "\n" and statement != " "
+                        and len(statement) > 1):
                     self._read_declare_statement(statement)
 
         return continue_declare
-            
+
     def _read_declare_statement(self, statement):
         """
         Reads single declare statements, which can have multiple
         variables.
         """
-        
+
         statement = statement.strip()
-            
+
         # Find type (same for all parameters in one statement)
         this_type = statement.split(" ", 1)[0]
         statement = statement.split(" ", 1)[1].strip()
-        
-        if this_type == "const": # other c keywords to consider?
+
+        if this_type == "const":  # other c keywords to consider?
             this_type += " " + statement.split(" ", 1)[0]
             statement = statement.split(" ", 1)[1].strip()
 
@@ -172,7 +176,7 @@ def _read_declare_statement(self, statement):
             fixed_variables = []
             array_mode = False
             for variable in variables:
-                
+
                 if "{" not in variable and array_mode is False:
                     fixed_variables.append(variable)
                 elif "{" in variable:
@@ -183,8 +187,8 @@ def _read_declare_statement(self, statement):
                 else:
                     temp_variable += variable
                     fixed_variables.append(temp_variable)
-                    array_mode = False    
-            
+                    array_mode = False
+
             variables = fixed_variables
 
         else:
@@ -194,7 +198,7 @@ def _read_declare_statement(self, statement):
         # Treat each variable independently
         for variable in variables:
             variable = variable.strip()
-            
+
             dynamic_size = False
             kw_args = {}
 
@@ -202,7 +206,7 @@ def _read_declare_statement(self, statement):
                 value = variable.split("=")[1].strip()
                 # remove the value part before proceeding
                 variable = variable.split("=")[0].strip()
-                
+
                 if "{" in value:
                     # handle array as value
                     value = value.split("{")[1]
@@ -218,9 +222,9 @@ def _read_declare_statement(self, statement):
                     try:
                         return_value = float(value)
                     except:
-                        value = value.replace('"',"\\\"")
+                        value = value.replace('"', "\\\"")
                         return_value = '"' + value + '"'
-                
+
                 kw_args["value"] = return_value
 
             # Handle array
@@ -228,31 +232,31 @@ def _read_declare_statement(self, statement):
                 array_sizes = []
                 array_size_strings = variable.split("[")
                 # remove the array size part before proceeding
-                variable = variable.split("[",1)[0].strip()
+                variable = variable.split("[", 1)[0].strip()
                 for array_size_string in array_size_strings:
                     if "]" in array_size_string:
                         this_size = array_size_string.split("]")[0]
-                        try: 
+                        try:
                             # Size declared normally
                             array_sizes.append(int(this_size))
                         except:
                             # No size declared means the size is automatic
                             dynamic_size = True
-                        
+
                 if len(array_sizes) > 1:
                     raise ValueError("Can not handle arrays with larger"
                                      + " than 1 dimension yet")
                 if not dynamic_size:
                     kw_args["array"] = array_sizes[0]
-                
+
             if dynamic_size:
                 # McStasScript needs size of array, so it is found manually
                 kw_args["array"] = len(kw_args["value"])
-                
+
             # value, array and typeremoved, all that remians is the name
             variable_name = variable
             self.Instr.add_declare_var(this_type, variable_name, **kw_args)
-            
+
             # Also write it to a file?
             write_string = []
             write_string.append(self.instr_name)
@@ -262,19 +266,18 @@ def _read_declare_statement(self, statement):
             write_string.append("\"" + variable_name + "\"")
             write_string.append(self._kw_to_string(kw_args))
             write_string.append(")\n")
-            
+
             # Write declare parameter to python file
             self._write_to_file(write_string)
-            
+
     def _write_declare_line(self, string):
-        
+
         string = string.rstrip()
-        
+
         write_string = []
         write_string.append(self.instr_name)
         write_string.append(".append_declare(")
         write_string.append("\"" + string + "\"")
         write_string.append(")\n")
-            
+
         self._write_to_file(write_string)
-        
\ No newline at end of file
diff --git a/mcstasscript/instr_reader/read_definition.py b/mcstasscript/instr_reader/read_definition.py
index d4ce1dd5..e4306097 100644
--- a/mcstasscript/instr_reader/read_definition.py
+++ b/mcstasscript/instr_reader/read_definition.py
@@ -1,30 +1,33 @@
 from mcstasscript.instr_reader.util import SectionReader
 
+
 class DefinitionReader(SectionReader):
     """
     Responsible for reading the defintion section of McStas instrument
     file. Contains instrument name and instrument parameters.
     """
-    
-    def __init__(self, Instr, write_file, product_filename, get_next_line, return_line):
-        
-        super().__init__(Instr, write_file, product_filename, get_next_line, return_line)
-        
+
+    def __init__(self, Instr, write_file, product_filename,
+                 get_next_line, return_line):
+
+        super().__init__(Instr, write_file, product_filename,
+                         get_next_line, return_line)
+
     def read_definition_line(self, line):
         """
         Reads line of instrument definition, returns bolean.  If it encounters
         the end of the definition section, it returns False, otherwise True.
-        
+
         The contents of the definition section is written to the McStasScript
         Instr object.
         """
-        
+
         continue_definition = True
 
         # Remove comments
         if "//" in line:
             line = line.split("//")[0]
-        
+
         if "(" in line:
             # Start of instrument definition, get name
             self.instr_name = line.split("(")[0].strip().split(" ")[-1]
@@ -36,7 +39,7 @@ def read_definition_line(self, line):
                 continue_definition = False
                 # these parameters are to be analyzed
                 parameters = parameters.split(")")[0]
-            
+
         elif ")" in line:
             # Found end of definition
             continue_definition = False
@@ -45,29 +48,29 @@ def read_definition_line(self, line):
         else:
             # Neither start or end on this line, analyze everything
             parameters = line
-        
+
         # Separate into individual parameters
         parameters = parameters.split(",")
         if "\n" in parameters:
             parameters.remove("\n")
-                
+
         for parameter in parameters:
             # Analyze individual parameter
             parameter = parameter.strip()
-            
+
             if parameter == "":
                 # If the parameter is empty, skip it.
                 continue
-            
+
             # Ready for keyword arguments
             kw_args = {}
-            
+
             # Default to double type if nothing else is set
             parameter_type = "double"
             if " " and "=" in parameter:
                 # Read parameter type
                 type_and_name = parameter.split("=", 1)[0].strip()
-                
+
                 if " " in type_and_name:
                     parameter_type = type_and_name.split(" ", 1)[0].strip()
                     parameter = parameter.split(" ", 1)[1].strip()
@@ -75,12 +78,12 @@ def read_definition_line(self, line):
                 # Read parameter type
                 parameter_type = parameter.split(" ", 1)[0].strip()
                 parameter = parameter.split(" ", 1)[1].strip()
-            
+
             if "=" in parameter:
                 # Read default value
                 parameter_name = parameter.split("=")[0].strip()
                 value = parameter.split("=")[1].strip()
-                
+
                 if parameter_type == "string":
                     if '"' in value:
                         value = value.replace('"', "\\\"")
@@ -97,10 +100,10 @@ def read_definition_line(self, line):
             else:
                 # No default value, just return the striped name
                 parameter_name = parameter.strip()
-                
+
             # Add this parameter to the object
             self.Instr.add_parameter(parameter_type, parameter_name, **kw_args)
-            
+
             # Also write it to a file?
             write_string = []
             write_string.append(self.instr_name)
@@ -110,34 +113,30 @@ def read_definition_line(self, line):
             write_string.append("\"" + parameter_name + "\"")
             write_string.append(self._kw_to_string(kw_args))
             write_string.append(")\n")
-            
+
             self._write_to_file(write_string)
-            
-            
+
         return continue_definition
-    
+
     def _start_py_file(self):
         write_string = []
-        
+
         # Write warning about robustness of this feature
         write_string.append("\"\"\"\n")
-        write_string.append("This McStasScript file was generated from a McStas\n")
-        write_string.append("instrument file. It is advised to check the content\n")
-        write_string.append("to ensure it is as expected.\n\"\"\"\n")
-        
+        write_string.append("This McStasScript file was generated from a\n")
+        write_string.append("McStas instrument file. It is advised to check\n")
+        write_string.append("the content to ensure it is as expected.\n")
+        write_string.append("\"\"\"\n")
+
         # import McStasScript
         write_string.append("from mcstasscript.interface ")
         write_string.append("import ")
         write_string.append("instr, plotter, functions")
         write_string.append("\n\n")
-        
+
         write_string.append(self.instr_name)
         write_string.append(" = instr.McStas_instr(")
         write_string.append("\"" + self.instr_name + "_generated\"")
         write_string.append(")\n")
-        
+
         self._write_to_file(write_string)
-        
-        
-        
-                
\ No newline at end of file
diff --git a/mcstasscript/instr_reader/read_finally.py b/mcstasscript/instr_reader/read_finally.py
index 0dae1be6..6e3d7140 100644
--- a/mcstasscript/instr_reader/read_finally.py
+++ b/mcstasscript/instr_reader/read_finally.py
@@ -1,55 +1,57 @@
 from mcstasscript.instr_reader.util import SectionReader
 
+
 class FinallyReader(SectionReader):
-    
-    def __init__(self, Instr, write_file, product_filename, get_next_line, return_line):
-        
-        super().__init__(Instr, write_file, product_filename, get_next_line, return_line)
-        
+
+    def __init__(self, Instr, write_file, product_filename,
+                 get_next_line, return_line):
+
+        super().__init__(Instr, write_file, product_filename,
+                         get_next_line, return_line)
+
     def read_finally_line(self, line):
-        
+
         continue_finally = True
-        
+
         # Remove comments
         if "//" in line:
             line = line.split("//", 1)[0].strip()
-            
+
         if line.startswith("FINALLY"):
             line = line.split("FINALLY", 1)[1].strip()
-            
+
         # Remove block opening
         if "%{" in line:
             line = line.split("%{", 1)[1].strip()
-            
+
         if "%}" in line:
             line = line.split("%}", 1)[0].strip()
             continue_finally = False
-            
+
         # If the line is just a new line quit
-        if line is "\n" or line is "":
+        if line == "\n" or line == "":
             return continue_finally
-        
+
         # Remove newline at the end of the line
         if line.endswith("\n"):
             line = line[:-1]
-            
+
         self.Instr.append_finally(line)
-        
+
         if self.write_file:
             # Cant get both \n and " to work in written string
-            write_line = line.replace("\\n","\\\\n")
-            #write_line = line.replace("\\n","test")
-            write_line = write_line.replace("\\t","\\\\t")
+            write_line = line.replace("\\n", "\\\\n")
+            write_line = write_line.replace("\\t", "\\\\t")
             # May need to expand to more cases
-            
+
             write_line = write_line.replace('"', '\\\"')
-            
+
             write_string = []
             write_string.append(self.instr_name)
             write_string.append(".append_finally(")
-            write_string.append("\"" + write_line + "\"") 
+            write_string.append("\"" + write_line + "\"")
             write_string.append(")\n")
-            
+
             self._write_to_file(write_string)
-        
-        return continue_finally
\ No newline at end of file
+
+        return continue_finally
diff --git a/mcstasscript/instr_reader/read_initialize.py b/mcstasscript/instr_reader/read_initialize.py
index c704e231..a16b0840 100644
--- a/mcstasscript/instr_reader/read_initialize.py
+++ b/mcstasscript/instr_reader/read_initialize.py
@@ -1,65 +1,68 @@
 from mcstasscript.instr_reader.util import SectionReader
 
+
 class InitializeReader(SectionReader):
     """
     Reads the initialize section of a McStas instrument file.
-    The initialize lines are added to the McStasScript instrument, and 
+    The initialize lines are added to the McStasScript instrument, and
     are sent to the function writing the lines to the python file.
     """
-    
-    def __init__(self, Instr, write_file, product_filename, get_next_line, return_line):
-        super().__init__(Instr, write_file, product_filename, get_next_line, return_line)
-        
+
+    def __init__(self, Instr, write_file, product_filename,
+                 get_next_line, return_line):
+        super().__init__(Instr, write_file, product_filename,
+                         get_next_line, return_line)
+
     def read_initialize_line(self, line):
         """
         Reads lines from INITIALIZE file and returns True as long as
         the stop characters has not been encountered. Comments are
         ignored with typical c syntax.
         """
-        
+
         continue_initialize = True
-        
+
         # Remove comments
         if "//" in line:
             line = line.split("//", 1)[0].strip()
-            
+
         if line.startswith("INITIALIZE"):
             line = line.split("INITIALIZE", 1)[1].strip()
-            
+
         if line.startswith("INITIALISE"):
             line = line.split("INITIALISE", 1)[1].strip()
-            
+
         # Remove block opening
         if "%{" in line:
             line = line.split("%{", 1)[1].strip()
-            
+
         if "%}" in line:
             line = line.split("%}", 1)[0].strip()
             continue_initialize = False
-            
+
         # If the line is just a new line quit
-        if line is "\n" or line is "":
+        if line == "\n" or line == "":
             return continue_initialize
-        
+
         # Remove newline at the end of the line
         if line.endswith("\n"):
             line = line[:-1]
-            
+
         self.Instr.append_initialize(line)
-        
+
         # Need to prepare string for being written again
-        write_line = line.replace("\\n","\\\\n")
-        write_line = write_line.replace("\\t","\\\\t")
+        write_line = line.replace("\\n", "\\\\n")
+        write_line = write_line.replace("\\t", "\\\\t")
         write_line = write_line.replace('"', '\\\"')
         # May need to expand to more cases
-        
+
         # Write line to Python file
         write_string = []
         write_string.append(self.instr_name)
         write_string.append(".append_initialize(")
-        write_string.append("\"" + write_line + " \"") 
+        write_string.append("\"" + write_line + " \"")
         write_string.append(")\n")
-        
+
         self._write_to_file(write_string)
-        
-        return continue_initialize
\ No newline at end of file
+
+        return continue_initialize
diff --git a/mcstasscript/instr_reader/read_trace.py b/mcstasscript/instr_reader/read_trace.py
index 62ca6733..f94aff80 100644
--- a/mcstasscript/instr_reader/read_trace.py
+++ b/mcstasscript/instr_reader/read_trace.py
@@ -1,5 +1,5 @@
 from mcstasscript.instr_reader.util import SectionReader
-from mcstasscript.helper import mcstas_objects
+
 
 class TraceReader(SectionReader):
     """
@@ -7,46 +7,48 @@ class TraceReader(SectionReader):
     component a McStasScript component instance is created and the
     parameters/keywords are applied to this instance. When the next
     component is found, the previous component is written to the
-    python file for reproduction of the McStas instrument.  
+    python file for reproduction of the McStas instrument.
     """
-    
-    def __init__(self, Instr, write_file, product_filename, get_next_line, return_line):
-        
-        super().__init__(Instr, write_file, product_filename, get_next_line, return_line)
-        
+
+    def __init__(self, Instr, write_file, product_filename,
+                 get_next_line, return_line):
+
+        super().__init__(Instr, write_file, product_filename,
+                         get_next_line, return_line)
+
         self.current_component = None
         self.in_component_mode = False
         self.EXTEND_mode = False
         self.component_copy_target = None
         self.SPLIT = 0
         self.stored_include = None
-        
+
     def sanitize_line(self, line):
         """
         Removes comments, the starting blok and newline characters
         """
-        
+
         line = line.strip()
-        
+
         # Remove comments
         if "//" in line:
             line = line.split("//", 1)[0].strip()
-            
+
         if line.startswith("TRACE"):
             line = line.split("TRACE", 1)[1].strip()
-        
+
         if "/*" in line:
             if "*/" in line:
                 line = line.split("/*", 1)[0] + line.split("*/", 1)[1]
             else:
                 line = line.split("/*", 1)[0]
-        
+
         # Remove newline at the end of the line
         if line.endswith("\n"):
             line = line[:-1]
-        
+
         return line
-        
+
     def read_trace_line(self, line):
         """
         Reads line of McStas file from TRACE section. Has the responsibility
@@ -54,33 +56,33 @@ def read_trace_line(self, line):
         section. May take extra lines through get_new_line if statements are
         spaced out over several lines.
         """
-        
+
         continue_trace = True
-        
+
         # Find stop characeters
         if line.startswith("FINALLY"):
             continue_trace = False
-        
+
         if line.startswith("END"):
             continue_trace = False
-            
+
         if line.strip().startswith("%include") or line.strip().startswith("#include"):
             # Handle include statement and attatch it to a component
-            if self.current_component != None:
+            if self.current_component is not None:
                 c_code_after = self.current_component.c_code_after + line + "\n"
                 self.current_component.set_c_code_after(c_code_after)
             else:
                 # If the include statement is before the first component,
                 # it is saved and attatched to the next component
-                line = line.replace('"',"\\\"")
+                line = line.replace('"', "\\\"")
                 self.stored_include = line.strip()
-        
+
         # If the line is just a new line quit
-        if line is "\n" or line is "":
+        if line == "\n" or line == "":
             return continue_trace
-        
+
         line = self.sanitize_line(line)
-            
+
         # Handle keywords that appear before components
         if line.startswith("SPLIT"):
             # Read split and save for the next component
@@ -89,7 +91,7 @@ def read_trace_line(self, line):
                 # Default split without indicating amount
                 self.SPLIT = "\"\""
             else:
-                try: 
+                try:
                     self.SPLIT = int(line.split(" ", 1)[0].strip())
                 except:
                     self.SPLIT = "\"" + line.split(" ", 1)[0].strip() + "\""
@@ -97,46 +99,46 @@ def read_trace_line(self, line):
                 if " " in line:
                     # If the line continues, remove the SPLIT number
                     line = line.split(" ", 1)[1].strip()
-                    
+
         # Read component definition (can be over several lines)
         if line.startswith("COMPONENT") or line.startswith("REMOVABLE COMPONENT"):
             # Start ned component, but write the previous component to file first
-            if self.stored_include != None and self.current_component != None:
+            if self.stored_include is not None and self.current_component is not None:
                 # In case an include statement was stored, include that statement
                 self.current_component.set_c_code_before(self.stored_include)
                 self.stored_include = None
             # write previous component
             self._write_component_to_py()
-            
+
             # start new component
             self.in_component_mode = True
             # Assume this is not a copy
             self.component_copy_target = None
-            
+
             # Remove COMPONENT from line
-            if line.startswith("COMPONENT"): 
+            if line.startswith("COMPONENT"):
                 line = line[9:].strip()
             elif line.startswith("REMOVABLE COMPONENT"):
                 line = line[19:].strip()
-            
+
             # Add new lines until the entire component definition is found
             full_component_line = False
             while not full_component_line:
-                
+
                 expected_end_parenthesis = 1 + line.count("COPY")
-                
+
                 if line.count("(") >= expected_end_parenthesis:
                     full_component_line = True
-                
+
                 if not full_component_line:
                     new_line = self.get_next_line()
-                    new_line = self.sanitize_line(new_line) 
+                    new_line = self.sanitize_line(new_line)
                     if new_line.startswith("AT") or new_line.startswith("WHEN"):
                         full_component_line = True
                         self.return_line()
                     else:
                         line += new_line
-            
+
             # Retrieve information from component definition
             instance_name = line.split("=", 1)[0].strip()
             component_name = line.split("=", 1)[1].split("(", 1)[0].strip()
@@ -150,26 +152,26 @@ def read_trace_line(self, line):
                     # Get the previous component name
                     last_component = self.Instr.get_last_component()
                     self.component_copy_target = last_component.name
-                
+
                 self.current_component = self.Instr.copy_component(instance_name,
-                                                                  self.component_copy_target)
-                 
+                                                                   self.component_copy_target)
+
             else:
                 # Normal component instance
                 self.current_component = self.Instr.add_component(instance_name,
                                                                   component_name)
-                
+
             # In case there are no parameters, stop in_component_mode
             if line.startswith(")"):
                 self.in_component_mode = False
                 line = line.split(")", 1)[1]
-                
+
             if self.SPLIT != 0:
                 self.current_component.set_SPLIT(self.SPLIT)
                 self.SPLIT = 0
-        
+
         # In case of COPY, there can be empty parameter lists
-        if self.in_component_mode: # and self.component_copy_target != None:
+        if self.in_component_mode:  # and self.component_copy_target != None:
             # Check if this line starts WHEN or AT
             if line.strip().startswith("AT"):
                 self.in_component_mode = False
@@ -179,12 +181,12 @@ def read_trace_line(self, line):
 
         # In component mode reads parameters of each new line read
         if self.in_component_mode:
-            
+
             par_line = line
             # check for parameters
             if par_line.strip().startswith("("):
                 par_line = line.split("(", 1)[1]
-            
+
             # _in_func like python in, but does not look inside parenthesis
             if self._in_func(line, ")"):
                 self.in_component_mode = False
@@ -193,7 +195,7 @@ def read_trace_line(self, line):
 
             # All parameters found saved in dictionary
             par_dict = {}
-            
+
             """
             A parameter line can contain a comma for separating parameters or
             inside of a string. This piece of code finds the next comma and
@@ -201,7 +203,7 @@ def read_trace_line(self, line):
             part, if not it increases the read part of the line to the next
             comma. In this way commas in strings do not separate parameters
             in the component input.
-            """ 
+            """
             while len(par_line) > 0:
                 # find the next parameter expression
                 if self._in_func_brack(par_line, ","):
@@ -218,9 +220,9 @@ def read_trace_line(self, line):
                         par_exp += ","
                         if "," in par_line:
                             # include up to the next comma in par_exp
-                            par_exp += par_line.split(",",1)[0]
+                            par_exp += par_line.split(",", 1)[0]
                             # remove the part of the par_line added to par_exp
-                            par_line = par_line.split(",",1)[1]
+                            par_line = par_line.split(",", 1)[1]
                         else:
                             # no commas left, must be end of par_line
                             par_exp += par_line.strip()
@@ -231,17 +233,16 @@ def read_trace_line(self, line):
                     # last parameter
                     par_exp = par_line
                     par_line = ""
-                    
+
                 if "=" in par_exp:
                     par_name = par_exp.split("=", 1)[0].strip()
                     par_value = par_exp.split("=", 1)[1].strip()
-                    
+
                     par_dict[par_name] = par_value
-                    
-            
+
             # Set all found parameters in the component
             self.current_component.set_parameters(par_dict)
-        
+
         # Read keywords given after parameters but before position (WHEN)
         if line.strip().startswith("WHEN"):
             if "(" in line:
@@ -253,14 +254,14 @@ def read_trace_line(self, line):
                     character_index += 1
                     if character == "(":
                         parenthesis_counter += 1
-                    if character == ")":    
+                    if character == ")":
                         parenthesis_counter -= 1
                     if parenthesis_counter == 0:
                         end_index = character_index
                         break
-                    
+
                 WHEN_statement = line[:character_index]
-                WHEN_statement = WHEN_statement.replace('"',"\\\"")
+                WHEN_statement = WHEN_statement.replace('"', "\\\"")
                 line = line[character_index+1:].strip()
             else:
                 # WHEN statement that does not use parenthesis
@@ -273,11 +274,10 @@ def read_trace_line(self, line):
 
             self.current_component.set_WHEN(WHEN_statement)
 
-        
         # Read component position
         if line.strip().startswith("AT"):
             # read AT statement
-            line = line.split("(",1)[1].strip()
+            line = line.split("(", 1)[1].strip()
             AT_data = []
             AT_data.append(self._split_func(line, ",", 1)[0])
             line = self._split_func(line, ",", 1)[1]
@@ -285,9 +285,9 @@ def read_trace_line(self, line):
             line = self._split_func(line, ",", 1)[1]
             AT_data.append(self._split_func(line, ")", 1)[0])
             line = self._split_func(line, ")", 1)[1]
-            
+
             if line.strip().startswith("ABSOLUTE"):
-                line = line.split(" ",1)[1].strip()
+                line = line.split(" ", 1)[1].strip()
                 relative_name = "ABSOLUTE"
                 # The line can continue, remove the used part
                 if " " in line.strip():
@@ -307,22 +307,22 @@ def read_trace_line(self, line):
                     line = ""
             else:
                 raise ValueError("Could not read: " + line)
-            
+
             self.current_component.set_AT(AT_data, RELATIVE=relative_name)
-            
+
         # Read component rotation
         if line.strip().startswith("ROTATED"):
             # read ROTATED statement
-            line = line.split("(",1)[1].strip()
+            line = line.split("(", 1)[1].strip()
             ROTATED_data = []
-            
+
             ROTATED_data.append(self._split_func(line, ",", 1)[0])
             line = self._split_func(line, ",", 1)[1]
             ROTATED_data.append(self._split_func(line, ",", 1)[0])
             line = self._split_func(line, ",", 1)[1]
             ROTATED_data.append(self._split_func(line, ")", 1)[0])
-            line = self._split_func(line, ")", 1)[1]            
-            
+            line = self._split_func(line, ")", 1)[1]
+
             if line.strip().startswith("ABSOLUTE"):
                 relative_name = "ABSOLUTE"
                 if " " in line.strip():
@@ -330,7 +330,7 @@ def read_trace_line(self, line):
                 else:
                     line = ""
             elif line.strip().startswith("RELATIVE"):
-                line = line.strip().split(" ",1)[1].strip()
+                line = line.strip().split(" ", 1)[1].strip()
                 relative_name = line.split(" ", 1)[0].strip()
                 # The line can continue, remove the used part
                 if " " in line:
@@ -339,39 +339,39 @@ def read_trace_line(self, line):
                     line = ""
             else:
                 raise ValueError("Could not read: " + line)
-            
+
             self.current_component.set_ROTATED(ROTATED_data,
                                                RELATIVE=relative_name)
-        
+
         # Read keywords after component position (GROUP, EXTEND, JUMP)
         if line.strip().startswith("GROUP"):
             line = line.strip()
-            
+
             group_name = line.split(" ", 1)[1].strip()
             #group_name = "\"" + group_name + "\""
             group_name = group_name
-            
-            line = "" 
-            
+
+            line = ""
+
             self.current_component.set_GROUP(group_name)
-                
+
         if line.strip().startswith("EXTEND"):
             line = line.split("EXTEND", 1)[1].strip()
             self.EXTEND_mode = True
-            
+
         if self.EXTEND_mode:
             if "%{" in line:
                 line = line.strip().split("%{", 1)[1].strip()
-            
+
             if "%}" in line:
                 line = line.strip().split("%}", 1)[0].strip()
                 self.EXTEND_mode = False
 
-            if len(line) > 0 and line != "\n": 
-                line = line.replace('\\n',"\\\\n")
-                line = line.replace('"',"\\\"")
+            if len(line) > 0 and line != "\n":
+                line = line.replace('\\n', "\\\\n")
+                line = line.replace('"', "\\\"")
                 self.current_component.append_EXTEND(line)
-                
+
         if line.strip().startswith("JUMP "):
             line = line.strip().split(" ", 1)[1]
             self.current_component.set_JUMP(line)
@@ -379,36 +379,37 @@ def read_trace_line(self, line):
         if not continue_trace:
             # write last component
             self._write_component_to_py()
-        
+
         return continue_trace
-                    
+
     def _write_component_to_py(self):
         # code for writing McStasScript python file
         if self.current_component is not None:
-            
+
             # Write the add_component statement
             write_string = ["\n"]
             write_string.append(self.current_component.name)
             write_string.append(" = ")
             write_string.append(self.instr_name)
-            if self.component_copy_target == None:
+            if self.component_copy_target is None:
                 write_string.append(".add_component(")
                 write_string.append("\"" + self.current_component.name + "\"")
                 write_string.append(", ")
-                write_string.append("\"" + self.current_component.component_name + "\"")
+                component_name = str(self.current_component.component_name)
+                write_string.append("\"" + component_name + "\"")
             else:
                 write_string.append(".copy_component(")
                 write_string.append("\"" + self.current_component.name + "\"")
                 write_string.append(", ")
                 write_string.append("\"" + self.component_copy_target + "\"")
             write_string.append(")\n")
-            
+
             self._write_to_file(write_string)
-            
+
             # Write all parameters as attribute updates
             for key in self.current_component.parameter_names:
                 val = getattr(self.current_component, key)
-                if val != None:
+                if val is not None:
                     write_string = []
                     write_string.append(self.current_component.name)
                     write_string.append(".")
@@ -422,29 +423,29 @@ def _write_component_to_py(self):
                         # If the value is a string, it needs quotes
                         if '"' in val:
                             # If it already has quotes, these need escapes
-                            val = val.replace('"','\\\"')
+                            val = val.replace('"', '\\\"')
                         val = '"' + val + '"'
 
                     write_string.append(val)
                     write_string.append("\n")
-                
+
                     self._write_to_file(write_string)
-                    
+
             # Write EXTEND block if present
             if self.current_component.EXTEND != "":
                 EXTEND = self.current_component.EXTEND
                 EXTEND_lines = EXTEND.split("\n")
                 EXTEND_lines = EXTEND_lines[:-1]
-                
+
                 for EXTEND_line in EXTEND_lines:
                     write_string = []
                     write_string.append(self.current_component.name)
                     write_string.append(".append_EXTEND(")
                     write_string.append("\"" + EXTEND_line + "\"")
                     write_string.append(")\n")
-                
+
                     self._write_to_file(write_string)
-                                        
+
             # Write WHEN statement if present
             if self.current_component.WHEN != "":
                 write_string = []
@@ -455,7 +456,7 @@ def _write_component_to_py(self):
                 WHEN = WHEN[:-1]
                 write_string.append("\"" + WHEN + "\"")
                 write_string.append(")\n")
-                
+
                 self._write_to_file(write_string)
 
             # Write SPLIT if present
@@ -465,9 +466,9 @@ def _write_component_to_py(self):
                 write_string.append(".set_SPLIT(")
                 write_string.append(str(self.current_component.SPLIT))
                 write_string.append(")\n")
-                
+
                 self._write_to_file(write_string)
-            
+
             # Write GROUP if present
             if self.current_component.GROUP != "":
                 write_string = []
@@ -475,39 +476,42 @@ def _write_component_to_py(self):
                 write_string.append(".set_GROUP(\"")
                 write_string.append(str(self.current_component.GROUP))
                 write_string.append("\")\n")
-                
+
                 self._write_to_file(write_string)
-            
+
             # Write JUMP if present
             if self.current_component.JUMP != "":
                 write_string = []
                 write_string.append(self.current_component.name)
                 write_string.append(".set_JUMP(")
-                write_string.append("\"" + str(self.current_component.JUMP) + "\"")
+                jump_string = str(self.current_component.JUMP)
+                write_string.append("\"" + jump_string + "\"")
                 write_string.append(")\n")
-                
+
                 self._write_to_file(write_string)
-                
+
             # Write c_code_before if present
             if self.current_component.c_code_before != "":
                 write_string = []
                 write_string.append(self.current_component.name)
                 write_string.append(".set_c_code_before(")
-                write_string.append("\"" + str(self.current_component.c_code_before) + "\"")
+                c_code_string = str(self.current_component.c_code_before)
+                write_string.append("\"" + c_code_string + "\"")
                 write_string.append(")\n")
-                
+
                 self._write_to_file(write_string)
-            
+
             # Write c_code_after if present
             if self.current_component.c_code_after != "":
                 write_string = []
                 write_string.append(self.current_component.name)
                 write_string.append(".set_c_code_after(")
-                write_string.append("\"" + str(self.current_component.c_code_after) + "\"")
+                c_code_string = str(self.current_component.c_code_after)
+                write_string.append("\"" + c_code_string + "\"")
                 write_string.append(")\n")
-                
+
                 self._write_to_file(write_string)
-            
+
             # Write AT
             write_string = []
             write_string.append(self.current_component.name)
@@ -517,12 +521,12 @@ def _write_component_to_py(self):
             if self.current_component.AT_relative == "ABSOLUTE":
                 write_string.append("\"" + "ABSOLUTE" + "\"")
             else:
-                relative = self.current_component.AT_relative.split(" ")[1] 
+                relative = self.current_component.AT_relative.split(" ")[1]
                 write_string.append("\"" + relative + "\"")
             write_string.append(")\n")
-            
+
             self._write_to_file(write_string)
-            
+
             # Write ROTATED
             write_string = []
             write_string.append(self.current_component.name)
@@ -532,32 +536,10 @@ def _write_component_to_py(self):
             if self.current_component.ROTATED_relative == "ABSOLUTE":
                 write_string.append("\"" + "ABSOLUTE" + "\"")
             else:
-                relative = self.current_component.ROTATED_relative.split(" ")[1] 
+                relative = self.current_component.ROTATED_relative.split(" ")
+                relative = relative[1]
                 write_string.append("\"" + relative + "\"")
             write_string.append(")\n")
-            
+
             if self.current_component.ROTATED_specified:
                 self._write_to_file(write_string)
-            
-            
-            
-            
-            
-            
-            
-            
-            
-            
-            
-            
-            
-            
-            
-            
-            
-            
-            
-            
-        
-        
-        
\ No newline at end of file
diff --git a/mcstasscript/instr_reader/util.py b/mcstasscript/instr_reader/util.py
index 38135579..97e8867b 100644
--- a/mcstasscript/instr_reader/util.py
+++ b/mcstasscript/instr_reader/util.py
@@ -3,61 +3,63 @@ class SectionReader:
     """
     Super class for the many necessary readers
     """
-    
-    def __init__(self, Instr, write_file, product_filename, get_next_line, return_line):
+
+    def __init__(self, Instr, write_file, product_filename,
+                 get_next_line, return_line):
         self.Instr = Instr
         self.write_file = write_file
         self.product_filename = product_filename
         self.instr_name = ""
         self.get_next_line = get_next_line
         self.return_line = return_line
-        
+
     def set_instr_name(self, name):
         self.instr_name = name
-        
+
     def _write_to_file(self, string_array):
         """
         In case a py file is being written, this function writes to the
         appropriate file.
         """
-        
+
         if self.write_file:
             with open(self.product_filename, "a") as product_file:
                 for string in string_array:
                     product_file.write(string)
-                    
+
     def _kw_to_string(self, kwargs):
         """
         Used when a dict containing keyword arguments need to be written
         to a string. This string can be used as argument in method call.
         """
-        
+
         output_string = ""
         for kwarg in kwargs:
             output_string += ", "
             output_string += kwarg + "=" + str(kwargs[kwarg])
-    
+
         return output_string
-    
+
     def _split_func(self, *args):
         """
         Returns list of strings seperated by commas that are not
-        within open parenthesis. 
+        within open parenthesis.
         """
-        
+
         string = args[0]
         split_character = args[1]
-        
+
         if len(args) == 3:
             limit = args[2]
         else:
             limit = -1
-        
+
         split_positions = []
         parenthesis = 0
-        for index in range(0,len(string)):
+        for index in range(len(string)):
             character = string[index]
-            if character == split_character and parenthesis == 0 and limit != 0:
+            if (character == split_character and parenthesis == 0
+                    and limit != 0):
                 split_positions.append(index)
                 limit -= 1
             else:
@@ -66,7 +68,7 @@ def _split_func(self, *args):
                 if character == ")":
                     parenthesis -= 1
 
-        split_positions.append(len(string)+1) # virtual comma at the end
+        split_positions.append(len(string)+1)  # virtual comma at the end
 
         result = []
         last_position = 0
@@ -75,28 +77,28 @@ def _split_func(self, *args):
             last_position = position + 1
 
         return result
-    
+
     def _split_func_brack(self, *args):
         """
         Returns list of strings seperated by commas that are not
-        within open parenthesis / brackets 
+        within open parenthesis / brackets
         """
-        
+
         string = args[0]
         split_character = args[1]
-        
+
         if len(args) == 3:
             limit = args[2]
         else:
             limit = -1
-        
+
         split_positions = []
         parenthesis = 0
         brackets = 0
-        for index in range(0,len(string)):
+        for index in range(len(string)):
             character = string[index]
-            if (character == split_character and parenthesis == 0  
-                    and brackets == 0and limit != 0):
+            if (character == split_character and parenthesis == 0
+                    and brackets == 0 and limit != 0):
                 split_positions.append(index)
                 limit -= 1
             else:
@@ -109,7 +111,7 @@ def _split_func_brack(self, *args):
                 if character == "}":
                     brackets -= 1
 
-        split_positions.append(len(string)+1) # virtual comma at the end
+        split_positions.append(len(string)+1)  # virtual comma at the end
 
         result = []
         last_position = 0
@@ -118,32 +120,25 @@ def _split_func_brack(self, *args):
             last_position = position + 1
 
         return result
-    
+
     def _in_func(self, string, character):
         """
         Returns true of character is in string when excluding occurances
-        within parenthesis. 
+        within parenthesis.
         """
-        
+
         if len(self._split_func(string, character, 1)) == 2:
             return True
         else:
             return False
-        
+
     def _in_func_brack(self, string, character):
         """
         Returns true of character is in string when excluding occurances
-        within parenthesis and brackets. 
+        within parenthesis and brackets.
         """
-        
+
         if len(self._split_func_brack(string, character, 1)) == 2:
             return True
         else:
             return False
-        
-        
-        
-        
-        
-    
-    
\ No newline at end of file
diff --git a/mcstasscript/integration_tests/test_complex_instrument.py b/mcstasscript/integration_tests/test_complex_instrument.py
index 50265d44..40f93df2 100644
--- a/mcstasscript/integration_tests/test_complex_instrument.py
+++ b/mcstasscript/integration_tests/test_complex_instrument.py
@@ -1,11 +1,10 @@
 import io
 import os
-import time
 import unittest
 import unittest.mock
 import matplotlib as plt
 
-from mcstasscript.interface import instr, functions, plotter
+from mcstasscript.interface import instr, functions
 
 
 def setup_complex_instrument():
@@ -91,10 +90,10 @@ def setup_complex_instrument():
     guide2.l = "guide_length"
     guide2.m = 4
     guide2.G = -9.82
-    
+
     guide2.set_SPLIT = 2
 
-    done = Instr.add_component("done", "Arm", RELATIVE="after_guide")
+    Instr.add_component("done", "Arm", RELATIVE="after_guide")
 
     PSD1 = Instr.add_component("PSD_1D_1", "PSDlin_monitor")
     PSD1.set_AT([0, 0, 0.2], RELATIVE="after_guide")
@@ -175,5 +174,6 @@ def test_complex_instrument(self, mock_stdout):
         # time.sleep(10)
         # plt.close()
 
+
 if __name__ == '__main__':
     unittest.main()
diff --git a/mcstasscript/integration_tests/test_simple_instrument.py b/mcstasscript/integration_tests/test_simple_instrument.py
index 58caad86..0e99a421 100644
--- a/mcstasscript/integration_tests/test_simple_instrument.py
+++ b/mcstasscript/integration_tests/test_simple_instrument.py
@@ -1,9 +1,8 @@
 import io
 import os
-import unittest
 import unittest.mock
 
-from mcstasscript.interface import instr, functions, plotter
+from mcstasscript.interface import instr
 
 
 def setup_simple_instrument():
@@ -30,6 +29,7 @@ def setup_simple_instrument():
 
     return Instr
 
+
 def setup_simple_instrument_input_path():
     THIS_DIR = os.path.dirname(os.path.abspath(__file__))
     input_path = os.path.join(THIS_DIR, "test_input_folder")
@@ -137,7 +137,8 @@ def test_simple_instrument_input(self, mock_stdout):
 
         Instr = setup_simple_instrument_input_path()
 
-        data = Instr.run_full_instrument(foldername="integration_test_simple_input",
+        foldername = "integration_test_simple_input"
+        data = Instr.run_full_instrument(foldername=foldername,
                                          ncount=1E6, mpi=1,
                                          increment_folder_name=True)
 
@@ -196,7 +197,8 @@ def test_slit_instrument(self, mock_stdout):
 
         Instr = setup_simple_slit_instrument()
 
-        data = Instr.run_full_instrument(foldername="integration_test_slit",
+        foldername = "integration_test_slit"
+        data = Instr.run_full_instrument(foldername=foldername,
                                          ncount=2E6, mpi=2,
                                          increment_folder_name=True)
 
@@ -238,5 +240,6 @@ def test_slit_moved_instrument(self, mock_stdout):
 
         self.assertTrue(1000*sum_outside_beam < sum_inside_beam)
 
+
 if __name__ == '__main__':
-    unittest.main()
\ No newline at end of file
+    unittest.main()
diff --git a/mcstasscript/interface/functions.py b/mcstasscript/interface/functions.py
index c4214982..62dafcc0 100644
--- a/mcstasscript/interface/functions.py
+++ b/mcstasscript/interface/functions.py
@@ -43,7 +43,7 @@ def name_search(name, data_list):
         for check in data_list:
             if check.metadata.filename == name:
                 list_result.append(check)
-    
+
     if len(list_result) == 0:
         raise NameError("No dataset with name: \""
                         + name
@@ -76,7 +76,7 @@ def name_plot_options(name, data_list, **kwargs):
         McStasPlotOptions
     """
     object_to_modify = name_search(name, data_list)
-    if type(object_to_modify) is not list:                     
+    if type(object_to_modify) is not list:
         object_to_modify.set_plot_options(**kwargs)
     else:
         for data_object in object_to_modify:
@@ -102,17 +102,17 @@ def load_data(foldername):
 class Configurator:
     """
     Class for setting the configuration file for McStasScript.
-    
+
     Attributes
     ----------
     configuration_file_name : str
         absolute path of configuration file
-        
+
     Methods
     -------
     set_mcstas_path(string)
         sets mcstas path
-        
+
     set_mcrun_path(string)
         sets mcrun path
 
@@ -121,16 +121,16 @@ class Configurator:
 
     set_mxrun_path(string)
         sets mxrun path
-        
+
     set_line_length(int)
         sets maximum line length to given int
-    
+
     _write_yaml(dict)
         internal method, writes a configuration yaml file with dict content
-        
+
     _read_yaml()
         internal method, reads a configuration yaml file and returns a dict
-    
+
     _create_new_config_file()
         internal method, creates default configuration file
 
@@ -141,7 +141,7 @@ def __init__(self, *args):
         Initialization of configurator, checks that the configuration file
         actually exists, and if it does not, creates a default configuration
         file.
-        
+
         Parameters
         ----------
         (optional) custom name : str
@@ -155,7 +155,7 @@ def __init__(self, *args):
 
         # check configuration file exists
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-        conf_file = os.path.join(THIS_DIR, ".." , name + ".yaml")
+        conf_file = os.path.join(THIS_DIR, "..", name + ".yaml")
         self.configuration_file_name = conf_file
         if not os.path.isfile(self.configuration_file_name):
             # no config file found, write default config file
@@ -166,7 +166,7 @@ def _write_yaml(self, dictionary):
         Writes a dictionary as the new configuration file
         """
         with open(self.configuration_file_name, 'w') as yaml_file:
-            yaml.dump(dictionary, yaml_file, default_flow_style=False)   
+            yaml.dump(dictionary, yaml_file, default_flow_style=False)
 
     def _read_yaml(self):
         """
@@ -184,19 +184,18 @@ def _create_new_config_file(self):
         mcstas = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/"
 
         mxrun = "/Applications/McXtrace-1.5.app" \
-                   + "/Contents/Resources/mcxtrace/1.5/mxrun"
+                + "/Contents/Resources/mcxtrace/1.5/mxrun"
         mcxtrace = "/Applications/McXtrace-1.5.app" \
                    + "/Contents/Resources/mcxtrace/1.5/"
 
-        default_paths = {"mcrun_path" : run,
-                         "mcstas_path" : mcstas,
-                         "mxrun_path" : mxrun,
-                         "mcxtrace_path" : mcxtrace}
+        default_paths = {"mcrun_path": run,
+                         "mcstas_path": mcstas,
+                         "mxrun_path": mxrun,
+                         "mcxtrace_path": mcxtrace}
 
-        default_other = {"characters_per_line" : 85}
+        default_other = {"characters_per_line": 85}
 
-        default_config = {"paths" : default_paths,
-                          "other" : default_other}
+        default_config = {"paths": default_paths, "other": default_other}
 
         self._write_yaml(default_config)
 
@@ -211,9 +210,10 @@ def set_mcstas_path(self, path):
         """
 
         if not os.path.isdir(path):
-            raise RuntimeError("Invalid path given to set_mcstas_path:" + str(path))
+            raise RuntimeError("Invalid path given to set_mcstas_path:"
+                               + str(path))
 
-        # read entire configuration file 
+        # read entire configuration file
         config = self._read_yaml()
 
         # update mcstas_path
@@ -233,9 +233,10 @@ def set_mcrun_path(self, path):
         """
 
         if not os.path.isdir(path):
-            raise RuntimeError("Invalid path given to set_mcrun_path:" + str(path))
+            raise RuntimeError("Invalid path given to set_mcrun_path:"
+                               + str(path))
 
-        # read entire configuration file 
+        # read entire configuration file
         config = self._read_yaml()
 
         # update mcstas_path
@@ -255,7 +256,8 @@ def set_mcxtrace_path(self, path):
         """
 
         if not os.path.isdir(path):
-            raise RuntimeError("Invalid path given to set_mcxtrace_path:" + str(path))
+            raise RuntimeError("Invalid path given to set_mcxtrace_path:"
+                               + str(path))
 
         # read entire configuration file
         config = self._read_yaml()
@@ -308,7 +310,7 @@ def set_line_length(self, line_length):
                              + " be positve, given length: "
                              + str(line_length))
 
-        # read entire configuration file 
+        # read entire configuration file
         config = self._read_yaml()
 
         # update mcstas_path
@@ -316,5 +318,3 @@ def set_line_length(self, line_length):
 
         # write new configuration file
         self._write_yaml(config)
-
-
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 5907cafc..f112ad0f 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -6,7 +6,6 @@
 import subprocess
 import copy
 
-from mcstasscript.data.data import McStasData
 from mcstasscript.helper.mcstas_objects import DeclareVariable
 from mcstasscript.helper.mcstas_objects import ParameterVariable
 from mcstasscript.helper.mcstas_objects import Component
@@ -158,13 +157,13 @@ class McCode_instr:
 
     set_component_SPLIT(instance_name, string)
         Sets SPLIT value for named component
-        
+
     set_component_c_code_before(instance_name, string)
         Sets c code before the component
-        
+
     set_component_c_code_after(instance_name, string)
         Sets c code after the component
-        
+
     set_component_comment(instance_name, string)
         Sets comment to be written before named component
 
@@ -264,7 +263,7 @@ def __init__(self, name, **kwargs):
                 raise RuntimeError("Given package_path does not point to "
                                    + "a folder:\"" + self.package_path + '"')
 
-        elif self.package_path is "":
+        elif self.package_path == "":
             raise NameError("At this stage of development "
                             + "McStasScript need the absolute path "
                             + "for the " + self.package_name +
@@ -273,7 +272,6 @@ def __init__(self, name, **kwargs):
 
         self.parameter_list = []
         self.declare_list = []
-        #self.declare_section = ""
         self.initialize_section = ("// Start of initialize for generated "
                                    + name + "\n")
         self.trace_section = ("// Start of trace section for generated "
@@ -373,7 +371,7 @@ def show_parameters(self, **kwargs):
         for parameter in self.parameter_list:
             print(str(parameter.type).ljust(longest_type), end=' ')
             print(str(parameter.name).ljust(longest_name), end=' ')
-            if parameter.value is "":
+            if parameter.value == "":
                 print("   ", end=' ')
             else:
                 print(" = ", end=' ')
@@ -450,18 +448,17 @@ def add_declare_var(self, *args, **kwargs):
     def append_declare(self, string):
         """
         Method for appending code to the declare section directly
-        
+
         This method is not meant for declaring simple variables which
         should be done using add_declare_var. This method can be used
         to declare functions, structures and unions directly.
-        
+
         Parameters
         ----------
         string : str
             code to be added to declare section
         """
 
-        #self.declare_section = self.declare_section + string + "\n"
         self.declare_list.append(string)
 
     def append_initialize(self, string):
@@ -721,7 +718,8 @@ def add_component(self, name, component_name, **kwargs):
 
             new_index = self.component_name_list.index(kwargs["after"])
 
-            new_component = self._create_component_instance(name, component_name,
+            new_component = self._create_component_instance(name,
+                                                            component_name,
                                                             **kwargs)
             self.component_list.insert(new_index + 1, new_component)
 
@@ -738,7 +736,8 @@ def add_component(self, name, component_name, **kwargs):
 
             new_index = self.component_name_list.index(kwargs["before"])
 
-            new_component = self._create_component_instance(name, component_name,
+            new_component = self._create_component_instance(name,
+                                                            component_name,
                                                             **kwargs)
             self.component_list.insert(new_index, new_component)
 
@@ -746,7 +745,8 @@ def add_component(self, name, component_name, **kwargs):
 
         # If after or before keywords absent, place component at the end
         else:
-            new_component = self._create_component_instance(name, component_name,
+            new_component = self._create_component_instance(name,
+                                                            component_name,
                                                             **kwargs)
             self.component_list.append(new_component)
             self.component_name_list.append(name)
@@ -1085,7 +1085,7 @@ def set_component_c_code_before(self, name, code):
         ----------
         name : str
             Unique name of component to modify
-            
+
         code : str
             Code to be pasted before component
         """
@@ -1101,7 +1101,7 @@ def set_component_c_code_after(self, name, code):
         ----------
         name : str
             Unique name of component to modify
-        
+
         code : str
             Code to be pasted after component
         """
@@ -1202,10 +1202,9 @@ def print_components(self, **kwargs):
         AT_pad = 6  # requires (, , ) in addition to data length
         RELATIVE_pad = 0
         ROTATED_pad = 6  # requires (, , ) in addition to data length
-        ROTATED_characters = 7 # ROTATED is 7 characters
-        AT_characters = 2 # AT is 2 characters
-        SPACING_between_strings = 7 # combining 8 strings, 7 spaces
-
+        ROTATED_characters = 7  # ROTATED is 7 characters
+        AT_characters = 2  # AT is 2 characters
+        SPACING_between_strings = 7  # combining 8 strings, 7 spaces
 
         # Check if longest line length exceeded
         longest_line_length = (longest_name + name_pad
@@ -1238,7 +1237,7 @@ def coordinates_to_string(data):
             longest_rotated_xyz_name = longest_at_xyz_name
             RELATIVE_pad = 0
 
-            SPACING_between_strings = 4 # combining 5 strings, 4 spaces
+            SPACING_between_strings = 4  # combining 5 strings, 4 spaces
 
             longest_line_length_at = (longest_name
                                       + comp_name_pad
@@ -1350,7 +1349,7 @@ def coordinates_to_string(data):
                           " ROTATED", p_ROTATED, p_ROTATED_RELATIVE)
                 else:
                     print(p_name + " ", p_comp_name, "\n",
-                      " AT     ", p_AT, p_AT_RELATIVE)
+                          " AT     ", p_AT, p_AT_RELATIVE)
 
     def write_c_files(self):
         """
@@ -1371,13 +1370,12 @@ def write_c_files(self):
                 print("Creation of the directory %s failed" % path)
 
         file_path = os.path.join(".", "generated_includes",
-                                self.name + "_declare.c")
+                                 self.name + "_declare.c")
         with open(file_path, "w") as fo:
             fo.write("// declare section for %s \n" % self.name)
 
-
-            file_path = os.path.join(".", "generated_includes",
-                                     self.name + "_declare.c")
+        file_path = os.path.join(".", "generated_includes",
+                                 self.name + "_declare.c")
         with open(file_path, "a") as fo:
             for dec_line in self.declare_list:
                 if isinstance(dec_line, str):
@@ -1460,7 +1458,6 @@ def write_full_instrument(self):
 
         # Write declare
         fo.write("DECLARE \n%{\n")
-        #fo.write(self.declare_section)
         for dec_line in self.declare_list:
             if isinstance(dec_line, str):
                 # append declare section parts written here
@@ -1504,12 +1501,11 @@ def _handle_parameters(self, given_parameters):
         Adds the given parameters to the default parameters, and ensures all
         required parameters are provided. Also checks all given parameters
         match an existing parameter.
-        
+
         Parameters
         ----------
         given_parameters: dict
             Parameters given by the user for simulation run
-        
         """
 
         if not isinstance(given_parameters, dict):
@@ -1541,7 +1537,7 @@ def _handle_parameters(self, given_parameters):
                                 + str(list(default_parameters.keys())))
 
         # Check if required parameters are provided
-        if len(given_parameters) is 0:
+        if len(given_parameters) == 0:
             if len(required_parameters) > 0:
                 # print required parameters and raise error
                 print("Required instrument parameters:")
@@ -1662,9 +1658,9 @@ def show_instrument(self, *args, **kwargs):
         bin_path = os.path.join(self.package_path, "bin", "")
         executable = "mcdisplay-webgl"
         if "format" in kwargs:
-            if kwargs["format"] is "webgl":
+            if kwargs["format"] == "webgl":
                 executable = "mcdisplay-webgl"
-            elif kwargs["format"] is "window":
+            elif kwargs["format"] == "window":
                 executable = "mcdisplay"
 
         full_command = (bin_path + executable + " "
@@ -2113,4 +2109,4 @@ def _read_calibration(self):
             # This happens in unit tests that mocks open
             self.executable_path = ""
             self.package_path = ""
-            self.line_limit = 180
\ No newline at end of file
+            self.line_limit = 180
diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index 861fe224..27f2daaa 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -7,8 +7,6 @@
 from matplotlib.colors import BoundaryNorm
 from matplotlib.ticker import MaxNLocator
 
-from mcstasscript.data.data import McStasMetaData
-from mcstasscript.data.data import McStasPlotOptions
 from mcstasscript.data.data import McStasData
 
 
@@ -74,6 +72,7 @@ def _find_min_max_I(data):
 
     return min_value, max_value
 
+
 def _plot_fig_ax(data, fig, ax, **kwargs):
     """
     Plots the content of a single McStasData object
@@ -257,6 +256,7 @@ def _handle_kwargs(data_list, **kwargs):
 
     return figsize, data_list
 
+
 def make_plot(data_list, **kwargs):
     """
     make_plot plots contents of McStasData objects given in list
@@ -279,6 +279,7 @@ def make_plot(data_list, **kwargs):
     else:
         plt.show()
 
+
 def make_sub_plot(data_list, **kwargs):
     """
     make_sub_plot plots contents of McStasData objects given in list
@@ -360,11 +361,11 @@ def make_animation(data_list, **kwargs):
             maximum_values.append(max_value)
 
     if is_1D and is_2D:
-        raise InputError(
+        raise ValueError(
             "Both 1D and 2D data in animation, only one allowed.")
 
     if len(minimum_values) == 0:
-        raise InputError(
+        raise ValueError(
             "No data found for animation!")
 
     maximum_value = np.array(maximum_values).max()
diff --git a/mcstasscript/interface/reader.py b/mcstasscript/interface/reader.py
index 2c8d4465..16748f00 100644
--- a/mcstasscript/interface/reader.py
+++ b/mcstasscript/interface/reader.py
@@ -2,24 +2,23 @@
 from mcstasscript.instr_reader.control import InstrumentReader
 from mcstasscript.interface.instr import McStas_instr
 
+
 class McStas_file:
     """
     Reader of McStas files, can add to an existing McStasScript
     instrument instance or create a corresponding McStasScript python
     file.
-    
-    
+
     Methods
     -------
-    
+
     add_to_instr(Instr)
         Add information from McStas file to McStasScript Instr instance
-        
+
     write_python_file(filename)
-        Write python file named filename that reproduce the McStas instr 
-    
+        Write python file named filename that reproduce the McStas instr
     """
-    
+
     def __init__(self, filename):
         """
         Initialization of McStas_file class, needs McStas instr filename
@@ -44,13 +43,13 @@ def add_to_instr(self, Instr):
         Parameters
         ----------
             Instr (McStasScript McStas_instr instance)
-                McStas_instr instance to add instrument information to    
+                McStas_instr instance to add instrument information to
         """
 
         # Check Instr
         if not isinstance(Instr, McStas_instr):
             raise TypeError("Given object is not of type McStas_instr!")
-        
+
         self.Reader.add_to_instr(Instr)
 
     def write_python_file(self, filename, **kwargs):
@@ -60,7 +59,7 @@ def write_python_file(self, filename, **kwargs):
         Parameters
         ----------
             filename (str)
-                Filename of python file to be written    
+                Filename of python file to be written
         """
 
         if "force" in kwargs:
@@ -73,9 +72,8 @@ def write_python_file(self, filename, **kwargs):
             if force:
                 os.remove(filename)
             else:
-                raise ValueError("Filename \"" + filename 
+                raise ValueError("Filename \"" + filename
                                  + "\" already exists, you can overwrite with "
                                  + "force=True")
 
         self.Reader.generate_py_version(filename)
-        
diff --git a/mcstasscript/tests/test_ComponentReader.py b/mcstasscript/tests/test_ComponentReader.py
index c512a437..b6c93f70 100644
--- a/mcstasscript/tests/test_ComponentReader.py
+++ b/mcstasscript/tests/test_ComponentReader.py
@@ -1,9 +1,7 @@
 import os
 import io
-import unittest
 import unittest.mock
 
-from mcstasscript.helper.component_reader import ComponentInfo
 from mcstasscript.helper.component_reader import ComponentReader
 
 
@@ -25,6 +23,7 @@ def setup_component_reader():
 
     return component_reader
 
+
 def setup_component_reader_input_path():
     """
     Sets up a component_reader instance with dummy mcstas
@@ -60,7 +59,7 @@ def test_ComponentReader_init_overwrite_message(self, mock_stdout):
         Here using default input path, which is current folder
         """
 
-        component_reader = setup_component_reader()
+        setup_component_reader()
 
         message = ("The following components are found in the work_directory "
                    + "/ input_path:\n     test_for_reading.comp\n"
@@ -76,7 +75,7 @@ def test_ComponentReader_init_overwrite_message_input(self, mock_stdout):
         Here using user defined input path, a directory in tests
         """
 
-        component_reader = setup_component_reader_input_path()
+        setup_component_reader_input_path()
 
         message = ("The following components are found in the work_directory "
                    + "/ input_path:\n     test_for_structure.comp\n"
@@ -115,27 +114,31 @@ def test_ComponentReader_init_component_paths(self, mock_stdout):
         self.assertEqual(n_components_found, 3)
 
         expected_path = os.path.join(THIS_DIR, "test_for_reading.comp")
-        self.assertIn("test_for_reading", component_reader.component_path)
-        self.assertEqual(component_reader.component_path["test_for_reading"],
-                         expected_path)
-        self.assertEqual(component_reader.component_category["test_for_reading"],
-                         "Work directory")
+        path = component_reader.component_path["test_for_reading"]
+        self.assertEqual(path, expected_path)
+
+        category = component_reader.component_category["test_for_reading"]
+        self.assertEqual(category, "Work directory")
 
         expected_path = os.path.join(dummy_path, "misc",
                                      "test_for_structure.comp")
         self.assertIn("test_for_structure", component_reader.component_path)
-        self.assertEqual(component_reader.component_path["test_for_structure"],
-                         expected_path)
-        self.assertEqual(component_reader.component_category["test_for_structure"],
-                         "misc")
 
+        path = component_reader.component_path["test_for_structure"]
+        self.assertEqual(path, expected_path)
+
+        category = component_reader.component_category["test_for_structure"]
+        self.assertEqual(category, "misc")
+
+        self.assertIn("test_for_structure2", component_reader.component_path)
+
+        path = component_reader.component_path["test_for_structure2"]
         expected_path = os.path.join(dummy_path, "sources",
                                      "test_for_structure2.comp")
-        self.assertIn("test_for_structure2", component_reader.component_path)
-        self.assertEqual(component_reader.component_path["test_for_structure2"],
-                         expected_path)
-        self.assertEqual(component_reader.component_category["test_for_structure2"],
-                         "sources")
+        self.assertEqual(path, expected_path)
+
+        category = component_reader.component_category["test_for_structure2"]
+        self.assertEqual(category, "sources")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_init_component_paths_input(self, mock_stdout):
@@ -154,29 +157,34 @@ def test_ComponentReader_init_component_paths_input(self, mock_stdout):
         n_components_found = len(component_reader.component_path)
         self.assertEqual(n_components_found, 3)
 
+        self.assertIn("test_for_reading", component_reader.component_path)
+
         expected_path = os.path.join(dummy_path, "misc",
                                      "test_for_reading.comp")
-        self.assertIn("test_for_reading", component_reader.component_path)
-        self.assertEqual(component_reader.component_path["test_for_reading"],
-                         expected_path)
-        self.assertEqual(component_reader.component_category["test_for_reading"],
-                         "misc")
+        path = component_reader.component_path["test_for_reading"]
+        self.assertEqual(path, expected_path)
+
+        category = component_reader.component_category["test_for_reading"]
+        self.assertEqual(category, "misc")
 
         expected_path = os.path.join(input_path, "test_for_structure.comp")
         self.assertIn("test_for_structure", component_reader.component_path)
-        self.assertEqual(component_reader.component_path["test_for_structure"],
-                         expected_path)
-        self.assertEqual(component_reader.component_category["test_for_structure"],
-                         "Work directory")
 
+        path = component_reader.component_path["test_for_structure"]
+        self.assertEqual(path, expected_path)
+
+        category = component_reader.component_category["test_for_structure"]
+        self.assertEqual(category, "Work directory")
+
+        self.assertIn("test_for_structure2", component_reader.component_path)
 
+        path = component_reader.component_path["test_for_structure2"]
         expected_path = os.path.join(dummy_path, "sources",
                                      "test_for_structure2.comp")
-        self.assertIn("test_for_structure2", component_reader.component_path)
-        self.assertEqual(component_reader.component_path["test_for_structure2"],
-                         expected_path)
-        self.assertEqual(component_reader.component_category["test_for_structure2"],
-                         "sources")
+        self.assertEqual(path, expected_path)
+
+        category = component_reader.component_category["test_for_structure2"]
+        self.assertEqual(category, "sources")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_init_categories(self, mock_stdout):
@@ -332,7 +340,7 @@ def test_ComponentReader_read_name_error(self, mock_stdout):
         component_reader = setup_component_reader()
 
         with self.assertRaises(NameError):
-            CompInfo = component_reader.read_name("no_such_comp")
+            component_reader.read_name("no_such_comp")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_read_name_success(self, mock_stdout):
diff --git a/mcstasscript/tests/test_Configurator.py b/mcstasscript/tests/test_Configurator.py
index 592b0746..42f68557 100644
--- a/mcstasscript/tests/test_Configurator.py
+++ b/mcstasscript/tests/test_Configurator.py
@@ -3,48 +3,50 @@
 
 from mcstasscript.interface.functions import Configurator
 
+
 def setup_expected_file(test_name):
     THIS_DIR = os.path.dirname(os.path.abspath(__file__))
     expected_file = os.path.join(THIS_DIR, "..", test_name + ".yaml")
-    
+
     if os.path.isfile(expected_file):
         os.remove(expected_file)
     return expected_file
 
+
 def setup_configurator(test_name):
-    
+
     setup_expected_file(test_name)
-    
+
     return Configurator(test_name)
-            
+
 
 class TestConfigurator(unittest.TestCase):
     """
     Tests for configurator class that handles yaml configuration file
     """
-    
+
     def test_simple_initialize(self):
         """
         Tests that initialization happens, new configuration file should be
         written.
         """
-        
+
         test_name = "test_configuration"
         expected_file = setup_expected_file(test_name)
-        
+
         # check the file did not exist before testing
         self.assertFalse(os.path.isfile(expected_file))
 
         # initialize the configurator
-        my_configurator = Configurator(test_name)
-        
+        Configurator(test_name)
+
         # check a new configuration file was made
         self.assertTrue(os.path.isfile(expected_file))
-        
+
         # remove the testing configuration file
         if os.path.isfile(expected_file):
             os.remove(expected_file)
-    
+
     def test_default_config(self):
         """
         This tests confirms the content of the default configuration file
@@ -52,14 +54,14 @@ def test_default_config(self):
 
         test_name = "test_configuration"
         expected_file = setup_expected_file(test_name)
-        
+
         # check the file did not exist before testing
         self.assertFalse(os.path.isfile(expected_file))
-        
+
         my_configurator = Configurator(test_name)
-        
+
         default_config = my_configurator._read_yaml()
-        
+
         run = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/"
         mcstas = "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/"
         mxrun = "/Applications/McXtrace-1.5.app" \
@@ -72,34 +74,34 @@ def test_default_config(self):
         self.assertEqual(default_config["paths"]["mxrun_path"], mxrun)
         self.assertEqual(default_config["paths"]["mcxtrace_path"], mcxtrace)
         self.assertEqual(default_config["other"]["characters_per_line"], 85)
-        
+
         # remove the testing configuration file
-        if os.path.isfile(expected_file): 
+        if os.path.isfile(expected_file):
             os.remove(expected_file)
-    
+
     def test_yaml_write(self):
         """
         This test checks that writing to the configuration file works
         """
         test_name = "test_configuration"
         my_configurator = setup_configurator(test_name)
-        
+
         config = my_configurator._read_yaml()
-        
+
         config["new_field"] = 123
-        config["paths"]["new_path"] = "/test/path/" 
-        
+        config["paths"]["new_path"] = "/test/path/"
+
         my_configurator._write_yaml(config)
-        
+
         new_config = my_configurator._read_yaml()
-        
+
         self.assertEqual(new_config["other"]["characters_per_line"], 85)
         self.assertEqual(new_config["new_field"], 123)
         self.assertEqual(new_config["paths"]["new_path"], "/test/path/")
-        
+
         # remove the testing configuration file
         setup_expected_file(test_name)
-        
+
     def test_set_mcrun_path(self):
         """
         This test checks that setting the mcrun path works
@@ -139,7 +141,7 @@ def test_set_mcstas_path(self):
         # remove the testing configuration file
         setup_expected_file(test_name)
 
-    def test_set_mcrun_path(self):
+    def test_set_mxrun_path(self):
         """
         This test checks that setting the mxrun path works
         """
@@ -158,7 +160,7 @@ def test_set_mcrun_path(self):
         # remove the testing configuration file
         setup_expected_file(test_name)
 
-    def test_set_mcstas_path(self):
+    def test_set_mcxtrace_path(self):
         """
         This test checks that setting the mcxtrace path works
         """
@@ -183,13 +185,13 @@ def test_set_line_length(self):
         This test checks that setting the line length works
         """
         test_name = "test_configuration"
-        my_configurator = setup_configurator(test_name) 
-        
+        my_configurator = setup_configurator(test_name)
+
         my_configurator.set_line_length(123)
-        
+
         new_config = my_configurator._read_yaml()
-        
-        self.assertEqual(new_config["other"]["characters_per_line"],123)
+
+        self.assertEqual(new_config["other"]["characters_per_line"], 123)
 
         # remove the testing configuration file
         setup_expected_file(test_name)
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index ced71652..1d2e7c60 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1,7 +1,6 @@
 import os
 import os.path
 import io
-import builtins
 import unittest
 import unittest.mock
 import datetime
@@ -23,10 +22,13 @@ def setup_instr_no_path():
     current_work_dir = os.getcwd()
     os.chdir(THIS_DIR)  # Set work directory to test folder
 
-    return McStas_instr("test_instrument")
+    instrument = McStas_instr("test_instrument")
 
     os.chdir(current_work_dir)
 
+    return instrument
+
+
 def setup_x_ray_instr_no_path():
     """
     Sets up a X-ray instrument without a package_path
@@ -36,10 +38,13 @@ def setup_x_ray_instr_no_path():
     current_work_dir = os.getcwd()
     os.chdir(THIS_DIR)  # Set work directory to test folder
 
-    return McXtrace_instr("test_instrument")
+    instrument = McXtrace_instr("test_instrument")
 
     os.chdir(current_work_dir)
 
+    return instrument
+
+
 def setup_instr_root_path():
     """
     Sets up a neutron instrument with root package_path
@@ -49,10 +54,13 @@ def setup_instr_root_path():
     current_work_dir = os.getcwd()
     os.chdir(THIS_DIR)  # Set work directory to test folder
 
-    return McStas_instr("test_instrument", package_path="/")
+    instrument = McStas_instr("test_instrument", package_path="/")
 
     os.chdir(current_work_dir)
 
+    return instrument
+
+
 def setup_x_ray_instr_root_path():
     """
     Sets up a X-ray instrument with root package_path
@@ -62,10 +70,13 @@ def setup_x_ray_instr_root_path():
     current_work_dir = os.getcwd()
     os.chdir(THIS_DIR)  # Set work directory to test folder
 
-    return McXtrace_instr("test_instrument", package_path="/")
+    instrument = McXtrace_instr("test_instrument", package_path="/")
 
     os.chdir(current_work_dir)
 
+    return instrument
+
+
 def setup_instr_with_path():
     """
     Sets up an instrument with a valid package_path, but it points to
@@ -78,9 +89,12 @@ def setup_instr_with_path():
     current_work_dir = os.getcwd()
     os.chdir(THIS_DIR)  # Set work directory to test folder
 
+    instrument = McStas_instr("test_instrument", package_path=dummy_path)
+
     os.chdir(current_work_dir)  # Return to previous workdir
 
-    return McStas_instr("test_instrument", package_path=dummy_path)
+    return instrument
+
 
 def setup_x_ray_instr_with_path():
     """
@@ -94,9 +108,12 @@ def setup_x_ray_instr_with_path():
     current_work_dir = os.getcwd()
     os.chdir(THIS_DIR)  # Set work directory to test folder
 
+    instrument = McXtrace_instr("test_instrument", package_path=dummy_path)
+
     os.chdir(current_work_dir)  # Return to previous workdir
 
-    return McXtrace_instr("test_instrument", package_path=dummy_path)
+    return instrument
+
 
 def setup_instr_with_input_path():
     """
@@ -104,7 +121,6 @@ def setup_instr_with_input_path():
     the dummy installation in the test folder. In addition the input_path
     is set to a folder in the test directory using an absolute path.
     """
-
     THIS_DIR = os.path.dirname(os.path.abspath(__file__))
     dummy_path = os.path.join(THIS_DIR, "dummy_mcstas")
     input_path = os.path.join(THIS_DIR, "test_input_folder")
@@ -112,11 +128,14 @@ def setup_instr_with_input_path():
     current_work_dir = os.getcwd()
     os.chdir(THIS_DIR)  # Set work directory to test folder
 
+    instrument = McStas_instr("test_instrument",
+                              package_path=dummy_path,
+                              input_path=input_path)
+
     os.chdir(current_work_dir)  # Return to previous workdir
 
-    return McStas_instr("test_instrument",
-                        package_path=dummy_path,
-                        input_path=input_path)
+    return instrument
+
 
 def setup_instr_with_input_path_relative():
     """
@@ -124,17 +143,18 @@ def setup_instr_with_input_path_relative():
     the dummy installation in the test folder. In addition the input_path
     is set to a folder in the test directory using a relative path.
     """
-
     THIS_DIR = os.path.dirname(os.path.abspath(__file__))
 
     current_work_dir = os.getcwd()
     os.chdir(THIS_DIR)  # Set work directory to test folder
 
+    instrument = McStas_instr("test_instrument",
+                              package_path="dummy_mcstas",
+                              input_path="test_input_folder")
+
     os.chdir(current_work_dir)  # Return to previous workdir
 
-    return McStas_instr("test_instrument",
-                        package_path="dummy_mcstas",
-                        input_path="test_input_folder")
+    return instrument
 
 
 def setup_populated_instr():
@@ -148,12 +168,13 @@ def setup_populated_instr():
     instr.add_declare_var("double", "two_theta")
     instr.append_initialize("two_theta = 2.0*theta;")
 
-    comp1 = instr.add_component("first_component", "test_for_reading")
-    comp2 = instr.add_component("second_component", "test_for_reading")
-    comp3 = instr.add_component("third_component", "test_for_reading")
+    instr.add_component("first_component", "test_for_reading")
+    instr.add_component("second_component", "test_for_reading")
+    instr.add_component("third_component", "test_for_reading")
 
     return instr
 
+
 def setup_populated_instr_with_dummy_path():
     """
     Sets up a neutron instrument with some features used and three components
@@ -192,12 +213,13 @@ def setup_populated_x_ray_instr():
     instr.add_declare_var("double", "two_theta")
     instr.append_initialize("two_theta = 2.0*theta;")
 
-    comp1 = instr.add_component("first_component", "test_for_reading")
-    comp2 = instr.add_component("second_component", "test_for_reading")
-    comp3 = instr.add_component("third_component", "test_for_reading")
+    instr.add_component("first_component", "test_for_reading")
+    instr.add_component("second_component", "test_for_reading")
+    instr.add_component("third_component", "test_for_reading")
 
     return instr
 
+
 def setup_populated_x_ray_instr_with_dummy_path():
     """
     Sets up a x-ray instrument with some features used and three components
@@ -237,14 +259,14 @@ def setup_populated_with_some_options_instr():
     instr.append_initialize("two_theta = 2.0*theta;")
 
     comp1 = instr.add_component("first_component", "test_for_reading")
-    comp1.set_AT([0,0,1])
+    comp1.set_AT([0, 0, 1])
     comp1.set_GROUP("Starters")
     comp2 = instr.add_component("second_component", "test_for_reading")
-    comp2.set_AT([0,0,2], RELATIVE="first_component")
-    comp2.set_ROTATED([0,30,0])
+    comp2.set_AT([0, 0, 2], RELATIVE="first_component")
+    comp2.set_ROTATED([0, 30, 0])
     comp2.set_WHEN("1==1")
-    comp2.yheight=1.23
-    comp3 = instr.add_component("third_component", "test_for_reading")
+    comp2.yheight = 1.23
+    instr.add_component("third_component", "test_for_reading")
 
     return instr
 
@@ -254,10 +276,6 @@ class TestMcStas_instr(unittest.TestCase):
     Tests of the main class in McStasScript called McStas_instr.
     """
 
-    #def test_show_test_folder(self):
-    #    os.system("tree")
-
-
     def test_simple_initialize(self):
         """
         Test basic initialization runs
@@ -278,8 +296,10 @@ def test_complex_initialize(self):
 
         self.assertEqual(my_instrument.author, "Mads")
         self.assertEqual(my_instrument.origin, "DMSC")
-        self.assertEqual(my_instrument.executable_path, "./dummy_mcstas/contrib")
-        self.assertEqual(my_instrument.package_path, "./dummy_mcstas/misc")
+        self.assertEqual(my_instrument.executable_path,
+                         "./dummy_mcstas/contrib")
+        self.assertEqual(my_instrument.package_path,
+                         "./dummy_mcstas/misc")
 
     def test_load_config_file(self):
         """
@@ -460,7 +480,7 @@ def test_simple_add_declare_parameter(self):
 
         self.assertEqual(instr.declare_list[0].name, "two_theta")
         self.assertEqual(instr.declare_list[0].comment, " // test par")
-        
+
     def test_simple_append_declare(self):
         """
         Appending to declare adds an object to the declare list, and the
@@ -475,11 +495,11 @@ def test_simple_append_declare(self):
 
         self.assertEqual(instr.declare_list[0],
                          "First line of declare")
-        self.assertEqual(instr.declare_list[1], 
+        self.assertEqual(instr.declare_list[1],
                          "Second line of declare")
-        self.assertEqual(instr.declare_list[2], 
+        self.assertEqual(instr.declare_list[2],
                          "Third line of declare")
-        
+
     def test_simple_append_declare_var_mix(self):
         """
         Appending to declare adds an object to the declare list, and the
@@ -496,8 +516,8 @@ def test_simple_append_declare_var_mix(self):
                          "First line of declare")
         self.assertEqual(instr.declare_list[1].name, "two_theta")
         self.assertEqual(instr.declare_list[1].comment, " // test par")
-        self.assertEqual(instr.declare_list[2], 
-                         "Third line of declare")        
+        self.assertEqual(instr.declare_list[2],
+                         "Third line of declare")
 
     def test_simple_append_initialize(self):
         """
@@ -652,7 +672,7 @@ def test_show_components_simple(self, mock_stdout):
                          + "work_directory / input_path:")
         self.assertEqual(output[1], "     test_for_reading.comp")
         self.assertEqual(output[2], "These definitions will be used "
-                                    +"instead of the installed versions.")
+                         + "instead of the installed versions.")
         self.assertEqual(output[3],
                          "Here are the available component categories:")
         self.assertEqual(output[4], " sources")
@@ -709,7 +729,7 @@ def test_show_components_input_path_simple(self, mock_stdout):
                          + "work_directory / input_path:")
         self.assertEqual(output[1], "     test_for_structure.comp")
         self.assertEqual(output[2], "These definitions will be used "
-                                    +"instead of the installed versions.")
+                         + "instead of the installed versions.")
         self.assertEqual(output[3],
                          "Here are the available component categories:")
         self.assertEqual(output[4], " sources")
@@ -736,7 +756,7 @@ def test_show_components_input_path_custom(self, mock_stdout):
                          + "work_directory / input_path:")
         self.assertEqual(output[1], "     test_for_structure.comp")
         self.assertEqual(output[2], "These definitions will be used "
-                                    +"instead of the installed versions.")
+                         + "instead of the installed versions.")
         self.assertEqual(output[3],
                          "Here are the available component categories:")
         self.assertEqual(output[4], " sources")
@@ -902,9 +922,9 @@ def test_add_component_simple_keyword(self, mock_stdout):
 
         instr = setup_instr_with_path()
 
-        comp = instr.add_component("test_component",
-                                   "test_for_reading",
-                                   WHEN="1<2")
+        instr.add_component("test_component",
+                            "test_for_reading",
+                            WHEN="1<2")
 
         self.assertEqual(len(instr.component_list), 1)
         self.assertEqual(instr.component_list[0].name, "test_component")
@@ -926,9 +946,9 @@ def test_add_component_simple_before(self):
 
         instr = setup_populated_instr()
 
-        comp = instr.add_component("test_component",
-                                   "test_for_reading",
-                                   before="first_component")
+        instr.add_component("test_component",
+                            "test_for_reading",
+                            before="first_component")
 
         self.assertEqual(len(instr.component_list), 4)
         self.assertEqual(instr.component_list[0].name, "test_component")
@@ -947,9 +967,9 @@ def test_add_component_simple_after(self):
 
         instr = setup_populated_instr()
 
-        comp = instr.add_component("test_component",
-                                   "test_for_reading",
-                                   after="first_component")
+        instr.add_component("test_component",
+                            "test_for_reading",
+                            after="first_component")
 
         self.assertEqual(len(instr.component_list), 4)
         self.assertEqual(instr.component_list[1].name, "test_component")
@@ -970,9 +990,9 @@ def test_add_component_simple_after_error(self):
         instr = setup_populated_instr()
 
         with self.assertRaises(NameError):
-            comp = instr.add_component("test_component",
-                                       "test_for_reading",
-                                       after="non_existent_component")
+            instr.add_component("test_component",
+                                "test_for_reading",
+                                after="non_existent_component")
 
     def test_add_component_simple_before_error(self):
         """
@@ -989,9 +1009,9 @@ def test_add_component_simple_before_error(self):
         instr = setup_populated_instr()
 
         with self.assertRaises(NameError):
-            comp = instr.add_component("test_component",
-                                       "test_for_reading",
-                                       before="non_existent_component")
+            instr.add_component("test_component",
+                                "test_for_reading",
+                                before="non_existent_component")
 
     def test_add_component_simple_double_naming_error(self):
         """
@@ -1002,7 +1022,7 @@ def test_add_component_simple_double_naming_error(self):
         instr = setup_populated_instr()
 
         with self.assertRaises(NameError):
-            comp = instr.add_component("first_component", "test_for_reading")
+            instr.add_component("first_component", "test_for_reading")
 
     def test_copy_component_simple(self):
         """
@@ -1010,7 +1030,7 @@ def test_copy_component_simple(self):
         """
 
         instr = setup_populated_with_some_options_instr()
-        
+
         comp = instr.copy_component("copy_of_second_comp", "second_component")
 
         self.assertEqual(comp.name, "copy_of_second_comp")
@@ -1028,7 +1048,7 @@ def test_copy_component_simple_fail(self):
         instr = setup_populated_with_some_options_instr()
 
         with self.assertRaises(NameError):
-            comp = instr.copy_component("copy_of_second_comp", "unknown_component")
+            instr.copy_component("copy_of_second_comp", "unknown_component")
 
     def test_copy_component_simple_object(self):
         """
@@ -1051,14 +1071,14 @@ def test_copy_component_keywords(self):
         """
         Checks that a component can be copied and that keyword
         arguments given under copy operation is successfully
-        applied to the new component. A check is also made to 
+        applied to the new component. A check is also made to
         ensure that the original component was not modified.
         """
 
         instr = setup_populated_with_some_options_instr()
 
         comp = instr.copy_component("copy_of_second_comp", "second_component",
-                                    AT=[1,2,3], SPLIT=10)
+                                    AT=[1, 2, 3], SPLIT=10)
 
         self.assertEqual(comp.name, "copy_of_second_comp")
         self.assertEqual(comp.yheight, 1.23)
@@ -1066,7 +1086,7 @@ def test_copy_component_keywords(self):
         self.assertEqual(comp.AT_data[1], 2)
         self.assertEqual(comp.AT_data[2], 3)
         self.assertEqual(comp.SPLIT, 10)
-        
+
         # ensure original component was not changed
         original = instr.get_component("second_component")
         self.assertEqual(original.name, "second_component")
@@ -1098,7 +1118,7 @@ def test_get_component_simple_error(self):
         instr = setup_populated_instr()
 
         with self.assertRaises(NameError):
-            comp = instr.get_component("non_existing_component")
+            instr.get_component("non_existing_component")
 
     def test_get_last_component_simple(self):
         """
@@ -1294,7 +1314,8 @@ def test_set_c_code_before(self):
 
         instr = setup_populated_instr()
 
-        instr.set_component_c_code_before("second_component", "%include before.instr")
+        instr.set_component_c_code_before("second_component",
+                                          "%include before.instr")
 
         comp = instr.get_component("second_component")
 
@@ -1308,7 +1329,8 @@ def test_set_c_code_after(self):
 
         instr = setup_populated_instr()
 
-        instr.set_component_c_code_after("second_component", "%include after.instr")
+        instr.set_component_c_code_after("second_component",
+                                         "%include after.instr")
 
         comp = instr.get_component("second_component")
 
@@ -1350,8 +1372,6 @@ def test_print_component(self, mock_stdout):
         self.assertEqual(output[3], "  " + par_name + warning)
 
         self.assertEqual(output[4], "AT [0, 0, 0] ABSOLUTE")
-        # Rotation not printed since it was never specified
-        #self.assertEqual(output[5], "ROTATED [0, 0, 0] ABSOLUTE")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_print_component_short(self, mock_stdout):
@@ -1512,7 +1532,7 @@ class for each component and aligns the data for display
                     + " ")
         self.assertEqual(output[0], expected)
 
-        expected = ("  AT      (-0.1, 12, dist) RELATIVE home")
+        expected = "  AT      (-0.1, 12, dist) RELATIVE home"
         self.assertEqual(output[1], expected)
 
         expected = (bcolors.BOLD
@@ -1525,10 +1545,10 @@ class for each component and aligns the data for display
                     + " ")
         self.assertEqual(output[2], expected)
 
-        expected = ("  AT      (0, 0, 0) ABSOLUTE ")
+        expected = "  AT      (0, 0, 0) ABSOLUTE "
         self.assertEqual(output[3], expected)
 
-        expected = ("  ROTATED (-4, 0.001, theta) RELATIVE etc")
+        expected = "  ROTATED (-4, 0.001, theta) RELATIVE etc"
         self.assertEqual(output[4], expected)
 
         expected = (bcolors.BOLD
@@ -1541,7 +1561,7 @@ class for each component and aligns the data for display
                     + " ")
         self.assertEqual(output[5], expected)
 
-        expected = ("  AT      (0, 0, 0) ABSOLUTE")
+        expected = "  AT      (0, 0, 0) ABSOLUTE"
         self.assertEqual(output[6], expected)
 
     @unittest.mock.patch('__main__.__builtins__.open',
@@ -1581,7 +1601,6 @@ def test_write_c_files_simple(self, mock_f):
         call = unittest.mock.call
         wrts = [
          call("// declare section for test_instrument \n"),
-         #call(""),
          call("double two_theta;"),
          call("\n"),
          call("// Start of initialize for generated test_instrument\n"
@@ -1666,7 +1685,6 @@ def test_write_full_instrument_simple(self, mock_f):
          my_call(")\n"),
          my_call("\n"),
          my_call("DECLARE \n%{\n"),
-         #my_call(""),
          my_call("double two_theta;"),
          my_call("\n"),
          my_call("%}\n\n"),
@@ -1862,7 +1880,8 @@ def test_run_full_instrument_complex(self, mock_sub, mock_stdout):
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     @unittest.mock.patch("subprocess.run")
-    def test_run_full_instrument_overwrite_default(self, mock_sub, mock_stdout):
+    def test_run_full_instrument_overwrite_default(self, mock_sub,
+                                                   mock_stdout):
         """
         Check that default parameters are overwritten by given
         parameters in run_full_instrument.
@@ -1894,7 +1913,7 @@ def test_run_full_instrument_overwrite_default(self, mock_sub, mock_stdout):
 
         expected_path = os.path.join(executable_path, "mcrun")
 
-        current_directory = os.getcwd()
+        os.getcwd()
         expected_folder_path = os.path.join(THIS_DIR, "test_data_set")
 
         # a double space because of a missing option
diff --git a/mcstasscript/tests/test_Instr_reader.py b/mcstasscript/tests/test_Instr_reader.py
index 3b89efc3..aa813f0f 100644
--- a/mcstasscript/tests/test_Instr_reader.py
+++ b/mcstasscript/tests/test_Instr_reader.py
@@ -4,15 +4,19 @@
 
 from mcstasscript.interface import instr
 from mcstasscript.instr_reader import control
-from mcstasscript.instr_reader import util
 
 
-# Disable print
 def blockPrint():
+    """
+    Disables print capability
+    """
     sys.stdout = open(os.devnull, 'w')
 
-# Restore print
+
 def enablePrint():
+    """
+    Restores print capability
+    """
     sys.stdout = sys.__stdout__
 
 
@@ -20,32 +24,35 @@ def set_dummy_dir():
     THIS_DIR = os.path.dirname(os.path.abspath(__file__))
     os.chdir(os.path.join(THIS_DIR, "dummy_instrument_folder"))
 
+
 def setup_standard(Instr):
     set_dummy_dir()
     filename = "Union_demonstration_test.instr"
     InstrReader = control.InstrumentReader(filename)
     InstrReader.add_to_instr(Instr)
-    
+
     return InstrReader
 
+
 def setup_standard_auto_instr():
     set_dummy_dir()
-    
+
     blockPrint()
     Instr = instr.McStas_instr("test_instrument")
     enablePrint()
 
     return setup_standard(Instr)
 
+
 class TestInstrReader(unittest.TestCase):
-    
+
     def test_read_instrument_name(self):
         """
         Check if the instrument name is read correctly
         """
 
         set_dummy_dir()
-        
+
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
@@ -57,286 +64,292 @@ def test_read_instrument_name(self):
         self.assertEqual(InstrReader.instr_name, "Union_demonstration")
 
     def test_read_input_parameter(self):
-    
+
         set_dummy_dir()
-        
+
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
-        InstrReader = setup_standard(Instr)
-        
+        setup_standard(Instr)
+
         self.assertEqual(Instr.parameter_list[0].name, "stick_displacement")
         # space in type inserted for easier writing by McStas_Instr class
         self.assertEqual(Instr.parameter_list[0].type, "double")
         self.assertEqual(Instr.parameter_list[0].value, 0)
-        
+
         self.assertEqual(Instr.parameter_list[1].name, "test_int")
         # space in type inserted for easier writing by McStas_Instr class
         self.assertEqual(Instr.parameter_list[1].type, "int")
         self.assertEqual(Instr.parameter_list[1].value, 3)
-        
+
         self.assertEqual(Instr.parameter_list[2].name, "test_str")
         # space in type inserted for easier writing by McStas_Instr class
         self.assertEqual(Instr.parameter_list[2].type, "string")
         self.assertEqual(Instr.parameter_list[2].value, "\"\\\"hurray\\\"\"")
 
     def test_read_declare_parameter(self):
-        
+
         set_dummy_dir()
-        
+
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
-        InstrReader = setup_standard(Instr)
-        
+        setup_standard(Instr)
+
         self.assertEqual(Instr.declare_list[0].name, "sample_1_index")
         self.assertEqual(Instr.declare_list[0].type, "int")
         self.assertEqual(Instr.declare_list[0].value, 27)
-        
+
         self.assertEqual(Instr.declare_list[8].name, "array")
         self.assertEqual(Instr.declare_list[8].type, "double")
         self.assertEqual(Instr.declare_list[8].vector, 3)
         self.assertEqual(Instr.declare_list[8].value, [0.1, 0.2, 0.3])
-        
+
         self.assertEqual(Instr.declare_list[9].name, "I_array")
         self.assertEqual(Instr.declare_list[9].type, "int")
         self.assertEqual(Instr.declare_list[9].vector, 4)
-        
+
         self.assertEqual(Instr.declare_list[10].name, "T_array")
         self.assertEqual(Instr.declare_list[10].type, "int")
         self.assertEqual(Instr.declare_list[10].vector, 5)
         self.assertEqual(Instr.declare_list[10].value, [1, 2, 3, 4, 5])
-        
+
         self.assertEqual(Instr.declare_list[11].name, "home")
         self.assertEqual(Instr.declare_list[11].type, "char")
         self.assertEqual(Instr.declare_list[11].vector, 20)
-        self.assertEqual(Instr.declare_list[11].value, "\"\\\"test_string\\\"\"")
-        
+        self.assertEqual(Instr.declare_list[11].value,
+                         "\"\\\"test_string\\\"\"")
+
     def test_read_initialize_line(self):
-    
+
         set_dummy_dir()
-        
+
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
-        InstrReader = setup_standard(Instr)
-        
-        self.assertEqual(Instr.initialize_section,
-                         "// Start of initialize for generated test_instrument\n"
-                         + "I_array[2] = 8;\n"
-                         + "printf(\"Hello world\\n\");\n")
-        
+        setup_standard(Instr)
+
+        initialize = ("// Start of initialize for generated test_instrument\n"
+                      + "I_array[2] = 8;\n"
+                      + "printf(\"Hello world\\n\");\n")
+
+        self.assertEqual(Instr.initialize_section, initialize)
+
     # Check a few components are read correctly
     def test_read_component_1(self):
-        
+
         set_dummy_dir()
-        
+
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
-        InstrReader = setup_standard(Instr)
-        
+        setup_standard(Instr)
+
         components = Instr.component_list
-        
+
         test_component = None
-        
+
         for component in components:
             if component.name == "Al":
                 test_component = component
-        
+
         self.assertEqual(test_component.component_name, "Union_make_material")
-        
+
         val = getattr(test_component, "my_absorption")
-        #self.assertEqual(val, "\"100*4*0.231/66.4\"")
         self.assertEqual(val, "100*4*0.231/66.4")
-        
+
         val = getattr(test_component, "process_string")
         self.assertEqual(val, "\"Al_incoherent,Al_powder\"")
-        
+
         self.assertEqual(test_component.AT_data, ["0", "0", "0"])
         self.assertEqual(test_component.AT_relative, "ABSOLUTE")
-        
+
         self.assertEqual(test_component.ROTATED_data, [0, 0, 0])
         self.assertEqual(test_component.ROTATED_relative, "ABSOLUTE")
-        
+
     def test_read_component_2(self):
-        
+
         set_dummy_dir()
-        
+
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
-        InstrReader = setup_standard(Instr)
-        
+        setup_standard(Instr)
+
         components = Instr.component_list
-        
+
         test_component = None
-        
+
         for component in components:
             if component.name == "sample_holder3":
                 test_component = component
-        
+
         self.assertEqual(test_component.component_name, "Union_box")
-        
+
         val = getattr(test_component, "xwidth")
         self.assertEqual(val, "0.0098")
-        
+
         val = getattr(test_component, "priority")
         self.assertEqual(val, "52")
-        
-        self.assertEqual(test_component.AT_data, ["0", "-0.03", "-0.03*0.35-0.004"])
-        self.assertEqual(test_component.AT_relative, "RELATIVE sample_rod_bottom")
-        
+
+        self.assertEqual(test_component.AT_data,
+                         ["0", "-0.03", "-0.03*0.35-0.004"])
+        self.assertEqual(test_component.AT_relative,
+                         "RELATIVE sample_rod_bottom")
+
         self.assertEqual(test_component.ROTATED_data, ["-25", "0", "0"])
-        self.assertEqual(test_component.ROTATED_relative, "RELATIVE sample_rod_bottom")
+        self.assertEqual(test_component.ROTATED_relative,
+                         "RELATIVE sample_rod_bottom")
 
     def test_read_component_WHEN(self):
-    
+
         set_dummy_dir()
-        
+
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
-        InstrReader = setup_standard(Instr)
-        
+        setup_standard(Instr)
+
         components = Instr.component_list
-        
+
         test_component = None
-        
+
         for component in components:
             if component.name == "outer_cryostat_vacuum":
                 test_component = component
-        
+
         self.assertEqual(test_component.component_name, "Union_cylinder")
-        
+
         val = getattr(test_component, "radius")
         self.assertEqual(val, "0.09")
-        
+
         val = getattr(test_component, "priority")
         self.assertEqual(val, "11")
-        
+
         self.assertEqual(test_component.WHEN, "WHEN (necessary == 1 )")
-        
+
         self.assertEqual(test_component.AT_data, ["0", "0.01", "0"])
         self.assertEqual(test_component.AT_relative, "RELATIVE beam_center")
-        
+
         self.assertEqual(test_component.ROTATED_data, ["0", "0", "0"])
-        self.assertEqual(test_component.ROTATED_relative, "RELATIVE beam_center")
-        
+        self.assertEqual(test_component.ROTATED_relative,
+                         "RELATIVE beam_center")
+
     def test_read_component_EXTEND(self):
-        
+
         set_dummy_dir()
-        
+
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
-        InstrReader = setup_standard(Instr)
-        
+        setup_standard(Instr)
+
         components = Instr.component_list
-        
+
         test_component = None
-        
+
         for component in components:
             if component.name == "test_sample":
                 test_component = component
-        
+
         self.assertEqual(test_component.component_name, "Union_master")
-        
+
         val = getattr(test_component, "history_limit")
         self.assertEqual(val, "1000000")
-        
+
         lines = test_component.EXTEND.split("\n")
         line0 = ("if (scattered_flag[sample_1_index] > 0) scattered_1 = 1;"
-                 +" else scattered_1 = 0;")
+                 + " else scattered_1 = 0;")
         line1 = ("if (scattered_flag[sample_2_index] > 0) scattered_2 = 1;"
-                 +" else scattered_2 = 0;")
+                 + " else scattered_2 = 0;")
         line2 = ("if (scattered_flag[sample_3_index] > 0) scattered_3 = 1;"
-                 +" else scattered_3 = 0;")
+                 + " else scattered_3 = 0;")
         line3 = ("if (scattered_flag[sample_4_index] > 0) scattered_4 = 1;"
-                 +" else scattered_4 = 0;")
-        
+                 + " else scattered_4 = 0;")
+
         self.assertEqual(lines[0], line0)
         self.assertEqual(lines[1], line1)
         self.assertEqual(lines[2], line2)
         self.assertEqual(lines[3], line3)
-        
+
         self.assertEqual(test_component.AT_data, ["0", "0", "0"])
         self.assertEqual(test_component.AT_relative, "RELATIVE beam_center")
-        
+
         self.assertEqual(test_component.ROTATED_data, ["0", "0", "0"])
-        self.assertEqual(test_component.ROTATED_relative, "RELATIVE beam_center")
-        
+        self.assertEqual(test_component.ROTATED_relative,
+                         "RELATIVE beam_center")
+
     def test_read_component_GROUP(self):
-        
+
         set_dummy_dir()
-        
+
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
-        InstrReader = setup_standard(Instr)
-        
+        setup_standard(Instr)
+
         components = Instr.component_list
-        
+
         test_component = None
-        
+
         for component in components:
             if component.name == "armA":
                 test_component = component
-        
+
         self.assertEqual(test_component.component_name, "Arm")
-        
+
         self.assertEqual(test_component.GROUP, "arms")
-        
+
         self.assertEqual(test_component.AT_data, ["0", "0", "0"])
         self.assertEqual(test_component.AT_relative, "ABSOLUTE")
-        
+
     def test_read_component_SPLIT(self):
-        
+
         set_dummy_dir()
-        
+
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
-        InstrReader = setup_standard(Instr)
-        
+        setup_standard(Instr)
+
         components = Instr.component_list
-        
+
         test_component = None
-        
+
         for component in components:
             if component.name == "sample_4_container":
                 test_component = component
-        
+
         self.assertEqual(test_component.component_name, "Union_cylinder")
-        
+
         self.assertEqual(test_component.AT_data, ["0", "0", "0"])
-        self.assertEqual(test_component.AT_relative, "RELATIVE sample_4")     
-    
+        self.assertEqual(test_component.AT_relative, "RELATIVE sample_4")
+
     def test_read_component_JUMP(self):
         """
         Check a JUMP and GROUP statement is read correctly
         """
-        
+
         set_dummy_dir()
-        
+
         blockPrint()
         Instr = instr.McStas_instr("test_instrument")
         enablePrint()
-        InstrReader = setup_standard(Instr)
-        
+        setup_standard(Instr)
+
         components = Instr.component_list
-        
+
         test_component = None
-        
+
         for component in components:
             if component.name == "armB":
                 test_component = component
-        
+
         self.assertEqual(test_component.component_name, "Arm")
-        
+
         self.assertEqual(test_component.GROUP, "arms")
         self.assertEqual(test_component.JUMP, "myself 2")
-        
+
         self.assertEqual(test_component.AT_data, ["0", "0", "0"])
         self.assertEqual(test_component.AT_relative, "ABSOLUTE")
 
@@ -344,67 +357,66 @@ def test_comma_split(self):
         """
         Test the Tracer_reader._split_func
         """
-        
+
         InstrReader = setup_standard_auto_instr()
-        
+
         test_string = "A,B,C,D(a,b),E"
-        
+
         result = InstrReader.Trace_reader._split_func(test_string, ",")
-        
-        self.assertEqual(result[0],"A")
-        self.assertEqual(result[1],"B")
-        self.assertEqual(result[2],"C")
-        self.assertEqual(result[3],"D(a,b)")
-        self.assertEqual(result[4],"E")
-    
+
+        self.assertEqual(result[0], "A")
+        self.assertEqual(result[1], "B")
+        self.assertEqual(result[2], "C")
+        self.assertEqual(result[3], "D(a,b)")
+        self.assertEqual(result[4], "E")
+
     def test_comma_split_limited(self):
         """
         Test the Tracer_reader._split_func
         """
-        
+
         InstrReader = setup_standard_auto_instr()
-        
+
         test_string = "A,B,C,D(a,b),E"
-        
+
         result = InstrReader.Trace_reader._split_func(test_string, ",",  2)
-        
-        self.assertEqual(result[0],"A")
-        self.assertEqual(result[1],"B")
-        self.assertEqual(result[2],"C,D(a,b),E")
-    
-        
+
+        self.assertEqual(result[0], "A")
+        self.assertEqual(result[1], "B")
+        self.assertEqual(result[2], "C,D(a,b),E")
+
     def test_parenthesis_split(self):
         """
         Test the Tracer_reader._split_func
         """
-        
+
         InstrReader = setup_standard_auto_instr()
-        
+
         test_string = "A)B)C)D(a,b))E"
-        
+
         result = InstrReader.Trace_reader._split_func(test_string, ")")
-        
-        self.assertEqual(result[0],"A")
-        self.assertEqual(result[1],"B")
-        self.assertEqual(result[2],"C")
-        self.assertEqual(result[3],"D(a,b)")
-        self.assertEqual(result[4],"E")
-        
+
+        self.assertEqual(result[0], "A")
+        self.assertEqual(result[1], "B")
+        self.assertEqual(result[2], "C")
+        self.assertEqual(result[3], "D(a,b)")
+        self.assertEqual(result[4], "E")
+
     def test_comma_split_brack(self):
         """
         Test the Tracer_reader._split_func
         """
 
         InstrReader = setup_standard_auto_instr()
-        
+
         test_string = "A,B{C,D(a,b)},E"
-        
+
         result = InstrReader.Trace_reader._split_func_brack(test_string, ",")
-        
-        self.assertEqual(result[0],"A")
-        self.assertEqual(result[1],"B{C,D(a,b)}")
-        self.assertEqual(result[2],"E")    
 
-    
+        self.assertEqual(result[0], "A")
+        self.assertEqual(result[1], "B{C,D(a,b)}")
+        self.assertEqual(result[2], "E")
+
+
 if __name__ == '__main__':
     unittest.main()
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index 3ed4d53b..cc6ae7c7 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -1,11 +1,6 @@
 import os
-import io
 import unittest
-from unittest import mock
-import numpy as np
 
-from mcstasscript.data.data import McStasMetaData
-from mcstasscript.data.data import McStasData
 from mcstasscript.helper.managed_mcrun import ManagedMcrun
 
 
@@ -151,37 +146,37 @@ def test_ManagedMcrun_init_no_folder_error(self):
         An error should occur if no filename is given
         """
         with self.assertRaises(NameError):
-            mcrun_obj = ManagedMcrun("test.instr", mcrun_path="")
+            ManagedMcrun("test.instr", mcrun_path="")
 
     def test_ManagedMcrun_init_invalid_ncount_error(self):
         """
         An error should occur if negative ncount is given
         """
         with self.assertRaises(ValueError):
-            mcrun_obj = ManagedMcrun("test.instr",
-                                     foldername="test_folder",
-                                     mcrun_path="",
-                                     ncount=-8)
+            ManagedMcrun("test.instr",
+                         foldername="test_folder",
+                         mcrun_path="",
+                         ncount=-8)
 
     def test_ManagedMcrun_init_invalid_mpi_error(self):
         """
         An error should occur if negative mpi is given
         """
         with self.assertRaises(ValueError):
-            mcrun_obj = ManagedMcrun("test.instr",
-                                     foldername="test_folder",
-                                     mcrun_path="",
-                                     mpi=-8)
+            ManagedMcrun("test.instr",
+                         foldername="test_folder",
+                         mcrun_path="",
+                         mpi=-8)
 
     def test_ManagedMcrun_init_invalid_parameters_error(self):
         """
         An error should occur if parameters is given as non dict
         """
         with self.assertRaises(RuntimeError):
-            mcrun_obj = ManagedMcrun("test.instr",
-                                     foldername="test_folder",
-                                     mcrun_path="",
-                                     parameters=[1, 2, 3])
+            ManagedMcrun("test.instr",
+                         foldername="test_folder",
+                         mcrun_path="",
+                         parameters=[1, 2, 3])
 
     @unittest.mock.patch("subprocess.run")
     def test_ManagedMcrun_run_simulation_basic(self, mock_sub):
@@ -204,7 +199,6 @@ def test_ManagedMcrun_run_simulation_basic(self, mock_sub):
 
         mcrun_obj.run_simulation()
 
-        current_directory = os.getcwd()
         expected_folder_path = os.path.join(THIS_DIR, "test_folder")
 
         # a double space because of a missing option
@@ -239,7 +233,6 @@ def test_ManagedMcrun_run_simulation_basic_path(self, mock_sub):
 
         mcrun_obj.run_simulation()
 
-        current_directory = os.getcwd()
         expected_folder_path = os.path.join(THIS_DIR, "test_folder")
 
         executable = os.path.join(executable_path, "mcrun")
@@ -362,7 +355,6 @@ def test_ManagedMcrun_run_simulation_compile(self, mock_sub):
 
         mcrun_obj.run_simulation()
 
-        current_directory = os.getcwd()
         expected_folder_path = os.path.join(THIS_DIR, "test_folder")
 
         executable = os.path.join(executable_path, "mcrun")
@@ -542,7 +534,7 @@ def test_ManagedMcrun_load_data_L_mon_direct_error(self):
 
         load_path = os.path.join(THIS_DIR, "non_existent_dataset")
         with self.assertRaises(NameError):
-            results = mcrun_obj.load_results(load_path)
+            mcrun_obj.load_results(load_path)
 
         os.chdir(current_work_dir)  # Reset work directory
 
@@ -564,7 +556,7 @@ def test_ManagedMcrun_load_data_L_mon_empty_error(self):
 
         load_path = os.path.join(THIS_DIR, "/dummy_mcstas")
         with self.assertRaises(NameError):
-            results = mcrun_obj.load_results(load_path)
+            mcrun_obj.load_results(load_path)
 
         os.chdir(current_work_dir)  # Reset work directory
 
diff --git a/mcstasscript/tests/test_Plotter.py b/mcstasscript/tests/test_Plotter.py
index 3a6c7d90..47f84d1e 100644
--- a/mcstasscript/tests/test_Plotter.py
+++ b/mcstasscript/tests/test_Plotter.py
@@ -21,6 +21,7 @@ def get_dummy_MetaData_1d():
 
     return meta_data
 
+
 def get_dummy_McStasData_1d():
     meta_data = get_dummy_MetaData_1d()
 
@@ -31,6 +32,7 @@ def get_dummy_McStasData_1d():
 
     return McStasData(meta_data, intensity, error, ncount, xaxis=axis)
 
+
 def get_dummy_MetaData_2d():
     meta_data = McStasMetaData()
     meta_data.component_name = "test a component"
@@ -42,6 +44,7 @@ def get_dummy_MetaData_2d():
 
     return meta_data
 
+
 def get_dummy_McStasData_2d():
     meta_data = get_dummy_MetaData_2d()
 
@@ -237,7 +240,7 @@ def test_find_min_max_I_log_with_zero_2D_case(self):
         """
 
         dummy_data = get_dummy_McStasData_2d()
-        dummy_data.Intensity[2,2] = 0
+        dummy_data.Intensity[2, 2] = 0
         dummy_data.set_plot_options(log=True)
         found_min, found_max = _find_min_max_I(dummy_data)
 
@@ -286,7 +289,7 @@ def test_find_min_max_I_log_orders_of_mag_2D_case(self):
         """
 
         dummy_data = get_dummy_McStasData_2d()
-        dummy_data.Intensity[2,2] = 10**6
+        dummy_data.Intensity[2, 2] = 10**6
         dummy_data.set_plot_options(log=True, orders_of_mag=3)
         found_min, found_max = _find_min_max_I(dummy_data)
 
@@ -303,8 +306,8 @@ def test_find_min_max_I_log_orders_of_mag_2D_with_zero_case(self):
         """
 
         dummy_data = get_dummy_McStasData_2d()
-        dummy_data.Intensity[2,2] = 10**6
-        dummy_data.Intensity[2,3] = 0
+        dummy_data.Intensity[2, 2] = 10**6
+        dummy_data.Intensity[2, 3] = 0
         dummy_data.set_plot_options(log=True, orders_of_mag=3)
         found_min, found_max = _find_min_max_I(dummy_data)
 
@@ -407,23 +410,32 @@ def test_handle_kwargs_all_simple(self):
             default_value = defaults[kw_option]
 
             dummy_data1 = get_dummy_McStasData_2d()
-            dummy_data2 = get_dummy_McStasData_2d()
+            data1_value = dummy_data1.plot_options.__getattribute__(kw_option)
+            self.assertEqual(data1_value, default_value)
 
-            self.assertEqual(dummy_data1.plot_options.__getattribute__(kw_option), default_value)
-            self.assertEqual(dummy_data2.plot_options.__getattribute__(kw_option), default_value)
+            dummy_data2 = get_dummy_McStasData_2d()
+            data2_value = dummy_data2.plot_options.__getattribute__(kw_option)
+            self.assertEqual(data2_value, default_value)
 
             data_list = [dummy_data1, dummy_data2]
 
             set_value = test_value[kw_option]
             given_option = {option: set_value}
             _handle_kwargs(data_list, **given_option)
-            self.assertEqual(dummy_data1.plot_options.__getattribute__(kw_option), set_value)
-            self.assertEqual(dummy_data2.plot_options.__getattribute__(kw_option), set_value)
+
+            data1_value = dummy_data1.plot_options.__getattribute__(kw_option)
+            self.assertEqual(data1_value, set_value)
+
+            data2_value = dummy_data2.plot_options.__getattribute__(kw_option)
+            self.assertEqual(data2_value, set_value)
 
             given_option = {option: [set_value, default_value]}
             _handle_kwargs(data_list, **given_option)
-            self.assertEqual(dummy_data1.plot_options.__getattribute__(kw_option), set_value)
-            self.assertEqual(dummy_data2.plot_options.__getattribute__(kw_option), default_value)
+
+            data_1_value = dummy_data1.plot_options.__getattribute__(kw_option)
+            self.assertEqual(data_1_value, set_value)
+            data_2_value = dummy_data2.plot_options.__getattribute__(kw_option)
+            self.assertEqual(data_2_value, default_value)
 
     def test_handle_kwargs_left_lim(self):
         """
@@ -541,8 +553,8 @@ def test_handle_kwargs_figsize_tuple(self):
         """
 
         dummy_data = get_dummy_McStasData_2d()
-        retrived_figsize, data_list = _handle_kwargs(dummy_data, figsize=(5,9))
-        self.assertEqual(retrived_figsize, (5, 9))
+        found_figsize, data_list = _handle_kwargs(dummy_data, figsize=(5, 9))
+        self.assertEqual(found_figsize, (5, 9))
 
     def test_handle_kwargs_figsize_list(self):
         """
@@ -551,8 +563,8 @@ def test_handle_kwargs_figsize_list(self):
         """
 
         dummy_data = get_dummy_McStasData_2d()
-        retrived_figsize, data_list = _handle_kwargs(dummy_data, figsize=[5,9])
-        self.assertEqual(retrived_figsize, (5, 9))
+        found_figsize, data_list = _handle_kwargs(dummy_data, figsize=[5, 9])
+        self.assertEqual(found_figsize, (5, 9))
 
     def test_handle_kwargs_single_element_to_list(self):
         """
@@ -607,4 +619,4 @@ def test_plot_function_2D_log(self):
         dummy_data = get_dummy_McStasData_2d()
 
         fig, ax0 = plt.subplots()
-        _plot_fig_ax(dummy_data, fig, ax0, log=True)
\ No newline at end of file
+        _plot_fig_ax(dummy_data, fig, ax0, log=True)
diff --git a/mcstasscript/tests/test_component.py b/mcstasscript/tests/test_component.py
index e0ea24cb..776811b4 100644
--- a/mcstasscript/tests/test_component.py
+++ b/mcstasscript/tests/test_component.py
@@ -1,5 +1,4 @@
 import io
-import builtins
 import unittest
 import unittest.mock
 
@@ -161,7 +160,6 @@ def test_Component_init_complex_call_relative(self):
         self.assertEqual(comp.SPLIT, 7)
         self.assertEqual(comp.comment, "test comment")
 
-
     def test_Component_basic_init_set_AT(self):
         """
         Testing set_AT method
@@ -283,7 +281,7 @@ def test_Component_basic_init_set_RELATIVE(self):
         self.assertEqual(comp.AT_relative, "RELATIVE sample")
         self.assertEqual(comp.ROTATED_relative, "RELATIVE sample")
 
-    def test_Component_basic_init_set_RELATIVE(self):
+    def test_Component_basic_object_ref_init_set_RELATIVE(self):
         """
         Testing set_RELATIVE method with Component object input
         """
@@ -364,7 +362,7 @@ def test_Component_basic_init_set_JUMP(self):
         self.assertEqual(comp.name, "test_component")
         self.assertEqual(comp.component_name, "Arm")
         self.assertEqual(comp.JUMP, "test jump")
-        
+
     def test_Component_basic_init_set_SPLIT(self):
         """
         Testing set_SPLIT method
@@ -454,18 +452,17 @@ def test_Component_write_to_file_simple(self, mock_f):
         handle.write.assert_has_calls(expected_writes, any_order=False)
 
     @unittest.mock.patch('__main__.__builtins__.open',
-                         new_callable=unittest.mock.mock_open)    
+                         new_callable=unittest.mock.mock_open)
     def test_Component_write_to_file_include(self, mock_f):
         """
         Testing that a Component can be written to file with the
         expected output. Here with simple input.
         """
-
         comp = Component("test_component", "Arm",
                          c_code_before="%include \"test.instr\"")
-        
+
         comp.set_c_code_after("%include \"after.instr\"")
-        
+
         comp._unfreeze()
         # Need to set up attribute parameters
         # Also need to categorize them as when created
@@ -514,7 +511,7 @@ def test_Component_write_to_file_complex(self, mock_f):
             comp.write_component(m_fo)
 
         my_call = unittest.mock.call
-        expected_writes = [my_call("SPLIT 7 "), 
+        expected_writes = [my_call("SPLIT 7 "),
                            my_call("COMPONENT test_component = Arm("),
                            my_call("\n"),
                            my_call(" new_par1 = 1.5"),
diff --git a/mcstasscript/tests/test_declare_variable.py b/mcstasscript/tests/test_declare_variable.py
index 2b537d5a..397d6cfc 100644
--- a/mcstasscript/tests/test_declare_variable.py
+++ b/mcstasscript/tests/test_declare_variable.py
@@ -1,5 +1,3 @@
-import io
-import builtins
 import unittest
 import unittest.mock
 
@@ -40,7 +38,7 @@ def test_DeclareVariable_init_basic_type_vector(self):
         """
 
         var = DeclareVariable("double", "test",
-                               array=6, value=[1, 2.2, 3, 3.3, 4, 4.4])
+                              array=6, value=[1, 2.2, 3, 3.3, 4, 4.4])
 
         self.assertEqual(var.name, "test")
         self.assertEqual(var.type, "double")  # space for easier writing
@@ -53,7 +51,7 @@ def test_DeclareVariable_init_basic_type_value_comment(self):
         """
 
         var = DeclareVariable("double", "test",
-                               value=518, comment="test comment /")
+                              value=518, comment="test comment /")
 
         self.assertEqual(var.name, "test")
         self.assertEqual(var.type, "double")  # Space for easier writing
@@ -89,9 +87,9 @@ def test_DeclareVariable_write_complex_float(self, mock_f):
         """
 
         var = DeclareVariable("double",
-                               "test",
-                               value=5.4,
-                               comment="test comment")
+                              "test",
+                              value=5.4,
+                              comment="test comment")
 
         with mock_f('test.txt', 'w') as m_fo:
             var.write_line(m_fo)
@@ -113,9 +111,9 @@ def test_DeclareVariable_write_complex_int(self, mock_f):
         """
 
         var = DeclareVariable("double",
-                               "test",
-                               value=5,
-                               comment="test comment")
+                              "test",
+                              value=5,
+                              comment="test comment")
 
         with mock_f('test.txt', 'w') as m_fo:
             var.write_line(m_fo)
@@ -136,9 +134,9 @@ def test_DeclareVariable_write_simple_array(self, mock_f):
         """
 
         var = DeclareVariable("double",
-                               "test",
-                               array=29,
-                               comment="test comment")
+                              "test",
+                              array=29,
+                              comment="test comment")
 
         with mock_f('test.txt', 'w') as m_fo:
             var.write_line(m_fo)
@@ -160,10 +158,10 @@ def test_DeclareVariable_write_complex_array(self, mock_f):
         """
 
         var = DeclareVariable("double",
-                               "test",
-                               array=3,
-                               value=[5, 4, 3.1],
-                               comment="test comment")
+                              "test",
+                              array=3,
+                              value=[5, 4, 3.1],
+                              comment="test comment")
 
         with mock_f('test.txt', 'w') as m_fo:
             var.write_line(m_fo)
diff --git a/mcstasscript/tests/test_functions.py b/mcstasscript/tests/test_functions.py
index f1357794..3f0f26f3 100644
--- a/mcstasscript/tests/test_functions.py
+++ b/mcstasscript/tests/test_functions.py
@@ -9,7 +9,7 @@
 from mcstasscript.interface.functions import load_data
 from mcstasscript.data.data import McStasData
 from mcstasscript.data.data import McStasMetaData
-from mcstasscript.data.data import McStasPlotOptions
+
 
 def set_dummy_MetaData_1d(name):
     """
@@ -159,7 +159,7 @@ def test_name_search_read(self):
         hero_object = name_search("Hero", data_list)
 
         self.assertEqual(hero_object.metadata.dimension, 123)
-        
+
     def test_name_search_filename_read(self):
         """
         Test that Hero object can be found and check the unique dimension
@@ -186,7 +186,7 @@ def test_name_search_read_repeat(self):
         hero_object = name_search("Big_Hero", data_list)
 
         self.assertEqual(hero_object.metadata.dimension, 123)
-        
+
     def test_name_search_read_duplicate(self):
         """
         Test simple case with duplicated name, search should return list
@@ -220,7 +220,7 @@ def test_name_search_read_error(self):
         data_list = setup_McStasData_array()
 
         with self.assertRaises(NameError):
-            hero_object = name_search("Hero8", data_list)
+            name_search("Hero8", data_list)
 
     def test_name_search_type_error_not_list(self):
         """
@@ -230,7 +230,7 @@ def test_name_search_type_error_not_list(self):
         data_list = set_dummy_McStasData_2d("Last_object_2d")
 
         with self.assertRaises(RuntimeError):
-            hero_object = name_search("Hero", data_list)
+            name_search("Hero", data_list)
 
     def test_name_search_type_error_not_McStasData(self):
         """
@@ -241,7 +241,7 @@ def test_name_search_type_error_not_McStasData(self):
         data_list = [1, 2, 3]
 
         with self.assertRaises(RuntimeError):
-            hero_object = name_search(1, data_list)
+            name_search(1, data_list)
 
 
 class Test_name_plot_options(unittest.TestCase):
@@ -261,7 +261,7 @@ def test_name_plot_options_simple(self):
         name_plot_options("Hero", data_list, colormap="Oranges")
         hero_object = name_search("Hero", data_list)
         self.assertEqual(hero_object.plot_options.colormap, "Oranges")
-        
+
     def test_name_plot_options_duplicate(self):
         """
         Test case where several McStasData objects are modified since
@@ -269,17 +269,17 @@ def test_name_plot_options_duplicate(self):
         """
 
         data_list = setup_McStasData_array()
-        
+
         hero_object = set_dummy_McStasData_2d("Hero")
         hero_object.metadata.dimension = 321
         hero_object.plot_options.colormap = "absurdly hot"
 
         data_list.append(hero_object)
-        
+
         name_plot_options("Hero", data_list, colormap="Blues")
-        
+
         results = name_search("Hero", data_list)
-        
+
         self.assertEqual(len(results), 2)
         self.assertEqual(results[0].plot_options.colormap, "Blues")
         self.assertEqual(results[1].plot_options.colormap, "Blues")
diff --git a/mcstasscript/tests/test_instrument.instr b/mcstasscript/tests/test_instrument.instr
index 2da161ee..07f05b12 100644
--- a/mcstasscript/tests/test_instrument.instr
+++ b/mcstasscript/tests/test_instrument.instr
@@ -14,8 +14,8 @@
 * Instrument test_instrument
 * 
 * %Identification
-* Written by: Python McStas Instrument Generator
-* Date: 15:36:48 on May 27, 2019
+* Written by: Python McXtrace Instrument Generator
+* Date: 12:22:07 on January 27, 2021
 * Origin: ESS DMSC
 * %INSTRUMENT_SITE: Generated_instruments
 * 
@@ -26,7 +26,8 @@
 ********************************************************************************/
 
 DEFINE INSTRUMENT test_instrument (
-double theta 
+double theta,
+double has_default = 37 
 )
 
 DECLARE 
@@ -41,4 +42,21 @@ two_theta = 2.0*theta;
 %}
 
 TRACE 
-COMPONENT first_component = test_for_reading(
\ No newline at end of file
+COMPONENT first_component = test_for_reading(
+ gauss = 1.2, test_string = a_string)
+AT (0,0,0) ABSOLUTE
+
+COMPONENT second_component = test_for_reading(
+ gauss = 1.4, test_string = b_string)
+AT (0,0,0) ABSOLUTE
+
+COMPONENT third_component = test_for_reading(
+ gauss = 1.6, test_string = c_string)
+AT (0,0,0) ABSOLUTE
+
+FINALLY 
+%{
+// Start of finally for generated test_instrument
+%}
+
+END
diff --git a/mcstasscript/tests/test_parameter_variable.py b/mcstasscript/tests/test_parameter_variable.py
index 68b81acb..c2d32bd2 100644
--- a/mcstasscript/tests/test_parameter_variable.py
+++ b/mcstasscript/tests/test_parameter_variable.py
@@ -1,5 +1,3 @@
-import io
-import builtins
 import unittest
 import unittest.mock
 
@@ -154,7 +152,7 @@ def test_ParameterVariable_write_complex_string(self, mock_f):
         """
 
         par = ParameterVariable("double", "test", value="\"Al\"",
-                                 comment="test comment")
+                                comment="test comment")
 
         with mock_f('test.txt', 'w') as m_fo:
             par.write_parameter(m_fo, ",")

From 247e94e14459e058a17e3ccaaa6df484daf68637 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Wed, 27 Jan 2021 20:45:23 +0100
Subject: [PATCH 138/403] Fixed two tests that broke during last commit as cwd
 was different in instrument setup and run instrument execution.

---
 mcstasscript/tests/test_Instr.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 1d2e7c60..b6ecd5e0 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -288,12 +288,19 @@ def test_complex_initialize(self):
         """
         Tests all keywords work in initialization
         """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
         my_instrument = McStas_instr("test_instrument",
                                      author="Mads",
                                      origin="DMSC",
                                      executable_path="./dummy_mcstas/contrib",
                                      package_path="./dummy_mcstas/misc")
 
+        os.chdir(current_work_dir)
+
         self.assertEqual(my_instrument.author, "Mads")
         self.assertEqual(my_instrument.origin, "DMSC")
         self.assertEqual(my_instrument.executable_path,
@@ -1728,9 +1735,12 @@ def test_run_full_instrument_required_par_error(self, mock_stdout):
         """
         instr = setup_populated_instr()
 
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
         with self.assertRaises(NameError):
             instr.run_full_instrument(foldername="test_data_set",
-                                      executable_path="dummy_mcstas")
+                                      executable_path=executable_path)
 
     def test_run_full_instrument_junk_par_error(self):
         """

From e68b1f6d0363ca7d0383a1c23f965c8025203149 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Thu, 28 Jan 2021 17:05:09 +0100
Subject: [PATCH 139/403] Further corrections to satisfy PEP-8 requirements

---
 mcstasscript/helper/component_reader.py  |  4 ++--
 mcstasscript/helper/managed_mcrun.py     | 14 ++++++--------
 mcstasscript/helper/mcstas_objects.py    |  3 ++-
 mcstasscript/tests/test_instrument.instr |  2 +-
 4 files changed, 11 insertions(+), 12 deletions(-)

diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index d5477a31..e9028ac0 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -373,8 +373,8 @@ def read_component_file(self, absolute_path):
                             if temp_par_type == "double":
                                 try:
                                     par_value = float(par_value)
-                                except:
-                                    # Could change the type
+                                except ValueError:
+                                    # value could be parameter name
                                     par_value = par_value
                             elif temp_par_type == "int":
                                 par_value = int(par_value)
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index b9817c02..d12d6b95 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -227,14 +227,11 @@ def run_simulation(self, **kwargs):
                         + self.name_of_instrumentfile
                         + parameter_string)
 
-        try:
-            process = subprocess.run(full_command, shell=True,
-                                     stdout=subprocess.PIPE,
-                                     stderr=subprocess.PIPE,
-                                     universal_newlines=True,
-                                     cwd=self.run_path)
-        except:
-            raise RuntimeError("Could not run McStas command.")
+        process = subprocess.run(full_command, shell=True,
+                                 stdout=subprocess.PIPE,
+                                 stderr=subprocess.PIPE,
+                                 universal_newlines=True,
+                                 cwd=self.run_path)
 
         if "suppress_output" in kwargs:
             if kwargs["suppress_output"] is False:
@@ -300,6 +297,7 @@ def load_results(data_folder_name):
 
         # Loop that reads mccode.sim sections
         metadata_list = []
+        current_object = None
         in_data = False
         for lines in f:
             # Could read other details about run
diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index dd65edb2..9acdbc11 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -242,7 +242,8 @@ def write_line(self, fo):
                 try:
                     fo.write("%s %s = %G;%s" % (self.type, self.name,
                                                 self.value, self.comment))
-                except:
+                except TypeError:
+                    # Value could not be converted to float, write as string
                     fo.write("%s %s = %s;%s" % (self.type, self.name,
                                                 self.value, self.comment))
         if self.value == "" and self.vector != 0:
diff --git a/mcstasscript/tests/test_instrument.instr b/mcstasscript/tests/test_instrument.instr
index 07f05b12..bbe65f75 100644
--- a/mcstasscript/tests/test_instrument.instr
+++ b/mcstasscript/tests/test_instrument.instr
@@ -15,7 +15,7 @@
 * 
 * %Identification
 * Written by: Python McXtrace Instrument Generator
-* Date: 12:22:07 on January 27, 2021
+* Date: 09:22:00 on January 28, 2021
 * Origin: ESS DMSC
 * %INSTRUMENT_SITE: Generated_instruments
 * 

From 0b5599a5004f3d479badecc100f24ea916166b91 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Fri, 29 Jan 2021 13:20:06 +0100
Subject: [PATCH 140/403] Fixed issue where generated_includes folder was
 created in whatever folder the unit tests were performed from.

---
 mcstasscript/tests/test_Instr.py | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index b6ecd5e0..ae6a865e 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1587,7 +1587,15 @@ def test_write_c_files_simple(self, mock_f):
         """
 
         instr = setup_populated_instr()
-        instr.write_c_files()
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        current_directory = os.getcwd()
+        os.chdir(THIS_DIR)
+
+        try:
+            instr.write_c_files()
+        finally:
+            os.chdir(current_directory)
 
         mock_f.assert_any_call("./generated_includes/"
                                + "test_instrument_declare.c", "w")

From f387f7c9e73c9b8de3e4d0be88a294cf9a5f7fb4 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Fri, 29 Jan 2021 13:29:51 +0100
Subject: [PATCH 141/403] Fixed small mistakes in Reader that affected the
 created objects.

---
 mcstasscript/instr_reader/read_declare.py    |  3 ++-
 mcstasscript/instr_reader/read_definition.py | 12 ++++++++++--
 2 files changed, 12 insertions(+), 3 deletions(-)

diff --git a/mcstasscript/instr_reader/read_declare.py b/mcstasscript/instr_reader/read_declare.py
index 63145bbb..28cf4559 100644
--- a/mcstasscript/instr_reader/read_declare.py
+++ b/mcstasscript/instr_reader/read_declare.py
@@ -223,7 +223,8 @@ def _read_declare_statement(self, statement):
                         return_value = float(value)
                     except:
                         value = value.replace('"', "\\\"")
-                        return_value = '"' + value + '"'
+                        #return_value = '"' + value + '"'
+                        return_value = value
 
                 kw_args["value"] = return_value
 
diff --git a/mcstasscript/instr_reader/read_definition.py b/mcstasscript/instr_reader/read_definition.py
index e4306097..52c0e766 100644
--- a/mcstasscript/instr_reader/read_definition.py
+++ b/mcstasscript/instr_reader/read_definition.py
@@ -86,8 +86,10 @@ def read_definition_line(self, line):
 
                 if parameter_type == "string":
                     if '"' in value:
-                        value = value.replace('"', "\\\"")
-                        value = "\"" + value + "\""
+                        pass
+                        # Value has to be normal for object version
+                        #value = value.replace('"', "\\\"")
+                        #value = "\"" + value + "\""
                 else:
                     if parameter_type == "int":
                         value = int(value)
@@ -104,6 +106,12 @@ def read_definition_line(self, line):
             # Add this parameter to the object
             self.Instr.add_parameter(parameter_type, parameter_name, **kw_args)
 
+            # Fix values for script version
+            if parameter_type == "string" and "value" in kw_args:
+                if isinstance(kw_args["value"], str):
+                    kw_args["value"] = kw_args["value"].replace('"', '\\\"')
+                    kw_args["value"] = "\"" + kw_args["value"] + "\""
+
             # Also write it to a file?
             write_string = []
             write_string.append(self.instr_name)

From 443e76549138c4af2a9d310f45adff532d42f295 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Fri, 29 Jan 2021 13:31:05 +0100
Subject: [PATCH 142/403] Added flake8 requirement to contributing as suggested
 by Celine Durniak.

---
 CONTRIBUTING.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 899b6523..6ec5f166 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -9,3 +9,4 @@ This document describes how to contribute to McStasScript. The preferred method
 * Note for updates required to manual 
 * Clear Jupyter Notebooks before committing
 * Check CI tests passes
+* Use flake8 to check code quality, reduce to reasonable number of issues

From 9099c5d27df108171975985c6f36186aa7af9227 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Wed, 3 Mar 2021 09:00:08 +0100
Subject: [PATCH 143/403] Update of instrument reader and fix of mcdisplay when
 using input_path. New version number.

---
 mcstasscript/interface/instr.py         | 7 ++++++-
 mcstasscript/tests/test_Instr.py        | 3 ++-
 mcstasscript/tests/test_Instr_reader.py | 4 ++--
 setup.py                                | 2 +-
 4 files changed, 11 insertions(+), 5 deletions(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index f112ad0f..22d19e8a 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1663,8 +1663,13 @@ def show_instrument(self, *args, **kwargs):
             elif kwargs["format"] == "window":
                 executable = "mcdisplay"
 
+        self.write_full_instrument()
+
+        instr_path = os.path.join(self.input_path, self.name + ".instr")
+        instr_path = os.path.abspath(instr_path)
+
         full_command = (bin_path + executable + " "
-                        + os.path.join(self.input_path, self.name + ".instr")
+                        + instr_path
                         + " " + parameter_string)
 
         process = subprocess.run(full_command, shell=True,
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index ae6a865e..d564f70b 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -2007,10 +2007,11 @@ def test_show_instrument_basic(self, mock_sub, mock_stdout):
         os.chdir(current_work_dir)
 
         expected_path = os.path.join(executable_path, "bin", "mcdisplay-webgl")
+        expected_instr_path = os.path.join(THIS_DIR, "test_instrument.instr")
 
         # a double space because of a missing option
         expected_call = (expected_path
-                         + " ./test_instrument.instr"
+                         + " " + expected_instr_path
                          + "  has_default=37 theta=1.2")
 
         mock_sub.assert_called_once_with(expected_call,
diff --git a/mcstasscript/tests/test_Instr_reader.py b/mcstasscript/tests/test_Instr_reader.py
index aa813f0f..85ae5d21 100644
--- a/mcstasscript/tests/test_Instr_reader.py
+++ b/mcstasscript/tests/test_Instr_reader.py
@@ -85,7 +85,7 @@ def test_read_input_parameter(self):
         self.assertEqual(Instr.parameter_list[2].name, "test_str")
         # space in type inserted for easier writing by McStas_Instr class
         self.assertEqual(Instr.parameter_list[2].type, "string")
-        self.assertEqual(Instr.parameter_list[2].value, "\"\\\"hurray\\\"\"")
+        self.assertEqual(Instr.parameter_list[2].value, "\"hurray\"")
 
     def test_read_declare_parameter(self):
 
@@ -118,7 +118,7 @@ def test_read_declare_parameter(self):
         self.assertEqual(Instr.declare_list[11].type, "char")
         self.assertEqual(Instr.declare_list[11].vector, 20)
         self.assertEqual(Instr.declare_list[11].value,
-                         "\"\\\"test_string\\\"\"")
+                         "\\\"test_string\\\"")
 
     def test_read_initialize_line(self):
 
diff --git a/setup.py b/setup.py
index ccf00909..7bc8431d 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
      name='McStasScript',
-     version='0.0.25',
+     version='0.0.27',
      author="Mads Bertelsen",
      author_email="Mads.Bertelsen@ess.eu",
      description="A python scripting interface for McStas",

From 8ba4fb4e05760fddcc055691764daaba586a02e4 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Tue, 6 Apr 2021 11:19:51 +0200
Subject: [PATCH 144/403] New license

Adding BSD license to McStasScript
---
 LICENSE | 703 +++-----------------------------------------------------
 1 file changed, 29 insertions(+), 674 deletions(-)

diff --git a/LICENSE b/LICENSE
index f288702d..544802a8 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,674 +1,29 @@
-                    GNU GENERAL PUBLIC LICENSE
-                       Version 3, 29 June 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
- Everyone is permitted to copy and distribute verbatim copies
- of this license document, but changing it is not allowed.
-
-                            Preamble
-
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-software and other kinds of works.
-
-  The licenses for most software and other practical works are designed
-to take away your freedom to share and change the works.  By contrast,
-the GNU General Public License is intended to guarantee your freedom to
-share and change all versions of a program--to make sure it remains free
-software for all its users.  We, the Free Software Foundation, use the
-GNU General Public License for most of our software; it applies also to
-any other work released this way by its authors.  You can apply it to
-your programs, too.
-
-  When we speak of free software, we are referring to freedom, not
-price.  Our General Public Licenses are designed to make sure that you
-have the freedom to distribute copies of free software (and charge for
-them if you wish), that you receive source code or can get it if you
-want it, that you can change the software or use pieces of it in new
-free programs, and that you know you can do these things.
-
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-these rights or asking you to surrender the rights.  Therefore, you have
-certain responsibilities if you distribute copies of the software, or if
-you modify it: responsibilities to respect the freedom of others.
-
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-freedoms that you received.  You must make sure that they, too, receive
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-giving you legal permission to copy, distribute and/or modify it.
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From 483c555d66d6f7d1005be7794d1a86d477421c98 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 6 Apr 2021 13:10:56 +0200
Subject: [PATCH 145/403] Split of load functionality allowing loading
 individual datasets instead of forcing the user to load an entire folder. The
 function loading an entire dataset is refactored to use the new function for
 loading a single data set.

---
 mcstasscript/helper/managed_mcrun.py | 100 +++++++++++++++++----------
 mcstasscript/interface/functions.py  |  32 +++++++++
 2 files changed, 96 insertions(+), 36 deletions(-)

diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index d12d6b95..1ab7e708 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -277,11 +277,34 @@ def load_results(data_folder_name):
     Parameters
     ----------
 
-    first argument : str
+    data_folder_name : str
         path to folder from which data should be loaded
 
     """
 
+    metadata_list = load_metadata(data_folder_name)
+
+    results = []
+    for metadata in metadata_list:
+        results.append(load_monitor(metadata, data_folder_name))
+
+    return results
+
+
+def load_metadata(data_folder_name):
+    """
+    Function that loads metadata from a mcstas simulation
+
+    Returns list of metadata objects corresponding to each monitor, all
+    information is taken from mccode.sim file.
+
+    Parameters
+    ----------
+
+    first argument : str
+        path to folder from which metadata should be loaded
+    """
+
     if not os.path.isdir(data_folder_name):
         raise NameError("Given data directory does not exist.")
 
@@ -325,42 +348,47 @@ def load_results(data_folder_name):
                 # Start recording data to metadata object
                 in_data = True
 
-    # Create a list for McStasData instances to return
-    results = []
+    return metadata_list
 
-    # Load datasets described in metadata list individually
-    for metadata in metadata_list:
-        # Load data with numpy
-        data = np.loadtxt(os.path.join(data_folder_name,
-                          metadata.filename.rstrip()))
-
-        # Split data into intensity, error and ncount
-        if type(metadata.dimension) == int:
-            xaxis = data.T[0, :]
-            Intensity = data.T[1, :]
-            Error = data.T[2, :]
-            Ncount = data.T[3, :]
-
-        elif len(metadata.dimension) == 2:
-            xaxis = []  # Assume evenly binned in 2d
-            data_lines = metadata.dimension[1]
-
-            Intensity = data[0:data_lines, :]
-            Error = data[data_lines:2*data_lines, :]
-            Ncount = data[2*data_lines:3*data_lines, :]
-        else:
-            raise NameError(
-                "Dimension not read correctly in data set "
-                + "connected to monitor named "
-                + metadata.component_name)
 
-        # The data is saved as a McStasData object
-        result = McStasData(metadata, Intensity,
-                            Error, Ncount,
-                            xaxis=xaxis)
+def load_monitor(metadata, data_folder_name):
+    """
+    Function that loads data given metadata and name of data folder
 
-        # Add this result to the results list
-        results.append(result)
+    Loads data for single monitor and returns a McStasData object
 
-    # Return list of McStasData objects
-    return results
+    Parameters
+    ----------
+
+    metadata : McStasMetaData object
+        McStasMetaData object corresponding to the monitor to be loaded
+
+    data_folder_name : str
+        path to folder from which metadata should be loaded
+    """
+    # Load data with numpy
+    data = np.loadtxt(os.path.join(data_folder_name,
+                                   metadata.filename.rstrip()))
+
+    # Split data into intensity, error and ncount
+    if type(metadata.dimension) == int:
+        xaxis = data.T[0, :]
+        Intensity = data.T[1, :]
+        Error = data.T[2, :]
+        Ncount = data.T[3, :]
+
+    elif len(metadata.dimension) == 2:
+        xaxis = []  # Assume evenly binned in 2d
+        data_lines = metadata.dimension[1]
+
+        Intensity = data[0:data_lines, :]
+        Error = data[data_lines:2 * data_lines, :]
+        Ncount = data[2 * data_lines:3 * data_lines, :]
+    else:
+        raise NameError(
+            "Dimension not read correctly in data set "
+            + "connected to monitor named "
+            + metadata.component_name)
+
+    # The data is saved as a McStasData object
+    return McStasData(metadata, Intensity, Error, Ncount, xaxis=xaxis)
diff --git a/mcstasscript/interface/functions.py b/mcstasscript/interface/functions.py
index 62dafcc0..6fab8b38 100644
--- a/mcstasscript/interface/functions.py
+++ b/mcstasscript/interface/functions.py
@@ -98,6 +98,38 @@ def load_data(foldername):
 
     return managed_mcrun.load_results(foldername)
 
+def load_metadata(data_folder_name):
+    """
+    Function that loads metadata from a mcstas simulation
+
+    Returns list of metadata objects corresponding to each monitor, all
+    information is taken from mccode.sim file.
+
+    Parameters
+    ----------
+
+    first argument : str
+        path to folder from which metadata should be loaded
+    """
+    return managed_mcrun.load_metadata(data_folder_name)
+
+def load_monitor(metadata, data_folder_name):
+    """
+    Function that loads data given metadata and name of data folder
+
+    Loads data for single monitor and returns a McStasData object
+
+    Parameters
+    ----------
+
+    metadata : McStasMetaData object
+        McStasMetaData object corresponding to the monitor to be loaded
+
+    data_folder_name : str
+        path to folder from which metadata should be loaded
+    """
+    return managed_mcrun.load_monitor(metadata, data_folder_name)
+
 
 class Configurator:
     """

From 8b06eab81f92442e72a1ae05d9324afa6cf0e821 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 27 Apr 2021 13:29:54 +0200
Subject: [PATCH 146/403] Added tests to new loader functions.

Can now load metadata or a monitor seperately.
---
 mcstasscript/tests/test_ManagedMcrun.py | 165 ++++++++++++++++++++++++
 mcstasscript/tests/test_functions.py    |  64 +++++++++
 2 files changed, 229 insertions(+)

diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index cc6ae7c7..71a34f4a 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -2,6 +2,9 @@
 import unittest
 
 from mcstasscript.helper.managed_mcrun import ManagedMcrun
+from mcstasscript.helper.managed_mcrun import load_results
+from mcstasscript.helper.managed_mcrun import load_metadata
+from mcstasscript.helper.managed_mcrun import load_monitor
 
 
 class TestManagedMcrun(unittest.TestCase):
@@ -560,6 +563,168 @@ def test_ManagedMcrun_load_data_L_mon_empty_error(self):
 
         os.chdir(current_work_dir)  # Reset work directory
 
+class Test_load_functions(unittest.TestCase):
+    """
+    Testing the load functions in managed_mcrun.
+    load_results loads all data in folder
+    load_metadata loads all metadata in folder
+    load_monitor loads one monitor given metadata and folder
+    These are used in ManagedMcrun
+    """
+    def test_mcrun_load_data_PSD4PI(self):
+        """
+        Use test_data_set to test load_data for PSD_4PI
+        """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
+        results = load_results("test_data_set")
+
+        os.chdir(current_work_dir)  # Reset work directory
+
+        self.assertEqual(len(results), 3)
+
+        PSD_4PI = results[0]
+
+        self.assertEqual(PSD_4PI.name, "PSD_4PI")
+        self.assertEqual(PSD_4PI.metadata.dimension, [300, 300])
+        self.assertEqual(PSD_4PI.metadata.limits, [-180, 180, -90, 90])
+        self.assertEqual(PSD_4PI.metadata.xlabel, "Longitude [deg]")
+        self.assertEqual(PSD_4PI.metadata.ylabel, "Latitude [deg]")
+        self.assertEqual(PSD_4PI.metadata.title, "4PI PSD monitor")
+        self.assertEqual(PSD_4PI.Ncount[4][1], 4)
+        self.assertEqual(PSD_4PI.Intensity[4][1], 1.537334562E-10)
+        self.assertEqual(PSD_4PI.Error[4][1], 1.139482296E-10)
+
+    def test_mcrun_load_data_PSD(self):
+        """
+        Use test_data_set to test load_data for PSD
+        """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
+        results = load_results("test_data_set")
+
+        os.chdir(current_work_dir)  # Reset work directory
+
+        self.assertEqual(len(results), 3)
+
+        PSD = results[1]
+
+        self.assertEqual(PSD.name, "PSD")
+        self.assertEqual(PSD.metadata.dimension, [200, 200])
+        self.assertEqual(PSD.metadata.limits, [-5, 5, -5, 5])
+        self.assertEqual(PSD.metadata.xlabel, "X position [cm]")
+        self.assertEqual(PSD.metadata.ylabel, "Y position [cm]")
+        self.assertEqual(PSD.metadata.title, "PSD monitor")
+        self.assertEqual(PSD.Ncount[27][21], 9)
+        self.assertEqual(PSD.Intensity[27][21], 2.623929371e-13)
+        self.assertEqual(PSD.Error[27][21], 2.765467693e-13)
+
+    def test_mcrun_load_metadata_PSD4PI(self):
+        """
+        Use test_data_set to test load_metadata for PSD_4PI
+        """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
+        metadata = load_metadata("test_data_set")
+
+        os.chdir(current_work_dir)  # Reset work directory
+
+        self.assertEqual(len(metadata), 3)
+
+        PSD_4PI = metadata[0]
+        self.assertEqual(PSD_4PI.dimension, [300, 300])
+        self.assertEqual(PSD_4PI.limits, [-180, 180, -90, 90])
+        self.assertEqual(PSD_4PI.xlabel, "Longitude [deg]")
+        self.assertEqual(PSD_4PI.ylabel, "Latitude [deg]")
+        self.assertEqual(PSD_4PI.title, "4PI PSD monitor")
+
+    def test_mcrun_load_metadata_L_mon(self):
+        """
+        Use test_data_set to test load_metadata for PSD_4PI
+        """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
+        metadata = load_metadata("test_data_set")
+
+        os.chdir(current_work_dir)  # Reset work directory
+
+        self.assertEqual(len(metadata), 3)
+
+        L_mon = metadata[2]
+        self.assertEqual(L_mon.dimension, 150)
+        self.assertEqual(L_mon.limits, [0.7, 1.3])
+        self.assertEqual(L_mon.xlabel, "Wavelength [AA]")
+        self.assertEqual(L_mon.ylabel, "Intensity")
+        self.assertEqual(L_mon.title, "Wavelength monitor")
+
+    def test_mcrun_load_monitor_PSD4PI(self):
+        """
+        Use test_data_set to test load_monitor for PSD_4PI
+        """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
+        metadata = load_metadata("test_data_set")
+        PSD_4PI = metadata[0]
+        monitor = load_monitor(PSD_4PI, "test_data_set")
+
+        os.chdir(current_work_dir)  # Reset work directory
+
+        self.assertEqual(monitor.name, "PSD_4PI")
+        self.assertEqual(monitor.metadata.dimension, [300, 300])
+        self.assertEqual(monitor.metadata.limits, [-180, 180, -90, 90])
+        self.assertEqual(monitor.metadata.xlabel, "Longitude [deg]")
+        self.assertEqual(monitor.metadata.ylabel, "Latitude [deg]")
+        self.assertEqual(monitor.metadata.title, "4PI PSD monitor")
+        self.assertEqual(monitor.Ncount[4][1], 4)
+        self.assertEqual(monitor.Intensity[4][1], 1.537334562E-10)
+        self.assertEqual(monitor.Error[4][1], 1.139482296E-10)
+
+    def test_mcrun_load_monitor_L_mon(self):
+        """
+        Use test_data_set to test load_monitor for PSD_4PI
+        """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
+        metadata = load_metadata("test_data_set")
+        L_mon = metadata[2]
+        monitor = load_monitor(L_mon, "test_data_set")
+
+        os.chdir(current_work_dir)  # Reset work directory
+
+        self.assertEqual(monitor.name, "L_mon")
+        self.assertEqual(monitor.metadata.dimension, 150)
+        self.assertEqual(monitor.metadata.limits, [0.7, 1.3])
+        self.assertEqual(monitor.metadata.xlabel, "Wavelength [AA]")
+        self.assertEqual(monitor.metadata.ylabel, "Intensity")
+        self.assertEqual(monitor.metadata.title, "Wavelength monitor")
+        self.assertEqual(monitor.xaxis[53], 0.914)
+        self.assertEqual(monitor.Ncount[53], 37111)
+        self.assertEqual(monitor.Intensity[53], 6.990299315e-06)
+        self.assertEqual(monitor.Error[53], 6.215308587e-08)
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/mcstasscript/tests/test_functions.py b/mcstasscript/tests/test_functions.py
index 3f0f26f3..cd10f340 100644
--- a/mcstasscript/tests/test_functions.py
+++ b/mcstasscript/tests/test_functions.py
@@ -7,6 +7,8 @@
 from mcstasscript.interface.functions import name_search
 from mcstasscript.interface.functions import name_plot_options
 from mcstasscript.interface.functions import load_data
+from mcstasscript.interface.functions import load_metadata
+from mcstasscript.interface.functions import load_monitor
 from mcstasscript.data.data import McStasData
 from mcstasscript.data.data import McStasMetaData
 
@@ -320,5 +322,67 @@ def test_mcrun_load_data_PSD4PI(self):
         self.assertEqual(PSD_4PI.Error[4][1], 1.139482296E-10)
 
 
+class Test_load_metadata(unittest.TestCase):
+    """
+    Testing the load metadata function which calls ManagedMcrun, which
+    was tested elsewhere. Since the load metadata is tested elsewhere,
+    this function has just a single test to check the interface.
+    """
+    def test_mcrun_load_metadata_PSD4PI(self):
+        """
+        Use test_data_set to test load_metadata for PSD_4PI
+        """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
+        metadata = load_metadata("test_data_set")
+
+        os.chdir(current_work_dir)  # Reset work directory
+
+        self.assertEqual(len(metadata), 3)
+
+        PSD_4PI = metadata[0]
+        self.assertEqual(PSD_4PI.dimension, [300, 300])
+        self.assertEqual(PSD_4PI.limits, [-180, 180, -90, 90])
+        self.assertEqual(PSD_4PI.xlabel, "Longitude [deg]")
+        self.assertEqual(PSD_4PI.ylabel, "Latitude [deg]")
+        self.assertEqual(PSD_4PI.title, "4PI PSD monitor")
+
+class Test_load_monitor(unittest.TestCase):
+    """
+    Testing the load monitor function which calls ManagedMcrun, which
+    was tested elsewhere. Since the load monitor is tested elsewhere, this
+    function has just a single test to check the interface.
+    """
+    def test_mcrun_load_monitor_PSD4PI(self):
+        """
+        Use test_data_set to test load_monitor for PSD_4PI
+        """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
+        metadata = load_metadata("test_data_set")
+        PSD_4PI = metadata[0]
+        monitor = load_monitor(PSD_4PI, "test_data_set")
+
+        os.chdir(current_work_dir)  # Reset work directory
+
+        self.assertEqual(monitor.name, "PSD_4PI")
+        self.assertEqual(monitor.metadata.dimension, [300, 300])
+        self.assertEqual(monitor.metadata.limits, [-180, 180, -90, 90])
+        self.assertEqual(monitor.metadata.xlabel, "Longitude [deg]")
+        self.assertEqual(monitor.metadata.ylabel, "Latitude [deg]")
+        self.assertEqual(monitor.metadata.title, "4PI PSD monitor")
+        self.assertEqual(monitor.Ncount[4][1], 4)
+        self.assertEqual(monitor.Intensity[4][1], 1.537334562E-10)
+        self.assertEqual(monitor.Error[4][1], 1.139482296E-10)
+
+
 if __name__ == '__main__':
     unittest.main()

From e04a270edd3519deb2f8b1418484eb1ca7e610ef Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Wed, 28 Apr 2021 13:38:39 +0200
Subject: [PATCH 147/403] Early version of simulation widget.

Run with:
from mcstasscript.jb_interface import simulation_interface
interface = simulation_interface.SimInterface(instrument_object)
interface.show_interface()

Can control a simulation with parameters, ncount, mpi.
Can select which monitor to plot and set log mode / orders_of_mag.

Running a simulation keeps the same plot options / choice of monitor,
which makes it quite convinient to see changes quickly.

Things to improve:
1) Error checking in entered data
2) Will move creation of widget elements to seperate classes / files
3) Colorbar for 2D plots to be added
4) Improve design
5) Provide method in instrument class to launch
---
 .../jb_interface/simulation_interface.py      | 497 ++++++++++++++++++
 1 file changed, 497 insertions(+)
 create mode 100644 mcstasscript/jb_interface/simulation_interface.py

diff --git a/mcstasscript/jb_interface/simulation_interface.py b/mcstasscript/jb_interface/simulation_interface.py
new file mode 100644
index 00000000..fa3099fe
--- /dev/null
+++ b/mcstasscript/jb_interface/simulation_interface.py
@@ -0,0 +1,497 @@
+import sys
+import os
+
+import ipywidgets as widgets
+from IPython.display import display
+
+import matplotlib.pyplot as plt
+
+from mcstasscript.interface.functions import name_search
+from mcstasscript.interface import plotter
+
+
+class HiddenPrints:
+    """
+    Environment which suppress prints
+    """
+    def __enter__(self):
+        self._original_stdout = sys.stdout
+        sys.stdout = open(os.devnull, 'w')
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        sys.stdout.close()
+        sys.stdout = self._original_stdout
+
+
+def parameter_has_default(parameter):
+    """
+    Checks if ParameterVariable has a default value, returns bool
+
+    Parameters
+    ----------
+
+    parameter: ParameterVariable
+        The parameter to check for default value
+    """
+    if parameter.value == "":
+        return False
+    return True
+
+
+def get_parameter_default(parameter):
+    """
+    Returns the default value of a parameter
+
+    Parameters
+    ----------
+
+    parameter: ParameterVariable
+        The parameter for which the default value is returned
+    """
+    if parameter.value != "":
+        if parameter.type == "string":
+            return parameter.value
+        elif parameter.type == "double" or parameter.type == "":
+            return float(parameter.value)
+        elif parameter.type == "int":
+            return int(parameter.value)
+        else:
+            raise RuntimeError("Unknown parameter type '"
+                               + parameter.type + "' of par named '"
+                               + parameter.name + "'.")
+    return None
+
+
+class SimInterface:
+    """
+    Class for setting up widget that controls McStasScript instrument and plot
+    """
+    def __init__(self, instrument):
+        """
+        Sets up widget where the user can input instrument parameters, run the
+        simulation, see plotted results and adjust the plots.
+
+        The parameters of the instrument model are displayed with name, default
+        value and comment. Can be adjusted with free text.
+
+        A run button starts a simulation, and basic settings can be adjusted.
+
+        A dropdown menu is available for selecting what monitor to view results
+        from, and basic settings related to the plot can be adjusted.
+
+        Show the interface with the show_interface method.
+
+        Parameters
+        ----------
+
+        instrument: McStas_instr or McXtrace_instr
+            instrument for which a widget should be created
+        """
+
+        self.instrument = instrument
+
+        self.fig = None
+        self.ax = None
+
+        self.log_mode = False
+        self.orders_of_mag = "disabled"
+
+        self.run_button = None
+
+        self.monitor_dropdown = None
+        self.current_monitor = None
+
+        self.ncount = "1E6"
+        self.mpi = "disabled"
+        self.data = None
+
+        self.parameters = {}
+        # get default parameters from instrument
+        for parameter in self.instrument.parameter_list:
+            if parameter_has_default(parameter):
+                self.parameters[parameter.name] = get_parameter_default(parameter)
+
+    def make_parameter_widgets(self):
+        """
+        Creates widgets for parameters using dedicated class ParameterTextbox
+        Preliminary check for parameter type disabled as the ParameterVariable
+        class does not set type for default case, will be fixed.
+
+        returns widget including all parameters
+        """
+        parameter_widgets = []
+        for parameter in self.instrument.parameter_list:
+            if True: #parameter.type != "":
+                par_widget = ParameterTextbox(parameter, self.parameters)
+            else:
+                raise RuntimeError("Unknown parameter type '"
+                                   + parameter.type + "' of par named '"
+                                   + parameter.name + "'.")
+
+            parameter_widgets.append(par_widget.make_widget())
+
+        return widgets.VBox(parameter_widgets)
+
+    def run_simulation(self, change):
+        """
+        Performs the simulation with current parameters and settings.
+
+        Changes icon on button to hourglass while simulation is running, then
+        returns to calculator icon.
+
+        Has dummy parameter change to allow the method to be used in on_click
+        method of the run button.
+
+        Parameters
+        ----------
+
+        change: widget change
+            Not used
+        """
+        run_arguments = {"foldername": "interface",
+                         "increment_folder_name": True,
+                         "parameters": self.parameters,
+                         "ncount": int(float(self.ncount))}
+        if self.mpi != "disabled":
+            run_arguments["mpi"] = self.mpi
+
+        self.run_button.icon = "hourglass"
+        print("Running with:", run_arguments)
+        with HiddenPrints():
+            data = self.instrument.run_full_instrument(**run_arguments)
+        self.run_button.icon = "calculator"
+
+        self.data = data
+        self.monitor_dropdown.set_data(data)
+        self.update_plot()
+
+    def make_run_button(self):
+        """
+        Creates a run button which perform the simulation
+        """
+        button = widgets.Button(
+            description='Run',
+            disabled=False,
+            button_style='',  # 'success', 'info', 'warning', 'danger' or ''
+            tooltip='Runs the simulation with current parameters',
+            icon='calculator'  # (FontAwesome names without the `fa-` prefix)
+        )
+        button.on_click(self.run_simulation)
+        return button
+
+    def make_ncount_field(self):
+        """
+        Creates field for ncount, links to update_ncount
+
+        The field supports scientific notation
+        """
+        description_layout = widgets.Layout(width='70px', height='32px',
+                                            display="flex",
+                                            justify_content="flex-end")
+        description = widgets.Label(value="ncount", layout=description_layout)
+        textbox = widgets.Text(value=str(self.ncount), layout=widgets.Layout(width='100px', height='32px'))
+        textbox.observe(self.update_ncount, "value")
+
+        return widgets.HBox([description, textbox])
+
+    def update_ncount(self, change):
+        """
+        Updates ncount variable from textbox input
+
+        Only updates when usable input is entered. Supports scientific
+        notation in input through conversion to float
+
+        Parameters
+        ----------
+
+        change: widget change
+            state change of widget
+        """
+        try:
+            self.ncount = int(float(change.new))
+        except ValueError:
+            pass
+
+    def make_mpi_field(self):
+        """
+        Creates field for mpi, links to update_mpi
+        """
+        description_layout = widgets.Layout(width='40px', height='32px',
+                                            display="flex",
+                                            justify_content="flex-end")
+        description = widgets.Label(value="mpi", layout=description_layout)
+        textbox = widgets.Text(value=str(self.mpi), layout=widgets.Layout(width='70px', height='32px'))
+        textbox.observe(self.update_mpi, "value")
+
+        return widgets.HBox([description, textbox])
+
+    def update_mpi(self, change):
+        """
+        Updates mpi value when integer or the word 'disabled' is given
+
+        Parameters
+        ----------
+
+        change: widget change
+            state change of widget
+        """
+        if change.new == "disabled":
+            self.mpi = change.new
+
+        try:
+            self.mpi = int(change.new)
+        except ValueError:
+            pass
+
+    def make_log_options(self):
+        """
+        Creates widget with options for log options, links to update_log
+        and update_orders_of_mag
+        """
+        description_layout = widgets.Layout(width='250px', height='32px',
+                                            display="flex",
+                                            justify_content="flex-start")
+        description_log = widgets.Label(value="Log plot", layout=description_layout)
+
+        log_check = widgets.Checkbox(
+            value=self.log_mode,
+            description='',
+            disabled=False,
+            indent=False,
+            layout=widgets.Layout(width='70px', height='32px')
+        )
+        log_check.observe(self.update_log, "value")
+
+        log_widget = widgets.HBox([description_log, log_check])
+
+        description_layout = widgets.Layout(width='250px', height='32px',
+                                            display="flex",
+                                            justify_content="flex-start")
+        description_orders = widgets.Label(value="Orders of magnitude", layout=description_layout)
+        textbox = widgets.Text(value=str(self.orders_of_mag),
+                               layout=widgets.Layout(width='70px', height='32px'))
+        textbox.observe(self.update_orders_of_mag, "value")
+
+        orders_of_mag_widget = widgets.HBox([description_orders, textbox])
+
+        return widgets.VBox([log_widget, orders_of_mag_widget])
+
+    def update_log(self, change):
+        """
+        Update log_mode and replot figure
+
+        Parameters
+        ----------
+
+        change: widget change
+            state change of widget
+        """
+        self.log_mode = change.new
+        self.update_plot()
+
+    def update_orders_of_mag(self, change):
+        """
+        Update orders_of_mag and replot
+
+        Parameters
+        ----------
+
+        change: widget change
+            state change of widget
+        """
+        if change.new == "disabled":
+            self.orders_of_mag = change.new
+            self.update_plot()
+            return
+
+        try:
+            self.orders_of_mag = float(change.new)
+        except ValueError:
+            return
+
+        self.update_plot()
+
+    def select_monitor(self, monitor):
+        """
+        Selects new monitor to plot and replots figure
+        The method is called from MonitorDropdown class
+
+        Parameters
+        ----------
+
+        monitor: str
+            Monitor name
+        """
+        self.current_monitor = monitor
+        self.update_plot()
+
+    def new_plot(self):
+        """
+        Sets up original plot with fig and ax
+        """
+        # fig, ax = plt.subplots(constrained_layout=True, figsize=(6, 4))
+        self.fig, self.ax = plt.subplots()
+
+        self.fig.canvas.toolbar_position = 'bottom'
+        self.ax.grid(True)
+
+        self.update_plot()
+
+    def update_plot(self):
+        """
+        Updates the plot with current data, monitor and plot options
+        """
+
+        self.ax.cla()
+
+        if self.data is None:
+            self.ax.text(0.4, 0.5, "No data available")
+            return
+
+        monitor = name_search(self.current_monitor, self.data)
+        plot_options = {"show_colorbar": False, "log": self.log_mode}
+        if self.orders_of_mag != "disabled":
+            plot_options["orders_of_mag"] = self.orders_of_mag
+        else:
+            plot_options["orders_of_mag"] = 300 # Default value in McStasPlotOptions
+
+        print("Plotting with: ", plot_options)
+        monitor.set_plot_options(**plot_options)
+        with HiddenPrints():
+            plotter._plot_fig_ax(monitor, self.fig, self.ax)
+
+        plt.tight_layout()
+
+    def show_interface(self):
+        """
+        Builds and shows widget interface
+        """
+        output = widgets.Output()
+
+        with output:
+            self.new_plot()
+
+        # Make parameter controls
+        parameter_widgets = self.make_parameter_widgets()
+
+        # Make simulation controls
+        self.run_button = self.make_run_button()
+        ncount_field = self.make_ncount_field()
+        mpi_field = self.make_mpi_field()
+
+        simulation_widget = widgets.HBox([self.run_button, ncount_field, mpi_field],
+                                         layout=widgets.Layout(border="solid"))
+
+        plot_box_monitor_label = widgets.Label(value="Choose monitor")
+        self.monitor_dropdown = MonitorDropdown(self.select_monitor)
+        monitor_dropdown_widget = self.monitor_dropdown.make_widget()
+        plot_box_options_label = widgets.Label(value="Plot options")
+        log_options = self.make_log_options()
+
+        plot_controls = widgets.VBox([plot_box_monitor_label, monitor_dropdown_widget,
+                                      plot_box_options_label, log_options],
+                                     layout=widgets.Layout(width="25%", border="solid"))
+        output.layout=widgets.Layout(width="75%")
+        plot_widget = widgets.HBox([output, plot_controls])
+
+        return widgets.VBox([parameter_widgets, simulation_widget, plot_widget])
+
+
+class MonitorDropdown:
+    """
+    Class for creating monitor dropdown menu
+    """
+    def __init__(self, update_function):
+        """
+        Sets up MonitorDropdown menu with given update function
+
+        Parameters
+        ----------
+
+        update_function: function
+            function called when dropdown menu item selected
+        """
+
+        self.update_function = update_function
+        self.data = None
+        self.widget = None
+
+    def set_data(self, data):
+        """
+        Updates the menu options given new data
+
+        Parameters
+        ----------
+
+        data: McStasData list
+            Data returned by McStasScript simulation
+        """
+        self.data = data
+
+        monitor_names = []
+        for data in self.data:
+            monitor_names.append(data.name)
+
+        self.widget.options = monitor_names
+
+    def make_widget(self):
+        """
+        Builds the widget for the dropdown menu and links to update method
+
+        """
+        self.widget = widgets.Dropdown(layout=widgets.Layout(width="98%"))
+
+        self.widget.observe(self.update, "value")
+
+        return self.widget
+
+    def update(self, change):
+        """
+        Calls update_function when dropdown menu is used
+
+        Parameters
+        ----------
+
+        change: widget change
+            state change of widget
+        """
+        self.update_function(change.new)
+
+
+class ParameterTextbox:
+    def __init__(self, parameter, parameters):
+
+        self.parameter = parameter
+        self.parameters = parameters
+
+        if parameter_has_default(parameter):
+            self.default_value = get_parameter_default(parameter)
+        else:
+            self.default_value = ""
+
+        self.name = parameter.name
+        self.comment = parameter.comment
+
+    def make_widget(self):
+        label = widgets.Label(value=self.name,
+                              layout=widgets.Layout(width='15%', height='32px'))
+        textbox = widgets.Text(value=str(self.default_value),
+                               layout=widgets.Layout(width='10%', height='32px'))
+        comment = widgets.Label(value=self.comment,
+                                layout=widgets.Layout(width='75%', height='32px'))
+
+        textbox.observe(self.update, "value")
+
+        return widgets.HBox([label, textbox, comment])
+
+    def update(self, change):
+        self.parameters[self.name] = change.new
+
+
+
+
+
+
+
+
+

From d848d6fdcd78fdefa5a4b66dc011fa945d2072d4 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Wed, 28 Apr 2021 17:25:37 +0200
Subject: [PATCH 148/403] Split the interface class into two, one for
 simulation control and one for plotting data. Now its possible to just use
 the interface for plotting data to explore data returned from normal
 simulations.

Experimented with moving widget code out of the interface class
and to seperate small classes, which does improve readability.
Will do the same for the simulation interface.

Moved some helper functions to seperate file.

To try the plot interface:

from mcstasscript.jb_interface import plot_interface
my_interface = plot_interface.PlotInterface()
my_interface.show_interface(data_from_simulation)
---
 mcstasscript/jb_interface/__init__.py       |   0
 mcstasscript/jb_interface/plot_interface.py | 240 ++++++++++++++++++++
 mcstasscript/jb_interface/widget_helpers.py |  53 +++++
 3 files changed, 293 insertions(+)
 create mode 100644 mcstasscript/jb_interface/__init__.py
 create mode 100644 mcstasscript/jb_interface/plot_interface.py
 create mode 100644 mcstasscript/jb_interface/widget_helpers.py

diff --git a/mcstasscript/jb_interface/__init__.py b/mcstasscript/jb_interface/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/mcstasscript/jb_interface/plot_interface.py b/mcstasscript/jb_interface/plot_interface.py
new file mode 100644
index 00000000..d08f4b52
--- /dev/null
+++ b/mcstasscript/jb_interface/plot_interface.py
@@ -0,0 +1,240 @@
+import sys
+import os
+
+import ipywidgets as widgets
+from IPython.display import display
+
+import matplotlib.pyplot as plt
+
+from mcstasscript.interface.functions import name_search
+from mcstasscript.interface import plotter
+
+from mcstasscript.jb_interface.widget_helpers import HiddenPrints
+
+
+class PlotInterface:
+    def __init__(self, data=None):
+        self.data = data
+
+        self.monitor_dropdown = None
+        self.current_monitor = None
+
+        self.log_mode = None
+        self.orders_of_mag = 300 # default value in McStasScript
+
+        self.fig = None
+        self.ax = None
+
+    def set_data(self, data):
+        self.data = data
+        self.monitor_dropdown.set_data(data)
+
+    def set_current_monitor(self, monitor):
+        self.current_monitor = monitor
+        self.update_plot()
+
+    def set_log_mode(self, log_mode):
+        self.log_mode = log_mode
+        self.update_plot()
+
+    def set_orders_of_mag(self, orders_of_mag):
+        self.orders_of_mag = orders_of_mag
+        self.update_plot()
+
+    def new_plot(self):
+        """
+        Sets up original plot with fig and ax
+        """
+        # fig, ax = plt.subplots(constrained_layout=True, figsize=(6, 4))
+        self.fig, self.ax = plt.subplots()
+
+        self.fig.canvas.toolbar_position = 'bottom'
+        self.ax.grid(True)
+
+        self.update_plot()
+
+    def update_plot(self):
+        """
+        Updates the plot with current data, monitor and plot options
+        """
+
+        self.ax.cla()
+
+        if self.data is None:
+            self.ax.text(0.4, 0.5, "No data available")
+            return
+
+        print("current monitor in update_plot:", self.current_monitor)
+        monitor = name_search(self.current_monitor, self.data)
+        plot_options = {"show_colorbar": False, "log": self.log_mode}
+        if self.orders_of_mag != "disabled":
+            plot_options["orders_of_mag"] = self.orders_of_mag
+        else:
+            plot_options["orders_of_mag"] = 300 # Default value in McStasPlotOptions
+
+        print("Plotting with: ", plot_options)
+        monitor.set_plot_options(**plot_options)
+        with HiddenPrints():
+            plotter._plot_fig_ax(monitor, self.fig, self.ax)
+
+        plt.tight_layout()
+
+    def show_interface(self):
+        # Set up plot area
+        output = widgets.Output()
+        with output:
+            self.new_plot()
+        output.layout = widgets.Layout(width="75%")
+
+        # could retrieve default plot options from data if given
+
+        plot_control_list = []  # Keep all control widgets in this list
+
+        plot_control_list.append(widgets.Label(value="Choose monitor"))
+
+        # Set up dropdown list for monitor choice
+        self.monitor_dropdown = MonitorDropdown(self.set_current_monitor)
+        plot_control_list.append(self.monitor_dropdown.make_widget())
+
+        plot_control_list.append(widgets.Label(value="Plot options"))
+
+        # Set up checkbox for log plotting
+        log_checkbox = LogCheckbox(self.log_mode, self.set_log_mode)
+        plot_control_list.append(log_checkbox.make_widget())
+
+        # Set up text field for orders of mag input
+        log_orders_of_mag = OrdersOfMagField(self.set_orders_of_mag)
+        plot_control_list.append(log_orders_of_mag.make_widget())
+
+        plot_controls = widgets.VBox(plot_control_list,
+                                     layout=widgets.Layout(width="25%", border="solid"))
+
+        if self.data is not None:
+            self.set_data(self.data)
+
+        return widgets.HBox([output, plot_controls])
+
+
+class MonitorDropdown:
+    """
+    Class for creating monitor dropdown menu
+    """
+    def __init__(self, set_current_monitor):
+        """
+        Sets up MonitorDropdown menu with given update function
+
+        Parameters
+        ----------
+
+        update_plot: function
+            function called to update plot
+        """
+
+        self.set_current_monitor = set_current_monitor
+        self.data = None
+        self.widget = None
+
+    def set_data(self, data):
+        """
+        Updates the menu options given new data
+
+        Parameters
+        ----------
+
+        data: McStasData list
+            Data returned by McStasScript simulation
+        """
+        self.data = data
+
+        monitor_names = []
+        for data in self.data:
+            monitor_names.append(data.name)
+
+        self.widget.options = monitor_names
+
+    def make_widget(self):
+        """
+        Builds the widget for the dropdown menu and links to update method
+
+        """
+        self.widget = widgets.Dropdown(layout=widgets.Layout(width="98%"))
+
+        self.widget.observe(self.update, "value")
+
+        return self.widget
+
+    def update(self, change):
+        """
+        Calls update_function when dropdown menu is used
+
+        Parameters
+        ----------
+
+        change: widget change
+            state change of widget
+        """
+        # can do input sanitation here
+        self.set_current_monitor(change.new)
+
+
+class LogCheckbox:
+    def __init__(self, log_mode, set_log_mode):
+        self.log_mode = log_mode
+        self.set_log_mode = set_log_mode
+
+    def make_widget(self):
+        description_layout = widgets.Layout(width='250px', height='32px',
+                                            display="flex",
+                                            justify_content="flex-start")
+        description_log = widgets.Label(value="Log plot", layout=description_layout)
+
+        log_check = widgets.Checkbox(
+            value=self.log_mode,
+            description='',
+            disabled=False,
+            indent=False,
+            layout=widgets.Layout(width='70px', height='32px')
+        )
+        log_check.observe(self.update, "value")
+
+        return widgets.HBox([description_log, log_check])
+
+    def update(self, change):
+        self.set_log_mode(change.new)
+
+
+class OrdersOfMagField:
+    def __init__(self, set_orders_of_mag):
+        self.set_orders_of_mag = set_orders_of_mag
+
+    def make_widget(self):
+        description_layout = widgets.Layout(width='250px', height='32px',
+                                            display="flex",
+                                            justify_content="flex-start")
+        description_orders = widgets.Label(value="Orders of magnitude", layout=description_layout)
+        textbox = widgets.Text(value=str("disabled"),
+                               layout=widgets.Layout(width='70px', height='32px'))
+        textbox.observe(self.update, "value")
+
+        return widgets.HBox([description_orders, textbox])
+
+    def update(self, change):
+        """
+        Update orders_of_mag and replot
+
+        Parameters
+        ----------
+
+        change: widget change
+            state change of widget
+        """
+        if change.new == "disabled":
+            self.set_orders_of_mag(change.new)
+            return
+
+        try:
+            float(change.new)
+        except ValueError:
+            return
+
+        self.set_orders_of_mag(float(change.new))
diff --git a/mcstasscript/jb_interface/widget_helpers.py b/mcstasscript/jb_interface/widget_helpers.py
new file mode 100644
index 00000000..7e6637ca
--- /dev/null
+++ b/mcstasscript/jb_interface/widget_helpers.py
@@ -0,0 +1,53 @@
+import sys
+import os
+
+class HiddenPrints:
+    """
+    Environment which suppress prints
+    """
+    def __enter__(self):
+        self._original_stdout = sys.stdout
+        sys.stdout = open(os.devnull, 'w')
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        sys.stdout.close()
+        sys.stdout = self._original_stdout
+
+
+def parameter_has_default(parameter):
+    """
+    Checks if ParameterVariable has a default value, returns bool
+
+    Parameters
+    ----------
+
+    parameter: ParameterVariable
+        The parameter to check for default value
+    """
+    if parameter.value == "":
+        return False
+    return True
+
+
+def get_parameter_default(parameter):
+    """
+    Returns the default value of a parameter
+
+    Parameters
+    ----------
+
+    parameter: ParameterVariable
+        The parameter for which the default value is returned
+    """
+    if parameter.value != "":
+        if parameter.type == "string":
+            return parameter.value
+        elif parameter.type == "double" or parameter.type == "":
+            return float(parameter.value)
+        elif parameter.type == "int":
+            return int(parameter.value)
+        else:
+            raise RuntimeError("Unknown parameter type '"
+                               + parameter.type + "' of par named '"
+                               + parameter.name + "'.")
+    return None
\ No newline at end of file

From 21a26d03c8f27c5b752a2f2791366ee3025ac2ce Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Wed, 28 Apr 2021 17:32:36 +0200
Subject: [PATCH 149/403] Split the interface class into two, one for
 simulation control and one for plotting data. Now its possible to just use
 the interface for plotting data to explore data returned from normal
 simulations.

Experimented with moving widget code out of the interface class
and to seperate small classes, which does improve readability.
Will do the same for the simulation interface.

Moved some helper functions to seperate file.

To try the plot interface:

from mcstasscript.jb_interface import plot_interface
my_interface = plot_interface.PlotInterface()
my_interface.show_interface(data_from_simulation)

The print output for all plot settings is temporary and for
debugging purposes.

Currently plot options are default regardless of the underlying
data, but could load the current plot options from the data.
---
 mcstasscript/jb_interface/plot_interface.py   |   5 +-
 .../jb_interface/simulation_interface.py      | 315 ++----------------
 2 files changed, 33 insertions(+), 287 deletions(-)

diff --git a/mcstasscript/jb_interface/plot_interface.py b/mcstasscript/jb_interface/plot_interface.py
index d08f4b52..a5ebd7bb 100644
--- a/mcstasscript/jb_interface/plot_interface.py
+++ b/mcstasscript/jb_interface/plot_interface.py
@@ -64,7 +64,10 @@ def update_plot(self):
             self.ax.text(0.4, 0.5, "No data available")
             return
 
-        print("current monitor in update_plot:", self.current_monitor)
+        if self.current_monitor is None:
+            self.ax.text(0.4, 0.5, "Select a monitor to plot")
+            return
+
         monitor = name_search(self.current_monitor, self.data)
         plot_options = {"show_colorbar": False, "log": self.log_mode}
         if self.orders_of_mag != "disabled":
diff --git a/mcstasscript/jb_interface/simulation_interface.py b/mcstasscript/jb_interface/simulation_interface.py
index fa3099fe..169d3f4b 100644
--- a/mcstasscript/jb_interface/simulation_interface.py
+++ b/mcstasscript/jb_interface/simulation_interface.py
@@ -8,58 +8,40 @@
 
 from mcstasscript.interface.functions import name_search
 from mcstasscript.interface import plotter
+from mcstasscript.jb_interface import plot_interface
+from mcstasscript.jb_interface.widget_helpers import HiddenPrints
+from mcstasscript.jb_interface.widget_helpers import parameter_has_default
+from mcstasscript.jb_interface.widget_helpers import get_parameter_default
 
 
-class HiddenPrints:
-    """
-    Environment which suppress prints
-    """
-    def __enter__(self):
-        self._original_stdout = sys.stdout
-        sys.stdout = open(os.devnull, 'w')
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        sys.stdout.close()
-        sys.stdout = self._original_stdout
-
+class ParameterTextbox:
+    def __init__(self, parameter, parameters):
 
-def parameter_has_default(parameter):
-    """
-    Checks if ParameterVariable has a default value, returns bool
+        self.parameter = parameter
+        self.parameters = parameters
 
-    Parameters
-    ----------
+        if parameter_has_default(parameter):
+            self.default_value = get_parameter_default(parameter)
+        else:
+            self.default_value = ""
 
-    parameter: ParameterVariable
-        The parameter to check for default value
-    """
-    if parameter.value == "":
-        return False
-    return True
+        self.name = parameter.name
+        self.comment = parameter.comment
 
+    def make_widget(self):
+        label = widgets.Label(value=self.name,
+                              layout=widgets.Layout(width='15%', height='32px'))
+        textbox = widgets.Text(value=str(self.default_value),
+                               layout=widgets.Layout(width='10%', height='32px'))
+        comment = widgets.Label(value=self.comment,
+                                layout=widgets.Layout(width='75%', height='32px'))
 
-def get_parameter_default(parameter):
-    """
-    Returns the default value of a parameter
+        textbox.observe(self.update, "value")
 
-    Parameters
-    ----------
+        return widgets.HBox([label, textbox, comment])
 
-    parameter: ParameterVariable
-        The parameter for which the default value is returned
-    """
-    if parameter.value != "":
-        if parameter.type == "string":
-            return parameter.value
-        elif parameter.type == "double" or parameter.type == "":
-            return float(parameter.value)
-        elif parameter.type == "int":
-            return int(parameter.value)
-        else:
-            raise RuntimeError("Unknown parameter type '"
-                               + parameter.type + "' of par named '"
-                               + parameter.name + "'.")
-    return None
+    def update(self, change):
+        self.parameters[self.name] = change.new
 
 
 class SimInterface:
@@ -90,20 +72,12 @@ def __init__(self, instrument):
 
         self.instrument = instrument
 
-        self.fig = None
-        self.ax = None
-
-        self.log_mode = False
-        self.orders_of_mag = "disabled"
+        self.plot_interface = None
 
         self.run_button = None
 
-        self.monitor_dropdown = None
-        self.current_monitor = None
-
         self.ncount = "1E6"
         self.mpi = "disabled"
-        self.data = None
 
         self.parameters = {}
         # get default parameters from instrument
@@ -161,9 +135,7 @@ def run_simulation(self, change):
             data = self.instrument.run_full_instrument(**run_arguments)
         self.run_button.icon = "calculator"
 
-        self.data = data
-        self.monitor_dropdown.set_data(data)
-        self.update_plot()
+        self.plot_interface.set_data(data)
 
     def make_run_button(self):
         """
@@ -243,133 +215,10 @@ def update_mpi(self, change):
         except ValueError:
             pass
 
-    def make_log_options(self):
-        """
-        Creates widget with options for log options, links to update_log
-        and update_orders_of_mag
-        """
-        description_layout = widgets.Layout(width='250px', height='32px',
-                                            display="flex",
-                                            justify_content="flex-start")
-        description_log = widgets.Label(value="Log plot", layout=description_layout)
-
-        log_check = widgets.Checkbox(
-            value=self.log_mode,
-            description='',
-            disabled=False,
-            indent=False,
-            layout=widgets.Layout(width='70px', height='32px')
-        )
-        log_check.observe(self.update_log, "value")
-
-        log_widget = widgets.HBox([description_log, log_check])
-
-        description_layout = widgets.Layout(width='250px', height='32px',
-                                            display="flex",
-                                            justify_content="flex-start")
-        description_orders = widgets.Label(value="Orders of magnitude", layout=description_layout)
-        textbox = widgets.Text(value=str(self.orders_of_mag),
-                               layout=widgets.Layout(width='70px', height='32px'))
-        textbox.observe(self.update_orders_of_mag, "value")
-
-        orders_of_mag_widget = widgets.HBox([description_orders, textbox])
-
-        return widgets.VBox([log_widget, orders_of_mag_widget])
-
-    def update_log(self, change):
-        """
-        Update log_mode and replot figure
-
-        Parameters
-        ----------
-
-        change: widget change
-            state change of widget
-        """
-        self.log_mode = change.new
-        self.update_plot()
-
-    def update_orders_of_mag(self, change):
-        """
-        Update orders_of_mag and replot
-
-        Parameters
-        ----------
-
-        change: widget change
-            state change of widget
-        """
-        if change.new == "disabled":
-            self.orders_of_mag = change.new
-            self.update_plot()
-            return
-
-        try:
-            self.orders_of_mag = float(change.new)
-        except ValueError:
-            return
-
-        self.update_plot()
-
-    def select_monitor(self, monitor):
-        """
-        Selects new monitor to plot and replots figure
-        The method is called from MonitorDropdown class
-
-        Parameters
-        ----------
-
-        monitor: str
-            Monitor name
-        """
-        self.current_monitor = monitor
-        self.update_plot()
-
-    def new_plot(self):
-        """
-        Sets up original plot with fig and ax
-        """
-        # fig, ax = plt.subplots(constrained_layout=True, figsize=(6, 4))
-        self.fig, self.ax = plt.subplots()
-
-        self.fig.canvas.toolbar_position = 'bottom'
-        self.ax.grid(True)
-
-        self.update_plot()
-
-    def update_plot(self):
-        """
-        Updates the plot with current data, monitor and plot options
-        """
-
-        self.ax.cla()
-
-        if self.data is None:
-            self.ax.text(0.4, 0.5, "No data available")
-            return
-
-        monitor = name_search(self.current_monitor, self.data)
-        plot_options = {"show_colorbar": False, "log": self.log_mode}
-        if self.orders_of_mag != "disabled":
-            plot_options["orders_of_mag"] = self.orders_of_mag
-        else:
-            plot_options["orders_of_mag"] = 300 # Default value in McStasPlotOptions
-
-        print("Plotting with: ", plot_options)
-        monitor.set_plot_options(**plot_options)
-        with HiddenPrints():
-            plotter._plot_fig_ax(monitor, self.fig, self.ax)
-
-        plt.tight_layout()
-
     def show_interface(self):
         """
         Builds and shows widget interface
         """
-        output = widgets.Output()
-
-        with output:
-            self.new_plot()
 
         # Make parameter controls
         parameter_widgets = self.make_parameter_widgets()
@@ -382,116 +231,10 @@ def show_interface(self):
         simulation_widget = widgets.HBox([self.run_button, ncount_field, mpi_field],
                                          layout=widgets.Layout(border="solid"))
 
-        plot_box_monitor_label = widgets.Label(value="Choose monitor")
-        self.monitor_dropdown = MonitorDropdown(self.select_monitor)
-        monitor_dropdown_widget = self.monitor_dropdown.make_widget()
-        plot_box_options_label = widgets.Label(value="Plot options")
-        log_options = self.make_log_options()
-
-        plot_controls = widgets.VBox([plot_box_monitor_label, monitor_dropdown_widget,
-                                      plot_box_options_label, log_options],
-                                     layout=widgets.Layout(width="25%", border="solid"))
-        output.layout=widgets.Layout(width="75%")
-        plot_widget = widgets.HBox([output, plot_controls])
+        self.plot_interface = plot_interface.PlotInterface()
+        plot_widget = self.plot_interface.show_interface()
 
         return widgets.VBox([parameter_widgets, simulation_widget, plot_widget])
 
 
-class MonitorDropdown:
-    """
-    Class for creating monitor dropdown menu
-    """
-    def __init__(self, update_function):
-        """
-        Sets up MonitorDropdown menu with given update function
-
-        Parameters
-        ----------
-
-        update_function: function
-            function called when dropdown menu item selected
-        """
-
-        self.update_function = update_function
-        self.data = None
-        self.widget = None
-
-    def set_data(self, data):
-        """
-        Updates the menu options given new data
-
-        Parameters
-        ----------
-
-        data: McStasData list
-            Data returned by McStasScript simulation
-        """
-        self.data = data
-
-        monitor_names = []
-        for data in self.data:
-            monitor_names.append(data.name)
-
-        self.widget.options = monitor_names
-
-    def make_widget(self):
-        """
-        Builds the widget for the dropdown menu and links to update method
-
-        """
-        self.widget = widgets.Dropdown(layout=widgets.Layout(width="98%"))
-
-        self.widget.observe(self.update, "value")
-
-        return self.widget
-
-    def update(self, change):
-        """
-        Calls update_function when dropdown menu is used
-
-        Parameters
-        ----------
-
-        change: widget change
-            state change of widget
-        """
-        self.update_function(change.new)
-
-
-class ParameterTextbox:
-    def __init__(self, parameter, parameters):
-
-        self.parameter = parameter
-        self.parameters = parameters
-
-        if parameter_has_default(parameter):
-            self.default_value = get_parameter_default(parameter)
-        else:
-            self.default_value = ""
-
-        self.name = parameter.name
-        self.comment = parameter.comment
-
-    def make_widget(self):
-        label = widgets.Label(value=self.name,
-                              layout=widgets.Layout(width='15%', height='32px'))
-        textbox = widgets.Text(value=str(self.default_value),
-                               layout=widgets.Layout(width='10%', height='32px'))
-        comment = widgets.Label(value=self.comment,
-                                layout=widgets.Layout(width='75%', height='32px'))
-
-        textbox.observe(self.update, "value")
-
-        return widgets.HBox([label, textbox, comment])
-
-    def update(self, change):
-        self.parameters[self.name] = change.new
-
-
-
-
-
-
-
-
 

From b162e8d48ef00f79df6aa9709b53a1fa2a257a19 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Thu, 29 Apr 2021 09:36:36 +0200
Subject: [PATCH 150/403] Now shows colorbar in plot interface. It was
 previously removed as weird things happened when switching between plots. Now
 a seperate ax is created for the colorbar and cleared with each transition.

---
 mcstasscript/interface/plotter.py           |  7 ++++++-
 mcstasscript/jb_interface/plot_interface.py | 14 ++++++++++----
 2 files changed, 16 insertions(+), 5 deletions(-)

diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index 27f2daaa..ba240179 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -164,8 +164,13 @@ def _plot_fig_ax(data, fig, ax, **kwargs):
 
         # Add the colorbar
         if data.plot_options.show_colorbar:
-            fig.colorbar(im, ax=ax,
+            cax = None
+            if "colorbar_axes" in kwargs:
+                cax = kwargs["colorbar_axes"]
+                print("colorbar axes set")
+            fig.colorbar(im, ax=ax, cax=cax,
                          format=matplotlib.ticker.FuncFormatter(_fmt))
+            cax.set_aspect(20)
 
         # Add a title
         ax.set_title(data.metadata.title)
diff --git a/mcstasscript/jb_interface/plot_interface.py b/mcstasscript/jb_interface/plot_interface.py
index a5ebd7bb..0b00f3ab 100644
--- a/mcstasscript/jb_interface/plot_interface.py
+++ b/mcstasscript/jb_interface/plot_interface.py
@@ -24,6 +24,7 @@ def __init__(self, data=None):
 
         self.fig = None
         self.ax = None
+        self.colorbar_ax = None
 
     def set_data(self, data):
         self.data = data
@@ -46,7 +47,7 @@ def new_plot(self):
         Sets up original plot with fig and ax
         """
         # fig, ax = plt.subplots(constrained_layout=True, figsize=(6, 4))
-        self.fig, self.ax = plt.subplots()
+        self.fig, (self.ax, self.colorbar_ax) = plt.subplots(ncols=2, gridspec_kw={'width_ratios': [4, 1]})
 
         self.fig.canvas.toolbar_position = 'bottom'
         self.ax.grid(True)
@@ -59,6 +60,9 @@ def update_plot(self):
         """
 
         self.ax.cla()
+        self.colorbar_ax.cla()
+        self.colorbar_ax.xaxis.set_ticks([])
+        self.colorbar_ax.yaxis.set_ticks([])
 
         if self.data is None:
             self.ax.text(0.4, 0.5, "No data available")
@@ -69,16 +73,18 @@ def update_plot(self):
             return
 
         monitor = name_search(self.current_monitor, self.data)
-        plot_options = {"show_colorbar": False, "log": self.log_mode}
+        plot_options = {"show_colorbar": True, "log": self.log_mode}
         if self.orders_of_mag != "disabled":
             plot_options["orders_of_mag"] = self.orders_of_mag
         else:
             plot_options["orders_of_mag"] = 300 # Default value in McStasPlotOptions
 
-        print("Plotting with: ", plot_options)
+        #print("Plotting with: ", plot_options)
         monitor.set_plot_options(**plot_options)
         with HiddenPrints():
-            plotter._plot_fig_ax(monitor, self.fig, self.ax)
+            plotter._plot_fig_ax(monitor, self.fig, self.ax, colorbar_axes=self.colorbar_ax)
+
+        self.colorbar_ax.set_aspect(20)
 
         plt.tight_layout()
 

From 74b3d451ee8978c6446fcd6ded3d45c86d03fff0 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Mon, 3 May 2021 14:53:34 +0200
Subject: [PATCH 151/403] Adds documentation and dropdown for parameters.

Documentation in the form of docstrings.

ParameterVariable can now have options as keyword argument, when
present, a dropdown menu will be made as a widget in jb interface.

Interface now integrated in instr and plotter interface.

instr.interface() creates simulation interface
plotter.interface(data) creates plotter interface with data
---
 mcstasscript/helper/mcstas_objects.py         |  12 +-
 mcstasscript/interface/functions.py           |   1 -
 mcstasscript/interface/instr.py               |  18 ++
 mcstasscript/interface/plotter.py             |  13 ++
 mcstasscript/jb_interface/plot_interface.py   | 212 +++++++++++++++++-
 .../jb_interface/simulation_interface.py      |  88 ++++++--
 6 files changed, 320 insertions(+), 24 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 9acdbc11..61de5a0d 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -90,7 +90,7 @@ def __init__(self, *args, **kwargs):
         self.value = ""
         if "value" in kwargs:
             if not isinstance(kwargs["value"], (str, int, float)):
-                raise RuntimeError("Given value for parameter has to be of"
+                raise RuntimeError("Given value for parameter has to be of "
                                    + "type str, int or float.")
             self.value = kwargs["value"]
 
@@ -102,6 +102,16 @@ def __init__(self, *args, **kwargs):
                                    + "comment that was not a string.")
             self.comment = "// " + self.comment
 
+        self.options = None
+        if "options" in kwargs:
+            self.options = kwargs["options"]
+            if self.value != "":
+                if (self.value not in self.options
+                        and self.value.strip("'") not in self.options
+                        and self.value.strip('"') not in self.options):
+                    raise RuntimeError("When giving both options and default, "
+                                       "the value has to be an option.")
+
     def write_parameter(self, fo, stop_character):
         """Writes input parameter to file"""
 
diff --git a/mcstasscript/interface/functions.py b/mcstasscript/interface/functions.py
index 6fab8b38..56d7903c 100644
--- a/mcstasscript/interface/functions.py
+++ b/mcstasscript/interface/functions.py
@@ -82,7 +82,6 @@ def name_plot_options(name, data_list, **kwargs):
         for data_object in object_to_modify:
             data_object.set_plot_options(**kwargs)
 
-
 def load_data(foldername):
     """
     Loads data from a McStas data folder including mccode.sim
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 22d19e8a..50c788fd 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -13,6 +13,7 @@
 from mcstasscript.helper.managed_mcrun import ManagedMcrun
 from mcstasscript.helper.formatting import is_legal_filename
 from mcstasscript.helper.formatting import bcolors
+from mcstasscript.jb_interface.simulation_interface import SimInterface
 
 
 class McCode_instr:
@@ -188,6 +189,9 @@ class McCode_instr:
     run_full_instrument(**kwargs)
         Writes instrument files and runs simulation.
         Returns list of McStasData
+
+    interface()
+        Shows interface with jupyter notebook widgets
     """
 
     def __init__(self, name, **kwargs):
@@ -1680,6 +1684,14 @@ def show_instrument(self, *args, **kwargs):
         print(process.stderr)
         print(process.stdout)
 
+    def interface(self):
+        """
+        Shows simulation interface in jupyter notebook interface
+
+        Needs "%matplotlib widget" in notebook to work correctly
+        """
+        interface = SimInterface(self)
+        return interface.show_interface()
 
 class McStas_instr(McCode_instr):
     """
@@ -1850,6 +1862,9 @@ class McStas_instr(McCode_instr):
     run_full_instrument(**kwargs)
         Writes instrument files and runs simulation.
         Returns list of McStasData
+
+    interface()
+        Shows interface with jupyter notebook widgets
     """
     def __init__(self, name, **kwargs):
         """
@@ -2068,6 +2083,9 @@ class McXtrace_instr(McCode_instr):
     run_full_instrument(**kwargs)
         Writes instrument files and runs simulation.
         Returns list of McStasData
+
+    interface()
+        Shows interface with jupyter notebook widgets
     """
     def __init__(self, name, **kwargs):
         """
diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index ba240179..fe5273ef 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -8,6 +8,7 @@
 from matplotlib.ticker import MaxNLocator
 
 from mcstasscript.data.data import McStasData
+from mcstasscript.jb_interface.plot_interface import PlotInterface
 
 
 def _fmt(x, pos):
@@ -412,3 +413,15 @@ def animate_2D(index):
         # check if imagemagick available?
         print("Saving animation with filename : \"" + filename + "\"")
         anim.save(filename, writer="imagemagick")
+
+def interface(data):
+    """
+    Shows plot interface to explore data
+
+    Parameters
+    ----------
+
+    data : List of McStasData objects
+    """
+    interface = PlotInterface(data)
+    return interface.show_interface()
\ No newline at end of file
diff --git a/mcstasscript/jb_interface/plot_interface.py b/mcstasscript/jb_interface/plot_interface.py
index 0b00f3ab..133e292b 100644
--- a/mcstasscript/jb_interface/plot_interface.py
+++ b/mcstasscript/jb_interface/plot_interface.py
@@ -1,6 +1,8 @@
 import sys
 import os
 
+from collections import OrderedDict
+
 import ipywidgets as widgets
 from IPython.display import display
 
@@ -11,46 +13,86 @@
 
 from mcstasscript.jb_interface.widget_helpers import HiddenPrints
 
-
 class PlotInterface:
+    """
+    Class for providing plotting interface given McStasScript data
+    """
     def __init__(self, data=None):
+        """
+        Initialize interface for exploring a dataset with plotting options
+
+        Parameters
+        ----------
+
+        data: List of McStasData objects
+            Optional to set the data, otherwise use set_data method
+        """
         self.data = data
 
+        # Variables related to monitor choice
         self.monitor_dropdown = None
         self.current_monitor = None
 
+        # default plotting
         self.log_mode = None
         self.orders_of_mag = 300 # default value in McStasScript
+        self.colormap = "jet"
 
+        # Matplotlib objects
         self.fig = None
         self.ax = None
         self.colorbar_ax = None
 
     def set_data(self, data):
+        """
+        Set a new dataset for the interface, and updates the plot
+
+        Parameters
+        ----------
+
+        data: List of McStasData objects
+            New dataset that will be plotted
+        """
         self.data = data
         self.monitor_dropdown.set_data(data)
+        self.update_plot()
 
     def set_current_monitor(self, monitor):
+        """
+        Selects a new monitor to be plotted
+        """
         self.current_monitor = monitor
         self.update_plot()
 
     def set_log_mode(self, log_mode):
+        """
+        Sets log mode for plotting, True or False
+        """
         self.log_mode = log_mode
         self.update_plot()
 
     def set_orders_of_mag(self, orders_of_mag):
+        """
+        Sets orders_of_mag value for logarithmic plots
+        """
         self.orders_of_mag = orders_of_mag
         self.update_plot()
 
+    def set_colormap(self, colormap):
+        """
+        Choose colormap, has to be available in matplotlib
+        """
+        self.colormap = colormap
+        self.update_plot()
+
     def new_plot(self):
         """
-        Sets up original plot with fig and ax
+        Sets up original plot with fig, ax and ax for colorbar
         """
         # fig, ax = plt.subplots(constrained_layout=True, figsize=(6, 4))
         self.fig, (self.ax, self.colorbar_ax) = plt.subplots(ncols=2, gridspec_kw={'width_ratios': [4, 1]})
 
         self.fig.canvas.toolbar_position = 'bottom'
-        self.ax.grid(True)
 
         self.update_plot()
 
@@ -59,21 +101,30 @@ def update_plot(self):
         Updates the plot with current data, monitor and plot options
         """
 
+        # Clear plot first
         self.ax.cla()
+        self.ax.xaxis.set_ticks([])
+        self.ax.yaxis.set_ticks([])
         self.colorbar_ax.cla()
         self.colorbar_ax.xaxis.set_ticks([])
         self.colorbar_ax.yaxis.set_ticks([])
 
+        # Display message if not data can be plotted
         if self.data is None:
-            self.ax.text(0.4, 0.5, "No data available")
+            self.ax.text(0.32, 0.5, "No data available")
+            return
+
+        if len(self.data) == 0:
+            self.ax.text(0.25, 0.5, "Simulation returned no data")
             return
 
         if self.current_monitor is None:
-            self.ax.text(0.4, 0.5, "Select a monitor to plot")
+            self.ax.text(0.3, 0.5, "Select a monitor to plot")
             return
 
+        # Get monitor and establish plot options
         monitor = name_search(self.current_monitor, self.data)
-        plot_options = {"show_colorbar": True, "log": self.log_mode}
+        plot_options = {"show_colorbar": True, "log": self.log_mode, "colormap": self.colormap}
         if self.orders_of_mag != "disabled":
             plot_options["orders_of_mag"] = self.orders_of_mag
         else:
@@ -89,6 +140,9 @@ def update_plot(self):
         plt.tight_layout()
 
     def show_interface(self):
+        """
+        Show the plot interface
+        """
         # Set up plot area
         output = widgets.Output()
         with output:
@@ -98,7 +152,6 @@ def show_interface(self):
         # could retrieve default plot options from data if given
 
         plot_control_list = []  # Keep all control widgets in this list
-
         plot_control_list.append(widgets.Label(value="Choose monitor"))
 
         # Set up dropdown list for monitor choice
@@ -115,15 +168,110 @@ def show_interface(self):
         log_orders_of_mag = OrdersOfMagField(self.set_orders_of_mag)
         plot_control_list.append(log_orders_of_mag.make_widget())
 
+        # Set up dropdown box for colormap
+        colormap_control = ColormapDropdown(self.set_colormap)
+        plot_control_list.append(colormap_control.make_widget())
+
         plot_controls = widgets.VBox(plot_control_list,
-                                     layout=widgets.Layout(width="25%", border="solid"))
+                                     #layout=widgets.Layout(width="25%", border="solid"))
+                                     layout=widgets.Layout(width="25%"))
 
+        # In case data is already supplied, set it
         if self.data is not None:
             self.set_data(self.data)
 
         return widgets.HBox([output, plot_controls])
 
 
+class ColormapDropdown:
+    """
+    Class for controlling dropdown menus for colormaps
+    """
+    def __init__(self, set_colormap):
+        """
+        Controls colormap dropdown menus with given set_colormap function
+
+        Creates dropdown widget and calls the given set_colormap function
+        when the user updates the colormap choice.
+        The colormap choice is given as two dropdown menus, one for selecting
+        the category of colormap, and the other to select the actual colormap.
+
+        The available colormaps are those supported in matplotlib
+
+        Parameters
+        ----------
+
+        set_colormap : function
+            Function called with colormap name as argument when changed
+
+        """
+        self.set_colormap = set_colormap
+        self.colormap_widget = None
+
+        # Default colormaps in matplotlib
+        self.cmaps = OrderedDict()
+        self.cmaps['Perceptually Uniform Sequential'] = [
+            'viridis', 'plasma', 'inferno', 'magma', 'cividis']
+        self.cmaps['Sequential'] = [
+            'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',
+            'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',
+            'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn']
+        self.cmaps['Sequential (2)'] = [
+            'binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink',
+            'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia',
+            'hot', 'afmhot', 'gist_heat', 'copper']
+        self.cmaps['Diverging'] = [
+            'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu',
+            'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic']
+        self.cmaps['Cyclic'] = ['twilight', 'twilight_shifted', 'hsv']
+        self.cmaps['Qualitative'] = ['Pastel1', 'Pastel2', 'Paired', 'Accent',
+                                'Dark2', 'Set1', 'Set2', 'Set3',
+                                'tab10', 'tab20', 'tab20b', 'tab20c']
+        self.cmaps['Miscellaneous'] = [
+            'flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern',
+            'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg',
+            'gist_rainbow', 'rainbow', 'jet', #'turbo',  # turbo reports errors
+            'nipy_spectral', 'gist_ncar']
+
+        self.categories = self.cmaps.keys()
+
+    def make_widget(self):
+        """
+        Creates the widget and sets appropriate update functions
+
+        The category dropdown will change options for the actual colormap
+        dropdown menu through the update_cmap_options method.
+        The colormap choice uses the update_cmap method and calls the
+        update_function held in attributes.
+        """
+
+        header = widgets.Label(value="Colormap category")
+        category = widgets.Dropdown(value="Miscellaneous", options=self.categories,
+                                    layout=widgets.Layout(width="98%"))
+        category.observe(self.update_cmap_options, "value")
+
+        description = widgets.Label(value="Colormap", layout=widgets.Layout(width="39%"))
+        default_options = self.cmaps[category.value]
+        self.colormap_widget = widgets.Dropdown(value="jet", options=default_options,
+                                                layout=widgets.Layout(width="58%"))
+        self.colormap_widget.observe(self.update_cmap, "value")
+
+        colormap = widgets.HBox([description, self.colormap_widget])
+
+        return widgets.VBox([header, category, colormap])
+
+    def update_cmap_options(self, change):
+        """
+        Updates the colormap options in the colormap widget
+        """
+        self.colormap_widget.options = self.cmaps[change.new]
+
+    def update_cmap(self, change):
+        """
+        Updates the colormap with the set_colormap function in attributes
+        """
+        self.set_colormap(change.new)
+
 class MonitorDropdown:
     """
     Class for creating monitor dropdown menu
@@ -187,11 +335,29 @@ def update(self, change):
 
 
 class LogCheckbox:
+    """
+    Class for widget with log mode checkbox
+    """
     def __init__(self, log_mode, set_log_mode):
+        """
+        Sets up checkbox with log mode, takes initial mode and update function
+
+        Parameters
+        ----------
+
+        log_mode : bool
+            Initial state of log checkbox
+
+        set_log_mode : function
+            Function which will be called with new log_mode
+        """
         self.log_mode = log_mode
         self.set_log_mode = set_log_mode
 
     def make_widget(self):
+        """
+        Creates the actual checkbox widget along with descriptive text
+        """
         description_layout = widgets.Layout(width='250px', height='32px',
                                             display="flex",
                                             justify_content="flex-start")
@@ -209,14 +375,44 @@ def make_widget(self):
         return widgets.HBox([description_log, log_check])
 
     def update(self, change):
+        """
+        Calls given update function with new log_mode
+
+        Parameters
+        ----------
+
+        change : change object
+            Change object from widget interaction
+        """
         self.set_log_mode(change.new)
 
 
 class OrdersOfMagField:
+    """
+    Class for handling orders_of_mag widget
+    """
     def __init__(self, set_orders_of_mag):
+        """
+        Widget for entering orders_of_mag used in log plotting
+
+        Orders_of_mag parameter controls how many orders of magnitude are
+        plotted when showing log scale, counting from the highest value and
+        down. The object needs an update function orders_of_mag.
+        The widget text field starts with the text "disabled" which
+        corresponds to the default value of 300 orders of magnitude.
+
+        Parameters
+        ----------
+
+        set_orders_of_mag : function
+            Function for updating orders_of_mag value
+        """
         self.set_orders_of_mag = set_orders_of_mag
 
     def make_widget(self):
+        """
+        Creates actual widget with descriptive text
+        """
         description_layout = widgets.Layout(width='250px', height='32px',
                                             display="flex",
                                             justify_content="flex-start")
diff --git a/mcstasscript/jb_interface/simulation_interface.py b/mcstasscript/jb_interface/simulation_interface.py
index 169d3f4b..e896f11a 100644
--- a/mcstasscript/jb_interface/simulation_interface.py
+++ b/mcstasscript/jb_interface/simulation_interface.py
@@ -14,8 +14,31 @@
 from mcstasscript.jb_interface.widget_helpers import get_parameter_default
 
 
-class ParameterTextbox:
+class ParameterWidget:
+    """
+    Widget for parameter object from McStasScript instrument
+    """
     def __init__(self, parameter, parameters):
+        """
+        Describes a widget for a parameter object given all parameters
+
+        When no options are given in ParameterVariable object, the widget will
+        be a textfield where the user can input the value. If the options
+        attribute is used, the widget will be a dropdown menu with available
+        options. The make_widget method returns the widget, and the update
+        function is called whenever the user interacts with the widget.
+
+        The widget shows parameter name, the interactive widget and a comment
+
+        Parameters
+        ----------
+
+        parameter: McStasScript ParameterVariable object
+            The parameter this widget should represent
+
+        parameters: dict of McStasScript ParameterVariable objects
+            Dict with all parameter objects of the instrument
+        """
 
         self.parameter = parameter
         self.parameters = parameters
@@ -29,19 +52,49 @@ def __init__(self, parameter, parameters):
         self.comment = parameter.comment
 
     def make_widget(self):
+        """
+        Returns widget with parameter name, interactive widget and comment
+        """
         label = widgets.Label(value=self.name,
                               layout=widgets.Layout(width='15%', height='32px'))
-        textbox = widgets.Text(value=str(self.default_value),
-                               layout=widgets.Layout(width='10%', height='32px'))
+        if self.parameter.options is not None:
+            par_widget = widgets.Dropdown(options=self.parameter.options,
+                                          layout=widgets.Layout(width='10%', height='32px'))
+            if self.default_value != "":
+                if self.default_value in self.parameter.options:
+                    par_widget.value = self.default_value
+                elif self.default_value.strip("'") in self.parameter.options:
+                    par_widget.value = self.default_value.strip("'")
+                elif self.default_value.strip('"') in self.parameter.options:
+                    par_widget.value = self.default_value.strip('"')
+                else:
+                    raise KeyError("default value not found in options for parameter: "
+                                   + str(self.parameter.name))
+
+        else:
+            par_widget = widgets.Text(value=str(self.default_value),
+                                      layout=widgets.Layout(width='10%', height='32px'))
         comment = widgets.Label(value=self.comment,
                                 layout=widgets.Layout(width='75%', height='32px'))
 
-        textbox.observe(self.update, "value")
+        par_widget.observe(self.update, "value")
 
-        return widgets.HBox([label, textbox, comment])
+        return widgets.HBox([label, par_widget, comment])
 
     def update(self, change):
-        self.parameters[self.name] = change.new
+        """
+        Update function called whenever the user updates the widget
+
+        When strings parameters are used, this function adds the necessary
+        quotation marks if none are provided.
+        """
+        new_value = change.new
+        if self.parameter.type == "string":
+            if type(new_value) is str:
+                if not (new_value[0] == '"' or new_value[0] == "'"):
+                    new_value = '"' + new_value + '"'
+
+        self.parameters[self.name] = new_value
 
 
 class SimInterface:
@@ -96,7 +149,7 @@ class does not set type for default case, will be fixed.
         parameter_widgets = []
         for parameter in self.instrument.parameter_list:
             if True: #parameter.type != "":
-                par_widget = ParameterTextbox(parameter, self.parameters)
+                par_widget = ParameterWidget(parameter, self.parameters)
             else:
                 raise RuntimeError("Unknown parameter type '"
                                    + parameter.type + "' of par named '"
@@ -130,9 +183,16 @@ def run_simulation(self, change):
             run_arguments["mpi"] = self.mpi
 
         self.run_button.icon = "hourglass"
-        print("Running with:", run_arguments)
-        with HiddenPrints():
-            data = self.instrument.run_full_instrument(**run_arguments)
+        #print("Running with:", run_arguments)
+
+        try:
+            with HiddenPrints():
+                data = self.instrument.run_full_instrument(**run_arguments)
+            #data = self.instrument.run_full_instrument(**run_arguments)
+        except NameError:
+            print("McStas run failed.")
+            data = []
+
         self.run_button.icon = "calculator"
 
         self.plot_interface.set_data(data)
@@ -161,7 +221,7 @@ def make_ncount_field(self):
                                             display="flex",
                                             justify_content="flex-end")
         description = widgets.Label(value="ncount", layout=description_layout)
-        textbox = widgets.Text(value=str(self.ncount), layout=widgets.Layout(width='100px', height='32px'))
+        textbox = widgets.Text(value=str(self.ncount), layout=widgets.Layout(width='100px', height='36px'))
         textbox.observe(self.update_ncount, "value")
 
         return widgets.HBox([description, textbox])
@@ -192,7 +252,7 @@ def make_mpi_field(self):
                                             display="flex",
                                             justify_content="flex-end")
         description = widgets.Label(value="mpi", layout=description_layout)
-        textbox = widgets.Text(value=str(self.mpi), layout=widgets.Layout(width='70px', height='32px'))
+        textbox = widgets.Text(value=str(self.mpi), layout=widgets.Layout(width='70px', height='36px'))
         textbox.observe(self.update_mpi, "value")
 
         return widgets.HBox([description, textbox])
@@ -228,8 +288,8 @@ def show_interface(self):
         ncount_field = self.make_ncount_field()
         mpi_field = self.make_mpi_field()
 
-        simulation_widget = widgets.HBox([self.run_button, ncount_field, mpi_field],
-                                         layout=widgets.Layout(border="solid"))
+        simulation_widget = widgets.HBox([self.run_button, ncount_field, mpi_field])
+                                         #layout=widgets.Layout(border="solid"))
 
         self.plot_interface = plot_interface.PlotInterface()
         plot_widget = self.plot_interface.show_interface()

From 4fa4aff0ad725700c5dc270f9cc21909900a9d25 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 4 May 2021 08:44:09 +0200
Subject: [PATCH 152/403] Added requirement of ipywidgets

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index 79e87c1e..a28ff210 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,4 @@
 numpy
 matplotlib
 PyYAML
+ipywidgets

From 697224750549d22b0887fe1d0aace68236bce70a Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 4 May 2021 08:49:22 +0200
Subject: [PATCH 153/403] Fixed bug in plotter that caused crash when calling
 without colorbar_ax keyword argument.

---
 mcstasscript/interface/plotter.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index fe5273ef..8a12ae59 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -168,10 +168,12 @@ def _plot_fig_ax(data, fig, ax, **kwargs):
             cax = None
             if "colorbar_axes" in kwargs:
                 cax = kwargs["colorbar_axes"]
-                print("colorbar axes set")
+
             fig.colorbar(im, ax=ax, cax=cax,
                          format=matplotlib.ticker.FuncFormatter(_fmt))
-            cax.set_aspect(20)
+            
+            if "colorbar_axes" in kwargs:
+                cax.set_aspect(20)
 
         # Add a title
         ax.set_title(data.metadata.title)

From 2c8e3fab2463900a4706d4d80b15a113b7544e7d Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 4 May 2021 09:49:46 +0200
Subject: [PATCH 154/403] Small fixes for jupyter widget interface and how it
 is used.

Added method to Instr class that returns data from interface.

Added example with calibration sample.
---
 examples/calibration_sample.ipynb             | 682 ++++++++++++++++++
 mcstasscript/interface/functions.py           |   5 +-
 mcstasscript/interface/instr.py               |  21 +-
 mcstasscript/interface/plotter.py             |   2 +-
 mcstasscript/jb_interface/plot_interface.py   |   6 +-
 .../jb_interface/simulation_interface.py      |  12 +-
 6 files changed, 711 insertions(+), 17 deletions(-)
 create mode 100644 examples/calibration_sample.ipynb

diff --git a/examples/calibration_sample.ipynb b/examples/calibration_sample.ipynb
new file mode 100644
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+++ b/examples/calibration_sample.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Demonstration of McStasScript\n",
+    "Here the McStasScript Python McStas API is demonstrated by creating a small simulation with an imaging calibration sample. A python function is defined in order to ease the task of adding the many materials, and a for loop is used for arranging the smaller cylinders which are embedded in a larger Aluminium cylinder. This demonstration will use widgets to control the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following components are found in the work_directory / input_path:\n",
+      "     Union_sphere.comp\n",
+      "     Union_box.comp\n",
+      "     Single_crystal_process.comp\n",
+      "     Union_logger_2D_kf.comp\n",
+      "     Template_process.comp\n",
+      "     Union_conditional_standard.comp\n",
+      "     Union_logger_2D_space.comp\n",
+      "     Union_conditional_PSD.comp\n",
+      "     Union_master.comp\n",
+      "     Union_logger_2D_kf_time.comp\n",
+      "     Union_cylinder.comp\n",
+      "     Powder_process.comp\n",
+      "     Union_make_material.comp\n",
+      "     Incoherent_process.comp\n",
+      "     Union_logger_1D.comp\n",
+      "     Union_logger_3D_space.comp\n",
+      "     Union_logger_2DQ.comp\n",
+      "     Union_logger_2D_space_time.comp\n",
+      "These definitions will be used instead of the installed versions.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import math\n",
+    "%matplotlib widget\n",
+    "\n",
+    "# Import McstasScript\n",
+    "from mcstasscript.interface import instr, plotter, functions\n",
+    "\n",
+    "# Create a McStas instrument\n",
+    "Instr = instr.McStas_instr(\"calibration_sample\", author = \"Mads Bertelsen\", origin = \"ESS DMSC\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding powder material definitions\n",
+    "Here we will be adding a few powder material definitions, and it is thus easier to create a small python function for the task. Normally the syntax would be like this for each material:\n",
+    "\n",
+    "`\n",
+    "Al_inc = Instr.add_component(\"Al_inc\",\"Incoherent_process\")\n",
+    "Al_pow = Instr.add_component(\"Al_pow\",\"Powder_process\")\n",
+    "Al_inc.sigma = 4*0.0082 # Incoherent cross section in Barns\n",
+    "Al_inc.unit_cell_volume = 66.4 # Unit cell volume in AA^3\n",
+    "Al_pow.reflections = \"\\\"Al.laz\\\"\" # Data file with powder lines\n",
+    "Al.my_absorption = \"100*4*0.231/66.4\" # Inverse penetration depth in 1/m\n",
+    "Al.process_string = \"\\\"Al_inc,Al_pow\\\"\" # Make a material with aluminium incoherent and aluminium powder` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_union_powder(name, data_name, sigma_inc, sigma_abs, unit_V, Instr):\n",
+    "    \"\"\"\n",
+    "    This function adds a Union material with incoherent scattering and powder lines\n",
+    "    \"\"\"\n",
+    "    material_incoherent = Instr.add_component(name + \"_inc\",\"Incoherent_process\")\n",
+    "    material_incoherent.sigma = sigma_inc\n",
+    "    material_incoherent.unit_cell_volume = unit_V\n",
+    "    material_powder = Instr.add_component(name + \"_pow\",\"Powder_process\")\n",
+    "    material_powder.reflections = \"\\\"\" + data_name + \"\\\"\"  # Need quotes when describing a filename\n",
+    "    material = Instr.add_component(name,\"Union_make_material\")\n",
+    "    material.my_absorption = 100*sigma_abs/unit_V\n",
+    "    material.process_string = \"\\\"\" + name + \"_inc,\" + name + \"_pow\" + \"\\\"\"\n",
+    "    \n",
+    "# Add a number of standard powders to our instrument (datafiles included with McStas)\n",
+    "add_union_powder(\"Al\", \"Al.laz\", 4*0.0082, 4*0.231, 66.4, Instr)\n",
+    "add_union_powder(\"Cu\", \"Cu.laz\", 4*0.55, 4*3.78, 47.24, Instr)\n",
+    "add_union_powder(\"Ni\", \"Ni.laz\", 4*5.2, 4*4.49, 43.76, Instr)\n",
+    "add_union_powder(\"Ti\", \"Ti.laz\", 2*2.87, 2*6.09, 35.33, Instr)\n",
+    "add_union_powder(\"Pb\", \"Pb.laz\", 4*0.003, 4*0.17, 121.29, Instr)\n",
+    "add_union_powder(\"Fe\", \"Fe.laz\", 2*0.4, 2*2.56, 24.04, Instr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Describing a simple instrument\n",
+    "Now we start describing an instrument, we start with the source. We would like to control the energy and energy spread at run time, so this will be described using input parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Since we want to change the energy and energy range at run time, we add these as instrument parameters\n",
+    "Instr.add_parameter(\"energy\", value=10, comment=\"[meV] Energy of source\")\n",
+    "Instr.add_parameter(\"delta_energy\", value=8, comment=\"[meV] Energy spread of source\")\n",
+    "\n",
+    "# Add a source to the McStas instrument\n",
+    "src = Instr.add_component(\"source\", \"Source_div\")\n",
+    "src.xwidth = 0.11\n",
+    "src.yheight = 0.11\n",
+    "src.focus_aw = 0.1\n",
+    "src.focus_ah = 0.1\n",
+    "src.E0 = \"energy\"\n",
+    "src.dE = \"delta_energy\"\n",
+    "src.flux = 1E13;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The source will illuminate the sample directly, but we may want to rotate the sample in its place. We define rotations around vertical and horizontal as further input parameters, and set up a few arms to act as reference points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now we want to set a position and rotation of our sample\n",
+    "# The rotation should be adjustable, so we add instrument parameters for controling the rotation\n",
+    "Instr.add_parameter(\"rotation_y\", value=180, comment=\"[deg] Rotation around vertical\")\n",
+    "Instr.add_parameter(\"rotation_x\", value=0, comment=\"[deg] Rotation around horizontal\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We add arms at the sample position, and a second arm with the correct rotation\n",
+    "sample_pos = Instr.add_component(\"sample_pos\", \"Arm\", AT=[0,0,1], AT_RELATIVE=\"source\")\n",
+    "sample_arm = Instr.add_component(\"sample_arm\", \"Arm\", AT=[0,0,0], AT_RELATIVE=\"sample_pos\")\n",
+    "sample_arm.set_ROTATED([\"rotation_x\", \"rotation_y\", 0], RELATIVE=\"sample_pos\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding the sample\n",
+    "The aim is to create a simple calibration sample which is made from cylinders of different materials embedded in a larger cylinder. To do so, we will need the McStas Union_cylinder component, so lets get a bit of help on its parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Union_cylinder ____________________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1mmaterial_string\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [] // material name of this volume, defined using \n",
+      "                          Union_make_material \n",
+      "\u001b[4m\u001b[1mpriority\u001b[0m\u001b[0m [1] // priotiry of the volume (can not be the same as another volume) \n",
+      "                A high priority is on top of low. \n",
+      "\u001b[4m\u001b[1mradius\u001b[0m\u001b[0m [m] // Outer radius volume in (x,z) plane\n",
+      "\u001b[4m\u001b[1myheight\u001b[0m\u001b[0m [m] // Cylinder height in (y) direction\n",
+      "\u001b[1mvisualize\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // set to 0 if you wish to hide this geometry in mcdisplay\n",
+      "\u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m\n",
+      "\u001b[1mtarget_x\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m\n",
+      "\u001b[1mtarget_y\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m // Position of target to focus at\n",
+      "\u001b[1mtarget_z\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m\n",
+      "\u001b[1mfocus_aw\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // horiz. angular dimension of a rectangular area\n",
+      "\u001b[1mfocus_ah\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // vert. angular dimension of a rectangular area\n",
+      "\u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // horiz. dimension of a rectangular area\n",
+      "\u001b[1mfocus_xh\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // vert. dimension of a rectangular area\n",
+      "\u001b[1mfocus_r\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // focusing on circle with this radius\n",
+      "\u001b[1mp_interact\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // probability to interact with this geometry [0-1]\n",
+      "\u001b[1mmask_string\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [] // Comma seperated list of geometry names which this \n",
+      "                      geometry should mask \n",
+      "\u001b[1mmask_setting\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [] // \"All\" or \"Any\", should the masked volume be simulated \n",
+      "                       when the ray is in just one mask, or all. \n",
+      "\u001b[1mnumber_of_activations\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // Number of subsequent Union_master components \n",
+      "                                   that will simulate this geometry \n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "Instr.component_help(\"Union_cylinder\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We add the large aluminium base that contains samples of the remaining materials\n",
+    "base_cyl = Instr.add_component(\"base_cyl\",\"Union_cylinder\", RELATIVE=\"sample_arm\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT base_cyl = Union_cylinder\n",
+      "  \u001b[1mmaterial_string\u001b[0m = \u001b[1m\u001b[92m\"Al\"\u001b[0m\u001b[0m []\n",
+      "  \u001b[1mpriority\u001b[0m = \u001b[1m\u001b[92m100\u001b[0m\u001b[0m [1]\n",
+      "  \u001b[1mradius\u001b[0m = \u001b[1m\u001b[92m0.04\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.02\u001b[0m\u001b[0m [m]\n",
+      "AT [0, 0, 0] RELATIVE sample_arm\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We set the parameters and confirm this looks appropriate with the print function\n",
+    "base_cyl.radius = 0.04\n",
+    "base_cyl.yheight = 0.02\n",
+    "base_cyl.priority = 100\n",
+    "base_cyl.material_string = \"\\\"Al\\\"\" # Select our Al material defined earlier\n",
+    "print(base_cyl)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next the internal cylinders are added. We use a small python for loop to add a cylinder of each material in succession. We add an additional sample with material chosen by the user from a list of our defined materials."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We define a string containing the names of the materials we wish to add\n",
+    "Instr.add_parameter(\"string\", \"material\", value='\"Pb\"',\n",
+    "                    comment=\"Material choice for extra material sample\",\n",
+    "                    options=[\"Cu\", \"Ni\", \"Ti\", \"Pb\", \"Fe\", \"Al\"])\n",
+    "\n",
+    "sample_strings = [\"Cu\", \"Ni\", \"Ti\", \"Pb\", \"Fe\", \"material\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Here we write a for loop that adds a small cylinder of this material inside the large Al cylinder\n",
+    "angle_seperation = 36\n",
+    "distance_from_center = 0.03\n",
+    "sample_radius = 0.007\n",
+    "counter = 0\n",
+    "for sample_string in sample_strings:\n",
+    "    x_position = distance_from_center * math.cos(counter*angle_seperation*3.14159/180)\n",
+    "    x_position = round(x_position, 5) # round to 4 digits for easier printing\n",
+    "    z_position = distance_from_center * math.sin(counter*angle_seperation*3.14159/180)\n",
+    "    z_position = round(z_position, 5) # round to 4 digits for easier printing\n",
+    "    this_sample = Instr.add_component(sample_string + \"_cyl\",\"Union_cylinder\",\n",
+    "                                      AT=[x_position, 0, z_position], RELATIVE=\"base_cyl\")\n",
+    "    this_sample.radius = sample_radius;\n",
+    "    this_sample.yheight = 0.019; # yheight must be different from base_cyl\n",
+    "    this_sample.priority = 150 + counter; # ensure higher priority than base\n",
+    "    if sample_string != \"material\":\n",
+    "        this_sample.material_string = \"\\\"\" + sample_string + \"\\\"\"\n",
+    "    else:\n",
+    "        # The user selectable material is a parameter, where the remaining are treated as strings.\n",
+    "        this_sample.material_string = sample_string\n",
+    "    counter = counter + 1\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Union Loggers for seeing scattering intensity in the sample\n",
+    "Now that the sample has been defined, a few loggers are added to investigate where neutrons are scattered within the sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A few Union loggers are set up for display of the scattering locations\n",
+    "space_2D_zx = Instr.add_component(\"logger_space_zx_all\", \"Union_logger_2D_space\", AT_RELATIVE=\"sample_pos\")\n",
+    "space_2D_zx.filename = \"\\\"space_zx.dat\\\"\"\n",
+    "space_2D_zx.D_direction_1 = \"\\\"z\\\"\"; space_2D_zx.n1 = 1000\n",
+    "space_2D_zx.D1_min = -0.05; space_2D_zx.D1_max = 0.05\n",
+    "space_2D_zx.D_direction_2 = \"\\\"x\\\"\"; space_2D_zx.n2 = 1000\n",
+    "space_2D_zx.D2_min = -0.05; space_2D_zx.D2_max = 0.05\n",
+    "\n",
+    "space_2D_zy = Instr.add_component(\"logger_space_zy_all\", \"Union_logger_2D_space\", AT_RELATIVE=\"sample_pos\")\n",
+    "space_2D_zy.filename = \"\\\"space_zy.dat\\\"\"\n",
+    "space_2D_zy.D_direction_1 = \"\\\"z\\\"\"; space_2D_zy.n1 = 1000\n",
+    "space_2D_zy.D1_min = -0.05; space_2D_zy.D1_max = 0.05\n",
+    "space_2D_zy.D_direction_2 = \"\\\"y\\\"\"; space_2D_zy.n2 = 1000\n",
+    "space_2D_zy.D2_min = -0.05; space_2D_zy.D2_max = 0.05\n",
+    "\n",
+    "space_2D_zy = Instr.add_component(\"logger_space_xy_all\", \"Union_logger_2D_space\", AT_RELATIVE=\"sample_pos\")\n",
+    "space_2D_zy.filename = \"\\\"space_xy.dat\\\"\"\n",
+    "space_2D_zy.D_direction_1 = \"\\\"x\\\"\"; space_2D_zy.n1 = 1000\n",
+    "space_2D_zy.D1_min = -0.05; space_2D_zy.D1_max = 0.05\n",
+    "space_2D_zy.D_direction_2 = \"\\\"y\\\"\"; space_2D_zy.n2 = 1000\n",
+    "space_2D_zy.D2_min = -0.05; space_2D_zy.D2_max = 0.05"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The Union master component\n",
+    "The Union master component is what will do the simulation of the collected calibration sample. It takes all the material information and physical volumes that we have described, and runs a simulation with full multiple scattering."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "master = Instr.add_component(\"calibration_sample\", \"Union_master\", AT_relative=\"sample_pos\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### McStas monitors\n",
+    "At the end we add a few McStas monitors to view the transmitted beam, including a PSD / energy monitor to see the Bragg edges of the different materials."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add position sensitive detector for transmission measurement\n",
+    "PSD = Instr.add_component(\"PSD\", \"PSD_monitor\", AT=[0,0,1], RELATIVE=\"sample_pos\") \n",
+    "PSD.xwidth = 0.1; PSD.yheight = 0.1; PSD.nx = 200; PSD.ny = 200;\n",
+    "PSD.filename = \"\\\"PSD.dat\\\"\"; PSD.restore_neutron = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Adds 1D position sensitive detector for transmission measurement\n",
+    "PSD = Instr.add_component(\"PSDlin\", \"PSDlin_monitor\", AT=[0,0,1], RELATIVE=\"sample_pos\") \n",
+    "PSD.xwidth = 0.1; PSD.yheight = 0.1; PSD.nx = 200;\n",
+    "PSD.filename = \"\\\"PSDlin.dat\\\"\"; PSD.restore_neutron = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add energy position monitor to see Bragg edges\n",
+    "EPSD = Instr.add_component(\"EPSD\", \"EPSD_monitor\", RELATIVE=\"PSD\")\n",
+    "EPSD.xwidth = 0.1; EPSD.yheight = 0.02; EPSD.nE = 300; EPSD.nx = 200;\n",
+    "EPSD.filename = \"\\\"EPSD.dat\\\"\"; EPSD.restore_neutron = 1;\n",
+    "EPSD.Emin = \"energy - delta_energy\"\n",
+    "EPSD.Emax = \"energy + delta_energy\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Last check \n",
+    "Before running the McStas simulation we do a last check to see that the McStas instrument looks as we expect. First the components are listed with their locations and rotations, then the available parameters are shown."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Al_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Al_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Al                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Cu_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Cu_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Cu                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Ni_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Ni_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Ni                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Ti_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Ti_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Ti                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Pb_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Pb_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Pb                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Fe_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Fe_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Fe                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "source              Source_div            AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "sample_pos          Arm                   AT      (0, 0, 1)                   RELATIVE source    \n",
+      "sample_arm          Arm                   AT      (0, 0, 0)                   RELATIVE sample_pos \n",
+      "                                          ROTATED (rotation_x, rotation_y, 0) RELATIVE sample_pos\n",
+      "base_cyl            Union_cylinder        AT      (0, 0, 0)                   RELATIVE sample_arm\n",
+      "Cu_cyl              Union_cylinder        AT      (0.03, 0, 0.0)              RELATIVE base_cyl  \n",
+      "Ni_cyl              Union_cylinder        AT      (0.02427, 0, 0.01763)       RELATIVE base_cyl  \n",
+      "Ti_cyl              Union_cylinder        AT      (0.00927, 0, 0.02853)       RELATIVE base_cyl  \n",
+      "Pb_cyl              Union_cylinder        AT      (-0.00927, 0, 0.02853)      RELATIVE base_cyl  \n",
+      "Fe_cyl              Union_cylinder        AT      (-0.02427, 0, 0.01763)      RELATIVE base_cyl  \n",
+      "material_cyl        Union_cylinder        AT      (-0.03, 0, 0.0)             RELATIVE base_cyl  \n",
+      "logger_space_zx_all Union_logger_2D_space AT      (0, 0, 0)                   RELATIVE sample_pos\n",
+      "logger_space_zy_all Union_logger_2D_space AT      (0, 0, 0)                   RELATIVE sample_pos\n",
+      "logger_space_xy_all Union_logger_2D_space AT      (0, 0, 0)                   RELATIVE sample_pos\n",
+      "calibration_sample  Union_master          AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "PSD                 PSD_monitor           AT      (0, 0, 1)                   RELATIVE sample_pos\n",
+      "PSDlin              PSDlin_monitor        AT      (0, 0, 1)                   RELATIVE sample_pos\n",
+      "EPSD                EPSD_monitor          AT      (0, 0, 0)                   RELATIVE PSD       \n"
+     ]
+    }
+   ],
+   "source": [
+    "Instr.print_components(line_length=117) # Show nice overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Running the simulation\n",
+    "The simulation can now be performed from the Jupyter Notebook using the widget interface."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf34078b8add43c189b04e53744c6bc3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(VBox(children=(HBox(children=(Label(value='energy', layout=Layout(height='32px', width='15%')),…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Instr.interface()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Getting data from the widget simulation\n",
+    "It is possible to grab the latest data produced by the widget simulation. Run these cells after a simulation has been performed with the above widget."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "McStasData: logger_space_zx_all type: 2D  I:4.46366e+09 E:1.38106e+07 N:167764, \n",
+      "McStasData: logger_space_zy_all type: 2D  I:4.46366e+09 E:1.38106e+07 N:167764, \n",
+      "McStasData: logger_space_xy_all type: 2D  I:4.46366e+09 E:1.38106e+07 N:167764, \n",
+      "McStasData: PSD type: 2D  I:4.35753e+10 E:5.06929e+07 N:738940, \n",
+      "McStasData: PSDlin type: 1D  I:4.35753e+10 E:5.06929e+07 N:738940, \n",
+      "McStasData: EPSD type: 2D  I:4.62274e+09 E:1.65109e+07 N:78398]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = Instr.get_interface_data()\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f97deafa40e44f4080ee340e89704057",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name EPSD\n"
+     ]
+    }
+   ],
+   "source": [
+    "if len(data) != 0:\n",
+    "    EPSD_data = functions.name_search(\"EPSD\", data)\n",
+    "    plotter.make_plot(EPSD_data, figsize=(9,6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using plotting interface without simulation interface\n",
+    "The plotting interface can be used for existing datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "21a32af6dc9749f4946010f7ae22ca6d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(Output(layout=Layout(width='75%')), VBox(children=(Label(value='Choose monitor'), Dropdown(layo…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotter.interface(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/mcstasscript/interface/functions.py b/mcstasscript/interface/functions.py
index 56d7903c..8d7a6600 100644
--- a/mcstasscript/interface/functions.py
+++ b/mcstasscript/interface/functions.py
@@ -26,7 +26,10 @@ def name_search(name, data_list):
     """
     if type(data_list) is not list:
         raise RuntimeError(
-            "name_search function needs list of McStasData as input")
+            "name_search function needs list of McStasData as input.")
+
+    if len(data_list) == 0:
+        raise RuntimeError("Given data list empty.")
 
     if not type(data_list[0]) == McStasData:
         raise RuntimeError(
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 50c788fd..372a97f3 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -291,6 +291,8 @@ def __init__(self, name, **kwargs):
                                                 input_path=self.input_path)
         self.component_class_lib = {}
 
+        self.widget_interface = None
+
     def _read_calibration(self):
         """
         Place holder method that should be overwritten by classes
@@ -1690,8 +1692,23 @@ def interface(self):
 
         Needs "%matplotlib widget" in notebook to work correctly
         """
-        interface = SimInterface(self)
-        return interface.show_interface()
+        self.widget_interface = SimInterface(self)
+        return self.widget_interface.show_interface()
+
+    def get_interface_data(self):
+        """
+        Returns data from last run performed with the widget interface
+        """
+
+        if self.widget_interface is None:
+            print("No widget interface initialized, use interface method.")
+            return []
+
+        if self.widget_interface.plot_interface.data is None:
+            print("No run has been performed with the interface widget yet")
+            return []
+
+        return self.widget_interface.plot_interface.data
 
 class McStas_instr(McCode_instr):
     """
diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index 8a12ae59..2e5d2162 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -171,7 +171,7 @@ def _plot_fig_ax(data, fig, ax, **kwargs):
 
             fig.colorbar(im, ax=ax, cax=cax,
                          format=matplotlib.ticker.FuncFormatter(_fmt))
-            
+
             if "colorbar_axes" in kwargs:
                 cax.set_aspect(20)
 
diff --git a/mcstasscript/jb_interface/plot_interface.py b/mcstasscript/jb_interface/plot_interface.py
index 133e292b..cda1878e 100644
--- a/mcstasscript/jb_interface/plot_interface.py
+++ b/mcstasscript/jb_interface/plot_interface.py
@@ -103,15 +103,15 @@ def update_plot(self):
 
         # Clear plot first
         self.ax.cla()
-        self.ax.xaxis.set_ticks([])
-        self.ax.yaxis.set_ticks([])
+        #self.ax.xaxis.set_ticks([])
+        #self.ax.yaxis.set_ticks([])
         self.colorbar_ax.cla()
         self.colorbar_ax.xaxis.set_ticks([])
         self.colorbar_ax.yaxis.set_ticks([])
 
         # Display message if not data can be plotted
         if self.data is None:
-            self.ax.text(0.32, 0.5, "No data available")
+            self.ax.text(0.3, 0.5, "No data available yet")
             return
 
         if len(self.data) == 0:
diff --git a/mcstasscript/jb_interface/simulation_interface.py b/mcstasscript/jb_interface/simulation_interface.py
index e896f11a..f13a33ae 100644
--- a/mcstasscript/jb_interface/simulation_interface.py
+++ b/mcstasscript/jb_interface/simulation_interface.py
@@ -140,21 +140,13 @@ def __init__(self, instrument):
 
     def make_parameter_widgets(self):
         """
-        Creates widgets for parameters using dedicated class ParameterTextbox
-        Preliminary check for parameter type disabled as the ParameterVariable
-        class does not set type for default case, will be fixed.
+        Creates widgets for parameters using dedicated class ParameterWidget
 
         returns widget including all parameters
         """
         parameter_widgets = []
         for parameter in self.instrument.parameter_list:
-            if True: #parameter.type != "":
-                par_widget = ParameterWidget(parameter, self.parameters)
-            else:
-                raise RuntimeError("Unknown parameter type '"
-                                   + parameter.type + "' of par named '"
-                                   + parameter.name + "'.")
-
+            par_widget = ParameterWidget(parameter, self.parameters)
             parameter_widgets.append(par_widget.make_widget())
 
         return widgets.VBox(parameter_widgets)

From 2b5edfc31360b68c7869a689c60caf1e6a996164 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Tue, 4 May 2021 09:56:29 +0200
Subject: [PATCH 155/403] Cleared cells in calibration example.

---
 examples/calibration_sample.ipynb | 253 +++++-------------------------
 1 file changed, 35 insertions(+), 218 deletions(-)

diff --git a/examples/calibration_sample.ipynb b/examples/calibration_sample.ipynb
index 63298e8f..68b81833 100644
--- a/examples/calibration_sample.ipynb
+++ b/examples/calibration_sample.ipynb
@@ -10,36 +10,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The following components are found in the work_directory / input_path:\n",
-      "     Union_sphere.comp\n",
-      "     Union_box.comp\n",
-      "     Single_crystal_process.comp\n",
-      "     Union_logger_2D_kf.comp\n",
-      "     Template_process.comp\n",
-      "     Union_conditional_standard.comp\n",
-      "     Union_logger_2D_space.comp\n",
-      "     Union_conditional_PSD.comp\n",
-      "     Union_master.comp\n",
-      "     Union_logger_2D_kf_time.comp\n",
-      "     Union_cylinder.comp\n",
-      "     Powder_process.comp\n",
-      "     Union_make_material.comp\n",
-      "     Incoherent_process.comp\n",
-      "     Union_logger_1D.comp\n",
-      "     Union_logger_3D_space.comp\n",
-      "     Union_logger_2DQ.comp\n",
-      "     Union_logger_2D_space_time.comp\n",
-      "These definitions will be used instead of the installed versions.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import math\n",
     "%matplotlib widget\n",
@@ -70,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -106,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -134,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,49 +139,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Union_cylinder ____________________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1mmaterial_string\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [] // material name of this volume, defined using \n",
-      "                          Union_make_material \n",
-      "\u001b[4m\u001b[1mpriority\u001b[0m\u001b[0m [1] // priotiry of the volume (can not be the same as another volume) \n",
-      "                A high priority is on top of low. \n",
-      "\u001b[4m\u001b[1mradius\u001b[0m\u001b[0m [m] // Outer radius volume in (x,z) plane\n",
-      "\u001b[4m\u001b[1myheight\u001b[0m\u001b[0m [m] // Cylinder height in (y) direction\n",
-      "\u001b[1mvisualize\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // set to 0 if you wish to hide this geometry in mcdisplay\n",
-      "\u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m\n",
-      "\u001b[1mtarget_x\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m\n",
-      "\u001b[1mtarget_y\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m // Position of target to focus at\n",
-      "\u001b[1mtarget_z\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m\n",
-      "\u001b[1mfocus_aw\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // horiz. angular dimension of a rectangular area\n",
-      "\u001b[1mfocus_ah\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // vert. angular dimension of a rectangular area\n",
-      "\u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // horiz. dimension of a rectangular area\n",
-      "\u001b[1mfocus_xh\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // vert. dimension of a rectangular area\n",
-      "\u001b[1mfocus_r\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // focusing on circle with this radius\n",
-      "\u001b[1mp_interact\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // probability to interact with this geometry [0-1]\n",
-      "\u001b[1mmask_string\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [] // Comma seperated list of geometry names which this \n",
-      "                      geometry should mask \n",
-      "\u001b[1mmask_setting\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [] // \"All\" or \"Any\", should the masked volume be simulated \n",
-      "                       when the ray is in just one mask, or all. \n",
-      "\u001b[1mnumber_of_activations\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // Number of subsequent Union_master components \n",
-      "                                   that will simulate this geometry \n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "Instr.component_help(\"Union_cylinder\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -218,23 +158,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT base_cyl = Union_cylinder\n",
-      "  \u001b[1mmaterial_string\u001b[0m = \u001b[1m\u001b[92m\"Al\"\u001b[0m\u001b[0m []\n",
-      "  \u001b[1mpriority\u001b[0m = \u001b[1m\u001b[92m100\u001b[0m\u001b[0m [1]\n",
-      "  \u001b[1mradius\u001b[0m = \u001b[1m\u001b[92m0.04\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.02\u001b[0m\u001b[0m [m]\n",
-      "AT [0, 0, 0] RELATIVE sample_arm\n",
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# We set the parameters and confirm this looks appropriate with the print function\n",
     "base_cyl.radius = 0.04\n",
@@ -253,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -267,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -306,7 +232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -343,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -360,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -372,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -384,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -406,54 +332,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Al_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Al_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Al                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Cu_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Cu_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Cu                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Ni_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Ni_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Ni                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Ti_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Ti_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Ti                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Pb_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Pb_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Pb                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Fe_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Fe_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Fe                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "source              Source_div            AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "sample_pos          Arm                   AT      (0, 0, 1)                   RELATIVE source    \n",
-      "sample_arm          Arm                   AT      (0, 0, 0)                   RELATIVE sample_pos \n",
-      "                                          ROTATED (rotation_x, rotation_y, 0) RELATIVE sample_pos\n",
-      "base_cyl            Union_cylinder        AT      (0, 0, 0)                   RELATIVE sample_arm\n",
-      "Cu_cyl              Union_cylinder        AT      (0.03, 0, 0.0)              RELATIVE base_cyl  \n",
-      "Ni_cyl              Union_cylinder        AT      (0.02427, 0, 0.01763)       RELATIVE base_cyl  \n",
-      "Ti_cyl              Union_cylinder        AT      (0.00927, 0, 0.02853)       RELATIVE base_cyl  \n",
-      "Pb_cyl              Union_cylinder        AT      (-0.00927, 0, 0.02853)      RELATIVE base_cyl  \n",
-      "Fe_cyl              Union_cylinder        AT      (-0.02427, 0, 0.01763)      RELATIVE base_cyl  \n",
-      "material_cyl        Union_cylinder        AT      (-0.03, 0, 0.0)             RELATIVE base_cyl  \n",
-      "logger_space_zx_all Union_logger_2D_space AT      (0, 0, 0)                   RELATIVE sample_pos\n",
-      "logger_space_zy_all Union_logger_2D_space AT      (0, 0, 0)                   RELATIVE sample_pos\n",
-      "logger_space_xy_all Union_logger_2D_space AT      (0, 0, 0)                   RELATIVE sample_pos\n",
-      "calibration_sample  Union_master          AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "PSD                 PSD_monitor           AT      (0, 0, 1)                   RELATIVE sample_pos\n",
-      "PSDlin              PSDlin_monitor        AT      (0, 0, 1)                   RELATIVE sample_pos\n",
-      "EPSD                EPSD_monitor          AT      (0, 0, 0)                   RELATIVE PSD       \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Instr.print_components(line_length=117) # Show nice overview"
    ]
@@ -468,24 +351,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bf34078b8add43c189b04e53744c6bc3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(VBox(children=(HBox(children=(Label(value='energy', layout=Layout(height='32px', width='15%')),…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "Instr.interface()"
    ]
@@ -500,23 +368,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[\n",
-      "McStasData: logger_space_zx_all type: 2D  I:4.46366e+09 E:1.38106e+07 N:167764, \n",
-      "McStasData: logger_space_zy_all type: 2D  I:4.46366e+09 E:1.38106e+07 N:167764, \n",
-      "McStasData: logger_space_xy_all type: 2D  I:4.46366e+09 E:1.38106e+07 N:167764, \n",
-      "McStasData: PSD type: 2D  I:4.35753e+10 E:5.06929e+07 N:738940, \n",
-      "McStasData: PSDlin type: 1D  I:4.35753e+10 E:5.06929e+07 N:738940, \n",
-      "McStasData: EPSD type: 2D  I:4.62274e+09 E:1.65109e+07 N:78398]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "data = Instr.get_interface_data()\n",
     "print(data)"
@@ -524,31 +378,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f97deafa40e44f4080ee340e89704057",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name EPSD\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "if len(data) != 0:\n",
     "    EPSD_data = functions.name_search(\"EPSD\", data)\n",
@@ -565,24 +397,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "21a32af6dc9749f4946010f7ae22ca6d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(Output(layout=Layout(width='75%')), VBox(children=(Label(value='Choose monitor'), Dropdown(layo…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plotter.interface(data)"
    ]

From 26bf9c1f63c88908a27fac7bde0ce4b3b0016ba3 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Wed, 2 Jun 2021 12:39:34 +0200
Subject: [PATCH 156/403] Updated version of jupyter interface. Now has a live
 mode where the run is split into 5 parts, and data is added as it is
 simulated. This is slower, but provides some data to plot earlier. This is
 accomplished using threading, so it is still possible to navigate the
 plotting while the calculation are performed.

---
 examples/calibration_sample.ipynb             | 857 +++++++++++++++++-
 mcstasscript/interface/instr.py               |   3 +
 mcstasscript/jb_interface/plot_interface.py   |  80 +-
 .../jb_interface/simulation_interface.py      | 301 +++---
 setup.py                                      |   2 +-
 5 files changed, 1080 insertions(+), 163 deletions(-)

diff --git a/examples/calibration_sample.ipynb b/examples/calibration_sample.ipynb
index 68b81833..a47af077 100644
--- a/examples/calibration_sample.ipynb
+++ b/examples/calibration_sample.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -119,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,16 +139,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Union_cylinder ____________________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1mmaterial_string\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [] // material name of this volume, defined using \n",
+      "                          Union_make_material \n",
+      "\u001b[4m\u001b[1mpriority\u001b[0m\u001b[0m [1] // priotiry of the volume (can not be the same as another volume) \n",
+      "                A high priority is on top of low. \n",
+      "\u001b[4m\u001b[1mradius\u001b[0m\u001b[0m [m] // Outer radius volume in (x,z) plane\n",
+      "\u001b[4m\u001b[1myheight\u001b[0m\u001b[0m [m] // Cylinder height in (y) direction\n",
+      "\u001b[1mvisualize\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // set to 0 if you wish to hide this geometry in mcdisplay\n",
+      "\u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m\n",
+      "\u001b[1mtarget_x\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m\n",
+      "\u001b[1mtarget_y\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m // Position of target to focus at\n",
+      "\u001b[1mtarget_z\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m\n",
+      "\u001b[1mfocus_aw\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // horiz. angular dimension of a rectangular area\n",
+      "\u001b[1mfocus_ah\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // vert. angular dimension of a rectangular area\n",
+      "\u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // horiz. dimension of a rectangular area\n",
+      "\u001b[1mfocus_xh\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // vert. dimension of a rectangular area\n",
+      "\u001b[1mfocus_r\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // focusing on circle with this radius\n",
+      "\u001b[1mp_interact\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // probability to interact with this geometry [0-1]\n",
+      "\u001b[1mmask_string\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [] // Comma seperated list of geometry names which this \n",
+      "                      geometry should mask \n",
+      "\u001b[1mmask_setting\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [] // \"All\" or \"Any\", should the masked volume be simulated \n",
+      "                       when the ray is in just one mask, or all. \n",
+      "\u001b[1mnumber_of_activations\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // Number of subsequent Union_master components \n",
+      "                                   that will simulate this geometry \n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
    "source": [
     "Instr.component_help(\"Union_cylinder\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -158,9 +191,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT base_cyl = Union_cylinder\n",
+      "  \u001b[1mmaterial_string\u001b[0m = \u001b[1m\u001b[92m\"Al\"\u001b[0m\u001b[0m []\n",
+      "  \u001b[1mpriority\u001b[0m = \u001b[1m\u001b[92m100\u001b[0m\u001b[0m [1]\n",
+      "  \u001b[1mradius\u001b[0m = \u001b[1m\u001b[92m0.04\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.02\u001b[0m\u001b[0m [m]\n",
+      "AT [0, 0, 0] RELATIVE sample_arm\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# We set the parameters and confirm this looks appropriate with the print function\n",
     "base_cyl.radius = 0.04\n",
@@ -179,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -193,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -232,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -269,7 +316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -286,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,7 +345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -310,7 +357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -332,11 +379,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Al_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Al_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Al                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Cu_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Cu_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Cu                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Ni_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Ni_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Ni                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Ti_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Ti_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Ti                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Pb_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Pb_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Pb                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Fe_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Fe_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "Fe                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "source              Source_div            AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "sample_pos          Arm                   AT      (0, 0, 1)                   RELATIVE source    \n",
+      "sample_arm          Arm                   AT      (0, 0, 0)                   RELATIVE sample_pos \n",
+      "                                          ROTATED (rotation_x, rotation_y, 0) RELATIVE sample_pos\n",
+      "base_cyl            Union_cylinder        AT      (0, 0, 0)                   RELATIVE sample_arm\n",
+      "Cu_cyl              Union_cylinder        AT      (0.03, 0, 0.0)              RELATIVE base_cyl  \n",
+      "Ni_cyl              Union_cylinder        AT      (0.02427, 0, 0.01763)       RELATIVE base_cyl  \n",
+      "Ti_cyl              Union_cylinder        AT      (0.00927, 0, 0.02853)       RELATIVE base_cyl  \n",
+      "Pb_cyl              Union_cylinder        AT      (-0.00927, 0, 0.02853)      RELATIVE base_cyl  \n",
+      "Fe_cyl              Union_cylinder        AT      (-0.02427, 0, 0.01763)      RELATIVE base_cyl  \n",
+      "material_cyl        Union_cylinder        AT      (-0.03, 0, 0.0)             RELATIVE base_cyl  \n",
+      "logger_space_zx_all Union_logger_2D_space AT      (0, 0, 0)                   RELATIVE sample_pos\n",
+      "logger_space_zy_all Union_logger_2D_space AT      (0, 0, 0)                   RELATIVE sample_pos\n",
+      "logger_space_xy_all Union_logger_2D_space AT      (0, 0, 0)                   RELATIVE sample_pos\n",
+      "calibration_sample  Union_master          AT      (0, 0, 0)                   ABSOLUTE           \n",
+      "PSD                 PSD_monitor           AT      (0, 0, 1)                   RELATIVE sample_pos\n",
+      "PSDlin              PSDlin_monitor        AT      (0, 0, 1)                   RELATIVE sample_pos\n",
+      "EPSD                EPSD_monitor          AT      (0, 0, 0)                   RELATIVE PSD       \n"
+     ]
+    }
+   ],
    "source": [
     "Instr.print_components(line_length=117) # Show nice overview"
    ]
@@ -351,13 +441,675 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d7fbe54d8a84ca48c37cb9eb9d0168a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(VBox(children=(HBox(children=(Label(value='energy', layout=Layout(height='32px', width='15%')),…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "Instr.interface()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "INFO: Using directory: \"calibration_sample_20210602_123159\"\n",
+      "INFO: Regenerating c-file: calibration_sample.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./calibration_sample.out\n",
+      "In file included from /Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../contrib/union/Incoherent_process.comp:62:0:\n",
+      "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../share/Union_functions.c: In function ‘write_tagging_tree’:\n",
+      "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../share/Union_functions.c:1377:82: warning: passing argument 4 of ‘qsort’ from incompatible pointer type [enabled by default]\n",
+      "qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "^\n",
+      "In file included from mccode-r.h:41:0:\n",
+      "/usr/include/stdlib.h:165:7: note: expected ‘int (*)(const void *, const void *)’ but argument is of type ‘int (*)(const struct saved_history_struct *, const struct saved_history_struct *)’\n",
+      "void  qsort(void *__base, size_t __nel, size_t __width,\n",
+      "^\n",
+      "In file included from /Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../contrib/union/Incoherent_process.comp:62:0:\n",
+      "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../share/Union_functions.c:1381:3: warning: passing argument 1 of ‘printf_history’ from incompatible pointer type [enabled by default]\n",
+      "MPI_MASTER(\n",
+      "^\n",
+      "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../share/Union_functions.c:1207:6: note: expected ‘struct dynamic_history_list *’ but argument is of type ‘struct saved_history_struct *’\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "^\n",
+      "INFO: ===\n",
+      "Warning: 17 events were removed in Component[33] PSD=PSD_monitor()\n",
+      "(negative time, miss next components, rounding errors, Nan, Inf).\n",
+      "instrument definition parsed\n",
+      "reading particle data...\n",
+      "\n",
+      "Component                         Al_inc AT (0,0,0)    0 m from origin\n",
+      "Component                         Al_pow AT (0,0,0)    0 m from origin\n",
+      "Component                             Al AT (0,0,0)    0 m from origin\n",
+      "Component                         Cu_inc AT (0,0,0)    0 m from origin\n",
+      "Component                         Cu_pow AT (0,0,0)    0 m from origin\n",
+      "Component                             Cu AT (0,0,0)    0 m from origin\n",
+      "Component                         Ni_inc AT (0,0,0)    0 m from origin\n",
+      "Component                         Ni_pow AT (0,0,0)    0 m from origin\n",
+      "Component                             Ni AT (0,0,0)    0 m from origin\n",
+      "Component                         Ti_inc AT (0,0,0)    0 m from origin\n",
+      "Component                         Ti_pow AT (0,0,0)    0 m from origin\n",
+      "Component                             Ti AT (0,0,0)    0 m from origin\n",
+      "Component                         Pb_inc AT (0,0,0)    0 m from origin\n",
+      "Component                         Pb_pow AT (0,0,0)    0 m from origin\n",
+      "Component                             Pb AT (0,0,0)    0 m from origin\n",
+      "Component                         Fe_inc AT (0,0,0)    0 m from origin\n",
+      "Component                         Fe_pow AT (0,0,0)    0 m from origin\n",
+      "Component                             Fe AT (0,0,0)    0 m from origin\n",
+      "Component                         source AT (0,0,0)    0 m from origin\n",
+      "Component                     sample_pos AT (0,0,1)    1 m from origin\n",
+      "Component                     sample_arm AT (0,0,1)    1 m from origin\n",
+      "Component                       base_cyl AT (0,0,1)    1 m from origin\n",
+      "Component                         Cu_cyl AT (-0.03,0,1)    1.03 m from origin\n",
+      "Component                         Ni_cyl AT (-0.02427,0,0.98237)    1.04854 m from origin\n",
+      "Component                         Ti_cyl AT (-0.00927,0,0.97147)    1.06708 m from origin\n",
+      "Component                         Pb_cyl AT (0.00927,0,0.97147)    1.08562 m from origin\n",
+      "Component                         Fe_cyl AT (0.02427,0,0.98237)    1.10416 m from origin\n",
+      "Component                   material_cyl AT (0.03,0,1)    1.1227 m from origin\n",
+      "Component            logger_space_zx_all AT (0,0,1)    1.1527 m from origin\n",
+      "Component            logger_space_zy_all AT (0,0,1)    1.1527 m from origin\n",
+      "Component            logger_space_xy_all AT (0,0,1)    1.1527 m from origin\n",
+      "Component             calibration_sample AT (0,0,0)    2.1527 m from origin\n",
+      "Component                            PSD AT (0,0,2)    4.1527 m from origin\n",
+      "Component                         PSDlin AT (0,0,2)    4.1527 m from origin\n",
+      "Component                           EPSD AT (0,0,2)    4.1527 m from origin\n",
+      "Opening input file '/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "Opening input file '/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../data/Cu.laz' (Table_Read_Offset)\n",
+      "Table from file 'Cu.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
+      "  '# TITLE *-Cu-[FM3-M] Otte, H.M.[1961];# CELL 3.615050 3.615050 3.615050 90. ...'\n",
+      "PowderN: Cu_pow: Reading 19 rows from Cu.laz\n",
+      "PowderN: Cu_pow: Read 19 reflections from file 'Cu.laz'\n",
+      "PowderN: Cu_pow: Vc=47.24 [Angs] sigma_abs=15.12 [barn] sigma_inc=2.2 [barn] reflections=Cu.laz\n",
+      "Opening input file '/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../data/Ni.laz' (Table_Read_Offset)\n",
+      "Table from file 'Ni.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
+      "  '# TITLE *Nickel-Ni-[FM3-M] Swanson, H.E.;Tatge, E.[1954] [carcinogen];# CEL ...'\n",
+      "PowderN: Ni_pow: Reading 19 rows from Ni.laz\n",
+      "PowderN: Ni_pow: Read 19 reflections from file 'Ni.laz'\n",
+      "PowderN: Ni_pow: Vc=43.76 [Angs] sigma_abs=17.96 [barn] sigma_inc=20.8 [barn] reflections=Ni.laz\n",
+      "Opening input file '/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../data/Ti.laz' (Table_Read_Offset)\n",
+      "Table from file 'Ti.laz' (block 1) is 72 x 18 (x=0:5), constant step. interpolation: linear\n",
+      "  '# TITLE *-Ti-[P63/MMC] Pawar, R.R.;Deshpande, V.T.[1968];# CELL 2.950800 2. ...'\n",
+      "PowderN: Ti_pow: Reading 72 rows from Ti.laz\n",
+      "PowderN: Ti_pow: Read 72 reflections from file 'Ti.laz'\n",
+      "PowderN: Ti_pow: Vc=35.33 [Angs] sigma_abs=12.18 [barn] sigma_inc=5.74 [barn] reflections=Ti.laz\n",
+      "Opening input file '/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../data/Pb.laz' (Table_Read_Offset)\n",
+      "Table from file 'Pb.laz' (block 1) is 41 x 18 (x=1:9), constant step. interpolation: linear\n",
+      "  '# TITLE *-Pb-[FM3-M] Bouad, N.; Chapon, L.; Marin-Ayral, R.-M.; B[2003] [to ...'\n",
+      "PowderN: Pb_pow: Reading 41 rows from Pb.laz\n",
+      "PowderN: Pb_pow: Read 41 reflections from file 'Pb.laz'\n",
+      "PowderN: Pb_pow: Vc=121.29 [Angs] sigma_abs=0.684 [barn] sigma_inc=0.012 [barn] reflections=Pb.laz\n",
+      "Opening input file '/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../data/Fe.laz' (Table_Read_Offset)\n",
+      "Table from file 'Fe.laz' (block 1) is 17 x 18 (x=1:5), constant step. interpolation: linear\n",
+      "  '# TITLE *Iron-Fe-[IM3-M] Swanson, H.E.;Tatge, E.[1954] [Iron alpha phase];# ...'\n",
+      "PowderN: Fe_pow: Reading 17 rows from Fe.laz\n",
+      "PowderN: Fe_pow: Read 17 reflections from file 'Fe.laz'\n",
+      "PowderN: Fe_pow: Vc=24.04 [Angs] sigma_abs=5.12 [barn] sigma_inc=0.8 [barn] reflections=Fe.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 12\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "name of process [2]: Cu_inc \n",
+      "component index [2]: 4 \n",
+      "name of process [3]: Cu_pow \n",
+      "component index [3]: 5 \n",
+      "name of process [4]: Ni_inc \n",
+      "component index [4]: 7 \n",
+      "name of process [5]: Ni_pow \n",
+      "component index [5]: 8 \n",
+      "name of process [6]: Ti_inc \n",
+      "component index [6]: 10 \n",
+      "name of process [7]: Ti_pow \n",
+      "component index [7]: 11 \n",
+      "name of process [8]: Pb_inc \n",
+      "component index [8]: 13 \n",
+      "name of process [9]: Pb_pow \n",
+      "component index [9]: 14 \n",
+      "name of process [10]: Fe_inc \n",
+      "component index [10]: 16 \n",
+      "name of process [11]: Fe_pow \n",
+      "component index [11]: 17 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 6\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 1.391570 \n",
+      "number of processes [0]: 2 \n",
+      "name of material    [1]: Cu \n",
+      "component index     [1]: 6 \n",
+      "my_absoprtion       [1]: 32.006800 \n",
+      "number of processes [1]: 2 \n",
+      "name of material    [2]: Ni \n",
+      "component index     [2]: 9 \n",
+      "my_absoprtion       [2]: 41.042000 \n",
+      "number of processes [2]: 2 \n",
+      "name of material    [3]: Ti \n",
+      "component index     [3]: 12 \n",
+      "my_absoprtion       [3]: 34.475000 \n",
+      "number of processes [3]: 2 \n",
+      "name of material    [4]: Pb \n",
+      "component index     [4]: 15 \n",
+      "my_absoprtion       [4]: 0.560640 \n",
+      "number of processes [4]: 2 \n",
+      "name of material    [5]: Fe \n",
+      "component index     [5]: 18 \n",
+      "my_absoprtion       [5]: 21.297800 \n",
+      "number of processes [5]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 6\n",
+      "\n",
+      "name of geometry    [0]: base_cyl \n",
+      "component index     [0]: 22 \n",
+      "Volume.name         [0]: base_cyl \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absoprtion       [0]: 1.391570 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [-1.000000 0.000000 -0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 -1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.040000 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.020000 \n",
+      "Volume.geometry.focus_data.Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: Cu_cyl \n",
+      "component index     [1]: 23 \n",
+      "Volume.name         [1]: Cu_cyl \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absoprtion       [1]: 32.006800 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: -0.030000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [-1.000000 0.000000 -0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 -1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.007000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.019000 \n",
+      "Volume.geometry.focus_data.Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: Ni_cyl \n",
+      "component index     [2]: 24 \n",
+      "Volume.name         [2]: Ni_cyl \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absoprtion       [2]: 41.042000 \n",
+      "Volume.p_physics.number of processes [2]: 2 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: -0.024270 \n",
+      "Volume.geometry.center.y             [2]: 0.000000 \n",
+      "Volume.geometry.center.z             [2]: 0.982370 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [-1.000000 0.000000 -0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 -1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.007000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.019000 \n",
+      "Volume.geometry.focus_data.Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: Ti_cyl \n",
+      "component index     [3]: 25 \n",
+      "Volume.name         [3]: Ti_cyl \n",
+      "Volume.p_physics.is_vacuum           [3]: 0 \n",
+      "Volume.p_physics.my_absoprtion       [3]: 34.475000 \n",
+      "Volume.p_physics.number of processes [3]: 2 \n",
+      "Volume.geometry.shape                [3]: cylinder \n",
+      "Volume.geometry.center.x             [3]: -0.009270 \n",
+      "Volume.geometry.center.y             [3]: 0.000000 \n",
+      "Volume.geometry.center.z             [3]: 0.971470 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [-1.000000 0.000000 -0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 -1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.007000 \n",
+      "Volume.geometry.geometry_parameters.height [3]: 0.019000 \n",
+      "Volume.geometry.focus_data.Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [4]: Pb_cyl \n",
+      "component index     [4]: 26 \n",
+      "Volume.name         [4]: Pb_cyl \n",
+      "Volume.p_physics.is_vacuum           [4]: 0 \n",
+      "Volume.p_physics.my_absoprtion       [4]: 0.560640 \n",
+      "Volume.p_physics.number of processes [4]: 2 \n",
+      "Volume.geometry.shape                [4]: cylinder \n",
+      "Volume.geometry.center.x             [4]: 0.009270 \n",
+      "Volume.geometry.center.y             [4]: 0.000000 \n",
+      "Volume.geometry.center.z             [4]: 0.971470 \n",
+      "Volume.geometry.rotation_matrix[0]           [4]: [-1.000000 0.000000 -0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 -1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.007000 \n",
+      "Volume.geometry.geometry_parameters.height [4]: 0.019000 \n",
+      "Volume.geometry.focus_data.Aim             [4]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [5]: Fe_cyl \n",
+      "component index     [5]: 27 \n",
+      "Volume.name         [5]: Fe_cyl \n",
+      "Volume.p_physics.is_vacuum           [5]: 0 \n",
+      "Volume.p_physics.my_absoprtion       [5]: 21.297800 \n",
+      "Volume.p_physics.number of processes [5]: 2 \n",
+      "Volume.geometry.shape                [5]: cylinder \n",
+      "Volume.geometry.center.x             [5]: 0.024270 \n",
+      "Volume.geometry.center.y             [5]: 0.000000 \n",
+      "Volume.geometry.center.z             [5]: 0.982370 \n",
+      "Volume.geometry.rotation_matrix[0]           [5]: [-1.000000 0.000000 -0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 -1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.007000 \n",
+      "Volume.geometry.geometry_parameters.height [5]: 0.019000 \n",
+      "Volume.geometry.focus_data.Aim             [5]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [6]: material_cyl \n",
+      "component index     [6]: 28 \n",
+      "Volume.name         [6]: material_cyl \n",
+      "Volume.p_physics.is_vacuum           [6]: 0 \n",
+      "Volume.p_physics.my_absoprtion       [6]: 0.560640 \n",
+      "Volume.p_physics.number of processes [6]: 2 \n",
+      "Volume.geometry.shape                [6]: cylinder \n",
+      "Volume.geometry.center.x             [6]: 0.030000 \n",
+      "Volume.geometry.center.y             [6]: 0.000000 \n",
+      "Volume.geometry.center.z             [6]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [6]: [-1.000000 0.000000 -0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 -1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.007000 \n",
+      "Volume.geometry.geometry_parameters.height [6]: 0.019000 \n",
+      "Volume.geometry.focus_data.Aim             [6]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 8\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      "Generating children lists --------------------------- \n",
+      "LIST: Children for Volume 0 = [1,2,3,4,5,6,7]\n",
+      "LIST: Children for Volume 1 (temporary_list) = [1,2,3,4,5,6,7]\n",
+      "LIST: Children for Volume 1 (permanent_list) = [2,3,4,5,6,7]\n",
+      "LIST: Children for Volume 2 (temporary_list) = [2]\n",
+      "LIST: Children for Volume 2 (permanent_list) = []\n",
+      "LIST: Children for Volume 3 (temporary_list) = [3]\n",
+      "LIST: Children for Volume 3 (permanent_list) = []\n",
+      "LIST: Children for Volume 4 (temporary_list) = [4]\n",
+      "LIST: Children for Volume 4 (permanent_list) = []\n",
+      "LIST: Children for Volume 5 (temporary_list) = [5]\n",
+      "LIST: Children for Volume 5 (permanent_list) = []\n",
+      "LIST: Children for Volume 6 (temporary_list) = [6]\n",
+      "LIST: Children for Volume 6 (permanent_list) = []\n",
+      "LIST: Children for Volume 7 (temporary_list) = [7]\n",
+      "LIST: Children for Volume 7 (permanent_list) = []\n",
+      "LIST: True children for Volume (post mask) 1 = [2,3,4,5,6,7]\n",
+      "LIST: True children for Volume (post mask) 2 = []\n",
+      "LIST: True children for Volume (post mask) 3 = []\n",
+      "LIST: True children for Volume (post mask) 4 = []\n",
+      "LIST: True children for Volume (post mask) 5 = []\n",
+      "LIST: True children for Volume (post mask) 6 = []\n",
+      "LIST: True children for Volume (post mask) 7 = []\n",
+      "\n",
+      "Generating overlap lists ---------------------------- \n",
+      "LIST: Overlaps for Volume 0 = [1,2,3,4,5,6,7]\n",
+      "LIST: Overlaps for Volume (pre mask) 1 = [0,1,2,3,4,5,6,7]\n",
+      "LIST: Overlaps for Volume (pre mask) 2 = [0,1,2]\n",
+      "LIST: Overlaps for Volume (pre mask) 3 = [0,1,3]\n",
+      "LIST: Overlaps for Volume (pre mask) 4 = [0,1,4]\n",
+      "LIST: Overlaps for Volume (pre mask) 5 = [0,1,5]\n",
+      "LIST: Overlaps for Volume (pre mask) 6 = [0,1,6]\n",
+      "LIST: Overlaps for Volume (pre mask) 7 = [0,1,7]\n",
+      "LIST: Overlaps for Volume (post mask) 1 = [0,2,3,4,5,6,7]\n",
+      "LIST: Overlaps for Volume (post mask) 2 = [0,1]\n",
+      "LIST: Overlaps for Volume (post mask) 3 = [0,1]\n",
+      "LIST: Overlaps for Volume (post mask) 4 = [0,1]\n",
+      "LIST: Overlaps for Volume (post mask) 5 = [0,1]\n",
+      "LIST: Overlaps for Volume (post mask) 6 = [0,1]\n",
+      "LIST: Overlaps for Volume (post mask) 7 = [0,1]\n",
+      "\n",
+      "Generating parents lists ---------------------------- \n",
+      "LIST: Parents for Volume 0 = []\n",
+      "LIST: Parents for Volume 1 = [0]\n",
+      "LIST: Parents for Volume 2 = [0,1]\n",
+      "LIST: Parents for Volume 3 = [0,1]\n",
+      "LIST: Parents for Volume 4 = [0,1]\n",
+      "LIST: Parents for Volume 5 = [0,1]\n",
+      "LIST: Parents for Volume 6 = [0,1]\n",
+      "LIST: Parents for Volume 7 = [0,1]\n",
+      "\n",
+      "Generating parents lists (ignoring masks) ----------- \n",
+      "LIST: Parents for Volume 0 = []\n",
+      "LIST: Parents for Volume 1 = [0]\n",
+      "LIST: Parents for Volume 2 = [0,1]\n",
+      "LIST: Parents for Volume 3 = [0,1]\n",
+      "LIST: Parents for Volume 4 = [0,1]\n",
+      "LIST: Parents for Volume 5 = [0,1]\n",
+      "LIST: Parents for Volume 6 = [0,1]\n",
+      "LIST: Parents for Volume 7 = [0,1]\n",
+      "\n",
+      "Generating parents lists ---------------------------- \n",
+      "LIST: Parents for Volume 0 = []\n",
+      "LIST: Parents for Volume 1 = [0]\n",
+      "LIST: Parents for Volume 2 = [0,1]\n",
+      "LIST: Parents for Volume 3 = [0,1]\n",
+      "LIST: Parents for Volume 4 = [0,1]\n",
+      "LIST: Parents for Volume 5 = [0,1]\n",
+      "LIST: Parents for Volume 6 = [0,1]\n",
+      "LIST: Parents for Volume 7 = [0,1]\n",
+      "\n",
+      "Generating parents lists (ignoring masks) ----------- \n",
+      "LIST: Parents for Volume 0 = []\n",
+      "LIST: Parents for Volume 1 = [0]\n",
+      "LIST: Parents for Volume 2 = [0,1]\n",
+      "LIST: Parents for Volume 3 = [0,1]\n",
+      "LIST: Parents for Volume 4 = [0,1]\n",
+      "LIST: Parents for Volume 5 = [0,1]\n",
+      "LIST: Parents for Volume 6 = [0,1]\n",
+      "LIST: Parents for Volume 7 = [0,1]\n",
+      "\n",
+      "Generating intersect check lists -------------------- \n",
+      "LIST: Intersect check list for Volume 0 = [1]\n",
+      "LIST: Mask intersect check list for Volume 0 = []\n",
+      "LIST: Intersect check list for Volume 1 = [2,3,4,5,6,7]\n",
+      "LIST: Mask intersect check list for Volume 1 = []\n",
+      "LIST: Intersect check list for Volume 2 = []\n",
+      "LIST: Mask intersect check list for Volume 2 = []\n",
+      "LIST: Intersect check list for Volume 3 = []\n",
+      "LIST: Mask intersect check list for Volume 3 = []\n",
+      "LIST: Intersect check list for Volume 4 = []\n",
+      "LIST: Mask intersect check list for Volume 4 = []\n",
+      "LIST: Intersect check list for Volume 5 = []\n",
+      "LIST: Mask intersect check list for Volume 5 = []\n",
+      "LIST: Intersect check list for Volume 6 = []\n",
+      "LIST: Mask intersect check list for Volume 6 = []\n",
+      "LIST: Intersect check list for Volume 7 = []\n",
+      "LIST: Mask intersect check list for Volume 7 = []\n",
+      "\n",
+      "Generating grandparents lists ----------------------- \n",
+      "LIST: Grandparents for Volume 0 = []\n",
+      "LIST: Grandparents for Volume 1 = []\n",
+      "LIST: Grandparents for Volume 2 = []\n",
+      "LIST: Grandparents for Volume 3 = []\n",
+      "LIST: Grandparents for Volume 4 = []\n",
+      "LIST: Grandparents for Volume 5 = []\n",
+      "LIST: Grandparents for Volume 6 = []\n",
+      "LIST: Grandparents for Volume 7 = []\n",
+      "grandparents_lists[0]->num_elements = 0 \n",
+      "\n",
+      "Generating grandparents lists ----------------------- \n",
+      "LIST: Grandparents for Volume 0 = []\n",
+      "LIST: Grandparents for Volume 1 = []\n",
+      "LIST: Grandparents for Volume 2 = []\n",
+      "LIST: Grandparents for Volume 3 = []\n",
+      "LIST: Grandparents for Volume 4 = []\n",
+      "LIST: Grandparents for Volume 5 = []\n",
+      "LIST: Grandparents for Volume 6 = []\n",
+      "LIST: Grandparents for Volume 7 = []\n",
+      "\n",
+      "Generating grandparents lists ----------------------- \n",
+      "LIST: Grandparents for Volume 0 = []\n",
+      "LIST: Grandparents for Volume 1 = []\n",
+      "LIST: Grandparents for Volume 2 = []\n",
+      "LIST: Grandparents for Volume 3 = []\n",
+      "LIST: Grandparents for Volume 4 = []\n",
+      "LIST: Grandparents for Volume 5 = []\n",
+      "LIST: Grandparents for Volume 6 = []\n",
+      "LIST: Grandparents for Volume 7 = []\n",
+      "\n",
+      "Generating grandparents lists ----------------------- \n",
+      "LIST: Grandparents for Volume 0 = []\n",
+      "LIST: Grandparents for Volume 1 = []\n",
+      "LIST: Grandparents for Volume 2 = []\n",
+      "LIST: Grandparents for Volume 3 = []\n",
+      "LIST: Grandparents for Volume 4 = []\n",
+      "LIST: Grandparents for Volume 5 = []\n",
+      "LIST: Grandparents for Volume 6 = []\n",
+      "LIST: Grandparents for Volume 7 = []\n",
+      "\n",
+      "Generating destinations lists ----------------------- \n",
+      "LIST: Destinations list for Volume 1 = [0]\n",
+      "LIST: Destinations list for Volume 2 = [1]\n",
+      "LIST: Destinations list for Volume 3 = [1]\n",
+      "LIST: Destinations list for Volume 4 = [1]\n",
+      "LIST: Destinations list for Volume 5 = [1]\n",
+      "LIST: Destinations list for Volume 6 = [1]\n",
+      "LIST: Destinations list for Volume 7 = [1]\n",
+      "\n",
+      "Generating reduced destination lists ----------------------- \n",
+      "LIST: Reduced destinations list for Volume 0 = []\n",
+      "LIST: Reduced destinations list for Volume 1 = []\n",
+      "LIST: Reduced destinations list for Volume 2 = [1]\n",
+      "LIST: Reduced destinations list for Volume 3 = [1]\n",
+      "LIST: Reduced destinations list for Volume 4 = [1]\n",
+      "LIST: Reduced destinations list for Volume 5 = [1]\n",
+      "LIST: Reduced destinations list for Volume 6 = [1]\n",
+      "LIST: Reduced destinations list for Volume 7 = [1]\n",
+      "\n",
+      "Generating direct children lists ----------------------- \n",
+      "LIST: Children list for Volume        0 = [1,2,3,4,5,6,7]\n",
+      "LIST: Direct_children list for Volume 0 = [1]\n",
+      "LIST: Children list for Volume        1 = [2,3,4,5,6,7]\n",
+      "LIST: Direct_children list for Volume 1 = [2,3,4,5,6,7]\n",
+      "LIST: Children list for Volume        2 = []\n",
+      "LIST: Direct_children list for Volume 2 = []\n",
+      "LIST: Children list for Volume        3 = []\n",
+      "LIST: Direct_children list for Volume 3 = []\n",
+      "LIST: Children list for Volume        4 = []\n",
+      "LIST: Direct_children list for Volume 4 = []\n",
+      "LIST: Children list for Volume        5 = []\n",
+      "LIST: Direct_children list for Volume 5 = []\n",
+      "LIST: Children list for Volume        6 = []\n",
+      "LIST: Direct_children list for Volume 6 = []\n",
+      "LIST: Children list for Volume        7 = []\n",
+      "LIST: Direct_children list for Volume 7 = []\n",
+      "LIST: Allowed starting volume logic list = [1,0,0,0,0,0,0,0]\n",
+      "\n",
+      "Generating start destinations list ------------------------------ \n",
+      "LIST: Starting destinations list = [1,2,3,4,5,6,7]\n",
+      "\n",
+      "Generating reduced start destination list ----------------------- \n",
+      "LIST: Reduced start destinations list = [1]\n",
+      "LIST: Start logic list = [0,1,1,1,1,1,1,1]\n",
+      "\n",
+      "Generating next volume list ------------------------------------- \n",
+      "LIST: Next volume list                0 = [1]\n",
+      "LIST: Next volume list                1 = [0,2,3,4,5,6,7]\n",
+      "LIST: Next volume list                2 = [1]\n",
+      "LIST: Next volume list                3 = [1]\n",
+      "LIST: Next volume list                4 = [1]\n",
+      "LIST: Next volume list                5 = [1]\n",
+      "LIST: Next volume list                6 = [1]\n",
+      "LIST: Next volume list                7 = [1]\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7]\n",
+      "LIST: Direct_children for Volume           0 = [1]\n",
+      "LIST: Intersect_check_list for Volume      0 = [1]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [1]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "LIST: Children for Volume                  1 = [2,3,4,5,6,7]\n",
+      "LIST: Direct_children for Volume           1 = [2,3,4,5,6,7]\n",
+      "LIST: Intersect_check_list for Volume      1 = [2,3,4,5,6,7]\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = []\n",
+      "LIST: Next_volume_list for Volume          1 = [0,2,3,4,5,6,7]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "LIST: Children for Volume                  2 = []\n",
+      "LIST: Direct_children for Volume           2 = []\n",
+      "LIST: Intersect_check_list for Volume      2 = []\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [1]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [1]\n",
+      "LIST: Next_volume_list for Volume          2 = [1]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "LIST: Children for Volume                  3 = []\n",
+      "LIST: Direct_children for Volume           3 = []\n",
+      "LIST: Intersect_check_list for Volume      3 = []\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [1]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = [1]\n",
+      "LIST: Next_volume_list for Volume          3 = [1]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "LIST: Children for Volume                  4 = []\n",
+      "LIST: Direct_children for Volume           4 = []\n",
+      "LIST: Intersect_check_list for Volume      4 = []\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [1]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = [1]\n",
+      "LIST: Next_volume_list for Volume          4 = [1]\n",
+      "      Is_vacuum for Volume                 4 = 0\n",
+      "      is_mask_volume for Volume            4 = 0\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = []\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 0\n",
+      "\n",
+      "LIST: Children for Volume                  5 = []\n",
+      "LIST: Direct_children for Volume           5 = []\n",
+      "LIST: Intersect_check_list for Volume      5 = []\n",
+      "LIST: Mask_intersect_list for Volume       5 = []\n",
+      "LIST: Destinations_list for Volume         5 = [1]\n",
+      "LIST: Reduced_destinations_list for Volume 5 = [1]\n",
+      "LIST: Next_volume_list for Volume          5 = [1]\n",
+      "      Is_vacuum for Volume                 5 = 0\n",
+      "      is_mask_volume for Volume            5 = 0\n",
+      "      is_masked_volume for Volume          5 = 0\n",
+      "      is_exit_volume for Volume            5 = 0\n",
+      "LIST: mask_list for Volume                 5 = []\n",
+      "LIST: masked_by_list for Volume            5 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
+      "      mask_mode for Volume                 5 = 0\n",
+      "\n",
+      "LIST: Children for Volume                  6 = []\n",
+      "LIST: Direct_children for Volume           6 = []\n",
+      "LIST: Intersect_check_list for Volume      6 = []\n",
+      "LIST: Mask_intersect_list for Volume       6 = []\n",
+      "LIST: Destinations_list for Volume         6 = [1]\n",
+      "LIST: Reduced_destinations_list for Volume 6 = [1]\n",
+      "LIST: Next_volume_list for Volume          6 = [1]\n",
+      "      Is_vacuum for Volume                 6 = 0\n",
+      "      is_mask_volume for Volume            6 = 0\n",
+      "      is_masked_volume for Volume          6 = 0\n",
+      "      is_exit_volume for Volume            6 = 0\n",
+      "LIST: mask_list for Volume                 6 = []\n",
+      "LIST: masked_by_list for Volume            6 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
+      "      mask_mode for Volume                 6 = 0\n",
+      "\n",
+      "LIST: Children for Volume                  7 = []\n",
+      "LIST: Direct_children for Volume           7 = []\n",
+      "LIST: Intersect_check_list for Volume      7 = []\n",
+      "LIST: Mask_intersect_list for Volume       7 = []\n",
+      "LIST: Destinations_list for Volume         7 = [1]\n",
+      "LIST: Reduced_destinations_list for Volume 7 = [1]\n",
+      "LIST: Next_volume_list for Volume          7 = [1]\n",
+      "      Is_vacuum for Volume                 7 = 0\n",
+      "      is_mask_volume for Volume            7 = 0\n",
+      "      is_masked_volume for Volume          7 = 0\n",
+      "      is_exit_volume for Volume            7 = 0\n",
+      "LIST: mask_list for Volume                 7 = []\n",
+      "LIST: masked_by_list for Volume            7 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
+      "      mask_mode for Volume                 7 = 0\n",
+      "\n",
+      "Union_master component calibration_sample initialized sucessfully\n",
+      "Detector: logger_space_zx_all_I=4.24506e+09 logger_space_zx_all_ERR=7.73168e+08 logger_space_zx_all_N=40 \"space_zx.dat\"\n",
+      "Detector: logger_space_zy_all_I=4.24506e+09 logger_space_zy_all_ERR=7.73168e+08 logger_space_zy_all_N=40 \"space_zy.dat\"\n",
+      "Detector: logger_space_xy_all_I=4.24506e+09 logger_space_xy_all_ERR=7.73168e+08 logger_space_xy_all_N=40 \"space_xy.dat\"\n",
+      "Detector: PSD_I=4.38373e+10 PSD_ERR=2.93556e+09 PSD_N=223 \"PSD.dat\"\n",
+      "Detector: PSDlin_I=4.38373e+10 PSDlin_ERR=2.93556e+09 PSDlin_N=223 \"PSDlin.dat\"\n",
+      "Detector: EPSD_I=2.75212e+09 EPSD_ERR=7.35534e+08 EPSD_N=14 \"EPSD.dat\"\n",
+      "\n",
+      "\n",
+      "Top 20 most common histories. Shows the index of volumes entered (VX), and the scattering processes (PX)\n",
+      "267\t N I=5.248679E+10 \t V0 \n",
+      "4\t N I=7.863190E+08 \t V0 -> V1 -> V0 \n",
+      "3\t N I=5.316424E+08 \t V0 -> V1 -> V5 -> P1 -> V1 -> V0 \n",
+      "3\t N I=5.228126E+08 \t V0 -> V1 -> V6 -> P1 -> V1 -> V0 \n",
+      "3\t N I=4.435212E+08 \t V0 -> V1 -> V5 -> V1 -> P1 -> V0 \n",
+      "2\t N I=3.931595E+08 \t V0 -> V1 -> V5 -> V1 -> V0 \n",
+      "2\t N I=2.714067E+08 \t V0 -> V1 -> P1 -> V0 \n",
+      "1\t N I=1.965797E+08 \t V0 -> V1 -> V2 -> V1 -> V0 \n",
+      "1\t N I=1.965797E+08 \t V0 -> V1 -> V7 -> V1 -> V0 \n",
+      "1\t N I=1.656375E+08 \t V0 -> V1 -> V4 -> V1 -> P1 -> P1 -> V0 \n",
+      "1\t N I=1.448487E+08 \t V0 -> V1 -> V4 -> V1 -> P1 -> V0 \n",
+      "1\t N I=1.323150E+08 \t V0 -> V1 -> V5 -> P1 -> P1 -> V1 -> V0 \n",
+      "1\t N I=1.289778E+08 \t V0 -> V1 -> P0 -> V0 \n",
+      "1\t N I=1.107849E+08 \t V0 -> V1 -> V3 -> P1 -> P1 -> P1 -> V1 -> V0 \n",
+      "1\t N I=1.023142E+08 \t V0 -> V1 -> V3 -> P1 -> V1 -> V0 \n",
+      "1\t N I=6.676574E+07 \t V0 -> V1 -> V6 -> P1 -> P1 -> V1 -> V0 \n",
+      "1\t N I=5.751379E+07 \t V0 -> V1 -> V6 -> P1 -> P1 -> P1 -> V1 -> V5 -> V1 -> V0 \n",
+      "1\t N I=5.535036E+07 \t V0 -> V1 -> V3 -> P0 -> V1 -> V0 \n",
+      "1\t N I=2.879975E+07 \t V0 -> V1 -> V4 -> P1 -> P0 -> V1 -> V0 \n",
+      "1\t N I=2.387658E+07 \t V0 -> V1 -> V4 -> P0 -> V1 -> V0 \n",
+      "\n",
+      "starting particle parsing\n",
+      "ended particle parsing\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "Instr.show_instrument()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -368,9 +1120,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "McStasData: logger_space_zx_all type: 2D  I:4.46932e+09 E:9.78589e+06 N:334938, \n",
+      "McStasData: logger_space_zy_all type: 2D  I:4.46932e+09 E:9.78589e+06 N:334938, \n",
+      "McStasData: logger_space_xy_all type: 2D  I:4.46932e+09 E:9.78589e+06 N:334938, \n",
+      "McStasData: PSD type: 2D  I:4.35683e+10 E:3.58426e+07 N:1.47764e+06, \n",
+      "McStasData: PSDlin type: 1D  I:4.35683e+10 E:3.58426e+07 N:1.47764e+06, \n",
+      "McStasData: EPSD type: 2D  I:4.64794e+09 E:1.1707e+07 N:157656]\n"
+     ]
+    }
+   ],
    "source": [
     "data = Instr.get_interface_data()\n",
     "print(data)"
@@ -378,9 +1144,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "842a963bb6c6401a9393877978ba1c10",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name EPSD\n"
+     ]
+    }
+   ],
    "source": [
     "if len(data) != 0:\n",
     "    EPSD_data = functions.name_search(\"EPSD\", data)\n",
@@ -397,9 +1185,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31522898410542958ea06e8ab06e057d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(Output(layout=Layout(width='75%')), VBox(children=(Label(value='Choose monitor'), Dropdown(layo…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plotter.interface(data)"
    ]
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 372a97f3..91a4ea02 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1590,6 +1590,8 @@ def run_full_instrument(self, **kwargs):
                 Sets parameters
             custom_flags : str
                 Sets custom_flags passed to mcrun
+            force_compile : bool
+                If True (default) new instrument file is written, otherwise not
             executable_path : str
                 Path to mcrun command, "" if already in path
         """
@@ -1692,6 +1694,7 @@ def interface(self):
 
         Needs "%matplotlib widget" in notebook to work correctly
         """
+        self.write_full_instrument()
         self.widget_interface = SimInterface(self)
         return self.widget_interface.show_interface()
 
diff --git a/mcstasscript/jb_interface/plot_interface.py b/mcstasscript/jb_interface/plot_interface.py
index cda1878e..0e70ada9 100644
--- a/mcstasscript/jb_interface/plot_interface.py
+++ b/mcstasscript/jb_interface/plot_interface.py
@@ -1,10 +1,11 @@
 import sys
 import os
+import threading
 
 from collections import OrderedDict
 
 import ipywidgets as widgets
-from IPython.display import display
+from IPython.display import display, clear_output
 
 import matplotlib.pyplot as plt
 
@@ -29,6 +30,8 @@ def __init__(self, data=None):
         """
         self.data = data
 
+        output = None
+
         # Variables related to monitor choice
         self.monitor_dropdown = None
         self.current_monitor = None
@@ -99,45 +102,52 @@ def new_plot(self):
     def update_plot(self):
         """
         Updates the plot with current data, monitor and plot options
+
+        Threading lock is used as this method is used in a threading context
+        and can easily fail if new data is written while plotting. The lock
+        prevents this from happening.
         """
+        lock = threading.Lock()
 
-        # Clear plot first
-        self.ax.cla()
-        #self.ax.xaxis.set_ticks([])
-        #self.ax.yaxis.set_ticks([])
-        self.colorbar_ax.cla()
-        self.colorbar_ax.xaxis.set_ticks([])
-        self.colorbar_ax.yaxis.set_ticks([])
+        with lock:
+            # Clear plot first
+            self.ax.cla()
+            #self.ax.xaxis.set_ticks([])
+            #self.ax.yaxis.set_ticks([])
+            self.colorbar_ax.cla()
+            self.colorbar_ax.xaxis.set_ticks([])
+            self.colorbar_ax.yaxis.set_ticks([])
 
-        # Display message if not data can be plotted
-        if self.data is None:
-            self.ax.text(0.3, 0.5, "No data available yet")
-            return
+            # Display message if not data can be plotted
+            if self.data is None:
+                self.ax.text(0.3, 0.5, "No data available yet")
+                return
 
-        if len(self.data) == 0:
-            self.ax.text(0.25, 0.5, "Simulation returned no data")
-            return
+            if len(self.data) == 0:
+                self.ax.text(0.25, 0.5, "Simulation returned no data")
+                return
 
-        if self.current_monitor is None:
-            self.ax.text(0.3, 0.5, "Select a monitor to plot")
-            return
+            if self.current_monitor is None:
+                self.ax.text(0.3, 0.5, "Select a monitor to plot")
+                return
 
-        # Get monitor and establish plot options
-        monitor = name_search(self.current_monitor, self.data)
-        plot_options = {"show_colorbar": True, "log": self.log_mode, "colormap": self.colormap}
-        if self.orders_of_mag != "disabled":
-            plot_options["orders_of_mag"] = self.orders_of_mag
-        else:
-            plot_options["orders_of_mag"] = 300 # Default value in McStasPlotOptions
+            # Get monitor and establish plot options
+            monitor = name_search(self.current_monitor, self.data)
+            plot_options = {"show_colorbar": True, "log": self.log_mode, "colormap": self.colormap}
+            if self.orders_of_mag != "disabled":
+                plot_options["orders_of_mag"] = self.orders_of_mag
+            else:
+                plot_options["orders_of_mag"] = 300 # Default value in McStasPlotOptions
 
-        #print("Plotting with: ", plot_options)
-        monitor.set_plot_options(**plot_options)
-        with HiddenPrints():
-            plotter._plot_fig_ax(monitor, self.fig, self.ax, colorbar_axes=self.colorbar_ax)
+            #print("Plotting with: ", plot_options)
+            monitor.set_plot_options(**plot_options)
+            with HiddenPrints():
+                plotter._plot_fig_ax(monitor, self.fig, self.ax, colorbar_axes=self.colorbar_ax)
 
-        self.colorbar_ax.set_aspect(20)
+            self.colorbar_ax.set_aspect(20)
 
-        plt.tight_layout()
+            plt.tight_layout()
+            self.fig.canvas.draw()
 
     def show_interface(self):
         """
@@ -305,6 +315,14 @@ def set_data(self, data):
 
         monitor_names = []
         for data in self.data:
+
+            # Ensure data names are unique
+            original_name = data.name
+            index = 1
+            while data.name in monitor_names:
+                data.name = original_name + "_" + str(index)
+                index += 1
+
             monitor_names.append(data.name)
 
         self.widget.options = monitor_names
diff --git a/mcstasscript/jb_interface/simulation_interface.py b/mcstasscript/jb_interface/simulation_interface.py
index f13a33ae..346b8151 100644
--- a/mcstasscript/jb_interface/simulation_interface.py
+++ b/mcstasscript/jb_interface/simulation_interface.py
@@ -1,5 +1,8 @@
 import sys
 import os
+import numpy as np
+import threading
+import copy
 
 import ipywidgets as widgets
 from IPython.display import display
@@ -14,89 +17,6 @@
 from mcstasscript.jb_interface.widget_helpers import get_parameter_default
 
 
-class ParameterWidget:
-    """
-    Widget for parameter object from McStasScript instrument
-    """
-    def __init__(self, parameter, parameters):
-        """
-        Describes a widget for a parameter object given all parameters
-
-        When no options are given in ParameterVariable object, the widget will
-        be a textfield where the user can input the value. If the options
-        attribute is used, the widget will be a dropdown menu with available
-        options. The make_widget method returns the widget, and the update
-        function is called whenever the user interacts with the widget.
-
-        The widget shows parameter name, the interactive widget and a comment
-
-        Parameters
-        ----------
-
-        parameter: McStasScript ParameterVariable object
-            The parameter this widget should represent
-
-        parameters: dict of McStasScript ParameterVariable objects
-            Dict with all parameter objects of the instrument
-        """
-
-        self.parameter = parameter
-        self.parameters = parameters
-
-        if parameter_has_default(parameter):
-            self.default_value = get_parameter_default(parameter)
-        else:
-            self.default_value = ""
-
-        self.name = parameter.name
-        self.comment = parameter.comment
-
-    def make_widget(self):
-        """
-        Returns widget with parameter name, interactive widget and comment
-        """
-        label = widgets.Label(value=self.name,
-                              layout=widgets.Layout(width='15%', height='32px'))
-        if self.parameter.options is not None:
-            par_widget = widgets.Dropdown(options=self.parameter.options,
-                                          layout=widgets.Layout(width='10%', height='32px'))
-            if self.default_value != "":
-                if self.default_value in self.parameter.options:
-                    par_widget.value = self.default_value
-                elif self.default_value.strip("'") in self.parameter.options:
-                    par_widget.value = self.default_value.strip("'")
-                elif self.default_value.strip('"') in self.parameter.options:
-                    par_widget.value = self.default_value.strip('"')
-                else:
-                    raise KeyError("default value not found in options for parameter: "
-                                   + str(self.parameter.name))
-
-        else:
-            par_widget = widgets.Text(value=str(self.default_value),
-                                      layout=widgets.Layout(width='10%', height='32px'))
-        comment = widgets.Label(value=self.comment,
-                                layout=widgets.Layout(width='75%', height='32px'))
-
-        par_widget.observe(self.update, "value")
-
-        return widgets.HBox([label, par_widget, comment])
-
-    def update(self, change):
-        """
-        Update function called whenever the user updates the widget
-
-        When strings parameters are used, this function adds the necessary
-        quotation marks if none are provided.
-        """
-        new_value = change.new
-        if self.parameter.type == "string":
-            if type(new_value) is str:
-                if not (new_value[0] == '"' or new_value[0] == "'"):
-                    new_value = '"' + new_value + '"'
-
-        self.parameters[self.name] = new_value
-
-
 class SimInterface:
     """
     Class for setting up widget that controls McStasScript instrument and plot
@@ -128,10 +48,17 @@ def __init__(self, instrument):
         self.plot_interface = None
 
         self.run_button = None
+        self.live_widget = None
+        self.progress_bar = None
+        self.sim_steps = 5
 
         self.ncount = "1E6"
         self.mpi = "disabled"
 
+        self.thread_data = None
+        self.thread = None
+        self.run_arguments = None
+
         self.parameters = {}
         # get default parameters from instrument
         for parameter in self.instrument.parameter_list:
@@ -151,15 +78,9 @@ def make_parameter_widgets(self):
 
         return widgets.VBox(parameter_widgets)
 
-    def run_simulation(self, change):
+    def run_simulation_thread(self, change):
         """
-        Performs the simulation with current parameters and settings.
-
-        Changes icon on button to hourglass while simulation is running, then
-        returns to calculator icon.
-
-        Has dummy parameter change to allow the method to be used in on_click
-        method of the run button.
+        Runs simulation as thread, allowing user to update plots simultaneously
 
         Parameters
         ----------
@@ -167,27 +88,67 @@ def run_simulation(self, change):
         change: widget change
             Not used
         """
-        run_arguments = {"foldername": "interface",
+
+        thread = threading.Thread(target=self.run_simulation_live)
+        thread.start()
+
+    def run_simulation_live(self):
+        """
+        Performs the simulation with current parameters and settings.
+
+        When live mode is used, updates plot as more data is added.
+
+        Changes icon on button to hourglass while simulation is running, then
+        returns to calculator icon.
+        """
+
+        lock = threading.Lock()
+
+        if self.live_widget.value:
+            sim_parts = self.sim_steps
+        else:
+            sim_parts = 1
+
+        part_ncount = int(float(self.ncount)/sim_parts)
+
+        run_arguments = {"foldername": "interface_" + self.instrument.name,
                          "increment_folder_name": True,
                          "parameters": self.parameters,
-                         "ncount": int(float(self.ncount))}
+                         "ncount": part_ncount,
+                         "force_compile": False}
         if self.mpi != "disabled":
             run_arguments["mpi"] = self.mpi
 
         self.run_button.icon = "hourglass"
         #print("Running with:", run_arguments)
 
-        try:
-            with HiddenPrints():
-                data = self.instrument.run_full_instrument(**run_arguments)
-            #data = self.instrument.run_full_instrument(**run_arguments)
-        except NameError:
-            print("McStas run failed.")
-            data = []
+        if self.live_widget.value:
+            self.progress_bar.layout.visibility = 'visible'
+        else:
+            self.progress_bar.layout.visibility = 'hidden'
 
-        self.run_button.icon = "calculator"
+        self.progress_bar.value = 0
+        plot_data = None
+        for index in range(sim_parts):
+            try:
+                with HiddenPrints():
+                    data = self.instrument.run_full_instrument(**run_arguments)
+            except NameError:
+                print("McStas run failed.")
+                data = []
+
+            self.progress_bar.value = index + 1
 
-        self.plot_interface.set_data(data)
+            if plot_data is None:
+                plot_data = data
+            else:
+                add_data(plot_data, data)
+
+            with lock:
+                sent_data = copy.deepcopy(plot_data)
+                self.plot_interface.set_data(sent_data)
+
+        self.run_button.icon = "calculator"
 
     def make_run_button(self):
         """
@@ -200,7 +161,8 @@ def make_run_button(self):
             tooltip='Runs the simulation with current parameters',
             icon='calculator'  # (FontAwesome names without the `fa-` prefix)
         )
-        button.on_click(self.run_simulation)
+        button.on_click(self.run_simulation_thread)
+
         return button
 
     def make_ncount_field(self):
@@ -267,6 +229,24 @@ def update_mpi(self, change):
         except ValueError:
             pass
 
+    def make_live_checkmark(self):
+        """
+        Makes widget for choosing live simulations on / off
+        """
+        widget = widgets.Checkbox(value=False, description="Live results")
+
+        return widget
+
+    def make_progress_bar(self):
+        """
+        Makes a progress bar for live simulations
+        """
+        widget = widgets.IntProgress(value=0, min=0, max=self.sim_steps,
+                                     description="Sim progress",
+                                     orientation="horizontal")
+
+        return widget
+
     def show_interface(self):
         """
         Builds and shows widget interface
@@ -276,11 +256,15 @@ def show_interface(self):
         parameter_widgets = self.make_parameter_widgets()
 
         # Make simulation controls
+        self.live_widget = self.make_live_checkmark()
+        self.progress_bar = self.make_progress_bar()
+        self.progress_bar.layout.visibility = "hidden"
         self.run_button = self.make_run_button()
         ncount_field = self.make_ncount_field()
         mpi_field = self.make_mpi_field()
 
-        simulation_widget = widgets.HBox([self.run_button, ncount_field, mpi_field])
+        simulation_widget = widgets.HBox([self.run_button, ncount_field, mpi_field,
+                                          self.live_widget, self.progress_bar])
                                          #layout=widgets.Layout(border="solid"))
 
         self.plot_interface = plot_interface.PlotInterface()
@@ -289,4 +273,113 @@ def show_interface(self):
         return widgets.VBox([parameter_widgets, simulation_widget, plot_widget])
 
 
+class ParameterWidget:
+    """
+    Widget for parameter object from McStasScript instrument
+    """
+    def __init__(self, parameter, parameters):
+        """
+        Describes a widget for a parameter object given all parameters
+
+        When no options are given in ParameterVariable object, the widget will
+        be a textfield where the user can input the value. If the options
+        attribute is used, the widget will be a dropdown menu with available
+        options. The make_widget method returns the widget, and the update
+        function is called whenever the user interacts with the widget.
+
+        The widget shows parameter name, the interactive widget and a comment
+
+        Parameters
+        ----------
+
+        parameter: McStasScript ParameterVariable object
+            The parameter this widget should represent
+
+        parameters: dict of McStasScript ParameterVariable objects
+            Dict with all parameter objects of the instrument
+        """
+
+        self.parameter = parameter
+        self.parameters = parameters
+
+        if parameter_has_default(parameter):
+            self.default_value = get_parameter_default(parameter)
+        else:
+            self.default_value = ""
+
+        self.name = parameter.name
+        self.comment = parameter.comment
+
+    def make_widget(self):
+        """
+        Returns widget with parameter name, interactive widget and comment
+        """
+        label = widgets.Label(value=self.name,
+                              layout=widgets.Layout(width='15%', height='32px'))
+        if self.parameter.options is not None:
+            par_widget = widgets.Dropdown(options=self.parameter.options,
+                                          layout=widgets.Layout(width='10%', height='32px'))
+            if self.default_value != "":
+                if self.default_value in self.parameter.options:
+                    par_widget.value = self.default_value
+                elif self.default_value.strip("'") in self.parameter.options:
+                    par_widget.value = self.default_value.strip("'")
+                elif self.default_value.strip('"') in self.parameter.options:
+                    par_widget.value = self.default_value.strip('"')
+                else:
+                    raise KeyError("default value not found in options for parameter: "
+                                   + str(self.parameter.name))
+
+        else:
+            par_widget = widgets.Text(value=str(self.default_value),
+                                      layout=widgets.Layout(width='10%', height='32px'))
+        comment = widgets.Label(value=self.comment,
+                                layout=widgets.Layout(width='75%', height='32px'))
+
+        par_widget.observe(self.update, "value")
+
+        return widgets.HBox([label, par_widget, comment])
+
+    def update(self, change):
+        """
+        Update function called whenever the user updates the widget
+
+        When strings parameters are used, this function adds the necessary
+        quotation marks if none are provided.
+        """
+        new_value = change.new
+        if self.parameter.type == "string":
+            if type(new_value) is str:
+                if not (new_value[0] == '"' or new_value[0] == "'"):
+                    new_value = '"' + new_value + '"'
+
+        self.parameters[self.name] = new_value
+
+
+def add_data(initial, new_data):
+    """
+    Method for adding new data to a data set
+
+    Updates Intensity, Error and Ncount
+
+    Updates all data except metadata info
+    """
+
+    for monitor in initial:
+        ref_ncount = float(monitor.metadata.info["Ncount"])
+
+        new_monitor = name_search(monitor.name, new_data)
+        new_ncount = float(new_monitor.metadata.info["Ncount"])
+
+        total_ncount = ref_ncount + new_ncount
+
+        scale_old = ref_ncount / total_ncount
+        scale_new = new_ncount / total_ncount
+
+        monitor.Intensity = scale_old*monitor.Intensity + scale_new*new_monitor.Intensity
+        monitor.Error = np.sqrt(scale_old**2*monitor.Error**2+scale_new**2*new_monitor.Error**2)
+        monitor.Ncount = monitor.Ncount + new_monitor.Ncount
+
+        monitor.metadata.info["Ncount"] = total_ncount
+
 
diff --git a/setup.py b/setup.py
index 7bc8431d..ed2cda63 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
      name='McStasScript',
-     version='0.0.27',
+     version='0.0.30',
      author="Mads Bertelsen",
      author_email="Mads.Bertelsen@ess.eu",
      description="A python scripting interface for McStas",

From ab10be4e54289fc7d0fa5432d51ab4c301df6a6c Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Wed, 16 Jun 2021 13:48:45 +0200
Subject: [PATCH 157/403] Update of plotting and simulation GUI along with unit
 tests.

Now include the live simulation, chopping the simulation into
smaller chunks that are displayed to the user earlier.

Unit tests now test a large part of the GUI code and underlying
methods that are called when using the widgets.

An integration test going through the GUI simulation interface
through the closest method to pressing the run button has also
been added.
---
 mcstasscript/helper/plot_helper.py            | 263 +++++++++++++++++
 .../test_complex_instrument.py                |  54 +++-
 mcstasscript/interface/plotter.py             | 268 +-----------------
 mcstasscript/jb_interface/plot_interface.py   |  11 +-
 .../jb_interface/simulation_interface.py      |   2 -
 mcstasscript/tests/test_add_data.py           | 236 +++++++++++++++
 mcstasscript/tests/test_plot_interface.py     | 222 +++++++++++++++
 .../tests/test_simulation_interface.py        | 208 ++++++++++++++
 mcstasscript/tests/test_widget_helpers.py     |  81 ++++++
 mcstasscript/tests/utilities.py               |  15 +
 10 files changed, 1090 insertions(+), 270 deletions(-)
 create mode 100644 mcstasscript/helper/plot_helper.py
 create mode 100644 mcstasscript/tests/test_add_data.py
 create mode 100644 mcstasscript/tests/test_plot_interface.py
 create mode 100644 mcstasscript/tests/test_simulation_interface.py
 create mode 100644 mcstasscript/tests/test_widget_helpers.py
 create mode 100644 mcstasscript/tests/utilities.py

diff --git a/mcstasscript/helper/plot_helper.py b/mcstasscript/helper/plot_helper.py
new file mode 100644
index 00000000..3e317510
--- /dev/null
+++ b/mcstasscript/helper/plot_helper.py
@@ -0,0 +1,263 @@
+import copy
+
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+
+from matplotlib.ticker import MaxNLocator
+from matplotlib.colors import BoundaryNorm
+
+from mcstasscript.data.data import McStasData
+
+def _fmt(x, pos):
+    """
+    Used for nice formatting of powers of 10 when plotting logarithmic
+    """
+    a, b = '{:.2e}'.format(x).split('e')
+    b = int(b)
+    if abs(float(a) - 1) < 0.01:
+        return r'$10^{{{}}}$'.format(b)
+    else:
+        return r'${} \times 10^{{{}}}$'.format(a, b)
+
+
+def _find_min_max_I(data):
+    """
+    Returns minimum and maximum intensity to plot given dataset
+
+    Uses the plot options embedded in McStasData to determine the proper
+    minimum and maximum intensity to display in a plot.
+
+    Have to take cut_min and cut_max into account that can cut parts of
+    the intensity away. When plotting logarithmic, orders_of_mags limits
+    the orders of magnitude shown.
+
+    Returns tuple of minimum and maximum, when no data is present the
+    function returns 0, 0.
+    """
+    cut_max = data.plot_options.cut_max  # Default 1
+    cut_min = data.plot_options.cut_min  # Default 0
+
+    to_plot = data.Intensity
+
+    min_value = to_plot.min()
+    max_value = to_plot.max()
+
+    if min_value == 0 and max_value == 0:
+        return 0, 0
+
+    if not data.plot_options.log:
+        # Linear, simple case
+        # Cut top and bottom of data as specified in cut variables
+        min_value = min_value + (max_value - min_value) * cut_min
+        max_value = max_value * cut_max
+
+    else:
+        # Logarithmic, minimum / maximum can not be zero
+        max_data_value = to_plot.max()
+        max_value = np.log10(max_data_value * cut_max)
+
+        min_value = np.min(to_plot[np.nonzero(to_plot)])
+        min_value = min_value + (max_data_value - min_value) * cut_min
+        min_value = np.log10(min_value)
+
+        # Take orders_of_mag into account (max / min in log10)
+        if max_value - min_value > data.plot_options.orders_of_mag:
+            min_value = max_value - data.plot_options.orders_of_mag
+
+        # Convert back from log10
+        min_value = 10.0 ** min_value
+        max_value = 10.0 ** max_value
+
+    return min_value, max_value
+
+
+def _plot_fig_ax(data, fig, ax, **kwargs):
+    """
+    Plots the content of a single McStasData object
+
+    Plotting is controlled through options assosciated with the
+    McStasData objects.
+
+    When plotting 2D objects, returns the pcolormesh object
+    """
+
+    print("Plotting data with name " + data.metadata.component_name)
+    if type(data.metadata.dimension) == int:
+
+        x_axis_mult = data.plot_options.x_limit_multiplier
+
+        x = data.xaxis * x_axis_mult
+        y = data.Intensity
+        y_err = data.Error
+
+        ax.errorbar(x, y, yerr=y_err)
+
+        if data.plot_options.log:
+            ax.set_yscale("log", nonposy='clip')
+
+        ax.set_xlim(data.metadata.limits[0] * x_axis_mult,
+                    data.metadata.limits[1] * x_axis_mult)
+
+        # Add a title
+        ax.set_title(data.metadata.title)
+
+        # Add axis labels
+        ax.set_xlabel(data.metadata.xlabel)
+        ax.set_ylabel(data.metadata.ylabel)
+
+        if data.plot_options.custom_xlim_left:
+            ax.set_xlim(left=data.plot_options.left_lim)
+
+        if data.plot_options.custom_xlim_right:
+            ax.set_xlim(right=data.plot_options.right_lim)
+
+    elif len(data.metadata.dimension) == 2:
+
+        min_value, max_value = _find_min_max_I(data)
+
+        if "fixed_minimum_value" in kwargs:
+            min_value = kwargs["fixed_minimum_value"]
+        if "fixed_maximum_value" in kwargs:
+            max_value = kwargs["fixed_maximum_value"]
+
+        # Set the axis
+        x_axis_mult = data.plot_options.x_limit_multiplier
+        y_axis_mult = data.plot_options.y_limit_multiplier
+
+        X = np.linspace(data.metadata.limits[0] * x_axis_mult,
+                        data.metadata.limits[1] * x_axis_mult,
+                        data.metadata.dimension[0] + 1)
+        Y = np.linspace(data.metadata.limits[2] * y_axis_mult,
+                        data.metadata.limits[3] * y_axis_mult,
+                        data.metadata.dimension[1] + 1)
+
+        # Create a meshgrid for both x and y
+        x, y = np.meshgrid(X, Y)
+
+        # Generate information on necessary colorrange
+        levels = MaxNLocator(nbins=150).tick_values(min_value, max_value)
+
+        # Select colormap
+        cmap = copy.copy(plt.get_cmap(data.plot_options.colormap))
+        if "no_data_to_black" in kwargs:
+            if kwargs["no_data_to_black"]:
+                cmap.set_bad((0, 0, 0))
+
+        norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
+
+        # Empty data, return without cmap or norm
+        if min_value == 0 and max_value == 0:
+            levels = MaxNLocator(nbins=150).tick_values(0.001, 1.0)
+            norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
+            im = ax.pcolormesh(x, y, data.Intensity, cmap=cmap, norm=norm)
+
+        # Plot the data on the meshgrids
+        elif data.plot_options.log:
+            color_norm = matplotlib.colors.LogNorm(vmin=min_value,
+                                                   vmax=max_value)
+            im = ax.pcolormesh(x, y, data.Intensity,
+                               cmap=cmap, norm=color_norm)
+        else:
+            im = ax.pcolormesh(x, y, data.Intensity, cmap=cmap, norm=norm)
+
+        # Add the colorbar
+        if data.plot_options.show_colorbar:
+            cax = None
+            if "colorbar_axes" in kwargs:
+                cax = kwargs["colorbar_axes"]
+
+            fig.colorbar(im, ax=ax, cax=cax,
+                         format=matplotlib.ticker.FuncFormatter(_fmt))
+
+            if "colorbar_axes" in kwargs:
+                cax.set_aspect(20)
+
+        # Add a title
+        ax.set_title(data.metadata.title)
+
+        # Add axis labels
+        ax.set_xlabel(data.metadata.xlabel)
+        ax.set_ylabel(data.metadata.ylabel)
+
+        if data.plot_options.custom_ylim_top:
+            ax.set_ylim(top=data.plot_options.top_lim)
+
+        if data.plot_options.custom_ylim_bottom:
+            ax.set_ylim(bottom=data.plot_options.bottom_lim)
+
+        if data.plot_options.custom_xlim_left:
+            ax.set_xlim(left=data.plot_options.left_lim)
+
+        if data.plot_options.custom_xlim_right:
+            ax.set_xlim(right=data.plot_options.right_lim)
+
+        return im
+    else:
+        print("Error, dimension not read correctly")
+
+
+def _handle_kwargs(data_list, **kwargs):
+    """
+    Handle kwargs when list of McStasData objects given.
+
+    Returns figsize and data_list
+
+    figsize has a default value, but can be changed with keyword argument
+    data_list is turned into a list if it isn't already
+
+    Any kwargs can be given as a list, in that case apply them to given
+    to the corresponding index.
+    """
+
+    if "fontsize" in kwargs:
+        used_fontsize = kwargs["fontsize"]
+    else:
+        used_fontsize = 11
+    plt.rcParams.update({'font.size': used_fontsize})
+
+    if isinstance(data_list, McStasData):
+        # Only a single element, put it in a list for easier syntax later
+        data_list = [data_list]
+
+    known_plotting_kwargs = ["log", "orders_of_mag",
+                             "top_lim", "bottom_lim",
+                             "left_lim", "right_lim",
+                             "cut_min", "cut_max",
+                             "colormap", "show_colorbar",
+                             "x_axis_multiplier",
+                             "y_axis_multiplier"]
+
+    for option in known_plotting_kwargs:
+        if option in kwargs:
+            given_option = kwargs[option]
+
+            if isinstance(given_option, list):
+                if len(data_list) < len(given_option):
+                    raise ValueError("Keyword argument " + option + " is "
+                                     + "given as a list, but this list has "
+                                     + "more elements than there are "
+                                     + "data sets to be plotted.")
+
+                index = 0
+                for per_list_option in given_option:
+                    input_kwarg = {option: per_list_option}
+                    data_list[index].set_plot_options(**input_kwarg)
+                    index += 1
+
+            else:
+                for data in data_list:
+                    input_kwarg = {option: given_option}
+                    data.set_plot_options(**input_kwarg)
+
+            # Remove option from kwargs
+            del kwargs[option]
+
+    if "figsize" in kwargs:
+        figsize = kwargs["figsize"]
+        if isinstance(figsize, list):
+            figsize = (figsize[0], figsize[1])
+    else:
+        figsize = (13, 7)
+
+    return figsize, data_list
\ No newline at end of file
diff --git a/mcstasscript/integration_tests/test_complex_instrument.py b/mcstasscript/integration_tests/test_complex_instrument.py
index 40f93df2..24537e93 100644
--- a/mcstasscript/integration_tests/test_complex_instrument.py
+++ b/mcstasscript/integration_tests/test_complex_instrument.py
@@ -3,9 +3,16 @@
 import unittest
 import unittest.mock
 import matplotlib as plt
+import threading
 
 from mcstasscript.interface import instr, functions
+from mcstasscript.jb_interface.simulation_interface import SimInterface
 
+class FakeChange:
+    def __init__(self, new=None, old=None, name=None):
+        self.new = new
+        self.old = old
+        self.name = name
 
 def setup_complex_instrument():
     """
@@ -133,7 +140,7 @@ class TestComplexInstrument(unittest.TestCase):
     correctly in order for these tests to succeed.
     """
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_complex_instrument(self, mock_stdout):
+    def test_complex_instrument_run(self, mock_stdout):
         """
         Test parameters can be controlled through McStasScript.  Here
         a slit is moved to one side and the result is verified.
@@ -174,6 +181,51 @@ def test_complex_instrument(self, mock_stdout):
         # time.sleep(10)
         # plt.close()
 
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_complex_instrument_interface(self, mock_stdout):
+        """
+        Test that a simulation can be performed through the simulation
+        interface, or as close as I can through scripting.
+        Need to join the simulation thread to the main thread in order
+        to wait for the completion as it is performed in a new thread.
+        """
+        CURRENT_DIR = os.getcwd()
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        os.chdir(THIS_DIR)
+
+        Instr = setup_complex_instrument()
+
+        interface = SimInterface(Instr)
+        interface.show_interface()
+
+        change = FakeChange()
+
+        interface.run_simulation_thread(change)
+
+        for thread in threading.enumerate():
+            if thread.name != "MainThread":
+                thread.join()
+
+        data = interface.plot_interface.data
+
+        os.chdir(CURRENT_DIR)
+
+        intensity_data_pos = functions.name_search("PSD_1D_1", data).Intensity
+        sum_outside_beam = sum(intensity_data_pos[0:50])
+        sum_inside_beam = sum(intensity_data_pos[51:99])
+        self.assertTrue(1000 * sum_outside_beam < sum_inside_beam)
+
+        intensity_data_neg = functions.name_search("PSD_1D_2", data).Intensity
+        sum_outside_beam = sum(intensity_data_neg[51:99])
+        sum_inside_beam = sum(intensity_data_neg[0:50])
+        self.assertTrue(1000 * sum_outside_beam < sum_inside_beam)
+
+        intensity_data_all = functions.name_search("PSD_1D", data).Intensity
+        sum_outside_beam = sum(intensity_data_all[49:51])
+        sum_inside_beam = (sum(intensity_data_all[0:45])
+                           + sum(intensity_data_all[56:99]))
+        self.assertTrue(1000 * sum_outside_beam < sum_inside_beam)
+
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index 2e5d2162..4c6bae29 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -1,270 +1,16 @@
 import math
-import copy
+
 import numpy as np
-import matplotlib
 import matplotlib.pyplot as plt
 import matplotlib.animation as animation
-from matplotlib.colors import BoundaryNorm
-from matplotlib.ticker import MaxNLocator
 
-from mcstasscript.data.data import McStasData
+from mcstasscript.helper.plot_helper import _fmt
+from mcstasscript.helper.plot_helper import _find_min_max_I
+from mcstasscript.helper.plot_helper import _plot_fig_ax
+from mcstasscript.helper.plot_helper import _handle_kwargs
 from mcstasscript.jb_interface.plot_interface import PlotInterface
 
 
-def _fmt(x, pos):
-    """
-    Used for nice formatting of powers of 10 when plotting logarithmic
-    """
-    a, b = '{:.2e}'.format(x).split('e')
-    b = int(b)
-    if abs(float(a) - 1) < 0.01:
-        return r'$10^{{{}}}$'.format(b)
-    else:
-        return r'${} \times 10^{{{}}}$'.format(a, b)
-
-
-def _find_min_max_I(data):
-    """
-    Returns minimum and maximum intensity to plot given dataset
-
-    Uses the plot options embedded in McStasData to determine the proper
-    minimum and maximum intensity to display in a plot.
-
-    Have to take cut_min and cut_max into account that can cut parts of
-    the intensity away. When plotting logarithmic, orders_of_mags limits
-    the orders of magnitude shown.
-
-    Returns tuple of minimum and maximum, when no data is present the
-    function returns 0, 0.
-    """
-    cut_max = data.plot_options.cut_max  # Default 1
-    cut_min = data.plot_options.cut_min  # Default 0
-
-    to_plot = data.Intensity
-
-    min_value = to_plot.min()
-    max_value = to_plot.max()
-
-    if min_value == 0 and max_value == 0:
-        return 0, 0
-
-    if not data.plot_options.log:
-        # Linear, simple case
-        # Cut top and bottom of data as specified in cut variables
-        min_value = min_value + (max_value - min_value) * cut_min
-        max_value = max_value * cut_max
-
-    else:
-        # Logarithmic, minimum / maximum can not be zero
-        max_data_value = to_plot.max()
-        max_value = np.log10(max_data_value * cut_max)
-
-        min_value = np.min(to_plot[np.nonzero(to_plot)])
-        min_value = min_value + (max_data_value - min_value) * cut_min
-        min_value = np.log10(min_value)
-
-        # Take orders_of_mag into account (max / min in log10)
-        if max_value - min_value > data.plot_options.orders_of_mag:
-            min_value = max_value - data.plot_options.orders_of_mag
-
-        # Convert back from log10
-        min_value = 10.0 ** min_value
-        max_value = 10.0 ** max_value
-
-    return min_value, max_value
-
-
-def _plot_fig_ax(data, fig, ax, **kwargs):
-    """
-    Plots the content of a single McStasData object
-
-    Plotting is controlled through options assosciated with the
-    McStasData objects.
-
-    When plotting 2D objects, returns the pcolormesh object
-    """
-
-    print("Plotting data with name " + data.metadata.component_name)
-    if type(data.metadata.dimension) == int:
-
-        x_axis_mult = data.plot_options.x_limit_multiplier
-
-        x = data.xaxis * x_axis_mult
-        y = data.Intensity
-        y_err = data.Error
-
-        ax.errorbar(x, y, yerr=y_err)
-
-        if data.plot_options.log:
-            ax.set_yscale("log", nonposy='clip')
-
-        ax.set_xlim(data.metadata.limits[0] * x_axis_mult,
-                    data.metadata.limits[1] * x_axis_mult)
-
-        # Add a title
-        ax.set_title(data.metadata.title)
-
-        # Add axis labels
-        ax.set_xlabel(data.metadata.xlabel)
-        ax.set_ylabel(data.metadata.ylabel)
-
-        if data.plot_options.custom_xlim_left:
-            ax.set_xlim(left=data.plot_options.left_lim)
-
-        if data.plot_options.custom_xlim_right:
-            ax.set_xlim(right=data.plot_options.right_lim)
-
-    elif len(data.metadata.dimension) == 2:
-
-        min_value, max_value = _find_min_max_I(data)
-
-        if "fixed_minimum_value" in kwargs:
-            min_value = kwargs["fixed_minimum_value"]
-        if "fixed_maximum_value" in kwargs:
-            max_value = kwargs["fixed_maximum_value"]
-
-        # Set the axis
-        x_axis_mult = data.plot_options.x_limit_multiplier
-        y_axis_mult = data.plot_options.y_limit_multiplier
-
-        X = np.linspace(data.metadata.limits[0] * x_axis_mult,
-                        data.metadata.limits[1] * x_axis_mult,
-                        data.metadata.dimension[0] + 1)
-        Y = np.linspace(data.metadata.limits[2] * y_axis_mult,
-                        data.metadata.limits[3] * y_axis_mult,
-                        data.metadata.dimension[1] + 1)
-
-        # Create a meshgrid for both x and y
-        x, y = np.meshgrid(X, Y)
-
-        # Generate information on necessary colorrange
-        levels = MaxNLocator(nbins=150).tick_values(min_value, max_value)
-
-        # Select colormap
-        cmap = copy.copy(plt.get_cmap(data.plot_options.colormap))
-        if "no_data_to_black" in kwargs:
-            if kwargs["no_data_to_black"]:
-                cmap.set_bad((0, 0, 0))
-
-        norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
-
-        # Empty data, return without cmap or norm
-        if min_value == 0 and max_value == 0:
-            levels = MaxNLocator(nbins=150).tick_values(0.001, 1.0)
-            norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
-            im = ax.pcolormesh(x, y, data.Intensity, cmap=cmap, norm=norm)
-
-        # Plot the data on the meshgrids
-        elif data.plot_options.log:
-            color_norm = matplotlib.colors.LogNorm(vmin=min_value,
-                                                   vmax=max_value)
-            im = ax.pcolormesh(x, y, data.Intensity,
-                               cmap=cmap, norm=color_norm)
-        else:
-            im = ax.pcolormesh(x, y, data.Intensity, cmap=cmap, norm=norm)
-
-        # Add the colorbar
-        if data.plot_options.show_colorbar:
-            cax = None
-            if "colorbar_axes" in kwargs:
-                cax = kwargs["colorbar_axes"]
-
-            fig.colorbar(im, ax=ax, cax=cax,
-                         format=matplotlib.ticker.FuncFormatter(_fmt))
-
-            if "colorbar_axes" in kwargs:
-                cax.set_aspect(20)
-
-        # Add a title
-        ax.set_title(data.metadata.title)
-
-        # Add axis labels
-        ax.set_xlabel(data.metadata.xlabel)
-        ax.set_ylabel(data.metadata.ylabel)
-
-        if data.plot_options.custom_ylim_top:
-            ax.set_ylim(top=data.plot_options.top_lim)
-
-        if data.plot_options.custom_ylim_bottom:
-            ax.set_ylim(bottom=data.plot_options.bottom_lim)
-
-        if data.plot_options.custom_xlim_left:
-            ax.set_xlim(left=data.plot_options.left_lim)
-
-        if data.plot_options.custom_xlim_right:
-            ax.set_xlim(right=data.plot_options.right_lim)
-
-        return im
-    else:
-        print("Error, dimension not read correctly")
-
-
-def _handle_kwargs(data_list, **kwargs):
-    """
-    Handle kwargs when list of McStasData objects given.
-
-    Returns figsize and data_list
-
-    figsize has a default value, but can be changed with keyword argument
-    data_list is turned into a list if it isn't already
-
-    Any kwargs can be given as a list, in that case apply them to given
-    to the corresponding index.
-    """
-
-    if "fontsize" in kwargs:
-        used_fontsize = kwargs["fontsize"]
-    else:
-        used_fontsize = 11
-    plt.rcParams.update({'font.size': used_fontsize})
-
-    if isinstance(data_list, McStasData):
-        # Only a single element, put it in a list for easier syntax later
-        data_list = [data_list]
-
-    known_plotting_kwargs = ["log", "orders_of_mag",
-                             "top_lim", "bottom_lim",
-                             "left_lim", "right_lim",
-                             "cut_min", "cut_max",
-                             "colormap", "show_colorbar",
-                             "x_axis_multiplier",
-                             "y_axis_multiplier"]
-
-    for option in known_plotting_kwargs:
-        if option in kwargs:
-            given_option = kwargs[option]
-
-            if isinstance(given_option, list):
-                if len(data_list) < len(given_option):
-                    raise ValueError("Keyword argument " + option + " is "
-                                     + "given as a list, but this list has "
-                                     + "more elements than there are "
-                                     + "data sets to be plotted.")
-
-                index = 0
-                for per_list_option in given_option:
-                    input_kwarg = {option: per_list_option}
-                    data_list[index].set_plot_options(**input_kwarg)
-                    index += 1
-
-            else:
-                for data in data_list:
-                    input_kwarg = {option: given_option}
-                    data.set_plot_options(**input_kwarg)
-
-            # Remove option from kwargs
-            del kwargs[option]
-
-    if "figsize" in kwargs:
-        figsize = kwargs["figsize"]
-        if isinstance(figsize, list):
-            figsize = (figsize[0], figsize[1])
-    else:
-        figsize = (13, 7)
-
-    return figsize, data_list
-
-
 def make_plot(data_list, **kwargs):
     """
     make_plot plots contents of McStasData objects given in list
@@ -422,8 +168,8 @@ def interface(data):
 
     Parameters
     ----------
-
     data : List of McStasData objects
     """
+
     interface = PlotInterface(data)
-    return interface.show_interface()
\ No newline at end of file
+    return interface.show_interface()
diff --git a/mcstasscript/jb_interface/plot_interface.py b/mcstasscript/jb_interface/plot_interface.py
index 0e70ada9..3247375c 100644
--- a/mcstasscript/jb_interface/plot_interface.py
+++ b/mcstasscript/jb_interface/plot_interface.py
@@ -10,7 +10,7 @@
 import matplotlib.pyplot as plt
 
 from mcstasscript.interface.functions import name_search
-from mcstasscript.interface import plotter
+from mcstasscript.helper.plot_helper import _plot_fig_ax
 
 from mcstasscript.jb_interface.widget_helpers import HiddenPrints
 
@@ -30,8 +30,6 @@ def __init__(self, data=None):
         """
         self.data = data
 
-        output = None
-
         # Variables related to monitor choice
         self.monitor_dropdown = None
         self.current_monitor = None
@@ -71,7 +69,7 @@ def set_log_mode(self, log_mode):
         """
         Sets log mode for plotting, True or False
         """
-        self.log_mode = log_mode
+        self.log_mode = bool(log_mode)
         self.update_plot()
 
     def set_orders_of_mag(self, orders_of_mag):
@@ -93,7 +91,7 @@ def new_plot(self):
         Sets up original plot with fig, ax and ax for colorbar
         """
         # fig, ax = plt.subplots(constrained_layout=True, figsize=(6, 4))
-        self.fig, (self.ax, self.colorbar_ax) = plt.subplots(ncols=2, gridspec_kw={'width_ratios': [4, 1]})
+        self.fig, (self.ax, self.colorbar_ax) = plt.subplots(ncols=2, gridspec_kw={'width_ratios': [6, 1]})
 
         self.fig.canvas.toolbar_position = 'bottom'
 
@@ -142,7 +140,8 @@ def update_plot(self):
             #print("Plotting with: ", plot_options)
             monitor.set_plot_options(**plot_options)
             with HiddenPrints():
-                plotter._plot_fig_ax(monitor, self.fig, self.ax, colorbar_axes=self.colorbar_ax)
+                #plotter._plot_fig_ax(monitor, self.fig, self.ax, colorbar_axes=self.colorbar_ax)
+                _plot_fig_ax(monitor, self.fig, self.ax, colorbar_axes=self.colorbar_ax)
 
             self.colorbar_ax.set_aspect(20)
 
diff --git a/mcstasscript/jb_interface/simulation_interface.py b/mcstasscript/jb_interface/simulation_interface.py
index 346b8151..deaa9f10 100644
--- a/mcstasscript/jb_interface/simulation_interface.py
+++ b/mcstasscript/jb_interface/simulation_interface.py
@@ -381,5 +381,3 @@ def add_data(initial, new_data):
         monitor.Ncount = monitor.Ncount + new_monitor.Ncount
 
         monitor.metadata.info["Ncount"] = total_ncount
-
-
diff --git a/mcstasscript/tests/test_add_data.py b/mcstasscript/tests/test_add_data.py
new file mode 100644
index 00000000..b21c3ced
--- /dev/null
+++ b/mcstasscript/tests/test_add_data.py
@@ -0,0 +1,236 @@
+import unittest
+import numpy as np
+import copy
+
+from mcstasscript.data.data import McStasData
+from mcstasscript.data.data import McStasMetaData
+from mcstasscript.jb_interface.simulation_interface import add_data
+
+def set_dummy_MetaData_1d():
+    """
+    Sets up simple McStasMetaData object with dimension, 1d case
+    """
+    meta_data = McStasMetaData()
+    meta_data.component_name = "component for 1d"
+    meta_data.filename = "data.dat"
+    meta_data.dimension = 50
+
+    meta_data.info = {"Ncount" : 40}
+
+    return meta_data
+
+
+def set_dummy_McStasData_1d():
+    """
+    Sets up simple McStasData object, 1d case
+    """
+    meta_data = set_dummy_MetaData_1d()
+
+    intensity = np.ones(20)
+    error = np.ones(20)
+    ncount = np.ones(20)
+    axis = np.arange(20)*5.0
+
+    return McStasData(meta_data, intensity, error, ncount, xaxis=axis)
+
+
+def set_dummy_MetaData_2d():
+    """
+    Sets up simple McStasMetaData object with dimensions, 2d case
+    """
+    meta_data = McStasMetaData()
+    meta_data.component_name = "test a component"
+    meta_data.filename = "data.dat"
+    meta_data.dimension = [50, 100]
+
+    meta_data.info = {"Ncount": 40}
+
+    return meta_data
+
+
+def set_dummy_McStasData_2d():
+    """
+    Sets up simple McStasData object, 2d case
+    """
+    meta_data = set_dummy_MetaData_2d()
+
+    intensity = np.ones(20).reshape(4, 5)
+    error = np.ones(20).reshape(4, 5)
+    ncount = np.ones(20).reshape(4, 5)
+
+    return McStasData(meta_data, intensity, error, ncount)
+
+class Test_add_data(unittest.TestCase):
+    def test_1d_updates_correctly(self):
+        """
+        Test that adding 1d dataset modifies only the intended dataset
+        """
+
+        data1 = set_dummy_McStasData_1d()
+        data1_original = copy.deepcopy(data1)
+
+        data2 = set_dummy_McStasData_1d()
+        data2_original = copy.deepcopy(data2)
+
+        add_data([data1], [data2])
+
+        # Data 2 should not be touched
+        self.assertTrue(np.array_equal(data2.Intensity, data2_original.Intensity))
+        self.assertTrue(np.array_equal(data2.Error, data2_original.Error))
+        self.assertTrue(np.array_equal(data2.Ncount, data2_original.Ncount))
+
+        # Data 1 Intensity should be unchanged, as data1 and data2 equal
+        self.assertTrue(np.array_equal(data1.Intensity, data1_original.Intensity))
+        # Data 1 should be updated
+        self.assertFalse(np.array_equal(data1.Error, data1_original.Error))
+        self.assertFalse(np.array_equal(data1.Ncount, data1_original.Ncount))
+
+    def test_1d_updates_different(self):
+        """
+        Test that adding 1d datasets work as expected when different
+        """
+        data1 = set_dummy_McStasData_1d()
+        data1.Intensity *= 2.0
+        data1.Intensity[10:] *= 2.0
+        data1.Error *= 1.5
+        data1.Ncount *= 4.0
+        data1.metadata.info["Ncount"] *= 4.0
+        data1_original = copy.deepcopy(data1)
+
+        data2 = set_dummy_McStasData_1d()
+        data2.Intensity *= 3.0
+        data2.Error *= 1.5
+        data2_original = copy.deepcopy(data2)
+
+        add_data([data1], [data2])
+
+        # Data 2 should not be touched
+        self.assertTrue(np.array_equal(data2.Intensity, data2_original.Intensity))
+        self.assertTrue(np.array_equal(data2.Error, data2_original.Error))
+        self.assertTrue(np.array_equal(data2.Ncount, data2_original.Ncount))
+
+        # 4 times more weight on data1, intensity 2 and 3
+        expected_low_intensity = 4/5*2.0 + 1/5*3.0
+        # 4 times more weight on data1, intensity 4 and 3
+        expected_high_intensity = 4/5*2.0*2.0 + 1/5*3.0
+        expected_error = np.sqrt((4 / 5) ** 2 * 1.5 ** 2 + (1 / 5) ** 2 * 1.5 ** 2)
+
+        for index in range(len(data1_original.Intensity)):
+            if index < 10:
+                self.assertEqual(data1.Intensity[index], expected_low_intensity)
+            else:
+                self.assertEqual(data1.Intensity[index], expected_high_intensity)
+
+            self.assertEqual(data1.Error[index], expected_error)
+            self.assertEqual(data1.Ncount[index], 5.0)
+
+        self.assertEqual(data1.metadata.info["Ncount"], 40*4+40)
+
+    def test_fail(self):
+        """
+        Test that adding datasets fail when they dont have the same monitors
+
+        Both 1d and 2d cases included.
+        """
+
+        data11 = set_dummy_McStasData_1d()
+        data11.name = "first monitor"
+        data11.filename = "first_monitor.dat"
+        data12 = set_dummy_McStasData_2d()
+        data12.name = "second monitor"
+        data12.filename = "second_monitor.dat"
+        data13 = set_dummy_McStasData_1d()
+        data13.name = "third monitor"
+        data13.filename = "third_monitor.dat"
+
+        data21 = set_dummy_McStasData_1d()
+        data21.name = "first monitor"
+        data21.filename = "first_monitor.dat"
+        data22 = set_dummy_McStasData_2d()
+        data22.name = "second monitor"
+        data22.filename = "second_monitor.dat"
+        data23 = set_dummy_McStasData_1d()
+        data23.name = "third monitor"
+        data23.filename = "third_monitor.dat"
+
+        # Should succeed, monitors match
+        add_data([data11, data12, data13], [data21, data22, data23])
+        # Should succeed, all monitors needed to update first argument present
+        add_data([data11, data12], [data21, data22, data23])
+
+        # Should fail if a monitor is missing
+        with self.assertRaises(NameError):
+            add_data([data11, data12, data13], [data21, data22])
+
+        data23.name = "different monitor"
+        # Should fail if name mismatch
+        with self.assertRaises(NameError):
+            add_data([data11, data12, data13], [data21, data22, data23])
+
+    def test_2d_updates_correctly(self):
+        """
+        Test that adding 1d dataset modifies only the intended dataset
+        """
+
+        data1 = set_dummy_McStasData_2d()
+        data1_original = copy.deepcopy(data1)
+
+        data2 = set_dummy_McStasData_2d()
+        data2_original = copy.deepcopy(data2)
+
+        add_data([data1], [data2])
+
+        # Data 2 should not be touched
+        self.assertTrue(np.array_equal(data2.Intensity, data2_original.Intensity))
+        self.assertTrue(np.array_equal(data2.Error, data2_original.Error))
+        self.assertTrue(np.array_equal(data2.Ncount, data2_original.Ncount))
+
+        # Data 1 Intensity should be unchanged, as data1 and data2 equal
+        self.assertTrue(np.array_equal(data1.Intensity, data1_original.Intensity))
+        # Data 1 should be updated
+        self.assertFalse(np.array_equal(data1.Error, data1_original.Error))
+        self.assertFalse(np.array_equal(data1.Ncount, data1_original.Ncount))
+
+    def test_2d_updates_different(self):
+        """
+        Test that adding 2d datasets work as expected when different
+        """
+        data1 = set_dummy_McStasData_2d()
+        data1.Intensity *= 2.0
+        data1.Intensity[1,:] *= 2.0
+        data1.Error *= 1.5
+        data1.Ncount *= 4.0
+        data1.metadata.info["Ncount"] *= 4.0
+        data1_original = copy.deepcopy(data1)
+
+        data2 = set_dummy_McStasData_2d()
+        data2.Intensity *= 3.0
+        data2.Error *= 1.5
+        data2_original = copy.deepcopy(data2)
+
+        add_data([data1], [data2])
+
+        # Data 2 should not be touched
+        self.assertTrue(np.array_equal(data2.Intensity, data2_original.Intensity))
+        self.assertTrue(np.array_equal(data2.Error, data2_original.Error))
+        self.assertTrue(np.array_equal(data2.Ncount, data2_original.Ncount))
+
+        # 4 times more weight on data1, intensity 2 and 3
+        expected_low_intensity = 4/5*2.0 + 1/5*3.0
+        # 4 times more weight on data1, intensity 4 and 3
+        expected_high_intensity = 4/5*2.0*2.0 + 1/5*3.0
+        expected_error = np.sqrt((4 / 5) ** 2 * 1.5 ** 2 + (1 / 5) ** 2 * 1.5 ** 2)
+
+        for index1 in range(len(data1_original.Intensity[:,0])):
+            for index2 in range(len(data1_original.Intensity[0, :])):
+
+                if index1 == 1:
+                    self.assertEqual(data1.Intensity[index1, index2], expected_high_intensity)
+                else:
+                    self.assertEqual(data1.Intensity[index1, index2], expected_low_intensity)
+
+            self.assertEqual(data1.Error[index1, index2], expected_error)
+            self.assertEqual(data1.Ncount[index1, index2], 5.0)
+
+        self.assertEqual(data1.metadata.info["Ncount"], 40*4+40)
+
diff --git a/mcstasscript/tests/test_plot_interface.py b/mcstasscript/tests/test_plot_interface.py
new file mode 100644
index 00000000..9a52ac63
--- /dev/null
+++ b/mcstasscript/tests/test_plot_interface.py
@@ -0,0 +1,222 @@
+import unittest
+import unittest.mock
+import io
+import numpy as np
+
+from mcstasscript.jb_interface.plot_interface import PlotInterface
+from mcstasscript.jb_interface.plot_interface import LogCheckbox
+from mcstasscript.jb_interface.plot_interface import ColormapDropdown
+from mcstasscript.jb_interface.plot_interface import OrdersOfMagField
+
+from mcstasscript.data.data import McStasMetaData
+from mcstasscript.data.data import McStasData
+
+import ipywidgets as widgets
+
+def set_dummy_MetaData_1d():
+    """
+    Sets up simple McStasMetaData object with dimension, 1d case
+    """
+    meta_data = McStasMetaData()
+    meta_data.component_name = "component for 1d"
+    meta_data.dimension = 50
+
+    meta_data.limits = [0, 1]
+    meta_data.xlabel = ""
+    meta_data.ylabel = ""
+    meta_data.title = ""
+    meta_data.filename = "dummy"
+
+    return meta_data
+
+
+def set_dummy_McStasData_1d():
+    """
+    Sets up simple McStasData object, 1d case
+    """
+    meta_data = set_dummy_MetaData_1d()
+
+    intensity = np.arange(20)
+    error = 0.5 * np.arange(20)
+    ncount = 2 * np.arange(20)
+    axis = np.arange(20)*5.0
+
+    return McStasData(meta_data, intensity, error, ncount, xaxis=axis)
+
+
+def set_dummy_MetaData_2d():
+    """
+    Sets up simple McStasMetaData object with dimensions, 2d case
+    """
+    meta_data = McStasMetaData()
+    meta_data.component_name = "test a component"
+    meta_data.dimension = [5, 4]
+
+    meta_data.limits = [0, 1, 0, 1]
+    meta_data.xlabel = ""
+    meta_data.ylabel = ""
+    meta_data.title = ""
+    meta_data.filename = "dummy"
+
+    return meta_data
+
+
+def set_dummy_McStasData_2d():
+    """
+    Sets up simple McStasData object, 2d case
+    """
+    meta_data = set_dummy_MetaData_2d()
+
+    intensity = np.arange(20).reshape(4, 5)
+    error = 0.5 * np.arange(20).reshape(4, 5)
+    ncount = 2 * np.arange(20).reshape(4, 5)
+
+    return McStasData(meta_data, intensity, error, ncount)
+
+
+def fake_data():
+    return [set_dummy_McStasData_1d(), set_dummy_McStasData_2d(), set_dummy_McStasData_2d()]
+
+
+class FakeChange:
+    def __init__(self, new=None, old=None, name=None):
+        self.new = new
+        self.old = old
+        self.name = name
+
+
+class TestPlotInterface(unittest.TestCase):
+    """
+    Test of PlotInterface, mainly the set functions.
+    Each set function does run the main update_plot function, ensuring
+    this does not throw obvious errors under a mixed set of circumstances.
+    The update_plot function is not tested directly.
+    """
+    def test_initialization_without_data(self):
+        """
+        Initialize PlotterInterface without arguments
+        """
+
+        interface = PlotInterface()
+        self.assertEqual(interface.data, None)
+
+    def test_initialization_with_data(self):
+        """
+        Initialize PlotterInterface with data
+        """
+        data = fake_data()
+        interface = PlotInterface(data)
+
+        self.assertEqual(len(interface.data), 3)
+        self.assertEqual(interface.data[0].Intensity[5], 5)
+        self.assertEqual(interface.data[1].Intensity[2, 3], 13)
+
+    def test_show_interface_return(self):
+        """
+        Ensure the show_interface method returns a widget of type HBox
+        """
+        interface = PlotInterface()
+        widget = interface.show_interface()
+
+        self.assertIsInstance(widget, widgets.widgets.widget_box.HBox)
+
+    def test_set_data(self):
+        """
+        Initialize PlotterInterface without data, add later and ensure
+        monitor_dropdown received the data and applied it to widget options.
+
+        Furthermore the widget options should be made unique as there are
+        two identical monitors in the fake data.
+        """
+
+        interface = PlotInterface()
+        self.assertEqual(interface.data, None)
+
+        interface.show_interface() # Needed to set up monitor_dropdown
+
+        interface.set_data(fake_data())
+        self.assertEqual(len(interface.data), 3)
+        self.assertEqual(interface.data[0].Intensity[5], 5)
+        self.assertEqual(interface.data[1].Intensity[2, 3], 13)
+
+        mon_drop = interface.monitor_dropdown
+        self.assertEqual(len(mon_drop.data), 3)
+        self.assertEqual(mon_drop.data[0].Intensity[5], 5)
+        self.assertEqual(mon_drop.data[1].Intensity[2, 3], 13)
+
+        self.assertEqual(mon_drop.widget.options[0], "component for 1d")
+        self.assertEqual(mon_drop.widget.options[1], "test a component")
+
+        # The last monitor is identical to the second, so the name is modified
+        self.assertEqual(mon_drop.widget.options[2], "test a component_1")
+
+    def test_set_current_monitor(self):
+        """
+        Check monitor can be set and that error occurs if wrong name given
+        """
+
+        interface = PlotInterface(fake_data())
+        interface.show_interface()
+
+        interface.set_current_monitor("test a component")
+        self.assertEqual(interface.current_monitor, "test a component")
+
+        with self.assertRaises(NameError):
+            interface.set_current_monitor("component that doesnt exist")
+
+    def test_set_log_mode(self):
+        """
+        Check that set_log_mode works, even through widget
+        """
+
+        interface = PlotInterface()
+        interface.show_interface()
+
+        self.assertFalse(interface.log_mode)
+        interface.set_log_mode(True)
+        self.assertTrue(interface.log_mode)
+
+        log_checkbox = LogCheckbox(interface.log_mode, interface.set_log_mode)
+
+        fake_change = FakeChange(new=False)
+
+        log_checkbox.update(fake_change)
+        self.assertFalse(interface.log_mode)
+
+    def test_set_orders_of_mag(self):
+        """
+        Check that set orders of mag works, even through widget
+        """
+        interface = PlotInterface()
+        interface.show_interface()
+
+        interface.set_orders_of_mag(37)
+        self.assertEqual(interface.orders_of_mag, 37)
+
+        log_orders_of_mag = OrdersOfMagField(interface.set_orders_of_mag)
+
+        fake_change = FakeChange(new=42)
+
+        log_orders_of_mag.update(fake_change)
+        self.assertEqual(interface.orders_of_mag, 42)
+
+    def test_set_colormap(self):
+        """
+        Check that set_colormap works, even through widget
+        """
+        interface = PlotInterface()
+        interface.show_interface()
+
+        interface.set_colormap("hot")
+        self.assertEqual(interface.colormap, "hot")
+
+        colormap_dropdown = ColormapDropdown(interface.set_colormap)
+
+        fake_change = FakeChange(new="Purples")
+        colormap_dropdown.update_cmap(fake_change)
+        self.assertEqual(interface.colormap, "Purples")
+
+
+
+
+
diff --git a/mcstasscript/tests/test_simulation_interface.py b/mcstasscript/tests/test_simulation_interface.py
new file mode 100644
index 00000000..17f5af05
--- /dev/null
+++ b/mcstasscript/tests/test_simulation_interface.py
@@ -0,0 +1,208 @@
+import os
+import io
+import unittest
+import unittest.mock
+import ipywidgets as widgets
+
+from mcstasscript.jb_interface.simulation_interface import SimInterface
+from mcstasscript.jb_interface.simulation_interface import ParameterWidget
+from mcstasscript.jb_interface.widget_helpers import parameter_has_default
+from mcstasscript.jb_interface.widget_helpers import get_parameter_default
+from mcstasscript.interface.instr import McStas_instr
+from mcstasscript.interface.instr import McXtrace_instr
+
+
+def setup_instr_root_path_McStas():
+    """
+    Sets up a neutron instrument with root package_path
+    """
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+    current_work_dir = os.getcwd()
+    os.chdir(THIS_DIR)  # Set work directory to test folder
+
+    instrument = McStas_instr("test_instrument", package_path="/")
+
+    os.chdir(current_work_dir)
+
+    return instrument
+
+def setup_populated_instr_McStas():
+    """
+    Sets up a neutron instrument with some features used and three components
+    """
+    instr = setup_instr_root_path_McStas()
+
+    instr.add_parameter("double", "theta")
+    instr.add_parameter("double", "has_default", value=37)
+    instr.add_declare_var("double", "two_theta")
+    instr.append_initialize("two_theta = 2.0*theta;")
+
+    instr.add_component("first_component", "test_for_reading")
+    instr.add_component("second_component", "test_for_reading")
+    instr.add_component("third_component", "test_for_reading")
+
+    return instr
+
+def setup_instr_root_path_McXtrace():
+    """
+    Sets up a neutron instrument with root package_path
+    """
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+    current_work_dir = os.getcwd()
+    os.chdir(THIS_DIR)  # Set work directory to test folder
+
+    instrument = McStas_instr("test_instrument", package_path="/")
+
+    os.chdir(current_work_dir)
+
+    return instrument
+
+def setup_populated_instr_McXtrace():
+    """
+    Sets up a neutron instrument with some features used and three components
+    """
+    instr = setup_instr_root_path_McXtrace()
+
+    instr.add_parameter("double", "theta")
+    instr.add_parameter("double", "has_default", value=37)
+    instr.add_declare_var("double", "two_theta")
+    instr.append_initialize("two_theta = 2.0*theta;")
+
+    instr.add_component("first_component", "test_for_reading")
+    instr.add_component("second_component", "test_for_reading")
+    instr.add_component("third_component", "test_for_reading")
+
+    return instr
+
+
+class FakeChange:
+    def __init__(self, new=None, old=None, name=None):
+        self.new = new
+        self.old = old
+        self.name = name
+
+
+class TestSimulationInterface(unittest.TestCase):
+    """
+    Tests of simulation interface
+    """
+    def test_initialization_McStas(self):
+        """
+        Checking that interface can initialize from instrument and retrieve the
+        parameters that has a default value.
+        """
+
+        sim_interface = SimInterface(setup_populated_instr_McStas())
+        self.assertEqual(sim_interface.parameters["has_default"], 37)
+
+    def test_show_interface_McStas(self):
+        """
+        Ensure that show_interface runs without errors and returns widget
+        """
+
+        sim_interface = SimInterface(setup_populated_instr_McStas())
+        widget = sim_interface.show_interface()
+
+        self.assertIsInstance(widget, widgets.widgets.widget_box.VBox)
+
+    def test_initialization_McXtrace(self):
+        """
+        Checking that interface can initialize from instrument and retrieve the
+        parameters that has a default value.
+        """
+
+        sim_interface = SimInterface(setup_populated_instr_McXtrace())
+        self.assertEqual(sim_interface.parameters["has_default"], 37)
+
+    def test_show_interface_McXtrace(self):
+        """
+        Ensure that show_interface runs without errors and returns widget
+        """
+
+        sim_interface = SimInterface(setup_populated_instr_McXtrace())
+        widget = sim_interface.show_interface()
+
+        self.assertIsInstance(widget, widgets.widgets.widget_box.VBox)
+
+    def test_update_ncount(self):
+        """
+        Check ncount can be set correctly
+        """
+        sim_interface = SimInterface(setup_populated_instr_McStas())
+        sim_interface.show_interface()
+
+        fake_change = FakeChange(new=100)
+        sim_interface.update_ncount(fake_change)
+        self.assertEqual(sim_interface.ncount, 100)
+
+    def test_update_mpi(self):
+        """
+        Check mpi can be set correctly
+        """
+        sim_interface = SimInterface(setup_populated_instr_McStas())
+        sim_interface.show_interface()
+
+        fake_change = FakeChange(new=3)
+        sim_interface.update_mpi(fake_change)
+        self.assertEqual(sim_interface.mpi, 3)
+
+        # Check input that wouldn't work is ignored
+        fake_change = FakeChange(new="wrong input")
+        sim_interface.update_mpi(fake_change)
+        self.assertEqual(sim_interface.mpi, 3)
+
+    def test_ParameterWidget(self):
+        """
+        Test that ParameterWidgets are initialized correctly
+
+        This code is part of the initialization for the simulation interface,
+        yet it is useful to have as its own test as if it fails its clear where
+        the overall problem is.
+        """
+
+        instrument = setup_populated_instr_McStas()
+
+        instrument.add_parameter("string", "choice", options=["A", "B", "Long"])
+
+        parameters = {}
+        # get default parameters from instrument
+        for parameter in instrument.parameter_list:
+            if parameter_has_default(parameter):
+                parameters[parameter.name] = get_parameter_default(parameter)
+
+        parameter_widgets = []
+        parameterwidget_objects = []
+        for parameter in instrument.parameter_list:
+            par_widget = ParameterWidget(parameter, parameters)
+            parameterwidget_objects.append(par_widget)
+            parameter_widgets.append(par_widget.make_widget())
+
+        self.assertEqual(parameterwidget_objects[0].name, "theta")
+        self.assertEqual(parameterwidget_objects[0].default_value, "")
+        # Parameter does not exist in parameter dict yet
+        with self.assertRaises(KeyError):
+            parameters[parameterwidget_objects[0].name]
+
+        change = FakeChange(new=222)
+        parameterwidget_objects[0].update(change)
+        self.assertEqual(parameters[parameterwidget_objects[0].name], 222)
+
+        self.assertEqual(parameterwidget_objects[1].name, "has_default")
+        self.assertEqual(parameterwidget_objects[1].default_value, 37)
+        self.assertEqual(parameters[parameterwidget_objects[1].name], 37)
+
+        change = FakeChange(new=227)
+        parameterwidget_objects[1].update(change)
+        self.assertEqual(parameters[parameterwidget_objects[1].name], 227)
+
+        self.assertEqual(parameterwidget_objects[2].name, "choice")
+        self.assertEqual(parameterwidget_objects[2].default_value, "")
+        with self.assertRaises(KeyError):
+            parameters[parameterwidget_objects[2].name]
+
+        change = FakeChange(new="test")
+        parameterwidget_objects[2].update(change) # Should add necessary extra quotation marks
+        self.assertEqual(parameters[parameterwidget_objects[2].name], "\"test\"")
+
diff --git a/mcstasscript/tests/test_widget_helpers.py b/mcstasscript/tests/test_widget_helpers.py
new file mode 100644
index 00000000..5f7a7ece
--- /dev/null
+++ b/mcstasscript/tests/test_widget_helpers.py
@@ -0,0 +1,81 @@
+import unittest
+import unittest.mock
+import io
+
+from mcstasscript.jb_interface.widget_helpers import HiddenPrints
+from mcstasscript.jb_interface.widget_helpers import parameter_has_default
+from mcstasscript.jb_interface.widget_helpers import get_parameter_default
+from mcstasscript.helper.mcstas_objects import ParameterVariable
+
+
+class TestWidgetHelpers(unittest.TestCase):
+    """
+    Tests of widget helpers
+    """
+
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_HiddenPrints(self, mock_stdout):
+        """
+        Checks HiddenPrints suppress output to stdout
+        """
+
+        print("test")
+
+        output = mock_stdout.getvalue()
+        self.assertEqual(output, "test\n")
+
+        with HiddenPrints():
+            print("Hello")
+
+        output = mock_stdout.getvalue()
+        self.assertEqual(output, "test\n")
+
+    def test_parameter_has_default_false(self):
+        """
+        Check for parameter that does not have default, should be false
+        """
+
+        par = ParameterVariable("test")
+        self.assertFalse(parameter_has_default(par))
+
+    def test_parameter_has_default_true(self):
+        """
+        Check for parameter that has default, should be true
+        """
+
+        par = ParameterVariable("test", value=8)
+        self.assertTrue(parameter_has_default(par))
+
+    def test_get_parameter_default_string(self):
+        """
+        Get the default for string parameter
+        """
+
+        par = ParameterVariable("string", "test", value="variable")
+        self.assertEqual(get_parameter_default(par), "variable")
+
+    def test_get_parameter_default_double_specified(self):
+        """
+        Get the default for specified double parameter
+        """
+
+        par = ParameterVariable("double", "test", value=5.5)
+        self.assertEqual(get_parameter_default(par), 5.5)
+
+    def test_get_parameter_default_double(self):
+        """
+        Get the default for parameter that was double by default
+        """
+
+        par = ParameterVariable("test", value=5.7)
+        self.assertEqual(get_parameter_default(par), 5.7)
+
+    def test_get_parameter_default_int(self):
+        """
+        Get default value from integer value, should be rounded
+        """
+
+        par = ParameterVariable("int", "test", value=5.7)
+        self.assertEqual(get_parameter_default(par), 5)
+
+
diff --git a/mcstasscript/tests/utilities.py b/mcstasscript/tests/utilities.py
new file mode 100644
index 00000000..7c1fbb9d
--- /dev/null
+++ b/mcstasscript/tests/utilities.py
@@ -0,0 +1,15 @@
+import os
+
+
+def work_dir_test(func):
+    def wrapper():
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        current_work_dir = os.getcwd()
+
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
+        func()
+
+        os.chdir(current_work_dir)  # Return to previous workdir
+
+    return wrapper

From d0fb259e6e33549c08e8fa21797c9ddeedcbc48a Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Wed, 16 Jun 2021 14:27:53 +0200
Subject: [PATCH 158/403] Added info on widgets and mcxtrace to readme.md

---
 README.md | 38 ++++++++++++++++++++++++++++++--------
 1 file changed, 30 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index 4d4f9e23..b5a3e254 100644
--- a/README.md
+++ b/README.md
@@ -1,20 +1,22 @@
 # McStasScript
-[McStas](http://www.mcstas.org) API for creating and running McStas instruments from python scripting
+[McStas](http://www.mcstas.org) API for creating and running McStas/McXtrace instruments from python scripting
 
 Prototype for an API that allow interaction with McStas through an interface like Jupyter Notebooks created under WP5 of PaNOSC.
 
 ## Installation
-McStasScript does not include the McStas installation, so McStas should be installed separately, link to instructions [here](https://github.com/McStasMcXtrace/McCode/tree/master/INSTALL-McStas).
+McStasScript does not include the McStas installation, so McStas/McXtrace should be installed separately, link to instructions [here](https://github.com/McStasMcXtrace/McCode/tree/master/INSTALL-McStas).
 McStasScript can be installed using pip from a terminal,
 
     python3 -m pip install McStasScript --upgrade
 
-After installation it is necessary to configure the package so the McStas installation can be found, here we show how the appropriate code for an Ubuntu system as an example. The configuration is saved permanently, and only needs to be updated when McStas or McStasScript is updated. This has to be done from a python terminal or from within a python environment.
+After installation it is necessary to configure the package so the McStas/McXtrace installation can be found, here we show how the appropriate code for an Ubuntu system as an example. The configuration is saved permanently, and only needs to be updated when McStas or McStasScript is updated. This has to be done from a python terminal or from within a python environment.
 
     from mcstasscript.interface import functions
     my_configurator = functions.Configurator()
     my_configurator.set_mcrun_path("/usr/bin/")
     my_configurator.set_mcstas_path("/usr/share/mcstas/2.5/")
+    my_configurator.set_mxrun_path("/usr/bin/")
+    my_configurator.set_mcxtrace_path("/usr/share/mcxtrace/1.5/")
 
 To get a python terminal, run the command python in a terminal and then copy, paste and execute the lines above one at a time. Exit with ctrl+D.
 
@@ -28,6 +30,8 @@ On a Mac OS X system, the paths to the mcrun executable and mcstas folder are th
 
     my_configurator.set_mcrun_path("/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/")
     my_configurator.set_mcstas_path("/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/")
+    my_configurator.set_mxrun_path("/Applications/McXtrace-1.5.app/Contents/Resources/mcxtrace/1.5/bin/")
+    my_configurator.set_mcxtrace_path("/Applications/McXtrace-1.5.app/Contents/Resources/mcxtrace/1.5/")
     
     
 ### Notes on windows installation
@@ -48,6 +52,8 @@ For a standard McStas installation on Windows, the appropriate configuration can
     my_configurator = functions.Configurator()
     my_configurator.set_mcrun_path("\\mcstas-2.6\\bin\\")
     my_configurator.set_mcstas_path("\\mcstas-2.6\\lib\\")
+    my_configurator.set_mxrun_path("\\mcxtrace-1.5\\bin\\")
+    my_configurator.set_mcxtrace_path("\\mcxtrace-1.5\\lib\\")    
     
 Double backslashes are necessary since backslash is the escape character in python strings.
 
@@ -58,11 +64,11 @@ Import the interface
 
     from mcstasscript.interface import instr, plotter, functions, reader
 
-Now the package can be used. Start with creating a new instrument, just needs a name
+Now the package can be used. Start with creating a new instrument, just needs a name. For a McXtrace instrument use McXtrace_instr instead.
 
     my_instrument = instr.McStas_instr("my_instrument_file")
 
-Then McStas components can be added, here we add a source and ask for help on the parameters
+Then McStas components can be added, here we add a source and ask for help on the parameters.
 
     my_source = my_instrument.add_component("source", "Source_simple")
     my_source.show_parameters() # Can be used to show available parameters for Source simple
@@ -129,6 +135,19 @@ In a python terminal this would display the data directly:
 Plotting is usually done in a subplot of all monitors recorded.    
 
     plot = plotter.make_sub_plot(data)
+    
+## Widgets in Jupyter Notebooks
+When using McStasScript in a jupyter notebook, it is possible to plot the data with a widget system instead.
+
+    plotter.interface(data)
+    
+There is also a widget solution for performing the simulation which works as an alternative to *run_full_instrument*, this method is also called *interface* and is available directly on the instrument. This interface includes setting parameters, simulation options and plotting of the resulting data.
+
+    my_instrument.interface()
+    
+The data from the latest run performed in the simulation widget can be retrieved with *get_interface_data*.
+
+    data = my_instrument.get_interface_data()
 
 ## Use in existing project
 If one wish to work on existing projects using McStasScript, there is a reader included that will read a McStas Instrument file and write the corresponding McStasScript python instrument to disk. Here is an example where the PSI_DMC.instr example is converted:
@@ -151,7 +170,9 @@ Here is a quick overview of the available methods of the main classes in the pro
         ├── append_initialize(str string) # Appends a line to initialize (c syntax)
         ├── print_components() # Prints list of components and their location
         ├── write_full_instrument() # Writes instrument to disk with given name + ".instr"
-        └── run_full_instrument() # Runs simulation. Options in keyword arguments. Returns list of McStasData
+        ├── run_full_instrument() # Runs simulation. Options in keyword arguments. Returns list of McStasData
+        ├── interface() # Shows widget interface in jupyter notebook
+        └── get_interface_data() # Returns data set from latest simulation performed in interface
         
     component # returned by add_component
     ├── set_AT(list at_list) # Sets component position (list of x,y,z positions in [m])
@@ -172,8 +193,9 @@ Here is a quick overview of the available methods of the main classes in the pro
     
     plotter
     ├── make_plot(list McStasData) # Plots each data set individually
-    └── make_sub_plot(list McStasData) # Plots data as subplot
-    
+    ├── make_sub_plot(list McStasData) # Plots data as subplot
+    └── interface(list McStasData) # Shows plotting interface in jupyter notebook
+
     reader
     └──  McStas_file(str filename) # Returns a reader that can extract information from given instr file
 

From 52e2faeda2bd1cf807829dc572016a9a0dd11a24 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <Mads.Bertelsen@ess.eu>
Date: Wed, 16 Jun 2021 14:53:57 +0200
Subject: [PATCH 159/403] Update of documentation, now mentions widget
 interface.

---
 McStasScript_documentation.pdf  | Bin 184620 -> 594931 bytes
 mcstasscript/interface/instr.py |   9 +++++++++
 setup.py                        |   2 +-
 3 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
index f1c876a952f59cac5590f349e98822e7ef376ef0..27fc59c8e322e55dbdd5a9fe36e16bfd238a3a85 100644
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zhC&nmGOXb=oCQ}dtDA4k=AGL!o>5^J*HkKgEk1Us=$Y|I#GmoX#`zt0DKonjs!|-j
zQb-$+)m#{?l??QPkrjuwJw=Els=MY)$?Bd8_fF<^UH?pc5f$|g!gD3kZeiB6_w-Gp
z<{7b0T<U_}aWNZnG&U8V5-cC%F{pk^-{i-|!=|&)sG+;UyHwYx?+=Tw(fDI%>ST$t
z;pU^QPQtN+h2>Sa-3*#@G`+h4k#nT|uTBQcI%Nupf5&%Y31w0%?X)|co?FiR?cdvC
zWhysdQLnGOPzx+WJf&(9Z<C^%Q8)Tsr%gEucg6QmxC>z^Ud^Wp*}806vIAGWf!;Np
z6m`WmcNL>x!EY6Nj93B!UrS@j85~p>ad1!mDr)gNeE2gBS1Fh3o!+`|&1AQoI)xiB
zx(7LygzA?0$&|iU-&mWvA6&T8;>A=fle`g6u8=w1i)p=R{-agbYp%MVl~^tr%<iLs
zgSP)HR)6<5eSQ9Jn${bbowR)gdOb){>N{=KA*VJNYIo<YAk<Hn-`sZa(_xOOTv*%}
z4`ieF^lx};v=Ql62BP$LL{*<=bp1q@I9=0mzKo6xkgEt(w)hBTo*C+EMs7qc>GTXw
z9RR!}9~L}`oo0B3Bdo2_!jhGda*!gNnNA#oV91e(%Upv1m6-OMjWwguSoXihFNB}O
z1i_I%{tu)g`~TvrFAc!B$%1kKWe{NLVOd!?{<%e2{?XO_5MZVM+v6+(YzOeQ9tJV-
z1Rb1}_5T%*@&f)-Df%DwQDzwl5<P+cA3UNUpj1CbM~$GUNUAU-;6}*SgIY<FP^VAg
zA)4g+H4eEGhRypgrbLQG$bx2G5T)5BDFP)Y2Rgq4-9*=2Q0#WSA@oGh3w|0Q1<kbK
z3|IhSl;;j%$aH|g(#-YVRM7eH-?(x?E9@+BNktSD6d-Fem{D!FNU#%Nz+H4$%>M_-
Co+l#!

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 91a4ea02..848d5fb1 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -192,6 +192,9 @@ class McCode_instr:
 
     interface()
         Shows interface with jupyter notebook widgets
+
+    get_interface_data()
+        Returns data set from latest simulation in widget
     """
 
     def __init__(self, name, **kwargs):
@@ -1885,6 +1888,9 @@ class McStas_instr(McCode_instr):
 
     interface()
         Shows interface with jupyter notebook widgets
+
+    get_interface_data()
+        Returns data set from latest simulation in widget
     """
     def __init__(self, name, **kwargs):
         """
@@ -2106,6 +2112,9 @@ class McXtrace_instr(McCode_instr):
 
     interface()
         Shows interface with jupyter notebook widgets
+
+    get_interface_data()
+        Returns data set from latest simulation in widget
     """
     def __init__(self, name, **kwargs):
         """
diff --git a/setup.py b/setup.py
index ed2cda63..edd37717 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
      name='McStasScript',
-     version='0.0.30',
+     version='0.0.31',
      author="Mads Bertelsen",
      author_email="Mads.Bertelsen@ess.eu",
      description="A python scripting interface for McStas",

From 1fa3a7a29b194e4e03607164f721534a32419955 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 27 Aug 2021 13:19:02 +0200
Subject: [PATCH 160/403] Addition of new datatype, event data.

Data loader now checks if Errors are available, if they are a
McStasDataBinned dataset is made, if not it is assumed the data
is events. In that case an McStasDataEvents dataset is created.

Added data set to test_data_set to test loader. Unit tests modified
to take this into account. New test added for Event data.
---
 mcstasscript/data/data.py                     |   268 +-
 mcstasscript/helper/managed_mcrun.py          |    45 +-
 mcstasscript/interface/functions.py           |     2 +-
 mcstasscript/tests/test_ManagedMcrun.py       |    57 +-
 mcstasscript/tests/test_McStasData.py         |    78 +-
 mcstasscript/tests/test_Plotter.py            |    96 +-
 mcstasscript/tests/test_add_data.py           |    46 +-
 .../event_dat_list.p.x.y.z.vx.vy.vz.t         | 12029 ++++++++++++++++
 mcstasscript/tests/test_data_set/mccode.sim   |    22 +
 mcstasscript/tests/test_functions.py          |    60 +-
 mcstasscript/tests/test_plot_interface.py     |    22 +-
 requirements.txt                              |     1 +
 12 files changed, 12556 insertions(+), 170 deletions(-)
 create mode 100644 mcstasscript/tests/test_data_set/event_dat_list.p.x.y.z.vx.vy.vz.t

diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index f7418bbf..218b307a 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -317,7 +317,7 @@ def set_options(self, **kwargs):
                                  + "given: " + str(self.right_lim))
 
 
-class McStasData:
+class McStasDataOld:
     """
     Class for holding full McStas dataset with data, metadata and
     plotting preferences
@@ -452,3 +452,269 @@ def __str__(self):
 
     def __repr__(self):
         return "\n" + self.__str__()
+
+class McStasData:
+    """
+    Class for holding full McStas dataset with data, metadata and
+    plotting preferences
+
+    Attributes
+    ----------
+    metadata : McStasMetaData instance
+        Holds the metadata for the dataset
+
+    name : str
+        Name of component, extracted from metadata
+
+    plot_options : McStasPlotOptions instance
+        Holds the plotting preferences for the dataset
+
+    Methods
+    -------
+    set_xlabel : string
+        sets xlabel of data for plotting
+
+    set_ylabel : string
+        sets ylabel of data for plotting
+
+    set_title : string
+        sets title of data for plotting
+
+    set_options : keyword arguments
+        sets plot options, keywords passed to McStasPlotOptions method
+    """
+
+    def __init__(self, metadata):
+        """
+        Initialize a new McStas dataset, 4 positional arguments, pass
+        xaxis as kwarg if 1d data
+
+        Parameters
+        ----------
+        metadata : McStasMetaData instance
+            Holds the metadata for the dataset
+        """
+
+        # attach meta data
+        self.metadata = metadata
+        # get name from metadata
+        self.name = self.metadata.component_name
+        # initialize PlotOptions
+        self.plot_options = McStasPlotOptions()
+
+        self.data_type = None
+
+    # Methods xlabel, ylabel and title as they might not be found
+    def set_xlabel(self, string):
+        self.metadata.set_xlabel(string)
+
+    def set_ylabel(self, string):
+        self.metadata.set_ylabel(string)
+
+    def set_title(self, string):
+        self.metadata.set_title(string)
+
+    def set_plot_options(self, **kwargs):
+        self.plot_options.set_options(**kwargs)
+
+    def __str__(self):
+        """
+        Returns string with quick summary of data
+        """
+
+        string = "McStasData: "
+        string += self.name + " "
+        if type(self.metadata.dimension) == int:
+            string += "type: 1D "
+        elif len(self.metadata.dimension) == 2:
+            string += "type: 2D "
+        else:
+            string += "type: other "
+
+        if "values" in self.metadata.info:
+            values = self.metadata.info["values"]
+            values = values.strip()
+            values = values.split(" ")
+            if len(values) == 3:
+                string += " I:" + str(values[0])
+                string += " E:" + str(values[1])
+                string += " N:" + str(values[2])
+
+        return string
+
+    def __repr__(self):
+        return "\n" + self.__str__()
+
+
+class McStasDataBinned(McStasData):
+    """
+    Class for holding full McStas dataset with data, metadata and
+    plotting preferences
+
+    Attributes
+    ----------
+    metadata : McStasMetaData instance
+        Holds the metadata for the dataset
+
+    name : str
+        Name of component, extracted from metadata
+
+    Intensity : numpy array
+        Intensity data [neutrons/s] in 1d or 2d numpy array, dimension in
+        metadata
+
+    Error : numpy array
+        Error data [neutrons/s] in 1d or 2d numpy array, same dimensions as
+        Intensity
+
+    Ncount : numpy array
+        Number of rays in bin, 1d or 2d numpy array, same dimensions as
+        Intensity
+
+    plot_options : McStasPlotOptions instance
+        Holds the plotting preferences for the dataset
+
+    Methods
+    -------
+    set_xlabel : string
+        sets xlabel of data for plotting
+
+    set_ylabel : string
+        sets ylabel of data for plotting
+
+    set_title : string
+        sets title of data for plotting
+
+    set_options : keyword arguments
+        sets plot options, keywords passed to McStasPlotOptions method
+    """
+
+    def __init__(self, metadata, intensity, error, ncount, **kwargs):
+        """
+        Initialize a new McStas dataset, 4 positional arguments, pass
+        xaxis as kwarg if 1d data
+
+        Parameters
+        ----------
+        metadata : McStasMetaData instance
+            Holds the metadata for the dataset
+
+        intensity : numpy array
+            Intensity data [neutrons/s] in 1d or 2d numpy array, dimension in
+            metadata
+
+        error : numpy array
+            Error data [neutrons/s] in 1d or 2d numpy array, same dimensions
+            as Intensity
+
+        ncount : numpy array
+            Number of rays in bin, 1d or 2d numpy array, same dimensions as
+            Intensity
+
+        kwargs : keyword arguments
+            xaxis is required for 1d data
+        """
+
+        super().__init__(metadata)
+
+        # three basic arrays from positional arguments
+        if not isinstance(intensity, np.ndarray):
+            raise ValueError("intensity should be numpy array!")
+        if not isinstance(error, np.ndarray):
+            raise ValueError("error should be numpy array!")
+        if not isinstance(ncount, np.ndarray):
+            raise ValueError("ncount should be numpy array!")
+
+        self.Intensity = intensity
+        self.Error = error
+        self.Ncount = ncount
+
+        if type(self.metadata.dimension) == int:
+            self.data_type = "Binned 1D"
+            if "xaxis" in kwargs:
+                self.xaxis = kwargs["xaxis"]
+            else:
+                raise NameError(
+                    "ERROR: Initialization of McStasData done with 1d "
+                    + "data, but without xaxis for " + self.name + "!")
+        elif len(self.metadata.dimension) == 2:
+            self.data_type = "Binned 2D"
+        else:
+            self.data_type = "Binned"
+
+class McStasDataEvent(McStasData):
+    """
+    Class for holding McStas event dataset with data, metadata and
+    plotting preferences
+
+    Attributes
+    ----------
+    metadata : McStasMetaData instance
+        Holds the metadata for the dataset
+
+    name : str
+        Name of component, extracted from metadata
+
+    Events : numpy array
+        Event data
+
+    plot_options : McStasPlotOptions instance
+        Holds the plotting preferences for the dataset
+
+    Methods
+    -------
+    set_xlabel : string
+        sets xlabel of data for plotting
+
+    set_ylabel : string
+        sets ylabel of data for plotting
+
+    set_title : string
+        sets title of data for plotting
+
+    set_options : keyword arguments
+        sets plot options, keywords passed to McStasPlotOptions method
+    """
+
+    def __init__(self, metadata, events, **kwargs):
+        """
+        Initialize a new McStas event dataset, 2 positional arguments
+
+        Parameters
+        ----------
+        metadata : McStasMetaData instance
+            Holds the metadata for the dataset
+
+        events : numpy array
+            event data
+        """
+
+        super().__init__(metadata)
+
+        # three basic arrays from positional arguments
+        if not isinstance(events, np.ndarray):
+            raise ValueError("events should be numpy array!")
+
+        self.Events = events
+        self.data_type = "Events"
+
+        # Intensity for compatability with plotting routine
+        data_lines = metadata.dimension[1]
+        self.Intensity = self.Events[0:data_lines, :]
+
+    def __str__(self):
+        """
+        Returns string with quick summary of data
+        """
+
+        string = "McStasDataEvent: "
+        string += self.name + " with "
+        string += str(len(self.Events)) + " events."
+        if "variables" in self.metadata.info:
+            string += " Variables: "
+            string += self.metadata.info["variables"].strip()
+
+        return string
+
+    def __repr__(self):
+        return "\n" + self.__str__()
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 1ab7e708..9e24db5a 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -1,9 +1,11 @@
 import os
 import numpy as np
 import subprocess
+import mmap
 
 from mcstasscript.data.data import McStasMetaData
-from mcstasscript.data.data import McStasData
+from mcstasscript.data.data import McStasDataBinned
+from mcstasscript.data.data import McStasDataEvent
 
 
 class ManagedMcrun:
@@ -339,7 +341,7 @@ def load_metadata(data_folder_name):
                 # This line contains info to be added to metadata
                 colon_index = lines.index(":")
                 key = lines[2:colon_index]
-                value = lines[colon_index+2:]
+                value = lines[colon_index+2:].strip()
                 current_object.add_info(key, value)
 
             if lines == "begin data\n":
@@ -367,8 +369,8 @@ def load_monitor(metadata, data_folder_name):
         path to folder from which metadata should be loaded
     """
     # Load data with numpy
-    data = np.loadtxt(os.path.join(data_folder_name,
-                                   metadata.filename.rstrip()))
+    filename = os.path.join(data_folder_name, metadata.filename.rstrip())
+    data = np.loadtxt(filename)
 
     # Split data into intensity, error and ncount
     if type(metadata.dimension) == int:
@@ -377,18 +379,37 @@ def load_monitor(metadata, data_folder_name):
         Error = data.T[2, :]
         Ncount = data.T[3, :]
 
-    elif len(metadata.dimension) == 2:
-        xaxis = []  # Assume evenly binned in 2d
-        data_lines = metadata.dimension[1]
+        # The data is saved as a McStasDataBinned object
+        return McStasDataBinned(metadata, Intensity, Error, Ncount, xaxis=xaxis)
 
-        Intensity = data[0:data_lines, :]
-        Error = data[data_lines:2 * data_lines, :]
-        Ncount = data[2 * data_lines:3 * data_lines, :]
+    elif len(metadata.dimension) == 2:
+        # Need to check if it is binned data or event data
+
+        with open(filename, 'rb', 0) as file, \
+                mmap.mmap(file.fileno(), 0, access=mmap.ACCESS_READ) as s:
+            if s.find(b'# Errors') != -1:
+                data_type = "Binned"
+            else:
+                data_type = "Events"
+
+        if data_type == "Events":
+            Events = data
+
+            return McStasDataEvent(metadata, Events)
+
+        elif data_type == "Binned":
+            # Binned 2D data
+            xaxis = []  # Assume evenly binned in 2d
+            data_lines = metadata.dimension[1]
+            Intensity = data[0:data_lines, :]
+            Error = data[data_lines:2 * data_lines, :]
+            Ncount = data[2 * data_lines:3 * data_lines, :]
+
+            # The data is saved as a McStasDataBinned object
+            return McStasDataBinned(metadata, Intensity, Error, Ncount, xaxis=xaxis)
     else:
         raise NameError(
             "Dimension not read correctly in data set "
             + "connected to monitor named "
             + metadata.component_name)
 
-    # The data is saved as a McStasData object
-    return McStasData(metadata, Intensity, Error, Ncount, xaxis=xaxis)
diff --git a/mcstasscript/interface/functions.py b/mcstasscript/interface/functions.py
index 8d7a6600..58010fcf 100644
--- a/mcstasscript/interface/functions.py
+++ b/mcstasscript/interface/functions.py
@@ -31,7 +31,7 @@ def name_search(name, data_list):
     if len(data_list) == 0:
         raise RuntimeError("Given data list empty.")
 
-    if not type(data_list[0]) == McStasData:
+    if not isinstance(data_list[0], McStasData):
         raise RuntimeError(
             "name_search function needs objects of type McStasData as input.")
 
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index 71a34f4a..d7debc10 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -396,7 +396,7 @@ def test_ManagedMcrun_load_data_PSD4PI(self):
         os.chdir(current_work_dir)  # Reset work directory
 
         # Check three data objects are loaded
-        self.assertEqual(len(results), 3)
+        self.assertEqual(len(results), 4)
 
         # Check properties of PSD_4PI data
         PSD_4PI = results[0]
@@ -432,7 +432,7 @@ def test_ManagedMcrun_load_data_PSD(self):
         os.chdir(current_work_dir)  # Reset work directory
 
         # Check three data objects are loaded
-        self.assertEqual(len(results), 3)
+        self.assertEqual(len(results), 4)
 
         # Check properties of PSD data
         PSD = results[1]
@@ -468,7 +468,7 @@ def test_ManagedMcrun_load_data_L_mon(self):
         os.chdir(current_work_dir)  # Reset work directory
 
         # Check three data objects are loaded
-        self.assertEqual(len(results), 3)
+        self.assertEqual(len(results), 4)
 
         # Check properties of L_mon
         L_mon = results[2]
@@ -519,6 +519,49 @@ def test_ManagedMcrun_load_data_L_mon_direct(self):
         self.assertEqual(L_mon.Intensity[53], 6.990299315e-06)
         self.assertEqual(L_mon.Error[53], 6.215308587e-08)
 
+        self.assertFalse(hasattr(L_mon, 'Events'))
+
+    def test_ManagedMcrun_load_data_Event(self):
+        """
+        Use test_data_set to test load_data for event data
+        """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        current_work_dir = os.getcwd()
+        os.chdir(THIS_DIR)  # Set work directory to test folder
+
+        mcrun_obj = ManagedMcrun("test.instr",
+                                 foldername="test_data_set",
+                                 executable_path=executable_path,
+                                 mcrun_path="path")
+
+        load_path = os.path.join(THIS_DIR, "test_data_set")
+        results = mcrun_obj.load_results(load_path)
+
+        os.chdir(current_work_dir)  # Reset work directory
+
+        # Check properties of event data file
+        mon = results[3]
+
+        self.assertEqual(mon.name, "monitor")
+        self.assertEqual(mon.metadata.dimension, [8, 12000])
+        self.assertEqual(mon.metadata.limits, [1.0, 12000.0, 1.0, 8.0])
+        self.assertEqual(mon.metadata.xlabel, "List of neutron events")
+        self.assertEqual(mon.metadata.ylabel, "p x y z vx vy vz t")
+        self.assertEqual(mon.metadata.title, "Intensity Position Position"
+                + " Position Velocity Velocity Velocity"
+                + " Time_Of_Flight Monitor (Square)")
+        self.assertEqual(mon.Intensity[12, 1], -0.006163896406)
+        self.assertEqual(mon.Events[12, 1], -0.006163896406)
+        self.assertEqual(mon.Events[43, 4], 22.06193582)
+
+        self.assertFalse(hasattr(mon, 'xaxis'))
+        self.assertFalse(hasattr(mon, 'Error'))
+        self.assertFalse(hasattr(mon, 'Ncount'))
+
+
     def test_ManagedMcrun_load_data_L_mon_direct_error(self):
         """
         Check an error occurs when directory has no mccode.sim
@@ -585,7 +628,7 @@ def test_mcrun_load_data_PSD4PI(self):
 
         os.chdir(current_work_dir)  # Reset work directory
 
-        self.assertEqual(len(results), 3)
+        self.assertEqual(len(results), 4)
 
         PSD_4PI = results[0]
 
@@ -613,7 +656,7 @@ def test_mcrun_load_data_PSD(self):
 
         os.chdir(current_work_dir)  # Reset work directory
 
-        self.assertEqual(len(results), 3)
+        self.assertEqual(len(results), 4)
 
         PSD = results[1]
 
@@ -641,7 +684,7 @@ def test_mcrun_load_metadata_PSD4PI(self):
 
         os.chdir(current_work_dir)  # Reset work directory
 
-        self.assertEqual(len(metadata), 3)
+        self.assertEqual(len(metadata), 4)
 
         PSD_4PI = metadata[0]
         self.assertEqual(PSD_4PI.dimension, [300, 300])
@@ -664,7 +707,7 @@ def test_mcrun_load_metadata_L_mon(self):
 
         os.chdir(current_work_dir)  # Reset work directory
 
-        self.assertEqual(len(metadata), 3)
+        self.assertEqual(len(metadata), 4)
 
         L_mon = metadata[2]
         self.assertEqual(L_mon.dimension, 150)
diff --git a/mcstasscript/tests/test_McStasData.py b/mcstasscript/tests/test_McStasData.py
index 0335dd37..81e58b85 100644
--- a/mcstasscript/tests/test_McStasData.py
+++ b/mcstasscript/tests/test_McStasData.py
@@ -2,10 +2,12 @@
 import numpy as np
 
 from mcstasscript.data.data import McStasData
+from mcstasscript.data.data import McStasDataBinned
+from mcstasscript.data.data import McStasDataEvent
 from mcstasscript.data.data import McStasMetaData
 
 
-def set_dummy_MetaData_1d():
+def set_dummy_MetaDataBinned_1d():
     """
     Sets up simple McStasMetaData object with dimension, 1d case
     """
@@ -16,21 +18,21 @@ def set_dummy_MetaData_1d():
     return meta_data
 
 
-def set_dummy_McStasData_1d():
+def set_dummy_McStasDataBinned_1d():
     """
     Sets up simple McStasData object, 1d case
     """
-    meta_data = set_dummy_MetaData_1d()
+    meta_data = set_dummy_MetaDataBinned_1d()
 
     intensity = np.arange(20)
     error = 0.5 * np.arange(20)
     ncount = 2 * np.arange(20)
     axis = np.arange(20)*5.0
 
-    return McStasData(meta_data, intensity, error, ncount, xaxis=axis)
+    return McStasDataBinned(meta_data, intensity, error, ncount, xaxis=axis)
 
 
-def set_dummy_MetaData_2d():
+def set_dummy_MetaDataBinned_2d():
     """
     Sets up simple McStasMetaData object with dimensions, 2d case
     """
@@ -41,17 +43,17 @@ def set_dummy_MetaData_2d():
     return meta_data
 
 
-def set_dummy_McStasData_2d():
+def set_dummy_McStasDataBinned_2d():
     """
     Sets up simple McStasData object, 2d case
     """
-    meta_data = set_dummy_MetaData_2d()
+    meta_data = set_dummy_MetaDataBinned_2d()
 
     intensity = np.arange(20).reshape(4, 5)
     error = 0.5 * np.arange(20).reshape(4, 5)
     ncount = 2 * np.arange(20).reshape(4, 5)
 
-    return McStasData(meta_data, intensity, error, ncount)
+    return McStasDataBinned(meta_data, intensity, error, ncount)
 
 
 class TestMcStasData(unittest.TestCase):
@@ -59,35 +61,35 @@ class TestMcStasData(unittest.TestCase):
     Various tests of McStasData class
     """
 
-    def test_McStasData_init_1d(self):
+    def test_McStasDataBinned_init_1d(self):
         """
         Test that newly created McStasMetaData has correct names, 1d case
         """
 
-        data = set_dummy_McStasData_1d()
+        data = set_dummy_McStasDataBinned_1d()
 
         self.assertEqual(data.name, "component for 1d")
         self.assertEqual(data.metadata.component_name, "component for 1d")
 
-    def test_McStasData_init_values(self):
+    def test_McStasDataBinned_init_values(self):
         """
-        Test that newly created McStasData has expected data, 1d case
+        Test that newly created McStasDataBinned has expected data, 1d case
         Here checking a single data point
         """
 
-        data = set_dummy_McStasData_1d()
+        data = set_dummy_McStasDataBinned_1d()
 
         self.assertEqual(data.Intensity[3], 3)
         self.assertEqual(data.Error[3], 1.5)
         self.assertEqual(data.Ncount[3], 6)
         self.assertEqual(data.xaxis[3], 15.0)
 
-    def test_McStasData_init_values_full(self):
+    def test_McStasDataBinned_init_values_full(self):
         """
-        Test that newly created McStasData has expected data, 1d case
+        Test that newly created McStasDataBinned has expected data, 1d case
         """
 
-        data = set_dummy_McStasData_1d()
+        data = set_dummy_McStasDataBinned_1d()
 
         intensity = np.arange(20)
         error = 0.5 * np.arange(20)
@@ -100,35 +102,35 @@ def test_McStasData_init_values_full(self):
             self.assertEqual(data.Ncount[index], ncount[index])
             self.assertEqual(data.xaxis[index], axis[index])
 
-    def test_McStasData_init_2d_names(self):
+    def test_McStasDataBinned_init_2d_names(self):
         """
         Test that newly created McStasMetaData has correct names, 1d case
         """
 
-        data = set_dummy_McStasData_2d()
+        data = set_dummy_McStasDataBinned_2d()
 
         self.assertEqual(data.name, "test a component")
         self.assertEqual(data.metadata.component_name, "test a component")
 
-    def test_McStasData_init_2d_values(self):
+    def test_McStasDataBinned_init_2d_values(self):
         """
-        Test that newly created McStasData has expected data, 2d case
+        Test that newly created McStasDataBinned has expected data, 2d case
         Here checking a single point
         """
 
-        data = set_dummy_McStasData_2d()
+        data = set_dummy_McStasDataBinned_2d()
 
         self.assertEqual(data.Intensity[2][3], 13)
         self.assertEqual(data.Error[2][3], 6.5)
         self.assertEqual(data.Ncount[2][3], 26)
 
-    def test_McStasData_init_2d_values_full(self):
+    def test_McStasDataBinned_init_2d_values_full(self):
         """
-        Test that newly created McStasData has expected data, 2d case
+        Test that newly created McStasDataBinned has expected data, 2d case
         Here checking a entire dataset
         """
 
-        data = set_dummy_McStasData_2d()
+        data = set_dummy_McStasDataBinned_2d()
 
         intensity = np.arange(20).reshape(4, 5)
         error = 0.5 * np.arange(20).reshape(4, 5)
@@ -146,35 +148,35 @@ def test_McStasData_init_2d_values_full(self):
                 self.assertEqual(data.Ncount[index1][index2],
                                  ncount[index1][index2])
 
-    def test_McStasData_set_info_title(self):
+    def test_McStasDataBinned_set_info_title(self):
         """
         Test that title can be set
         """
-        data = set_dummy_McStasData_2d()
+        data = set_dummy_McStasDataBinned_2d()
         data.set_title("title_test")
         self.assertEqual(data.metadata.title, "title_test")
 
-    def test_McStasData_set_xlabel(self):
+    def test_McStasDataBinned_set_xlabel(self):
         """
         Test that xlabel can be set
         """
-        data = set_dummy_McStasData_2d()
+        data = set_dummy_McStasDataBinned_2d()
         data.set_xlabel("xlabel test")
         self.assertEqual(data.metadata.xlabel, "xlabel test")
 
-    def test_McStasData_set_ylabel(self):
+    def test_McStasDataBinned_set_ylabel(self):
         """
         Test that ylabel can be set
         """
-        data = set_dummy_McStasData_2d()
+        data = set_dummy_McStasDataBinned_2d()
         data.set_ylabel("ylabel test")
         self.assertEqual(data.metadata.ylabel, "ylabel test")
 
-    def test_McStasData_set_log(self):
+    def test_McStasDataBinned_set_log(self):
         """
         Test that log setting has correct type regardless of how it is given
         """
-        data = set_dummy_McStasData_2d()
+        data = set_dummy_McStasDataBinned_2d()
         data.set_plot_options(log=True)
         self.assertIsInstance(data.plot_options.log, bool)
         self.assertTrue(data.plot_options.log)
@@ -187,11 +189,11 @@ def test_McStasData_set_log(self):
         self.assertIsInstance(data.plot_options.log, bool)
         self.assertTrue(data.plot_options.log)
 
-    def test_McStasData_set_show_colorbar(self):
+    def test_McStasDataBinned_set_show_colorbar(self):
         """
         Test that log setting has correct type regardless of how it is given
         """
-        data = set_dummy_McStasData_2d()
+        data = set_dummy_McStasDataBinned_2d()
         data.set_plot_options(show_colorbar=True)
         self.assertIsInstance(data.plot_options.show_colorbar, bool)
         self.assertTrue(data.plot_options.show_colorbar)
@@ -204,19 +206,19 @@ def test_McStasData_set_show_colorbar(self):
         self.assertIsInstance(data.plot_options.show_colorbar, bool)
         self.assertTrue(data.plot_options.show_colorbar)
 
-    def test_McStasData_set_orders_of_mag(self):
+    def test_McStasDataBinned_set_orders_of_mag(self):
         """
         Test that orders_og_mag can be set correctly
         """
-        data = set_dummy_McStasData_2d()
+        data = set_dummy_McStasDataBinned_2d()
         data.set_plot_options(orders_of_mag=5.2)
         self.assertEqual(data.plot_options.orders_of_mag, 5.2)
 
-    def test_McStasData_set_colormap(self):
+    def test_McStasDataBinned_set_colormap(self):
         """
         Test that colormap can be set correctly
         """
-        data = set_dummy_McStasData_2d()
+        data = set_dummy_McStasDataBinned_2d()
         data.set_plot_options(colormap="hot")
         self.assertIs(data.plot_options.colormap, "hot")
 
diff --git a/mcstasscript/tests/test_Plotter.py b/mcstasscript/tests/test_Plotter.py
index 47f84d1e..a0b4fd0d 100644
--- a/mcstasscript/tests/test_Plotter.py
+++ b/mcstasscript/tests/test_Plotter.py
@@ -3,14 +3,14 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-from mcstasscript.data.data import McStasData
+from mcstasscript.data.data import McStasDataBinned
 from mcstasscript.data.data import McStasMetaData
 from mcstasscript.interface.plotter import _find_min_max_I
 from mcstasscript.interface.plotter import _handle_kwargs
 from mcstasscript.interface.plotter import _plot_fig_ax
 
 
-def get_dummy_MetaData_1d():
+def get_dummy_MetaDataBinned_1d():
     meta_data = McStasMetaData()
     meta_data.component_name = "component for 1d"
     meta_data.dimension = 50
@@ -22,18 +22,18 @@ def get_dummy_MetaData_1d():
     return meta_data
 
 
-def get_dummy_McStasData_1d():
-    meta_data = get_dummy_MetaData_1d()
+def get_dummy_McStasDataBinned_1d():
+    meta_data = get_dummy_MetaDataBinned_1d()
 
     intensity = np.arange(20) + 5
     error = 0.5 * np.arange(20)
     ncount = 2 * np.arange(20)
     axis = np.arange(20)*5.0
 
-    return McStasData(meta_data, intensity, error, ncount, xaxis=axis)
+    return McStasDataBinned(meta_data, intensity, error, ncount, xaxis=axis)
 
 
-def get_dummy_MetaData_2d():
+def get_dummy_MetaDataBinned_2d():
     meta_data = McStasMetaData()
     meta_data.component_name = "test a component"
     meta_data.dimension = [5, 4]
@@ -45,14 +45,14 @@ def get_dummy_MetaData_2d():
     return meta_data
 
 
-def get_dummy_McStasData_2d():
-    meta_data = get_dummy_MetaData_2d()
+def get_dummy_McStasDataBinned_2d():
+    meta_data = get_dummy_MetaDataBinned_2d()
 
     intensity = np.arange(20).reshape(4, 5) + 5
     error = 0.5 * np.arange(20).reshape(4, 5)
     ncount = 2 * np.arange(20).reshape(4, 5)
 
-    return McStasData(meta_data, intensity, error, ncount)
+    return McStasDataBinned(meta_data, intensity, error, ncount)
 
 
 class TestPlotterHelpers(unittest.TestCase):
@@ -66,7 +66,7 @@ def test_find_min_max_I_simple_1D_case(self):
         and maximum value to plot for a given McStasData set
         """
 
-        dummy_data = get_dummy_McStasData_1d()
+        dummy_data = get_dummy_McStasDataBinned_1d()
         found_min, found_max = _find_min_max_I(dummy_data)
 
         # np.arange(20) + 5: min = 5, max = 5+19 = 24
@@ -80,7 +80,7 @@ def test_find_min_max_I_cut_max_1D_case(self):
         Here cut_max is used to limit the maximum plotted.
         """
 
-        dummy_data = get_dummy_McStasData_1d()
+        dummy_data = get_dummy_McStasDataBinned_1d()
         dummy_data.set_plot_options(cut_max=0.8)
         found_min, found_max = _find_min_max_I(dummy_data)
 
@@ -95,7 +95,7 @@ def test_find_min_max_I_cut_min_1D_case(self):
         Here cut_min is used to limit the minimum plotted.
         """
 
-        dummy_data = get_dummy_McStasData_1d()
+        dummy_data = get_dummy_McStasDataBinned_1d()
         dummy_data.set_plot_options(cut_min=0.2)
         found_min, found_max = _find_min_max_I(dummy_data)
 
@@ -112,7 +112,7 @@ def test_find_min_max_I_log_with_zero_case(self):
         ignored.
         """
 
-        dummy_data = get_dummy_McStasData_1d()
+        dummy_data = get_dummy_McStasDataBinned_1d()
         dummy_data.Intensity[5] = 0
         dummy_data.set_plot_options(log=True)
         found_min, found_max = _find_min_max_I(dummy_data)
@@ -129,7 +129,7 @@ def test_find_min_max_I_log_cut_max_1D_case(self):
         log mode is enabled.
         """
 
-        dummy_data = get_dummy_McStasData_1d()
+        dummy_data = get_dummy_McStasDataBinned_1d()
         dummy_data.set_plot_options(cut_max=0.8, log=True)
         found_min, found_max = _find_min_max_I(dummy_data)
 
@@ -145,7 +145,7 @@ def test_find_min_max_I_log_cut_min_1D_case(self):
         log mode is enabled.
         """
 
-        dummy_data = get_dummy_McStasData_1d()
+        dummy_data = get_dummy_McStasDataBinned_1d()
         dummy_data.set_plot_options(cut_min=0.2, log=True)
         found_min, found_max = _find_min_max_I(dummy_data)
 
@@ -161,7 +161,7 @@ def test_find_min_max_I_log_orders_of_mag_1D_case(self):
         while log mode is enabled.
         """
 
-        dummy_data = get_dummy_McStasData_1d()
+        dummy_data = get_dummy_McStasDataBinned_1d()
         dummy_data.Intensity[5] = 10**6
         dummy_data.set_plot_options(log=True, orders_of_mag=3)
         found_min, found_max = _find_min_max_I(dummy_data)
@@ -178,7 +178,7 @@ def test_find_min_max_I_log_orders_of_mag_1D_with_zero_case(self):
         zero intensity, which should be ignored.
         """
 
-        dummy_data = get_dummy_McStasData_1d()
+        dummy_data = get_dummy_McStasDataBinned_1d()
         dummy_data.Intensity[5] = 10**6
         dummy_data.Intensity[6] = 0
         dummy_data.set_plot_options(log=True, orders_of_mag=3)
@@ -193,7 +193,7 @@ def test_find_min_max_I_simple_2D_case(self):
         and maximum value to plot for a given McStasData set
         """
 
-        dummy_data = get_dummy_McStasData_2d()
+        dummy_data = get_dummy_McStasDataBinned_2d()
         found_min, found_max = _find_min_max_I(dummy_data)
 
         # np.arange(20) + 5: min = 5, max = 5+19 = 24
@@ -207,7 +207,7 @@ def test_find_min_max_I_cut_max_2D_case(self):
         Here cut_max is used to limit the maximum plotted.
         """
 
-        dummy_data = get_dummy_McStasData_2d()
+        dummy_data = get_dummy_McStasDataBinned_2d()
         dummy_data.set_plot_options(cut_max=0.8)
         found_min, found_max = _find_min_max_I(dummy_data)
 
@@ -222,7 +222,7 @@ def test_find_min_max_I_cut_min_2D_case(self):
         Here cut_min is used to limit the minimum plotted.
         """
 
-        dummy_data = get_dummy_McStasData_2d()
+        dummy_data = get_dummy_McStasDataBinned_2d()
         dummy_data.set_plot_options(cut_min=0.2)
         found_min, found_max = _find_min_max_I(dummy_data)
 
@@ -239,7 +239,7 @@ def test_find_min_max_I_log_with_zero_2D_case(self):
         ignored.
         """
 
-        dummy_data = get_dummy_McStasData_2d()
+        dummy_data = get_dummy_McStasDataBinned_2d()
         dummy_data.Intensity[2, 2] = 0
         dummy_data.set_plot_options(log=True)
         found_min, found_max = _find_min_max_I(dummy_data)
@@ -256,7 +256,7 @@ def test_find_min_max_I_log_cut_max_2D_case(self):
         log mode is enabled.
         """
 
-        dummy_data = get_dummy_McStasData_2d()
+        dummy_data = get_dummy_McStasDataBinned_2d()
         dummy_data.set_plot_options(cut_max=0.8, log=True)
         found_min, found_max = _find_min_max_I(dummy_data)
 
@@ -272,7 +272,7 @@ def test_find_min_max_I_log_cut_min_2D_case(self):
         log mode is enabled.
         """
 
-        dummy_data = get_dummy_McStasData_2d()
+        dummy_data = get_dummy_McStasDataBinned_2d()
         dummy_data.set_plot_options(cut_min=0.2, log=True)
         found_min, found_max = _find_min_max_I(dummy_data)
 
@@ -288,7 +288,7 @@ def test_find_min_max_I_log_orders_of_mag_2D_case(self):
         while log mode is enabled.
         """
 
-        dummy_data = get_dummy_McStasData_2d()
+        dummy_data = get_dummy_McStasDataBinned_2d()
         dummy_data.Intensity[2, 2] = 10**6
         dummy_data.set_plot_options(log=True, orders_of_mag=3)
         found_min, found_max = _find_min_max_I(dummy_data)
@@ -305,7 +305,7 @@ def test_find_min_max_I_log_orders_of_mag_2D_with_zero_case(self):
         zero intensity, which should be ignored.
         """
 
-        dummy_data = get_dummy_McStasData_2d()
+        dummy_data = get_dummy_McStasDataBinned_2d()
         dummy_data.Intensity[2, 2] = 10**6
         dummy_data.Intensity[2, 3] = 0
         dummy_data.set_plot_options(log=True, orders_of_mag=3)
@@ -323,7 +323,7 @@ def test_find_min_max_I_fail_case(self):
         zero intensity, which should be ignored.
         """
 
-        dummy_data = get_dummy_McStasData_2d()
+        dummy_data = get_dummy_McStasDataBinned_2d()
         dummy_data.Intensity = np.zeros((5, 5))
         dummy_data.set_plot_options(log=True, orders_of_mag=3)
         found_min, found_max = _find_min_max_I(dummy_data)
@@ -338,8 +338,8 @@ def test_handle_kwargs_log(self):
         Keyword args can be set for all by normal use, or individual
         data sets by using a list. Both are checked here.
         """
-        dummy_data1 = get_dummy_McStasData_2d()
-        dummy_data2 = get_dummy_McStasData_2d()
+        dummy_data1 = get_dummy_McStasDataBinned_2d()
+        dummy_data2 = get_dummy_McStasDataBinned_2d()
         self.assertEqual(dummy_data1.plot_options.log, False)
         self.assertEqual(dummy_data2.plot_options.log, False)
 
@@ -359,8 +359,8 @@ def test_handle_kwargs_oders_of_mag(self):
         Keyword args can be set for all by normal use, or individual
         data sets by using a list. Both are checked here.
         """
-        dummy_data1 = get_dummy_McStasData_2d()
-        dummy_data2 = get_dummy_McStasData_2d()
+        dummy_data1 = get_dummy_McStasDataBinned_2d()
+        dummy_data2 = get_dummy_McStasDataBinned_2d()
         self.assertEqual(dummy_data1.plot_options.orders_of_mag, 300)
         self.assertEqual(dummy_data2.plot_options.orders_of_mag, 300)
 
@@ -409,11 +409,11 @@ def test_handle_kwargs_all_simple(self):
 
             default_value = defaults[kw_option]
 
-            dummy_data1 = get_dummy_McStasData_2d()
+            dummy_data1 = get_dummy_McStasDataBinned_2d()
             data1_value = dummy_data1.plot_options.__getattribute__(kw_option)
             self.assertEqual(data1_value, default_value)
 
-            dummy_data2 = get_dummy_McStasData_2d()
+            dummy_data2 = get_dummy_McStasDataBinned_2d()
             data2_value = dummy_data2.plot_options.__getattribute__(kw_option)
             self.assertEqual(data2_value, default_value)
 
@@ -444,8 +444,8 @@ def test_handle_kwargs_left_lim(self):
         Keyword args can be set for all by normal use, or individual
         data sets by using a list. Both are checked here.
         """
-        dummy_data1 = get_dummy_McStasData_2d()
-        dummy_data2 = get_dummy_McStasData_2d()
+        dummy_data1 = get_dummy_McStasDataBinned_2d()
+        dummy_data2 = get_dummy_McStasDataBinned_2d()
         self.assertEqual(dummy_data1.plot_options.custom_xlim_left, False)
         self.assertEqual(dummy_data2.plot_options.custom_xlim_left, False)
 
@@ -469,8 +469,8 @@ def test_handle_kwargs_right_lim(self):
         Keyword args can be set for all by normal use, or individual
         data sets by using a list. Both are checked here.
         """
-        dummy_data1 = get_dummy_McStasData_2d()
-        dummy_data2 = get_dummy_McStasData_2d()
+        dummy_data1 = get_dummy_McStasDataBinned_2d()
+        dummy_data2 = get_dummy_McStasDataBinned_2d()
         self.assertEqual(dummy_data1.plot_options.custom_xlim_right, False)
         self.assertEqual(dummy_data2.plot_options.custom_xlim_right, False)
 
@@ -494,8 +494,8 @@ def test_handle_kwargs_top_lim(self):
         Keyword args can be set for all by normal use, or individual
         data sets by using a list. Both are checked here.
         """
-        dummy_data1 = get_dummy_McStasData_2d()
-        dummy_data2 = get_dummy_McStasData_2d()
+        dummy_data1 = get_dummy_McStasDataBinned_2d()
+        dummy_data2 = get_dummy_McStasDataBinned_2d()
         self.assertEqual(dummy_data1.plot_options.custom_ylim_top, False)
         self.assertEqual(dummy_data2.plot_options.custom_ylim_top, False)
 
@@ -519,8 +519,8 @@ def test_handle_kwargs_bottom_lim(self):
         Keyword args can be set for all by normal use, or individual
         data sets by using a list. Both are checked here.
         """
-        dummy_data1 = get_dummy_McStasData_2d()
-        dummy_data2 = get_dummy_McStasData_2d()
+        dummy_data1 = get_dummy_McStasDataBinned_2d()
+        dummy_data2 = get_dummy_McStasDataBinned_2d()
         self.assertEqual(dummy_data1.plot_options.custom_ylim_bottom, False)
         self.assertEqual(dummy_data2.plot_options.custom_ylim_bottom, False)
 
@@ -542,7 +542,7 @@ def test_handle_kwargs_figsize_default(self):
         Tests handle_kwargs delivers default figsize
         """
 
-        dummy_data = get_dummy_McStasData_2d()
+        dummy_data = get_dummy_McStasDataBinned_2d()
         retrived_figsize, data_list = _handle_kwargs(dummy_data)
         self.assertEqual(retrived_figsize, (13, 7))
 
@@ -552,7 +552,7 @@ def test_handle_kwargs_figsize_tuple(self):
         using tuple as input
         """
 
-        dummy_data = get_dummy_McStasData_2d()
+        dummy_data = get_dummy_McStasDataBinned_2d()
         found_figsize, data_list = _handle_kwargs(dummy_data, figsize=(5, 9))
         self.assertEqual(found_figsize, (5, 9))
 
@@ -562,7 +562,7 @@ def test_handle_kwargs_figsize_list(self):
         using tuple as input
         """
 
-        dummy_data = get_dummy_McStasData_2d()
+        dummy_data = get_dummy_McStasDataBinned_2d()
         found_figsize, data_list = _handle_kwargs(dummy_data, figsize=[5, 9])
         self.assertEqual(found_figsize, (5, 9))
 
@@ -572,7 +572,7 @@ def test_handle_kwargs_single_element_to_list(self):
         and turn it into a list.
         """
 
-        dummy_data = get_dummy_McStasData_2d()
+        dummy_data = get_dummy_McStasDataBinned_2d()
         self.assertFalse(isinstance(dummy_data, list))
         figsize, data_list = _handle_kwargs(dummy_data)
         self.assertTrue(isinstance(data_list, list))
@@ -583,7 +583,7 @@ def test_plot_function_1D_normal(self):
         result.
 
         """
-        dummy_data = get_dummy_McStasData_1d()
+        dummy_data = get_dummy_McStasDataBinned_1d()
 
         fig, ax0 = plt.subplots()
         _plot_fig_ax(dummy_data, fig, ax0)
@@ -594,7 +594,7 @@ def test_plot_function_1D_log(self):
         result. Here with logarithmic y axis.
 
         """
-        dummy_data = get_dummy_McStasData_1d()
+        dummy_data = get_dummy_McStasDataBinned_1d()
 
         fig, ax0 = plt.subplots()
         _plot_fig_ax(dummy_data, fig, ax0, log=True)
@@ -605,7 +605,7 @@ def test_plot_function_2D_normal(self):
         result.
 
         """
-        dummy_data = get_dummy_McStasData_2d()
+        dummy_data = get_dummy_McStasDataBinned_2d()
 
         fig, ax0 = plt.subplots()
         _plot_fig_ax(dummy_data, fig, ax0)
@@ -616,7 +616,7 @@ def test_plot_function_2D_log(self):
         result. Here the intensity coloraxis is logarithmic.
 
         """
-        dummy_data = get_dummy_McStasData_2d()
+        dummy_data = get_dummy_McStasDataBinned_2d()
 
         fig, ax0 = plt.subplots()
         _plot_fig_ax(dummy_data, fig, ax0, log=True)
diff --git a/mcstasscript/tests/test_add_data.py b/mcstasscript/tests/test_add_data.py
index b21c3ced..9208e29d 100644
--- a/mcstasscript/tests/test_add_data.py
+++ b/mcstasscript/tests/test_add_data.py
@@ -2,11 +2,11 @@
 import numpy as np
 import copy
 
-from mcstasscript.data.data import McStasData
+from mcstasscript.data.data import McStasDataBinned
 from mcstasscript.data.data import McStasMetaData
 from mcstasscript.jb_interface.simulation_interface import add_data
 
-def set_dummy_MetaData_1d():
+def set_dummy_MetaDataBinned_1d():
     """
     Sets up simple McStasMetaData object with dimension, 1d case
     """
@@ -20,21 +20,21 @@ def set_dummy_MetaData_1d():
     return meta_data
 
 
-def set_dummy_McStasData_1d():
+def set_dummy_McStasDataBinned_1d():
     """
     Sets up simple McStasData object, 1d case
     """
-    meta_data = set_dummy_MetaData_1d()
+    meta_data = set_dummy_MetaDataBinned_1d()
 
     intensity = np.ones(20)
     error = np.ones(20)
     ncount = np.ones(20)
     axis = np.arange(20)*5.0
 
-    return McStasData(meta_data, intensity, error, ncount, xaxis=axis)
+    return McStasDataBinned(meta_data, intensity, error, ncount, xaxis=axis)
 
 
-def set_dummy_MetaData_2d():
+def set_dummy_MetaDataBinned_2d():
     """
     Sets up simple McStasMetaData object with dimensions, 2d case
     """
@@ -48,17 +48,17 @@ def set_dummy_MetaData_2d():
     return meta_data
 
 
-def set_dummy_McStasData_2d():
+def set_dummy_McStasDataBinned_2d():
     """
     Sets up simple McStasData object, 2d case
     """
-    meta_data = set_dummy_MetaData_2d()
+    meta_data = set_dummy_MetaDataBinned_2d()
 
     intensity = np.ones(20).reshape(4, 5)
     error = np.ones(20).reshape(4, 5)
     ncount = np.ones(20).reshape(4, 5)
 
-    return McStasData(meta_data, intensity, error, ncount)
+    return McStasDataBinned(meta_data, intensity, error, ncount)
 
 class Test_add_data(unittest.TestCase):
     def test_1d_updates_correctly(self):
@@ -66,10 +66,10 @@ def test_1d_updates_correctly(self):
         Test that adding 1d dataset modifies only the intended dataset
         """
 
-        data1 = set_dummy_McStasData_1d()
+        data1 = set_dummy_McStasDataBinned_1d()
         data1_original = copy.deepcopy(data1)
 
-        data2 = set_dummy_McStasData_1d()
+        data2 = set_dummy_McStasDataBinned_1d()
         data2_original = copy.deepcopy(data2)
 
         add_data([data1], [data2])
@@ -89,7 +89,7 @@ def test_1d_updates_different(self):
         """
         Test that adding 1d datasets work as expected when different
         """
-        data1 = set_dummy_McStasData_1d()
+        data1 = set_dummy_McStasDataBinned_1d()
         data1.Intensity *= 2.0
         data1.Intensity[10:] *= 2.0
         data1.Error *= 1.5
@@ -97,7 +97,7 @@ def test_1d_updates_different(self):
         data1.metadata.info["Ncount"] *= 4.0
         data1_original = copy.deepcopy(data1)
 
-        data2 = set_dummy_McStasData_1d()
+        data2 = set_dummy_McStasDataBinned_1d()
         data2.Intensity *= 3.0
         data2.Error *= 1.5
         data2_original = copy.deepcopy(data2)
@@ -133,23 +133,23 @@ def test_fail(self):
         Both 1d and 2d cases included.
         """
 
-        data11 = set_dummy_McStasData_1d()
+        data11 = set_dummy_McStasDataBinned_1d()
         data11.name = "first monitor"
         data11.filename = "first_monitor.dat"
-        data12 = set_dummy_McStasData_2d()
+        data12 = set_dummy_McStasDataBinned_2d()
         data12.name = "second monitor"
         data12.filename = "second_monitor.dat"
-        data13 = set_dummy_McStasData_1d()
+        data13 = set_dummy_McStasDataBinned_1d()
         data13.name = "third monitor"
         data13.filename = "third_monitor.dat"
 
-        data21 = set_dummy_McStasData_1d()
+        data21 = set_dummy_McStasDataBinned_1d()
         data21.name = "first monitor"
         data21.filename = "first_monitor.dat"
-        data22 = set_dummy_McStasData_2d()
+        data22 = set_dummy_McStasDataBinned_2d()
         data22.name = "second monitor"
         data22.filename = "second_monitor.dat"
-        data23 = set_dummy_McStasData_1d()
+        data23 = set_dummy_McStasDataBinned_1d()
         data23.name = "third monitor"
         data23.filename = "third_monitor.dat"
 
@@ -172,10 +172,10 @@ def test_2d_updates_correctly(self):
         Test that adding 1d dataset modifies only the intended dataset
         """
 
-        data1 = set_dummy_McStasData_2d()
+        data1 = set_dummy_McStasDataBinned_2d()
         data1_original = copy.deepcopy(data1)
 
-        data2 = set_dummy_McStasData_2d()
+        data2 = set_dummy_McStasDataBinned_2d()
         data2_original = copy.deepcopy(data2)
 
         add_data([data1], [data2])
@@ -195,7 +195,7 @@ def test_2d_updates_different(self):
         """
         Test that adding 2d datasets work as expected when different
         """
-        data1 = set_dummy_McStasData_2d()
+        data1 = set_dummy_McStasDataBinned_2d()
         data1.Intensity *= 2.0
         data1.Intensity[1,:] *= 2.0
         data1.Error *= 1.5
@@ -203,7 +203,7 @@ def test_2d_updates_different(self):
         data1.metadata.info["Ncount"] *= 4.0
         data1_original = copy.deepcopy(data1)
 
-        data2 = set_dummy_McStasData_2d()
+        data2 = set_dummy_McStasDataBinned_2d()
         data2.Intensity *= 3.0
         data2.Error *= 1.5
         data2_original = copy.deepcopy(data2)
diff --git a/mcstasscript/tests/test_data_set/event_dat_list.p.x.y.z.vx.vy.vz.t b/mcstasscript/tests/test_data_set/event_dat_list.p.x.y.z.vx.vy.vz.t
new file mode 100644
index 00000000..989f9608
--- /dev/null
+++ b/mcstasscript/tests/test_data_set/event_dat_list.p.x.y.z.vx.vy.vz.t
@@ -0,0 +1,12029 @@
+# Format: McCode list with text headers
+# URL: http://www.mccode.org
+# Creator: McStas 2.7 - Nov. 27, 2020
+# Instrument: event_test.instr
+# Ncount: 12000
+# Trace: no
+# Gravitation: no
+# Seed: 1630142078
+# Directory: event_data
+# Date: Fri Aug 27 12:51:34 2021 (1630061494)
+# type: array_2d(8, 12000)
+# Source: event_test (event_test.instr)
+# component: monitor
+# position: 0 0 2
+# title:  Intensity Position Position Position Velocity Velocity Velocity Time_Of_Flight Monitor (Square)
+# Ncount: 12000
+# filename: event_dat_list.p.x.y.z.vx.vy.vz.t
+# statistics: X0=0; dX=0; Y0=0; dY=0;
+# signal: Min=2.49999e-06; Max=2.49999e-06; Mean=0;
+# values: 0 0 0
+# xvar: List
+# yvar: p
+# xlabel: List of neutron events
+# ylabel: p x y z vx vy vz t 
+# zvar: I
+# zlabel: Signal per bin
+# xylimits: 1 12000 1 8
+# variables: p x y z vx vy vz t 
+# Data [monitor/event_dat_list.p.x.y.z.vx.vy.vz.t] I:
+2.499985086e-06 0.005476124948 -0.00688575492 0 1.632069004 -0.5240574608 992.5004585 0.002015112419 
+2.499984569e-06 -0.001733593824 0.004791744661 0 1.381763976 -0.8502880853 923.5218743 0.00216562277 
+2.496856437e-06 0.0008841058053 -0.004494516801 0 18.94603399 -17.15675038 1018.890295 0.001962919864 
+2.497044251e-06 0.007899970417 -0.009599994554 0 21.68522323 1.791436417 894.5392473 0.002235787872 
+2.497127939e-06 0.002746270066 0.004339154698 0 8.944170969 23.42568419 1045.788193 0.001912433142 
+2.498591539e-06 0.005766389919 -0.003936564261 0 15.10675725 2.503815482 912.1717984 0.00219256943 
+2.494442331e-06 0.008963741655 -0.01207258223 0 13.64230533 -30.81198184 1009.875948 0.001980441264 
+2.496267853e-06 -0.006437451518 -0.003652858904 0 -28.78474349 -4.489612533 1065.722258 0.001876661564 
+2.496786367e-06 -0.003534191852 0.01211220376 0 -24.34546891 6.840194821 996.9980213 0.002006022035 
+2.499201739e-06 -0.007529071802 -0.004231258454 0 4.483371261 -12.06810202 1018.764369 0.001963162494 
+2.497732451e-06 -0.00276489717 -0.006969841509 0 19.23156592 12.21812856 1069.545763 0.001869952712 
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+2.4976693e-06 -0.002373360475 0.002229967879 0 22.20328402 -0.05705883536 1028.036621 0.001945455988 
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diff --git a/mcstasscript/tests/test_data_set/mccode.sim b/mcstasscript/tests/test_data_set/mccode.sim
index 5891d96b..fcbe76de 100644
--- a/mcstasscript/tests/test_data_set/mccode.sim
+++ b/mcstasscript/tests/test_data_set/mccode.sim
@@ -88,3 +88,25 @@ begin data
   xlimits: 0.7 1.3
   variables: L I I_err N
 end data
+
+begin data
+  Date: Fri Aug 27 12:51:34 2021 (1630061494)
+  type: array_2d(8, 12000)
+  Source: event_test (event_test.instr)
+  component: monitor
+  position: 0 0 2
+  title:  Intensity Position Position Position Velocity Velocity Velocity Time_Of_Flight Monitor (Square)
+  Ncount: 12000
+  filename: event_dat_list.p.x.y.z.vx.vy.vz.t
+  statistics: X0=0; dX=0; Y0=0; dY=0;
+  signal: Min=2.49999e-06; Max=2.49999e-06; Mean=0;
+  values: 0 0 0
+  xvar: List
+  yvar: p
+  xlabel: List of neutron events
+  ylabel: p x y z vx vy vz t
+  zvar: I
+  zlabel: Signal per bin
+  xylimits: 1 12000 1 8
+  variables: p x y z vx vy vz t
+end data
diff --git a/mcstasscript/tests/test_functions.py b/mcstasscript/tests/test_functions.py
index cd10f340..f13e4b34 100644
--- a/mcstasscript/tests/test_functions.py
+++ b/mcstasscript/tests/test_functions.py
@@ -9,11 +9,11 @@
 from mcstasscript.interface.functions import load_data
 from mcstasscript.interface.functions import load_metadata
 from mcstasscript.interface.functions import load_monitor
-from mcstasscript.data.data import McStasData
+from mcstasscript.data.data import McStasDataBinned
 from mcstasscript.data.data import McStasMetaData
 
 
-def set_dummy_MetaData_1d(name):
+def set_dummy_MetaDataBinned_1d(name):
     """
     Sets up a dummy MetaData object for a 1d dataset
 
@@ -32,7 +32,7 @@ def set_dummy_MetaData_1d(name):
     return meta_data
 
 
-def set_dummy_McStasData_1d(name):
+def set_dummy_McStasDataBinned_1d(name):
     """
     Sets up a dummy McStasData object for a 1d dataset
 
@@ -42,17 +42,17 @@ def set_dummy_McStasData_1d(name):
     name : str
         base for filename, .dat will be appended
     """
-    meta_data = set_dummy_MetaData_1d(name)
+    meta_data = set_dummy_MetaDataBinned_1d(name)
 
     intensity = np.arange(20)
     error = 0.5 * np.arange(20)
     ncount = 2 * np.arange(20)
     axis = np.arange(20)*5.0
 
-    return McStasData(meta_data, intensity, error, ncount, xaxis=axis)
+    return McStasDataBinned(meta_data, intensity, error, ncount, xaxis=axis)
 
 
-def set_dummy_MetaData_2d(name):
+def set_dummy_MetaDataBinned_2d(name):
     """
     Sets up a dummy MetaData object for a 2d dataset
 
@@ -70,7 +70,7 @@ def set_dummy_MetaData_2d(name):
     return meta_data
 
 
-def set_dummy_McStasData_2d(name):
+def set_dummy_McStasDataBinned_2d(name):
     """
     Sets up a dummy McStasData object for a 2d dataset
 
@@ -81,13 +81,13 @@ def set_dummy_McStasData_2d(name):
         base for filename, .dat will be appended
     """
 
-    meta_data = set_dummy_MetaData_2d(name)
+    meta_data = set_dummy_MetaDataBinned_2d(name)
 
     intensity = np.arange(20).reshape(4, 5)
     error = 0.5 * np.arange(20).reshape(4, 5)
     ncount = 2 * np.arange(20).reshape(4, 5)
 
-    return McStasData(meta_data, intensity, error, ncount)
+    return McStasDataBinned(meta_data, intensity, error, ncount)
 
 
 def setup_McStasData_array():
@@ -97,20 +97,20 @@ def setup_McStasData_array():
 
     data_list = []
 
-    data_list.append(set_dummy_McStasData_1d("A_1d_thing"))
-    data_list.append(set_dummy_McStasData_2d("A_2d_thing"))
-    data_list.append(set_dummy_McStasData_1d("Another_1d_thing"))
-    data_list.append(set_dummy_McStasData_2d("Another_2d_thing"))
-    data_list.append(set_dummy_McStasData_2d("A_third_2d_thing"))
+    data_list.append(set_dummy_McStasDataBinned_1d("A_1d_thing"))
+    data_list.append(set_dummy_McStasDataBinned_2d("A_2d_thing"))
+    data_list.append(set_dummy_McStasDataBinned_1d("Another_1d_thing"))
+    data_list.append(set_dummy_McStasDataBinned_2d("Another_2d_thing"))
+    data_list.append(set_dummy_McStasDataBinned_2d("A_third_2d_thing"))
 
-    hero_object = set_dummy_McStasData_2d("Hero")
+    hero_object = set_dummy_McStasDataBinned_2d("Hero")
     hero_object.metadata.dimension = 123
     hero_object.plot_options.colormap = "very hot"
 
     data_list.append(hero_object)
 
-    data_list.append(set_dummy_McStasData_2d("After_hero_2d"))
-    data_list.append(set_dummy_McStasData_2d("Last_object_2d"))
+    data_list.append(set_dummy_McStasDataBinned_2d("After_hero_2d"))
+    data_list.append(set_dummy_McStasDataBinned_2d("Last_object_2d"))
 
     return data_list
 
@@ -124,20 +124,20 @@ def setup_McStasData_array_repeat():
 
     data_list = []
 
-    data_list.append(set_dummy_McStasData_1d("A_1d_thing"))
-    data_list.append(set_dummy_McStasData_2d("A_2d_thing"))
-    data_list.append(set_dummy_McStasData_1d("Another_1d_thing"))
-    data_list.append(set_dummy_McStasData_2d("Another_2d_thing"))
-    data_list.append(set_dummy_McStasData_2d("Hero"))
+    data_list.append(set_dummy_McStasDataBinned_1d("A_1d_thing"))
+    data_list.append(set_dummy_McStasDataBinned_2d("A_2d_thing"))
+    data_list.append(set_dummy_McStasDataBinned_1d("Another_1d_thing"))
+    data_list.append(set_dummy_McStasDataBinned_2d("Another_2d_thing"))
+    data_list.append(set_dummy_McStasDataBinned_2d("Hero"))
 
-    hero_object = set_dummy_McStasData_2d("Big_Hero")
+    hero_object = set_dummy_McStasDataBinned_2d("Big_Hero")
     hero_object.metadata.dimension = 123
     hero_object.plot_options.colormap = "very hot"
 
     data_list.append(hero_object)
 
-    data_list.append(set_dummy_McStasData_2d("After_hero_2d"))
-    data_list.append(set_dummy_McStasData_2d("Last_object_2d"))
+    data_list.append(set_dummy_McStasDataBinned_2d("After_hero_2d"))
+    data_list.append(set_dummy_McStasDataBinned_2d("Last_object_2d"))
 
     return data_list
 
@@ -197,7 +197,7 @@ def test_name_search_read_duplicate(self):
         data_list = setup_McStasData_array_repeat()
 
         # Adds another dataset with a name already in the data_list
-        hero_object = set_dummy_McStasData_2d("Big_Hero")
+        hero_object = set_dummy_McStasDataBinned_2d("Big_Hero")
         hero_object.metadata.dimension = 321
         hero_object.plot_options.colormap = "very hot"
 
@@ -229,7 +229,7 @@ def test_name_search_type_error_not_list(self):
         Check error is given even when data list is just single object
         """
 
-        data_list = set_dummy_McStasData_2d("Last_object_2d")
+        data_list = set_dummy_McStasDataBinned_2d("Last_object_2d")
 
         with self.assertRaises(RuntimeError):
             name_search("Hero", data_list)
@@ -272,7 +272,7 @@ def test_name_plot_options_duplicate(self):
 
         data_list = setup_McStasData_array()
 
-        hero_object = set_dummy_McStasData_2d("Hero")
+        hero_object = set_dummy_McStasDataBinned_2d("Hero")
         hero_object.metadata.dimension = 321
         hero_object.plot_options.colormap = "absurdly hot"
 
@@ -307,7 +307,7 @@ def test_mcrun_load_data_PSD4PI(self):
 
         os.chdir(current_work_dir)  # Reset work directory
 
-        self.assertEqual(len(results), 3)
+        self.assertEqual(len(results), 4)
 
         PSD_4PI = results[0]
 
@@ -342,7 +342,7 @@ def test_mcrun_load_metadata_PSD4PI(self):
 
         os.chdir(current_work_dir)  # Reset work directory
 
-        self.assertEqual(len(metadata), 3)
+        self.assertEqual(len(metadata), 4)
 
         PSD_4PI = metadata[0]
         self.assertEqual(PSD_4PI.dimension, [300, 300])
diff --git a/mcstasscript/tests/test_plot_interface.py b/mcstasscript/tests/test_plot_interface.py
index 9a52ac63..94e41ccf 100644
--- a/mcstasscript/tests/test_plot_interface.py
+++ b/mcstasscript/tests/test_plot_interface.py
@@ -9,11 +9,11 @@
 from mcstasscript.jb_interface.plot_interface import OrdersOfMagField
 
 from mcstasscript.data.data import McStasMetaData
-from mcstasscript.data.data import McStasData
+from mcstasscript.data.data import McStasDataBinned
 
 import ipywidgets as widgets
 
-def set_dummy_MetaData_1d():
+def set_dummy_MetaDataBinned_1d():
     """
     Sets up simple McStasMetaData object with dimension, 1d case
     """
@@ -30,21 +30,21 @@ def set_dummy_MetaData_1d():
     return meta_data
 
 
-def set_dummy_McStasData_1d():
+def set_dummy_McStasDataBinned_1d():
     """
     Sets up simple McStasData object, 1d case
     """
-    meta_data = set_dummy_MetaData_1d()
+    meta_data = set_dummy_MetaDataBinned_1d()
 
     intensity = np.arange(20)
     error = 0.5 * np.arange(20)
     ncount = 2 * np.arange(20)
     axis = np.arange(20)*5.0
 
-    return McStasData(meta_data, intensity, error, ncount, xaxis=axis)
+    return McStasDataBinned(meta_data, intensity, error, ncount, xaxis=axis)
 
 
-def set_dummy_MetaData_2d():
+def set_dummy_MetaDataBinned_2d():
     """
     Sets up simple McStasMetaData object with dimensions, 2d case
     """
@@ -61,21 +61,23 @@ def set_dummy_MetaData_2d():
     return meta_data
 
 
-def set_dummy_McStasData_2d():
+def set_dummy_McStasDataBinned_2d():
     """
     Sets up simple McStasData object, 2d case
     """
-    meta_data = set_dummy_MetaData_2d()
+    meta_data = set_dummy_MetaDataBinned_2d()
 
     intensity = np.arange(20).reshape(4, 5)
     error = 0.5 * np.arange(20).reshape(4, 5)
     ncount = 2 * np.arange(20).reshape(4, 5)
 
-    return McStasData(meta_data, intensity, error, ncount)
+    return McStasDataBinned(meta_data, intensity, error, ncount)
 
 
 def fake_data():
-    return [set_dummy_McStasData_1d(), set_dummy_McStasData_2d(), set_dummy_McStasData_2d()]
+    return [set_dummy_McStasDataBinned_1d(),
+            set_dummy_McStasDataBinned_2d(),
+            set_dummy_McStasDataBinned_2d()]
 
 
 class FakeChange:
diff --git a/requirements.txt b/requirements.txt
index a28ff210..dd4a1c13 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,3 +2,4 @@ numpy
 matplotlib
 PyYAML
 ipywidgets
+mmap

From 104cc69ab05ed7600d0daa4ae00c334a49a1ee00 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 31 Aug 2021 13:39:39 +0200
Subject: [PATCH 161/403] Removed old McStasData class as it has been split
 into a binned version and an event version.

---
 mcstasscript/data/data.py | 142 ++------------------------------------
 1 file changed, 4 insertions(+), 138 deletions(-)

diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index 218b307a..5039535b 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -317,142 +317,6 @@ def set_options(self, **kwargs):
                                  + "given: " + str(self.right_lim))
 
 
-class McStasDataOld:
-    """
-    Class for holding full McStas dataset with data, metadata and
-    plotting preferences
-
-    Attributes
-    ----------
-    metadata : McStasMetaData instance
-        Holds the metadata for the dataset
-
-    name : str
-        Name of component, extracted from metadata
-
-    Intensity : numpy array
-        Intensity data [neutrons/s] in 1d or 2d numpy array, dimension in
-        metadata
-
-    Error : numpy array
-        Error data [neutrons/s] in 1d or 2d numpy array, same dimensions as
-        Intensity
-
-    Ncount : numpy array
-        Number of rays in bin, 1d or 2d numpy array, same dimensions as
-        Intensity
-
-    plot_options : McStasPlotOptions instance
-        Holds the plotting preferences for the dataset
-
-    Methods
-    -------
-    set_xlabel : string
-        sets xlabel of data for plotting
-
-    set_ylabel : string
-        sets ylabel of data for plotting
-
-    set_title : string
-        sets title of data for plotting
-
-    set_options : keyword arguments
-        sets plot options, keywords passed to McStasPlotOptions method
-    """
-
-    def __init__(self, metadata, intensity, error, ncount, **kwargs):
-        """
-        Initialize a new McStas dataset, 4 positional arguments, pass
-        xaxis as kwarg if 1d data
-
-        Parameters
-        ----------
-        metadata : McStasMetaData instance
-            Holds the metadata for the dataset
-
-        intensity : numpy array
-            Intensity data [neutrons/s] in 1d or 2d numpy array, dimension in
-            metadata
-
-        error : numpy array
-            Error data [neutrons/s] in 1d or 2d numpy array, same dimensions
-            as Intensity
-
-        ncount : numpy array
-            Number of rays in bin, 1d or 2d numpy array, same dimensions as
-            Intensity
-
-        kwargs : keyword arguments
-            xaxis is required for 1d data
-        """
-
-        # attach meta data
-        self.metadata = metadata
-        # get name from metadata
-        self.name = self.metadata.component_name
-        # three basic arrays from positional arguments
-        if not isinstance(intensity, np.ndarray):
-            raise ValueError("intensity should be numpy array!")
-        if not isinstance(error, np.ndarray):
-            raise ValueError("error should be numpy array!")
-        if not isinstance(ncount, np.ndarray):
-            raise ValueError("ncount should be numpy array!")
-
-        self.Intensity = intensity
-        self.Error = error
-        self.Ncount = ncount
-
-        if type(self.metadata.dimension) == int:
-            if "xaxis" in kwargs:
-                self.xaxis = kwargs["xaxis"]
-            else:
-                raise NameError(
-                    "ERROR: Initialization of McStasData done with 1d "
-                    + "data, but without xaxis for " + self.name + "!")
-
-        self.plot_options = McStasPlotOptions()
-
-    # Methods xlabel, ylabel and title as they might not be found
-    def set_xlabel(self, string):
-        self.metadata.set_xlabel(string)
-
-    def set_ylabel(self, string):
-        self.metadata.set_ylabel(string)
-
-    def set_title(self, string):
-        self.metadata.set_title(string)
-
-    def set_plot_options(self, **kwargs):
-        self.plot_options.set_options(**kwargs)
-
-    def __str__(self):
-        """
-        Returns string with quick summary of data
-        """
-
-        string = "McStasData: "
-        string += self.name + " "
-        if type(self.metadata.dimension) == int:
-            string += "type: 1D "
-        elif len(self.metadata.dimension) == 2:
-            string += "type: 2D "
-        else:
-            string += "type: other "
-
-        if "values" in self.metadata.info:
-            values = self.metadata.info["values"]
-            values = values.strip()
-            values = values.split(" ")
-            if len(values) == 3:
-                string += " I:" + str(values[0])
-                string += " E:" + str(values[1])
-                string += " N:" + str(values[2])
-
-        return string
-
-    def __repr__(self):
-        return "\n" + self.__str__()
-
 class McStasData:
     """
     Class for holding full McStas dataset with data, metadata and
@@ -642,10 +506,12 @@ def __init__(self, metadata, intensity, error, ncount, **kwargs):
         else:
             self.data_type = "Binned"
 
+
 class McStasDataEvent(McStasData):
     """
     Class for holding McStas event dataset with data, metadata and
-    plotting preferences
+    plotting preferences. Usually data the first one million events
+    is plotted.
 
     Attributes
     ----------
@@ -698,7 +564,7 @@ def __init__(self, metadata, events, **kwargs):
         self.Events = events
         self.data_type = "Events"
 
-        # Intensity for compatability with plotting routine
+        # Intensity for compatibility with plotting routine
         data_lines = metadata.dimension[1]
         self.Intensity = self.Events[0:data_lines, :]
 

From d93dfce3f148d178b6f08ab1663b94cd534d190d Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 31 Aug 2021 13:56:48 +0200
Subject: [PATCH 162/403] Update of documentation to include Event data.

---
 McStasScript_documentation.pdf | Bin 594931 -> 595674 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
index 27fc59c8e322e55dbdd5a9fe36e16bfd238a3a85..f20483e67d2047373eb89c29664efd8b194731df 100644
GIT binary patch
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V&89++eBAy(kn;h{^rK>&q5yy!$fp1R


From f862e95f6a83bebe53d58b32e478bc486c0c3bed Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 31 Aug 2021 14:04:37 +0200
Subject: [PATCH 163/403] Updated version.

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index edd37717..444d20e6 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
      name='McStasScript',
-     version='0.0.31',
+     version='0.0.32',
      author="Mads Bertelsen",
      author_email="Mads.Bertelsen@ess.eu",
      description="A python scripting interface for McStas",

From cc3b91dd48f520d4facaf5da46286973b50cbacb Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 3 Sep 2021 17:33:25 +0200
Subject: [PATCH 164/403] Small update adding knowledge of the data location to
 McStasData.

Retrieve it with get_data_location()
---
 mcstasscript/data/data.py            | 7 +++++++
 mcstasscript/helper/managed_mcrun.py | 4 +++-
 2 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index 5039535b..2824377a 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -367,6 +367,7 @@ def __init__(self, metadata):
         self.plot_options = McStasPlotOptions()
 
         self.data_type = None
+        self.original_data_location = None
 
     # Methods xlabel, ylabel and title as they might not be found
     def set_xlabel(self, string):
@@ -381,6 +382,12 @@ def set_title(self, string):
     def set_plot_options(self, **kwargs):
         self.plot_options.set_options(**kwargs)
 
+    def set_data_location(self, data_location):
+        self.original_data_location = data_location
+
+    def get_data_location(self):
+        return self.original_data_location
+
     def __str__(self):
         """
         Returns string with quick summary of data
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 9e24db5a..0185b1b7 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -288,7 +288,9 @@ def load_results(data_folder_name):
 
     results = []
     for metadata in metadata_list:
-        results.append(load_monitor(metadata, data_folder_name))
+        result = load_monitor(metadata, data_folder_name)
+        result.set_data_location(data_folder_name)
+        results.append(result)
 
     return results
 

From 1d4a76f7a5fa57e637132739b49acfabe57b0d7a Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Tue, 21 Sep 2021 09:17:41 +0200
Subject: [PATCH 165/403] Updated link to McStas windows installation

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index b5a3e254..9c20d1c2 100644
--- a/README.md
+++ b/README.md
@@ -35,7 +35,7 @@ On a Mac OS X system, the paths to the mcrun executable and mcstas folder are th
     
     
 ### Notes on windows installation
-McStasScript was tested on Windows 10 installed using this [guide](https://github.com/McStasMcXtrace/McCode/blob/master/INSTALL-McStas/Windows/README.md), it is necessary to include MPI using MSMpiSetup.exe and msmpisdk.msi located in the extras folder.
+McStasScript was tested on Windows 10 installed using this [guide](https://github.com/McStasMcXtrace/McCode/blob/master/INSTALL-McStas-2.x/Windows/README.md), it is necessary to include MPI using MSMpiSetup.exe and msmpisdk.msi located in the extras folder.
 
 Open the McStas-shell cmd (shortcut should be available on desktop) and install McStasScript / jupyter notebook with these commands:
 

From 86a7c49b29a0a6fe6e7d8e9672040f6e461cc94d Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 24 Sep 2021 09:57:11 +0200
Subject: [PATCH 166/403] Fixes to GUI system.

Fixed problem with colorbar axes being visible when they shouldn't.
Fixed problem when switching between runs with and without mpi.
Fixed problem with use of depricated keyword in plotting.

Removed live mode as it was failing.
Still some issues with plotting in simulation widget, since it is
working with the plotting interface it is most likely from threading.

Added ipywidgets to requirements.
---
 mcstasscript/helper/plot_helper.py            |  2 +-
 mcstasscript/jb_interface/plot_interface.py   | 64 ++++++++++++++-----
 .../jb_interface/simulation_interface.py      | 33 +++++++---
 3 files changed, 75 insertions(+), 24 deletions(-)

diff --git a/mcstasscript/helper/plot_helper.py b/mcstasscript/helper/plot_helper.py
index 3e317510..697f102e 100644
--- a/mcstasscript/helper/plot_helper.py
+++ b/mcstasscript/helper/plot_helper.py
@@ -94,7 +94,7 @@ def _plot_fig_ax(data, fig, ax, **kwargs):
         ax.errorbar(x, y, yerr=y_err)
 
         if data.plot_options.log:
-            ax.set_yscale("log", nonposy='clip')
+            ax.set_yscale("log", nonpositive='clip')
 
         ax.set_xlim(data.metadata.limits[0] * x_axis_mult,
                     data.metadata.limits[1] * x_axis_mult)
diff --git a/mcstasscript/jb_interface/plot_interface.py b/mcstasscript/jb_interface/plot_interface.py
index 3247375c..1254ba3f 100644
--- a/mcstasscript/jb_interface/plot_interface.py
+++ b/mcstasscript/jb_interface/plot_interface.py
@@ -113,20 +113,29 @@ def update_plot(self):
             #self.ax.xaxis.set_ticks([])
             #self.ax.yaxis.set_ticks([])
             self.colorbar_ax.cla()
-            self.colorbar_ax.xaxis.set_ticks([])
-            self.colorbar_ax.yaxis.set_ticks([])
+            #self.colorbar_ax.xaxis.set_ticks([])
+            #self.colorbar_ax.yaxis.set_ticks([])
 
             # Display message if not data can be plotted
             if self.data is None:
                 self.ax.text(0.3, 0.5, "No data available yet")
+                self.colorbar_ax.set_axis_off()
+                self.ax.xaxis.set_ticks([])
+                self.ax.yaxis.set_ticks([])
                 return
 
             if len(self.data) == 0:
                 self.ax.text(0.25, 0.5, "Simulation returned no data")
+                self.colorbar_ax.set_axis_off()
+                self.ax.xaxis.set_ticks([])
+                self.ax.yaxis.set_ticks([])
                 return
 
             if self.current_monitor is None:
                 self.ax.text(0.3, 0.5, "Select a monitor to plot")
+                self.colorbar_ax.set_axis_off()
+                self.ax.xaxis.set_ticks([])
+                self.ax.yaxis.set_ticks([])
                 return
 
             # Get monitor and establish plot options
@@ -140,11 +149,18 @@ def update_plot(self):
             #print("Plotting with: ", plot_options)
             monitor.set_plot_options(**plot_options)
             with HiddenPrints():
-                #plotter._plot_fig_ax(monitor, self.fig, self.ax, colorbar_axes=self.colorbar_ax)
                 _plot_fig_ax(monitor, self.fig, self.ax, colorbar_axes=self.colorbar_ax)
 
             self.colorbar_ax.set_aspect(20)
 
+            # Show colorbar if something is present, otherwise hide it
+            if self.colorbar_ax.lines or self.colorbar_ax.collections:
+                self.colorbar_ax.set_axis_on()
+            else:
+                self.colorbar_ax.set_axis_off()
+
+            #self.ax.set_axis_on()
+
             plt.tight_layout()
             self.fig.canvas.draw()
 
@@ -297,6 +313,7 @@ def __init__(self, set_current_monitor):
         """
 
         self.set_current_monitor = set_current_monitor
+        self.last_monitor = None
         self.data = None
         self.widget = None
 
@@ -310,21 +327,34 @@ def set_data(self, data):
         data: McStasData list
             Data returned by McStasScript simulation
         """
-        self.data = data
 
-        monitor_names = []
-        for data in self.data:
+        lock = threading.Lock()
+        with lock:
 
-            # Ensure data names are unique
-            original_name = data.name
-            index = 1
-            while data.name in monitor_names:
-                data.name = original_name + "_" + str(index)
-                index += 1
+            self.data = data
 
-            monitor_names.append(data.name)
+            if data is None:
+                self.widget.options = []
+                return
 
-        self.widget.options = monitor_names
+            monitor_names = []
+            for data in self.data:
+
+                # Ensure data names are unique
+                original_name = data.name
+                index = 1
+                while data.name in monitor_names:
+                    data.name = original_name + "_" + str(index)
+                    index += 1
+
+                monitor_names.append(data.name)
+
+            self.widget.options = monitor_names
+
+            # Go to the last set monitor if possible
+            if self.last_monitor is not None:
+                if self.last_monitor in self.widget.options:
+                    self.set_current_monitor(self.last_monitor)
 
     def make_widget(self):
         """
@@ -348,7 +378,11 @@ def update(self, change):
             state change of widget
         """
         # can do input sanitation here
-        self.set_current_monitor(change.new)
+        lock = threading.Lock()
+        with lock:
+            self.set_current_monitor(change.new)
+            if change.new is not None:
+                self.last_monitor = change.new
 
 
 class LogCheckbox:
diff --git a/mcstasscript/jb_interface/simulation_interface.py b/mcstasscript/jb_interface/simulation_interface.py
index deaa9f10..6d57011e 100644
--- a/mcstasscript/jb_interface/simulation_interface.py
+++ b/mcstasscript/jb_interface/simulation_interface.py
@@ -54,6 +54,7 @@ def __init__(self, instrument):
 
         self.ncount = "1E6"
         self.mpi = "disabled"
+        self.last_mpi_on = None
 
         self.thread_data = None
         self.thread = None
@@ -106,6 +107,7 @@ def run_simulation_live(self):
 
         if self.live_widget.value:
             sim_parts = self.sim_steps
+            #self.plot_interface.set_data(None)
         else:
             sim_parts = 1
 
@@ -114,10 +116,22 @@ def run_simulation_live(self):
         run_arguments = {"foldername": "interface_" + self.instrument.name,
                          "increment_folder_name": True,
                          "parameters": self.parameters,
-                         "ncount": part_ncount,
-                         "force_compile": False}
+                         "ncount": part_ncount}
         if self.mpi != "disabled":
             run_arguments["mpi"] = self.mpi
+            mpi_on = True
+        else:
+            mpi_on = False
+
+        # McStas does not recognize if the instrument was compiled with or without mpi
+        # Ensure it is compiled when switching to and from mpi
+        # This also ensures the instrument is compiled at the first run
+        if mpi_on == self.last_mpi_on:
+            run_arguments["force_compile"] = False
+        else:
+            run_arguments["force_compile"] = True
+
+        self.last_mpi_on = mpi_on
 
         self.run_button.icon = "hourglass"
         #print("Running with:", run_arguments)
@@ -137,19 +151,21 @@ def run_simulation_live(self):
                 print("McStas run failed.")
                 data = []
 
-            self.progress_bar.value = index + 1
+            with lock:
+                self.progress_bar.value = index + 1
 
-            if plot_data is None:
-                plot_data = data
-            else:
-                add_data(plot_data, data)
+                if plot_data is None:
+                    plot_data = data
+                else:
+                    add_data(plot_data, data)
 
-            with lock:
                 sent_data = copy.deepcopy(plot_data)
+                # This happens in a thread, maybe it should be in Main?
                 self.plot_interface.set_data(sent_data)
 
         self.run_button.icon = "calculator"
 
+
     def make_run_button(self):
         """
         Creates a run button which perform the simulation
@@ -234,6 +250,7 @@ def make_live_checkmark(self):
         Makes widget for choosing live simulations on / off
         """
         widget = widgets.Checkbox(value=False, description="Live results")
+        widget.layout.visibility = "hidden"
 
         return widget
 

From fac5063cee1488e88221a77edc69612fe1f5851c Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 24 Sep 2021 10:00:17 +0200
Subject: [PATCH 167/403] New setup.py now added.

---
 setup.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 444d20e6..3e3309e5 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
      name='McStasScript',
-     version='0.0.32',
+     version='0.0.36',
      author="Mads Bertelsen",
      author_email="Mads.Bertelsen@ess.eu",
      description="A python scripting interface for McStas",
@@ -13,7 +13,7 @@
      long_description=long_description,
      long_description_content_type="text/markdown",
      url="https://github.com/PaNOSC-ViNYL/McStasScript",
-     install_requires=['numpy', 'matplotlib', 'PyYAML'],
+     install_requires=['numpy', 'matplotlib', 'PyYAML', "ipywidgets"],
      packages=setuptools.find_packages(),
      classifiers=[
          "Programming Language :: Python :: 3",

From fc495085bcbd3c83fa09ffd0496418f75ab55f17 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Mon, 29 Nov 2021 09:45:33 +0100
Subject: [PATCH 168/403] Updates for reading parameters from mccode.sim file.
 Added a metadata field. Added unittests.

Also added __repr__ to configurator class.
---
 mcstasscript/data/data.py               |  3 +++
 mcstasscript/helper/managed_mcrun.py    |  9 +++++--
 mcstasscript/interface/functions.py     | 15 +++++++++++
 mcstasscript/tests/test_ManagedMcrun.py | 35 ++++++++++++++++++++++++-
 4 files changed, 59 insertions(+), 3 deletions(-)

diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index 2824377a..36d0c306 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -87,6 +87,9 @@ def extract_info(self):
         if "component" in self.info:
             self.component_name = self.info["component"].rstrip()
 
+        if "Parameters" in self.info:
+            self.parameters = self.info["Parameters"]
+
         # Extract filename
         if "filename" in self.info:
             self.filename = self.info["filename"].rstrip()
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index c791d43d..60ececbf 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -346,8 +346,13 @@ def load_metadata(data_folder_name):
                 
             if in_sim:
                 if "Param" in lines:
-                    parm_lst=lines.split(':')[1].split('=')
-                    instrument_parameters[parm_lst[0]] = parm_lst[1]
+                    parm_lst = lines.split(':')[1].split('=')
+                    try:
+                        value = float(parm_lst[1].strip())
+                    except ValueError:
+                        value = parm_lst[1].strip()
+
+                    instrument_parameters[parm_lst[0].strip()] = value
                     
             if in_data:
                 # This line contains info to be added to metadata
diff --git a/mcstasscript/interface/functions.py b/mcstasscript/interface/functions.py
index 58010fcf..7a98fa13 100644
--- a/mcstasscript/interface/functions.py
+++ b/mcstasscript/interface/functions.py
@@ -352,3 +352,18 @@ def set_line_length(self, line_length):
 
         # write new configuration file
         self._write_yaml(config)
+
+    def __repr__(self):
+        string = "Configurator:\n"
+        config = self._read_yaml()
+        if "paths" in config:
+            string += " paths:\n"
+            for key, value in config["paths"].items():
+                string += "  " + str(key) + ": " + str(value) + "\n"
+
+        if "other" in config:
+            string += " other:\n"
+            for key, value in config["other"].items():
+                string += "  " + str(key) + ": " + str(value) + "\n"
+
+        return string
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index d7debc10..fd5c57a2 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -1,5 +1,6 @@
 import os
 import unittest
+import unittest.mock
 
 from mcstasscript.helper.managed_mcrun import ManagedMcrun
 from mcstasscript.helper.managed_mcrun import load_results
@@ -407,6 +408,9 @@ def test_ManagedMcrun_load_data_PSD4PI(self):
         self.assertEqual(PSD_4PI.metadata.xlabel, "Longitude [deg]")
         self.assertEqual(PSD_4PI.metadata.ylabel, "Latitude [deg]")
         self.assertEqual(PSD_4PI.metadata.title, "4PI PSD monitor")
+        expected_parameters = {"wavelength": 1.0}
+        self.assertEqual(PSD_4PI.metadata.info["Parameters"], expected_parameters)
+        self.assertEqual(PSD_4PI.metadata.parameters, expected_parameters)
         self.assertEqual(PSD_4PI.Ncount[4][1], 4)
         self.assertEqual(PSD_4PI.Intensity[4][1], 1.537334562E-10)
         self.assertEqual(PSD_4PI.Error[4][1], 1.139482296E-10)
@@ -443,6 +447,9 @@ def test_ManagedMcrun_load_data_PSD(self):
         self.assertEqual(PSD.metadata.xlabel, "X position [cm]")
         self.assertEqual(PSD.metadata.ylabel, "Y position [cm]")
         self.assertEqual(PSD.metadata.title, "PSD monitor")
+        expected_parameters = {"wavelength": 1.0}
+        self.assertEqual(PSD.metadata.info["Parameters"], expected_parameters)
+        self.assertEqual(PSD.metadata.parameters, expected_parameters)
         self.assertEqual(PSD.Ncount[27][21], 9)
         self.assertEqual(PSD.Intensity[27][21], 2.623929371e-13)
         self.assertEqual(PSD.Error[27][21], 2.765467693e-13)
@@ -479,6 +486,9 @@ def test_ManagedMcrun_load_data_L_mon(self):
         self.assertEqual(L_mon.metadata.xlabel, "Wavelength [AA]")
         self.assertEqual(L_mon.metadata.ylabel, "Intensity")
         self.assertEqual(L_mon.metadata.title, "Wavelength monitor")
+        expected_parameters = {"wavelength": 1.0}
+        self.assertEqual(L_mon.metadata.info["Parameters"], expected_parameters)
+        self.assertEqual(L_mon.metadata.parameters, expected_parameters)
         self.assertEqual(L_mon.xaxis[53], 0.914)
         self.assertEqual(L_mon.Ncount[53], 37111)
         self.assertEqual(L_mon.Intensity[53], 6.990299315e-06)
@@ -514,6 +524,9 @@ def test_ManagedMcrun_load_data_L_mon_direct(self):
         self.assertEqual(L_mon.metadata.xlabel, "Wavelength [AA]")
         self.assertEqual(L_mon.metadata.ylabel, "Intensity")
         self.assertEqual(L_mon.metadata.title, "Wavelength monitor")
+        expected_parameters = {"wavelength": 1.0}
+        self.assertEqual(L_mon.metadata.info["Parameters"], expected_parameters)
+        self.assertEqual(L_mon.metadata.parameters, expected_parameters)
         self.assertEqual(L_mon.xaxis[53], 0.914)
         self.assertEqual(L_mon.Ncount[53], 37111)
         self.assertEqual(L_mon.Intensity[53], 6.990299315e-06)
@@ -553,6 +566,9 @@ def test_ManagedMcrun_load_data_Event(self):
         self.assertEqual(mon.metadata.title, "Intensity Position Position"
                 + " Position Velocity Velocity Velocity"
                 + " Time_Of_Flight Monitor (Square)")
+        expected_parameters = {"wavelength": 1.0}
+        self.assertEqual(mon.metadata.info["Parameters"], expected_parameters)
+        self.assertEqual(mon.metadata.parameters, expected_parameters)
         self.assertEqual(mon.Intensity[12, 1], -0.006163896406)
         self.assertEqual(mon.Events[12, 1], -0.006163896406)
         self.assertEqual(mon.Events[43, 4], 22.06193582)
@@ -561,7 +577,6 @@ def test_ManagedMcrun_load_data_Event(self):
         self.assertFalse(hasattr(mon, 'Error'))
         self.assertFalse(hasattr(mon, 'Ncount'))
 
-
     def test_ManagedMcrun_load_data_L_mon_direct_error(self):
         """
         Check an error occurs when directory has no mccode.sim
@@ -638,6 +653,9 @@ def test_mcrun_load_data_PSD4PI(self):
         self.assertEqual(PSD_4PI.metadata.xlabel, "Longitude [deg]")
         self.assertEqual(PSD_4PI.metadata.ylabel, "Latitude [deg]")
         self.assertEqual(PSD_4PI.metadata.title, "4PI PSD monitor")
+        expected_parameters = {"wavelength": 1.0}
+        self.assertEqual(PSD_4PI.metadata.info["Parameters"], expected_parameters)
+        self.assertEqual(PSD_4PI.metadata.parameters, expected_parameters)
         self.assertEqual(PSD_4PI.Ncount[4][1], 4)
         self.assertEqual(PSD_4PI.Intensity[4][1], 1.537334562E-10)
         self.assertEqual(PSD_4PI.Error[4][1], 1.139482296E-10)
@@ -666,6 +684,9 @@ def test_mcrun_load_data_PSD(self):
         self.assertEqual(PSD.metadata.xlabel, "X position [cm]")
         self.assertEqual(PSD.metadata.ylabel, "Y position [cm]")
         self.assertEqual(PSD.metadata.title, "PSD monitor")
+        expected_parameters = {"wavelength": 1.0}
+        self.assertEqual(PSD.metadata.info["Parameters"], expected_parameters)
+        self.assertEqual(PSD.metadata.parameters, expected_parameters)
         self.assertEqual(PSD.Ncount[27][21], 9)
         self.assertEqual(PSD.Intensity[27][21], 2.623929371e-13)
         self.assertEqual(PSD.Error[27][21], 2.765467693e-13)
@@ -692,6 +713,9 @@ def test_mcrun_load_metadata_PSD4PI(self):
         self.assertEqual(PSD_4PI.xlabel, "Longitude [deg]")
         self.assertEqual(PSD_4PI.ylabel, "Latitude [deg]")
         self.assertEqual(PSD_4PI.title, "4PI PSD monitor")
+        expected_parameters = {"wavelength": 1.0}
+        self.assertEqual(PSD_4PI.info["Parameters"], expected_parameters)
+        self.assertEqual(PSD_4PI.parameters, expected_parameters)
 
     def test_mcrun_load_metadata_L_mon(self):
         """
@@ -715,6 +739,9 @@ def test_mcrun_load_metadata_L_mon(self):
         self.assertEqual(L_mon.xlabel, "Wavelength [AA]")
         self.assertEqual(L_mon.ylabel, "Intensity")
         self.assertEqual(L_mon.title, "Wavelength monitor")
+        expected_parameters = {"wavelength": 1.0}
+        self.assertEqual(L_mon.info["Parameters"], expected_parameters)
+        self.assertEqual(L_mon.parameters, expected_parameters)
 
     def test_mcrun_load_monitor_PSD4PI(self):
         """
@@ -738,6 +765,9 @@ def test_mcrun_load_monitor_PSD4PI(self):
         self.assertEqual(monitor.metadata.xlabel, "Longitude [deg]")
         self.assertEqual(monitor.metadata.ylabel, "Latitude [deg]")
         self.assertEqual(monitor.metadata.title, "4PI PSD monitor")
+        expected_parameters = {"wavelength": 1.0}
+        self.assertEqual(monitor.metadata.info["Parameters"], expected_parameters)
+        self.assertEqual(monitor.metadata.parameters, expected_parameters)
         self.assertEqual(monitor.Ncount[4][1], 4)
         self.assertEqual(monitor.Intensity[4][1], 1.537334562E-10)
         self.assertEqual(monitor.Error[4][1], 1.139482296E-10)
@@ -764,6 +794,9 @@ def test_mcrun_load_monitor_L_mon(self):
         self.assertEqual(monitor.metadata.xlabel, "Wavelength [AA]")
         self.assertEqual(monitor.metadata.ylabel, "Intensity")
         self.assertEqual(monitor.metadata.title, "Wavelength monitor")
+        expected_parameters = {"wavelength": 1.0}
+        self.assertEqual(monitor.metadata.info["Parameters"], expected_parameters)
+        self.assertEqual(monitor.metadata.parameters, expected_parameters)
         self.assertEqual(monitor.xaxis[53], 0.914)
         self.assertEqual(monitor.Ncount[53], 37111)
         self.assertEqual(monitor.Intensity[53], 6.990299315e-06)

From bd94ebc6cac290c3c9662871df40d76edbe4a44e Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 1 Dec 2021 11:50:53 +0100
Subject: [PATCH 169/403] Update of version number and now default
 configuration is mcstas 2.7.1 on OS X.

---
 mcstasscript/configuration.yaml | 4 ++--
 setup.py                        | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/mcstasscript/configuration.yaml b/mcstasscript/configuration.yaml
index 457fa45b..008534d1 100644
--- a/mcstasscript/configuration.yaml
+++ b/mcstasscript/configuration.yaml
@@ -1,7 +1,7 @@
 other:
   characters_per_line: 85
 paths:
-  mcrun_path: /Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/
-  mcstas_path: /Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5
+  mcrun_path: /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/
+  mcstas_path: /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/
   mcxtrace_path: /Applications/McXtrace-1.5.app/Contents/Resources/mcxtrace/1.5/
   mxrun_path: /Applications/McXtrace-1.5.app/Contents/Resources/mcxtrace/1.5/bin/
diff --git a/setup.py b/setup.py
index 3e3309e5..385e622c 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
      name='McStasScript',
-     version='0.0.36',
+     version='0.0.37',
      author="Mads Bertelsen",
      author_email="Mads.Bertelsen@ess.eu",
      description="A python scripting interface for McStas",

From 48a99b48b68b45d3888ccdace6b931e887f38ea9 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 2 Dec 2021 16:37:49 +0100
Subject: [PATCH 170/403] Use of libpyvinyl Parameter class and
 CalculatorParameters class.

Use these as baseclasses for the appropriate classes in McStasScript,
and have made all required changes for the package to be functional.
Unit tests are updated and everything seems to be in order.

Am using direct access to underlying attributes a lot, so it is
hard to change the underlying implementation in libpyvinyl without
breaking this implementation. Will probably need a better contract
with libpyvinyl to be more confident in the features I use will not
be changed.
---
 examples/calibration_sample.ipynb             | 856 +-----------------
 mcstasscript/helper/mcstas_objects.py         | 161 +++-
 mcstasscript/interface/instr.py               | 100 +-
 .../jb_interface/simulation_interface.py      |  25 +-
 mcstasscript/jb_interface/widget_helpers.py   |   7 +-
 mcstasscript/tests/test_Instr.py              |  56 +-
 mcstasscript/tests/test_Instr_reader.py       |  20 +-
 mcstasscript/tests/test_instrument.instr      |   2 +-
 mcstasscript/tests/test_parameter_variable.py |  17 +-
 .../tests/test_simulation_interface.py        |   8 +-
 requirements.txt                              |   1 +
 setup.py                                      |   2 +-
 12 files changed, 260 insertions(+), 995 deletions(-)

diff --git a/examples/calibration_sample.ipynb b/examples/calibration_sample.ipynb
index a47af077..c521a8d2 100644
--- a/examples/calibration_sample.ipynb
+++ b/examples/calibration_sample.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -119,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,49 +139,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Union_cylinder ____________________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1mmaterial_string\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [] // material name of this volume, defined using \n",
-      "                          Union_make_material \n",
-      "\u001b[4m\u001b[1mpriority\u001b[0m\u001b[0m [1] // priotiry of the volume (can not be the same as another volume) \n",
-      "                A high priority is on top of low. \n",
-      "\u001b[4m\u001b[1mradius\u001b[0m\u001b[0m [m] // Outer radius volume in (x,z) plane\n",
-      "\u001b[4m\u001b[1myheight\u001b[0m\u001b[0m [m] // Cylinder height in (y) direction\n",
-      "\u001b[1mvisualize\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // set to 0 if you wish to hide this geometry in mcdisplay\n",
-      "\u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m\n",
-      "\u001b[1mtarget_x\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m\n",
-      "\u001b[1mtarget_y\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m // Position of target to focus at\n",
-      "\u001b[1mtarget_z\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m\n",
-      "\u001b[1mfocus_aw\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // horiz. angular dimension of a rectangular area\n",
-      "\u001b[1mfocus_ah\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // vert. angular dimension of a rectangular area\n",
-      "\u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // horiz. dimension of a rectangular area\n",
-      "\u001b[1mfocus_xh\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // vert. dimension of a rectangular area\n",
-      "\u001b[1mfocus_r\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // focusing on circle with this radius\n",
-      "\u001b[1mp_interact\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // probability to interact with this geometry [0-1]\n",
-      "\u001b[1mmask_string\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [] // Comma seperated list of geometry names which this \n",
-      "                      geometry should mask \n",
-      "\u001b[1mmask_setting\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [] // \"All\" or \"Any\", should the masked volume be simulated \n",
-      "                       when the ray is in just one mask, or all. \n",
-      "\u001b[1mnumber_of_activations\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // Number of subsequent Union_master components \n",
-      "                                   that will simulate this geometry \n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Instr.component_help(\"Union_cylinder\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -191,23 +158,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT base_cyl = Union_cylinder\n",
-      "  \u001b[1mmaterial_string\u001b[0m = \u001b[1m\u001b[92m\"Al\"\u001b[0m\u001b[0m []\n",
-      "  \u001b[1mpriority\u001b[0m = \u001b[1m\u001b[92m100\u001b[0m\u001b[0m [1]\n",
-      "  \u001b[1mradius\u001b[0m = \u001b[1m\u001b[92m0.04\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.02\u001b[0m\u001b[0m [m]\n",
-      "AT [0, 0, 0] RELATIVE sample_arm\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# We set the parameters and confirm this looks appropriate with the print function\n",
     "base_cyl.radius = 0.04\n",
@@ -226,21 +179,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# We define a string containing the names of the materials we wish to add\n",
     "Instr.add_parameter(\"string\", \"material\", value='\"Pb\"',\n",
     "                    comment=\"Material choice for extra material sample\",\n",
-    "                    options=[\"Cu\", \"Ni\", \"Ti\", \"Pb\", \"Fe\", \"Al\"])\n",
+    "                    options=['\"Cu\"', '\"Ni\"', '\"Ti\"', '\"Pb\"', '\"Fe\"', '\"Al\"'])\n",
     "\n",
     "sample_strings = [\"Cu\", \"Ni\", \"Ti\", \"Pb\", \"Fe\", \"material\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -279,7 +232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -316,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -333,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -345,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -357,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -379,54 +332,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Al_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Al_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Al                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Cu_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Cu_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Cu                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Ni_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Ni_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Ni                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Ti_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Ti_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Ti                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Pb_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Pb_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Pb                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Fe_inc              Incoherent_process    AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Fe_pow              Powder_process        AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "Fe                  Union_make_material   AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "source              Source_div            AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "sample_pos          Arm                   AT      (0, 0, 1)                   RELATIVE source    \n",
-      "sample_arm          Arm                   AT      (0, 0, 0)                   RELATIVE sample_pos \n",
-      "                                          ROTATED (rotation_x, rotation_y, 0) RELATIVE sample_pos\n",
-      "base_cyl            Union_cylinder        AT      (0, 0, 0)                   RELATIVE sample_arm\n",
-      "Cu_cyl              Union_cylinder        AT      (0.03, 0, 0.0)              RELATIVE base_cyl  \n",
-      "Ni_cyl              Union_cylinder        AT      (0.02427, 0, 0.01763)       RELATIVE base_cyl  \n",
-      "Ti_cyl              Union_cylinder        AT      (0.00927, 0, 0.02853)       RELATIVE base_cyl  \n",
-      "Pb_cyl              Union_cylinder        AT      (-0.00927, 0, 0.02853)      RELATIVE base_cyl  \n",
-      "Fe_cyl              Union_cylinder        AT      (-0.02427, 0, 0.01763)      RELATIVE base_cyl  \n",
-      "material_cyl        Union_cylinder        AT      (-0.03, 0, 0.0)             RELATIVE base_cyl  \n",
-      "logger_space_zx_all Union_logger_2D_space AT      (0, 0, 0)                   RELATIVE sample_pos\n",
-      "logger_space_zy_all Union_logger_2D_space AT      (0, 0, 0)                   RELATIVE sample_pos\n",
-      "logger_space_xy_all Union_logger_2D_space AT      (0, 0, 0)                   RELATIVE sample_pos\n",
-      "calibration_sample  Union_master          AT      (0, 0, 0)                   ABSOLUTE           \n",
-      "PSD                 PSD_monitor           AT      (0, 0, 1)                   RELATIVE sample_pos\n",
-      "PSDlin              PSDlin_monitor        AT      (0, 0, 1)                   RELATIVE sample_pos\n",
-      "EPSD                EPSD_monitor          AT      (0, 0, 0)                   RELATIVE PSD       \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Instr.print_components(line_length=117) # Show nice overview"
    ]
@@ -441,671 +351,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6d7fbe54d8a84ca48c37cb9eb9d0168a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(VBox(children=(HBox(children=(Label(value='energy', layout=Layout(height='32px', width='15%')),…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Instr.interface()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "INFO: Using directory: \"calibration_sample_20210602_123159\"\n",
-      "INFO: Regenerating c-file: calibration_sample.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./calibration_sample.out\n",
-      "In file included from /Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../contrib/union/Incoherent_process.comp:62:0:\n",
-      "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../share/Union_functions.c: In function ‘write_tagging_tree’:\n",
-      "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../share/Union_functions.c:1377:82: warning: passing argument 4 of ‘qsort’ from incompatible pointer type [enabled by default]\n",
-      "qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "^\n",
-      "In file included from mccode-r.h:41:0:\n",
-      "/usr/include/stdlib.h:165:7: note: expected ‘int (*)(const void *, const void *)’ but argument is of type ‘int (*)(const struct saved_history_struct *, const struct saved_history_struct *)’\n",
-      "void  qsort(void *__base, size_t __nel, size_t __width,\n",
-      "^\n",
-      "In file included from /Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../contrib/union/Incoherent_process.comp:62:0:\n",
-      "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../share/Union_functions.c:1381:3: warning: passing argument 1 of ‘printf_history’ from incompatible pointer type [enabled by default]\n",
-      "MPI_MASTER(\n",
-      "^\n",
-      "/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../share/Union_functions.c:1207:6: note: expected ‘struct dynamic_history_list *’ but argument is of type ‘struct saved_history_struct *’\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "^\n",
-      "INFO: ===\n",
-      "Warning: 17 events were removed in Component[33] PSD=PSD_monitor()\n",
-      "(negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "instrument definition parsed\n",
-      "reading particle data...\n",
-      "\n",
-      "Component                         Al_inc AT (0,0,0)    0 m from origin\n",
-      "Component                         Al_pow AT (0,0,0)    0 m from origin\n",
-      "Component                             Al AT (0,0,0)    0 m from origin\n",
-      "Component                         Cu_inc AT (0,0,0)    0 m from origin\n",
-      "Component                         Cu_pow AT (0,0,0)    0 m from origin\n",
-      "Component                             Cu AT (0,0,0)    0 m from origin\n",
-      "Component                         Ni_inc AT (0,0,0)    0 m from origin\n",
-      "Component                         Ni_pow AT (0,0,0)    0 m from origin\n",
-      "Component                             Ni AT (0,0,0)    0 m from origin\n",
-      "Component                         Ti_inc AT (0,0,0)    0 m from origin\n",
-      "Component                         Ti_pow AT (0,0,0)    0 m from origin\n",
-      "Component                             Ti AT (0,0,0)    0 m from origin\n",
-      "Component                         Pb_inc AT (0,0,0)    0 m from origin\n",
-      "Component                         Pb_pow AT (0,0,0)    0 m from origin\n",
-      "Component                             Pb AT (0,0,0)    0 m from origin\n",
-      "Component                         Fe_inc AT (0,0,0)    0 m from origin\n",
-      "Component                         Fe_pow AT (0,0,0)    0 m from origin\n",
-      "Component                             Fe AT (0,0,0)    0 m from origin\n",
-      "Component                         source AT (0,0,0)    0 m from origin\n",
-      "Component                     sample_pos AT (0,0,1)    1 m from origin\n",
-      "Component                     sample_arm AT (0,0,1)    1 m from origin\n",
-      "Component                       base_cyl AT (0,0,1)    1 m from origin\n",
-      "Component                         Cu_cyl AT (-0.03,0,1)    1.03 m from origin\n",
-      "Component                         Ni_cyl AT (-0.02427,0,0.98237)    1.04854 m from origin\n",
-      "Component                         Ti_cyl AT (-0.00927,0,0.97147)    1.06708 m from origin\n",
-      "Component                         Pb_cyl AT (0.00927,0,0.97147)    1.08562 m from origin\n",
-      "Component                         Fe_cyl AT (0.02427,0,0.98237)    1.10416 m from origin\n",
-      "Component                   material_cyl AT (0.03,0,1)    1.1227 m from origin\n",
-      "Component            logger_space_zx_all AT (0,0,1)    1.1527 m from origin\n",
-      "Component            logger_space_zy_all AT (0,0,1)    1.1527 m from origin\n",
-      "Component            logger_space_xy_all AT (0,0,1)    1.1527 m from origin\n",
-      "Component             calibration_sample AT (0,0,0)    2.1527 m from origin\n",
-      "Component                            PSD AT (0,0,2)    4.1527 m from origin\n",
-      "Component                         PSDlin AT (0,0,2)    4.1527 m from origin\n",
-      "Component                           EPSD AT (0,0,2)    4.1527 m from origin\n",
-      "Opening input file '/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "Opening input file '/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../data/Cu.laz' (Table_Read_Offset)\n",
-      "Table from file 'Cu.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
-      "  '# TITLE *-Cu-[FM3-M] Otte, H.M.[1961];# CELL 3.615050 3.615050 3.615050 90. ...'\n",
-      "PowderN: Cu_pow: Reading 19 rows from Cu.laz\n",
-      "PowderN: Cu_pow: Read 19 reflections from file 'Cu.laz'\n",
-      "PowderN: Cu_pow: Vc=47.24 [Angs] sigma_abs=15.12 [barn] sigma_inc=2.2 [barn] reflections=Cu.laz\n",
-      "Opening input file '/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../data/Ni.laz' (Table_Read_Offset)\n",
-      "Table from file 'Ni.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
-      "  '# TITLE *Nickel-Ni-[FM3-M] Swanson, H.E.;Tatge, E.[1954] [carcinogen];# CEL ...'\n",
-      "PowderN: Ni_pow: Reading 19 rows from Ni.laz\n",
-      "PowderN: Ni_pow: Read 19 reflections from file 'Ni.laz'\n",
-      "PowderN: Ni_pow: Vc=43.76 [Angs] sigma_abs=17.96 [barn] sigma_inc=20.8 [barn] reflections=Ni.laz\n",
-      "Opening input file '/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../data/Ti.laz' (Table_Read_Offset)\n",
-      "Table from file 'Ti.laz' (block 1) is 72 x 18 (x=0:5), constant step. interpolation: linear\n",
-      "  '# TITLE *-Ti-[P63/MMC] Pawar, R.R.;Deshpande, V.T.[1968];# CELL 2.950800 2. ...'\n",
-      "PowderN: Ti_pow: Reading 72 rows from Ti.laz\n",
-      "PowderN: Ti_pow: Read 72 reflections from file 'Ti.laz'\n",
-      "PowderN: Ti_pow: Vc=35.33 [Angs] sigma_abs=12.18 [barn] sigma_inc=5.74 [barn] reflections=Ti.laz\n",
-      "Opening input file '/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../data/Pb.laz' (Table_Read_Offset)\n",
-      "Table from file 'Pb.laz' (block 1) is 41 x 18 (x=1:9), constant step. interpolation: linear\n",
-      "  '# TITLE *-Pb-[FM3-M] Bouad, N.; Chapon, L.; Marin-Ayral, R.-M.; B[2003] [to ...'\n",
-      "PowderN: Pb_pow: Reading 41 rows from Pb.laz\n",
-      "PowderN: Pb_pow: Read 41 reflections from file 'Pb.laz'\n",
-      "PowderN: Pb_pow: Vc=121.29 [Angs] sigma_abs=0.684 [barn] sigma_inc=0.012 [barn] reflections=Pb.laz\n",
-      "Opening input file '/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../data/Fe.laz' (Table_Read_Offset)\n",
-      "Table from file 'Fe.laz' (block 1) is 17 x 18 (x=1:5), constant step. interpolation: linear\n",
-      "  '# TITLE *Iron-Fe-[IM3-M] Swanson, H.E.;Tatge, E.[1954] [Iron alpha phase];# ...'\n",
-      "PowderN: Fe_pow: Reading 17 rows from Fe.laz\n",
-      "PowderN: Fe_pow: Read 17 reflections from file 'Fe.laz'\n",
-      "PowderN: Fe_pow: Vc=24.04 [Angs] sigma_abs=5.12 [barn] sigma_inc=0.8 [barn] reflections=Fe.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 12\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "name of process [2]: Cu_inc \n",
-      "component index [2]: 4 \n",
-      "name of process [3]: Cu_pow \n",
-      "component index [3]: 5 \n",
-      "name of process [4]: Ni_inc \n",
-      "component index [4]: 7 \n",
-      "name of process [5]: Ni_pow \n",
-      "component index [5]: 8 \n",
-      "name of process [6]: Ti_inc \n",
-      "component index [6]: 10 \n",
-      "name of process [7]: Ti_pow \n",
-      "component index [7]: 11 \n",
-      "name of process [8]: Pb_inc \n",
-      "component index [8]: 13 \n",
-      "name of process [9]: Pb_pow \n",
-      "component index [9]: 14 \n",
-      "name of process [10]: Fe_inc \n",
-      "component index [10]: 16 \n",
-      "name of process [11]: Fe_pow \n",
-      "component index [11]: 17 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 6\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 1.391570 \n",
-      "number of processes [0]: 2 \n",
-      "name of material    [1]: Cu \n",
-      "component index     [1]: 6 \n",
-      "my_absoprtion       [1]: 32.006800 \n",
-      "number of processes [1]: 2 \n",
-      "name of material    [2]: Ni \n",
-      "component index     [2]: 9 \n",
-      "my_absoprtion       [2]: 41.042000 \n",
-      "number of processes [2]: 2 \n",
-      "name of material    [3]: Ti \n",
-      "component index     [3]: 12 \n",
-      "my_absoprtion       [3]: 34.475000 \n",
-      "number of processes [3]: 2 \n",
-      "name of material    [4]: Pb \n",
-      "component index     [4]: 15 \n",
-      "my_absoprtion       [4]: 0.560640 \n",
-      "number of processes [4]: 2 \n",
-      "name of material    [5]: Fe \n",
-      "component index     [5]: 18 \n",
-      "my_absoprtion       [5]: 21.297800 \n",
-      "number of processes [5]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 6\n",
-      "\n",
-      "name of geometry    [0]: base_cyl \n",
-      "component index     [0]: 22 \n",
-      "Volume.name         [0]: base_cyl \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absoprtion       [0]: 1.391570 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [-1.000000 0.000000 -0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 -1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.040000 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.020000 \n",
-      "Volume.geometry.focus_data.Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: Cu_cyl \n",
-      "component index     [1]: 23 \n",
-      "Volume.name         [1]: Cu_cyl \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absoprtion       [1]: 32.006800 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: -0.030000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [-1.000000 0.000000 -0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 -1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.007000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.019000 \n",
-      "Volume.geometry.focus_data.Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: Ni_cyl \n",
-      "component index     [2]: 24 \n",
-      "Volume.name         [2]: Ni_cyl \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absoprtion       [2]: 41.042000 \n",
-      "Volume.p_physics.number of processes [2]: 2 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: -0.024270 \n",
-      "Volume.geometry.center.y             [2]: 0.000000 \n",
-      "Volume.geometry.center.z             [2]: 0.982370 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [-1.000000 0.000000 -0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 -1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.007000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.019000 \n",
-      "Volume.geometry.focus_data.Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: Ti_cyl \n",
-      "component index     [3]: 25 \n",
-      "Volume.name         [3]: Ti_cyl \n",
-      "Volume.p_physics.is_vacuum           [3]: 0 \n",
-      "Volume.p_physics.my_absoprtion       [3]: 34.475000 \n",
-      "Volume.p_physics.number of processes [3]: 2 \n",
-      "Volume.geometry.shape                [3]: cylinder \n",
-      "Volume.geometry.center.x             [3]: -0.009270 \n",
-      "Volume.geometry.center.y             [3]: 0.000000 \n",
-      "Volume.geometry.center.z             [3]: 0.971470 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [-1.000000 0.000000 -0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 -1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.007000 \n",
-      "Volume.geometry.geometry_parameters.height [3]: 0.019000 \n",
-      "Volume.geometry.focus_data.Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [4]: Pb_cyl \n",
-      "component index     [4]: 26 \n",
-      "Volume.name         [4]: Pb_cyl \n",
-      "Volume.p_physics.is_vacuum           [4]: 0 \n",
-      "Volume.p_physics.my_absoprtion       [4]: 0.560640 \n",
-      "Volume.p_physics.number of processes [4]: 2 \n",
-      "Volume.geometry.shape                [4]: cylinder \n",
-      "Volume.geometry.center.x             [4]: 0.009270 \n",
-      "Volume.geometry.center.y             [4]: 0.000000 \n",
-      "Volume.geometry.center.z             [4]: 0.971470 \n",
-      "Volume.geometry.rotation_matrix[0]           [4]: [-1.000000 0.000000 -0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 -1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.007000 \n",
-      "Volume.geometry.geometry_parameters.height [4]: 0.019000 \n",
-      "Volume.geometry.focus_data.Aim             [4]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [5]: Fe_cyl \n",
-      "component index     [5]: 27 \n",
-      "Volume.name         [5]: Fe_cyl \n",
-      "Volume.p_physics.is_vacuum           [5]: 0 \n",
-      "Volume.p_physics.my_absoprtion       [5]: 21.297800 \n",
-      "Volume.p_physics.number of processes [5]: 2 \n",
-      "Volume.geometry.shape                [5]: cylinder \n",
-      "Volume.geometry.center.x             [5]: 0.024270 \n",
-      "Volume.geometry.center.y             [5]: 0.000000 \n",
-      "Volume.geometry.center.z             [5]: 0.982370 \n",
-      "Volume.geometry.rotation_matrix[0]           [5]: [-1.000000 0.000000 -0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 -1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.007000 \n",
-      "Volume.geometry.geometry_parameters.height [5]: 0.019000 \n",
-      "Volume.geometry.focus_data.Aim             [5]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [6]: material_cyl \n",
-      "component index     [6]: 28 \n",
-      "Volume.name         [6]: material_cyl \n",
-      "Volume.p_physics.is_vacuum           [6]: 0 \n",
-      "Volume.p_physics.my_absoprtion       [6]: 0.560640 \n",
-      "Volume.p_physics.number of processes [6]: 2 \n",
-      "Volume.geometry.shape                [6]: cylinder \n",
-      "Volume.geometry.center.x             [6]: 0.030000 \n",
-      "Volume.geometry.center.y             [6]: 0.000000 \n",
-      "Volume.geometry.center.z             [6]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [6]: [-1.000000 0.000000 -0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 -1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.007000 \n",
-      "Volume.geometry.geometry_parameters.height [6]: 0.019000 \n",
-      "Volume.geometry.focus_data.Aim             [6]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 8\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      "Generating children lists --------------------------- \n",
-      "LIST: Children for Volume 0 = [1,2,3,4,5,6,7]\n",
-      "LIST: Children for Volume 1 (temporary_list) = [1,2,3,4,5,6,7]\n",
-      "LIST: Children for Volume 1 (permanent_list) = [2,3,4,5,6,7]\n",
-      "LIST: Children for Volume 2 (temporary_list) = [2]\n",
-      "LIST: Children for Volume 2 (permanent_list) = []\n",
-      "LIST: Children for Volume 3 (temporary_list) = [3]\n",
-      "LIST: Children for Volume 3 (permanent_list) = []\n",
-      "LIST: Children for Volume 4 (temporary_list) = [4]\n",
-      "LIST: Children for Volume 4 (permanent_list) = []\n",
-      "LIST: Children for Volume 5 (temporary_list) = [5]\n",
-      "LIST: Children for Volume 5 (permanent_list) = []\n",
-      "LIST: Children for Volume 6 (temporary_list) = [6]\n",
-      "LIST: Children for Volume 6 (permanent_list) = []\n",
-      "LIST: Children for Volume 7 (temporary_list) = [7]\n",
-      "LIST: Children for Volume 7 (permanent_list) = []\n",
-      "LIST: True children for Volume (post mask) 1 = [2,3,4,5,6,7]\n",
-      "LIST: True children for Volume (post mask) 2 = []\n",
-      "LIST: True children for Volume (post mask) 3 = []\n",
-      "LIST: True children for Volume (post mask) 4 = []\n",
-      "LIST: True children for Volume (post mask) 5 = []\n",
-      "LIST: True children for Volume (post mask) 6 = []\n",
-      "LIST: True children for Volume (post mask) 7 = []\n",
-      "\n",
-      "Generating overlap lists ---------------------------- \n",
-      "LIST: Overlaps for Volume 0 = [1,2,3,4,5,6,7]\n",
-      "LIST: Overlaps for Volume (pre mask) 1 = [0,1,2,3,4,5,6,7]\n",
-      "LIST: Overlaps for Volume (pre mask) 2 = [0,1,2]\n",
-      "LIST: Overlaps for Volume (pre mask) 3 = [0,1,3]\n",
-      "LIST: Overlaps for Volume (pre mask) 4 = [0,1,4]\n",
-      "LIST: Overlaps for Volume (pre mask) 5 = [0,1,5]\n",
-      "LIST: Overlaps for Volume (pre mask) 6 = [0,1,6]\n",
-      "LIST: Overlaps for Volume (pre mask) 7 = [0,1,7]\n",
-      "LIST: Overlaps for Volume (post mask) 1 = [0,2,3,4,5,6,7]\n",
-      "LIST: Overlaps for Volume (post mask) 2 = [0,1]\n",
-      "LIST: Overlaps for Volume (post mask) 3 = [0,1]\n",
-      "LIST: Overlaps for Volume (post mask) 4 = [0,1]\n",
-      "LIST: Overlaps for Volume (post mask) 5 = [0,1]\n",
-      "LIST: Overlaps for Volume (post mask) 6 = [0,1]\n",
-      "LIST: Overlaps for Volume (post mask) 7 = [0,1]\n",
-      "\n",
-      "Generating parents lists ---------------------------- \n",
-      "LIST: Parents for Volume 0 = []\n",
-      "LIST: Parents for Volume 1 = [0]\n",
-      "LIST: Parents for Volume 2 = [0,1]\n",
-      "LIST: Parents for Volume 3 = [0,1]\n",
-      "LIST: Parents for Volume 4 = [0,1]\n",
-      "LIST: Parents for Volume 5 = [0,1]\n",
-      "LIST: Parents for Volume 6 = [0,1]\n",
-      "LIST: Parents for Volume 7 = [0,1]\n",
-      "\n",
-      "Generating parents lists (ignoring masks) ----------- \n",
-      "LIST: Parents for Volume 0 = []\n",
-      "LIST: Parents for Volume 1 = [0]\n",
-      "LIST: Parents for Volume 2 = [0,1]\n",
-      "LIST: Parents for Volume 3 = [0,1]\n",
-      "LIST: Parents for Volume 4 = [0,1]\n",
-      "LIST: Parents for Volume 5 = [0,1]\n",
-      "LIST: Parents for Volume 6 = [0,1]\n",
-      "LIST: Parents for Volume 7 = [0,1]\n",
-      "\n",
-      "Generating parents lists ---------------------------- \n",
-      "LIST: Parents for Volume 0 = []\n",
-      "LIST: Parents for Volume 1 = [0]\n",
-      "LIST: Parents for Volume 2 = [0,1]\n",
-      "LIST: Parents for Volume 3 = [0,1]\n",
-      "LIST: Parents for Volume 4 = [0,1]\n",
-      "LIST: Parents for Volume 5 = [0,1]\n",
-      "LIST: Parents for Volume 6 = [0,1]\n",
-      "LIST: Parents for Volume 7 = [0,1]\n",
-      "\n",
-      "Generating parents lists (ignoring masks) ----------- \n",
-      "LIST: Parents for Volume 0 = []\n",
-      "LIST: Parents for Volume 1 = [0]\n",
-      "LIST: Parents for Volume 2 = [0,1]\n",
-      "LIST: Parents for Volume 3 = [0,1]\n",
-      "LIST: Parents for Volume 4 = [0,1]\n",
-      "LIST: Parents for Volume 5 = [0,1]\n",
-      "LIST: Parents for Volume 6 = [0,1]\n",
-      "LIST: Parents for Volume 7 = [0,1]\n",
-      "\n",
-      "Generating intersect check lists -------------------- \n",
-      "LIST: Intersect check list for Volume 0 = [1]\n",
-      "LIST: Mask intersect check list for Volume 0 = []\n",
-      "LIST: Intersect check list for Volume 1 = [2,3,4,5,6,7]\n",
-      "LIST: Mask intersect check list for Volume 1 = []\n",
-      "LIST: Intersect check list for Volume 2 = []\n",
-      "LIST: Mask intersect check list for Volume 2 = []\n",
-      "LIST: Intersect check list for Volume 3 = []\n",
-      "LIST: Mask intersect check list for Volume 3 = []\n",
-      "LIST: Intersect check list for Volume 4 = []\n",
-      "LIST: Mask intersect check list for Volume 4 = []\n",
-      "LIST: Intersect check list for Volume 5 = []\n",
-      "LIST: Mask intersect check list for Volume 5 = []\n",
-      "LIST: Intersect check list for Volume 6 = []\n",
-      "LIST: Mask intersect check list for Volume 6 = []\n",
-      "LIST: Intersect check list for Volume 7 = []\n",
-      "LIST: Mask intersect check list for Volume 7 = []\n",
-      "\n",
-      "Generating grandparents lists ----------------------- \n",
-      "LIST: Grandparents for Volume 0 = []\n",
-      "LIST: Grandparents for Volume 1 = []\n",
-      "LIST: Grandparents for Volume 2 = []\n",
-      "LIST: Grandparents for Volume 3 = []\n",
-      "LIST: Grandparents for Volume 4 = []\n",
-      "LIST: Grandparents for Volume 5 = []\n",
-      "LIST: Grandparents for Volume 6 = []\n",
-      "LIST: Grandparents for Volume 7 = []\n",
-      "grandparents_lists[0]->num_elements = 0 \n",
-      "\n",
-      "Generating grandparents lists ----------------------- \n",
-      "LIST: Grandparents for Volume 0 = []\n",
-      "LIST: Grandparents for Volume 1 = []\n",
-      "LIST: Grandparents for Volume 2 = []\n",
-      "LIST: Grandparents for Volume 3 = []\n",
-      "LIST: Grandparents for Volume 4 = []\n",
-      "LIST: Grandparents for Volume 5 = []\n",
-      "LIST: Grandparents for Volume 6 = []\n",
-      "LIST: Grandparents for Volume 7 = []\n",
-      "\n",
-      "Generating grandparents lists ----------------------- \n",
-      "LIST: Grandparents for Volume 0 = []\n",
-      "LIST: Grandparents for Volume 1 = []\n",
-      "LIST: Grandparents for Volume 2 = []\n",
-      "LIST: Grandparents for Volume 3 = []\n",
-      "LIST: Grandparents for Volume 4 = []\n",
-      "LIST: Grandparents for Volume 5 = []\n",
-      "LIST: Grandparents for Volume 6 = []\n",
-      "LIST: Grandparents for Volume 7 = []\n",
-      "\n",
-      "Generating grandparents lists ----------------------- \n",
-      "LIST: Grandparents for Volume 0 = []\n",
-      "LIST: Grandparents for Volume 1 = []\n",
-      "LIST: Grandparents for Volume 2 = []\n",
-      "LIST: Grandparents for Volume 3 = []\n",
-      "LIST: Grandparents for Volume 4 = []\n",
-      "LIST: Grandparents for Volume 5 = []\n",
-      "LIST: Grandparents for Volume 6 = []\n",
-      "LIST: Grandparents for Volume 7 = []\n",
-      "\n",
-      "Generating destinations lists ----------------------- \n",
-      "LIST: Destinations list for Volume 1 = [0]\n",
-      "LIST: Destinations list for Volume 2 = [1]\n",
-      "LIST: Destinations list for Volume 3 = [1]\n",
-      "LIST: Destinations list for Volume 4 = [1]\n",
-      "LIST: Destinations list for Volume 5 = [1]\n",
-      "LIST: Destinations list for Volume 6 = [1]\n",
-      "LIST: Destinations list for Volume 7 = [1]\n",
-      "\n",
-      "Generating reduced destination lists ----------------------- \n",
-      "LIST: Reduced destinations list for Volume 0 = []\n",
-      "LIST: Reduced destinations list for Volume 1 = []\n",
-      "LIST: Reduced destinations list for Volume 2 = [1]\n",
-      "LIST: Reduced destinations list for Volume 3 = [1]\n",
-      "LIST: Reduced destinations list for Volume 4 = [1]\n",
-      "LIST: Reduced destinations list for Volume 5 = [1]\n",
-      "LIST: Reduced destinations list for Volume 6 = [1]\n",
-      "LIST: Reduced destinations list for Volume 7 = [1]\n",
-      "\n",
-      "Generating direct children lists ----------------------- \n",
-      "LIST: Children list for Volume        0 = [1,2,3,4,5,6,7]\n",
-      "LIST: Direct_children list for Volume 0 = [1]\n",
-      "LIST: Children list for Volume        1 = [2,3,4,5,6,7]\n",
-      "LIST: Direct_children list for Volume 1 = [2,3,4,5,6,7]\n",
-      "LIST: Children list for Volume        2 = []\n",
-      "LIST: Direct_children list for Volume 2 = []\n",
-      "LIST: Children list for Volume        3 = []\n",
-      "LIST: Direct_children list for Volume 3 = []\n",
-      "LIST: Children list for Volume        4 = []\n",
-      "LIST: Direct_children list for Volume 4 = []\n",
-      "LIST: Children list for Volume        5 = []\n",
-      "LIST: Direct_children list for Volume 5 = []\n",
-      "LIST: Children list for Volume        6 = []\n",
-      "LIST: Direct_children list for Volume 6 = []\n",
-      "LIST: Children list for Volume        7 = []\n",
-      "LIST: Direct_children list for Volume 7 = []\n",
-      "LIST: Allowed starting volume logic list = [1,0,0,0,0,0,0,0]\n",
-      "\n",
-      "Generating start destinations list ------------------------------ \n",
-      "LIST: Starting destinations list = [1,2,3,4,5,6,7]\n",
-      "\n",
-      "Generating reduced start destination list ----------------------- \n",
-      "LIST: Reduced start destinations list = [1]\n",
-      "LIST: Start logic list = [0,1,1,1,1,1,1,1]\n",
-      "\n",
-      "Generating next volume list ------------------------------------- \n",
-      "LIST: Next volume list                0 = [1]\n",
-      "LIST: Next volume list                1 = [0,2,3,4,5,6,7]\n",
-      "LIST: Next volume list                2 = [1]\n",
-      "LIST: Next volume list                3 = [1]\n",
-      "LIST: Next volume list                4 = [1]\n",
-      "LIST: Next volume list                5 = [1]\n",
-      "LIST: Next volume list                6 = [1]\n",
-      "LIST: Next volume list                7 = [1]\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7]\n",
-      "LIST: Direct_children for Volume           0 = [1]\n",
-      "LIST: Intersect_check_list for Volume      0 = [1]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [1]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "LIST: Children for Volume                  1 = [2,3,4,5,6,7]\n",
-      "LIST: Direct_children for Volume           1 = [2,3,4,5,6,7]\n",
-      "LIST: Intersect_check_list for Volume      1 = [2,3,4,5,6,7]\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = []\n",
-      "LIST: Next_volume_list for Volume          1 = [0,2,3,4,5,6,7]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "LIST: Children for Volume                  2 = []\n",
-      "LIST: Direct_children for Volume           2 = []\n",
-      "LIST: Intersect_check_list for Volume      2 = []\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [1]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [1]\n",
-      "LIST: Next_volume_list for Volume          2 = [1]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "LIST: Children for Volume                  3 = []\n",
-      "LIST: Direct_children for Volume           3 = []\n",
-      "LIST: Intersect_check_list for Volume      3 = []\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [1]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = [1]\n",
-      "LIST: Next_volume_list for Volume          3 = [1]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "LIST: Children for Volume                  4 = []\n",
-      "LIST: Direct_children for Volume           4 = []\n",
-      "LIST: Intersect_check_list for Volume      4 = []\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [1]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = [1]\n",
-      "LIST: Next_volume_list for Volume          4 = [1]\n",
-      "      Is_vacuum for Volume                 4 = 0\n",
-      "      is_mask_volume for Volume            4 = 0\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = []\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 0\n",
-      "\n",
-      "LIST: Children for Volume                  5 = []\n",
-      "LIST: Direct_children for Volume           5 = []\n",
-      "LIST: Intersect_check_list for Volume      5 = []\n",
-      "LIST: Mask_intersect_list for Volume       5 = []\n",
-      "LIST: Destinations_list for Volume         5 = [1]\n",
-      "LIST: Reduced_destinations_list for Volume 5 = [1]\n",
-      "LIST: Next_volume_list for Volume          5 = [1]\n",
-      "      Is_vacuum for Volume                 5 = 0\n",
-      "      is_mask_volume for Volume            5 = 0\n",
-      "      is_masked_volume for Volume          5 = 0\n",
-      "      is_exit_volume for Volume            5 = 0\n",
-      "LIST: mask_list for Volume                 5 = []\n",
-      "LIST: masked_by_list for Volume            5 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
-      "      mask_mode for Volume                 5 = 0\n",
-      "\n",
-      "LIST: Children for Volume                  6 = []\n",
-      "LIST: Direct_children for Volume           6 = []\n",
-      "LIST: Intersect_check_list for Volume      6 = []\n",
-      "LIST: Mask_intersect_list for Volume       6 = []\n",
-      "LIST: Destinations_list for Volume         6 = [1]\n",
-      "LIST: Reduced_destinations_list for Volume 6 = [1]\n",
-      "LIST: Next_volume_list for Volume          6 = [1]\n",
-      "      Is_vacuum for Volume                 6 = 0\n",
-      "      is_mask_volume for Volume            6 = 0\n",
-      "      is_masked_volume for Volume          6 = 0\n",
-      "      is_exit_volume for Volume            6 = 0\n",
-      "LIST: mask_list for Volume                 6 = []\n",
-      "LIST: masked_by_list for Volume            6 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
-      "      mask_mode for Volume                 6 = 0\n",
-      "\n",
-      "LIST: Children for Volume                  7 = []\n",
-      "LIST: Direct_children for Volume           7 = []\n",
-      "LIST: Intersect_check_list for Volume      7 = []\n",
-      "LIST: Mask_intersect_list for Volume       7 = []\n",
-      "LIST: Destinations_list for Volume         7 = [1]\n",
-      "LIST: Reduced_destinations_list for Volume 7 = [1]\n",
-      "LIST: Next_volume_list for Volume          7 = [1]\n",
-      "      Is_vacuum for Volume                 7 = 0\n",
-      "      is_mask_volume for Volume            7 = 0\n",
-      "      is_masked_volume for Volume          7 = 0\n",
-      "      is_exit_volume for Volume            7 = 0\n",
-      "LIST: mask_list for Volume                 7 = []\n",
-      "LIST: masked_by_list for Volume            7 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
-      "      mask_mode for Volume                 7 = 0\n",
-      "\n",
-      "Union_master component calibration_sample initialized sucessfully\n",
-      "Detector: logger_space_zx_all_I=4.24506e+09 logger_space_zx_all_ERR=7.73168e+08 logger_space_zx_all_N=40 \"space_zx.dat\"\n",
-      "Detector: logger_space_zy_all_I=4.24506e+09 logger_space_zy_all_ERR=7.73168e+08 logger_space_zy_all_N=40 \"space_zy.dat\"\n",
-      "Detector: logger_space_xy_all_I=4.24506e+09 logger_space_xy_all_ERR=7.73168e+08 logger_space_xy_all_N=40 \"space_xy.dat\"\n",
-      "Detector: PSD_I=4.38373e+10 PSD_ERR=2.93556e+09 PSD_N=223 \"PSD.dat\"\n",
-      "Detector: PSDlin_I=4.38373e+10 PSDlin_ERR=2.93556e+09 PSDlin_N=223 \"PSDlin.dat\"\n",
-      "Detector: EPSD_I=2.75212e+09 EPSD_ERR=7.35534e+08 EPSD_N=14 \"EPSD.dat\"\n",
-      "\n",
-      "\n",
-      "Top 20 most common histories. Shows the index of volumes entered (VX), and the scattering processes (PX)\n",
-      "267\t N I=5.248679E+10 \t V0 \n",
-      "4\t N I=7.863190E+08 \t V0 -> V1 -> V0 \n",
-      "3\t N I=5.316424E+08 \t V0 -> V1 -> V5 -> P1 -> V1 -> V0 \n",
-      "3\t N I=5.228126E+08 \t V0 -> V1 -> V6 -> P1 -> V1 -> V0 \n",
-      "3\t N I=4.435212E+08 \t V0 -> V1 -> V5 -> V1 -> P1 -> V0 \n",
-      "2\t N I=3.931595E+08 \t V0 -> V1 -> V5 -> V1 -> V0 \n",
-      "2\t N I=2.714067E+08 \t V0 -> V1 -> P1 -> V0 \n",
-      "1\t N I=1.965797E+08 \t V0 -> V1 -> V2 -> V1 -> V0 \n",
-      "1\t N I=1.965797E+08 \t V0 -> V1 -> V7 -> V1 -> V0 \n",
-      "1\t N I=1.656375E+08 \t V0 -> V1 -> V4 -> V1 -> P1 -> P1 -> V0 \n",
-      "1\t N I=1.448487E+08 \t V0 -> V1 -> V4 -> V1 -> P1 -> V0 \n",
-      "1\t N I=1.323150E+08 \t V0 -> V1 -> V5 -> P1 -> P1 -> V1 -> V0 \n",
-      "1\t N I=1.289778E+08 \t V0 -> V1 -> P0 -> V0 \n",
-      "1\t N I=1.107849E+08 \t V0 -> V1 -> V3 -> P1 -> P1 -> P1 -> V1 -> V0 \n",
-      "1\t N I=1.023142E+08 \t V0 -> V1 -> V3 -> P1 -> V1 -> V0 \n",
-      "1\t N I=6.676574E+07 \t V0 -> V1 -> V6 -> P1 -> P1 -> V1 -> V0 \n",
-      "1\t N I=5.751379E+07 \t V0 -> V1 -> V6 -> P1 -> P1 -> P1 -> V1 -> V5 -> V1 -> V0 \n",
-      "1\t N I=5.535036E+07 \t V0 -> V1 -> V3 -> P0 -> V1 -> V0 \n",
-      "1\t N I=2.879975E+07 \t V0 -> V1 -> V4 -> P1 -> P0 -> V1 -> V0 \n",
-      "1\t N I=2.387658E+07 \t V0 -> V1 -> V4 -> P0 -> V1 -> V0 \n",
-      "\n",
-      "starting particle parsing\n",
-      "ended particle parsing\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Instr.show_instrument()"
    ]
@@ -1120,23 +377,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[\n",
-      "McStasData: logger_space_zx_all type: 2D  I:4.46932e+09 E:9.78589e+06 N:334938, \n",
-      "McStasData: logger_space_zy_all type: 2D  I:4.46932e+09 E:9.78589e+06 N:334938, \n",
-      "McStasData: logger_space_xy_all type: 2D  I:4.46932e+09 E:9.78589e+06 N:334938, \n",
-      "McStasData: PSD type: 2D  I:4.35683e+10 E:3.58426e+07 N:1.47764e+06, \n",
-      "McStasData: PSDlin type: 1D  I:4.35683e+10 E:3.58426e+07 N:1.47764e+06, \n",
-      "McStasData: EPSD type: 2D  I:4.64794e+09 E:1.1707e+07 N:157656]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = Instr.get_interface_data()\n",
     "print(data)"
@@ -1144,31 +387,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "842a963bb6c6401a9393877978ba1c10",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name EPSD\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "if len(data) != 0:\n",
     "    EPSD_data = functions.name_search(\"EPSD\", data)\n",
@@ -1185,24 +406,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "31522898410542958ea06e8ab06e057d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(Output(layout=Layout(width='75%')), VBox(children=(Label(value='Choose monitor'), Dropdown(layo…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plotter.interface(data)"
    ]
@@ -1294,7 +500,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 61de5a0d..05203bed 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -1,8 +1,11 @@
 from mcstasscript.helper.formatting import bcolors
 from mcstasscript.helper.formatting import is_legal_parameter
 
+from libpyvinyl.Parameters.Parameter import Parameter
+from libpyvinyl.Parameters.Collections import CalculatorParameters
 
-class ParameterVariable:
+
+class ParameterVariable(Parameter):
     """
     Class describing an input parameter in McStas instrument
 
@@ -66,9 +69,10 @@ def __init__(self, *args, **kwargs):
                 sets comment displayed next to declaration
 
         """
+
         if len(args) == 1:
             self.type = ""
-            self.name = str(args[0])
+            name = str(args[0])
         if len(args) == 2:
             specified_type = args[0]
             allowed_types = {"double", "int", "string"}
@@ -79,38 +83,40 @@ def __init__(self, *args, **kwargs):
                                    + str(allowed_types) + ".")
 
             self.type = specified_type
-            self.name = str(args[1])
+            name = str(args[1])
 
-        if not is_legal_parameter(self.name):
+        if not is_legal_parameter(name):
             raise NameError("The given parameter name: \""
-                            + self.name
+                            + name
                             + "\" is not a legal c variable name, "
                             + " and cannot be used in McStas.")
 
-        self.value = ""
-        if "value" in kwargs:
-            if not isinstance(kwargs["value"], (str, int, float)):
-                raise RuntimeError("Given value for parameter has to be of "
-                                   + "type str, int or float.")
-            self.value = kwargs["value"]
-
-        self.comment = ""
+        comment = None
         if "comment" in kwargs:
-            self.comment = kwargs["comment"]
-            if not isinstance(self.comment, str):
+            comment = kwargs["comment"]
+            if not isinstance(comment, str):
                 raise RuntimeError("Tried to create a parameter with a "
                                    + "comment that was not a string.")
-            self.comment = "// " + self.comment
 
-        self.options = None
+        unit = None
+        if "unit" in kwargs:
+            unit = kwargs["unit"]
+            if not isinstance(unit, str):
+                raise RuntimeError("Unit has to be a string")
+
+        super().__init__(name=name, unit=unit, comment=comment)
+
         if "options" in kwargs:
-            self.options = kwargs["options"]
-            if self.value != "":
-                if (self.value not in self.options
-                        and self.value.strip("'") not in self.options
-                        and self.value.strip('"') not in self.options):
-                    raise RuntimeError("When giving both options and default, "
-                                       "the value has to be an option.")
+            options = kwargs["options"]
+
+            self.add_option(options)
+
+        if "value" in kwargs:
+            if not isinstance(kwargs["value"], (str, int, float)):
+                raise RuntimeError("Given value for parameter has to be of "
+                                   + "type str, int or float.")
+
+            self.value = kwargs["value"]
 
     def write_parameter(self, fo, stop_character):
         """Writes input parameter to file"""
@@ -120,7 +126,7 @@ def write_parameter(self, fo, stop_character):
                                + "a string.")
 
         fo.write("%s %s" % (self.type, self.name))
-        if self.value != "":
+        if self.value is not None:
             if isinstance(self.value, int):
                 fo.write(" = %d" % self.value)
             elif isinstance(self.value, float):
@@ -128,9 +134,111 @@ def write_parameter(self, fo, stop_character):
             else:
                 fo.write(" = %s" % str(self.value))
         fo.write(stop_character)
-        fo.write(self.comment)
+
+        if self.comment is None:
+            c_comment = ""
+        else:
+            c_comment = "// " + self.comment
+
+        fo.write(c_comment)
         fo.write("\n")
 
+class ParameterContainer(CalculatorParameters):
+    def __init__(self, parameters=None):
+        super().__init__(parameters)
+
+    def show_parameters(self, line_limit=100):
+
+        """
+        Method for displaying current instrument parameters
+
+        line_limit : int
+            Maximum line length for terminal output
+        """
+
+        if len(self.parameters) == 0:
+            print("No instrument parameters available")
+            return
+
+        # Find longest fields
+        types = []
+        names = []
+        values = []
+        comments = []
+        for parameter in self.parameters.values():
+            types.append(str(parameter.type))
+            names.append(str(parameter.name))
+            values.append(str(parameter.value))
+            if parameter.comment is None:
+                comments.append("")
+            else:
+                comments.append(str(parameter.comment))
+
+        longest_type = len(max(types, key=len))
+        longest_name = len(max(names, key=len))
+        longest_value = len(max(values, key=len))
+        # In addition to the data 11 characters are added before the comment
+        comment_start_point = longest_type + longest_name + longest_value + 11
+        longest_comment = len(max(comments, key=len))
+        length_for_comment = line_limit - comment_start_point
+
+        # Print to console
+        for parameter in self.parameters.values():
+            print(str(parameter.type).ljust(longest_type), end=' ')
+            print(str(parameter.name).ljust(longest_name), end=' ')
+            if parameter.value is None:
+                print("  ", end=' ')
+                print(" ".ljust(longest_value + 1), end=' ')
+            else:
+                print(" =", end=' ')
+                print(str(parameter.value).ljust(longest_value + 1), end=' ')
+
+            if parameter.comment is None:
+                c_comment = ""
+            else:
+                c_comment = "// " + str(parameter.comment)
+
+            if (length_for_comment < 5
+                    or length_for_comment > len(c_comment)):
+                print(c_comment)
+            else:
+                # Split comment into several lines
+                comment = c_comment
+                words = comment.split(" ")
+                words_left = len(words)
+                last_index = 0
+                current_index = 0
+                comment = ""
+                iterations = 0
+                max_iterations = 50
+                while words_left > 0:
+                    iterations += 1
+                    if iterations > max_iterations:
+                        #  Something went long, print on one line
+                        break
+
+                    line_left = length_for_comment
+
+                    while line_left > 0:
+                        if current_index >= len(words):
+                            current_index = len(words) + 1
+                            break
+                        line_left -= len(str(words[current_index])) + 1
+                        current_index += 1
+
+                    current_index -= 1
+                    for word in words[last_index:current_index]:
+                        comment += word + " "
+                    words_left = len(words) - current_index
+                    if words_left > 0:
+                        comment += "\n" + " " * comment_start_point
+                        last_index = current_index
+
+                if not iterations == max_iterations + 1:
+                    print(comment)
+                else:
+                    print(c_comment.ljust(longest_comment))
+
 
 class DeclareVariable:
     """
@@ -242,6 +350,7 @@ def write_line(self, fo):
         fo : file object
             File the line will be written to
         """
+
         if self.value == "" and self.vector == 0:
             fo.write("%s %s;%s" % (self.type, self.name, self.comment))
         if self.value != "" and self.vector == 0:
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 848d5fb1..af2a6104 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -9,6 +9,7 @@
 from mcstasscript.helper.mcstas_objects import DeclareVariable
 from mcstasscript.helper.mcstas_objects import ParameterVariable
 from mcstasscript.helper.mcstas_objects import Component
+from mcstasscript.helper.mcstas_objects import ParameterContainer
 from mcstasscript.helper.component_reader import ComponentReader
 from mcstasscript.helper.managed_mcrun import ManagedMcrun
 from mcstasscript.helper.formatting import is_legal_filename
@@ -55,7 +56,7 @@ class McCode_instr:
     executable_path : str
         absolute path of mcrun command, or empty if it is in path
 
-    parameter_list : list of ParameterVariable instances
+    instrument_parameters : ParameterContainer
         contains all input parameters to be written to file
 
     declare_list : list of DeclareVariable instances
@@ -277,7 +278,7 @@ def __init__(self, name, **kwargs):
                             + " installation as keyword named "
                             + "package_path or in configuration.yaml")
 
-        self.parameter_list = []
+        self.instrument_parameters = ParameterContainer()
         self.declare_list = []
         self.initialize_section = ("// Start of initialize for generated "
                                    + name + "\n")
@@ -338,7 +339,7 @@ def add_parameter(self, *args, **kwargs):
                 Comment displayed next to declaration of parameter
         """
         # ParameterVariable class documented independently
-        self.parameter_list.append(ParameterVariable(*args, **kwargs))
+        self.instrument_parameters.add(ParameterVariable(*args, ** kwargs))
 
     def show_parameters(self, **kwargs):
         """
@@ -353,78 +354,7 @@ def show_parameters(self, **kwargs):
         else:
             line_limit = self.line_limit
 
-        if len(self.parameter_list) == 0:
-            print("No instrument parameters available")
-            return
-
-        # Find longest fields
-        types = []
-        names = []
-        values = []
-        comments = []
-        for parameter in self.parameter_list:
-            types.append(str(parameter.type))
-            names.append(str(parameter.name))
-            values.append(str(parameter.value))
-            comments.append(str(parameter.comment))
-
-        longest_type = len(max(types, key=len))
-        longest_name = len(max(names, key=len))
-        longest_value = len(max(values, key=len))
-        # In addition to the data 11 characters are added before the comment
-        comment_start_point = longest_type + longest_name + longest_value + 11
-        longest_comment = len(max(comments, key=len))
-        length_for_comment = line_limit - comment_start_point
-
-        # Print to console
-        for parameter in self.parameter_list:
-            print(str(parameter.type).ljust(longest_type), end=' ')
-            print(str(parameter.name).ljust(longest_name), end=' ')
-            if parameter.value == "":
-                print("   ", end=' ')
-            else:
-                print(" = ", end=' ')
-            print(str(parameter.value).ljust(longest_value+1), end=' ')
-            if (length_for_comment < 5
-                    or length_for_comment > len(str(parameter.comment))):
-                print(str(parameter.comment))
-            else:
-                # Split comment into several lines
-                comment = str(parameter.comment)
-                words = comment.split(" ")
-                words_left = len(words)
-                last_index = 0
-                current_index = 0
-                comment = ""
-                iterations = 0
-                max_iterations = 50
-                while(words_left > 0):
-                    iterations += 1
-                    if iterations > max_iterations:
-                        #  Something went long, print on one line
-                        break
-
-                    line_left = length_for_comment
-
-                    while(line_left > 0):
-                        if current_index >= len(words):
-                            current_index = len(words) + 1
-                            break
-                        line_left -= len(str(words[current_index])) + 1
-                        current_index += 1
-
-                    current_index -= 1
-                    for word in words[last_index:current_index]:
-                        comment += word + " "
-                    words_left = len(words) - current_index
-                    if words_left > 0:
-                        comment += "\n" + " "*comment_start_point
-                        last_index = current_index
-
-                if not iterations == max_iterations + 1:
-                    print(comment)
-                else:
-                    print(str(parameter.comment).ljust(longest_comment))
+        self.instrument_parameters.show_parameters(line_limit)
 
     def add_declare_var(self, *args, **kwargs):
         """
@@ -1458,10 +1388,11 @@ def write_full_instrument(self):
         fo.write("DEFINE INSTRUMENT %s (" % self.name)
         fo.write("\n")
         # Add loop that inserts parameters here
-        for variable in self.parameter_list[0:-1]:
+        parameter_list = list(self.instrument_parameters.parameters.values())
+        for variable in parameter_list[0:-1]:
             variable.write_parameter(fo, ",")
-        if len(self.parameter_list) > 0:
-            self.parameter_list[-1].write_parameter(fo, " ")
+        if len(parameter_list) > 0:
+            parameter_list[-1].write_parameter(fo, " ")
         fo.write(")\n")
         fo.write("\n")
 
@@ -1524,12 +1455,11 @@ def _handle_parameters(self, given_parameters):
         required_parameters = []
         default_parameters = {}
 
-        for index in range(len(self.parameter_list)):
-            if self.parameter_list[index].value == "":
-                required_parameters.append(self.parameter_list[index].name)
+        for name, parameter in self.instrument_parameters.parameters.items():
+            if parameter.value is None:
+                required_parameters.append(name)
             else:
-                default_parameters.update({self.parameter_list[index].name:
-                                           self.parameter_list[index].value})
+                default_parameters.update({name: parameter.value})
 
         # Check if all given parameters correspond to legal parameters
         for given_par in given_parameters:
@@ -1751,7 +1681,7 @@ class McStas_instr(McCode_instr):
     executable_path : str
         absolute path of mcrun command, or empty if it is in path
 
-    parameter_list : list of ParameterVariable instances
+    instrument_parameters : ParameterContainer instance
         contains all input parameters to be written to file
 
     declare_list : list of DeclareVariable instances
@@ -1975,7 +1905,7 @@ class McXtrace_instr(McCode_instr):
     executable_path : str
         absolute path of mcrun command, or empty if it is in path
 
-    parameter_list : list of ParameterVariable instances
+    instrument_parameters : ParameterContainer
         contains all input parameters to be written to file
 
     declare_list : list of DeclareVariable instances
diff --git a/mcstasscript/jb_interface/simulation_interface.py b/mcstasscript/jb_interface/simulation_interface.py
index 6d57011e..0eef0b4c 100644
--- a/mcstasscript/jb_interface/simulation_interface.py
+++ b/mcstasscript/jb_interface/simulation_interface.py
@@ -62,7 +62,7 @@ def __init__(self, instrument):
 
         self.parameters = {}
         # get default parameters from instrument
-        for parameter in self.instrument.parameter_list:
+        for parameter in self.instrument.instrument_parameters.parameters.values():
             if parameter_has_default(parameter):
                 self.parameters[parameter.name] = get_parameter_default(parameter)
 
@@ -73,7 +73,7 @@ def make_parameter_widgets(self):
         returns widget including all parameters
         """
         parameter_widgets = []
-        for parameter in self.instrument.parameter_list:
+        for parameter in self.instrument.instrument_parameters.parameters.values():
             par_widget = ParameterWidget(parameter, self.parameters)
             parameter_widgets.append(par_widget.make_widget())
 
@@ -322,7 +322,7 @@ def __init__(self, parameter, parameters):
         if parameter_has_default(parameter):
             self.default_value = get_parameter_default(parameter)
         else:
-            self.default_value = ""
+            self.default_value = None
 
         self.name = parameter.name
         self.comment = parameter.comment
@@ -333,17 +333,22 @@ def make_widget(self):
         """
         label = widgets.Label(value=self.name,
                               layout=widgets.Layout(width='15%', height='32px'))
-        if self.parameter.options is not None:
+        if len(self.parameter.options) > 0:
             par_widget = widgets.Dropdown(options=self.parameter.options,
                                           layout=widgets.Layout(width='10%', height='32px'))
-            if self.default_value != "":
+            if self.default_value is not None:
                 if self.default_value in self.parameter.options:
                     par_widget.value = self.default_value
-                elif self.default_value.strip("'") in self.parameter.options:
-                    par_widget.value = self.default_value.strip("'")
-                elif self.default_value.strip('"') in self.parameter.options:
-                    par_widget.value = self.default_value.strip('"')
-                else:
+
+                if isinstance(self.default_value, str):
+
+                    if self.default_value.strip("'") in self.parameter.options:
+                        par_widget.value = self.default_value.strip("'")
+                    elif self.default_value.strip('"') in self.parameter.options:
+                        par_widget.value = self.default_value.strip('"')
+
+                if par_widget.value is None:
+                    print(self.parameter.options)
                     raise KeyError("default value not found in options for parameter: "
                                    + str(self.parameter.name))
 
diff --git a/mcstasscript/jb_interface/widget_helpers.py b/mcstasscript/jb_interface/widget_helpers.py
index 7e6637ca..1bc5595d 100644
--- a/mcstasscript/jb_interface/widget_helpers.py
+++ b/mcstasscript/jb_interface/widget_helpers.py
@@ -24,7 +24,7 @@ def parameter_has_default(parameter):
     parameter: ParameterVariable
         The parameter to check for default value
     """
-    if parameter.value == "":
+    if parameter.value is None:
         return False
     return True
 
@@ -39,7 +39,7 @@ def get_parameter_default(parameter):
     parameter: ParameterVariable
         The parameter for which the default value is returned
     """
-    if parameter.value != "":
+    if parameter.value is not None:
         if parameter.type == "string":
             return parameter.value
         elif parameter.type == "double" or parameter.type == "":
@@ -50,4 +50,5 @@ def get_parameter_default(parameter):
             raise RuntimeError("Unknown parameter type '"
                                + parameter.type + "' of par named '"
                                + parameter.name + "'.")
-    return None
\ No newline at end of file
+
+    return None
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index d564f70b..1e18f0d0 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -399,8 +399,9 @@ def test_simple_add_parameter(self):
 
         instr.add_parameter("double", "theta", comment="test par")
 
-        self.assertEqual(instr.parameter_list[0].name, "theta")
-        self.assertEqual(instr.parameter_list[0].comment, "// test par")
+        parameter = instr.instrument_parameters["theta"]
+        self.assertEqual(parameter.name, "theta")
+        self.assertEqual(parameter.comment, "test par")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_show_parameters(self, mock_stdout):
@@ -409,9 +410,9 @@ def test_show_parameters(self, mock_stdout):
         """
         instr = setup_instr_root_path()
 
-        instr.add_parameter("double", "theta", comment="test par")
-        instr.add_parameter("double", "theta", comment="test par")
-        instr.add_parameter("int", "theta", value=8, comment="test par")
+        instr.add_parameter("theta", comment="test par")
+        instr.add_parameter("double", "par_double", comment="test par")
+        instr.add_parameter("int", "int_par", value=8, comment="test par")
         instr.add_parameter("int", "slits", comment="test par")
         instr.add_parameter("string", "ref",
                             value="string", comment="new string")
@@ -420,11 +421,11 @@ def test_show_parameters(self, mock_stdout):
 
         output = mock_stdout.getvalue().split("\n")
 
-        self.assertEqual(output[0], "double theta             // test par")
-        self.assertEqual(output[1], "double theta             // test par")
-        self.assertEqual(output[2], "int    theta  =  8       // test par")
-        self.assertEqual(output[3], "int    slits             // test par")
-        self.assertEqual(output[4], "string ref    =  string  // new string")
+        self.assertEqual(output[0], "       theta                 // test par")
+        self.assertEqual(output[1], "double par_double            // test par")
+        self.assertEqual(output[2], "int    int_par     = 8       // test par")
+        self.assertEqual(output[3], "int    slits                 // test par")
+        self.assertEqual(output[4], "string ref         = string  // new string")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_show_parameters_line_break(self, mock_stdout):
@@ -436,9 +437,9 @@ def test_show_parameters_line_break(self, mock_stdout):
         """
         instr = setup_instr_root_path()
 
-        instr.add_parameter("double", "theta", comment="test par")
-        instr.add_parameter("double", "theta", comment="test par")
-        instr.add_parameter("int", "theta", value=8, comment="test par")
+        instr.add_parameter("theta", comment="test par")
+        instr.add_parameter("double", "par_double", comment="test par")
+        instr.add_parameter("int", "int_par", value=8, comment="test par")
         instr.add_parameter("int", "slits", comment="test par")
         instr.add_parameter("string", "ref",
                             value="string", comment="new string")
@@ -456,23 +457,20 @@ def test_show_parameters_line_break(self, mock_stdout):
 
         output = mock_stdout.getvalue().split("\n")
 
-        self.assertEqual(output[0], "double theta             // test par")
-        self.assertEqual(output[1], "double theta             // test par")
-        self.assertEqual(output[2], "int    theta  =  8       // test par")
-        self.assertEqual(output[3], "int    slits             // test par")
-        self.assertEqual(output[4], "string ref    =  string  // new string")
-        comment_line = "This is a very long comment meant for testing "
-        self.assertEqual(output[5], "double value  =  37      // "
-                                    + comment_line)
-        comment_line = "the dynamic line breaking that is used in this "
-        self.assertEqual(output[6], "                            "
-                                    + comment_line)
-        comment_line = "method. It needs to have many lines in order to "
-        self.assertEqual(output[7], "                            "
-                                    + comment_line)
-        comment_line = "ensure it really works. "
-        self.assertEqual(output[8], "                            "
+        self.assertEqual(output[0], "       theta                 // test par")
+        self.assertEqual(output[1], "double par_double            // test par")
+        self.assertEqual(output[2], "int    int_par     = 8       // test par")
+        self.assertEqual(output[3], "int    slits                 // test par")
+        self.assertEqual(output[4], "string ref         = string  // new string")
+        comment_line = "This is a very long comment meant for "
+        self.assertEqual(output[5], "double value       = 37      // "
                                     + comment_line)
+        comment_line = "testing the dynamic line breaking that is "
+        self.assertEqual(output[6], " "*33 + comment_line)
+        comment_line = "used in this method. It needs to have many "
+        self.assertEqual(output[7], " "*33 + comment_line)
+        comment_line = "lines in order to ensure it really works. "
+        self.assertEqual(output[8], " "*33 + comment_line)
 
     def test_simple_add_declare_parameter(self):
         """
diff --git a/mcstasscript/tests/test_Instr_reader.py b/mcstasscript/tests/test_Instr_reader.py
index 85ae5d21..ced82f17 100644
--- a/mcstasscript/tests/test_Instr_reader.py
+++ b/mcstasscript/tests/test_Instr_reader.py
@@ -72,20 +72,22 @@ def test_read_input_parameter(self):
         enablePrint()
         setup_standard(Instr)
 
-        self.assertEqual(Instr.parameter_list[0].name, "stick_displacement")
+        parameter_list = list(Instr.instrument_parameters.parameters.values())
+
+        self.assertEqual(parameter_list[0].name, "stick_displacement")
         # space in type inserted for easier writing by McStas_Instr class
-        self.assertEqual(Instr.parameter_list[0].type, "double")
-        self.assertEqual(Instr.parameter_list[0].value, 0)
+        self.assertEqual(parameter_list[0].type, "double")
+        self.assertEqual(parameter_list[0].value, 0)
 
-        self.assertEqual(Instr.parameter_list[1].name, "test_int")
+        self.assertEqual(parameter_list[1].name, "test_int")
         # space in type inserted for easier writing by McStas_Instr class
-        self.assertEqual(Instr.parameter_list[1].type, "int")
-        self.assertEqual(Instr.parameter_list[1].value, 3)
+        self.assertEqual(parameter_list[1].type, "int")
+        self.assertEqual(parameter_list[1].value, 3)
 
-        self.assertEqual(Instr.parameter_list[2].name, "test_str")
+        self.assertEqual(parameter_list[2].name, "test_str")
         # space in type inserted for easier writing by McStas_Instr class
-        self.assertEqual(Instr.parameter_list[2].type, "string")
-        self.assertEqual(Instr.parameter_list[2].value, "\"hurray\"")
+        self.assertEqual(parameter_list[2].type, "string")
+        self.assertEqual(parameter_list[2].value, "\"hurray\"")
 
     def test_read_declare_parameter(self):
 
diff --git a/mcstasscript/tests/test_instrument.instr b/mcstasscript/tests/test_instrument.instr
index bbe65f75..307a993d 100644
--- a/mcstasscript/tests/test_instrument.instr
+++ b/mcstasscript/tests/test_instrument.instr
@@ -15,7 +15,7 @@
 * 
 * %Identification
 * Written by: Python McXtrace Instrument Generator
-* Date: 09:22:00 on January 28, 2021
+* Date: 16:27:39 on December 02, 2021
 * Origin: ESS DMSC
 * %INSTRUMENT_SITE: Generated_instruments
 * 
diff --git a/mcstasscript/tests/test_parameter_variable.py b/mcstasscript/tests/test_parameter_variable.py
index c2d32bd2..468a3081 100644
--- a/mcstasscript/tests/test_parameter_variable.py
+++ b/mcstasscript/tests/test_parameter_variable.py
@@ -51,7 +51,7 @@ def test_ParameterVariable_init_basic_type_value_comment(self):
         self.assertEqual(par.name, "test")
         self.assertEqual(par.type, "double")
         self.assertEqual(par.value, 518)
-        self.assertEqual(par.comment, "// test comment /")
+        self.assertEqual(par.comment, "test comment /")
 
     def test_ParameterVariable_init_basic_value_comment(self):
         """
@@ -64,7 +64,20 @@ def test_ParameterVariable_init_basic_value_comment(self):
         self.assertEqual(par.name, "test")
         self.assertEqual(par.type, "")
         self.assertEqual(par.value, 518)
-        self.assertEqual(par.comment, "// test comment /")
+        self.assertEqual(par.comment, "test comment /")
+
+    def test_ParameterVariable_init_options_initialize(self):
+        """
+        Initialization with value and comment
+        """
+
+        par = ParameterVariable("test", value=2,
+                                options=[1, 2, "horse"])
+
+        self.assertEqual(par.name, "test")
+        self.assertEqual(par.type, "")
+        self.assertEqual(par.value, 2)
+        self.assertEqual(par.options, [1, 2, "horse"])
 
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
diff --git a/mcstasscript/tests/test_simulation_interface.py b/mcstasscript/tests/test_simulation_interface.py
index 17f5af05..b93a8c6d 100644
--- a/mcstasscript/tests/test_simulation_interface.py
+++ b/mcstasscript/tests/test_simulation_interface.py
@@ -168,19 +168,19 @@ def test_ParameterWidget(self):
 
         parameters = {}
         # get default parameters from instrument
-        for parameter in instrument.parameter_list:
+        for parameter in instrument.instrument_parameters.parameters.values():
             if parameter_has_default(parameter):
                 parameters[parameter.name] = get_parameter_default(parameter)
 
         parameter_widgets = []
         parameterwidget_objects = []
-        for parameter in instrument.parameter_list:
+        for parameter in instrument.instrument_parameters.parameters.values():
             par_widget = ParameterWidget(parameter, parameters)
             parameterwidget_objects.append(par_widget)
             parameter_widgets.append(par_widget.make_widget())
 
         self.assertEqual(parameterwidget_objects[0].name, "theta")
-        self.assertEqual(parameterwidget_objects[0].default_value, "")
+        self.assertEqual(parameterwidget_objects[0].default_value, None)
         # Parameter does not exist in parameter dict yet
         with self.assertRaises(KeyError):
             parameters[parameterwidget_objects[0].name]
@@ -198,7 +198,7 @@ def test_ParameterWidget(self):
         self.assertEqual(parameters[parameterwidget_objects[1].name], 227)
 
         self.assertEqual(parameterwidget_objects[2].name, "choice")
-        self.assertEqual(parameterwidget_objects[2].default_value, "")
+        self.assertEqual(parameterwidget_objects[2].default_value, None)
         with self.assertRaises(KeyError):
             parameters[parameterwidget_objects[2].name]
 
diff --git a/requirements.txt b/requirements.txt
index dd4a1c13..9f879ef5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,3 +3,4 @@ matplotlib
 PyYAML
 ipywidgets
 mmap
+libpyvinyl
diff --git a/setup.py b/setup.py
index 385e622c..cb570994 100644
--- a/setup.py
+++ b/setup.py
@@ -13,7 +13,7 @@
      long_description=long_description,
      long_description_content_type="text/markdown",
      url="https://github.com/PaNOSC-ViNYL/McStasScript",
-     install_requires=['numpy', 'matplotlib', 'PyYAML', "ipywidgets"],
+     install_requires=['numpy', 'matplotlib', 'PyYAML', "ipywidgets", "libpyvinyl"],
      packages=setuptools.find_packages(),
      classifiers=[
          "Programming Language :: Python :: 3",

From e1ae499e97bb1db2c784838a05e6266395daee9d Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 8 Dec 2021 11:11:59 +0100
Subject: [PATCH 171/403] This commit breaks backwards compatability with
 scripts. Syntax for running simulations have changed.

Instrument class now based on libpyvinyl calculator.
Jupyter notebook example available in example folder.

Still todo:
Update doc strings
Update unit tests
Update documentation
---
 examples/libpyvinyl_example.ipynb             | 479 ++++++++++++++++++
 mcstasscript/interface/instr.py               | 111 ++--
 .../jb_interface/simulation_interface.py      |   8 +-
 3 files changed, 564 insertions(+), 34 deletions(-)
 create mode 100644 examples/libpyvinyl_example.ipynb

diff --git a/examples/libpyvinyl_example.ipynb b/examples/libpyvinyl_example.ipynb
new file mode 100644
index 00000000..33698151
--- /dev/null
+++ b/examples/libpyvinyl_example.ipynb
@@ -0,0 +1,479 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "thirty-runner",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "deluxe-scroll",
+   "metadata": {},
+   "source": [
+    "### Calculator is what I call an instrument object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "earned-ribbon",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following components are found in the work_directory / input_path:\n",
+      "     Union_sphere.comp\n",
+      "     Union_cone.comp\n",
+      "     Union_box.comp\n",
+      "     Single_crystal_process.comp\n",
+      "     Union_logger_2D_kf.comp\n",
+      "     Template_process.comp\n",
+      "     PhononSimple_process.comp\n",
+      "     Union_conditional_standard.comp\n",
+      "     Union_logger_2D_space.comp\n",
+      "     Union_conditional_PSD.comp\n",
+      "     Union_master.comp\n",
+      "     AF_HB_1D_process.comp\n",
+      "     Union_logger_2D_kf_time.comp\n",
+      "     Union_cylinder.comp\n",
+      "     Powder_process.comp\n",
+      "     Union_make_material.comp\n",
+      "     Incoherent_process.comp\n",
+      "     Union_logger_1D.comp\n",
+      "     Union_logger_3D_space.comp\n",
+      "     Union_logger_2DQ.comp\n",
+      "     Union_mesh.comp\n",
+      "     Union_logger_2D_space_time.comp\n",
+      "These definitions will be used instead of the installed versions.\n"
+     ]
+    }
+   ],
+   "source": [
+    "calculator = instr.McStas_instr(name=\"demo_instrument\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "inappropriate-impossible",
+   "metadata": {},
+   "source": [
+    "### Can define parameters (libpyvinyl parameter object and collection)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "parental-albert",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "par = calculator.add_parameter(\"double\", \"source_energy\", unit=\"meV\", comment=\"Source mean energy\")\n",
+    "calculator.parameters[\"source_energy\"].add_legal_interval(3, None)\n",
+    "\n",
+    "calculator.add_parameter(\"double\", \"source_height\", unit=\"cm\", comment=\"Height of source\")\n",
+    "calculator.parameters[\"source_height\"].add_legal_interval(0.01, 0.2)\n",
+    "\n",
+    "calculator.add_parameter(\"sample_height\", unit=\"cm\", comment=\"Height of sample\")\n",
+    "calculator.parameters[\"sample_height\"].add_legal_interval(0.0, 0.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "incident-nitrogen",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "double source_energy          // Source mean energy\n",
+      "double source_height          // Height of source\n",
+      "       sample_height          // Height of sample\n"
+     ]
+    }
+   ],
+   "source": [
+    "calculator.show_parameters()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "subjective-battle",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " - Parameters object -\n",
+      "source_energy       [meV]     Source mean energy   L[3, inf] \n",
+      "source_height       [cm]      Height of source   L[0.01, 0.2]\n",
+      "sample_height       [cm]      Height of sample   L[0.0, 0.05]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(calculator.parameters)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "weird-dependence",
+   "metadata": {},
+   "source": [
+    "### Can add components to the simulation as usual"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "accomplished-twelve",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "src = calculator.add_component(\"source\", \"Source_div\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "foreign-developer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "src.xwidth = 0.12\n",
+    "src.yheight = \"source_height\"\n",
+    "src.E0 = \"source_energy\"\n",
+    "src.dE = 3\n",
+    "src.focus_aw = 3.0\n",
+    "src.focus_ah = 4.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "champion-knife",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = calculator.add_component(\"powder\", \"PowderN\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "wrong-retailer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample.set_AT(1, RELATIVE=src)\n",
+    "sample.reflections='\"Ni.laz\"'\n",
+    "sample.radius = 0.01\n",
+    "sample.yheight = \"sample_height\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "anticipated-tampa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are all components in the monitors category.\n",
+      " Brilliance_monitor      Monitor_Sqw           Pol_monitor\n",
+      " Cyl_monitor             Monitor_nD            PreMonitor_nD\n",
+      " DivLambda_monitor       PSD_TOF_monitor       Res_monitor\n",
+      " DivPos_monitor          PSD_monitor           Sqq_w_monitor\n",
+      " Divergence_monitor      PSD_monitor_4PI       Sqw_monitor\n",
+      " EPSD_monitor            PSD_monitor_TOF       TOF2E_monitor\n",
+      " E_monitor               PSD_monitor_psf       TOF2Q_cylPSD_monitor\n",
+      " Event_monitor_simple    PSD_monitor_psf_eff   TOFLambda_monitor\n",
+      " Hdiv_monitor            PSDcyl_monitor        TOF_PSD_monitor_rad\n",
+      " L_monitor               PSDlin_diff_monitor   TOF_cylPSD_monitor\n",
+      " MeanPolLambda_monitor   PSDlin_monitor        TOF_monitor\n",
+      " Monitor                 PolLambda_monitor     TOFlog_monitor\n",
+      " Monitor_4PI             PolTOF_monitor        \n"
+     ]
+    }
+   ],
+   "source": [
+    "calculator.show_components(\"monitors\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "experienced-arizona",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Cyl_monitor _______________________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1mnr\u001b[0m = \u001b[1m\u001b[94m20.0\u001b[0m\u001b[0m [1] // Number of pixel (radial) columns\n",
+      "\u001b[1mfilename\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [string] // Name of file in which to store the detector image\n",
+      "\u001b[1myheight\u001b[0m = \u001b[1m\u001b[94m10.0\u001b[0m\u001b[0m [m] // Height of detector\n",
+      "\u001b[1mradius\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [m] // Radius of detector\n",
+      "\u001b[1mrestore_neutron\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // If set, the monitor does not influence the neutron \n",
+      "                             state \n",
+      "\u001b[1mthmin\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m\n",
+      "\u001b[1mthmax\u001b[0m = \u001b[1m\u001b[94m360.0\u001b[0m\u001b[0m\n",
+      "\u001b[1mnowritefile\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [1] // If set, monitor will skip writing to disk\n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "calculator.component_help(\"Cyl_monitor\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "wooden-helicopter",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cyl = calculator.add_component(\"cyl_monitor\", \"Cyl_monitor\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "preliminary-training",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cyl.nr = 200\n",
+    "cyl.filename = '\"cylinder.dat\"'\n",
+    "cyl.yheight = 0.2\n",
+    "cyl.radius = 0.5\n",
+    "cyl.restore_neutron = 1\n",
+    "cyl.set_AT(0, RELATIVE=sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "official-withdrawal",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mon = calculator.add_component(\"acceptance_horizontal\", \"DivPos_monitor\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "colored-policy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mon.xwidth = 0.2\n",
+    "mon.yheight = 0.2\n",
+    "mon.maxdiv_h = 30.0\n",
+    "mon.filename = '\"acceptance_h.dat\"'\n",
+    "mon.restore_neutron = 1\n",
+    "mon.nh = 300\n",
+    "mon.ndiv = 300\n",
+    "mon.set_AT(0.1, RELATIVE=sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "based-torture",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "source                Source_div     AT      (0, 0, 0)   ABSOLUTE       \n",
+      "powder                PowderN        AT      (0, 0, 1)   RELATIVE source\n",
+      "cyl_monitor           Cyl_monitor    AT      (0, 0, 0)   RELATIVE powder\n",
+      "acceptance_horizontal DivPos_monitor AT      (0, 0, 0.1) RELATIVE powder\n"
+     ]
+    }
+   ],
+   "source": [
+    "calculator.print_components()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "august-words",
+   "metadata": {},
+   "source": [
+    "### Could set parameters with a syntax like this"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "smooth-cholesterol",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calculator.prepare_run(parameters={\"source_height\": 0.03, \"sample_height\": 0.04, \"source_energy\": 300}, \n",
+    "                       ncount=5E6, foldername=\"path_to_output\", increment_folder_name=True,\n",
+    "                       force_compile=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dependent-garlic",
+   "metadata": {},
+   "source": [
+    "### Run with backengine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "daily-louisiana",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/McStasScript_tests/path_to_output_13\"\n",
+      "INFO: Using existing c-file: ./demo_instrument.c\n",
+      "INFO: Using existing binary: ./demo_instrument.out\n",
+      "INFO: ===\n",
+      "Warning: 24259 events were removed in Component[4] acceptance_horizontal=DivPos_monitor()\n",
+      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
+      "INFO: Placing instr file copy demo_instrument.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/McStasScript_tests/path_to_output_13\n",
+      "\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Ni.laz' (Table_Read_Offset)\n",
+      "Table from file 'Ni.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
+      "  '# TITLE *Nickel-Ni-[FM3-M] Swanson, H.E.;Tatge, E.[1954] [carcinogen];# CEL ...'\n",
+      "PowderN: powder: Reading 19 rows from Ni.laz\n",
+      "PowderN: powder: Read 19 reflections from file 'Ni.laz'\n",
+      "PowderN: powder: Vc=43.76 [Angs] sigma_abs=17.96 [barn] sigma_inc=20.8 [barn] reflections=Ni.laz\n",
+      "Detector: cyl_monitor_I=0.030668 cyl_monitor_ERR=0.000134901 cyl_monitor_N=134610 \"cylinder.dat\"\n",
+      "Detector: acceptance_horizontal_I=0.744367 acceptance_horizontal_ERR=0.000360233 acceptance_horizontal_N=4.72627e+06 \"acceptance_h.dat\"\n",
+      "PowderN: powder: Info: you may highly improve the computation efficiency by using\n",
+      "    SPLIT 19 COMPONENT powder=PowderN(...)\n",
+      "  in the instrument description demo_instrument.instr.\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "calculator.backengine()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "talented-poison",
+   "metadata": {},
+   "source": [
+    "### Get data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "global-loading",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = calculator.data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "engaged-syria",
+   "metadata": {},
+   "source": [
+    "### Plot data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "retired-brick",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name cyl_monitor\n",
+      "Plotting data with name acceptance_horizontal\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotter.make_sub_plot(data, log=True, cut_max=0.011, orders_of_mag=1.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "affiliated-british",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "pleasant-machine",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index af2a6104..92e0dad8 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -6,6 +6,8 @@
 import subprocess
 import copy
 
+from libpyvinyl.BaseCalculator import BaseCalculator
+
 from mcstasscript.helper.mcstas_objects import DeclareVariable
 from mcstasscript.helper.mcstas_objects import ParameterVariable
 from mcstasscript.helper.mcstas_objects import Component
@@ -17,7 +19,7 @@
 from mcstasscript.jb_interface.simulation_interface import SimInterface
 
 
-class McCode_instr:
+class McCode_instr(BaseCalculator):
     """
     Main class for writing a McCode instrument using McStasScript
 
@@ -26,7 +28,8 @@ class McCode_instr:
     The class also holds methods for writing the finished instrument
     file to disk and to run the simulation. This is meant as a base class
     that McStas_instr and McXtrace_instr inherits from, these have to provide
-    some attributes.
+    some attributes. Inherits from libpyvinyl BaseCalculator in order to
+    harmonize input with other simulation engines.
 
     Required attributes in superclass
     ---------------------------------
@@ -198,7 +201,7 @@ class McCode_instr:
         Returns data set from latest simulation in widget
     """
 
-    def __init__(self, name, **kwargs):
+    def __init__(self, name, parameters=None, dumpfile=None, **kwargs):
         """
         Initialization of McStas Instrument
 
@@ -221,6 +224,17 @@ def __init__(self, name, **kwargs):
                 Work directory, will load components from this folder
         """
 
+        super().__init__(name, parameters, dumpfile, **kwargs)
+
+        # If no parameters given, initialize parameter container
+        if self.parameters is None:
+            self.parameters = ParameterContainer()
+        else:
+            if not isinstance(self.parameters, ParameterContainer):
+                # Need to convert McStasScript parameters
+                # todo
+                pass
+
         # Check required attributes has been set by class that inherits
         if not (hasattr(self, "particle") or
                 hasattr(self, "executable") or
@@ -228,8 +242,6 @@ def __init__(self, name, **kwargs):
             raise AttributeError("McCode_instr is a base class, use "
                                  + "McStas_intr or McXtrace_instr instead.")
 
-        self.name = name
-
         if not is_legal_filename(self.name + ".instr"):
             raise NameError("The instrument is called: \""
                             + self.name
@@ -274,11 +286,14 @@ def __init__(self, name, **kwargs):
         elif self.package_path == "":
             raise NameError("At this stage of development "
                             + "McStasScript need the absolute path "
-                            + "for the " + self.package_name +
+                            + "for the " + self.package_name
                             + " installation as keyword named "
                             + "package_path or in configuration.yaml")
 
-        self.instrument_parameters = ParameterContainer()
+
+        self.current_run_options = None
+
+        #self.instrument_parameters = ParameterContainer()
         self.declare_list = []
         self.initialize_section = ("// Start of initialize for generated "
                                    + name + "\n")
@@ -339,7 +354,7 @@ def add_parameter(self, *args, **kwargs):
                 Comment displayed next to declaration of parameter
         """
         # ParameterVariable class documented independently
-        self.instrument_parameters.add(ParameterVariable(*args, ** kwargs))
+        return self.parameters.add(ParameterVariable(*args, ** kwargs))
 
     def show_parameters(self, **kwargs):
         """
@@ -354,7 +369,7 @@ def show_parameters(self, **kwargs):
         else:
             line_limit = self.line_limit
 
-        self.instrument_parameters.show_parameters(line_limit)
+        self.parameters.show_parameters(line_limit)
 
     def add_declare_var(self, *args, **kwargs):
         """
@@ -1388,7 +1403,7 @@ def write_full_instrument(self):
         fo.write("DEFINE INSTRUMENT %s (" % self.name)
         fo.write("\n")
         # Add loop that inserts parameters here
-        parameter_list = list(self.instrument_parameters.parameters.values())
+        parameter_list = list(self.parameters.parameters.values())
         for variable in parameter_list[0:-1]:
             variable.write_parameter(fo, ",")
         if len(parameter_list) > 0:
@@ -1455,7 +1470,7 @@ def _handle_parameters(self, given_parameters):
         required_parameters = []
         default_parameters = {}
 
-        for name, parameter in self.instrument_parameters.parameters.items():
+        for name, parameter in self.parameters.parameters.items():
             if parameter.value is None:
                 required_parameters.append(name)
             else:
@@ -1496,19 +1511,9 @@ def _handle_parameters(self, given_parameters):
             default_parameters.update(given_parameters)
             return default_parameters
 
-    def run_full_instrument(self, **kwargs):
+    def prepare_run(self, **kwargs):
         """
-        Runs McStas instrument described by this class, returns list of
-        McStasData
-
-        This method will write the instrument to disk and then run it
-        using the mcrun command of the system. Options are set using
-        keyword arguments.  Some options are mandatory, for example
-        foldername, which can not already exist, if it does data will
-        be read from this folder.  If the mcrun command is not in the
-        path of the system, the absolute path can be given with the
-        executable_path keyword argument.  This path could also already
-        have been set at initialization of the instrument object.
+        Sets parameters and options for McStas run
 
         Parameters
         ----------
@@ -1528,6 +1533,7 @@ def run_full_instrument(self, **kwargs):
             executable_path : str
                 Path to mcrun command, "" if already in path
         """
+
         # Make sure executable path is in kwargs
         if "executable_path" not in kwargs:
             kwargs["executable_path"] = self.executable_path
@@ -1562,19 +1568,59 @@ def run_full_instrument(self, **kwargs):
 
         kwargs["parameters"] = self._handle_parameters(given_parameters)
 
-        # Write the instrument file
-        compile = True
-        if "force_compile" in kwargs:
-            compile = kwargs["force_compile"]
-        if compile:
+        if "force_compile" not in kwargs:
+            kwargs["force_compile"] = True
+
+        self.current_run_options = kwargs
+
+    def backengine(self):
+        """
+        Runs McStas instrument described by this class, returns list of
+        McStasData
+
+        This method will write the instrument to disk and then run it
+        using the mcrun command of the system. Options are set using
+        keyword arguments.  Some options are mandatory, for example
+        foldername, which can not already exist, if it does data will
+        be read from this folder.  If the mcrun command is not in the
+        path of the system, the absolute path can be given with the
+        executable_path keyword argument.  This path could also already
+        have been set at initialization of the instrument object.
+
+        Parameters
+        ----------
+        Keyword arguments
+            foldername : str
+                Sets data_folder_name
+            ncount : int
+                Sets ncount
+            mpi : int
+                Sets thread count
+            parameters : dict
+                Sets parameters
+            custom_flags : str
+                Sets custom_flags passed to mcrun
+            force_compile : bool
+                If True (default) new instrument file is written, otherwise not
+            executable_path : str
+                Path to mcrun command, "" if already in path
+        """
+
+        if self.current_run_options is None:
+            raise RuntimeError("Need to prepare run first!")
+
+        if self.current_run_options["force_compile"]:
             self.write_full_instrument()
 
         # Set up the simulation
-        simulation = ManagedMcrun(self.name + ".instr", **kwargs)
+        simulation = ManagedMcrun(self.name + ".instr", **self.current_run_options)
 
         # Run the simulation and return data
-        simulation.run_simulation(**kwargs)
-        return simulation.load_results()
+        simulation.run_simulation(**self.current_run_options)
+
+        # Load data and store in __data
+        data = simulation.load_results()
+        self._set_data(data)
 
     def show_instrument(self, *args, **kwargs):
         """
@@ -1646,6 +1692,9 @@ def get_interface_data(self):
 
         return self.widget_interface.plot_interface.data
 
+    def saveH5(self, filename: str, openpmd: bool = True):
+        pass
+
 class McStas_instr(McCode_instr):
     """
     Main class for writing a McStas instrument using McStasScript
diff --git a/mcstasscript/jb_interface/simulation_interface.py b/mcstasscript/jb_interface/simulation_interface.py
index 0eef0b4c..05add7a5 100644
--- a/mcstasscript/jb_interface/simulation_interface.py
+++ b/mcstasscript/jb_interface/simulation_interface.py
@@ -62,7 +62,7 @@ def __init__(self, instrument):
 
         self.parameters = {}
         # get default parameters from instrument
-        for parameter in self.instrument.instrument_parameters.parameters.values():
+        for parameter in self.instrument.parameters.parameters.values():
             if parameter_has_default(parameter):
                 self.parameters[parameter.name] = get_parameter_default(parameter)
 
@@ -73,7 +73,7 @@ def make_parameter_widgets(self):
         returns widget including all parameters
         """
         parameter_widgets = []
-        for parameter in self.instrument.instrument_parameters.parameters.values():
+        for parameter in self.instrument.parameters.parameters.values():
             par_widget = ParameterWidget(parameter, self.parameters)
             parameter_widgets.append(par_widget.make_widget())
 
@@ -146,7 +146,9 @@ def run_simulation_live(self):
         for index in range(sim_parts):
             try:
                 with HiddenPrints():
-                    data = self.instrument.run_full_instrument(**run_arguments)
+                    self.instrument.prepare_run(**run_arguments)
+                    self.instrument.backengine()
+                    data = self.instrument.data
             except NameError:
                 print("McStas run failed.")
                 data = []

From 502b0179c8101c1424150abe0fbbc81e219fc594 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 8 Dec 2021 13:41:12 +0100
Subject: [PATCH 172/403] Allows return of parameter from add_parameter. Allow
 components to use parameter objects for input. Updated example to show the
 new use.

---
 mcstasscript/helper/mcstas_objects.py | 4 ++++
 mcstasscript/interface/instr.py       | 4 +++-
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 05203bed..eccf4efd 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -143,6 +143,7 @@ def write_parameter(self, fo, stop_character):
         fo.write(c_comment)
         fo.write("\n")
 
+
 class ParameterContainer(CalculatorParameters):
     def __init__(self, parameters=None):
         super().__init__(parameters)
@@ -872,6 +873,9 @@ def write_component(self, fo):
                                     + " not set.")
                 else:
                     continue
+            elif isinstance(val, ParameterVariable):
+                # Extract the parameter name
+                val = val.name
 
             component_parameters[key] = val
 
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 92e0dad8..f156374b 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -354,7 +354,9 @@ def add_parameter(self, *args, **kwargs):
                 Comment displayed next to declaration of parameter
         """
         # ParameterVariable class documented independently
-        return self.parameters.add(ParameterVariable(*args, ** kwargs))
+        par = ParameterVariable(*args, ** kwargs)
+        self.parameters.add(par)
+        return par
 
     def show_parameters(self, **kwargs):
         """

From 5e6af8ccf8bf1db8de61283c1bb25ca147a34e85 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 8 Dec 2021 17:10:09 +0100
Subject: [PATCH 173/403] Using iterators from CalculatorParameters to simplify
 syntax.

---
 mcstasscript/interface/instr.py                   | 8 ++++----
 mcstasscript/jb_interface/simulation_interface.py | 4 ++--
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index f156374b..54ac3f4c 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1405,7 +1405,7 @@ def write_full_instrument(self):
         fo.write("DEFINE INSTRUMENT %s (" % self.name)
         fo.write("\n")
         # Add loop that inserts parameters here
-        parameter_list = list(self.parameters.parameters.values())
+        parameter_list = list(self.parameters)
         for variable in parameter_list[0:-1]:
             variable.write_parameter(fo, ",")
         if len(parameter_list) > 0:
@@ -1472,11 +1472,11 @@ def _handle_parameters(self, given_parameters):
         required_parameters = []
         default_parameters = {}
 
-        for name, parameter in self.parameters.parameters.items():
+        for parameter in self.parameters:
             if parameter.value is None:
-                required_parameters.append(name)
+                required_parameters.append(parameter.name)
             else:
-                default_parameters.update({name: parameter.value})
+                default_parameters.update({parameter.name: parameter.value})
 
         # Check if all given parameters correspond to legal parameters
         for given_par in given_parameters:
diff --git a/mcstasscript/jb_interface/simulation_interface.py b/mcstasscript/jb_interface/simulation_interface.py
index 05add7a5..ecaf6e3b 100644
--- a/mcstasscript/jb_interface/simulation_interface.py
+++ b/mcstasscript/jb_interface/simulation_interface.py
@@ -62,7 +62,7 @@ def __init__(self, instrument):
 
         self.parameters = {}
         # get default parameters from instrument
-        for parameter in self.instrument.parameters.parameters.values():
+        for parameter in self.instrument.parameters:
             if parameter_has_default(parameter):
                 self.parameters[parameter.name] = get_parameter_default(parameter)
 
@@ -73,7 +73,7 @@ def make_parameter_widgets(self):
         returns widget including all parameters
         """
         parameter_widgets = []
-        for parameter in self.instrument.parameters.parameters.values():
+        for parameter in self.instrument.parameters:
             par_widget = ParameterWidget(parameter, self.parameters)
             parameter_widgets.append(par_widget.make_widget())
 

From e07a8c725895e4d6306f6f4f26c60c15459cf4d8 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 10 Dec 2021 11:21:48 +0100
Subject: [PATCH 174/403] Figured out how to dump McStas instrument with
 BaseCalculator dump method. It was required to register dynamic classes to
 globals.

Avoid overwriting loaded classes by checking if attribute already
defined.

Updated example to show dump and load from dump.
---
 examples/libpyvinyl_example.ipynb | 174 ++++++++++++++++++++++++------
 1 file changed, 144 insertions(+), 30 deletions(-)

diff --git a/examples/libpyvinyl_example.ipynb b/examples/libpyvinyl_example.ipynb
index 33698151..97b492bd 100644
--- a/examples/libpyvinyl_example.ipynb
+++ b/examples/libpyvinyl_example.ipynb
@@ -30,24 +30,33 @@
      "text": [
       "The following components are found in the work_directory / input_path:\n",
       "     Union_sphere.comp\n",
+      "     Texture_process.comp\n",
       "     Union_cone.comp\n",
       "     Union_box.comp\n",
       "     Single_crystal_process.comp\n",
+      "     Union_abs_logger_2D_space.comp\n",
       "     Union_logger_2D_kf.comp\n",
       "     Template_process.comp\n",
       "     PhononSimple_process.comp\n",
       "     Union_conditional_standard.comp\n",
+      "     Union_abs_logger_1D_space.comp\n",
+      "     Union_abs_logger_event.comp\n",
+      "     NCrystal_process.comp\n",
+      "     Union_abs_logger_1D_space_event.comp\n",
+      "     Union_abs_logger_1D_space_tof.comp\n",
       "     Union_logger_2D_space.comp\n",
       "     Union_conditional_PSD.comp\n",
       "     Union_master.comp\n",
       "     AF_HB_1D_process.comp\n",
       "     Union_logger_2D_kf_time.comp\n",
       "     Union_cylinder.comp\n",
+      "     Union_abs_logger_1D_space_tof_to_lambda.comp\n",
       "     Powder_process.comp\n",
       "     Union_make_material.comp\n",
       "     Incoherent_process.comp\n",
       "     Union_logger_1D.comp\n",
       "     Union_logger_3D_space.comp\n",
+      "     IncoherentPhonon_process.comp\n",
       "     Union_logger_2DQ.comp\n",
       "     Union_mesh.comp\n",
       "     Union_logger_2D_space_time.comp\n",
@@ -74,14 +83,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "par = calculator.add_parameter(\"double\", \"source_energy\", unit=\"meV\", comment=\"Source mean energy\")\n",
-    "calculator.parameters[\"source_energy\"].add_legal_interval(3, None)\n",
+    "source_energy = calculator.add_parameter(\"double\", \"source_energy\", unit=\"meV\", comment=\"Source mean energy\")\n",
+    "source_energy.add_interval(3, None, intervals_are_legal=True)\n",
     "\n",
-    "calculator.add_parameter(\"double\", \"source_height\", unit=\"cm\", comment=\"Height of source\")\n",
-    "calculator.parameters[\"source_height\"].add_legal_interval(0.01, 0.2)\n",
+    "source_height = calculator.add_parameter(\"double\", \"source_height\", unit=\"cm\", comment=\"Height of source\")\n",
+    "source_height.add_interval(0.01, 0.2, intervals_are_legal=True)\n",
     "\n",
-    "calculator.add_parameter(\"sample_height\", unit=\"cm\", comment=\"Height of sample\")\n",
-    "calculator.parameters[\"sample_height\"].add_legal_interval(0.0, 0.05)"
+    "sample_height = calculator.add_parameter(\"sample_height\", unit=\"cm\", comment=\"Height of sample\")\n",
+    "sample_height.add_interval(0.0, 0.05, intervals_are_legal=True)"
    ]
   },
   {
@@ -152,8 +161,8 @@
    "outputs": [],
    "source": [
     "src.xwidth = 0.12\n",
-    "src.yheight = \"source_height\"\n",
-    "src.E0 = \"source_energy\"\n",
+    "src.yheight = source_height\n",
+    "src.E0 = source_energy\n",
     "src.dE = 3\n",
     "src.focus_aw = 3.0\n",
     "src.focus_ah = 4.0"
@@ -179,7 +188,7 @@
     "sample.set_AT(1, RELATIVE=src)\n",
     "sample.reflections='\"Ni.laz\"'\n",
     "sample.radius = 0.01\n",
-    "sample.yheight = \"sample_height\""
+    "sample.yheight = sample_height"
    ]
   },
   {
@@ -326,13 +335,21 @@
   {
    "cell_type": "code",
    "execution_count": 17,
+   "id": "defensive-phrase",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calculator.set_parameters({\"source_energy\":350, \"source_height\":0.025, \"sample_height\":0.04})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
    "id": "smooth-cholesterol",
    "metadata": {},
    "outputs": [],
    "source": [
-    "calculator.prepare_run(parameters={\"source_height\": 0.03, \"sample_height\": 0.04, \"source_energy\": 300}, \n",
-    "                       ncount=5E6, foldername=\"path_to_output\", increment_folder_name=True,\n",
-    "                       force_compile=False)"
+    "calculator.prepare_run(ncount=5E6, foldername=\"path_to_output\", increment_folder_name=True)"
    ]
   },
   {
@@ -345,7 +362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "daily-louisiana",
    "metadata": {},
    "outputs": [
@@ -353,22 +370,27 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/McStasScript_tests/path_to_output_13\"\n",
-      "INFO: Using existing c-file: ./demo_instrument.c\n",
-      "INFO: Using existing binary: ./demo_instrument.out\n",
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/path_to_output_0\"\n",
+      "INFO: Regenerating c-file: demo_instrument.c\n",
+      "CFLAGS=\n",
+      "INFO: Recompiling: ./demo_instrument.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "1 warning generated.\n",
       "INFO: ===\n",
-      "Warning: 24259 events were removed in Component[4] acceptance_horizontal=DivPos_monitor()\n",
+      "Warning: 23922 events were removed in Component[4] acceptance_horizontal=DivPos_monitor()\n",
       "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "INFO: Placing instr file copy demo_instrument.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/McStasScript_tests/path_to_output_13\n",
+      "INFO: Placing instr file copy demo_instrument.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/path_to_output_0\n",
       "\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Ni.laz' (Table_Read_Offset)\n",
+      "  Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Ni.laz' (Table_Read_Offset)\n",
       "Table from file 'Ni.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
       "  '# TITLE *Nickel-Ni-[FM3-M] Swanson, H.E.;Tatge, E.[1954] [carcinogen];# CEL ...'\n",
       "PowderN: powder: Reading 19 rows from Ni.laz\n",
       "PowderN: powder: Read 19 reflections from file 'Ni.laz'\n",
       "PowderN: powder: Vc=43.76 [Angs] sigma_abs=17.96 [barn] sigma_inc=20.8 [barn] reflections=Ni.laz\n",
-      "Detector: cyl_monitor_I=0.030668 cyl_monitor_ERR=0.000134901 cyl_monitor_N=134610 \"cylinder.dat\"\n",
-      "Detector: acceptance_horizontal_I=0.744367 acceptance_horizontal_ERR=0.000360233 acceptance_horizontal_N=4.72627e+06 \"acceptance_h.dat\"\n",
+      "Detector: cyl_monitor_I=0.026388 cyl_monitor_ERR=0.000115993 cyl_monitor_N=140247 \"cylinder.dat\"\n",
+      "Detector: acceptance_horizontal_I=0.621105 acceptance_horizontal_ERR=0.000301518 acceptance_horizontal_N=4.75183e+06 \"acceptance_h.dat\"\n",
       "PowderN: powder: Info: you may highly improve the computation efficiency by using\n",
       "    SPLIT 19 COMPONENT powder=PowderN(...)\n",
       "  in the instrument description demo_instrument.instr.\n",
@@ -391,7 +413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "global-loading",
    "metadata": {},
    "outputs": [],
@@ -409,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "retired-brick",
    "metadata": {},
    "outputs": [
@@ -423,7 +445,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 936x504 with 3 Axes>"
       ]
@@ -438,21 +460,113 @@
     "plotter.make_sub_plot(data, log=True, cut_max=0.011, orders_of_mag=1.5)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "relevant-employer",
+   "metadata": {},
+   "source": [
+    "### Store in dump file"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "affiliated-british",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'dump_file.dmp'"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "calculator.dump(\"dump_file.dmp\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "integrated-theory",
+   "metadata": {},
+   "source": [
+    "### Load from dump file"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "pleasant-machine",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following components are found in the work_directory / input_path:\n",
+      "     Union_sphere.comp\n",
+      "     Texture_process.comp\n",
+      "     Union_cone.comp\n",
+      "     Union_box.comp\n",
+      "     Single_crystal_process.comp\n",
+      "     Union_abs_logger_2D_space.comp\n",
+      "     Union_logger_2D_kf.comp\n",
+      "     Template_process.comp\n",
+      "     PhononSimple_process.comp\n",
+      "     Union_conditional_standard.comp\n",
+      "     Union_abs_logger_1D_space.comp\n",
+      "     Union_abs_logger_event.comp\n",
+      "     NCrystal_process.comp\n",
+      "     Union_abs_logger_1D_space_event.comp\n",
+      "     Union_abs_logger_1D_space_tof.comp\n",
+      "     Union_logger_2D_space.comp\n",
+      "     Union_conditional_PSD.comp\n",
+      "     Union_master.comp\n",
+      "     AF_HB_1D_process.comp\n",
+      "     Union_logger_2D_kf_time.comp\n",
+      "     Union_cylinder.comp\n",
+      "     Union_abs_logger_1D_space_tof_to_lambda.comp\n",
+      "     Powder_process.comp\n",
+      "     Union_make_material.comp\n",
+      "     Incoherent_process.comp\n",
+      "     Union_logger_1D.comp\n",
+      "     Union_logger_3D_space.comp\n",
+      "     IncoherentPhonon_process.comp\n",
+      "     Union_logger_2DQ.comp\n",
+      "     Union_mesh.comp\n",
+      "     Union_logger_2D_space_time.comp\n",
+      "These definitions will be used instead of the installed versions.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from_dump = instr.McStas_instr(\"\", dumpfile='dump_file.dmp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "specified-study",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "source                Source_div     AT      (0, 0, 0)   ABSOLUTE       \n",
+      "powder                PowderN        AT      (0, 0, 1)   RELATIVE source\n",
+      "cyl_monitor           Cyl_monitor    AT      (0, 0, 0)   RELATIVE powder\n",
+      "acceptance_horizontal DivPos_monitor AT      (0, 0, 0.1) RELATIVE powder\n"
+     ]
+    }
+   ],
+   "source": [
+    "from_dump.print_components()"
+   ]
   }
  ],
  "metadata": {

From 5c912d1731e14cbbf9917213f0edcc20afd419c1 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 10 Dec 2021 14:07:16 +0100
Subject: [PATCH 175/403] Allow to add parameter object with libpyvinyl origin
 at initialization.

McStasScript use a parameter class that inherits from the libpyvinyl
parameter class as more checks and methods are needed. When importing
from libpyvinyl versions, all attributes are included and the checks
are made.
---
 mcstasscript/helper/mcstas_objects.py | 40 +++++++++++++++++++
 mcstasscript/interface/instr.py       | 57 +++++++++++++++++----------
 2 files changed, 76 insertions(+), 21 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index eccf4efd..0fad8030 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -146,8 +146,48 @@ def write_parameter(self, fo, stop_character):
 
 class ParameterContainer(CalculatorParameters):
     def __init__(self, parameters=None):
+        """
+        McStasScript version of libpyvinyls CalculatorParameters
+
+        Expanded with ability to import standard libpyvinyl parameters to
+        McStasScript and show parameter method.
+        """
         super().__init__(parameters)
 
+    def import_parameters(self, parameters):
+        """
+        Imports libpyvinyl parameters to this ParameterContainer
+
+        There are further requirements for parameters in McStasScript which
+        need to be checked on import, and a subclass of Parameter is used
+        to store these with additional functionality.
+        """
+        if isinstance(parameters, ParameterContainer):
+            for parameter in parameters:
+                self.add(parameter)
+            return
+
+        if not isinstance(parameters, CalculatorParameters):
+            raise RuntimeError("Uknown parameter class given.")
+
+        # Code for loading from CalculatorParameters
+        for parameter in parameters:
+            try:
+                mcstas_par = ParameterVariable(parameter.name,
+                                               unit=parameter.unit,
+                                               comment=parameter.comment)
+            except:
+                raise NameError("Imported parameter did not have McStas "
+                                + "legal name")
+
+            # Ensure strings get appropriate McStas type.
+            if isinstance(parameter.value, str):
+                mcstas_par.type = "string"
+
+            mcstas_par.__dict__.update(parameter.__dict__)
+
+            self.add(mcstas_par)
+
     def show_parameters(self, line_limit=100):
 
         """
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 54ac3f4c..5ece9732 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -7,6 +7,7 @@
 import copy
 
 from libpyvinyl.BaseCalculator import BaseCalculator
+from libpyvinyl.Parameters.Collections import CalculatorParameters
 
 from mcstasscript.helper.mcstas_objects import DeclareVariable
 from mcstasscript.helper.mcstas_objects import ParameterVariable
@@ -232,8 +233,14 @@ def __init__(self, name, parameters=None, dumpfile=None, **kwargs):
         else:
             if not isinstance(self.parameters, ParameterContainer):
                 # Need to convert McStasScript parameters
-                # todo
-                pass
+                if isinstance(self.parameters, CalculatorParameters):
+                    mcstasscript_parameters = ParameterContainer()
+                    mcstasscript_parameters.import_parameters(self.parameters)
+                    self.parameters = mcstasscript_parameters
+
+                else:
+                    raise RuntimeError("Given parameters object not"
+                                       + " recognized.")
 
         # Check required attributes has been set by class that inherits
         if not (hasattr(self, "particle") or
@@ -290,28 +297,28 @@ def __init__(self, name, parameters=None, dumpfile=None, **kwargs):
                             + " installation as keyword named "
                             + "package_path or in configuration.yaml")
 
-
-        self.current_run_options = None
-
-        #self.instrument_parameters = ParameterContainer()
-        self.declare_list = []
-        self.initialize_section = ("// Start of initialize for generated "
-                                   + name + "\n")
-        self.trace_section = ("// Start of trace section for generated "
-                              + name + "\n")
-        self.finally_section = ("// Start of finally for generated "
-                                + name + "\n")
-        # Handle components
-        self.component_list = []  # List of components (have to be ordered)
-        self.component_name_list = []  # List of component names
-
         # Read info on active McStas components
         self.component_reader = ComponentReader(self.package_path,
                                                 input_path=self.input_path)
-        self.component_class_lib = {}
 
+        self.component_class_lib = {}
         self.widget_interface = None
 
+        # Avoid initializing if loading from dump
+        if not hasattr(self, "current_run_options"):
+            self.current_run_options = None
+
+            self.declare_list = []
+            self.initialize_section = ("// Start of initialize for generated "
+                                       + name + "\n")
+            self.trace_section = ("// Start of trace section for generated "
+                                  + name + "\n")
+            self.finally_section = ("// Start of finally for generated "
+                                    + name + "\n")
+            # Handle components
+            self.component_list = []  # List of components (have to be ordered)
+            self.component_name_list = []  # List of component names
+
     def _read_calibration(self):
         """
         Place holder method that should be overwritten by classes
@@ -592,9 +599,13 @@ def _create_component_instance(self, name, component_name, **kwargs):
             input_dict["category"] = comp_info.category
             input_dict["line_limit"] = self.line_limit
 
-            self.component_class_lib[component_name] = type(component_name,
-                                                            (Component,),
-                                                            input_dict)
+            dynamic_component_class = type(component_name, (Component,),
+                                           input_dict)
+
+            # add this class to globals to allow for pickling
+            globals()[component_name] = dynamic_component_class
+
+            self.component_class_lib[component_name] = dynamic_component_class
 
         return self.component_class_lib[component_name](name, component_name,
                                                         **kwargs)
@@ -1120,6 +1131,10 @@ def print_components(self, **kwargs):
             Maximum line length in console
         """
 
+        if len(self.component_name_list) == 0:
+            print("No components added to instrument object yet.")
+            return
+
         if "line_length" in kwargs:
             line_limit = kwargs["line_length"]
         else:

From 41e82c49260a4b8b675d88654b8df609ac7d095c Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 10 Dec 2021 17:15:17 +0100
Subject: [PATCH 176/403] Have warning for using run_full_instrument.

Now use output_path from BaseCalculator for folder name

Set increment_folder_name to True as default.
---
 mcstasscript/helper/managed_mcrun.py | 14 +++---
 mcstasscript/interface/instr.py      | 70 ++++++++++++++++++++++++++--
 2 files changed, 72 insertions(+), 12 deletions(-)

diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 60ececbf..b696ea79 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -56,7 +56,7 @@ def __init__(self, instr_name, **kwargs):
             Name of instrument file to be simulated
 
         kwargs : keyword arguments
-            foldername : str, required
+            output_path : str, required
                 Sets data_folder_name
             ncount : int, default 1E6
                 Sets ncount
@@ -68,8 +68,8 @@ def __init__(self, instr_name, **kwargs):
                 Sets custom_flags passed to mcrun
             executable_path : str
                 Path to mcrun command, "" if already in path
-            increment_folder_name : bool, default False
-                If True, automatically appends foldername to make it unique
+            increment_folder_name : bool, default True
+                If True, automatically appends output_path to make it unique
             force_compile : bool, default True
                 If True, forces compile. If False no new instrument is written
             run_folder : str
@@ -86,7 +86,7 @@ def __init__(self, instr_name, **kwargs):
         self.custom_flags = ""
         self.executable_path = ""
         self.executable = ""
-        self.increment_folder_name = False
+        self.increment_folder_name = True
         self.compile = True
         self.run_path = "."
         # executable_path always in kwargs
@@ -96,11 +96,11 @@ def __init__(self, instr_name, **kwargs):
         if "executable" in kwargs:
             self.executable = kwargs["executable"]
 
-        if "foldername" in kwargs:
-            self.data_folder_name = kwargs["foldername"]
+        if "output_path" in kwargs:
+            self.data_folder_name = kwargs["output_path"]
         else:
             raise NameError(
-                "ManagedMcrun needs foldername to load data, add "
+                "ManagedMcrun needs output_path to load data, add "
                 + "with keyword argument.")
 
         if "ncount" in kwargs:
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 5ece9732..6fdf9210 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -5,6 +5,7 @@
 import yaml
 import subprocess
 import copy
+import warnings
 
 from libpyvinyl.BaseCalculator import BaseCalculator
 from libpyvinyl.Parameters.Collections import CalculatorParameters
@@ -1528,14 +1529,14 @@ def _handle_parameters(self, given_parameters):
             default_parameters.update(given_parameters)
             return default_parameters
 
-    def prepare_run(self, **kwargs):
+    def settings(self, **kwargs):
         """
         Sets parameters and options for McStas run
 
         Parameters
         ----------
         Keyword arguments
-            foldername : str
+            output_path : str
                 Sets data_folder_name
             ncount : int
                 Sets ncount
@@ -1578,12 +1579,17 @@ def prepare_run(self, **kwargs):
                                    + "directory: \""
                                    + str(kwargs["run_path"]) + "\"")
 
+        if "output_path" not in kwargs:
+            kwargs["output_path"] = self.output_path
+
+        """
         if "parameters" in kwargs:
             given_parameters = kwargs["parameters"]
         else:
             given_parameters = {}
 
         kwargs["parameters"] = self._handle_parameters(given_parameters)
+        """
 
         if "force_compile" not in kwargs:
             kwargs["force_compile"] = True
@@ -1598,7 +1604,7 @@ def backengine(self):
         This method will write the instrument to disk and then run it
         using the mcrun command of the system. Options are set using
         keyword arguments.  Some options are mandatory, for example
-        foldername, which can not already exist, if it does data will
+        output_path, which can not already exist, if it does data will
         be read from this folder.  If the mcrun command is not in the
         path of the system, the absolute path can be given with the
         executable_path keyword argument.  This path could also already
@@ -1607,7 +1613,7 @@ def backengine(self):
         Parameters
         ----------
         Keyword arguments
-            foldername : str
+            output_path : str
                 Sets data_folder_name
             ncount : int
                 Sets ncount
@@ -1629,8 +1635,19 @@ def backengine(self):
         if self.current_run_options["force_compile"]:
             self.write_full_instrument()
 
+        parameters = {}
+        for parameter in self.parameters:
+            if parameter.value is None:
+                raise RuntimeError("Unspecified parameter: '" + parameter.name
+                                   + "' set with set_parameters.")
+
+            parameters[parameter.name] = parameter.value
+
+        options = self.current_run_options
+        options["parameters"] = parameters
+
         # Set up the simulation
-        simulation = ManagedMcrun(self.name + ".instr", **self.current_run_options)
+        simulation = ManagedMcrun(self.name + ".instr", **options)
 
         # Run the simulation and return data
         simulation.run_simulation(**self.current_run_options)
@@ -1639,6 +1656,49 @@ def backengine(self):
         data = simulation.load_results()
         self._set_data(data)
 
+    def run_full_instrument(self, **kwargs):
+        """
+        Runs McStas instrument described by this class, returns list of
+        McStasData
+
+        This method will write the instrument to disk and then run it
+        using the mcrun command of the system. Options are set using
+        keyword arguments.  Some options are mandatory, for example
+        output_path, which can not already exist, if it does data will
+        be read from this folder.  If the mcrun command is not in the
+        path of the system, the absolute path can be given with the
+        executable_path keyword argument.  This path could also already
+        have been set at initialization of the instrument object.
+
+        Parameters
+        ----------
+        Keyword arguments
+            output_path : str
+                Sets data_folder_name
+            ncount : int
+                Sets ncount
+            mpi : int
+                Sets thread count
+            parameters : dict
+                Sets parameters
+            custom_flags : str
+                Sets custom_flags passed to mcrun
+            force_compile : bool
+                If True (default) new instrument file is written, otherwise not
+            executable_path : str
+                Path to mcrun command, "" if already in path
+        """
+        warnings.warn(
+            "run_full_instrument will be removed in future version of McStasScript. \n"
+            + "Instead supply parameters with set_parameters, set settings with "
+            + "settings and use backengine() to run. See examples in package.")
+
+        self.prepare_run(**kwargs)
+        if "parameters" in kwargs:
+            self.set_parameters(kwargs["parameters"])
+        self.backengine()
+        return self.data
+
     def show_instrument(self, *args, **kwargs):
         """
         Uses mcdisplay to show the instrument in web browser

From 0e940b34d607fd2906a34677c6bd0410b5f9730b Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 10 Dec 2021 17:18:34 +0100
Subject: [PATCH 177/403] Updated example to new use of settings.

---
 examples/libpyvinyl_example.ipynb | 141 ++++++++++++++++++++++++++----
 1 file changed, 125 insertions(+), 16 deletions(-)

diff --git a/examples/libpyvinyl_example.ipynb b/examples/libpyvinyl_example.ipynb
index 97b492bd..ab21f99d 100644
--- a/examples/libpyvinyl_example.ipynb
+++ b/examples/libpyvinyl_example.ipynb
@@ -84,7 +84,7 @@
    "outputs": [],
    "source": [
     "source_energy = calculator.add_parameter(\"double\", \"source_energy\", unit=\"meV\", comment=\"Source mean energy\")\n",
-    "source_energy.add_interval(3, None, intervals_are_legal=True)\n",
+    "source_energy.add_interval(3.1, None, intervals_are_legal=True)\n",
     "\n",
     "source_height = calculator.add_parameter(\"double\", \"source_height\", unit=\"cm\", comment=\"Height of source\")\n",
     "source_height.add_interval(0.01, 0.2, intervals_are_legal=True)\n",
@@ -124,7 +124,7 @@
      "output_type": "stream",
      "text": [
       " - Parameters object -\n",
-      "source_energy       [meV]     Source mean energy   L[3, inf] \n",
+      "source_energy       [meV]     Source mean energy   L[3.1, inf]\n",
       "source_height       [cm]      Height of source   L[0.01, 0.2]\n",
       "sample_height       [cm]      Height of sample   L[0.0, 0.05]\n",
       "\n"
@@ -335,11 +335,11 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "defensive-phrase",
+   "id": "premier-eleven",
    "metadata": {},
    "outputs": [],
    "source": [
-    "calculator.set_parameters({\"source_energy\":350, \"source_height\":0.025, \"sample_height\":0.04})"
+    "calculator.set_parameters({\"source_energy\": 320, \"source_height\":0.025, \"sample_height\":0.04})"
    ]
   },
   {
@@ -349,7 +349,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "calculator.prepare_run(ncount=5E6, foldername=\"path_to_output\", increment_folder_name=True)"
+    "calculator.settings(ncount=5E6, mpi=2, output_path=\"path_to_output\")"
    ]
   },
   {
@@ -370,27 +370,33 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/path_to_output_0\"\n",
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/path_to_output_4\"\n",
       "INFO: Regenerating c-file: demo_instrument.c\n",
       "CFLAGS=\n",
       "INFO: Recompiling: ./demo_instrument.out\n",
+      "mccode-r.c:1880:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "} /* mcsiminfo_init */\n",
+      "^\n",
       "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
       "  *t0;\n",
       "  ^~~\n",
-      "1 warning generated.\n",
+      "2 warnings generated.\n",
       "INFO: ===\n",
-      "Warning: 23922 events were removed in Component[4] acceptance_horizontal=DivPos_monitor()\n",
+      "Warning: 12132 events were removed in Component[4] acceptance_horizontal=DivPos_monitor()\n",
       "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "INFO: Placing instr file copy demo_instrument.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/path_to_output_0\n",
+      "Warning: 12144 events were removed in Component[4] acceptance_horizontal=DivPos_monitor()\n",
+      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
+      "INFO: Placing instr file copy demo_instrument.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/path_to_output_4\n",
       "\n",
-      "  Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Ni.laz' (Table_Read_Offset)\n",
+      "  Simulation 'demo_instrument' (demo_instrument.instr): running on 2 nodes (master is 'CI0021617', MPI version 3.1).\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Ni.laz' (Table_Read_Offset)\n",
       "Table from file 'Ni.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
       "  '# TITLE *Nickel-Ni-[FM3-M] Swanson, H.E.;Tatge, E.[1954] [carcinogen];# CEL ...'\n",
       "PowderN: powder: Reading 19 rows from Ni.laz\n",
       "PowderN: powder: Read 19 reflections from file 'Ni.laz'\n",
       "PowderN: powder: Vc=43.76 [Angs] sigma_abs=17.96 [barn] sigma_inc=20.8 [barn] reflections=Ni.laz\n",
-      "Detector: cyl_monitor_I=0.026388 cyl_monitor_ERR=0.000115993 cyl_monitor_N=140247 \"cylinder.dat\"\n",
-      "Detector: acceptance_horizontal_I=0.621105 acceptance_horizontal_ERR=0.000301518 acceptance_horizontal_N=4.75183e+06 \"acceptance_h.dat\"\n",
+      "Detector: cyl_monitor_I=0.02597 cyl_monitor_ERR=0.000114161 cyl_monitor_N=136619 \"cylinder.dat\"\n",
+      "Detector: acceptance_horizontal_I=0.620813 acceptance_horizontal_ERR=0.000300875 acceptance_horizontal_N=4.73624e+06 \"acceptance_h.dat\"\n",
       "PowderN: powder: Info: you may highly improve the computation efficiency by using\n",
       "    SPLIT 19 COMPONENT powder=PowderN(...)\n",
       "  in the instrument description demo_instrument.instr.\n",
@@ -445,7 +451,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 936x504 with 3 Axes>"
       ]
@@ -462,7 +468,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "relevant-employer",
+   "id": "applied-florence",
    "metadata": {},
    "source": [
     "### Store in dump file"
@@ -491,7 +497,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "integrated-theory",
+   "id": "rotary-military",
    "metadata": {},
    "source": [
     "### Load from dump file"
@@ -550,13 +556,14 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "specified-study",
+   "id": "experienced-reminder",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "demo_instrument\n",
       "source                Source_div     AT      (0, 0, 0)   ABSOLUTE       \n",
       "powder                PowderN        AT      (0, 0, 1)   RELATIVE source\n",
       "cyl_monitor           Cyl_monitor    AT      (0, 0, 0)   RELATIVE powder\n",
@@ -565,8 +572,110 @@
     }
    ],
    "source": [
+    "print(from_dump.name)\n",
     "from_dump.print_components()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "talented-cheese",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/********************************************************************************\n",
+      "* \n",
+      "* McStas, neutron ray-tracing package\n",
+      "*         Copyright (C) 1997-2008, All rights reserved\n",
+      "*         Risoe National Laboratory, Roskilde, Denmark\n",
+      "*         Institut Laue Langevin, Grenoble, France\n",
+      "* \n",
+      "* This file was written by McStasScript, which is a \n",
+      "* python based McStas instrument generator written by \n",
+      "* Mads Bertelsen in 2019 while employed at the \n",
+      "* European Spallation Source Data Management and \n",
+      "* Software Center\n",
+      "* \n",
+      "* Instrument demo_instrument\n",
+      "* \n",
+      "* %Identification\n",
+      "* Written by: Python McStas Instrument Generator\n",
+      "* Date: 17:07:30 on December 10, 2021\n",
+      "* Origin: ESS DMSC\n",
+      "* %INSTRUMENT_SITE: Generated_instruments\n",
+      "* \n",
+      "* \n",
+      "* %Parameters\n",
+      "* \n",
+      "* %End \n",
+      "********************************************************************************/\n",
+      "\n",
+      "DEFINE INSTRUMENT demo_instrument (\n",
+      "double source_energy = 320,// Source mean energy\n",
+      "double source_height = 0.025,// Height of source\n",
+      " sample_height = 0.04 // Height of sample\n",
+      ")\n",
+      "\n",
+      "DECLARE \n",
+      "%{\n",
+      "%}\n",
+      "\n",
+      "INITIALIZE \n",
+      "%{\n",
+      "// Start of initialize for generated demo_instrument\n",
+      "%}\n",
+      "\n",
+      "TRACE \n",
+      "COMPONENT source = Source_div(\n",
+      " xwidth = 0.12, yheight = source_height,\n",
+      " focus_aw = 3, focus_ah = 4,\n",
+      " E0 = source_energy, dE = 3)\n",
+      "AT (0,0,0) ABSOLUTE\n",
+      "\n",
+      "COMPONENT powder = PowderN(\n",
+      " reflections = \"Ni.laz\", radius = 0.01,\n",
+      " yheight = sample_height)\n",
+      "AT (0,0,1) RELATIVE source\n",
+      "\n",
+      "COMPONENT cyl_monitor = Cyl_monitor(\n",
+      " nr = 200, filename = \"cylinder.dat\",\n",
+      " yheight = 0.2, radius = 0.5,\n",
+      " restore_neutron = 1)\n",
+      "AT (0,0,0) RELATIVE powder\n",
+      "\n",
+      "COMPONENT acceptance_horizontal = DivPos_monitor(\n",
+      " nh = 300, ndiv = 300,\n",
+      " filename = \"acceptance_h.dat\", xwidth = 0.2,\n",
+      " yheight = 0.2, maxdiv_h = 30,\n",
+      " restore_neutron = 1)\n",
+      "AT (0,0,0.1) RELATIVE powder\n",
+      "\n",
+      "FINALLY \n",
+      "%{\n",
+      "// Start of finally for generated demo_instrument\n",
+      "%}\n",
+      "\n",
+      "END\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"demo_instrument.instr\", \"r\") as f:\n",
+    "    instrument_file = f.read()\n",
+    "print(instrument_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "collectible-patrick",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From b392bb8d7b7be14c3c4a3a346cab9dfb82049eb3 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Mon, 13 Dec 2021 19:38:44 +0100
Subject: [PATCH 178/403] Bug fixes that ensure unit tests run. Fixed unit
 tests.

---
 mcstasscript/helper/mcstas_objects.py         | 10 +++--
 .../test_complex_instrument.py                |  2 +
 mcstasscript/interface/instr.py               | 21 ++++++---
 .../jb_interface/simulation_interface.py      | 16 +++----
 mcstasscript/tests/test_Instr.py              | 45 +++++++++++--------
 mcstasscript/tests/test_Instr_reader.py       |  2 +-
 mcstasscript/tests/test_ManagedMcrun.py       | 40 ++++++++---------
 mcstasscript/tests/test_parameter_variable.py |  2 +-
 .../tests/test_simulation_interface.py        |  4 +-
 9 files changed, 83 insertions(+), 59 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 0fad8030..8c9ca946 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -109,7 +109,7 @@ def __init__(self, *args, **kwargs):
         if "options" in kwargs:
             options = kwargs["options"]
 
-            self.add_option(options)
+            self.add_option(options, True)
 
         if "value" in kwargs:
             if not isinstance(kwargs["value"], (str, int, float)):
@@ -125,7 +125,11 @@ def write_parameter(self, fo, stop_character):
             raise RuntimeError("stop_character in write_parameter should be "
                                + "a string.")
 
-        fo.write("%s %s" % (self.type, self.name))
+        if not self.type == "":
+            fo.write("%s %s" % (self.type, self.name))
+        else:
+            fo.write(self.name)
+
         if self.value is not None:
             if isinstance(self.value, int):
                 fo.write(" = %d" % self.value)
@@ -913,7 +917,7 @@ def write_component(self, fo):
                                     + " not set.")
                 else:
                     continue
-            elif isinstance(val, ParameterVariable):
+            elif isinstance(val, (ParameterVariable, DeclareVariable)):
                 # Extract the parameter name
                 val = val.name
 
diff --git a/mcstasscript/integration_tests/test_complex_instrument.py b/mcstasscript/integration_tests/test_complex_instrument.py
index 24537e93..3fd6d1d9 100644
--- a/mcstasscript/integration_tests/test_complex_instrument.py
+++ b/mcstasscript/integration_tests/test_complex_instrument.py
@@ -210,6 +210,8 @@ def test_complex_instrument_interface(self, mock_stdout):
 
         os.chdir(CURRENT_DIR)
 
+        print(data)
+
         intensity_data_pos = functions.name_search("PSD_1D_1", data).Intensity
         sum_outside_beam = sum(intensity_data_pos[0:50])
         sum_inside_beam = sum(intensity_data_pos[51:99])
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 6fdf9210..8538f9d5 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -61,7 +61,7 @@ class McCode_instr(BaseCalculator):
     executable_path : str
         absolute path of mcrun command, or empty if it is in path
 
-    instrument_parameters : ParameterContainer
+    parameters : ParameterContainer
         contains all input parameters to be written to file
 
     declare_list : list of DeclareVariable instances
@@ -305,6 +305,10 @@ def __init__(self, name, parameters=None, dumpfile=None, **kwargs):
         self.component_class_lib = {}
         self.widget_interface = None
 
+        # Ensure output_path field exist
+        if not hasattr(self, "output_path"):
+            self.output_path = None
+
         # Avoid initializing if loading from dump
         if not hasattr(self, "current_run_options"):
             self.current_run_options = None
@@ -407,7 +411,9 @@ def add_declare_var(self, *args, **kwargs):
         """
 
         # DeclareVariable class documented independently
-        self.declare_list.append(DeclareVariable(*args, **kwargs))
+        declare_par = DeclareVariable(*args, **kwargs)
+        self.declare_list.append(declare_par)
+        return declare_par
 
     def append_declare(self, string):
         """
@@ -1423,7 +1429,7 @@ def write_full_instrument(self):
         # Add loop that inserts parameters here
         parameter_list = list(self.parameters)
         for variable in parameter_list[0:-1]:
-            variable.write_parameter(fo, ",")
+            variable.write_parameter(fo, ", ")
         if len(parameter_list) > 0:
             parameter_list[-1].write_parameter(fo, " ")
         fo.write(")\n")
@@ -1693,7 +1699,10 @@ def run_full_instrument(self, **kwargs):
             + "Instead supply parameters with set_parameters, set settings with "
             + "settings and use backengine() to run. See examples in package.")
 
-        self.prepare_run(**kwargs)
+        if "foldername" in kwargs:
+            kwargs["output_path"] = kwargs["foldername"]
+
+        self.settings(**kwargs)
         if "parameters" in kwargs:
             self.set_parameters(kwargs["parameters"])
         self.backengine()
@@ -1807,7 +1816,7 @@ class McStas_instr(McCode_instr):
     executable_path : str
         absolute path of mcrun command, or empty if it is in path
 
-    instrument_parameters : ParameterContainer instance
+    parameters : ParameterContainer instance
         contains all input parameters to be written to file
 
     declare_list : list of DeclareVariable instances
@@ -2031,7 +2040,7 @@ class McXtrace_instr(McCode_instr):
     executable_path : str
         absolute path of mcrun command, or empty if it is in path
 
-    instrument_parameters : ParameterContainer
+    parameters : ParameterContainer
         contains all input parameters to be written to file
 
     declare_list : list of DeclareVariable instances
diff --git a/mcstasscript/jb_interface/simulation_interface.py b/mcstasscript/jb_interface/simulation_interface.py
index ecaf6e3b..3a35d9fd 100644
--- a/mcstasscript/jb_interface/simulation_interface.py
+++ b/mcstasscript/jb_interface/simulation_interface.py
@@ -113,7 +113,7 @@ def run_simulation_live(self):
 
         part_ncount = int(float(self.ncount)/sim_parts)
 
-        run_arguments = {"foldername": "interface_" + self.instrument.name,
+        run_arguments = {"output_path": "interface_" + self.instrument.name,
                          "increment_folder_name": True,
                          "parameters": self.parameters,
                          "ncount": part_ncount}
@@ -146,7 +146,7 @@ def run_simulation_live(self):
         for index in range(sim_parts):
             try:
                 with HiddenPrints():
-                    self.instrument.prepare_run(**run_arguments)
+                    self.instrument.settings(**run_arguments)
                     self.instrument.backengine()
                     data = self.instrument.data
             except NameError:
@@ -335,22 +335,22 @@ def make_widget(self):
         """
         label = widgets.Label(value=self.name,
                               layout=widgets.Layout(width='15%', height='32px'))
-        if len(self.parameter.options) > 0:
-            par_widget = widgets.Dropdown(options=self.parameter.options,
+        if len(self.parameter._options) > 0:
+            par_widget = widgets.Dropdown(options=self.parameter._options,
                                           layout=widgets.Layout(width='10%', height='32px'))
             if self.default_value is not None:
-                if self.default_value in self.parameter.options:
+                if self.default_value in self.parameter._options:
                     par_widget.value = self.default_value
 
                 if isinstance(self.default_value, str):
 
-                    if self.default_value.strip("'") in self.parameter.options:
+                    if self.default_value.strip("'") in self.parameter._options:
                         par_widget.value = self.default_value.strip("'")
-                    elif self.default_value.strip('"') in self.parameter.options:
+                    elif self.default_value.strip('"') in self.parameter._options:
                         par_widget.value = self.default_value.strip('"')
 
                 if par_widget.value is None:
-                    print(self.parameter.options)
+                    print(self.parameter._options)
                     raise KeyError("default value not found in options for parameter: "
                                    + str(self.parameter.name))
 
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 1e18f0d0..28410aa2 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -399,7 +399,7 @@ def test_simple_add_parameter(self):
 
         instr.add_parameter("double", "theta", comment="test par")
 
-        parameter = instr.instrument_parameters["theta"]
+        parameter = instr.parameters["theta"]
         self.assertEqual(parameter.name, "theta")
         self.assertEqual(parameter.comment, "test par")
 
@@ -1687,7 +1687,7 @@ def test_write_full_instrument_simple(self, mock_f):
          my_call("DEFINE INSTRUMENT test_instrument ("),
          my_call("\n"),
          my_call("double theta"),
-         my_call(","),
+         my_call(", "),
          my_call(""),
          my_call("\n"),
          my_call("double has_default"),
@@ -1745,7 +1745,8 @@ def test_run_full_instrument_required_par_error(self, mock_stdout):
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
         with self.assertRaises(NameError):
-            instr.run_full_instrument(foldername="test_data_set",
+            instr.run_full_instrument(output_path="test_data_set",
+                                      increment_folder_name=False,
                                       executable_path=executable_path)
 
     def test_run_full_instrument_junk_par_error(self):
@@ -1757,8 +1758,9 @@ def test_run_full_instrument_junk_par_error(self):
 
         pars = {"theta": 2, "junk": "test"}
 
-        with self.assertRaises(NameError):
-            instr.run_full_instrument(foldername="test_data_set",
+        with self.assertRaises(KeyError):
+            instr.run_full_instrument(output_path="test_data_set",
+                                      increment_folder_name=False,
                                       parameters=pars)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
@@ -1779,7 +1781,8 @@ def test_x_ray_run_full_instrument_basic(self, mock_sub, mock_stdout):
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
         instr = setup_populated_x_ray_instr_with_dummy_path()
-        instr.run_full_instrument(foldername="test_data_set",
+        instr.run_full_instrument(output_path="test_data_set",
+                                  increment_folder_name=False,
                                   executable_path=executable_path,
                                   parameters={"theta": 1})
 
@@ -1787,13 +1790,14 @@ def test_x_ray_run_full_instrument_basic(self, mock_sub, mock_stdout):
 
         expected_path = os.path.join(executable_path, "mxrun")
 
-        expected_folder_path = os.path.join(THIS_DIR, "test_data_set")
+        expected_folder_path = os.path.join(THIS_DIR,
+                                            "test_data_set")
 
         # a double space because of a missing option
         expected_call = (expected_path + " -c -n 1000000 "
                          + "-d " + expected_folder_path
                          + "  test_instrument.instr"
-                         + " has_default=37 theta=1")
+                         + " theta=1 has_default=37")
 
         expected_run_path = os.path.join(THIS_DIR, ".")
 
@@ -1822,9 +1826,11 @@ def test_run_full_instrument_basic(self, mock_sub, mock_stdout):
 
         instr = setup_populated_instr_with_dummy_path()
 
-        instr.run_full_instrument(foldername="test_data_set",
-                                  executable_path=executable_path,
-                                  parameters={"theta": 1})
+        instr.set_parameters({"theta": 1})
+        instr.settings(output_path="test_data_set",
+                       increment_folder_name=False,
+                       executable_path=executable_path)
+        instr.backengine()
 
         os.chdir(current_work_dir)
 
@@ -1836,7 +1842,7 @@ def test_run_full_instrument_basic(self, mock_sub, mock_stdout):
         expected_call = (expected_path + " -c -n 1000000 "
                          + "-d " + expected_folder_path
                          + "  test_instrument.instr"
-                         + " has_default=37 theta=1")
+                         + " theta=1 has_default=37")
 
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
@@ -1867,7 +1873,8 @@ def test_run_full_instrument_complex(self, mock_sub, mock_stdout):
         instr.add_parameter("A")
         instr.add_parameter("BC")
 
-        instr.run_full_instrument(foldername="test_data_set",
+        instr.run_full_instrument(output_path="test_data_set",
+                                  increment_folder_name=False,
                                   executable_path=executable_path,
                                   mpi=7,
                                   ncount=48.4,
@@ -1886,7 +1893,7 @@ def test_run_full_instrument_complex(self, mock_sub, mock_stdout):
         expected_call = (expected_path + " -c -n 48 --mpi=7 "
                          + "-d " + expected_folder_path
                          + " -fo test_instrument.instr "
-                         + "has_default=37 A=2 BC=car theta=\"toy\"")
+                         + "theta=\"toy\" has_default=37 A=2 BC=car")
 
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
@@ -1915,7 +1922,8 @@ def test_run_full_instrument_overwrite_default(self, mock_sub,
         instr.add_parameter("A")
         instr.add_parameter("BC")
 
-        instr.run_full_instrument(foldername="test_data_set",
+        instr.run_full_instrument(output_path="test_data_set",
+                                  increment_folder_name=False,
                                   executable_path=executable_path,
                                   mpi=7,
                                   ncount=48.4,
@@ -1936,7 +1944,7 @@ def test_run_full_instrument_overwrite_default(self, mock_sub,
         expected_call = (expected_path + " -c -n 48 --mpi=7 "
                          + "-d " + expected_folder_path
                          + " -fo test_instrument.instr "
-                         + "has_default=10 A=2 BC=car theta=\"toy\"")
+                         + "theta=\"toy\" has_default=10 A=2 BC=car")
 
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
@@ -1963,7 +1971,8 @@ def test_run_full_instrument_x_ray_basic(self, mock_sub, mock_stdout):
 
         instr = setup_populated_x_ray_instr_with_dummy_path()
 
-        instr.run_full_instrument(foldername="test_data_set",
+        instr.run_full_instrument(output_path="test_data_set",
+                                  increment_folder_name=False,
                                   executable_path=executable_path,
                                   parameters={"theta": 1})
 
@@ -1977,7 +1986,7 @@ def test_run_full_instrument_x_ray_basic(self, mock_sub, mock_stdout):
         expected_call = (expected_path + " -c -n 1000000 "
                          + "-d " + expected_folder_path
                          + "  test_instrument.instr"
-                         + " has_default=37 theta=1")
+                         + " theta=1 has_default=37")
 
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
diff --git a/mcstasscript/tests/test_Instr_reader.py b/mcstasscript/tests/test_Instr_reader.py
index ced82f17..390c8ec3 100644
--- a/mcstasscript/tests/test_Instr_reader.py
+++ b/mcstasscript/tests/test_Instr_reader.py
@@ -72,7 +72,7 @@ def test_read_input_parameter(self):
         enablePrint()
         setup_standard(Instr)
 
-        parameter_list = list(Instr.instrument_parameters.parameters.values())
+        parameter_list = list(Instr.parameters)
 
         self.assertEqual(parameter_list[0].name, "stick_displacement")
         # space in type inserted for easier writing by McStas_Instr class
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index fd5c57a2..caac66ce 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -30,7 +30,7 @@ def test_ManagedMcrun_init_simple(self):
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_folder",
+                                 output_path="test_folder",
                                  executable_path=executable_path,
                                  executable="mcrun")
 
@@ -55,7 +55,7 @@ def test_ManagedMcrun_init_defaults(self):
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_folder",
+                                 output_path="test_folder",
                                  executable_path=executable_path,
                                  executable="mcrun")
 
@@ -79,7 +79,7 @@ def test_ManagedMcrun_init_set_values(self):
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_folder",
+                                 output_path="test_folder",
                                  executable="mcrun",
                                  executable_path=executable_path,
                                  run_path="test_data_set",
@@ -110,7 +110,7 @@ def test_ManagedMcrun_init_set_parameters(self):
                      "string_par": "\"Car\""}
 
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_folder",
+                                 output_path="test_folder",
                                  executable_path=executable_path,
                                  executable="",
                                  parameters=par_input)
@@ -136,7 +136,7 @@ def test_ManagedMcrun_init_set_custom_flags(self):
         custom_flag_input = "-p"
 
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_folder",
+                                 output_path="test_folder",
                                  executable="mcrun",
                                  executable_path=executable_path,
                                  custom_flags=custom_flag_input)
@@ -158,7 +158,7 @@ def test_ManagedMcrun_init_invalid_ncount_error(self):
         """
         with self.assertRaises(ValueError):
             ManagedMcrun("test.instr",
-                         foldername="test_folder",
+                         output_path="test_folder",
                          mcrun_path="",
                          ncount=-8)
 
@@ -168,7 +168,7 @@ def test_ManagedMcrun_init_invalid_mpi_error(self):
         """
         with self.assertRaises(ValueError):
             ManagedMcrun("test.instr",
-                         foldername="test_folder",
+                         output_path="test_folder",
                          mcrun_path="",
                          mpi=-8)
 
@@ -178,7 +178,7 @@ def test_ManagedMcrun_init_invalid_parameters_error(self):
         """
         with self.assertRaises(RuntimeError):
             ManagedMcrun("test.instr",
-                         foldername="test_folder",
+                         output_path="test_folder",
                          mcrun_path="",
                          parameters=[1, 2, 3])
 
@@ -195,7 +195,7 @@ def test_ManagedMcrun_run_simulation_basic(self, mock_sub):
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_folder",
+                                 output_path="test_folder",
                                  executable_path=executable_path,
                                  executable="mcrun",)
 
@@ -229,7 +229,7 @@ def test_ManagedMcrun_run_simulation_basic_path(self, mock_sub):
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_folder",
+                                 output_path="test_folder",
                                  executable_path=executable_path,
                                  executable="mcrun",)
 
@@ -266,7 +266,7 @@ def test_ManagedMcrun_run_simulation_no_standard(self, mock_sub):
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_folder",
+                                 output_path="test_folder",
                                  executable_path=executable_path,
                                  executable="mcrun",
                                  mpi=7,
@@ -303,7 +303,7 @@ def test_ManagedMcrun_run_simulation_parameters(self, mock_sub):
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_folder",
+                                 output_path="test_folder",
                                  executable_path=executable_path,
                                  executable="mcrun",
                                  mpi=7,
@@ -344,7 +344,7 @@ def test_ManagedMcrun_run_simulation_compile(self, mock_sub):
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_folder",
+                                 output_path="test_folder",
                                  executable_path=executable_path,
                                  executable="mcrun",
                                  mpi=7,
@@ -388,7 +388,7 @@ def test_ManagedMcrun_load_data_PSD4PI(self):
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_data_set",
+                                 output_path="test_data_set",
                                  executable_path=executable_path,
                                  mcrun_path="path")
 
@@ -427,7 +427,7 @@ def test_ManagedMcrun_load_data_PSD(self):
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_data_set",
+                                 output_path="test_data_set",
                                  executable_path=executable_path,
                                  mcrun_path="path")
 
@@ -466,7 +466,7 @@ def test_ManagedMcrun_load_data_L_mon(self):
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_data_set",
+                                 output_path="test_data_set",
                                  executable_path=executable_path,
                                  mcrun_path="path")
 
@@ -506,7 +506,7 @@ def test_ManagedMcrun_load_data_L_mon_direct(self):
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_data_set",
+                                 output_path="test_data_set",
                                  executable_path=executable_path,
                                  mcrun_path="path")
 
@@ -546,7 +546,7 @@ def test_ManagedMcrun_load_data_Event(self):
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_data_set",
+                                 output_path="test_data_set",
                                  executable_path=executable_path,
                                  mcrun_path="path")
 
@@ -589,7 +589,7 @@ def test_ManagedMcrun_load_data_L_mon_direct_error(self):
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_data_set",
+                                 output_path="test_data_set",
                                  executable_path=executable_path,
                                  mcrun_path="path")
 
@@ -611,7 +611,7 @@ def test_ManagedMcrun_load_data_L_mon_empty_error(self):
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
         mcrun_obj = ManagedMcrun("test.instr",
-                                 foldername="test_data_set",
+                                 output_path="test_data_set",
                                  executable_path=executable_path,
                                  mcrun_path="path")
 
diff --git a/mcstasscript/tests/test_parameter_variable.py b/mcstasscript/tests/test_parameter_variable.py
index 468a3081..6266c5e1 100644
--- a/mcstasscript/tests/test_parameter_variable.py
+++ b/mcstasscript/tests/test_parameter_variable.py
@@ -77,7 +77,7 @@ def test_ParameterVariable_init_options_initialize(self):
         self.assertEqual(par.name, "test")
         self.assertEqual(par.type, "")
         self.assertEqual(par.value, 2)
-        self.assertEqual(par.options, [1, 2, "horse"])
+        self.assertEqual(par._options, [1, 2, "horse"])
 
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
diff --git a/mcstasscript/tests/test_simulation_interface.py b/mcstasscript/tests/test_simulation_interface.py
index b93a8c6d..96983c00 100644
--- a/mcstasscript/tests/test_simulation_interface.py
+++ b/mcstasscript/tests/test_simulation_interface.py
@@ -168,13 +168,13 @@ def test_ParameterWidget(self):
 
         parameters = {}
         # get default parameters from instrument
-        for parameter in instrument.instrument_parameters.parameters.values():
+        for parameter in instrument.parameters:
             if parameter_has_default(parameter):
                 parameters[parameter.name] = get_parameter_default(parameter)
 
         parameter_widgets = []
         parameterwidget_objects = []
-        for parameter in instrument.instrument_parameters.parameters.values():
+        for parameter in instrument.parameters:
             par_widget = ParameterWidget(parameter, parameters)
             parameterwidget_objects.append(par_widget)
             parameter_widgets.append(par_widget.make_widget())

From 8c41b1d50637464a9ec1269531eaddc086769c5e Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 14 Dec 2021 18:03:06 +0100
Subject: [PATCH 179/403] Update of the documentation for areas affected to
 libpyvinyl transition. Still work to do on documentation, need to ensure all
 doc strings are up to date, handle README and the pdf documentation.

---
 mcstasscript/helper/mcstas_objects.py    |  10 +-
 mcstasscript/interface/instr.py          | 177 +++++++-----
 mcstasscript/tests/test_dump_and_load.py | 335 +++++++++++++++++++++++
 mcstasscript/tests/test_instrument.instr |   4 +-
 4 files changed, 447 insertions(+), 79 deletions(-)
 create mode 100644 mcstasscript/tests/test_dump_and_load.py

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 8c9ca946..7c89b58b 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -13,7 +13,8 @@ class ParameterVariable(Parameter):
     cast.  If two positional arguments are given, the first is the
     type, and the second is the parameter name.  With one input, only
     the parameter name is read.  It is also possible to assign a
-    default value and a comment through keyword arguments.
+    default value and a comment through keyword arguments. Inherits from the
+    libpyvinyl Parameter.
 
     Attributes
     ----------
@@ -26,6 +27,9 @@ class ParameterVariable(Parameter):
     value : any
         Default value/string of parameter, converted to string
 
+    unit : str
+        String descrbing the unit for this variable
+
     comment : str
         Comment displayed next to the parameter, could contain units
 
@@ -165,6 +169,10 @@ def import_parameters(self, parameters):
         There are further requirements for parameters in McStasScript which
         need to be checked on import, and a subclass of Parameter is used
         to store these with additional functionality.
+
+        Parameters:
+            parameters: ParameterContainer
+                libpyvinyl ParameterContainer for conversion
         """
         if isinstance(parameters, ParameterContainer):
             for parameter in parameters:
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 8538f9d5..b0d40bd2 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -58,6 +58,9 @@ class McCode_instr(BaseCalculator):
     input_path : str, default "."
         directory in which simulation is executed, uses components found there
 
+    output_path : str
+        directory in which the data is written
+
     executable_path : str
         absolute path of mcrun command, or empty if it is in path
 
@@ -91,6 +94,12 @@ class McCode_instr(BaseCalculator):
     package_path : str
         Path to mccode package containing component folders
 
+    current_run_settings : dict
+        Dict of options set with settings
+
+    data : list
+        List of McStasData objects produced by last run
+
     Methods
     -------
     add_parameter(*args, **kwargs)
@@ -192,9 +201,17 @@ class McCode_instr(BaseCalculator):
     show_instrument()
         Shows instrument using mcdisplay
 
+    set_parameters(dict)
+        Inherited from libpyvinyl BaseCalculator
+
+    settings(**kwargs)
+        Sets settings for performing simulation
+
+    backengine()
+        Performs simulation, saves in data attribute
+
     run_full_instrument(**kwargs)
-        Writes instrument files and runs simulation.
-        Returns list of McStasData
+        Depricated method for performing the simulation
 
     interface()
         Shows interface with jupyter notebook widgets
@@ -213,6 +230,12 @@ def __init__(self, name, parameters=None, dumpfile=None, **kwargs):
             Name of project, instrument file will be name + ".instr"
 
         keyword arguments:
+            parameters : ParameterContainer or CalculatorParameters
+                Parameters for this instrument
+
+            dumpfile: str
+                File path to dump file to be loaded
+
             author : str
                 Name of author, written in instrument file
 
@@ -310,8 +333,8 @@ def __init__(self, name, parameters=None, dumpfile=None, **kwargs):
             self.output_path = None
 
         # Avoid initializing if loading from dump
-        if not hasattr(self, "current_run_options"):
-            self.current_run_options = None
+        if not hasattr(self, "current_run_settings"):
+            self.current_run_settings = None
 
             self.declare_list = []
             self.initialize_section = ("// Start of initialize for generated "
@@ -1537,19 +1560,25 @@ def _handle_parameters(self, given_parameters):
 
     def settings(self, **kwargs):
         """
-        Sets parameters and options for McStas run
+        Sets settings for McStas run performed with backengine
+
+        Some options are mandatory, for example output_path, which can not
+        already exist, if it does data will be read from this folder. If the
+        mcrun command is not in the path of the system, the absolute path can
+        be given with the executable_path keyword argument. This path could
+        also already have been set at initialization of the instrument object.
 
         Parameters
         ----------
         Keyword arguments
             output_path : str
                 Sets data_folder_name
+            increment_folder_name : bool
+                Will update output_path if folder already exists, default True
             ncount : int
                 Sets ncount
             mpi : int
                 Sets thread count
-            parameters : dict
-                Sets parameters
             custom_flags : str
                 Sets custom_flags passed to mcrun
             force_compile : bool
@@ -1563,8 +1592,8 @@ def settings(self, **kwargs):
             kwargs["executable_path"] = self.executable_path
         else:
             if not os.path.isdir(str(kwargs["executable_path"])):
-                raise RuntimeError("The executable_path provided to "
-                                   + "run_full_instrument does not point to a"
+                raise RuntimeError("The executable_path provided in "
+                                   + "settings does not point to a"
                                    + "directory: \""
                                    + str(kwargs["executable_path"]) + "\"")
 
@@ -1581,64 +1610,32 @@ def settings(self, **kwargs):
         else:
             if not os.path.isdir(str(kwargs["run_path"])):
                 raise RuntimeError("The run_path provided to "
-                                   + "run_full_instrument does not point to a"
+                                   + "settings does not point to a"
                                    + "directory: \""
                                    + str(kwargs["run_path"]) + "\"")
 
         if "output_path" not in kwargs:
             kwargs["output_path"] = self.output_path
 
-        """
-        if "parameters" in kwargs:
-            given_parameters = kwargs["parameters"]
-        else:
-            given_parameters = {}
-
-        kwargs["parameters"] = self._handle_parameters(given_parameters)
-        """
-
         if "force_compile" not in kwargs:
             kwargs["force_compile"] = True
 
-        self.current_run_options = kwargs
+        self.current_run_settings = kwargs
 
     def backengine(self):
         """
-        Runs McStas instrument described by this class, returns list of
-        McStasData
+        Runs McStas instrument described by this class, saves data in
+        data attribute
 
         This method will write the instrument to disk and then run it
-        using the mcrun command of the system. Options are set using
-        keyword arguments.  Some options are mandatory, for example
-        output_path, which can not already exist, if it does data will
-        be read from this folder.  If the mcrun command is not in the
-        path of the system, the absolute path can be given with the
-        executable_path keyword argument.  This path could also already
-        have been set at initialization of the instrument object.
-
-        Parameters
-        ----------
-        Keyword arguments
-            output_path : str
-                Sets data_folder_name
-            ncount : int
-                Sets ncount
-            mpi : int
-                Sets thread count
-            parameters : dict
-                Sets parameters
-            custom_flags : str
-                Sets custom_flags passed to mcrun
-            force_compile : bool
-                If True (default) new instrument file is written, otherwise not
-            executable_path : str
-                Path to mcrun command, "" if already in path
+        using the mcrun command of the system. Settings are set using
+        settings methods.
         """
 
-        if self.current_run_options is None:
+        if self.current_run_settings is None:
             raise RuntimeError("Need to prepare run first!")
 
-        if self.current_run_options["force_compile"]:
+        if self.current_run_settings["force_compile"]:
             self.write_full_instrument()
 
         parameters = {}
@@ -1649,14 +1646,14 @@ def backengine(self):
 
             parameters[parameter.name] = parameter.value
 
-        options = self.current_run_options
+        options = self.current_run_settings
         options["parameters"] = parameters
 
         # Set up the simulation
         simulation = ManagedMcrun(self.name + ".instr", **options)
 
         # Run the simulation and return data
-        simulation.run_simulation(**self.current_run_options)
+        simulation.run_simulation(**self.current_run_settings)
 
         # Load data and store in __data
         data = simulation.load_results()
@@ -1801,22 +1798,16 @@ class McStas_instr(McCode_instr):
     origin : str, default "ESS DMSC"
         origin of instrument file (affiliation)
 
-    executable : str
-        Name of executable, mcrun or mxrun
-
-    particle : str
-        Name of probe particle, "neutron" or "x-ray"
-
-    package_name : str
-        Name of package, "McStas" or "McXtrace"
-
     input_path : str, default "."
         directory in which simulation is executed, uses components found there
 
+    output_path : str
+        directory in which the data is written
+
     executable_path : str
         absolute path of mcrun command, or empty if it is in path
 
-    parameters : ParameterContainer instance
+    parameters : ParameterContainer
         contains all input parameters to be written to file
 
     declare_list : list of DeclareVariable instances
@@ -1846,6 +1837,12 @@ class McStas_instr(McCode_instr):
     package_path : str
         Path to mccode package containing component folders
 
+    current_run_settings : dict
+        Dict of options set with settings
+
+    data : list
+        List of McStasData objects produced by last run
+
     Methods
     -------
     add_parameter(*args, **kwargs)
@@ -1947,9 +1944,17 @@ class McStas_instr(McCode_instr):
     show_instrument()
         Shows instrument using mcdisplay
 
+    set_parameters(dict)
+        Inherited from libpyvinyl BaseCalculator
+
+    settings(**kwargs)
+        Sets settings for performing simulation
+
+    backengine()
+        Performs simulation, saves in data attribute
+
     run_full_instrument(**kwargs)
-        Writes instrument files and runs simulation.
-        Returns list of McStasData
+        Depricated method for performing the simulation
 
     interface()
         Shows interface with jupyter notebook widgets
@@ -1967,6 +1972,12 @@ def __init__(self, name, **kwargs):
             Name of project, instrument file will be name + ".instr"
 
         keyword arguments:
+            parameters : ParameterContainer or CalculatorParameters
+                Parameters for this instrument
+
+            dumpfile: str
+                File path to dump file to be loaded
+
             author : str
                 Name of author, written in instrument file
 
@@ -2025,18 +2036,12 @@ class McXtrace_instr(McCode_instr):
     origin : str, default "ESS DMSC"
         origin of instrument file (affiliation)
 
-    executable : str
-        Name of executable, mcrun or mxrun
-
-    particle : str
-        Name of probe particle, "neutron" or "x-ray"
-
-    package_name : str
-        Name of package, "McStas" or "McXtrace"
-
     input_path : str, default "."
         directory in which simulation is executed, uses components found there
 
+    output_path : str
+        directory in which the data is written
+
     executable_path : str
         absolute path of mcrun command, or empty if it is in path
 
@@ -2055,7 +2060,7 @@ class McXtrace_instr(McCode_instr):
     finally_section : str
         string containing entire finally section to be written
 
-    component_list : list of Component instances
+    component_list : list of component instances
         list of components in the instrument
 
     component_name_list : list of strings
@@ -2070,6 +2075,12 @@ class McXtrace_instr(McCode_instr):
     package_path : str
         Path to mccode package containing component folders
 
+    current_run_settings : dict
+        Dict of options set with settings
+
+    data : list
+        List of McStasData objects produced by last run
+
     Methods
     -------
     add_parameter(*args, **kwargs)
@@ -2171,9 +2182,17 @@ class McXtrace_instr(McCode_instr):
     show_instrument()
         Shows instrument using mcdisplay
 
+    set_parameters(dict)
+        Inherited from libpyvinyl BaseCalculator
+
+    settings(**kwargs)
+        Sets settings for performing simulation
+
+    backengine()
+        Performs simulation, saves in data attribute
+
     run_full_instrument(**kwargs)
-        Writes instrument files and runs simulation.
-        Returns list of McStasData
+        Depricated method for performing the simulation
 
     interface()
         Shows interface with jupyter notebook widgets
@@ -2191,6 +2210,12 @@ def __init__(self, name, **kwargs):
             Name of project, instrument file will be name + ".instr"
 
         keyword arguments:
+            parameters : ParameterContainer or CalculatorParameters
+                Parameters for this instrument
+
+            dumpfile: str
+                File path to dump file to be loaded
+
             author : str
                 Name of author, written in instrument file
 
@@ -2198,7 +2223,7 @@ def __init__(self, name, **kwargs):
                 Affiliation of author, written in instrument file
 
             executable_path : str
-                Absolute path of mxrun or empty if already in path
+                Absolute path of mcrun or empty if already in path
 
             input_path : str
                 Work directory, will load components from this folder
diff --git a/mcstasscript/tests/test_dump_and_load.py b/mcstasscript/tests/test_dump_and_load.py
new file mode 100644
index 00000000..37dbe3d4
--- /dev/null
+++ b/mcstasscript/tests/test_dump_and_load.py
@@ -0,0 +1,335 @@
+import os
+import os.path
+import io
+import unittest
+import unittest.mock
+import datetime
+
+from mcstasscript.interface.instr import McStas_instr
+from mcstasscript.interface.instr import McXtrace_instr
+from mcstasscript.helper.mcstas_objects import ParameterContainer
+from mcstasscript.helper.mcstas_objects import ParameterVariable
+
+
+run_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '.')
+
+def setup_instr_no_path():
+    """
+    Sets up a neutron instrument without a package_path
+    """
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+    current_work_dir = os.getcwd()
+    os.chdir(THIS_DIR)  # Set work directory to test folder
+
+    instrument = McStas_instr("test_instrument")
+
+    os.chdir(current_work_dir)
+
+    return instrument
+
+
+def setup_x_ray_instr_no_path():
+    """
+    Sets up a X-ray instrument without a package_path
+    """
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+    current_work_dir = os.getcwd()
+    os.chdir(THIS_DIR)  # Set work directory to test folder
+
+    instrument = McXtrace_instr("test_instrument")
+
+    os.chdir(current_work_dir)
+
+    return instrument
+
+
+def setup_instr_root_path():
+    """
+    Sets up a neutron instrument with root package_path
+    """
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+    current_work_dir = os.getcwd()
+    os.chdir(THIS_DIR)  # Set work directory to test folder
+
+    instrument = McStas_instr("test_instrument", package_path="/")
+
+    os.chdir(current_work_dir)
+
+    return instrument
+
+
+def setup_x_ray_instr_root_path():
+    """
+    Sets up a X-ray instrument with root package_path
+    """
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+    current_work_dir = os.getcwd()
+    os.chdir(THIS_DIR)  # Set work directory to test folder
+
+    instrument = McXtrace_instr("test_instrument", package_path="/")
+
+    os.chdir(current_work_dir)
+
+    return instrument
+
+
+def setup_instr_with_path():
+    """
+    Sets up an instrument with a valid package_path, but it points to
+    the dummy installation in the test folder.
+    """
+
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+    dummy_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+    current_work_dir = os.getcwd()
+    os.chdir(THIS_DIR)  # Set work directory to test folder
+
+    instrument = McStas_instr("test_instrument", package_path=dummy_path)
+
+    os.chdir(current_work_dir)  # Return to previous workdir
+
+    return instrument
+
+
+def setup_x_ray_instr_with_path():
+    """
+    Sets up an instrument with a valid package_path, but it points to
+    the dummy installation in the test folder.
+    """
+
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+    dummy_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+    current_work_dir = os.getcwd()
+    os.chdir(THIS_DIR)  # Set work directory to test folder
+
+    instrument = McXtrace_instr("test_instrument", package_path=dummy_path)
+
+    os.chdir(current_work_dir)  # Return to previous workdir
+
+    return instrument
+
+
+def setup_instr_with_input_path():
+    """
+    Sets up an instrument with a valid package_path, but it points to
+    the dummy installation in the test folder. In addition the input_path
+    is set to a folder in the test directory using an absolute path.
+    """
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+    dummy_path = os.path.join(THIS_DIR, "dummy_mcstas")
+    input_path = os.path.join(THIS_DIR, "test_input_folder")
+
+    current_work_dir = os.getcwd()
+    os.chdir(THIS_DIR)  # Set work directory to test folder
+
+    instrument = McStas_instr("test_instrument",
+                              package_path=dummy_path,
+                              input_path=input_path)
+
+    os.chdir(current_work_dir)  # Return to previous workdir
+
+    return instrument
+
+
+def setup_instr_with_input_path_relative():
+    """
+    Sets up an instrument with a valid package_path, but it points to
+    the dummy installation in the test folder. In addition the input_path
+    is set to a folder in the test directory using a relative path.
+    """
+    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+    current_work_dir = os.getcwd()
+    os.chdir(THIS_DIR)  # Set work directory to test folder
+
+    instrument = McStas_instr("test_instrument",
+                              package_path="dummy_mcstas",
+                              input_path="test_input_folder")
+
+    os.chdir(current_work_dir)  # Return to previous workdir
+
+    return instrument
+
+
+def setup_populated_instr():
+    """
+    Sets up a neutron instrument with some features used and three components
+    """
+    instr = setup_instr_root_path()
+
+    instr.add_parameter("double", "theta")
+    instr.add_parameter("double", "has_default", value=37)
+    instr.add_declare_var("double", "two_theta")
+    instr.append_initialize("two_theta = 2.0*theta;")
+
+    instr.add_component("first_component", "test_for_reading")
+    instr.add_component("second_component", "test_for_reading")
+    instr.add_component("third_component", "test_for_reading")
+
+    return instr
+
+
+def setup_populated_instr_with_dummy_path():
+    """
+    Sets up a neutron instrument with some features used and three components
+
+    Here uses the dummy mcstas installation as path and sets required
+    parameters so that a run is possible.
+    """
+    instr = setup_instr_with_path()
+
+    instr.add_parameter("double", "theta")
+    instr.add_parameter("double", "has_default", value=37)
+    instr.add_declare_var("double", "two_theta")
+    instr.append_initialize("two_theta = 2.0*theta;")
+
+    comp1 = instr.add_component("first_component", "test_for_reading")
+    comp1.gauss = 1.2
+    comp1.test_string = "a_string"
+    comp2 = instr.add_component("second_component", "test_for_reading")
+    comp2.gauss = 1.4
+    comp2.test_string = "b_string"
+    comp3 = instr.add_component("third_component", "test_for_reading")
+    comp3.gauss = 1.6
+    comp3.test_string = "c_string"
+
+    return instr
+
+
+def setup_populated_x_ray_instr():
+    """
+    Sets up a X-ray instrument with some features used and three components
+    """
+    instr = setup_x_ray_instr_root_path()
+
+    instr.add_parameter("double", "theta")
+    instr.add_parameter("double", "has_default", value=37)
+    instr.add_declare_var("double", "two_theta")
+    instr.append_initialize("two_theta = 2.0*theta;")
+
+    instr.add_component("first_component", "test_for_reading")
+    instr.add_component("second_component", "test_for_reading")
+    instr.add_component("third_component", "test_for_reading")
+
+    return instr
+
+
+def setup_populated_x_ray_instr_with_dummy_path():
+    """
+    Sets up a x-ray instrument with some features used and three components
+
+    Here uses the dummy mcstas installation as path and sets required
+    parameters so that a run is possible.
+    """
+    instr = setup_x_ray_instr_with_path()
+
+    instr.add_parameter("double", "theta")
+    instr.add_parameter("double", "has_default", value=37)
+    instr.add_declare_var("double", "two_theta")
+    instr.append_initialize("two_theta = 2.0*theta;")
+
+    comp1 = instr.add_component("first_component", "test_for_reading")
+    comp1.gauss = 1.2
+    comp1.test_string = "a_string"
+    comp2 = instr.add_component("second_component", "test_for_reading")
+    comp2.gauss = 1.4
+    comp2.test_string = "b_string"
+    comp3 = instr.add_component("third_component", "test_for_reading")
+    comp3.gauss = 1.6
+    comp3.test_string = "c_string"
+
+    return instr
+
+
+def setup_populated_with_some_options_instr():
+    """
+    Sets up a neutron instrument with some features used and two components
+    """
+    instr = setup_instr_root_path()
+
+    instr.add_parameter("double", "theta")
+    instr.add_parameter("double", "has_default", value=37)
+    instr.add_declare_var("double", "two_theta")
+    instr.append_initialize("two_theta = 2.0*theta;")
+
+    comp1 = instr.add_component("first_component", "test_for_reading")
+    comp1.set_AT([0, 0, 1])
+    comp1.set_GROUP("Starters")
+    comp2 = instr.add_component("second_component", "test_for_reading")
+    comp2.set_AT([0, 0, 2], RELATIVE="first_component")
+    comp2.set_ROTATED([0, 30, 0])
+    comp2.set_WHEN("1==1")
+    comp2.yheight = 1.23
+    instr.add_component("third_component", "test_for_reading")
+
+    return instr
+
+
+class WorkInTestDir:
+    """
+    Simple class that enables working in test directory
+    """
+    def __enter__(self):
+        self.current_work_dir = os.getcwd()
+        os.chdir(os.path.dirname(os.path.abspath(__file__)))
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        os.chdir(self.current_work_dir)
+
+
+class TestDumpAndLoad(unittest.TestCase):
+    def test_dump_simple(self):
+        """
+        Test a simple instrument can be dumped using with environment
+        """
+        my_instrument = setup_populated_instr_with_dummy_path()
+
+        with WorkInTestDir() as handler:
+            dump_name = "test_McStasScript_dump.dmp"
+
+            if os.path.isfile(dump_name):
+                os.remove(dump_name)
+
+            # Ensure dump file does not exist
+            self.assertFalse(os.path.isfile(dump_name))
+
+            # Write dump file
+            my_instrument.dump(dump_name)
+
+            # Check it was written
+            self.assertTrue(os.path.isfile(dump_name))
+
+            # Delete dump file
+            os.remove(dump_name)
+
+    def test_load_simple(self):
+        """
+        Test a simple instrument can be loaded from dump
+        """
+        my_instrument = setup_populated_instr_with_dummy_path()
+        my_instrument.add_parameter("check", comment="for testing")
+
+        with WorkInTestDir() as handler:
+            dump_name = "test_McStasScript_dump.dmp"
+            my_instrument.dump(dump_name)
+
+            loaded_instrument = McStas_instr("", dumpfile=dump_name)
+            os.remove(dump_name)
+
+            self.assertTrue(loaded_instrument, McStas_instr)
+            self.assertTrue(loaded_instrument.name, my_instrument.name)
+
+            self.assertEqual(len(loaded_instrument.component_list), 3)
+            self.assertEqual(loaded_instrument.component_list[0].name, "first_component")
+            self.assertEqual(loaded_instrument.component_list[2].name, "third_component")
+            self.assertEqual(loaded_instrument.parameters["check"].name, "check")
+            self.assertEqual(loaded_instrument.parameters["check"].comment, "for testing")
+            self.assertEqual(loaded_instrument.parameters["has_default"].value, 37)
+
diff --git a/mcstasscript/tests/test_instrument.instr b/mcstasscript/tests/test_instrument.instr
index 307a993d..145148d7 100644
--- a/mcstasscript/tests/test_instrument.instr
+++ b/mcstasscript/tests/test_instrument.instr
@@ -15,7 +15,7 @@
 * 
 * %Identification
 * Written by: Python McXtrace Instrument Generator
-* Date: 16:27:39 on December 02, 2021
+* Date: 10:17:47 on December 14, 2021
 * Origin: ESS DMSC
 * %INSTRUMENT_SITE: Generated_instruments
 * 
@@ -26,7 +26,7 @@
 ********************************************************************************/
 
 DEFINE INSTRUMENT test_instrument (
-double theta,
+double theta = 1, 
 double has_default = 37 
 )
 

From b01637aed968f783eabaf3dc4f77978f563bbd83 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 14 Dec 2021 21:06:10 +0100
Subject: [PATCH 180/403] Update of README to new run syntax.

---
 README.md | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 9c20d1c2..eb351968 100644
--- a/README.md
+++ b/README.md
@@ -114,11 +114,16 @@ A monitor is added as well to get data out of the simulation (few bins so it is
     PSD.yheight = 0.1
     PSD.nx = 5
     PSD.ny = 5
-    PSD.filename = "\"PSD.dat\""
+    PSD.filename = '"PSD.dat"'
 
-This simple simulation can be executed from the 
+Settings for the simulation can be adjusted with the *settings* method, an output_path for the data is needed.
 
-    data = my_instrument.run_full_instrument(foldername="first_run", increment_folder_name=True)
+    my_instrument.settings(output_path="first_run, ncount=1E7)
+
+The simulatiuon is performed with the *backengine* method. After this call, the data can be retrieved from the data attribute.
+
+    my_instrument.backengine()
+    data = my_instrument.data
 
 Results from the monitors would be stored as a list of McStasData objects in the returned data. The counts are stored as numpy arrays. We can read and change the intensity directly and manipulate the data before plotting.
 
@@ -170,7 +175,9 @@ Here is a quick overview of the available methods of the main classes in the pro
         ├── append_initialize(str string) # Appends a line to initialize (c syntax)
         ├── print_components() # Prints list of components and their location
         ├── write_full_instrument() # Writes instrument to disk with given name + ".instr"
-        ├── run_full_instrument() # Runs simulation. Options in keyword arguments. Returns list of McStasData
+        ├── settings(kwargs) Settings as keyword arguments.
+        ├── backengine() # Runs simulation.
+        ├── data # Data attribute, contains list of McStasData objects
         ├── interface() # Shows widget interface in jupyter notebook
         └── get_interface_data() # Returns data set from latest simulation performed in interface
         

From 6d4098b6749653f2f5b093371f93f4e997f4f585 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 14 Dec 2021 21:07:30 +0100
Subject: [PATCH 181/403] Fixed typo in README.

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index eb351968..6c2678da 100644
--- a/README.md
+++ b/README.md
@@ -118,7 +118,7 @@ A monitor is added as well to get data out of the simulation (few bins so it is
 
 Settings for the simulation can be adjusted with the *settings* method, an output_path for the data is needed.
 
-    my_instrument.settings(output_path="first_run, ncount=1E7)
+    my_instrument.settings(output_path="first_run", ncount=1E7)
 
 The simulatiuon is performed with the *backengine* method. After this call, the data can be retrieved from the data attribute.
 

From 1cd7c6cff68935a8968a0a7f535d00147a216998 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 15 Dec 2021 14:36:48 +0100
Subject: [PATCH 182/403] Update of tutorials and examples (still lack union
 tutorial)

Ensured declare and parameter variables print nicely in component print
---
 examples/McStasScript_demo.ipynb              | 432 +++----------
 examples/calibration_sample.ipynb             |  99 +--
 examples/libpyvinyl_example.ipynb             | 123 +++-
 mcstasscript/helper/managed_mcrun.py          |   5 +
 mcstasscript/helper/mcstas_objects.py         |  20 +-
 mcstasscript/interface/instr.py               |  32 +-
 .../McStasScript_tutorial_1_the_basics.ipynb  | 571 +++++++++++++++---
 tutorial/McStasScript_tutorial_2_SPLIT.ipynb  |  56 +-
 ...tasScript_tutorial_3_EXTEND_and_WHEN.ipynb |  26 +-
 tutorial/McStasScript_tutorial_4_JUMP.ipynb   |  18 +-
 10 files changed, 778 insertions(+), 604 deletions(-)

diff --git a/examples/McStasScript_demo.ipynb b/examples/McStasScript_demo.ipynb
index d4be9e59..2c5b5a3d 100644
--- a/examples/McStasScript_demo.ipynb
+++ b/examples/McStasScript_demo.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,88 +26,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are the available component categories:\n",
-      " sources\n",
-      " optics\n",
-      " samples\n",
-      " monitors\n",
-      " misc\n",
-      " contrib\n",
-      " union\n",
-      " obsolete\n",
-      "Call show_components(category_name) to display\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Instr.show_components() # Shows available McStas component categories in current installation"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are all components in the sources category.\n",
-      " Adapt_check     Monitor_Optimizer   Source_div      Virtual_output\n",
-      " ESS_butterfly   Source_Maxwell_3    Source_gen      \n",
-      " ESS_moderator   Source_Optimizer    Source_simple   \n",
-      " Moderator       Source_adapt        Virtual_input   \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Instr.show_components(\"sources\") # Display all McStas source components "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Source_simple _____________________________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1mradius\u001b[0m = \u001b[1m\u001b[94m0.1\u001b[0m\u001b[0m [m] // Radius of circle in (x,y,0) plane where neutrons are generated.\n",
-      "\u001b[1myheight\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Height of rectangle in (x,y,0) plane where neutrons are generated.\n",
-      "\u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Width of rectangle in (x,y,0) plane where neutrons are generated.\n",
-      "\u001b[1mdist\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Distance to target along z axis.\n",
-      "\u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[94m0.045\u001b[0m\u001b[0m [m] // Width of target\n",
-      "\u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[94m0.12\u001b[0m\u001b[0m [m] // Height of target\n",
-      "\u001b[1mE0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Mean energy of neutrons.\n",
-      "\u001b[1mdE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Energy half spread of neutrons (flat or gaussian sigma).\n",
-      "\u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [AA] // Mean wavelength of neutrons.\n",
-      "\u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [AA] // Wavelength half spread of neutrons.\n",
-      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1/(s*cm**2*st*energy unit)] // flux per energy unit, Angs or meV if flux=0, \n",
-      "                                           the source emits 1 in 4*PI whole space. \n",
-      "\u001b[1mgauss\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Gaussian (1) or Flat (0) energy/wavelength distribution\n",
-      "\u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1] // relative index of component to focus at, e.g. next is +1 this \n",
-      "                        is used to compute 'dist' automatically. \n",
-      "---------------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Instr.component_help(\"Source_simple\") # Displays help on the Source_simple component"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -116,48 +62,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Lets add a parameter to the instrument to control the wavelength of the source\n",
-    "Instr.add_parameter(\"double\", \"wavelength\", value=3,\n",
-    "                    comment=\"[AA] Wavelength emmited from source\")\n",
-    "source.xwidth = 0.06; source.yheight = 0.08;\n",
-    "source.dist = 2; source.focus_xw = 0.05; source.focus_yh = 0.05\n",
-    "source.lambda0 = \"wavelength\"; source.dlambda = 0.05; source.flux = 1E8"
+    "wavelength = Instr.add_parameter(\"double\", \"wavelength\", value=3,\n",
+    "                                 comment=\"[AA] Wavelength emmited from source\")\n",
+    "source.xwidth = 0.06\n",
+    "source.yheight = 0.08;\n",
+    "source.dist = 2\n",
+    "source.focus_xw = 0.05\n",
+    "source.focus_yh = 0.05\n",
+    "source.lambda0 = wavelength # Can provide a parameter object, in this way spelling is checked\n",
+    "source.dlambda = 0.05\n",
+    "source.flux = 1E8"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT Source = Source_simple\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.08\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.06\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mdist\u001b[0m = \u001b[1m\u001b[92m2\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[92mwavelength\u001b[0m\u001b[0m [AA]\n",
-      "  \u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [AA]\n",
-      "  \u001b[1mflux\u001b[0m = \u001b[1m\u001b[92m100000000.0\u001b[0m\u001b[0m [1/(s*cm**2*st*energy unit)]\n",
-      "AT [0, 0, 0] ABSOLUTE\n",
-      "ROTATED [0, 0, 0] ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "source.print_long() # Verify that the information is correct"
+    "print(source) # Verify that the information is correct"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,102 +100,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Guide_gravity _____________________________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[4m\u001b[1mw1\u001b[0m\u001b[0m [m] // Width at the guide entry\n",
-      "\u001b[4m\u001b[1mh1\u001b[0m\u001b[0m [m] // Height at the guide entry\n",
-      "\u001b[1mw2\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Width at the guide exit. If 0, use w1.\n",
-      "\u001b[1mh2\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Height at the guide exit. If 0, use h1.\n",
-      "\u001b[4m\u001b[1ml\u001b[0m\u001b[0m [m] // length of guide\n",
-      "\u001b[1mR0\u001b[0m = \u001b[1m\u001b[94m0.995\u001b[0m\u001b[0m [1] // Low-angle reflectivity\n",
-      "\u001b[1mQc\u001b[0m = \u001b[1m\u001b[94m0.0218\u001b[0m\u001b[0m [AA-1] // Critical scattering vector\n",
-      "\u001b[1malpha\u001b[0m = \u001b[1m\u001b[94m4.38\u001b[0m\u001b[0m [AA] // Slope of reflectivity\n",
-      "\u001b[1mm\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // m-value of material. Zero means completely absorbing. m=0.65  glass/SiO2 \n",
-      "               Si   Ni  Ni58  supermirror Be    Diamond m=  0.65       0.47 1   1.18  2-6   \n",
-      "                     1.01  1.12 for glass/SiO2, m=1 for Ni, 1.2 for Ni58, m=2-6 for \n",
-      "               supermirror. m=0.47 for Si \n",
-      "\u001b[1mW\u001b[0m = \u001b[1m\u001b[94m0.003\u001b[0m\u001b[0m [AA-1] // Width of supermirror cut-off\n",
-      "\u001b[1mnslit\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // Number of vertical channels in the guide (>= 1) (nslit-1 vertical \n",
-      "                   dividing walls). \n",
-      "\u001b[1md\u001b[0m = \u001b[1m\u001b[94m0.0005\u001b[0m\u001b[0m [m] // Thickness of subdividing walls\n",
-      "\u001b[1mmleft\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // m-value of material for left.   vert. mirror\n",
-      "\u001b[1mmright\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // m-value of material for right.  vert. mirror\n",
-      "\u001b[1mmtop\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // m-value of material for top.    horz. mirror\n",
-      "\u001b[1mmbottom\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // m-value of material for bottom. horz. mirror\n",
-      "\u001b[1mnhslit\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // Number of horizontal channels in the guide (>= 1). (nhslit-1 \n",
-      "                    horizontal dividing walls). this enables to have nslit*nhslit \n",
-      "                    rectangular channels \n",
-      "\u001b[1mG\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m/s2] // Gravitation norm. 0 value disables G effects.\n",
-      "\u001b[1maleft\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // alpha-value of left  vert. mirror\n",
-      "\u001b[1maright\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // alpha-value of right vert. mirror\n",
-      "\u001b[1matop\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // alpha-value of top   horz. mirror\n",
-      "\u001b[1mabottom\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // alpha-value of left  horz. mirror\n",
-      "\u001b[1mwavy\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // Global guide waviness\n",
-      "\u001b[1mwavy_z\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // Partial waviness along propagation axis\n",
-      "\u001b[1mwavy_tb\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // Partial waviness in transverse direction for top/bottom mirrors\n",
-      "\u001b[1mwavy_lr\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // Partial waviness in transverse direction for left/right mirrors\n",
-      "\u001b[1mchamfers\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Global chamfers specifications (in/out/mirror sides).\n",
-      "\u001b[1mchamfers_z\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Input and output chamfers\n",
-      "\u001b[1mchamfers_lr\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Chamfers on left/right mirror sides\n",
-      "\u001b[1mchamfers_tb\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Chamfers on top/bottom mirror sides\n",
-      "\u001b[1mnelements\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // Number of sections in the guide (length l/nelements).\n",
-      "\u001b[1mnu\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Hz] // Rotation frequency (round/s) for Fermi Chopper approximation\n",
-      "\u001b[1mphase\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // Phase shift for the Fermi Chopper approximation\n",
-      "\u001b[1mreflect\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [str] // Reflectivity file name. Format <q(Angs-1) R(0-1)>\n",
-      "---------------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "guide.show_parameters() # Lets view the parameters available in our guide component"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "guide.set_parameters({\"w1\" : 0.05, \"w2\" : 0.05, \"h1\" : 0.05, \"h2\" : 0.05,\n",
-    "                      \"l\" : 8, \"m\" : 3.5, \"G\" : -9.2})"
+    "guide.set_parameters({\"w1\" : 0.05, \"w2\" : 0.05, \"h1\" : 0.05, \"h2\" : 0.05, \"l\" : 8, \"m\" : 3.5, \"G\" : -9.2})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT Guide = Guide_gravity\n",
-      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mw2\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mh2\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92m8\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92m3.5\u001b[0m\u001b[0m [1]\n",
-      "  \u001b[1mG\u001b[0m = \u001b[1m\u001b[92m-9.2\u001b[0m\u001b[0m [m/s2]\n",
-      "AT [0, 0, 2] RELATIVE Source\n",
-      "ROTATED [0, 0, 0] RELATIVE Source\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "guide.print_long() # Verify the information on this component is correct"
+    "print(guide) # Verify the information on this component is correct"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -272,43 +137,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Set parameters corresponding to a copper cylinder\n",
-    "sample.radius = 0.015; sample.yheight = 0.05; sample.reflections = \"\\\"Cu.laz\\\"\""
+    "sample.radius = 0.015\n",
+    "sample.yheight = 0.05\n",
+    "sample.reflections = '\"Cu.laz\"'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are all components in the monitors category.\n",
-      " Brilliance_monitor      Monitor           PSD_monitor_psf_eff   TOF2E_monitor\n",
-      " DivLambda_monitor       Monitor_4PI       PSDcyl_monitor        TOF2Q_cylPSD_monitor\n",
-      " DivPos_monitor          Monitor_Sqw       PSDlin_diff_monitor   TOFLambda_monitor\n",
-      " Divergence_monitor      Monitor_nD        PSDlin_monitor        TOF_PSD_monitor_rad\n",
-      " EPSD_monitor            PSD_TOF_monitor   PolLambda_monitor     TOF_cylPSD_monitor\n",
-      " E_monitor               PSD_monitor       Pol_monitor           TOF_monitor\n",
-      " Hdiv_monitor            PSD_monitor_4PI   PreMonitor_nD         TOFlog_monitor\n",
-      " L_monitor               PSD_monitor_TOF   Res_monitor           \n",
-      " MeanPolLambda_monitor   PSD_monitor_psf   Sqq_w_monitor         \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Instr.show_components(\"monitors\") # Monitors are needed to record information"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -318,46 +168,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT PSD_4PI = PSD_monitor_4PI\n",
-      "  \u001b[1mnx\u001b[0m = \u001b[1m\u001b[92m300\u001b[0m\u001b[0m [1]\n",
-      "  \u001b[1mny\u001b[0m = \u001b[1m\u001b[92m300\u001b[0m\u001b[0m [1]\n",
-      "  \u001b[1mfilename\u001b[0m = \u001b[1m\u001b[92m\"PSD_4PI.dat\"\u001b[0m\u001b[0m [string]\n",
-      "  \u001b[1mradius\u001b[0m = \u001b[1m\u001b[92m1\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mrestore_neutron\u001b[0m = \u001b[1m\u001b[92m1\u001b[0m\u001b[0m [1]\n",
-      "AT [0, 0, 0] RELATIVE sample\n",
-      "ROTATED [0, 0, 0] RELATIVE sample\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "sphere.nx = 300; sphere.ny = 300\n",
-    "sphere.radius = 1; sphere.restore_neutron = 1\n",
-    "sphere.filename = \"\\\"PSD_4PI.dat\\\"\" # filenames need printed quotes, use \\\"\n",
-    "sphere.print_long() # Verify that monitors have filenames that are strings when printed"
+    "sphere.nx = 300\n",
+    "sphere.ny = 300\n",
+    "sphere.radius = 1\n",
+    "sphere.restore_neutron = 1\n",
+    "sphere.filename = '\"PSD_4PI.dat\"' # filenames need printed quotes, use both ' and \"\n",
+    "print(sphere) # Verify that monitors have filenames that are strings when printed"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Add PSD monitor to see the direct beam after the sample\n",
     "PSD = Instr.add_component(\"PSD\", \"PSD_monitor\", AT=[0,0,1], RELATIVE=\"sample\") \n",
-    "PSD.xwidth = 0.1; PSD.yheight = 0.1; PSD.nx = 200; PSD.ny = 200\n",
-    "PSD.filename = \"\\\"PSD.dat\\\"\"; PSD.restore_neutron = 1"
+    "PSD.xwidth = 0.1\n",
+    "PSD.yheight = 0.1\n",
+    "PSD.nx = 200\n",
+    "PSD.ny = 200\n",
+    "PSD.filename = '\"PSD.dat\"'\n",
+    "PSD.restore_neutron = 1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -366,79 +207,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Since the wavelength is an instrument parameter, it can be used when setting parameters\n",
-    "L_mon.Lmin = \"wavelength - 0.1\"; L_mon.Lmax = \"wavelength + 0.1\"; L_mon.nL = 150\n",
-    "L_mon.xwidth = 0.1; L_mon.yheight = 0.1\n",
-    "L_mon.filename = \"\\\"wave.dat\\\"\"; L_mon.restore_neutron = 1\n",
+    "L_mon.Lmin = \"wavelength - 0.1\"\n",
+    "L_mon.Lmax = \"wavelength + 0.1\"; L_mon.nL = 150\n",
+    "L_mon.xwidth = 0.1\n",
+    "L_mon.yheight = 0.1\n",
+    "L_mon.filename = '\"wave.dat\"'\n",
+    "L_mon.restore_neutron = 1\n",
     "L_mon.comment = \"Wavelength monitor for narrow range\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "// Wavelength monitor for narrow range\n",
-      "COMPONENT L_mon = L_monitor\n",
-      "  \u001b[1mnL\u001b[0m = \u001b[1m\u001b[92m150\u001b[0m\u001b[0m [1]\n",
-      "  \u001b[1mfilename\u001b[0m = \u001b[1m\u001b[92m\"wave.dat\"\u001b[0m\u001b[0m [string]\n",
-      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mLmin\u001b[0m = \u001b[1m\u001b[92mwavelength - 0.1\u001b[0m\u001b[0m [AA]\n",
-      "  \u001b[1mLmax\u001b[0m = \u001b[1m\u001b[92mwavelength + 0.1\u001b[0m\u001b[0m [AA]\n",
-      "  \u001b[1mrestore_neutron\u001b[0m = \u001b[1m\u001b[92m1\u001b[0m\u001b[0m [1]\n",
-      "AT [0, 0, 0] RELATIVE PSD\n",
-      "ROTATED [0, 0, 0] RELATIVE PSD\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "L_mon.print_long()"
+    "print(L_mon)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Source  Source_simple   AT (0, 0, 0) ABSOLUTE        ROTATED (0, 0, 0) ABSOLUTE\n",
-      "Guide   Guide_gravity   AT (0, 0, 2) RELATIVE Source ROTATED (0, 0, 0) RELATIVE Source\n",
-      "sample  PowderN         AT (0, 0, 9) RELATIVE Guide  ROTATED (0, 0, 0) RELATIVE Guide\n",
-      "PSD_4PI PSD_monitor_4PI AT (0, 0, 0) RELATIVE sample ROTATED (0, 0, 0) RELATIVE sample\n",
-      "PSD     PSD_monitor     AT (0, 0, 1) RELATIVE sample ROTATED (0, 0, 0) RELATIVE sample\n",
-      "L_mon   L_monitor       AT (0, 0, 0) RELATIVE PSD    ROTATED (0, 0, 0) RELATIVE PSD\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Instr.print_components() # Lets get an overview of the instrument so far"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "double  wavelength  =  3  // [AA] Wavelength emmited from source\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Instr.show_parameters()"
    ]
@@ -453,51 +258,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"jupyter_demo_13\"\n",
-      "INFO: Regenerating c-file: jupyter_demo.c\n",
-      "CFLAGS=\n",
-      "INFO: Recompiling: ./jupyter_demo.out\n",
-      "INFO: ===\n",
-      "Warning: 509477 events were removed in Component[5] PSD=PSD_monitor()\n",
-      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "Warning: 509083 events were removed in Component[5] PSD=PSD_monitor()\n",
-      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "Warning: 510047 events were removed in Component[5] PSD=PSD_monitor()\n",
-      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "Warning: 509125 events were removed in Component[5] PSD=PSD_monitor()\n",
-      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "INFO: Placing instr file copy jupyter_demo.instr in dataset jupyter_demo_13\n",
-      "\n",
-      "Simulation 'jupyter_demo' (jupyter_demo.instr): running on 4 nodes (master is 'CI0020872', MPI version 2.1).\n",
-      "Opening input file '/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/tools/Python/mcrun/../mccodelib/../../../data/Cu.laz' (Table_Read_Offset)\n",
-      "Table from file 'Cu.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
-      "  '# TITLE *-Cu-[FM3-M] Otte, H.M.[1961];# CELL 3.615050 3.615050 3.615050 90. ...'\n",
-      "PowderN: sample: Reading 19 rows from Cu.laz\n",
-      "PowderN: sample: Read 19 reflections from file 'Cu.laz'\n",
-      "PowderN: sample: Vc=47.24 [Angs] sigma_abs=15.12 [barn] sigma_inc=2.2 [barn] reflections=Cu.laz\n",
-      "Detector: PSD_4PI_I=42440.9 PSD_4PI_ERR=25.8046 PSD_4PI_N=8.59445e+06 \"PSD_4PI.dat\"\n",
-      "Detector: PSD_I=35456.6 PSD_ERR=24.9783 PSD_N=4.48913e+06 \"PSD.dat\"\n",
-      "Detector: L_mon_I=35456.6 L_mon_ERR=24.9783 L_mon_N=4.48913e+06 \"wave.dat\"\n",
-      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
-      "    SPLIT 8 COMPONENT sample=PowderN(...)\n",
-      "  in the instrument description jupyter_demo.instr.\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# With increment_folder_name enabled, a new folder with incremented number is created\n",
-    "data = Instr.run_full_instrument(foldername=\"jupyter_demo\",\n",
-    "                                 parameters={\"wavelength\" : 1.5},\n",
-    "                                 mpi=4, ncount=2E7,\n",
-    "                                 increment_folder_name = True)"
+    "# output_path specifies the foldername, if it already exists an index is added\n",
+    "Instr.settings(output_path=\"jupyter_demo\", mpi=4, ncount=2E7)\n",
+    "\n",
+    "# Input parameters are set with set_parameters\n",
+    "Instr.set_parameters(wavelength=1.5)\n",
+    "\n",
+    "# The simulation is performed by calling backengine()\n",
+    "Instr.backengine()\n",
+    "\n",
+    "# The data is retrieved from the data attribute\n",
+    "data = Instr.data"
    ]
   },
   {
@@ -511,21 +286,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.494, 474.8222215]\n",
-      "[1.495333333, 474.7230047]\n",
-      "[1.496666667, 465.981932]\n",
-      "[1.498, 476.3501473]\n",
-      "[1.499333333, 470.3317335]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "wavelength_data = functions.name_search(\"L_mon\", data)\n",
     "wavelength_intensity = wavelength_data.Intensity\n",
@@ -545,32 +308,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "number of elements in data list = 3\n",
-      "Plotting data with name PSD_4PI\n",
-      "Plotting data with name PSD\n",
-      "Plotting data with name L_mon\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 6 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Adjusting PSD_4PI plot\n",
     "functions.name_plot_options(\"PSD_4PI\", data, log=1, colormap=\"hot\", orders_of_mag=5)\n",
@@ -595,7 +335,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/examples/calibration_sample.ipynb b/examples/calibration_sample.ipynb
index c521a8d2..c0ca65fd 100644
--- a/examples/calibration_sample.ipynb
+++ b/examples/calibration_sample.ipynb
@@ -74,7 +74,7 @@
    "metadata": {},
    "source": [
     "### Describing a simple instrument\n",
-    "Now we start describing an instrument, we start with the source. We would like to control the energy and energy spread at run time, so this will be described using input parameters"
+    "Now we start describing an instrument, we start with the source. We would like to control the energy and energy spread at run time, so this will be described using input parameters. These are added with the *add_parameter* method, which return a ParameterVariable object that can be used to refer to this variable."
    ]
   },
   {
@@ -84,8 +84,8 @@
    "outputs": [],
    "source": [
     "# Since we want to change the energy and energy range at run time, we add these as instrument parameters\n",
-    "Instr.add_parameter(\"energy\", value=10, comment=\"[meV] Energy of source\")\n",
-    "Instr.add_parameter(\"delta_energy\", value=8, comment=\"[meV] Energy spread of source\")\n",
+    "energy = Instr.add_parameter(\"energy\", value=10, comment=\"[meV] Energy of source\")\n",
+    "delta_energy = Instr.add_parameter(\"delta_energy\", value=8, comment=\"[meV] Energy spread of source\")\n",
     "\n",
     "# Add a source to the McStas instrument\n",
     "src = Instr.add_component(\"source\", \"Source_div\")\n",
@@ -93,8 +93,8 @@
     "src.yheight = 0.11\n",
     "src.focus_aw = 0.1\n",
     "src.focus_ah = 0.1\n",
-    "src.E0 = \"energy\"\n",
-    "src.dE = \"delta_energy\"\n",
+    "src.E0 = energy\n",
+    "src.dE = delta_energy\n",
     "src.flux = 1E13;"
    ]
   },
@@ -113,8 +113,8 @@
    "source": [
     "# Now we want to set a position and rotation of our sample\n",
     "# The rotation should be adjustable, so we add instrument parameters for controling the rotation\n",
-    "Instr.add_parameter(\"rotation_y\", value=180, comment=\"[deg] Rotation around vertical\")\n",
-    "Instr.add_parameter(\"rotation_x\", value=0, comment=\"[deg] Rotation around horizontal\")"
+    "rot_y = Instr.add_parameter(\"rotation_y\", value=180, comment=\"[deg] Rotation around vertical\")\n",
+    "rot_x = Instr.add_parameter(\"rotation_x\", value=0, comment=\"[deg] Rotation around horizontal\")"
    ]
   },
   {
@@ -126,7 +126,7 @@
     "# We add arms at the sample position, and a second arm with the correct rotation\n",
     "sample_pos = Instr.add_component(\"sample_pos\", \"Arm\", AT=[0,0,1], AT_RELATIVE=\"source\")\n",
     "sample_arm = Instr.add_component(\"sample_arm\", \"Arm\", AT=[0,0,0], AT_RELATIVE=\"sample_pos\")\n",
-    "sample_arm.set_ROTATED([\"rotation_x\", \"rotation_y\", 0], RELATIVE=\"sample_pos\")"
+    "sample_arm.set_ROTATED([rot_x, rot_y, 0], RELATIVE=\"sample_pos\")"
    ]
   },
   {
@@ -237,21 +237,21 @@
    "outputs": [],
    "source": [
     "# A few Union loggers are set up for display of the scattering locations\n",
-    "space_2D_zx = Instr.add_component(\"logger_space_zx_all\", \"Union_logger_2D_space\", AT_RELATIVE=\"sample_pos\")\n",
+    "space_2D_zx = Instr.add_component(\"logger_space_zx_all\", \"Union_logger_2D_space\", AT_RELATIVE=sample_pos)\n",
     "space_2D_zx.filename = \"\\\"space_zx.dat\\\"\"\n",
     "space_2D_zx.D_direction_1 = \"\\\"z\\\"\"; space_2D_zx.n1 = 1000\n",
     "space_2D_zx.D1_min = -0.05; space_2D_zx.D1_max = 0.05\n",
     "space_2D_zx.D_direction_2 = \"\\\"x\\\"\"; space_2D_zx.n2 = 1000\n",
     "space_2D_zx.D2_min = -0.05; space_2D_zx.D2_max = 0.05\n",
     "\n",
-    "space_2D_zy = Instr.add_component(\"logger_space_zy_all\", \"Union_logger_2D_space\", AT_RELATIVE=\"sample_pos\")\n",
+    "space_2D_zy = Instr.add_component(\"logger_space_zy_all\", \"Union_logger_2D_space\", AT_RELATIVE=sample_pos)\n",
     "space_2D_zy.filename = \"\\\"space_zy.dat\\\"\"\n",
     "space_2D_zy.D_direction_1 = \"\\\"z\\\"\"; space_2D_zy.n1 = 1000\n",
     "space_2D_zy.D1_min = -0.05; space_2D_zy.D1_max = 0.05\n",
     "space_2D_zy.D_direction_2 = \"\\\"y\\\"\"; space_2D_zy.n2 = 1000\n",
     "space_2D_zy.D2_min = -0.05; space_2D_zy.D2_max = 0.05\n",
     "\n",
-    "space_2D_zy = Instr.add_component(\"logger_space_xy_all\", \"Union_logger_2D_space\", AT_RELATIVE=\"sample_pos\")\n",
+    "space_2D_zy = Instr.add_component(\"logger_space_xy_all\", \"Union_logger_2D_space\", AT_RELATIVE=sample_pos)\n",
     "space_2D_zy.filename = \"\\\"space_xy.dat\\\"\"\n",
     "space_2D_zy.D_direction_1 = \"\\\"x\\\"\"; space_2D_zy.n1 = 1000\n",
     "space_2D_zy.D1_min = -0.05; space_2D_zy.D1_max = 0.05\n",
@@ -273,7 +273,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "master = Instr.add_component(\"calibration_sample\", \"Union_master\", AT_relative=\"sample_pos\")"
+    "master = Instr.add_component(\"calibration_sample\", \"Union_master\", AT_relative=sample_pos)"
    ]
   },
   {
@@ -281,7 +281,7 @@
    "metadata": {},
    "source": [
     "### McStas monitors\n",
-    "At the end we add a few McStas monitors to view the transmitted beam, including a PSD / energy monitor to see the Bragg edges of the different materials."
+    "At the end we add a few McStas monitors to view the transmitted beam, including a PSD / energy monitor to see the Bragg edges of the different materials. Notice in the EPSD monitor we use the input parameters in a small c expression to calculate the minimum and maximum energy simulated by the source."
    ]
   },
   {
@@ -291,7 +291,7 @@
    "outputs": [],
    "source": [
     "# Add position sensitive detector for transmission measurement\n",
-    "PSD = Instr.add_component(\"PSD\", \"PSD_monitor\", AT=[0,0,1], RELATIVE=\"sample_pos\") \n",
+    "PSD = Instr.add_component(\"PSD\", \"PSD_monitor\", AT=[0,0,1], RELATIVE=sample_pos) \n",
     "PSD.xwidth = 0.1; PSD.yheight = 0.1; PSD.nx = 200; PSD.ny = 200;\n",
     "PSD.filename = \"\\\"PSD.dat\\\"\"; PSD.restore_neutron = 1"
    ]
@@ -303,7 +303,7 @@
    "outputs": [],
    "source": [
     "# Adds 1D position sensitive detector for transmission measurement\n",
-    "PSD = Instr.add_component(\"PSDlin\", \"PSDlin_monitor\", AT=[0,0,1], RELATIVE=\"sample_pos\") \n",
+    "PSD = Instr.add_component(\"PSDlin\", \"PSDlin_monitor\", AT=[0,0,1], RELATIVE=sample_pos) \n",
     "PSD.xwidth = 0.1; PSD.yheight = 0.1; PSD.nx = 200;\n",
     "PSD.filename = \"\\\"PSDlin.dat\\\"\"; PSD.restore_neutron = 1"
    ]
@@ -315,7 +315,7 @@
    "outputs": [],
    "source": [
     "# Add energy position monitor to see Bragg edges\n",
-    "EPSD = Instr.add_component(\"EPSD\", \"EPSD_monitor\", RELATIVE=\"PSD\")\n",
+    "EPSD = Instr.add_component(\"EPSD\", \"EPSD_monitor\", RELATIVE=PSD)\n",
     "EPSD.xwidth = 0.1; EPSD.yheight = 0.02; EPSD.nE = 300; EPSD.nx = 200;\n",
     "EPSD.filename = \"\\\"EPSD.dat\\\"\"; EPSD.restore_neutron = 1;\n",
     "EPSD.Emin = \"energy - delta_energy\"\n",
@@ -361,7 +361,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "Instr.show_instrument()"
@@ -413,69 +415,6 @@
     "plotter.interface(data)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/examples/libpyvinyl_example.ipynb b/examples/libpyvinyl_example.ipynb
index ab21f99d..478d98bc 100644
--- a/examples/libpyvinyl_example.ipynb
+++ b/examples/libpyvinyl_example.ipynb
@@ -90,7 +90,10 @@
     "source_height.add_interval(0.01, 0.2, intervals_are_legal=True)\n",
     "\n",
     "sample_height = calculator.add_parameter(\"sample_height\", unit=\"cm\", comment=\"Height of sample\")\n",
-    "sample_height.add_interval(0.0, 0.05, intervals_are_legal=True)"
+    "sample_height.add_interval(0.0, 0.05, intervals_are_legal=True)\n",
+    "\n",
+    "source_energy_spread = calculator.add_declare_var(\"double\", \"source_energy_spread\")\n",
+    "calculator.append_initialize(\"source_energy_spread = 0.02*source_energy;\")"
    ]
   },
   {
@@ -163,7 +166,7 @@
     "src.xwidth = 0.12\n",
     "src.yheight = source_height\n",
     "src.E0 = source_energy\n",
-    "src.dE = 3\n",
+    "src.dE = source_energy_spread\n",
     "src.focus_aw = 3.0\n",
     "src.focus_ah = 4.0"
    ]
@@ -335,7 +338,60 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "premier-eleven",
+   "id": "virtual-cleaners",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/mcstasscript/interface/instr.py:1693: UserWarning: run_full_instrument will be removed in future version of McStasScript. \n",
+      "Instead supply parameters with set_parameters, set settings with settings and use backengine() to run. See examples in package.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/run_full_instrument_path_3\"\n",
+      "INFO: Regenerating c-file: demo_instrument.c\n",
+      "CFLAGS=\n",
+      "INFO: Recompiling: ./demo_instrument.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "1 warning generated.\n",
+      "INFO: ===\n",
+      "Warning: 9688 events were removed in Component[4] acceptance_horizontal=DivPos_monitor()\n",
+      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
+      "INFO: Placing instr file copy demo_instrument.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/run_full_instrument_path_3\n",
+      "\n",
+      "  Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Ni.laz' (Table_Read_Offset)\n",
+      "Table from file 'Ni.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
+      "  '# TITLE *Nickel-Ni-[FM3-M] Swanson, H.E.;Tatge, E.[1954] [carcinogen];# CEL ...'\n",
+      "PowderN: powder: Reading 19 rows from Ni.laz\n",
+      "PowderN: powder: Read 19 reflections from file 'Ni.laz'\n",
+      "PowderN: powder: Vc=43.76 [Angs] sigma_abs=17.96 [barn] sigma_inc=20.8 [barn] reflections=Ni.laz\n",
+      "Detector: cyl_monitor_I=0.0552829 cyl_monitor_ERR=0.000384292 cyl_monitor_N=54838 \"cylinder.dat\"\n",
+      "Detector: acceptance_horizontal_I=1.32373 acceptance_horizontal_ERR=0.00101418 acceptance_horizontal_N=1.89423e+06 \"acceptance_h.dat\"\n",
+      "PowderN: powder: Info: you may highly improve the computation efficiency by using\n",
+      "    SPLIT 19 COMPONENT powder=PowderN(...)\n",
+      "  in the instrument description demo_instrument.instr.\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "old_data = calculator.run_full_instrument(parameters={\"source_energy\": 320, \"source_height\":0.025, \"sample_height\":0.04},\n",
+    "                               ncount=2E6, foldername=\"run_full_instrument_path\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "closed-promise",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -344,7 +400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "smooth-cholesterol",
    "metadata": {},
    "outputs": [],
@@ -362,7 +418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "daily-louisiana",
    "metadata": {},
    "outputs": [
@@ -370,7 +426,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/path_to_output_4\"\n",
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/path_to_output\"\n",
       "INFO: Regenerating c-file: demo_instrument.c\n",
       "CFLAGS=\n",
       "INFO: Recompiling: ./demo_instrument.out\n",
@@ -382,11 +438,11 @@
       "  ^~~\n",
       "2 warnings generated.\n",
       "INFO: ===\n",
-      "Warning: 12132 events were removed in Component[4] acceptance_horizontal=DivPos_monitor()\n",
+      "Warning: 11956 events were removed in Component[4] acceptance_horizontal=DivPos_monitor()\n",
       "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "Warning: 12144 events were removed in Component[4] acceptance_horizontal=DivPos_monitor()\n",
+      "Warning: 12035 events were removed in Component[4] acceptance_horizontal=DivPos_monitor()\n",
       "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "INFO: Placing instr file copy demo_instrument.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/path_to_output_4\n",
+      "INFO: Placing instr file copy demo_instrument.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/path_to_output\n",
       "\n",
       "  Simulation 'demo_instrument' (demo_instrument.instr): running on 2 nodes (master is 'CI0021617', MPI version 3.1).\n",
       "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Ni.laz' (Table_Read_Offset)\n",
@@ -395,8 +451,8 @@
       "PowderN: powder: Reading 19 rows from Ni.laz\n",
       "PowderN: powder: Read 19 reflections from file 'Ni.laz'\n",
       "PowderN: powder: Vc=43.76 [Angs] sigma_abs=17.96 [barn] sigma_inc=20.8 [barn] reflections=Ni.laz\n",
-      "Detector: cyl_monitor_I=0.02597 cyl_monitor_ERR=0.000114161 cyl_monitor_N=136619 \"cylinder.dat\"\n",
-      "Detector: acceptance_horizontal_I=0.620813 acceptance_horizontal_ERR=0.000300875 acceptance_horizontal_N=4.73624e+06 \"acceptance_h.dat\"\n",
+      "Detector: cyl_monitor_I=0.0553181 cyl_monitor_ERR=0.000243053 cyl_monitor_N=136639 \"cylinder.dat\"\n",
+      "Detector: acceptance_horizontal_I=1.32423 acceptance_horizontal_ERR=0.000641726 acceptance_horizontal_N=4.73644e+06 \"acceptance_h.dat\"\n",
       "PowderN: powder: Info: you may highly improve the computation efficiency by using\n",
       "    SPLIT 19 COMPONENT powder=PowderN(...)\n",
       "  in the instrument description demo_instrument.instr.\n",
@@ -419,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "global-loading",
    "metadata": {},
    "outputs": [],
@@ -437,7 +493,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "retired-brick",
    "metadata": {},
    "outputs": [
@@ -451,7 +507,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 936x504 with 3 Axes>"
       ]
@@ -468,7 +524,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "applied-florence",
+   "id": "surgical-footwear",
    "metadata": {},
    "source": [
     "### Store in dump file"
@@ -476,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "affiliated-british",
    "metadata": {},
    "outputs": [
@@ -486,7 +542,7 @@
        "'dump_file.dmp'"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -497,7 +553,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "rotary-military",
+   "id": "lovely-cigarette",
    "metadata": {},
    "source": [
     "### Load from dump file"
@@ -505,7 +561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 28,
    "id": "pleasant-machine",
    "metadata": {},
    "outputs": [
@@ -555,8 +611,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "experienced-reminder",
+   "execution_count": 30,
+   "id": "expensive-tenant",
    "metadata": {},
    "outputs": [
     {
@@ -564,6 +620,9 @@
      "output_type": "stream",
      "text": [
       "demo_instrument\n",
+      "double source_energy  = 320    // Source mean energy\n",
+      "double source_height  = 0.025  // Height of source\n",
+      "       sample_height  = 0.04   // Height of sample\n",
       "source                Source_div     AT      (0, 0, 0)   ABSOLUTE       \n",
       "powder                PowderN        AT      (0, 0, 1)   RELATIVE source\n",
       "cyl_monitor           Cyl_monitor    AT      (0, 0, 0)   RELATIVE powder\n",
@@ -573,13 +632,15 @@
    ],
    "source": [
     "print(from_dump.name)\n",
-    "from_dump.print_components()"
+    "from_dump.show_parameters()\n",
+    "from_dump.print_components()\n",
+    "from_dump.write_full_instrument()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "talented-cheese",
+   "execution_count": 31,
+   "id": "authorized-cigarette",
    "metadata": {},
    "outputs": [
     {
@@ -603,7 +664,7 @@
       "* \n",
       "* %Identification\n",
       "* Written by: Python McStas Instrument Generator\n",
-      "* Date: 17:07:30 on December 10, 2021\n",
+      "* Date: 12:21:44 on December 15, 2021\n",
       "* Origin: ESS DMSC\n",
       "* %INSTRUMENT_SITE: Generated_instruments\n",
       "* \n",
@@ -614,25 +675,27 @@
       "********************************************************************************/\n",
       "\n",
       "DEFINE INSTRUMENT demo_instrument (\n",
-      "double source_energy = 320,// Source mean energy\n",
-      "double source_height = 0.025,// Height of source\n",
-      " sample_height = 0.04 // Height of sample\n",
+      "double source_energy = 320, // Source mean energy\n",
+      "double source_height = 0.025, // Height of source\n",
+      "sample_height = 0.04 // Height of sample\n",
       ")\n",
       "\n",
       "DECLARE \n",
       "%{\n",
+      "double source_energy_spread;\n",
       "%}\n",
       "\n",
       "INITIALIZE \n",
       "%{\n",
       "// Start of initialize for generated demo_instrument\n",
+      "source_energy_spread = 0.02*source_energy;\n",
       "%}\n",
       "\n",
       "TRACE \n",
       "COMPONENT source = Source_div(\n",
       " xwidth = 0.12, yheight = source_height,\n",
       " focus_aw = 3, focus_ah = 4,\n",
-      " E0 = source_energy, dE = 3)\n",
+      " E0 = source_energy, dE = source_energy_spread)\n",
       "AT (0,0,0) ABSOLUTE\n",
       "\n",
       "COMPONENT powder = PowderN(\n",
@@ -672,7 +735,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "collectible-patrick",
+   "id": "fixed-address",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index b696ea79..7891bede 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -50,6 +50,11 @@ class ManagedMcrun:
 
     def __init__(self, instr_name, **kwargs):
         """
+        Performs call to McStas with given options
+
+        Uses subprocess to call mcrun / mxrun to perform simulation of given
+        instrument file.
+
         Parameters
         ----------
         instr_name : str
diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 7c89b58b..8177a5d5 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -732,6 +732,11 @@ def set_AT(self, at_list, RELATIVE=None):
             raise RuntimeError("Position data given to set_AT should "
                                + "either be of length 3 or just a float.")
 
+        # If parameter objects given, take their name instead
+        for index, element in enumerate(at_list):
+            if isinstance(element, (ParameterVariable, DeclareVariable)):
+                at_list[index] = element.name
+
         self.AT_data = at_list
         if RELATIVE is not None:
             self.set_AT_RELATIVE(RELATIVE)
@@ -763,6 +768,11 @@ def set_ROTATED(self, rotated_list, RELATIVE=None):
             raise RuntimeError("Rotation data given to set_ROTATED should "
                                + "be of length 3.")
 
+        # If parameter objects given, take their name instead
+        for index, element in enumerate(rotated_list):
+            if isinstance(element, (ParameterVariable, DeclareVariable)):
+                rotated_list[index] = element.name
+
         self.ROTATED_data = rotated_list
         self.ROTATED_specified = True
         if RELATIVE is not None:
@@ -1067,9 +1077,17 @@ class is used as a superclass for classes describing each
                 unit = ""
                 if key in self.parameter_units:
                     unit = "[" + self.parameter_units[key] + "]"
+                if isinstance(val, ParameterVariable):
+                    #val_string = val.print_line() # too long
+                    val_string = val.name
+                elif isinstance(val, DeclareVariable):
+                    val_string = val.name
+                else:
+                    val_string = str(val)
+
                 value = (bcolors.BOLD
                          + bcolors.OKGREEN
-                         + str(val)
+                         + val_string
                          + bcolors.ENDC
                          + bcolors.ENDC)
                 string += "  " + parameter_name
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index b0d40bd2..ae847c09 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -254,24 +254,26 @@ def __init__(self, name, parameters=None, dumpfile=None, **kwargs):
         # If no parameters given, initialize parameter container
         if self.parameters is None:
             self.parameters = ParameterContainer()
-        else:
-            if not isinstance(self.parameters, ParameterContainer):
-                # Need to convert McStasScript parameters
-                if isinstance(self.parameters, CalculatorParameters):
-                    mcstasscript_parameters = ParameterContainer()
-                    mcstasscript_parameters.import_parameters(self.parameters)
-                    self.parameters = mcstasscript_parameters
 
-                else:
-                    raise RuntimeError("Given parameters object not"
-                                       + " recognized.")
+        if not isinstance(self.parameters, ParameterContainer):
+            # Need to convert McStasScript parameters
+            if isinstance(self.parameters, CalculatorParameters):
+                mcstasscript_parameters = ParameterContainer()
+                mcstasscript_parameters.import_parameters(self.parameters)
+                self.parameters = mcstasscript_parameters
+
+            else:
+                raise TypeError("Input parameter 'parameters' must be of "
+                                + "type CalculatorParameters or "
+                                + "ParameterContainer, not "
+                                + "%s", type(self.parameters))
 
         # Check required attributes has been set by class that inherits
         if not (hasattr(self, "particle") or
                 hasattr(self, "executable") or
                 hasattr(self, "package_name")):
             raise AttributeError("McCode_instr is a base class, use "
-                                 + "McStas_intr or McXtrace_instr instead.")
+                                 + "McStas_instr or McXtrace_instr instead.")
 
         if not is_legal_filename(self.name + ".instr"):
             raise NameError("The instrument is called: \""
@@ -1564,7 +1566,7 @@ def settings(self, **kwargs):
 
         Some options are mandatory, for example output_path, which can not
         already exist, if it does data will be read from this folder. If the
-        mcrun command is not in the path of the system, the absolute path can
+        mcrun command is not in the PATH of the system, the absolute path can
         be given with the executable_path keyword argument. This path could
         also already have been set at initialization of the instrument object.
 
@@ -1629,7 +1631,7 @@ def backengine(self):
 
         This method will write the instrument to disk and then run it
         using the mcrun command of the system. Settings are set using
-        settings methods.
+        settings method.
         """
 
         if self.current_run_settings is None:
@@ -1776,8 +1778,12 @@ def get_interface_data(self):
         return self.widget_interface.plot_interface.data
 
     def saveH5(self, filename: str, openpmd: bool = True):
+        """
+        Not relevant, but required from BaseCalculator, will be removed
+        """
         pass
 
+
 class McStas_instr(McCode_instr):
     """
     Main class for writing a McStas instrument using McStasScript
diff --git a/tutorial/McStasScript_tutorial_1_the_basics.ipynb b/tutorial/McStasScript_tutorial_1_the_basics.ipynb
index c0e80693..4a99ca95 100644
--- a/tutorial/McStasScript_tutorial_1_the_basics.ipynb
+++ b/tutorial/McStasScript_tutorial_1_the_basics.ipynb
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,13 +74,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "configurator = functions.Configurator()\n",
-    "configurator.set_mcrun_path(\"/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin/\")\n",
-    "configurator.set_mcstas_path(\"/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5\")"
+    "configurator.set_mcrun_path(\"/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\")\n",
+    "configurator.set_mcstas_path(\"/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\")"
    ]
   },
   {
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,27 +112,75 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are the available component categories:\n",
+      " sources\n",
+      " optics\n",
+      " samples\n",
+      " monitors\n",
+      " misc\n",
+      " contrib\n",
+      " union\n",
+      " obsolete\n",
+      "Call show_components(category_name) to display\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.show_components()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are all components in the sources category.\n",
+      " Adapt_check     Moderator           Source_Optimizer   Source_gen\n",
+      " ESS_butterfly   Monitor_Optimizer   Source_adapt       Source_simple\n",
+      " ESS_moderator   Source_Maxwell_3    Source_div         \n"
+     ]
+    }
+   ],
    "source": [
     "instrument.show_components(\"sources\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Source_div ________________________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[4m\u001b[1mxwidth\u001b[0m\u001b[0m [m] // Width of source\n",
+      "\u001b[4m\u001b[1myheight\u001b[0m\u001b[0m [m] // Height of source\n",
+      "\u001b[4m\u001b[1mfocus_aw\u001b[0m\u001b[0m [deg] // FWHM (Gaussian) or maximal (uniform) horz. width divergence\n",
+      "\u001b[4m\u001b[1mfocus_ah\u001b[0m\u001b[0m [deg] // FWHM (Gaussian) or maximal (uniform) vert. height divergence\n",
+      "\u001b[1mE0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Mean energy of neutrons.\n",
+      "\u001b[1mdE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Energy half spread of neutrons.\n",
+      "\u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Ang] // Mean wavelength of neutrons (only relevant for E0=0)\n",
+      "\u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Ang] // Wavelength half spread of neutrons.\n",
+      "\u001b[1mgauss\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [0|1] // Criterion\n",
+      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1/(s cm 2 st energy_unit)] // flux per energy unit, Angs or meV\n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.component_help(\"Source_div\")"
    ]
@@ -153,9 +201,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "source Source_div AT (0, 0, 0) ABSOLUTE\n"
+     ]
+    }
+   ],
    "source": [
     "src = instrument.add_component(\"source\", \"Source_div\")\n",
     "instrument.print_components()"
@@ -166,16 +222,30 @@
    "metadata": {},
    "source": [
     "## Working with component objects\n",
-    "The src object created by *add_component* can be used to modify the component. It also holds the information on the component, which can be shown with the *print_long* method. This will tell us for example if any required parameters are yet to be set and the position of the component."
+    "The src object created by *add_component* can be used to modify the component. It also holds the information on the component, which can be shown by printing the object. This will tell us for example if any required parameters are yet to be set and the position of the component."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT source = Source_div\n",
+      "  \u001b[1mxwidth\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1myheight\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1mfocus_aw\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1mfocus_ah\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "AT [0, 0, 0] ABSOLUTE\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "src.print_long()"
+    "print(src)"
    ]
   },
   {
@@ -188,16 +258,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT source = Source_div\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_aw\u001b[0m = \u001b[1m\u001b[92m1.2\u001b[0m\u001b[0m [deg]\n",
+      "  \u001b[1mfocus_ah\u001b[0m = \u001b[1m\u001b[92m2.3\u001b[0m\u001b[0m [deg]\n",
+      "AT [0, 0, 0] ABSOLUTE\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "src.xwidth = 0.1\n",
     "src.yheight = 0.05\n",
     "src.focus_aw = 1.2\n",
     "src.focus_ah = 2.3\n",
     "\n",
-    "src.print_long()"
+    "print(src)"
    ]
   },
   {
@@ -205,14 +289,36 @@
    "metadata": {},
    "source": [
     "### Getting status of all parameters\n",
-    "Using *print_long* on a component only show the required parameters and user specified parameters, but it is also possible to see all parameters with the *show_parameters* method. This reminds us to set an energy or wavelength range for the source, as it is necessary to set one of these even though they are technically not required parameters."
+    "Printing a component only show the required parameters and user specified parameters, but it is also possible to see all parameters with the *show_parameters* method. This reminds us to set an energy or wavelength range for the source, as it is necessary to set one of these even though they are technically not required parameters."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Source_div ________________________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[4m\u001b[1mxwidth\u001b[0m\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m] // Width of source\n",
+      "\u001b[4m\u001b[1myheight\u001b[0m\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m] // Height of source\n",
+      "\u001b[4m\u001b[1mfocus_aw\u001b[0m\u001b[0m = \u001b[1m\u001b[92m1.2\u001b[0m\u001b[0m [deg] // FWHM (Gaussian) or maximal (uniform) horz. width \n",
+      "                        divergence \n",
+      "\u001b[4m\u001b[1mfocus_ah\u001b[0m\u001b[0m = \u001b[1m\u001b[92m2.3\u001b[0m\u001b[0m [deg] // FWHM (Gaussian) or maximal (uniform) vert. height \n",
+      "                        divergence \n",
+      "\u001b[1mE0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Mean energy of neutrons.\n",
+      "\u001b[1mdE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Energy half spread of neutrons.\n",
+      "\u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Ang] // Mean wavelength of neutrons (only relevant for E0=0)\n",
+      "\u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Ang] // Wavelength half spread of neutrons.\n",
+      "\u001b[1mgauss\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [0|1] // Criterion\n",
+      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1/(s cm 2 st energy_unit)] // flux per energy unit, Angs or meV\n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
    "source": [
     "src.show_parameters()"
    ]
@@ -222,19 +328,28 @@
    "metadata": {},
    "source": [
     "### Adding an instrument parameter to control wavelength\n",
-    "Controlling the wavelength range emitted by the source is best done with an instrument parameter, then this same parameter can be used to for example rotate a monochromator or set the range for an wavelength sensitive monitor. Adding an instrument parameter is done using the instrument method *add_parameter*, and it is possible to set a default value and comment. The current instrument parameters can be viewed with the *show_parameters* method on the isntrument object.\n",
+    "Controlling the wavelength range emitted by the source is best done with an instrument parameter, then this same parameter can be used to for example rotate a monochromator or set the range for an wavelength sensitive monitor. Adding an instrument parameter is done using the instrument method *add_parameter*, and it is possible to set a default value and comment. The method returns a parameter object that can be used to assign the parameter to a component. The current instrument parameters can be viewed with the *show_parameters* method on the isntrument object.\n",
     "\n",
     "The default type for instrument parameters is a double (floating point number), but other types can be selected if necessary by providing a type string before, here we also provide an example of an integer."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
-    "instrument.add_parameter(\"int\", \"order\", value=1, comment=\"Monochromator order, integer\")\n",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    wavelength  = 5.0  // Wavelength in [Ang]\n",
+      "int order       = 1    // Monochromator order, integer\n"
+     ]
+    }
+   ],
+   "source": [
+    "wavelength = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "order = instrument.add_parameter(\"int\", \"order\", value=1, comment=\"Monochromator order, integer\")\n",
     "instrument.show_parameters()"
    ]
   },
@@ -247,13 +362,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "src.lambda0=\"wavelength\"\n",
-    "src.dlambda=\"0.01*wavelength\"\n",
-    "src.print_long()"
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT source = Source_div\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_aw\u001b[0m = \u001b[1m\u001b[92m1.2\u001b[0m\u001b[0m [deg]\n",
+      "  \u001b[1mfocus_ah\u001b[0m = \u001b[1m\u001b[92m2.3\u001b[0m\u001b[0m [deg]\n",
+      "  \u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[92mwavelength     5.0            Wavelength in [Ang]   \u001b[0m\u001b[0m [Ang]\n",
+      "  \u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[92m0.01*wavelength\u001b[0m\u001b[0m [Ang]\n",
+      "AT [0, 0, 0] ABSOLUTE\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "src.lambda0 = wavelength\n",
+    "src.dlambda = \"0.01*wavelength\" # When performing math use a string and the parameter name\n",
+    "print(src)"
    ]
   },
   {
@@ -273,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -289,9 +420,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT guide = Guide_gravity\n",
+      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mw2\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mh2\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92m8.0\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92m3.5\u001b[0m\u001b[0m [1]\n",
+      "  \u001b[1mG\u001b[0m = \u001b[1m\u001b[92m-9.82\u001b[0m\u001b[0m [m/s2]\n",
+      "AT [0, 0, 2] RELATIVE source\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "guide.w1 = 0.05\n",
     "guide.w2 = 0.05\n",
@@ -301,7 +449,7 @@
     "guide.m = 3.5\n",
     "guide.G = -9.82\n",
     "\n",
-    "guide.print_long()"
+    "print(guide)"
    ]
   },
   {
@@ -311,22 +459,22 @@
     "## Adding calculations to an instrument file\n",
     "One of the advantages of McStas is the ease of adding calculations to the instrument. Here we calculate the rotation of a monochromator so that its scatters the wavelengths from our source. We need to declare variables using *add_declare_var* and append C code to initialize using *append_initialize*.\n",
     "\n",
-    "For *add_declare_var* the first argument is the C type, usually double or int, the next is the variable name. A default value can be specified with the value keyword.\n",
+    "For *add_declare_var* the first argument is the C type, usually double or int, the next is the variable name. A default value can be specified with the value keyword. Like when adding a parameter, a *add_declare* also returns an object that can be used to refer to this variable later.\n",
     "\n",
     "*append_initialize* just adds the given C code to the initialize section of the McStas instrument file. It is necessary to follow C syntax, for example remember semicolon at the end of statements."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument.add_declare_var(\"double\", \"mono_Q\", value=1.714) # Q for Ge 311\n",
+    "mono_Q = instrument.add_declare_var(\"double\", \"mono_Q\", value=1.714) # Q for Ge 311\n",
     "instrument.add_declare_var(\"double\", \"wavevector\")\n",
     "instrument.append_initialize(\"wavevector = 2.0*PI/wavelength;\")\n",
     "\n",
-    "instrument.add_declare_var(\"double\", \"mono_rotation\")\n",
+    "mono_rotation = instrument.add_declare_var(\"double\", \"mono_rotation\")\n",
     "instrument.append_initialize(\"mono_rotation = asin(mono_Q/(2.0*wavevector))*RAD2DEG;\")\n",
     "instrument.append_initialize('printf(\"monochromator rotation = %g deg\\\\n\", mono_rotation);')"
    ]
@@ -343,7 +491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -352,24 +500,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
     "mono.zwidth = 0.05\n",
     "mono.yheight = 0.08\n",
-    "mono.Q = \"mono_Q\"\n",
+    "mono.Q = mono_Q\n",
     "mono.set_AT([0, 0, 8.5], RELATIVE=guide)\n",
-    "mono.set_ROTATED([0, \"mono_rotation\", 0], RELATIVE=\"guide\")"
+    "mono.set_ROTATED([0, mono_rotation, 0], RELATIVE=\"guide\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT mono = Monochromator_flat\n",
+      "  \u001b[1mzwidth\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.08\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mQ\u001b[0m = \u001b[1m\u001b[92m<mcstasscript.helper.mcstas_objects.DeclareVariable object at 0x7fab109241f0>\u001b[0m\u001b[0m [1/angstrom]\n",
+      "AT [0, 0, 8.5] RELATIVE guide\n",
+      "ROTATED [0, 'mono_rotation', 0] RELATIVE guide\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "mono.print_long()"
+    "print(mono)"
    ]
   },
   {
@@ -382,12 +544,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
     "beam_direction = instrument.add_component(\"beam_dir\", \"Arm\", AT_RELATIVE=\"mono\")\n",
-    "beam_direction.set_ROTATED([0, \"mono_rotation\", 0], RELATIVE=\"mono\")"
+    "beam_direction.set_ROTATED([0, mono_rotation, 0], RELATIVE=\"mono\")"
    ]
   },
   {
@@ -400,16 +562,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
-    "sample = instrument.add_component(\"sample\", \"PowderN\", AT=[0,0,1.1], RELATIVE=\"beam_dir\")"
+    "sample = instrument.add_component(\"sample\", \"PowderN\", AT=[0, 0, 1.1], RELATIVE=beam_direction)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -420,9 +582,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT sample = PowderN\n",
+      "  \u001b[1mreflections\u001b[0m = \u001b[1m\u001b[92m\"Na2Ca3Al2F14.laz\"\u001b[0m\u001b[0m []\n",
+      "  \u001b[1mradius\u001b[0m = \u001b[1m\u001b[92m0.015\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "AT [0, 0, 1.1] RELATIVE beam_dir\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "sample.print_long()"
    ]
@@ -439,7 +614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -461,7 +636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -470,7 +645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -493,9 +668,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "source   Source_div         AT      (0, 0, 0)             ABSOLUTE         \n",
+      "guide    Guide_gravity      AT      (0, 0, 2)             RELATIVE source  \n",
+      "mono     Monochromator_flat AT      (0, 0, 8.5)           RELATIVE guide    \n",
+      "                            ROTATED (0, mono_rotation, 0) RELATIVE guide\n",
+      "beam_dir Arm                AT      (0, 0, 0)             RELATIVE mono     \n",
+      "                            ROTATED (0, mono_rotation, 0) RELATIVE mono\n",
+      "sample   PowderN            AT      (0, 0, 1.1)           RELATIVE beam_dir\n",
+      "banana   Monitor_nD         AT      (0, 0, 0)             RELATIVE sample  \n",
+      "monitor  PSD_monitor        AT      (0, 0, 0.1)           RELATIVE sample  \n"
+     ]
+    }
+   ],
    "source": [
     "instrument.print_components()"
    ]
@@ -505,23 +696,77 @@
    "metadata": {},
    "source": [
     "## Running the simulation\n",
-    "The instrument object has a method called *run_full_instrument* to execute the simulation and return the data. A number of keyword arguments are available to control the execution of the simulation.\n",
+    "Running the simulation is done in three steps\n",
+    "- Setting the parameters with *set_parameters*\n",
+    "- Setting the settings with *settings*\n",
+    "- Running the McStas simulation with *backengine*\n",
+    "\n",
+    "The *set_parameters* method takes a value for each of the parameters defined in the instrument, here wavelength.\n",
+    "\n",
+    "Settings adjust settings for the simulations, a few examples can be seen here\n",
     "- ncount sets the number of rays\n",
     "- mpi sets the number of CPU cores used for execution (requires mpi installed)\n",
-    "- foldername sets the name of the output folder\n",
-    "- increment_folder_name if set to True, automatically changes the foldername if it already exists.\n",
-    "- parameters allows setting instrument parameters using a python dictionary"
+    "- output_path sets the name of the output folder\n",
+    "- increment_folder_name if set to True, automatically changes the foldername if it already exists (default).\n",
+    "- parameters allows setting instrument parameters using a python dictionary\n",
+    "\n",
+    "The *backengine* method takes no parameters and just performs the simulation"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/tutorial/data_folder/mcstas_basics_0\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS=\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "1 warning generated.\n",
+      "INFO: ===\n",
+      "Warning: 64159 events were removed in Component[7] monitor=PSD_monitor()\n",
+      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/tutorial/data_folder/mcstas_basics_0\n",
+      "\n",
+      "      monochromator rotation = 22.4519 deg\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "Detector: banana_I=1.35163e-06 banana_ERR=2.28321e-08 banana_N=10521 \"banana.dat\"\n",
+      "Detector: monitor_I=4.0821e-05 monitor_ERR=2.69721e-07 monitor_N=50009 \"psd.dat\"\n",
+      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
+      "    SPLIT 47 COMPONENT sample=PowderN(...)\n",
+      "  in the instrument description python_tutorial.instr.\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.set_parameters(wavelength=2.8) # Set parameters\n",
+    "\n",
+    "instrument.settings(ncount=5E6, output_path=\"data_folder/mcstas_basics\") # Settings\n",
+    "\n",
+    "instrument.backengine() # Perform simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = instrument.run_full_instrument(ncount=5E6, foldername=\"data_folder/mcstas_basics\",\n",
-    "                                      increment_folder_name=True,\n",
-    "                                      parameters={\"wavelength\" : 2.8})"
+    "data = instrument.data # The data is available in the data attribute"
    ]
   },
   {
@@ -534,9 +779,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name banana\n",
+      "Plotting data with name monitor\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plotter.make_sub_plot(data)"
    ]
@@ -560,9 +826,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name banana\n",
+      "Plotting data with name monitor\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "functions.name_plot_options(\"monitor\", data, log=True)\n",
     "functions.name_plot_options(\"banana\", data, left_lim=90, right_lim=150)\n",
@@ -579,9 +866,111 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/********************************************************************************\n",
+      "* \n",
+      "* McStas, neutron ray-tracing package\n",
+      "*         Copyright (C) 1997-2008, All rights reserved\n",
+      "*         Risoe National Laboratory, Roskilde, Denmark\n",
+      "*         Institut Laue Langevin, Grenoble, France\n",
+      "* \n",
+      "* This file was written by McStasScript, which is a \n",
+      "* python based McStas instrument generator written by \n",
+      "* Mads Bertelsen in 2019 while employed at the \n",
+      "* European Spallation Source Data Management and \n",
+      "* Software Center\n",
+      "* \n",
+      "* Instrument python_tutorial\n",
+      "* \n",
+      "* %Identification\n",
+      "* Written by: Python McStas Instrument Generator\n",
+      "* Date: 12:59:09 on December 15, 2021\n",
+      "* Origin: ESS DMSC\n",
+      "* %INSTRUMENT_SITE: Generated_instruments\n",
+      "* \n",
+      "* \n",
+      "* %Parameters\n",
+      "* \n",
+      "* %End \n",
+      "********************************************************************************/\n",
+      "\n",
+      "DEFINE INSTRUMENT python_tutorial (\n",
+      "wavelength = 2.8, // Wavelength in [Ang]\n",
+      "int order = 1 // Monochromator order, integer\n",
+      ")\n",
+      "\n",
+      "DECLARE \n",
+      "%{\n",
+      "double mono_Q = 1.714;\n",
+      "double wavevector;\n",
+      "double mono_rotation;\n",
+      "%}\n",
+      "\n",
+      "INITIALIZE \n",
+      "%{\n",
+      "// Start of initialize for generated python_tutorial\n",
+      "wavevector = 2.0*PI/wavelength;\n",
+      "mono_rotation = asin(mono_Q/(2.0*wavevector))*RAD2DEG;\n",
+      "printf(\"monochromator rotation = %g deg\\n\", mono_rotation);\n",
+      "%}\n",
+      "\n",
+      "TRACE \n",
+      "COMPONENT source = Source_div(\n",
+      " xwidth = 0.1, yheight = 0.05,\n",
+      " focus_aw = 1.2, focus_ah = 2.3,\n",
+      " lambda0 = wavelength, dlambda = 0.01*wavelength)\n",
+      "AT (0,0,0) ABSOLUTE\n",
+      "\n",
+      "COMPONENT guide = Guide_gravity(\n",
+      " w1 = 0.05, h1 = 0.05,\n",
+      " w2 = 0.05, h2 = 0.05,\n",
+      " l = 8, m = 3.5,\n",
+      " G = -9.82)\n",
+      "AT (0,0,2) RELATIVE source\n",
+      "\n",
+      "COMPONENT mono = Monochromator_flat(\n",
+      " zwidth = 0.05, yheight = 0.08,\n",
+      " Q = mono_Q)\n",
+      "AT (0,0,8.5) RELATIVE guide\n",
+      "ROTATED (0,mono_rotation,0) RELATIVE guide\n",
+      "\n",
+      "COMPONENT beam_dir = Arm()\n",
+      "AT (0,0,0) RELATIVE mono\n",
+      "ROTATED (0,mono_rotation,0) RELATIVE mono\n",
+      "\n",
+      "COMPONENT sample = PowderN(\n",
+      " reflections = \"Na2Ca3Al2F14.laz\", radius = 0.015,\n",
+      " yheight = 0.05)\n",
+      "AT (0,0,1.1) RELATIVE beam_dir\n",
+      "\n",
+      "COMPONENT banana = Monitor_nD(\n",
+      " xwidth = 2, yheight = 0.3,\n",
+      " restore_neutron = 1, options = \"theta limits=[5 175] bins=150, banana\",\n",
+      " filename = \"banana.dat\")\n",
+      "AT (0,0,0) RELATIVE sample\n",
+      "\n",
+      "COMPONENT monitor = PSD_monitor(\n",
+      " nx = 100, ny = 100,\n",
+      " filename = \"psd.dat\", xwidth = 0.05,\n",
+      " yheight = 0.08, restore_neutron = 1)\n",
+      "AT (0,0,0.1) RELATIVE sample\n",
+      "\n",
+      "FINALLY \n",
+      "%{\n",
+      "// Start of finally for generated python_tutorial\n",
+      "%}\n",
+      "\n",
+      "END\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "with open(\"run_folder/python_tutorial.instr\") as file:\n",
     "    data = file.read()\n",
@@ -612,7 +1001,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/tutorial/McStasScript_tutorial_2_SPLIT.ipynb b/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
index 706f580d..28abd363 100644
--- a/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
+++ b/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
@@ -31,7 +31,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\", output_path=\"data_folder/mcstas_SPLIT\")"
    ]
   },
   {
@@ -46,11 +46,11 @@
     "src.focus_aw = 1.2\n",
     "src.focus_ah = 2.3\n",
     "\n",
-    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
-    "src.lambda0=\"wavelength\"\n",
-    "src.dlambda=\"0.03*wavelength\"\n",
+    "wavelength = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = wavelength\n",
+    "src.dlambda = \"0.03*wavelength\"\n",
     "\n",
-    "guide = instrument.add_component(\"guide\", \"Guide_gravity\", AT=[0,0,2], RELATIVE=\"source\")\n",
+    "guide = instrument.add_component(\"guide\", \"Guide_gravity\", AT=[0,0,2], RELATIVE=src)\n",
     "guide.w1 = 0.05\n",
     "guide.w2 = 0.05\n",
     "guide.h1 = 0.05\n",
@@ -59,25 +59,25 @@
     "guide.m = 3.5\n",
     "guide.G = -9.82\n",
     "\n",
-    "instrument.add_declare_var(\"double\", \"mono_Q\", value=1.714) # Q for Ge 311\n",
+    "mono_Q = instrument.add_declare_var(\"double\", \"mono_Q\", value=1.714) # Q for Ge 311\n",
     "instrument.add_declare_var(\"double\", \"wavevector\")\n",
     "instrument.append_initialize(\"wavevector = 2.0*PI/wavelength;\")\n",
     "\n",
-    "instrument.add_declare_var(\"double\", \"mono_rotation\")\n",
+    "mono_rotation = instrument.add_declare_var(\"double\", \"mono_rotation\")\n",
     "instrument.append_initialize(\"mono_rotation = asin(mono_Q/(2.0*wavevector))*RAD2DEG;\")\n",
     "instrument.append_initialize('printf(\"monochromator rotation = %g deg\\\\n\", mono_rotation);')\n",
     "\n",
     "mono = instrument.add_component(\"mono\", \"Monochromator_flat\")\n",
     "mono.zwidth = 0.05\n",
     "mono.yheight = 0.08\n",
-    "mono.Q = \"mono_Q\"\n",
+    "mono.Q = mono_Q\n",
     "mono.set_AT([0, 0, 8.5], RELATIVE=guide)\n",
-    "mono.set_ROTATED([0, \"mono_rotation\", 0], RELATIVE=\"guide\")\n",
+    "mono.set_ROTATED([0, mono_rotation, 0], RELATIVE=guide)\n",
     "\n",
-    "beam_direction = instrument.add_component(\"beam_dir\", \"Arm\", AT_RELATIVE=\"mono\")\n",
+    "beam_direction = instrument.add_component(\"beam_dir\", \"Arm\", AT_RELATIVE=mono)\n",
     "beam_direction.set_ROTATED([0, \"mono_rotation\", 0], RELATIVE=\"mono\")\n",
     "\n",
-    "sample = instrument.add_component(\"sample\", \"PowderN\", AT=[0,0,1.1], RELATIVE=\"beam_dir\")\n",
+    "sample = instrument.add_component(\"sample\", \"PowderN\", AT=[0,0,1.1], RELATIVE=beam_direction)\n",
     "sample.radius = 0.015\n",
     "sample.yheight = 0.05\n",
     "sample.reflections = '\"Na2Ca3Al2F14.laz\"'\n",
@@ -104,9 +104,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_low = instrument.run_full_instrument(ncount=1E6, foldername=\"data_folder/mcstas_SPLIT\",\n",
-    "                                          increment_folder_name=True,\n",
-    "                                          parameters={\"wavelength\" : 2.8})\n",
+    "instrument.settings(ncount=1E6)\n",
+    "\n",
+    "instrument.set_parameters(wavelength=2.8)\n",
+    "                          \n",
+    "instrument.backengine()\n",
+    "data_low = instrument.data\n",
     "\n",
     "plotter.make_sub_plot(data_low)"
    ]
@@ -134,9 +137,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_reasonable = instrument.run_full_instrument(ncount=1E6, foldername=\"data_folder/mcstas_SPLIT\",\n",
-    "                                                increment_folder_name=True,\n",
-    "                                                parameters={\"wavelength\" : 2.8})\n",
+    "# No need to set settings or parameters as these have not changed\n",
+    "instrument.backengine()\n",
+    "data_reasonable = instrument.data\n",
     "\n",
     "plotter.make_sub_plot(data_reasonable)"
    ]
@@ -156,9 +159,11 @@
    "outputs": [],
    "source": [
     "sample.set_SPLIT(10000)\n",
-    "data_unreasonable = instrument.run_full_instrument(ncount=1E3, foldername=\"data_folder/mcstas_SPLIT\",\n",
-    "                                                   increment_folder_name=True,\n",
-    "                                                   parameters={\"wavelength\" : 2.8})\n",
+    "\n",
+    "instrument.settings(ncount=1E3) # Change settings to lower ncount, but keep parameters\n",
+    "                          \n",
+    "instrument.backengine()\n",
+    "data_unreasonable = instrument.data\n",
     "\n",
     "plotter.make_sub_plot(data_unreasonable)"
    ]
@@ -178,9 +183,12 @@
    "outputs": [],
    "source": [
     "sample.set_SPLIT(1)\n",
-    "data_ref = instrument.run_full_instrument(ncount=5E7, foldername=\"data_folder/mcstas_SPLIT\",\n",
-    "                                      increment_folder_name=True,\n",
-    "                                      parameters={\"wavelength\" : 2.8})"
+    "instrument.settings(ncount=5E7)\n",
+    "                          \n",
+    "instrument.backengine()\n",
+    "data_ref = instrument.data\n",
+    "\n",
+    "plotter.make_sub_plot(data_unreasonable)"
    ]
   },
   {
@@ -259,7 +267,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb b/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
index 3938a566..4ee501ab 100644
--- a/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
+++ b/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
@@ -23,7 +23,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+    "instrument = instr.McStas_instr(\"python_tutorial\",\n",
+    "                                input_path=\"run_folder\",\n",
+    "                                output_path=\"data_folder/mcstas_EXTEND_WHEN\")"
    ]
   },
   {
@@ -49,9 +51,9 @@
     "src.dist = 1.5\n",
     "src.flux = 1E13\n",
     "\n",
-    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
-    "src.lambda0=\"wavelength\"\n",
-    "src.dlambda=\"0.001*wavelength\""
+    "wavelength = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = wavelength\n",
+    "src.dlambda = \"0.001*wavelength\""
    ]
   },
   {
@@ -96,9 +98,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = instrument.run_full_instrument(ncount=5E6, foldername=\"data_folder/mcstas_EXTEND_WHEN\",\n",
-    "                                      increment_folder_name=True,\n",
-    "                                      parameters={\"wavelength\" : 2.8})\n",
+    "instrument.set_parameters(wavelength=2.8)\n",
+    "instrument.settings(ncount=5E6)\n",
+    "instrument.backengine()\n",
+    "data = instrument.data\n",
     "\n",
     "plotter.make_sub_plot(data)"
    ]
@@ -176,9 +179,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = instrument.run_full_instrument(ncount=5E6, foldername=\"data_folder/mcstas_EXTEND_WHEN\",\n",
-    "                                      increment_folder_name=True,\n",
-    "                                      parameters={\"wavelength\" : 2.8})\n",
+    "instrument.set_parameters(wavelength=2.8)\n",
+    "instrument.settings(ncount=5E6)\n",
+    "instrument.backengine()\n",
+    "data = instrument.data\n",
     "\n",
     "plotter.make_sub_plot(data)"
    ]
@@ -227,7 +231,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/tutorial/McStasScript_tutorial_4_JUMP.ipynb b/tutorial/McStasScript_tutorial_4_JUMP.ipynb
index 7f39406e..47b109db 100644
--- a/tutorial/McStasScript_tutorial_4_JUMP.ipynb
+++ b/tutorial/McStasScript_tutorial_4_JUMP.ipynb
@@ -65,9 +65,8 @@
     "src.dist = 1.5\n",
     "src.flux = 1E13\n",
     "\n",
-    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
-    "src.lambda0=\"wavelength\"\n",
-    "src.dlambda=\"0.001*wavelength\"\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.dlambda = \"0.001*wavelength\"\n",
     "\n",
     "guide = instrument.add_component(\"guide\", \"Guide_gravity\", AT=[0,0,1.5], RELATIVE=src)\n",
     "guide.w1 = guide_opening_w\n",
@@ -104,7 +103,8 @@
     "start_arm = instrument.add_component(\"split_arm\", \"Arm\")\n",
     "start_arm.set_AT([0,0, guide_length + 3E-3], RELATIVE=guide)\n",
     "start_arm.set_JUMP(\"target_arm WHEN (x<0)\")\n",
-    "start_arm.print_long()"
+    "\n",
+    "print(start_arm)"
    ]
   },
   {
@@ -201,9 +201,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = instrument.run_full_instrument(ncount=5E6, foldername=\"data_folder/mcstas_JUMP\",\n",
-    "                                      increment_folder_name=True,\n",
-    "                                      parameters={\"wavelength\" : 2.8})"
+    "instrument.set_parameters(wavelength=2.8)\n",
+    "instrument.settings(ncount=5E6, output_path=\"data_folder/mcstas_JUMP\")\n",
+    "instrument.backengine()\n",
+    "\n",
+    "data = instrument.data"
    ]
   },
   {
@@ -247,7 +249,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,

From 3b34672b06b065e72b7fdbb4ccafddcf81cda51d Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 4 Jan 2022 12:24:28 +0100
Subject: [PATCH 183/403] Update of how settings are set in instrument class.
 Settings are now a dict which is updated by the settings method. The settings
 method contains input sanitation and is used in init.

All examples and tutorials were checked for proper new usage.

The components can now have parameters set with set_parameters
using both keyword and dict argument.
---
 examples/McStasScript_demo.ipynb              |  28 +-
 examples/libpyvinyl_example.ipynb             | 257 ++----
 examples/manual_code.ipynb                    | 276 +-----
 examples/random_demonstration.py              |  28 +-
 mcstasscript/helper/mcstas_objects.py         |  11 +-
 mcstasscript/interface/instr.py               | 241 ++++--
 mcstasscript/tests/test_Instr.py              |  12 +-
 .../McStasScript_tutorial_1_the_basics.ipynb  | 508 ++---------
 tutorial/McStasScript_tutorial_2_SPLIT.ipynb  |   4 +-
 ...tasScript_tutorial_3_EXTEND_and_WHEN.ipynb |   6 +-
 tutorial/McStasScript_tutorial_4_JUMP.ipynb   |  19 +
 ...n_tutorial_1_processes_and_materials.ipynb | 788 +++++++++++++++++-
 tutorial/Union_tutorial_2_geometry.ipynb      |  46 +-
 tutorial/Union_tutorial_3_loggers.ipynb       |  55 +-
 tutorial/Union_tutorial_4_conditionals.ipynb  |  36 +-
 tutorial/Union_tutorial_5_masks.ipynb         |  31 +-
 ...ial_6_Exit_and_number_of_activations.ipynb |  31 +-
 .../Union_tutorial_7_Tagging_history.ipynb    |  21 +-
 18 files changed, 1252 insertions(+), 1146 deletions(-)

diff --git a/examples/McStasScript_demo.ipynb b/examples/McStasScript_demo.ipynb
index 2c5b5a3d..76a9b5db 100644
--- a/examples/McStasScript_demo.ipynb
+++ b/examples/McStasScript_demo.ipynb
@@ -14,10 +14,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys\n",
-    "# Path to McStasScript pythoon file\n",
-    "sys.path.append('/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript')\n",
-    "\n",
     "from mcstasscript.interface import instr, plotter, functions\n",
     "\n",
     "# Creating the instance of the class, insert path to mcrun and to mcstas root directory\n",
@@ -57,7 +53,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "source = Instr.add_component(\"Source\",\"Source_simple\") # Adds an instance of Source_simple"
+    "source = Instr.add_component(\"Source\", \"Source_simple\") # Adds an instance of Source_simple"
    ]
   },
   {
@@ -113,7 +109,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "guide.set_parameters({\"w1\" : 0.05, \"w2\" : 0.05, \"h1\" : 0.05, \"h2\" : 0.05, \"l\" : 8, \"m\" : 3.5, \"G\" : -9.2})"
+    "guide.set_parameters(w1=0.05, w2=0.05, h1=0.05, h2=0.05, l=8, m=3.5, G=-9.2)"
    ]
   },
   {
@@ -264,10 +260,26 @@
    "source": [
     "# output_path specifies the foldername, if it already exists an index is added\n",
     "Instr.settings(output_path=\"jupyter_demo\", mpi=4, ncount=2E7)\n",
-    "\n",
+    "Instr.show_settings() # Check settings are correct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# Input parameters are set with set_parameters\n",
     "Instr.set_parameters(wavelength=1.5)\n",
-    "\n",
+    "Instr.show_parameters()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# The simulation is performed by calling backengine()\n",
     "Instr.backengine()\n",
     "\n",
diff --git a/examples/libpyvinyl_example.ipynb b/examples/libpyvinyl_example.ipynb
index 478d98bc..5f6c4f54 100644
--- a/examples/libpyvinyl_example.ipynb
+++ b/examples/libpyvinyl_example.ipynb
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "parental-albert",
    "metadata": {},
    "outputs": [],
@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "incident-nitrogen",
    "metadata": {},
    "outputs": [
@@ -118,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "subjective-battle",
    "metadata": {},
    "outputs": [
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "accomplished-twelve",
    "metadata": {},
    "outputs": [],
@@ -158,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "foreign-developer",
    "metadata": {},
    "outputs": [],
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "champion-knife",
    "metadata": {},
    "outputs": [],
@@ -183,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "wrong-retailer",
    "metadata": {},
    "outputs": [],
@@ -196,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "anticipated-tampa",
    "metadata": {},
    "outputs": [
@@ -227,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "experienced-arizona",
    "metadata": {},
    "outputs": [
@@ -256,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "wooden-helicopter",
    "metadata": {},
    "outputs": [],
@@ -266,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "preliminary-training",
    "metadata": {},
    "outputs": [],
@@ -281,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "official-withdrawal",
    "metadata": {},
    "outputs": [],
@@ -291,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "colored-policy",
    "metadata": {},
    "outputs": [],
@@ -308,7 +308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "based-torture",
    "metadata": {},
    "outputs": [
@@ -332,20 +332,20 @@
    "id": "august-words",
    "metadata": {},
    "source": [
-    "### Could set parameters with a syntax like this"
+    "### Old syntax"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "virtual-cleaners",
+   "execution_count": 18,
+   "id": "prescription-whole",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/mcstasscript/interface/instr.py:1693: UserWarning: run_full_instrument will be removed in future version of McStasScript. \n",
+      "/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/mcstasscript/interface/instr.py:1696: UserWarning: run_full_instrument will be removed in future version of McStasScript. \n",
       "Instead supply parameters with set_parameters, set settings with settings and use backengine() to run. See examples in package.\n",
       "  warnings.warn(\n"
      ]
@@ -354,7 +354,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/run_full_instrument_path_3\"\n",
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/run_full_instrument_path_0\"\n",
       "INFO: Regenerating c-file: demo_instrument.c\n",
       "CFLAGS=\n",
       "INFO: Recompiling: ./demo_instrument.out\n",
@@ -363,9 +363,9 @@
       "  ^~~\n",
       "1 warning generated.\n",
       "INFO: ===\n",
-      "Warning: 9688 events were removed in Component[4] acceptance_horizontal=DivPos_monitor()\n",
+      "Warning: 9658 events were removed in Component[4] acceptance_horizontal=DivPos_monitor()\n",
       "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "INFO: Placing instr file copy demo_instrument.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/run_full_instrument_path_3\n",
+      "INFO: Placing instr file copy demo_instrument.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/run_full_instrument_path_0\n",
       "\n",
       "  Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Ni.laz' (Table_Read_Offset)\n",
       "Table from file 'Ni.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
@@ -373,8 +373,8 @@
       "PowderN: powder: Reading 19 rows from Ni.laz\n",
       "PowderN: powder: Read 19 reflections from file 'Ni.laz'\n",
       "PowderN: powder: Vc=43.76 [Angs] sigma_abs=17.96 [barn] sigma_inc=20.8 [barn] reflections=Ni.laz\n",
-      "Detector: cyl_monitor_I=0.0552829 cyl_monitor_ERR=0.000384292 cyl_monitor_N=54838 \"cylinder.dat\"\n",
-      "Detector: acceptance_horizontal_I=1.32373 acceptance_horizontal_ERR=0.00101418 acceptance_horizontal_N=1.89423e+06 \"acceptance_h.dat\"\n",
+      "Detector: cyl_monitor_I=0.0559433 cyl_monitor_ERR=0.00038608 cyl_monitor_N=55188 \"cylinder.dat\"\n",
+      "Detector: acceptance_horizontal_I=1.32435 acceptance_horizontal_ERR=0.00101504 acceptance_horizontal_N=1.89432e+06 \"acceptance_h.dat\"\n",
       "PowderN: powder: Info: you may highly improve the computation efficiency by using\n",
       "    SPLIT 19 COMPONENT powder=PowderN(...)\n",
       "  in the instrument description demo_instrument.instr.\n",
@@ -388,19 +388,27 @@
     "                               ncount=2E6, foldername=\"run_full_instrument_path\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "domestic-bearing",
+   "metadata": {},
+   "source": [
+    "### New libpyvinyl syntax"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "closed-promise",
+   "execution_count": 22,
+   "id": "independent-millennium",
    "metadata": {},
    "outputs": [],
    "source": [
-    "calculator.set_parameters({\"source_energy\": 320, \"source_height\":0.025, \"sample_height\":0.04})"
+    "calculator.set_parameters(source_energy=320, source_height=0.025, sample_height=0.04)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 23,
    "id": "smooth-cholesterol",
    "metadata": {},
    "outputs": [],
@@ -418,7 +426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 24,
    "id": "daily-louisiana",
    "metadata": {},
    "outputs": [
@@ -426,7 +434,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/path_to_output\"\n",
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/path_to_output_0\"\n",
       "INFO: Regenerating c-file: demo_instrument.c\n",
       "CFLAGS=\n",
       "INFO: Recompiling: ./demo_instrument.out\n",
@@ -438,11 +446,11 @@
       "  ^~~\n",
       "2 warnings generated.\n",
       "INFO: ===\n",
-      "Warning: 11956 events were removed in Component[4] acceptance_horizontal=DivPos_monitor()\n",
+      "Warning: 11913 events were removed in Component[4] acceptance_horizontal=DivPos_monitor()\n",
       "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "Warning: 12035 events were removed in Component[4] acceptance_horizontal=DivPos_monitor()\n",
+      "Warning: 11974 events were removed in Component[4] acceptance_horizontal=DivPos_monitor()\n",
       "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "INFO: Placing instr file copy demo_instrument.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/path_to_output\n",
+      "INFO: Placing instr file copy demo_instrument.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/path_to_output_0\n",
       "\n",
       "  Simulation 'demo_instrument' (demo_instrument.instr): running on 2 nodes (master is 'CI0021617', MPI version 3.1).\n",
       "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Ni.laz' (Table_Read_Offset)\n",
@@ -451,8 +459,8 @@
       "PowderN: powder: Reading 19 rows from Ni.laz\n",
       "PowderN: powder: Read 19 reflections from file 'Ni.laz'\n",
       "PowderN: powder: Vc=43.76 [Angs] sigma_abs=17.96 [barn] sigma_inc=20.8 [barn] reflections=Ni.laz\n",
-      "Detector: cyl_monitor_I=0.0553181 cyl_monitor_ERR=0.000243053 cyl_monitor_N=136639 \"cylinder.dat\"\n",
-      "Detector: acceptance_horizontal_I=1.32423 acceptance_horizontal_ERR=0.000641726 acceptance_horizontal_N=4.73644e+06 \"acceptance_h.dat\"\n",
+      "Detector: cyl_monitor_I=0.0551286 cyl_monitor_ERR=0.000242487 cyl_monitor_N=136616 \"cylinder.dat\"\n",
+      "Detector: acceptance_horizontal_I=1.32419 acceptance_horizontal_ERR=0.000641536 acceptance_horizontal_N=4.7368e+06 \"acceptance_h.dat\"\n",
       "PowderN: powder: Info: you may highly improve the computation efficiency by using\n",
       "    SPLIT 19 COMPONENT powder=PowderN(...)\n",
       "  in the instrument description demo_instrument.instr.\n",
@@ -475,7 +483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 25,
    "id": "global-loading",
    "metadata": {},
    "outputs": [],
@@ -493,7 +501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 26,
    "id": "retired-brick",
    "metadata": {},
    "outputs": [
@@ -507,7 +515,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 936x504 with 3 Axes>"
       ]
@@ -524,7 +532,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "surgical-footwear",
+   "id": "medieval-packaging",
    "metadata": {},
    "source": [
     "### Store in dump file"
@@ -532,28 +540,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "affiliated-british",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'dump_file.dmp'"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "calculator.dump(\"dump_file.dmp\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "lovely-cigarette",
+   "id": "spatial-peace",
    "metadata": {},
    "source": [
     "### Load from dump file"
@@ -561,75 +558,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "pleasant-machine",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The following components are found in the work_directory / input_path:\n",
-      "     Union_sphere.comp\n",
-      "     Texture_process.comp\n",
-      "     Union_cone.comp\n",
-      "     Union_box.comp\n",
-      "     Single_crystal_process.comp\n",
-      "     Union_abs_logger_2D_space.comp\n",
-      "     Union_logger_2D_kf.comp\n",
-      "     Template_process.comp\n",
-      "     PhononSimple_process.comp\n",
-      "     Union_conditional_standard.comp\n",
-      "     Union_abs_logger_1D_space.comp\n",
-      "     Union_abs_logger_event.comp\n",
-      "     NCrystal_process.comp\n",
-      "     Union_abs_logger_1D_space_event.comp\n",
-      "     Union_abs_logger_1D_space_tof.comp\n",
-      "     Union_logger_2D_space.comp\n",
-      "     Union_conditional_PSD.comp\n",
-      "     Union_master.comp\n",
-      "     AF_HB_1D_process.comp\n",
-      "     Union_logger_2D_kf_time.comp\n",
-      "     Union_cylinder.comp\n",
-      "     Union_abs_logger_1D_space_tof_to_lambda.comp\n",
-      "     Powder_process.comp\n",
-      "     Union_make_material.comp\n",
-      "     Incoherent_process.comp\n",
-      "     Union_logger_1D.comp\n",
-      "     Union_logger_3D_space.comp\n",
-      "     IncoherentPhonon_process.comp\n",
-      "     Union_logger_2DQ.comp\n",
-      "     Union_mesh.comp\n",
-      "     Union_logger_2D_space_time.comp\n",
-      "These definitions will be used instead of the installed versions.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from_dump = instr.McStas_instr(\"\", dumpfile='dump_file.dmp')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "id": "expensive-tenant",
+   "execution_count": null,
+   "id": "modified-brooks",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "demo_instrument\n",
-      "double source_energy  = 320    // Source mean energy\n",
-      "double source_height  = 0.025  // Height of source\n",
-      "       sample_height  = 0.04   // Height of sample\n",
-      "source                Source_div     AT      (0, 0, 0)   ABSOLUTE       \n",
-      "powder                PowderN        AT      (0, 0, 1)   RELATIVE source\n",
-      "cyl_monitor           Cyl_monitor    AT      (0, 0, 0)   RELATIVE powder\n",
-      "acceptance_horizontal DivPos_monitor AT      (0, 0, 0.1) RELATIVE powder\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(from_dump.name)\n",
     "from_dump.show_parameters()\n",
@@ -639,93 +581,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "id": "authorized-cigarette",
+   "execution_count": null,
+   "id": "divine-digit",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/********************************************************************************\n",
-      "* \n",
-      "* McStas, neutron ray-tracing package\n",
-      "*         Copyright (C) 1997-2008, All rights reserved\n",
-      "*         Risoe National Laboratory, Roskilde, Denmark\n",
-      "*         Institut Laue Langevin, Grenoble, France\n",
-      "* \n",
-      "* This file was written by McStasScript, which is a \n",
-      "* python based McStas instrument generator written by \n",
-      "* Mads Bertelsen in 2019 while employed at the \n",
-      "* European Spallation Source Data Management and \n",
-      "* Software Center\n",
-      "* \n",
-      "* Instrument demo_instrument\n",
-      "* \n",
-      "* %Identification\n",
-      "* Written by: Python McStas Instrument Generator\n",
-      "* Date: 12:21:44 on December 15, 2021\n",
-      "* Origin: ESS DMSC\n",
-      "* %INSTRUMENT_SITE: Generated_instruments\n",
-      "* \n",
-      "* \n",
-      "* %Parameters\n",
-      "* \n",
-      "* %End \n",
-      "********************************************************************************/\n",
-      "\n",
-      "DEFINE INSTRUMENT demo_instrument (\n",
-      "double source_energy = 320, // Source mean energy\n",
-      "double source_height = 0.025, // Height of source\n",
-      "sample_height = 0.04 // Height of sample\n",
-      ")\n",
-      "\n",
-      "DECLARE \n",
-      "%{\n",
-      "double source_energy_spread;\n",
-      "%}\n",
-      "\n",
-      "INITIALIZE \n",
-      "%{\n",
-      "// Start of initialize for generated demo_instrument\n",
-      "source_energy_spread = 0.02*source_energy;\n",
-      "%}\n",
-      "\n",
-      "TRACE \n",
-      "COMPONENT source = Source_div(\n",
-      " xwidth = 0.12, yheight = source_height,\n",
-      " focus_aw = 3, focus_ah = 4,\n",
-      " E0 = source_energy, dE = source_energy_spread)\n",
-      "AT (0,0,0) ABSOLUTE\n",
-      "\n",
-      "COMPONENT powder = PowderN(\n",
-      " reflections = \"Ni.laz\", radius = 0.01,\n",
-      " yheight = sample_height)\n",
-      "AT (0,0,1) RELATIVE source\n",
-      "\n",
-      "COMPONENT cyl_monitor = Cyl_monitor(\n",
-      " nr = 200, filename = \"cylinder.dat\",\n",
-      " yheight = 0.2, radius = 0.5,\n",
-      " restore_neutron = 1)\n",
-      "AT (0,0,0) RELATIVE powder\n",
-      "\n",
-      "COMPONENT acceptance_horizontal = DivPos_monitor(\n",
-      " nh = 300, ndiv = 300,\n",
-      " filename = \"acceptance_h.dat\", xwidth = 0.2,\n",
-      " yheight = 0.2, maxdiv_h = 30,\n",
-      " restore_neutron = 1)\n",
-      "AT (0,0,0.1) RELATIVE powder\n",
-      "\n",
-      "FINALLY \n",
-      "%{\n",
-      "// Start of finally for generated demo_instrument\n",
-      "%}\n",
-      "\n",
-      "END\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "with open(\"demo_instrument.instr\", \"r\") as f:\n",
     "    instrument_file = f.read()\n",
@@ -735,7 +594,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fixed-address",
+   "id": "knowing-solution",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/examples/manual_code.ipynb b/examples/manual_code.ipynb
index 2a957194..078483d3 100644
--- a/examples/manual_code.ipynb
+++ b/examples/manual_code.ipynb
@@ -2,13 +2,10 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys\n",
-    "sys.path.append('/Users/madsbertelsen/PaNOSC/McStasScript') # Path to McStasScript pythoon file\n",
-    "\n",
     "from mcstasscript.interface import instr, plotter, functions\n",
     "\n",
     "# Creating the instance of the class, insert path to mcrun and to mcstas root directory\n",
@@ -19,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,20 +50,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<mcstasscript.interface.instr.Guide_gravity at 0x11d4f45f8>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "detector.add_component(\"Origin\",\"Arm\")\n",
     "src = detector.add_component(\"source\",\"Source_simple\",RELATIVE=\"Origin\")\n",
@@ -75,20 +61,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<mcstasscript.interface.instr.Slit at 0x11d4ea6d8>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "detector.add_component(\"pre_guide_slit\",\"Slit\",before=\"beam_extraction\",\n",
     "                       AT=[0,0,1],RELATIVE=\"source\",comment=\"Slit before the guide\")"
@@ -96,27 +71,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Origin            Arm             AT  [0, 0, 0]     ABSOLUTE          ROTATED  [0, 0, 0]     ABSOLUTE\n",
-      "source            Source_simple   AT  [0, 0, 0]     RELATIVE Origin   ROTATED  [0, 0, 0]     RELATIVE Origin\n",
-      "pre_guide_slit    Slit            AT  [0, 0, 1]     RELATIVE source   ROTATED  [0, 0, 0]     RELATIVE source\n",
-      "beam_extraction   Guide_gravity   AT  [0, 0, 2]     RELATIVE source   ROTATED  [0, 0, 0]     RELATIVE source\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "detector.print_components()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,29 +90,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT source = Source_simple\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.12\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.12\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mE0\u001b[0m = \u001b[1m\u001b[92menergy\u001b[0m\u001b[0m [meV]\n",
-      "AT [0, 0, 0] RELATIVE Origin\n",
-      "ROTATED [0, 0, 0] RELATIVE Origin\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "detector.print_component(\"source\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -157,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -175,43 +126,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Origin            Arm             AT  [0, 0, 0]        ABSOLUTE                  ROTATED  [0, 0, 0]       ABSOLUTE\n",
-      "source            Source_simple   AT  [0.01, 0, 0]     RELATIVE Origin           ROTATED  [0, 0, 0]       RELATIVE Origin\n",
-      "pre_guide_slit    Slit            AT  [0, 0, 1]        RELATIVE source           ROTATED  [0, 0, 0]       RELATIVE source\n",
-      "beam_extraction   Guide_gravity   AT  [0, 0, 2]        RELATIVE pre_guide_slit   ROTATED  [0, 2.0, 0]     RELATIVE pre_guide_slit\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "detector.print_components()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT beam_extraction = Guide_gravity\n",
-      "  \u001b[1mw1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1mh1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1ml\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "WHEN (vx > 0)\n",
-      "AT [0, 0, 2] RELATIVE pre_guide_slit\n",
-      "ROTATED [0, 2.0, 0] RELATIVE pre_guide_slit\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "detector.set_component_WHEN(\"beam_extraction\",\"vx > 0\")\n",
     "detector.print_component(\"beam_extraction\")"
@@ -219,26 +145,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT beam_extraction = Guide_gravity\n",
-      "  \u001b[1mw1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1mh1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1ml\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "WHEN (vx > 0)\n",
-      "AT [0, 0, 2] RELATIVE pre_guide_slit\n",
-      "ROTATED [0, 2.0, 0] RELATIVE pre_guide_slit\n",
-      "EXTEND %{\n",
-      "n_scattering = SCATTERED - 2\n",
-      "%}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "detector.append_component_EXTEND(\"beam_extraction\",\"n_scattering = SCATTERED - 2\")\n",
     "detector.print_component(\"beam_extraction\")"
@@ -246,27 +155,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT beam_extraction = Guide_gravity\n",
-      "  \u001b[1mw1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1mh1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1ml\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "WHEN (vx > 0)\n",
-      "AT [0, 0, 2] RELATIVE pre_guide_slit\n",
-      "ROTATED [0, 2.0, 0] RELATIVE pre_guide_slit\n",
-      "GROUP guides\n",
-      "EXTEND %{\n",
-      "n_scattering = SCATTERED - 2\n",
-      "%}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "detector.set_component_GROUP(\"beam_extraction\",\"guides\")\n",
     "detector.print_component(\"beam_extraction\")"
@@ -274,28 +165,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT beam_extraction = Guide_gravity\n",
-      "  \u001b[1mw1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1mh1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1ml\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "WHEN (vx > 0)\n",
-      "AT [0, 0, 2] RELATIVE pre_guide_slit\n",
-      "ROTATED [0, 2.0, 0] RELATIVE pre_guide_slit\n",
-      "GROUP guides\n",
-      "EXTEND %{\n",
-      "n_scattering = SCATTERED - 2\n",
-      "%}\n",
-      "JUMP myself iterate 3\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "detector.set_component_JUMP(\"beam_extraction\",\"myself iterate 3\")\n",
     "detector.print_component(\"beam_extraction\")"
@@ -303,29 +175,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "// Simulating severe misalignment\n",
-      "COMPONENT beam_extraction = Guide_gravity\n",
-      "  \u001b[1mw1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1mh1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1ml\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "WHEN (vx > 0)\n",
-      "AT [0, 0, 2] RELATIVE pre_guide_slit\n",
-      "ROTATED [0, 2.0, 0] RELATIVE pre_guide_slit\n",
-      "GROUP guides\n",
-      "EXTEND %{\n",
-      "n_scattering = SCATTERED - 2\n",
-      "%}\n",
-      "JUMP myself iterate 3\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "detector.set_component_comment(\"beam_extraction\",\"Simulating severe misalignment\")\n",
     "detector.print_component(\"beam_extraction\")"
@@ -333,85 +185,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are the availalbe component categories:\n",
-      " sources\n",
-      " optics\n",
-      " samples\n",
-      " monitors\n",
-      " misc\n",
-      " contrib\n",
-      " union\n",
-      " obsolete\n",
-      "Call show_components(category_name) to display\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "detector.show_components()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are all components in the samples category.\n",
-      " Incoherent      Phonon_simple   Res_sample      Single_crystal\n",
-      " Isotropic_Sqw   Powder1         Sans_spheres    TOFRes_sample\n",
-      " Magnon_bcc      PowderN         SasView_model   Tunneling_sample\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "detector.show_components(\"samples\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Phonon_simple _________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[4m\u001b[1mradius\u001b[0m\u001b[0m [m] // Outer radius of sample in (x,z) plane\n",
-      "\u001b[4m\u001b[1myheight\u001b[0m\u001b[0m [m] // Height of sample in y direction\n",
-      "\u001b[4m\u001b[1msigma_abs\u001b[0m\u001b[0m [barns] // Absorption cross section at 2200 m/s per atom\n",
-      "\u001b[4m\u001b[1msigma_inc\u001b[0m\u001b[0m [barns] // Incoherent scattering cross section per atom\n",
-      "\u001b[4m\u001b[1ma\u001b[0m\u001b[0m [AA] // fcc Lattice constant\n",
-      "\u001b[4m\u001b[1mb\u001b[0m\u001b[0m [fm] // Scattering length\n",
-      "\u001b[4m\u001b[1mM\u001b[0m\u001b[0m [a.u.] // Atomic mass\n",
-      "\u001b[4m\u001b[1mc\u001b[0m\u001b[0m [meV/AA^(-1)] // Velocity of sound\n",
-      "\u001b[4m\u001b[1mDW\u001b[0m\u001b[0m [1] // Debye-Waller factor\n",
-      "\u001b[4m\u001b[1mT\u001b[0m\u001b[0m [K] // Temperature\n",
-      "\u001b[1mtarget_x\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // position of target to focus at . Transverse coordinate\n",
-      "\u001b[1mtarget_y\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // position of target to focus at. Vertical coordinate\n",
-      "\u001b[1mtarget_z\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // position of target to focus at. Straight ahead.\n",
-      "\u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [1] // relative index of component to focus at, e.g. next is +1\n",
-      "\u001b[1mfocus_r\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // Radius of sphere containing target.\n",
-      "\u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // horiz. dimension of a rectangular area\n",
-      "\u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [m] // vert.  dimension of a rectangular area\n",
-      "\u001b[1mfocus_aw\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // horiz. angular dimension of a rectangular area\n",
-      "\u001b[1mfocus_ah\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [deg] // vert.  angular dimension of a rectangular area\n",
-      "\u001b[1mgap\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [meV] // Bandgap energy (unphysical)\n",
-      "-------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "detector.component_help(\"Phonon_simple\")"
    ]
@@ -433,7 +227,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/examples/random_demonstration.py b/examples/random_demonstration.py
index e9f1a540..9a838282 100644
--- a/examples/random_demonstration.py
+++ b/examples/random_demonstration.py
@@ -1,10 +1,8 @@
 # Demonstration of McStasScript, an API for creating and running McStas instruments from python scripts
 # Written by Mads Bertelsen, ESS DMSC
 import random
-import sys
-sys.path.append('/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript')
-from mcstasscript.interface import instr, plotter, functions
 
+from mcstasscript.interface import instr, plotter, functions
 
 # Create a McStas instrument
 Instr = instr.McStas_instr("random_demo",
@@ -26,15 +24,15 @@
 Cu.process_string = "\"Cu_incoherent,Cu_powder\""
 
 # Add neutron source
-Source = Instr.add_component("source", "Source_div", AT=[0,0,0])
+Source = Instr.add_component("source", "Source_div")
 # Add parameter to select energy at run time
-Instr.add_parameter("double","energy", value=10, comment="[meV] source energy")
+energy = Instr.add_parameter("double", "energy", value=10, comment="[meV] source energy")
 
 Source.xwidth = 0.12
 Source.yheight = 0.12
 Source.focus_aw = 0.1
 Source.focus_ah = 0.1
-Source.E0 = "energy"
+Source.E0 = energy
 Source.dE = 0.0
 Source.flux = 1E13
 
@@ -42,7 +40,7 @@
 material_name_list = ["Cu", "Vacuum"]
 
 # Wish to set up a number of random boxes, here the number is chosen at random
-number_of_volumes = random.randint(30,40)
+number_of_volumes = random.randint(30, 40)
 
 # Initialize the priority that needs to be unique for each volume
 current_priority = 99
@@ -65,9 +63,9 @@
 
     # Add a McStas Union geometry with unique name
     this_geometry = Instr.add_component("volume_" + str(volume), "Union_box")
-    this_geometry.xwidth = random.uniform(0.01,max_side_length)
-    this_geometry.yheight = random.uniform(0.01,max_side_length)
-    this_geometry.zdepth = random.uniform(0.01,max_side_length)
+    this_geometry.xwidth = random.uniform(0.01, max_side_length)
+    this_geometry.yheight = random.uniform(0.01, max_side_length)
+    this_geometry.zdepth = random.uniform(0.01, max_side_length)
     this_geometry.material_string = "\"" + volume_material + "\""
     this_geometry.priority = current_priority
     this_geometry.p_interact = 0.3
@@ -112,7 +110,7 @@
 PSD.filename = "\"PSD.dat\""
 PSD.restore_neutron = 1
 
-big_PSD = Instr.add_component("large_detector","PSD_monitor", AT=[0,0,2])
+big_PSD = Instr.add_component("large_detector", "PSD_monitor", AT=[0,0,2])
 big_PSD.xwidth = 1.0
 big_PSD.yheight = 1.0
 big_PSD.nx = 500
@@ -123,9 +121,11 @@
 Instr.print_components()
 
 # Run the McStas simulation, a unique foldername is required for each run
-data = Instr.run_full_instrument(foldername="demonstration",
-                                 parameters={"energy": 600},
-                                 mpi=2, ncount=5E7)
+Instr.settings(output_path="demonstration", mpi=2, ncount=5E7)
+Instr.set_parameters(energy=600)
+
+Instr.backengine()
+data = Instr.data
 
 # Set plotting options for the data (optional)
 functions.name_plot_options("logger_space_zx_all", data, log=1, orders_of_mag=3)
diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 8177a5d5..bc91fbe6 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -811,9 +811,9 @@ def set_RELATIVE(self, relative):
             self.AT_relative = "RELATIVE " + relative
             self.ROTATED_relative = "RELATIVE " + relative
 
-    def set_parameters(self, dict_input):
+    def set_parameters(self, args_as_dict=None, **kwargs):
         """
-        Set Component parameters from dictionary input
+        Set Component parameters from dictionary input or keyword arguments
 
         Relies on attributes added when McStas_Instr creates a subclass from
         the Component class where each component parameter is added as an
@@ -821,7 +821,12 @@ def set_parameters(self, dict_input):
 
         An error is raised if trying to set a parameter that does not exist
         """
-        for key, val in dict_input.items():
+        if args_as_dict is not None:
+            parameter_dict = args_as_dict
+        else:
+            parameter_dict = kwargs
+
+        for key, val in parameter_dict.items():
             if not hasattr(self, key):
                 raise NameError("No parameter called "
                                 + key
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index ae847c09..d0f42490 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -94,7 +94,7 @@ class McCode_instr(BaseCalculator):
     package_path : str
         Path to mccode package containing component folders
 
-    current_run_settings : dict
+    run_settings : dict
         Dict of options set with settings
 
     data : list
@@ -220,7 +220,7 @@ class McCode_instr(BaseCalculator):
         Returns data set from latest simulation in widget
     """
 
-    def __init__(self, name, parameters=None, dumpfile=None, **kwargs):
+    def __init__(self, name, parameters=None, dumpfile=None, executable=None, **kwargs):
         """
         Initialization of McStas Instrument
 
@@ -242,11 +242,20 @@ def __init__(self, name, parameters=None, dumpfile=None, **kwargs):
             origin : str
                 Affiliation of author, written in instrument file
 
+            executable : str
+                Name of simulation executable (mcrun or mxrun)
+
             executable_path : str
                 Absolute path of mcrun or empty if already in path
 
             input_path : str
                 Work directory, will load components from this folder
+
+            ncount : int
+                Number of rays to simulate
+
+            mpi : int
+                Number of mpi threads to use in simulation
         """
 
         super().__init__(name, parameters, dumpfile, **kwargs)
@@ -270,7 +279,6 @@ def __init__(self, name, parameters=None, dumpfile=None, **kwargs):
 
         # Check required attributes has been set by class that inherits
         if not (hasattr(self, "particle") or
-                hasattr(self, "executable") or
                 hasattr(self, "package_name")):
             raise AttributeError("McCode_instr is a base class, use "
                                  + "McStas_instr or McXtrace_instr instead.")
@@ -293,6 +301,12 @@ def __init__(self, name, parameters=None, dumpfile=None, **kwargs):
         else:
             self.origin = "ESS DMSC"
 
+        self._run_settings = {}  # Settings for running simulation
+
+        # Sets max_line_length and adds paths to run_settings
+        self._read_calibration()
+
+        # Settings that can't be changed later
         if "input_path" in kwargs:
             self.input_path = str(kwargs["input_path"])
             if not os.path.isdir(self.input_path):
@@ -300,44 +314,50 @@ def __init__(self, name, parameters=None, dumpfile=None, **kwargs):
                                    + "folder:\"" + self.input_path + '"')
         else:
             self.input_path = "."
+        self._run_settings["run_path"] = self.input_path
 
-        self._read_calibration()
+        if "package_path" in kwargs:
+            if not os.path.isdir(str(kwargs["package_path"])):
+                raise RuntimeError("The package_path provided to mccode_instr "
+                                   + " does not point to a + directory: \""
+                                   + str(kwargs["package_path"]) + "\"")
+            self._run_settings["package_path"] = kwargs["package_path"]
+
+        # Settings for run that can be adjusted by user
+        provided_run_settings = {"executable": executable}
 
         if "executable_path" in kwargs:
-            self.executable_path = str(kwargs["executable_path"])
-            if not os.path.isdir(self.executable_path):
-                raise RuntimeError("Given executable_path does not point to "
-                                   + "a folder:\"" + self.executable_path
-                                   + '"')
+            provided_run_settings["executable_path"] = str(kwargs["executable_path"])
 
-        if "package_path" in kwargs:
-            self.package_path = str(kwargs["package_path"])
-            if not os.path.isdir(self.package_path):
-                raise RuntimeError("Given package_path does not point to "
-                                   + "a folder:\"" + self.package_path + '"')
-
-        elif self.package_path == "":
-            raise NameError("At this stage of development "
-                            + "McStasScript need the absolute path "
-                            + "for the " + self.package_name
-                            + " installation as keyword named "
-                            + "package_path or in configuration.yaml")
+        if "force_compile" in kwargs:
+            provided_run_settings["force_compile"] = kwargs["force_compile"]
+        else:
+            provided_run_settings["force_compile"] = True
+
+        if "ncount" in kwargs:
+            provided_run_settings["ncount"] = kwargs["ncount"]
+
+        if "mpi" in kwargs:
+            provided_run_settings["mpi"] = kwargs["mpi"]
+
+        # Set run_settings, perform input sanitation
+        self.settings(**provided_run_settings)
 
         # Read info on active McStas components
-        self.component_reader = ComponentReader(self.package_path,
-                                                input_path=self.input_path)
+        package_path = self._run_settings["package_path"]
+        run_path = self._run_settings["run_path"]
+        self.component_reader = ComponentReader(package_path,
+                                                input_path=run_path)
 
         self.component_class_lib = {}
         self.widget_interface = None
 
-        # Ensure output_path field exist
+        # Ensure output_path field exist (not ensured by BaseCalculator)
         if not hasattr(self, "output_path"):
-            self.output_path = None
+            self.output_path = self.name + "_data"
 
         # Avoid initializing if loading from dump
-        if not hasattr(self, "current_run_settings"):
-            self.current_run_settings = None
-
+        if not hasattr(self, "declare_list"):
             self.declare_list = []
             self.initialize_section = ("// Start of initialize for generated "
                                        + name + "\n")
@@ -1589,55 +1609,122 @@ def settings(self, **kwargs):
                 Path to mcrun command, "" if already in path
         """
 
-        # Make sure executable path is in kwargs
-        if "executable_path" not in kwargs:
-            kwargs["executable_path"] = self.executable_path
-        else:
+        if "run_path" in kwargs:
+            raise RuntimeError("Can not change run_path for instrument as the "
+                               + "available components could change along "
+                               + "with their inputs. Create new instrument "
+                               + "object with the desired input_path")
+
+        if "package_path" in kwargs:
+            raise RuntimeError("Can not change package_path for instrument as "
+                               + "the available components could change "
+                               + "along with their inputs. Update "
+                               + "configuration and create a new instrument "
+                               + "object which will then have the new "
+                               + "package_path.")
+
+        if "executable_path" in kwargs:
             if not os.path.isdir(str(kwargs["executable_path"])):
                 raise RuntimeError("The executable_path provided in "
                                    + "settings does not point to a"
                                    + "directory: \""
                                    + str(kwargs["executable_path"]) + "\"")
 
-        if "executable" not in kwargs:
-            kwargs["executable"] = str(self.executable)
-        else:
+        if "executable" in kwargs:
             # check provided executable can be converted to string
             str(kwargs["executable"])
 
-        if "run_path" not in kwargs:
-            # path where mcrun is executed, will load components there
-            # if not set, use input_folder given
-            kwargs["run_path"] = self.input_path
-        else:
-            if not os.path.isdir(str(kwargs["run_path"])):
-                raise RuntimeError("The run_path provided to "
-                                   + "settings does not point to a"
-                                   + "directory: \""
-                                   + str(kwargs["run_path"]) + "\"")
+        if "force_compile" in kwargs:
+            if not isinstance(kwargs["force_compile"], bool):
+                raise TypeError("force_compile must be a bool.")
 
-        if "output_path" not in kwargs:
-            kwargs["output_path"] = self.output_path
+        if "increment_folder_name" in kwargs:
+            if not isinstance(kwargs["increment_folder_name"], bool):
+                raise TypeError("increment_folder_name must be a bool.")
 
-        if "force_compile" not in kwargs:
-            kwargs["force_compile"] = True
+        if "ncount" in kwargs:
+            if not isinstance(kwargs["ncount"], (float, int)):
+                raise TypeError("ncount must be a number.")
 
-        self.current_run_settings = kwargs
+        if "mpi" in kwargs:
+            if not isinstance(kwargs["mpi"], (type(None), int)):
+                raise TypeError("mpi must be an integer or None.")
 
-    def backengine(self):
+        if "output_path" in kwargs:
+            self.output_path = kwargs["output_path"]
+
+        self._run_settings.update(kwargs)
+
+    def settings_string(self):
+        """
+        Returns a string describing settings stored in this instrument object
         """
-        Runs McStas instrument described by this class, saves data in
-        data attribute
 
-        This method will write the instrument to disk and then run it
-        using the mcrun command of the system. Settings are set using
-        settings method.
+        variable_space = 20
+        description = "Instrument settings:\n"
+
+        if "ncount" in self._run_settings:
+            value = self._run_settings["ncount"]
+            description += "  ncount:".ljust(variable_space)
+            description += "{:.2e}".format(value) + "\n"
+
+        if "mpi" in self._run_settings:
+            value = self._run_settings["mpi"]
+            description += "  mpi:".ljust(variable_space)
+            description += str(int(value)) + "\n"
+
+        description += "  output_path:".ljust(variable_space)
+        description += str(self.output_path) + "\n"
+
+        if "increment_folder_name" in self._run_settings:
+            value = self._run_settings["increment_folder_name"]
+            description += "  increment_folder_name:".ljust(variable_space)
+            description += str(value) + "\n"
+
+        if "run_path" in self._run_settings:
+            value = self._run_settings["run_path"]
+            description += "  run_path:".ljust(variable_space)
+            description += str(value) + "\n"
+
+        if "package_path" in self._run_settings:
+            value = self._run_settings["package_path"]
+            description += "  package_path:".ljust(variable_space)
+            description += str(value) + "\n"
+
+        if "executable_path" in self._run_settings:
+            value = self._run_settings["executable_path"]
+            description += "  executable_path:".ljust(variable_space)
+            description += str(value) + "\n"
+
+        if "executable" in self._run_settings:
+            value = self._run_settings["executable"]
+            description += "  executable:".ljust(variable_space)
+            description += str(value) + "\n"
+
+        if "force_compile" in self._run_settings:
+            value = self._run_settings["force_compile"]
+            description += "  force_compile:".ljust(variable_space)
+            description += str(value) + "\n"
+
+        return description.strip()
+
+    def show_settings(self):
         """
+        Prints settings stored in this instrument object
+        """
+        print(self.settings_string())
 
-        if self.current_run_settings is None:
-            raise RuntimeError("Need to prepare run first!")
+    def backengine(self):
+        """
+        Runs McStas instrument described by this class, saves data in data
+        attribute
+
+        This method will write the instrument to disk and then run it using
+        the mcrun command of the system. Settings are set using settings
+        method.
+        """
 
-        if self.current_run_settings["force_compile"]:
+        if self._run_settings["force_compile"]:
             self.write_full_instrument()
 
         parameters = {}
@@ -1648,14 +1735,15 @@ def backengine(self):
 
             parameters[parameter.name] = parameter.value
 
-        options = self.current_run_settings
+        options = self._run_settings
         options["parameters"] = parameters
+        options["output_path"] = self.output_path
 
         # Set up the simulation
         simulation = ManagedMcrun(self.name + ".instr", **options)
 
         # Run the simulation and return data
-        simulation.run_simulation(**self.current_run_settings)
+        simulation.run_simulation(**self._run_settings)
 
         # Load data and store in __data
         data = simulation.load_results()
@@ -1727,7 +1815,8 @@ def show_instrument(self, *args, **kwargs):
                                 + "="
                                 + str(val))  # parameter value
 
-        bin_path = os.path.join(self.package_path, "bin", "")
+        package_path = self._run_settings["package_path"]
+        bin_path = os.path.join(package_path, "bin", "")
         executable = "mcdisplay-webgl"
         if "format" in kwargs:
             if kwargs["format"] == "webgl":
@@ -1843,7 +1932,7 @@ class McStas_instr(McCode_instr):
     package_path : str
         Path to mccode package containing component folders
 
-    current_run_settings : dict
+    run_settings : dict
         Dict of options set with settings
 
     data : list
@@ -1997,10 +2086,10 @@ def __init__(self, name, **kwargs):
                 Work directory, will load components from this folder
         """
         self.particle = "neutron"
-        self.executable = "mcrun"
         self.package_name = "McStas"
+        executable = "mcrun"
 
-        super().__init__(name, **kwargs)
+        super().__init__(name, executable=executable, **kwargs)
 
     def _read_calibration(self):
         this_dir = os.path.dirname(os.path.abspath(__file__))
@@ -2012,13 +2101,13 @@ def _read_calibration(self):
             config = yaml.safe_load(ymlfile)
 
         if type(config) is dict:
-            self.executable_path = config["paths"]["mcrun_path"]
-            self.package_path = config["paths"]["mcstas_path"]
+            self._run_settings["executable_path"] = config["paths"]["mcrun_path"]
+            self._run_settings["package_path"] = config["paths"]["mcstas_path"]
             self.line_limit = config["other"]["characters_per_line"]
         else:
             # This happens in unit tests that mocks open
-            self.executable_path = ""
-            self.package_path = ""
+            self._run_settings["executable_path"] = ""
+            self._run_settings["package_path"] = ""
             self.line_limit = 180
 
 
@@ -2081,7 +2170,7 @@ class McXtrace_instr(McCode_instr):
     package_path : str
         Path to mccode package containing component folders
 
-    current_run_settings : dict
+    run_settings : dict
         Dict of options set with settings
 
     data : list
@@ -2235,10 +2324,10 @@ def __init__(self, name, **kwargs):
                 Work directory, will load components from this folder
         """
         self.particle = "x-ray"
-        self.executable = "mxrun"
         self.package_name = "McXtrace"
+        executable = "mxrun"
 
-        super().__init__(name, **kwargs)
+        super().__init__(name, executable=executable, **kwargs)
 
     def _read_calibration(self):
         this_dir = os.path.dirname(os.path.abspath(__file__))
@@ -2250,11 +2339,11 @@ def _read_calibration(self):
             config = yaml.safe_load(ymlfile)
 
         if type(config) is dict:
-            self.executable_path = config["paths"]["mxrun_path"]
-            self.package_path = config["paths"]["mcxtrace_path"]
+            self._run_settings["executable_path"] = config["paths"]["mxrun_path"]
+            self._run_settings["package_path"] = config["paths"]["mcxtrace_path"]
             self.line_limit = config["other"]["characters_per_line"]
         else:
             # This happens in unit tests that mocks open
-            self.executable_path = ""
-            self.package_path = ""
+            self._run_settings["executable_path"] = ""
+            self._run_settings["package_path"] = ""
             self.line_limit = 180
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 28410aa2..9f830ad1 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -303,9 +303,9 @@ def test_complex_initialize(self):
 
         self.assertEqual(my_instrument.author, "Mads")
         self.assertEqual(my_instrument.origin, "DMSC")
-        self.assertEqual(my_instrument.executable_path,
+        self.assertEqual(my_instrument._run_settings["executable_path"],
                          "./dummy_mcstas/contrib")
-        self.assertEqual(my_instrument.package_path,
+        self.assertEqual(my_instrument._run_settings["package_path"],
                          "./dummy_mcstas/misc")
 
     def test_load_config_file(self):
@@ -344,8 +344,8 @@ def test_load_config_file(self):
         # Check the value matches what is loaded by initialization
         my_instrument = setup_instr_no_path()
 
-        self.assertEqual(my_instrument.executable_path, correct_mcrun_path)
-        self.assertEqual(my_instrument.package_path, correct_mcstas_path)
+        self.assertEqual(my_instrument._run_settings["executable_path"], correct_mcrun_path)
+        self.assertEqual(my_instrument._run_settings["package_path"], correct_mcstas_path)
         self.assertEqual(my_instrument.line_limit, correct_n_of_characters)
 
     def test_load_config_file_x_ray(self):
@@ -384,8 +384,8 @@ def test_load_config_file_x_ray(self):
         # Check the value matches what is loaded by initialization
         my_instrument = setup_x_ray_instr_no_path()
 
-        self.assertEqual(my_instrument.executable_path, correct_mxrun_path)
-        self.assertEqual(my_instrument.package_path, correct_mcxtrace_path)
+        self.assertEqual(my_instrument._run_settings["executable_path"], correct_mxrun_path)
+        self.assertEqual(my_instrument._run_settings["package_path"], correct_mcxtrace_path)
         self.assertEqual(my_instrument.line_limit, correct_n_of_characters)
 
     def test_simple_add_parameter(self):
diff --git a/tutorial/McStasScript_tutorial_1_the_basics.ipynb b/tutorial/McStasScript_tutorial_1_the_basics.ipynb
index 4a99ca95..b6716104 100644
--- a/tutorial/McStasScript_tutorial_1_the_basics.ipynb
+++ b/tutorial/McStasScript_tutorial_1_the_basics.ipynb
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,75 +112,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are the available component categories:\n",
-      " sources\n",
-      " optics\n",
-      " samples\n",
-      " monitors\n",
-      " misc\n",
-      " contrib\n",
-      " union\n",
-      " obsolete\n",
-      "Call show_components(category_name) to display\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_components()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are all components in the sources category.\n",
-      " Adapt_check     Moderator           Source_Optimizer   Source_gen\n",
-      " ESS_butterfly   Monitor_Optimizer   Source_adapt       Source_simple\n",
-      " ESS_moderator   Source_Maxwell_3    Source_div         \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_components(\"sources\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Source_div ________________________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[4m\u001b[1mxwidth\u001b[0m\u001b[0m [m] // Width of source\n",
-      "\u001b[4m\u001b[1myheight\u001b[0m\u001b[0m [m] // Height of source\n",
-      "\u001b[4m\u001b[1mfocus_aw\u001b[0m\u001b[0m [deg] // FWHM (Gaussian) or maximal (uniform) horz. width divergence\n",
-      "\u001b[4m\u001b[1mfocus_ah\u001b[0m\u001b[0m [deg] // FWHM (Gaussian) or maximal (uniform) vert. height divergence\n",
-      "\u001b[1mE0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Mean energy of neutrons.\n",
-      "\u001b[1mdE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Energy half spread of neutrons.\n",
-      "\u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Ang] // Mean wavelength of neutrons (only relevant for E0=0)\n",
-      "\u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Ang] // Wavelength half spread of neutrons.\n",
-      "\u001b[1mgauss\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [0|1] // Criterion\n",
-      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1/(s cm 2 st energy_unit)] // flux per energy unit, Angs or meV\n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "instrument.component_help(\"Source_div\")"
    ]
@@ -201,17 +153,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "source Source_div AT (0, 0, 0) ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "src = instrument.add_component(\"source\", \"Source_div\")\n",
     "instrument.print_components()"
@@ -227,23 +171,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT source = Source_div\n",
-      "  \u001b[1mxwidth\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1myheight\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1mfocus_aw\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1mfocus_ah\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "AT [0, 0, 0] ABSOLUTE\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(src)"
    ]
@@ -258,23 +188,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT source = Source_div\n",
-      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mfocus_aw\u001b[0m = \u001b[1m\u001b[92m1.2\u001b[0m\u001b[0m [deg]\n",
-      "  \u001b[1mfocus_ah\u001b[0m = \u001b[1m\u001b[92m2.3\u001b[0m\u001b[0m [deg]\n",
-      "AT [0, 0, 0] ABSOLUTE\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "src.xwidth = 0.1\n",
     "src.yheight = 0.05\n",
@@ -294,31 +210,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Source_div ________________________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[4m\u001b[1mxwidth\u001b[0m\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m] // Width of source\n",
-      "\u001b[4m\u001b[1myheight\u001b[0m\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m] // Height of source\n",
-      "\u001b[4m\u001b[1mfocus_aw\u001b[0m\u001b[0m = \u001b[1m\u001b[92m1.2\u001b[0m\u001b[0m [deg] // FWHM (Gaussian) or maximal (uniform) horz. width \n",
-      "                        divergence \n",
-      "\u001b[4m\u001b[1mfocus_ah\u001b[0m\u001b[0m = \u001b[1m\u001b[92m2.3\u001b[0m\u001b[0m [deg] // FWHM (Gaussian) or maximal (uniform) vert. height \n",
-      "                        divergence \n",
-      "\u001b[1mE0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Mean energy of neutrons.\n",
-      "\u001b[1mdE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Energy half spread of neutrons.\n",
-      "\u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Ang] // Mean wavelength of neutrons (only relevant for E0=0)\n",
-      "\u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Ang] // Wavelength half spread of neutrons.\n",
-      "\u001b[1mgauss\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [0|1] // Criterion\n",
-      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1/(s cm 2 st energy_unit)] // flux per energy unit, Angs or meV\n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "src.show_parameters()"
    ]
@@ -335,18 +229,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    wavelength  = 5.0  // Wavelength in [Ang]\n",
-      "int order       = 1    // Monochromator order, integer\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "wavelength = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
     "order = instrument.add_parameter(\"int\", \"order\", value=1, comment=\"Monochromator order, integer\")\n",
@@ -362,25 +247,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT source = Source_div\n",
-      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mfocus_aw\u001b[0m = \u001b[1m\u001b[92m1.2\u001b[0m\u001b[0m [deg]\n",
-      "  \u001b[1mfocus_ah\u001b[0m = \u001b[1m\u001b[92m2.3\u001b[0m\u001b[0m [deg]\n",
-      "  \u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[92mwavelength     5.0            Wavelength in [Ang]   \u001b[0m\u001b[0m [Ang]\n",
-      "  \u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[92m0.01*wavelength\u001b[0m\u001b[0m [Ang]\n",
-      "AT [0, 0, 0] ABSOLUTE\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "src.lambda0 = wavelength\n",
     "src.dlambda = \"0.01*wavelength\" # When performing math use a string and the parameter name\n",
@@ -404,7 +273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -420,26 +289,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT guide = Guide_gravity\n",
-      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mw2\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mh2\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92m8.0\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92m3.5\u001b[0m\u001b[0m [1]\n",
-      "  \u001b[1mG\u001b[0m = \u001b[1m\u001b[92m-9.82\u001b[0m\u001b[0m [m/s2]\n",
-      "AT [0, 0, 2] RELATIVE source\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "guide.w1 = 0.05\n",
     "guide.w2 = 0.05\n",
@@ -466,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -491,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -500,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -513,23 +365,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT mono = Monochromator_flat\n",
-      "  \u001b[1mzwidth\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.08\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mQ\u001b[0m = \u001b[1m\u001b[92m<mcstasscript.helper.mcstas_objects.DeclareVariable object at 0x7fab109241f0>\u001b[0m\u001b[0m [1/angstrom]\n",
-      "AT [0, 0, 8.5] RELATIVE guide\n",
-      "ROTATED [0, 'mono_rotation', 0] RELATIVE guide\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(mono)"
    ]
@@ -544,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -562,7 +400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -571,7 +409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -582,22 +420,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT sample = PowderN\n",
-      "  \u001b[1mreflections\u001b[0m = \u001b[1m\u001b[92m\"Na2Ca3Al2F14.laz\"\u001b[0m\u001b[0m []\n",
-      "  \u001b[1mradius\u001b[0m = \u001b[1m\u001b[92m0.015\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "AT [0, 0, 1.1] RELATIVE beam_dir\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "sample.print_long()"
    ]
@@ -614,7 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -636,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -645,7 +470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -668,25 +493,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "source   Source_div         AT      (0, 0, 0)             ABSOLUTE         \n",
-      "guide    Guide_gravity      AT      (0, 0, 2)             RELATIVE source  \n",
-      "mono     Monochromator_flat AT      (0, 0, 8.5)           RELATIVE guide    \n",
-      "                            ROTATED (0, mono_rotation, 0) RELATIVE guide\n",
-      "beam_dir Arm                AT      (0, 0, 0)             RELATIVE mono     \n",
-      "                            ROTATED (0, mono_rotation, 0) RELATIVE mono\n",
-      "sample   PowderN            AT      (0, 0, 1.1)           RELATIVE beam_dir\n",
-      "banana   Monitor_nD         AT      (0, 0, 0)             RELATIVE sample  \n",
-      "monitor  PSD_monitor        AT      (0, 0, 0.1)           RELATIVE sample  \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.print_components()"
    ]
@@ -708,61 +517,42 @@
     "- mpi sets the number of CPU cores used for execution (requires mpi installed)\n",
     "- output_path sets the name of the output folder\n",
     "- increment_folder_name if set to True, automatically changes the foldername if it already exists (default).\n",
-    "- parameters allows setting instrument parameters using a python dictionary\n",
     "\n",
-    "The *backengine* method takes no parameters and just performs the simulation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/tutorial/data_folder/mcstas_basics_0\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS=\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "1 warning generated.\n",
-      "INFO: ===\n",
-      "Warning: 64159 events were removed in Component[7] monitor=PSD_monitor()\n",
-      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/tutorial/data_folder/mcstas_basics_0\n",
-      "\n",
-      "      monochromator rotation = 22.4519 deg\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "Detector: banana_I=1.35163e-06 banana_ERR=2.28321e-08 banana_N=10521 \"banana.dat\"\n",
-      "Detector: monitor_I=4.0821e-05 monitor_ERR=2.69721e-07 monitor_N=50009 \"psd.dat\"\n",
-      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
-      "    SPLIT 47 COMPONENT sample=PowderN(...)\n",
-      "  in the instrument description python_tutorial.instr.\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+    "The *backengine* method takes no parameters and just performs the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=2.8) # Set parameters\n",
-    "\n",
+    "instrument.show_parameters()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "instrument.settings(ncount=5E6, output_path=\"data_folder/mcstas_basics\") # Settings\n",
-    "\n",
+    "instrument.show_settings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "instrument.backengine() # Perform simulation"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -779,30 +569,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name banana\n",
-      "Plotting data with name monitor\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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+1t3HAlgMYA8eWAohhBCrM31q5lIIIYQQQgghqiGHjnPnonfR4FIIIYToBoqzl11ApY/qLB9ZNVQqz8smeUnfBxn7P56qn5U6l5cKfpCxP73Edl5GuZEZZTZI1d+H9HzStCzVyKvntMTVNk825bQ9kfbP/hZt8FJWamuz1JLNhbfSBvV59KSg36MiK3npLYAJY4NexH3cks5JfVlEj/Hfk01hDi+F5WW1vBSWK/C156XCQGLp8Qg6/7Ks54vZJrX9BGm+p75OmocW6WXEfIyXIbM3M2u566JUW7yk+imUh/vI16XS8mp+fWctKa7n63p3LZHlpbtpH7EQWhYrhBBCCCGEEKIL0MylEEIIIYQQot+hZbF9D81cCiGEEEIIIYRoGA0uhRBCCCGEEEI0jJbFCiGEEH2anso9x18JOOBI1vl5MVo6bd2ojHJjSVebE3AkysN95GAr6WAxWbkSSfsi2r9lxv5U27OpX7YD1aHAO1tTEJ/nUk0lcj3SY36Pdm9F+t1UQCBOC+58gILNLKR+HZVRFwCWkR5BQXUm0f4736eNsUGOTwXhmb+ofLvg4EiUPzNxHV5KtjWC+r+MAxo9hurga8Ztc0AeDvTD50gvtuT7je/drPyhHDSI77s0WcGsBmWUSVMph2aJSu8htb6/9K0gPloW2/fQzKUQQgghhBBCiIbR4FIIIYQQQgghRMNoWawQQgghhBCi36FlsX0PDS6FEEKIAUn6K0CW9yorsXqlJO9ZnjT2oW2bUaaSB4zbPYg0++E2SNVhv9vYjHNyX9iPl26LPHy70O7ZGWWeI5+fsecQgJN/c2vyLbI3k9tNf4PekPRo0muST3F+W9DX8jVKewDp8S+jft1J/TJ67tjjOT/VFOic/HxzH9/g6z2T9KRkU8uQAfliR4yl8u+nyl1O+oSMtrLujzTse0yfpwTfL+z93TZV7qGM+p8izfd62tOcRdbX+kqvqazXtxD1ocGlEEIIIYQQQogEZjYawBWIfoVZB8BR7r6yUh15LoUQQgghhBD9jtKy2K7+qwYzm25m/2dmT5vZI2b2mVrLmdlXi/ufMbNnzeyAcm1Ui5lNM7OFZuZmNiF17JNmNsvM/ln8v3lWO8TWAB5y92MRhTPerLMKmrkUQgghhBBCiNo4wt2XA4CZ7QfgGnRcA51ZzswMwA0AdnH3uWa2DYDHzGy6uxdKlc1sCIAN3f0V2rcWgHXc/fXUuaYD+A2AR8r04woAl7r7jWZ2OIArAXy52N644jbzAIDfAfi+me0IIA/gxc4uigaXQgghRL+lEb9UtT6sas5X7deJeRma5wp2TdXhFVhjST+Wsb/Siq1FpL9Oupl0hbyB7O97hNvi/IbU1giaGFj2bKox8jMmfIvkARxPHr6xSMKnf4/0G5zPkSptRnphqrERGf1ag9vN6GMHXyq1vTVdi+fIi2lUxkmPTzW1gPQXyMv6yBNBJ3yW7OkFEj5Lnm9JPH5+7vnasUcVSD5JY1Eevvey8qsC2a8XPifXr/Z1Xs9rUj7LeikNGIsMB1Coo1yhuA8A1gXwBg8si0wAcLOZ7ePu/zCz4YiSsl4D4OrUuR4FgGjcGjCzUYgGvrsVd90M4BIz+5i7v+3uC5BMGFyqdwqAq9x9upmdDmBPAPeWe5wlNLgUQgghhBBC9DsMyZ8HupD1zYxDal3l7ld1OL/Z1QB2L3Zlz6zGypVzdzezgwDcZWYfAVgbwFfSdd19jpl9G8C9ZnYkgAsA3OjuV6fLVmATAK+5e77YZt7MXi/uf7tCvXsBTDWz3QBsBOD6zk6kwaUQXcDBV84CANx63E693BMhhBBCCNEg77j7xM4Kufu3AMDMvgHgfAB7V1vOzAYB+BGA/dz9MTPbGcCtZvZpd/8wVf+h4szhwwDOd/eLG3hsVePuLwKoyQeqgD5CCCGEEEIIUSfufgOAL5nZejWU+wyAjdz9seKxxwB8hESunQgz2wDA2QCmAjjQzD5XYxdfBbCxmTUV22tCNBP5ao3tdIoGl0IIIYQQQghRJWa2lpltQtv7IDJsL62h3GIAY8xsi+KxLRFlsF2QamM0gBkA/svdzwRwIIBbzOzz1fbX3ZcAeAbAocVdhwJ42t0rLYmtCy2LFUIIIfottQbjqBQYJOvYxzPaSgc8qQaO2MIBUjh4CQfqSdd5inS5oIzlGJtRJ6v/OwdpqQAxq3jjfZTlUjrfSRTEZ+I2yXKzFwW9kgICTaYAOXxZ1k+dZ23Sh5J+hoIIzaD93Pf0Yj9OSHAz6Q7hPUptUR/HpI6tJAccP8Wb0f6Fi4IeOjboj1JtcUAhdr8N3YHOR/sTgYkALJtJ5xxJB/j7P19k6leHwFBHZpRjzYF7+BxLUm3xay0rCFBWf9NwW1wnfc5q6ve/4D6G6lOHdDFrAvijma2JKIrqUgD7FH2U9wI4y91nVyoH4E0zOwHA7WZWCuJzlLunn/ChAH7i7tMBwN2fMrMpiAaiCczsYkTLWDcEMMPM3nX3rYqHjwdwvZmdBWAZgG92yZVIocGlEEIIIYQQQlSJu78FYMeMY3tXU654/A8A/tDJuV4G8HJq37MA0iGo4e7fA/C9jHZeBLBDuWNdiZbFCiGEEEIIIYRoGM1cCiGEEEIIIfodOfTasliRgQaXQgghxGpNPQnYGfYdPpfRbpq0X60EW4lGZuj0V0Xy9yXaZf/kTNJjU/UXkWaf5T5BGvkBnVaaOZ8bwHjafo48lGwLncsVqEwiRAcAkM9yBJ2fvZTTSXuqOp/zq6RnkmbP5BakH0i1xXX4ej0/NuitqAh7Ocel2nquLeih7LN8KegRZPJkL2h68eCd5GEcQdd+GReiMu+lni++r8bTczF/JpXhe3IS6UWptu4jPY8034eccZE9j+nAnlx/LGm6dniRdLVf1yt5M6uhf/svRd9Ay2KFEEIIIYQQQjSMZi6FEEIIIYQQ/Q4ti+17aOZSCCGEEEIIIUTDaOZSCCGEWO2oxzvFdXgugH2Wa2eUSc8dZHkux5J+KqPMXqltzh3I/rY7SJ9Amn1rAEZQ5P1l5KdM+Cy5AnlMN0t5+OaiPOyn5FyNm5FO+wnn0vmH0H72GR5F57829bjY3/co7eZ8kJwnMuspAZL+zUljg/4b7X+PNNlV8edUW5OpX4tof558luzTnEGPtz11vYfS9rq0nz2Xh1KZW1J94USb87lt8rsmfJJchn2RQPb9/hbKcxjpdE5V9hhn+SyzGJXazvIxZ/kv0+8HjXgr08MI+TSFBpdCCCGEEEKIfoiWxfY9tCxWCCGEEEIIIUTDaHAphBBCCCGEEKJhtCxWCCGEEEII0e/Qsti+hwaXQgghxGoNf9RzwI8l6YJEVvSXD0jzV7p0eQ6M8j5pDuLDwT+4/BOptpZmlNuSNAf34WAtAJZx/UVBOgV7wQGk6Rq9merKBNIc52hD0qeRPpX0eqm2FpNeRfon9Bjvo/0jOPAMkg9/Juk9ST9DmuMkcbtAMiDRG6T58d5Dz+NQCno0JdXWnaR3IT2J9DTS4+nx3pNqi4PlLKRyI6jIzek6zD4Z+/k+4oA8fGH4tZJmLOm/k/44ab7v0sGBsoLwcLAefn3yk50VQCjdbhaVvvrXGpBHAXxER7QsVgghhBBCCCFEw2jmUgghhBBCCNHvMCSTyYjeRzOXQgghhBBCCCEaRjOXQgghRL+FP8bbMzTDPq50MvasY1n7V2ZoIOnH3Jn0DNLse1tEOu11Yz8knyftYyt3biDpV+O+kG8wcX4+XaqtITRHcijtf4k02/Ymk34s1fbx5U+JWaTZCzkuVY4v5ZGkx5Neg/R1pNkvCgCjSU8kzf7Nz9D1Yr/otW3Jtk6ja/QM7b8k4xyPkuZrCgAv0T2yCe2/k865C53vkVT9rDktI+38HG9LelGqEm/zvfO58ufAa6S3TB1Lt12u3bGk+V7P8kMDPe+BTA8j5MEUGlwKIYQQQggh+iEGYGh3jGY0Tq4bLYsVQgghhBBCCNEwGlwKIYQQQgghhGgYLYsVQggh+i2NrN1K57kclHGM92fl50vzCunXMksF2Ee2beoY5/XjJIpnkO6QIJGgfo4nT9v8mUFvPSnoIVQ13fXZ5PXbi/x8N7PvkPafTLu3TrV1C2nOjcm5NdkzmYZ9i9zWr0l/SDrL4wkAC0lzmtGLSOdJ/5L0F1K+xutIs2ezhfRJpDmvZprtSV+ecY3n0u7NUvX5cbHPcd2xQS9jkyx7IzknK5D06/LrgNpKvCa4ftpwy+fhPLBcP8vTXOl1l5Xnsp73iSw/d6Ptdi25HDB0SOflaqb3H1q/RTOXQtTJwVfOwsFXzuq8oBBCCCGEEAMADS6FEEIIIYQQQjSMlsUKIYQQQggh+h05A4ZWWj5eLx91Q5sDBM1cCiGEEEIIIYRoGM1cCiGEEKICnGR+Y9LzSe+TqsPBTDgxPLfFgUw2IJ2OosN1diXNgWQOIH1dsvrobYKe/yztnxQ0B4VxCiA0mfuVOuc5tPsE2j+d9nOcoR+lmqLTg2PKrE36ENLrp+pvSPpm0meT5sA7D5F+NNUWB0Q5lDQ/3ZeS5qfoklRbO5JeRXoH0keRnkH6/0u19S7pzegar0X7n6OLN2mvZP0JpO+h+2gZ3XuTD6a+cPCoVFvggEJ8T3IdfiKZnVPb80hXE7hnPOkXU23xDcNRaBr9is9t8Tk+aLBdsbqjwaUQQgghhBCi/5FD8scR0etocCmEEEJ0EWY2HVFChAKiRBDfdfdnerNPQgghRE+hwaUQQgjRdRzh7ssBwMz2A3ANOiZuFEIIIVZLNLgUQgghuojSwLLIcEQzmL1MNcnQ04wizYnZ2WeZlTAeSHoo30d52CfJHrT5qXLsbyNP2mja/QZ5KUccmaz+xqLybb1Bu40rUN/npLqyjPRPSLNnkyNXzqfH/mf2niLp2Tye9Nak2Wp3f6ov7FU8kvR3g5yw75Ohi3/Yvny7aaZ40N+nC3NLC/WF1iFOSdV/j/Q7pCeSnkz6GdJ0CgDA10j/gTR7ObmxV1P1Z5NPchd6Xh+h/TPo3gH5c8H7gaQfchFpfl75nub79reptvj3Jm6L66S9lSU+XqFfrLP8l5XeA7LK9WGfpUGjmT6Gng4hhBCiCzGzqwHsjuhrz5693B0hhBCix+jWVCRmtsjMXjSzZ4p/e3Tn+YRYHTj4ylk4+MpZvd0NIUSduPu33H1TAGcAOD993MyONbPZZjYbWNHzHRRCCCG6iZ6YuTzQ3ed2XkwIIYRYfXD3G8zsKjNbz93fpf1XAbgKAMw28swGhBBCVMagaLF9DC2LFUIIIboAM1sLwAh3f7W4vQ8iw+LSihW7nSyPVforAJfjLrNBjz1dnOyQ/ZdAMvcft7Vxxn72mrHpMH0eqs+eSW5r2Uup+txn8t2NoN2cD5F5I7X9a9J/I/1cRv3HyI93eOrYNNLnkf4p6dPIhLhj6hv0GNLkQRy1779iPXc38ln+hspPT/WFF28vJp/lFNo/jc4/Cdl8gfSapK8mvQlp9o6m17fxbcF5PseSXsT5TlOMoGNpK28M+yzJI8v5UYHUvbCINN9fnOeSfcdbZp08dYy9x/z65Ndg+u0kKzfmEtKVfJZZ/moh6qMnBpd/MDNDlLL3DHd/jw+a2bEAjgWATTfdtAe6I4QQQnQLawL4o5mtCSCP6JvaPu6u2UkhhBADgu4eXO7i7q+a2RBEv/ddgtRvd7w8aOLEifoAFkII0S9x97cA7Njb/RBCiAFDDskIzaLX6daAPqWlQe7eAuAyADt35/mEEEIIIYQQQvQO3TZzWVwWNMjdlxeXxR6CZDYjIYQQQgghhBB9EDMbDeAKREb6dQAc5e4rK9XpzmWxGwD4k5k1AWgC8AKAE7vxfEIIIYQAkJ0MvVIydT7GkVSyIqFw/cmpYxylhRPGjyU9L2M/nxtIBvR5DWXZZ1LQ6fj0TRlN89ejR0hzoJ9jUm3dTnoK6SdJjyZ9POn1kM2dpP9fkKM2CoGRlsxPxaWYSJoC5yy5hsp9jMpcW+H8N5PejPRaQQ49e1msV95IF+m9VFsUXIhvgzF/CffREIRARQte2Cq7X+eQ5pg2z5Ael109EfSIgy5tRoF+FtL+ERSA6Q0OSgXAKECP70UH+D5uI80dfizVMW6bg/BkBefh12Cl4Dxcv9JrnVlS4VhnVAoK1kP0YrRYM5uO6BVTAPAhgO+6+zNlyn0SwPWI3gXeBfBNd3+ps2N19mkagK8helPdmjN21HmurQE85O4XmtnFxcf7QqUK3Ta4dPeXAXy2u9oXQgghhBBCiF7iCHdfDgBmth+Aa5D8Na3EFQAudfcbzexwAFcC+HIVx1BsewiADd39Fdq3FoB13P311LmmI4oN/Qg6knkuMxtX3GYeAPA7AN83sx0RBap7MetilOhWz6UQQgghhBBCrG6UBpZFhiOawUxgZqMQDThL6wNuBrCtmX2s0rFUMxMA/MXMtii2ORzAgwD2LtOnR0sxb6rtR7HeAnefnPo7H8DRAK5y94MAPIVk4qKyKM+lEEIIIYQQov/Rfcti1zez2bR9VTHDRfL0ZlcD2L3Yk3IDr00AvObueQBw97yZvV7cbxWOvV1qwN3nmNm3AdxrZkcCuADAje5+NaqnUj/erlDvXgBTzWw3ABshWlZbEQ0uhRBCiNWCaj1WWXCdLO9X1v6nUm2tzDjGXrMDSLMn7aFUW/tntEv+uHtQJc8GOXSboI2KTCH9TKr61qT/TPqCjNNNJ53+2sne0HVJXxLkB++vHTYeTdVfRvoo0vR18xs3/S7Wty49ONatk+jaAcB0ygR3P12MCUGuvJ18lmwBpDIAkh7MDYNcfAp5ED9DZXhgwH5XIHKxxR0gzT7LQ0nzcwIAC0jzc3QqFyKf5BjyYo7bAAlmszWNj+2A8nCH90odm0l6LGn2FLNBmF9rSzPOV4lRpBvxWKbpBY9lz/GOu0/srJC7fwsAzOwbAM5HmdnErsDdHzKz0wE8DOB8d7+4O85T5rwvIvlm3SlaFiuEEEIIIYQQdeLuNwD4kpmlQ3e9CmDjYoBTFP9vVNxf6VgCM9sAwNkApgI40Mw+V2MXqz5Xo2hwKYQQQgghhOh/5ACs0Q1/nWBma5nZJrS9D6Jp5cTUsrsvQbQGojTHfiiAp9397UrHUucajSj28n+5+5kADgRwi5l9vvOedt6PatuoFi2LFUIIIYQQQojqWRPAH81sTURRVJcC2Mfd3czuBXCWu5c8m8cDuN7MzkK0oP2b1E6lYyWGAviJu08HAHd/ysymILHwPKKYLuSA4rEZZvauu5fy/VRzrobR4FIIIYQYMFTySGXlueQf4tszyqRzao/MKMdmvTtIs4ftp8mmOG/kG0+UbzdRJtWVfUivtQ3Kcj/prNyIQLL7fLnYD3l/xv7DU229F+Tup9wd6wcv3DfWK+eSCTGVv3PCTSG55tzTt4/1mAtDJ2+4+9uhwjtU+QupvqwKPsstT3w61vOeooxyk8iXOYZ8mZQLEwC2/HyoP5aSSN73MNm2plCF6aTfS/XrQNJ/I83XglOqpm9v7hs/F5yWdQbd3+/QffjG+6nGyKdqpJ3vSb7v2Qi8Zaot8nZiEWm2tt2XUZ/zagLZHkq+QSv5NGv1ajfq7V49cPe3AOyYcWzv1PaLyDDnVjpGZV4G8HJq37NImMjj/d8D8L16z9UVaFmsEEIIIYQQQoiG0cylEEIIIYQQov9h0Gimj6GnQ4gu4LX3VuKjloG7PEQIIYQQQggNLoXoAt5f2YZVbYXe7oYQQgghhBC9hgaXQnQBq9oLcHjnBYUQokvpymAclOQ+EbmmUuCeEpUChuycsZ+DmjyWfY43uF9jST9FZf5O+1MBge5ZRBsbB/kQnX9dKnIj6VSwmkTAmUdIX9IS9JghQd9C+yfSfgA4J8gHX6aoQxyQ6GrS06gtAHMfDkF88FGQix+m68VP42LS6VQLdGzeLRTEZysq8wQF8XmT9n+YbGre+qH+vEOorb9QIQrCM2FbCkz03/SYAOB20nwbcNClW0hPSFbHcxSU57lFQQ/lwE50772xLZ1vbLKtsRTEZ8YTKA+/DjgIzyJkw68vbndJhv54qj6/pkeiPNyv9Ff/rPeNURnn72OrtAzAkE5LiR5EAX2EaJDW9gJa2zVrKYQQQgghBjYaXArRIK+9F/3K7pq4FEIIIYQQAxgtixWiQf61dEVvd0EIIYQQYuCRg5bF9jE0uBSiQf71bmR20cSlEKIx6klOnlWunrayfJYfZLSb5S8DEt5GNtiNODPoZYuozHOk2T8JYP+9gp5O+79A+x9hr1yqK/PZx0Z+zmlHBn3PuVTmu6TfSrZ18uZ0zluDPuLgoP8flXdaIMaXFABOY01+xpPJW7kxfWsek/wGveXPn471vLnkbeSnaBXpK0ivTD2uAzcI+hdtQY8mX+qeVP4LpNdLNoVD7gv6WnpedqFzkEd07v3ks2QvJQBMIv2L64Ief2TQTs/d8XR/AcBs8kk+Rz7L2TOp0AFBHkW7r7082dZ88h1eSr7ek+g+wItBbk1lnku1hcmkbyPNr9VdST9EOu1vzvJKZ1Ht+wH7LNl/yefvY/5L0SfQslghGqQ0c+laFyuEEEIIIQYwmrkUokG0LFYIIYQQohcwdIx+LHoVzVwK0SCvvFuauezljgghhBBCCNGLaOZSiAZwd7xaWhbby30RQvR3GvUvZeW7y8pRxz6qdLkPUB42DvI5Uh6+hA+M8lwmfJbsy+RTpPJU3vkz2qBjj/wM5UnVH01+yDeeDXouF2KvHre7f7Kt6aQfo3YTqTy5/smkOa8ngGVU7kzKXXjtkUE/T+UvSVaftxv5LBNpQsnzuBn5Uk+iMnnyPwJJC+Cl1E/2hV5L3tUJOwQ9Gyn4vqBcpJOoL7+gxz6en9NU/shH6DyHHhn0zRnPEdl7OzD7JdogD+Nmk4K+lts9LNXATUGew/vZb0yey+foXku8tgCAvKCJm2dehmbS5l2GX99rZ9RJ9yXLn83103WEyEaDSyEaoL3g+Kg1j6acIV9wuDvMrPOKQgghhBCiMRQtts+hZbFCNMCqtgIAYI1B0UupoOlLIYQQQggxQNHgUogGaGnPAwDWaG4CAOQ1uhRCCCGEEAMULYsVogFaSjOXzaWZSw0uhRBCCCF6BINGM30MPR1CNMCq9jw2XGcN5Io+S81cCiHqJyuwRrVwnXSi9XLnSAfpyEqUzgFLOHDP0Iwylc7PQXxWkj4+yImpKo/wxqLyzZ5MQWEuSgb62dL3jfU8+79wYOGdQY/mIECk30idZyEFadn5nqCNAgLdT/WnUN2VqWA1m1G5hXRsPgXkmU9BcNLpFjYn/V+kF1OdW2j/NNLpa8zBgjjQEQfIeZGC6xxD+3dJtTWCyo2j/RsGOca/HrrL8X+wAxIcSvrmmUH/hK4dB9e5MxVYah8KXHQUXbBrKYDSL6n8IVz5tWRb59I5z8wIMgW6pzAf2XAEJr4A25Lm1xfda0gFY8oM/MPway391T8riE9WUC8hKqNlsUI0QEt7AZuOHIZSDJ98gzOX7o4331+FtnyhC3onhBBCCCFEz6GZSyEaYFVbHpuMHIa33l8FACg0OHP5xvJVeOXdFVC8WSGEEEKITjAoWmwfQzOXQtRJoeBoyzs+vt4wlEaDjS6LbW2PZizl3RRCCCGEEP0NzVwKUSctxYHgpiOHxTONjS6LbS9EbWpVrBADkXp8lrW2VekcWT7JNtLsA2NPWrou+f5wB+mHUJ6Tg3zkZ9nHcBHpPYNkS1qKeXY3bR1GmpLXv/E+nY4S3F80M9HWlj48tLs9+Sw3oUJvkmY/4KMpP+Gd5K1cQI9lGq1d2Y+uxT7s7QOG3x5OtPLDYbFunUb9P40qjCe9brIr7KE8w8+K9dTtfx4OnEvlTyDNjxdI+iy3J/1ekItHUmd+TGXSvtKT+N4jb+IvaPdRpKen/IjsGd0Z5Tf49ppM13gG+WuBpGc1waIgd6H6j7D/ky8+kPQoP0WavZHULiaTTnss+TX9KdLzM8pUgn2W7L/kfnXl+5RYHdHgUog6aS2OADdad2g8uCw0OChsy0eDU81cCiGEEEJ0Qg4df5QQvYqWxQpRJ6UB4OBBOZQi+jQ8c1kcXCrqrBBCCCGE6G9ocClEnZTGkYNyRjOXjQ0K2wryXAohhBBCiP6JlsUK0SBNueCPaXTGUTOXQoj6qeYjnb1f6Tx8nG+PPVYvkuZcmFyevZgA8AfSlLvvefJibsXeSvZSUs7L9DHOR/kG1f8o5WdkOJ/kKtr/Hml+uBex125Soql5Rvko2au3FukjqF9z6NzXJbs1vOWzsV5/8AuxXrD+VqEQ+SxH3f2vRP0ltiltkX+Tn5ebyQu7T7j27NcEgOUzQxLKqfuSz3JBkJN2vz/WM6eQR5SvHQD8hDT5FAcvDr7W1tnkC2XPZbotNAd5wTZBn0qP9wt0T53Tkqy+8XlBD6Xn4lDKecmpVxO+Ss5ZCWAMnX8ZH7g+yJPYc3lF0JOTflnMYC8pG0P59TmWdCV/M7/us3yWWTls0+WYfpLnUtFi+xyauRSiTrw4uzgoZ12W57I9r5lLIYQQQgjRP9HgUog6KQ3/mrp0WWxp5rKhZoQQQgghhOhxtCxWiDoJnsuuDOijmUshhBBCiKpQtNg+hwaXQtRJPHPZFGYuG/VKtslzKYQAkP3xXG2OOS7HbbGpLe2TRMYxNqVtkC5Ypl0gM4/fVn8PegT50JZRnsmE/xJIeDD5S+REqs+WtLRnc2FG2+zB24zL82NM5To08nY20f5nSPPj2o48mhckfaHLvxJ8jssPDRozqdAj18Vyj0S+UeBp/0ys5w4j3+FErk/exLPp3DvT+YBkns47qQP7TArdOoB8lsF+Ceyaykv6C7r+54Zr2fot8lneSdd1F/Iyrp9sCndS26fS+U+gx8u5PI9Jme/Yo3vPTDpA9/QI8l/iVtK7Jtt6jvpyLrV7NZU5JJ2jtciMjP0Asl+ffJGzvM5A8ubnY1l5a9P1l5Dm3JZZnkvub7XvRxpuDCS0LFaIemHPZXFXo3ku2xUtVgghhBBC9FP0U4IQdcKeS3RZQB/NXAohhBBCVIWixfY5NHMpRJ2Uy3PZ+LLY0sxl48GBhBBCCCGE6Ek0uBSiTrw4d9mUM1gxoE+jy1nbaUC5qj3fUFtCCCGEEEL0JFoWK0SdlMaRTTmL9zU6c9lOOUg+aslj2GC9RIXoL5jZegBuADAOQAuijObHufvbtbdWbaCMaupwAI9XSKffX6pJms5J2ncm/VSqHAf04cTwLwa5jAPUUKCdC1IJ50+lYCgLaf9CCvAymoLlPJAKOrTHS7RBba9Lu88m/UOqPynV1s2hL8PPPS7Wyw+hADm/pvLvUL9+nGwKKynYzl4UoIb6tZN/PNY3PP6lZP3DSXNQm1+QPora/X6Qxzx5aaKp8fS8/sjouZhChWaTnkH6oeTzNXhCCKDUuh49x9ZMpSiIDz2NeAdJDqW2+fpNIz2O+5gKwPTGPNrg5/K5IJdx4BsOqPMQMrmT9MK3ypf5PfX9mEoBfYZm6KzP/1dS29V8T8h47B3qV/Me0JXvTV2AQaOZPoZmLoWok27Jc5kP9Ve2auZSiH6GA/iVu2/h7tsAWADgvF7ukxBCCNFjaHApRJ1wnkvrqoA+FG72o9Zu/KVPCNHluPtSd59Ju/4G4OMZxYUQQog+jZmNNrO7zOwqM7vFzCrlsAKgwaUQDRANJGlVbJfluQSAFRpcCtFvMbMcgBMA3N3bfRFCiNWWUrTYrv7r7LRm65nZvWb2DzN71szuMLOPZZRdw8wuN7OXzOw5M7uKjk03s/8zs6fN7BEz+0w9l4Ham2ZmC83MzWxC6tgnzWyWmf2z+H/zrHaIrQE85O7HIkqKulkn5bVKWYh6cY/e08y6MKBPYnCpZbFC9GN+C+BDAJekD5jZsQCOjbaGI3wU8w9K9SQqz4I9ZY1+7HNfMrxmABI+yxHblC/yZ9Js3zw15U8bQd61ZeyfvCnIVcHbOG735xPVF9y1VdjYjzx5X6d+HUH759D+tAfw5tCXLw2+JdbTjz8klGFv4jmk2VsIAAcHP+SYg4PncTFdu1lTg89y6HeWJaoPfrEl1jsM/nusJ/88GCJ/cNnFocKYIH8/7CRkwlZDfixUH2eSl3I6eymB1uPXoa0ngjyS/KfXcVukb0GSW0m/F+TQX4drsfLGEeX7CwCbHRz0QvZjLiLNHsRdgxx/ZLKt+YtQHvZpHh8k+ywPTfmIb+Z7/BTS5MNNeKX5Nfyp1PnJx5yos4Q0P8b0ewBPRGV5Lteuoky5tkuslj+Wl2wQMwHAzM5HZIM4pkzZXwFYBeCT7u5mxibYI9x9ebGN/QBcA2BbrmxmQwBs6O6v0L61AKzj7q+nzjUdwG8APFKmH1cAuNTdbzSzwwFcCeDLxfbGFbeZBwD8DsD3zWxHAHkkb7iyaOZSiDpxIM5vGVKRNNZmYllsiwaXQvRHzGwagM0BHOzuHd4V3P0qd5/o7hOBNXu+g0IIIRqiWhtEcRD4TQA/cY9mINw9/mWuNLAsMhxAuW+SEwD8xcy2KLY5HMCDAPYu069H3f3VMv0YhWjQenNx180Ati3Ntrr7AnefnPo7H8DRAK5y94MQRW5L/0zWAc1cClEnpZlLRstihRjYmNm5ALYD8BV3b+msvBBCiAbIoaplrHWwvpnxXPhV7n5VuYKd2CDGAXgXwE/N7EuIVrT82N0fpfpXA9gd0dfKDoM3d59jZt8GcK+ZHQngAgA3uvvVNTyeTQC85u75Ypt5M3u9uL9SRPN7AUw1s90AbATg+s5OpMGlEHXi8Hg5bCmgT+PLYsMPVloWK0T/wsy2AnAGgH8CeLz4/rDQ3ffv1Y4JIYSolXei1SVVkWmDQDTW+gSAp939dDPbAcA9Zjbe3d8HAHf/FgCY2TcAnI/yM5IPmdnpAB4GcL67X5wu0x24+4sADqiljgaXQtRJcuYyUg3nuSw4DNGSW81cCtG/cPfn0XFBQ2e1UN6P1JWv/2rbYi/Xa6TZ78XeK/aacS5LIJH3chnnxqTvKDu/T/vJq4Yrkk3NpQngjdnfd0SQ6wW5wAYn63P4idHkp0ykeiTf2XbUrxHsH0Ti2Z2eI58lr447bVLQPAfx/WRTnA9z8Ud0/U4lnyKCT3ElyFsIYCWd5vzPnx7rf3s9eAu3O/GxWM/Znoyt/NQBUciOEuQY2/Klp2M976zPhgPP0/PwXqqt20k/Tz7L+2n/GqRXkT5kZqoxMoDeGX6jWbkPPY/3kJfy9yl/L887TaRjx3A58vGOoPgmu6W6cv3YoHdexAfoHOSt/Anp/Srkufw96WP4idkrXbLIH1Lbo0inn9hypN8P2EOZ5fWupky57dUfskHsU84GgSgxaTuKy1Hd/QkzewfAJ5FyCbv7DcWorOu5+7up82yAKCPvVACHmtnt7v53VM+rADY2s6birGUTopnIDktoG0WeSyEawFKey0ZnLtvyBTQVw89e//giHHzlrIbaE0IIIYRYbTFEP1Z09V81pw42iClZNgh3fwfA/6L4c4WZfRLRLwLzzWwtM9uE2tsH0a95/IsezGw0gBkA/svdzwRwIIBbzOzz1fUUcPclAJ4BcGhx16GIZlMrLYmtC81cClEn7g5DcllswzOXeYdZlN6kwaaEEEIIIUQ3UMkGYWb3AjjL3Uszk8cDuMbMLgDQBuAb7v5ecTbyj2a2JqJIrEsRzYCmvwEORRQQaDoAuPtTZjYFwIZl+nUxoiUiGwKYYWbvunspZPbxAK43s7MALEMUaKjL0eBSiDrhaLElGg/oU0DODG6NtyWEEEIIIbqeSjYId987tf0ygEllyr0FYMcqzvUygJdT+54F8GyZst8D8L2Mdl4Er7XvJrQsVog6Yc9lVwX0aSt6LptypsGlEEIIIYToV2jmUog6cbDnshTQp7E22/MFmEWtNTpQFUKIjlQKxsG5sbOChLDm5Otp+BgH7mkrX3wy5RR/L5Vw/hDeoOApo6ncfA6Yckay/kIK2DKaArbwgrKF/Lg4YMoJyba4Ds83bDgp6Ado/3MhOM+oO0Ynmlqy56Zh406+LhT55mSaZDiTA/0A+Ek49m87/3fYPzTkZZizLgXx+THV3SXZVGJOZUqQ84ZREJ/5HFjpvKA3Sz1fnwlyp0//b6xnfeFL4cAtQU7Y98lYr8THEk0tsM+FDQ7iQ2FQhreEe3V5OrbnWNKX032wGd0HC6nMMrrGl49MtnU5BTGaSA1zSJbZdB/uh+o45nLa4NfNW6QfQzZZr0+O0rQo4xwAsIR0VkCeSu8bvUz3pSIRdaLBpRB1wp7LEvmGA/oEz6VmLoUQQgghRH9Cy2KFaIB45rK0LLbhVCQFGICcmQL6CCGEEEKIfoVmLoWok4Tnsvi/a6LFGppyhvb2BtfYCiGEEEKszpRSkYg+gwaXQtRJIlpsVwX0yReKy2IV0EcI0R1U8kvxVwL2m7EnizxweIr05FRbXH8G6Ywk7zMqJJnP4g2qcyj5/sanyv3iJqrz8ViO8S/EevGF5Od7lPQlqdR1i8jctTP3ec8gLyCf5KlBLnmKPJYAcA5vkJ9vOj0WahYTUkEerwtyjK8X68W8Jo2vBfkccWSyKcwkPQ1lGbrWiliv5PtgQqrgtPDZNcuGhf37UBnyI86dsz1tpM+aD/Ie9iBeEavla9D18tR9tJj9oJQ6cGHGczeZrnGle/Ioapc9l4nX0HdJX5SsP579wufSAX7dkA8ZW5JOv4b4unB6xHmkK/mjmbVJf5BRplr/ZR/2aYpuRctihaiTRJ7LOKBPo8tiFS1WCCGEEEL0TzRzKUSdJKPFRjQa0KcULTbyXGpwKYQQQgiRiQFo6u1OCEYzl0LUCXsu0UUBfdryju3HjsBB22+Cgkezo0IIIYQQQvQHNHMpRJ04HGbR7zMhoE9jbbYXChiUy2HNwdHPcFoZK4RIUuljm31N1Xy8p31QvE0eQHwqo/7XSf8hdWxnlGcd0pz/cn/SdyarnED+tMvJB3cB7T+VchiCPJZAMg8j5bxcvBEZEtnbuC7pjf+RbGto8GMOfvfkWLd+ix7XqYtieYb/T6ynHpbyTG5M+l3S5OHba/c7Yr1+ohDw1tHBk7culsX6tnH0uNYN8oy7zwp9ufXnyb4ECyNuW7FvrA/a7e5Yr1xjRCg0eq9YTrg75KkEgLkHkIfyenrMx1Oh35N+B+XLAMAD5H/lfKfL6Dk9kvazlxEAFpNO5K08LMhdKOflIpQvAwCHUrmUhTKwbcb+VFuJvKycj5LznV6fUSaVfzPhuUwbjkuwF3NJRhkg0xOdoNL7THuGFgOJHpm5NLOfmpmbWdr2LUT/hWYubz1uJwBdsSzWMajJMCweXGp0KYQQQghRlhyiaLFd/SfqptsHl2a2LYAdAfyru88lRE/C0WIjn2QXLIstFNDclMOwwdGvgQrqI4QQQggh+gvdOrg0syEALgVwIorfxYVYXUh4LlGM8NoVM5c5w5pDopnLrhpcHnzlLBx85awuaUsIIYQQQohydLfn8ucAbnT3hWZWtoCZHQvgWADYdNNNy5YRoi8SeS7DfZ0z65KAPoOachhanLnUslghhBBCiAwMwJBOS4kepNsGl2a2E4DtAfywUjl3vwrAVQAwceJEfZMW/YayM5cN57ksoLnJ4oA+jQYIEkL0V7ISkNcTJIPrcHCe+RXqcGARTuD+GOmnSKcDmXBgEApAcijtvjkrKsr+yc3LrytfbFpG9UR0HgCreGNskG9QUJX7KRDMOVR8JgWUAYD1gtx65HOxnsMBjLYO55h6IgXOmZhs6rZTKHDOJ0LgHEwO8r6pB5TvF5B8/BSQZ8uXno71gqXjYn01vhUKHfJEsi0KvHPQZdSXTajMo6Rnt8TyPVCgHwBb3hHOPy/32XDgair0a9J8G26V7FbC+zabPl9PC5++W15D5zuRzgcAl/N9TIGeRtDz/QgH1wmBdwa/uwGY1vWepa1U0KmYvwe5Swh6hDHrJIvdTHo8PcfzLy/bl+RF4tcgkHztcZCsFzP6mGZUxn4O/JMVxEdBe0RHunNZ7K6IPsUWmtkiAGMAPGBmu3fjOYXoUXhCvsm6alls8Fxq5lIIIYQQQvQXum3m0t3PA3Beabs4wPyqu8/trnMK0ZOkZy5zua5YFhvNXA4b3LWeSyGEEEKI1Y5StFjRZ+iRVCRCrI6kPZddEtCnUExFMkSpSIQQQgghRP+iuwP6xLj72J46lxA9QXrclzNryCPp7sgXomWxa2pZrBADnGq9TOyXyvJIMZV8llxnLGlOwD40ozzvB5KeS6p/889Qln3IAzcvdWw++9UosfwbGR64k6ktAJhOenxz0A+cFfQ4auuY4DGd4n9KNnXWIbGeczr5LDmL97pBnnjZhbG+7BOnJNq6/ZSvhY2FdGBMkN8443ehu2fskag/DgtiPev+L8X6A6wd69b1wsVcsuBzsd7OX020NYdslrid9D3Bwzf43bC79cXwnCw+YnyirV/95Xux/sGj5IG8kQodT5q8nGNuSt6fi42NnmNRjnn2VtgYnfRcjvN/xnrBiXRfXD4z6KG0f+MgW9d7KXUmvg+PCHIE9es7VOQXfK+fnGqLfMXz+T7m19FzGfvJwwwAoMeP11CerPcJAGBv6XPonCz/ZfqYGKj02OBSiNUNR8pzmWssz2VbPqrb3GQY2qxlsUIIIYQQFVG02D6HlsUKUSfuDiPXZaMBfdoL0bTnoKYccjlDzhQtVgghhBBC9B80uBSiTtIzl40G9CnNXA7KRY3mzLQsVgghhBBC9Bu0LFaIeimX57KRmcviNGVzUy60p2WxQogEa6e22T9VzUc6e6LSbX1Amr1b7OninHrsqySzGoCkT5M8YieEfIpYTEWmkL4n7cvcmjR53U4bG/QfKB/lRSmv3GabBz2fjo2jPlro1+WFo0J3p16bbOsZ0lNIf4Y05dW87AXyWS5MPq7bDiCv36V0gCyMT1PDS7bfNFH/l0/+KNaz5gbP5eLtqYFDSd8f5JwdyS8KABzHfxLpGcFn2Hpk2D3u7udjPeUvyZyPP7j14rBxCOWZ3J/8rrdQBfIpLj4s6d8E/hDkCOrAMi5D50jlEl1wNCXOvJa8jXdNCno/qsD3x/503wDAnYtog7y/y8hA+gtKOJogndOVc1jOIM3+R0p4ijtIpz2X/FrbOGN/pfeJ5zKOVeOf7AMeS4NGM30MzVwKUQfu3tFzaY0NBtuLdQc1aeZSCCGEEEL0PzS4FKIOwhgyjC5zDc40tpVmLnOauRRCCCGEEP0PTSQLUQelQd9hO4RlSg3PXObTM5c8iBVCCCGEEAkULbbPoZlLIeqgNIhsyiVnLhtZxlqKFivPpRBCCCGE6I9o5lKIOojThtDgsinXWF5KznMJyHMphCjHBxWOVUpuXo6Vqe2Pk55HmoOsLCI9ljQFawGQSPo+lILtXE5BbUZTQJtEEB8O4AMkgwhRIJVpFNxnNPVlQSoozB688RT16+CgV4ZgLycYJbznqG1AFCa8xD23xnJL/2Ss573+6VBmDE+p0OMFgOmk77wu6JOPjOV7u48I+0OcIQDA7fha2Fj4RCx3fzkEYHpw831DGQ6i84VkW4ngSguD3NKfjvW8yz4b671xb6ynY39kwkF8+NadRPrD7H7tflM454NGQW0mHxDLE//y21hf9iAFUAKAPUmfQPchXwvwvUcVxiabwgO0Yzo9l5enA1CVzlehzGQKFjSDA+rwvX416S1Jv1/+fACSgbgYDt6Vft0z9Lqt+F5TIv0+0wcC/IheR4NLIeqg3MxllOey/jbjZbHyXAohhBBCdE4OwBq93QnBaFmsEHUQR3ZNzFw2lueytRjQR9FihRBCCCFEf0SDSyHqIMxchpdQozONHfNcRgF9GhmwCiGEEEII0VNoWawQdVBu5jJnhnxDAX2SbeaKSTRb2gsYOrip7naFEAOdLC/mxqlyS1Ee9nFxAvf5pBel6pBHbOUdKMsbGV61DpDPcjz52OYHzyO+PjbocT/Pbupkqv8Z2n8EPfahk2K53YrHEtXn3Lpz2LgreDbPxIGxPvyc20OZ2VT5uGRXBj8QvHOt1x0Z6z+fMjnWp+P8UGFMsv59lwXfIS4IMmE7HE3VXwrP1+LdUr5U6vJe24bn676H6RwnBV/nb086PexPXqLk7fJqkLc9GfyfB/333eEA356nJZt6cEyoM7zlzVgvPzOUufZ9MqPS5QIAHB/kQZeF++i2x8mv+7dwT5z48oWxvsz4Xgdw0U0oz6eCNPLxss/y0pTfdk/6rjCOfY5PkGafJfskN0id/0XSWV/rK3kpB1U41hm977F0A9oVLbZPoZlLIeogX/RH5rpwWWxbvCw2elkWx5YNDViFEEIIIYToKTS4FKIOykeLbXDmMhUtthSmMN9IlCAhhBBCCCF6CC2LFaIOyua5tMZmLsOAtThzWTqXZi6FEEIIITrgOaBlSHfMlRW6oc2BgQaXQtRBacDXlTOX6TyXpWWxpUGnEEJUTzU+y3S+Oz72YsZ+zme5Dmn2dAFJ/+ZbKMsD5EPbg8uk8vgddQDKMp/6OG1R0ONT/rY8abYakh9v6PJ/i/XK74f9c6wt2dYCeo8/5JVYHr4xmRavC/LEy8jDNzuZg3HvkSFX5GGnBD/fV4fNCIX40i9IduWXhZCP81KcFOtvUX7EF8i3d9vDwWd46l/OSbR1wYU/jvXjEz4fDrzH5/9c+f0vJfuVyJlJKTAPev2PYWNyS9AbPxP09Tsk2zoyyOXLNizb7sqrKRfo88nqw/8n+DRvs0V0hLyN48M5Lzv9lPJlAACHBXkU5am8lry/zj5iusFOSvuL+R7lJ5l9lpxr9q0MDQDkHwX7m9k/ya/PSl/9s471vrdS9B80uBSiDkpLWNMzl/kGxoHxzGVTcuZSY0shhBBCCNGTmNloAFcg+kVjHQBHuXv6V8kOaHApRB2UlsUOauKZy8bShpRmLuPZ0OLUpWYuhRBCCCE64mZoHdId4WIrj6HMbD0ANwAYB6AFUfjs49z97WrLmNkiAKuKfwDwn+7+QCO9NrNpAL4GYCyArd19Lh37JKLw2+sBeBfAN909vfaA2RrAQ+5+oZldDGAzAC901gcF9BGiDtqz8lx2SUAfzVwKIYQQQvRhHMCv3H0Ld98G0cL18+ooc6C7f6b4V3ZgaWZDzOzjqX1rmdlGZYpPB/BFAK+UOXYFgEvd/ZMALgVwZbGtcWY2I/V3OoC/A5hsZrcB+BiSfolMNLgUog7yGXkuuySgT8pzqYA+QgghhBB9B3df6u4zadffAHy81jJVMgHAX8xsCwAws+EAHgSwd5l+Perur6b3m9koANsCuLm462YA25rZx9x9gbtPTv2dD+BoAFe5+0EAngKwZzWd1bJYIeqgNBBs6o6APulosZq6FELUzNqkObAHB9pJL/taQnpr0m0ZmtvaNtUWH+Nk9HSOPThwD+tUv65dFPT4sUFvRkFRQtwWYH4qeMpEKncSrQA7OQRlWTmcvotZCBAzzj+WaGqBPRfrSf56rGca9QvPxuqybShAzAXJbk3f/JCgXwsas6nQVlwjGWDmYewS68WXhUhFZ59E0W4eCsFqBk8I1/iCl89MdubUIPc95Z5Yv7Dvp2M95+GdYz3qi/+K9ZKrN022dTn183kK0PMdWroYLiO+6w/H+reHpQL68FfZt4Pc644QuOa+UyjgU2pOaPmQcL/9yUO/vmYUnGc+VZhG987WqcBQzy0Kmu/JRBCeSSjPEaltvkd5rMH1OaAPv7743OltDtzDgYL49VgpOM8o0ksyS/UlHIY8mrqj6fXNjF+NV7n7VeUKmlkOwAkA7s5qrEKZP5iZAXgUwBnu/l66rrvPMbNvA7jXzI5E9G5yo7tfnS5bgU0AvObu+WKbeTN7vbj/7Yw69wKYama7AdgI0ZLaTtHgUog6KDdz2WQW76+H9nxy5rI0ddlIkCAhhBBCCFEz77j7xCrL/hbAhwAuqbHMLu7+qpkNAfDr4rHDy1V294eKS1UfBnC+u19cZd/qxt1fBJARrjsbLYsVog7ay+W5zDW6LDYZJMji/Y2PLl94/X0seX9V5wWFEHVjZtPMbKGZuZlN6O3+CCGE6F6KAXQ2B3Cwu5f9wpZVprR81d1bAFwGYOdy9YttbADgbABTARxoZp/LKpvBqwA2NrOmYntNiGYjOyyhbRTNXApRB/kyqUiarNFlsdH7Tbwstth0o2PLtnwBH7S0Y9iQblk2IoQITAfwGwCP9HI/hBBiQOAwtKA7osV+2GkJMzsXwHYAvlIcIFZdxszWBDDI3ZcXl8UeAuCZjDZGI/JYnufufzCzPwG43cwOd/fHq3k07r7EzJ4BcCiAG4v/n+botl2FBpdC1EHWzGVDeS7zjpxF7QBdN3P53orII6W4QEJ0L+7+KACYWWdFa4Q/qqtNZv5BFfvTXwHYp5nl9+IE7uw1ey3VFvu92EPJPrbgOxveclyslw9pTjY1cWzQs9PJ6EuQz9BSXrlEnU8FeUvwXGL/bYJeI8gFuYTpMXGamSOpDvksMYL2P0f+w1VJP+G4l54P5zmdzsMuqqNIP5Ks/xybBdeiAw9QuR8H2frIOrE+1c9JtHXBA6HgCwg+y88jfG/9zy/+V6wP2jzYxk586cJEW7dfdmCsJ5FF67ax5Du8M3h3Z4NWHs5FkltIbxWu5ddxU6zzF4b7+MGR+6YauDNWU0E+06H03K+k52483TvPpe615+kYWWST5Y4nfQWyYQ/mzCBHjw36jW9RmXVIX5dqK8vfzLB/Mv265zrcVpb/kuunz1ett7P/Y2ZbATgDwD8BPF5831/o7vub2b0AzkL05le2DIANAPypOIPYhCjFx4kZpxsK4CfuPh0A3P0pM5sCYMMy/boY0VLWDQHMMLN33b30BnM8gOvN7CwAywB8s6GLkIEGl0LUQfBccioSoNDIzGWhgEFNvFI9+oLaSJsAsGxFKwDANboUQgghhGgYd38eYR4gfYyjuGaVeRnAZ6s818sAXk7texaJX7Xi/d8D8L2Mdl4EsEO5Y12JBpdC1EFp+WuHZbENBfRxNFN7pcmPUv7Leln2UXFw2VArQoiuwsyOBXBstDW8V/sihBBCdCUaXApRB6X0IIO6MqBPPjlzGaci6aKZywa6JoToQorh7K8CALON9MoUQog6KSCHVgzu7W4IQoNLIeqgvTsC+hQczU0dZy4bmQ0FgKUflTyX+g4rRP+EvUvp/NuvZZRr1KdJ3kT8nTTnv1xEemyqLc63R7kxn6fdZDNcPuTK7G5xprmTyfd2EXndTiOf40ep+hMy8lyyJY5DMHHXv5pqa2aQ45aSZ/LEcP5Zl4WVbjtd+HSstzwlaACYZ63lz3k/6V+QTtok8SzCOTc7JKyYW35gsGHt9PD/hn7t+6VYX/D66cnGxgT5wvvBc/nqOpvE+ur3gwfwly+dHOvXsVGiqSWbh7yX058gk+oMLhV8uLM2D/1K3F5AKj1kuEiH73Z72P0NKjIlVf/+8NzPSSxOfAll2Yf0RaljWy2ijTaUh28qyqVJHtEIboue/De4zH2kDybN/ksg6Y9mDyR7ojmZZ5osn2SWF5PLVJsLU8ONgYRSkQhRB/lU2hAgGmg2mueSPZzpc9VL8Fw21IwQohPM7GIzW4zoq/oMM3u+szpCCCHE6oR+ShCiDjLzXDYwgmvPe2KwWoo4WW+bB185CwAwYePI06WxpRDdS6VACkIIIbqeKBWJlsX2JTRzKUQdlI0W22BAn2hZbEfPZcMBfRQtVgghhBBC9AAaXApRB9kzl/UP4qJlsR09lw2nIvlIy2KFEEIIIUT3k7ks1syqcem+6e7bdF5MiNWLctFim+JlrEBT2axGlWnLeyrPZUR7owF9VkSBBwpaGCvEasBrqe1qgvVkfdSPT22/mKGZ90lzwJCVqXKPkabAIFtRIJSHKNDOo1T8zLeSTU3cIOhbMrr1AOl3koeGnrcs9HL+5rH2NejHvGncMD328dRHAIPnh8e/wN4OB0J8G+x0DQXuWT/IefumUtqdG+Tvz/h6rJ/BZ2L92/0p8E5LsvrIs1aEjYlBPnV3CMjzBKW0G393COpyw8shOA+AROCcTz/5QqzXw7uxfvBv+8b6hd3DOTq0lRE7ZsKzT8Z67tTtw4EfUyHaDQC4lT63jqOvm3x7fYz0zcm0f7/0a2P98/fPivXKI8N9gDu5k6RPSD73uHwRHaP6c6ncIxRkCnTfduChII3q+x0Z9Z8gvXGqLY4G9RRpfh1xcJ70a5UDefF7RdZ7AJN+b2k0qFjtOAytGNJt7YvaqeS5fBvA3hWOG4C7u7Y7QvQPys1clsaF+YIn9lffZiEZLba4MLbhgD6auRRCCCGEED1ApcHlWe7+SqXKZvazSseFWF3Jl0lFkss1FoCnPe9ll8VqcCmEEEIIIfoDmYNLd/9TZ5WrKSPE6kg8c2kdl8XWOxhsyxcSy2JLLTcyuCy444OWaDmKa1msEEIIIVYjHIY8mnq7G4LoNBWJma0B4OsAxnF5d/9BN/ZLiD5NwR05C7OVQJjFzNc7c1lwrNFMnssumLnkSLOauRSiL2IIH63V+JKq9S5lleOP/UqJ1fcnfQ9pTuDOydvZfwkk/WLs7Zwa5K4VTs/MPj6Wv/TzYv0j2zGUeS4spDrGyegI4PdGJsyTg7/NptCb4miq8MbMoNnOBuCckcEg+IN9Lo71hAvJT7gvGQe3CHLC3aEMAMx9IZQ75qw/hAPPUKExpC9P9gWnkb46yG2nB88kriWP66HNQadse6Oe/Fes5zy+c6xv+3zwWT64Z9C3v3dgqHxLygayIFzXdUe+F+u5l9F1+UKQOxX+N9azXv98oqk/bRTuw0NvDb7Y1vUWhUIfBi/mXp68p3+00UVh442X6Agbc8k/eQ7tTllJgeuDvPww2k/XFbT/UnqtnJRui5hG+lS+4dhYyq+vtFd6EWn2Vi7NOGHac9mIN7JS+e7zWYq+TTV5Lv8IYDAiN3FLJ2WFGBC0FzyRhgQAcqWAPnUOBtvzBQwaEl6SseeykdyZxcBDOdPgUgghhBBCdC/VDC7Hu3v6J0khBjTlgvbEM5d1Di5b86k8l104czl4UK7hfJlCCCGEEH0Jh6FF0WL7FNUMLl82s7Xd/YPOiwoxMEgH3wHCEtm6l8Xmk9FiSzQyuGwr1m1uyqEtn6+7HSH6O2ZWKfp5iVXu/tdu74wQQgixmlLN4HI5gNlm9gCAVaWd8lyKgUy+UEBTaiAY57ks1NdmeyGZ57JrZi6jzgxuyuEjl/9BDGhuBfAkQqyscmwGYGyP9CbGUZs3qVJeuWrqVMp3x36veSjPc6Q/TnrbVDn2e/F5ziBN/kscT5pzaQLYJfg3f7Q9eegSXrMDYvX7keyBA4Dgx/zGhb+L9Q3XfTsUOY+KXz0p6LnJln7w+vkox9xryE/IPsldqIwlZ1eGt7wZ6+W/oOD8E0NuStwzM+hzqV8Ahp9G9WdvGOuzP//DoCfSA2NvX8pPuOTBTVGOU3Bh2Jgd5N/WCX7Xf1v8j2SlcWEuYsnJ1O4kKkO22FkjvxQ2OM8kgK/Nvbdsv7AL5bykx3KfpUyyJ5C+nHzA55Ifkm2a65E+k3NOpmHv8fvli5zECRUOSx28KchT76P9bIblx5LK/ZoJ1+HXHb8e0+8ZWUOBrP36LiEqU83g8h/FPyFEkchzmV4WG/2vd+ayLV9AM6ci6YI8l+3xzKXJcykGOvPd/cuVCpjZ0z3VGSGEEI3jMLRicG93QxCdDi7dXbkshUhRznPZeEAfx6AmHlwWz9VIQJ98AWsNGYRczuAA3B1mlSZuhFht+VwXlRFCCCFEBpmDSzP7D3f/o5mdWO64u1/Wfd0Som9TLlpsowF92gvJPJdxKpIGAvG05R0j1myGF5fq5gvJAawQAwV3b+uKMkIIIYTIptLM5QREaUi2L3NMC+zEgCZfcKTGlg3nuWzLe2JZ7K3H7oRPnHFvg6lIHCOHDcbSj1rj7UHKNSwGMGa2MyKHXSl3swFwdx/Vqx0TQghRMwXk0KJlsX2KzMGlu/+0+P+onuuOEP2DbstzSTOXuZzBrPGAPiPWHIxlK6LBZWu+gDWaNboUA5prAZwJYA6AfhhCudpgGllBfCoF9qgmKDy3O5b0Y6lykzOOcRCfXUlTUJStKak9ADxC7pzNfkoHngryFurLJqmufDXUueETtP+9IC//Zviqc8K614YDRyabGrVRCKyyZAsKVnNMkHt5CARz3+Yh0NCWnrzd5r0+Imw8FALy4NEgBz8QAiW1poILLR8Sfow8xi+N9dn/TUF8Tgryux6CEe2AJxJtHZ67PWzQBP7iOeNjfeq258T633YLoTim/OWWRFv/uGyLWM/b7bPhwEXhnGf4/8R66jk/D2VmIMky6udRFOjoO1QmdAtjPHnvLN6NNvahIDxnvkQHqM7WtHv/A5DgTgpadQEFBzr1iqBvofvzkJOpMgeiqsTYDP0H0vwaBpJBfFhzfX4NVhNuJQ2/hyypo74YSHR6h5nZIADHAvgyohnLvwL4nbtCT4qBS75QyM5zWe/MZZklq4Ny1nBAn5HDBmNhcY2tcl0KgWXu/sfe7oQQQgixOlLNzxeXIvr543pEy4cOB/BvSMYNF2JAkS8TLbY0c1m35zJfQHOZ2dCG8lwWZy5LMXza8nXmSRFi9eEmMzsewG1Iptda0XtdEkIIUQ9RtNghnRcUPUY1g8svAtjKPQoJYma3IpnoSogBR7losaXtevJcFgqOgqNLZy4LHrU5YlhzHCFWg0shsATA7xD9cAoUPZcAtF5cCCGEaJBqBpeLAQxByMbaDODVbuuREP2Ars5z2VYckTY3pWYucxbnqqy5j8UlsCPWHBynNWnTslghfokonftTpR9N+zfVeCuRUWZl6hgncM+qz1Qqw8e4XfKk7ZJRNd2tXagO+y9xRJCH0P7x7MsEsOzWoI88OJYTLnwy1iccFnyW290U/GlzJuycaGrJheSz/D0deK0llq+S6XOvl8h/eWHSwzfmlPmxXvyF4G287eV9Y30KLgxl1iLPIIAJ/q9Y37D0G+EAXZZv+O9i/dtrTg/6U4mmMHzVm7Fe/jfyfx4X5AW/+XHYmBG8kNPtkERbg98N/tnh/0PtDtky1lMtPMnsBf0tQh8BAGuQz3I27f8CyvLu++sld/yENNe/Z2zQo2n/eNJ3Jn2pOJT6cuoiOkBGzUPuoP08D8P+SwDg5/Jy0m+RZv8kv27TcLlqXvdpqqnDPstKQ4csx1w9Pk/RX6mUiqSUgmQhgFnFGUsA+A8AD3d3x4Toy1TKc1nPTGNpINhxwGoo1OnhbC8OWEcOG4xSs+2auRTidXef3XkxIYQQfR2HKVpsH6PSTwmcguRpAJ8s6v9D8icXIQYc7fnsPJf1DAbjwWVq5nJQAzOXbTxzGS+L1cylGPD8PzP7LwC3Ium5fKH3uiSEEEKsHlQaXP7K3ef1WE+E6EeU9Vw2MHMZlsV2nA2tO7VJsd5IBfQRgjm8+P8g2ucAPlGmrBBCCCFqoNLg8mIz2wjAvQDuAvCYewPZ3IVYjWgvFDCkOfnyycUBfRpZFttx5rKR6LMAMGLYYJz5lU/jiGv+Hi+VFWKg4u6b9XYfkhjCR3E1fqV0Gd7O8k5l+R/T+eqy6nOdsaTZk5X+LZqMk3dRHsH9Qg5FrN8c9J2U57JSTsDR5Kd8IyP/5XzOYQjgqOCzxHfC++lcW5faDXLORuSzfIP6CwCPkD6U+r9niFY595Cw8GvunbQI7NBkU4svCwa/MS8H/+VBl90dCp1EftFd2BAIfPHh0JmVI4fFesFPtor1DXd/O7R79PWxfqB1j0RbyycHn+VtDwfP50GzT4l124QvxbrZ6HPpkkRT2GRkCMux4MHQF/YzHvPS87GewTlRT3o22dgt28Rywk3BIzt3X7quZwe58j7KHQoADwT5jWuC//SGS8J1wSIqfyfpx8hjCSS8rFEChRL7B3la6C+msV83dU8fSvfrzbR/6JFBr2TPJ7/WXkN18OuZ689Plct638l6P6gnE2H3ZS90GPLydPYpclkH3H03AJ9HtCT2ewDmm9k1ZrafmQ3NqifEQKBStNi6AvoUB4LpaLG5hgaXUb11hzWjudi31vbKbR185SwcfOWsus4nRH/AzCab2XDaXtfMvtybfRJCCCFWFyoO9d19OYCbEOUFGwzg3wHsB+BCM5vr7vv1QB+F6HO0FzxeBluioYA+xTrpZbGDclbXYBWIlto2maG5KYfmQbniecrPXGpAKQYQ5wPYlrbfBzAttU8IIYQQdVD1PLK7twK4r/gHM9uhcg0hVl8q5rmsK6BPceYy13WpSAoFoNRcKQqtPJdCwNji4e4FM1OOSyGE6Ic4DK2KFtunyFwWW8LMLjCz4WY2yMweMbOPzOxwd3+is7pCrK60F7zDEtYQ0Kf29kpRXMvNXNYb0MfhsGKGy1L+TEWLFQIf8I+jRf1RL/ZHCCGEWG2oZuZysrufamZfQeQiPgTA/wC4sVt7JkQfplBwNHWYZYz+17csNmPm0uqfuXRHHCW2NLhs1+BSiB8AmG5mpYginwZwQIXy3YwjBLvICtxTbTAMLjcqo8wrFcpsQPq5CuVKcFay9KrimUHux2Ea6FLfSQF58LmMcwCYSMFPZi8KmoP47EjlF1EAoVRXsAf9gPfapnSgJVbjNgoBTxacRQFpAGBD0nTOwWNDQKK9R94b6+n/e0goNCX5/nv2J34U61sRgg4NPXxZrFdeS8GIJiS7clkuBNs5u/DDWF/04+/H+oeD/yvWP3qQgsrcn2wLvw7yIAsBhab4LbH+Iv5a9nxnb3Reoql1T3wv1qN2/1esl6wbrvcQut6vt1I0pQf4AgMYzwGYtgj7b6Ey55CekqyOa2fG8oZrXw/79wnywZd3ifXuFCQpcd8AwPxFtMHRfSi4z0wK6DORXk+zT0629TbpC04I+tT0SUs8lbEfACiwVCIID78fcPCuSl/9+bWeDvhVDZWCj4mBQi3hlb4I4A53f83M9A1VDGjaCx4vNS3RyLLYtjjPZWrmsqmRmUseXGpZrBAA4O6zzOzTAHZCFKr1cXdf1kk1IYQQfZACcmjBkM4Lih6jmsHlEjP7HYDdAZxnZoOqrAczmw5gMwAFAB8C+K67P1NfV4XoO1TKc1nPTGPJc1maYeQ265+5LLcsVoNLIYqDyXs7LSiEEEKImujUcwngMADPAzi4+IE8BsAFVbZ/hLv/m7t/FlE0vmvq66YQfYv2QqHDzGVDeS4LpTyXHdusZyYUSM5cDopnLrXoQAxMzOzRrigjhBBCDBTMbLSZ3WVmV5nZLdWko8ycgTSz2wBMB3Cvu/+6tN/dFwG4rpoOFVOZlBiOaAZTiH5PpZnLejyXIc9l8veeQTmr2yfpDpR6GHsuM1KRCDEA2Kr4uVaJjXqkJ5mwR6ke7xL7pdh7tXFGGU6sDgBtpLcm/VYV5057wsaSfoz0VNK7Bjl5UtAz/p5sajbHDySz4C/Jc3nIouy+NJHP80e0f2FIwTTJV8V6ptH5LOW5/HGQp24bzH7TfvmTUOWMv4VCZKfDzORnxtR1z4j1R4Pi1Kv47Drh8c/df/uy5wYQ/YJY5Oxh5HskD+LtpxwY6wd3J2/hWuQtRNIz+n8efIOfui94dO2v4YSzDv1SqMy3F4A5D+4cNujpmvXkZ2M9DCti/frg8LKbPpc8qgAwha7Z6ODxHXUweTlvJe/s8cnqOHRS0GuQvjbc6//R+sewf9eXqPJNybbG0/02n/3CdNLZvJ8Y8dPk9gwq9xYfm0eafczsb07DTwDXX5s0v9ZfS9Xn95pqfJaVFi/2vM+yN6PFmtk0AF9D9Ia3tbvPzSj3VQC/QPS1LAfgbHe/o7NjXd0nM/skIpPwegDeBfBNd3+pXDvE1gAecvcLzexiRCtSX6hUodId8htE1ugfm9nrAO4CcLe7v1KhTgfM7GpES2oNwJ611BWir1LJc1lPXsqp90YfCOlosTmrP8+lu8OKA97mnKLFigHP96soc3t3d0IIIcRqw3RE46VHsgpY9EXsBgC7uPtcM9sGwGNF66BnHXP3ArUxBMCGPAYzs7UArOPuryNJpT5dAeBSd7/RzA4HcCWAL5vZuKJmHnD38wH8HcD3zWxHAHkAL1a8IqgwuHT3xxD93Hh6MfjBfgBuNbPBAO4GcJe7P93ZCdz9WwBgZt9AlLx6bz5uZscCOBYANt100w71heiL5PPlosXWvyy2NH5MR4sd1GRY1VbfbGMioM8gBfQRAxt3v77zUkIIIUR1uPujAOIf8itQQLSCEwDWBfBGMceyZR1L1Z8A4GYz28fd/2FmwwHch8hueHU1fTKzUYimw3cr7roZwCVm9jF3XwBgckbfjwZwlbtPN7PTEU0UVoxZUFVgHnd/AdEU6C/NbDSigea5SA0UO2njhuJ63fXc/V3afxWAqwBg4sSJmlYR/YJyeS4HNTBzWcrpXnbmss6APqBUJKVBa7sGl0IIIYRYTXAYWrpnWez6Zjabtq8qjllqwt3dzA4CcJeZfYRovfJXOjuWamOOmX0bwL1mdiSi2Dc3uvvV6bIV2ATAa+6eL7aZL65M3QTJ5Dhp7gUw1cx2Q2Qd6fSH2mqjvg5DFMinVP5hd7+ikzprARjh7q8Wt/dBZAJZWqmeEP2BfMGRs44DQaDOmcvi/3Key3oD+hTgGGRRe6VBa6uWxQrRT8jyLqU/trNy2THsZuH69XwcLyK9skJb80mz34v7+FCQM9g3ljLO7UL5Ah8hfSmVOXRs0HuSBpK5KTm/45hJsZxpGV65I9Pb4T30gqODCfKCa/cNZR6iXId8GS5PNvXJo/8R65aW8N7/RVrNNvcS8lw6e08BTN4hlgf9JXzfu+3EkIOR/Y+b7P5qrCd9Ppno8on3Q1tbsuf0Abrem1GF7UifluzWL3cPOR1/tMdRsd5pfVrsdidVoMV4J55yYaKty04NuTzZWvje0nWpLXruJqe8jTfPpA2+D0NMkuVDyFvJ9Wfsn2xrfsY9gvdJ0717Ml27i7gMkPAbP0d9HEEm3WXXUXl+rZGnFUDSZ8mxVrJ8lmmvNb92s3zb1ebdXa3yXL7j7hMbbaSYZeNHAPZz98fMbGdEK0E/DWBV1jF3/5DbcfeHijOHDwM4390vbrRv1eDuL6LGXNCdDi7N7CQA5yG6y0rTHg7gE51UXRPAH81sTURrdJcC2Me9zm/KQvQhykWLjT2XDS2L7dhmvQF9QAF9zKwYHKjvzVwefGUUVOPW43bq5Z4IIYQQQnQpnwGwUdFuiOIg8iMAWyIaT2Ude5IbMbMNAJyNKCraoWZ2u7unop9V5FUAG5tZU3HWsgnRTOSrndSrmWpmLk8FMKHWQD7u/haAHevqlRB9GHdHwZEdLbaOsWBYFpvKc9lFqUiAyL8pz6UQZVfjlOwfQggh+hEF5NCKIb3djUosBjDGzLYo+iW3RLSeYgGAwRWOxRQtiQ8COM/d/2BmfwJwu5kd7u6PV9MJd19iZs8AOBTAjcX/T7t7pSWxdVHN4PLNWgeWQqzO5DNzUkb/61kWWxrypX2cTTmLc2DWinvS0N3clFO0WDHgaWA1jhBCCBFTTM1xAKIB4Qwze9fdtzKzewGc5e6z3f1NMzsB0WCw9JlzlLsvLbaReYwYCuAn7j4dANz9KTObguSi/4p9Kh4+HsD1ZnYWgGUAvtkV1yFNNYPLv5jZrwDcgmhtMAD9yisGLqXBXlOZgSBQb0Cf6H9zLj1zmatrsBq16eAeNjfllOdSiDpX4wghhBCMu38PwPfK7N87tf0HAH/IaCPzGJV5GcDLqX3PAni22j4Vj70IYIdyx7qSagaXpVHtf9A+/corBiyZM5fWiOeyuCx2UHqpLeqfuURyWWxzk6GtXTOXYsCj1TgxQ1PbHFSnmfRbpLMSu49PtZVO1F6CA/qcTPoi2p0KynJRRiCVR0LgGmw9Nuj0b/nTSbNZJ6vd5+n8l6SOHUxvqpNo/7kUxGct2v/nIMc8ydF9gLn7hmA9j9/9+Vg/jF1CoTWowqGp74TnhPfzPfBgrG+bGK7L7rvfHestT18U6o5ONjXhlGDvmsvfYfc8OJZ/LoRMBbNPDHFOTms5P9HWWo/nw8bWfBLSFCbl7G1/GPTU85IdmxPkN7b9XaxvOOzbsR7lIWjQknRGiNGTgubnflyQo86n+htRGaPnFADWpW1qFnfeRxtkgduB72O6vwEkg1ZRfMxlXIYD9zxHOv3a4tcqv4Z58otfn8n7MDsoWFUxPyu0JQYqnd457r5ZZ2WEGEjEM5cdZhm7IFpsmZnLelOROAX0KbXdpplLIbQaRwghViPyaOrtLggic3BpZuu4ezp2cs1lhFjdyJq5DAF9ui7PZVOuvplQAHB4wnM5eJA8l0JAq3GEEEKIbqPSzOVMJNe/1FtGiNWKkm8xHS0218jMZbGKWUcfZz2D1VKbyZnLvpmKhFFaEtHdaDWOEEII0X1UGlxuYWaV8qcYgPW6uD9C9HmyZi6B+geDaX9kor2GosWG7UFNOaUiEQJAMXn1lxC99P5aDHLQVW1/EsD1iD4f3wXwTXd/qava7+hpyvoYz/I+8f6NU8fYizUyQw/N0OlFTFuSpiTv+5AP7R6+LORBu4g9nqljiWTyxHOLSF+fOLSXB+PffY9TLvA9qS98iq0oRsbvU767W0ifRpo8jCdue2GsL8MpsV58TcqXek9bLHd/6pFYj9r2X0G/HPSS3TZNVB8+JlynYzaneCDnBLk1xft48NJ9w4H5LYm23sOIsLFZ8FmyT/KrRufYZYNYXvbwiYm28A7p++k8G88K+oFJsTx7GPksVz6RbOtvwWd6w6PBZ4ljglySo+uyT7I6fkj66iD3Ov+OWN9nZAz9CZX/xa3JtpaR53XC2KDvpOd14l5Bn0B1R6R8xMvY7/s50uyzpNdNgvRrgMtleS6ZkantanyW1b7PDMo4Vo9/szoKyKGlb6ciGXBUerb3rnCsRGtXdUSI/kJ7vuS5LDO4NEM947d0ZNdke120LLbJtCxWDHjM7BsA/gvA/xR3nWFm/1mM2NcVXAHgUne/0cwOB3AlgC93UdtCCCFEnyZzcOnuD/VkR4ToL+QL2YPLXA4o1JmKJFdm6rJLA/ooFYkQQDTftK27vwkAZrYhgAfQSSj4ajCzUYisIrsVd90M4BIz+1h3JKoWQggh+hrdN08txGpKe4XBZb0zjdnLYhsJ6KNUJEKUozSwLOm017kBNgHwmrvni23nzez14n4NLoUQootxGFoxuLe7IQgNLoWokeC5zHU4lqvTIxkti82YuaxjJrTUB26zuSmHD9uVg0oMeBaY2c8QLVd1AMcilZy6uzGzY4vnBTAc4aO4ntdnI6/pdL67DJ9kgqdIU57LrfdKFnuuQ27viHtm0gbHA2R/WNIzmYRyW+4/NmjObTk96W+7j3MX7kn6Wvb3bUCafJbsqwQw/M34dwmsP/jdWC/44laxvuyR4LPEuUHOOvqzibZ2Gv902Ngu9GWUh8+WuRZyYfrfkp8RdiB9NpDPkq2wa+PDsPFokJdvxCZT4ISjr431d18OeStXYFisf3/NSaFCuAxYsk3SC/rLZ0P+0h89TPkdL50U9B507R8gL+MeKT/hFkGOupv8p/vSOW+m8imbJF4lTX7Z+66lvJEPkA+X84pOJu8pAOy6KOhfkObXwWzyUm5G9+HCdE7VPVEebpfzWbKnme9VIPt1m7U/7blkst5PsryUlepXW0esbnT8diyEqEilZbFNOatrWWzBu3bmMl7+mpi5VEAfIRCFbtkCwLPFv08BOK6L2n4VwMZm1gQAxf8bIfkVF+5+lbtPdPeJwJpddGohhBCi9+l05tLM7gdwCYD/ca8zJ4IQqxEVo8V2+bLYyHPp7h3SlFSiFHSIuxilItFLWAxs3H0JgEO6q20zewbAoQBuLP5/Wn5LIYToHhyGFi2L7VNUsyz2KgDfB3CxmV0J4Gp3f7dyFSFWX+I8l03lAvrUN3OZuSy2OKAsOFDmdBX6GPXhiM+HlH7NTTm0auZSDFDMbGd3f8zMykZCd/d7u+hUxwO43szOArAMwDe7qF0hhBCiz9Pp4NLd7wBwh5ltAeAkAM+b2YMAfuPuc7q7g0L0NXpy5nJQcUSZL3jZZbhZtBcHkc1N7LnUzKUY0BwJ4DEAp5c55gC6ZHBZzJm5Q6cFhRBCiNWQegL6tAJYBeC/zex+dz+1i/skRJ+mYrTYXL15LlE2z2UpPUmtA9ZyvtBBTbl40JnmjeWrsKotDwfQnDMUCo5cDYNZIfo67v7t4v8v9XZfkjgaC3ZRbXLzcoxPbb9FetuM/c2kFwX53MxUW+lE7yUmk55avshpqYTz0+g8HOznTtp9F9X5VrL6r7b9Xqx/YN8NB4x+A+CF0jdTMKJVFNwHwPK5IXLQ8lVB//rhYNv9aWsI3rL8K6HM4WfcmGhrry/eEevHWz4f6yWcQpwerm2yKlGfY8KMOjgEu7mfDmx7zQuh0PpBvrVtMijMqGtC/Rn0HM37YghC5NeEzwS7jD6Txia79aPDQhCfKTeFKDrTHw0X2e/dMdZDJi6Pdevzk5KN3R/kkqkUxOeetqDfoHuSY+AA0TqCEolbMgQdwh4vBb3L5tRuqq27xga93yI6cEWQozmID79u6HwAAAp0hMNI0zkSAbcmkeZAVOly/JrOCuJDEZ86sCtpzkjYaLCx7osfGkWLHdJt7Yva6TSgj5kdYGZ/BXAHgBcBfNrdjwXwbwC+1s39E6LPUTlabL15Lst7KpuKp6g1YmxbsY/N1MdoWWzHdtryBfxr6Qq882ELln7UiteXr8K/lq6o6XyNIju36CnM7NFq9gkhhBCidqr5KeEYAP/l7g/wTndvN+OfAIUYGPRsnstocJivcTlrqTz3sbnJQhRZoqU92jdmxFAMbsph/tsf9WhU2ZWtebzwxvuY98b7nRcWonGG8UYxomul2PxCCCGEqJJqUpHckh5YmtnhAODu93RLr4Tow+SLA7RynstczurKS5m1LLZkmax95rLYx4TnMoe29o6DxtbiPjOLZ097MvDPqvY82guOG//2So+dUww8zOx0M3sbwAQzW1L6A7AcwCO93D0hhBB14DDk0dTlf6J+qpm5PBnADal9pyAKsy7EgKO9zKxgiSaL/Iq14u6wMstsm4rrYsvNOFai3NLdQU0WL5dlWtrzAKJfmkqzpz0Z+Kd0ve7+v9fxqQ3WltdTdBdXAfgjotRalA0e77v7st7pUldQja9pFOmlpF9Mlfs46b9ntLszaUpE38HHNYk0e7/YZ3lG+f3TUqsYNhsb9LfI03YJlSEP3BhP+sN+8Pr5dCykHF18dCgz/Io3Y738ZurvyqTnEtNJXx3k92+/Mmz8jcqsHeR2mJ1o6rZtjggbbEVdRPq0IH+50Q8T9e8+cd9Yz7o1WIm3PYQ68FpL0O8FX9qr2CTR1h4Icwg3jPx2OHBMkLaEPhcuujWWg9/dK9FW69x1Yj19avBZjjkjeAOHLA0+y71Hhlha0/+SyhK0MWm+xbYmnyWfnp4TAIl7ZJQHX+mSB8m/uceiWJ768DmxvmCjHyfbSvgsyQzLHuFpwW+L8bR/PnlEO/AUaTaN8oO/j3TSLwtsmVGH/dF8fvZoAsn3h4dQnmo9k1nvR434ykV/I/NuMbOJiCLerW9mJ9Kh4YASyoiBS7lgOSWigD5duCy2lIqkxonE0rLWxMxlLld2uWti5rI4f9qTy2JL49gPVrVj6YpWrL+WjPmi63H35YhmKb/a230RQgghVlcq/RSxMYCJANYEsD3tfx9RSHchBiTtFVKR5KzePJfll8WWzlHrsthy6VKam3Jw75jWpDS4zFkY4Lb1wszliGHNePuDFg0uRbdgZje4+zfM7ElEv+ckcPfP9UK3hBBCNIDD0KJosX2KzMGlu98F4C4z293dH+zBPgnRpykNHrt25rJ8tNjSEtFaA/qUBoeDmpLLYqNjBTTlgp+gJR5cGnLx4LLnZi5L1/Pg7TfFFQ8tQEtbvsfOLQYUvy7+P61SISGEEELUT6VlsTu7+2MABpnZ3unj7t4lCaeF6G+U/IjlU5EY6pn064mZy8HFgWZbvoA1mjsOLs0QD3Br9Xg2Qmlw+fUdosHl2x+2dlJDiNpx9znF/7GpyMwGAxjp7m9mVuzzZPmayOyXyJ23JKMMAHBQLW6Xc15mxfHbObVNOf5GkyluR9J3kj+NGbFOcpuTnp1JdbYmT9sbY2O52FLtUu7BxWtQHsCFIb/h8vEhv+Gv/LFY/8AOSLY1hXxs7PmkxdZjlgZP2+JV4Xy3nU4eSwCDZwZv6RnrDY/12ReE9/stP/90rH/0OOdGBHBekMNvJ88oB0R+hGZ0tg/t/t7Ydgzg0oz+jwz9H3U+eRYfOpgqJz2yU74Yclv+44tbxHre1JAz8xtn/C7WD2CPUDlluRz8bmi7dT0yXU6m54EthJT7EwCwRpBLTgw+y3GXPR/rBZODr/aCo8lj+0bIQwoA4HthuwyfJTM/Yz8AgO+FrGDVi0izKTftmeQEnuzfZG8m10n7H/k9oRqfpPyTojKVlsUeCeAxAKeXOeYANLgUA5LYc9lUZubSUGdAn/Key3jmssbBXnu+Y0Tb0sxlOlhPHNDHLB7gtrb33LLYfCFakrvJyGFYZ41BWPqRBpei+zCzWwAcB6AVwP8hiisw1d2n9W7PhBBC1EoBObQqFEyfotKy2G8X/38pq4wQA5FKnsuGlsWWmbuMZy5rnEiM+5hKRQJ0XPKaTEVSqt+zM5e54omHDm7CCs1ciu5lC3dfbmYHAvgroujnfwOgwaUQQgjRIJ3muTSzL5rZWkV9jJldYWabdX/XhOiblGYRy3kuc9ZAnstyM5d1LlNtj3Nxhpd4c8lzWUjPXIaAPvH5ejKgj4cAQ00WDc69jmsoRJWU1tPtCuBed18BoOd+TRFCCCFWY6pJXHMJgH8zs60AnIoov+XvAXy5OzsmRF+ls5nL0kxgLWSlIimdo/ZUJB2DDpUGmm3t5WcuE8tiezIVSSHMXDblDI5owMu+UCG6kBfM7EEAnwLwQzNLJ2jsRbL8TpU+qqvxP7EPa1RmqWS+PPJMZnrCtib9XOoY1XmDvGd3kldtMukZlMdvWcqrNm3/oC9grxuVYUvajFRXbiF9HemF5FUbF+QPbL1YP+RfSDS1a+6JsMFJbdYKcvEXydc5hsqk0ia2XhG8pWdzAOMpQX8PF8f6hOOvTTYQUjLinVWjY72ZBy/p4qnkX32NPmROSDaFKSEf5uKX6WL8OcglOcoNeXyQ37gsmQr983g81mvjg1jPOzN4Lv/7oGNjbUfzj4m3gmn9Fnk7+TNyc9Ls09z1WSRhP2K4Jxc8uVXYPTtcLxxKDY9I+W05FBhnxp3N/kl+O+HXHb+2kn3B1vQcPTeTyiwizT5kzhubhu69RH0+f/q1mvW+k5Uft1p6xpsZRYvVsti+RKczlwDaPZpG2AvA5e4+FcCI7u2WEH2XTvNc1jVzWX5ZbOkctc5clvrYTNFimwflyrbVWi6gT4/OXAKlCdbS4/1glQIGiG7jCACXAdjV3T9C9C3vh5WrCCGEEKIaqhlcDjKznQGU/CkAoCkFMWC5/vFFADKixZrVF9AHlQP61Jo7s+Sr5AFwc66UiiRrWaxRnsse9FwWHE00cwkAH7ZocCm6B3dfCeARAJ8uRkJf5e7393K3hBBCiNWCapbF/gRRkOq/uvvzZvZJdIyDLMSAoTQ0KzO2bGDmsnIqklpnEsPMZTUBfaJosdHMJcqW6U7y7hhcvJjx4FIzl6KbMLM9ENk7nkb0stvGzA5397/0bs+EEELUisPQiiGdFxQ9RqeDS3e/C8BdtP1PAAdk1xBi9aY0dsyauax1XFYKXlMpoE+tA9b2cp7Lps5nLkuky3QnBfd4hrY0g/lBS1uPnV8MOM4F8EV3nwcAZvYpRINNDS6FEEKIBul0cGlmawD4OiK7e1ze3X/Qjf0Sog8TDbzKWC7RlKs9z2VpIGdlRpelAWGt6U3ay3guB3eSioQfT3uPLouN8oMCmrkUPUJzaWAJAO7+opk1V6rQczSatPzjpDkAR3vG/o1T9TkACdcZS3pohk4H/eGgIxwYhIL1cOAdCu4z9E87JlpaOZzSE526iI5cH+R1of5Bf+H+ArcZBWzBTUEuOCtojtszNLR1XiJoEYB1ST8a5JZLn471vMNC4BosDtL/nnyPt3XpfX2fIKd8IgS1OeGwEMTnu8+en6j/WwtpyJvvCm2N2vdfKMeEU56M9XO3fC7Zl1tC/T+dsnesv/YepTSnxeN/2p3KWDLt+e/3OSn05e7Ql0m0+twupMd+HenxFMAHwK/vOC7W37/wynBgLhWaQnrONon6mE76FyFI1fDH3oz18nMoiM8vqPxPkk0lgvhM46BTFHDqqLFBP7dX0LNTQar2p8BUd86kAxxIi4PwvEaag/akj2UtLOR2P546xsGCONjPkoy21ia9MnVMn92iumWxfwQwGMATAFo6KSvEak9pCWu5wWA9y2JLA8dyy2LjmcuaB5cdPZeDioPL9BLbUmRYM4tnUXvUc8kzl/Jciu7nbTM70t2vAwAzOwLA273bJSGEEPVQQE7RYvsY1Qwux7t7OoayEAMWB8qPBFFfQJ+2QojWmmZQnQF9SgPIZlq6G5bFJgeOLe2FeNbSiulI0rkwu5O8J1ORABpcim7lOAB/MLMrEL2cn0G0OkcIIYQQDVLN4PJlM1vb3T/ovKgQqz9ZwXeAOmcuS8tiK6UiqdEDWZq5HEQBfSoti+VZWLOOuTC7i3zB4Y4Og0ulIhHdhbsvALCjma0FwPTZJoQQQnQd1QwulwOYbWYPAFhV2inPpRioOLzsklggCkhT6xLWSjOXTfXOXJbJxVkaaLan+tfSnk/4Lc2sQ5nuYmVbFKm2ZA3NFWdONXMpupNiCpIvA3Az+6u739fbfaof/hh/pca61SZGX0Sa4/nNzCiTbvsg0uRvI58h7glPwcp/J68aAJxA+nI+z55BBpsfbgMntQdwWpB7nR88lPeNpDe+7YIc/j/Bj3ff619BFoPnvx/reU+Rz/Icev+8hH64ez35vvrgGbvE+oQTr4j1fIyL9ak3nRPrC3b7caL+iX5hrC+78JRYL7FNQ6G7gvwZgs/PTky9x09nGTyEU7a9Jezf95BY37o7eSPDw4gYE+SSqaEvH3yHvHrkVx1zyoJYL3YyqQL4/mHks/wbyrNGhgaSFsShG8Ry+adofyK5Hnkjt/opEhyyiDZ2JU2+wz1o97Xv08ZhybbuZA8md2YD0nw+9kWy1xkA2DLelrGfPZdpnyRv8/tJ1udwpd/jqqnf9eSrGs6InqKaZ+MfxT8hBCrPXOZytS+LjT2XFQaXtQ724mWxFNAnKxVJtCyWvgAh+DC7mxWt0YfPt78Yvkw15UwBfUS3YWbnIhrW3ILodv+lmX3e3dPhO4QQQghRI9WkIvlZZ2WEGGhkTFxGM5d1+iMrLYutN1osz0iW/JfpNCPRstiwbdZz0WJXtUbnGdYcfjpuyplmLkV38h8APuvuHwGAmf0aUZhUDS6FEEIIwsxGA7gC0fT3OgCOcvf09HeCMmngOzQ6ysxuNLOHi9vbmNnxXdFhIfoj7l52IAhEM5e1jsva8hWWxdYbLTZfQHOTJZbvNg+qFNAnlMuZ9VieyxVt0SBy2ODk4FKeS9GNvAZgBW2vAvB6L/VFCCFEAxSQQysGd/lfNZjZNDNbaGZuZhMqlFtkZi+a2TPFvz3o2CfNbJaZ/bP4f/OsdhrtU53n2hrAQ+5+LKL8NJt1VqGaZbG/A3AfgBOL2y8iSjh9RWYNIVZjHBVmLnO1+yMrpSKpd+YyX/CE3xIABuVKqUjKBfQJ22Y9l4pkRWvkuVxjcHrmsi2rihCN8gyA+8yslCTxGwAeK/ow4e73ZlXsG6Q/tqv5IYbz2rEvM/3jM+fPe5E0+8Bmkmb/JefaA4D3yx+7gL7LnJqxMGrdlOfy8lBuku8QemJPUKGwH/N5P4BLw7H7/hH6vNPS/431rH2/VLYrO230eGJ71oRQrnW9RbHe0vOx3oKcRP973qRQftWQRFu7X/NI2XMOPTAkVFx7nT+FA1cmPwcuveXUoL8RtM0P5Q7aN+QCvRrfivWvln4v0dYuCH3Z6YWQs/OXnz451tPPDp7LfXFPrG/7VsrjegRd/3PDtR+3TjBAfumOmbH+7b4hX2eHHI43Pxu0UQ5LZw9huD8nfPpJMHM/3D5srKR+vUf3y+VBDt4tPN7W9ZJdwbljgz6T2tqF/KfnUvmJ5JP8DXsmAex8Mm1wwlfOF7szac5ByzlkAWDbjLY4j21Wzspq6R0vZT9gOoDfACj/Yk5yoLvPLbP/CgCXuvuNZnY4gCsRxQSIMbMhADZ091do31oA1nH39I+jlfqUeS4zG1fcZh5ANA78vpntCCCP5IdDWTqduQSwsbtfUWwQ7t4KoOeS4AnRx6g0dqwroE9pWWxG3kyg9sFlW94TaUgAoLkY0Kc1NSvZ0p5Hjoa2BuuxweXK4uAysSzWtCxWdCufATAEwLHFv6GIvsWdjkToFyGEECIbd3/U3V+tt76ZjUL068DNxV03A9jWzD6WKjoBwF/MbItiveEAHgSwd7V96uxc7r7A3Sen/s4HcDSAq9z9IES/cuyZbjtNNTOXiW95ZrYusuOZCLHaU2nmspGAPv+556c6HIsHlzXPhhbQ1JTsZCmgTzUzl7WmPqmXeHA5OLwVKaCP6E7cvfwUlRBCiH6Hw9CCIZ0XrJ31zWw2bV/l7lc10N4fLJpFeBTAGe7+HoBNALzm7qUJvLyZvV7c/3aporvPMbNvA7jXzI4EcAGAG9396hrOX9W5ynAvgKlmthuAjQBcX6EsgOoGl38ysysBrF18QCcCuKaKekKsllTyXNYT0KeUiiQ9GAQamLkseLwMtkRWKpLWfCparEX1e4IVxVQkQweHviqgjxBCCCF6mXfcfWIXtbWLu79aXN76awCXADi8lgbc/SEzOx3AwwDOd/eLu6hvnZ33RSQ9EJ3S6bLY4pTowwDmIJp+vdjdf1NXD4VYTcj2XNa+LLZUPr2MFag/oE8+7xiU8lyW2m9tTwX0aSskosrmYGhr76llsdEgcmhx5vLW43bC/tturIA+QgghhFgtKC1TdfcWAJchGGpfBbCxmTUBQPH/RsX9CcxsAwBnA5gK4EAz+1yN3aj6XI3S6cylmX3Z3f8A4A+pfX/t6s4I0R/oNM9lrTOXxWWq6QA8QJjNrH3mshDPVHLfmnKG9kJqWWy+kPB7mqFDme6inOdy7SGD0NJeQGt7AYMHVWMLF0JUhoP4VBuYg8txkBEOJnIH6VTAkkQdCr5y6lvolG+ktmfsH8uZn6CgLpR8/lcvhQA1P7BUJJaTKHhLiLuDWVyO8tIvHxKCn8z6fWoVNYcy/HHoyzx6uFsvDUFoln9lw3Ag9bP8dkc/FuuTcVGsD7/w9tDWKRTQZlzyPd1Anwv704HvB3nbbiHYzol/uTDWP9goNenxZpBTCrfE+lKcFA5MD/Lwm0Mff3V+MjjQD9althcHOfeLIbjO3D0p0M4UqvznZLf2KoQgQPfZ0lhP8lWxnnlWsIDNtY+SDQzljfuD/DHdE4e8FMtWLn5a6p4+cxFtUEyTRygw1bk/pfJ0r3NsHgCYQ0GytuNOzifNrzUOqpV4UADuJM2v22aUp1JQsKz3h2p/8M0qV81CyfpwWNXRXXsDM1sTwCB3X15cFnsIosBycPclZvYMgEMRBUs9FMDT7v52qo3RiDyW57n7H8zsTwBuN7PD3T0ZdSyDas/VFVTzzW1amX3nd3VHhOgvOJA5uqwnoE/J39hcbllsvTOXhY4zl6VzpNOMtLQV8O9bjsKtx+0EIAoslA76012EZbFhcLnWkOhD6CMtjRVdiJkd19t9EEIIsfpgZheb2WIAYwDMMLPni/vvNbPSktoNAMw0s2cBzAXwSYQMHABwPIDvmtk/AXy3uJ1mKICfFCf74O5PIfpZZq1q+1TDuRom86cEMxuP6AKsUwrRXmQ4gGHd0Rkh+gIHXzkLAOLBVprO8lwWvFgma+1sitLAcVBTmWWxdQb0ac972faac7kOkWBb8wUMGRQGd1FAn56buTQDhtAM5VprRL+2ftjSjhFr9t1fI0W/40AzOwDA0e6ezpshhBBC1IS7fw/A98rs35v0ywA+W6GNF5HIpVS2zMsAXk7texbAs2XKlu1TtefqCirNU+8M4EhEI25OQvQ+FK5dDGAq5rmkmcb0stQsSoO9cjON8eCyxpnE9kKhbHuDmjqmGeHlp7cetxOOv2EOXn7nw5rOVy8rW/MY1tyUGIiXZi7luxRdibvvVpy9nGVmP3H3TiPeCSGE6NsUYGjpw8tiByKZg8viB+/1Znaku1/Xc10Som/jDpQZtwEASpOFefeqHQZh5rLCsti6Zi7LLYvNdUgz0tKeT8wcDmqyHktFsqItn1gSC4TBpSLGiq7G3a80s78CeNLMLkCUs9miQz6qd3uXph6PUq2JztPn4NzYfDlWkuaE7UMzygBJjxgZEifTfs73Dkoq/9t0P6ntxCKw92P1A/tu2H3L5snqLaSPIJ/oCAqAuGxm0PtMCvrRZFPsk5xz59qxPtXvjvUFtm+o8PsgD/p08veM27YJfsiLeP4h2AnxtX3vDRupt/QPV5KdYE4eZVkQ5GUXnhLrWa8nJ1J2uuzpWE+fekjZvuAd0hTn8gdnpfybU0jvF54j/CR4GMecEbyFi28dH8qcmmzqvgPCc3QQ/R5029Hh2mEPqjB6UrKB2fTkTyc/5EnBZ4mj6H7hvtNlAAA8Pzborej8u9D+M8l/uQudb/1UW9vR+bEx6cdI8+uLvcrphRecxoxfw+zZrPTewK/1JWiMRj2bYnWg008vd7/OzMYBGMfl3f3e7FpCrL44HGbl7cq54qizlng4pbQf5WYaczmDWe2ey/aCo6lM9NnmplzCc1koONryngicM7gph9YeXBbbYXC5Rmlw2dYjfRADBzPbHsC1AG5CFE8g4xu5EEIIIeqhmmixUwF8G9FPIKUPYkeUVFOIgUeFaLH1zDTmC6VlseUHrIPqSG/SXiigOTOgTxg4lgaRg3tr5rK1HcOak29DWhYrugMzOw/AfwA4zt1ndFZeCCGEELVTzbqbgwCMc/f3Oy0pxACgYrTYXPBcVktpJjHLo5mrMwJtudQmg5pyiTQjLcV8lhzQJ5rd7KGZy7YC1kjNXK69hpbFim7hYwA+4+4f9HZHhBBCdBWGfDemOhG1U82z8YYGlkIEKuW5bIqXxVY/GCzNEmbNXDbVNXPpGNrc1GF/c1MOre2hrdb2jjOXPTq4bG1P5LgEyHOpmUvRhbj7Mb3dh9qo5/7PqvNx0pUC5fJXgqWkF5FmbyX7KtmLCSQ8YptRcMIZP0NZtqacgvunjs2mPpM9D/Mp5+Vceo/8Qqr+e6TZZzmF9l8bfKGT7g75EGcetieYOduEhIU7+f/G+oKRP4717/3rsT7mlDhFON7DiGS/xgY5jvIbzhlHSRHZ8zgmWX2tC2hV95kzg956UixHvfyvWC95fNNYH44bk41RnktMJj2J9Dkozy9Sz+kv6Jo9FJ777b4Y/IRrI/zGs3gaPamzOwS/jLltJPkc2Qs7gbSzlxHAxjcFPZ48kEPJZ0npLxPXeN1UB7Yiv+5Euo8e4XNSZge+dc7MuO8BJD2PfPH5tcr+ybGp+lkWEvZsct7ZV1Ll+LX+OdJ/z2iXqSdnpljdqWZwOcvMbgbwR9DbnDyXYqASeS7LDy/rSR0SL4vNmLlsyhnaa565LKBpSMeXd3OTpWYuoy8nQxKDy465MLuLFa15bLhOMtHzsMFNMNPMpRBCCCFEf6OaweX2xf8Uik2eSzFwqTRzmbPaZy7jZbEZIWibcoZCrdFiC47mMoPVl9/+CIve+Sjebo2XxbLnMrl0tjtZ2ZbvsCzWzLDWkEG4bfar+PvCpZn5RoUQQggxsHEYWpWKpE9RTbTYL/VER4ToT2Tmuaxr5rLkucwO6FP7zGV5z6VZ0TNapOS5HNyUXhbrcM+eoe0qSnku06w9ZFCPRawVQgghhBBdQ+bg0sw+Xamiu7/Q9d0Rou9T0XNpdQT0iaPFZgf0qWUmFIiixZYbrBoAbiqeuWymwWWxH1mzn13JitY8hg3uOLhca41BePuDljI1hBBCCCFEX6XSzOX/VDjmAD7RxX0Rol9QyXNZT57L9k6WxdaXisTL5800iz2eAKUiaaJoscUlsm35ApozZlO7inLLYoEoqM+by1eVqSGEqB0ODMKBPdIJ07MC/1CwnUSQEOap7NMvvC5oDqpyMpU5aVHQz3HwEgB4Lsg79wn6AvJrj6PgIydQACEAOIT0fNLfKddZYOZGFIllYurglCA/i6fDBsVEOeYTIYgPHg0/kr2KTRJNbXd3CHBzm1GwmjlB/vrg42h3sjM3DPs2bVEApQ+DXHJZCOLDgY0W7LxVoq3B74a4ja3X0fN9XZBTvnlLrKcfFi7qGZ5MFzt1arj+4774fOi/7YxOOXebxObQ7yyL9crhFOznC1RuSpBb3kEPHsA8OyxszKfAO4+FgD67f/7uWD+4+b6hzBupIDzn0r27Bu2fza+PK4K8hMpXhIIDYSZpjkewF2kKLAQg+Zr+FGkOvpUO4pNF1us4KzhP78dGKCCHFgzp7W4IInNw6e6b9WRHhOgvVFrxWhqLfffmp3DXd9IhC8tTWvJabhkrEA1Y60lFUi76bIdlsW0dZy5Lg9LuDuqTLzha2wsd8lwCwFprNNe0tFgIIYQQQvQ+3TstIcRqSMEdGePAOKBPLeOi9nwBg3KWORs6KGc1D7TaC4WyM5dmyeBArfnoF2f2XJbSkrR3s+dxRWv0i2e5ZbFrDxlU84BaiN7EzA43s2fNrN3MMubEhBBCiNUbZR0VokbcwyAyTWn20VFbQJ+sNCRANHNZa0CfrDYNyYFvaeaS81yWZjy7e+ZyZVs0sM1aFqvBpehnPINoAeYPe7kfQggxYFC02L6HBpdC1EC+EA0bM/Nc1hFdtS1jCWuJQbnaA/pEbWZEi+WAPvmOqUhKQXzaunnmcmVrNLgsFy12rTU0uBT9C3efCwBm1s1hjtdObX9AOusjnX1R7LMclSqXlXT9fdKccD3LKwYkfZobBMmex5PI03YL+dMOSfk6h5LfLGkVDIwmn+X9qWOXs3eO/JS/pzrH3Bn0FPLzXU4+PwC4J8jLfkEJ738S6gyeTf7FK8J1mHdkKk7iFeQT25r2k23w+6dcGTYmJKvjx6SvPTh0+aWjYn2CHR/K7EKPN/Xx0PqX0M9fnfK9WP/g4YtjfSIujfX0c8L5ph7980Rbe10TPIH3bR/ukRP9wlhf9uApoQI9JZid7NfKL4wIG/uQnkKFjgnP0Ty7GwlG033FNv4Xg3zwRfJZ7kZl5pNfEwDOpHthIntDr0BZ1id9SMp/eVEbbSwivSVp9j0zI1PbXG48afZcpl/rDL+m+b0iy2dZaeiQ5cfUcGMg0W3LYs1sPTO718z+UVwqdIeZfay7zidET1CKrpq5LDZX+7LYfKFQeebS6p25LOe5tMSsapyKJDG4DAF9upMVpcFlxsxlwQGX71IIIYQQot/QnT8lOIBfuftMADCz8wGcB+CYbjynEN1KS3s0IMpcFlvyXNbQZltGZNcSg5rqmbnM8FwitSy2lIpkUBjglQa6tQ5oa6U0uBxaznO5RvTWpKA+oq9gZk8B2DTj8AbuqZCZlds6FsCx0dbwhvsmhBADFXdDS6uWxfYlum1w6e5LkVwn8zcAJ3TX+YToKirNlpVmLrNWv8YRX2sO6JO9iKCp7pnLcqlIkAzoU2HmsnSsu1hV9FwOLbcsdkhxcKmlsaKP4O7bdmFbVwG4CgDMNtJNLoQQYrWhRxZBm1kO0cDy7jLH4l9wN90060dhIXqOeW9+gDUHl39ptMTLYivnuawloE97FQF9CjXM4Lk72guOprKpSCyZiqQ4E1vOc9lTM5fDylzrtdbQ4FIMRKrxTH6QUSZdLqst3p/OWcmGSPZukUEt4flk3xj7uwCAxuLjyZ82P5U7sMQhvH/r5LGVlOdyNnnXZi8KevTYoCel2n4zI9/gMZT3EPsHyT7Lo5J5FxOwh+8XQbb+gvJ0DqWcj2fw9QUWfCEYSHf/OeVaNPKrzpkU9PTU+emcCOkkMZvzYW4Wzn/Qw9fH+rYXKK8mAIQUlniL/Xl0zt2ve6Rseax8ItHUfd8hn+WT5LPcl3yW95CP91J6vGR9jdomPSnIcUeHB7xgHj1H09iziMxcpjiG+8wexpuCvCt13/DTdyrdI89Tua3oPn6O7q/nxqY6MDXIfaj+Pfya4pyT7HuuBHudX8wok35vqDVXZT25LXs/H6boOXrKYftbRBb1S9IH+BfciRMn6puk6HVa2grIF9rKH4uXxZavW8+y2PaM4DslBuUM7TVEbi0NyJqrWBYbz1w29bzn8rz7oi9g5ZbFauZS9DfM7FAA5wMYAWA/M/shgN3d/YXe7ZkQQqy+FAo5tK4a0nlB0WN0++DSzKYB2BzAPu7ezVH0hGicgjtWthXQni/g61dHv27eetxOAIBVbaVlsVkzl0VRU0Cf8sF34jattjyXpRnHpnKpSIr9LhQcuZyhtb2A5iaLZ1wBTkXSvS/X0rixoudSg0vRT3D3mwHc3Nv9EEIIIXqTbosWCwBmdi6A7QBMcfeW7jyXEF2FI5rdW/Tuig7HSqk7unLmMiv4TolaA/q0xzOX5ZbFFs9ZiB5HS3shEcwHAAYPKqUi6d6BXekxlU1FMqQZANDNXRBCCCGEEF1It81cmtlWAM4A8E8AjxdnTBa6+/4VK4oe5eArZwEIM3MiBLz5x5sdvU0tbZU9l011pSLpxHNZY0Cf9uIAuKnMgLW0qy3vGDIoWhbLwXyAMHPZ3s0zl6XZ2LLLYjVzKYQQQojOcCDf3vF7hOg9ujNa7PPokKZXiL6Nu8cDw3+8VWZwWfRcZkWLDctLqx8URalIshcRDKoxoE88c1luWWzxJVkaOLa05xN+y6heTy2Ljfo5ZFDHxy7PpRiYdFfQCw7Cw0F8spK0A8noJczKjDIHpMq9FeS7Wec4njQnok8H5h1fVmJ+CFCDrSgoyrWpoEHX07Ef0n5+KHMoKMzfaP9JqbZOprYe4f2kL6JrPCPIBSduBWbcZSEozYMH7BsOcLAc7svMZFc4bg+2Cgd/P/6ksH9MkLedGIL4/OmyvRNNfe2+e2N9wVY/jnXb2eFzpHkavR+vpIA2W4egQel+XnZOCOIz/PY3Y738kA1DoT2p3fnZXxsHHxmC2iw4mq7lBC41FQl+TM+XUxCf06jP0ziwE7HfotQOjsVAgXM4iA8Hhtpn86DvSbd1Bh27NehLDw76pA2oPAf3ScMBibhc1lf8kantpVWUW1Lh/EIk6dZlsUL0N3gp6D/e7BidrbNosfUF9Km8LLapxoA+pbLlfJylbpeW97a2FzCkOT247LllsTkr71/V4FIIIYQQov/RU9FihegXtNJs3T/e/AAbrLNG4ning8s6lsV2loqkqeaZy+xlsaVulwagrflCL85clu8jEO23VE5OIYQQQgjGXdFi+xqauRSCKKXmGJQzvLJ0RYeZs9LxzGWxWQcqEM1cZr8Um3K1ei47XxZbGji2tHWcuSwNdGuZLa2HfMErXq+c1TaoFoGDr5wV+6mFEEIIIXoKzVwKQZQGj2sOGYTlK9uwsi0fL9EEOM9lJzOXNZyzs4A+TblcXdFimypFi60wc1nabu0Bz2WlwWU6J6cQAwv+eG7P2J+mPUOzubCjl7w8Hyf9CunJpNnfRR5LAAlP2jL2Zh5GmryJE8kbNzvlc2QWkDcSwUOIGeyb43MAOJv0FNL3kyduJu0/NXjgBr/LZkqgdT0qOHRSaPbJYJScvuiQUOZGqnx5Mn/y3pcFn+N6d9wQ64mYHeuv/jeZNh9FkmlB7vRkeLOcNYzKnEOaLIe/xfeSbc0mG8iPwnPXfGv5N+GD/OlY3zZsm8Sx7U55LNZz1tg51stvJJ/l96nCe/Q5cF3qROQ/bZ1I99SOVOYQSkbAHksAWMn3BXkIp5HPkT2+R6XqM2kvb8zx5Xffw+WPSB7bujno52j/Sdwv9rKOzNgPABeSznqv4P3V+ifr8VlmnVMMJDS4FIKIB5eDm7B8ZRtWtKYGl3G02PL143FaDaOitrxX9lwaapu5LC6LbS67LNYSZVraykSLbeqZaLEFd1SYsNXMpRBCCCEqUzBg1eDe7oUgtCxWCKI1H81MDh3chDWac1jZmvzlrSVeFlt+MJirI6BPvpNosbmc1RTYprSctVIqklXFQXJLvmOey9Jy2loGtPWQL4QASOXImWYuhRBCCCH6ExpcCkFwwJ7NR62NFa35xPHW9s5mLmsP6NNWKFRcFjuo1sFlnIqk48u7NEO67KNWAOXzXDb3lWWxmrkUQgghhOhXaFmsEAQPHrfYcG28mEpH0tKeh6HzmctaaO9sWWzOkK9hkJWvEC22NHB858PIo9LSnu+QZ7LUl+4O6BMti9XMpRDlqdavVKuvKSvnJZD0WLHPclfSnHtvS9KPIcnXSf+KNHs2LwpyNqpjPr0pjGPPJ/nmbGx2/etIsxV1LhcKj+vFkZ9KVP/Eoa+HjZuDh3L69uSz3IQqsOXvJ+SzA/Dbl0+L9e6fuCfWZ3/ivFCIczh+FUmmBzlr/S+FjT2DPOjgkAv0tk2C72/m61QeAM6laJuHhGu85SeeQTlewKdj/d0V5yeOnYWfx/pjM8jj+wUqtGvw6H7D7471DeO+nTzRHuRbPDf4IW8848BYH27fCWVGT0rWX0k+zRF07y6jMpuR93YP2n9I2mPJXl5+LvlG4tcUl6ecrEDSZ5n4Kk6vKb6Pnb+L3JfqF/sx2VN9EOmbSI9K1ec8l1nvJ1leynRbvZAP0wGsqv27l+g+NHMpBNFKy14/teHaaMt7IiVHS3shM1IswAF9ahkMetmclNxmLTOXbXGey0qDy+yZy1IakO5ORZIvOCpM2GrmUgghhBCin6HBpRBEaSloaeYSAFbS0tiW9nzF2cm6lsXmC50E9KnPc1nOx9mUM+QMeDeeuSx0mLk0MzTncvEgtbtwz54BBqLnoJttn0IIIYQQogvRslghCJ65XG/NaJkQB7ZpaSt0mpuxVro+FUkxV2dGm81NuXhZbGt7x4A+URnr9pnLajyXXujePgghhBCiH1MAsKq3OyEYDS4HOG9/0IIVrcpFVKKFPJfDBkeDLl6a2ZqvcllszTOXlZbF1piKJJ65LN/RQU2GdysE9InK5HogFUl2YCRAM5dCCCGEEP0NDS4HOO+taMUHLRpcluCZy6HFwWW+hpnLprpTkVSeuawloE9pIJo1YG1uyuHtD7ID+pTKtHbjstj2fIjKm4WZ1eRdFWJgUM/7NQf84IAbH6TK8VcCrvMQ6XkZ59g2Yz8A7FN+9whKWL/sLTqwMlWQgplMo/eMQw8IeiYVXzNV/SPSPyZ9JiWsvzYEUhn87thYf8LuTzTlvwnnt/vp/SnE5sG4g5+P9YKntgoHnkn1a3Jo68Fv7RvOPzs83taD10EW3304BNL5B7YIbU0IbX1AAZx+9fnvlS0PAL9fdVLYWBz69fqY0bFePmRYrPfyGbH+7bDTE239djJtr08HLiF9Wgiuc8OFFMRnr0RTmPDk3rGee2HYf7jdHjZGUwV6HgAA11EQn+c4ulII4nPqy+fE+gLjCPFHIMFmY4Ne+GzQu2wT9CPpIEBZUNtZ7ToH7uHXYzLQYJKPk55Bml/b6aA7gzJ0Ne81vRDAR/QoZjYawBUA3gKwDoCj3D39Jp1AnssBTt41O8TE0WKBeHDJ1yfyXGbXD+O5GgLwdBrQBzWmIulkWWwuh3c/akV7voCCA4PLnLu5ybp15jKeIa7wDpQzQKtiBzYHXzkLB185q7e7IYQQoq9SWhbb1X9VYGbTzGyhmbmZTainnJlNN7P/M7OnzewRM/tMtQ+9jnN90sxmmdk/i/83z2qH2BrAQ+5+LKJfEzbrrIIGlwOcQsFr8vOt7rTkaeayuTi45JnL9kLFIDT1LItt7yygTy6HfMHhVTaaL1ReFtvcZFj6UStWtkW/0JZbFtvclOtWz2ULzRBnkdPMpRBCCCH6LtMBfBHJ3E21ljvC3f/N3T8LYBqAa9IFzGyImX08tW8tM9uoxnNdAeBSd/8kgEsBXEntjTOzGam/0wH8HcBkM7sNwMcAvNjJY9XgcqCT9+jre3f76/oLpZnL6476HJqbcjCkPJfthU58grUtiy0UHAXPnmUEwlLban8DaKsQLRaIBo75guPrv3sCAMouix3UZGjrxh8dWtqjgW2la2nyXK72aGZSCCFEf8XdH3X3Vxsp5+7LaXM4ornYNBMA/MXMtgAAMxsO4EEAe6cLZp3LzEYh8i/cXNx1M4BtzexjxXoL3H1y6u98AEcDuMrdDwLwFBKZdMsjz+UApzRwWtmWx9oVlmYOFEqDy9JsXi5nCb9jS3snnsvSaKnKQVF7J7OMQBh45gse2q9AvtNosdH+UtqVwWWixQ5uyqGtvRtnLtuKM5foZOZSeS6FqJMsj1WWrzJdLqsOw8nXx6aOzUBZNiN/2sJq/WnEmG06LzM/1e548nayz/KBg4OeHmQr+/ZGJ89nz9B70iQuF+SCkeSzXBbk4HeTXrnWbwU/5XfPCP7J3z5InsWhQY66+1+J+r/NUbnFLUGvG/p434XBl3ofgj7mlEsTbQ3/8ZuxXn7/hkHfGDQmBvlD/FfZdgEAU0jPJj0uyDHnzw9df3x8ODAz2VSe771T6bm7lJ67O6nCG8n6IMslniN9aJAX2HZ0YDLp15JtTSH9CPss30Kn3PXT5PZ+dI8u5ANcjl9fdCOgOdU4l+PXdNZk2qjUdjW+yT4em6N7ure+mfEdfJW7X9UdJzKzqwHsDsBQZvDm7nPM7NsA7jWzIwFcAOBGd7+6htNsAuA1d88X28yb2evF/W9XqHcvgKlmthuAjQBc39mJNLgc4JSWUK5szWPtNdJvWKsnpZmSW4/bqcOxDoPLlO+vpT1fOVpsjTOX8RLWCgP70mC2Wt9lW6fRYqNzlZamZs1c1hKhtlaC57JCQB9EM5fuXnH5rOhIW75Qk09XCCGEEAnecfeJnRdrHHf/FgCY2TcAnI/yM5IPFZepPgzgfHe/uIf69iKQ/gWpMpqqGuCUvn+W/HcDndZ8Hk05i2cIc2aJZbGdzVyGwVKVA8HSLGNFz2X0v9qIsZ0NWOOZy/be9Fx2viy2dJ27c5C7urJ42Uq8+GY6EqgQQggh+irufgOAL5nZeuljZrYBgLMBTAVwoJl9rsbmXwWwsZk1FdtrQjQT2emy3lrR4HKAUwpWs6JVg0ugmPeRBmVNudTgsq2Af99yVNlZzxKG6gP65DuZZYz6kEuU7YzSoDBrCW1zauay7OAy1zMBfSoN1L+xU+Rdb+3G5bmrK635AlrbC6vtsmJ5NYUQQgDo1WixjVIMyrMJbe+DaJ3z0lS50Yi8Bv/l7mcCOBDALWb2+WrP5e5LECVEKi0KPxTA0+5eaUlsXWhZ7ACmtb0Qz69p5jKitb2QGGzlzBLLC1vzBQwp41FkaglEE89cVkpFUhx/1Tpz2ZzhuRxUnJltrbAstnmQYVVbD3guK8xclp6H1vYC1hzSbV1ZLcnno0BdK1rzWHOI3ub7P5Vyz2Uda88ow56sxPcXAJ8iPZ/0+Iz9O5NO+87Gkia/GPssjyJ/2bW0f7OUP41zUx6zKOOc9LhGpOofRXoxefWuo/2cznIZ50NMpnPb8prwWTnPyET4TPBPjloavJFL7NpYt66f6he9pf+WMwZQoP9EDsYcXwgAR5KeQW+Sv6X9sylXooUkkr8/NbXa7y7yVh7C9UlPD3JXo5NXWjjIxy4PcvEBdE99gcqcybkdgdPOuDHWx+zzh1gPPoRygZ60KFSYwSZLIGHiPJmeexo8DG/5bKyXD+E8rmzmBDA/9fyVMDpn4mP65CDZYwkkfcDzKbclFpHme+8e0umcslmeS4Z9lpXyXPZxb2Ufw8wuRrRkdEMAM8zsXXffyszuBXCWu8+uVA5RVt4/mtmaAPKInsx9vOMvw0MB/MTdpwOAuz9lZlOK7VXVp+Lh4wFcb2ZnIXKEf7OrrgWjbx0DmJU0W7lSM5cASoNHHlym8ly25csOxhirIYVGezUzl8WBZ3uVSR9Ly0izZi7NDCPXHIwPVrYBKD9zOSiXQ3u++z5kWvOlZbHZjzseXCqScc2U7oFlK1r75OBSs45CCCH6O+7+PQDfK7N/7yrLvQVgxyrO8zKAl1P7ngXwbJmyZc9VPPYigB06O1+j9L1vHaLH+Kg1DB60LDaiJT1zmTO007LM9PFy5KyGZbFVBPSJU5FUOcYqDVibM1KRAMB6aw7GOx9G0QXLzcQ2N1kcGKg7KM1cVvJclpYnt3TjDOrqSumHiGUftWHMiF7ujBBCCNFdlJbFij6DPJcDGB5QallsRHrw2GQW+1Lb8wW0F7zzZbGoPoVGyddYMRVJ8Vi1y2LbCwWYVY7E+rG1h8QD4LLLYrs9oE/nnsswc6l7s1Z45lIIIYQQoqfQ4HIAk1wWq3X2QMeAPv++5SisO2xwdKw42BrS3Nmy2Oo9l2HmsvMItNUG9GkveMVZSyCauSxRPhVJrptTkUT33mWHb5dZpjSIb1FAn5pY1ZaPfzjQ4FIIIYQQPYmWxQ5geFmsPJcRre1Jz+WwwYOwonidKgXAYXJWy8xlyXOZ3WbNM5f5QqbfssT6a4XgD+VTkVi3RmmtlGOzxBAK6COqZ3nRSwsA761oq1BS9B8q/fiXFaAna386iA8yjm1MmnMgc3AfDn7y9VRbi0hzEKAzgrz2fZRlYSo40GwO0sL5uykoygO0e49U8JSrqdykIIdesSzsXud/Y33fvpTSLfWw5tFlHePhcS0eGQL6LJm6aSh0KJ17crItvBfkqFO2ivWSo8P+C6ZSEJ/pqfq8fQPpliCn+PJQnN5uD/IXE03ddmKwYQ1/781YL5/ZIV5IxAMnBH1+6tifg/zVHcH69YPPZKTlo2BKW3ryfMc8GIL4cLut64frvbsvivWDlrq/N6MgPpNoPwXYWX75rmH/eCq0/TbJtm6mQE8TNw+aAxLxPflIxv0NAPPp2FF0nms5mBTX5yA+ySBTSfi1mhWgi4P7AB0D/JSjnqA/ChQ0UNHgcgDDA8oVWhYLoGO02DWam+KoqWFAVEW02CrPF89cVhgMxjOXNQT0qTQTCgDrdTa4zOWqDiBUDyUfZaXB5WANLuuCB5RLP+rfM5fu3q1Ri4UQQvRzHJXH26LH0bLYAcwKRYvtQGs+ObgcNrgJrfkC2vOFeEDUWUAfqyGgT0hFUoXnsoaAPpUGqwCw/lq8LLZMQJ9B3RzQp7gsttJAvXSdtSy2Nt6jpbDv9aFlseVyUxbc8fYHLbGvOc2yFW149rXlWPD2hz3RRSGEEEI0iGYuBzBaFtuR1vYC1lkjvCyGNkeDn5VteRoQdbIstoaAPu1VLIstBb2pJRVJVvTZW4/bCQDwvy+GZTBZqUi6O6CPWXYuTiBEi9XMZW3wsthlfXxZ7Fvvr8K/lq7EEwuXYqdx63U4XnpfeuZf7+GMO54DEO5hIYQQQvQ9NLgcwJS+uOVMy2JLpJfFDh3Mg8vqPJe1BPRpr2LmsuSfrD4VSaGKmcuwLLbc4xk8qHsHlyVvq1WIFlsKnKQ8l7XxXnFwOShnfTqgj7vjrfcjc9iidz8qO7gs/aAz9/XlHY6J/7+9M4+TpCzy/i/qruq7e+4ZZnoEhvsQBhG8Zlc8VxRPwBOPFVDXVRff3RV1XV133Vc8XhU51gPWg8OD8VhBRXdQEYHhHpgBBqbn7jn67ror83n/yCsyO7Mqq7t6qro7vp/PfCY688nMJ6syqyoy4hdhEaSX6g0xpqPKvrhei2srueYyzWymjZty/Fcxm2vCBgLmda37z2uYfTnTtF3DtJV38Ab3b3VvfwGzv+Jo2vLdjm7u9kGms/zFJtv8zM+ZIBDAZ37wBdveE2GvxcnM/MT9tr0lc5Ztv+OH/+Xa1/e+/Le2ffDLjk7z5O+w7b/sbI89cNMNfxY75sYXX+T88S3HvPWWd7m34S/5Vx3zEy//tG3/+82fdVa8go3/vOf4V95um//nCaazvJSNOcoxl/x0l21vpUG4OJ/Zir3f5Lzfv3nxawPn0nUF049ewvScd7Hr5SVM57idHWMvv6YA4IeO+Thbl+ca31OYzUW2TGsMwHUfPMaXc21kJ7NPYPa9COaxgOX8ft7mWbeG2TsDtp+OZvII6Sx1uHTGQvORtNgFjJUW29uWREEilwCstFgnVdOOXJaYcxmvpbmcTuSydlps2MilFkpz6aTF+h07FiF7brNBsaLX1K5K5HJ6jJvOZSoebWnnciRXtu+pnUM53zHW+i17xbkUBEEQhLmAOJcLmFypAiKgJxN36S8XMt5WJO7IZci0WDL05WFwWpFUSYu1IpdhK9DqqmqaLeA4l0TwjR7GzVYkYZ3keilWtJqvoxT0mR5WQZ9UPIKRbOumxR4YLyARjSAZi2DnUNZ3jOVcPr5vfNauRUEQBEEQGoekxS5gciUNmXgUmURU0mJNigFpsTkWuaxd0IfCO4JmymeoyGXISKKm106LTcaiVduVWFrIsqaQiFXf13QolvWar6NT0EeuzXoYzZcQixBiEWqpgj6cXKmC8UIFR/WkMVGo+EYuK5qOUkVHPErIlTQUyrp9PwqCIAgCAONpvnQ6aSnEuVzA5EoaMskY0onogkqLHc+X7Wigl5InomalxRZKWqj2GQBACF8t1olcVmlFQvX1uSxrqmafS8BwILUAcWjcjKSWtdpO4HQoevqJ+pGMRu2xQnjG8hVEI4RYNIJsSTOjxK3llB0YL4IIWNyRREnTsXMoC6WUK4o+OF6AAtDblsCB8SKypQrSiSg0XWHr/nEs7Uw17wTmBEH9LLn2aq9nXdBPAt7zkmvCuP6y37NNgB7z9aw/4mHWK/CPfFuPHpBzzTX+y3/B7MuPda/7Covgf56tY9pAgPXWXH4JW+7WXHL95sk/Z9rIPzjayGfGnddlVc55vb5Hnr6J65n9gGNu2cR0lvv9xwBw9bk84bUP2fZWOtq2/05dZ9tff+vH/Y/tYezfHG3iv192pW3/5Duvtu033vwrZ4N/C97XCSeyeZ3yXGfFRsc8eKqjN00Mdbu2L7E2lVju1UCaML0rVrlXuXSWN7G+kTex9/vd7Jr47usdOz/gOdDzHPMqtviD/Hpl1/0pTDP5GO9fCbj0m5vZfvl1CN7fdROC4Z4V72HJPwO8OkuO93PAb19hemEKgoGkxS5gcqUKMoko0vEocuWF89jn2cNZ7Bnxb4rkdXoyLHJpFZap9UM9QvWlsALVq8Vajmc9BX3iVdJsLeKR4II6VpruTHSXfq0nLMI4PFLQZ3qM5kqIRcmOXo+2WMXYwbECDk0WsagtiXg0glQsimxJw5CnJ6d1j3an40jEInbq/h+ePoTxwsL5vBIEQRCEuYQ4lwuYXElDOh5FJhFbMK1IJosVFCu6b8ROKTWlz6WrFUk5nObSKOgTbj6VEGmx02lFEiZymYhFEA1wLhOmQztbjl2xotvOYxBS0Gd6jOfLOHNNL/7PK40IVasV9fnKb58CFLCy24g8pszrwKu73D2cM9dHccKyDmSLhkP5o827EYsQujNxCIIgCAscBaAwC/+EaSNpsQuYfElDWzKGVDw6751LK4L28VccB8A/smgUsIF/QR9X5LJxBX0qIdJiY3UW9Kloqmr/SIujetOBabF25DJsuLROiuXaabERUzcozmV9jObLWNPXZjtfrVTUJ1fS8KMHdmNpZ9KuumxFsD/+o0exuCOJWy49Bxdedw/2jBjOZSIWwbFLO7Bl3zjKmo7fPnEAi9oT9kMXQRAEQRBaB4lcLmCyZlrsQiros3W/obnwc6pKPgV7XJHLSri02HoK+lhpp9XSWKN1F/SpXS0WMM4jk/B/vmRrLiuzVC1Wq92KBDDeC9Fc1sdorozuTBy9bUZF4FaKXO4ZySGTiGFFt6NLSsYjiNDUwk1Fs3JzhAgnr+iCpivsHc2jrCks7kh6dy0IgiAIQgsgkcsFTL6kYXF7EpnE/I9cWjxRp3NpOV/5smanutZK56yvoI9xzGpprNG6W5HoSMZndmvb1WJnLXKpIRnCQUjGIhK5rANdVxgvlNGVjqMn01rO5UShjJFcGVe8fB3++PRhe3mECMu70iiUpzqXVnT75JVGYYwD40WcuqrLfugzPyA4X8WN1JIGFeDgBXm8x+MFPIIKgPDPlqCG74CrsMlyVsTnNj6GFVg5jxVreT3cfJfZm7lenjWmv5gt/hxvag/g12zfr2CFVda+yrFfycbf4JifiXzBvS/lFFzZ8l72GcZqG73p6R/b9ve+/LfOivN5ASQgcYNz/qW+rc6K4862zQ0/dwoKbfp3PkkAn3PMrRewYjnnO+bXP+1fxOflH/u5a1e/ef5rnT9esMmxJzfY5hu/yor4/Ilt/IJ7Xfs6UznXwgN/YPP6rlNA6WS1yLa3kFPAqNQHN3cx+5PM/uOjjj3KCiW9y/PeEy8C9BVmv5XNi10TvOiS8uzrS2xfH+TreEGfTY75WLUiOnyO7DpUA2zFg8zmxX3YtQIA6GA2L+ITptCPdxyHf4bw+77FNO86JI21xZDI5QImW6rYabFBOsT5xhP7jC9zP0fNSnvlzqX14zZXcvpcJmoUy7Gru4Z4PctW5LJKpNGOXIZ8f4zI5cxSBq3I50wK+lSjVNHttMhqJMS5rIuJQgVKAV3puJ0W2yoFfYYmS4gQ8J4Xrp2yrn9RBgVv5LLs6HKPW9YB64p+8/qjZnuqgiAIgiBME3EuFzD5koa0mRYLYErkYL6hlMK2wQkAgK6mOn925JI5j5EIIRWPoGCmxSaikcA2JhaWFKwcohiOpWmMVtFIWs5lWOffaEUys1vb6XM5ewV9ajnpgOlcSrXY0IzlDUeyO5Mw056jGMm2RuSyrCskYhHfVOzVvW12qx/AePhT0pzIZTIWRToRBRHw2tNWHLE5C4IgCIJQH5IWu4DJlTRk4lG7aE3OLPAzXymUdRQruqExLWnIlSroSDkVJ4s+abGAkRqbK1WQiEZD9Xy02nsUKzpSNaJzdkGfammxdURCjXF6qII+1eB9LmeDYkWrmV4MGI6+RC7DM5o3HMmutHFd92QS2PjwXjy2dwy3XHpOM6eGiqYHXuf9fRlUdGU/bCn56JtXdKVQ0ZV9boIgCIIgabGtx/z1JISq6LoynMtkzNYvzffIZbZk6ATakzHTudRczqXzg9bt9KTjUeRLOlS8dqVYwKgWC4SMXGohnMs6I5cVLVwrkmo4zuUsFfQJUS0WABKx6JRCL0IwVgqslRLb0xbHnuHW0MdouppSuMpyeO/YMggAdvTSetDz+defgnOONkRYfe2tXcSHiK4G8FIARQCTAP5eKbW5ubOqhfcnANdiHc/skNoxF0wXtv9OtjzgemRDErd81LWqVGB6zld9zLG5tpLpD5HmOjsAFzB7/bGOvZnp9q5hWrsb2Xi+XwDoZtq3zewc2ZS/F2E6y39g2x7n3lVpGzuvtKOzxLsdcxMxneXNnrm8iM+L2ac7pjrkfBfQJufz/DeLmMYSAL7F7PM22OY7vvxftv3nL59r289ETnLGX87mDuAB9hbBtcrRFm75NFv8OLO73ff5BSuck974xxPhy3nMLnje+zuY3c3W7WfXzvlsOdeSjniO8yfU5ksXOvY//GvwOH5/qWvY8v6A8VyvO+5Zx+/bdMByr86SE+QKVAJsQaiOpMUuUCx9k1Et1vhgyc3zoj65koZ4lNBuRmetvnkWfppLwGhHki9XDJ1gqMil8X+4tFjjyz5MQZ/QzqXPj/h6sVqjVGazz2WIarFJqRZbF1ZaLI9czlY7mWpceN09dvsfi2r9V9f0ZQA4D7isBwqretK+41uU2wGcopQ6DcB/ALilyfMRBEEQhCOORC4XKJYjmUlEkU5YRWvm95OpXEnDsUs6bIfJ60w7mku305M2+4DGIpFQRWjILD0SJp2zohkprFSlZ5/tXIZub6I3LHI5G3pHXXfr6aohBX3qY9TSXJrOZXcmYUefLWfP6iNp2UeKiq4C+7nazqX5XlsRzOVdqSMzuQaglPol+/MeAKuIKKKUkgtYEARhtlCQtNgWQ5zLBUquaDmXMaTjTruN+UyuVMGJKzqxZe8YAJ/IpZUWG58aucyVNCRikVBFaOpJi9WqRHMsphe5nKlzWV9vzXqwHNYwmstkLILJ4vx+6NEoLrzuHuwdNdo0dJrOZW8mHrrKcLX9AjNzRDVdVe2/mknEEI8Sinbk0nj4EJthBL6JfAjA/4hjKQiCICw0xLlcoOTKxg92I3JpROPmc6/LUkVHWVM4YXkntpq9LrOeSG1Qq5F0PIrRXAnFSjSUQ2RFIUuVcK1IqrUhAZyCPt/+4w6885z+mvusln4YlkYU9MmVKr7zKPoUawkiEY24qogK1aloCql4xC4k1Z1JQNMVlFJVo+OzzbgZUa2mLU7FosiVjZY/xZAp6EcSInoQwOqA1UuVUpo57iIYTfReXGVf7wfwfuOvLjRPz1TtuFyjdQqzeY+9M5jNe/IBeNGVjs2Vp/kgHZrTd7DU59WHsb9P6WfLeX9B1msxz/oWetn8Q99jgl2aS965y7YPTnre8g8x+/MbHJv373wps09g+33PLnAO9rJ9L2MrLmP2l5j9z+6p4E72HTPKTuAzjklvYmN4Fx82BgBwhWMe/QFHBPk9YhrVtKOzTI86gsT8JT3ufXGd49sd8wPqy7b9Tf5R8LkXOPbFbv3mxu6LnD9Yu1SXBHEDO8f3eT5jLmD2X+APzzlQ/Npxa39xG9Pouq49JtLlGlvXfcNtwKWtJFakTPGeofye4jrLvQhHL7N5z8olnnFBPXGnQwv3wxSOGOJcLlCslFDeimQ+Ry6tlN8Tl3dio/kjO1sMSIudUi02iv1jGtrq1FyGSSmt6HrVNiSA06YkbPzJqMrZoFYkM4h6bT+Yta8tjuXEh06LlVYkodF0Hd3phP13j1nYpxHR7JlgpesGpcUCQFsyisHxIs76tzuRLVWwqC0ROLYZKKXOqDWGiF4P4PMAXqqUOlBlX9cDuN7YZsX8bzAsCIIwW+gA8s2ehMAR53KBYqXFtiWcarHzuaBPwYx+rVvaDisw6dWYBhb0iRtpscWKbr9W1YhQ+B6RlSqpghZW5FKF1VzqqmqEKAxW5PKrv31q2n0FK7qOYsUncln2f539SM4xzWUztIyciq7Q1+48Be8xHbSKphDi0p01RnNGixTruvR7fVb3ZtCVjuOE5Z34+SP70J6aW19PRPQaAF8G8DKl1ECTpyMIgiAITWFufXsLDcNyrBZKWqylO+tKx21nzRu5tPtcetJiU4koCma6XneIHnuWO1UOWdCnliNYb4qrUThlptVije1nElLRdOXrYBcDWr74IQV96qOiK1tvCRjVYo3lOoDmeZdW5DJa5UEKEaE7k8BXL3ou9o3mq6bxWs7prZcFDmkG3wVQAvBjNveXKqWGmjclQRAEQTiyiHO5QLFSYBdKWqyuFCJkOE0R01mbErkMcHoyVuSyrIeKttmay7CRy1ppsRErcllzd8Y+QzistbBSKMNGS70opaAr/+itkxYbQnM5R9NimxXBrGjK9QDEdi5DFGaazTmP5WqnxXKaqQ+dLkqpxc2egyAIwoJDAZi/P1/nJOJcLlCyRacVSSo2/9NiNV3Z6ao/uuxcrLvydkyG1FwafS7DFxmx/LpwrUhq96S002Jr7s1o86Er4LaH9uKKVxxXe4MArCJD0/Qt7TRkXRkR8TTTXhYDqvL6kYhG7QqiQm0qum73uASAbqa5bCbetFghiNkqhuEt4GHR6/k7qOk6L/jB5zjAbE9P0j/yNp8BgijevP4XvNCPNyTN5KuPsWO+vt+xT2b256o0ryd2TH5bsII6B2nU+eMuT0GfbzD7g6z4yutZIZrbNjn2nY5U9+CHPPvKs/MqLHVsVlwHH2Tn8jM2dwA4+j72Bzv+crb4Fz+1zTOVc4wHvsuK6ADABsd85j1O4R78ms/FMfMbWRGfT7p39Xc//aJtf/0NH7ftbx7+GBvFzutyNvdrvgwX57Ft7uTFblhhqdQljq02ubd/4QbHvo1vzwrsKDaXHvYaj/DxgLtSURjOc8y7O92rXsCOqfg9xd8XVg3KVUjLe9/y+7MjYDn/DPAWzOLM9DNIivgIQGuV4xOOGE5abAyRCCEVj9gNzOcj3pYfmWQ0vOYyEYVSwHihHCra9uULTwcAu79gPfPyw4q0qhDupeVEzDTwE49ZabFOj0QrshUGXon38GTRtc7SXEpBn8bA3xtNV7ZDCTiay5lU/a3n+EFYabG3NkmLKgiCIAjCkUEilwsUS19ppcRmErEpztZ8QlduJ64tEQuuFuvTigQAxvLlUNG2etp4lEOmsBIQKnRp9cKcaXzImtN0A15cv3twooijejP23/WkxSZjEZQ1BV1XtpMt+GNFrbszTpXVtkQUEQLGCxUsD2hHUs9DA+82YVNox/JldKRic7lvpSAIgtCKKACFZk9C4IhzuUDJljTEo2Q7QlZF1PkKT4sFDKfaT3Np6TI5lgOuVLhom6VXDJUWG0JzCQCgcGmxZd045owjl9GZpcVWi1wGaVv9sKLIJU1HKtLEcqdzACtqzQv6EBFW9WSwaziHfWMFrOxOB20eyHi+jPZkfV8VXq3uWK7siqhOh2ZV4BUEQRAEITziXC5Q8qUKMgnn7U+bFVHnK5pyt/xoS8aQ9TjTJc2/YE+K9XAIU9CHO0S1CNOKBDAikWEcPU2zIpcz8y6dIkLT8y55VPjQhCct1nQuUyGiwEnuXDazl8YcwHIuvRWNl3UmkS1WsGckjzbzPs+VKqGuu8GxArYOTqC/L1NzLOeJ/RPoYK1ERvNlV/9NIYjZyh4JapJerXk612hxzeREwHKvrrIzwN7pmFxneR7Tut3p1Uwe75jPvMWxj2bjbmPbH+PRJm6/xrEvYstvZvb+px2bTnXslzzq3td6to5r8K56nmOft4HNiw2/zvN5mur2n9eHHHOVeptt76l6G7LX4pfs/Le8wTYfGx53lm8P3lPiKmfc0b3P2PbW7SVn0LscneTR6nHX9l8/1dFZul5jLqV9EZvj89nya7guE4Br1/zhGLuOeNTq6g3u7bku1qVV5PZKx+xji0eYDhYArmBzvmrAsT/V79guve9XHPMFH0UwfC5svy7NJR/j1Uzyn/ITAcsbCf9sqPYZIixUxLlcoORKmqvB/XyPXOo6wANfbckocsWpkUu/VE3uhIeqcFpHWmxF00M1tyeiUJrLeiKXtSJBFDJa6keumubSTj8OVy0WMHWaqWlOZoFQMa+3Lo9zSURYu6gN+bKGpw5M4sRP3wHdjMLXengwMJQF4BRoCkNZ0zFZrLiuwdFcacaRS0EQBEGYgg5Ji20xRACzQMl5KnimE9F53edySkGfRAyTHueyWNF8I5NpFjELlxZrRttCpsWG6WNZt+ayAfJEI1o6PfeSP6iYGrk0NZehqsWGjwK3AoNjBWSLzdEuD2VLIAAnreicsi4aIaxb0o7FHUl86K+PxdLOJIoVHQ/vHq26z93DOQBAqRL+s8HZxnnPRvNlV7quIAiCIAjzE3EuFyi5UgVtLCKXMdttzFe0KQV9pkZqixV9SjEfAEgnnGX1OJfhI5fhbsMwbl6lQWmxABAhmrbm0opcEs2sWqzlgIZx1L3UW+F2phyaKGLncA5bByeOeHGsiq7j8GQRfe0J9LUnfcck41GsXdSGj71sHVZ1p0EAfvno/qr73T1ipDoW63j9dxw2op2lim4/7BjLlaek6wqCIAiC0NoQ0Toi+ikRfZOI/i3MNpIWu0CZErmc92mxnoI+yanVcUsBfSzTcec2SYRIi7UL+jSoFQlgpqiGcPQa1YrE2sd044WW5jIViwZqLsOlGBtjpuNc+lFvldN62LJ3DIAR7d02OIETl0+NIM4WhydK0BWwtDNc7nAsGkF3Jo5fProPa3ozdhVZb6R6z4gZhawjcmw5lwrAgfEClnelDM1ljbRYKdgDhOsxV+1rm28TtC++3Nsvj8N1XQH6NJdwz7uvAcdce7ljr3qVY/+R6dPuZHpAL8dc6NhHs/3ircxmmsntx7q358e/iY1bzsbt/6Fjv5TrP/vd+3o9szez46/neeBsjGKav0tZP0cAKPo/CDpad4SGz9Cks+KBY9wDL2T742/FR5j9R6eXZgmOhrCrOOja1diVTqPP0k+cz66tb38uG+Xsa4Ny9KabyNMP8hTWJ/Mwn8uAY5/f79jvYmN+5t7Vya+937a30FnOiiuY9pX/3B3xaGQvZq/RRrac9TXFDn7tsGvyZo929yKup2QXQlBfVVfPTO8YrsG823971z1YRSTruieDdNDVelvWy2z2yZwGTaoWS0T9cF9V3QA6lVJTPliJaADGLK2Z/qNS6tfmunUAboSh+B0C8E6l1NPefdQxr6sAvBFAP4BTlFJb2LrpHOtvANyolPoZEX2ViM5USj1QbQOJXC5Qpmgu53FabLGiQQFTIpd+rUh802IT9aXFEhES0UjIViRhC/oQwsQuLd1dI5p2EGgGabHGl0oyHsHhyZJrnZUWW1dxpAY5l7PJY6ZzecKyTigFbBucOCIpskopHJgooC0Zrauqa29bAgfGi5goGHM8OFHEQ7tHMZYr22P2DBs/TsqaCiz45b1GLOcSAPaO5jFZrBj9N6WgjyAIgjBPUEoNKKVOt/7BcDR/WGWTN7Hxv2bLrwVwtVJqHYCrAVzn3ZCIkkS0xrOsnYhW+BxnI4AXw1X1qvaxiOhoIrrT8+/jAP4bwHlE9CUYTzL6q5wjAHEuFyzetNh0fP6mxeZMJ/K9L1xrL2tLxpAva3baHhBcLZY74WF0goARvQynuQzZ5zJsKxIrLbYBoUu/aGnYVFMrCv765670jVzGoxQqYmsX9KlD89csHts7hlQ8gvZUDM9Z3IZiRcd9Oxr5tNif8UIFhbKOZSGjlhY9mQRS8QiGsiVkixUMHM6irCk8vm/MHrN7JGdHwfePuR8NK6VwzaZnsHnnCMbzjkO643DWvqb3jOQwZq7rqqOgzy2XniORTEEQBGFOQEQJAG8D8J06t1sC4AwAN5mLbgJwBhEt9gw9GcBvieg4c7suAL8B8GrvPpVSf1JK7a73WEqpZ5RS53n+fVEpNaSU+jul1D8AmATwRK3zmjXnkoiuIqIdRKSI6OTZOo5QPxdedw92D+ddEbnMPI5cWj0XuTNt2Tw1thSguUy5CvqEa4eRiIWLXIbuc4mQrUistNhQe6zOzKrFakjHo1jamUK+rLkieEFVef1I1FEc6Ujg51xbyx7bM2ZfV52pOAjA/QOz71weGC8gFiH0ttUXGYxGCC89YSmGsyU8fXDSdvafPGCUsi9WNAyOF+xo6N4RJ8WqWNGwdXAC/3nHNujKKNhjMXA4i86Us82oGQkVzaUgCILQcKxqsY3+Bywios3s3/urzOK1APYqpR6sMuYHRPSoqV3sNpcdZW6nAYD5/z5zuY2Zhvq3AH5FRC8C8FsANyulvlX9xXER6lheiGglEX2biL4N4FGl1NZaB5rNyOVGBIdlhSajKTWlFUm+rEHXp+tONI5GF2Kx0l/bWMpgJmmcO9eZBqXFZupMiwWMoj7hCvqoUAV9Qkcu62hFUgujoM90+1xW0JaMYpFZXIYX9SlWtNCvoxUpLrZ4tdhSRcfgeMG+xqIRQiYZxf0Dw7NaWEgphbF8GX1tCZemOCznn7oCFV2hVNGxbmk7YhHCk4OGc7l/tACl4DiKozl7u+0Hs8gWK7jqzaehPRm1U2vzJQ37xgrIJGKIRQh7R/N25LI7I2mxgiAIwpzhsFJqPft3fZWx70H1qOWLlFKnATgLxvP/b9Q7GaXUXQA+DuAPADYppb5W7z6mg1Jqr1Lqvea/r9TeYhYL+iil/gQ0Jj1PaDy6rlz9G9OmXazorojmfMCKXFoOJeBELl0RNU1He2rqLRGPRmzFYxidoLVNmAqboQv6hNQ/Oq1IGpAWi3DRUj8MTW8MizsM5/LQRBFr+toAGNViw76OrRa5DMKJjjvXWGcqjkd2j+G0VV2IhHiPp8OhiSJ0BaSmec9uOG4x2pMxLGpPoCMVRzoRtSOXu81iPh2pOIAC9o4aj3JLFR3ZYgXLulJ405mrcNWvt+HARBGlim73xUzFI0jGItjDI5fS5zIE09HoBm0TZrm3AXpHiO33Mps3eT/FM+5Ox9xxDbM9jeltqvxm4UVW8EpmO0VlcAornvLYgHv7Hf3sDyaJ2n+ZYxMv4sOKwtzICscAwLucKD2OYQWBguqtfJQVlPmKp/DN+Wwde7meibCCOGk2/kzP9ut5gSBmr+KD/F/vsaS3Ago7r5+xe/WTbMiLnH1tyrD34XL+ngDYw+yLmM2L+LyNLd/A7Nf91LWrLek32PbL1c9t+zcfe60ziCsCetzv16ofOm/MnggriLQjoAgP56IBzwJeQIpdR69n185tTtGjqUV8OPx65/cOPyYvyMPx1ovhF8le+FPts2UJs72fCfXSnFZczcTUPb4EwDuCxlhpqkqpIhF9E4B1Me8GsJKIokopjYiiAFaYy73HWQrgMwD+HcDFRPRjpdR9dUw19LFmimguFyC6UlBwR+Qs+0i3UDgSWA4kL3binK8nchkQRbScg9ARt1jE1j9Wo6zpdnXZaoStFmsVXWmEL0NE0GdQ0CeTiGJRuxGtckcu/avy+pGcIwV9skUNRO7oeHsyhpKm247nbDAwZDiAqZCvp5dUPIqTVnTaVWYziSieGpyAUgq7zWI+qXgE8SjZabFPH5yAguNIt6fiUAp4/TfvxodvesjebzIWMdJi80ZBJ0mLFQRBEBqOguHTNvpfeC4B8D9KqSG/lUTUZmokQcaT/4sAPAwASqmDpn2xOfxiAA8ppQ559rEcxiOo/1RKXQngTQBuJqJzw04y7LEaQdOdSyJ6v5XPfOhQw89v3jOdlDsr9dWbFgsA773hft9t5jJWWiw/X8vR9GoBgyJqlrMWVisYj0ZQbmDkMkoELYSjZ53rdFIkvcxUc5lJRO3I5Rdu32Zfp0ZabHjtqrFNizuXpQqes6jN9V52mFHw8cLsOZc77Uhh8OtZT3GcTDyKbEnD3tE89ozkEIsQfnL5C3Dqqm47LfbxfUbLCCvzwbqXJgsVuyhYKh5FMh7FntE8RrKGc9kpzqUgCIIw/7gEnpRYIvoVEa03/1wKYBMRPQpgC4B1AD7Ahl8G4O+I6CkAf2f+7SUN4FNKqR8AgKntvABAu3cgEX2NiPbAyGG4k4geZ6vDHGvGNL3PpZnDfD0ArF+/vvmCvwWAJat0p8UaP05DBNvmHDmfgj6ZpFXQx4lcFqs6l0ZibOhqsTEK2YpED9WKJBIhu81INfJl41zDOKy1IBivye7hXM2xXgzNZQy9mQSI4HotihU99OvY6FYkY/kyorOQqp8tVvBXxy3BvlEnjSkejeCYJe047KmW20h2mpHLsGnGtbA+B95zw/0YmixhRXca0QhhZXcaD+8eBQA8sW8cETIimoARXY5HCZPFCoiAJR1JRCOERCyCUkXHs4eySMUjVR1gQRAEQZiLmG09vMtezexnATzXO4at3wZXXrvvmGcBPOtZ9iiAR33GfhjAh6d7rEbQdOdSOPJYETC/yOV00yBbGSs6yVMWrZS+SRa5LFapYmo5a0Fps17i0UioxvMVXYVqRRIh56FANRoZuVzckcSzh7P46y9tQm9bAqt6MqHmChhO++KOJGLRCPraEq4U4WK5nrRY4/0oNagVycDhLOIhj21FWmtF/UoVHWVN4eSVXfjKhae7tj2rvxe3bt497cJItdg5nEMyFmnI+w04zmW+pKFQ0XD8ckODt6I7jV88sg9vufbPAAiZRMyl6+1IxjBZrCAejeCkFZ245dJzcOcTB/C+/96M27cMulr+CDPFGwmfSdNy70+ACWZz/SXXfnG9Fy8ayLRmAIyK9xZMUOjSdLFG9LgtYLl33R3wZZL/0e9ZGdAjfC3TI+5gv9HOZ7q9d3l/u7EK0NvZa3Ee2+bjbPgiZl/r+U33C7bv17PtuQJqM9NZft6z/RZmX+6Y33/Pm2z77a/7sbPiIrYvqvL7kuseydE8YjMbcyazr3FrQf+vEVwBAPyfZ/+fs+IK9jnFa1zy1+v17HiA663/TS/TWY4wHW8PO/n3ujff8wGms/wIW3EB00lewJaP8GvFe02z68Wl8eVjBpj9UWbf7tkXf/03wR9+Px/PbK/A92DAuOEA2/s5Ua/OstrnzEw+j6aJjmB5qtAUZs25JKKvAXgDgGUwwrJDSqmTamwmHAGstFhvKxK+bj6RLVnVYtn52pFLnhYbXMXUTosNG3GLRkL2uVSIhXBYo5FwabHW+TQicrmoPYmOVBwnLO/ETfftQkVTOGbJlAyMgHlodmR8UXsSB8ad4hHFiuaKmlfDjlzWWS1WKYXB8QL6WHsOpRSKmo5Kg69xS1N5ysquKeuet7YHN923yxUhbyS7hrL2NVvLCebrg8bGIhGs6EohV9JQLOs4qicDAFjZk4YCUNIUth+ccD2YAoD2VAzDuTLKmo7nLDYKN63qNYpM5Mua/fBKEARBEIT5zaxpLpVSH1ZKrVJKxZRSy8SxbB0sJ6XNJy12vkYuieD6gWtFLq1IH2A4MNXTYmehz2XIgj4RolCOv+XENKo4aTIWwX+84RSs7E5jKFvCaK4UajuroA9gREC5c1jSwkcurUhxsVyfc7lnJI+dQzlXIaGhbAlKGQ49X14PFU2fco9YkfGTVnROGb9+jRHZmCjOzhPcgaFcw9NN1y3rQLZYQUVXWNVjOIgru42CPxOFMibNNjMcS3epK6DfrAq8stupYBi2l6sgCIIgCHObphf0EY48fpHL+ay5zBY1tHnS+KzImTty2bhqsWHSYnVdQVfhooyRkJVbcyUNEWp8C6AV3Smk41HsOJwLleKYLWp2GvKi9iQq3rTYsNpV0ympN3JpOY+8END+USd6+vSBySnb1ELTFR7dO4bdwzlXIa2JQgXpeNSVdm2xqieNRDRi94FsJKO5EsbyZVv72CiOW9aBgvm6HdVrRi67jf+t4jw88nzLpefgpx94Aawrbu0iw7nsSMXtHplh06kFQRAEQZjbiOZyjlPW9LpT7iznwNWaI25GHlogLbZY0VCqNG4eudLUSEsiFkEiGrFTZo2IVHBhlCjV51wmohGUa5yDlZ4ZD5UWa0SFdF1V7ZmYK1Uapr/jRIiwdlEGT+yfwJ6R6gV+NF0hX9amRC4t3WGxihPvhYiQjIVLMeaMmBFWHvHcy4rtPH1wAucc3VfXPp8cnEBZUxjKlrC6NwMiwki2hPFCBSu6Uq6xPO20LRlFfhbSYq1iPmGj6bWw5vzTB50mdavMtNgVZuRyNF9GNELIeKKlqXgUmWQU2aJmp8Va2z+xfzxU0SphuszkwYW3Xx4njA6L97lMe9YNMJvp3sA1df/ObK6z9PZm5D9V+L6G4cunPH9/7kH2B9PK7fg8W84aL/6Cae1u9vS55H0fu5nNZaW82x3XXOZv8czzQtu85rPvtu3L3/pdNohp8670akfZ67TRyZz4p/d8wVn+Ipal8W62r+96tKQfZed5NnuP+Nu6idn8pfukW7/5f97A/r6KfQ/+hQ1ayezfMvto97Rcxx8ZcOwrmM7ySTbmKu9rxMV47Byv5UPY+/KiCxEIfy9vYy/AeVc6dpad+/6geQDAjcz+BLO5GJXfn7za9vmefd3NbN7nMoyGGnB/hoTRTFbri9mEdnYKwOzVzROmgTiXc5wD40Zz87FcGV0hm5RbQSTucKUSxo+/VkiL3TtawHC2BKVUQyJwk8WKKwXYwvgxbHwQWpGxaq1ICOEjgvEQabGWkx82cgkY+jW/CJlFrqg1RG/pR0cqjiUdSQyOF3FooogP/dD4webV71ntKCznclF7Ako51109rUgA4z2ptxXJ0KTpXPLI5ZjxhUoAnjow4bdZVe4fMH7IljWFyWIFHak47txq/AjtYdpOL4lYpOHtSC687h4MmdHZRkcu1y11foQcZeomO1JxQ/erKxy3pN33AUdHMo5cUbOjnYCh1Xxi/7ikxQqCIAjCAkGcyzmOVYXz/oFhnHei92mvP76Ry4SlmWq+c1nWdGi6wv6xAlZ0e5+GuwlT0TNX0pBJTnVm2hIxW3NpRcaCImqxaKQupy0epZoOUVk31oeqFmuOyZWqO5fZWYpcWixqT+LgRNF2tPywUo2ta8rqdWm1UqmnFQlgRIvrTYsdzlrOpWY/pNg3mgeR8b4/VUdarHWNLe5IImYWVhrOltCRiuPXjx9AIhqxNbx+JKIRaLoydagz+8jlPW0LZlS2UZFLC6toExGwuD1pL0/GIsiVNJy0ohNfNqviclZ2p9DXnnDNx9JdztYDD0EQBEEQWgtxLptM2HYHQViO4n11OJdW6it3UqxiN3X+hp8VLH3eUwcmajqXYQiMXCaitiNkO5cBkcvlXSlX5dFaJENELq3zDJUWa0Uua6RX5koaZjMDsS0ZRYSA+3ZUcS6L7uq8i0wHxXIQ62lFAhjOWb0FfSznUlfASK6M3rYE9o0WkIxFkI5Hsf3gZF33nlIK9w8MoysdN53LMlb1KPzh6UPoaYtXjWhb7+/gWAHPWRyu2m4YChUNSzuT+PHl585oP97zT8WjdjSUn1fCdC5P9ClcBBgPYNo917JVEEg0l4IgCMKsIK1IWg5xLuc4lm7v3meHQm+jKQUit1MTjRAihJboR2dFuJ46MIENxy2pMbo2uVIFSzpSU5ZnkjFbc1ms4VzGo5FQTiAfX9O5NCOXodJirchluXp6Za6k2Y7obBAhQnsyhvsHhl2Rb07WE7lc1mm89qWKobucTlpsvZHLoaxT1Xb3cM5wLsfySEQjSCei2DWcQ1nTA9/TA+MFbNk7hqMXtyOdiKJY0XFgvIj+vgwiRBjNZbF3NI9SRUdvpvpDB+uaGhxvrHNZLOs4Ybm/ozdTlnamDB0Lw3ogEORc+mE7l3XcO0K98PuQP4wL+rUVNkU7SFfF98sb/D3Psz2/Tni/QC5OfGvAvriGDAjUdi5n+rgdTGv3OU8fwGPYuJex5XcwrdwOtvxnzOYSOADYPu7Yn2fneAobcxWzL2H2eo+er98xL3+C6SwvCtj+3ce6t3+fY15w7s22vfHjbAdrHfMz3/knx34F02UCANv1mWc4r/8Dx76AHd8xu64YtO2xkWXufV3M7KPZ9xH/anols7mEcKN7V/y12PCdbba9KdLvrFBMP3qxRyN7M7MvYDbXf17A3pebWD/KK17l3tdVvFcl00PyS9rVz5ILS70P//m96r3e/caUmf0Lz7iVqE1Y74t/PvCLmt+f9fbFBMTdWFjIN/4cRzMdlC37xvGma/7sSpsL3kb5OiDRSLiKpLNNWbcil/VX9PSDVy7ltCejyHk0l/VE1KphOJc1CvrYkcvazqA1pFbxpmyxMuspiB2pGLbuHw/sF2nN0dJcWhq8YtnoMamr+l7nZCyKUqW+gjgj2RKsS3zPiPGlum80j2QsYs+r2mt5zzNDyJY07DN1mla1145UDD2ZOAjA/rECetsS6EhV/9K0Uq15r89GUKhoWNOXqT1wGizrTGGZp0hROm5ErU9aPrWfZxCnrup2veaCIAiCIMxvxLmc41R0ZejAdBW63YGmK18HJErU9MhlxdRbAvUVXeGtIbxkixVfTVwmEcMT+8dx4XX31NRc1ks8WrvCqVPQp/YxIyHTYvNlDS9et3jaadZh6EjFoStgslD2Xe84l4bTlYpHkYhGUKhodoS4Hs1lYhrVYoeyJduh2T2SQ6mi4+BEEW89ew1ufI8RYbEKD/mxdb8RnRiaLKFU0TFRrKAjFUM6HkUsGkFn2iiedd4JS2oWebIjl2ONK2en6QplTc2ac+nH4o4kTl3VFbpwGACs6E7j9KO6XT1mBUEQBKFhWNViG/1PmDbiXM5xNF2hKx1HLEKYCPixP2UbFeBcRprvXI7mjXOIkNGLsBGtUXIlzbeQSlsiap9vaRpOTzWsVE5VJRJspc2GiVzygj7VyBb9z7WRtCdjiEYo8GGGFQ3m1YiT8QgKZd15nWc5LXY4W0IqFkU0QtgzksOB8QKUMgrMLOlIoiMVq+qobx2cQDxKUDDSWScKZaxf02M7kr2m/vaVJy8L3IdFNEKIRqihkcuiGcld09dWY2TjiBA1vHiQIAiCIAjzC3Eum0yxomPXcLjG9H5EiPCmM1fh5JVd9UUufaItkRZwLq0m7e3JGPJlzU5pnC5KKWRLFbT7VIvNJGP2+dqay2hjfjwnTIcxKHUUqK8VifV+WQWIgsiV/KO0jSQaIZy8ojPwerN0rLyIUioWRaGs2U5RkLbVj+kW9IlHjR6Zu4fz2Gf2uFzenQIRYd3SjqqRy237x9GVjqM3E8eB8QIKZR1nrXU0NovbE1i3pB1/FVITnIhGMDjWOOfSqhR7JCOXgiAIgiAItRCFbZMZzhaxf6yAnUPZuot9GD/WdXSm4zh7bS8e2T0aKtLX39eGvvapRUiiEao7/bDRjOSMyGVnKo7xQgVPHZjA6io/oLPFClLxaKCDli9rUMpwJL20JaK2xvTTP9sCoD6npxpWoZhSJbhojKXJDNNg3hpSLdqm6wr5snZE9G1n9ffi0b1jvhpdywFOs3mk4hFUdGX3n6yrWmwsYhcJCkOxomGyWEF3Jo5kLII9IznsNx07q/rwuqXteGT3qG9keWiyiIMTRazuzaA9GcOweU2e1d+LD2wwGrhfeN096GlLgIhqpiDfcuk5eMe378VggyKXulJ2lsKa3iMXuRRaFX5vhJES8M/CaoU5+IMTXqznwYDx456/efp03n8csSoyildFqVYl/AzHHGSLl7N97e93b8L7yu9hNr8lP8rsTcy+wHP4PCviw/f1CmafzOz9zOb1UQBgK7NHHXPVx5yCRHv+dIyz4g735iefe79t/3rcmUDin53XuPRVZ76f2fevCCLR72zzwL+zIj63sEGbHHPsDpa14X27+Ov6ALMfZvY1zP4cs1mRIgCu4kqbzmJVgG5iY97Niviwuk4AgH9g9iZmX8DnwgpOrWVFfK5ihYIAuAtLcfhBWaEf1wtTregOH8dee1ehH75fdg8AcN+T/Di8qnu171D+mcDHbfUOnAGN7fXsQqrFthwSuWwyVgRiLB8upZUzbv7A7EzHcfZzeqFgtN2oRbZY8S1w0wqaS6uFhFUk5amDwT+W8iUNj+8bx47D2cAxk3aKpl8rkhh0ZUQ3LSepUc6ltZ9qFWOtarGh0mKpdlpsoRLsSDeas9b2QinjWvKS84tcmpq7p833s570ymSdmkvrGopHjDTOPSN57DUjlyu6jC/xY5d0oKIrVHQ1Ra+7bdCYYyYRRUcqhvZkDETAqavCF7LxsrQzNeO02GJFw47DWTy4axSD40Wk49G69I+CIAiCIAizjUQum0zBTM0bnY5zmTd+2HemYjhzjfFUazxEauxksYJ2H11eK2guR3JmZCsexYquFL79xx2468lDvtGhvaM5KBjFW5bky3aRFY7dc9Enmme10jAcTGNZIwv6AKiqFaxMIy22Wiqn49Qdmcgl4H+95YoVEMHulQg4kconByddf4eh3oI+lnP5j686AQfGC/iXnz+OR3aPoicTt6Op65Z2GHMtaehKu+diFfOxIsDPWdyGYrm+9ilelnWmcHCiGFhMKwwP7xrFwYkiejJxLOlI+l7vzWQ2i0gJgiAIgjA3kMhlk7G0fuMzjFx2pY0UwGrOh0W2WEG7T/uEVnAuLcfg5vc/H8fW0MXtNvWYRMDO4ZxviqO35yInY+owNX0WIpcsLTaIK28z+kaF6Z9562XnIELVNZeWI52e5YI+gFHQJhWP+EbKsyUNbYmYq4qqFbm0KgDXWy22OA3nsrctgaN6jUjl77cdtFNiAeCUlV0gcsZaXHjdPbj2rmewuCNpvy/peBTdNXpZ1mJpVwqarjA0Of0SdNb1vro3g+5Mwo5mC4IgCMKCRQHQZuGfMG0kctlEShXd/tE8rbTYvKNPBIz0ykqNqpq6rowf/35psRGjOma9Te4byYjZQiIVj+K4ZR34w9OHAiuuWsV+VvdksHM4h4MTU3+4Z02Hq91Xc2ks05SyI5eN6nPppMUGO+vWMcNEsogImUSsalqs5UgficglYDhdBZ9CO7lSxaW3BIxzjEcJTw5OLy12us5lt5k2WtEVlnc5zmVXJo6+tgQOTxraSv4e5Eoa1vf3ohjiQU1YlnUaPSMHxwtY0pmqMdqf3cM5AI17AFINiUIuVIL0mFy7xTSA2IZgmI7NJYja4JjqBrb8vIDjAS692TGsGf32TY7NtY1TNGksyr+ZLV7PbD6VkQHHvqLfvSt+WkzmiWeY/Ulm/xuz13qmxTPl2Vz2fNN5jdNvH7Ht/J4e1+Zb3nqW88dlbMWAY57w2Ydse+sTz3X2+6oRcPJXsX13sxVMV/qWj91o278Yf62z7Ub3vE44lx3zDc4x8Ro2iOtP+evwONzwdXybIfjj3Z7/zSSbuGbAsanfsXfczpYz/aUXxa7JNNPh5rn+kl/32+GG6xm5zpJd32DzcjEQPC/XMfnD3yBd5Uzx/raaRW2lMGcQ57KJ7BnJ2fZobjqRS+Mm7kobb2MsEqmq8QOAXNlytqb+uP/bFz0H//LzxzFZqCDZ3ngHxdK1VfvhOpwroceMEh27pB1KAYUAx2LPSA4EYGlnEiO5EnaP5KfoSe3IpV+1WNMB0mchcmlFvaq9H5ZzGUZzCRgFcgoh0mK9jl0j4e9dIhbBWL4MpZQrSpkrab4ObjIWtbWP9Tjxv9920E6XDoNVNKivLeGKkK7sdjt1SzqSODxZwtBk0Xb4lFLIlzScsKwD//xqo3hDUP/UerCdy7ECTl01vX3sHskhEY3MuYilOKqCIAiCsHAQ57KJ7BxynMtGRC6jEUK+XD2tNVulwI0V3ZssVtDXnqx7Po1gJFuyewget8zQxQVVSN07kkcyFgERYVlnCk8dnMSTByZwxmrnKap1vr6RS3OZpqtZ0FwaDkC1tFgFS3MZ7piZRLRq5NJKmfV7b2eKn4OQjEWgK+MhRxfT//n12rzl0nPwD7c+gp88aDwGryctNkJUtV+ol+FsCdEIoSsdRyRCiEUIFV250mIB45pIx6M4MFHE4g7jes+XdSgAxy/vCH28MCztMvZfq2JstQcwe4bzDYusTwdxEgVBEISWQ8Ed4RaajjiXTWRgyKhymopHpuVcWttYhT1iUaraVxGA3ZuwmrMVpuLsbDGcK6PHdC6PWWK0ZglyLveM5O1Io6Xp2zmUdTmXlg7Rrz1Hm13Qp3bkst4f1vFY7YI+lr8UC1ngJR2v7lxmq5zrdKh1zgkztXXvSN7lXOZKFbT5RIr7WUuZepx4glF0KSxG9NtwLAHDka0UNSz3OJdEhKWdSQwM5ezenHnTQT9+WSeqUe/1sKgtiViEZtTrcvdIri6nXBAEQRAE4UgjzmUT2TmUQ1siiqN6M9NMiy0jEYvYjlUsEoGmK1Q0HbGAH+925NKn6IvV/mMyRMXZ2WI0V7KdkEwihniUUAxw0PaM5G3tnvWje8fhnGuMo0P073MJAAMsgtyotNiklRZbNXJpEKagD2A4jdX6XObLwcWLZgMrirZvNI8TVzjOWLakuZxNizWLnJ6MyXh4B9hKA612XXOGJ53otzHPKLJFbUpaLAD0tSexazhntwnJljQQgKPr7Dlbi0iEsKQjiVs378YDO0fqdk6LFQ2D4wWs6JqeXlNYKITRVfF+ed4+l3wbLnAL6ne3htleTRmHa8o2MZv3DeQPdG71bM+aH3KdJddmpt/g2N6ed3nWrzDP5jnKtnFJENn3sVfbdwmzuX6TS+0uYjZPg+f9LwHgH5nNtI1dX3AaeB6V2G3bW45hGksA4B9Ti/yXb913ovPHqGNqyzyfwVxKe5jZ7CPn1j+/C750u//c+h2ms2Sa0yUn7rLtg1tWOyuuDZgH4NbF8tePa1n5x2KfZ3t+WW5kdrof/jCd5UWeVd8d8N/epbPkQlym30S133hcY8wfgt7H7OOrbM8J6l07nd91YbYRjaUwFXkM3kR2DmWxpq8NXen49KrF5it2SizgRMCqtSMJmxbbLIazjuYSMBwvP91ioazh8GTRdiojREjGItg55O55We18n7O4Hcu7UkjHo3YaZdgoYi3iIQr6WKHLsK0pjII+1d7bI9eKBHCcS0tHaZEvVXznwCOX9aR3WhLDsEV9vNeQdSxvWixg3DOL2g3t5flf/xNGciWkE9FZKZqztCtVV0sVzr7RApSqrxCSIAiCIMx7dBhpsY3+J0wbcS6byM6hHNb0ZdCVjk9Pc1koozPtOE0ffdk6AEb0LwjLcezwaUXS7LTYsqZjolBxRZ1iEfJ1Lq1KsVe8/Dg7CpSMRTBw2ONcljTEo+TrLEQjhNW9GRy3rAOnrOzC8cs6XIVpZoLT5zI40qhPo6BPtbRYK6qZmQXNpR+xCIHIiFxy/DSXALCml0Uu63DerMhlWMdsKFtEX7tzDfW2JbC4PYGlHf5Rv9W9GazuzUBBoVDWp0S5b7n0nIboDZd1pqqmSVfDqhTbTM2lIAiCIAhCLSQttklousLukRxeftIyDGeLeGRP+GqYFuP5sity2WW2XRit4qhmqxR9sdNiZ8G5LFV07BnJ2VUz/bAqgva0uSOXBZ/5WJV2V/U40ahUPOpKcQUwpXrskcLpc1lbLBg+chmt2vfTem/TdaSczgQiQjIawR6PcxmkuezKxO3iOvVE4Cx/P6xjNpx1p8W2J2NoX9xuazC9RCOE5V0p3HLpOXjdN/4UOk25XpbNIHK527zev33JWb4RWEEQBEEQhFZAnMsmsW80j7Km0N+Xgabr04xcuqt0dpv2WBX95qSVOunz499Oi50FzeXd2w9j72jB1of6MZI15t3LUhoTsQjKOX1Kuwsrcrmqx0m1TMUjODhRxEi2ZDuo2aLmq7ecbRIxY67VWpFYRYTq0VxWrxarIRWPhHZWG0EiFpkauSxpge1QrOI6YaO1QH2RS01XGM2X0ds2vWrH1a7PmbKsMwVdGXMMwnudW+weziMeJSydZo9MQRAEQZiXWGmxQssgzmWTsNqQrO7L4PBkEYWyjmJFqyuiM5EvY3Wv41x1m07ZaD44ClqtNUcmEQXR7EQuH9hpVEuo5iAMZ63IpeMwx6MEpYw5dZhR2guvuwe7hnOIR40iKRaWYzAwlLWdy99vO4BCeXrRopkQps9loawhQsCikG1f0vFY1YI+uVLliDvSyVgEe0cc57Ki6ShVpqaWWqRiRlGietKPHc1l8LlbjOZKUMrocVkLK9W1EX0sw7DMLMYTdA8cnixi884RHLtkajGh3SM5rOxOH9EHB8J8Jajghxf+kJJ/J2xjNr/P3+LZ/k5m84JAvQH2poDlXlhxIJ/CYQYDnr/58fsdk3/08ltLsaIs3h+tm5g9ymxeOIYXguHH4EVoAHftlg2OOfaNZY69wbGP/sDjrs2f+c5J/vO6oOjYA2wCbI6l3Z6K2JPw52ZmX+GY6VVOBaT8sm73NgX2Yv6TYx48nRXx+Sobfyazv+Q5/iizP8ns/cw+j9l/5dn+ygHHvqjfsfl58Wd2eTb+u+OenbEHqXnvOot7/cfz624KB5jNr1V+f/ELLOxvtKACX97v53p/84UpHCYsZETA0ySsNiT9fW3oMp3CeqOXY/kyOpl20opcVqs8my1WECH/1EkiQnsiNivO5YO7TOeyirNlpcXylMaPnGfoSA9Puh3mUkXHiu60K9UxFXOcSwtdKYRsI9lQLI1nNWc6W9KQSUTrSovNlSqBPR9zxeCI4WyRiEVxcKJoO365cvV2KH3tCSyus4eq5agfHC/WGAlc8t37AbivoVbBijoG3QOP7B6FroARn/t3z3AOR7EHSYIgCIIgCK2IOJdN4ur/3Q4iI1WuK0Q6qxellFnQx3ly2xnCuZwsGtGtoMhReyrW8LTYiqbj4d2jAEJGLllarBXVOzzpdiyKFc2lt7zl0nPwo8vPAREwwNqRaDoQbVCRnnqoFbnUdYVcQOGbINKJKHQVXDU1e4Qjl7dceg4+cp7xhN/q35iz067959GTSaCftSQJg/Ug5OmDQY/WHSrm692KzqWlNw66B7YNTgBwetFydo/kXSngQutBRFcS0aNE9BARPUxEFzZ7ToIgCPMeBakW22KIc9kkCmUNyVgEEbMFBlBf5LJQ1lHWlKugTzRC6EzFqu5nslC9wE17svGRy22DE7ZWsFrk0qpy251xzsl2Lie8zqWOVd3uH9vJWBQrutKuyKWmq6akEjrVYv2jjLtHctCU8tW+BmFFA4NSY3NVtI6zxUqzuIzVjsQqKhQUuZwO8SghGiE8dWCi5tiyqWdsRedyeXcKkSpp50/sN9Ks8mXN9aApW6xgOFvCUb1SyKfF+YZS6lSl1HMBvBrAfxFRT7MnJQiCIAhHEtFcNolCWbc1gt3TcC7HC8ZY3ooEMHSX1faTLVXQ7tOGxKI91Xjn0kqJ7UzFqlY7Hc6W0Z6MuXSnizoMJ4FHLnVdoawpV+TSYu2iNty59QAuvO4e3HLpOUZabBMil061WH9nestew5GoJ9JoOWy5soYeOFpBSzuYK2l1OauNwKpc+snbtmBxRxKfes2J5lwb99FCREjHo3VFLrnmshFtRBpBMhZFVzqOkVwJuq6mVK/dtn/crqa7eecwXnrCUgDARdcb7/NRErlsaZRSY+zPdhjP0xv0ADesxmkm2ikvXOPFx3E9JNeU3VplX3ybfmY/GDD+VZ6/ub6NadLyXKvGNIRpz/Z5dkzu7g8wm+vujmH2056pPMPsUfizkdkbmL3eM46/xJ9j9nXMfpgdegvTWAJuWSvf952O9CB9AdNGXsVO3lsb7JXM5rLvPv/55gtV9nUVs1cx+2Fmb4Q/v/D8/RNm72D2WmaPBhwbgOva49GolzJ7gNnbVzp2ut+9q/zt7A82Dnczm2mC3Rt7/uaaV65j7gjYnj9crXbf8nttmNlrmL3Ts03Q/oI+T0RnKVRHnMsmoJRCsaKhy3QMu0Kks3oZNx1IHrkEjKhf9T6X2hGPXD6wcwRLO5OIRgjjhQoqmo6YT4XUkVzJVcwHMCrHEgGHmObSSgtd5RPJWdOXwV+eHbL/Dhu5bLQDYmkug9JiH983BgLqijSmTYctX/J/f7LFCnrbjqwDsrzb+FVhvScf/9EjAIC2BkdQ04kotnucS69zDQAVM1LcU0fkkm8/245ob1sCI7kyHto9ijPXOD/OCmUNOw5nsbQzhcGxAu4fGLGdS/t693mYIrQWRHQZgI8AOArAe5RSQ9W3EARBEGZM7a5vwhFE0mKbwFC2BF3BjtBNJy3WiVy6nbGudLx6n8tiBe1VolvtycZqLi+87h7csWUQZ67pwQc2GI+DvcV5LH6/7SCGPeti0Qh6MwkcYmmxVvGYld1THam1i9pQ0ZUdwdJUc9JioxFChPydywuvuwc/uHcX0oloXVHVjBnpDmpHki9rDU1HDUMyFsXijiRK5ntitVfJNLi3aDoexXC2hKHJ6kV9yrqOaIRmrVflTOnOxEEAPvTDB11Vap86MAFdGfdfWzKG+wecJ85Fs9qxFPRpLkT0IBEdDvgXBQCl1LVKqeMBPB/AlUTUF7Cv9xPRZiLaDOT8hgiCIAjCnKQ1f4HNc6ziJ1Z0q3M6zmXecAC7fJzLaoWBssXqRV8aHbksVXQUKzrOWN1jFzQZHPdXSgdFNBe1J11psaUqkZw1fUaxmEJFR0XToRSakhYLGLpLv7RYpRSyxUrdjqCdFhvgXGbrLBDUKFZ2p+3omtXDsdFOrlXU56kD1VNjK5pCvIXbdcQiEXSm4xjOllxVf7eaestMIoqOVAwP7hzBm6/5MwDjYUqEwrVXEWYPpdQZSqlFAf80z9jHAOyDOymSr79eKbVeKbUekIcGgiAIwvxB0mKbwAHTubKiK9EIoaNGIR4vY3ZarFdzWT1yOVms+Pa4tGi05tLa1xlremwd4uBYwUga81DRFdKJqY7B4g63c1ms6CDAt6H82kXGD7VCWcMBM9pZLXI5m2mQiVjEt4BRWVOo6Kruyq7pGgV98qVKw9NRw7CyO41tpnNk+paBzuV0X29rf9sPTuCco32DQQCMSLHfA4pWorctgR2Hs66HBFv3TyAdj+K2D7wAv992EO/7782YNNOfixUdyVi0rt6gwpGHiE5QSm017bUAngvgicbsfaY96vg2XFC4zTswBFzHxfVd3oJbS5h9ArMfDFjONWy8158XPo7r26r1IGT7Gzmb2ey7ktiDWv4M6zHP9+l6No4/3+S9LUeZvZHZ3ueqhwK2uYPZ3cz+oGf7u5jN+zbe4Jj5vwRoI5fBzZ4Qx9/LbP4aed+u7UwLe/dSx34TG/N8Zn+X2d5nxl9lNtelbmY214h2e7bn2sbbBtjyfmaz5cvZ8v0/de+q5w2OPcLX8X1x/WXQ9Q24NcrHMzuonyW/n/g9CLjvQ97Hlm/j1Vlygj5DpJ+lMD3EuawDP43XdLAid4mY82OxKx1vSFpsdzqB0YCCIYAZuQyhuVRKNeTH7EShAiLgpBWddouFAwGRy7KmI+Yz50XtCezc5VSALVR0JGIRX6fRatcwni/jHd+6FxGa6oAfKRLRiG9arFVRtd7iO+kqkUtdV8g1IS0WAFb2pFHUdCil7Mhlo1uixKOEjmSsalEfXVfIlrSWj/D1ZOLYAWCYaaO37h/Hccs6EImQrcWcKFTwzKFJo31Qg9OMhVnhX4noJABlABqAD1vOpiAIgiAsFOQXSxM4YDaD57qw7kydzqU5tsMncqkrYLJUmVLsBzBSJ2s5l0pZlUfd46bjXFt9NZOxKOKZCOJR8k2LLVY06ArBabETzg/xfEmz0yS9pOJRJGIRHJosoVDRcfyyjqb9MI9HIyhXpqrMrV6Qt152btUospdM3Bib8ynoU6hoUKrxWscwrOhKQSkj8pwzqwE3siWKdb29/pt3Y+NDe/Hk4ITvNfjs4UlouqrrNW0G8WgEHakYRrLGPfyWa/+MB3aN4i3rjXB+T1sC6XgUhyeLuOAbd0Mpp+WL0Loopd7S7DkIgiAIQqMgonUAvgBgEMCwUuqTYbZr7fyxecqBsQIWdyTxo8vOtZd1patXefUyXqggFY+42nZY+wHgq7ssVjSUNH2KQ8qx2pQ0IjW2VNGN1ifmj/1IhLCkI2VrTjlWpVzfyGVHEvmyhmyxgkJZq1m4JhOPIhYh3Pz+56PDx8E+UgSlxWZLFaRikbqdIDst1qedixXNbEparBkt3jY4gUMTRfS1Jew2O41k3ZIOV9R2slixI6UA8OCuUQBoeecSMPST+bKG2x/bj5KmQ9MVTlzulKDvSMVQKOtY3ZfBySs758Q5CYIgCMKRR8FIGGn0v9oQ0UYieoSIHiKiPxLR6Z71/UT0MPs3QETDbP0AEW1j618x7ZfB2edVRLSDiBQRnexZt46I7iGip8z/j62xu78BcKNS6gMA2onozDBzkF8sTWBwvGAXt7HoSsd9na4gxvNl38hkd8ZICRzNlXFUr3tdtljbAbF+xE4WK1gaOCoc2wbHoRRcfTWXdfk7l8NZw7H2q/K5qN3o2XV4smin1lZzLo9e3AYF4KQVXTOZ/oyJR8l2LnnUN1vSpuUsVCvoY0VD000q6AMYTm9/XwZLOpI1tpgexy5tR0VXKGs6Hto1gsf3jWN5l3MfPbRrBNEIIRVv3WdmVsT1zdf+GYcmivjEbY/Z9+zxyx1t0PKuFBKxCH582bm45Lv3NWWugiAIgiBU5V1Wj2Mieh2A7wA4w1qplBoAcLr1NxF9FVN9rzcppbYEHYCIkgCWKaV2smXtADqVUvt8NtkI4P8B+KPPumsBXK2U+j4RvR1GV92/JqKj4e6wCwC/Ns/nM0T0YhjC4X4ADwTN1UKcyyZwYLxgawMtDM1l+GjheKE8pVIsYKTFAsBofmoUNFu0tH7V02IBNKQdySO7R419MkdwWWfKrozJsbR0/s6l8eP78GQRzx4ytJfVqqK2SkEXIy3WHbl8dM8oShUd7Z31R/bSVVqR2DrOJkQuj1/WgTW9GXSl4w1Nh/VyzBKjakO+pOGzvzTqpIyYVVeJCA/tGkV7MjYnCt9EiHD04nZsOzCBsbxxTR+3zIlcpuJRrOxOz+rrKcxVvJ/NYYpu8OXTKeLDC4McDBjj/UzmRUcehD9ckjrA7H7POF4MZTtq0+n+s4cX8XmUrTjVMbmCgd92az3fs5vhzyJm82I5o8w+z7MN/znJ68Dcxmzefeliz/a8iA9/ZtvP7PsDtr/I09bpheyh4A72Gr2IvUajbPwqZn/KM6872KPpF7LlihX6uZKl+l/B3q+rPfv6OrP58T/PbJ6ox48BAGn+mJxHozYxe8Ax929gyz1FeEbuZX+cwmz+5vEiU/y69zztd41jx3dVNAq67r3FgYKK9QQV+glLvcXCwm4zf7AcS5MuAP7NzQEQUQLA2wDUG508GcBNRHS+UupJIuoCcDsMx+9bPnP6k3k87/GXwHB8X2YuugnAN4hosVLqGUz9hLL4O3P77yJkkTpxLpvA4HgB6/t7XMu60gmM5UuhC+mM5ytTivkAQHeVtiZWqmvVarHJxqXFPrR7FPEo2S1XACNy+b9PHrTP04rodaXjiEfJtwenFbk8NFHC1v0TiBBaOjpl4U2LVUrhn3/6GOJRwuJpRPciZlQubzqSulLQzbRQy+FshjMSiRCWdU2t3Ntojl1qOF97R/MYL1TQkYxholjB0wcnsbwrhScPTGBF19zRJqYTUfzjK4/H5375BJKxiG8mgiAIgiAI1dDhqhbdOBYZvYhtrldKXe8dRETfAvByAATglVX291oAe5VS3qdtPyDjh/+fAHxCKTXKVyqlHiCivwXwKyK6BMCXAHxfKTXFsazBUebxNXO/GhHtM5cf8tuAiFYC+Kz556Nhi9SJc3mEKZQ1jObKvmmxZU2ZesLab8tYvmxH9Fz7sSKXPprLUJHLBmouH9k9ijZPJGlZZwq5koaJolNwqKLr2PTkIfS1JXwda8sROzxZxLbBcaTjc6Mtg7da7IHxInYO53DMknbEItNzjjOJmO1I7h8rYHCsgIqm20V+5nNV0RVdKUTI0BuftKIT8WgED+8exa+3DOKMNT1GCnadFXibzbvP7cc3fv80krHWf1giCIIgCAuIw0Yv4uoopd4HAET0DgBfBPDqgKHvgRFt5LxIKbXbTH39KoBvAHi7zzHuIqKPA/gDgC8qpb4W+ixmgFJqL4D31rvd/P0l2qJYbTi8PRq7WMQxjHM5XijjOYvbpizvChG5PBJpsWP5Mp45lMUVL1+HD/21oxdeaka4BscKtnM5ki2jpOnoa/OP5vW2JUAEHJooYuv+8VCvTyvAq8UWKzp2j+TwknWL7cjjdEjHo3afy2yxgoqu8OzhrK2nbUYrkiMFESGdiCJb1PDp15yIL//2KbQnY7jmrmfQa+oWb73sXN908VYlEiGsW9pRdcxs9mIVBEEQBGHmKKW+R0TXE1GfUmqIryOiFQBeAuAdnm12m/8XieibAH7ut28iWgrgMwD+HcDFRPRjpVS9BRl2A1hJRFEzahkFsMJc3lDmxq/0FuHAeAGHJouBPSTDYBWz8aYRWlrJsXwZy0Ok9u0dydtOBicZiyKTiPpWnrUckJmmxdZqSXLhdffYzu1pR3W71lkR28Gxgv2jeihbxOreTGDfx3g0gp5MAo/vG8dIrow1vRnfca1GPBZBznwd9o7koQB87nUn4+M/fmTa+8wkonbk0qoau2XvGKxAbrMd79l2hJZ0pFBMaTj7OX0AjJ6Ru0fyUKqEY5a0zxnHstbrJA6lEJ6ZPAisppeq97OkmhaUp6xxHRjXZZ4QsNy7PaOH6QFHBhw73e8eF6SzxNPMZkUTt/O6AJ65rPfs22KC2R9h9gZmP+zZ5nRmX8DsyYAx3/ds/3xm/4XZRzN7L7M3MfsfPNkSPD7Tx14jrq3sZjbXex7jmdceZvcze5DpHz/Ilm9ktldh8buA418VsE3e8/uJb3MMe48f498VZzCbv9/8xQPcJ8rHdQYs53rPsC1v+bXO98XvL6/Gkmui+TZcX83vR6/+czp6TL95NYtZS4utillUp8dyEInofBhvgPcDDAAuAfA/3OkkojYAMaXUmJkWexGmfkqAiJYD+A2ALyilfkBEPwHwYyJ6u1Lqz2Hnq5Q6SEQPw1Bff9/8/yGllG9K7EwQ57IODk+WkC1qeHjPKM5Y3VN7Ax+sHo9+abGAfzqrF6UUKrrybdsBGLrLammx7UegFYm1/amrul3LbefSfB3Kmo6xfAVvf/4abB4YCdzfovYE/vKscU/WE51r5o/0BCvoM1Esozsdx+q+mTnGmUQUubKGiqajWDb2/djeMRy92Ch204yCPkcSbyXa3rYEdo/kkS9rOGN1d3MmJQiCIAjCQqMNwI9MJ1GD4VSer5RSRPQrAJ9WSlmazUsAfNiz/VIAPzEjiFEYxXI+4HOcNIBPKaU2AoBS6kEiugDu0mE2RPQ1AG8w199JRENKqZPM1ZcBuJGIPg1gBMA76z7rEIhzGZLJYsV2zn7z+IFpO5d2WmxXcFpsLazIVTTAuezKJDDqs58Jy7msEt1KxqJIRCOBzmW2WMG+sYLx/2geKwKau08WKzh6cduUSNKSTsM5OGBGcK0WJOeftqKqczk4VrDndPOl51SNULVK1CcRI5Q1HYWyhkJZR2/bVI1svaQTUeRLFSNaZy57fO+4XcgmM481l16s9/mET92BfFnDc6d5TwqCIAiCINSDUuoA3PkDfN2rPX+v8xnzLIDnhjjOswCe9Sx7FMCjAeM/jKmOrLVuG4Cz/dY1EqkiEZL7dgxBAYhFCL95YnDa+xkcKyKTiKLD4wTU41yOF4wxsWhw5HKsakGf6tGttmR0iuZS0xWePjiJLfvGMZoroaTp+PTPtkApNWV7pRSyxcqUlFjAaLEQixC+95ed0HSFQxNFpONRHL+sE7dcek6gY2i1KFnZnZ4zqY9xs6DPM4eMHKdMfOZRRaugzzNm65a2RBSP7xuzHxykG3CMuUZvm3E9TPeBT7Opdt0LgiAIglANBSMtttH/hOmycMIcM+Tu7UMgAlZ0p/DsoSy2H5y0++7Vw4HxApZ1pqZUO7WqvPo5hV52DeUA+PeEBAz9puXQcLLFCpKxSM0+kO2p2JTI5bbBcQxnS1jSkcRRPWkcmizizq0H8bKv/AF9bQnXj+NSRUdZU3iuj3MJmC06Kjo+98snkC1pWLtoamEiL3HTkT5hefXiJ61EImqc55ODhhgnbUaMZ+JIGJFLDc8eNt7fvvYEdg3n8cS+caTikcBo9mzTTOdoWVca6XjU1SdSEITpwO+hicBRDlyr5f05wTWUvPdgkP6S6xz5tl7Y9iO3+2+fH/Bss4HZrFchsQf4im3DNZvLPD0zvTI8i1Fm89qK/Pnr6Z5tHnPMJU/vsu2DL1vtrOC9ML3b896W/OfIYma/j9n3MPtLnoe0f4I/XGb4MLP7mO3tjvc5ZvPemhuZ/Qyzt7PfPes98+Lt2vn58oRA1/N+j9xtP+tBuZ+fDO9NuYHZm5j9Kve+XNtwnSbXU/YGLOfjAfd9EOTI8HOpppn0k/gBwf1pg8YLQmOQyGVI7t5+GB3JmJ3a+O7v3mcXtqmHwfHClEqxgJGqGqFwkct7dxgfDN7op0V3xl9zOVmsVC3mY88lGZ/iXD6w00hZXdGVQiwawbLOFE5Z2YWdQ1lUWLsN6zjA1GI+FoloBGP5Mm748wCWdaam6Oj8sBzpE5Z31hjZOsRjEZQ0hScHJ0BoTG/OTNwo6PPsoSxiEbIr7m7eOYy2OVJFt9HEIoS+9vr7hgqCIAiCIAiNRZzLEBj9FSfQmY4jGYvi1FVdGPGpxurHhdfd43JCB8cKvg3nIxFChAi3bq5dEfi+HcPIJKKBEcjOdByj+fKUlNVssRKqD2JHMjYlLfb+gRHEo4RELIJbLj0Ht152Lr7wxlNQ1hT2jLifuk0WNRABx3uf+JrEYxEoAK86eRlW94Zrem85l0H7bEWsPpdPHphAOhFFpAG9OY1qsRU8c2gS6XgU6YShkR3NlZGe58V8BEEQBEEQhNZmYYY66uTPzxhVSi2t38tPXIqrfjOGUkWvttkUdF3h4IR/5BIwCvRo+lQNI6es6Xhg5wg608FvXXc6gVJFx5uvvcdOk7zl0nMwWdRCOZdtySgOT7qd580Dw+hIxV3pvCet6EJvWwIjnihptlhBWyKGREBj+N5MAkopfOXCsnLufgAAIIhJREFU0/Gu74Rr09OejCEdj+KstXNHVxePkp0W+/ITl+KrF9XUbdcknYghXzYil6m44bAev7wDj+4Zw9BkuAcecxnRJgqCIAiC4KAA1M76E44c88q5rNV/cbr8efthdKRidpuHl5+0DFf95qmq0Uu/lNnhXAllTWFZp38KXyxCqNRwLh/dM4Z8WcOX33IaXnXKct8xVs/Miq5cGrz7dgzBp/7OFNpTcQyYuk4A2Duax/6xgm9/yY5kDMPZkl05tqLpyJYqVVNduzNxdGfiSNVRfCadMCLGSzr8HfNWJBGLIF/WkB/TcFyDIq6ZRBRlTWEoW7Kjviev7MKje8YQWaB5COJwCoIgCIIgtAbzyrnkNNLRvO2hvcgkorj1snMBGNVQk7HIFH2k3zGLZQ2j+TJKFR2DZvsNv7RYwHBGskUNSqkpBX8s7t1hRFGft9Yr6HboNiOsmq6DZz5rugosAsRpT7oL+mweMDWePv0xrb6YD+4awYruNJ48MAFdIVSEdL7DX+vjG1Rshvf4tJzzk1d0AQCiDUi7FQRhIeFtOcUlDvwzPKjJOh8TsgiPy+5n9kCV7XkVHV4Y5UFm80IwG9yb849GxY7vetjKvlPzrDjQjirFT9b2O3aWLV/EbF7s5nDwrg5GWBGfC9iK7cHbgD/DZcWBcDKzNwaM8SpSeK0bvv0NzF7PbF4o6BLPvk5h9s3M5q93UL2oZzx/H81s/loM8P2y96un3729q8MZLyy1gdl8mxcETAxwv2h8AuzhcQ/b7wi/PnlxH8B9H6wMOB6/P5cEjKk2LqjgVrU+5mHHCUIwCyrW4dU/hmHnUBbFio5O1v6CiGwHzK8VBwDoSmHfaB6P7h3DwFAOX//9006Py4C02K50HCVNxzOHsr7rAUNvecyS9qoFTKzKs4PjRdccNaUQCVFNtN3TimTzwAjaElGXY2Ox8YMvQCoesQv+PLx71NyHOJfcuWxUJVOuq7zuHetxy6Xn4OSVxhdbsyrFCoIgCIIgNAcd0oqktZhXHoBSChNVHL7pcMcWo8Z1t6e3YnsqhqFsCXtH81jVY6SLVjQdhydL+PubH8Iju8dQ0nT0ZOIgAN/c9Ax6M0al2aDIpXWMu5465NvmpKLp2DwwgtedvqLqnE9a0YWudByHJoo4NFFENEJ43Tf+hGJFR0eytgPyq8cGkS9rqGg6YtEI7h8Yxhlrenw1pvFoBKet6saDu0YBAI/sHkUsQkgG6C0Bd2R3Pqc0Wq9BRyqG5QHveb1YDn48Sjiqx3iKum5pBwhoSMEgQRAEQRAEQZgu8ypyuW+sgK37J3D59x+sWRgnLP/z2H60JaJT9IFWGxDLqQKAHUM57BzO4S/PDqEtGcVxS9uxbmkH1i5uw5KOJA5NFgEAiwOijhs/9EIcvbgNdz11yF7Go62vu/puTBYrOPs5fb7bW3Sl4zh+WQfOXN2N5yxqQ19bwqx0G6laCMjCioBlixre+M27sW1wAmeuCS6kc8aaHjy+dwyFsoaHd4/ihccuslOIFzLf+8tOAMBxSzsC05zrJR033r9ohOxqwal4FMu6Uugx2+QIgiAIgiAIQjOYN5HLQlnDgfECkrEIfvPEIJKxKNYtdUf/cqUKDk4UUdb0UNrDXUM5PLpnDJ949fF4/4uPdq3b+MEX4JTP/AYP7hzBa09bgUJZw2iuhMXtSdz7ifNc6bexSAT/+cZT8c7v3Id4lAJbiADAS9YtwQ/u3YlCWZvi0E4UDI3n2VX0lpxYNILFHUks7kjie+89O3RKsOVcPn1wwtZentXfi3vMqrlezlzdg2t0hXueHcLTByfx6oBCQzNhLkY4rXe5USmxgBO5THuujdU+xZYEQRAaQ1ADd65H5II+7zZBLaeYVs4lwvPq03hBtAPwpecNjj1yjXudeh/8GfC3aQPbtkoxtuczeyOzL2D2b5nt/QoNSmi5I2D5MZ6/u5l9MbM3VdnGYlXAcgD4KrMnmb2F2X9kNtdoesddwGx+iYwyez3LDNvs2dfIgGNTv2MzEzvY9TLCV3g5m9m3MJtrHu9m9iWe7bmumNfcYMcc+Ve2/CXM9l63PO1yG/zh9xC/17xaSP4bI4zO0vvTvxJgzxWstFihVZg3kcvbHtqLsqawdlEbvvfes1HWdGwdnECuZNwoSinsOJzFgfEifrfVfZMHaTH/57H9AODrLMWiEZy6qgsP7jK0hn95dgi6Anrb4lPGAsCL1y3Gyu4UemtEl15y3GIUKzruZUUEShUdGx/ai0MTJSRjkUDNZqNoT8YQIeBN196DHYeNqrGnH9UdOP65q411N9w9AKWqj11IWNHKRhXzARznsp5Ku4IgCIIgCIJwJGi5yGXYKq98nK4r/Ncfn0UmEUVnKoYXHLMIxy5tx9b9E/ja77bjn151PH756H5MFjUQgB/cuwuvPNlxGHWlECGacuxfPbYfpx/VbWsqvZyxpgf/9YdnUShr+N3Wg4gQ0JmKB84/aD+cs9f2IhmL4K4nD+El6xZj72gee0byeOiWhxGLEI4KGaGyjl9vASPAcGBOP6obLzxmEa7e9IzRhiUZC3xP+tqTWLvISec9bVV33cecj1j1ddYtbZxzaVXn9UYuBUEQBEEQBKHZtJxzWQ3LUbrxPc/DULYEXVcYyZbwluvuwbOHsjhmcZsdLepMxbG4PYFv/fFZ/M0py/Gfd2xDJhFFTyaOPz59GDuHsljT14bH943hgZ0jSMWj6GtLoK/diCzuHMrisb1juPLVweXVz1jdg4qu8MjuUdy59QBeduJSXPeO9YHjw5CKR3H2c/rww/t24vfbDmDPSB69bQnc8O6z8G+/fKJu7d50i+fEoxF87OXHBabCennu6m7sOJxFMhYR7Z9JVzqOld1pnFFFr1ovxy3twFpTRysIgiAIgrCwkbTYVqOlnMtSRccT+8fRkYzh5V+5C+3JGH76gRfgwuvuga4UxvJlDE2WcObnfotsSQMAnPX5OwEYlTnv+MiLXXrGo3ozqOhZvO7qP0FXRnpiOh7F4HgRP7xvFz72snX4h1sfQTRCIAJ2j+SxeySP9924GQ/vNtJdX3XKssD5nmGmg37/3l3YP1bAx162rur5hXXuXrJuMf7w1CEMDOXQnYnj6MVtOHVVd8OKwtRDmNYlAHDmmh789MG90oKEEYtGsKonHUrfGxYiwpKOqQWh5qImVRCEZuPNqpgIWMeX8x9xYXVcXKfJt+f9K3lzRHcPabdeje+LCQpHuIbO25uS6ehog2OrTWwMe5CsfsqW80aVANJMg8m1kfy0rhlgf/QH7sp1yncym/fD3I9geE/Ix5nNJYRc/8hfx2s8Othj2HnxbmgnMXuA2aMBNgDsYPYgs/Ps+Oml/svPZ8sBYLTfsfm57GDbHMO0lNsf9Uxmq7+dvpAdn2/D+1wOePbl/duP5wUcuxprmM3viSB9c9hsqCD9ZCP7XFa774WFSkt5ApquAF1h/1gBasxYtv7ffotiRUe+pKGiK0QjhDefuQoPmy0vXnDMItx4zwBWdKWnFMqJRyP4xKtPwMd//Ci603Hc8ZEXAwAu/d5mfPuPO/Czh/ZhcLyAdUvb0ZNJoFDWcGiiiId2jWAoW0JbMlo1lbWvPYn+vgx+8cg+RAj46+OrNbkNz4bjFuNzvzQq0h67uN1uMXGkHIjpHOesfuND0ErbFARBEARBEARhYdFSnkA6EcXRK7ug6QrZUgXZooaz1/biV1v2oysdR197Al3pOL7wxlPtFNl/fvUJeHj3aOA+33TmKlyz6Rl0sT6Vbzt7DX79+AEMjhfw5jNX4YtvPg2AkXZ7VG8G//3e5+FvvvanULo2q5rqmWt60BfQYqRejl7cjpNWdCIdj+JHlze3pUdYR3Pd0g4ct7Td1pwKs/cwQKKUgiAIgiAIgKTFth4t5VxaRCOEzlQcnak4vvjm07Br2KhYGqQf9PuxzZct9qQRvvCYRUjGIlAK+NT5J/puc+fHXoIwtCdjODxZwnknLK09uA7mYnppd0Z0gIIgCIIgCIKwUGk5D2Y2ojLefUYihBOXd4JYddfp0pNJYDhbwutOX1l7cB3MxejUXJyzIAiCIAiCIAiNoeWcSz9mw2lJxBpTZCURi+CE5Z1Y1jW7vScFQRAEYXoEFemY8A5k8DQzvv1EwHIvvFrNYyHmwov29FeZCy9s8iCzeSEWbyYRe/irxv2Xu7Y5g9mewjcFPi2+L1YQxzX/Ace8ky8HcCc/Z378TY55zAZmuzd3FRTihep54Rs+31OqZFjxl3iUz5EXuznVMXn77/33uvd1Hiuww7c/n23/i01sXxvYcs+81jJ7hG3D36PtQa8j4H4v2Lg8nzM/+WqBAj6OF+tJewea8G4DBzzrvEWn/I6/k9nV7rWge5UTttBOvQV5GlkcaLooSFpsazGrziURrQNwI4A+AEMA3qmUeno2jxmWRjmsEq0TBEEQBEEQBEEAGtcjwZ9rAVytlFoH4GoA183y8QRBEARBEARBEIQmMGuRSyJaAiNv4WXmopsAfIOIFiulDs3WcQVBEARBEARBWAgoTO2NKzST2UyLPQrAXqWUBgBKKY2I9pnLbeeSiN4P4P0AsHr16lmcjiAIgiAsRGZL78S1Zr2edUE6Sz6O6+MOBNiAW8/IdY5cZ8nnwjR/AIAbmM31lHczmxf34z9UPVouxf4+hc1rO98k5A/dtez8+9jyxzc49jNVtuc6y/3MHmX2mcx+gNnKsy/i69j8XTpJNoZLA/dzbSHcOssXnQp/+h1zNGAeU+AHDdLYDXj+DtJD8nH8+rq1yrb82s0HjONj+PXFrzvArbkM0lby3ukHA45djaD73vvTf7Y+H2ZTZym0MrOdFlsTpdT1Sqn1Sqn1ixcvbvZ0BEEQBEEQBEEQhGkwm5HL3QBWElHUjFpGAawwlwuCIAiCIAiCIMwAHVIttrWYtcilUuoggIcBXGwuuhjAQ6K3FARBEARBEARBmH/Mdp/LywDcSESfBjAC4J2zfDxBEARBEEKxxPM313WF0UvxaMFOz7owPy8GmM21anurbMPXnRCw3KuVCxrXH3AMrhc91r2KawIfu4Ed8hK2IqBvondaO5i2dJDpL/O8tySb795XubfnesxuZvOOb5uZlvSYfsfe7tGFqjh8+QXXuA6w/bJzPN+jcb2T6Sz/yFeMw5dqQafBoIH9zOZz3+TZAdf18mvsjBBjvNch7235Fmbza+TzzOZv+H2effF77/iAYwb1wvReSPx1CaOzFC2kMLvMqnOplNqGqcp6QRAEQRAEQRCEGSJpsa1G0wv6CIIgCIIgCIIgCHMfcS4FQRAEQRAEQRCEGTPbmktBEARBEARBEIRZQNJiWw1xLgVBEARhQRJUMKQa0ykMwsfxYiT8+NWK+PBteHEevj0vtuKFFc5BZ8B++wPGs+I4AKBY8ZdjLnHs7bwIz4PMfptj5vl+AVeBnDyby3JWEGeU2d2ezfffwsZdyFbwAjPsGHyO69l+AeBxZqdYgZyROx37PFZQ6E5WEOgXnuJA/JjE5qLYa7+c2ft/yrY9z72rPHstaQPbF38teUEe/v4C7vf1BwHHuZvZvDCV9ydyULGf7QFj+LwGEAzfnt8rvOgPL7bldaTSAesqAXaHZ/uJKnMThPqRtFhBEARBEARBEARhxkjkUhAEQRAEQRCEOYikxbYaErkUBEEQBEEQBEEQZoxELgVBEAShQRDRBgC/A/D3SqlvNHc2taimmeQ/D7iOLKxOk2/D9WJcE8d1a8dU2Rc/ZlCE4gxm3+VZ97wQ23BtJZ8718MBLj3l9n62nNt8jnzufL9emGZ0P9+GzTE/7tmG6SzVAFvO9YhMj0dMZ7nZo//sYfrAkU1sxQsc8xkEMOD5m+ksu9niEaat3M9fC/4+ePe10jEVn9cGZvPl3u35e/G2gHH8+FzTW03Hy7fn13GQvpjbQPB91BEwptrP9SDNZJA+WjSWwlSIKAXgWgDjAHSl1Eemuy+JXAqCIAhCAyCiDgD/CeD2Zs9FEARhYaBgOM+N/lcbIlpHRPcQ0VPm/8fWOy7sPsJCRFcR0Q4iUkR0ch3zfQOAu5RSHwaQI6L1052DOJeCIAiC0Bi+DOCLAA43eyKCIAjCrHMtgKuVUusAXA3gummMq7kPIkoS0RrPsnYiWuFzrI0AXgx36eMwx1oDJyS/A+50jLoQ51IQBEEQZggRvQpAt1Lqx82eiyAIgjC7ENESGDnVN5mLbgJwBhEtDjsu7D4AnAzgt0R0nLnPLgC/AfBq77yUUn9SSu2exnx3wXAwYf7v55yGoqU0lw888MBhIpr2yTSYRZi7T59l7keeuTpvQObeDObqvIHWmvua2kMaAxE9CGB1wOrjAHwBwMtC7uv9AN5v/jkJfObJmc9w2rTS+3mkOQLn/oHZ3X09qClLnPMfCbH9jmkcM8x+m8NCvu6B5p9/Az+79/8a+Myixu3PJkVEm9nf1yulrmd/HwVgr1JKAwCllEZE+8zlh0KOozD7UEo9QER/C+BXRHQJgC8B+L5S6lt1nE+t+f4UwDVEdCqAqFLq/jr27aKlnEullNdTbxpEtFkpNe1842Yicz/yzNV5AzL3ZjBX5w3M7bnPBKXUGUHriOiFAJYDuI+IAOOH2/lE1KuU+qzPvq4HcL13eTNYqO8nsLDPHVjY57+Qzx2YX+evlHpls+dwJFBK3UVEHwfwBwBfVEp9rcH7zwO4pBH7ainnUhAEQRDmGkqpPwFYYv1NRDcA2Nz61WIFQRCEabIbwEoiippRwCiAFebysOMo5D5AREsBfAbAvwO4mIh+rJS6bxbmO2NEcykIgiAIgiAIghASpdRBAA8DuNhcdDGAh5RSh8KOC7sPIloO4E4A/6mUuhLAmwDcTETnNnq+jUAil8G0RMrSNJG5H3nm6rwBmXszmKvzBub23I8ISqlLmj2HOljI7+dCPndgYZ//Qj53QM6/UVwG4EYi+jQMhfE7AYCIfgXg00qpzdXGhVhnkQbwKaXURgBQSj1IRBcAWOYdSERfg9FWZBmAO4loSCl1Uh3HmjGk1FSFtyAIgiAIgiAIgiDUg6TFCoIgCIIgCIIgCDNmwTuXRNRHRL8ioieJ6FEi+qnV84WI1hHRPUT0lPn/sc2erx9E9C9EpIjoZPPvlp83EaWI6BoiepqIHiOi683lc2HuryGih4joYfOaeYO5vOXmTkRXEdEOfn2YywPn2grn4Tfvavdqq8w7aO6e9f9Sz/txJKlyvfjer600d2H6ENHVRLSNiB4horuJaF5UkQwDEb3d/DypENGHmj2fI8FCvmdrfT7PZ2p9hwpCo1jwziWMzk//Vyl1nFLqVADPwOhXBgDXArhaKbUOwNUArmvSHAMhojMAPB9G81OLlp83gP8LoABgnVLqFACfMpe39NyJiAB8D8A7lFKnA3g7jPz1CFpz7hsBvBhTm+FWm2srnMdGTJ13tXsVaI15A8GvedD9CrT+3IPuV6B15i5Mn9sBnKKUOg3AfwC4pcnzOZI8DOAiAD9s8jyOJAv5nt2IgM/nBUCt71BBaAiiufRARG8EcDmAtwJ4CkAfK9k7BODY2aisNB2IKAlgE4y5/i+A1wA4iNafdzuAPQBWKaUm2fIlaP25E4zGw69VSt1NRC8G8C0AL0QLz52IBgC8Rim1pdrrDKMsdsucB5+3z7o3ArhcKXVeK1473rn73a+13o9WmHvQ/WqOa7m5CzODiPoA7AOQVkrpzZ7PkYIWSPsYuWcNqn23LBT4d2iz5yLMLyRyyTCjT5cD+DmAowDsVUppAGD+v89c3ip8FsD3lVI72LK5MO+jYXyZ/QsRbSaiTWQ0IW/5uSvjacxbAPyMiHbCeAr6LsyBuTOqzXVOnIfnXgXmxrz97leg9ecedL8CrT93oX4+BOB/FpJjucCQe1bw+w4VhIYhzqWbrwOYBNDyTy6J6BwAZwH4ZrPnMg1iAJ4Do7/OegD/COCnANqbOqsQEFEMwD8DeJ1Sag2A82GkkLX83OcZc+ZeBebn/UpEnc2dlhAWInqQiA4H/IuycRfBiKxf3rzZNpaw5y4IC4w59R0qzC2kz6UJEV0FIy3wfKWUTkS7AawkoihLHVkBYHdTJ+rwEgDHA9hhZGpiFYBfA/goWnvegKF1qAC4CQCUUvcS0WEAebT+3E8HsEIpdTcAmKmxWRh6tFafu0W1a5uqrGsJvPequXhO3q9E9G4Ymq9WnnvQ/boOhna0lecuAFBKnVFrDBG9HsDnAbxUKXVg9md1ZAhz7guMVv+sFGaZgO9QQWgYErkEQESfB3AmgAuUUkUAUEodhPGj72Jz2MUwnty3hCZBKfUFpdQKpVS/UqofhibqFUqpW9HC8wYApdRhGJqzlwFG5ToAlg7kYbTw3GFqz4joOAAgohNgNKp9Gq0/dwDVr+1Wv+797lVgTt+vv5kDcw+6X7e3+tyFcBDRawB8GcY1OdDk6QiziNyzC5ug71BBaCQLvqAPEZ0EYAsMxyZvLt6hlHo9ER0P4EYAPQBGALxTKfVkc2ZaHU8BjpafNxE9B8B3APQBKAO4Uil1+xyZ+9sA/BMA64nfvyilNrbi3InoawDeAMMBPgxgSCl1UrW5tsJ5+M0bhtbV915tlXmb8/B9zT1jBuAu9tPScw+6X1tp7sL0IaJDAEoAuIPxUqXUUJOmdMQgoosBfBHG9VsCkAXwcqXUE02d2CyykO/ZMJ/P85Vqv3ebNythPrLgnUtBEARBEARBEARh5kharCAIgiAIgiAIgjBjxLkUBEEQBEEQBEEQZow4l4IgCIIgCIIgCMKMEedSEARBEARBEARBmDHiXAqCIAiCIAiCIAgzRpxLYcFCRANEdPIROM5rieiLpt1PRO+fwb4UEbXXuc0NRLTHLMHut34DEW2e7pzMfZxLRA9PZ36CIAjzCSLqJaLdRHQWW3YlEf3kCBz7MiL6qGmfTkRv8ax/mIjSDTxe1e+Xae5Tvk8EYQ4Ta/YEBGG+o5T6OYCfm3/2A3g/gOuP8DS+oJT6xmztXCn1ZwCnE5H0NhIEYUGjlBomog8BuJGIngtgHYAPAHjuETj2tezP0wG8BsCtbP3ps3DYhn6/yPeJIMxtJHIpCB6I6J1E9BgRPUpEtxHREnP5JUT0GyK6hYgeJ6K7iWiZuS5BRNcT0VNE9Cci+gYR/Zht92Nz91cDONF8Kmutdz2d5X8T0RuIaBsR/ZmIPumZ59lE9L9E9ID572/qOMd/I6LtRHQXgL/xrHsXEd1r7vP3RHRcrXMUBEEQHJRSPwPwIIAvALgRwEeVUgf9xpqf+Z8xP+efJKI3snWvJKKHzO+j3xHRMeby44joHiJ6hIi2ENEV5vLPENFVRNQH4LMAzjO/b77GjmV9v5xl7uNR8/+zzOX9RHSYiD5vHvtJInph2HMnoveY83qEiO4noqVsn/9h7nMbEZ1JRP9lHv9e6/tUEIS5jTiXgsAgI032CwBerpQ6FcAWAF9nQ84CcIVS6iQATwD4O3P5pQBWAzgRwHkA1gcc4oMAnlBKna6UelONuSwB8F8AXqeUOhdAia3rBnAtgLcqpc6E8XT6OnN5rXM8H8BrYTzV/msAx7N1LwLwFgAvNvf7RQDfqfMcBUEQBOP74b0Adiqlbq0xVjc/518L4HoiWmJ+B3wPwNvM76MfAviBOf4DAH6llDpNKXUygG/znSmlhgB8GsCd5vfNh/l6IkoA+AmAT5n7/iSAn5jLAaAPwD1KqefCcFL/M8wJE9EGAJ8A8Aql1GkA/grAGNvnn8x9fhvA7wBcbR7/AQAfCnMMQRBaG3EuBcHNX8H4wt5v/n0dDEfK4m6l1G7T/guAo9l231NKVZRSBQA3NWAuzwfwoFLqSfNvnkp7LoC1AG4noocB3A5AATgmxH7/CsAtSqlJpZQG94+S8wGcBuBec79fAHAU267R5ygIgjBf+WsA4wCOI6JkjbHfBgDz8/5BGJ//ZwN4RCn1hDnmuzDSRTsA/AHAe4joc0T01wBG65zbcQBKSqk7zeP+DsYDzOPM9ZNKqV+aNv+uq8XfAPhvpdSgud9J8/vC2uf/mPaDAPYopR42/34A4b6/BEFoccS5FAQ3BMNJ4/C/C8zW4OiW/bYLiwbzXiSilGcuQRCAR80n0ta/o5RSYQrz1Nrvd9g+T1NKrWbrRAMjCIJQAyJaBOD/wXC2NgP413o2h/FZG/iZq5T6CYAXAHgGwD/BiHDWNcWAfVvLimwZ/64Ls98gvPsM+j4VBGEOI86lILj5HYBXM+3H3wK4M8R2/wvg7UQUMx3ECwPGjQPo8ix7Bka6LQC8lS2/B8BziehY8+/3sXV/BnAsEf2VtcDUz1T7Yrf4HYC3EFEbEUUBvJut+wWAdxLRKnOfUSI6s85zFARBWOh8E8B/KaUeAfD3AN5KrHqsD+8GAPPz/nQA98L4DjidiCzpwrsAPKSUmjC1l4NKqRtgOK7P89mn3/eNxTYASes7xPw/DuCp0Gfoj/UdstTcb3uIqK0gCPMIeUokLHTuJKIK+/sUAP8M4LdkVKp7FobWsBbXwkgnfRzAbhgpPhmfcY8CeJKItgDYZuouPwpDL7kfgJWGBKXUQTLalvyCiIbhrvg3QkSvBfBFIvoqgIQ51/NRI7qolPolEZ0D4GEA+2A4jSvNdX8goisB/Nx0PBMAfmSeT9hzFARBWLCQ0f5jHYC3Afbn9QcBfIeIzlRKlXw2KxLR3QAWAbjUKv5DRO8A8EMiigE4BODt5vi3AHgbEZVgfOb/vc8+fwfgCiJ6BMBdXHeplCqZhYO+RkRtALIA3mQun/a5K6XuIqL/gPHdqsOIVp4/7R0KgjDnIKUky00QGgERdZhPlJMwWo/8SCn1rRaY1w0ANjeiVHytczQd8g6l1ORMjyUIgrAQmMufm438fvHZ95x9XQRhISNpsYLQOO40i+A8AuBpADc0dTYOYwA+TI1pcu17jmQ2vQZwAIDegOMIgiAIrU8jv18AyPeJIMx1JHIpCIIgCIIgCIIgzBiJXAqCIAiCIAiCIAgzRpxLQRAEQRAEQRAEYcaIcykIgiAIgiAIgiDMGHEuBUEQBEEQBEEQhBkjzqUgCIIgCIIgCIIwY8S5FARBEARBEARBEGbM/wfJ+hl68sBb/wAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 936x504 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plotter.make_sub_plot(data)"
    ]
@@ -826,30 +595,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name banana\n",
-      "Plotting data with name monitor\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "functions.name_plot_options(\"monitor\", data, log=True)\n",
     "functions.name_plot_options(\"banana\", data, left_lim=90, right_lim=150)\n",
@@ -866,111 +614,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/********************************************************************************\n",
-      "* \n",
-      "* McStas, neutron ray-tracing package\n",
-      "*         Copyright (C) 1997-2008, All rights reserved\n",
-      "*         Risoe National Laboratory, Roskilde, Denmark\n",
-      "*         Institut Laue Langevin, Grenoble, France\n",
-      "* \n",
-      "* This file was written by McStasScript, which is a \n",
-      "* python based McStas instrument generator written by \n",
-      "* Mads Bertelsen in 2019 while employed at the \n",
-      "* European Spallation Source Data Management and \n",
-      "* Software Center\n",
-      "* \n",
-      "* Instrument python_tutorial\n",
-      "* \n",
-      "* %Identification\n",
-      "* Written by: Python McStas Instrument Generator\n",
-      "* Date: 12:59:09 on December 15, 2021\n",
-      "* Origin: ESS DMSC\n",
-      "* %INSTRUMENT_SITE: Generated_instruments\n",
-      "* \n",
-      "* \n",
-      "* %Parameters\n",
-      "* \n",
-      "* %End \n",
-      "********************************************************************************/\n",
-      "\n",
-      "DEFINE INSTRUMENT python_tutorial (\n",
-      "wavelength = 2.8, // Wavelength in [Ang]\n",
-      "int order = 1 // Monochromator order, integer\n",
-      ")\n",
-      "\n",
-      "DECLARE \n",
-      "%{\n",
-      "double mono_Q = 1.714;\n",
-      "double wavevector;\n",
-      "double mono_rotation;\n",
-      "%}\n",
-      "\n",
-      "INITIALIZE \n",
-      "%{\n",
-      "// Start of initialize for generated python_tutorial\n",
-      "wavevector = 2.0*PI/wavelength;\n",
-      "mono_rotation = asin(mono_Q/(2.0*wavevector))*RAD2DEG;\n",
-      "printf(\"monochromator rotation = %g deg\\n\", mono_rotation);\n",
-      "%}\n",
-      "\n",
-      "TRACE \n",
-      "COMPONENT source = Source_div(\n",
-      " xwidth = 0.1, yheight = 0.05,\n",
-      " focus_aw = 1.2, focus_ah = 2.3,\n",
-      " lambda0 = wavelength, dlambda = 0.01*wavelength)\n",
-      "AT (0,0,0) ABSOLUTE\n",
-      "\n",
-      "COMPONENT guide = Guide_gravity(\n",
-      " w1 = 0.05, h1 = 0.05,\n",
-      " w2 = 0.05, h2 = 0.05,\n",
-      " l = 8, m = 3.5,\n",
-      " G = -9.82)\n",
-      "AT (0,0,2) RELATIVE source\n",
-      "\n",
-      "COMPONENT mono = Monochromator_flat(\n",
-      " zwidth = 0.05, yheight = 0.08,\n",
-      " Q = mono_Q)\n",
-      "AT (0,0,8.5) RELATIVE guide\n",
-      "ROTATED (0,mono_rotation,0) RELATIVE guide\n",
-      "\n",
-      "COMPONENT beam_dir = Arm()\n",
-      "AT (0,0,0) RELATIVE mono\n",
-      "ROTATED (0,mono_rotation,0) RELATIVE mono\n",
-      "\n",
-      "COMPONENT sample = PowderN(\n",
-      " reflections = \"Na2Ca3Al2F14.laz\", radius = 0.015,\n",
-      " yheight = 0.05)\n",
-      "AT (0,0,1.1) RELATIVE beam_dir\n",
-      "\n",
-      "COMPONENT banana = Monitor_nD(\n",
-      " xwidth = 2, yheight = 0.3,\n",
-      " restore_neutron = 1, options = \"theta limits=[5 175] bins=150, banana\",\n",
-      " filename = \"banana.dat\")\n",
-      "AT (0,0,0) RELATIVE sample\n",
-      "\n",
-      "COMPONENT monitor = PSD_monitor(\n",
-      " nx = 100, ny = 100,\n",
-      " filename = \"psd.dat\", xwidth = 0.05,\n",
-      " yheight = 0.08, restore_neutron = 1)\n",
-      "AT (0,0,0.1) RELATIVE sample\n",
-      "\n",
-      "FINALLY \n",
-      "%{\n",
-      "// Start of finally for generated python_tutorial\n",
-      "%}\n",
-      "\n",
-      "END\n",
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "with open(\"run_folder/python_tutorial.instr\") as file:\n",
     "    data = file.read()\n",
diff --git a/tutorial/McStasScript_tutorial_2_SPLIT.ipynb b/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
index 28abd363..ae9308d2 100644
--- a/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
+++ b/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
@@ -40,6 +40,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "instrument.add_component(\"Origin\", \"Progress_bar\")\n",
+    "\n",
     "src = instrument.add_component(\"source\", \"Source_div\")\n",
     "src.xwidth = 0.1\n",
     "src.yheight = 0.05\n",
@@ -188,7 +190,7 @@
     "instrument.backengine()\n",
     "data_ref = instrument.data\n",
     "\n",
-    "plotter.make_sub_plot(data_unreasonable)"
+    "plotter.make_sub_plot(data_ref)"
    ]
   },
   {
diff --git a/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb b/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
index 4ee501ab..99101f13 100644
--- a/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
+++ b/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
@@ -181,10 +181,10 @@
    "source": [
     "instrument.set_parameters(wavelength=2.8)\n",
     "instrument.settings(ncount=5E6)\n",
-    "instrument.backengine()\n",
-    "data = instrument.data\n",
+    "instrument.show_settings()\n",
     "\n",
-    "plotter.make_sub_plot(data)"
+    "instrument.backengine()\n",
+    "plotter.make_sub_plot(instrument.data)"
    ]
   },
   {
diff --git a/tutorial/McStasScript_tutorial_4_JUMP.ipynb b/tutorial/McStasScript_tutorial_4_JUMP.ipynb
index 47b109db..74e18eff 100644
--- a/tutorial/McStasScript_tutorial_4_JUMP.ipynb
+++ b/tutorial/McStasScript_tutorial_4_JUMP.ipynb
@@ -225,6 +225,25 @@
     "We see that each daughter instrument have beam and show the different powder patterns as expected."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The McStas instrument file\n",
+    "We here show the generated McStas instrument file in order to clarify how this would be accomplished without the McStasScript API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"run_folder/python_tutorial.instr\") as file:\n",
+    "    instrument_string = file.read()\n",
+    "    print(instrument_string)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/tutorial/Union_tutorial_1_processes_and_materials.ipynb b/tutorial/Union_tutorial_1_processes_and_materials.ipynb
index afea0cd4..b49e9067 100644
--- a/tutorial/Union_tutorial_1_processes_and_materials.ipynb
+++ b/tutorial/Union_tutorial_1_processes_and_materials.ipynb
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,18 +47,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are all components in the Work directory category.\n",
+      "No components found in this category! Available categories:\n",
+      " sources\n",
+      " optics\n",
+      " samples\n",
+      " monitors\n",
+      " misc\n",
+      " contrib\n",
+      " union\n",
+      " obsolete\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.show_components(\"Work directory\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Incoherent_process ________________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1msigma\u001b[0m = \u001b[1m\u001b[94m5.08\u001b[0m\u001b[0m [barns] // Incoherent scattering cross section\n",
+      "\u001b[1mf_QE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Fraction of quasielastic scattering (rest is elastic)\n",
+      "\u001b[1mgamma\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Lorentzian width of quasielastic broadening (HWHM)\n",
+      "\u001b[1mpacking_factor\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // How dense is the material compared to optimal 0-1\n",
+      "\u001b[1munit_cell_volume\u001b[0m = \u001b[1m\u001b[94m13.8\u001b[0m\u001b[0m [AA^3] // Unit_cell_volume\n",
+      "\u001b[1minteract_fraction\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // How large a part of the scattering events \n",
+      "                                should use this process 0-1 (sum of all processes \n",
+      "                                in material = 1) \n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.component_help(\"Incoherent_process\")"
    ]
@@ -74,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,9 +128,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Union_make_material _______________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1mprocess_string\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated names of physical processes\n",
+      "\u001b[4m\u001b[1mmy_absorption\u001b[0m\u001b[0m [1/m] // Inverse penetration depth from absorption at standard \n",
+      "                       energy \n",
+      "\u001b[1mabsorber\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [0/1] // Control parameter, if set to 1 the material will have \n",
+      "                        no scattering processes \n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.component_help(\"Union_make_material\")"
    ]
@@ -111,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,21 +226,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
     "src = instrument.add_component(\"source\", \"Source_div\")\n",
     "\n",
-    "instrument.add_parameter(\"source_width\", value=0.15, comment=\"Width of source in [m]\")\n",
-    "src.xwidth = \"source_width\"\n",
+    "source_width = instrument.add_parameter(\"source_width\", value=0.15, comment=\"Width of source in [m]\")\n",
+    "src.xwidth = source_width\n",
     "src.yheight = 0.03\n",
     "src.focus_aw = 0.01\n",
     "src.focus_ah = 0.01\n",
     "\n",
-    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
-    "src.lambda0=\"wavelength\"\n",
-    "src.dlambda=\"0.001*wavelength\""
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.dlambda = \"0.001*wavelength\""
    ]
   },
   {
@@ -205,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -241,9 +290,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Union_logger_2D_space _____________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1mtarget_geometry\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated list of geometry names \n",
+      "                                     that will be logged, leave empty for all \n",
+      "                                     volumes (even not defined yet) \n",
+      "\u001b[1mtarget_process\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated names of physical \n",
+      "                                    processes, if volumes are selected, one can \n",
+      "                                    select Union_process names \n",
+      "\u001b[1mD_direction_1\u001b[0m = \u001b[1m\u001b[94m\"x\"\u001b[0m\u001b[0m [string] // Direction for first axis (\"x\", \"y\" or \"z\")\n",
+      "\u001b[1mD1_min\u001b[0m = \u001b[1m\u001b[94m-5.0\u001b[0m\u001b[0m [1] // histogram boundery, min position value for first axis\n",
+      "\u001b[1mD1_max\u001b[0m = \u001b[1m\u001b[94m5.0\u001b[0m\u001b[0m [1] // histogram boundery, max position value for first axis\n",
+      "\u001b[1mn1\u001b[0m = \u001b[1m\u001b[94m90.0\u001b[0m\u001b[0m [1] // number of bins for first axis\n",
+      "\u001b[1mD_direction_2\u001b[0m = \u001b[1m\u001b[94m\"z\"\u001b[0m\u001b[0m [string] // Direction for second axis (\"x\", \"y\" or \"z\")\n",
+      "\u001b[1mD2_min\u001b[0m = \u001b[1m\u001b[94m-5.0\u001b[0m\u001b[0m [1] // histogram boundery, min position value for second axis\n",
+      "\u001b[1mD2_max\u001b[0m = \u001b[1m\u001b[94m5.0\u001b[0m\u001b[0m [1] // histogram boundery, max position value for second axis\n",
+      "\u001b[1mn2\u001b[0m = \u001b[1m\u001b[94m90.0\u001b[0m\u001b[0m [1] // number of bins for second axis\n",
+      "\u001b[1mfilename\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Filename for logging output\n",
+      "\u001b[1morder_total\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time, 0 for \n",
+      "                         all orders \n",
+      "\u001b[1morder_volume\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time in the \n",
+      "                          same geometry \n",
+      "\u001b[1morder_volume_process\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time \n",
+      "                                  in the same geometry, using the same process \n",
+      "\u001b[1mlogger_conditional_extend_index\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // If a conditional is used with \n",
+      "                                              this logger, the result of each \n",
+      "                                              conditional calculation can be made \n",
+      "                                              available in extend as a array called \n",
+      "                                              \"logger_conditional_extend\", and one \n",
+      "                                              would then acces \n",
+      "                                              logger_conditional_extend[n] if \n",
+      "                                              logger_conditional_extend_index is \n",
+      "                                              set to n \n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.component_help(\"Union_logger_2D_space\")"
    ]
@@ -257,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -294,16 +383,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Union_master ______________________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1mverbal\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [bool] // Toogles terminal output describing the defined simulation\n",
+      "\u001b[1mlist_verbal\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Toogles information of all internal lists in \n",
+      "                            intersection network \n",
+      "\u001b[1mfinally_verbal\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Toogles information about cleanup performed in \n",
+      "                               finally section \n",
+      "\u001b[1mallow_inside_start\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Set to 1 to allow rays to start inside the \n",
+      "                                   defined geometry \n",
+      "\u001b[1menable_tagging\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Enable tagging of ray history (geometry, \n",
+      "                               scattering process) \n",
+      "\u001b[1mhistory_limit\u001b[0m = \u001b[1m\u001b[94m300000.0\u001b[0m\u001b[0m [bool] // Limit the number of unique histories that \n",
+      "                                   are saved \n",
+      "\u001b[1menable_conditionals\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [bool] // Use conditionals with this master\n",
+      "\u001b[1minherit_number_of_scattering_events\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Inherit the number of \n",
+      "                                                    scattering events from last \n",
+      "                                                    master \n",
+      "\u001b[1mrecord_absorption\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Toggles logging of absorption\n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.component_help(\"Union_master\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -320,13 +435,287 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Instrument settings:\n",
+      "  ncount:           5.00e+06\n",
+      "  output_path:      data_folder/union_materials\n",
+      "  run_path:         run_folder\n",
+      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
+      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
+      "  executable:       mcrun\n",
+      "  force_compile:    True\n",
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/tutorial/data_folder/union_materials_5\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
+      "                                                ^~~~~~~~\n",
+      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
+      "#define filename mccabs_logger_space_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
+      "                                                ^\n",
+      "                                                \"%s\", \n",
+      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
+      "#define filename mccabs_logger_space_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "14 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/tutorial/data_folder/union_materials_5\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Cu.laz' (Table_Read_Offset)\n",
+      "Table from file 'Cu.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
+      "  '# TITLE *-Cu-[FM3-M] Otte, H.M.[1961];# CELL 3.615050 3.615050 3.615050 90. ...'\n",
+      "PowderN: powder: Reading 19 rows from Cu.laz\n",
+      "PowderN: powder: Read 19 reflections from file 'Cu.laz'\n",
+      "PowderN: powder: Vc=47.24 [Angs] sigma_abs=15.12 [barn] sigma_inc=2.2 [barn] reflections=Cu.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: incoherent \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: powder \n",
+      "component index [1]: 4 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 3\n",
+      "name of material    [0]: inc_material \n",
+      "component index     [0]: 2 \n",
+      "my_absoprtion       [0]: 0.000000 \n",
+      "number of processes [0]: 1 \n",
+      "name of material    [1]: abs_material \n",
+      "component index     [1]: 3 \n",
+      "my_absoprtion       [1]: 3.000000 \n",
+      "number of processes [1]: 0 \n",
+      "name of material    [2]: powder_material \n",
+      "component index     [2]: 5 \n",
+      "my_absoprtion       [2]: 1.200000 \n",
+      "number of processes [2]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 3\n",
+      "\n",
+      "name of geometry    [0]: box_inc \n",
+      "component index     [0]: 7 \n",
+      "Volume.name         [0]: box_inc \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.000000 \n",
+      "Volume.p_physics.number of processes [0]: 1 \n",
+      "Volume.geometry.shape                [0]: box \n",
+      "Volume.geometry.center.x             [0]: 0.040000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: box_powder \n",
+      "component index     [1]: 8 \n",
+      "Volume.name         [1]: box_powder \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absorption       [1]: 1.200000 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: box \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: box_abs \n",
+      "component index     [2]: 9 \n",
+      "Volume.name         [2]: box_abs \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absorption       [2]: 3.000000 \n",
+      "Volume.p_physics.number of processes [2]: 0 \n",
+      "Volume.geometry.shape                [2]: box \n",
+      "Volume.geometry.center.x             [2]: -0.040000 \n",
+      "Volume.geometry.center.y             [2]: 0.000000 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 4\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           0 = [1,2,3]\n",
+      "LIST: Intersect_check_list for Volume      0 = [1,2,3]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [1,2,3]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for box_inc with box shape made of inc_material\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = []\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = []\n",
+      "LIST: Next_volume_list for Volume          1 = [0]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for box_powder with box shape made of powder_material\n",
+      "LIST: Children for Volume                  2 = []\n",
+      "LIST: Direct_children for Volume           2 = []\n",
+      "LIST: Intersect_check_list for Volume      2 = []\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = []\n",
+      "LIST: Next_volume_list for Volume          2 = [0]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for box_abs with box shape made of abs_material\n",
+      "LIST: Children for Volume                  3 = []\n",
+      "LIST: Direct_children for Volume           3 = []\n",
+      "LIST: Intersect_check_list for Volume      3 = []\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = []\n",
+      "LIST: Next_volume_list for Volume          3 = [0]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_I=3.1635e-09 logger_space_ERR=3.59609e-12 logger_space_N=790141 \"logger.dat\"\n",
+      "Detector: abs_logger_space_I=1.70717e-09 abs_logger_space_ERR=1.53146e-12 abs_logger_space_N=1.81278e+06 \"abs_logger.dat\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "data = instrument.run_full_instrument(ncount=5E6, foldername=\"data_folder/union_materials\",\n",
-    "                                      increment_folder_name=True,\n",
-    "                                      parameters={\"wavelength\" : 8.0})"
+    "instrument.set_parameters(wavelength=8.0)\n",
+    "instrument.settings(ncount=5E6, output_path=\"data_folder/union_materials\")\n",
+    "instrument.show_settings()\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data = instrument.data"
    ]
   },
   {
@@ -341,9 +730,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space\n",
+      "Plotting data with name abs_logger_space\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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7V7qudI2nUkpdhXQ7FaWUUurCXaJ2M0ZEqro8ftUY8+rZEopIGPBDYON58psA/NEY8zvncbzz+M8Axpg/i4jfOX+880nGmGoRmQ5sFpEC4AXgDWPMLy7y9VxQeWd5bUuBr4nIMuA5bOc6HzvD+UvAvgspXDueSimllFJKKfWXmo0xGReY9iXgA+Dl86T5Hs5o58UyxvynM/V1B/BTY0zRp8nnU5b9IPBgl1OHgfKLzUen2iqllFJKKaXUpyQii4FBwD8YYzrOkcYDfBNY1eV0I/AVEbnOSXMd4HHOd3/+zcCTwDNAvojc/imqesHlXQo64qmUUlchnWqrlFJKXbgr1W6KyEIgHbjDGBM8T9IC4C1jTEvnCWNMk4jUAHcDbzg/9xhjPjbtVUT6Ydd0LjLGrBKRXwMlIvKPxpjKC63rhZZ3qeiIp1JKKaWUUkpdJBEZAjyBHTWsdLZLWe9c2ywiXafpFnD2abb3AzNE5CAww3ncXW9gnjFmFYCzRvRO4MZz1KtIRI4A/YF3RGT/RZZ3SeiIp1JKKaWUUkpdJGPMfmyAnbNdG9ftcfI50v0e+OtPKOc94L1u5/YCe8+RfiYw89OWd6lox1Mppa5COtVWKaWUunCCdmyudjrVVimllFJKKaXUJaUdT6WUUkoppZRSl5SOSCullFJKKaU+13SJytVPO55KKXUV0gZUKaWUUtcSnWqrlFJKKaWUUuqS0hFPpZRSXygiEgX8CzDWGBPvnLsVeBi4Dts2TjPGmCtXS6WUUhcjDLjhSldCnZeOeCql1FWoswHtyeNiiMh8ETEiknqO69eLyM9F5JCIeEXk1S7X/lZE9jgbae8VkUkXWXz3shaLyOGz1UdEkkXktyJy0Pk56JPyM8a0GmO+B9R2OfeuMabQGPN9IMI5lFJKKdVDdMRTKaXUx4jIbUAm0HCeZM8DHwHJxhgjIjc7zxXgdeB/GWP2ichQYKeIbDDGdHQpwwXEGWP+0OXcjUCkMcbfrawNwM+A/3OWeiwDlhpj3hCRfwSKgdFOfgOdx12VGmN+eo7XnQN8H2gGPjzPa1dKKaXURbriI54XcrdaRK4TkaUiUi8idSLy/W7X73LuuO9zft58+V6BUkpdO5wO4VLgAeCsU02dDuJ3gXmd01GNMX/qkqQDiHJ+/zJwtGun05EKbBWRFCfPKOBtYFz38owxFcaYxrPUIxa4DVjjnFoD3CYifZ3n1Rtj/qbbcdZOp5O+zBgzFTgNDDtXuitN202llPpLAvTu1bOH6llXw1t6zrvVXUwFkoBBQDSwR0TeMcb4RCQDeBIYbYw55nx5CV6+6iulVM/rbEB71GliRKSqy5lXjTGvdkv1FPCGMeawHbw8q4FACzDfGSX8APgnp4NoROQu4N9F5CTgBu7onoExplpEpgObRaQAeMEp9xcX8YrigT8aY/7s5PlnEfE754+f74kishT4mogsA54DvgrkY9/6LwH7LqIel5u2m0oppT53rmjHs8vd6rHOqTXAyyLS1xjT9UvDPwDLnTvmx0VkA/D3wE+B/w0sNsYcA7t253LVXymlPmeajTEZ57ooIt8Avg489gn59AJuAfYYY34sIn8NbBKRJOwU1ceBvzPG7BSREcD/JyK3GmM+6JqJMeY/ReTHwA7gp8aYok//0i6OMeZB4MEupw4D5Zer/E9L202llFKfV1d6xPNC71YnAH/o8rjBSQNwK3BYRHYANwLrgIUajVAppS7aN4GvYf9PBegPlIrINGPM213S/QE7HXUNgDHm/4pIM5DsXPcYY3Y613Y6I5+Dgf/XtTBneueTwDPA3SJSYozZfRH1bQS+IiLXOe3HdYDHOX+t0nZTKaXOIiwMert6ONPTPZzfF9wVX+PZA3oBQ7F3f78J5AH3nC2hiPxARKrsVDONG6GUUl0ZYxYZYzzGmERjTCJwBMjt1unEGNMMlOGMuolIMhAL1DnP6d9l7eZgIA6o75qHiPQD3gGeM8bMxU5zXSsiWRdR3yagBrjbOXU3dhT2vNNslbabSimlLr8rPeJ5oXerG7Drbzrvlne9k/sHoMQYEwSCIvLvwO3Ar7oX5qxlehVAxKN3dpVSV62r7c6tiGwGfmKM6Vwjej/wryLyAvA/wD3GmP920v4QKBGRzoBC04wx73fLsjc2ONEGAGPM70TkTmwntXvZRcAk59o7ItJijBnSpR6vichPgBPYoEfXMm03lVJKfS5d0Y6nMaZJRGqwd6nf4Nx3q/8NmC4i67BBEu4ERjnXVgPjROR17OsZA5Rc+torpdS1zRn17Px9XLdr7wHZ53jeKmDVJ+T9HvBet3N7gb1nSTsTmHmOfH4P/PX5yrqWaLuplFJnJwK9rvSQmjqvq2Gq7f3ADBE5CMxwHiMim53Ie2D3hHsPOATsAp5yvrQArAWagHexU672A7+8bLVXSimlLi9tN5VSSn3uXPH7Aue6W9317roTROGH53h+BzDLOZRS6poQJtD7+h7O9GQP56euCG03lVJKfR5d8Y6nUkoppZRSSn0WIvClno6NoHrU1TDVVimllFJKKaXUNUxHPJVS6moUBuidW6WUUkpdI7TjqZRSSimllPp8CwN6OjaC6lE61VYppZRSSiml1CWlHU+llFJKKaWUUpeUTrVVSqmrkaBThpRSSqkLJWhshKucjngqpZRSSimllLqktOOplFJKKaWUUuqS0qm2qkfNMDcAsJHxtHwYQ/QNzUxlNSuYxjRWECScDKoBWMgTLOJxJr2/jvybSijtyCU3rJSS9/PJuqkSPx4AsikDwE2ATUwgmhZ++24OP7r1n9nEBAD8H3rIuaGMTdv/nvTRO6n+3Qg4Bj8a98+046KUXADS8OIljYOVQ/lGVhkBbiSHckrJ5eDmoQCkj9tJdeUIO83xywZ+I3z1gd/zQYeb5LCDHOxIBqBlw1dgmGH8LSVs2v734IPogj/S4vOQeksVAPu++3VuXNZM+0fhtP8mEoB+Uw5zdPMAm3+isW/cRwIfQfSwP9p8a4BvA8eArzlvbg3w387jY0AcsMt5nBkEnzO/pPM584G7gTXAYLjxsWY+eDjGpvnFgs/2QatLT7dTUUoppS6cRrW96okx5krX4YoQ8RgovNLVuOaY554E4NicKOL2tcJKoAC2po7Ew1GGrK2HBidxLKwtuJPJ0zfAJDg52g7AV7sySMMbyrPP06fYP2+gfW4s8BSc3ghFkQ+QTwkA4QRxBwN4XUMp4TvkUM4d27exdfRIvAwlA9sRDBLO2O0VLB99DzmU4cdDM9FkUE0J+QBE08y9W95kV94wNjGeIOFMYBPhtFNKLokcBiCHclYzhXSqqGQELoL0w8+vnXwA+uGnhRgSOUw7LtLwMuvDJRTesAwX7aHX6cdDLckcxUMaXtKp4t4Pf8XMG4poJhqAg6RQ+X4W2TeVs4jHuIO3WM50mommnByqSAfARTt+PBx9L5Fv3FLOb9/L5ke3LOSFd/+J7Ft/A0C5/N8e/NTVxz1ZbYzJ+Ky5ZPQWUzWgJ+pzhhygR+qmvpi03VRKXRo91G7eIKYqqSfqc4Z4td3sSTriqXrWJPujhRjibm6F+4FDMHZzBYyB1ybfxb3H37SJPoIUaiENjuVFEdPWysbIPCYN3wJvg9NXhCpoJxxGAK3Av0FZ5EjS2EvCliabxguL5jzCY/texJPqt6OlCfASM3mNe9nIeACmtr0Jg2znMmnfEcpSc0Kdv84OpYejbM0bydiiCkpn5hJOO9G0sInxFLKMx1kEQCVZzOQlyslm/vHnoAEmpL9JASs4SAoAAdxkUB3q5AZwk3aDFxftpFMVGrFNw8sAfLhop5YUntn+FE+Ofgw//Wghxqmfj8SbfNQ6eedTggc/AdxUkU4KBwFwESSAm8RbynERJPkWLy/87p/od9thyt/9tvOmasdTKaWUUkpdPtrxVEqpq5FG51NKKaXUNUQ7nqpHLUp6BIC/khcZcgjwwv6JA0kZUU9t5EA74pjpJF4O7vgA3A0xba2URY5kUuMW+DfY2nckY/9UYdONg+RgLbvih5EZrGFd3zxiaGHU9t3U5fUHIHHEEdJDQ6Tgwc/J+DBe4176HD/FvV47yrpudB7VkRks3P40y0ffE5o668cTGjFczRSyqGTtzDtZzRRq6/6KtUl3kk4VvyafKawCoJwcWoimigzW9fWT1beSAlbg4WgorxUUEE0ze0nj1+SThhcXQbKoxEtaaArwYRLJ59csZjZLmIV7dAA3AaoZj5sAAD4ScRNgAIeZxRLGs4mNTMCDn/sppgo7E8RNgFxKqSSLdsLxvZ/Is7f9b8JpZ8WtBQDs6/FPXimllFLqCrvuSldAnY9GtVU9anbbi8xue5G8zcAK4BAMmVtPr6Cd3tqrCSh1jiAkrT9CQ99YqiKHMba4Ag4BP4MUDtJQEEtDQSwnCnvjCnaQebyG07F2beWo9buhCqJpIZoWgq4wPBzlrdQxJBxvIuF4EwddKQQJh3W2HhyCAfjIpoz9owdSRTrRNNNCDGnBvZSQTwn5ZFNGLSkk4qP2+F/xWtJd+PGEgiKVk0M5ORRSjB8PU1hFCzE8w1xSOMh8zgTu8XCUSXVb8DGAAlYQTjuvcS9BwmkmmiJmUsRMjuJhE+PJoYzVTCGaZtwEmM1ibBfUzTomkU4Vs1nMGiazggIC3MhqpvBTZuMlDS9pgJ3iO55NFFKM+8sBllGIj8RQXkoppZRSSl1O2vFUPWpF5D2siLwH1sHpR4E0IArY5iSoAirscSKvN+yDhMYmMp+vYX/hQE6OCIM59pynzR597jrFxsg8+BMEXWH0mXuKHRNv5/T90KfuFH3qThGxpoNakrmjeBtr+94J22B48QHiprdysiDMrj2dBEFcjC2uoJRc5vIMbgJUkY7XNZQ09pLGXlI4SAvRuAiyq+8wqslg1vFXqCKdAG6yKSObslBHzkU75WSThpcAbtYy2ekOR+OnH2lJu0mhlhZiKCebhMYmsqgki0pms5jZLGY8m5y3J8NG3SUFD368pFFIMYUUU0U61WQQxMWPWMIENuHhKIn4yKDa6VIGCOCmjGzaCWcT4wn8t5uheHnpdz++An8RSimllFJK6VRb1cM6A/QwCXoFYX+enWYbiOxN3NpW9kweTDjtAAyprodB2E5pFAzZWc/JjDBOxPemz8pT9IoilNekfVvgEARS3UQEW/Hgt6OnnWmutyORMYUt5LdtsGvjBsHywnvIoIo01wEAAtzIjsLbSWMvpeSSz69JC+6l2pWBF7udio8BFFKMlzSChJNPCUv73scqpvIrvkuzE+zHRRAfiXhJ49XgdMpdOVSSRWZdDZVJWQDkUkoG1STio5Rcsilnf/xAvKSR37aB+ZHzAKglBTcBomkmhYN48FNGNkBoqm0G1UTTQim5ZFHJYRtuKNTh7BztjKYFNwH8eNjkn0CBZwUAa26byGqmAPCHHv3U1SWhYeGVUkqpC6ft5lVPO56qR4WmcVYB26B9cTi9GsCbmsao2N0Mrz5wJlrtGNgzeTCJ+GghmmZiyNxeQ0TTKfYUDGb4dNtZpAkbzbYAu0WLC5KKj0AGnIx3Bu0nwrPBxzjoSsEbOZjhaQc4lhTF9MbXoRVeS70LsFFtSyLvZPL2Dewa/QF++hHjaiY9WIXfZfcNTaGWEvKZtfMVjo2IwscAcimlmRj6Bf3c6LIdwZVMA8BLGotdPw51HN9KGkOwS1QYH4kECaeFaLKoZAUFpOFlYeSjdiowNkKtj0TARvAtJZdng4/xjGsuRcwEoJRcPPjJoIoS8vGRSAEreImZZFNGLqWAjbZr13iOIPz6IOEE8TKUAG42/e7vnVrpPp5KKaWUUury0Y6n6lGTGrfYXwqAZdiOphdG3bMbBkHDm7E4202ScLyJ4UUH4CPoM+4IScEjkACnM2D4zgMcW26HM12006fulO2wNgJpsH/yQIYU13Mw3W4tcphEJmVvIby8nSHH6zmWFEXc2lYYBFvTR3JvsQ0utKdwMJPv2kDDm7G8xAxeYBarmUqaay8e/IANZLTKNYUdI2531noeptyZurraNTW0/UoupZSQTyI+3ATw4A9tdeKnHwAxtPDY8RdZ1zePNLzccXwbiX191JJMIodDnUUfA8iikoU8EXq8yTWB4o5C0sJsedE0M5OXWMzs0J6dB0khnSqO4rFbyGD3Ki0hnxhaiL6phVffm8nvbhnCqA93wJe/mPv2KqWUUkqpT0dEooB/AcYaY+Kdc7cCD2NDOvUCphljzvtFUzueqkftjx8IwJDn62GS7fTlDKqwf2hNkLCviZODnFHKCmAzMMf+XDvnTlKoZfjcA+xaOIzMlTUA7Ci4nfCkdjKratiTN5hoWkgMHoaoMyOsKRyEXzjTd7dB3Bg7rTeIi7H7KkL7iw5ff4DTv7DTV1etv4/9EweSTwkJO5tYNyIPgIjGDhY3zOPE6N6hslwuOz04mhYqsdNos6gkl1LSg1WUunJJ4WBoymsMLQCsYBrRfRfjYwC1JJPW10spufhIxI+Ho05nsZIsXuYhUjjIFFYxlq0sZjavhk0P7QlaSi61JDObxQQJ5yApBHDjZSj5lJBNeShdNM18gBs/HpJv8fJdXmPKDavgFvs+vNrjn7zqcbqdilJKKXXhrkC7KSLRwOvAQCAI1AGFxpjj53nOfOBJIM0Ys885lwy8BkQDLcB3jTGHPkO9FgPfARK7lvNpyzLGtALfE5F3upx7Fyh08vw3IAL44Hz5aHAh1aOGbK9nyPZ6ts4ZCU/B2C0VVEUOg0bYM2cwrIeItg4i2jrsP89YWDAG6ub0Z/J3NzB8+wG4HjJvrYE2oA2SqSVzSw37Jw+kH34SqpuIaOrg2OQostp2k9W2m1qS4TmYmz7PltMAw1cewEUQbobTLnvQCs2RUXZa6yFb31qS4Xo7OhlDC7OTnoZD0Of5U7QQTbHr/tCaSRsF1wYO2sR4Rm3fzXzXAibN3kI74fjxEMTFwn1Ps3Df04xnI6Xk0k4491NMADcp1IaObMrJppxw2qkincXMJq6xlQXMD3Uum4mmmWgKWEEpuRQxg6N4SKeKMrKJoZliCiliBkXMYDaLKSeHG52ouPmU4OEoB0kJBTVSSimllFKfmQGeN8akGGOGAvXAonMlFpHbsBsLNnS7tAxYaoxJBpYCxWd5rktEvtrt3I0i4jlLURuAUZw9rMc5yxKRgSLyTrfjnNEpRSRHRFYBzcCH50rXSTueqkftHz2Q/aMHMnZ2BVv/fSRUQGZxDRyyU1hZBuv65rGubx6nx8HsXz3N/FsgqfoIu341DNYDsXDs3SiOzbQHwI682xmypR4X7exJHwxr7DTToCuMoCvMbnWSAQv/7mmGbzlAw8RYyIDhdQfY2nckvRqgVwM0FMRy1OkcnpjTm2Ojo2jHxY702xnVuJtRjbtJw8uxwijbMcbuCbqRCaThxR0MMIXVTGE1hRRzOgMWV88jf/EbgF0fOoGNFKa+SGHqi3ZdJeOdSLM5lJNNADfNxBBNC3764acfWVTiop1neYxj8VFkUM1UVrOCaaFtXjz4yaKSSkbQDz8v/O6fmM1iprCacGdv0CwqSaEWD35yKA8FHIqhmVpSQlvBKKWUUkqpTxQjIlVdjh90vWiMed8YU97l1C7gY53DTiLiwnb0HsB2WDvPxwK3AWucU2uA20Skb7csUoGtIpLiPC8KeBsY170sY0yFMabxLHU4b1nGmHpjzN90O356ttfjpC8zxkwFTgPDzpWuk061VT0qMehEtXVBJSMYe3MFxwqjiGtqJeJnHbzf5EyLBXqtg8Xr58GbQBRkTq+BBGAcxNW1hu4Fnc4AV6SXhrxYooPNJFMLYyCrbTeByN6AXS+6q3AYmdtqOJYXRUJdE3tSBwPYNZlO9NuE6iYSEprgT7ArdRgtRJMdLMPnGsDW+JGADUDUywtMhFGNuzkWH0U2Zbhop93lCkWZBZgV+Txp6V4KWcbw7QdYNzqPZqKZ4GyPktu2DXdkgKxgJSWufLykhbZk6dwXFGAhWayggCwq8ZLGVFbjx0M+JaG1mw+xlAyqGMBhNjGBKbf9K5uYgJsAGVSHpvdOYh21pFBMIW4Cdm3quu/Rb9JhVr5yv1OiBhe66ml0PqWUUurCXZp2s9kYk3EhCUUkDPghsPEcSZ4C3jDGHBaRrufjgT8aY/4MYIz5s4j4nfOhKbvGmGoRmQ5sFpEC4AUnv19cxOu5oLLO8fqWAl8TkWXAc9gOdj52kvOXgH3neTqgHU/Vwypddv3j2DEVzG17jhMzexPXaCPR7phzO9FzWhhSVA/AnpmDGZ5wwM7HX4HtdKYCT8HW5SMZW1UBQK+VEF4YJNwVTsRTHTZdAvRaBsHlNirsadcpMo/XwDM2ymxcVAXD6w5wOhZ67bTbugAkxh4m4HIT19pK5r4a9qfa8wHctDsLA7yRg/GP8FBLCjmUAbCXNPKDJax2TQ11PH0kUlQ9h7fSx+DmA7aOHomLdsIJhjrXQVcYXoZS7sohm7JQAKNNTMBFeyiq7QQ20Y6LRHzMOv4Ke/oOJoVagrhCU2M7RzxrSaGZGJ7lcWaxhBRqCafdTivGrkPtHAHdyHhiaCF10v8jg2pWDuvseCqllFJKqR70EnaN48vdL4jIN4CvA499lgKMMf/pTH3dAfzUGFP0WfK7yLIfBB7scuowOAFGLpBOtVU9auy+ChvMZzP0qoI+2afsPp3Xw6h9uxnydD2sA9ZBP/x2Out27Ihkq/25dvmdjP1GBcRij3F2XWjc9FZIgx2Ft9sRy+XDWM1UVjOVXq1AFaxNupOxxyvY03cwDUmxeCMHU5fXnyHH6xlyvJ6I9R0cJIVpST9nR+rtePCHOpNpeEnDSz/8JOIjhzKG1x0gmhaO4qHd5WL67NedXTPdpFPFjvTbScNL5vEaOxWXAGOLK0LTYw+6UignmzS8tBBDADcZVOPBj5tAKF0VGbgJkMhh1va9k2UUkkwtKyhwVpTa/TsryWJm2yt48DOWrbQTzkYm4Kcf49nEeDZRSzIejoZGStPwMpXVANw4rJkbhzVfgb8MpZRSSqlrkxPMZxDwD8aYjrMk+SbwNeCwiPiA/kCpiHwLu2fDV0TkOiev6wCPc757OTdjAxM9A+SLyO0XWdULLutS0I6nUkoppZRS6vOtM6ptTx4XUqzIQuxmgXcaY4JnS2OMWWSM8RhjEo0xicARINcY87YxpgmoAe52kt8N7OkeGVdE+gHvAM8ZY+Zip7muFZGsC6spXGhZl4pOtVU9qnPq6hCX3daEl4F7gGlwOgG7dtKZf19JFpM+2mL35yzAjngegslNG2AiNIyOBSChqCk08nliYm+y2nZDEALcyKznXwHg9P1QmjcGD37q+vZn+L4DEATPoCZ6NcCi1EcAmDHRzkhY3vZDmiOjCOCmhWjcBEg43gRAQ99YSsgnhzLWJt1JOEGCuPCRyPzF0yiqmwNAftIblKz8RxgJi5IewY8HNwGCheHMqrP12pU0DD8eSsllReMPWRD/aChSbVrbAWIi7bpML2kUOkHFVjOFCWyinBwWMD+0fYuvIxF/mIeqyGG8HHyQl1wzqSQLD34qGRGa3jsAH2l4Q3uAVpHBb3+Xww9u+xnuG+w04fPGulZXB91ORSmllLqqicgQ4AngIFDprN08bIyZKCKbgZ8YY6ouIKv7gddE5CfACeC7Z0nTG5hnjNkAYIz5nYjcCcSdpV5F2M0E44B3RKTFGDPkIsq6JOQT9vm8Zol4jLP1jOpB5tCT9pcKqCvoTzQt9Fl7CsYAb8FbBWO4Y+U2m+Z6YCW89pu7uHffm5wcFEbEemd2wlOw691hAGTW1ZwpoBVogtMjoDLy9tA+nnfM3gaxcGJOb/oUnWLJzAeYte8VuBmO9Y1iNVMBaCGaXEoJp5204F58rgFE00xcYyvr4u0+ni7aSaGWvaTh4SiZjbb8HfG3E047mVvs49l5T3M/xdSSgpc0wmknl1LaCbfbwgCLRj8CQD4lBHDbfT1pIZHDePCHOot+PBRTyDRWUEYO7YSTTC3tuIjGTo2tJIsUDuIljRaimcFLTGYNuZSSSykvMROwa0EPMyAU3daPhwBufr1uKt+ZtAqAX0vdZ/2o1Tk9WX2hgQjOJ+PLYqq+2RP1OUM20iN1U19M2m4qpS4NbTe/KHTEU/Wok/F29nZ7gYtVTGV+9XM2YNA9wOtwR902WOkkjgIW2hG6Y6lRxK1stedHAi8TCpaDC7vNSqv9ff+cgQypq8cdGWBU3W6bJgOOTY4ips1Gw51V9wp7UgcTxEUih8mnBLCjp2tn3snktRvYOnkkWcFKIp7qYN3CPHKDpQBUuzJoJoZ2XGxiPA/Fv0zV9pGEx7dTSi61eckALD4+j4a+sdxRt43EJB/FFNo9QYHhsbbjmU05KdSymNk0E80Hzj6eHvy0EANOx3MT45nCKgK4SWNvqOxvURra/iSaFnwk0kI0NxKglNxQp/MHHcvJDbP1/xaltONiPgtCUXLHs5EfTfpnij/sDC6kHU+llFJKXUME7dlc5fTjUT3qcZfdM7eocQ7zNz8HI5wLL2ODCrngWLnd2yRuXytshlENu2E9HPtVFC7a6bP2FKfHERo1pAm7XLsCGAdD1tfD9RCd1BLacqVw8ou8eOMj9NoFZACtMLzxAEvj7/vYiOmJmb3x4OfkxDCygpUEXG42LbQRZ0MReVdW0FBgp/mOD24k0eWDVrvdS9rLe3EF7ajs8r730EI0j0W9CEAiPlqIIYMqGlLt81uIppRcFrQ9TWnkGCrJoiC4gmLX/bQQHYpyu7D6aVLS/4sprKaadNwEqCWF2SzmbXIBcBOgigzyKSGcIJuYQAq1VJLFjLCXePJd+96H39pONM3O3qBBCinmR6uXQqaBjz4WvltdzcLQqbZKKaWUumZox1P1qCVtdv0jm7Ezy/8EVEFhwYsURz0CH0FctTOy2WqPJRMfYFbrK8Td1QptsP83A+2WK52d1ueh7jf9Cea5GLKlnoaJsXjamogONttygOLNj3Dab8tiJez5jd3D88G1v6Rhcmxo/Waf6lNEp7fgcw1gSGM9vvgYJu/bEFqbCkAGtBOOn35kNtWQEl8LGbBr4jASOUyxy07bzaGMaFo47SK0JrSKDMrIYVajXePpidpG3I1HSIz0EU0L2ZRR7LqfaJrJp4SxbAWgML2YrYyllFzaCeeJ4EJ8rgF48DODlwC79jOHMtLw8hAvk0UllWQRxEUMzRTcugzA2bvTxTRWUEmWHYWNg4Jbiln5r7qdilJKKaWUuvw0qq3qUb2W2YNDsKfvYNgJbIfi6Y/AW0ArnB5kDw7B6UchgyqbbhI0/CaWlLZ6Oz23czuVyZC09gjRNLMnbzAJW5qojRxIiSufBYsfZcHiR5m7eB69vHBsdBQ8DMNX2m1QaIKE7U12ZLQBSLCdSg9+TsaGkdJWz8lBYXjw46IdF+3QaNdcTmjbQkN8LKOqd3M6ygYzau82BOUjkZLIOwGoJQU3AWatf4Xl8fewPP4eApG9+Y+wv8VNgMwtNbj5gAlsxEU7VaTzMg/yMg8ya/srPMTLjGcjydSy2PVjhqytZwXTnE1e0siikiwqKSeb2SzGSxpPsJA09lJFBh78ePBTRQZBwpnFklAn9Bujy2yQojjOsgRdKaWUUupzrnOm0GWOaqsunI54qh61a84wADJn1zD8rgOQAPwEG+9rDDTEnxl9pBV6rYEv37+bPWYww58+QDhBApG98Y/2hAIHZd5cw/68gQxZX8/BiSkwCIZsr2dIghM5F+zoaBDcwQDr8vKYdHwLfjwkjG6iITUWNzaaqx8P/fBTSwqZ62vYM3kwycFavKSF9r3cnzeQRHwUR95HQXAFJ1PDiNjZQcrogyTsbMI1wq49Hd54gD3xg4mmmQwCRNOCmwALJj4aKm8sW8mllHxKeCtvDFlU4iORRHzE0Ey/oJ1qu2j0IxSwgpVMox9+Uqhl0eRHqCQrlFcaXvx4aCaa2W0vMj9yAV6GAvAsj3GQFMB25FuIJoCbjc50XO+HaSTecBgSL8nHri4FIRQBWvUsEYkC/gUYa4yJd87dCjwMXIdtG6eZL2r0PaWUUuoS0BFP1aMyj9eQebyGY4ujIAG2Lh4JDXDydfunllDUZDuhT2A7i9tg6FMwvPoATLTrPvsUn2JIdT2ZjTVkNtZwcnQYQwbWQxSUkRMavTwZH3ZmVHSnLT+isYPcYCm7+g4js66GutT+tBBNFelUkU474cTV2am+DZNj6YefclcOoxp3h15DC9Ek7GsigJuIn3UQsbODtaPvxE2AuhH9KSGfEvI57dRn7PEK0qnCRyLTWY6PRMrJppxsnmAh0bRQQj4bGU8xhVSRQUZbDcsopMSVT4krH4C7319LM9EMxctqppCIjyXMIpmDJHMwNA23nBwmR75BOEFqSWYTE6gmI1SvIOEkOluq/HZ1jn3eR+Hs8w+Fj7CHUp9AROaLiBGR1HNcXywih8+WRkSuF5Gfi8ghEfGKyKufsS7nKytZRH4rIgedn4M+KT9jTKsx5ntAbZdz7xpjCo0x3wcinEMppZRSPURHPNUlEVfUCndjtwtZCZWjs8iKrcRV0EGvlU6i7wIvn9kaZdQ3dsNP4ERhb/pUn7KRbIGIyA5O1Pemz9xTzH/qORuoaBBEzO6AcU5eLiAK1iXl0UIM07e/Djshel4LSSuPcKzACWi0pRXWQcryWvo0ngLgjqZtEAWTGzcAsC4+j5ODwsiikpMPh1HssusiO6fSrmMSAEFXGLmUctoFq5lKGnuZxgo2MZ5XmQ7AUTx4OEoMzRQxk8fqXqQuqT+rIu9iKqu5g7cAWMB8ZtxUxGwWh7Z+6YyEu4tMwE4RfqzxRaLjW4immfks4H6K8XCUcrJD+4B2bsuSRSWuKUGmsJrHbnqWHMpZff0UJy+lzk1EbgMyCYXvOqsNwM+A/3OWa89jb3EkG2OMiNx8ljJcQJwx5g9dzt0IRBpj/BdR1jJgqTHmDRH5R6AYGO3kN9B53FWpMeanZ3tBIpIDfB9oBj48WxqllFJXKd3/+qqnI56qZ/3IOe4GYiFhSxMzf/U8Y5+uoN3lotdOwOscBcBH0KsVRs3YDRvt4z4rT3EivfeZdZlR0GfLKZiG3Q/0ZmAZvPXSGLsWNNXuD7og/VFiaGH6+tfhEBAPfYpPQQLE7WwlbmcrJ0eHcWJ5b/x42Bo/0nZuo4AK2+FcF5/HpKItVLqyyGrbTakr1wki1MwmxjtBh+xqyhZXDPNZQNAVRmFwGS3E4MfDura7cAcDuIMBVjElFKG2kGK2Jo2klhRyKWU+CxjPRsazESC0R6ePxFAHdwSVbGICm5hAIcXMjn8aH4k8xFKOdnjYSxrVpDODotDWKguYj5c02gnnMIncXbme+ykmhVraP3LR/pH+r/wFFiMiVV2OH3RP4HQIlwIPAOecamqMqTDGNJ7l+TdibyvN65yqaoz501mySAW2ikiK87wo4G3O3E66kLJigduANc6pNcBtItLXeV69MeZvuh1n7XQ66cuMMVOB08Cwc6VTSiml1MXTEU/Vo/b8ykaTdRMg6fgR+AiK1s+xncDqU/ZOlBNY9Vh6lB2BnAD8G2emgLZBn+2nOP0T+7BXFXY6LYDLBi0aPvIAd0zfZrdaAaL/vYUMqhi1djc0wekC7MhqOrw24i7uXfkmAJUjshiAjwBuWojh2Mwo2nGxImka84ufs5mlQjsu/JF2bejwpw/Qb56fe7e8yda8kYwtrrDpxsG0+BW85JpJOlVMrtvA3KR5+CNjSVhpKzahYBOrmUIytZSRTQwtZFNO3PFW1kROZpNrAgAB3NSSTCm5pFCLj0SKmEE61aQ4swFLyaWEfLuOlPm4wwJ4SaPswxwO35DITCf6rZsApR25jA/bxDRW8kymh8c3/wvfGFcGH4X34KetLqlLs51K8wVshP0U8IYx5rDIp9p+ZyDQAsx3RhA/AP7JGFPRNZExplpEpgObRaQAeMEp9xcXUVY88EdjzJ+dPP8sIn7n/PHzPVFElgJfE5FlwHPAV4F87D3zLwH7LqIeSimllPoE2vFUPaozQM8d1dvsqGMTkAonx4URsaaDk3eHEfF3dh/MuJdbbXCguwEvnJjYmz7rT9ktUu7ukmkQdqUOI3N7Dafvh+H7DthJd4WEvhqmUGuDER2CE/N64247BaPh5KAwBuBjT4HtEGdQTZ/tp0iqOkLlnCyeYS5Fx+eQ0beK006Zvbxwx/ptkAYJ25rYMe92goTTkneUcnLwFQ5w8qrq8pozGFtVQXKS7TR6JtmOZzjtPBFcyEuumUxjJV7ScBNgSd8HbDpnH08vaaRTzeLGeUyL/znfocR2fvEwqWMdALPDFuOiPTRamkItU1jNyzc8yFE85FAGwCqm8kTYM7QQzWqmEB3WwnfGleAmwMHEZMD2CpTqTkS+AXwdeOwzZNMLuAXYY4z5sYj8NbBJRJKMMW1dExpj/lNEfgzsAH5qjCn6DOVeFGPMg8CDXU4dBsovV/lKKaV6WBgalO8qp1NtlVJKdfom8DXgsIj4gP5AqYh86yLy+AN2quoaAGPM/8WumUzuntBZ+/kk8AyQLyK3X2R9G4GviMh1Tn7XAR7nvFJKKaWuIjriqXpU5xYoIR8BOyFiZwfEg9c1lMzUGnvtEHaV1xggE/rMPgW7YN3MPCZVb6HXz87kkZh3GKZDLxfsf3cgQ0bX261anBHPPttP0WffqdDqMG/kYIYfOkDEXR1UvptFPiUABAln1+gUMhtqmLX2FbvWdD1k7ykjENkbAN+IRPrhpx0XCTThx8P44EZWu6byRHBhaHpsCfnkU+JEkN0L8XaK7mTW8nKkHUiZtG8LC1IfZX7dc7yVNIZsytjEBDz4KSGfJcwCoIAVxK1tZdHkR6glBR+JNBPNBDYxPmwTYLdTmcYKomlhCqs4SAotRANQRQa1znYqK/3TeNbzGCuYRiI+ZrOY+SwgER8tv/9Kz3zQ6tK7AtupGGMWAYtCVbCdz781xlzwtFNjTLOIlAFjgbdFJBk7Wb6uazoR6Ydd07nIGLNKRH4NlIjIPxpjKi+wrCYRqcHOkXjD+bnHGHPeabZKKaWUuvy046l61PigDZRDAlCF7RgeAh6GtyaP4Y7Z286EDllmr20tGMnYX1RwIq83fRpPMenWLfZr6gtOuiiIa2xlR/3tjHp+N0Om207nrvRheNLtVNVwghx19v4ctXI3/oJ2To4Lo3piBml4Sdp3xOZ1PZB0mNOT7NrR049CL2fN6WqmOHm1M73xdRbFP8Jja14kel4zR10esqjkoCuFNLyA7QimtNVTFpmDjwFsHWHP/Y7hPMRSACbdvIV8SliXlEc7Ll5iJml4WcgTLGEWpeQ6ee2lcnIWHo6yhFm8xAyyKaedcHIpDb3GZGop5n6W8iDTWc5GJtBOONW/G8GM22zMlOi45tDnMYGNLGY2hRTTQjS/JafHPmv1xSIim4GfGGOqnMdFwCQgDnhHRFqMMUOc5PcD/yoiLwD/A9xjjPnvbln2xgYg2gBgjPmdiNzp5Ne97E8q6zUR+QlwAhvYSCml1BeNRrW96mnHU/Wogy476jbcewAGAevhZHkYEVUd3LF2m/2K+FyXJzwKY9dWQCuEB4MwHXgd2/H8kZNmHFANo1p3s+dXgxk+9wAn54SRebyGE33tKGWfLadoz3Mx/PkDsAaGRNWzbmIek57eQt28/tSl9gcgaf0R4hpamTD6TV4e/RC1JDO2qoKIhg4eTPglAGvj72Rm/PMsYD675g3jICkEcDOpbgu02g4vQCKH8UX2J5HDTNqyhWN5UXYNKIm87CwdW973HsazES/5JHKYRHx4SWMCm/AyFDcBADYxgVxKmXx8A/l93whFuh2AL9Q5dRMgiIsWoikll1xKScPLLJbwg9t+RiI+AIIfueAGSMRHOO0c/N1QSm9rYQCH4b979ONW1zhjTGKX38d1uzYTmHmO570HZH9C3u8B73U7txfYe5a05yvr98Bfn68spZRSSl152vFUParI+W64YvMPObkwjIg/dRCxuYOGibGEEyRubaudWgvQNbZmFUQ81MGC3zxKCrVMnr6BE2/aTmWQcOL2tcIcGH78AKRBxMoOCBL6KnoirzfhBCEDGubEUsQMFh+fx8x5z/Ns8DG8rqEA9Bvn56ArhWzKSVjbxIrJ0xhLBcdGROHDBg1KxMfkfRtYm3onbgIUtv2S+ZHzIAkm1W0hc3sNADtG30447cTQwom83pSSi48BTGAjxU7o3vnHn2Nd3zwyqKKSLMaziSwqGVJXz2tJdzG1zUbbfTDyRTKo4q2+Y0ihFi9pVJPBDIrYyHgAprES4GMdTj8efO8nknVTJY87MySX3DCLdsKZSRE+EglPbMNFkDJy+GrW7wG7CE8ppZRSSqnLRZxt1r5wRDzGhkVVPek/jN3f/Y712+z+mMuANGAidpprBWfCfowDDsHSyffxYNEv4SPYP2cgQ4rq7fXOyLYVwGY4/QL0+j6c/oXd+3N//ED89APAzQckcpi4462hvT/JBBZ2KQvsvp1/goaFsZSSi5tAaHpwZ7Tdt34zxkbl3QynHwZ/ZCybGE847bbTWG3rtyt9GGnBvVS6smjHRQA3k49v4FjfKI460X0DuAmnnYy2Gqoih5FCLbNYQhpeEjmMh6MAlJNNrTOy6sGPD7vO9CgeClgBQDV22rAfDy6CNgous/Hgp5Ks0IhnFpX46YePAXhJYxorQlFzi5gBwB/k//uMn7Q6tyerL2DLkk+UESem6h97oj5nyAv0SN3UF5O2m0qpS0PbzS8KjWqrelQiPtsBSgCet51E7sZ2Og9hRymjnONHwAjIphwi7bkhs+s5WRgGI4B1znG9PXo1Ac9Ac2QUNMGQ7fUMwMcAfGQ+XUNcdSu8BSdTw6AV9jcPhBGwv3Cgja+5BhgNJxb2JqGoiQyqWM0UInZ2ELG9gyW/eYAlv3mAO4q3cSw9ym6j4gojYV8TWdhYJ0NW1rM8/R6Wp99jt3BxuRm7tgI3AVYzhbf6juEoHvaSxl7SACimkOLI+ygnmxaiSaGWRA5zkBRKyaWUXNLw8nLwQRI5zBRWk4aXXEpZwHwqyaKSLBZWPx3a0zOAm8XM5tfvTSFIOEHCSaGWFGp5puMJYmghDS+vcS9e0simnEqyLs8fgVJKKaWUUt3oVFvVo4Y874xWBoE50GsztpPZAHsKBxPERebaGgB2lN/OqC27GbKznhMFvekz/BQ8ChFtHdSl9ycpygkIVAGrXoapPwHugbhxrWydOZKx364gONpZRR4PDemxJLQ2EbG+g/2TB+LBz8lBYQyZ60TABdgO/lQP4YWHGb72AOvG3QU7YdG8R2wHGCADFjObGFrABaOCuzlMIvmUMLPgeQopBmwwoiAupk1ewUbGM55N3FG9jf3pA/E6nc4cyknERzrVPM6zuAlQSRb98FPACtqdVfDzWYDbFSCLSoopJIVa5rOAAc46TYBp6T+304mBV9c9TMGkZcy4ZTFlZDONlaEIt+vCJjGd5UTTwjPvP0H+TTair5uAfU3oVFullFJKKXV56VRb1aP2mdcBSGmrp9caOFYYZdd1gl3b+SLwiH142gW9crFrPVuBh4Gd2Gmxazgz1XYdNthQk/N4nJMuljPTdlNh3eg8JhVtYf/MgQzZXs+O0bczav1uqLKjnAArmMYUVhFX1wqH7NpQd9spmiOjaCEGgCFb6iEeTqTaayWRd5LftgFv5GAA0toOAHbkNe54Kwv6Pko+JZSSywQ2UksKdzRus1WPzwNs8KAsKtnEeDY23sWS+AdIYy8pHATs1iwugiyjkFxKOYqHftiIvYvr5gGwP2kgJeTTQjRPsJBKsignBzcBfCSGtrLZSxoejtJOOB78JFNLCflMYyUrKAB0qu2l1UNThvqJqfpeT9TnDHlGpwypT0/bTaXUpaHt5heFTrVVPWoT49nEeHo1AFUQl90K24AM2NN3MNwMHLRHWeRIWI6dVtsEJ9J7287kU9AwL5ZdScPYlTSM0/djO5pjgElwOgFOFoRRN7G/7YSOs8+fdHwLb80cw5DsepgOo7bvtqOtYz5ex7jnW6EC6vL6U0kW3sjB/FlaCeC2nbdUmJn6PH3WnwIgmmZ6tUK0M1roi+yPL7I/LcRQ2PdF5q99jpS2embVvUI47VSRYbeSqQIPR6kmgyXYgD/P8jhb40cyq/EVGxjImZw8s+0VVjCNfH7NNFYyhdUsCM5nAD4WJT3CoqRHeJxnWc0UUqjlXn5FOy58JOLHE5rinIiPCWyihWimsJrZLOYF/49ZwiyqSSeGltCop1JKKaWUUpeLTrVVPapzj0ueAn6CHblMAypgeMMBThaGEdFkg/iMfbrCdhpToeE3sSSsbbKd1IWQsKWJhFRniHMNnFjemz51p6AKenmh17gOkg4dwZnRCl5gBNyxfZudwjt3tw0ydDPMzns6NGo4y/sKu+YMI7OxhqS6IwSTXAzZV88S8wAeZ4Qx81AN6fFVnB4D3sjBZAUrYR8kXN9Ebd9kkoN2naXPNYDiukfgIyiOvI/wyHbyKSGRwzRMjAWglFwKKaaEfG4kgJc0G0Qo3s1RPOQH7TTYxZGPkEspE9iIBz9FzCDa1UI47TQ7U2jHs4nDJBLEFRo9PUwi49kU6oAClHdk80TYM/zo3aWEx7WR7SkLRbztnKqrlFJKKaXU5XTFO54ikgy8BkQDLcB3jTGHuqW5DigCvg0YYJEx5hfd0qQAe4BXjDGzL0fd1V+6o9pOMeVR2J86kCGD6mmYHEs74SStPELEvg7WzbTTTydFbbEjmZGQ0Nhkp9tmYNeHrsM+xol0e2s9pxqgdynQAA0jYgmMcJ9ZUzrGpq8b3Z9RRbs5sbA3fbacYk/eYBa1zeNkvB3cj6joIHNlDTTByYfDaCcc1kNuamloT81jo6PIoBpfZH+G1x2Aj2BdXh6T1m7BPfmD0NYstSTjSfLTx3WK71BCXF0rhUkvMrSz8w3MdLZD8eAnSDhuAvjJJpdS2nGF8sqmHA9+/Hjos/0U00av5HGepZDij41QLuUhysghhmYAdpLFaqacGa0FssIqqSadNbdOZDVTcBMIdV73vfJ1J6fNPfBpq0sqDBtYS6lutN1USqmz0Hbzqnc1TLVdBiw1xiQDS8GJ3PJxU4EkYBDwDeBJEUnsvOg0sMXAhktdWXV+u9KHsSt9GHhhyK31sN12KpN2HoF9cGJmbyYVbWFS0Rb2zBxsO6FRcHoE0GrXhLINu3bTiX47ZG49DILezwBB22FM2NLEkPX1LJrzCIvmPGLTPgVBXJwugD7FpziWF8XwxgOURY4koq2DiLYOuB62FoyEcRDxfAfDZxzgxLzeVJEeih67kmkM2VJPLSnQAEtSH2DSvi28NXkMmftqQpNap7a9SRnZ0ARxja28lTSG+ymmmRj8ePDjoYXoUIChAfjwMYAB+GgnnGzKaCGaFqJxEQxFnZ07eh5e0oimBS9p9MNPP/y0EB1aC9q5X+oOvkkupSxhFs/yGM/yGAHc+PGwmilkU863KGUAPva9+3W7xUzmZf+zUEr1LG03lVJKfe5c0RFPEYkFbgPGOqfWAC+LSF9jzPEuSf8BWG6M6QCOi8gG4O+BnzrXHwP+A7jROdQV0jkSRxvwOuAC9jkXCyBIOIywayeHNx5g+OYDsA16ZTgjm8X1ds3nJOw6SZzn/wTwwq7Rw2xU3HVQ92Z/Htv3ImA7vJnUMOR4PQ19Y0kY0YQ7GLDPjQc+cvJ6DryThzK2sYIT83rT52lblxZiyKHszAtJtWs6l4++hzT20pAayx3Ht7EndXBoZLEo8gFmHX+FRemP4MdDOlX48ZBDmR1VBXYVDKOdcKJpIYCbaW2v44vsz17SyGprxR1pR1mTg7VEu1pIWN9E5cQsfCQ6+3z6cTlRbTs7oQAZVLGCAmJoJoiLEvJtRxk7eprG3lDgoYL31vKtWzaRfetvQhFy3/4sH7JS6orRdlMppdTn1ZUe8YwH/miM+TOA89PvnO8qgY/vANHQmUZEhgK5wL98UmEi8gMRqRKRKviwB6qvlFJKXVbabiql1NkIdsCjJw/Vo674Gs/PQkS+hI2LOs0Y82cROW96Y8yrwKv2uZ4v5j4yl1hStbP35hogFWiAtwrGcMfwbZz+T4jb3sqx0VEAxE1vtWszfwI74m9n1L7dds3nNtjz5mCSR9sgPhGHbDCiuoL+ZNbVQCqcnBjGXtJIwpaX+XwNvAC7+g4jwI12zeggWJp3Hw9u/+WZMh9uJZ8S+Aj6bDkFE+3P3LzS0GvIpgw2Q2ZCDZmuGlgDdcv7s6vvMGJoZjwbAfAxAFphtutFem2DXROHUU424bSzq2CYkyaRLCpJON4EFfW8NXEMZWSTwkE2RuYx6fgWAPb3tXt/lk5000I0WVRSSi4FwRWUu3IA6Iefdlwk4qOWlNCeoFNYTRUZzKQIgEqyKCUXHwPwksb4W0pIxEcaXkrI7/HPXF0inQ2oUj1I202llFJXypXueDYCXxGR65wG8DrAw5ndGTs1AF8F/p/zuPNObj9gILDZaTy/DIiIRBpjfnAZ6q+6s/07tv52JGOrKyAD0qmCZ5x9Lwe1EldsowbtXz6QlLZ6aiMH2ii0g7Cd1bthePWBM1+6vYALkjYf4di8KOKebyUi2IFv3gCWpD4AwMyEV+gVhMyiGhgH+/NsRy6LylCnE+BYQRTRwWa7+HwQcMiuLx3yjXr2/3YgACuZhrswQCI+wgkydnMF/YJ+khqPcDoWKiNvByCAm2NJUfgYQMrEWmpJJp8Sfsps27kF/Hjs1NyPbEfYRyKLd86jYUSsXdPpxCGaP3oBs1lMCd9hApsIp51prOAZ19xQYKBObgKk4aWFaIK48JLm7OmZDdjot7WkkEItZWRTSwppeFnBNFpCee3pkY9bKXXZabuplFLqc+mKdjyNMU0iUgPcDbzh/NzTbZ0KwL8B00VkHTaK353AKGNMAxDTmUhEngRu1Oh8V97YpyvsusqRNvDOydFhtsPZZTePIcPrYRoMcdWz6hmYmgsUYLdimQQnRzuRaB/tgBXANLsH5/45A4mmmVlPv2K3Y+nkwj5eA655QSZv38D+0QNxBwMEXHZdZtyWVrbmjSRnRAXNkVFUJmUxqWgL/ASGTLcRctuXlzL8+AGO9Y3icRYxdlwFi10/Zn7rc/iS+jOqbjcAe5IGA9BCNCuYxqzqV1iXnsdUVrOCaQBMYwUtRDM7voBp8Sv5DiVMG/FzPPgJpz3UKS5Z+Y8sL7gHgGaiqSYDD36SqSXo7BkTwE0O5cxnAR78FFJMGdkEcOMmwGwWAzbabiKHaSGGoXgJ4Gbx+7NJvqk2tBepUurzSdtNpZQ6B41qe9W70ms8Ae4HZojIQWCG8xgR2SwiGU6a14H3gEPALuApY8x7V6Ky6hN85BxrsKOfTwAuiPi7Dk7fjV1hNNEeJ3eF2V099sHUBvu8Y5Oj4BmgFSKGdxAxvMNurTINGpJibQCilfW2E+t0MlkDNMDpBGAznJwTRtLxIxwbHYWffriCHWxiApuYAF5w8wG9lkFccStZVEKqHfU8/YI9hq+1W6i4aCcRH7hsMJ+30sfQTAxbk0ayNWkke0mjmgzuWLuNRA6zI/12juJh1PbdTtxbH24CjKrejYt2htTVU0kWC5hPOO3kUIY7GMAdDLC04D7aCWcAPobiJZsy/HjwMQA3H+DmA3IoZyFP4MGPmwBBwnHRbqflfphLCd+hhO9wFA/tznDxdyghgJv2mkgmsIl2wu0WMurq19mA9uShrhXabiqllPrcudJTbTHG/B7467OcH9fl9z8DP7yAvJ7s0cqpi7Y29U4Aot9tZuzKCthIaFprr51AFWydOBKAsdsr7HTXEXAivjd9xp2ykWiXAbE4X6WgYWYsCWubSFjRBNc3wd1wIqk3fb59irrf9Acg6a4j9NoJJxeGEfF8B3Xz+pNUfQRXejW9tsH0jNdtXnPsnqJ75gxm+PMHcAcD7Bk92O7X6Tg5MYyIpg42Mp751c+xY8TtbGQ8uZTiwU8V6QDce+ub7Hp3GMcmR+EmEBqhXDD6UeZveQ6AY3lRrE2/k/FsYl1SHj4GOJ3FnZSSy3zXAgBe4177vgWbOeryUMz9TGMFXtLIphyARA4TJJxqMggnyCyWkEM5QcLJv6GEauz3zWzKyaKSe99/jfCbgpSv+zZfnfR7VjGFP6z+mvMq3+nJj10pdRlpu6mUUurz6Ip3PNW1JdrZTmXs2gpIw3Y6D2LvuT8BvA1j76oA4P31cNM4eH8Z3JRxCqIgYl+HnTi2gtAITXSw2a6FnIRdpfQW9HGdgj9B0pYjZwqPhYiqDqiCwyQSnt5OwpYmW3bnMs948OAniAvGQcDlJjlYC8tgyWK7XjSFWu5o3cbQeC+nB0FW227ckQH8eFjBNApZBsCxd6NI5DAxba2sjJxGOtXUksxUVrMnz07DrSKDxzueJSuskmzKSaGWcIJ4OMrCfU+zPNVOry0hnywqGeX6TxbxOIUso5IRtBBNLcmAnUL7ayfdYRLJoJo0vHgZShXp5DgdVIDFzCbxJh8txMD1dl3oXJ7hockvA9AytWc+b6WUUkqpq8Z1V7oC6ny046l6VFpntJxlwMtwMjKMyhFZjJ1TYc+9Bc7yR27C/n5TFHa6rBdoBQ7BuuV5of0y7137pu3E7sR2PIOwK34YLXuiuWP7NpvZUuf5h4BfwNgtFRzLs73NrXNGMva47ewmrGyCDOBmexzFQ9zzBzi2OCo0BTWaFlgDw68/wPJ59xDuLEz1MYD5K59jbsE8ABZueZpdecNwu/ZSSDGJ+HicZxl1fLcN6wEcTk/kP8L+lhUUkMhhmom2nUHgmdQneIJnABuJdjVTmMpqEvHhJY0MqqgkKzSSWUY2C5hPOTlMZTVl5IQ6+lNZTREzAZjNYrIpI4Cbg6Tw1XG/52iHh7vL10PcZ/2E1WWjUW2VUkopdQ3RjqfqUXF1NmIt90Ndan+S1h9h7PUVNlqt02kM7TY3DjvV9mGgCtiO7WAGYVL2ltBUW6KwX8AbYE/fwSQW+shcWWM7mXfbJCcjw+y2KyOAj2BP3mCGrz3A8sn3MK3tdXb0tZFoR92xG/4Ec/vOI50qDpLC8KgDxG1vZXbGiwD0OgQNC2OJDjaTQZUdEQW8rqHQCDOcbUt25Q0js7qG04MIdRAKKWZB30cp7GtHRVuIIYtKiqsfYUn6A2RRSeaWN1madx+rtt/H8tF2xDORwxQxgwXMJ6OthvLI7FDHu7PjO5dnKCeHNLwUMZPXgt/lXtev+PX2qfzH6L8hBaeepNFOODcSIIiLGFpwhbUzZfRqSskF7CC0UkoppZRSl8vVEFxIXUPWJt3J2qQ7OTkxjKS6I7ZjmQH8CBtUqIkzAYEGYUc4n8Ou6fwJtjP5ArZTGmuPXXnD7J6cc+6zgX+ArQUjmbtwXihoSsTsDpu+CdgMifjYP3kgaXgJRPYmSDhBwuFF2JM6mGmswEU72ZRzugC2jh5JryroVQUnU8NIeL6JdpeLKme0MeJnHbQQzaJ5j1BNBtVkkLm9hq3pI+nVYKfK3strJOKjkGUcJIWDpODHQyVZ7Ei/nRaiyayuYUfe7axiKlNH/5Lpx19n+vHXWc1UBuDDw1FKI8fQTDRTWAWAl6F4GUolWXaPUSCLSsa63qGSLN4e/b9YzGxqSaGWFCrJYuOHE6gkCzcBArhZznSqyHBS1F7qPwOllFJKqctLg/Jd9XTEU/WoRHwARGzvsCc+wnYGH8VGp80AZ3cQGpJiSShush3NndjRyruBbXD6fuhlBw0JjL6R/fEDebD4lzQUxgIwtriC8sIcTiT1BqDPT07ZUVMXHCuMIm5LK1V5/Rh7vILTLhjbaqfa8oidSts5ipi5vgbSICOy2j4fiIjq4OTDYfQ5forcvqW0u1xEjDuFHw+PVb/IiXRb5rrReQC8lTqGFGpJpyoUHGjUTrvlSvOIaErJxcNRZlDE0vT7COBmKquIpoXZfZ8G7BrMBcynlmQ8+EnhIKXkkuzkC3D3+2vx3ZRINuW0E06OM522HRdTWB36DGpJJuaGFpKpZT4LcBNgIXMB2OSf4KR691N+wkoppZRSSl08HfFUPcqDHw9+aCW0xpJD2E5n59TaQ/ZImN5kp9HuhCWTH7DnAUZArybsWtBpMHZlBUP21bO08D4S9jWRMKOJhsJYprCaPo2n6NN4Ch4EGuBkRhgxba0QDxlUs6Pv7RRFPmC3bdkMJ/r2pphC4qpbuWPfNmiCrUkj6bP9FHvmDGbPnMEcS48ioq0DKgjtk0mjHdXs7HQCrGYqbgKkU0U25Xjw43UNJZx2Zo94mtkjnmYl08inhBaiiWtsZTyb8OMhnWoS8TmbvGwiER9lZBNDC1VkUEou+cESAAbgYwA+nr3pcdwEyKKSdKpwEwhNu/XjYRPj2cR43HxAFRl853ebmclLuAngIkgMzfDfLnuoq18Ydgp3Tx5KKaWUUhdJRKJE5F9FpLHLuVtFpFhEfiEiK0VEPikfHfFUPSo6aIPd4IK4la12tHMcsA1oBJ7HjmyCnVpbBccmRjFr/StsnTySsUUVEImd3uB0RI/NiyKuqJUHo34Js4FWSChqgtgmTk60904iHu2AjyCi0RlpfQr6PHyK9IwqRh3azY5Cu8azlhTGs4k96YMZvvIAywvvwU2AkyPCGP60nca7Z95gvH3TGDDRx73b3wQXnMjrzXKmU0Y2XoYCsIRZRAeb8bkGUEouhcFlVLsyKKaQuU7QoCJmUkI+xXWPQAVsKhiPmwAtRFNJFrWkADCejRzFg5c0WoghnxKKXfezmikUUhx6f8vIIZmDuAhSSi5ZVJJBFTmU4acfAO2Ek0ItL9/2fTKo4gfvvcYHX+nFva5fwYae+6yVUkoppa4anTdsLyMRicbumzwQG8mkDig0xhw/S9rFwHeARCDNGLOvy7Vk4DUgGmgBvmuMOdQ9j4uoV4+WZYxpBb4nIu90OfcuUOjk+W9ABPDB+fLREU+llFJKKaWUungGeN4Yk2KMGQrUA4vOkXYDMAr4w1muLQOWGmOSsXs1FHdPICIuEflqt3M3ioinp8oSkYEi8k6348fneD2ISI6IrAKagQ/Pla6TdjxVjyp35VDuyrGBfm7GRrK9HjvyCez692HsnzOQ/XMGcjI2jLqJ/e2FQ9COi/d/ZJ+3f/JAG4xoIrzETPbMHAwTsBFxX4b9Mwd+/K7WU7BgxKN26u4K7JrSYlufY6lRoSnAbgK0E05ysJY9BYOZvvZ1JtdtIOJnHfa5UbCKKYytqyCp8Qh7Rg+GNqginYSdTcTQQj4l5FOCHw8RbR20E05hcBktrhhKySWc9lAwowyqmMlL7E8ayI6C20nEx3g2cceWbUxjRaj6HvwkU0sWlWRTRiVZlJPNOiaFptC6CDKbxeRSShAXQVxE04IfD6XkEkMLMbQANrLtg8d/SRUZjL+lhPmuBVSRzneeWMV3nlh1Cf8CVI8RNEiCUkopdWXFiEhVl+MHXS8aY943xpR3ObUL+FjnsEvaCmNMY/fzIhIL3IYNvYnz8zYR6dstaSqwVURSnOdFAW9j5xb2SFnGmHpjzN90O356ttfjpC8zxkwFTgPDzpWuk061VT0qtK/mZs5sodIKvAysgcydNbZDCbAPklxH7O93wx3Tt9lrKyBlRL3tCAIL1z5tp90GsYGJomDI8/XsmjOMRA4DEDGolfnHn+O0y26HQhWwEHLbttGrAeam2j0up7CaIY317Ii/nVHFuzl9NwRdYQTmuIl73m4FM5vFrEvKY9L2Lbaj+xG4aKduRH+Su0SE9eDnZGQYzUST6HIRHWxmYdPTcD3s6jsMsEGDSskll1JGVe+mLr2/XcOZl8vctudwRwYAKCeHfEqIpoUq0pnCalKo5V5eC23fUk0GBazgcRYxjRWUkssmxjObxfjx4MbmtZopuAmwvO89zGcBR/0e0j1VuGininSn9nWf/cNWSimllLpadN6w7VnNxpiMCypeJAz4IbDxIsuIB/5ojPkzgDHmzyLid86HpuwaY6pFZDqwWUQKsOE53zDG/KKnyzobEVkKfE1ElmH3pPgqkI99578E7DvP0wHteKoetmv0MAAyq2o4UdCbPtNP2YBCzv6cu14aRubzNTZxE/zxBbgpAno3YNd8NsGS9AeYdfwVTsQ7EWtdp+w/hwI4WRhGRFMHJEBmdQ0nU+2g/f7FAxkyvZ5ek7CTBhqAh6DXRDhWEBUaXUxqPGKvxcPswqdZvH0evYIduEa0cmKOLc8dDDDU5QWXXaO5gh8CcJhEWoghw4kyW0kaA1w+XLSzgmn4Xf2YEL+JUnJZWG2j1b6Wfhc5lOFyoug2E4OXNMazicrI220gJiCZWgK4mc5yXuYhXARZyBMkU4vbmS4fwE0LMYQTZDrLWcckiilkI+OpJiO01UotKSTiYzVTmMYKKj12W5VcSnnodxfzf5NSSimllLpAL2HXOL58qQowxvynM/V1B/BTY0zRpSrrLGU/iA3n2ekwUH4xeWjHU/Wol5gBQOaY++gz+xQ8A7yFnXqbAJnba2y0WoDn4CtPYYMNBeFkbBgRwQ6iaYbnoA+nAGhYHEvC+iYYBBE7neBBqcA+iEiwj4ccrIcCm8+evoOp7ZvC5Ps3cCKvN+5gwAY6wtaBKkgeUcvixt2sHX0nifjIXF9Dn0O2vNP3w0bXBGZtfoUpI1bBTiibmEMupYytq2BX0jDA7qXpJY0B+HATYPHxV9jR93byKWFR+iOh96TdmRN8OP0wAdykU4UHPw/xMtnOv9eXmMkC5odGMtsJJ5dSNjGBFRQAdtS1iBnkUgrYKLte0sillA9w42MAAIUUs5opeN9PI3iTizT2chQPs95fwrduszfh3v6Mn7O6DK5AkASllFJKXTwnmM8gYLwxpuMin94IfEVErnNGIK8DPM757uXcDDyJ/YZ9t4iUGGN2X4qyLgVd46l61KrZ97Fq9n12TWcqbO070u7RGQUnZva2U3CrnGMQcD92Gm0VBFxu9icN5N6n37SdyHH2SKhu4vQYIAjLR9/D1tEjYQ3smTzYdmrfstuo0GTzOUyiHZWsgD4zTtHiioEx2CPW1iWuuhX22c5j5vYaWxdnHVzQFUZhcBnHFkYxdl8F+xcPxEWQzH01rEvKC73Wg6SwiQn0C9r1o1v7jqSKDAK4nRWlHtLwkk8JiRzGj4fcYClZVFLEDNa13UUGVWRQxRRWc0fjNsJpJ8CNFHcUUumE/3UTCEXCzaAaL0NZ/eFUZlJE5ftZHCaRLCqJpplomllBAS/zELk3lRLgRrwMpR9+nrjpGXwk4iPxMvwlKKWUUkpdRldoGzIRWQikA3caY4IXW21jTBNQg93NHufnnu6RcUWkH/AO8JwxZi52mutaEcnq6bIuFe14qh51cmEYJxeGwUewp2AwGVTD3XAyPow+209BApweYQ+ehx197TYnpEJcUWtonSJ/RyjYD62wKvIueAqmtb3O2OMVMAmCuOz9mUZod7nYNXEYjLMjg0l1R+AROL0QPG1Ndp1pK+xJH8zJwjBIgF15w2ynNgO43gYs2j9zIBFtHXhdQ4nb3squ1GEMqa4nDS9bU0eSwsFQoKJkalnSNod2l8vurxmsZFbjKwAUbZlD0ZY5pFBLFemMravAxwBecs2kn/9PVJNBUeQDpAerSA/aPTmXxt8H2DWmL4fZ6bbPBh/jKB6OOp1YL2nkUMbcGxaymNmk3eTl1x35pOFlAfNZwHzcfMAzPEEVGRx8P4UMqqgkCx+JpOElDe9l/ItQSimllLo2icgQ4AnsqGGliNSIyHrn2mYRyeiStkhEjgD9gXdEZH+XrO4HZojIQWCG87i73sA8Y8wqAGPM74A7gRvPUq/PWtYlIcaYy1XWVUXEY5ytZ1QPMhVP2l+2O/tvVrdCFJyOhV73ABnYNZZgRx+bsB3TEWFUurIY21hhrx0itI/nicLeuNtO0Wtll+dMxO4N2vnP2emg0gSzRz/NTF4iYXoTRNnOcMDlBmA1UykMLqPElc+9099k6/KReDhKOdk8WPdLAJYm3ceD1b9kV/owMo/XMLvv08xmMXFbWtmaN5Kx020dG5bHUkK+7WxeDzwHWxePZOzxChr6xgJ2XWZi8DDtLhfTWc5sFuMljemNrzMz/nkC2Hql4cVHIuHOWtBprMRLGqXk4iUNgKrqkbgGtDLjpiJaiAk9J0g4lWSF1oJmUMVGxhNDC+lU8YEzZgpQ2WFvirVcp2s9L50nqy80EMH5ZCSJqTpnHLlPRybRI3VTX0zabiqlLo0eajcHiala0hP1OUMmaLvZk3SNp+pRa0fcCcDk4Abivt0KE+HUj6D3Rjj5ZhiPuxZRtHMOAMdGROEOBogo7iBiUAcD4n12Wu4IaBgdi3u0Hf10t52i11zgI3uNddiOZyPsLxgI2A5e5vU1sBMWp82zndudwEK7pUqKE402l1IiGju41/smJ5b3JoYWhuyst/k6y0AL234JrRBDMycjw6gmg1JyIQ82MYGY5XbLkuHVB8hPL4EmG0To3offZGxjBQviHw0FDQrgxuPyO5FqV5HZWEN7fDh74gdTSRbpTqCiNPaSQi3NRONjAOVkk4aXKtJZwixb9/R/D0W4BUIjmO2E4yaAh6MAbGI8AEFnneh8FuDmA3IpxR9mt3pq6aHPW11ClyY6n1JKKXXt0p7NVU0/HtWjOgPf8DNgDtAEvXcBOyFiegdFk+fYAD9A3M5Wu9YzCo7NjCJp9hG7/+Y68NzdRK/OGaHXc2ZrlnjgeZgZ/zxFd88hMehsp7K5wwZxjsWu+2wD7oflE+9h+srXzyyZdgIZsRP6jDxFn9YDsBaqRqTjT+8HQAbV9Ek7RTMx+F0etrbl4YvsT9LxI+T2LQ0F8ZmbPo9EfKSk13Lv8Tfhenir7xhSqCURH4AzEmk70O242Bo/kkpG4CbAEyzkICmhMiexjiwq8eOx0WjJYgHzedzZhzhIOCnUMo0VBHDzmHPeR2JonWenBcxnAD6KmEkMLYTTjp9+VG8f4aR457N8zEoppZRSSl0UXeOpelQRMyliph3pnIDdlnYzMAZOvusEAGqwx8mHw+zWJ2MgbkurDf6TCbRCWeTIM2s8wXY818Cx1Ch4HYq2z6EhKZaIQx1EHOrgrYljWDvvTsiAk3eHcWxmFAyC6TNehzQ77ffYvChogmN5UXYd6gpgGzS8FMu91W86IXzc9Kk+xWkXZNbVkNW2m7LIkSQVHWFt3ztZyTQyG2vIbKyx25/UvU4z0baOf4JwgmRTRoAbCXAjbgLcsW8bGVQzuXED7bjw4GdW9SusZmpozWXc+8d4lsdD02c3MZ4sKjlICoUUU0gxc3mGWlLoh59iCpnKKgK4CaedaaxkAD4G4COA20axZQkZVJFNOZUfZlFNBv1GH6bf6MOX9W9CqauNiESJyL+KSGOXc7eKSLGI/EJEVoqIXMk6KqWUUtca7XiqHjX/6eeY//RzMAh6/wQ7wjgJOAQR6zvsiOZIe5S6cm0HdGKYXas5CJbX38PJh8MYu70iFDiIzdgO6MN2j00Ondkv9ERqb06k9iadKiYXb+BYehRe11BbmUjY8dLtnEwNI66ulbi6VvZPHkg5ORx1eey+neOglmQWpT9COTmUk8PJ1DB8kf0BqI20U3kZDdmUUUgxx+KjOBYfRS6l7E8ayKTtW9jTdzB7Ugfjx05lddGOi3YSOczW1JGMD25kT/xg0vBSSRYkQDTNTrczjcKbinERpJRcomlhL2lMYh3NRIei2gYJx4Ofamdhaym5JOIjl1KKnG1sADz4OUwi+ZSwgmnE0MyCG+YDkEMZOc5+n+oqd+Wi820Qkf8SkT0i8n9EZNg50l0vIj8XkUMi4hWRV8+SZr6IGBFJveDXffayFovI4bPlJSLJIvJbETno/Bz0SfkZY1qNMd8DZw6+PfeuMabQGPN9IMI5lFJKfV5coXZTXTjteKoetXbenXbk8XrsdNtD2FhfsZwJLBRvj0lFW2AzRHylA7xwLCmK6Y2vE9HWAeuBSHucnBMGK2FH+u1EzO2ADMhoqyGcIOFBe8Tta+VEYW/ijtvIuEedDuCo4t1EZHeERlndBJhctIHEtiMEcFMX35+x+yp4bPaLPMFCnmAhm1wTbAfSBS3OaObW1JEcJIXHeNYZVxxAGdlE08yx0VEcJpFoWtjEBIq5n1E7dzNq527acVFODmNd7+DHw2qmMJeFzOz7PEvbHiGLytCxgmm0E040zfy6I5+q4yNx8wG1pFBLCkOdbmoZ2WRTThaVVJPOD373K/Ip4bAz5TYNLxlU00w0HvyE085iZtNMNKu3f4/V2793mf8q1OfMvcaYvzLGDAcWA/96jnTPY1deJxtj0oB5XS+KyG3YOQwNZ3kuIuISka92O3ejiHjOknwDMAr4w1muLQOWGmOSsXMoirvkN1BE3ul2/PgcrwcRyRGRVUAz8OG50imllFLq4ukaT9WjJjdusL80Ab8AfgSnGqD3UuCgc77JJjk2M4q477bCT4AE8DGAuMwa+3gycLNN1+KKIWJEE6OKdtvpuFHQaxnERbXaqbwAL8NGxtPe10UaXjLX1/DaxLu4N+pNdhXa6LQACd9o4q3fjuGOum0kBJvgT7Y+JxeGEbfPRheafP0G9ifZkc4g4WRQTTGFTGE1M3mJ1UwBYCqrQ+s9J+3bQkNqLL9+bwrTbhnLnhGDAULRZWNoxoOfO45vg1bwJqUxP3Ie0U6YnwlsJIVagrhoIZqpYatZ2/dODpIcikjrIkgytaE1o1lUUuWMflaTEVrjmYiPFqJx0Q7A4x3P8h9hf8tDvEy7M8326Gf6lNW1zBjT2uVhFPAXG2GLyI3Ad4H+xgmNboz5U5frLmwncAqcc4g9FVgjIuONMbUiEgVswXZ0PxZ22RhT4eTbvR6xwG3AWOfUGuBlEelrjDlujKkH/uZCXrdTThlQJiIvAcOA313oc5VSSil1ftrxVEqpq5FwKab5xIhIVZfHrxpjzjZF9hfAt5xafPss+QzEBkeeLyI5wAfAP3V2EIGngDeMMYfPtVTSGFMtItOBzSJSALzgPOdi9vqJB/5ojPmzk+efRcTvnD/vZtgishT4mogsA54DvordjFuAL2HDlSmllPq8uDTtpupBOtVW9aynnGMdMAi4H3q/gB3tDAIuaJgcS8PkWOKKWmEcnC4AfgSZMTXwn0AGvDViTGh+fcL6JtZNzINIOJHX207dHQekwpLyB1hS/gDcDPc+/SbT614nc20Nb00cQwwt0AqZ1TV27GIYsBHuWLuNhqRY1vXN4+SgMNaNziPgcnMsNcoGL4qyU2y3xo8khhb6zD1FAStoIZohjfVMYTVTWE0VGWTuq8FPP6iy25i8fcsoiphJNC1E02K3MMHDxn13EcTF0r73wUeQTTnpVDmpogngJogLL2mUkE8z0SzkCbKoJBEfifioJAsX7fhIJJdSSsjHg59v3baRZGopJZdSclnBNLKoZPH7swFIDjto15FSHFovqr6wmo0xGV2Ov+h0Ahhjvm+MScD+azvbbqK9gFuAPc7+Zo8C60QkUkS+AXwdeOWTKmOM+U/gx8AOoNwYU/QJT+kxxpgHjTH9jTH3G2MOG2PKjTEPOed/YIxpv1x1UUoppb4IdMRT9ag9y+0U0+HHD0ArLJr5CI/NfRGaYP+8gSQGD5Ow1plr6wKiwBs5mOHBA3aKLdCQHsvoG7fZ8Q+AWGghBtKgz9xT8BM4HWUD/4QC5TyIDWL0EWydPJI7nt/Gujl5bB0xkrHfrYD7nbwa4MTk3iRsaSI8r5KDrhS8DGVS3ZbQXbIl8Q8wa8srkArH4qNgHJSTg5c0muPLQlNfp7W9zq7UYbQQw2sFd1EQXEGlK4vZ/BRPm32NKZEHmdT4Q2amPk8AN8vbfkhdan8yqKKWlNA+ng+ylCx2kkF1aF/Ot/hbJrGO5UwHoJloVjINgId4maBT4Ria8TGANOz+M24CFFPIEzc9g49ESj7MZ9MN40nDS0tHdE9+3OoaZ4x5XUReFZFoY0zX7V//AJzGmexujPm/ItIMJAPfBL4GdI529gdKRWSaMebtrvmLyM3Ak8AzwN0iUmKM2X0RVWwEviIi1zmjndcBHs5soKSUUkqpq4R2PFWPCnfWFbLN/nis9UVYD3veHczwGQfABbsWDwMgM7uGXeXDyNxXw1u/GUMWlQRwk7CvCTYSCkmyZ+JgxrMRWrERcYPQqxWGbK63UXMB2mD/5IEMOV5vO6nj7LrHhXVPn4ms66glhbTRe4mra6U9ycU0VrAraRgpToDLFGppyIslYWcT5fE5tIyIJptyomm20XbTnIxaIRB5I2l4cRPgoCuFFA4SHWzmwcgXAZjLMyyNv49syoihhcmRb5BBFY81vog3Pi1Up3xKmMIqfk0+NxLAxwCKuZ9w2lnhdDansYIZvEQ52fjx0EI0aXgJp53H33+W9JuqQ3nZTm01taRQeMMyjuIhSDgtvrPFbVHKctZu9jHGNDqPxwPvO0eIMaZZRMqwayvfFpFkbAixOmNMFTibzNo8fMDfGmM+NnVVRPoBbwOLjDGrROTXQImI/KMxppILYIxpEpEa4G7gDefnHmPMeafZKnV1cTs/dTaKUp9JGDa4pbpqacdT9SgXQftLK/ZrKMAI2Esaw8fZjmctyQBkbKyxUWNvhjuqt0Es9Dl0CnbC6YehV4J9+vC1ByAWTmdA0BVGxPoOTkzubdPGO2UsBx+JDDlYz+SqDRAL49kEFXD67jPbojzDE6yquw+q4K3JYwgnSAA3k3ZuYceI2wGIpoWE6U0sWP4os4N2lmG5Kwc/HhaNzuCx2S/aMgvgGebyGveSsL6J1ybexVE8BFxulrY9EnpPkiNthzYNL0uYRQA3hfEvsoD53M1ap/rTKeZ+SsklhzJqSaaQZaHRVbDbp5SRw1wWMp3lePCTTTn/P3t3HxfldSZ8/HfQzoAwvDjjiAgUZYQYwcVCoxXXRa1rNKvV1KbGJI0mtWZra/Pppi/bPGma5rFPmthuappuzEtjmsTY1KobNxqbqNQVQyxUVjAEMigFRBxnFBgUZ6qc549zi8T6go0ikuv7+dwfmPs+99so3FxzzrkuGyH+38B/73zvdzEeB0FKyQVM0iYHQcLYscWbP2xkDOF14No8QKOB3ymlooHTmIBzptZaK6U2AT+0Aksw4wh+rZT6GfBX4C6tdfNlnCsKeEhrvQFAa/1npdRsIPHchkqpFZiPjxKBd5RSAa31qC7X8ZJS6ofAMUzSIyGuIxJwCiE+GSTwFFeUZ1uD+WYVsAwoAtxw9wOvm6Gsg+GvU14HYIuewi31pmv0eFYE0Xd10PR6HImbWngjdjrT7FsAsLs76F8O/etgV1YeE4t2kzClHRZYxwcIwS1shRAUT8/BTsgEYkXQfwKUxJogbD6r4UVoWhaHkwC7GM80tlCX7+6c+zimvpK5z73C2iN3smPQTUys3834lF2UWIHc8WVmanS5fTQ/YBkAT8+5Fz8uHmz9KY2xbvpb8ffcQa8wl7U4CBLAiae0gUdyrTmYPNCZifYNZjGeImpJI4MqVrGQTKpNzU/LfF6likxWspg8SnASoJY0q8annRdZAMBCVtHIEA6RRBgb5WR3Zs8dMrAROH9NCiGszLTjLrBtxjmv9wMF3Thm2gXW7wf2n7NuL7D3PG2XAksvcJwPgLGXug4hei/p8RRCfDJI4Cmujm/Bjsk3MTF7t6n0tx6OLY0ihI1b/81Uaxi4eSs8A/v+K51R9TXwQwhj59jyKKaFtlBrN6VKwnYbY75QCYWQQRV1T5mu1KRWH1XzTE9mZmsN/X8M3AfjvGUQB8WDcsy8UR+dQeUtm7fCQkg80kL5oBjm8yrVZDLKW0OqVedlj2cka9ffCRUwMWs3C+f8J9mUs5AXGUYt9pCpLrHLPp6lrSZ/yhLfCzR54ujvg9Q6H49l3Q/A2m13smPyTVSRyXYKWO57iCAxBHCxkFUsxyQAshHGZQWSq1hILiWs4Jv8hrt5hsWdb6sTP2HsOAmQSRUBnGxnUme9UTA9ygFcBHFQ+ud8nDkHKYjYzkJW8bn9Uh1CCCF6Fwk4hbgiJKttryeBp7iyzgyvTYUkGqEFdnhuYmLWbhLWt4O7nR1/NUNaJ640dTlHPVoDeUAJ7MoaTxq1jKsoY9QvasyxJgPfBSIhsaKFuiw3qdt8HM+P6Bxe2r8OM+CuDo7nR7DLPp6p3p1QDuTBrNbN5lhZcCwlitXMZyYb2cI07t78OqSANysZgO/zGFte+wLMgEfmfI9HeBgbIRK87eAJsCvWXH8AJ2/ETieJQzTGmh7GJXEv8KZnChnWfNG3J09gJfeRxgEeCT3M0umPk015Z09rgZUcKZNqtjCNDKpYfeIOnAMC3MFq3mCWmbMKrGIhD7CctcylnGxc+GkkiSoymEQhjZj5m6uOLmTIwEbC2Jn5md+RRi1P7foOgfEuGWp7PZEHqBBCCCH6EAk8xRX1YNZDACxb8yienWbY7cTXdpvAMhu8nmQmbrOSVuZ12fHHwHdhXv0GUz0vC5MmBMAO+xakU0hBZwIi2+QQjtDZT4mPZUXhaG3nRc9dLNr2MvbJYdgEvAbPvXsX0zDDdsOxNjyrGliS9wLHsqIYRq2ZJxqicz7llke/QN3rbiugM/Mt7YSp9pg2adQCZs5mJtWUkMshkpjPan4+6Os0MoQkDgFmeGweJZSTzWr7Hayo/y5NKXGdQ2/LrUxFYezsYjw/4Cc4Bpj5mMs7HiA7opy5rAXM3NO9ZFNLGltOTOOlfl/hM/Y9zGc1DoK8wUwA/nvgLewin0aGUE0mqzvm87nxJsANF8Z+/H9kIYQQQgghLpMEnuKK6kyGMwL4EKiDpofiAEhc34KnvIE9c0zJFQdBPEesOaGpQBEcy48iYVM7b0+fwNRIU4v+lN0kLVrifYFiTw7ZlJOwsh1SwbPV7F+33E3CpnYW5b0MbtObemxpFI1Lk8ijpHN+posAnsEN7Mi6CRthJj6wG74FbIUhuWb+4wMPPcpSnsJBG1Vk0IaDJRUvsC5rOrNaN/Ni7F0ATMKUVqkmkx+ElhG0O8hmL078nUN7V3MHuxhPLqUUUkBaygGSOMRa5pJNeWdA7CLAJLbzKnewlKcAaIxIIpMqMy8Vk1yolDyCOPhwgIdS8ljMSsLYyGZv57GW8x0COEmikSQaCUXYcRA078G40JX/RxdCCCGEuNYUEtn0cvLPI66oM7UksWMCzzhI/GaL6VWcYdaNWVRp2izA9Eq2Qt1TblLrfSR426lb7GZq6U44kzvzdvAUNXAsP8rM3wTWLJ7NvMc3wBTTJLXUB1lQ7Mlh3OYy9iweSRAHE4/s5nhsROfQ16fsS5nIbkLYmPjAbtYsn40TP64FAQ6QBphhr0EcjFtZhmNxkEaG8POsr5NEI8tj7+f79U+aa0iZTSZVPNL6KN+OfZzx7KKWNAoo7OzJnMR2nPgJ4Op8b8qtXks7IYLEAKb0ywMsx4WfW/hvZrGRB1nGdiaxiOcAk7X3babyMI+wnAcIY2c+q3mRBRwgrXNIbjZ7O7/fwjSSaOQP789iwY3PsKr5hiv1Ty2uNkkLL4QQQog+RAJPcUUtWvWy+SYSjj0URUJRuwlA84FtViOrpua+/HRGbauhbpmbAE5SW3wwDcoPZpPaspW6xWbCaAAnYwZX4mhth8NAPdg8IRPI+qxjlgBZMK6ijKen38uSIy9wbFAUxYNyGLeizCQ4Ah6470nWTJ/NvNINLF1ugsU8SlnBUh4+8lPTqMUkGPIuTqaRIQRwdfZgZlDFnhTTYzuNLWyngGdiF+MgSBWZBHASJIZFReZ9eDN/Ci4re+4sNlLLMLIpp6R+Am+nTGA1dwB0DpW1E+YOVlNONi+ykKf2P0DBcNOT+QDLeZhHSKIRB0H8uGhkCGBKpsziDQC+f+Ix8gaUkE05adTyc77NrhvHs5a55N5o0gCXXqF/byGEEEIIIbpDAk9xRa1ZMBswczUTNrdzfHIE0fUdkAEMBraebTtqvUkelFrhI9XngzrgVrhlxVb2LB1JyMqsMu5IGZyE7bETcOS3Wb2K2yEO02MK1H3XTeoDPo4tjyKEneOxESRUtBPOsvHcUjPvE6D/L2Be3gaKp+dQwHYyqaaRJBwEeW6QGUK7qPxlxtgreSnlNgBGU04QB7mhEp6yL+XWIyZR0Sk7uGID/Dz0bb5hf7pziOxKFuPIbwNMgqUqMgF4g5mmliYhXCl+1jK3c3hsI0msZS6Leaaz93M5DxCTGOhss5r5ZFPOdiaRRCNO/Pw7j5FJFS78xFjB8cIBL1oBsAMXflaymFrSyKSqsydWCCGEEKJPkaR8vV7Etb4AIYQQQgghhBB9m/R4iivqTEmP4+4Ious6KLXnsXzeA7wWmkf0ix2QD6esTrcXY+9iUf3L8CHsm5NOWugA0a0dsA7GzKg8+6lVpFmSOMSoIzWMe62MY0ujAHj7uxMAmFq0kweXP8RCXgRgtf0OFvEyDoIs2vYyeyab4bFMBj9OptbvZGHKYoaxgoxQFQG7k28/+qvONsfdEbThIJtynAR4g1lk2KtYwIumPiimN7ORJJx2a/gsuZQzmiQaGVdfBsDClP/kRe+/kuapZS1f5A5Wsp1J7CWbNhydvZkDd53gm+OfIJNq7q5/nUkpm5nGFvYOMENuAd7kFpbxYGdSozPvdRAHDoL8hAcByKSKvWRz6GgSPx/47c4e3acalzIzyQzHlaG21wH55FYIIYQQfYgEnuKKSsJkho1e3wFZUEWmCTorOiAEDIaQ3XS0z+QNUzLlSzAqtQZOwptLpzB+6S4cre2UxOYAMG6lSRY05rZKeBqYA8v4AcvrHmJqyGS+ZTDMZCNDQo3k2UuY+Ohu6h5yM2ZNJUyhswRKwpF2lg56nKkf7mRxykrGFFVSnJ/DstJHeeyh+wHIpYQkDhFDED9OQth4IPQEq+1mPub80KsAlNtHU0sa09hCGBvljMaFnwIKWZzyJAAPsoxTbigll1lsxI8TJ35WsRAb4c7EQT8bv4RCCiikgG+n/JxJbGcX45lEIU4CgKkvWkUGAZwMoZFpbGEX48mmnC1M4xEeBmAX4ykhj/kDVzOERpbxIE78/CjpYX60//9Z/1I/vuL/9kIIIYQQ14wk5ev1JPAUV9S8+g3mmxHASUySnfWYDLa3A4fhkMf01A0JNXI8NoKHCx/hDlYzZkwlSXsaSTjSTtOguM4gljoIYWff6+mdyXSWb36IfdPTceIHILG+hbzWMvpvgolTdrPmIZP11vvdZDwrGli7dC4A2YPKmckbvDT5NmaxEYpgbf4X2ZU7nvHsAiCEjSAO5obWUmrPI4SNXfbxhLFRQh42uylJks8uhtDIN/gli1mJHyeZVLOI51jMMwA8w2Lmxv6evWRjI0whBTzCwwRwkUQjazHXlWll3f1Jxw8ojhjHEzxAEAerO+ZTEFEIQBgbDtrYuH8uBcO3UMgkfskSnmIpIWxstOp4zmQjqzvmUxuRRil5pFFL+YlsflT2GEPGHwCwqowKIYQQQgjRM5TW+lpfwzWhVJKGxdf6Mvqef3z47PftmCy0mUCU9frMV4Ay6/VYoBb4HLAKKACauxwzp8vrQ1a7Zmv9mU+2nrHWdT3Xe8CPgM3WdQDcDxQCrwH+v0Lep6DKWv+fVhv/X+Hz1voc6zoHAyOt/aKsdkHgm9Z+Q7pcb30lfN4a2vvOXyH7U1DZZb9p1jFnA8utdZ4u71UZcKrSnNADeP9s2jg+A/HWNW22jhdv7fPOYXANNu2awaoMA17rHsusttZl8T+PIK6WH5VqrfM+7lHycpQueftKXM9Zys0VuTbxySTPTSHE1XGFnpv/oHTJW1fies5SSfLcvJIk8BRCiCtKAk/RN8lzUwhxdVy55+aftl263eWIcMpz80qSrLZCCCGEEEIIIa4qmeMphBC9kFamVqwQQgghRF8ggacQQgghhBDiuqYVnJbIpleTobZCCCGEEEIIIa4qCTyFEEIIIYQQQlxV0iEthBC9kI6AkP1KfzbYcYWPJ4QQQvQO8tzs/aTHUwghhBBCCCHEVSWBpxBCCCGEEEKIq0qG2gohRC+klSJsv9L1VNqv8PGEEEKI3kGem73fNe/xVEplKKXeVUpVW19HnKdNP6XU00qpGqWUVyn11S7bHlJK7VNK/a9SqlQpNa1n70AIIYToOfLcFEIIcT265oEn8AzwtNY6A3gaWHmeNncAHmAE8DngR0qpNGvbbuCzWut/AO4BfquUirrqVy2EEEJcG/LcFEII0WOUUnFKqV8rpeq7rLtRKbVSKfW8UmqVUkpd6jjXNPBUSrmBzwCvWateAz6jlBp0TtMvA89prTu01keADcCXALTWW7TWJ6x2ewEFOK/2tQshhBA9TZ6bQghxfh1EEMJ2RZfuUEotV0odUEpppVTWRdr9i1Jqj1KqTCm1Vyl1a5dtlxzJcjkudk1/z7m01i1a63uAqi7r3tdaL9ZafxWItpaLutY9ninAQa31aQDra6O1vqtU4C9dXtedpw3AV4AarXXD+U6mlPqaUqpEKVUCJ87XRAgheoVr9QAVvZ48N4UQonfZAEzko79zP8LqDXwZuEtrnQPcCbyklDoTi11yJItSyq6U+vQ562KUUkmXeU3nPZdSKl0p9c45y3cufNuglJqklHoV8NONh8S1DjyvGKXUPwGPArdfqI3W+lmtdZ7WOg8G9NzFCSGEEL2MPDeFEOLj01rv1FrXX7olHUCc9X08cEhr3XEZI1mygLeVUplghr8CfwBmdPeaLnYurXWN1vrz5yxPXOyGtNbbtdZ3AKeAnEvc/zXPalsPDFVK9dNan1ZK9QOSrPVd1QGfBv5kvf7IJ7lKqc8BrwBf0FpXIYQQQvRN8twUQojz0CjCXOmstrjMiI9Oz2qtn73cg2ittVLqNuC/lFLHAQdwi7X5b0ayKKXOjGQ50uUYpUqpRcAmpdQC4GfAK1rr5y/jUrp1rvNRSj0N3KCUegb4KeYZMxczXeNTQMWlTn5NA0+ttU8pVYb5tPUV6+seaz5KV78DFiml1mHmoczGdB+jlPos8Ftgrtb6zz106UIIcVVdpQeouM7Jc1MIIXqU34z4+HiUUv2Bf8d82FeklMrHJHa78XKOo7X+ozX8dQfwhNZ6xce9tss49xJgSZdVB4DCyzlGbxhqex/wTaVUNfBN6zVKqU1KqTP/0C8D+4EPgWLgx1rr/da2XwFRwEprsm6ZUiq7R+9ACCGE6Dny3BRCiOtLDpCktS4CsL4eB0bSZSQLmHJYnH8kC0qpwcCPgJ8Ac5VSN13mdXT7XFfDtR5qi9b6A2DsedbP6PL9aeBfL7D/Z6/e1QkhhBC9izw3hRDib2kUp+l3rS/jQhqAZKVUpta6Sik1EkjEJHc72p2RLEqpIZg5nY9prV9VSv0eWKuUulNrvas7F3EZo2auimseeAohhPhbZqitZKIVQgghejOl1ArgVkwg+Y5SKqC1HqWU2gT8UGtdorVuUkr9KyZQ7LB2Xai1Pmp9fx8my+0PgWOYjOPnigIe0lpvANBa/1kpNds6b7eu6TLOdVVI4CmEEEIIIYQQfwet9VJg6XnWzzjn9avAqxc4xnlHspzTZj9mCkXXdXsx9Zi7dU3dPdfVIoGnEEIIIYQQ4rqmUVKzupeTwFMIIcQnilX77D+AqVrrFGvdjcC3gH6YZ+NCrbW+dlcphBBC9C29IautEEKIc3QQQQj7FV26Qym1QSn1v0qpPUqp/1FK5Vyg3XKl1AGllFZKZXVZ77Syq1YppfYqpdadpwj2ZbnQuaxtGUqpd5VS1dbXEZc6nta6RWt9D1DVZd37WuvFWuuvAtHWIoQQQogrRAJPIYQQXd2ttf4HrfUYYDnw6wu024CpC/mXc9Zr4HGtdabWejRQAzx27s5KKbtS6tPnrItRSiVdxrkAngGe1lpnAE8DK7scL10p9c45y3cucD8opSYppV4F/MCJC7UTQgjR+5ypf30lF3FlSeAphBCfHC6lVEmX5WvnNtBat3R5GQd0nNvGardTa/03db+01ke11oVdVhUDnz63HZAFvK2UyoTO4a9/AGac2/BC51JKuYHPAK9Zq14DPnOmh1VrXaO1/vw5yxPnux+r/Xat9R3AKUzNNSGEEEJcITLHUwgheqGrVE7Fr7XOu1QjpdTzwD8DCrj57z2ZUioCU0vyjXO3aa1LlVKLgE1KqQXAz4BXtNbPX8YpUoCDVs1KtNanlVKN1vqL1iRTSj0N3KCUegb4KSY4nou5508BFZdxHUIIIYS4BAk8hRBCfIQ1zxGl1F3AE5ynF7KbngLagF9e4Dx/tIa+7gCe0Fqv+DvPc9m01kuAJV1WHQAKe+r8QgghrqwOyWrb60ngKfqsz+mJ/Iav4FnRAJuslQ/BsfwoEh5vh0hgBHinJ3OANOyEAZhYuhs2wb6H0qkiAxcBHAR5lfncwWrSqO08R0JpO3tyRxLE0blkU04jQwCoJpMgDp5hMX9Rv+3ZN0CIj0lr/bJS6lmllFNrHbicfZVSy4ERwEyt9XmH6yqlBgM/An4C3K6UWqu13n0Zp6kHhiql+lm9nf2AJGu9EOJy3fUwLIGRY/fgwQvAro7xBLxDGZmxB4AATnx7U6EB8xwFYsb58QzwUvbeODgIxGP+wnwX+BwfKW8fk+ynrcRlBrSftBYX5iMqgCbMtreA9Y9c1dsVQvQsCTxFn1VLGp7HG0xfTb61Mg4S1rSz5ruzmRkyo/9shNnCNB5rfQiAUyOgfwqUk00eJTgJEMDJNLYQxNF5/ITN7Twy/XvMZS1+nARw4cTPqNIaVuYuBmAxKwlj49/f/4+evHXRB5h6ZD2b2EApFQMknJlPqZSaCRy1lss5zjIgF7hFax26QJshmDmdj2mtX1VK/R5Yq5S6U2u9qzvn0Vr7lFJlwO3AK9bXPVrriw6zFUJcQJsJOutPpFBPCgDhkzaSM7xUrhsDOV0qDJUDd5kf77YmJ8Hhh03QORRIAI4B2Zgg0uLOqMO3ItWUrvcDzUA8xOT5aXvKZRoVACFM2jAhRJ8igafosw69P8w89ELQlBsHQKK3hbp5btKoJWB3kdTqI3W9j+UtD1G8OAeATKrYsmAa8x7fYPprJkDCyQZKUvKYt34DxXNMu6Tpjbjw08gQ8ihlLXNpJAlXboAkGgEI4qCWNHJvLKK0x98BIS5bNPA7pVQ0cBoTcM7UWmul1Cbgh1rrEgCl1ArgVkxfxjtKqYDWepRSahTwA6Aa2KWUAjigtZ5zzrmigIe01hsAtNZ/VkrN5iN9I8aFzmVtvg94SSn1Q8yful+5Qu+FEJ88taZHE6DNawJBW3IrDXs9Vq+kghhN8mgv9tEharZZP4bJULNuFCNv3WN6RBsHY3OFCB+MJXmGl4YdHgB8J1MhBjgOeDCpxxqgrdhlglWsbU2Y9GN7eui+RZ+giZBMtL2cBJ6iz8q9sQgGAS3gajWJOvd50hm1uYbUET5oAeywY95NTNy2m3ErywA4viCCeSUbWPPd2cxbs8F8qpsK2ZSzY85NZFil/0rJo4BCdjGeMHYWh54h2tfBnpSRZFMOwFN8k/F0q/NGiGtOa30YGHeBbTPOeb0UWHqedvswCXouda79wP5z1u0F9p6n7XnPZW37ANN/IoT4uLIwQWNkCGJM72b4w1jcY+toPhqPLdJMSWnY5sE9uY70yfsAqNl/I87ZjVSuHkPyfC/ORD/BZgcMNW1Jto7vxQScXswQ2wIgJgRe+9k271hthBB9jpRTEX1WCJsprlAH/VvM4sRP8fQc3vRMYUfuTbSPg4mbd7NvcjqUACUQXd/BqWwoYDveecnwoTlGOdkk0Uji5hYSN7cwnl3YCbFo/cvYCBG0OyAStjOpc77n/+PfyaVUJrsLIYS4LoxMep/4gc3Y4oPY4oOQAL4dqYSLY2krdvGVAb/BPbkO33upuPHhxoctPkigyQV50PCeh/iIZuIHNpsqvi4zxNadUWcCSjsk3+M180NPWif9gLPzPechgacQfZT0eIo+K49SM1T2NUzqEiCxtIWNudksKnoZQlDX5ib1AR+jXqsxxRyAukFugjgYVVQDg1tgBuxLSWc8uwjiYN306QDcun4zwTlh3pwzhVtKt0IqNA2K49ulv+Kx3PsBmMYWqsig+mgmZzMcCXFpV6mcihBCXFiyGWp7I+9TeHISYCUD2unCueAgAL/a+20+N3o7N459n8JG0wa/HdrB+dmDBJsd1B9NIfxhrBku2w6+danm8Ld6adjroWGTh5gCP23NDvDbcX71IIHnh5pjZWESFzX07K2LvuE0/a71JYiLkMBT9FlbmAZb4fgvI4j+sZVUcwIs+ubL5sEGpG7ywbegKSWOxG1mOG5qqg8+9EEWFKfk0MgQpoW2EF3Swbr8XBwEzc7ZEMbGFqbhzA2QxgG2MI1puVv4vvdJANZ4ZpPPLtIG1lLdw/cvhBBCXJYySKGewuqbyc0oAqCeFLLv2U4z8YSxkTa6ltKjuYT9sTg9JhgNRjqIH9iMr3GwGTbrB3I0zrRGAhuGdma/9R11QztQDm2RLjNvtAICDMU2txWAcGEspIM11VQI0YfIUFshhBBCCCGEEFeV9HiKPms8u6AFttincWveZrMyBZgMdXPcpHp9kA97UkYyZk0lx+ec/Rxml2c840O7aGQIDoJEP9BB01NxuDibsfaUGzz1DRSkbGectwzscEfc6yyJfZKV9vsBsBFiL9lU/3o0sL5H719c3zqIkLnBQoieNcqUGKMCDmcMBuDEiQHUDEjHtzcVW3IrhyMHE66IJXmil4b96Z27+opjzdxMPxAJnxteyLvVk0xNz2jTJtzswDailfDBWGw5Vg9njANeUYS/2iUb6UGgsAfuV/Qp8tzs/STwFH1WNuVwK2Zo7ASz7pQd+n8IqZt9HJ8cQfSqDkJZdvbNS2eUt8Y0+jFMXbATKmDa4i0A1D3lJrXeR2Ld7s4hQ6TCmylTuLV+M/s86ZSTzbwjG1hZej9v5k4B4NYjm3l60L04FxwkcG8PvwFCCCHE5UiBGtIhEhoaTR1PTtqIH96Me3Qdvv0ppAx/nzZcNLznwTbCBI/jB+6iPC0bd4SPyv45ALy7dxK25FYcniChkyaobGt2EC6OxVbQauaAHgTGhYh5IEhboVXHc1wI3rLDZ4HNPXv7QoirSwJP0WcFiYEsmPqVnZ2Z8/pnA3FALgTtDgoX5+EkwKj1Nbw05zYA7l72uukFraik0D6JW1ZtJXqED1qBPNg36OwnvEk08lLKbWRSjYMgOwbdRNKgxs4SKusGTaeRJBZHrOQnMuFdCCFEb9ZuyqkUzHirc1Uz8YSw4+twQ5uiZtMoiIHksV4aVpv0s7tuHk+4Ihb3RB98oEifsY/mjnj6RZw28z7rrd7MEBAD4bdiTVXeSOADO23R9rOZbIvtcAS4GQk8hehjJPAUfdY0trAuZTq3ztkM2dbKHwPfBSrMy1vWbIUsODUF7n78dQDu+O4LPMLDkA+3bNvKvgXpjDpSw7FBUSSsaKdx6RAAphbt5FQ2OGKD2Ajjws92JuEkYBIbAUEcLCt9FNWigUd69P7F9c1ktZVC2EKIHvRZoNiO91aPSQSE6c0sOZFH/IBmiITkGV6aT8TTsD+dnPnFAJR9ZxzMBV+HG/eMOmreGwUpIWizMzJjD5VlYwBw3nyQQG0S2BVEYRIN1WCSCZWcvYyYb/pp+wdXD9646Avkudn7SXIh0WcVMolpoS3mE9Y6s3h/k2webimQ+GAL+IAi6F8OpJrl1c330kgSP8/9Ot7JyQRxQJ1VF3QTNJJEI0k8ln8//V8zdTsDOBkSMj2do1bWMG/9Buat30ASjTTlxplPdoUQQojerALI0QDEDzS1OAv33szgAYdp74giN6MIB0FSBtTjTGvEjws/LtxP1MFxCDw/FN97qRCCmHiTAd6DF5qBZrM9ffj7JthsBgZpSIf0sftInu8leb4X4qGtwQWOa/EGCCGuJunxFH1WCXlEP9gB94HXkwyAZ7NVGKwCuM983TH9JrIpxxYKAVBon4SNEN9+9FcwB2xZYUiFxKIWit/Koc16Gs5lLXWL3SyqeJnHsu4nZLcz7kgZuOHnc77e2cZOGOJDPXz3QgghxGXymqRApUdzzRxMwD22DoD4iGbKj2absinbUiEZ6G+CVD5QEAk5XyvGe8JDm9dFW7MDp+cgG3d8qXO6C/8ANXtHETPbT9srLkCBJ0QQB75fm1qf5Fltz+RTEEL0GdLjKfqscrJNvc7DdH4qe3xyBGsWzDYNxgElMNG7mxJyiX68g+jHTb3PqZt3UvxQDsVZOaSu8XE8NqLzIZhBFRlU4fE2EMTBmqzZ5FLCOG8Z6wZN57k5d9HIEBoZgo2QGXb7HzL0QwghRC/XAD7cOOKDZrRQCHzVqdSsGwXAlwf+liQaTUZaL+QMf4+c4e8B4J5cR9m2cbSVuEge7YW2Ls+9NLPYRrRCO7RtcIEHbFmtUGw3QecRzBKJ+XC4uCdvXPQFGkUI2xVdxJUlPZ6iTzu2IIqEbe2M+0KZWfFLmPfaBpgCdQfdpK7wcTwlgqkrdvLYQ/cDMJONHJsexbiKMqgHRkB0hQlIx32uDN4wh3rTM4Vb1m9lVF4NrIeXlt5GEo2UM5o7WA1A4pEWHIOCMLIHb1r0CWceoEII0ZNqqkfh9Bzknyeah13h0QJuvPV9vCc8vOxdxMjRewg3Oxg5Yw9lz44zO/2DCVBjxvkZMOAEzSfiiUn2EzppZ/bENWxonANAuDiW5BleGlJSGJn0PpWrx5ipKA3A56wLiAlBpB2cmNIsQog+Q3o8RZ/1CA+T8JV2dky+yQzdyQOKrI3lkFrhgyyILuqgbqmbxaxkMSsZ5a0hoaKduiw3j02/n2O5UVAEe3JHsu/ddCg3++dSwp45IzkVB+uWTieTauyEyWYvuxjPLsbDSaucS+W1ex+EEEKIbvkXKMh4i0DhUHy48eHGPdBHCDuDBxwmxuPH1+HGmdZI5d4xUAAUmJ7MmGQ/bV4XvmdTaWtwkTKgnrYSFxvem4c76TDupMPggYYdHjhpo3LTmLP5D5IBr7WA6fUM9PztCyGuLgk8RZ+1nAfgZ1ZSoDmYZQQmw209sAlwA3GQutJHEIdJJNRi9k894uP7pU+S8Hg7TIYxFZWEu/RAhbEzhEbKY0cSxk4SjVSRSRKHWHLkBZYceYE9KaarM+uJP/XovQshhBCXbTMUNk4CwHvCg/eEh+YT8QRw4iCIZ4CX0Ek7oZN2Ro7eY+ZungRbZNjs7woRc6efkRl7qD+RQkyeH0JwuqMfpzv6mfbxQIMy37uAJuvrzSGzeK0hul/s4XsX170OIghjv6KLuLIk8BR9loMgvGnma7ISswBN0+MgH/OJahzghh2LbyK11EdqqQ/ccCoVeMZsP3UfMBj40OxfN9lN3WQ3qdt8lJLHmPpKtlNAUquPRaUvE8DJS4Nu46VBpi6oHycVqz/bw3cvrnfyABVC9LgoSE/y4p5cR/aAcrIHlANmGG0jSYSw09bsIHzSRuW2McR4/MR4/LQ1O2hrcpKTtMds259DW7PDlGABAhVDCVQMxZl1EBqsuZ0VQIwmZrYf2oBCu1nAZLx97xrcvxDiqpLAU/RZ09gC2bBveroJGq3AMbG0BV4DUuBYSpRJMHRkN8/l3sVzuXdBCPpvgnUPTedtzwR2xd4E1UCcKaWylrmsZS4vTb6NW1Zs5e2UCTzIT7g19nWwQy1ptOGgDQdDaCSPUmw3t17Dd0IIIYTohjw4fGIwvvdSrZR8ZpJlTLKfG3mfAE5osxM/sBluCNG2xkXbGhe2yBDJw2soWzeOcFks1CqciX4zSqgNU8asBAJrhjJyxh7TZjbwpCJ80mZ6Pa3eU+IBj7UIIfoUSS4k+qwXWcgy+6OMqq8xQ2wB7EAIdjx3E2nUknrEB9lQN8jNogdeBuDUD6F/HAyjFgdB0lobYLApyXLLo1vJfKgKgAOkUbfUTSNJTGrdyRsf3sae3JHMZCPL+EHndXyRtYQLY3v47oUQQojLVAjhO22QEsKP66Obtt1shsS6QjQfjQe/nc/dsx2Ad/cXEB7YbLbbgUGaYLOD8Iex5Mwopuw9KwlRADM3tAlI08Q8FqCtxGVyMGzocrI8zs73FEL0GRJ4CiFELyRZbYUQQojLc5p+1/oSxEVI4Cn6rKWs4PiICKI3dcAMa2Wkmb/pIEjqep/JcpsKtUvTWLt8LgBzWUsqPrJbTSra/j5gHVR9N5PGh5LIDZUAYLOHSa33cXfodU654dQIeJX5LOUpZrERgBLy2MgsbAWthHv4/oUQQojLcjOEmx0kD6+hhnQAwidthE/aoT8kj/YymMOcYACVZWN491mTiAirQxNPCE7asMUHSR9YQ2VgDGXbxkGaNtujFLhC2G4OEbbmhfIuMB3Tywmmp7MEM9S2vqduXAjREyTwFH3Wv2/7D75f96R5mBVYK5+D/pEwZHojTXPicMwIYg91MLFoN9n5JolCgrcd6qB8+kjG1FfytmcCU9mJjRATvbs55TaHCtttvJRyG3mUkhY6wCF7ErPYiIMguZjg1I+TSRQS/kCG2gohhOjlnoD0r73P4ROD+fKA3wKwceBMAHz+VBr2emiIMQGps+AggeShANhcrTgI4vOmgidEuDgW+4yQ2ZbTSrjZYY7fDhTaCafbYZCGIwo+i0n2d2ZOZzOQBTzZA/crhOhREniKPitj8l7YBm9mTSHXbwJBRyjIIXsSNqv/8ZDdQaM9iYmRu0mobwfgeEoE0TM6GEIjx91W/q0pMHXFTk4tMImHAPzzXAyjllrSCNid2AgzsXQ3AC/lmgd1PrtYzgPE5Php67lbF0IIIS5fDjR3xNNW6OIFzxKzrr8VIEZZbdoU+CGUaIcYE1yGmx3UnPSYupwnzRQB7wkPI2fsoXJ/DsnDawBoaLDaBIDjCoCYcSaBUdsGa05pOma+50enmApxSR1EEJIM7r2aBJ6iz1rIi9RNdnNL0VZOZZt1/VvAE2rgmCeKhDHt8C2IWRAEH531O6MjO9iXkk4YG4m+Soal1MJJeHvpBKYW7cQ7LxmA7NBeAKrtmYSws4VpuHL9eI40mIy6QCl5JNFIW608QcXl0SgpgSKE6FlTTekT580HCdQmmXWRYWwjQoT9sdyW8RKl5FGzdxQDBpzAHmkCz0CTC+rtEAI8YXDasUeGqNw0BufNB2l4z+rOTLaG3DYoOAVUQFu0C1JCHx1qm4jp+RRC9ClSTkX0WS+yEAdBivNzCNkjCNkjeDNlCqfc1nDaGXD89ghcrS14pyfDVsxSAqMer2FMaSXFKTl4vA1QatUDjYQQdkLYCdodrLIvJISdMDYebP0pQ0KNvDloCst5gOU8wIssZAEvQqS+1m+HEEIIcXFvA5EQ2DkUTimzlNkJNzuwuVqpJpOa/TdCjMa3N5WUiHpSIupxJx0mZ2wxMXl++MCObUQr7ggf7hl1hE52+QDtlIJCK+gE+BcNCUCZHdZjlu2cnR4jhOhTpMdT9Flp1JJQ1M64uDLYZtZlLy2nfwmcyoPyZSPJCFWxPXYCmVTjXWZ6Mp0ECE53sJLFLORFjnmiCC51kNTqwx8bRxKNAFSRiZMA4+rLeC7lLiYW7aZ/XgfZg8rZbj015/MqrtYWbPFBSS4khBCidxsCTs9BQsl2UgaYzD6VJ8fgHl6Pb38KZRXjTK/lBwqSOVsmJQF8J1NhM1AAYX8slQ1jIEabobkJ1vGbMHU6bwjBW3bck+vxNQ6GZDuUW20+hxneG6968MZFX2BGCkk2+N5MAk/RZwVwwuPAt4A6sy51mw/qoH8RZHy3iujXOhi2oJbUVT68C0zgmVDaTkJLO9+cvILElS0wA1anzOeLsWsB2MI0APIoYVxRGW/mTyGNAzw3/S6COMijhPtYCZgg9o3Y6YQbZcikuDwdRMgDVAjRs5ohN6KUsgE5DOAEAO7RdTgIkjT8PcoYS/rw96lpG0X66H3UvDcKgJhkPwMGnMBHqlnf6IGddphgBY8V1vE94JxwkMBbQyERfL9ONb2fHs72ciYAZRJ0CtEXSeAp+qyqE5lmvkkR8D1rZTlwC7w9aAKTWnfCevBkN0AqDAmZnkw2ASNMIqLixTlkh/YyjS3UMowkGnEQBGAtc/l+6EkAsilnasVO8MFzk++iETM3ppY0MqjCFhmSHk8hhBC9WwPYCZFEI6WNZtKlM9FPTZMHPrBDmuZG3uewZzBBHCYzLeAZ4MWPC2I0NTtGmV7RUZzt4Yy0jl8M/UafNt8nQ0yBFbCuTj3b5n+RxEJC9DJKqTjgP4CpWusUa92NmO6dfpiYcqHW+qJzyy4aeCqlHu/GtbRqrf9vt65aiB6UOaCK4rdyGLe5DKu6CRyGNydPwUmAqth0kv6rkYRF7RAJhyabYNHT0gBuiN7WwbiTZbw5Zwq5lOA50oB3UDJOAgAmgdAIE3Su5g7Ks7J58fF/ZZH7ZX6e9XUAFvAiW5hmaqAJIfo8eW6K61oybNzxJdwT63AnHQZM3etA2VATGB5RbDz1JQoy3iKEHd+RVAD6DT9Ne0cU7uH1MBx8m1JNL2ZMyCQdOhNU3gDNR+MhGSiGtjUuPD8p5sTsAbS9YkWbBZhn9smevHHRF2gUoWswUkgptRz4IpAGZGutKy7QrhbzP/vM/+7vaa23WNsygJcAJybv81e01h9ejWv6e86ltW4B7lFKvdNl3fvAYuuYvwOi4eJFHC7V43kX8Mwl2nwFkAeo6HVC2MikCtzAN6yVi6EgtJ3oig7wwWPT7+f733sSDoPnSINpkwU7Jt9ECBtT63dyy4qtMAJ2TL+JiUd2UzUoE4BbirbCYCgnm28/8Cu4D9787hRqSePbFb8C4O2sCYzunLgiRPeZB6h8YHEdkuemuH61A4lw4sQAPj/A/H1ZTjbcoIlJDJjeyRWp7HKNxxEfNNlogWbiCRQONX9O54QYOWMPAZz4tqWa+Zxl5neZ8+aDBJsd0AC5Xyui/Gg2ZZvGQRPE3GmVVSl0mcBUiOvHBuAXwP90o+3cCwSmzwBPa61fUUrdCawEJndtoJSyA4la6790WRcDxGqtGy/jmi54LqVUuvW6qy1a6yfOdzNKqUnAVwE/WOPzL+JSgecftNaPXKyBUmr4pU4ixLUwiUISjrTDh1ifxwA+iF7VwZ7FI0mjlrmspckTRyIt8JppUrw0h4ne3Xg9yfAh1C11k1rhY2LFbo5lRZnhRcBz+XexqP5l8/p78PSge1my/gXq5rhZkzUbgCAOpm7bScy4oNTxFOKTQZ6b4vqVDcSEiB/QbAJOwEbYzOvcMYq2RGCsSR4UBEYmvQ9A5Y4x2HJaCX8YizPRT+XeMdiSW4kZ56etwdXZvxNYMxQKQtCMGcr7lp3ke7w07PWcreMJuGfU4YtM7dFbF+ICXEqpki6vn9VaP9u1gdZ6J4BSf9/cZKWUG/gMMNVa9RrwS6XUIK31kS5Ns4DXlFIztdZV1vDXzcCvgee7c02XOpfWugb4fHevXWu9HdiulHoKyAH+fLH2Fy2norW+uxsnvGQbIa6FF08shDcBO7DKWkbAscVRjLm5ElsohGdzA2Hs7PGM7Nxv3OYysIOnogHirJUl8FLWbVSRydzWDcxt3cCiR1/muZS7SKKRHYNuYj6rqZvjppEkysmmnGxKyOWOyS/Q9pZMWBHik0Cem+K6VgzpSV4A6ylWThAHNdWjmDnxd9Bf4x5bBychXBHbuZt7Yh3hk6aMypnyKSkD600w2QDkaMjR5MwvhkK7qdP5gR0mQMNej6nZ6bcWL/i+k2rqeQpxGUxSPvsVXQC/1jqvy/Lspa7jEl5VSu1VSv1KKRVvrUsBDmqtTwNYXxut9Z201qXAImCTUuofMQWQ1mitPxJ0XkK3znU+SqmngRuUUs8opYYppQqUUr+01ts5m0bsgrqdXEgpNQAz+KFzH2tsrxBCCCHOIc9NIYQQXfyj1rreGjL7JPBL4M7LOYDW+o9Kqe8AO4AntNYrrvxlXvDcS4AlXVYdAAov5xjdCjyVUkuBZcBRoOPM+QEZLiR6rUkDtoMP02v5M7NuTe5s0qhl3OQyoks68E5PxrOmgdQ8H8eWRgGQsLKd45MjiA518FzWXcwPvcrbCyZw97bXeXPyFKpi0wEIP2RjJm/gam1hS+wUtlPAeHYRxsZ8VgNQQi4Aq0/e09O3L65zUo/s+ibPTXFdGmWSCYWw884JM9qubYOZc1mfkYIzrRHfe6kkj/XiO+qmsnoMAOkZ+/C1pUJkiLY1LijQ1GwbhXOeVTrliBnuV9YwzgwWjLFqgVZgXp8CxlrXUGN9/W0P3rcQPUBrXW99DSmlfgW8YW2qB4YqpfpprU8rpfoBSdb6j1BKDQZ+BPwEuF0ptVZrvfsyLqPb57oaLjrUtov7gUyt9ae11sOsRR6eolc7k322aU4cbAW2wrwjG8zGEcB6SGu1htOehISidhKK2tmx+CZq7cNgGswPvUrQ7mDqkZ0wAm6p2EoSjSTRSHZrJYdIYkXs13EQZDTlrGIhfpxUkUEVGYSxc3fp6yanmBDik+R+5LkprjefghB2Gqo9pAyoJ2VAvZmTeQoaSSItohYGacLYsEWGcXoO4vQcpGbbKDgGXx74W5gAnFJwQ4hAk4uYAr+pzZkAeMz8Tt5RJtPtUEx/STNw0FqAmNl+U4ZFiMt0mn5XdLlSlFLR1pxMlJl4OQ8oA9Ba+6zvb7ea3w7sOWd+J0qpIcA7wE+11g8Cc4E1Sqnx3b2O7p7raulu4NlwnmxJQvRqAZycug8SK1pMoDnCrB+3sgzcsGP5TfS/zaw/lhUFqUAqTHx0N0k0whb4if1BDpEEJ6EpJY4dWTeRsKadhDXtBGOjGLOtkm97f0UatXjqG/j+kSeZFtpCOaMpZzRO/Dyde+/ZVPJCiE8KeW6K608bVO7PISbZTxBHZzK9kZP3cLqjH6Xb8pk5fC3NR+Npa3ARaDJLzuRiSIDfHv0yvIWp3wnQZqet2EVyhpfkDC+02XFPrMM2txVcYBvRCv8SMnNAGzBLpCmzIpXmxfVCKbVCKdWAmVrxjlJqn7V+k1Iqz2o2GChUSu3F9PVnAF/vcpj7gG8qpaqBb1qvzxUFPKS1fhVAa/1nYDYQ091ruoxzXRXd/bF+WCn1PLCJLpWVtNabrspVCXEFbD8xif6rMNOl11srIzE/7lNgYv1u1rw1m3lHNpDwQPvZz35GmFIsTR4by448CoehLstNGBsTj+zm2DxrSO62dvZMHomTAKnbfDw3+S4Wlb5MdGoHcwetBUzwe2v9ZlZ8ZinVPXr34nrXQYSUU7lKrlQh7EuQ56a4/nhh5PAygjjwWNl9wkk24mmmpjkdbgixcd2XYFyI3Iwiqk6Y8mJ+TObasD8W5oaIiQ/S5nWZ8iw3hGio9gDgzqjDtyMV4sGZdZDAqqHEzPPT1uyAsdbvu+PAuBBstsOenn8LhLhcWuulwNLzrJ/R5fv9wJiLHOMDzg44v1Cb/cD+c9btBfZ295q6e66rpbuB50xryQBOW+s05oH6sXSniKk1/ngFcLN13sfOZHC62DbxyfbzAd82VYkiMb2ZwLHcKILTHaRW+OBFmLZsC+yEUz+E/tb/unXzpltzNe0cj40gurwDGyFSvT5oAQaZdk2T40ijFlsoxNuTJzCzc6g+nXPzMqji6ZR7qd41mrPRrxC9k1LKCbwMpAMhTF7JxecbgqOUisQEb5/HBFbvaq2/Zm37RBTCvgR5borrz51QfyKF8EkbzZHxALQ1uPCdTCXG4yd7YDmlWfm4kw5Tun88MYlmSkvDOg94ML8J+tsYMOAEbZFgS27FFhmmrc0Elb7qVJPRtr82cz/zupw7ZH1NBt6yQ1XP3LLoO+QD296vu0Nt5wBpWuuJWutJ1jL5knt1z5kiphnA0/xt0VKAOzC/0kYAnwN+pJRK68Y28Qn2tcbnTA3PfzMB57HcKBLWt5ug8zUgzyQSwgf9i6A4N4fi3BwcBEmsbyGIg+jWDqgDV2sL2MGbm0wIGyErsEx4oB0waefLyYaT8NKg2zqHKDlCQWay8dq9CUJcHg08rrXO1FqPxqT5eOwCbR/H/JmZobXOBh7qsu2Sv9eVUnal1KfPWRejlEo6z7k2ABOBv5xn2wXPpZRKV0q9c87ynQvcD0qpSUqpV+lmIexLkOemuP48D/EDmikYWEhbg4u2BjNM1pbcSuaAKupJIT1jHw6CuIfX01bioq3EZeZjukKmhzMyjIMgxJhIsq3cquNp9fvnZhSZb5KBBrBHhkyJlVOYpb/+aEAqhOgzuht47gf+eqVP3qWI6WvWqteAzyilBp3T9MvAc1rrDuuT9w3Al7qxTXyCfS3pGRN43n42cRAjMHm7BsO+OemQD7iBn9KZNGjqtp0cS4liJYs5ZccEpq/BcXcEB0ijlmHUMszM/fweRLd2sIVpuAiwLz/9TN0nwMwRPROQCtELuJRSJV2Wr3XdqLU+qrUu7LKqGPhIcAgmQAS+gplroq19D1vbuvt7PQt4WymVae0XB/wBmHFOO7TWO89kAzznOi56Lq11jdb68+csT1zozdFab9da34H58zfnQu26SZ6b4vpTYLLavs+NnYmDfEfdxA9s5gQD8L2XyuETgwlhZzy7TMAZD86Cg9Bm7yys4CBIepKXcLPD9Mk3W0sTlDbmQWTYTHuJgcCfhn70Gn6nzFzP9p65ZSFEz+nuUNsPgW1KqQ18dK7Krz7m+f+miKlS6kwR065Du1L56CfddZwtdHqxbeITbDTlJqiMA1qtlZFQN91Nap2PUetrIBu8c5Lx5DXgDPlNG7cpg7KUFbwRO51p39pC9IcdRJd0kJ1fTpuVbCGttYE3B02hhDzyKMFBEM+2BpImN9KI6bSxETbHvCGEEJfjKpVT8Wutu9WXoJSKAP4VuowhPysd8+fkw0qpSZghqf9Ha72Tbv5e11qXKqXOFMJegCl69MrHLYR9gWfI+e6vsxA28FNMgD0XUMCn6EYh7EuQ56a4/iSbqSJhbPSLMCPEbZFhfHtTcYwO4h5bR/PReBrKPfhGuE1ZFCB00m6CxQKg2E5ZzlhiEgM40xoJnBp69icgRkOZ3Qzi9wDx4B5dhy8hFY5ZbaKsr4PAmmYqRLdIGbLer7uBpx0z5Cq7y7qPk3ThmrA+3bc+4Y+7ptcirr43mMUSXoCT8NycuwBYdNvLJD3vo3hxDuPWlHHKDTEEOZYSRcIa6+PVPCglj6nbduIZ0QAfQt1kN6leH4nbWjieHwTMnNBbUreSOagKT1EDYNqtZr75JBiYxHZKyINamXMgrjtPYQLKX55nW39MPco9WuvvKKXGAhuVUp7LOcH1Xgj7EuS5Ka4/ZeaLjTANvzY/zv98zxv8IWYmNTtGkTzR6sVMMImEkjNMZOg76gYvuL9Wh69xMM5EP4GKoYSTW3F6DhKoNR/GxiQGaIt0QLIdZ5apneLbm2p+8rKsa8jCBJwfZ4a1EKJX6lbgqbVeeJXO390ipnWYT6P/ZL3u+mntxbZ9hNb6WeBZAKWSrrs/AMTlaWQIbIO3fzOBuZgss/wEgrFRjDtSBv8G/VPAld1C/2mczWq7Dr4f9ySE4Hh+RGcPaGrcZpomx5H4zRYAji+PIGB3sZ1JFOWHGE05Q2gkgyozvwUoIY9s9pIxfq9ktRXXDSuZzwhgpta64zxN/oIZjvoagNb6PaWUH5NIp45PSCHsi5HnprguHYIU6incdLOpxwkUHi0wdTk9ISaxnU1pUQRqk5g9/LdUYbLaOgYGCX/Nho0wvpMpJtD0QthlJ1ARy+dGbwfg3f0F0KagBAIVQ808z0TM1zN/kXqt15lAeQ/euxDiqrvoHE+l1CUTIXSnzYVcRhHT3wGLlFIR1jyW2cDvu7FNfIJNopCm38SRRykJ3nYSvKZHM2FNO7yJyd3ZCrWxybAM3lw6hTeXToHb4dTtQAlEr+ogurWDW1dthmrM/M18IB+CdpNAaNGal6kmkzGllQRwMYxaVrCUFSwljxJqGUampOcTl0mjOhNZXamlO5RSy4BcYLbW+rxjxLXWfmA7MNXaJwMzsN37SSqEfT7y3BTXtWwo3HszeMDmasXmMvNUkjO8pCd52cI0MwT3iOJ/Ov6Ryk1jqNw0hpqj6dTsv5HBHGbk8DKIDJN+6z6ciWYKSz0p1JNiAthmSL7HC/UQk+c3Q2xdmPqfb2GG4DZhclgLcRmu1XNTdN+lkgv9XCkVpZQacKEFMy/n4zhvEdNziq6+jEnU8CEm2cWPrVo2l9omPsECOEmsbzHzLUswyzcAN9QtcLN08uNgB8+aBnBDGrWkUQu/gJLYHI4/FwH5sGbQbMiD43kRNJLEwnn/ycJ5/0lifQthbBybF8V4dvHz3K8zqqKGIA7m8yrzeZUx9ZVkUsXGXZK3Q/R+SqlRwA8wPWi7lFJlSqn11rauv5PB/K7+gVKqHFgD3KW1bu6yrc8Xwr4AeW6K61fQlEDhGLgH+nAP9DFt4BbaO6Ko2TsK349NbbLksV4zr9MJOCF7YDk0KAr3T6P+RAoU2zl8YrDp+WyGhh94aPiBpzOQbaj2QDa0veIiJttvxk9MsBZPyASdW67JOyCEuIouNdR2NGaUvTrPNm2tb/o4F3ChIqbnFF09jUlycb79L7hNCCFE92mt93H+3/cf+Z1svd6PSSVyvrafiELYFyDPTSGEEOI8Lhp4aq27W25FiF6nhDyoA3tKyAyPBd6cN4VbNm8ltcLHim3fha1Q/F85uPAzak0NAHuWj2RcRRnerGQ8v2gg9JyNuiw3W5jGNLbwc74NwHMpd5HGAULYuaVoK9Oyt7IvK53tTDLzS4FgioNpoS3E5PglT4K4LCY7nySlut7Ic1Nc12rBER8kQKxJGARsLP4S7hl1xHj8pPzQDJr9w7ZZEG16PgFKd+QTk+enzesie0A5756cRJvXZXJDZwELzOHDb8WaOZ1N4LzZJB1qK7faNVvXcNIOORqSlczxFJdFnpu9X3ez2gpx3Ql0OMEHnlUNNC0w2Rj9OCHL1OSMpoOmpXFkh/YSXdLBsXkmh/uYikqOj4ggiAN+CMOoJYiZz3kmaRCYup9TV+2keEEOL+XfRil5pFHLwxU/pTgrB4Ba0thin0bbaleP378QQghxWY5DVEQ76WP3UVM9yqxrhuaj8YSbHdQnQqV3DKRpnGmNZsgsZq5mW5MTmqH0aK4JLtsxff+RXY4fD+mT91GzYxSBNUNNIqFEiPmqn7YS6znpB8rUxy9oJITodSTwFH3WzIiN5oFXAolfMJlo7/7u6+CD6JYOTt0KbThIfLAF8iHhAaucSgjCT9kZU1QJwMTQbmiBzBG/ImSPILrEJPm85cOtHL89gioyuHvN60yaV4gz5GdN1uzOADWJRt5gJkPmH+DQHT3+FgghhBDdl2PqeA7mMP+cYUr4lnpy6RdxGsfAemr23wjtkDt6F4cZTG5GEWDKr7x7ahLOCaZESjDH1P9sG+QEwJnWCEB8RrMJaBsgeb6Xhr0eiNG0veU6G6DGY/It59EL8lMLIa4kCTxFn9VIEsemR5HwYXtnWvgd+Tcx8chumgbFkbi+Bc+EBpgB2GHf8nQARh2pwdHajjc/mbTWBkL2CDZ6ZjG3dQNb7NO4NXWzOVgkVNszuXvN65BvHtYb7bNIo5Zx3jLT5jC8mj+fQ98Z1uP3L4QQQlyWNvBVp+LMCJgstECgcCjcEMLXlkpyhpeGyBRqO9IAKP2TNY8lJQT9NYE/DYVBpupOuEzBuBAU2wlEmt7MULwdmkzQeWYoL2UK4sGWYxIPOeKDBN4ZCht66qZFX6FRnKbftb4McRF/d+CplIq4QH03IXqFhbxIwoPt7Fh2U2cP5MTNuyELXK0tHJ8RQdDuILGkBdbDqMlmjueOZSY4LSEPT10D/UMdzKvbwL456eRRyrEUMyQ3oaSdMSWVvLl4CplUsZ1JzOQNEotaKM7PAaDRM4RMqhnyxAEOLb8mb4O4TnUQIanc+xh5boperwByM4oo3Zbf+Reie3IdvsbB0F93Dq11R/jIpIr6sSY4Ld2WDzeEoMZOTHYAgPhbm2l4z2PKo7SZeXfxSfUMnniYmk2jTEbcfUAeOLMOEtg5FICAP9ZktZ0NrO+h+xZC9IhuJUFQSq1WSkV3eZ2EqeEmRK/1bX4OI8y8zjGLKhmzqNKUUklx038VHLIn4QhZczbnwKnvmWVixW54E2aG3uBYlgkym+bEMaq0hnKySVjRTsIK04u6b3E6t9RvxY+LmbzB3fwGIs0Q2yQaOUSS+fq+9HgK8Ukiz01xXXoeDjMYmqFg4lsUTHyLEycGgN9u6nOeNM3shKgik9L94yndP56YcX7Sk7wwTtPWYHo3G6o9OD97EBpgZMYeRmbsoaExhZr3RmEb1wohzGik5zGlWeIxSzOmqFJtD9+7EOKq6272vWqgRCmVo5SaDrwLrLpqVyXEFZBGLU0L4hhNOdyKWVLNENympXF4ihoI2h1mXokP+j9jFgCyIbqowyQY2gSJFS08l3sXtxRt5fjiCI4vjoDXMNsjYdy2MsrJZiEv0pQbR+pKH6krfSwIvci8ig0fs3iCEOI6JM9Ncf1xWTU2h8L73Mj73EhbswP8ULl3DM4sM4czhJ0BnGDk8DITkAKHTwzGPbweToJngJeYZD+BNUNx3nyQyv05VO7PYWTS+3AcwiftuCfWmeRCBdDmdTFy9B5Gjt4DBZqY2X665PITols0ihD2K7qIK6tbQ2211j9SShUBRcAxYKrWuvKqXpkQH1OQGBKPtEBdC8emW8NjV7UzLruMfbnpbMyfxfzQqxxfHEH0gx0c+65pE8JGGDsOgoSxse+hdMrJppEkTmVDwG4+zT201Ma49WU0zYlj1+Tx5FHKWkZziCTKF2cD4KANe1YI/nxt3gNx/ZK08Nc3eW6K61I7EBMiJjmIrzoVgPSMfRyOH0xbuYvAqqFQoKncn0Pu8F1UVo8x+1k9oW2R5hhl742Dg4AfArVJ0GbK2lbuHWOG7lan4luXaobhFgMuqNxkHSsS02sa1YP3LYToEd0KPJVSKcCPgdeBUcA3lVL3a63DV/PihPg4XATgSTi2LIqE20zG2j2vj2TM5krC2Fi07WUIwfHJEZz6ISRss7LaftgOPnjsoftxEGTJihfIXFBDMDYKf2wcqfU+wAzZbZoTxyGSAHCG/OTZS3AQZAgmg18peWZ+abzu8fsXQlw78twU16V4yEnaQ1n1uM6stn/YMQv3xDraQuBccJB+Eafx7U+hdP943Bl1AFbBsSBlz46D/jDynj1U7s+BLGWCTlfIHN9vx7c39WzNzhgNHgV2c24AGjAjkWJ66J6FED2mu0Nti4Bfaq0XAvnAX4H3rtpVCXEFFDZOggmml7Pp9TiaXo9jTH0lxydHmGDQboLO6G0dlMTmmCQG6+HU7cAc+L73SZYceQGAFbFfJ+HxdkrJozglh+KUHABqGYYfJ7cWbWa1/Q4mHtmNjTCJm1tI3NxCGrXYCEOhulZvgxDi2pDnprj+7DPTUdIz9vGHvbP4w95Z2LJa8e1P4cwADN/+FDOktlaRTg3p1FCz/0a8JzzY5rbCzVaQ+Y5iZMYeE0h+YDcLmKCzDZyzD5pn4w0hiAL36Drco+tM0qH+wIc9fvfiOtdBBGFsV3QRV1Z3s9pO1VpXAWit/wp8Syn1hat3WUJ8fP+ctAV2APWQuMLU8Xx76QSmVuzEE9nA2/kTyKSa6BE+xj1aBt8y+/UvArJgTcps5q3YAOUwizdY993p3Fq6mVMjzp4jGOvAhZ+m/Dic+Nk3KJ1R3hrenm7qt2xkFplU9eRtiz7CDLWVh951TJ6b4vqTZ2pyOgia+ZZA5bYxxIzzE463EXhrKCRD89F4ciYXU3o0FwD38HqT+bbYzshb9zCYw1T+A1SuG0PMzX4zTxTgpA2aFSRDwDvU9HLW27GNaMW3zQztZSeQ3ON3LoToAd2d4/k3fzlrrf/ryl+OEFfOH/48C0owAaWJO8mk2hSkfg2mfmunCSLjgMlwzGNNKPFAwvp25sZt4NQC6L8KYggSwAWp0L/ENPNOTuZFFnAHqzlAGrNaN9O/Dp7LuotGa/jtNLbgJAA39OSdCyGuNXluiuvSThjMYUob87BFWj2XLhgw4AR5A0ooKcijrclJ+KSd+o4Uwv5YAHz+WFPjMyfdDLEdXmbmaJ6EtmYHzkQ/gKnz+S7wOaANyNHEJAZoW+MyrwHyMMNshwD+nrt1IcTV93fX8RSit/u3z/xfnvvMXSxa9XLnkJ2SZbk4pheSkNcO5dD/Q6AO6ua4SfWauZucxAySmwL964DDkLithbTJB+CnULfcFL32VDQwPmsXVWQSwEljbBJ38zrZlDONLQCsZj4h7Hx6/Af8paffACGEEOJyfBlKf51P+j37iLcmYpYW5uOLTMUXMxh30mFINIGob28qtuRWAGyRYQZzmAbSscUHsRMytTkbhpKbVELp3nwAYrL9tB10mYCyGdP7ORuTZCjeuoZizHPYA5T32J2LPsBktZWRQr1Zd+d4CiGEEEIIIYQQfxfp8RR91lrmUvu5kax5d3ZnD6SdMAmPtkMKkA3UQdOcOFK3+dgzeSQAY7yVFC/PYdyRMoiE4z+MIHpTB1Mf2Anfgl2MB8A54g0mUch2CvDjYj6rKc7KIY0D/J65ndewmJX8ZZ2MtRWXp4MIqSEmhOhZ78HsdWvYsG4eZFnrIiEno5ggDg6fGEz8gGYa9npwj67Dt8Oal5nVSmlJPnjM8Nyy/WNJHl7D52Zs593G8aZeJ9AW6TLH9WKG1k6AthKX6e20prGwEyjo8loI0WdI4Cn6rLmshWVQwPbOOZe3VGzl1Leg/y/gzQVTuOXkVhLrW2ArjHFbJfbiYNwDZRxbHkXC4XaiWztMrc45JuDcyEwA0uy1uPAzLbQF7PAGM7m7/nUeSHmUpTwFwJLNL7B4+pM9fu9CCCHEZRtrfbjqwVSfBWIK/JTtH0vO8PeoKRyF/eYQtuRWUqjnRN4AAMInbeROLKK0Oh8iQxAZpqHaQ0OFxxzkXev4Lkx23Bs0nFRQArabWwk/FwvTTRP3PXX4fpDagzct+gqpf937yVBb0Wc9dXQprDcP0VFFNYwqqoEiKI8dCXPglm1bIRWKU3LYsewmMw/0Q8AH+5an84YVYFIOYewEcTAttIVf8g1+yTcIY+MAaXzD/jSzWjczN7SWt1MmcB8rO6/h6en3mtItkdfkLRBCCCG6bwukUwNecH72IM7PHmTwgMPQoPCe8OCeUUegyUW4IpbSbfnED2gmfkAztsgwpe/lm0AyMmySCZ3ELDkaFmGWU0AAeF6Z9eM04bJY08Np8f061ST9k/hBiD5HAk/RZ6UNrIUFcKt3M3X5bury3VAHY9ZXQgXwIBCCMDbGt+7m2Jwojs2JYk/uSEZV1HC393WasuJ4bPL9pBb5uHvz6+yyjyfhSDsJR9pZyWKSOMTP+TYlsTlstM8CoIQ8AjgJ4GRJ6QtMovBs0gQhuulMORWpRyaE6DFOKD+Rja2glUDFUAIVQ4mnmeSJXuIHNDOeXSbb7SkgTZtezWoPbSUuYrL92G5upa3BReD5oTizDuKeXwcfKIgJmeUdcw4WAA0KypQJTg9iJRuCmHl+MxUm6hq9B0KIq0YCT9FnZVIFPjieEoGDIA6CHP9hhAk63eB9N5kmTxxp1NK/BbZTwHYKGLO50nzaCtQyjO+vfJI9+SOpm+5m6pqd7Bk0kj2DRvJq0b0EcVBONtmhvYxnFw7amMYW/Djx4+TnuV8njVqs5IBCCCFE7zXEZKwN+2PNSJ1IKN0/Hhd+2jui2PDePLPNBbb4oHmeVoB7Yt3ZWp1NkP61fQR2DsW3N5XkGV7w2sFrx/l/D0IIaACStRnSG4OZ93nKLG2vuExw2n5N3gFxHdMoTtPvii7iypLAU/RZDoKwCaJ/0UGCt50EbzuH7EkwGU7lQRAHid4WUkt9UAJ5lJJHKRw2+6/xzCaJRsiHMfWVbKeA43Mi2Es2e8lmXf50kmhkLXOptmeS1OpjC9OoIrPzGvIooZxsPjdj+zV6F4QQQohuijFDbXMyirG5WrG5WuGIor4jhUBtEoTA5molxuPHPdBnAkePed7aIkOE34qFeDh8YrBJNFQBDfvTTaDZAIENQ80IoD9hejxjNJQBTZwdmuvB9IAWXIs3QAhxNUngKfosPy6YAbiBOrP4cUEG9F9nNTpMZ+9mI0k0ksTx2yM47o5gXukGbIRgE5yKg0MkEb2tg1lsZBYbubV0MwGcpkcTaIx1cwevUkUG1WRSTSZrmUseJVR3ZPT4/Yvrm6lHZr+iixBCXFQb1JBOEAfxA5uJH9gMxyHQ5CJ9+PsAOOKDhE9aw/ebgWaoqR5FuNlBzGw/RIJngBdO2qAN3MPrIQ/Ig5ib/WafhDPnUzAWE5g2WctOYCgm+BRC9CmS1Vb0WaUduTACTuVDVWw6AOO+Uga3w44FNzFx827WTZ/OrRWbeXPOFNO7CUSXdLAvP51R1JD4aAvHvxtB9MoO5i9dzdPT7+0MNP25TlwE+PbmX8EITGmWyXGs5g621H8BgOPuCJbbv0OgeOg1eAeEEEKIy+CF5qPx+JpT4IgCoGDyW7zPjdRsG4V7ch2+danE3OzHtynVDJMFnJ6DBP40FBJN6ZWy/WMZObyMynFjTLKgRNPuTOkU99fqaD4aT/zAZnx7U6EYbD9qBSDc7ID/ViYYFeIynPnAVvReEniKPqsgohDWQf8RwBxrpfX7aBfjGZ+/m1s3b4ZYKAhtp9puhsi+nT+BYdTizU0mmOtgF+MZsrSRYdRSQGFngNpIEqOO1JiaoM8A34Kf8CBpHODBlIcAGM8u5rKWH6U90pO3LoQQQly+UXDjwPfxD3TRcMTz0W1e8KWl4L61jtMd/Wi7QZvhskDgraHghLZyF2UhF3ihsmyMKZ8yirM9nMeAlBDNR+PJHVhqanxuAFyYkipgEguNBdb0wP0KIXqUDLUVfVYtaZ1DbQspoJACkyxhJ3z/tifp/1Monp4D9RD9eEdn7s+p23ZygDQ8FQ04CLJk5QskcYjs1krC2DqH5DoIsm9QOnuyRrJm+WxeSrmNuazFRYBsysmmnCAOtjDNpJYXQggherPD4D3hob0jCvYB+yCEnRTqKfjaW+QO34VvRypREe3kDH/PfJhrB/eMOghATLYf7DDynj0m6EzWJpnQMWuJAurthCtieXfdJCi0wwTMvM8UazkJ/C+SDV6IPkh6PEWfZSPMvqx0Rh2p4YusBeDUMgjGRpHgbYd14MJvHnQjYNTKGgCaFsdhJ0xdltsM2ciCAE7KY0cyhEYCuAAT2AZxkEsJY7yVvOmZQhWZzGVt51CPKjJYHHqGlfbFBK7JuyCuVx1ESAkUIUSPaytx0eYJmYAQeHd/AZxS2Fyt3DjwfbBDe0cU3pMe0sfuA8wcTyKhzWuej74ON7RhekQTMUEngJ/OBEK2rFbCxbFQC+R0uYAG4PMa3lZX/V5F3yLPzd5PAk/RZw3jAJmtNdACieUtZmUqJPy4Hb4FzIAhoUaa8uNwhIJEuzsASCxqIXHNbt5+agJTi3ZyKhtC2Ez9zw/B/t0wAKNW1HBsaRQJ29oh1WTJTaKRIGZ4Lpjg95A9iep1o4H11+BdEEIIIbopGfCEsEWGcFgjdYLNDrIHllPamIf3hAcSIFAxFGI0bZGmhIrTc5CMjGre3TYJZ8FBkwH3JCTP8OIgSGX1GABGjt1DZfUY3BlmjicnMYHpKcDb5TraFLzXg/cthOgREngKIYQQQgghhPgbSqk44D+AqVrrFGvdjZhunH6YeHKh1lpf6lgSeIo+y4+L8tiRjPFVwipr5e2YQtXr4djSKII4SF3jgyngTUkGwBNqADtUk0lt/jAWVbzMrb7N7JuTzi7GU4UpjbLc/RC7GE/u5BICuJjbuoEXY+/CRohpbAHgYR5hPLsgrafvXlzvJDufEKLHtQFeO2SFyI4oB+D9gTfSTDwjk943PZcnIXm0l4bGFPCb31HBSAfhgTaohcCaodhubsUxO0jDex6oAerN4YPfc5gkRaSa4bc5GgqVyb9wg3UNa63vXWf3E6I7rtVzUym1HPgi5q+9bK11xTnbncDLQDpm1rMXWKy1PmJtzwBeApxAAPiK1vrDq3VNl3s+rXULcI9S6p0u694HFlvH+x0QjfkNclESeIo+q5EhHCCNMa9VcuqXZl1tbDKe+gb2paQzan0NoTk2dsy7iYlrduNxW7nbW4B8WOJ9geMpEbyZNYVcSghjY2Hry/T3mWZ189yEsBHAhRM/y2K/xyS2kxsqIdpnhu0+kLLczCON7Pn7F0IIIS7LIcBvMtsWNk4y6+rt+FJCUG8neayXhmoPDds8JE/20p4YBUC/iNMcZjC22a2Emx2Ei2MJJMeaP7ELzNBdgIa9HjO89hgm0+1/K/NhcJqGSDONhdl2SSwkrjcbgF8A/3OB7Rp4XGtdCKCUegJ4DLjX2v4M8LTW+hWl1J3ASmDyuQdRStmBRK31X7qsiwFitdaNl3FN5z2fUird+r6rLVrrJ853U0qpScBXMbO3T1zg3j9CAk/RZx3qSOLW/ZvNQ83i2Wx6MzPjzNzPajKZWLqb43MiOGRPAsy8zNT1PrDDRvssxrPLzPsc3MKDnodYVvcoANsp4O4jr/PYoPsBeLjop3jzk3nKvhRnikkl1EiSme8ZecnRB0IIIcS1dRxsBa34rSR6AO6xpnxK4GQSdkLQADHj/DTsTzdzMS3Jo61JmmUKPODMOkiw2UH4lVhsXw0CEK4ACkLw36ZXyvkNq/7nOwr6Wz1VbZj+IPnAVvQOLqVUSZfXz2qtn+3aQGu9E0Cp8yfE0lofBQq7rCoG/tXaxw18BphqbXsN+KVSatCZHtEusoDXlFIztdZV1hDYzcCvgee7c02XOF8N8Pnz3sT572s7sF0p9RQmRdifL7WPBJ6izwqdtLPPk86oD2vof+ZXRh6wFfq/BsWLc5i4fjd1c9ykbvbhOWn1ePrg2OIok6G2dQP+2Dj25I9kjLeSKjJ5LOt+AL5f8SRNWXGdmW3X5M/GiUnGsKjoZQAeyf+eqftZK9n5xOXRKMnOJ4ToceEPYwmPbcaddBgAX+NgkpPqSR/+PjU7RpE82UvDXg/u0XWd+zgImsy2DZjhs22KwM6h2LJaoR7aXjGBbMydfpP59hgwAgIbhnb2bjoXHAQg8MuhEAOc+ye3EJdwlZ6bfq113pU6mFIqAhN0vmGtSgEOaq1PA2itTyulGq31H/kp0FqXKqUWAZuUUguAnwGvaK0/EnReQrfPd851Pw3coJR6Bvgp8GlgLqCATwEVF9q3K6njKfqspQNWMGpVDcTC8fwIjudHsGPQTZBnAstxK8rgAVPyZMf0m87uOAISStsZ92AZK2PNKIgxmysBeISHCWE3cwjiINHbQggbLgJkUkUjScxlLU/n38vT+fcyizcoYLsZRiSEEEL0Zv8E6WP3cbqjH77qVHzVqfCBHd9RNzXVoxg5cQ+T2A4N4NuW2rlbc0c8Mcl+cicXmTmbgHuiFZjOxQyvPQltzQ5sya1mfTxmHmcT8A8QWDWUwKqhpt8kCxjUQ/csRM96CtOv/8u/Z2et9R+B7wA7gEKt9YoreG0XO+8SrXWy1vo+rfUBrXWh1vob1vqvaa3D3TmO9HiKPmsX400FkziIDpk5l42TkyAOU8fTDsyDqUd2csoOxXNyABjnLQM7eJcls+TBF2haFgex5jhhbJ2Jg6iDp/PvJYlDZFBFYlELafm1rGApQWIAcBKglLyzc1eEEEKI3soL6dQQjrBR7jGrAhVDCZ+0Y3OZgPF9boScEJy04dthgk9bltlW6s0n954iSqvzOd3Rj/BJu5nnaU15cSb6TXCZALjMcNyAdyi8BbRb1xCPJBUSfZKV8GcEMFNr3WGtrgeGKqX6Wb2P/YAkLvBToJQaDPwI+Alwu1JqrdZ692VcxmWd70qTwFP0WYWNk3jpv27jbu/r7PCYHs2ZoTdoGhRHYkkLxxZHkbCynR2DbiKbctI4AGCG55bW0JiSBMvMsfblu7ATYgiNbGQWAPb8EGnUcsuRrVAH3vxk5rGGuaw1CYUw9T+FEL3LlUwNL0Sfsg/+sHoWtptbCZfFmnU5GtrsxCcdprJ6DOkZ++ADu6n5mWiahD+MJSbbT9gPpe/lA5i5m2ACSWu6S2DnULNPToiY+KBp8zRmGsyZHs6TmGBViMukUZym37W+jPNSSi0DcoFbtNad/8O11j6lVBmm7sIr1tc955nfiVJqCPAH4DGt9atKqd8Da5VSd2qtd3XnOi7nfFeDDLUVfZYz0c/dpa/DOphYv5uJ9buJ3tRB4lda4KQZTlu8OIcDVq2TNhy04WCUt4YduTfhJMCQUCN7ySaIAycBqskkm3KyKWcv2WRTzs8HfZ03c6fgx8UPWEYGVZ3XkEQjM3kDiqUshrg8HShC2K7ocilKKadSapNSqkoptVcptU4pdd4Bb0qpDKXUu0qpauvriC7b/kUptUcpVWYd59aP814opZYrpQ4opbRSKuucbRe8jgvRWrdore+Bsz+sWuv3tdaLtdZfxaSFj/441yzEdSkeYmb7uXHg+9hyWrHltJI8vIaCjLewESYm2U/NtlFmeCyYuZrHwDailbZil/mpGaThICZ4TMDku/RYSwMmYP1vO22FLtNmtrWuyzWQZ7UV4jqglFqhlDrzv/sdpdQ+a/0mpVSeUmoU8ANMz+Iu69m4vssh7gO+qZSqBr5pvT6fKOAhrfWrAFrrP2N+gmK6e02Xeb4rTgJP0WeNj7A+/BkBp+LMQhzwQ7P61AgTGN7tfZ2EI+28wSzeYBZPe+5l4srdpIUOsNp+B7du3oydEAnrzTgguzXLM5NqwtiYxHa2U0AAJ9Vkkkk1VWRSRSa7yMdBm9TxFNeLMynfM7XWozEV+B67QNsz6dgzMH0WKwGUSaH3MnCX1joHuBN4yUqo0EkpZVdKffqcdTFKqaTznGsDMBH4y3m2nfc6rOOlK6XeOWf5zoVuXik1SSn1KpeRGl6IPsUaXuvHZcqiNDto74hi19HxALQ1OcEFtptbISYE/wv8L4Q3xJI+eZ8JOgsV7lvN/M7kDO9Hj5+MGVJ7A1COmenmB4ZiAs0GTEbbGOiSWFeIXk1rvdSa/9hfa52otR5lrZ+htS7RWu/TWivr2ZpjLXO67P+B1nqs1jrD+lp1gfPs11pvOGfdXq31H7p7TZdzvqtBAk/RZ2VQxY7cmyAVgrFRBGOjeHPyFCiHPXNG0t8HqUU+ij050AKT2M4ktrOk/gXIg+gPO8ikinXTp5MRquK5OXeRGyrBSQAnAQopoIpMbIR5gOWsZS7ff/BJVvBNCthOAdvJo8TM94yXEXui99NaHz1TZ8xSjMlc9xFd0rG/Zq16DfhMl97RDszHPGD6Lw51mc9yRhbwtlIq0zpmHGYI0YzzXNdOrfXfzD+51HVorWu01p8/ZzlvPTKr/Xat9R3AKUyKEyE+WYZCW7GL5hPxcErBKUXgraG4B/po2OHBFh/EmXWQcEWsqc15A2a5OcThE4OJSQxAGvjWpUIiNPzaY5LrxWOWDzDl6u3AHGAn5H6tCLZgfiNkYYLfU0g5FXHZNKozAeSVWsSVJXM8RZ+1qmMhywsfYs/kkVSRCUA25TABxhyphK2wY/FNJNHIMU9U535NKXEktrRQnJVDI0nMe3QDuMG2OEStfVhnyZQCCkmikbXMJYgDgGPLojhEEqMpB+BV7iCAk9zhuyjt2dsX1zlNBOEr/9C7ZD2yM86T8r2rC6Zj11ofUUrdBvyXUuo44ABuOfcA1zItvHV/VyQ1vBB9yh/B/b06kzTIb60bCg2NKeAJkTKw3gy1dUHYH3t2vzY7xAdpK3FBM+TcWkwQBzUlo+CIMkNuwQSVLsyczzazqrQ6H4J09rZSCDRbr9+9qncrhOhhEniKPivQ73kUPzpnbc5HX3ZjVPvtZ/a5D+DGi7Q8zCq+B7zPRr7aZf0J4J1Ln0iIq+9y6pH9XSnflVL9gX8HvqC1LlJK5QO/VUrdqLVu69pWa/1Ha+jrDuCJnkoLb517CbCky6oDfLTAtxCfPJsfwXeRstM1AKw977auP9xlnd+dv+1H/D/r6/JLNxVCXN8k8BRCCPERF0j53tXF0rHnAEla6yIAK/g8DowE/nTOea7rtPBCCCF6D40iLNUEejWZ4ymEEL2QvgZZbeEjKd9nd035/pFr09qH6dS43VrVNR17A5DcZe7mSEwBhZpzzjMEMxTgp1rrBzHDXNcopcZ3+z26+HUIIYQQoheRwFMIIQQAF0v5fiYtfJfm503HrrVuwswNXauU+l9gDaYm5tFzTnfdp4UXQgghRPfJUFshhBAAaK33YZLrnG/bjHNefwCMvUDbV4FXL3Gu/cD+c9btBfaep+1SYOkFjnPB6xBCCPHJ0UGEZKLt5aTHUwghhBBCCCHEVSU9nkII0QuZJAnyya0QQggh+gYJPIUQQgghhBDXNclq2/vJUFshhBBCCCGEEFeV9HgKIUQv1EEE4Q755FYIIYQQfYMEnkIIIYQQQojrmkZxuqPftb4McRHXbKitUmqAUuq3SimvUuoDpdS/XKTtIqtdjVLql0qpCGv9F5RSpUqpCqXUPqXUv/XcHQghhBA9R56bQgghrmfXssfzASCotfYopUYA/6OU8mit27o2UkoNAx4GxgABYDNwJ/AboAmYqbVuVErFAaVKqd1a6//p0TsRQogrTHcoQiclq634CHluCiGEuG5dy+RCXwaeAdBafwiUANPP024usEFrfURr3QE8Z+2L1vo9rXWj9X0LUAl8ugeuXQghhOhp8twUQogLOPOB7ZVcxJV1LQPPVOAvXV7XASl/bzul1A3AOGDbhU6olPqaUqpEKVUCJ/6uixZCCCGuEXluCiGEuG5dtaG2Sqk/Yx5+5zP4Cp9rCPBfwJIzn+Sej9b6WeBZs0+SvpLXIIQQQnwc8twUQgjRl121wFNr/ZmLbVdK1WGG9xyxVqUC28/T9Ew7urSr73IcN/AO8ITW+vWPc81CCNFb6A5F+KSUU/kkkeemEEL8/eS52ftdy6G2vwMWA1hJEj4LvHWedr8HZiulBllZ+RYBr1v7OYG3gV9qrZ/vkasWQgghrg15bgohhLhuXcvA8wkgXinlBf4b+JrWOgiglPqxUuo+AK31fuBRoBj4ENgPvGId4/tABrBYKVVmLQt7+D6EEEKIniDPTSGEENeta1ZORWt9HPjSBbb98JzXK4GV52n3HeA7V+UChRDiGtI6grBk1BNdyHNTCCEuTJ6bvd+17PEUQgghhBBCCPEJIIGnEEIIIYQQQoir6poNtRVCCCGEEEKIK6JDgWS17dUk8BRCiN5IHqBCCCGE6ENkqK0QQgghhBBCiKtKejyFEEIIIYQQ1zcNnFLX+irERUjgKYQQvZEGTsoDVAghhBB9gwy1FUIIIYQQQghxVUmPpxBCCCHEtfKLh83XEmABUGF97wGKgUign9V2CLAB+CowAXgNmMPZ/Qut77OAHOv7I8AgIM06ttdafzOwvss2gJNAPTDS2g8gCmi2rme9dQ1zgbeABqvNbOu6PEA80ATkWevirfNinbvA2vY/wD8Cq4AvA36rTTOwz9pvnPUe5AAfAgeBfKtdCGi3vvqtcz8FvNDlugqBZGAwUGTd013Aw9b7duY9egpIsd4HJ/CedewR1r0A7HkE0ctpzP9h0WtJ4CmEEL1RB/IAFUIIIUSfIYGnEEIIIcQ1onOtudxbgduBb2B6+bIwPYS/AJ6xGs/G9OCFgAfhr5Xwqf6YXsB4zvYaZsLxOyKIntZBU2EciUUtUArsB/5qtVlhneMYcALTE1gJRAOTrHMABKyvZZieSYAPoekncSRWtJjXfwQegWeGLeC+B1ZBCRz7SRQJn2uHl6FpRRyAaf8ekAR1S90ktfrYszSLXYzHZZ3oy62/p98peGfgBOpJ5Z6K1RCCotxcTtOPiRt3A1A0M5exraU8H7sAN4cB8KyowUGQXYwHIOWH9exiPOPZxcSK3TRlxZG4rYVf6/kM5jA26yanfnYnb06fgo0QNXhwc5jB+MgJ7eFW+3oA/iBT7oX42CTwFEIIIYS4RpryraAsvwWGAb8DZsHRB6H9FAwFsx7gx7D3jxAE8m+HTw3DjIyoBOIwQSPAOIh+vAO9DhLXtoAd2APHn4kg+vkO08YOpAJJcGxuFAlfbIcfAC3AJuAV61hjgW3AMuv4VkCauLGFupluAFL/6OPAsCHct22VCZ6XWPuehFP3QeIhE6B+kPVpbjjwF4iF1FIfAI7cIN8qepYD+UMACNkjKI3NY+qBnTwzbAGUwd47M0ihntQiX+c95oT24I+N476iVRAJH+R+msH4+A13saT1WQD6ByA0zEZuqIS6LDfOkJ8dk29iMIfJprwz8Fwy/WdM4R0OM5gbeZ94mhldUc2bWVPIowSAP3SOdxa9VgdmiLTotSTwFEIIIYS4RhI3Wr2GlcCtQAiO/gIGzsL0Nr7B2XmN2TCiBKKeAZ632hdjgs7hmKAPOJYbRcLWdtQU4EHQ+aB+YoJR7rSO9QXMfMZhkPB4u5k3WgkcsLafCR7zgO9B05A4Eu9vQf8Q1DbAB6kbTfD4wZJPc8MLf+H4nRFEV3TA45AwpZ26uW5SD/k4MMQElTdU/IVT/wT9VwEDzXXfEP0XeBqGDT9kztcCE0/t5viICHIpoe5ON+khLzV2D6mtPt6cPgWAWw5tJbqxhVPZ4I+No4wxzNu8Ad/0wfQ7ZQ61Y9hNDMZHvT2FelLAnkEpeSaIPeTjwSEPAfBNVhDAxbTQFsJ2O83E81jW/QRxMI0tAPyEGX//P7IQApDAUwgheieZ4ynEJ4K2gkr1Q+AQUNkl6PwqHF1oYjQA1kHUCExvZB6mJ/QwHPweDJ1FZ29gQlI7zLBe3wDqQUzPZT4mWAX4L2A+JpHQ00AhHBg2hGELD5l1ZxTBr++dTx4lJNa3cLo/9G8BJmN6Z4EbWv4CkyH6lQ6a7o0jcVMLRXNzGX+0FE6CHycAjVlJjG0thZlwbFgUCZvbabohjvrVKXz2gMlAtOOGm4jiROfpy8nmlsatOIYFoRHGs8tc1pBcnEP8hLHzG+5iMD7qprspYDvK6pWd+Ohu9j6UQT0p5FHKb/kybnxkU87eIRldgsoHKaCQensKuxjPWN7je0efZPnAb37kWoT4pFJKxQH/AUzVWqecs+0l4K9a669e6jgSeAohhBBCXCNqm/laVAb50zBDYOdgejF/BgPXYTK/gslkOwzTC2oHKuDgFhh6H+ADkqx2hzFDZo9jekaTrP3swEKrzdPWOfZghtFGQj9Om6BzJGeH0Zb6uOeV1aZ39AfQvw7TMzoWjn/LVOXz2QfTj9OkBnwkbmvhsYfu5/svPAmTQcfBZ1dYaW1nYnpUK8C7NJ3B032sZzaz2MgpE5sy8dBuCMCOrJuYeGg3bw+J54NhnyaMnR33Wj2XmLmg22Mn4GMwX+FlyskG4PNHd3b2sA770iG2U8A0tpBwqJ3mIfGMZxejK6r5ddZ8xlMEwF38hiAOqsnkByyjkEnUD3STRCMv8xXrDZMAtNfTnJ2b3IOUUrcAjwKfAo4CC7TWB87TLgN4CfPTFAC+orX+8FLb/s5rWg58EZOrOVtrXdFl22WfS2vdAtyjlHrnnPN8A9iC+SjqkqSOpxBCCCHEtbLHLPk/Bb4ETMEk8vkjJqnQMExSoP1ALKZsylcxQ2yXwFA3MBCOr44wbYcBjVb7aExAOoezw2jzrWWr9fWr1tf9kLrZZ4LUpyF1m4/UbT6acuN4+84JJmjdZB3zn4AQRG/sIHpjB8M2HiL1FV9n7+n3Nz7Jn+7NYs2w2aifQNPSOJqWxkEjHJg8hGNLoyhnNKkbfXyFl3GHDrMldgpbYqdwfGAEOGHiB7s5PjCCqU/vJIwdB0HGt+5mPLsYzy7ei83Fx2D+kf/BSzpRnOAwbpTP9LD6cbLkhp919pDuHZJBFCeoJ4W9WRkf+ScYjI+X+QrfP/QkNXgA09MaxEE6XtI7a9AI8VFKqQRMEDdPa50NPAf85wWaPwM8rbXOwHz0s7Kb286cy66U+vQ562KUUknntsUUM5oI/OVyrkMpla6Ueuec5TsXuPdcYABYP2TdIIGnEEL0RmfqkV3JRQjR+xRZSyVm6OqZwBFgGvDA2abHn4kwwZ0T01v5IOb1cYj+QoeZ5zncajzFbNv4Gqb+Zsja53lr+Tc49lCUSRx0FBPs7reu48mz11VKHlMrdqJzMGN+GzG9qeus1wMxwepITJbcJdA0M47PllZwmn7ULXeTeKCFxAMtHM+LYFjpIWyhkMlWCyRsbif6QAe3bNvKLdu2sss+nmNDogAT2DISmoknpfUQ9bFDCOIgiIP8baXMDq0nqdWHnTDVZPLZQxW8fcMEPNTgoYb/y/+hmkze50YcBBlDGRlU4SWdr7SuJp5m4mnmfW7kAZbz4JCHyD9Uylje4zCDyabcOpLnCvxDiz7KAxzWWldbrzcB05RSrq6NlFJu4DOY6rtYXz+jlBp0sW3nnCsLeFsplWkdMw74A/ztBGSt9U6tdf256y91Lq11jdb68+csT1zg3m/BVLtdBuQrpf7xAu06yVBbIYQQQohr5cyff/cBQzCBnd36mgPMwmSbxQrE8oAbMF8PmHZFf4T854Ezg+U+wMzl9MHMX2B6R4dh/urLO3vqhPutFKBJmGG52zC9rgfoHDh3ywtbYT8oMMHrP0HTnXEMPtrC4YFdyqTUY4LXG6CdAbC1hTsG/h5K4U9zswATQE717eR9+418Ntoa+XeDOe/b904AwE6YZuI5fIOblGH11NtT+Meju3lx4Hw8eAniAGC0u5roog7qJrs7M9G+PWQCA2hnC9MAOE0/5h/9PYcHxhHGjh8nedRSzmjeiJ3OVj4PwGzWk46XbMo5FQ2NJBEkBgfBLr2d5+tUEr2KBk5d8aO6lFIlXV4/q7V+tsvraiBRKfVZrfWfgDus9amcLXAEkAIc1FqfBtBan1ZKNVrr1UW2HTlzAK11qVJqEbBJKbUA+Bnwitb6+cu4n4tdx5GL7aiUehq4QSn1DPBTrfWPrfVpwP/RWv/PpU4ugacQQgghxLWy1fr6z5jg7z1M/8F9mJ7IH3A25mnB1PXsh+m9tIbQ5v8MqMP0ZILJXPtLTLKiG+Cvm+BTS4D9cPyrZrBb9I87INvszw3m3H/6bhaffbzCjJCYYh2r0fr+qNmf9yDRZ0q/JDaajLynUqG/DxMwx0E8zTAc9g7LYHRkNTeG3gdgi30aH0z/NB5qWDLsZ/zfYf8HR2s7/e0w9cBOAD4YZkYSZh79C5sGTqEgtB21HhbOWc3hgXH4GAzw/9u7+/iqqjPh+78FkUNI8wo9yYlN4AiEkDtkggnQSZAKaLmJgwXEQS16a6VqB8XO1GqfOr5Ub/pUxbtKpaUOVqfiCxWBWx6SQXlRJBmBRBiSiSGAgaTmkFMIeWmIJ4as549r5yRSXgIkJMr1/Xz25+Tsvc7eK+ckWbn2Wuta5KdmBJdDqSMKFy3E4qeUFA4yDICr+JCNMdIDuoXJAITTyCCOc31DHi0RLgDWMovdjGU4+9kekcFxQgnnr2zh6mBwCqXn9LGqr40j1trM0x201tYbY+YCvzbGDATygDo6VsztVtbaD5yhr1uBZ6y1S3riOqe59gI68l133n8QGbR/VjrUViml+qL2rLY61LbbGWMijTF/MMZUddqXYoz5vTFmuTHmFWOMLhevLo6pzrYCGd6agAy5fQjyi5EZWOnO9gKQ4TwOBgbCnleAEPjsXiR4/S4y3LYWGQY7DS67GRgNzQ9JwBn2RJv0iEZC2eKh0sPpgnFFJRJoliBBcBMS7Hqdc42WcvxEzku9bCH7gFwomzcU/BBV2wxhEGAAhz2RhO1rI2yfrB8aRR3RRc0srfgJVSRQFeGhYp6HMu9QyrxDcdFCKMc5EQLXrdrEOtf1MAVMPsRV1JNWUU5aRTmFZNDMIMblleCihYxAIfsZTgJV3M7L3M7LFDOGa8u2sYt0cljPLNbwe+5mAtvZH9ExVe4+5H/3aYENDHd6VV0EqCOaIwzhCF8aNanUl1hrN1prJzoB6gtAKHKbprMq4HJjTH8A5zHe2X+mY19ijIkFHgd+Ccwxxow/x+p2+Vo9QQNPpZRSQcaYxcaYCmOMNcaknqHcQWNMmTFmt7NN63RsoDHmd8aYfcaYYmPMi6c7z4XWyRiTZIz5T2NMufM48mzns9bWW2t/AOzttK/UWnu3kw4+jODCFEr1sKdke+0jJLgLID2Xn0L2C0jv51hnGw2tz0Pt1chQ2F2Q9ipQCJffTjAjLbuA+wkGlIwGdkPofCQz7kSwTpbb5FWHpPc0BMl460eSDVU4WyKQD62ZSK/qJ8j8zkflnOyG1pFADiS/JHlMzAogHUZwgNja+uC3OjsvjzhfvXyfuZBWUU6AAZSSEizjLfER96N6+reCnQLxVGMjgTCo9LrZ401ijzeJBKqY5NtB/vQMJvl2cNQ1hLHsJruiiAd5hgd5hiwK2Jo8ngOMoJgxrGEWt/A6u0hnLbMYQIABBGhB1u6sciVQRCYtuAjlOFnk8ww/5RlOmVtF9TW9dMPWGBPnPPZDAsJl1tqmzmWstX7kN8ZZbZebgV3W2r+c6dhJ1/EAG5Fhrg8Dc4A3jTFZXavpmevR1XNcCA08lVJKdbaW02fCO9kca226s23otP9ppMlOcrL8PXLyC/tadj6n/GRjzGvIvBxdO0FdFK1PyPb9aUjQORWZhxkLDAReRYLPRcACCHkUPm5FhtwGkN7I9hloLzhbrbM/XrbKhW6OLQ6V8i7ZqmLcEgB64XBGpAz5fRlww7F5oV/+xzsbQvKRntTZyNInAYI9sSFHkd5RnOt+3lEnE0CWg3nFKf8srPdOlWC4BI4yhKsDW3DRgosWKlI9HP5dJL+PuZ2CmAwZtgswWjLNpvnKSfOVM7soj4WepzlOaPBaBWSx2jud3wXu4XeBe6jBzaTNOxjMUa4t2sYI9pNY4aeYNKaykTqiqSOa/QwnijoOMIIo6oLzOltwsYaZrGHm+X686tLwv40xnyCzrFuAnwEYY3KNMZ2H6d4D3GeMKQfuc5535Vi7UOARa+1rANbaj4GZwDdOLmiMWWKM+TPwLWCjMea/z/FaPULneCql1KXjbEkSsNZuAzjfkabGmG8AtwHfstZa55w1pyiaCrxhjJlhrd3rZOfLA/5Ax0y1M9apU3a+a51dbwAvGGO+6dxFPgDBCVpnZa3dAmwxxvwG+Rf5466+Viml1KXJGSlzqv05Jz0vAyacpuxpj3Uq076wUud9e4A9pyi7EFh4vtfqKRp4KqVUX9S+nEr3OmOShPPwmjMXchvwc2ttHTAcWZD6MWPMZOCvSLa7bZ1f2Ney8wFDkWFLBlkEvOQML1eq24Q85XxxP9ReDzE5yG+UGz5bBjFhENr+W7sbmArXXIEs2nAzsqRKPdJLGumUq3KOTwDehUSXH54A/hHpKQUSN/k5vCiSuJfqJUlQKxzLCCX682aiX2ru+Pd2NtKbmY70i1wPIWHO+YudMvcgPaCbgKNQ8aCHUI4TV1LPntQk0qbKShOrPdMZsvgo15Vtkt7YfMiuKIJqqM6WwQ4n6M+ksh3cU/0KAHumJGE2w9Y543FTE1xqZlnG7cxnOYVkghcGEOAqPiTR5w/+7bwmcht7piQxir20joT+nOAZ7308EHiGla6buIaNAKxkLgBZFPB77iaDQmLxs5dRzGItAM8y4xw/WXXR9Uy7qbqRDrVVSil1Pq6y1v4dMA4J1l5w9ocgA/J2OUHuQ8BqY0zEySew1n4AtGfne/9iZ+ez1n7LWnuPtbbCWvu+tfZeZ/9d1tqWi1UXdYlrn5f5KsQsQILIgbBnGVz+AoTe36lMOuyZkdTxz/W9yNDWm5HbPU6yHybA4QWRMh+zPzII/Zd0ZNAFuALiflwv8zl3g30UBgQCMq8zGyoXualc5KYiwwMVyNqay5zruZDcls5QXiKd/T8BpkoCoRZcEnQuLefw9EgOT4/k+oY8ktgLLmjy9mPZnbdLIBmQgPME/Zm0eQdlyUPJn5IBQCkp2CkSNA7hKFszxrM1Yzw55FJHFGPYA1WyDEshGVR4PDTF96Mpvh/3xzzNAALEU83PIp7kIMOYwyoaXeGMYD8vcwcvcwcT2M5gjrKSuWRRwDW129jINYxiLyuZGwxMlVIXRns8lVJKnbP2hamttQFjzG+Bd5xDh5CV1N5wjm83xhwBkuiYiQb8TXa+m40xq6y1O86hGsHsfE5v50XNzqdUd9rzBoyOhMt+AqyGtIfBWTlE1tcE8MPwzP2y5Eo2smz8d4Fq+HgqXNn+G7Ya4m6ol+VWvMicT5AAcZHzdRVwFKwbTCSYagiraAuu6Zn4Y7+Umwit34Xosc3yW50PuGDZlNu5p+IVACo9bgbHHJHMtU5W2+jdzSRG+iFWgkKAgKsfcUX17MlIAuAe3yuszp7O7CInIEWSGCWvOwTeQ3KN/FcgAAlTqsglh/kNcs3+rVAQk0UWBRybHkqAAVSRwOySPF5LvQGAuaykBReNhLOQ33CcUBbzAM83/ISVEelkOn+SqkigigRGsZdQjrMk5i4ms4UCsqgiwXnDgrnIlFLnSQNPpZTqi9qz8/VBxpgwIMRZv8wANyGDALHWHjHGbEHmXb5rjEkC3BBchb39HB7gXeBX1trXjDFvA6uMMfOstQVdqYe11m+M2Y3096zgImfnU6pbDJaHtGmwZwOkpSMDz2Ng340w8mY68k9+CmH/2AY3Iim8bkSSDw2EK6vpmB09GglKV4DNAdOADJnNwbklhPRsLgDzSyRADUF+i73I8N2fO+U+gpBssJtkSRNuhNZ4uGfpKxxbEApAYoUEqcdSQ4l+shkTD4enROKihShvs/SkAmGb27ATZJmVcBqp9LiZ/XQe5EDcZif7bSSUzRhKLH7u8b3C4exIWnCRSw5uaghxhgrnP5LBnIa1LI24i0yKGMwREqiiLHWoDMkFPuQqruJDsouKaErtR51rBGMopjQiiQUNL1IaIQFwDbE8EHiGIlemk+22hSMMZgAB5rISgLWkn+8nrC6WPtxuKqGBp1JKqSBjzBLkX9Q4JBPeUWvt/zDG5AKPWmsLkX6Yt50exv7Iyur/1Ok09wB/MMY8iyyifasz/7Oz9ux8a0Gy8xljZjrX7VKdOl3r340xjwLHkMRGSn117HMevZD2bSQwDACFMOIo8ENk+RKASMh/B7IrgQdh5a0wdxMSaD7lvA4kW+2PgP8L5hYkQH2BjqATpOdyNjAVmjL7EfZWm6wf+joSdFY65aYCr4LJQZZaaV/QKB3qiAIgenMzTfP6Ee1rhnQJTAF5/jkcjZF1MKumh5K87hDjAiUcmxNKdEkzhx+MJK6onsopbgAGB46QEKii1JVCi8fFcUKZ3LCNaREb2EU6ex6RYHE4+ymIkCUMB3OEQTQzuySPstShXLtZppRf+8k27M3wTMZ9zGUlxYwhg0I2MI01EbMY5CSvTmcXv3UtYDBHSaGU/pxwcuy6OmW0PdjFD1QpdTo6x1MppVSQtXahM/cxxFob1x7gWWtznKATa+2n1tqx1to0a+3/sNbeaK31dTrHp9baq621Y6y1V1pr805xnU/bg85O+/ZYa9/tap2cY2XW2gnW2iTnUcfDqa+WGc6WCvwbMj7gF0A8mDeACGRZlYHArZC9CQkYl8LcQuAd+Cwe6UpoX3szHfgd8ADwHJIcqB7pOc1xtheQ3iE3hO1rI39ehgzDvVfOUbnATeUCt6TZynfq+gnwFhyJiGRndirezT68m30wAcJK2uQaQHWEm7iH61nvmcphbyTHCeU4obTgovU7UiZ6STNbU8cTHmiUeaSdtLhcjPOVEE81oyinMSKU7Uxgdl4ejYTTSDhxFfXsZwT3F73IqNpD7CWJ11JvwE8sTdn9aMrux84Fqbwccwu38kcAvu9723nL3yGBKiawnQlsp4BsJrCdAwznKj6kmDGE00g81cTiJxb/hX/OSint8VRKqT5JhwwpdUk47JVUtHG/r5e5nAnArUhwuA+a34LQkU7hGmQ4rQt4FEnmcy9cPhHpzZzilItE5oF6kV7Mz+lIAtS+Um4+kAx8AofnRUp2WSdBUOUMd3D4bNnCoSTcXcVR1xAS3X4ZFru0nrjv1Et5gCKonCM9lseTQ0l+8hCMlkyzcWX1VCXLPMkhHKV/q/N9L4wkI1BIoyuc7UxgMEcAGOMKEFdUL0GsBwrJYFZtHjdtXwutBMvZSPiB73VaR8KWiIlkBQqociXwPpMZ5joIyDqcgzhOOaMI5Ti5nhwSqGIT15BFAQVkyTXZwxEGU0MsH3IVNcRSSCZ3s4xGwp1vctj5fLzqYtJ2s8/THk+llFJKqV4S96N64n5UD1Pgi03ASGA+0nO5HUIHIwFjJDAa9m1wjhfKczYRzGTLJ84WQJZDud15XSyUzRkq/5RvcrZqgj2kcevqpVf0FnltYoVfMuEuheRVhwhb0UbiZj+MgaaR/bA3Ax90vJ5keU3iw36iqJOsuPPcXFO7TYJkxxEGczDGQ+UcN0Ma6il1pdDMIAAmN2xjcsM24pbWsz5jKjZd5oK24MJsh2em34fNJjgEtjgmCXZLkqFrarex2zWW5HWHcFNDLjnkkkO2T+Z+bmEya5nF3bWvsIFpJLGXYRx0UgpVkUIpLlrIpJAxFDOZLWRSyC95mDqigkOKlVIXRns8lVJKKaV6y2jn8XO4bD4QC/k3QvZPkGG3m5HeTaB2GoxsT/rzFjKXM1te23oPhCxzju1Ghub6kV7JkZB8xyHJbnujU+ZpZD5nPMG1MfkdNE+D0HuQ4BagBCru9OBd6pMlWoCw2nr4DrQmSpGQYiRL7lgY0lBPaybU4Cax2s/O1FQSnETTBWQRTiOjag9xMMbDCA5QSAYplLIhYioAgQUDZGmUmFRacJFFAVunjyeBKqpi3BQzBoDvP/A2hxdHEltbT11MKNklRRCPlHMy0W71jKeUFB4IPEOLy8XvY27nn/k13hIfz6TexzQ2APBbFjCXlUxrkPVmtkRczX5GkEUBH3KV80Y0nuMHq5Q6mfZ4KqWUUkr1lhpnW4EsRPQ5ZKcjPZU3wi+KkcWK3oGYl5HUXiORYHQwwYy3IROBXGdzwbFHQiXQ/CXSK/koEkw680UrD7klQB2N9JBuB8rgsmp4b/FE6QFtAjzgzfNBIsRV1EsPZT4QJoFuyDKn7unALqiK8BDyDozLL2FnairjykqIK6onrqieLApILjkEyDnDG5oZRDPFjAn2Pl7fkEcWBYzzlVBHFIlFfrIadjC3di0DCHBL7dvcUvs2LJDkRrkxU4mqlfmi1hn6O4IDjOAAV9XuoIoEClxZhDc0E04jjYSzPlWC3P0MZz/DWbL5QfYyii0REzkSEUkmRQzieLBOCbpC01eDRX6HunNT3Up7PJVSqi+y6FwVpS4Fkc7jSOAKpIfyBPC59D4+9imwzimTjgSSYUgvZSuSQOhG4EE6ei4rIPqlZhl+i7M/HQlKnR7PxCK/BJ35QDxUTneTUOvnSESkBFqfOK+th8MLIokrqYcy8Fb4pKe1CSoflHmdiRV+KjwevLN9rGIOCfOquGnpWsZVlNB0Yz9aXDLeNi6vHiLAhAFuCMmHCdlFHI8IZYCTkrcqwoP3SR+rH5nOCA6wLON2MihkBAd4ldu4NUYSBbliWniQZ3in7B8pSx5KVsMO1sRMJ4EqGe4L3B/zNP/EUpIrDnHMG0oOufyRW0mhlBRKKSQTgANTRpDOLmLxU0UCBxlGFgUMZz8vc4fzRhw5zw9YKdVOezyVUkoppXrL9c42AbnZdC8QD5/9EkInImtqxjgbyPDbaud5KtRuQpIEVSM9jyuAo07ZB5zzfoost+ICnne23Ugg+ilQDIk/9mNWQFxZPcn5hzrmlQJVJMiwWhcytzQX2AyJPj+JPj80OQHpJvjpj3/DcQZJoJsJrkAbNbipwS2vjwcq4L2MidgJsCFiKteu28ZuxrKbsXh9PpgHs2rzKCWFu2tf4QAj+F/8Oz+t+A3SRzqIOqJ4p+If2ZmcSix+3omYzuyyPMb5SsiqLSKrtogZvMNRhnDMG0oVCexnOAB+YgGYy5vM5U0OMozdjGUAAQrIYgzFHCcUP7FMZguT2dJ9n7dSlzDt8VRKKaWUUkp9telIoT5PA0+llOqLNC28UpeG9qyv7UtFTgWq4fL7kWVVPkGWTXGONb3cj7CHnTUzZ0FMDpIoKAa43ylXjfRWhjnnzaGjR7S9zFHg24AH8DnJifJl//rsqVyXL4l2CINY/JRGJJEWKMfOhw9jxjMpbwetYVJkv2eozN3MkYRDd9S+jjNylpC3IOrOOnnihfXeqeREbmJywzaMH67bvInKOW7mNKzteE+KoHBOKvFUUxiTSjq7CKeRSq8bd6AGgN2usbjja2jBxRauZnZeHjunp3KAEZygPwAzA2socslw2jqi2M8I/MQSzl9pJJwW582fy0qqiXcy2xaxn+H4iaWRcEpJOddPVCl1Ghp4KqWUUkr1lkDHlxuflZG0afc7+3+JrOt5rxz/4lkIe6iNL16By6Yia3pmApXIENZc50Tt62vOBzYgCYWqgd9BU6bMsgpb1yZDdF8BboaQp5A5nxFI0HmFc45PZD5o4mpZw/NECEy6ZQfcDI0RoQAkFx2CMDiWHEr0A80SPHuhMtlNgttPnK8egDLvULIowPghxAVN3n4cTR4iw3WlCDuTUxnnKWGcr0See1JJCFSBCxJf8rPzzlRA1vMscmWSESikypXAe9MnBpMK5ZIDwAbXNDIp4o/cSjFpxFJDgAG4CDCNDTL8F0hibzDjLoCLFlIoJSNQSJ0r2nkjks75o1VKfZnO8VRKKaWU6mW1z8M190Paq3Qsg+KCfc8TXJ/zsvlAPVwWCbyABJslcHhXpJRPcLZEYBEyf3M7suTKNGANhD3RRtgTbbBNnjNLzkmF1GNndqos4dKeIdfrHL8VKrPdhLwF9gXgFSgkg0IyIAbykzPkBDnQmi3PE29wunGPyraBaVSRwPrkqcHrJVb4Zd6oM6d0BAd4L3silR43fCq9ra5AG2uZRcWdnmBw6SeWSXk7qHIlUEoKWYEC3IEaGgmnPyfozwkSqOIE/RnLbhayhBxymctKoqijmDE0Ek4j4fiJJYCLXHLYz3ASqCKU4+x2jWU36ewmvds/b9UD2oDmbt5Ut9LAUyml+qL2obbduSml+p5C2WKuR4KxpfBFLtKbGQkjf4IEmPFId+hUYAE0jwTKgDCIW1IvvZUTnW0TtH6AZMN9lI6strcj5UYjgWg8EmTmI8NuP4FxL5VIsOkEgk3x/eBVOWdihR/SwQSAn8O1P9rGtT/aBgMlAdGAQECy1T6BDI19240JwHupE3kvVXokGwmnigSasvvR6ArnsDcS/PCeZyLveSYSYADX+rZRQBb4ZD3QkEKYyRq8RT6iS5qJLmkmI1BI/vQMoqhj9kt5vO+ajN8Vy8vcwTQ2MI0NpARKJVkR4KaG7HVF5HIdAQbQSDhFZFJEJgEGMJktRFHHEI6SVlTO77mHcBqDC6oopS6cDrVVSimllOoto51HZ1kTgMvikYA0BgkA28u84WwnINTp/eRV2FmTyrgflUjPJsCNztDZGcA+JJB8FRk+62SqbXXKhjyKZNVdCvwL8CQyD1RGoRL2dFvH+p/PI4EvSDDbfkPrCbjpxrXymsFyjTRfOXwO+d4MXLQA0puZ0OAju6mInZ5Uxj1dwtYHx+O6s5ijDAGgnFGUe+BqtrB1zniOMJjiKWkM4jjJuYdodeaoVrkSKGYMNbiZNm8DjYQDkqk2eoV0VTXd2A+OwhHPYBrJJDBjAI/lP0VTZj8GNbVRECO9tO3zQm/lj4yqPcRtGS/yCx4DCC7zopS6cBp4KqWUUkr1ltWdvj5KcJ1NFgEPw8er4Uon2Mp/A7I/AZ6C2qUQUww0wLjvlUgioo+c145GAsx1SPD6EdLzmY8MsQVCRgI/hmP/EUp0fnPHvNApgBesE3iaT+Q5AWh6SgK2uphQonc34yyDKdcrQdYiHQx2ARg/cAdkLypizxSZH+nNk95HEmBcbgmt98CkFTtourEfxa4xAExgO95VPtgGcbfuwHrheFg/DrhGwDz4Y8QtAFzDRtzU4KKFUlcKWRQwgACLeYCH5j0FwHFCSfYfopg07mYZK5nLsuzbAchwFTKc/QCkUMpy5nNL7SF+GPMCUdRRTTwn6M+Q4No0qs+zoPcJ+jYNPJVSSimlests5/EnSI/lUvjsDbj8LWAeXJlAMBjNDkGCxwqIyUbW+FyAzMX8HGhyzhUJHIXK/3CTWObHusG0r9k53ynjkutFVzTDcmha1o+wt9qk93M5HFzsAcAb75Nezt1wwDWCtIpyoqubOXxnZDArbGKZM5ezCXgL6haGsj9mOOMWybDdtJJyAMqmDw0mIsILVREe3pk3g2lsYNHmJwGomOKBavjDc7fwg7LX2RszlAKyyaSQw95IssgHZH5pM4NIoZRSUhhXUgJlkDCnKpjVdlTtISqmeJjFGt5nMveXvcgzyfeRzi6GcDTYExve0MzgiKMYP8yNeZNBNHOcUI4yhO1McN6wjRf0MSulNPBUSqm+SdcjU+rS0L6cSgD2/VKmbc64WXY1PwH7miBtmlOmBJmrOdV5XQBJPOQswRIcBvsq8BQkPu2H5XC8uB9hmW3Sc1nrlLkC+RszEI69HEp0SbOcuwRYAN51Tu/ku3B4cSRxhfWk5ZfLcOB9EOevl+sAzAebDmY3cCNEr2smdoYfXDLUtj0QnOTbwS8yHuKxzU/BaAgwgPmB5bgCbcGeqioS6L/wBLc1vE5rPAyimWvYSBUJNBLOhIYiKRgBCYEqClxZpFDKntQkAqkDSKAqGCz2jyng1/wzw9nPZN7ncHIk81nOBqZxrW8bezxJwXMN4jhbk8dTRCYJVFFFAgFcTGA7AEXn+fEqpTpo4KmUUkop1UtaE+UxZBGMXArf2uAccEPoNEibAMHRnh7gWSQRUKcEQGwAqiCYAycemY+ZDUxF1v1Md46tcR4/QDLafgrRnzfL3Mw7nGuE0TFkMVXWwKybEcUgmhlAgNjIegkyH3XKFIGJh51TUiU5kRsSf+SH6yF7VZGcEznnw2ESdG71jCcjUMgB1wgCrgGMS5DlUyat28HqGdMhAibzPolL/TAbEvP9VMzxsCpiJgBza9dyMMbDKMqpwU0dUUxu2EZzxKBgMqAa3MxhFfsZQTFjKOBuruJDUihlq0fmjwKsZRa38keS2Msq5vBAw3MsiniIBKo4wHCn8o3n+Mmqi07Xv+7zNKutUkoppVQvCXlHNpYChdAcQILJKqfAcmR4bT7QgAzNdZIQsct5zXLgO0jwONg5nokMwb0RCVSrnfP+i7NNdcqtQYLUT2DPfyRJT+ifkGG5nwI/h+R1hxhVe4jEfD9xq+oxT8hrbaRsW+eMhxIYl18CE4AYWP276ZAMTTP60ToGWsfAsSmhhBTLeYdxkA2uadTgZtzmkuD7cXhGJAnON19KCnwblnluZ8+cJHaR7iyA0khVjBtvno9En58DjCArUMDPIp4kwAC+73ub7/ve5gAj6M8JxrCH/pxgLisZQIA1zCLAgOA1MyhkDMU0M4gsClgU8RB3swyAKAm7L/hzVkppj6dSSvVN7euRKaW+1lqvl8eQQuBfIOYOZFmVVKRXcx8yjxNk6ZNPkd7K2UAkfPY8XJ6IZLhtz37rDKElgAyHzQTGIMNzVzhlwpCe0vvhmDeUqNpm0srKKXtkKMmbD1E2ZSgAyXmHgomHWsfIeUKqgSZn3igw6ekd7NyTKvMsw6Se4dmNEIBBTW1UxUimosQVfirmefAW+Yhv8OOKaOHaFdtgKrSGybkOMIL0wC5wIXNIP4cccjlOKLOX5LFnoQyPTaiVpV2e8dzHNDZQ5MpkLLtJoIrnPXcBcBuvUkUCdUSRzi5acBHKcTIp4mXuIIf1gCQXKmYMLlqoI4pR7CWuoh6/183gYHdz6Pl9wEqpIA08lVJKKaV6SUh7QqB44F6k9/F54C0ks20GHb2f9Ugw+awzpzIXLi8EXkCCyvVOuQogB+kFzaYjm+0VUPa7TgHlp8ByiKZZ5n5mw4j4QxybEtrRy9cEx1JDiV7RTEgtHF4YyYGFI0ihlOiP5O5Y2Z6hjG0ooTURQvbBsYWhXJu/jYpsD94yH4nbJfnQnnlJpK0qh4+gYPF4xlAM2XDYE0mRkyJ3WsMmjkRI4qLBHGFr9ngmvbQDOwvyF3bMF62LCaWOKG7lj7hoYQPT+KfAUqpcCWRRAMD9PM/T/JSUhnIaI0JpJJyEWj97Y4bycMNTwQy5AD8oeZ1jqVKmkEx+4X2IMeyR9UQBOHEBn7JSCjTwVEoppZTqPfXO44JIWAAAR6pJREFU4xXAQPgsB47jJBl6C1mi5HanTCyy3MlSMN8BapAeTBfSq+n0Gh5+OZK4dfUSgD6BZLLdBHwbkh84JIXccj0qkPmfE4FtEBIJ0ZXN7Fwgcxur5iQwzlcC6RKAxq2oJ25gEXvmJMECiYiTnzwEo8FOkbpEv9TMzjtTyawtIT85g+x9kponLa8c6qH1URhDMaWkkJjvp8Cbxex1eQAcmxGKn1iyHyiiaVE/jrqGgBuqYtwMZz9DGtrfMCACcskhlOP8c8NvCKmEtamzCDgZm7IoYEhDPT+LeJLFeY9QPH0MVTEJDOYIRyIiGcRxAG6qWEtZ6lB+w0LmspLHyp7iV8k/JoVSDjDCudjg8/2E1cVigS96uxLqTHptjqcxZpAxZqUxZr8xpswY8w9nKPtDp9wBY8wLxph+Jx0faIwpNcYU9nzNlVLqImhfj6w7N/WVpu2mUkqpr7LeTC70ANBorR0BzACWG2O+cXIhY4wXeAz4e+S+30hg3knFFgH/2bPVVUoppXqVtptfZ6sBFxwERl4PMxKRG0ajYY9fNjKQIbkRSOKgHCRjrBe4R8oyGuJK6qGSjqVWPgH7MjI3NN3ZrkCG2j4L+Q9mwDtID2g88B0Yd0cJ4+4oYQQHpFd2O0Q/0MzOeamQLb2X0XnNROc1S4/q52DygSqouNNDCy72xgwlPbCLyhluKme4ac2Gpnn9CNkniYNSKIWBMDsvT64bL2tqDg/s5w+LbyHsrTYSqGL1jOl8yFXUEcUfI27hjxG3EHJUymZRwGTepzEilAWpz/JQ7XPEUkMsNUxjA6URSbho4c3pM8kIyH2W5cyniEyyKCCLApZ5b2dU7SFu4XV2Oel/b+dlisjkIMM4yLAe+9iVupT0ZuA5FyRlmLV2H1AITD9FuTnAWmvtX6y1bcC/Oa8FwBhzFdKovnqK1yqllFJfF9pufp3FQ3MhZP8cuF6eMw+ohrRE2QAJJq9Dgs41yLzQeOc1Fc72AJKUqB6a7u4nczxBlk9xAjw+AeZLUp/sq4vk9dlyPV5BkhfNliVJOOq8ZjRk1pZAPqyfPlWWb0mACo+HX837MXtmJEGCZILNLikieckhwkranLywUVRHuAl7t435GS+QvVmG3+6Zk4SdAOszprI+YyrVEW42uKZxDRshHeIb/GRSxFQ2spz5ZJFPFvls9Y4n5Ch4N/ucpVKy+N/8K3UxocxlJXNZiTtQQykp1BDLAAKsdN1EVm0RQzjK/uAyKVLfD2PGE081LlpYnTw9eM4k9pLE3m7/uFUP0JFCfV5vzvFMBA51el5JxwpUXSpnjAkDnkP+XI482wWNMXcBkuqMyHOvsVJKKdV7tN38OqpwHjdBaCbyz64XCfSqkfmbU5wyE4Fc+OxGuHwkEnzmI72X2wlmvz18ZyRDGuoJWQZ7B7ZxZS6YJ8A+CuYO51xPyRYyH3gIyqYPJfnpQ3KudGj6rvRNJK84JNMb9wE3gnkDiITrHtgErc65noOfLX0OSoB5MCAQkO8rHppS+5FWUR78dtfPmMpy370QL+uDjqktpyrGLb2fwODAEVJcpSRW+NmaOp5JJTsoTM2gjmjmsjJ4niT2sto7ndmb8wh15mrW4KaITGYGZLHSsNo2wj2NzGINCVSxn+Hkxkwlh1zSNpezfspUAKaxgQ1M4x1mBDPb9ucEU9lIOaPO73NVSv2NHgs8jTEfI43fqcR202WeAZZaaz8zxpy1AbXWvgi8KPWLt91UB6WU6n66nMolR9vNS5OdII/mBOz7AEZGImt33g74kaVRJjqFPwHccPkLBANBQIK87yA9nUDcC/XwcyAAV1YDu+UcZjk4yWMl2dC9SGA7GJKXHIJsJBPtwz7CCtukXPuSLDmw05PKuKYSuVYFtK804q3wSQ9pDjAQXIG2YOA8qKmNY15ZiqSRcDIo5D3PRDI9RQyr9bExZiLX+rbJ9wlQCy0ZLl7z3iA9jQHwE0sCVYzzlQSXXSmNSGK2L4/Dd0ZSziiyKCCU4zQSzv/j+hUAS44+SKEnkwMMJ4NCCshiLispZgwDpgQodN6Mx/gFv+MeBnOEQTRzgv4UkMVYdpPLdU7Fjpz7h6uU+pIeCzyttVee6bgxphIYCvzF2ZUIbDlF0fZydCrXnlh8IpBjjHkUmZkQbYzZY61Nu5C6K6WUUhebtpuXJrPb+SIMRj6PzPV8Cj4eDVc+hQSh9zplvEgvaHswOdV5rAe+DThBLLuR4bifAjcDo+HY70IpIIvrhm6SMsuQ3tIaaH0IQm6Ua3kDPg4viiQur1P22PmyfEsLLvY8mERaXjlbHxnPVbU7ACR4LNsmPaC1EBIDlRluEl1+CmIyCKcRADc1bOIavl/2NluTxzOpfgejYso55pGlUQAC3gEEGMD3897GToA9GUlkUEgdUfzBcwtj2APAOF8J+Z4MAGYG1tDoCieuqJ66jCj+38DPAFifOpXHfE9x2BNJAVlM412qkOVWGgnH5YylnME6CsjCRQsBBlBHNFEcYyVz8eN23gQNPPs8vWHb5xlre+cGpjHmceBya+0Pnbuu24AR1trGk8pdAWwFxiL31vKA1621/35SuauBxdbaTLpA7tzefaHfhlJKneTxoq7+HToT881My8xuTji63HRL3VTv0Hbz68lWPy5f/ATphZwN1EPtWxCzDBnh/IRT+FFkCZXngYeQJVE+QgLQ5XTMSYtwzud3nuc6ZSG45AqxzrkSkW6IbUAJVPxfD8NqfZj2uLMW6d2scV5TD2TDseRQol9y/st3yxDa68o2cTg5ktjaeqpi3ITTSHRZM5XJcvHEzX5ww9bU8YRynHHrSiAedmakMrahJPieNEaEspz5PFD7G8wn8F72REZRTi455JALEJw3GspxXLQ41aghPNBIoyscgLiKeo55ZW3O+AY/jRGh5JLDzMAaVrpuCgaebmqCyYaS2EtcRT17vEkMD+znCpeMhfabl7v6kapzpu3mpaI353g+A7xijNmPrMp7V3vjaYx5Aqi21i6z1n5qjHkS+dMK8C6yapVSSn19taGJDdTJtN38OnrWeaxGei1TgX0QM9XZ/xGsK5YvZ7yBDGfNRno3fwmfvQGXx0Ll624Sff6Oc26mI4lQEzJP04UkJAIZiusCYpC+74lyfe/DPgl2b3XKvQrcjwSg9VA2ZyjJZYeI3typaykGrsvbBEedjLoeGJx5hLDqNnDBAOePWcUUD6EcZ5JvB61h8PyMuxjLbsZQTFWEB4AjDGbckhLmLlzJwRgPXnyMQd6ALApYwn0AzGUlYyhmC1eTQimDaKYFF8tdc7m/4kUAVnunk0AVLbj4MOIqAEI5zjrX9WSRT0JABgI85voFmRRRRQJHGEymtyi4P53dgPwSKaUuTK8FntbaJjpmJ5x87NGTnv8e+P1Zzvc+HTMXlFJKqa8VbTe/ptydvg4Am+Gz1XB5IvA5NC+DGbnO8WVIIDkRWTDHC5dXA09AYpG/I1GRFxlG+xNo/a7sCnkVaII9byc5l60hLr9eyt6KBLQ3I0uuZCKBMMA02ONNIq26HCIh+aVDkrwovp6QJilyLDuU6BXN5M/LIDuvCCJht2ss2flFsrwLTvfp0nr2LE5iSFg970RM5/7/+SLMh9VzpjPblwdAqOc4TIHfczeLfE/ycPYjLMp/kvXZUwmnketZB0h23b0xQ8migFyuYwx7yKwtITOmiNe8NwCQQBVrmUUGhbipoZFwEqjiACNw0cJR1xA5F0XEO99wFHW8zi248XM96yjSX5GvDr1h2+f1Zo+nUkoppdSlbYzzmAs8CNSCexPSq1kIoY/iLKKD/FPtRno7I+HYnFCi/76ZfR/ByI+QjLQgQ23dQAWExCO9ltuBZDoyzNYCNWDjwcyXXZWpbhJz/LAcml6WrLZhz7eRllwuvaIVkjE3Lr9eelOd/yKjf9wM10N2URGt2VAQMZ6shh3snJfKEI4SRZ2U8zaT9nQ5ZELVlAQq/8NNYoWfOqIp88i05EbCKUjN4gEW0xTTj0W+JyEeUijFu9nH4SmSXdmsgZY7XTQziLmBN/G7YjHVcDBmWHAtzijqWLTuSSpnuDlBf7ybffxqyo+Zy0qOMDj4EeSSwx8r7uJX3h+TQy7VxDOVjdQRxXyWA/B+8INSSp0vDTyVUqovsmiSBKUuBe1LkswH7gCehct+DoxG0kTFIkEjSDC6GZm72QrRNc2QAyM/R4bptmeG3Y30YLqc54Od69wLvOBc9iEIiXQy3W4H+2+QmOaXtUNvhbAn2jrqOBAJNHcjSYe2QdOj/QgLSJk9zyWR9qNyyIHtGRlMKtqB9cL7TOanJb+htT1Xcy3Sm+qC+0telPmmJZDjXU+cT3pFj3lCg3M2q1wJrPTcxB28zH6G43X7WOksSXu/90WKGSPJglwFVBNPVOoGABbw22DVX5txAzMDazjgGsHqKelUE+8svzI4uFTKAyxmtXc6WRQQRR3/xFJKSSEWfzCIldHtSl2ajDGRwK+Ba621CScd+3fgC2vt/LOdp18P1U8ppZRSSp2FzZaNADLf8gUkcFwNXzyMJBza5my5sG8DEmSGIMNh65GAMgzJZHsdHZlwM6HS44ajYF92zp0jW8g+55oBIALMZji8J1KCTJCAN1YCVO6Qa+MF3gEiocXlosnbjyZvP9LyyuVYJmT7iiAf6mJCmcMqCEDIUdmYJedYnz1Vgulq2DkjlSEN9Rz2RHLYE0kdUQwP7Gc/w3mfycxiDSfoz+SGbbyZOpP5geXMDyxnz5QkktjLVDZSRQI35a+lgCwCuPC+5MP7ko9dpJPgJHT+I7cylt0sqXiQf2UR4TRSQBYFZJHSUE4zgwjlOMWM4QAjaGYQxwnlACM4wIge+exVN2u/YdudWxcYYwYaY35njNlnjCk2xrx4mnJJxpj/NMaUO48ju3LsfBhjFhtjKowx1hiT2tV6nI61tt5a+wNg70nnuhfY0NV6aeCplFJKKdVLqmLcVMW4JdnPaKTn0w37XoHLRiPzNmc7WyQMAskwCxJEVgDFQDqwy9k2IQmG3oLEVX4IIFlqneGyVCDB6SxoerAf/KOcM7a2XoLfdDi8MJLDCyOpjnCz+j+mwxQk6Mx0rguEvdVG2FuSQIhqCXLzPRngRXooX/LBG9AU34+m+H4cSw2FdyRJEK9KPVy0sCFiKkVkUkQm3iIfYSVtbOIahrOfOqIYVusjpBhO0J+wd9sIe7eNAAMYwlGKyCSHXCqz3VxXsolZrKHiTg8Vd3qYXZZHI+Gsc11POH9lP8P5g/cWrmEjLlq4ig+5ig8JOQpzG96mmUHk1G6ikEwyKGQts5DFXXTioDqjp5HxBknW2jHAI6cptwxZRzkJWMqX5+Gf6RgAxhiXMWboSfu+YYyJP8W11gKTgEPnUg9jzHBjzMaTtp+e6psxxmQgf5IKTvP9/g0daquUUuqScqohQ8aYFCR3Z3+kbbzD9tZ6Y0oppfqKIcaYzmu0vGitDfZoGmO+AdwGfKu9zbDW1px0DowxbuBK4Fpn1xvAC8aYbwLmdMestX/pdJpU4A1jzAxr7V6nLcsD/gDOZGSHtXabc90u18Na+xdr7QHgmi68LyDjKxKARcCVxpirrLUfnukFGngqpVRf1EvZ+Ywxi4EbgGHAGGttyWnKJQH/jsweOwrcZq3dd1KZx4DHz3SeC61TV+pxMmttPfADY8zGTvtKcRapNMa8hQxc/Ov51lmprkq8w1kCZSqy7Ek+EA8j7wfKkB7MeU7hfLj8HmRxnPnI0ijtczk/QeZQAhWLPHjX+eSnuFDOxx1IT2d7XuQFwHIIe6hNEhG5wTwBlc+5SczzEzfNyUR7NyQOzIOjcOw5WROzBjfjflQit2qQ65c9N5Tklw4RfmcjW2eM56raHQCULR5KcoV0uoQ1NZP/YAYDCDBuUQkMlgRAbmqIq5DrHc6IJM5XTw657Gc4LbTwYcx4qrPj+X7J27R+Ry5Zzihi+TDYK+rd7GPPlCTCacRb5gPgteQbSKCKRsLJYT1DOMogmlnCfUxgO1XIVLXWwfDHiFu4reF1Xo+5gSzyWcf1zGQNuxkLwJ86JSNSfZSlJ6biHjnLOp7DkbbnMWPMZKTd+Nf2wK+TBOAza+0JAGvtCWNMtbPfnOFYMPC01hYZY34I5BpjbkfGNayw1i6n685Uj7+c6YXGmKVAsjFmGfCUtfYJZ/8w53s+Y9AJGngqpZT6srXI8vRna0Dah+qsMMbMQ4bqTGk/aIy5EpmJVnmqFxtjXECctfZQp33fACKstdUnFT9TnU5bD2PMcP52uNIGa+0zp6nTZOTf+SPA8VOVUarb/dJ5XA68AHv8kHYzEiRWQ3MThL7llNmN/ITOd47dD6G/RIatzkaWQwG8T/skkH0VuYFVDTyEDMF1yvCsc57lSNKigASWiSv8MiT3dafcYLAumQMaYACJS/0k5vslcM13ynwCyVMPQTrUEcWkh3dweFEkcVPqicXPau90AGbn5ZEdVgSbYc+dSaRVlJNY5qcseSjFXskae+3mbTRly0ywBKpICZQyqKmNEyFAGMH1Pr9f8jYLU5/mn1hKI+Hg9ZHSUM6iiIeITZYOp1sDf+RV120MoIVxm0vkvC74Ff8PCQ0+UiJKAcnCew0bKYgYD0BWoIBSVwotuNgY7PzZdR4frroEhCD5pHdZa39qjJkArDPGjLDWNnT3xay1HzhDX7cCz1hrl3T3Nc5w7QXIb/7J+w8if03OSud4KqWUCrLWbrPWVp2pTKehOm84u95Ahtl80znuQuaN/BNyD/pUUoH3jDGjnNdEImu053S1Tmerh7X2gLX2mpO2UwadTvkt1trvI/k/08/0HijVXaxLNuKBmyHtBaTnEyAHQl9BgsipSAKfKXAsIxRynWMvO/s/Qtbg/AD5Nzgf6d2sBkZC5XS3BJ3VzuZDgtIpQCTwKkSvaqb1emS22g9lO+YJxawAYmQ+JlORuagDgWTZ8hdnQASszpjOsFqfBJ0/rodaCG9oJpxGwmmUntUnoPVGSNtczk5vKmXJQ0kuO4SLFly08Icpt/Cq6zbS8sspZxSDmtown0jAWeH1MKzWx7BaHztTU0mhNNhrOcv7BqsiZhJLDRkUkkEhVa4EZrKGUexlz5Qk/K5YFvMApaSwMuIGp1bhvMMMPuQqoqiTY66bGMtuAEaxl1FfzqeiVGeHkDbjDQBr7Xbk5mXSSeWqgMuNMf0BnMd4Z/+Zjn2JMSYWGUn0S2COMWb8Oda3y9fqCRp4KqVUX9RGT2TnG2KMKey03XWetfuboTrIv7LtKdafQIb/VJzm9Vhri5B/bXONMVcB7wFvXuiQoZPqcVqdhwwZY7zGmKuNMS84+13AeQ8NVupcmKmysRzJUDsamjchAWE8Mly2PaCMBB6G6B81S2D5EfIT3yCvo8TZNiGzlTcjt3hiIbHEL+eqdLYHkZ/0ALS257T0QkilnLPp/X40vd+P6AeaIRv2TEkiekkzrfFIgqF4JHj1QfbTRRAJs5fkYeolQN3zXBLESDbba8u2cW3ZtmCipJB98vohHCW56FDHkjJIoJdFAc9n38X3fW9jKuDN7JlEUYc3zxdMxpRZW8LVbGEU5exnOGt8N3NT2dpgMFpFAslLDvEKd3CC/kRRRyPh3MLrbOFq3NQQTzXxVDOZ9wnlOLnk8IuGJyllNAVksZK5TGMD07qetFP1pjbkpkl3bmdhrT0CbMGZM+lM/3AD+08q50fGLLSPObgZ6SX9y5mOdT6HMcYDbESGuT4MzAHeNMZknb2mZ69HV89xIXSorVJKXTrONlflghlj/h4YB/zsbGX72JChCuD9i3V9pdod2xUKQPRLzZKd9lUInQgUATP4csqQ+UgAugkJKKuBR5FlT74LIY865UKg9VYIeR4Yi/TFpCNzRZ01NSumePDe4oP50L898NuE9GjGQ9hyZx3PCsAPY2rL4UYJTH8x/SEeu+UpKl6XYa/epT75j7JGykc3NVOX2sgxb6h8X0flVK33wKqImdyUv5atGeOZtGIH782byFGGcNOKtVJoKqz3TCWFUrZ6xtPoCSeBKrZwNSOmHwhmmP0wZjwFZDGGYlIopcLjodSTQjiNjOAAAHsWJpFDLmNqyymMSaUFF3VEMYu11BFFLDK/tooEkthLCqUsj7id61lHFHVMZSOvcpvz5ujoe3Va9wB/MMY8C3wB3GqtrTPG5AKPWmsLO5X7d2PMo8AxCP5wne1Yu1DgEWvtWgBr7cfGmJlA3MkFjTFLkAH4ccBGY8xRa+3/OIdr9QgNPJVSSp2r4FAdJzFB56E6dyID8CqcbHrfAjYYY+6w1r7b+SQnDRm62Rizylq7o5vqodRXQh1RAES/1SyB5Xqk59ONJAb6FHAS6rDUOTYVKfsWklAoEkJeQX76HSFPIENw2+d1fo4Esw6vzydBa4Wz1MoCqMj24C3xSc9qtlOwCXgKzKNSJxsPj730FFTLnE8AcmCnN5VxOZIwqDLZzbBaH2tipjP707zg7K8jEZHcVLEWOxqGcZBj80KJoo5r87bJEF+gNCKJMRST6PNz2BPJkIZ6Qo5ChddDI+HBNTWzKCCKuuA6nfsZThR11OAm1AkSh3CUKOo4GONhE9dwN78nuqyZJm8/VrpuCg7TnckaNnENbmooJJM6opjqLLmyl1HOG6FzPNWpWWs/Ba4+xf6ck56XARNOc47THjvpOp+etG8PsOcUZRcCC8/3Wj1FA0+llFLnxFrrN8bsRv6dXcGXh+r8ytkAMMYcBP7h5Ky2zpChd4FfWWtfM8a8Dawyxsyz1nZpTbCz1EOprwTvKsnAigvpmXQhAWETNN8OoQ85+0DmbvZHfnOmIfM7I51jv0Ru+eDs88o5yEEC1mzkX9bRThmP89iK/PbkOHX5HAlmP3KON8Gx90OJqm3GbAaTC9wKpENyieQGq0x1E04jTZn9CCtpYy9JJC71Mzs9T1KMDWz/FltgIJjtUDU9gXiqGcJRWrMh5AMpkxZSDslwzBuKixb2RwylMSIcFy3U4A4GmgEGMKa2HBOQ9UPHlZXQ5O3Hq67bKHeCxQcanmNXRCrljKI/JygkgyxvAa5AG1GuYxSQFazXLtJx0cJc3mQQzaxkLg/zSyazBYB3nRsEqg+zdGl4rOo9OsdTKaX6Iosk/ujOrQuMMUuMMX9Geio3GmP+29mfa4zpPEz3HuA+Y0w5cJ/z/Fy0Dxl6DWTIEDAT+EZX69RN9VCqd8U4mxcZavsJwVQloQ/BZ48iiwUNRv6pXg61uXTM75yPDIctRALTKwA/EvB9gvSQLnKu8ZNO130L6QGNkae135bhusfmhcr80lhnmwbRK5ox+VA5xw2Z0BrmXDNXtvgGP7H48btiqcxwc+2qbTAf1s+YSuUMN6s901ntmU50RTPHPKEQAdmbi2QplUANIR/AazNu4LUZN9A0Rf41LSCL6IpmjjIEP7HBREDt8zd/zz2cCIHVnunUEcX65Kk0usKJoo4sCsiigC0RE9nENSRQxVxWkhUowO+KJaRaekNT+IQUPuGP3Mos1tJIOGMoZg0zmcz7LGEhy5nP8q4l7FRKnYX2eCqllAo63fCccxkydFK5YafZ/5UfMqRUt2if/bUbGIMMq12KzO8cCJffjASQANXw8W648idIEJoLrEbW+1wNlRluABJv8EuKrwfBjnaG0hYCiXD4QekijXupXobz+oDREFMMZEL0smZaM6G/s3boiRDJKBtgAMkrDnF4XiRxefXsnJMa/BaKSeMHvtep80TxIVeRNacAb56P4Z79hNPI7JI8AFoTkaVPsiHa10z0S82svnM65TNGMRVZVrfF5eKoN5zrlm6SINoLYygOXutDrgJgFmsIaYKxEbvxrvKxcs5crlu3ib0zRgXLprOLyQ3bKI2QBKPrXNeTQinPJ89gFmspJQWAWPx8yFVMYwPljOIBFvMbFlJDLDNZA8jqM0qpC6OBp1JKKaVUb2mfSxlAhtR+ivRibkIyzy7vVAa4shAJRJ/v9DgSuB0SfyLJcmgFnpPXmgeQQegNQAjE3VAvZX5Ox3DaQjg8J5K4l+tluOzDbcEhvCGzwBtwhgNnwpCGekiQYC3RJ9cb90IJOxelMq6iBG/+28GlX5LLDtEaL0NxAY4TSvLmQ7w3ZSLXVm+DbMmEy3fzqEiWsb/hDc3sjxjOiQX98b7kI7uiiAqvB6/PR50nKhiENhLOYU8kVSSwfc4ErmYL+TMyiKUmOBz3VW4jI6KQERygBjfFjGEwR4jFzxpmcj3rAPBW+DjsjZShwEAuORSSycMs4o/BvCtHzvWTVRdbe1Zb1Wdp4KmUUn1R+3IqSqmvt3znsRo+XgZXzpavNy6Daz5HMtG2Z7adivRyRiKB6nzn6xXAK8CfnHJ+4GkkYL0VuMMpF0HHCrV+pEfR6XGNy6uHagiraJNhuu3zSuuda9UCyc5SKDGQmO+XnlakHuNWlcj1IoHPIT85g+wSyWaUuNQJiK+AiukervVtgxjY6h2PO7mG5cxncckjgCQvGsJRGgnnvTuHE4ufNF85Oz2puGgJJjSqIoE1zGQEB8iigCoScNFCDrmcoD8Av2h4kl0RqURRRy45ZFBIAdnksJ6bytbyfLKsKDXNu4ECsjnKYBKoopp4nuGn/JFbOcBw55vUwFOpC6WBp1JKKaVUbxnsPNbDlSPhi01wWTJc8x1kMYQAEjiCJFYdjQSPq5E5niFIAHoPBJebfNUpF0CWMklGAtAfIfM3QXpWY+GLZ+GyT5zXDEYSAX2KrPnpaP2Ok/wnxNn/AJADh+90hu1urpf99Uhm3RKI91ZDLux/cCjJJyQJEU2STXenJ5VxPy5h0kM7sC54hkfYkyrDYd3UEMpxvD4fWz1RpDSUQy4cuHMEBWRxK38M1qsGN25qCKcRgFCOc5BhwSG0GRGFjCsrYWvyeFIopZFwssinmUEcSw6ljmgAdjPW6Smt5AAjOMgwBhAgFj8DnF5QpdSF08BTKaWUUqqX2FnyaG6Bj/fBlYlIT+KtSBD3MNLrCXAdkvn2VaQH8lZYNx9mNAGzkIRBIJlsnUy1lBEMQo/tCSV6qAyl2HpoPJOKdvBJXRI1uLl29Da5XhiypIuTiZZ34ciMSOJy6+U82cgw3WqIe1qG7VY86MFb4ZNAGKic4ca7zkfrPZC87hB/WHgLAD9Y8TpbPeOZlLeDw8/J0Nb9To/iuFVO4msvUAE756QyjIMciYjklTvvIIdcAFICpQCsdN3E/UtfJH9BBgAZFBIeaGS5az6zWAtAOI08kPwki0se4bXUG5gZWMNR1xAOMoxccpjlzN/cwtWksws/sdQRRQ65HGAEM3iHYsacz8eqeoMOte3zNPBUSqm+qD2rrVJKKaXU14Cx1vZ2HXqFMfEW7u7taiilvnYeL7LWZp693JmZQZmWEYVnL3guik231E1dmrTd7Bn2548D8MVSuOxZJKlQPJJxthqooiP/82ykt/MKoB6aiyH0KaR3cgrSOwrS09k+zPZGJEtuNTJ3s7NPgEzpdTX5yLzPdCAXKh9xMuQ68zOPLQglelWzDPGdj8wN7ZzrukyWYymIGM+kl3bANuAfkbmi7fNFB0J+RgbhNJJAFdFLmyEdjmWHEl0iPbGVqW4OMowAA7imdhu5MVMZQIBMiqgjiv6cAOBl7mAub3KUIXzIVWQ4k1X9xDK34W0AGiNCAUkWlEUB7zCDsezGTQ1FZHLEGedcRQKx+LmaLYTTSCPhrGUW4TQG1/pcZ0rP9lGq89aN7WZyN7ebu7Td7E7a46mUUkop1VvulYfLyuDj+XDlp7DvChj5BLLECuDEWvL8ISQQHQihbiQAHIvMA53qlGsfovsysA54FxmueyMdS7M0Af2BCWCqgWeh9R3o3wo1j0R2JAQaCLhl+ZOdc1LJnFLCwRgP3kifzOkESSiUAcsjbueevFc4dmco0d5mVk+ZDsDsvLxgvYaznxZcRJc184cFtzCKvWQXFdE6suMtqSKBmYE11MWEBoNOAG+Zj/xkGVo7lzdpwUUdUfzspedYfed0pgU2EFbdRlN8x1qgAQYwhmJ2kc5YdrOdCaSzi2EcDGbILWYMUdRRRQIHGEECVcxkDc0MIom9AKzTVZu+Glp7uwLqTDTwVEoppZTqJa1OEp8vNsCVucAKGPkUEtRlIkFje6KfdOAt+OIjuGw+Mg/0DWi6ux9hqW3BOZaABJpNznnm0ZE0qD3wvAJZM3Q2siTLo1AdIb2c8Q1+mc8JElTGgHXBuKdL4ArwenyyPMtRp8xg2Dp9PPeseoWKOR68T0pQOis9j6oYN9aJ2Uw+4IXBgSOwCe5wv87emKE8n3EXt/EqACfozxiKaXG5qMFNI+FElzRTmepmgDcQXCplI9dwDRtpJJytd44HICy/jbIpQ2WtUCCLAqJXNNN6PRABdURxnEFUkUgW+VSRAMA7zGABvyWBOly00Eg4VSRQRCbDOXA+H6tS6hT69XYFlFJKnUL7cirduSml+pyQMbKFfgC8gyyd8gnUPo0EfU6vZLDDzQuXLUcC0k2yP/CNNlmWpcLZ8p3XrkaCzXwkI25Zx3VJd56Pccq8BYnr/ITTSMi78OaUmbw5ZaYka1kHxTFJVD7oliG0/8d5fbyzFcKksh3YKbImJkeBBWDekGVXTD2YetgzI4nwQCNhJW1sXTAeUw3JFYeYzPtEP9lM9JPNhHIcgFJSaMFFM4NYnTqdxAo/LS4X8Q1+4hv83FH7Oku4jzqiSKCKTIrYOSWVUlIYwQFGcIBF/Jyd81L5dcR9DHAmzc9iDYM4Tikpwe3N2tvZzgTqiKKGWGpwU0UC09jASuaykrnd+pkrdanSHk+llFJKqd7yoPO4G754Ay5bAFwBMU5PI+nI8FqAJ4BUJOtsBjLH0ivFyAQn8WvHUipjgSLnGrnQeiOEVDplCuHwnEgib68ntAnpFQ1B5nF+CjetWgvA+jlTua5hE2mby2nK7ifzO8uAF6BykfSQ1kxxkxIoJWxdm9RvJOzxJhG+oBFvkY8y71AA0srKafL2AxdkBApl+ZZ6yT77/COypmYWBcTix00Nm7iG75e9DS5Y75W5nrERMgR4TG05Y9nNGIopJINY/JygP2PZHcyUu4DfUkoKc1jFPSxjMlvIooC9jOIaNnJ9gwwBLojJoIAsfrr5NxyZMpgojlFFAiuZy90sA+BdHWrb9+n6132e9ngqpZRSSvWSpvn9aJrfDz5xkgvVIj2Z25GgczfSc7kaCSBjkWGytyLJgJY7ZXKRJVVmIb2QTznH5juP34aQV53X+OG9GROJq6gn9C049h+hcs0EZFjvZrBTZLuuYhMV0z3wCbzvmgwfIMHlzZDo85Po8zPOV0LYijZZCuVd2LpgPGm+cqKoAxcMoplBNFOZ7Ga5az7HUkPZ4JrGMU8o1g2NhLOw9kUW1r5IM4OIb/ATt6KeW2rfxroh35vBcPbjoiXYS2kC8H3f2ySW+YnFz3D2E2AAjYQzrqyEcWUleCt8BBhAf06woeJ75JDLr/lnEqjiCR5lacRdLI24i/TALv6ZX/OLKQ9RRzSbuIZmBhHKceTssT38U6DUpUGz2iqlVLfqpux8AzItcd2cna9Ks/Op86ftZs+wuY/LF39C5mHWIwmD2oexuulIMpSJBHf5QBN88QEU18PYo2CeACdJK589CpcnQlN5P8KeboPPofUhCCmU1wGwFOzrYLY716sHFkBTfD/CStrk2gCvgp0PpgKsV3aZH4L9N9gbIz2ZLlrw5vkgHQ57Iokrqqcyw03iEr+s++nMK906bzyTluyQOan5bbJ/HzQ91Y+wwjYAmjL78b5rMsPZzyCaOcgwACZt3sHzU+7i/ooX5X2LhMKYVIpJI4f1FJFJFHXsZRQtDAAgh1wOMoxhHKSOqGDG2v0Mp5lBVDvfZAqljKGYXHKoI4qr+JDh7CeX64LzQB83oef9Gauz6aZ2c2CmJaGb28392m52Jx1qq5RSSinVW9oDwVw6ljuZivRwDga2g10vu81mZM7laCAeWv7UjysT22AF0rP5ipSL+rwf5LcR9r026cH8OYR8AHxExxIoOWACSK9qKxJ4NkGLy0WYq1mCW6DiQQ/edT7wSpBaOd1N4gsyb3MUh6Re+UAqVHrchNMIFZBY7+fwwkjKGUVohszbDOU46xdOJYEqEqZUsWXK1SRQhYsWBmTLHMzkzYcYMCWAixbCaSQjUEiLy0XZlKHcv/lFHp7yCACLVj1J8Zw0RrGXFlxc9/AmXlt0Az/If116hIGdnlSWM59wGnmEJ8jlOvy4yaKA7YwgnV0AXFuxjee9d0ldOU4dUVSRQBTHWM585w3beiGfsroY2tD1r/s47fFUSqlupT2e6utJ282eYTc9Ll/UIkl+tsO+1TKN8n9Gwrt1U7nue5ukTBiShTYMnCSwEqR+imS0bV9OJRMJHNvXz6xHgtMcgj2ZlXe6SXzYL/+o54BNl15NKpzztf+lqEYC3RhkvmiNc51qqJjiAWAX6WRSRHyDn6oID8NqfRwP60eLy0Uj4ZLFFihyZTKGYqIrmsn3ZpBCKdG+Zgmma+Vyr2XcQAJVTPLt4LAnkgOMYAABhnCUYbW+YC9rI+Fk1pZQHJNEWkU5O72pVJFACy5SkDU3C8iikXB+WvYb9iRLuT3eJKKoYyPXMJktgGS1HcJRtjCZQRxnAtvZzwj8uIOf029N4zl9rupcaLt5qdAeT6WU6ossmiRBqUtBewA5EAkWU2HkzTDSBdwK192wCdrXuHTBxzfKl1cuRwLJadC6zZm/OdAptxz4iXPugUiwmIMEeM5w2cSn/TARDk+PJG5zvfRaBpCkRZ/S0XOUDbyFBKJhwBVg3WBKwLvCB4B3qg9egJ2LUhmXV4KdAK5AG2EVzURHNgeXeRmWcZDokmYqUj2E00gpKQz2HKGITLK8BYDM2zzmCYXPIa6ongMZMC6vBICd01MZ4SxvMqLhECec/2KPeUNJCZTicrUQYEBwyZUUSqkmnopkD/sZTlptOWu8s3jspacYcef+4DDaERygkXCWl9zL4dRIZ9juMebyJg/yjPNGlJ77Z6uU+hINPJVSSimlesuj8rDvChgWCZeNRJL8ZELz9RD6Dh3BqQ+GhUDMg8iyJk8B8RCyD3gacIJS3kKSBbVnt52FBLU3AiVOmYHAOxBXWC9Lr3yOLK1SApUPuqkjCoD9DGf26DwYDU0x/fC7YvGWyNDbYxky7zG6ohkmSgBHtfScbs/IIPtokQwZjpRLJlb4ZZ3PEh+E+MhPzmAlN/FYxVPBt2OnN5VxFSVs9Y4nyltHFQkcni5Bop9YWpxu3BMR/ZmUt4O66VGkNJSzPSKD44TiooVcZzzxzMAaolwjqCKB6xvyeC9jItewkQV3PksUdcxkDQDbmUAKpaxOnU4zgwjgIosCBtHMGIoBWEf/C/qY1UWgN2z7PA08lVJKKaV6yxPyMPLbyJImbyEJhQZC6D1Ib+FEp6wbYu5H1vssAl5GhtCGAa+DE0fBs3QkIWrv5axFht46PZ5kIkHht4EYOOyNxEUL0RXNxDf4SWySZUvSqsslE+5uCGtqwxvm49j0UKJvaSY6x/kvfyrQBNGbm2m9EfZHDJVexwrYuSCVcT6Jdis9brxFPjmfG44TShb5EmyukjLjaktgE4x5sFiG6XKE8EAjuOC6pzfxqwd/DMADDc+xfvpU3NRQEDGeauKZEXiHA64Rkk0XWO6az1h2M6liB+u9U2kknCKGMZeVrGIOuVwHwDVsJDu/iPXZMlb5CIMJp5E1zDzfT1UpdQq6nIpSSimllFJKqR6lPZ5KKdUXaXY+pS4NP3EeH6BjLuW7yNzKWciQ2PYyzzpfZwLP0dHDeQ8yh3OB87wEyWDrZMBtHQMhDUivZ65TZjUwFngD+A7ELa8P9qyWTk8i7a1yeVINrfdDSDHYCZLZNnpdM2WvDw0Oey1mDMyB7xe9zf6IoSQvPYS9GY5NCWVcUQlbM8YDMKlsB9YLJkbW5pzcsI2QJ2Dr4vG0flcu178VzEAYEAiQ0NRMccwY3ndNJosCdj6Yyi28LtWKcNNIONet2MTqedO5qWwth5MjqcFNFYmADP3NatjBHm8SS1jIBt/3sC5ZBmY+y3FTE6z/nmxJOnSC/qRQSrLvEC2e91nEz5037OC5f7bq4tJ2s8/TwFMppZRSqpdYJ8usyUGGzr6DBJ2FyDDbTyC4oscbyD/WbuDHyBzPViTA/ISO+ZshcHhBJHFL6yEMQo46r8lFhuUCTc85a2emdnpdMpAPab5y+RrYc6cEZOHZjUT/WOZy4oUWXKStk+A0zV8OU6B1JCQvPQRTZZ5neEgzxzJCCUWWU2EZFD6XiiumheyiInDBzsWpTMrfwdZsCU6zGnbQfx78xrWQHFcuWRQQndeMnQB1MVHEIkOA7+UF7mMJb86byQS20+TtR1xRPWszRgSz2gKEVENCRBWT2cIxTyjFjGEX6Uzm/WDgvJKbyGE9zQwilONsZwIrPYO4m2U0M+j8P1yl1Jdo4KmUUkop1UuOh8msp7DKNum1XOEcuIKOeZrtPZ63Iut+vuE8dyPLn/wjMn/zDmf/RxD3o3rp9bwX6UlNBT4F+6wUCfteG8f+I5TokmZZYmU5sBmYggS9Tu9n2qryjnmhEwku0ZKWXw675eumB/sRtqKNI3dGUrQgkywKaCScxCI/ZBDMRGsfhXG+ErZ6xkM8bPWMx00NW7PHk9WwA5CezDqi+FnRcxzOkKxETVP64Qq0ce3mbcFERf87418pJYUT9KeRcLwlPp7PuIs6ooMJgT7kKhqTw4mijqv4kBrcuKkhnL/KeqOOQRwniwKqZIVRUiilxklkdB9LAFjHNV38RJVSp6OBp1JK9UVtaHY+pS4BYflt8kUF0ns5Gj57BS4H6Z28lY7lVKYCR4EGJAD9CNiHrNM5mo61N99Cekm9SG/o53BsQSjR+c3BJUhCFkP0LTJkNplDVO5xk7jKD9VwbGGoZKoF2AyVGW4Sf+SXnthPgXgoy5bXAYRVt2FnQdyKenJyNlEV4yah1g8uqMFN8goplz8vgwEEmLRqB1vnjGdSxQ62esczqWiH1BM4kd0fNzWsz5gqvZ0VzVR4PXj3+Tg2JTS4BEraqnLq5kQRYAABBvBaxg1czzr2MzzY45lCKcWMYTEPMJ/lZPuK+JXnx/zM9xxvemYS76zzMp/lvMptzOAdRnCAOqJwUxNcF1R9RVjkd0j1WRp4KqWUUkr1tiYkUNwEl9/sBJ+RSM9nhVNmA9I7ORa+eBQui0Gy0n5wUrlspFfU7TzPlHmZ+CGkvUc1DJgPyTccgmRIrPXDg8ALEH1js2TNBciFxHS/BLL18pztkPwvhziW7SynkteMqYamef0IK2kjsd6PjewYRrxnXhIAw9nP+0wmc0oJ/TkBtZDgraIiw0M18cEycRX1uL0y/3KZ93YaCWdu6kr2ksQoZHjv4TmRwey1UdQxLr+EPdlJXJu3jYXTnwbgFzwGQDzV5NRuYqcnlTEU8wfPLQziePD1dURxnEEklxzicKqcdyVzmctKFvOA80bsPeePVCn1ZcZa29t16BXGxFu4u7eroZT62nm8yFqbefZyZ2ZMpiWksDsq1KHVdEvd1KVJ282e8biVnsUojjGCA4TTSBJ7yeU6XAQYxkEKyQCgmDT8uMmigAwKKSCbEewngSr2M4JBzlzKXHLIoJADjGAAAYZwlCMMpgVXsDdwCQvJYT0pfIIfN/05IUmCgOEcCJ5rI9cQSw0DaCGKYzQziFJSqCOKYU7CnQEE8BPLcA7gx00CVWxnAnNZyRpmBb/XFgYwge1UkUAUdYyhmCUsJJYaDrYNA6B/vxMcPz6IqEF1hNPILNZQRQKlpABw3Jlz6W9zc3T/5QxP+m9qjseSNahA6nU8imsGbQRgbfUsZsavoZBMWhiA/9ME0q/Yzu7yb/ONbx3hr4cHA/CNuKO4BkpWmqPvX843vn2E2EE1hNPI7uqxUvnLf9W9H7zqpJvazX6ZloHd3G42a7vZnTTwVEqpbtWNgSfd3ICiDag6f9puKqV6Rje2m6ab202r7WZ30nU8lVJKKaWUUkr1KA08lVJKKaWUUkr1KE0upJRSSimllPrquzRnEH5laI+nUkoppZRSSqkepYGnUkoppZRSSqkepYGnUkoppZRSSqkepXM8lVKqT7LAF71dCaWUUkqpbqE9nkoppZRSSimlTskYE2mM+YMxpuoUx/7dGLO8K+fRwFMppZRSSin1FdcGNHfzdnbGmLXGmP8yxuwyxnxojEk/TbkkY8x/GmPKnceRXTl2Powxi40xFcYYa4xJ7Wo9TsdaW2+t/QGw96Rz3Qts6Gq9ei3wNMYMMsasNMbsN8aUGWP+4Qxlf+iUO2CMecEY06/TsXRjzFZjTKmzTb8434FSSvWk3mlAv+66665tb9B2Uyml+qT/Za39O2vtWGAx8IfTlFsGLLXWJgFLgd938RgAxhiXMWboSfu+YYyJP8W11gKTgEPnUg9jzHBjzMaTtp+e6psxxmQAg4CC03y/f6M3ezwfABqttSOAGcByY8w3Ti5kjPECjwF/D4x0tnnOsTBgNfCgtTYFSAN2XJzqK6XU148x5qAT1Ox2tmknHR9sjMk1xuw1xuwxxqw2xnyz0/FuvWvrnLPb7tx2113bXqLtplJKXVxDjDGFnba7Ti5gra3v9DQSuXP8JcYYN3Al8Iaz6w3gSmPMN8907KTTpALvGWNGOeeMBN4Fck5Rp23W2lPdYD3jtay1B6y115y0PXOqNwa4DmlfFgHZxpirTlMuqDcDz7lIxI21dh9QCJzqruscYK219i/W2jbg35zXAtwCbLPWfuScp9Vae7THa66UUl9vc6y16c52cjBmgaettaOstWnAAeBXnY6f9a4t9Pyd256+a9tLtN1USqnTak/K150bR6y1mZ22F091ZWPMcmNMJRKE/a9TFEkAPrPWngBwHqud/Wc61vHdWVsE/BDIdYK894A3rbXnMlKnS9c6zfe4FEg2xiwzxnittU9Ya38IPAzkW2s/PNs5ejPwTOTL/0RUcupv+kzlUoAvnLvvu40xLxljonuktkop9dV31ju3Z2OtrbXWvt9p10fAUDj7ndST9Oid256+a9tLtN1USqk+yFo731qbCPwcOF1b0x3X+QD4KbAVeN9au6SnrnWKay+w1n7LWnuPtbai0/6D1tr5XTlHjy2nYoz5GGn8TiW2my4TAkxFhhPVAP8HeBb4wWnqdBfg/KMV2U1VUEqpntA+x7NbHbHWZnah3GvGGANsA35ura07VSFn3uCPgHecXX9zJ9UY034n9S+dX2utLTLGtN+5vR35273iQu/cnu56J9U7eNcWeMpa+4Szfxjwr125a9sTtN1USqmvNmvtq8aYF40xg08aTVIFXG6M6e+0Vf2BeGe/OcOxLzHGxAKPA78EbjbGrLLWnst0iTPVo8f1WI+ntfZKa+2Q02wnkDuwnYdZJXLqb/pM5Q4Bm621Pmc40evA+DPU6cX2rnIZVaWUUuokV1lr/w4YhzSGL5yh7G+Av56lzGn11p3b7rhr20P10nZTKaXO28VPyudMEUno9HwGUOtsQdZaP7AbuNnZdTOwy5kScdpjJ13LA2xEbpg+jEyreNMYk3XWinahHl09x4XozaG2bwF3AzjJIMYB/3GKcm8DM53Jt/2Qsc1/co79CRhvjAl3nv9P4L96tNZKKfU11j6k1VobAH4LZJ+qnDFmMTI8da4TwECnO6lOmTPeST3pzu0cY8xpA6DTOKfrfQ1ou6mUUn1LGPCWMabYGLMb+GdghrXWOlMaOo8yuge4zxhTDtznPO/KsXahwCPW2tcArLUfAzOBUyWZW2KM+TPwLWCjMea/z/FaPaLHhtp2wTPAK8aY/cAJ4C5rbSOAMeYJoNpau8xa+6kx5klkHhHIHKAVANbaSmPM08B/GmPagAqCQ4KUUuqrzHKxl0BxMp6GWGvrnaG2NyF3Rk8utwjIAK5zAlRA7qQ6De/NyN/p095Jde7cvgv8ylr7mjHmbWCVMWaetbZLSX7O5XpfE9puKqVUH2KtrQG+fZpjOSc9LwMmnKbsaY91KvMp8OlJ+/YAe05RdiGw8Hyv1VOMtbY3rtvrjIm3zo1jpZTqRo8XdXEe5RkZk2Y7pk52F+8Z62aMuQLpLevvbKXAQmutzxiTCzyKRMMlQDkdkXGFtXaWc45k4N+BaOAYcJu19ktLl3S6Vpq1dm2nfWlAnLX23ZPKLgFmA3HAEeCotfZ/nMv11IXTdlMp1TO+uu2mOje92eOplFKqD3Hupo49zbHOd27NGc7RpTupX4c7t0oppZTqOg08lVKqT+qRrLZKKaWUUr1CA0+llFJKKaXUV5zesO3rejOrrVJKKaWUUkqpS4AGnkoppZRSSimlepQOtVVKqT5JhwwppZRSXaftZl+nPZ5KKaWUUkoppXqUBp5KKaWUUkoppXqUDrVVSqk+SYcMKaWUUl1ngS96uxLqDLTHUymllFJKKaVUj9LAUymllFJKKaVUj9Khtkop1SfpUFullFKq67Td7Ou0x1MppZRSSimlVI/SwFMppZRSSimlVI/SobZKKaWUUkqprzgdatvXaeCplFJ9kkUbUKWUUkp9XehQW6WUUkoppZRSPUp7PJVSSimllFJfcTpSqK/TwFMppfqkNuB4b1dCKaWUUqpb6FBbpZRSSimllFI9Sns8lVJKKaWUUl9xmtW2r9MeT6WUUkoppZRSPUp7PJVSqk/SO7dKKaWU+vrQwFMppZRSSin1FWeBL3q7EuoMdKitUkoppZRSSqkepT2eSinVJ+lQW6WUUkp9fWjgqZRSSimllPqK0xu2fZ0OtVVKKaWUUkop1aO0x1MppfokvXOrlFJKqa8PDTyVUkoppZRSX3F6w7av06G2SimllFJKKaV6lAaeSimllFJKKaV6lA61VUqpPkmHDCmllFJdp+1mX6c9nkoppZRSSimlepT2eCqllPpaMsYMBJYBDUCbtfbHvVsjpZRSqu/q6XZTezyVUqpPskBrN29nZ4xJMsb8pzGm3Hkcea7lunqOrjLGLDbGVBhjrDEm9RzqOxv4wFq7EDhujMm8kHoopZTqy7Td7HS+PtluauCplFKqs2XAUmttErAU+P15lDvrOYwxLmPM0JP2fcMYE3+Ka60FJgGHzrEeQ4GDztcVwLDTfC9KKaXU+dJ2s4s08FRKKQWAMcYNXAm84ex6A7jSGPPNrpbr6jmAVOA9Y8wo55yRwLtAzsn1stZus9ZWnUd9K5FGFOfxVA2wUkopdV603Tw3l/AcT99f4fG9vV0LxxDgSG9XwqF1ObW+Upe+Ug/QupzOqO45jW8DPD6ke84VNNAYU9jp+YvW2hc7PU8APrPWngCw1p4wxlQ7+//SxXKmK+ew1hYZY34I5BpjbgeeBVZYa5efw/dztvquBn5njEkD+ltrd57DudXf0HbzNLQuf6uv1AO0LqfTl+qi7eYl0m5ewoEne621fWK+jzGmUOvyt7QufbceoHU5nZMaqPNmrf2f3XGevsxa+4Ex5qfAVuAZa+2Sbj5/M3B7d57zEqft5iloXfpuPUDrcjp9rS7dcR5tN7vl/D3abupQW6WUUu2qgMuNMf0BnMd4Z39Xy3X1HBhjYoHHgV8Cc4wx43uovkoppVRP0HbzHGjgqZRSCgBrrR/YDdzs7LoZ2GWt/UtXy3X1HMYYD7AReMpa+zAwB3jTGJPV3fVVSimleoK2m+fmUg48Xzx7kYtG63JqWpe/1VfqAVqX0+lLdTkf9wD3GWPKgfuc5xhjck9Kq37Kcl041i4UeMRa+xqAtfZjYCbwjZMLGmOWGGP+DHwL2GiM+e9zvJbqHn3pZ1vrcmp9pS59pR6gdTkdrUv30Xazi4y19mJdSymllFJKKaXUJehS7vFUSimllFJKKXURfG0DT2PMIGPMSmPMfmNMmTHmH85Q9odOuQPGmBeMMf1OOj7QGFN6vlm3uqMuxph0Y8zHxpjdxpj/Nsa8aIxx9VJdvmeMKTLGlDh1+cm51qMb63K5MWaLMab+XD8fY0ySMeY/jTHlzuPIU5Tpb4xZ6lx3vzFmfleOnatuqMt3jTGFxpiAMWbx+dajm+ryiPNz8V/Oz8m0XqzLHcaYPc7vTbExZmFv1aVTmVHGmOMX+jkp1d26429yp+Pabmq7qe1m1+ui7aa2m5cGa+3XcgMeBZY7X48EDgPfOEU5L/Bn4JtIIL4BuO2kMs8CLwGFvVUXZFz3AOfrfsDbwMJeqssEIN75OhLYD1zVS3WJBCYB/3Cunw+wGZjnfD0P2HyKMrc51+vnXP/PwLCzHTuP9+JC6zICGAv8b2DxBf7uXGhdpgGDnK//DqgDQnupLhF0TCkIRxZCTuuNujjH+wPvA69f6Oekm27dvXXH3+ROZbTd1HZT282u10XbTW03L4nta9vjCcwFlgFYa/cBhcD0U5SbA6y1klWqDfg357UAGGOuQv64v9qbdbHWNltrW5xylyENalsv1WW7tbba+boe+AQY2kt1qbfWbgX+ei4XNsa4gSuBN5xdbwBXGmO+eYo6/pu1ts1Kxq+1wI1dOHZR62Kt3W+t3QW0nuv1e6AuG6y1x51ye5CFkQf3Ul0arLXtE9kHIb875zyxvZt+XgB+Bvx/QPm51kGpi0DbzZ6ri7abZz92Ueui7aa2m+ri+zoHnonIXZp2lUDCuZQzxoQBzwE/6u26OPWJN8bsBo4AjZxfFrBuqUunOiUD30buavVqXc5RAvCZtfYEgPNYfYrznuna3VWv7qhLd+nuutwGHLDW/rm36mKMud5INrdDyGLLxb1RF2NMGnJX+9fncX2lLgZtN3uwLp3qpO3mhddL280erIu2m6qnhPR2Bc6XMeZj5If1VGK76TLPAEuttZ+dalz6Ra4Lzt3SdKdhXwHMBt7sjbo41/IA/xdY0H4nt7fqovoeY8x3gCeBa3uzHtbad4B3jDGJwFpjTK61du/FrIMx5jKkx+EOa+0JY8zFvLxSgLabaLup+jhtNztou/n19JUNPK21V57puDGmEhnG0r4gaiKw5RRF28vRqVyV8/VEIMcY8ygwEIg2xuyx1qb1Ql06X6/JGLMS+D4nNaAXqy7OEIqNyJ2wP53qWhf7fTlHVcDlxpj+zh+0/kD8Kc7bfu2dna59qAvHLnZduku31MUY8/fIP3nfu4DGqlvfF2ttpTFmBzKv6VzrdKF18QDDgVyn8YwCjDEmwlp71znWRanzou2mtptnOncXaLvZg3XRdlPbzUuC7QMTTXtiAx5HxoyDzDWpAcJPUe4K/nYC/v86RbmrOf8kCRdcF+dYe5KEAchY+V/2Ul0GA/8F/KivfEbn8/kgk9U7T3rfcooyt/O3k96vONux83gvLqguJ72nF5ok4ULfl3FIQzLhQurRTXVJ7lRuCNJwfrc3P6Pu+px00627t+78m+yUO+e/y91ZF7Td1HZT201tN3XrU1uvV6DHvjEIA95CMsftRe4gtR97Arin0/O7gQPO9jug/ynOd85/oLuzLs4vbDHScJUASzmPjGfdVJdngGZgd6ftjl6qS3/nj9RfgBbn68e7eP1kYDsyYX07MMrZnwtkdjr/7zpd+65Orz/tsfN4Ly60LhOd770Bmcf0Z2BaL9Vlp/N5dP75GNNLdfk18N9OHf4LuK+3PqOTzvU42oDq1sc2tN3sybpou3mWY+fxXmi72TN10XZTtx7b2tMlK6WUUkoppZRSPeLrnNVWKaWUUkoppVQfoIGnUkoppZRSSqkepYGnUkoppZRSSqkepYGnUkoppZRSSqkepYGnUkoppZRSSqkepYGnUkoppZRSSqkepYGnUicxxrxvjPnUGPOz83jtQmPMfmNMYU/UTSmllOprtN1USnWFBp5KndpCa+2vzvVF1tolwPweqI9SSinVl2m7qZQ6Iw081SXDGJNjjNndaWsxxnyvC6+73RjzrjHmT8aYMmPMJmNMijFmvTGm3BjzmjHGXIzvQSmllLpYtN1USnUnDTzVJcNam2utTbfWpgNLgUJgQxdfPg74F2ttMtAMvA7cAqQAY4Cp3V9jpZRSqvdou6mU6k4aeKpLjjFmGvAT4HvW2s+7+LJ8a+2fna93AdustfXW2lbgv4ARPVBVpZRSqtdpu6mU6g4hvV0BpS4mY8zfAcuAadbav5zDSzs3tCdO8Vx/l5RSSn3taLuplOou2uOpLhnGmMuBt4FbrbXlvV0fpZRSqi/TdlMp1Z008FSXkvnAN4EXOiVKmNzblVJKKaX6KG03lVLdxlhre7sOSvUpxpj3gcXW2v/vPF9/tfP6zG6sllJKKdUnabuplOoK7fFU6m/VAs+c70LYwG+BI91eK6WUUqpv0nZTKXVW2uOplFJKKaWUUqpHaY+nUkoppZRSSqkepYGnUkoppZRSSqkepYGnUkoppZRSSqkepYGnUkoppZRSSqkepYGnUkoppZRSSqke9f8DxgNeC3m9aX8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 936x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plotter.make_sub_plot(data)"
    ]
@@ -360,20 +770,315 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Instrument settings:\n",
+      "  ncount:           5.00e+06\n",
+      "  output_path:      data_folder/union_materials\n",
+      "  run_path:         run_folder\n",
+      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
+      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
+      "  executable:       mcrun\n",
+      "  force_compile:    True\n",
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/tutorial/data_folder/union_materials_6\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
+      "                                                ^~~~~~~~\n",
+      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
+      "#define filename mccabs_logger_space_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
+      "                                                ^\n",
+      "                                                \"%s\", \n",
+      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
+      "#define filename mccabs_logger_space_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "14 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/tutorial/data_folder/union_materials_6\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Cu.laz' (Table_Read_Offset)\n",
+      "Table from file 'Cu.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
+      "  '# TITLE *-Cu-[FM3-M] Otte, H.M.[1961];# CELL 3.615050 3.615050 3.615050 90. ...'\n",
+      "PowderN: powder: Reading 19 rows from Cu.laz\n",
+      "PowderN: powder: Read 19 reflections from file 'Cu.laz'\n",
+      "PowderN: powder: Vc=47.24 [Angs] sigma_abs=15.12 [barn] sigma_inc=2.2 [barn] reflections=Cu.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: incoherent \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: powder \n",
+      "component index [1]: 4 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 3\n",
+      "name of material    [0]: inc_material \n",
+      "component index     [0]: 2 \n",
+      "my_absoprtion       [0]: 0.000000 \n",
+      "number of processes [0]: 1 \n",
+      "name of material    [1]: abs_material \n",
+      "component index     [1]: 3 \n",
+      "my_absoprtion       [1]: 3.000000 \n",
+      "number of processes [1]: 0 \n",
+      "name of material    [2]: powder_material \n",
+      "component index     [2]: 5 \n",
+      "my_absoprtion       [2]: 1.200000 \n",
+      "number of processes [2]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 3\n",
+      "\n",
+      "name of geometry    [0]: box_inc \n",
+      "component index     [0]: 7 \n",
+      "Volume.name         [0]: box_inc \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.000000 \n",
+      "Volume.p_physics.number of processes [0]: 1 \n",
+      "Volume.geometry.shape                [0]: box \n",
+      "Volume.geometry.center.x             [0]: 0.040000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: box_powder \n",
+      "component index     [1]: 8 \n",
+      "Volume.name         [1]: box_powder \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absorption       [1]: 1.200000 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: box \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: box_abs \n",
+      "component index     [2]: 9 \n",
+      "Volume.name         [2]: box_abs \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absorption       [2]: 3.000000 \n",
+      "Volume.p_physics.number of processes [2]: 0 \n",
+      "Volume.geometry.shape                [2]: box \n",
+      "Volume.geometry.center.x             [2]: -0.040000 \n",
+      "Volume.geometry.center.y             [2]: 0.000000 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 4\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           0 = [1,2,3]\n",
+      "LIST: Intersect_check_list for Volume      0 = [1,2,3]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [1,2,3]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for box_inc with box shape made of inc_material\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = []\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = []\n",
+      "LIST: Next_volume_list for Volume          1 = [0]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for box_powder with box shape made of powder_material\n",
+      "LIST: Children for Volume                  2 = []\n",
+      "LIST: Direct_children for Volume           2 = []\n",
+      "LIST: Intersect_check_list for Volume      2 = []\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = []\n",
+      "LIST: Next_volume_list for Volume          2 = [0]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for box_abs with box shape made of abs_material\n",
+      "LIST: Children for Volume                  3 = []\n",
+      "LIST: Direct_children for Volume           3 = []\n",
+      "LIST: Intersect_check_list for Volume      3 = []\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = []\n",
+      "LIST: Next_volume_list for Volume          3 = [0]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_I=1.13342e-09 logger_space_ERR=5.90802e-13 logger_space_N=3.86971e+06 \"logger.dat\"\n",
+      "Detector: abs_logger_space_I=4.16101e-11 abs_logger_space_ERR=2.36615e-14 abs_logger_space_N=4.12627e+06 \"abs_logger.dat\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "data = instrument.run_full_instrument(ncount=5E6, foldername=\"data_folder/union_materials\",\n",
-    "                                      increment_folder_name=True,\n",
-    "                                      parameters={\"wavelength\" : 2.8, \"source_width\" : 0.03})"
+    "instrument.set_parameters(wavelength=2.8, source_width=0.03)\n",
+    "instrument.settings(ncount=5E6, output_path=\"data_folder/union_materials\")\n",
+    "instrument.show_settings()\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data = instrument.data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space\n",
+      "Plotting data with name abs_logger_space\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "functions.name_plot_options(\"logger_space\", data, log=True)\n",
     "functions.name_plot_options(\"abs_logger_space\", data, log=True)\n",
@@ -401,7 +1106,8 @@
     "All libraries for McStas are found here: https://github.com/McStasMcXtrace/McCode/tree/master/mcstas-comps/share but only three are needed for the Union components:\n",
     "- Union_initialization.c\n",
     "- Union_functions.c\n",
-    "- Geometry_functions.c"
+    "- Geometry_functions.c\n",
+    "- Union_last_functions.c (if on McStas 3.X)"
    ]
   },
   {
@@ -428,7 +1134,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/tutorial/Union_tutorial_2_geometry.ipynb b/tutorial/Union_tutorial_2_geometry.ipynb
index abadba76..5a07c758 100644
--- a/tutorial/Union_tutorial_2_geometry.ipynb
+++ b/tutorial/Union_tutorial_2_geometry.ipynb
@@ -89,10 +89,8 @@
     "src.yheight = 0.035\n",
     "src.focus_aw = 0.01\n",
     "src.focus_ah = 0.01\n",
-    "\n",
-    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
-    "src.lambda0=\"wavelength\"\n",
-    "src.dlambda=\"0.01*wavelength\"\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.dlambda = \"0.01*wavelength\"\n",
     "src.flux = 1E13"
    ]
   },
@@ -282,9 +280,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_geometry\",\n",
-    "                                      increment_folder_name=True,\n",
-    "                                      parameters={\"wavelength\" : 3.0})"
+    "instrument.set_parameters(wavelength=3.0)\n",
+    "instrument.settings(ncount=1E7, output_path=\"data_folder/union_geometry\")\n",
+    "instrument.show_settings()\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data = instrument.data"
    ]
   },
   {
@@ -374,18 +375,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "logger_zx.D1_min = -0.12\n",
-    "logger_zx.D1_max = 0.12\n",
-    "logger_zx.D2_min = -0.12\n",
-    "logger_zx.D2_max = 0.12\n",
-    "logger_zy.D1_min = -0.12\n",
-    "logger_zy.D1_max = 0.12\n",
-    "logger_zy.D2_min = -0.12\n",
-    "logger_zy.D2_max = 0.12\n",
-    "logger_xy.D1_min = -0.12\n",
-    "logger_xy.D1_max = 0.12\n",
-    "logger_xy.D2_min = -0.12\n",
-    "logger_xy.D2_max = 0.12"
+    "logger_zx.set_parameters(D1_min=-0.12, D1_max=0.12, D2_min=-0.12, D2_max=0.12)\n",
+    "logger_zy.set_parameters(D1_min=-0.12, D1_max=0.12, D2_min=-0.12, D2_max=0.12)\n",
+    "logger_xy.set_parameters(D1_min=-0.12, D1_max=0.12, D2_min=-0.12, D2_max=0.12)"
    ]
   },
   {
@@ -393,7 +385,7 @@
    "metadata": {},
    "source": [
     "### Run the updated instrument file\n",
-    "Run the simulation with the added cryostat. If mpi is installed, one can add mpi=N where N is the number of cores available to speed up the simulation."
+    "Run the simulation with the added cryostat, since no parameters or settings are changed it is enough to just call the backengine function and grab the new data."
    ]
   },
   {
@@ -402,9 +394,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_cryo = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_geometry\",\n",
-    "                                           increment_folder_name=True,\n",
-    "                                           parameters={\"wavelength\" : 3.0})"
+    "instrument.backengine()\n",
+    "data_cryo = instrument.data"
    ]
   },
   {
@@ -412,7 +403,7 @@
    "metadata": {},
    "source": [
     "### Plot the data from the new simulation\n",
-    "Here we increase the orders of magnitude of intensity plotted. Try to play with these values to see how it changes the plots."
+    "Here we increase the orders of magnitude of intensity plotted on the log plots. Try to play with these values to see how it changes the plots."
    ]
   },
   {
@@ -421,10 +412,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "functions.name_plot_options(\"logger_space_zx\", data_cryo, log=True, orders_of_mag=5)\n",
-    "functions.name_plot_options(\"logger_space_zy\", data_cryo, log=True, orders_of_mag=5)\n",
-    "functions.name_plot_options(\"logger_space_xy\", data_cryo, log=True, orders_of_mag=5)\n",
-    "plotter.make_sub_plot(data_cryo)"
+    "plotter.make_sub_plot(data_cryo, log=[True, True, True, False], orders_of_mag=5)"
    ]
   },
   {
@@ -487,7 +475,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/tutorial/Union_tutorial_3_loggers.ipynb b/tutorial/Union_tutorial_3_loggers.ipynb
index e1e1035f..253246e0 100644
--- a/tutorial/Union_tutorial_3_loggers.ipynb
+++ b/tutorial/Union_tutorial_3_loggers.ipynb
@@ -59,10 +59,8 @@
     "src.yheight = 0.035\n",
     "src.focus_aw = 0.01\n",
     "src.focus_ah = 0.01\n",
-    "\n",
-    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
-    "src.lambda0=\"wavelength\"\n",
-    "src.dlambda=\"0.01*wavelength\"\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.dlambda = \"0.01*wavelength\"\n",
     "src.flux = 1E13\n",
     "\n",
     "sample_geometry = instrument.add_component(\"sample_geometry\", \"Union_cylinder\")\n",
@@ -209,9 +207,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_loggers\",\n",
-    "                                      increment_folder_name=True,\n",
-    "                                      parameters={\"wavelength\" : 3.0})"
+    "instrument.set_parameters(wavelength=3.0)\n",
+    "instrument.settings(ncount=1E7, output_path=\"data_folder/union_loggers\")\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data = instrument.data"
    ]
   },
   {
@@ -220,9 +220,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
-    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
-    "plotter.make_sub_plot(data)"
+    "plotter.make_sub_plot(data, log=True, orders_of_mag=4)"
    ]
   },
   {
@@ -252,10 +250,8 @@
     "logger_zx.target_geometry = '\"outer_cryostat_wall,cryostat_wall\"'\n",
     "logger_zy.target_geometry = '\"sample_geometry\"'\n",
     "\n",
-    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_loggers\",\n",
-    "                                      increment_folder_name=True,\n",
-    "                                      parameters={\"wavelength\" : 3.0})\n",
-    "\n"
+    "instrument.backengine()\n",
+    "data = instrument.data"
    ]
   },
   {
@@ -264,9 +260,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
-    "functions.name_plot_options(\"logger_space_zy\", data, log=False)\n",
-    "plotter.make_sub_plot(data)"
+    "plotter.make_sub_plot(data, log=[True, False], orders_of_mag=4)"
    ]
   },
   {
@@ -293,9 +287,8 @@
     "logger_zy.target_geometry = '\"NULL\"'\n",
     "logger_zy.order_total = 2\n",
     "\n",
-    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_loggers\",\n",
-    "                                      increment_folder_name=True,\n",
-    "                                      parameters={\"wavelength\" : 3.0})"
+    "instrument.backengine()\n",
+    "data = instrument.data"
    ]
   },
   {
@@ -304,9 +297,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=3)\n",
-    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=3)\n",
-    "plotter.make_sub_plot(data)"
+    "plotter.make_sub_plot(data, log=True, orders_of_mag=3)"
    ]
   },
   {
@@ -383,9 +374,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_loggers\",\n",
-    "                                      increment_folder_name=True,\n",
-    "                                      parameters={\"wavelength\" : 3.0})"
+    "instrument.backengine()\n",
+    "data = instrument.data"
    ]
   },
   {
@@ -470,9 +460,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_loggers\",\n",
-    "                                      increment_folder_name=True,\n",
-    "                                      parameters={\"wavelength\" : 3.0})"
+    "instrument.backengine()\n",
+    "data = instrument.data"
    ]
   },
   {
@@ -521,9 +510,9 @@
    "outputs": [],
    "source": [
     "log_2D_st.time_bins = 128\n",
-    "#data = instrument.run_full_instrument(ncount=2E8, foldername=\"data_folder/union_loggers\",\n",
-    "#                                      increment_folder_name=True, mpi=4,\n",
-    "#                                      parameters={\"wavelength\" : 3.0})"
+    "instrument.settings(ncount=2E8, mpi=4)\n",
+    "#instrument.backengine()\n",
+    "#data = instrument.data"
    ]
   },
   {
@@ -573,7 +562,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/tutorial/Union_tutorial_4_conditionals.ipynb b/tutorial/Union_tutorial_4_conditionals.ipynb
index 66465ba5..bdef0734 100644
--- a/tutorial/Union_tutorial_4_conditionals.ipynb
+++ b/tutorial/Union_tutorial_4_conditionals.ipynb
@@ -69,8 +69,8 @@
     "src.focus_ah = 0.01\n",
     "\n",
     "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
-    "src.lambda0=\"wavelength\"\n",
-    "src.dlambda=\"0.01*wavelength\"\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.dlambda = \"0.01*wavelength\"\n",
     "src.flux = 1E13\n",
     "\n",
     "# At a reference point to build the cryostat around\n",
@@ -78,11 +78,11 @@
     "cryostat_center.set_AT([0, 0, 1], RELATIVE=src)\n",
     "\n",
     "# Parameter for controlling sample rotation\n",
-    "instrument.add_parameter(\"A3_angle\", value=0)\n",
+    "A3_angle = instrument.add_parameter(\"A3_angle\", value=0)\n",
     "\n",
     "sample = instrument.add_component(\"sample\", \"Union_box\")\n",
     "sample.set_AT([0, 0, 0], RELATIVE=cryostat_center)\n",
-    "sample.set_ROTATED([0, \"A3_angle\", 0], RELATIVE=cryostat_center)\n",
+    "sample.set_ROTATED([0, A3_angle, 0], RELATIVE=cryostat_center)\n",
     "sample.xwidth = 0.015\n",
     "sample.yheight = 0.032\n",
     "sample.zdepth = 0.012\n",
@@ -203,7 +203,7 @@
    "outputs": [],
    "source": [
     "import math\n",
-    "wavelength = 4\n",
+    "wavelength = 4.0\n",
     "theta = 180/3.14159*math.asin(wavelength/2.0/3.8843)\n",
     "print(theta)"
    ]
@@ -214,9 +214,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_conditionals\",\n",
-    "                                      increment_folder_name=True,\n",
-    "                                      parameters={\"wavelength\" : wavelength, \"A3_angle\" : theta})"
+    "instrument.set_parameters(wavelength=wavelength, A3_angle=theta)\n",
+    "instrument.settings(ncount=1E7, output_path=\"data_folder/union_conditionals\")\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data = instrument.data"
    ]
   },
   {
@@ -340,10 +342,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_con = instrument.run_full_instrument(ncount=3E7, foldername=\"data_folder/union_conditionals\",\n",
-    "                                      increment_folder_name=True, #mpi=2,\n",
-    "                                      parameters={\"wavelength\" : wavelength, \"A3_angle\" : theta,\n",
-    "                                                  \"tag_angle\" : -95, \"tag_time\" : 0.00188, \"tag_interval\" : 9E-5})"
+    "instrument.set_parameters(wavelength=wavelength, A3_angle=theta, \n",
+    "                          tag_angle=-95, tag_time=0.00188, tag_interval=9E-5)\n",
+    "instrument.settings(ncount=3E7) # Can add mpi to improve speed for this longer simulation\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data_con = instrument.data"
    ]
   },
   {
@@ -431,10 +435,8 @@
    "source": [
     "PSD_conditional.overwrite_logger_weight = 1\n",
     "\n",
-    "data_con_f = instrument.run_full_instrument(ncount=3E7, foldername=\"data_folder/union_conditionals\",\n",
-    "                                      increment_folder_name=True, #mpi=2,\n",
-    "                                      parameters={\"wavelength\" : wavelength, \"A3_angle\" : theta,\n",
-    "                                                  \"tag_angle\" : -95, \"tag_time\" : 0.00188, \"tag_interval\" : 9E-5})"
+    "instrument.backengine()\n",
+    "data_con_f = instrument.data"
    ]
   },
   {
@@ -516,7 +518,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/tutorial/Union_tutorial_5_masks.ipynb b/tutorial/Union_tutorial_5_masks.ipynb
index 56e95d4c..e8daeb60 100644
--- a/tutorial/Union_tutorial_5_masks.ipynb
+++ b/tutorial/Union_tutorial_5_masks.ipynb
@@ -58,9 +58,9 @@
     "src.focus_aw = 0.01\n",
     "src.focus_ah = 0.01\n",
     "\n",
-    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
-    "src.lambda0=\"wavelength\"\n",
-    "src.dlambda=\"0.01*wavelength\"\n",
+    "\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.dlambda = \"0.01*wavelength\"\n",
     "src.flux = 1E13\n",
     "\n",
     "wall = instrument.add_component(\"wall\", \"Union_cylinder\")\n",
@@ -97,9 +97,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = instrument.run_full_instrument(ncount=2E6, foldername=\"data_folder/union_masks\",\n",
-    "                                      increment_folder_name=True, #mpi=2,\n",
-    "                                      parameters={\"wavelength\" : 3.0})"
+    "instrument.settings(ncount=2E6, output_path=\"data_folder/union_masks\")\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data = instrument.data"
    ]
   },
   {
@@ -163,9 +164,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = instrument.run_full_instrument(ncount=2E6, foldername=\"data_folder/union_masks\",\n",
-    "                                      increment_folder_name=True, #mpi=2,\n",
-    "                                      parameters={\"wavelength\" : 3.0})"
+    "instrument.backengine()\n",
+    "data = instrument.data"
    ]
   },
   {
@@ -220,9 +220,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = instrument.run_full_instrument(ncount=2E6, foldername=\"data_folder/union_masks\",\n",
-    "                                      increment_folder_name=True, #mpi=2,\n",
-    "                                      parameters={\"wavelength\" : 3.0})"
+    "instrument.backengine()\n",
+    "data = instrument.data"
    ]
   },
   {
@@ -312,9 +311,9 @@
     "\n",
     "master = instrument.add_component(\"master\", \"Union_master\")\n",
     "\n",
-    "data = instrument.run_full_instrument(ncount=2E6, foldername=\"data_folder/union_masks\",\n",
-    "                                      increment_folder_name=True, #mpi=2,\n",
-    "                                      parameters={\"wavelength\" : 3.0})"
+    "\n",
+    "instrument.backengine()\n",
+    "data = instrument.data"
    ]
   },
   {
@@ -350,7 +349,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb b/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
index 9e1b32f5..aa08a55b 100644
--- a/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
+++ b/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
@@ -58,10 +58,8 @@
     "src.yheight = 0.035\n",
     "src.focus_aw = 0.01\n",
     "src.focus_ah = 0.01\n",
-    "\n",
-    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
-    "src.lambda0=\"wavelength\"\n",
-    "src.dlambda=\"0.01*wavelength\"\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.dlambda = \"0.01*wavelength\"\n",
     "src.flux = 1E13\n",
     "\n",
     "sample_volume = instrument.add_component(\"sample_volume\", \"Union_cylinder\")\n",
@@ -135,9 +133,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_empty = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_external\",\n",
-    "                                           increment_folder_name=True, #mpi=2,\n",
-    "                                           parameters={\"wavelength\" : 3.0})"
+    "instrument.set_parameters(wavelength=3.0)\n",
+    "instrument.settings(ncount=1E7, output_path=\"data_folder/union_external\")\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data_empty = instrument.data"
    ]
   },
   {
@@ -175,9 +175,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_external\",\n",
-    "                                      increment_folder_name=True, #mpi=2,\n",
-    "                                      parameters={\"wavelength\" : 3.0})"
+    "instrument.backengine()\n",
+    "data = instrument.data"
    ]
   },
   {
@@ -227,9 +226,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_wrong = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_external\",\n",
-    "                                            increment_folder_name=True,\n",
-    "                                            parameters={\"wavelength\" : 3.0})"
+    "instrument.backengine()\n",
+    "data_wrong = instrument.data"
    ]
   },
   {
@@ -292,9 +290,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_external\",\n",
-    "                                      increment_folder_name=True,\n",
-    "                                      parameters={\"wavelength\" : 3.0})"
+    "instrument.backengine()\n",
+    "data = instrument.data"
    ]
   },
   {
@@ -377,7 +374,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/tutorial/Union_tutorial_7_Tagging_history.ipynb b/tutorial/Union_tutorial_7_Tagging_history.ipynb
index 644aad52..b0479bd9 100644
--- a/tutorial/Union_tutorial_7_Tagging_history.ipynb
+++ b/tutorial/Union_tutorial_7_Tagging_history.ipynb
@@ -61,10 +61,8 @@
     "src.yheight = 0.035\n",
     "src.focus_aw = 0.01\n",
     "src.focus_ah = 0.01\n",
-    "\n",
-    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
-    "src.lambda0=\"wavelength\"\n",
-    "src.dlambda=\"0.01*wavelength\"\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.dlambda = \"0.01*wavelength\"\n",
     "src.flux = 1E13\n",
     "\n",
     "sample_geometry = instrument.add_component(\"sample_geometry\", \"Union_cylinder\")\n",
@@ -146,9 +144,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_tagging\",\n",
-    "                                      increment_folder_name=True,\n",
-    "                                      parameters={\"wavelength\" : 3.0})"
+    "instrument.set_parameters(wavelength=3.0)\n",
+    "instrument.settings(ncount=1E7, output_path=\"data_folder/union_tagging\")\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data = instrument.data"
    ]
   },
   {
@@ -247,9 +247,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = instrument.run_full_instrument(ncount=1E7, foldername=\"data_folder/union_tagging\",\n",
-    "                                      increment_folder_name=True,\n",
-    "                                      parameters={\"wavelength\" : 3.0})"
+    "instrument.backengine()\n",
+    "data = instrument.data"
    ]
   },
   {
@@ -295,7 +294,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,

From 52d69e2410b96ef3638bce2532f755de4e1474ce Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 4 Jan 2022 17:58:30 +0100
Subject: [PATCH 184/403] Made settings use positional arguments so
 misspellings found. Avoided using parameters in settings, these have to go
 through libpyvinyl parameters now. This changed interface a bit, but no
 issue.

---
 mcstasscript/interface/instr.py               | 73 ++++++++++---------
 .../jb_interface/simulation_interface.py      |  3 +-
 2 files changed, 41 insertions(+), 35 deletions(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index d0f42490..aa06b339 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1580,7 +1580,9 @@ def _handle_parameters(self, given_parameters):
             default_parameters.update(given_parameters)
             return default_parameters
 
-    def settings(self, **kwargs):
+    def settings(self, ncount=None, mpi="not_set", force_compile=None,
+                 output_path=None, increment_folder_name=None,
+                 custom_flags=None, executable=None, executable_path=None):
         """
         Sets settings for McStas run performed with backengine
 
@@ -1601,59 +1603,58 @@ def settings(self, **kwargs):
                 Sets ncount
             mpi : int
                 Sets thread count
-            custom_flags : str
-                Sets custom_flags passed to mcrun
             force_compile : bool
                 If True (default) new instrument file is written, otherwise not
+            custom_flags : str
+                Sets custom_flags passed to mcrun
+            executable : str
+                Name of the executable
             executable_path : str
                 Path to mcrun command, "" if already in path
         """
 
-        if "run_path" in kwargs:
-            raise RuntimeError("Can not change run_path for instrument as the "
-                               + "available components could change along "
-                               + "with their inputs. Create new instrument "
-                               + "object with the desired input_path")
-
-        if "package_path" in kwargs:
-            raise RuntimeError("Can not change package_path for instrument as "
-                               + "the available components could change "
-                               + "along with their inputs. Update "
-                               + "configuration and create a new instrument "
-                               + "object which will then have the new "
-                               + "package_path.")
-
-        if "executable_path" in kwargs:
-            if not os.path.isdir(str(kwargs["executable_path"])):
+        settings = {}
+        if executable_path is not None:
+            if not os.path.isdir(str(executable_path)):
                 raise RuntimeError("The executable_path provided in "
                                    + "settings does not point to a"
                                    + "directory: \""
-                                   + str(kwargs["executable_path"]) + "\"")
+                                   + str(executable_path) + "\"")
+            settings["executable_path"] = executable_path
 
-        if "executable" in kwargs:
+        if executable is not None:
             # check provided executable can be converted to string
-            str(kwargs["executable"])
+            str(executable)
+            settings["executable"] = executable
 
-        if "force_compile" in kwargs:
-            if not isinstance(kwargs["force_compile"], bool):
+        if force_compile is not None:
+            if not isinstance(force_compile, bool):
                 raise TypeError("force_compile must be a bool.")
+            settings["force_compile"] = force_compile
 
-        if "increment_folder_name" in kwargs:
-            if not isinstance(kwargs["increment_folder_name"], bool):
+        if increment_folder_name is not None:
+            if not isinstance(increment_folder_name, bool):
                 raise TypeError("increment_folder_name must be a bool.")
+            settings["increment_folder_name"] = increment_folder_name
 
-        if "ncount" in kwargs:
-            if not isinstance(kwargs["ncount"], (float, int)):
+        if ncount is not None:
+            if not isinstance(ncount, (float, int)):
                 raise TypeError("ncount must be a number.")
+            settings["ncount"] = ncount
 
-        if "mpi" in kwargs:
-            if not isinstance(kwargs["mpi"], (type(None), int)):
+        if mpi != "not_set":  # None is a legal value for mpi
+            if not isinstance(mpi, (type(None), int)):
                 raise TypeError("mpi must be an integer or None.")
+            settings["mpi"] = mpi
+
+        if custom_flags is not None:
+            str(custom_flags)  # Check a string is given
+            settings["custom_flags"] = custom_flags
 
-        if "output_path" in kwargs:
-            self.output_path = kwargs["output_path"]
+        if output_path is not None:
+            self.output_path = output_path
 
-        self._run_settings.update(kwargs)
+        self._run_settings.update(settings)
 
     def settings_string(self):
         """
@@ -1788,10 +1789,14 @@ def run_full_instrument(self, **kwargs):
 
         if "foldername" in kwargs:
             kwargs["output_path"] = kwargs["foldername"]
+            del kwargs["foldername"]
 
-        self.settings(**kwargs)
         if "parameters" in kwargs:
             self.set_parameters(kwargs["parameters"])
+            del kwargs["parameters"]
+
+        self.settings(**kwargs)
+
         self.backengine()
         return self.data
 
diff --git a/mcstasscript/jb_interface/simulation_interface.py b/mcstasscript/jb_interface/simulation_interface.py
index 3a35d9fd..17a276ab 100644
--- a/mcstasscript/jb_interface/simulation_interface.py
+++ b/mcstasscript/jb_interface/simulation_interface.py
@@ -115,7 +115,7 @@ def run_simulation_live(self):
 
         run_arguments = {"output_path": "interface_" + self.instrument.name,
                          "increment_folder_name": True,
-                         "parameters": self.parameters,
+                         #"parameters": self.parameters,
                          "ncount": part_ncount}
         if self.mpi != "disabled":
             run_arguments["mpi"] = self.mpi
@@ -147,6 +147,7 @@ def run_simulation_live(self):
             try:
                 with HiddenPrints():
                     self.instrument.settings(**run_arguments)
+                    self.instrument.set_parameters(self.parameters)
                     self.instrument.backengine()
                     data = self.instrument.data
             except NameError:

From 3b61980c8d71d4ad3d5dd9e23dde642004d5e220 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 5 Jan 2022 12:59:02 +0100
Subject: [PATCH 185/403] Changed behavior when running simulation with
 exisiting foldername and increment_folder_name set to False. Before it would
 load such a data file, which made unittests easier but was confusing for
 users. Now it will provide an error if the folder already exists.
 Furthermore, if a simulation does not produce a folder, a warning is shown,
 and the data returned is None.

Tests updated to take this into account.
Started helper file for tests.
---
 mcstasscript/helper/managed_mcrun.py     |  26 +-
 mcstasscript/tests/helpers_for_tests.py  |  13 +
 mcstasscript/tests/test_Instr.py         | 283 ++++++++++-----------
 mcstasscript/tests/test_ManagedMcrun.py  | 310 +++++++++--------------
 mcstasscript/tests/test_dump_and_load.py |  15 +-
 5 files changed, 291 insertions(+), 356 deletions(-)
 create mode 100644 mcstasscript/tests/helpers_for_tests.py

diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 7891bede..d1124263 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -2,6 +2,7 @@
 import numpy as np
 import subprocess
 import mmap
+import warnings
 
 from mcstasscript.data.data import McStasMetaData
 from mcstasscript.data.data import McStasDataBinned
@@ -198,14 +199,18 @@ def run_simulation(self, **kwargs):
                          + "-n " + str(self.ncount)  # Set ncount
                          + mpi_string)
 
-        if self.increment_folder_name and os.path.isdir(self.data_folder_name):
-            counter = 0
-            new_name = self.data_folder_name + "_" + str(counter)
-            while os.path.isdir(new_name):
-                counter = counter + 1
+        if os.path.exists(self.data_folder_name):
+            if self.increment_folder_name:
+                counter = 0
                 new_name = self.data_folder_name + "_" + str(counter)
+                while os.path.isdir(new_name):
+                    counter = counter + 1
+                    new_name = self.data_folder_name + "_" + str(counter)
 
-            self.data_folder_name = new_name
+                self.data_folder_name = new_name
+            else:
+                raise NameError("output_path already exists and "
+                                + "increment_folder_name was set to False.")
 
         if len(self.data_folder_name) > 0:
             option_string = (option_string
@@ -248,6 +253,9 @@ def run_simulation(self, **kwargs):
             print(process.stderr)
             print(process.stdout)
 
+        if not os.path.isdir(self.data_folder_name):
+            warnings.warn("Simulation did not create data folder, most likely failed.")
+
     def load_results(self, *args):
         """
         Method for loading data from a mcstas simulation
@@ -271,7 +279,11 @@ def load_results(self, *args):
             raise RuntimeError("load_results can be called "
                                + "with 0 or 1 arguments")
 
-        return load_results(data_folder_name)
+        if os.path.isdir(data_folder_name):
+            return load_results(data_folder_name)
+        else:
+            warnings.warn("No data available to load.")
+            return None
 
 
 def load_results(data_folder_name):
diff --git a/mcstasscript/tests/helpers_for_tests.py b/mcstasscript/tests/helpers_for_tests.py
new file mode 100644
index 00000000..4d0e62b5
--- /dev/null
+++ b/mcstasscript/tests/helpers_for_tests.py
@@ -0,0 +1,13 @@
+import os
+
+class WorkInTestDir:
+    """
+    Simple class that enables working in test directory
+    """
+    def __enter__(self):
+        self.current_work_dir = os.getcwd()
+        os.chdir(os.path.dirname(os.path.abspath(__file__)))
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        os.chdir(self.current_work_dir)
\ No newline at end of file
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 9f830ad1..98d61351 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -8,23 +8,17 @@
 from mcstasscript.interface.instr import McStas_instr
 from mcstasscript.interface.instr import McXtrace_instr
 from mcstasscript.helper.formatting import bcolors
-
+from .helpers_for_tests import WorkInTestDir
 
 run_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '.')
 
-
 def setup_instr_no_path():
     """
     Sets up a neutron instrument without a package_path
     """
-    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-
-    current_work_dir = os.getcwd()
-    os.chdir(THIS_DIR)  # Set work directory to test folder
-
-    instrument = McStas_instr("test_instrument")
 
-    os.chdir(current_work_dir)
+    with WorkInTestDir() as handler:
+        instrument = McStas_instr("test_instrument")
 
     return instrument
 
@@ -33,14 +27,9 @@ def setup_x_ray_instr_no_path():
     """
     Sets up a X-ray instrument without a package_path
     """
-    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-
-    current_work_dir = os.getcwd()
-    os.chdir(THIS_DIR)  # Set work directory to test folder
 
-    instrument = McXtrace_instr("test_instrument")
-
-    os.chdir(current_work_dir)
+    with WorkInTestDir() as handler:
+        instrument = McXtrace_instr("test_instrument")
 
     return instrument
 
@@ -49,14 +38,8 @@ def setup_instr_root_path():
     """
     Sets up a neutron instrument with root package_path
     """
-    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-
-    current_work_dir = os.getcwd()
-    os.chdir(THIS_DIR)  # Set work directory to test folder
-
-    instrument = McStas_instr("test_instrument", package_path="/")
-
-    os.chdir(current_work_dir)
+    with WorkInTestDir() as handler:
+        instrument = McStas_instr("test_instrument", package_path="/")
 
     return instrument
 
@@ -65,14 +48,8 @@ def setup_x_ray_instr_root_path():
     """
     Sets up a X-ray instrument with root package_path
     """
-    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-
-    current_work_dir = os.getcwd()
-    os.chdir(THIS_DIR)  # Set work directory to test folder
-
-    instrument = McXtrace_instr("test_instrument", package_path="/")
-
-    os.chdir(current_work_dir)
+    with WorkInTestDir() as handler:
+        instrument = McXtrace_instr("test_instrument", package_path="/")
 
     return instrument
 
@@ -83,15 +60,10 @@ def setup_instr_with_path():
     the dummy installation in the test folder.
     """
 
-    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-    dummy_path = os.path.join(THIS_DIR, "dummy_mcstas")
-
-    current_work_dir = os.getcwd()
-    os.chdir(THIS_DIR)  # Set work directory to test folder
-
-    instrument = McStas_instr("test_instrument", package_path=dummy_path)
-
-    os.chdir(current_work_dir)  # Return to previous workdir
+    with WorkInTestDir() as handler:
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        dummy_path = os.path.join(THIS_DIR, "dummy_mcstas")
+        instrument = McStas_instr("test_instrument", package_path=dummy_path)
 
     return instrument
 
@@ -105,12 +77,8 @@ def setup_x_ray_instr_with_path():
     THIS_DIR = os.path.dirname(os.path.abspath(__file__))
     dummy_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-    current_work_dir = os.getcwd()
-    os.chdir(THIS_DIR)  # Set work directory to test folder
-
-    instrument = McXtrace_instr("test_instrument", package_path=dummy_path)
-
-    os.chdir(current_work_dir)  # Return to previous workdir
+    with WorkInTestDir() as handler:
+        instrument = McXtrace_instr("test_instrument", package_path=dummy_path)
 
     return instrument
 
@@ -121,18 +89,15 @@ def setup_instr_with_input_path():
     the dummy installation in the test folder. In addition the input_path
     is set to a folder in the test directory using an absolute path.
     """
+
     THIS_DIR = os.path.dirname(os.path.abspath(__file__))
     dummy_path = os.path.join(THIS_DIR, "dummy_mcstas")
     input_path = os.path.join(THIS_DIR, "test_input_folder")
 
-    current_work_dir = os.getcwd()
-    os.chdir(THIS_DIR)  # Set work directory to test folder
-
-    instrument = McStas_instr("test_instrument",
-                              package_path=dummy_path,
-                              input_path=input_path)
-
-    os.chdir(current_work_dir)  # Return to previous workdir
+    with WorkInTestDir() as handler:
+        instrument = McStas_instr("test_instrument",
+                                  package_path=dummy_path,
+                                  input_path=input_path)
 
     return instrument
 
@@ -143,16 +108,11 @@ def setup_instr_with_input_path_relative():
     the dummy installation in the test folder. In addition the input_path
     is set to a folder in the test directory using a relative path.
     """
-    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-
-    current_work_dir = os.getcwd()
-    os.chdir(THIS_DIR)  # Set work directory to test folder
-
-    instrument = McStas_instr("test_instrument",
-                              package_path="dummy_mcstas",
-                              input_path="test_input_folder")
 
-    os.chdir(current_work_dir)  # Return to previous workdir
+    with WorkInTestDir() as handler:
+        instrument = McStas_instr("test_instrument",
+                                  package_path="dummy_mcstas",
+                                  input_path="test_input_folder")
 
     return instrument
 
@@ -1770,28 +1730,28 @@ def test_x_ray_run_full_instrument_basic(self, mock_sub, mock_stdout):
         Tests x-ray run_full_instrument
 
         Check a simple run performs the correct system call.  Here
-        the target directory is set to the test data set so that some
-        data is loaded even though the system call is not executed.
+        the output_path is set to a name that does not correspond to a
+        existing file so no error is thrown.
         """
 
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
-
-        instr = setup_populated_x_ray_instr_with_dummy_path()
-        instr.run_full_instrument(output_path="test_data_set",
-                                  increment_folder_name=False,
-                                  executable_path=executable_path,
-                                  parameters={"theta": 1})
-
-        os.chdir(current_work_dir)
+        with WorkInTestDir() as handler:
+            new_folder_name = "folder_name_which_is_unused"
+            if os.path.exists(new_folder_name):
+                raise RuntimeError("Folder_name was supposed to not "
+                                   + "exist before "
+                                   + "test_run_backengine_basic")
+            instr = setup_populated_x_ray_instr_with_dummy_path()
+            instr.run_full_instrument(output_path=new_folder_name,
+                                      increment_folder_name=False,
+                                      executable_path=executable_path,
+                                      parameters={"theta": 1})
 
         expected_path = os.path.join(executable_path, "mxrun")
 
-        expected_folder_path = os.path.join(THIS_DIR,
-                                            "test_data_set")
+        expected_folder_path = os.path.join(THIS_DIR, new_folder_name)
 
         # a double space because of a missing option
         expected_call = (expected_path + " -c -n 1000000 "
@@ -1807,36 +1767,59 @@ def test_x_ray_run_full_instrument_basic(self, mock_sub, mock_stdout):
                                          stderr=-1, stdout=-1,
                                          universal_newlines=True)
 
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_run_backengine_existing_folder(self, mock_stdout):
+        """
+        Test neutron run of backengine fails if using existing folder
+        for output_path and with increment_folder_name disabled.
+        """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        with WorkInTestDir() as handler:
+            instr = setup_populated_instr_with_dummy_path()
+
+            instr.set_parameters({"theta": 1})
+            instr.settings(output_path="test_data_set",
+                           increment_folder_name=False,
+                           executable_path=executable_path)
+
+            with self.assertRaises(NameError):
+                instr.backengine()
+
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     @unittest.mock.patch("subprocess.run")
-    def test_run_full_instrument_basic(self, mock_sub, mock_stdout):
+    def test_run_backengine_basic(self, mock_sub, mock_stdout):
         """
         Test neutron run_full_instrument
 
-        Check a simple run performs the correct system call.  Here
-        the target directory is set to the test data set so that some
-        data is loaded even though the system call is not executed.
+        Check a simple run performs the correct system call. Here
+        the output_path is set to a name that does not correspond to a
+        existing file so no error is thrown.
         """
 
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
+        with WorkInTestDir() as handler:
+            new_folder_name = "folder_name_which_is_unused"
+            if os.path.exists(new_folder_name):
+                raise RuntimeError("Folder_name was supposed to not "
+                                   + "exist before "
+                                   + "test_run_backengine_basic")
 
-        instr = setup_populated_instr_with_dummy_path()
+            instr = setup_populated_instr_with_dummy_path()
 
-        instr.set_parameters({"theta": 1})
-        instr.settings(output_path="test_data_set",
-                       increment_folder_name=False,
-                       executable_path=executable_path)
-        instr.backengine()
-
-        os.chdir(current_work_dir)
+            instr.set_parameters({"theta": 1})
+            instr.settings(output_path=new_folder_name,
+                           increment_folder_name=True,
+                           executable_path=executable_path)
+            instr.backengine()
 
         expected_path = os.path.join(executable_path, "mcrun")
 
-        expected_folder_path = os.path.join(THIS_DIR, "test_data_set")
+        expected_folder_path = os.path.join(THIS_DIR, new_folder_name)
 
         # a double space because of a missing option
         expected_call = (expected_path + " -c -n 1000000 "
@@ -1856,38 +1839,39 @@ def test_run_full_instrument_complex(self, mock_sub, mock_stdout):
         """
         Test neutron run_full_instrument in more complex case
 
-        Check a complex run performs the correct system call.  Here
-        the target directory is set to the test data set so that some
-        data is loaded even though the system call is not executed.
+        Check a complex run performs the correct system call. Here
+        the output_path is set to a name that does not correspond to a
+        existing file so no error is thrown.
         """
 
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
+        with WorkInTestDir() as handler:
+            new_folder_name = "folder_name_which_is_unused"
+            if os.path.exists(new_folder_name):
+                raise RuntimeError("Folder_name was supposed to not "
+                                   + "exist before "
+                                   + "test_run_backengine_basic")
 
-        instr = setup_populated_instr_with_dummy_path()
-
-        # Add some extra parameters for testing
-        instr.add_parameter("A")
-        instr.add_parameter("BC")
+            instr = setup_populated_instr_with_dummy_path()
 
-        instr.run_full_instrument(output_path="test_data_set",
-                                  increment_folder_name=False,
-                                  executable_path=executable_path,
-                                  mpi=7,
-                                  ncount=48.4,
-                                  custom_flags="-fo",
-                                  parameters={"A": 2,
-                                              "BC": "car",
-                                              "theta": "\"toy\""})
+            # Add some extra parameters for testing
+            instr.add_parameter("A")
+            instr.add_parameter("BC")
 
-        os.chdir(current_work_dir)
+            instr.run_full_instrument(output_path=new_folder_name,
+                                      increment_folder_name=False,
+                                      executable_path=executable_path,
+                                      mpi=7,
+                                      ncount=48.4,
+                                      custom_flags="-fo",
+                                      parameters={"A": 2,
+                                                  "BC": "car",
+                                                  "theta": "\"toy\""})
 
         expected_path = os.path.join(executable_path, "mcrun")
-
-        expected_folder_path = os.path.join(THIS_DIR, "test_data_set")
+        expected_folder_path = os.path.join(THIS_DIR, new_folder_name)
 
         # a double space because of a missing option
         expected_call = (expected_path + " -c -n 48 --mpi=7 "
@@ -1913,32 +1897,32 @@ def test_run_full_instrument_overwrite_default(self, mock_sub,
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
+        with WorkInTestDir() as handler:
+            new_folder_name = "folder_name_which_is_unused"
+            if os.path.exists(new_folder_name):
+                raise RuntimeError("Folder_name was supposed to not "
+                                   + "exist before "
+                                   + "test_run_backengine_basic")
 
-        instr = setup_populated_instr_with_dummy_path()
+            instr = setup_populated_instr_with_dummy_path()
 
-        # Add some extra parameters for testing
-        instr.add_parameter("A")
-        instr.add_parameter("BC")
-
-        instr.run_full_instrument(output_path="test_data_set",
-                                  increment_folder_name=False,
-                                  executable_path=executable_path,
-                                  mpi=7,
-                                  ncount=48.4,
-                                  custom_flags="-fo",
-                                  parameters={"A": 2,
-                                              "BC": "car",
-                                              "theta": "\"toy\"",
-                                              "has_default": 10})
+            # Add some extra parameters for testing
+            instr.add_parameter("A")
+            instr.add_parameter("BC")
 
-        os.chdir(current_work_dir)
+            instr.run_full_instrument(output_path=new_folder_name,
+                                      increment_folder_name=False,
+                                      executable_path=executable_path,
+                                      mpi=7,
+                                      ncount=48.4,
+                                      custom_flags="-fo",
+                                      parameters={"A": 2,
+                                                  "BC": "car",
+                                                  "theta": "\"toy\"",
+                                                  "has_default": 10})
 
         expected_path = os.path.join(executable_path, "mcrun")
-
-        os.getcwd()
-        expected_folder_path = os.path.join(THIS_DIR, "test_data_set")
+        expected_folder_path = os.path.join(THIS_DIR, new_folder_name)
 
         # a double space because of a missing option
         expected_call = (expected_path + " -c -n 48 --mpi=7 "
@@ -1959,28 +1943,29 @@ def test_run_full_instrument_x_ray_basic(self, mock_sub, mock_stdout):
         Test x-ray run_full_instrument
 
         Check a simple run performs the correct system call.  Here
-        the target directory is set to the test data set so that some
-        data is loaded even though the system call is not executed.
+        the output_path is set to a name that does not correspond to a
+        existing file so no error is thrown.
         """
 
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
-
-        instr = setup_populated_x_ray_instr_with_dummy_path()
+        with WorkInTestDir() as handler:
+            new_folder_name = "folder_name_which_is_unused"
+            if os.path.exists(new_folder_name):
+                raise RuntimeError("Folder_name was supposed to not "
+                                   + "exist before "
+                                   + "test_run_backengine_basic")
 
-        instr.run_full_instrument(output_path="test_data_set",
-                                  increment_folder_name=False,
-                                  executable_path=executable_path,
-                                  parameters={"theta": 1})
+            instr = setup_populated_x_ray_instr_with_dummy_path()
 
-        os.chdir(current_work_dir)
+            instr.run_full_instrument(output_path=new_folder_name,
+                                      increment_folder_name=False,
+                                      executable_path=executable_path,
+                                      parameters={"theta": 1})
 
         expected_path = os.path.join(executable_path, "mxrun")
-
-        expected_folder_path = os.path.join(THIS_DIR, "test_data_set")
+        expected_folder_path = os.path.join(THIS_DIR, new_folder_name)
 
         # a double space because of a missing option
         expected_call = (expected_path + " -c -n 1000000 "
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index caac66ce..e632bd74 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -6,7 +6,7 @@
 from mcstasscript.helper.managed_mcrun import load_results
 from mcstasscript.helper.managed_mcrun import load_metadata
 from mcstasscript.helper.managed_mcrun import load_monitor
-
+from .helpers_for_tests import WorkInTestDir
 
 class TestManagedMcrun(unittest.TestCase):
     """
@@ -26,15 +26,11 @@ def test_ManagedMcrun_init_simple(self):
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
-
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 output_path="test_folder",
-                                 executable_path=executable_path,
-                                 executable="mcrun")
-
-        os.chdir(current_work_dir)
+        with WorkInTestDir() as handler:
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     output_path="test_folder",
+                                     executable_path=executable_path,
+                                     executable="mcrun")
 
         self.assertEqual(mcrun_obj.name_of_instrumentfile, "test.instr")
 
@@ -51,15 +47,11 @@ def test_ManagedMcrun_init_defaults(self):
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
-
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 output_path="test_folder",
-                                 executable_path=executable_path,
-                                 executable="mcrun")
-
-        os.chdir(current_work_dir)
+        with WorkInTestDir() as handler:
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     output_path="test_folder",
+                                     executable_path=executable_path,
+                                     executable="mcrun")
 
         self.assertEqual(mcrun_obj.mpi, None)
         self.assertEqual(mcrun_obj.ncount, 1000000)
@@ -75,18 +67,14 @@ def test_ManagedMcrun_init_set_values(self):
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
-
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 output_path="test_folder",
-                                 executable="mcrun",
-                                 executable_path=executable_path,
-                                 run_path="test_data_set",
-                                 mpi=4,
-                                 ncount=128)
-
-        os.chdir(current_work_dir)
+        with WorkInTestDir() as handler:
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     output_path="test_folder",
+                                     executable="mcrun",
+                                     executable_path=executable_path,
+                                     run_path="test_data_set",
+                                     mpi=4,
+                                     ncount=128)
 
         self.assertEqual(mcrun_obj.mpi, 4)
         self.assertEqual(mcrun_obj.ncount, 128)
@@ -101,21 +89,17 @@ def test_ManagedMcrun_init_set_parameters(self):
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
-
         par_input = {"A_par": 5.1,
                      "int_par": 1,
                      "define_par": "Bike",
                      "string_par": "\"Car\""}
 
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 output_path="test_folder",
-                                 executable_path=executable_path,
-                                 executable="",
-                                 parameters=par_input)
-
-        os.chdir(current_work_dir)
+        with WorkInTestDir() as handler:
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     output_path="test_folder",
+                                     executable_path=executable_path,
+                                     executable="",
+                                     parameters=par_input)
 
         self.assertEqual(mcrun_obj.parameters["A_par"], 5.1)
         self.assertEqual(mcrun_obj.parameters["int_par"], 1)
@@ -130,18 +114,13 @@ def test_ManagedMcrun_init_set_custom_flags(self):
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
-
         custom_flag_input = "-p"
-
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 output_path="test_folder",
-                                 executable="mcrun",
-                                 executable_path=executable_path,
-                                 custom_flags=custom_flag_input)
-
-        os.chdir(current_work_dir)
+        with WorkInTestDir() as handler:
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     output_path="test_folder",
+                                     executable="mcrun",
+                                     executable_path=executable_path,
+                                     custom_flags=custom_flag_input)
 
         self.assertEqual(mcrun_obj.custom_flags, custom_flag_input)
 
@@ -191,15 +170,11 @@ def test_ManagedMcrun_run_simulation_basic(self, mock_sub):
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
-
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 output_path="test_folder",
-                                 executable_path=executable_path,
-                                 executable="mcrun",)
-
-        os.chdir(current_work_dir)
+        with WorkInTestDir() as handler:
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     output_path="test_folder",
+                                     executable_path=executable_path,
+                                     executable="mcrun",)
 
         mcrun_obj.run_simulation()
 
@@ -225,15 +200,11 @@ def test_ManagedMcrun_run_simulation_basic_path(self, mock_sub):
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas", "")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
-
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 output_path="test_folder",
-                                 executable_path=executable_path,
-                                 executable="mcrun",)
-
-        os.chdir(current_work_dir)
+        with WorkInTestDir() as handler:
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     output_path="test_folder",
+                                     executable_path=executable_path,
+                                     executable="mcrun",)
 
         mcrun_obj.run_simulation()
 
@@ -262,18 +233,14 @@ def test_ManagedMcrun_run_simulation_no_standard(self, mock_sub):
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
-
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 output_path="test_folder",
-                                 executable_path=executable_path,
-                                 executable="mcrun",
-                                 mpi=7,
-                                 ncount=48.4,
-                                 custom_flags="-fo")
-
-        os.chdir(current_work_dir)
+        with WorkInTestDir() as handler:
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     output_path="test_folder",
+                                     executable_path=executable_path,
+                                     executable="mcrun",
+                                     mpi=7,
+                                     ncount=48.4,
+                                     custom_flags="-fo")
 
         mcrun_obj.run_simulation()
 
@@ -299,21 +266,17 @@ def test_ManagedMcrun_run_simulation_parameters(self, mock_sub):
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
-
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 output_path="test_folder",
-                                 executable_path=executable_path,
-                                 executable="mcrun",
-                                 mpi=7,
-                                 ncount=48.4,
-                                 custom_flags="-fo",
-                                 parameters={"A": 2,
-                                             "BC": "car",
-                                             "th": "\"toy\""})
-
-        os.chdir(current_work_dir)  # Reset work directory
+        with WorkInTestDir() as handler:
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     output_path="test_folder",
+                                     executable_path=executable_path,
+                                     executable="mcrun",
+                                     mpi=7,
+                                     ncount=48.4,
+                                     custom_flags="-fo",
+                                     parameters={"A": 2,
+                                                 "BC": "car",
+                                                 "th": "\"toy\""})
 
         mcrun_obj.run_simulation()
 
@@ -340,22 +303,18 @@ def test_ManagedMcrun_run_simulation_compile(self, mock_sub):
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
-
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 output_path="test_folder",
-                                 executable_path=executable_path,
-                                 executable="mcrun",
-                                 mpi=7,
-                                 ncount=48.4,
-                                 force_compile=False,
-                                 custom_flags="-fo",
-                                 parameters={"A": 2,
-                                             "BC": "car",
-                                             "th": "\"toy\""})
-
-        os.chdir(current_work_dir)
+        with WorkInTestDir() as handler:
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     output_path="test_folder",
+                                     executable_path=executable_path,
+                                     executable="mcrun",
+                                     mpi=7,
+                                     ncount=48.4,
+                                     force_compile=False,
+                                     custom_flags="-fo",
+                                     parameters={"A": 2,
+                                                 "BC": "car",
+                                                 "th": "\"toy\""})
 
         mcrun_obj.run_simulation()
 
@@ -384,17 +343,13 @@ def test_ManagedMcrun_load_data_PSD4PI(self):
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
-
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 output_path="test_data_set",
-                                 executable_path=executable_path,
-                                 mcrun_path="path")
+        with WorkInTestDir() as handler:
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     output_path="test_data_set",
+                                     executable_path=executable_path,
+                                     mcrun_path="path")
 
-        results = mcrun_obj.load_results()
-
-        os.chdir(current_work_dir)  # Reset work directory
+            results = mcrun_obj.load_results()
 
         # Check three data objects are loaded
         self.assertEqual(len(results), 4)
@@ -423,17 +378,13 @@ def test_ManagedMcrun_load_data_PSD(self):
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
-
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 output_path="test_data_set",
-                                 executable_path=executable_path,
-                                 mcrun_path="path")
+        with WorkInTestDir() as handler:
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     output_path="test_data_set",
+                                     executable_path=executable_path,
+                                     mcrun_path="path")
 
-        results = mcrun_obj.load_results()
-
-        os.chdir(current_work_dir)  # Reset work directory
+            results = mcrun_obj.load_results()
 
         # Check three data objects are loaded
         self.assertEqual(len(results), 4)
@@ -462,17 +413,13 @@ def test_ManagedMcrun_load_data_L_mon(self):
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
-
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 output_path="test_data_set",
-                                 executable_path=executable_path,
-                                 mcrun_path="path")
+        with WorkInTestDir() as handler:
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     output_path="test_data_set",
+                                     executable_path=executable_path,
+                                     mcrun_path="path")
 
-        results = mcrun_obj.load_results()
-
-        os.chdir(current_work_dir)  # Reset work directory
+            results = mcrun_obj.load_results()
 
         # Check three data objects are loaded
         self.assertEqual(len(results), 4)
@@ -502,18 +449,14 @@ def test_ManagedMcrun_load_data_L_mon_direct(self):
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
-
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 output_path="test_data_set",
-                                 executable_path=executable_path,
-                                 mcrun_path="path")
+        with WorkInTestDir() as handler:
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     output_path="test_data_set",
+                                     executable_path=executable_path,
+                                     mcrun_path="path")
 
-        load_path = os.path.join(THIS_DIR, "test_data_set")
-        results = mcrun_obj.load_results(load_path)
-
-        os.chdir(current_work_dir)  # Reset work directory
+            load_path = os.path.join(THIS_DIR, "test_data_set")
+            results = mcrun_obj.load_results(load_path)
 
         # Check properties of L_mon
         L_mon = results[2]
@@ -542,18 +485,14 @@ def test_ManagedMcrun_load_data_Event(self):
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
-
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 output_path="test_data_set",
-                                 executable_path=executable_path,
-                                 mcrun_path="path")
+        with WorkInTestDir() as handler:
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     output_path="test_data_set",
+                                     executable_path=executable_path,
+                                     mcrun_path="path")
 
-        load_path = os.path.join(THIS_DIR, "test_data_set")
-        results = mcrun_obj.load_results(load_path)
-
-        os.chdir(current_work_dir)  # Reset work directory
+            load_path = os.path.join(THIS_DIR, "test_data_set")
+            results = mcrun_obj.load_results(load_path)
 
         # Check properties of event data file
         mon = results[3]
@@ -577,49 +516,46 @@ def test_ManagedMcrun_load_data_Event(self):
         self.assertFalse(hasattr(mon, 'Error'))
         self.assertFalse(hasattr(mon, 'Ncount'))
 
-    def test_ManagedMcrun_load_data_L_mon_direct_error(self):
+    def test_ManagedMcrun_load_data_nonexisting(self):
         """
-        Check an error occurs when directory has no mccode.sim
+        If folder does not exists, a warning should be shown and None returned
         """
 
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
-
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 output_path="test_data_set",
-                                 executable_path=executable_path,
-                                 mcrun_path="path")
+        with WorkInTestDir() as handler:
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     output_path="test_data_set",
+                                     executable_path=executable_path,
+                                     mcrun_path="path")
 
         load_path = os.path.join(THIS_DIR, "non_existent_dataset")
-        with self.assertRaises(NameError):
-            mcrun_obj.load_results(load_path)
 
-        os.chdir(current_work_dir)  # Reset work directory
+        with self.assertWarns(Warning):
+            result = mcrun_obj.load_results(load_path)
 
-    def test_ManagedMcrun_load_data_L_mon_empty_error(self):
+        self.assertIsNone(result)
+
+    def test_ManagedMcrun_load_data_no_mcsim_file(self):
         """
-        Check an error occurs when pointed to empty directory
+        Check an error occurs when pointed to directory without mcsim file
         """
 
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        current_work_dir = os.getcwd()
-        os.chdir(THIS_DIR)  # Set work directory to test folder
+        with WorkInTestDir() as handler:
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     output_path="test_data_set",
+                                     executable_path=executable_path,
+                                     mcrun_path="path")
 
-        mcrun_obj = ManagedMcrun("test.instr",
-                                 output_path="test_data_set",
-                                 executable_path=executable_path,
-                                 mcrun_path="path")
+            load_path = os.path.join(THIS_DIR, "dummy_mcstas")
 
-        load_path = os.path.join(THIS_DIR, "/dummy_mcstas")
         with self.assertRaises(NameError):
             mcrun_obj.load_results(load_path)
 
-        os.chdir(current_work_dir)  # Reset work directory
 
 class Test_load_functions(unittest.TestCase):
     """
diff --git a/mcstasscript/tests/test_dump_and_load.py b/mcstasscript/tests/test_dump_and_load.py
index 37dbe3d4..c90c99e9 100644
--- a/mcstasscript/tests/test_dump_and_load.py
+++ b/mcstasscript/tests/test_dump_and_load.py
@@ -10,6 +10,8 @@
 from mcstasscript.helper.mcstas_objects import ParameterContainer
 from mcstasscript.helper.mcstas_objects import ParameterVariable
 
+from .helpers_for_tests import WorkInTestDir
+
 
 run_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '.')
 
@@ -271,19 +273,6 @@ def setup_populated_with_some_options_instr():
     return instr
 
 
-class WorkInTestDir:
-    """
-    Simple class that enables working in test directory
-    """
-    def __enter__(self):
-        self.current_work_dir = os.getcwd()
-        os.chdir(os.path.dirname(os.path.abspath(__file__)))
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        os.chdir(self.current_work_dir)
-
-
 class TestDumpAndLoad(unittest.TestCase):
     def test_dump_simple(self):
         """

From 5e6fa5eee162e6b374cc6e3bde4e3dc3a43cc0f9 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 10:07:09 +0100
Subject: [PATCH 186/403] Added seed to allowed settings in ManagedMcrun and
 Instr settings. Tests include a case of setting seed.

Updated interface to disable threading as it caused crashes.
---
 mcstasscript/helper/managed_mcrun.py          | 18 ++++++++++---
 mcstasscript/interface/instr.py               | 10 +++++---
 .../jb_interface/simulation_interface.py      | 25 ++++++++++---------
 mcstasscript/tests/test_Instr.py              |  3 ++-
 mcstasscript/tests/test_ManagedMcrun.py       |  4 +--
 5 files changed, 38 insertions(+), 22 deletions(-)

diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index d1124263..603ef6a1 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -95,6 +95,7 @@ def __init__(self, instr_name, **kwargs):
         self.increment_folder_name = True
         self.compile = True
         self.run_path = "."
+        self.seed = None
         # executable_path always in kwargs
         if "executable_path" in kwargs:
             self.executable_path = kwargs["executable_path"]
@@ -130,6 +131,9 @@ def __init__(self, instr_name, **kwargs):
                     raise ValueError("MPI should be an integer larger than"
                                      + " 0, was " + str(self.mpi))
 
+        if "seed" in kwargs:
+            self.seed = kwargs["seed"]
+
         if "parameters" in kwargs:
             self.parameters = kwargs["parameters"]
 
@@ -191,13 +195,19 @@ def run_simulation(self, **kwargs):
             option_string = "-c "
 
         if self.mpi is not None:
-            mpi_string = " --mpi=" + str(self.mpi) + " "  # Set mpi
+            mpi_string = "--mpi=" + str(self.mpi) + " "  # Set mpi
+        else:
+            mpi_string = ""
+
+        if self.seed is not None:
+            seed_string = "--seed=" + str(self.seed) + " " # Set seed
         else:
-            mpi_string = " "
+            seed_string = ""
 
         option_string = (option_string
-                         + "-n " + str(self.ncount)  # Set ncount
-                         + mpi_string)
+                         + "-n " + str(self.ncount) + " " # Set ncount
+                         + mpi_string
+                         + seed_string)
 
         if os.path.exists(self.data_folder_name):
             if self.increment_folder_name:
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index aa06b339..a03b825a 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1580,9 +1580,10 @@ def _handle_parameters(self, given_parameters):
             default_parameters.update(given_parameters)
             return default_parameters
 
-    def settings(self, ncount=None, mpi="not_set", force_compile=None,
-                 output_path=None, increment_folder_name=None,
-                 custom_flags=None, executable=None, executable_path=None):
+    def settings(self, ncount=None, mpi="not_set", seed=None,
+                 force_compile=None, output_path=None,
+                 increment_folder_name=None, custom_flags=None,
+                 executable=None, executable_path=None):
         """
         Sets settings for McStas run performed with backengine
 
@@ -1651,6 +1652,9 @@ def settings(self, ncount=None, mpi="not_set", force_compile=None,
             str(custom_flags)  # Check a string is given
             settings["custom_flags"] = custom_flags
 
+        if seed is not None:
+            settings["seed"] = seed
+
         if output_path is not None:
             self.output_path = output_path
 
diff --git a/mcstasscript/jb_interface/simulation_interface.py b/mcstasscript/jb_interface/simulation_interface.py
index 17a276ab..673b49c1 100644
--- a/mcstasscript/jb_interface/simulation_interface.py
+++ b/mcstasscript/jb_interface/simulation_interface.py
@@ -90,10 +90,10 @@ def run_simulation_thread(self, change):
             Not used
         """
 
-        thread = threading.Thread(target=self.run_simulation_live)
+        thread = threading.Thread(target=self.run_simulation_live, args=[1])
         thread.start()
 
-    def run_simulation_live(self):
+    def run_simulation_live(self, change):
         """
         Performs the simulation with current parameters and settings.
 
@@ -149,26 +149,26 @@ def run_simulation_live(self):
                     self.instrument.settings(**run_arguments)
                     self.instrument.set_parameters(self.parameters)
                     self.instrument.backengine()
-                    data = self.instrument.data
             except NameError:
                 print("McStas run failed.")
                 data = []
 
             with lock:
                 self.progress_bar.value = index + 1
+                data = self.instrument.data
 
-                if plot_data is None:
-                    plot_data = data
-                else:
-                    add_data(plot_data, data)
+                if data is not None:
+                    if plot_data is None:
+                        plot_data = data
+                    else:
+                        add_data(plot_data, data)
 
-                sent_data = copy.deepcopy(plot_data)
-                # This happens in a thread, maybe it should be in Main?
-                self.plot_interface.set_data(sent_data)
+                    sent_data = copy.deepcopy(plot_data)
+                    # This happens in a thread, maybe it should be in Main?
+                    self.plot_interface.set_data(sent_data)
 
         self.run_button.icon = "calculator"
 
-
     def make_run_button(self):
         """
         Creates a run button which perform the simulation
@@ -180,7 +180,8 @@ def make_run_button(self):
             tooltip='Runs the simulation with current parameters',
             icon='calculator'  # (FontAwesome names without the `fa-` prefix)
         )
-        button.on_click(self.run_simulation_thread)
+        #button.on_click(self.run_simulation_thread)
+        button.on_click(self.run_simulation_live)
 
         return button
 
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 98d61351..7876e7e2 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1864,6 +1864,7 @@ def test_run_full_instrument_complex(self, mock_sub, mock_stdout):
                                       increment_folder_name=False,
                                       executable_path=executable_path,
                                       mpi=7,
+                                      seed=300,
                                       ncount=48.4,
                                       custom_flags="-fo",
                                       parameters={"A": 2,
@@ -1874,7 +1875,7 @@ def test_run_full_instrument_complex(self, mock_sub, mock_stdout):
         expected_folder_path = os.path.join(THIS_DIR, new_folder_name)
 
         # a double space because of a missing option
-        expected_call = (expected_path + " -c -n 48 --mpi=7 "
+        expected_call = (expected_path + " -c -n 48 --mpi=7 --seed=300 "
                          + "-d " + expected_folder_path
                          + " -fo test_instrument.instr "
                          + "theta=\"toy\" has_default=37 A=2 BC=car")
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index e632bd74..c102cf11 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -238,7 +238,7 @@ def test_ManagedMcrun_run_simulation_no_standard(self, mock_sub):
                                      output_path="test_folder",
                                      executable_path=executable_path,
                                      executable="mcrun",
-                                     mpi=7,
+                                     mpi=7, seed=300,
                                      ncount=48.4,
                                      custom_flags="-fo")
 
@@ -248,7 +248,7 @@ def test_ManagedMcrun_run_simulation_no_standard(self, mock_sub):
 
         # a double space because of a missing option
         executable = os.path.join(executable_path, "mcrun")
-        expected_call = (executable + " -c -n 48 --mpi=7 "
+        expected_call = (executable + " -c -n 48 --mpi=7 --seed=300 "
                          + "-d " + expected_folder_path + " -fo test.instr")
 
         mock_sub.assert_called_once_with(expected_call,

From 51d180564deb17e786c0b9e72352d2dd95bba64d Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 10:11:33 +0100
Subject: [PATCH 187/403] Trying github actions

---
 .github/workflows/github-actions-demo.yml | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)
 create mode 100644 .github/workflows/github-actions-demo.yml

diff --git a/.github/workflows/github-actions-demo.yml b/.github/workflows/github-actions-demo.yml
new file mode 100644
index 00000000..fd357f7a
--- /dev/null
+++ b/.github/workflows/github-actions-demo.yml
@@ -0,0 +1,18 @@
+name: GitHub Actions Demo
+on: [push]
+jobs:
+  Explore-GitHub-Actions:
+    runs-on: ubuntu-latest
+    steps:
+      - run: echo "🎉 The job was automatically triggered by a ${{ github.event_name }} event."
+      - run: echo "🐧 This job is now running on a ${{ runner.os }} server hosted by GitHub!"
+      - run: echo "🔎 The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}."
+      - name: Check out repository code
+        uses: actions/checkout@v2
+      - run: echo "💡 The ${{ github.repository }} repository has been cloned to the runner."
+      - run: echo "🖥️  The workflow is now ready to test your code on the runner."
+      - name: List files in the repository
+        run: |
+          ls ${{ github.workspace }}
+      - run: echo "🍏 This job's status is ${{ job.status }}."
+

From 89f5d6c7bab586b1f344accd98a8c668162ad8e4 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 10:44:28 +0100
Subject: [PATCH 188/403] First attempt at unittesting on github actions

---
 .github/workflows/test_ubuntu.yaml | 29 +++++++++++++++++++++++++++++
 1 file changed, 29 insertions(+)
 create mode 100644 .github/workflows/test_ubuntu.yaml

diff --git a/.github/workflows/test_ubuntu.yaml b/.github/workflows/test_ubuntu.yaml
new file mode 100644
index 00000000..35b03132
--- /dev/null
+++ b/.github/workflows/test_ubuntu.yaml
@@ -0,0 +1,29 @@
+jobs:
+  build:
+    strategy:
+      matrix:
+        python-version: [3.8.8]
+    runs-on: ubuntu-latest
+    
+steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+          
+      - name: Switch to Current Branch
+        run: git checkout ${{ env.BRANCH }}
+              
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v1
+        with:
+          python-version: ${{ matrix.python-version }}
+          
+      - name: Install dependencies
+              run: |
+                python -m pip install --upgrade pip
+                pip install -r requirements.txt
+                pip install -e .
+
+      - name: Run unit tests
+              run: python -m pytest mcstasscript/tests/

From 3699195b22a7907a7446eaf4f73127453a47c352 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 10:48:51 +0100
Subject: [PATCH 189/403] Fix of indenting

---
 .github/workflows/test_ubuntu.yaml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/test_ubuntu.yaml b/.github/workflows/test_ubuntu.yaml
index 35b03132..da46ef29 100644
--- a/.github/workflows/test_ubuntu.yaml
+++ b/.github/workflows/test_ubuntu.yaml
@@ -20,10 +20,10 @@ steps:
           python-version: ${{ matrix.python-version }}
           
       - name: Install dependencies
-              run: |
-                python -m pip install --upgrade pip
-                pip install -r requirements.txt
-                pip install -e .
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+          pip install -e .
 
       - name: Run unit tests
-              run: python -m pytest mcstasscript/tests/
+        run: python -m pytest mcstasscript/tests/

From 1371ab67fd770847f6a0cf296cfe69caf67b946b Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 10:50:06 +0100
Subject: [PATCH 190/403] Added name and on condition.

---
 .github/workflows/test_ubuntu.yaml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/test_ubuntu.yaml b/.github/workflows/test_ubuntu.yaml
index da46ef29..ae0d6bf2 100644
--- a/.github/workflows/test_ubuntu.yaml
+++ b/.github/workflows/test_ubuntu.yaml
@@ -1,3 +1,5 @@
+name: McStasScript unit tests on Linux
+on: [push]
 jobs:
   build:
     strategy:

From 0ff6b6d5fc2eabf478092c9a74be3185c749ab4e Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 10:54:37 +0100
Subject: [PATCH 191/403] Getting indentation right

---
 .github/workflows/test_ubuntu.yaml | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/test_ubuntu.yaml b/.github/workflows/test_ubuntu.yaml
index ae0d6bf2..7601c38f 100644
--- a/.github/workflows/test_ubuntu.yaml
+++ b/.github/workflows/test_ubuntu.yaml
@@ -1,26 +1,27 @@
 name: McStasScript unit tests on Linux
 on: [push]
 jobs:
+  Linux-Unit-Test
   build:
     strategy:
       matrix:
         python-version: [3.8.8]
     runs-on: ubuntu-latest
     
-steps:
+    steps:
       - name: Checkout
         uses: actions/checkout@v2
         with:
           fetch-depth: 0
-          
+              
       - name: Switch to Current Branch
         run: git checkout ${{ env.BRANCH }}
-              
+                  
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v1
         with:
           python-version: ${{ matrix.python-version }}
-          
+              
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip

From 2e42a3afd224aa5bd7acb5b854a7d8e66875f77e Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 10:55:59 +0100
Subject: [PATCH 192/403] Added colon

---
 .github/workflows/test_ubuntu.yaml | 48 +++++++++++++++---------------
 1 file changed, 24 insertions(+), 24 deletions(-)

diff --git a/.github/workflows/test_ubuntu.yaml b/.github/workflows/test_ubuntu.yaml
index 7601c38f..8c1aaa87 100644
--- a/.github/workflows/test_ubuntu.yaml
+++ b/.github/workflows/test_ubuntu.yaml
@@ -1,32 +1,32 @@
 name: McStasScript unit tests on Linux
 on: [push]
 jobs:
-  Linux-Unit-Test
-  build:
-    strategy:
-      matrix:
-        python-version: [3.8.8]
-    runs-on: ubuntu-latest
+  Linux-Unit-Test:
+    build:
+      strategy:
+        matrix:
+          python-version: [3.8.8]
+      runs-on: ubuntu-latest
     
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v2
-        with:
-          fetch-depth: 0
+      steps:
+        - name: Checkout
+          uses: actions/checkout@v2
+          with:
+            fetch-depth: 0
               
-      - name: Switch to Current Branch
-        run: git checkout ${{ env.BRANCH }}
+        - name: Switch to Current Branch
+          run: git checkout ${{ env.BRANCH }}
                   
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v1
-        with:
-          python-version: ${{ matrix.python-version }}
+        - name: Set up Python ${{ matrix.python-version }}
+          uses: actions/setup-python@v1
+          with:
+            python-version: ${{ matrix.python-version }}
               
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install -r requirements.txt
-          pip install -e .
+        - name: Install dependencies
+          run: |
+            python -m pip install --upgrade pip
+            pip install -r requirements.txt
+            pip install -e .
 
-      - name: Run unit tests
-        run: python -m pytest mcstasscript/tests/
+        - name: Run unit tests
+          run: python -m pytest mcstasscript/tests/

From d3f1b65d5f137fc070da6ef96664a76b69ae3632 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 11:13:45 +0100
Subject: [PATCH 193/403] Small changes to syntax

---
 .github/workflows/test_ubuntu.yaml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/test_ubuntu.yaml b/.github/workflows/test_ubuntu.yaml
index 8c1aaa87..4520486e 100644
--- a/.github/workflows/test_ubuntu.yaml
+++ b/.github/workflows/test_ubuntu.yaml
@@ -3,11 +3,11 @@ on: [push]
 jobs:
   Linux-Unit-Test:
     build:
+      runs-on: ubuntu-latest
       strategy:
         matrix:
-          python-version: [3.8.8]
-      runs-on: ubuntu-latest
-    
+          python-version: [3.6, 3.7, 3.8]
+      
       steps:
         - name: Checkout
           uses: actions/checkout@v2
@@ -18,7 +18,7 @@ jobs:
           run: git checkout ${{ env.BRANCH }}
                   
         - name: Set up Python ${{ matrix.python-version }}
-          uses: actions/setup-python@v1
+          uses: actions/setup-python@v2
           with:
             python-version: ${{ matrix.python-version }}
               

From f8497f929217b569777ecd05d92c08894f959e8b Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 11:15:40 +0100
Subject: [PATCH 194/403] removed name

---
 .github/workflows/test_ubuntu.yaml | 47 +++++++++++++++---------------
 1 file changed, 23 insertions(+), 24 deletions(-)

diff --git a/.github/workflows/test_ubuntu.yaml b/.github/workflows/test_ubuntu.yaml
index 4520486e..509351d1 100644
--- a/.github/workflows/test_ubuntu.yaml
+++ b/.github/workflows/test_ubuntu.yaml
@@ -1,32 +1,31 @@
 name: McStasScript unit tests on Linux
 on: [push]
 jobs:
-  Linux-Unit-Test:
-    build:
-      runs-on: ubuntu-latest
-      strategy:
-        matrix:
-          python-version: [3.6, 3.7, 3.8]
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.6, 3.7, 3.8]
       
-      steps:
-        - name: Checkout
-          uses: actions/checkout@v2
-          with:
-            fetch-depth: 0
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
               
-        - name: Switch to Current Branch
-          run: git checkout ${{ env.BRANCH }}
+      - name: Switch to Current Branch
+        run: git checkout ${{ env.BRANCH }}
                   
-        - name: Set up Python ${{ matrix.python-version }}
-          uses: actions/setup-python@v2
-          with:
-            python-version: ${{ matrix.python-version }}
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
               
-        - name: Install dependencies
-          run: |
-            python -m pip install --upgrade pip
-            pip install -r requirements.txt
-            pip install -e .
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+          pip install -e .
 
-        - name: Run unit tests
-          run: python -m pytest mcstasscript/tests/
+      - name: Run unit tests
+        run: python -m pytest mcstasscript/tests/

From e0e1402a00439d74c602c0a4b6885f421aa3b1bc Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 11:20:05 +0100
Subject: [PATCH 195/403] Removed mmap from requirements as its a default
 package

---
 requirements.txt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index dd4a1c13..a28ff210 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,3 @@ numpy
 matplotlib
 PyYAML
 ipywidgets
-mmap

From 4e985b678f2580947f8b8868c1343f299aedddbf Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 11:20:56 +0100
Subject: [PATCH 196/403] Removed demo

---
 .github/workflows/github-actions-demo.yml | 18 ------------------
 1 file changed, 18 deletions(-)
 delete mode 100644 .github/workflows/github-actions-demo.yml

diff --git a/.github/workflows/github-actions-demo.yml b/.github/workflows/github-actions-demo.yml
deleted file mode 100644
index fd357f7a..00000000
--- a/.github/workflows/github-actions-demo.yml
+++ /dev/null
@@ -1,18 +0,0 @@
-name: GitHub Actions Demo
-on: [push]
-jobs:
-  Explore-GitHub-Actions:
-    runs-on: ubuntu-latest
-    steps:
-      - run: echo "🎉 The job was automatically triggered by a ${{ github.event_name }} event."
-      - run: echo "🐧 This job is now running on a ${{ runner.os }} server hosted by GitHub!"
-      - run: echo "🔎 The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}."
-      - name: Check out repository code
-        uses: actions/checkout@v2
-      - run: echo "💡 The ${{ github.repository }} repository has been cloned to the runner."
-      - run: echo "🖥️  The workflow is now ready to test your code on the runner."
-      - name: List files in the repository
-        run: |
-          ls ${{ github.workspace }}
-      - run: echo "🍏 This job's status is ${{ job.status }}."
-

From 4d18f082888995f45b019b30c98de907e33dd351 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 11:21:44 +0100
Subject: [PATCH 197/403] installed pytest

---
 .github/workflows/test_ubuntu.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/test_ubuntu.yaml b/.github/workflows/test_ubuntu.yaml
index 509351d1..4e35267c 100644
--- a/.github/workflows/test_ubuntu.yaml
+++ b/.github/workflows/test_ubuntu.yaml
@@ -26,6 +26,7 @@ jobs:
           python -m pip install --upgrade pip
           pip install -r requirements.txt
           pip install -e .
+          pip install pytest
 
       - name: Run unit tests
         run: python -m pytest mcstasscript/tests/

From 55bafdafb0016ae71fd1c33342e8664f14d4f643 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 11:31:22 +0100
Subject: [PATCH 198/403] Show file structure to ensure all folders are present

---
 .github/workflows/test_ubuntu.yaml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.github/workflows/test_ubuntu.yaml b/.github/workflows/test_ubuntu.yaml
index 4e35267c..8879551b 100644
--- a/.github/workflows/test_ubuntu.yaml
+++ b/.github/workflows/test_ubuntu.yaml
@@ -27,6 +27,9 @@ jobs:
           pip install -r requirements.txt
           pip install -e .
           pip install pytest
+          
+      - name: Show file structure
+        run: tree .
 
       - name: Run unit tests
         run: python -m pytest mcstasscript/tests/

From a8c7b7d1f3292756dc01977936ffcb3b26646063 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 12:00:16 +0100
Subject: [PATCH 199/403] Updated show_categories so that their output is in
 alphabetic order. This improves readability but also consistency accros
 platforms, as the order dependent on which order the folders were read by the
 system.

---
 mcstasscript/helper/component_reader.py    | 12 ++++++++----
 mcstasscript/tests/test_ComponentReader.py | 17 +++++++++--------
 2 files changed, 17 insertions(+), 12 deletions(-)

diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index e9028ac0..aad30062 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -98,7 +98,7 @@ def __init__(self, mcstas_path, input_path="."):
                     overwritten_components.append(file)
 
                 self.component_path[component_name] = abs_path
-                self.component_category[component_name] = "Work directory"
+                self.component_category[component_name] = "work directory"
 
         # Report components found in the work directory and install to the user
         if len(overwritten_components) > 0:
@@ -114,12 +114,16 @@ def show_categories(self):
         """
         Method that will show all component categories available
 
+        Sorted alphabetically for easier readability and consistency
         """
         categories = []
         for component, category in self.component_category.items():
             if category not in categories:
                 categories.append(category)
-                print(" " + category)
+
+        categories.sort()
+        for category in categories:
+            print(" " + category)
 
     def show_components_in_category(self, category_input, **kwargs):
         """
@@ -215,8 +219,8 @@ def read_name(self, component_name):
         output = self.read_component_file(self.component_path[component_name])
 
         # Category loaded using path, in case of Work directory it fails
-        if self.component_category[component_name] == "Work directory":
-            output.category = "Work directory"  # Corrects category
+        if self.component_category[component_name] == "work directory":
+            output.category = "work directory"  # Corrects category
 
         return output
 
diff --git a/mcstasscript/tests/test_ComponentReader.py b/mcstasscript/tests/test_ComponentReader.py
index b6c93f70..d9cc7156 100644
--- a/mcstasscript/tests/test_ComponentReader.py
+++ b/mcstasscript/tests/test_ComponentReader.py
@@ -118,7 +118,7 @@ def test_ComponentReader_init_component_paths(self, mock_stdout):
         self.assertEqual(path, expected_path)
 
         category = component_reader.component_category["test_for_reading"]
-        self.assertEqual(category, "Work directory")
+        self.assertEqual(category, "work directory")
 
         expected_path = os.path.join(dummy_path, "misc",
                                      "test_for_structure.comp")
@@ -174,7 +174,7 @@ def test_ComponentReader_init_component_paths_input(self, mock_stdout):
         self.assertEqual(path, expected_path)
 
         category = component_reader.component_category["test_for_structure"]
-        self.assertEqual(category, "Work directory")
+        self.assertEqual(category, "work directory")
 
         self.assertIn("test_for_structure2", component_reader.component_path)
 
@@ -202,7 +202,7 @@ def test_ComponentReader_init_categories(self, mock_stdout):
         categories as well because of the dummy installation.
         """
         category = component_reader.component_category["test_for_reading"]
-        self.assertEqual(category, "Work directory")
+        self.assertEqual(category, "work directory")
         category = component_reader.component_category["test_for_structure"]
         self.assertEqual(category, "misc")
         category = component_reader.component_category["test_for_structure2"]
@@ -224,7 +224,7 @@ def test_ComponentReader_show_categories(self, mock_stdout):
 
         self.assertEqual(len(output), 7)
         self.assertIn(" sources", output)
-        self.assertIn(" Work directory", output)
+        self.assertIn(" work directory", output)
         self.assertIn(" misc", output)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
@@ -243,10 +243,11 @@ def test_ComponentReader_show_categories_ordered(self, mock_stdout):
         output = mock_stdout.getvalue()
         output = output.split("\n")
 
+        self.assertEqual(len(output), 7)
         # Ignoring message about overwritten components, starting from 3
-        self.assertEqual(output[3], " sources")
-        self.assertEqual(output[4], " Work directory")
-        self.assertEqual(output[5], " misc")
+        self.assertEqual(output[3], " misc")
+        self.assertEqual(output[4], " sources")
+        self.assertEqual(output[5], " work directory")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_show_components_short(self, mock_stdout):
@@ -355,7 +356,7 @@ def test_ComponentReader_read_name_success(self, mock_stdout):
         CompInfo = component_reader.read_name("test_for_reading")
 
         self.assertEqual(CompInfo.name, "test_for_reading")
-        self.assertEqual(CompInfo.category, "Work directory")
+        self.assertEqual(CompInfo.category, "work directory")
         self.assertIn("dist", CompInfo.parameter_names)
         self.assertIn("dist", CompInfo.parameter_defaults)
         self.assertIn("dist", CompInfo.parameter_types)

From de8cc839634b79ac092a99a7c0ead4f84419a788 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 12:03:06 +0100
Subject: [PATCH 200/403] Missed a file in last commit.

---
 mcstasscript/tests/test_Instr.py | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index d564f70b..3767ba3b 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -682,9 +682,9 @@ def test_show_components_simple(self, mock_stdout):
                          + "instead of the installed versions.")
         self.assertEqual(output[3],
                          "Here are the available component categories:")
-        self.assertEqual(output[4], " sources")
-        self.assertEqual(output[5], " Work directory")
-        self.assertEqual(output[6], " misc")
+        self.assertEqual(output[4], " misc")
+        self.assertEqual(output[5], " sources")
+        self.assertEqual(output[6], " work directory")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_show_components_folder(self, mock_stdout):
@@ -699,7 +699,7 @@ def test_show_components_folder(self, mock_stdout):
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
-        instr.show_components("Work directory")
+        instr.show_components("work directory")
 
         os.chdir(current_work_dir)
 
@@ -713,7 +713,7 @@ def test_show_components_folder(self, mock_stdout):
         self.assertEqual(output[2], "These definitions will be used "
                          + "instead of the installed versions.")
         self.assertEqual(output[3],
-                         "Here are all components in the Work directory "
+                         "Here are all components in the work directory "
                          + "category.")
         self.assertEqual(output[4], " test_for_reading")
         self.assertEqual(output[5], "")
@@ -739,9 +739,9 @@ def test_show_components_input_path_simple(self, mock_stdout):
                          + "instead of the installed versions.")
         self.assertEqual(output[3],
                          "Here are the available component categories:")
-        self.assertEqual(output[4], " sources")
-        self.assertEqual(output[5], " misc")
-        self.assertEqual(output[6], " Work directory")
+        self.assertEqual(output[4], " misc")
+        self.assertEqual(output[5], " sources")
+        self.assertEqual(output[6], " work directory")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_show_components_input_path_custom(self, mock_stdout):
@@ -766,9 +766,9 @@ def test_show_components_input_path_custom(self, mock_stdout):
                          + "instead of the installed versions.")
         self.assertEqual(output[3],
                          "Here are the available component categories:")
-        self.assertEqual(output[4], " sources")
-        self.assertEqual(output[5], " misc")
-        self.assertEqual(output[6], " Work directory")
+        self.assertEqual(output[4], " misc")
+        self.assertEqual(output[5], " sources")
+        self.assertEqual(output[6], " work directory")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_component_help(self, mock_stdout):
@@ -846,7 +846,7 @@ def test_create_component_instance_simple(self, mock_stdout):
         comment = ("Radius of circle in (x,y,0) plane where "
                    + "neutrons are generated.")
         self.assertEqual(comp.parameter_comments["radius"], comment)
-        self.assertEqual(comp.category, "Work directory")
+        self.assertEqual(comp.category, "work directory")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_create_component_instance_complex(self, mock_stdout):
@@ -883,7 +883,7 @@ def test_create_component_instance_complex(self, mock_stdout):
         comment = ("Radius of circle in (x,y,0) plane where "
                    + "neutrons are generated.")
         self.assertEqual(comp.parameter_comments["radius"], comment)
-        self.assertEqual(comp.category, "Work directory")
+        self.assertEqual(comp.category, "work directory")
 
         # The keyword arguments of the call should be passed to the
         # new instance of the component. This is checked by reading

From ab45030b51bb8b35b0e6dd200891913ec3959668 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 12:10:45 +0100
Subject: [PATCH 201/403] Attempting to add windows to the text matrix

---
 .github/workflows/test_ubuntu.yaml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/test_ubuntu.yaml b/.github/workflows/test_ubuntu.yaml
index 8879551b..676928e3 100644
--- a/.github/workflows/test_ubuntu.yaml
+++ b/.github/workflows/test_ubuntu.yaml
@@ -2,10 +2,11 @@ name: McStasScript unit tests on Linux
 on: [push]
 jobs:
   build:
-    runs-on: ubuntu-latest
     strategy:
       matrix:
         python-version: [3.6, 3.7, 3.8]
+        os: [linux-latest, windows-latest]
+    runs-on: ${{ matrix.os }}
       
     steps:
       - name: Checkout

From 35915067feba1e1759525f8acdd4ef85d1ee0986 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 12:24:41 +0100
Subject: [PATCH 202/403] Fixed unit test that would fail on windows due to
 hardcoded slash

---
 mcstasscript/tests/test_Instr.py | 26 +++++++++++++++-----------
 1 file changed, 15 insertions(+), 11 deletions(-)

diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 3767ba3b..b89979a6 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1597,16 +1597,19 @@ def test_write_c_files_simple(self, mock_f):
         finally:
             os.chdir(current_directory)
 
-        mock_f.assert_any_call("./generated_includes/"
-                               + "test_instrument_declare.c", "w")
-        mock_f.assert_any_call("./generated_includes/"
-                               + "test_instrument_declare.c", "a")
-        mock_f.assert_any_call("./generated_includes/"
-                               + "test_instrument_initialize.c", "w")
-        mock_f.assert_any_call("./generated_includes/"
-                               + "test_instrument_trace.c", "w")
-        mock_f.assert_any_call("./generated_includes/"
-                               + "test_instrument_component_trace.c", "w")
+        base_path = os.path.join(".", "generated_includes")
+        expected_path = os.path.join(base_path, "test_instrument_declare.c")
+        mock_f.assert_any_call(expected_path, "w")
+        mock_f.assert_any_call(expected_path, "a")
+
+        expected_path = os.path.join(base_path, "test_instrument_initialize.c")
+        mock_f.assert_any_call(expected_path, "w")
+
+        expected_path = os.path.join(base_path, "test_instrument_trace.c")
+        mock_f.assert_any_call(expected_path, "w")
+
+        expected_path = os.path.join(base_path, "test_instrument_component_trace.c")
+        mock_f.assert_any_call(expected_path, "w")
 
         # This does not check that the right thing is written to the
         # right file. Can be improved by splitting the method into
@@ -1728,7 +1731,8 @@ def test_write_full_instrument_simple(self, mock_f):
          my_call("%}\n"),
          my_call("\nEND\n")]
 
-        mock_f.assert_called_with("./test_instrument.instr", "w")
+        expected_path = os.path.join(".", "test_instrument.instr")
+        mock_f.assert_called_with(expected_path, "w")
         handle = mock_f()
         handle.write.assert_has_calls(wrts, any_order=False)
 

From 7452ebd6c5a3b0631835eb4b04c4aa034851d425 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 12:29:22 +0100
Subject: [PATCH 203/403] Fixed mistake in workflow that caused unix part not
 to run.

---
 .github/workflows/{test_ubuntu.yaml => unit_tests.yaml} | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
 rename .github/workflows/{test_ubuntu.yaml => unit_tests.yaml} (95%)

diff --git a/.github/workflows/test_ubuntu.yaml b/.github/workflows/unit_tests.yaml
similarity index 95%
rename from .github/workflows/test_ubuntu.yaml
rename to .github/workflows/unit_tests.yaml
index 676928e3..a391077b 100644
--- a/.github/workflows/test_ubuntu.yaml
+++ b/.github/workflows/unit_tests.yaml
@@ -5,7 +5,7 @@ jobs:
     strategy:
       matrix:
         python-version: [3.6, 3.7, 3.8]
-        os: [linux-latest, windows-latest]
+        os: [ubuntu-latest, windows-latest]
     runs-on: ${{ matrix.os }}
       
     steps:

From f054b108fb7e731c7275648386b277388c2bbe5c Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 12:35:32 +0100
Subject: [PATCH 204/403] Added macos and cache system to unittest workflow

---
 .github/workflows/unit_tests.yaml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/unit_tests.yaml b/.github/workflows/unit_tests.yaml
index a391077b..48fd3110 100644
--- a/.github/workflows/unit_tests.yaml
+++ b/.github/workflows/unit_tests.yaml
@@ -5,7 +5,7 @@ jobs:
     strategy:
       matrix:
         python-version: [3.6, 3.7, 3.8]
-        os: [ubuntu-latest, windows-latest]
+        os: [ubuntu-latest, macos-latest, windows-latest]
     runs-on: ${{ matrix.os }}
       
     steps:
@@ -21,6 +21,7 @@ jobs:
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
+          cache: 'pip'
               
       - name: Install dependencies
         run: |

From fef851866994cbc381b634e5c9904f0da69666a7 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 13:24:20 +0100
Subject: [PATCH 205/403] Moved to find instead of tree to show files to ensure
 compatability between linux, os x and windows

---
 .github/workflows/unit_tests.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/unit_tests.yaml b/.github/workflows/unit_tests.yaml
index 48fd3110..71fc2582 100644
--- a/.github/workflows/unit_tests.yaml
+++ b/.github/workflows/unit_tests.yaml
@@ -31,7 +31,7 @@ jobs:
           pip install pytest
           
       - name: Show file structure
-        run: tree .
+        run: find .
 
       - name: Run unit tests
         run: python -m pytest mcstasscript/tests/

From 5c01de8a4b71aef4f845f70375e6dbf15e2996a5 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 13:31:15 +0100
Subject: [PATCH 206/403] tree . on windows and unix find . on os x

---
 .github/workflows/unit_tests.yaml | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/.github/workflows/unit_tests.yaml b/.github/workflows/unit_tests.yaml
index 71fc2582..1465eb20 100644
--- a/.github/workflows/unit_tests.yaml
+++ b/.github/workflows/unit_tests.yaml
@@ -30,8 +30,13 @@ jobs:
           pip install -e .
           pip install pytest
           
+      - name: Show file structure
+        run: tree .
+        if: ${{ matrix.os }} == ubuntu-latest || ${{ matrix.os }} == windows-latest
+        
       - name: Show file structure
         run: find .
+        if: ${{ matrix.os }} == macos-latest
 
       - name: Run unit tests
         run: python -m pytest mcstasscript/tests/

From 38976a4fdcfbb6d286d869d1a7b9012b70e9c8b8 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 13:40:49 +0100
Subject: [PATCH 207/403] reorder lines for if

---
 .github/workflows/unit_tests.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/unit_tests.yaml b/.github/workflows/unit_tests.yaml
index 1465eb20..a5c12b01 100644
--- a/.github/workflows/unit_tests.yaml
+++ b/.github/workflows/unit_tests.yaml
@@ -31,12 +31,12 @@ jobs:
           pip install pytest
           
       - name: Show file structure
-        run: tree .
         if: ${{ matrix.os }} == ubuntu-latest || ${{ matrix.os }} == windows-latest
+        run: tree .
         
       - name: Show file structure
-        run: find .
         if: ${{ matrix.os }} == macos-latest
+        run: find .
 
       - name: Run unit tests
         run: python -m pytest mcstasscript/tests/

From ce06820daf4cb49975f440645529a8cfe34ba4fd Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 13:42:44 +0100
Subject: [PATCH 208/403] made comparisons strings

---
 .github/workflows/unit_tests.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/unit_tests.yaml b/.github/workflows/unit_tests.yaml
index a5c12b01..c871e7c7 100644
--- a/.github/workflows/unit_tests.yaml
+++ b/.github/workflows/unit_tests.yaml
@@ -31,11 +31,11 @@ jobs:
           pip install pytest
           
       - name: Show file structure
-        if: ${{ matrix.os }} == ubuntu-latest || ${{ matrix.os }} == windows-latest
+        if: ${{ matrix.os }} == "ubuntu-latest" || ${{ matrix.os }} == "windows-latest"
         run: tree .
         
       - name: Show file structure
-        if: ${{ matrix.os }} == macos-latest
+        if: ${{ matrix.os }} == "macos-latest"
         run: find .
 
       - name: Run unit tests

From d29a866dd94ff074b2347d6f7c0f656c51f96b58 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 13:46:58 +0100
Subject: [PATCH 209/403] Changed to one workflow per operating system.

---
 .../{unit_tests.yaml => unit_tests_mac.yaml}  |  8 +----
 .github/workflows/unit_tests_ubuntu.yaml      | 36 +++++++++++++++++++
 .github/workflows/unit_tests_windows.yaml     | 36 +++++++++++++++++++
 3 files changed, 73 insertions(+), 7 deletions(-)
 rename .github/workflows/{unit_tests.yaml => unit_tests_mac.yaml} (75%)
 create mode 100644 .github/workflows/unit_tests_ubuntu.yaml
 create mode 100644 .github/workflows/unit_tests_windows.yaml

diff --git a/.github/workflows/unit_tests.yaml b/.github/workflows/unit_tests_mac.yaml
similarity index 75%
rename from .github/workflows/unit_tests.yaml
rename to .github/workflows/unit_tests_mac.yaml
index c871e7c7..0acf876d 100644
--- a/.github/workflows/unit_tests.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -5,8 +5,7 @@ jobs:
     strategy:
       matrix:
         python-version: [3.6, 3.7, 3.8]
-        os: [ubuntu-latest, macos-latest, windows-latest]
-    runs-on: ${{ matrix.os }}
+    runs-on: macos-latest
       
     steps:
       - name: Checkout
@@ -29,13 +28,8 @@ jobs:
           pip install -r requirements.txt
           pip install -e .
           pip install pytest
-          
-      - name: Show file structure
-        if: ${{ matrix.os }} == "ubuntu-latest" || ${{ matrix.os }} == "windows-latest"
-        run: tree .
         
       - name: Show file structure
-        if: ${{ matrix.os }} == "macos-latest"
         run: find .
 
       - name: Run unit tests
diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
new file mode 100644
index 00000000..ebfb547d
--- /dev/null
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -0,0 +1,36 @@
+name: McStasScript unit tests on Linux
+on: [push]
+jobs:
+  build:
+    strategy:
+      matrix:
+        python-version: [3.6, 3.7, 3.8]
+    runs-on: ubuntu-latest
+      
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+              
+      - name: Switch to Current Branch
+        run: git checkout ${{ env.BRANCH }}
+                  
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+              
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+          pip install -e .
+          pip install pytest
+        
+      - name: Show file structure
+        run: tree .
+
+      - name: Run unit tests
+        run: python -m pytest mcstasscript/tests/
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
new file mode 100644
index 00000000..4053c00b
--- /dev/null
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -0,0 +1,36 @@
+name: McStasScript unit tests on Linux
+on: [push]
+jobs:
+  build:
+    strategy:
+      matrix:
+        python-version: [3.6, 3.7, 3.8]
+    runs-on: windows-latest
+      
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+              
+      - name: Switch to Current Branch
+        run: git checkout ${{ env.BRANCH }}
+                  
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+              
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+          pip install -e .
+          pip install pytest
+        
+      - name: Show file structure
+        run: tree .
+
+      - name: Run unit tests
+        run: python -m pytest mcstasscript/tests/

From 40c830450d8811f142dbfc0d96a3ebd3c84fe34c Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 14:02:53 +0100
Subject: [PATCH 210/403] Checking checkout of libpyvinyl on workflow

---
 .github/workflows/unit_tests_ubuntu.yaml | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index ebfb547d..23b7f25f 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -21,6 +21,15 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
+          
+      - name: Temporary developer libpyvinyl checkout
+        uses: actions/checkout@v2
+        with:
+          repository: PaNOSC-ViNYL/libpyvinyl
+          ref: develop
+          
+      - name: Show file structure
+        run: tree .
               
       - name: Install dependencies
         run: |

From 609942dd15e67177a8cfc7ebf20a4a711913f22b Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 14:07:41 +0100
Subject: [PATCH 211/403] Trying libpyvinyl build first, then mcstasscript

---
 .github/workflows/unit_tests_mac.yaml | 23 ++++++++++++++---------
 1 file changed, 14 insertions(+), 9 deletions(-)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index 0acf876d..761b7779 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -1,4 +1,4 @@
-name: McStasScript unit tests on Linux
+name: McStasScript unit tests on OS X
 on: [push]
 jobs:
   build:
@@ -8,20 +8,25 @@ jobs:
     runs-on: macos-latest
       
     steps:
-      - name: Checkout
-        uses: actions/checkout@v2
-        with:
-          fetch-depth: 0
-              
-      - name: Switch to Current Branch
-        run: git checkout ${{ env.BRANCH }}
-                  
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
               
+      - name: Install dependencies (libpyvinyl)
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+          pip install -e .
+          pip install pytest
+          
+      - name: Checkout
+        uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+          ref: ${{ env.BRANCH }}
+          
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip

From 33f98e93a2aa4b3077310677ce801e31868ab7ba Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 14:09:26 +0100
Subject: [PATCH 212/403] Forgot checkout of libpyvinyl

---
 .github/workflows/unit_tests_mac.yaml | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index 761b7779..13dc10e1 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -8,6 +8,12 @@ jobs:
     runs-on: macos-latest
       
     steps:
+      - name: Temporary developer libpyvinyl checkout
+        uses: actions/checkout@v2
+        with:
+          repository: PaNOSC-ViNYL/libpyvinyl
+          ref: develop
+    
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v2
         with:

From 1587455be33ece29119b2ee5e33aaa0c41772118 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 14:13:15 +0100
Subject: [PATCH 213/403] All workflows now get libpyvinyl develop from github.

---
 .github/workflows/unit_tests_ubuntu.yaml  | 30 ++++++++++++-----------
 .github/workflows/unit_tests_windows.yaml | 25 +++++++++++++------
 2 files changed, 34 insertions(+), 21 deletions(-)

diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index 23b7f25f..9fc22982 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -1,4 +1,4 @@
-name: McStasScript unit tests on Linux
+name: McStasScript unit tests on ubuntu
 on: [push]
 jobs:
   build:
@@ -7,30 +7,32 @@ jobs:
         python-version: [3.6, 3.7, 3.8]
     runs-on: ubuntu-latest
       
-    steps:
-      - name: Checkout
+     steps:
+      - name: Temporary developer libpyvinyl checkout
         uses: actions/checkout@v2
         with:
-          fetch-depth: 0
-              
-      - name: Switch to Current Branch
-        run: git checkout ${{ env.BRANCH }}
-                  
+          repository: PaNOSC-ViNYL/libpyvinyl
+          ref: develop
+    
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
+              
+      - name: Install dependencies (libpyvinyl)
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+          pip install -e .
+          pip install pytest
           
-      - name: Temporary developer libpyvinyl checkout
+      - name: Checkout
         uses: actions/checkout@v2
         with:
-          repository: PaNOSC-ViNYL/libpyvinyl
-          ref: develop
+          fetch-depth: 0
+          ref: ${{ env.BRANCH }}
           
-      - name: Show file structure
-        run: tree .
-              
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
index 4053c00b..0daf85da 100644
--- a/.github/workflows/unit_tests_windows.yaml
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -1,4 +1,4 @@
-name: McStasScript unit tests on Linux
+name: McStasScript unit tests on windows
 on: [push]
 jobs:
   build:
@@ -8,20 +8,31 @@ jobs:
     runs-on: windows-latest
       
     steps:
-      - name: Checkout
+      - name: Temporary developer libpyvinyl checkout
         uses: actions/checkout@v2
         with:
-          fetch-depth: 0
-              
-      - name: Switch to Current Branch
-        run: git checkout ${{ env.BRANCH }}
-                  
+          repository: PaNOSC-ViNYL/libpyvinyl
+          ref: develop
+    
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
               
+      - name: Install dependencies (libpyvinyl)
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+          pip install -e .
+          pip install pytest
+          
+      - name: Checkout
+        uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+          ref: ${{ env.BRANCH }}
+          
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip

From f3e094cfa3465d1e8eccede296596a9b0a0e0de8 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 14:18:06 +0100
Subject: [PATCH 214/403] Fix of indentation error on ubuntu workflow

---
 .github/workflows/unit_tests_ubuntu.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index 9fc22982..7f0de378 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -7,7 +7,7 @@ jobs:
         python-version: [3.6, 3.7, 3.8]
     runs-on: ubuntu-latest
       
-     steps:
+    steps:
       - name: Temporary developer libpyvinyl checkout
         uses: actions/checkout@v2
         with:

From c86c020b9710aa0a60d4c8289ce3cfb39ae17aa5 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 14:23:27 +0100
Subject: [PATCH 215/403] Added note on unused threading.

---
 mcstasscript/jb_interface/simulation_interface.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/mcstasscript/jb_interface/simulation_interface.py b/mcstasscript/jb_interface/simulation_interface.py
index 673b49c1..718facff 100644
--- a/mcstasscript/jb_interface/simulation_interface.py
+++ b/mcstasscript/jb_interface/simulation_interface.py
@@ -83,6 +83,10 @@ def run_simulation_thread(self, change):
         """
         Runs simulation as thread, allowing user to update plots simultaneously
 
+        The use of this method has caused crashes, temporarily circumvented by
+        calling run_simulation_live on button instead. Now plots can now be
+        updated while a simulation is running.
+
         Parameters
         ----------
 

From 0925b954abdfa6fca151e3cd9366ac169724f6c4 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 14:31:17 +0100
Subject: [PATCH 216/403] Updates to test workflows so developer version of
 libpyvinyl is used

---
 .github/workflows/unit_tests_mac.yaml     | 25 ++++++++++++++++-------
 .github/workflows/unit_tests_ubuntu.yaml  | 25 ++++++++++++++++-------
 .github/workflows/unit_tests_windows.yaml | 25 ++++++++++++++++-------
 3 files changed, 54 insertions(+), 21 deletions(-)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index 0acf876d..13dc10e1 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -1,4 +1,4 @@
-name: McStasScript unit tests on Linux
+name: McStasScript unit tests on OS X
 on: [push]
 jobs:
   build:
@@ -8,20 +8,31 @@ jobs:
     runs-on: macos-latest
       
     steps:
-      - name: Checkout
+      - name: Temporary developer libpyvinyl checkout
         uses: actions/checkout@v2
         with:
-          fetch-depth: 0
-              
-      - name: Switch to Current Branch
-        run: git checkout ${{ env.BRANCH }}
-                  
+          repository: PaNOSC-ViNYL/libpyvinyl
+          ref: develop
+    
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
               
+      - name: Install dependencies (libpyvinyl)
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+          pip install -e .
+          pip install pytest
+          
+      - name: Checkout
+        uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+          ref: ${{ env.BRANCH }}
+          
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index ebfb547d..7f0de378 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -1,4 +1,4 @@
-name: McStasScript unit tests on Linux
+name: McStasScript unit tests on ubuntu
 on: [push]
 jobs:
   build:
@@ -8,20 +8,31 @@ jobs:
     runs-on: ubuntu-latest
       
     steps:
-      - name: Checkout
+      - name: Temporary developer libpyvinyl checkout
         uses: actions/checkout@v2
         with:
-          fetch-depth: 0
-              
-      - name: Switch to Current Branch
-        run: git checkout ${{ env.BRANCH }}
-                  
+          repository: PaNOSC-ViNYL/libpyvinyl
+          ref: develop
+    
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
               
+      - name: Install dependencies (libpyvinyl)
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+          pip install -e .
+          pip install pytest
+          
+      - name: Checkout
+        uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+          ref: ${{ env.BRANCH }}
+          
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
index 4053c00b..0daf85da 100644
--- a/.github/workflows/unit_tests_windows.yaml
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -1,4 +1,4 @@
-name: McStasScript unit tests on Linux
+name: McStasScript unit tests on windows
 on: [push]
 jobs:
   build:
@@ -8,20 +8,31 @@ jobs:
     runs-on: windows-latest
       
     steps:
-      - name: Checkout
+      - name: Temporary developer libpyvinyl checkout
         uses: actions/checkout@v2
         with:
-          fetch-depth: 0
-              
-      - name: Switch to Current Branch
-        run: git checkout ${{ env.BRANCH }}
-                  
+          repository: PaNOSC-ViNYL/libpyvinyl
+          ref: develop
+    
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
               
+      - name: Install dependencies (libpyvinyl)
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+          pip install -e .
+          pip install pytest
+          
+      - name: Checkout
+        uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+          ref: ${{ env.BRANCH }}
+          
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip

From 04f53df97659a7d4c3f54049119377082a4ed2f6 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 14:46:30 +0100
Subject: [PATCH 217/403] Removed cache from libpyvinyl install

---
 .github/workflows/unit_tests_mac.yaml     | 1 -
 .github/workflows/unit_tests_ubuntu.yaml  | 1 -
 .github/workflows/unit_tests_windows.yaml | 1 -
 3 files changed, 3 deletions(-)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index 13dc10e1..11795848 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -18,7 +18,6 @@ jobs:
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
-          cache: 'pip'
               
       - name: Install dependencies (libpyvinyl)
         run: |
diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index 7f0de378..def17c8a 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -18,7 +18,6 @@ jobs:
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
-          cache: 'pip'
               
       - name: Install dependencies (libpyvinyl)
         run: |
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
index 0daf85da..cbe47c46 100644
--- a/.github/workflows/unit_tests_windows.yaml
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -18,7 +18,6 @@ jobs:
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
-          cache: 'pip'
               
       - name: Install dependencies (libpyvinyl)
         run: |

From da974e59bd266bdd09b3ffa3aa908b7b0e8b276f Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 15:10:08 +0100
Subject: [PATCH 218/403] setting up a path for each library.

---
 .github/workflows/unit_tests_mac.yaml     | 2 ++
 .github/workflows/unit_tests_ubuntu.yaml  | 3 ++-
 .github/workflows/unit_tests_windows.yaml | 2 ++
 3 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index 11795848..72d310c9 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -13,6 +13,7 @@ jobs:
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
           ref: develop
+          path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v2
@@ -31,6 +32,7 @@ jobs:
         with:
           fetch-depth: 0
           ref: ${{ env.BRANCH }}
+          path: mcstasscript
           
       - name: Install dependencies
         run: |
diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index def17c8a..f7a40cc2 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -13,6 +13,7 @@ jobs:
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
           ref: develop
+          path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v2
@@ -29,8 +30,8 @@ jobs:
       - name: Checkout
         uses: actions/checkout@v2
         with:
-          fetch-depth: 0
           ref: ${{ env.BRANCH }}
+          path: mcstasscript
           
       - name: Install dependencies
         run: |
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
index cbe47c46..226062b8 100644
--- a/.github/workflows/unit_tests_windows.yaml
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -13,6 +13,7 @@ jobs:
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
           ref: develop
+          path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v2
@@ -31,6 +32,7 @@ jobs:
         with:
           fetch-depth: 0
           ref: ${{ env.BRANCH }}
+          path: mcstasscript
           
       - name: Install dependencies
         run: |

From 69dad371cd27c7da7e1837bf3ed26205506f08f1 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 15:13:05 +0100
Subject: [PATCH 219/403] Trying to cd into libpyvinyl and mcstasscript folders

---
 .github/workflows/unit_tests_ubuntu.yaml  | 2 ++
 .github/workflows/unit_tests_windows.yaml | 2 ++
 2 files changed, 4 insertions(+)

diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index f7a40cc2..575142f6 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -22,6 +22,7 @@ jobs:
               
       - name: Install dependencies (libpyvinyl)
         run: |
+          cd libpyvinyl
           python -m pip install --upgrade pip
           pip install -r requirements.txt
           pip install -e .
@@ -35,6 +36,7 @@ jobs:
           
       - name: Install dependencies
         run: |
+          cd mcstasscript
           python -m pip install --upgrade pip
           pip install -r requirements.txt
           pip install -e .
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
index 226062b8..1ea86157 100644
--- a/.github/workflows/unit_tests_windows.yaml
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -22,6 +22,7 @@ jobs:
               
       - name: Install dependencies (libpyvinyl)
         run: |
+          cd libpyvinyl
           python -m pip install --upgrade pip
           pip install -r requirements.txt
           pip install -e .
@@ -36,6 +37,7 @@ jobs:
           
       - name: Install dependencies
         run: |
+          cd mcstasscript
           python -m pip install --upgrade pip
           pip install -r requirements.txt
           pip install -e .

From 9c753b76f8a37da9be226f0f2feb4e369dc2f7f1 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 15:20:31 +0100
Subject: [PATCH 220/403] Changed directory structure of workflows. Now only
 libpyvinyl is in a folder.

---
 .github/workflows/unit_tests_mac.yaml     | 3 +--
 .github/workflows/unit_tests_ubuntu.yaml  | 2 --
 .github/workflows/unit_tests_windows.yaml | 3 ---
 3 files changed, 1 insertion(+), 7 deletions(-)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index 72d310c9..1a584ad9 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -22,6 +22,7 @@ jobs:
               
       - name: Install dependencies (libpyvinyl)
         run: |
+          cd libpyvinyl
           python -m pip install --upgrade pip
           pip install -r requirements.txt
           pip install -e .
@@ -30,9 +31,7 @@ jobs:
       - name: Checkout
         uses: actions/checkout@v2
         with:
-          fetch-depth: 0
           ref: ${{ env.BRANCH }}
-          path: mcstasscript
           
       - name: Install dependencies
         run: |
diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index 575142f6..4741b7ef 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -32,11 +32,9 @@ jobs:
         uses: actions/checkout@v2
         with:
           ref: ${{ env.BRANCH }}
-          path: mcstasscript
           
       - name: Install dependencies
         run: |
-          cd mcstasscript
           python -m pip install --upgrade pip
           pip install -r requirements.txt
           pip install -e .
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
index 1ea86157..630e6a0e 100644
--- a/.github/workflows/unit_tests_windows.yaml
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -31,13 +31,10 @@ jobs:
       - name: Checkout
         uses: actions/checkout@v2
         with:
-          fetch-depth: 0
           ref: ${{ env.BRANCH }}
-          path: mcstasscript
           
       - name: Install dependencies
         run: |
-          cd mcstasscript
           python -m pip install --upgrade pip
           pip install -r requirements.txt
           pip install -e .

From e915fb4b6ebba12cbbf741d99b03df8ba9bfb3fe Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 6 Jan 2022 15:27:27 +0100
Subject: [PATCH 221/403] Changed directory to avoid python setup getting
 requirements from mcstasscript before libpyvinyl is installed manually.

---
 .github/workflows/unit_tests_mac.yaml     | 16 +++++++++-------
 .github/workflows/unit_tests_ubuntu.yaml  | 16 +++++++++-------
 .github/workflows/unit_tests_windows.yaml | 16 +++++++++-------
 3 files changed, 27 insertions(+), 21 deletions(-)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index 1a584ad9..db26727b 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -8,6 +8,12 @@ jobs:
     runs-on: macos-latest
       
     steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+        with:
+          ref: ${{ env.BRANCH }}
+          path: mcstasscript
+    
       - name: Temporary developer libpyvinyl checkout
         uses: actions/checkout@v2
         with:
@@ -28,13 +34,9 @@ jobs:
           pip install -e .
           pip install pytest
           
-      - name: Checkout
-        uses: actions/checkout@v2
-        with:
-          ref: ${{ env.BRANCH }}
-          
-      - name: Install dependencies
+      - name: Install dependencies (mcstasscript)
         run: |
+          cd mcstasscript
           python -m pip install --upgrade pip
           pip install -r requirements.txt
           pip install -e .
@@ -44,4 +46,4 @@ jobs:
         run: find .
 
       - name: Run unit tests
-        run: python -m pytest mcstasscript/tests/
+        run: python -m pytest mcstasscript/mcstasscript/tests/
diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index 4741b7ef..c14c2541 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -8,6 +8,12 @@ jobs:
     runs-on: ubuntu-latest
       
     steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+        with:
+          ref: ${{ env.BRANCH }}
+          path: mcstasscript
+    
       - name: Temporary developer libpyvinyl checkout
         uses: actions/checkout@v2
         with:
@@ -28,13 +34,9 @@ jobs:
           pip install -e .
           pip install pytest
           
-      - name: Checkout
-        uses: actions/checkout@v2
-        with:
-          ref: ${{ env.BRANCH }}
-          
-      - name: Install dependencies
+      - name: Install dependencies (mcstasscript)
         run: |
+          cd mcstasscript
           python -m pip install --upgrade pip
           pip install -r requirements.txt
           pip install -e .
@@ -44,4 +46,4 @@ jobs:
         run: tree .
 
       - name: Run unit tests
-        run: python -m pytest mcstasscript/tests/
+        run: python -m pytest mcstasscript/mcstasscript/tests/
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
index 630e6a0e..55415dfa 100644
--- a/.github/workflows/unit_tests_windows.yaml
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -8,6 +8,12 @@ jobs:
     runs-on: windows-latest
       
     steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+        with:
+          ref: ${{ env.BRANCH }}
+          path: mcstasscript
+    
       - name: Temporary developer libpyvinyl checkout
         uses: actions/checkout@v2
         with:
@@ -28,13 +34,9 @@ jobs:
           pip install -e .
           pip install pytest
           
-      - name: Checkout
-        uses: actions/checkout@v2
-        with:
-          ref: ${{ env.BRANCH }}
-          
-      - name: Install dependencies
+      - name: Install dependencies (mcstasscript)
         run: |
+          cd mcstasscript
           python -m pip install --upgrade pip
           pip install -r requirements.txt
           pip install -e .
@@ -44,4 +46,4 @@ jobs:
         run: tree .
 
       - name: Run unit tests
-        run: python -m pytest mcstasscript/tests/
+        run: python -m pytest mcstasscript/mcstasscript/tests/

From 8de9af14806e79edf830626c2723160b41386774 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 12 Jan 2022 15:26:35 +0100
Subject: [PATCH 222/403] Started sphinx documentation

Added show_variables method to instrument object
Made sure NameError is raised if trying to declare a variable with
a name that was already used.
---
 docs/Makefile                                 |   20 +
 docs/make.bat                                 |   35 +
 .../mcstasscript.data.data.McStasData.rst     |   29 +
 ...cstasscript.data.data.McStasDataBinned.rst |   29 +
 ...mcstasscript.data.data.McStasDataEvent.rst |   29 +
 .../mcstasscript.data.data.McStasMetaData.rst |   28 +
 ...stasscript.data.data.McStasPlotOptions.rst |   24 +
 .../_autosummary/mcstasscript.data.data.rst   |   35 +
 .../source/_autosummary/mcstasscript.data.rst |   32 +
 ....helper.component_reader.ComponentInfo.rst |   23 +
 ...elper.component_reader.ComponentReader.rst |   29 +
 .../mcstasscript.helper.component_reader.rst  |   32 +
 ...mcstasscript.helper.formatting.bcolors.rst |   36 +
 ...pt.helper.formatting.is_legal_filename.rst |    6 +
 ...t.helper.formatting.is_legal_parameter.rst |    6 +
 .../mcstasscript.helper.formatting.rst        |   39 +
 ...ript.helper.managed_mcrun.ManagedMcrun.rst |   25 +
 ...ipt.helper.managed_mcrun.load_metadata.rst |    6 +
 ...ript.helper.managed_mcrun.load_monitor.rst |    6 +
 ...ript.helper.managed_mcrun.load_results.rst |    6 +
 .../mcstasscript.helper.managed_mcrun.rst     |   40 +
 ...script.helper.mcstas_objects.Component.rst |   44 +
 ....helper.mcstas_objects.DeclareVariable.rst |   24 +
 ...lper.mcstas_objects.ParameterContainer.rst |   34 +
 ...elper.mcstas_objects.ParameterVariable.rst |   38 +
 .../mcstasscript.helper.mcstas_objects.rst    |   34 +
 .../mcstasscript.helper.plot_helper.rst       |   23 +
 .../_autosummary/mcstasscript.helper.rst      |   36 +
 ....instr_reader.control.InstrumentReader.rst |   26 +
 .../mcstasscript.instr_reader.control.rst     |   31 +
 ...nstr_reader.read_declare.DeclareReader.rst |   25 +
 ...mcstasscript.instr_reader.read_declare.rst |   31 +
 ...eader.read_definition.DefinitionReader.rst |   25 +
 ...tasscript.instr_reader.read_definition.rst |   31 +
 ...nstr_reader.read_finally.FinallyReader.rst |   25 +
 ...mcstasscript.instr_reader.read_finally.rst |   31 +
 ...eader.read_initialize.InitializeReader.rst |   25 +
 ...tasscript.instr_reader.read_initialize.rst |   31 +
 ...pt.instr_reader.read_trace.TraceReader.rst |   26 +
 .../mcstasscript.instr_reader.read_trace.rst  |   31 +
 .../mcstasscript.instr_reader.rst             |   38 +
 ...script.instr_reader.util.SectionReader.rst |   24 +
 .../mcstasscript.instr_reader.util.rst        |   31 +
 .../mcstasscript.integration_tests.rst        |   33 +
 ...sts.test_complex_instrument.FakeChange.rst |   23 +
 ...mplex_instrument.TestComplexInstrument.rst |   98 ++
 ...egration_tests.test_complex_instrument.rst |   39 +
 ...ex_instrument.setup_complex_instrument.rst |    6 +
 ...simple_instrument.TestSimpleInstrument.rst |  101 ++
 ...tegration_tests.test_simple_instrument.rst |   40 +
 ...ple_instrument.setup_simple_instrument.rst |    6 +
 ...ent.setup_simple_instrument_input_path.rst |    6 +
 ...nstrument.setup_simple_slit_instrument.rst |    6 +
 ...cript.interface.functions.Configurator.rst |   28 +
 ...asscript.interface.functions.load_data.rst |    6 +
 ...ript.interface.functions.load_metadata.rst |    6 +
 ...cript.interface.functions.load_monitor.rst |    6 +
 ....interface.functions.name_plot_options.rst |    6 +
 ...script.interface.functions.name_search.rst |    6 +
 .../mcstasscript.interface.functions.rst      |   42 +
 ...tasscript.interface.instr.McCode_instr.rst |   76 +
 ...tasscript.interface.instr.McStas_instr.rst |   76 +
 ...sscript.interface.instr.McXtrace_instr.rst |   76 +
 .../mcstasscript.interface.instr.rst          |   33 +
 ...stasscript.interface.plotter.interface.rst |    6 +
 ...cript.interface.plotter.make_animation.rst |    6 +
 ...stasscript.interface.plotter.make_plot.rst |    6 +
 ...script.interface.plotter.make_sub_plot.rst |    6 +
 .../mcstasscript.interface.plotter.rst        |   33 +
 ...tasscript.interface.reader.McStas_file.rst |   25 +
 .../mcstasscript.interface.reader.rst         |   31 +
 .../_autosummary/mcstasscript.interface.rst   |   35 +
 ...erface.plot_interface.ColormapDropdown.rst |   26 +
 ...b_interface.plot_interface.LogCheckbox.rst |   25 +
 ...terface.plot_interface.MonitorDropdown.rst |   26 +
 ...erface.plot_interface.OrdersOfMagField.rst |   25 +
 ...interface.plot_interface.PlotInterface.rst |   31 +
 ...stasscript.jb_interface.plot_interface.rst |   35 +
 .../mcstasscript.jb_interface.rst             |   34 +
 ...e.simulation_interface.ParameterWidget.rst |   25 +
 ...face.simulation_interface.SimInterface.rst |   34 +
 ...nterface.simulation_interface.add_data.rst |    6 +
 ...ript.jb_interface.simulation_interface.rst |   39 +
 ..._interface.widget_helpers.HiddenPrints.rst |   23 +
 ...e.widget_helpers.get_parameter_default.rst |    6 +
 ...e.widget_helpers.parameter_has_default.rst |    6 +
 ...stasscript.jb_interface.widget_helpers.rst |   39 +
 docs/source/_autosummary/mcstasscript.rst     |   38 +
 ....tests.helpers_for_tests.WorkInTestDir.rst |   23 +
 .../mcstasscript.tests.helpers_for_tests.rst  |   31 +
 .../_autosummary/mcstasscript.tests.rst       |   52 +
 ...st_ComponentReader.TestComponentReader.rst |  115 ++
 ...cstasscript.tests.test_ComponentReader.rst |   39 +
 ...ComponentReader.setup_component_reader.rst |    6 +
 ...ader.setup_component_reader_input_path.rst |    6 +
 ...sts.test_Configurator.TestConfigurator.rst |  104 ++
 .../mcstasscript.tests.test_Configurator.rst  |   39 +
 ...s.test_Configurator.setup_configurator.rst |    6 +
 ....test_Configurator.setup_expected_file.rst |    6 +
 ...ript.tests.test_Instr.TestMcStas_instr.rst |  163 ++
 .../mcstasscript.tests.test_Instr.rst         |   50 +
 ...t.tests.test_Instr.setup_instr_no_path.rst |    6 +
 ...tests.test_Instr.setup_instr_root_path.rst |    6 +
 ...test_Instr.setup_instr_with_input_path.rst |    6 +
 ...r.setup_instr_with_input_path_relative.rst |    6 +
 ...tests.test_Instr.setup_instr_with_path.rst |    6 +
 ...tests.test_Instr.setup_populated_instr.rst |    6 +
 ....setup_populated_instr_with_dummy_path.rst |    6 +
 ...etup_populated_with_some_options_instr.rst |    6 +
 ...test_Instr.setup_populated_x_ray_instr.rst |    6 +
 ..._populated_x_ray_instr_with_dummy_path.rst |    6 +
 ...s.test_Instr.setup_x_ray_instr_no_path.rst |    6 +
 ...test_Instr.setup_x_ray_instr_root_path.rst |    6 +
 ...test_Instr.setup_x_ray_instr_with_path.rst |    6 +
 ...ests.test_Instr_reader.TestInstrReader.rst |  111 ++
 ...ipt.tests.test_Instr_reader.blockPrint.rst |    6 +
 ...pt.tests.test_Instr_reader.enablePrint.rst |    6 +
 .../mcstasscript.tests.test_Instr_reader.rst  |   42 +
 ....tests.test_Instr_reader.set_dummy_dir.rst |    6 +
 ...tests.test_Instr_reader.setup_standard.rst |    6 +
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 ...sts.test_ManagedMcrun.TestManagedMcrun.rst |  117 ++
 ....test_ManagedMcrun.Test_load_functions.rst |  102 ++
 .../mcstasscript.tests.test_ManagedMcrun.rst  |   32 +
 ...t.tests.test_McStasData.TestMcStasData.rst |  109 ++
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 ...StasData.set_dummy_McStasDataBinned_1d.rst |    6 +
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 ...test_McStasMetaData.TestMcStasMetaData.rst |  105 ++
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 ...cStasPlotOptions.TestMcStasPlotOptions.rst |  120 ++
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 ....tests.test_Plotter.TestPlotterHelpers.rst |  128 ++
 ..._Plotter.get_dummy_McStasDataBinned_1d.rst |    6 +
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 ...ript.tests.test_add_data.Test_add_data.rst |  101 ++
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 ...ipt.tests.test_component.TestComponent.rst |  127 ++
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 ...test_dump_and_load.setup_instr_no_path.rst |    6 +
 ...st_dump_and_load.setup_instr_root_path.rst |    6 +
 ...p_and_load.setup_instr_with_input_path.rst |    6 +
 ...d.setup_instr_with_input_path_relative.rst |    6 +
 ...st_dump_and_load.setup_instr_with_path.rst |    6 +
 ...st_dump_and_load.setup_populated_instr.rst |    6 +
 ....setup_populated_instr_with_dummy_path.rst |    6 +
 ...etup_populated_with_some_options_instr.rst |    6 +
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 ..._populated_x_ray_instr_with_dummy_path.rst |    6 +
 ...ump_and_load.setup_x_ray_instr_no_path.rst |    6 +
 ...p_and_load.setup_x_ray_instr_root_path.rst |    6 +
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 ...t.tests.test_formatting.TestFormatting.rst |  104 ++
 .../mcstasscript.tests.test_formatting.rst    |   31 +
 ...pt.tests.test_functions.Test_load_data.rst |   97 ++
 ...ests.test_functions.Test_load_metadata.rst |   97 ++
 ...tests.test_functions.Test_load_monitor.rst |   97 ++
 ....test_functions.Test_name_plot_options.rst |   98 ++
 ....tests.test_functions.Test_name_search.rst |  103 ++
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 ...unctions.set_dummy_McStasDataBinned_1d.rst |    6 +
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 ..._functions.set_dummy_MetaDataBinned_1d.rst |    6 +
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 ....test_functions.setup_McStasData_array.rst |    6 +
 ...unctions.setup_McStasData_array_repeat.rst |    6 +
 ...ameter_variable.Test_ParameterVariable.rst |  106 ++
 ...asscript.tests.test_parameter_variable.rst |   31 +
 ...t.tests.test_plot_interface.FakeChange.rst |   23 +
 ....test_plot_interface.TestPlotInterface.rst |  104 ++
 ...pt.tests.test_plot_interface.fake_data.rst |    6 +
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 ...nterface.set_dummy_McStasDataBinned_1d.rst |    6 +
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 ..._interface.set_dummy_MetaDataBinned_1d.rst |    6 +
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 ...s.test_simulation_interface.FakeChange.rst |   23 +
 ...tion_interface.TestSimulationInterface.rst |  103 ++
 ...script.tests.test_simulation_interface.rst |   42 +
 ...interface.setup_instr_root_path_McStas.rst |    6 +
 ...terface.setup_instr_root_path_McXtrace.rst |    6 +
 ...interface.setup_populated_instr_McStas.rst |    6 +
 ...terface.setup_populated_instr_McXtrace.rst |    6 +
 ....test_widget_helpers.TestWidgetHelpers.rst |  103 ++
 ...mcstasscript.tests.test_widget_helpers.rst |   31 +
 .../mcstasscript.tests.utilities.rst          |   30 +
 ...asscript.tests.utilities.work_dir_test.rst |    6 +
 docs/source/_templates/class.rst              |   29 +
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 docs/source/_templates/module.rst             |   60 +
 docs/source/conf.py                           |   82 +
 .../source/getting_started/installation.ipynb |  149 ++
 docs/source/getting_started/overview.ipynb    |   96 ++
 docs/source/getting_started/quick_start.ipynb |  590 +++++++
 docs/source/index.rst                         |   62 +
 .../McStasScript_tutorial_1_the_basics.ipynb  | 1059 ++++++++++++
 .../McStasScript_tutorial_2_SPLIT.ipynb       |  315 ++++
 ...tasScript_tutorial_3_EXTEND_and_WHEN.ipynb |  263 +++
 .../McStasScript_tutorial_4_JUMP.ipynb        |  281 ++++
 ...n_tutorial_1_processes_and_materials.ipynb | 1151 +++++++++++++
 .../tutorial/Union_tutorial_2_geometry.ipynb  |  492 ++++++
 .../tutorial/Union_tutorial_3_loggers.ipynb   |  591 +++++++
 .../Union_tutorial_4_conditionals.ipynb       |  539 ++++++
 .../tutorial/Union_tutorial_5_masks.ipynb     |  374 +++++
 ...ial_6_Exit_and_number_of_activations.ipynb |  399 +++++
 .../Union_tutorial_7_Tagging_history.ipynb    | 1487 +++++++++++++++++
 docs/source/tutorial/data_folder/note.txt     |    1 +
 docs/source/tutorial/run_folder/note.txt      |    2 +
 docs/source/user_guide/component_object.ipynb |  808 +++++++++
 .../source/user_guide/instrument_object.ipynb |  819 +++++++++
 .../source/user_guide/instrument_reader.ipynb |   42 +
 .../user_guide/parameters_and_variables.ipynb |  506 ++++++
 docs/source/user_guide/plotting.ipynb         |   42 +
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 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_instr_no_path.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_instr_root_path.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_instr_with_input_path.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_instr_with_input_path_relative.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_instr_with_path.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_populated_instr.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_populated_instr_with_dummy_path.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_populated_with_some_options_instr.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_populated_x_ray_instr.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_populated_x_ray_instr_with_dummy_path.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_x_ray_instr_no_path.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_x_ray_instr_root_path.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_x_ray_instr_with_path.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_formatting.TestFormatting.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_formatting.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_functions.Test_load_data.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_functions.Test_load_metadata.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_functions.Test_load_monitor.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_functions.Test_name_plot_options.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_functions.Test_name_search.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_functions.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_functions.set_dummy_McStasDataBinned_1d.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_functions.set_dummy_McStasDataBinned_2d.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_functions.set_dummy_MetaDataBinned_1d.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_functions.set_dummy_MetaDataBinned_2d.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_functions.setup_McStasData_array.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_functions.setup_McStasData_array_repeat.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_parameter_variable.Test_ParameterVariable.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_parameter_variable.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_plot_interface.FakeChange.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_plot_interface.TestPlotInterface.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_plot_interface.fake_data.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_plot_interface.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_plot_interface.set_dummy_McStasDataBinned_1d.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_plot_interface.set_dummy_McStasDataBinned_2d.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_plot_interface.set_dummy_MetaDataBinned_1d.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_plot_interface.set_dummy_MetaDataBinned_2d.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.FakeChange.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.TestSimulationInterface.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.setup_instr_root_path_McStas.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.setup_instr_root_path_McXtrace.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.setup_populated_instr_McStas.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.setup_populated_instr_McXtrace.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_widget_helpers.TestWidgetHelpers.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.test_widget_helpers.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.utilities.rst
 create mode 100644 docs/source/_autosummary/mcstasscript.tests.utilities.work_dir_test.rst
 create mode 100644 docs/source/_templates/class.rst
 create mode 100644 docs/source/_templates/custom-class-template.rst
 create mode 100644 docs/source/_templates/custom-module-template.rst
 create mode 100644 docs/source/_templates/module.rst
 create mode 100644 docs/source/conf.py
 create mode 100644 docs/source/getting_started/installation.ipynb
 create mode 100644 docs/source/getting_started/overview.ipynb
 create mode 100644 docs/source/getting_started/quick_start.ipynb
 create mode 100644 docs/source/index.rst
 create mode 100644 docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
 create mode 100644 docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
 create mode 100644 docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
 create mode 100644 docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb
 create mode 100644 docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
 create mode 100644 docs/source/tutorial/Union_tutorial_2_geometry.ipynb
 create mode 100644 docs/source/tutorial/Union_tutorial_3_loggers.ipynb
 create mode 100644 docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
 create mode 100644 docs/source/tutorial/Union_tutorial_5_masks.ipynb
 create mode 100644 docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
 create mode 100644 docs/source/tutorial/Union_tutorial_7_Tagging_history.ipynb
 create mode 100644 docs/source/tutorial/data_folder/note.txt
 create mode 100644 docs/source/tutorial/run_folder/note.txt
 create mode 100644 docs/source/user_guide/component_object.ipynb
 create mode 100644 docs/source/user_guide/instrument_object.ipynb
 create mode 100644 docs/source/user_guide/instrument_reader.ipynb
 create mode 100644 docs/source/user_guide/parameters_and_variables.ipynb
 create mode 100644 docs/source/user_guide/plotting.ipynb

diff --git a/docs/Makefile b/docs/Makefile
new file mode 100644
index 00000000..d0c3cbf1
--- /dev/null
+++ b/docs/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = source
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/docs/make.bat b/docs/make.bat
new file mode 100644
index 00000000..6fcf05b4
--- /dev/null
+++ b/docs/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.https://www.sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
diff --git a/docs/source/_autosummary/mcstasscript.data.data.McStasData.rst b/docs/source/_autosummary/mcstasscript.data.data.McStasData.rst
new file mode 100644
index 00000000..9a95b288
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.data.data.McStasData.rst
@@ -0,0 +1,29 @@
+mcstasscript.data.data.McStasData
+=================================
+
+.. currentmodule:: mcstasscript.data.data
+
+.. autoclass:: McStasData
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~McStasData.__init__
+      ~McStasData.get_data_location
+      ~McStasData.set_data_location
+      ~McStasData.set_plot_options
+      ~McStasData.set_title
+      ~McStasData.set_xlabel
+      ~McStasData.set_ylabel
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.data.data.McStasDataBinned.rst b/docs/source/_autosummary/mcstasscript.data.data.McStasDataBinned.rst
new file mode 100644
index 00000000..71b2c76a
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.data.data.McStasDataBinned.rst
@@ -0,0 +1,29 @@
+mcstasscript.data.data.McStasDataBinned
+=======================================
+
+.. currentmodule:: mcstasscript.data.data
+
+.. autoclass:: McStasDataBinned
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~McStasDataBinned.__init__
+      ~McStasDataBinned.get_data_location
+      ~McStasDataBinned.set_data_location
+      ~McStasDataBinned.set_plot_options
+      ~McStasDataBinned.set_title
+      ~McStasDataBinned.set_xlabel
+      ~McStasDataBinned.set_ylabel
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.data.data.McStasDataEvent.rst b/docs/source/_autosummary/mcstasscript.data.data.McStasDataEvent.rst
new file mode 100644
index 00000000..6984859d
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.data.data.McStasDataEvent.rst
@@ -0,0 +1,29 @@
+mcstasscript.data.data.McStasDataEvent
+======================================
+
+.. currentmodule:: mcstasscript.data.data
+
+.. autoclass:: McStasDataEvent
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~McStasDataEvent.__init__
+      ~McStasDataEvent.get_data_location
+      ~McStasDataEvent.set_data_location
+      ~McStasDataEvent.set_plot_options
+      ~McStasDataEvent.set_title
+      ~McStasDataEvent.set_xlabel
+      ~McStasDataEvent.set_ylabel
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.data.data.McStasMetaData.rst b/docs/source/_autosummary/mcstasscript.data.data.McStasMetaData.rst
new file mode 100644
index 00000000..188a1b20
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.data.data.McStasMetaData.rst
@@ -0,0 +1,28 @@
+mcstasscript.data.data.McStasMetaData
+=====================================
+
+.. currentmodule:: mcstasscript.data.data
+
+.. autoclass:: McStasMetaData
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~McStasMetaData.__init__
+      ~McStasMetaData.add_info
+      ~McStasMetaData.extract_info
+      ~McStasMetaData.set_title
+      ~McStasMetaData.set_xlabel
+      ~McStasMetaData.set_ylabel
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.data.data.McStasPlotOptions.rst b/docs/source/_autosummary/mcstasscript.data.data.McStasPlotOptions.rst
new file mode 100644
index 00000000..b224de26
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.data.data.McStasPlotOptions.rst
@@ -0,0 +1,24 @@
+mcstasscript.data.data.McStasPlotOptions
+========================================
+
+.. currentmodule:: mcstasscript.data.data
+
+.. autoclass:: McStasPlotOptions
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~McStasPlotOptions.__init__
+      ~McStasPlotOptions.set_options
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.data.data.rst b/docs/source/_autosummary/mcstasscript.data.data.rst
new file mode 100644
index 00000000..6c2fabdf
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.data.data.rst
@@ -0,0 +1,35 @@
+mcstasscript.data.data
+======================
+
+.. automodule:: mcstasscript.data.data
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      McStasData
+      McStasDataBinned
+      McStasDataEvent
+      McStasMetaData
+      McStasPlotOptions
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.data.rst b/docs/source/_autosummary/mcstasscript.data.rst
new file mode 100644
index 00000000..cc6e60e1
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.data.rst
@@ -0,0 +1,32 @@
+mcstasscript.data
+=================
+
+.. automodule:: mcstasscript.data
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: custom-module-template.rst
+   :recursive:
+
+   mcstasscript.data.data
+
diff --git a/docs/source/_autosummary/mcstasscript.helper.component_reader.ComponentInfo.rst b/docs/source/_autosummary/mcstasscript.helper.component_reader.ComponentInfo.rst
new file mode 100644
index 00000000..8c5c4145
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.helper.component_reader.ComponentInfo.rst
@@ -0,0 +1,23 @@
+mcstasscript.helper.component\_reader.ComponentInfo
+===================================================
+
+.. currentmodule:: mcstasscript.helper.component_reader
+
+.. autoclass:: ComponentInfo
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~ComponentInfo.__init__
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.helper.component_reader.ComponentReader.rst b/docs/source/_autosummary/mcstasscript.helper.component_reader.ComponentReader.rst
new file mode 100644
index 00000000..f675b66f
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.helper.component_reader.ComponentReader.rst
@@ -0,0 +1,29 @@
+mcstasscript.helper.component\_reader.ComponentReader
+=====================================================
+
+.. currentmodule:: mcstasscript.helper.component_reader
+
+.. autoclass:: ComponentReader
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~ComponentReader.__init__
+      ~ComponentReader.correct_for_brackets
+      ~ComponentReader.load_all_components
+      ~ComponentReader.read_component_file
+      ~ComponentReader.read_name
+      ~ComponentReader.show_categories
+      ~ComponentReader.show_components_in_category
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.helper.component_reader.rst b/docs/source/_autosummary/mcstasscript.helper.component_reader.rst
new file mode 100644
index 00000000..dde2cd53
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.helper.component_reader.rst
@@ -0,0 +1,32 @@
+mcstasscript.helper.component\_reader
+=====================================
+
+.. automodule:: mcstasscript.helper.component_reader
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      ComponentInfo
+      ComponentReader
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.helper.formatting.bcolors.rst b/docs/source/_autosummary/mcstasscript.helper.formatting.bcolors.rst
new file mode 100644
index 00000000..2d0a6bbe
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.helper.formatting.bcolors.rst
@@ -0,0 +1,36 @@
+mcstasscript.helper.formatting.bcolors
+======================================
+
+.. currentmodule:: mcstasscript.helper.formatting
+
+.. autoclass:: bcolors
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~bcolors.__init__
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~bcolors.BOLD
+      ~bcolors.ENDC
+      ~bcolors.FAIL
+      ~bcolors.HEADER
+      ~bcolors.OKBLUE
+      ~bcolors.OKGREEN
+      ~bcolors.UNDERLINE
+      ~bcolors.WARNING
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.helper.formatting.is_legal_filename.rst b/docs/source/_autosummary/mcstasscript.helper.formatting.is_legal_filename.rst
new file mode 100644
index 00000000..5a0a4c19
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.helper.formatting.is_legal_filename.rst
@@ -0,0 +1,6 @@
+mcstasscript.helper.formatting.is\_legal\_filename
+==================================================
+
+.. currentmodule:: mcstasscript.helper.formatting
+
+.. autofunction:: is_legal_filename
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.helper.formatting.is_legal_parameter.rst b/docs/source/_autosummary/mcstasscript.helper.formatting.is_legal_parameter.rst
new file mode 100644
index 00000000..2f16d73c
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.helper.formatting.is_legal_parameter.rst
@@ -0,0 +1,6 @@
+mcstasscript.helper.formatting.is\_legal\_parameter
+===================================================
+
+.. currentmodule:: mcstasscript.helper.formatting
+
+.. autofunction:: is_legal_parameter
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.helper.formatting.rst b/docs/source/_autosummary/mcstasscript.helper.formatting.rst
new file mode 100644
index 00000000..cce17dcd
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.helper.formatting.rst
@@ -0,0 +1,39 @@
+mcstasscript.helper.formatting
+==============================
+
+.. automodule:: mcstasscript.helper.formatting
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      is_legal_filename
+      is_legal_parameter
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      bcolors
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.helper.managed_mcrun.ManagedMcrun.rst b/docs/source/_autosummary/mcstasscript.helper.managed_mcrun.ManagedMcrun.rst
new file mode 100644
index 00000000..f8cf9353
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.helper.managed_mcrun.ManagedMcrun.rst
@@ -0,0 +1,25 @@
+mcstasscript.helper.managed\_mcrun.ManagedMcrun
+===============================================
+
+.. currentmodule:: mcstasscript.helper.managed_mcrun
+
+.. autoclass:: ManagedMcrun
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~ManagedMcrun.__init__
+      ~ManagedMcrun.load_results
+      ~ManagedMcrun.run_simulation
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.helper.managed_mcrun.load_metadata.rst b/docs/source/_autosummary/mcstasscript.helper.managed_mcrun.load_metadata.rst
new file mode 100644
index 00000000..b9847efd
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.helper.managed_mcrun.load_metadata.rst
@@ -0,0 +1,6 @@
+mcstasscript.helper.managed\_mcrun.load\_metadata
+=================================================
+
+.. currentmodule:: mcstasscript.helper.managed_mcrun
+
+.. autofunction:: load_metadata
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.helper.managed_mcrun.load_monitor.rst b/docs/source/_autosummary/mcstasscript.helper.managed_mcrun.load_monitor.rst
new file mode 100644
index 00000000..2a4167a4
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.helper.managed_mcrun.load_monitor.rst
@@ -0,0 +1,6 @@
+mcstasscript.helper.managed\_mcrun.load\_monitor
+================================================
+
+.. currentmodule:: mcstasscript.helper.managed_mcrun
+
+.. autofunction:: load_monitor
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.helper.managed_mcrun.load_results.rst b/docs/source/_autosummary/mcstasscript.helper.managed_mcrun.load_results.rst
new file mode 100644
index 00000000..0b1ef42d
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.helper.managed_mcrun.load_results.rst
@@ -0,0 +1,6 @@
+mcstasscript.helper.managed\_mcrun.load\_results
+================================================
+
+.. currentmodule:: mcstasscript.helper.managed_mcrun
+
+.. autofunction:: load_results
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.helper.managed_mcrun.rst b/docs/source/_autosummary/mcstasscript.helper.managed_mcrun.rst
new file mode 100644
index 00000000..c4574f67
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.helper.managed_mcrun.rst
@@ -0,0 +1,40 @@
+mcstasscript.helper.managed\_mcrun
+==================================
+
+.. automodule:: mcstasscript.helper.managed_mcrun
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      load_metadata
+      load_monitor
+      load_results
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      ManagedMcrun
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.helper.mcstas_objects.Component.rst b/docs/source/_autosummary/mcstasscript.helper.mcstas_objects.Component.rst
new file mode 100644
index 00000000..9a47c11f
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.helper.mcstas_objects.Component.rst
@@ -0,0 +1,44 @@
+mcstasscript.helper.mcstas\_objects.Component
+=============================================
+
+.. currentmodule:: mcstasscript.helper.mcstas_objects
+
+.. autoclass:: Component
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Component.__init__
+      ~Component.append_EXTEND
+      ~Component.print_long
+      ~Component.print_long_deprecated
+      ~Component.print_short
+      ~Component.set_AT
+      ~Component.set_AT_RELATIVE
+      ~Component.set_GROUP
+      ~Component.set_JUMP
+      ~Component.set_RELATIVE
+      ~Component.set_ROTATED
+      ~Component.set_ROTATED_RELATIVE
+      ~Component.set_SPLIT
+      ~Component.set_WHEN
+      ~Component.set_c_code_after
+      ~Component.set_c_code_before
+      ~Component.set_comment
+      ~Component.set_keyword_input
+      ~Component.set_parameters
+      ~Component.show_parameters
+      ~Component.show_parameters_simple
+      ~Component.write_component
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.helper.mcstas_objects.DeclareVariable.rst b/docs/source/_autosummary/mcstasscript.helper.mcstas_objects.DeclareVariable.rst
new file mode 100644
index 00000000..7e5d2526
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.helper.mcstas_objects.DeclareVariable.rst
@@ -0,0 +1,24 @@
+mcstasscript.helper.mcstas\_objects.DeclareVariable
+===================================================
+
+.. currentmodule:: mcstasscript.helper.mcstas_objects
+
+.. autoclass:: DeclareVariable
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~DeclareVariable.__init__
+      ~DeclareVariable.write_line
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.helper.mcstas_objects.ParameterContainer.rst b/docs/source/_autosummary/mcstasscript.helper.mcstas_objects.ParameterContainer.rst
new file mode 100644
index 00000000..0c6a3c6b
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.helper.mcstas_objects.ParameterContainer.rst
@@ -0,0 +1,34 @@
+mcstasscript.helper.mcstas\_objects.ParameterContainer
+======================================================
+
+.. currentmodule:: mcstasscript.helper.mcstas_objects
+
+.. autoclass:: ParameterContainer
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~ParameterContainer.__init__
+      ~ParameterContainer.add
+      ~ParameterContainer.check_list_type
+      ~ParameterContainer.check_type
+      ~ParameterContainer.from_dict
+      ~ParameterContainer.from_json
+      ~ParameterContainer.import_parameters
+      ~ParameterContainer.new_parameter
+      ~ParameterContainer.print_indented
+      ~ParameterContainer.show_parameters
+      ~ParameterContainer.to_dict
+      ~ParameterContainer.to_json
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.helper.mcstas_objects.ParameterVariable.rst b/docs/source/_autosummary/mcstasscript.helper.mcstas_objects.ParameterVariable.rst
new file mode 100644
index 00000000..e12ec0b0
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.helper.mcstas_objects.ParameterVariable.rst
@@ -0,0 +1,38 @@
+mcstasscript.helper.mcstas\_objects.ParameterVariable
+=====================================================
+
+.. currentmodule:: mcstasscript.helper.mcstas_objects
+
+.. autoclass:: ParameterVariable
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~ParameterVariable.__init__
+      ~ParameterVariable.add_interval
+      ~ParameterVariable.add_option
+      ~ParameterVariable.clear_intervals
+      ~ParameterVariable.clear_options
+      ~ParameterVariable.from_dict
+      ~ParameterVariable.is_legal
+      ~ParameterVariable.print_line
+      ~ParameterVariable.print_paramter_constraints
+      ~ParameterVariable.write_parameter
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~ParameterVariable.value
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.helper.mcstas_objects.rst b/docs/source/_autosummary/mcstasscript.helper.mcstas_objects.rst
new file mode 100644
index 00000000..89382169
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.helper.mcstas_objects.rst
@@ -0,0 +1,34 @@
+mcstasscript.helper.mcstas\_objects
+===================================
+
+.. automodule:: mcstasscript.helper.mcstas_objects
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      Component
+      DeclareVariable
+      ParameterContainer
+      ParameterVariable
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.helper.plot_helper.rst b/docs/source/_autosummary/mcstasscript.helper.plot_helper.rst
new file mode 100644
index 00000000..736b3c80
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.helper.plot_helper.rst
@@ -0,0 +1,23 @@
+mcstasscript.helper.plot\_helper
+================================
+
+.. automodule:: mcstasscript.helper.plot_helper
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.helper.rst b/docs/source/_autosummary/mcstasscript.helper.rst
new file mode 100644
index 00000000..d01c6fc2
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.helper.rst
@@ -0,0 +1,36 @@
+mcstasscript.helper
+===================
+
+.. automodule:: mcstasscript.helper
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: custom-module-template.rst
+   :recursive:
+
+   mcstasscript.helper.component_reader
+   mcstasscript.helper.formatting
+   mcstasscript.helper.managed_mcrun
+   mcstasscript.helper.mcstas_objects
+   mcstasscript.helper.plot_helper
+
diff --git a/docs/source/_autosummary/mcstasscript.instr_reader.control.InstrumentReader.rst b/docs/source/_autosummary/mcstasscript.instr_reader.control.InstrumentReader.rst
new file mode 100644
index 00000000..1576bcac
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.instr_reader.control.InstrumentReader.rst
@@ -0,0 +1,26 @@
+mcstasscript.instr\_reader.control.InstrumentReader
+===================================================
+
+.. currentmodule:: mcstasscript.instr_reader.control
+
+.. autoclass:: InstrumentReader
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~InstrumentReader.__init__
+      ~InstrumentReader.add_to_instr
+      ~InstrumentReader.generate_py_version
+      ~InstrumentReader.update_file_name
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.instr_reader.control.rst b/docs/source/_autosummary/mcstasscript.instr_reader.control.rst
new file mode 100644
index 00000000..449be958
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.instr_reader.control.rst
@@ -0,0 +1,31 @@
+mcstasscript.instr\_reader.control
+==================================
+
+.. automodule:: mcstasscript.instr_reader.control
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      InstrumentReader
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.instr_reader.read_declare.DeclareReader.rst b/docs/source/_autosummary/mcstasscript.instr_reader.read_declare.DeclareReader.rst
new file mode 100644
index 00000000..2d30fc1b
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.instr_reader.read_declare.DeclareReader.rst
@@ -0,0 +1,25 @@
+mcstasscript.instr\_reader.read\_declare.DeclareReader
+======================================================
+
+.. currentmodule:: mcstasscript.instr_reader.read_declare
+
+.. autoclass:: DeclareReader
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~DeclareReader.__init__
+      ~DeclareReader.read_declare_line
+      ~DeclareReader.set_instr_name
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.instr_reader.read_declare.rst b/docs/source/_autosummary/mcstasscript.instr_reader.read_declare.rst
new file mode 100644
index 00000000..39190174
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.instr_reader.read_declare.rst
@@ -0,0 +1,31 @@
+mcstasscript.instr\_reader.read\_declare
+========================================
+
+.. automodule:: mcstasscript.instr_reader.read_declare
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      DeclareReader
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.instr_reader.read_definition.DefinitionReader.rst b/docs/source/_autosummary/mcstasscript.instr_reader.read_definition.DefinitionReader.rst
new file mode 100644
index 00000000..63180b97
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.instr_reader.read_definition.DefinitionReader.rst
@@ -0,0 +1,25 @@
+mcstasscript.instr\_reader.read\_definition.DefinitionReader
+============================================================
+
+.. currentmodule:: mcstasscript.instr_reader.read_definition
+
+.. autoclass:: DefinitionReader
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~DefinitionReader.__init__
+      ~DefinitionReader.read_definition_line
+      ~DefinitionReader.set_instr_name
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.instr_reader.read_definition.rst b/docs/source/_autosummary/mcstasscript.instr_reader.read_definition.rst
new file mode 100644
index 00000000..a85a4a4e
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.instr_reader.read_definition.rst
@@ -0,0 +1,31 @@
+mcstasscript.instr\_reader.read\_definition
+===========================================
+
+.. automodule:: mcstasscript.instr_reader.read_definition
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      DefinitionReader
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.instr_reader.read_finally.FinallyReader.rst b/docs/source/_autosummary/mcstasscript.instr_reader.read_finally.FinallyReader.rst
new file mode 100644
index 00000000..82a514fa
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.instr_reader.read_finally.FinallyReader.rst
@@ -0,0 +1,25 @@
+mcstasscript.instr\_reader.read\_finally.FinallyReader
+======================================================
+
+.. currentmodule:: mcstasscript.instr_reader.read_finally
+
+.. autoclass:: FinallyReader
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~FinallyReader.__init__
+      ~FinallyReader.read_finally_line
+      ~FinallyReader.set_instr_name
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.instr_reader.read_finally.rst b/docs/source/_autosummary/mcstasscript.instr_reader.read_finally.rst
new file mode 100644
index 00000000..aa324d55
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.instr_reader.read_finally.rst
@@ -0,0 +1,31 @@
+mcstasscript.instr\_reader.read\_finally
+========================================
+
+.. automodule:: mcstasscript.instr_reader.read_finally
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      FinallyReader
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.instr_reader.read_initialize.InitializeReader.rst b/docs/source/_autosummary/mcstasscript.instr_reader.read_initialize.InitializeReader.rst
new file mode 100644
index 00000000..8a853cd5
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.instr_reader.read_initialize.InitializeReader.rst
@@ -0,0 +1,25 @@
+mcstasscript.instr\_reader.read\_initialize.InitializeReader
+============================================================
+
+.. currentmodule:: mcstasscript.instr_reader.read_initialize
+
+.. autoclass:: InitializeReader
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~InitializeReader.__init__
+      ~InitializeReader.read_initialize_line
+      ~InitializeReader.set_instr_name
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.instr_reader.read_initialize.rst b/docs/source/_autosummary/mcstasscript.instr_reader.read_initialize.rst
new file mode 100644
index 00000000..9ce7df19
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.instr_reader.read_initialize.rst
@@ -0,0 +1,31 @@
+mcstasscript.instr\_reader.read\_initialize
+===========================================
+
+.. automodule:: mcstasscript.instr_reader.read_initialize
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      InitializeReader
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.instr_reader.read_trace.TraceReader.rst b/docs/source/_autosummary/mcstasscript.instr_reader.read_trace.TraceReader.rst
new file mode 100644
index 00000000..96205481
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.instr_reader.read_trace.TraceReader.rst
@@ -0,0 +1,26 @@
+mcstasscript.instr\_reader.read\_trace.TraceReader
+==================================================
+
+.. currentmodule:: mcstasscript.instr_reader.read_trace
+
+.. autoclass:: TraceReader
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~TraceReader.__init__
+      ~TraceReader.read_trace_line
+      ~TraceReader.sanitize_line
+      ~TraceReader.set_instr_name
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.instr_reader.read_trace.rst b/docs/source/_autosummary/mcstasscript.instr_reader.read_trace.rst
new file mode 100644
index 00000000..f7569ae1
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.instr_reader.read_trace.rst
@@ -0,0 +1,31 @@
+mcstasscript.instr\_reader.read\_trace
+======================================
+
+.. automodule:: mcstasscript.instr_reader.read_trace
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      TraceReader
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.instr_reader.rst b/docs/source/_autosummary/mcstasscript.instr_reader.rst
new file mode 100644
index 00000000..9d60864b
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.instr_reader.rst
@@ -0,0 +1,38 @@
+mcstasscript.instr\_reader
+==========================
+
+.. automodule:: mcstasscript.instr_reader
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: custom-module-template.rst
+   :recursive:
+
+   mcstasscript.instr_reader.control
+   mcstasscript.instr_reader.read_declare
+   mcstasscript.instr_reader.read_definition
+   mcstasscript.instr_reader.read_finally
+   mcstasscript.instr_reader.read_initialize
+   mcstasscript.instr_reader.read_trace
+   mcstasscript.instr_reader.util
+
diff --git a/docs/source/_autosummary/mcstasscript.instr_reader.util.SectionReader.rst b/docs/source/_autosummary/mcstasscript.instr_reader.util.SectionReader.rst
new file mode 100644
index 00000000..3572e07c
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.instr_reader.util.SectionReader.rst
@@ -0,0 +1,24 @@
+mcstasscript.instr\_reader.util.SectionReader
+=============================================
+
+.. currentmodule:: mcstasscript.instr_reader.util
+
+.. autoclass:: SectionReader
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~SectionReader.__init__
+      ~SectionReader.set_instr_name
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.instr_reader.util.rst b/docs/source/_autosummary/mcstasscript.instr_reader.util.rst
new file mode 100644
index 00000000..b888ab1b
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.instr_reader.util.rst
@@ -0,0 +1,31 @@
+mcstasscript.instr\_reader.util
+===============================
+
+.. automodule:: mcstasscript.instr_reader.util
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      SectionReader
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.integration_tests.rst b/docs/source/_autosummary/mcstasscript.integration_tests.rst
new file mode 100644
index 00000000..6e92a287
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.integration_tests.rst
@@ -0,0 +1,33 @@
+mcstasscript.integration\_tests
+===============================
+
+.. automodule:: mcstasscript.integration_tests
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: custom-module-template.rst
+   :recursive:
+
+   mcstasscript.integration_tests.test_complex_instrument
+   mcstasscript.integration_tests.test_simple_instrument
+
diff --git a/docs/source/_autosummary/mcstasscript.integration_tests.test_complex_instrument.FakeChange.rst b/docs/source/_autosummary/mcstasscript.integration_tests.test_complex_instrument.FakeChange.rst
new file mode 100644
index 00000000..cfe3a107
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.integration_tests.test_complex_instrument.FakeChange.rst
@@ -0,0 +1,23 @@
+mcstasscript.integration\_tests.test\_complex\_instrument.FakeChange
+====================================================================
+
+.. currentmodule:: mcstasscript.integration_tests.test_complex_instrument
+
+.. autoclass:: FakeChange
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~FakeChange.__init__
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.integration_tests.test_complex_instrument.TestComplexInstrument.rst b/docs/source/_autosummary/mcstasscript.integration_tests.test_complex_instrument.TestComplexInstrument.rst
new file mode 100644
index 00000000..97423d77
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.integration_tests.test_complex_instrument.TestComplexInstrument.rst
@@ -0,0 +1,98 @@
+mcstasscript.integration\_tests.test\_complex\_instrument.TestComplexInstrument
+===============================================================================
+
+.. currentmodule:: mcstasscript.integration_tests.test_complex_instrument
+
+.. autoclass:: TestComplexInstrument
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~TestComplexInstrument.__init__
+      ~TestComplexInstrument.addClassCleanup
+      ~TestComplexInstrument.addCleanup
+      ~TestComplexInstrument.addTypeEqualityFunc
+      ~TestComplexInstrument.assertAlmostEqual
+      ~TestComplexInstrument.assertAlmostEquals
+      ~TestComplexInstrument.assertCountEqual
+      ~TestComplexInstrument.assertDictContainsSubset
+      ~TestComplexInstrument.assertDictEqual
+      ~TestComplexInstrument.assertEqual
+      ~TestComplexInstrument.assertEquals
+      ~TestComplexInstrument.assertFalse
+      ~TestComplexInstrument.assertGreater
+      ~TestComplexInstrument.assertGreaterEqual
+      ~TestComplexInstrument.assertIn
+      ~TestComplexInstrument.assertIs
+      ~TestComplexInstrument.assertIsInstance
+      ~TestComplexInstrument.assertIsNone
+      ~TestComplexInstrument.assertIsNot
+      ~TestComplexInstrument.assertIsNotNone
+      ~TestComplexInstrument.assertLess
+      ~TestComplexInstrument.assertLessEqual
+      ~TestComplexInstrument.assertListEqual
+      ~TestComplexInstrument.assertLogs
+      ~TestComplexInstrument.assertMultiLineEqual
+      ~TestComplexInstrument.assertNotAlmostEqual
+      ~TestComplexInstrument.assertNotAlmostEquals
+      ~TestComplexInstrument.assertNotEqual
+      ~TestComplexInstrument.assertNotEquals
+      ~TestComplexInstrument.assertNotIn
+      ~TestComplexInstrument.assertNotIsInstance
+      ~TestComplexInstrument.assertNotRegex
+      ~TestComplexInstrument.assertNotRegexpMatches
+      ~TestComplexInstrument.assertRaises
+      ~TestComplexInstrument.assertRaisesRegex
+      ~TestComplexInstrument.assertRaisesRegexp
+      ~TestComplexInstrument.assertRegex
+      ~TestComplexInstrument.assertRegexpMatches
+      ~TestComplexInstrument.assertSequenceEqual
+      ~TestComplexInstrument.assertSetEqual
+      ~TestComplexInstrument.assertTrue
+      ~TestComplexInstrument.assertTupleEqual
+      ~TestComplexInstrument.assertWarns
+      ~TestComplexInstrument.assertWarnsRegex
+      ~TestComplexInstrument.assert_
+      ~TestComplexInstrument.countTestCases
+      ~TestComplexInstrument.debug
+      ~TestComplexInstrument.defaultTestResult
+      ~TestComplexInstrument.doClassCleanups
+      ~TestComplexInstrument.doCleanups
+      ~TestComplexInstrument.fail
+      ~TestComplexInstrument.failIf
+      ~TestComplexInstrument.failIfAlmostEqual
+      ~TestComplexInstrument.failIfEqual
+      ~TestComplexInstrument.failUnless
+      ~TestComplexInstrument.failUnlessAlmostEqual
+      ~TestComplexInstrument.failUnlessEqual
+      ~TestComplexInstrument.failUnlessRaises
+      ~TestComplexInstrument.id
+      ~TestComplexInstrument.run
+      ~TestComplexInstrument.setUp
+      ~TestComplexInstrument.setUpClass
+      ~TestComplexInstrument.shortDescription
+      ~TestComplexInstrument.skipTest
+      ~TestComplexInstrument.subTest
+      ~TestComplexInstrument.tearDown
+      ~TestComplexInstrument.tearDownClass
+      ~TestComplexInstrument.test_complex_instrument_interface
+      ~TestComplexInstrument.test_complex_instrument_run
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~TestComplexInstrument.longMessage
+      ~TestComplexInstrument.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.integration_tests.test_complex_instrument.rst b/docs/source/_autosummary/mcstasscript.integration_tests.test_complex_instrument.rst
new file mode 100644
index 00000000..c4278c98
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.integration_tests.test_complex_instrument.rst
@@ -0,0 +1,39 @@
+mcstasscript.integration\_tests.test\_complex\_instrument
+=========================================================
+
+.. automodule:: mcstasscript.integration_tests.test_complex_instrument
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      setup_complex_instrument
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      FakeChange
+      TestComplexInstrument
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.integration_tests.test_complex_instrument.setup_complex_instrument.rst b/docs/source/_autosummary/mcstasscript.integration_tests.test_complex_instrument.setup_complex_instrument.rst
new file mode 100644
index 00000000..91b6982c
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.integration_tests.test_complex_instrument.setup_complex_instrument.rst
@@ -0,0 +1,6 @@
+mcstasscript.integration\_tests.test\_complex\_instrument.setup\_complex\_instrument
+====================================================================================
+
+.. currentmodule:: mcstasscript.integration_tests.test_complex_instrument
+
+.. autofunction:: setup_complex_instrument
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.integration_tests.test_simple_instrument.TestSimpleInstrument.rst b/docs/source/_autosummary/mcstasscript.integration_tests.test_simple_instrument.TestSimpleInstrument.rst
new file mode 100644
index 00000000..09e4dc2a
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.integration_tests.test_simple_instrument.TestSimpleInstrument.rst
@@ -0,0 +1,101 @@
+mcstasscript.integration\_tests.test\_simple\_instrument.TestSimpleInstrument
+=============================================================================
+
+.. currentmodule:: mcstasscript.integration_tests.test_simple_instrument
+
+.. autoclass:: TestSimpleInstrument
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~TestSimpleInstrument.__init__
+      ~TestSimpleInstrument.addClassCleanup
+      ~TestSimpleInstrument.addCleanup
+      ~TestSimpleInstrument.addTypeEqualityFunc
+      ~TestSimpleInstrument.assertAlmostEqual
+      ~TestSimpleInstrument.assertAlmostEquals
+      ~TestSimpleInstrument.assertCountEqual
+      ~TestSimpleInstrument.assertDictContainsSubset
+      ~TestSimpleInstrument.assertDictEqual
+      ~TestSimpleInstrument.assertEqual
+      ~TestSimpleInstrument.assertEquals
+      ~TestSimpleInstrument.assertFalse
+      ~TestSimpleInstrument.assertGreater
+      ~TestSimpleInstrument.assertGreaterEqual
+      ~TestSimpleInstrument.assertIn
+      ~TestSimpleInstrument.assertIs
+      ~TestSimpleInstrument.assertIsInstance
+      ~TestSimpleInstrument.assertIsNone
+      ~TestSimpleInstrument.assertIsNot
+      ~TestSimpleInstrument.assertIsNotNone
+      ~TestSimpleInstrument.assertLess
+      ~TestSimpleInstrument.assertLessEqual
+      ~TestSimpleInstrument.assertListEqual
+      ~TestSimpleInstrument.assertLogs
+      ~TestSimpleInstrument.assertMultiLineEqual
+      ~TestSimpleInstrument.assertNotAlmostEqual
+      ~TestSimpleInstrument.assertNotAlmostEquals
+      ~TestSimpleInstrument.assertNotEqual
+      ~TestSimpleInstrument.assertNotEquals
+      ~TestSimpleInstrument.assertNotIn
+      ~TestSimpleInstrument.assertNotIsInstance
+      ~TestSimpleInstrument.assertNotRegex
+      ~TestSimpleInstrument.assertNotRegexpMatches
+      ~TestSimpleInstrument.assertRaises
+      ~TestSimpleInstrument.assertRaisesRegex
+      ~TestSimpleInstrument.assertRaisesRegexp
+      ~TestSimpleInstrument.assertRegex
+      ~TestSimpleInstrument.assertRegexpMatches
+      ~TestSimpleInstrument.assertSequenceEqual
+      ~TestSimpleInstrument.assertSetEqual
+      ~TestSimpleInstrument.assertTrue
+      ~TestSimpleInstrument.assertTupleEqual
+      ~TestSimpleInstrument.assertWarns
+      ~TestSimpleInstrument.assertWarnsRegex
+      ~TestSimpleInstrument.assert_
+      ~TestSimpleInstrument.countTestCases
+      ~TestSimpleInstrument.debug
+      ~TestSimpleInstrument.defaultTestResult
+      ~TestSimpleInstrument.doClassCleanups
+      ~TestSimpleInstrument.doCleanups
+      ~TestSimpleInstrument.fail
+      ~TestSimpleInstrument.failIf
+      ~TestSimpleInstrument.failIfAlmostEqual
+      ~TestSimpleInstrument.failIfEqual
+      ~TestSimpleInstrument.failUnless
+      ~TestSimpleInstrument.failUnlessAlmostEqual
+      ~TestSimpleInstrument.failUnlessEqual
+      ~TestSimpleInstrument.failUnlessRaises
+      ~TestSimpleInstrument.id
+      ~TestSimpleInstrument.run
+      ~TestSimpleInstrument.setUp
+      ~TestSimpleInstrument.setUpClass
+      ~TestSimpleInstrument.shortDescription
+      ~TestSimpleInstrument.skipTest
+      ~TestSimpleInstrument.subTest
+      ~TestSimpleInstrument.tearDown
+      ~TestSimpleInstrument.tearDownClass
+      ~TestSimpleInstrument.test_simple_instrument
+      ~TestSimpleInstrument.test_simple_instrument_input
+      ~TestSimpleInstrument.test_simple_instrument_mpi
+      ~TestSimpleInstrument.test_slit_instrument
+      ~TestSimpleInstrument.test_slit_moved_instrument
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~TestSimpleInstrument.longMessage
+      ~TestSimpleInstrument.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.integration_tests.test_simple_instrument.rst b/docs/source/_autosummary/mcstasscript.integration_tests.test_simple_instrument.rst
new file mode 100644
index 00000000..886d97e4
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.integration_tests.test_simple_instrument.rst
@@ -0,0 +1,40 @@
+mcstasscript.integration\_tests.test\_simple\_instrument
+========================================================
+
+.. automodule:: mcstasscript.integration_tests.test_simple_instrument
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      setup_simple_instrument
+      setup_simple_instrument_input_path
+      setup_simple_slit_instrument
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      TestSimpleInstrument
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.integration_tests.test_simple_instrument.setup_simple_instrument.rst b/docs/source/_autosummary/mcstasscript.integration_tests.test_simple_instrument.setup_simple_instrument.rst
new file mode 100644
index 00000000..f01243a0
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.integration_tests.test_simple_instrument.setup_simple_instrument.rst
@@ -0,0 +1,6 @@
+mcstasscript.integration\_tests.test\_simple\_instrument.setup\_simple\_instrument
+==================================================================================
+
+.. currentmodule:: mcstasscript.integration_tests.test_simple_instrument
+
+.. autofunction:: setup_simple_instrument
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.integration_tests.test_simple_instrument.setup_simple_instrument_input_path.rst b/docs/source/_autosummary/mcstasscript.integration_tests.test_simple_instrument.setup_simple_instrument_input_path.rst
new file mode 100644
index 00000000..71fa9e01
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.integration_tests.test_simple_instrument.setup_simple_instrument_input_path.rst
@@ -0,0 +1,6 @@
+mcstasscript.integration\_tests.test\_simple\_instrument.setup\_simple\_instrument\_input\_path
+===============================================================================================
+
+.. currentmodule:: mcstasscript.integration_tests.test_simple_instrument
+
+.. autofunction:: setup_simple_instrument_input_path
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.integration_tests.test_simple_instrument.setup_simple_slit_instrument.rst b/docs/source/_autosummary/mcstasscript.integration_tests.test_simple_instrument.setup_simple_slit_instrument.rst
new file mode 100644
index 00000000..3cca106b
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.integration_tests.test_simple_instrument.setup_simple_slit_instrument.rst
@@ -0,0 +1,6 @@
+mcstasscript.integration\_tests.test\_simple\_instrument.setup\_simple\_slit\_instrument
+========================================================================================
+
+.. currentmodule:: mcstasscript.integration_tests.test_simple_instrument
+
+.. autofunction:: setup_simple_slit_instrument
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.interface.functions.Configurator.rst b/docs/source/_autosummary/mcstasscript.interface.functions.Configurator.rst
new file mode 100644
index 00000000..06328851
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.interface.functions.Configurator.rst
@@ -0,0 +1,28 @@
+mcstasscript.interface.functions.Configurator
+=============================================
+
+.. currentmodule:: mcstasscript.interface.functions
+
+.. autoclass:: Configurator
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Configurator.__init__
+      ~Configurator.set_line_length
+      ~Configurator.set_mcrun_path
+      ~Configurator.set_mcstas_path
+      ~Configurator.set_mcxtrace_path
+      ~Configurator.set_mxrun_path
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.interface.functions.load_data.rst b/docs/source/_autosummary/mcstasscript.interface.functions.load_data.rst
new file mode 100644
index 00000000..b9ef5a89
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.interface.functions.load_data.rst
@@ -0,0 +1,6 @@
+mcstasscript.interface.functions.load\_data
+===========================================
+
+.. currentmodule:: mcstasscript.interface.functions
+
+.. autofunction:: load_data
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.interface.functions.load_metadata.rst b/docs/source/_autosummary/mcstasscript.interface.functions.load_metadata.rst
new file mode 100644
index 00000000..5706faaf
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.interface.functions.load_metadata.rst
@@ -0,0 +1,6 @@
+mcstasscript.interface.functions.load\_metadata
+===============================================
+
+.. currentmodule:: mcstasscript.interface.functions
+
+.. autofunction:: load_metadata
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.interface.functions.load_monitor.rst b/docs/source/_autosummary/mcstasscript.interface.functions.load_monitor.rst
new file mode 100644
index 00000000..49cea964
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.interface.functions.load_monitor.rst
@@ -0,0 +1,6 @@
+mcstasscript.interface.functions.load\_monitor
+==============================================
+
+.. currentmodule:: mcstasscript.interface.functions
+
+.. autofunction:: load_monitor
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.interface.functions.name_plot_options.rst b/docs/source/_autosummary/mcstasscript.interface.functions.name_plot_options.rst
new file mode 100644
index 00000000..d16ed3c6
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.interface.functions.name_plot_options.rst
@@ -0,0 +1,6 @@
+mcstasscript.interface.functions.name\_plot\_options
+====================================================
+
+.. currentmodule:: mcstasscript.interface.functions
+
+.. autofunction:: name_plot_options
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.interface.functions.name_search.rst b/docs/source/_autosummary/mcstasscript.interface.functions.name_search.rst
new file mode 100644
index 00000000..467c69ab
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.interface.functions.name_search.rst
@@ -0,0 +1,6 @@
+mcstasscript.interface.functions.name\_search
+=============================================
+
+.. currentmodule:: mcstasscript.interface.functions
+
+.. autofunction:: name_search
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.interface.functions.rst b/docs/source/_autosummary/mcstasscript.interface.functions.rst
new file mode 100644
index 00000000..de157e23
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.interface.functions.rst
@@ -0,0 +1,42 @@
+mcstasscript.interface.functions
+================================
+
+.. automodule:: mcstasscript.interface.functions
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      load_data
+      load_metadata
+      load_monitor
+      name_plot_options
+      name_search
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      Configurator
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.interface.instr.McCode_instr.rst b/docs/source/_autosummary/mcstasscript.interface.instr.McCode_instr.rst
new file mode 100644
index 00000000..a2389736
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.interface.instr.McCode_instr.rst
@@ -0,0 +1,76 @@
+mcstasscript.interface.instr.McCode\_instr
+==========================================
+
+.. currentmodule:: mcstasscript.interface.instr
+
+.. autoclass:: McCode_instr
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~McCode_instr.__init__
+      ~McCode_instr.add_component
+      ~McCode_instr.add_declare_var
+      ~McCode_instr.add_parameter
+      ~McCode_instr.append_component_EXTEND
+      ~McCode_instr.append_declare
+      ~McCode_instr.append_finally
+      ~McCode_instr.append_finally_no_new_line
+      ~McCode_instr.append_initialize
+      ~McCode_instr.append_initialize_no_new_line
+      ~McCode_instr.append_trace
+      ~McCode_instr.append_trace_no_new_line
+      ~McCode_instr.backengine
+      ~McCode_instr.component_help
+      ~McCode_instr.copy_component
+      ~McCode_instr.dump
+      ~McCode_instr.get_component
+      ~McCode_instr.get_interface_data
+      ~McCode_instr.get_last_component
+      ~McCode_instr.interface
+      ~McCode_instr.print_component
+      ~McCode_instr.print_component_short
+      ~McCode_instr.print_components
+      ~McCode_instr.run_from_cli
+      ~McCode_instr.run_full_instrument
+      ~McCode_instr.saveH5
+      ~McCode_instr.set_component_AT
+      ~McCode_instr.set_component_GROUP
+      ~McCode_instr.set_component_JUMP
+      ~McCode_instr.set_component_RELATIVE
+      ~McCode_instr.set_component_ROTATED
+      ~McCode_instr.set_component_SPLIT
+      ~McCode_instr.set_component_WHEN
+      ~McCode_instr.set_component_c_code_after
+      ~McCode_instr.set_component_c_code_before
+      ~McCode_instr.set_component_comment
+      ~McCode_instr.set_component_parameter
+      ~McCode_instr.set_parameters
+      ~McCode_instr.settings
+      ~McCode_instr.settings_string
+      ~McCode_instr.show_components
+      ~McCode_instr.show_instrument
+      ~McCode_instr.show_parameters
+      ~McCode_instr.show_settings
+      ~McCode_instr.show_variables
+      ~McCode_instr.write_c_files
+      ~McCode_instr.write_full_instrument
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~McCode_instr.data
+      ~McCode_instr.parameters
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.interface.instr.McStas_instr.rst b/docs/source/_autosummary/mcstasscript.interface.instr.McStas_instr.rst
new file mode 100644
index 00000000..e63ff84e
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.interface.instr.McStas_instr.rst
@@ -0,0 +1,76 @@
+mcstasscript.interface.instr.McStas\_instr
+==========================================
+
+.. currentmodule:: mcstasscript.interface.instr
+
+.. autoclass:: McStas_instr
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~McStas_instr.__init__
+      ~McStas_instr.add_component
+      ~McStas_instr.add_declare_var
+      ~McStas_instr.add_parameter
+      ~McStas_instr.append_component_EXTEND
+      ~McStas_instr.append_declare
+      ~McStas_instr.append_finally
+      ~McStas_instr.append_finally_no_new_line
+      ~McStas_instr.append_initialize
+      ~McStas_instr.append_initialize_no_new_line
+      ~McStas_instr.append_trace
+      ~McStas_instr.append_trace_no_new_line
+      ~McStas_instr.backengine
+      ~McStas_instr.component_help
+      ~McStas_instr.copy_component
+      ~McStas_instr.dump
+      ~McStas_instr.get_component
+      ~McStas_instr.get_interface_data
+      ~McStas_instr.get_last_component
+      ~McStas_instr.interface
+      ~McStas_instr.print_component
+      ~McStas_instr.print_component_short
+      ~McStas_instr.print_components
+      ~McStas_instr.run_from_cli
+      ~McStas_instr.run_full_instrument
+      ~McStas_instr.saveH5
+      ~McStas_instr.set_component_AT
+      ~McStas_instr.set_component_GROUP
+      ~McStas_instr.set_component_JUMP
+      ~McStas_instr.set_component_RELATIVE
+      ~McStas_instr.set_component_ROTATED
+      ~McStas_instr.set_component_SPLIT
+      ~McStas_instr.set_component_WHEN
+      ~McStas_instr.set_component_c_code_after
+      ~McStas_instr.set_component_c_code_before
+      ~McStas_instr.set_component_comment
+      ~McStas_instr.set_component_parameter
+      ~McStas_instr.set_parameters
+      ~McStas_instr.settings
+      ~McStas_instr.settings_string
+      ~McStas_instr.show_components
+      ~McStas_instr.show_instrument
+      ~McStas_instr.show_parameters
+      ~McStas_instr.show_settings
+      ~McStas_instr.show_variables
+      ~McStas_instr.write_c_files
+      ~McStas_instr.write_full_instrument
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~McStas_instr.data
+      ~McStas_instr.parameters
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.interface.instr.McXtrace_instr.rst b/docs/source/_autosummary/mcstasscript.interface.instr.McXtrace_instr.rst
new file mode 100644
index 00000000..69b3a723
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.interface.instr.McXtrace_instr.rst
@@ -0,0 +1,76 @@
+mcstasscript.interface.instr.McXtrace\_instr
+============================================
+
+.. currentmodule:: mcstasscript.interface.instr
+
+.. autoclass:: McXtrace_instr
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~McXtrace_instr.__init__
+      ~McXtrace_instr.add_component
+      ~McXtrace_instr.add_declare_var
+      ~McXtrace_instr.add_parameter
+      ~McXtrace_instr.append_component_EXTEND
+      ~McXtrace_instr.append_declare
+      ~McXtrace_instr.append_finally
+      ~McXtrace_instr.append_finally_no_new_line
+      ~McXtrace_instr.append_initialize
+      ~McXtrace_instr.append_initialize_no_new_line
+      ~McXtrace_instr.append_trace
+      ~McXtrace_instr.append_trace_no_new_line
+      ~McXtrace_instr.backengine
+      ~McXtrace_instr.component_help
+      ~McXtrace_instr.copy_component
+      ~McXtrace_instr.dump
+      ~McXtrace_instr.get_component
+      ~McXtrace_instr.get_interface_data
+      ~McXtrace_instr.get_last_component
+      ~McXtrace_instr.interface
+      ~McXtrace_instr.print_component
+      ~McXtrace_instr.print_component_short
+      ~McXtrace_instr.print_components
+      ~McXtrace_instr.run_from_cli
+      ~McXtrace_instr.run_full_instrument
+      ~McXtrace_instr.saveH5
+      ~McXtrace_instr.set_component_AT
+      ~McXtrace_instr.set_component_GROUP
+      ~McXtrace_instr.set_component_JUMP
+      ~McXtrace_instr.set_component_RELATIVE
+      ~McXtrace_instr.set_component_ROTATED
+      ~McXtrace_instr.set_component_SPLIT
+      ~McXtrace_instr.set_component_WHEN
+      ~McXtrace_instr.set_component_c_code_after
+      ~McXtrace_instr.set_component_c_code_before
+      ~McXtrace_instr.set_component_comment
+      ~McXtrace_instr.set_component_parameter
+      ~McXtrace_instr.set_parameters
+      ~McXtrace_instr.settings
+      ~McXtrace_instr.settings_string
+      ~McXtrace_instr.show_components
+      ~McXtrace_instr.show_instrument
+      ~McXtrace_instr.show_parameters
+      ~McXtrace_instr.show_settings
+      ~McXtrace_instr.show_variables
+      ~McXtrace_instr.write_c_files
+      ~McXtrace_instr.write_full_instrument
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~McXtrace_instr.data
+      ~McXtrace_instr.parameters
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.interface.instr.rst b/docs/source/_autosummary/mcstasscript.interface.instr.rst
new file mode 100644
index 00000000..02536e4d
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.interface.instr.rst
@@ -0,0 +1,33 @@
+mcstasscript.interface.instr
+============================
+
+.. automodule:: mcstasscript.interface.instr
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      McCode_instr
+      McStas_instr
+      McXtrace_instr
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.interface.plotter.interface.rst b/docs/source/_autosummary/mcstasscript.interface.plotter.interface.rst
new file mode 100644
index 00000000..7c1dd213
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.interface.plotter.interface.rst
@@ -0,0 +1,6 @@
+mcstasscript.interface.plotter.interface
+========================================
+
+.. currentmodule:: mcstasscript.interface.plotter
+
+.. autofunction:: interface
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.interface.plotter.make_animation.rst b/docs/source/_autosummary/mcstasscript.interface.plotter.make_animation.rst
new file mode 100644
index 00000000..51b83934
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.interface.plotter.make_animation.rst
@@ -0,0 +1,6 @@
+mcstasscript.interface.plotter.make\_animation
+==============================================
+
+.. currentmodule:: mcstasscript.interface.plotter
+
+.. autofunction:: make_animation
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.interface.plotter.make_plot.rst b/docs/source/_autosummary/mcstasscript.interface.plotter.make_plot.rst
new file mode 100644
index 00000000..4e9253c7
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.interface.plotter.make_plot.rst
@@ -0,0 +1,6 @@
+mcstasscript.interface.plotter.make\_plot
+=========================================
+
+.. currentmodule:: mcstasscript.interface.plotter
+
+.. autofunction:: make_plot
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.interface.plotter.make_sub_plot.rst b/docs/source/_autosummary/mcstasscript.interface.plotter.make_sub_plot.rst
new file mode 100644
index 00000000..5727a25d
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.interface.plotter.make_sub_plot.rst
@@ -0,0 +1,6 @@
+mcstasscript.interface.plotter.make\_sub\_plot
+==============================================
+
+.. currentmodule:: mcstasscript.interface.plotter
+
+.. autofunction:: make_sub_plot
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.interface.plotter.rst b/docs/source/_autosummary/mcstasscript.interface.plotter.rst
new file mode 100644
index 00000000..a799b32a
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.interface.plotter.rst
@@ -0,0 +1,33 @@
+mcstasscript.interface.plotter
+==============================
+
+.. automodule:: mcstasscript.interface.plotter
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      interface
+      make_animation
+      make_plot
+      make_sub_plot
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.interface.reader.McStas_file.rst b/docs/source/_autosummary/mcstasscript.interface.reader.McStas_file.rst
new file mode 100644
index 00000000..78a3ce68
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.interface.reader.McStas_file.rst
@@ -0,0 +1,25 @@
+mcstasscript.interface.reader.McStas\_file
+==========================================
+
+.. currentmodule:: mcstasscript.interface.reader
+
+.. autoclass:: McStas_file
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~McStas_file.__init__
+      ~McStas_file.add_to_instr
+      ~McStas_file.write_python_file
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.interface.reader.rst b/docs/source/_autosummary/mcstasscript.interface.reader.rst
new file mode 100644
index 00000000..88f65cc5
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.interface.reader.rst
@@ -0,0 +1,31 @@
+mcstasscript.interface.reader
+=============================
+
+.. automodule:: mcstasscript.interface.reader
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      McStas_file
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.interface.rst b/docs/source/_autosummary/mcstasscript.interface.rst
new file mode 100644
index 00000000..048758cc
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.interface.rst
@@ -0,0 +1,35 @@
+mcstasscript.interface
+======================
+
+.. automodule:: mcstasscript.interface
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: custom-module-template.rst
+   :recursive:
+
+   mcstasscript.interface.functions
+   mcstasscript.interface.instr
+   mcstasscript.interface.plotter
+   mcstasscript.interface.reader
+
diff --git a/docs/source/_autosummary/mcstasscript.jb_interface.plot_interface.ColormapDropdown.rst b/docs/source/_autosummary/mcstasscript.jb_interface.plot_interface.ColormapDropdown.rst
new file mode 100644
index 00000000..f79ebc3b
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.jb_interface.plot_interface.ColormapDropdown.rst
@@ -0,0 +1,26 @@
+mcstasscript.jb\_interface.plot\_interface.ColormapDropdown
+===========================================================
+
+.. currentmodule:: mcstasscript.jb_interface.plot_interface
+
+.. autoclass:: ColormapDropdown
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~ColormapDropdown.__init__
+      ~ColormapDropdown.make_widget
+      ~ColormapDropdown.update_cmap
+      ~ColormapDropdown.update_cmap_options
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.jb_interface.plot_interface.LogCheckbox.rst b/docs/source/_autosummary/mcstasscript.jb_interface.plot_interface.LogCheckbox.rst
new file mode 100644
index 00000000..d2ff0f1e
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.jb_interface.plot_interface.LogCheckbox.rst
@@ -0,0 +1,25 @@
+mcstasscript.jb\_interface.plot\_interface.LogCheckbox
+======================================================
+
+.. currentmodule:: mcstasscript.jb_interface.plot_interface
+
+.. autoclass:: LogCheckbox
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~LogCheckbox.__init__
+      ~LogCheckbox.make_widget
+      ~LogCheckbox.update
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.jb_interface.plot_interface.MonitorDropdown.rst b/docs/source/_autosummary/mcstasscript.jb_interface.plot_interface.MonitorDropdown.rst
new file mode 100644
index 00000000..778002c3
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.jb_interface.plot_interface.MonitorDropdown.rst
@@ -0,0 +1,26 @@
+mcstasscript.jb\_interface.plot\_interface.MonitorDropdown
+==========================================================
+
+.. currentmodule:: mcstasscript.jb_interface.plot_interface
+
+.. autoclass:: MonitorDropdown
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~MonitorDropdown.__init__
+      ~MonitorDropdown.make_widget
+      ~MonitorDropdown.set_data
+      ~MonitorDropdown.update
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.jb_interface.plot_interface.OrdersOfMagField.rst b/docs/source/_autosummary/mcstasscript.jb_interface.plot_interface.OrdersOfMagField.rst
new file mode 100644
index 00000000..d03b870a
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.jb_interface.plot_interface.OrdersOfMagField.rst
@@ -0,0 +1,25 @@
+mcstasscript.jb\_interface.plot\_interface.OrdersOfMagField
+===========================================================
+
+.. currentmodule:: mcstasscript.jb_interface.plot_interface
+
+.. autoclass:: OrdersOfMagField
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~OrdersOfMagField.__init__
+      ~OrdersOfMagField.make_widget
+      ~OrdersOfMagField.update
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.jb_interface.plot_interface.PlotInterface.rst b/docs/source/_autosummary/mcstasscript.jb_interface.plot_interface.PlotInterface.rst
new file mode 100644
index 00000000..e05ae3c4
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.jb_interface.plot_interface.PlotInterface.rst
@@ -0,0 +1,31 @@
+mcstasscript.jb\_interface.plot\_interface.PlotInterface
+========================================================
+
+.. currentmodule:: mcstasscript.jb_interface.plot_interface
+
+.. autoclass:: PlotInterface
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~PlotInterface.__init__
+      ~PlotInterface.new_plot
+      ~PlotInterface.set_colormap
+      ~PlotInterface.set_current_monitor
+      ~PlotInterface.set_data
+      ~PlotInterface.set_log_mode
+      ~PlotInterface.set_orders_of_mag
+      ~PlotInterface.show_interface
+      ~PlotInterface.update_plot
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.jb_interface.plot_interface.rst b/docs/source/_autosummary/mcstasscript.jb_interface.plot_interface.rst
new file mode 100644
index 00000000..873329b2
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.jb_interface.plot_interface.rst
@@ -0,0 +1,35 @@
+mcstasscript.jb\_interface.plot\_interface
+==========================================
+
+.. automodule:: mcstasscript.jb_interface.plot_interface
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      ColormapDropdown
+      LogCheckbox
+      MonitorDropdown
+      OrdersOfMagField
+      PlotInterface
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.jb_interface.rst b/docs/source/_autosummary/mcstasscript.jb_interface.rst
new file mode 100644
index 00000000..edd731de
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.jb_interface.rst
@@ -0,0 +1,34 @@
+mcstasscript.jb\_interface
+==========================
+
+.. automodule:: mcstasscript.jb_interface
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: custom-module-template.rst
+   :recursive:
+
+   mcstasscript.jb_interface.plot_interface
+   mcstasscript.jb_interface.simulation_interface
+   mcstasscript.jb_interface.widget_helpers
+
diff --git a/docs/source/_autosummary/mcstasscript.jb_interface.simulation_interface.ParameterWidget.rst b/docs/source/_autosummary/mcstasscript.jb_interface.simulation_interface.ParameterWidget.rst
new file mode 100644
index 00000000..830f8c0b
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.jb_interface.simulation_interface.ParameterWidget.rst
@@ -0,0 +1,25 @@
+mcstasscript.jb\_interface.simulation\_interface.ParameterWidget
+================================================================
+
+.. currentmodule:: mcstasscript.jb_interface.simulation_interface
+
+.. autoclass:: ParameterWidget
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~ParameterWidget.__init__
+      ~ParameterWidget.make_widget
+      ~ParameterWidget.update
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.jb_interface.simulation_interface.SimInterface.rst b/docs/source/_autosummary/mcstasscript.jb_interface.simulation_interface.SimInterface.rst
new file mode 100644
index 00000000..58c4f624
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.jb_interface.simulation_interface.SimInterface.rst
@@ -0,0 +1,34 @@
+mcstasscript.jb\_interface.simulation\_interface.SimInterface
+=============================================================
+
+.. currentmodule:: mcstasscript.jb_interface.simulation_interface
+
+.. autoclass:: SimInterface
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~SimInterface.__init__
+      ~SimInterface.make_live_checkmark
+      ~SimInterface.make_mpi_field
+      ~SimInterface.make_ncount_field
+      ~SimInterface.make_parameter_widgets
+      ~SimInterface.make_progress_bar
+      ~SimInterface.make_run_button
+      ~SimInterface.run_simulation_live
+      ~SimInterface.run_simulation_thread
+      ~SimInterface.show_interface
+      ~SimInterface.update_mpi
+      ~SimInterface.update_ncount
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.jb_interface.simulation_interface.add_data.rst b/docs/source/_autosummary/mcstasscript.jb_interface.simulation_interface.add_data.rst
new file mode 100644
index 00000000..d47dcbef
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.jb_interface.simulation_interface.add_data.rst
@@ -0,0 +1,6 @@
+mcstasscript.jb\_interface.simulation\_interface.add\_data
+==========================================================
+
+.. currentmodule:: mcstasscript.jb_interface.simulation_interface
+
+.. autofunction:: add_data
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.jb_interface.simulation_interface.rst b/docs/source/_autosummary/mcstasscript.jb_interface.simulation_interface.rst
new file mode 100644
index 00000000..36110602
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.jb_interface.simulation_interface.rst
@@ -0,0 +1,39 @@
+mcstasscript.jb\_interface.simulation\_interface
+================================================
+
+.. automodule:: mcstasscript.jb_interface.simulation_interface
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      add_data
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      ParameterWidget
+      SimInterface
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.jb_interface.widget_helpers.HiddenPrints.rst b/docs/source/_autosummary/mcstasscript.jb_interface.widget_helpers.HiddenPrints.rst
new file mode 100644
index 00000000..2b4d5708
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.jb_interface.widget_helpers.HiddenPrints.rst
@@ -0,0 +1,23 @@
+mcstasscript.jb\_interface.widget\_helpers.HiddenPrints
+=======================================================
+
+.. currentmodule:: mcstasscript.jb_interface.widget_helpers
+
+.. autoclass:: HiddenPrints
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~HiddenPrints.__init__
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.jb_interface.widget_helpers.get_parameter_default.rst b/docs/source/_autosummary/mcstasscript.jb_interface.widget_helpers.get_parameter_default.rst
new file mode 100644
index 00000000..a32a7179
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.jb_interface.widget_helpers.get_parameter_default.rst
@@ -0,0 +1,6 @@
+mcstasscript.jb\_interface.widget\_helpers.get\_parameter\_default
+==================================================================
+
+.. currentmodule:: mcstasscript.jb_interface.widget_helpers
+
+.. autofunction:: get_parameter_default
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.jb_interface.widget_helpers.parameter_has_default.rst b/docs/source/_autosummary/mcstasscript.jb_interface.widget_helpers.parameter_has_default.rst
new file mode 100644
index 00000000..236c530b
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.jb_interface.widget_helpers.parameter_has_default.rst
@@ -0,0 +1,6 @@
+mcstasscript.jb\_interface.widget\_helpers.parameter\_has\_default
+==================================================================
+
+.. currentmodule:: mcstasscript.jb_interface.widget_helpers
+
+.. autofunction:: parameter_has_default
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.jb_interface.widget_helpers.rst b/docs/source/_autosummary/mcstasscript.jb_interface.widget_helpers.rst
new file mode 100644
index 00000000..46adefe8
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.jb_interface.widget_helpers.rst
@@ -0,0 +1,39 @@
+mcstasscript.jb\_interface.widget\_helpers
+==========================================
+
+.. automodule:: mcstasscript.jb_interface.widget_helpers
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      get_parameter_default
+      parameter_has_default
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      HiddenPrints
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.rst b/docs/source/_autosummary/mcstasscript.rst
new file mode 100644
index 00000000..4295f961
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.rst
@@ -0,0 +1,38 @@
+mcstasscript
+============
+
+.. automodule:: mcstasscript
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: custom-module-template.rst
+   :recursive:
+
+   mcstasscript.data
+   mcstasscript.helper
+   mcstasscript.instr_reader
+   mcstasscript.integration_tests
+   mcstasscript.interface
+   mcstasscript.jb_interface
+   mcstasscript.tests
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.helpers_for_tests.WorkInTestDir.rst b/docs/source/_autosummary/mcstasscript.tests.helpers_for_tests.WorkInTestDir.rst
new file mode 100644
index 00000000..15539529
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.helpers_for_tests.WorkInTestDir.rst
@@ -0,0 +1,23 @@
+mcstasscript.tests.helpers\_for\_tests.WorkInTestDir
+====================================================
+
+.. currentmodule:: mcstasscript.tests.helpers_for_tests
+
+.. autoclass:: WorkInTestDir
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~WorkInTestDir.__init__
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.helpers_for_tests.rst b/docs/source/_autosummary/mcstasscript.tests.helpers_for_tests.rst
new file mode 100644
index 00000000..43572439
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.helpers_for_tests.rst
@@ -0,0 +1,31 @@
+mcstasscript.tests.helpers\_for\_tests
+======================================
+
+.. automodule:: mcstasscript.tests.helpers_for_tests
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      WorkInTestDir
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.rst b/docs/source/_autosummary/mcstasscript.tests.rst
new file mode 100644
index 00000000..6c41dc46
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.rst
@@ -0,0 +1,52 @@
+mcstasscript.tests
+==================
+
+.. automodule:: mcstasscript.tests
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: custom-module-template.rst
+   :recursive:
+
+   mcstasscript.tests.helpers_for_tests
+   mcstasscript.tests.test_ComponentReader
+   mcstasscript.tests.test_Configurator
+   mcstasscript.tests.test_Instr
+   mcstasscript.tests.test_Instr_reader
+   mcstasscript.tests.test_ManagedMcrun
+   mcstasscript.tests.test_McStasData
+   mcstasscript.tests.test_McStasMetaData
+   mcstasscript.tests.test_McStasPlotOptions
+   mcstasscript.tests.test_Plotter
+   mcstasscript.tests.test_add_data
+   mcstasscript.tests.test_component
+   mcstasscript.tests.test_declare_variable
+   mcstasscript.tests.test_dump_and_load
+   mcstasscript.tests.test_formatting
+   mcstasscript.tests.test_functions
+   mcstasscript.tests.test_parameter_variable
+   mcstasscript.tests.test_plot_interface
+   mcstasscript.tests.test_simulation_interface
+   mcstasscript.tests.test_widget_helpers
+   mcstasscript.tests.utilities
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_ComponentReader.TestComponentReader.rst b/docs/source/_autosummary/mcstasscript.tests.test_ComponentReader.TestComponentReader.rst
new file mode 100644
index 00000000..35f0ebe2
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_ComponentReader.TestComponentReader.rst
@@ -0,0 +1,115 @@
+mcstasscript.tests.test\_ComponentReader.TestComponentReader
+============================================================
+
+.. currentmodule:: mcstasscript.tests.test_ComponentReader
+
+.. autoclass:: TestComponentReader
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~TestComponentReader.__init__
+      ~TestComponentReader.addClassCleanup
+      ~TestComponentReader.addCleanup
+      ~TestComponentReader.addTypeEqualityFunc
+      ~TestComponentReader.assertAlmostEqual
+      ~TestComponentReader.assertAlmostEquals
+      ~TestComponentReader.assertCountEqual
+      ~TestComponentReader.assertDictContainsSubset
+      ~TestComponentReader.assertDictEqual
+      ~TestComponentReader.assertEqual
+      ~TestComponentReader.assertEquals
+      ~TestComponentReader.assertFalse
+      ~TestComponentReader.assertGreater
+      ~TestComponentReader.assertGreaterEqual
+      ~TestComponentReader.assertIn
+      ~TestComponentReader.assertIs
+      ~TestComponentReader.assertIsInstance
+      ~TestComponentReader.assertIsNone
+      ~TestComponentReader.assertIsNot
+      ~TestComponentReader.assertIsNotNone
+      ~TestComponentReader.assertLess
+      ~TestComponentReader.assertLessEqual
+      ~TestComponentReader.assertListEqual
+      ~TestComponentReader.assertLogs
+      ~TestComponentReader.assertMultiLineEqual
+      ~TestComponentReader.assertNotAlmostEqual
+      ~TestComponentReader.assertNotAlmostEquals
+      ~TestComponentReader.assertNotEqual
+      ~TestComponentReader.assertNotEquals
+      ~TestComponentReader.assertNotIn
+      ~TestComponentReader.assertNotIsInstance
+      ~TestComponentReader.assertNotRegex
+      ~TestComponentReader.assertNotRegexpMatches
+      ~TestComponentReader.assertRaises
+      ~TestComponentReader.assertRaisesRegex
+      ~TestComponentReader.assertRaisesRegexp
+      ~TestComponentReader.assertRegex
+      ~TestComponentReader.assertRegexpMatches
+      ~TestComponentReader.assertSequenceEqual
+      ~TestComponentReader.assertSetEqual
+      ~TestComponentReader.assertTrue
+      ~TestComponentReader.assertTupleEqual
+      ~TestComponentReader.assertWarns
+      ~TestComponentReader.assertWarnsRegex
+      ~TestComponentReader.assert_
+      ~TestComponentReader.countTestCases
+      ~TestComponentReader.debug
+      ~TestComponentReader.defaultTestResult
+      ~TestComponentReader.doClassCleanups
+      ~TestComponentReader.doCleanups
+      ~TestComponentReader.fail
+      ~TestComponentReader.failIf
+      ~TestComponentReader.failIfAlmostEqual
+      ~TestComponentReader.failIfEqual
+      ~TestComponentReader.failUnless
+      ~TestComponentReader.failUnlessAlmostEqual
+      ~TestComponentReader.failUnlessEqual
+      ~TestComponentReader.failUnlessRaises
+      ~TestComponentReader.id
+      ~TestComponentReader.run
+      ~TestComponentReader.setUp
+      ~TestComponentReader.setUpClass
+      ~TestComponentReader.shortDescription
+      ~TestComponentReader.skipTest
+      ~TestComponentReader.subTest
+      ~TestComponentReader.tearDown
+      ~TestComponentReader.tearDownClass
+      ~TestComponentReader.test_ComponentReader_find_components_categories
+      ~TestComponentReader.test_ComponentReader_find_components_names
+      ~TestComponentReader.test_ComponentReader_init_categories
+      ~TestComponentReader.test_ComponentReader_init_component_paths
+      ~TestComponentReader.test_ComponentReader_init_component_paths_input
+      ~TestComponentReader.test_ComponentReader_init_filenames
+      ~TestComponentReader.test_ComponentReader_init_overwrite_message
+      ~TestComponentReader.test_ComponentReader_init_overwrite_message_input
+      ~TestComponentReader.test_ComponentReader_load_all_components
+      ~TestComponentReader.test_ComponentReader_read_component_category
+      ~TestComponentReader.test_ComponentReader_read_component_int
+      ~TestComponentReader.test_ComponentReader_read_component_required
+      ~TestComponentReader.test_ComponentReader_read_component_standard
+      ~TestComponentReader.test_ComponentReader_read_component_string
+      ~TestComponentReader.test_ComponentReader_read_name_error
+      ~TestComponentReader.test_ComponentReader_read_name_success
+      ~TestComponentReader.test_ComponentReader_show_categories
+      ~TestComponentReader.test_ComponentReader_show_categories_ordered
+      ~TestComponentReader.test_ComponentReader_show_components_short
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~TestComponentReader.longMessage
+      ~TestComponentReader.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_ComponentReader.rst b/docs/source/_autosummary/mcstasscript.tests.test_ComponentReader.rst
new file mode 100644
index 00000000..5136d839
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_ComponentReader.rst
@@ -0,0 +1,39 @@
+mcstasscript.tests.test\_ComponentReader
+========================================
+
+.. automodule:: mcstasscript.tests.test_ComponentReader
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      setup_component_reader
+      setup_component_reader_input_path
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      TestComponentReader
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_ComponentReader.setup_component_reader.rst b/docs/source/_autosummary/mcstasscript.tests.test_ComponentReader.setup_component_reader.rst
new file mode 100644
index 00000000..36c09c77
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_ComponentReader.setup_component_reader.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_ComponentReader.setup\_component\_reader
+=================================================================
+
+.. currentmodule:: mcstasscript.tests.test_ComponentReader
+
+.. autofunction:: setup_component_reader
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_ComponentReader.setup_component_reader_input_path.rst b/docs/source/_autosummary/mcstasscript.tests.test_ComponentReader.setup_component_reader_input_path.rst
new file mode 100644
index 00000000..90dfc13c
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_ComponentReader.setup_component_reader_input_path.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_ComponentReader.setup\_component\_reader\_input\_path
+==============================================================================
+
+.. currentmodule:: mcstasscript.tests.test_ComponentReader
+
+.. autofunction:: setup_component_reader_input_path
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Configurator.TestConfigurator.rst b/docs/source/_autosummary/mcstasscript.tests.test_Configurator.TestConfigurator.rst
new file mode 100644
index 00000000..27632f5e
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Configurator.TestConfigurator.rst
@@ -0,0 +1,104 @@
+mcstasscript.tests.test\_Configurator.TestConfigurator
+======================================================
+
+.. currentmodule:: mcstasscript.tests.test_Configurator
+
+.. autoclass:: TestConfigurator
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~TestConfigurator.__init__
+      ~TestConfigurator.addClassCleanup
+      ~TestConfigurator.addCleanup
+      ~TestConfigurator.addTypeEqualityFunc
+      ~TestConfigurator.assertAlmostEqual
+      ~TestConfigurator.assertAlmostEquals
+      ~TestConfigurator.assertCountEqual
+      ~TestConfigurator.assertDictContainsSubset
+      ~TestConfigurator.assertDictEqual
+      ~TestConfigurator.assertEqual
+      ~TestConfigurator.assertEquals
+      ~TestConfigurator.assertFalse
+      ~TestConfigurator.assertGreater
+      ~TestConfigurator.assertGreaterEqual
+      ~TestConfigurator.assertIn
+      ~TestConfigurator.assertIs
+      ~TestConfigurator.assertIsInstance
+      ~TestConfigurator.assertIsNone
+      ~TestConfigurator.assertIsNot
+      ~TestConfigurator.assertIsNotNone
+      ~TestConfigurator.assertLess
+      ~TestConfigurator.assertLessEqual
+      ~TestConfigurator.assertListEqual
+      ~TestConfigurator.assertLogs
+      ~TestConfigurator.assertMultiLineEqual
+      ~TestConfigurator.assertNotAlmostEqual
+      ~TestConfigurator.assertNotAlmostEquals
+      ~TestConfigurator.assertNotEqual
+      ~TestConfigurator.assertNotEquals
+      ~TestConfigurator.assertNotIn
+      ~TestConfigurator.assertNotIsInstance
+      ~TestConfigurator.assertNotRegex
+      ~TestConfigurator.assertNotRegexpMatches
+      ~TestConfigurator.assertRaises
+      ~TestConfigurator.assertRaisesRegex
+      ~TestConfigurator.assertRaisesRegexp
+      ~TestConfigurator.assertRegex
+      ~TestConfigurator.assertRegexpMatches
+      ~TestConfigurator.assertSequenceEqual
+      ~TestConfigurator.assertSetEqual
+      ~TestConfigurator.assertTrue
+      ~TestConfigurator.assertTupleEqual
+      ~TestConfigurator.assertWarns
+      ~TestConfigurator.assertWarnsRegex
+      ~TestConfigurator.assert_
+      ~TestConfigurator.countTestCases
+      ~TestConfigurator.debug
+      ~TestConfigurator.defaultTestResult
+      ~TestConfigurator.doClassCleanups
+      ~TestConfigurator.doCleanups
+      ~TestConfigurator.fail
+      ~TestConfigurator.failIf
+      ~TestConfigurator.failIfAlmostEqual
+      ~TestConfigurator.failIfEqual
+      ~TestConfigurator.failUnless
+      ~TestConfigurator.failUnlessAlmostEqual
+      ~TestConfigurator.failUnlessEqual
+      ~TestConfigurator.failUnlessRaises
+      ~TestConfigurator.id
+      ~TestConfigurator.run
+      ~TestConfigurator.setUp
+      ~TestConfigurator.setUpClass
+      ~TestConfigurator.shortDescription
+      ~TestConfigurator.skipTest
+      ~TestConfigurator.subTest
+      ~TestConfigurator.tearDown
+      ~TestConfigurator.tearDownClass
+      ~TestConfigurator.test_default_config
+      ~TestConfigurator.test_set_line_length
+      ~TestConfigurator.test_set_mcrun_path
+      ~TestConfigurator.test_set_mcstas_path
+      ~TestConfigurator.test_set_mcxtrace_path
+      ~TestConfigurator.test_set_mxrun_path
+      ~TestConfigurator.test_simple_initialize
+      ~TestConfigurator.test_yaml_write
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~TestConfigurator.longMessage
+      ~TestConfigurator.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Configurator.rst b/docs/source/_autosummary/mcstasscript.tests.test_Configurator.rst
new file mode 100644
index 00000000..0a5e435c
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Configurator.rst
@@ -0,0 +1,39 @@
+mcstasscript.tests.test\_Configurator
+=====================================
+
+.. automodule:: mcstasscript.tests.test_Configurator
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      setup_configurator
+      setup_expected_file
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      TestConfigurator
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Configurator.setup_configurator.rst b/docs/source/_autosummary/mcstasscript.tests.test_Configurator.setup_configurator.rst
new file mode 100644
index 00000000..fbc4687f
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Configurator.setup_configurator.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Configurator.setup\_configurator
+=========================================================
+
+.. currentmodule:: mcstasscript.tests.test_Configurator
+
+.. autofunction:: setup_configurator
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Configurator.setup_expected_file.rst b/docs/source/_autosummary/mcstasscript.tests.test_Configurator.setup_expected_file.rst
new file mode 100644
index 00000000..28cbbe61
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Configurator.setup_expected_file.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Configurator.setup\_expected\_file
+===========================================================
+
+.. currentmodule:: mcstasscript.tests.test_Configurator
+
+.. autofunction:: setup_expected_file
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr.TestMcStas_instr.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr.TestMcStas_instr.rst
new file mode 100644
index 00000000..565aa1c4
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr.TestMcStas_instr.rst
@@ -0,0 +1,163 @@
+mcstasscript.tests.test\_Instr.TestMcStas\_instr
+================================================
+
+.. currentmodule:: mcstasscript.tests.test_Instr
+
+.. autoclass:: TestMcStas_instr
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~TestMcStas_instr.__init__
+      ~TestMcStas_instr.addClassCleanup
+      ~TestMcStas_instr.addCleanup
+      ~TestMcStas_instr.addTypeEqualityFunc
+      ~TestMcStas_instr.assertAlmostEqual
+      ~TestMcStas_instr.assertAlmostEquals
+      ~TestMcStas_instr.assertCountEqual
+      ~TestMcStas_instr.assertDictContainsSubset
+      ~TestMcStas_instr.assertDictEqual
+      ~TestMcStas_instr.assertEqual
+      ~TestMcStas_instr.assertEquals
+      ~TestMcStas_instr.assertFalse
+      ~TestMcStas_instr.assertGreater
+      ~TestMcStas_instr.assertGreaterEqual
+      ~TestMcStas_instr.assertIn
+      ~TestMcStas_instr.assertIs
+      ~TestMcStas_instr.assertIsInstance
+      ~TestMcStas_instr.assertIsNone
+      ~TestMcStas_instr.assertIsNot
+      ~TestMcStas_instr.assertIsNotNone
+      ~TestMcStas_instr.assertLess
+      ~TestMcStas_instr.assertLessEqual
+      ~TestMcStas_instr.assertListEqual
+      ~TestMcStas_instr.assertLogs
+      ~TestMcStas_instr.assertMultiLineEqual
+      ~TestMcStas_instr.assertNotAlmostEqual
+      ~TestMcStas_instr.assertNotAlmostEquals
+      ~TestMcStas_instr.assertNotEqual
+      ~TestMcStas_instr.assertNotEquals
+      ~TestMcStas_instr.assertNotIn
+      ~TestMcStas_instr.assertNotIsInstance
+      ~TestMcStas_instr.assertNotRegex
+      ~TestMcStas_instr.assertNotRegexpMatches
+      ~TestMcStas_instr.assertRaises
+      ~TestMcStas_instr.assertRaisesRegex
+      ~TestMcStas_instr.assertRaisesRegexp
+      ~TestMcStas_instr.assertRegex
+      ~TestMcStas_instr.assertRegexpMatches
+      ~TestMcStas_instr.assertSequenceEqual
+      ~TestMcStas_instr.assertSetEqual
+      ~TestMcStas_instr.assertTrue
+      ~TestMcStas_instr.assertTupleEqual
+      ~TestMcStas_instr.assertWarns
+      ~TestMcStas_instr.assertWarnsRegex
+      ~TestMcStas_instr.assert_
+      ~TestMcStas_instr.countTestCases
+      ~TestMcStas_instr.debug
+      ~TestMcStas_instr.defaultTestResult
+      ~TestMcStas_instr.doClassCleanups
+      ~TestMcStas_instr.doCleanups
+      ~TestMcStas_instr.fail
+      ~TestMcStas_instr.failIf
+      ~TestMcStas_instr.failIfAlmostEqual
+      ~TestMcStas_instr.failIfEqual
+      ~TestMcStas_instr.failUnless
+      ~TestMcStas_instr.failUnlessAlmostEqual
+      ~TestMcStas_instr.failUnlessEqual
+      ~TestMcStas_instr.failUnlessRaises
+      ~TestMcStas_instr.id
+      ~TestMcStas_instr.run
+      ~TestMcStas_instr.setUp
+      ~TestMcStas_instr.setUpClass
+      ~TestMcStas_instr.shortDescription
+      ~TestMcStas_instr.skipTest
+      ~TestMcStas_instr.subTest
+      ~TestMcStas_instr.tearDown
+      ~TestMcStas_instr.tearDownClass
+      ~TestMcStas_instr.test_add_component_simple
+      ~TestMcStas_instr.test_add_component_simple_after
+      ~TestMcStas_instr.test_add_component_simple_after_error
+      ~TestMcStas_instr.test_add_component_simple_before
+      ~TestMcStas_instr.test_add_component_simple_before_error
+      ~TestMcStas_instr.test_add_component_simple_double_naming_error
+      ~TestMcStas_instr.test_add_component_simple_keyword
+      ~TestMcStas_instr.test_append_component_EXTEND
+      ~TestMcStas_instr.test_complex_initialize
+      ~TestMcStas_instr.test_component_help
+      ~TestMcStas_instr.test_copy_component_keywords
+      ~TestMcStas_instr.test_copy_component_simple
+      ~TestMcStas_instr.test_copy_component_simple_fail
+      ~TestMcStas_instr.test_copy_component_simple_object
+      ~TestMcStas_instr.test_create_component_instance_complex
+      ~TestMcStas_instr.test_create_component_instance_simple
+      ~TestMcStas_instr.test_get_component_simple
+      ~TestMcStas_instr.test_get_component_simple_error
+      ~TestMcStas_instr.test_get_last_component_simple
+      ~TestMcStas_instr.test_load_config_file
+      ~TestMcStas_instr.test_load_config_file_x_ray
+      ~TestMcStas_instr.test_print_component
+      ~TestMcStas_instr.test_print_component_short
+      ~TestMcStas_instr.test_print_components_complex
+      ~TestMcStas_instr.test_print_components_complex_2lines
+      ~TestMcStas_instr.test_print_components_complex_3lines
+      ~TestMcStas_instr.test_print_components_simple
+      ~TestMcStas_instr.test_run_backengine_basic
+      ~TestMcStas_instr.test_run_backengine_existing_folder
+      ~TestMcStas_instr.test_run_full_instrument_complex
+      ~TestMcStas_instr.test_run_full_instrument_junk_par_error
+      ~TestMcStas_instr.test_run_full_instrument_overwrite_default
+      ~TestMcStas_instr.test_run_full_instrument_required_par_error
+      ~TestMcStas_instr.test_run_full_instrument_x_ray_basic
+      ~TestMcStas_instr.test_set_c_code_after
+      ~TestMcStas_instr.test_set_c_code_before
+      ~TestMcStas_instr.test_set_component_AT
+      ~TestMcStas_instr.test_set_component_GROUP
+      ~TestMcStas_instr.test_set_component_JUMP
+      ~TestMcStas_instr.test_set_component_RELATIVE
+      ~TestMcStas_instr.test_set_component_ROTATED
+      ~TestMcStas_instr.test_set_component_SPLIT
+      ~TestMcStas_instr.test_set_component_WHEN
+      ~TestMcStas_instr.test_set_component_comment
+      ~TestMcStas_instr.test_set_component_parameter
+      ~TestMcStas_instr.test_set_component_parameter_error
+      ~TestMcStas_instr.test_show_components_folder
+      ~TestMcStas_instr.test_show_components_input_path_custom
+      ~TestMcStas_instr.test_show_components_input_path_simple
+      ~TestMcStas_instr.test_show_components_simple
+      ~TestMcStas_instr.test_show_instrument_basic
+      ~TestMcStas_instr.test_show_parameters
+      ~TestMcStas_instr.test_show_parameters_line_break
+      ~TestMcStas_instr.test_simple_add_declare_parameter
+      ~TestMcStas_instr.test_simple_add_parameter
+      ~TestMcStas_instr.test_simple_append_declare
+      ~TestMcStas_instr.test_simple_append_declare_var_mix
+      ~TestMcStas_instr.test_simple_append_finally
+      ~TestMcStas_instr.test_simple_append_finally_no_new_line
+      ~TestMcStas_instr.test_simple_append_initialize
+      ~TestMcStas_instr.test_simple_append_initialize_no_new_line
+      ~TestMcStas_instr.test_simple_append_trace
+      ~TestMcStas_instr.test_simple_append_trace_no_new_line
+      ~TestMcStas_instr.test_simple_initialize
+      ~TestMcStas_instr.test_write_c_files_simple
+      ~TestMcStas_instr.test_write_full_instrument_simple
+      ~TestMcStas_instr.test_x_ray_run_full_instrument_basic
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~TestMcStas_instr.longMessage
+      ~TestMcStas_instr.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr.rst
new file mode 100644
index 00000000..77a32905
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr.rst
@@ -0,0 +1,50 @@
+mcstasscript.tests.test\_Instr
+==============================
+
+.. automodule:: mcstasscript.tests.test_Instr
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      setup_instr_no_path
+      setup_instr_root_path
+      setup_instr_with_input_path
+      setup_instr_with_input_path_relative
+      setup_instr_with_path
+      setup_populated_instr
+      setup_populated_instr_with_dummy_path
+      setup_populated_with_some_options_instr
+      setup_populated_x_ray_instr
+      setup_populated_x_ray_instr_with_dummy_path
+      setup_x_ray_instr_no_path
+      setup_x_ray_instr_root_path
+      setup_x_ray_instr_with_path
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      TestMcStas_instr
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_instr_no_path.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_instr_no_path.rst
new file mode 100644
index 00000000..21ef5103
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_instr_no_path.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Instr.setup\_instr\_no\_path
+=====================================================
+
+.. currentmodule:: mcstasscript.tests.test_Instr
+
+.. autofunction:: setup_instr_no_path
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_instr_root_path.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_instr_root_path.rst
new file mode 100644
index 00000000..7f0b149c
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_instr_root_path.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Instr.setup\_instr\_root\_path
+=======================================================
+
+.. currentmodule:: mcstasscript.tests.test_Instr
+
+.. autofunction:: setup_instr_root_path
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_instr_with_input_path.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_instr_with_input_path.rst
new file mode 100644
index 00000000..03e734b0
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_instr_with_input_path.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Instr.setup\_instr\_with\_input\_path
+==============================================================
+
+.. currentmodule:: mcstasscript.tests.test_Instr
+
+.. autofunction:: setup_instr_with_input_path
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_instr_with_input_path_relative.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_instr_with_input_path_relative.rst
new file mode 100644
index 00000000..568e549f
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_instr_with_input_path_relative.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Instr.setup\_instr\_with\_input\_path\_relative
+========================================================================
+
+.. currentmodule:: mcstasscript.tests.test_Instr
+
+.. autofunction:: setup_instr_with_input_path_relative
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_instr_with_path.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_instr_with_path.rst
new file mode 100644
index 00000000..e59bb9fb
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_instr_with_path.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Instr.setup\_instr\_with\_path
+=======================================================
+
+.. currentmodule:: mcstasscript.tests.test_Instr
+
+.. autofunction:: setup_instr_with_path
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_populated_instr.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_populated_instr.rst
new file mode 100644
index 00000000..92c82a2c
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_populated_instr.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Instr.setup\_populated\_instr
+======================================================
+
+.. currentmodule:: mcstasscript.tests.test_Instr
+
+.. autofunction:: setup_populated_instr
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_populated_instr_with_dummy_path.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_populated_instr_with_dummy_path.rst
new file mode 100644
index 00000000..77c8a8fb
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_populated_instr_with_dummy_path.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Instr.setup\_populated\_instr\_with\_dummy\_path
+=========================================================================
+
+.. currentmodule:: mcstasscript.tests.test_Instr
+
+.. autofunction:: setup_populated_instr_with_dummy_path
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_populated_with_some_options_instr.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_populated_with_some_options_instr.rst
new file mode 100644
index 00000000..3cd026f6
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_populated_with_some_options_instr.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Instr.setup\_populated\_with\_some\_options\_instr
+===========================================================================
+
+.. currentmodule:: mcstasscript.tests.test_Instr
+
+.. autofunction:: setup_populated_with_some_options_instr
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_populated_x_ray_instr.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_populated_x_ray_instr.rst
new file mode 100644
index 00000000..28d0e459
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_populated_x_ray_instr.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Instr.setup\_populated\_x\_ray\_instr
+==============================================================
+
+.. currentmodule:: mcstasscript.tests.test_Instr
+
+.. autofunction:: setup_populated_x_ray_instr
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_populated_x_ray_instr_with_dummy_path.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_populated_x_ray_instr_with_dummy_path.rst
new file mode 100644
index 00000000..7f30098d
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_populated_x_ray_instr_with_dummy_path.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Instr.setup\_populated\_x\_ray\_instr\_with\_dummy\_path
+=================================================================================
+
+.. currentmodule:: mcstasscript.tests.test_Instr
+
+.. autofunction:: setup_populated_x_ray_instr_with_dummy_path
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_x_ray_instr_no_path.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_x_ray_instr_no_path.rst
new file mode 100644
index 00000000..f4140434
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_x_ray_instr_no_path.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Instr.setup\_x\_ray\_instr\_no\_path
+=============================================================
+
+.. currentmodule:: mcstasscript.tests.test_Instr
+
+.. autofunction:: setup_x_ray_instr_no_path
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_x_ray_instr_root_path.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_x_ray_instr_root_path.rst
new file mode 100644
index 00000000..ba4c5ec3
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_x_ray_instr_root_path.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Instr.setup\_x\_ray\_instr\_root\_path
+===============================================================
+
+.. currentmodule:: mcstasscript.tests.test_Instr
+
+.. autofunction:: setup_x_ray_instr_root_path
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_x_ray_instr_with_path.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_x_ray_instr_with_path.rst
new file mode 100644
index 00000000..22d13c0c
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr.setup_x_ray_instr_with_path.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Instr.setup\_x\_ray\_instr\_with\_path
+===============================================================
+
+.. currentmodule:: mcstasscript.tests.test_Instr
+
+.. autofunction:: setup_x_ray_instr_with_path
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.TestInstrReader.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.TestInstrReader.rst
new file mode 100644
index 00000000..3a55f03a
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.TestInstrReader.rst
@@ -0,0 +1,111 @@
+mcstasscript.tests.test\_Instr\_reader.TestInstrReader
+======================================================
+
+.. currentmodule:: mcstasscript.tests.test_Instr_reader
+
+.. autoclass:: TestInstrReader
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~TestInstrReader.__init__
+      ~TestInstrReader.addClassCleanup
+      ~TestInstrReader.addCleanup
+      ~TestInstrReader.addTypeEqualityFunc
+      ~TestInstrReader.assertAlmostEqual
+      ~TestInstrReader.assertAlmostEquals
+      ~TestInstrReader.assertCountEqual
+      ~TestInstrReader.assertDictContainsSubset
+      ~TestInstrReader.assertDictEqual
+      ~TestInstrReader.assertEqual
+      ~TestInstrReader.assertEquals
+      ~TestInstrReader.assertFalse
+      ~TestInstrReader.assertGreater
+      ~TestInstrReader.assertGreaterEqual
+      ~TestInstrReader.assertIn
+      ~TestInstrReader.assertIs
+      ~TestInstrReader.assertIsInstance
+      ~TestInstrReader.assertIsNone
+      ~TestInstrReader.assertIsNot
+      ~TestInstrReader.assertIsNotNone
+      ~TestInstrReader.assertLess
+      ~TestInstrReader.assertLessEqual
+      ~TestInstrReader.assertListEqual
+      ~TestInstrReader.assertLogs
+      ~TestInstrReader.assertMultiLineEqual
+      ~TestInstrReader.assertNotAlmostEqual
+      ~TestInstrReader.assertNotAlmostEquals
+      ~TestInstrReader.assertNotEqual
+      ~TestInstrReader.assertNotEquals
+      ~TestInstrReader.assertNotIn
+      ~TestInstrReader.assertNotIsInstance
+      ~TestInstrReader.assertNotRegex
+      ~TestInstrReader.assertNotRegexpMatches
+      ~TestInstrReader.assertRaises
+      ~TestInstrReader.assertRaisesRegex
+      ~TestInstrReader.assertRaisesRegexp
+      ~TestInstrReader.assertRegex
+      ~TestInstrReader.assertRegexpMatches
+      ~TestInstrReader.assertSequenceEqual
+      ~TestInstrReader.assertSetEqual
+      ~TestInstrReader.assertTrue
+      ~TestInstrReader.assertTupleEqual
+      ~TestInstrReader.assertWarns
+      ~TestInstrReader.assertWarnsRegex
+      ~TestInstrReader.assert_
+      ~TestInstrReader.countTestCases
+      ~TestInstrReader.debug
+      ~TestInstrReader.defaultTestResult
+      ~TestInstrReader.doClassCleanups
+      ~TestInstrReader.doCleanups
+      ~TestInstrReader.fail
+      ~TestInstrReader.failIf
+      ~TestInstrReader.failIfAlmostEqual
+      ~TestInstrReader.failIfEqual
+      ~TestInstrReader.failUnless
+      ~TestInstrReader.failUnlessAlmostEqual
+      ~TestInstrReader.failUnlessEqual
+      ~TestInstrReader.failUnlessRaises
+      ~TestInstrReader.id
+      ~TestInstrReader.run
+      ~TestInstrReader.setUp
+      ~TestInstrReader.setUpClass
+      ~TestInstrReader.shortDescription
+      ~TestInstrReader.skipTest
+      ~TestInstrReader.subTest
+      ~TestInstrReader.tearDown
+      ~TestInstrReader.tearDownClass
+      ~TestInstrReader.test_comma_split
+      ~TestInstrReader.test_comma_split_brack
+      ~TestInstrReader.test_comma_split_limited
+      ~TestInstrReader.test_parenthesis_split
+      ~TestInstrReader.test_read_component_1
+      ~TestInstrReader.test_read_component_2
+      ~TestInstrReader.test_read_component_EXTEND
+      ~TestInstrReader.test_read_component_GROUP
+      ~TestInstrReader.test_read_component_JUMP
+      ~TestInstrReader.test_read_component_SPLIT
+      ~TestInstrReader.test_read_component_WHEN
+      ~TestInstrReader.test_read_declare_parameter
+      ~TestInstrReader.test_read_initialize_line
+      ~TestInstrReader.test_read_input_parameter
+      ~TestInstrReader.test_read_instrument_name
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~TestInstrReader.longMessage
+      ~TestInstrReader.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.blockPrint.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.blockPrint.rst
new file mode 100644
index 00000000..2e42db31
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.blockPrint.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Instr\_reader.blockPrint
+=================================================
+
+.. currentmodule:: mcstasscript.tests.test_Instr_reader
+
+.. autofunction:: blockPrint
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.enablePrint.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.enablePrint.rst
new file mode 100644
index 00000000..7d8860ac
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.enablePrint.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Instr\_reader.enablePrint
+==================================================
+
+.. currentmodule:: mcstasscript.tests.test_Instr_reader
+
+.. autofunction:: enablePrint
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.rst
new file mode 100644
index 00000000..f57ac30f
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.rst
@@ -0,0 +1,42 @@
+mcstasscript.tests.test\_Instr\_reader
+======================================
+
+.. automodule:: mcstasscript.tests.test_Instr_reader
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      blockPrint
+      enablePrint
+      set_dummy_dir
+      setup_standard
+      setup_standard_auto_instr
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      TestInstrReader
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.set_dummy_dir.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.set_dummy_dir.rst
new file mode 100644
index 00000000..6d4f0c33
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.set_dummy_dir.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Instr\_reader.set\_dummy\_dir
+======================================================
+
+.. currentmodule:: mcstasscript.tests.test_Instr_reader
+
+.. autofunction:: set_dummy_dir
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.setup_standard.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.setup_standard.rst
new file mode 100644
index 00000000..1742a64d
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.setup_standard.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Instr\_reader.setup\_standard
+======================================================
+
+.. currentmodule:: mcstasscript.tests.test_Instr_reader
+
+.. autofunction:: setup_standard
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.setup_standard_auto_instr.rst b/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.setup_standard_auto_instr.rst
new file mode 100644
index 00000000..703d60ea
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Instr_reader.setup_standard_auto_instr.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Instr\_reader.setup\_standard\_auto\_instr
+===================================================================
+
+.. currentmodule:: mcstasscript.tests.test_Instr_reader
+
+.. autofunction:: setup_standard_auto_instr
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_ManagedMcrun.TestManagedMcrun.rst b/docs/source/_autosummary/mcstasscript.tests.test_ManagedMcrun.TestManagedMcrun.rst
new file mode 100644
index 00000000..4ca2316e
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_ManagedMcrun.TestManagedMcrun.rst
@@ -0,0 +1,117 @@
+mcstasscript.tests.test\_ManagedMcrun.TestManagedMcrun
+======================================================
+
+.. currentmodule:: mcstasscript.tests.test_ManagedMcrun
+
+.. autoclass:: TestManagedMcrun
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~TestManagedMcrun.__init__
+      ~TestManagedMcrun.addClassCleanup
+      ~TestManagedMcrun.addCleanup
+      ~TestManagedMcrun.addTypeEqualityFunc
+      ~TestManagedMcrun.assertAlmostEqual
+      ~TestManagedMcrun.assertAlmostEquals
+      ~TestManagedMcrun.assertCountEqual
+      ~TestManagedMcrun.assertDictContainsSubset
+      ~TestManagedMcrun.assertDictEqual
+      ~TestManagedMcrun.assertEqual
+      ~TestManagedMcrun.assertEquals
+      ~TestManagedMcrun.assertFalse
+      ~TestManagedMcrun.assertGreater
+      ~TestManagedMcrun.assertGreaterEqual
+      ~TestManagedMcrun.assertIn
+      ~TestManagedMcrun.assertIs
+      ~TestManagedMcrun.assertIsInstance
+      ~TestManagedMcrun.assertIsNone
+      ~TestManagedMcrun.assertIsNot
+      ~TestManagedMcrun.assertIsNotNone
+      ~TestManagedMcrun.assertLess
+      ~TestManagedMcrun.assertLessEqual
+      ~TestManagedMcrun.assertListEqual
+      ~TestManagedMcrun.assertLogs
+      ~TestManagedMcrun.assertMultiLineEqual
+      ~TestManagedMcrun.assertNotAlmostEqual
+      ~TestManagedMcrun.assertNotAlmostEquals
+      ~TestManagedMcrun.assertNotEqual
+      ~TestManagedMcrun.assertNotEquals
+      ~TestManagedMcrun.assertNotIn
+      ~TestManagedMcrun.assertNotIsInstance
+      ~TestManagedMcrun.assertNotRegex
+      ~TestManagedMcrun.assertNotRegexpMatches
+      ~TestManagedMcrun.assertRaises
+      ~TestManagedMcrun.assertRaisesRegex
+      ~TestManagedMcrun.assertRaisesRegexp
+      ~TestManagedMcrun.assertRegex
+      ~TestManagedMcrun.assertRegexpMatches
+      ~TestManagedMcrun.assertSequenceEqual
+      ~TestManagedMcrun.assertSetEqual
+      ~TestManagedMcrun.assertTrue
+      ~TestManagedMcrun.assertTupleEqual
+      ~TestManagedMcrun.assertWarns
+      ~TestManagedMcrun.assertWarnsRegex
+      ~TestManagedMcrun.assert_
+      ~TestManagedMcrun.countTestCases
+      ~TestManagedMcrun.debug
+      ~TestManagedMcrun.defaultTestResult
+      ~TestManagedMcrun.doClassCleanups
+      ~TestManagedMcrun.doCleanups
+      ~TestManagedMcrun.fail
+      ~TestManagedMcrun.failIf
+      ~TestManagedMcrun.failIfAlmostEqual
+      ~TestManagedMcrun.failIfEqual
+      ~TestManagedMcrun.failUnless
+      ~TestManagedMcrun.failUnlessAlmostEqual
+      ~TestManagedMcrun.failUnlessEqual
+      ~TestManagedMcrun.failUnlessRaises
+      ~TestManagedMcrun.id
+      ~TestManagedMcrun.run
+      ~TestManagedMcrun.setUp
+      ~TestManagedMcrun.setUpClass
+      ~TestManagedMcrun.shortDescription
+      ~TestManagedMcrun.skipTest
+      ~TestManagedMcrun.subTest
+      ~TestManagedMcrun.tearDown
+      ~TestManagedMcrun.tearDownClass
+      ~TestManagedMcrun.test_ManagedMcrun_init_defaults
+      ~TestManagedMcrun.test_ManagedMcrun_init_invalid_mpi_error
+      ~TestManagedMcrun.test_ManagedMcrun_init_invalid_ncount_error
+      ~TestManagedMcrun.test_ManagedMcrun_init_invalid_parameters_error
+      ~TestManagedMcrun.test_ManagedMcrun_init_no_folder_error
+      ~TestManagedMcrun.test_ManagedMcrun_init_set_custom_flags
+      ~TestManagedMcrun.test_ManagedMcrun_init_set_parameters
+      ~TestManagedMcrun.test_ManagedMcrun_init_set_values
+      ~TestManagedMcrun.test_ManagedMcrun_init_simple
+      ~TestManagedMcrun.test_ManagedMcrun_load_data_Event
+      ~TestManagedMcrun.test_ManagedMcrun_load_data_L_mon
+      ~TestManagedMcrun.test_ManagedMcrun_load_data_L_mon_direct
+      ~TestManagedMcrun.test_ManagedMcrun_load_data_PSD
+      ~TestManagedMcrun.test_ManagedMcrun_load_data_PSD4PI
+      ~TestManagedMcrun.test_ManagedMcrun_load_data_no_mcsim_file
+      ~TestManagedMcrun.test_ManagedMcrun_load_data_nonexisting
+      ~TestManagedMcrun.test_ManagedMcrun_run_simulation_basic
+      ~TestManagedMcrun.test_ManagedMcrun_run_simulation_basic_path
+      ~TestManagedMcrun.test_ManagedMcrun_run_simulation_compile
+      ~TestManagedMcrun.test_ManagedMcrun_run_simulation_no_standard
+      ~TestManagedMcrun.test_ManagedMcrun_run_simulation_parameters
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~TestManagedMcrun.longMessage
+      ~TestManagedMcrun.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_ManagedMcrun.Test_load_functions.rst b/docs/source/_autosummary/mcstasscript.tests.test_ManagedMcrun.Test_load_functions.rst
new file mode 100644
index 00000000..4faea0b1
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_ManagedMcrun.Test_load_functions.rst
@@ -0,0 +1,102 @@
+mcstasscript.tests.test\_ManagedMcrun.Test\_load\_functions
+===========================================================
+
+.. currentmodule:: mcstasscript.tests.test_ManagedMcrun
+
+.. autoclass:: Test_load_functions
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Test_load_functions.__init__
+      ~Test_load_functions.addClassCleanup
+      ~Test_load_functions.addCleanup
+      ~Test_load_functions.addTypeEqualityFunc
+      ~Test_load_functions.assertAlmostEqual
+      ~Test_load_functions.assertAlmostEquals
+      ~Test_load_functions.assertCountEqual
+      ~Test_load_functions.assertDictContainsSubset
+      ~Test_load_functions.assertDictEqual
+      ~Test_load_functions.assertEqual
+      ~Test_load_functions.assertEquals
+      ~Test_load_functions.assertFalse
+      ~Test_load_functions.assertGreater
+      ~Test_load_functions.assertGreaterEqual
+      ~Test_load_functions.assertIn
+      ~Test_load_functions.assertIs
+      ~Test_load_functions.assertIsInstance
+      ~Test_load_functions.assertIsNone
+      ~Test_load_functions.assertIsNot
+      ~Test_load_functions.assertIsNotNone
+      ~Test_load_functions.assertLess
+      ~Test_load_functions.assertLessEqual
+      ~Test_load_functions.assertListEqual
+      ~Test_load_functions.assertLogs
+      ~Test_load_functions.assertMultiLineEqual
+      ~Test_load_functions.assertNotAlmostEqual
+      ~Test_load_functions.assertNotAlmostEquals
+      ~Test_load_functions.assertNotEqual
+      ~Test_load_functions.assertNotEquals
+      ~Test_load_functions.assertNotIn
+      ~Test_load_functions.assertNotIsInstance
+      ~Test_load_functions.assertNotRegex
+      ~Test_load_functions.assertNotRegexpMatches
+      ~Test_load_functions.assertRaises
+      ~Test_load_functions.assertRaisesRegex
+      ~Test_load_functions.assertRaisesRegexp
+      ~Test_load_functions.assertRegex
+      ~Test_load_functions.assertRegexpMatches
+      ~Test_load_functions.assertSequenceEqual
+      ~Test_load_functions.assertSetEqual
+      ~Test_load_functions.assertTrue
+      ~Test_load_functions.assertTupleEqual
+      ~Test_load_functions.assertWarns
+      ~Test_load_functions.assertWarnsRegex
+      ~Test_load_functions.assert_
+      ~Test_load_functions.countTestCases
+      ~Test_load_functions.debug
+      ~Test_load_functions.defaultTestResult
+      ~Test_load_functions.doClassCleanups
+      ~Test_load_functions.doCleanups
+      ~Test_load_functions.fail
+      ~Test_load_functions.failIf
+      ~Test_load_functions.failIfAlmostEqual
+      ~Test_load_functions.failIfEqual
+      ~Test_load_functions.failUnless
+      ~Test_load_functions.failUnlessAlmostEqual
+      ~Test_load_functions.failUnlessEqual
+      ~Test_load_functions.failUnlessRaises
+      ~Test_load_functions.id
+      ~Test_load_functions.run
+      ~Test_load_functions.setUp
+      ~Test_load_functions.setUpClass
+      ~Test_load_functions.shortDescription
+      ~Test_load_functions.skipTest
+      ~Test_load_functions.subTest
+      ~Test_load_functions.tearDown
+      ~Test_load_functions.tearDownClass
+      ~Test_load_functions.test_mcrun_load_data_PSD
+      ~Test_load_functions.test_mcrun_load_data_PSD4PI
+      ~Test_load_functions.test_mcrun_load_metadata_L_mon
+      ~Test_load_functions.test_mcrun_load_metadata_PSD4PI
+      ~Test_load_functions.test_mcrun_load_monitor_L_mon
+      ~Test_load_functions.test_mcrun_load_monitor_PSD4PI
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~Test_load_functions.longMessage
+      ~Test_load_functions.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_ManagedMcrun.rst b/docs/source/_autosummary/mcstasscript.tests.test_ManagedMcrun.rst
new file mode 100644
index 00000000..5c82724a
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_ManagedMcrun.rst
@@ -0,0 +1,32 @@
+mcstasscript.tests.test\_ManagedMcrun
+=====================================
+
+.. automodule:: mcstasscript.tests.test_ManagedMcrun
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      TestManagedMcrun
+      Test_load_functions
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_McStasData.TestMcStasData.rst b/docs/source/_autosummary/mcstasscript.tests.test_McStasData.TestMcStasData.rst
new file mode 100644
index 00000000..781dcb99
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_McStasData.TestMcStasData.rst
@@ -0,0 +1,109 @@
+mcstasscript.tests.test\_McStasData.TestMcStasData
+==================================================
+
+.. currentmodule:: mcstasscript.tests.test_McStasData
+
+.. autoclass:: TestMcStasData
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~TestMcStasData.__init__
+      ~TestMcStasData.addClassCleanup
+      ~TestMcStasData.addCleanup
+      ~TestMcStasData.addTypeEqualityFunc
+      ~TestMcStasData.assertAlmostEqual
+      ~TestMcStasData.assertAlmostEquals
+      ~TestMcStasData.assertCountEqual
+      ~TestMcStasData.assertDictContainsSubset
+      ~TestMcStasData.assertDictEqual
+      ~TestMcStasData.assertEqual
+      ~TestMcStasData.assertEquals
+      ~TestMcStasData.assertFalse
+      ~TestMcStasData.assertGreater
+      ~TestMcStasData.assertGreaterEqual
+      ~TestMcStasData.assertIn
+      ~TestMcStasData.assertIs
+      ~TestMcStasData.assertIsInstance
+      ~TestMcStasData.assertIsNone
+      ~TestMcStasData.assertIsNot
+      ~TestMcStasData.assertIsNotNone
+      ~TestMcStasData.assertLess
+      ~TestMcStasData.assertLessEqual
+      ~TestMcStasData.assertListEqual
+      ~TestMcStasData.assertLogs
+      ~TestMcStasData.assertMultiLineEqual
+      ~TestMcStasData.assertNotAlmostEqual
+      ~TestMcStasData.assertNotAlmostEquals
+      ~TestMcStasData.assertNotEqual
+      ~TestMcStasData.assertNotEquals
+      ~TestMcStasData.assertNotIn
+      ~TestMcStasData.assertNotIsInstance
+      ~TestMcStasData.assertNotRegex
+      ~TestMcStasData.assertNotRegexpMatches
+      ~TestMcStasData.assertRaises
+      ~TestMcStasData.assertRaisesRegex
+      ~TestMcStasData.assertRaisesRegexp
+      ~TestMcStasData.assertRegex
+      ~TestMcStasData.assertRegexpMatches
+      ~TestMcStasData.assertSequenceEqual
+      ~TestMcStasData.assertSetEqual
+      ~TestMcStasData.assertTrue
+      ~TestMcStasData.assertTupleEqual
+      ~TestMcStasData.assertWarns
+      ~TestMcStasData.assertWarnsRegex
+      ~TestMcStasData.assert_
+      ~TestMcStasData.countTestCases
+      ~TestMcStasData.debug
+      ~TestMcStasData.defaultTestResult
+      ~TestMcStasData.doClassCleanups
+      ~TestMcStasData.doCleanups
+      ~TestMcStasData.fail
+      ~TestMcStasData.failIf
+      ~TestMcStasData.failIfAlmostEqual
+      ~TestMcStasData.failIfEqual
+      ~TestMcStasData.failUnless
+      ~TestMcStasData.failUnlessAlmostEqual
+      ~TestMcStasData.failUnlessEqual
+      ~TestMcStasData.failUnlessRaises
+      ~TestMcStasData.id
+      ~TestMcStasData.run
+      ~TestMcStasData.setUp
+      ~TestMcStasData.setUpClass
+      ~TestMcStasData.shortDescription
+      ~TestMcStasData.skipTest
+      ~TestMcStasData.subTest
+      ~TestMcStasData.tearDown
+      ~TestMcStasData.tearDownClass
+      ~TestMcStasData.test_McStasDataBinned_init_1d
+      ~TestMcStasData.test_McStasDataBinned_init_2d_names
+      ~TestMcStasData.test_McStasDataBinned_init_2d_values
+      ~TestMcStasData.test_McStasDataBinned_init_2d_values_full
+      ~TestMcStasData.test_McStasDataBinned_init_values
+      ~TestMcStasData.test_McStasDataBinned_init_values_full
+      ~TestMcStasData.test_McStasDataBinned_set_colormap
+      ~TestMcStasData.test_McStasDataBinned_set_info_title
+      ~TestMcStasData.test_McStasDataBinned_set_log
+      ~TestMcStasData.test_McStasDataBinned_set_orders_of_mag
+      ~TestMcStasData.test_McStasDataBinned_set_show_colorbar
+      ~TestMcStasData.test_McStasDataBinned_set_xlabel
+      ~TestMcStasData.test_McStasDataBinned_set_ylabel
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~TestMcStasData.longMessage
+      ~TestMcStasData.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_McStasData.rst b/docs/source/_autosummary/mcstasscript.tests.test_McStasData.rst
new file mode 100644
index 00000000..d07f116e
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_McStasData.rst
@@ -0,0 +1,41 @@
+mcstasscript.tests.test\_McStasData
+===================================
+
+.. automodule:: mcstasscript.tests.test_McStasData
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      set_dummy_McStasDataBinned_1d
+      set_dummy_McStasDataBinned_2d
+      set_dummy_MetaDataBinned_1d
+      set_dummy_MetaDataBinned_2d
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      TestMcStasData
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_McStasData.set_dummy_McStasDataBinned_1d.rst b/docs/source/_autosummary/mcstasscript.tests.test_McStasData.set_dummy_McStasDataBinned_1d.rst
new file mode 100644
index 00000000..94ba6ae9
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_McStasData.set_dummy_McStasDataBinned_1d.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_McStasData.set\_dummy\_McStasDataBinned\_1d
+====================================================================
+
+.. currentmodule:: mcstasscript.tests.test_McStasData
+
+.. autofunction:: set_dummy_McStasDataBinned_1d
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_McStasData.set_dummy_McStasDataBinned_2d.rst b/docs/source/_autosummary/mcstasscript.tests.test_McStasData.set_dummy_McStasDataBinned_2d.rst
new file mode 100644
index 00000000..8cc7ea05
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_McStasData.set_dummy_McStasDataBinned_2d.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_McStasData.set\_dummy\_McStasDataBinned\_2d
+====================================================================
+
+.. currentmodule:: mcstasscript.tests.test_McStasData
+
+.. autofunction:: set_dummy_McStasDataBinned_2d
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_McStasData.set_dummy_MetaDataBinned_1d.rst b/docs/source/_autosummary/mcstasscript.tests.test_McStasData.set_dummy_MetaDataBinned_1d.rst
new file mode 100644
index 00000000..7cb2e196
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_McStasData.set_dummy_MetaDataBinned_1d.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_McStasData.set\_dummy\_MetaDataBinned\_1d
+==================================================================
+
+.. currentmodule:: mcstasscript.tests.test_McStasData
+
+.. autofunction:: set_dummy_MetaDataBinned_1d
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_McStasData.set_dummy_MetaDataBinned_2d.rst b/docs/source/_autosummary/mcstasscript.tests.test_McStasData.set_dummy_MetaDataBinned_2d.rst
new file mode 100644
index 00000000..a62b8c52
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_McStasData.set_dummy_MetaDataBinned_2d.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_McStasData.set\_dummy\_MetaDataBinned\_2d
+==================================================================
+
+.. currentmodule:: mcstasscript.tests.test_McStasData
+
+.. autofunction:: set_dummy_MetaDataBinned_2d
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_McStasMetaData.TestMcStasMetaData.rst b/docs/source/_autosummary/mcstasscript.tests.test_McStasMetaData.TestMcStasMetaData.rst
new file mode 100644
index 00000000..e20bd396
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_McStasMetaData.TestMcStasMetaData.rst
@@ -0,0 +1,105 @@
+mcstasscript.tests.test\_McStasMetaData.TestMcStasMetaData
+==========================================================
+
+.. currentmodule:: mcstasscript.tests.test_McStasMetaData
+
+.. autoclass:: TestMcStasMetaData
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~TestMcStasMetaData.__init__
+      ~TestMcStasMetaData.addClassCleanup
+      ~TestMcStasMetaData.addCleanup
+      ~TestMcStasMetaData.addTypeEqualityFunc
+      ~TestMcStasMetaData.assertAlmostEqual
+      ~TestMcStasMetaData.assertAlmostEquals
+      ~TestMcStasMetaData.assertCountEqual
+      ~TestMcStasMetaData.assertDictContainsSubset
+      ~TestMcStasMetaData.assertDictEqual
+      ~TestMcStasMetaData.assertEqual
+      ~TestMcStasMetaData.assertEquals
+      ~TestMcStasMetaData.assertFalse
+      ~TestMcStasMetaData.assertGreater
+      ~TestMcStasMetaData.assertGreaterEqual
+      ~TestMcStasMetaData.assertIn
+      ~TestMcStasMetaData.assertIs
+      ~TestMcStasMetaData.assertIsInstance
+      ~TestMcStasMetaData.assertIsNone
+      ~TestMcStasMetaData.assertIsNot
+      ~TestMcStasMetaData.assertIsNotNone
+      ~TestMcStasMetaData.assertLess
+      ~TestMcStasMetaData.assertLessEqual
+      ~TestMcStasMetaData.assertListEqual
+      ~TestMcStasMetaData.assertLogs
+      ~TestMcStasMetaData.assertMultiLineEqual
+      ~TestMcStasMetaData.assertNotAlmostEqual
+      ~TestMcStasMetaData.assertNotAlmostEquals
+      ~TestMcStasMetaData.assertNotEqual
+      ~TestMcStasMetaData.assertNotEquals
+      ~TestMcStasMetaData.assertNotIn
+      ~TestMcStasMetaData.assertNotIsInstance
+      ~TestMcStasMetaData.assertNotRegex
+      ~TestMcStasMetaData.assertNotRegexpMatches
+      ~TestMcStasMetaData.assertRaises
+      ~TestMcStasMetaData.assertRaisesRegex
+      ~TestMcStasMetaData.assertRaisesRegexp
+      ~TestMcStasMetaData.assertRegex
+      ~TestMcStasMetaData.assertRegexpMatches
+      ~TestMcStasMetaData.assertSequenceEqual
+      ~TestMcStasMetaData.assertSetEqual
+      ~TestMcStasMetaData.assertTrue
+      ~TestMcStasMetaData.assertTupleEqual
+      ~TestMcStasMetaData.assertWarns
+      ~TestMcStasMetaData.assertWarnsRegex
+      ~TestMcStasMetaData.assert_
+      ~TestMcStasMetaData.countTestCases
+      ~TestMcStasMetaData.debug
+      ~TestMcStasMetaData.defaultTestResult
+      ~TestMcStasMetaData.doClassCleanups
+      ~TestMcStasMetaData.doCleanups
+      ~TestMcStasMetaData.fail
+      ~TestMcStasMetaData.failIf
+      ~TestMcStasMetaData.failIfAlmostEqual
+      ~TestMcStasMetaData.failIfEqual
+      ~TestMcStasMetaData.failUnless
+      ~TestMcStasMetaData.failUnlessAlmostEqual
+      ~TestMcStasMetaData.failUnlessEqual
+      ~TestMcStasMetaData.failUnlessRaises
+      ~TestMcStasMetaData.id
+      ~TestMcStasMetaData.run
+      ~TestMcStasMetaData.setUp
+      ~TestMcStasMetaData.setUpClass
+      ~TestMcStasMetaData.shortDescription
+      ~TestMcStasMetaData.skipTest
+      ~TestMcStasMetaData.subTest
+      ~TestMcStasMetaData.tearDown
+      ~TestMcStasMetaData.tearDownClass
+      ~TestMcStasMetaData.test_McStasMetaData_add_info
+      ~TestMcStasMetaData.test_McStasMetaData_add_info_len
+      ~TestMcStasMetaData.test_McStasMetaData_add_info_title
+      ~TestMcStasMetaData.test_McStasMetaData_add_info_xlabel
+      ~TestMcStasMetaData.test_McStasMetaData_add_info_ylabel
+      ~TestMcStasMetaData.test_McStasMetaData_init
+      ~TestMcStasMetaData.test_McStasMetaData_long_read_1d
+      ~TestMcStasMetaData.test_McStasMetaData_long_read_2d
+      ~TestMcStasMetaData.test_McStasMetaData_return_type
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~TestMcStasMetaData.longMessage
+      ~TestMcStasMetaData.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_McStasMetaData.rst b/docs/source/_autosummary/mcstasscript.tests.test_McStasMetaData.rst
new file mode 100644
index 00000000..97ad837e
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_McStasMetaData.rst
@@ -0,0 +1,31 @@
+mcstasscript.tests.test\_McStasMetaData
+=======================================
+
+.. automodule:: mcstasscript.tests.test_McStasMetaData
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      TestMcStasMetaData
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_McStasPlotOptions.TestMcStasPlotOptions.rst b/docs/source/_autosummary/mcstasscript.tests.test_McStasPlotOptions.TestMcStasPlotOptions.rst
new file mode 100644
index 00000000..0f87fdbe
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_McStasPlotOptions.TestMcStasPlotOptions.rst
@@ -0,0 +1,120 @@
+mcstasscript.tests.test\_McStasPlotOptions.TestMcStasPlotOptions
+================================================================
+
+.. currentmodule:: mcstasscript.tests.test_McStasPlotOptions
+
+.. autoclass:: TestMcStasPlotOptions
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~TestMcStasPlotOptions.__init__
+      ~TestMcStasPlotOptions.addClassCleanup
+      ~TestMcStasPlotOptions.addCleanup
+      ~TestMcStasPlotOptions.addTypeEqualityFunc
+      ~TestMcStasPlotOptions.assertAlmostEqual
+      ~TestMcStasPlotOptions.assertAlmostEquals
+      ~TestMcStasPlotOptions.assertCountEqual
+      ~TestMcStasPlotOptions.assertDictContainsSubset
+      ~TestMcStasPlotOptions.assertDictEqual
+      ~TestMcStasPlotOptions.assertEqual
+      ~TestMcStasPlotOptions.assertEquals
+      ~TestMcStasPlotOptions.assertFalse
+      ~TestMcStasPlotOptions.assertGreater
+      ~TestMcStasPlotOptions.assertGreaterEqual
+      ~TestMcStasPlotOptions.assertIn
+      ~TestMcStasPlotOptions.assertIs
+      ~TestMcStasPlotOptions.assertIsInstance
+      ~TestMcStasPlotOptions.assertIsNone
+      ~TestMcStasPlotOptions.assertIsNot
+      ~TestMcStasPlotOptions.assertIsNotNone
+      ~TestMcStasPlotOptions.assertLess
+      ~TestMcStasPlotOptions.assertLessEqual
+      ~TestMcStasPlotOptions.assertListEqual
+      ~TestMcStasPlotOptions.assertLogs
+      ~TestMcStasPlotOptions.assertMultiLineEqual
+      ~TestMcStasPlotOptions.assertNotAlmostEqual
+      ~TestMcStasPlotOptions.assertNotAlmostEquals
+      ~TestMcStasPlotOptions.assertNotEqual
+      ~TestMcStasPlotOptions.assertNotEquals
+      ~TestMcStasPlotOptions.assertNotIn
+      ~TestMcStasPlotOptions.assertNotIsInstance
+      ~TestMcStasPlotOptions.assertNotRegex
+      ~TestMcStasPlotOptions.assertNotRegexpMatches
+      ~TestMcStasPlotOptions.assertRaises
+      ~TestMcStasPlotOptions.assertRaisesRegex
+      ~TestMcStasPlotOptions.assertRaisesRegexp
+      ~TestMcStasPlotOptions.assertRegex
+      ~TestMcStasPlotOptions.assertRegexpMatches
+      ~TestMcStasPlotOptions.assertSequenceEqual
+      ~TestMcStasPlotOptions.assertSetEqual
+      ~TestMcStasPlotOptions.assertTrue
+      ~TestMcStasPlotOptions.assertTupleEqual
+      ~TestMcStasPlotOptions.assertWarns
+      ~TestMcStasPlotOptions.assertWarnsRegex
+      ~TestMcStasPlotOptions.assert_
+      ~TestMcStasPlotOptions.countTestCases
+      ~TestMcStasPlotOptions.debug
+      ~TestMcStasPlotOptions.defaultTestResult
+      ~TestMcStasPlotOptions.doClassCleanups
+      ~TestMcStasPlotOptions.doCleanups
+      ~TestMcStasPlotOptions.fail
+      ~TestMcStasPlotOptions.failIf
+      ~TestMcStasPlotOptions.failIfAlmostEqual
+      ~TestMcStasPlotOptions.failIfEqual
+      ~TestMcStasPlotOptions.failUnless
+      ~TestMcStasPlotOptions.failUnlessAlmostEqual
+      ~TestMcStasPlotOptions.failUnlessEqual
+      ~TestMcStasPlotOptions.failUnlessRaises
+      ~TestMcStasPlotOptions.id
+      ~TestMcStasPlotOptions.run
+      ~TestMcStasPlotOptions.setUp
+      ~TestMcStasPlotOptions.setUpClass
+      ~TestMcStasPlotOptions.shortDescription
+      ~TestMcStasPlotOptions.skipTest
+      ~TestMcStasPlotOptions.subTest
+      ~TestMcStasPlotOptions.tearDown
+      ~TestMcStasPlotOptions.tearDownClass
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_default_bottom_lim
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_default_colormap
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_default_cut_max
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_default_cut_min
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_default_left_lim
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_default_log
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_default_orders_of_mag
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_default_right_lim
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_default_show_colorbar
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_default_top_lim
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_default_x_axis_multiplier
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_default_y_axis_multiplier
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_set_bottom_lim
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_set_colormap
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_set_cut_max
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_set_cut_min
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_set_left_lim
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_set_log
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_set_orders_of_mag
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_set_right_lim
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_set_show_colorbar
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_set_top_lim
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_set_x_axis_multiplier
+      ~TestMcStasPlotOptions.test_McStasPlotOptions_set_y_axis_multiplier
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~TestMcStasPlotOptions.longMessage
+      ~TestMcStasPlotOptions.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_McStasPlotOptions.rst b/docs/source/_autosummary/mcstasscript.tests.test_McStasPlotOptions.rst
new file mode 100644
index 00000000..a33c7975
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_McStasPlotOptions.rst
@@ -0,0 +1,31 @@
+mcstasscript.tests.test\_McStasPlotOptions
+==========================================
+
+.. automodule:: mcstasscript.tests.test_McStasPlotOptions
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      TestMcStasPlotOptions
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Plotter.TestPlotterHelpers.rst b/docs/source/_autosummary/mcstasscript.tests.test_Plotter.TestPlotterHelpers.rst
new file mode 100644
index 00000000..8ff72964
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Plotter.TestPlotterHelpers.rst
@@ -0,0 +1,128 @@
+mcstasscript.tests.test\_Plotter.TestPlotterHelpers
+===================================================
+
+.. currentmodule:: mcstasscript.tests.test_Plotter
+
+.. autoclass:: TestPlotterHelpers
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~TestPlotterHelpers.__init__
+      ~TestPlotterHelpers.addClassCleanup
+      ~TestPlotterHelpers.addCleanup
+      ~TestPlotterHelpers.addTypeEqualityFunc
+      ~TestPlotterHelpers.assertAlmostEqual
+      ~TestPlotterHelpers.assertAlmostEquals
+      ~TestPlotterHelpers.assertCountEqual
+      ~TestPlotterHelpers.assertDictContainsSubset
+      ~TestPlotterHelpers.assertDictEqual
+      ~TestPlotterHelpers.assertEqual
+      ~TestPlotterHelpers.assertEquals
+      ~TestPlotterHelpers.assertFalse
+      ~TestPlotterHelpers.assertGreater
+      ~TestPlotterHelpers.assertGreaterEqual
+      ~TestPlotterHelpers.assertIn
+      ~TestPlotterHelpers.assertIs
+      ~TestPlotterHelpers.assertIsInstance
+      ~TestPlotterHelpers.assertIsNone
+      ~TestPlotterHelpers.assertIsNot
+      ~TestPlotterHelpers.assertIsNotNone
+      ~TestPlotterHelpers.assertLess
+      ~TestPlotterHelpers.assertLessEqual
+      ~TestPlotterHelpers.assertListEqual
+      ~TestPlotterHelpers.assertLogs
+      ~TestPlotterHelpers.assertMultiLineEqual
+      ~TestPlotterHelpers.assertNotAlmostEqual
+      ~TestPlotterHelpers.assertNotAlmostEquals
+      ~TestPlotterHelpers.assertNotEqual
+      ~TestPlotterHelpers.assertNotEquals
+      ~TestPlotterHelpers.assertNotIn
+      ~TestPlotterHelpers.assertNotIsInstance
+      ~TestPlotterHelpers.assertNotRegex
+      ~TestPlotterHelpers.assertNotRegexpMatches
+      ~TestPlotterHelpers.assertRaises
+      ~TestPlotterHelpers.assertRaisesRegex
+      ~TestPlotterHelpers.assertRaisesRegexp
+      ~TestPlotterHelpers.assertRegex
+      ~TestPlotterHelpers.assertRegexpMatches
+      ~TestPlotterHelpers.assertSequenceEqual
+      ~TestPlotterHelpers.assertSetEqual
+      ~TestPlotterHelpers.assertTrue
+      ~TestPlotterHelpers.assertTupleEqual
+      ~TestPlotterHelpers.assertWarns
+      ~TestPlotterHelpers.assertWarnsRegex
+      ~TestPlotterHelpers.assert_
+      ~TestPlotterHelpers.countTestCases
+      ~TestPlotterHelpers.debug
+      ~TestPlotterHelpers.defaultTestResult
+      ~TestPlotterHelpers.doClassCleanups
+      ~TestPlotterHelpers.doCleanups
+      ~TestPlotterHelpers.fail
+      ~TestPlotterHelpers.failIf
+      ~TestPlotterHelpers.failIfAlmostEqual
+      ~TestPlotterHelpers.failIfEqual
+      ~TestPlotterHelpers.failUnless
+      ~TestPlotterHelpers.failUnlessAlmostEqual
+      ~TestPlotterHelpers.failUnlessEqual
+      ~TestPlotterHelpers.failUnlessRaises
+      ~TestPlotterHelpers.id
+      ~TestPlotterHelpers.run
+      ~TestPlotterHelpers.setUp
+      ~TestPlotterHelpers.setUpClass
+      ~TestPlotterHelpers.shortDescription
+      ~TestPlotterHelpers.skipTest
+      ~TestPlotterHelpers.subTest
+      ~TestPlotterHelpers.tearDown
+      ~TestPlotterHelpers.tearDownClass
+      ~TestPlotterHelpers.test_find_min_max_I_cut_max_1D_case
+      ~TestPlotterHelpers.test_find_min_max_I_cut_max_2D_case
+      ~TestPlotterHelpers.test_find_min_max_I_cut_min_1D_case
+      ~TestPlotterHelpers.test_find_min_max_I_cut_min_2D_case
+      ~TestPlotterHelpers.test_find_min_max_I_fail_case
+      ~TestPlotterHelpers.test_find_min_max_I_log_cut_max_1D_case
+      ~TestPlotterHelpers.test_find_min_max_I_log_cut_max_2D_case
+      ~TestPlotterHelpers.test_find_min_max_I_log_cut_min_1D_case
+      ~TestPlotterHelpers.test_find_min_max_I_log_cut_min_2D_case
+      ~TestPlotterHelpers.test_find_min_max_I_log_orders_of_mag_1D_case
+      ~TestPlotterHelpers.test_find_min_max_I_log_orders_of_mag_1D_with_zero_case
+      ~TestPlotterHelpers.test_find_min_max_I_log_orders_of_mag_2D_case
+      ~TestPlotterHelpers.test_find_min_max_I_log_orders_of_mag_2D_with_zero_case
+      ~TestPlotterHelpers.test_find_min_max_I_log_with_zero_2D_case
+      ~TestPlotterHelpers.test_find_min_max_I_log_with_zero_case
+      ~TestPlotterHelpers.test_find_min_max_I_simple_1D_case
+      ~TestPlotterHelpers.test_find_min_max_I_simple_2D_case
+      ~TestPlotterHelpers.test_handle_kwargs_all_simple
+      ~TestPlotterHelpers.test_handle_kwargs_bottom_lim
+      ~TestPlotterHelpers.test_handle_kwargs_figsize_default
+      ~TestPlotterHelpers.test_handle_kwargs_figsize_list
+      ~TestPlotterHelpers.test_handle_kwargs_figsize_tuple
+      ~TestPlotterHelpers.test_handle_kwargs_left_lim
+      ~TestPlotterHelpers.test_handle_kwargs_log
+      ~TestPlotterHelpers.test_handle_kwargs_oders_of_mag
+      ~TestPlotterHelpers.test_handle_kwargs_right_lim
+      ~TestPlotterHelpers.test_handle_kwargs_single_element_to_list
+      ~TestPlotterHelpers.test_handle_kwargs_top_lim
+      ~TestPlotterHelpers.test_plot_function_1D_log
+      ~TestPlotterHelpers.test_plot_function_1D_normal
+      ~TestPlotterHelpers.test_plot_function_2D_log
+      ~TestPlotterHelpers.test_plot_function_2D_normal
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~TestPlotterHelpers.longMessage
+      ~TestPlotterHelpers.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Plotter.get_dummy_McStasDataBinned_1d.rst b/docs/source/_autosummary/mcstasscript.tests.test_Plotter.get_dummy_McStasDataBinned_1d.rst
new file mode 100644
index 00000000..bfb09573
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Plotter.get_dummy_McStasDataBinned_1d.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Plotter.get\_dummy\_McStasDataBinned\_1d
+=================================================================
+
+.. currentmodule:: mcstasscript.tests.test_Plotter
+
+.. autofunction:: get_dummy_McStasDataBinned_1d
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Plotter.get_dummy_McStasDataBinned_2d.rst b/docs/source/_autosummary/mcstasscript.tests.test_Plotter.get_dummy_McStasDataBinned_2d.rst
new file mode 100644
index 00000000..8299482c
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Plotter.get_dummy_McStasDataBinned_2d.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Plotter.get\_dummy\_McStasDataBinned\_2d
+=================================================================
+
+.. currentmodule:: mcstasscript.tests.test_Plotter
+
+.. autofunction:: get_dummy_McStasDataBinned_2d
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Plotter.get_dummy_MetaDataBinned_1d.rst b/docs/source/_autosummary/mcstasscript.tests.test_Plotter.get_dummy_MetaDataBinned_1d.rst
new file mode 100644
index 00000000..a727390a
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Plotter.get_dummy_MetaDataBinned_1d.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Plotter.get\_dummy\_MetaDataBinned\_1d
+===============================================================
+
+.. currentmodule:: mcstasscript.tests.test_Plotter
+
+.. autofunction:: get_dummy_MetaDataBinned_1d
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Plotter.get_dummy_MetaDataBinned_2d.rst b/docs/source/_autosummary/mcstasscript.tests.test_Plotter.get_dummy_MetaDataBinned_2d.rst
new file mode 100644
index 00000000..7c5d48cb
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Plotter.get_dummy_MetaDataBinned_2d.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_Plotter.get\_dummy\_MetaDataBinned\_2d
+===============================================================
+
+.. currentmodule:: mcstasscript.tests.test_Plotter
+
+.. autofunction:: get_dummy_MetaDataBinned_2d
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_Plotter.rst b/docs/source/_autosummary/mcstasscript.tests.test_Plotter.rst
new file mode 100644
index 00000000..ad6ba15a
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_Plotter.rst
@@ -0,0 +1,41 @@
+mcstasscript.tests.test\_Plotter
+================================
+
+.. automodule:: mcstasscript.tests.test_Plotter
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      get_dummy_McStasDataBinned_1d
+      get_dummy_McStasDataBinned_2d
+      get_dummy_MetaDataBinned_1d
+      get_dummy_MetaDataBinned_2d
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      TestPlotterHelpers
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_add_data.Test_add_data.rst b/docs/source/_autosummary/mcstasscript.tests.test_add_data.Test_add_data.rst
new file mode 100644
index 00000000..a147251f
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_add_data.Test_add_data.rst
@@ -0,0 +1,101 @@
+mcstasscript.tests.test\_add\_data.Test\_add\_data
+==================================================
+
+.. currentmodule:: mcstasscript.tests.test_add_data
+
+.. autoclass:: Test_add_data
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Test_add_data.__init__
+      ~Test_add_data.addClassCleanup
+      ~Test_add_data.addCleanup
+      ~Test_add_data.addTypeEqualityFunc
+      ~Test_add_data.assertAlmostEqual
+      ~Test_add_data.assertAlmostEquals
+      ~Test_add_data.assertCountEqual
+      ~Test_add_data.assertDictContainsSubset
+      ~Test_add_data.assertDictEqual
+      ~Test_add_data.assertEqual
+      ~Test_add_data.assertEquals
+      ~Test_add_data.assertFalse
+      ~Test_add_data.assertGreater
+      ~Test_add_data.assertGreaterEqual
+      ~Test_add_data.assertIn
+      ~Test_add_data.assertIs
+      ~Test_add_data.assertIsInstance
+      ~Test_add_data.assertIsNone
+      ~Test_add_data.assertIsNot
+      ~Test_add_data.assertIsNotNone
+      ~Test_add_data.assertLess
+      ~Test_add_data.assertLessEqual
+      ~Test_add_data.assertListEqual
+      ~Test_add_data.assertLogs
+      ~Test_add_data.assertMultiLineEqual
+      ~Test_add_data.assertNotAlmostEqual
+      ~Test_add_data.assertNotAlmostEquals
+      ~Test_add_data.assertNotEqual
+      ~Test_add_data.assertNotEquals
+      ~Test_add_data.assertNotIn
+      ~Test_add_data.assertNotIsInstance
+      ~Test_add_data.assertNotRegex
+      ~Test_add_data.assertNotRegexpMatches
+      ~Test_add_data.assertRaises
+      ~Test_add_data.assertRaisesRegex
+      ~Test_add_data.assertRaisesRegexp
+      ~Test_add_data.assertRegex
+      ~Test_add_data.assertRegexpMatches
+      ~Test_add_data.assertSequenceEqual
+      ~Test_add_data.assertSetEqual
+      ~Test_add_data.assertTrue
+      ~Test_add_data.assertTupleEqual
+      ~Test_add_data.assertWarns
+      ~Test_add_data.assertWarnsRegex
+      ~Test_add_data.assert_
+      ~Test_add_data.countTestCases
+      ~Test_add_data.debug
+      ~Test_add_data.defaultTestResult
+      ~Test_add_data.doClassCleanups
+      ~Test_add_data.doCleanups
+      ~Test_add_data.fail
+      ~Test_add_data.failIf
+      ~Test_add_data.failIfAlmostEqual
+      ~Test_add_data.failIfEqual
+      ~Test_add_data.failUnless
+      ~Test_add_data.failUnlessAlmostEqual
+      ~Test_add_data.failUnlessEqual
+      ~Test_add_data.failUnlessRaises
+      ~Test_add_data.id
+      ~Test_add_data.run
+      ~Test_add_data.setUp
+      ~Test_add_data.setUpClass
+      ~Test_add_data.shortDescription
+      ~Test_add_data.skipTest
+      ~Test_add_data.subTest
+      ~Test_add_data.tearDown
+      ~Test_add_data.tearDownClass
+      ~Test_add_data.test_1d_updates_correctly
+      ~Test_add_data.test_1d_updates_different
+      ~Test_add_data.test_2d_updates_correctly
+      ~Test_add_data.test_2d_updates_different
+      ~Test_add_data.test_fail
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~Test_add_data.longMessage
+      ~Test_add_data.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_add_data.rst b/docs/source/_autosummary/mcstasscript.tests.test_add_data.rst
new file mode 100644
index 00000000..73b3caa5
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_add_data.rst
@@ -0,0 +1,41 @@
+mcstasscript.tests.test\_add\_data
+==================================
+
+.. automodule:: mcstasscript.tests.test_add_data
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      set_dummy_McStasDataBinned_1d
+      set_dummy_McStasDataBinned_2d
+      set_dummy_MetaDataBinned_1d
+      set_dummy_MetaDataBinned_2d
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      Test_add_data
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_add_data.set_dummy_McStasDataBinned_1d.rst b/docs/source/_autosummary/mcstasscript.tests.test_add_data.set_dummy_McStasDataBinned_1d.rst
new file mode 100644
index 00000000..61cee01d
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_add_data.set_dummy_McStasDataBinned_1d.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_add\_data.set\_dummy\_McStasDataBinned\_1d
+===================================================================
+
+.. currentmodule:: mcstasscript.tests.test_add_data
+
+.. autofunction:: set_dummy_McStasDataBinned_1d
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_add_data.set_dummy_McStasDataBinned_2d.rst b/docs/source/_autosummary/mcstasscript.tests.test_add_data.set_dummy_McStasDataBinned_2d.rst
new file mode 100644
index 00000000..bfefcf20
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_add_data.set_dummy_McStasDataBinned_2d.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_add\_data.set\_dummy\_McStasDataBinned\_2d
+===================================================================
+
+.. currentmodule:: mcstasscript.tests.test_add_data
+
+.. autofunction:: set_dummy_McStasDataBinned_2d
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_add_data.set_dummy_MetaDataBinned_1d.rst b/docs/source/_autosummary/mcstasscript.tests.test_add_data.set_dummy_MetaDataBinned_1d.rst
new file mode 100644
index 00000000..af88bf87
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_add_data.set_dummy_MetaDataBinned_1d.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_add\_data.set\_dummy\_MetaDataBinned\_1d
+=================================================================
+
+.. currentmodule:: mcstasscript.tests.test_add_data
+
+.. autofunction:: set_dummy_MetaDataBinned_1d
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_add_data.set_dummy_MetaDataBinned_2d.rst b/docs/source/_autosummary/mcstasscript.tests.test_add_data.set_dummy_MetaDataBinned_2d.rst
new file mode 100644
index 00000000..8eb9cbad
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_add_data.set_dummy_MetaDataBinned_2d.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_add\_data.set\_dummy\_MetaDataBinned\_2d
+=================================================================
+
+.. currentmodule:: mcstasscript.tests.test_add_data
+
+.. autofunction:: set_dummy_MetaDataBinned_2d
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_component.TestComponent.rst b/docs/source/_autosummary/mcstasscript.tests.test_component.TestComponent.rst
new file mode 100644
index 00000000..ac14da9c
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_component.TestComponent.rst
@@ -0,0 +1,127 @@
+mcstasscript.tests.test\_component.TestComponent
+================================================
+
+.. currentmodule:: mcstasscript.tests.test_component
+
+.. autoclass:: TestComponent
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~TestComponent.__init__
+      ~TestComponent.addClassCleanup
+      ~TestComponent.addCleanup
+      ~TestComponent.addTypeEqualityFunc
+      ~TestComponent.assertAlmostEqual
+      ~TestComponent.assertAlmostEquals
+      ~TestComponent.assertCountEqual
+      ~TestComponent.assertDictContainsSubset
+      ~TestComponent.assertDictEqual
+      ~TestComponent.assertEqual
+      ~TestComponent.assertEquals
+      ~TestComponent.assertFalse
+      ~TestComponent.assertGreater
+      ~TestComponent.assertGreaterEqual
+      ~TestComponent.assertIn
+      ~TestComponent.assertIs
+      ~TestComponent.assertIsInstance
+      ~TestComponent.assertIsNone
+      ~TestComponent.assertIsNot
+      ~TestComponent.assertIsNotNone
+      ~TestComponent.assertLess
+      ~TestComponent.assertLessEqual
+      ~TestComponent.assertListEqual
+      ~TestComponent.assertLogs
+      ~TestComponent.assertMultiLineEqual
+      ~TestComponent.assertNotAlmostEqual
+      ~TestComponent.assertNotAlmostEquals
+      ~TestComponent.assertNotEqual
+      ~TestComponent.assertNotEquals
+      ~TestComponent.assertNotIn
+      ~TestComponent.assertNotIsInstance
+      ~TestComponent.assertNotRegex
+      ~TestComponent.assertNotRegexpMatches
+      ~TestComponent.assertRaises
+      ~TestComponent.assertRaisesRegex
+      ~TestComponent.assertRaisesRegexp
+      ~TestComponent.assertRegex
+      ~TestComponent.assertRegexpMatches
+      ~TestComponent.assertSequenceEqual
+      ~TestComponent.assertSetEqual
+      ~TestComponent.assertTrue
+      ~TestComponent.assertTupleEqual
+      ~TestComponent.assertWarns
+      ~TestComponent.assertWarnsRegex
+      ~TestComponent.assert_
+      ~TestComponent.countTestCases
+      ~TestComponent.debug
+      ~TestComponent.defaultTestResult
+      ~TestComponent.doClassCleanups
+      ~TestComponent.doCleanups
+      ~TestComponent.fail
+      ~TestComponent.failIf
+      ~TestComponent.failIfAlmostEqual
+      ~TestComponent.failIfEqual
+      ~TestComponent.failUnless
+      ~TestComponent.failUnlessAlmostEqual
+      ~TestComponent.failUnlessEqual
+      ~TestComponent.failUnlessRaises
+      ~TestComponent.id
+      ~TestComponent.run
+      ~TestComponent.setUp
+      ~TestComponent.setUpClass
+      ~TestComponent.shortDescription
+      ~TestComponent.skipTest
+      ~TestComponent.subTest
+      ~TestComponent.tearDown
+      ~TestComponent.tearDownClass
+      ~TestComponent.test_Component_basic_init
+      ~TestComponent.test_Component_basic_init_defaults
+      ~TestComponent.test_Component_basic_init_set_AT
+      ~TestComponent.test_Component_basic_init_set_AT_Component
+      ~TestComponent.test_Component_basic_init_set_AT_Component_keyword
+      ~TestComponent.test_Component_basic_init_set_EXTEND
+      ~TestComponent.test_Component_basic_init_set_GROUP
+      ~TestComponent.test_Component_basic_init_set_JUMP
+      ~TestComponent.test_Component_basic_init_set_RELATIVE
+      ~TestComponent.test_Component_basic_init_set_ROTATED
+      ~TestComponent.test_Component_basic_init_set_ROTATED_Component
+      ~TestComponent.test_Component_basic_init_set_ROTATED_Component_keyword
+      ~TestComponent.test_Component_basic_init_set_SPLIT
+      ~TestComponent.test_Component_basic_init_set_WHEN
+      ~TestComponent.test_Component_basic_init_set_comment
+      ~TestComponent.test_Component_basic_new_attribute_error
+      ~TestComponent.test_Component_basic_object_ref_init_set_RELATIVE
+      ~TestComponent.test_Component_freeze
+      ~TestComponent.test_Component_init_complex_call
+      ~TestComponent.test_Component_init_complex_call_relative
+      ~TestComponent.test_Component_print_long
+      ~TestComponent.test_Component_print_short_longest_name
+      ~TestComponent.test_Component_print_short_standard
+      ~TestComponent.test_Component_show_parameters
+      ~TestComponent.test_Component_show_parameters_simple
+      ~TestComponent.test_Component_write_Component_required_parameter_error
+      ~TestComponent.test_Component_write_to_file_complex
+      ~TestComponent.test_Component_write_to_file_complex_SPLIT_string
+      ~TestComponent.test_Component_write_to_file_include
+      ~TestComponent.test_Component_write_to_file_simple
+      ~TestComponent.test_component_basic_init_set_parameters
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~TestComponent.longMessage
+      ~TestComponent.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_component.rst b/docs/source/_autosummary/mcstasscript.tests.test_component.rst
new file mode 100644
index 00000000..229ca9f4
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_component.rst
@@ -0,0 +1,40 @@
+mcstasscript.tests.test\_component
+==================================
+
+.. automodule:: mcstasscript.tests.test_component
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      setup_Component_all_keywords
+      setup_Component_relative
+      setup_Component_with_parameters
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      TestComponent
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_component.setup_Component_all_keywords.rst b/docs/source/_autosummary/mcstasscript.tests.test_component.setup_Component_all_keywords.rst
new file mode 100644
index 00000000..21647c5f
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_component.setup_Component_all_keywords.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_component.setup\_Component\_all\_keywords
+==================================================================
+
+.. currentmodule:: mcstasscript.tests.test_component
+
+.. autofunction:: setup_Component_all_keywords
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_component.setup_Component_relative.rst b/docs/source/_autosummary/mcstasscript.tests.test_component.setup_Component_relative.rst
new file mode 100644
index 00000000..4264aca1
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_component.setup_Component_relative.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_component.setup\_Component\_relative
+=============================================================
+
+.. currentmodule:: mcstasscript.tests.test_component
+
+.. autofunction:: setup_Component_relative
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_component.setup_Component_with_parameters.rst b/docs/source/_autosummary/mcstasscript.tests.test_component.setup_Component_with_parameters.rst
new file mode 100644
index 00000000..adf923ce
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_component.setup_Component_with_parameters.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_component.setup\_Component\_with\_parameters
+=====================================================================
+
+.. currentmodule:: mcstasscript.tests.test_component
+
+.. autofunction:: setup_Component_with_parameters
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_declare_variable.Test_DeclareVariable.rst b/docs/source/_autosummary/mcstasscript.tests.test_declare_variable.Test_DeclareVariable.rst
new file mode 100644
index 00000000..ac3b4442
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_declare_variable.Test_DeclareVariable.rst
@@ -0,0 +1,105 @@
+mcstasscript.tests.test\_declare\_variable.Test\_DeclareVariable
+================================================================
+
+.. currentmodule:: mcstasscript.tests.test_declare_variable
+
+.. autoclass:: Test_DeclareVariable
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Test_DeclareVariable.__init__
+      ~Test_DeclareVariable.addClassCleanup
+      ~Test_DeclareVariable.addCleanup
+      ~Test_DeclareVariable.addTypeEqualityFunc
+      ~Test_DeclareVariable.assertAlmostEqual
+      ~Test_DeclareVariable.assertAlmostEquals
+      ~Test_DeclareVariable.assertCountEqual
+      ~Test_DeclareVariable.assertDictContainsSubset
+      ~Test_DeclareVariable.assertDictEqual
+      ~Test_DeclareVariable.assertEqual
+      ~Test_DeclareVariable.assertEquals
+      ~Test_DeclareVariable.assertFalse
+      ~Test_DeclareVariable.assertGreater
+      ~Test_DeclareVariable.assertGreaterEqual
+      ~Test_DeclareVariable.assertIn
+      ~Test_DeclareVariable.assertIs
+      ~Test_DeclareVariable.assertIsInstance
+      ~Test_DeclareVariable.assertIsNone
+      ~Test_DeclareVariable.assertIsNot
+      ~Test_DeclareVariable.assertIsNotNone
+      ~Test_DeclareVariable.assertLess
+      ~Test_DeclareVariable.assertLessEqual
+      ~Test_DeclareVariable.assertListEqual
+      ~Test_DeclareVariable.assertLogs
+      ~Test_DeclareVariable.assertMultiLineEqual
+      ~Test_DeclareVariable.assertNotAlmostEqual
+      ~Test_DeclareVariable.assertNotAlmostEquals
+      ~Test_DeclareVariable.assertNotEqual
+      ~Test_DeclareVariable.assertNotEquals
+      ~Test_DeclareVariable.assertNotIn
+      ~Test_DeclareVariable.assertNotIsInstance
+      ~Test_DeclareVariable.assertNotRegex
+      ~Test_DeclareVariable.assertNotRegexpMatches
+      ~Test_DeclareVariable.assertRaises
+      ~Test_DeclareVariable.assertRaisesRegex
+      ~Test_DeclareVariable.assertRaisesRegexp
+      ~Test_DeclareVariable.assertRegex
+      ~Test_DeclareVariable.assertRegexpMatches
+      ~Test_DeclareVariable.assertSequenceEqual
+      ~Test_DeclareVariable.assertSetEqual
+      ~Test_DeclareVariable.assertTrue
+      ~Test_DeclareVariable.assertTupleEqual
+      ~Test_DeclareVariable.assertWarns
+      ~Test_DeclareVariable.assertWarnsRegex
+      ~Test_DeclareVariable.assert_
+      ~Test_DeclareVariable.countTestCases
+      ~Test_DeclareVariable.debug
+      ~Test_DeclareVariable.defaultTestResult
+      ~Test_DeclareVariable.doClassCleanups
+      ~Test_DeclareVariable.doCleanups
+      ~Test_DeclareVariable.fail
+      ~Test_DeclareVariable.failIf
+      ~Test_DeclareVariable.failIfAlmostEqual
+      ~Test_DeclareVariable.failIfEqual
+      ~Test_DeclareVariable.failUnless
+      ~Test_DeclareVariable.failUnlessAlmostEqual
+      ~Test_DeclareVariable.failUnlessEqual
+      ~Test_DeclareVariable.failUnlessRaises
+      ~Test_DeclareVariable.id
+      ~Test_DeclareVariable.run
+      ~Test_DeclareVariable.setUp
+      ~Test_DeclareVariable.setUpClass
+      ~Test_DeclareVariable.shortDescription
+      ~Test_DeclareVariable.skipTest
+      ~Test_DeclareVariable.subTest
+      ~Test_DeclareVariable.tearDown
+      ~Test_DeclareVariable.tearDownClass
+      ~Test_DeclareVariable.test_DeclareVariable_init_basic_type
+      ~Test_DeclareVariable.test_DeclareVariable_init_basic_type_value
+      ~Test_DeclareVariable.test_DeclareVariable_init_basic_type_value_comment
+      ~Test_DeclareVariable.test_DeclareVariable_init_basic_type_vector
+      ~Test_DeclareVariable.test_DeclareVariable_write_basic
+      ~Test_DeclareVariable.test_DeclareVariable_write_complex_array
+      ~Test_DeclareVariable.test_DeclareVariable_write_complex_float
+      ~Test_DeclareVariable.test_DeclareVariable_write_complex_int
+      ~Test_DeclareVariable.test_DeclareVariable_write_simple_array
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~Test_DeclareVariable.longMessage
+      ~Test_DeclareVariable.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_declare_variable.rst b/docs/source/_autosummary/mcstasscript.tests.test_declare_variable.rst
new file mode 100644
index 00000000..5cdc84dc
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_declare_variable.rst
@@ -0,0 +1,31 @@
+mcstasscript.tests.test\_declare\_variable
+==========================================
+
+.. automodule:: mcstasscript.tests.test_declare_variable
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      Test_DeclareVariable
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.TestDumpAndLoad.rst b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.TestDumpAndLoad.rst
new file mode 100644
index 00000000..c86fdfce
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.TestDumpAndLoad.rst
@@ -0,0 +1,98 @@
+mcstasscript.tests.test\_dump\_and\_load.TestDumpAndLoad
+========================================================
+
+.. currentmodule:: mcstasscript.tests.test_dump_and_load
+
+.. autoclass:: TestDumpAndLoad
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~TestDumpAndLoad.__init__
+      ~TestDumpAndLoad.addClassCleanup
+      ~TestDumpAndLoad.addCleanup
+      ~TestDumpAndLoad.addTypeEqualityFunc
+      ~TestDumpAndLoad.assertAlmostEqual
+      ~TestDumpAndLoad.assertAlmostEquals
+      ~TestDumpAndLoad.assertCountEqual
+      ~TestDumpAndLoad.assertDictContainsSubset
+      ~TestDumpAndLoad.assertDictEqual
+      ~TestDumpAndLoad.assertEqual
+      ~TestDumpAndLoad.assertEquals
+      ~TestDumpAndLoad.assertFalse
+      ~TestDumpAndLoad.assertGreater
+      ~TestDumpAndLoad.assertGreaterEqual
+      ~TestDumpAndLoad.assertIn
+      ~TestDumpAndLoad.assertIs
+      ~TestDumpAndLoad.assertIsInstance
+      ~TestDumpAndLoad.assertIsNone
+      ~TestDumpAndLoad.assertIsNot
+      ~TestDumpAndLoad.assertIsNotNone
+      ~TestDumpAndLoad.assertLess
+      ~TestDumpAndLoad.assertLessEqual
+      ~TestDumpAndLoad.assertListEqual
+      ~TestDumpAndLoad.assertLogs
+      ~TestDumpAndLoad.assertMultiLineEqual
+      ~TestDumpAndLoad.assertNotAlmostEqual
+      ~TestDumpAndLoad.assertNotAlmostEquals
+      ~TestDumpAndLoad.assertNotEqual
+      ~TestDumpAndLoad.assertNotEquals
+      ~TestDumpAndLoad.assertNotIn
+      ~TestDumpAndLoad.assertNotIsInstance
+      ~TestDumpAndLoad.assertNotRegex
+      ~TestDumpAndLoad.assertNotRegexpMatches
+      ~TestDumpAndLoad.assertRaises
+      ~TestDumpAndLoad.assertRaisesRegex
+      ~TestDumpAndLoad.assertRaisesRegexp
+      ~TestDumpAndLoad.assertRegex
+      ~TestDumpAndLoad.assertRegexpMatches
+      ~TestDumpAndLoad.assertSequenceEqual
+      ~TestDumpAndLoad.assertSetEqual
+      ~TestDumpAndLoad.assertTrue
+      ~TestDumpAndLoad.assertTupleEqual
+      ~TestDumpAndLoad.assertWarns
+      ~TestDumpAndLoad.assertWarnsRegex
+      ~TestDumpAndLoad.assert_
+      ~TestDumpAndLoad.countTestCases
+      ~TestDumpAndLoad.debug
+      ~TestDumpAndLoad.defaultTestResult
+      ~TestDumpAndLoad.doClassCleanups
+      ~TestDumpAndLoad.doCleanups
+      ~TestDumpAndLoad.fail
+      ~TestDumpAndLoad.failIf
+      ~TestDumpAndLoad.failIfAlmostEqual
+      ~TestDumpAndLoad.failIfEqual
+      ~TestDumpAndLoad.failUnless
+      ~TestDumpAndLoad.failUnlessAlmostEqual
+      ~TestDumpAndLoad.failUnlessEqual
+      ~TestDumpAndLoad.failUnlessRaises
+      ~TestDumpAndLoad.id
+      ~TestDumpAndLoad.run
+      ~TestDumpAndLoad.setUp
+      ~TestDumpAndLoad.setUpClass
+      ~TestDumpAndLoad.shortDescription
+      ~TestDumpAndLoad.skipTest
+      ~TestDumpAndLoad.subTest
+      ~TestDumpAndLoad.tearDown
+      ~TestDumpAndLoad.tearDownClass
+      ~TestDumpAndLoad.test_dump_simple
+      ~TestDumpAndLoad.test_load_simple
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~TestDumpAndLoad.longMessage
+      ~TestDumpAndLoad.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.rst b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.rst
new file mode 100644
index 00000000..cf236593
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.rst
@@ -0,0 +1,50 @@
+mcstasscript.tests.test\_dump\_and\_load
+========================================
+
+.. automodule:: mcstasscript.tests.test_dump_and_load
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      setup_instr_no_path
+      setup_instr_root_path
+      setup_instr_with_input_path
+      setup_instr_with_input_path_relative
+      setup_instr_with_path
+      setup_populated_instr
+      setup_populated_instr_with_dummy_path
+      setup_populated_with_some_options_instr
+      setup_populated_x_ray_instr
+      setup_populated_x_ray_instr_with_dummy_path
+      setup_x_ray_instr_no_path
+      setup_x_ray_instr_root_path
+      setup_x_ray_instr_with_path
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      TestDumpAndLoad
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_instr_no_path.rst b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_instr_no_path.rst
new file mode 100644
index 00000000..b5169c3c
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_instr_no_path.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_dump\_and\_load.setup\_instr\_no\_path
+===============================================================
+
+.. currentmodule:: mcstasscript.tests.test_dump_and_load
+
+.. autofunction:: setup_instr_no_path
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_instr_root_path.rst b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_instr_root_path.rst
new file mode 100644
index 00000000..e32846fd
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_instr_root_path.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_dump\_and\_load.setup\_instr\_root\_path
+=================================================================
+
+.. currentmodule:: mcstasscript.tests.test_dump_and_load
+
+.. autofunction:: setup_instr_root_path
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_instr_with_input_path.rst b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_instr_with_input_path.rst
new file mode 100644
index 00000000..8467ec9e
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_instr_with_input_path.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_dump\_and\_load.setup\_instr\_with\_input\_path
+========================================================================
+
+.. currentmodule:: mcstasscript.tests.test_dump_and_load
+
+.. autofunction:: setup_instr_with_input_path
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_instr_with_input_path_relative.rst b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_instr_with_input_path_relative.rst
new file mode 100644
index 00000000..e0cbfec2
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_instr_with_input_path_relative.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_dump\_and\_load.setup\_instr\_with\_input\_path\_relative
+==================================================================================
+
+.. currentmodule:: mcstasscript.tests.test_dump_and_load
+
+.. autofunction:: setup_instr_with_input_path_relative
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_instr_with_path.rst b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_instr_with_path.rst
new file mode 100644
index 00000000..43f0a23d
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_instr_with_path.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_dump\_and\_load.setup\_instr\_with\_path
+=================================================================
+
+.. currentmodule:: mcstasscript.tests.test_dump_and_load
+
+.. autofunction:: setup_instr_with_path
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_populated_instr.rst b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_populated_instr.rst
new file mode 100644
index 00000000..2aff5aab
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_populated_instr.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_dump\_and\_load.setup\_populated\_instr
+================================================================
+
+.. currentmodule:: mcstasscript.tests.test_dump_and_load
+
+.. autofunction:: setup_populated_instr
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_populated_instr_with_dummy_path.rst b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_populated_instr_with_dummy_path.rst
new file mode 100644
index 00000000..8c3ef682
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_populated_instr_with_dummy_path.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_dump\_and\_load.setup\_populated\_instr\_with\_dummy\_path
+===================================================================================
+
+.. currentmodule:: mcstasscript.tests.test_dump_and_load
+
+.. autofunction:: setup_populated_instr_with_dummy_path
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_populated_with_some_options_instr.rst b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_populated_with_some_options_instr.rst
new file mode 100644
index 00000000..aa75b9ed
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_populated_with_some_options_instr.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_dump\_and\_load.setup\_populated\_with\_some\_options\_instr
+=====================================================================================
+
+.. currentmodule:: mcstasscript.tests.test_dump_and_load
+
+.. autofunction:: setup_populated_with_some_options_instr
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_populated_x_ray_instr.rst b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_populated_x_ray_instr.rst
new file mode 100644
index 00000000..c3e25cb8
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_populated_x_ray_instr.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_dump\_and\_load.setup\_populated\_x\_ray\_instr
+========================================================================
+
+.. currentmodule:: mcstasscript.tests.test_dump_and_load
+
+.. autofunction:: setup_populated_x_ray_instr
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_populated_x_ray_instr_with_dummy_path.rst b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_populated_x_ray_instr_with_dummy_path.rst
new file mode 100644
index 00000000..5c2d1bbd
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_populated_x_ray_instr_with_dummy_path.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_dump\_and\_load.setup\_populated\_x\_ray\_instr\_with\_dummy\_path
+===========================================================================================
+
+.. currentmodule:: mcstasscript.tests.test_dump_and_load
+
+.. autofunction:: setup_populated_x_ray_instr_with_dummy_path
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_x_ray_instr_no_path.rst b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_x_ray_instr_no_path.rst
new file mode 100644
index 00000000..55134cfb
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_x_ray_instr_no_path.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_dump\_and\_load.setup\_x\_ray\_instr\_no\_path
+=======================================================================
+
+.. currentmodule:: mcstasscript.tests.test_dump_and_load
+
+.. autofunction:: setup_x_ray_instr_no_path
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_x_ray_instr_root_path.rst b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_x_ray_instr_root_path.rst
new file mode 100644
index 00000000..1cf1d3ac
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_x_ray_instr_root_path.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_dump\_and\_load.setup\_x\_ray\_instr\_root\_path
+=========================================================================
+
+.. currentmodule:: mcstasscript.tests.test_dump_and_load
+
+.. autofunction:: setup_x_ray_instr_root_path
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_x_ray_instr_with_path.rst b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_x_ray_instr_with_path.rst
new file mode 100644
index 00000000..ca614de0
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_dump_and_load.setup_x_ray_instr_with_path.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_dump\_and\_load.setup\_x\_ray\_instr\_with\_path
+=========================================================================
+
+.. currentmodule:: mcstasscript.tests.test_dump_and_load
+
+.. autofunction:: setup_x_ray_instr_with_path
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_formatting.TestFormatting.rst b/docs/source/_autosummary/mcstasscript.tests.test_formatting.TestFormatting.rst
new file mode 100644
index 00000000..4e00ae61
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_formatting.TestFormatting.rst
@@ -0,0 +1,104 @@
+mcstasscript.tests.test\_formatting.TestFormatting
+==================================================
+
+.. currentmodule:: mcstasscript.tests.test_formatting
+
+.. autoclass:: TestFormatting
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~TestFormatting.__init__
+      ~TestFormatting.addClassCleanup
+      ~TestFormatting.addCleanup
+      ~TestFormatting.addTypeEqualityFunc
+      ~TestFormatting.assertAlmostEqual
+      ~TestFormatting.assertAlmostEquals
+      ~TestFormatting.assertCountEqual
+      ~TestFormatting.assertDictContainsSubset
+      ~TestFormatting.assertDictEqual
+      ~TestFormatting.assertEqual
+      ~TestFormatting.assertEquals
+      ~TestFormatting.assertFalse
+      ~TestFormatting.assertGreater
+      ~TestFormatting.assertGreaterEqual
+      ~TestFormatting.assertIn
+      ~TestFormatting.assertIs
+      ~TestFormatting.assertIsInstance
+      ~TestFormatting.assertIsNone
+      ~TestFormatting.assertIsNot
+      ~TestFormatting.assertIsNotNone
+      ~TestFormatting.assertLess
+      ~TestFormatting.assertLessEqual
+      ~TestFormatting.assertListEqual
+      ~TestFormatting.assertLogs
+      ~TestFormatting.assertMultiLineEqual
+      ~TestFormatting.assertNotAlmostEqual
+      ~TestFormatting.assertNotAlmostEquals
+      ~TestFormatting.assertNotEqual
+      ~TestFormatting.assertNotEquals
+      ~TestFormatting.assertNotIn
+      ~TestFormatting.assertNotIsInstance
+      ~TestFormatting.assertNotRegex
+      ~TestFormatting.assertNotRegexpMatches
+      ~TestFormatting.assertRaises
+      ~TestFormatting.assertRaisesRegex
+      ~TestFormatting.assertRaisesRegexp
+      ~TestFormatting.assertRegex
+      ~TestFormatting.assertRegexpMatches
+      ~TestFormatting.assertSequenceEqual
+      ~TestFormatting.assertSetEqual
+      ~TestFormatting.assertTrue
+      ~TestFormatting.assertTupleEqual
+      ~TestFormatting.assertWarns
+      ~TestFormatting.assertWarnsRegex
+      ~TestFormatting.assert_
+      ~TestFormatting.countTestCases
+      ~TestFormatting.debug
+      ~TestFormatting.defaultTestResult
+      ~TestFormatting.doClassCleanups
+      ~TestFormatting.doCleanups
+      ~TestFormatting.fail
+      ~TestFormatting.failIf
+      ~TestFormatting.failIfAlmostEqual
+      ~TestFormatting.failIfEqual
+      ~TestFormatting.failUnless
+      ~TestFormatting.failUnlessAlmostEqual
+      ~TestFormatting.failUnlessEqual
+      ~TestFormatting.failUnlessRaises
+      ~TestFormatting.id
+      ~TestFormatting.run
+      ~TestFormatting.setUp
+      ~TestFormatting.setUpClass
+      ~TestFormatting.shortDescription
+      ~TestFormatting.skipTest
+      ~TestFormatting.subTest
+      ~TestFormatting.tearDown
+      ~TestFormatting.tearDownClass
+      ~TestFormatting.test_is_legal_filename_reject_backwards_dash
+      ~TestFormatting.test_is_legal_filename_reject_forward_dash
+      ~TestFormatting.test_is_legal_filename_rekect_space
+      ~TestFormatting.test_is_legal_filename_simple
+      ~TestFormatting.test_is_legal_parameter_reject_empty
+      ~TestFormatting.test_is_legal_parameter_reject_first_number
+      ~TestFormatting.test_is_legal_parameter_reject_space
+      ~TestFormatting.test_is_legal_parameter_simple
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~TestFormatting.longMessage
+      ~TestFormatting.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_formatting.rst b/docs/source/_autosummary/mcstasscript.tests.test_formatting.rst
new file mode 100644
index 00000000..f3e641b4
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_formatting.rst
@@ -0,0 +1,31 @@
+mcstasscript.tests.test\_formatting
+===================================
+
+.. automodule:: mcstasscript.tests.test_formatting
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      TestFormatting
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_functions.Test_load_data.rst b/docs/source/_autosummary/mcstasscript.tests.test_functions.Test_load_data.rst
new file mode 100644
index 00000000..b5ea20cd
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_functions.Test_load_data.rst
@@ -0,0 +1,97 @@
+mcstasscript.tests.test\_functions.Test\_load\_data
+===================================================
+
+.. currentmodule:: mcstasscript.tests.test_functions
+
+.. autoclass:: Test_load_data
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Test_load_data.__init__
+      ~Test_load_data.addClassCleanup
+      ~Test_load_data.addCleanup
+      ~Test_load_data.addTypeEqualityFunc
+      ~Test_load_data.assertAlmostEqual
+      ~Test_load_data.assertAlmostEquals
+      ~Test_load_data.assertCountEqual
+      ~Test_load_data.assertDictContainsSubset
+      ~Test_load_data.assertDictEqual
+      ~Test_load_data.assertEqual
+      ~Test_load_data.assertEquals
+      ~Test_load_data.assertFalse
+      ~Test_load_data.assertGreater
+      ~Test_load_data.assertGreaterEqual
+      ~Test_load_data.assertIn
+      ~Test_load_data.assertIs
+      ~Test_load_data.assertIsInstance
+      ~Test_load_data.assertIsNone
+      ~Test_load_data.assertIsNot
+      ~Test_load_data.assertIsNotNone
+      ~Test_load_data.assertLess
+      ~Test_load_data.assertLessEqual
+      ~Test_load_data.assertListEqual
+      ~Test_load_data.assertLogs
+      ~Test_load_data.assertMultiLineEqual
+      ~Test_load_data.assertNotAlmostEqual
+      ~Test_load_data.assertNotAlmostEquals
+      ~Test_load_data.assertNotEqual
+      ~Test_load_data.assertNotEquals
+      ~Test_load_data.assertNotIn
+      ~Test_load_data.assertNotIsInstance
+      ~Test_load_data.assertNotRegex
+      ~Test_load_data.assertNotRegexpMatches
+      ~Test_load_data.assertRaises
+      ~Test_load_data.assertRaisesRegex
+      ~Test_load_data.assertRaisesRegexp
+      ~Test_load_data.assertRegex
+      ~Test_load_data.assertRegexpMatches
+      ~Test_load_data.assertSequenceEqual
+      ~Test_load_data.assertSetEqual
+      ~Test_load_data.assertTrue
+      ~Test_load_data.assertTupleEqual
+      ~Test_load_data.assertWarns
+      ~Test_load_data.assertWarnsRegex
+      ~Test_load_data.assert_
+      ~Test_load_data.countTestCases
+      ~Test_load_data.debug
+      ~Test_load_data.defaultTestResult
+      ~Test_load_data.doClassCleanups
+      ~Test_load_data.doCleanups
+      ~Test_load_data.fail
+      ~Test_load_data.failIf
+      ~Test_load_data.failIfAlmostEqual
+      ~Test_load_data.failIfEqual
+      ~Test_load_data.failUnless
+      ~Test_load_data.failUnlessAlmostEqual
+      ~Test_load_data.failUnlessEqual
+      ~Test_load_data.failUnlessRaises
+      ~Test_load_data.id
+      ~Test_load_data.run
+      ~Test_load_data.setUp
+      ~Test_load_data.setUpClass
+      ~Test_load_data.shortDescription
+      ~Test_load_data.skipTest
+      ~Test_load_data.subTest
+      ~Test_load_data.tearDown
+      ~Test_load_data.tearDownClass
+      ~Test_load_data.test_mcrun_load_data_PSD4PI
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~Test_load_data.longMessage
+      ~Test_load_data.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_functions.Test_load_metadata.rst b/docs/source/_autosummary/mcstasscript.tests.test_functions.Test_load_metadata.rst
new file mode 100644
index 00000000..50b98783
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_functions.Test_load_metadata.rst
@@ -0,0 +1,97 @@
+mcstasscript.tests.test\_functions.Test\_load\_metadata
+=======================================================
+
+.. currentmodule:: mcstasscript.tests.test_functions
+
+.. autoclass:: Test_load_metadata
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Test_load_metadata.__init__
+      ~Test_load_metadata.addClassCleanup
+      ~Test_load_metadata.addCleanup
+      ~Test_load_metadata.addTypeEqualityFunc
+      ~Test_load_metadata.assertAlmostEqual
+      ~Test_load_metadata.assertAlmostEquals
+      ~Test_load_metadata.assertCountEqual
+      ~Test_load_metadata.assertDictContainsSubset
+      ~Test_load_metadata.assertDictEqual
+      ~Test_load_metadata.assertEqual
+      ~Test_load_metadata.assertEquals
+      ~Test_load_metadata.assertFalse
+      ~Test_load_metadata.assertGreater
+      ~Test_load_metadata.assertGreaterEqual
+      ~Test_load_metadata.assertIn
+      ~Test_load_metadata.assertIs
+      ~Test_load_metadata.assertIsInstance
+      ~Test_load_metadata.assertIsNone
+      ~Test_load_metadata.assertIsNot
+      ~Test_load_metadata.assertIsNotNone
+      ~Test_load_metadata.assertLess
+      ~Test_load_metadata.assertLessEqual
+      ~Test_load_metadata.assertListEqual
+      ~Test_load_metadata.assertLogs
+      ~Test_load_metadata.assertMultiLineEqual
+      ~Test_load_metadata.assertNotAlmostEqual
+      ~Test_load_metadata.assertNotAlmostEquals
+      ~Test_load_metadata.assertNotEqual
+      ~Test_load_metadata.assertNotEquals
+      ~Test_load_metadata.assertNotIn
+      ~Test_load_metadata.assertNotIsInstance
+      ~Test_load_metadata.assertNotRegex
+      ~Test_load_metadata.assertNotRegexpMatches
+      ~Test_load_metadata.assertRaises
+      ~Test_load_metadata.assertRaisesRegex
+      ~Test_load_metadata.assertRaisesRegexp
+      ~Test_load_metadata.assertRegex
+      ~Test_load_metadata.assertRegexpMatches
+      ~Test_load_metadata.assertSequenceEqual
+      ~Test_load_metadata.assertSetEqual
+      ~Test_load_metadata.assertTrue
+      ~Test_load_metadata.assertTupleEqual
+      ~Test_load_metadata.assertWarns
+      ~Test_load_metadata.assertWarnsRegex
+      ~Test_load_metadata.assert_
+      ~Test_load_metadata.countTestCases
+      ~Test_load_metadata.debug
+      ~Test_load_metadata.defaultTestResult
+      ~Test_load_metadata.doClassCleanups
+      ~Test_load_metadata.doCleanups
+      ~Test_load_metadata.fail
+      ~Test_load_metadata.failIf
+      ~Test_load_metadata.failIfAlmostEqual
+      ~Test_load_metadata.failIfEqual
+      ~Test_load_metadata.failUnless
+      ~Test_load_metadata.failUnlessAlmostEqual
+      ~Test_load_metadata.failUnlessEqual
+      ~Test_load_metadata.failUnlessRaises
+      ~Test_load_metadata.id
+      ~Test_load_metadata.run
+      ~Test_load_metadata.setUp
+      ~Test_load_metadata.setUpClass
+      ~Test_load_metadata.shortDescription
+      ~Test_load_metadata.skipTest
+      ~Test_load_metadata.subTest
+      ~Test_load_metadata.tearDown
+      ~Test_load_metadata.tearDownClass
+      ~Test_load_metadata.test_mcrun_load_metadata_PSD4PI
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~Test_load_metadata.longMessage
+      ~Test_load_metadata.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_functions.Test_load_monitor.rst b/docs/source/_autosummary/mcstasscript.tests.test_functions.Test_load_monitor.rst
new file mode 100644
index 00000000..97bc8099
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_functions.Test_load_monitor.rst
@@ -0,0 +1,97 @@
+mcstasscript.tests.test\_functions.Test\_load\_monitor
+======================================================
+
+.. currentmodule:: mcstasscript.tests.test_functions
+
+.. autoclass:: Test_load_monitor
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Test_load_monitor.__init__
+      ~Test_load_monitor.addClassCleanup
+      ~Test_load_monitor.addCleanup
+      ~Test_load_monitor.addTypeEqualityFunc
+      ~Test_load_monitor.assertAlmostEqual
+      ~Test_load_monitor.assertAlmostEquals
+      ~Test_load_monitor.assertCountEqual
+      ~Test_load_monitor.assertDictContainsSubset
+      ~Test_load_monitor.assertDictEqual
+      ~Test_load_monitor.assertEqual
+      ~Test_load_monitor.assertEquals
+      ~Test_load_monitor.assertFalse
+      ~Test_load_monitor.assertGreater
+      ~Test_load_monitor.assertGreaterEqual
+      ~Test_load_monitor.assertIn
+      ~Test_load_monitor.assertIs
+      ~Test_load_monitor.assertIsInstance
+      ~Test_load_monitor.assertIsNone
+      ~Test_load_monitor.assertIsNot
+      ~Test_load_monitor.assertIsNotNone
+      ~Test_load_monitor.assertLess
+      ~Test_load_monitor.assertLessEqual
+      ~Test_load_monitor.assertListEqual
+      ~Test_load_monitor.assertLogs
+      ~Test_load_monitor.assertMultiLineEqual
+      ~Test_load_monitor.assertNotAlmostEqual
+      ~Test_load_monitor.assertNotAlmostEquals
+      ~Test_load_monitor.assertNotEqual
+      ~Test_load_monitor.assertNotEquals
+      ~Test_load_monitor.assertNotIn
+      ~Test_load_monitor.assertNotIsInstance
+      ~Test_load_monitor.assertNotRegex
+      ~Test_load_monitor.assertNotRegexpMatches
+      ~Test_load_monitor.assertRaises
+      ~Test_load_monitor.assertRaisesRegex
+      ~Test_load_monitor.assertRaisesRegexp
+      ~Test_load_monitor.assertRegex
+      ~Test_load_monitor.assertRegexpMatches
+      ~Test_load_monitor.assertSequenceEqual
+      ~Test_load_monitor.assertSetEqual
+      ~Test_load_monitor.assertTrue
+      ~Test_load_monitor.assertTupleEqual
+      ~Test_load_monitor.assertWarns
+      ~Test_load_monitor.assertWarnsRegex
+      ~Test_load_monitor.assert_
+      ~Test_load_monitor.countTestCases
+      ~Test_load_monitor.debug
+      ~Test_load_monitor.defaultTestResult
+      ~Test_load_monitor.doClassCleanups
+      ~Test_load_monitor.doCleanups
+      ~Test_load_monitor.fail
+      ~Test_load_monitor.failIf
+      ~Test_load_monitor.failIfAlmostEqual
+      ~Test_load_monitor.failIfEqual
+      ~Test_load_monitor.failUnless
+      ~Test_load_monitor.failUnlessAlmostEqual
+      ~Test_load_monitor.failUnlessEqual
+      ~Test_load_monitor.failUnlessRaises
+      ~Test_load_monitor.id
+      ~Test_load_monitor.run
+      ~Test_load_monitor.setUp
+      ~Test_load_monitor.setUpClass
+      ~Test_load_monitor.shortDescription
+      ~Test_load_monitor.skipTest
+      ~Test_load_monitor.subTest
+      ~Test_load_monitor.tearDown
+      ~Test_load_monitor.tearDownClass
+      ~Test_load_monitor.test_mcrun_load_monitor_PSD4PI
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~Test_load_monitor.longMessage
+      ~Test_load_monitor.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_functions.Test_name_plot_options.rst b/docs/source/_autosummary/mcstasscript.tests.test_functions.Test_name_plot_options.rst
new file mode 100644
index 00000000..bb500a01
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_functions.Test_name_plot_options.rst
@@ -0,0 +1,98 @@
+mcstasscript.tests.test\_functions.Test\_name\_plot\_options
+============================================================
+
+.. currentmodule:: mcstasscript.tests.test_functions
+
+.. autoclass:: Test_name_plot_options
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Test_name_plot_options.__init__
+      ~Test_name_plot_options.addClassCleanup
+      ~Test_name_plot_options.addCleanup
+      ~Test_name_plot_options.addTypeEqualityFunc
+      ~Test_name_plot_options.assertAlmostEqual
+      ~Test_name_plot_options.assertAlmostEquals
+      ~Test_name_plot_options.assertCountEqual
+      ~Test_name_plot_options.assertDictContainsSubset
+      ~Test_name_plot_options.assertDictEqual
+      ~Test_name_plot_options.assertEqual
+      ~Test_name_plot_options.assertEquals
+      ~Test_name_plot_options.assertFalse
+      ~Test_name_plot_options.assertGreater
+      ~Test_name_plot_options.assertGreaterEqual
+      ~Test_name_plot_options.assertIn
+      ~Test_name_plot_options.assertIs
+      ~Test_name_plot_options.assertIsInstance
+      ~Test_name_plot_options.assertIsNone
+      ~Test_name_plot_options.assertIsNot
+      ~Test_name_plot_options.assertIsNotNone
+      ~Test_name_plot_options.assertLess
+      ~Test_name_plot_options.assertLessEqual
+      ~Test_name_plot_options.assertListEqual
+      ~Test_name_plot_options.assertLogs
+      ~Test_name_plot_options.assertMultiLineEqual
+      ~Test_name_plot_options.assertNotAlmostEqual
+      ~Test_name_plot_options.assertNotAlmostEquals
+      ~Test_name_plot_options.assertNotEqual
+      ~Test_name_plot_options.assertNotEquals
+      ~Test_name_plot_options.assertNotIn
+      ~Test_name_plot_options.assertNotIsInstance
+      ~Test_name_plot_options.assertNotRegex
+      ~Test_name_plot_options.assertNotRegexpMatches
+      ~Test_name_plot_options.assertRaises
+      ~Test_name_plot_options.assertRaisesRegex
+      ~Test_name_plot_options.assertRaisesRegexp
+      ~Test_name_plot_options.assertRegex
+      ~Test_name_plot_options.assertRegexpMatches
+      ~Test_name_plot_options.assertSequenceEqual
+      ~Test_name_plot_options.assertSetEqual
+      ~Test_name_plot_options.assertTrue
+      ~Test_name_plot_options.assertTupleEqual
+      ~Test_name_plot_options.assertWarns
+      ~Test_name_plot_options.assertWarnsRegex
+      ~Test_name_plot_options.assert_
+      ~Test_name_plot_options.countTestCases
+      ~Test_name_plot_options.debug
+      ~Test_name_plot_options.defaultTestResult
+      ~Test_name_plot_options.doClassCleanups
+      ~Test_name_plot_options.doCleanups
+      ~Test_name_plot_options.fail
+      ~Test_name_plot_options.failIf
+      ~Test_name_plot_options.failIfAlmostEqual
+      ~Test_name_plot_options.failIfEqual
+      ~Test_name_plot_options.failUnless
+      ~Test_name_plot_options.failUnlessAlmostEqual
+      ~Test_name_plot_options.failUnlessEqual
+      ~Test_name_plot_options.failUnlessRaises
+      ~Test_name_plot_options.id
+      ~Test_name_plot_options.run
+      ~Test_name_plot_options.setUp
+      ~Test_name_plot_options.setUpClass
+      ~Test_name_plot_options.shortDescription
+      ~Test_name_plot_options.skipTest
+      ~Test_name_plot_options.subTest
+      ~Test_name_plot_options.tearDown
+      ~Test_name_plot_options.tearDownClass
+      ~Test_name_plot_options.test_name_plot_options_duplicate
+      ~Test_name_plot_options.test_name_plot_options_simple
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~Test_name_plot_options.longMessage
+      ~Test_name_plot_options.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_functions.Test_name_search.rst b/docs/source/_autosummary/mcstasscript.tests.test_functions.Test_name_search.rst
new file mode 100644
index 00000000..468dfa63
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_functions.Test_name_search.rst
@@ -0,0 +1,103 @@
+mcstasscript.tests.test\_functions.Test\_name\_search
+=====================================================
+
+.. currentmodule:: mcstasscript.tests.test_functions
+
+.. autoclass:: Test_name_search
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Test_name_search.__init__
+      ~Test_name_search.addClassCleanup
+      ~Test_name_search.addCleanup
+      ~Test_name_search.addTypeEqualityFunc
+      ~Test_name_search.assertAlmostEqual
+      ~Test_name_search.assertAlmostEquals
+      ~Test_name_search.assertCountEqual
+      ~Test_name_search.assertDictContainsSubset
+      ~Test_name_search.assertDictEqual
+      ~Test_name_search.assertEqual
+      ~Test_name_search.assertEquals
+      ~Test_name_search.assertFalse
+      ~Test_name_search.assertGreater
+      ~Test_name_search.assertGreaterEqual
+      ~Test_name_search.assertIn
+      ~Test_name_search.assertIs
+      ~Test_name_search.assertIsInstance
+      ~Test_name_search.assertIsNone
+      ~Test_name_search.assertIsNot
+      ~Test_name_search.assertIsNotNone
+      ~Test_name_search.assertLess
+      ~Test_name_search.assertLessEqual
+      ~Test_name_search.assertListEqual
+      ~Test_name_search.assertLogs
+      ~Test_name_search.assertMultiLineEqual
+      ~Test_name_search.assertNotAlmostEqual
+      ~Test_name_search.assertNotAlmostEquals
+      ~Test_name_search.assertNotEqual
+      ~Test_name_search.assertNotEquals
+      ~Test_name_search.assertNotIn
+      ~Test_name_search.assertNotIsInstance
+      ~Test_name_search.assertNotRegex
+      ~Test_name_search.assertNotRegexpMatches
+      ~Test_name_search.assertRaises
+      ~Test_name_search.assertRaisesRegex
+      ~Test_name_search.assertRaisesRegexp
+      ~Test_name_search.assertRegex
+      ~Test_name_search.assertRegexpMatches
+      ~Test_name_search.assertSequenceEqual
+      ~Test_name_search.assertSetEqual
+      ~Test_name_search.assertTrue
+      ~Test_name_search.assertTupleEqual
+      ~Test_name_search.assertWarns
+      ~Test_name_search.assertWarnsRegex
+      ~Test_name_search.assert_
+      ~Test_name_search.countTestCases
+      ~Test_name_search.debug
+      ~Test_name_search.defaultTestResult
+      ~Test_name_search.doClassCleanups
+      ~Test_name_search.doCleanups
+      ~Test_name_search.fail
+      ~Test_name_search.failIf
+      ~Test_name_search.failIfAlmostEqual
+      ~Test_name_search.failIfEqual
+      ~Test_name_search.failUnless
+      ~Test_name_search.failUnlessAlmostEqual
+      ~Test_name_search.failUnlessEqual
+      ~Test_name_search.failUnlessRaises
+      ~Test_name_search.id
+      ~Test_name_search.run
+      ~Test_name_search.setUp
+      ~Test_name_search.setUpClass
+      ~Test_name_search.shortDescription
+      ~Test_name_search.skipTest
+      ~Test_name_search.subTest
+      ~Test_name_search.tearDown
+      ~Test_name_search.tearDownClass
+      ~Test_name_search.test_name_search_filename_read
+      ~Test_name_search.test_name_search_read
+      ~Test_name_search.test_name_search_read_duplicate
+      ~Test_name_search.test_name_search_read_error
+      ~Test_name_search.test_name_search_read_repeat
+      ~Test_name_search.test_name_search_type_error_not_McStasData
+      ~Test_name_search.test_name_search_type_error_not_list
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~Test_name_search.longMessage
+      ~Test_name_search.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_functions.rst b/docs/source/_autosummary/mcstasscript.tests.test_functions.rst
new file mode 100644
index 00000000..6f93bcac
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_functions.rst
@@ -0,0 +1,47 @@
+mcstasscript.tests.test\_functions
+==================================
+
+.. automodule:: mcstasscript.tests.test_functions
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      set_dummy_McStasDataBinned_1d
+      set_dummy_McStasDataBinned_2d
+      set_dummy_MetaDataBinned_1d
+      set_dummy_MetaDataBinned_2d
+      setup_McStasData_array
+      setup_McStasData_array_repeat
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      Test_load_data
+      Test_load_metadata
+      Test_load_monitor
+      Test_name_plot_options
+      Test_name_search
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_functions.set_dummy_McStasDataBinned_1d.rst b/docs/source/_autosummary/mcstasscript.tests.test_functions.set_dummy_McStasDataBinned_1d.rst
new file mode 100644
index 00000000..4fcf4154
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_functions.set_dummy_McStasDataBinned_1d.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_functions.set\_dummy\_McStasDataBinned\_1d
+===================================================================
+
+.. currentmodule:: mcstasscript.tests.test_functions
+
+.. autofunction:: set_dummy_McStasDataBinned_1d
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_functions.set_dummy_McStasDataBinned_2d.rst b/docs/source/_autosummary/mcstasscript.tests.test_functions.set_dummy_McStasDataBinned_2d.rst
new file mode 100644
index 00000000..2730ecba
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_functions.set_dummy_McStasDataBinned_2d.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_functions.set\_dummy\_McStasDataBinned\_2d
+===================================================================
+
+.. currentmodule:: mcstasscript.tests.test_functions
+
+.. autofunction:: set_dummy_McStasDataBinned_2d
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_functions.set_dummy_MetaDataBinned_1d.rst b/docs/source/_autosummary/mcstasscript.tests.test_functions.set_dummy_MetaDataBinned_1d.rst
new file mode 100644
index 00000000..96de12c2
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_functions.set_dummy_MetaDataBinned_1d.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_functions.set\_dummy\_MetaDataBinned\_1d
+=================================================================
+
+.. currentmodule:: mcstasscript.tests.test_functions
+
+.. autofunction:: set_dummy_MetaDataBinned_1d
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_functions.set_dummy_MetaDataBinned_2d.rst b/docs/source/_autosummary/mcstasscript.tests.test_functions.set_dummy_MetaDataBinned_2d.rst
new file mode 100644
index 00000000..08635b61
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_functions.set_dummy_MetaDataBinned_2d.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_functions.set\_dummy\_MetaDataBinned\_2d
+=================================================================
+
+.. currentmodule:: mcstasscript.tests.test_functions
+
+.. autofunction:: set_dummy_MetaDataBinned_2d
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_functions.setup_McStasData_array.rst b/docs/source/_autosummary/mcstasscript.tests.test_functions.setup_McStasData_array.rst
new file mode 100644
index 00000000..2ddba636
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_functions.setup_McStasData_array.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_functions.setup\_McStasData\_array
+===========================================================
+
+.. currentmodule:: mcstasscript.tests.test_functions
+
+.. autofunction:: setup_McStasData_array
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_functions.setup_McStasData_array_repeat.rst b/docs/source/_autosummary/mcstasscript.tests.test_functions.setup_McStasData_array_repeat.rst
new file mode 100644
index 00000000..d187ac71
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_functions.setup_McStasData_array_repeat.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_functions.setup\_McStasData\_array\_repeat
+===================================================================
+
+.. currentmodule:: mcstasscript.tests.test_functions
+
+.. autofunction:: setup_McStasData_array_repeat
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_parameter_variable.Test_ParameterVariable.rst b/docs/source/_autosummary/mcstasscript.tests.test_parameter_variable.Test_ParameterVariable.rst
new file mode 100644
index 00000000..ab3370c4
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_parameter_variable.Test_ParameterVariable.rst
@@ -0,0 +1,106 @@
+mcstasscript.tests.test\_parameter\_variable.Test\_ParameterVariable
+====================================================================
+
+.. currentmodule:: mcstasscript.tests.test_parameter_variable
+
+.. autoclass:: Test_ParameterVariable
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Test_ParameterVariable.__init__
+      ~Test_ParameterVariable.addClassCleanup
+      ~Test_ParameterVariable.addCleanup
+      ~Test_ParameterVariable.addTypeEqualityFunc
+      ~Test_ParameterVariable.assertAlmostEqual
+      ~Test_ParameterVariable.assertAlmostEquals
+      ~Test_ParameterVariable.assertCountEqual
+      ~Test_ParameterVariable.assertDictContainsSubset
+      ~Test_ParameterVariable.assertDictEqual
+      ~Test_ParameterVariable.assertEqual
+      ~Test_ParameterVariable.assertEquals
+      ~Test_ParameterVariable.assertFalse
+      ~Test_ParameterVariable.assertGreater
+      ~Test_ParameterVariable.assertGreaterEqual
+      ~Test_ParameterVariable.assertIn
+      ~Test_ParameterVariable.assertIs
+      ~Test_ParameterVariable.assertIsInstance
+      ~Test_ParameterVariable.assertIsNone
+      ~Test_ParameterVariable.assertIsNot
+      ~Test_ParameterVariable.assertIsNotNone
+      ~Test_ParameterVariable.assertLess
+      ~Test_ParameterVariable.assertLessEqual
+      ~Test_ParameterVariable.assertListEqual
+      ~Test_ParameterVariable.assertLogs
+      ~Test_ParameterVariable.assertMultiLineEqual
+      ~Test_ParameterVariable.assertNotAlmostEqual
+      ~Test_ParameterVariable.assertNotAlmostEquals
+      ~Test_ParameterVariable.assertNotEqual
+      ~Test_ParameterVariable.assertNotEquals
+      ~Test_ParameterVariable.assertNotIn
+      ~Test_ParameterVariable.assertNotIsInstance
+      ~Test_ParameterVariable.assertNotRegex
+      ~Test_ParameterVariable.assertNotRegexpMatches
+      ~Test_ParameterVariable.assertRaises
+      ~Test_ParameterVariable.assertRaisesRegex
+      ~Test_ParameterVariable.assertRaisesRegexp
+      ~Test_ParameterVariable.assertRegex
+      ~Test_ParameterVariable.assertRegexpMatches
+      ~Test_ParameterVariable.assertSequenceEqual
+      ~Test_ParameterVariable.assertSetEqual
+      ~Test_ParameterVariable.assertTrue
+      ~Test_ParameterVariable.assertTupleEqual
+      ~Test_ParameterVariable.assertWarns
+      ~Test_ParameterVariable.assertWarnsRegex
+      ~Test_ParameterVariable.assert_
+      ~Test_ParameterVariable.countTestCases
+      ~Test_ParameterVariable.debug
+      ~Test_ParameterVariable.defaultTestResult
+      ~Test_ParameterVariable.doClassCleanups
+      ~Test_ParameterVariable.doCleanups
+      ~Test_ParameterVariable.fail
+      ~Test_ParameterVariable.failIf
+      ~Test_ParameterVariable.failIfAlmostEqual
+      ~Test_ParameterVariable.failIfEqual
+      ~Test_ParameterVariable.failUnless
+      ~Test_ParameterVariable.failUnlessAlmostEqual
+      ~Test_ParameterVariable.failUnlessEqual
+      ~Test_ParameterVariable.failUnlessRaises
+      ~Test_ParameterVariable.id
+      ~Test_ParameterVariable.run
+      ~Test_ParameterVariable.setUp
+      ~Test_ParameterVariable.setUpClass
+      ~Test_ParameterVariable.shortDescription
+      ~Test_ParameterVariable.skipTest
+      ~Test_ParameterVariable.subTest
+      ~Test_ParameterVariable.tearDown
+      ~Test_ParameterVariable.tearDownClass
+      ~Test_ParameterVariable.test_ParameterVariable_init_basic
+      ~Test_ParameterVariable.test_ParameterVariable_init_basic_type
+      ~Test_ParameterVariable.test_ParameterVariable_init_basic_type_value
+      ~Test_ParameterVariable.test_ParameterVariable_init_basic_type_value_comment
+      ~Test_ParameterVariable.test_ParameterVariable_init_basic_value_comment
+      ~Test_ParameterVariable.test_ParameterVariable_init_options_initialize
+      ~Test_ParameterVariable.test_ParameterVariable_write_basic
+      ~Test_ParameterVariable.test_ParameterVariable_write_complex_float
+      ~Test_ParameterVariable.test_ParameterVariable_write_complex_int
+      ~Test_ParameterVariable.test_ParameterVariable_write_complex_string
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~Test_ParameterVariable.longMessage
+      ~Test_ParameterVariable.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_parameter_variable.rst b/docs/source/_autosummary/mcstasscript.tests.test_parameter_variable.rst
new file mode 100644
index 00000000..f0bc5a2c
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_parameter_variable.rst
@@ -0,0 +1,31 @@
+mcstasscript.tests.test\_parameter\_variable
+============================================
+
+.. automodule:: mcstasscript.tests.test_parameter_variable
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      Test_ParameterVariable
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.FakeChange.rst b/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.FakeChange.rst
new file mode 100644
index 00000000..b6467595
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.FakeChange.rst
@@ -0,0 +1,23 @@
+mcstasscript.tests.test\_plot\_interface.FakeChange
+===================================================
+
+.. currentmodule:: mcstasscript.tests.test_plot_interface
+
+.. autoclass:: FakeChange
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~FakeChange.__init__
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.TestPlotInterface.rst b/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.TestPlotInterface.rst
new file mode 100644
index 00000000..d60c50ff
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.TestPlotInterface.rst
@@ -0,0 +1,104 @@
+mcstasscript.tests.test\_plot\_interface.TestPlotInterface
+==========================================================
+
+.. currentmodule:: mcstasscript.tests.test_plot_interface
+
+.. autoclass:: TestPlotInterface
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~TestPlotInterface.__init__
+      ~TestPlotInterface.addClassCleanup
+      ~TestPlotInterface.addCleanup
+      ~TestPlotInterface.addTypeEqualityFunc
+      ~TestPlotInterface.assertAlmostEqual
+      ~TestPlotInterface.assertAlmostEquals
+      ~TestPlotInterface.assertCountEqual
+      ~TestPlotInterface.assertDictContainsSubset
+      ~TestPlotInterface.assertDictEqual
+      ~TestPlotInterface.assertEqual
+      ~TestPlotInterface.assertEquals
+      ~TestPlotInterface.assertFalse
+      ~TestPlotInterface.assertGreater
+      ~TestPlotInterface.assertGreaterEqual
+      ~TestPlotInterface.assertIn
+      ~TestPlotInterface.assertIs
+      ~TestPlotInterface.assertIsInstance
+      ~TestPlotInterface.assertIsNone
+      ~TestPlotInterface.assertIsNot
+      ~TestPlotInterface.assertIsNotNone
+      ~TestPlotInterface.assertLess
+      ~TestPlotInterface.assertLessEqual
+      ~TestPlotInterface.assertListEqual
+      ~TestPlotInterface.assertLogs
+      ~TestPlotInterface.assertMultiLineEqual
+      ~TestPlotInterface.assertNotAlmostEqual
+      ~TestPlotInterface.assertNotAlmostEquals
+      ~TestPlotInterface.assertNotEqual
+      ~TestPlotInterface.assertNotEquals
+      ~TestPlotInterface.assertNotIn
+      ~TestPlotInterface.assertNotIsInstance
+      ~TestPlotInterface.assertNotRegex
+      ~TestPlotInterface.assertNotRegexpMatches
+      ~TestPlotInterface.assertRaises
+      ~TestPlotInterface.assertRaisesRegex
+      ~TestPlotInterface.assertRaisesRegexp
+      ~TestPlotInterface.assertRegex
+      ~TestPlotInterface.assertRegexpMatches
+      ~TestPlotInterface.assertSequenceEqual
+      ~TestPlotInterface.assertSetEqual
+      ~TestPlotInterface.assertTrue
+      ~TestPlotInterface.assertTupleEqual
+      ~TestPlotInterface.assertWarns
+      ~TestPlotInterface.assertWarnsRegex
+      ~TestPlotInterface.assert_
+      ~TestPlotInterface.countTestCases
+      ~TestPlotInterface.debug
+      ~TestPlotInterface.defaultTestResult
+      ~TestPlotInterface.doClassCleanups
+      ~TestPlotInterface.doCleanups
+      ~TestPlotInterface.fail
+      ~TestPlotInterface.failIf
+      ~TestPlotInterface.failIfAlmostEqual
+      ~TestPlotInterface.failIfEqual
+      ~TestPlotInterface.failUnless
+      ~TestPlotInterface.failUnlessAlmostEqual
+      ~TestPlotInterface.failUnlessEqual
+      ~TestPlotInterface.failUnlessRaises
+      ~TestPlotInterface.id
+      ~TestPlotInterface.run
+      ~TestPlotInterface.setUp
+      ~TestPlotInterface.setUpClass
+      ~TestPlotInterface.shortDescription
+      ~TestPlotInterface.skipTest
+      ~TestPlotInterface.subTest
+      ~TestPlotInterface.tearDown
+      ~TestPlotInterface.tearDownClass
+      ~TestPlotInterface.test_initialization_with_data
+      ~TestPlotInterface.test_initialization_without_data
+      ~TestPlotInterface.test_set_colormap
+      ~TestPlotInterface.test_set_current_monitor
+      ~TestPlotInterface.test_set_data
+      ~TestPlotInterface.test_set_log_mode
+      ~TestPlotInterface.test_set_orders_of_mag
+      ~TestPlotInterface.test_show_interface_return
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~TestPlotInterface.longMessage
+      ~TestPlotInterface.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.fake_data.rst b/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.fake_data.rst
new file mode 100644
index 00000000..a3b03d77
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.fake_data.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_plot\_interface.fake\_data
+===================================================
+
+.. currentmodule:: mcstasscript.tests.test_plot_interface
+
+.. autofunction:: fake_data
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.rst b/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.rst
new file mode 100644
index 00000000..e315e7e2
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.rst
@@ -0,0 +1,43 @@
+mcstasscript.tests.test\_plot\_interface
+========================================
+
+.. automodule:: mcstasscript.tests.test_plot_interface
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      fake_data
+      set_dummy_McStasDataBinned_1d
+      set_dummy_McStasDataBinned_2d
+      set_dummy_MetaDataBinned_1d
+      set_dummy_MetaDataBinned_2d
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      FakeChange
+      TestPlotInterface
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.set_dummy_McStasDataBinned_1d.rst b/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.set_dummy_McStasDataBinned_1d.rst
new file mode 100644
index 00000000..d2fb46f5
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.set_dummy_McStasDataBinned_1d.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_plot\_interface.set\_dummy\_McStasDataBinned\_1d
+=========================================================================
+
+.. currentmodule:: mcstasscript.tests.test_plot_interface
+
+.. autofunction:: set_dummy_McStasDataBinned_1d
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.set_dummy_McStasDataBinned_2d.rst b/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.set_dummy_McStasDataBinned_2d.rst
new file mode 100644
index 00000000..2b8a7c3d
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.set_dummy_McStasDataBinned_2d.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_plot\_interface.set\_dummy\_McStasDataBinned\_2d
+=========================================================================
+
+.. currentmodule:: mcstasscript.tests.test_plot_interface
+
+.. autofunction:: set_dummy_McStasDataBinned_2d
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.set_dummy_MetaDataBinned_1d.rst b/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.set_dummy_MetaDataBinned_1d.rst
new file mode 100644
index 00000000..7c9ffd17
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.set_dummy_MetaDataBinned_1d.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_plot\_interface.set\_dummy\_MetaDataBinned\_1d
+=======================================================================
+
+.. currentmodule:: mcstasscript.tests.test_plot_interface
+
+.. autofunction:: set_dummy_MetaDataBinned_1d
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.set_dummy_MetaDataBinned_2d.rst b/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.set_dummy_MetaDataBinned_2d.rst
new file mode 100644
index 00000000..14180919
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_plot_interface.set_dummy_MetaDataBinned_2d.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_plot\_interface.set\_dummy\_MetaDataBinned\_2d
+=======================================================================
+
+.. currentmodule:: mcstasscript.tests.test_plot_interface
+
+.. autofunction:: set_dummy_MetaDataBinned_2d
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.FakeChange.rst b/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.FakeChange.rst
new file mode 100644
index 00000000..7185e840
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.FakeChange.rst
@@ -0,0 +1,23 @@
+mcstasscript.tests.test\_simulation\_interface.FakeChange
+=========================================================
+
+.. currentmodule:: mcstasscript.tests.test_simulation_interface
+
+.. autoclass:: FakeChange
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~FakeChange.__init__
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.TestSimulationInterface.rst b/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.TestSimulationInterface.rst
new file mode 100644
index 00000000..440b2252
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.TestSimulationInterface.rst
@@ -0,0 +1,103 @@
+mcstasscript.tests.test\_simulation\_interface.TestSimulationInterface
+======================================================================
+
+.. currentmodule:: mcstasscript.tests.test_simulation_interface
+
+.. autoclass:: TestSimulationInterface
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~TestSimulationInterface.__init__
+      ~TestSimulationInterface.addClassCleanup
+      ~TestSimulationInterface.addCleanup
+      ~TestSimulationInterface.addTypeEqualityFunc
+      ~TestSimulationInterface.assertAlmostEqual
+      ~TestSimulationInterface.assertAlmostEquals
+      ~TestSimulationInterface.assertCountEqual
+      ~TestSimulationInterface.assertDictContainsSubset
+      ~TestSimulationInterface.assertDictEqual
+      ~TestSimulationInterface.assertEqual
+      ~TestSimulationInterface.assertEquals
+      ~TestSimulationInterface.assertFalse
+      ~TestSimulationInterface.assertGreater
+      ~TestSimulationInterface.assertGreaterEqual
+      ~TestSimulationInterface.assertIn
+      ~TestSimulationInterface.assertIs
+      ~TestSimulationInterface.assertIsInstance
+      ~TestSimulationInterface.assertIsNone
+      ~TestSimulationInterface.assertIsNot
+      ~TestSimulationInterface.assertIsNotNone
+      ~TestSimulationInterface.assertLess
+      ~TestSimulationInterface.assertLessEqual
+      ~TestSimulationInterface.assertListEqual
+      ~TestSimulationInterface.assertLogs
+      ~TestSimulationInterface.assertMultiLineEqual
+      ~TestSimulationInterface.assertNotAlmostEqual
+      ~TestSimulationInterface.assertNotAlmostEquals
+      ~TestSimulationInterface.assertNotEqual
+      ~TestSimulationInterface.assertNotEquals
+      ~TestSimulationInterface.assertNotIn
+      ~TestSimulationInterface.assertNotIsInstance
+      ~TestSimulationInterface.assertNotRegex
+      ~TestSimulationInterface.assertNotRegexpMatches
+      ~TestSimulationInterface.assertRaises
+      ~TestSimulationInterface.assertRaisesRegex
+      ~TestSimulationInterface.assertRaisesRegexp
+      ~TestSimulationInterface.assertRegex
+      ~TestSimulationInterface.assertRegexpMatches
+      ~TestSimulationInterface.assertSequenceEqual
+      ~TestSimulationInterface.assertSetEqual
+      ~TestSimulationInterface.assertTrue
+      ~TestSimulationInterface.assertTupleEqual
+      ~TestSimulationInterface.assertWarns
+      ~TestSimulationInterface.assertWarnsRegex
+      ~TestSimulationInterface.assert_
+      ~TestSimulationInterface.countTestCases
+      ~TestSimulationInterface.debug
+      ~TestSimulationInterface.defaultTestResult
+      ~TestSimulationInterface.doClassCleanups
+      ~TestSimulationInterface.doCleanups
+      ~TestSimulationInterface.fail
+      ~TestSimulationInterface.failIf
+      ~TestSimulationInterface.failIfAlmostEqual
+      ~TestSimulationInterface.failIfEqual
+      ~TestSimulationInterface.failUnless
+      ~TestSimulationInterface.failUnlessAlmostEqual
+      ~TestSimulationInterface.failUnlessEqual
+      ~TestSimulationInterface.failUnlessRaises
+      ~TestSimulationInterface.id
+      ~TestSimulationInterface.run
+      ~TestSimulationInterface.setUp
+      ~TestSimulationInterface.setUpClass
+      ~TestSimulationInterface.shortDescription
+      ~TestSimulationInterface.skipTest
+      ~TestSimulationInterface.subTest
+      ~TestSimulationInterface.tearDown
+      ~TestSimulationInterface.tearDownClass
+      ~TestSimulationInterface.test_ParameterWidget
+      ~TestSimulationInterface.test_initialization_McStas
+      ~TestSimulationInterface.test_initialization_McXtrace
+      ~TestSimulationInterface.test_show_interface_McStas
+      ~TestSimulationInterface.test_show_interface_McXtrace
+      ~TestSimulationInterface.test_update_mpi
+      ~TestSimulationInterface.test_update_ncount
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~TestSimulationInterface.longMessage
+      ~TestSimulationInterface.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.rst b/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.rst
new file mode 100644
index 00000000..5983b3fa
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.rst
@@ -0,0 +1,42 @@
+mcstasscript.tests.test\_simulation\_interface
+==============================================
+
+.. automodule:: mcstasscript.tests.test_simulation_interface
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      setup_instr_root_path_McStas
+      setup_instr_root_path_McXtrace
+      setup_populated_instr_McStas
+      setup_populated_instr_McXtrace
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      FakeChange
+      TestSimulationInterface
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.setup_instr_root_path_McStas.rst b/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.setup_instr_root_path_McStas.rst
new file mode 100644
index 00000000..a7d070d6
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.setup_instr_root_path_McStas.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_simulation\_interface.setup\_instr\_root\_path\_McStas
+===============================================================================
+
+.. currentmodule:: mcstasscript.tests.test_simulation_interface
+
+.. autofunction:: setup_instr_root_path_McStas
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.setup_instr_root_path_McXtrace.rst b/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.setup_instr_root_path_McXtrace.rst
new file mode 100644
index 00000000..7a1f0a3e
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.setup_instr_root_path_McXtrace.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_simulation\_interface.setup\_instr\_root\_path\_McXtrace
+=================================================================================
+
+.. currentmodule:: mcstasscript.tests.test_simulation_interface
+
+.. autofunction:: setup_instr_root_path_McXtrace
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.setup_populated_instr_McStas.rst b/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.setup_populated_instr_McStas.rst
new file mode 100644
index 00000000..768aaf1d
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.setup_populated_instr_McStas.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_simulation\_interface.setup\_populated\_instr\_McStas
+==============================================================================
+
+.. currentmodule:: mcstasscript.tests.test_simulation_interface
+
+.. autofunction:: setup_populated_instr_McStas
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.setup_populated_instr_McXtrace.rst b/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.setup_populated_instr_McXtrace.rst
new file mode 100644
index 00000000..b0ea1152
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_simulation_interface.setup_populated_instr_McXtrace.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.test\_simulation\_interface.setup\_populated\_instr\_McXtrace
+================================================================================
+
+.. currentmodule:: mcstasscript.tests.test_simulation_interface
+
+.. autofunction:: setup_populated_instr_McXtrace
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_widget_helpers.TestWidgetHelpers.rst b/docs/source/_autosummary/mcstasscript.tests.test_widget_helpers.TestWidgetHelpers.rst
new file mode 100644
index 00000000..cb5ebbcf
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_widget_helpers.TestWidgetHelpers.rst
@@ -0,0 +1,103 @@
+mcstasscript.tests.test\_widget\_helpers.TestWidgetHelpers
+==========================================================
+
+.. currentmodule:: mcstasscript.tests.test_widget_helpers
+
+.. autoclass:: TestWidgetHelpers
+   :members:
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~TestWidgetHelpers.__init__
+      ~TestWidgetHelpers.addClassCleanup
+      ~TestWidgetHelpers.addCleanup
+      ~TestWidgetHelpers.addTypeEqualityFunc
+      ~TestWidgetHelpers.assertAlmostEqual
+      ~TestWidgetHelpers.assertAlmostEquals
+      ~TestWidgetHelpers.assertCountEqual
+      ~TestWidgetHelpers.assertDictContainsSubset
+      ~TestWidgetHelpers.assertDictEqual
+      ~TestWidgetHelpers.assertEqual
+      ~TestWidgetHelpers.assertEquals
+      ~TestWidgetHelpers.assertFalse
+      ~TestWidgetHelpers.assertGreater
+      ~TestWidgetHelpers.assertGreaterEqual
+      ~TestWidgetHelpers.assertIn
+      ~TestWidgetHelpers.assertIs
+      ~TestWidgetHelpers.assertIsInstance
+      ~TestWidgetHelpers.assertIsNone
+      ~TestWidgetHelpers.assertIsNot
+      ~TestWidgetHelpers.assertIsNotNone
+      ~TestWidgetHelpers.assertLess
+      ~TestWidgetHelpers.assertLessEqual
+      ~TestWidgetHelpers.assertListEqual
+      ~TestWidgetHelpers.assertLogs
+      ~TestWidgetHelpers.assertMultiLineEqual
+      ~TestWidgetHelpers.assertNotAlmostEqual
+      ~TestWidgetHelpers.assertNotAlmostEquals
+      ~TestWidgetHelpers.assertNotEqual
+      ~TestWidgetHelpers.assertNotEquals
+      ~TestWidgetHelpers.assertNotIn
+      ~TestWidgetHelpers.assertNotIsInstance
+      ~TestWidgetHelpers.assertNotRegex
+      ~TestWidgetHelpers.assertNotRegexpMatches
+      ~TestWidgetHelpers.assertRaises
+      ~TestWidgetHelpers.assertRaisesRegex
+      ~TestWidgetHelpers.assertRaisesRegexp
+      ~TestWidgetHelpers.assertRegex
+      ~TestWidgetHelpers.assertRegexpMatches
+      ~TestWidgetHelpers.assertSequenceEqual
+      ~TestWidgetHelpers.assertSetEqual
+      ~TestWidgetHelpers.assertTrue
+      ~TestWidgetHelpers.assertTupleEqual
+      ~TestWidgetHelpers.assertWarns
+      ~TestWidgetHelpers.assertWarnsRegex
+      ~TestWidgetHelpers.assert_
+      ~TestWidgetHelpers.countTestCases
+      ~TestWidgetHelpers.debug
+      ~TestWidgetHelpers.defaultTestResult
+      ~TestWidgetHelpers.doClassCleanups
+      ~TestWidgetHelpers.doCleanups
+      ~TestWidgetHelpers.fail
+      ~TestWidgetHelpers.failIf
+      ~TestWidgetHelpers.failIfAlmostEqual
+      ~TestWidgetHelpers.failIfEqual
+      ~TestWidgetHelpers.failUnless
+      ~TestWidgetHelpers.failUnlessAlmostEqual
+      ~TestWidgetHelpers.failUnlessEqual
+      ~TestWidgetHelpers.failUnlessRaises
+      ~TestWidgetHelpers.id
+      ~TestWidgetHelpers.run
+      ~TestWidgetHelpers.setUp
+      ~TestWidgetHelpers.setUpClass
+      ~TestWidgetHelpers.shortDescription
+      ~TestWidgetHelpers.skipTest
+      ~TestWidgetHelpers.subTest
+      ~TestWidgetHelpers.tearDown
+      ~TestWidgetHelpers.tearDownClass
+      ~TestWidgetHelpers.test_HiddenPrints
+      ~TestWidgetHelpers.test_get_parameter_default_double
+      ~TestWidgetHelpers.test_get_parameter_default_double_specified
+      ~TestWidgetHelpers.test_get_parameter_default_int
+      ~TestWidgetHelpers.test_get_parameter_default_string
+      ~TestWidgetHelpers.test_parameter_has_default_false
+      ~TestWidgetHelpers.test_parameter_has_default_true
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~TestWidgetHelpers.longMessage
+      ~TestWidgetHelpers.maxDiff
+   
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/mcstasscript.tests.test_widget_helpers.rst b/docs/source/_autosummary/mcstasscript.tests.test_widget_helpers.rst
new file mode 100644
index 00000000..8981d181
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.test_widget_helpers.rst
@@ -0,0 +1,31 @@
+mcstasscript.tests.test\_widget\_helpers
+========================================
+
+.. automodule:: mcstasscript.tests.test_widget_helpers
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      TestWidgetHelpers
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.utilities.rst b/docs/source/_autosummary/mcstasscript.tests.utilities.rst
new file mode 100644
index 00000000..03b15f80
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.utilities.rst
@@ -0,0 +1,30 @@
+mcstasscript.tests.utilities
+============================
+
+.. automodule:: mcstasscript.tests.utilities
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      work_dir_test
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/_autosummary/mcstasscript.tests.utilities.work_dir_test.rst b/docs/source/_autosummary/mcstasscript.tests.utilities.work_dir_test.rst
new file mode 100644
index 00000000..290c7dd6
--- /dev/null
+++ b/docs/source/_autosummary/mcstasscript.tests.utilities.work_dir_test.rst
@@ -0,0 +1,6 @@
+mcstasscript.tests.utilities.work\_dir\_test
+============================================
+
+.. currentmodule:: mcstasscript.tests.utilities
+
+.. autofunction:: work_dir_test
\ No newline at end of file
diff --git a/docs/source/_templates/class.rst b/docs/source/_templates/class.rst
new file mode 100644
index 00000000..0f7d6f32
--- /dev/null
+++ b/docs/source/_templates/class.rst
@@ -0,0 +1,29 @@
+{{ fullname | escape | underline}}
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+
+   {% block methods %}
+   .. automethod:: __init__
+
+   {% if methods %}
+   .. rubric:: {{ _('Methods') }}
+
+   .. autosummary::
+   {% for item in methods %}
+      ~{{ name }}.{{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block attributes %}
+   {% if attributes %}
+   .. rubric:: {{ _('Attributes') }}
+
+   .. autosummary::
+   {% for item in attributes %}
+      ~{{ name }}.{{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
diff --git a/docs/source/_templates/custom-class-template.rst b/docs/source/_templates/custom-class-template.rst
new file mode 100644
index 00000000..893443b1
--- /dev/null
+++ b/docs/source/_templates/custom-class-template.rst
@@ -0,0 +1,30 @@
+{{ fullname | escape | underline }}
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+   :members:
+
+   {% block methods %}
+   .. automethod:: __init__
+
+   {% if methods %}
+   .. rubric:: {{ _('Methods') }}
+
+   .. autosummary::
+   {% for item in methods %}
+      ~{{ name }}.{{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block attributes %}
+   {% if attributes %}
+   .. rubric:: {{ _('Attributes') }}
+
+   .. autosummary::
+   {% for item in attributes %}
+      ~{{ name }}.{{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
diff --git a/docs/source/_templates/custom-module-template.rst b/docs/source/_templates/custom-module-template.rst
new file mode 100644
index 00000000..f46f9f63
--- /dev/null
+++ b/docs/source/_templates/custom-module-template.rst
@@ -0,0 +1,66 @@
+{{ fullname | escape | underline}}
+
+.. automodule:: {{ fullname }}
+
+   {% block attributes %}
+   {% if attributes %}
+   .. rubric:: {{ _('Module Attributes') }}
+
+   .. autosummary::
+      :toctree:
+   {% for item in attributes %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block functions %}
+   {% if functions %}
+   .. rubric:: {{ _('Functions') }}
+
+   .. autosummary::
+      :toctree:
+   {% for item in functions %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block classes %}
+   {% if classes %}
+   .. rubric:: {{ _('Classes') }}
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   {% for item in classes %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block exceptions %}
+   {% if exceptions %}
+   .. rubric:: {{ _('Exceptions') }}
+
+   .. autosummary::
+      :toctree:
+   {% for item in exceptions %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+{% block modules %}
+{% if modules %}
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: custom-module-template.rst
+   :recursive:
+{% for item in modules %}
+   {{ item }}
+{%- endfor %}
+{% endif %}
+{% endblock %}
diff --git a/docs/source/_templates/module.rst b/docs/source/_templates/module.rst
new file mode 100644
index 00000000..e74c012f
--- /dev/null
+++ b/docs/source/_templates/module.rst
@@ -0,0 +1,60 @@
+{{ fullname | escape | underline}}
+
+.. automodule:: {{ fullname }}
+
+   {% block attributes %}
+   {% if attributes %}
+   .. rubric:: {{ _('Module Attributes') }}
+
+   .. autosummary::
+   {% for item in attributes %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block functions %}
+   {% if functions %}
+   .. rubric:: {{ _('Functions') }}
+
+   .. autosummary::
+   {% for item in functions %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block classes %}
+   {% if classes %}
+   .. rubric:: {{ _('Classes') }}
+
+   .. autosummary::
+   {% for item in classes %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block exceptions %}
+   {% if exceptions %}
+   .. rubric:: {{ _('Exceptions') }}
+
+   .. autosummary::
+   {% for item in exceptions %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+{% block modules %}
+{% if modules %}
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :recursive:
+{% for item in modules %}
+   {{ item }}
+{%- endfor %}
+{% endif %}
+{% endblock %}
diff --git a/docs/source/conf.py b/docs/source/conf.py
new file mode 100644
index 00000000..d0fb33ee
--- /dev/null
+++ b/docs/source/conf.py
@@ -0,0 +1,82 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+import os
+import sys
+sys.path.insert(0, os.path.abspath('../../mcstasscript'))
+sys.path.insert(0, os.path.abspath('../..'))
+
+print(sys.path)
+
+# -- Project information -----------------------------------------------------
+
+project = 'McStasScript'
+copyright = '2022, Mads Bertelsen'
+author = 'Mads Bertelsen'
+
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = ['sphinx.ext.autodoc',
+              'sphinx.ext.autosummary',
+              'sphinx.ext.napoleon',
+              #'nbsphinx',
+              'myst_nb',
+              ]
+
+autosummary_generate = True
+
+# Add any paths that contain templates here, relative to this directory.
+#templates_path = ['source/_templates']
+templates_path = ['_templates']
+
+master_doc = 'index'
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = []
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = 'sphinx_book_theme'
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+#
+html_theme_options = {
+    #"logo_only": True,
+    "repository_url": "https://github.com/PaNOSC-ViNYL/McStasScript",
+    "repository_branch": "mcstasscript_on_libpyvinyl",
+    "path_to_docs": "docs",
+    "use_repository_button": True,
+    "use_issues_button": True,
+    "use_edit_page_button": True,
+    "show_toc_level": 2,  # Show subheadings in secondary sidebar
+}
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+#man_pages = [(master_doc, 'McStasScript', u'McStasScript Documentation', 'Mads Bertelsen', 1)]
diff --git a/docs/source/getting_started/installation.ipynb b/docs/source/getting_started/installation.ipynb
new file mode 100644
index 00000000..7735bef1
--- /dev/null
+++ b/docs/source/getting_started/installation.ipynb
@@ -0,0 +1,149 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "decent-error",
+   "metadata": {},
+   "source": [
+    "# Installation\n",
+    "McStasScript needs to be installed and then configured. There are two ways to install, either through pip or from the source code directly. The configuration can be done after installation and changed at any point.\n",
+    "\n",
+    "Before installing McStasScript, install McStas or McXtrace as described on [www.mcstas.org](https://www.mcstas.org) or [www.mcxtrace.org](https://www.mcxtrace.org)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "crude-continuity",
+   "metadata": {},
+   "source": [
+    "## Pip\n",
+    "The standard way to install McStasScript is through pip. For Unix and OS X, use this line in the terminal. For windows, use the McStas-shell or McXtrace-shell to run the install."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ceramic-community",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "pip install McStasScript --upgrade\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "introductory-monitor",
+   "metadata": {},
+   "source": [
+    "## From source\n",
+    "A developer might one to install directly from source, this makes it easier to contribute to the project and see how changes to the code affect the package.\n",
+    "\n",
+    "Open a terminal and go to the location where the source code should be located, and use these commands."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lesser-identifier",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "git clone https://github.com/PaNOSC-ViNYL/McStasScript.git\n",
+    "cd McStasScript\n",
+    "pip install -r requirements.txt\n",
+    "pip install -e .\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sound-familiar",
+   "metadata": {},
+   "source": [
+    "## Configuration\n",
+    "The McStasScript package has a configuration file in the source directory that can be changed with a text editor or through a configurator class included in the package. The configuration needs the path of the mcrun / mxrun executable and the base directory of the McStas/McXtrace installation.\n",
+    "\n",
+    "### Typical Mac OS configuration\n",
+    "\n",
+    "```\n",
+    "from mcstasscript.interface import functions\n",
+    "my_configurator = functions.Configurator()\n",
+    "my_configurator.set_mcrun_path(\"/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\")\n",
+    "my_configurator.set_mcstas_path(\"/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/\")\n",
+    "my_configurator.set_mxrun_path(\"/Applications/McXtrace-1.5.app/Contents/Resources/mcxtrace/1.5/bin/\")\n",
+    "my_configurator.set_mcxtrace_path(\"/Applications/McXtrace-1.5.app/Contents/Resources/mcxtrace/1.5/\")\n",
+    "print(my_configurator)\n",
+    "```\n",
+    "\n",
+    "### Typical Unix configuration\n",
+    "\n",
+    "```\n",
+    "from mcstasscript.interface import functions\n",
+    "my_configurator = functions.Configurator()\n",
+    "my_configurator.set_mcrun_path(\"/usr/bin/\")\n",
+    "my_configurator.set_mcstas_path(\"/usr/share/mcstas/2.7.1/\")\n",
+    "my_configurator.set_mxrun_path(\"/usr/bin/\")\n",
+    "my_configurator.set_mcxtrace_path(\"/usr/share/mcxtrace/1.5/\")\n",
+    "print(my_configurator)\n",
+    "```\n",
+    "\n",
+    "### Typical Windows configuration\n",
+    "\n",
+    "```\n",
+    "from mcstasscript.interface import functions\n",
+    "my_configurator = functions.Configurator()\n",
+    "my_configurator.set_mcrun_path(\"\\\\mcstas-2.7.1\\\\bin\\\\\")\n",
+    "my_configurator.set_mcstas_path(\"\\\\mcstas-2.7.1\\\\lib\\\\\")\n",
+    "my_configurator.set_mxrun_path(\"\\\\mcxtrace-1.5\\\\bin\\\\\")\n",
+    "my_configurator.set_mcxtrace_path(\"\\\\mcxtrace-1.5\\\\lib\\\\\")    \n",
+    "print(my_configurator)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "stainless-class",
+   "metadata": {},
+   "source": [
+    "## Tests\n",
+    "In order to ensure the installation and configuration was succesful, one can run the test suite. Navigate to the folder containing the McStasScript source code and run:\n",
+    "```\n",
+    "pytest\n",
+    "```\n",
+    "\n",
+    "If pytest has not been installed yet, it can be installed with\n",
+    "```\n",
+    "pip install pytest\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "creative-prison",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/getting_started/overview.ipynb b/docs/source/getting_started/overview.ipynb
new file mode 100644
index 00000000..702ad3ea
--- /dev/null
+++ b/docs/source/getting_started/overview.ipynb
@@ -0,0 +1,96 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ecological-candidate",
+   "metadata": {},
+   "source": [
+    "# Overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "above-sport",
+   "metadata": {},
+   "source": [
+    "## McStas / McXtrace simulations\n",
+    "McStasScript is a python API for writing and running [McStas](https://www.mcstas.org) / [McXtrace](https://www.mcxtrace.org) simulations. These are sister packages meant for simulation of neutron and x-ray scattering instrumentation respectively and share a common syntax. The packages are used widely in the field and come with a large repository of components that describe smaller parts of the beamline. The community of users contribute such components to the packages, and they have in this way grown over the years.\n",
+    "\n",
+    "McStas and McXtrace simulations are described by an *instrument file* which is a custom language built on C. Here a number of components are placed in simulated space to describe the physical instrument, along with a number of monitors that record the properties of the beam. The instrument file is used to generate a c code, which is then compiled to an executable on the users system. The simulation itself is a monte carlo ray-tracing simulation that tracks individual rays from the source, through the instrument, through any scattering events and deposits the rays intensity onto any monitors along the way."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "nasty-promotion",
+   "metadata": {},
+   "source": [
+    "## McStasScript\n",
+    "McStasScript provides a pythonic alternative to writing the instrument file as a textfile in the simulation meta language. A McStasScript instrument object is create which in turn can generate the instrument file and perform the simulation. The user still has to understand the underlying software and logic in McStas / McXtrace to make meaningful simulations, and it is possible to add snippets of C code to the generated instrument, so some C knowledge is still an advantage.\n",
+    "\n",
+    "McStasScript is developed with the intent of use in Jupyter Notebooks, with for example available widgets for performing the simulation and plotting the results. The package can be used in python scripts, but won't have the interfaces.\n",
+    "\n",
+    "McStasScript is developed under [PaNOSC](https://www.panosc.eu) and specifically WP5 on simulations. Our collected github repo can be found [here](https://github.com/PaNOSC-ViNYL). McStasScript uses [libpyvinyl](https://github.com/PaNOSC-ViNYL/libpyvinyl) which imposes some standards on how parameters are handled along with the syntax for constructing and running the simulation, and thus using McStasScript is similar to other packages that follow this standard."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "corresponding-inspector",
+   "metadata": {},
+   "source": [
+    "### The instrument class\n",
+    "The central class of McStasScript is the *McCode_instr* class that describes the instrument object, from which both the *McStas_instr* and *McXtrace_instr* classes are derived. The *McCode_instr* class itself inherits from the libpyvinyl *BaseCalculator* that provides some basic functionality, such as loading from and dumping to file.\n",
+    "\n",
+    "Upon initialization an instrument object reads the available component database along with components in the work directory, and from this information can guide the user and check for errors. When adding a component to the instrument, a component object is returned which can then be customized further.\n",
+    "\n",
+    "It is possible to add practically anything to the instrument object that one would normally add to a instrument file, which includes parameters, declared variables, lines of initialize code and lines of finally code.\n",
+    "\n",
+    "The instrument object has methods for adjusting settigns of the simulation such as the number of rays to simulate and setting the parameters. The *backengine* method runs the currently specified simulation, and loads the data into the *data* attribute which can the be accessed by the user. The data is loaded as a McStasData object for each monitor output, these objects contain the data itself as numpy arrays and relevant metadata."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aboriginal-server",
+   "metadata": {},
+   "source": [
+    "### Component objects\n",
+    "Component objects belong to an instrument but are returned to the user whenever one is added to an instrument. At that point a custom class is generated for this kind of component, and the attributes will correspond to the parameters of the component. This is achieved by reading the McStas components in the users installation of McStas, and any custom components they may have added. Because the attributes correspond with the component parameters, it is not allowed to create new attributes in component objects, and in this way any misspelling of parameters are caught early. Since the component object is aware of the allowed inputs, it can provide help and for example show when any required parameters have not yet been specified."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "prospective-tiger",
+   "metadata": {},
+   "source": [
+    "### Plotting tools\n",
+    "McStasScript includes tools for plotting the resulting simulation data, providing a convinient way to quickly see the results from the performed simulation. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "advance-picnic",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/getting_started/quick_start.ipynb b/docs/source/getting_started/quick_start.ipynb
new file mode 100644
index 00000000..21f6cf9b
--- /dev/null
+++ b/docs/source/getting_started/quick_start.ipynb
@@ -0,0 +1,590 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "delayed-biodiversity",
+   "metadata": {},
+   "source": [
+    "# Quick start\n",
+    "This section is a quick start guide that will show the basic functionality of McStasScript. It assumes the user is already familiar with McStas itself, if this is not the case, it is recommended to start with the tutorial which can serve as an introduction to both McStas and McStasScript.\n",
+    "\n",
+    "## Importing the package\n",
+    "The package includes an interface folder that contains the modules which the  user is meant to use directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "dense-internet",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "exotic-sandwich",
+   "metadata": {},
+   "source": [
+    "## Creating the first instrument object\n",
+    "Now the package can be used. Start with creating a new instrument, just needs a name. For a McXtrace instrument use McXtrace_instr instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "disabled-canon",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"first_instrument\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "known-depression",
+   "metadata": {},
+   "source": [
+    "### Finding a component\n",
+    "The instrument object loads the available McStas components, so it can show these in order to help the user."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "adult-assignment",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are the available component categories:\n",
+      " contrib\n",
+      " misc\n",
+      " monitors\n",
+      " obsolete\n",
+      " optics\n",
+      " samples\n",
+      " sources\n",
+      " union\n",
+      "Call show_components(category_name) to display\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.show_components()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "fatal-climate",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are all components in the sources category.\n",
+      " Adapt_check     Moderator           Source_Optimizer   Source_gen\n",
+      " ESS_butterfly   Monitor_Optimizer   Source_adapt       Source_simple\n",
+      " ESS_moderator   Source_Maxwell_3    Source_div         \n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.show_components(\"sources\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "powerful-rover",
+   "metadata": {},
+   "source": [
+    "### Adding the first component\n",
+    "McStas components can be added to the instrument, here we add a source and ask for help on the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "instructional-liquid",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Source_simple _____________________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1mradius\u001b[0m = \u001b[1m\u001b[94m0.1\u001b[0m\u001b[0m [m] // Radius of circle in (x,y,0) plane where neutrons are \n",
+      "                    generated. \n",
+      "\u001b[1myheight\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Height of rectangle in (x,y,0) plane where neutrons are \n",
+      "                     generated. \n",
+      "\u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Width of rectangle in (x,y,0) plane where neutrons are \n",
+      "                    generated. \n",
+      "\u001b[1mdist\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Distance to target along z axis.\n",
+      "\u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[94m0.045\u001b[0m\u001b[0m [m] // Width of target\n",
+      "\u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[94m0.12\u001b[0m\u001b[0m [m] // Height of target\n",
+      "\u001b[1mE0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Mean energy of neutrons.\n",
+      "\u001b[1mdE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Energy half spread of neutrons (flat or gaussian sigma).\n",
+      "\u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [AA] // Mean wavelength of neutrons.\n",
+      "\u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [AA] // Wavelength half spread of neutrons.\n",
+      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1/(s*cm**2*st*energy unit)] // flux per energy unit, Angs or meV if \n",
+      "                                           flux=0, the source emits 1 in 4*PI whole \n",
+      "                                           space. \n",
+      "\u001b[1mgauss\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Gaussian (1) or Flat (0) energy/wavelength distribution\n",
+      "\u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1] // relative index of component to focus at, e.g. next is \n",
+      "                        +1 this is used to compute 'dist' automatically. \n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "source = instrument.add_component(\"source\", \"Source_simple\")\n",
+    "source.show_parameters()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "noble-colleague",
+   "metadata": {},
+   "source": [
+    "### Set parameters\n",
+    "The parameters of the component object are adjustable directly through the attributes of the object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "continued-denial",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT source = Source_simple\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mdist\u001b[0m = \u001b[1m\u001b[92m5\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[92m0.01\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[92m0.01\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[92m3\u001b[0m\u001b[0m [AA]\n",
+      "  \u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[92m2.2\u001b[0m\u001b[0m [AA]\n",
+      "AT [0, 0, 0] ABSOLUTE\n"
+     ]
+    }
+   ],
+   "source": [
+    "source.xwidth = 0.03\n",
+    "source.yheight = 0.03\n",
+    "source.lambda0 = 3\n",
+    "source.dlambda = 2.2\n",
+    "source.dist = 5\n",
+    "source.focus_xw = 0.01\n",
+    "source.focus_yh = 0.01\n",
+    "print(source)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "interior-better",
+   "metadata": {},
+   "source": [
+    "### Instrument parameters\n",
+    "It is possible to add instrument parameters that can be adjusted when running the simulation or adjusted using the widget interface."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "atlantic-capital",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT source = Source_simple\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mdist\u001b[0m = \u001b[1m\u001b[92m5\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[92m0.01\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[92m0.01\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[92mwavelength\u001b[0m\u001b[0m [AA]\n",
+      "  \u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[92m0.1*wavelength\u001b[0m\u001b[0m [AA]\n",
+      "AT [0, 0, 0] ABSOLUTE\n"
+     ]
+    }
+   ],
+   "source": [
+    "wavelength = instrument.add_parameter(\"wavelength\", value=3, comment=\"Wavelength in AA\")\n",
+    "source.lambda0 = wavelength\n",
+    "source.dlambda = \"0.1*wavelength\"\n",
+    "print(source)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "practical-somewhere",
+   "metadata": {},
+   "source": [
+    "### Inserting a sample component\n",
+    "A sample component is added as any other component, but here we place it relative to the source. A SANS sample is used, it focuses to a detector (chosen with target_index) with a width of focus_xw and height of focus_yh."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "changing-funds",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = instrument.add_component(\"sans_sample\", \"Sans_spheres\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "after-reliance",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT sans_sample = Sans_spheres\n",
+      "  \u001b[1mR\u001b[0m = \u001b[1m\u001b[92m120\u001b[0m\u001b[0m [AA]\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.01\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.01\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mzdepth\u001b[0m = \u001b[1m\u001b[92m0.01\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[92m1\u001b[0m\u001b[0m [1]\n",
+      "  \u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[92m0.5\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[92m0.5\u001b[0m\u001b[0m [m]\n",
+      "AT [0, 0, 3] RELATIVE source\n"
+     ]
+    }
+   ],
+   "source": [
+    "sample.set_AT(3, RELATIVE=source)\n",
+    "sample.set_parameters(R=120, xwidth=0.01, yheight=0.01, zdepth=0.01,\n",
+    "                      target_index=1, focus_xw=0.5, focus_yh=0.5)\n",
+    "print(sample)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "million-shoot",
+   "metadata": {},
+   "source": [
+    "### Adding a monitor\n",
+    "The monitor can be placed relative to the sample, and even use the attributes from the sample to define its size so that the two always match. When setting a filename, it has to be a string also in the generated code, so use double qoutation marks as shown here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "characteristic-spectacular",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PSD = instrument.add_component(\"PSD\", \"PSD_monitor\")\n",
+    "PSD.set_AT([0, 0, 5], RELATIVE=sample)\n",
+    "PSD.set_parameters(xwidth=sample.focus_xw, yheight=sample.focus_yh, filename='\"PSD.dat\"')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "mechanical-burlington",
+   "metadata": {},
+   "source": [
+    "## Setting up the simulation\n",
+    "The instrument now contains a source, a sample and a monitor, this is enough for a simple demonstration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "wicked-terrorism",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " wavelength  = 3  // Wavelength in AA\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.show_parameters()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "postal-advocate",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.set_parameters(wavelength=4)\n",
+    "instrument.settings(ncount=2E6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "requested-translation",
+   "metadata": {},
+   "source": [
+    "### Performing the simulation\n",
+    "In order to start the simulation the *backengine* method is called. If the simulation is succesful, the data will be placed in the *data* attribute, otherwise this attribute will contain None."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "durable-printer",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/getting_started/first_instrument_data_4\"\n",
+      "INFO: Regenerating c-file: first_instrument.c\n",
+      "CFLAGS=\n",
+      "INFO: Recompiling: ./first_instrument.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "1 warning generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy first_instrument.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/getting_started/first_instrument_data_4\n",
+      "\n",
+      "Detector: PSD_I=1.13331e-05 PSD_ERR=1.27748e-08 PSD_N=1.99997e+06 \"PSD.dat\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.backengine()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "detailed-ontario",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "frozen-circle",
+   "metadata": {},
+   "source": [
+    "## Plot the data\n",
+    "The data can be plotted with the *make_sub_plot* function from the plotter module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "christian-detail",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name PSD\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotter.make_sub_plot(data, log=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "critical-rebound",
+   "metadata": {},
+   "source": [
+    "### Access the data\n",
+    "The data is a list of McStasData objects and can be accessed directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "gothic-water",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "McStasData: PSD type: 2D  I:1.13331e-05 E:1.27748e-08 N:1.99997e+06]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "common-overhead",
+   "metadata": {},
+   "source": [
+    "It is possible to search through the data list with the *name_search* function to retrieve the desired data object. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "colonial-thompson",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PSD_data = functions.name_search(\"PSD\", data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "american-crash",
+   "metadata": {},
+   "source": [
+    "The intensities can then be accessed directly, along with Error and Ncount. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "exempt-lincoln",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[3.44527442e-29, 2.85790145e-29, 3.31769558e-29, ...,\n",
+       "        2.91848858e-29, 3.46035438e-29, 3.18800919e-29],\n",
+       "       [2.99309240e-29, 2.71389219e-29, 4.51518249e-29, ...,\n",
+       "        3.88940177e-29, 2.79711427e-29, 3.13858152e-29],\n",
+       "       [3.09932004e-29, 3.65012990e-29, 4.19605051e-29, ...,\n",
+       "        6.17328696e-29, 4.54832798e-29, 2.95402503e-29],\n",
+       "       ...,\n",
+       "       [2.26834139e-29, 3.81964364e-29, 5.66352294e-29, ...,\n",
+       "        6.09311571e-29, 4.78664320e-29, 3.58314124e-29],\n",
+       "       [3.37192383e-29, 2.95154846e-29, 3.68253460e-29, ...,\n",
+       "        4.11785300e-29, 3.48591918e-29, 2.20162822e-29],\n",
+       "       [2.39943987e-29, 2.44856071e-29, 2.61788386e-29, ...,\n",
+       "        3.09847097e-29, 2.78252273e-29, 2.38869215e-29]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PSD_data.Intensity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unavailable-secretariat",
+   "metadata": {},
+   "source": [
+    "Metadata is also available as a dict."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "mediterranean-guatemala",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date : Tue Jan 11 12:39:32 2022 (1641901172)\n",
+      "type : array_2d(90, 90)\n",
+      "Source : first_instrument (first_instrument.instr)\n",
+      "component : PSD\n",
+      "position : 0 0 8\n",
+      "title : PSD monitor\n",
+      "Ncount : 2000000\n",
+      "filename : PSD.dat\n",
+      "statistics : X0=0.000187759; dX=0.61402; Y0=0.00177741; dY=0.612948;\n",
+      "signal : Min=2.20163e-29; Max=1.15483e-06; Mean=1.39915e-09;\n",
+      "values : 1.13331e-05 1.27748e-08 1.99997e+06\n",
+      "xvar : X\n",
+      "yvar : Y\n",
+      "xlabel : X position [cm]\n",
+      "ylabel : Y position [cm]\n",
+      "zvar : I\n",
+      "zlabel : Signal per bin\n",
+      "xylimits : -25 25 -25 25\n",
+      "variables : I I_err N\n",
+      "Parameters : {'wavelength': 4.0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "info_dict = PSD_data.metadata.info\n",
+    "for field, info in info_dict.items():\n",
+    "    print(field, \":\", info)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "coupled-sailing",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/index.rst b/docs/source/index.rst
new file mode 100644
index 00000000..2f3355f2
--- /dev/null
+++ b/docs/source/index.rst
@@ -0,0 +1,62 @@
+Welcome to McStasScript's documentation!
+========================================
+
+**McStasScript** is a Python API for `McStas <https://mcstas.org>`_, which allows the user to get help, build their instrument, perform simulations and plot the resulting data.
+This site serves as the documentation for the package and contains conceptual explanations of how the package is meant to be used, tutorials and a reference for all internal functions/methods.
+
+Documentation
+=============
+
+.. toctree::
+   :caption: Getting started
+   :maxdepth: 2
+   
+   getting_started/overview
+   getting_started/installation
+   getting_started/quick_start
+   
+.. toctree::
+   :caption: User guide
+   :maxdepth: 2
+   
+   user_guide/instrument_object
+   user_guide/component_object
+   user_guide/parameters_and_variables
+   user_guide/plotting
+   user_guide/instrument_reader
+
+.. toctree::
+   :caption: McStasScript Tutorial
+   :maxdepth: 1
+   
+   tutorial/McStasScript_tutorial_1_the_basics
+   tutorial/McStasScript_tutorial_2_SPLIT.ipynb
+   tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
+   tutorial/McStasScript_tutorial_4_JUMP.ipynb
+   
+.. toctree::
+   :caption: McStas Union Tutorial
+   :maxdepth: 1
+   
+   tutorial/Union_tutorial_1_processes_and_materials.ipynb
+   tutorial/Union_tutorial_2_geometry.ipynb
+   tutorial/Union_tutorial_3_loggers.ipynb
+   tutorial/Union_tutorial_4_conditionals.ipynb
+   tutorial/Union_tutorial_5_masks.ipynb
+   tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
+   tutorial/Union_tutorial_7_Tagging_history.ipynb
+   
+.. autosummary::
+   :toctree: _autosummary
+   :template: custom-module-template.rst
+   :caption: Reference
+   :recursive:
+   
+   mcstasscript
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
diff --git a/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb b/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
new file mode 100644
index 00000000..a1ccc6ae
--- /dev/null
+++ b/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
@@ -0,0 +1,1059 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# McStasScript introduction\n",
+    "This notebook shows how to use McStas and McStasScript to perform a basic simulation of a neutron diffractometer. The following software is required:\n",
+    "- McStas (www.mcstas.org)\n",
+    "- McStasScript (can be installed with python -m pip install McStasScript)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Anatomy of a McStas instrument\n",
+    "\n",
+    "In McStas a simulation is described using an instrument file. Such an instrument has five sections where code can be added to define the simulation to be perfomed.\n",
+    "\n",
+    "- Instrument definition\n",
+    "- Declare section\n",
+    "- Initialize section\n",
+    "- Trace section\n",
+    "- Finally section\n",
+    "\n",
+    "##### Instrument definition\n",
+    "In the instrument definition it is possible to define *instrument parameters* which can be specified at run time and used in the remaining sections for either calculations or as direct input to the components.\n",
+    "\n",
+    "##### Declare section\n",
+    "Here internal variables can be declared with C syntax.\n",
+    "\n",
+    "##### Initialize section\n",
+    "The initialize section is used for performing calculations, typically using both instrument parameters and declared variables to calculate for example chopper phases, angles and similar. The calculations are performed using C syntax. These calculations are performed before the raytracing simulation, and thus only performed once in a given simulation.\n",
+    "\n",
+    "##### Trace section\n",
+    "In the trace section McStas *components* are added, these are the building blocks of the simulation and correspond to different c codes that describe parts of neutron instruments or samples. Each component have a set of available parameters, some of which may be required. These will set the behavior of a component, a guide component may for example have parameters describing the physical shape and mirror reflectivity. Components also need to be placed in 3D space, and can be placed either in the absolute coordinate system or relative to a previously defined component.\n",
+    "\n",
+    "##### Finally section\n",
+    "The finally section is very similar to the initialize section, here calculations can be performed after the raytracing has been completed, again using C syntax. This may be some brief data analysis or print of some status.\n",
+    "\n",
+    "### McStasScript python package and this tutorial\n",
+    "The McStasScript python package provides an API to build and run such instruments files, but it is still necessary to have a basic understanding of the structure of the underlying instrument file and its capabilities and limitations. These tutorials will teach basic use of McStas through the McStasScript API without assuming expertise in the underlying McStas software."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the McStasScript package\n",
+    "The McStasScript modules intended for normal use is located in the interface submodule, and one usually imports the necessary modules from there."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### McStasScript configuration\n",
+    "Before the first use of McStasScript it is necessary to configure the package so it can locate the McStas installation and call the binaries. One way to find the path is to open a terminal with the McStas environment and run:\n",
+    "\n",
+    "which mcrun\n",
+    "\n",
+    "This should return the path for the binary, and the mcstas path is usually just one step back."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "configurator = functions.Configurator()\n",
+    "configurator.set_mcrun_path(\"/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\")\n",
+    "configurator.set_mcstas_path(\"/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create an instrument object\n",
+    "A McStas instrument is described with a McStas instrument object which is created using the *McStas_instr* method on the instr class. Creating an instrument object also reads available components, both in the work folder and from the McStas installation. By default, the work folder is the current work directory, but using the input_path keyword argument this can be change to avoid cluttering the folder containing notebooks.\n",
+    "\n",
+    "Here our instrument object for this tutorial is created, we give it the name python_tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Requesting help on source components\n",
+    "The main building blocks used for creating a McStas simulation are the components. One can ask an instrument object which components are available, and get help for each component. Here we check what sources are available, and ask for help on the Source_div component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are the available component categories:\n",
+      " contrib\n",
+      " misc\n",
+      " monitors\n",
+      " obsolete\n",
+      " optics\n",
+      " samples\n",
+      " sources\n",
+      " union\n",
+      "Call show_components(category_name) to display\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.show_components()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are all components in the sources category.\n",
+      " Adapt_check     Moderator           Source_Optimizer   Source_gen\n",
+      " ESS_butterfly   Monitor_Optimizer   Source_adapt       Source_simple\n",
+      " ESS_moderator   Source_Maxwell_3    Source_div         \n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.show_components(\"sources\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Source_div ________________________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[4m\u001b[1mxwidth\u001b[0m\u001b[0m [m] // Width of source\n",
+      "\u001b[4m\u001b[1myheight\u001b[0m\u001b[0m [m] // Height of source\n",
+      "\u001b[4m\u001b[1mfocus_aw\u001b[0m\u001b[0m [deg] // FWHM (Gaussian) or maximal (uniform) horz. width divergence\n",
+      "\u001b[4m\u001b[1mfocus_ah\u001b[0m\u001b[0m [deg] // FWHM (Gaussian) or maximal (uniform) vert. height divergence\n",
+      "\u001b[1mE0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Mean energy of neutrons.\n",
+      "\u001b[1mdE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Energy half spread of neutrons.\n",
+      "\u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Ang] // Mean wavelength of neutrons (only relevant for E0=0)\n",
+      "\u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Ang] // Wavelength half spread of neutrons.\n",
+      "\u001b[1mgauss\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [0|1] // Criterion\n",
+      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1/(s cm 2 st energy_unit)] // flux per energy unit, Angs or meV\n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.component_help(\"Source_div\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding a component\n",
+    "Now we are ready to add a component to our simulation which is done with the *add_component* method on our instrument. This method requires two inputs:\n",
+    "\n",
+    "- Nickname for the component used to refer to this component instance\n",
+    "- Name of the component type to be used\n",
+    "\n",
+    "Here we want to make a component nicknamed \"source\" of type \"Source_div\".\n",
+    "\n",
+    "We also use the *print_components* method to confirm our component was added successfully. Running this code block multiple times result in an error, as McStas does not allow two components with the same nickname."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "source Source_div AT (0, 0, 0) ABSOLUTE\n"
+     ]
+    }
+   ],
+   "source": [
+    "src = instrument.add_component(\"source\", \"Source_div\")\n",
+    "instrument.print_components()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with component objects\n",
+    "The src object created by *add_component* can be used to modify the component. It also holds the information on the component, which can be shown by printing the object. This will tell us for example if any required parameters are yet to be set and the position of the component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT source = Source_div\n",
+      "  \u001b[1mxwidth\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1myheight\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1mfocus_aw\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1mfocus_ah\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "AT [0, 0, 0] ABSOLUTE\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(src)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Modifying a component object\n",
+    "The parameters of a component object can be modified as attributes. From the above print we know there are four required parameters, so we start by setting these and then print the resulting component status."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT source = Source_div\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_aw\u001b[0m = \u001b[1m\u001b[92m1.2\u001b[0m\u001b[0m [deg]\n",
+      "  \u001b[1mfocus_ah\u001b[0m = \u001b[1m\u001b[92m2.3\u001b[0m\u001b[0m [deg]\n",
+      "AT [0, 0, 0] ABSOLUTE\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "src.xwidth = 0.1\n",
+    "src.yheight = 0.05\n",
+    "src.focus_aw = 1.2\n",
+    "src.focus_ah = 2.3\n",
+    "\n",
+    "print(src)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Getting status of all parameters\n",
+    "Printing a component only show the required parameters and user specified parameters, but it is also possible to see all parameters with the *show_parameters* method. This reminds us to set an energy or wavelength range for the source, as it is necessary to set one of these even though they are technically not required parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Source_div ________________________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[4m\u001b[1mxwidth\u001b[0m\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m] // Width of source\n",
+      "\u001b[4m\u001b[1myheight\u001b[0m\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m] // Height of source\n",
+      "\u001b[4m\u001b[1mfocus_aw\u001b[0m\u001b[0m = \u001b[1m\u001b[92m1.2\u001b[0m\u001b[0m [deg] // FWHM (Gaussian) or maximal (uniform) horz. width \n",
+      "                        divergence \n",
+      "\u001b[4m\u001b[1mfocus_ah\u001b[0m\u001b[0m = \u001b[1m\u001b[92m2.3\u001b[0m\u001b[0m [deg] // FWHM (Gaussian) or maximal (uniform) vert. height \n",
+      "                        divergence \n",
+      "\u001b[1mE0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Mean energy of neutrons.\n",
+      "\u001b[1mdE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Energy half spread of neutrons.\n",
+      "\u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Ang] // Mean wavelength of neutrons (only relevant for E0=0)\n",
+      "\u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Ang] // Wavelength half spread of neutrons.\n",
+      "\u001b[1mgauss\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [0|1] // Criterion\n",
+      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1/(s cm 2 st energy_unit)] // flux per energy unit, Angs or meV\n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "src.show_parameters()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding an instrument parameter to control wavelength\n",
+    "Controlling the wavelength range emitted by the source is best done with an instrument parameter, then this same parameter can be used to for example rotate a monochromator or set the range for an wavelength sensitive monitor. Adding an instrument parameter is done using the instrument method *add_parameter*, and it is possible to set a default value and comment. The method returns a parameter object that can be used to assign the parameter to a component. The current instrument parameters can be viewed with the *show_parameters* method on the isntrument object.\n",
+    "\n",
+    "The default type for instrument parameters is a double (floating point number), but other types can be selected if necessary by providing a type string before, here we also provide an example of an integer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    wavelength  = 5.0  // Wavelength in [Ang]\n",
+      "int order       = 1    // Monochromator order, integer\n"
+     ]
+    }
+   ],
+   "source": [
+    "wavelength = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "order = instrument.add_parameter(\"int\", \"order\", value=1, comment=\"Monochromator order, integer\")\n",
+    "instrument.show_parameters()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now our source component can have its parameters assigned to a instrument parameter, or even a mathematical expression using the variable. This allows us to set a reasonable wavelength range for our source component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT source = Source_div\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_aw\u001b[0m = \u001b[1m\u001b[92m1.2\u001b[0m\u001b[0m [deg]\n",
+      "  \u001b[1mfocus_ah\u001b[0m = \u001b[1m\u001b[92m2.3\u001b[0m\u001b[0m [deg]\n",
+      "  \u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[92mwavelength\u001b[0m\u001b[0m [Ang]\n",
+      "  \u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[92m0.01*wavelength\u001b[0m\u001b[0m [Ang]\n",
+      "AT [0, 0, 0] ABSOLUTE\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "src.lambda0 = wavelength\n",
+    "src.dlambda = \"0.01*wavelength\" # When performing math use a string and the parameter name\n",
+    "print(src)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using keyword arguments when adding a component\n",
+    "When adding a component, several keyword arguments are available, for example for setting the position of the component.\n",
+    "\n",
+    "- AT set position with list of x,y,z coordinates\n",
+    "- AT_RELATIVE set reference point for position (name of component instance or object)\n",
+    "- ROTATED set rotation around x,y,z axis\n",
+    "- ROTATED_RELATIVE set reference rotation (name of component instance or object)\n",
+    "- RELATIVE set both reference position and rotation (name of component instance or object)\n",
+    "\n",
+    "We use this to set up a guide 2 meters after the source. The McStas coordinate system convention is such that the nominal beam direction is in the Z direction and with Y vertical against gravity. We use the component instance name as a string to refer to our source. The RELATIVE could also have been specified as src, which is our source object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "guide = instrument.add_component(\"guide\", \"Guide_gravity\", AT=[0,0,2], RELATIVE=\"source\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we set the parameters for our guide component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT guide = Guide_gravity\n",
+      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mw2\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mh2\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92m8.0\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92m3.5\u001b[0m\u001b[0m [1]\n",
+      "  \u001b[1mG\u001b[0m = \u001b[1m\u001b[92m-9.82\u001b[0m\u001b[0m [m/s2]\n",
+      "AT [0, 0, 2] RELATIVE source\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "guide.w1 = 0.05\n",
+    "guide.w2 = 0.05\n",
+    "guide.h1 = 0.05\n",
+    "guide.h2 = 0.05\n",
+    "guide.l = 8.0\n",
+    "guide.m = 3.5\n",
+    "guide.G = -9.82\n",
+    "\n",
+    "print(guide)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding calculations to an instrument file\n",
+    "One of the advantages of McStas is the ease of adding calculations to the instrument. Here we calculate the rotation of a monochromator so that its scatters the wavelengths from our source. We need to declare variables using *add_declare_var* and append C code to initialize using *append_initialize*.\n",
+    "\n",
+    "For *add_declare_var* the first argument is the C type, usually double or int, the next is the variable name. A default value can be specified with the value keyword. Like when adding a parameter, a *add_declare* also returns an object that can be used to refer to this variable later.\n",
+    "\n",
+    "*append_initialize* just adds the given C code to the initialize section of the McStas instrument file. It is necessary to follow C syntax, for example remember semicolon at the end of statements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mono_Q = instrument.add_declare_var(\"double\", \"mono_Q\", value=1.714) # Q for Ge 311\n",
+    "instrument.add_declare_var(\"double\", \"wavevector\")\n",
+    "instrument.append_initialize(\"wavevector = 2.0*PI/wavelength;\")\n",
+    "\n",
+    "mono_rotation = instrument.add_declare_var(\"double\", \"mono_rotation\")\n",
+    "instrument.append_initialize(\"mono_rotation = asin(mono_Q/(2.0*wavevector))*RAD2DEG;\")\n",
+    "instrument.append_initialize('printf(\"monochromator rotation = %g deg\\\\n\", mono_rotation);')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding the monochromator\n",
+    "Here the monochromator is added, and we use the declared variables *mono_Q* and *mono_rotation* prepared above. Setting position and rotation can also be done using the *set_AT* and *set_ROTATED* methods on the component objects. Here it is also demonstrated how one can use either component objects or component names for the relative keyword.\n",
+    "\n",
+    "Rotation is specified around each axis, so rotation of our monochromator should be around the Y axis in order to keep the beam in the usual X-Z plane."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mono = instrument.add_component(\"mono\", \"Monochromator_flat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mono.zwidth = 0.05\n",
+    "mono.yheight = 0.08\n",
+    "mono.Q = mono_Q\n",
+    "mono.set_AT([0, 0, 8.5], RELATIVE=guide)\n",
+    "mono.set_ROTATED([0, mono_rotation, 0], RELATIVE=\"guide\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT mono = Monochromator_flat\n",
+      "  \u001b[1mzwidth\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.08\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mQ\u001b[0m = \u001b[1m\u001b[92mmono_Q\u001b[0m\u001b[0m [1/angstrom]\n",
+      "AT [0, 0, 8.5] RELATIVE guide\n",
+      "ROTATED [0, 'mono_rotation', 0] RELATIVE guide\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(mono)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using an arm to define the beam direction\n",
+    "As the beam changes direction at the monochromator, we wish to define the new direction to simplify adding latter components. This can be done with an Arm component, which performs no simulation but can be used as new coordinate reference. The outgoing direction correspond to one more rotation of *mono_rotation*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "beam_direction = instrument.add_component(\"beam_dir\", \"Arm\", AT_RELATIVE=\"mono\")\n",
+    "beam_direction.set_ROTATED([0, mono_rotation, 0], RELATIVE=\"mono\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding a sample\n",
+    "We now add a powder sample using the PowderN component placed relative to our newly defiend beam direction. The chosen powder is Na2Ca3Al2F14 which is a standard sample due to its large number of available reflections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = instrument.add_component(\"sample\", \"PowderN\", AT=[0, 0, 1.1], RELATIVE=beam_direction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample.radius = 0.015\n",
+    "sample.yheight = 0.05\n",
+    "sample.reflections = '\"Na2Ca3Al2F14.laz\"'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT sample = PowderN\n",
+      "  \u001b[1mreflections\u001b[0m = \u001b[1m\u001b[92m\"Na2Ca3Al2F14.laz\"\u001b[0m\u001b[0m []\n",
+      "  \u001b[1mradius\u001b[0m = \u001b[1m\u001b[92m0.015\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "AT [0, 0, 1.1] RELATIVE beam_dir\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "sample.print_long()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding a cylindrical monitor\n",
+    "The flexible Monitor_nD component can be used to add a banana monitor (part of a cylinder). The component shape is specified using an option string. The restore_neutron parameter is set to 1 to allow other monitors to record each neutron.\n",
+    "\n",
+    "We have to specify a filename and option string here, and if we just use a string like \"banana.dat\" it would be interpreted as an instrument parameter called *banana.dat* and fail, so it is necessary to add single quotes around, '\"banana.dat\"'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "banana = instrument.add_component(\"banana\", \"Monitor_nD\", RELATIVE=sample)\n",
+    "banana.xwidth = 2.0\n",
+    "banana.yheight = 0.3\n",
+    "banana.restore_neutron = 1\n",
+    "banana.filename = '\"banana.dat\"'\n",
+    "banana.options = '\"theta limits=[5 175] bins=150, banana\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding a psd monitor\n",
+    "We also add a simple PSD (position sensitive detector) monitor to see the transmitted beam."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mon = instrument.add_component(\"monitor\", \"PSD_monitor\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mon.nx = 100\n",
+    "mon.ny = 100\n",
+    "mon.filename = '\"psd.dat\"'\n",
+    "mon.xwidth = 0.05\n",
+    "mon.yheight = 0.08\n",
+    "mon.restore_neutron = 1\n",
+    "mon.set_AT([0,0,0.1], RELATIVE=sample)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Print the components contained in an instrument\n",
+    "Before performing the simulation, it is a good idea to check that the instrument contains the expected components and that they are appropriately placed in space. The *print_components* method is useful for this purpose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "source   Source_div         AT      (0, 0, 0)             ABSOLUTE         \n",
+      "guide    Guide_gravity      AT      (0, 0, 2)             RELATIVE source  \n",
+      "mono     Monochromator_flat AT      (0, 0, 8.5)           RELATIVE guide    \n",
+      "                            ROTATED (0, mono_rotation, 0) RELATIVE guide\n",
+      "beam_dir Arm                AT      (0, 0, 0)             RELATIVE mono     \n",
+      "                            ROTATED (0, mono_rotation, 0) RELATIVE mono\n",
+      "sample   PowderN            AT      (0, 0, 1.1)           RELATIVE beam_dir\n",
+      "banana   Monitor_nD         AT      (0, 0, 0)             RELATIVE sample  \n",
+      "monitor  PSD_monitor        AT      (0, 0, 0.1)           RELATIVE sample  \n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.print_components()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Running the simulation\n",
+    "Running the simulation is done in three steps\n",
+    "\n",
+    "- Setting the parameters with *set_parameters*\n",
+    "- Setting the settings with *settings*\n",
+    "- Running the McStas simulation with *backengine*\n",
+    "\n",
+    "The *set_parameters* method takes a value for each of the parameters defined in the instrument, here wavelength.\n",
+    "\n",
+    "Settings adjust settings for the simulations, a few examples can be seen here\n",
+    "\n",
+    "- ncount sets the number of rays\n",
+    "- mpi sets the number of CPU cores used for execution (requires mpi installed)\n",
+    "- output_path sets the name of the output folder\n",
+    "- increment_folder_name if set to True, automatically changes the foldername if it already exists (default).\n",
+    "\n",
+    "The *backengine* method takes no parameters and just performs the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    wavelength  = 2.8  // Wavelength in [Ang]\n",
+      "int order       = 1    // Monochromator order, integer\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.set_parameters(wavelength=2.8) # Set parameters\n",
+    "instrument.show_parameters()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Instrument settings:\n",
+      "  ncount:           5.00e+06\n",
+      "  output_path:      data_folder/mcstas_basics\n",
+      "  run_path:         run_folder\n",
+      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
+      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
+      "  executable:       mcrun\n",
+      "  force_compile:    True\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.settings(ncount=5E5, output_path=\"data_folder/mcstas_basics\") # Settings\n",
+    "instrument.show_settings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_basics_7\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS=\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "1 warning generated.\n",
+      "INFO: ===\n",
+      "Warning: 64538 events were removed in Component[7] monitor=PSD_monitor()\n",
+      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_basics_7\n",
+      "\n",
+      "      monochromator rotation = 22.4519 deg\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "Detector: banana_I=1.35081e-06 banana_ERR=2.24941e-08 banana_N=10752 \"banana.dat\"\n",
+      "Detector: monitor_I=4.07521e-05 monitor_ERR=2.69874e-07 monitor_N=49651 \"psd.dat\"\n",
+      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
+      "    SPLIT 47 COMPONENT sample=PowderN(...)\n",
+      "  in the instrument description python_tutorial.instr.\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.backengine() # Perform simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = instrument.data # The data is available in the data attribute"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting the data\n",
+    "The *run_full_instrument* method returned a list of McStasData objects which can be plotted by the McStasScript plotter module. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name banana\n",
+      "Plotting data with name monitor\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adjusting plots\n",
+    "The McStasData objects contain preferences for how the data should be plotted, which can be modified using the functions module and the *name_plot_options* function. The function arguments are the name of the monitor component and a list of McStasData objects, then options are provided with the keyword arguments.\n",
+    "\n",
+    "The following plot options are often useful:\n",
+    "- log [True or False] For plotting on logarithmic axis\n",
+    "- orders_of_mag [number] When using logarithmic plotting, limits the maximum orders of magnitudes shown\n",
+    "- left_lim [number] lower limit of plot x axis\n",
+    "- right_lim [number] upper limit of plot x axis\n",
+    "- bottom_lim [number] lower limit of plot y axis\n",
+    "- top_lim [number] upper limit of plot y axis\n",
+    "- colormap [string] name of matplotlib colormap to use"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name banana\n",
+      "Plotting data with name monitor\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "functions.name_plot_options(\"monitor\", data, log=True)\n",
+    "functions.name_plot_options(\"banana\", data, left_lim=90, right_lim=150)\n",
+    "plotter.make_sub_plot(data, fontsize=16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Behind the scenes \n",
+    "McStasScript writes the instrument file and uses mcrun to compile and run it. The file can be found in the input_path selected when the instrument object were created. We can print it here to see what was done behind the scenes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/********************************************************************************\n",
+      "* \n",
+      "* McStas, neutron ray-tracing package\n",
+      "*         Copyright (C) 1997-2008, All rights reserved\n",
+      "*         Risoe National Laboratory, Roskilde, Denmark\n",
+      "*         Institut Laue Langevin, Grenoble, France\n",
+      "* \n",
+      "* This file was written by McStasScript, which is a \n",
+      "* python based McStas instrument generator written by \n",
+      "* Mads Bertelsen in 2019 while employed at the \n",
+      "* European Spallation Source Data Management and \n",
+      "* Software Center\n",
+      "* \n",
+      "* Instrument python_tutorial\n",
+      "* \n",
+      "* %Identification\n",
+      "* Written by: Python McStas Instrument Generator\n",
+      "* Date: 11:20:52 on January 07, 2022\n",
+      "* Origin: ESS DMSC\n",
+      "* %INSTRUMENT_SITE: Generated_instruments\n",
+      "* \n",
+      "* \n",
+      "* %Parameters\n",
+      "* \n",
+      "* %End \n",
+      "********************************************************************************/\n",
+      "\n",
+      "DEFINE INSTRUMENT python_tutorial (\n",
+      "wavelength = 2.8, // Wavelength in [Ang]\n",
+      "int order = 1 // Monochromator order, integer\n",
+      ")\n",
+      "\n",
+      "DECLARE \n",
+      "%{\n",
+      "double mono_Q = 1.714;\n",
+      "double wavevector;\n",
+      "double mono_rotation;\n",
+      "%}\n",
+      "\n",
+      "INITIALIZE \n",
+      "%{\n",
+      "// Start of initialize for generated python_tutorial\n",
+      "wavevector = 2.0*PI/wavelength;\n",
+      "mono_rotation = asin(mono_Q/(2.0*wavevector))*RAD2DEG;\n",
+      "printf(\"monochromator rotation = %g deg\\n\", mono_rotation);\n",
+      "%}\n",
+      "\n",
+      "TRACE \n",
+      "COMPONENT source = Source_div(\n",
+      " xwidth = 0.1, yheight = 0.05,\n",
+      " focus_aw = 1.2, focus_ah = 2.3,\n",
+      " lambda0 = wavelength, dlambda = 0.01*wavelength)\n",
+      "AT (0,0,0) ABSOLUTE\n",
+      "\n",
+      "COMPONENT guide = Guide_gravity(\n",
+      " w1 = 0.05, h1 = 0.05,\n",
+      " w2 = 0.05, h2 = 0.05,\n",
+      " l = 8, m = 3.5,\n",
+      " G = -9.82)\n",
+      "AT (0,0,2) RELATIVE source\n",
+      "\n",
+      "COMPONENT mono = Monochromator_flat(\n",
+      " zwidth = 0.05, yheight = 0.08,\n",
+      " Q = mono_Q)\n",
+      "AT (0,0,8.5) RELATIVE guide\n",
+      "ROTATED (0,mono_rotation,0) RELATIVE guide\n",
+      "\n",
+      "COMPONENT beam_dir = Arm()\n",
+      "AT (0,0,0) RELATIVE mono\n",
+      "ROTATED (0,mono_rotation,0) RELATIVE mono\n",
+      "\n",
+      "COMPONENT sample = PowderN(\n",
+      " reflections = \"Na2Ca3Al2F14.laz\", radius = 0.015,\n",
+      " yheight = 0.05)\n",
+      "AT (0,0,1.1) RELATIVE beam_dir\n",
+      "\n",
+      "COMPONENT banana = Monitor_nD(\n",
+      " xwidth = 2, yheight = 0.3,\n",
+      " restore_neutron = 1, options = \"theta limits=[5 175] bins=150, banana\",\n",
+      " filename = \"banana.dat\")\n",
+      "AT (0,0,0) RELATIVE sample\n",
+      "\n",
+      "COMPONENT monitor = PSD_monitor(\n",
+      " nx = 100, ny = 100,\n",
+      " filename = \"psd.dat\", xwidth = 0.05,\n",
+      " yheight = 0.08, restore_neutron = 1)\n",
+      "AT (0,0,0.1) RELATIVE sample\n",
+      "\n",
+      "FINALLY \n",
+      "%{\n",
+      "// Start of finally for generated python_tutorial\n",
+      "%}\n",
+      "\n",
+      "END\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"run_folder/python_tutorial.instr\") as file:\n",
+    "    data = file.read()\n",
+    "    print(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb b/docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
new file mode 100644
index 00000000..f7a581a4
--- /dev/null
+++ b/docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
@@ -0,0 +1,315 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Advanced McStas features:  SPLIT\n",
+    "McStas uses the Monte Carlo ray-tracing technique, which allows some tricks in how the physics is sampled as long as the resulting probability distributions matches the physics. This is possible as each ray has a weight, corresponding to how much intensity this ray represent. The SPLIT keyword can be used to split a ray into many equal parts, which can be useful if the remaining instrument has many different simulated and random outcomes. In this tutorial we will use the SPLIT keyword on a powder sample, as there are many powder Bragg peaks each ray could select, and splitting the ray samples this more efficiently."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting up an example instrument\n",
+    "First we set up an example instrument, this is taken from the basic tutorial and correspond of source, guide, monochromator, sample and banana detector."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\", output_path=\"data_folder/mcstas_SPLIT\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.add_component(\"Origin\", \"Progress_bar\")\n",
+    "\n",
+    "src = instrument.add_component(\"source\", \"Source_div\")\n",
+    "src.xwidth = 0.1\n",
+    "src.yheight = 0.05\n",
+    "src.focus_aw = 1.2\n",
+    "src.focus_ah = 2.3\n",
+    "\n",
+    "wavelength = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = wavelength\n",
+    "src.dlambda = \"0.03*wavelength\"\n",
+    "\n",
+    "guide = instrument.add_component(\"guide\", \"Guide_gravity\", AT=[0,0,2], RELATIVE=src)\n",
+    "guide.w1 = 0.05\n",
+    "guide.w2 = 0.05\n",
+    "guide.h1 = 0.05\n",
+    "guide.h2 = 0.05\n",
+    "guide.l = 8.0\n",
+    "guide.m = 3.5\n",
+    "guide.G = -9.82\n",
+    "\n",
+    "mono_Q = instrument.add_declare_var(\"double\", \"mono_Q\", value=1.714) # Q for Ge 311\n",
+    "instrument.add_declare_var(\"double\", \"wavevector\")\n",
+    "instrument.append_initialize(\"wavevector = 2.0*PI/wavelength;\")\n",
+    "\n",
+    "mono_rotation = instrument.add_declare_var(\"double\", \"mono_rotation\")\n",
+    "instrument.append_initialize(\"mono_rotation = asin(mono_Q/(2.0*wavevector))*RAD2DEG;\")\n",
+    "instrument.append_initialize('printf(\"monochromator rotation = %g deg\\\\n\", mono_rotation);')\n",
+    "\n",
+    "mono = instrument.add_component(\"mono\", \"Monochromator_flat\")\n",
+    "mono.zwidth = 0.05\n",
+    "mono.yheight = 0.08\n",
+    "mono.Q = mono_Q\n",
+    "mono.set_AT([0, 0, 8.5], RELATIVE=guide)\n",
+    "mono.set_ROTATED([0, mono_rotation, 0], RELATIVE=guide)\n",
+    "\n",
+    "beam_direction = instrument.add_component(\"beam_dir\", \"Arm\", AT_RELATIVE=mono)\n",
+    "beam_direction.set_ROTATED([0, \"mono_rotation\", 0], RELATIVE=\"mono\")\n",
+    "\n",
+    "sample = instrument.add_component(\"sample\", \"PowderN\", AT=[0,0,1.1], RELATIVE=beam_direction)\n",
+    "sample.radius = 0.015\n",
+    "sample.yheight = 0.05\n",
+    "sample.reflections = '\"Na2Ca3Al2F14.laz\"'\n",
+    "\n",
+    "banana = instrument.add_component(\"banana\", \"Monitor_nD\", RELATIVE=sample)\n",
+    "banana.xwidth = 2.0\n",
+    "banana.yheight = 0.3\n",
+    "banana.restore_neutron = 1\n",
+    "banana.filename = '\"banana.dat\"'\n",
+    "banana.options = '\"theta limits=[5 175] bins=150, banana\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Running the simulation\n",
+    "Here we run the simulation with very few neutrons to show problematic sampling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "instrument.settings(ncount=1E5)\n",
+    "\n",
+    "instrument.set_parameters(wavelength=2.8)\n",
+    "                          \n",
+    "instrument.backengine()\n",
+    "data_low = instrument.data\n",
+    "\n",
+    "plotter.make_sub_plot(data_low)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding the SPLIT keyword\n",
+    "Here we add the SPLIT keyword to the sample, we choose to split each ray into 30."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample.set_SPLIT(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# No need to set settings or parameters as these have not changed\n",
+    "instrument.backengine()\n",
+    "data_reasonable = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(data_reasonable)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding the SPLIT keyword\n",
+    "It is however possible to mismanage splitting, mainly by simulating a too few rays and splitting too much. Here we do this on purpose to see how such data would look. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "sample.set_SPLIT(10000)\n",
+    "\n",
+    "instrument.settings(ncount=1E3) # Change settings to lower ncount, but keep parameters\n",
+    "                          \n",
+    "instrument.backengine()\n",
+    "data_unreasonable = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(data_unreasonable)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparison with high statistics run\n",
+    "We here compare the different runs to a reference. The reference run is set up to have 50 times more rays than the earlier runs with 5E7 instead of 1E6 rays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "sample.set_SPLIT(1)\n",
+    "instrument.settings(ncount=1E5) #1E7\n",
+    "                          \n",
+    "instrument.backengine()\n",
+    "data_ref = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(data_ref)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting data on same plot\n",
+    "Here we only have one monitor in each data list, but we still use the *name_search* function to retrieve the correct data object from each. This avoids the code breaking in case additional monitors are added.\n",
+    "\n",
+    "Once we have the objects, we use the *xaxis*, *Intensity* and *Error* attributes to plot the data with matplotlib."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "banana_low = functions.name_search(\"banana\", data_low)\n",
+    "banana_reasonable = functions.name_search(\"banana\", data_reasonable)\n",
+    "banana_unreasonable = functions.name_search(\"banana\", data_unreasonable)\n",
+    "banana_ref = functions.name_search(\"banana\", data_ref)\n",
+    "\n",
+    "plt.figure(figsize=(14,6))\n",
+    "plt.errorbar(banana_low.xaxis, banana_low.Intensity, yerr=banana_low.Error, fmt=\"r\")\n",
+    "plt.errorbar(banana_ref.xaxis, banana_ref.Intensity, yerr=banana_ref.Error, fmt=\"b\")\n",
+    "plt.xlabel(\"2Theta [deg]\")\n",
+    "plt.ylabel(\"Intensity [n/s]\")\n",
+    "plt.legend([\"Low statistics\", \"High statistics reference\"])\n",
+    "\n",
+    "plt.figure(figsize=(14,6))\n",
+    "plt.errorbar(banana_reasonable.xaxis, banana_reasonable.Intensity, yerr=banana_reasonable.Error, fmt=\"r\")\n",
+    "plt.errorbar(banana_ref.xaxis, banana_ref.Intensity, yerr=banana_ref.Error, fmt=\"b\")\n",
+    "plt.xlabel(\"2Theta [deg]\")\n",
+    "plt.ylabel(\"Intensity [n/s]\")\n",
+    "plt.legend([\"Low statistics with SPLIT\", \"High statistics reference\"])\n",
+    "\n",
+    "plt.figure(figsize=(14,6))\n",
+    "plt.errorbar(banana_unreasonable.xaxis, banana_unreasonable.Intensity, yerr=banana_unreasonable.Error, fmt=\"r\")\n",
+    "plt.errorbar(banana_ref.xaxis, banana_ref.Intensity, yerr=banana_ref.Error, fmt=\"b\")\n",
+    "plt.xlabel(\"2Theta [deg]\")\n",
+    "plt.ylabel(\"Intensity [n/s]\")\n",
+    "plt.legend([\"Very low statistics with unreasonable SPLIT\", \"High statistics reference\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretation of the data\n",
+    "We see that with low statistics, the data quality is so bad that noise can be mistaken for peaks. Using SPLIT improves the situation a lot, and the data is very similar to the high statistics reference which takes longer to compute. The situation with a low number of simulated rays and very high SPLIT have some erratic behavior, showing some very different peak intensities than the reference, and some peaks that shouldn't be there at all."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb b/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
new file mode 100644
index 00000000..c37d5b61
--- /dev/null
+++ b/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
@@ -0,0 +1,263 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Advanced McStas features: EXTEND and WHEN\n",
+    "In this tutorial we will look at two advanced features in McStas, the EXTEND block and WHEN condition. Here we will use them to flag certain neutrons with EXTEND, and only record them in monitors when the flag is set using a WHEN condition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"python_tutorial\",\n",
+    "                                input_path=\"run_folder\",\n",
+    "                                output_path=\"data_folder/mcstas_EXTEND_WHEN\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up an example McStas instrument\n",
+    "First we set up an example instrument conisiting of a source, a guide and a position/divergence monitor. The guide is set up such that it only has mirrors on the left and right side, and absorbs neutrons if they hit the top or bottom. This is done to look at the horizontal behavior independently from the vertical, as this is easier to analyze."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "src = instrument.add_component(\"source\", \"Source_simple\")\n",
+    "\n",
+    "src.xwidth = 0.02\n",
+    "src.yheight = 0.02\n",
+    "src.focus_xw = guide_opening_w = 0.05\n",
+    "src.focus_yh = guide_opening_h = 0.06\n",
+    "src.dist = 1.5\n",
+    "src.flux = 1E13\n",
+    "\n",
+    "wavelength = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = wavelength\n",
+    "src.dlambda = \"0.001*wavelength\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "guide = instrument.add_component(\"guide\", \"Guide_gravity\", AT=[0,0,1.5], RELATIVE=src)\n",
+    "guide.w1 = guide_opening_w\n",
+    "guide.h1 = guide_opening_h\n",
+    "guide.w2 = guide_opening_w\n",
+    "guide.h2 = guide_opening_h\n",
+    "guide.l = guide_length = 15\n",
+    "guide.mleft = 4.0\n",
+    "guide.mright = 4.0\n",
+    "guide.mtop = 0.0\n",
+    "guide.mbottom = 0.0\n",
+    "guide.G = -9.82"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "acceptance = instrument.add_component(\"acceptance\", \"DivPos_monitor\")\n",
+    "acceptance.set_AT([0,0, guide_length + 0.1], RELATIVE=guide)\n",
+    "acceptance.nh = 200\n",
+    "acceptance.ndiv = 200\n",
+    "acceptance.filename = '\"acceptance.dat\"'\n",
+    "acceptance.xwidth = 0.08\n",
+    "acceptance.yheight = 0.05\n",
+    "acceptance.maxdiv_h = 1.5\n",
+    "acceptance.restore_neutron = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "instrument.set_parameters(wavelength=2.8)\n",
+    "instrument.settings(ncount=5E5)\n",
+    "instrument.backengine()\n",
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpreting the data\n",
+    "Here we see an acceptance monitor, with position along the x-axis and divergence along the y-axis. The guide is under illuminated by the small source, so there are gaps in the acceptance diagram. We see the position and divergence of the beam consist of a large number of stripes, the ones with lowest divergence has the largest intensity.\n",
+    "\n",
+    "## Add an flag\n",
+    "A flag is just a name for a variable that records some information on the neutron during the simulation, and can be used later to make a decision. Here we could check how many times the ray was reflected in the guide.\n",
+    "\n",
+    "We use an EXTEND block after a component to access variables internal to the component in the instrument scope. We declare a variable in the instrument scope called *n_reflections*. In the component scope, one can use the SCATTERED variable which contains the number of times the ray has encountered the SCATTER keyword within the component. Usually this is done when entering and leaving, and under each scattering / reflection, so the number of reflections is SCATTERED - 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.add_declare_var(\"int\", \"n_reflections\")\n",
+    "guide.append_EXTEND(\"n_reflections = SCATTERED - 2;\")\n",
+    "guide.print_long()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use the flag to limit what is recorded in a monitor\n",
+    "A WHEN statement can be used to activate / deactivate a component when some condition is true / false. For example we could require 0 reflection in our guide. We add a few monitors similar to the original, with the only difference being WHEN statements requiring 0, 1 or 2 reflections in the guide for the component to be active. We use a for loop to add the similar components, only changing the component instance name, filename and WHEN statement between each."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reflection_numbers = [0, 1, 2]\n",
+    "\n",
+    "for reflections in reflection_numbers:\n",
+    "    reflections_string = str(reflections)\n",
+    "    \n",
+    "    acceptance = instrument.add_component(\"acceptance_\" + reflections_string, \"DivPos_monitor\")\n",
+    "    acceptance.filename = '\"acceptance_' + reflections_string + '.dat\"'\n",
+    "    acceptance.set_WHEN(\"n_reflections == \" + reflections_string)\n",
+    "    \n",
+    "    acceptance.set_AT([0,0, guide_length + 0.1], RELATIVE=guide)\n",
+    "    acceptance.nh = 200\n",
+    "    acceptance.ndiv = 200\n",
+    "    acceptance.xwidth = 0.08\n",
+    "    acceptance.yheight = 0.05\n",
+    "    acceptance.maxdiv_h = 1.5\n",
+    "    acceptance.restore_neutron = 1\n",
+    "    \n",
+    "    acceptance.print_long()\n",
+    "    print(\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Running the simulation\n",
+    "We now run the simulation with the new monitors to see how they differ from the original version."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "instrument.set_parameters(wavelength=2.8)\n",
+    "instrument.settings(ncount=5E5)\n",
+    "instrument.show_settings()\n",
+    "\n",
+    "instrument.backengine()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(instrument.data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretation of the data\n",
+    "The original monitor is unchanged as it was not modified. On the monitors with different numbers of reflections, we see the middle line correspond to zero reflections, the two lines around those are for one reflection and so forth. This explains why the lines further from the center has lower intensity, as they underwent more reflections while also having a larger angle of incidence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The McStas instrument file\n",
+    "We here show the generated McStas instrument file in order to clarify how this would be accomplished without the McStasScript API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"run_folder/python_tutorial.instr\") as file:\n",
+    "    instrument_string = file.read()\n",
+    "    print(instrument_string)"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb b/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb
new file mode 100644
index 00000000..8e679da4
--- /dev/null
+++ b/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb
@@ -0,0 +1,281 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Advanced McStas features: JUMP\n",
+    "In this notebook we will look at JUMP and how it can be used to control the sequence of execution of components. One instance where this is useful is if a guide splits into two. Consider an instrument with the following components:\n",
+    "\n",
+    "- source\n",
+    "- main guide\n",
+    "- guide1\n",
+    "- sample1\n",
+    "- detector1\n",
+    "- guide2\n",
+    "- sample2\n",
+    "- detector2\n",
+    "\n",
+    "After the main guide, if the ray hits the opening of guide1 the ray will continue to sample1 and detector1 as expected, but if it misses the opening of guide1, it will just be absorbed and never reach guide2 later in the component sequence. One possible solution is to use a JUMP statement, which jumps to another place in the component sequence. The target component must be an Arm, and no coordinate transformations are done, so the simplest solution is to have the Arm conincide with the component with the JUMP statement.\n",
+    "\n",
+    "- source\n",
+    "- main guide\n",
+    "- arm A JUMP arm B WHEN ray hits guide2 entrance \n",
+    "- guide1\n",
+    "- sample1\n",
+    "- detector1\n",
+    "- arm B (same position and rotation of arm A)\n",
+    "- guide2\n",
+    "- sample2\n",
+    "- detector2\n",
+    "\n",
+    "Here we build such an instrument with a few notes on the syntax along the way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "src = instrument.add_component(\"source\", \"Source_simple\")\n",
+    "\n",
+    "src.xwidth = 0.12\n",
+    "src.yheight = 0.12\n",
+    "src.focus_xw = guide_opening_w = 0.1\n",
+    "src.focus_yh = guide_opening_h = 0.06\n",
+    "src.dist = 1.5\n",
+    "src.flux = 1E13\n",
+    "\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.dlambda = \"0.001*wavelength\"\n",
+    "\n",
+    "guide = instrument.add_component(\"guide\", \"Guide_gravity\", AT=[0,0,1.5], RELATIVE=src)\n",
+    "guide.w1 = guide_opening_w\n",
+    "guide.h1 = guide_opening_h\n",
+    "guide.w2 = guide_opening_w\n",
+    "guide.h2 = guide_opening_h\n",
+    "guide.l = guide_length = 15\n",
+    "guide.m = 4.0\n",
+    "guide.G = -9.82"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding the reference arm\n",
+    "We here add an arm just after the exit of the main guide which will be the component that performs the JUMP under certain circumstances. The McStas syntax for such a JUMP statement would be:\n",
+    "\n",
+    "JUMP *reference* WHEN *condition*\n",
+    "\n",
+    "We will call the arm we jump to for *target_arm*, and our condition is that the neutron is on the left side, so x<0. That means our JUMP statement would be:\n",
+    "\n",
+    "JUMP target_arm WHEN (x<0)\n",
+    "\n",
+    "In McStasScript this is added with the *set_JUMP* method, that takes a string for what to set after JUMP."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start_arm = instrument.add_component(\"split_arm\", \"Arm\")\n",
+    "start_arm.set_AT([0,0, guide_length + 3E-3], RELATIVE=guide)\n",
+    "start_arm.set_JUMP(\"target_arm WHEN (x<0)\")\n",
+    "\n",
+    "print(start_arm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding the first daughter instrument\n",
+    "We then add the left side, which correspond to x>0, so this is the case where no jump was performed and the sequence of components runs as normal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "guide1 = instrument.add_component(\"guide1\", \"Guide_gravity\")\n",
+    "guide1.set_AT([0.25*guide_opening_w,0,0], RELATIVE=start_arm)\n",
+    "guide1.set_ROTATED([0, 1, 0], RELATIVE=start_arm)\n",
+    "guide1.w1 = 0.5*guide_opening_w\n",
+    "guide1.h1 = 0.5*guide_opening_h\n",
+    "guide1.w2 = 0.5*guide_opening_w\n",
+    "guide1.h2 = 0.5*guide_opening_h\n",
+    "guide1.l = guide1_length = 10\n",
+    "guide1.m = 2.5\n",
+    "guide1.G = -9.82\n",
+    "\n",
+    "sample1 = instrument.add_component(\"sample1\", \"PowderN\")\n",
+    "sample1.set_AT([0,0,guide1_length+0.5], RELATIVE=guide1)\n",
+    "sample1.radius = 0.015\n",
+    "sample1.yheight = 0.05\n",
+    "sample1.reflections = '\"Na2Ca3Al2F14.laz\"'\n",
+    "\n",
+    "banana1 = instrument.add_component(\"banana1\", \"Monitor_nD\", RELATIVE=sample1)\n",
+    "banana1.xwidth = 2.0\n",
+    "banana1.yheight = 0.3\n",
+    "banana1.filename = '\"banana1.dat\"'\n",
+    "banana1.options = '\"theta limits=[5 175] bins=150, banana\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding the second daughter instrument\n",
+    "Now we need to add the target_arm that rays jump to when they go to the right side of the guide split. This is in the exact same position of the previous arm, to avoid the need for a coordinate transformation which is not performed automatically when using JUMP statements.\n",
+    "\n",
+    "After that we add a second daughter instrument with a different sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_arm = instrument.add_component(\"target_arm\", \"Arm\")\n",
+    "target_arm.set_AT([0,0,0], RELATIVE=start_arm)\n",
+    "\n",
+    "guide2 = instrument.add_component(\"guide2\", \"Guide_gravity\")\n",
+    "guide2.set_AT([-0.25*guide_opening_w,0,0], RELATIVE=target_arm)\n",
+    "guide2.set_ROTATED([0, -1, 0], RELATIVE=target_arm)\n",
+    "guide2.w1 = 0.5*guide_opening_w\n",
+    "guide2.h1 = 0.5*guide_opening_h\n",
+    "guide2.w2 = 0.5*guide_opening_w\n",
+    "guide2.h2 = 0.5*guide_opening_h\n",
+    "guide2.l = guide1_length = 15\n",
+    "guide2.m = 2.5\n",
+    "guide2.G = -9.82\n",
+    "\n",
+    "sample2 = instrument.add_component(\"sample2\", \"PowderN\")\n",
+    "sample2.set_AT([0,0,guide1_length+0.5], RELATIVE=guide2)\n",
+    "sample2.radius = 0.015\n",
+    "sample2.yheight = 0.05\n",
+    "sample2.reflections = '\"Cu.laz\"'\n",
+    "\n",
+    "banana2 = instrument.add_component(\"banana2\", \"Monitor_nD\", RELATIVE=sample2)\n",
+    "banana2.xwidth = 2.0\n",
+    "banana2.yheight = 0.3\n",
+    "banana2.filename = '\"banana2.dat\"'\n",
+    "banana2.options = '\"theta limits=[5 175] bins=150, banana\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Running the simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "instrument.set_parameters(wavelength=2.8)\n",
+    "instrument.settings(ncount=5E5, output_path=\"data_folder/mcstas_JUMP\")\n",
+    "instrument.backengine()\n",
+    "\n",
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interpretation of the data\n",
+    "We see that each daughter instrument have beam and show the different powder patterns as expected."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The McStas instrument file\n",
+    "We here show the generated McStas instrument file in order to clarify how this would be accomplished without the McStasScript API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"run_folder/python_tutorial.instr\") as file:\n",
+    "    instrument_string = file.read()\n",
+    "    print(instrument_string)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb b/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
new file mode 100644
index 00000000..9584bf80
--- /dev/null
+++ b/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
@@ -0,0 +1,1151 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The Union components\n",
+    "This tutorial is the first in a series showing how the Union components are used. This notebook focuses on setting up material definitions that are used to provide scattering physics to geometries. There are several kinds of Union components, and they need to be used in conjunction with one another to function.\n",
+    "- Process components: Describe individual scattering phenomena, such as incoherent, powder, single crystal scattering\n",
+    "- Make_material component: Joins several processes into a material definition\n",
+    "- Geometry components: Describe geometry, each is assigned a material definition\n",
+    "- Union logger components: Records information for each scattering event and plots it\n",
+    "- Union abs logger components: Records information for each absorption event and plots it\n",
+    "- Union conditional components: Modifies a logger or abs logger so it only records when certain final condition met\n",
+    "- Union master component: Performs simulation described by previous Union components\n",
+    "\n",
+    "In this notebook we will focus on setting up materials using process components and the *Union_make_material* component, but the Union components can not work individually, so it will also be necessary to add a geometry and the *Union_master*. First we import McStasScript and create a new instrument object.\n",
+    "\n",
+    "In case of any issues with running the tutorial notebooks there is troubleshooting at the end of this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Process components\n",
+    "In this notebook we will focus on exploring how to build different physical descriptions of materials, and checking that they behave as expected. We start by looking at the process component for incoherent scattering, Incoherent_process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are all components in the Work directory category.\n",
+      "No components found in this category! Available categories:\n",
+      " sources\n",
+      " optics\n",
+      " samples\n",
+      " monitors\n",
+      " misc\n",
+      " contrib\n",
+      " union\n",
+      " obsolete\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.show_components(\"Work directory\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Incoherent_process ________________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1msigma\u001b[0m = \u001b[1m\u001b[94m5.08\u001b[0m\u001b[0m [barns] // Incoherent scattering cross section\n",
+      "\u001b[1mf_QE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Fraction of quasielastic scattering (rest is elastic)\n",
+      "\u001b[1mgamma\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Lorentzian width of quasielastic broadening (HWHM)\n",
+      "\u001b[1mpacking_factor\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // How dense is the material compared to optimal 0-1\n",
+      "\u001b[1munit_cell_volume\u001b[0m = \u001b[1m\u001b[94m13.8\u001b[0m\u001b[0m [AA^3] // Unit_cell_volume\n",
+      "\u001b[1minteract_fraction\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // How large a part of the scattering events \n",
+      "                                should use this process 0-1 (sum of all processes \n",
+      "                                in material = 1) \n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.component_help(\"Incoherent_process\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The process components in general have few parameters as they just describe a single physical phenomena. The incoherent process here is described adequately by just the cross section *sigma* and volume of the unit cell, *unit_cell_volume*.\n",
+    "\n",
+    "Two parameters are available for all processes, *packing_factor* and *interact_fraction*. The packing factor describes how dense the material is, and can make it easier to mix for example different powders. It is implemented as a simple factor on the scattering strength. The interact fraction is used to balance many processes when they are used in one material. Normally processes are sampled according to they natural probability for scattering, but this can be overwritten using the *interact_fraction*, which provides the sampling probability directly, they just have to sum to 1 within a material."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "incoherent = instrument.add_component(\"incoherent\", \"Incoherent_process\")\n",
+    "incoherent.sigma = 2.5\n",
+    "incoherent.unit_cell_volume = 13.8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Making a material\n",
+    "In order to collect processes into a material, one uses the *Union_make_material* component. Here are the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Union_make_material _______________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1mprocess_string\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated names of physical processes\n",
+      "\u001b[4m\u001b[1mmy_absorption\u001b[0m\u001b[0m [1/m] // Inverse penetration depth from absorption at standard \n",
+      "                       energy \n",
+      "\u001b[1mabsorber\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [0/1] // Control parameter, if set to 1 the material will have \n",
+      "                        no scattering processes \n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.component_help(\"Union_make_material\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A material definition thus consists of a number of processes given with the *process_string* parameter, and a description of the absorption in the material given with the inverse penetration depth at the standard neutron speed of 2200 m/s. For our first test material, lets just set absorption to zero and set our process_string to incoherent, referring to the process we created above.\n",
+    "\n",
+    "The name of the material is now inc_material, which will be used in the future to refer to this material."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inc_material = instrument.add_component(\"inc_material\", \"Union_make_material\")\n",
+    "inc_material.my_absorption = 0.0\n",
+    "inc_material.process_string = '\"incoherent\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If the material contains no physical processes, it is necessary to set the *absorber* parameter to 1, as it will just have an absorption description. Here we make a material called abs_material. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "absorber = instrument.add_component(\"abs_material\", \"Union_make_material\")\n",
+    "absorber.absorber = 1\n",
+    "absorber.my_absorption = 3.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The primary reason for having both process components and a make_material component is that it is possible to add as many processes in one material as necessary. Here we create a powder process, and then make a material using the powder and previously defined incoherent processes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "powder = instrument.add_component(\"powder\", \"Powder_process\")\n",
+    "powder.reflections = '\"Cu.laz\"'\n",
+    "\n",
+    "inc_material = instrument.add_component(\"powder_material\", \"Union_make_material\")\n",
+    "inc_material.my_absorption = 1.2\n",
+    "inc_material.process_string = '\"incoherent,powder\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At this point we have three materials defined\n",
+    "\n",
+    "| Material name   | Description                                                      |\n",
+    "|-----------------|------------------------------------------------------------------|\n",
+    "| inc_material    | Has one incoherent process and no absorption                     |\n",
+    "| abs_material    | Only has absorption                                              |\n",
+    "| powder_material | Has both incoherent and powder process in addition to absorption |\n",
+    "\n",
+    "Let us defined a quick test instrument to see these materials are behaving as expected. First we add a source."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "src = instrument.add_component(\"source\", \"Source_div\")\n",
+    "\n",
+    "source_width = instrument.add_parameter(\"source_width\", value=0.15, comment=\"Width of source in [m]\")\n",
+    "src.xwidth = source_width\n",
+    "src.yheight = 0.03\n",
+    "src.focus_aw = 0.01\n",
+    "src.focus_ah = 0.01\n",
+    "\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.dlambda = \"0.001*wavelength\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding geometries that use the material definitions\n",
+    "Here we add three boxes, each using a different material definition and placed next to one another. The *material_string* parameter is used to specify the material name. The *priority* parameter will be explained later, as it is only important when geometries overlap, here they are spatially separated, yet the priorties must still be unique.\n",
+    "\n",
+    "It is important to note that these three boxes will be simulated simultaneously in the McStas simulation flow, so no need for GROUP statements to have these in parallel. Because they are simulated simultaneously, a ray can go from one to another, which would not be possible with a standard GROUP."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "box_inc = instrument.add_component(\"box_inc\", \"Union_box\", AT=[0.04,0,1], RELATIVE=src)\n",
+    "box_inc.xwidth = 0.03\n",
+    "box_inc.yheight = 0.03\n",
+    "box_inc.zdepth = 0.03\n",
+    "box_inc.material_string = '\"inc_material\"'\n",
+    "box_inc.priority = 10\n",
+    "\n",
+    "box_inc = instrument.add_component(\"box_powder\", \"Union_box\", AT=[0,0,1], RELATIVE=src)\n",
+    "box_inc.xwidth = 0.03\n",
+    "box_inc.yheight = 0.03\n",
+    "box_inc.zdepth = 0.01\n",
+    "box_inc.material_string = '\"powder_material\"'\n",
+    "box_inc.priority = 11\n",
+    "\n",
+    "box_inc = instrument.add_component(\"box_abs\", \"Union_box\", AT=[-0.04,0,1], RELATIVE=src)\n",
+    "box_inc.xwidth = 0.03\n",
+    "box_inc.yheight = 0.03\n",
+    "box_inc.zdepth = 0.03\n",
+    "box_inc.material_string = '\"abs_material\"'\n",
+    "box_inc.priority = 12"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding loggers that show scattering and absorption\n",
+    "In order to check the three materials behave as expected, we add spatial loggers for scattering and absorption. These are called loggers and abs_loggers, here is the parameters for a logger."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Union_logger_2D_space _____________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1mtarget_geometry\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated list of geometry names \n",
+      "                                     that will be logged, leave empty for all \n",
+      "                                     volumes (even not defined yet) \n",
+      "\u001b[1mtarget_process\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated names of physical \n",
+      "                                    processes, if volumes are selected, one can \n",
+      "                                    select Union_process names \n",
+      "\u001b[1mD_direction_1\u001b[0m = \u001b[1m\u001b[94m\"x\"\u001b[0m\u001b[0m [string] // Direction for first axis (\"x\", \"y\" or \"z\")\n",
+      "\u001b[1mD1_min\u001b[0m = \u001b[1m\u001b[94m-5.0\u001b[0m\u001b[0m [1] // histogram boundery, min position value for first axis\n",
+      "\u001b[1mD1_max\u001b[0m = \u001b[1m\u001b[94m5.0\u001b[0m\u001b[0m [1] // histogram boundery, max position value for first axis\n",
+      "\u001b[1mn1\u001b[0m = \u001b[1m\u001b[94m90.0\u001b[0m\u001b[0m [1] // number of bins for first axis\n",
+      "\u001b[1mD_direction_2\u001b[0m = \u001b[1m\u001b[94m\"z\"\u001b[0m\u001b[0m [string] // Direction for second axis (\"x\", \"y\" or \"z\")\n",
+      "\u001b[1mD2_min\u001b[0m = \u001b[1m\u001b[94m-5.0\u001b[0m\u001b[0m [1] // histogram boundery, min position value for second axis\n",
+      "\u001b[1mD2_max\u001b[0m = \u001b[1m\u001b[94m5.0\u001b[0m\u001b[0m [1] // histogram boundery, max position value for second axis\n",
+      "\u001b[1mn2\u001b[0m = \u001b[1m\u001b[94m90.0\u001b[0m\u001b[0m [1] // number of bins for second axis\n",
+      "\u001b[1mfilename\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Filename for logging output\n",
+      "\u001b[1morder_total\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time, 0 for \n",
+      "                         all orders \n",
+      "\u001b[1morder_volume\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time in the \n",
+      "                          same geometry \n",
+      "\u001b[1morder_volume_process\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time \n",
+      "                                  in the same geometry, using the same process \n",
+      "\u001b[1mlogger_conditional_extend_index\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // If a conditional is used with \n",
+      "                                              this logger, the result of each \n",
+      "                                              conditional calculation can be made \n",
+      "                                              available in extend as a array called \n",
+      "                                              \"logger_conditional_extend\", and one \n",
+      "                                              would then acces \n",
+      "                                              logger_conditional_extend[n] if \n",
+      "                                              logger_conditional_extend_index is \n",
+      "                                              set to n \n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.component_help(\"Union_logger_2D_space\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The parameters for the abs_logger are very similar, so the two are added here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger = instrument.add_component(\"logger_space\", \"Union_logger_2D_space\", RELATIVE=\"box_powder\")\n",
+    "logger.D_direction_1 = '\"z\"'\n",
+    "logger.D1_min = -0.04\n",
+    "logger.D1_max = 0.04\n",
+    "logger.n1 = 250\n",
+    "logger.D_direction_2 = '\"x\"'\n",
+    "logger.D2_min = -0.075\n",
+    "logger.D2_max = 0.075\n",
+    "logger.n2 = 400\n",
+    "logger.filename = '\"logger.dat\"'\n",
+    "\n",
+    "logger = instrument.add_component(\"abs_logger_space\", \"Union_abs_logger_2D_space\", RELATIVE=\"box_powder\")\n",
+    "logger.D_direction_1 = '\"z\"'\n",
+    "logger.D1_min = -0.04\n",
+    "logger.D1_max = 0.04\n",
+    "logger.n1 = 250\n",
+    "logger.D_direction_2 = '\"x\"'\n",
+    "logger.D2_min = -0.075\n",
+    "logger.D2_max = 0.075\n",
+    "logger.n2 = 400\n",
+    "logger.filename = '\"abs_logger.dat\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding the Union master component\n",
+    "The Union master component is what actually executes the simulation, and so it takes information from all Union components defined before and performs the described simulation. This is the component that matters in terms of order of execution within the sequence of McStas components. As all the previous components have described the what the master component should simulate, it has no required parameters. It also does not matter where it is located in space, as it will grab the locations described by all previous Union components that need a spatial location, such as the geometries and loggers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Union_master ______________________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1mverbal\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [bool] // Toogles terminal output describing the defined simulation\n",
+      "\u001b[1mlist_verbal\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Toogles information of all internal lists in \n",
+      "                            intersection network \n",
+      "\u001b[1mfinally_verbal\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Toogles information about cleanup performed in \n",
+      "                               finally section \n",
+      "\u001b[1mallow_inside_start\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Set to 1 to allow rays to start inside the \n",
+      "                                   defined geometry \n",
+      "\u001b[1menable_tagging\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Enable tagging of ray history (geometry, \n",
+      "                               scattering process) \n",
+      "\u001b[1mhistory_limit\u001b[0m = \u001b[1m\u001b[94m300000.0\u001b[0m\u001b[0m [bool] // Limit the number of unique histories that \n",
+      "                                   are saved \n",
+      "\u001b[1menable_conditionals\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [bool] // Use conditionals with this master\n",
+      "\u001b[1minherit_number_of_scattering_events\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Inherit the number of \n",
+      "                                                    scattering events from last \n",
+      "                                                    master \n",
+      "\u001b[1mrecord_absorption\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Toggles logging of absorption\n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.component_help(\"Union_master\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "master = instrument.add_component(\"master\", \"Union_master\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Running the simulation\n",
+    "Here the McStas simulation is executed as normal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Instrument settings:\n",
+      "  ncount:           5.00e+06\n",
+      "  output_path:      data_folder/union_materials\n",
+      "  run_path:         run_folder\n",
+      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
+      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
+      "  executable:       mcrun\n",
+      "  force_compile:    True\n",
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/tutorial/data_folder/union_materials_5\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
+      "                                                ^~~~~~~~\n",
+      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
+      "#define filename mccabs_logger_space_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
+      "                                                ^\n",
+      "                                                \"%s\", \n",
+      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
+      "#define filename mccabs_logger_space_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "14 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/tutorial/data_folder/union_materials_5\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Cu.laz' (Table_Read_Offset)\n",
+      "Table from file 'Cu.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
+      "  '# TITLE *-Cu-[FM3-M] Otte, H.M.[1961];# CELL 3.615050 3.615050 3.615050 90. ...'\n",
+      "PowderN: powder: Reading 19 rows from Cu.laz\n",
+      "PowderN: powder: Read 19 reflections from file 'Cu.laz'\n",
+      "PowderN: powder: Vc=47.24 [Angs] sigma_abs=15.12 [barn] sigma_inc=2.2 [barn] reflections=Cu.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: incoherent \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: powder \n",
+      "component index [1]: 4 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 3\n",
+      "name of material    [0]: inc_material \n",
+      "component index     [0]: 2 \n",
+      "my_absoprtion       [0]: 0.000000 \n",
+      "number of processes [0]: 1 \n",
+      "name of material    [1]: abs_material \n",
+      "component index     [1]: 3 \n",
+      "my_absoprtion       [1]: 3.000000 \n",
+      "number of processes [1]: 0 \n",
+      "name of material    [2]: powder_material \n",
+      "component index     [2]: 5 \n",
+      "my_absoprtion       [2]: 1.200000 \n",
+      "number of processes [2]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 3\n",
+      "\n",
+      "name of geometry    [0]: box_inc \n",
+      "component index     [0]: 7 \n",
+      "Volume.name         [0]: box_inc \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.000000 \n",
+      "Volume.p_physics.number of processes [0]: 1 \n",
+      "Volume.geometry.shape                [0]: box \n",
+      "Volume.geometry.center.x             [0]: 0.040000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: box_powder \n",
+      "component index     [1]: 8 \n",
+      "Volume.name         [1]: box_powder \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absorption       [1]: 1.200000 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: box \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: box_abs \n",
+      "component index     [2]: 9 \n",
+      "Volume.name         [2]: box_abs \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absorption       [2]: 3.000000 \n",
+      "Volume.p_physics.number of processes [2]: 0 \n",
+      "Volume.geometry.shape                [2]: box \n",
+      "Volume.geometry.center.x             [2]: -0.040000 \n",
+      "Volume.geometry.center.y             [2]: 0.000000 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 4\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           0 = [1,2,3]\n",
+      "LIST: Intersect_check_list for Volume      0 = [1,2,3]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [1,2,3]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for box_inc with box shape made of inc_material\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = []\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = []\n",
+      "LIST: Next_volume_list for Volume          1 = [0]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for box_powder with box shape made of powder_material\n",
+      "LIST: Children for Volume                  2 = []\n",
+      "LIST: Direct_children for Volume           2 = []\n",
+      "LIST: Intersect_check_list for Volume      2 = []\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = []\n",
+      "LIST: Next_volume_list for Volume          2 = [0]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for box_abs with box shape made of abs_material\n",
+      "LIST: Children for Volume                  3 = []\n",
+      "LIST: Direct_children for Volume           3 = []\n",
+      "LIST: Intersect_check_list for Volume      3 = []\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = []\n",
+      "LIST: Next_volume_list for Volume          3 = [0]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_I=3.1635e-09 logger_space_ERR=3.59609e-12 logger_space_N=790141 \"logger.dat\"\n",
+      "Detector: abs_logger_space_I=1.70717e-09 abs_logger_space_ERR=1.53146e-12 abs_logger_space_N=1.81278e+06 \"abs_logger.dat\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.set_parameters(wavelength=8.0)\n",
+    "instrument.settings(ncount=3E5, output_path=\"data_folder/union_materials\")\n",
+    "instrument.show_settings()\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interpreting the results\n",
+    "The first logger shows scattering, and since the top box has incoherent, and the middle both powder and incoherent, we expect those to show up. We can see the beam attenuation, as the beam originates from the left side.\n",
+    "\n",
+    "The second logger shows absorption, and here the top box is absent as it has no absorption cross section. The bottom box is however visible now, as it has absorption but no scattering. As the absorber is quite strong, we see the attenuation here as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space\n",
+      "Plotting data with name abs_logger_space\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Alternative run to show powder properties\n",
+    "In order to see the scattering from the powder sample, we restrict the source size to only illuminate the center box with a powder material. A wavelength with powder lines close to 90 deg is selected to ensure the scattering from the center box hits the surrounding boxes.\n",
+    "\n",
+    "We choose to show the data with logarithmic colorscale using the *name_plot_options* method on functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Instrument settings:\n",
+      "  ncount:           5.00e+06\n",
+      "  output_path:      data_folder/union_materials\n",
+      "  run_path:         run_folder\n",
+      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
+      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
+      "  executable:       mcrun\n",
+      "  force_compile:    True\n",
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/tutorial/data_folder/union_materials_6\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
+      "                                                ^~~~~~~~\n",
+      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
+      "#define filename mccabs_logger_space_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
+      "                                                ^\n",
+      "                                                \"%s\", \n",
+      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
+      "#define filename mccabs_logger_space_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "14 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/tutorial/data_folder/union_materials_6\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Cu.laz' (Table_Read_Offset)\n",
+      "Table from file 'Cu.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
+      "  '# TITLE *-Cu-[FM3-M] Otte, H.M.[1961];# CELL 3.615050 3.615050 3.615050 90. ...'\n",
+      "PowderN: powder: Reading 19 rows from Cu.laz\n",
+      "PowderN: powder: Read 19 reflections from file 'Cu.laz'\n",
+      "PowderN: powder: Vc=47.24 [Angs] sigma_abs=15.12 [barn] sigma_inc=2.2 [barn] reflections=Cu.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: incoherent \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: powder \n",
+      "component index [1]: 4 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 3\n",
+      "name of material    [0]: inc_material \n",
+      "component index     [0]: 2 \n",
+      "my_absoprtion       [0]: 0.000000 \n",
+      "number of processes [0]: 1 \n",
+      "name of material    [1]: abs_material \n",
+      "component index     [1]: 3 \n",
+      "my_absoprtion       [1]: 3.000000 \n",
+      "number of processes [1]: 0 \n",
+      "name of material    [2]: powder_material \n",
+      "component index     [2]: 5 \n",
+      "my_absoprtion       [2]: 1.200000 \n",
+      "number of processes [2]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 3\n",
+      "\n",
+      "name of geometry    [0]: box_inc \n",
+      "component index     [0]: 7 \n",
+      "Volume.name         [0]: box_inc \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.000000 \n",
+      "Volume.p_physics.number of processes [0]: 1 \n",
+      "Volume.geometry.shape                [0]: box \n",
+      "Volume.geometry.center.x             [0]: 0.040000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: box_powder \n",
+      "component index     [1]: 8 \n",
+      "Volume.name         [1]: box_powder \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absorption       [1]: 1.200000 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: box \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: box_abs \n",
+      "component index     [2]: 9 \n",
+      "Volume.name         [2]: box_abs \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absorption       [2]: 3.000000 \n",
+      "Volume.p_physics.number of processes [2]: 0 \n",
+      "Volume.geometry.shape                [2]: box \n",
+      "Volume.geometry.center.x             [2]: -0.040000 \n",
+      "Volume.geometry.center.y             [2]: 0.000000 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 4\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           0 = [1,2,3]\n",
+      "LIST: Intersect_check_list for Volume      0 = [1,2,3]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [1,2,3]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for box_inc with box shape made of inc_material\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = []\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = []\n",
+      "LIST: Next_volume_list for Volume          1 = [0]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for box_powder with box shape made of powder_material\n",
+      "LIST: Children for Volume                  2 = []\n",
+      "LIST: Direct_children for Volume           2 = []\n",
+      "LIST: Intersect_check_list for Volume      2 = []\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = []\n",
+      "LIST: Next_volume_list for Volume          2 = [0]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for box_abs with box shape made of abs_material\n",
+      "LIST: Children for Volume                  3 = []\n",
+      "LIST: Direct_children for Volume           3 = []\n",
+      "LIST: Intersect_check_list for Volume      3 = []\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = []\n",
+      "LIST: Next_volume_list for Volume          3 = [0]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_I=1.13342e-09 logger_space_ERR=5.90802e-13 logger_space_N=3.86971e+06 \"logger.dat\"\n",
+      "Detector: abs_logger_space_I=4.16101e-11 abs_logger_space_ERR=2.36615e-14 abs_logger_space_N=4.12627e+06 \"abs_logger.dat\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.set_parameters(wavelength=2.8, source_width=0.03)\n",
+    "instrument.settings(ncount=5E6, output_path=\"data_folder/union_materials\")\n",
+    "instrument.show_settings()\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space\n",
+      "Plotting data with name abs_logger_space\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "functions.name_plot_options(\"logger_space\", data, log=True)\n",
+    "functions.name_plot_options(\"abs_logger_space\", data, log=True)\n",
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretation of the data\n",
+    "Now that the direct beam only hits the center box, all rays that enter the surrounding boxes are scattered from that center box. Since the center box contains a powder, the scattered beam is not homogeneous and most of it is in the form of Bragg peaks with certain scattering angles, and we can see two of these intersecting the surrounding geometries."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Troubleshooting\n",
+    "In case of issues with the notebooks concerning the Union components or McStasScript it is recommended to:\n",
+    "- Update McStasScript with python -m pip install --upgrade mcstasscript\n",
+    "- Get newest version of Union components (Both library files and components themselves)\n",
+    "\n",
+    "Since the Union components need to collaborate, it is important to have the same version of the libraries and components. The newest version of the components can be found here: https://github.com/McStasMcXtrace/McCode/tree/master/mcstas-comps/contrib/union\n",
+    "All libraries for McStas are found here: https://github.com/McStasMcXtrace/McCode/tree/master/mcstas-comps/share but only three are needed for the Union components:\n",
+    "- Union_initialization.c\n",
+    "- Union_functions.c\n",
+    "- Geometry_functions.c\n",
+    "- Union_last_functions.c (if on McStas 3.X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/tutorial/Union_tutorial_2_geometry.ipynb b/docs/source/tutorial/Union_tutorial_2_geometry.ipynb
new file mode 100644
index 00000000..a15aae94
--- /dev/null
+++ b/docs/source/tutorial/Union_tutorial_2_geometry.ipynb
@@ -0,0 +1,492 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Advanced geometry using the Union components\n",
+    "The Union components allow the user to construct advanced geometry from simple shapes. Each available shape has their own component, here are the currently available geometry components.\n",
+    "- Union_box\n",
+    "- Union_sphere\n",
+    "- Union_cylinder\n",
+    "- Union_cone\n",
+    "\n",
+    "They differ in their parameters describing the geometry, but are otherwise identical. In this notebook we will show how to construct hollow geometries with several layers, and that multiple scattering between these quickly result in complex behavior."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting up some standard materials\n",
+    "Before setting up the geometry, we need some material definition, here we set up aluminium and a sample powder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
+    "Al_inc.sigma = 4*0.0082 # 4 atoms per unit cell\n",
+    "Al_inc.unit_cell_volume = 66.4\n",
+    "\n",
+    "Al_pow = instrument.add_component(\"Al_pow\", \"Powder_process\")\n",
+    "Al_pow.reflections = '\"Al.laz\"'\n",
+    "\n",
+    "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
+    "Al.process_string = '\"Al_inc,Al_pow\"'\n",
+    "Al.my_absorption = 100*4*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "\n",
+    "Sample_inc = instrument.add_component(\"Sample_inc\", \"Incoherent_process\")\n",
+    "Sample_inc.sigma = 4*3.4176\n",
+    "Sample_inc.unit_cell_volume = 1079.1\n",
+    "\n",
+    "Sample_pow = instrument.add_component(\"Sample_pow\", \"Powder_process\")\n",
+    "Sample_pow.reflections = '\"Na2Ca3Al2F14.laz\"'\n",
+    "\n",
+    "Sample = instrument.add_component(\"Sample\", \"Union_make_material\")\n",
+    "Sample.process_string = '\"Sample_inc,Sample_pow\"'\n",
+    "Sample.my_absorption = 100*4*2.9464/1079.1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up source\n",
+    "We will also need a source, and allow the wavelength to be tuned with a instrument parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "src = instrument.add_component(\"source\", \"Source_div\")\n",
+    "\n",
+    "src.xwidth = 0.01\n",
+    "src.yheight = 0.035\n",
+    "src.focus_aw = 0.01\n",
+    "src.focus_ah = 0.01\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.dlambda = \"0.01*wavelength\"\n",
+    "src.flux = 1E13"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Describing the geometry of a simple cryostat\n",
+    "A cryostat is a complex geometry with several layers to consider. The way geometry is described in the Union components aims to make it easy to describe such systems. This is aciheved by allowing the simple geometries to overlap, and having a value called the priority to determine which is active in a given volume. If two geometries overlap, the overlapping region gets the physics from the geometry with the highest priority. In that way a cryostat model can be created by having a high priority for the sample in the center, and decreasing the priority as we move out.\n",
+    "\n",
+    "The ray tracing algorithm can however not handle if two geometries overlap perfectly, even with a single side. This could be two boxes sharing a side.\n",
+    "\n",
+    "Let us look at the parameters for a Union geometry component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.component_help(\"Union_cylinder\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The geometry components have many parameters due to their flexibility, but only a few are needed for basic use.\n",
+    "- material_string : string for selecting an available material\n",
+    "- priority : number, in case of overlap the geometry with highest priority decides the material properties\n",
+    "- geometrical parameters : Here radius and yheight\n",
+    "\n",
+    "In addition there is a focusing system where scattering of physical processes that support this can be forced to a certain direction, this is controlled with these parameters, but are rarely used:\n",
+    "- target_index : relative component index of target\n",
+    "- target_x : if target_index not set, relative x coordinate of target\n",
+    "- target_y : if target_index not set, relative y coordinate of target\n",
+    "- target_z : if target_index not set, relative z coordinate of target\n",
+    "- focus_aw : angular width of focusing cone (either specify angular, box or circular)\n",
+    "- focus_ah : angular height of focusing cone \n",
+    "- focus_xw : spatial width of focusing cone (box type focusing)\n",
+    "- focus_xh : spatial height of focusing\n",
+    "- focus_r : spatial radius of focusing cone (circular)\n",
+    "\n",
+    "Finally there is p_interact, which is used for controlling Monte Carlo sampling frequency of the geometry, as it controls the probability for scattering occurring for any path before or after scattering.\n",
+    "\n",
+    "The remaining parameters including masks and number_of_activations are for advanced rules which will be described in a later tutorial."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### First geometry, a sample in a container\n",
+    "\n",
+    "We have defined the following materials that are available to us:\n",
+    "- Al\n",
+    "- Sample\n",
+    "\n",
+    "Lets start by building a simple powder container with a lid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_geometry = instrument.add_component(\"sample_geometry\", \"Union_cylinder\")\n",
+    "sample_geometry.yheight = 0.03\n",
+    "sample_geometry.radius = 0.0075\n",
+    "sample_geometry.material_string='\"Sample\"' \n",
+    "sample_geometry.priority = 100\n",
+    "sample_geometry.set_AT([0,0,1], RELATIVE=src)\n",
+    "\n",
+    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\", RELATIVE=sample_geometry)\n",
+    "container.yheight = 0.03+0.003 # 1.5 mm top and button\n",
+    "container.radius = 0.0075 + 0.0015 # 1.5 mm sides of container\n",
+    "container.material_string='\"Al\"' \n",
+    "container.priority = 99\n",
+    "\n",
+    "container_lid = instrument.add_component(\"sample_container_lid\", \"Union_cylinder\")\n",
+    "container_lid.set_AT([0, 0.0155, 0], RELATIVE=container)\n",
+    "container_lid.yheight = 0.004\n",
+    "container_lid.radius = 0.013\n",
+    "container_lid.material_string='\"Al\"' \n",
+    "container_lid.priority = 98"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up loggers to check what is going on\n",
+    "In order to view what geometry we have set up, we set up three loggers that view the scattering projected onto three different planes. These record the spatail distribution of scattering events."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zx.D_direction_1 = '\"z\"'\n",
+    "logger_zx.D1_min = -0.02\n",
+    "logger_zx.D1_max = 0.02\n",
+    "logger_zx.n1 = 300\n",
+    "logger_zx.D_direction_2 = '\"x\"'\n",
+    "logger_zx.D2_min = -0.02\n",
+    "logger_zx.D2_max = 0.02\n",
+    "logger_zx.n2 = 300\n",
+    "logger_zx.filename = '\"logger_zx.dat\"'\n",
+    "\n",
+    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zy.D_direction_1 = '\"z\"'\n",
+    "logger_zy.D1_min = -0.02\n",
+    "logger_zy.D1_max = 0.02\n",
+    "logger_zy.n1 = 300\n",
+    "logger_zy.D_direction_2 = '\"y\"'\n",
+    "logger_zy.D2_min = -0.02\n",
+    "logger_zy.D2_max = 0.02\n",
+    "logger_zy.n2 = 300\n",
+    "logger_zy.filename = '\"logger_zy.dat\"'\n",
+    "\n",
+    "logger_xy = instrument.add_component(\"logger_space_xy\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_xy.D_direction_1 = '\"x\"'\n",
+    "logger_xy.D1_min = -0.02\n",
+    "logger_xy.D1_max = 0.02\n",
+    "logger_xy.n1 = 300\n",
+    "logger_xy.D_direction_2 = '\"y\"'\n",
+    "logger_xy.D2_min = -0.02\n",
+    "logger_xy.D2_max = 0.02\n",
+    "logger_xy.n2 = 300\n",
+    "logger_xy.filename = '\"logger_xy.dat\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add master component\n",
+    "We need to remember to add a master component to actually perform the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "master = instrument.add_component(\"master\", \"Union_master\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add banana monitor\n",
+    "We are also interested in viewing some scattering data, here we add a banana monitor using the Monitor_nD component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "banana = instrument.add_component(\"banana\", \"Monitor_nD\", RELATIVE=sample_geometry)\n",
+    "banana.xwidth = 1.5\n",
+    "banana.yheight = 0.4\n",
+    "banana.restore_neutron = 1\n",
+    "banana.options = '\"theta limits=[5 175] bins=250, banana\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run simulation\n",
+    "Now we need to run the simulation to view the geometry we have built."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "instrument.set_parameters(wavelength=3.0)\n",
+    "instrument.settings(ncount=3E5, output_path=\"data_folder/union_geometry\")\n",
+    "instrument.show_settings()\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting the data\n",
+    "Due to the large differences between the scattered intensity from parts in the direct beam and outside, we use a logarithmic axis to display scattered intensity. We limit it to 4 orders of magnitude below the maximum intensity, otherwise a single very low intensity event can draw the intensity axis out to a large interval making it difficult to see the important nuances."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
+    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
+    "functions.name_plot_options(\"logger_space_xy\", data, log=True, orders_of_mag=4)\n",
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretation of results\n",
+    "The beam is narrower than the sample, but taller than the can, so some parts of the sample powder are not directly illuminated, and can thus be seen as a intensity area especially on the zx logger image. The aluminum scatters less, and so lower intensity still."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding a cryostat around the sample can\n",
+    "We can add a crude model of a cryostat around our sample can by adding more Union geometry components. They have to be before the Union_master in the McStas instrument file, so we use the keyword argument *before* in the *add_component* method to specify this when adding the components.\n",
+    "\n",
+    "We also need to designate areas as empty, this is done using the default material Vacuum which has no absorption or scattering processes. In this way we can create several layers by decreasing the priority when going out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inner_wall = instrument.add_component(\"cryostat_wall\", \"Union_cylinder\", before=\"master\")\n",
+    "inner_wall.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "inner_wall.yheight = 0.12\n",
+    "inner_wall.radius = 0.03\n",
+    "inner_wall.material_string='\"Al\"' \n",
+    "inner_wall.priority = 80\n",
+    "\n",
+    "inner_wall_vac = instrument.add_component(\"cryostat_wall_vacuum\", \"Union_cylinder\", before=\"master\")\n",
+    "inner_wall_vac.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "inner_wall_vac.yheight = 0.12 - 0.008\n",
+    "inner_wall_vac.radius = 0.03 - 0.002\n",
+    "inner_wall_vac.material_string='\"Vacuum\"' \n",
+    "inner_wall_vac.priority = 81\n",
+    "\n",
+    "outer_wall = instrument.add_component(\"outer_cryostat_wall\", \"Union_cylinder\", before=\"master\")\n",
+    "outer_wall.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "outer_wall.yheight = 0.15\n",
+    "outer_wall.radius = 0.1\n",
+    "outer_wall.material_string='\"Al\"' \n",
+    "outer_wall.priority = 60\n",
+    "\n",
+    "outer_wall_vac = instrument.add_component(\"outer_cryostat_wall_vacuum\", \"Union_cylinder\", before=\"master\")\n",
+    "outer_wall_vac.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "outer_wall_vac.yheight = 0.15 - 0.01\n",
+    "outer_wall_vac.radius = 0.1 - 0.003\n",
+    "outer_wall_vac.material_string='\"Vacuum\"' \n",
+    "outer_wall_vac.priority = 61"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adjusting the logger view to see the larger cryostat area\n",
+    "The loggers were only viewing a small area around the sample can, but this can be expanded as we still have access to the component objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger_zx.set_parameters(D1_min=-0.12, D1_max=0.12, D2_min=-0.12, D2_max=0.12)\n",
+    "logger_zy.set_parameters(D1_min=-0.12, D1_max=0.12, D2_min=-0.12, D2_max=0.12)\n",
+    "logger_xy.set_parameters(D1_min=-0.12, D1_max=0.12, D2_min=-0.12, D2_max=0.12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run the updated instrument file\n",
+    "Run the simulation with the added cryostat, since no parameters or settings are changed it is enough to just call the backengine function and grab the new data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "instrument.backengine()\n",
+    "data_cryo = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the data from the new simulation\n",
+    "Here we increase the orders of magnitude of intensity plotted on the log plots. Try to play with these values to see how it changes the plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(data_cryo, log=[True, True, True, False], orders_of_mag=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpreting the data\n",
+    "The different layers of the cryostat both result in scattering from the aluminium the beam has to move through, but also some increase intensity where it illuminated by scattering from the sample.\n",
+    "\n",
+    "### Comparing situation with and without cryostat\n",
+    "It could be interesting to see what difference adding the cryostat did to the measured signal in the banana monitor, here we extract the numpy arrays and plot them manually with matplotlib for at direct comparison. Ensure you run the two simulations with the same wavelength in order for a comparison to be meaningful."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "banana_can = functions.name_search(\"banana\", data)\n",
+    "banana_cryo = functions.name_search(\"banana\", data_cryo)\n",
+    "\n",
+    "import copy\n",
+    "import numpy as np\n",
+    "banana_diff = copy.deepcopy(banana_cryo)\n",
+    "banana_diff.Intensity = banana_cryo.Intensity - banana_can.Intensity\n",
+    "banana_diff.Error = np.sqrt(banana_cryo.Error**2 + banana_can.Error**2)\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.figure(figsize=(14,6))\n",
+    "plt.plot(banana_can.xaxis, banana_can.Intensity, \"r\",\n",
+    "         banana_cryo.xaxis, banana_cryo.Intensity, \"b\",\n",
+    "         banana_diff.xaxis, banana_diff.Intensity-10.0, \"k\")\n",
+    "plt.xlabel(\"2Theta [deg]\")\n",
+    "plt.ylabel(\"Intensity [n/s]\")\n",
+    "plt.legend([\"Sample in can\", \"Sample in can in cryostat\", \"Difference displaced to -10\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/tutorial/Union_tutorial_3_loggers.ipynb b/docs/source/tutorial/Union_tutorial_3_loggers.ipynb
new file mode 100644
index 00000000..d57308e0
--- /dev/null
+++ b/docs/source/tutorial/Union_tutorial_3_loggers.ipynb
@@ -0,0 +1,591 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualizing what happens in Union master\n",
+    "One disadvantage to collecting all the simulation in the Union_master component, is that it is not possible to insert monitors between the parts to check on the beam. This issue is addressed by adding logger components that can record scattering and absorption events that occurs during the simulation. This notebook will show examples on the usage of loggers and their features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up materials and geometry to investigate\n",
+    "First we set up the same mock cryostat we created in the advanced geometry tutorial to have an interesting system to investigate using the loggers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter\n",
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
+    "Al_inc.sigma = 0.0082\n",
+    "Al_inc.unit_cell_volume = 66.4\n",
+    "\n",
+    "Al_pow = instrument.add_component(\"Al_pow\", \"Powder_process\")\n",
+    "Al_pow.reflections = '\"Al.laz\"'\n",
+    "\n",
+    "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
+    "Al.process_string = '\"Al_inc,Al_pow\"'\n",
+    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "\n",
+    "Sample_inc = instrument.add_component(\"Sample_inc\", \"Incoherent_process\")\n",
+    "Sample_inc.sigma = 3.4176\n",
+    "Sample_inc.unit_cell_volume = 1079.1\n",
+    "\n",
+    "Sample_pow = instrument.add_component(\"Sample_pow\", \"Powder_process\")\n",
+    "Sample_pow.reflections = '\"Na2Ca3Al2F14.laz\"'\n",
+    "\n",
+    "Sample = instrument.add_component(\"Sample\", \"Union_make_material\")\n",
+    "Sample.process_string = '\"Sample_inc,Sample_pow\"'\n",
+    "Sample.my_absorption = 100*2.9464/1079.1\n",
+    "\n",
+    "src = instrument.add_component(\"source\", \"Source_div\")\n",
+    "src.xwidth = 0.01\n",
+    "src.yheight = 0.035\n",
+    "src.focus_aw = 0.01\n",
+    "src.focus_ah = 0.01\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.dlambda = \"0.01*wavelength\"\n",
+    "src.flux = 1E13\n",
+    "\n",
+    "sample_geometry = instrument.add_component(\"sample_geometry\", \"Union_cylinder\")\n",
+    "sample_geometry.yheight = 0.03\n",
+    "sample_geometry.radius = 0.0075\n",
+    "sample_geometry.material_string='\"Sample\"' \n",
+    "sample_geometry.priority = 100\n",
+    "sample_geometry.set_AT([0,0,1], RELATIVE=src)\n",
+    "\n",
+    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\", RELATIVE=sample_geometry)\n",
+    "container.yheight = 0.03+0.003 # 1.5 mm top and button\n",
+    "container.radius = 0.0075 + 0.0015 # 1.5 mm sides of container\n",
+    "container.material_string='\"Al\"' \n",
+    "container.priority = 99\n",
+    "\n",
+    "container_lid = instrument.add_component(\"sample_container_lid\", \"Union_cylinder\")\n",
+    "container_lid.set_AT([0, 0.0155, 0], RELATIVE=container)\n",
+    "container_lid.yheight = 0.004\n",
+    "container_lid.radius = 0.013\n",
+    "container_lid.material_string='\"Al\"' \n",
+    "container_lid.priority = 98\n",
+    "\n",
+    "inner_wall = instrument.add_component(\"cryostat_wall\", \"Union_cylinder\")\n",
+    "inner_wall.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "inner_wall.yheight = 0.12\n",
+    "inner_wall.radius = 0.03\n",
+    "inner_wall.material_string='\"Al\"' \n",
+    "inner_wall.priority = 80\n",
+    "\n",
+    "inner_wall_vac = instrument.add_component(\"cryostat_wall_vacuum\", \"Union_cylinder\")\n",
+    "inner_wall_vac.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "inner_wall_vac.yheight = 0.12 - 0.008\n",
+    "inner_wall_vac.radius = 0.03 - 0.002\n",
+    "inner_wall_vac.material_string='\"Vacuum\"' \n",
+    "inner_wall_vac.priority = 81\n",
+    "\n",
+    "outer_wall = instrument.add_component(\"outer_cryostat_wall\", \"Union_cylinder\")\n",
+    "outer_wall.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "outer_wall.yheight = 0.15\n",
+    "outer_wall.radius = 0.1\n",
+    "outer_wall.material_string='\"Al\"' \n",
+    "outer_wall.priority = 60\n",
+    "\n",
+    "outer_wall_vac = instrument.add_component(\"outer_cryostat_wall_vacuum\", \"Union_cylinder\")\n",
+    "outer_wall_vac.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "outer_wall_vac.yheight = 0.15 - 0.01\n",
+    "outer_wall_vac.radius = 0.1 - 0.003\n",
+    "outer_wall_vac.material_string='\"Vacuum\"' \n",
+    "outer_wall_vac.priority = 61\n",
+    "\n",
+    "instrument.print_components()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.show_components(\"Work directory\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding Union logger components\n",
+    "Union logger components need to be added before the *Union_master* component, as the master need to record the necessary information when the simulation is being performed. There are two different kind of Union logger components, the *loggers* that record scattering and the *abs_loggers* that record absorption. They have similar parameters and user interface. Here is a list of the currently available loggers:\n",
+    "\n",
+    "- Union_logger_1D\n",
+    "- Union_logger_2D_space\n",
+    "- Union_logger_2D_space_time\n",
+    "- Union_logger_3D_space\n",
+    "- Union_logger_2D_kf\n",
+    "- Union_logger_2D_kf_time\n",
+    "- Union_logger_2DQ\n",
+    "\n",
+    "- Union_abs_logger_1D_space\n",
+    "- Union_abs_logger_1D_space_tof\n",
+    "- Union_abs_logger_2D_space\n",
+    "\n",
+    "The most commonly used logger is probably the *Union_logger_2D_space*, this component records spatial distribution of scattering, here are the available parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.component_help(\"Union_logger_2D_space\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting up a 2D_space logger\n",
+    "One can select which two axis to record using *D_direction_1* and *D_direction_2*, and the range with for example *D1_min* and *D1_max*. When spatial information is recorded it is also important to place the logger at an appropriate position, here we center it on the sample position."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zx.D_direction_1 = '\"z\"'\n",
+    "logger_zx.D1_min = -0.12\n",
+    "logger_zx.D1_max = 0.12\n",
+    "logger_zx.n1 = 300\n",
+    "logger_zx.D_direction_2 = '\"x\"'\n",
+    "logger_zx.D2_min = -0.12\n",
+    "logger_zx.D2_max = 0.12\n",
+    "logger_zx.n2 = 300\n",
+    "logger_zx.filename = '\"logger_zx.dat\"'\n",
+    "\n",
+    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zy.D_direction_1 = '\"z\"'\n",
+    "logger_zy.D1_min = -0.12\n",
+    "logger_zy.D1_max = 0.12\n",
+    "logger_zy.n1 = 300\n",
+    "logger_zy.D_direction_2 = '\"y\"'\n",
+    "logger_zy.D2_min = -0.12\n",
+    "logger_zy.D2_max = 0.12\n",
+    "logger_zy.n2 = 300\n",
+    "logger_zy.filename = '\"logger_zy.dat\"'\n",
+    "\n",
+    "master = instrument.add_component(\"master\", \"Union_master\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Running the simulation\n",
+    "If mpi is installed, one can add mpi=N where N is the number of cores available to speed up the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "instrument.set_parameters(wavelength=3.0)\n",
+    "instrument.settings(ncount=3E5, output_path=\"data_folder/union_loggers\")\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(data, log=True, orders_of_mag=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpreting the data\n",
+    "The zx logger views the cryostat from the top, while the zy loggers shows it from the side. These are histograms of scattered intensity, and it is clear the majority of the scattering happens in the direct beam. There are however scattering events in all parts of our mock cryostat, as neutrons that scattered in either the sample or cryostat walls could go in any direction due to the incoherent scattering. The aluminium and sample also have powder scattering, so some patterns can be seen from the debye scherrer cones."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Logger targets\n",
+    "It is possible to attach a logger to a certain geometry, or even a list of geometries using the *target_geometry* parameter. In that way one can for example view the scattering in the sample environment, while ignoring the sample. It is also possible to select a number of specific scattering processes to investigate with the *target_process* parameter. This is especially useful when working with a single crystal process, that only scatters when the Bragg condition is met.\n",
+    "\n",
+    "Let us modify our existing loggers to view certain parts of the simulated system, and then rerun the simulation.  If mpi is installed, one can add mpi=N where N is the number of cores available to speed up the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "logger_zx.target_geometry = '\"outer_cryostat_wall,cryostat_wall\"'\n",
+    "logger_zy.target_geometry = '\"sample_geometry\"'\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(data, log=[True, False], orders_of_mag=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scattering order\n",
+    "All loggers also have the option to only record given scattering orders. For example only record the second scattering.\n",
+    "- order_total : Match given number of scattering events, counting all scattering events in the system\n",
+    "- order_volume : Match given number of scattering events, only counting events in the current volume\n",
+    "- order_volume_process : Match given number of scattering events, only counting events in current volume with current process\n",
+    "\n",
+    "We can modify our previous loggers to test out these features. The zx logger viewing from above will keep the target, but we remove the sample target on the zy logger, which is done by setting the *taget_geometry* to NULL. We choose to look at the second scattering event."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger_zx.order_total = 2\n",
+    "\n",
+    "logger_zy.target_geometry = '\"NULL\"'\n",
+    "logger_zy.order_total = 2\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(data, log=True, orders_of_mag=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Demonstration of additional logger components\n",
+    "Here we add a few more loggers to showcase what kind of information that can be displayed.\n",
+    "- 1D logger that logs scattered intensity as function of time\n",
+    "- 2D abs_logger that logs absorption projected onto the scattering plane\n",
+    "- 2DQ logger that logs scattering vector projected onto the scattering plane\n",
+    "- 2D kf logger that logs final wavevector projected onto the scattering plane"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger_1D = instrument.add_component(\"logger_1D\", \"Union_logger_1D\", before=\"master\")\n",
+    "logger_1D.variable = '\"time\"'\n",
+    "logger_1D.min_value = 0.0006\n",
+    "logger_1D.max_value = 0.0012\n",
+    "logger_1D.n1 = 300\n",
+    "logger_1D.filename = '\"logger_1D_time.dat\"'\n",
+    "\n",
+    "abs_logger_zx = instrument.add_component(\"abs_logger_space_zx\", \"Union_abs_logger_2D_space\",before=\"master\")\n",
+    "abs_logger_zx.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "abs_logger_zx.D_direction_1 = '\"z\"'\n",
+    "abs_logger_zx.D1_min = -0.12\n",
+    "abs_logger_zx.D1_max = 0.12\n",
+    "abs_logger_zx.n1 = 300\n",
+    "abs_logger_zx.D_direction_2 = '\"x\"'\n",
+    "abs_logger_zx.D2_min = -0.12\n",
+    "abs_logger_zx.D2_max = 0.12\n",
+    "abs_logger_zx.n2 = 300\n",
+    "abs_logger_zx.filename = '\"abs_logger_zx.dat\"'\n",
+    "\n",
+    "logger_2DQ = instrument.add_component(\"logger_2DQ\", \"Union_logger_2DQ\", before=\"master\")\n",
+    "logger_2DQ.Q_direction_1 = '\"z\"'\n",
+    "logger_2DQ.Q1_min = -5.0\n",
+    "logger_2DQ.Q1_max = 5.0\n",
+    "logger_2DQ.n1 = 200\n",
+    "logger_2DQ.Q_direction_2 = '\"x\"'\n",
+    "logger_2DQ.Q2_min = -5.0\n",
+    "logger_2DQ.Q2_max = 5.0\n",
+    "logger_2DQ.n2 = 200\n",
+    "logger_2DQ.filename = '\"logger_2DQ.dat\"'\n",
+    "\n",
+    "logger_2D_kf = instrument.add_component(\"logger_2D_kf\", \"Union_logger_2D_kf\", before=\"master\")\n",
+    "logger_2D_kf.Q_direction_1 = '\"z\"'\n",
+    "logger_2D_kf.Q1_min = -2.5\n",
+    "logger_2D_kf.Q1_max = 2.5\n",
+    "logger_2D_kf.n1 = 200\n",
+    "logger_2D_kf.Q_direction_2 = '\"x\"'\n",
+    "logger_2D_kf.Q2_min = -2.5\n",
+    "logger_2D_kf.Q2_max = 2.5\n",
+    "logger_2D_kf.n2 = 200\n",
+    "logger_2D_kf.filename = '\"logger_2D_kf.dat\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Runnig the simulation\n",
+    "We now rerun the simulation with the new loggers. If mpi is installed, one can add mpi=N where N is the number of cores available to speed up the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "instrument.backengine()\n",
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=3)\n",
+    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=3)\n",
+    "functions.name_plot_options(\"abs_logger_space_zx\", data, log=True, orders_of_mag=3)\n",
+    "functions.name_plot_options(\"logger_1D\", data, log=True, orders_of_mag=3)\n",
+    "functions.name_plot_options(\"logger_2DQ\", data, log=True, orders_of_mag=3)\n",
+    "functions.name_plot_options(\"logger_2D_kf\", data, log=True, orders_of_mag=3)\n",
+    "\n",
+    "plotter.make_sub_plot(data[0:2])\n",
+    "plotter.make_sub_plot(data[2:4])\n",
+    "plotter.make_sub_plot(data[4:6])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interpreting the data\n",
+    "We see the scattered intensity as a function of time, here the peaks correspond to the direct beam intersecting the sides of the cryostat and sample. The source used release all neutrons at time 0, so it is a perfect pulse.\n",
+    "\n",
+    "The absorption monitor shows an image very similar to the scattered intensity, but this could be very different, for example when using materials meant as shielding.\n",
+    "\n",
+    "The 2D scattering vector is interesting, it shows a small sphere made of vertical lines, these are powder Bragg peaks. Since the wavevector is almost identical for all incoming neutrons, the first scattering can only access this smaller region of the space. The larger circle is incoherent scattering from second and later scattering events, where the incoming wavevector could be any direction since a scattering already happened.\n",
+    "\n",
+    "The 2D final wavevector plot shows mainly the powder Bragg peaks."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Animations and time series\n",
+    "Several of the Union loggers sets up more than one McStas monitor, these include:\n",
+    "- Union_logger_3D_space\n",
+    "- Union_logger_2D_space_time\n",
+    "- Union_logger_2D_kf_time\n",
+    "\n",
+    "The Union_logger_2D_space_time for example sets up a number of Union_logger_2D_space monitors that are limited to specific time intervals. This can be used to make an animation of the monitor, which we will demonstrate here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "log_2D_st = instrument.add_component(\"logger_2D_space_time\", \"Union_logger_2D_space_time\", before=\"master\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "log_2D_st.time_bins = 36\n",
+    "log_2D_st.time_min = 0.0007\n",
+    "log_2D_st.time_max = 0.0011\n",
+    "\n",
+    "log_2D_st.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "log_2D_st.D_direction_1 = '\"z\"'\n",
+    "log_2D_st.D1_min = -0.12\n",
+    "log_2D_st.D1_max = 0.12\n",
+    "log_2D_st.n1 = 300\n",
+    "log_2D_st.D_direction_2 = '\"x\"'\n",
+    "log_2D_st.D2_min = -0.12\n",
+    "log_2D_st.D2_max = 0.12\n",
+    "log_2D_st.n2 = 300\n",
+    "log_2D_st.filename = '\"logger_2D_space_time.dat\"'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "instrument.backengine()\n",
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating an animation\n",
+    "The plotter in McStasScript can create animations when supplied with many McStasData objects. We use name_search to find all the data from the relevant logger, and then a for loop to set plot options for each of them. Then the plotter can make an animation, which is saved as a gif."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ani_data = functions.name_search(\"logger_2D_space_time\", data)\n",
+    "for frame in ani_data:\n",
+    "    frame.set_plot_options(log=True, colormap=\"jet\")\n",
+    "    \n",
+    "plotter.make_animation(ani_data, filename=\"animation_demo\", fps=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Viewing the animation\n",
+    "Some problem in the jupyter notebook prevents playing the gif directly, but it can be played from markdown. One has to refresh this cell when a new animation is written. It should be visible that the beam enters the cryostat from the left, scatters of the sample and illuminates the entire cryostat. Running this simulation with a larger ncount and more time_bins in the monitor will reveal more details in what happens. This is available below, but commented out as the simulation can take some time.\n",
+    "\n",
+    "![SegmentLocal](animation_demo.gif \"Animation\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Optional: Increasing resolution\n",
+    "Running this simulation with a larger ncount and more time_bins in the monitor will reveal more details in what happens. This is available below, but commented out as the simulation can take some time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "log_2D_st.time_bins = 128\n",
+    "instrument.settings(ncount=2E8, mpi=4)\n",
+    "#instrument.backengine()\n",
+    "#data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#ani_data = functions.name_search(\"logger_2D_space_time\", data)\n",
+    "#for frame in ani_data:\n",
+    "#    frame.set_plot_options(log=True, colormap=\"jet\", orders_of_mag=6)\n",
+    "#    \n",
+    "#plotter.make_animation(ani_data, filename=\"animation_demo_long\", fps=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Viewing the animation\n",
+    "In the longer animation it is more evident that scattering from the aluminium hits the top and bottom of the outer cylinder.\n",
+    "\n",
+    "![SegmentLocal](animation_demo_long.gif \"Animation\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb b/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
new file mode 100644
index 00000000..3946d819
--- /dev/null
+++ b/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
@@ -0,0 +1,539 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using conditional component to modify loggers\n",
+    "Even with the results from the loggers, it can still be difficult to explain all the features in the resulting scattering pattern. The conditional components can modify a logger so that it only records events when the final state of the neutron satisfy some condition. The condition could be leaving with a certain energy or in a specified direction. Before demonstrating these conditional components, we will set up an interesting sample and sample environment, including a few loggers and a time of flight 2theta detector. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up an example instrument\n",
+    "First an example instrument is made, again with a cryostat but this time with a box shaped single crystal of YBaCuO. Since the *single_crystal_process* have quite a few parameters, we use the *set_parameters* method that allows setting parameters using a dictionary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter\n",
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up Al material with incoherent and powder\n",
+    "Al_incoherent = instrument.add_component(\"Al_incoherent\", \"Incoherent_process\")\n",
+    "Al_incoherent.sigma = \"4*0.0082\"\n",
+    "Al_incoherent.packing_factor = 1\n",
+    "Al_incoherent.unit_cell_volume = 66.4\n",
+    "\n",
+    "Al_powder = instrument.add_component(\"Al_powder\", \"Powder_process\")\n",
+    "Al_powder.reflections = \"\\\"Al.laz\\\"\"\n",
+    "\n",
+    "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
+    "Al.process_string = '\"Al_incoherent,Al_powder\"'\n",
+    "Al.my_absorption = \"100*4*0.231/66.4\"\n",
+    "\n",
+    "# Set up YBaCuO with incoherent and single crystal\n",
+    "YBaCuO_incoherent = instrument.add_component(\"YBaCuO_incoherent\", \"Incoherent_process\")\n",
+    "YBaCuO_incoherent.sigma = 2.105\n",
+    "YBaCuO_incoherent.unit_cell_volume = 173.28\n",
+    "\n",
+    "YBaCuO_crystal = instrument.add_component(\"YBaCuO_crystal\", \"Single_crystal_process\")\n",
+    "YBaCuO_crystal.set_parameters(\n",
+    "{\"ax\" : 3.816, \"ay\" : 0, \"az\" : 0,\n",
+    " \"bx\" : 0, \"by\" : 3.886, \"bz\" : 0,\n",
+    " \"cx\" : 0, \"cy\" : 0, \"cz\" : 11.677,\n",
+    " \"delta_d_d\" : 5E-4, \"mosaic\" : 30, \"barns\" : 1,\n",
+    " \"reflections\" : '\"YBaCuO.lau\"'})\n",
+    "\n",
+    "YBaCuO = instrument.add_component(\"YBaCuO\", \"Union_make_material\")\n",
+    "YBaCuO.process_string = '\"YBaCuO_incoherent,YBaCuO_crystal\"'\n",
+    "YBaCuO.my_absorption = 100*14.82/173.28\n",
+    "\n",
+    "src = instrument.add_component(\"source\", \"Source_div\")\n",
+    "src.xwidth = 0.01\n",
+    "src.yheight = 0.035\n",
+    "src.focus_aw = 0.01\n",
+    "src.focus_ah = 0.01\n",
+    "\n",
+    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.dlambda = \"0.01*wavelength\"\n",
+    "src.flux = 1E13\n",
+    "\n",
+    "# At a reference point to build the cryostat around\n",
+    "cryostat_center = instrument.add_component(\"cryostat_center\", \"Arm\")\n",
+    "cryostat_center.set_AT([0, 0, 1], RELATIVE=src)\n",
+    "\n",
+    "# Parameter for controlling sample rotation\n",
+    "A3_angle = instrument.add_parameter(\"A3_angle\", value=0)\n",
+    "\n",
+    "sample = instrument.add_component(\"sample\", \"Union_box\")\n",
+    "sample.set_AT([0, 0, 0], RELATIVE=cryostat_center)\n",
+    "sample.set_ROTATED([0, A3_angle, 0], RELATIVE=cryostat_center)\n",
+    "sample.xwidth = 0.015\n",
+    "sample.yheight = 0.032\n",
+    "sample.zdepth = 0.012\n",
+    "sample.material_string = '\"YBaCuO\"'\n",
+    "sample.priority = 200\n",
+    "\n",
+    "# Setting up two layers of cryostat\n",
+    "inner_cryostat_wall = instrument.add_component(\"inner_cryostat_wall\", \"Union_cylinder\")\n",
+    "inner_cryostat_wall.material_string = \"\\\"Al\\\"\"\n",
+    "inner_cryostat_wall.priority = 12\n",
+    "inner_cryostat_wall.radius = 0.0621\n",
+    "inner_cryostat_wall.yheight = 0.16\n",
+    "inner_cryostat_wall.p_interact = 0.20\n",
+    "inner_cryostat_wall.set_AT([0, 0.01, 0], RELATIVE=cryostat_center)\n",
+    "\n",
+    "inner_cryostat_vacuum = instrument.add_component(\"inner_cryostat_vacuum\", \"Union_cylinder\")\n",
+    "inner_cryostat_vacuum.material_string = \"\\\"Vacuum\\\"\"\n",
+    "inner_cryostat_vacuum.priority = 13\n",
+    "inner_cryostat_vacuum.radius = 0.06\n",
+    "inner_cryostat_vacuum.yheight = 0.15\n",
+    "inner_cryostat_vacuum.set_AT([0, 0.01, 0], RELATIVE=cryostat_center)\n",
+    "\n",
+    "outer_cryostat_wall = instrument.add_component(\"outer_cryostat_wall\", \"Union_cylinder\")\n",
+    "outer_cryostat_wall.material_string = \"\\\"Al\\\"\"\n",
+    "outer_cryostat_wall.priority = 10\n",
+    "outer_cryostat_wall.radius = 0.180\n",
+    "outer_cryostat_wall.yheight = 0.355\n",
+    "outer_cryostat_wall.p_interact = 0.20\n",
+    "outer_cryostat_wall.set_AT([0, 0.032, 0], RELATIVE=cryostat_center)\n",
+    "\n",
+    "outer_cryostat_vacuum = instrument.add_component(\"outer_cryostat_vacuum\", \"Union_cylinder\")\n",
+    "outer_cryostat_vacuum.material_string = \"\\\"Vacuum\\\"\"\n",
+    "outer_cryostat_vacuum.priority = 11\n",
+    "outer_cryostat_vacuum.radius = 0.178\n",
+    "outer_cryostat_vacuum.yheight = 0.355\n",
+    "outer_cryostat_vacuum.set_AT([0, 0.037, 0], RELATIVE=cryostat_center)\n",
+    "\n",
+    "# Set up loggers\n",
+    "logger_space_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\")\n",
+    "logger_space_zx.n1 = 150\n",
+    "logger_space_zx.n2 = 150\n",
+    "logger_space_zx.D1_min = -0.2\n",
+    "logger_space_zx.D1_max = 0.2\n",
+    "logger_space_zx.D2_min = -0.2\n",
+    "logger_space_zx.D2_max = 0.2\n",
+    "logger_space_zx.D_direction_1 = '\"z\"'\n",
+    "logger_space_zx.D_direction_2 = '\"x\"'\n",
+    "logger_space_zx.filename = '\"logger_zx.dat\"'\n",
+    "logger_space_zx.logger_conditional_extend_index = 1\n",
+    "logger_space_zx.set_AT([0, 0, 0], RELATIVE=cryostat_center)\n",
+    "\n",
+    "logger_space_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\")\n",
+    "logger_space_zy.n1 = 150\n",
+    "logger_space_zy.n2 = 150\n",
+    "logger_space_zy.D1_min = -0.2\n",
+    "logger_space_zy.D1_max = 0.2\n",
+    "logger_space_zy.D2_min = -0.15\n",
+    "logger_space_zy.D2_max = 0.2\n",
+    "logger_space_zy.D_direction_1 = '\"z\"'\n",
+    "logger_space_zy.D_direction_2 = '\"y\"'\n",
+    "logger_space_zy.filename = '\"logger_zy.dat\"'\n",
+    "logger_space_zy.logger_conditional_extend_index = 1\n",
+    "logger_space_zy.set_AT([0, 0, 0], RELATIVE=cryostat_center)\n",
+    "\n",
+    "logger_2DQ = instrument.add_component(\"logger_2DQ_sample\", \"Union_logger_2DQ\")\n",
+    "logger_2DQ.Q_direction_1 = '\"z\"'\n",
+    "logger_2DQ.Q1_min = -4.0\n",
+    "logger_2DQ.Q1_max = 4.0\n",
+    "logger_2DQ.n1 = 100\n",
+    "logger_2DQ.Q_direction_2 = '\"x\"'\n",
+    "logger_2DQ.Q2_min = -4.0\n",
+    "logger_2DQ.Q2_max = 4.0\n",
+    "logger_2DQ.n2 = 100\n",
+    "logger_2DQ.target_geometry = '\"sample\"'\n",
+    "logger_2DQ.filename = '\"logger_2DQ_sample.dat\"'\n",
+    "\n",
+    "logger_2DQ = instrument.add_component(\"logger_2DQ_environment\", \"Union_logger_2DQ\")\n",
+    "logger_2DQ.Q_direction_1 = '\"z\"'\n",
+    "logger_2DQ.Q1_min = -4.0\n",
+    "logger_2DQ.Q1_max = 4.0\n",
+    "logger_2DQ.n1 = 100\n",
+    "logger_2DQ.Q_direction_2 = '\"x\"'\n",
+    "logger_2DQ.Q2_min = -4.0\n",
+    "logger_2DQ.Q2_max = 4.0\n",
+    "logger_2DQ.n2 = 100\n",
+    "logger_2DQ.target_geometry = '\"inner_cryostat_wall,outer_cryostat_wall\"'\n",
+    "logger_2DQ.filename = '\"logger_2DQ_all.dat\"'\n",
+    "\n",
+    "logger_time_all = instrument.add_component(\"logger_time_all\", \"Union_logger_1D\")\n",
+    "logger_time_all.n1 = 600\n",
+    "logger_time_all.min_value = 0.0008\n",
+    "logger_time_all.max_value = 0.0015\n",
+    "logger_time_all.filename = '\"scattering_time.dat\"'\n",
+    "\n",
+    "master = instrument.add_component(\"master\", \"Union_master\")\n",
+    "\n",
+    "# Adding a banana - tof detector\n",
+    "banana_detector = instrument.add_component(\"banana_detector\", \"Monitor_nD\", RELATIVE=cryostat_center)\n",
+    "banana_detector.xwidth = 1\n",
+    "banana_detector.yheight = 0.2\n",
+    "banana_detector.restore_neutron = 1\n",
+    "banana_detector.options = '\"banana, theta limits=[-180,180] bins=361, t limits=[0.0011 0.0025] bins=500\"'\n",
+    "banana_detector.filename = '\"tof_b.dat\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculating theta\n",
+    "Our YBaCuO sample has the 010 axis along the z axis and have 010 allowed with d = 3.8843. Here we calculate the necessary rotation of the crystal for satisfying the Bragg condition. This could also be done within the initialize section of the McStas instrument, but here we wish to preserve control over the A3_angle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "wavelength = 4.0\n",
+    "theta = 180/3.14159*math.asin(wavelength/2.0/3.8843)\n",
+    "print(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "instrument.set_parameters(wavelength=wavelength, A3_angle=theta)\n",
+    "instrument.settings(ncount=3E5, output_path=\"data_folder/union_conditionals\")\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger_zx = functions.name_search(\"logger_space_zx\", data)\n",
+    "logger_zx.set_plot_options(log=True, orders_of_mag=9, colormap=\"jet\")\n",
+    "\n",
+    "logger_zy = functions.name_search(\"logger_space_zy\", data)\n",
+    "logger_zy.set_plot_options(log=True, orders_of_mag=9, colormap=\"jet\")\n",
+    "\n",
+    "logger_2DQ_sample = functions.name_search(\"logger_2DQ_sample\", data)\n",
+    "logger_2DQ_sample.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
+    "\n",
+    "logger_2DQ_env = functions.name_search(\"logger_2DQ_environment\", data)\n",
+    "logger_2DQ_env.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
+    "\n",
+    "plotter.make_sub_plot([logger_zx, logger_zy, logger_2DQ_sample, logger_2DQ_env], fontsize=10)\n",
+    "\n",
+    "time = functions.name_search(\"logger_time_all\", data)\n",
+    "time.set_plot_options(log=True)\n",
+    "plotter.make_sub_plot([time], fontsize=18)\n",
+    "\n",
+    "banana = functions.name_search(\"banana_detector\", data)\n",
+    "banana.set_plot_options(log=True, orders_of_mag=7, cut_max=0.001)\n",
+    "plotter.make_sub_plot([banana], fontsize=18)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretation of the data\n",
+    "The data from the two spatial loggers show scattering location within the cryostat, and we see one beam entering the cryostat, yet two beams leaving as we satisfy the Bragg condition for the sample. This also result in scattering from when the scattered beam intersect the sample environment.\n",
+    "\n",
+    "We have two 2DQ loggers recording the scattering vector, one just for the sample and one for the sample environment. On the sample monitor we see that many Bragg peaks scatter some intensity, but 010 and 0-10 have the most intensity, they are at [0.9, -1.5] and [1.5, -0.9]. The two circles are incoherent scattering from the two most common wavevectors, the initial beam and the beam scattered from 010. On the 2DQ logger for the sample environment, we mainly see the Debye-Scherrer cones as lines within the circles defined by the two predominant wavevectors. It seems the used wavelength allows two different Bragg conditions to be met in the aluminium.\n",
+    "\n",
+    "The time logger show a surprising amount of complexity. The 5 peaks from entering and exiting two layers and intersecting the sample are clear, but all structure after 0.0012 is a surprise. There is also an unexpected peak at 0.00115, this may be the scattered beam intersecting the outer layer of the cryostat, this happens a bit sooner than the direct beam because the path is shorter when scattered from the front of the sample.\n",
+    "\n",
+    "The time of flight vs 2theta monitor also has a large amount of complexity that is not simple to explain. The bright spot at [0, 0.00155] is the direct beam, while the spot at [60, 0.00155] is the scattered beam. The horizontal line at t=0.00155 must be incoherent scattering from the sample, as it must have had the same distance to all points on the detector. The curved lower branch could be incoherent scattering from where the beam enters the cryostat, as that is closest to the 180 deg point on the detector. The remaining hot spots are all some Debye Scherrer cones from a beam entering or exiting the sample environment, and the more blurry spots may be of even higher order."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conditional components\n",
+    "We can use conditional components to investigate peaks in the final scattering pattern, by limiting the loggers to only recording events that for example end in a certain spot. Currently there are two available\n",
+    "- Union_conditional_standard\n",
+    "- Union_conditional_PSD\n",
+    "\n",
+    "The standard version allows limits for energy, time and number of scattering events for the neutron when it exits the *Union_master* simulation, in this case when it leaves the sample environment.\n",
+    "\n",
+    "The PSD version propagates the final neutron states to a given rectangular surface and it is possible to filter the logger events on the neutron state when it reaches this surface, and ignores all events that misses. This is what we will use here to investigate a peak in the scattering pattern. We also set a time limit as our detector is time of flight sensitive.\n",
+    "\n",
+    "Here are the important parameters for the Union_conditional_PSD component\n",
+    "- target_loggers : comma separated string of logger names this conditional should affect\n",
+    "- xwidth : width of rectangle\n",
+    "- yheight : height of rectangle\n",
+    "- time_min : lower time limit for condition\n",
+    "- time_max : upper time limit for condition\n",
+    "- overwrite_logger_weight : If set to 1, will weight logger results with the final ray weight"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up instrument parameters describing what spot to investigate\n",
+    "instrument.add_parameter(\"tag_angle\", value=-95)\n",
+    "instrument.add_parameter(\"tag_time\", value=0.00188)\n",
+    "instrument.add_parameter(\"tag_interval\", value=9E-5)\n",
+    "\n",
+    "# Set up an arm pointing to the relevant spot\n",
+    "spot_dir = instrument.add_component(\"spot_dir\", \"Arm\", RELATIVE=cryostat_center, before=\"master\")\n",
+    "spot_dir.set_ROTATED([0, \"tag_angle\", 0], RELATIVE=cryostat_center)\n",
+    "\n",
+    "# Set up a conditional component targeting all our loggers\n",
+    "PSD_conditional = instrument.add_component(\"space_all_PSD_conditional\", \"Union_conditional_PSD\", before=\"master\")\n",
+    "PSD_conditional.target_loggers = '\"logger_space_zx,logger_space_zy,logger_time_all,logger_2DQ_sample,logger_2DQ_environment\"'\n",
+    "PSD_conditional.xwidth = 0.2\n",
+    "PSD_conditional.yheight = 0.2\n",
+    "PSD_conditional.time_min = \"tag_time-0.5*tag_interval\"\n",
+    "PSD_conditional.time_max = \"tag_time+0.5*tag_interval\"\n",
+    "# Ensure the position of the conditional rectangle is on the detector surface\n",
+    "PSD_conditional.set_AT([0, 0, 0.5], RELATIVE=spot_dir) \n",
+    "\n",
+    "# Add a monitor with flag that is only active when the condition in the conditional is true\n",
+    "instrument.add_declare_var(\"int\", \"flag1\")\n",
+    "logger_space_zx.logger_conditional_extend_index = 1\n",
+    "master.append_EXTEND(\"flag1 = logger_conditional_extend_array[1];\")\n",
+    "\n",
+    "# Copy of our banana detector, but with WHEN condition to verify we are investigating the right peak\n",
+    "banana_detector = instrument.add_component(\"banana_detector_limited\", \"Monitor_nD\", RELATIVE=cryostat_center)\n",
+    "banana_detector.xwidth = 1\n",
+    "banana_detector.yheight = 0.2\n",
+    "banana_detector.restore_neutron = 1\n",
+    "banana_detector.options = '\"banana, theta limits=[-180,180] bins=361, t limits=[0.0011 0.0025] bins=500\"'\n",
+    "banana_detector.filename = '\"tof_b_limited.dat\"'\n",
+    "banana_detector.set_WHEN(\"flag1 > 0\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run the simulation\n",
+    "We run the simulation again with a larger ncount, as the loggers now only record a small fraction of the scattered neutrons. You can investigate other areas of interest by changing *tag_angle*, *tag_time* and *tag_interval* to another interesting location on the time of flight detector.\n",
+    "\n",
+    "If MPI is installed add mpi=N where N is the number of CPU cores available in your computer to speed up the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "instrument.set_parameters(wavelength=wavelength, A3_angle=theta, \n",
+    "                          tag_angle=-95, tag_time=0.00188, tag_interval=9E-5)\n",
+    "instrument.settings(ncount=3E5) # Can add mpi to improve speed for this longer simulation\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data_con = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Confirm our conditional is on the desired peak\n",
+    "First we plot our 2theta / time of flight detector and the version limited to what the conditional records to confirm that we selected an appropriate region to investigate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "banana = functions.name_search(\"banana_detector\", data_con)\n",
+    "banana.set_plot_options(log=True, orders_of_mag=7, cut_max=0.001)\n",
+    "\n",
+    "banana_limited = functions.name_search(\"banana_detector_limited\", data_con)\n",
+    "banana_limited.set_plot_options(log=False)\n",
+    "\n",
+    "plotter.make_sub_plot([banana, banana_limited], fontsize=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting the loggers, now limited by the conditional\n",
+    "We use a different colormap here instead of the standard jet, because in jet the lowest and highest intensity are both dark colors. By choosing a colormap where zero intensity is white, it blends in with white from lack of data which is beneficial in this situation for clearly seeing the hotspots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger_zx = functions.name_search(\"logger_space_zx\", data_con)\n",
+    "logger_zx.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
+    "\n",
+    "logger_zy = functions.name_search(\"logger_space_zy\", data_con)\n",
+    "logger_zy.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
+    "\n",
+    "logger_2DQ_sample = functions.name_search(\"logger_2DQ_sample\", data_con)\n",
+    "logger_2DQ_sample.set_plot_options(log=True, orders_of_mag=7, colormap=\"YlOrRd\")\n",
+    "\n",
+    "logger_2DQ_env = functions.name_search(\"logger_2DQ_environment\", data_con)\n",
+    "logger_2DQ_env.set_plot_options(log=True, orders_of_mag=7, colormap=\"YlOrRd\")\n",
+    "\n",
+    "plotter.make_sub_plot([logger_zx, logger_zy, logger_2DQ_sample, logger_2DQ_env], fontsize=10)\n",
+    "\n",
+    "time = functions.name_search(\"logger_time_all\", data_con)\n",
+    "time.set_plot_options(log=True)\n",
+    "\n",
+    "plotter.make_sub_plot([time], fontsize=18)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interpreting the data from loggers with conditional\n",
+    "Now that the loggers only show scattering from neutrons that end in our specific peak of interest, we can explain the origin of this peak. The spatial loggers show that scattering primarily happens in the sample, and in the outer part of the cryostat where the scattered beam leaves the sample environment. It is in this case obvious the first scattering is in the sample, as the exit area is not within the direct beam, but one can create individual loggers for each scattering order in order to confirm this.\n",
+    "\n",
+    "On the 2DQ logger for the sample it is clear the scattering is from the main Bragg peak (010 and 0-10). We see two peaks as a ray scattered from a Bragg peak will fulfil the Bragg condition of the opposite reciprocal indices. On the 2DQ logger for the sample environment, we see the brightest spot is a small part of a Debye-Scherrer cone. Scattered neutrons from other parts of this cone will not hit our conditional PSD, some may not even hit the detector at all.\n",
+    "\n",
+    "That means this specific peak was an uneven number of single crystal scattering events in 010/0-10 in the sample followed by a powder scattering in the outer wall of the sample environment. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using the final weight option\n",
+    "Here we rerun the simulation with the overwrite_logger_weight option in the conditional turned on to see the effect. Without it a ray is recorded in the loggers if it satisfy the conditional, but it does not matter how large the final weight is. For this reason, some rays with high sampling probability to reach the condition but low weight are represented more than is appropriate. This is mainly important when shielding is simulated, as ray that pass through the shielding needs can be heavily overrepresented."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "PSD_conditional.overwrite_logger_weight = 1\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data_con_f = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger_zx = functions.name_search(\"logger_space_zx\", data_con_f)\n",
+    "logger_zx.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
+    "\n",
+    "logger_zy = functions.name_search(\"logger_space_zy\", data_con_f)\n",
+    "logger_zy.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
+    "\n",
+    "logger_2DQ_sample = functions.name_search(\"logger_2DQ_sample\", data_con_f)\n",
+    "logger_2DQ_sample.set_plot_options(log=True, orders_of_mag=7, colormap=\"YlOrRd\")\n",
+    "\n",
+    "logger_2DQ_env = functions.name_search(\"logger_2DQ_environment\", data_con_f)\n",
+    "logger_2DQ_env.set_plot_options(log=True, orders_of_mag=7, colormap=\"YlOrRd\")\n",
+    "\n",
+    "plotter.make_sub_plot([logger_zx, logger_zy, logger_2DQ_sample, logger_2DQ_env], fontsize=10)\n",
+    "\n",
+    "time = functions.name_search(\"logger_time_all\", data_con_f)\n",
+    "time.set_plot_options(log=True)\n",
+    "\n",
+    "plotter.make_sub_plot([time], fontsize=18)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparison between normal and overwrite_logger_weight\n",
+    "Here we make a direct comparison, and see only a slight difference in this case. No shielding is simulated here which would cause a much more clear difference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger_zx = functions.name_search(\"logger_space_zx\", data_con)\n",
+    "logger_zx.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
+    "\n",
+    "logger_zy = functions.name_search(\"logger_space_zy\", data_con)\n",
+    "logger_zy.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
+    "\n",
+    "logger_zx_f = functions.name_search(\"logger_space_zx\", data_con_f)\n",
+    "logger_zx_f.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
+    "\n",
+    "logger_zy_f = functions.name_search(\"logger_space_zy\", data_con_f)\n",
+    "logger_zy_f.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
+    "\n",
+    "plotter.make_sub_plot([logger_zx, logger_zy, logger_zx_f, logger_zy_f], fontsize=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/tutorial/Union_tutorial_5_masks.ipynb b/docs/source/tutorial/Union_tutorial_5_masks.ipynb
new file mode 100644
index 00000000..d7a1d2a5
--- /dev/null
+++ b/docs/source/tutorial/Union_tutorial_5_masks.ipynb
@@ -0,0 +1,374 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Union tutorial on masks\n",
+    "There are some geometries that are impossible to build using only the priority based system geometry system, for example making part of a cylinder thinner, which is needed for a cryostat window. In many such cases, masks can be used to solve the problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting up an example without masks\n",
+    "First we set up an example with a thick and hollow Al cylinder and a logger to view the spatial distribution of scattering."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
+    "Al_inc.sigma = 0.0082\n",
+    "Al_inc.unit_cell_volume = 66.4\n",
+    "\n",
+    "Al_pow = instrument.add_component(\"Al_pow\", \"Powder_process\")\n",
+    "Al_pow.reflections = '\"Al.laz\"'\n",
+    "\n",
+    "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
+    "Al.process_string = '\"Al_inc,Al_pow\"'\n",
+    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "\n",
+    "src = instrument.add_component(\"source\", \"Source_div\")\n",
+    "\n",
+    "src.xwidth = 0.2\n",
+    "src.yheight = 0.035\n",
+    "src.focus_aw = 0.01\n",
+    "src.focus_ah = 0.01\n",
+    "\n",
+    "\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.dlambda = \"0.01*wavelength\"\n",
+    "src.flux = 1E13\n",
+    "\n",
+    "wall = instrument.add_component(\"wall\", \"Union_cylinder\")\n",
+    "wall.set_AT([0,0,1], RELATIVE=src)\n",
+    "wall.yheight = 0.15\n",
+    "wall.radius = 0.1\n",
+    "wall.material_string='\"Al\"' \n",
+    "wall.priority = 10\n",
+    "\n",
+    "wall_vac = instrument.add_component(\"wall_vacuum\", \"Union_cylinder\")\n",
+    "wall_vac.set_AT([0,0,0], RELATIVE=wall)\n",
+    "wall_vac.yheight = 0.15 + 0.01\n",
+    "wall_vac.radius = 0.1 - 0.02\n",
+    "wall_vac.material_string='\"Vacuum\"' \n",
+    "wall_vac.priority = 50\n",
+    "\n",
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=wall)\n",
+    "logger_zx.D_direction_1 = '\"z\"'\n",
+    "logger_zx.D1_min = -0.12\n",
+    "logger_zx.D1_max = 0.12\n",
+    "logger_zx.n1 = 300\n",
+    "logger_zx.D_direction_2 = '\"x\"'\n",
+    "logger_zx.D2_min = -0.12\n",
+    "logger_zx.D2_max = 0.12\n",
+    "logger_zx.n2 = 300\n",
+    "logger_zx.filename = '\"logger_zx.dat\"'\n",
+    "\n",
+    "master = instrument.add_component(\"master\", \"Union_master\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "instrument.settings(ncount=2E5, output_path=\"data_folder/union_masks\")\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Masks\n",
+    "All Union geometries can act as a mask for a list of other already defined geometries. The geometries affected by a mask will only exist inside the mask, while the parts outside will not have any effect on this simulation. This provides some interesting geometrical capabilities, for example by defining two spheres with some overlap and making one a mask of the other, a classical lens shape can be created.\n",
+    "\n",
+    "The relevant parameters of all geometry components are:\n",
+    "- mask_string : comma separated list of geometry names the mask should be applied to\n",
+    "- mask_setting : selects between \"ANY\" or \"ALL\" mode. Default mode is \"ALL\".\n",
+    "\n",
+    "The mask mode is only important if several masks affect the same geometry, per default just having any of the masks overlap the target geometry allow it to exists, which correspond to the \"ANY\" mode. If the \"ALL\" mode is selected, the target geometry will only exists in regions where all the masks and itself overlap.\n",
+    "\n",
+    "Note that a unique priority is still necessary, but it is not used."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding a window using masks\n",
+    "Here we add a window to one side of the cylinder by inserting a larger vacuum cylinder, but mask it so that it is only active in the area around the window. In this way we get a nice curved window. We chose a box shape for the mask, but we could also have chosen a cylinder to get a round window."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "window = instrument.add_component(\"window\", \"Union_cylinder\", before=\"master\")\n",
+    "window.set_AT([0,0,0], RELATIVE=wall)\n",
+    "window.yheight = 0.15 + 0.02\n",
+    "window.radius = 0.1 - 0.01\n",
+    "window.material_string='\"Vacuum\"' \n",
+    "window.priority = 25\n",
+    "\n",
+    "mask = instrument.add_component(\"mask\", \"Union_box\", before=\"master\")\n",
+    "mask.xwidth = 0.1\n",
+    "mask.yheight = 0.2\n",
+    "mask.zdepth = 0.09\n",
+    "mask.priority = 1\n",
+    "mask.mask_string='\"window\"'\n",
+    "mask.set_AT([0,0,-0.1], RELATIVE=wall)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "instrument.backengine()\n",
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding an external window using a mask\n",
+    "It is also possible to create a thinner section where the material is reduced from the outside. Here we need to add both a vacuum and an aluminium geometry, both of which need to have a priority lower than the original inner vacuum. One mask can handle several geometries, just include both names in the *mask_string* parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "o_window = instrument.add_component(\"outer_window\", \"Union_cylinder\", before=\"master\")\n",
+    "o_window.set_AT([0,0,0], RELATIVE=wall)\n",
+    "o_window.yheight = 0.15 + 0.03\n",
+    "o_window.radius = 0.1 + 0.01\n",
+    "o_window.material_string='\"Vacuum\"' \n",
+    "o_window.priority = 30\n",
+    "\n",
+    "o_window_al = instrument.add_component(\"outer_window_Al\", \"Union_cylinder\", before=\"master\")\n",
+    "o_window_al.set_AT([0,0,0], RELATIVE=wall)\n",
+    "o_window_al.yheight = 0.15 + 0.04\n",
+    "o_window_al.radius = 0.1 - 0.01\n",
+    "o_window_al.material_string='\"Al\"' \n",
+    "o_window_al.priority = 31\n",
+    "\n",
+    "mask = instrument.add_component(\"mask_outer\", \"Union_box\", before=\"master\")\n",
+    "mask.xwidth = 0.12\n",
+    "mask.yheight = 0.2\n",
+    "mask.zdepth = 0.09\n",
+    "mask.priority = 2\n",
+    "mask.mask_string='\"outer_window,outer_window_Al\"'\n",
+    "mask.set_AT([0,0,0.1], RELATIVE=wall)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "instrument.backengine()\n",
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Masks are flexible\n",
+    "Masks can be used to create many interesting shapes with few geometries. Below we create a octagon with rounded corners using just three geometries, two of these being masks. Using masks expands the space of possible geometries greatly, and in many cases can also be a performance advantage when they reduce the number of geometries needed to describe the desired geometry."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")\n",
+    "\n",
+    "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
+    "Al_inc.sigma = 0.0082\n",
+    "Al_inc.unit_cell_volume = 66.4\n",
+    "\n",
+    "Al_pow = instrument.add_component(\"Al_pow\", \"Powder_process\")\n",
+    "Al_pow.reflections = '\"Al.laz\"'\n",
+    "\n",
+    "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
+    "Al.process_string = '\"Al_inc,Al_pow\"'\n",
+    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "\n",
+    "src = instrument.add_component(\"source\", \"Source_div\")\n",
+    "\n",
+    "src.xwidth = 0.2\n",
+    "src.yheight = 0.035\n",
+    "src.focus_aw = 0.01\n",
+    "src.focus_ah = 0.01\n",
+    "\n",
+    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0=\"wavelength\"\n",
+    "src.dlambda=\"0.01*wavelength\"\n",
+    "src.flux = 1E13\n",
+    "\n",
+    "box = instrument.add_component(\"box\", \"Union_box\")\n",
+    "box.set_AT([0,0,1], RELATIVE=src)\n",
+    "box.xwidth = 0.2\n",
+    "box.yheight = 0.1\n",
+    "box.zdepth = 0.2\n",
+    "box.material_string='\"Al\"' \n",
+    "box.priority = 10\n",
+    "\n",
+    "# Cut the corners by using an identical box rotated 45 deg around y\n",
+    "box_mask = instrument.add_component(\"box_mask\", \"Union_box\")\n",
+    "box_mask.set_AT([0,0,0], RELATIVE=box)\n",
+    "box_mask.set_ROTATED([0,45,0], RELATIVE=box)\n",
+    "box_mask.xwidth = 0.2\n",
+    "box_mask.yheight = 0.11 # Have to increase yheight to avoid perfect overlap\n",
+    "box_mask.zdepth = 0.2\n",
+    "box_mask.mask_string='\"box\"' \n",
+    "box_mask.priority = 50\n",
+    "\n",
+    "# Round the corners with a cylinder mask\n",
+    "cyl_mask = instrument.add_component(\"cylinder_mask\", \"Union_cylinder\")\n",
+    "cyl_mask.set_AT([0,0,0], RELATIVE=box)\n",
+    "cyl_mask.radius = 0.105\n",
+    "cyl_mask.yheight = 0.12\n",
+    "cyl_mask.mask_string='\"box\"' \n",
+    "cyl_mask.priority = 51\n",
+    "\n",
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=box)\n",
+    "logger_zx.D_direction_1 = '\"z\"'\n",
+    "logger_zx.D1_min = -0.12\n",
+    "logger_zx.D1_max = 0.12\n",
+    "logger_zx.n1 = 300\n",
+    "logger_zx.D_direction_2 = '\"x\"'\n",
+    "logger_zx.D2_min = -0.12\n",
+    "logger_zx.D2_max = 0.12\n",
+    "logger_zx.n2 = 300\n",
+    "logger_zx.filename = '\"logger_zx.dat\"'\n",
+    "\n",
+    "master = instrument.add_component(\"master\", \"Union_master\")\n",
+    "\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb b/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
new file mode 100644
index 00000000..b1d26615
--- /dev/null
+++ b/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
@@ -0,0 +1,399 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Advanced geometry component concepts: Exit geometry and number of activations\n",
+    "This notebook explains the concept of exit geometry and the activation counter both of which are tied to the geometry components and how they are treated by the *Union_master*.\n",
+    "\n",
+    "An exit geometry is created by setting the *material_string* of a geometry to \"Exit\", and if a ray enters such a geometry, it is immediately released from the master component. Normally this only happens when the ray does not intersect any geometries. There are several uses for this, for example inserting a monitor within a Union geometry ensemble."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up an example with empty sample container\n",
+    "First we set up an example with an empty sample container."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
+    "Al_inc.sigma = 0.0082\n",
+    "Al_inc.unit_cell_volume = 66.4\n",
+    "\n",
+    "Al_pow = instrument.add_component(\"Al_pow\", \"Powder_process\")\n",
+    "Al_pow.reflections = '\"Al.laz\"'\n",
+    "\n",
+    "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
+    "Al.process_string = '\"Al_inc,Al_pow\"'\n",
+    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "\n",
+    "src = instrument.add_component(\"source\", \"Source_div\")\n",
+    "src.xwidth = 0.01\n",
+    "src.yheight = 0.035\n",
+    "src.focus_aw = 0.01\n",
+    "src.focus_ah = 0.01\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.dlambda = \"0.01*wavelength\"\n",
+    "src.flux = 1E13\n",
+    "\n",
+    "sample_volume = instrument.add_component(\"sample_volume\", \"Union_cylinder\")\n",
+    "sample_volume.yheight = 0.03\n",
+    "sample_volume.radius = 0.0075\n",
+    "sample_volume.material_string='\"Vacuum\"' \n",
+    "sample_volume.priority = 100\n",
+    "sample_volume.set_AT([0,0,1], RELATIVE=src)\n",
+    "\n",
+    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\", RELATIVE=sample_volume)\n",
+    "container.yheight = 0.03+0.003 # 1.5 mm top and button\n",
+    "container.radius = 0.0075 + 0.0015 # 1.5 mm sides of container\n",
+    "container.material_string='\"Al\"' \n",
+    "container.priority = 99\n",
+    "\n",
+    "container_lid = instrument.add_component(\"sample_container_lid\", \"Union_cylinder\")\n",
+    "container_lid.set_AT([0, 0.0155, 0], RELATIVE=container)\n",
+    "container_lid.yheight = 0.004\n",
+    "container_lid.radius = 0.013\n",
+    "container_lid.material_string='\"Al\"' \n",
+    "container_lid.priority = 98\n",
+    "\n",
+    "inner_wall = instrument.add_component(\"cryostat_wall\", \"Union_cylinder\")\n",
+    "inner_wall.set_AT([0,0,0], RELATIVE=sample_volume)\n",
+    "inner_wall.yheight = 0.12\n",
+    "inner_wall.radius = 0.03\n",
+    "inner_wall.material_string='\"Al\"' \n",
+    "inner_wall.priority = 80\n",
+    "\n",
+    "inner_wall_vac = instrument.add_component(\"cryostat_wall_vacuum\", \"Union_cylinder\")\n",
+    "inner_wall_vac.set_AT([0,0,0], RELATIVE=sample_volume)\n",
+    "inner_wall_vac.yheight = 0.12 - 0.008\n",
+    "inner_wall_vac.radius = 0.03 - 0.002\n",
+    "inner_wall_vac.material_string='\"Vacuum\"' \n",
+    "inner_wall_vac.priority = 81\n",
+    "\n",
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=sample_volume)\n",
+    "logger_zx.D_direction_1 = '\"z\"'\n",
+    "logger_zx.D1_min = -0.04\n",
+    "logger_zx.D1_max = 0.04\n",
+    "logger_zx.n1 = 300\n",
+    "logger_zx.D_direction_2 = '\"x\"'\n",
+    "logger_zx.D2_min = -0.04\n",
+    "logger_zx.D2_max = 0.04\n",
+    "logger_zx.n2 = 300\n",
+    "logger_zx.filename = '\"logger_zx.dat\"'\n",
+    "\n",
+    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\", RELATIVE=sample_volume)\n",
+    "logger_zy.D_direction_1 = '\"z\"'\n",
+    "logger_zy.D1_min = -0.04\n",
+    "logger_zy.D1_max = 0.04\n",
+    "logger_zy.n1 = 300\n",
+    "logger_zy.D_direction_2 = '\"y\"'\n",
+    "logger_zy.D2_min = -0.06\n",
+    "logger_zy.D2_max = 0.06\n",
+    "logger_zy.n2 = 300\n",
+    "logger_zy.filename = '\"logger_zy.dat\"'\n",
+    "\n",
+    "master = instrument.add_component(\"master\", \"Union_master\")\n",
+    "\n",
+    "banana = instrument.add_component(\"banana\", \"Monitor_nD\", RELATIVE=sample_volume)\n",
+    "banana.xwidth = 1.5\n",
+    "banana.yheight = 0.4\n",
+    "banana.restore_neutron = 1\n",
+    "banana.options = '\"theta limits=[5 175] bins=250, banana\"'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "instrument.set_parameters(wavelength=3.0)\n",
+    "instrument.settings(ncount=3E5, output_path=\"data_folder/union_external\")\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data_empty = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions.name_plot_options(\"logger_space_zx\", data_empty, log=True, orders_of_mag=4)\n",
+    "functions.name_plot_options(\"logger_space_zy\", data_empty, log=True, orders_of_mag=4)\n",
+    "plotter.make_sub_plot(data_empty[0:2])\n",
+    "plotter.make_sub_plot(data_empty[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding an exit volume\n",
+    "Now we switch the sample_volume material from Vacuum to exit, ejecting rays from the simulation when they encounter it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_volume.material_string='\"Exit\"' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "instrument.backengine()\n",
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
+    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
+    "plotter.make_sub_plot(data[0:2])\n",
+    "plotter.make_sub_plot(data[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding an external component in the gap\n",
+    "We can now see any part of the beam that intersected the exit volume is basically removed from the simulation. It is now however possible to insert another component within that exit volume, for example a sample not available as a Union process. Here we just use a PowderN sample in order to demonstrate. We select the same dimensions as the exit volume, but subtract 10 micrometer to avoid a perfect overlap."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = instrument.add_component(\"sample\", \"PowderN\", after=\"master\")\n",
+    "sample.set_AT([0,0,0], RELATIVE=sample_volume)\n",
+    "sample.radius = sample_volume.radius - 1E-5\n",
+    "sample.yheight = sample_volume.yheight - 2E-5\n",
+    "sample.reflections = '\"Na2Ca3Al2F14.laz\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Running the simulation again\n",
+    "We run the simulation again, but know that the scattering within the sample wont be directly visible in the loggers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "instrument.backengine()\n",
+    "data_wrong = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions.name_plot_options(\"logger_space_zx\", data_wrong, log=True, orders_of_mag=4)\n",
+    "functions.name_plot_options(\"logger_space_zy\", data_wrong, log=True, orders_of_mag=4)\n",
+    "plotter.make_sub_plot(data_wrong[0:2])\n",
+    "plotter.make_sub_plot(data_wrong[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretation of the data\n",
+    "Now we have added a sample inside the Union geometry, but when the neutron reaches that sample, it is ignored by the sample environment leading to unphysical behavior. Here the beam does not illuminate the sample environment on the way out, and all rays scattered by the PowderN sample are not attenuated by the "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Allowing the rays to return to the Union_master\n",
+    "Now we could recreate the entire sample environment with new geometries and insert an additional unit master to grab the neutrons after the external sample, yet this would be error prone as all geometries would need to be exactly the same. Instead it is possible to tell Union geometries that they should be simulated in several of the next *Union_master* components using the *number_of_activation* parameter on each Union geometry, which is 1 per default.\n",
+    "\n",
+    "Setting it to 2, we tell the geometries that they should be simulated in the two next *Union_master* components. We do not update the sample_volume which is an exit volume, as this would allow the ray to escape once more. Instead we will replace it with Vacuum, but one could also have placed something closer to the actual sample.\n",
+    "\n",
+    "One last necessary detail is to set the *allow_inside_start* parameter on the second *Union_master* component. This disables an error message that would occur if a neutron starts inside a Union geometry, as this is most likely an error. Here we want to do this on purpose, and we need to let the *Union_master* component know this is allowed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "container.number_of_activations = 2\n",
+    "container_lid.number_of_activations = 2\n",
+    "inner_wall.number_of_activations = 2\n",
+    "inner_wall_vac.number_of_activations = 2\n",
+    "\n",
+    "sample_replacement = instrument.add_component(\"sample_volume_replace\", \"Union_cylinder\")\n",
+    "sample_replacement.yheight = sample_volume.yheight\n",
+    "sample_replacement.radius = sample_volume.radius\n",
+    "sample_replacement.material_string='\"Vacuum\"' \n",
+    "sample_replacement.priority = 101\n",
+    "sample_replacement.set_AT([0,0,0], RELATIVE=sample_volume)\n",
+    "\n",
+    "master_2 = instrument.add_component(\"master_after_sample\", \"Union_master\", after=\"sample\")\n",
+    "master_2.allow_inside_start=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "instrument.backengine()\n",
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
+    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
+    "plotter.make_sub_plot(data[0:2])\n",
+    "plotter.make_sub_plot(data[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretation of the data\n",
+    "Now we see evidence of the beam leaving the sample environment after interacting with the sample, and also elevated scattering in comparison to the empty can. This is now a reasonable simulation containing an external component inside a Union geometry ensemble, but there is still one problem, if the ray leaves the external component and reenters later, it will find a Vacuum instead of that sample. This can be fixed to some extend by adding a second copy of the external component and a third *Union_master* component, while incrementing the *number_of_activations* on all geometries, then the ray would be able to leave and enter the external component once before the external component effectively disappears. Even with this assumption, it is still a reasonable approximation and a flexible approach to add for example a mirror inside a sample environment."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparison of the three datasets\n",
+    "Here we compare the three datasets, the empty sample environment, the wrong simulation where rays scattered in the sample could not interact with the sample environment, and the full simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "banana_empty = functions.name_search(\"banana\", data_empty)\n",
+    "banana_wrong = functions.name_search(\"banana\", data_wrong)\n",
+    "banana_sample = functions.name_search(\"banana\", data)\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.figure(figsize=(14,6))\n",
+    "plt.plot(banana_empty.xaxis, banana_empty.Intensity, \"r\",\n",
+    "         banana_wrong.xaxis, banana_wrong.Intensity, \"b\",\n",
+    "         banana_sample.xaxis, banana_sample.Intensity, \"k\")\n",
+    "plt.xlabel(\"2Theta [deg]\")\n",
+    "plt.ylabel(\"Intensity [n/s]\")\n",
+    "plt.legend([\"No sample\", \"Wrong simulation, no exit\", \"Full simulation\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretation of the data\n",
+    "We see that the wrong simulation have slightly lower background, and more peak intensity. We also see the peak shape is different near the aluminium Bragg peaks. Since the Union components contain a powder process, one can also recreate this example without using an external PowderN component to check the accuracy of the approach. It is however still not perfect, as the Union powder process will perform multiple scattering where PowderN will not. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/tutorial/Union_tutorial_7_Tagging_history.ipynb b/docs/source/tutorial/Union_tutorial_7_Tagging_history.ipynb
new file mode 100644
index 00000000..d81e6c8a
--- /dev/null
+++ b/docs/source/tutorial/Union_tutorial_7_Tagging_history.ipynb
@@ -0,0 +1,1487 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Union tagging system\n",
+    "The *Union_master* is capable of recording histories for each neutron in a tree like fashion and add the total intensities for each unique history together. At the end of the simulation these are sorted by intensity and written to file, and the top 20 are shown in the terminal. This system does not work with MPI, if MPI is used only part of the data is written to disk. The system can take up a large amount of memory when used, so it is disabled per default.\n",
+    "\n",
+    "First we set up a simple instrument with sample, container and a layer of cryostat."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
+    "Al_inc.sigma = 0.0082\n",
+    "Al_inc.unit_cell_volume = 66.4\n",
+    "\n",
+    "Al_pow = instrument.add_component(\"Al_pow\", \"Powder_process\")\n",
+    "Al_pow.reflections = '\"Al.laz\"'\n",
+    "\n",
+    "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
+    "Al.process_string = '\"Al_inc,Al_pow\"'\n",
+    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "\n",
+    "Sample_inc = instrument.add_component(\"Sample_inc\", \"Incoherent_process\")\n",
+    "Sample_inc.sigma = 3.4176\n",
+    "Sample_inc.unit_cell_volume = 1079.1\n",
+    "\n",
+    "Sample_pow = instrument.add_component(\"Sample_pow\", \"Powder_process\")\n",
+    "Sample_pow.reflections = '\"Na2Ca3Al2F14.laz\"'\n",
+    "\n",
+    "Sample = instrument.add_component(\"Sample\", \"Union_make_material\")\n",
+    "Sample.process_string = '\"Sample_inc,Sample_pow\"'\n",
+    "Sample.my_absorption = 100*2.9464/1079.1\n",
+    "\n",
+    "src = instrument.add_component(\"source\", \"Source_div\")\n",
+    "src.xwidth = 0.01\n",
+    "src.yheight = 0.035\n",
+    "src.focus_aw = 0.01\n",
+    "src.focus_ah = 0.01\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.dlambda = \"0.01*wavelength\"\n",
+    "src.flux = 1E13\n",
+    "\n",
+    "sample_geometry = instrument.add_component(\"sample_geometry\", \"Union_cylinder\")\n",
+    "sample_geometry.yheight = 0.03\n",
+    "sample_geometry.radius = 0.0075\n",
+    "sample_geometry.material_string='\"Sample\"' \n",
+    "sample_geometry.priority = 100\n",
+    "sample_geometry.set_AT([0,0,1], RELATIVE=src)\n",
+    "\n",
+    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\", RELATIVE=sample_geometry)\n",
+    "container.yheight = 0.03+0.003 # 1.5 mm top and button\n",
+    "container.radius = 0.0075 + 0.0015 # 1.5 mm sides of container\n",
+    "container.material_string='\"Al\"' \n",
+    "container.priority = 99\n",
+    "\n",
+    "inner_wall = instrument.add_component(\"cryostat_wall\", \"Union_cylinder\")\n",
+    "inner_wall.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "inner_wall.yheight = 0.12\n",
+    "inner_wall.radius = 0.03\n",
+    "inner_wall.material_string='\"Al\"' \n",
+    "inner_wall.priority = 80\n",
+    "\n",
+    "inner_wall_vac = instrument.add_component(\"cryostat_wall_vacuum\", \"Union_cylinder\")\n",
+    "inner_wall_vac.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
+    "inner_wall_vac.yheight = 0.12 - 0.008\n",
+    "inner_wall_vac.radius = 0.03 - 0.002\n",
+    "inner_wall_vac.material_string='\"Vacuum\"' \n",
+    "inner_wall_vac.priority = 81\n",
+    "\n",
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zx.D_direction_1 = '\"z\"'\n",
+    "logger_zx.D1_min = -0.04\n",
+    "logger_zx.D1_max = 0.04\n",
+    "logger_zx.n1 = 300\n",
+    "logger_zx.D_direction_2 = '\"x\"'\n",
+    "logger_zx.D2_min = -0.04\n",
+    "logger_zx.D2_max = 0.04\n",
+    "logger_zx.n2 = 300\n",
+    "logger_zx.filename = '\"logger_zx.dat\"'\n",
+    "\n",
+    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zy.D_direction_1 = '\"z\"'\n",
+    "logger_zy.D1_min = -0.04\n",
+    "logger_zy.D1_max = 0.04\n",
+    "logger_zy.n1 = 300\n",
+    "logger_zy.D_direction_2 = '\"y\"'\n",
+    "logger_zy.D2_min = -0.06\n",
+    "logger_zy.D2_max = 0.06\n",
+    "logger_zy.n2 = 300\n",
+    "logger_zy.filename = '\"logger_zy.dat\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding the Union_master with tagging\n",
+    "There are two important parameters to consider when setting a *Union_master* up for tagging:\n",
+    "- enable_tagging [default 0] 0 for disable, 1 for enable\n",
+    "- history_limit [default 300000] Limit of unique histories recorded\n",
+    "\n",
+    "As the *Union_master* component records each ray in succession, their unique history is added to the history tree. If a ray takes the same path in the tree, the intensity gets added to that unique history. When the history limit is reached, the tree is not built out further, but if already recorded histories occur, their intensity is still added to the existing tree."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "master = instrument.add_component(\"master\", \"Union_master\")\n",
+    "master.enable_tagging = 1\n",
+    "master.history_limit = 300000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_tagging_3\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:12948:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:12948:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:13191:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:13191:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "14 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_tagging_3\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: Sample_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: Sample_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: Sample_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 4\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "name of process [2]: Sample_inc \n",
+      "component index [2]: 4 \n",
+      "name of process [3]: Sample_pow \n",
+      "component index [3]: 5 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 2\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 0.347892 \n",
+      "number of processes [0]: 2 \n",
+      "name of material    [1]: Sample \n",
+      "component index     [1]: 6 \n",
+      "my_absoprtion       [1]: 0.273042 \n",
+      "number of processes [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 2\n",
+      "\n",
+      "name of geometry    [0]: sample_geometry \n",
+      "component index     [0]: 8 \n",
+      "Volume.name         [0]: sample_geometry \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.273042 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: sample_container \n",
+      "component index     [1]: 9 \n",
+      "Volume.name         [1]: sample_container \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: cryostat_wall \n",
+      "component index     [2]: 10 \n",
+      "Volume.name         [2]: cryostat_wall \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
+      "Volume.p_physics.number of processes [2]: 2 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: 0.000000 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.030000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.120000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: cryostat_wall_vacuum \n",
+      "component index     [3]: 11 \n",
+      "Volume.name         [3]: cryostat_wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [3]: 1 \n",
+      "Volume.p_physics.my_absorption       [3]: 0.000000 \n",
+      "Volume.p_physics.number of processes [3]: 0 \n",
+      "Volume.geometry.shape                [3]: cylinder \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: 0.000000 \n",
+      "Volume.geometry.center.z             [3]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.028000 \n",
+      "Volume.geometry.geometry_parameters.height [3]: 0.112000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 5\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4]\n",
+      "LIST: Direct_children for Volume           0 = [3]\n",
+      "LIST: Intersect_check_list for Volume      0 = [3]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [3]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for sample_geometry with cylinder shape made of Sample\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = []\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [2]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = [2]\n",
+      "LIST: Next_volume_list for Volume          1 = [2]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for sample_container with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  2 = [1]\n",
+      "LIST: Direct_children for Volume           2 = [1]\n",
+      "LIST: Intersect_check_list for Volume      2 = [1]\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [4]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [4]\n",
+      "LIST: Next_volume_list for Volume          2 = [4,1]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for cryostat_wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  3 = [1,2,4]\n",
+      "LIST: Direct_children for Volume           3 = [4]\n",
+      "LIST: Intersect_check_list for Volume      3 = [4]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = []\n",
+      "LIST: Next_volume_list for Volume          3 = [0,4]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  4 = [1,2]\n",
+      "LIST: Direct_children for Volume           4 = [2]\n",
+      "LIST: Intersect_check_list for Volume      4 = [2]\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [3]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = [3]\n",
+      "LIST: Next_volume_list for Volume          4 = [3,2]\n",
+      "      Is_vacuum for Volume                 4 = 1\n",
+      "      is_mask_volume for Volume            4 = 0\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = []\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_zx_I=18017.8 logger_space_zx_ERR=32.4189 logger_space_zx_N=879822 \"logger_zx.dat\"\n",
+      "Detector: logger_space_zy_I=18017.8 logger_space_zy_ERR=32.4189 logger_space_zy_N=879822 \"logger_zy.dat\"\n",
+      "\n",
+      "\n",
+      "Top 20 most common histories. Shows the index of volumes entered (VX), and the scattering processes (PX)\n",
+      "1859607\t N I=3.965281E+04 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "506763\t N I=1.050335E+04 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "226314\t N I=4.825743E+03 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
+      "166837\t N I=3.557502E+03 \t V0 -> V3 -> V4 -> V3 -> V0 \n",
+      "70469\t N I=1.466246E+03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "31797\t N I=5.940376E+02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "23495\t N I=4.091492E+02 \t V0 -> V3 -> P1 -> V0 \n",
+      "17274\t N I=3.516535E+02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "13839\t N I=2.758026E+02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "11994\t N I=2.441687E+02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "9842\t N I=2.293453E+02 \t V0 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "10180\t N I=2.028735E+02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "8250\t N I=1.719896E+02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "5405\t N I=1.056087E+02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "4597\t N I=8.898223E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "3735\t N I=5.656959E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "2454\t N I=4.841669E+01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "2882\t N I=4.699149E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "2334\t N I=4.194865E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "2056\t N I=4.159660E+01 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.set_parameters(wavelength=3.0)\n",
+    "instrument.settings(ncount=3E5, output_path=\"data_folder/union_tagging\")\n",
+    "\n",
+    "instrument.backengine()\n",
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space_zx\n",
+      "Plotting data with name logger_space_zy\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
+    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Finding the history file\n",
+    "A file called Union_history.dat is written in the run folder with all the unique histories. In some cases a bug happens that causes the file to be unreadable, for now the best fix is to rerun the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "History file written by the McStas component Union_master \n",
+      "When running with MPI, the results may be from just a single thread, meaning intensities are divided by number of threads\n",
+      "----- Description of the used volumes -----------------------------------------------------------------------------------\n",
+      "V0: Surrounding vacuum \n",
+      "V1: sample_geometry  Material: Sample   P0:  P1: Sample_pow\n",
+      "V2: sample_container  Material: Al   P0: 4 P1: Al_pow\n",
+      "V3: cryostat_wall  Material: Al   P0: 4 P1: Al_pow\n",
+      "V4: cryostat_wall_vacuum  Material: Vacuum  \n",
+      "----- Histories sorted after intensity ----------------------------------------------------------------------------------\n",
+      "1859607\t N I=3.965281E+04 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "506763\t N I=1.050335E+04 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "226314\t N I=4.825743E+03 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
+      "166837\t N I=3.557502E+03 \t V0 -> V3 -> V4 -> V3 -> V0 \n",
+      "70469\t N I=1.466246E+03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "31797\t N I=5.940376E+02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "23495\t N I=4.091492E+02 \t V0 -> V3 -> P1 -> V0 \n",
+      "17274\t N I=3.516535E+02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "13839\t N I=2.758026E+02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "11994\t N I=2.441687E+02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "9842\t N I=2.293453E+02 \t V0 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "10180\t N I=2.028735E+02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "8250\t N I=1.719896E+02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "5405\t N I=1.056087E+02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "4597\t N I=8.898223E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "3735\t N I=5.656959E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "2454\t N I=4.841669E+01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "2882\t N I=4.699149E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "2334\t N I=4.194865E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "2056\t N I=4.159660E+01 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "1531\t N I=3.104778E+01 \t V0 -> V3 -> V4 -> V3 -> P1 -> V0 \n",
+      "1529\t N I=2.937892E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1605\t N I=2.843064E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1094\t N I=2.371972E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "1056\t N I=2.251227E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "927\t N I=1.618954E+01 \t V0 -> V3 -> P1 -> P1 -> V0 \n",
+      "750\t N I=1.509174E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "623\t N I=1.063780E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "559\t N I=1.026990E+01 \t V0 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "556\t N I=9.481044E+00 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "589\t N I=9.135234E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "298\t N I=6.872061E+00 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "388\t N I=6.792255E+00 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "434\t N I=6.645396E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "435\t N I=6.588350E+00 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "273\t N I=5.790274E+00 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "366\t N I=5.672326E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "285\t N I=5.647409E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "352\t N I=5.331544E+00 \t V0 -> V3 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "265\t N I=4.655556E+00 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "267\t N I=4.518687E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "186\t N I=3.656094E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "281\t N I=3.513630E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "182\t N I=3.302548E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "160\t N I=3.112205E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "163\t N I=3.046436E+00 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "144\t N I=3.000987E+00 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "163\t N I=2.972572E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "211\t N I=2.805841E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "183\t N I=2.789270E+00 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "113\t N I=2.753453E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "175\t N I=2.589555E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "134\t N I=2.491092E+00 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "137\t N I=2.457782E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "103\t N I=2.004625E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "115\t N I=1.942657E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "97\t N I=1.776228E+00 \t V0 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "84\t N I=1.619118E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "85\t N I=1.601890E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "25\t N I=1.378346E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "98\t N I=1.373456E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "95\t N I=1.349907E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "69\t N I=1.326884E+00 \t V0 -> V3 -> V4 -> V2 -> P0 -> V4 -> V3 -> V0 \n",
+      "78\t N I=1.312686E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "40\t N I=1.202410E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "72\t N I=1.113576E+00 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "38\t N I=1.054350E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "57\t N I=9.793343E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "64\t N I=9.571626E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "57\t N I=9.514756E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "63\t N I=9.241653E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "48\t N I=9.188580E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "44\t N I=9.071488E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "37\t N I=8.697112E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "53\t N I=8.477626E-01 \t V0 -> V3 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "41\t N I=8.158051E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "60\t N I=8.021806E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "52\t N I=7.809131E-01 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "40\t N I=7.692616E-01 \t V0 -> V3 -> V4 -> V2 -> P0 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "53\t N I=7.261881E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "42\t N I=7.178343E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "36\t N I=6.980954E-01 \t V0 -> V3 -> P1 -> P1 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
+      "29\t N I=6.689273E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "34\t N I=6.539172E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P0 -> V0 \n",
+      "34\t N I=6.538197E-01 \t V0 -> V3 -> P0 -> V0 \n",
+      "42\t N I=6.024936E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "35\t N I=5.834859E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "36\t N I=5.775684E-01 \t V0 -> V3 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "48\t N I=5.733596E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "30\t N I=5.577867E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "39\t N I=5.534476E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "60\t N I=5.273567E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "29\t N I=4.431660E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "22\t N I=4.231337E-01 \t V0 -> V3 -> P0 -> V4 -> V3 -> V0 \n",
+      "25\t N I=4.180681E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
+      "20\t N I=4.001083E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "15\t N I=3.996063E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "21\t N I=3.907685E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "9\t N I=3.873613E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "19\t N I=3.872826E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "35\t N I=3.741849E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "19\t N I=3.653692E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P0 -> V4 -> V3 -> V0 \n",
+      "10\t N I=3.577379E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "27\t N I=3.383308E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "2\t N I=3.171137E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "19\t N I=2.898560E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "8\t N I=2.891570E-01 \t V0 -> V3 -> V4 -> V2 -> P0 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "17\t N I=2.822871E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "20\t N I=2.790372E-01 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "8\t N I=2.645994E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "22\t N I=2.587804E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "17\t N I=2.515664E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "13\t N I=2.500189E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P0 -> V4 -> V3 -> V0 \n",
+      "2\t N I=2.410002E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "16\t N I=2.356043E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "12\t N I=2.307864E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P0 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "19\t N I=2.306564E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "5\t N I=2.259507E-01 \t V0 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "19\t N I=2.122730E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "11\t N I=2.028933E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "13\t N I=2.024403E-01 \t V0 -> V3 -> P1 -> P1 -> P1 -> V0 \n",
+      "13\t N I=2.008074E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P0 -> V4 -> V3 -> V0 \n",
+      "19\t N I=1.999939E-01 \t V0 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "15\t N I=1.997969E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "9\t N I=1.826944E-01 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "2\t N I=1.791654E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "19\t N I=1.772469E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "17\t N I=1.688696E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "20\t N I=1.670351E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "15\t N I=1.626160E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "11\t N I=1.559772E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "13\t N I=1.500194E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "10\t N I=1.424290E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "9\t N I=1.375370E-01 \t V0 -> V3 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "11\t N I=1.361135E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> P1 -> V0 \n",
+      "8\t N I=1.350152E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "7\t N I=1.336463E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "8\t N I=1.285106E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "3\t N I=1.221980E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "10\t N I=1.210789E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "5\t N I=1.201056E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "9\t N I=1.200436E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "3\t N I=1.182892E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "7\t N I=1.175995E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "9\t N I=1.142555E-01 \t V0 -> V3 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "6\t N I=1.083372E-01 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "5\t N I=1.079352E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "4\t N I=1.064247E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P0 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "7\t N I=1.036886E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "10\t N I=1.017997E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "4\t N I=1.017446E-01 \t V0 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "7\t N I=1.014091E-01 \t V0 -> V3 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "10\t N I=9.965093E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "6\t N I=9.662395E-02 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "8\t N I=9.191802E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "7\t N I=9.091205E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "4\t N I=8.802686E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "5\t N I=8.753136E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "4\t N I=8.713988E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "7\t N I=8.673562E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "6\t N I=8.473219E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "5\t N I=8.459402E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "7\t N I=8.453155E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "5\t N I=8.202485E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
+      "7\t N I=8.179178E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "5\t N I=8.008056E-02 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
+      "6\t N I=7.787455E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "4\t N I=7.691362E-02 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P0 -> V0 \n",
+      "6\t N I=7.687598E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "3\t N I=7.361250E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "6\t N I=7.285952E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "8\t N I=7.219209E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=7.037750E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P0 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "6\t N I=7.035304E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "20\t N I=6.930433E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "5\t N I=6.736602E-02 \t V0 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=6.721552E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "9\t N I=6.695282E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P0 -> V0 \n",
+      "6\t N I=6.648583E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "5\t N I=6.543821E-02 \t V0 -> V3 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "5\t N I=6.229937E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "4\t N I=5.973815E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "4\t N I=5.969631E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=5.951830E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "5\t N I=5.841225E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "6\t N I=5.788928E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "3\t N I=5.768873E-02 \t V0 -> V3 -> P0 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "3\t N I=5.767973E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P0 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "3\t N I=5.764591E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "2\t N I=5.744261E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "6\t N I=5.505677E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "4\t N I=5.463461E-02 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "3\t N I=5.433915E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "6\t N I=5.383604E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "3\t N I=5.197286E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "4\t N I=4.987311E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "3\t N I=4.856827E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "3\t N I=4.831851E-02 \t V0 -> V3 -> P1 -> P0 -> V0 \n",
+      "2\t N I=4.609026E-02 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "2\t N I=4.605094E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "4\t N I=4.514625E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "6\t N I=4.274886E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "4\t N I=4.227548E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "2\t N I=4.209610E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P0 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "2\t N I=4.207892E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
+      "5\t N I=4.207512E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "2\t N I=4.205341E-02 \t V0 -> V3 -> P1 -> P0 -> V4 -> V3 -> V0 \n",
+      "1\t N I=4.200370E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "3\t N I=4.167133E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "3\t N I=4.160331E-02 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> P1 -> P1 -> V0 \n",
+      "3\t N I=4.157004E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "2\t N I=4.111478E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "7\t N I=4.002838E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "2\t N I=3.845955E-02 \t V0 -> V3 -> V4 -> V3 -> P0 -> V0 \n",
+      "2\t N I=3.844839E-02 \t V0 -> V3 -> V4 -> V3 -> P0 -> V4 -> V3 -> V0 \n",
+      "3\t N I=3.799710E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=3.771217E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "2\t N I=3.767373E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P0 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "2\t N I=3.632067E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P0 -> V4 -> V3 -> V0 \n",
+      "2\t N I=3.623438E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "2\t N I=3.619957E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "2\t N I=3.553759E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "3\t N I=3.532011E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "2\t N I=3.448112E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "3\t N I=3.315148E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "3\t N I=3.309992E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "2\t N I=3.276986E-02 \t V0 -> V3 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "2\t N I=3.269722E-02 \t V0 -> V3 -> P1 -> P1 -> P1 -> P1 -> V0 \n",
+      "2\t N I=3.240473E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P0 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "9\t N I=3.154916E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "3\t N I=3.119187E-02 \t V0 -> V3 -> P1 -> P1 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "4\t N I=3.025105E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P0 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=3.002264E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> P1 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.825316E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=2.823280E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "2\t N I=2.815913E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> P1 -> P1 -> V0 \n",
+      "1\t N I=2.789046E-02 \t V0 -> V3 -> P1 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.788551E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "4\t N I=2.644613E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "2\t N I=2.618939E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "4\t N I=2.600572E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.559786E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.545367E-02 \t V0 -> V3 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.503821E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.436294E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "3\t N I=2.362027E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "2\t N I=2.353812E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "4\t N I=2.332097E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "2\t N I=2.272885E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P0 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "2\t N I=2.260732E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.229209E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P0 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "3\t N I=2.226424E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.177959E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "2\t N I=2.173728E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "4\t N I=2.137424E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.134416E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> P0 -> V0 \n",
+      "2\t N I=2.124210E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P0 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "2\t N I=2.114286E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "1\t N I=2.106589E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P0 -> V0 \n",
+      "1\t N I=2.105620E-02 \t V0 -> V3 -> V4 -> V2 -> P0 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.105417E-02 \t V0 -> V3 -> P0 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.102680E-02 \t V0 -> V3 -> V4 -> V2 -> P0 -> P1 -> V4 -> V3 -> V0 \n",
+      "3\t N I=2.101774E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.101559E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P0 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.099840E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P0 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.099810E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.099241E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P0 -> V4 -> V3 -> V0 \n",
+      "3\t N I=2.073353E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> P1 -> V0 \n",
+      "4\t N I=2.016148E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.008879E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "3\t N I=1.989747E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.986359E-02 \t V0 -> V3 -> P1 -> P1 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "2\t N I=1.942461E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.923702E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P0 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.922269E-02 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P0 -> V4 -> V3 -> V0 \n",
+      "5\t N I=1.834890E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.814081E-02 \t V0 -> V3 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=1.813887E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.813885E-02 \t V0 -> V3 -> P1 -> P1 -> P1 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.812308E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.812027E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "1\t N I=1.810402E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.808167E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.807547E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.807263E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "2\t N I=1.739841E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "5\t N I=1.714379E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P0 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.621517E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=1.618113E-02 \t V0 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.617426E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.616066E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.611050E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.609214E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.527837E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=1.388429E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P0 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.377018E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=1.367051E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P0 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.365858E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P0 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.365367E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P0 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.365050E-02 \t V0 -> V3 -> V4 -> V2 -> P0 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=1.364029E-02 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> P0 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.363490E-02 \t V0 -> V3 -> P0 -> P1 -> V0 \n",
+      "1\t N I=1.302289E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=1.299298E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=1.289116E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=1.289094E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=1.286992E-02 \t V0 -> V3 -> P1 -> P1 -> P1 -> V4 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=1.283121E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.271447E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.242743E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=1.202185E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.193356E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=1.192701E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "2\t N I=1.180068E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=1.179451E-02 \t V0 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.178828E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.177854E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=1.177829E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.176821E-02 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=1.176692E-02 \t V0 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.176084E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=1.175595E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.175161E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.174237E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.174026E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.077195E-02 \t V0 -> V3 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.050128E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P0 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.049533E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.047453E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "2\t N I=9.194804E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=8.828024E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> P0 -> V4 -> V3 -> V0 \n",
+      "1\t N I=8.616066E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=8.382155E-03 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=8.360062E-03 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> P1 -> P1 -> P1 -> V0 \n",
+      "1\t N I=7.974062E-03 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P0 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=7.660077E-03 \t V0 -> V3 -> V4 -> V3 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=7.647885E-03 \t V0 -> V3 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "5\t N I=7.331964E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=7.128787E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=6.530489E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> P1 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "2\t N I=6.369831E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "1\t N I=6.350997E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> P1 -> P1 -> P1 -> V0 \n",
+      "1\t N I=5.952547E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "1\t N I=5.855741E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=5.839953E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> P1 -> P1 -> V0 \n",
+      "1\t N I=5.634308E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
+      "6\t N I=5.502077E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=5.452432E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P0 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=5.319210E-03 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> P1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "2\t N I=4.920947E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=4.135698E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=4.100827E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=3.945110E-03 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P0 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "3\t N I=3.705516E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=3.669367E-03 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=3.648249E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=3.477421E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "2\t N I=2.474695E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P0 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=2.304728E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.255269E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.011449E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P0 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.869764E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.847745E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P0 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.528149E-03 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=1.056831E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P0 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=9.166647E-04 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "4\t N I=7.810965E-04 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=5.888911E-04 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=4.802447E-04 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=4.204479E-04 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=3.772470E-04 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=3.676614E-04 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.357966E-04 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "1\t N I=9.527289E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=9.238563E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "1\t N I=9.057649E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=8.667397E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=4.666257E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P0 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=4.519254E-05 \t V0 -> V3 -> V4 -> V2 -> P0 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=3.945415E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=3.325231E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P0 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=3.066219E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.534252E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P0 -> V0 \n",
+      "1\t N I=1.606027E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=9.518509E-06 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=5.475630E-06 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=5.457173E-06 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=4.436696E-06 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V2 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.717751E-06 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.038084E-30 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.945785E-50 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"run_folder/Union_history.dat\") as file:\n",
+    "    instrument_string = file.read()\n",
+    "    print(instrument_string)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpreting the histories\n",
+    "The history with highest intensity in my run is: V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0, which means:\n",
+    "- Neutron entered Volume0 (Surrounding vacuum)\n",
+    "- Neutron entered Volume3 (Cryostat wall)\n",
+    "- Neutron entered Volume4 (Cryostat vacuum)\n",
+    "- Neutron entered Volume2 (Container)\n",
+    "- Neutron entered Volume1 (Sample)\n",
+    "- Neutron entered Volume2 (Container)\n",
+    "- Neutron entered Volume4 (Cryostat vacuum)\n",
+    "- Neutron entered Volume3 (Cryostat wall)\n",
+    "- Neutron entered Volume0 (Surrounding vacuum)\n",
+    "\n",
+    "So the most likely occurrence is that the ray is propagated through all geometries. The next most likely history is:\n",
+    "\n",
+    "V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0\n",
+    "\n",
+    "- Neutron entered Volume0 (Surrounding vacuum)\n",
+    "- Neutron entered Volume3 (Cryostat wall)\n",
+    "- Neutron entered Volume4 (Cryostat vacuum)\n",
+    "- Neutron entered Volume2 (Container)\n",
+    "- Neutron entered Volume1 (Sample)\n",
+    "- Neutron scattered on Process1 (Since we are in Volume1, that would be Sample_pow)\n",
+    "- Neutron entered Volume2 (Container)\n",
+    "- Neutron entered Volume4 (Cryostat vacuum)\n",
+    "- Neutron entered Volume3 (Cryostat wall)\n",
+    "- Neutron entered Volume0 (Surrounding vacuum)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use conditional component to filter tagging\n",
+    "Just like a logger can be modified by a conditional component to only record events that satisfy that condition, the tagging system of the *Union_master* component can also be modified by a conditional component. In that case, the tagging system will only record events that satisfy the condition imposed by the conditional component.\n",
+    "\n",
+    "This can be useful to explain an unexpected feature, as the conditional components can filter for energy, time, direction or any combination of these. Here we demonstrate this feature by adding a *Union_conditional_PSD* outside of the direct beam and enabling the *master_tagging* control parameter, which causes the condition to be applied to the tagging system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up an arm pointing to the relevant spot\n",
+    "spot_dir = instrument.add_component(\"spot_dir\", \"Arm\", RELATIVE=sample_geometry, before=\"master\")\n",
+    "spot_dir.set_ROTATED([0, 60, 0], RELATIVE=sample_geometry)\n",
+    "\n",
+    "# Set up a conditional component targeting all our loggers\n",
+    "PSD_conditional = instrument.add_component(\"space_all_PSD_conditional\", \"Union_conditional_PSD\", before=\"master\")\n",
+    "PSD_conditional.xwidth = 0.2\n",
+    "PSD_conditional.yheight = 0.2\n",
+    "PSD_conditional.master_tagging = 1\n",
+    "PSD_conditional.set_AT([0, 0, 0.5], RELATIVE=spot_dir) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_tagging_4\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:13237:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:13237:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:13480:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:13480:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "14 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_tagging_4\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: Sample_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: Sample_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: Sample_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "starded conditional initialize \n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 4\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "name of process [2]: Sample_inc \n",
+      "component index [2]: 4 \n",
+      "name of process [3]: Sample_pow \n",
+      "component index [3]: 5 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 2\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 0.347892 \n",
+      "number of processes [0]: 2 \n",
+      "name of material    [1]: Sample \n",
+      "component index     [1]: 6 \n",
+      "my_absoprtion       [1]: 0.273042 \n",
+      "number of processes [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 2\n",
+      "\n",
+      "name of geometry    [0]: sample_geometry \n",
+      "component index     [0]: 8 \n",
+      "Volume.name         [0]: sample_geometry \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.273042 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: sample_container \n",
+      "component index     [1]: 9 \n",
+      "Volume.name         [1]: sample_container \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: cryostat_wall \n",
+      "component index     [2]: 10 \n",
+      "Volume.name         [2]: cryostat_wall \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
+      "Volume.p_physics.number of processes [2]: 2 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: 0.000000 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.030000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.120000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: cryostat_wall_vacuum \n",
+      "component index     [3]: 11 \n",
+      "Volume.name         [3]: cryostat_wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [3]: 1 \n",
+      "Volume.p_physics.my_absorption       [3]: 0.000000 \n",
+      "Volume.p_physics.number of processes [3]: 0 \n",
+      "Volume.geometry.shape                [3]: cylinder \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: 0.000000 \n",
+      "Volume.geometry.center.z             [3]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.028000 \n",
+      "Volume.geometry.geometry_parameters.height [3]: 0.112000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 5\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4]\n",
+      "LIST: Direct_children for Volume           0 = [3]\n",
+      "LIST: Intersect_check_list for Volume      0 = [3]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [3]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for sample_geometry with cylinder shape made of Sample\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = []\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [2]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = [2]\n",
+      "LIST: Next_volume_list for Volume          1 = [2]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for sample_container with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  2 = [1]\n",
+      "LIST: Direct_children for Volume           2 = [1]\n",
+      "LIST: Intersect_check_list for Volume      2 = [1]\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [4]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [4]\n",
+      "LIST: Next_volume_list for Volume          2 = [4,1]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for cryostat_wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  3 = [1,2,4]\n",
+      "LIST: Direct_children for Volume           3 = [4]\n",
+      "LIST: Intersect_check_list for Volume      3 = [4]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = []\n",
+      "LIST: Next_volume_list for Volume          3 = [0,4]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  4 = [1,2]\n",
+      "LIST: Direct_children for Volume           4 = [2]\n",
+      "LIST: Intersect_check_list for Volume      4 = [2]\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [3]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = [3]\n",
+      "LIST: Next_volume_list for Volume          4 = [3,2]\n",
+      "      Is_vacuum for Volume                 4 = 1\n",
+      "      is_mask_volume for Volume            4 = 0\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = []\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_zx_I=18092.6 logger_space_zx_ERR=33.1511 logger_space_zx_N=879706 \"logger_zx.dat\"\n",
+      "Detector: logger_space_zy_I=18092.6 logger_space_zy_ERR=33.1511 logger_space_zy_N=879706 \"logger_zy.dat\"\n",
+      "\n",
+      "\n",
+      "Top 20 most common histories. Shows the index of volumes entered (VX), and the scattering processes (PX)\n",
+      "5892\t N I=4.578027E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "895\t N I=2.001013E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "143\t N I=3.463667E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "108\t N I=2.251620E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "66\t N I=1.949978E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "65\t N I=1.194341E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "37\t N I=7.657474E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "14\t N I=3.683233E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "19\t N I=3.455680E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "17\t N I=2.855082E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "13\t N I=2.822139E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "9\t N I=2.778669E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "17\t N I=2.373749E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "18\t N I=2.297456E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.200556E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "11\t N I=1.857153E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "10\t N I=1.571294E-01 \t V0 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "6\t N I=9.084604E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "5\t N I=8.596945E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "4\t N I=8.405909E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.backengine()\n",
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "History file written by the McStas component Union_master \n",
+      "When running with MPI, the results may be from just a single thread, meaning intensities are divided by number of threads\n",
+      "----- Description of the used volumes -----------------------------------------------------------------------------------\n",
+      "V0: Surrounding vacuum \n",
+      "V1: sample_geometry  Material: Sample   P0:  P1: Sample_pow\n",
+      "V2: sample_container  Material: Al   P0:  P1: Al_pow\n",
+      "V3: cryostat_wall  Material: Al   P0:  P1: Al_pow\n",
+      "V4: cryostat_wall_vacuum  Material: Vacuum  \n",
+      "----- Histories sorted after intensity ----------------------------------------------------------------------------------\n",
+      "5892\t N I=4.578027E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "895\t N I=2.001013E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "143\t N I=3.463667E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "108\t N I=2.251620E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "66\t N I=1.949978E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "65\t N I=1.194341E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "37\t N I=7.657474E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "14\t N I=3.683233E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "19\t N I=3.455680E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "17\t N I=2.855082E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "13\t N I=2.822139E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "9\t N I=2.778669E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "17\t N I=2.373749E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "18\t N I=2.297456E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.200556E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "11\t N I=1.857153E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "10\t N I=1.571294E-01 \t V0 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "6\t N I=9.084604E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "5\t N I=8.596945E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "4\t N I=8.405909E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "4\t N I=7.833313E-02 \t V0 -> V3 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "4\t N I=7.512586E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "5\t N I=7.432999E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "3\t N I=6.171041E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "5\t N I=5.967012E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "2\t N I=4.201788E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=4.079794E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "2\t N I=3.763233E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=3.552621E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "2\t N I=3.316035E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "2\t N I=3.311965E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "2\t N I=3.308331E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "2\t N I=3.307357E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.793967E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.790450E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.762920E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "2\t N I=2.734459E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "2\t N I=2.732315E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.380510E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "2\t N I=2.150892E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.923950E-02 \t V0 -> V3 -> P0 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.921815E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P0 -> V0 \n",
+      "1\t N I=1.921198E-02 \t V0 -> V3 -> V4 -> V2 -> P0 -> V4 -> V3 -> V0 \n",
+      "3\t N I=1.884439E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.811470E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=1.658620E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.657351E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
+      "1\t N I=1.654961E-02 \t V0 -> V3 -> P1 -> P1 -> V0 \n",
+      "1\t N I=1.653411E-02 \t V0 -> V3 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "5\t N I=1.536141E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.479142E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "2\t N I=1.377721E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
+      "1\t N I=1.072312E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "3\t N I=6.601606E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=4.722113E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=4.516881E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=4.340485E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
+      "1\t N I=3.643161E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=3.424502E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
+      "2\t N I=2.831503E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=2.169313E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=1.570388E-03 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "4\t N I=1.393536E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=4.412262E-04 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "1\t N I=7.660815E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"run_folder/union_history.dat\") as file:\n",
+    "    instrument_string = file.read()\n",
+    "    print(instrument_string)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpreting the data\n",
+    "Now all the histories contain a scattering process as this is necessary to reach the rectangle placed by the Union_conditional_PSD component set at 2theta = 60 deg. We also observe the intensities are much lower, simply because only a fraction of the simulated rays satisfy this condition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/tutorial/data_folder/note.txt b/docs/source/tutorial/data_folder/note.txt
new file mode 100644
index 00000000..fa985a4a
--- /dev/null
+++ b/docs/source/tutorial/data_folder/note.txt
@@ -0,0 +1 @@
+This folder will contain data files from McStas simulations performed in the notebook.
diff --git a/docs/source/tutorial/run_folder/note.txt b/docs/source/tutorial/run_folder/note.txt
new file mode 100644
index 00000000..1a8cb02d
--- /dev/null
+++ b/docs/source/tutorial/run_folder/note.txt
@@ -0,0 +1,2 @@
+McStas components, library functions and data files placed here can be used in tutorial notebooks.
+This folder will also contain generated instrument files.
diff --git a/docs/source/user_guide/component_object.ipynb b/docs/source/user_guide/component_object.ipynb
new file mode 100644
index 00000000..e953898b
--- /dev/null
+++ b/docs/source/user_guide/component_object.ipynb
@@ -0,0 +1,808 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "valid-array",
+   "metadata": {},
+   "source": [
+    "# Component object\n",
+    "McStas components are the essential building blocks used to make McStas simulations. This sections focuses on how to work with the component objects in McStasScript.\n",
+    "\n",
+    "Since a component is always part of an instrument, new components are created in the instrument object so that they may immediately be registred and controlled by that instrument object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "widespread-maple",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr\n",
+    "\n",
+    "instrument = instr.McStas_instr(\"component_examples\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "piano-anderson",
+   "metadata": {},
+   "source": [
+    "## Creating a component object\n",
+    "A component object is returned from the *add_component* and *copy_component* instrument object methods. When creating a component object, it needs an instance name and the name of the component in the library. When copying a component, the same library component is used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "documentary-import",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT source = Source_div\n",
+      "  \u001b[1mxwidth\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1myheight\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1mfocus_aw\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1mfocus_ah\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "AT [0, 0, 0] ABSOLUTE\n"
+     ]
+    }
+   ],
+   "source": [
+    "source = instrument.add_component(\"source\", \"Source_div\")\n",
+    "print(source)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "confidential-active",
+   "metadata": {},
+   "source": [
+    "## Setting component parameters\n",
+    "Since the underlying McStas component is read, McStasScript is aware of the parameters, including which are required from the user. These can be set directly as attributes or with the *set_parameters* method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "dynamic-holmes",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT source = Source_div\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_aw\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1mfocus_ah\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "AT [0, 0, 0] ABSOLUTE\n"
+     ]
+    }
+   ],
+   "source": [
+    "source.xwidth = 0.1\n",
+    "source.set_parameters(yheight=0.03)\n",
+    "print(source)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "naked-harbor",
+   "metadata": {},
+   "source": [
+    "Trying to set a parameter that does not exist would lead to an error. The full list of parameters in a component can be shown with *show_parameters*. This also show the current state of the component, including default values and values set by the user."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "respective-spectrum",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Source_div ________________________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[4m\u001b[1mxwidth\u001b[0m\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m] // Width of source\n",
+      "\u001b[4m\u001b[1myheight\u001b[0m\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m] // Height of source\n",
+      "\u001b[4m\u001b[1mfocus_aw\u001b[0m\u001b[0m [deg] // FWHM (Gaussian) or maximal (uniform) horz. width divergence\n",
+      "\u001b[4m\u001b[1mfocus_ah\u001b[0m\u001b[0m [deg] // FWHM (Gaussian) or maximal (uniform) vert. height divergence\n",
+      "\u001b[1mE0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Mean energy of neutrons.\n",
+      "\u001b[1mdE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Energy half spread of neutrons.\n",
+      "\u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Ang] // Mean wavelength of neutrons (only relevant for E0=0)\n",
+      "\u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Ang] // Wavelength half spread of neutrons.\n",
+      "\u001b[1mgauss\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [0|1] // Criterion\n",
+      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1/(s cm 2 st energy_unit)] // flux per energy unit, Angs or meV\n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "source.show_parameters()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "liable-spokesman",
+   "metadata": {},
+   "source": [
+    "## Setting the component position and rotation\n",
+    "The default location of a component is at the origin of the absoslute coordinate system used in McStas. The placement of components in space is very important, and McStas provides easy coordinate transfers when placing each component, which are accessible from McStasScript. In McStas one specifies the position with **AT** and orientation with **ROTATED**. Each of these can be relative to a reference, which is set with **RELATIVE**. This behavior is adopted in McStasScript where *set_AT* and *set_ROTATED* methods are available.\n",
+    "\n",
+    "If for example we want to shift our source a bit horizontally, we would use a list as a vector. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "respiratory-springer",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT source = Source_div\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_aw\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1mfocus_ah\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "AT [0.02, 0, 0] ABSOLUTE\n"
+     ]
+    }
+   ],
+   "source": [
+    "source.set_AT([0.02, 0, 0])\n",
+    "print(source)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "twelve-roommate",
+   "metadata": {},
+   "source": [
+    "In McStas convention the beam direction is along z, and it is common to place components some distance down beam, and so if a number is given instead of a list, it is assumed to be along z."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "compatible-romance",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT source = Source_div\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_aw\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1mfocus_ah\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "AT [0, 0, 0.01] ABSOLUTE\n"
+     ]
+    }
+   ],
+   "source": [
+    "source.set_AT(0.01)\n",
+    "print(source)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "wrong-garbage",
+   "metadata": {},
+   "source": [
+    "The position and rotation can also be specified when creating a new component with the *AT* and *RELATIVE* keywords. With the **RELATIVE** option we are now in the coordinate system of the reference component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "familiar-joshua",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT PSD = PSD_monitor\n",
+      "AT [0, 0, 2] RELATIVE source\n"
+     ]
+    }
+   ],
+   "source": [
+    "monitor = instrument.add_component(\"PSD\", \"PSD_monitor\", AT=[0, 0, 2], RELATIVE=source)\n",
+    "print(monitor)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "expressed-supplement",
+   "metadata": {},
+   "source": [
+    "Setting the rotation is similar with the *set_ROTATED* method. The rotation is specified with euler angles in degrees."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "recreational-overhead",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT PSD = PSD_monitor\n",
+      "AT [0, 0, 2] RELATIVE source\n",
+      "ROTATED [0, 4, 0] RELATIVE source\n"
+     ]
+    }
+   ],
+   "source": [
+    "monitor.set_ROTATED([0, 4, 0])\n",
+    "print(monitor)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "prepared-harrison",
+   "metadata": {},
+   "source": [
+    "Here McStasScript assumed that the rotation should be relative to the source, but it is possible to have another reference for the rotation as shown here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "automotive-racing",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT PSD = PSD_monitor\n",
+      "AT [0, 0, 2] RELATIVE source\n",
+      "ROTATED [0, 4, 0] ABSOLUTE\n"
+     ]
+    }
+   ],
+   "source": [
+    "monitor.set_ROTATED([0, 4, 0], RELATIVE=\"ABSOLUTE\")\n",
+    "print(monitor)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tired-breakfast",
+   "metadata": {},
+   "source": [
+    "When setting both position and rotation at initialization one can distinguish between the reference for position and rotation with the *AT_RELATIVE* and *ROTATED_RELATIVE* keywords."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "beautiful-marking",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT guide = Guide\n",
+      "  \u001b[1mw1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1mh1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "  \u001b[1ml\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "AT [0, 0, 2] RELATIVE source\n",
+      "ROTATED [0, 0, 0] RELATIVE PSD\n"
+     ]
+    }
+   ],
+   "source": [
+    "guide = instrument.add_component(\"guide\", \"Guide\",\n",
+    "                                 AT=2, AT_RELATIVE=source, \n",
+    "                                 ROTATED=[0,0,0], ROTATED_RELATIVE=monitor)\n",
+    "print(guide)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "opened-athletics",
+   "metadata": {},
+   "source": [
+    "## Using parameters in a component\n",
+    "It is common to use defined parameters and declare variables in a component, and they can even be defined directly where needed. The parameter section of the documentation explain the details of setting up parameters, but here it is shown how they are used in components.\n",
+    "\n",
+    "After creating a parameter, it can be used when assigning component parameters. Just use a string with the same name, and it can even contain basic math."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "better-massachusetts",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT guide = Guide\n",
+      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92mguide_width\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m1.5*guide_width\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1ml\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
+      "AT [0, 0, 2] RELATIVE source\n",
+      "ROTATED [0, 0, 0] RELATIVE PSD\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.add_parameter(\"guide_width\")\n",
+    "guide.w1 = \"guide_width\"\n",
+    "guide.h1 = \"1.5*guide_width\"\n",
+    "print(guide)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "capable-touch",
+   "metadata": {},
+   "source": [
+    "When creating a parameter or variable, an object is returned which can be brovided to the component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "accomplished-currency",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT guide = Guide\n",
+      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92mguide_width\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m1.5*guide_width\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92mguide_length\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92mguide_m_value\u001b[0m\u001b[0m [1]\n",
+      "AT [0, 0, 2] RELATIVE source\n",
+      "ROTATED [0, 0, 0] RELATIVE PSD\n"
+     ]
+    }
+   ],
+   "source": [
+    "guide_length = instrument.add_declare_var(\"double\", \"guide_length\")\n",
+    "guide.l = guide_length\n",
+    "\n",
+    "guide.m = instrument.add_parameter(\"guide_m_value\")\n",
+    "\n",
+    "print(guide)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "large-auditor",
+   "metadata": {},
+   "source": [
+    "These features cover the majority of usecases for McStas components."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "discrete-celebrity",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "// This guide points in the same direction as the PSD monitor\n",
+      "COMPONENT guide = Guide\n",
+      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92mguide_width\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m1.5*guide_width\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92mguide_length\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92mguide_m_value\u001b[0m\u001b[0m [1]\n",
+      "AT [0, 0, 2] RELATIVE source\n",
+      "ROTATED [0, 0, 0] RELATIVE PSD\n"
+     ]
+    }
+   ],
+   "source": [
+    "guide.set_comment(\"This guide points in the same direction as the PSD monitor\")\n",
+    "print(guide)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "removable-national",
+   "metadata": {},
+   "source": [
+    "## Advanced features\n",
+    "In McStas / McXtrace there are a number of advanced features which can be used to control component behavior in the simulation. In the tutorial section these keywords are explained and demonstrated, here it is shown how they are applied. Most of these can be set when creating a component or through component methods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "technological-garden",
+   "metadata": {},
+   "source": [
+    "### SPLIT\n",
+    "The split keyword instructs the simulation to split the ray into multiple smaller rays to improve statistis. This is done before the component that has the SPLIT keyword applied. The split value must be an integer. Use the SPLIT keyword to set split while creating a new component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "equal-tactics",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "// This guide points in the same direction as the PSD monitor\n",
+      "SPLIT 150 COMPONENT guide = Guide\n",
+      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92mguide_width\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m1.5*guide_width\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92mguide_length\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92mguide_m_value\u001b[0m\u001b[0m [1]\n",
+      "AT [0, 0, 2] RELATIVE source\n",
+      "ROTATED [0, 0, 0] RELATIVE PSD\n"
+     ]
+    }
+   ],
+   "source": [
+    "guide.set_SPLIT(150)\n",
+    "print(guide)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "adequate-small",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SPLIT 180 COMPONENT Splitter = Arm\n",
+      "AT [0, 0, 0] ABSOLUTE\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(instrument.add_component(\"Splitter\", \"Arm\", SPLIT=180))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "registered-roommate",
+   "metadata": {},
+   "source": [
+    "The split keyword can be removed from a component by setting split to 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "french-bangladesh",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "// This guide points in the same direction as the PSD monitor\n",
+      "COMPONENT guide = Guide\n",
+      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92mguide_width\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m1.5*guide_width\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92mguide_length\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92mguide_m_value\u001b[0m\u001b[0m [1]\n",
+      "AT [0, 0, 2] RELATIVE source\n",
+      "ROTATED [0, 0, 0] RELATIVE PSD\n"
+     ]
+    }
+   ],
+   "source": [
+    "guide.set_SPLIT(0)\n",
+    "print(guide)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "opened-stewart",
+   "metadata": {},
+   "source": [
+    "### EXTEND\n",
+    "A component can be extended with additional C code that has a special scope. It can access both parameters and variable from the component, and the instrument. Lines of extend code can be added with the *append_EXTEND* method. Beware that this code is executed for each neutron that reach the component and during the ray-tracing simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "academic-outside",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "// This guide points in the same direction as the PSD monitor\n",
+      "COMPONENT guide = Guide\n",
+      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92mguide_width\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m1.5*guide_width\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92mguide_length\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92mguide_m_value\u001b[0m\u001b[0m [1]\n",
+      "AT [0, 0, 2] RELATIVE source\n",
+      "ROTATED [0, 0, 0] RELATIVE PSD\n",
+      "EXTEND %{\n",
+      "  if (x>0) flag = 1;\n",
+      "  else flag = 0;\n",
+      "%}\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.add_declare_var(\"int\", \"flag\")\n",
+    "guide.append_EXTEND(\"  if (x>0) flag = 1;\")\n",
+    "guide.append_EXTEND(\"  else flag = 0;\")\n",
+    "print(guide)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "initial-federation",
+   "metadata": {},
+   "source": [
+    "### WHEN\n",
+    "The WHEN keyword can be used to set a condition for when a component should be applied to the ray. The input for a WHEN condition is a logical c expression that can use variables in the instrument scope. Since just the expression is needed, there is no terminating semicolon.\n",
+    "\n",
+    "Below a monitor is created which checks the flag defined during the extend example. This monitor will thus only record rays that had a positive x coordinate after the guide. WHEN and EXTEND are often used in conjuction with each other in this way, this is also explored in the tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "flying-olympus",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT Left_side_PSD = PSD_monitor\n",
+      "WHEN (flag == 1)\n",
+      "AT [0, 0, 0] ABSOLUTE\n"
+     ]
+    }
+   ],
+   "source": [
+    "left_PSD = instrument.add_component(\"Left_side_PSD\", \"PSD_monitor\")\n",
+    "left_PSD.set_WHEN(\"flag == 1\")\n",
+    "print(left_PSD)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "honey-notebook",
+   "metadata": {},
+   "source": [
+    "The WHEN keyword can also be applied when adding a component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "immediate-ivory",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT Right_side_PSD = PSD_monitor\n",
+      "WHEN (flag == 0)\n",
+      "AT [0, 0, 0] ABSOLUTE\n"
+     ]
+    }
+   ],
+   "source": [
+    "right_PSD = instrument.add_component(\"Right_side_PSD\", \"PSD_monitor\", WHEN=\"flag == 0\")\n",
+    "print(right_PSD)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cutting-spring",
+   "metadata": {},
+   "source": [
+    "### JUMP\n",
+    "The JUMP keyword allows the user to modify the order in which components are executed on a per neutron level. Using JUMP is complex with several pitfalls, but the syntax is simple. The system is explored in the tutorial on this site, but for full documentation refer to the McStas / McXtrace documentation.\n",
+    "\n",
+    "Here the jump keyword is used to jump from the guide component to the target_arm in case the y component of the velocity is positive. That would skip the monitors!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "seven-kingdom",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "// This guide points in the same direction as the PSD monitor\n",
+      "COMPONENT guide = Guide\n",
+      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92mguide_width\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m1.5*guide_width\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92mguide_length\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92mguide_m_value\u001b[0m\u001b[0m [1]\n",
+      "AT [0, 0, 2] RELATIVE source\n",
+      "ROTATED [0, 0, 0] RELATIVE PSD\n",
+      "EXTEND %{\n",
+      "  if (x>0) flag = 1;\n",
+      "  else flag = 0;\n",
+      "%}\n",
+      "JUMP target_arm WHEN (vy>0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.add_component(\"target_arm\", \"Arm\", RELATIVE=guide)\n",
+    "guide.set_JUMP(\"target_arm WHEN (vy>0)\")\n",
+    "print(guide)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "valid-latitude",
+   "metadata": {},
+   "source": [
+    "### GROUP\n",
+    "The GROUP keyword allows the user to set a number of components in parallel, but only allows a ray to interact with one of them. When two components have the same group name, this behavior is applied."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "consecutive-request",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT mono_1 = Monochromator_flat\n",
+      "AT [0.01, 0, 5] ABSOLUTE\n",
+      "GROUP monochromator\n",
+      "COMPONENT mono_2 = Monochromator_flat\n",
+      "AT [-0.01, 0, 5] ABSOLUTE\n",
+      "GROUP monochromator\n"
+     ]
+    }
+   ],
+   "source": [
+    "crystal_1 = instrument.add_component(\"mono_1\", \"Monochromator_flat\")\n",
+    "crystal_1.set_GROUP(\"monochromator\")\n",
+    "\n",
+    "crystal_2 = instrument.add_component(\"mono_2\", \"Monochromator_flat\", GROUP=\"monochromator\")\n",
+    "\n",
+    "# Place two crystals in parallel\n",
+    "crystal_1.set_AT([0.01, 0, 5]) \n",
+    "crystal_2.set_AT([-0.01, 0, 5])\n",
+    "\n",
+    "print(crystal_1)\n",
+    "print(crystal_2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "educated-commons",
+   "metadata": {},
+   "source": [
+    "### Set c code\n",
+    "It is possible to add C code between components in the instrument trace section. In McStasScript this is attached to component objects as one can insert code before or after a component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "assigned-content",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "// code before\n",
+      "COMPONENT code_arm = Arm\n",
+      "AT [0, 0, 0] ABSOLUTE\n",
+      "// code after\n"
+     ]
+    }
+   ],
+   "source": [
+    "code_arm = instrument.add_component(\"code_arm\", \"Arm\", c_code_before=\"// code before\")\n",
+    "code_arm.set_c_code_after(\"// code after\")\n",
+    "print(code_arm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "verbal-gibson",
+   "metadata": {},
+   "source": [
+    "Here this is used to set comments, but there is also the option of setting a comment directly with the comment keyword."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "nuclear-bottle",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "// This is a comment!\n",
+      "COMPONENT commented_component = Arm\n",
+      "AT [0, 0, 0] ABSOLUTE\n"
+     ]
+    }
+   ],
+   "source": [
+    "commented_component = instrument.add_component(\"commented_component\", \"Arm\", comment=\"This is a comment!\")\n",
+    "print(commented_component)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "stable-blond",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/user_guide/instrument_object.ipynb b/docs/source/user_guide/instrument_object.ipynb
new file mode 100644
index 00000000..433a6177
--- /dev/null
+++ b/docs/source/user_guide/instrument_object.ipynb
@@ -0,0 +1,819 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "important-midwest",
+   "metadata": {},
+   "source": [
+    "# Instrument object\n",
+    "This section shows the majority of the features implemented for the instrument object in McStasScript."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eastern-distance",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "When an instrument object is created the only required argument is the name of the instrument which will be used for the instrument filename. There are however a number of keyword arguments that can be used to provide more informaton and alter the behavior.\n",
+    "\n",
+    "| Keyword argument | Type | Default | Description |\n",
+    "| --- | --- | --- | --- |\n",
+    "| author | str |\"Python Instrument Generator\" | Name that will apear as author in instrument files |\n",
+    "| origin | str |\"ESS DMSC\" | String that will appear as origin in instrument files |\n",
+    "| input_path | str | \".\" | Folder which is considered workspace for McStas / McXtrace |\n",
+    "| output_path | str | instrument_name | Name of data folder written by simulation |\n",
+    "| package_path | str | | Can be set to manually specify location of McStas/McXtrace installation |\n",
+    "| executable_path | str |  | Can be set to manually specify location of mcrun/mxrun executable |\n",
+    "| ncount | int, float | 1E6 | Sets the ncount used for simulations |\n",
+    "| mpi | int |  | Sets the number of MPI threads used for simulations |\n",
+    "| force_compile | bool | True | Whether to force compilation before each run or not |\n",
+    "| parameters | ParameterContainer |  | Set of parameters for initialized instrument |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "liable-newton",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "experienced-locator",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument = instr.McStas_instr(\"instr_name\", author=\"Mads Bertelsen\", origin=\"DMSC\")\n",
+    "instrument_w_settings = instr.McStas_instr(\"instr_name\", ncount=3E6, output_path=\"new_folder\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "universal-saudi",
+   "metadata": {},
+   "source": [
+    "### Using settings method\n",
+    "The instrument object has a settings method which can update some settings after initialization. The current settings can always be viewed with *show_settings*.\n",
+    "\n",
+    "| Keyword argument | Type | Default | Description |\n",
+    "| --- | --- | --- | --- |\n",
+    "| output_path | str | instrument_name | Name of data folder written by simulation |\n",
+    "| package_path | str | | Can be set to manually specify location of McStas/McXtrace installation |\n",
+    "| executable_path | str |  | Can be set to manually specify location of mcrun/mxrun executable |\n",
+    "| ncount | int, float | 1E6 | Sets the ncount used for simulations |\n",
+    "| mpi | int |  | Sets the number of MPI threads used for simulations |\n",
+    "| seed |  |  | Sets the seed of the simulation |\n",
+    "| force_compile | bool | True | Whether to force compilation before each run or not |\n",
+    "| custom_flags | str |  | String with custom flags for mcrun/mxrun command |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "sapphire-pasta",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Instrument settings:\n",
+      "  output_path:      instr_name_data\n",
+      "  run_path:         .\n",
+      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
+      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
+      "  executable:       mcrun\n",
+      "  force_compile:    True\n",
+      "Instrument settings:\n",
+      "  ncount:           3.00e+06\n",
+      "  output_path:      new_folder\n",
+      "  run_path:         .\n",
+      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
+      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
+      "  executable:       mcrun\n",
+      "  force_compile:    True\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.show_settings()\n",
+    "instrument_w_settings.show_settings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "assumed-boundary",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Instrument settings:\n",
+      "  mpi:              4\n",
+      "  seed:             300\n",
+      "  output_path:      instr_name_data\n",
+      "  run_path:         .\n",
+      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
+      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
+      "  executable:       mcrun\n",
+      "  force_compile:    True\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.settings(mpi=4, seed=300)\n",
+    "instrument.show_settings()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unsigned-ideal",
+   "metadata": {},
+   "source": [
+    "## Parameters\n",
+    "Instrument parameters can be added with *add_parameters* which returns a parameter object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "signed-distributor",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parameter named: 'wavelength' without set value.\n",
+      " Wavelength in AA\n",
+      "\n",
+      "Parameter named: 'wavelength' with value: 5\n",
+      " Wavelength in AA\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "wavelength = instrument.add_parameter(\"wavelength\", comment=\"Wavelength in AA\")\n",
+    "print(wavelength)\n",
+    "wavelength.value = 5\n",
+    "print(wavelength)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fourth-glasgow",
+   "metadata": {},
+   "source": [
+    "## Initialize section\n",
+    "One of the great advantages for the McStas / McXtrace packages is the initialize section of the instrument where calculations can be performed before the ray-tracing simulation starts. One could for example calculate appropriate angles to reach a certain bragg peak at a given wavelength. This would involve defining some declare variables, using these in the initialize section and then assigning them as component inputs.\n",
+    "\n",
+    "In McStasScript many calculations can be performed directly in Python, and so typically the initialize section is used less, but it is still useful and available through McStasScript.\n",
+    "\n",
+    "The instrument object has the method *append_initialize* which adds a line of code to the initialize. This line is copied directly into the instrument file, so it follows C syntax. Remember the semicolon! In addition there is *add_declare_var* to specify the declared variables needed. When declare variables are defined an object is returned which can be used when refering to that variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "spare-button",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wavenumber = instrument.add_declare_var(\"double\", \"wavenumber\")\n",
+    "instrument.append_initialize(\"wavenumber = 2*PI/wavelength;\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "twelve-tuition",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "// Start of initialize for generated instr_name\n",
+      "wavenumber = 2*PI/wavelength;\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(instrument.initialize_section)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "occupational-budget",
+   "metadata": {},
+   "source": [
+    "## Finally section\n",
+    "The finally section works exactly as the initialize section, but is executed after the ray-tracing simulation. Add a line to it with *append_finally*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "cultural-workplace",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "// Start of finally for generated instr_name\n",
+      "printf(\"Thanks for using McStasScript!\\n\");\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.append_finally('printf(\\\"Thanks for using McStasScript!\\\\n\\\");')\n",
+    "print(instrument.finally_section)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "threaded-double",
+   "metadata": {},
+   "source": [
+    "## Help features\n",
+    "There are a few methods built into the instrument class that helps the user, these are:\n",
+    "\n",
+    "- *show_components*\n",
+    "- *component_help*\n",
+    "\n",
+    "### show_components\n",
+    "The *show_components* method shows the component categories, and if called with the name of a category, will show all components in the specified category. The categories can include the work directory if any components are located there."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "foreign-chosen",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are the available component categories:\n",
+      " contrib\n",
+      " misc\n",
+      " monitors\n",
+      " obsolete\n",
+      " optics\n",
+      " samples\n",
+      " sources\n",
+      " union\n",
+      "Call show_components(category_name) to display\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.show_components()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "regulated-hollywood",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are all components in the optics category.\n",
+      " Absorber                 Guide_gravity          Pol_bender\n",
+      " Arm                      Guide_simple           Pol_constBfield\n",
+      " Beamstop                 Guide_tapering         Pol_guide_mirror\n",
+      " Bender                   Guide_wavy             Pol_guide_vmirror\n",
+      " Collimator_linear        He3_cell               Pol_mirror\n",
+      " Collimator_radial        Mask                   Refractor\n",
+      " Derotator                Mirror                 Rotator\n",
+      " Diaphragm                Monochromator_curved   Selector\n",
+      " DiskChopper              Monochromator_flat     Set_pol\n",
+      " Elliptic_guide_gravity   Monochromator_pol      Slit\n",
+      " FermiChopper             PolAnalyser_ideal      V_selector\n",
+      " Filter_gen               Pol_Bfield             Virtual_mcnp_ss_Guide\n",
+      " Guide                    Pol_Bfield_stop        Vitess_ChopperFermi\n",
+      " Guide_anyshape           Pol_FieldBox           \n",
+      " Guide_channeled          Pol_SF_ideal           \n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.show_components(\"optics\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ultimate-junction",
+   "metadata": {},
+   "source": [
+    "### component_help\n",
+    "The *component_help* method can show the parameters of any component the instrument object knows about, although not necessarily used in the instrument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "dominican-dubai",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Guide _____________________________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1mreflect\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [str] // Reflectivity file name. Format <q(Angs-1) R(0-1)>\n",
+      "\u001b[4m\u001b[1mw1\u001b[0m\u001b[0m [m] // Width at the guide entry\n",
+      "\u001b[4m\u001b[1mh1\u001b[0m\u001b[0m [m] // Height at the guide entry\n",
+      "\u001b[1mw2\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Width at the guide exit\n",
+      "\u001b[1mh2\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Height at the guide exit\n",
+      "\u001b[4m\u001b[1ml\u001b[0m\u001b[0m [m] // length of guide\n",
+      "\u001b[1mR0\u001b[0m = \u001b[1m\u001b[94m0.99\u001b[0m\u001b[0m [1] // Low-angle reflectivity\n",
+      "\u001b[1mQc\u001b[0m = \u001b[1m\u001b[94m0.0219\u001b[0m\u001b[0m [AA-1] // Critical scattering vector\n",
+      "\u001b[1malpha\u001b[0m = \u001b[1m\u001b[94m6.07\u001b[0m\u001b[0m [AA] // Slope of reflectivity\n",
+      "\u001b[1mm\u001b[0m = \u001b[1m\u001b[94m2.0\u001b[0m\u001b[0m [1] // m-value of material. Zero means completely absorbing. glass/SiO2 \n",
+      "               Si Ni Ni58 supermirror Be Diamond m=  0.65 0.47 1 1.18 2-6 1.01 1.12 \n",
+      "\u001b[1mW\u001b[0m = \u001b[1m\u001b[94m0.003\u001b[0m\u001b[0m [AA-1] // Width of supermirror cut-off\n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.component_help(\"Guide\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "studied-victorian",
+   "metadata": {},
+   "source": [
+    "## Adding components\n",
+    "One adds components to the instrument using *add_component* which takes the name of the component instance for the instrument, followed by the name of the component in the library. Notice that it is not allowed to add two components with the same instance name, meaning rerunning this cell would raise an exception. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "literary-nirvana",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source = instrument.add_component(\"source\", \"Source_div\")\n",
+    "source.set_parameters(xwidth=0.1, yheight=0.1, focus_aw=3.0, focus_ah=2.0, \n",
+    "                      lambda0=wavelength, dlambda=\"0.1*wavelength\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "loving-raleigh",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT source = Source_div\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_aw\u001b[0m = \u001b[1m\u001b[92m3.0\u001b[0m\u001b[0m [deg]\n",
+      "  \u001b[1mfocus_ah\u001b[0m = \u001b[1m\u001b[92m2.0\u001b[0m\u001b[0m [deg]\n",
+      "  \u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[92mwavelength\u001b[0m\u001b[0m [Ang]\n",
+      "  \u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[92m0.1*wavelength\u001b[0m\u001b[0m [Ang]\n",
+      "AT [0, 0, 0] ABSOLUTE\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(source)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "unnecessary-helen",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "source Source_div AT (0, 0, 0) ABSOLUTE\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.print_components()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "outdoor-motor",
+   "metadata": {},
+   "source": [
+    "There are a number of keyword arguments allowed when adding a component. These will mainly be discussed in connection to the *component* object, but a few are relevant for the instrument, because the handle in what order components are sequenced in the instrument. To illustrate this we add a slit and a guide to the instrument at reasonable positions. Notice these new components are added at the end of the instrument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "informative-council",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slit = instrument.add_component(\"source_slit\", \"Slit\", AT=2, RELATIVE=source)\n",
+    "slit.set_parameters(xwidth=0.015, yheight=0.015)\n",
+    "\n",
+    "guide = instrument.add_component(\"guide\", \"Guide\", AT=0.1, RELATIVE=slit)\n",
+    "guide.set_parameters(w1=0.03, h1=0.03, l=10.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "moral-apartment",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "source      Source_div AT      (0, 0, 0)   ABSOLUTE            \n",
+      "source_slit Slit       AT      (0, 0, 2)   RELATIVE source     \n",
+      "guide       Guide      AT      (0, 0, 0.1) RELATIVE source_slit\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.print_components()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "least-circulation",
+   "metadata": {},
+   "source": [
+    "The order of components is important in a McStas/McXtrace simulation as each will affect the ray state in the sequence shown with *print_components*. If one wants to add a component between the source and the slit, this can be done with the *before* or *after* keyword."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "absent-pizza",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "source      Source_div  AT      (0, 0, 0)   ABSOLUTE            \n",
+      "PSD         PSD_monitor AT      (0, 0, 1.9) RELATIVE source     \n",
+      "source_slit Slit        AT      (0, 0, 2)   RELATIVE source     \n",
+      "guide       Guide       AT      (0, 0, 0.1) RELATIVE source_slit\n"
+     ]
+    }
+   ],
+   "source": [
+    "monitor = instrument.add_component(\"PSD\", \"PSD_monitor\", after=\"source\")\n",
+    "monitor.set_AT(1.9, RELATIVE=source)\n",
+    "monitor.set_parameters(xwidth=0.1, yheight=0.1, filename='\"PSD.dat\"')\n",
+    "\n",
+    "instrument.print_components()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "municipal-prior",
+   "metadata": {},
+   "source": [
+    "The PSD monitor was inserted after the source, this could also be accomplished with the before keyword argument.\n",
+    "```\n",
+    "before=\"source_slit\"\n",
+    "```\n",
+    "It is important to note that the McStas instrument file is read sequentially, so the position of the PSD monitor can not be relative to a later component, but must only refer to earlier components. At this point in development it is not possible to reorder components in the instrument object."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "distinct-thousand",
+   "metadata": {},
+   "source": [
+    "## Making a component copy\n",
+    "It is possible to copy an existing component using the *copy_component* method. This can reduce both the amount of typing necessary, but also the risk of making a mistake. Here the guide is copied and placed a bit after the end of the first guide, with a small rotation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "figured-electronics",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT guide_2 = Guide\n",
+      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92m10.0\u001b[0m\u001b[0m [m]\n",
+      "AT [0, 0, 10.01] RELATIVE guide\n",
+      "ROTATED [0, 0.5, 0] RELATIVE guide\n"
+     ]
+    }
+   ],
+   "source": [
+    "guide2 = instrument.copy_component(\"guide_2\", \"guide\")\n",
+    "guide2.set_AT(guide.l + 0.01, RELATIVE=guide)\n",
+    "guide2.set_ROTATED([0, 0.5, 0], RELATIVE=guide)\n",
+    "print(guide2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "parental-theme",
+   "metadata": {},
+   "source": [
+    "## Getting components"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hidden-shopping",
+   "metadata": {},
+   "source": [
+    "It is always possible to retrieve the component objects corresponding to components in the instrument with the *get_component* and *get_last_component* methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "military-biotechnology",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT source = Source_div\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_aw\u001b[0m = \u001b[1m\u001b[92m3.0\u001b[0m\u001b[0m [deg]\n",
+      "  \u001b[1mfocus_ah\u001b[0m = \u001b[1m\u001b[92m2.0\u001b[0m\u001b[0m [deg]\n",
+      "  \u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[92mwavelength\u001b[0m\u001b[0m [Ang]\n",
+      "  \u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[92m0.1*wavelength\u001b[0m\u001b[0m [Ang]\n",
+      "AT [0, 0, 0] ABSOLUTE\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_source = instrument.get_component(\"source\")\n",
+    "print(my_source)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "ambient-naples",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT guide_2 = Guide\n",
+      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92m10.0\u001b[0m\u001b[0m [m]\n",
+      "AT [0, 0, 10.01] RELATIVE guide\n",
+      "ROTATED [0, 0.5, 0] RELATIVE guide\n"
+     ]
+    }
+   ],
+   "source": [
+    "last_component = instrument.get_last_component()\n",
+    "print(last_component)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cardiovascular-hughes",
+   "metadata": {},
+   "source": [
+    "## Run the simulation\n",
+    "The simulation is executed with a call to the *backengine* method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "intense-chapter",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/user_guide/instr_name_data_6\"\n",
+      "INFO: Regenerating c-file: instr_name.c\n",
+      "CFLAGS=\n",
+      "INFO: Recompiling: ./instr_name.out\n",
+      "mccode-r.c:1880:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "} /* mcsiminfo_init */\n",
+      "^\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "2 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy instr_name.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/user_guide/instr_name_data_6\n",
+      "\n",
+      "Simulation 'instr_name' (instr_name.instr): running on 4 nodes (master is 'CI0021617', MPI version 3.1).\n",
+      "Detector: PSD_I=0.114429 PSD_ERR=0.000144614 PSD_N=626113 \"PSD.dat\"\n",
+      "Thanks for using McStasScript!\n",
+      "Thanks for using McStasScript!\n",
+      "Thanks for using McStasScript!\n",
+      "Thanks for using McStasScript!\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.backengine()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "spread-douglas",
+   "metadata": {},
+   "source": [
+    "## Get the data\n",
+    "The data is held in the data attribute of the instrument. If the simulation failed, or did not output any data, the data attribute will contain None."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "honey-conference",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "McStasData: PSD type: 2D  I:0.114429 E:0.000144614 N:626113]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = instrument.data\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "varying-clearing",
+   "metadata": {},
+   "source": [
+    "## Visualizing the instrument\n",
+    "It is possible to visualize the instrument using mcdisplay, yet this opens a new window and is not yet implemented in such a way that it is shown in the notebook. The *show_instrument* method is used, and one can choose the format. Be aware that the parameters set for the insturment is used for the visualization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "headed-simulation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.show_instrument()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "italic-madagascar",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.show_instrument(format=\"webgl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dressed-branch",
+   "metadata": {},
+   "source": [
+    "## Dump and load an instrument object\n",
+    "It is possible to save an instrument object to disk and load it later. For now the name is still a required parameter, but it is overwritten by the loading process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "casual-blame",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'dump_file_name.dmp'"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "instrument.dump(\"dump_file_name.dmp\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "advance-soldier",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded_instrument = instr.McStas_instr(\"\", dumpfile=\"dump_file_name.dmp\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "european-pepper",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "source      Source_div  AT      (0, 0, 0)     ABSOLUTE            \n",
+      "PSD         PSD_monitor AT      (0, 0, 1.9)   RELATIVE source     \n",
+      "source_slit Slit        AT      (0, 0, 2)     RELATIVE source     \n",
+      "guide       Guide       AT      (0, 0, 0.1)   RELATIVE source_slit\n",
+      "guide_2     Guide       AT      (0, 0, 10.01) RELATIVE guide       \n",
+      "                        ROTATED (0, 0.5, 0)   RELATIVE guide\n",
+      "Instrument settings:\n",
+      "  output_path:      instr_name_data\n",
+      "  run_path:         .\n",
+      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
+      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
+      "  executable:       mcrun\n",
+      "  force_compile:    True\n"
+     ]
+    }
+   ],
+   "source": [
+    "loaded_instrument.print_components()\n",
+    "loaded_instrument.show_settings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "independent-blend",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/user_guide/instrument_reader.ipynb b/docs/source/user_guide/instrument_reader.ipynb
new file mode 100644
index 00000000..8a95e5e4
--- /dev/null
+++ b/docs/source/user_guide/instrument_reader.ipynb
@@ -0,0 +1,42 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "stretch-villa",
+   "metadata": {},
+   "source": [
+    "# Instrument reader\n",
+    "How to use instrument reader (not it is unfinished)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "alpine-yeast",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/user_guide/parameters_and_variables.ipynb b/docs/source/user_guide/parameters_and_variables.ipynb
new file mode 100644
index 00000000..6e455703
--- /dev/null
+++ b/docs/source/user_guide/parameters_and_variables.ipynb
@@ -0,0 +1,506 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "smooth-monaco",
+   "metadata": {},
+   "source": [
+    "# Parameters and variables\n",
+    "At the instrument level there is a destingsion between parameters and variables. Parameters are inputs to the instrument that can be changed at run time, while variables are defined internally within the insturment. This section of the documentation covers these two as they have some similarities."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "strong-muslim",
+   "metadata": {},
+   "source": [
+    "## Parameters\n",
+    "Instrument parameters are built on the libpyvinyl parameters, and have some interesting features. First a simple parameter is added."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "acceptable-emperor",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parameter named: 'first_par' without set value.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter\n",
+    "instrument = instr.McStas_instr(\"parameters_and_variables\")\n",
+    "\n",
+    "first_par = instrument.add_parameter(\"first_par\")\n",
+    "print(first_par)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "alive-cholesterol",
+   "metadata": {},
+   "source": [
+    "It is a good habit to add a comment to each parameter when creating it. Since two parameters can not have the same name, creates "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "adverse-default",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "second_parameter = instrument.add_parameter(\"first_par\", comment=\"My first parameter!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "downtown-pittsburgh",
+   "metadata": {},
+   "source": [
+    "### Types\n",
+    "If only one argument is given, it is assumed the type is a C *double*, so a floating point number. It is also allowed to have integers and strings. No other types are supported by McStas / McXtrace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cellular-server",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       first_par           // My first parameter!\n",
+      "double double_par          // Typed double\n",
+      "int    int_par             // Typed int\n",
+      "string string_par          // Typed string\n"
+     ]
+    }
+   ],
+   "source": [
+    "double_par = instrument.add_parameter(\"double\", \"double_par\", comment=\"Typed double\")\n",
+    "int_par = instrument.add_parameter(\"int\", \"int_par\", comment=\"Typed int\")\n",
+    "string_par = instrument.add_parameter(\"string\", \"string_par\", comment=\"Typed string\")\n",
+    "\n",
+    "instrument.show_parameters()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "southern-shoulder",
+   "metadata": {},
+   "source": [
+    "### Value\n",
+    "It is common to set the value of a parameter when it is created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "rental-indication",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Parameter named: 'par_with_value' with value: 5.2\n",
+       " Added value at creation"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "instrument.add_parameter(\"par_with_value\", value=5.2, comment=\"Added value at creation\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "expressed-nancy",
+   "metadata": {},
+   "source": [
+    "The value can always be changed, either directly or through the instrument object. When using the instrument object, use the name in the instrument file as the keyword argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "narrow-guard",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "double_par.value = 1.2\n",
+    "instrument.set_parameters(int_par=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "thirty-sunrise",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       first_par               // My first parameter!\n",
+      "double double_par      = 1.2   // Typed double\n",
+      "int    int_par         = 3     // Typed int\n",
+      "string string_par              // Typed string\n",
+      "       par_with_value  = 5.2   // Added value at creation\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.show_parameters()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cardiac-apparel",
+   "metadata": {},
+   "source": [
+    "### Parameter restrictions\n",
+    "Since the parameter object in McStasScript is derived from libpyvinyl, some functionality is inherited in terms of setting parameter restrictions. This comes in the forms of intervals and options, which can both be legal and illegal.\n",
+    "\n",
+    "Here is an example of adding legal intervals to the *double_par* parameter. None can be used if the interval should extend to infinity in the given direction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "portuguese-circuit",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parameter named: 'double_par' with value: 1.2\n",
+      " Typed double\n",
+      "  Legal intervals:\n",
+      "    [0,5]\n",
+      "    [7,inf]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "double_par.add_interval(0, 5, intervals_are_legal=True)\n",
+    "double_par.add_interval(7, None, intervals_are_legal=True)\n",
+    "print(double_par)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "virtual-judges",
+   "metadata": {},
+   "source": [
+    "Now only values between 0 and 5 or from 7 to 8 are accepted. Trying to set a different value will raise a ValueError."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "unknown-edition",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parameter named: 'double_par' with value: 1.8\n",
+      " Typed double\n",
+      "  Legal intervals:\n",
+      "    [0,5]\n",
+      "    [7,inf]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "double_par.value = 1.8\n",
+    "print(double_par)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "familiar-church",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parameter named: 'double_par' with value: 10\n",
+      " Typed double\n",
+      "  Legal intervals:\n",
+      "    [0,5]\n",
+      "    [7,inf]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    double_par.value = 10\n",
+    "except:\n",
+    "    print(\"Failed to set value!\")\n",
+    "    \n",
+    "print(double_par)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "psychological-voluntary",
+   "metadata": {},
+   "source": [
+    "Setting *intervals_are_legal* to False means that only values outside the defined intervals are allowed.\n",
+    "\n",
+    "Options work in a similar way, but for specific values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "honey-theta",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Failed to set value!\n",
+      "Parameter named: 'int_par' with value: 3\n",
+      " Typed int\n",
+      "  Allowed values:\n",
+      "    1\n",
+      "    2\n",
+      "    3\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "int_par.add_option(1, options_are_legal=True)\n",
+    "int_par.add_option(2, options_are_legal=True)\n",
+    "int_par.add_option(3, options_are_legal=True)\n",
+    "\n",
+    "try:\n",
+    "    int_par.value = 10\n",
+    "except:\n",
+    "    print(\"Failed to set value!\")\n",
+    "    \n",
+    "print(int_par)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "worse-square",
+   "metadata": {},
+   "source": [
+    "Adding restrictions to parameters is a healthy habit that can make the produced instrument more resilient to errors. Ensure the given wavelength is a positive number to catch errors early instead of trying to understand what happened when a simulation fails."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "spread-parade",
+   "metadata": {},
+   "source": [
+    "## Declared variables\n",
+    "It is possible to declare variables that are used internally in the instrument and as such not exposed to the user. This is done thrugh the instrument objects *add_declare_var* method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "confidential-village",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Declare variable: 'declared_var' of type double\n"
+     ]
+    }
+   ],
+   "source": [
+    "declared_var = instrument.add_declare_var(\"double\", \"declared_var\", comment=\"Declared variable\")\n",
+    "print(declared_var)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "valuable-sigma",
+   "metadata": {},
+   "source": [
+    "There are no restrictions on the type for declared variables, typically double and int is used.\n",
+    "\n",
+    "Declared variables have no methods for additional input, all information must be given at initialization."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "capital-fraction",
+   "metadata": {},
+   "source": [
+    "The declared variable can be initialized to a given value by using the *value* keyword."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "valid-offense",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Declare variable: 'integer' of type int with value: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "declared_var = instrument.add_declare_var(\"int\", \"integer\", value=5, comment=\"Declared integer\")\n",
+    "print(declared_var)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "identified-butter",
+   "metadata": {},
+   "source": [
+    "Declared variables can also be one dimensional arrays using the *array* keyword. In the full McStas / McXtrace its possible to make arrays of any dimensionality but only one dimensional is supported in McStasScript. If a value of array is smaller than the length of the given value, the simulation will fail as it would write outside of the declared memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "polish-integration",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Declare variable: 'declared_array' of type double with value: [2, 3, 4]. Array with length 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "var = instrument.add_declare_var(\"double\", \"declared_array\", value=[2, 3, 4], array=3)\n",
+    "print(var)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "overall-differential",
+   "metadata": {},
+   "source": [
+    "The declared variables of the instrument can be displayed with *show_variables*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "after-reply",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "type    variable name   value      array length  \n",
+      "-----------------------------------------------\n",
+      "double  declared_var                             \n",
+      "int     integer         5                        \n",
+      "double  declared_array  [2, 3, 4]  3             \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.show_variables()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "affected-street",
+   "metadata": {},
+   "source": [
+    "## Using parameters and variables\n",
+    "Instrument parameters and variables are can both be used when setting component attributes. One can use either the name in a string, or the object directly. When using a string, it is allowed to do basic math and use several parameter / variables. It is also possible to select a certain element from an array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "dangerous-telling",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT source = Source_simple\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92minteger\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92mdouble_par\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mdist\u001b[0m = \u001b[1m\u001b[92m5.0*first_par\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[92mdeclared_array[1]\u001b[0m\u001b[0m [m]\n",
+      "AT [0, 0, 0] ABSOLUTE\n"
+     ]
+    }
+   ],
+   "source": [
+    "source = instrument.add_component(\"source\", \"Source_simple\")\n",
+    "source.xwidth = double_par\n",
+    "source.yheight = declared_var\n",
+    "source.dist = \"5.0*first_par + 0.1*double_par\"\n",
+    "source.focus_xw = \"declared_array[0]\"\n",
+    "print(source)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "biological-engine",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/user_guide/plotting.ipynb b/docs/source/user_guide/plotting.ipynb
new file mode 100644
index 00000000..60fa5924
--- /dev/null
+++ b/docs/source/user_guide/plotting.ipynb
@@ -0,0 +1,42 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "adult-childhood",
+   "metadata": {},
+   "source": [
+    "# Plotting\n",
+    "All there is to know about plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "initial-enlargement",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From b703622ab974a51624761420ef4842b4218504ec Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 15 Mar 2022 11:22:56 +0100
Subject: [PATCH 223/403] Updates to documentation, including longer run
 lengths.

Start of relying on release version of libpyvinyl, but still issues. Tests does not run.
Syntax will change as a result of release version of libpyvinyl data handling.
backengine will return data, and not in the data attribute anymore.
---
 docs/requirements.txt                         |   2 +
 docs/source/conf.py                           |   1 +
 .../source/getting_started/installation.ipynb |   2 +-
 docs/source/getting_started/overview.ipynb    |   6 +-
 docs/source/getting_started/quick_start.ipynb |   6 +-
 docs/source/index.rst                         |  10 +
 .../McStasScript_tutorial_1_the_basics.ipynb  |  14 +-
 .../McStasScript_tutorial_2_SPLIT.ipynb       | 339 ++++++-
 ...tasScript_tutorial_3_EXTEND_and_WHEN.ipynb |  13 +-
 .../McStasScript_tutorial_4_JUMP.ipynb        |   5 +-
 ...n_tutorial_1_processes_and_materials.ipynb |  18 +-
 .../tutorial/Union_tutorial_2_geometry.ipynb  | 956 +++++++++++++++++-
 .../tutorial/Union_tutorial_3_loggers.ipynb   |  74 +-
 .../Union_tutorial_4_conditionals.ipynb       |  47 +-
 .../tutorial/Union_tutorial_5_masks.ipynb     |  47 +-
 ...ial_6_Exit_and_number_of_activations.ipynb |  37 +-
 .../Union_tutorial_7_Tagging_history.ipynb    |  40 +-
 docs/source/tutorial/animation_demo.gif       | Bin 0 -> 203149 bytes
 docs/source/tutorial/animation_demo_long.gif  | Bin 0 -> 1413612 bytes
 docs/source/user_guide/component_object.ipynb |  23 +-
 .../source/user_guide/instrument_object.ipynb | 164 ++-
 .../source/user_guide/instrument_reader.ipynb | 159 ++-
 docs/source/user_guide/laue_example.py        |  48 +
 .../user_guide/parameters_and_variables.ipynb |  16 +-
 docs/source/user_guide/plotting.ipynb         | 293 +++++-
 mcstasscript/data/McStasDataFormat.py         |  51 +
 mcstasscript/data/data.py                     |  86 ++
 mcstasscript/data/pyvinylData.py              |  51 +
 mcstasscript/helper/mcstas_objects.py         |  24 +-
 mcstasscript/interface/instr.py               | 259 +++--
 mcstasscript/tests/test_Instr.py              |   6 +-
 31 files changed, 2508 insertions(+), 289 deletions(-)
 create mode 100644 docs/requirements.txt
 create mode 100644 docs/source/tutorial/animation_demo.gif
 create mode 100644 docs/source/tutorial/animation_demo_long.gif
 create mode 100644 docs/source/user_guide/laue_example.py
 create mode 100644 mcstasscript/data/McStasDataFormat.py
 create mode 100644 mcstasscript/data/pyvinylData.py

diff --git a/docs/requirements.txt b/docs/requirements.txt
new file mode 100644
index 00000000..39e17c5a
--- /dev/null
+++ b/docs/requirements.txt
@@ -0,0 +1,2 @@
+nbsphinx
+sphinx-book-theme
diff --git a/docs/source/conf.py b/docs/source/conf.py
index d0fb33ee..72362af7 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -14,6 +14,7 @@
 import sys
 sys.path.insert(0, os.path.abspath('../../mcstasscript'))
 sys.path.insert(0, os.path.abspath('../..'))
+sys.path.insert(0, os.path.abspath('/Users/madsbertelsen/PaNOSC/libpyvinyl/github/libpyvinyl'))
 
 print(sys.path)
 
diff --git a/docs/source/getting_started/installation.ipynb b/docs/source/getting_started/installation.ipynb
index 7735bef1..a887698b 100644
--- a/docs/source/getting_started/installation.ipynb
+++ b/docs/source/getting_started/installation.ipynb
@@ -105,7 +105,7 @@
    "metadata": {},
    "source": [
     "## Tests\n",
-    "In order to ensure the installation and configuration was succesful, one can run the test suite. Navigate to the folder containing the McStasScript source code and run:\n",
+    "In order to ensure the installation and configuration was successful, one can run the test suite. Navigate to the folder containing the McStasScript source code and run:\n",
     "```\n",
     "pytest\n",
     "```\n",
diff --git a/docs/source/getting_started/overview.ipynb b/docs/source/getting_started/overview.ipynb
index 702ad3ea..e9d5940b 100644
--- a/docs/source/getting_started/overview.ipynb
+++ b/docs/source/getting_started/overview.ipynb
@@ -16,7 +16,7 @@
     "## McStas / McXtrace simulations\n",
     "McStasScript is a python API for writing and running [McStas](https://www.mcstas.org) / [McXtrace](https://www.mcxtrace.org) simulations. These are sister packages meant for simulation of neutron and x-ray scattering instrumentation respectively and share a common syntax. The packages are used widely in the field and come with a large repository of components that describe smaller parts of the beamline. The community of users contribute such components to the packages, and they have in this way grown over the years.\n",
     "\n",
-    "McStas and McXtrace simulations are described by an *instrument file* which is a custom language built on C. Here a number of components are placed in simulated space to describe the physical instrument, along with a number of monitors that record the properties of the beam. The instrument file is used to generate a c code, which is then compiled to an executable on the users system. The simulation itself is a monte carlo ray-tracing simulation that tracks individual rays from the source, through the instrument, through any scattering events and deposits the rays intensity onto any monitors along the way."
+    "McStas and McXtrace simulations are described by an *instrument file* which is a custom language built on C. Here a number of components are placed in simulated space to describe the physical instrument, along with a number of monitors that record the properties of the beam. The instrument file is used to generate a c code, which is then compiled to an executable on the users system. The simulation itself is a Monte Carlo ray-tracing simulation that tracks individual rays from the source, through the instrument, through any scattering events and deposits the rays intensity onto any monitors along the way."
    ]
   },
   {
@@ -44,7 +44,7 @@
     "\n",
     "It is possible to add practically anything to the instrument object that one would normally add to a instrument file, which includes parameters, declared variables, lines of initialize code and lines of finally code.\n",
     "\n",
-    "The instrument object has methods for adjusting settigns of the simulation such as the number of rays to simulate and setting the parameters. The *backengine* method runs the currently specified simulation, and loads the data into the *data* attribute which can the be accessed by the user. The data is loaded as a McStasData object for each monitor output, these objects contain the data itself as numpy arrays and relevant metadata."
+    "The instrument object has methods for adjusting settings of the simulation such as the number of rays to simulate and setting the parameters. The *backengine* method runs the currently specified simulation, and loads the data into the *data* attribute which can the be accessed by the user. The data is loaded as a McStasData object for each monitor output, these objects contain the data itself as numpy arrays and relevant metadata."
    ]
   },
   {
@@ -62,7 +62,7 @@
    "metadata": {},
    "source": [
     "### Plotting tools\n",
-    "McStasScript includes tools for plotting the resulting simulation data, providing a convinient way to quickly see the results from the performed simulation. "
+    "McStasScript includes tools for plotting the resulting simulation data, providing a convenient way to quickly see the results from the performed simulation. "
    ]
   },
   {
diff --git a/docs/source/getting_started/quick_start.ipynb b/docs/source/getting_started/quick_start.ipynb
index 21f6cf9b..5745c0f3 100644
--- a/docs/source/getting_started/quick_start.ipynb
+++ b/docs/source/getting_started/quick_start.ipynb
@@ -269,7 +269,7 @@
     }
    ],
    "source": [
-    "sample.set_AT(3, RELATIVE=source)\n",
+    "sample.set_AT(5, RELATIVE=source)\n",
     "sample.set_parameters(R=120, xwidth=0.01, yheight=0.01, zdepth=0.01,\n",
     "                      target_index=1, focus_xw=0.5, focus_yh=0.5)\n",
     "print(sample)"
@@ -281,7 +281,7 @@
    "metadata": {},
    "source": [
     "### Adding a monitor\n",
-    "The monitor can be placed relative to the sample, and even use the attributes from the sample to define its size so that the two always match. When setting a filename, it has to be a string also in the generated code, so use double qoutation marks as shown here."
+    "The monitor can be placed relative to the sample, and even use the attributes from the sample to define its size so that the two always match. When setting a filename, it has to be a string also in the generated code, so use double quotation marks as shown here."
    ]
   },
   {
@@ -340,7 +340,7 @@
    "metadata": {},
    "source": [
     "### Performing the simulation\n",
-    "In order to start the simulation the *backengine* method is called. If the simulation is succesful, the data will be placed in the *data* attribute, otherwise this attribute will contain None."
+    "In order to start the simulation the *backengine* method is called. If the simulation is successful, the data will be placed in the *data* attribute, otherwise this attribute will contain None."
    ]
   },
   {
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 2f3355f2..bbe83c6f 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -22,7 +22,9 @@ Documentation
    user_guide/instrument_object
    user_guide/component_object
    user_guide/parameters_and_variables
+   user_guide/data
    user_guide/plotting
+   user_guide/functions
    user_guide/instrument_reader
 
 .. toctree::
@@ -53,6 +55,14 @@ Documentation
    :recursive:
    
    mcstasscript
+   
+.. autosummary::
+   :toctree: _autosummary
+   :template: custom-module-template.rst
+   :caption: Reference (libpyvinyl)
+   :recursive:
+   
+   libpyvinyl
 
 Indices and tables
 ==================
diff --git a/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb b/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
index a1ccc6ae..a06e0cad 100644
--- a/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
+++ b/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
@@ -351,8 +351,11 @@
     }
    ],
    "source": [
-    "wavelength = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
-    "order = instrument.add_parameter(\"int\", \"order\", value=1, comment=\"Monochromator order, integer\")\n",
+    "wavelength = instrument.add_parameter(\"wavelength\", value=5.0,\n",
+    "                                      comment=\"Wavelength in [Ang]\")\n",
+    "\n",
+    "order = instrument.add_parameter(\"int\", \"order\", value=1,\n",
+    "                                 comment=\"Monochromator order, integer\")\n",
     "instrument.show_parameters()"
    ]
   },
@@ -570,7 +573,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sample = instrument.add_component(\"sample\", \"PowderN\", AT=[0, 0, 1.1], RELATIVE=beam_direction)"
+    "sample = instrument.add_component(\"sample\", \"PowderN\",\n",
+    "                                  AT=[0, 0, 1.1], RELATIVE=beam_direction)"
    ]
   },
   {
@@ -733,7 +737,7 @@
     }
    ],
    "source": [
-    "instrument.set_parameters(wavelength=2.8) # Set parameters\n",
+    "instrument.set_parameters(wavelength=2.8)\n",
     "instrument.show_parameters()"
    ]
   },
@@ -758,7 +762,7 @@
     }
    ],
    "source": [
-    "instrument.settings(ncount=5E5, output_path=\"data_folder/mcstas_basics\") # Settings\n",
+    "instrument.settings(ncount=5E6, output_path=\"data_folder/mcstas_basics\")\n",
     "instrument.show_settings()"
    ]
   },
diff --git a/docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb b/docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
index f7a581a4..b10b7f2b 100644
--- a/docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
+++ b/docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,16 +27,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\", output_path=\"data_folder/mcstas_SPLIT\")"
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\",\n",
+    "                                output_path=\"data_folder/mcstas_SPLIT\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,21 +103,77 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_116\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS=\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "1 warning generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_116\n",
+      "\n",
+      "      monochromator rotation = 22.4519 deg\n",
+      "[python_tutorial] Initialize\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "\n",
+      "Save [python_tutorial]\n",
+      "Detector: banana_I=2.57019e-06 banana_ERR=1.14784e-07 banana_N=1447 \"banana.dat\"\n",
+      "\n",
+      "Finally [python_tutorial: /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_116]. Time: 0 [s] \n",
+      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
+      "    SPLIT 47 COMPONENT sample=PowderN(...)\n",
+      "  in the instrument description python_tutorial.instr.\n",
+      "loading system configuration\n",
+      "\n",
+      "Plotting data with name banana\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "instrument.settings(ncount=1E5)\n",
+    "instrument.settings(ncount=1E6)\n",
     "\n",
     "instrument.set_parameters(wavelength=2.8)\n",
     "                          \n",
     "instrument.backengine()\n",
-    "data_low = instrument.data\n",
-    "\n",
+    "data_low = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "plotter.make_sub_plot(data_low)"
    ]
   },
@@ -130,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,13 +196,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_117\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "Info:    Defining SPLIT from sample=PowderN() to END in instrument python_tutorial\n",
+      "CFLAGS=\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "1 warning generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_117\n",
+      "\n",
+      "      monochromator rotation = 22.4519 deg\n",
+      "[python_tutorial] Initialize\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "\n",
+      "Save [python_tutorial]\n",
+      "Detector: banana_I=2.58648e-06 banana_ERR=2.11397e-08 banana_N=44235 \"banana.dat\"\n",
+      "\n",
+      "Finally [python_tutorial: /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_117]. Time: 1 [s] \n",
+      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
+      "    SPLIT 47 COMPONENT sample=PowderN(...)\n",
+      "  in the instrument description python_tutorial.instr.\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# No need to set settings or parameters as these have not changed\n",
     "instrument.backengine()\n",
@@ -154,9 +248,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name banana\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plotter.make_sub_plot(data_reasonable)"
    ]
@@ -165,19 +279,55 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Adding the SPLIT keyword\n",
+    "### Caution on split value\n",
     "It is however possible to mismanage splitting, mainly by simulating a too few rays and splitting too much. Here we do this on purpose to see how such data would look. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_118\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "Info:    Defining SPLIT from sample=PowderN() to END in instrument python_tutorial\n",
+      "CFLAGS=\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "1 warning generated.\n",
+      "INFO: ===\n",
+      "Warning: Number of events 2.99e+06 reaching SPLIT position Component[6] sample=PowderN()\n",
+      "         is probably too low. Increase Ncount.\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_118\n",
+      "\n",
+      "      monochromator rotation = 22.4519 deg\n",
+      "[python_tutorial] Initialize\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "\n",
+      "Save [python_tutorial]\n",
+      "Detector: banana_I=2.56161e-06 banana_ERR=3.73111e-08 banana_N=14737 \"banana.dat\"\n",
+      "\n",
+      "Finally [python_tutorial: /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_118]. Time: 0 [s] \n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "sample.set_SPLIT(10000)\n",
     "\n",
@@ -189,9 +339,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name banana\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plotter.make_sub_plot(data_unreasonable)"
    ]
@@ -206,16 +376,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_119\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "Info:    Defining SPLIT from sample=PowderN() to END in instrument python_tutorial\n",
+      "CFLAGS=\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "1 warning generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_119\n",
+      "\n",
+      "      monochromator rotation = 22.4519 deg\n",
+      "[python_tutorial] Initialize\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "\n",
+      "Save [python_tutorial]\n",
+      "Detector: banana_I=2.60536e-06 banana_ERR=2.63437e-08 banana_N=29248 \"banana.dat\"\n",
+      "\n",
+      "Finally [python_tutorial: /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_119]. Time: 10 [s] \n",
+      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
+      "    SPLIT 47 COMPONENT sample=PowderN(...)\n",
+      "  in the instrument description python_tutorial.instr.\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "sample.set_SPLIT(1)\n",
-    "instrument.settings(ncount=1E5) #1E7\n",
+    "instrument.settings(ncount=2E7)\n",
     "                          \n",
     "instrument.backengine()\n",
     "data_ref = instrument.data"
@@ -223,9 +430,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name banana\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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dsbMtERFRbWLgSUREVef4QKzQ0RYAXPmMJ2d5EhER1SYGnkREVFUiySwGohls7pwQeCpWqS0DTyIiolrEwJOIiKrKsf4YAEzKeKqKgBDAOEttiYiIahIDTyIiqirHBqYHnkIINPrdzHgSERHVKAaeRERUVY71x9Dgc6GrwTfp9uaAh11tiYiIahQDTyIiqirH+s3GQkKISbc3BdwY5xxPIiKimsTAk4iIqoaUEsemdLS1NPndzHgSERHVKAaeRERUNbK6RCydm9TR1tIc8GAswYwnERFRLWLgSUREVSOVzQEANnc1TLuvkaW2RERENYuBJxERVY1kVgeAWTOe8UwO2ZxR7mURERHRIjHwJCKiqpHSdHQ1+NAYcE+7rzl/G7OeREREtYeBJxERVY1kVsemGRoLAUBjwAMAiLDBEBERUc1h4ElERFVBSomUpmPLLIGnlfEcSzLjWS579u7Dnr37Kr0MIiKqAww8iYioKqQ1A1ICm2bY3wmYezwBZjyJiIhqEQNPIiKqCknNbCw0W8az0W9mPCPMeBIREdUcBp5ERFQV0vnAc117cMb7m4NmxnOMGU8iIqKaw8CTiIiqQjZnwKUIBDyuGe8PelS4VYEIu9oSERHVHAaeRERUFTI5A17X7KclIQQa/R7u8SQiIqpBDDyJiKgqZHI6PHMEnoDZ2XYswYwnERFRrXE08BRCnBVCHBVCPJP/eIWTxyMiotokpUR2nownADQF3IikmPG0Uyan4/hADIlMrtJLISKiOjbzRhp7vUFK+XwZjkNERDUqktRgSMDjUud8XFPAgwujyTKtamk4P5LEWFKDW2URFBEROYdnGSIiqrieSAoA5s14NgfcVTlOZc/efdizd1+ll7Egw3EzgzyWzMIwZIVXQ0RE9aocgee3hRDPCSG+IIRomnqnEOK9Qoj9Qoj9Q0NDZVgOERFVm9584DnfHs+mgIfjVGw2ksgAADRd4mDP+IKfp5aDbyIicp7TgecLpZQ7AVwDQAD43NQHSCnvkFLuklLuam9vd3g5RERUjebKeN55+27ceftuAOYez0zOQCqrl3V99Ww0cSmQv/fIQAVXQkRE9czRwFNKeSH/ZwbAFwDc4OTxiIioNvVGUlAE4FLEnI9rDngAgA2GbGSV2oa8Lvz6MANPIiJyhmOBpxAiKIRozP9dAHgzgGecOh4REdWunkgKHpcC83Qxuya/GwA4UsVGI/EMXIpAS9CNo/0xNm8iIiJHOJnx7ATwgBDiOQDPA9gE4P0OHo+IiGpUTyQNTxFdVZusjGcyyz2FNhlNZOFWlUI2+T6W2xIRkQMcG6cipTwN4Eqnnp+IiOpHz1gK3nlGqQBAc9DMeEZSzHjaZSSehUsV8LlVrG8P4t4jg3jXDWsrvSwiIqozHKdCRESOKSYrmdZ0DMcz845SAYAmv5mVY2db+wwnMoUZni/b1oXHTo9gnIE9ERHZjIEnERFVVN94GsD8o1QAs6stgKqc5VmrRhNZuPNNnV62rQM5Q+K3xznejIiI7MXAk4iIKqp3jlEqU/ncKvxuFRFmPG2h6QYiSQ2ufMbzipXNaA16cC+72xIRkc0YeBIRUUVZMzyLyXgCZtZzjBlPW4zlZ3i6VTPjqSoCL97Sgd8cG4SmG5VcGhER1RkGnkREVFE9YykIUUrg6WGprU1GCoHnpdf+pds6EUvn8OSZ0Uoti4iI6hADTyIiKqupDYd6Iyl0hL1Q5pnhaWnyu1lqa5ORuPk6utRLr/0LN7bB61Lwa45VISIiGzHwJCKiiuqJpNDd5C/68c1BN7va2mQkkQEAuJVLbwcCHhdu3NCGe48MQEpZqaUREVGdYeBJREQV1RtJYXkJgWdTwMNxHzaxMp5udXK2+aXbOnFhNIWUpldiWUREVIcYeBIRUcUYhkRvJI3u5hICT78bkaTGbJwNRhIZuBQBVZkceL5kSwcAsIkTERHZhoEnERFVzHAig6xulFZqG/AgZ0jojDsXbSSeRXPQAzFlf21Hgw87VzaxpJmIiGzDwJOIiCqmN5IGgJICz6aAGwCQ47iPRRtJZNEa9Mx438u2diCR0ZHN8XUmIqLFY+BJREQV0zNmzvAsdY8nAOQMpjwXaySeQVvIO+N9N282y22jaZbbEhHR4jHwJCKiiumNlB54Nhcyngw8F2skkUXLLBnPzV1hCAEkMmwwREREi8fAk4iIKqYnkkLY60Kj313051zKeLIEdLFG41m0hmYOPN2qgoBHRSKbK/OqiIioHjHwJCKiiukpcZQKMGGPJ0ttFyWT0xHL5GYttQWAoMeFZEaHMeG13rN3H/bs3VeOJRIRUR1h4ElERBXTM5YqaZQKYI5TAWqj1Laag7TRhNmxdrZSWwAIel3QpcS50WS5lkVERHWKgScREVVM73gKy5t8JX2OS1UQ9rpYartII3Ez8Jytqy0ABD0qAOBgz3hZ1kRERPWLgScREVVEIpNDJKmhuylQ8uc2Bd1VlfGUUmI8pUHK6lnTfIbjGQBA6xyltn6PCgHgeQaeRES0SAw8iYioIi51tC0t4wkAzQFPVe3xjKQ0HO2P4bfHhyq9lKJZpbZzZTwVIRDwqDh4kYEnEREtDgNPIiKqiJ584LmixD2eANDod1dVqa01cuSBY7UTeBZKbWfpamsJel14vnd8wdncat7nSkRE5eOq9AKIiGhp6lnADE9Lc8BTVaW2yfzIkYdO1E7gOZzIwONSEPK6cOftu2d9XNCjYjCWwbmRJNa0Bcu4QiIiqifMeBIRUUX0RlJwKQId4YWU2rqrqtQ2mTUznqeGEoWAutqNxrNoDXoghJjzcUGveY2aDYaIiGgxGHgSEVFF9Iyl0NXog6rMHfjMpDHggW7IqmjmE8/kkMkZhb2SD9bIPs+RRHbeMlvAbDDkUZV5GwxlNB29kVRVfE+IiKj6MPAkIqKK6I2kF1RmC5gZTwBVkfU81h8FYO6VXNboq53AM55Ba3D2jrYWRQhs7grPm/HsHU/jwlgKxwfidi2RiIjqCANPIiKqiJ5ICisWHHiambpqCDwP98UAAAGPihdubMMjJ4eR06un8dFsRhLZOTvaTrS9uxHP98zeYCinG4UuuU+eHbVtjUREVD8YeBIRUdlJKdEfXXjGs9HKeFZBg6GjfVGoioBHVXDTpnZE0zk8WwPjR0bixZXaAsDl3Y2IpnO4MDrz/tUnzowWLgLsZ+BJREQzYFdbIiJyhJQS8XQOQa867b6sbkA3JLonjFKZq7PqVJcynpXPLB7piyLgUSGEwI0b2iCEuc/z6tXNlV7arJLZHFKajtbQ/KW2gBl4ArM3GLr7YB8UATT43Hjy7Jht6yQiovrBjCcRETniUG8Uh/qi6BtPT7svmzMDxoVmPJv8lct4TpxLaRgSR/tjCHjM4Lop4MGOFU1VP1bFmuHZUmSp7aauENyqmDHw1A2JXx7qR1PAg0a/Gz2RFHprpLMvERGVDwNPIiJyxEDUDDh7IimcH0lOui+TDzy7a3yP5/nRJJJZHQHPpQKiF21swzMXIhhPahVc2dxG8vsx24ostfW6VGzuCs/Y2fbxMyMYjmfREvQg7DNfh/3nmPUkIqLJGHgSEZEjIvnAy5DAX//o4KTGNJlCxrP0GZ4ACgFOpUttj+Y72loZTwC4aVM7DAk8cmq4Usua10g8AwBoKaKrreXy7kYcnKHB0D0H++B3q2jyuxHwqAh4VO7zJCKiaRh4EhGRI8ZTZuC5osmPh04M48fP9Bbuy+YMNAfckzKFpVAUAZciKt5c6HBfDIoAAu5LgefOlU0Ie11VXW5rZTyL7WoLmJ1tx1Na4aIBYJbZ/uL5frx4SwdURUAIgatWNWM/93kSEdEUDDyJiMgRkXzguazJhytXNeEffnYYY/mAJ5MzJjUWWgiXKipeanukL4q1bUEoiijc5lYVvGBDKx48Pjzr+JFKs/Z4FtvVFgC2LzcbDCWyeuE2q8z21ZcvK9y2a00zjvZHEU1Xb6kxERGVHwNPIiJyxHgyC1URUITAP7/+ckRTGv7xniMAzIzn8sZFBp6KUvF5mUf6oti6rGHa7TdtakdPJIW0VvmuuzMZiWfgd6slZZw3d4XhUgQSmVzhtnsO9sHnVnDLlvbCbdesaYEhgQPnI3YumYiIahwDTyIicsR4SoMrnwnc0tWA21+0Dt9/6mK+XFNffMZTqWzGM5rWcHEsNXPgudEMxKxy42ozmih+hqfF51axqTNcCDzNMtsBvGRL56QA9oqVTVAVwX2eREQ0CQNPIiJyRGRC4AkAf/rijVjTGsDpoQQMufCOthZVEdArGHge648BALYuC0+7b2VLAGvbglUbeA4nskXP8Jzo8u5GJLM6pJR44swohuOZSWW2ABD0urBtWQOeZOBJREQTMPAkIiJHjKc0qBMCT59bxT+97nJk9cWNUrEoioBRwT2UR/rMjrYzZTwB4KaNbYimtYqucTYj8UxJjYUs21c0ImdIZHPGjGW2ll1rmvHMhUhhXisREREDTyIicsR4UoNLnXyaecGGtsLsyMWW2irCHNVSKUf6omgKuNHVMPNIGGusSiydm/H+ShpNZBcUeF7ebTYYimd1/DzfzXamfaLXrGlBWjNwqHf63E8iIlqaFtbHnoiIaB7jU0ptLatbgwj73IUuqQuliEpnPGPY0hWGENO/RgC4fl0rBKprn+eevfsgpcRIfGGltlu6whAA+sfTiGdy08psLbtWNwMAnjrHsSpERGRixpOIiGwnpZy2x9PiUgQ6wt5JI0gWQhGAlKjIyBIpJY71x2YtswXMvY4hn6uqAk8A0KVEVjcWlPH0uVX4PSrimRx8bgUv3tIx4+M6GnxY3RrgPk8iIipgxpOIiGyXyOrQDQmXurjgci5KPtOYyRnwuVXHjjOTdM5AStPnDDwBoMnvxoWxFIbjGbQtIMPohJxuBurFdLW98/bd024LelQkszpu2Txzma3l6tXN+O2xIaxvD86aFSYioqWDGU8iIrJdJJkFAKiKc6cZK/BMa7pjx5hNMmsec2vX3IFn0GsGZsfzHXCrgZZv7rSQUlvg0td0646Zy2wt16xpwUgiizQbDBERERh4EhGRA6zy0plKbe1iPXWmAoFNMpuDqghs7AzN+Th/PhN7cihejmUVRbMyngsotbU+r7vJh5dt65zzcdesMfd5VmNzJSIiKj8GnkREZLvxpPOBp6hwxnNdW3DeEl+3KqAI4MRA9QSeuULGc2GBp0tVsKI5AK9r7q99fXsIzQE34unq2uNKRESVwT2eRERku0LG08k9nooVeFYg45nRsXXjpTLbmfZCAmZw7PeoODlYPYGnlp9B07LAjGexhBC4enULHj4x5OhxiIioNjDjSUREtovkA09n93iaf5Y745nTDWR1A1uWhYt6vN+tVlWpbU43EPa65s1Y2uGaNc1I54zCvlIiIlq6GHgSEZHtImUotZ3Y1bacCo2F5uloa/G7VQzFMoXy40rTdLngMttS7eI+TyIiymPgSUREthtPafCoChyMOyuW8bQCz23FBp4eq8FQdXS21XRjwR1tS7W9uxFCMPAkIiIGnkRE5IDxVBYNfrej8xsrNU4lmdXhUgQ6wsUFb4XOtlWyzzNnSMf3d1q8LhUhjwuxTHVke4mIqHIYeBIRke3GUxqaAm5Hj1G5UtscAh616KDa61LgcSlVE3hquoG2MpXaAkDI50IyoyPLeZ5EREsaA08iIrJdJKmh0e904Gn+Wc6Mp25IpDQdAU/xjXmEEFjXFsSJKgg8pZTQ9PJlPAEg4FEhAZwZTpTtmEREVH0YeBIRke3GUxqaHA48C3M8y5hJOz+ahCGBgKe0aWQbO8NVkfHU86NUWoPl2eMJXCo1Pj5QHXtciYioMhh4EhGR7cqS8cyfwTJlzHhGklkAgLvE+aQb2kPoiaSQypZ3P+pUmp4PPMtYamsFnicYeBIRLWmlXbIlIiIqQjSloTHgRk8k5dgxKrHHM5HRJx27WBs6QpASODUUx/buRuzZuw8AcOftu21f41yseZpOZDxn+1oURcDnVnCMgScR0ZLGjCcREdkqpxuIZXJo8jubVbNCv3Lu8UxkzbEgaolzYjZ0hABUvrNtzih/xhMws54nBipfakxERJXDwJOIiGwVzc9sbPQ7W1QjhIAiyhx4ZhYWeK5pC0BVRMUDz0LGs8yBZ8Cj4uxIouyjb4iIqHqUJfAUQvy9EEIKIbaX43hERFQ51j7IpoAHd96+29FyUkUIpLVyltrmCscthdelYnVLoAoCTzPj2Rwof8bTyJcaExHR0uR44CmEuArA9QDOO30sIiKqvEhKAwDHmwsBZgCYyZWz1NY8VqkZTwBY3xHCyQoHXjndgEsRcKvlLXjye6wGQww8iYiWKkfPPEIIL4DPA3g/AOnksYiIqDqMW4FnwPnAUwhUKONZ+udu7Ajh7HCiUO5aCZoh4SqxI68dfG4VLkVMajC0Z+++QpOlereUvlYiotk4fcnzHwB8S0p5ZrYHCCHeK4TYL4TYPzQ05PByiIjIaePJ8mY8y7lvMJ7JQRGXZoiWYkNHCDlD4txIwoGVFUfTDbiV8rd3UITAuvYgR6oQES1hjp19hBC7AVwD4AtzPU5KeYeUcpeUcld7e7tTyyEiojKxMp5N5Qg8lfKOU0lm9AWV2QLV0dk2p1cm4wkAGzvDOM5SWyKiJcvJy54vArAFwBkhxFkAKwD8UgjxcgePSUREFRbJZzwb6jHjmc0tOPBc3175wFPTjbLv77Rs6gjj/GgSyfxIGiIiWlpm7XUvhBgs4vP7pZQ7ZrpDSvkJAJ+Y8HxnAbxGSvl8qYskIqLaMZ7SEPK6yhLgKEIgXdaMZw7qAspsASDodaG7yY8TFQo8c7qBnCHhrlDGc3PXpcB7x4qmiqyBiIgqZ64ha0MAXj3H/QLAT+xdDhER1bpIKluW/Z2A2eQnU9Y5njqUBWY8gXxn28E4Ql5nZ5zOZDRhjrmpVMZzY2cYAHB8gIEnEdFSNNeZ7++klOfm+mQhxMeKPZCUck2xjyUiotoVTWllDDxFWfd4xjMLL7UFgA3tITxxZgQ7uhsX1KBoMQZjGQCVCzxXtwTgURUcZ4MhIqIladazj5TyB/N9cjGPISKipSWSLGfgibLu8UxmF15qC5gNhtKaUdZg2TIYSwMAPBUKPF2qgvUdIQaeRERL1Ly1PkIIH4C3Alg/8fFSyo84uC4iIqpR4ymt0MHVaaLs41RK62p75+27J/17Y6f5uqQ1HT63auva5jMYzWc8XZXZ4wkAmzpD2H92rGLHJyKiyinmsuf3ALwJQA5AYsIHERHRNJGUhqZAfZbaJjI5KIvJeOY726bKGCxbKl1qCwCbOsPoiaQQS2sVWwMREVVGMd0NNkgptzq+EiIiqnlSSoyntLKMUgHMOZ5pTYeU0vE9k7ohkdJ0tAQX/rU1Bz1oDXqQylam1NaliEUFzou1Kd9gqFKdfYmIqHKKCTxPCyHCUkpuyiAiojmlNQPZnIEmv6csx1OEgCEBTZfwOFxCas2fXExzIcDc53moN2rHkkoyFMvYku2cWj5cik35UuPj/XxLQUS01BQTeI4D2C+E+CWAtHUj93gSEdFUkZQ5smOu5kKLCVymsmLAdE6Hx+VsCWkio+ePufjAc/+5MUgp7VhW0QZjmYrN8LSsbA7A51ZwfIAZTyKipaaYwPNY/oOIiGhO4ylz714593gCQEYzAJ+zx0rYmPHUDQlNL3PgGc04HpzPR1EENnaEcWKQGU8ioqVm3sBTSln0rE4iIlraIkkz8CzHOJU7b9+N7+2/gA9//7mydLZNZOwLPIHyNhiSUmIolkFrqDwl0HPZ1BnGQyeGsLYtWOmlEBFRGc0aeAoh3iil/J4Q4v0z3S+l/IJzyyIiolpkZTzLNcfTmx9Jksk5H8TF84HnYkttN3aYDXbKGXiOpzRkdaOiHW0tmzpD+MHTF7Gy2Q9XFayHiIjKY66M53aYo1SumeG+8tYHERFRTRgvY8YTAHz50tG05nyX2GR+j+diM56dDV4oAkhnyxd4DuVHqXgqvMcTuNTZNqXpCDPwnNGevfsA2Lsfmoio0mYNPKWUf5//893lWw4REdWycu/x9JUx41nY47nIjKcQAn6PWtaMZzXM8LRs6jIDz2RWR9hXnp8TIiKqvHn3eAohXADeC+DFMDOd9wP4spQy5/DaiIioxkRSWaiKQMhbTO+6xfOWMeNpdbW94x270NW4uE5GfrdaCNLLYTBmNqV3V7i5EAAsb/Qh5HWVNfCuNCklS8WIaMkr5p3B5wGsAfB1AALA2wDsBPA+55ZFRES1aDylodHvhlhkVrBYVsaznM2Fgl510c/lc6sYjmeR1vTC1+CkwWj1ZDyFENjQEcLpoaUzUuXsSLIsP6NERNWsmMDzJgCXSSkNABBC3AngoKOrIiKimhRJamgq0/5OYGKprfMZT6u5UMCz+GyutU80ls6VJ/CMZeB3q6iCLZ4AgM2dYTzfM17pZZRNPJNDWtOhG3LRe4SJiGpVMWfPiwC8AFL5f7sBXHBsRUREVLPGUxoayhp4WqW2zmeTktmcGbzZEDi4CoGnhvawd9HPN5+hWAYdDd6yZaInmqlBzsbOEHKGhKY7f8GgGmRzBgwJXBhNYg3HyBDREjXXOBVrjMoZAPvymU4AeCOAB51eGBER1Z7xlIaWYPlmRXpdVqltOTKeOoI27V2dmPEsh8FYGh1lCHCLtXlCg6F6F8/kkDPMHZ7HBmIMPIloyZprs8c1+Q83gAMANuU/ngXQ4PzSiIio1lh7PMulnBnPRCaHkA37O4FLnXHLF3hm0BFeXEMkO00cqVLvesZShb8f749VcCVERJU116XbT0opj5RtJUREVPPqeY9nMpuzZX8nMDHjWZ7OtkPRDG7a6MVwPFOW482nI+yFqgiklkDGsyeSLPz92AADTyJauubKeP6HEOKQEOJTQogbRSU2hhARUc0wDIlourwZz0vjVJwPYOKZnG1jYlxlLLVNZXXEMrmy7CUtlhACAbe6NALPfMYz6FFxnIEnES1hswaeUsqXAXgBzDLbDwA4KYT4ihDitUIIf7kWSEREtSGWzkFKoDFQvj2eQgh4XArSuXKU2uoI2FVqmw88o2XIeA7FzCxnNe3xBACvuzzft0q7GElBAGjwu3F6KIFsGbLzRETVaM6BXlLKcSnld6SUbwKwFcD3ALwKwPNCiB+XY4FERFQbIqksAJQ14wkAPpeCTBmaCyWyuZpsLjQYSwMAOhqqZ48nYM4UtZru1LOLYyl4XAoCHhU5Q+LsSKLSSyIiqoiiz6BSyiyAn+c/IIS4zqlFERFR7RlPmdm7cu7xBMx9npmyZDxzCNm0x1MIAUWUK/CszoynSxGQ0iwF9nucn2VaKT1jKXjzgScAHOuPFZorEREtJXNmPAFACPFpIUSjEMIlhHhICJEQQrxNSvl4ORZIRES1IZI0A8/GQHkDT69bKcs4FTtLbQEz61mO5kKDUTPjWU17PIFL+1zHktkKr8RZPREz8PTlZ8BynycRLVXzBp4AXiqlHAfwCgA9MEeqfMjRVRERUc2pWMbTpTreXEhKiUTWvuZCAOBSlLJlPF2KQEvAgztv3407b9/t+DGL4VLNtyD1HHimNR1DsQw8LhWKEFjTGsAxjlQhoiWqmMDTchOAu6SUPQDqf1MGERGVJJIPPMu+x9OtOj5OJaXpkBK27fEE8hnPTBkynrEM2kJeKEp1Nae3Mp5Wprwe9UbMjrZW9+XNXWFmPIloySom8BwUQnwZwJsB/FoI4UIJe0OJiGhpiOYDz4ayB56K4xnPeMbMTAZt3Itolto6n/EcimXQ0VBdZbYA4FLrv9S2Z0rguakzjHOjyXnHyMTSWln2LRMRlVMxgedbABwCsEdKOQZgBYBPO7oqIiKqOZFkFn63Cp+7vI1ivGUotU1mzOe3PeNZplLbamssBJilxgAwVscZT2uGZyHj2RmGlMDJwfisn5PWdBztj6Enki7LGomIymXWwFMI8V0hxFsAaFLKf5dSPgYAUsqzUsqvlWuBRERUG8ZTWtnLbAEr4+lsqW0h42nrHs/yNBcaiqWrrrEQcCnjGUnUd8ZTEYDbynh2md1s5yq3feLMKAwJaDrnfRJRfZkr4/kZAFcCeFQIca8Q4k+FEKvLtC4iIqoxkaSGpjJ3tAUAbxnGqSTzpZFBm8apAGbGM+pwxjOnGxhJZNEerq4ZngCg5EfKjNZxqe3FsRSWNfqhCDPIXt0SgMelzBl4PnBsCACQ09lOg4jqy6yBp5TyESnlh6WU2wB8AEAIwJ1CiKeFEB8VQlxZtlUSEVHVi6S0su/vBMwyRqcznolCxtPePZ7ZnOFo0Dwcz0LK6pvhaXGrSl03F+oZS6G7yV/4t0tVsKE9hGNzBJ6/PT4IAMgZzHgSUX0pqqutlPKwlPKfpZTXA7gVQD+Af3R0ZUREVFOiKa3so1QAq6tteZoL2TlORc13dXVyn+dQLAOgegNPlyLqvrlQd7N/0m2bOkM4PstIlQujSZwaSkAIZjyJqP4UFXgKIQJCiE1CiG0AmgE8KKV8tbNLIyKiWrFn7z6cGopXZo+nS0XG4YxnMmsGhwE7A0/hfOA5GDMb1HQ0VF+pLWDu86zX5kI53UB/ND0p4wmY+zx7x9OIpjXs2bsPe/buK9z3wHGzzLY54EHOkDAMBp9EVD/mPYMKIf4YwCcAjAKwzuwSwDoH10VERDVGN2RF9nj63ArSjmc8zecP2bjH01XIeDoXeA3mM57V2FwIMDvbRuo049k3noZuSKxo9uNDr9hduH1zp9lg6MQM5ba/PTaIFc3mntDRRBbRtIamgKdsayYiclIxGc8/B7BdSrlaSrk2/8Ggk4iICgwpYUhUJOPpdanQdAndweyQtcczYPMeT8DhjGc0H3iGqjTwVAXG6rSrrTXDc3qprRl4HuufPFIlk9Px6KkR3Ly5He7CjNP6zAYT0dJUTODZL6U85/hKiIioZln70RorkJ3xuc1TmZOzPBPZHDwuBW61qB0qRVHLkvFMoznghsdl37rt5Mp39s3V4egQa4bn1FLb7iY/gh51WmfbJ8+MIZnVcfOmjkI2vJ73vxLR0lPMmejXQohPCiGuEkJssz4cXxkREdWMXD7bWJk5nmYWMpNzLnhJZHK2NhYCLgWeTo5UGYpl0FGFo1QsrnwgP56qv8yelfFcPiXwVBSBjZ1hHJvSYOiBY4PwqApesKG18LrUazaYiJamYs6i78j/+cYJt3GPJxERFej50Q+V6WpbhoxnRrd1lMqdt+9GJJnFFf/wa4ebC2XQ0VCdZbYAJmT2NLRWaTnwQl0cS6I97C1cGJloc2cY9x0dwPr2UOG2B44P4dq1LQh4XJNeFyKiejFvxnPCvs613ONJREQzqWTG0+sy39g7G3jmELSxsRBwaTSLk6W2Q7FM1e7vBMzmQgDqssFQTyQ1rczWsqkrjOF4Flq+xPjiWBInB+O4eXM7AHPvK1CfrwsRLV2znkWFEA1Syuhcn1zMY4iIqP5Zezwr1dUWcLjUNptD0OZSW5eqIOBRHcl47tm7D1JKM/Cs4oyn1URntA5LSnvGUrisu3HG+zZ1mpnOZFZHo1/Bb/NjVKzAUxUCAvX5uhDR0jVXxvOBIj6/mMcQEVGdszKeTf7yNxfyup3PeMYzOgIe+0ptLWGfy7GMZ86QyOpGde/xVKzMXn2VlBqGRG8kjRXNM2c8rZEqqaz5M/vAsSF0N/kLpbdCiLqecUpES9Ncl283CyGemON+AaDV5vUQEVENsvZ4hn32ZgWL4XVZezydy3gmMzksb7Q/gAv73I7t8bTKODuqdIYncKm5UL11bx2KZ5DVDayYpdS2PexFU8CNlKbDkBKPnhzG717ZDSFE4TH1POOUiJamud4hvLqIz+dvRCKiOrRn7z4AZhOcYuQMCVURUBQx/4NtZjVvSecc3uNpc6ktYGU8nQo8zSx0NQeeijDLbests3dxbOYZnhYhBDZ1hnG4N4pYOodEVsfNmzsmPcalCpbaElFdmfUsKqX8bTkXQkREtSuny0LZZLn58s2FMg5mPOOZHIKOlNq6Me5QViub3/Pa0VC9pbZCCDQFPHWX2bNGqXQ3BWZ9zObOMJ46O4ZIUjPHqKyfXETmUkTdlSAT0dJWnROliYiopuSMCgaeheZCzmQ8pZRIZvUazHiagWd7FWc8AaA54K67UtuLY0kAs2c8AbOzrS4lhuMZXLO2edrPl0tR6u51IaKljYEnEREtWs4woFYo8HS6uVAmZyBnSEcCzwafC1EHA8+ARy2MbalWTQFP3ZXa9oyl0BRwz/naWw2GcobEiza1T7rvztt34w27VmAsmYWU0tG1EhGVS3WfjYiIqCbohoTXgVLUYvgcbi6UzHcedarU1qmutlldVvX+TktLwIPTw/FKL8MW1t5ov0eddYanxRqpAmDa/k7AzARrukQiq1f9xQMiomLMm/EUQvxCCPEaMbHVGhER0QQV3eOZz3g6VWqbyJgZSUdKbb0uZHJGYT+mnbQqH6Vy5+27ceftu9EcdNdlxnO+wLMp4IFbFfCoCjZ2hGa8HwDG2GCIiOpEMaW2dwD4MwCnhBB/IYTgCBUiIiqQUlZ0j6fT41Ti+cDTiayTNX7Giaynphtob6j+jGdTwIOxRP2UlEopcXEshRXNszcWsixv9GNFsx8zXdtvsQJP7vMkojoxb+AppbxLSvlSAK8C0A3gkBDiG0KIqx1fHRERVT0rMHOplQk8XaoClyIc2+OZzJpfX8CRwNMNAI40GMrmDLSHqj/wbA64kTNk4eeo1uUMiZSmz9lYyNLV6Ju1+VNz0PzZqLdsMBEtXQtpLpQFkAbwDSHEp21eDxER1Rhr1qBLqVy/Op9bRcaBclUAiGfMgDbkdWKPp5XxtDfo0g0JQwIdNZLxBFA3o0Ossun5Sm3nc+l1YcaTiOpDMXs8Xy+EuB/AXQCOAtgmpXwvgJ0Afs/h9RERUZU7O2KOjvC6Kxl4Ko5lPB3d41nIeNobdFmjVKp5j6eluc5KSq0LICuKyHjOxSq1HeUeTyKqE8WcRd8D4P+TUv5y4o1SypwQ4k+dWRYREdWKM0NmR1K/uzJdbQHA61Id2+NZCDw9zu3xtHukyqXAs/ozns2B+ioptZpcLTbwbPC7IUT9vC5ERMVcnv6fqUGnEOJtACCl/KkjqyIioppxZjgBVYiKNRcCzGxruga72jY4lPHM6majntoqta2PzF4mZyDoUdHody/qeVRFoNHvrpvXhYiomMDz/8xw2wftXggREdWm08MJ+NzKjJ05y8XnUpFxKuNpzfGsoT2eWr7cs1aaCwH1MzYkmzPQPUun2lI1BzwstSWiujHr5VshxC4A1wFoE0K8f8JdjQA8Ti+MiIhqw5nhRGGWZqV43Yqjczxdijlv0W4hhwLPrG5A4NL+yWrWWGclpZmcUdQolWI0B9x103SJiGiuuqFuALsABAFcM+H2KIB3FfPkQogfAVgLwAAQB/CnUspnFrBOIiKqQmlNR08kheWNlW1i43OpjjYXCnpdjmR03aoCv1t1pLmQW1WgVLD8uVguVUGDr35KSjM5Y9EdbS3NAQ/6o2lbnouIqNJmDTyllD8G8GMhxMullL9a4PO/U0o5DgBCiNcC+AqAqxb4XEREVCaH+6JoKmKP2vnRJKRExTOePreCkYQzcyDjGR1Bj3NfX9jnsr/UVpdwV2iu6kI0B9wYrYPMXs6Q0A1Z1AzPYjQFPDjSF7XluYiIKm2uUtsbpJSPAHAJIV499X4p5T3zPbkVdOY1wsx8EhFRFdMNWXQgdHooAaAaAk/nMp7JbM6RxkKWsM+FWMb+jKfHVbnxNqVqCnjqIuOZzZd725XxbAm666YEmYhorjPpuwA8AuDDM9wnAcwbeAKAEOI/AbwcgADwyhnufy+A9wLAqlWrinlKIiJy0Eg8AwBIZnVIKecsMT0zbAWelQ1yvC7FsXEq8YyzgWeD3+1AxtNwdM12aw64MZT/uatlds3wtDQFPEhpOtKaXvGLO0REizVXqe0f5v+8ZTEHkFL+AQAIId4O4FMAXj3l/jsA3AEAu3btkos5FhERLZ61p0w3JPqjaSxrnP1N9JnhONpCXriUygaeTmY8zT2eTpbaujGesjerlTNkRcfblKo54MHxgXill1G0PXv3AQDuvH33pNutwNOuUlurOdRYMjvn/0Miolow7zsFIcRNQohQ/u/vEUJ8SQixttQDSSm/CeAWIUTrAtZJRERl0j9+qZnJ0b7YnI89M5zAurag00ual8+tFt702y2Z1RH0OFxqa2NzoUxOh5TmHMhaUS+lthlNhxBAW9CeMTYtQWvUDMttiaj2FXOJ+nMAEkKIywD8OYDzAP5rvk8SQoSEECsn/Ps2AKP5DyIiqlIDsUslj4fnaWxyZjiBtVUQeHrdimMZz3gmh5CTpbY2NxeynquWAs/mgBuJrI6sQxcPykHTDYwmNIS9Ltu6CTdNyHgSEdW6YgLPnJRSAngVgC9KKf8JQHMRnxcE8D0hxEEhxDMA/g+A2/LPRUREVWogn/H0qAqO9s+e8YymNQzHs1jbXgWBp8vMeDpxiklmdQQcLrW1M+NZCDwdGP/ilKagGWDVctbzV4cGkNUNdNk4WqiZgScR1ZFiLuG6hBA3AHgDgPfkb5v3DCylHABw/SLWRkREFdAfTcOtCgS86pyjHM7mGwutbQviN+Va3Cys5kaZnGF7ExanmwuFvS6kNaMwe3Ox4vnAs7b2eOZLSpMaOhoqOxN2ob726Bl4XUpRY4gsU/eITtUcvPS6EBHVumLOcH8L4PMAHpVSHhJCbAJw0tllERFRpQxE0/C4FAQ8Kk4PxWctYbU62lbFHk+XGWxmbO5sq+kGsjkDIYf3eAKwrdzWyp7WUqltS41n9p7vGceTZ8fQ2eCbswt0qZr8+dclUZuvCxHRRPMGnlLKH0spr5BSfjD/7+NSytc7vzQiIqqEgWgaHlVBwOOCIYGTgzN3Gz09lIAQwKrWQJlXOJ03n/FM5+zd55nMmM8XcHSOp5nVsqvcNlqDezytvYy1Wmr7tUfPIuBR0R7y2Pq8HpeCkNdVswE5EdFE855JhRA+AG8FsH7i46WUH3FwXUREVCH942n43SoCHjOLeLgviu3djdMed2Y4gRXNfnhdlZ8vaGU87W4wFM+aQVzI0T2ezHhaJaWjNdi9dTiewU+e6cWea1bi+MDcXaAXojnoRoSltkRUB4optf0egDcByAFITPggIqI6k8rqiKZzcLsU+FwKfG5l1pEqZkfbUJlXODNrX6fdI1USGTMYdHSPZz7jGbUp41mbXW1rt9T2f544j6xu4J0vWO3I8zcHPBhlqS0R1YFizqQbpJRbHV8JERFV3ED0UkdbIQQ2dzXM2GBISokzwwlcvdpscj5fkxSnWc2F7M54FgLPmtrjWXuBp8+twudWaq7UVtMNfPOxc3jhxjZs6Ag7cox6mXFKRFRMxvO0EMKZ36ZERFRV+q3A02WeHrZ2hXG0PzptTMlQPIN4JlcVMzwBFMp90zY3F0rk93g6mfFsKOzxtK/UVhGAUkPjVAAzs1dr3Vt/8Xw/BqIZvPuGNY4doyXgrrnXhYhoJsWcSccB7BdC/BJA2rqRezyJiOrPxIwnAGxd1oD/efICBmMZdE4Yc3Fm6NIolWrgVMYzXii1LcceT3uCi3gmV1PZTkstZva+9uhZrG4N4OZNHY4doyngYVdbIqoLxQSex/IfRERU56zA053PeG7pMgteDvdFJweew9UWeDqzxzOZdb7UNuRAqW0tBp7NNZbZi2dyONQbxd++ZhsUB1/v5oAHsUzOtjmvRESVMu+ZVEr5sXIshIiIKq9/PIOAR8X337cbQgiM5wOBo30x3LL5UlbnzHACHpeC5U3+Si11Eq/L4T2eDpbaulUFfreKaMqucSpajQaeHhzpn76fuFoNRNMIeFS8cdcKR4/Tku/4G0lqaA97HT0WEZGTihmn0gHgXwGsklLeJITYAeAFUsovOb46IiIqq4FoGl0NPoj8/sDGgBvdTf5Cg6E9e/cBABr8bqxpDVRNgGNlPO0vtbX2eDo7Mibsc9mb8ayx/Z1AbY0N0XQDI/Es3r57dWGPrlOaJnT8ZeBJRLWsmJqNLwN4GEBT/t9HAbzfqQUREVHlDETTk0pqAbPc9uiUTJQ5SqU6ymwBwJvf4+lEqa0iAL+7DIFnxq5xKhpcVXJBoBTN+T2ehiHnf3CFDcczkADesXuN48cqjJrhPk8iKpM9e/cVLjTbqZjAszuf3dQBQEqZBWDvmZ2IiKpCfzSNzobJWZWtyxpwaiiBTM7M/kkpcW6kemZ4Ak5mPHMIelyFDLBTwj63zXs8a28vYFPAA0PaN8/USYmMDo+qYEOH8/8HmvOltrW0/5WIaCbFnJkmnQmFEE0Aau9SKhERzUlKicFoBp2NUzKey8LQDYkTA3EAZlZR0yXWVVPG0+VQxjOjI+BwmS1gZjyjbC4EoDYCrGRWR8Dj/M8FMCHjWWMdf4mo/iw2E1pMt4QfCCH2AggLId4Fs8z2Kws+IhERVaWxpIasbqBrWqltAwDgaH8MwKWs4tr26gk8PaoCIRzIeGZzjjYWsjT43OiJpBb9PDndQErT0RL02LCq8poYYK1F9fxsTaXpBtKajqbA9L2dd96+2/bjMfAkonoxb8ZTSvkpAA8CeArAqwH8h5TyM04vjIiIyqt/3BylMnWP59q2ILwupdBgKK0ZhdurhRACPpdqe8YzkckhVIbA067mQtbc0Vrc42kFcn/5g+cc2VtklzPDCUigbBlPv0eFz63UTOMlIqLZFNPV9sVSym8D+PaU2+53dGVERFRW1gzPqYGnqghsntBgKK3pCPtcaK2yrJrPrdie8UxmylNSaQaeiw8srOD1T168AW/ctXLRz1dOVmYvp1d3cyEr8+90w6mJmgMejLK5EFFZWBe+nKhgqJRq+ZqK2eP5LzPc9im7F0JERJVlBZ5dU/Z4AsDWrgYc6YtBSomUpmNdW9Dxhjul8rpUR5oLlSfj6UZaM6Dpi8vYWo15wj7n12y3QuBZ5V1tj+UvwPjLlPEEzMZLEZbaElGNm/XMJITYAGATgAYhxKsn3NUIIOD0woiIyD7FXO3szwee7aHpswK3LAvjzv0XoLUEkM4ZVVVmazEznjaX2pZpj6cVKMbSuUXtz7QynmGHZ0s6IexzQRHmPtVqdqw/Bp9bgVLGCy8tQXdNNF0iIprLXGfTGwC8C0AngA9PuD0K4EMOromIiCpgIJpGW8gDj2t6MYzVYCieySGbM6pqlIrF51YLI1/sksjoCHjKk/EEzBmc9gSetZfxVBSB5oCn+jOeA7Gy/ExM1BTw4EhvdP4HEhFVsVl/c0opvw7g60KId0kpv1a+JRERUSUMRDPT9ndati4LA7jUWXNNW/UVvnhdDmQ8MzmEyjROBcCiGwzFM1apbe1lPAGzwVA1Z/bimRwujKawoslf1uM2B9zsaktENW/eS3ZSyq8JIdYDWD/x8VLKe5xcGBERlVf/eHrG/Z2AmXFZ1ujDYCwDAFhXhRlPr9vePZ66Ye5nLWepbXSRDYZqOeMJmPs8h/I/Y9Xo+EC+sVAZ93cCQEvAg/GUBt2QNTmjlYgIKK6r7T8B+EMARwBYZ3QJgIEnEVEdGYimsXNl06z3b13WgL7xQQDVmfH0uVVEU/Zly5JZM4gLlqGssqFQaru4jGetB55NVV5qeyzf0bZco1QsTQEPDAlEUxqaq6ybNBFRsYo5M70JwHopJTcXEBHVqWzOwEgii65ZSm0BYEtXGPcfHYRbFVVZyulzKRi0MeOZyJjPVe7mQosRTWvwuBR4XeUNjOzSHHAvurOvk471xxDwqPDOsA/aSc1B8//bWDLLwJOIHGUYEmeGE1CEwG+ODuLatS22nQeLeZY+Bp1ERPVtMGaNUpne0dayZZnZYMhXxvmFpfC6VWRy9gUt8Uw+41mWPZ6XmgstRiydQ0ONZjsBoDlY/RnPjZ3hso8SskbNVPP+VyKqDxfHUoVtNe/+2pNwKQJXrGzCCza0IZbWFnXhuZiz0z4hxH8D+B6AtHUj93gSEdUPa4ZnxxwZz235BkO+Ks2m+VyKrXs8y1lqa1fGM5Yuz9xRpzQF3JDS3F9bbaSUODYQw8u2duLsSKKsxy4Engk2GCKqNsWMK6slp4bjAMwqp799zTY8fHIYj54cxufuPwFDAuvbFz5OrZiz0zX5P/90wm3c40lEVEcGoubVzblKbde0BhH0qGj0V2dg43Ms4+n81+tWFfjcig0Zz8Vdja40K8DKGdVXbjscz2I0kcWmrnDlAk92tiUih50eMn+/BTwqbtjQhhs2tAEAxpMarv/n+9A3noaUckGVH8V0tb2l5GclIqKa0j+eL7WdI/B0qQq2dzeWa0kl89qc8fzojw8BKE+pLWCW2y56nEo6V7ONhQBzjycA5PTqy3hajYW2dIXxnhvXlvXY1h7PCEttichhp4fiUBUB15QO2o0BNzobvDg7ksSBCxFctaoZQGkZ31l3xwshts31sZgviIiIqstANA2PS0FToHazZb78OBUp7Qla9PzzlCPjCZjltnaU2tZy4NlUyHhWX+B5tN9sd7G5K1z2Y4e8LrgUga88cqbwJo+InBFJZpHK2ncRsxqMJLJFV9ScGorD71ZmzGi2hbxQBPDNfecWtI65zk53z3GfBLBuQUckIqKqMxBNo7PBW/amKXbyuRUY0gxa3Orivw5rn2G59kyGfW4b5njWdqltS75ja64KO9se64+hLeRBW2j2BlxOEUJU/agZonqQzOZwfDCOxir/PVpKljGV1XF6KF70uez0UGLWJoKqItAe9uLu5/rw17duLfn34awZTynl2jk+GHQSEdWR/mh6zjLbWmCdKO0qtzXyGc9yzWxsYMazkHGvxgDr+EAMmzrLn+20tATdVRmQE9WTh08MQ0pzNFXWxp4BlfSbY4MwJJAsIosbS2sYjGXm7F7fGfYhqxu488kLJa+lvIOoiIioKg1EM3N2tK0F1mzFtGbPmwUr41mOrraAVWq78IynYUjEs7mazng2+a2MZ3UFnoYhcXwgXpEyWwsznkTOu+/IIADAkMDT58cqvBp73HOwD4B5QW84npnzsWeGzcZC/jkCT79Hxe51rfjO4+dL7kDOwJOIaAmIpjSMzjKKQUqJgTrIeHptznjqBqAIQFHKU34c9rpxfjS54D188WwOUgLhGh6n4nEpUET1dbU9P5pEStOxpYKBZ3PAXXUBOVE9MQyJ+44OFjq3P3xiuMIrWry0puP+o4Pwuc2Qz2qSNhuro631+Nm8Y/dq9ERSuO/IQEnrYeBJRLQEnB9N4vRwApnc9KAslskhmdVrPvC0SoPsGqliSAm1TEEnYGY8FzO/0irTreVSW8AcLaNVSYC1Z+8+7Nm7D0fzb9Y2dzVUbC0tQU/VBeRE9eRgzziG4xm0Br0IeV146GTtB54PHBtEMquju8kPAIXfZbM5PRSHIjBnqS0AvGxbJ7oafPjmY6U1GWLgSURU59KajmRWh25IPHh8+ol0ID9KpaOh/E1T7HSp1NaujKeEUsZmS2GfG4a8tLe0VPFC4Fm7pbYA4FJE1ZWUHh8w36xt7AhVbA1NAQ9yurStazMRTXbfkQEowtxr3uh34eDFCMZrfITR3Qf70RL0oDXogUsROJbvzj2bU8MJrGgOzHvuc6kK3nLdKjx0YhipEs65DDyJ6oR1ZZ5oqoM947Deqv702d5p9/dH55/hWQsuZTztCzzLnfG0jrsQ1v7Q+sh4Vldm71h/DKtaAmUbrTOT5oAbEkCVJIOJ6s69RwZx9epmuFUFjX7zQuCjp2or6znxvWBa03H/kQG84rJOCCEQ8KhFldquaw8Wdaw3X7sSblVgMP8eohgMPImI6tzT58wGCc0BN359eADJ7OTOqQNRs9lAV2ONB552NxeSEmpZM56LDTzro9TW61aQ0Yyqyuwd7Y9WtLEQADQHqnfUDFGt642kcLgvipds7QRgzm8uptw2ldVxYjCORGZxHcmd8NvjQ0hkdbxq+zIAZof24wNxGLOcYwxD4sxwHOvaiqvs6Aj78MrtyzAUzxZ93mLgSURU5w6cj8DrUtDV4EMq32jAsmfvPvz7vccBAJ3MeE5ilD3jaZbILjTwjBYynrVdaut1qdClRKRKStwMQ+LsSLKijYWACYHnDD8frHghKt3E/zf35c+LL93aAQBQhMD161rmbTD0k2d7MJrI4sJYytnFLsA9B/vQFHBj9/pWAIDf40JK03F+NDnj4/uiaaQ1A+vag7jz9t1FzQh9x+7V0A2JkXm65VoYeBIR1TEpJZ4+P4aQ14Wwz4WOsBc/eWZyuW02Z6DR7563mUC187rtz3iWq6MtYM7xBBaf8Wyo8Yynlbme7c1RuaU0c390JWd4AkBzMD/jlBlPWiLKeUHlviMDWN0awPr2S9m+Gze04fxoEudHZv5dJKXEN/aZzXXGUxqO9E3eP1nJC0JpTcd9Rwbxim1dcKvm71RrJvXRWfZ5nh6KA8CcpbZTA9Jdq5sR8KgYiGWKqlJh4ElEVMd6x9MYjGUQ8rkghMCtO5bhgWNDhewYAGi6UfP7OwHA57J7nEq5S23zgcUiA89QjQee1gWEagk8raHr1ZzxJKKFS2ZzePTUCF6yxdwLablxYzsA4KGTQzN+3tPnIzjUG8WKZj8UAdzx4OmyrBcwm9DN9bvgoRPDiGdyePWOZYXb/G4VQsze2dYapTIx+J6PEAKdYS+SWR1PnZt/7ikDTyKiOmbt77RmO/7OzuXI6gZ+dejS7K1szqj5jraA/eNUzOZCtjxVUexoLqQqYs7B37XAm7+AUC2BZ0rT4VEVrGkrruGGUy7t8WTgSWSnh04MI5szCmW2lvXtQSxr9BXKbadmML/12DmEvC50NfjMaqJne3FxrDy/t04NJfDMhciszYLuOdiHRr8bL8iX2QKAqgisbgnM+jmnh+IIelR0hEt7P9Aa8mJFsx9ri/gdycCTiKiOPX1+DD63An++xOaKlU1Y0eyf1N02Wy8ZT7d941SklDAkaqqrbTyTQzif2a5lqiLgVgUuVEngmczqWN8RKpSrVUqj3w1VERhPV8feV6JKsbuE9b4jAwj7XLhmbcuk24UQuHFDGx49NTLt9/JwPIO7n+vD713VDVUR6Gr0QQD4ysNnbVvXbM6NJDCaMBv6vOMrj0/7XWlIiXsPD+Dl2zqn/d7a3BWePfAcTmBde6jkc4iqCHQ3+dEamj9gZeBJRFTHDpyPYEd3U2EmlxACt+1cjodPDmM0kYWUEpoui+5oW2zDgUrwFkptF5/xtMoryz3HE1jcHs9a72hr8bpUnJtlX1W5pbI6NndWbn6nRVEEljX6EElqeOLMaKWXQ7Qgldj3ONcxpZS4/+gQXrSpvRCkTTzP3bixDeMpDQd7xid93p1PXkBWN/D23asBmL+zfmfncvzPk+cRSWYdXftXHj4DAbP8P5XV8fb/ehzDE5r7jKc0xKaU2Vpf0+auBpwdScx4gbaUUSoLxcCTiKhOpTUdh3rHceXqpkm337ZjOXRD4p6DfdDyZXsddZDx9Lrsy3has03LmeXyuBQIYTY1WohYWkPYW9sdbS1el1IVpbY53UBWN7C5q6HSSwEALGvwwaMq+Md7jlTVuBmiWpXI6BiOZ/DS/BiVqW7Y0AYAePjEpX2euiHxncfP4wXrW7Gh49Le7/e+aB2SWR3feuycY+sdT2r47v6LaA150Oh346vvvgb90TTe+ZUnCrOcRxNZNPhcuGF927TP39IVhiGBEwPxSbensjp6IqmiR6ksFANPIqI6dag3Ck2XuGpV86Tbty4LY317ED99thfZfIfMeii1VRQBj0uxZY/nuRGzyYJVvlsuLkUsuHlMtI4ynj63gr7xFLI27dddqKRWHY2FLIoisKLZj2cvRPCz5/oqvRyimjeWzEIRwIs2tc94f1vIi23LGvDQhLEq9x0ZQE8khXfks52WLV0NuHlzO7726NmSL4AWmwn+zhPnkdL0wjn76tUt+OLbrsax/hj+4Ov7kTMkxpIaXn5ZFzyu6ecvax7x1M62Z4bNcx4znkREtCAHzpuNha5c1TTpdqvc9omzo4Wh1/UQeAJmpsyOjOfZYTPbVu4RM6oiFllqWy8ZTxWGNIe6V1IqX3K9qUoCTwBoC3mwpSuMT/7yqG0za4mWqkhKw67VLWgOemZ9zAs3tuHp82OF383ffOwcljX6ZsyS3n7TegzHs/jB0xdtX2s2Z+Brj57BDRtaEfReush4y+YOfPpNO/H4mVEc6h2HbkjcevmyGZ9jTWsQXpcybZ/nqSJGqdiBgScRUZ16+vwYVjT70RGeHlTetnM5pLxUUtpZB11tATNQtOPN+LmRBFRFwFXG5kIAoIrFBJ5a3WQ8q2WkSkrToQhgeZF7oMtBCIG/evVWXBhN4VuPna/0cohqVkbTkczqeMmUbrZT3bixDZouEUtrSGk6HjoxjLdcuwquGbZiXL+uBTtXNOLLD562vRz+noN9GIhm8Ac3rpt232uv6MZHb9uGtGZAVUShRHgqVRHY2BnCsYHJgac1SqWYzrSLwcCTqE5IKTGWzMLgjLclbWK5zoHzEVw5pczWsr49hMuWNxQa8RTTja4W+NwKMjY0FzozkoTPpZS9Q6yZ8VzY+q2utvXAVyUjVVJZPT/7rro6Bd+0qR0v3NiGz95/AuMpdrklWoix/P+dl8yyv9NyzZoWeFwKxlMaBqNpuFWBN1+7asbHCiFw+4vW4+xIEmNJ+/5vSinx5YdOY0NHaNay4HfdsBZrWwNY3RKYsczWsrmzYdosz9PDcXQ3+RHwOHsOYeBJVCfGkhqOD8Txy0P9lV4KVYG+8RT6xtO4Kl9mO1M32tt2LgcAeFSlrGNDnORzqUjblPH0VmAeprrAPZ5SyrrqautWzf26lR6pktL0wiiiavN/X7UV4ykNX/jNyUovhajmpLI6hmMZeF0K1s9TXupzq7h2TQvGkhqG4lm8avsytM8x6/IVl3VhdWsAvZGUbVnPx06P4lBvFO+5cS2UOc7XHQ2+OdcGmHvWh2IZjCYudd8tR0dbgIEnUd2w9iLddaCnwiuhanDgfAQApjUWmsjaA+J21UfQCZglmosdp6LpBi6OpeCb44qxU1wL3OOZ0nTohqybPZ5CCKxs9lc04xlNa9B0CX8FLkAUY9vyBrz+yhX46qNnkbFhXzNRtUlrOk4PxXG4Nzr/g0sQz+Tw7q89gURWx4pmf1EVDTdubEMmZ0A3ZGGEimXqhV1VEfjDF65DIqsjms7Zsub/evg0WoIevO7K7kU/15ZlkxsMSSlxeiiOdQ6X2QIMPInqRir/xuOBY4MYS9g3Q4pq09PnxuB1Kdi6bPYxECtbAmgKuBH21keWDMhnPBf5JrxnLAXdkGVvLAQAHpcKTZclfw2x/Jubesl4AsCqlkBFA89Tg2azjUr8HBTrz1++CQLAhbHKNmEickJPJIWheBav+exD+PsfP29LWfl4SsM7/utxPHl2DOvbg2grcpvJjfk9kwGPil2rZ7+ga3nD1SvgUc2qjYlboBYyxzSl6bj3yCDedv1qW34fWZ1trQZDg7EMElkd69qdn1fMwJOoTiSzOrwuBZoucfdBttlf6g5ciODy7sY593kAwObOMFa3On+Vs1zM5kKLy3ierdAoFQDw549pNXooljW/rV4ynkA+8BxJVmxe5cl84FmtpbYAsLzJj9+/cS1GEtlCh2qiejCayGIkkUVbyIO3Xb8a33zsHF78Lw/gu09eWPDvhLFEFm/9z8dwsGccn3/LlUUHnQCwbVkDmgPuojOkPreKFc1+JLL6ot+T9Y+n4XEpePv1q+d/cBHaQ160BD041h/Dnr378M6vPAHA+Y62AANPorqQ0w2kNR0tQQ82dYbwwwnltgu5uka1zZASB3vGp41RWQrsGKdydtgKPMsfcFhBzsmh+DyPnCxahxnPlS0BxDI5RGxs0FGKk0NxCKAiJdeluP0ms8NlhE2GqI7c+eQFSAksa/ThH167HT/90xuxpi2Ij/zgORzui5Z8oUXTDbz5jsdwfCCOO96+C6/cPvO4kdkoisCmzjCaA7OPXZmqLeSB363iU788tuCZxJpuYDieweuu6J60d3Omvg3FEkJgc2e40GDIOmcy40lERTk3moQE4HereN2VK/DUuTGcH6lsUw6qnGRGRzZnzLm/s1753IsvtT07kkTQo5Z9lApwqZurVeZZrEKpbR2VTa9qCQCoXGfbkwNx+Kqwo+1UTQEPvC4FySz3eVJ103QDF0aT04KwqRfIdUPiW4+dQ9jnKnRZvWx5I753+278yxt3Iq0ZONQXRd94cSXm2ZyBw31RnB9N4qvvuga3bJl7fIpdhBBY1WLuVf/24+fmfbyUEj2RFH59eACfufcE3vuN/TjYMw5DAu954Vpb17a5K4zjAzFIaW7t8LkVLFvgPO9SgmAGnkR14ER+HpPfo+K1V5idSn/IJkNLVjx/JfiqIvah1Bs7mgudG0lgdWuwIgGHogh4XUrJGc94IeNZR6W2rRUOPIfiVV1mO5HfoxYazBFVq6fOjaF3PI0zI3NvJbj/6CB6Iil0TQmEFEXgDVevwGXLGwAJ3PHg6XmPKaXEicE4sjkD33jPtbPOt3RKo9+N3eta8dn7Txa2RMxkKJbB0+cjuOET9+MPv7Ef/37fcZwcjCPsc2N9exCbOsO2rmtLVxjJrI5MzkBKM7C2LTRnt1y71M+lUaIl7PhAfi+SW8XyJj+uX9eCHz3Tgw+8ZEOFV0aVEMvksLzRh84FXr2sZXbs8Tw3ksTWZQ0YjmdsWlVp/G51ARlPa49n7Z/WrSvnyawZTFci8ExrOi6MJrGssbr+D82WVQi4VUSSGjI5HV5XbQTLtPQc6TO7qEaSGh44NoibN8+cefzGvrPoavChOTDzhTSfW0VryIP/fuI8/viWDYW9mlbWdOL/kweODSGeyWFNawDXrGmx88spihAC//fVW/A7n3sEe387c6D87cfP4fRwAmGvCx955WZsW96ILV1hBL0ux7ZKWQ2GklkdaU0vy/5OwMGMpxCiVQhxjxDimBDiOSHEXUKImSeeEtGiHB+Iweu6NIvx9VeuwJnhBJ69OF7hlVElxDM5XLkEs52AucdzMaMlcrqBC2NJrM5n2yrB51ZxejhR0liVeuxqG/C40BbyVmSW5+mhBAyJqh2lMlXAY5Vol9aUiqicDvdG4VIEfG4FH/vpYWRmmLl8eiiOh04M463XrZqz6mR5ox+ZnIGvPHxm1scYhsSnfnkMXpcy72xLJ+1Y0YTbdi7Hfz58elqZ8dceOYO//uHzaPK7saUrjLfvXoOrVzcj6PC2CSuDmsjmkMkZWF+GUSqAs6W2EsAnpZSbpZQ7AJwC8AkHj0e0ZJ0cjE96g/TKy7vgdSn44dMXK7gqqoRszkA2Z+DKlU2VXkpF+Nwq0jO8mSlWbyQNTZdYU8FOv36PimzOwMWx4gOuWFqDEEDQUz+BJ4DC/qhys0qda6nUFgCODdg775DITof7ogh4VKxuCeDMcAJfefjstMd887FzcKsCb7521ZzP5feoePXly/CNfecwPksDsnue78PhvihWNPuhVHiv9odfvhm6IXExcmlf6pcfPI2P/vQwXr6tExs7y1Pqagl6XVjVEsBYwnztytFYCHAw8JRSjkopH5hw02MA7OkDTEQFOd3A6aHEpDdIDT43XrqtEz99rg9GhUYRUGUs5f2dgNmcR9Ml3vSlRxf0+dYolUpmPK2RKidLKLeNpnMIeV1lfeNSDpWa5XlyMA5FAN973wsW3DmynHxuFQIodKkkqjaabuDEQBxBrwtNAQ9etq0Tn73/BPrH04XHJDI5fH//Rbz68mVFZSj/+OYNiGdy+Pq+s9Puy+kG/vXXx7GpM4TWYPGdaJ2yqjWAt163GkOxDFJZHZ//zUn84z1HcOvly/D5t15VkcB4c1e4MAO+5kttJxJCKAD+CMBPZrjvvUKI/UKI/UNDQ+VYDlFdOTuSRFY3ppWEve6KbowmsrYMXKbaEUvnIACz+cISZM3eLKFKdZJz+cBzTVtwUe3qF8P6v3yqhAZDsXQODXXUWMiyqiWA3kgKmr64fbulOjUYx6qWQEVG6iyEIgT8HrUwEJ6o2pwaiiOrG4Wy8L+9dRtyhsQ/3XOk8JgfPdODWCaHd+wuLk+1bXkDXrKlA1955My08Sp3HejB6aEEPviyzVXTmfpPX7wBijAvEH3ql8fwu1csx2fefAXcqn3hWCnnrS1dlxoWra2DUtuJPgsgDuBzU++QUt4hpdwlpdzV3s4toESlOjl4qaPtRDdtakdzwI3heLYSy6IKMAyJ0UQWDX73km0w4nVZgefCIs+zI0n43So6KrgfyKUqaAt5Ssp4xtIaQnU0SsWysiUAQwK9keLGJtjlxGAMGzrKU3pmF7+bgSdVr8O9Zhm4NR5lVWsA77tpHX7ybC+iKQ1SSnxz3zlsW9ZQ0iiwP37xBkSS2qRxJZmcjs/cewI7VjTiFZd12vuFLEJryIvlTX5kdQNvuHoFPv2mK+CyMegsldVgyK2KsnVEd/yrFUL8C4CNAPZIKct7yZJoCbA62n7vfZOvcnlcCm7buRxjySxyC03/UE3Zf24MWd1AW6jyZUWVYmWoFhp4mqNUAhW/Qr6+PYRTQ8U3iolncnXVWMhSiVmeOd3AmeEE1tdY4BnwqOgbT8+6342okg73RuFxKYWtBADwRzdvQHeTH+dGk4imczjaH8M7X7C6pN+/V61qxgvWt+LLD52BkX+v8z9PXEBPJIUPv2LubGclqlqWN/qwtSuMT/7ejkJDyEqxMp7lrOxwNPAUQvwjgKsB/K6UsjJ96Ynq3PGBGFa2+AtXESf63Su7ISUwlmDWcyn44YEeKAJoDjDwXOjW5jPDiYo2FrKs7wjh5GAcssgvJJau08CzArM8z48moekSG8rUbMMugUKDIWY9qfoc6Y9iS1d4UiDo96j4m1u3IpnVcXIwjka/G7+zs7twf7GB4Z+8eAOGYhkMxTPQDYnP3n8S161twY1lntk51UzrF0Kgwe+uiv34a1qDUIQ5jqlcnBynchmAvwKwHMCjQohnhBA/dOp4REvVycE4NnbMPFj4ypVN8LqUis0jpPLJ5HTcc7APzQFPxa+iVtJiSm11Q+LCaAqr2yrXWMiyoT2E8ZRWdKl8LK2VrVSqnDrDPnhUBedHyhd4WiXOG20e2O60Qmfbfna2LZc9e/c5NmfRaeVcu5QSh3uj2LZseu+BV27vQoPPhZwh8aZdKxbUSXr3ulZctaoJvZE0+sfTGI5n5s12LkSl9v07xaUq2NrVgO5mf9mO6WRX20NSSpEfp3JF/uN1Th2PaCmyOtpu7Jz5yrwQAm0hD6LpHEaZ9awrU980PHBsCOMpreQy23o7kRZKbRewsaNvPIWsblRFxtPaX1hsg6F6zXgqisCK/EiVcr1RPpEPPNeXqcujXTyqgrDPxc62VHX6o2mMJTVsnSHwFEJgTVsQDT4X3nXD2gU9vxACf/LiDcjqBi5GUnjxlg7sWtOy2GUvCSGfy9bmRvOp3I5WIlo0q6PtplkynsCljfzlbs5B5fXjZ3rQFvKg0V9/Wa9SeN0Lz3iey2fVqiHwtPYXFttgyAw86/N7v7rMI1VODcbR1eCruddTCIEtXWE2GKKqc6TPzMJvm6Xbut+tYuuyBnQ3LTzzdsvmjkK5+Z+/fNOCn6fa1NvF4fq7PEq0hFgdbWfLeAIoXMkairHctl5F0xruPTKIt1y7qnCCX6oW01zozLA1SqXypbbLG30IeNSiAs+0piOrG3WZ8QTMBkP7z45h67JwWZo+nRyK11xHW8vmrjB+/EwvpJQVb5BFS4NVhTBXcGR1tN3SFbYliJrpOYQQWNcWRDKr47LljYs+RrnVU3A5l/o8SxEtEVZH27neJHlU880HA8/69YuD/cjmDPzuld1LPvC8tMez9M89N5KA16WgM+yzeVWlE0LkO9vOH3jG8/Pr6jXwXNkSQCyTg25IuFRngykpJU4OxvGmXSsdPY7drDet33zsHGLp8+gdTy8qe0S1p5gAsFIO90WxqiXgeBVB0OtCcIaxUtX4mixV9XmWIloi5upoaylkPNlgqG798EAP1rYFsXNF7V3ltdtiMp5nR5JY3RqoaLfBiW+Q1rcH8cSZ0Xk/J5au78DTGqmSzhkIObwXqXc8jWRWr9mMpzUe4Vh/lIEnVY3ZGgvR0sM9nkQ17MRAfM79nYDZnENVBDOedapvPIXHzozgtVcsZ2kdJgaepX+uOcOz8vs7LRs6QugdTyORz2jOJpY25zaGvbW1J7FY1kiVjKY7fiyrtLlWA89N+U68S63B0GyNp2q562y9iGdyODeanHV/Jy0tDDyJalRON3B6OI4Nc+zvtLhVBp716ifP9EJK4Hev6J7/wUvAQsepGIbEuZEk1rZVT+C5Pj9H8vRQYs7H1XvGc2VzPvDMLaBVcYkKo1RqNPBs9LuxvNHHBkNUNY71RyElmPEkAAw8iWrW2RFzyPl8GU/ALLdl4FmffvRML65Y2YQ1VRQwVZKV8ZQlBp790TQyOQOrWyvfWMhiZd1ODs0dRBQynjXWhbVYQa8LbSEP0mUKPJsDbrSGvI4fyymb2dmWqojVWGgrM54E7vEkqlknBsw3FpuKGHLuVhXu8axDyWwOR/qi+NjvXFbppVQNn5XxLCFG2bN3H8ZTZvBWDaNULKtbg1AVMW9n22idZzwBs8HQqSJHyyyEVY5pSFmzZbaWzV0NePjkMDTdKOt8vqVoKJaBWsE94YthGHJBe+FLdbgvVsjEExsd1e9ZiqiO7dm7Dz1j5lzO9R3zv1H2sNS2Lg3Hs1AVgVt3LCvctuRPaqoCgdJLba39g9WU8fS4FKxuCeDUYGLOjpVWqW1DnWY8AbPBkJU5cdLJwTheub3L8eM4aeuyMDRd4vRQApu75r8wSZeU0hm2N5LCmeEE3KoCw5AVbUq2EKeGE4ilNSQyuUmdYO3ujnu4z2wsxB4EBLDUlqhmJTV93o62FreqIJ7JIZmdu0kJ1Q4pJUbiWdy0sQ1tNVwW6AQhSg880zkDHlXBssbq6gS6viOEk/OMVInnA8+gVy3HkipiVUsAmZzhaIZG0w2MJTVsKGL7QjWzgs2j/Ut7tJLTPv+bk5AAsrqBp8+PVXo5JbkwmsRoIgtNl/jiA6ccO05ON3C0L4qt3N9Zte68fXdZL1gz8CSqUamsXtT+TuDSSJXhWNbJJVEZxdI5ZHVzdidNpghRclfbtKZjVWug6srmNnSEcHY4MWfAFUtrCHhUuOq4rHJlfqRK1sF9nqmsmfWu9VLbdW0huBSBY/0xdnW1ydTX8eJYEt/dfwGtQQ+EAH76bG8FV1e6b+w7CwBo8Llwx0OncXEs6chxzo4kkMkZ7GhLBfV7liKqY1JKpDW9qI62gNnVFgCG4mknl0VlFElpEABetq2z0kupOmbgWWLGUzOwporKbC3r20PIGRIZbfaAK5bO1fX+TgBY3eJ8Z9uUVh+Bp8elYH17iA2GHPT535yEgMDKFj+a/W7cfbAf+kJmONlI0w0MxzPTGqtNDZoTmRz+58kLaAl6sK49CEUAn/j50QUft388Xai6mHrc933zKQDzd7Qtd9aNKoeBJ1ENSmsGJFBUxvPO23fjX/dcAQDc51lHEpkcAh61qFLrpUZRSpvjKaVEJqdX1QxPixUEpeaYYRnLaHXb0dZizfJMOzjLM6XpCHrUumiCsrkrvORmeZbLhdEkvrf/It587Up4XSpaQl4MxzN4/PRIRdfVG0nh1FACP3uub87H/eDpi4ilc+hq8MLrUnH7Tevxs+f6sP/saMnHPNofxbnRJI4PxjCamF5RlcjqcKuibBdzGMBWPwaeRDXIehNaTEdbAGgPm3sAGXjWByklElkdAS+DzpmUmvHUdAlDokoznmYwPFfAtRQynp1hHwQcznhmdazvCNVFE5TNXWH0RFLIVTgLVy5SSoynNPzi+T785Nle3PX0RXz3yQsYiKYxMkMWcDE+d/9JKIrA+2/eAABo8rsR9Kj46XOVK7fN5gwMx83A7+N3H0Y8M3M/B8OQ+NojZ7FzRSNC+fPH7S9ah64GH/7hZ4dhlPjz8u3HzkMAyOkSf/Ojg9Ne52RWx4aOMDwuhhtk4k8CUQ2y9iIV09EWAFqDXiiCgWe96ImkoBsSQU/9NpNZDEWgpDdQ6UJH2+rLeIZ9bnQ2eOfMeEbTubrPeCqKgNetzPk6LFZaM7ChvbbLbC1b8g2GUjXcUK6U/aljSQ1H+2N437eexgf++wA++N1n8ZEfPIezI0mcHErgl4f6bVnT+ZEkvv/0Rbzl2lXoymfGVUXgZds68fPn+x3dgzyXB44NImdILG/0YSCawWfvOzHj4357fAinhxP4/RvXFi6wBDwu/MWrNuO5i+P44YGeoo+ZyOTwwwM9aA150N3sxz0H+/GTKXtdk9ncvGW2tLQw8CSqQUlNh9elFF1mqSoCLUEvZ3nWied7zG6VQWY8Z6QqSkmZnnT+zeLatuoLPAGz3Na62DSTWFqr+4wnAIS8LsTTOVuzV5acIZHVDayv8f2dFquzbXKOn5t6MpY0R0vd/YEbce8Hb8IDH7oZj/zli3Hlyib43So++ctjyOmLDwo/e/8JuBSBP7p5/aTbb9u5HJGkhkdODi/6GAvxg6cvwqUIdDf7sWfXSvzXw2dwcnB6qfVXHjmDjrAXr9q+bNLtr93ZjZ0rGvHJXx4teq/qj5/pRTyTQ0fYi+WNPly5qgl/9+NDGIiavSSyOQOaLtlYiCZh4ElUg1JZHX53admu9rCXGc86cah3HAAQKPFnYKnwuxWkNb3orGda0yEALKvSvX0b2kNIafqsAVc8nUN4CVyECHldyBkS50ft78BpZb031kng2d3kR9jrWhKBp25IRJIamvxuXLa8ERs6wljTFkR3kx8el4IVzX6cHkrge09dLHzOQrr9pjUddx3owVuuW4XOhsm/K164sR0NPteCu9supvvwSDyD+44Moi3khSIEPvLKzQh4VPzdjw9N+p1xYiCGh04M4x27V08rfVUUgb+7bRsGohn0jafmPaaUEt967By2LmtAyOuCEAKffuNOZHI6/uIHz0FKWRjftnVZbY8nInsx8CSqMTndQFrT4S+xzJKBZ/14vmccfrdacwPLy8XnVmFIoLeIN1AAkNF0eN1K1Y4jWd8RgiHNvagzWQp7PAEUvkYnZibG8l05t3c32v7clSCEwKau8JyZ8nrxzIUIcoZEU2DmcvPmgBtXr27Gv/36+LyvR89YCs9dHMcnf3EUz/eMTwrceiIpM9v5ovXTPs/jUvDK7V341eEBpDW9rGNsfvJsL3KGRHvYAwBoDXnx4VdsxqOnRnD3wUuNhr766Fl4XAr+17WrZnyeq1e34Lady9E7nkYmN/11mvg1PXMhgsN9Ubz1ulWFkt117SH85Su34IFjQ7jzyQuFix4staWJqvMsS0SzOj+ahARKz3iGGHjWi+d7owh6me2cjXVR5uRgvKjHp3MGfK7qfT2tfYcz7W/UdAMpTa/7PZ6A+TtPEcBT5+wPPKNpDV6XguVNftufu1I2d4WRnCNTXi/uOzIAwGzyMxMhBP7yVVswGMvgq4+emfV5fn6wDxcjKRhSYu+Dp/Gazz6Mm//lAXzi50cxmshiOJ7F269fjY4J2c6JXVRv27kc8UwODxwbsvGrm98Pnr6Iy5Y3TNp685brVuOy5Q34+M+OQDckcrqBu56+iNdd0Y3WkHfW5/qLV24GAJwbSc75c/Otx84j6FGnzZF+x+412L2uFf/vZ4cRSWrwqAqaAp5FfoVUTxh4EtWY/nFz/4S3xC5x7WFzj2e9vwmpd4PRNIZiGQQ5RmVW1kWZYgJPwzBn4nrd1Xs6XD/HSBUrA7EUMp5CCIS8Ljx9LmLr8+qGRCydQ0OdBe9busLQDYnsLJnyarHY7OC9RwYQ9rnmrFi4Zk0LXrq1A1984BTGZhj7cXIwhg9971kEvSp2rGjEk3/9Unzi9ZdjVUsAX37oNE4MxqEI4PYZsp2W3eta0Rr0ON7dduLrdbQ/iud7onjD1SsmPUZVBP7htdvRH02jJ5LCYCyDtGbg3TeuKTxmptEjK5oDWNHkx1hSwzf2nZvx+JFkFj97rhevu6q70BnXoigCn3rjDgghEMvkEOAFUpqies+0RDQjq0GQu8SywPawF5putpyn2nWo12wsxBP67NyqApcicGJg/sDzwlgShgQCVdwhuCPshSrEjGWCViOQpZDxBMwA+2h/FIlZxkUsxJG+KHRDosFfX8H7li6zxLGWO9vO58JoEscH4miepcx2og+/YgsSmRy+8MDJSbfH0hre+82n4Peo2NgRhiIEWoIevPnaVfjme67D/r9+Kda2BbGxI1QYTTYTl6rg1Zcvw31HBopu0LNYP3jqItyqwGuv6J5239Wrm/GmXSvQP55G33gaL1jfWviZmMuyRh+a/G58/O7DM1YXfP+pi8jkDLz1utUzfv6K5gD+7rZtAMDO6zQNA0+iGmOVy7rV0vb3cZZnfXi+x2wsxIzn3PweFSdm6Oo41ZG+fCBfxa+nEAI+jzJP4Fm967dTyOuCIYFnL0Rse859p0YAoO4ynlY30Ximfvd53psvs20uopxzc1cYr79qBb7+6LnCHkbDkPjgd5/FuZEkPveWq2asJGoOetAR9hZVMnrbzuVIawbGktOzqnbL6QZ+eKAXt2zuQEtw5rX9xSu3QFEEcobEu29YW9TzCiGwvj2IrkYf/vjbT2NkQjd8KSW+8/h5XL26GVvn2Lv5xqtXYH17cFoTJiIGnkQ1ZiiWgRBmKU0p2kMMPOvB873jWNcWLPn7v9T43SpODMbnLS0/bGWQq7xDcJPfg1gmh/1nRyfdvhQDT8DeBkP7To/A51bqbsh9yOuCz63Ymh2uNvcdGcT69iB8Rf7//T8v2wQI4OKY2Xjsi789hV8fHsBfvXorrl/Xuuj17FrdjK4GH0ZmKOe124MnhjAczxTKbGcqnW0NebGmNYDmgBsv3tJR9HO7VAVffOvVGE1m8YH/OVD4PRNN53B6OIG3XjdzgyKLEAJtIW/JlVlU//gTQVRjBmMZeFSl0EmuWIWMJ2d51rTne6K4rE46bzrJ71ERS+fmvdByuC8Gn1up+g7Byxp98KgK/v4nhyaV8RUCT299Zetm41IVbOwI2dZgKKcbeOLMaN1lOy0hrwuJrDOzT50ipcQvD/Xj+Z5xjM4RwEXTGh4/M4KXbu0s+rm7m/x45+7VGI5n0Teewr/86hh+Z+dy/P4Na2xYubnH8dYdyzCe1GyZGzqX7z91ES1BD27ePHdA2RbyYlNnuOSLldu7G/Hx127HIydH8G+/Pg7A7DHQHHDj1ZdfmgM6U8BLNBsGnkQ1ZiiWKbnMFmCpbT0YS2TRE0lh+/IGnuznYTUYOjFPg6EjfdGaKFtWFYFVLX4c6o3izicvFG7PLbGMJwBctaoZBy5EbAmmDvVGEc/k0FCnr1/Q64KmS/Tlm9JVuzPDCbzrq0/i9m8+hURWx9mRxKwZ2wePD0HTJV66bfbAc6bfk++/eQNUReD8aAqbO8P4xO9dXvKF3LnctnM5JICxZHH9FDTdwMWx1IwjTGaT0w3ce3gQr71iuaOZ+jddsxJ7dq3E535zEoOxNMaSGt64a2XRGWaiqRh4EtUYM/As/b9ug88Fj0thxrOGWY2FLlvOjOd8CoHnwOz7PMeTGnoiqapuLDRRS9CDa9e24FO/PIrx/JtaXS69wPPq1c2IJDWcHk4s+rn2nc7v75xlFEetC+Uvqjx3MVLZhWDu7rW6IfEvvzyGV/zbg3jq3Bj+5tat2LosDE2X+PxvTs74PPcdGURzwI2rVjWXdCGuOejBimY/PKqCvW+/2vb93TtXNMLnUjAQTRd1ceSnz/aiJ5IqdKwvxkgii6xuTOtm64SPvfYyXLa8AWeGzVFus80BJSoGA0+iGjMUX1jgKYTgLM8ad6jXbCx02XIO5J6PWxVo8LnmzHge6bcaC9VG4CmEwEdvuwzjKQ3/dq9Z+rbUutoCwFWrmwDYM89z36kRbOgI4a7331CXFQQBjwoB4NmL45VeyqzGklk8d3Ecn/vNSdy6Yxnu//MX4Q9euA4NPjdagx7850NncG5k8kUGKSV+c2wQt2zuWNB+964GH65Y2YjVrUG7vowCIQSWNfqQyOp45OTInI81DIkvPnAKgBlMGkV2wx2KZbClK1yWi5A+t4ovvvVqqIpAo9+NtW32v2a0dDDwJKoh2ZyB0UR2wRv228MMPGvZ871RdDf50TxLB0O6RAiBjZ3hOWd5FhoL1UCprWXb8ga89brV+OZj53C03xwDIgTqrjHOXNa1hdDgc+HAIhsMabqBJ8+OYrcNTWWqlaII+D1qVWQ8Z7L3t6dwfCAOVRG4873X49/2XIGOCZ1QV7UE4FIFPn73kUmfF8vkEElqeEkJ+zunsrO8dqq2sBduVeBzvzkx5+PuPTKAE4NxNPnd0HRZVNOsZFZHIquXJdtpWdUawM4VjdiYnylMtFBL50xF5IDFDr4u1UjCDBo9C9jjCZhNBhh41q5DPePY3s1sZ7E2doTmDDyP9EXRFvLUXND2wZdtQtjnwsd+chi6IeGq8sZIdlMUgatWN+Ppc5FFPc9zF8eRzOrYvb5+A0/AbDD03MXxorNp5WAYEv9492H888+PoiXowfbuBlw3wwUAj0vBH9+yAb8+PICHTgwVbo8kNbhVgZs2tdm6Lrv2zitCYFmjH4+dHsVT50ZnfIyUEl944BRWtvixvj0IIYCfPdc373MPxdIQAH73yumzO53kVhV2U6dFq62zLdESZwWNf3XrtgWdHNvDXgxzj2dNiqXNPW3bub+zaBs6QhhJZCfNoZvocF90zll01ao56MGfv3wz9p0ewWgiuyTfDF61qhnHB2OIprUFXwB8LL+/87q1LXYvr6oEvS7E0jmcHVn8nlg7aLqBD33vWXz5oTN45+7V2NAehDJH9vE9N67FqpYA/uGnh6HlO8WOJbO4fl1r2UrMFxKQdoS9aA648fnfnJrx/n2nR/DMhQjee9N6uFQFTX437jnYN6lr9VRpTcdQPIvmoAdt+RFpRLWEgSdRDbECT6tDbanaw16MJLKOt3kn+x3pM5vkbOcolaJtyJeFzZT11HQDJwbi2FaDgScAvOXaVdi6rAE5Q0J1sGSwmkx883/VqmZICTxzPrLg53vs9Ag2d4bRWudv4ENecw/zc2Xa5zlfE6E//MZ+3HWgBx96+SZ89Hcum7fk1edW8Te3bsWJwTi+9dg5pDUdac3AS0qYS1kJqiLw+zesxf1HBwv78ye+Nl984BTaQl68MV8y2xr0YDCWmTardyIrMO1Y4HuAYrBjOjmJgSdRDbECz4WedNrDXkiJOWejUXV6voeNhUq1sTMMADg5ND3wPD2UQFY3ajLjCZhvaj9627bC35eanSsboQgUtSduqj179+GNX3oU+8+O1X2Z7Z2378YP338DfG4Fz1Z4n6emGzjSH8WDx4fwz6+/HH/y4o1F77N82bZOvHBjG/7t18cL58HF7O8sl3e8YA1CXhe+8MDkrOfBi+N46MQw3nPj2sJokqaAB16XgrsPzl5u+53Hz8PnVmpi/A8DWJoJA0+iGjKYP+G2hhbWXKY9f2V/kPs8a4Z1hfxQbxTtYe+kxhs0t+WNPgQ9Kk4MTA88D/eZgfy2Gg7kr1vXiu4m34J/H9SysM+NTZ3hBXe2TWRySGk6rq/jxkIWl6pg+/JG2zOepZQ4D0TTONwXRTKr44tvu7rkkRxCCPzda7YhkdXRO56G361iZUtgIcsuq0a/G2/fvRr3HOzDqQkXwL7wwEmEfS687fpLr4OqCLx4SwfuOdg/Y7ntsf4Y9p8bQ0fY62hjJCInMfAkqiFDsQyaAm54XQsb/2CV6HKWZ+051DuO7TUcJFWCEALrZ2kwdKQvBo9Lwbq2YE1fmV/RHEBHeGlejLhqdTOeuRApalbiVNFUDkIA16+r7/2dlh0rmnCod3xB2ywW20Tv4lgSb9q7D9mcgS2dYbzisq4FPc/GzjDesXs1AKA5UDvjg95z41p4VAVfymc9U1kdvzjUj3fuXjNtj+qtO5ZhOJ7BE2eml9t+5/Fz8KhKyXs7a/n3G9Wf6s/VE1WxaEpDStMhpZx0BdI6Sdv9y34olilkLRfCKtFlZ9vaYhgSJwbjeNm26i8tqzYbOkJ45OTwtNsP90axuTMM1wJHE1HlXb2qGd95/DxSml7ySJxoWsOWrgY0BZZGtnjnykZ85REDxwfiZc3ynxtJ4C1ffrzweodLKBGd6fz5Zy/dhJ8+27vgPgflMnHtbSEv/te1q/Ctx85h+/IG9I2n4HUpePcNa6Y9PpnNwe9WcffBXuxe31p4L/HVd1+Duw704NWXd6FvPF3Wr4XITjzjEi3CxbEUzo4k8YlfHF3QVfdSDcUz6GhY+AnXulLKwLO2JDUduiHLMiy83mzsCGMgmkE0rRVuk1LiSF8UW5eFK7gyWqyrVjcDAOLpXEmfZ0iJWCZX1/M7p9qxogkAyjrP8+RgDG/80j4kszn89x9eX1LQOZtGvxvr20OFfZELVe4s4HtvWgcAODeaxHA8izdfs2rGplYBjwsv3tqBXzzfPyk7/bNn+xBL5/CW61aXbc1ETmDGk2iBcrqBRDYHlyKw97en4VUVfPDlmx095mAsjatXNS/48/0eFWGvi4FnjUlkzDfWnOFZuo0TOttelf+/MxjLYCSRranGQiyVm25NawAtQQ/imRxK6W8aT+cgJeq+sdBEa1oDaPC58OzFcbz5WuePl8jksGfvYxBC4H/euxubu2a/yLMUfraXN/nx+qu68d39FyEA/GE+EJ3Jay5fhruf65tUbvvtJ85jY0cI16xZ+Pl/sZbC94mcx8CTaIGODcRgSGBtWwDXrGnBf9x/Ei5VwQdestGR40kpzVLbRZYYtYe93ONZYxJZHY1+N7qb/JVeSk2Y+AapMFJl4FLgebgvCgA1O0qFTEIIXLWqCQ/PUEo9Fyv7fW2dz++cSAiBHSuaypLxjKU1HBuIoyPsxbf/4Dqsaw85fsxa8Ec3b8B3919Ea8gz5+/ymzd3IOBR8dPnzO62iUwOz/dG8fe3bYMQggEg1TQGnkQLdCA/Py7kdeGfXnc5NF3iX399HB6XMxXs8UwOac1YdODZFvYy41ljkpkcrljVxE6GC7CyJQCPS8GJwVhhv9SLNrcDALYw8Kx5V65qxr1HBktqmhNN5xDwqGj0106DGjvsWNGIOx48jbSmL7pUdSopJR48MYw7HjyFw30xeF0Kvnv77proPFsua9uCuGx5A3zuud8j+D0qXrK1E794vg8bO0IYjGXgdSl4/ZUryrRSIucw8CRaoGcuROBSBLwuBYoi8Mk37EDOMPCJnx/FqpYAljXa22nSChbtyHge6Y3asSQqA0NKJLM6tnN/54KoisD69smdbQ/3RrGi2b/kAo96dLW1zzNT3D7PtKYjns6hcwmOJdqxogk5Q+L1X3gUYZ/LlsyZISXuevoi7njwNI72x9AR9mJlsx8dDV4GnTMIeYt7233r5cvw02d7MZbUMBzP4PVXrUBjDXXyJZoNA0+iBTpwfgwhr6uQhVIVgU+/cSc03cA9B/ttH+puzd5c7OiE9pAXDzLjWTNSWR0SwGXdDDwXamNHCE+fHyuUt5mNhZjtrAc7Vpj/L6JFNhj69eEBSGBJXnTYudJ8rRKZXEmNfmJpDX3jabz3G/shBKAIAUUInBiMI57O4cmzz2JTZwifesMOvPaKbrz9vx536ktYMm7e3I6gR8WZ4QQMCbzlutLmnhJVKwaeRAswntJwaiiBFc2T92m4VAWfefOV+O3xX6Hf5pbndmY8Y5kcUlkdfo+95VZkv/GUuR/tcgaeC7ahI4SfPNuLrnyW68xwAq/ZsbzCqyI7BDwuNAfcGIpliioh/ea+c/C6FDT6l97bn64GH9rDXiSyxXcBjiSzODEYh5TAhbEUpJQwpIQh86M/PAr27rkaL9rUDmWei63cm1g8n1vFS7d14sfP9CLgUXHlyqZKL4nIFkvvNy+RDZ69EAEAfOL1O3DjxrZJ97lVBU1+N3oiaSSzuZLny82mEHguYo4ncClwHY5nWApVIqfms85mLJFF73gajX431rYFy3LMemR1tk1rZvbYkGDGs450NvgwlozhJ8/24k27Vs76uKP9UTxxdhQrm/1Lcr+0EAI7VzTO2oxppt9v/+9nR6DpEtuXN+BnH3jhjI+/ZUspPYWpWK/ZsRw/fqYXHWHvkvx5pfrEOZ5EC/DMhQiEAHasnDkLFcwHm0f67NtLORTPwK0KNC1yn4cVeA6y3Lbqfea+E9ANiVUt7Ga7GBs7zcAzmdWRzO8FZEfb+tHgc8HvVvHVR87OOU/ZynYutmqklu1Y0YS0ZiBnzD93+oFjg/jB0xexvNGHYJF7E8uh3DM4K+WlWzuwqTOEjiX880r1h4En0QIcOD+GDe0hNPhmDgKtk/TBi+O2HXMolkF7aPFXPq2MKTvbVpc9e/cVMggAcHoojm89dg4dYa9tWfOlanVrEC5FIK3pSGR1hL2uaWXyVLuEEOhq9OJIX3TS7MOJomkNPzzQg9t2LodbXbpvfaw9sYl5mjHF0hr+6q6D2NARQjf/r1SEEALNAQ+znVRXlu5vX6IFklLimQsRXDHHngu3KuBWBQ722JfxHLRhhieAwtVTzvKsbv/886PwuVUGSDZwqwrWtAWR0nQkszq2LAvPux+tViyV7M98WoNeNAXc+OojZ2e8/4dP9yCZ1fGO3avLu7Aqs2NFE4D5A8//7xdH0RdN45Nv2AGFgY9t+P+VljoGnkQlOjeSxFhSw5X5YfQzEUIg6HHh+R6bM542BJ4tQQ+EYMZzLlOzj+X26Klh/PrwAP7o5vVLOjtjp40dIbPUNptjmW0dUhWB/3XtKvzqcD8ujiUn3SelxDcfO4edKxoLgddS1RL0wOtS5gw8Hzs9gm89dh7vuWEtrprjPEdEVCq+oyEq0TP5xkJzZTwBIOhVcWIwhlRWt+W4dgWeLlVBa9DDwLNK6YbEx392BN1NfrznxrWVXk7d2NgRQiZnsLFQHXv79ashhMA3950DcOkC0r5TIzg5GMfbd6+p7AKrRNDrQnyW85JuSPzFD57D6tYA/vzlmxf0/MzqEdFsGHhSzal0NuqZCxEEPCo25RuWzOTO23fj7267DIYEjvQvvtxWNyRGExm0L3KGp6Ut5GXgWSIpJcZTGjJa8RcSFvKzetfTF3G4L4qPvHLzvKMhqHgbOsOFvzPwrE/Lm/x45WVd+O8nziM5YWTIN/adQ1PAjdfsWFbB1VWPkFdFNmfgn39+BD860IPDvVFkcubvtYtjKZwbSeITr9/BcVtEZDt2rCAq0YHzY7i8uxGueUogrbmLz/eML7pcaSSegSEXP8PT0h72co9nkXRD4p6DffjCA6dwtD+GhhIGry/kWJ/65TFcsbIJv7OTcybttKH90oWizV3hOR5JtexdN6zB3Qf78MMDPQCATM7Ar48M4A9uXFu4kLPUs3FNAQ+G41n810NnCt1tVcXsS5DWDLz1ulXYvb618Pil/noRkX0YeBKVIK3pONwXxXtuXDfvY5c1+tAa9NjS2XbQphmelvawF6eHErY8Vy1YyPxNQ0rc+eR5fOm3p3FmOIH17UE0BdyIJDX0RFLoblp405/Z1tM3nsJgLIMvvu0qdjK02bp2cw6qz60wk1zHdq1uxvbuBnztkbNoDrgxFEvDkBJvu35pNxWayO9WcXl3I775nutwZjiB4wMxHB+I4VuPnYPXpeIvX7WlqOdhQEpEpWLgSVVrtjfnhiFRqffkh3qj0HQ57/5OwGwwtL27EQdtaDBkZSdtzXjGMpBSMsCZwVgyi7PDSTx5dgzbuxvwxbdehVdc1oXXfeERRJLj+NGBHvzxLRtsPWYqq6NvPI1bdyzD1atbCrfzzZ09fG4VAY9aVfMIyX5CCLzrBWvxoe89i82dIQzGMrhlcwdWtgQqvbSq43Ep2NwVLlQAWKNowrOMCaPy4+9/qjc8A1NN+f5TF7H//BhWNVfmTYTVWOjKVU1FPf7y7kY8fHIYaU1fVJbF2o9p1yDp9pAXWd1ANJVDY4BvMqY6P5qEIoCv//61uGljWyE497lVhL0u3PX0Rbz/5vW2Be0PHBvEob4oFCHwl68sLttApdu6rIGNDerQ1Dfnt+1chk/8/AhODSWQMyTevsRHqBARVQueg6kmZHI6/vqHB/Gh7z0LKSs3g/LA+TEsb/Shs6G4Jj/buxuhGxJH+hbXYMgKPO3MeALAUDxty/OVm5MNpsaTGtKagbawFy/a1D4tuGwLeXBqKGFLJltKif986DR+/2tPwutSsH15AzMzDnIpom7md9LsvC4Vb7l2FXKGhNel4EUb2yu9JCIiAgNPqgF94yns2fsYvv34edz+onVY1eJHMqvj5GC87Gt55kJkzvmdU23vNrtnPt+7+MAz7HPZtjfNCjwH2dl2mmcvRgAAoVlKMluCHnhcCu56umdRxzGkxEe+/xw+fvcRvHxbF7Yta4CXew+JbPG261dDEUBXo48XG4iIqoRjgacQ4l+EEGeEEFIIsd2p41B9G09peM1/PIwTAzF88a1X4f++aitag2bQdPdzfWVdy1Asg4tjqaL2d1q6m/xoDrjx/CIbDNk1w9Nilez+7Y+et+0564VVTh30zhwEulQFL93agZ8+2wtNNxZ0DE03cKQvhu89dREfeMlGfOGtV0Hlm2Mi23Q0+HDVqmZ02vh7k4iIFsfJPZ4/AvAZAA85eAyqYwPRNM6OJLGhI4Qvve1qbOgwxyF4XArCPhfuPtiL//3SjWVbT6n7OwH7GgwNxTK2dbQFgPaQWSqs6dK256wXz1yIwOdW4FJmvy73+itX4J6D/Xjw+BBesrVzxsfohkTfeAopzcD//p8DSGV1pDQdaU3HcxfHYUiJz7/lKtzK2YJEjuDFHCKi6uJY4CmlfBgAO2bSguR0A+dGk2jwufCjP75hWtljS9CD4wNxHB+IYVOn8zP59uzdhwujSbgUM5AsxeXdjbjjwdOLajA0FM+UfNy5NPhdEMCCM3b1SkqJZy5EZi2ztZqYaLqBlqAHdx3omTXw/PJDp3F+NAW3KvDMhQj8bhV+jwq/W0XY50J3k59BJxERES0ZFe9qK4R4L4D3AsCqVasqvBqqFqeHE5DS3Is4NQi48/bdGIylcd0/3Ye7n+vDppeVZxh8PJPD1mUNJQePl3c3ImdIHOuPYWcJZboTDUbTaN/csaDPnYkQAm5VqdnA0zAkRpNZZHMGPK75dwxouoFsbv6v9cJoCqOJLNa0zt3gx60quG3HMvz3kxcQTWtomDJ+4PmecXz6V8fQHHBjY0cI333fCybd71RjJCKiheLoDiJyWsWbC0kp75BS7pJS7mpvZ+c5Mh3ON+MJeGYO8jrCPly3tgV3H+yDlM6Xi0opEc/kStrfabEylQstt01kckhkdVv3eAKA2yVqNvDsj6ZxaiiBv//J8/N+/8dTGg73RXGoL4pYWpvzsQcujAEAPveWq+Z9E/a6q1YgmzPw84OT9xqnsjr+9/8cQEvQg7VtQVZ9EFFVufP23QwyiagiKh54Es3kSF8UQmDO7OKtO5bj5GAcxwec726b0nQYEgsKPFc0+9Hod+NQ78ICz+G4vaNULGbGs/b2eEopMRTPQBHAfz9xAV995Oysj9V0A3/ynaeR1gxICTxycnjO57b2d27pmj+LvnNFI9a1B/GDp3smjXf55/z8wE+/8Qq41eJ/xfLNIBEREdUzBp5UlQ73ReF3q1DmyBa9ansXFAH87Llex9cTz+gASmssZBFC4PJFNBiyZnh2OBB4FlN+Wm2e74kirRlY1RLAKy7rxMfvPozfHBsEMHm+p5QSH/3JITx0YhhrWwNQFYH7jw7O+dzPXIhgR3cTXEUEjEIIvP7KbjxxZhQZzfz5+M3RQXxj3zm858a1uHFj2yK/UiIiIqL64eQ4lf8QQlwEsALAvUKIQ04di+qLlBKHe6MIzlJma2kLebF7fSvufs75cttEJgdVEVjbFlzQ52/vbsSx/hgyOb3kz7UCT7sznh5VIGdIpLXS11RJdx24CAGgNejBv+25Alu6GvCB7xzAiYHYpMd99ZGz+Pbj5/G+F61HR4MPjX43fnNsCIYx889KNmfgUG8UV5RwceG1V3QDAIYTWWi6gQ9//zls6Qrjw6/YvNAvj4hswioCIqLq4ljgKaX8gJRyhZTSJaXsklJe5tSxqL4MxjIYSWQR8Mzf++rWy5fj9HACR/pi8z52MVJZHX63uuD9epd3N0LTJY73l14WPOhQ4Gm9vsf6nX3t7KTpBn7yTC+aAm64VAUBjwv/+c5d8LpVvOfr+wt7Vu8/OoCP330Yr7isEx/JB4FNfjeGYhkc7ovO+NxH+qLI5oySyqlXtgRw7doWDMczOD2cQDSt4d/ffMWCuxcTERER1SuW2lLVsRoLfeqNO+e9Wv2KyzqhKgJ3H3Su3FZKiZSmz9roqBiXL6LB0FAsA1URaAl4Fnz8mVgZ5ecWOWO0nB46MYSRRBZtE2aaLm/y4453XI3+aBonBuOIZ3L40+8cwLblDfi3PVdAyc/yawq4IQRmLbe15rSWuo/3967qRlozEElq+ItXbsGWroZ5P4eZGCIiIlpqGHhS1bEyUluWzd/gpTXkxQscLrcdimeQMyT8i8hirWzxo8Hnwr/fe7zkURpDsQzaQp5CAGUXj0uBSxE4eDFi6/M66a6ne9AccKMpMHl8yVWrmvGpN+xALJ3D4d4owj43/uud10zKmrtVBTtWNM0ZeHaEvVjW6CtpTa+6fBkUATT4XHj3C9aU/DVR+TDgJyIiqhwGnlR1DvdFsaolMG024mxuvXwZzo4kcdtnH3ZkPuKJfNdc/yIynkIIbO9uRCKTK/lzh+IZ28tsrTUFvS48d7E2Mp7RtIZfHR7AbTuX43vve8G0AOK1V3Sju8kHVRH4z3fuQmfD9ADyls3tePZiBCP5TsETPXMhgitWNpVcTt3gc2P78kZs6gxPuzjAQIeIiIjIxMCTqs6R3ii2FpHttLzisi64FIGRRNaR9RzPN61ZTMYTMMttk1kdRomZ2cFYGu0h+wNPAAh6VRwfiCGVrf4GQz8/2IdszsDrruye9TErmgO4alVTYXbqVC/e0gEpgd8eH5p0eySZxZnhREmNhSbye1SoNmekiYiIiOoJA0+qKolMDmdGEti2bObAYSbNQQ9u2NCG0UTWkXLb4wMxNAXcuOv9L1jU82zvboQESg7yhmLOZDwBIOhxwZDA4b7qz3re9XQP1rYF592DOVfGcvvyRrSFvNPKbRe6v5OIiIiIijN/21CiMjraH4OUwLbl8zdomejWHcvw2+NDSDiQuTs+EMemjvCCO9parAZDiWzx5baGITEczzoWeIa85q+A5y6O4+rVLY4cY7H27N2HjKbjmYvj+POXbVrQ92FiuevNm9vxq0P9yOlGYV7ngf+/vTuPr6q69z7++Z0p8wRJgBAGQQYHJpnEWlFbO6qt1opYqtdapXa8em9v23uf2/Zpb/v4au3TOhbUOvZxrnWqPrVOOCEIEkDmGUIgIZB5zjnr/nFOcgNkzjnJCXzfr5evcPbeZ++18zPJ/p211m/tLccMpuZnRqvZIiIiItKGejwlrrQUFupp4nnRacMAqKxrimp7nHNsLa5i4vDUPp9rzNBkvB6jur77iWdZbSPBkCM3rWcFb7rjycXz+Ot3PkFuWgLr+zDPc8HS5TGZW9tWaWQY9Zc7GWbbXRdOzqWyvpmP9pa3bivYV87E3LTWRFxEREREoktPWRJXNhZVkpHkJ6+HlUWzUgIk+jxU96J4T2eKKxuoqm9m4rDuzzntiJmRlezncE0jR2oaGZLS9fIosVrDs62p+RlxvaSKc47S6gbmjB3CqCHJfT7fuROy8XmMNzaXMOeUITjnWFtYzmdPH97rc6qAkIiIiEjn1OMpcWXjgUpOH5Heq+GUKQm+qCeeLYWFJuT2PfEEGJGRSMjBw+/v7tbxh/oh8ZwyMpMdh6qj/r2LlprGIPVNIS47q+vezu5UkU1P9DNrbBZvbQnP89x9uJby2qZeFxYSERERka4p8ZS4EQw5thys5LQRPRtm2yI1wUdT0HGgoi5qbWpJPCcO6/tQW4DkgI/MZD8PL9/draVVWhPPGFW1hXCPp3OwIU57PUurGzCDL0wZEbVzXjg5l80Hq9hfXkfBvjJAhYVEREREYkmJp8SNXaU11DeFejy/s0VqYnjk+NpIhdJo2FZczdCUAEOjmPjlZSRSXtvEEx/u6/LYQ9Wx7/FsWXpkfRwmnk3BEIerG8lKDpCR1L11Xbvjgkm5ALy5uYSCveUkB7xRGU4tIiIiIu1T4ilxo7WwUC97PJ/99jkEvB7WRDHx3FpSxYQo9Xa2SEv0M/eUIdz/zk4am0OdHvvQe7vwWHgYcazkpCWQl5HIuj4UGIqVl9cfoDnkyE7tej5sT5yam0p+VhK3vbqFZ1YXMjU/Q+twioiIiMSQEk+JGxuLKvF7jVNze5foJfi8nJaXHrUeT+cc24qrY9ITdtP54zlQUc/zBfuBjivDNgUdfm/sf0yn5GfEXY9nKOS4+83tJPm9ZEaxtxPChZ4unJxLZV0TtY1Bpo/Kiur5RURERORoSjwlbmw8UMmE3DQCvt7/bzk9P4P1hRUEQ67P7SmqqKe6oZkJUUw8W4rfzJ+Yw2kj0lmybAehTtraGAwR6IfEc2p+JrtKa6joxXI0zaFQp/fQW3/fcJCtxdXkZSb2eQ3V9lwwKZeQA4fmd4qIiIjEmhJPiRsbiyp7Pb+zxbRRmdQ0BtleUt3n9rQWFuplD2xnzIybzh/PjkM1/GNTcYfHNQVD+PuQiHfX1PzwPM+Pe9jrGQw5NhRVsuFAZZfDhnvCOcedb2znlOwUhnZj2ZnemDd+KC2ja2eooq2IiIhITCnxlLhQUlVPaXVDryvatmjpuWqpVNoX21or2sam6MwXzhzO6CHJ3PPWDpxrv8cwPNQ29nMPp0QKDPV0nuf///gg9U0hahuD3PH6tqi15/VNJWw8UMm3zx8fk95OgES/l/QkPwk+D8PSe7ZurIiIiIj0jBJPiQubDoSTvN4WFmoxdmgK6Yk+Cvb1fb7i1uJqctISyIpRj5vP6+HG88axdl85VfVHL63S0Bzk+YL9BEP9M8czMznA6CHJrN9f3u33OOdYsmwHiX4P2akB7nlrO2v29j3hD/d2biM/K4kvz+h67c6+GJedwuThqmYrIiIiEmuxK5Up0gMbi/pW0baFx2NMG5VJQRQKDG0rrora+p0duWJmPn94bRtFFfWkJ/nZd6SWx1bu5akP93G4ppEEnyfqhXU6MiU/o0eFmZbvPMz6/RWcMjSZIakBEnxe/uWptfzt+58kKeDtdTve3lbK2sIKfn3ZFPxeD08untfrc3XF7/Xg731TRURERKSb1OMpcWHjgUpGZiaRkdz3JGv6qEy2FldR29jc9cEdCIUc20qqmZAb296wRL+Xb5w7loq6JjYdqOS8377J0mU7OGtMFg9/Yw7T8jNiupRKW1NHZlBYVseRmsZuHb902U6yUxPITk3A5/HwmyumsrO0ht/8fXOvrr9g6XKuXPI+d76+jREZiXxlZmx7O0VERESk/yjxlLiwsaiiz4WFWkwflUkw5Ph4f2Wvz7G/vI7axmDM5ne2tejsMfg8Rl1TkO9dcCrv/uhC7rtmFvMn5sRsfmN7pkQKDHVnWZVNBypZtvUQ131iLJ5IhZ5PnJrNtfPG8OB7u3l/R2mHS8R0tB2gsr6ZVXvK+Nb88ST41BUpIiIicqJQ4ikDrraxmZ2lNX0uLNRiWqTAUF/W89xW0lJYKLZDbQHSE/1MG5XJ9FGZ3PKZSeRlJsX8mu1pKTC0vrC8y2Pve3snyQEvi+aOOWr7jz9/Gqdkp/DDp9fR3IslVorK68hJS2DB7FE9fm9vtCxvIyIiIiKxpcRTBtxX/7gc5/o+v7NFdmoC+VlJfZrnubU4vBxLNNfw7IzPY3j6sXezPWmJfsblpPCnd3d12CMJ4d7gF9YWsXDO6OOGRicFvNz21WkcqKhj7+GaHl2/qr6JyvpmFp83jkRNvBQRERE5oai4kAy4mshczDOiNNQWwr2eBXvLe/3+rQerGJaeQEY/FfaJF1NHZvDy+oOdHvPAu7sA+Ma5p7S7f+aYLBbPH88f39qBWQ1Llu1gSHKArJQAWcl+6hqDYOF1UpuCIZqDjuZQiMKyOnwe4+q5o6N+XyIiIiIysJR4yoCrbQziNSM/K3pDTGeMyuRv6w5wqKqBnLSEHr9/a0lVv8zv7Ep/DwOdkp/JcwVFNDaH2t1fUdvE4yv3cum0PEZGhgS318Z//vQEHluxl0NVDdz6SvvFhj7z+7eP2zYqK4nkgH4tiYiIiJxo9IR3gmsZMhnP89hqGppJDnijWkin7TzPT58+rEfvDYUc20uquXrOmK4PPsFMjRQYqmmnIvCCpctbiy7dcN64Ts+T4PMyeXgazjkevG4OR2oaKa9t4khtI794cQMAN180EZ/Hg99r+Lwebn1lE2n9VMFXRERERPqXnvLi1GBIGKNhd2kNNY3BqPZ2ApyZl4HXYxT0IvHcV1ZLfVOoXwoLxZuWebY1DccnnqGQo7iynvkTc7pdCMrMSEnwkZLgY9SQ8Lbs1HAP9MVT84469p43t/eh5SIiIiISz5R4yoD6y0eFAOSk9nw4bGeSAl4mDUtjbTcqtB6rpbDQxOH9N9Q2Xj5gSEnwkeT3UlnfzAc7D1PXFKS+MUhdU5C9R2ppCjoWz++8t1NERERE5FhKPE9wdY1BEvzxWbw4FHL8ZXUhGUl+Ar7ot3H66ExeXFtEKORa15rsjq3F4aVUJuSefD2eAKkJPg5VN3DVvR+0u2/euKHdOk+8JNMiIiIiMvCUeJ7A1hWWs25/BRlJPhqbQzFJ7vpi+c7DFFXUc8fCGVw6La/rN/TQ9PxMHluxl12Haxif0/0kcltxFXkZiaQlnlwVbVuMHpLEkBQ/P7v0DJL8XpICXpL8Xv75iQL8XuvzXFwlpCIiIiInHyWeg0xP5n7e9upWPAYVdc38y9NruX3B9B71/MXaM6sLSUv08ZkezsHsrumjMwG44eFV5KQldOt7tmDpctbvr2D22CExadNg4PN6yEwOcM747KO2x/qDCyWkIiIiIieu+OoCk6hZuesIb289xMjMJEZlJfHi2iJ+/uIGnHMD3TQAquqbeOXjA1wyLY9Evzcm1xifk0pKwNtuoZyOOOeoawqelIWFRERERERiRT2eAywW1Wudc9z29y3kpCUwLD0Rj8Hnp4zg3rd3kpUc4OaLJkbtWr318voD1DeFuGJmfsyu4fUYU/MzWdeDAkMNzSGcgwlxsIZnvFGPpIiIiIj0lhLPQcQ5R1MwhK+L4bLvbCtl5e4j/OJLZ/C3dQcA+MnnJ3OkppHbX9/GkJQAL68Pb2+bTPTnEi7PrC5kXE4KMyLrbcbKtFGZfLDzMKFQ93p6axuDAExU4ikiIiIiEjVKPONUUzBETUMz97+zk23F1WwrqWJ7STWV9c2kJviobgh/PZZzjtte3cLIzCQWzB7FNfPGtu679fIplNc28fMXNzAuO6V1PcX+tru0hg93l/Fvn5vU50I1XZk+KhMHHKys77K67bKth9hVWoPXYxpqKyIiIiISRZrjGUecc3y0t4ybnyxgzd5ythRX819/28Trm4vxez1cOj2PvIxEqhuaueHhVdQ3BY87x6sbi1lXWMEPPjWBBN/Rcyd9Xg93XT2D2WOHsPNQDdtLqnn2o0IOVzd02q4FS5e39oZGw18+KsRjcPmM2A2zbXH+pBwykvzsK6tjwb3L2XkovEZn23tqCoa49ZXNXPvASvxeD6ePSCc5cPJ+JvPk4nkaVisiIiIiURWXT9f9OeSzv3R2T8GQ46kP9/HIB7v5eH8lqQk+ctMTGJIc4LEbziYrJfA/5yleTlLAywe7DvPt//cRSxbNbK02Ggw5/u+rWxmXncLlZ41stx2Jfi/3XzuLT922jLLaRm55ai1mMDU/k9KqBjKSfByqamBoSiAmFXBb1u48d0IOwzMSo37+YyX6vUwalkppdSNbDlbxudvf4ZaLJuKcw8woLKvl+4+v4aO95SycM5rtxVVxVflXREREROREEJeJ58niYEU9e4/UUlLVwKo9ZUwclsovv3wml80YyfUPfQhwVNLZIjs1gevPHce//3U9Nz9ZwO1XTcfn9fDSuiK2FFdxx8IZ+Lwdd2anJ/oZl5OCc8n858Vn8OaWEt7cUsL+8jr2l8PsX72Gz2NkpyYwLD2BPUdqSfJ7qW1s7lZPYGdJdsvanT/+wmnd/C71nZmRk5bAEzeezX8+/zG3vrKZlICXnLQEvnjHuwRDrnUt0Wj27IqIiIiISFhcJp6V9U3UNgRbe6V6K1o9p9Hugd18sJJ7397JCwVFNIccQ5L93LNoJnNPGdLt+7167mhqGpr51cubSAp4+fVlU/j9P7YyeXgaF08Z0eX7297LlPwMvv+pCVx+z3tU1Tfz9XljKK6sp7iygZKqBhqKqymvbWLR/St48J/mkJHs79V9L1i6nO0l1TFdu7MzuemJLFk0k5fXH+QHT6xh9+FapozM4M6FMxibnQKcWL3sIiIiIiLxIi4Tz9KqRg5VN3DLU2v59WVTSAqE5yoO5iG4zjkq65u55oGVvL31EEl+L4vOHsOavWUk+r2cPW5ot87T9t5vOG8cNY3N/OG1bbyy/gA1jUHuu2ZWr4eK+r0ehqQEjipIBOHv+5GaRj7eX8mCe5fzyPVzyE3reJhsQ3OIusYgByvqGZae0JpMN4ccZbWNXDVndMzW7mxP2++ZmfHFqSP407s7qahr4pmb5h03F1ZERERERKIrLhPPU7KTSfB5eK5gP5sPVrF00UxGD03u8XlqG5vxeQa+flJ9U5BtJdWU1TZRWt3IDz87ia/NHU1mcqDPQzt/8KkJ1DQ0c987u0hJ8PLp03Kj1OqjDUkJ8NNLTmfxo6v56pLl/Pn6uYwacnRMKmqbuOet7awtLMc5OPv/vE5Wsp/Jw9M5bUQ6+47UEnLEdO3O7vJ7PWSnJijpFBERERHpB3GZeJoZI7OS+OVlZ/KDx9dwyV3vcvtV03t0jsdW7GX9/krM4H+/uIGbzh/faS9dR0oq69l3pJYh7cy17I6ymka++cgqymqbGJWVxD9umR/V3j4z49+/cBqvbSomLdEf0+VJPjkhhz9/cy7XPfghVyx5n9y0BJIDPh65fg6PLt/DnW9sp7K+iaEpAbJTE1g4ZzSbDlSy6WAVj63cQ31TiCS/N+Zrd4qIiIiISHyJy8Sz7dDIF793LosfXc11D33IyIwk8jI7Tx5DIcdvX93CH9/aQUaSn4DXeGT5Hh5bsZevnz2GxfPH893HPjruOu35aG8Z33p0NSVVDRRV1HPzkwXcctHE43r6OrLvSC3XPriSwrI6Ts1NZWhKICZDTM2sV0n1sTr6frTdftboLJ5aPI+v/2kFmw5UkZeZyKd+t4zCsjrmT8zhx5+fzM9f2ADAteeMbX1fMOS47O738Hkt5mt3ioiIiIhIfInLxLOtMUNT+Ou3P8FPnl3HcwVFVNQ38e62Uj5x6tDjEpiG5iD/+vQ6XlxbxNVzw0tjmBlPf2Uqd76xnQfe28WfV+whKznA8PTOE7UnVu7lp89vYFhGAqeNSKO8tomX1x/gpXVFfG3uGNYVluP3ejpM1tYXVnDdQx/SFAzx5+vn8rtXt0Tte9Ke/pz3Oml4Gs986xwu+v0y9h6p44y8dG69fCrnTsju8D1ej7XO1Y0Hg3GesIiIiIjIYBX3iSdAUsDL7xdMp2BfOfvL61j0pxWcPiKdG88bxxenjmDR/StoDobwej2s3HWEH31uMt+aP46r7v0AgLHZKfzuyml854Lx3PnGdv66Zj8HK+r57mMfce05Y5k1Jqs1iW1sDvHLlzby6Ad7+OSEbO5cOIPFj64mPdHPs98+h9tf28Yjy3cDMCw9kZfWFTE8PZFh6YnkpCVw7QMrKa9tZF9ZHVnJAZ64cS6n5qZ1eG+DNQEaPTSZM/LSqWlo5sXvnntUQaPBek8iIiIiIhIb5pwb6Da0mjVrllu1alWH+xcsXU4o5LhiVj73vbOL7SXV5GUk4vEYpdUNhEJw25XTuHRaXqfX+dJd71JcWU9tY5DK+mYmD0/j2nPG8sTKvew8VENVQzM3njeOf/vsJHxez3HVdLeXVPPVJe9TVtt03Ll9HqM55Dh9RDoPXjebYV30rA5mg7nKsIiIiIiIRJeZrXbOzWpv36Do8WzL4zEWzB7NV2eO4q2tJSxdtpMVu47g9RiPfXMuc7uxLEmi38uYoSk8eN1sni8o4pHle/jJs+vD5ze4/arpfGn6yNbjj02sTs1NZeKwNJqDIX51+RQOVtRTUtlAcWU9j36wB4/Bk4vPJi2xd+tdDhZKOEVEREREpDsGXeLZwuMxLpw8jAsnD+PiO97B57VuJZ1wdMK0cM5orpo9itV7yvje42sYmhI4KunsjM/rYfLwdCYPT2/d9u72UoATPukUERERERHprkGbeLaVktC32zAzZo0dwuhuVquF7lWAFRERERERkUE2x1NERERERETiU2dzPD393RgRERERERE5uSjxFBERERERkZhS4ikiIiIiIiIxpcRTREREREREYkqJp4iIiIiIiMSUEk8RERERERGJqZgmnmY20cyWm9nWyNcJsbyeiIiIiIiIxJ9Y93guAe52zk0E7gaWxvh6IiIiIiIiEmdilniaWS5wFvB4ZNPjwFlmlhOra4qIiIiIiEj8iWWP5yhgv3MuCBD5WhTZ3srMbjSzVWa26tChQzFsjoiIiIiIiAyEAS8u5Jy71zk3yzk3KydHnaEiIiIiIiInmlgmnvuAkWbmBYh8zYtsFxERERERkZNEzBJP51wJUAAsjGxaCKxxzmk8rYiIiIiIyEnEF+Pzfwt42Mx+CpQB18T4eiIiIiIiIhJnYpp4Ouc2A3NjeQ0RERERERGJbwNeXEhERERERERObEo8RUREREREJKaUeIqIiIiIiEhMKfEUERERERGRmDLn3EC3oZWZHQL2DHQ7BqFsoHSgGyEdUnzim+IT3xSf+Kb4xDfFJ74pPvFN8emdMc65nPZ2xFXiKb1jZqucc7MGuh3SPsUnvik+8U3xiW+KT3xTfOKb4hPfFJ/o01BbERERERERiSklniIiIiIiIhJTSjxPDPcOdAOkU4pPfFN84pviE98Un/im+MQ3xSe+KT5RpjmeIiIiIiIiElPq8RQREREREZGYUuI5iJjZUDN72cy2mNk6M3vWzHIi+yaa2XIz2xr5OmGg23syM7OfmZkzszMjrxWfOGBmiWb2RzPbZmbrzezeyHbFJw6Y2cVmtsbMCiK/4y6PbFd8BoCZ3WZmu9r+Lots7zAeilX/aS8+nT0nRPYrPv2ko5+fNvt/1pOfLYmuTn6/tfucENmn+PSREs/BxQG/cc5Ncs5NBXYAt0b2LQHuds5NBO4Glg5QG096ZnYWcDawt81mxSc+/AaoByY656YA/xnZrvgMMDMz4FHg68656cAi4GEz86D4DJTngPM4fn3tzuKhWPWf5zg+Pp09J4Di05+eo/2fn46eE0Dx6U/P0X58OnpOAMWnzzTHcxAzs68ANwFXA1uBoc65oJl5gcPABOfcoYFs48nGzBKAtwjH5E3gYqAExWfAmVkqUAjkO+eq22zPRfEZcJHEsxS41Dn3npmdB9wPnIviM6DMbDdwsXPu485+XgDraJ9iFTtt49POvq8ANznnPq3fdQPj2Pi095zQ1c+W4hM7x/x+a/c5IXKc4hMF6vEcpCK9ADcBLwCjgP3OuSBA5GtRZLv0r18Af3bO7WqzTfGJD+MJ/5H4mZmtMrO3zOxcFJ+44MKfgl4JPG9mewh/Gn0tik+86SweilUcOeY5ARSfeNHecwIoPvGgo+cEUHyiQonn4HUnUA3cNdANkTAzmwfMBu4Z6LZIu3zAOGCNc24W8CPgWSB1QFslAJiZD/gJ8CXn3BjgEuBJFB+R3tJzQpzRc0Lca/c5wczSB7ZZJw4lnoOQmd1GeFjTAudcCNgHjIx0+xP5mhfZLv1nPjAZ2BUZupEP/J3wJ2iKz8DbAzQDjwM451YQHtpZh+ITD6YDec659wAiX2sIz7VRfOJHZ39v9LcoTrTznACKTzxo9znBzD6D4hMPOnpOmIjiExVKPAcZM/sVMBP4snOuAcA5VwIUAAsjhy0k/GmNxpz3I+fcrc65POfcWOfcWMLzBD7rnHsKxWfAOedKCc+nuQjC1emAljkbBSg+A60QyDezSQBmdhowHNiG4hM3Ovt7o79F8aG95wTQs0I86OQ54VXFZ+B18pywXfGJDhUXGkTM7AzgY8IPynWRzbucc5eZ2WTgYSALKAOucc5tGZiWChw3YV3xiQNmNg54ABgKNAH/4Zx7RfGJD2b2NeDHQEsPzc+cc88pPgPDzO4ALif8AUApcNg5d0Zn8VCs+k978SE8T7rd54TIexSfftLRz88xx+zm6MJDik8/6eT3W7vPCZH3KD59pMRTREREREREYkpDbUVERERERCSmlHiKiIiIiIhITCnxFBERERERkZhS4ikiIiIiIiIxpcRTREREREREYkqJp4iIDGpmttvMzuyH61xqZr+N/Husmd3Yh3M5M0vt4XseMrPCyDIA7e0/38xW9bZNkXOcY2YFvWmfiIhIZ5R4ioiIdINz7gXn3A8jL8cCvU48++BW59z3Y3Vy59z7zrnpsTq/iIicvJR4iojICcnMrjGz9Wa2zsz+ama5ke3/ZGavmtmTZrbBzN4zs+GRfQEzu9fMtprZu2Z2l5k90+Z9z0ROfzdweqR3sGX/Ub2EbV+b2eVmttnM3jez/3VMO+ea2Ztmtjry3xd7cI//ZWbbzWwZ8MVj9l1rZisi53zDzCZ1dY8iIiKxosRTREROOJGht7cCn3HOTQU+Bu5sc8hs4F+dc2cAG4HvRbYvBkYDpwOfBmZ1cInvABudc9Odc1d00ZZc4D7gS865c4DGNvsygSXA1c65mcDFwNLI9q7u8RLgUmA6cCEwuc2+TwJXAudFzvtb4IEe3qOIiEjUKPEUEZET0QXAy865A5HXSwknWS3ec87ti/z7A2B8m/c96pxrds7VA49HoS1nAx8557ZEXt/bZt85wCnAK2ZWALwCOODUbpz3AuBJ51y1cy4I/KnNvkuAacCKyHlvBUa1eV+071FERKRTvoFugIiISAwY4QSurbav69v8O8j//D1s733dFSTyga6ZJR7Tlo4YsM45d14vrtfVeR9wzv20g329vUcREZFeUY+niIiciF4HvtAydxO4AXitG+97E1hkZr5I8rigg+MqgYxjtu0gPIQX4Oo225cDM8xsQuT1N9vsex+YYGYXtGwws9lm1llS2eJ14EozSzEzL3Bdm30vAteYWX7knF4zm9nDexQREYka9XiKiMiJ4DUza27zegrwE+AfZuaAnYTnNnZlCeEhqhuAfcBqILmd49YBW8zsY2BzZJ7nzYTnZx4AXmo50DlXEll65UUzOwI81WZfmZldCvzWzP4ABCJtvYQueiWdcy+Z2TygACginFCOjOx728z+A3ghkpQGgKcj99PdexQREYkac06jbURERFqYWZpzrsrMEoAXgKedc/fHQbseAlY55+6Kwrk6vcdIsp7mnKvu67VERERAPZ4iIiLHei2SkCUSHp770MA2p1UF8H0zmxiFtTzbvUczOwe4BygGQn28hoiISCv1eIqIiIiIiEhMqbiQiIiIiIiIxJQSTxEREREREYkpJZ4iIiIiIiISU0o8RUREREREJKaUeIqIiIiIiEhMKfEUERERERGRmPpvUeqbkDNtoiUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 936x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plotter.make_sub_plot(data_ref)"
    ]
@@ -242,9 +469,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7ffe9077da30>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Sk7TWFe6NlFJXA1cDjB071o/DEUII0YLn6iajDwchhBiq3Bf8bjt37mx3u7vvvpu4uDi2b9/OkSNHOOaYY/j+97/veX3NmjWsXr2amJgYTj/9dJYtW8ZVV13V4hgfffQRpaWlbN26FYDS0lIAHnvsMW655RbWrFnj2fa2227zBB7PPPMMv/3tb3nllVd47LHHmD17NtkdRIa33normzdvZsyYMdTX1+N0OvnDH/7AP//5T1599VVSU1Pb7PP888/z9ttv89VXXxEdHU1JSQkWi4XHH3+cs846i9tvv73FeNvjcrk85XxXXnklJ510Es888wwul4slS5bw7LPPctVVV7Fr164WQdWpp57K7bffzoIFC7Db7SxatIg5c+Zw6qmnApCfn89nn30GwJ///GdiYmJYtWoVAL/97W+55557+Mtf/gLA5s2bWb58ORaLhZkzZ7J8+XJOPfVUbrjhBiIjI9mwYQMWi4Xi4mIA7rvvvk6P1xv+DG72Aw7gZQCt9XdKqWJgCuD5H6S1fhp4GmD27Nnaj+MRQgghhBjyupNZcWdsGq+dfar1Bf/s2bPb3e6zzz7jH//4BwDx8fGcd955LV4//fTTiY2NBeC4445jz549bY6Rnp7O9u3bufbaa8nMzOR73/teh+P64IMPeOyxx6iqqsLhcHj9/SxcuJCf/vSnnHvuuXzve99jwoQJXe7z7rvv8otf/ILo6GgAhg0bBsCCBQu45ZZbsNvtnHzyyZx88skdHuMnP/mJ5/O3336bVatW8eCDDwImYzV69Og2+1RXV5OVlUVRUZHnucrKSrZt2+YJbi677LIWx62oqODVV18FoL6+nvT0dM/r5513nidQnTVrFnv27OHUU0/l3XffZe3atVgsZpp/QkKCV8frDb8FN1rrYqXUZ8CpwMdKqSlAErDbX+cUQgghhBCDi9YapVSHrzfP/litVmpra9tsM2HCBLZt28aKFSv44IMP+P3vf8+mTZvabLd//35PGVVKSgpff/01l1xyiVfjfP3111m9ejWffvopJ598Mk8++SRnnnlml99be77//e8zb948Pv74Y+69916effZZ/v3vf7e7bfNSLq01b775ZpeBlcvlQinF6tWrCQoK8uq4jz/+OAsXLmx329b/Bl0FhV0drzf83S3tGuD3SqlNwCvAj7XWZX4+pxBCCCGEGCROPvlknn/+ecCUZ7311lvdPsahQ4ewWq2cd955PPzwwxQVFXHkyBGio6MpLy/3bFdRUUFwcDAjRozA5XLx5JNPel6Ljo6mpqam3Qt3h8PB3r17OfbYY7nttts47bTTWL9+vWe/5udo7uyzz+aJJ56gsrISgJKSEgB2797NiBEjuPzyy/nTn/7kKd/qyjnnnMO9996L0+kEoLi4mJycnDbbRUVFceKJJ3Lvvfd6njt48CAFBQUdHvehhx7yBI7uLE9XFi9ezP333+8J4txlaT09njf8GtxorfdqrTO11mla61la6w/8eT4hhBBCCDG43HHHHRw+fJjp06dz6aWXMn/+fGJiYrp1jE2bNjFv3jzS09M59thj+d3vfseoUaOYMWMGU6dOJTU1lQsvvJC0tDQuuugipk+fzsKFC0lJSfEcIz4+niVLlpCWlsbxxx/f4vjuSf9paWmkp6eTn5/Pz3/+cwB+9atf8dOf/pSMjAzPnB+3yy67jLPPPpu5c+eSnp7Oueee62lGkJaWxsyZM7n++ut55JFHvPo+ly5ditVqJT09nbS0NM444wxyc3Pb3XbZsmVs3bqVtLQ00tLS+OEPf0hZWVm72952222kp6czZ84cZsyYwQknnOBVMPLwww9TWVlJamoq6enpni54PT2eN1RH6bC+MHv2bN18QpcQQgj/yYzNBiCrLKNPxyGE8L9t27Yxbdq0bu/nzzk33mpoaMDpdBIaGkpFRQUnnHACDz30EKecckrfDUoETHv/d5VSa7XW7U7SGjhNq4UQQgghRED1ZVDjVlpayplnnonT6aSuro5LLrlEAhvRIQluhBBCCCFEv5WUlMTatWv7ehhigPB3QwEhhBBCCNEP9KepCEJ4oyf/ZyW4EUIIIYQY5EJDQykpKZEARwwYWmtKSkpatJn2hpSlCSGEEEIMcqNHj+bQoUMtFm0Uor8LDQ1tdxHSzkhwI4QQQggxyAUFBbVoayzEYCVlaUIIIYQQQohBQYIbIYQYIjIzm9asEEIIIQYjCW6EEEIIIYQQg4IEN0IIIYQQQohBQRoKCCHEEFXnCqLUEdXXwxBCCCF8RjI3QggxROXVJ7CzdixlZX09EiGEEMI3JLgRQoghqsoVBkBxcR8PRAghhPARCW6EEGII0hqqnBLcCCGEGFwkuBFCiCGooAAadBAgwY0QQojBQ4IbIYQYgjZsaPpcghshhBCDhQQ3QggxBGVnN30uwY0QQojBQlpBCyHEUOGJaDLIzoZQVU+9DqK4WO5zCSGEGBzkL5oQQgxB2dkQZa0mVNVL5kYIIcSgIcGNEEIMMdXVsHMnHKe/ZaLeTXFBQ18PqV/KzDQfQgghBg4JboQQYojZvNm0gj5Rf0EiRRTn2vt6SEIIIYRPSHAjhBBDjHvqzZmu90igmOIiV5+ORwghhPAVCW6EEGKI2bABYqKcpLLZBDdl0ltGCCHE4CDBjRBCDDHZ2ZA+qggFJFDMkeoQnM6+HpUQQgjRexLcCCHEEKI1bNwIGUFbqSISpUBjobS0r0cmhBBC9J4EN0IIMYTUukKoroaM8s/ZZksFpQBZyFMIIcTgIMGNEEIMIdWuUADSD73HVmsaLouZb1NcpPtyWEIIIYRPSHAjhBBDSJUzDJvVxdF6M1usM7BbwgAozqns45EJIYQQvSfBjRBCDCFVzjCmJRYTSj3bralUWyIBKN5R0scjE0IIIXpPghshhBhCqpxhpNu2wpQpVFhiqbDGAZK5EUIIMThIcCOEEEOE3WXFroPJKMuCuXMBKLKOIIwaSnJr+3ZwQgghhA9IcCOEEENEtcvMr8mo+tIT3NSqCBIsRygukIVuhBBCDHwS3AghxBBR5TTBTTobPMENQEJoFcVH5M+BEEKIgU/+mgkhxBBR5QwjniMkhNdCWprn+YSoeoqrQswXmZnmQwghhBiAJLgRQoghotoZRhqbYM4csNk8zyfEuyiuiwKXqw9HNzBI7CeEEP2bBDdCCDEE1NVBjSuUuXzToiQNIGG4jWKGQV5eH41OCCGE8A0JboQQYgjYuhU0ilmsaxvcjAmjjDgadu3rm8ENJNnZ5kMIIUS/JMGNEEIMAbt3m8ep7IDjjmvxWkJKFABHNkvmRgghxMDm1+BGKbVPKbVdKZXd+HG6P88nhBCiffv2mcdwamHkyBavJUyJB6B4e3GARzUIyCQcIYToV2xdb9JrF2qtNwfgPEIIITqQkwNxHOFgUAqTW72WMDIIgOI95YEfWD9WXg5hYX09CiGEEN0hZWlCCDEE7NtRRwo5bLXOaPNaQoJ5LD5QE+BR9V8uF2zcCHv39vVIhBBCdEcggptlSqmNSqnHlVKxATifEEKIVnJ2O0khh93WKW1e8wQ3BY4Aj8roj5VdR46YAKekBOz2vh6NEEIIb/k7uDlRa50OzAEU8GjrDZRSVyul1iil1hQVFfl5OEIIMfS4XLCvIJQUcihV8W1eHzbMPBaXWmWtm0YFBebR4YDPPuvevv0xWBNCiKHCr8GN1vpg42M98Dgwv51tntZaz9Zaz05MTPTncIQQYkgqLIT6Bivj2Ue5ivM8n5VxA1kZNxASAlGhdrPWTX19H460/ygsbPr8tdf6bhxCCCG6x2/BjVIqQikV0/i5Ai4Gsv11PiGEEO3LyTGPKeRQqaLb3SYhzkkxCVBbG8CR9V/u4CYyEt54w2RwhBBC9H/+zNwMB7KUUhuBzcAU4Jd+PJ8QQoh2uIObBIpxqmZNMrOyzAeQkGShmAQe2H0umdlLAz3Efscd3IwaBcXF8MUXfTseIYQQ3vFbcKO13qu1nqm1nqG1nq61vkhrne+v8wkhxFDV1RwP9xo3Maqiw20SRgZTTCIjXbm+HNqAVVAASkFSEoSHw6uv9vWIhBBCeENaQQshxCCXkwPDg49gt4R2uE1CoqLYNlyCm0aFhRAcDFYrnHUWvP46OJ19PSohhBBdkeBGCCEGuZwcSLEdbNFMoLWEBCjWwxjhkgQ7NAU3ABdeaL7+6qu+HZMQQoiuSXAjhBCD3L59MF7nUN7JUmMJCVDlDCfWWRywcfVnhYUQFGQ+P+ssCA2VrmlCCDEQSHAjhBCDkHsejtMJBw5oUup3UG6J7XB790KeGkWolo5pzTM3UVFw+ukmuNG6b8clhBCicxLcCCHEIJabCw6HIsW1m7IuytIAiklghCsvQKPrn1yulsENmNK03FyocIb33cCEEEJ0SYIbIYQYxNxtoM0CnrEdbtc8uBnqTQWOHDEZL3dZGsDZZ0NQkGZc7XZOtn8k3QWEEKKfkuBGCCEGseYLeHbVUADcmZuhHdy417hpnrmJiYHTMg6z3pXOHbW3wbRp8MwzJs0jhBCi35DgRgghBrF9+0ApzVgOeFWWlsfIIZ+5aS+4Afj+uLUcYBzXhTwN0dFw1VXw3XeQLx3mhBCiv5DgRgghBrGcHEiOqyGYhk4bCsTHm8f9ajwjh/icG3dw07wsDeAM/QEAH6mzYPVq+PhjCAmBnTshb2j/zIQQor+Q4EYIIQaxnBxIiSkF6HTOjc0GcXGQy2iSdEGARtc/FTR++60zNyP3fsVwCkxTAaXg1FMhJcW8uHVrYAcphBCiXRLcCCHEILZvH6REmNZfNUR0um1CApQwjFBdF5jBuWVnm49+wt0pzWZr9qTTCdu2MU4doNLR1DHtgpwHzCfbtgV2kEIIIdolwY0QQgxSLhccOgTjg3JN5KJUp9snJEApcYT08To37jV6+kphISQltfpx7d0LdXUMs5ZTp0MoKjJPH1EJVBEpwY0QQvQTEtwIIcQgVV9vFp1MYV9Tx4BOJCRAOTGEEtjMzWF7LA7df/4cFRbC8OGtnty8GWjK5qxa1fi8Uuy3pkhwI4QQ/UT/+WsihBDCp+oaY5QU+w5ITOxy+4QEKNfRAS1L274dttWO51B9UsDO2ZWCgnaCmy1bAKgNigI0333X9NJ+ywQJboQQop+Q4EYIIQYpd3Azvmar15mbSqIIoS5g67esXWsejziiA3I+bxQWwogRrZ7cvBlSUmiwhBJhqWsZ3FhTzE6lpR0es69L7YQQYqiQ4EYIIQapujpTRjW6bLPXwU0DwdQQ3hQZ+dm6deax0hneL5aLcbng8GGTucnKMh+AydxMnw5AtLWaVatMyR/AAUtjxzTJ3gghRJ+T4EYIIQapujoYO1ZjLS32OrgB0zGNmho/j85Ytw5sygHA++83PtmH3dNKS8HhaFWW1tAAO3ZAaioAUbYayspg1y7z8v5Wwc3Bg7B7d+DGLIQQookEN0IIMUjV1sL4UQ3mCy/n3AAUkxCQ4MblMsFNYlAZIcrOu+/6/ZRdci/g2SK42bXLBDiNmZsoq/nZuJsKFFhGmcU8t22joQEOHDBregYoPhRCCNGMBDdCCDFI1ddDyvBq80VCAmRkmI8OBDq4ycmBigoTLAwLquCTT8yY+5J7Ac8Wc24aO6W5MzcRljoiIvDMu3EpK0ydCtu389FHJvOjNXz7beDGLYQQwrB1vYkQQoj+rKioaf6Hm9MJdjukxJaZJxISmuaPdCAQwY17Un1WVtN8m0hrLcHKQV51Ap9/7pfTeq1F5sY92AULwGKBo44CtqMUzJ5tghvPcp7TpsHq1SxbZuY5ORzme1y4MNDfgRBCDG2SuRFCiAHuwAHTUnnDhqbnPJ3SIhpXm+yHZWlr10JQkMmExNoqCQujz0vT2i1L27IFJk2C0FDPU8cdZ6YFuXTjSp/TplG5t4i33tIkJUFUFHz2WcCGLYQQopEEN0IIMcC5y6Auu8xka6DZGjcheeYTLxoKxMaCwhWw4GbdOlPpZVEaq9IsWmSCm9ZZqEAqLDQBV1xcsyc3b/aUpLkdd5yZhlPlDDNPTJvGG5xHba0iqXwnMfWFfPedzLsRQohAk+BGCCEGuIYGCA+HjRvh7rvNc57gRu0znwwb1uVxLBYIUXYT3FRX+2ewjbQ2wc2sWU3PLV5s5uHUuEL8eu7OFBZCUhKoxoQMLpdpfdbYTMDt2GPNY4WzsTBt2jSWsYSUpCqirTXE2qpoaIBvvgnc2IUQQkhwI4QQA5rdbubXJCbC5ZfDvfeaLl51deYCfXjdfoiONt28vBBMQ0AyNwcPQklJy+DmrLPMY18u6FlQ0KqZQE2NCXDcmZvGpgyjR8OoUWZ9HoCC6Cks5xSWTF2LUhBjq8Zqpct5TkIIIXxLghshhBjASkvNY1AQLF0KI0fCT35irslDQ8FSUuRVSZpbkMURkODG3UzgmKOqWVZ5DgvtHzBmDKSnQ0lDjF/P3ZnCwlbzbdwZrFaZGzClaZXOCABeeSMEF1aWhL8BgE25OOYYCW6EECLQJLgRQogBrKTEPAYFQUwM/OtfprnAkSON89+Luhfc2JSTI8QHJLixWmGG3kCy6yA31f4VDhxg8WIod0bQ4LL69fwdaTe4CQqCyZPbbHvssVDrCqFhegbLlsGsmN0clbvC83pmJjLvRgghAkyCGyGEGMDcwY2tsbH/aafBL35hPg8NBYqLveqU1kRRS1hAgptp0yBsj1lDJph6uOIKFp/lAhSljii/nr89LhccPtxOcDN1KgQHt9n+uOPMY2EhrFkDS2Zug507sWoHYIIbmXcjhBCBJcGNEEIMYM0zN2733Wc6n8XHY4KbbmRulCJgwc2sWcDWrdQSxqOht8Ly5cxZ9xRBykFJgOfdZMZmc2LsJhoaWgU3NTXtlqSBWesGYP9+83O7+OxqsNsZ4coF4IQTkHk3QggRYBLcCCHEANZecBMZaeauJCTQ7bI0i3JRQ7hfg5v6esjPbwpu9ltTeDv4QjjjDKy/vYXR1nyOOKJxOPw2hHbZtUl/eRoKOJ2mM0OrNtBuUVGmS53DAYsWwajjxwMwzpXjeX32bAluhBAikCS4EUKIAezIEfPoLktrLsRZA7W13SpLs6D9nrmpqjKP7uBmn2WCSX088wwEB3ON81Ec2tZiUdJAsGsTIXoyN+00E8jKahmsRDcmmJYswdTZ0RTcQNO8G6fTL0MWQgjRigQ3QggxgJWUmLjA2s78+5iGYvNJNzM3tYShq/0b3CgFGSnlkJvLfusE80JyMjz6KOfqNwGzdmYgNbhMhOgJbtwBXgeZGzDLB4WFwQUXYDo6jBzJOGfL4KahASoq/DNmIYQQLUlwI4QQA1hJiSlJ8yw62UxsQ5H5pBvBjRUXGgv1lXYfjbCtykqYMgWiDm0DMJkbt0su4ZB1LCHUsfnbKr+NoT3usrQWmRuLBSZM6HCfhATTNc2dwWHaNMY2y9zMn28Cz7Iy/4xZCCFESxLcCCHEAFZS0n5JGjTL3HSnLE1pAGqr/FdHVVXVVJIGcE/qS2Rl3GBeVIpXQy9jGtvY/F2gg5sgbDaIi2t8orraTKppLy3WkWnTTOZGm5+je96NBDdCCBEYEtwIIcQAduRIy2YCzfWoLG1MMuC/4KahwTQU8AQ3oaGNPaub5FgnMZ3NbNod7pcxdDg2l43hw02yBpfLBDcREd07yLRpRFBNgi7yPJWZabJVMu9GCCH8T4IbIYQYwDrL3PSoLK0xSVFTrXs5svZVVppHT3Bz1FFtaurqVBhj1SFyK6MpLfXLMNpl17amkrRnnwW7vVkax0uepgJ7PU+dfLJJ5Mi8GyGE8D8JboQQYgBzz7lpT0xDsYlWYmO9Pp6l8a9CbY1/ght3p7SZMzHBzdFHt7tdlMV0KtuyxS/DaJddB5ngprAQbr3VNAhISureQRqDm7HOlvNuoJ3StMxM8yGEEMJnJLgRQogBSutOgpvsbGIKtpt2Xhbv3+o9wU2t/4Kb0FCIC6oyq192ENwoqxnI5tW1fhlHe9xladx4o+mUNmVK+50aOjNiBFVEtmgHHRlpPtxZKyGEEP4jwY0QQgxQ1dWmcqrDhgKu0m6VpEFTcFNT658/D9XV5kKf7dvNEx0EN6XWRKIpZ9MXgalL09qUpY2o2Qsvvwy//71pJtBdSrHfmtKiHTSYqTvuZXOEEEL4jwQ3QggxQLkX8OywLE2XdatTGjTNuamt62bGwksNDRAcjKdTWkfBzV7bFFLZzObN/skgtebQVjQWhn/ybzMP6LbbenysPMsYRrjyWjwXEWEC0aKiDnYSQgjhExLcCCHEAFVSYh47DW56mLmprVeedsa+5HA0BlBbt5qBT5zY7nZFKonUkN1sPhiD1v6fnuJZ46Z0Gzz1FISE9PhYpw7fyEjyW/z8IiPN48aNvRqmEEKILgQkuFFK/UkppZVSHS/zLIQQolvcwU2H3dJ0L8rSXKEm1eBDdru53rcezjfBzdSpZvBZWeajOaVIHVvBkfpICgp8Oox2JTnyARh+1mxYsKB3BwsONt9oebnnKXdHaQluhBDCv/we3CilZgFzgQP+PpcQQgwl7uBm2bJ2YgPtIlqX97wsjTAzqd6H3BPqbcrZaac0t9R0M5hN2f5fIGaO/SsAhv/hyt4fLDjYPObnt3gqOBg2bOj94YUQQnTMr8GNUioEeAz4JRCYwmkhhBgi3MHNsGFtX4vUlVhx9bwszY/BTQj1sHdv18HNgngANmcV+3Qc7XFoM8doxNSY3h/MHdy0SjlFRLTM3GRmLyUze2nvz9cHpIu1EKK/6qCYwWfuBv6ttc5RHbTTVEpdDVwNMHbsWD8PRwghBg93Q4H4+LavxerGLmM9LUsj3OfBjXsRyxHu+ShdBDeJJx7FcArY/F0NZGc3Ppvh0zG5VelIrDiIi/PBn8VOgpstW0xThY7mSQkhhOidDjM3SqlwLz5CO9l/HjAHeLyzAWitn9Zaz9Zaz07sZvmEEEIMZSUlZqK6+1q6uRhdZj7pR5kbd3CT7DpoPukiuOHoo0lVW9i808+RgNaUE0M0ld1ZEqhj7mYEzcrSwPxb2e2wc6cPziGEEKJdnb2NVwGVjY+tP9zP7+lk/5OAo4AcpdQ+YDTwkVLqtN4PWwghRElJ+yVpADHuzE03bxopZebE+CNz4y5LS9E5ZnLP5Mmd7xAcTNqwPLYcTvRH47Ym5eUUkUiE8tFCNFar+UG2k7kBmXcjhBD+1Flws0FrbdVaW9r5sGqtLcDhjnbWWt+rtR6ltR6vtR4PHAJO11p/7OPvQQghhqTOgpuelqUBWC0uv2ZuJrh2m8CmvZRTK6mT6qlxhlLn6nrbHjt8mDxGEa5qfXM8pUz2plXmJjzclKNJxzQhhPCfzoKbX3qxvzfbCCGE8INOMzeuMvNJD4Ibm0X7taHAVNe2zkvSMjLMB5A6JwwA7XD4dCwtFBWRxyhClQ9bXwcFtcncWCzm25bMjRBC+E+HwY3W+puudvZmm2bbjtdab/Z2eyGEEJ07cqSzsrQyagk16YJusli0XxsKTNY7Og1umi97c/SpyQAEO+t8OpbmGvKLOUwSNqvLdwdtJ3MDkJ4umRshhPCnLqdOKqUeVErFKKVsSqkvlVLVSqlLAzE4IYQQHSspab9TGpg5N+UqtkfHtViVydxU+2gOSiN35iaGcpg+3at9oualksJe6pz+ayqQv7sajcWUkzXX3uKi3goObpO5AZgxA/LyoNj/3a2FEGJI8qYvzCla63LgdCAXmAzc4tdRCSGE6JTTCaWlnWduylVcj45tsSi/zbkJpt6sv9NVpzS3hARSQ3ZT7OogivOBvH2mHM1l8eHqCMHBJvq0tyx1S083j1KaJoQQ/tGdppcLgNe11nnIgpxCCNGnysrMUjEdBTfzwjYwNaZt5sArVovfytLCqcWJBaZM8Xq/1ORSchmFS7e/Xlpv5R405WhWa/eP32Fyx90sobCwxdMzZpjHjavqeLTqJ0x07uj2OYUQQnTMm+DmsFLqn8DFwCdKKRv+X/xTCCFEJ0pKzGNHwU1vVopUVovfGgpEUUGeZTSEdrhMWhup0zUOgmhw+mIRmrbyCq0ABPuyoYA7uGk17yYpCUaMgA1flpPq3MgMx3rfnVMIIYRXwc0lwBbgh1rrUsx6NQ/6dVRCCCE6deSIefR1cJOVBQsXKmqVfzI3sZSx35LSrf3STowFINRR5dPxuOWWhGCjgSDl9N1B3cFNB/NuNm419wjjdInvzimEEKLj4EYp9V+l1CVAg9Z6qdb6WwCt9T6t9XOBGqAQQoi23JmbdhsK7N5tJuV0IzvSXFgY1KgIv2Ru4jlCoWVkt/abevp4bDSA0z/toHPLI0mguE0/gV7pIHMDZt7NlkMxNGAjznXEhyftfzIzzYcQQgRKZ5mbR4CZwNdKqeVKqeuVUuMCNC4hhBCdaFGW1voK8uWXzWNiYo+OHR6OfxoKlLuIpYxS1VG6qX3B0yczmV1UObvf1tobeTWxxKky3x0wKwu++MJ0X+sgc2N32tjJFMncCCGEj3W2zs1XWutbtdZHA78CIoH/KKXWKaXuVErNDNgohRBCtNDhnBut4aWXICbGrLXSA2Fh/gluKstcRFPBleM+6d6OVivj1X7y9QifjgcArcmtTyQKH5e8BQWZBVQ7yNwAbCCdOD0wMzf19W16JQghRL/g1exMrfVWrfU9Wuu5wPeAAuAvfh2ZEEKIDpWUmBXvY2JavbBhA2zfbmau91BYGNS4Qv0w50YTTUWP5gKFq1qK6V7Gx8tBkcdIIqIskJHh22OPGNFu5mbqVAjCzkZmDNiytLz1BWzfLgGOEKL/8Sq4UUqFK6WmKKWOBuKAL7TWZ/l3aEIIITpy5IiZb2Np/S7+8stgs/W4JA1MWZqDIByVtb0bZCuV1RaiqGyaj9INVuWinlDq6nw6JCpziqkkuidD6trIkZ7MTfOW0cGuOo5mK9mkD9iytFqXyQrKej1CiP6my+BGKXUtUAh8ArzX+PGun8clhBCiEyUl7ZSkuVzwyitw6qk9bgMNJnMDUFvt6vkAW6mvB7vDajI3PYgkgpRpJlBW1OCzMQHkbqsAwBbqhxUOOsjcsG8fM9hINrMIow6qq31/bj+rc5l/QwluhBD9jTeZm5uBVK31OK11SuPHBH8PTAghRMdKStrplPbNN3DgAPzoR706tju4qanyXXBTYWKIHmdubO7gJt+32aS8PeZ4KqxnneU6NXKkCW50q3Wv9+4lnQ0UMtyU2g3A2i4JboQQ/ZU3wU2B1nq/30cihBDCa+1mbl5+2bR/Pu+8Xh07vLEpWW2N7nzDbqisNI/RqqqdWrquWZUZS1mBb+vScnPMwp3OsEifHhcwmRu7HUpLWz6/dy8z2AjARmYMuOCmqgoatMkMSnAjhOhvvPkL84lS6j6l1Cyl1NHuD7+PTAghRIeOHGkV3GgN//sfLF4MUVG9OranLM2HwY0ncxPUs+DEQmNwc7jeV0MCIC/XHLc+Is6nxwVM5gbadkzbu5epoQcA2MPEARfc5OSYxzBLPdu3m5JDIYToL7wJbi4DLgJeQ+bcCCFEv9Amc1NaCocP97okDZqVpdX6blVLT+YmqGcd2JTFBCGlhT6ec1NoI5pynMF+WENnRGPr6tbzbnJySEwxmaJiEsy/2wDiDm6G2cpwOGDr1r4djxBCNNdlcNNsnk2KzLkRQoi+V19v5qC3CG4OH4boaDir940sPWVpdb4LbjyZG1sPy8qU+XNVVuL00YiM3JJQkoP8FFx0krkJm5SMFacJbgZY5mbvXvOYEFQOSGmaEKJ/6TC4UUpFd7WzN9sIIYTwLfcCnp6GAi4XFBfDBReYOTe95MncOILA4ej18aBZ5ia4Z8GNdgc3R3zX5AAgryKS5LAjLVo1+0x7mRutTXQwYQI25SSPUQMuuMnJAStOoq01hIdLcCOE6F86y9xkebG/N9sIIYTwIXdw48nclJSA0+mTkjRoNueGMKj1TXeyihJTThYd0rMJGg5LCCHUUVbqu3lAALk1sYyKqvTpMT2iokwarHnmprjYzMifMIFgi4MCRgy4srS9eyHUYkcpSEvrPLix203sLYQQgdJZY/+pSqlVnbyuwB/LRQshhOjMkcZF7T3BTVGRWddm4UKfHN9TlkaYqX/rZYMCgIr8aiCWqBA7ENbt/WtVOLGUUVbuu1I5lwvyGxJIjvdte2kPpdqudeOu6ZowAZtycJikAZm5ibWUc379K2ydkc6rrym0Nt9uc/X1sGYNjBrVN+MUQgxNnQU33hRu2301ECGEEN5pk7mprITYWLD5ZiFKT1ka4VDTswYArVUW1qCIJuKBu+C8c7q9fy2hJripsPpkPABFhzUOghiV5Nt5PC2MHNkyc+MOblJSCB4WzZGixAEV3GhtgpuTXev5dd3feDzm5zx9JIXcXBg9uuW2n38ODQ0+S/4JIYRXOvxLqLX+PJADEUII4Z0WwU19PdTVwfDhPjt+i7I0HwU3FUV1RFKFZWTPxunJ3FQl+WQ8ALm7aoAIkpN9dsi2Roxo2U6sWXBjs0GJjhlQwc3hw+a/xHS1GYD0ba8Av2PDhrbBzdtvm8cG3za4E0KITnV/JTUhhBB9qkVDgd27zRdh3S/16kiLsjRfZW6ONBBNRY+DMDshxFBGWXWQT8YDkLfDzLVJHuebjFe7WmducnJMwBMeTnAw1OkQasvqzOSUAcAdm83R3+FCMePTpQBkZwOZmeYDk+GR4EYI0Rf8+I4uhBDC1zIzYc8e0xQtPBzYscO8EO67dVr8UZZWUeoiikoYPr5nB1CKKKrYVxvik/GAO3MDoyb6LjBsY8QIKCsz2bXQUE+nNDDTpMCsdTPm8OG2qY9+yL3GzdFs5Z3g73Nu7atMSChnw4YYMrOXAqbTUHY2HDwIFosEN0KIwJLMjRBCDDAOR7P5Nu7gpnXmphe9ja1WCLK5fJu5qYRoS3XPM0wZGURaaiir632ra7e8Aw0oXIyY3PuGCR1yr3XjLj3rILgZKKVp7szNePZxbupemDmTdPsaNmxo2cXu7bdNg4HERAluhBCB1WVwo5T6UCm1WKnWfVCEEEL0hYaGVsFNcLDPmgm4hYdp3865qbYQ1cM20G4RllrK6sPQPuoGnZsLwynENsp383jacK91c/75sGCBSWekpABNwU0RiQOmHXRODoyIqCBc1UFEBFx9NekVX7BrFzh10yXF22/DvHkmoehymaZ7QggRCN5kbp4GbgD2KKV+q5SS9s9CCNGH2gQ3Ppxv4xYWqn1bllYbRHRY7xYEDVd12F1B1PVsHdA2cguDSCbXpBf8xZ25sdtN8weXa8BnbibYDprAxmKBSy4hPWQHWiuqnSardugQrFsH55zTLIAr6sNBCyGGlC6DG63161rrU4AzgWRgi1LqBaXUMX4fnRBCiDYcjsZmAlqb4MaH823cwsN93FDAHkJ0VM9Xc8zKgoljzKT70lKfDIm80lBGWQrNhbq/uDM3dntTT+RWwU0RA6cddE6OJqV2K0RGmieio0k/z2Si7E5T4PHOO+al5sFNcXGgRyqEGKp6MufGDtQBLyilHvTxeIQQQnTBk7kpLjZX+n4IbsLCVdMinj5Q4QgnKrp31c2xEWbyRllZ78bibuqVWxFFcviR3h2sK0lJJsNht+NJOTUGN+5KwmLbyAER3DQ0mKq6CfZtLRZ2HX/TBURTTkyDaeP39tsweTIcdZSpmATJ3AghAsebOTcXKKU+BV4HtgNHa62vBtKB7/t5fEIIIZrRullDgY6aCfhAWLjFZ2Vp2t5ApY4kOrZ3C3DGRpnFNnsb3ICpDiupjyI5uqr3B+uM1WrK3tzBTXAwjBoFmAn3QUFQFDZ2QMy5OXAAXC5FCjlNmRtAzZnNjIg91DmDcLgUn35qsjbu7w8kuBFCBI43mZsrgL9pradrrR/XWlcBaK0dwPV+HZ0QQogWnE4T4LQIbvxRlhahqFW+CW7qDhzGQRBRw4J7dZy4aN8FN/WNvQ1GxftoAk9nRo5sKksbP95kchoFBUFx0IgBkblxd0qboPa1LOVTivTZQexiMmH2Cux2E9yABDdCiMDzJrh5RWv9UfMnlFKXAmit3/HLqIQQYghqtgZih9xtdYcNA3buNJmAUN+1R3YLC4NaS4RPgpvKfaZcKTqpd+OMjTWPvghu3GtmJg/vXZMDr4wY0ZS5aSxJcwsKgiKVNCCCG/caNymTrPDlly1ajadfMJFKosm1JxAfD8cfb563Wk0GR+bcCCECxZvg5sZ2nrvJ1wMRQgjRNUfjtbgnczNpkrl69LGwMKhRvgluKvaZeS1Rw3uXYYqNM9+nbzI3pp/0qOQArHLgztx0ENwUO+MHRFna3r0QhJ3kY5PbvJY+1/zbbtPT+N73tGc+kaf0TjI3QogA6XBhBKXUbOA4IEEp9ctmL8UAvastEEII0SPuzE18PCa4mTYNXn/d5+cJD8dnZWkVhyoAiE7u3WKZMfFmzk7ZERe9XYNa19mBEJLHB/XqOF4ZMaKpDq69zE1NNFQUmZpDa+/mJflLZibs3lzLOA5hnT2zzeupqaBwobFw7vwSIAEwyZ30dAluhBCB09lfh2RgNhABzGn2MQK43O8jE0II0YYncxPjgD17YOpUv5wnLAxqtG+Cm8q8SgCix8T06jihsaGEUktZce9LyXRtLaHUEje2dwGXV9xr3UCL4CYrC372MyipDcfl0lBS4v+x9IKzpp4J7IVZs9q8Fh4O0aqSYOo5LfSLFq8lJkpwI4QInA4zN1rrt4C3lFKnaa0/DuCYhBBCdMAz56b6gPnCj8FNrQ71Team0KzvEpXUy65ukZHEUkZZce8LCJz1TkaRhxqe1LsxecO91g20ydwkJoJLWygljmGFhaZ1dH+UnU1V7STTKS3jknY3GRZcydn1/0fUhgPABZ7nExNh7doAjVMIMeR1VpY2X2v9FWBTSp3V+nWt9ft+HZkQQog23MFNXME284mfgpvwcKjVIb7J3BSZjmTR0b08UESECW6ORHa9bRfq62E8uebK29+aZ25SUlq8lGCqtygmgWH9eN6NQ1uoIpIJwyo6/IccE1rMzxwvwLctG0ckJEjmRggROB0GN5jSs6+AW9t5TQMS3AghRIDV15sGabbd280Tfszc1LlCcFXV9HJ2C1QcMRFZVG8rwNyZm9K2E9q7q6YhiFHkQeKxvT5Wl9yZG5utTWDgjq2KSGRqP+6YVucymbKUqZ3PUdpincGMtS+b/6ghIYD5HsvKTGAeFIApTkKIoa2zsrSrGh9PDtxwhBBCdKa6unFZmx07zC3x+Hi/nMe9LmhdtZPerqJTWeYCfJe5KSnvXYczraHCEUZyoDI37uCmncVWm2du+nU76MbJXhOO6fz/21ZbGtQ8D9nZcNxxQNOPuLi4ZRJLCCH8ocsbckqpBUqpyMbPr1BKPamUSulqPyGEEL6ltakS8wQ3fsraQNO6oLXVrl4fq6JCY1Gu3q812pi5KS3vXS7Jmb2JBh1EsrUQIntf4talyEjTBa2d9Yg8mRvriH7dDjrCZTrepWSO63S7rdY088k333ieax7cCCGEv3nzF+JRoFopNR24GTgA/Mubgyul3lRKbVBKrVdKfamUyuj5UIUQYmgrLDTdggMR3LiTDL2ectPQQEVtEFHB9t4vx+Oec1PVWUV11+pdpjZqVGy1X9YIaldKCiS3LafzZG4ixvXrzI1yOomhjLgFaZ1uV2JJgrFj4dtvPc+5v0eZdyOECARvghuH1loDZwJPaK3/CsR5efyfaK3TtdYzgQeAZ3s4TiGEGPRqasDVSaJkW2MPgfi8TeZCOADBTW1tLw9UVEQlUUSF9759s2fOTXUQWvf8MO7gJnlYXe/H5K3kZIhp2wo7LAwiIqAodEy/Dm5qXSGM4RAqYVjXG8+d2yK48WSnJLgRQgSAN8GNTSk1H7gQ+LTxOa9WGdNalzf7MgbofX2DEEIMQmVlsGYN5OV1vI07uJmid5pPAlGWVq86j7i6UlhIBdFER/rg7T8igjhKcTgtvcoo2XVj5mZ4//iTlJAAxbb+XZZWouOJV0c63ygjw3zMmwf790N+PtBOcJOZaT6EEMIPvAlubgceA77WWm9RSk0Bdnt7AqXUM0qpA8BfgJ/0bJhCCDG47dvXONG9ouNttm0DK06mav+2gYZmZWmEQ10vMhyFhVQSRXS0D8q/GjM3YILBnqp3mbK2UckBKknrQmIiFKmkfpu5cZVXkksyYRa7dzvMnWseG7M38fGm+k/m3AghAqHL4EZr/ZbWOkNrfVPj1zu11hd0tV+z/a/UWo8Ffg/c3/p1pdTVSqk1Sqk1RZKzFkIMUYcOmceqqo632b4dwq11jNP7zQT1VgtC+pKnLI2w3k28aczcRMV6lfDvXHi4T4Ibh7YRQRVhI2N7PyYfSEiAYmec+U9w0kl9PZw2iZWCFVuoIwyXxcu5TjNnmn7ljU0FbDaIi5OyNCFEYHT5TqWUCgWWABObb6+1/k13TqS1flEp9bRSapjWuqTZ808DTwPMnj27F1XUQggxcB08aB5ra6Gysv01YbZtg3BLPWOc+0xgExzst/F4ytJ8ENxUcgzJCT4Yq8VCbEgd1PcuuNEaEyQlJfV+TD6QmAjb7dFmYE5nXw+njQ1v7wfmUmHzcrptSAjMmmUyN41RUmJilgQ3QoiA8KYs7X/ADwAHUN3so1NKqUil1JhmX58NHGn8EEII0Yw7uAHYsKHt6xUVkJsL4ZY6xrj2+7UkDVqVpVV3+ZbfsYICKlQMUXG963DmFhtWD/QuuEG7THATiDVuvJCQAEXVjdGk3cvSrwB65L1JJFBEcHcW4Jw710wia5yvlZgomRshRGB489dmktZ6Wg+OHQH8TykVATgxQc3ZjZ3XhBBCNHPokKk0czph3To44YSWr2/fbh7DLbWMdh2Aqef4dTw+zdyo6N4v4NkoNtIBZb3M3LjoV8FNYiJU1wdRSyhh/Sy42bDexUfFx/Bjy785oFI73TYrq9kXc+fC0qUmMI6KIjERdu3y50iFEMLwJrjZq5SK0lpXdufAWutCYG7PhiWEEEPLwYOmJXBtrQluWnMHN2MthwihPmCZm14FN5mZ6OwNVLgi2y2z64nYSNNSujfBjVNb+lVw41nrhgTGNDT07WBauf/2CiKxMi44nwN0Hty0MG+eeayogKgoEhLg66/9M0YhRC+4J9i1uDsxsHkT3JQDa5RSHwGeljndnXMjhBCiY4cOmakKNluz4KbZH51t2yAoCI5y+b9TGrQqS+tF5qamIQgXVp9lbmKiTfK/N8FNA7bG4OYon4ypt9wxVjEJjLF30lEiwPbvh1c+iObXPMzuoG4WcIwZAyNHmuAmOZnERNMtzeXyrh5eCCF6ypv3mB3AS0AJ3ZhzI4QQwjtaNwU3kZGwdWvb7svbtsGkSTBO55gn/Bzc+KosrdIeArTfIKEnQqKCCbPU9a4VtA4mhvJ+01DAnbkpIhH6UeZm6VJQ2sUNSS+Tr5K7t7NSJnvT2Ns8MdGUXJav3W36njt8sKirEEK0o8vMjdb6rkAMRAghhqriYhPMhISYD6cTNm2COc222bYNUlM1v859EnSI3y/Mg4LAYtHUunoR3GhNhcOkgHyVuSEyklhLBWVloT0dErWEEUUHLen6gCdzYx0B9s19O5hGDQ3wz3/Cj8LfYsxJE+DjHqwJNHcuvP462O0kBpcDMRTNPZs4134I7dm/nxBCdKXLzI1SKkkp9W+l1BeNX89QSl3j/6EJIcTQ4F7jxp25gZbzbux22LMHpqkd5k74uHHmzrgfKQVhobp3ZWkNDVRiAgifBTcREcSqCkpLe7Z7dTW4sBKM3e8/Q295MjfWEf2mW1penvlZ3Vp9J8yf37ODuBfz3LWLhJvNGt5FZ//MPNebhWGFEKIT3pSl/RNYCcQ2fr0d+KW/BiSEEEONuw10SIi5oR0bC+vXN72+e7fJ5kxb+U9TLzZiREDGFR7ey7I0u50KTFTjsyRJZCSxlPa4LM29n031n/Vk4uLAYoFi1T/K0pxO03b8zIw80tjctnWft2bPNgFkcTGJx08GoOjyW836TPX1PhyxEEI08aahQLLW+kml1M8BtNZ2pZTLz+MSQoghw525ef99E7csWtSYuWmc97KtsYfAUYVZkDohYBmHsDDVmLnJ79kB7Ha/ZG7iXEcoLOvZ7u7gxtI/kjaACWyGDYOiyqR+kbkpLDQx1m/G/w92RUB6OmT0YJ2isDCYPh2Cgkh87n4Ya0owCQmRzI0Qwm+8ebdqMetPKRUL9KM/C0IIMbAdPGjmuCT98GRQmllzsvjHP2BByFIsCk7JrgdCOOqERLDUBmxcYeFQq3qxiKe/MjfOYnaUaXryp8gd3GgV4J5dXbRZTUyE4sqEPs/cuJtbREXBSTnPmaYAtl4swDpsGNCs9K4Ik56s6j9d4YQYyjKzlwKQ1aej8C1v3t1fU0o9BUQppS4HPgae9euohBBiCDl4EJKTwaJMm+OZM03VTo3LTLre/uZ2xrKfiAfvDug8kfBwRa0lsldzbtzBjU/n3FDW87K0ElOO5rL04oLdDxISoIgEUxPWm0VTe+ngQbPW0pjEWtSmjT2fb9NKWJhZx6moiKbMjazpLUSfm+lYxfOVF/Suv34/02Vwo7W+H/gCWAucBfxda/2IvwcmhBBDxaFDMHp009ezZpnHKmcYw1yH2bbFxVEjyuDYYwM6rrAwqLH2Irix26lU/phzY4Kbnlwbl+WajEGDCvLRgHwjMRGKXfHmi8LCPhtHdrZ5zMh93yxK09P5Nu1ITGyWudEaDh/22bGFED1ziv0DxrlyYMUKz3OZmU3LrA1E3nRLW6i1Xqa1/qHW+gda638rpRYGYnBCCDEUHDxo1jx0mzzZ3OWucoZzWe3TbNdTmHbGuICPKyyssSytNw0FLHFYrU2LgvZaY+bG6VQ9qpYrO2R2qrOG+2hAvpGQAPvsjWvJFBT02ThMcKM5SX8GViscdxxgqup6u4C5eyFPQszaRxw40LsDCjFU+DHamOlYbT5pFtwMdN6UpT3QznP3+3ogQggxpDT+sXLPcWieubFaISPDrHM4w7GGGiKYNjc24EMMD+99cFNpjSEqyofVdI2ZG+hZFUVZvpmz9NdJz/loQL6RkAC1hOJCQX4PGzj4QHY2hFnqme1cZf4T+nAtoISEZpkbgP37fXZsIUQP5OQwSufixArLl/f1aHymw+BGKTVJKXUWEK2UOqvZx4/w9PARQgjRG0VFpkFW88wNwMyp1VS5wviOeQAcdVTgxxYWRu/Wuamvp0LF+m6+DXgyN9DD4KawnnCqCQ4NcEOBLiQmgsZCKXF9HtxEWao52rnJpyVp0Kws7ZNPzBMS3IgBaCCVbHU51sZszXvB58GuXYMmm9rZu/t84FZgeOOj++Ni4Bb/D00IIQaHzv7AuNe4aRHc1NUxK+shqonkLtvdAEyb1viaL+qDvBQWBrU6tGfBjdZQX0+livZtcNPbzE2J0+wfHOzDQfWeu5tYIcP7LLgpK4OcHBivDhBKnc+aCbh5gpvYWJMRGiQXUkIMWJ9+SrFK4I3gH5qvB0lpWofBjdb6ea31ycD1WuuTm32cq7V+L4BjFEKIQcu9xo2nLE1ruOIKZu19FYDdzhTi482FYaCFh/ciuKmsBJeLChXty8qm3mduyrTZ32r14aB6z/3vu4eJfRbcbNxoHmfTWIPvh+CmthZqahWMHSuZGyH6ktbw6aestx1LjmUSDB8+aErTuuyFqbV+Tik1EZjYfHut9fv+HJgQQgwFbTI3Bw7AF19w9N33oO5w4dA2pk0LaAdoj7AwqHGG9Cy4yc0FoEJHEdePMjflFRZig2vh8899OKjec2duDljGQ/6ePhmDu1PaKa6PybWMJnnUqN4ftFmWsflaN+PGjZPMjRB9acsWKCxkXdgc8wdm0SKTudE9W0OsP/GmW9pfgW+BP9JUmiZlaUII4QPuBTwTEzFXffv2waWXEvTH3xJpNau498V8G2gsS3OFoKt60JasMbip1JF+y9yUlnZ/97KaIGLC6n04IN9wZ24OMrbPMjfZ2TB8uOYk12dssmb4/Pju77GoCMncCNHXPv0UgO+s8yiwx7FhykW4Cg+boGeA82YVsx8AE7XWFf4ejBBCDDWHDsHoZBeWu++GbdvMapf//CcoRaSlhkpneNN8mwALDweXttBQ00C3Z6jk5QFQ4Yry+ZybGMqBHpal1YUxOdHhwwH5hjurkcfIPg1uMiZUEF94hGzbHM7w8fFbBDfjxsGRI1BVBZGRPj6TEIOI1uZOTmxs9/d1p2PJaPvaihUwcSKbcqezu240GXeOI5YSTrz4CAdr+6YU2le8aReTL4GNEEL4x8ENRxhTsAbuustc4aamelrlRlpN2+K+Cm7ca9P0qFmaO3PjCvd55iaYBsKD7D0LbhoiiI12+XBAvhEWBhacFKoRZnFLR2ADMLvd3LDNUBtxYuEb24k+P4f7Yqm4GJO5ASlNE4OSTzuqffCBmRB35IiPDoh5f8nKgoULqY4bjc0GL7wAF0Z/wvZ9oezdC+vWmTlyHgOoTZw3wc03SqmXlVIXNG8J7feRCSHEYFZfD1u2cGhLGaODCs1EzqOPNjVqjRKCyhkRVMKJvr/O9Ep4Y9P/2monnHRS93bOzaWCKCqcEb7N3NhsEBJCXEhtt4Mb7XBSpqOJjetfbaDdgpSTEj3M3Kk9fDig596+3QQ4GYfeZbM1nXJLnM/P0XzODeMaF6WV0jQxGPjzwv/NN81jZaVvjpeZCcceCxUVsGgR1dXmvf7HP4Z/LslipzqKjPAdNDSYgMezW/ZSMrOX+mYMfubNO/wcYARwPTLnRgghes/lgo0bcZWUcsg6jjE/P9NM5mwl2OJgavhB32Y+usGduaklrHGSaTfk5nJAjQeUb4MbMPNugqu7HdzUHCzBQRCxid5UZAdesMVBKbHmiwCXprmrV9IPvE36uHKyMm7w+TliYkzsvnQpXHRLY3AjmRsxwJSVmWrKgHC54J13zOfV3Z/7WGiPpdoZ2vaFxjdPffJCamogIqLx+VNOgaoqjucboqw1PPSQGcJA4023tJMDMRAhhBgyPvsMamo4POkEGnZbGT2urwfUPk9ZGuHgdHZv57w89kdPh3KfLnJvREYSW1fV7eCmbHcxkETs8BAfD8g3bMpBpW6MBPsguAkLamBKw05ImO2XcyhlsjcNDVASPNJk4SRzIwaQhgZTvul+b/S71auhoMC0rm8WUbmTRJ0teZafD9trx5EUVNb2xdJSmDGDwzoRh6MpS09mJijFbOd3fBacybad43n/fVi82DffTqB0mLlRSh3d2UcgBymEEIPKP/8JNhuHIk0btBYLePYjnrI0wrp/+y43l1zbeAD/ZG4sld0PbvaZHWJHhne+YR8JVg6qdePY+iC4SQvbjfXoo/x65ZaYaC4QXcpqFneSzI0YQD7/3ExXqaxsnDvmb2+9ZQKbkSOhrs6UknnplVcAFFXOVr/PLpc5zsKFbN1qnvIEN/HxcMwxzHJ8R0JQGWPGwIMP+uIbCazOytLe6+TjXf8PTQghBofqtdspWp1jvigqgtdfh+HDOdgwHGi2gGdrGRnmo4/0OHPjdEJBAfk2M2ncL5kbVc7Wrd0rcy87aGrWY8f6OtryDVtSPPWqsYQkgMGN1pC93kVG5Zdw7rl+PZc7cwNIO2gx4Lz+etPnH33UyYbZ2c06lTXqybyct9+GBQtMTSfApk1e77psmXmscYW0bApTXm4CnEWLPMGNpywN4JRTmO7cRAQ1/OpXJju0bl33ht3XOgxutNYpnXxMCOQghRBiIMupG8m2mvFmIvXzz5uru5EjOVRv2kf118xNizk33cncHD4MTif5tmTAT5kbyrrdUKws19Ssx46P9fGAfCM4GJwuRe2w0QELbjIzYd48KC2zkKHX+z24cWduANNUQDI3YoBwueCNN0yAHhQE773n5xPu2WNq4M45p6ld+oYNXu26YwesXQvH8h2g2Hz/B003qNwp7wUL2LrVJIaCm/f6X7QIGw5mONZy1VXm5tRDDzg5xvEt0x3ZPvrm/Kt/towRQohBQmsod0agUbz8koann4YTToCICA7WJREc3H/XE2hRltadzE1jG+hii8lM+SVz4zqCw9G9PgdlBWZR1NjkiC627BvuRnmHE44OXOYmO5uqTXsByIg/CHPmmFu1nRXz90KL4GbsWPN/JcBtr4UAup1J+eYbM/0lMdFUb334YbO3xbo638+8f/tt83jOORASYuaodRLcNP92XnoJlNI8xE0ArL/zTUhLg1dfNfNtoqIgOppt28z7vFLNDjR/PnaCOalhBTGvPcuVw9/hPy+7uLH6z/yo/nnffo9+IsGNEAPMAGo1LzAtdh3aBmief6wKdu2Cq64C4GB9EqNHt/rD0o+0KEvrzh/uxuCmxJIE+Clz4ywBuhdzlRWbq+rYuP75Aw9p7HOQEzUjoGVptc5gFC7SzkkBi38vCxITTSzjcmEyN06n5/+LEP3Za6+ZDEd8vPkoLYXvvgP27YNVq0wZWuPvrd1lY3dtMuXlvTjhW2+Zdc8mTDCTfebP9ypzo7UpSVsYtZpklY8VB9mn/sa8eNFFZsJQnGn1vnVrq5I0gLAwNlkzOKvhLbjiCn5V+RdcysqFtjf5c/hfe/ENBY4EN0II4UdffmkeRwUXs25XFJuj5sFFF5GZvZT3jxzXb0vSoFVZWg8yN6UqHvBT5qahCOjeTf+yIybN4y5f72/cmbLdQdMCGtxYHfVMZheRF57h93O517pxOPCsdXP9uQfkho3ol9w3E7U2821OPdUkUOLiTDnX++8D//qX2aC6GmbPhjVrOFifRK49kYce6uFJ58+HlStblommp5s5N128F69aZSrallQ8wZshPyTSWkd25USz7wsvQGwsDB9OSQkUFsItt7RN1D4V9mueCL0BsrMZn/8NF15kYY1jJlX0z6x3axLcCCGEH335JQSpBlKDdmKjgRcm3+2JGupdwS2bCfixHKgnetwtLS8PrFYqMVGNXzI3DWaRy+4EN+XlmjBLvSdD0t+EhJgs3i7XRFP/0t21hXqowhlJhnVzu2st+Zq7BNNux5SlASPqpamA8B1/VDesW2d6X3z/++broCA4/nh4/z1tgpv4eJg5E4KCqDjhLIrsMYDmkUeapri0uwhmR4M9csQEMeec0/RcejrU1JjIpRPLlkGIxc4FkZ/wUfBiIq21bNwITqxmpc70dAgPZ9s2s/20aW2P8fQxT/OL49abbZXippvM/gX2+K5/WP2ABDdCCOFHK1dCjLWas51vcCYf8O+DJ+F0mutWuw4aEJmbbndLy82FESNocFpQCt8HE5GRxNYVAt3M3FTZiA2t9fFgfEcp8zPfXZvcuBhMid/P6XApChlOxlG1ENrOYn8+5g5uGhrwBDdJddJUQPQDnURFr79uMjXNY42zzoL12Yq8fPhd3R1k7n4GVq/mmVF3UE8IP7c+Q3k5/P0na+HRR/lB/QssaFju3ViKi03759nN1pxKTzePGzZw+LBZAsfd7QyA7Gz0+mz+87KTs/U7xPz0AmpVBJHWWmpqTEV0c+59j/ZicZfjjoMYaxV1ruCuN+4HJLgRQgg/OXTIlGPHWKs42/46P5nyLflFQSxfDvajM9Cofh3cuK91u525yc2F5GScTnNB4PM5RRERxLrMhb/XwY3TSVltKDFhDV1v24dCQ2FXWWPtVgBK0+IbM2AZp4/w+7nAU4lGaSkmNZiQwHDJ3Ag/6002R2sz3yYzE4YNa3r+rLPM44exP2KVbT4AjrhEHnFey3S1hSedV3Mub/Lw2xMov/4P/LLuYe6uuRX+/OfOT+hymV+Qs89uOQdu+nSwWmlYt4mcHJPEWbjQzOt0K3VEcbjYyhL9Ilx7LQCRVtMHunVn6q1bza9g4z2GLs2I2M2ksDzvNu5jEtwIIYSfuOfbHKPWMs6Vw+KbpxIXZ8qe6+vNax2ucUPfV6lZLBAaqrs/5yYvD5KTcThMfbrPRUYSRynQjeCmuJgyYoiN6sb30QfCwmB3YRQaAhLcRDeYlQgzfjbL7+cCSEmB4cNN4L97NzBuHMMlcyO85M+GOu2WjWGCiB074IILzNdZZJJFJmnR+0nmEO+P/BlOZd7oXnsNDhxQEBrKxVHvcvsbx1BGHI/+Lo/vRX/Bh0GL4fbb4f77Ox5IWZl5v2023yYzEzLPCIWpU3nlw1jq6kyfAa1NgLNzp9muqCGGWFXGmac4YOpUsjJu4OuZ1xEU1H5wM22a9z1ELP2zD0u7JLgRQgg/WbnSLE+wxPE81UQQsuRCLr7YrJXgXlStP2duwFxs96hb2qhR1Pqr0qlxnRvoRnBTWEgZscTG+mE8PhQWBrX1VvIYFZDgpsYVSjwljDg6cLX0KY1N2W68ERg7VjI3IvAqKmDjRq9+x4qLTfb5/PNbPq+e/Rdn8QEfH5yGS5tA48EHYfJkGBZUSYElmWPOG8PixfDQUxGUE8N9YXfCxRfDb34DjzzS7vne3HEUtYSaqKUVV/pM7tl8NhER5sbYp5+a98CTT4YqZyhHGqK5UP+PkF9fY3bIyiL4i+VMnw7r17c81rZt3pWkDUQS3Agx0LS38rHol778Eo6f5+Ik52d8HXQSRETwk59Aba25cw2dZ276g7AwRW135tzU1EBZGXpUMrW1TU0JfCoykhhMj9VuBzfD+vefPfc8p11M9n9w43CwT48jWeUFtB15SIgpT3v3XXjfdQbD6/YHrHmCEAB89JEp/dq500TZnbyRFBebhW5Hjmz2pDaNBM6aVUBllYUKZyQVzghWrzaHa/77dMcdpj9AXn0CLmU1qfvzz4cbbjBZbjCp/KwsuP12FjZ8xGrb8e3eGXrT9n22OSYzJbkKpUyl2ooVZvf1VVOwE8ySEZ/CmWe22G/mTBPcuH/NKhzhHDrUzeAmI8N8DAD9+11eCNGGS64B+jV32URpKWzeDCeMOUCMLmNlUCYAxx4LU6aYrqEWS1Nr3P4qPBxqVTeCm8Y20EXRE3E4mi7WfSoiAhtOgizO7gc3if17QqynHXRIqt+DG+e2nWxjGjHWKr+epz3JyTB1Kvz6q4uwuBxEO44EfAxiCPvgA1Mzm5wMS5fC4sVE6so2m9Wu20pVVVOXNI+SEsjLY9GtswgKgpK4SRwMnUx8PPzkJy03nTPHxBoH7Uk4tcW0WnvlFVi82Mzyz842vaVPPhn++lcOWcbyQuhVbcaiNfzlm4VMYhcnhK7xPJ+WZgKcIBoYwwEW3DTbTHZsJiMDiopME0aAbTVm8ptkboQQfW7HDlhZMYMyx8DoNT+Uff21+WN0YsV7oBR3zXwLMHf03H/83K1/+7OwMKhREd6nSBrvRO5wTAT8l7kBCLE2eB1z6fwCE9yM9H9HsN4ICTELBe4KT/d7cLPnk73UEYayBv5SwGIxVTm7i+N4mBsZvvHjgI9B9JG+Xolaa/jwQxNQTJoETz8NK1bweNVlJDtblki6Wx+3LkkjPx9GjiTqwtNZsAAOHzbxzi9+0f573h13mMWcc+2Nd7OCg+F//4OkJPPeeuWV8OabUFLCLyNfZLf1qDbHKC2FdbtjuI17mVzdcjHP9HT4p+0XfMgZWK74aZt93QmX9euBrCy2Xv8E0Elw09cTPntJghshBpCVK0FjIc/ez2/3C7O+TZDm2NWPmT+izWbWX3qpeQzu30kEoHEOiAr3PrhpzNzsqBzl2d/nGpfUDrPYTUthL9TmHqGBYGKT+ukiN42UMhOFd1un+D242fylacpQZ4v063k6cvrpcO5JZfyZPxLmaHvXXIieyMszk+Xd67i0sWGD+d2Kb5xndtVVsGIF0bqMJ6t+DMuXmyzJX+BA/QgSbGWkpDTbv67O1JldcQXYbJx1llm3SSlPgzKyMm4gK+MGzy5z50KcrYJD9UmUlzc+GRpqZvTPng1//7tpINDJpMADB0wZ848TP2Jiq+CGjRu5xPE8m4NnNX1fzbi7SLsr2rduNTdSWnxfg4gEN0IMIJs2mcfihhiKi/t2LKJzX34Jx0yrIXz/tja1Z2PHmmoI95of/ZkpSwvD6yjCHdwUxaOUnxoKNGZu4oMqqfVy2ZqyQ6b0Kjaun6fKMDeTdzWk+D+42QwKFyHWbiwW5GMP3e/EgY2v7XP6bAxi8KiqgpwcU4KVlga//KXJqrTwwQfmsXkQsGAB10T+myLLcJynn8V1J23kj3+EpKAjTAtv1fDC/Xt5xRVAU0vopKRW83JaSQnNp0Fb+X//r+m5jjq0tVZWBuXlcOutEJxxNBOrmgU3WsPNN1Olongh9Op294+JMTdNmgc3U6e2qV4bNCS4EWIA2bjRrHavsfDvf/f1aERHnE6zwNqJURvM7bzmiyM0mjTJBDj9nadbWnfK0iIj2bk/hLAwP5XdNWZuRgUXUVvbuNp9F8ryTHu6/t4tDUy3pd3VI9B5+X6daL/pUCwjKcCq+m4i34TZ8fyKv7PWNdMdFwvRY88+a96qUlNNidjTT5v32gMHmk0b/OADM8O+Veq8wJLMNRHP84OkLB7/cga3zPiY6aF7sLh/P1atMqt4Hjhg3tPHjwfgqKNMAmbChGYHa6esK+qYqYwYoXjkEVNi3h0HDpipOldeCaSnk1K1Gev6NU3fz/LlPB9yNVUqusNjZGSY4CYz03RZG6zzbUCCGyEGDK1h40bNFOseoizV/Otf5rm+Ll8WrWRnU7l+Fw0NcGL+f+H44wdG/VkHwsMbF/HsTlnaqFHs2OGn+TbgydyMsRWgdduVt9tTdthEQAMhuJk0CWodweTVxkKln8q1iorYXDeJBEuJf47vLaWYq1YBsG5d3w5F9DOt/rhVVppSs+bZ2uabOBzw8MMQHW1ij3/8A7ZsMR2Vc3Ia14IpKzMTIlt1EwNwaAurambwesHxPLTwXe7feDoP1vyS4xq+hNNOg+OOg6++MkHNUS3nxCQlefc2n5Ji3hdvvLHjbepdNoobYrj7brjoInOq0lJTkhYeDqSnE4ydFVN+YTLqN98MU6bwVvBFnZ575kzzXmm3m8q67gY3A2kajt+CG6XUMKXU+0qpHUqpjUqp15VSA6AIQ4j+KT8fSkoU1zge5Qfqv2zebLID0hq6/yl3mIvv4/e+2GIhtoGoRebGm7VucnNpGDmWPXv8NN8GPJmbcTbTT3vz5q53KSsxt20HQnAzebJ53M0kv5Wm1a3exC4mE5YQ2eftXeMsZShc8jYmOpWVZUrMduxo/63ojTdg376Wa4dNnWrm6Y8ZY/bd8Mxqk8I588w2JWH76kZS7ozg5ZfhxhWL4cUXme7cyN9qfmXKJu6/H/bvN33Me7g6cXAw/OlPJtny3nttX3/rLVhVeTRbalK4807TAGDaNBMUeZYNcE+gqaoyqant2+H++3GqoE7P7f41Lyoyj5K56RkN3Ke1nqq1ngHsAe714/mEGNTc823S2MSDzhsIC3bwr3/17ZhE+8qdEUwfUcIwjsB55/X1cHolLAxqdePEGc9M2E7k5pITk4HD4cfMTXAw2GyMt5iV7bds6WJ7p9Mz9P4c3LjvjE6aZL7251o3O1YcwokNR2zbkkl/a30HuMoSwwT2SnAjOrWmsQqrvJw2f/vcC2hOnNhuFTBjx5p45I5/JJg3gblzW6zbsnMn5NkTGBlcwsUXN+506aVcG/kc94XdYVI/t9ziyRr3xnXXmaDrxhvBpZvqdp95Bi64ACKiLMycaTJVu3eboG3sWNNhEDCpHKXMD+JPfzItpM8+u8vzuoObwkLzKMFND2itj2its5o99S0wzl/nE2Kw27ix8ROLlXzraC7S/+Pll1ymb77oN7SGCkcEJ1q+Mn89Jk8eWPn8VsLDmwU3R7pYi0RryMtjh226Z1+/UAoiIojW5YSFeZG5KS6mTJta9P4c3LiNHQtBNu3X4GbTd2YOkoruuEY/UA5bRjCLdWSv9yIzKAa+4mJTbVBT063d1q417ykxMWZiffNfja+/hu++a7uAppttczajrXm8fWAmq465pk3m5Te/AQsuxocUtHh+l3Ua7wef79M0dHCwWVpn1y44NCwdnZ7BX/9qmradeqpJzERHexLUbQUFmRfz88178kMPeTW5MTnZ9LaprDSbu2+iDEYBuSpSSlmAXwBvB+J8QgxGG7+pZjQH2Rw8kzvD7+eKkH9TWWWh1C5r3vQnVa4wnFg5If9/Az5rA+ZveqWr8f9YaWnnGxcXQ0MDO50TPfv6TWQkYc4qIiK8CG4aF/AEc2HU31mtMCHF5deytM07gwlSDXz9dd/H3futKcxkPXtzLF4lB8UAV1hosg6PPNKt3dauhagoswhyXR38+tdNrz34oGl+dvnlHe9/ou0bEiji9oLrWjyflWXKwcaGFBJsCUznwDPOMMmWAwdM1ugPf4AlS+Dtt73sYOaOfH7606aUTLNMVHuUano5LMzESINVoG75/gOoAh5t/YJS6mql1Bql1JoidyGgEKKNjavrmMFGVtuOp8AyihNf+gWT2Ymur+/roYlmjjSYO+En6c8GTXDjwooTS9fBjbsNdPVoEhLM3Fu/XThHRBDmrCYiAvbsofOW0AVmAc+QYJd/WlP7weSpFnYpP611Y7ezuXg4RyUU94sLnI3WWWSQDZglSMQgpnVTeeu995qVL6HLzjh5eVBQYKrCwsPh9tvNGpjFxSYB9Oabpjtah9kO4ATH59zGvXy8JZkvvmgazs03N7bnnzMqoPPPHnrIzB0qKDAZpxde6Ebvmbg4E5006yvtTYGA+9vzW1a9n/B7cKOUegCYDPxQa90m56y1flprPVtrPTtxICz6IEQfaGiAbbnRpEXt47lZfycr4wbU2Yv52Ul72aqPZmbd1309REHjHyp7PLPUOkYnA8cc09dD6jX3H8FawrouS8vLA2BH8TCmTvXzwJplblwuM6e2Q42Zm9jogVP2NHmyYjeTTDtoX9u2jc16OqmTveihHQBllngSlPm/JfNuBrmtW80ftNGjzYT4v/zFq93WrjWPUVHm8dZbTbvn3bub2iRf507IdNBk51jHV/xyxleMHAl//KMJbAoLTZe+v/61/YxJ68U4fWnSJDP3ZsoUk3mydOeKfPhwmDcPRo3q1jkluPEBpdRfgGOA87TWcntZiB7ascVBgw5ixjFBLWprf/LiKVhxYK2vMXl60ac+/xzqdAjX6X+YLmnd+mvVP7lLy2oI9z5zkxvh/+AmIoJQZ7Xnj3SnTQXcwc0AWMDTbdIkqNVh5O33cvHUbqj4div7GU/asf6sG+yePNsYktRh1q8bOAFonxqoawC4UwujRpkassceMy3OWissNGm8xveUtWvN26l7Pn9wsGkUVl9vNl2yBEaMaDxFOwFJpK4kzbmBsO8t5I9/NIssl5SYPgFz5sCPftTN76FVisSbrEl72wwfbhb+7NF6YD3Yae5c89gPptr5lT9bQU8Hfg+MAr5WSmUrpd7w1/mEGMw2vrkXgBlnj2/x/MgxNqZY9rCMJThzC9rZU/hb82uMZ5+FEOq5mFe6LEkbKD0G3MFNLWFeBTflxFBYZA1M5sZV5akd73DeTWYmPPIIZZZ4YuMHTrDpbgf95ZoQn1/DbskyJeCpJwW+U1pHNtlmMVOvI/tbuUnjDW9Xtg+0LmOuzz+HkBAIDYW77jIRyx//2HKbw4fN4jRlZSY188UXrF1rmoRZN2V7sjLNExc33dT5uI5xfIsV0wL6iitMN+etW82aLw89NCjuQ3ll4kQT4MTH9/VI/Muf3dK2aK1VYyvojMaP8/11PiEGs42fFBKEnamXzmlzVTzamk8BIync1kXJkPCr8nJ49VU42ZIFKDjppL4ekk+4MyNlxHZelpaZCU8/zc54c2swUJkbi8Vc9HSaubHbKbMlEhs7sDI3AMUNsT4/9uZsM2k6Nd2bmcuBscFm5t1s2R2CvX9Uy/neQM22+IrWJriJjTVZh9Gj4YYbYNmypsVqi4qaVt2cPt3cXbn4YtascrZb5TtpkllbMzW181Mf2/AVVUTCvHmEhMAdd5jhJCTACSd0smM/uQvly2GEhPQwUzSADJFYVYiBbeMWK9PC9xOUFNfmNWUx71J5O6sCPSzRzCuvmMrA6/XfWWeb042Zof2bO3NTbBvRdeamvp4dUbMBU0fuV41zbsBcA3XaMa2hgTJL3IBoA+02diwEWZ0cco0ieP23vjuw1mzeF0mErY5x/WRxhqyMG/jfrHvJSMilwWll27a+HpHwi+3bTVamecvC3/4W4uNZtTGEc9fdAYsWwd698O67JvI4+mjySkIoOGzlmFmuNiVnakM2oduzOz5nSQnccgvfc7xNZGKYpwX0ZZeZwMidIe1P+kk8NaBJcCNEf1dczMbyscyY1H47KKfVtDvK2yvlHH3p2WchbWo9Z+r3WW07vq+H4zPu4OaINcm74CY4FavVlD/4VUQEwyOqycoyd2337TPzk9tlt1PmihlQwY3VChMSK9nFZOJ1ie8OnJ/PprpJpCaX9btSnIz5ptXV+nW6j0ciWvNJ0sl9xd78FzE2Fv7wB451fMNTVUvM4i/vvGOyNwCRkay98gkAjtnyovfncjpNs4IJE+DhhyEpqcXCLjabWfel+T0oCSoGj3721iaEaCEzkyPzzyaX0cxYENvuJnaL6W2bfzAw/flFW9XVsGoV/Cx1FQpYbZvX10PyGXdZ2hFbQtfd0ux2djgnk5ISgMRVZKQnmplu1gxl69b2N9X1dsocEQMquAGYPNbOLiYzzOWjZRIyM+H009lMKqmp/SiAaLyqnPy9KYRTTfZnXQTRA0B7wUCP58m0PpjWzHSswqp932zCr7KyTCnat9+2jCKuvZYCNZI4XWoCm0WLWuy2NuF0FC4y/nW9mYfTGZfLtE9ftcrM5Tn5ZLMC9lFHDZpsOiCRWBckuBGin9tUZFrAzDgzud3XlcWCwkVe/iAvou3H8vPNpPYllU+RaxlDrnVsXw/JZ9yZm1JLQueZG5cLGhrYUT3a//NtwCxoYbdDQ4On3r7d0jStqXNYsbuCBlxwM2ky7GYS8a5inx3zcEUoRSSROj/WZ8f0FWvmicxgI9nfSRa6U3feycPVP2dJ/bN9PRLvuefbZGa2nfAREsJNEU9xdeQyOOWUNruuXac4aqomcvJI2LbNBDi6neB8yxYz13HnTtOw4KuvzAI47rsfYsiQ4EaI/kxrNlaMB2DGzPYn/yoFCZSQVzyI7koNIC6XKSM/Z7GLxK/eZNUgytpAU3BTbonrNHNzcfZtuFDsKg3AGjfQ1BO2upqUFDPOdpsKNDSYZggw4IKbyamh1BKOzeW7Gfaby8cAkDqn/7SB9pg0iYywnWTnxLR77TrQjXXmtH9R3h3LlqHvvpv/8APOqH+ri9VrA6/Db2/HDtOzuYNGK3nWMey3tl/LunYtzD7WalbtdLlMi+ijjoIHHyTGVUqwroM//MEs4rJ1q+lmkpEBx/f/8mBJwPiHBDdC9GfV1Wx0Hs2wyDpPD/82MjIYZiklv3yQr8rVT5WUmDXpfjZnE1RXc/7kLX5b9K0vuMvSylVcp5mbBNdhDjGaWrvN/80EoGkp8upqrFaYNs1kbtqUA9ntlGMmMA+44GamCeDsLptPjpeZvZTPK9KBrrtL9QmlyDjaTnlDBPtyBll08957vFB1Acc7Pu90s07ntnz1FfzsZ7wy7W4u5j+8yI/hxW7MQ/G1VoMtLYXvvjOLarbZxH0F352JO1lZ5L+cRX5+43rIM2aYPsZTp5pmA7fcwv8qT2dZ5TlmFc4lS0zTghEjBn87MNEpCW6E6Mee3HICG5nBjBmq0/fqWGsledUxHW8g/KagwJRyn176ipmlOtCuoLvgztxUWGLMHJeG9uv8h+kidmBSNgHN3DTOu0lN7SBzU18/YDM3k6aYP9FVLt/cuAjWdeTqZBLCqxk+3CeH9LmZJ8cCkP2Jj+YZ9Qdaw913A3BSw/KeHaO2Fs47j/oxk/hDze8BeIar0A88aLIZ/cDjj5tFNfftM30BALMmTXa2KUlLTu52p5G1a82jpw201WqCl6++gk2beDv4QvZbJ8Cnn8Jzz0Fiok++l8FsKGSLJLgRoh87xv4tm0hjxpyQTreLDKojz95/FuQbKoqKTKXW8OFg/eRDmD8fVq4cVH853MFNpWoMnjvI3iS6Dgc2uGmWuQFTVp+b207sVVs7YIObsWMhiAZKXb65cZHi3MMWppOaUt1vb2ynXjQNC07+cd12MmOz+3o4vvHJJ7BqFaUqjrmOleDoXvOXxev/H/vWFIHTyVM/+oyc/VYSbGXsZhLrdkWaSfh+5nB0XgFXWwuPPGJWvrdY4Nprm5WoaW3eE086qcOMSq0rmGpny79zmZlw3XVml4yMdnZKTeXCeXnMnh9qGgcI0UiCGyH6K4eDCFc5tYQzY0bHm2VlwfFzHBzWiThqB1j3nAHu28blR1KiS8zdyTPO6NPx+IM7uKlSjZmSDoKbeFcxW5lGVJTuuITSl9rJ3ADU1LTarqaGMosJ/AdacGO1wkjyKNRJPjleinOX6ZQ2q//OzwuffTRTLbupdob29VB8t+jmn/8Mo0fzj9DfEKPLzQ0QbzU08Kea35DsOkjFv9/m/z2ZxMKFMCX8IBZcvBB1HTzwgN8XCN2zB9asMXP13Zp3f/u//zM3e1JSzMcnn5gpMgBjXPtNiruD8blcsKl6AuurprBnT8vXKivN9Br3r3uPtZOuGAoZjKFKghsh+qu8PLYzDYC0tM43HZWs0Fgo3OK7rkqiHa0uIFavBtCcvOdp88Tpp/fBoPwrKMjcOa2m8+BmmC5iC6lMndp5CaXPtJO5afZlk+pqyoJNcDDQghuAJFVELsm9n4gOxDqLqSKK1HnRPhiZn1gsZIws5JBuvztkX/M2hti/38xtvzCziHO/vJmzor/kiYYr2cMEeOutjnd0l3GB+Te/+mrmOL7lwbDbuf+bEyguhr/9DYJmpjEs0cJLrh/SsPJbqKjo/TfXgbIy0zTF5YKrr25bBedwwP33w7x5Zn3OUaNg1iy44QZwaAvpzsbasg5+cO+/D7WuUJxY+NGPTBNEt6qqZiVpQnhJghsh+qv9+9nIDEB32cly5DhzJzZvu//+wIm2Vq2CCEsd851fmkXi0tP7ekh+YbFAtWoMJjromJbgKmI3kwLTTACabuXecgtgSrgiI1sFN1qbzE2QqcOPGYDT0mItFexgKjU5hb0+lstpAqS09P79p3/mTMgjmQhnWV8Ppa3mwUcHdu0y807Ki+1sW1PNPtskDoeM5bAjnivVsya48SZYvesueO45/i/kGt6yXcBDD8EPfwizZ5uXhw+H4upwPoy4EA4e7O131qF//9sENKOCi/j8c/jXvxpf0Bq05n//M9/vbbeB2pCN2pDNE0+YZM2+uhFkONbAyJEtFtFsbulSCAmBadMUq1eb5WnAzN+x23sQ3EhKZsjr3+9wQgxljcFNtKrydKzqyKiJpnYob1fr29bCX7Q2mZtoazVzHN/AaafR75Z895HQUDhijzJfdJC5CXdVks/IwMy3gabMjdMJmOxSamqrsrSiInA4KLMOIzjYfB8DTaStDgdBfPd6bu8OVF/PEVcc0P+X/cg4w9Q1Jtjz+3gkPfPaa+bxotB32FKdwoa/vs+adRbGhhSSpU/is5xxsHlzu1kgrcGllanzuusu+OlPeT7kavbXj8Buh7/8pWnbuDgzf/750X+A4uK2k2LaOUF3q9e0hqeegihrDZNCczn5ZLj1VsjbUMRT1Ut4oOoa/naPi6OPhsWLm/Y79lj4+c8h156ItaGu/fVtgE2bYMUKk+1JSjL73H8/fPSRp+K0ZXAjgYvwwuD8SyzEYLB/P5tII8za9RoXo6aZW9L5++r9PSrRaO9ek8SYZNlDrC4blPNt3CIi4EhtY2TQQXBTqmPRWAIX3LgzN43BDZiL9haZm23bACizxBEbOzC7wzYEhaNw8cWKXs6n27iRb5hHvDrS7zNY6edPAEA5uzfxvr94/XWIslbzi4a/Q3w8/OIXAIyeM5LwECc3sBTH62+32a++HjZWT+SbiqM5/4o4npt+P8V/eYoaVyj59mFcc03LZmMWC1xyCbyTM50CkngjO8Xn38u335oW6yODi1EKnn4a6us0152wnvHOvRx2xbNhk4Vbb3K2ubfz179CKHX8gb/gWpDpeb55gLV0qZnXN3Kk+frhh83v8WWXNSWJZ870+bclBjkJboTopxpyDrGHiYRau76oSTo6AQtO8nIH2doQ/ZiZbwOZrs/MJ6ee2neD8bOICKizWykjpv2ytNpaMy+EAHVKcw8KWgQ3qammW5qnZt8d3BA3IOfbAFRZYkhlM19u7N08mfqv1pBFJnG2Sh+NzH+SRtlIpIgyZ9/ODbpy7S+o+nwtnHUWvPCCV/NaDhww7w1TrbuZ7/gCbrzRE4hbrTB+opWNpPPMsy0vv7Q2WYsyZxTn8harbfP46ZZbGD46iOzqSVhxcfvtTdu7Exg/+QnY7Yr/Z7mTs+xvNvVj9pGnnjLDTwoqA2CSNYe7wv/GG1WncV7I+/za8iijOcglX/6iTaldXNYb/IPr+Ybj+fPeH7U59uHDsGyZ+R6CgsxzYWHwn/+YH3Venllnq9NmApLJEe2Q4EaIfqpgVyUaCyGWrjM3tthIkigir9AagJENYVp7ZtOuWmXKnM5xvs4OyzRTUzFIueOIzWHHtp+5KShgIzOw4ApccONu49YqcwPNsjfbtlFDOB+XHzdggxuUIlVt4ZuClI6WGPLKNx+UUUMEobaBkQ0ZZclnF5M67z/sZwsalhNGjQmSf/ITSEriruqbmdPwdYf7vP66ebzB+RBVRJpexs0kJMCC8Qf444GriK7O8zz/t7/B88/DzTzA4+o6Du51sGYN/OEPEGppICU0v923mIwME9S/wsXstE4zPZinTDETY3q4/k1mbDaZsdmUlppAY8kSsCoXo537YcECbtIPMnNqNZ/Un8Ru10RuOnUzwc//E/7wB7IybiAr/demQ9wFFzDP8h1jbHn86f4oXn215XmeesrEYb/6Vcvnp083baXBB13SxJAkwY0Q/VTePhPUBE8c69X2o4KLyDsyACcVDBTV1WZFuS+/hOBgVv/jG2bpNcx0rWV10Ly+Hp1fuYObTaFz2s/c5OezlmOID6nyxBx+p5S5Fd4qcwPNbrBv3cqBqKNxhEcP3OAGSLYWUOMKY93anmdmP14bjxUnsUEDY15etLWKbUyj/JutfTaGWY5V7LBONzWo33wD11xDqnMjf6u5Dr77rt19XnsNZkysZonzBV4PubhNiz6l4JEHGjhCPOXr90J2Nq+9Br/7Hfxo7Eru5TbuDP8banQyxxxj1v6cFbmT5JD2O2Eq1VjC5YrjitBl8OGHptPAlVeaFFJxzztoLlsGdXUmozTFuZW/V18B9fXYPl/BMy9F4MSCTTm46rUzTBu1e+4xqatt2+D22+HSS7kx8hnGhxcxb54Z5/r15tgul0kynXEGTJvW9txXXWVK8EaP7vHwxRAmwY0QvuaL9Qa0Jq/A/HoGe7kkxajwcvIrI3p33qGqvX+z1s/ddZcJcMaMwXHjrazlGOakFPN50Cm8H3RewIbaF0JCTByxyZLebuZG55ngJi4ywFkBq7XF3emRI80k64MHTUkL27axP3waDsfAbAPtFmk12Ysv3inv2QHKy/mkZBYpKgeb6h+r2XfFYrOhsbD6rbyuN/aHigqmObew1naciSDmzoWlS/le2Ar+wfXmir/VYpwFBfDVV5rvV/wfBWoEL4X8rN1DZ1wwgSuj/8tXzrkU2uP48Y9h3sRCnj1wCs+G/IKttlZdFzMyOljF0liyBEBT6Ig37ei/+w7eftv8fmzdaj66yd1IYPYsJzOX3cLTVUuID6qEL76AGTOYNQumhe3nqLD9REYpE6lccAHk5JhGHvfdBy+8gF2FYFGaN94wWatzzjHZmqIi8/O68cb2z6+UCWyioro9dCEkuBGiXyoqIs9uFh4MCeli20YjY6rJq43z46CGsOxseOghGDECJkxg65K/UOsI5tjbz2Dh8fW8dMyDfT1Cv1LKZG82O45qN7jJ3VrOYYYTGhUU2IFZLC0yNwCTJ5sLs1/9ogFyc9kffvSAD26KbSOYwg6+XNHJXIpObqqUrMhmLceQmNjRUu/9j8NmstDffN1Hwdjnn2PFaYIbzOKV550H39XM4AYe4a0N45pqpxq98QZorbig6EkeCbuNOtVBGlMp/rxkOxFUs712HMPj7byZeyyhi07g5ZCfdnuoo0ZBnK2SQnu8ifWVgrPPhhkzTIBz7bWe+TCHD0O5FzFyhTOczZvh5/t+Dw8+aO4czJ5tVtRslBRcxrCgxjlcVqtJ9SQnm4XZbr21RQeP4cNNvHXkCGzZAocOmYyNe6qiTJ0RviTBjRD90f795DEKhcsz0bIro4bZOewY1qu6fNEOp9PcpR02DCaYLk6rVpmX5szpw3EFWEQEbKqZgC5pW5a2bpMNAFtMgDOHNhut/8OHhcG4cfDa20G8w2L2h0/D6RzYwc0+y0QW8AVfbozp0TSKFf87gsaCGp7o+8H5ic3iYgJ7+WZXQsDO2SI+XL6cOkJZr2bxq1+ZeSCffgrjQ/KIsNRyXegzVN5+H+zf75mj8vqyWqaonUw/bwr3zHmDrIwbWhy/+QV80o8WcR+/IYoK3g37AUlRtfDii2jVs8uyEUFHqNfBvPlmsyeDgyElxZz05ZfZuxe2bzfr8HQmVNeSVLOPKCq4OOlTU4o7ZQpd/jEKDTVr2cTHt/tyRoZZM6ey0rR5vuGGgdnBUPR/EtwI0R81BjfhQQ6v3/xHjTBXPYV5zi62FN3yxBMmmnn4Yc8f99WrzcVyB2vSDUoREVDWEEluSdt5XWt3xWDBSURUgP+kREaaCTatyoNGj4bpyaVcx6PsDJ6OyzWAg5uMDCpnncSJMZsoqw1l8+buH+KTbyKJsVTw+broAXV3fJzlAN+WT8NV3wd3bJYv51HL9XxVlc5jj5kpLLt2wbjQw0wOO8ihukT+1PBHT1akwWnhs6+D+b7tbdQ//t718Y8/nh+q/1LIcKbvfst0E3D3Q+6BxDnjCQ+HO+5olcx0Z1xuvpk/32FHa1Ndu3Fjx8c6uf5DvtAnsmTOTiI3fAUnnND+hl2Uy7Xn/PPNXJqoKLj00m7tKoTXJLgRwpccDlO/UNnLdquNwU1QiPe/oqPGmE5pedu7blcqvFRfD7//vVmg80dNrUxXrTJZm6F019HTVKC07QzftbkjmGDZhzXQzfri482VnLsvdyOLBZ7OfJkDjOPLQybbNmCDm0YL0kw54Jdfdm8/reHjQ9NYmLwDm80PA/OjGGs1pcSz86OcgJ5X5+Xz4NYz+I3rb4RHWdm40dzjGD68cVy2Gn7+c3jEeS3r38tlgWMFw+x5OLWV71830rtZ8FYr39gWEEYd3HQTnHkmAFllGWSVZXR7zEqZjOWWLfDf/7Z64Ykn2F0QyQsvWUkKOoJCs2xZx8cqsUdSRxhXPzXb+0mfHWknABo9GmbNosvFqYXoKQluhOiFNmXur78O+fmmsLk39u8nzzKazFOsXt9pHTneTM6R4MaHdu82ZU+PP+6JZGqdwWzaZFbgHko8wU3D1DbtedeWTWBS0P7AD8odsXz8cZuXjq/4kGvi/0NhkaXFpgPVuGMSGK0O8cXnuls9S3atLOSAczSnze3/69u0kJGBc4qZ3/HN20UBO63LBT9fUsUtPMj42DLS05tajLvHRUYG99wDiUmKq8P+zS9rHqDObmV8cC6z/vZDs50Xk0j+G/Jj/hN8qeky1glv56MkJprpLnfe2SqZOXs2/2/KiwTrehYFfUmcrYJly9pMVwPAue8gr+kLmKj2yOKZYsCS4EYIX3JPMK2p6d1x9u8nTyUzapT3qYFRU8yCAHl7+m5diAHL6TQtU5ctM+tDPPYY7NljnvvTn1osC76+ajJO59CabwOmIi85torNpLZoKpCXBwWOREZ30KrW74OKioKPPmr72rZt3HPiB54bzwM1uHFf2Kq0VBboz/nyc2frtRI79fELBQCc+oOB12ykMn48MZTx7Xe+T5G2FyA2NMCmTfDPrMn8PvQhxqbFdJiNjIuDpUsVa2qn8wxX8jkLuOBCCyrI+/RYjnUST4Td3PvsSCOlTOvonTvNW1kmWWSSxY4d8O/dx/HLsOf4Q/3tjAgqITcXPv+87THe+/N69jKRkSGt5tb1cMZ/bxoFSJMB0VMS3AjhK6tXw9dfm5qY6t6tJVGXk88RZyyjRnm/T9LUOCw4yT84MBbpCxhvbnPn5Zl6jksvNcX1111n2vlER8PNNzdtl5XF6ssfA5oFN0PoL3Da+Eo2kdZirZu135r5EAlhfbR+SlycaX1bVuZ5KthVB3v3Eps+zjMvKiWlb4bnM6mpnMiX5B+2UVfXzuvFxS2aK7j/23+SFcQE9jBxcTuLifRjWVnw2RdWjovZzjc5w/1+vn37zBos5eWa5+Ju5C+LvzHv5Z344Q9Nxert/Bk7IVzwy57PmfGVc8+FY44xnevdzSfuvhtCQxW/+dswpjs3cYleRlSUmdzf2iP/G8VI8rAEB7jzoRA+JMGNEL3Q4g7qI4+Yu8jJyWauRlVVj4+bv8+0fO1OcGMdNZzhFJKX3+PTDl1lZabN1s6dZhG6w4dh/nxTgtKsQ1BmJvz1r+afuDv/NoNF2pR6tjENR1FT5mbtlzUoXPzxj30U48XHm6u4Tz/1PDW6Zqd5bto0EhPNfOhmHWwHpqOPZgFfAO208l23zgTnO3e2eNrlgs9yxnFqQrbpZDUAzZtcwubqFCrKvGsT582SVa1lZ8O8eSY2XDTlED8pXQqnnNLluVTj8i4WXASrBub1g7V83dmbnByzjkx1Nbz8srlfk3TdD9hsncHV9f/gwvMdvPpqyyKDTZ8U8GnFbOZaV3s1n7Dd+zpD6GaP6L8kuBGiF7ZsgQ0boHxHgZnF+bOf8ceSxlXJerBwGgAVFeRVmhKzbl1Ax8YySuWTVyR33LrF5TIdt2JizCIpY8aY4nWbrd2OAZWVQ68kzS31aE09oeza2pQhWLfGxVFsJyIlqW8GFRVlPprNuxlXs818cvTRAIFvdOAPUVFMG1vDsODK5kkq48UXzWNxMbz7rufpygoXlc4ITp1VErBh+tq8+RY0Fla9XeCX43/6KSxYYH7dMzLgLOc75oVFi9rdvvW1+8SJMC18H1PCDnaV6Gl7rIwb2rSL7qnm4zrzTBOsHThggpyICLPsDEqROh0SdRE/jn6bykp4552m4O/vtxcRRg2W9LQBsx6SEO2R4EaIHqqqgpISc9P/lJOdlDREw/XXkzN9sdlgy5aeHbixUxp0M7hRilEhJeSXdbBwnGjf1q1m9m1MTJebNjSYufRDrZmAW9osEzhv3toU9K3dGsoxrO1VG9tesVhg4UIz70ZrsrLgTz/YZp6fMqVvxuQnKnU6J4aubpm5cTjgpZfMOkzh4eYWfXU1ZGfj2roDC04WnhPZZ2PurePOM/+vvnmv7fpKvZKdzeFV+zjjDBg7Fr75xgQBx5QtN23HJk70OgmREFTBsKD+08hFKfh//88UEJSUwK9+BQnu5YLi4iAmhpP+dx2jk12e0rSGBvj3qin8eNgHFEdN8Ov4JLkj/E2CGyF66LvvzOPYUQ425Q8jM2oNBRETyQ+bQL0lNPDBDTAqsoK8quienXeoWrnSPHoR3Lg7fA+1zI37YmTanEisONi0ywTQBQWQdySs74Ib98BOO81Mmtizxzy/dauZZDNAS7E6lJrKidUfUldnLlwBk7E6fBhGjDCZx/37zZUtUOqIYg6riTs5o8+G3Fux86ZxNFv4dq3v+liXlsK+uhFsqx3P3Lmmvfbo0WBZv5aZxctNSVog+rz78Sp/4ULzlma1tpw2CMD48VgK87lkylo+/BDsdqg4UEadDuH6JUck+BADngQ3QvTQV18BaH565AHe43vsbRjDSSdBTb2VA+FH9Tq4CQ7WHS303KGRsXUU2WOx23t26iFp5UrTrciLC2F3cDN7tp/H1E+FJkUzmV1sOhBNZiaceqp5/hi1HpL6qCwNTHADTaVp27bBtIE1gd4r06ezwGnmFnmyNy++aOYdxceblnA//Sk8+CCjHPvJ1aM4LfhzmDq1z4bcayEhzIvfwbcHRnWrS1x7Nm2Cn//czJnbXz+CxKBSPv7YJDMAJju3E0VlhyVpHerBYpb+ppSpypw1i7Z/R2Jj4ZRTuDT7FhwOExsfyreyiOWkXn9yXwxXCJ+S4EaIHlq5EiIsdVxq/z8WpRXx8ScWCgrM5NRVISf2LrixjGbUqO7fPByVZOZCFPinPH3gOXzY/IMUFna8zcqV5hanFz/sykrTd2CgthXuNYuFtKAdbMpPBMzPQ+FiZlJu305smTjRZGo++ghOOsn87jXOtxlUUlPJIJtgS4OZd1NRAW++CRdf3NTZ6777IDqa1JrVuLBw6vS8AT/paO5R5RxpiOa4Y3WXjQ/t6zaz99tCfv1ruPFGk7XYs8e8DcyYAS+8AJdcAsdEbufo8P0t7mkc42hMxy9c6K9vJaCCg9tZKNOdlrnrLtJKvyB9ZCE5e5yUOaP49YR38bQXFGIAk+BGiB5wOEyN9gRLDpNcO+HXv2b+CYoVK0y5yDLHD+HgQXPx0V3795MXktKtNW7cRjVWBuXn9fIW50DVvC1SVRUsXmxucR882P72Bw+aMp7orkv5ysrMoaKifDXYgSk1ch97K4bhdJof8dSIQ0Qmd13S51dKmezNp5+a9k9aD87MzbRp2JSLmaHbOXwYyl58B+rq4Mc/btomIQF93/28ps9nKtuZf+rAXwZ+3gIz18t1pKzT7Vwu2FY7joP1STz/PDz7LDz9tOn0Xl8Pf/ub6fD+zDNwqeN5bq25ywSDb70F27ZxdegLZuLNcP+3nu5zxx8PZ5zBpeWP48LKRHbzvStG9PWohPAJCW6E6IFNm8yF3W9D/27a7FxyCWDKlSIjYZujcSJzTzqmeRbw7P6uo8aauvS83b1cRHSAysxeSmb2UjM79gc/gLVrzUTr6ur2/y3c823++9+2ReatCs9vv90EtaNH+2nwA0RafC4aC9XVJnNzTPCmvmsm0Nxpp5lfSnfacjAGN2FhMHEiV9Q/htMJTz1UZebZHHdci82+mHA56ziGm3gIy3EDf4LYtDPNYp6O0rY3i5rfz/j736HMEcWUsIOemxGVlXDiieZH9JvfmLcD9u3j+rr7WdjwIfz2t3DeeSbTV14+tNKyd9/NJTX/JIwabuEBLD+8qK9HJIRPSHAjRA+Y+TZwQvVHZq5BWFOHsqgoyK2Lx4XqWWna/v3kNST2KLgZOdHcpc3b2fM1dgaMjhav0Bquvho++ACeeqqpY9Z//tN225UrTTQ6Y0anh1+/3qxnMWqUZG7SRhYDJpNlt8Oshu/6x6I/Cxea0qz8xoWeBvzCNh1ITeU0/THDrcUs3XsOdRdfbjJXzYLxh5ZaCKGecdbc7s8f6YcsM9M5ju8orup4XtzmzXDbbZBgK+V09TGdTtD5299wYuHHUW+Z/8irVpm5S+PH9+juxYCdgD9nDqPOnk0BI8hUX5jyTiEGAQluxODhzUr0PvLVV5CcWM9Yva/Nnb7oaGhwWtkRkt794KaujqqCSioawnt0MzxxUgxWHJ5FQIeiK+ofh+eegzvvhCuvNIXnsbHwyittL3hWrjQLQtg67sTkcsG115o7vuPH+3HgA8SEUXWEqVrPNKZjqr7oH5mb2Fhze97pNP/mjd3vBuyFZ0emT2e06wA/tvybAkby7/CrWry8c6dZu2RESCn3RP7Fqy6A/V5UFPPid5LXkIjD0fZllwuWLDHf6p2Wu/l7zVXmPaA9ubnw7LN8GHwOxZYks9OcOXDppaYF9CDqsOfV//277iKSKj4LOSMAIxIiMCS4EaIHVq6EE0bsQUGbiwf3nf1VI87pfnBz8CD5mAvFntwMt44azggKyDvk7P7OA4ynBA1M0LJ5M7+ofYgf1z9jMjd33NG0cVKSuerLzm56rqzM1BeeeGKn53nuOTO/6r77IEjWR8USH8t063bPyuYzWdc/ghuA0083j21mUQ8iqalYcXJHw+3MitjB/f+XiLPZr/vSpeb/6ajg4j4boj/MS61EY6Eye3eb13JyYONGePaBI/zM/iROLHDLLaahSGsPPghOJy+F/DQAox4AZs7k8shXeTX4R309EiF8RoIbMWhkrn+YU9ff5/fzHDhgJqXOb8gyF1HBwS1eDw83zYm+C1nQ/eCmF2vcAJCUxEjyySsYAr/aWpPmWG8uYiZPhrQ0Ztm/YZntMnjssZbdzxISTHbmlVeanvv6axMUnXBCh6doaDAl+fPnw2WX+fF7GUji40lzZgMQE1JHNJX9oywNmlpCR0T07Tj8KTUVgCiq+M2PDrJzJ7z9tklaz59vgvFLL4VgSzspjgHs2EwTsB6uj+G992DvXpOkKy0178e/+AV879vbCcLBH8MfMpNtbrqp5UGKiuDJJ+HSS3ll1v1kZdwQ+G+kHzpgnYBTyZ0bMXgMgSsgMVSc2vA+b1QsMn/A/Mg9B/2Eff82a0q0yvt//jksWACrqo82JRBlZd4fvLfBTUICo8gnrySkBzsPLJfXP8U/qn+G/vs/WB7/A85K3c8UdnGV6ym0tVWZWVCQWZSlsTQtMxP+fc1KE/Ace2yH59i3D44cMbGSRd4tjbg40vRGAJJDG7MD/SVzM2eOCWQ9y7EPQlOm4MCGnSC+/9djSEkxXcC0Nl3BamvNNX1Wxg2D6uI9bv7RnM6HFDgSWbzYTA+JjDRzbcLC4IFrc+Dpp7GNTOSeOW/A734Hy5bBJ5+YjG12Njz8sOku97vfDcJ6RSGEm/y5FgNW6yk2pzW8SyRV8N57fj3vV19BZLiTtLpVZk2Ndhx3HGwoGE4toWZBQW81dkqDHgY3Viujwo6QXzGw71x3OX3q0CEuqn+R2yx/I31KDaeu/itrD49lmK2MWlcoX37ZbFv3RczFF5u027ffQnY2aXkfmhXuIiLaPV9lpblYvO46SE9veaghLS6ONDYBMCGoscV2fwlubDaYPn1wd7wKDmabdTqfBZ2GLTGOW26B774zGYy8PFOZN316Xw/SD2bO5EPO5G8hf2TlSvjnP81cuPh40+gs/J7bzU2McePM9r/7nWkmcs01ZKVdT9b0a+HRR+Giizpe1FR+wYUYFCS4EYOCPlLK145jWcw71Lz+oV/PtXIlzEs+gA1nh8HNsceCw2khm4z/396dx1Vd5v0ff11wQBRBQVGUzCVNSwUVUnMvs6Zs0WpmKttLm2pqnJoWa6q77Z4Wf03TVFOWLXNrTrs11tRUphbuuKBl7hvijiibrNfvj+uoqOxw4ADv5+PBAzjf5bqOF5fnfM7nWio3NG3rVlJDT6dp06rPA24flsm+w2HkNuQ1BR57jJe4i2eL7seaQN56y21Xc0azrQRSyJtvlnDNZZdBkybw/vsE2TzOKPyp1CFpc+e66ThBQfD44759KvVOZCSDmM9pkWkMb7LQDf9rDPuC+JF7QqfwXNPHALjxRpeoWrPGrV53773ekxraG/WoKHYEdOCmvCkMDl7CrbfC5MnQM38FsWs/gPfeg7vvdhPk5sxxCwO8/robv7Z1q8uiZ2TAww/X9TPxO3PS+zAnvU9dV0Okxii4kXpv/34Yc14mE/kbX3Axn/23qRt64AMHD7o3vYNtottHo02bEs87su3EoqChlQ5udoZ0pn3746eMVEa7VnnAse0+/EmNLGj300/wzjvMMOMIDcghOdmNDgwJgUBjaRt8gA8/LGE0YIsWcNFF8MEH9ChcTTB5Jy0mYK0b4nPuuS4JEBfXsJMAVRIRQTNyuPiUlXQvWgNRUVppoZZ90/d+vut7H+Dm+N19t9uDKTQUzjuvjivnQ480m0wBQa7fTpt29PFbD7/i+vcDDxx/wYgRcPPNbrPelBS49NISl30XkYbFZ8GNMWayMWazMcYaY3r5qhxp3NLT3RvQr1ZG82TgY4QF5TAt90r4/nuflLdggXsDPDjl/VKzNuCGlMXEwOLm51Y+cxNQtQ08j5bdxk0kPrLdR4NQPCp68EEKmrfkZ3sGLTyZxweBffoQ3as1hw+7D3JPctVVsHMn1+VOdb8PHnz0UEGB28vvwQfhiiuOjliTE0VEABBekEar3J3+MyStEbvjDjfvpGPHqn8oUh9sCjyd3zWf5pZvv+46uO8+4gqWMqhgngtsvH+bx3n+eRd8FxYqayPSSPgyczMTGAZs9WEZ0khZ6yZ7r1wJzZoWsSBoOGe2TaNV2yC+5gL2vO+b4CYxEQIDLQMOzyk3BTFgACzK71fx4KawEFJSSM1vU73g5hTXrW+5per38Ftz58KsWSRfP5lCPIR3iTrplLAw6NuXkoemjR5NYbMwNhecwiPmSZ56PYqHHoINGyApCb780i2l+/77ZW5907hFRgIQVnCAVnkKbvxBq1ZuKGzUyd2hwTkYEAH//a+bDDd5Ms9n3cl+09qlr0pyZFLOaaeVuXiIiDQcPnv5ttb+CGAa8sdIUmeWLHHDqKOiIOnxLwgbt4D/a/0UrYM9bEmBf80M4W5ra/RjzBEj3II7cdG7CduRWWbmBtzr6CeftGFfZi6t09PLH9+UmootKCA1uwUXVyO4adfJrZRWdDgX8LNV047uM9On8tdaC/ffDzExJHYeB5w8L+nIFINXX3WTjZctcxmYo0JDeaLL2zyx+gqwwCMuiLHWTceZOxcGDap81RoV76fjzQsO0CovFdrH1XGFpNEJCoK//x1iY7ET7uKNkN/zYFl7G7VsqfGlIo1Inc+5McZMMMYsNcYs3evjJXyl4Vi0yH0/7TQI+88HEBnJX5OGsWQJ9D11P9MOXnz8ho01oKjIzUcdErTYrbYTHV3m+Ufm3SzhrIplb7ZuJYMwsnKDqpW5ieoSRiAFmJzsqt/ER/bmt+Dn7I5kZhZ78ISJOBkZsHYtHDp0wsX79sHixfDkkyQuDiYmxg0TLMk117g5OCdmb775Bp786XJu4B0eDXmGvDy3l82QIW4VYQU2FRAaCkFBtMjfR0Tebv/L3DS0ifRyTJ8+7uuI8eNpMrQ/D/b3TaZeROqnOg9urLVTrLUJ1tqEqMaQU5cakZTkPrxr5smDWbPcRFHvOKJrbwlmCf1Z+1ZijZaZmekCnMG7Pyk3awMQHw/GWBYxoGLBzfjx1dvjxiugXVvasZOi3Pyq38QHrIXNh9uxNz+CK690QcWJcnNh7Fi3GML27cUOFBW5bch79YLrrycx0U2XKS0x17KlW/F1+nQ393jECLdY0rhxbpncmJA0EoNHah58VRgDERGcmr2WQIr8L7hpDBppAFfi09YGVCJyAv2vIPVSUpJ3bsXBOW5VgcsvP3rs6vFhBFDItI9CarTMI5mEwTnfVGjJr7Aw90Z6MQPgiSfKL+Dw4RoJbmjThvakkudnS0H/+CPkFIVwHt/y9ddwa6+F2Bf+yqQlY7l4+RMUFcH118N337nkQFqa25CQggKXysnJgWefZduOQFJSjlsLoES33urabO9eF1hdfTVkZ8OHHxoSm5xLQTk7cjfS948VExFBp2xvwF6tP1aRWqDOLNKoKLiReicrC37+2e1OPXTfp+6d8KhRR4+3awfnddvGtF0jsdtTaqzcQ4cgIvAQMaRWKHMD0L+/YbHpj83MKv/k3FxSA08Fqvl+sW1bOrCdjLzgatyk5r35ah5hHOJl83seb/86/1w3kIfvzeEv2RN599Dl/OHKHXzwgVvcqIvdSFERfPffQrfO85490LkzXHQRid6EXClb1Bw1dCh06+ayQJs3ww8/wJQp0KOH759rgxcZSbucTe5nZW78gt6/i4g4vlwK+iVjTApwCvCtMaYS6+GKlG7lSjdK6aUXi7jMznR7l4Qcn6W59uZgttCZxL8trbFys7LgzIA17h1zBaOPAQNgv23Fpuyy5+cALnMT2AGo5vvFNm2IJ4l9+S1JS6vGfWpQejp8+JHhGt7j+dD/4ZGU25gw3vIXHuKc4B95xjzAy5/GcO/wpfzpXktLTyaBFDLzvh/dfhadOsGpLvBLTHTxbHnbVRjjsjcHD7ohbrfd5ubiSA2IiHBD0kDBjdQtRXUicgKfBTfW2ruttadYaz3W2mhrbU9flSWNS1KS+54QsMx9LD927EnnjL2zPc1MNtM+qJnsxeHDkJNtOTt/XoWzNnBs5dFF+X0pM9LwrlaQ6jmVsDA3pK3KgoPpzSrAzb/3BzPeyCSnIIi+gclsCOyBMfDKq4ZLL4U5eYOYbO/jug5zeG5uf/jtb2lOBkNMIp+vP4PCx55wG3h4JSbCwIEVW6r5hhtckNO8uVviuTR6f1RJxfcTKWdhDRERkdqkYWlS7yQlQdu20P7Ose6d6+jRJ53TPMwwtvvPfLB9IOeHL6x2mWvWgMVwFksqNN/miF69wEM+i+kPy5eXfuLPP0NBAamBp9TIFIbOZisBFLKw+k+9Rrz5/w4Sxwom9FnCnD4TAReczJgBEZ4MWnvSmbphOAHPPQsff8wHhy7kDvsye2nDgvMeOXqfjAxITi5/vs0Rbdu6PW9iY49P7s3pM/FoPaQKvHvd4PG4NbRFRET8hIIbqXeWLoX4eIvZv899ghweXuJ5190UxAEiCc/dU+0yV6503+NYWanMjccDYYHZLGQgzJ5d+onz5gGQatvXSHCTHxDMaWz0i+Bm2bdpLNsdw63xKzBLlxyXImnWDGIHh9NzcEuCgg3cdx98+y0ZJpy04GiCgy0zZ3I0tbJwoXfFugoGN+CyYCetiqZUTfUcydwosBERET+j4Ebqlawsl0VJiNnlxoq1bl3quSPvOpM27CY1v/pLjCcnQwg5dG2SAqecUqlrQwNzWU5f8r6ZW/pJP/wAwcGkFkTVSHCzO6AdA1nIokUuGKhLU/+0hiYcZtyrFYxIzjmHdsO68buzkxk50jBzplvtDNyQtIAANyxN6tCR4CbYvxatEBERUXAj9cqRxQTi937lhqSVEdx4mgbRP2AJybY3Bfm2WuUmryyiF6sJjGxR6WvDPVnk0YQfl4a42e0nshbmzcOOvbzGMjcXnPoL5/A96emwbl3FrzthP81qGdFyBZeFfcv0lT25sstyIvp3q/Q9xoyBjRuPbROUmAi9e5earJPacmRYmoIbERHxMwpupF5Z6l38LH7hK+4NVjm7MDYPPEwWzVn9781VLtNaWLmskFiSj59IXUGRnkM0M9k8bSfB3BKyN5s3Q2oqBxJGkZtbQ9uGhIUxEDcmrS6HpnXMWctBWnLrU52qdP2ll7oYduZMt93NwoWVG5IGGoHmE8rciIiIn1JwI/VKUhK0jcil/a4kaNOm3POzgloCMP+D7WWfWIbdu2FfehBxJJccnJQjsG8cp3QOZjYjmffPLSefcGS+TddhQA0FN82b0521tGhyuM6Cm6iiXawo7E3X8N0Mv6r05YLLCj6io90QtJkzYdUqyMysfHAjPqDgRkRE/JSCG6lXkpIgoflaTPPmLo1TzkfyB+NH0pbdJM6v+p96crL7Hts1u8xhcGVp095DdPB+HvtPCZNF5s2DyEhSQ7oANRTcBAYS0Kwp/cPWsGhRNe9VxbFqCYd/5AeGcevtwRhTyYuLRTxjxrh2/9e/3CEFN36gc2eXUgsNreuaiIiIHEfBjdQbbjEBS/ye/7i9bZo1K/eaufMMQztuY/6OU6s8s37lkjwAev8qpkrXAwQGwoPnL2dOdn/mfHLCfjc//ABDh3L3RNcdayS4mTMHfv1rBuZ8T3KyJSurgtetWOG+qquwkKT8WJqQyw0TKz+Ur7gxY9z3yZMhJuboXp5Sl2JiYNCgKg3TFBER8SUFN1JvrFgBRUWG+NxEGDeuwtcNGgRbijqSOvuXKpWbPHsfMaTQ6uKzq3T9kSTEhAciaEcq//Pn/GPJkJ07YcMGGDaM3Fx3fo1t+B4fz8CsbykqMkfnKpXHWsgqDDn2S1ISbNoE27ZVquif3lrER1xJvGdFtfd4PP10F8ceWQK60lkg8Y2K7KIqIiJSyxTcSL2RlOS+J7TaAiNHVvi6wVd1AGD+9KotKpC8CuLMKhgypErXH9H07D5Mavoic9e05cAB74M//OC+Dx1KXp57v9i0abWKOSYhgQG4MWkVnXezJTeapZk9eH/Me9C1KyQkwPbtbtGD1asrXPSjT3hoThZBCXFVqflJWrVy3zUkTURERMqi4EbKVpNrA1dT0oI8otlJ+2tGVOpT4z6/iibEHGb+vIJKl5mXB2v2RRHbIa368wsCAxk/aivtA3exdYt1e7fMmwehoeT17Et2dg3Pz46Lo1XgQbpF7qtQcHNg0wH254ZhKGLCZ6PZHDMEpk6Fs85ym8tMnlyhYpfMzuCTlP6MDpsLTUKq+SSctm0hJAQuuqhGbiciIiINlIIbqTeSfsginqRKDUkDFzCc1XYbiVtjoLCwUteuXXiAfBtE7IDy5/dURMj5w5hU+BQHDxnS0yH5651MbPV/tD/VQ3o6NG9eI8U4zZpBz54MDFnJwoXHNsIs0Tff8FKfqWQRyp2eKZjwMK7Oe5f8625292nXDqZPd1mccvz5rnRas5eQLjUxecgJDYUBA1wySURERKQ0Cm6kfGW+K64dWVmwZkc48RGboX//Sl8/+Kx8lhXGkbNwZaWuW/mh2wEzdkyXSpdZonPP5VbepIUnk59WW+I2fMw/dlzCyJFuc8oePWqmmKPi4xl46L/s2lXKtJlhw+CUUzh4/pW8mDWejoEprAodyJtTA1i0CB55BDdhKDHR/R389a+lFjViBPTpA//9uQOTot/h7eV9tb+MiIiI1CoFN1K29HQ3YaPa6wlXz4pv91FEIAnnR1ZpRvmgK9pRQBBLpq+r1HXJ89IJJpful/esdJkl6tGDkHaR3N30DdqFHOAl7iL108W8/77bk7TGJ8snJDAg81ughHk3GRmwbBns2MHfB0wnvagFrZq6ZdWuvBImTIBnn4XYWBhxQ0e4+mqYMgXS0kocrWgtpG7IJoYUbp/YpEafjDbiFBERkYpQcCNl27rVTTy59loqvp5wzUu65VUA4u8YUKXrzx4dCcD873IqdV3y+qb0bJGCJ6SGVoYyBkaO5J7MJ5hcMJG7gqfQalS/mrl3SRISiCWZkODCk+PTf/4TsrLI6J7AC+su5uKLISy+u0u/4JI0PXvCL7+4PwHuv9/9DfzjHyUWlZYGe7Oa8ah5iqY3/tZ3z0n8gyJOERHxQwpupHQrVrjMTevWsHEj3HdfnVVl9v44othL+2FVm3TRujV0b7mL+RvbQn5+xS7avJnknK7Eds+tUpmlGjmSljadC/L/7YbYhbhJ9z55rxgbS5AHEtpsZ+rUYtkWa+G11yAsjJfzxnPgADz66PGXNmsG77/vpiktXw4XT+rNDTHf8MenW5GyOZ9t2+DJJ911kybBxo2WzmzmptF73AoAIiIiIrVMGxVI6V54gRya8uu8T5j1x5nwwgtwySVw4YW1W4/ly1lPV9oE7AWiqnybwX1z+Oz7/tilSZizB5Z7/t6ZiezkWmKH1/BnAN5lrFvadDfnxZdCQqB3bwamLSExo9OxfUznz4fVq3my6zs8seUGLrzQLYp2YnDVsyeccQbs2OG25FldMIS0nP5kbAsCjgVEHg94CnL5O78n6Jbxvn1OIiIiIqVQ5qaRqPSKzjt2wIwZfBEznsy+Q+Hpp9073Ztvhv37fVTLkq179lN+pieFQdVbVnjQpa3ZT2vWfViBRQVGjGDV458AEDuqhrMQHTqwPeBU9/PQoTV775LExzNw/xdYC5mZ3sdeew3Cw5lWcDUFBSdnbYpr3Rri4tw+Q1t2NuHQgPPZZLowLGwZ+fluc838fPjKczGDzAIYPdr3z0lERESkBApupGQvvwxFRXwc8wf3e0gITJvmApvbb6+9FdQyMpjySWsCKSCiX/VWLBt0QRgAiV9llH+ytazMcOXF9anpWf7QIbrAzb8ZNKjG732ShAQGZn4DuClU996Rwx3vDeGm9l+zKSWYiAgYWH4iyzEG7r+fznYzkw4/hueWGzDnjIDOnRlaMJtvgy6CoCAfPRERERGRsim4kZNlZrpP9seOZUNRF1avdpPK6dMHHn8cPvwQzjyzVqpy+J1/8U7+NXRsebDaG1x27w6RIVnMX9cacsuZR5OVRXJRT6JbZBNV9ZFwpevUyaVDwsN9cPMTJCQQQyrtQtJIS4PXpwbyYdHlzD7Yj5AQ6FJOzHjSXKDLLmNDQDdG5X8Js2dDQQEMGsT0JrfwXpObfPhERERERMqm4KYRK3Wo2jvvQHo69p572bDBJWvOPRfWrcOtmBUeDhs2wJ49vq2gtXwyeRP7ac3rH0RWe7J9QACc3TOD+YX93Y6QZdm5k2Riie3joy4SFAQtWvjm3ifq1QuCg3m+9XMMH1pEZsde7B1yOVtTg4mPr8LGoYGBdB3SjsChg92mnj/+CNOnc93A9Xza70mfPAURERGRilBwI8crLHRrAA8cyJcHziY9HWJi3Ifz55wDGzYHuhRIYSE8+KBv67JoEa9v+xWnRR3k3JE1MzRs8EUtWMOZvLzmnNJP2rqVnNT9rKI3sWdVb56PX2jSBHr3pkfmUvod/B7Wr4ff/a569zTGRYvFaWlgERERqWMKbhqgSi8eUFxcHGzaRMHEP3HffdC0qRu29N13biTXOefApn8tdstCv/02LFhQgzU/3ppnPmMew5lwV8hJ76OratDIpgBE5213y3+V5KmnWMfp5BNMXFzNlFvnEhLonrGUy1L/Aa1awRVXAIpHREREpGFRcOPPqhWlHK+gwG2y+PLLcNddcP75sGheLusX7KWwEDh82G1msm0bhITw5v6xrFnjApuAAOjd2wU42dluus3w2Y+6lM6dd0JhYU1W1UlLY8qs9gQFFHDjbU1q7LZnnQWGIj7gNxy+/Y8nn7B+PXvf+je3mLcB6Nu3xoo+Xm1HFQkJNC88yPB9H8NNNx3dW6fKFBWJiIiIH1JwU1cqEg1Yy7GNSSrPWjcd4sYbXYJl1SoX2Lz7Lhw4AB1MCql5UVwb8QUFoS2gXz/IyOBQzBk89ngAw4a5a468h42Lg2+/dSPS5q8I5apT5zN1eV+2/WV6lZ9iaXLenM67heO4fFQGbdpU7R4ladYM2scE8CG/oddnT/H1Iz8Cx+o6a/xn9C5ayXL6cdppbvXrBiEh4djPEybUXT1EREREfEibeJbiyJtyn304nZ9f9pK5ycluY5HCQjdHolu3Uk89sa75y1axKz+SM86IYe1aCAtzG8ZHRcFXX0F0W4t59BFY+jSP8ARPZTxCwRlJvPfnNQS9+DzP7r6LPXtg1iw3taK4vn1dkJOSAvO2dOB9psIjEBpSQGi4h3vugehoV17akg209GQBlRzbZS0f/XU7B4jktgcqd2lFdO0KUREFZK/38KunhnDlqjyys4NJ33yAS/b9ibg2qZzSwVR+or0/69nTpeDCw8v8WxIRERGpz5S5KUXKoh3kLvup0teVm63IyYG773Y7xK9c6VIrxRUVwQsvuPFT+fkuuBk0CJYsKbfspCSXpVmUcQabDscQxV7efsuycyecfjpEREC7NoWYO++Ap59mVtBYEsMv4oUX4KM1vfjtR79m44zFvLDnOq65xlWhJM2bQ48esGOHYfVnG/lrwL2caX8iIwPeeAMeeMDVY1V2V9ZnRrN7TVqp/0Al/nv17cvruy6lW9uDNTvUrZgWrTwkzznAU+YRZs1y/7wb94UzKXgyi5Y3aViBDbhAumdP94cgIiIi0kApc1OCLVtg2+EotudG8eWXcNFFpWRyKpveWbkSxo2Dn35yaZT0dLdD/ahR3DF/HHsC2vHRgOfd2K/LLnNLLRcUwL59bib/xx/DBRccd0tr3WkpKW7kUWhIAeN5g/FMod/aFfDWEIj7G9APT1EejLse3n8fHniAyQv+Asbwxz+Cx+Nirs8/d/HV//5v6U+j+NPteelp9Lw3kInP9+G1kD/wu18fIvObBexOyWMBZ3Nb0esMPHMPX3YcxxnDomDHDrdKwS+/QMeOpC9NIb2gOe/9zUOvzIV03/gli1eGk8gQnv9D/kmZo5pwrP59efhPM7jm+e7cHP4xTx26i8GTRkH7Vg1zOklkZF3XQERERMS3rLV+8xUfH2/9wubNNpletjtrLFh7773WDh1q7fDhJ5zXv7+13btbe+iQtdbavDxrO3SwNjra2jVrip03bJi1XbpYGxzsDn71lbvZkCHWTp5sbVSUzSXIrqKntc2aWTtlirVFRe6c4cOt3bnT2rg4az0ea3v0OFqRnBxrx42zFqxt2tTav01YbdM9rezqwN52dPg8d5/Wra01xs4KGmMXeAa7k599tsSn/eqr7nCHDpX89zp0yO4xbdzFkZHWXnGFta+8Ym9o/pG9KuQTGx2WYVsGZdjZEZe7c8AmcrYdyTdHfj36FUi+DSfdBlJg9+6tZD2qIivL2tNOsxbsQRNubXp6LRQqIiIiIlUFLLWlxBN1HtAU//Kb4MZa+3CzF2wmTe2dHT6zYG1YmItljlqyxNqgIHvk4J7xD9kRA7ItWGsotMYU2ctHHbKL75lhVwf2dueNGWNLese+cVWWPdVstWDtqMGZNjGxhAqlp1s7YoS7T3S03fHvJNu/f5EFazt1svbeXl+54CkhwY4efOBYIHbggLX33GPz8dgCAqx9440yn3f//i4Wq7QBA6zt18/awsJjj3mDsy1brD3zTGuDgopsr+C19tTA7RasbROaYW8JmGo/9Vxhkye9Z2c8s8U+PKnQtvIcsF1CUqpQiSqaPdsWEGBfDZlYe2WKiIiISJWUFdwYd9w/JCQk2KVLl9Z1NZwRI9w+KOvW8enQF/jNgolYa5g4Ee6I/ZEud17ohoyddhorO17KZV9OYBfRPBAwmc6BW9gYGsfL6deSTgSD+ZFuwVt5I/MaPEHHj7P6+GO4+WbIPlRAdHAaeS3bsGcPjBoFe/e6TeyPDpHKzYUuXVi6M4bL7KccNC2Zdu3XrPhoA5NyHqVJnzPgu+8YcbkbflR8aNW4sM8It4f4R+Z1tfCPd7L0dLe1yuzZ4DEFPP2MhzvvhNExKwCYk97n6Lk+X8yhJGefDcHBMHduLRYqIiIiIpVljEmy1iaUeEzBTTn+8hd46CH+0fZ/eKzoUQ6kWQoLYXTYD6TF9CI9oBVbtkBEi0Jmjn6TLlMfoqnNpuklozg0/BKm7Luch58NJ88G0b6922Lk5pvdhPtNm9wUlLPOcquSNW0KX3wBr70Gzz3n5tI0awZDhkC7dm4Vshkv7iIlN4oOrbL5vMPviV3xTwA2BXSly+4F0Lp1iU+jTgKGE+TludWmW7Y8eR0FEREREZGKUHBTHdbCPffAiy8y3zOMjgUbmHLq07x++Hp273GLzQ0a5DIw0dHA8OHumnnzjt5i2DC3gWanTvCf/7gJ+8HB7s3+xInw7LNuU004FnxkZUFsLBw86DbS3LXLfeXnu2zO+vVuTQLWr4cLL4Q2bdwKbH7OH4IsEREREam/FNxUV1ER34Rcwqj8L2HMGJgxg1wTQp8+cPiwW/irSRPvueW8e9++Hd55x632HBMDq1eXXuyJtyoqcoureTwaPSUiIiIijZOCmxowssUS4gqX8UL6LS66oHpZCGUwREREREQqT8GNiIiIiIg0CGUFNwG1XRkRERERERFfUHAjIiIiIiINgoIbERERERFpEHwa3BhjTjfGLDDGrPN+7+bL8kREREREpPHydebmNeAVa+3pwCvA6z4uT0REREREGimfBTfGmDZAP2CG96EZQD9jTJSvyhQRERERkcbLl5mbDsAOa20hgPd7qvdxERERERGRGlXnCwoYYyYYY5YaY5bu3bu3rqsjIiIiIiL1lC+Dm+1AjDEmEMD7vb338aOstVOstQnW2oSoKI1YExERERGRqvFZcGOt3QOsAK72PnQ1sNxaq/SMiIiIiIjUOI+P7/874F1jzKPAAeB6H5cnIiIiIiKNlE+DG2vtL8AAX5YhIiIiIiICfrCggIiIiIiISE1QcCMiIiIiIg2CsdbWdR2OMsbsBbZW4pLWwD4fVUdqhtqoflA7+T+1kf9TG/k/tZH/Uxv5P39oo47W2hKXWfar4KayjDFLrbUJdV0PKZ3aqH5QO/k/tZH/Uxv5P7WR/1Mb+T9/byMNSxMRERERkQZBwY2IiIiIiDQI9T24mVLXFZByqY3qB7WT/1Mb+T+1kf9TG/k/tZH/8+s2qtdzbkRERERERI6o75kbERERERERoB4HN8aY040xC4wx67zfu9V1nRozY0wrY8yXxpi1xphkY8wnxpgo77EtxphfjDErvF8X1HV9G6vS2kL9yX8YYzoVa58V3jZL8x5TX6ojxpjJxpjNxhhrjOlV7PFS+476Ve0qqY3Kem3yHlefqkVl9KNS20H9qHaV0o9KfV3yHverflRvh6UZY2YDb1lrpxljrgVuttaeW9f1aqyMMZFArLV2jvf354FIa+0txpgtwMXW2tV1WEXB/QdECW2h/uS/jDEvAh5r7e/Vl+qOMWYIbh+2HyjWBmX1HfWr2lVSG5X12uT9fQvqU7WmjH60hVLaQf2odpXWRiec8yLe1yXv71tKO7cu1MvMjTGmDdAPmOF9aAbQr/inMVK7rLVpR148vBYCHeuoOlIJ6k/+yxgTDIwD3qrrujR21tofrbXbiz9WVt9Rv6p9JbWRXpv8S0ltVBb1o9pXXhvVh9elehncAB2AHdbaQgDv91Tv41LHjDEBwO3A58Uenu4dEvCqMaZl3dRMvE5sC/Un/3Uprm2WFXtMfcl/lNV31K/8TCmvTaA+5S9Kagf1I/9T0usS+FE/qq/Bjfi3vwOZwMve34daa+OAswBT7HGpfWqL+uVmjv90TO0nUnUnvjaB+pS/UDvUHye+LoGftV99DW62AzHGmEAA7/f23selDhljJgPdgN9aa4sAjqQ3rbW5wKvA4LqrYeNWSluoP/khY0x7YDgw/chj6kt+p6y+o37lR0p6bQL1KX9RRjuoH/mRkl6XwP/6Ub0Mbqy1e4AVwNXeh64Glltr99ZZpQRjzNNAPDDG+weOMSbUGNPC+7MBrsK1ndSy0tpC/clv3Qh8Ya3dD+pL/qisvqN+5T9Kem3yPq4+5QfKagf1I79zI8Vel8A/+1F9Xi2tB/AuEAEcAK631q6t21o1XsaYnsBqYB2Q4314M3Av8DEQ6P36GbjbWruzLurZmBljulBKW6g/+R9jzDpc+3zl/b3U9qu7WjYexpiXgMuBaGAfsN9a27OsvqN+VbtKaiPgN5Tw2mStHas+VftKaaNLKKMd1I9qV2n/13mPHfe65H3M7/pRvQ1uREREREREiquXw9JEREREREROpOBGREREREQaBAU3IiIiIiLSICi4ERERERGRBkHBjYiIiIiINAgKbkRE5DjGmFbGmC+NMWuNMcnGmE+MMVHeY4uMMSuMMT8bYwq8P68wxrxtjBlhjFlahfL6GGN+U8W6zjHGbDLGPFjK8RuNMR9V5d7F7nGl9/nuq859RETE9xTciIjIiSzwnLW2u7U2FtgIPANgrR1gre0DXASkW2v7eL9uqkZ5fXD7kVTV3dbaZ6pxfZmstR/hnq+IiPg5BTciInIca22atXZOsYcWAh0reLnHGPO6N+Oz0hhzxpEDxpgbvJmfJGPMbGNMd2NMK+AJ4DxvBugl77nTjTFLjTGrjDGfGmMiKlK4MSbYW/5aY8xsoP8Jx+83xiw2xiwzxvzbGBPtfbyFMeZjY8wvxpjvjDH/NMZMruBzFhERP6HgRkRESmWMCQBuBz6v4CU9gde8GZ8PgD977zMUl50ZZq2NB54H3rLW7gceBb71ZoDu9t7nD9baBGttb+An4IEKln8b0BnoBVxMseDGGHMt0BUYaK3tB3wJ/D/v4UeBA9baHsCvgaEVLE9ERPyIp64rICIifu3vQCbwcgXPX2utXe79eSFwiffnS4A4YJExBsAAZWVjrjfGjAOCgVBgXQXLPwd411qbD+QbY6YBQ7zHLgUSgGXeOniAg8Wuuwtc5soYM7OC5YmIiB9RcCMiIiXyDsvqBlxirS2q4GWHi/1cyLHXGYPL1DxagXKH4rJFg6y1e40x1wATKlrtco49Za19q5RjtoJliIiIn9KwNBEROYkx5mkgHhhjrc2tgVv+G5eNOcV7/0BjTLz32CGgRbFzW+IyKvuNMU2AmytRznfAdcYYjzGmKXBNsWOfA3ccmb9jjGlijInzHvseuMH7eARwWWWenIiI+AcFNyIichxjTE/gIaA9MN870f/T6tzTWjsPeBj43BizEljNsQDiOyDUuwDBS8B/cCu0/eL9eVklipoCbMPN05kFzC1Wh/8DpgNzjTHJQBIw2Hv4CaCNMeYnYBqQyLEhayIiUk8Ya5WFFxGR+skYMweYbK2dVc37BAGB1trDxphw4EfgHmvtt97jnYCl1trW1ayyiIj4kDI3IiJSn6UBz5e2iWclRACJxpgVwGLgo2KBzZW4YXW7q1mGiIj4mDI3IiIiIiLSIChzIyIiIiIiDYKCGxERERERaRAU3IiIiIiISIOg4EZERERERBoEBTciIiIiItIgKLgREREREZEG4f8DSNzqJHJNdhkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1008x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "banana_low = functions.name_search(\"banana\", data_low)\n",
@@ -260,18 +534,20 @@
     "plt.legend([\"Low statistics\", \"High statistics reference\"])\n",
     "\n",
     "plt.figure(figsize=(14,6))\n",
-    "plt.errorbar(banana_reasonable.xaxis, banana_reasonable.Intensity, yerr=banana_reasonable.Error, fmt=\"r\")\n",
+    "plt.errorbar(banana_reasonable.xaxis, banana_reasonable.Intensity,\n",
+    "             yerr=banana_reasonable.Error, fmt=\"r\")\n",
     "plt.errorbar(banana_ref.xaxis, banana_ref.Intensity, yerr=banana_ref.Error, fmt=\"b\")\n",
     "plt.xlabel(\"2Theta [deg]\")\n",
     "plt.ylabel(\"Intensity [n/s]\")\n",
     "plt.legend([\"Low statistics with SPLIT\", \"High statistics reference\"])\n",
     "\n",
     "plt.figure(figsize=(14,6))\n",
-    "plt.errorbar(banana_unreasonable.xaxis, banana_unreasonable.Intensity, yerr=banana_unreasonable.Error, fmt=\"r\")\n",
+    "plt.errorbar(banana_unreasonable.xaxis, banana_unreasonable.Intensity,\n",
+    "             yerr=banana_unreasonable.Error, fmt=\"r\")\n",
     "plt.errorbar(banana_ref.xaxis, banana_ref.Intensity, yerr=banana_ref.Error, fmt=\"b\")\n",
     "plt.xlabel(\"2Theta [deg]\")\n",
     "plt.ylabel(\"Intensity [n/s]\")\n",
-    "plt.legend([\"Very low statistics with unreasonable SPLIT\", \"High statistics reference\"])"
+    "l = plt.legend([\"Very low statistics with unreasonable SPLIT\", \"High statistics reference\"])"
    ]
   },
   {
@@ -308,6 +584,11 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.8.8"
+  },
+  "metadata": {
+   "execution": {
+    "timeout": 100
+   }
   }
  },
  "nbformat": 4,
diff --git a/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb b/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
index c37d5b61..1db44f13 100644
--- a/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
+++ b/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
@@ -23,8 +23,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\",\n",
-    "                                input_path=\"run_folder\",\n",
+    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\",\n",
     "                                output_path=\"data_folder/mcstas_EXTEND_WHEN\")"
    ]
   },
@@ -51,7 +50,8 @@
     "src.dist = 1.5\n",
     "src.flux = 1E13\n",
     "\n",
-    "wavelength = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "wavelength = instrument.add_parameter(\"wavelength\", value=5.0,\n",
+    "                                      comment=\"Wavelength in [Ang]\")\n",
     "src.lambda0 = wavelength\n",
     "src.dlambda = \"0.001*wavelength\""
    ]
@@ -103,7 +103,7 @@
    "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=2.8)\n",
-    "instrument.settings(ncount=5E5)\n",
+    "instrument.settings(ncount=5E6)\n",
     "instrument.backengine()\n",
     "data = instrument.data"
    ]
@@ -160,7 +160,8 @@
     "for reflections in reflection_numbers:\n",
     "    reflections_string = str(reflections)\n",
     "    \n",
-    "    acceptance = instrument.add_component(\"acceptance_\" + reflections_string, \"DivPos_monitor\")\n",
+    "    component_name = \"acceptance_\" + reflections_string\n",
+    "    acceptance = instrument.add_component(component_name, \"DivPos_monitor\")\n",
     "    acceptance.filename = '\"acceptance_' + reflections_string + '.dat\"'\n",
     "    acceptance.set_WHEN(\"n_reflections == \" + reflections_string)\n",
     "    \n",
@@ -195,7 +196,7 @@
    "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=2.8)\n",
-    "instrument.settings(ncount=5E5)\n",
+    "instrument.settings(ncount=5E6)\n",
     "instrument.show_settings()\n",
     "\n",
     "instrument.backengine()"
diff --git a/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb b/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb
index 8e679da4..563bd6a1 100644
--- a/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb
+++ b/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb
@@ -65,7 +65,8 @@
     "src.dist = 1.5\n",
     "src.flux = 1E13\n",
     "\n",
-    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0,\n",
+    "                                       comment=\"Wavelength in [Ang]\")\n",
     "src.dlambda = \"0.001*wavelength\"\n",
     "\n",
     "guide = instrument.add_component(\"guide\", \"Guide_gravity\", AT=[0,0,1.5], RELATIVE=src)\n",
@@ -206,7 +207,7 @@
    "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=2.8)\n",
-    "instrument.settings(ncount=5E5, output_path=\"data_folder/mcstas_JUMP\")\n",
+    "instrument.settings(ncount=5E6, output_path=\"data_folder/mcstas_JUMP\")\n",
     "instrument.backengine()\n",
     "\n",
     "data = instrument.data"
diff --git a/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb b/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
index 9584bf80..3c4c662f 100644
--- a/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
+++ b/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
@@ -232,13 +232,15 @@
    "source": [
     "src = instrument.add_component(\"source\", \"Source_div\")\n",
     "\n",
-    "source_width = instrument.add_parameter(\"source_width\", value=0.15, comment=\"Width of source in [m]\")\n",
+    "source_width = instrument.add_parameter(\"source_width\", value=0.15,\n",
+    "                                        comment=\"Width of source in [m]\")\n",
     "src.xwidth = source_width\n",
     "src.yheight = 0.03\n",
     "src.focus_aw = 0.01\n",
     "src.focus_ah = 0.01\n",
     "\n",
-    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0,\n",
+    "                                       comment=\"Wavelength in [Ang]\")\n",
     "src.dlambda = \"0.001*wavelength\""
    ]
   },
@@ -247,7 +249,7 @@
    "metadata": {},
    "source": [
     "## Adding geometries that use the material definitions\n",
-    "Here we add three boxes, each using a different material definition and placed next to one another. The *material_string* parameter is used to specify the material name. The *priority* parameter will be explained later, as it is only important when geometries overlap, here they are spatially separated, yet the priorties must still be unique.\n",
+    "Here we add three boxes, each using a different material definition and placed next to one another. The *material_string* parameter is used to specify the material name. The *priority* parameter will be explained later, as it is only important when geometries overlap, here they are spatially separated, yet the priorities must still be unique.\n",
     "\n",
     "It is important to note that these three boxes will be simulated simultaneously in the McStas simulation flow, so no need for GROUP statements to have these in parallel. Because they are simulated simultaneously, a ray can go from one to another, which would not be possible with a standard GROUP."
    ]
@@ -350,7 +352,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "logger = instrument.add_component(\"logger_space\", \"Union_logger_2D_space\", RELATIVE=\"box_powder\")\n",
+    "logger = instrument.add_component(\"logger_space\", \"Union_logger_2D_space\")\n",
+    "logger.set_RELATIVE(\"box_powder\")\n",
     "logger.D_direction_1 = '\"z\"'\n",
     "logger.D1_min = -0.04\n",
     "logger.D1_max = 0.04\n",
@@ -361,7 +364,8 @@
     "logger.n2 = 400\n",
     "logger.filename = '\"logger.dat\"'\n",
     "\n",
-    "logger = instrument.add_component(\"abs_logger_space\", \"Union_abs_logger_2D_space\", RELATIVE=\"box_powder\")\n",
+    "logger = instrument.add_component(\"abs_logger_space\", \"Union_abs_logger_2D_space\")\n",
+    "logger.set_RELATIVE(\"box_powder\")\n",
     "logger.D_direction_1 = '\"z\"'\n",
     "logger.D1_min = -0.04\n",
     "logger.D1_max = 0.04\n",
@@ -715,7 +719,7 @@
    ],
    "source": [
     "instrument.set_parameters(wavelength=8.0)\n",
-    "instrument.settings(ncount=3E5, output_path=\"data_folder/union_materials\")\n",
+    "instrument.settings(ncount=3E6, output_path=\"data_folder/union_materials\")\n",
     "instrument.show_settings()\n",
     "\n",
     "instrument.backengine()\n",
@@ -1111,7 +1115,7 @@
     "- Get newest version of Union components (Both library files and components themselves)\n",
     "\n",
     "Since the Union components need to collaborate, it is important to have the same version of the libraries and components. The newest version of the components can be found here: https://github.com/McStasMcXtrace/McCode/tree/master/mcstas-comps/contrib/union\n",
-    "All libraries for McStas are found here: https://github.com/McStasMcXtrace/McCode/tree/master/mcstas-comps/share but only three are needed for the Union components:\n",
+    "All libraries for McStas are found here: https://github.com/McStasMcXtrace/McCode/tree/master/mcstas-comps/share but only a few are needed for the Union components:\n",
     "- Union_initialization.c\n",
     "- Union_functions.c\n",
     "- Geometry_functions.c\n",
diff --git a/docs/source/tutorial/Union_tutorial_2_geometry.ipynb b/docs/source/tutorial/Union_tutorial_2_geometry.ipynb
index a15aae94..30ea3e15 100644
--- a/docs/source/tutorial/Union_tutorial_2_geometry.ipynb
+++ b/docs/source/tutorial/Union_tutorial_2_geometry.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,7 +55,7 @@
     "\n",
     "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
     "Al.process_string = '\"Al_inc,Al_pow\"'\n",
-    "Al.my_absorption = 100*4*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "Al.my_absorption = 100*4*0.231/66.4 # barns [m^2 E-28]*Å^3 [m^3 E-30]=[m E-2]\n",
     "\n",
     "Sample_inc = instrument.add_component(\"Sample_inc\", \"Incoherent_process\")\n",
     "Sample_inc.sigma = 4*3.4176\n",
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,7 +89,8 @@
     "src.yheight = 0.035\n",
     "src.focus_aw = 0.01\n",
     "src.focus_ah = 0.01\n",
-    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0,\n",
+    "                                       comment=\"Wavelength in [Ang]\")\n",
     "src.dlambda = \"0.01*wavelength\"\n",
     "src.flux = 1E13"
    ]
@@ -108,9 +109,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Union_cylinder ____________________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1mmaterial_string\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [] // material name of this volume, defined using \n",
+      "                          Union_make_material \n",
+      "\u001b[4m\u001b[1mpriority\u001b[0m\u001b[0m [1] // priotiry of the volume (can not be the same as another volume) \n",
+      "                A high priority is on top of low. \n",
+      "\u001b[4m\u001b[1mradius\u001b[0m\u001b[0m [m] // Outer radius volume in (x,z) plane\n",
+      "\u001b[4m\u001b[1myheight\u001b[0m\u001b[0m [m] // Cylinder height in (y) direction\n",
+      "\u001b[1mvisualize\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // set to 0 if you wish to hide this geometry in mcdisplay\n",
+      "\u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [1] // Relative index of component to focus at, e.g. next is +1\n",
+      "\u001b[1mtarget_x\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m]\n",
+      "\u001b[1mtarget_y\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Position of target to focus at\n",
+      "\u001b[1mtarget_z\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m]\n",
+      "\u001b[1mfocus_aw\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // horiz. angular dimension of a rectangular area\n",
+      "\u001b[1mfocus_ah\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // vert. angular dimension of a rectangular area\n",
+      "\u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // horiz. dimension of a rectangular area\n",
+      "\u001b[1mfocus_xh\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // vert. dimension of a rectangular area\n",
+      "\u001b[1mfocus_r\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // focusing on circle with this radius\n",
+      "\u001b[1mp_interact\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // probability to interact with this geometry [0-1]\n",
+      "\u001b[1mmask_string\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [] // Comma seperated list of geometry names which this \n",
+      "                      geometry should mask \n",
+      "\u001b[1mmask_setting\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [] // \"All\" or \"Any\", should the masked volume be simulated \n",
+      "                       when the ray is in just one mask, or all. \n",
+      "\u001b[1mnumber_of_activations\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // Number of subsequent Union_master components \n",
+      "                                   that will simulate this geometry \n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.component_help(\"Union_cylinder\")"
    ]
@@ -155,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,7 +200,8 @@
     "sample_geometry.priority = 100\n",
     "sample_geometry.set_AT([0,0,1], RELATIVE=src)\n",
     "\n",
-    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\", RELATIVE=sample_geometry)\n",
+    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\")\n",
+    "container.set_RELATIVE(sample_geometry)\n",
     "container.yheight = 0.03+0.003 # 1.5 mm top and button\n",
     "container.radius = 0.0075 + 0.0015 # 1.5 mm sides of container\n",
     "container.material_string='\"Al\"' \n",
@@ -190,11 +225,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\")\n",
+    "logger_zx.set_RELATIVE(sample_geometry)\n",
     "logger_zx.D_direction_1 = '\"z\"'\n",
     "logger_zx.D1_min = -0.02\n",
     "logger_zx.D1_max = 0.02\n",
@@ -205,7 +241,8 @@
     "logger_zx.n2 = 300\n",
     "logger_zx.filename = '\"logger_zx.dat\"'\n",
     "\n",
-    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\")\n",
+    "logger_zy.set_RELATIVE(sample_geometry)\n",
     "logger_zy.D_direction_1 = '\"z\"'\n",
     "logger_zy.D1_min = -0.02\n",
     "logger_zy.D1_max = 0.02\n",
@@ -216,7 +253,8 @@
     "logger_zy.n2 = 300\n",
     "logger_zy.filename = '\"logger_zy.dat\"'\n",
     "\n",
-    "logger_xy = instrument.add_component(\"logger_space_xy\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_xy = instrument.add_component(\"logger_space_xy\", \"Union_logger_2D_space\")\n",
+    "logger_xy.set_RELATIVE(sample_geometry)\n",
     "logger_xy.D_direction_1 = '\"x\"'\n",
     "logger_xy.D1_min = -0.02\n",
     "logger_xy.D1_max = 0.02\n",
@@ -238,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -255,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -276,16 +314,320 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Instrument settings:\n",
+      "  ncount:           3.00e+06\n",
+      "  output_path:      data_folder/union_geometry\n",
+      "  run_path:         run_folder\n",
+      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
+      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
+      "  executable:       mcrun\n",
+      "  force_compile:    True\n",
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_geometry_49\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:14851:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:14851:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:15094:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:15094:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:15337:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_xy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:15337:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_xy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "15 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_geometry_49\n",
+      "\n",
+      "          Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: Sample_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: Sample_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: Sample_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 4\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "name of process [2]: Sample_inc \n",
+      "component index [2]: 4 \n",
+      "name of process [3]: Sample_pow \n",
+      "component index [3]: 5 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 2\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 1.391570 \n",
+      "number of processes [0]: 2 \n",
+      "name of material    [1]: Sample \n",
+      "component index     [1]: 6 \n",
+      "my_absoprtion       [1]: 1.092170 \n",
+      "number of processes [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 2\n",
+      "\n",
+      "name of geometry    [0]: sample_geometry \n",
+      "component index     [0]: 8 \n",
+      "Volume.name         [0]: sample_geometry \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 1.092170 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: sample_container \n",
+      "component index     [1]: 9 \n",
+      "Volume.name         [1]: sample_container \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absorption       [1]: 1.391570 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: sample_container_lid \n",
+      "component index     [2]: 10 \n",
+      "Volume.name         [2]: sample_container_lid \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absorption       [2]: 1.391570 \n",
+      "Volume.p_physics.number of processes [2]: 2 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: 0.015500 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 4\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           0 = [2,3]\n",
+      "LIST: Intersect_check_list for Volume      0 = [2,3]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [2,3]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for sample_geometry with cylinder shape made of Sample\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = []\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [2,3]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
+      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for sample_container with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  2 = [1]\n",
+      "LIST: Direct_children for Volume           2 = [1]\n",
+      "LIST: Intersect_check_list for Volume      2 = [1]\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [0,3]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [3]\n",
+      "LIST: Next_volume_list for Volume          2 = [0,3,1]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for sample_container_lid with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  3 = []\n",
+      "LIST: Direct_children for Volume           3 = []\n",
+      "LIST: Intersect_check_list for Volume      3 = [2]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = []\n",
+      "LIST: Next_volume_list for Volume          3 = [0,2]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_zx_I=17768.4 logger_space_zx_ERR=30.9103 logger_space_zx_N=950589 \"logger_zx.dat\"\n",
+      "Detector: logger_space_zy_I=17768.4 logger_space_zy_ERR=30.9103 logger_space_zy_N=950589 \"logger_zy.dat\"\n",
+      "Detector: logger_space_xy_I=17768.4 logger_space_xy_ERR=30.9103 logger_space_xy_N=950589 \"logger_xy.dat\"\n",
+      "Detector: banana_I=1937.6 banana_ERR=10.3152 banana_N=108657 \"banana_1643033933.th\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.set_parameters(wavelength=3.0)\n",
-    "instrument.settings(ncount=3E5, output_path=\"data_folder/union_geometry\")\n",
+    "instrument.settings(ncount=3E6, output_path=\"data_folder/union_geometry\")\n",
     "instrument.show_settings()\n",
     "\n",
     "instrument.backengine()\n",
@@ -302,9 +644,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space_zx\n",
+      "Plotting data with name logger_space_zy\n",
+      "Plotting data with name logger_space_xy\n",
+      "Plotting data with name banana\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 7 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
     "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
@@ -332,32 +697,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
-    "inner_wall = instrument.add_component(\"cryostat_wall\", \"Union_cylinder\", before=\"master\")\n",
+    "inner_wall = instrument.add_component(\"cryostat_wall\", \"Union_cylinder\",\n",
+    "                                      before=\"master\")\n",
     "inner_wall.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
     "inner_wall.yheight = 0.12\n",
     "inner_wall.radius = 0.03\n",
     "inner_wall.material_string='\"Al\"' \n",
     "inner_wall.priority = 80\n",
     "\n",
-    "inner_wall_vac = instrument.add_component(\"cryostat_wall_vacuum\", \"Union_cylinder\", before=\"master\")\n",
+    "inner_wall_vac = instrument.add_component(\"cryostat_wall_vacuum\", \"Union_cylinder\",\n",
+    "                                          before=\"master\")\n",
     "inner_wall_vac.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
     "inner_wall_vac.yheight = 0.12 - 0.008\n",
     "inner_wall_vac.radius = 0.03 - 0.002\n",
     "inner_wall_vac.material_string='\"Vacuum\"' \n",
     "inner_wall_vac.priority = 81\n",
     "\n",
-    "outer_wall = instrument.add_component(\"outer_cryostat_wall\", \"Union_cylinder\", before=\"master\")\n",
+    "outer_wall = instrument.add_component(\"outer_cryostat_wall\", \"Union_cylinder\",\n",
+    "                                      before=\"master\")\n",
     "outer_wall.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
     "outer_wall.yheight = 0.15\n",
     "outer_wall.radius = 0.1\n",
     "outer_wall.material_string='\"Al\"' \n",
     "outer_wall.priority = 60\n",
     "\n",
-    "outer_wall_vac = instrument.add_component(\"outer_cryostat_wall_vacuum\", \"Union_cylinder\", before=\"master\")\n",
+    "outer_wall_vac = instrument.add_component(\"outer_cryostat_wall_vacuum\", \"Union_cylinder\",\n",
+    "                                          before=\"master\")\n",
     "outer_wall_vac.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
     "outer_wall_vac.yheight = 0.15 - 0.01\n",
     "outer_wall_vac.radius = 0.1 - 0.003\n",
@@ -375,7 +744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -384,6 +753,45 @@
     "logger_xy.set_parameters(D1_min=-0.12, D1_max=0.12, D2_min=-0.12, D2_max=0.12)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Viewing the instrument\n",
+    "The instrument can be viewed with the *show_instrument* method. The mock cryostat and detector can be seen in a 3D view."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"800\"\n",
+       "            height=\"450\"\n",
+       "            src=\"run_folder/python_tutorial_mcdisplay_0/index.html\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x7fc898bb3a00>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "instrument.show_instrument()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -394,13 +802,445 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_geometry_50\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:15483:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:15483:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:15726:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:15726:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:15969:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_xy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:15969:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_xy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "15 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_geometry_50\n",
+      "\n",
+      "          Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: Sample_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: Sample_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: Sample_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 4\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "name of process [2]: Sample_inc \n",
+      "component index [2]: 4 \n",
+      "name of process [3]: Sample_pow \n",
+      "component index [3]: 5 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 2\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 1.391570 \n",
+      "number of processes [0]: 2 \n",
+      "name of material    [1]: Sample \n",
+      "component index     [1]: 6 \n",
+      "my_absoprtion       [1]: 1.092170 \n",
+      "number of processes [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 2\n",
+      "\n",
+      "name of geometry    [0]: sample_geometry \n",
+      "component index     [0]: 8 \n",
+      "Volume.name         [0]: sample_geometry \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 1.092170 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: sample_container \n",
+      "component index     [1]: 9 \n",
+      "Volume.name         [1]: sample_container \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absorption       [1]: 1.391570 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: sample_container_lid \n",
+      "component index     [2]: 10 \n",
+      "Volume.name         [2]: sample_container_lid \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absorption       [2]: 1.391570 \n",
+      "Volume.p_physics.number of processes [2]: 2 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: 0.015500 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: cryostat_wall \n",
+      "component index     [3]: 14 \n",
+      "Volume.name         [3]: cryostat_wall \n",
+      "Volume.p_physics.is_vacuum           [3]: 0 \n",
+      "Volume.p_physics.my_absorption       [3]: 1.391570 \n",
+      "Volume.p_physics.number of processes [3]: 2 \n",
+      "Volume.geometry.shape                [3]: cylinder \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: 0.000000 \n",
+      "Volume.geometry.center.z             [3]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
+      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [4]: cryostat_wall_vacuum \n",
+      "component index     [4]: 15 \n",
+      "Volume.name         [4]: cryostat_wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [4]: 1 \n",
+      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
+      "Volume.p_physics.number of processes [4]: 0 \n",
+      "Volume.geometry.shape                [4]: cylinder \n",
+      "Volume.geometry.center.x             [4]: 0.000000 \n",
+      "Volume.geometry.center.y             [4]: 0.000000 \n",
+      "Volume.geometry.center.z             [4]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
+      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [5]: outer_cryostat_wall \n",
+      "component index     [5]: 16 \n",
+      "Volume.name         [5]: outer_cryostat_wall \n",
+      "Volume.p_physics.is_vacuum           [5]: 0 \n",
+      "Volume.p_physics.my_absorption       [5]: 1.391570 \n",
+      "Volume.p_physics.number of processes [5]: 2 \n",
+      "Volume.geometry.shape                [5]: cylinder \n",
+      "Volume.geometry.center.x             [5]: 0.000000 \n",
+      "Volume.geometry.center.y             [5]: 0.000000 \n",
+      "Volume.geometry.center.z             [5]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.100000 \n",
+      "Volume.geometry.geometry_parameters.height [5]: 0.150000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [6]: outer_cryostat_wall_vacuum \n",
+      "component index     [6]: 17 \n",
+      "Volume.name         [6]: outer_cryostat_wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [6]: 1 \n",
+      "Volume.p_physics.my_absorption       [6]: 0.000000 \n",
+      "Volume.p_physics.number of processes [6]: 0 \n",
+      "Volume.geometry.shape                [6]: cylinder \n",
+      "Volume.geometry.center.x             [6]: 0.000000 \n",
+      "Volume.geometry.center.y             [6]: 0.000000 \n",
+      "Volume.geometry.center.z             [6]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.097000 \n",
+      "Volume.geometry.geometry_parameters.height [6]: 0.140000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 8\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7]\n",
+      "LIST: Direct_children for Volume           0 = [6]\n",
+      "LIST: Intersect_check_list for Volume      0 = [6]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [6]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for sample_geometry with cylinder shape made of Sample\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = []\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [2,3]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
+      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for sample_container with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  2 = [1]\n",
+      "LIST: Direct_children for Volume           2 = [1]\n",
+      "LIST: Intersect_check_list for Volume      2 = [1]\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [3,5]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [5]\n",
+      "LIST: Next_volume_list for Volume          2 = [3,5,1]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for sample_container_lid with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  3 = []\n",
+      "LIST: Direct_children for Volume           3 = []\n",
+      "LIST: Intersect_check_list for Volume      3 = [2]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [5]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = [5]\n",
+      "LIST: Next_volume_list for Volume          3 = [5,2]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "List overview for cryostat_wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  4 = [1,2,3,5]\n",
+      "LIST: Direct_children for Volume           4 = [5]\n",
+      "LIST: Intersect_check_list for Volume      4 = [5]\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [7]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = [7]\n",
+      "LIST: Next_volume_list for Volume          4 = [7,5]\n",
+      "      Is_vacuum for Volume                 4 = 0\n",
+      "      is_mask_volume for Volume            4 = 0\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = []\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 0\n",
+      "\n",
+      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  5 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           5 = [2,3]\n",
+      "LIST: Intersect_check_list for Volume      5 = [2,3]\n",
+      "LIST: Mask_intersect_list for Volume       5 = []\n",
+      "LIST: Destinations_list for Volume         5 = [4]\n",
+      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
+      "LIST: Next_volume_list for Volume          5 = [4,2,3]\n",
+      "      Is_vacuum for Volume                 5 = 1\n",
+      "      is_mask_volume for Volume            5 = 0\n",
+      "      is_masked_volume for Volume          5 = 0\n",
+      "      is_exit_volume for Volume            5 = 0\n",
+      "LIST: mask_list for Volume                 5 = []\n",
+      "LIST: masked_by_list for Volume            5 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
+      "      mask_mode for Volume                 5 = 0\n",
+      "\n",
+      "List overview for outer_cryostat_wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  6 = [1,2,3,4,5,7]\n",
+      "LIST: Direct_children for Volume           6 = [7]\n",
+      "LIST: Intersect_check_list for Volume      6 = [7]\n",
+      "LIST: Mask_intersect_list for Volume       6 = []\n",
+      "LIST: Destinations_list for Volume         6 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 6 = []\n",
+      "LIST: Next_volume_list for Volume          6 = [0,7]\n",
+      "      Is_vacuum for Volume                 6 = 0\n",
+      "      is_mask_volume for Volume            6 = 0\n",
+      "      is_masked_volume for Volume          6 = 0\n",
+      "      is_exit_volume for Volume            6 = 0\n",
+      "LIST: mask_list for Volume                 6 = []\n",
+      "LIST: masked_by_list for Volume            6 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
+      "      mask_mode for Volume                 6 = 0\n",
+      "\n",
+      "List overview for outer_cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  7 = [1,2,3,4,5]\n",
+      "LIST: Direct_children for Volume           7 = [4]\n",
+      "LIST: Intersect_check_list for Volume      7 = [4]\n",
+      "LIST: Mask_intersect_list for Volume       7 = []\n",
+      "LIST: Destinations_list for Volume         7 = [6]\n",
+      "LIST: Reduced_destinations_list for Volume 7 = [6]\n",
+      "LIST: Next_volume_list for Volume          7 = [6,4]\n",
+      "      Is_vacuum for Volume                 7 = 1\n",
+      "      is_mask_volume for Volume            7 = 0\n",
+      "      is_masked_volume for Volume          7 = 0\n",
+      "      is_exit_volume for Volume            7 = 0\n",
+      "LIST: mask_list for Volume                 7 = []\n",
+      "LIST: masked_by_list for Volume            7 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
+      "      mask_mode for Volume                 7 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_zx_I=20806.8 logger_space_zx_ERR=38.1707 logger_space_zx_N=1.17952e+06 \"logger_zx.dat\"\n",
+      "Detector: logger_space_zy_I=20806.8 logger_space_zy_ERR=38.1707 logger_space_zy_N=1.17952e+06 \"logger_zy.dat\"\n",
+      "Detector: logger_space_xy_I=20806.8 logger_space_xy_ERR=38.1707 logger_space_xy_N=1.17952e+06 \"logger_xy.dat\"\n",
+      "Detector: banana_I=1899.84 banana_ERR=9.43645 banana_N=112573 \"banana_1643033946.th\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.backengine()\n",
     "data_cryo = instrument.data"
@@ -416,9 +1256,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space_zx\n",
+      "Plotting data with name logger_space_zy\n",
+      "Plotting data with name logger_space_xy\n",
+      "Plotting data with name banana\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 7 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plotter.make_sub_plot(data_cryo, log=[True, True, True, False], orders_of_mag=5)"
    ]
@@ -436,9 +1299,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "banana_can = functions.name_search(\"banana\", data)\n",
     "banana_cryo = functions.name_search(\"banana\", data_cryo)\n",
@@ -456,15 +1332,8 @@
     "         banana_diff.xaxis, banana_diff.Intensity-10.0, \"k\")\n",
     "plt.xlabel(\"2Theta [deg]\")\n",
     "plt.ylabel(\"Intensity [n/s]\")\n",
-    "plt.legend([\"Sample in can\", \"Sample in can in cryostat\", \"Difference displaced to -10\"])"
+    "l = plt.legend([\"Sample in can\", \"Sample in can in cryostat\", \"Difference displaced to -10\"])"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -485,6 +1354,11 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.8.8"
+  },
+  "metadata": {
+   "execution": {
+    "timeout": 100
+   }
   }
  },
  "nbformat": 4,
diff --git a/docs/source/tutorial/Union_tutorial_3_loggers.ipynb b/docs/source/tutorial/Union_tutorial_3_loggers.ipynb
index d57308e0..158b4faf 100644
--- a/docs/source/tutorial/Union_tutorial_3_loggers.ipynb
+++ b/docs/source/tutorial/Union_tutorial_3_loggers.ipynb
@@ -28,9 +28,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'instrument' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-5353298b0c38>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mAl_inc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstrument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_component\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Al_inc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Incoherent_process\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mAl_inc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msigma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0082\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mAl_inc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_cell_volume\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m66.4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mAl_pow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstrument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_component\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Al_pow\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Powder_process\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'instrument' is not defined"
+     ]
+    }
+   ],
    "source": [
     "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
     "Al_inc.sigma = 0.0082\n",
@@ -41,7 +53,7 @@
     "\n",
     "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
     "Al.process_string = '\"Al_inc,Al_pow\"'\n",
-    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28]*Å^3[m^3 E-30]=[m E-2], factor 100\n",
     "\n",
     "Sample_inc = instrument.add_component(\"Sample_inc\", \"Incoherent_process\")\n",
     "Sample_inc.sigma = 3.4176\n",
@@ -59,7 +71,8 @@
     "src.yheight = 0.035\n",
     "src.focus_aw = 0.01\n",
     "src.focus_ah = 0.01\n",
-    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0,\n",
+    "                                       comment=\"Wavelength in [Ang]\")\n",
     "src.dlambda = \"0.01*wavelength\"\n",
     "src.flux = 1E13\n",
     "\n",
@@ -70,7 +83,8 @@
     "sample_geometry.priority = 100\n",
     "sample_geometry.set_AT([0,0,1], RELATIVE=src)\n",
     "\n",
-    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\", RELATIVE=sample_geometry)\n",
+    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\")\n",
+    "container.set_RELATIVE(sample_geometry)\n",
     "container.yheight = 0.03+0.003 # 1.5 mm top and button\n",
     "container.radius = 0.0075 + 0.0015 # 1.5 mm sides of container\n",
     "container.material_string='\"Al\"' \n",
@@ -164,11 +178,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'instrument' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-c11f0150980d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlogger_zx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstrument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_component\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"logger_space_zx\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Union_logger_2D_space\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mlogger_zx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_RELATIVE\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_geometry\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mlogger_zx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mD_direction_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'\"z\"'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mlogger_zx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mD1_min\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.12\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mlogger_zx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mD1_max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.12\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'instrument' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\")\n",
+    "logger_zx.set_RELATIVE(sample_geometry)\n",
     "logger_zx.D_direction_1 = '\"z\"'\n",
     "logger_zx.D1_min = -0.12\n",
     "logger_zx.D1_max = 0.12\n",
@@ -179,7 +206,8 @@
     "logger_zx.n2 = 300\n",
     "logger_zx.filename = '\"logger_zx.dat\"'\n",
     "\n",
-    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\")\n",
+    "logger_zy.set_RELATIVE(sample_geometry)\n",
     "logger_zy.D_direction_1 = '\"z\"'\n",
     "logger_zy.D1_min = -0.12\n",
     "logger_zy.D1_max = 0.12\n",
@@ -212,7 +240,7 @@
    "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=3.0)\n",
-    "instrument.settings(ncount=3E5, output_path=\"data_folder/union_loggers\")\n",
+    "instrument.settings(ncount=3E6, output_path=\"data_folder/union_loggers\")\n",
     "\n",
     "instrument.backengine()\n",
     "data = instrument.data"
@@ -287,7 +315,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
     "logger_zx.order_total = 2\n",
@@ -333,7 +365,8 @@
     "logger_1D.n1 = 300\n",
     "logger_1D.filename = '\"logger_1D_time.dat\"'\n",
     "\n",
-    "abs_logger_zx = instrument.add_component(\"abs_logger_space_zx\", \"Union_abs_logger_2D_space\",before=\"master\")\n",
+    "abs_logger_zx = instrument.add_component(\"abs_logger_space_zx\", \"Union_abs_logger_2D_space\",\n",
+    "                                         before=\"master\")\n",
     "abs_logger_zx.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
     "abs_logger_zx.D_direction_1 = '\"z\"'\n",
     "abs_logger_zx.D1_min = -0.12\n",
@@ -356,7 +389,8 @@
     "logger_2DQ.n2 = 200\n",
     "logger_2DQ.filename = '\"logger_2DQ.dat\"'\n",
     "\n",
-    "logger_2D_kf = instrument.add_component(\"logger_2D_kf\", \"Union_logger_2D_kf\", before=\"master\")\n",
+    "logger_2D_kf = instrument.add_component(\"logger_2D_kf\", \"Union_logger_2D_kf\",\n",
+    "                                        before=\"master\")\n",
     "logger_2D_kf.Q_direction_1 = '\"z\"'\n",
     "logger_2D_kf.Q1_min = -2.5\n",
     "logger_2D_kf.Q1_max = 2.5\n",
@@ -441,7 +475,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "log_2D_st = instrument.add_component(\"logger_2D_space_time\", \"Union_logger_2D_space_time\", before=\"master\")"
+    "log_2D_st = instrument.add_component(\"logger_2D_space_time\", \"Union_logger_2D_space_time\",\n",
+    "                                     before=\"master\")"
    ]
   },
   {
@@ -584,6 +619,11 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.8.8"
+  },
+  "metadata": {
+   "execution": {
+    "timeout": 100
+   }
   }
  },
  "nbformat": 4,
diff --git a/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb b/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
index 3946d819..4568bcd3 100644
--- a/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
+++ b/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
@@ -69,7 +69,8 @@
     "src.focus_ah = 0.01\n",
     "\n",
     "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
-    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0,\n",
+    "                                       comment=\"Wavelength in [Ang]\")\n",
     "src.dlambda = \"0.01*wavelength\"\n",
     "src.flux = 1E13\n",
     "\n",
@@ -180,11 +181,13 @@
     "master = instrument.add_component(\"master\", \"Union_master\")\n",
     "\n",
     "# Adding a banana - tof detector\n",
-    "banana_detector = instrument.add_component(\"banana_detector\", \"Monitor_nD\", RELATIVE=cryostat_center)\n",
+    "banana_detector = instrument.add_component(\"banana_detector\", \"Monitor_nD\",\n",
+    "banana_detector.set_RELATIVE(cryostat_center)\n",
     "banana_detector.xwidth = 1\n",
     "banana_detector.yheight = 0.2\n",
     "banana_detector.restore_neutron = 1\n",
-    "banana_detector.options = '\"banana, theta limits=[-180,180] bins=361, t limits=[0.0011 0.0025] bins=500\"'\n",
+    "options = '\"banana, theta limits=[-180,180] bins=361, t limits=[0.0011 0.0025] bins=500\"'\n",
+    "banana_detector.options = options\n",
     "banana_detector.filename = '\"tof_b.dat\"'"
    ]
   },
@@ -219,7 +222,7 @@
    "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=wavelength, A3_angle=theta)\n",
-    "instrument.settings(ncount=3E5, output_path=\"data_folder/union_conditionals\")\n",
+    "instrument.settings(ncount=3E6, output_path=\"data_folder/union_conditionals\")\n",
     "\n",
     "instrument.backengine()\n",
     "data = instrument.data"
@@ -292,9 +295,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-1-926d3f87d6e5>, line 30)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-1-926d3f87d6e5>\"\u001b[0;36m, line \u001b[0;32m30\u001b[0m\n\u001b[0;31m    banana_detector.xwidth = 1\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
    "source": [
     "# Set up instrument parameters describing what spot to investigate\n",
     "instrument.add_parameter(\"tag_angle\", value=-95)\n",
@@ -302,12 +314,16 @@
     "instrument.add_parameter(\"tag_interval\", value=9E-5)\n",
     "\n",
     "# Set up an arm pointing to the relevant spot\n",
-    "spot_dir = instrument.add_component(\"spot_dir\", \"Arm\", RELATIVE=cryostat_center, before=\"master\")\n",
+    "spot_dir = instrument.add_component(\"spot_dir\", \"Arm\",\n",
+    "                                    RELATIVE=cryostat_center, before=\"master\")\n",
     "spot_dir.set_ROTATED([0, \"tag_angle\", 0], RELATIVE=cryostat_center)\n",
     "\n",
     "# Set up a conditional component targeting all our loggers\n",
-    "PSD_conditional = instrument.add_component(\"space_all_PSD_conditional\", \"Union_conditional_PSD\", before=\"master\")\n",
-    "PSD_conditional.target_loggers = '\"logger_space_zx,logger_space_zy,logger_time_all,logger_2DQ_sample,logger_2DQ_environment\"'\n",
+    "PSD_conditional = instrument.add_component(\"space_all_PSD_conditional\", \"Union_conditional_PSD\",\n",
+    "                                           before=\"master\")\n",
+    "\n",
+    "loggers = '\"logger_space_zx,logger_space_zy,logger_time_all,logger_2DQ_sample,logger_2DQ_environment\"'\n",
+    "PSD_conditional.target_loggers = loggers\n",
     "PSD_conditional.xwidth = 0.2\n",
     "PSD_conditional.yheight = 0.2\n",
     "PSD_conditional.time_min = \"tag_time-0.5*tag_interval\"\n",
@@ -321,11 +337,13 @@
     "master.append_EXTEND(\"flag1 = logger_conditional_extend_array[1];\")\n",
     "\n",
     "# Copy of our banana detector, but with WHEN condition to verify we are investigating the right peak\n",
-    "banana_detector = instrument.add_component(\"banana_detector_limited\", \"Monitor_nD\", RELATIVE=cryostat_center)\n",
+    "banana_detector = instrument.add_component(\"banana_detector_limited\", \"Monitor_nD\")\n",
+    "banana_detector.set_RELATIVE(cryostat_center)\n",
     "banana_detector.xwidth = 1\n",
     "banana_detector.yheight = 0.2\n",
     "banana_detector.restore_neutron = 1\n",
-    "banana_detector.options = '\"banana, theta limits=[-180,180] bins=361, t limits=[0.0011 0.0025] bins=500\"'\n",
+    "options = '\"banana, theta limits=[-180,180] bins=361, t limits=[0.0011 0.0025] bins=500\"'\n",
+    "banana_detector.options = options\n",
     "banana_detector.filename = '\"tof_b_limited.dat\"'\n",
     "banana_detector.set_WHEN(\"flag1 > 0\")"
    ]
@@ -352,7 +370,7 @@
    "source": [
     "instrument.set_parameters(wavelength=wavelength, A3_angle=theta, \n",
     "                          tag_angle=-95, tag_time=0.00188, tag_interval=9E-5)\n",
-    "instrument.settings(ncount=3E5) # Can add mpi to improve speed for this longer simulation\n",
+    "instrument.settings(ncount=3E6) # Can add mpi to improve speed for this longer simulation\n",
     "\n",
     "instrument.backengine()\n",
     "data_con = instrument.data"
@@ -532,6 +550,11 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.8.8"
+  },
+  "metadata": {
+   "execution": {
+    "timeout": 100
+   }
   }
  },
  "nbformat": 4,
diff --git a/docs/source/tutorial/Union_tutorial_5_masks.ipynb b/docs/source/tutorial/Union_tutorial_5_masks.ipynb
index d7a1d2a5..9d9dbc44 100644
--- a/docs/source/tutorial/Union_tutorial_5_masks.ipynb
+++ b/docs/source/tutorial/Union_tutorial_5_masks.ipynb
@@ -36,9 +36,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'instrument' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-02960afe2720>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mAl_inc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstrument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_component\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Al_inc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Incoherent_process\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mAl_inc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msigma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0082\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mAl_inc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_cell_volume\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m66.4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mAl_pow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstrument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_component\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Al_pow\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Powder_process\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'instrument' is not defined"
+     ]
+    }
+   ],
    "source": [
     "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
     "Al_inc.sigma = 0.0082\n",
@@ -49,7 +61,7 @@
     "\n",
     "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
     "Al.process_string = '\"Al_inc,Al_pow\"'\n",
-    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28]*Å^3 [m^3 E-30]=[m E-2], factor 100\n",
     "\n",
     "src = instrument.add_component(\"source\", \"Source_div\")\n",
     "\n",
@@ -59,7 +71,8 @@
     "src.focus_ah = 0.01\n",
     "\n",
     "\n",
-    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0,\n",
+    "                                       comment=\"Wavelength in [Ang]\")\n",
     "src.dlambda = \"0.01*wavelength\"\n",
     "src.flux = 1E13\n",
     "\n",
@@ -77,7 +90,8 @@
     "wall_vac.material_string='\"Vacuum\"' \n",
     "wall_vac.priority = 50\n",
     "\n",
-    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=wall)\n",
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\")\n",
+    "logger_zx.set_RELATIVE(wall)\n",
     "logger_zx.D_direction_1 = '\"z\"'\n",
     "logger_zx.D1_min = -0.12\n",
     "logger_zx.D1_max = 0.12\n",
@@ -101,7 +115,7 @@
    },
    "outputs": [],
    "source": [
-    "instrument.settings(ncount=2E5, output_path=\"data_folder/union_masks\")\n",
+    "instrument.settings(ncount=2E6, output_path=\"data_folder/union_masks\")\n",
     "\n",
     "instrument.backengine()\n",
     "data = instrument.data"
@@ -255,13 +269,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'instr' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-4-95b89adce35b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minstrument\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMcStas_instr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"python_tutorial\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"run_folder\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mAl_inc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstrument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_component\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Al_inc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Incoherent_process\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mAl_inc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msigma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0082\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mAl_inc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_cell_volume\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m66.4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'instr' is not defined"
+     ]
+    }
+   ],
    "source": [
     "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")\n",
     "\n",
@@ -274,7 +300,7 @@
     "\n",
     "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
     "Al.process_string = '\"Al_inc,Al_pow\"'\n",
-    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28]*Å^3 [m^3 E-30]=[m E-2], factor 100\n",
     "\n",
     "src = instrument.add_component(\"source\", \"Source_div\")\n",
     "\n",
@@ -314,7 +340,8 @@
     "cyl_mask.mask_string='\"box\"' \n",
     "cyl_mask.priority = 51\n",
     "\n",
-    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=box)\n",
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\")\n",
+    "logger_zx.set_RELATIVE(box)\n",
     "logger_zx.D_direction_1 = '\"z\"'\n",
     "logger_zx.D1_min = -0.12\n",
     "logger_zx.D1_max = 0.12\n",
diff --git a/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb b/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
index b1d26615..6239cd04 100644
--- a/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
+++ b/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
@@ -38,9 +38,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'instrument' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-e9c217e34638>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mAl_inc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstrument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_component\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Al_inc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Incoherent_process\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mAl_inc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msigma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0082\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mAl_inc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_cell_volume\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m66.4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mAl_pow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstrument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_component\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Al_pow\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Powder_process\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'instrument' is not defined"
+     ]
+    }
+   ],
    "source": [
     "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
     "Al_inc.sigma = 0.0082\n",
@@ -51,14 +63,15 @@
     "\n",
     "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
     "Al.process_string = '\"Al_inc,Al_pow\"'\n",
-    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28]*Å^3 [m^3 E-30]=[m E-2], factor 100\n",
     "\n",
     "src = instrument.add_component(\"source\", \"Source_div\")\n",
     "src.xwidth = 0.01\n",
     "src.yheight = 0.035\n",
     "src.focus_aw = 0.01\n",
     "src.focus_ah = 0.01\n",
-    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0,\n",
+    "                                       comment=\"Wavelength in [Ang]\")\n",
     "src.dlambda = \"0.01*wavelength\"\n",
     "src.flux = 1E13\n",
     "\n",
@@ -69,7 +82,8 @@
     "sample_volume.priority = 100\n",
     "sample_volume.set_AT([0,0,1], RELATIVE=src)\n",
     "\n",
-    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\", RELATIVE=sample_volume)\n",
+    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\")\n",
+    "container.set_RELATIVE(sample_volume)\n",
     "container.yheight = 0.03+0.003 # 1.5 mm top and button\n",
     "container.radius = 0.0075 + 0.0015 # 1.5 mm sides of container\n",
     "container.material_string='\"Al\"' \n",
@@ -96,7 +110,8 @@
     "inner_wall_vac.material_string='\"Vacuum\"' \n",
     "inner_wall_vac.priority = 81\n",
     "\n",
-    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=sample_volume)\n",
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\")\n",
+    "logger_zx.set_RELATIVE(sample_volume)\n",
     "logger_zx.D_direction_1 = '\"z\"'\n",
     "logger_zx.D1_min = -0.04\n",
     "logger_zx.D1_max = 0.04\n",
@@ -107,7 +122,8 @@
     "logger_zx.n2 = 300\n",
     "logger_zx.filename = '\"logger_zx.dat\"'\n",
     "\n",
-    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\", RELATIVE=sample_volume)\n",
+    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\")\n",
+    "logger_zy.set_RELATIVE(sample_volume)\n",
     "logger_zy.D_direction_1 = '\"z\"'\n",
     "logger_zy.D1_min = -0.04\n",
     "logger_zy.D1_max = 0.04\n",
@@ -138,7 +154,7 @@
    "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=3.0)\n",
-    "instrument.settings(ncount=3E5, output_path=\"data_folder/union_external\")\n",
+    "instrument.settings(ncount=3E6, output_path=\"data_folder/union_external\")\n",
     "\n",
     "instrument.backengine()\n",
     "data_empty = instrument.data"
@@ -392,6 +408,11 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.8.8"
+  },
+  "metadata": {
+   "execution": {
+    "timeout": 100
+   }
   }
  },
  "nbformat": 4,
diff --git a/docs/source/tutorial/Union_tutorial_7_Tagging_history.ipynb b/docs/source/tutorial/Union_tutorial_7_Tagging_history.ipynb
index d81e6c8a..5b5ecb59 100644
--- a/docs/source/tutorial/Union_tutorial_7_Tagging_history.ipynb
+++ b/docs/source/tutorial/Union_tutorial_7_Tagging_history.ipynb
@@ -32,7 +32,19 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'instrument' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-2785ca2919cd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mAl_inc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstrument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_component\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Al_inc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Incoherent_process\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mAl_inc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msigma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0082\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mAl_inc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_cell_volume\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m66.4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mAl_pow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstrument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_component\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Al_pow\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Powder_process\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'instrument' is not defined"
+     ]
+    }
+   ],
    "source": [
     "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
     "Al_inc.sigma = 0.0082\n",
@@ -43,7 +55,7 @@
     "\n",
     "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
     "Al.process_string = '\"Al_inc,Al_pow\"'\n",
-    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28]*Å^3 [m^3 E-30]=[m E-2], factor 100\n",
     "\n",
     "Sample_inc = instrument.add_component(\"Sample_inc\", \"Incoherent_process\")\n",
     "Sample_inc.sigma = 3.4176\n",
@@ -61,7 +73,8 @@
     "src.yheight = 0.035\n",
     "src.focus_aw = 0.01\n",
     "src.focus_ah = 0.01\n",
-    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0,\n",
+    "                                       comment=\"Wavelength in [Ang]\")\n",
     "src.dlambda = \"0.01*wavelength\"\n",
     "src.flux = 1E13\n",
     "\n",
@@ -72,7 +85,8 @@
     "sample_geometry.priority = 100\n",
     "sample_geometry.set_AT([0,0,1], RELATIVE=src)\n",
     "\n",
-    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\", RELATIVE=sample_geometry)\n",
+    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\")\n",
+    "container.set_RELATIVE(sample_geometry)\n",
     "container.yheight = 0.03+0.003 # 1.5 mm top and button\n",
     "container.radius = 0.0075 + 0.0015 # 1.5 mm sides of container\n",
     "container.material_string='\"Al\"' \n",
@@ -92,7 +106,8 @@
     "inner_wall_vac.material_string='\"Vacuum\"' \n",
     "inner_wall_vac.priority = 81\n",
     "\n",
-    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\")\n",
+    "logger_zx.set_RELATIVE(sample_geometry)\n",
     "logger_zx.D_direction_1 = '\"z\"'\n",
     "logger_zx.D1_min = -0.04\n",
     "logger_zx.D1_max = 0.04\n",
@@ -103,7 +118,8 @@
     "logger_zx.n2 = 300\n",
     "logger_zx.filename = '\"logger_zx.dat\"'\n",
     "\n",
-    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\")\n",
+    "logger_zy.set_RELATIVE(sample_geometry)\n",
     "logger_zy.D_direction_1 = '\"z\"'\n",
     "logger_zy.D1_min = -0.04\n",
     "logger_zy.D1_max = 0.04\n",
@@ -995,11 +1011,13 @@
    "outputs": [],
    "source": [
     "# Set up an arm pointing to the relevant spot\n",
-    "spot_dir = instrument.add_component(\"spot_dir\", \"Arm\", RELATIVE=sample_geometry, before=\"master\")\n",
+    "spot_dir = instrument.add_component(\"spot_dir\", \"Arm\", RELATIVE=sample_geometry,\n",
+    "                                    before=\"master\")\n",
     "spot_dir.set_ROTATED([0, 60, 0], RELATIVE=sample_geometry)\n",
     "\n",
     "# Set up a conditional component targeting all our loggers\n",
-    "PSD_conditional = instrument.add_component(\"space_all_PSD_conditional\", \"Union_conditional_PSD\", before=\"master\")\n",
+    "PSD_conditional = instrument.add_component(\"space_all_PSD_conditional\", \"Union_conditional_PSD\",\n",
+    "                                           before=\"master\")\n",
     "PSD_conditional.xwidth = 0.2\n",
     "PSD_conditional.yheight = 0.2\n",
     "PSD_conditional.master_tagging = 1\n",
@@ -1356,7 +1374,11 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [
     {
      "name": "stdout",
diff --git a/docs/source/tutorial/animation_demo.gif b/docs/source/tutorial/animation_demo.gif
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diff --git a/docs/source/user_guide/component_object.ipynb b/docs/source/user_guide/component_object.ipynb
index e953898b..d64b33c3 100644
--- a/docs/source/user_guide/component_object.ipynb
+++ b/docs/source/user_guide/component_object.ipynb
@@ -6,9 +6,9 @@
    "metadata": {},
    "source": [
     "# Component object\n",
-    "McStas components are the essential building blocks used to make McStas simulations. This sections focuses on how to work with the component objects in McStasScript.\n",
+    "McStas components are the essential building blocks used to make McStas simulations. This sections focuses on how to work with [component objects](../_autosummary/mcstasscript.helper.mcstas_objects.Component.rst) in McStasScript.\n",
     "\n",
-    "Since a component is always part of an instrument, new components are created in the instrument object so that they may immediately be registred and controlled by that instrument object."
+    "Since a component is always part of an instrument, new components are created in the instrument object so that they may immediately be registered and controlled by that instrument object."
    ]
   },
   {
@@ -29,7 +29,7 @@
    "metadata": {},
    "source": [
     "## Creating a component object\n",
-    "A component object is returned from the *add_component* and *copy_component* instrument object methods. When creating a component object, it needs an instance name and the name of the component in the library. When copying a component, the same library component is used."
+    "A [component object](../_autosummary/mcstasscript.helper.mcstas_objects.Component.rst) is returned from the *add_component* and *copy_component* instrument object methods. When creating a [component object](../_autosummary/mcstasscript.helper.mcstas_objects.Component.rst), it needs an instance name and the name of the component in the library. When copying a component, the same library component is used."
    ]
   },
   {
@@ -134,7 +134,7 @@
    "metadata": {},
    "source": [
     "## Setting the component position and rotation\n",
-    "The default location of a component is at the origin of the absoslute coordinate system used in McStas. The placement of components in space is very important, and McStas provides easy coordinate transfers when placing each component, which are accessible from McStasScript. In McStas one specifies the position with **AT** and orientation with **ROTATED**. Each of these can be relative to a reference, which is set with **RELATIVE**. This behavior is adopted in McStasScript where *set_AT* and *set_ROTATED* methods are available.\n",
+    "The default location of a component is at the origin of the absolute coordinate system used in McStas. The placement of components in space is very important, and McStas provides easy coordinate transfers when placing each component, which are accessible from McStasScript. In McStas one specifies the position with **AT** and orientation with **ROTATED**. Each of these can be relative to a reference, which is set with **RELATIVE**. This behavior is adopted in McStasScript where *set_AT* and *set_ROTATED* methods are available.\n",
     "\n",
     "If for example we want to shift our source a bit horizontally, we would use a list as a vector. "
    ]
@@ -321,7 +321,7 @@
    "metadata": {},
    "source": [
     "## Using parameters in a component\n",
-    "It is common to use defined parameters and declare variables in a component, and they can even be defined directly where needed. The parameter section of the documentation explain the details of setting up parameters, but here it is shown how they are used in components.\n",
+    "It is common to use defined [parameters](../_autosummary/mcstasscript.helper.mcstas_objects.ParameterVariable.rst) and [declare variables](../_autosummary/mcstasscript.helper.mcstas_objects.DeclareVariable.rst) in a component, and they can even be defined directly where needed. The parameter section of the documentation explain the details of setting up parameters, but here it is shown how they are used in components.\n",
     "\n",
     "After creating a parameter, it can be used when assigning component parameters. Just use a string with the same name, and it can even contain basic math."
    ]
@@ -357,7 +357,7 @@
    "id": "capable-touch",
    "metadata": {},
    "source": [
-    "When creating a parameter or variable, an object is returned which can be brovided to the component."
+    "When creating a [parameter](../_autosummary/mcstasscript.helper.mcstas_objects.ParameterVariable.rst) or [declare variable](../_autosummary/mcstasscript.helper.mcstas_objects.DeclareVariable.rst), an object is returned which can be provided to the component."
    ]
   },
   {
@@ -394,7 +394,7 @@
    "id": "large-auditor",
    "metadata": {},
    "source": [
-    "These features cover the majority of usecases for McStas components."
+    "These features cover the majority of use-cases for McStas components."
    ]
   },
   {
@@ -438,7 +438,7 @@
    "metadata": {},
    "source": [
     "### SPLIT\n",
-    "The split keyword instructs the simulation to split the ray into multiple smaller rays to improve statistis. This is done before the component that has the SPLIT keyword applied. The split value must be an integer. Use the SPLIT keyword to set split while creating a new component."
+    "The split keyword instructs the simulation to split the ray into multiple smaller rays to improve statistics. This is done before the component that has the SPLIT keyword applied. The split value must be an integer. Use the SPLIT keyword to set split while creating a new component."
    ]
   },
   {
@@ -571,7 +571,7 @@
     "### WHEN\n",
     "The WHEN keyword can be used to set a condition for when a component should be applied to the ray. The input for a WHEN condition is a logical c expression that can use variables in the instrument scope. Since just the expression is needed, there is no terminating semicolon.\n",
     "\n",
-    "Below a monitor is created which checks the flag defined during the extend example. This monitor will thus only record rays that had a positive x coordinate after the guide. WHEN and EXTEND are often used in conjuction with each other in this way, this is also explored in the tutorial."
+    "Below a monitor is created which checks the flag defined during the extend example. This monitor will thus only record rays that had a positive x coordinate after the guide. WHEN and EXTEND are often used in conjunction with each other in this way, this is also explored in the tutorial."
    ]
   },
   {
@@ -801,6 +801,11 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.8.8"
+  },
+  "metadata": {
+   "execution": {
+    "timeout": 100
+   }
   }
  },
  "nbformat": 4,
diff --git a/docs/source/user_guide/instrument_object.ipynb b/docs/source/user_guide/instrument_object.ipynb
index 433a6177..2043a0d0 100644
--- a/docs/source/user_guide/instrument_object.ipynb
+++ b/docs/source/user_guide/instrument_object.ipynb
@@ -15,11 +15,11 @@
    "metadata": {},
    "source": [
     "## Initialization\n",
-    "When an instrument object is created the only required argument is the name of the instrument which will be used for the instrument filename. There are however a number of keyword arguments that can be used to provide more informaton and alter the behavior.\n",
+    "An instrument object is created with the [McStas_instr](../_autosummary/mcstasscript.interface.instr.McStas_instr.rst) or [McXtrace_instr](../_autosummary/mcstasscript.interface.instr.McXtrace_instr.rst) class in the instr module. When an instrument object is created the only required argument is the name of the instrument which will be used for the instrument filename. There are however a number of keyword arguments that can be used to provide more information and alter the behavior.\n",
     "\n",
     "| Keyword argument | Type | Default | Description |\n",
     "| --- | --- | --- | --- |\n",
-    "| author | str |\"Python Instrument Generator\" | Name that will apear as author in instrument files |\n",
+    "| author | str |\"Python Instrument Generator\" | Name that will appear as author in instrument files |\n",
     "| origin | str |\"ESS DMSC\" | String that will appear as origin in instrument files |\n",
     "| input_path | str | \".\" | Folder which is considered workspace for McStas / McXtrace |\n",
     "| output_path | str | instrument_name | Name of data folder written by simulation |\n",
@@ -58,7 +58,7 @@
    "metadata": {},
    "source": [
     "### Using settings method\n",
-    "The instrument object has a settings method which can update some settings after initialization. The current settings can always be viewed with *show_settings*.\n",
+    "The instrument object has a *setting* method which can update some settings after initialization. The current settings can always be viewed with *show_settings*.\n",
     "\n",
     "| Keyword argument | Type | Default | Description |\n",
     "| --- | --- | --- | --- |\n",
@@ -173,11 +173,11 @@
    "metadata": {},
    "source": [
     "## Initialize section\n",
-    "One of the great advantages for the McStas / McXtrace packages is the initialize section of the instrument where calculations can be performed before the ray-tracing simulation starts. One could for example calculate appropriate angles to reach a certain bragg peak at a given wavelength. This would involve defining some declare variables, using these in the initialize section and then assigning them as component inputs.\n",
+    "One of the great advantages for the McStas / McXtrace packages is the initialize section of the instrument where calculations can be performed before the ray-tracing simulation starts. One could for example calculate appropriate angles to reach a certain Bragg peak at a given wavelength. This would involve defining some declare variables, using these in the initialize section and then assigning them as component inputs.\n",
     "\n",
     "In McStasScript many calculations can be performed directly in Python, and so typically the initialize section is used less, but it is still useful and available through McStasScript.\n",
     "\n",
-    "The instrument object has the method *append_initialize* which adds a line of code to the initialize. This line is copied directly into the instrument file, so it follows C syntax. Remember the semicolon! In addition there is *add_declare_var* to specify the declared variables needed. When declare variables are defined an object is returned which can be used when refering to that variable."
+    "The instrument object has the method *append_initialize* which adds a line of code to the initialize. This line is copied directly into the instrument file, so it follows C syntax. Remember the semicolon! In addition there is *add_declare_var* to specify the declared variables needed. When declare variables are defined an object is returned which can be used when referring to that variable."
    ]
   },
   {
@@ -363,7 +363,7 @@
    "metadata": {},
    "source": [
     "## Adding components\n",
-    "One adds components to the instrument using *add_component* which takes the name of the component instance for the instrument, followed by the name of the component in the library. Notice that it is not allowed to add two components with the same instance name, meaning rerunning this cell would raise an exception. "
+    "One adds components to the instrument using *add_component* which takes the name of the component instance for the instrument, followed by the name of the component in the library. When adding a component, a [component](../_autosummary/mcstasscript.helper.mcstas_objects.Component.rst) object is returned, and how these can be manipulated is discussed on the [component object page](component_object.ipynb). Notice that it is not allowed to add two components with the same instance name, meaning rerunning this cell would raise an exception. "
    ]
   },
   {
@@ -426,7 +426,7 @@
    "id": "outdoor-motor",
    "metadata": {},
    "source": [
-    "There are a number of keyword arguments allowed when adding a component. These will mainly be discussed in connection to the *component* object, but a few are relevant for the instrument, because the handle in what order components are sequenced in the instrument. To illustrate this we add a slit and a guide to the instrument at reasonable positions. Notice these new components are added at the end of the instrument."
+    "There are a number of keyword arguments allowed when adding a component. These will mainly be discussed on the [component object page](component_object.ipynb), but a few are relevant for the instrument, because the handle in what order components are sequenced in the instrument. To illustrate this we add a slit and a guide to the instrument at reasonable positions. Notice these new components are added at the end of the instrument."
    ]
   },
   {
@@ -628,7 +628,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/user_guide/instr_name_data_6\"\n",
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/user_guide/instr_name_data_23\"\n",
       "INFO: Regenerating c-file: instr_name.c\n",
       "CFLAGS=\n",
       "INFO: Recompiling: ./instr_name.out\n",
@@ -640,7 +640,7 @@
       "  ^~~\n",
       "2 warnings generated.\n",
       "INFO: ===\n",
-      "INFO: Placing instr file copy instr_name.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/user_guide/instr_name_data_6\n",
+      "INFO: Placing instr file copy instr_name.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/user_guide/instr_name_data_23\n",
       "\n",
       "Simulation 'instr_name' (instr_name.instr): running on 4 nodes (master is 'CI0021617', MPI version 3.1).\n",
       "Detector: PSD_I=0.114429 PSD_ERR=0.000144614 PSD_N=626113 \"PSD.dat\"\n",
@@ -692,7 +692,7 @@
    "metadata": {},
    "source": [
     "## Visualizing the instrument\n",
-    "It is possible to visualize the instrument using mcdisplay, yet this opens a new window and is not yet implemented in such a way that it is shown in the notebook. The *show_instrument* method is used, and one can choose the format. Be aware that the parameters set for the insturment is used for the visualization."
+    "It is possible to visualize the instrument using the visualization features in McStas / McXtrace. If done in a Jupyter Notebook, the visualization can be viewed in the notebook. The *show_instrument* method is used, and one can choose the format with the keyword *format* which can be either \"window\" or \"webgl\". When using window format a new window is opened with 2D visualization, and with the webgl format a 3D viewer is opened in a new browser tab. Be aware that the parameters set for the instrument is used for the visualization."
    ]
   },
   {
@@ -700,19 +700,145 @@
    "execution_count": 23,
    "id": "headed-simulation",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "loading system configuration\n",
+      "\n",
+      "INFO: Using directory: \"instr_name_mcdisplay\"\n",
+      "INFO: Regenerating c-file: instr_name.c\n",
+      "CFLAGS=\n",
+      "INFO: Recompiling: ./instr_name.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "*t0;\n",
+      "^~~\n",
+      "1 warning generated.\n",
+      "INFO: ===\n",
+      "instrument definition parsed\n",
+      "reading particle data...\n",
+      "\n",
+      "Component                         source AT (0,0,0)    0 m from origin\n",
+      "Component                            PSD AT (0,0,1.9)    1.9 m from origin\n",
+      "Component                    source_slit AT (0,0,2)    2 m from origin\n",
+      "Component                          guide AT (0,0,2.1)    2.1 m from origin\n",
+      "Component                        guide_2 AT (0,0,12.11)    12.11 m from origin\n",
+      "Detector: PSD_I=0.116358 PSD_ERR=0.00841937 PSD_N=191 \"PSD.dat\"\n",
+      "Thanks for using McStasScript!\n",
+      "loading system configuration\n",
+      "\n",
+      "starting particle parsing\n",
+      "ended particle parsing\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"800\"\n",
+       "            height=\"450\"\n",
+       "            src=\"./instr_name_mcdisplay/index.html\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x7f91502c15b0>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "instrument.show_instrument()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "italic-madagascar",
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/../tools/Python/mcdisplay/pyqtgraph/mcdisplay.py:292: DeprecationWarning: GraphicsWindow is deprecated, use GraphicsLayoutWidget instead,will be removed in 0.13\n",
+      "  window = pg.GraphicsWindow()\n",
+      "\n",
+      "loading system configuration\n",
+      "\n",
+      "INFO: Using directory: \"instr_name_mcdisplay_0\"\n",
+      "INFO: Regenerating c-file: instr_name.c\n",
+      "CFLAGS=\n",
+      "INFO: Recompiling: ./instr_name.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "*t0;\n",
+      "^~~\n",
+      "1 warning generated.\n",
+      "INFO: ===\n",
+      "instrument definition parsed\n",
+      "reading particle data...\n",
+      "\n",
+      "Component                         source AT (0,0,0)    0 m from origin\n",
+      "Component                            PSD AT (0,0,1.9)    1.9 m from origin\n",
+      "Component                    source_slit AT (0,0,2)    2 m from origin\n",
+      "Component                          guide AT (0,0,2.1)    2.1 m from origin\n",
+      "Component                        guide_2 AT (0,0,12.11)    12.11 m from origin\n",
+      "Detector: PSD_I=0.127933 PSD_ERR=0.0152909 PSD_N=70 \"PSD.dat\"\n",
+      "Thanks for using McStasScript!\n",
+      "loading system configuration\n",
+      "\n",
+      "starting particle parsing\n",
+      "ended particle parsing\n",
+      "\n",
+      "q            - quit\n",
+      "p            - save png\n",
+      "s            - save svg\n",
+      "space        - next ray\n",
+      "click        - enter subplot\n",
+      "right-click  - exit subplot\n",
+      "h/F1         - show component list\n",
+      "\n",
+      "q            - quit\n",
+      "p            - save png\n",
+      "s            - save svg\n",
+      "space        - next ray\n",
+      "click        - enter subplot\n",
+      "right-click  - exit subplot\n",
+      "h/F1         - show component list\n",
+      "wrapped C/C++ object of type ScatterPlotItem has been deleted\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.show_instrument(format=\"window\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "common-clause",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "instrument.show_instrument(format=\"webgl\")"
+    "When using the 3D view, use these controls to manipulate the view:\n",
+    "\n",
+    "| Action | Effect on view |\n",
+    "| --- | --- |\n",
+    "| Hold left click and drag | Rotate |\n",
+    "| Hold right click and drag | Move |\n",
+    "| Hold mouse wheel and drag up/down | Zoom in/out |\n"
    ]
   },
   {
@@ -726,7 +852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "casual-blame",
    "metadata": {},
    "outputs": [
@@ -736,7 +862,7 @@
        "'dump_file_name.dmp'"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -796,6 +922,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -812,6 +939,11 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.8.8"
+  },
+  "metadata": {
+   "execution": {
+    "timeout": 100
+   }
   }
  },
  "nbformat": 4,
diff --git a/docs/source/user_guide/instrument_reader.ipynb b/docs/source/user_guide/instrument_reader.ipynb
index 8a95e5e4..76f7f155 100644
--- a/docs/source/user_guide/instrument_reader.ipynb
+++ b/docs/source/user_guide/instrument_reader.ipynb
@@ -6,15 +6,170 @@
    "metadata": {},
    "source": [
     "# Instrument reader\n",
-    "How to use instrument reader (not it is unfinished)"
+    "When McStasScript performs a McStas simulation, a traditional instrument file is generated and used for the simulation. In that way a user can easily go from a McStasScript instrument description to a regular instrument file. McStasScript does contain an instrument reader that can attempt to do the reverse, read a regular instrument file and create the McStasScript object or even a file with the python code that would create such an object. The [instrument reader](../_autosummary/mcstasscript.interface.reader.rst) contains the interface, but the system is not yet finished and can be somewhat unreliable. \n",
+    "\n",
+    "The instrument reader is a good way to transfer a project to McStasScript, but may require fixing of a few issues created in the transfer process."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "neutral-moore",
+   "metadata": {},
+   "source": [
+    "## Create a reader object\n",
+    "The first step is to create a reader object with [*McStas_file*](../_autosummary/mcstasscript.interface.reader.McStas_file.rst), this requires just a path to the instrument file."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "alpine-yeast",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter, reader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "burning-romance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument_reader = reader.McStas_file(\"data_example.instr\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "corporate-offer",
+   "metadata": {},
+   "source": [
+    "## Create instrument object\n",
+    "The [*McStas_file*](../_autosummary/mcstasscript.interface.reader.McStas_file.rst) object contains a method called *add_to_instr* that can add the instrument information to an [instrument](../_autosummary/mcstasscript.interface.instr.McCode_instr) object. Here a [McStas_instr](../_autosummary/mcstasscript.interface.instr.McStas_instr) object is initialized, and then the information to the instrument file is added with the instrument reader object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "living-inflation",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "double wavelength  = 1.5  \n",
+      "source Source_simple AT (0, 0, 0)   ABSOLUTE       \n",
+      "sample PowderN       AT (0, 0, 5)   RELATIVE source\n",
+      "banana Monitor_nD    AT (0, 0, 0)   RELATIVE sample\n",
+      "PSD    PSD_monitor   AT (0, 0, 0.1) RELATIVE sample\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument = instr.McStas_instr(\"reader_test\")\n",
+    "instrument_reader.add_to_instr(instrument)\n",
+    "\n",
+    "instrument.show_parameters()\n",
+    "instrument.print_components()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "modern-happiness",
+   "metadata": {},
+   "source": [
+    "## Create a python script file\n",
+    "It is also possible to get a script file from the [McStas_file](../_autosummary/mcstasscript.interface.reader.McStas_file.rst) object. A name for the script needs to be provided. The force keyword can be used to overwrite the file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "australian-possession",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument_reader.write_python_file(\"generated_python_script.py\", force=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "religious-lotus",
+   "metadata": {},
+   "source": [
+    "The generated file is loaded and displayed to show that the python script is of reasonable quality, but still has room for improvement. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "operational-earthquake",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\"\"\"\n",
+      "This McStasScript file was generated from a\n",
+      "McStas instrument file. It is advised to check\n",
+      "the content to ensure it is as expected.\n",
+      "\"\"\"\n",
+      "from mcstasscript.interface import instr, plotter, functions\n",
+      "\n",
+      "data_example = instr.McStas_instr(\"data_example_generated\")\n",
+      "data_example.add_parameter(\"double\", \"wavelength\", value=1.5)\n",
+      "\n",
+      "source = data_example.add_component(\"source\", \"Source_simple\")\n",
+      "source.yheight = 0.03\n",
+      "source.xwidth = 0.05\n",
+      "source.dist = 5\n",
+      "source.focus_xw = 0.015\n",
+      "source.focus_yh = 0.03\n",
+      "source.lambda0 = \"wavelength\"\n",
+      "source.dlambda = 0.02\n",
+      "source.set_AT(['0', '0', '0'], RELATIVE=\"ABSOLUTE\")\n",
+      "\n",
+      "sample = data_example.add_component(\"sample\", \"PowderN\")\n",
+      "sample.reflections = \"\\\"Na2Ca3Al2F14.laz\\\"\"\n",
+      "sample.radius = 0.015\n",
+      "sample.yheight = 0.024\n",
+      "sample.set_AT(['0', '0', '5'], RELATIVE=\"source\")\n",
+      "\n",
+      "banana = data_example.add_component(\"banana\", \"Monitor_nD\")\n",
+      "banana.xwidth = 1.5\n",
+      "banana.yheight = 0.4\n",
+      "banana.restore_neutron = 1\n",
+      "banana.options = \"\\\"theta limits=[5 175] bins=250, banana\\\"\"\n",
+      "banana.filename = \"\\\"banana.dat\\\"\"\n",
+      "banana.set_AT(['0', '0', '0'], RELATIVE=\"sample\")\n",
+      "\n",
+      "PSD = data_example.add_component(\"PSD\", \"PSD_monitor\")\n",
+      "PSD.nx = 100\n",
+      "PSD.ny = 100\n",
+      "PSD.filename = \"\\\"psd.dat\\\"\"\n",
+      "PSD.xwidth = 0.075\n",
+      "PSD.yheight = 0.048\n",
+      "PSD.restore_neutron = 1\n",
+      "PSD.set_AT(['0', '0', '0.1'], RELATIVE=\"sample\")\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"generated_python_script.py\") as file:\n",
+    "    script = file.read()\n",
+    "print(script)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "expected-lambda",
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],
diff --git a/docs/source/user_guide/laue_example.py b/docs/source/user_guide/laue_example.py
new file mode 100644
index 00000000..b905beeb
--- /dev/null
+++ b/docs/source/user_guide/laue_example.py
@@ -0,0 +1,48 @@
+from mcstasscript.interface import instr, functions, plotter
+
+def create_data(path):
+    instrument = instr.McStas_instr("laue_example", output_path=path)
+    
+    source = instrument.add_component("source", "Source_simple")
+    source.set_parameters(xwidth=0.01, yheight=0.01,
+                          dist=5, focus_xw=0.01, focus_yh=0.01)
+    source.lambda0 = instrument.add_parameter("wavelength", value=3.0)
+    source.dlambda = "0.95*wavelength"
+
+    sample = instrument.add_component("sample", "Single_crystal")
+    sample.set_parameters(radius=source.focus_xw, yheight=source.focus_yh,
+                          reflections='"YBaCuO.lau"', mosaic=30)
+    sample.set_AT(source.dist, RELATIVE=source)
+    sample.set_ROTATED([8, 4, 0])
+                      
+    mon = instrument.add_component("PSD", "PSD_monitor")
+    mon.set_AT(source.dist + 0.1, RELATIVE=source)
+    mon.set_parameters(nx=300, ny=300, filename='"psd.dat"',
+                       xwidth=1.0, yheight=1.0,
+                       restore_neutron=1)
+                       
+    lmon = instrument.add_component("div_lambda_h", "DivLambda_monitor")
+    lmon.set_AT(0, RELATIVE=mon)
+    lmon.set_parameters(nL=100, nh=70, maxdiv_h=80, filename='"div_lambda_h.dat"',
+                        Lmin="0.05*wavelength", Lmax="1.95*wavelength",
+                        xwidth=mon.xwidth, yheight=mon.yheight,
+                        restore_neutron=1)
+                        
+    lmon = instrument.add_component("div_lambda_v", "DivLambda_monitor")
+    lmon.set_AT(0, RELATIVE=mon)
+    lmon.set_ROTATED([0, 0, 90], RELATIVE=mon)
+    lmon.set_parameters(nL=100, nh=70, maxdiv_h=80, filename='"div_lambda_v.dat"',
+                        Lmin="0.05*wavelength", Lmax="1.95*wavelength",
+                        xwidth=mon.xwidth, yheight=mon.yheight,
+                        restore_neutron=1)
+                        
+    lmon = instrument.add_component("lambda", "L_monitor")
+    lmon.set_AT(0, RELATIVE=mon)
+    lmon.set_parameters(nL=300, filename='"lambda_transmission.dat"',
+                        Lmin="0.05*wavelength", Lmax="1.95*wavelength",
+                        xwidth=0.015, yheight=0.015,
+                        restore_neutron=1)
+
+    instrument.settings(ncount=5E6)
+    instrument.backengine()
+    return instrument.data
diff --git a/docs/source/user_guide/parameters_and_variables.ipynb b/docs/source/user_guide/parameters_and_variables.ipynb
index 6e455703..d1cd5b62 100644
--- a/docs/source/user_guide/parameters_and_variables.ipynb
+++ b/docs/source/user_guide/parameters_and_variables.ipynb
@@ -6,7 +6,7 @@
    "metadata": {},
    "source": [
     "# Parameters and variables\n",
-    "At the instrument level there is a destingsion between parameters and variables. Parameters are inputs to the instrument that can be changed at run time, while variables are defined internally within the insturment. This section of the documentation covers these two as they have some similarities."
+    "At the instrument level there is a distinction between parameters and variables. Parameters are inputs to the instrument that can be changed at run time, while variables are defined internally within the instrument. This section of the documentation covers these two as they have some similarities."
    ]
   },
   {
@@ -15,12 +15,12 @@
    "metadata": {},
    "source": [
     "## Parameters\n",
-    "Instrument parameters are built on the libpyvinyl parameters, and have some interesting features. First a simple parameter is added."
+    "Instrument parameters are built on the libpyvinyl parameters, and have some interesting features. First a simple [parameter](../_autosummary/mcstasscript.helper.mcstas_objects.ParameterVariable.rst) is added."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "acceptable-emperor",
    "metadata": {},
    "outputs": [
@@ -46,12 +46,12 @@
    "id": "alive-cholesterol",
    "metadata": {},
    "source": [
-    "It is a good habit to add a comment to each parameter when creating it. Since two parameters can not have the same name, creates "
+    "It is a good habit to add a comment to each [parameter](../_autosummary/mcstasscript.helper.mcstas_objects.ParameterVariable.rst) when creating it. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "adverse-default",
    "metadata": {},
    "outputs": [],
@@ -319,7 +319,7 @@
    "metadata": {},
    "source": [
     "## Declared variables\n",
-    "It is possible to declare variables that are used internally in the instrument and as such not exposed to the user. This is done thrugh the instrument objects *add_declare_var* method."
+    "It is possible to declare variables that are used internally in the instrument and as such not exposed to the user. This is done through the instrument objects *add_declare_var* method. It returns a [DeclareVariable](../_autosummary/mcstasscript.helper.mcstas_objects.DeclareVariable.rst) object that can be used to refer to this variable."
    ]
   },
   {
@@ -346,9 +346,7 @@
    "id": "valuable-sigma",
    "metadata": {},
    "source": [
-    "There are no restrictions on the type for declared variables, typically double and int is used.\n",
-    "\n",
-    "Declared variables have no methods for additional input, all information must be given at initialization."
+    "There are no restrictions on the type for declared variables, typically double and int is used. Declared variables have no methods for additional input, all information must be given at initialization."
    ]
   },
   {
diff --git a/docs/source/user_guide/plotting.ipynb b/docs/source/user_guide/plotting.ipynb
index 60fa5924..e1debe7c 100644
--- a/docs/source/user_guide/plotting.ipynb
+++ b/docs/source/user_guide/plotting.ipynb
@@ -6,19 +6,303 @@
    "metadata": {},
    "source": [
     "# Plotting\n",
-    "All there is to know about plotting"
+    "McStasScript includes a [plotting](../_autosummary/mcstasscript.interface.plotter.rst) module meant for plotting McStasScript data objects. \n",
+    "\n",
+    "## Example instrument\n",
+    "First an example instrument is constructed to provide some data for plotting examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "perfect-hours",
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter\n",
+    "instrument = instr.McStas_instr(\"data_example\")\n",
+    "\n",
+    "source = instrument.add_component(\"source\", \"Source_simple\")\n",
+    "source.set_parameters(xwidth=0.05, yheight=0.03, dlambda=0.02,\n",
+    "                      dist=5, focus_xw=0.015, focus_yh=0.03)\n",
+    "source.lambda0 = instrument.add_parameter(\"wavelength\", value=2.0)\n",
+    "\n",
+    "sample = instrument.add_component(\"sample\", \"PowderN\")\n",
+    "sample.set_parameters(radius=source.focus_xw, yheight=0.8*source.focus_yh,\n",
+    "                      reflections='\"Na2Ca3Al2F14.laz\"')\n",
+    "sample.set_AT(source.dist, RELATIVE=source)\n",
+    "\n",
+    "banana = instrument.add_component(\"banana\", \"Monitor_nD\", RELATIVE=sample)\n",
+    "banana.set_parameters(xwidth=1.5, yheight=0.4, restore_neutron=1, filename='\"banana.dat\"')\n",
+    "banana.options = '\"theta limits=[5 175] bins=250, banana\"'\n",
+    "\n",
+    "mon = instrument.add_component(\"PSD\", \"PSD_monitor\")\n",
+    "mon.set_AT(0.1, RELATIVE=sample)\n",
+    "mon.set_parameters(nx=100, ny=100, filename='\"psd.dat\"',\n",
+    "                   xwidth=5*sample.radius, yheight=2*sample.yheight, restore_neutron=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "catholic-distribution",
+   "metadata": {},
+   "source": [
+    "### Performing the simulation\n",
+    "Here the simulation is performed to provide the example data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "general-chance",
+   "metadata": {
+    "scrolled": true,
+    "tags": [
+     "scroll-output",
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/user_guide/plotting_example_12\"\n",
+      "INFO: Regenerating c-file: data_example.c\n",
+      "CFLAGS=\n",
+      "INFO: Recompiling: ./data_example.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "1 warning generated.\n",
+      "INFO: ===\n",
+      "Warning: 263901 events were removed in Component[4] PSD=PSD_monitor()\n",
+      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
+      "INFO: Placing instr file copy data_example.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/user_guide/plotting_example_12\n",
+      "\n",
+      "      Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "Detector: banana_I=1.13353e-07 banana_ERR=8.60125e-10 banana_N=61381 \"banana.dat\"\n",
+      "Detector: PSD_I=9.76188e-06 PSD_ERR=2.05626e-08 PSD_N=570953 \"psd.dat\"\n",
+      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
+      "    SPLIT 263 COMPONENT sample=PowderN(...)\n",
+      "  in the instrument description data_example.instr.\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.settings(ncount=1E6, output_path=\"plotting_example\")\n",
+    "instrument.set_parameters(wavelength=1.5)\n",
+    "instrument.backengine()\n",
+    "data = instrument.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "retired-tiffany",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "McStasData: banana type: 1D  I:1.13353e-07 E:8.60125e-10 N:61381, \n",
+      "McStasData: PSD type: 2D  I:9.76188e-06 E:2.05626e-08 N:570953]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "alpine-arnold",
+   "metadata": {},
+   "source": [
+    "## make_plot and make_sub_plot\n",
+    "The first available plotter is [*make_plot*](../_autosummary/mcstasscript.interface.plotter.make_plot.rst), it makes a single figure for each [McStasData](../_autosummary/mcstasscript.data.data.McStasData.rst) set given in the input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "literary-costs",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name banana\n",
+      "Plotting data with name PSD\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotter.make_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "norwegian-growth",
+   "metadata": {},
+   "source": [
+    "The second available plotter is [*make_sub_plot*](../_autosummary/mcstasscript.interface.plotter.make_sub_plot.rst), it makes a single figure that includes plot of all given [McStasData](../_autosummary/mcstasscript.data.data.McStasData.rst) as subplots. This works best with between 1 and 9 monitors, as more tend to become difficult to read."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "pretty-transcription",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name banana\n",
+      "Plotting data with name PSD\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotter.make_sub_plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "behind-pleasure",
+   "metadata": {},
+   "source": [
+    "## Customizing plots\n",
+    "As described in the data section of the documentation, the [McStasData](../_autosummary/mcstasscript.data.data.McStasData.rst) objects contain preferences on how they should be plotted. The same keywords can however be given to the plotters to override these preferences. When plotting a list of [McStasData](../_autosummary/mcstasscript.data.data.McStasData.rst) objects, a list can be given for each keyword."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "natural-transition",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name banana\n",
+      "Plotting data with name PSD\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotter.make_sub_plot(data, log=[False, True], fontsize=15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "backed-mirror",
+   "metadata": {},
+   "source": [
+    "### Available keywords\n",
+    "The keywords available to customize plotting are the same as can be set for *plot_options* in [McStasData](../_autosummary/mcstasscript.data.data.McStasData.rst). There are however a few additional keywords for controlling figure size and fontsize.\n",
+    "\n",
+    "| Keyword argument | Type | Default | Description |\n",
+    "| --- | --- | --- | --- |\n",
+    "| figsize | tuple | (13, 7) | Tuple describing figure size |\n",
+    "| fontsize | int | 11 | Fontsize to use in plotting |\n",
+    "| log | bool | False | Logarithmic axis for y in 1D or z in 2D |\n",
+    "| orders_of_mag | float | 300 | Maximum orders of magnitude to plot in 2D |\n",
+    "| colormap | str | \"jet\" | Matplotlib colormap to use |\n",
+    "| show_colorbar | bool | True | Show the colorbar |\n",
+    "| x_axis_multiplier | float | 1 | Multiplier for x axis data |\n",
+    "| y_axis_multiplier | float | 1 | Multiplier for y axis data |\n",
+    "| cut_min | float | 0 | Unitless lower limit normalized to data range |\n",
+    "| cut_max | float | 1 | Unitless upper limit normalized to data range |\n",
+    "| left_lim | float | | Lower limit to plot range of x axis |\n",
+    "| right_lim | float | | Upper limit to plot range of x axis|\n",
+    "| bottom_lim | float | | Lower limit to plot range of y axis|\n",
+    "| top_lim | float | | Upper limit to plot range of y axis|"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "initial-enlargement",
+   "id": "declared-welsh",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dedicated-addiction",
    "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -35,6 +319,11 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.8.8"
+  },
+  "metadata": {
+   "execution": {
+    "timeout": 100
+   }
   }
  },
  "nbformat": 4,
diff --git a/mcstasscript/data/McStasDataFormat.py b/mcstasscript/data/McStasDataFormat.py
new file mode 100644
index 00000000..296f8da7
--- /dev/null
+++ b/mcstasscript/data/McStasDataFormat.py
@@ -0,0 +1,51 @@
+from libpyvinyl.BaseFormat import BaseFormat
+from mcstasscript.helper.managed_mcrun import load_results
+
+
+class McStasFormat(BaseFormat):
+    def __init__(self) -> None:
+        super().__init__()
+
+    @classmethod
+    def format_register(self):
+        key = "sim"
+        desciption = "sim format for McStasData"
+        file_extension = ".sim"
+        read_kwargs = [""]
+        write_kwargs = [""]
+        return self._create_format_register(
+            key, desciption, file_extension, read_kwargs, write_kwargs
+        )
+
+    @staticmethod
+    def direct_convert_formats():
+        # Assume the format can be converted directly to the formats supported by these classes:
+        # AFormat, BFormat
+        # Redefine this `direct_convert_formats` for a concrete format class
+        return []
+
+    @classmethod
+    def read(cls, filename: str) -> dict:
+        """Read the data from the file with the `filename` to a dictionary. The dictionary will
+        be used by its corresponding data class."""
+
+        data = load_results(filename)
+        data_dict = {"data": data}
+        return data_dict
+
+    @classmethod
+    def write(cls, object, filename: str, key: str = None):
+        """Don't have a way to write McStasData"""
+        pass
+
+    """
+    #def write(cls, object: NumberData, filename: str, key: str = None):
+        #Save the data with the `filename`.
+        data_dict = object.get_data()
+        arr = np.array([data_dict["number"]])
+        np.savetxt(filename, arr, fmt="%.3f")
+        if key is None:
+            original_key = object.key
+            key = original_key + "_to_TXTFormat"
+        return object.from_file(filename, cls, key)
+    """
\ No newline at end of file
diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index 36d0c306..7455662b 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -57,6 +57,16 @@ def __init__(self):
         """Creating a new instance, no parameters"""
         self.info = {}
 
+        self.component_name = None
+        self.parameters = None
+        self.filename = None
+        self.dimension = None
+        self.limits = []
+
+        self.xlabel = None
+        self.ylabel = None
+        self.title = None
+
     def add_info(self, key, value):
         """Adding information to info dict"""
         self.info[key] = value
@@ -135,6 +145,48 @@ def set_ylabel(self, string):
         """Sets ylabel for plotting"""
         self.ylabel = string
 
+    def __repr__(self):
+        string = "metadata object\n"
+        if self.component_name is not None:
+            string += "component_name: " + self.component_name + "\n"
+
+        if self.filename is not None:
+            string += "filename: " + str(self.filename) + "\n"
+
+        if self.dimension is not None:
+            if isinstance(self.dimension, int):
+                string += "1D data of length " + str(self.dimension) + "\n"
+                if self.limits is not None:
+                    string += "  [" + str(self.limits[0]) + ": "
+                    string += str(self.limits[1]) + "]"
+                if self.xlabel is not None:
+                    string += " " + self.xlabel + "\n"
+                if self.ylabel is not None:
+                    string += " " + self.ylabel + "\n"
+
+            elif len(self.dimension) == 2:
+                string += "2D data of dimension (" + str(self.dimension[0])
+                string += ", " + str(self.dimension[1]) + ")\n"
+                if self.xlabel is not None:
+                    if self.limits is not None:
+                        string += "  [" + str(self.limits[0]) + ": "
+                        string += str(self.limits[1]) + "]"
+                    string += " " + self.xlabel + "\n"
+
+                if self.ylabel is not None:
+                    if self.limits is not None and len(self.limits) == 4:
+                        string += "  [" + str(self.limits[2]) + ": "
+                        string += str(self.limits[3]) + "]"
+                    string += " " + self.ylabel + "\n"
+
+        if self.parameters is not None and len(self.parameters)>0:
+            string += "Instrument parameters: \n"
+            for key in self.parameters:
+                string += " " + str(key) + " = "
+                string += str(self.parameters[key]) + "\n"
+
+        return string
+
 
 class McStasPlotOptions:
     """
@@ -202,6 +254,11 @@ def __init__(self, *args, **kwargs):
         self.custom_xlim_left = False
         self.custom_xlim_right = False
 
+        self.top_lim = None
+        self.bottom_lim = None
+        self.left_lim = None
+        self.right_lim = None
+
     def set_options(self, **kwargs):
         """
         Set custom values for plotting preferences
@@ -319,6 +376,35 @@ def set_options(self, **kwargs):
                 raise ValueError("right_lim has to be a number, was "
                                  + "given: " + str(self.right_lim))
 
+    def __repr__(self):
+
+        string = "plot_options"
+
+        string += " log: " + str(self.log) + "\n"
+        if self.log:
+            string += " orders_of_mag: " + str(self.orders_of_mag) + "\n"
+
+        string += " colormap: " + str(self.colormap) + "\n"
+        string += " show_colorbar: " + str(self.show_colorbar) + "\n"
+        string += " cut_min: " + str(self.cut_min) + "\n"
+        string += " cut_max: " + str(self.cut_max) + "\n"
+        string += " x_limit_multiplier: " + str(self.x_limit_multiplier) + "\n"
+        string += " y_limit_multiplier: " + str(self.y_limit_multiplier) + "\n"
+
+        if self.custom_xlim_left:
+            string += "manual x lower limit: " + str(self.left_lim)
+
+        if self.custom_xlim_right:
+            string += "manual x upper limit: " + str(self.right_lim)
+
+        if self.custom_ylim_bottom:
+            string += "manual y lower limit: " + str(self.bottom_lim)
+
+        if self.custom_ylim_bottom:
+            string += "manual y upper limit: " + str(self.top_lim)
+
+        return string
+
 
 class McStasData:
     """
diff --git a/mcstasscript/data/pyvinylData.py b/mcstasscript/data/pyvinylData.py
new file mode 100644
index 00000000..1a261fbd
--- /dev/null
+++ b/mcstasscript/data/pyvinylData.py
@@ -0,0 +1,51 @@
+from libpyvinyl.BaseData import BaseData
+from mcstasscript.data.McStasDataFormat import McStasFormat
+
+
+class pyvinylMcStasData(BaseData):
+    def __init__(
+        self,
+        key,
+        data_dict=None,
+        filename=None,
+        file_format_class=None,
+        file_format_kwargs=None,
+    ):
+
+        expected_data = {}
+
+        ### DataClass developer's job start
+        expected_data["data"] = None
+        ### DataClass developer's job end
+
+        super().__init__(
+            key,
+            expected_data,
+            data_dict,
+            filename,
+            file_format_class,
+            file_format_kwargs,
+        )
+
+    @classmethod
+    def supported_formats(self):
+        format_dict = {}
+        ### DataClass developer's job start
+        self._add_ioformat(format_dict, McStasFormat)
+        ### DataClass developer's job end
+        return format_dict
+
+    @classmethod
+    def from_file(cls, filename: str, format_class, key, **kwargs):
+        """Create the data class by the file in the `format`."""
+        return cls(
+            key,
+            filename=filename,
+            file_format_class=format_class,
+            file_format_kwargs=kwargs,
+        )
+
+    @classmethod
+    def from_dict(cls, data_dict, key):
+        """Create the data class by a python dictionary."""
+        return cls(key, data_dict=data_dict)
diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index bc91fbe6..ce6d72d5 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -434,6 +434,21 @@ def write_line(self, fo):
                     fo.write("%G," % self.value[i])
                 fo.write("%G};%s" % (self.value[-1], self.comment))
 
+    def __repr__(self):
+        string = "Declare variable: '"
+        string += str(self.name)
+        string += "' of type "
+        string += str(self.type)
+
+        if self.value != "":
+            string += " with value: "
+            string += str(self.value)
+
+        if self.vector != 0:
+            string += ". Array with length "
+            string += str(self.vector)
+
+        return string
 
 class Component:
     """
@@ -1121,7 +1136,7 @@ class is used as a superclass for classes describing each
         if len(self.c_code_after) > 1:
             string += self.c_code_after + "\n"
 
-        return string
+        return string.strip()
 
     def print_long(self):
         """
@@ -1220,7 +1235,12 @@ def show_parameters(self, **kwargs):
                 characters_from_value = 3 + len(this_default)
 
             if getattr(self, parameter) is not None:
-                this_set_value = str(getattr(self, parameter))
+                parameter_input = getattr(self, parameter)
+                # Use name when an par/var object is found
+                if isinstance(parameter_input, (ParameterVariable, DeclareVariable)):
+                    parameter_input = parameter_input.name
+
+                this_set_value = str(parameter_input)
                 value = (" = "
                          + bcolors.BOLD
                          + bcolors.OKGREEN
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index a03b825a..767bd644 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -7,9 +7,13 @@
 import copy
 import warnings
 
+from IPython.display import IFrame
+
 from libpyvinyl.BaseCalculator import BaseCalculator
 from libpyvinyl.Parameters.Collections import CalculatorParameters
 
+from mcstasscript.data.pyvinylData import pyvinylMcStasData
+
 from mcstasscript.helper.mcstas_objects import DeclareVariable
 from mcstasscript.helper.mcstas_objects import ParameterVariable
 from mcstasscript.helper.mcstas_objects import Component
@@ -220,7 +224,7 @@ class McCode_instr(BaseCalculator):
         Returns data set from latest simulation in widget
     """
 
-    def __init__(self, name, parameters=None, dumpfile=None, executable=None, **kwargs):
+    def __init__(self, name, parameters=None, dumpfile=None, executable=None, author=None,  **kwargs):
         """
         Initialization of McStas Instrument
 
@@ -258,12 +262,12 @@ def __init__(self, name, parameters=None, dumpfile=None, executable=None, **kwar
                 Number of mpi threads to use in simulation
         """
 
-        super().__init__(name, parameters, dumpfile, **kwargs)
-
-        # If no parameters given, initialize parameter container
-        if self.parameters is None:
-            self.parameters = ParameterContainer()
+        super().__init__(name, input=[],
+                         output_keys=["simulation_data"],
+                         output_data_types=[pyvinylMcStasData],
+                         parameters=parameters)
 
+        # Libpyvinyl uses CalculatorParameters, replace with ParameterContainer
         if not isinstance(self.parameters, ParameterContainer):
             # Need to convert McStasScript parameters
             if isinstance(self.parameters, CalculatorParameters):
@@ -340,6 +344,12 @@ def __init__(self, name, parameters=None, dumpfile=None, executable=None, **kwar
         if "mpi" in kwargs:
             provided_run_settings["mpi"] = kwargs["mpi"]
 
+        if "seed" in kwargs:
+            provided_run_settings["seed"] = str(kwargs["seed"])
+
+        if "custom_flags" in kwargs:
+            provided_run_settings["custom_flags"] = kwargs["custom_flags"]
+
         # Set run_settings, perform input sanitation
         self.settings(**provided_run_settings)
 
@@ -369,6 +379,12 @@ def __init__(self, name, parameters=None, dumpfile=None, executable=None, **kwar
             self.component_list = []  # List of components (have to be ordered)
             self.component_name_list = []  # List of component names
 
+    def init_parameters(self):
+        """
+        Create empty ParameterContainer for new instrument
+        """
+        self.parameters = ParameterContainer()
+
     def _read_calibration(self):
         """
         Place holder method that should be overwritten by classes
@@ -415,7 +431,7 @@ def add_parameter(self, *args, **kwargs):
         self.parameters.add(par)
         return par
 
-    def show_parameters(self, **kwargs):
+    def show_parameters(self, line_length=None):
         """
         Method for displaying current instrument parameters
 
@@ -423,12 +439,65 @@ def show_parameters(self, **kwargs):
             line_length : int
                 Maximum line length for terminal output
         """
-        if "line_length" in kwargs:
-            line_limit = kwargs["line_length"]
-        else:
-            line_limit = self.line_limit
+        if line_length is None:
+            line_length = self.line_limit
+
+        self.parameters.show_parameters(line_length)
+
+    def show_variables(self):
+        """
+        Shows declared variables in instrument
+        """
+
+        type_heading = "type"
+        variable_types = [x.type for x in self.declare_list]
+        variable_types.append(type_heading)
+        max_type_length = len(max(variable_types, key=len))
 
-        self.parameters.show_parameters(line_limit)
+        name_heading = "variable name"
+        variable_names = [x.name for x in self.declare_list]
+        variable_names.append(name_heading)
+        max_name_length = len(max(variable_names, key=len))
+
+        value_heading = "value"
+        variable_values = [str(x.value) for x in self.declare_list]
+        variable_values.append(value_heading)
+        max_value_length = len(max(variable_values, key=len))
+
+        vector_heading = "array length"
+        variable_vector = [str(x.vector) for x in self.declare_list]
+        variable_vector.append(vector_heading)
+        max_vector_length = len(max(variable_vector, key=len))
+
+        padding = 2
+        string = ""
+        string += type_heading.ljust(max_type_length + padding)
+        string += name_heading.ljust(max_name_length + padding)
+        string += value_heading.ljust(max_value_length + padding)
+        string += vector_heading.ljust(max_vector_length + padding)
+        string += "\n"
+        string += "-"*(max_type_length + max_name_length + max_value_length
+                       + max_vector_length + 3*padding)
+        string += "\n"
+
+        for variable in self.declare_list:
+            string += str(variable.type).ljust(max_type_length + padding)
+            string += str(variable.name).ljust(max_name_length + padding)
+
+            if variable.value != "":
+                value_string = str(variable.value)
+            else:
+                value_string = ""
+            string += value_string.ljust(max_value_length + padding)
+
+            if variable.vector != 0:
+                vector_string = str(variable.vector)
+            else:
+                vector_string = ""
+            string += vector_string.ljust(max_vector_length + padding)
+            string += "\n"
+
+        print(string)
 
     def add_declare_var(self, *args, **kwargs):
         """
@@ -457,6 +526,13 @@ def add_declare_var(self, *args, **kwargs):
 
         # DeclareVariable class documented independently
         declare_par = DeclareVariable(*args, **kwargs)
+
+        names = [x.name for x in self.declare_list
+                 if isinstance(x, DeclareVariable)]
+        if declare_par.name in names:
+            raise NameError("Variable with name '" + declare_par.name
+                            + "' already present in instrument!")
+        
         self.declare_list.append(declare_par)
         return declare_par
 
@@ -1517,69 +1593,6 @@ def write_full_instrument(self):
 
         fo.close()
 
-    def _handle_parameters(self, given_parameters):
-        """
-        Internal helper function that handles which parameters to pass
-        when given a certain set of parameters and values.
-
-        Adds the given parameters to the default parameters, and ensures all
-        required parameters are provided. Also checks all given parameters
-        match an existing parameter.
-
-        Parameters
-        ----------
-        given_parameters: dict
-            Parameters given by the user for simulation run
-        """
-
-        if not isinstance(given_parameters, dict):
-            raise RuntimeError("Given parameters must be a dict.")
-
-        # Find required parameters
-        required_parameters = []
-        default_parameters = {}
-
-        for parameter in self.parameters:
-            if parameter.value is None:
-                required_parameters.append(parameter.name)
-            else:
-                default_parameters.update({parameter.name: parameter.value})
-
-        # Check if all given parameters correspond to legal parameters
-        for given_par in given_parameters:
-            if not isinstance(given_par, str):
-                raise NameError("Given parameter must be a string.")
-            if (given_par not in required_parameters
-                    and given_par not in default_parameters):
-                raise NameError("Given parameter: \"" + str(given_par)
-                                + "\" did not match any in instrument. "
-                                + "Currently available parameters: \n"
-                                + "  Required parameters:"
-                                + str(required_parameters) + "\n"
-                                + "  Default parameters: "
-                                + str(list(default_parameters.keys())))
-
-        # Check if required parameters are provided
-        if len(given_parameters) == 0:
-            if len(required_parameters) > 0:
-                # print required parameters and raise error
-                print("Required instrument parameters:")
-                for name in required_parameters:
-                    print("  " + name)
-                raise NameError("Required parameters not provided.")
-            else:
-                # If all parameters have defaults, just run with the defaults.
-                return default_parameters
-        else:
-            for name in required_parameters:
-                if name not in given_parameters:
-                    raise NameError("The required instrument parameter "
-                                    + str(name)
-                                    + " was not provided.")
-            # Overwrite default parameters with given parameters
-            default_parameters.update(given_parameters)
-            return default_parameters
-
     def settings(self, ncount=None, mpi="not_set", seed=None,
                  force_compile=None, output_path=None,
                  increment_folder_name=None, custom_flags=None,
@@ -1678,6 +1691,11 @@ def settings_string(self):
             description += "  mpi:".ljust(variable_space)
             description += str(int(value)) + "\n"
 
+        if "seed" in self._run_settings:
+            value = self._run_settings["seed"]
+            description += "  seed:".ljust(variable_space)
+            description += str(int(value)) + "\n"
+
         description += "  output_path:".ljust(variable_space)
         description += str(self.output_path) + "\n"
 
@@ -1721,8 +1739,7 @@ def show_settings(self):
 
     def backengine(self):
         """
-        Runs McStas instrument described by this class, saves data in data
-        attribute
+        Runs instrument with McStas / McXtrace, saves data in data attribute
 
         This method will write the instrument to disk and then run it using
         the mcrun command of the system. Settings are set using settings
@@ -1751,8 +1768,20 @@ def backengine(self):
         simulation.run_simulation(**self._run_settings)
 
         # Load data and store in __data
+        #data = simulation.load_results()
+        #self._set_data(data)
+
         data = simulation.load_results()
-        self._set_data(data)
+        data_dict = {"data": data}
+        key = self.output_keys[0]
+        output_data = self.output[key]
+        output_data.set_dict(data_dict)
+
+        if "data" not in self.output.get_data():
+            print("\n\nNo data returned.")
+            return None
+        else:
+            return self.output.get_data()["data"]
 
     def run_full_instrument(self, **kwargs):
         """
@@ -1801,20 +1830,32 @@ def run_full_instrument(self, **kwargs):
 
         self.settings(**kwargs)
 
-        self.backengine()
-        return self.data
+        return self.backengine()
 
-    def show_instrument(self, *args, **kwargs):
+    def show_instrument(self, format="webgl", width=800, height=450):
         """
         Uses mcdisplay to show the instrument in web browser
+
+        If this method is performed from a jupyter notebook and use the webgl
+        format the interface will be shown in the notebook using an IFrame.
+
+        Keyword arguments
+        -----------------
+            format : str
+                'webgl' or 'window' format for display
+            width : int
+                width of IFrame if used in notebook
+            height : int
+                height of IFrame if used in notebook
         """
 
-        if "parameters" in kwargs:
-            given_parameters = kwargs["parameters"]
-        else:
-            given_parameters = {}
+        parameters = {}
+        for parameter in self.parameters:
+            if parameter.value is None:
+                raise RuntimeError("Unspecified parameter: '" + parameter.name
+                                   + "' set with set_parameters.")
 
-        parameters = self._handle_parameters(given_parameters)
+            parameters[parameter.name] = parameter.value
 
         # add parameters to command
         parameter_string = ""
@@ -1826,12 +1867,20 @@ def show_instrument(self, *args, **kwargs):
 
         package_path = self._run_settings["package_path"]
         bin_path = os.path.join(package_path, "bin", "")
-        executable = "mcdisplay-webgl"
-        if "format" in kwargs:
-            if kwargs["format"] == "webgl":
-                executable = "mcdisplay-webgl"
-            elif kwargs["format"] == "window":
-                executable = "mcdisplay"
+
+        if format == "webgl":
+            executable = "mcdisplay-webgl"
+        elif format == "window":
+            executable = "mcdisplay"
+
+        dir_name_original = self.name + "_mcdisplay"
+        dir_name = dir_name_original
+        index = 0
+        while os.path.exists(os.path.join(self.input_path, dir_name)):
+            dir_name = dir_name_original + "_" + str(index)
+            index += 1
+
+        dir_control = "--dirname " + dir_name + " "
 
         self.write_full_instrument()
 
@@ -1839,6 +1888,7 @@ def show_instrument(self, *args, **kwargs):
         instr_path = os.path.abspath(instr_path)
 
         full_command = (bin_path + executable + " "
+                        + dir_control
                         + instr_path
                         + " " + parameter_string)
 
@@ -1847,8 +1897,29 @@ def show_instrument(self, *args, **kwargs):
                                  stderr=subprocess.PIPE,
                                  universal_newlines=True,
                                  cwd=self.input_path)
-        print(process.stderr)
-        print(process.stdout)
+
+        try:
+            shell = get_ipython().__class__.__name__
+            is_notebook = shell == "ZMQInteractiveShell"
+        except:
+            is_notebook = False
+
+        if not is_notebook or executable != "mcdisplay-webgl":
+            print(process.stderr)
+            print(process.stdout)
+            return
+
+        html_path = os.path.join(self.input_path, dir_name, "index.html")
+        if not os.path.exists(html_path):
+            print(process.stderr)
+            print(process.stdout)
+            print("")
+            print("mcdisplay run failed.")
+            return
+
+        # Create IFrame in ipython that shows instrument
+
+        return IFrame(src=html_path, width=width, height=height)
 
     def interface(self):
         """
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 48a87c99..77e0e891 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1999,7 +1999,8 @@ def test_show_instrument_basic(self, mock_sub, mock_stdout):
 
         instr = setup_populated_instr_with_dummy_path()
 
-        instr.show_instrument(parameters={"theta": 1.2})
+        instr.set_parameters(theta=1.2)
+        instr.show_instrument()
 
         os.chdir(current_work_dir)
 
@@ -2008,8 +2009,9 @@ def test_show_instrument_basic(self, mock_sub, mock_stdout):
 
         # a double space because of a missing option
         expected_call = (expected_path
+                         + " --dirname test_instrument_mcdisplay"
                          + " " + expected_instr_path
-                         + "  has_default=37 theta=1.2")
+                         + "  theta=1.2 has_default=37")
 
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,

From 74fdc15d07ad6987315e378bd5d1730d20b0a280 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Mon, 21 Mar 2022 14:17:46 +0100
Subject: [PATCH 224/403] First version of McStasScript working with something
 very close to the released libpyvinyl version. Have proposed the small
 necessary changes.

Need to get options from parameters, get_options proposed.
Need set_parameters on calculators, set_parameters method proposed.

Introduce a custom unpickle call that creates the dynamic component
classes on the unpicklers end, which means component incompatabilities
are caught if they have different parameters.
---
 mcstasscript/helper/unpickler.py              | 47 ++++++++++++++
 .../test_complex_instrument.py                |  4 ++
 mcstasscript/interface/instr.py               | 65 ++++++++++++++++---
 .../jb_interface/simulation_interface.py      | 14 ++--
 mcstasscript/tests/test_dump_and_load.py      |  2 +-
 mcstasscript/tests/test_parameter_variable.py |  4 +-
 6 files changed, 118 insertions(+), 18 deletions(-)
 create mode 100644 mcstasscript/helper/unpickler.py

diff --git a/mcstasscript/helper/unpickler.py b/mcstasscript/helper/unpickler.py
new file mode 100644
index 00000000..502295ad
--- /dev/null
+++ b/mcstasscript/helper/unpickler.py
@@ -0,0 +1,47 @@
+import pickle
+
+
+class CustomMcStasUnpickler(pickle.Unpickler):
+    """
+    Helps pickle find dynamic classes on users computer, McStas version
+
+    Usage:
+    pickle_data = CustomUnpickler(open('filename.dill', 'rb')).load()
+    """
+
+    def find_class(self, module, name):
+        if not hasattr(self, "mcstasscript_instr"):
+            from mcstasscript.interface import instr
+
+            self.mcstasscript_instr = instr.McStas_instr("dummy")
+            self.component_list = list(self.mcstasscript_instr.component_reader.component_category.keys())
+
+        if str(module) == 'mcstasscript.interface.instr':
+            if name in self.component_list:
+                self.mcstasscript_instr._create_component_instance("dummy", name)
+
+        return super().find_class(module, name)
+
+
+class CustomMcXtraceUnpickler(pickle.Unpickler):
+    """
+    Helps pickle find dynamic classes on users computer, McXtrace version
+
+    Usage:
+    pickle_data = CustomUnpickler(open('filename.dill', 'rb')).load()
+    """
+
+    def find_class(self, module, name):
+        if not hasattr(self, "mcstasscript_instr"):
+            from mcstasscript.interface import instr
+
+            self.mcstasscript_instr = instr.McXtrace_instr("dummy")
+            self.component_list = list(self.mcstasscript_instr.component_reader.component_category.keys())
+
+        if str(module) == 'mcstasscript.interface.instr':
+            if name in self.component_list:
+                self.mcstasscript_instr._create_component_instance("dummy", name)
+
+        return super().find_class(module, name)
+
+
diff --git a/mcstasscript/integration_tests/test_complex_instrument.py b/mcstasscript/integration_tests/test_complex_instrument.py
index 3fd6d1d9..d9f5cdad 100644
--- a/mcstasscript/integration_tests/test_complex_instrument.py
+++ b/mcstasscript/integration_tests/test_complex_instrument.py
@@ -200,11 +200,15 @@ def test_complex_instrument_interface(self, mock_stdout):
 
         change = FakeChange()
 
+        """
         interface.run_simulation_thread(change)
 
         for thread in threading.enumerate():
             if thread.name != "MainThread":
                 thread.join()
+        """
+
+        interface.run_simulation_live(change)
 
         data = interface.plot_interface.data
 
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 767bd644..303eb58e 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -23,6 +23,7 @@
 from mcstasscript.helper.formatting import is_legal_filename
 from mcstasscript.helper.formatting import bcolors
 from mcstasscript.jb_interface.simulation_interface import SimInterface
+from mcstasscript.helper.unpickler import CustomMcStasUnpickler, CustomMcXtraceUnpickler
 
 
 class McCode_instr(BaseCalculator):
@@ -224,7 +225,8 @@ class McCode_instr(BaseCalculator):
         Returns data set from latest simulation in widget
     """
 
-    def __init__(self, name, parameters=None, dumpfile=None, executable=None, author=None,  **kwargs):
+    def __init__(self, name, parameters=None, dumpfile=None, executable=None,
+                 author=None, origin=None,  **kwargs):
         """
         Initialization of McStas Instrument
 
@@ -294,16 +296,16 @@ def __init__(self, name, parameters=None, dumpfile=None, executable=None, author
                             + self.name + ".instr"
                             + "\" which is not a legal filename")
 
-        if "author" in kwargs:
-            self.author = str(kwargs["author"])
-        else:
+        if author is None:
             self.author = "Python " + self.package_name
             self.author += " Instrument Generator"
-
-        if "origin" in kwargs:
-            self.origin = str(kwargs["origin"])
         else:
+            self.author = str(author)
+
+        if origin is None:
             self.origin = "ESS DMSC"
+        else:
+            self.origin = str(origin)
 
         self._run_settings = {}  # Settings for running simulation
 
@@ -717,7 +719,6 @@ def _create_component_instance(self, name, component_name, **kwargs):
         if component_name not in self.component_class_lib:
             comp_info = self.component_reader.read_name(component_name)
 
-            input_dict = {}
             input_dict = {key: None for key in comp_info.parameter_names}
             input_dict["parameter_names"] = comp_info.parameter_names
             input_dict["parameter_defaults"] = comp_info.parameter_defaults
@@ -2190,6 +2191,30 @@ def _read_calibration(self):
             self._run_settings["package_path"] = ""
             self.line_limit = 180
 
+    @classmethod
+    def from_dump(cls, dumpfile: str):
+        """Load a dill dump from a dumpfile.
+
+        Overwrites a libpyvinyl method to load McStas components
+
+        :param dumpfile: The file name of the dumpfile.
+        :type dumpfile: str
+        :return: The calculator object restored from the dumpfile.
+        :rtype: CalcualtorClass
+        """
+
+        with open(dumpfile, "rb") as fhandle:
+            try:
+                # Loads necessary component classes from unpackers installation
+                tmp = CustomMcStasUnpickler(fhandle).load()
+            except:
+                raise IOError("Cannot load calculator from {}.".format(dumpfile))
+
+            if not isinstance(tmp, cls):
+                raise TypeError(f"The object in the file {dumpfile} is not a {cls}")
+
+        return tmp
+
 
 class McXtrace_instr(McCode_instr):
     """
@@ -2427,3 +2452,27 @@ def _read_calibration(self):
             self._run_settings["executable_path"] = ""
             self._run_settings["package_path"] = ""
             self.line_limit = 180
+
+    @classmethod
+    def from_dump(cls, dumpfile: str):
+        """Load a dill dump from a dumpfile.
+
+        Overwrites a libpyvinyl method to load McStas components
+
+        :param dumpfile: The file name of the dumpfile.
+        :type dumpfile: str
+        :return: The calculator object restored from the dumpfile.
+        :rtype: CalcualtorClass
+        """
+
+        with open(dumpfile, "rb") as fhandle:
+            try:
+                # Loads necessary component classes from unpackers installation
+                tmp = CustomMcXtraceUnpickler(fhandle).load()
+            except:
+                raise IOError("Cannot load calculator from {}.".format(dumpfile))
+
+            if not isinstance(tmp, cls):
+                raise TypeError(f"The object in the file {dumpfile} is not a {cls}")
+
+        return tmp
diff --git a/mcstasscript/jb_interface/simulation_interface.py b/mcstasscript/jb_interface/simulation_interface.py
index 718facff..ee0a5ced 100644
--- a/mcstasscript/jb_interface/simulation_interface.py
+++ b/mcstasscript/jb_interface/simulation_interface.py
@@ -159,7 +159,7 @@ def run_simulation_live(self, change):
 
             with lock:
                 self.progress_bar.value = index + 1
-                data = self.instrument.data
+                data = self.instrument.output.get_data()["data"]
 
                 if data is not None:
                     if plot_data is None:
@@ -341,22 +341,22 @@ def make_widget(self):
         """
         label = widgets.Label(value=self.name,
                               layout=widgets.Layout(width='15%', height='32px'))
-        if len(self.parameter._options) > 0:
-            par_widget = widgets.Dropdown(options=self.parameter._options,
+        if len(self.parameter.get_options()) > 0:
+            par_widget = widgets.Dropdown(options=self.parameter.get_options(),
                                           layout=widgets.Layout(width='10%', height='32px'))
             if self.default_value is not None:
-                if self.default_value in self.parameter._options:
+                if self.default_value in self.parameter.get_options():
                     par_widget.value = self.default_value
 
                 if isinstance(self.default_value, str):
 
-                    if self.default_value.strip("'") in self.parameter._options:
+                    if self.default_value.strip("'") in self.parameter.get_options():
                         par_widget.value = self.default_value.strip("'")
-                    elif self.default_value.strip('"') in self.parameter._options:
+                    elif self.default_value.strip('"') in self.parameter.get_options():
                         par_widget.value = self.default_value.strip('"')
 
                 if par_widget.value is None:
-                    print(self.parameter._options)
+                    print(self.parameter.get_options())
                     raise KeyError("default value not found in options for parameter: "
                                    + str(self.parameter.name))
 
diff --git a/mcstasscript/tests/test_dump_and_load.py b/mcstasscript/tests/test_dump_and_load.py
index c90c99e9..c0998526 100644
--- a/mcstasscript/tests/test_dump_and_load.py
+++ b/mcstasscript/tests/test_dump_and_load.py
@@ -309,7 +309,7 @@ def test_load_simple(self):
             dump_name = "test_McStasScript_dump.dmp"
             my_instrument.dump(dump_name)
 
-            loaded_instrument = McStas_instr("", dumpfile=dump_name)
+            loaded_instrument = McStas_instr.from_dump(dump_name)
             os.remove(dump_name)
 
             self.assertTrue(loaded_instrument, McStas_instr)
diff --git a/mcstasscript/tests/test_parameter_variable.py b/mcstasscript/tests/test_parameter_variable.py
index 6266c5e1..53c7c7e9 100644
--- a/mcstasscript/tests/test_parameter_variable.py
+++ b/mcstasscript/tests/test_parameter_variable.py
@@ -72,12 +72,12 @@ def test_ParameterVariable_init_options_initialize(self):
         """
 
         par = ParameterVariable("test", value=2,
-                                options=[1, 2, "horse"])
+                                options=[1, 2, 3.1])
 
         self.assertEqual(par.name, "test")
         self.assertEqual(par.type, "")
         self.assertEqual(par.value, 2)
-        self.assertEqual(par._options, [1, 2, "horse"])
+        self.assertEqual(par.get_options(), [1, 2, 3.1])
 
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)

From 919c61e69276cd5055973c88660fd65d2656ed4d Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Mon, 21 Mar 2022 14:32:10 +0100
Subject: [PATCH 225/403] Change libpyvinyl version in github workflows.

---
 .github/workflows/unit_tests_mac.yaml     | 2 +-
 .github/workflows/unit_tests_ubuntu.yaml  | 2 +-
 .github/workflows/unit_tests_windows.yaml | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index db26727b..c83c1793 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -18,7 +18,7 @@ jobs:
         uses: actions/checkout@v2
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
-          ref: develop
+          ref: 1.1.0
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}
diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index c14c2541..0324a91d 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -18,7 +18,7 @@ jobs:
         uses: actions/checkout@v2
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
-          ref: develop
+          ref: 1.1.0
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
index 55415dfa..6c49e111 100644
--- a/.github/workflows/unit_tests_windows.yaml
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -18,7 +18,7 @@ jobs:
         uses: actions/checkout@v2
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
-          ref: develop
+          ref: 1.1.0
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}

From 7b1eee3088f961f7a7a2daa37259e74750226409 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Mon, 21 Mar 2022 15:11:12 +0100
Subject: [PATCH 226/403] Update of documentation to reflect that backengine
 now returns the data from the simulation.

---
 docs/source/getting_started/quick_start.ipynb |  285 +---
 .../McStasScript_tutorial_1_the_basics.ipynb  |   13 +-
 .../McStasScript_tutorial_2_SPLIT.ipynb       |  318 +----
 ...tasScript_tutorial_3_EXTEND_and_WHEN.ipynb |    7 +-
 .../McStasScript_tutorial_4_JUMP.ipynb        |    4 +-
 ...n_tutorial_1_processes_and_materials.ipynb |    6 +-
 .../tutorial/Union_tutorial_2_geometry.ipynb  |    6 +-
 .../tutorial/Union_tutorial_3_loggers.ipynb   |   15 +-
 .../Union_tutorial_4_conditionals.ipynb       |    9 +-
 ...ial_6_Exit_and_number_of_activations.ipynb |   12 +-
 .../Union_tutorial_7_Tagging_history.ipynb    | 1226 +----------------
 docs/source/user_guide/data.ipynb             |  617 +++++++++
 .../source/user_guide/instrument_object.ipynb |   14 +-
 docs/source/user_guide/plotting.ipynb         |    3 +-
 14 files changed, 715 insertions(+), 1820 deletions(-)
 create mode 100644 docs/source/user_guide/data.ipynb

diff --git a/docs/source/getting_started/quick_start.ipynb b/docs/source/getting_started/quick_start.ipynb
index 5745c0f3..ff1df342 100644
--- a/docs/source/getting_started/quick_start.ipynb
+++ b/docs/source/getting_started/quick_start.ipynb
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "dense-internet",
    "metadata": {},
    "outputs": [],
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "disabled-canon",
    "metadata": {},
    "outputs": [],
@@ -52,48 +52,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "adult-assignment",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are the available component categories:\n",
-      " contrib\n",
-      " misc\n",
-      " monitors\n",
-      " obsolete\n",
-      " optics\n",
-      " samples\n",
-      " sources\n",
-      " union\n",
-      "Call show_components(category_name) to display\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_components()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "fatal-climate",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are all components in the sources category.\n",
-      " Adapt_check     Moderator           Source_Optimizer   Source_gen\n",
-      " ESS_butterfly   Monitor_Optimizer   Source_adapt       Source_simple\n",
-      " ESS_moderator   Source_Maxwell_3    Source_div         \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_components(\"sources\")"
    ]
@@ -109,39 +81,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "instructional-liquid",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Source_simple _____________________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1mradius\u001b[0m = \u001b[1m\u001b[94m0.1\u001b[0m\u001b[0m [m] // Radius of circle in (x,y,0) plane where neutrons are \n",
-      "                    generated. \n",
-      "\u001b[1myheight\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Height of rectangle in (x,y,0) plane where neutrons are \n",
-      "                     generated. \n",
-      "\u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Width of rectangle in (x,y,0) plane where neutrons are \n",
-      "                    generated. \n",
-      "\u001b[1mdist\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Distance to target along z axis.\n",
-      "\u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[94m0.045\u001b[0m\u001b[0m [m] // Width of target\n",
-      "\u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[94m0.12\u001b[0m\u001b[0m [m] // Height of target\n",
-      "\u001b[1mE0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Mean energy of neutrons.\n",
-      "\u001b[1mdE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Energy half spread of neutrons (flat or gaussian sigma).\n",
-      "\u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [AA] // Mean wavelength of neutrons.\n",
-      "\u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [AA] // Wavelength half spread of neutrons.\n",
-      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1/(s*cm**2*st*energy unit)] // flux per energy unit, Angs or meV if \n",
-      "                                           flux=0, the source emits 1 in 4*PI whole \n",
-      "                                           space. \n",
-      "\u001b[1mgauss\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Gaussian (1) or Flat (0) energy/wavelength distribution\n",
-      "\u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1] // relative index of component to focus at, e.g. next is \n",
-      "                        +1 this is used to compute 'dist' automatically. \n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "source = instrument.add_component(\"source\", \"Source_simple\")\n",
     "source.show_parameters()"
@@ -158,26 +101,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "continued-denial",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT source = Source_simple\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mdist\u001b[0m = \u001b[1m\u001b[92m5\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[92m0.01\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[92m0.01\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[92m3\u001b[0m\u001b[0m [AA]\n",
-      "  \u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[92m2.2\u001b[0m\u001b[0m [AA]\n",
-      "AT [0, 0, 0] ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "source.xwidth = 0.03\n",
     "source.yheight = 0.03\n",
@@ -200,26 +127,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "atlantic-capital",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT source = Source_simple\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mdist\u001b[0m = \u001b[1m\u001b[92m5\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[92m0.01\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[92m0.01\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[92mwavelength\u001b[0m\u001b[0m [AA]\n",
-      "  \u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[92m0.1*wavelength\u001b[0m\u001b[0m [AA]\n",
-      "AT [0, 0, 0] ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "wavelength = instrument.add_parameter(\"wavelength\", value=3, comment=\"Wavelength in AA\")\n",
     "source.lambda0 = wavelength\n",
@@ -238,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "changing-funds",
    "metadata": {},
    "outputs": [],
@@ -248,26 +159,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "after-reliance",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT sans_sample = Sans_spheres\n",
-      "  \u001b[1mR\u001b[0m = \u001b[1m\u001b[92m120\u001b[0m\u001b[0m [AA]\n",
-      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.01\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.01\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mzdepth\u001b[0m = \u001b[1m\u001b[92m0.01\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[92m1\u001b[0m\u001b[0m [1]\n",
-      "  \u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[92m0.5\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[92m0.5\u001b[0m\u001b[0m [m]\n",
-      "AT [0, 0, 3] RELATIVE source\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "sample.set_AT(5, RELATIVE=source)\n",
     "sample.set_parameters(R=120, xwidth=0.01, yheight=0.01, zdepth=0.01,\n",
@@ -286,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "characteristic-spectacular",
    "metadata": {},
    "outputs": [],
@@ -307,25 +202,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "wicked-terrorism",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " wavelength  = 3  // Wavelength in AA\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_parameters()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "postal-advocate",
    "metadata": {},
    "outputs": [],
@@ -340,48 +227,17 @@
    "metadata": {},
    "source": [
     "### Performing the simulation\n",
-    "In order to start the simulation the *backengine* method is called. If the simulation is successful, the data will be placed in the *data* attribute, otherwise this attribute will contain None."
+    "In order to start the simulation the *backengine* method is called. If the simulation is successful, the data will be returned, otherwise the method returns None."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "durable-printer",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/getting_started/first_instrument_data_4\"\n",
-      "INFO: Regenerating c-file: first_instrument.c\n",
-      "CFLAGS=\n",
-      "INFO: Recompiling: ./first_instrument.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "1 warning generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy first_instrument.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/getting_started/first_instrument_data_4\n",
-      "\n",
-      "Detector: PSD_I=1.13331e-05 PSD_ERR=1.27748e-08 PSD_N=1.99997e+06 \"PSD.dat\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "instrument.backengine()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "detailed-ontario",
-   "metadata": {},
    "outputs": [],
    "source": [
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -395,30 +251,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "christian-detail",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name PSD\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plotter.make_sub_plot(data, log=True)"
    ]
@@ -434,19 +270,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "gothic-water",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[\n",
-      "McStasData: PSD type: 2D  I:1.13331e-05 E:1.27748e-08 N:1.99997e+06]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(data)"
    ]
@@ -461,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "colonial-thompson",
    "metadata": {},
    "outputs": [],
@@ -479,33 +306,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "exempt-lincoln",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[3.44527442e-29, 2.85790145e-29, 3.31769558e-29, ...,\n",
-       "        2.91848858e-29, 3.46035438e-29, 3.18800919e-29],\n",
-       "       [2.99309240e-29, 2.71389219e-29, 4.51518249e-29, ...,\n",
-       "        3.88940177e-29, 2.79711427e-29, 3.13858152e-29],\n",
-       "       [3.09932004e-29, 3.65012990e-29, 4.19605051e-29, ...,\n",
-       "        6.17328696e-29, 4.54832798e-29, 2.95402503e-29],\n",
-       "       ...,\n",
-       "       [2.26834139e-29, 3.81964364e-29, 5.66352294e-29, ...,\n",
-       "        6.09311571e-29, 4.78664320e-29, 3.58314124e-29],\n",
-       "       [3.37192383e-29, 2.95154846e-29, 3.68253460e-29, ...,\n",
-       "        4.11785300e-29, 3.48591918e-29, 2.20162822e-29],\n",
-       "       [2.39943987e-29, 2.44856071e-29, 2.61788386e-29, ...,\n",
-       "        3.09847097e-29, 2.78252273e-29, 2.38869215e-29]])"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "PSD_data.Intensity"
    ]
@@ -520,37 +324,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "mediterranean-guatemala",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date : Tue Jan 11 12:39:32 2022 (1641901172)\n",
-      "type : array_2d(90, 90)\n",
-      "Source : first_instrument (first_instrument.instr)\n",
-      "component : PSD\n",
-      "position : 0 0 8\n",
-      "title : PSD monitor\n",
-      "Ncount : 2000000\n",
-      "filename : PSD.dat\n",
-      "statistics : X0=0.000187759; dX=0.61402; Y0=0.00177741; dY=0.612948;\n",
-      "signal : Min=2.20163e-29; Max=1.15483e-06; Mean=1.39915e-09;\n",
-      "values : 1.13331e-05 1.27748e-08 1.99997e+06\n",
-      "xvar : X\n",
-      "yvar : Y\n",
-      "xlabel : X position [cm]\n",
-      "ylabel : Y position [cm]\n",
-      "zvar : I\n",
-      "zlabel : Signal per bin\n",
-      "xylimits : -25 25 -25 25\n",
-      "variables : I I_err N\n",
-      "Parameters : {'wavelength': 4.0}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "info_dict = PSD_data.metadata.info\n",
     "for field, info in info_dict.items():\n",
diff --git a/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb b/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
index a06e0cad..5aeb1faa 100644
--- a/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
+++ b/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
@@ -719,7 +719,7 @@
     "- output_path sets the name of the output folder\n",
     "- increment_folder_name if set to True, automatically changes the foldername if it already exists (default).\n",
     "\n",
-    "The *backengine* method takes no parameters and just performs the simulation."
+    "The *backengine* method takes no parameters and just performs the simulation and returns the generated data."
    ]
   },
   {
@@ -810,16 +810,7 @@
     }
    ],
    "source": [
-    "instrument.backengine() # Perform simulation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = instrument.data # The data is available in the data attribute"
+    "data = instrument.backengine() # Perform simulation"
    ]
   },
   {
diff --git a/docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb b/docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
index b10b7f2b..b756fa92 100644
--- a/docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
+++ b/docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -103,69 +103,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_116\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS=\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "1 warning generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_116\n",
-      "\n",
-      "      monochromator rotation = 22.4519 deg\n",
-      "[python_tutorial] Initialize\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "\n",
-      "Save [python_tutorial]\n",
-      "Detector: banana_I=2.57019e-06 banana_ERR=1.14784e-07 banana_N=1447 \"banana.dat\"\n",
-      "\n",
-      "Finally [python_tutorial: /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_116]. Time: 0 [s] \n",
-      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
-      "    SPLIT 47 COMPONENT sample=PowderN(...)\n",
-      "  in the instrument description python_tutorial.instr.\n",
-      "loading system configuration\n",
-      "\n",
-      "Plotting data with name banana\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.settings(ncount=1E6)\n",
     "\n",
     "instrument.set_parameters(wavelength=2.8)\n",
     "                          \n",
-    "instrument.backengine()\n",
-    "data_low = instrument.data"
+    "data_low = instrument.backengine()"
    ]
   },
   {
@@ -187,7 +137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -196,81 +146,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_117\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "Info:    Defining SPLIT from sample=PowderN() to END in instrument python_tutorial\n",
-      "CFLAGS=\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "1 warning generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_117\n",
-      "\n",
-      "      monochromator rotation = 22.4519 deg\n",
-      "[python_tutorial] Initialize\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "\n",
-      "Save [python_tutorial]\n",
-      "Detector: banana_I=2.58648e-06 banana_ERR=2.11397e-08 banana_N=44235 \"banana.dat\"\n",
-      "\n",
-      "Finally [python_tutorial: /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_117]. Time: 1 [s] \n",
-      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
-      "    SPLIT 47 COMPONENT sample=PowderN(...)\n",
-      "  in the instrument description python_tutorial.instr.\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# No need to set settings or parameters as these have not changed\n",
-    "instrument.backengine()\n",
-    "data_reasonable = instrument.data"
+    "data_reasonable = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name banana\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plotter.make_sub_plot(data_reasonable)"
    ]
@@ -285,83 +177,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_118\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "Info:    Defining SPLIT from sample=PowderN() to END in instrument python_tutorial\n",
-      "CFLAGS=\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "1 warning generated.\n",
-      "INFO: ===\n",
-      "Warning: Number of events 2.99e+06 reaching SPLIT position Component[6] sample=PowderN()\n",
-      "         is probably too low. Increase Ncount.\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_118\n",
-      "\n",
-      "      monochromator rotation = 22.4519 deg\n",
-      "[python_tutorial] Initialize\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "\n",
-      "Save [python_tutorial]\n",
-      "Detector: banana_I=2.56161e-06 banana_ERR=3.73111e-08 banana_N=14737 \"banana.dat\"\n",
-      "\n",
-      "Finally [python_tutorial: /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_118]. Time: 0 [s] \n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "sample.set_SPLIT(10000)\n",
     "\n",
     "instrument.settings(ncount=1E3) # Change settings to lower ncount, but keep parameters\n",
     "                          \n",
-    "instrument.backengine()\n",
-    "data_unreasonable = instrument.data"
+    "data_unreasonable = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name banana\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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iopZjZzwFM55EREQtgRlPIiJqOQlNhyIEpAAznkRERC2AgScREbWchJaFagWezHgSERE1P5baEhFRy0loOhR7uBAznkRERE2PgScREbWchKZDFYCiCKSZ8SQiImp6LLUlIqKWk0hnzYynZMaTiIioFTDjSURELSeR0aEKs9SWGU8iIqLmx8CTiIhaTiJt93hyjycREVErYOBJREQtJaMb0HQDquAeTyIiolbBwJOIiFpKQjMDTWY8iYiIWgcDTyIiailJK/BUBaAIwYwnERFRC2DgSURELSWuZQEgt8eTGU8iIqLmx8CTiIhaip3xVIRZaqsbEhmdwScREVEzY+BJREQtJZ42M56qIqBYZzGW2xIRETU3Bp5ERNRSEpnljKcQAgDLbYmIiJodA08iImopibQ1XEgxg0+AGU8iIqJmx8CTiIhaSsIeLiTM4UIAkMow40lERNTMGHgSEVFLsfd4qtYeTwBIZ5nxJCIiamYMPImIqKUkVk21NV/GjCcREVFzY+BJREQtJallIQSgCDDjSURE1CIYeBIRUUuJazravCqEWFFqy4wnERFRU2PgSURELSWh6WjzewAAIldqy4wnERFRM2PgSURELSWhZdHmUwEAisI9nkRERK2AgScREbWUhKajzWdmPBVmPImIiFoCA08iImopqzKeghlPIiKiVsDAk4iIWoqZ8VwdeDLjSURE1NwYeBIRUUtJpFcGnubLuMeTiIiouXkafQBERESVSGSyCPk8uOMt1wEA9v7xd7jHk4iIqMkx40lERC0lkdYRtDKeAOD3KMx4EhERNTkGnkRE1FISmo6Qf7lgJ+BVmfEkIiJqcgw8iYioZRiGRDKjI+hdzngGvCoznkRERE2OgScREbWMpDW9NuRfU2rLjCcREVFTY+BJREQtI65lAQBB33Kprd+rIs2MJxERUVNj4ElERC0jqVkZzzXDhdjjSURE1NwYeBIRUcuIp80As823ssdTYcaTiIioyTHwJCKilpHMmKW2bb7VU23Z40lERNTcGHgSEVHLyJfxNPd4MvAkIiJqZgw8iYioZSQ0O/Bcu8eTpbZERETNjIEnERG1jIRml9oy40lERNRKGHgSEVHLyGU8/SuHCzHjSURE1OwYeBIRUctYzniuGS7EjCcREVFTY+BJREQtw854Br1rS20NSCkbdVhERERUAgNPIiJqGQlNR8CrQFVE7mUBKwjVdJbbEhERNSsGnkRE1DISWhahFWW2gJnxBIBUhoEnERFRs2LgSURELSOR1hFcMdEWAPxWxjOdZZ8nERFRs2LgSURELSOh6esyngEr45lmxpOIiKhpMfAkIqKWEdeyBTOenGxLRETUvBh4EhFRy0hqOkL+1YFnLuPJXZ5ERERNi4EnERG1jLimI+hdU2rLjCcREVHTY+BJREQtI6ll12U8/cx4EhERNT0GnkRE1DLimo62NT2ezHgSERE1PwaeRETUMpKajra1ezy93ONJRETU7Bh4EhFRS5BSIq5l12c8PdzjSURE1OwYeBIRUUtIZw1IiXUZz+VSW2Y8iYiImpWrgacQIiCE+KgQ4lkhxJNCiI+7+XxERLRxxdNZAFiX8bSHC7HHk4iIqHl5Sr9JTf4WQArApVJKKYTY4vLzERFRE7ntjiMAgDtvP1zzYyU0M7AsNFyIU22JiIial2uBpxCiHcBbAQxJKSUASCkn3Xo+IiLa2JYDzzXDhZjxJCIianpultruATAL4H8LIR4WQtwjhHju2jcSQrzTev3D09PTLh4OERG1soRmldqu2eOpKAI+VWHGk4iIqIm5GXh6AOwG8JiU8joAfwTgP4UQHSvfSEr5cSnldVLK6/r6+lw8HCIiamW5jKdXXfc6v1dhxpOIiKiJuRl4ngeQBfBFAJBSPgBgBsClLj4nERFtUHbgGfKv7xLxe1SuUyEiImpirgWeUsoZAD8G8BIAEEJcCqAfwCm3npOIiDYuu9Q26Fuf8Qx4FaS5ToWIiKhpuT3V9jcBfEoI8fcAMgDeIqWMuvycRES0AeUynr58GU8FKWY8iYiImpargaeU8gyAW918DiIi2hzsPZ75M54qM55ERERNzM0eTyIiIsckC+zxBMzAkxlPIiKi5sXAk4iIWkJc0+FTFXjV9acuv0dBihlPIiKipsXAk4iIWkJSy67b4WkLeDnVloiIqJkx8CQiopYQ1/S8OzwBZjyJiIiaHQNPIiJqCUlNR1ueHZ4AM55ERETNjoEnERG1hLiWzTtYCDD3eDLjSURE1LwYeBIRUUtIaHrBwNPvUZHKMONJRETUrBh4EhFRS0hoWbT58pfa+r0K0llmPJ122x1HcNsdRxp9GEREtAHkP4MTERE1CTvwKZXx1LIGDENCUUQ9D4+IiIjKwIwnERG1hES6cOAZ8JqnM01n1pOIiKgZMfAkIqKWUKzUNuAxA1L2eVaHJbVEROQ2Bp5ERNQSipbaWhlPTrYlIiJqTgw8iYio6RlSImtIhArt8bQyntzlSURE1JwYeBIRUdMzDAkACHoL9XjapbbMeBIRETUjBp5ERNT0dGkGniF/oam25umMGU8iIqLmxMCTiIianmElMoOFhgu1WMaTw3yIiGizKbjHUwgxVcb7T0gpr3TweIiIiNbJZTxLDhdixpOIiKgZFQw8AUwDeEWR1wsA33D2cIiIiNbL9XgW2uOZGy7UGhlPIiKizaZY4PlnUsrzxd5ZCPHnDh8PERHROssZz0Kltsx4EhERNbOCPZ5Syq+Ueudy3oaIiKhWdsaz4B5PZjyJiIiaWrGMJwBACBEA8KsA9qx8eynlf3fxuIiIiHJ0M+5EW6E9nsx4EhERNbWSgSeALwPwAXgAQNrdwyEiIlovl/EssMfTzngy8CQiImpO5QSee6WUB1w/EiIi2lBG5hJ46NwcLt/aUfNj2T2ebYX2eHrtPZ4stSUiImpG5ezxPCOECLt+JEREtKGcn03AkM7s1jQMCVUR8Kn5T1t+jxV4MuNJRETUlMrJeC4AeFgI8X0AKfuF7PEkIqJi4loWAKBbZbK1MKREm0+FECLv64UQ8HsUZjyJiIiaVDmB50nrDxERUdkSDgaeulF4oq0t4FXZ40lERNSkSgaeUkru6iQioorF0mYQaPdn1sKQEmGft+jb+D2KI2W9TrrtjiMAgDtvP9zgIyEiImqsgoGnEOJXpJRfFkL8Vr7XSyn/xb3DIiKiVpdIO5nxlAiWkfFMZ5nxJCIiakbFMp4HYa5SuT7P62q/iiAiog0trlkZT4d6PEO+4kU6AW/zZTyJiIjIVPAsLqX839bfb6/f4RAR0UZR74yn38OMJxERUbMq2eMphPAAeCeAF8LMdN4N4F+llFmXj42IiFpYLuPpSI8nECqww9PGjCcREVHzKmeq7T8D2AngMwAEgDcDOATgN907LCIianVxpzOe3uKnLL9Hza1wISIiouZSTuD5fACXSykNABBC3AngSVePioiIWp6T61QMKcvKeM7FmfEkIiJqRkoZb3MRgH/F/70ARtw5HCIi2ijiaeeGC+myjB5Pr4oUezyJiIiaUrF1KvYalbMAjliZTgD4FQA/dfvAiIiotdkZT6PGHk8pJaREyam2fo+CNHs8q5LRDaiKaPRhEBHRBlbsLL5yjcpjAC61/v0EgA7XjoiIiDaEmEM9nvZwojbu8XRFNKHhsQtRCAG87f89iOfs6cVz9vZi/0C40YdGREQbSLHA82+llMfrdiRERLShJKyptoYEsroBj1pOd8d6hpXEbCsj48mptpWbjWuQANr9HozMJfBXJ81Tf0/IBwDY2dPWwKMjIqKNothZ/MNCiK0AvgPg6wDuldKBmfhERLQp2FNtzX/riLRVF3gy4+mupHWDYKAjgB+85xaMLyRx36lZ/PD4JL771AS6rQCUiIioFgWvAqSULwFwM8wy23cDOCWE+JQQ4tVCiGC9DpCIiFqPlBIJTYdXNfsGl9KZqh/LMMoMPD0qMrp0ZJjRZmIH63aL52AkiP9y7RD+4tUHAQCaziwyERHVrujtZynlgpTy36SUrwdwAMCXAbwcwFNCiK/X4wCJiKj1aLqBrCHhtcprY+nq92suZzxLlNp6zedKZZj1rERSMwNLZc1wIbvUNpNl4ElERLUrZ48nAEBKqQH4rvUHQogb3TooIiJqbfYqFZ+qIAF9VdltpXIZz1J7PD1m4JnOGgj5i75pw2V1A3GtOQJkO1BXxerAU1EEvKpAhhlPIiJyQMmGGyHE3wshIkIIjxDiZ0KIuBDizVLKB+pxgERE1HrsQNNrBYNLqVoynubfpUpt/V7z9a2Q8TwzE8eJiSUspaovQXZK0vp8CbF+nYpPVaDptZUu33bHEdx2x5GaHoOIiFpfOZMeXiylXADwMgCjMNeq/IGrR0VERC3Nnmjrs3o8aym1tTOepfZ4BrzLGc9m9vhIFPMJM+A8OxNv8NEsB55qnjWeXo/CjCcRETmikhGDzwfwn1LKUQCc3EBERAXFNSvjafV41lJqa/d4BssYLgQ0d8ZTSokPfPc47OTi6elYYw8IQNr6fK3t8QSsjGeTB/JERNQaygk8p4QQ/wrgDQB+IITwoILeUCIi2nwSdo+nA6W25WY8W2G40E+emcb9Z+Yw3GUOhz891TwZTyVPqa1XVZA1JINPIiKqWTmB55sAHANwm5RyHsAQgL939aiIiKil2aW1yxnP6oNBw8p42qW0hdgZz2YttTUMib/53kkMdwexpSOAgFdpioxnbqptnlJbu1R6Jpau5yEREdEGVPAsLoT4khDiTQAyUsp/lFLeDwBSynNSyk/X6wCJiKj1JKxSW48ioAggVsMez4wuoSoi7/CblZp9uNA3j47h+Pgi3vuSfVCEQMCrNkXgmcrqEMg/XMgeDjW5mKrzURER0UZT7PbxhwBcDeA+IcQPhRC/I4TYUafjIiKiFmavClGEgKqImoYLpbMG/J7SBTp+T/MOF9KyBv7+rmdwYLADv3RoKwAg6FVxbiYB3Wjs2ISkpuft7wTMHk8AmFpixpOIiGpT8EwupbxXSvmHUsrLALwbQDuAO4UQjwoh3ieEuLpuR0lERC0lYQWaqmIHntVnIdMZPbejs5hAE2c8v/jgBVyYS+CPfmFfLsgLelVouoGL84mGHlsqo+ctswWWS6UZeBIRUa3KmmorpXxaSvnXUsqbAPwigAkAf+XqkRERUctazngCqhCIVbmvUjekmfH0Fp9oC6zIeGaaK+OpGxIfuftZ3LS7G7dc2pd7ud2z2uhyWzPwzB95eq0ezymW2hIRUY3KCjyFEG1CiEuFEJcB6ALwUynlK9w9NCIialXxdBZtPhWixlLb8YUkJFBRxjOdba6M5/hCCjMxDX/0C/tX9VEGreNt9GTbZJHAUwgBryowtciMJxER1abkWhQhxG8D+ACAOQD2bWQJYLeLx0VERC0soWUR8punmFpKbS/MmmWo5WQ8A7l1Ks2T8czoBsYXkviFywdw9fauVa/zqAp6Qr6GZzyTGQNKkbjeqyqYXGLGk4iIalPOPs73AjgopTzv9sEQEdHGEE/rCPnMYFERouqpthfmzMCznIyn39N8Gc/5RAaGBH7nRXvzvn5PX3vDA89ipbaAOWCIGU8iIqpVOaW2Eww6iYioEgktizafB3fefhivuGIQsVR1pbbn5xIQAHxlBJ5e1Vzd0kwZT90wj2VnTyjv6/f0h3B6urGltqUCT69H4XAhIiKqWTmB5w+EEH8rhLhGCHGZ/cf1IyMiopYVT+sI+c0MZMjvQbzaUtu5BHwepeQOT8DsR/R71KaaapvVzVUpbb78pcJ7+toxF9cwH9fqeVirJLXCU20BwKcKzMbTyOrNE9ATEVHrKafU9q3W37+y4mXs8SQiooLiWhbdIR8AIBzwQNMNpLN6rhy2XBdmE7mhQeUIeBV844kxPDm6gDtvP1zRc7lBNyQ8iigYOO/pawcAnJmJ4dpQdz0PLSeVLZHxVBVICczENAxEAnU8MiIi2khKZjyllLvy/GHQSUREBcXTWYR85r3NdmvIUDXltudn47k1KeUIeFVIKSt+HrdkDQm1SDrRDjztyba33XEEt91xpC7HZktqRtFj9Fm7PCe5UoWIiGpQ8GwuhOgo9c7lvA0REW0+CU3PlZfa020rLbddSGSwmMrmptWWw+9RYDRP3JnLeBayrSsIn0dp6IAhs8ez8Ou9VuDPPk8iIqpFsbP5PWW8fzlvQ0REm0w8vbxOxc54LlU42fb8nJkFrKQ8N+BVYbRQxlNVBHb1hJog8Cyd8ZziShUiIqpBsR7PfUKIB4u8XgDocfh4iIioxUkpV2U8w4HyMp52iandm3ne2uHZ6hlPb4nj39MfwvHxpTod0WoZ3UDWkCV6PAWEACa5UoWIiGpQLPB8RRnv37gxfERE1JTSWTOYsTOe9t+V7vK0d3hWkvH0N13G04BHKT7Hb09fO75/bLIh+0eT1gRgpUhsLIRAT8iPaWY8iYioBgXPhlLKn9TzQIiIaGNIaGYwE7IynrlS2wqHC12YTaC33V+0VHWtZiu11UuU2gJm4KkbEhesDG892atn3vH8PXjLTTsKvl1/2M+MJxER1aT8+iUiIqIyxNNmgNlmBZzlltqudX4uju3dwYrex+8xV380g4xuwJAoOlwIWDHZdjpej8NaJaWZuzmDJVbW9Hf42eNJREQ1YeBJRESOWs541lZqOzKXxI6eUEXvE/CqMJqkydPO8Kpq8cBzd5/5MTZiwFDKKu8tFXhuCQcwxYwnERHVgIEnERE5Kq7ZGU8zmGnzqhCisj2e6ayOsYUktne3VfTczTRcaDFpBtqlMp4hvweDkUBDAs+kdZOg1ACn/g4/ZmJp6M3yySUiopZTMvAUQnxPCPFKIYqMvCMiIrLYpbZ2b6eiCIR8HsQqKLW9OJ+ElKg48Ax4labp8VxMmYFnOT2qu/tCDSm1tYcLlSy1DfthSGA2xqwnERFVp5yM58cB/B6A00KIPxJCcIUKEREVZPdy2utUADMIraTU1p5ou6OnwsDT0zzDhRaTZgBeKuMJmH2eZ6ZikHU+dnu4UMBXqsczAKC6lSrzCa2he0qJiKg5lAw8pZT/KaV8MYCXA9gG4JgQ4rNCiGtdPzoiImo5CavU1u7xBID2gAexdPmltvaE1+0VBp5+bxOV2laQ8dzT146ldBYZvUGBZ4mVNf1hPwBUNWBoLq5hJqZhYoHDicp12x1HcnttiYg2iuLLxfLTAKQAfFYI8T0p5XsdPiYiImphcatv0O7xBMw+xkpKbc/PJhD0quhr91f03HYA1QxZT7vHU82zJPPO2w+v+r892TaZ0eHz1G/8Qq7UtkDG0z7OsWgSADC1VHnGM5UxJ+c+cTGKgchA7uV2YLX2c0FERBtTOT2erxVC3A3gPwGcAHCZlPKdAA4B+C8uHx8REbUYu8dzZcYz7Pcglqqs1HZ7dxsqHS/gt4bkNEPW0854llVq229OtrUzkGu5lQGzg8JSPZ691g2AycXKs5b2x3T0YrTi9yUioo2jnIznbwD4Gynl91e+UEqZFUL8jjuHRURErSqRzkKI1cFMu9+D6QqyZRfm4hWvUgHMdSoA6t4rmY/d41lG3ImBjgDafGrBwNMt5U619XkUdId8FWc8l1IZZK27AEcvLlR3kEREtCGUU8/z72uDTiHEmwFASvlNV46KiIhaVlzT0eZVoayIuMxS2/J6PKWUuYxnpXKltk2Q8lxMZeBRRFlZWyEE9vS1I2llIOvFLrUNlMh4Amaf51SFGc/zVq+uRxE4enGhKW4IEBFRY5QTeP5+npe9x+kDISKijSGhZdHmX11QEw54sFRmqe30UhqpjFHxRFugyUptk5myBgvZdveFcoFgvaQzOoQw95+W0t8RqDjjeW7WXBHT0+7DQjKTC0SJiGjzKVhqK4S4DsCNAHqFEL+14lURAD63D4yIiFpTPK0jtGZYTbvfg7imQ0pZMgN43lqlUk3G099Mw4VS2bL6O217+tqhZQ3odYyakxkdAY9aVlZ2S9iPZyaWKnp8O9DsCfkwuZjGExej2NlbeQk1ERG1vmK3OLcBuA5ACMD1K/4MAHib60dGREQtKZ7OIrQm4xnye6AbMjfMphg7WKkq8MxlPJsg8Kww42lPtq1nn2cyoxecaLtWf4cf07E0Xv+x+8oedHRuJg6vKhDye+D3KOzzJCLaxApmPKWUXwfwdSHES6WUd9XxmIiIqIXFteyqibaAuccTAJbSmZKBzoW5BBQBDHXV0OPZ+LgTi6kKA88Sk23dkMoYCJS5vqU/HIBuSGQNCa9a3sd1fjaBgEeFIgQu39rBybZERJtYsVLb50gp7wXgEUK8Yu3rpZTfcfXIiIioJSU0Hd2h1R0Z7dZOz3haB8LF3//CbByDkWBV+yzt6azNMMRmMZmFJ88Oz0J2WlN86zlgKJnRESgz47mlw1ypomUNeNXyPq6zs/Hc1+TKoU7c+dAIsroBT5nvT0REG0exdSpvA3AvgD/M8zoJgIEnERGtE09nMbwmW9nu9wIAYqnSk23PVznRFljZ41nVuztqMZVBR8Bb9tsHvCr8HqW+GU9NL7nD09YXDgAAMnp5gXE8ncX0UhpDXUEAwKHhCD593zmcmo5h/0BHdQdMREQtq1ip7Tusv19Qv8MhIqJWl9B0tOUZLgSYpbaljMwl8OIDW6p67kCT9HhmdMPM/LZVNosv6FXrOtk2ldXLWqUCrMh46uV9bu1eXfvxrxzqBAAcHVlg4ElEtAmVrHURQjxfCNFu/fs3hBAfE0Lscv/QiIioFcXyDBeyA894unhQpRsSMzEN26tYpQIsBzmNDjyXrMzuO56/G3fefrjs9wv5VSQ0HeetNSRuS1aU8TQDz0y2vIyn/THYPaS7ekII+z14gn2eRESbUjlNFv8EIC6EuBzAewFcAPBJV4+KiIhakpQSCU1HyL8m42kNF4qVyHjaZabVl9o2xx7PxaT5cXYEi3W0rNcfDkAA+MTPzrpwVOslM0bZGU+/R0VnmxdamaW256yMp996fEURuGIowsm2RESbVDmBZ1aaUxpeDuCjUsr3A+iq5EmEEP9bCCGFEAerOUgiImoNaWsPZdvaqbZWxrNUj2fayqbt6K5u12Mu49ngyHMxZQWeFfR4AoDPo6C33Y8vPTyC2VjajUNbJZ3Rc+XJ5dgSDpTd43luJo7edt+qXaZXDnXixMQi0tn6lRMTEVFzKOds4xFCPAfA6wDcbb2svNujAIQQ1wC4CWamlIiINrCEZgYUoQI9nrESpbZ2QFJtqW3zZDzNALsjWFngCQCDkQA03cBn7jvn8FGtl8yUX2oLmLs8y894xnOTem2HhiLI6BLHx5cqOk4iImp95QSe/wvAPwO4T0p5TAhxKYBT5Ty4EMJvve9vwZyES0REG1g8bQZcbWt6PANeBaoiyii1NRAJehGpImADAI+qQKDx61SqzXgCQNCn4iUHtuAzR87nPp9uSWb0kntVV+oL+5HJlj9caMeawPPK4U4A4D5PIqJNqGTgKaX8upTyKinle6z/PyOlfG2Zj/8XAD4vpSzYrCKEeKcQ4mEhxMPT09NlPiwRETWjuGYGSu1rAk8hBNr9npLDhdJZHTvWZDvvvP1wRQN6hGj8cKFqezxtt9+yBwvJDL708IiTh7VOKlP+VFsA2NJhltqWCuyTmo6JxRR2rvlabo0E0NvuwxMjhfs8b7vjCG6740jZx0RERK2hnKm2AWua7fuFEH9r/ynj/Q4DuB7AvxR7Oynlx6WU10kpr+vr6yv/yImIqOnYgeXadSqAGYwulejxTGWMqgcL2RQhGl9qW0PGEwCu3dGF63d24RM/O1t2T2WlpJRIVTBcCAD6w35IANkSn+ALc+ZgoR29qzOeQghcOdTJjGcJI3MJzMW1Rh8GEZGjyim1/TKA1wPIAoiv+FPKLQD2AzgrhDgHYAjA94UQL63uUImIqNklrIzn2nUqgBl4Fiu1lVJCyzoVeDY645mFqoi8AXi5bn/+HoxGk/jOk+MOHtkye5BTJT2eWzoCAJA3GF6ZqTw7Y14mrM14AsCVQxGcmo5Bb/TdgSZlGBLjCylMLbk/XIqIqJ7KqQHaK6U8UOkDSyk/AOAD9v+t4POVUsqnKn0sIiJqDUUznoHipbbprAEJrCu1rZSiNMFwoVQGHQEPhBCl37iAF+7vxyX97fjYT84g7Fdreqx8ktYgqEqm2vZbuzy1Ers87R2ea3s8AeDQUCekNPuBqxm+tNFNLqUgsXwTh4hooyjnbHNGCBF2/UiIiKjlJQr0eAJmFnSpyLAcOwM3vCEynpmagypFEXjn83fj+PgiFpLFhzJVI2ntTK1oqm3YzngW//yem02gO+TLOyTqyqEIgOV+YFrtgrX/NKNLTDPrSUQbSDkZzwUADwshvg8gZb9QSvnfK3kiKeXOyg6NiIhaTW6qrW/96SXs92B0PlHwfdNWIJQvS1YJRTTDHs9s1f2dK736qm34u7tOYnwhhc42nwNHtixlB54VlAP3d1gZzxJ9p+dn4wUz1z3tfmzrDJZcrbNZjcwnc/8+Pr6IvjDnXxDRxlBOxvMkgH8DMIvKejyJiGiTidt7PP3rg5mQXy1aapvKGhAABqw+wmo1xXChZKbqibYr+TwKfuO5u7CYyiLm8GoVO+Pp95QfeAa8KlRFIFOy1DaxbofnSlcNd7q+KqZV2YOZAODp8cUGHgkRkbNKnhWllH9ejwMhIqLWl0hnIQQQyBPMtPu9RYOnVEaH39r3WQtVEchk3JkEW67FVAZ7wu2OPNYbb9iOv/nuSUwspEq/cQWqyXgCgE9VimY8UxkdYwvJor26Vw5F8O0nx12b2NvKLs4l4FPNvMBxBp5EtIGUs06lXwjxeSHET63/XymE+E33D42IiFpNXNPR5lWh5Ake2wMexNLZgmWw6axRUfatEI+iIGs0OPBMOlNqCwDhgBcdQY/jPZGpTOVTbQHAq4qiAePIXAJSArt6C2c8rxzqBABmPfO4MJeA36ugza/i6TEGnkS0cZRTavuvAH4OoNP6/wkAv+XWARERUetKaNm8q1QAoN0qv01k1pfbSimRzhgVTVgtxKOKknsm3baYcqbU1uZRFMfXj1Qz1RYAvKoCrchwoXPWcBy7V/fO2w/jztsPr3qbK+wBQ+zzXGdkPgG/R0HIp+L0dCyXmSYianXlnG22SSk/BkAHACmlBoC1MUREtE4srRcJPM0MYCy1Pss1n8hAl9KRjKeqCEiJhl2wZ3QDCU13LOMJmB+T44FnFVNtAbPvNJM1IAtMDrZXqeTb4Wlr93sQ8CqIcbLtKqmMjsnFNAIeFW0+DwwJnJxYavRhERE5opzAc9VZQQjRCcDZZWJERLQhJNLZvDs8AbPUFkDePk97oErA40DG0yrzdWMFSTmWrMDayR2VHsUcmORkT6QdmAcqLrVVIAFEE/k/v+dm44gEvSWn8Ib9HiylsszorXDRmmjr9yq5nyP2eRLRRlHOGf4rQog7AISFEG8DcBeAT7l6VERE1JLiWhahPKtUgOVS23yBp50l8ztRatvgwHPRel4nS23tgUv5ssXVqjbw9Fs3B45P5A+Izs0kimY7bT3tfuiGxF1PT1b0/BvZiHUDxu9R4PcoaPd7ONmWiDaMkmd4KeUHAfwUwCMAXgHgw1LKD7l9YERE1HoSmo62PKtUgOKlthdm7QtuB4YLWRNBC2Xk3GYHvE6X2gJm76hTklVOtY0EvfAoAh/7yZm8rz83G8fOIoOFbB0BD3weBV96aKSi59/IRuaXfw6EEDgwGOaAISLaMMqZavtCKeUXpJS3SSlfL6X8vBDihfU4OCIiai3xdLHhQsVLbb2qqHmVCrAcpDUs45myM57OB55LjmY8zbLdSsubVUVgMBLAT5+ZxuMj0VWvM6TEWDSZGyxUjBACfe1+3Ht6Jpfp2+xG5szBQl7V/HofGOzA8fHFgpOgiah6t91xBLfdcaTRh7GplHO2+bs8L/ug0wdCREStL57WESrU41kk8Dw/l8i7+7MajS+1tXo8Hcx4elzKeHpVkcsQV2JLRwCdbV585EfPrnp5OmPAkMUHC63U1272gX7l0YsVH8NGdGEugeHuNghhfr0vG+xAXNNzmVAiolZW8GwjhNgrhHgFgA4hxCtW/HkjgPLOKEREtKnEtSzaCvV42sOF8gRPF2YTjvR3Ak0QeKbc6/F0MuOZ1PSK+ztXHs9/fe4u/OjEFJ4aXci9PJU1y3fLyXgCgN+r4rl7e/Hlhy8yqwdgZC6J4a5g7v+Xbe0AAJbbEtGGUOws/xwAfwhgi/W3/ecNAP7A/UMjIqJWIqVEQtMRKtDjab88rq2eYprK6JhYTDnS3wmsKLVNaI48XqUWXezxdDLwTGerDzwB4K0370RHwIMPr8h62uW75WY8AeBXrhvGaDSJ+07PVn0sG4GUEiNzCWzvXv7cXbolDFURHDBERBtCwcBTSvkZKeULAPyOlPIFK/68Wkr57ToeIxERtYB01oBuyIIZT79HhU9V1gVPF60ywoBDGU8hzF7RRmY8VUUUXCtTjdxwIQc/pqSmV7zDc6WOgBdvf84u3PX0ZG7lRzqjIxzwoDtUfJXKSi+9bAsiQS++9PDmHjK0kMxgKZ3F8IrAM+BVsbs3xIwnEW0IJeuApJSfFkLsAbBn5dtLKb/j5oEREZH77MEKd95+uObHilu9m+0FhgsBZrltLL06eDo/u7xCwimeRgaeySw6Ap5cn14lCn0dXCm1zdQWeALArz9nFz7587P4p7tPAQBSWQM7e0IVfewBr4pfvmorvvjQCBYaNIm4Gdi7bFcGnoBZbvvQ2TkAzv68EhHVWzlTbd8P4H4Af4rlcluW2hIR0SoJq4S2WKYv5FcRT68utbUvuGsp+1yr0RlPJyfaAoAiBBQBLDk4XCiVMWrOMkfavHjbzTvxnafGkdB0pDI6dlRQZmv7leuGoWUNfP2J0ZqOp5WNzCUBAMNdawLPwQ6MLaQwHy9cOs7pnETUCsqZfPB6AHuklKzzICKiguKamY0rtE4FMHd5rs3anZ9NIORTc0OBnNDYjGfG0f5Om6oIxzOeTgT7v/7cXfjUvWcxGk0ibWU8K3VwWwSXb+3Alx4eQahAqfZGt5zxDK7KaNoDho6zz5OIWlw5tzrHGXQSEVEpdiazWMYz7F9fart2hYQTPKqCaMMynllHJ9raVEVgKe3cx5TO6Ag60IfaHfLhLYd3YM7KyFWT8QSA1183jKdGF3Ml25vNyHwCXW1ehNfctDgwaE22ZeBJRC2unMDziBDii0KI165cq+L6kRERUUtJaKV7PAuV2lYbrBTiUYSjg3gq4VbG0/yYHM54OjRJ+B3P2w07Yb2rt/KMJwC8+qqt8HkUTMfSjhxTq1k70dbW2+5Hf9jPwJOIWl45gef1AAYA/A7Y40lERAXYmapCU20BoD3gRWxFRsswpBV4VhesFGL3eEpZ/92Qiym3Sm0VR3s8kw5lPAE7OAoAqD7w7Gzz4WWXD2Ampm3KnZ4jcwkM5Qk8AbPclpNtiajVlQw816xSsf+8sB4HR0RErcPOZBba4wmY2dCL84ncIJSppTS0rLFukmetPIpARpdIZvTSb+ywxaSLpbYO9niaw4WcG+g03B3Ewa0d6Gn3V/0Yr79uCLohMd+gHayNohsSo9Fk3ownYA4YOjUVg9GAGylE1HxadaBYwTOjEOKyYu8opXza+cMhIqJWZZfaFs14+lXoK7JZ52fjAIAdLgSegLkbsdjxOE3LGkhmdPdKbZ0MPDXdsd2pgDl5t9hgqXI8Z08vfKqCmdjmCjwnFlPI6HLdRFvbgcEOZA2JpKbX/DkmImqUYr+9vl3kdRLAboePhYiIWlhcKyfj6YUhkSuBPW9N8nS6x1NVzYAqmshgMBJ09LGLsUthnV6nAljlw0VWalTKiT2eTlMUgY6gp2ETiRtlxPo5KJjxtCbbJhh4ElELK/jbS0q5q54HQkRErS2RzkIIFA1m7KDUznqOzCWgKgJbO50NDldmPOvJzki6VWqbzhpIZ3X4axwKlNENZA3ZdIEnYGbMZ2IaZmJp9NZQtttKVq5SyWdnTwhBr2pVFWyOzwkRbTzO1dgQEdGmFkvrCPk8RdeihANmQKbbGc/ZBLZ2BuBVnT0dNSzwtJ7PrT2eABzp80xZva9O9ng6xV7Hc3JiqcFHUj8X5xJQBAregFEVgf2D4VxVARHVzjBk3kFmhfonW7Wvspkw8CQiIkcktGzRHZ6AWWoLLGc8z88lsKPb2Ym2wHKQVv+Mp3ulth4HA0976FLAoam2TrK/h05sosDzwlwCg5Fg0RswBwY7kND0hkxqJtqInpmK4dmpWKMPY1Nh4ElERI6Il9F/lq/U1umJtgDgUa3AM1HvjKdVautwxvPO2w/jf778AAA4slIlnTEAFC+LbhSvqsCjCJzYRHsrR+aTBctsbZcNdkA3JLSsUaejItrYUhkd0WQGZ2fijT6UTYOBJxEROSKRLp3xzJXaGhJLqQzm4prjg4UAQBUCimhkxtP5Hk/7c+doxtPBqbZOavOpODm5uTKehQYL2ewBQyy3JXJG1roB+sUHLzT4SDaP5jzjEBFRy4lr2ZIZz1yprTT7OwHnV6kAgBACHUHvhurxDFuPuejAx5S0gpdmzHgCZuD5zOTSqtU7G9FtdxzB6z56H6aX0gVXqdj2D4QhhPlz8+l7z+b6dImocrohc79f/uORi0hn+fNUDww8iYjIEfG0jlCJjOfKUtuR3CRP5wNPAIg0IvBMZaAqomTmtxpOZjztoKV5A08PUhkjt+e1UeoxTCRtlc5uL5H5b/N5sH9LGH6Pgvd982k892/uxsd+chqxtHO7XYk2C7tloTPoxVxcw/ePTTb4iDYHLoMiItrEZmLp3CCeWsW1LLb7i188h1cMF3Jrh6etIYFnMouOQPHJvtWyBxYtOtDjaZfa+qsIPO+8/XDNz19KcMVk29197a4/XyPZmZahEhlPwPweuCzoxXtecin+6cen8IHvnsBH7zmNdr/q+Eoioo1s3ur/7w750BH04t8eOI9fOrS1wUe18THwJCLaxEbnk44FnokKM57nZxPoDvlyJaROBzSRoBfRBmQ83ZhoCwDt/s2T8Qx6VSjCnGz78isGG304rrIHPZXq8Vzpxt09uHF3D54YieIjdz+LHx6fqnm3K9FmEk1oAMxBdK+7bgh/+72TOD0dw54NfqOr0VhqS0S0SUkpoekGEhkdWb32SZlxLYs2X/H7mR5VgSIAQ0rXJtraIkGvI/2QlVhMZlzp7wTMFTHtfo8jGc+UPdW2CdepAObHurMnhBMTy5NtN+oOvXRWR9CrorfdV/H7HhruxEfffK31OJx2S1Qu+6akR1HwumuH4FEEvvhA6wwZklK25GolBp5ERJvUYjILQwJSAudq7KWTUiKh6bmsXDGqIqxS27grg4VsjenxzLoy0dYWDng2zFTbO28/XDTLvX8wjJNl7PJs9YA0nTUw3B2sujzbXj+TceDmEdFmsTLj2R8O4KWXb8F/PHqxZYZ2XZxP4unx1pv8zcCTiGiTmlhM5f59bKy2nYnprAHdkGjzl86gqUIgqxsYi6Zc6+8ElgPPet4VdjPjCdiB58afagsA+7Z04PxcAgltYw/PSWeNkhNtS/GqCjQGnkRliybsjKd5w+dNN+xANJHB956aaORhlS2uZVvydyMDTyKiTWpl4Pn0eG2BZ9yarBkqUWoLAIoikMjo0A3paqltZ5sXuiHrOvVzMeVu4NkR8DrT45m1M55NHHgOhCEl8MxkrNGH4hopJVIZveafA5+HGU+iSqwNPG/e04MdPW34txYpt9WyEoZcvonYKhh4EhFtUpMLZuDpUQSO11iyk7BOfuWsEVEVkesxdKPU1i7hjFhDfupZbruYbI1S25SmQwjA72ney4ADg2EAwMmJ2m6KNLOsYV481hp4elUFmWzr9XsRNUo0oUFVRK7EXVEE3njDdjx4bi53PmtmdoXDbDzd4COpTPOecYiIyFV2xjMS9OLpGktt41bJT7k9nrYdPaGanreYegeeWtZAMqO7XGrrdWydSsCjurL2xSnDXW1o86k13xRpZrkdnk4EnrrRksNGiBohmszksp221107BK8qML2UKvBezSGezkI3zJ/12ZjW4KOpDANPIqJNamIxBY81KXUmlsZUDSfbeNrKeJYTeFrBjs+joD/sr/o5S+moc+Bp9166tU4FcDDjmTEaOlioHIoicMmW8gYMtaq0NchkuLu2HZxeVYHEcvkgERUXTawPPHvb/XjZ5QOYjmkwjOa9ibOyTWYmxownERG1gImFFHweJVceW0vWc7nHs7xSW8DM8igO7RDNx8541mulyqIVELpbauvFUqr2gUnJjN7Ug4VsBwbCODGxuGEzeXbGs9bhQj7V/DmabrGLUKJGiSY0eNT1YdCbbtwO3ZAYjSYxsdCcmc+Vx9VqGU/3zo5ERNTUJhZS8KnLgefx8SXcuq+/qseyp+uV2uMJLAeebq5SAYDONnMvYj2yQLfdcSQ3xMjV4UJBDzK6RDpr1DQYKJXREWjSHZ4r7RsI498fGsH00sYMqFJZAx5FIFRGpUAxXusCemoxjUu3hJ04NGoC9pqgYmuHqDr5Sm0B4PDuHrT7PRhbSOGmv/4RdveFcPOeHjxnTy8yupH7WWuklYHnTIv1eDLwJCLapCYXzYynR1WwrTNY02Tbv/3eSQBAqJx1KtbJ3s2JtkD9ezztnht3S22tLG4qU3vg6Wn+wHP/QAcA4MQGLbdNZ3T4HSh59lpDomoplyfaTKKJTN52AyEELhsMI6HpePVV23Df6Rl89dFRfP5+c9rtnj735hKUyy61FWDGk4iIWkA6q2M2rmFbp9lbdmCwA0+PLVT9eIZVCllO5sbu8XRzhydglv2qiqhb4Jm1A09X16mYn9/FZBb9NSS2khkdQYcznm5kZfYPmB/kiQZNts0aBubiGqSUrgxiSmeNmrOdAOCzsjAbNTNM5CTdkFhMZfBrN1+C97zk0nWvF8KsQnjH83fjHc/fjYxu4OjFKF5/x/25lopGmlgw5zOoisBsi5XXM/AkItqEphbNk5XPypRctrUDd5+YRFKrLiCxs33l7PHMldq6HHgKIRAJeuuX8bTG27u9TgVYHmRUrVTGaIkez66QD/1hf8MynjNLGs7PJXB6Ooa9tUT6eRiGhJY10B0qP+NZKLhXhPlnioEnUUmLyQykBDrLrE7xqgqu3dGNgFfJDQRrpHFrPoMigJkWy3g2vlCZiIjqbtIq1bGHklw22AFDAicnq7vA162MZzmTUoNeFYows6xu66xj4FmfjKf52LVOtk1qetNPtbXtH+zAiQatVElaF5mjUedLWKdjaUg4s0tVCAGvqjDjSVSGqHVO6ApV9rs64FFzA8EaaXIxBa9qtslwqi0RETW9cWs4QS7jaQWB1U62NQwJRaCscsT2gAfX7ejCYKS2FRLl6KhnxtOQUBWRG9bkhrBDgWcqo9fUI1pP+wfCODUda8hk25QVeI5Hk44/9sV58zF9DgSegJmVYY8nUWnzCTNL2Bn0VfR+fo+CdNZAVm9s8GlnPL2Kgtk4M55ERNTkljOe5mlgqCuIsN+D49aAodvuOJKbqFgO3VguoS2HG/1y+dSz1DZrSESCXlc/NudKbVtjnQpgBp5a1kAqU/+LPTvjOebCWoVRK5h1IuMJmNULLLUlKm3BmnTe2VZZxtNv/c4cb+CaFS1rYDaehk9V4FUF5uLNvXN0LQaeRESb0MRCCgGvkgsWFUWYA4aqnGxrSFlR4Fkvde3xNGRu+I9b7MBzscbAM9lCGc991oAhe2VPvcTSWWR084JuzIWM5+i8HXg683XwelhqS1SOXMazrfKMJwCMzCUcP6ZyTS2lICXw7hftxX993m7ohqzbOc4JHC5ERLQJTSymMNARwJd+8+bcyw4MhvHlRy5WdfdUNySUOmUxK1H3wNPFVSqAObxJCAd6PF2YauuUtQN09va3Q1UEEpqOnjoex9npeO7f4wsuBJ7RBFRrMmWt7rz9MP75x6fwwe+fbKkyaqJGsHc7lztcyBawA8/5xgWedrXSlo5AbsLuTCyNrlBlQXQpbu2QZcaTiGgTmlxMYUtHYNXLLtvagYSm43wVd3P1Js14draZgWc9SpGyhnR1sBBgZqbDfk9NgaeUEqmM0TLBid+jYndvCIk6T5M8MxMDYA7DGndhuNDofNKxMlsA6Av7AXClClEp0WQGQlS+c9nux77QwIynXeY7GAmi1wo2W2myLQNPIqJNaGIxhYHImsBzMAIAuT7PShiGzO3nbCaRoBdSAktp98s0zYyn+4VE4YC3plJbeypjq0y1Bcxy26RW38Dz9JQZeHYEPRiNJh0fbjQadSfw5IAhouKiCQ0dAW/FN0uFEPB7FIzMOV8BUa4JK/Ac6Aigp938mZ+Nt87NptY56xARkSOklJhcSK8LPC/ZYpY0VjPZVpcSShNmPO072ot1KLfNGobrGU/A7PNcTFYfSNsBXKsMFwLM1TvprJFbWVMPp2fi8HuU3AqF+YRz30NSSozOJx2baAsA/cx40gZT6ZC7ckUTmYoHC9n8HqWhGc+JhRSCXhUdQQ962s2M5ywznkRE1Kzm4ho03cDAmlLbgFfFnr5QVQOG9CbOeAKoS59nPXo8AXOXZy1TbVPZ1gs8920xBwwlVwwYOjmxhPd/5zgeH4m6Emydnooh6FVzwaGTA4YWkhnENd2ljCcDz40gqxuYWEi11MTSVhFNZioeLGTze1VcbGCPp12tJIRAV5sPQgCzLbTLk8OFiIg2mYnF5VKdtS4b7MADZ+ewvbutosc0DEBtwluZnXUKPA0pYUi4PtUWMDOetYzztzOerdLjCSxPtl1MZfHpe8/iK4+O4snRBXgUAd2QNU/5XcswJM7OxNHV5lsVeB7cFnHk8S86PNEWAHpCfigCmFpsnYtQKuzBc3M4P5eAR22+G3qtLprQ0FVt4OlRcHE+iXg6i5C//mHUxEIqd+5WFYHuNh9mWmiXZxNeJhARkZtyU/EieQLPrR0YX0ghU8GCbCmlWWrbjBlPq5wq6mCZZD66lZWoS8Yz6MVSuvqPx95N2UqB51BXEIowA7b3ffNp6IbEn73yMtz/xy9Cm1+FlnV2x+doNIl01kDQq+Sykk7u7rN3eDpZaqsqAr3tfpbabhD2yo5UnYdqbQa1lNo2erLt2vkMve1+zLTQzzwznkREm8zEgnmSypfxPDDYAQBIaDoiwfIuiu1hNc041bZepba5wLNOPZ61TLVNZcyvV7OuU8lHCIGhrjZoWQOfetv1uGxrR+51PlXJBdNOOTNjrlIJ+FR4FAGfqmDMwZUqyzs8nb3/3xf2c7jQBmH3EdZ7qNZmUFPG07phNzKXxP6BjhJv7SzDkJhcE3j2tPsw20IZTwaeRESbzMRiCkIs94SttBx4ZnNBWylxa2JsMw4Xqlfgmc1lPOtTaruUykJKCVFFltnOoAQcDnrcNmhdbK0MOgEza1jLsKV87Im2Qa8KIQQGIgFHV6qMRpMIeBV85V03V/U1LKQ/7Md0C/V7UWH25NRkxtls/maX1Q0spso/v63lb+BKlbmEhowuV9007mn348mL0bofS7UYeBIRbTKTCyn0tfvhzdOU2dvux5YOPxIV3GW377Z6mjDwDHpVeFXhfuCpm4FnuC4ZTy90QyKh6VX1GOWm2rZQxrMYn6pAl9LRnqszMzF0BDy5wPANHz/i6HCh0fkktnUGHQ06AfNm0rEqplJT87mwotQ2qxvwNGMTfQtatKpFipXa3nn74YKv8ygCIZ+aK4Wup9wqlZUZz5CPU22JiKh5jefZ4bnSgcEOxNPlB56nVmSHmo0QApGgDwtJd0/MWcPMSnSHqivfqkTYGmBUbbltK061Lcbuk7SHZjnh9FQce/rbc4Hh1kjQ8R7PbV2VDfAqR384gNm4liv9ptZ1cT4BRQASwMh84/ZGbjTRhHkuqLbUVgiB4e62xgaeHSt7PH1YSmdbpheYgScR0SYzuZDCljz9nbbLBjuQyugwZHkXr3bg2azDaiJBj+sZz4yV8ewNrS9fdprdR1rtSpVWnGpbjJ25n3QwMDwzE8Pu3vbc/wc7A5hYTDkW0I1GzYyn0/rCfuiGxFwL9XzRegkti5mYlisHtX/HUu3sfbyRKoYL3Xn7Ydx5+2Fs725ryHChcevm2uCa4UIAWqbPk4EnEdEmM7GYyjtYyHbZ1g5IlD/U4tmpGPwepSmHCwFmn6f7pbYGBOrX4wksl4xVKtWCU22LcTrjGUtnMbmYxp7+UO5lWzuD0A3pyOCehJbFXFzDUJfzgWd/bpcnBwy1Mru/s5OBp+Ps6pfOGiaQmxnPJGSZN2edMrmQgqoI9LQv3+C0/90quzwZeBIRbSKpjI6FZKZoqe1lKybbluPUVKypyzbrEXhmdAmPKhzv2cvH7iOtdndlK061LcanOht4npk2L/JXZjy3RswgccyBAUN2r6gbGc/+DvMilCtVWptdxtnm98CrCgaeDrJXa1VbagsA27vbkMzomKlzb+X4Qgpbwv5VN3l72s2Po1X6PBl4EhFtInaPSLFS2x09ISgCiGulM2q6IXF6OtbU2bPONp/7gadh5B3W5IaOGns8ky061bYQVRFQFeFYqe2ZaXOVyt4VGc/BTvPnZdyBlSoXrX49NzKefe3mcU4x8Gxp9mAhv0dB0Kvi1DQDT6fYpbbV7vEEgOFu82e33pNtJxdT6/Zv2+0dMw5mPA1D4txMPDex3kkb46xDRERlmcjTI7KWqgi0+VQkyhgwdHE+AS1rNHX2LBL05u5yuyWrG3Wb6huuscczldHhVcWGmpLpUxXHMp6np2NQFYHt3atLbQE4Mtl21M54uhF4hpnx3AhG5hMIWTtkg14Vp6didS/r3KgWEhqEqG0C+fZuczDYxTr3eY4vJNedu3MZTwd7PGfiaUwupR0d2GbbOGcdIiIqqZyMJwC0+TyIa1kYJYapPDtpT7Rt3tNJR9CLpVTW1UmfGV3WL+MZrD3jGfA0742CanhVgYlFZ4KtM9NxbO9uy/WOAuZAp3a/x5FS29H5JDyKQH+4+M9gNYI+FWG/h4FnixuZS2C4uw1CCAR8aq7veLNJZ3Wcm40jozu3yzSazCAS9NY0k2DImkh9YbbeGc/0unN3yO9B0KtixsGfeXs4WTSRKXkNUKnmvVIgIiLH2Xcwi/V4AkDIp8KQKDm5zy4Ba/YeT6D6DGE5MroBr1qfjGfQq0JVRE0Zz0ATZ6ir4fMojpXanp6OYXdvaN3LByMBR0ptR6NJDHYGXBvG1dfhZ+DZ4kbmkhi2smr279bN2Oc5H89gcjGNkxNLzj1mIlPTYCHAHMzWH/bXdbLtUiqDWDqbt1qpp93naMZzzuoXzRoST1yMOva4AANPIqJNZWIhhXa/B+3+4tNX26zXl1pG/+xkDP1hf1OXbdqBp1t9nubqGdTtcyCEQDjgwWKy2qm2RlPfKKiGT1UwHUvXnNXWDYmzM+YOz7W2dgYdy3i6MVjI1tfuLzrV9rY7juC2O4649vxUGyklLswlMNzVhjtvP4zP/voNAIBTU84FX61CszKdFx3cYxpNaIjUMFjINtzdVtcez8nFwtVKPe1+R3s8VwaxPz4x5djjAgw8iYg2lcnFFLZ0lN412WYFJsfGFoq+3anpGPbmuUhvJvbd7d/6wqOuXHDbZUneOq6TCQc8Ne3xDDRxaXQ1fB4FuiFrvvgaiyaRzhp5M55bO53LeG7rbKv5cQrp7whwuFCduBHEz8Y1JDM6tlsDbPrCfoT9nk05YCidNQNPJ3qrbQvJDLpqGCxk226tVKmX8QV7PsP6m1a9IZ+jU23t1SwBr4K7TzLwJCKiKk0spkqW2QKAYg21eLpIxlNKidNTMVxSYeBpL+GuF3tReFbPnw2r9eLRDjzrmfUN+7019XhutIyn3V87UWO57Wnr4j5fxnMwEsRMTEM6W96aoXwyuoHJxZQrg4Vsfe0stW1l9ioVu9RWCIE9/e04PRVv5GE1hP2zNupg4Dmf0GoutQWA4a4gxheSjvafFmP/bsu3g9sstXW+x7O33Y+nRhdz2VYnMPAkItpEJhdSGOgo76I35FeLltpOLKYQS2ebPuNpl9rqhjsXCHaWrV49noA5YKjawDOV0Zt6/U017EFAtV4gnbZWqeTPeJo/N7UEtxMLKRgSGHKx1La/w4+EpiPmwioEcp9dvmlPTgWAvf3tmzLjqVkZz1FHS20z6HSo1NaQzmZji7F/7/TnqVjqafdjNqY5NghoNq7Bo4hcZtjJclsGnkREm4RuSEwupTEQKV1qC5iTbaeW0gWzJ/awi739YceO0Q124Jl1aaptrtS2nhnPgBeLtQwX2miBp1pb4Glnvc9Mx9DZ5kV3aP2F6VarUqCW7Is9jMTNjGc/V6q0tOU9r6sDz+mltOv7iJtJOqsjY1WpOJXxzOoGllLZmnZ42uyMdL36PCcWU+gO+fL+7u5t9yNryKrPCWvNxTV4VXOH7GAkgLsZeBIRUaVmreEr+Up18mnzFe/ztFeptErG063A0+6t8dQx42n2eLLU1uZVBVRF1Lx3zp5oK8T6r+WglaUcr2HAkJ25cXW4kBV4Trmwg4/cd2E2gd52/6rdyHv7zN+xm2myrZ3hU4RzgacduDtRamtnpOvV5zmxkCp47u61dnnOONTnORvT4FEFhBB4wf5+/PzUTK7sudbWFAaeRESbxESRqXj5hKwLn6fH85fbnpqOIRL05k56zSrgVeG3hs+4YTauQQBQ8wQrbumoKeNprLqo3QiEEOgP+zGxUFuW78x0HHv68t9IsdcY1DJgyL6AHux0foenzd4POu3glEuqn5H5RG6wkM2+uXd6EwWe9s9Ku9+DubiGhFZ76XjUCjy78lQ0VGpLRwBeVdQ141loPkNPyLzZNOvQz/xsPJ0blvei/f1IaDoePDvnyGMz8CQi2gRuu+MI3vulJwCU3uFp86gKhrqCBfs8T02ag4XyZYeaTSToLThcqFazsXTu7nC9hAMexNLZqnp6kpmNNdXWHla1pSNQU49n1pCYWkpjd4HAM+BV0RPyYbTGjGd/2A+/x73AfznjycCzFV2YS+TKOG3D3W3weZRN1edpry4KB7yr/l+LaMIMPCMOZDxVRWCoq61uuzwnFooEntbNX6d2ec7FtdywvJv39MLvURwrt904Zx4iIirKnr5XbqktAFy+taPgZNuVq1TqPam2UpGgF1mXhgvZ/TD11BHwQkogXkUWIKVtvB5PwPy+rqXUNpUxS8n29K0fLGQbrHGlymg06Wp/J2CWEXpVwZUqLSijGxhfSK0aLASYQc7u3tCmKrW1y9LDAXOntBPlttGEGZg5MVwIMG8IjNQh45nO6piNawXP3T25Utvaf+azuoH5RCY3LC/oU3F4T49jA4YYeBIRbRJa1oCqCPS0lzdcCAAuG4zg7Ex83YTM2Vgac3Gt6fs7bZGg17VS25m4hmt3dNU18LYvxhar6PNMZTdejydgZvIna5g4m9TMwLNQxhMwV6rU1OMZTbra3wmYq5B6uVKlJY1HU9ANieGu9Xte9/S3b6rAcyyahFcVueoMJybb2hlPJ/Z4AuZKlZG5hCv7XFeyqxcKZTy723wQwpkez3nrc7RyPdgL9/fj3GwCZxzIuDPwJCLaJDTdwJawH6pSfkno5Vs7AAAn1vR5Lk+0bY3As7PN6+JU2zR6HOgZqoRdfrZUYZ9nRjeQ0eWGzHhu6QhgKZ1FvMo1IqmMDo8isKNn/UW/bVtnsOr1CYYhMR51d4enrT/sx9QShwu5LZaq/vstH7tsc22pLWAOGBqZT+Qy8xvd2EISPo8Cn6pAVQRGo7VnFqO54ULO/L7e3t2G+UTGtWoam13JUSjj6VEVdLX5HOnxzE1pX3Gd8IJ9/QDgSLktA08ioibm5J1ULWtgS5n9nbbLt5mB59o+z2etwPOSLc29SsXW4WqPp1ZRFtkJdsaz0sm29kXrxsx4ml+Dasttkxkd27vbipZND0bM4LbSgB8wh/1ouuHqDk9bXzhQMOMZS2dx9OJC7gKTqndmJo5nJmOOBYN22eZw9/rvkb397ZDSHIC1GYzOJ+H3qBBCYKAj4FDGU4Miln9/1sq+QZDOuBt4jluVHINFzt89IV9uwnotZuP2Xurl34PD3W24pL8dPz5Ze+DpzGeeiIianqYbFfV3AuYd1u6Qb91KlVNTMbT51Nxuw2bnVo9nKqMjoel59z66aTnwrCwASlkXSIEWnGpbqpTZntY8uZAqOJm2mFTGwO4i/Z3AipUqC6lc1rkct91xJPe1qkfGsy/sx2MX5vO+bmIhhWRGx/HxRTxnb6/rx+I0+0Zco3vKDUMildUhJfCFBy7gN567q+bHvDCXgEcRGIzkDzwB4N1ffBQ97f6Gf/xuklJiNJpEl9WLua0r6FCPZwaRoBdKBVU/xdi9uOmsgZBL9x5vu+NIrq+82I3jnnZfLmishR28fuiNV+PSFTeWX7i/H5/8+VkcGu6Ep4bPHzOeRESbhJY1yl6lYg8LEkLgssGOdRnP09ZgoVaYaAuYgachzQsaJ9lTBOu9UqYjaJfaVpfxDHg23unfvqlSTcZTSjOIKBWwbrPWoFRzEaxlDesxCpfyOqU/7MdcQssNFLPNx7VcprPakmEyTSymICUgAHz0nlOrSm6rrVQZmTeHT+Vrh9jVG4IizMx8KyvnczMX15DOGrj9lt248/bDGOoMOjPVNplxbLAQgFwvrr3j0i1a1kDIpyLsL5wv7Gn3O9Ljaf9+WHsz9QX7+5E1JBaT1a3xsm28Mw8REa2jGxKGLH+VykqXb+3As5MxaFkjd9Hw7GQst9S8Fdjj853u87R7arrdut1dQG64UIUXAfZF60bb4wksZzyrCTzTWQNSonTG08pEVTNgKG0HnnXKeEqJdaV3X31sFPZPwEQNg5gIODdrlrxu6wpiJqbh0/edq/kxL8wl8g4WAsx1PsPdbUi6XNbZDOwg0x7Eta0riInFFLJ6bR97NKE5skrFFmnzoiPgcb3UVtMltkQCRW/09rX7HZlqOxvXIARy2WbbtTu6EA54cpOBq8XAk2iTcnsKGzUXO9tSaaktAFy2tQOabuQGCmUNiYnFFPZuaZ3As7OtcOCZzOhVT7y1M5499c54WmWelU613cg9niG/B2G/p6rJtvbnpdhEW8DMJCoC+KcfP1vx78901kAk6EV7kayFU/rtXZ4rBgxJKfHvD11AyKfCowiMMfCsyflZsx+zN+TDi/b3446fnMZCjdmgi3l2eK60p689N315I7MHCW21As+tnUHo1nmnFtFExrGJtrbh7jaksi4HnlmjaH8nYPZ4LqWyNWdf5+JpdLX51mXdvaqC51/ah2gyU1PlkGuBpxCiRwjxHSHESSHEUSHEfwoh+tx6PiIiKkyzd3hWlfGMAECuzzNlXfi0YsZTXzNg6CfPTOPJiwu4WOXgCjujVO+ptn6PAq8qKi61tS9aN+JUW8Dsgarm4tTOBJf6nvaoCgY6ArkbOZXQsobrq1RsfVbguXLA0GMjUTwzGUN/2A+fR6lpHymZGU8BwOdR8J6XXorFVBaf+NmZqh8vns5iNq7lHSxk29vfbvWVujMorVmMrs14Wn/XOmAomtQcLbUFzD7PepTalmqTsQfc1To0bDamFZxZ8MJ9/cjoEvEabn64mfGUAP5WSrlPSnklgNMAPuDi8xERUQG1ZDx39YYQ9Kq5Ps/cRXqLrFIBVpbaLgcMj49E8a7PPwIJYCldXaZizhrmUO+ptkIIhAPeiocL2V+7jRp4DnQEMLFYeblZMmPAowh0lXEDYbAzWFXgmc4adSmzBYB+6+f8L7/19PIwngdH0OZT0dPuh9+jsNS2RudnEvB7FQghcPnWCH7xykF86udnq15pYa9S2V4k47m3z5xsm3Y5w9ZoY9Ek2nxqrlLF/rmpdcBQNJ5xtNQWMDOeZqm+OzcDpJTI6GVkPK2qm1on287GCweez7vEHEZW6Q3PlVwLPKWUc1LKe1a86H4AO9x6PiLKjyW1BNSW8VQVgf2DYTw9vhx4+lSl6AVSs1nb43l6OoZf//RD6Gn3oSfkQyKtV3XXejamwedREGpAz2RHwFPFcCHz+2AjltoCZp9nVaW2ml7252QwEih44V/o962UEumsXreMpz3sKmNl+GPpLL55dAyvvHIQqiLgUxUOF6rRudn4qhs4v//iS5HM6PjoPaererwLVuluoR5PANhj3exr9QFDpYxFk9jaGcz1NNo/N7V8z2Z0A0vp7LrexVoNd7dByuWfNadldAmJ0jeN7Z/56Rr7POfiWsEKnv6OALyqqGl3bV16PIUQCoB3AfhGnte9UwjxsBDi4enp6XocDhHRpqNlDaiKqDrTdfnWDhwfW4SUEklNx67eEDxF9h02m4i1MFw3JCYXU3jrJx+EAPC5X78RXSEfJIAT40sVP+5sXENvyNeQ6b7hgBeLFa9TsTOerfO1q8RAxI/pWLrint1kRi97xcy2ziA0vbIMhz3ca6hOGU+/x8wW2TecvvnEGBKajjfcsB2AWR66mMrWdAG5mUkpcX42sWo69N7+drz2miF89v7zVWXER6wy0mI9nnaVyUbv8xy1Ak9bwKuit91XU8bTHsTW6XSPp/Uz7Va57fJN4+K/O3qsAXe1Zjzn4lrRmQXtfg/iWpMHngA+AiAG4J/WvkJK+XEp5XVSyuv6+tgCSkTkhnTWgK+GQPGywQiW0lmkswaSGb2lBgsByxnPVMbAWz/5IKIJDZ9++w3Y2RtCu98MOB4fiVb8uLOxNLrrPFjIFq4q47lxp9oCZlZAN2RF5Y5zcQ1ZQ1aU8ZSysgnJuYm2dcp4AuaAIXudyr8/NIJLt7Tj6uFOAGbgCSwvpqfKTC2lzZsVa75nfvdFl+R2UFZqZC6Bdr+n6PCbSNALryo2RcbTXl1k29YZrLoXHwDmE+4Ennblj1s3A8ptk+kN24Fn9RlP3ZCYT2hFp7SHfB6kMkbFNz1trgeeQoi/A3AJgNuklBu7KJ2ogVhSS4VoWQNLqUxuBUc1Lt/aAcAs2UtnjZYaLASYF9qKMFdtnJmJ4Y63XIcrhsyhST7VHNRTTeBpliXVt7/TZgaeVa5T2cCltkBlK1VOT5vTmoNlZoEHreCxkqxWPVep2PqswDOhZfHESBRvuH57LjNv34RqtQFD0YSG4+OLDc/UnpsxV6n4Pat/joa72/CG67djeimdu8lTrpG5BIa6giWrJ4JetakCT6evPVIZHTMxbd1Nmq2dwZoyngtJMxPo9HChnT3mDITxxVTV09GLKbdNJuRT4fcouUnr1ZhPaJCy+LC8kHWj9qnRhaqew9XAUwjxVwCuBfDLUsral8sQEVHFHj43B0PWdqd330AYqiJyZTyXtFjGE0BuPPw/vP4qPNcakgCYg3pCfg+eqCLwnIkV7odxWzjgxbmZREUXfRt+uJB1cVbJ4JzT1pqgcj8n9gVxJQNetIZkPAPQdImppTR8qoLXXL0t97pcxrOKfaSN9M2j41hMZXGxwf2p9iqVf33rdbjz9sOrXvffXrgXEJXvkx2ZT5TVNx/wqkhp7g2zaTS7j3Prmp+VbZ1BjEWTVX/cUTvj6fBwIUUR2NYVRCpj4FtHxxx9bMAMxIUoPTldCIHeGnd52hNxCw0XAsy1VUATBp5CiMsB/DGArQDuE0I8LoT4qlvPR0RE+d3zzDQElnc/ViPgVbG3rz23p66VJtraBiMB7O4N4VWHtq57XbvfgzMzcSwkKssgluqHcVNHwLtqSm85vnD/eQDmOpaNyC5Hm6zgov/UVAxClP85sadLVpbx1KGI4hd0TusL+5HJGpiJaXjZwYFVE3tbtdT2W0+YF/bRRAYnJyrvyXbKudk4PIrA1s71WagtHQGE/ZWVwb/+Y/fh1FSsaH+nLehToUvzhsJGNGbdDFkXeFrBXbUZPbdKbQGgu82LoFfFh374LLL68u+FWrPBuiExF9fQGfRCUUrPEehp99XU45lbD1bknOZVFfg8Co5ebLLAU0p5TEoprHUqV1l/XuPW8xERUX73nJxCOOBZtxC6Updt7YB9r3lXb6j2A6uzwUgwt99wrXbrLu7jF6NlP15CyyKZ0Yv2w7gpHPDAkKgoA2BIQBFoyDCkeuhp90NVRMWltkGvWvbnpDvkgxDLJXDFGIbE1x8fxWxcg99T/nM4oT/sh4R58frG64dzL7/z9sP48m/ejN52f0uV2k4tpvDguTls6fBDEcAdP6lueqwTzs8mMNzdVnDAWjjgQULTkShzCEvWGj5VTsazzcrMH7emjLeSjG5gNJrM9R7nY2c811YH1DrZNppwp9QWMH+fDnUFcWYmjm884VzW88jpWWR0id4y13X1hHw1ZTxn7fVgJc5pIZ+KJ5st40lERLVbTGbw1OgCYlX2NI1Fk3hmMubIXV67zzPgUdb1NrW6kF+FEKio3Lacu8Nusnt2K+kr0g0JZYMGnYBZTt0f9mNiofyLr9PT8YpKj4UQ8KtK0VJbKSW+f2wCL//Qz/C7//44vKqCXb31XT9k32TxexTctLtn3eu3dgYw1kIZz+88OQ4pzRLi/rAfX39iDCNzZslrvWccnJuNY2dP4a+nfSPriZHyLs7tftDh7tKl2G019tg10s+fncHF+WTR4Gg0moQQ63sac7s8qxwwtJDMQBFA2F/9rINiutq8uGywAx/+0eqsZy2+9vgoVCHKLg/ubffXlPEsp9QWMMttz88mKq4QAhh4Em06Uko8MRLF2Zk4Tk3FNmyfyEYxMp9EXNPx+IVoVe9/z0lzTVVnsPbg6DI78NyAE1E9ioI9fe0VDRiyS74a1eNpl05XEnhKubEDT8Da5VlmxjOV0TEyn6h42JLPo+QttZVSIprQ8Op/vhe3f+4RZHQDH3nj1Ti4tQPhGkrdq9EfNi/c+8L+vGV6g5EAJuqY8aw1OPzW0XHs2xJGm0/FQCQIRQCf+NkZB4+wPPYqlR09has+7D64x0bmy3pM+yZGORlPj6Ig4FGqzjg10j0npwAAi8nCN1JHo0lsCQfgXZNNHupsy72+GvMJDZEyS1arIYTA77/kUpybTeCrj43W/HipjI7vPTWB7lD5x9zT7sfkYgqv/9h9VT2nHbQWm6wMLN9YqeZ70J2wn4iazkwsja89NoovPTyCZyZjuZdHE5lVvT/UGPYF2cpBFY+cn8tlOp8aW1g1EKdc95ycwrbOoCN7Gy8fNKfAtm3QwTRXDXfi7hNTkFKWVRI5Z5cllVkG5TQ741nJWg9dAsoGv+U80BHAqelY6TcEcHYmDimB//Hy/Xl7fwvxeRQsJDO479QMTk3H8OxkDKemYnhsJIqMLrGtM4i/fd2VeO3V2+BRFXze6q2tp2t3dGGoK4gtHfm/PwcjQdx3arbOR1WdsWgSD5+fx3tfcil+fmoGfo85LOnfHxrB77zoEseeJ9/v4bVmYhpi6WzRjKdXVRDwKniszBuG6YwZeA51lZcVb/N78NRo65Xa/uQZ80boUioL3ZB52z/Gosm8vbMdQQ9CPrXqlSrRRAZdLpTZrvTiA/24YlsEH777WfzyimFe1bj7xBRi6SyGusJlv09vuy9XXl+NubiGrjZvyR3dIevm89HRaMXXJRv89EO0ucXTWczGNTwzuYSb3v8j/OW3j6PN58H7X3NFrkev1YZLbCZ3/OQMPIqAVxVV3VnUsgbuPTWDW/b1OdJbFmnzYv9AuORY91Z1aLgTc3Gt7AubmViDM57ByjOexibIeA5EApgs8/eavUql0mFZfo+CjC7xpk88gD/7+jF87fFRpLI6OoNe7Optw91/cAtef91wyQs4N/k8CrZ1BuEpcKdhMBLAUjpb8UqeRvjOk+MAgFeuuDnwzufvgaYb+Mx95+p6LOdnzVUqO0r0ubf7PXjswnxZVUXprA6vKsou+Q75VIxGk7nSyFZwbiaOc7PmrlJdyoI9qmPRJLblCcCFMKfHVpvxXEhmEHFhsNBKZtbzEozMJfGVRy7W9Fhfe2wU/WE/OipYg2a3fWSqDDxn4+myBqB5VAXbu9uqKvdmxpNog1lKZXD3iSl858lx3HNyGumsAa8q8OvP3YVfuXYIl2wx757Z0y0nFpO5EkpqHqenY/jB8UlsjQSQ0HQcK/MX/Mo79g+fn0Nc03HrpX25lRG1ijg8ir6ZXD3cCQB4bCRa1nRJ+6KvlXo8jQ3e4wkA/R1+LKWziKezuZLHQuyJtpUOy9rSEYBPVfB/fvkKXLKlHf1hP4QQuZ+/VuiBtveRTiyk6l4GXKlvHh3H5Vs7Vn2d9va342WXDeAz953DpVvCNQ9PK9c5a5XKziKltoAZeJ6bTeDifLLk75N01qjoeya0otTxlkv7yn6/RrKzncNdQRyfWML9Z2ZxcFtk1dsYhsRYNIWXHcx/c3NbZ7DqHs/5hJYrP3fTC/b149BwJz5y9yls7QxU9ft2IZHBPSen8ZbDOyoK7uyhQMWGNxUzGyu9l9quBvjtf3u0qhVkzHgSbRAZ3cAzk0u49v/8EL/774/j8ZEo3nD9MA4MhHH1cCf++BUHckEn0Lrj9DeLT/zsDHyqgi0dAYSsC5jFCjMTPzk5Da8qcPPeykt0N6N9A2H4PUrZ/bSzsTQCXgVtvsbcw7WDhUpKbe2pthuZvVLljR+/v2RP4enpOIa6ghXvNfWqCvo7AnjuJb3Y0hFoySnB9lqYZh8wNDKXwBMjUbzyyvWl0L956x4sprKYWqrfx3B+Ng5VESV3stp9cI9eKN3nmcoaFa04sksdW2nA0D0np7Czpw0dQS8CHgX3n5lb9zYz8TQ03Sj4ud3WFcRYlX3J0UTG8R2e+Qgh8PsvvgSj0SSmK1h5s7IH+jtPjUPTDfzyVZWV69rTb7N69aW25d5IvXJbBBfnK8+6M/Ak2iCiiQzmExm8/vohfOVdh3Hkf7wIf/7qg+gIevNeFHlV82WVLFqn+phaSuErj47iv1w7BK+qVH2Rcc/JaVy/szt3AUT53Xn7Ydx5+2F4VQUHt0XwRJkrVWbjpe8OuymX8axonYp0bbhGs7ADz3LWnZyeimFvX+vtpHWCHXiOV1m66Db7QvxbR60y2ysH173NVcOduHlPD8YXUjDqNCjv3GwC2zqDuZu3hbT5VAS9ask+z4xuQMsaFfXhe1QFO3ra8GSVuxSdYhgST44ulFxxksroOHJmNpedDQe9ePDs7LpqjdwOz0iBwLOzDdFEBvEqprwvJNwvtbXdcmkfrtneibHo+u/LcoZsff3xUezuC+Hgtsqq0XrtUttqM55xrexdw1cMmdnqStuAGHgSbRDJjA4B4H+/6nJcu6O75MWlEAI+VWHGswl95r5zyOgG3vG83QCWy6qOVTBMYnwhiZOTS7h1X2uUYTWLq4Y78dToAjK6UfICYTZW/t1hN1RVarsJejy3WAFVvqmzKxmGxJmZGPZs0sDTzNQ2f9XLt46O4dBwZ65c1b5RZHvXrXuQ0WWu59pt52fj2FFksBBgHuOXfvNmXDkUwWMlMp5Pj5m/1yvNuh/cFmn4ZNuTk0tIaDrGF1JIZ/WCb/fg2TmkMgZu3dcPAOgIeLCYyq7r88zt8OwqnPEEKp9sm9ENLKWzrg8Xsgkh8N6X7oOmGxUPQxpfSOKBs3P45au2VVxJYQ+KzFSR8dQNifmEVvbMArtM+skKdl8DDDyJNoyEpiPoU9eNIC/G51GY8Wwy8XQWnztyHi+7bCDXz+RVFQxGAhVdZPzEWqNin+ipPFcNdyKdNXBifKnk287Fyz9Ju8HvMXePVtbjuXlKbUvd9R+NJpHKGNhT4WChjcKrKuhr92O8jitVKpXK6Dg2tohX5cl22p67txdtPhXjC8l1PwtO7/eUUuLsTLzsnuBrdnTh2Nhibk9nPt98YgwCqLgM9IptEYxGk5hv4IChe0/NADDL/b/31ETBt7vn5DR8K/bJ2m0CD5xdXW5r929uLVRqa027rbTP0/4ecGKfdT5rb4YAwHP29qI/7Mf4Qgrfe2q87Mf6xuNjkBL4JWuQVr7HLsSrKvAooqqMZzShQcrSOzxtHQEvdvWGcLTCrDsDT6INIqnpVe2ia+aLjs3o3x8awWIqi9tv2b3q5Qe3RfDUWPm/4O85OY2tkQAusS6qKzl5bWZXWQOGHi/jLu5sLI3uBpbaAoBHERX2eG78jGfI70HY7ylZanuqyom2G8lgZ7BuGc/xhVTFQZK9V/AVVxQOPIUQ2NoZRCpj4NMuT7idT2SwlMoW3eG50tXDncgaEscK/O42DIlvHR1HpIwVFmtdYWWcKjkvOO3np2YQ8Cjwe4qvDPrJM1O4cVc3glbbiN9jlgrff2b1Op/RaBLtfk/BSa7brF2eFyvMeNo9j/Uejrejpw0hv4o/+PJRnClzxdPXHx/DVcOd2FnhwDObR60u8Fwellf+Oe2KbZGKW4AYeBJtAAvJDDTdQJuvwsBTFRhfSJU17p3cZ0iJT/38LG7Y2Y2rt3etet3BrRGcnYnn9nqWehxzjUp/Sw49aaShriB6Qr6SA4aklJiNa7memkZRFVFxqW29pn820pZIoGSprT3pebOW2gLA1kigLoHn1GIKF+YSGF+s7Llm4xqu29FVMANm627zorPNi7/57omaBu4ktCxmYoUHwpyzVqkU2+G50lXbOwGgYJ/nw+fnMbGYqqpy4uDW6nrsnKJlDTxwZg6RoBdbOvx46Nx83hUpI3MJnJ6Or6u+uWlXDx48Owdjxe8ve4dnofNWf9gPryoqznhmDfN3Qb1KbW2KELikvx1eVeC3vvAoklrhzDdgVq49Pb6IX76q/J3Ca/lUBQlNX7cmqVT2v5r1YFcORTC2kKpoiBIDT6IN4JlJsywwWGng6TF/QS2mKm/UJ+fNxTWMRpPrsp0AcMVQB6Rc7gcqJpbKYimdbZkx+81ECIGrhjvx+Ejxvqy4piOdNcouS3KLV1VKBlg2w5CbYqotYJbblsp4np6Oo6vN2/CvoZtKVToMRAIYjyZdv/n4lUdHAZiVOeU+V0LTkczoeYcKrSWEwO7eELpCXrz7i49VNXwGAC7OJ3F6Ol7wQjq3w7PMjGd/OIChrmDBybbffGIMAa9SUUBkf00jbd6qdyk64bEL80hmdHQEvehr98PvUfC5PFlPe43K2vPRjbu7sZDM4MTEclvDaDRZdFqwoggMRoIlhxmtZVeFuFVqW4zfo+If33A1Tk4u4U+++mTR7//ZWBqqIvCLeSY4l2trZxDprIHf+eJjyFaQ+bQznt0V3Ey1s+5v/kTpCeI2Bp5EG8BJ6xd3W6WltlZpD/s8G09KifGFFPb2t+MFefoyc438ZVxkRJMZeBSB5+ztcfw4N4NDw504PR3P3SXPZy5WeVmSG0I+D+JatqzSqjMzZoavFXZM1mpLRwCZbPEA5/RUzPEy20KBXrOWum+NBBF3+eajlBJfengEgBkATJWZHZmLm29XrMx2Ja+q4P/edhXOzsbx5988VtVxLlmfh28fHcv7NudmEhACGO4unoFd6ZrtXXkznlndwHeeHMeLDmypugrhigYOGLr39CwUYQ4K8qgKfunQVnztsdHc2i87w3bPyWkMdQWxp88M1u2fhRutfs8Hzi6X25oZz+Kf222dwYLDhQpl9exS285gY24y3XJpH373RZfgPx8bLfj9b1fRPGdvL/rC1Z9XIkEvdva04Z6T0/jLbx8v+/3sn7dKbsRdvi0CIYBYungmdyUGnkQbwMmJJSgCJce7r7W8y5N9nrVwYoBFLJ1FQtPxzuftzjuRuD8cQH/Yj2NlXGQsJDO4bmdX0y+Fb1Z2n2e8yMl0xjpJN3K4EAC0+1VIuXzzqRj74je0CdbrDET80HSjaHbh9PTmnWhrG7SGtbh58/HBs3M4OxNHn5VJyVeOuZZ9ER4OeNBvDYsqx817evHbt+7Flx6+iG88kT94LMS82WR+vxR63/OzcWyNBCu6eXP19k6ML6TWnWePnJnFbFzDq2rIbh3cFsHIXBLRRP0HDN17agZXDnXmelPfcngHEpqOr1rZbcAs7b/v9AxuubRvXfnsts4ghruDuT7PhJbFfCJTOvDsCuLoxWhF51z7JmJnqHHnxHe/8BLccmkfzs8mci0zWtbAqaklfP/YBEbmk0hnjZrKbG1bOgL4r8/dhU/fdw6fvvdsWe8za2c8K8i+t/s92NPXjrhW/o0rBp5EG8DJySW0+TwV9fPdefth/OuvXQ+AGc9mYN8xfOGBwlNoy7m7rWUNJDSd02xrcGioEwCK9tMuZzwbG3jaQWQ5u0cfH4lCFQLBCvYFtqrlybb5A8/5uIbZuMbA09qXOObizcc7Hx5Bu9+DbV1mX2Q5N0nGFlJIZYyKLoJtv/viS3DN9k78yX8+WXSa7FoPnTMnrPa1+/DohShG5hLr3ubcbAI7e8vr77TZ/fprs57ffGIM7X5PTSuv7D2PT1WwassJS6kMHh+J4rl7e3Mvu3KoE4eGO/G5+8/nbvgspbJFz0c37erBA2fn8PqP3Yc33HE/ABQttQXMUtKMLiva22pnPMMNvOmmKAL/eNtV8KoKTk4s4ZYP/hj7/9d38eJ/+Clu/9wjGF9Ioc2n4qWXDzjyfP/zFQfw4gNb8Bffeho/PjFV8u1nYxo6qxhydeW2SEWl7Rv/7EO0wUkp8czkUsHBQsVKvPrD/pbY47YZJLQsvKpA75rSzZVfv8u3RXB6OoZEkbuL0aRZ5sT9ndWLtHmxuzdUNPCcraIsyQ1+jzk+/4mRaMm3fexCFCG/uikGTm2xAs9CfZ6nOdEWADBo7Twdj7pzDlhMZfCdJ8fxqkNb4fco8KqirMDT3g3YXmC6aTFeVcGH3nA1APPrXG6A8tDZOXgUkQt88mU9z83Gy+7vtF022AGfR1m1zzOd1fG9pybw0su3VLy/c6VGDRh64MwcdEPiOSsCTwB4y007cGoqhiNWFnMhmYFXFbh5T/62j5t29yCayCCZ0XN7QAvt8LQNWV+fcnvbAbPE26OIhv/u6wr5cOmWdrT5VBzcGsFvv2Av/u9th/D1334Ort3RiSu2RdDuUHCsKgIfesNVODDYgf/2b48WvW4AzB7Pas5nVwxFkNFl2V8PBp5ELW5qKY1oIoPfunVPxT1E9h43ZjwbL1HGOpwrtkVgyOKlanPxNHyqgn1bwk4fIoDm7VVz2lXDnYinswVLNe2ypJ4Gr1MRQiDk9+CJkeIXnklNx8nJJccuaprdgBVQFboYsgPPzZ7x7A/7oQhgwqWM5zefGEMqY+C264cBAG0+D46XEXgevbgAgcrnFtiGu9vw/tdegVhaL3sC6oPn5hAOeOD3qrh2Rxe+8fjqwDOa0BBNZLCrwsDT51FwcGsHHrsQzbVl/OyZGSymsnjVodrKKrtCPgx1Bes+YOjnp2YQ8Cq4Zkfnqpe/8spBdLZ5c6tVookMrt/ZXbC8/8bd3QCAxWQ297NaTqktAKTLDHSmFlOIpbPwqM1xwy3k9+DAYAf++VevwXtfug+vuXoIh4Y74VGcD8lCfg8++WvXoz3gwcmJWNHgcDaerqp1xB4wVG65LQNPohZn3z3eN9BR1fsPRgIVj7gnZ2V1A0lNL9l7V6qs6txMHAvJrJXJbo6TbKu6antn0bu4szENbT614knSbmj3q3hmaqlohvbJ0QXohqwqg9SK7FLbfKWWt91xBP/4w2fh8yglsyuFbJQbMB5VwZaOAMYcvPm4suf9Sw+NYN+WMA4NmRenbT4Vp6diJYdhPTm6gKBPzdvvXq5XHdqKvnY/xhZSuWm0hYxFk7g4n0SH1Rf/6qu24uTkEk5MLP+uPT9rlt7uKHOVykrXbO/Ck6MLuezrN54YQ1ebN1eqWsv3UyMGDN17agY37OqB36OuOvaAV8XrrxvG949NIpbOIpnRi1bfDHW1YagriMVUBmndgKoIbCkxWGdbmRlPw5D4wgPn8aJ/+AmSGR1bI9X9rNeiGX5PDEQC+OSvXY+sYeBCnvJx21xcq+pG6mVbzeuScsttGXgStbjlwLO6DNdAJODa3W4qz9mZOCRQMuM50BFAb7uv4EXGvz14AQKoaSIemUr1ec7FtYb3d9ra/R5IiaJZD3s9zGbJePaF/Wj3ezC+kMpN2VwpqenY3RvaFDtNSxmIBFwZMHd8fBFPXFzAbdcP526EtflUaLqBczOFA0EpJY5eXHBkCNa2LvMGxHefmij6dnZ/Z9i6MfOKKwahKmJV1jO3w7O3sownYPZ5pq3+e92Q+MHTk3j5FYPwVthPl8/BbRFcmEtgIbH++xxwZvjdSpOLKTw7FcNzC0xN/9Ubt8OQMrcn95ZLi88buHFXD5ZSWaQzBgY6AiV7DO2BWMUyns9OLuH1dxzBn3z1KRzcGsGV2yKb+rx4cFsEXW0+LKYyhat4YlpFq1RsbT4Pgl616DC+lRh4ErW4k5NL6Av7q+41G4wE2ePZYHbpWaE+XZsQApdvjeQNMFIZHV96eARdbd6KpxvTegcGO6AqItczu9ZMLI3uBpfZ2nIDhor0eT4+EsVQV9CRC91WIITAzp42ZA2JD//w2XWvT2Z07Nnk/Z22rS6dA+58aAQ+VcFrrt5m/v/2w/jIG68BgKLltiNzSSwkM2ivoJqgUGbJ71ER8qklA88Hz86h3e/J/Q7ubffjOXt78Y0nxnIX6nbGc3t35RnPq7d3AjB3LEcTGpIZvaZptivZpY7HxuqT9bz31AwArOvvtO3oCeGWS/uQyhrwqQou3VL85+ym3d3IGhKLqQy2dpaeYOz3qPCqAlp2faBjGBIX5xN4xYd/hlPTMXzwdVfi395xY019tBtFe8CDjC5xMU/puWFIzCe0qqe0h/wqYkVaU1baHGcgog3s5MRSTf18A5EAllLZomV6VFxCyxbd+VjK8fFFCKCsss0rtkXw7FQMv/LR+1bdxf720XFEE5mKVg9sNpWUPfk8CrravJhPZPKWdM3FNfQ2eLCQzasqGO4OFp1s+9iFaG665mYR8nvQF/bj0/edw6mp5UDHMCTSWWPT93faBiMBjEdTZV00lsuQEl97fBQvuXwLulb8nOzpD8GjCJycKNynfnQ0CsC5tT/dIR+eGIlirMDuR8DMeF67o2tVi8IvHdqKi/NJPGr1Zn72yDkMRgJVBTGDkQC2dPgRS2cxG9fQH/bjhl3dVX08a11RwY5nJ/z81Ay6Qz4cKNLe85abdgAwd0qWavu4ydrnmdFlyf5Om8+jrMp4npuJ46+/cxyPjUQxGk3hlVduxY/ecwt+5brhTdd2Uug8Z0/0ffj83LrXRZMZGLL6YXkhvwdZQ5Z1A4uBJ1EL0w2JZ6eWqi6zBZanGrLctjqxdBbHxhbLHmCRz4nxRQS8KpQyTpAHt0WgGxIJbfXd3s8/cB67+0Lo2CQ9fPXQHfJBN2TuDv9Ks7HqJgC65dBQZ8EBQ5OLKYwvpHL7STeT4a4g2nwq3veNp3OBVcrKlGz2iba2gUgAyYyOhQLZ/WrMx81BPLddN7zq5X6Pit19oaKTbZ+8uACfqjjWP20Hvt8/lj/rOR/X8MxkDDfs6l510f6yy7fA51HwTWu6bSpjVNXfCZgZ+KuHu7CYyiCazOAXrxx0rMy7K+TDts4g7vjpGUdLate67Y4jeP3H7sO9p2Zw856eov23t+7rx2AkgIFI6aqQoa4gfFYlRrmBp9+jIp018L2nJvCWTz6AW//uHnzi52cRDnhwYCCM/3vbVehpb46KlGbR5lOhCoGHz82ve92cvZe6ys9Zu88DRSxXBRTDwJOohV2YSyCVMWrKeNp73FhuW52fnJyGIYGFZPUZ4xMThdfhrGUPGFo5Qe7Y2AIeuxDFr964Y9Pd3XVTJOiFKgS+/eT4qpdLKa0ez+a5sLlquBOj0SSml9LrXmfvD9yMgadXVfCel1yKn5+awfePTQIAkhkzU7Knr/JevY3Ivth38hwwtZTGts7gqj2Ptn0DHTg+XjjwPHpxAQcGw2XdiCtH0Kvi0i3t+F6BctuHz5sX4tfvXJ2BDAe8eNH+fnzrqFlum8ro2FnhRNuVrtlhDiyTEjVPs13rigp3KVYrlTEwuZjO+3VdSVUEtne3oc1X+kaoEAIdQfPtSu3wtPmtjOdvfv4RnJqK4T0vuRT3/Y8X4tItYXQEvWU9xmYjhEB7wJM38Jyx91LXUGp73Y4uHC6wNmclBp5ELazWwULAij1uDDyrctfT5sVMMqNj3lqxUYloQsstji7Hts4gutq8qxr5P3//BQS8Cl53zVDFz0+FKUKgK+TFXccmVpXbLqWz0HSj6pO0Gw5ZQeXRPOW2j49E4VUFLt9a3eTrVvfmm3Zg35Yw/vLbTyOV0ZG0qgV29zLjCaw8B+Sv2qh0OE06o2MxlcXrrh3KmxXbPxDGaDSZd+iTYUg8NbqAK4Yijk4E/YWDg3jo3BxmYutvzDx0bg4+VcGV1uTdlV591VbMxDTMJTLIGrLiHZ4r2aXufo+Cqx2+CXTFUATprFFTy0c57Kx4of7OaoWtacLl9HgCZoDUE/LhX996HX7231+Ad7/oktzu3lZVjwm4Yb8Hz0wtratumLOuXaqt4hGi/B2pDDyJWtjJiSUIAVxSonm/mP4OM2vDXZ6Vy+gG7j4xhYDX/FX64Ln1vROl2Hf+yw08hRA4uC2Sy3gupjL42mOj+KVDWxFp451ep3WHfFhMZXHv6eVy2zn77nCTTLUFgMu3msOQ8g0YenxkHgcGOzbtgA2PquB9v3Q5Ls4nccdPziCV0eH3OFfK2ersqpexqDPngGkruPuV6/LfCNtv3Sh9Jk+57bnZOJbSWVy5rdORY7H9wuUDMCTwg6cn173uwbNzODQcyfvzceu+foT9nlx/6K7e6kptATMrqQigt93neGXKQXuXYpmTRau1kMpgR08bhqsYsFRMT8iHwUgAh3eXF9CG/B7s7W/HSy7bUnIKbrNp5IqV9oA5Af3RC6uznst7qd0/p7XWV4uoRTg9vryQZyaXyi5nKcTvUdHb7mPGswoPnJnDUiqLoa42CGH+v1L2nrhKvoYHt0WQ1HQYUuKrj44imdHxZmuYAzkrEvQi7PfgO0eXy21nrX6YZujxtC9i2nweXNLfjscvru7z1A1zNYXTGZZWc3hPD37xykH8yz2nsJTKbtogPJ++sB8eRThy81FKidmYho6AB0Nd+YMTu0LnRJ7A0x6Qc0We7GMtDgyGsaOnbd1024SWxVOjC+vKbG0Br4qXHRzI9dTXkvEMeFUcGuosu4+xEgetaoaEtrrcNpXRMbWYKjpYqVxSSiwmM45nO4Hl0lzeDHLPnbcfxn/+1s1QFYGH19wkt2+mdtXhnMYpFEQt7MTEIi6tob/Txl2e1fnB0xMIeBV0WsHJg+dmK36ME+NL6An58J+/dXPZd8EPbo1AAkhoOj5//3lcORTBldbeSXKWIgRectkW3PX0JP4qa8DnUTBrnaR7m6jHEzB7OL/71ASklLnvpWcml5DQdFxlrXNo9DLzRvrjVxzAj45PIp010O1ldYBNVQS2dAQw5sA54NRUDKmsgYFI4bLHbZ1BhP2evAOGjl5cgN+j4BKHBz8JIfALlw/gkz8/i4VkBhGrD/DxC1FkDYnri0yY/aVDW/Efj1wEgKqHC9ncWnXV0+6HT1VyGc+ppRQ+f+Q8Pv/AhVwZ5dGL0ZrOE7F0FoZEyf7OZrSZf++t1Obz4PKtHev6PGfjaUSC3rqs22LGk6iOnMyEpjI6zs0mcmVLtRjo4C7PSklpLgF//iV9UBWBcMCLp8cW8/YtFXN8YhH7B8MVlV7Z4/MnFsxF3m++kdlON73iikEsJDO5ctu//s5xAM2R8Vzp0HAnFpKZVZMFH7dKb68a3lyrVPLZ1hnEb9+6FwCY8VxjwFqpUqu7rFLWrrbCPxtCCOwbCOeqPVZ68uICLt/a4Vj55Mqyxl84OICsIfGj48vltg+em4MQwLU7Cv983LynBx5FwKuKmqqL3Bbyq1hKZfGHX34Cz/3Aj/GRH5/CNdu7sG9LOxQBfPbI+Zoef9EaoHd4d+kBMtS8rt3RhScuRlfNLZiNV7/Ds1IMPIla1OnpGHRDOpLxHIwEMLHIwLMSx8YWMbaQwksu2wIACAc8MCTWlbAUoxsSJyeWsL/IPrR8hruDUBWB2bhZ0rZyQmIj+0c2qudd2ruq3DZjmGs5mi7wtLIZT1yM5m5yPX4his42L3bWmKnZKN7x/N0Y6go23deu0Zw6B9x1bAIhv1oys2cGnkurdofqhsRTYwuuVW8cGurEQEcA33tqIvfz8eDZORwY6EBHoHAG3KMqGO4KNv3wmpDfA0038K2j43jDDcO4+7234hO/dh0623zobffjm0+MVTUAz7aQzCDkU+tSjknuuX5nN1IZA8fGltsy5uq4HoyBJ1GLembSLFNyJOMZCSCayOSmPVJpdx2bgCKAFx2wAk+/Bz5VwQNnyw88z83Gkc4aFX8NhRAIWb0wr7t2mH0xLvN7VLzYKrfN6AayugFFNF/W7NIt7Qh4lVyWEzAznoeGOrlmxxLwqtjWGaxLSVkr2doZxFg0uSoQrNT4QhJPXFxAd5Fsp23/YAeWUtlVlTZnZ2JIaHquosNpiiLwCwcH8JNnpqEbEoaUeOxCFDcUKbO19XcEyl710Shbwn7s7g3hyP98If7i1Qexq3e5H3VLhx/prIEvPTxS1WOfmY5hKZ0tmsluBrzxWtp1Vnb/kfPL5baz8XTdhuXxNy9RGSoeJ5/VEU9nazqJl3JiYgleVWBnb+276Oxx+sx6lu+upydx/c5udId8uPP2w/jyu27GoeFIRQOGTlgTbQ8MVr7mIuQ3S75+9abtFb8vVe4X7XLbUzPI6LIpAxePquCKbZHcZFvdkHhmamlT7u+kygx0BJDOGphPVNYqsNIPyiizte3PDRhaLrc9ag3GyrfWxCkvu3wA6ayBaDKDRFpHMqMXHCzUajyqgr6wH515Pv9tPg9u2NWNz91/HrpR+XXJFx64AAFzEFW5GAQ2p/6OALZ3t63q85yLa+gO1WdmQfOdOYlKqNfE2Fo8MxnDU2OLuPkDd+PPvv4Ufvbs9Kp6ekeeY2IJe/raHbkAHiixx41WuzCbwImJpVyZre2GXd14cnSh7EXex8cXoSoCe6sYpDEYCWD/QBh7+riL0C0rL5xy5bZPjiOjG/CqzZlBPDTUiWNjizCkRCydhZTA1dZgoc2GF77ls/cnvvWTD6x73UIygwtziZI3Uu86NondfaGyKjDsFpGVk22PXlxAm0/Fbhd/p92wqxs9IR/m4xqW0maQff2uzdH//NbDO3BxPol7Tk5V9H5JTceXHx5BV8jn2nAkqq/rdnTh4fNzkFLCMCTmE5maejwr+V3L7yAih12YTSCh6egJ+XDlUARfengEb/nkg7j2L3+AU1Ox3Fj2Wtx2xxHce2o2N5a+VvYeNyeGS2wGdz1tjuR/6WUDq15+464e6IZctyOrkBMTi9jdG6qqZNOrKrnJjOQ+u9z2+8cmkdFl0+6OOzTciXTWQFLTEbNugDDjSaXY5wBNX32D9JnJJTwzuYTxhRTueWa64PsvJDK4/8zsut+JhUSCXmzrDK6abPvk6AIObo1AVdy7qaMq5pTq+YSGhWQWO3va0B9u7t5Np7zs8gH0h/0VDxn6xhOjWExlsaWCbCc1t2t3dmEmpuH8bAILyQx0Q9at1LZ5x3MRtSg7KBnqCuKOt1yHVEbHz5+dwV1PT+Arj4wina098MwaBjTdcCzwHOjY2KW2dobcqezHD56exP6BMLavGdhy7Y4uqIrAA2fm8LxL+ko+zvHxpaLTFKm5vOKKQXz1sVEAQLu/OU+fdpAZS2cRT2exqzeUt/SOaCW73WJlZc5CMoN3fvZhqIqABwIf+dGzuPXSvrz9wj8+OYWsIfHSy7fgf7x8f1nPuW8gnGs3yOrmsJNfrcOE7pcdHMC/PzSChWQGL7t8S+l3cFAjM/BeVcEbb9iOD/3oWZybieOPvnK05DFJKfHZI+exb0sY4UDz/M5jJUNt7PLyh87N5SpiOFyIqELNUoL7/WMTaPOpuSxWwGtmSv72dYewtTOAWFrHuZl4WY9V6GOyhwDtc2CiLQAEfSo627xNWWrbLF9XwDyW1/7LvXjo3Bxeetn6C5aQ34OD2yJ44GzpfZ4LyQxGo0nsH3Tma0jue94lvbmAs1lLbe2JrfF0FrF0ltlOKktvux8Cy4GnYUj8/p2P4+J8Epf0t2NrZwCPXojivtP5f7fd9fQE+sJ+XFXBRNp9A2Gcno5Byxo4NR1DKmO42t9pe86eXqhW8LxR+juLWVkG+aYbt8OjCHz+/vKyno+PRHFsbBFvPrzD1QFlLIuvr7197egIePDI+fncXuoe9njSZtdMAUe5ppfSePj8fMHhCj3WwvmvPT5a0/PY5bpOZTwBs9Rqgrs8S4omMjAk8JICJWU37erGEyMLSGWKZ7btErMDFa5SocYJeNVcX2+zltoKIXBoKIL5RAYZXTLwpLIoioDPoyBtBZ7/+KNncfeJKfzvV12GcMCLvnY/tnT48eEfPbvufVMZHfecnMZLLtsCpYIy2f0DYWQNiTMzsdxgoYMuTbRdyedR0NlmtimUM9F2I9nSEcDLDg7gSw+PlDVk6HP3n0fIp+I1V2+rw9FRvSiKwLU7uvDw+Xn86deeAsCMJ1Fe0hqYUeqivlF+eHwSUgLdofy9d36Pgo6AB199bLSmibfJjA5FwNHx7oORwKrR9pTffELDYCSAg9vyB4w37u6Gpht47EK06OPY0xyrmWhLjfOKKwYBNG/GEwCuHOpE1rqoZOBJ5fKpCjTdwA+ensSHf/QsXnftEN58k1n6qigC73z+Hjxwdg4PrlkZdd/pGSQ0PW8VSDH2/uIT40t48uIC2v0e7OqpfUp7ObZ1BjHcFcT27vL227ZCRq7cY3zrTTuwmMpitsROz7m4hm8dHcdrrxlq2tYCqt51O7txaiqGpHU93cseT6LVnhpdwF99+ziOjZkX7C/4u3vw/Et6ccu+Pty0u8fV555PaJiLazAMWfSO7l3HJjDcHUSwyLCY3nY/zszE8dhIFNdsr66/L6HpaPN5HC19GYgEcmsYWpXTvZxr6YbEQjKDVx3aWvBzf+2ObggBPHB2Fof3FP6+PD6+hM42L7Z0cGBDK3nh/n7s7Glr6n12drApBG9sUPl8HgULyQx+/87HccW2CP7ylw+u+j33phu246P3nMJH7n4Wn/uNG3Mvv+vYJNr9nqK/7/LZ3ReCVxX4m++dwGIqgyu2RSrKmNYi6FMR9AU35X7bG3Z1Y9+WMEbmE+jLE2zY59EX7O+HljXwlsPmzYdmD7ypMvY+z3nrBkRXnTKeDDyp6U0spPB3d53EVx69iM6gF9u72yAA7Oxpw50Pj+AzR87DpyoIeBXsdmCnZT7TS2nMJzL44fFJvPTy/CWWS6kM7j01i7ce3oE/feVlBR+rK+SDP5rEVx8drSrwlFIiqemO/5IY7AhgNq4hldGrmrLazLSskSshq8Vi0iyzLTa5MRL04rLBjnVZgbWOjy9i/0B4U174tDJVEdjS0dxTMO0+uZDPw/UHVDafR0HWkPB5FHzsLdeuOw8EfSr+6/N24wPfPYHHR6L46+8ch5QSZ2biuHVfH/yeys4bXlXBnr52jEWTSGg6rqygP5SqJ4TAWw7vwJ9+7anc5Ou1pJT4wgPnceOu7tzqG9pYDg13wqsKxDUdqiLqtpuaZyRqWrohcXE+gRf83T34xuNjeMfzduOeP3wBBiMBDEQC+H9vvwGP/9lL8fnfuBFve85OLKWzGHOpVNTuqfynH58qWCJ7z8lpaLqBlx0sPk7eY41z/9bRsYp2e2Z0A49emMdH7j6FrCHR5nBwaO/ynFpMO/q4jXbPySk8ObqAp8cX8fTYYul3KGI+oUFVBG7cXbwv6MZdPXj0wnzBr+/rP3Yfjl6MMhtFruhp9yMS9BQs+d/sWqFsshGCXhUCwD+98eqCbRxvvmkHOtu8+IjV6xlLZzET0wrekC1l/0AYiylz3+wVdejvJNNrrt4GVQiMRVNIaOuDz4VkBiNzyVy2kzaegFfN9VR761RpADDwpCZ2fHwRo9EUXnigHz967y3441ccWLe3MOBV8dxLevHHrziA3pAPM7F0wTt41VpIZJDOGgh6VRy9uICfPjuT9+2+f2wCve2+ollM+4Lntddsw3wig58U2YsGAAkti7FoEm/91IM49Od34bX/ch/+4QfPIOhVc8MRnJLb5dmEk22rYRgS//jDZ/D2Tz8Er6rAowj8r68/BaOMgQr5RBMa5uIZdAa9Je8M3rCrG6mMgV/+55/nHZCVzhowJAcLkXv2D3TkfqaJytHb7sPV2ztx897egm/T7vfgN56zCz86MYV4Oou5eAZeVeDWfaXXR+Wzb8XvwHpMtCVTyO/BYCSAaDKD5/3Nj3HHT06vCkAnF9PoC/vL3stKrckut63nsDyW2lJTGosmEdd0DHcF8c9vuqas9+kLBzAd0/CNx8fwphu3O3Ysx8bNaXvD3UHEUll85EfP4vmX9K4qkUxnzal+r7xysKzl18+7pA/dIR++9thobkrmWvedmsGTo2aGrs3nweuuHcJNu3tww65u/PYXHnXgI1vNznhuhF2ec3ENv3fn4/jpM9N47TXbcGE2gbm4hkfOz+M/Hr2I1183XPFjfvynZ6BLia2dpcss7UmJi6kswoH1NwjsDHotq1SYsSEiJwkh8g7NWvu75teesxMf/9kZjFolsof39KIjz++5cti/A1VFlD3oh5yxrSuIjqAHveEA/vq7J/Dxn57BO5+/Gwkti2gyg3e/cC9L9Te463Z2419/drauw/L4HUVN6ZHz8wCwLsNZTLtfRdCr4osPXnD0WI5ZwV+734Pbb9mDh8/P44F1U/1mEUtn8bIyy428qoJXXTmIHxyfxGIqs+71s7E0fu/OxxHwKrhmeye+//vPx1+8+iBeccUgetvdGUZjB56tPtk2ls7iVR/5Oe4/PYv3v+YK/P2vHIKqCPS2+3Dtji584LsnsJBY/zkvZnopjf937zn0hHxo85W+X9cd8mHfljCWUvmz7/adZfbOEFGr6Qh48fabd2LeqgaqdJrtSvutlWAhn8p+9wYIB7z47K/fgK+862Zcvi2Cv/7uidwN7zc6eAOfmtO1Dch4MvCkpvTI+XkowhxmUC4hBPo7/HhydAFPWjvBnPDU2AJ8qgKvquC264fR2+7HP919atXb3HVsAiGfipv3lj/V7zXXDEHLGvjuk+OrXi6lxB/+x1FEkxns7WuvW8N3u9+DcMCD8WjrltrOxNK5Ps7/eNdhvOnG7bmLGSEE/s+rDyKa0PDBu04AKH9X7L/ccwqabmCoq/zSxRt2dWMplcnbE5zQdAS8yoYb4kREm8Pbn7MLdnFPLYHnQEcAAa/ieOtIKezzXe3aHV1WAHoYnUEvBjoCLNXfBHrb/dgaCaCnThNtAQae1KQeOT+Pdr8HSp47oIVOGHfefhjf+G/Phd+j4IsPOZf1fGp0AW1+M0AIeFW88/m78PNTM3j0gpmV1Q2JHzw9iVv391c01e/QUAS7ekP46mOjqwKgT917DnefmMKfvOIAQnXendVsuzwXkhmcm43nHX6w1ueOnMPp6TjCAQ++/e7n5p2QeNnWDvzazTvxhQcu4OjFaFnHMBZN4gv3X8DrrhmqKFC8cXc3DAlEkxmks6v3ztrrcIiIWlFXyIfhrjb0h/3or2HKsxACh4Y6GeQ0iWt3dGPfQBg7elj2vFkMd7dVVF1YKwae1HQSWhZPjy9WtbA4EvTilVduxdcfG0XcgSFD8XQWZ2biCK0IEn71RnOq3z/ffQq33XEEr/rIzzAT08ous7UJIfCaq7fh/jNzucDkqdGF/9/encdHXd37H399kslkT0ggEMK+hS0iQkCliuLW1n2pVq3iviutXW9rvVZ7+7te9dfWWlsF69pKca96XepWNxQIArKIiGyBsBOyJ5OZOfePTGiAyUomM4T38/HwEfNdz+TDJN/PnHM+h7tf/4KTx/RhehSqyeVmJsfUHM9HP1rL1vI6lpWUM3v+hj29h00TdeccD763mtv/sZweKQmM7JNOjxbWWLz15Hx6pSVy+0vLmq1Q3NQDod7tGSeNaFfbjxzS0Pu9amslo25/g6P+3zuc/9BcfjhnMXX+ICnt6M0XEYk1uZlJDInQEmYi0j0p8ZSYs7h4N4Gg4zfnHtahoTAXHzmAKl+AV5aUHHBbvthcjnNw55lj97QlNUxVP298HNM6UNXv7PH9ANhZ6SMQdNwyexE9UxO557xxUZnv0jcjdno8a3wBnvxkHRlJHtITPfz8haXM+PtiKprMiXXOcfcbK7n3zS85e3weI3qnhV2AvGkveUZSAredOpolG8vYXtHy0jHrd1bxbFExF00e0OzyAs3JSU/ksH4ZDO2VyowTRjBleE8M45M1OzGDjCT1eB6sNExPRESk/fTkIzHns1BhoQkDml+WpCUTBmaR3yeN2fM3cOHkA5scvzw0X7Bgn/XFpk8ZzMwP1uypvjtleM+w1UtbM7BnChMHZbGipJya+gC7qnw8fc1RZHXhePumcjOT2FFZh88fjHo1u+cWFlNaXc/ovumkJ3o4bmRvfvvWKpZu3E1aoocUbzy3vbSMp+dt4JKjBnLXmQVcNOvTNl37rPF5zJ6/gYXrS8P+rBt7U/N6JOOJN246YTjQ/kqyKV4PKV4Pt56cv9f2Cx6aq0IaIiISNfrwTKJBPZ4Sc4rWl5LfJ43MDhYbMDMumjyQJRvLWLbpwIoMLdtURq80L30y9q4km5mcwPQpg9i1p6pfx9e6OueIftTUB9hR6ePmE0Zw1NDWCxRFqsclr0cSzsG2iuj2egaCjkc+Wsv4AT1IT/RgZtw0bTh/v/YoauuDLC8p54vNFTw9bwM3HD+MX59VELanszlmxq/PLiAQdKzdUcWWML281T4/Ly3exGVTBtM7veNzmJq7v4hILFFPvohEmhJPiSnBoOOz9aV7Sjx31DlH9CPRE8ffF2xoc+XScJaVlDM2LzNsonBlk6p+za3F2RanHdYXM0hP9DAj1LPWqKsfBHJDBR7CJWJd6c3lW1i/s5rrpg7d62c/aXA2r33/WDKTE6io8/PTb43kZ98a1aFELr9POv2zkimtrufYe97lh3MW76mIC7CxtIZUr4frpw7rlNckIiL7U8IbWxQPiSQNtZWYsnp7JeW1fiYOyj6g6/RI8XLaYX15aVEJo3LTiW9Hb1ij2voAX22taHbuZs+0RPpnpVBbHyAnveNra2alehnbN4NET1yXrqUUTt8YWMvTOcfDH6xhcM8UThmby+Nz1+21PzvVS36fNHwBx43HDw9/kTbK65FMdqqXCYOymLOgmBcWbeKY4b3YUl5LaXU93z9xRNSGPYuIxDIlJyLSXko8JaYsDM3vPNAeT2hY/PiFRZvYWeWjdwcSw1VbK/AH3X7zO5tqTNQOVFcvm9Kc3NDruffNlZxxeF5U2jB/7S6WFO/m12cXNPuBgZmR6AlfRKi9khLiueOMsfzgxHyenr+Bx+c2VNKNjzOuOnZIu693oO0R6Qz6tyciIq3p6r8VsfG0KxJStK6UnqleBnfCGlKFg7IY3juNzWU1HUo8l20KFRbKaz7x7G7SEz3EGfj8wS6/d+Nw6LRED9mpXs6f2L9L75+ZksANxw/jqmOG8O37P8AbH0dGBwpGiYiIiMj+lHhKTPlsQykTBmV1SvGVxiJDv351BdW+9q/puaykjPQkDwOyD52Frc0MrycuKoknQLUvwLy1u/jBSSNISojOOpdeTxy90jo+dLot1BslIiIihxoVF5KYsaOyjrU7qijshGG2jc44vC8ApdX1rRy5v+Ul5RQ0U1ioO/PGx1EXiE7iuaWshqSEOKYfPbjF41T8QEREROTgoh7Pbq5x+OLB8JD+WSfO72zUOz2JFG88ZTXtSzzrA0G+2FzOZUcPavG4zvq5xlJ8Ej1xlFbXU+cPkOjpul5Hnz/Ijkoflxw1iOwuKugTSz93ERERke5MiafEjIXrS/HGx7VYzKcjeiQnsLmsloraetLbOGfv6+2V+PzBTm/LwaBnWiLbK3387dMNXHlM68V1OuvDjS3ltTjg6n0K+kQjOVRCKiIiItK5NNRWOl1H181cuL6Ugn4ZnT63LzM5AQfM/Xpnm89pLCw0Ni+jU9tyMMhMTiAjycOD762msq79c2M7oqymnm0VdWSnJDCoZ2qX3FNEREREuo4ST4kJdf4An28qo3Dwga3fGU5aUkOl1vdXbW/zOcs2lZGcEM+QXmmd3p6DQf+sFHZW+Xj0o7UdvkZ7PoC4982VBIKOvB6HTiEnERERkUOJEk+JCcs2lePzB5kwsPPmdzZ69vopnDS6D+9/uR3nXJvOWV5Sxpi8jGbXkezu0pM8nDymD7M+WENpla/Z4+r8AVZuqWBJ8W5+OGcxT326nuUlZfjbUZxo0YZS/jZvA7kZiTGznqmIiIiIdC4lnt3YipJyFhXvZnNZbZuO7+gQ2XDamuA1Wrh+F9C5hYWaOm5kDpt21/D19qpWjw0GHStKyik4BIfZNvXjU0ZS6fPz0Ptfh93vnOO2F5dRVlNPYkIcH3y1g9tfWsZpf/iIcXf+ky82l7O7uvmkFcAfCPKLF5fRJz2J/lkHvnariIiIiMQmJZ5R1lnJ3r7XWbO9kumPzsPnD7JhVzUvLyk54Hu0RcnuGlZsLufzTWXU+QNtPm/h+lIG9UwhJz0y6ydOHZEDwAdtGG67bmcVVb4AYw/BwkLw76VKRuamc874fjw+dx1bwnx4MevDNTy3cCP9eiQxKjeDBbedyIc/ncb9F47n/In9qfMHWbW1ko9X72j2Xo/PXccXm8v51ZljDtneZREREZFDgRLPbmjT7houeWQezkFBXgbpiR5+/MwSPl3TcnGdal+ADbuqKWvjmpf7Jrv/XL6Fb9//IZV1fmrrg8xZUNym6zjnWLi+NGK9nQADslMYmpPa6jzP7z78Cdc+WQRAQd6hmXg2devJ+QSd4w/vfrXX9ne+2Mp/v76S0w7rS7/QvEwzY0B2CmeN78edZxVQkNdQKOraJ4tYUrx7v2tv2l3Db99axYmjevPNsbld8XJEREREJEqUeHYzOyrruPSReVTU+XniysmkJnrI75PGgOxkrn2yiK+2Vux3jnOOZxYUs7ykjM1ltdz5yvJ23bO2PsAd/1jGtU8tZGB2CuP6ZZKe6OGBd1dT42u913PDrmp2VPoimngCHJefw6drdlJb33KbqnwBvPFxjOhzaBYWampAdgoXTR7IMwuKWbejYZjyl1sqmDF7EQV5mdx3/uGYhe+p9MTHMSo3new0L5c/Np/V2/b+t/erl5fjHNx51thmryEiIiIi3YMSz27EHwgy/S/zKSmr4bHLJ+1Zg9ITH8fjV0wmMSGeyx9bwLby2j29lRW19fxgzmJ++vznpCV66JORyAuLNvH2iq1tumeNL8A5f5rLE5+s5+pjhvD8DVNISoinf1Yy2yvqeOrTda1e44rHFgBQOKjzK9o2dVx+DnX+IPPW7mrxuKo6PyNz00mI19sD4OZpw/HEG+f9eS7n/uljrnpiAamJHmZNLyTZG79naO6+5lx3NC/e9A2euvJI4uPiuOSR+WwsrQYaesffWrGV7580Ys/czuauIyIiIiIHPz1ZdxOBoOPLrZV8ta2Chy8t3G9ZkgHZKTx2+SRKq31c8fgCAkFHVZ2fMx74iFeWlPDjU/IZlZvOwOwURuWm8/MXl7ZaGGZXlY9lJWVsLa/l0csL+eXpY/B6Gv5JZSQncOyIXvz5X1+3uhZkZZ2f+DhjRO/I9jAeOaQnXk9ci/M8nXNU+QIU9Du0Cws11TsjiSu+MYSdVT5Wbqlge0Uds6YXkpuZ1KbzB/dK5amrJlPl83PpX+Zz1h8/4uanFzEqN52rjhkS4daLiIiISCxQ4tnJmisW1Nx2f9C1a+mJcHZU1rFqawWVdX7uv/AIjsvP2bOvaS9SQb9MHvzeBFZuqWDF5nKWl5RT5w8y57qjufmEETxz/RSevX4K951/OKVVPu58ZUWz93x96Wa+2lZJsjee12Ycywmj+ux3zx+dMpLS6noea2EtyPU7q9hV5SM90UNchIvLJHvjOXJIdovzPH3+IIGgY6zmd+7l+qnDiI8zqn0B7j3/cA4f0KNd54/um8Fjl09ic1kNSzeV4QsE+c05BepVFhERETlE6KkvCipq63lx0UaufmIBn60vZemm8lZ7BRvtm8C+tnQzp/zuAypq/Qztlcqph/Vt8fxpI3vzm7MLqPYFyExJ4LUZxzJpn97Rgn6Z3DhtOC8u2sRbYYbcvrFsC7fMXkRaoodRuRnN9nyNH9CDk8f0YeaHa8IWLNpeUcf0R+cDMDC7a5bSOC4/h9XbKtm0uybs/qrQnNSCQ7SibXMyUxIYnpPKsJxUzjw8r0PXKByczZ8vmYhz0Ds9kYkRHlotIiIiIrHjoEo829ubGEv8gSA7Kuu45skiJv7X29w6ZwkrSsrplZaILxDk92+tatf1Sqt83DJ7ETf+7TP6ZyVT0C+zzUuRXDh5IEcM6EF+7zSyUr1hj7l52nBG983gF/sMuf3n8i3c/PRnHNY/k5G56Xha6aX84cn5VNb5mfnh3mtBVtb5ufLxBWwtryW/TzrJ3vg2tf1AHT+y+WVVdlTWsWFXNZ44Y1Ruepe052DSI8VLr7QDW+5m2sjejB/Qg8E9tWaniIiIyKHEE+0GdIRzjjp/kKo6P9W+ANU+/565hbFiY2k1RetKmb9uF0XrdrFqayUAVXUBLjlyEKeN68sRA3pw0axPMYPH5q7jvIn9Gd239bmFpdU+Tvn9B+yu9vGjk/O54fhhfO+Ree1qX2s/L68njvvOH8dZf/yYE///+wzvncbVxw7lpqc/o6BfJk9cOZlrnihq9T6j+2Zw+rg8Hvt4HVd8Y0hDou0PcsNfF7Jiczmzpk/ca5hupA3LSSMvM4n3v9zORZMH7tle7fNz1eMLqA8EGZ3bsAyIREasvVdFREREJPIOmsRzwbpdrNzSMI9xxG2v4w+6vfbHGdz5ynKuOmbIniqZjb2g+1bKbO/2tqrzByjZXcPW8jqO+Z/3AEhP9DBhUBbVvgAZSR5eveXY/eYyDshKJhB03PbiUp67fkqzcx0DQcfaHVVsq6hjdN8MnrhiMmPyOlYEpy2vcWxeJjdNG87973xFws4qbvzbQsbkZfLkVZPJSEpo871+cNII/vfzEk7/w4cMzE4hr0cyH361g3u+M65Lk05oWGvyuJE5vLpkM/WBIAnxcfgDQWbMXsTSTWUMy0kjLemgeVt0qc6qOKvKtSIiIiKHnph8wm6aAM5fu4v731nFx6t34okzeqZ6Ob+wPyleD2mJHlK88Tz8/teUVtfz1CfrefKT9Zx2WF+unTq0XfesDwSp8wfxxneswM27K7dy1ysrKC6tITPZwy9OGsWkIdmMys0gPs72vKZwSaUnPo6fnzqaHz+7hGeKirmwSU9co2DQ8YsXlrKtoo6+mUn846ZvdEnP0U3ThjPrwzVsKa/j8P6ZPHnlv5POtiYQw3LSOHdCf57/bCOBoGP+ulJ+8s2RXFA4IJJNb9Zx+TnMnl/Mog27mTQ4i1+9spy3v9jGXWeN5X8/3xyVNomIiIiIdGcxmXgClNfUc9HMT/lkzU56pSXyy9NG88ayLcTHGT/55qi9jn1u4UZ6piXyu++O59GP1jJ7/gZeXlJCRpKHjOQEHvt4LaleDymJ8aR6PZTV1OPzB7nnjZWs3lbJ19srWb+zGn/QkZWSQFl1PZkpLffoNSaSd583jl+/uoJ3V25jaE4qI/uk0SPFy+XfaN8yEedN6MczRcXc/cZKThmbS3aTuZfOOe58ZTlziorp1yOJ/lkpXTZc0euJY3hOGtsqannyqiPJTG57T2dT3z9xBM8v3Mj2Sh/Tjx7EjccP6+SWtt2U4b0A+MHfFzF9ymD++ukGrps6lOlHD1biKSIiIiISATGZeDYOJ81JT+T208dw8eSBJHvjw1ZYhb173n55+hhuOXEET8/bwG/f+pLyWn+zy4LM/GANg3qmMLx3Gt8qyOXFzzaxuayW0x74kAcvntDikhH+QJCSslpO+d37JHriue3U0Vw2ZTCX/iX8XMvWegfNjN+cXcC37/+Q/37tC+49/3CgIem8+42VPPHJeq45dghLind36PoHItkbz6CeqR1OOqFhHdEB2cn4/EHuOGMsZpFdOqUlGUkJpCd62FZRx92vr+SMw/P42bdGtX6iiIiIiIh0SEwmnj1SEkhOiOfNW6fuVeSlrclVZnICNxw/jPdWbsUBMy8tpMrXWIgowH88/zne+Diev3HKXusIFq0rJSvVy+7qer7z0Fx+edoYph89aK8kaf3OKh79aC2LincTdPCdif356bdG0js9/JIi7TGiTzrXTB3Kn//1NRdMGsCkwdn84Z3VPPz+Gi45aiC/OHU0F8789IDvEy19M5MBiI/wep1tkZmSQEWpn8lDsrnv/HF7hkBr/qGIiIiISOeLycQzK6VhmOmBVhY1MwzISvXutWxIY8/dvovXNyYdu6t9/OiZJdzx8nLmr93FlvIaanwBBman8uaKLXjijOxUL30zk7gv1DPZUfsmOjNOGMHLi0uY/pf59ErzUlxaw3cm9ueuMwsws6gkRt0xGeudnkgg6Jh1aSGJHlWwFRERERGJpJhMPKNdPbNHipdZ0wuZ9eEa7nnzS5xzBB1s2l3LDccN47Ipg5kxe1Gn3rNRsjeeO88cy9VPFlFcWsPp4/ryP+eNa7bS7cEklhLYhPg4BmantDqXV0REREREDlxMJp6R1pYEKC7OuO64YUwclMU1TxaRkZzA698/lhSvp83X6KiTxvShd3oiQef43XfHx8TQVBERERERkY46JBPP9igcnE1+n3SAPUlnVxjSKxXYfziwdI5Y6n0VEREREenuzDkX7TbsUVhY6IqKiqLdDBEREREREWknM1vonCsMt0/daSIiIiIiIhJREU08zSzfzD4xs1WhryMieT8RERERERGJPZHu8XwIeNA5lw88CDwc4fuJiIiIiIhIjIlY4mlmvYEJwOzQptnABDPLidQ9RUREREREJPZEssdzALDJORcACH0tCW3fw8yuNbMiMyvavn17BJsjIiIiIiIi0RD14kLOuZnOuULnXGFOjjpDRUREREREuptIJp7FQD8ziwcIfc0LbRcREREREZFDRMQST+fcNmAxcFFo00XAIuecxtOKiIiIiIgcQjwRvv71wBNm9p9AKTA9wvcTERERERGRGBPRxNM5txI4MpL3EBERERERkdgW9eJCIiIiIiIi0r0p8RQREREREZGIUuIpIiIiIiIiEaXEU0RERERERCJKiaeIiIiIiIhElBJPERERERERiSglniIiIiIiIhJRSjxFREREREQkopR4ioiIiIiISESZcy7abdjDzLYD66PdjoNQL2BHtBshzVJ8YpviE9sUn9im+MQ2xSe2KT6xTfHpmEHOuZxwO2Iq8ZSOMbMi51xhtNsh4Sk+sU3xiW2KT2xTfGKb4hPbFJ/Ypvh0Pg21FRERERERkYhS4ikiIiIiIiIRpcSze5gZ7QZIixSf2Kb4xDbFJ7YpPrFN8Yltik9sU3w6meZ4ioiIiIiISESpx1NEREREREQiSonnQcTMeprZa2b2pZl9bmYvmFlOaF++mX1iZqtCX0dEu72HMjO7w8ycmRWEvld8YoCZJZnZn83sKzNbamYzQ9sVnxhgZqeb2SIzWxz6HXduaLviEwVmdp+ZrW36uyy0vdl4KFZdJ1x8WnpOCO1XfLpIc++fJvvvaM97SzpXC7/fwj4nhPYpPgdIiefBxQH3OOdGOufGAV8Dd4f2PQQ86JzLBx4EHo5SGw95ZjYBOArY0GSz4hMb7gFqgXzn3GHA7aHtik+UmZkBTwGXOufGA5cAT5hZHIpPtLwETGX/9bVbiodi1XVeYv/4tPScAIpPV3qJ8O+f5p4TQPHpSi8RPj7NPSeA4nPANMfzIGZm5wE3ABcDq4CezrmAmcUDO4ERzrnt0WzjocbMEoF/0RCT94DTgW0oPlFnZmnARqC/c66yyfbeKD5RF0o8dwBnOuc+NrOpwCPAMSg+UWVm64DTnXPLWnq/ANbcPsUqcprGJ8y+84AbnHMn6XdddOwbn3DPCa29txSfyNnn91vY54TQcYpPJ1CP50Eq1AtwA/AyMADY5JwLAIS+loS2S9e6C/irc25tk22KT2wYRsMfiTvMrMjM/mVmx6D4xATX8CnoBcA/zGw9DZ9GX4biE2taiodiFUP2eU4AxSdWhHtOAMUnFjT3nACKT6dQ4nnwegCoBP4Y7YZIAzM7GpgE/CnabZGwPMBQYJFzrhD4GfACkBbVVgkAZuYBfg6c5ZwbBJwBzEHxEekoPSfEGD0nxLywzwlmlhHdZnUfSjwPQmZ2Hw3Dmr7rnAsCxUC/ULc/oa95oe3SdY4DRgFrQ0M3+gNv0vAJmuITfesBPzAbwDk3j4ahnTUoPrFgPJDnnPsYIPS1ioa5NopP7Gjp743+FsWIMM8JoPjEgrDPCWZ2CopPLGjuOSEfxadTKPE8yJjZb4CJwNnOuToA59w2YDFwUeiwi2j4tEZjzruQc+5u51yec26wc24wDfMEvumcewbFJ+qccztomE9zMjRUpwMa52wsRvGJto1AfzMbCWBmo4Fc4CsUn5jR0t8b/S2KDeGeE0DPCrGgheeEfyo+0dfCc8JqxadzqLjQQcTMxgLLaHhQrgltXuucO8fMRgFPAFlAKTDdOfdldFoqsN+EdcUnBpjZUOBRoCdQD9zmnHtd8YkNZvY94D+Axh6aO5xzLyk+0WFmfwDOpeEDgB3ATufc2JbioVh1nXDxoWGedNjnhNA5ik8Xae79s88x69i78JDi00Va+P0W9jkhdI7ic4CUeIqIiIiIiEhEaaitiIiIiIiIRJQSTxEREREREYkoJZ4iIiIiIiISUUo8RUREREREJKKUeIqIiIiIiEhEKfEUEZGDmpmtM7OCLrjPmWZ2b+j/B5vZtQdwLWdmae0853Ez2xhaBiDc/uPNrKijbQpdY4qZLe5I+0RERFqixFNERKQNnHMvO+d+Evp2MNDhxPMA3O2cmxGpizvn5jrnxkfq+iIicuhS4ikiIt2SmU03s6Vm9rmZvWhmvUPbLzezf5rZHDNbbmYfm1luaJ/XzGaa2Soz+8jM/mhmzzU577nQ5R8ExoR6Bxv379VL2PR7MzvXzFaa2Vwz++U+7TzSzN4zs4Wh/05rx2v8LzNbbWbvA6fts+8yM5sXuua7ZjaytdcoIiISKUo8RUSk2wkNvb0bOMU5Nw5YBjzQ5JBJwI+dc2OBFcAtoe3XAQOBMcBJQGEzt7gJWOGcG++c+04rbekNzALOcs5NAXxN9vUAHgIuds5NBE4HHg5tb+01ngGcCYwHTgBGNdl3LHABMDV03XuBR9v5GkVERDqNEk8REemOpgGvOec2h75/mIYkq9HHzrni0P9/Cgxrct5Tzjm/c64WmN0JbTkK+Mw592Xo+5lN9k0BhgCvm9li4HXAAcPbcN1pwBznXKVzLgD8pcm+M4DDgXmh694NDGhyXme/RhERkRZ5ot0AERGRCDAaErimmn5f2+T/A/z772G489oqQOgDXTNL2qctzTHgc+fc1A7cr7XrPuqc+89m9nX0NYqIiHSIejxFRKQ7egc4tXHuJnAN8HYbznsPuMTMPKHk8bvNHFcOZO6z7WsahvACXNxk+yfAEWY2IvT91U32zQVGmNm0xg1mNsnMWkoqG70DXGBmqWYWD1zRZN8rwHQz6x+6ZryZTWznaxQREek06vEUEZHu4G0z8zf5/jDg58BbZuaANTTMbWzNQzQMUV0OFAMLgZQwx30OfGlmy4CVoXmet9IwP3Mz8Grjgc65baGlV14xs13AM032lZrZmcC9ZvZ7wBtq6xm00ivpnHvVzI4GFgMlNCSU/UL7PjCz24CXQ0mpF3g29Hra+hpFREQ6jTmn0TYiIiKNzCzdOVdhZonAy8CzzrlHYqBdjwNFzrk/dsK1WnyNoWQ93TlXeaD3EhERAfV4ioiI7OvtUEKWRMPw3Mej25w9yoAZZpbfCWt5hn2NZjYF+BOwFQge4D1ERET2UI+niIiIiIiIRJSKC4mIiIiIiEhEKfEUERERERGRiFLiKSIiIiIiIhGlxFNEREREREQiSomniIiIiIiIRJQSTxEREREREYmo/wMJsQXPJ1q8VgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 936x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plotter.make_sub_plot(data_unreasonable)"
    ]
@@ -376,83 +211,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_119\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "Info:    Defining SPLIT from sample=PowderN() to END in instrument python_tutorial\n",
-      "CFLAGS=\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "1 warning generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_119\n",
-      "\n",
-      "      monochromator rotation = 22.4519 deg\n",
-      "[python_tutorial] Initialize\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "\n",
-      "Save [python_tutorial]\n",
-      "Detector: banana_I=2.60536e-06 banana_ERR=2.63437e-08 banana_N=29248 \"banana.dat\"\n",
-      "\n",
-      "Finally [python_tutorial: /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_SPLIT_119]. Time: 10 [s] \n",
-      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
-      "    SPLIT 47 COMPONENT sample=PowderN(...)\n",
-      "  in the instrument description python_tutorial.instr.\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "sample.set_SPLIT(1)\n",
     "instrument.settings(ncount=2E7)\n",
     "                          \n",
-    "instrument.backengine()\n",
-    "data_ref = instrument.data"
+    "data_ref = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name banana\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plotter.make_sub_plot(data_ref)"
    ]
@@ -469,56 +246,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7ffe9077da30>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "banana_low = functions.name_search(\"banana\", data_low)\n",
diff --git a/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb b/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
index 1db44f13..3d2ee193 100644
--- a/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
+++ b/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
@@ -104,8 +104,7 @@
    "source": [
     "instrument.set_parameters(wavelength=2.8)\n",
     "instrument.settings(ncount=5E6)\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -199,7 +198,7 @@
     "instrument.settings(ncount=5E6)\n",
     "instrument.show_settings()\n",
     "\n",
-    "instrument.backengine()"
+    "new_data = instrument.backengine()"
    ]
   },
   {
@@ -208,7 +207,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter.make_sub_plot(instrument.data)"
+    "plotter.make_sub_plot(new_data)"
    ]
   },
   {
diff --git a/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb b/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb
index 563bd6a1..012472e2 100644
--- a/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb
+++ b/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb
@@ -208,9 +208,7 @@
    "source": [
     "instrument.set_parameters(wavelength=2.8)\n",
     "instrument.settings(ncount=5E6, output_path=\"data_folder/mcstas_JUMP\")\n",
-    "instrument.backengine()\n",
-    "\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
diff --git a/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb b/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
index 3c4c662f..ef9c2b69 100644
--- a/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
+++ b/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
@@ -722,8 +722,7 @@
     "instrument.settings(ncount=3E6, output_path=\"data_folder/union_materials\")\n",
     "instrument.show_settings()\n",
     "\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -1061,8 +1060,7 @@
     "instrument.settings(ncount=5E6, output_path=\"data_folder/union_materials\")\n",
     "instrument.show_settings()\n",
     "\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
diff --git a/docs/source/tutorial/Union_tutorial_2_geometry.ipynb b/docs/source/tutorial/Union_tutorial_2_geometry.ipynb
index 30ea3e15..5e47afe2 100644
--- a/docs/source/tutorial/Union_tutorial_2_geometry.ipynb
+++ b/docs/source/tutorial/Union_tutorial_2_geometry.ipynb
@@ -630,8 +630,7 @@
     "instrument.settings(ncount=3E6, output_path=\"data_folder/union_geometry\")\n",
     "instrument.show_settings()\n",
     "\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -1242,8 +1241,7 @@
     }
    ],
    "source": [
-    "instrument.backengine()\n",
-    "data_cryo = instrument.data"
+    "data_cryo = instrument.backengine()"
    ]
   },
   {
diff --git a/docs/source/tutorial/Union_tutorial_3_loggers.ipynb b/docs/source/tutorial/Union_tutorial_3_loggers.ipynb
index 158b4faf..284a5ea8 100644
--- a/docs/source/tutorial/Union_tutorial_3_loggers.ipynb
+++ b/docs/source/tutorial/Union_tutorial_3_loggers.ipynb
@@ -242,8 +242,7 @@
     "instrument.set_parameters(wavelength=3.0)\n",
     "instrument.settings(ncount=3E6, output_path=\"data_folder/union_loggers\")\n",
     "\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -286,8 +285,7 @@
     "logger_zx.target_geometry = '\"outer_cryostat_wall,cryostat_wall\"'\n",
     "logger_zy.target_geometry = '\"sample_geometry\"'\n",
     "\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -420,8 +418,7 @@
    },
    "outputs": [],
    "source": [
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -511,8 +508,7 @@
    },
    "outputs": [],
    "source": [
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -566,8 +562,7 @@
    "source": [
     "log_2D_st.time_bins = 128\n",
     "instrument.settings(ncount=2E8, mpi=4)\n",
-    "#instrument.backengine()\n",
-    "#data = instrument.data"
+    "#data = instrument.backengine()"
    ]
   },
   {
diff --git a/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb b/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
index 4568bcd3..a0fef557 100644
--- a/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
+++ b/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
@@ -224,8 +224,7 @@
     "instrument.set_parameters(wavelength=wavelength, A3_angle=theta)\n",
     "instrument.settings(ncount=3E6, output_path=\"data_folder/union_conditionals\")\n",
     "\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -372,8 +371,7 @@
     "                          tag_angle=-95, tag_time=0.00188, tag_interval=9E-5)\n",
     "instrument.settings(ncount=3E6) # Can add mpi to improve speed for this longer simulation\n",
     "\n",
-    "instrument.backengine()\n",
-    "data_con = instrument.data"
+    "data_con = instrument.backengine()"
    ]
   },
   {
@@ -465,8 +463,7 @@
    "source": [
     "PSD_conditional.overwrite_logger_weight = 1\n",
     "\n",
-    "instrument.backengine()\n",
-    "data_con_f = instrument.data"
+    "data_con_f = instrument.backengine()"
    ]
   },
   {
diff --git a/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb b/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
index 6239cd04..67fd914e 100644
--- a/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
+++ b/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
@@ -156,8 +156,7 @@
     "instrument.set_parameters(wavelength=3.0)\n",
     "instrument.settings(ncount=3E6, output_path=\"data_folder/union_external\")\n",
     "\n",
-    "instrument.backengine()\n",
-    "data_empty = instrument.data"
+    "data_empty = instrument.backengine()"
    ]
   },
   {
@@ -199,8 +198,7 @@
    },
    "outputs": [],
    "source": [
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -254,8 +252,7 @@
    },
    "outputs": [],
    "source": [
-    "instrument.backengine()\n",
-    "data_wrong = instrument.data"
+    "data_wrong = instrument.backengine()"
    ]
   },
   {
@@ -322,8 +319,7 @@
    },
    "outputs": [],
    "source": [
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
diff --git a/docs/source/tutorial/Union_tutorial_7_Tagging_history.ipynb b/docs/source/tutorial/Union_tutorial_7_Tagging_history.ipynb
index 5b5ecb59..3e6a675e 100644
--- a/docs/source/tutorial/Union_tutorial_7_Tagging_history.ipynb
+++ b/docs/source/tutorial/Union_tutorial_7_Tagging_history.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,21 +30,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'instrument' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-3-2785ca2919cd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mAl_inc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstrument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_component\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Al_inc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Incoherent_process\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mAl_inc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msigma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0082\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mAl_inc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_cell_volume\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m66.4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mAl_pow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstrument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_component\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Al_pow\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Powder_process\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'instrument' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
     "Al_inc.sigma = 0.0082\n",
@@ -145,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,379 +144,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_tagging_3\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:12948:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:12948:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:13191:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:13191:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "14 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_tagging_3\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: Sample_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: Sample_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: Sample_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 4\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "name of process [2]: Sample_inc \n",
-      "component index [2]: 4 \n",
-      "name of process [3]: Sample_pow \n",
-      "component index [3]: 5 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 2\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 0.347892 \n",
-      "number of processes [0]: 2 \n",
-      "name of material    [1]: Sample \n",
-      "component index     [1]: 6 \n",
-      "my_absoprtion       [1]: 0.273042 \n",
-      "number of processes [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 2\n",
-      "\n",
-      "name of geometry    [0]: sample_geometry \n",
-      "component index     [0]: 8 \n",
-      "Volume.name         [0]: sample_geometry \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.273042 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: sample_container \n",
-      "component index     [1]: 9 \n",
-      "Volume.name         [1]: sample_container \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: cryostat_wall \n",
-      "component index     [2]: 10 \n",
-      "Volume.name         [2]: cryostat_wall \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
-      "Volume.p_physics.number of processes [2]: 2 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: 0.000000 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.030000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.120000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: cryostat_wall_vacuum \n",
-      "component index     [3]: 11 \n",
-      "Volume.name         [3]: cryostat_wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [3]: 1 \n",
-      "Volume.p_physics.my_absorption       [3]: 0.000000 \n",
-      "Volume.p_physics.number of processes [3]: 0 \n",
-      "Volume.geometry.shape                [3]: cylinder \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: 0.000000 \n",
-      "Volume.geometry.center.z             [3]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.028000 \n",
-      "Volume.geometry.geometry_parameters.height [3]: 0.112000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 5\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4]\n",
-      "LIST: Direct_children for Volume           0 = [3]\n",
-      "LIST: Intersect_check_list for Volume      0 = [3]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [3]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for sample_geometry with cylinder shape made of Sample\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = []\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [2]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = [2]\n",
-      "LIST: Next_volume_list for Volume          1 = [2]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for sample_container with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  2 = [1]\n",
-      "LIST: Direct_children for Volume           2 = [1]\n",
-      "LIST: Intersect_check_list for Volume      2 = [1]\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [4]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [4]\n",
-      "LIST: Next_volume_list for Volume          2 = [4,1]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for cryostat_wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  3 = [1,2,4]\n",
-      "LIST: Direct_children for Volume           3 = [4]\n",
-      "LIST: Intersect_check_list for Volume      3 = [4]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = []\n",
-      "LIST: Next_volume_list for Volume          3 = [0,4]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  4 = [1,2]\n",
-      "LIST: Direct_children for Volume           4 = [2]\n",
-      "LIST: Intersect_check_list for Volume      4 = [2]\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [3]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = [3]\n",
-      "LIST: Next_volume_list for Volume          4 = [3,2]\n",
-      "      Is_vacuum for Volume                 4 = 1\n",
-      "      is_mask_volume for Volume            4 = 0\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = []\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_zx_I=18017.8 logger_space_zx_ERR=32.4189 logger_space_zx_N=879822 \"logger_zx.dat\"\n",
-      "Detector: logger_space_zy_I=18017.8 logger_space_zy_ERR=32.4189 logger_space_zy_N=879822 \"logger_zy.dat\"\n",
-      "\n",
-      "\n",
-      "Top 20 most common histories. Shows the index of volumes entered (VX), and the scattering processes (PX)\n",
-      "1859607\t N I=3.965281E+04 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "506763\t N I=1.050335E+04 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "226314\t N I=4.825743E+03 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
-      "166837\t N I=3.557502E+03 \t V0 -> V3 -> V4 -> V3 -> V0 \n",
-      "70469\t N I=1.466246E+03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "31797\t N I=5.940376E+02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "23495\t N I=4.091492E+02 \t V0 -> V3 -> P1 -> V0 \n",
-      "17274\t N I=3.516535E+02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "13839\t N I=2.758026E+02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "11994\t N I=2.441687E+02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "9842\t N I=2.293453E+02 \t V0 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "10180\t N I=2.028735E+02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "8250\t N I=1.719896E+02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "5405\t N I=1.056087E+02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "4597\t N I=8.898223E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "3735\t N I=5.656959E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "2454\t N I=4.841669E+01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "2882\t N I=4.699149E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "2334\t N I=4.194865E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "2056\t N I=4.159660E+01 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=3.0)\n",
     "instrument.settings(ncount=3E5, output_path=\"data_folder/union_tagging\")\n",
     "\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space_zx\n",
-      "Plotting data with name logger_space_zy\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
     "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
@@ -545,417 +179,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "History file written by the McStas component Union_master \n",
-      "When running with MPI, the results may be from just a single thread, meaning intensities are divided by number of threads\n",
-      "----- Description of the used volumes -----------------------------------------------------------------------------------\n",
-      "V0: Surrounding vacuum \n",
-      "V1: sample_geometry  Material: Sample   P0:  P1: Sample_pow\n",
-      "V2: sample_container  Material: Al   P0: 4 P1: Al_pow\n",
-      "V3: cryostat_wall  Material: Al   P0: 4 P1: Al_pow\n",
-      "V4: cryostat_wall_vacuum  Material: Vacuum  \n",
-      "----- Histories sorted after intensity ----------------------------------------------------------------------------------\n",
-      "1859607\t N I=3.965281E+04 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "506763\t N I=1.050335E+04 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "226314\t N I=4.825743E+03 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
-      "166837\t N I=3.557502E+03 \t V0 -> V3 -> V4 -> V3 -> V0 \n",
-      "70469\t N I=1.466246E+03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "31797\t N I=5.940376E+02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "23495\t N I=4.091492E+02 \t V0 -> V3 -> P1 -> V0 \n",
-      "17274\t N I=3.516535E+02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "13839\t N I=2.758026E+02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "11994\t N I=2.441687E+02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "9842\t N I=2.293453E+02 \t V0 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "10180\t N I=2.028735E+02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "8250\t N I=1.719896E+02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "5405\t N I=1.056087E+02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "4597\t N I=8.898223E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "3735\t N I=5.656959E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "2454\t N I=4.841669E+01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "2882\t N I=4.699149E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "2334\t N I=4.194865E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "2056\t N I=4.159660E+01 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "1531\t N I=3.104778E+01 \t V0 -> V3 -> V4 -> V3 -> P1 -> V0 \n",
-      "1529\t N I=2.937892E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1605\t N I=2.843064E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1094\t N I=2.371972E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "1056\t N I=2.251227E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "927\t N I=1.618954E+01 \t V0 -> V3 -> P1 -> P1 -> V0 \n",
-      "750\t N I=1.509174E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "623\t N I=1.063780E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "559\t N I=1.026990E+01 \t V0 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "556\t N I=9.481044E+00 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "589\t N I=9.135234E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "298\t N I=6.872061E+00 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "388\t N I=6.792255E+00 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "434\t N I=6.645396E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "435\t N I=6.588350E+00 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "273\t N I=5.790274E+00 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "366\t N I=5.672326E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "285\t N I=5.647409E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "352\t N I=5.331544E+00 \t V0 -> V3 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "265\t N I=4.655556E+00 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "267\t N I=4.518687E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "186\t N I=3.656094E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "281\t N I=3.513630E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "182\t N I=3.302548E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "160\t N I=3.112205E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "163\t N I=3.046436E+00 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "144\t N I=3.000987E+00 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "163\t N I=2.972572E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "211\t N I=2.805841E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "183\t N I=2.789270E+00 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "113\t N I=2.753453E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "175\t N I=2.589555E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "134\t N I=2.491092E+00 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "137\t N I=2.457782E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "103\t N I=2.004625E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "115\t N I=1.942657E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "97\t N I=1.776228E+00 \t V0 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "84\t N I=1.619118E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "85\t N I=1.601890E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "25\t N I=1.378346E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "98\t N I=1.373456E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "95\t N I=1.349907E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "69\t N I=1.326884E+00 \t V0 -> V3 -> V4 -> V2 -> P0 -> V4 -> V3 -> V0 \n",
-      "78\t N I=1.312686E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "40\t N I=1.202410E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "72\t N I=1.113576E+00 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "38\t N I=1.054350E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "57\t N I=9.793343E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "64\t N I=9.571626E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "57\t N I=9.514756E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "63\t N I=9.241653E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "48\t N I=9.188580E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "44\t N I=9.071488E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "37\t N I=8.697112E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "53\t N I=8.477626E-01 \t V0 -> V3 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "41\t N I=8.158051E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "60\t N I=8.021806E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "52\t N I=7.809131E-01 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "40\t N I=7.692616E-01 \t V0 -> V3 -> V4 -> V2 -> P0 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "53\t N I=7.261881E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "42\t N I=7.178343E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "36\t N I=6.980954E-01 \t V0 -> V3 -> P1 -> P1 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
-      "29\t N I=6.689273E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "34\t N I=6.539172E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P0 -> V0 \n",
-      "34\t N I=6.538197E-01 \t V0 -> V3 -> P0 -> V0 \n",
-      "42\t N I=6.024936E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "35\t N I=5.834859E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "36\t N I=5.775684E-01 \t V0 -> V3 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "48\t N I=5.733596E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "30\t N I=5.577867E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "39\t N I=5.534476E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "60\t N I=5.273567E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "29\t N I=4.431660E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "22\t N I=4.231337E-01 \t V0 -> V3 -> P0 -> V4 -> V3 -> V0 \n",
-      "25\t N I=4.180681E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
-      "20\t N I=4.001083E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "15\t N I=3.996063E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "21\t N I=3.907685E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "9\t N I=3.873613E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "19\t N I=3.872826E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "35\t N I=3.741849E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "19\t N I=3.653692E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P0 -> V4 -> V3 -> V0 \n",
-      "10\t N I=3.577379E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "27\t N I=3.383308E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "2\t N I=3.171137E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "19\t N I=2.898560E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "8\t N I=2.891570E-01 \t V0 -> V3 -> V4 -> V2 -> P0 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "17\t N I=2.822871E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "20\t N I=2.790372E-01 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "8\t N I=2.645994E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "22\t N I=2.587804E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "17\t N I=2.515664E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "13\t N I=2.500189E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P0 -> V4 -> V3 -> V0 \n",
-      "2\t N I=2.410002E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "16\t N I=2.356043E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "12\t N I=2.307864E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P0 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "19\t N I=2.306564E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "5\t N I=2.259507E-01 \t V0 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "19\t N I=2.122730E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "11\t N I=2.028933E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "13\t N I=2.024403E-01 \t V0 -> V3 -> P1 -> P1 -> P1 -> V0 \n",
-      "13\t N I=2.008074E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P0 -> V4 -> V3 -> V0 \n",
-      "19\t N I=1.999939E-01 \t V0 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "15\t N I=1.997969E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "9\t N I=1.826944E-01 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "2\t N I=1.791654E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "19\t N I=1.772469E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "17\t N I=1.688696E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "20\t N I=1.670351E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "15\t N I=1.626160E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "11\t N I=1.559772E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "13\t N I=1.500194E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "10\t N I=1.424290E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "9\t N I=1.375370E-01 \t V0 -> V3 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "11\t N I=1.361135E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> P1 -> V0 \n",
-      "8\t N I=1.350152E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "7\t N I=1.336463E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "8\t N I=1.285106E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "3\t N I=1.221980E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "10\t N I=1.210789E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "5\t N I=1.201056E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "9\t N I=1.200436E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "3\t N I=1.182892E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "7\t N I=1.175995E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "9\t N I=1.142555E-01 \t V0 -> V3 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "6\t N I=1.083372E-01 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "5\t N I=1.079352E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "4\t N I=1.064247E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P0 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "7\t N I=1.036886E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "10\t N I=1.017997E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "4\t N I=1.017446E-01 \t V0 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "7\t N I=1.014091E-01 \t V0 -> V3 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "10\t N I=9.965093E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "6\t N I=9.662395E-02 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "8\t N I=9.191802E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "7\t N I=9.091205E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "4\t N I=8.802686E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "5\t N I=8.753136E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "4\t N I=8.713988E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "7\t N I=8.673562E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "6\t N I=8.473219E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "5\t N I=8.459402E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "7\t N I=8.453155E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "5\t N I=8.202485E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
-      "7\t N I=8.179178E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "5\t N I=8.008056E-02 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
-      "6\t N I=7.787455E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "4\t N I=7.691362E-02 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P0 -> V0 \n",
-      "6\t N I=7.687598E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "3\t N I=7.361250E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "6\t N I=7.285952E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "8\t N I=7.219209E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=7.037750E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P0 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "6\t N I=7.035304E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "20\t N I=6.930433E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "5\t N I=6.736602E-02 \t V0 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=6.721552E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "9\t N I=6.695282E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P0 -> V0 \n",
-      "6\t N I=6.648583E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "5\t N I=6.543821E-02 \t V0 -> V3 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "5\t N I=6.229937E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "4\t N I=5.973815E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "4\t N I=5.969631E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=5.951830E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "5\t N I=5.841225E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "6\t N I=5.788928E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "3\t N I=5.768873E-02 \t V0 -> V3 -> P0 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "3\t N I=5.767973E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P0 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "3\t N I=5.764591E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "2\t N I=5.744261E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "6\t N I=5.505677E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "4\t N I=5.463461E-02 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "3\t N I=5.433915E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "6\t N I=5.383604E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "3\t N I=5.197286E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "4\t N I=4.987311E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "3\t N I=4.856827E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "3\t N I=4.831851E-02 \t V0 -> V3 -> P1 -> P0 -> V0 \n",
-      "2\t N I=4.609026E-02 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "2\t N I=4.605094E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "4\t N I=4.514625E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "6\t N I=4.274886E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "4\t N I=4.227548E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "2\t N I=4.209610E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P0 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "2\t N I=4.207892E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
-      "5\t N I=4.207512E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "2\t N I=4.205341E-02 \t V0 -> V3 -> P1 -> P0 -> V4 -> V3 -> V0 \n",
-      "1\t N I=4.200370E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "3\t N I=4.167133E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "3\t N I=4.160331E-02 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> P1 -> P1 -> V0 \n",
-      "3\t N I=4.157004E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "2\t N I=4.111478E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "7\t N I=4.002838E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "2\t N I=3.845955E-02 \t V0 -> V3 -> V4 -> V3 -> P0 -> V0 \n",
-      "2\t N I=3.844839E-02 \t V0 -> V3 -> V4 -> V3 -> P0 -> V4 -> V3 -> V0 \n",
-      "3\t N I=3.799710E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=3.771217E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "2\t N I=3.767373E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P0 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "2\t N I=3.632067E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P0 -> V4 -> V3 -> V0 \n",
-      "2\t N I=3.623438E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "2\t N I=3.619957E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "2\t N I=3.553759E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "3\t N I=3.532011E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "2\t N I=3.448112E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "3\t N I=3.315148E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "3\t N I=3.309992E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "2\t N I=3.276986E-02 \t V0 -> V3 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "2\t N I=3.269722E-02 \t V0 -> V3 -> P1 -> P1 -> P1 -> P1 -> V0 \n",
-      "2\t N I=3.240473E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P0 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "9\t N I=3.154916E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "3\t N I=3.119187E-02 \t V0 -> V3 -> P1 -> P1 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "4\t N I=3.025105E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P0 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=3.002264E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> P1 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.825316E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=2.823280E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "2\t N I=2.815913E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> P1 -> P1 -> V0 \n",
-      "1\t N I=2.789046E-02 \t V0 -> V3 -> P1 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.788551E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "4\t N I=2.644613E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "2\t N I=2.618939E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "4\t N I=2.600572E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.559786E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.545367E-02 \t V0 -> V3 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.503821E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.436294E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "3\t N I=2.362027E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "2\t N I=2.353812E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "4\t N I=2.332097E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "2\t N I=2.272885E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P0 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "2\t N I=2.260732E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.229209E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P0 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "3\t N I=2.226424E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.177959E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "2\t N I=2.173728E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "4\t N I=2.137424E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.134416E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> P0 -> V0 \n",
-      "2\t N I=2.124210E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P0 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "2\t N I=2.114286E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "1\t N I=2.106589E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P0 -> V0 \n",
-      "1\t N I=2.105620E-02 \t V0 -> V3 -> V4 -> V2 -> P0 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.105417E-02 \t V0 -> V3 -> P0 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.102680E-02 \t V0 -> V3 -> V4 -> V2 -> P0 -> P1 -> V4 -> V3 -> V0 \n",
-      "3\t N I=2.101774E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.101559E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P0 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.099840E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P0 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.099810E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.099241E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P0 -> V4 -> V3 -> V0 \n",
-      "3\t N I=2.073353E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> P1 -> V0 \n",
-      "4\t N I=2.016148E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.008879E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "3\t N I=1.989747E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.986359E-02 \t V0 -> V3 -> P1 -> P1 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "2\t N I=1.942461E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.923702E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P0 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.922269E-02 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P0 -> V4 -> V3 -> V0 \n",
-      "5\t N I=1.834890E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.814081E-02 \t V0 -> V3 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=1.813887E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.813885E-02 \t V0 -> V3 -> P1 -> P1 -> P1 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.812308E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.812027E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "1\t N I=1.810402E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.808167E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.807547E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.807263E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "2\t N I=1.739841E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "5\t N I=1.714379E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P0 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.621517E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=1.618113E-02 \t V0 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.617426E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.616066E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.611050E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.609214E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.527837E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=1.388429E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P0 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.377018E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=1.367051E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P0 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.365858E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P0 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.365367E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P0 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.365050E-02 \t V0 -> V3 -> V4 -> V2 -> P0 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=1.364029E-02 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> P0 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.363490E-02 \t V0 -> V3 -> P0 -> P1 -> V0 \n",
-      "1\t N I=1.302289E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=1.299298E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=1.289116E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=1.289094E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=1.286992E-02 \t V0 -> V3 -> P1 -> P1 -> P1 -> V4 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=1.283121E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.271447E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.242743E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=1.202185E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.193356E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=1.192701E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "2\t N I=1.180068E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=1.179451E-02 \t V0 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.178828E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.177854E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=1.177829E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.176821E-02 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=1.176692E-02 \t V0 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.176084E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=1.175595E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.175161E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.174237E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.174026E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.077195E-02 \t V0 -> V3 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.050128E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P0 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.049533E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.047453E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "2\t N I=9.194804E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=8.828024E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> P0 -> V4 -> V3 -> V0 \n",
-      "1\t N I=8.616066E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=8.382155E-03 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=8.360062E-03 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> P1 -> P1 -> P1 -> V0 \n",
-      "1\t N I=7.974062E-03 \t V0 -> V3 -> V4 -> V2 -> V4 -> V3 -> P0 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=7.660077E-03 \t V0 -> V3 -> V4 -> V3 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=7.647885E-03 \t V0 -> V3 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "5\t N I=7.331964E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=7.128787E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=6.530489E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> P1 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "2\t N I=6.369831E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "1\t N I=6.350997E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> P1 -> P1 -> P1 -> V0 \n",
-      "1\t N I=5.952547E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "1\t N I=5.855741E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=5.839953E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> P1 -> P1 -> V0 \n",
-      "1\t N I=5.634308E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
-      "6\t N I=5.502077E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=5.452432E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P0 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=5.319210E-03 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> P1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "2\t N I=4.920947E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=4.135698E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=4.100827E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=3.945110E-03 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P0 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "3\t N I=3.705516E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=3.669367E-03 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=3.648249E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=3.477421E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "2\t N I=2.474695E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P0 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=2.304728E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.255269E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.011449E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P0 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.869764E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.847745E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P0 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.528149E-03 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=1.056831E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P0 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=9.166647E-04 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "4\t N I=7.810965E-04 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=5.888911E-04 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=4.802447E-04 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=4.204479E-04 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=3.772470E-04 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=3.676614E-04 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.357966E-04 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "1\t N I=9.527289E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=9.238563E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "1\t N I=9.057649E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=8.667397E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=4.666257E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P0 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=4.519254E-05 \t V0 -> V3 -> V4 -> V2 -> P0 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=3.945415E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=3.325231E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P0 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=3.066219E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.534252E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P0 -> V0 \n",
-      "1\t N I=1.606027E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=9.518509E-06 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=5.475630E-06 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=5.457173E-06 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=4.436696E-06 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V2 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.717751E-06 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.038084E-30 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.945785E-50 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "with open(\"run_folder/Union_history.dat\") as file:\n",
     "    instrument_string = file.read()\n",
@@ -1006,7 +232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1026,442 +252,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_tagging_4\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:13237:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:13237:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:13480:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:13480:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "14 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_tagging_4\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: Sample_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: Sample_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: Sample_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "starded conditional initialize \n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 4\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "name of process [2]: Sample_inc \n",
-      "component index [2]: 4 \n",
-      "name of process [3]: Sample_pow \n",
-      "component index [3]: 5 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 2\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 0.347892 \n",
-      "number of processes [0]: 2 \n",
-      "name of material    [1]: Sample \n",
-      "component index     [1]: 6 \n",
-      "my_absoprtion       [1]: 0.273042 \n",
-      "number of processes [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 2\n",
-      "\n",
-      "name of geometry    [0]: sample_geometry \n",
-      "component index     [0]: 8 \n",
-      "Volume.name         [0]: sample_geometry \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.273042 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: sample_container \n",
-      "component index     [1]: 9 \n",
-      "Volume.name         [1]: sample_container \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: cryostat_wall \n",
-      "component index     [2]: 10 \n",
-      "Volume.name         [2]: cryostat_wall \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
-      "Volume.p_physics.number of processes [2]: 2 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: 0.000000 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.030000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.120000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: cryostat_wall_vacuum \n",
-      "component index     [3]: 11 \n",
-      "Volume.name         [3]: cryostat_wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [3]: 1 \n",
-      "Volume.p_physics.my_absorption       [3]: 0.000000 \n",
-      "Volume.p_physics.number of processes [3]: 0 \n",
-      "Volume.geometry.shape                [3]: cylinder \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: 0.000000 \n",
-      "Volume.geometry.center.z             [3]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.028000 \n",
-      "Volume.geometry.geometry_parameters.height [3]: 0.112000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 5\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4]\n",
-      "LIST: Direct_children for Volume           0 = [3]\n",
-      "LIST: Intersect_check_list for Volume      0 = [3]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [3]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for sample_geometry with cylinder shape made of Sample\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = []\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [2]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = [2]\n",
-      "LIST: Next_volume_list for Volume          1 = [2]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for sample_container with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  2 = [1]\n",
-      "LIST: Direct_children for Volume           2 = [1]\n",
-      "LIST: Intersect_check_list for Volume      2 = [1]\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [4]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [4]\n",
-      "LIST: Next_volume_list for Volume          2 = [4,1]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for cryostat_wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  3 = [1,2,4]\n",
-      "LIST: Direct_children for Volume           3 = [4]\n",
-      "LIST: Intersect_check_list for Volume      3 = [4]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = []\n",
-      "LIST: Next_volume_list for Volume          3 = [0,4]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  4 = [1,2]\n",
-      "LIST: Direct_children for Volume           4 = [2]\n",
-      "LIST: Intersect_check_list for Volume      4 = [2]\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [3]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = [3]\n",
-      "LIST: Next_volume_list for Volume          4 = [3,2]\n",
-      "      Is_vacuum for Volume                 4 = 1\n",
-      "      is_mask_volume for Volume            4 = 0\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = []\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_zx_I=18092.6 logger_space_zx_ERR=33.1511 logger_space_zx_N=879706 \"logger_zx.dat\"\n",
-      "Detector: logger_space_zy_I=18092.6 logger_space_zy_ERR=33.1511 logger_space_zy_N=879706 \"logger_zy.dat\"\n",
-      "\n",
-      "\n",
-      "Top 20 most common histories. Shows the index of volumes entered (VX), and the scattering processes (PX)\n",
-      "5892\t N I=4.578027E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "895\t N I=2.001013E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "143\t N I=3.463667E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "108\t N I=2.251620E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "66\t N I=1.949978E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "65\t N I=1.194341E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "37\t N I=7.657474E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "14\t N I=3.683233E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "19\t N I=3.455680E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "17\t N I=2.855082E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "13\t N I=2.822139E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "9\t N I=2.778669E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "17\t N I=2.373749E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "18\t N I=2.297456E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.200556E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "11\t N I=1.857153E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "10\t N I=1.571294E-01 \t V0 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "6\t N I=9.084604E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "5\t N I=8.596945E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "4\t N I=8.405909E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "History file written by the McStas component Union_master \n",
-      "When running with MPI, the results may be from just a single thread, meaning intensities are divided by number of threads\n",
-      "----- Description of the used volumes -----------------------------------------------------------------------------------\n",
-      "V0: Surrounding vacuum \n",
-      "V1: sample_geometry  Material: Sample   P0:  P1: Sample_pow\n",
-      "V2: sample_container  Material: Al   P0:  P1: Al_pow\n",
-      "V3: cryostat_wall  Material: Al   P0:  P1: Al_pow\n",
-      "V4: cryostat_wall_vacuum  Material: Vacuum  \n",
-      "----- Histories sorted after intensity ----------------------------------------------------------------------------------\n",
-      "5892\t N I=4.578027E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "895\t N I=2.001013E+01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "143\t N I=3.463667E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "108\t N I=2.251620E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "66\t N I=1.949978E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "65\t N I=1.194341E+00 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "37\t N I=7.657474E-01 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "14\t N I=3.683233E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "19\t N I=3.455680E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "17\t N I=2.855082E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "13\t N I=2.822139E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "9\t N I=2.778669E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "17\t N I=2.373749E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "18\t N I=2.297456E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.200556E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "11\t N I=1.857153E-01 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "10\t N I=1.571294E-01 \t V0 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "6\t N I=9.084604E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "5\t N I=8.596945E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "4\t N I=8.405909E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "4\t N I=7.833313E-02 \t V0 -> V3 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "4\t N I=7.512586E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "5\t N I=7.432999E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "3\t N I=6.171041E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "5\t N I=5.967012E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "2\t N I=4.201788E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=4.079794E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "2\t N I=3.763233E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=3.552621E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "2\t N I=3.316035E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "2\t N I=3.311965E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "2\t N I=3.308331E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "2\t N I=3.307357E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.793967E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.790450E-02 \t V0 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.762920E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "2\t N I=2.734459E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "2\t N I=2.732315E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.380510E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "2\t N I=2.150892E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.923950E-02 \t V0 -> V3 -> P0 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.921815E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P0 -> V0 \n",
-      "1\t N I=1.921198E-02 \t V0 -> V3 -> V4 -> V2 -> P0 -> V4 -> V3 -> V0 \n",
-      "3\t N I=1.884439E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> V2 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.811470E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=1.658620E-02 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.657351E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> V2 -> P1 -> V1 -> V2 -> V4 -> V3 -> P1 -> V0 \n",
-      "1\t N I=1.654961E-02 \t V0 -> V3 -> P1 -> P1 -> V0 \n",
-      "1\t N I=1.653411E-02 \t V0 -> V3 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "5\t N I=1.536141E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.479142E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "2\t N I=1.377721E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> P1 -> V0 \n",
-      "1\t N I=1.072312E-02 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "3\t N I=6.601606E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P0 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=4.722113E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P0 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=4.516881E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=4.340485E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> P1 -> V4 -> V3 -> V0 \n",
-      "1\t N I=3.643161E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=3.424502E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V4 -> V3 -> P1 -> V0 \n",
-      "2\t N I=2.831503E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=2.169313E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> V2 -> P1 -> P1 -> V1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=1.570388E-03 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "4\t N I=1.393536E-03 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=4.412262E-04 \t V0 -> V3 -> V4 -> V2 -> P1 -> V1 -> P1 -> P1 -> P1 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "1\t N I=7.660815E-05 \t V0 -> V3 -> V4 -> V2 -> V1 -> P1 -> P0 -> P1 -> V2 -> V4 -> V3 -> V0 \n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "with open(\"run_folder/union_history.dat\") as file:\n",
     "    instrument_string = file.read()\n",
diff --git a/docs/source/user_guide/data.ipynb b/docs/source/user_guide/data.ipynb
new file mode 100644
index 00000000..daef4c70
--- /dev/null
+++ b/docs/source/user_guide/data.ipynb
@@ -0,0 +1,617 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "russian-transparency",
+   "metadata": {},
+   "source": [
+    "# Data\n",
+    "Data from simulations performed by McStasScript is returned as [McStasData](../_autosummary/mcstasscript.data.data.McStasData.rst) objects. This section will explore what these contain and how one can manipulate them. First a small instrument is written that will supply data to investigate.\n",
+    "\n",
+    "## Example instrument\n",
+    "The instrument will consist of a source, a powder sample and some monitors that will record data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "substantial-weekly",
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from mcstasscript.interface import instr, functions, plotter\n",
+    "\n",
+    "instrument = instr.McStas_instr(\"data_example\")\n",
+    "\n",
+    "source = instrument.add_component(\"source\", \"Source_simple\")\n",
+    "source.set_parameters(xwidth=0.05, yheight=0.03, dlambda=0.1,\n",
+    "                      dist=5, focus_xw=0.015, focus_yh=0.03)\n",
+    "source.lambda0 = instrument.add_parameter(\"wavelength\", value=1.2)\n",
+    "\n",
+    "sample = instrument.add_component(\"sample\", \"PowderN\")\n",
+    "sample.set_parameters(radius=source.focus_xw, yheight=source.focus_yh,\n",
+    "                      reflections='\"Na2Ca3Al2F14.laz\"', barns=0)\n",
+    "sample.set_AT(source.dist, RELATIVE=source)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sized-henry",
+   "metadata": {},
+   "source": [
+    "### Example monitors\n",
+    "Here three monitors are defined, a 2D PSD monitor, a 1D banana monitor and an event monitor. Monitor_nD is used for the last two, where the option string describes the geometry and what is to be recorded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "apart-duration",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "banana = instrument.add_component(\"banana\", \"Monitor_nD\", RELATIVE=sample)\n",
+    "banana.set_parameters(xwidth=1.5, yheight=0.4, restore_neutron=1, filename='\"banana.dat\"')\n",
+    "banana.options = '\"theta limits=[5 175] bins=250, banana\"'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "collaborative-police",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "event = instrument.add_component(\"events\", \"Monitor_nD\", RELATIVE=sample)\n",
+    "event.set_parameters(xwidth=0.5, yheight=0.5, restore_neutron=1, filename='\"events.dat\"')\n",
+    "event.options = '\"list all auto, x y z vx vy vz t\"'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "urban-traffic",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mon = instrument.add_component(\"PSD\", \"PSD_monitor\")\n",
+    "mon.set_AT(0.1, RELATIVE=sample)\n",
+    "mon.set_parameters(nx=100, ny=100, filename='\"psd.dat\"',\n",
+    "                   xwidth=3*sample.radius, yheight=2*sample.yheight, restore_neutron=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "center-middle",
+   "metadata": {},
+   "source": [
+    "## Generating data\n",
+    "The simulation is executed using the *backengine* method with a low number of neutrons. The data is returned by the *backengine* method, but will contain None if the simulation failed. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "finished-employment",
+   "metadata": {
+    "scrolled": true,
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/user_guide/data_example_21\"\n",
+      "INFO: Regenerating c-file: data_example.c\n",
+      "CFLAGS=\n",
+      "INFO: Recompiling: ./data_example.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "1 warning generated.\n",
+      "INFO: ===\n",
+      "Warning: 54849 events were removed in Component[5] PSD=PSD_monitor()\n",
+      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
+      "INFO: Placing instr file copy data_example.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/user_guide/data_example_21\n",
+      "\n",
+      "      Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: sample: Powder file probably of type Lazy Pulver (laz)\n",
+      "WARNING: but F2 unit is set to barns=0 (fm^2). Intensity might be 100 times too low.\n",
+      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "Detector: banana_I=3.09053e-07 banana_ERR=7.2496e-09 banana_N=13127 \"banana.dat\"\n",
+      "Events:   \"events_dat_list.p.x.y.z.vx.vy.vz.t\"\n",
+      "Detector: PSD_I=5.12526e-05 PSD_ERR=5.1007e-07 PSD_N=10736 \"psd.dat\"\n",
+      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
+      "    SPLIT 504 COMPONENT sample=PowderN(...)\n",
+      "  in the instrument description data_example.instr.\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.settings(ncount=1E5, output_path=\"data_example\")\n",
+    "data = instrument.backengine()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "charged-recovery",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "McStasData: banana type: 1D  I:3.09053e-07 E:7.2496e-09 N:13127, \n",
+      "McStasDataEvent: events with 43619 events. Variables: p x y z vx vy vz t, \n",
+      "McStasData: PSD type: 2D  I:5.12526e-05 E:5.1007e-07 N:10736]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "interracial-lancaster",
+   "metadata": {},
+   "source": [
+    "## McStasData objects\n",
+    "The data retrieved from the instrument object is in the form of a list that contains [McStasData](../_autosummary/mcstasscript.data.data.McStasData.rst) and [McStasDataEvent](../_autosummary/mcstasscript.data.data.McStasDataEvent.rst) objects. The McStasScript module [*functions*](../_autosummary/mcstasscript.interface.functions.rst) contains a function called [*name_search*](../_autosummary/mcstasscript.interface.functions.name_search.rst) which can be used to select a certain element of such a data list. It will match the component name first and if no match is found it will check for match with the filename. Here [*name_search*](../_autosummary/mcstasscript.interface.functions.name_search.rst) is used to retrieve the PSD [McStasData](../_autosummary/mcstasscript.data.data.McStasData.rst) object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "knowing-schedule",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PSD = functions.name_search(\"PSD\", data)\n",
+    "banana = functions.name_search(\"banana\", data)\n",
+    "events = functions.name_search(\"events\", data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "competitive-conversion",
+   "metadata": {},
+   "source": [
+    "### Accessing metadata\n",
+    "The McStasData object carries relevant metadata in a *metadata* attribute. Using the python print function this object can display basic information on the contained data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "official-major",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "metadata object\n",
+      "component_name: PSD\n",
+      "filename: psd.dat\n",
+      "2D data of dimension (100, 100)\n",
+      "  [-2.25: 2.25] X position [cm]\n",
+      "  [-3.0: 3.0] Y position [cm]\n",
+      "Instrument parameters: \n",
+      " wavelength = 1.2\n",
+      "\n",
+      "metadata object\n",
+      "component_name: banana\n",
+      "filename: banana.dat\n",
+      "1D data of length 250\n",
+      "  [5.0: 175.0] Longitude [deg]\n",
+      " Intensity [n/s/bin]\n",
+      "Instrument parameters: \n",
+      " wavelength = 1.2\n",
+      "\n",
+      "metadata object\n",
+      "component_name: events\n",
+      "filename: events_dat_list.p.x.y.z.vx.vy.vz.t\n",
+      "2D data of dimension (8, 43619)\n",
+      "  [1.0: 43619.0] List of neutron events\n",
+      "  [1.0: 8.0] p x y z vx vy vz t\n",
+      "Instrument parameters: \n",
+      " wavelength = 1.2\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(PSD.metadata)\n",
+    "print(banana.metadata)\n",
+    "print(events.metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "virgin-profit",
+   "metadata": {},
+   "source": [
+    "The metadata object has attributes which can be accessed as well. The info attribute is a dict with the raw metadata read from the file.\n",
+    "\n",
+    "- component_name\n",
+    "- dimension\n",
+    "- filename\n",
+    "- limits\n",
+    "- parameters\n",
+    "- info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "previous-theme",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Date': 'Wed Jan 19 16:25:40 2022 (1642605940)',\n",
+       " 'type': 'array_2d(100, 100)',\n",
+       " 'Source': 'data_example (data_example.instr)',\n",
+       " 'component': 'PSD',\n",
+       " 'position': '0 0 5.1',\n",
+       " 'title': 'PSD monitor',\n",
+       " 'Ncount': '100000',\n",
+       " 'filename': 'psd.dat',\n",
+       " 'statistics': 'X0=0.00170905; dX=0.444962; Y0=-0.0029441; dY=0.887821;',\n",
+       " 'signal': 'Min=0; Max=8.13188e-08; Mean=5.12526e-09;',\n",
+       " 'values': '5.12526e-05 5.1007e-07 10736',\n",
+       " 'xvar': 'X',\n",
+       " 'yvar': 'Y',\n",
+       " 'xlabel': 'X position [cm]',\n",
+       " 'ylabel': 'Y position [cm]',\n",
+       " 'zvar': 'I',\n",
+       " 'zlabel': 'Signal per bin',\n",
+       " 'xylimits': '-2.25 2.25 -3 3',\n",
+       " 'variables': 'I I_err N',\n",
+       " 'Parameters': {'wavelength': 1.2}}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PSD.metadata.info"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acknowledged-orlando",
+   "metadata": {},
+   "source": [
+    "### Accessing the data\n",
+    "McStasData objects stores the data as [Numpy arrays](https://numpy.org/doc/stable/reference/generated/numpy.array.html), these can be accessed as attributes.\n",
+    "\n",
+    "- Intensity: Holds the intensity, sum of all ray weights\n",
+    "- Error: Error on intensity\n",
+    "- Ncount: Number of rays that reached "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "composite-property",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intensity\n",
+      "[[0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
+      "  0.00000000e+00 0.00000000e+00]\n",
+      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
+      "  0.00000000e+00 0.00000000e+00]\n",
+      " [0.00000000e+00 3.16421435e-13 0.00000000e+00 ... 0.00000000e+00\n",
+      "  0.00000000e+00 0.00000000e+00]\n",
+      " ...\n",
+      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
+      "  0.00000000e+00 0.00000000e+00]\n",
+      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
+      "  0.00000000e+00 0.00000000e+00]\n",
+      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
+      "  0.00000000e+00 1.83098259e-10]]\n",
+      "Error\n",
+      "[[0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
+      "  0.00000000e+00 0.00000000e+00]\n",
+      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
+      "  0.00000000e+00 0.00000000e+00]\n",
+      " [0.00000000e+00 3.16421435e-13 0.00000000e+00 ... 0.00000000e+00\n",
+      "  0.00000000e+00 0.00000000e+00]\n",
+      " ...\n",
+      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
+      "  0.00000000e+00 0.00000000e+00]\n",
+      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
+      "  0.00000000e+00 0.00000000e+00]\n",
+      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
+      "  0.00000000e+00 1.83098259e-10]]\n",
+      "Ncount\n",
+      "[[0. 0. 0. ... 0. 0. 0.]\n",
+      " [0. 0. 0. ... 0. 0. 0.]\n",
+      " [0. 1. 0. ... 0. 0. 0.]\n",
+      " ...\n",
+      " [0. 0. 0. ... 0. 0. 0.]\n",
+      " [0. 0. 0. ... 0. 0. 0.]\n",
+      " [0. 0. 0. ... 0. 0. 1.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Intensity\")\n",
+    "print(PSD.Intensity)\n",
+    "\n",
+    "print(\"Error\")\n",
+    "print(PSD.Error)\n",
+    "\n",
+    "print(\"Ncount\")\n",
+    "print(PSD.Ncount)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "central-democracy",
+   "metadata": {},
+   "source": [
+    "McStasDataEvent objecst stores event data, and for this reason does not have *Error* or *Ncount*. The event information is contained in a 2D Numpy array in the *Intensity* and *Events* attributes. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "closed-tumor",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Events [[ 3.19443415e-13 -1.72869932e-02 -4.51773809e-03 ... -2.41580555e+03\n",
+      "  -3.05806649e+02  1.40746641e-03]\n",
+      " [ 2.13584425e-12  1.71666512e-02 -8.63270069e-03 ... -3.19302620e+01\n",
+      "  -8.06158169e+02  1.61182199e-03]\n",
+      " [ 1.19125080e-12  4.48926741e-02  3.57122451e-03 ... -1.91672521e+02\n",
+      "   2.31988770e+02  1.58734495e-03]\n",
+      " ...\n",
+      " [ 1.32055701e-12 -1.69617301e-03  3.22148392e-03 ... -7.69868816e+02\n",
+      "  -1.41856315e+03  1.53521303e-03]\n",
+      " [ 1.24293709e-12  1.20333693e-01  8.91293709e-02 ...  1.99940422e+03\n",
+      "   3.26712779e+02  1.54102054e-03]\n",
+      " [ 1.51518794e-12  2.36891432e-02 -5.80047664e-03 ... -1.15832262e+03\n",
+      "  -1.66223218e+03  1.39916492e-03]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Events\", events.Events)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "registered-vocabulary",
+   "metadata": {},
+   "source": [
+    "The original path to the data is also contained within the McStasData object and can be returned with get_data_location."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "adverse-demonstration",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/user_guide/data_example_21'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PSD.get_data_location()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dated-heritage",
+   "metadata": {},
+   "source": [
+    "### Plotting \n",
+    "[McStasData](../_autosummary/mcstasscript.data.data.McStasData.rst) objects contain information on how the data should be plotted, including for example if it should be on a logarithmic axis. This information is contained in the *plot_options* attribute of a [McStasData](../_autosummary/mcstasscript.data.data.McStasData.rst) object. The plotting are described in more detail on the [plotting page](plotting.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "furnished-juice",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "plot_options log: False\n",
+       " colormap: jet\n",
+       " show_colorbar: True\n",
+       " cut_min: 0\n",
+       " cut_max: 1\n",
+       " x_limit_multiplier: 1\n",
+       " y_limit_multiplier: 1"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PSD.plot_options"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "educated-template",
+   "metadata": {},
+   "source": [
+    "The plotter module of McStasScript can plot a McStasData object directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "religious-chambers",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name PSD\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotter.make_plot(PSD)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "green-nancy",
+   "metadata": {},
+   "source": [
+    "The *plot_options* can be updated with *set_plot_options* that takes keyword arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "blessed-sandwich",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name PSD\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "PSD.set_plot_options(log=True, top_lim=1.5, bottom_lim=-1.5, colormap=\"hot\", orders_of_mag=2)\n",
+    "plotter.make_plot(PSD)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "northern-ethernet",
+   "metadata": {},
+   "source": [
+    "The *set_plot_options* takes the following keyword arguments. Some will only apply for 2D data, for example *orders_of_mag*.\n",
+    "\n",
+    "| Keyword argument | Type | Default | Description |\n",
+    "| --- | --- | --- | --- |\n",
+    "| log | bool | False | Logarithmic axis for y in 1D or z in 2D |\n",
+    "| orders_of_mag | float | 300 | Maximum orders of magnitude to plot in 2D |\n",
+    "| colormap | str | \"jet\" | Matplotlib colormap to use |\n",
+    "| show_colorbar | bool | True | Show the colorbar |\n",
+    "| x_axis_multiplier | float | 1 | Multiplier for x axis data |\n",
+    "| y_axis_multiplier | float | 1 | Multiplier for y axis data |\n",
+    "| cut_min | float | 0 | Unitless lower limit normalized to data range |\n",
+    "| cut_max | float | 1 | Unitless upper limit normalized to data range |\n",
+    "| left_lim | float | | Lower limit to plot range of x axis |\n",
+    "| right_lim | float | | Upper limit to plot range of x axis|\n",
+    "| bottom_lim | float | | Lower limit to plot range of y axis|\n",
+    "| top_lim | float | | Upper limit to plot range of y axis|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "frozen-participation",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "banner-fitting",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/user_guide/instrument_object.ipynb b/docs/source/user_guide/instrument_object.ipynb
index 2043a0d0..6746a189 100644
--- a/docs/source/user_guide/instrument_object.ipynb
+++ b/docs/source/user_guide/instrument_object.ipynb
@@ -615,7 +615,7 @@
    "metadata": {},
    "source": [
     "## Run the simulation\n",
-    "The simulation is executed with a call to the *backengine* method."
+    "The simulation is executed with a call to the *backengine* method, which will return the generated data. If the simulation fails, the method returns None."
    ]
   },
   {
@@ -654,16 +654,7 @@
     }
    ],
    "source": [
-    "instrument.backengine()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "spread-douglas",
-   "metadata": {},
-   "source": [
-    "## Get the data\n",
-    "The data is held in the data attribute of the instrument. If the simulation failed, or did not output any data, the data attribute will contain None."
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -682,7 +673,6 @@
     }
    ],
    "source": [
-    "data = instrument.data\n",
     "print(data)"
    ]
   },
diff --git a/docs/source/user_guide/plotting.ipynb b/docs/source/user_guide/plotting.ipynb
index e1debe7c..3c777204 100644
--- a/docs/source/user_guide/plotting.ipynb
+++ b/docs/source/user_guide/plotting.ipynb
@@ -103,8 +103,7 @@
    "source": [
     "instrument.settings(ncount=1E6, output_path=\"plotting_example\")\n",
     "instrument.set_parameters(wavelength=1.5)\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {

From 0af03de53c8913663d4024f5110f551ea9c41049 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 22 Mar 2022 08:11:50 +0100
Subject: [PATCH 227/403] Updates to documentation. Bug in reading
 union_histories fixed.

---
 .../tutorial/Union_tutorial_3_loggers.ipynb   | 2606 ++++++++++++++++-
 .../tutorial/Union_tutorial_5_masks.ipynb     | 1225 +++++++-
 ...ial_6_Exit_and_number_of_activations.ipynb | 1773 ++++++++++-
 .../Union_tutorial_7_Tagging_history.ipynb    |    4 +-
 .../user_guide/parameters_and_variables.ipynb |  208 +-
 5 files changed, 5495 insertions(+), 321 deletions(-)

diff --git a/docs/source/tutorial/Union_tutorial_3_loggers.ipynb b/docs/source/tutorial/Union_tutorial_3_loggers.ipynb
index 284a5ea8..1205f357 100644
--- a/docs/source/tutorial/Union_tutorial_3_loggers.ipynb
+++ b/docs/source/tutorial/Union_tutorial_3_loggers.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -28,18 +28,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'instrument' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-5353298b0c38>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mAl_inc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstrument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_component\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Al_inc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Incoherent_process\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mAl_inc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msigma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0082\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mAl_inc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_cell_volume\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m66.4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mAl_pow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstrument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_component\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Al_pow\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Powder_process\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'instrument' is not defined"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mAl_inc\u001b[0m  \u001b[1mIncoherent_process\u001b[0m \n",
+      "  AT      (0, 0, 0) ABSOLUTE\n",
+      "\u001b[1mAl_pow\u001b[0m  \u001b[1mPowder_process\u001b[0m \n",
+      "  AT      (0, 0, 0) ABSOLUTE\n",
+      "\u001b[1mAl\u001b[0m  \u001b[1mUnion_make_material\u001b[0m \n",
+      "  AT      (0, 0, 0) ABSOLUTE\n",
+      "\u001b[1mSample_inc\u001b[0m  \u001b[1mIncoherent_process\u001b[0m \n",
+      "  AT      (0, 0, 0) ABSOLUTE\n",
+      "\u001b[1mSample_pow\u001b[0m  \u001b[1mPowder_process\u001b[0m \n",
+      "  AT      (0, 0, 0) ABSOLUTE\n",
+      "\u001b[1mSample\u001b[0m  \u001b[1mUnion_make_material\u001b[0m \n",
+      "  AT      (0, 0, 0) ABSOLUTE\n",
+      "\u001b[1msource\u001b[0m  \u001b[1mSource_div\u001b[0m \n",
+      "  AT      (0, 0, 0) ABSOLUTE\n",
+      "\u001b[1msample_geometry\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0, 1) RELATIVE source\n",
+      "\u001b[1msample_container\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0, 0) RELATIVE sample_geometry\n",
+      "\u001b[1msample_container_lid\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0.0155, 0) RELATIVE sample_container\n",
+      "\u001b[1mcryostat_wall\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0, 0) RELATIVE sample_geometry\n",
+      "\u001b[1mcryostat_wall_vacuum\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0, 0) RELATIVE sample_geometry\n",
+      "\u001b[1mouter_cryostat_wall\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0, 0) RELATIVE sample_geometry\n",
+      "\u001b[1mouter_cryostat_wall_vacuum\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0, 0) RELATIVE sample_geometry\n"
      ]
     }
    ],
@@ -130,9 +153,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are all components in the Work directory category.\n",
+      "No components found in this category! Available categories:\n",
+      " contrib\n",
+      " misc\n",
+      " monitors\n",
+      " obsolete\n",
+      " optics\n",
+      " samples\n",
+      " sources\n",
+      " union\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.show_components(\"Work directory\")"
    ]
@@ -161,9 +201,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Union_logger_2D_space _____________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1mtarget_geometry\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated list of geometry names \n",
+      "                                     that will be logged, leave empty for all \n",
+      "                                     volumes (even not defined yet) \n",
+      "\u001b[1mtarget_process\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated names of physical \n",
+      "                                    processes, if volumes are selected, one can \n",
+      "                                    select Union_process names \n",
+      "\u001b[1mD_direction_1\u001b[0m = \u001b[1m\u001b[94m\"x\"\u001b[0m\u001b[0m [string] // Direction for first axis (\"x\", \"y\" or \"z\")\n",
+      "\u001b[1mD1_min\u001b[0m = \u001b[1m\u001b[94m-5.0\u001b[0m\u001b[0m [1] // histogram boundery, min position value for first axis\n",
+      "\u001b[1mD1_max\u001b[0m = \u001b[1m\u001b[94m5.0\u001b[0m\u001b[0m [1] // histogram boundery, max position value for first axis\n",
+      "\u001b[1mn1\u001b[0m = \u001b[1m\u001b[94m90.0\u001b[0m\u001b[0m [1] // number of bins for first axis\n",
+      "\u001b[1mD_direction_2\u001b[0m = \u001b[1m\u001b[94m\"z\"\u001b[0m\u001b[0m [string] // Direction for second axis (\"x\", \"y\" or \"z\")\n",
+      "\u001b[1mD2_min\u001b[0m = \u001b[1m\u001b[94m-5.0\u001b[0m\u001b[0m [1] // histogram boundery, min position value for second axis\n",
+      "\u001b[1mD2_max\u001b[0m = \u001b[1m\u001b[94m5.0\u001b[0m\u001b[0m [1] // histogram boundery, max position value for second axis\n",
+      "\u001b[1mn2\u001b[0m = \u001b[1m\u001b[94m90.0\u001b[0m\u001b[0m [1] // number of bins for second axis\n",
+      "\u001b[1mfilename\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Filename for logging output\n",
+      "\u001b[1morder_total\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time, 0 for \n",
+      "                         all orders \n",
+      "\u001b[1morder_volume\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time in the \n",
+      "                          same geometry \n",
+      "\u001b[1morder_volume_process\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time \n",
+      "                                  in the same geometry, using the same process \n",
+      "\u001b[1mlogger_conditional_extend_index\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // If a conditional is used with \n",
+      "                                              this logger, the result of each \n",
+      "                                              conditional calculation can be made \n",
+      "                                              available in extend as a array called \n",
+      "                                              \"logger_conditional_extend\", and one \n",
+      "                                              would then acces \n",
+      "                                              logger_conditional_extend[n] if \n",
+      "                                              logger_conditional_extend_index is \n",
+      "                                              set to n \n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.component_help(\"Union_logger_2D_space\")"
    ]
@@ -178,21 +258,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'instrument' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-2-c11f0150980d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlogger_zx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstrument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_component\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"logger_space_zx\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Union_logger_2D_space\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mlogger_zx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_RELATIVE\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_geometry\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mlogger_zx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mD_direction_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'\"z\"'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mlogger_zx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mD1_min\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.12\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mlogger_zx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mD1_max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.12\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'instrument' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\")\n",
     "logger_zx.set_RELATIVE(sample_geometry)\n",
@@ -231,13 +299,424 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_loggers_118\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:14067:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:14067:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:14310:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:14310:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "14 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_loggers_118\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: Sample_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: Sample_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: Sample_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 4\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "name of process [2]: Sample_inc \n",
+      "component index [2]: 4 \n",
+      "name of process [3]: Sample_pow \n",
+      "component index [3]: 5 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 2\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 0.347892 \n",
+      "number of processes [0]: 2 \n",
+      "name of material    [1]: Sample \n",
+      "component index     [1]: 6 \n",
+      "my_absoprtion       [1]: 0.273042 \n",
+      "number of processes [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 2\n",
+      "\n",
+      "name of geometry    [0]: sample_geometry \n",
+      "component index     [0]: 8 \n",
+      "Volume.name         [0]: sample_geometry \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.273042 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: sample_container \n",
+      "component index     [1]: 9 \n",
+      "Volume.name         [1]: sample_container \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: sample_container_lid \n",
+      "component index     [2]: 10 \n",
+      "Volume.name         [2]: sample_container_lid \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
+      "Volume.p_physics.number of processes [2]: 2 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: 0.015500 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: cryostat_wall \n",
+      "component index     [3]: 11 \n",
+      "Volume.name         [3]: cryostat_wall \n",
+      "Volume.p_physics.is_vacuum           [3]: 0 \n",
+      "Volume.p_physics.my_absorption       [3]: 0.347892 \n",
+      "Volume.p_physics.number of processes [3]: 2 \n",
+      "Volume.geometry.shape                [3]: cylinder \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: 0.000000 \n",
+      "Volume.geometry.center.z             [3]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
+      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [4]: cryostat_wall_vacuum \n",
+      "component index     [4]: 12 \n",
+      "Volume.name         [4]: cryostat_wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [4]: 1 \n",
+      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
+      "Volume.p_physics.number of processes [4]: 0 \n",
+      "Volume.geometry.shape                [4]: cylinder \n",
+      "Volume.geometry.center.x             [4]: 0.000000 \n",
+      "Volume.geometry.center.y             [4]: 0.000000 \n",
+      "Volume.geometry.center.z             [4]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
+      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [5]: outer_cryostat_wall \n",
+      "component index     [5]: 13 \n",
+      "Volume.name         [5]: outer_cryostat_wall \n",
+      "Volume.p_physics.is_vacuum           [5]: 0 \n",
+      "Volume.p_physics.my_absorption       [5]: 0.347892 \n",
+      "Volume.p_physics.number of processes [5]: 2 \n",
+      "Volume.geometry.shape                [5]: cylinder \n",
+      "Volume.geometry.center.x             [5]: 0.000000 \n",
+      "Volume.geometry.center.y             [5]: 0.000000 \n",
+      "Volume.geometry.center.z             [5]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.100000 \n",
+      "Volume.geometry.geometry_parameters.height [5]: 0.150000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [6]: outer_cryostat_wall_vacuum \n",
+      "component index     [6]: 14 \n",
+      "Volume.name         [6]: outer_cryostat_wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [6]: 1 \n",
+      "Volume.p_physics.my_absorption       [6]: 0.000000 \n",
+      "Volume.p_physics.number of processes [6]: 0 \n",
+      "Volume.geometry.shape                [6]: cylinder \n",
+      "Volume.geometry.center.x             [6]: 0.000000 \n",
+      "Volume.geometry.center.y             [6]: 0.000000 \n",
+      "Volume.geometry.center.z             [6]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.097000 \n",
+      "Volume.geometry.geometry_parameters.height [6]: 0.140000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 8\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7]\n",
+      "LIST: Direct_children for Volume           0 = [6]\n",
+      "LIST: Intersect_check_list for Volume      0 = [6]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [6]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for sample_geometry with cylinder shape made of Sample\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = []\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [2,3]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
+      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for sample_container with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  2 = [1]\n",
+      "LIST: Direct_children for Volume           2 = [1]\n",
+      "LIST: Intersect_check_list for Volume      2 = [1]\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [3,5]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [5]\n",
+      "LIST: Next_volume_list for Volume          2 = [3,5,1]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for sample_container_lid with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  3 = []\n",
+      "LIST: Direct_children for Volume           3 = []\n",
+      "LIST: Intersect_check_list for Volume      3 = [2]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [5]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = [5]\n",
+      "LIST: Next_volume_list for Volume          3 = [5,2]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "List overview for cryostat_wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  4 = [1,2,3,5]\n",
+      "LIST: Direct_children for Volume           4 = [5]\n",
+      "LIST: Intersect_check_list for Volume      4 = [5]\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [7]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = [7]\n",
+      "LIST: Next_volume_list for Volume          4 = [7,5]\n",
+      "      Is_vacuum for Volume                 4 = 0\n",
+      "      is_mask_volume for Volume            4 = 0\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = []\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 0\n",
+      "\n",
+      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  5 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           5 = [2,3]\n",
+      "LIST: Intersect_check_list for Volume      5 = [2,3]\n",
+      "LIST: Mask_intersect_list for Volume       5 = []\n",
+      "LIST: Destinations_list for Volume         5 = [4]\n",
+      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
+      "LIST: Next_volume_list for Volume          5 = [4,2,3]\n",
+      "      Is_vacuum for Volume                 5 = 1\n",
+      "      is_mask_volume for Volume            5 = 0\n",
+      "      is_masked_volume for Volume          5 = 0\n",
+      "      is_exit_volume for Volume            5 = 0\n",
+      "LIST: mask_list for Volume                 5 = []\n",
+      "LIST: masked_by_list for Volume            5 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
+      "      mask_mode for Volume                 5 = 0\n",
+      "\n",
+      "List overview for outer_cryostat_wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  6 = [1,2,3,4,5,7]\n",
+      "LIST: Direct_children for Volume           6 = [7]\n",
+      "LIST: Intersect_check_list for Volume      6 = [7]\n",
+      "LIST: Mask_intersect_list for Volume       6 = []\n",
+      "LIST: Destinations_list for Volume         6 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 6 = []\n",
+      "LIST: Next_volume_list for Volume          6 = [0,7]\n",
+      "      Is_vacuum for Volume                 6 = 0\n",
+      "      is_mask_volume for Volume            6 = 0\n",
+      "      is_masked_volume for Volume          6 = 0\n",
+      "      is_exit_volume for Volume            6 = 0\n",
+      "LIST: mask_list for Volume                 6 = []\n",
+      "LIST: masked_by_list for Volume            6 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
+      "      mask_mode for Volume                 6 = 0\n",
+      "\n",
+      "List overview for outer_cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  7 = [1,2,3,4,5]\n",
+      "LIST: Direct_children for Volume           7 = [4]\n",
+      "LIST: Intersect_check_list for Volume      7 = [4]\n",
+      "LIST: Mask_intersect_list for Volume       7 = []\n",
+      "LIST: Destinations_list for Volume         7 = [6]\n",
+      "LIST: Reduced_destinations_list for Volume 7 = [6]\n",
+      "LIST: Next_volume_list for Volume          7 = [6,4]\n",
+      "      Is_vacuum for Volume                 7 = 1\n",
+      "      is_mask_volume for Volume            7 = 0\n",
+      "      is_masked_volume for Volume          7 = 0\n",
+      "      is_exit_volume for Volume            7 = 0\n",
+      "LIST: mask_list for Volume                 7 = []\n",
+      "LIST: masked_by_list for Volume            7 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
+      "      mask_mode for Volume                 7 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_zx_I=20561.2 logger_space_zx_ERR=35.2947 logger_space_zx_N=1.0129e+06 \"logger_zx.dat\"\n",
+      "Detector: logger_space_zy_I=20561.2 logger_space_zy_ERR=35.2947 logger_space_zy_N=1.0129e+06 \"logger_zy.dat\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.set_parameters(wavelength=3.0)\n",
     "instrument.settings(ncount=3E6, output_path=\"data_folder/union_loggers\")\n",
@@ -247,9 +726,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space_zx\n",
+      "Plotting data with name logger_space_zy\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plotter.make_sub_plot(data, log=True, orders_of_mag=4)"
    ]
@@ -274,13 +774,424 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_loggers_119\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:14067:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:14067:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:14310:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:14310:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "14 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_loggers_119\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: Sample_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: Sample_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: Sample_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 4\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "name of process [2]: Sample_inc \n",
+      "component index [2]: 4 \n",
+      "name of process [3]: Sample_pow \n",
+      "component index [3]: 5 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 2\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 0.347892 \n",
+      "number of processes [0]: 2 \n",
+      "name of material    [1]: Sample \n",
+      "component index     [1]: 6 \n",
+      "my_absoprtion       [1]: 0.273042 \n",
+      "number of processes [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 2\n",
+      "\n",
+      "name of geometry    [0]: sample_geometry \n",
+      "component index     [0]: 8 \n",
+      "Volume.name         [0]: sample_geometry \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.273042 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: sample_container \n",
+      "component index     [1]: 9 \n",
+      "Volume.name         [1]: sample_container \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: sample_container_lid \n",
+      "component index     [2]: 10 \n",
+      "Volume.name         [2]: sample_container_lid \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
+      "Volume.p_physics.number of processes [2]: 2 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: 0.015500 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: cryostat_wall \n",
+      "component index     [3]: 11 \n",
+      "Volume.name         [3]: cryostat_wall \n",
+      "Volume.p_physics.is_vacuum           [3]: 0 \n",
+      "Volume.p_physics.my_absorption       [3]: 0.347892 \n",
+      "Volume.p_physics.number of processes [3]: 2 \n",
+      "Volume.geometry.shape                [3]: cylinder \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: 0.000000 \n",
+      "Volume.geometry.center.z             [3]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
+      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [4]: cryostat_wall_vacuum \n",
+      "component index     [4]: 12 \n",
+      "Volume.name         [4]: cryostat_wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [4]: 1 \n",
+      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
+      "Volume.p_physics.number of processes [4]: 0 \n",
+      "Volume.geometry.shape                [4]: cylinder \n",
+      "Volume.geometry.center.x             [4]: 0.000000 \n",
+      "Volume.geometry.center.y             [4]: 0.000000 \n",
+      "Volume.geometry.center.z             [4]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
+      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [5]: outer_cryostat_wall \n",
+      "component index     [5]: 13 \n",
+      "Volume.name         [5]: outer_cryostat_wall \n",
+      "Volume.p_physics.is_vacuum           [5]: 0 \n",
+      "Volume.p_physics.my_absorption       [5]: 0.347892 \n",
+      "Volume.p_physics.number of processes [5]: 2 \n",
+      "Volume.geometry.shape                [5]: cylinder \n",
+      "Volume.geometry.center.x             [5]: 0.000000 \n",
+      "Volume.geometry.center.y             [5]: 0.000000 \n",
+      "Volume.geometry.center.z             [5]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.100000 \n",
+      "Volume.geometry.geometry_parameters.height [5]: 0.150000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [6]: outer_cryostat_wall_vacuum \n",
+      "component index     [6]: 14 \n",
+      "Volume.name         [6]: outer_cryostat_wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [6]: 1 \n",
+      "Volume.p_physics.my_absorption       [6]: 0.000000 \n",
+      "Volume.p_physics.number of processes [6]: 0 \n",
+      "Volume.geometry.shape                [6]: cylinder \n",
+      "Volume.geometry.center.x             [6]: 0.000000 \n",
+      "Volume.geometry.center.y             [6]: 0.000000 \n",
+      "Volume.geometry.center.z             [6]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.097000 \n",
+      "Volume.geometry.geometry_parameters.height [6]: 0.140000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 8\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7]\n",
+      "LIST: Direct_children for Volume           0 = [6]\n",
+      "LIST: Intersect_check_list for Volume      0 = [6]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [6]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for sample_geometry with cylinder shape made of Sample\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = []\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [2,3]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
+      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for sample_container with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  2 = [1]\n",
+      "LIST: Direct_children for Volume           2 = [1]\n",
+      "LIST: Intersect_check_list for Volume      2 = [1]\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [3,5]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [5]\n",
+      "LIST: Next_volume_list for Volume          2 = [3,5,1]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for sample_container_lid with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  3 = []\n",
+      "LIST: Direct_children for Volume           3 = []\n",
+      "LIST: Intersect_check_list for Volume      3 = [2]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [5]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = [5]\n",
+      "LIST: Next_volume_list for Volume          3 = [5,2]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "List overview for cryostat_wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  4 = [1,2,3,5]\n",
+      "LIST: Direct_children for Volume           4 = [5]\n",
+      "LIST: Intersect_check_list for Volume      4 = [5]\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [7]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = [7]\n",
+      "LIST: Next_volume_list for Volume          4 = [7,5]\n",
+      "      Is_vacuum for Volume                 4 = 0\n",
+      "      is_mask_volume for Volume            4 = 0\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = []\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 0\n",
+      "\n",
+      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  5 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           5 = [2,3]\n",
+      "LIST: Intersect_check_list for Volume      5 = [2,3]\n",
+      "LIST: Mask_intersect_list for Volume       5 = []\n",
+      "LIST: Destinations_list for Volume         5 = [4]\n",
+      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
+      "LIST: Next_volume_list for Volume          5 = [4,2,3]\n",
+      "      Is_vacuum for Volume                 5 = 1\n",
+      "      is_mask_volume for Volume            5 = 0\n",
+      "      is_masked_volume for Volume          5 = 0\n",
+      "      is_exit_volume for Volume            5 = 0\n",
+      "LIST: mask_list for Volume                 5 = []\n",
+      "LIST: masked_by_list for Volume            5 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
+      "      mask_mode for Volume                 5 = 0\n",
+      "\n",
+      "List overview for outer_cryostat_wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  6 = [1,2,3,4,5,7]\n",
+      "LIST: Direct_children for Volume           6 = [7]\n",
+      "LIST: Intersect_check_list for Volume      6 = [7]\n",
+      "LIST: Mask_intersect_list for Volume       6 = []\n",
+      "LIST: Destinations_list for Volume         6 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 6 = []\n",
+      "LIST: Next_volume_list for Volume          6 = [0,7]\n",
+      "      Is_vacuum for Volume                 6 = 0\n",
+      "      is_mask_volume for Volume            6 = 0\n",
+      "      is_masked_volume for Volume          6 = 0\n",
+      "      is_exit_volume for Volume            6 = 0\n",
+      "LIST: mask_list for Volume                 6 = []\n",
+      "LIST: masked_by_list for Volume            6 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
+      "      mask_mode for Volume                 6 = 0\n",
+      "\n",
+      "List overview for outer_cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  7 = [1,2,3,4,5]\n",
+      "LIST: Direct_children for Volume           7 = [4]\n",
+      "LIST: Intersect_check_list for Volume      7 = [4]\n",
+      "LIST: Mask_intersect_list for Volume       7 = []\n",
+      "LIST: Destinations_list for Volume         7 = [6]\n",
+      "LIST: Reduced_destinations_list for Volume 7 = [6]\n",
+      "LIST: Next_volume_list for Volume          7 = [6,4]\n",
+      "      Is_vacuum for Volume                 7 = 1\n",
+      "      is_mask_volume for Volume            7 = 0\n",
+      "      is_masked_volume for Volume          7 = 0\n",
+      "      is_exit_volume for Volume            7 = 0\n",
+      "LIST: mask_list for Volume                 7 = []\n",
+      "LIST: masked_by_list for Volume            7 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
+      "      mask_mode for Volume                 7 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_zx_I=3800.09 logger_space_zx_ERR=10.0702 logger_space_zx_N=200291 \"logger_zx.dat\"\n",
+      "Detector: logger_space_zy_I=14815.6 logger_space_zy_ERR=30.8042 logger_space_zy_N=713524 \"logger_zy.dat\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "logger_zx.target_geometry = '\"outer_cryostat_wall,cryostat_wall\"'\n",
     "logger_zy.target_geometry = '\"sample_geometry\"'\n",
@@ -290,9 +1201,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space_zx\n",
+      "Plotting data with name logger_space_zy\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plotter.make_sub_plot(data, log=[True, False], orders_of_mag=4)"
    ]
@@ -312,28 +1244,459 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_loggers_120\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:14067:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:14067:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:14310:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:14310:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "14 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_loggers_120\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: Sample_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: Sample_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: Sample_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 4\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "name of process [2]: Sample_inc \n",
+      "component index [2]: 4 \n",
+      "name of process [3]: Sample_pow \n",
+      "component index [3]: 5 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 2\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 0.347892 \n",
+      "number of processes [0]: 2 \n",
+      "name of material    [1]: Sample \n",
+      "component index     [1]: 6 \n",
+      "my_absoprtion       [1]: 0.273042 \n",
+      "number of processes [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 2\n",
+      "\n",
+      "name of geometry    [0]: sample_geometry \n",
+      "component index     [0]: 8 \n",
+      "Volume.name         [0]: sample_geometry \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.273042 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: sample_container \n",
+      "component index     [1]: 9 \n",
+      "Volume.name         [1]: sample_container \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: sample_container_lid \n",
+      "component index     [2]: 10 \n",
+      "Volume.name         [2]: sample_container_lid \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
+      "Volume.p_physics.number of processes [2]: 2 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: 0.015500 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: cryostat_wall \n",
+      "component index     [3]: 11 \n",
+      "Volume.name         [3]: cryostat_wall \n",
+      "Volume.p_physics.is_vacuum           [3]: 0 \n",
+      "Volume.p_physics.my_absorption       [3]: 0.347892 \n",
+      "Volume.p_physics.number of processes [3]: 2 \n",
+      "Volume.geometry.shape                [3]: cylinder \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: 0.000000 \n",
+      "Volume.geometry.center.z             [3]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
+      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [4]: cryostat_wall_vacuum \n",
+      "component index     [4]: 12 \n",
+      "Volume.name         [4]: cryostat_wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [4]: 1 \n",
+      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
+      "Volume.p_physics.number of processes [4]: 0 \n",
+      "Volume.geometry.shape                [4]: cylinder \n",
+      "Volume.geometry.center.x             [4]: 0.000000 \n",
+      "Volume.geometry.center.y             [4]: 0.000000 \n",
+      "Volume.geometry.center.z             [4]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
+      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [5]: outer_cryostat_wall \n",
+      "component index     [5]: 13 \n",
+      "Volume.name         [5]: outer_cryostat_wall \n",
+      "Volume.p_physics.is_vacuum           [5]: 0 \n",
+      "Volume.p_physics.my_absorption       [5]: 0.347892 \n",
+      "Volume.p_physics.number of processes [5]: 2 \n",
+      "Volume.geometry.shape                [5]: cylinder \n",
+      "Volume.geometry.center.x             [5]: 0.000000 \n",
+      "Volume.geometry.center.y             [5]: 0.000000 \n",
+      "Volume.geometry.center.z             [5]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.100000 \n",
+      "Volume.geometry.geometry_parameters.height [5]: 0.150000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [6]: outer_cryostat_wall_vacuum \n",
+      "component index     [6]: 14 \n",
+      "Volume.name         [6]: outer_cryostat_wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [6]: 1 \n",
+      "Volume.p_physics.my_absorption       [6]: 0.000000 \n",
+      "Volume.p_physics.number of processes [6]: 0 \n",
+      "Volume.geometry.shape                [6]: cylinder \n",
+      "Volume.geometry.center.x             [6]: 0.000000 \n",
+      "Volume.geometry.center.y             [6]: 0.000000 \n",
+      "Volume.geometry.center.z             [6]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.097000 \n",
+      "Volume.geometry.geometry_parameters.height [6]: 0.140000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 8\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7]\n",
+      "LIST: Direct_children for Volume           0 = [6]\n",
+      "LIST: Intersect_check_list for Volume      0 = [6]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [6]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for sample_geometry with cylinder shape made of Sample\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = []\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [2,3]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
+      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for sample_container with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  2 = [1]\n",
+      "LIST: Direct_children for Volume           2 = [1]\n",
+      "LIST: Intersect_check_list for Volume      2 = [1]\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [3,5]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [5]\n",
+      "LIST: Next_volume_list for Volume          2 = [3,5,1]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for sample_container_lid with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  3 = []\n",
+      "LIST: Direct_children for Volume           3 = []\n",
+      "LIST: Intersect_check_list for Volume      3 = [2]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [5]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = [5]\n",
+      "LIST: Next_volume_list for Volume          3 = [5,2]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "List overview for cryostat_wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  4 = [1,2,3,5]\n",
+      "LIST: Direct_children for Volume           4 = [5]\n",
+      "LIST: Intersect_check_list for Volume      4 = [5]\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [7]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = [7]\n",
+      "LIST: Next_volume_list for Volume          4 = [7,5]\n",
+      "      Is_vacuum for Volume                 4 = 0\n",
+      "      is_mask_volume for Volume            4 = 0\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = []\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 0\n",
+      "\n",
+      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  5 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           5 = [2,3]\n",
+      "LIST: Intersect_check_list for Volume      5 = [2,3]\n",
+      "LIST: Mask_intersect_list for Volume       5 = []\n",
+      "LIST: Destinations_list for Volume         5 = [4]\n",
+      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
+      "LIST: Next_volume_list for Volume          5 = [4,2,3]\n",
+      "      Is_vacuum for Volume                 5 = 1\n",
+      "      is_mask_volume for Volume            5 = 0\n",
+      "      is_masked_volume for Volume          5 = 0\n",
+      "      is_exit_volume for Volume            5 = 0\n",
+      "LIST: mask_list for Volume                 5 = []\n",
+      "LIST: masked_by_list for Volume            5 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
+      "      mask_mode for Volume                 5 = 0\n",
+      "\n",
+      "List overview for outer_cryostat_wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  6 = [1,2,3,4,5,7]\n",
+      "LIST: Direct_children for Volume           6 = [7]\n",
+      "LIST: Intersect_check_list for Volume      6 = [7]\n",
+      "LIST: Mask_intersect_list for Volume       6 = []\n",
+      "LIST: Destinations_list for Volume         6 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 6 = []\n",
+      "LIST: Next_volume_list for Volume          6 = [0,7]\n",
+      "      Is_vacuum for Volume                 6 = 0\n",
+      "      is_mask_volume for Volume            6 = 0\n",
+      "      is_masked_volume for Volume          6 = 0\n",
+      "      is_exit_volume for Volume            6 = 0\n",
+      "LIST: mask_list for Volume                 6 = []\n",
+      "LIST: masked_by_list for Volume            6 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
+      "      mask_mode for Volume                 6 = 0\n",
+      "\n",
+      "List overview for outer_cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  7 = [1,2,3,4,5]\n",
+      "LIST: Direct_children for Volume           7 = [4]\n",
+      "LIST: Intersect_check_list for Volume      7 = [4]\n",
+      "LIST: Mask_intersect_list for Volume       7 = []\n",
+      "LIST: Destinations_list for Volume         7 = [6]\n",
+      "LIST: Reduced_destinations_list for Volume 7 = [6]\n",
+      "LIST: Next_volume_list for Volume          7 = [6,4]\n",
+      "      Is_vacuum for Volume                 7 = 1\n",
+      "      is_mask_volume for Volume            7 = 0\n",
+      "      is_masked_volume for Volume          7 = 0\n",
+      "      is_exit_volume for Volume            7 = 0\n",
+      "LIST: mask_list for Volume                 7 = []\n",
+      "LIST: masked_by_list for Volume            7 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
+      "      mask_mode for Volume                 7 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_zx_I=732.116 logger_space_zx_ERR=5.1384 logger_space_zx_N=39935 \"logger_zx.dat\"\n",
+      "Detector: logger_space_zy_I=2799.48 logger_space_zy_ERR=16.3849 logger_space_zy_N=140144 \"logger_zy.dat\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "logger_zx.order_total = 2\n",
     "\n",
     "logger_zy.target_geometry = '\"NULL\"'\n",
     "logger_zy.order_total = 2\n",
     "\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space_zx\n",
+      "Plotting data with name logger_space_zy\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plotter.make_sub_plot(data, log=True, orders_of_mag=3)"
    ]
@@ -352,7 +1715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -410,22 +1773,574 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_loggers_121\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:20436:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:20436:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:20679:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:20679:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_1D.comp:479:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:20918:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_1D_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_1D.comp:479:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:20918:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_1D_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
+      "                                                ^~~~~~~~\n",
+      "./python_tutorial.c:21109:18: note: expanded from macro 'filename'\n",
+      "#define filename mccabs_logger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
+      "                                                ^\n",
+      "                                                \"%s\", \n",
+      "./python_tutorial.c:21109:18: note: expanded from macro 'filename'\n",
+      "#define filename mccabs_logger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2DQ.comp:535:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:21333:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_2DQ_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2DQ.comp:535:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:21333:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_2DQ_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_kf.comp:536:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:21561:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_2D_kf_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_kf.comp:536:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:21561:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_2D_kf_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "18 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_loggers_121\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: Sample_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: Sample_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: Sample_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 4\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "name of process [2]: Sample_inc \n",
+      "component index [2]: 4 \n",
+      "name of process [3]: Sample_pow \n",
+      "component index [3]: 5 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 2\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 0.347892 \n",
+      "number of processes [0]: 2 \n",
+      "name of material    [1]: Sample \n",
+      "component index     [1]: 6 \n",
+      "my_absoprtion       [1]: 0.273042 \n",
+      "number of processes [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 2\n",
+      "\n",
+      "name of geometry    [0]: sample_geometry \n",
+      "component index     [0]: 8 \n",
+      "Volume.name         [0]: sample_geometry \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.273042 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: sample_container \n",
+      "component index     [1]: 9 \n",
+      "Volume.name         [1]: sample_container \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: sample_container_lid \n",
+      "component index     [2]: 10 \n",
+      "Volume.name         [2]: sample_container_lid \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
+      "Volume.p_physics.number of processes [2]: 2 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: 0.015500 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: cryostat_wall \n",
+      "component index     [3]: 11 \n",
+      "Volume.name         [3]: cryostat_wall \n",
+      "Volume.p_physics.is_vacuum           [3]: 0 \n",
+      "Volume.p_physics.my_absorption       [3]: 0.347892 \n",
+      "Volume.p_physics.number of processes [3]: 2 \n",
+      "Volume.geometry.shape                [3]: cylinder \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: 0.000000 \n",
+      "Volume.geometry.center.z             [3]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
+      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [4]: cryostat_wall_vacuum \n",
+      "component index     [4]: 12 \n",
+      "Volume.name         [4]: cryostat_wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [4]: 1 \n",
+      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
+      "Volume.p_physics.number of processes [4]: 0 \n",
+      "Volume.geometry.shape                [4]: cylinder \n",
+      "Volume.geometry.center.x             [4]: 0.000000 \n",
+      "Volume.geometry.center.y             [4]: 0.000000 \n",
+      "Volume.geometry.center.z             [4]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
+      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [5]: outer_cryostat_wall \n",
+      "component index     [5]: 13 \n",
+      "Volume.name         [5]: outer_cryostat_wall \n",
+      "Volume.p_physics.is_vacuum           [5]: 0 \n",
+      "Volume.p_physics.my_absorption       [5]: 0.347892 \n",
+      "Volume.p_physics.number of processes [5]: 2 \n",
+      "Volume.geometry.shape                [5]: cylinder \n",
+      "Volume.geometry.center.x             [5]: 0.000000 \n",
+      "Volume.geometry.center.y             [5]: 0.000000 \n",
+      "Volume.geometry.center.z             [5]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.100000 \n",
+      "Volume.geometry.geometry_parameters.height [5]: 0.150000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [6]: outer_cryostat_wall_vacuum \n",
+      "component index     [6]: 14 \n",
+      "Volume.name         [6]: outer_cryostat_wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [6]: 1 \n",
+      "Volume.p_physics.my_absorption       [6]: 0.000000 \n",
+      "Volume.p_physics.number of processes [6]: 0 \n",
+      "Volume.geometry.shape                [6]: cylinder \n",
+      "Volume.geometry.center.x             [6]: 0.000000 \n",
+      "Volume.geometry.center.y             [6]: 0.000000 \n",
+      "Volume.geometry.center.z             [6]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.097000 \n",
+      "Volume.geometry.geometry_parameters.height [6]: 0.140000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 8\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7]\n",
+      "LIST: Direct_children for Volume           0 = [6]\n",
+      "LIST: Intersect_check_list for Volume      0 = [6]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [6]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for sample_geometry with cylinder shape made of Sample\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = []\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [2,3]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
+      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for sample_container with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  2 = [1]\n",
+      "LIST: Direct_children for Volume           2 = [1]\n",
+      "LIST: Intersect_check_list for Volume      2 = [1]\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [3,5]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [5]\n",
+      "LIST: Next_volume_list for Volume          2 = [3,5,1]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for sample_container_lid with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  3 = []\n",
+      "LIST: Direct_children for Volume           3 = []\n",
+      "LIST: Intersect_check_list for Volume      3 = [2]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [5]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = [5]\n",
+      "LIST: Next_volume_list for Volume          3 = [5,2]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "List overview for cryostat_wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  4 = [1,2,3,5]\n",
+      "LIST: Direct_children for Volume           4 = [5]\n",
+      "LIST: Intersect_check_list for Volume      4 = [5]\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [7]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = [7]\n",
+      "LIST: Next_volume_list for Volume          4 = [7,5]\n",
+      "      Is_vacuum for Volume                 4 = 0\n",
+      "      is_mask_volume for Volume            4 = 0\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = []\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 0\n",
+      "\n",
+      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  5 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           5 = [2,3]\n",
+      "LIST: Intersect_check_list for Volume      5 = [2,3]\n",
+      "LIST: Mask_intersect_list for Volume       5 = []\n",
+      "LIST: Destinations_list for Volume         5 = [4]\n",
+      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
+      "LIST: Next_volume_list for Volume          5 = [4,2,3]\n",
+      "      Is_vacuum for Volume                 5 = 1\n",
+      "      is_mask_volume for Volume            5 = 0\n",
+      "      is_masked_volume for Volume          5 = 0\n",
+      "      is_exit_volume for Volume            5 = 0\n",
+      "LIST: mask_list for Volume                 5 = []\n",
+      "LIST: masked_by_list for Volume            5 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
+      "      mask_mode for Volume                 5 = 0\n",
+      "\n",
+      "List overview for outer_cryostat_wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  6 = [1,2,3,4,5,7]\n",
+      "LIST: Direct_children for Volume           6 = [7]\n",
+      "LIST: Intersect_check_list for Volume      6 = [7]\n",
+      "LIST: Mask_intersect_list for Volume       6 = []\n",
+      "LIST: Destinations_list for Volume         6 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 6 = []\n",
+      "LIST: Next_volume_list for Volume          6 = [0,7]\n",
+      "      Is_vacuum for Volume                 6 = 0\n",
+      "      is_mask_volume for Volume            6 = 0\n",
+      "      is_masked_volume for Volume          6 = 0\n",
+      "      is_exit_volume for Volume            6 = 0\n",
+      "LIST: mask_list for Volume                 6 = []\n",
+      "LIST: masked_by_list for Volume            6 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
+      "      mask_mode for Volume                 6 = 0\n",
+      "\n",
+      "List overview for outer_cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  7 = [1,2,3,4,5]\n",
+      "LIST: Direct_children for Volume           7 = [4]\n",
+      "LIST: Intersect_check_list for Volume      7 = [4]\n",
+      "LIST: Mask_intersect_list for Volume       7 = []\n",
+      "LIST: Destinations_list for Volume         7 = [6]\n",
+      "LIST: Reduced_destinations_list for Volume 7 = [6]\n",
+      "LIST: Next_volume_list for Volume          7 = [6,4]\n",
+      "      Is_vacuum for Volume                 7 = 1\n",
+      "      is_mask_volume for Volume            7 = 0\n",
+      "      is_masked_volume for Volume          7 = 0\n",
+      "      is_exit_volume for Volume            7 = 0\n",
+      "LIST: mask_list for Volume                 7 = []\n",
+      "LIST: masked_by_list for Volume            7 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
+      "      mask_mode for Volume                 7 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_zx_I=718.303 logger_space_zx_ERR=5.06453 logger_space_zx_N=39477 \"logger_zx.dat\"\n",
+      "Detector: logger_space_zy_I=2765.06 logger_space_zy_ERR=15.8723 logger_space_zy_N=140097 \"logger_zy.dat\"\n",
+      "Detector: logger_1D_I=20449.7 logger_1D_ERR=32.9156 logger_1D_N=1.01088e+06 \"logger_1D_time.dat\"\n",
+      "Detector: abs_logger_space_zx_I=956.183 abs_logger_space_zx_ERR=1.34122 abs_logger_space_zx_N=1.01088e+06 \"abs_logger_zx.dat\"\n",
+      "Detector: logger_2DQ_I=20449.7 logger_2DQ_ERR=32.9156 logger_2DQ_N=1.01088e+06 \"logger_2DQ.dat\"\n",
+      "Detector: logger_2D_kf_I=20449.7 logger_2D_kf_ERR=32.9156 logger_2D_kf_N=1.01088e+06 \"logger_2D_kf.dat\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space_zx\n",
+      "Plotting data with name logger_space_zy\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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yI7JYLBaLxeKHNS4tFovFEpcKzB05UUTGAbOVUrMr5hQWi8VisRxZauq8aY1Li8ViscSlAsN7vldK3VgxXVssFovFUjnU1HnTCvpYLBaLxWKxWCwWi6XMWM+lxWKxWOJSUyXVLRaLxWIpDTV13rSeS4vFYrFYLBaLxWKxlBnrubRYLBZLXGrqCqzFYrFYLKWhps6b1ri0WCwWS0LYCcNisVgslsSpifOmDYu1WCwWi8VisVgsFkuZqYkGtcVisViSpKbW67JYLBaLpTTYsFiLxWKxWI48Vbpel8VisVgslsSxxqXFYrFY4lKBxaAtFovFYjnqqKnzZk28ZovFYrEkSU0N77FYLBaLpTTU1HnTCvpYLBaLxWKxWCwWi6XMWM+lxWKxWOJSU8N7LBaLxWIpDRU4b1ZpITx7r2CxWCwWi8VisVgs1YMqLYRnjUuLxWKxxKWm5o5YLBaLxVIaauq8aY1Li8ViscSlpob3WCwWi8VSGmpqOklNvGaLxWKxVB2qdHiPxWKxWCyWxKl0tVgROV1E/i0iX5m/bXzaXCoihSKyX0TGhu1LEZFnRWSdiKwVkaFHbvQWi8VSM3DCe8r7YUkeO29aLBZL1aemzpuVblwCLwDPKqVOB54FXvRp8x/gBuBRn325QGugDfAr4EERSauYoVosFovFUunYedNisVgsVZJKNS5FpDGQAbxhNr0BZIjISd52Sqm1SqllwEGfbvoDLymlDiulvgPeBK6suFFbLBZLzcPJHSnvhyU57LxpsVgs1YOaOm9WtueyGbBJKXUIwPzdbLYnSnNgvef1hiSPt1hispZmrKUZOeTzFt3JZQJN2EABveAdYRp9yGUCfC18ylnMogdPchM/7k/hUYYhRSACG2jCBprAZ6IfK4U0ikmjmAnkMoFct83HnKPbfqfbsVIq+22wWCxVAztvWiwWi6XKUh0M4HJDRG4EbgRo3rx5JY/GYrFYqg81VVK9pmPnTYvFYikdNXXerGzP5UbgVBFJAS0yADQ12xNlA9DC87p5tOOVUuOUUplKqcyTTjrJr4mlBnMdL5BGMbIKdpHK21xEGsWVPSyXr2nKW3TnLbqTRjHNWEt/JtKfiZU9NEsNoKYKE1RB7LxpsVgs1YCaOm9WqudSKbVNRJYDVwGvm7/LTA5IokwDbhCRAqAhcAXQpZyHajkKkWz9t6igHafvXw3HPuvbbhXtAJhefDU8CZdnvcen17an3f5V+pYMmPzUEE4Ztpl2fMn1T+WzZlgzTvjiEDQ6yDcZjTj18x2IKNarJmz4ZWOaz9yGZCu+UY3c85zadQcAUxZewVl8TvNF2yju0oL0V9ZDnzgXs1Zo1noNL3AzAJczv/RvjMViqbLYedNisVgsVZmqEBZ7MzBRREYAu4BBACIyBxihlCoUkQuAKUBdvUsGANcrpeYBrwFZwBrT30il1H+O9EVYqg8T6U933gO2h2zvzGIe5W4abf+BT2nPZVsWUvKvM/h2wIm6wZfQ7vkipnElnWaupLhvC9K/X89sevPdsDqcx8e8evg6uA3uHPYYvTOmMWv678ht/jL5J14PQIvntvLNLUGD8jaeBGD6dVfDM/BY+1toxyou4202bmtD+pT1+tZxG2ylCTfzAseynyyW8Cx/oPahA+xNqRNxjTnk05bVAOwjFYDHubec30lLTaMqTBgWwM6bFovFUi2oifNmpV+zUqoYPcmFb+/lef4hcFqU4w8Bv6+wAVqOWh4suJebeIGT137PstZncMP016Ax8DF0aryS/c1h3oBLmUp/APKXX8+q/RlwPtABmh7awl1ZD/FagxuYtPpG/nbS3eyo1YiFqiu9mMN0cuB8OIvPoRDUctG6jZ8BbWC9agJAc7by2CuLAbjznec4eB5sHN+GO+78K92Zz27qkdNwBk3ZzEecD2ij9Lj9+zh+9mFW5TTj1ON3sHFLG/afCANS3yCFQ0f8/bRYSsmJIjIOmK2Uml3Zg6kO2HnTYrFYLFWVys65tFgqHLVTUDuFEQxnz6FjK3s4lca95LGdOgxnBMMZUdnDsVQzBPjZMeX/AL5XSt1oDUuLxWKxHE1U4LxZpakGQ7RY4tOLAgBGcT8pHKLV/rUcv+cww096gIfD2vY99CbrUloRmPIItIcJ7QdyDstYkXM6zdhI/R0/0T39n3RmMSO/Hk2PJu/oA68ENsMdzf5KL94i69An7KARr+/MIZ9+3MljHMc+ACYXDQHgnIw0AO5t84Tu45fKHYdXd/FOTL5n0+c4ZgPccedf6ctMUjjE5Ze9x8dvd2A39bhs5kLUr6F/g6m8eWxfctNnUIe9HDwejvkc7j3/rwxlPJ9zFlcwE4BjOETrRd9wb5cHQ9+Itaa8SWuFxRIPETimImYMvyqMFovFYrFUc2rqvGmNS8tRz/0NHmAZHVhFOyalXMP6onT+lZEFHeDd9AtoxyreohetWccvi1bzTt8LmYA2DmmpcH2d7UGM5MXGnc14MeVmppMLRkQn13vSDOfJeaUas86P1DmSU97uC8AAZpLWt5hMlgLQn6l81L4jHfYv45iPgGaQSSGXL32Pjh0LATiZ3Vrxtgu8zWU02vID7U5ZZcNmLRaLxWKxWCzljg2LtVRbJppCHBWJyChERkXdn0M+OeTrtg30I41iRPSKValZKWygCRtoUoZO4H06x20zhjvYRSqyCGRRmU5nOYoRgZ+llP/DYrFYLJajkZo6b1rPpaX6ca9QMKYnoFVSJ9KfOYv+offNgg1jG3P85sNMbPk710P3EjfyIjdxgNqMafYgX9AKuU/RoqCY9UXpbtc9Mwq4hA8AEMmLOLXaCVIEM0T7Kfup/LjDTaMY2n9JCelx29JewcpQq3QmV7CUTO7nC0o4k/4sAeB3RbMAuDRjFvSEOuzlOl5hTMfbGc9QXuIGADJZyghGcohjyDnldR7lblqu3QLA8NYPUIe9IefryxRz3gHxx2uxWCwWi8ViOZJUaSE8a1xaqi2D+/2DiTN+B8DELvrvoPb/oPmibXAGDP7uH3CSgq+FTS0bAlCbA8hNOsfw9YIcALZmNOFP0pie6iyfsxxEqUDIFpUBuGmKnmDYXfpPCeme/aWgvT64OdCfiRG7pzIYgCUZxSHbC+lIIR0pIZ17gRxudfeVkMZe6jD9u6t566SLGd96KI3ZCsAd+57g7tS/wUG4otuUMgzccjRTYbkjFovFYrEchVTgvPm9UurGCum5HLC3CpbqwVrhx2a1SP3xMGWJNk0ERxwoHCc8VqnhANxkalS+yG0h7Zww2emhWZiefuaafnr67ncMSseITJagQVq2f29Jha773nbrZC6xNdZrNI7qncVisVgslvjU1HmzBl6ypbrwLENZTGcm7LsebwGRiWN+p0M5R8Dgz/5Br1/OYM7KfsEGKfDFSa0AOBOgpeJUYKTZPbJAh7ZeXTSdnhnGkFyazdwi7ZV0jMtwj6XDveTxiMCNqp67TYqghSr2bZ9QOGwUwg3M/kzkkPm3LXGNV91/GsXkMpm16GsPNW5zacZarm6svbP9VGvyGUguk1mUegH7qc2N3Z5kG01YRgfWp0aOWRaA6lbqS7FYLBaLxWKxHOVY49JS9VgkcCyQdb27aULrgQyZPpl6fbaw+6NTGHH+ffBLyP79XKY9cSWs0e3e7tsVgMt437frNBwDUBtPq2jn5ly2yChGitKBbB36GoZSw83xUwHYSDPAeCKX9mR9UTotMord0FTnXOHGZTSPpUO4QemI+jQ3YayxyGcgWSYnM5yNtAZjMA9nBG1ZzemsZiEX0YgdADRiBzlM5859j7EttTkLUy+DD4Hdug8xJdrVnLhDsRxtCFANhAQsFovFYqkS1NB50xqXlirLrWtf5tZZL/PWnRczZMtkvshpxa6dp0AxjCweDcfDhOcHAjCkrw5FvSzBvh3jMQ1tVGpjMD4lpJNDa/opmE42AMJc6AjXqhd4n65JXGHyxAqVdYzY/lGMSz8GG0M5h3wWHu7KCbX2Mor7OXQ4hRv3PclrewbRu+4s3jvcnR3Fp5Zt8BaLxWKxWCyWoxpbisRSZdhFKmyp6IzKxBjBcN/tTj4laC9o0BN6ZHiMW3nMI9TjjEGKdGhuNIYzguGMKLdxSGqwz0aHN9Ho8KZy69tSRRH0cmR5P4zqnYj0PnIXY7FYLBZLBVNx82aVphoM0XK0I5KHeuNBGHAcACO63Mc6WpH/7PXQAC5f+x7LWp/BOZ99CcCG6xuz15QhGYJ/KRAnb3KO8S46lHjyE72vAdZn6+cPFGjDUiaAGhLZdyEdQ15/qX4PwEOM4HwWk8+QiL5DcEqNtI8uKduUrwHYTEv+wDMAzIraOkgPZgH1mUefmO1GMZI0islHe22nk+tZappObi2QVbfRMH0T22hC41rb6NFuHsyByak+bwpwIe/yAZckMEpLtcSZJMufKq16Z7FYLBZLqai4ebNKUwMv2VJVafCfH/nuF3V5n658MPxS9oyszSMp9zBqy0Ocs+VL+GWwREdFoFyR2FHIBP82fgqwR9J3eSfPhrwuIV17MjP0vh5RjhvlyhkljmoHqXtSWTj+MugArbutpTYH3P2je+k+fzdnIptpmnT/FovFYrFYLJajC2tcWioNGUjSltkXtAbgTNaGbPeGsY5kVML9pVEcU3wnmgc0WvtoOCGrfkJB5YUO2a0Tsb2Zea82mvcuHOc9KA+u4wXXqxytFIulGmNnDIvFYrFYEqcGzps18JItlY3MAn4LXGU2nBMAHqTFL1bT6LsfGHTSa3AdrEppx6i/PcSyP5+hmyVxjnBjMByvQemHGqIN1vAQWIB8coCH3dfplHAOHwOwjPPiDy5KOKxXFXYzLd3ts7iSDTQhjbPcsTu05gv2c4erEBsrHNbJuRwtWrqsn2rtXl933mI+l0cck3rCPlJv38f2WqcCVyKPAP/U+47L3MXDde9nLHexpaglD2bcS4lHFulJbgLgNl6MOiaLxWKxWCwWy9GDNS4tlcaCyZ3plrEYnoGCzj0pWXAGad2+pGSJNibPa70M/hxqVIZ7LB2S8VY6eI20l7jBPPvA3fZQtu4zGC4bZBCvmWeJnbciPZYAS8iK6plM4aDvdsez2J23ovarjcog6p7gc1lVnz/Ve4zaP9/PtRkvcBYryGIJHVhGT97mQhYleRWWKk0NlVS3WCwWi6VU1NB506rFWo4IInmIgCQhBltALwroxRe0dsNhASbSn4n0j3lsvDZpFJsx5ZFLZIKl9k56x56HyKgIRdbwa0pUQXYDTVxPZUXQnbdiGo0QqnwbC1mgH6VlaFieqKWaUkNV7ywWi8ViKRU1dN6sBkO0VHdkctiG7dBKfUEr1nFPUR4XGm/hW90upoR0hmc9AEBHCo/YGF/lOlfl1eFvBcP4c4OnItpeXTSdFhmhBuQu6sUNtY1Hc7aGvPbmdZ7Hx3RmMQB9WQ7ATAawljPj9uuEzDqiPqNU0LDMIZ/u7vscGRYbC9UOMimk5HAaOUwnQB69mU0zNvI6V3MDL9GOVcCRyTm1WCwWi8VisVQu1ri0HBH6qdZAPtPJRbrAukZn0oKt0Ahu6dyEFnILAMqTjpjNnFKfbzBTY+4vIR1UIOr+XKYDcPdOAN1Oi/sUmHxOE1KrCDEoExXGCTck46FzMHUeZl+mxG3vlz/ppS2rWU1bAArJjCm+o7rFPldarRIuZz6/2XCQnObT+YALWUIWo4zI0nU+nmFLNaSGSqpbLBaLxVIqaui8acNiLRWCZOiHI3Tj8OP+5ILPw8NMHfXW8iC8b5FRiETmUI5geIgabQ75CYeUViUcQR+RvKhtGh3eVKrrk9TE2rU2Qc4Wi8VisVgslqMPa1xayp0I4+UHmDEwl4n7BwFw6aJZMB1oArwALQZt5VJ1DpcqLd2Tz0DyGRi1/6n0j+uZTJYQI7PIv00mS0NUaNMoRhpAfXZTn90hx0frw3tsvLzLEtIjPKFN+ZpCMikkE9BlRpqxNsJQ9hpxUxlMa9aFXOdoSWE6ucyQtcwQf5GkWEaoO8bDaSxNPV+/mH4Mf0l9nKZsJo8ArVhHtyWLeYHfc2PGk7TKsEZltSelAh4Wi8VisRyt1MB50xqXlnJFupgnvwFKYPnmc+A3+2ENtDl2LW2OXcs7sgxey0NlgZqkH168RlUJ6QziNddzmMlSMllaLmNdX5TO+qKg8dZC9YOl+jxpFLtGYltWuw+Azix28x9B51vuol7C501UyCeaB/EQKRzLft/w2Ggex1GMdMNgW6j+KBMSrFTAfQ6wI2V8TIPTj4Y/bELtA7oCH8Jvi+axj+O4gZf4e9atrKId40bcRkcKWSfTWSfTE+7bYrFYLBaLxVJ9qIGRwJaK5nTVl6/mwCkjv2ZLt5bwDGz5tB4AJ7PbN9cxVn1GL6UpOZIIrocwgwhRHif/0uFPDbT6qdoJ7IQc19jVfSQiWrOIXyeddwk699IbVppJIW8WDaBVxhd0MEI/QFShn0yWxsyvdLhPHUpoPN5SJc51p1HMZbxtBI5OZ31qOnTVRvkqNTShfi1VkBqaO2KxWCwWS6moofNmDbxkS0UgkmfCWhMzEhPBya/M9NnnlBlJJDzW8XpGM0y9SqZSBC0SVDQVCRUgitcWQH0u0L5xYgd5cAzKRNRhHfoz0X0+lcER+71qtOWBPKL/trgndjuAJ7mJT8hiBWexUrRwk4ohsGSpAtTQSdJisVgsllJRQ+dNGxZrKTPiNcay4Ku6Z9OvVz5bRrSk54ICLm03i8FM0l7LUjCSUVENw1j1LMOFeMJRGUSUFAEdLltCOiojzAu5UuhFAT13FsCu4Obp5LKP41xjOEIoKCz/svnKbb5ey/Aw2OnkksLBUE/qSuEsPucsPmcmA5jJAFQGHCSFQjqyhKyo17uXOuylTkzBHidMdrSkMFriB/Y7NTC9gj7rH0ln/SPBsGa1D8iE20e8SMm+lvRhtn9fI+KeznJ0cqKIjBOR3pU9EIvFYrFYLGWjBtrTlgphdYB3VgE3ABfCDMmFa2DOzH56f98EXXwevOI54QxmKr0ooH8CnstYobQlpJPmGpjpCddhXKlam2cJ5iYudQzV5N8H0MZjPA/jobB/53BvpRN67BiWuTgFSEdG9FUqL2Ie2pD0QY0EuRJ+GNyI6dNy+EpmwvpBsPYY/qZe488jIuuJWqogFSMk8L1S6sYK6dlisVgslsqkGgjwlDfWc2kpFdJJ11tMRFE0qX6NiI7jkfQqr8byRJa2RImfqusXRms1nucTIr2U5cJK0Y8wwoWA+pt3KRyRvJDPJfx1MjieydJQqpIm5fx9slgsFovFYrEcOazn0lJ2GsI/Tu/DsexnOjn0Pn02KPiddEReezBhL1is8h0F9ALOcV9/QWseBc5kbVTDMlHxn/VFsT2WQYPnQRaosQAEyKOQjm4br5c13MP4TIYWsdlgwkGjCfkEhXaudrct5nw2rmzDBpOn2az9GjbSmjSKyWKJ2+5WngPgz0WJewAd9dhEkAWgupnnns/J2cY+HRorqf7eyxzy6TdNP5+xoT+8fDbnNl/E8hM6MJ0cSAOu1++zLAHGmv6nJTxES0VTQ3NHLBaLxWIpFTV03qyBl2wpDz77tC2/lIH8SaXw969gPEN5p1Mfen86jde4hllcCZTOC6UNvamuETMjoxcQ3ViMFT6b2LlCOZO1iNPlQvBex2aaspq2tGOVa1yWlyCOQ+v2K4HQgNvmK7fRrP2akHZLyHLPfTfaqPwzwNJAyHVdofyNyERUY1W30nsuo/Wvmh+DpMEnJ3WB38Mnl3eh8ZANbFseIIEhWSqLGjpJWiwWi8VSKmrovGnDYi1J8y4Xluo4EecRO0xTeymj4w1V/YLWMVpGxwl9DacsYa5tWUFbViAyF5G5CR3jtPVrfwdj4h7/JDfxJDdxFw9zFw+724dTenWcHsyiB7Mitvdlim9tzWSR2B9vaNs7y3w6i8VisVgsFssRogba05bSIJ3033M/XcTDwCSu4VzVhWOZD23zqKNac+6ni9hKE5bQBUhcFMYxJlXGnBDDcn2GzjFsUbTV7A8eM5X+CYn5+F5LEawM81g6nsoWJsL2gQJtwD6UPYp+BUEjdDj9WS9TGaiacT6LuYXn+Ag4n8Ko5+sdJxw2FjlMJ6f96wB05iMAmpICHMuWopYQdh33ZQSNymA9zLMSKmESNEhDi784oa99nQ27/Y+PJuYjqcDDxiV5P9AVGAsXfPcuAB9wCdAcuXd/8KDpP8IdDfTxZiHCliqpZGroCqzFYrFYLKWi4ubNE0VkHDBbKeUvwV+J2FsFS9Kcw3Lq8AM5zOBXk5YBecyQtSgVO6YxWBMyupGQzRz3eTRjbCSj6EUBhXQsdUhs+yIddBoeFltCug7H9WyfkZ2LKgjWhVQq4NatvEU957b7KvtsmJkHSxM3gpTqGbEtaAjqv+H1KJvytdv29qIXURm6TVu+ItMYuaMYyVTXuEyOeVFqlc5kgH7SzXd3bO7Xxqf8T79U9wBc4ttUNT0W1LHaqDzLGpQWi8VisVgsHqq0yroNi7VUGDJB18B0wmETOqaodKGpE13tVP+al+CvNluasNpi0iK2VZTKabjaaqJhqa0TMCyjKcH2YBaNDm+i0eFNsY8vIqYIE5SuduUgXkLeidx+Dh8n35mlfEmpgIfFYrFYLEcrNXDetJ5LS0xStv7I1iYn0/vTCfRgHqewmce5g4FMZggTYDxwYQC1SLd3jEgVo5xjAb3oN0F7KNcPaUImsUNGX81wDMZgGKzjsQwahx1JFJWhDU1vWK0q0KGxkq2fY8SDHpqZxwPqACOATNrxsDFOW6hi2rGKUdzHXPlcdzITWqj+QHGIyE9ztrKBJmygSalCYzNZGiKOk8USltGBFhnB8+i/6fRlH28WDaBVhjYuveGw4eGljr3f6PAmttc6FYDCsJDY0hI0inPhLrh25AtcB/S7p05wezjfHss1GS8FX88L8K9LswC4jScjxmo5wtiwWIvFYrFYEqeGzps18JItidKHaeDJgbyY+ZzRqYSXP82l/aC1nDFpGT5OPBc1JGjAqCGxz+XkWq7PWOrmWDqhqdcWaSNwcIxyIetoHbf0SPj+qfRnJKNIo5gRPATGIBLJ883va8cqLuNtPU6Zwfq+w7WabP1s1E5tWK+XdFqoste9dDy3d2CsdmOMzWQAzVhLCgd9j0vhEH/NuAOAv0g9hOlAZL6iSB4NDw11X/dgFplAJoWMYmRCMQ0qQ4vzCKDmRGkUloo6nVztMSVUiVZ1M/0tuEFvOAau7/Yso4wxv3zEeTR8cJM7dr9rslgsFovFYrFULta4tMSke5P32EsdruBN5tGDzz5tyzx68LdJw3T5i0mh7WN5LB2ymeMxNrUhWWiMy+ZsjVlzMpwzWRsR6poIfoboywW5XC+nh2xrofrzkMxwXyuVjojn2K6Op1Nfe7Rw3uafbdNPfpn0UCNw6lz6oT2c2hD9S1gpGO1VjWR7rVPpwVIKyYyab5kshXQkk6X0m5PPjF65vNrr5ugGqAflyeeUG26FN6H9d5/CFtiReSq9i6ZR5TLXawo1dAXWYrFYLJZSUUPnzRp4yZZ4vEV38+ymmO1EIo1JMYah8snF20AT93mi4aHxcvrKSr0D31KvduT2EQyP6wmNhRT519B03gPn+r3viXd7suQyAYB8tNUebnx6X5clP7TR4U3seCJ6WGowHDbxMGWLxWKxWCwWy9GBNS4tLsMZwehOI/nnp/r17PuuRJ0tLBpwLqnsc0th/FmeSqg/0fZO1JBYGWT2TwpViXXYv08Abb3G8mb6GYFOLuaZrI07zt0H6jGy9gMAPKPGstUYfPnkMAroqLyxncNRSntKPzL5iSK6SIdSw4NqswbnucpwrPBQYzKcNJOvGczZfDFm+75MoZBMU/wllBaqPyWkhxiW62WqGWtoSOk7C4zHMkkl2Ib/dER/TkV6QT/zMTo5oAARH2294FMV5XwyAjgF1HcgJ3Wi43f6u7ePVPop/dmKzDbX0ju5QVtKh1AthAQsFovFYqkS1NB50xqXlgi67n8fADVI+Dr9FHZTj8kM5Kq7Z+rtMUJf/TyWDqXxyrXIcAyj9JjtSsuw2k/z1IE/hmxrwlZuZTz55JSqz1iGcPOVJjy2vXnteU+SUcgtIZ3hjGAV7QBYRBc2epRvvYJC3udEyVOMZuSFs73WqXCnfu6nNDujV27c8Ndkwp7BGJgjzofngQeA2xzPa5IdWcpGDQ3vsVgsFoulVNTQedOWIrGUGyJ5MUMunZIkIkGvZbIMNwVFvPSigF4UlK7DKJQmj1Nkrn4kUKIjFjnkw0qBlUIO+eSQTzPW0iwBL2w0wsu7+JV78b63zn5vuZJY1xW+PY1i7mCM73lkBEiqfpQHD3NX+XRksViqDNLFPGQ8IuP923jmHBliHhsOIveBjIMLeReZA/IfhYwGGQ1N2OD2Kd3g2J179HF3m4fpUyQPKQDpprd15y392tm3eb/uu49+nMWniPwPGab7vIMxyFSdSnAdL+jfva/0ORod3qT7nWB+VyUPGWHGIv/T7STP/f3VY16o/zbXv68i/2Moz+q+nHl1835a84W+/kla7V1G6n238hjdeYsRDOdWHnPnlnYs4zpe4FYe0+/3SP1owgaG8ix19m2nzr7tRu98A6Df12aspQ/T6MM03qI7uUxgOCN4m4t4jFt5jFuRRSAr9DjO4lPasoJMPuIxbmUoz+rrWIJ+jNbXfCuPIbNAHoG2rEAGwmPcyjl8rD9H83DO24QNFNCLjzmHXaTSg1m6jyXmc79bv89DeRa5W5e0kiL9ubbmC3pRoNt309saHd6ErNIid635gs68r9/3PiBT9XssI/X3S+aAyJrQ74zzqOX5TjU3j3fMo0Bfb+qeXfqzPls/0ihGVoHIfESWuN9ZkfHIU0bRPiOYfuT2fze6vd84bgFpZx6SF/w/8X6Pb0E/otzDBe/dFiKyMP7/rqedfn/W6Of/Ucjk4DbpZMbeIOz+0H3Evqe0VF1qoD1t8eMtutOKJvzvPeEYYwus+OXpNGQHF++fz6JjL+RPjz7M3wfdjwzSoazxiKcQCzBjUi/C4yalCGZk9OKD1L+yvsh43XycVOEGpWMQjmRUQuGw2ui5hu9PPpnvORmAPzCeB3bqfmbS1/073ad0xvlGCtXx5IrMDb0Ov7Is7aO7fR0PYw5L44xZt81nIAA/HT5O74yyVCSS54r5DGeEVoNNEllASDgrON5OnX8pAA9rT3NalHGXJOF9VuFD3AJsz+P5YSX8/rY0eCDALSMf4xQ2A7ZEyRHDzhiWSkLfoE6Osi8PrrPq0UeCpnxNq8oehAUZBhxbQX2fbZ5cEGW/MRyV6oro4CnUqmjtzD2CrIF1ydcV18cmJhZZZamB86aoav2JlZ7MzExVWFgYv2EN4S26c/nS97i149/d+o+55PNNJ/1joEwepjdPsrQ4q24zirRCbHi+pWtc0oUniu7V54thXM4hO8TTmKgQj9dQCz+/c05HoMbPuIxFo8Ob2JGif1ST/RdLo5hMlnKA2qzgLDKNsTmd3JAxRxt/OM7K333qEACjGJlUOQ9vuZBExh4+pkTHmQhyEtz43ZO0pASAe3mcRod13qc1LiMRkaVKqXIpXprZUFTh5eXRUyjyGuU2RkvFUlnzpuP58DUuJQAqD2ihX794LZwG/BzIAeZCx6yPSDO/GTOKcrkg4112U4+VT3XSbfutgTpt4FXTZxpQArwKZ8xaxpcF51C76x6OO2Efhw4eQ53UvWyZ2hI2waV3zgJgyYFz+f7YF2FeQN9MrgV+sx9OHQO9A7Sa9QXbDzTk+9dPpuOQj1gq82F1gN+dPpF/dBqsz1uYD//KhWPhlKyv2TKnpd7+DXBTCfwqDf6dx+9UGp9zNtsON2bvbl07+MDuOlzzi/Gczmq20YRX9l3Hgf/W5m8N7mY551CHvXzAhSxfdB6Mhz9OehSApy+5mxvffZKz+ZxCMmnLav4i9SAvAIFXaay6sa1Bc97Y2ZcXuYnVtGXr1sYMbaK9yc3YSBZLmE4O1/EqqzmdL026xhmsog576bdhFl2bz2fR1gvp3uQ9VtGOJmxlqcznWtWEQjqyUtozUL3BGaxiKZm8uWIAfz37DmbRh3xy2UoTAkYBPYWD1Gc39djNCs4ih+ncPuFF/jZkGI3ZSn128yy3spwObJvQnNuHjGE2vdm8pyk/ba8Hxxzi2ubj2UgzNtKMfQTDaPozhTlcTltWM39fd9JSv2bl6E6cct/XbFnRklpNfuTwyWOpveMODqTV1bqHL6O/R58vAUCpLKQAGIv+LjhVv54AngFmQodHP2b5O+fBLmDAGvqpT5gxIVcv4r4BTF8C7IUXu8Ny4PnHoP6dsMt48v4YgKd9vHpDAzB+hT7JAwF4yLTJC0BgITzZFYrN97sE+NLs7xmAd4GJwBjTl2NcpgMvAF86J1lorjMR4xL4v67wW/M/PKYNRr5DC9v/n3n+BkAe1Df3JLucxfqe5lyR/VcUdt4sOzXQnrZEsEigy8Vl6uJI1R6ciPbAaYPrbwkd4zUW/bjXTFhjCB27zrns675uywoAvnKFZIaTRjHb9jThpbq6PuPVoutKNjwUeZ5wpdgIVhpXZ/sv/ffHQIqC+am55gbM8WxWJnpc5vljoO4MNVblsWBbdWfy/esw3tiqxpZyouKECU4UkXHAbKWUrTRjcQkalRZL9UEaAP6R3EcVIsAZ5vndwM/MjjeBL+cCx1XGsKoWVtDHUtNoxlqK97fleKAxW9l/BgxkMofMf0I+uXT5dEnIMbUe/REAkbGAvzGZslW3OXzyWDhH7/cK/TjPo2VJqgxIQ1sdD2Q4HslRbKcOmHqYAI/yZzf8tTRlQ/KMMemssnrP74j5xPJYikCLKKtp22udSpryFyMSgfUquqHpKK3mkE8mS+nBPG4ILyhK4p5ARzXWi1IBNx8y1+MN8AuZTVTsRxuK6XC//j1V+/T29UXpzkIncifwm8T680N9ByK76a06s4t6dOED+tfSnnaRvRW+uFGjqThhgu+VUjdWSM+Wo4f6bWCXEw0yVOdhnjMUlq2BJ83//WnA68ATB2nVfDXr/nkmvbOmkcIhd17jB1hHa/buq6Pbbwf+0gYGANOBDmgP5mXA7Dy+fCoAP4cDaXU5kFmX2tP3sO+Y4+ja/2020ox3Jmml7faDPuX7swLQfj/881j4L/yu6RT+kR+AYlh30pn0+y6fdUNasVROhRcDND59A//oNjjoKTVeS6bDlvyWXPqU9oqmso83b1oN/wY4n3+M6w43OV6rm+mo1rL02vNZNqsDcw734o+1nqZP6iy2pzZiPEM5wLF8NeFszhiyjDO6LOPLJ87h6UV3A/C7dyeym3rspQ4pHOIvix7nXLWIoQxi5ogrmHtJc+gIV10ykwvefZdWrOXRJnczn+60YxUX8gHPcQv9mcpY7iKH6WykGQCraUs7VvFy88Gkso+cJtP5w+Zn6dB0GftI5W9qB7s5xDZ6MVC9QQqHyGIJzdgIZ8Ne6vDvFRdx3tkf83/8lhQOAjB3cjbH/WYX+3Y04N2WF/Act9JiSDFLyCKVfWSxhBQO8lf+wh9ynmE2venMYrbXbUj9uruZ/NQQ9g6rw3sbevCr5h+QSSHzTQm2JWTx5X86kPKLg3ROXcw74/rAFbBlZEsYAIdPOR7OCFCn3iZ27N0KddpQ68sfOTzqeHg9C36ZB2RBvyVAA+Bt+MGIBl4BHASugeXtzoPzgS+A99qwly+hEdrjPd148K6FE67ezg+3N4J/3am9h28G4Ke84G3Fhea7/yDaM7oJ+P3ZwNkwFxgVgELQ4vb/gu1d4XmvxzMAfzdP5+YRvOUJwOcAeTo64Alw5RP+0FX/PRN4GPO99OMnoDXMB/7YxowBTpz+LQDf33syfOu0/Uaf5wS09/a0nq64vno3Wv/VgBoq6FMDL9kSzo9ZtVhMZ0anDqcjhTzQaay7zwmHdTjU5HhfMZ4jdVN/OXNoxN6kjonmsXRCbAbxWsS+XKZHbBthDK98NZCFey7ipxNhvaSbcA3dfngUgxK0IRmelxlCWD5mIR0ZzuiQbdEMSpUBUpTOfRlakGdUmap0lh/9MkxYcUauNixBh/gAdCudt9L5rmXyEaOvHKknYovFctShVBtAC5rQoQ0wFOVEPqih2rNZv01QQfqiAI0XbGDbnObsa55KrVY/cgqbKaEl78zRRuCJ3b9lS3ZLOhZ8xNJ/N4I2aOPul6aPl0244Q8BTlGD2FJg9mcC6XCga10OUJeFuZdx8T1vse6HMwFYuaSTNh7GHwuB/3HCj9/zj3cG6/DcAcBDU5lRN5dWe77guO9PpF7dr9myuCXsBcYreFXg13mQGYCpClrt4p0/mBJRbXfCRQPg/TzOVbX5RKDhoaHsOPZUWv3vC5a+cz7kwMqBneAPsLBzV9qxinNNuOqHnS6h96fTWEtrvpQTuEU9RkN2ALCcDvRgnr502Q4zoDVrWUhX5pBN23dX0JtZrsHYkhKufmQ67e/5lN7MotueBTxc937m0IvuzGc5HThoDPl67OapfcPISw3wOWfxxIa7uKD5+3xFW7atas6+dlNZSyuOYx97qUMP5nHlgWlk1f6EEtJ4U/rRUxVwOXOYz8V0ZjEA3Qfq0NqhdZ+hP1NYTVtKlp7Bpo4N+ZyzuHLfNH74tiGHfnEMfevOZPLdQ1iXcya/y5pIB5Yz+TSYsSSXB7KGs4OGjN85lN4NdNDEZprS+xfT6cpCltOBX934Pv+WRRz3/TB+OvEp+H0A/gA7hp4KeUAjODzjeD3l3w9wmV60uDkLvgN+1gYcB3x34GQFPwi13v+Rwy8fDycB82Fzt1O0cTkFuCsAzwInwA85jeCnfHghF/6nv4PsDUAAbYCuBLUTRD7SHQGcEdDnKswDJ4q9Q0CHvnYFzgvA5Xn6Wp7Pgz/dAWfUhaUBvTgD8F9cI7TV4S9Yd4v+nqvnQJwQ2IlAP/P8ZfP3ZHQ47T97wvMLgckwNECLs4tZPysdfruG77/R/9c0Ing/cMZpuv9VnnDaz/9ldtpF4+qGVYu1xEQmBJW7gAjDMmXrj66nsjtvVRllr9IoyDrhs8XGp5fMcX5qqcmygSZGvy+xEiiO2p8fXpXWsiiuedViQeeSOjmO5YmM0A+nf79zSAMTbhSDMdxR7mOzGJwV2PJ+WCxJILIGuZMqM9cczTzKsMoegiUBnJriR+Rcsyq4/6PNKqmh82Y1GKKlvBHJQ21+kMxT3uD4BYfZ3vMEhu4fT6NjdzCJQVz66Szm0QfpFKWDJuiVta0AJkz2Brj4Jb378MljtXcpzJtZ4AlphUghHy8lpIfkKH5Bay7nU7MvVMwnWeod0HEYu2unI0XwkMfHl0Yxb4e1dwzHFhkPA/AWv2F53Q6sVm1pxVquLZrqtl0vM3Rb1c+8/po0FbwmzdyIcNimfM3HYefV7XU4bGu+AGAtZ0ZcTxrFrqru6KKRntqgoXTnLQDmc7nbfw75rKZt0oJFfrheSI83csZjul/ZDvwc1OiIw5JGrjTnm3Y+WdMW8YnAQ2ovx6ATXTONWkAh55f9ZBaLpVIIEY+7yiiivLEzrNVk2G08MgA/h21fNYc06M0sXv3ZdSymM+v2tKbhZZ4Fq5mwdMT5MHYn8LQWQWGgVrNsZYzWTNjSrSW8n6fDbscr+E6gGE5Z8DVb/pPGe+Mux0RT6tD/K6Bxxga2ndacH6Y0gieg44qPWCrnc7o6gzrmt2ndD6356bz6WkwlPwCtBP6CFl3ZDl1/MY+Fv7oM2pqxzAhowZM/BvhEgBzY0f9UmAjrfnEmxy3fBcBfB93LWO5m4S2XkfXcEubRg5aU8OFV0Ip1fE0aK1VPurHAFe6bLavYq3JYRTtqfduQw6vhfS4CoINxK73GILatas7p7VbQmcWceMe3ZPEJr3IdvevOojVrCezLY2jqeNqymnMJptPUSd3LZk7huZ238Ezzm1lMZ5qzkQvbfUAd9lKPXeygEbMXXMlZ3T5nUO3X2E9tPucsitQv2UcqM7mCHsxjCVkA7KAhY7iXlvu+ZvLBgRTVPYeCjj25aucUejWYw9DU8Vz0i/dZxjnM52J6PzqN2UVXspv6zKc7/bLzmTErlxQO8TVp3NPgER7aoN/r65u/yMsbbuLr5mm0w7jozgjw0+6DXKrOYeHOPRxYWBcaAv+FX93yPv9edBH8eiusawyZWRD4BrJOgx3AzDWQ38b9ftJqG2yqx+EfUzntvrV8c0trTn9uBSulkxagugEdfnoz2gM6FuB83BuTXZ5Fld8H3FBTmB/0RN5ljusZ0KGrq4FAHnwUgPPztNDPGQH0TcevgSL4sit0XKi7mtgVBufBW/r/ap0shCfBXXN/xnzfv3S++F3193P+XHhPC/Bor+ev9eOXa1h/RjoMBl5sAz+YfgqBq9GpMg8DHzrX0tX81Z7LI6XpYSk/rFpsDcP9J938IHsa16butgO8e8oFbl7Gm3cP0PsfJWhcdtWvw/vgGvOP/lqemZzhlJe+BmCLTIr4IUjUuHQ8bov4NQBd+BdvmUS9M1nLCIZTSEegrMblya7h+GVGGgCX8bZrqDnhtE4bJ/9zEtdQgjZM6eiEuYa6LpXSbUXm0kJpxb/18rVnf0+PgI+iKXpfbfYDkeGviRqXbv9hocDDGeFOzGuNkHy5qLcaQZ5o4a2uYM9283c5qChrCk7dSydXM+o5XeNS/32B61hGB9bRmveuvJyO06xx6VCuqneNRRX2L4+eQpFnqrbqnSXIkZw3vcalyHjoOVTnhNX3zCtOTYzCJfBFlg5LvAt9o/oxnPDNdn64uhEtCjy/kfejb3K3ovPfvgEez4Pept8P0aGHf8CEKMKvurzPv0dexAUj3qWElnwjjSGvLgTy6Ke0ovqMd3Khh7khvxfIgUtHzOId6cqJ+/fx/dCT9VwJnLj/Jr5/4WRYCD0LClhFO9bPSafhZZvY9V09Dp92PLxHUBTmtTxYbcb3rRlXPfQ9eI+FMKUrt/fXEp/raMUSskijhOt4hd/PeoWL+7zFXYzl6sM65nFgrcksowOgw1wPkcLkVUO0LTBDvwdP3HcTgQN6vP1rT2Xc6Nt4+b5c1tGK8Qxl29TmsA5evi+X3dSjHrvZRmMAdpu6VQdJoQfzeI5b6cUc/nL4rzxQayRTGcC/b7iI6196lqZs5qErR3HptFk0Yjtrac09jOFN+lKbAxwihRQO8TlnMQatHt+YbbSvtZaehwu4mRf5bcE81mQ3o03RRm3sLz8I24/hVxnvcyz76cByNyR3G01ozFaW7skk5ZhD9Ep9i84s5gO6ALCa01m5oBO1O+zhlAabacZGNtKM9bIPPsuAegfh3mPgjTwYE9A5jrnoENHBwHlL4KoseGOF+fB+QudeAjlt4AvouOojlhadDx2B9Qe5sfmzjLvhNrgKbYCuRRt3LwZMbu1Aau9owoFGdeF2830F4DI4Rs/rHDTbHjDfk4c8RqiTk3wbQB68F9CZPAPA3GaFMpEQ45K70AblxQvh9131ttboef08XE0FHl9orhcclVcdO3wu1O8Jl0HHyR/pMG7MtabpPFo6GF/XLu9AnPurT4AjZ1zaebPsWOOyhiGSx5fqFdbRmlW04+4tTyP9FKctXss3YsqOeL4SonP+Q4xLvz4BnasCcH3ZVpnCS1ekUcwPh7Xc+vZap4bUsywvnDDY2egcl7t5KqKNV5HVNS6B41rv4qcTnwOCRqWXPYd0MaoTj3mTEFltj3HpUNrSJ85n4NSzDDcchzMiQj32SBiXIW3N2kJZjcto3IQuUfK1+Sxf1DNpjcZOkpby5Igalx5vhZtbCcZzcy767lbnZYp8o/e9eJr28CxE5z++CYyBDjd+zPJx5+k2a9D5YW+icxzHBOARtDH5UNDIVLP0GGrv0KH2+xvURbqhvUu3mjmvdwBmm+f55riBZuxDTXRPVzOeRsAr/6Oj+oSl/+kMa83vf488+CwQzPncdC+8diwhGQ/Xwil3fs0WWYaO3dG6A/1Ua/ZxHHPlLBjVBtoDP4fTL13BV0+dTeNhG9g2obn2bpl7eO6B2lP3cKChzjG8Vu11xWzqsNcVupts5ot5e3qQVXcJCy+5jOvffRaAl7Nv5dKCWSzc2ZVrGrzGak7nw6cuYeCwCUyeNIR+g/TgZ6zI5fSzV5DFEl5rcAPs+h+N1RY6UsjcJdkcd8YuflpZH36AVpd+QS/m8PSGO+jXfCpbacyHAy/hnsl5zKQvXzU4m547dcTS3EnZNLx6E2fUWqVLymR34v8KerCYzjxydwAuQi8SrIQOsz5mM03Z9k5zbQS+Bxec/S4fLr6Enp0LaMoWXr7vVpxslOsznmU+F7N+dDpcDGdkLePLdufoz3YViPwE/E0baKDtn7GOIefkbZwL9bNg1xL4P238KZM+K6vQCwR/AL40yZiZbaAO2ksOOu/2EtPVX/U1K5Wtv1dGJJFlofdZ+v8lgHYn/sPdJ+0w5UYuM9uyTPuFOJ5BJ6cxWLPb/x5O92VevKf/uKrvf1pojukacbyj+uzmUDuhtdeaPnaG1rENHjfXvHYM1SODnTfLztEW3WyJwXW8UC79yITyj/EvMFmS4ZQmdzFRnFzJ8HzJhI6N4jAVmRsU7Vki+uHHyijbk+BC3uVCSi+j5uRlhudVepEFyeVZOnmg3pzPI4HjzQzZNkc/LOWEI6le3g+LJQYisWs62NxLi6Vq4grzeLfF0U1ItJ9qQw2dN63nsoaQxSLasYpXZSu8HODDIZkspjOtWEu/u/UdeCzvpEOIUXl9XsTKVhaL+GSCDi9RQ6L34xiSTmhs+Otww8RZTR3MVGLxhVnNPpO1un4nQBf/77jXqPSGkY5gOK1MiZPZ9HFDcEHnVLZQ/VifnU6LgmKzTYe0KtUzdKXNGJYbsnSoUAt5NbjPx2vpJdx76+DNm3QMyw+4hB7M4hyW8YjHZm2lgsJAaznTvQlzwric61q/ICyk1lN6RBZAw67asNxeS5cBiFabUh6Dfnfmh/RdQnpcj2WieOtjRuy7Es6dtgiADizjTfqybU5z3T5yzaLGUK4rsCeLKry6PHoKRf5etVdgLUGO9LypvRmOd+XXhHs2dJtIL4sTcWNSB12c3wL3BvdNXDVNv+gL6WL2LQrbbrxQKoa4iZxt2qyIPs5Y2/W+oPtSKZO/bsaiukATNrBN9pv9bWjCBgC2NWge7MTY5iobxOS8q/vCzmO21755D/sb1I1+UU578/6pnc4418Bb2isV/nvreKkcz533eHZF3kNUJCEe8BSC3kGTz+hcT8RxzpyzPLSdmMpdakRpxhK6KBLrO+0uZs/0evNjR4g5Iozht/je88Z676MdHwsxVd1CFF9/31WHcQPs8rz/zhg873lotIIzzl+bbV0TH0gZsfNm2bGCPjWIVbQDtvKPIX14jlv4u1FeScSojOD68l0tjpZ/GTSuYhuVpSFaiZJ4ZLKUUQX3A6ZkiQoaZyHhG1nOr3KTyH1RjMpk+MCdGWEefYA+jFGJr+Q7gj6Z3ZYynVxf76U25E5NeEwzHsstVXmRsqLzL/Wd4LsmbLpOL6dkzf1HfkAWi6Wc8CaFDXRD62KhHjU35E644t/NTbSz0LXTGGm/QdcHjNbPouj7yotYN/iOQZkoW2leKs+Qug+O3bkn8fZ+RtjlaxL6bJzjK8XjnGPG9x5wCJQp8ewX+VLRxP7c/fdVL0GbrvB7n827Ev+eaJzFpa5lHpHlyGHDYmsAFf0jXqZSF56V3xW0ZQVtQ/ZPIJcJ5aBkCtqr6Xg2Q8ZQpD2FIqMQKX0eZ7zjpRSRsNHKjTjvufO+92VKzBDf0oSohpQzSQ3mRIaMI8r2RHDKj1Q0i4yQkaWM1FBJdUvlUOo5RfJiGljSzeOJqkGUtYSEyBpkdOR873irytR3AmWm4vaR7Z+uEm07gIzTD999pbyvEVmCyJI4bda4eYjJ9f100seEHl8x94Jyi34k3L5B+Xzm1YIaOm9WgyFayoPezGIpmfCrAJOZQn+mspCLGMDM5DpaiE7ozgygPg3dFQxp6BKzC8kAmBNzxdhV9kvSu1iHvcEXUcJhRzA8phjQQ0WjUBm6XV9m0peZ2kMJYAR7BqFVZxIxe5uz1dewjBb66pDJ0oht9djNDFkb83z3qUOMYqT7Ood8CulIC9Vfh6mazylH5YcIB/mFm/oSxREYzWPphMNKEToMrWvofsfAVCOJSbTxyUlm/3f6O/i6asJCEw/3EjcAUGffdvamNop9AovFUsWYHAwNDLsRdUMzo3lzZnqUZR8GxoTtfxCtLPohqORKIgdFfGIUd3fCYd3XZfQ6+YVBbqU5hE1zUUM7Z6EFfw76708kHDZ8PO5YzmhjQiEjx+gNhw0ZZ5LvRzLlKMIVxUEbmKrAGJoXh41lGjFRUTQJkg2HDRp3A2O2g6BBrArQ5UzCiqT5h1EH36NY4axO2GnIZxjRJu4QI49Z5X+8TPG22gSEei61ob0WONeN8AoX9bFUL6xxWUNYR2velNWsUc1o858NvEl/1C/KLioDwIXmx+mD5FbFwieds1ntPndUWYf4eO1i8b6xXAb77HPKmESMIwOkKB2WRu5bTdskVGmjW8Kl+aEupGOE4Tmd3IibCYCZDIAMrQobznRyPdtHolTA1xsajvfc0RRcS6vsCkFjsry9l49zB/8yIbJt9sU2xC1JUg2EBCxHB+E3vdGMpujHefK2XvT0VS+sfbKGZTVF9QEpw7W6Sp/PBGCLJ29vVbQjkug7wc82Zh9Rrs01KL38CMxdA9OJGqJZ2sUAR401dptkwkKd9skd499PxYTVqufi7HdyVDu1gcK1sGvuEVeArVRq4LxpjcujnKE8G3VfFjqhZAmxPY2xkAzghCj7jPhPNGGfDTShhYmliJqUHkV0J5xe6JnFq/g80byKJwIUD2fFM4d8ZmRrT981ZiK7lzzG+Kxe+4WhJlL6I2j0dYzZLmKMksff1A6cO6cezDJ5mJE0OryJrrWCx4FnlTCGaI7vec3nU5r8Vae2J7T07zt8bO+Yc12a/LkAxnCH+/xeHi9dJzUZJ7zHYjkChHhugPByBkdsHAmI90B4yKGjmKOVciJFfPLN9rKnfKRs/ZHDZxyv+4vmtTSCPc4UlLpnF/vq1i/zuf0FWKK0baDrjgL8cPyz7nF+fZUXUhdU4qmkkcfHElsK+34eSSrivSoLbkjsQv0n0QWHoFcyMl3pqKGGzps18JJrDu9yITCAi1UmmznEFaoebSZtBEANwjcTrZlRSd3o88+exSLOneRjjP4AqggkI7EfOmUMkg0x2qwXYxAu9e9zgxHJac7WkO1T6c8cso3iayThXkgpAjrm0Vhdx2NG4OjqouluW2cCCff0TeIG7iU4maZRTAvVkhLSyScHHYcVimw4iGoe/JeLZWw6IbE5aLEd7/Xmm4Jcbqiuh3rsdmuWpVHsnmN1WC7rwsNd2V7rVIT43uZwg9PrrfQa//HCfFUGIc7dps72OOGwsiDS2A0J//nOcw53st3NRPrzTOqtDGZqiGEJkMlHFHJ+7BNbLJZKRxpEGk3haqV+xzAG6BuAe8zGz8xfb7hslP7jkUhYYSJEO943vLQcDAmVrQ3LMvWxGUK8wuH7vQaj0ZxTpa+Y5duvg7OoPIdQ16Sapg1L93VBMPcyaAwaT6xpl4wRKg0olb5MdGVXfxXYyDHHGFNc9djQ/Ym2K1fWAfV7Rv6/1W8Du6KVO/vE/K1Bns6jAGtcHqXIYninM4zhXjbTlK005tLOH3DuYkf+rktcj2V/JgIwlcHGA3qW7tsjLa6KIo8TY0D47XPbDALYGjVc1PvD5pQpGYEuZOw1EDfQhOZsjZhcQIcC+4W0RpuQ7uQxthU1D9n2QMFwo7KLzr8s0PmXaRTzNq+YVqE3Kq/vhFHcTy7Tgytz6y8hEaaT6xppfjmXXhzjUYqApQGepZhzWM47RX3csGI/nJIifqhuRqSHyJBXP69mLI/lHSbJ6XHu9d2/OYrH0uWBgDuBl+YmbjBTkTnwdK+PnPrYTHefWZKmhq7AWiqRhfqPSFBhUmQ81B9qnse5Eb4MLTTuXe/qa26uC4JGqlteaSH6N8ecV80KeiyjlSVxOcv0G5JrGd1wTIQmbNB5lTE41OR44iW4eEuPlIfHEnyMyO6BmEak2gnSoFHEsX79lcv4yuCxdPuIFTWzUP+RkcnnXpaFxIzIy3ByNI+Ed9MJi01UtNCJBqB3T9Qs839YvyvsWhPyv35UUEPnzRp4yTWHP/Mor3M1v5SBFKmpFC1uxznEjlfw81g6nMXnjOfW6BPZb4B/lnq4SdGcra43z49E8yRVhv7f3yav6B9hH2OpHasYySh6cRzgL+JTQrp7o+J4PnMzcPMKgmPd6nN0ZF+a4B2R10PrGLfRuCJjis7B9DA9ivRQIhOPY0z6lSrxkkjYb9IsBB6KvBlLdsJszDaeIyhnZ72WFkvVJjwcNoRd41FqaPSb2V3j4SZ0mzvBXd+6Maxd1zKMr4w37fFKUTSJGdtTPQg3Nssjt9KL36Jy1LFEy8kshREaXueyrET1UM+MLxwVflwiHuVEx1LeuO9bnyjbff6fq0roryU5bCmSo5Bky07I3bhFp5ux1g2NTejYCeaRxGqs+AilbaCJDjMa5Hg1y5e3uYi3wytqh/Et9fiWejGlyyEyRDZW+RFvOQ/ALReSa4qsJEMfptHo8KaQ/nrgnwg0nBEh4j6yILZx6LdfHjOPOOVGpFfw4Vc6xbu/vJErzUPykJNwlWPjHjdHPyxJUEMl1S1HHr/fYGeucV/L+OT79ZbQSKAMgowsPwOiPBHBt1TGkSjvUB6/ndHKfDglOryltqQBoa+TudeIVYYkOzh/HElEno4oKVKWcm6Jn7fs50i25EjE8X0iDUvv/WfU447A+1Nh1NB5sxoM0VIq3oZbOj9H+85rGacGscyElJ5jdqds/RGA65q8Ctzq28VUj+bqeE8bR+VVOhFaJPchHbro9A3Hh5SZcH8crgpoOfQ38mBSgAJ6kRl27mUmFDWjaBUtMh5zt5eQ7iq+jkzAC+hH+Grnv5T/TJlGMa/wPuMZSi4TqA/kM4Qc8slkKZfxNutlRvCA+fBghl4iD4Tp3ofnhkbD8XDexpMAER5IL4fMv++lGbN4p6gP64vSycpYwgGOjXrMw9zF/YyNOQYnHFYeC9ueZG24x7k3YYPSN8fIuakbWfqVS9UL4Hxkzvnc00ufoxkbWWo9l6WjBqreWSoXpdqEGAjaYznePA8tUxL0jHnyt1LQHkrHC+TNuZxvjiswv0G9A7DQEwrrKFrHC4kF+Dy2l0nkf2GvPYqrPt6ZeOGwbj8NgF3Gu1u/csIJHQ9lZQnNRIg/Je7QjN6nJxczmohcvHBYp95lIgqyvv37vo+XJXhM7HMGvYSl/8ycGqeOgE94mlNUEa7ZC1Gqq25zt9N2LpjoMGjj+f84CjyXNXDetMblUcj6Wek0fHATc+gF/86jiTFs+jDPt/31jz7rGo+xwmJ9eR5d73JIEj8Ab0SuQBXSMeTHbVnY/tLWvXS4jPfjtjmZ3YCeoGJ5fx3F2BYFxbyhCt3tOv8w1KgMDxV1chRFTgEgP4kSJbO40ifWQH9efTOm8GaRNkRrsz+kziVAv275dIh4Vz3j8jEc3bqVUepXuu1CbPPcGPvKn2B9stjfP9ULMo3QkfPaYrFUTSJCGGfmwUyvGMnQ2Md7F6kedXK6AjHDMn1vrp11uA5xh5wEoUqypaGx2gBcx1aah3gqyzvs1I/y+O2MXlvRMULC95e/gaEKjrzXEkCpP/psS/z6ytdQTbKPOCVH4h4/C0QWhm57FM93+KcoJUrOLduJLUcca1weRaRRrFVW/y/5H5HhjOA4tMsqmmfLW7pEeye1/LkbqnS9NhprfXsXQEibaIjkMcNbbNeIABWV0ogsCyMYnkRNy1De5UIeN4qkq2jnGsMqI7hCWJpal/Hoy5T4jcLwU3VNtgQJBENl/WpdPmYWK+6MUQrHT8rdG5qbyFgSXSkXyaOjChqXdfZpSfy3Ui8HtLyVJQ41VJjAUnmE51v6eUK80RGut8N4JtXOxL1YCXknMWF9C03bchCNiXmuON7N6MdVXNkWp6yJVySoTP3dDYw9smVm3O9VTuLni6dQHOy7bN7K6P06/wuTTf+BmPNfsl7kZNrHC41NVNgH/MO4q20IrB81dN6sgZd89PNAn+E89NwoVt/SlmvVbmbSl93UC6l6eKiJY/RpIyAkP+9uvZoUj1r//FEr1YWlDjp9p2z9MSjGMjKAd/VRqeAPSD/JivhBO4dVurxHRjEtKaFlRgl/5ClgToQB+IXx3p2ZRK6ol+E8zEXO3QJ6XDvVI2YcRazPDhphk01oVQula2gOYKYp+ZIAK80vbnsVtYDwU/yRsSsfYFn7DjG7CjcqZzKAtIxilpDF+qJ0/kGokqsW9MmNyIeMR7zyItGOGWae55AfVUzIQU4CGumSIvFEg9xjSjH5rN6ny7HclTo2ojSLzLHeTIulqhAh4tM3ADPHh+wPMUTO01ESidzUxjPYQkpTdDXPnTDZydH7jVezUamfhR1Rek+SEzKbxSLO3ZlcreryMg5lICjzfgSN2QB19m2nzr7t7E1tFLbPqP3GqA9ZWqM4qlhPAcg4/VBGzClksWH6GmjUBjkJt6SVO74YCwdOuLQTFhvNMItnYDq5l0r9MeT5kSC42B3QxuLzic2p0o6EhbDCP0ett+GjVr9rDX61LoMiRbYcSXXDGpdHGX9SKezjOG6/ZQz3mBDN3/NC3OOcMMpYidXeCSxonIIaYp6EhcYeanI8xFHEi+BN8zdDGzRpFPM1aW4NyorgAy4BQkuFnLpnE43r6nDiEM+az83LJXxg/nrwGHZKBVfF4zGW++nfvhlZLAnJeY2GNyezhHTuYAxPRCn9ATFUY5PMpwR/j6XDn3o9S785sQ1ZNz/GI8KT7DhKo4CXSW8uTNXuiXwGMm7ObcmdtKZSQ1dgLZXI+pZwu/7SOaGw4YanN/Te9eZc69mWYJF7x2MZUh/RTy9tQJTtFUBpQxkr0gOo7sNXlK/U/T0KPOqUmfFRBi5Nn/E+875tfOefmH0mGnIs5euxdM/vEzIcT3E44b6fA55L4ru2UOdZOjmXkedOvCv9vrYJ+ewjjfRqnHdZQ+fNGnjJRycraAv8X8w2jrLovBAfJuThL0Lj4A2HBSPkg8m1JCgOdLinNjhVUfxQUBkEalLYthh1MRPBEcPxiucExX9GMRHtbRzM1KT6depJ+lGaAtzuseZ6HQ+j41X05n47Hsra7AeIMDi9tSRzyKdZnHNG80Q64j0qCRs+/JhkVYpLQzyPpUgeNDIhcd9V+HBqHjVQmMBSNRDJc2tURm0TFmLn9ZIlq1YtZqFL+URTyCNm3z2hwmfhv5/e+rwi+vddqdiRHOWFNMBdrFU+i5vH7tzD/gZ1I3cke45E2zq/3X2d9yN6PcPweTVm+GfYGBKdj6VT8B7GOUd5hJeWByHXtOvIhgzHIhml2Lj3gGaBQk0OXdA5KqmB86Y1Lo8icpnMdhrSg3kc4hg+oAtXMouZZr+3bIX3eWdPH4mEw5YGkTy4JoEf5yuCxpY2Fv8FwJ3oGTybSCPyTNbGrHmZCCFG19IAP62Fkoz6oWVEsuF19TkA+ZzF3AZJ1NhaBBB9OS+NYlcxN5bH0hnPITIpoC+TjSeyKV/TGdhIM/plOB5Dva8Hs6jDXrOtY0Sf2qjVbRsd3sT2WqciZhVfdUm+duWWnU2p/foeCulIJkvd0FhvXmdIjucNSZ8CchLIC4lyU1DI+UyjD43QeZd9e73J/TxMHRM264RzWSyWykGpNvoGu4PPzr5t8GQxRP6f9w1AiTEozU+0NADOMwf0DMQUGlN7nD59fmOc9dc49qE3Hy4ckf+5IbJlNTiTCYd1x3afNiwdUvfsAmBf3foAbn3NbZfo0Fv1bvA9bqyuI40SSO9C7xHTmN3AKOJMacO5/RdxK7PpnNqaxfs6U2ffdn5Y2whWt4G2nvdiZh6gLQuZA9ysN3fc8BG0Ph926NcjGM5zh29hx/JgGOWjJuHiRW5i3Ya2HFdvL1A/5Ppkhf577tl6Ems4tBU7XjgV+Y+CViM54cdb+eHXJmR3KnT87iOWipYNlkeAmWtgTBsdPvytp19Zo9V4d+XBcQFnkPo9UgG3bI06HPPtDwn99YbAHqlw2MjxJGk8d8UV9nFUYiP7jHLsG+Y99BO8rdNG/+8lmNtqqdpY4/IoYTo5jJYUYDfjyHJzAr1iaOEeS4fFdI66DyInMO9qH8Dh32iP5blFjhJClAnvNb0yGK2OpTdH0GERv6Y5W+N6xaKV+vDmZybrsfTj6uzpqALIp4CeOwuSKuIcTvj1FtIxwqPohL3e4eNVzmYmG2ntvjeL6cxmWsbMq4zmge13Zz4LD3dNYvSRq/QlpCOpUPubilO5cL0AUVT+QvOeovdzpVlcuYkn6evGYltiUnHhPSeKyDhgtlJqdoWcwVLtCPVcjQdO1XmX9aMrxfrekC40+5JQro6qZrrCZ+OfPIbTndFDFpXKjShHUpHEujkvq8fSpSOc8u7XbJnaMna7/AB/H3grf7rhWRifxxVqKQBvzon0xt2480mWu0XTNKeoQVzE+7iWp4df7dRK8P/uc5FW9/1vAuNuBuR4Xn8cCFmwCKUcY4ATJPSzq3yPpUOyarFRw2PfDmtXwcJYlUoNDYuNKGxgqV5IEVAc/S66GWtJ2fqjG7ralykJK4wmUtzW9zjPvFWa4wH2HPKv1XgkVcRymRC3TUWN5zFudRVXk8H72UpR2UON/fqSBebRK/FQs2iLA04d1ITHcVLiOTJR+zAFmdvid6doqQS+V0rdaA1Li4PIeLeWZVLHNYgequn8Vssc8/Ar6N4tGA4LkW1kiX6E9+l9XZY5wXt8eRWOl0v0I/65/Q3fege+9d1uKRuJfr6STUKKx4n0l8x3Sm4JDYOVp/QjXrtkEVmDdAqmW1mODmqgPX108grX8Xf1OH9a8CxcnMd6mYpSAZpFUVD1eiqHM4LhFDK6kxb1CfdMQtBo8QrIOATbdwkq7Sk8xXEBAu4qVkSu5SD/7fUO7aZuyv6I87WQW3hAHQBP6RBvWGw0L2YsHPEgrVAL57PY3d6WFbRlBacXwAhGukGkV8tZABSTxiMmb/UVbiaNYt42cR/plCRUisTxKDpex85h+x/nXvLJYTa9Q9r3ZQqHyCSFQ7RDx6gcIoVlnniyd4r6+HqFHWYU5QZX94030snRcY3Hh83rEfgqxTnjVvtyyUHbCIUmBNf5q7ppj6M8D7xnDvwDcAyokUTgToIPmBxKT5tgfcvoJBLm0533mE4OHVjO0oXn641WNdafGroCa6k8nDw4V8wnLGTO9WiCb0SMGxo7M4GbeMmDi5Ip9xGpOhs9FLbEtEsLHV+S4bDh5wwPaQx/f3SIa/OYfe6rW981LNMoZps4ET6tOXH/xQCcrvry1Z1ns20BbPu4OS1GFNOKdVAHUjgEv4c6/feSf2AgTWtv4brUVwB4+pdXUXtHXQ6cXJc/SWN+pyZy4KW2vHn3ABj7PCyF322YyOecTQeWU2fNXsaN0wJru2+sx46UxtynRlBCGpN/MYQV/9E3G+1Yxd7mddh2ZXOWPHMujZrsoPGsDfyVv7CYzrw87lY+6dpFz2vfAvOhVr99HF4/nB9ePwZm5nHilJv4fvzJLB2if/ePe0KHB/90qA18Zt6CNwDMm3lWFjQC3g/AT5HzuUpwodRVzU2iXEdZiWVQ+uaZlsJYFAm+J9HudZTyCPdc1ca8v54+OuvvrjaC/b/j1Y4aOm/WwEs+OrjIiSvYfRmDMsYxlPEMYhLjuw2lmQqGlGykNY7d1Z23qJPEOTo8+rF5dp677SX0DH4Dk3yOAJY7vyri5m+KKZvpeE8hVG3Wi+PhWt9JG5XKeMtaUgJAifmBcYR6Jpjkl+6E4hXyKQ2Ti7QE7mTg9KiG2bl8qX5fqv7jsdiYl97I09w4RT3m0YcLeZembCaLJZRkBENgGx3eBMD2WuZGLLyuZVe4MeNJIIpy6kJteMqb5vlIkCjhR4WevM54ZUj8jMqY7RMQ6Yk3GYWEzg7zyD7e7GwD5bNCW+MRaqQwgeXIo9TQoAcvG2gYuj/Cq9nXhMr+JrhIGWJU1vf8FnxjzuGn+Pp+mKjLzWHjChcCreMNw/8fcJ9PyZHYiLwa7F9FD6tNrk+PWM5F8drmwf8FaNHHG11yMzCf7z8+mdO6rOWr0WdDCbAW+t2Xz4wluTyx7l76FZh0irvgvSWX07DTJjYfOIXNtZvqbv7vNDo0WMSgbpP4w1vj+UcXuHbRC7R4tJi+j37PE0vgH1ICqwdzz+lj+InjWHBjZ27gJfozlXv+O4YTGhzihG+289f/3OEuoO6lDl+Txra/N+XwW8fTbsgHLHznMq4/OZ/GZ2+AH4ALgNfg4kff4r2cyzn8oy7MfNqNazl04yC2ZJ8MXaHVhC9Yl30mP91scjdbA/eijdI3gEbmQz8BeD9opEm3QIjYU7L1LYMLEmaDOX14OLOrahvXyPJLZIx+XodY/XrDYNWwiN1um0QNZW1YroWrvCVFJsO6AH4ZOyJzE+u4KlPN5k0R+TnwArAHOKyUur00/VjjspqzsltrbuAlejCPefSgLzNZRRR9aPSP8nwuD9nmlCFZ9akTUhnpnXQ8li+RROKKwVnFSonmUOwav4/3w344h6JvLuYbs9Jr0MRep42O471cn53O6wU51OYAvyuaxVdFZ6MyYP8+ITf1dUAbTblKHyUyF+rrH8tXdjpexRK33wvUu+ZZ/NgkxxhLtB5luCf5gwTOEY7K0LmHvvvmEKKGSD3PPo+Hb8YCY0QaYzULJ3YsdMZIxOPotj0CK5WOESnD4JR2X7NlWJzcIYvFckRQKqCjX9Z5trkeS6fN0KAibJT1zghuMgbCjWUrd5BUqYcwj2VpiVeiQXt9giUdttIcEqxnuX5Ruo4kAeAF+ChA185vs9CJA14/nPbNlzFz6xXUSvuRu7PG8khRgF9lvM/m+/bSmnW8t+JyHjh7OA9t1m680/qsJZV9/EGaAXlAA2YfzubCWh/wRMG9nJL9NVvqB+DnBxnPUJbvO4drU1/hdFbzkNTmLHU5vXdO43RW8zln8Zd3HgfgikunsPK+TnAy8HPIIwCXBqjNAS7Z966+p3gYTpu1lvcaXA4rD8LKY+Bq+Ob21tqAfGY/Fzedz3udLoe/mPcv2xhbDznvw0C4tg1MBb7xGISlKN1VmST93S6v3EpfWgfrowIQ0DmYxcH/b+2xnOs+t5QOERkL9APSgLOUUivN9tOBiehlux3AIKWU88ORDfxLKfWKiIwWkUylVGGy57Y5l9WQ0uTRPcxdsfv0xLuXNtfSyyBeCsn1TAS/sKZE8h6TxS8XUYqImYPnlDEJ7+NC3o1yRLBdosyiB7Po4WtYNmNtSIiz93UPZnEHY7iDMaaHWeSQ7/bjDYmRRbhKsPHGLUWhOZWO/H4sHI9x3P5HlCLf8kqiCvkkkovp5JuEhwjV2bc9oq3fthqPE95T3g+LpZwI/o8nXi8xoVy16Hp3pcqNLK98Skv5IqPj7O8CkmS0TXkg3kTfcumvcr9/0XKjRdZE/O9W9ljLTOXOm2+iFTbXh21/AXhWKXU68CzwomdfC4Leka/RhmnS2Km9mnIXOtb0ALWZxpX8xHGADp2MRtCbdDnDGcHou0fy0KOO0TmWNzsNQH3qrCbB8rvPiyhN4hcO6w2rUL/QR/sJwnpDYcWEmqow42v9IO3pmlHUixkeObfw0A3HUzmEfGPQpLm1LCfSn5FGGTbZ8NhjOWDCUKa7huGrGdqwbJv6Jetlht641HtUT9gFXyod1Jvu8VqCLg8SvA5nNa4nrDTvdHv/Zb/lJm9SG4mlK8zsGJg7UsZz3PfBuBYnHDahfIb5xoP5CPA/UPcEd3lrXTrvtfbcJlfCxDE0nTBZJ2zXsem94bCyAHgemB459tLmZ7z6VH+WkglPwSv7rkvqWIvFUv5IA4JRLeZnl5f0HycHE4iu8jlzDYwKoO4jWJJE8nSI7K4838Wt8N8P1SvSsHQW3CLVZ0dHhhyaxTAnYkOXHvldSBulro1yAaWkflBd1FkkjllirHcAfpsPebnwDJyQeSs/HP8snJ9HL7WDhX95Cv6aBzcfw1ev63JNh5cczwuX3QT1FM3YwL8bXMT6XVPhs8t56D8P849f/BaA382aRbM+G6F3gIGzJjD5uSHseBPe7Lea3uZNmb1rEnwY4MPcxTDjEv6c/SjnsoR/qg8Ywz20YxVN2cJu6sOJeshvzhrANaP1l2EePZhHD0YvGAnPoEMQdwAfwjfjWtNvZz4zJJv2ahkrp3eCD+GKgVN4c84A3nv4crgLGAru+vvSQDDPZizwOtrTmQ6sNNu7huZYak96FpCHyNtR5x+/+cnr9XPEe5xQ2FAuc0Nu/UJw44XjRjPQkp0rxQTGecuQJKIr4cvbwK65kNkTCkONSqV6mr4/SbLTGkMjEfF6FMcppcZ5GyilPgQQT9yyiDQGMgiG0r0BPCMiJymlvgM2oA1MzN/lpRmcNS6rISoD9hz6AIDbUp7kla9/T6MWOpFkexRf9P3GGI3ap0fEpzxqXU7iBiYlWHpSDfH88In+ocuctJRMliYkzpNsXqWvuM3D8BVnh/yoqwyYQl8ABjBTV59U4Z65S3DKV0YrlrK+KD2moI5Dn5XvADCp/SDXsIzGRlq7z7U4k74DckqWPCH76ad0m36qNYV0ZNseOK7DLreeWSzc8XpuoLxGpR/JfA7J5loCrjHJ731yQ4zxGSv3I9okOjA1n6cZRg/mMZO+3JPqlH2J/T9TI7EzhqUSSLTmnRu2mA1u4WCMsE8pb659czOTON6f5HMzE6U09QE7qFbAxyw/+zx+oJErpPJnaagNhtHONRrFvj4gi06GH+AfxYN1OO7oAPwT1H0CTh3t3+bxb0Cpi4Ah5N8CMhC4KuBW2hbyIDcPRgWgGJZxHnAekn0bzMzjQwAaA9vDjJcbXON91LSRjOqGm5YBwQWFGffmwl2w8u5O+t6mG8iQAfCKqTf5XBvkRfQi6gITVnxhG/ggD65H35OoJXBFljYwnzYnSDLypiwkmseZeH9VJNR0l1ls/xSilVypMmMtCxUzb25XSmXGbxZBM2CTUuoQgFLqkIhsNtu/AwqA50XkbCBFKT+Jz/jYsNhqxgRyY9Z8HMMdIa8f5q64IbGgFWP98AttfZKbeJKbItpKAgZUskhGaL/hYaZfeIyscEKu6TvRj7KMJYYkuCOYAzCNPkwLqxvqhK364Q1hjYcTDuuEv5YnTokRgEw+IpOPYrdPjdyWQ74OeX1HPxI5V6Jji7ovSrhrNG4y3+B42NBYi6Vq4BtCFxZaJyZfLrxtWVM8pA9uCRJvSKRfKZOk+o3zm5VU2QiT7uCEFSYTFhzt3H7PQYeOhoePeudFKYge9ugl2u+rxBBNiDb/RpSMiZI+UZ7EU1QVWWIekZ+j/7ayfWbVmaMyHLaaopT6SSl1rVLqTqVUFIXH+Nh16GpEZ97HCQaanqJDRoczihEt7+MWniOVfQn18zB3cRyOh3Ksa4Q5Rmg8L2c4SgXiGpbh5Ua84bApW3+EcwLu9jSKaVEU6rH8UM3mgqJgBEA2c1zDciL9qcNeAOqYfQ6HSDGCRdGTKaQIuD/UoylF0CKjmPUNZgIwIMpq8ET60533gM+44PBH3FLrOc7ic3e/06dj8rdQQcGYnPavh/SV0/51VyX2QpzEyDNDFFfDS8ukUeyWJQFdsiSHfPqpoDiQ/oHWx8UThIiFNwTW5X44Yft2oBFN+TqihIqDYwjPI8rd2ENmEhlpvgfOCrTjkVwAvBfQYbLPB8cuJwHbIycg5+bixmlP6nBXoCP+Oekvugq55zOfRTxQMJYTLtM3PlKgRR4sUFMl1S2Vg9oZaTxqD6TZECsAYy/wPjgBINIAqB/wePViK0nHDK8/T4dE+hmWcrbpx6Tvq2kgz+GW+nBKjyRz01yWUgyJRCHVfnUPy2UeOgJuXuJ9dzFGpvOzeh7wX/Nw+D895tQ9uzh0MIUDy+vCUDjhPI9h+Za5rlfAydwJGhoD6ak+Z658Dn11u1wmkM8QmrCBxtNgW4PmWhnWIHWBHgTDpbsSzChz3o9XAWkDV4CMAwaAutHpYTJ8QFBheDfA23Db23oMa/MiomeCHtU4XvA4JWuYucaU63D2X2aOC/VcJurJDElZ8pT1cEr8xD3eow3phMF6w2GD40loOOba5sKutSbstWeIUSkNgF3hRncpwm2rElVv3twInCoiKcZrmQI0NdvLjap1yZa4tGYtj5kCFdnMoYBe1NO/ftzJsxHtEzEUHbVYPw+nX8mQ20JyfzXhuZPJUmvujyHnapFhjCbT70fAhxmZnO8xEM40BlNhmNiOg3NdAJxUul+nFjuDxpt/HoTms1q/5FS2MwJ4hWsB8C6e3oFWuXvc1MOE0DIdjudyMy15jFsB/89zI63dtl6F3Fh4J5WY/Mb83QdLU893n0elK5wwPXT1eTGdEwp5dY1HzxgTau9XiqRRwA2Ldb7DD8RZuX4xSsmVl7iBXy5cTcpvDvL9P0+O3UlNo5pJqluqP646rM8Cjzf8UyQPZmLyKcfDfKD+UL2qd3UZx+AJEJFR/ttL3XeM372Ef7fBrUuM8g8tjEftHXewv4EOeXUMYWdBNmKM34QdvNDz11SICf1s1sD3jUIO2ZsafO2qjvdyjAsTrupeSxukr/4CDCyIFPjzDQOeB2qPMTTn6+eJElEz9DCIHAfz/qxlULoHYA24IvBR+3EMwEhD0LfsRyk/u+qGk0sZQf02Ye0CR7QWaIVRxeZNpdQ2EVkOXIXOKL4KWGbyLcsNa1xWY4bybEi9d8eL1Zcp7EPHLHo9RY7XKzKg1Z9wT2ZC4gA++KnAJkIaxeQYgaK+SRy3wRT2jJevGVSA1eI/IqNgaWy10z5MY7acYF4V8Wocm9UJhb3DZ58T3lwSQ/zGOf5x7qUvulRM+O/UcEYwukhbdInkdvrhVYX10ujwJrc2Ziy0wav9lolcV8LjMgqw5fuzhxvyW8j5cdv2ooA5WPelxVIZyOQY+5qjaxom26fjEXW8JBeZiIkF/l4lt/TECcHX3hqHCZ/3kcjc9WieyaZ8nVzfEm6UJTm2usDePPxql7jvSd+ALv8Rh2N3xrbmgp7cYN5pRDiz93PoG/reaGX3esG2fl7kup7nVxK3DJbU9TdCo4WrlsWjnCxiFg9UIkrvSbSN2odPyK84tZ+drKznF+rzqK56fxKiPuJoC8bIPNHv71GQb1mJiMhT6NIiJwPzRWSHUupMdCHbiSIyAtiFvwZnmbDGZTUihYOcxeeM5S62GgPqA/QvyUyuAJyb/FDzwzFKHIUDrzfT2TeTAW74YLI0Yy3fDNIhqt5aYyFG5WsmHMP8WHjbhXtHnXqTXs73CWksMKb1YE8YrGNYJoPKiMzlBBjhFruKng/5+z0vAPDTiU8Bo6C+XtpusdM/LzaNYhbxa/PqX+52rxfzAy4EYDH5Hp3ZIG1ZzVpakcUSWnuLwAH9mciMBYP1i27BczriPjHpilaFLQI+hIYdNoXsdsJhvYaoswKdA3RmMTMey6XFnaHXrvMl+0R4K0PalGKivoknufE77YUMGov6u32/eyPh9VAGDclo+aRdDvyLGU/1YimZbM9uGKL0W+OpeuE9lppCDvAHENGusRC1WHBDXmUOMH6o9qKNQXvZuiZ+Gq9B4+tdmkXMGodOOKzbnxMmq34WVsrpsojzhY/hFDWIU9QgNtPSd3+s38pYhqaTL6mMDbm/QV2a8jVbHLEeRsMXASj27/+4Mbt0Pw3qa7/H69r7KJIHz5tjugbbK9UGGWJevAnxAm506OT/QreZPM7JDYbQc6cOIarHbr5qcLZWt/eExbrnXmj+9jDXfSVBUbgXzTgfNG3mmzYm6kaNQN9GbTeGZf0/6/LfxrBWqyhXr5rrJfXUeQzZ34VSEfs7ksCc27V0502Kq8x39A3nf+Fc8/cTIC/Eg18txX0qcd5USg0DhvlsL6a0JQgSxN4qVCMe5c/Uf+cnsi5dwgrOcrffz1geJxhaOZMBvsdvjCF+43dceEhtqVVkvwQyA3BG4oc4BmZnFid1qkTUZR0GG4+li8drqT2AkUblLK7U6nbAy+or4A9Mpx9znQa7RtFC9XPbO0a/ExbrJRHP3kaauYbnTAa4+bFTGUwaxSwhixLSGZWhDUuIDDkFHUIb63yRsvok5LF0cI3jOyHZMiSxKG+PpUM0j+Xu2icDcxi8bzspxxykde11vu0sFsuRQQ2MIQ6z0eemcygwczy8PdT/GPDNvYxlWHq9lKXxWALB6cRHddvvpjncqIxHWcIqt/RpqQ2JfwfgFODMrabPJu74nPrIP504Ber/3r+j+XnQPcCB/nVDw0YHAD1MGZiloR5Ld/wR4a2hirpqp44iAViNttpd++6g+WvWqb1eSmkAXBzWtZOd8nPzdy/gF4HcqA0cMs93PQ0n/DE4niNo6CTjhYzVNm7Op9PuOf3X68FU4SbKc13D+k5wgBBU3AXAlCLJDJhyJI6uxLmRx1m8nCgi44DZSqnZlT2YcCpdLVZETheRf4vIV+ZvxC+kiKSIyLMisk5E1orIUM++B0Vkm4gsN4/IRLWjgEUxFhkqWlWrL1PoyxS68xbSCVrzBa35okx9Jl10usg5LrEVwy/MKAG39iXoME+vsmv08Y1yFVPTKHYf5Ymft9RPNdZ5/xPuV/KijrciriMaY7gj6sp8tM8/XKWwLN/rRBRv/cjz5MVawqi8YtAWD0f7vCmyRz9iKHRHHHNn/Da+x3WL4kEcia86bGkUYsM9cdHbRf7+JaMqK7ImQt29PIgf5ho9dNT3vb3SPJL4fJMh/LMr9/49467o+6+jTTU1nopwRPuj4dorZt78Xil1Y1U0LKFqTO0vAM8qpV4XkavRul7hvpdcdBnbNkBDYJmIzFdKlZj9k5RS8ettVFNyyGcYcN6W5Wy/9AR+e988+o32GiF6pedQlKxhxyPppwYbzcuZDN8Mau0WF/aqgqpJIBPQ4ZZDgu0jJbj1X+/Kl2MAZTPHNcL0tqBXzKsKW1acXEXnvOujtJtGH/6m0mjKZgBymc4QAJUdkmsoq8yq6pl6vM3VVqQI+mXkcyePMaMoN6LvNIrJBJbRgSlcBcDYMJEl72eci05GmshqAKYy1feHOJbH0iknovaFPe+lx6/m4JYBcTyiXi+nYwxP95TI6cubAMEA5YXmb5RwMjkJaBRwVV/7EJocE03ZzivKEyt30ik7Ek3Ex4+UYw4yqvb9bljsTSYsO5k+jjqqmDBBDadmzZu7Ij2UkQqw6PDGEvN85ngt6rNrPLwY9GImFlaaB3mxPVOx+pERuArYrjDJ5/9zPXGxxF5Ke04vtb5t6ivGB8Fw2BDGAGeaPMjvfoZ0awLv5yOLcmE31L4Aarffw4GGW4HuwePGAzPzgIHmMRmW+vT/KFoIxxEfeCXsusJzYF1Cr3PuScaiMxEtIcJBpvKY8hiWku0RhXLmRudzdfL9nn4acv4YLBU/cwkSeBsdutxAC810BWbu1J7yFwPwts81JkC0z88dY1h4bHn07UeiYlGOBzPyXE4/5rVRlfVTkvWlPkE16OuNwM/LQO82QBt9z7A3doh6taGGzpuValyKSGMgA7jEbHoDeEZETgpTLuoPvKSUOgx8JyJvosU4SxuoWW24i4eBljRkBy+dcg2Aa1i6oYgK5D5405k0RoN0gis+DXq79pFqMvngBa7j5vBfd6KXiwgxQD+FHibPL4sdxsjyN1CzWAQmJ1Qkzy034iBhwm9+P45OSRCH8NCLXhTQ34S3esNcz/SU7BjMVNd7ub1WaCjsCHQo7EhGMZH+jDIxMLlqujH2lrKdhr7XF876bGPEeVRl1yvHxNJhRjOKcrUhm+F4EUONv0I6cgyHuJrXWcuZRs5I05+JbqBUX6bgUQkH9Od3uzoW0MKJTr9N2MCFfABACZFGbXkgI6BF2EpxIZn0UwD5ITmlbmjOSWGd5AToPW1aSLt4K/bhfYaeXxucN0UpQeLFK/ATYAwP/hP+YKQPe2YX0Kx8VbotllJTE+ZNpYJqLIl6OpQplRTiCbseGLsJbsrTojA3+hy3AHzDY501WKdcsiMc9BvcskyJ45TC8jFEw8pFRIwjDt5j4oXGxhZdiT7GAx/WNQvIdWEGOtcSaFywgW0NPO13DcRZDxVZo8uMXO65DsfG3xI610f7jKUBvorBzvOeBcHJVo30NFju08eLAYwQu+bpNcS2Et/Wc5A7tgbAr/XiRTrw1zxTJqN8DB+Rp6H+HyO2J5s3Gb4YG9WgrWCDTWSh51xdg8+NUem+ry+jS8O8DMw2CUZ1ekKdQFIqv5aqRWV7LpsBm5RShwBMzZXNZrt3kmxOqDNpg2njMEBELgW+BQJKqX9X7LCPLHfwOAvpyq3DX+aLUa0Y5aPm1mJ0Mevvi53r9gFd6MvMMo/HMT6zTC3GKyZN8RigoYbpuUMW8Ym8H9rBOQFUUdC4VAot/vNasEkJ6RFho+WhPloaXKMRuLIguv58iwLHCE5HtTOGcdFW1ysaT8nVEfrpRvSknq9JYyqD6csUVtHOfd+dhYHHuTepsFe1L8pzj3cylhCPW0sTWD8i3ZQhCYaV+oX5RozBqWU5AmaPuDKklEl5TICl8TY6tS3r7NvO3GH6hXoqxgE1ASvoU1WoUfOmNi58xHWieHd0yaihyBJgOhFKown/pjwY1q/5WXbr/cYqIzISt2ZvUueMQ4UZBAOAszx9v28Mki4Eaw+OCsAoOD17BWTrvMcI+bzubWC448n08KKCm6Lns/jmwPoZnL9K6Go0HxDMlwSYAvQ0fx3ytDGnRnhDexsEa1w6LASldFsZgVtyJVnifn67nDEknz+bTOma8iZhj6Uf13tffKL/dIhSrqQ6UkPnzUrPuSwHXgBaKqXORq/I/p+I+LqaRORGESkUkcLvvqsgpZAjTHnk0MXL6fPbr6XA/UnZ+mPcvA/p5Hnu84Pod01SZB4ZoTmVFcEuU8ol1v5i1/eILgwcBSkCPhP4TBCZi8jchD4zb25rtEmjv/HJel+7510QDGl1GM4IcsiPMPqc99bJSe3M+3TmfXd7SNtewUc4uUwgl8haZBD9exHhwfRs9+6LlXuSyUc6D8bk8lQEMoyghLrFUr2p0vNmeC6eyJroeX2mrf59iN4u4jiv96QsY+1iHqXMxywPmrCBJmwIHVeC1xeeDyoeQ8FbzqMi8M/JD/0MRdYkeB0xviOl/JzFsyCsv18+QglRx7MEkSXm+dPuI7RN5DZ3Xzl9PysSaRcMiU34mAT/P3XboyvftCZR2fb0RuBUEUkxq68pQFOz3csGoAXwqXntrsgqpb51Giml3hWRjUB7vHUegvvHAeMAMjMzk9G2qhSkCNZnPE3zv27jwF9q8+2oE7mcf7r7tcHXAYBzWM51o1/lwc5jkM7Q+9NpHCKFWVzptt1HKjPpGxH26jCPPgxnBMMpZBQjXWVSPAGY3XnLPGvGbuqxhC5IhsnP8/ndXUIXBO25dPa7huWXwFizQjoJ5LXI4wGu41UeLBrju2/uoGzmks36SbFLkAxmKiPM1Y1ElwvRynMd3RqG12ZP5cOCTNqhZ1cnd7BfgQ7rLHazPkt8z7G+yHg4zYTUIqM4uC0B7kQviS8wqVPhHsxD5t+1GWv5RnSe5e9UGqA/uzSKyWIJWSzhH1IC7+lV0m0LmkM3XcIknCTmSt0+FV958vtGjiA/fLUamJFqPJv/hFpnhu5T34Uaj16PZTjhwjwhE05OAEoh3OOlkPO5iSe5iUJe5DZd0gDg7UZwGZzRaxlfDjtHn3tYDfVi1tAV2CpIjZ83o5YkcfbHEIlxfzvCvVMOdUy5rDvD2t9ltpciqDhaaKJIPtAapfzSFUx5L9995nhjfDROMEfPCYcNFeg5Dj7/H7wVqeAKBPMgh+u/X401oawdQX+lTN9O+GqBuUZHifXFACee9i28Bd+ffLKePxYGQvIrxeT2Ka86qVGHnStGGX8huLLs5+lj5+BVP9XtJJtQr+XFwHvAcs8YRwO0CQt99hZUDWgvYlobSAOWeeabh4qAOpQ3Sv1Rj32mMZLra++lNzQ48pjw7/BlCbQpBxyx4IXRmyjVNbr44q41wGT9Pzh2DSxsA62AQnMP8YHZb4imu1AtqKHzZqVeslJqm4gsB65CR/FfBSwLyxsBmAbcICIFaGGCKzDJfCJyqlJqk3neAf1TEHkXXU1pXrwNsuCip/7N9mEnUEgnGrHX9SSe400u8OAYlV4uxNGojpFTmSCOFLgfh5oc74bMgk+s/6cgk4DPgbsCvpN1CemkeXItH8y4lwDGwCwCR8Ro5aTW1GFv0uP35X64oKhQ172UPG5XRK1zuJXGPGcSOJ4ClOrpejEdRYkS0l2pdOkQvCdzJvigl1EboHP29KJx3a104V+UkO7JGnWMyINMZTDNWEs/1Zq2rCY/TBDCMUCVCoR4R6eTS6PDm/hbrT/TinUMCfNcBkN2Tw396xfKuzA0hNYhl8mMIrqFePiL4yNCbGOVGgnf11F19xfumZ5HIYGQXNfyQj0Fw3iUp+fcreftUgo5HDXUQGGCqkZNmDdV2P9yQmU2+gZ0PcRxBP9PZ5r4xXMijdDSiKb4UZZi9TH7jWFUhrPVY+S5xyd6fXV+xnHf7KJZXb02sXlfU/jxVvamNjLe0OvY1qB5RJ9yiU9fAM8TFO4xfP/qyYSIcHcFLvA3Fryf9VIyoX7zyAXNHL9jJ0Nvs/1C9FIJRuDHXYw0CwT3OdscYywrRMBJjQAnNFVuAZYNhAvbwAdOPwPhotjGTlC0KRhSG9nGfzt4BG+qINGEfhLHY8jXbwPrnO9Wm2AY9tFExcybthRJHG4G/igiXwF/NK8RkTkikmnavAb8B1gDfAyMVEr9x+wbLSIrReQz4CXgGu+qbE0l6HWMTjPW0izEhEkcJ8cv6MnUiOSFhZKQUOmQ0D78PWrh2zZEZnxUGPKfoHH4aGRN2qRCN/xCmf1yEx/jVh4LUSBI/Dzh/ccrvxIePuvbxkfEwi/ENmYf4zzPHZn9EbhKfxaLJSGO6nlTZA9yiTZg5Gf6AZGhgkmXtEriZj3pviVPq4SvCtuWQD/lFfrnhMc2YUNC6Skxx+Sf3RC9fYPk53pwvIiePpww58hAmMhjS1HKRGRJ3GOCJWCWJNd5UuOIHhIbbTzJ9R+lDEwCfZUm3DX+eGJ/P3TKUOIhsxZbiiQmSqlifPS4lVK9PM8PEXTEh7cbXHGjq0SKhX9lnAtfw55f12Z3t3qAEzqZT3+jI3o3j/Ikw7iTx1hGB9Rif8NSeyf9PZRyN7z8qF4lHeUxFOYbyfFP7taKr40e2QR0oGOtUK3xi4veYj6Xh2z7JKMLLIt+eWqQ5/xOmOw1ZiXQhMc6IaWBjDFRwzdXczrjjQRdeGGS8PdhlAmHdXDCYR0cVVqRGTygDjCJ/qxvkA6F2rA8Q24A4G9qK2dIiTlqLnkqFbiHM+Q6sy3PJNfr8yk1HFSkwTd15bXQPmi09qo7h+krrw7Z5nCIY5gheiEgk0IK6UgKB8liCVMJ/gtspbH7PNezOvgiNwHw58N/A2CIs6x0P4RVO4nNw8FwMS+raQsQWo5lAfAhWlRjPriO3SSQEejw6enz9QYV6rksz1CZpWS6z5Unn3TSgWvgv3Ba9lroBWexgl5Efn+OempoeE9VpCbOm15PmRMOK5KnS2EY0R7XaFho/tYfqj1At0C4rIB0MlE0npBVv5tupQLuopjyUZtNiLMCKF+ZguDirg6RDXos9evgfr/futJ6Xw8s1MmUKhukA/z0m/p89cEcYC2NlZ7HfgDtsdxl7gm65+rP4EU9P6l3g5ZCiNE+H9htnhd6QpA/BM7Mg5lwwo/BRdMfnmik248lgm0Dm4eGsr4HDNfzay8KmJttxNZUQKdszAd+CvVSOvsj6BuAmU5O5Jem3Rn6tbf588YAXNlGX8euL4GD0BBUaNWshAh+x0JXOtz3sH4b95qDZVT8+llixuz1kCZX2iZkPGf4KBl7w5QT8FY6CrGOOqyfKrFSbZAGMebtywACqMlh46uu1NB5swZecvWhy+xP4Bg4tskBmh7cpjeGJdg/yt2soh1DGe/Wr4wVmuhlo8npiLaYtMQpIxK23ZuzGW5UlsfNvls7yWNQ+imtNjfBQOV1i19CumsQOrTYWRxUqVU6Z/N9utJTFTCHbPdHf6d6hPrsi/lD6PSTQ742Ij1MJxdWhr7Td/Js6H7zvsQSX9pIM4+qbvB7MKpW0Hu5vdapoQeNDVWHjUbD2x3vZ/D46WGlTWIJFUXcmJnQpli5llH7qoT8i2+GtabjU2XL7bRYLPEJKUWSDU7mQ7gxpVTANShVQQwv1vPeHM1AiKCc//nDUjkSMCp9jT8VQKJnkIS2k8QjQKIREh6baGCPCfW8VJ3DOyMDbBsJBDwG9yXxw3N/tfN99nICACsbdPKEOXqM/vvRirOF8MPVwdBnGYsOg3RyYGcG8w29n3cwlFUzh+yQVIj2+3Rq8crU+OGqriF3ljHGPv/Sv/FVWUCWa+hozign0bidCc9jpZnvoh2TUB1M4333GpdlJcTQ3LXGs72NEUQ0Ao1vmM94cjXNsbQAVSMs1lJOJBri2oNZbljrwz5uKydcthlrffcfCcJDXsNVS53gH4ACelGAj3RpGCtoywrjYfMjmmHkhGI5yq3u9hghHsVJuulCQks9RqajGDvB6LAmwrtcGBF6m8j7E46kGgGfaPsf0Q/QIbPesNl40USlXY30HqcFeJ6M2T6TjyLEgJIhXv81CmcFtrwfFosPIlMRmRq/Ybx+yjFvzVGEjaUM6/wWliUs0bffBI7zU42tSETyYr6/qXt2lYsh1oQNEQsHvczMDyDvJN5XNHVyd3+Uu2IdxpoXXd21VvRjy5uK+v5UBrFCYavieJOmhs6b1WCINZAFAscC+4E2cG/qXyOEZa7qPBO1OGiULAmLkHqYu/jAeB6jqcM6PPToXUylPwBDouzXXtGgx8rJ5TjU5PiELsmvvTihsUbVTU1KqKuE6UghANkmYHYF+kZlornWa4tCb1xaZEAL1Y/1MoNJXANAJktZ76mvOZmBdPCIKD2gPiTXVJSeGz7JLh0e8jJo7HUkrf2XlJDuGqwdQG9bqcNyuEtgrL8w40wG0Iy1LOZ8NtLa/QFuofrrUOIwL2+O0XEdV+sG+kkWeItuh3ksndxLv9qWO54wn39YWKykAg/r5+tHpOM4s1U3c80ZeruMCPNSTk9AAe6hPHggtE0mH7FU5nOjqhf72FJSZ992APamNgKgf+2pLM8+B7Lhk2FdaPGU/hxzmUC+73/MUUoNDe+xVAHqRIr8gFlwbGWejyYYDrtrPNQfGhThqm/CaH0MIb/fIKlLQgXcncU1dU+MRmnx+9HjcMJhY4fp+o7D3KA3Vscmdi6PoeZcv9wNLR4tZv2qdB1Vcma+9qb21uNSs7TK7OkNtO6TFATdv+vMh7CV5sgUoMcaROC47/VvKFnAIwQ/n7vM5zU8eK3hyrA9z9ZLonMXZNPYmY+MaFPEtc9rw8p3OmmF2reBJ4CD4e2MUdjoj1pgZXse7DI7LzTvgfv9MAmfb+j3VU4NE5Sa/nRMMZ5YRP1cd5nV2b53hnjjo/N2zPkzmkKx33gg6KX0hr8mK9zjhMPGpb7n/dy1BmgNF7WBa4HBkcm21VYpFmrsvFkDL7macKL+83X6KeQd0v9YZ6d8BsD6zkFjZzq5PMxddGCZGxbrh+OpnEefEGMzsFX/AHVv8p67zQmX7cEsPqALqfhIgxoe5i7f8yb6wxaN5mxFnaQ9eHfwV1TGvRH7HdryVcg+x3h0xMIdj19WSEXlSEpIZyL9ubbvVC7iBfaaHpzQnI9MbuMF8pDesBQeKjJhtN2dXvS/1BnyPDAXpSKLAZeQ7llfDirpZLJU51vepa87jWJKOJPWfMFo7mMtZ0b05TdJXSrdgak8oA6AyTOdTK6bs5kwno/O8WCqfYTWt3Su25O7GSvtQxZoo9P3hk7y3PIpEGbgPhBAjQwtSTJOdsf9fvmpyzreyBe5zbdtHba727xtxEz064elM/CpJNUuLBZLcnTvH1HxQRrgKoe6N95XEani/NFQOH8NvIGbw+Y1LP1yFd3fUlOKRPqgcwE/CM5lalbYMY/4jPtB3PZ+JDonRvy2/ysAv85DjDqt6hJ5zDbZb3aaPjxt3ZxVxyD2Cu9NBbbC+g2tdV4k4IYpNvKMZ0aAlcd0Chpu3U25qxEmHDcsxeGnE7cD23XePZPd3Nhk2baguWuYOqU55o7MhoDxej1jGnoDrY4Bdkd6KtV3xiB1xjIzT7dd7jQwfd5lDKCxeUAA7vojjH0a0F+e8PIY6rAZn8d76WxzcAzcyJI0Jm94YZQ3IAzHQPW7z6oIb5+f4Rm1rQm88su39KV+GzjZ83piGxicWG1TS9XFGpdVkYOwvf0JNPr6B12bMqUvTxFcJVOLQ5v7GXexDE0//Lyb8+gTM78vr0niE4Wfh1NNMt7L9lG8ls0UdzjlR2JwN1qkJjxt0PFYOsblmaxlBNqbOJJRDM6AL2jtqR0aNNrfN3cwJZ5t7riNwShF0CHjY05hM+wkJAczmlEZjmMw7t9ndNNTc2EopKVHyQExeD2WIRNVBgxTqdRzFRU8eZEJ/Nj7eSxjMh/oGj1n073mkSSkSBsxnrBJ2DUWVaTRWF44HsuIsWQD2XrynvwHPa780ytsGFUTW4rEcoRQ7ybQyFnc2gXUN8c5IigQ4iFJVPxG7Yke8hrR1sdjmYjH020bJU8TIhdoHUPRvx9TNqMsapuXg2p+TFBj4Y+58HQejSdsYNuGpvAKrhGgOuu/8rz+W/t256Lrat3ACM9U6LU6dTYPeIJ75pAdMkdJg2yTi5kd7Mfb7xNBT+1WjFEz2xg1xvAWz3wW4Wmcuca8b4Ewg8h5L4NGmnq0jO9tMsx03q8Eyu/EICJvOJ4Hs8zlRRLH/R+9G9iEDoobuwZTEj1I/UC5lQyqVGrgvGlzLqsaU+JrefvlBjo5dn5x9dEMRMeb6W3XlylIJ9xHMsjd5sfChywWhdS+TIRY4jDRqHcgqKY/kf6uFzOcL0w2Y7zzefM6vNv82vqJSYTniobLcfuVJUmWHsxyS4KElwWJVSpEFmiRn3hlSpLFe00yQq8cl8awTAQ5KX4OTbmdqy405esjczKLpQZTlnxLkUivRzShn1LnP47Uj4jtZ5OQgE/oGPLLLOYTXqIlqWM901sy74X4eE5j5Xu6JUZ85gIndzPRa4jVzpmzo12Ld77zyweVKz0htAmcr6oQ/l0uVfmSUpYgEVnoKsUGt801Qj1+7atm/qel/LCey6rI8dDoox+gKbTet47LUyNj+zt7lnh68RYQWSTaSyr7TDmSULo3eY9MCo0/L/JXZTNNQ16H5k76e0eTMSIdj6UzUYUXpJ7JFawvSmdqxtdmPC3dfU49TVUUeudw24En+aj2BRTSEYAh5MNaASZT2HqGDj81nMlaStAGYIuMYgLk0a9AT/SPcSc387zbNpfXaUewiFmLjGKWcV7wB7Qv0He4b55EytYfOXzyIkB7NFsUbTVhSdoIa5v6pRuWS/rvgrmXwLqzHgSgvypkKoPdEiu/U63Y7SzXe9hMU2bRO+Q6S4OzKi+paBl5Z7vHS+ncKETkU+IY62He2oVAFO9oRC5K2EqrE87qlAwJD3l1Qma922/iyYj24eGw8QiG4urjF6h5PGWEoa5jN6/oEoNHPzU0d8RSheiq/zi/sWpnmOE40+Ndqt8GrgfW6ZchnqgY+Ywh2/MC0D2AilKHN6GcS5++43mR/NqpLkT8JoYT7u3yhs7G9ASfAsd12MVP5mVjdR3bRgAPBNi2AhqfvQHUdWwzOglygmk4X4/zwgaZfI7Ol6y9dg8HWhu13/EAbSJuT2r//AA/nOYfIeLw15138Jdxj8NNns8jxVz/IWi8c4MulQKwU3sdIxYRGqIja6YZL+b7a2h4KFWHXprqgLqkhxNX7fP+Zgb0d6h+G9jluNFC47B9y4KEhcTq/f55mqHfh9DPMN53JpqHMpbxVta0pVi4av+J1DwdmwdXBbT30vFKe5VkjwavZcXNmyeKyDhgdlWsdWlvFaoax8K355/IyVu+Z7/Ju/SGU0bzcrmlSHx+LPyMSj/eFJ2o742VT6Zqkno0eMyl6pyQfU5Zk2RwrjuZusy7a59MLwq4m79FrUNYSEdGhtW81IZeOjnkU0hHFvFrILKW4dwG2by+M4ers3WdUQp0CGwxaZ46l57PoCPU+tZbyHouLI0MmY2gvXJVY5UK0J+Jvs3eKdLxW06NTuc9S6PYNa6joUNgT43ZJh6qmzYsvThGbSEdaTGymJKR5nOMcoMW0l+Sk536LqnmZULtAVnQkuu6TeB8FlObA0fu5BZLjcKn9lQUQkqQrG8Jt5vbmlbB/RD9ZjdmaGqUMkmOsRmRcxnbXopCkrnwPpTlJlyZUP+fng3AswG4NY9t8kqwPEpObtCI25UHn5kSUotAsgOwF967RBuvInm6TubO3W5ZFBFTZmShOX5hgB/ebARvAk8E6FlQwNwG2RHXcS+P85d7CZYoAdp/Z8qNXNmJbVc2j7zut2FuL/NluMv0+Whokx0p+/Q9jmMoh30vdO3KPyJNgf/iLk6EnITKF5mJF/ZaqvIlq+K38R9LV59tMe5zPJ8p3wAfOAblZCCAz5q5JZLvlSp15d0Kx4bFViEWRTHl5D79N1p4o0N33gKC4a2l5dk4XlAvD3OXW66kL1Ncr5rDOXxcaonu8JBSiAwrjcUEcllB24iyJsnwKMN4lGGlOjZ87OXBXY4sawzGcIfv9lghsl5ilSApzTXFKmdSFo50aI2TJ1RjqaGS6pbKR57TD/d1dvRQ14hjs5MPa4z3uxJeeilif13zkDydGmBexztn+HmdbeXxOycFoSGw8XBUs6P2VxHCMZKX3GcraxLKhYwIk24Acov/Neh7jJoVslnacNiK5qgIn62h82Y1GGLNII1iJgHshJOLvwfg2VNu4U/3PRvSLjzUsZCOjGcorSOX10IUYr049TA3utv131Eq0rAM9zhmNnHOH90T2Z35zA/Kp0ZFTEjsuYMWce4ifS6/cI1LM2bxzoo+tDg71Guripwfn9D2c8iOqAm5oXVjmrPVFf1xDE6v6qw2nHIpymjHDhqymM4hfayXGQBcLWcBWimuHcsAWEUJSgXzPVyP51I4vOl47VlUTkhvsa+4D8BgPHlG7ZXxVBdTwuAIw3Iqg5maEVxUmM/l9GAWF5n9uQQrP4uk0E+F5pj6ofNRfLyZFxASGuul38h8ZjyWizwG6k4tIOTeeN3vadjV/3gnZzKeF3Kc7NbtjJiPMD9kf3g4LCQfAivmC6I8irhOv8eyh791G8af5XzWL9UlSF7PyCGf+eQyPanzVFtqoDCB5cgTTcxEFRCiGBtCK1yvpW+7MHGQhMIJA+b5CBNG29scY/oND4dVTppAFEMympc08gba+S0LqmOL5GnFWPBVii0TOQHcQJe+AU54XRuWx33fi311QXbpRcnG6jq2eQ2/m6HFpc68rOc0x+vp4nipdnmu8Yrg67mCFu3ZFWZYjzZhz47K7UmwcmAn1GSQtNDhSzY0LthA4336/ACcF3D3AbBcf6/kFghZe78oC8gKVSwF2JIHT5qxP+hsfBuOM/2a9A61IHo4rF+4bDj6sx9o2rVxDWHnu1oe5UYiOCPsu37GkRP7EVkTGgJ7Whu40LxeGdCh7C+X3/mqBDVw3rTGZRWiy0ef6C/hMbCr9XFcxys8NdqJ0U9nOrncwRi35qWjAvoW3bnV1eL+f/bOPc6mev//z48RGdcZMkXMOGYyig5jSofSkHLp5BimQ8aRyul6ujn6JWK3ifTN0e0oSiUhitHhREqaFCc1JifK1JAZQibGfRwyPr8/Pp9123vt2xi37NfjsR+z91qftdZnrb1nfdb78369Xy+LBusr2BMO7lNFEmHDrkq7gP6moI98BjOL+fXxqyLuByiqqv0cir9JDVve+vYwMnThIJYy85pIOTJEaydkmipqZ00PisVmoAeJ0hkgBwoy3TBRR2q+2eGPRZ4+oDKY/ITOer8Wp2s8XuaLjWENQvUPbmNXFSvAlIGdaEIiscx68IhYiTYETjUt6Uh8HeAFHpFwKV9Tn+Az+1FEEUUlwZgj0pYI5DoplCb11Ve1OhelXto//AdkR51joDq3oSH2YZIc9PYB6LUmWuug8RvjuMbkrPpbGdkbGSwbeD3wMeZD8MHuDajx7z3W+n9kw/+g5BFUEP8awAjo9ivFxoSo9LgHz6VoP+csdh2tz77qPiq4+tx6SFWzuRgVWPrtZ7bt/TM6aByVAut92rlRms3AVffP+D28pNvarBWlBCEGgG1yFoCLUarEh73Q9iz2XXRBRemwJ4y4FNNLVNW0FsJE++TSb+s6n0uI0mJ/gwim2mpHEzaaWcxevEsv3g3aJtjxfNVgszkxH8AcetKWL/yWJ1EQVAUW3BVeIbh6bDDcxhTzvRDjEGKcazvfetigFNL/+nN7Z5HFJO5jEvcBgVV+7bCrl9pV+IKp0J4MhVizD6MD11aqLPP/IcT/OZY510egVlgJlJl0Vjq8M8OB2//DidDQzxqco/SeKE49RDyI8/RrRJjb2IInEca8qogHIaYhhJo8tN9P7HRU0QlXZdRIIHoR0N7Eri4rxK8I8Wvk+19BUKuSE4F4Jcx2mqIqxlvBoXjapS41kmM38vkcZHwx2wSwxQIcyuJCrPaj1Z6I6m5lIJzntoDbBhkPwx0rzxR6rP17OOupsefouHkWdPHcwBvcDr8DagExlkprEamIEer3mTi+gHSdtbSLtdzIMm502eeHHfRoZvPFzGSO1s6EPPNd+AglzHPlMytI1g/f40JN165Tf74UWvlWdkJKjw6KFJ3kw/xe0M6LQd319WqU0sNoRjKakX4iPYHwJv2Yq1Vg7ePQqDSVmczUgWk6a6jNAXO9PVibRRYDxTxmSuOYiha5RCh/SpGJooSu6aHEgmQPkihQKrDl/n1K5lvcbp9tWcvzPAjsJJlvOUYMMIAiLT4EHbhIDqIaRyheDu31dfsey4BxJKMZIWF8zzGInlD/39b+jXpIIztpz1j6rgMrYDYMuFVmubY5k28O/Nq2M1BmVggvjNKzktfohQv0OlMF2Gp/F89zp6znSnM1zKztFCGjnV1B1v7QYae92mFfbtQcuflebhA/0FJeQjvyeCwML9YooojiBNDaeitLrYdP0y/PCCwN4s1gYDrwBRhzg+Jlp1pspHAY1dsYOo6gMVcvD7M8WwivI2tp1xOw38+MY1tWH00D7jOBLZSsUOtlJ2fZREDM1H/3AuvUcQ/XBbgfpgJ/nwVx2YoOvBWVvcwr0hsNAL7026WqUVf84E2XXmZWW7jRkUW8EsszqaAjbIHlMkVHVueuRYJuRl3rY7rNQSjp0hQt/6CCyCN6X/sBPFZg6Ut/RWVFnUHlB4BHZ8z172YPij66YTV8vRoy27sqwzvPLbQkot/v6g5/EaJwto0E8rsToNTa4LQfydD7C9TW/3gqsK+vPtwBvOajlHsWWMBE4Y5ocHmGIIkiNRjuBIbAAWq7tgulAArAecFXW6qwlorsQlwMn4CtPl6QblA3QhV0tmcFG0mukDqsHUOZRLFI1f1UNyMjuPPgDUonDaQSeytzg2YujeD0O02pnR+vaMd2+lWi7EsRqcwiC8BUjc3OgRncylCpig3X9AGexDH4FJEKraQpquNbpefBy8b1OiprNZkF9A8pRtSBVeZvQnaBLBc7GV/sztWjfCXTVAHTjkR8GKjFYefHUR6bhYnHDCzDOlYQO4Fw4WtnEg62kowgmSxGkktnHsWYmg9PlfmsRdSKJIpTBD8VUBeapB+MIaEAmK7vvb3Utkbtm/8xLI0BNzqsEF74zHvCFHwZLJO6ztq/lCEG70C4VmffAtSqhsRnv6IucjIwwtGXa/iIz+/aqKilGz2wTinClhiB8LgUGPkl8CVSqjHTED+r9tf9ynZk+GTYEIRm7KZ2exWwwHl/bzhmCztpqoLLPV5Y5L/PQL7H8hcbVTYXDC18Ixiy90EIoDPwiVUPaeIPLvWZlYRwg0p3dA+83zB/v6eaHmvQX0U8MLEQhqWov2DWZZ5uVd4Txjk6bkZpsWcAgil4hlStGxGaNhQurWgko8OiwRroxsKgdZ0xOw+ZvpjhQAxSL7/lo/2vwxYSXAMvkUZEAYodgdRUw1VZdUOgfobTl0jXu/1WAlFjg6EDn3ANlimaIc7jS0eyq8Depwm9bu0qCuP35WZ0DTiWGwI+4UIsVtu4bRdoech9nu30nVA4R+k9UZx6iOv1K96gr85FiLn+7eLdA0dQ1MxA/5PhqoyeCCpKcQ28v9AlBOGcU1feN7OZQEAFWXG5k7IbVh8XB94f2L7PxfolvA4VW+O8rAytyz58xJJEJxuN2WVe2VCqD/RcYIwjoov1+i3BVYU4TOqruNd6mctuV6/gx8z1yWr69+c3P14aOEfHzbOgi+cGvqcFMY+Vk1BWwp7YuiSxmav4AphL3/GzzOyU3UPQDUJ4uVjPHm4l2QwsDergAvojHlPLDAsRuyiPHUYdmSEQ9AwPMHznUwCUJ9R0HlfThK7UM2/q4dyaFTaClc+43lwmnwHxV//jmllJ6QxUisVcWKNnsfRAIdKgON/gQlm02G9J5jKXILlc/+SX5PdhVNpIvkFdjMv53myTpXOK8zOyuTrHCrIuxZjWS6UdeWyQSbTMLzLXd2I1WloHscCfomvUUuKjQgtwjBjaspbMVm8TY+PN2tVsN3KZX1BpiDoZ9ZbzxUYaytuogcVlnWUo0S3WAWGuRXX1FesRy+EPenCtf3Abu5/zUY59D1umUdFvi2NTSS7TgWVPIB343JbFjIB+Y6fCdrMtf+XmB7X/mA+yPNz5buBA0J6ZtFNeg9XlGHCjw9rRiO2sogMDymdTt2qYMzhRRBFFhRGQJrdgNRS3gw+qqsylT8WHoidaFMVQQZgQ+dAsDZp5kD/qZfr+IRc7M0xBs5IhEOye6LbO9J/0XWFT3ywRRxzUxI+FLpEIJoY3/zz4twfe8HKxLOanK5JNWunnf72eVrIe68ViRZnN9FDyHfAtSkV8IiivzgF+uz3arQ6tv1oJsitrru+Ij9i9A36BxkQJE0dD8zHQAuW1qWmwaizwuNfCXqOUY0t6NYV/+6x7DsXerKeFe4zAUnihs/16/wqfFCql2z2FwGxFidUZS5lj1SeeNjEcH0jZXgdsH7j+doxnQBMtPY6+GwFkMOEr/6Ayw3b8UD28Uv91UqjFUJR6MCmwDbhF/5bfNuqffwPZy5ODukKIV4BFUspFp7szvogGl2cAvHi4bMkmaAYsho+HdmWkzXZCqXxm8y3JePA6AsvE8SozJUa400TleOdNWymN+tdCdmMh6cAgZoSuldTwtTgBqybzLr7m+IUqaBXpHq7+ytnOrC2xOZ/IGcY63d81HhLHFFCcnwq9PfRNm8V8HXw0ZSffaspuYtpO6udtA7YxKv8l+uGc4baL+yzWwdkdPv02bEhkGiTRjgd4EYDP+1zPOznWed7MQkQf2KB3adQeGhnCIlJ5mAkkyr4AdKKvXm5h3vqB0Mr/Tvw1bcx9+OJ57lLbMtU5g41twPjYQ2KXAg4Dh4k1TaytgDTVEUx25gNyTal0jwo8/w0/HFcPI7uqNAabKqL0+VnIMshiDella9hJAvN7qkC37xh1PCHCNwd3syP5UCibl3ZS12T4UFitYFPVV04NMrj5Brgq0HTWb1o1lqHtS+6Vkygiid4sYFnMdeRL6zcn8j3m7+K3BnkOSqpHceohPwIhbIWLcQHKGTKA3PaqZvAh2/KJel2mx7UuLpDVSSAI4YUeLg/s+uFcumSCKkJxdas3t/bnpO0aIj4OW5J067wMUR37uSrRnxud2zyBWWcJ8JOYBa09NPxGZQ9LXmrK+peugKVXqAZTUHoCG1E1jiONe2uKecyj83R68WlYE9/RsjnZ46XWofs4WLOcWodi4NB9HMwFbtTHj/NwR+lkXhNHaCi3UlYWy8FmHlUydLXeRz19LrZ7uuij7+s5Vp19yedNVRBp0F2/1sfI1ZTnm8FRffSJNeEg5VAr05mRAm088DPwcqE+dgq01P3QmVG7BUkwVEatYyT7cS0f2eCjnhsIL9uOl4uiKgPSRa9RiCVI2UN/yvBbp/ClCtib64+G3sL56k+tf+7iYM09MCEFfu+B4ZbAlnEuZyNO0ri5T0p550nZcyUgGlyeAUg6spktPRrStKCESUPvZRpDKO7grDe0wwh8YLIZiBixmuNGYtZxOPcxYvxoxosYRj1V27Euj3RHwLjAp4bsEV7gkQAMT99agak8yFQJQo9H9oylY7tXgVdD3DCGQN98FbAUpxkd2Mlc+jEqXwn5NNA39peO38uYKqGFfdTD/zhwEQFS13QyQ82HEv+p6ZZ9ioIW8xetb+lc0EoylMmw3l8l1jhmRSisBgybjyRwZC3Bym6GhT/CrjIXn8sAmB+bTd8y9d1IMxuos6mc3QNCMExmKD3JYQ792Li/JZ/UUU8+N+R/dpp7FkUoCCHqAs8C10spm5zu/kQRHFIGMI20QfRBlZx9YFuYG6CWLywsgs2LnPeuJSdee3myYAobXVHBHbRWtZT8S5/f41ByeVNogMr2GYHdYMw6SLMudaIt6DWYUkbN/T+BDLgoZzM7tLF1+bEYauyL4UBsnGq7VjVtKG+jxCczHRtbxoEfbfubSUiYbJVA3/1DWL6VB/Qk5eu+AZjH9NEmyQNrUXWa96So39hmdA2pFVyeCQhY0xoOa8glY2lkIw0arW9Qaa43g0fn8oDYpH6zikFQB9gPTVLUpNCEFCXgNRO4xaPEls4F+uxvDGfQv8W5iQeoeAW3b+2EL0ZqHdVgCFZj6VszmckcPqAzH9C5Yh0OghOplYwEX3NpQKsSwBTqscOw9Zilqy8NGCq1dmymke/mJnxvwGDVc1qUWXU8Y3kmc+jHm+a6QN+Vm/VIJDdkuzWJGK3os/aaSsd+l+vXpPD2LT4kiMBP5UHcjF99ZkWsRk5ku98ypIDyqpX/CgdCiIlCiM1CCCmEaGVbfokQ4j9CiB/035ApKSnlPinl7WDjwkdxRsFhD2Gbl3TUYRpWBUH8G92sJSrDsiHg8erYXp2MesDKrb0Mqx+nQGVTiFkI4SzTqHf0Z+od/TnM7cO71naLM2tb6/dhr88UXfR+fb73QHW5xjpfuqeqyY3sdxDsGO7tCxF9nL9f389uxwh1HCFWIkT4Y1ck9iNC2FhnYbVf4vrccy7hdI6bpxNnQRd/+zhcPZamv5QgG8LQ1S8xlJdIWrWBdNYgRqgskKK/qgKCPNqRzCZze6FL+KS2HDFmqdwCS4Py+p2cY6rGgqIgBprdMgLMXgnOZb0SFpmWKQYd1mhr1GRe+ZUxMFg8HDVYaPsRH1qGEF7+oDk71fU0dPG0VOavUtchr0M7Zc+Sr87GoB8aFhpbSOBbzXUxai4N9divgyipTk/rR1VbreNoRtrox87tjIylcXU/4hqKxQggHyQ8wAv0bDUfgCnco89e05dbL7ZRR5wo+kVlO5Mu2OC3LoES8/3FMputJJvBpG9QOYi3GCuqmZ+zmEXu8QwA7qoyFVC/g3XHW0MNQ2Fum6qxfCL8rCUAT6rMqIgFoetbjCyqObMdJLB0o8MaMLc36nJf9dlWB5K+tZhCeKGBBzKg3bvOfQVDoBpLoz7T175kyeI+jO05jKl17uAZXb98R9pkvfY+fnMQp3VQew94HvBNDU8BJkspZwohBqKME7oACCGa6892LJXyxDQZozhFqFrHl13nBxGP1eZCTCsQJqAyTLl+m1jbimlIOcQMQnyznL72RoAp9iJ1jbsbHTbg8S4HWujt3O5Z9uNohRsp+wRsY1Bb7f2XX9k+u5SBm9uMt7V5EviTrjk0SFFJwN3AQNSyfqiyHYBMfS9doAPLPd9QbXcS4vo61DXmujXp6aLSzeyIb2ZmLQEO133BUTcqRwAjrGv9moABUv2bzxa3kySLoJuiSP9ZKr+qd97qAAygoawO6BpL8YZP3aQOQjON+abucEt7lYmtB8zzWssBbvMgX3eZlC1A3WUe0tdEW0ubKrPHIwss1e/Kv+bX8Vs2lrnYgdn3UxGIS/GrtwQcoj2+WUyjbSSBpWN785nH7dlnv6oZ7gcHh+gxeLihXgzi7bOc/XR6x83ThnPwlM8s9GQxO0mgwdKDCGDSwHtDbgPw8Uvas+o+L/zB3b8qWO3kAvqbBf6BZuiMANEIGLfTCA9eHUiGpwL75RV6NPsqeDuTggKssmVGkyggMU3VXSamVZw2CtBWC/K4abncir8SoQGrBtJpHmJYl3xkFg6k0YztPscwHg4KSJTN/OopLcpqNr0uyLAdT7XLZA5HqU5/bQK5wMxiJpv7std7Gu9HyaNh+36CCs5FPlatUoad5uqEScHtYnwfgW1hAOQNYXfjhCDf9f8tr7m5o7sQUAiEZVFyIXQml400ZwXXAlCOUVzxGwwuTyOklJ8DCNvTjRCiIUray8htvQ38UwhxgZTyFynlJqDrqe5rFJWEY4XIj3wS0XZBmLtRD/25mmK3EHXvsjsyZOBXvqAe7qfhhhO2HLGViRpiM0btZaTKqxXuQ0WowP/yULf7z+yrrudibrJdh/n6+vrZwWyETz1w7Tfmkn03XqjqZW2tLirdzI4Ben9vB/MaNQSBZrOwrBcHa6pAYzWdQOL0E22QQsNfVMZyJ00t+5FPbPf/PS9j/vtrT0oxAPX7+BnAY04S2GEF+FjB6hPAAWDDy2BO9NprYEP7WTqPEYBgkRv8+wvPNzMyey0zcAzj0TMQ3TXQhHmg5a6YWAgXpYScUIri7ECUFhtFpcDXRsR3hisgDSmQPLjbDOIJIkcTYoPhGj7yywSONgnGIwNs5YS9rcjHYeth0Fd9j2GnuwZqEwq+7SfwMBN4OKJ9VAZET0xlxbC3ifenSZ1OqXJDKr+iOJH62TMVUsCxmCqV/gIaCCHybK9wRQqaANuklOUA+u92vTwohBCTgVQhxBQhRLNQ7aM4PRDxKmgUC4NTPUP9rwoxzTWgDPf+Eu69SNQhLAsPcQGufox+lhHC67f8hGi71xPUKiQQglmDnAgM6vAJ7SNM65BAdFPxkkGDtVGxT+G442uhIvpUbCyMdLwMlwob9v4q4bsMuO+zuObyJI6bZzSimcvTiCxmMW99rrUgRdFiaS8ZimKm9B0/i/liI8VPAVlqpqzvu7NIv3eNynrda82eGbYjUs8wZqOqr2fhbkpktK975K6g/eyVoFSODYGfTObQK0F99q0BLE+oaQaWmczhuq9q87HIc7T5UmhKbFs9Q+jibWmgOD+Vh9Im8NyQ4RSTSmJ+AawWKuna3ppGswK/IY6M3Zv0Y5NWlW0T4BhGwNnHlm+cwV8oItXVx9OeKQS4ns+QEls46C+g04StbPV55jVqEYzZvQzN4fqG1mYbX1GlVS4ZNcNeJUbrtM/gLxTnp/JUmgos08ljXpVsGhzfxtTjd5kU4l1VGmPX/pFpQBmacuwP0RNFoTLEHUiFw15EDQ9Xl33E50Lzsn1UFU1GlBbLNjKJ4mbULOXH7sczcOerz7ODRvQCFnKzWQt557vG78pSdw2UeTB8K6f6KMSGAzsdNgmtXoy6XsO5iadz1DE39Eki9ZdiV1pzFEGxS0qZHrpZ5UFKeR/R9PKZjTiX7M4eIE6/v/sYFFS1qK9VUerjRgypbSPUg6kv3d+qMbdninwpslYWSz/c/lG306QgGUzSoB4OmxL5jW0/DZz3KV9FTyn7hP1ArYRRvIgVTrpsUKSCybjVQ3j5saqK8tpGfW7XcyVrnuyo2FHcBiP1DTzuJvW3qwfGQrXdSRytvxPYCcwmdv8D1CiCsjpxiD7N0KQb9b3FeZClUL3USPEqsaYEttiosB4O1gQw6lTPU8Htojc4oMdG+QuIPk2t82kNZHjA47WyrouOIWWKqa+wpE8f+B/q95KhmohGKOVbG4Rd5PeTXP1Gz4K3vQfmqecOIXKRMsPv0lYYGSrL7giCDxcqFd4K0lEN2CnV8jv/wNIU7DmJtirGOUiJkxaclQLz9OcJKPpx3Hlwh/4eJ569geW5jGhweZqR0KqYnf9NVGpk3ZzrTHqkDxXBX3Lm5MI3wLFjqw7csEoqqPLzITMgXcaNxPyc4bqtIbfuFlzK24HbjQF4OM/qtklh99offVwJsU7soBFVKWcjl5nLVtPe1XYlEOzBrUwDkX+9+d4OYRRvYFxHVa83lMAwr7fLMkOUZ1eVxnosfNbRzggqQ6GiNhqJsh/FF6TCkjBlzo3jmQ93/g9cRlB4quFbXxkIT+d7eLSPl8G8oRZsgQUXGE8HZ4gBWiVBCkF51ZMxZByt6IZbgcZCiBgpZbkQIgZopJdHcbbjCfVH9tKUyOaonHSuXp84Xf29ZYgq4U8B7KVsP2NTNrV5XlEB+usoGz0S0CXWfjBosSLEcBGsxtzts1+QW0HIj1yWDVX7Pfh3veAJD/zeC6O70u7elayZ46FkNDCqKYz1UuunPwDQKHY7e7UvSDUplIVJsGO70T2HQ8OevllRy5bEjobyNhZjBZR2unMy37JJqLIVuRCEeNFct0Q0ZrD8msE5U3iDu9U1/MyDOYd7n4uqeZxH1Y2+nKs+z6mjqmJWo3w3dRlEZcDvt9gcGOZRNNFAbcLZT7C2J2Fokv66S5FhQgrc6oVbPGbNruwFPHMW11tyRo6bpwRnfm41ChNuVElDVTQYginCVjbclN0M9MK/+C1Y1tKv7TcuyyJQLwumEuu333z3DF6grJ6jjaFiKJztt5DAFty9XJL5lmS+NT8nUUAmczSxdULQ4z3MBMdDR6BsaxIFQSmrDY5vM19nKiJRcA1EPYui4iiPian0V0UhpSxBTcvdohfdAnwtpdujexRnKxyZpMrcrw8VsnL2GZ4yrFuQWFllAEIU6pFGBW1ivFu9pL399BM+ZqB+OD6PB/F05AGykx7q/5353uMdFNPQLjaO45xq+qU//dVfGOhEfqNu9Nqg7SNQjnUe5+SowooBuk72LMeZNG6eKkQzl6cJ4hH49zNvMK9gIHwKVEepmI3zr5jeg+EJoSKVQL6F0mcAMeiw9sDSeL+VZHqPV4qx+55Ss16GumwwISAInskESE9YwwL6KysTDlGeUNMZWA5TM1EqEO0EbwHpQLoH6SP840tBLc5PRZwn/bJrgcRrBpW+A4CIl36B5bc649fHdn02iffUmzUqc/lhfC9GlY5kNMZNfxyzGKj22UfRMk0qy4Iik7IlxDgIUKOZxSzmrRyI/BySOm4giQLzH7Efb3KU6gQm8QZGuyqW2oV9wmH+8mwSw6xLMWDYjEhbGtUQ+BEZekE6UE95gH0+6HrkDOAXED19ZuD1BIK41agf0vScIEI7RoC81IfGagSXSnDHXbjACkAjEzYIB0WkmgwpAxnkMlf/T3jqPk2bUkWLzY5/PSAlPYrIIIR4AaWOdSGwTAixW0p5GUrW5U0hxGgUaTKC6aoozljckgIPzoWq/dTnRVpwrV4/aAcsmwvUhrh+ShV2CfBfYBOWoM9wzLHGF771dWbWZ491jwKbMqsxJNqGRjFavaTLcCn95/fUg73hJ4n1oB9RximIj2G49iNG0LnTlgUE4BoPbACuA/7rYY2Rfe2NlS2+yUP5sT0cHhhHo4V5/CAWwGMeJawU54HroKyO7foOQamtDnQe6mj9Z2G+B/oWmvI4sw3CQROP8sdEZ60FtEONtUtsJSN2bLrgMhh3GYzUwVPm/bAX9GZM73U3AG8s1P0sxxL/GeZRQlHG52NedSd5Wbc9AOxGCZ3WQ9Gue2QAmB6X8rh/n6xJb/XsELboz8TVqu0zKZU2+eFLuwZMAR/5EoomnFsph3KFn9KxTdBIDAC8KfA06nq/Xaik2UDdB6I4KxENLk8jNtIc6n6syg7CoOH5Ko1C4EDTDjcqpYHeskXIYDESfPmILvjwEftfdIX2jfgK5DO2DOd6oC1+QaUrfu8/GIc067VhMX1CtnlHruYR/g8ooDje/3qroDbPbzkAmTrQFOMcfTMC5Kbs1A39M80b57SiX//p5ue2rA37e3mW4SyS37IJOEYMoxnLYp8fVPHyVGQXEEEoqwZtVjwNnIRsgdsAFynCUnE1jncK8lcPpU1gKJNYqGnTxxqq5bfWee3kH/wUQyJsarin+NhSPgA84LK8AIhMqjGKswcZ+u8y/deYP4vr56yV7IP1cHwAp6psUMw+kd75wVCGDYh13oAU1xNWqtXXQwgoideBYynWtXOxJlHHHay8RJdNg081ffgtuGThNxygtsNGBODwYGXzlPtAd6A5PKXrAu0ew0ttQcHPwFX6WF2AR23naQ8yBKbNifShFS8R69T1cRnv//LLq7x1gfarGudBjrBEfhRNub2zjnGPFzUD0R2GqVuHXA7iPN2vFN3ngcBMoDYmdRY8irZpPFJ94N+fE4FvABpQVTbs/flTqwMiAz8LkvCPE4EqrC8+AN4uRUXwwMP6nD9DBZoAs8/eIPN0jpunE1Fa7BkMX8prFrNMmsMk7jNf4cLww4wE7VnhoLrG7DxEzM5Dpn5qIBj2JQDi9TD6JvyPZacAP+lT5BIpheVbTTy1fzYwmpF0Y6ErndQXs06g4jWJgrBotW7w7V9F1GSDQSxXL7/lsfoVpgLsiaovVoSaNEv/p4QLsVhlN9NZ6UeVDlcltrKv/zmOukKIV4QQN53ujkRx9kDEg3iFoKbzjrbB1GZPotJlpKioMqhYoV6nA/ZxLZACfNDtw7jn26m+Qff1ivXel60USFk2VI1syGPqJ2khVuvXi/5txIuuy0H9hsP5HZ9sBKPEBipBCkWJDfbd+sxbRBEZzuhxM5q5PE2Qz8AW3lOWSVXhi+Q2gDnB50CcKekZuLYykzlAaMrqTx2SkVrU0972SYZRA3iciY5l2IRs1GfnjWK4/jwBD91YyA3PwFJ6EaOTdJkJ7zGfbLgHqtx4iJidcHxiTUCP5Nepa2G/aRk3o0TZj+JBOns4A/hLIIpTYHqRiDemOt2zt3Pp5/h8Mwu5lGTm0o/vStWdtgXfu26rspRehIDpZoX8XKQcqYfBFwBYgcpaGsHI/PxsqDuQ5FbrqUo5bVjLkV4wo2wwN8e+Q7UghdpN2GiqwtpxhOrM0IzALTThJhZa3p22AdWcNTcCSa1+yL9tOzO+4gCiFX0XW7/DeWTbJkCyHeuF2GgqHBsU2HCFKT4UX1d4Jr+dmVm2spyhVGJ9qbR2+q0RbLqJ/CygN4N4iyZlPwFQdQPsaV+DezGmgH87tFiJ4NjJmYHdJ6UM134kinMAcjaID/pp+itQtR/SVsoYu38Ph5PirAU6EcUHWBnMPYXKt/ceZ9ZDxANxKTrT58umUOOElKEZQXKMCkoM5VjjtizHKLqs8d5sH0SwByKvRQzYL59sl+HlaARm7TVNU4zX2c2JwJ5ZEKezltdOh8zB/NBJ+6nEeVQGb4reYQzwnn7/WopaB/AYigYL3HDDQpbSywqY9Nxf7Z92cbDmZKU0C9Rfuo3dH9iE5nKtt8b1aChvc5xPe1bwZbx+fpgJ8FeTpZLFLOa/kg1roUdpjt7PYX0eNVS1yZsepVyei1Ii1eqy9t+XEKvhxQ8wGT7ve+DGQvXE/BGmX6eUHjOw9MeLqNQxgPvMhi+dWcT7qBf7PNsYNZmhKLbhUK7tWUr5UojAshJsSwL2ZY+hFJsCwwvh2UIYlqIyyJvO3oylgXN13IwGl6cBSRSQxTwmbi2BHOAYXDVyrV+9ZR7tzPeiA/RdBX2lGvyGBlDiDHRTcRZ1B/5Hf5JhjgDzJhZymFhGsowagDfBo9erUdMILlVwq2pDxRUANZFfwW0cYPDtSqXNCDjtkJo+qyikasAQKKuSIlIRb3lJlP2YxhBV04eeJV3jiUjV9DIfQSPjszH2G1YmWRzAC/RjLvPIItm23UCh6j2yg1BxRTzIfgJebuhYvoUE0plEetoa5q1XBSj55WncHqPSutmxM/32Zccmmvsty2Y29iKgLdrqxKO/k9m2TOdSepmzy27XTeoA1J69lGOs3Rs1mOCkYqv22eb2jPIwPx9o5zOrvv0IslF1a8F/A9cOBYOz5lIhm3mONkYgGSgoVJ/V9oYabYkWWjLaBvLFMDziZB816VG7bBfP8jCZsUpvf3X79tTmANlb56uGIR0Xzy6UR4eMKE4BxABgOkA/6KuX9QGzMqQgTlFmh+vPuajP3YHz9bKfUuAz/eDaH3i5UE1iulmcfOb1UyeFitVF+m4f6bYVVQZ1syDxtVYJibX67zFUfeHntnV3o6iiF6MsI37W+16ur9NFHvgb3DDCGndkjg60xxa6X3eNHrI1H5d2BfZzJN6pwtOOPJaIddQ6dB+XxrrLnIqXQN4L8+vosamqotLqvau+lIK4HaVIiqESEw+LXkTK+xHCLhZXFfVjKgRK4cZjcEtH9TvbUQiddbA3xqfeUmia7fEImfq5kTUPFxH9/jJCrG8JNAZxvVN1OBglVmU0r0TKHn7Wa4C2HUlRAeZ/9bK4FHgNNfGtA08hZuttz07l2HNx3Dz3zvhMwiHgBpAt1UdfxoFbjaU94LTDyEIKKjb7OUrUZqw84Fz2iAoyRzyjpmHtQaeBCTrIzWQOsZSxgP5+52GgPKGmevNMgAYavjeQYjE3+AxcJdxwDEGgLGbhwWteZ/t30FMawkqB6xgG679NXyhRQk03AIdgy9CGzGs8EAaD/Ds0jS/k2/LLuNRmVzGGcWYW0LeWdiOXOQSXfCnJqq52LlnMIh3lgdqNHwKfcD3d/zKXdf+2gk3zPIP5o9jbGbGu7bqI7Uf8G47Vf4OI+pwK+AaiRqmqPXgNx5Zkpw5Oh+yfTtV9WA9pv7HgMoooTjXkr/6UQZmj6Y+GeM/FenlP4E4bxTUzBX7W7V/Wy/YYRpgqzea8h4fOWDr6EaCSQo6JPBN5sh+cS3SmcuEInbE0ajBHgHHeLfgGHkjjB+FV9Y+tUOI6DwG1dPskMObzpL3kZUchjIQPJ2q2k09AW+unXRy8uIEK4jWRb1eVxogbC1kyR33B0kEkUgHgYlIQcX04eDF8uUdNPEsdSYsB2HyXUZnJ9fr9PR6MygVpmzR1wj3qtmcGhUE+OR/lk8lsZX3jdQnwXYR9QiFY4O+f7Q4vaK2IvkGwekuDmCWuj2iXoY9p1AnHp+j6yjrW/3Qb4N96QmJP5R43ipOPaHB5FkAFHNkuyxRCifoYlFk77DWMo0TtiPrzjNbVeETTPj/iGiL1IzdqMs2AU8O4kTsGLSCHnmH5VNrxrQ8V1jd7GQ6SKOBupgJwl2258bAjuwmlrpeHlhWJQGHIB7JUQLxLBnO9DtdbjQKUoqwvyfcBM2JvBMCrDCKQI6pYjhlcmsuMuPnfVAhJFFBEqiM4Nge3bcODbGnrE/5B7elAMBqsHYrm3CBom0NHYqhZvbxyOnaaca4KE0RxehFJLVpl1K25ZSuNDJBBgZWjNZvjoPW5MhGoD77LAm+vM7ZBsoWOtv9Q7S4JcxLRsf29kW9jbusjWCvGo2i6NjKKEO5Z5YiPJVbDbe7BmZ+9iVDZzMpGuN9hMPprOOy0yurHyUYkv9OzFefquBkNLk8DikjlExbDEdjVsRYNlqgRKqlHgSNTZtTopQP8x8t8AYnSPXNpwG0w6i1b0Fu24D2hagd9M5QGfDOTRsaynU0dNZM5GLpA9oDvXiZzPZ+pFRnWPqaLnVT5eRjTJ+v+BXc5sfXdoMrCfLnattwLazyO4xvekZYaq6K5OqspFTXV3saOh5lAbQ5wKTA2fxz/SLuPF3AOLlO5i++4lBlbBYGCyJ/2CLxTwNMaaASr74bLa0KtR0tUVmwqiOnwbWkKdVrBmEPjjQ4jb4B5vyjK7MYLhlONowxlkjnObqQ5R6lONY6wkebsJY5uqNoWA0UkcZcOhu1ZUehl0mHFcpRM+x9VttyRvZwIcrEVbNY/qDwvTSVZn1oi2cX6ndonPOwDnYMOaywLI2OZzkrWiGW0k10BlU2MSC3WJzisXbbLfH8g1hkU1sow1gUPFqXPw2v9WLVdj3w1oz4zLYvr6izjws37ONQ+qpcWRRQVwp+AXViTXQv2q79xijIpDC0tI8thWF1k+HzOte/0U/13G8R5/OrbIoEQXvhH8Adzv4d/XcIov3G+d+wT3zH819AKtGFCBlCLteOHH1vDewLm6w7+B2UVlgF4vMp2xKifL9R9vsejXi/re/4en3Mfq5YfHOiBPV5qHbqPg7kNFM0WVSMqnoaHHp3Ac+NtE5FxtqzVHi9M8NDjUVWaYgYlr+k2dxQi7oOGsjoljzRVCq8AL+v60k46SNsLZHpUJjsewKN+Q28Xwhd6bLnqRb0Nii4NwADVn2XABx74CZURD5DtizR4q0iwJ8Qs0BPmwSxqKhN2Oqxff1x+03YKrPE+8LmmwJtYCrw7sOoxK2FyIYpTi2hweZpQj72wBRrk6MDSuyFo+74y2SdD6U+ZDQQjqDTgG0Q+HmGy7QjVHIGLGVRqyBC0V/DPWJrbvu6vSNb3dRXASoOe0s7rCDgDYS79TLqrEYCGixe4nwX0YQGZlBFr7mcQr6q+CKFozdOAhvDNLXD5zdA4BzyPQuE6SNkN7a+Fwk8hpSlsWAjJdeG8FMjfrp+DDMboNBCfAtv1539ZfUlqtYFxPA5ANY4wl1sZyWjybJWBL/AIH3ENs48PYFwV9RSRaaouOGHWV9qXlRGWGqwbjlKdDiiVKOM3atCzwx3czoSMpYFwaLAAu8tUIHogtoHpfdmV28h+bT7f3HEJO1F1t5XMJDptOFdnYKM4Teh+RD34/606tQ4pkbMDsThVX/WDqCzVy5ujvC4/UFRYKiB3Ecy+wchQCg/w98jrKSPpgxC/+i0Lf3sVdIXjfSlliqKXpgLNxyjfx3nAf7ymT6gcDSLPo4L9Gj47eFlndV/GFTfItnzYx0WOdYrzXjubATQcsYWSkW+YQnDcAeAx5/ysOkp3v8skiij5vqnyDe8B3NReBZT4ZwFlqabVYl0v9eF+f+XglimKHpyBugbX6knqlyJ7rgh7PAxCf1W/jcDijhWt2T1tMLOWhfCQei9L9QTCBL3u6dPTtcrAuTpuRqfWTzEe4Jmw5LSDwdeixI4hTGYIk8O2HRHCSyZzyGQOtzHFfO8Gt+V26xBQYj7iEfUKtJ9Q/XF7D1TYxsMXOfTkTfrxpl9uMwQ2B6omPbXwva7Bfg92iHz9irXRYE8yfCXzxQXqVVHULtvlyEBWxLokXBh2JaEQypKksn63UURxriNQsOS2PFx7ByEKIzKrd9zPJgVpWMk4Wfc68WF4xw62zLcWz7B+CRXciusjr02tKES8dQ39xiXhtWy3TvLYGNSSJAgtOdzv3+38HMvvjZzKbNiQCJGLELmRbezYT2DLEke7HyteXhTFmYFo5vI0oe2cDdASdt2kquTdxHuKSCWJAvJoRxGpTOI+VhFm1AjwB49pO+LrExkOxtmUSJ1WJ0o8yB5Yikfc99FbtuC9R2pSZdghvc0abtIqpr4ZVCtj6eVK2ZkvhbIjgQKK8/X10ZTYO48rumafKrhSXY1Mo4FAdFgwHv6HMzMti5E8SWJagT7nHJqwVTW6XMC/YMZDd8JKlA5AQ6Au4IHLr4Jt70LjR2HOD73pv+g9aArP/15NnT/Lw0poZ+ZA+g2cztzhg5VYwgLdiR+BZsBKOPw/SKz3E+fdDfN6DoRPocFIZXVxTRWVJR4vYoCvAUtB+Ho+I6PKLGV1Ajy316IYbe/yJnCr47yNbKXoqaiwcrFznbo2WiZeZ+bcaM3baUaWzlwaSNTKDMVirssV94dhwG2ny+bREaQ7DbYiKoy+VFg3GIGrva2hKOtrZ3IgtoEZWBp/M1nHt3c0p4gkWrOO3+Vv57eEkzQDW1cI8QqwSEq56GQcIIqzDP21HQHAATi4rIGllgaY4tmboMZ0my3JJr18GPjrz20z3xmZTocNhI2u5/YQ73e/0bRYuy+uG+vBjS5Ib71OJ/TkQuO43yDEN1gDAxBAGd6AMVm9E2cBY0RKsYVoQZXGsAb4T746vwzgSd1mkb4mj+n+rINW+oTXG8Hlv/S6wdauPxS94HmUIE53DwdrepW1x7O24w/3kvWo4rK+NM6jNAwWeNV3mOXh6mcUH/Pz4TrV2CRFucboxxopUxBXwJdXpDD4K+WZMv3mu9V+dgSp2Xzbi6tNiEGHNbbbsBKWdCRxv7rPF0nn85rx7CLNmGgAkUBKjxlYhvreAtFKw7X5cuxLB5nBxHzc+2Cdq9ApaynvCXPrK4Ev1Vt7reW/UlSW2aDFpqfARZH160zFuZi5jAaXpxiN2K488FoBmyHv94ra2D34Zn4IJOIzTQvrTFvlutofj3lAB45vGEUQLnDzz/zyCs0d+cpaJvX79qxgO4348hHVpjyhpl+W0x0e6watFeGu4SOapG3l87+qEUy+CtDYdetw0IwiANo6ahIVxvE4A/NVlaN8Xyi1Tz07/WtbOO86oCnKn7QNTHj5IR77cRI7fhfH/2MSs+Nvp2/pLObdlMX8HwfwEE/znFBBXtHSlmQPfI2qlDNowiu0YS2zB6pB6DbeYCG9yPlnJjXzjqvBvhBV73IEdo27GFqBeFXy58Vv8meZxFxuRQgv88VGc2CZRzbZaa+zkg4UL3cOgH9Oe5PVtIcy98kMN0Ri9+KKj30GwF9ObHcHYhs46zlPIr0nz7QryQvR0kILvmc39em19UMmN7mD/DTDIMxdQv9swrnq1xXFacAt2o7gC6h1sZrwOUgDq6ZyU4DtctUfmYOy+bIhcF2appCGS0zRNhSGgrY9uAwXcowOOpMi2OZk3eumA5moYH5WinJp+mQD/DtNUWWHABP0OV+iNhGzgVke1puBzhWIeI8KKu9QL6M8RgjgQX0MO5YVmuclxsNLxn19HJAOMscK8j+/+XrkuyDQGeatXtjqQ2E2n0PuNk+LjlBt6sMc/Sdawdtj+lM7EW9NqNrX7TEC044wsZDiQ3rcDBGM+XqNOte5CwVJ/ZwhpgfaLrzg8WTUX0pp/H9kVHgfaj89nOO3qRaLNSGRCzTX8rAvxkNecBubMx0ncdw8oxGlxZ4l8KU9BqJBBqO12iE6hD/DFbPzkKnuahyjm81DUflaRo5A2c5P6MAnQTK02bwecJ2Bb0n2U4sN1O5shZ3WGw4tNhTE0+oF0OD4Nhoc3+bXxk7BDZeO6zjGzVaWsrIQLn01iiiiODfQlfcBd2qmHyWyKQ7V0kDjohvVsHrp/rD7FIiuGO52wfqhllv03lClN2KhekUK8dcAy13mjcULhEU3FuPdlyfzbehtDfptH2jNV+r9lmNh1ZqeCNzOFzDprnbqazi0a/M80pzLgm4jVpuqsicLFh32ZO3/5JW1RHF6Ec1cnmKUEavEfGoCDZXoDFiZSzs90FKLXQNgqpcanwN5IgJ4Gc5hXXk/AY+Dgqo8EjW/8SkvZePbBuyvCvLcJcJmfpXFDAbx4RW9IBfGfjWMJ1F019V00jeNT2Cqh0zm0AgjA9rJNbA0MpafGNfCiCHvWEWVn4cx4FUrqDSsL9yUYtUyp71KT3JYvFG5cbdNdvL5+6bNYh2tGcNo8o+05eBlMdS87bj673gbuFa1K1oHKWth5CZlCTK+zxiu5iOu/t0ybtcBr/xIsFGbru38XUMeZQKPygmk8TUiX0I+/JosaFankE/ozNb1akYuq9VMlv7yJ4wkWdbgmdzFFK6//3N1/JnAIZAD1P5X6uBb/vgEWc2c9iWz85XykSGSk0QB75WqaeOj07VBtZ51lxWYdQcQy6FvEBEeg9Kd2KUgvCzpPGOAqTwxAiPgDEdhNhhl1pcOa/xfGudlULZlGoj8N9iQlgTAfZtf49tmzfmtQAkTRIeMKE4B3p4Lt/SDJ+DoP6txtGsdleVLxfQupD3wNBYlFmA6XNfLCiyDwcyaNHVZ53KPMemIy63P1XY/TLWr9nMkXqvY6turtMWcBh3W7wF9nRfW+QoIaQ4tlzuO6QajNGAnTbXAy5Vq+Qpo2Ansem6BqLOAevi4tRC66uzQp8CabDX2Xaj3c6MXhujM2QzV7KJBm9kR44H+uo/vAZ/rfRqemADo7F8uKvF1v0dRHzclKwEhfc9vLpV11kacgaV8F3rxrtJEj9MPSA95aDh6CyXBvmM9phytra/vAvVZDPXokzY2VjxMYWQjv9WU3T/ozz+jaNj9t8H5KRal2UZfVZk99zrKiJEEmlzlCiuDqZVwT4DJEwkdVspAa0oiO6ZbH/d44U39u+gODItXXqjX6qA8vFLNMxLn6rh57p3xacYgZrCLBhxJgOoz4Y0P74FRwYuX55EddoZoAf0RHaDNqrVB241YNdqsqbRnId2wLOF6Ottq6t67QlFkl37VS4W1mo7ypM92UnqU0bULginKdsaf09skYSsr6UACJaYKZzDU5kDQOksDl7HRtPqYRRY1Zx6H0fDrITivJ/B3KNVs4ZS31N/x+TowX+Dl83wPiWkFxHLY3GeKUHWa6ma8VxexFyBlMwCO7Yet61PIajWTzFZvm9ttuaChUrgDVtOecmJ46cX7GMBsbm69CJ6GXRNqkfzfn0j+4h24QbUN5XNaRKo1jgYRDJCPWu8N6xFf5NHOpNvOX55tBrBufXALKgNZkJwIZScSe5LKRhGpTtXdNLiGV1nY5E/ErTnM980Uj+yy09O9KKI4O/Gvfqr+6iE4OqSORR8t0JRXDZEE/OEYtKkKE4Cf4eP4G60GGWEca2uYytY3+a8/Wv/Z8O9d/3DSacOB274D1YTyRYqpPl4i3gi4PYC0i7c+hKIhDwGu0/v9m4c/PKOmef/zRGcgEaY57VdiKOfifhv5aZ9HfS93ghgLdIZqA3V0PTCBo/08YFiRXeKSqUwF+nq5aYRhWXUZG7ksaGDdY3SOTTkWaONh/Zgr9IRBVcQeL3+RyvN5BkZdYEPI1HWB97cHQ902JUUFkL64YyeJUtEzi0UMcFip6B5z75Od7up8Hz6lU2rxN+vcA/+2pGx/0rN+gQPKSkKcR01EbPWq3yGoQPM1PRkxIUVd87/Zac9RnA2I0mLPAHTlfZPGY8CuMCnC8KcSL9lm3oIgGGW2GwsDBprB1gU6hp1KGwgjGa0zqf7HC4X95f7+ieHCVy12NCMrvK9AEGKc37JILVHCwasMYhL3kUSBq3JpoOW+EKMxfSzFcvUy100KMYv+IabqoBgR/Dd7F887jmUsM4RzTgUq43hu1zXQdf6tUH/Kian0VxRRRAIRH/oec7YiHJpgZdxLKkqLPRVIZyU9bQWzkZxvAlv8KMHBFFhPBQLRYgMvd1d5DbXMbbnrtveeHDqqm4psuP0Ma/8nmep8MnEujpvRzOUphFgOb3dJZzuNuGrxWgpGJTrXvwR0Bq5Sn43MjwDmj8gmcbx6cJ1HtnNg9ZkJVAqxEwL24zsudQj0fIcSHTG8KzOZY64fxAzHuicZBuf5yfABEMthPuMa1edHoMowqPKnQ9rT0l8QSAgYYZsZs9du3vAV0EZ/WOOhWAfbxVVTIQ/23aoCy6bsNOmz8nb0oPQyi3Fq0C+mDyRLxCDoMSPHDCtz6EmfRUvgphFkz5wPi4G/wXlfw863IWZ2LS54/4Bq/BcjWFR/1QzyEooB4nqQWFpAQbtENsgkWuriFNEHyOyhRATyoTgNHqnzFEmtNnMNKxzqv4/wfwB8hhIyek98z59lEptoTvLv13Pv7JfoyjK+/31tOm5fA6thW//6/l9EO3XTzvLzRg0Mu6S+iMUyL/dBsZgLPWyzqV2gH/5KtADigsgFfIJRfMIxmnZTe40UhkBHIL/LIlJ5nWzGoOjRhrqw4T27kD8Rt/4we9rVoD1fcrFwF3A423Cu+nVFcRrwE9AVWKuzkkCtn7Swz8XW//bV/VTJxpqidA4PttFjjeHvA/zhoxwatvegTzDmd3/qCVxtlRoITXAxvDF9M5YmvdFNTTac/jjotNZ9XsQDcR6H2qgrHdbAnkJolQIPQYZsT+5iVaTzn286A9Bw4RZKRLHynvwPii76dy8/3QpXyw4k3bmZz8UquNMDbx6BxhM4as+4tYEqddVEsxD62eFfHlgvoIkH+nqhq4fnrod2H1m18z2k8rJcItaxSHwH4zza9xKWdOoD1/RBrtBjrEdTb40Jy9s8vCW8kOlRWe+H74F0IFtTV1ver2iXAAsUvVTeq7wlxQzgGmB+AsVNE6i2dj9MraMosXeDOaT+U19vTU+1ZxIjYeIEUj4P3xPTancqJzENX2z1HJRbsZ3s8WJmZ5/Qy7Z64OEj8Kfq0Bb4L+7/x2cJTuK4eUarrEeDy1OMF3iAV/kru7Jq0YDdACxD0XgMet0NacYoprgrfx+vCKdFNCOPdpoi6xM0GIWKAfyLjJtOb9kCsKxJ7LWYS+nll9ncagreJFt2JjZvZ6P9pXxHfeAaPuPvjygOjAoqA/RH11wqSw0Y50O/+PCKXpDn5SI5CIAd+YpSmnh5AcWDU6n7zyMmhSQU3qQfncnlALVRFR2q1nUxfchB3SHH3DZeDT7xKIePv8GedxIppyr/l/OAua+eqCeHu3mZ25hCsb6xJ8YrCm7L/CKeSnuYHrIlU7iHxD4qI7qSdCCPxPyd0A5kiaDfBdMBaM+XrNb1MnuJozXr6Mn7vCgfYA0wl1tRerPqum6jAXt61CBu7GEar97NX89/i9t+/zKdyWUYE7lR/tusy02igIPHVf2p/f4vu2AK9pgU2HqYhtP8Uf/VCnpyKDBUPwzZgq4kCmgP/PmGN3kn9lZMQTlNYRHCaxpiG5TYqTzIVB9Lk6k8GJmEeogAEPzpsnZ/zAOxD/o2jwhjGEVxTiokWcGleAAgldkvDOA+XiNux2GKLkpigGxyQseKIopzDm/oV5Oq6n6Sq+1Ipuv1U7SdyCvaA+Ni1aZ5qarV29TnMjWhpx9K3R7ghSh0UBYjDQwM8TN7OUHQ9uJXvf/zAqx3z/AYqMhkWyA4aLEADYDDkDu3OxjuUVoIouSnpkohdzfWuKDxucjAmN0WfYD06lTb/TCwn6Mf1IFsLwzzcPzCic6g/k+2jN0wD0z0AgNYk9NRBZsGkj2Q3Af+gmktI0v188MaTbFdUAgMgD2FlDyQAi9qqfU4D9QHDqAovQBe3Yc5YD7vz9FBpZFFHJKi2sd54CAczaijJjsuRAWqPdyvqV1cx/HdCHdarJQp7mNeGJMfoexygv025EvAS6F/M+J3uv2PLutELvTIsB03w7+RrZ++/39mP5sCB1Hn/ATwKNQYtYd6dfay4/5myr7mA8xJhSgcOKNV1qPB5SnET10asIzrSDqymZorj/Nwl6cAy+5JmoGh753fH33Hz3JmpR6reL+22hRTywjsIPw4E10prAZ2U59xjGFokHpKO6r8fAgYZgahdk69ogV7MFwCk9IsuqFvUClvt977Zix9sX5GMpfpABOg25Glyu5jDbACJbQEiFqS1/T1feQXW6H+BUYUrGbM+xj0nT1exrEO0iCbeUyhgE58iswxAksrCCkmldEXjOCd/Fv5R5qyjlmNGuCMwu9uLGWT0NWg0lmx15hdiOWwY1Q9Lpy6D5rCG/H38G0TJR6jMt6pJkWzVpUDfrYkvnDMqo9x0rL94FNcu5r2FJHKO/aFVwBLKjaLeiJS6ieSsTSPEyRgtSOxj/WbLH7Aur73MQ0Ovcabrf7MrTve4YaLluo1t3M2Q8LJklQ/o2dgozgNSAEehIz2KjrM7dNdWRXcDaSCNNg6hpVvhvqzqY/zXulmOyFLbYFEJcJXHE0GHiqd7QIJ/gRqH8KSIixfSzuyUmAP6hmiv9eqLf23kbXUhZytU+Dvs8AcP63nhobyNkr6AHlwJL6OsioBmOWB7F9VAHkTMFbt+w8f6XrOhZ3p0SsHnmnNkg9TzPMTVwAX6PNZbGWBXTEsRVmYNgBe9DqDs93OptIox8jCz7NZ2MlVtT2qtnKg7TSNcW8gKiB6w02ptbtfLaQ8HrjrrsFfmN/fybThCgtLcgMGlRGhFsgtOuM+vJDDF6Vw+NZd1Dq0i4OeBkoo6rkTP8zpwkkcN89oRIPLMxxZzIrECgvwV7MMB2ZWUlMyfWFlKC2oQPNS1/b27bajCuu/FJ9YK4bZqBw6iymfgUZs1kubWevzIbGCfov2ukoD39CCy/kepgu4JbKy457k0I+5KPUDdwixiEQ9Iz6IV7kroiP4I4tZzBcbuVhmc5+hjOBwoHbCsmoJbOcSCGISsAxzIDXqKOUNEe/Kff/GA4OmywZSdPWdebW3U+/DF/GxZywj3cYIVsPJlEZRYZzRM7BRRBEMwjYX7EudPaX9EF4aytsARYMVWmzNULd1tF1IQGGaE+5HGpiPExXZ/voQ6/ug1Fsrsm/hhVFOtVUp21dsZ2Edz5jEMObMDuhjuqgRx6Npori2sQLW7np9+wDrA/TFh9UWiVJs0P2KJbo/PSqcTT+b6ymjcEc0uDyF+Cuv0Igd3Lr/HWgKz258DHxsMQwIHRf0XWXRYQG68jEAr710n0mjlfcS0uze/5/dmqaL2XkIr9aZWWrLmrZHmTmttgWc468Yw4ivrCnZe3Ww8zhGPaJaZyjRBuzPM2CkCY3zEFfARTp7OT+tJ+msMQXUi/NTldXDI6p9MLVZ1QtFRzXGoKbsZAsJ7KWeWlCgjlrziuOqVK696s43ryp1z4eYQCYLWEAmr18wgNuZ5RAZGMI0PKgb6UL+xHD5FDGspQlKKbZYFHKn/JHDxDKVu1iha1EnMZQ+65aQydv0TZvFKjowjIlspQlbUfTJ+WIjWXIeWRK6sBzt/uzAn7u8yYM8z9wbBisz6Zpw2epNHGpTBbTOUQt+MNtnd5nNOMaYN3/RwwNPKjpsizQ1df49l1sHeFzPGH9oLbLXYhoqsUadYRIFJJZhZkjV+sgGmDtlvbDbtuu5kjWLOyIWhx/wVUZW00AvFvFivs1PZ7D1VuTAj30u4tZf3uFQfBWSAzq+n204NyXVozj1aD77W2IoJzenOzf1eVfdn23P0vWO/sy+6RdCnoRfhKrLMpCr/oh4IwtkBBMWDdagJPpmC93q1/we8nV9ukGHtQeXjnZ6uRFwBqLD+qIitXZ+x47H1CywW2a44mG4pMM3NGQnn9/RmOsWKnHBj1+5kZK5TWEO1M38GfiZfRdqttQeL5Cta1s9lNgDm7Yeq2b/F4B34JZspQXQVfX5P+M7w0hFX12CVn39bx/4u3p751fP8x6ZeidNwaODtExNMX1Nr1qr+jJYJjC9yt3KRuQy3ZcsD6YUPN3hlvbwthfGKgquea22H4G7qys6LMB5qOzky16Y44H+UO3J/RzdUEfRcw2sa4/8CoSen7ZnKK3scuAM+YnQmitj+1Bwo8Oa62RGSP9LK+gMoni7RV+juBR4FBheH14E0lM4mAf1n97G7tzGFZ5MODNwbo6b594Zn0Z8ebw9taocgJ3APr0wOdgWSrzHrkC5jOsoItW8t7qhWOiiiQhuOp6d3qA1kqYA0FeALXD8SAdNRgA6kmWO7f6iBYFu3vkuVX5u536MXOvtdp2xtKnNhwycg+FWs4BEBZiWrIFQ3pE9UQPJ+ZCUvAGAjftb8gyPcWud12huCwyMOsbB4nJoPZLEb6zvZUl8H5bQx/S8AqVIup1GJGhLlEnaBySr1UyGMZGOC9bwRWYbcx9N2MqzDEfgJVHcywhZTj/mMo4xNJGKijScZ/WgUsRDsjrid0+QL+fSdscG2AA1zz/OXb+fagaq2cwLTGXWAWQL3+VdLZpspBlLK+gMsN5H4MeesbT7SfoOSPZ2xvswWWQVCip9twkWwCamFZhelwaazdzBsV5Q/chxEqqHtsQ5GxAV9IniVMH0g24M9djLHx79hP+8ogRm6g62+UakCyX6cxGYjzO5zn0pD8LKp8Ga2Hvydh0JzIDGCCYNP06XrJBvzaUQl6upyC/gCB/x+YrrobtObSaOY99rHmr130Wtn3bpe6Ou+TNoyTd5VDA71kuN3D0c7q/FlZbpQK+dDuiMz509VNutZrSPxHvU9/N7TWnd4+UVAaroFsdzjN2GRvVb7e8N7ma69CrPp2Ee0ydSSuP828PbqzFoqwDCJF0dce70VeCYFprZC0yBowPrKDLVByjBxbH+19QNVk1vaMlaWQqij8cUGPLfl28m84OA613rMV+yn3PlQcoetvcVqP+VKap+efhOiEuAf6D0H9bC7imNuWH2Qj7cGLpU7EzFuTpuRq1IziCIfP9at0nc59cuXM9LO+yWH08yjCcZRjcWOlRnA9mCiA7+bXzbZTInqM2JHe1ZYWZFDaVXt3Xm8Qf5LzdunpFYpJhYGW5YYsGimgaHiFfU3orapDx/wiRan/7Yfk9uvxsRIO6MpJ19IAtkeyJuVi87wv3uTrZNSe2yXQGps+IBRYkVi11XRxFFFGcwhJgWWfv4wBQ9P5uHOuoV9r4DtHe1j0jT9FJHu1kIMSvgdkb/rfXuVhd+x4+QkhjpeYfcX07oNvZjgz5329Nra0NBzrd9IBuMCyLpYWQQVXD0LaxtKtEa5ET3I0RuxdVfo4hCI5q5PIWoVeUA35T/HhbBN48p+uXlAdoqOxGYBDzAi/w9X9FP+6apwaXvvbMC20z8IfDsUSZzNMHWwvHnayLHYzo9NmEjW0lmNZ3MwLI9K6jHXi1NY+2rHKWkGkM5YNmaGPvoo9VVL0/4Qm91lWN76A/3KFEeYWMZ9n1dP83nOvsqnwl+87wPxVmarGm621AZqMao4GF/eXXqgDrZZkAMeFs55f7O26hSj/+X9gCTuZeOqC9jAb0B2CBvpuVLI017lsv5XgkirEHJ52s8zLN05wM20pyOrOJxxtGC7xmh+zYpUxVBDGUyz/ImmbzHThrSXGYB0JrHzX2lkwdAFqvpqy1GhuPlUQmZ5MBF0H/wXCasfIJOH37JVzeUcWW+ohuNzx9DYloB6awxt+3MBzRkJ++MvpUfRl9uihvIoYr2JSbhb/Y9LHBmsljMhaUeEm9wCSxtirGBYHhq2vdv1Fn6/l4NtOtpyNaHX38ZDsKxM1nITaZAUxGpoB8ERT6QBFPSBtObBVy4eh/Jx34C4OuOl9JW/2bOVpyLM7BRnHrseLoZHR6dRYf2q1hFB2Ipgy+AN/az765YmFxHKXcuAXZUVQqea4/BQ7ZHmj0qsAwcPA2AdJ1ZyvMPwgJmYXKxbLIAPrMFhpNstgpt1DohwsvouCuAjjDptEZgGci6wrGvUhtl8b2Qh+Y6+T5FJFGDMj6Pvx76Qt3Ju9iXfKFqcAwO1moA0qvEbkBlGdugqKmfQ++Fc1g2/D6g3FKU/5du+5P+/LjHHNOP1n8W5uv1/0gxKbHEeZRA0wJ1LRLYomiTOpHquEZCt+UrWsmerK9zhVIUNqp05gEPoZRmr2kPn4Hpa/EL8DTwQXVYpL5/KVPgVX3tWup9bMiFf2ZQd/rP7PtJ1w951cUVqwFpZBojq910/Q4XFALxZsYzqOprqN+A2zaVOPwYDK3A6wPI6rrhaWBUAvwHEjsVULwwFa6WVPvjAT6c0cuyIjlLK/PPxXEzGlyeYhyIqU2dg7v5Wo9O9uDSjf45VNc0Dk1zGrSH619oYBxjECOg9/g5rCHdormOd7ZpYlNSBSvINW6beaSbdZmZzCGGct77UYnmyN8JxCNw8TPOffjCoNBmMofet89xeG4aNYurb1czt4n5BRSLuXz5kL5RpgGvWTfNV/mrfhcBbWIsHP4carQBRsOzRx8CoF61vQD0SFNTqQOYzRDUA0qc4cmxXlDQKhGAJS/1MRV+pVR1ni1Kvyd7/XxEa3Xn7SvVA8FKOpDOGuatHqg2aC95zicbuoDezOVWHgS/jN4C+ofMDE/45QkmdHyI4Vuf44rN683zAENF16JufqJFAd7JCLpLC8HafaztWLoEEZKa5/9AtDTM78xOl7XDVwSooggWRMoXItuX8T+cw04uXLEPakJ2+9cYZrP8iSKKKEIgE5LZyNPfePjz5W/yzo+DVJAUp1NXy1B1WLpqQVEKqyqtk+6g5KGGWBRRm+WIG3wf5t3qxU3aqXHbeshQHNXLJwU/Jbk/0LFdLBqEFxjh0y47qDUJOOsqQ52zfRuxvSt3NppCbQ6w/m9XwD9hX+cLVU3lv3VA+HcAZRtS5edhHG9ZE66GEctHm5PKJgz6620eZUcyxAM1gIEgjYce3X8vw3likjYm3VMIzFbHyfQgc3Rwuccn6J7god2janLxGhUx8lwdxdE1Si9EH5SFyjFUgDnWRjfd5TMe3e5yrTZomm5cBvwM+y67UDFo70YFO1r3wVdYJ5g6bCBI6VH9xZ/6W5F9ndj2GSfWgRPBGij+JhU2QI2MvfSrM5fpF98d/PkjijMSUVrsaUZQywcUJVW4iH4aNArfmU43SmK4uI0prsu78n5Y2z/pIxEnBunXI7guNz+HKAwPhGAZzBx6mh6WBnZRO/KD2PCNf3UiSRTgZThes/gkTCwP/4S7sdD0pDTgOwkQCmJViPU9rYxlZcKPtqV/s+msNDOTwdqHgp0yK4Q3bAprMCrsydjutwCjdqSyX1FEcaIQr/h8vheHOmaktMOwKaVd9L3zFKtIO2i0K9RLLQ+v39VL91O9NEC0e5Jh3J/F9YRUhq3wMcL4rn1LPYT4yXydTgT6rVYmdTb8vuRWCk3W3nd/GvhJrIc+A3CujpvRzOUpwn1M4j6KaFS6G/4Gt+54B7HDn1dgtxFR751ZoDyTJGgFF7PIYqBoDUCi7EfiKqfAyEYME/etvDeiv+URpmHUwg1+F7qyTCuqOpWGanPAL9O0gP7E7DwE/QRjv1KBpXwGMjWF8yfbPvpq2Tbx+lWO7QFTmKXKsEPMv0JnZL8CmQ8iLRXaepRSrPDSXnaGNp20QIAS6QGlHgjQoVqSo48GHdZAnUePQkOocYv6nNRkA/vEVL6Uqn/VOUptDlCNozSeupvRd6nZ47H58N80+KbVJfw+/ns12xsP2B5gZmgpuYGd5nG1/Mhcnkc7VnAtAC3a/5fvl/8egFncTi/epRfv8p2DcKyyes/zPhN5xKTftiOPaQwxlceeXu7h7S6ZtGEttTnA+xdcxzK6cn/DFyipnsCrmkNy8Sp1DYxsqP17lF1cHowy/LPobnRYYROylUNB6N9cVtos5i+3ZdYbhEEJ6+L/QGAK95ygKl4guxM7jImVN7jbNZNpPBDVylB/ix9INU3GcXmw7PvAYuTvBTITRjGG7prXU1SREzhDIBHnpF9XFKceV1/yEXmk0+byL9T9Ll2oLNrFKLGPacBuaJizhZL4pup/sTng85xqZPJcJy/jUlTmc08hyljTX+XTN1Nofl6E5Qdp4O+2+5RZUuBiOaHvF76+mM7jjdD7cirM2vsQjB5rV8Y1l+kAVHbybTuNarv/TDlV+ccrj1Nr+C4OXtVAUVm7H4OroUr1IypTuccL//Vw/MIiYCLkeRgvYsDb3/Ii3GN5TTYcsAUG3EaJKISPU+B/qkky36LUd+CJ8TprucYDPwMDVaa0VelXiAuuoOEv2kdT7KDWISUbGhu7hTVjOsITsKa3vq/rp1lzgtQQxqnXXv0+hM4wPtHeQbGV0oPwZah09sAnKFH9l73wybWAUleneQrch6JyglN9EOO3pryxpbyfcGBkLf3FqIIoAldgXDQEfQLRY43/k1CU16DHsFmT+K/zurwfoP4HxwK3pVAl4RDHL6pJ6zrryKMdN3V5l0U1b/bb19mCc3XcjAaXpwAiH+41arJWwzc9VL2lvCj0tomrVLCZhKVWmkc7RsmjALTge0f7IlJDZkMD4Q3uNqmXRhZyEb2A2nRlGSNZbVqMWBTNmwAYdcVEpK6pLyMWADnDvveJlnhPhqqxNGDdyGriW5cv89UNz56RvTJtBY9Ki+IiBkFdrduw6mgH9la7EOhjrv+WZC7L2ESDRkBd4BAwGJK6KHVYufYJRL6kbisVoI6sNp4D1Ia7VFD9//LVyFNODGnx6q6cQ0/ml0Kv/Us4b7g0bS97pOVQVJoKXM8WEhjKJNJZQzYz+TxfTdNmd1Fav0eYBVRznK/Ih4vT1MRBDA8TwzFqc4B6VfaylramEvBzcjufdenEEaoxePlc/t1FFXu2ZzV3Vn+VEYwnqXQHAAfbxVDr23LHcYxM6K4qjZXlSE8c/paAsh4BZFmYA9nV0LfMRzRIB5btfnF6U7oGemEEob6YyoNmFrTWofuAXSHVYVWg2MBUgBWLYXAEdibiAZ/96f83P1p7XVgQ34MfaMG7GIPjuvAOFEUU5zAasZ0ESgBVKjE/bwANf6dKJkr6NKXG9D0cToqD11FBZyHKnmKm3oGuPkB7+fkpbRqy4XusaFSJ/2xztA/EqJHSgzDq1HUGTHzjEkja7EjMh+keFbWemGUd2x70dgq0ReCA8ki8U5HnkvjvGcI0ZvcfwMFd9aDVMahfFdpUhT3TOH7PEOgLbPJgSgHE6WAQwOOFWzzQCsADI1X/SgRQPBLYDFlY3w9Y1NllIBc/gUA/CMwE2VLQmi/VPi5oypJfOtODCaruE4g9rkzK7LWZZrnMXuAfHkYMHc34eJsl2hSgq4Q5Qj35jjSC9EJ4PgUuuli186vqGACPpag60tugVtIuDtacDFNP3ALErAfOUH/s1jmVgYB2OkY5Twivy0goskZQ6b4fj6MvjonkuBQ1IfGm6mNCQgk7dijXgAbspjYHYHfY3YjiDEE0uDxF6MliVU+43RK/cYO9Xi2dNbZMpTuMjKUDhreTfthNGaEGZd+MpYF73zWm+oZyL5NZHUFhenmCf0DoV3+hYQ8oK4Q1Hr40bko+M2v7hlzoE8y64BBQE7bNrw9Y2eGk32+gBzlmvx9BBZOjGckBajMqbSRjGEcSORzcoWagltINgGV1MmAOMAd6lDqnMJuyk0kkkBi/E7pD4uwCBvEWY9aPZ1arviwgk29Q359XDyiDbdYpBh7gBRbr9JiUHoei7CJ6QRb8kWXIUvij8FK/vBtPVnkcoRXWay44juwiYDPQLMCUpGb1RmL7IruAL1tmfmy2Ckav0yI+GdDuXRv9NUgA6WtTYi4/wRqScGozpy++O6jdiH2dbx2m22SOfAGOlKnJh6pbljC51R0AXBFOh89gnCS/rrpCiFeARVLKRSFbR/GbRzKbaM4m9lKPvdSjWr0DKkM5HRgMh8fGwR2oZTaYE0bhHKQWyNJQtZieoJ8d6wKp8/m2C0LbrwyPS7Xe/7wCBqG3DCGZOTzLQ3Sts4xFS26GPKxg8KchcL5uO83YfxJClFKjSHC4LiqwBOQIFTDVPaLGqNHVxrKdCWTK94ihnO+4lBIa0o+5NFu7A34Ergby4Im04XjWP23atK3bfCWzfulL9tb5fEAGcstVfNtEZS5rc4Cy0TXIHd2ZFnzPFpown3dJoogWfM80PdubUfoBufndlYciwF062JzvUa++WsjnAcA2aWgGP1U9wGz4ykPd93+mdbV17KUe69/0WEJJPiIzarI8vIyluU0YdZaV4WdpZCztlHHnMU74EAGFfFxri43gOs6j6mKbw46kZvAH2HS8Oa2rrGM7jU6aeN+pwrnocxmtuTyNyGIWWcxCPOJfl1hZcHvwNes1feo5w7US8d3Gnu30rbs0t3kdP9sRcYV6+cLNjiQQfOsqI6lLCFSfanwvFYUQXkVzmVmBQtIQmGMaS1cMl/L1CW0/SRO8oWKWOJHgZFuQhAuRg6tc/jV8xDV85L8C/LKbBk6kJvo3jH1SyjujgWUUJwrRx0YxtH0OtxaxUvsS5jgkRlv1f4G0FCKttQtmqVIZCNSfyq7ZP1UQ94JYZb3OVJyOukt1XGcWv6I6GWpb9994FL8tnHvh9GlAYVoTOrKSHPpAE6hPcEEQi2an6tbu4nmW0o35I9TnxPEFZn1fovRX5/TLUGYB7bwkje/nerzJDDUDxFw6A5j0V8sMwzntWU87R6v6TCe277xIZTRd4Fb3FwjiCiBP3YCKhYdEWUCxpr70FTBfT1NPn6HOqyfW+Rm1FJO4j0zKVEnNBqAlNB6+m6wJFj/nff7II/yfWdtoYBPNTa58EgUUi/nUYjR9ZTJPMpKW4jYSZT/6lqogy1DwFTbqY9OZJcqm5G3Imj2PMmqQ1GoD5VTlJ1EIbNYzfXMZzUiVJV0/HvbB8I5PMI8s+hzP4Zoqn9HPltX8TH8fIxlH7dID6rjL71aUk3z4Hds5eJnqe8kdCWylCXfrusLaZbs4EKvqR+zUV1/Ylxkzjb6+q33HKEsc0QeVnc0pIAlUhjIXGGarnVwOzHWv3zSCSDdl2Lt43lyezkra6ZreqTyoKa4dVcYiFlPGabGmRdcu20WLWEUdN/rhm6EMlrEMB4YlSZL+bNQ7V88DYiCz49s8xXCz7vJsxblqBh3FqccKrmEjzclmNn/lVY4+VYdqG/dzdJ6mc26CGtP3wCgUPRacipITgX8HOcBWf1VYKYdE1EeDDhsUi2x1kT41mqZX5EOBN3c+gG/0oxf6tvPNCAWjzJp4u5BPpmcwstp4FtOTi/ptJqHfTtZuSYenq6r7uF2t9RqPtvR4UWUt3/dQK0M90xyc0gD2wL7qUwEYc2QUvaotIpYyyoilJ+9TnaPElh2GGNQT6G6gFTxc/iyUAx8C14FsBQNK5wPQ/cNcsJG06h/ZRdOtxylP/ozPuIbmbKQv8zhKdT7jGvoxl80kKZZYO1X/yJD7LeuTXfjV57riRVRW9u1Z7BuczZop1Tl8MJbEQQUUP+Siig6mx2Ug1VghbPWYe/x/h+HCj+rq8rvwaxOi5jIcBKaKB7cecQ0m9+i/cai62OEoe6GW8GiVp/mEDEpIoP5ZzIs9V8fNaHB5CvA+PenGUjr+sgYSrIDMtBN5JvQ+urGUqeNTI858GEI4jmUu9ApDXGckoUdMZU9yt9/yx5moBH5ccOXtK8yA1NyPu+8xX17hPiIWi7kkyn4UC3VTNzw0QQUVAD1ka5YIZ11bs6t3wH6gGewfV43bY1QK1QjKv0VRkDfR3NxmttjBAHkRczcOJilZ1WYmyr7mNuLebGAcxX1SWZlztd5KfZ+GrLjIkSo6X+N/Lunkke5DQRnDOFjvvHNvEvN4Su4FoD8LAHieu7iGFaxCpZCHMI2rl+eZ2xjUVrFdRYdLGnXnUr7ju/+mUTslAPc0TBjWOBDcDkf+AuLpgKvDwlQePCMyl7KP+/LPsKQO/f4vB8MXaW1IYjPv7r+FqiVQREtIrgTe0WnCuTpIRnHqsZsGrDvamkbVdtCBVRQ9s521r1wF70KN+Xs4TByHB8epiVNQgWV38Ju/ydV/45w00cqgGIaCH6V2YYB2trLAoLTbCPpstyQJiVtSiK22mb3UYxf12RHfjB00o03pF6xNugpeBvZ5FH31c0i8swDoR/Glqcq2+kYvB2/ywCIvfOyhodyCV0uXbedFYihnCnfRhK20Ll9HndKj8DXQFL6+qSVtV2+AQqhT9SjUQT1aFILYhxo7U4H9UHBRIpcVKO+ZY42AfTCPLC7lOzaRzC7qU52jdGAV9djLNIbQnE2AvhjTvIrWO0zZqagDzYbWHqW0ZvyWMOoC84El8HYPoCtkweHcOO7v9QxbaULx+e7BZSSI9Hd4Kn637scNb1l4+3KpTR6g3ywB/gD8HTb2ak5b1lKf3ZTQUDeIwHLuDMG5Om5GabFnMHzpmYECyyQKAq4LZVkRiMpqp7s2YaPrfjKZgxjhvt53+0Cw0yvEIH/q7InAlzLriy0k+GXiwoEQG9yX96kYpXYS98FmoV4niNGMBGCYXZknDPhabIh8d0o1YFqWBKNAGQrEEIRCdYF+hUmP8Q00fS1B3KxNKhMiX/2vGTTZYNco4D4C0GWjiCKK04fKounZ74uii3o51o/BFAKq0P4DWDtZnwvNv/ZXFGc+TjaVOex+COcriigqgmjm8hQgnTU8uP4VslrNZN64gex9Li74Bk9B4rtWsNiCHwAlIlN8cyp933XSMH0DS1/1yotlNjEco63hG+Fmcq8xjjFh1176IpM5lCf01++tZZDOdhqx2odaq4JaF3P5e5T4j7qxeZgve3JjmeD8uyXFg6DKzyo7KsREPcMI0MecSROv9WH97cl8zyUM9bykhGzqwrZ/1edicT+w0VTbBUg6spnm1TfSnI148HI3U+kr04Ej7Emuwdek0ZZ8fUwv8Gd4eQPEjYRc+Cs+JmtgKtiNylHHmc0A3iOT5XThH/mPU6y/g3+kOYNb0foJAP4sk3iG/8csuZLh69Ws++hWsayjNV/zMKAyuR+Xq5q/+hnbuLeKkn4Ty+HvXaBvI6XsdJRqNF2klBcPNLoAgIdjn+JZhivxHVuAJGLdKbJiNIpWDGpm0QeGIMEDqLK5F0lVohDzLNl8ALI83NXFCBItCuwrYq/6Lm1BqXzXosmKTnDnCmXH8orYq5Vh/SmtJSQ4BHyUcmxw9VgDtct20T5WqRAv40br3I2AcLD7dmIx1MqwjmH8313D//HZ6hs41KYKR5pArUcCC3mdLTgXJdWjOPXoxlK6VVtKI7bzNI+y9pGrlBpsBhyeGQd/PEaN/gdU9vIOvZGRtczFylju0VYTQinRiHhFfbUsSpyBl9EuEEXWj36qqa3SxzLSPvEmctzZD77buMFSrXUGkW5CQxUOIrvDMCbShK1spQnppWuYvuJu1va5CtoAc4GHoG7yz+z7/EKKX0+FO7xqQ2NMGAYNF95GifBSwgDu6foGAE999DCX8h1dWcb3tOD7mEtocsFWLjx/H3wKbUduUONEdZSSe30UXbYQqIn1hNoGUnOKTbGfqulAI/B8/DTcDO83u451tOYitrOdFsRwjAHMZi794P1ecKNx7brrrCVwUwKgM67HPMrixvEolQZ4gR7AFHhcifgs5Cb1LBVAWyYQHdZxwW0wv9e4yLOSkdqRnAgd1sISoAdCqOxlMOsRx7Ftv2Xjs+N9PPB2IfwrhcSvCricdewkgQR2EovLQ8lZhHNx3IwGl6cAHQvW8G2r5sxbPRCuhf68DYT2vMujnaZhqiDyBQocQacB35pLO7Y6/Cr9240StQF43EZxMCiy7vtwtus2fiEfiq/pLVv4rTOC1HTy/PZpYKwcxuM6wJQzbDNltxs3LugrFvOQnGDWppQn1Aw6y6xUaTeylc6KBlMXuEUFgT2kqscbzUju5mUAhlafzK16W/X3Wd6lF7fufxPqwEo6OA+wpqU6Tpq6sS7JV08OM3Oy6Kmpus/LWC7O38WYqQJx92JK5RXE55eR2G46sMR2I1Y002HNlC/N3yVsNX1JYSUdyWo1k3JiaK1rObOZDcA4zacybEVeOm5JwP1juaqWlV2gwfEMPr3pWi5buQn+Bw83ecp5Po/DQRo4PLaCqcbKoYHXbaeRavOuscQ5oDj284uzTSi80ulB+Eztx81uJBxV2BOBuiapdpcb0//SDZ9xPXwIP7RvQW0OUPhCE5LZelL7GEUUvwUsoDed+YQZ/IV+zGXBqEwOZ8bBelQQuawqh9GTtAad0ffZdo89GBsSNCvkZ49QQcihOuB8AvgjViG2sT6cOs2gcI7Fzvq6FNf3odB80Ldsojmt+YZ67GX28QHUv3obu+s15g+Xf8J/xMWQm0LVXeXUH7KNXVUaw+0eZXMyBbhbqcuXXN8UPvbA25jWEc/yMPfyEr1YSDM2E8thLvxlH9s61afx87vhCzi8D2o0gtK1EF8Xfj0G51WFDYXQMgVoiPreS7A8hfNQAWg68Du4puln1I45wFK60YFVbKUJq7mSH3IuV0Fjpsd6/JnSHq4DbkHZq+BRBxNu1+xKWAl0HAD/lmT8bilN2EoSRbyX7v5MU1FERGV2276SKLOVoRhbIfwTOJYCn0HxrlSO3F6d1bTnALV5bruaUL+70Wnq25mJM1plPUqLPQPgq9pqx8lU5Aw3Q2mnuIaCUbPppgJrQMxQr4rCnqmtyH72aB9OA6MZadJJH2ZC2Ne8wPfJIRKsFw7l1Ypggs5gBsPJUO8TsdYrrPYBHtoMf8oK9SEIjS3Ufn0pwBA8SAzYhwr4yW4hIfKNzhCo2pGqlf6KIoozGW7qlqdb6fJ0qm2KV5xlD79FVIQOKqpYYj6V25czR1k11HURYonjpZaF7r/4DQeNJ3HcPKNV1qMj+ylAQWoily3ZpGge26Focku4L/D0kHwXRH4qfdOsIOdhJpCJmtG1MpoB0M7wggw9k9VbtuC9TuHNwBkBpj0LmckCMiW8QWe6sszR/r0r+ptqr76+lICZsXTgL9ZbIbww1cOAO1+nhIamKifYKBYzgDeBW70mjRbg132C7lej/LkagrhEwlyY3k8pyj7MsyyjKwDrtAxdEgUmnXcu/filegPu5FU6soq+2jy077eL2XFpPS7qsxdygDU9mJmWxcAr5jGQeQz+SimyPsoE5AXCnGmNzy9jVNpIkDBWjHOc8iyyaAuM1HWSRaTShI0MxYoM3xPfs04O4AjVWUdrhmjDsSQKuKvKbDNjevXyPEZ0UQH++Ekqs7n7uca8MPR+RnccS+PNu3l29WMAbGw/mWS2uvquCd1vY51ddMI3qLTTsOfHZ8M8EPirwlozqw/6BX/iAt0mkNel6UwTOBPqi2AKtG7rD+Y2YFnPG/3aGb6WBj3W4XN5ofrTNdb67Yt89f9xddpHfPIgNC/fROyho2yv05AooogiNIrzUylLi6Uta/mOSzn8U5xSKM0A7poL7/eDddDq0a9YH69nMjNwivrkehzZIJMKa69ti0sxl/tmL8O5x9iprUZ9ZTh010gRSBnUsM2QHfzXhZvJ2rT8MjK6PEY5VXnxw0fgQki8vIDdT8B//thZ0ZHv8LI7BmAETNUbXutV6qu7gL76Wv3Fo4RzLlLHLhFvMFsO4InFE9T6Nkd4pdFf+WvBWyojeQPUWAv5ayGpKhTthqSmcHg3tGwIOwsh4QjQCtXeUNjdjqKl6kqDF2LupyrljF88hrpdf2Zf1wtpvuJbMNjNe7zQVvXp4lKlAfFTh2SrYmKuylrKLvqZ4lYAr1LGfRbgGDUa7CV3QHeum/0+7VnNPx8dwt9EE8TwCMWWZApCvIgQHwClJh3WtNBZsBrjRxzazzT0cX0VYiuqGGsFlj1QdGEAT0g6rLW9P63bpMMCDENVymwDWkMsZcRSxl7qMbbRSN3I5ZkxijMS0eDyFCD1tWIODaxCzRJ3Mr508VVSFLzAapzBEMmNLpzAcgH9EcLrR321oyvLmMEgAMYBvb+aw8KdN9Eowf8c5KDAx7pyhhFFWPWZs1+5HdKV5cPWnZoymuDcl7gVf/wPuAuYDtIr4DvJm6iaiVuZizH5GkNPZutrvZv65NEOrw5iGlLCNXbPzWdh96sNWJ+TTCpFPJE2nL/uf5UaH++hrE4ct+lmYxkNTWAWtyMlrEBRhwbxJomygGe0yXIjtpu7LhbaakR62LoohS03NeRBm4hNP+bSmnXcItowRd5lLm/ITvJox4NMhT/CeMYwomw0I4aONi1l4EG+Yho0g8aHdrOx1cUBv4NQeKfMrtgWQAIxBE6UwhrsN27s+y7yXNcblNq7bMsqYkdiqvLmw3v5/a3PtjZVN0OdDUfZ1r++SRl22r6fPThXVe+iOPVol7aScmJoxHb1f6M9sWrcs4fDxX2BY1C/KutXX4Es1cGiUcqWW/Hjut1XTpdCZzCYk6uV4cmYBYtLe1KfXTS/4Vs2/XCZGosyPaq28l/6/Md5YOSvcNcSaNkD3vfYahmTYVsW9S/cBgOHsMvM4HkQlwM/6X6XVmcWZYo2WwKsA/ZB8vlw4H9QA8jdAhkNgaoqzojfB+floW6cuuaSBLWeusBB6Mgq7mYKLXt+zQZxPsRdyKb4y2wTCkoRVn1OVtTlVsAuL3zvgd56v9+pZwoxAVVP2t3o+zvKdmWph500ZDuNGC+c98LQtZYVw5n2+zPKldzX+QeaYVHO/4fSNPiv+lgraxdbaUI1jtCadaYy/tmIc3XcjNJiTyImM4TJ5tSZE+Iln88BqLEVUR+tbPgp1AWh8UaKbiykPSsQVwSn0vr1Kb9iap0GvuZShPCGVJQNBj8hJU0DsaNAa/lGgnDsYNxgUHsdfcpXvyG3dRA+vVUsVy9fGPWeAbfzORU/etlo/zagsorh2JCIxRWjtJ6MfbnRbd1wny0jfbahnJhKf0URxYnAzPhEuO6Ejhmh6mswdW0hfkWIX4NvH4JaKITXT23UtHc4DRBCPbOIR5zLG7H51PbjXuf3pK5jkPYRlHu4bn8SUUmWOwABAABJREFUVXqFWI0Qq09sH/eGbhNF5eNcHDejmctTgRug5vrjKsnzNxDvKo6ouNkufOIPe1D5LMMB2GouC89jyVc51o4F9IcVRk2lzlB2wDKjfso2mKV7eO8KFM1Vq4W+TjbjND1iK8mMA7ryPl35nmX0hwRob2bm3EWBfLGaTog0EDFwkRxEAl8AsDb/KqVSB0rBzn6OaUBbD1Kf6xYSqLoCRUOeCT2/U0bMi4HB4lL4i4f8GZc69pFHOwDKiGUSQ7mU79havQnP5Q+nfpoVMCS+WkArcRuJsi8Ans1P42mmKD8CmC4SgS/VrC8wKkeNbG9wm5kNBXig7EVaxFqWJiu4lmKpDKG/41JG3jSKahylDWvxMAGkCmaLxVz+IUuAFymjBrEcppyqvMD9DOItrit7n48n3cgsBlCc7/yNXLliHfILNbImF/7kWNfguBZq0DDosKIn+DrV3KyzlUZgaf9tyVKL4opBcR3l4c4x/sGiEF4Ypa7Jnb88H5C+GgmM4FD29KfD2o8/lQcDHq922S5XwSD5AqYNiexjTS7ItFRHUCnTVD8+X3w9+7tVo06do8RwjKvWrOW+dv840VP8LeKMFiaI4tSjA6s4RgwxlNOcjYzKGcnY1eM4vLc2PFQVHgVsPro1ivZweEOcWpah1KsDBZbhCqe4UlENP0evtc5QjAVgmApmAgn3mNRZl4k6NzqrW0BpBG5SZiM7+LeR0mMGlmFRZIcp0b0rWc2LpQ8oYZU1HlVe870HWqh9tBzxNRtGaqbKhi/hRqCGBw7r/TT2svsmDyzSgeV4vbwbMNEItKoqtk5ToAkYjwfrSqDj72DDjypruawEujZUVNnz6qvlLX8HhZ+q9sVA1xEoddchsIUmHKA2O8Qx8KYooZ9ptnPco87TZFUeAJ4ErouHkahz/qOt/QY9UTzSuLYDYGkKF9+wkb3EsZCbQPuVhxJPMgJCKduby6S8HyFe1H3zmt7YCuFRYq39O+mmvp/96K8Zert7QfokOYLBLvRzIv6cxkSJSY01mAcxwO+Bi+DgxQ3IK00nhmPU5gDVORLR8aI4/YgGlycRvXlPvSmBD9pl0P1YLqwDmSoQLzuLEO3U2Cxm0YStoFVD59+czYR3DfGW9IDHCzaQmDecx/Q/9Hi/JnRjIW4mtb6S0QZ8LSlVTWbtgP2z+2ka9ZZL9fFEAJeGnSSQTh5ruUrLn6MGPnClIQLUK9+rBpaWwHpYfHlf+EayhQR6yJdZIrxUm3GU9fItLtMenEMp4FX+ylTuZhpDiGOva3+KxVxmynXA12RvVkGrGWR83JMtXRpSRg1mc5Sx8eNoGV8Ea48xuOk0pjFEf6/QP/Ztij5siej2hDq+nKSv4QIu5Tu+wwp+GxzfRo0qh/lpuQoW/y5U7d5YeYAyajBNZ8fHMI4kCkgcqvpj1Kjm0Y75sdlcWbaCLZ0a0nRsiaIkAWRA/X+rIFGMxhx83exIfGso61fZzQ/LL0ccg743OO1xzG30rLFBQW0nVZ2r+Xsc6+VOWQ9QQjwGpTVUoFm7bBcDYmdxp0sQ6YZ0Vurpg8CQPf29MwNB5EOi/v0lUUD9WB/VZl33daBnbeoU7ubCNftgH+xsd3YL+pwkSfV9Uso7T8aOozg7UY0jNGC3muSjCX0XL+YPPT/hP490huZYyrAZ6s/hOXFWraXxL5wb/BiG7Qjp2p7kK2zZJqXIHfAh2uMN+oDtF0QuC9g0QN/CE3BxtDPVcT3wtg/bSA+SUqJUXgFpVJ6MLOTIiOqspS3XxH9GzAvlfLilp6pRPAhMNc7za3hNW3YMAfbMomHZFgBqUEaxMRCPs40BE720lL3gGajNAb58vRNNbt+qlF63wv61ig7bGBVAVgW+KYErzwfKYf8xYIuiy/66AVJ02XpKXWCO7sc+GFT6DhPjh7FjU7pSlc9DTQLQHYgHkQKdgc4eTGn4xwFKlV1WucdS9u0KvFgDuBZuylDLBgMT4Kc9yTzRbzjf0ZKtR7r5fR/2QDM8NlY8UqoSGZlDCEXj9o7PFRH5MYLJk5HBDGciw62mWUoP7CkEUuAPUP/qbdQadIAG7KIfc9lOI+oFeB47G3ASx80zGtHg8iSi8S9aj/tD6L47F8bDF7ltAP8HdTfEUsZbN//Vscz34T0UAgVgdiygvw4sfeo/x9tlzv1vGLczC48OzgzU5oBD8MfX2zJgP+301uvUn+00AyCJI/Ac8BcP0kUd1rGtgX1w+GOosTSswwNKNj2Ww2ZgV7SjJbMuuoMkbebVhK1cLSEbD0kUMLLZkzqgsOixTZeXQBfJGGBIaQKJ8TuhTVXSS1X932dcz7H96tvYckNDiuVLNGWnPs8C5udnMyJNTXuv9U3R2s9ZeszvzM2/tFjMpa+0ssVFZalAJ9hRAoeAhrAnswb8C3b/sXHgOYvAcxmB++YiyvPK5Q/CDmj3i1PIx/hdVVQ11u/YQVjOa0gPWevplrF07F/PLhuMgI6s8reqQWc5H4Aikmhcuhv2gfeuR+mA8Q9WsXrqKKI4F7CDRhygNhtJVvfjgfCfJUpcpv6ubey+pjH8aT8sACnrIF5BxREzrX2cqLWDL6wx0DkWGgI+YgxWZsznvikXg+gF1ALpU6Yu5Xmu+w3cDxcdA9+6tjgrKwSBa+QAyEyhPrtIoISPn77RygjPhBrJezicrixfivonWRO8DAD+TIkAGA+MAEYgF+oA6X9WPzZcDywrhPfb0vD2LcyhH4+X/gPyoE680uhZvR2OAfHAl0BKDGzYrR5Q4+uq5at3Q8vz1W7rNEV5Y04B6sC6jpewXlwAWmNASg9isb4GtjFBCC+01Nf5P98oQaK+S2BBLnycoZZfp7OW9+gZjDmo4HLmEXirOnupRzlV2ddfq7nlBLm2oM8IwBkcGkGlH+I8Ef123TxPQ24TQcbylGGiCjB312vMkeRdXBm7mgd4ntGMpRpHQ24exZmFaM3lWQbf+ksxQr8qSaray3C8moLri0zmaNOO0DWBz3NXWDN3kUq8+7UP09Yk0H63kBCxHUioOh6RD6wPMJr/4r98JKMjrqt1O5836RfRPiqKM6EO2I5wajN9UZm1mq77f+Dk7ft0IWpFEsWZCLHlWPD1ffzv2RXKDJ4kiMvVyzpm4Hurmy2Ksdx8HyT7JVwYS5UJ8cPJ3X+4sFuliEfUNTlT7DxOJ8SlmGqxQduJEBMSjrZhWI1EsL/fGs7VcfPM7+FZjC0XKA5H030lakb1LfieSwC4Ksh288gmi1nk0Y7EdwtIZw1TuIt01oQ8ZqBZK1NdzrgJjHd+ltKDGAEdxjvl55rozOSlfOfjDqnw0xU6O/aVqlcMBlfrEQ0hbJz+ibqPz9jOZT20yv+K9W8FPQQAZTE1qFP1KDWuAjwgPn7Csb6HbE2r/I0Up1kUxRncShmxrKIDl6KKFLIveo3ZYgcwn3fkao5SDYDPuZ7iPqnIHOhJDh8wFICCLon8P54BndW6no9ILFVZzXas4TA1AEius8H8LlvwvdmHdNbQIu0H8kinHntYTXsmMZR1VVozijEMvs45KiSxmc7kcsvyBUzvogLL4jEqe9lQ3sb8WJsuaRkM40n+cYdE3ihgO8QVHEZOEogyadVNjsGUICoi1WFB4ovWrONIF3VN8kKRTtd5dTuPqsls0BH5ixUY5gWhtrpZiqgM44NhB5bBMpZ2KmyozKWJn5VdQjGpDEgzCOKpDoGpxBcKeIl76djyDtgJnklPkzM0PNn2MxVng5BAFGc/mrCVeuwllwyasJVLSr9hL/U4UFSb3e81VmUPxbbx5mLU08xMI9BUjzYiHqvGzGh+vrGRFiPL80K6B3GFYRPh/qAcivYnR4PIhWrzVCrz6PQ6iEkgh+r1toyl0NlB+WjQy+Dcv4vqpn1cbyiVVnmJDi6rbDiEEh7QdFgdWEofMlG7nJVs4FJa8AMDHn2d2X+4nXadVvL1zjYcnhNHlR2HOH7hRA5P81Bt98Mcrf8sijbcEbxdVcK17XnwtaY2LsOyQzuMKsVZNpuMnu3JFat5vOQfbMlqSNMxJfA/2FAI7RvB4X2w7BD8+Xz46ZDKaJYBvx6D86pD+6Zq9wCsRblhLIJv72tOPfbSQ+azRHhUbWUR8AawFKcsOMAG3TfhURYqRibzOn2d6AGZKFr1Bi/ggb8BydVhCmzV5UoG7Vp0QtGHcbKo5Fd6vcgkXJxIxrIiqKgtiRsis2PxbTsbbvGoa3oTHHy8AR9P6koW8ygnJiiL62zAuThuRoPLk4imY3Vh204U52M87H45zIdXG4xg03hvhxyva/7G9wvufRkGLh6/kWkMYWsA8Z0yYm3WFvr4X7nv60Gm8mCAdY7tpccxo+U2gBoWHevwgHSmQt1sTS7squrbaAM5r/agmJdITNtJooBRUgV16WlrmMYQxjCOFzSttYhUuqM8RRfQm+/LWtJT9jUpGdU4ys0s5FiOkSGc6zjuatqzKP9maOdF8ASJspq57qoP19Lvhum0i1/DOB4nj3QW0Nu0ImnEdhaQycM8y2dcA8A1rKC/STKey+PSSUFeDCzmRmQXyEbVf8jRKnPalWXM/vx2v2vz98VPsocaxL12GKqD2CP92oRCpNTsk4lXLlAB59QA/piBUBHrkUDbB7oaxQ+k0vGFVeoBpxyoQ1gTRFFEca6jOZsoIoneLGAxN9KErfwQf7kKKtOPKaWXf1dVTzAfoGrvjoFs6vJIk6tFQ+xzn5p66BZInuhD+9GsOsjlwUtRHMf7xnfJxpD9CEaFFK2gyruH/LcZ4b6vXdTnGDFsojmbaQZJx9hFfY43qwmHC2l0p+CnLA88AUf//qy1oberDtw7wt0AKfAzcG0hfKr7Mxa4Tl3jXLEa4jx0vSCdN7kVGsHhz5VQz8ofVRyaChw7Bin1gRiYW6JKIbeVQI2qUKhKPGnfE/YPgzoj4LJJm/hk6B9oz5csYR187LFfRqW9sGcn8GeIa6mW7fGC1IHlzyjBH/NaopXfe6AiZy/suBuGJah6zO1q8oP1WmSmf3X3C2vur+K2X5WN8IPIXP03I/Q+w6Hh6seMoNnLt73AAJiSAk+oOt567OUzrrGVk3QPsoMoziREabFnGNzsNewUxCRtbuGGZL4lmW+tfYWgK9jprW7WIiLAYGRHJnMQr4OwqfsY2U47hecTOvCJrk1rwkbEaGjPCtrbPSRdYL8WQnj9zl+kqdcWEthCAhuNWcUwYd/Xw0wI2f4jHfgBbNa+hScTWwgtAPO8bWrW/v3bEc65BYJ4gaBS+sFosmK0k6J0JiCYzUkouqzv7+9EruvZBsOv61yTVI/i7IFBgT0RCxIhljhspULagFzuT211bddL1136bBvsGIFosBVB9dL9VC/df8L7+S1DPB3ec09Y+wr4nb6orUqs9QZtNxy7kWBtAh7ThQ4bCU1ViFzzFUVkOFfHzWjm8mRCT5KxHdgPNIQs5umFkwECWoXMI5skChyZDmMZqIdcI1Np/LUHFt0CmNv7zjKNkOWM76CykW4ZS4MiupRePMkwPkDNXHbnE7+2S+llBpYR4S/qT0Da0RoPVRorak5Y2AfUh6RXN+isZ3vkeAE3w5v82Wzm0TYql/IdLbCKRRbQm9uYzoTYR/H88jRiq552a+flQ7mMTSSzmJ504jMm8zdzu8HiUsDLKHmUIUwDXiIxfyfz03rS64Z3qMZR/hrzCuVU5RpWkM4aLWmuMIgZ7KY+eaTzDI/YZuusgP11FPXphp6f0XfxLK7RwXlv3mOx9uxsnvYtA5jNgLTZ3Mc/OXhcKfhOP34bu3Mbs6NLI2ZtvwMWqRufYrRK08dSdrEy4GIo2G06fX+vYhL0Heq8/GI55my1YTVClse03bEL/oRSerX/JtJZyZqbFb3VbV+hYASNd/pkLQ/ENghbJdaOJArIBBpSYloFATBd9+0FeJOliob3P+BTaNpNsxnCc+aJIopzEgvoTWdymUcWHVmlFLFnAsegedPv2fTQZVSbpumnx+pAq2PKosQHdpqhXQHb8jvU9kt5hkpsiqON3/7cDOFb28arWnpbHUT6iveYGP4rtD7PdVWgTFDoujY9wfdpNse/r2nSXw0F3Gq73Scqd5c1YG9sHJtJogG7uK7pUiWitF7Czyn89ANKoOgCoKvKYALg0f3J8qgnyYtR97nJKZawUQ3gHg/0Br6Gmx59lwHMpvGC3bAbDh+BGvFQ+0fV9DwgviEs0QI/7YHzakLj6rCyBFoblOZSqNMQddxjUIPDrKM13JINbxfCaynqvjsPbaU1RW2XpFVh93rgE69FkQWYYLvuj/VQVmyZHvibR41n//bAFCVauJY2JDZSnp1FK4IzxoRY7af0Gg7CspE5UWxwWowYkDLjhHZrTM5I2UN/DudcBkBcisqC/wxtL1nLJXxPAiUsM1QeozhrEDS4FEL8Xxj72C+lfLKS+vPbgjFerYTDPwJL4YJ/lFDjYGAqoj3IVEFjatjiKRu5zPH5BtnWtPpwg0FxHbfKfb2yKwm8PWivTBv7Mp08/S4ZKT1mdrSzLVDaSjLeMephfC1t6D1jjvLYdKmnNK5HzE6o8vMwioXhlaGV4PIhh57k0Y4+LIZJQkvMqWaJUtGFhVDXY4C+lrcyl7nkMIuBZDOTNnztOO4bDKY4PxVPE5uZ2hoP1+PhBnEYOtdg1fIOZv3FY+IG1R/Zg9GMdOyrb/5iRqWNZAjTSMzfyZ/T3mQVHfzqFC/he3bTgId5lv4ssN2QrTY3xH7GTWXv0nexOo8HmYpYDoldCshmtil5fR//NLfZvbYxz+ngR3aBdFrAtcC7YBP2DYwi0E4pfll1gPmTss26Ij+MDS7ZfzrwyuIH/WixRq1l7QwjyHSnr/tSzxfQOygd/VbmQqt34GOgLeovnJXBpTEDG0VgRMfMykEHVvEXZlBEM2YzgKL9SST2LKB4dSqbVl8GXeFoYR34r95gR1VlLaGvqgyp4OmE3ULCeiD+MlBz57Z+tFb/DCU4A01LIdb4HJ5WQlj30t76r0/93pH4Or4tAUiO3cg1rKCIZtRnN4t+zFKCdJ+BKUQ+Wf/NBXM+9Z8q2KJjIcxT1i2M0/27StFwRS/gJ9Q4cyEsyryZf17yN7Zl1qfx5N3EN4PVeapEssf58On/YON2yKgJSw6piqI6JRA/BNLeUsEooKy0uqAm7oeqmrZneIT5V2fD1SnI23UtZAMsi5a2um8LrIDS6b9YaFuegnjKOE9g23DIg7rdfyaJItbRmuLtSs0+MIEpHrjSb6mlEmtT4y8F0ccDC4JnLdX2gQPVgL8jFzqsW1AZeL8Z4TeuEAZAegpkoX5TT0qOUJ33yOQ6llHbKGo9C3GujpuhaLF/QRkXBHu5VL1FUVmw0+6CBZludNpIYSjPBlrniycZ5vCu9IU9izmHTDKZY7789u+TuR3JaLOtGw1YqY+FRxdyaxPoWgaiHJv7ClGzIMS4kP0JCza12X68GbBZg+PbItrtBB6uUHeES42ikekERX89UURC/wpGbQ3rWCGUYiPZfzCq+m8Nx4ip9NdvDNEx8wyFiLdeQdefAA1V1FGvCvcxLOXNcMe9QkfAdKpxeo8dbjtNO473XR7BsSqhuEy8EmK9jR5dGRRpcS9BvS7dKLBRWmzFcS6Om6FosR9KKYP+koUQv6vE/vy2oL0si0ZAUgrg77nrR4e1QwgvD8nqNGErC+hNOmt8BEFSEYZfkY/8bKCMpRBebpBtzTZGoPee+F6pugXBqBETdTYzMBb4pMJmMIhneMSv3ROiBlV+HkbV88qthW09yHyI2XmI4T4MniYJWynOT4U1HjWbeoe1rq9oz3T5HW/Sj1vXQeEXkLIFihq3hG0SAayXbwFvkaA9JRfRi1gOE7f5MLHNDrOIXprKqpRjAeql7UXk6+m9rlhG2N/WIPHSAgbGK4rzh6XX8KHMowlbaSn+ytg+48jKmcd2GrEhLYmW+UWMeU1duIvv2MhoxjKNIWTynqU8p/s0b+tARetFZV3bo2Yyq3OE5mzimbJHWEdrvqcFucczABjVZSQz+IsSGdDoxSJenKSv++P6OpWpgHqNWAZvA7sBI0DsiMN7VSxHzVLXAjMZDTyXpuo7s+gEZEMb/M3KX8ZBgz0R2Gdi8+iosq34CwD6wgge7RlK432wwNLIYIbaPyi1WIDENCuwbJe2kl1p9dVxHkhFvgAYk9FvAbeEseMozmZEx8xKwFaasINGbKUJDdlJmzpr+c/izor+Oq8qfI7KiCWh7m/GHOyj4JbYCWbPEQiBsoZSeswA0vC49NtWZymFzqAa/riVDcu2RE/kan9LZUqv8WkKwbD1eBM+rtKV61imVDmrlnNDr8V8uLeXUm7P9FjPFxOAu3yopDelwCJNxbgK9VR5rT7+Iq/ykjQIIT9B0SVJdPrvlyrreAjat4SiDSprmaw3r1ETWh6CX4HCY9A+D0rtGkUXoTKzBUA5fE8LVtMe7vPCZH0NPpsEHAD0d5YC3IYam0boulxbFpPJ+joZzmQ32Z6Hbq0OywrZ92kKhzvVoJwYmjfayKYqTraYE6UotakwRWi6g8yxfryRMn5OJo020qDSyP5bnwP3SQgvdPbAhcA8SPyqgOIrUin/Kob7eYHZZFOdIwG3j+LMRNDgUkp5a6gdhNPmnMVD6k/jurCzEBJqRr6L58QRXpM/sMDkurgjWJAaLnqPNzKK/lxJt6AymLUIKMVSgFseWWDt5xm096WPpLpP1nX8IG2GbPOwFEP0m78Br4G87Am1/KonrEZrlYQ5tms9XX5HHu24lblsIYGhWMo0u5qpIplh+lyKSCWJN3mG/8dOEuiRlsOSeP1k0M7LdPmdWat5delHAFzPZ2yjAbfzOjdIWLpAIIRErlPTnzINSJP6mkxhIsOYUnYPw2OfclBj01lDVpOZlDeJ4WstvT1XB7rJfEtP3mcnDSkjliZsZVeVxkziPvZSj3TWUI+9TOAxblm+gPZdHqf90CyymQdDVYYz93gG86pkM1wCc56ATwmr6lragrEHmWpmfft2mcU8shFGfeWYExvUIhkUQ9VqBj1OGEqxkezfCDJJUwGwyMesuwSUTH0h0Ar4IuzdnnEw/LqiCIzomFk5MKxIltKNXixkL/VYe3Ubatc5QEmtptbzevdjQFX1YBpK+2zPNPOtFTjalpk00shN6U1cHX5TN4SnvBlERbYUxApU2QOa3hlcM4/d3RqT8VEuB6lNDOWwtiplTWuoGsp7PCriW6AtOfSs20VyEDvqNMMsNR+ejZ6b5YZOC1kr2wBQIgZwZZ8VfJmkCkBbpn1NbQ6o8Xk3kAJFK6E+SpZiI7r2sgRW6vdXAhyB+JrKogRQ+f/WqFrQcdDsjc2spIOi5e4FpgJfqVoN+areZqMXs2JlhL6GmR51bntQgakJjzk2yhwQg4AJKTTv9K352zxCdVoe/5rvaOv+XUhPSHEes+2dIdZXcsAoXwqxPgAN9qTRYy8E3i6kpTxIbQ5wcHVtNtGcL2lPEptZQ/rJOe4pwLk6boad0BdCxAohLhFCXGq8TmbHfssQLykFzVOlohmIijqH8P2XMplDEzYyktEOlVk7JvCwSbsMRpd1g6/irLn8CjWzdQ0fRbQ/A9tstXPhKK+uIPLC+1CKoZfyNYwUTGZI0HZuGMnogJTLQN+DHdm87lTXneRO/Q0kAHUiqHf0Z9fl4VB7AtFS7+J50llJOisRi0+cHgsqu2BkGMJBJDTYUPTbKH67iI6ZpwehKIZmuwC0TV86aWVREYP3xeug7IaiQAoxy5a1jCIY1HXc77csku0jP+aLCPFiGP3yBqVqVwQOCm04KsYBlGQjoQZb2zmVlsPpaxS/TYQVTgshHgDGofL8x/ViCUTpPcHwo/pz4BAsAQZvh0Y7f4QQs0YG7LNVY/TDrJHpCiQiYjz0Fo9Q6+0ZR6MG8kPxNW/LtQC0I09lIKWqq+w9fo4jkPuM68PrrA23MYXmQHM28jgTeeMZ5/oq/z7E8R4ejl/oVQ6SwzyWsi5QnlCTmGfUFOXxP1opyH1fKT+puF1qijknoQfprKFYvsQBlCLqyrVa3HQEbBtc39y2tqbH2LGb+pQRyySG0olPTToswCaaE0M5LfiBFqUTuI/JpORvZXA+rE9LplX8RoozUnkoRwWWF8cr3k+P0hx6Zs4HwNvK3yF7ev7d9E2bRYvYDYDle7iWNo7v1hAFMgrBN4l5PEQjU60wUfYjm9nspj4rtD1KGcrI7R9d7mMnCayxZUV3D2usZqINbEEJI26Abl/9y6+fsgsmrdtOt8liloPKaywHVcc6v6dyfaz7nntgaUc6KxVFF5TiIFSYSisWq4ykEWjKnhXPbEJ4dLbEtAKd6TYCTf0/lwakQe2yXRzMbQDrUdoOi+AsnoA9Z4UJKoLomHliqM0BVtGB0YxhLv2YflypZJdsaQQXQ42r93B4TpxFke2O8it0gfHgLqXb5N61DjEfv21tD78O2uEBbSGh2RpSz/NJn8mkcOiwoR7+zWO6Ls/266fsBAmyOrt21idm5yG4djt8qjKYshNmJtNQk6U1HKCWFvTZRZX2h/h8hh73X7YH4PoY//KwYwZK6OZxlOoqqCzgV3CgS21KxBvmVl8KD8QBe7wckNm0Xb1BKbleB6yE+PMh8X8Qi6LB1qkKRcdUwjQJSKoPOeugI5DSVO2zdD3UKIQavYAhsJMEVtFB3YKNZPRE4DJ7/7vDPe3hZd3fBRZt1q+2wwvU068FmKUlm3cm8UlCZxqyk9w+On0eZGIyEpVYEe9UN3ZtcwLUVyOANMR9gtVc+sI3Y6n6caVeZwhgLXFp495Xv6DybS+ke9gQD7wHl3T6hjk7+3FRwnbSWeMo+TnbcK6Om+Hmah8CWkgpt5/Evvz2oK9ufDMlqLayBN4Vv+PqyRI6gwzyz20XnPGlTvqqjILKgro9mGeilFib2IyZhfByi2jDWKlurOY/eoiay3TyqIHSczf2Z7cvGa5tSm5jCptI5g3lrOyOVkArD71nzOG9R+DK2w3uTidHM/mVmkH7XKyijjyi+zoRKT3kgKUSu1JNs+WiFOYanw+N5+yG/tIMGYWm2EpDTmOJ2iarx0wA3tA2H/bz/YTODEJt2CMth8Ur+sIKaxAQQtWMzpRZtCOPln2KYME4YBxv8ReuYxnfrU+DW+AZhpGYVkB1jvAAL7KXegDM4C8ULVfR9cguo+hHczroIpUHmco4YKS01aZqzGIAAMViLiNkOWMYBcCr/JUben6mznWxnnD4I/yhyyf8cPwSDh6vzZ7/V4O4aYfhEGSiaMtieS9n3eVoYIPtczz0LVWUtXlkK+9LcFWK3VvtQsQFer2PXYhhK+IGITYgZcuAtFRjuZH1n9rzwbCzg3a7EVMdtmwXtbpbn6127mqxgE9A6aTF+qJWxi64B2iICub3hdfXMxUnaZCsK4R4BVgkpVx0Mg5wGvAQ0TGzwlhKNybzN9bSho00p2+VebzFIDimfn+H58TR/M5v2fSKrnnrfgw+sB5ngj2oW8Gme1DpttzXgsSk1Y7x2beWOmi4cAslvVQkFNCOxI7WiobpWx4CgR7SnZZfRv+ECK7ObafIihwd/PZWNa7JbGQMoxmSMI29g+rxzoBbYZy+FiPvAF5T7+dA89nfsknoa1/VA8f0tRnu5T+1PWD6V9v74uGnb6CgfSKpK4rhQ9i2WynFtqwK3xxTt8ifjimDmEVo0feakLwbEhpiKrPGp6BotdcChyCWMjXB/Dhwo16n0VxmAVvZJNrDL8AXHvgrSgNjJVYV0IMZ+k2utfFeVB1rb2BDIccnpBD7aJkau23NzGtqZLzF/f4rHe28GNxuWRo6cxkuvRacv1U3JWM/uBCvIlGSjQRBM5WbgObQstPXxHCMbglLqc1BYilTNjNnMaLBZWD8FB0kI8c22xXbBmR0PLH92X0uDch7nfRaM6M5HgcV1s3D0qiZHKVnJFWW092bwhLqUX+nBvGzDBpUGsiw3vZ+Zg7vva73r21Nym0FqvbB0v6+D7ao4kP15zDKK8uMXYJYbfTsMZ8lg/rwVI+HSSePKdxFJu+RyXtmoNwZEPlKAXZ6Wj/Ec+qua8z6Sum0HZE5wBw9zclIttAF8Y6EsUsoltcylEmspj2zVtxBVqeZrhMFAC/yAIAZYtkzr/+QJcCLvIBzAMtmNhmG/6gPK3lEFzW1vqpKZ8R38OylDzPm/PHQGu6ePB3RMshoMsqjvq8sKLKbXrqg72JVhxkKeeh/Bmn9UwixIUDr4LDXUJ5ILaYdtbrvcgScQY8foN7Z3D4dfv1Y+bUROElyLmOflKGqjs46RMfME8DlrKOIJGpzgAbsZgaDODw4jr/kvMrS33Wj5JWmKpj4AHjumNro93rjHoH2GjkCjT3mskpQyZbf4KeWfqLYSVOzBjVBVge2UHJ9U/T8oyWwA3AQmlHElaymH3N5ccvD8FNVGIwKoAqA2hcrv0fUc4IQ84B5StBnuBe8el2ODroNURy8lsgQhfB7OCBrKxPLG6D2Akj6H+w/prQSdqNcRuoD9+uAs2gLtKwLO0vUC9SpxdeF87YAdaHXjg+ZdtEQJTC3SR/uKqB2CpuErhdFTcAL4YUGHpiuJj4NpXMroMowxyIpWyIeRMWBV6XAEdhMEtU56hqUGZDHA69zRUZ4zYLW24bIZvrakRh1l6ICFTHB+9EjjDbOdSIeeBS4HjYMaEub2V/QmVyz7nrR0/oh158MFsUZinCDS49Qle+LUTa5AEjpSwKJojIgRkDfEKqsoeo1hfDSW7Yw9+cmyCM6gAzgcWnHSEYzXqiZl2A3DLdsZkWgBH9U1tIOo57TCPzCwS5qc7dhoBxG0GP2YQTKcykA9hBLGbEcpgbJT/2kFj7mDNAW0k3L8lQe+vFmRFWhvpMRqrYyuHcpEJGtjdD6SH0DeV36tj9Jqna36e/Zd3LDsl+xJgKszGmLEzpmez1Dv1pn3I3rVhkCW2caJOKskEA/QxAdM88SGKI+Ug5xvHdvG4Lqd5P7Pc3IcJ5IMBouzVCsQKX9UAJ6lY1gtlMG9fZMhW+m2bWN+D/gJvXeX+g+suMZqibSP+tttrFRp40MpUGnVZ/DV5ytzBpGQyE2lIiPPx12id6uh89y/76Zy+Iq91ngTMK5Om6GG1zepF+XAAY/TwInPFAKIS4B3kRNVu0GBkkpC33axAAvoP7DJDBBSjkt1LrTjca65G/DbliHNTkVjA5rwMho2a1HspjFFF1Q0MPIUGHRYUUHVB2ERkB7kcc8DkpHJA/5RqC6lWREBxAZavkN4xcCzqpw48bsO8CVJ9SE29WDeQdW8f9ef0GtcElcuQ2orsHnZvVnJ2oGdPiLTwCWCiyAyY/VtNjvuJTiGQl04lOT2vhs3GNsa1af50wZPKCeChoH/ziHDTlJ/p3UKCCJufSH/o/Slq8BuJuX6TtmFvPHxpMoppMom/EALzKp0720ZzXvlg4EILnLeo4RQ1X979WOPOauH0y3Vv9iALNoQjkL9YD3d9FQH3EuL8sikHA3b5DFLMYvHwN/hOvK3mcjzc2+FZHELk31lJdCa3ozZvt4Dv8INW4AWSjgLnWeKmDqpKhAeSCNAbkUwMo09h2qqNvzJ6mg3S8LmxHwUrlCypYB1xm1lEZmsu4so8DqQtv66kH33yL2e79lbtlJY5mdbuurMFuco34vCX3UVHoSBQHroIlRddfxzVDy+1GcCzhpYyb8tsdNgGoc5QC1ySjPZU9MPcavGkNiTgFvbbnNpL+WvNIUhqA+/x4rY5mhGSQavvRY+2e7WmwkCEg/XaQCzAP7a1Nj5h7K6sQp2xLNJBG93GmydjpsOAFCQBrsp/592omi51afq0RtjsT7GHHOhAfaPc/ohLE0Yju1GuzlIPXgf1VVnX4tlKLqFA/sKYVRNg5nHqrW0lMEnyYBkMAWGkpVYnIbb/D0jxKaL6DKz924MWExJSTA9vUUvq1qKOv8D/bvh/3/U8yjeNSz0/Td6se9Dfh1n1pn4BiaCVIV+AJ+7l+XJmyl1jSVkj14cQNlH7ZAlxfFAXtWgzE1+98j+lrb0o8OtfNFqCN64DHMKolLhn5DU7bSkBI+/ulGx2UUVQCRYmYthVgNwn0q2Glz0149j2W4NtX4IORzWkUDS+ky12y3HgkWZIZTe+l3vEDnMbxQqQ+vSWHz0STeqHYbXjx0YylNHjVqLsNPLERxehFucJkJJEkpD4dsGTmmAJOllDOFEANRItJdfNpko+q7U1D3m6+FEMuklEUh1p1W7NS8/3xUobr5iBECKsuoRF3GvqQomfJeyGIN07TiaCjhk6A3otzw+mFgHGPIkwv5cETwjNelGLyL8DOX88hC3u6cIfTNWBoYzrOBM2rai7EENSQcI4Z/vP44T+d62DAjiXKqMt+k8lrXZiiT8OJhUZo6tyxmcinfMX18P27d+A5fJbcyBWz2Ekd3PmAK91BEM3qI/6f6K3tQ4EMX/VP+UmjnpYeEMmqwXt6PBy95NOMF7jfr9ibHWzMNZnCyXtCv1XTH/gYunwfXeWkjuzFCbjIzyXfzhqPdhi5JUAapFJnLxHfQ8dJVfDipl3HSrBc/sv98qNNRXzQfXJm2gi97hjcFbdRcihEWJTto+5PgwwVQjSOutFgj0z3PVueZxwly1DVW08mRHa6VavDNbEFrKcR3hP2roY5TC+mswrkqqV5BnMwxE37D4ybAXurxPS2oHXOAvcTBAtiR2kgFPh80gIfgjk6TeW3LXTCtqrL6uQOLEhkhjExloIyl1c4T8CHeeV+Lc66cSAUm28K/T/q2Tei0Rb9rai7zCyo1EmcXMJLxHKA2z/IwtWMPcPBYfa6+5CM+H349LChUmaWrgUXxymf6Jo8SJ/Mo3+y1tKFkKPCsF+RtlAhV8/80yVBcDsP6cPweWJR3M28X9oeWkPJ3lG9xUzi2VlWR1gHSqsKy3WquoBT17JRwPtSpg1lzSRvUd54H3AJv8f/ZO/c4m8r9j78fRK4xJlMuMyMzGULMTE2HaEhEh8I4ZEolpXJSiV9y2223dEilo9JRSUi5Hk5EQsRJjeFE7mrGrWZiyDXC8/vjedbaa+299mUubtmf12u/ZvZaz1rr2WuvvZ71fb6f7+fTncNU5NhDlvvuCpAyXdmIAHJKisoSdnCpwNPvuVTjuhBu9cx2FnjeDX9xsf0/Dai/bKPy1Nzkfx+ena3VWUh/gkyWIDPZBckpyIHW9SmQTwX7QNfo+cG3XvRxZ166I63c+PzGfEW/ePgYzrpL0KfkeA5Smfm0owufFHmvLxSu1HEzVCuSH9HxUVFCCFEFJYPxsV70MZAohLjWq2kX4F9SynNSyl+BeUDnENZd8mjNfNMGIj8WB/mBaKyzmsHa9XemgYiB9vftmUlLPgv5+NbP6HjcECWv80PVDIS2zGEOIZgdhohpgfizflDDT82qVcgpFLzDI4hlIJbZl0ee26eNYXwLQyLP7fO7P+/9gJ2C7XSsYCiQlPu16nUh8AjvmJTaYBCZ/q/DQOf1z4KzFC/y158U52XMhPC4GQr8WVsF3U5M8slm2qwdvNRjC/sQL8Qf59XqRKwEcZfKJkaxO/gGfzJYn2eCUWINSw4x7MKMPf6+d9HRTpVVbdfmS9TH3O4p8qUKq46V78NYtvV8pkC2JMGsdv5suBLHzVDD6R3AMiHEPOz1IyGaavhFDWCflEoKU0p5VgixXy+3akxGA9mW97t1m2DrLioMZ6XSqCeBLdpA3Yn8J96CGH0TMALMDpTn2adG83rmAPVA+7KiHzplLc0faZL/GU+D0nrfmhm000UZYqAKZpxqMq1YMrA9cpQKLA3cueYzUlnOSW2BMRL73btYv+OcG1sWf/h2mCUzNhaGy37qH/DMNjp8ltu/U1YpQqzxrK+n/tTfomg1T70/mOt7/ETOPVWYRRrd+YjfzqgP2VU/kxmU44d+/pSH+JTY6xUvt50OhNPj3uMMxfk0U/Fp2yXOpAEbieUn6jyVpVRA31aZ5ToRWdAVHnxLOTb/mFiVmh/9zBfaVfsmdrJKD+6G4u5ZSrDH5VF4iXVvIRmYVq8Tn8x9GIAl9ddTRnbg2RajQZYil83UZyMfS3wU1Ga3TWc26cQs9Jqg+B2mZ/ZgYN+hjGQYkeeg8tnblZF1Lhhxv9Asn2+H6u+lH/C2z+n3HE/oANkoNbXE2KZowgHP4GGtT0xmNUk63WzNNnpqIVVmUVyrRBe8M5KHS15nex+KkE9+s5XeVFjbuo6eQPPZxHl8dO5BAEpdfYoDxarZG8fD9+8oKld9nSW+Kl89CeMyxPkaM+FPPm6CUi+tyn5qsYvSnISnz3B6VgVO34yiwv4X3nu9txLzef0MNCyhMoMr9A7+6tmXEEBTYJVv3VuwTKUT7JRGT825iZf036m6/RFCghB/2Paf33543mt29FfO6mGGgItBhzx8uiIbS9anNYvpxTsMzxsK/QRf//0u+Dtwu97P85Pgm57KYCcW5eLRz8US8T3wAdVlOns3uGjHBNDj07eiGcSgtlkBVITXSj3H4N9fVaJ7JYDfYMsZlZ8rgfq/PipruQVoeTXs+x0yf4dUw6p6HSqLeQPwMbR7YD6LaU2bmXNY1KIjHALtTgYf7QC3/gxz3fCkS31HN+9QGdhyqLHQW8A80qXKHbMAHoXroPKsfTRmDXuowX+LN7edf4T9e8uPDYmt7CcE+xornFSMzWvBT2DpbUvi2ZfxX2pQxVgVON6KlG381l0G6qsNrwPP6u+oKRybFcnG7vWpy2Z681ZQIcErFJe0ynqowWUpFOHE+jR7nsSKzx+EEI8DjwNER0cHaV14xF6j/q7+TdUCNnA424tpj9CPGwYt0gguX9N1f6/rtqF4ADpSD9bYs49K+VXJqD5qbeen+N8aeC4Y2Jnqo1RQ0SvEbM+S/u0d933fUKVmu39oVTOgC4RnE0fz+i0D+Jq7IMPrJqUf2k+iBqROPVTQPCsqHSHKM4SnzHPThS4cpTwnKA3A6utVreBQhtPj5+mkXT+VcfRlCa15g2f4pMLDAMSyhc+9Cusny+9ZTnMezZvAe+IAp96KpT0LuCFzP7Ky4K5FX0Mb9VN5hA9oq0uuMkhmFU0Z51Z3/zI64MwgieKcYX6HdvoIWQAssFBDFtCOafRgI0N5cuUHvNpCSfM9/9cJxJxQ186BYtUQmZ660YGJHhUGM/hpiKoBXAwiUf+cK1pqLMGXaLfCs9z7WvMRsTkQWBY/EJJ+Xc26a4uGuhoMIakba/gT6nmw2Efm79UH2n4kArgq7HN5peBPMWbChR83AYpzVtk4UZfDVKRy9RwOUo2YlK1kr02AVKjywm7Kc5Rda2/ybJhqr7fML5yCTf8ek/oB3ju4zNfxrsKgTBrBZVHBFNb5Aqz0WG/89vl15LSvwmEqsoFG9IyYxFtzjyr/aWO46wLVZSp7W6BmyTagg/Vu5n72imlQycV7n/Tm1i7aiqQSoK2MGQv8HbrzkYocd0POfrW7OiUgoqqqrdzxm5qQL4H68eT8rqzF+F0fG7UyZzFE1VCNNtCIFNYyqtswRd+9HcoNOECZMifIHTvdl5X6DsB0WOAbpOPS1NJrgS1uoAk81xK2QuViB9lMXTbQUAXYrwb5EgqCFb6L8hOo2rYr4FRWQa1IFCXXWdDHqa35f3egO4gpcGv7leRQhViyOEUp9lNVTTBdpjiP4+YlrbIeEi1WSvmIw6tHERx/D1BNiwsYIgNV9XIrdqPmvwxEW9oEWuf9Od6VUiZLKZOvvfYCce4cIDrrlzfNxou6msY0H5rsOHozjt74g4eS4DbpsN5U14LQEYRwM8Lb3yJAW+/9d2CGuTzy3L58UQgLQxf+wVL/mWJ6b11YxLI133TXYAhEMwYQfrJuXfgQsQxHumx+YXyfzzGaNKY5fkYhfCfU/HlcBkMv3kBcq/4aAj+hbpef9sEQiBoL6vsO1iaMPzfO45gJV+i4mR+ESosVEQT1GDTbOoxrgZRTzTbjfAPQK4kSeKlD/CgRP9qjKcOvtEj2L95EiDcDrNcUUUc6rDMlO/gxHa7VAlBkQztWYAEf/9tZPls+fodhXB4ImLkUQrSQUgassAqljT9IKXOFEBuA+1EkkvuB9boGxIqZwGNCiDko8YH7gGYhrLuo2KgzFqWBu4ASfs62fMoeWFoVYgFeTVTBpL8JUvEWcL8LPg5+8/HxydRZScMTc24gY0hg+Kh+Jg3W+Au+lFhQqrBVx/xECs61AsaxiuccJ4Mkm7clH+kg+SOIkV0Alclt+J363/B9NB4gpBJ/IwqV62tqCSQ3yY/ogedJ4+H+n5A9JoqH31eZwM09FEdkPvcifpbEXL+VsfRjNY25PvMwzyaqQKwD87ibz/mcu/nfW7W5WXTn4bc/4VapjtVGHuDTiI7M2Pkw3WIFbRNns9mioLuTOCbRk25MZzxPk8w6VtEUUBSi2mznXzwGQDQ5xPEDfPkQJfkQNy4GMQKA2mwHYFTmMCgHz7edQKeF0xh4YqhnuRdGieL8TcbaF+6HFbshdQLIRwUkSkRbEGAKHsi+nqy3HAXBJhCNAFPNYGrqtYW21Ys3WIdK33lTWcVCaNVWqWDF8hNJv2YUyrcyvwGlEfSGSp+NSVS/pVyUem+6eY15zRI1hOozIaIOLP1SLQqgJ3FJI5y5DIzzPWbCn3/cBKjPRvZQg0ZsYCFtqVjsMAfvjiJ7dxy8AvwOufdFk7sDeA9FidVZNvEuticbKfWDayWXqRQbzAIhVNsPhrjgMPA1il5pgUE7DTWzKeVV+mEbqK/pjd97H9u4v6ZbtvOmYwY20/VRB30T2rZfyEqaspYU9r4VB58Bc4GKKIbLpB3sbRSvMpagVOlXAd/Ew236fIzW/ejq5ttP9P+HdsCIeJj7Bwy5Cp53s63vjUQfz4VTSsTHmP5duxsalIUGQDXUuuolYM4Z6FkWzpzBI+gDRDVEierUhNpsYyP1GThd26bVcXGsRCTHrJ8zzQWzdsDbO6BNPLRxafq0EVjqPr+Kyh7+BVhwK/CHosfeB9t/rE/JG05Rgz3quqsIMs/pGlHBpJRPO6zzXFvCEEGywFvdOBjyyw4SdX3psJ595ee4dnVYj7+lc8YypOD4oR1svK8+VSrkcJTyZBFLU1axRo+pRTU7d6FxJY6bwWix44QQf0E/b/rBq0CjQvThCeBDIcRQFFO+O4AQYiEwVEqZAXyEouQbUuvDpJQ/6v8DrbuoMMoDTqLK2p7UsZNBb42li9nW6jdpNaEfR2/G8zR9UDcr40fmYyl4O8jpVsUu3xuOEVgWhKr4nlQBTQ8dPBhZTGuA6e/Ya0lhPzVpKQ0RoA0s5R6f7bz3YdyM/Fo8WKFZI9W+UYNTOb4FoD//YJHYCCw3+/XqmN6mSm0a05idqc73zT2VVUVWZgIiM4GYxK10S3yftlq8qFXmKmIStzKcobqP6lYXea4WtxbT/lR5ABKZJ+jORwzgZUDRXbPFJwyRp2mdt4LsrASyaujq22uNO/pNxHKj/szqAat+C6VMt9aiGLeLWqpeMckNpV20OjGfo5RnmqYoyUQYt1BNSPRlAmKoCtA/1XYrsgWsZwqc0bV/vYKfXhOlPf9K6UKMg9fHeRRj7dlL39RdYYLFiTzDxF+BfO6jMMd0gkxUWcnszARkIqRjN4vNIkEVWRvYoP7s22hPFYXxp8SFGDPhTzxuApykNNupzc9UpSKH2bW7NrWit7Gr7U0qBrhWUu+GDCI5CGPgAMr3ayO3qODyDIRCGMvvg7zP9sNAeJUO+NRYrtB/rYP2q+dHMbtAGAIlOEtLvmQzdUl+KoMcovhvbHN4BGRdEN3iVbD5BPCyG3CpwPw2S6BkMGQruZRSax5AvA7sr0IOg9rDOnCaVXANUA+a/ARHclVNJUDpssBx9b8RdKbr56a9Z6CEJr1UqIIq2bhTtV+qp+tGtR+mav+/BFxueMSlXkf1zpp6Bd4HgLHGT1UHP88bK5XFBu3aqM+dBnVGbaALn7CKZqFaTxJI10uOA8ZpWq6DSVFBy0ocy6Pe8tRaFiWC0V992zsF4znm/yfnVSI7thIlm53iQaawgUa0ZrFe65+1F8alhWDBZQPgGM4DpdTLf3FYFzKklFtx0FqW0iOnoYULnvSzvd91VxpsGSZjmU6iWIPXQLOyg3TgNJJhZgA52OoV6e/YXn5bBhX3+gAmzqI7sAmKWVgVjr6WOkYprDG9eB+G9BhExSDt5tMazJtZAEwQiCGSygc8i2LZygBeyVe81oEZxJKli9YbmssKinTe93acDAmKamuf1g5VGdaYuAhUUhjouruUUP6E+kKdPDDBTneNZatPntM4FyFNilxGuFLNoPOJ8z5mQnjc9AcRAcEY/6axfSj7K6wabAtsGU0nemyo90Mja1kYlMrzeF0a/ysDkADHjSBoICUcUkqmqJvxfhjcGAKN+HKFcV0ZPpchbaPFl6zZZjHUS/egIH15CnNSw1+WsigRcqaf/F3zfwZcqeNmwOBSSpmP23AY3qima+hX7Fa37wXa99Ij1eL78GkNpMRb8OpTyj9yKXdSRnMSrZlN0LRa4YYX/f9gxVsgn1LHsz71GD/8gTKwCeejWlW2hw5cBzOWQQwlg2SWiPUALNdZ1eaWTE5xi7mnka3swAwdPCla7Lm1emrSEtdYb0DGOVF/td9REsyWbZndoy2dxEJFlUJRaSKAxK0b2JlQnfE8Q/x79lKi59+fAMCaHtMYQ38GJY5kJIMgU2UYAf6d2JpTlOJvIoXV8l/mtiU4y/SOPWCum2n/VMu+LtaEuCPZxFXYYrabGPEwa0kha3wd6vZRH6CTjGO4iGA4C5ESPuNO07fUQNYmlc3sUO9j5o67n7i+myjBWU5RihTWUhuVXd1MXarLdFJ0prAMJ4gihxM6k7yOJFbTmPFsJWmYuvCy5+mDJMIHPEKtvP+jSTxwHFqn/FutW6H+SD2LKpYBLVW2E/RMfVtPTae3Mu1ssZMY2cVGv/YRTPAD9VhsD24Nm5d0ZpnLQqWv5jdjue1E7Xy1N2ix0zN72CY+jM8uFurP9BscOQOxd+LJE12GuFL9uvKD8JhZNMghihrsoRvTmU87Xo7uz4v9X4NvgP8BCDZl3QK3AQ97bfy5+iO0vpbMc8hQCpdjEOCp++oGTHfsm8+DsfZYtvpY2mr2LMvNffS13w/9PZw7ivzUT/ddZuzjK02n9SI4G5+rpJMDVnXJBzzCC4xmDzWI5CCbT9fhmrhf+O06pcpd+p1DnKxeCbPCJV33/S8ujMeYKtN3k7sy2rc8JxVjzpRUlqsxrCxwNZw8CEd/V+yuCkBenuFTDeWvhrzfIecMlDgODariEfT5HUWJzVX77r/zTd6Ie1zZCz8LzHKrDOo8TNVeJqFEiJ50wdtu4A7omuqh+tZxmYGY6IjK1IIKrO8B2MGWHxuy4YbN1GAPpd85pBt4eZqaGylI2de2xmBlqe+8m+V4b8IQXxptMDipxfpFarB9rdD7CdLQz/EDLbey0Zy36Qb3x1P6PnVey3CS8hxjG7V5jeeAy9Mr6UodN6+8T3whoekd9VEq1+mVPasM70A5E5NOIi1jmaoXUw/HRt2eUYvZnX/xkdhvtpXS5V/Rbg2mGi2oB9/h8qhjW7DXTpbAOeA0smrz+g+j8iv7qHw2iVrFdmHN9skxHgGdpqxkEKd99s8WoLi9BsTbADiWrcQkwjj60ilTRTwxiVvJJoFOwmNOvPP/qgNQ5YW9fAskvgJx7IUPJLIHpo/h8PdHwqN6/z3i6M8Y2jGf0QygJV9Ql80c0qp5NdhDG1kH2KzOfaKqjaw/ZyPzbnEh/l4CGEkCWYhrFsGXCUxtkcY2VJDyAqM51Kc0q2lCIy+KqApAVHSazDrmch8prKVLvcl8wkPMBWL7NqQEZ9m5SfmsxNXbRB/Gmzfavcvi2HvnNDrJOEpzgnfbPkOnhdNY7VXzZ1DGYoapoKc12+nHfCo0AQ7C8YbFmHOqA+VWn8Vbs8kIKsFOITZgTJAks5p1YilOKKqMpRBu+MxFUogWpd5BqFVIKFBgamQshVadlF4iC0YgGYuixqr/PcF0FgkmxUnMAXkNxFaGLV96cgReZiVhhBGGBU1ZRTIZbKQ+ZTjJYlrDBqiX9x2bXrnFE+RUBxZBlRRl85TbUc3oyjn+BUIcg0rD27KSMdk3XY9FO2zvA2KFosQKS52kd2WtLc4YEnh/6r7pkXmXMh3RwLedk3+lp98alTyZsVMRFWx/xdfQ+IY15BJFGU4yL7MrzyaOZg2N+fbQdYCbk9dUg5Y9YaklKPhS9z8NOOQm9wMod7y3rc6xzq/rKc4ZNkXcAlPh3R/70PyGFcTV2AtnYPVxNRlcHiUgW/qMsnCrdhvs+Abi28KRZUoxNi/Xo1tRIR7yNkDEO8Dv8E1cQ96kD7Xe/4Fd02/y1NKmqvX0xBNYva1pvU+iAtFDbqhjV/6Vc1QGkeFu6H0rxLWh3P8qUaPMBnMS9+RwHVRqdpZxXdmCRrEjYA2slPGmiI9TfWZ+YUzK+1WJXWFpq8V9isQcCfuzm00J1uF/x+CyUjx8Dic3VIJysCexButIIo6dlORU0XQyjAuGcHB5PqHHhQavQOwROKKDzUAkFOPBtRfAXkzhF/Bk1ZqxKl/dkF4KYZPoaQYFIXtqOfhgqgByl/l+v7XaHuVn2E+L0FhhCPkIP8lSpz51ZCEy0Vo3CjZ98bV79TF1ZtiuicQHPMFuonD3cBHdI0cr7uZyluLkEkUf3qAD8xh94kUAjpUpz99FTWALQ+QJ305OBBhky1jtaFGDeLHHPHa3xGn01TJMWTPq0LTrEuSwVowb8hTjUQOJ8d32ZyyjvawsskigAzPoU+8fzKcdJThLD97n8LmKHCw+SdWVaCymNYxQ18ixc+U5+Ho1Kj/rUeS1ei+2Zjt3/eNrqAJsgL+XmuD7+QJhhC9F+QCViZFdyBafkC0+Ud9hiBnLQLBmLA2sW9gkoAdlQeGPBusPRm2lmIO9xhLM/ok5qKemGhB3Bq66ME4O5w1XojBBGBcehq9dTX6iKSv5iAepvHgfh6kEr0CVvN0qkIwFDkLuwmjYC9xtqbVMzf9xZZ53Jig+OOPCycdyBEiHQDD/GGV/OP/ef0tbKYn3ylpQcrEfw82HYVdeLRqynvbMJ7dhFV7vP4BHx0zgW5brRndAG6C0CxZMgok94U43bHMRk7eV7pxmeN+RHJsB6sFHPTBsaduIRxdOICXvWybRm7bM5TAVlQVWWWhZBbbkQnwVyMyFatfAVb/BJ98oP/B9C5WNW+KdKEsnLZLIfogYiBoDS8DB3pW5HjXhvrPbTYidqHrKUsB/UQGmzmgbgSQlUIElKIW1WK/zOQwY5kKMB96EY2UnsOV+F4OmjySDZAiomzQdq02LN2zfaSGsc6z7CmWcDUaRzU/G0vv4vrg1X9sIsUMFmPdB6axDZBFLXTZzlPIMwngAnZLv/l0KuBLHzTCF5yKguXmXKxoEsj3wJ2GdX3sPD5U1hP7cov6Gav0hblGv9sykPTNNWwvR3dJGuG3S8nPIX3Rh3bYthbybWzCUQSZ1s6iQxjRqsJMaOHGYCoZp+qyGimAKh8b105LPaMlnPuuFH8cCMRS/8v29eCPfsuuP8I6Zlc4PnMQTQoH370b0Kdh+wggjjEsb/u5D+bk/iQr2l7/jFHT/zvsLzr0XK9XrQsLbEu28HacIgnoh9HNVIe0/LhWIuudHzMd2DIsliQgkZwb5HufDuPxQ4MylEKKYlPkpXb4C8ZP6k5MLV5WAiAhonqNlm2d6mhl0WJO++gF0+i6STqOm0VjXLz7ffwIxY9RD7QnK0EnGMYt0TVUM3hUxEJaMakq2UKpqhr1HIBylvOPyJF1kMoIMqmoJl7l0pQP7mXeL3cpkFc1Y60fhXmZCDXay95Y4x/UA2d0TbDPQ2e8nMLuHJ7DcrTV5o1OU4mrKDYIj+1FKcsBPZjZ1PzFakayNnMNS7mQhHTl1Qt0Fi5c5S9+16gtIS5lKL95hi1Q0zzqZWebxJieq8zZvRFeGzBnk6VilNkyhO0PkIJqyktps5wneJp3ppG+aDfVg1ZJWkKIUf/vwJuN5mgyS2HZC1Vn2LjPJxyuyOGfZT1Wyhao1bSUbQTFoK/cDvciVUWwDcz2yCw8W+4iKfQ/z0m6VNY6J9gSpoi1U/o+W+ykBdIYPnnySyAl7MXWbLD4ZJk2or671TfS1s8FyfNN82gLHWUrdxsc8IQBCFr7QgaNs28Rrmcp4hhpYetNh/WIF4Ket7AgsByrAVfGoGrHLFOfRDPpPByHE1VLK3y92Py5XjMwZyIyo+zlFKSbyBKcoRVKxdaw9fSuVD+wj991oas35gV0LbwIUU8CkwerMZbCMkNFe5oGUntp370yQv/uOP2ES2UDVXAo/zHsjyJRHAh+jsGUEBh1TeThKkwbrjXp535HKCoYzlNYspnixs5R88Qjvze+tdI0TgHvclHuiN8ee1yyXDajaxdp5ZFOJ4a+OhKVoWw2XenWAa2b8wnvFeoN0Qz9Y1L8jaWNmK3bPFpQJTi58n6tqLef8phKCsSgl82qVYe1BSNyCGq+M4fxOYAnwABCllNV/piq7GqjrgRXAcjeq9vFz83wKAfwKNAXe1IHN0y71jGHUzr7sdYKe8QRADad/wyzSVN3oCr3wcRDCY7dmyyAXUy9nKrZdzMekxxZw7tvvdeonqCwMHdYeFN7q9X9gSxLHfT1nsa7pF0/9Chu5j7nUZTMZJKlMMWijtssLV+q4GVJwKYSYDjwmpTyu31cFPgbuOI99u/yhBXyi2kLeEqAcLK/VBnYFNxOafUs6nb6bxhoaM7u/qnPzCAB5hIBmkY5I0j90PwOUoSJrRShKltb6yGD+l4AZWMrv1Ht/QaUVe4iD71SWM4co+zZT9AzYR1BLpgE/sCvzJjplLvSvHNsEKpwBvvJafjsU++U4P0TdxN1+MsfpKe8xbeujzNr6ACRIfiCO/vyDLYmxAPRlHJ/QhYV05OG5MFyMBFSwSioM7zhS1WrMAVlLUObmkyqwBMbVewrqwfOZE+jEND7iQQAzsLSiMWuYtekBANLqKTUCY+Awgs9nmGgq+84WO+EbF7/XF9SmC7XZRg5R/DvaY/dSnqN0PqdmNA6uqGYGlnwJkeP22o7vpT/gA+PaiWUrO6mlagyHeI4VKGA0VPDE277rJvIME/PhswWK7pxfFIZS6/276TZepcSnBXLgqgf8iBKguIwLEa5U1bsCIksoec+3pZRFR0G4QjAm6v/IpQpruZW6bGFJ//aUHqKEPn67pjj0g25MZ3g94yG3RFCV2PzCr46Bly5AQAwC+YJVmOeqoMcoqqAyYBvLMLsp4hYO51WkJ5OYQRdyP4n2BFrvoc7rNhfHugL9XDDWTZW3dpO7ORreVhZpHHNBGsR834Xs+cBkrwMKFyVfPMLpTRVIJkNFkG2BjyHuGtjym6pD/wMVZFYB4pvA96uVZsWW/aqcqHyu2t2ZDIhohRrnO6pJ2C58wqiuw2CQxR5FrMUurqO/u7lAUxesGueZTP1G/30SeBvl/IoL0Bm5iW0oxxecpTixZDnQru9GSh8B50sG51s1Nj+WJLYA3Ou3VGfMeg5SmVKcZg2Nac1iU2ficsSVOm6GSovdDmQIIRoKIdqgWOyTz1uv/uTwzk4VBlaxHmsQWVDKgTcd07qfUKmaRtBj0F3zi0uBLrHVuwDDAhEgoxWMDhIIzwV4OurChxfsnBjZSit91VCIFUNV1tGanbT9b/nuREfPuQpEhw2EXryhxa0C0NQWFpzmWlSYaZK6vaxcwrTZUHCNEOJdIUS74E0vGzRA+U9+KYRYJIT4qxCFuTuEEQhivuX/js73BBFhedmUWid5BH0uAQgxLSTbEe929s+0w3w5bqsnQf/MKGq6cWjHXGvLYjq3sffDuCb9vbduE+pnCLldM/Uqagjhtu3b5zOHabF/eoQ0jy6lfEkIsRolenoIuEtKuSXIZmFoAR92QISeqUsbO9Vc7c8Pr8136q5vKHPKAF6RAExwKTEDP5Cj1I+51ctqiq66TKcGO1XWEBigzYNHY58xNTwyvTGRJ+jOFNO4+NvGzWANFPvPcQZEveLT3snuxKYaC1TVxfjFc47btpPShciEXZmK7mLYPwiThpmDN/Z9DNWS4Y3vHufZ+RPhXpW1nBnVmb6MY5x2sxaJC+mUqb6PdsxnNY2JTdhCMusYwSCKc4aZp5Ss70OlplCGkya9YapMI+3EbPaXuR6AZ+acZYGWAL694xc0ZQlfZ96FIJ0hiYPoySQW0I5/JvZkDP3IzkxgcmIXZpT5G68wgNOUpB0LyCCJaSce4NQNMKHMUxQ3tde9zukyyGuheKw75dt8eW4f44s9zTvaum4pLdmMyop+uvYhhqQM4qlib/HWf57i4WIfwDsoi4xXS3Jwr6IWy4UOtZaHUXTZVN8+ZF+bAAe0l9nbKmMphjv09THges93SpqiIRnUcCN4tFqHGNfM47Ki4+cPBCMzafWr9JetDOZpGQzT6EEsW7mPef4bZaA4HpWxGHRfnjhPkuq/SRmK5f3lAyllLjBCCPEycC/wFnBWCPFPYEKYMhsYB6lMTyaZpRltxsxh0bsdaff4TJCwoGNnhnccye1zvuDr+Xepja4jf1Peh9x46zZ701Qd7RS8vSotQ5nUE2hyvof+Kj4Dml6F1DWOjgJADjCynVJepd8bgaQxwavG7xyiHWxNLGIytXYGzGbWyVOZooasZ+Pp+tze5QuqdtnPQSL5ctc9MAh1n1/ngqQdgIvcV4ABbmiunxnOwFPDxvHWyr5wr6cvv13nouSBI5yeV4HT8yqo72clKg25WlmPHDyjPlEsKmtp9HTfaoi9Gq4qDqWrwpE9sE//aupUQYn7aAX+xbRmDY2V5clsl7J8GuCGxSnQ2iuA6eCC4vpAt/dVrNm3LW0StJLs28rOpOTOJpyu/BrXP57ALuLoxUSe3P0vuPsM/FxCf0cp+tzbA8tAhWMmHdagZ+tg35/KcShwCtbkZk/NZVFlLwuaXfcbTL6mJ0EqxbOlPzQc8w3LSWUPNYhjF9Hscd7uMkHYisQPhBA1gGHAp8BNwNNCiGellKfPZ+cuexiZRBdQE/gGs4YSL69KAHqrH97Cp/QP1x/10wJDCdYjaqJogt4//lAVxXqZ+9EFeH9xIXWXjWBUYRgjsQeg564ryyiGaSVXDz02lPn6uXQ1A8tAtKPszATlAepvR279uLAfDlOJl9oPwCVHs5sbAKU6Cx0ds8fZmQl0SlR2G30SxzOXDry29kXSmk21tbtvjhI2urqsmhrPlg/jxsWD8iP+9th8mrOC4ZkjkccEopw6GZPoybAlSvGsd6tJ7E5UAZ2VoruAdpylBOll1PEMP9MufMinIouP5QYOkwXAbK/r585iS5nAU1TlZwCKc8ZUov1LynKW0pLt524EtEJtW+ArWFW8Kf+Jvpt7UPWlVkqsHAaijP7fkgWIHWa13XC+zkDLuT9m2VcBZimtAadfmtp5UI7ND9Tk0HzHdTHjt7KPylQbdFB5tBmJhJsvVO/CuFgQQpQBHgSeQj0/TwKaAwsxq8LDcEIN9lCRw6ynIZEcVA9nkbBgbWflabxCtdtObYjCKPMy/SxNdon13nDIk52U0mWrubyUIGV6SJlLKdMt/3vfG6fbPqPPthb2TSPWcytrieQgT5ScyCsRugbxJUyqsbxRPz80jYdVbuq90JZN21zwV2AWsBPeGtYXXG5ulB3Yvkar6qyA05VzgBylBBoLe6hBgyPbAUVtPbIQSqMeRlNKQERNOLkfTp6Ck79DFtDgaqhQE04aKY2zqO+9DrAbKnKIbkxnxT0eGuzDMorJiYDbBa+jAsoDbpjrpvRvfTh5zXjlC+qtU/i2WwWob7vgEJyuDJXP9uTnV6px+wtfUJwzvBQ9mJfW+jKNQqHF+jybeV1/wa7HUAK7kF0Azpuo062wapFJlQ1Ux+zzXPAYbOh2G7Wm76IlX1KZA+p3HsZlhVBpsauBf0opHwGaoKjxgXP/YRQZxtGbcfQ2lSoNNVUndGAGYqCiyF5I2oGV/jqa5xitOfL+aLGGcpxIhNbMp7Xl4dyq7BoyBeQ80TvOB7rwYUjtBjHUpBhfykhjmg9VtqhgpcVeKjB+h+VPHDAzn1cCDGGCon79GSGEeBMVUDYCukkpW0gpp0spHwMvz6Ywigw+FMMQKK9OAZgaO30ppSGNR+3Vy3G5+MNSfwmiAUHVTYuaClkQhCmMhYMQb5qvwu+rEErCAVRjhViBECsCvC9c2Y9vdv3KwJU6boaaq71LSrkNQEr5B/CMEOLe89etPwkMz/Y7UIXnd0LfrbpIMmGCDx22jrSPSEYA6YedaqPVfsATfq1CxEBFja3BTu6UyaRor6DWJOuLNJmlKEGWb/HMvA1cYwQ2dgqruAVu/U5Neck1zcwAUkoVCJ+mpL0DyS6qf7eTUf3tqrAj6IdHohTO9S9r0i9ny7V0EqovMYlbyX5f+3L2MI6lZr68bUUG1HyJ0RkvwRlwNVIU3d3rPyCDJDbQCBjEMEbSjvm0y5zPAtrTDvVXrhc8l/gyspJA5sEdWWtxN3HRhU/M/W+kPsNFGdbLEShhJTcZJBHHLmqzjex/RRG9KZe0xFl8z41sIZbhOkBMazWVWf0egFbQn38whv9TqnPAGzpDZ/W/3Eh9QGVgpXxInXudSRx4Yihj6cdIhvFpGfV+LSlUZT8dmMt82vF6psc38y+Jy7mx2HbW0Jzv2azU+krAPY99Cf+Stn1bbT2dLD6DiUEZ1+0s0pH/8iw3KK4TLW29ZzKNQPJxWZGJPGOrt7S2CwVWqqsT9da7TVHAm+qenZlAtcUH1Sx7CfghrRag6B+XG65U1bsCYjdQV0p52GFd8wvcl8sS82nHQSKpxGGWfN9eUVLaABvOUC96PZvW3qK8LkGNGy9gZjBZgS1TqTKZdv1BmecJLEUEpuehjTkj1P3GoDcK4Ybn7Sqx0s/8nzziHGSGAiMraffcTPd5QDf6WkU+Ymnn8gTZh4wAOR6h2cNVvtgNKDotwPT5PajY/jD12chqGsMD8Oh4pQr7nrgHrJOhq9TxN4mFwEKqvP8IuQ/r72CEhLEutlcAjlqCh9U6w3loB0/nzSOHKDW9sh/+2KGylikloEQJ2PI7HN0BLaOhdCmYtAN63gAchz8OKs9LgDMHNUPpR2AgRJHLz1RVlFeAuW4miwjgTXjiaZWB7eXp08lrxqt/7nAr38sJerve+nx10pTf5W7o6eJg8dfgBxcHiWQdydRiJ6XrHNJ7q+Tz/RlBpJRP+6wLBEWr9ajbGnBinjmNicb14K1kbNBhz7cdiacf+VCKBZXVBngdylU/wDEiqcEeKnOAXcRR2VDHvAxxpY6bodZcbnNY9u+i786fCxP6PgpA79T34A2UGtlvvu08D6SNbMuS9f/PD5yAHAVr9EN7BkmOxzMVXQ2/2VG+SrHbqM1S7rFlCsFhJkna10npsvhWelKEI+hHq++a6f9XAqUZzFhG0I8R9OO+75J1n5OpPmYnyVpY2kCxRccpg4pgnp2iaCZNjeNscxFzo7MXp+gOt0/5gvJAG1mfRe+rwFM85mL00JdUTeEp2P3fKj7bfkgXdhHHZtSdtgwnqcEeYh9VnJv2NedzhuJ0ipjGNZmnzO3+kajUWe6TtRhHC9bKFBqynjoRWWzJi7UdYxZpDO87kn+O60lj1vD3iEl0yptG7FhPqXIzvmIG9wOwn6q2oLwkp0gig4neaqgn3XSSniBdZYhfM9/PFjuZvW4WQxIHMSRxELtQwcxP1ARgJSk0e3m7omnr/IkoYw8iRVsPDVYsU39lC0/QaL3+ssclAOnE/KprYTsn0Mlis2OFd2AXDMmsJkNv8y75n+mMPLcPgAPF7HVVjjWepn1J8P0ePBHJsa0qKC2XoLKXlcs4ZzAnJ3ZREv0JeCabwvjTQ0r/lfJSyp8vZF8uR5yiJAeoTHsWkEWsR8kzFXi2BD/PsiR/X1B/YlK2kj01QdlTgGkvYlo8WOoOrQ/mvuIpqq4QvIJKnCe3rAGknI+9Pj0ZU3lVzgcftdjvfXZnX+9Q3uIv62MNMPODO9t/RlNWkUUsUeRSb/x3vLewN9e0/AWSr6PcV705VvYQoD3TXnTBVOAdyBVuYmQXWvIl74kD8KpLl4S64O/ATW5oooLf3G7xvPl9f35tUF5VEdwGV20AflN1l7ln4NZrIPc3VAlBKW1yUQH2bYBq8VBBPz9Vux+PFclCKN5b6Tn8c05P/i5q2IJMInW70i7oCWyCkrNU4evpyltgy1rkUylemWqXspDiVo92xjew5eqGpN0wi5Kc4uQsHVQGEAoXYgtS1rEFmbZrryMmxdubDluQCdVgCFRzKWVq4G3zqeRuRdBMpZ7YKda6H8d6RpI0fTUHqcwu4ijJKRagtN6CCNmHcQnhyqsyvYDoZtyM26JuljPhp15K2aRmCNvP7pzuqW4HZr+VTsxTziJAocBaM2kEcIN15lDoEbC6pY5jJMOcC8S/A2hmKscuEevVPgPcfJItAeU6ks1AeHAUoP836LCv9dAZt2P4ZCy9sUh0VHLpFizv+heaZ/wXfoPoPbnsrlGFZNbRkYV8iMffM4MkxvB/lj6uI4MkHuF9sjMTyEysyzuJTzCWfoASHSrOWfZQg5qzfqYm8xCdJcNlPzVrCjRnDcvrNWYKD7JpnDrf/fkHALMj0umWpz7krZpVfhvq3K3UGeOxm4Ywrt5TZOkrZBs32j6blC7i+MF2XmNObKUyB5V3pnyaGnzB8MyRdEv08Iv/m9kcbgdOYNYhfX5zKm3KLDfb3Hjie7a3LZgDdQprWas/w+zO6TYf10DwHjiNgC/ZKxIr7ACb38A2EIzAUiZCeYfMrhUt+RI6w4Ga5Sje6qxpQVPErgkXDFfiDGwYFx5f0pIH+YjN1KUum9XkzH9RgaQbDr5QjVpjfmDX66oWy+rly1SvSaK5xhjmp2bbfKg3skPOCquhwsfKKdl/drNQx9H3RONvFLs968zP5HmAkF+g29nx5Y9tOXpDeXoyicasUcF8dWhWchULdnXmWNl50K4n4FK2SqBsOrYCdCNbwHtUhH694WpUyaPxqPGDC25ykys+UAHf1co/OzLlmGJzlYLSV0NUWYiPAM4qGxJKqNc+oMF+5XdJHsTqJOnJ+VC6vmpPZSjFKXP8gf2qb1cDlXQg7OUvfLrya54A1DyfWmBHrAXcyv/yzW/h8zZwv0vto9ZONsiGavzupzd0fDa5G/yI8QWCqtk0xIEcnr0cspmB2lxq8KcFAup3d+66/fBhPOu6NaHK9BzuZCnfkkJrFutWd3M54kocN0OtuQyjiGHUbVmtRKzLvVFYjrpfG4f37TWOBn4gjh8swah3pjPgsfoHb9OBGX5pvKBqMa0wz1ei77qQ+3UBeP6BbEoKgzh+CHrOihJimSdrWeT77ux7PV6Mmp5LsZ4zjDDCKDxEBDYrpIDtbDWa+bsH+SjH+qm3DLYupGMFuEcGsrawtRvlvPxKx6VQUxr0WrXafIVgeeK4jwA1l/nel1hke9nX+XnedLAgEWIHoptP0zAuc4Qzl+cRs0gD4LEbPoIIoC3UXKcZURZma/ZbCbAcBBCjMz5ZJMBMFVR1YB6vvwz0dpM91aPeCgWnTgxmrKLM6vhRSpcWj5niybiiFNf266zcWk2HHYOih3anIicpw9syC4CRDDK3G9J/LMX6Hccdpfo1mUfYJWbptS7ukypIEv3xWK08qm+cjwKNFIVFZupg0mt2MGbKVlZxF3yjK8xTVNo0+19RxOzeh9x9lTKt/wfE/JbDr1OUpP1D//sUgAM3l6MP43lCq+OeoDQt+ZLGrGEPNSAROjCHumw2VVcX6pTf9MwevJA2Wim97pIM+XEM1BLQAX1++pEtEqjXQc2md5ozjU5506ih5bRj+QmAZ8V9PLNR9d8gGqfVU0qxcezklWV6ZrqKIK7eJvOz12IXRynPYtrzAy5ebPsaBxcqTXZjhm9PYg2mZ3pO2q2JK/n282Y0e+9b+Bq4A+5ev8JGh91GA6VjiSWwXKHfjwVIp9PCaSSzjlmkqzYNgS7w6csPIX8FLBlLw9dS2kt2Q8I6sVRv3MRnnfc1b2Q5M7C3PVCsmhLd4YCtttLIYloDy3KpBq01eA2mtExuHC0TiciEyokeWmx2nwQEIMdjUr4jNx1jXL2nmKEz55dj5vJKNYMO48KjMgfZQw1OU5IcqsBwYOkR+KYCuOD5lBG82mewacGVvSLBQ0ddYd+X89hYDSr1NN/ZRX+mB9jOAakqWymmoJRTrccOkLEs6Njtr32u+EBRLYNsnzsqGmktl+ki6PPdeHYSx3S6sb17A66Z9AsLBnZW1iG/9FT1iqtd8Dvq/N5nbGw8K7hgrFJhBTh5jb4fdlDZwGK/9AOOc65OWabldWPw2VfV+t9UnWXe71DtoKq/rF4Wju6H8mWhTTTs2w2lS0BENBxRjmVUaAscRFU2V4XyHKUyB9lveF4loDPWLmVRM/cI1K8AbwK4oZ9L0cfmetTaTXyms4f3ANwKhxZBWhv12f8dTwYlqEKuov1aIHSqRp6zU7C94aSc6hRYKgr02gIFvQW9tgzBHikD02StQj/25b5BppSugDWjpm1OcrxKtL+pbO2as4KN1Kc0J1iuy9QHcPnhSh03w8HleYRR00c9lIDKQcz6i+zRmu45U2VyDGS/pemuT3mWzeU+psrBPNDYa+QKAYbHJThQErTIj0IcIxnGCPoxl/sYzFhqsJOWLDWDIm8YXpXvYK/1kGMghZVk5CThynFzNqosk/mBWjKNnaaUSdfAGU6v32J2YgJMghcy3eYDuhOiySE7Osqj3fA1yH8KWA1D5GlEf0mrMfOZ5mXlsZCOHD/VmZOlynBt5lHTTzOXKIZvHulpqH22EsgiC5hxQwc+oQtTf0+n3NXDgEEsEh3Jlop8FJOZwwlKA/AaA2jEN/QjgwfEoz59H1fvKRqzhrWq0oQXWri196hkJ/A+o9jP9ZxEK+/QnpvYCYvU97uY1oAKsqzfeS/e4N1lz8CdbmWG0BrOaNqY3xvABvVHDgORCRZWsw/kr/7XObafCd4UtVaykWPb8wkrVbYwwj4q2FS/W2/WQaVFJ9W1XB+iTDmKyxNKmCA8ZIRx/lGV/USRQwprOUAk9b74ju15tTn9MvD0GV7dPUCNq/3OQEOvazIV0zMwFMg860O1/5pGx229KbBpILv7aXws9D7lFza6ZAArC1tQaSAV1tOQMpykLps5O6U4u969SY0BL2cCC6Cli5IJRzjdsALscatgv5865l9kM2A5kdRlgRgPLV3QspJJwxXtXZwzxO2vhppkqTrGmrDvS0iJhqzdivKatRvmHIf6QINr4I/foFpDVXO570eoVkfvp6ranlPAbjhAJJupy5KI9kpfZ64bJrrgF3RA+RpsNM6ThYr5Tz2B668WsZIWpumkr4kXXQxtP4xP6ALDNX16mP9AMlTk53oFgz5bgOMUkc+lQmmkTPUJKMH/78ivJd7H6lwW+09Vzi0uS6VrDzOBp+jJJFbRjMNULMqOX1BcqeNmmBZ7icKf1YixLtB60NTDxth8KGuw0xJMerVvHJwS1JLPaMln5vtQaZrWNims9KVF+LErAdjtUyFi6bO/QbwAWEn+btYt+cxWvxkKCmIrYnxnTtt+T23b92HAu17xfMBqO2J8nyZ12YHyattWuBFDPZnNywlijnqFEUYYly4Mga6Q2gYZ9wJRUYVw+9BdjffiLXzKXgLtx9/7wNtN0y/P//Y2O2w1pGKlel0I9GPERaeaekM8pV991MtcfhH7KToGpjIXyTH05w65vZcNSWjbXFrfdRgXF1deOH0B8ZqRxJ/5IoeGlKbSjpNmRsgqRm8VPxGNodMaNUDEspUsEhDCzQPUp5OcxuyBnoxbGtPoJOOY5ZWF84aULp0d9QSWNkqGJQAdIhR9dLBUAkCicZxpSZJFLAD9UTLeRtD45MAPALhv1AxTqGctzUzlgA5AfTaa9FoTY92QrGayiuccp9gv/TgbVRahFfSKRSmZtuxMTxbwlUyXci/52A0PuhATJGyB2d+pVFynxIXMzkxiV+/DJPX7LxXuAG4E0Vry78zW/HtMa9aSwh5q0GjZFmbxALEtlILrTaV+IDszgSGJg1hIW6qQw6LMjixL9Jyg/VTlAVEfpFLBPUVJ6rKZcuXOwvWYwgGT6MkUHoSkRZSXR6mKokO3ZSEPZM5C7hbE1dhEHJtoyAZOU5L1Wi24FKfoySRaiZZUt4gkGbTcFNbassmdZBzbUFTSTjLO/J5AXUN92M7tLb5g1caXVAbyN2hWQfFe12CpG2qo/sgWnv9BZebECPX/LNI9gWUXiPl1K9k4Qw7zosF0BmbZByClCNuExdiLkQLReazrevEGSfgX7MlPRtIfvdYJItNOj7VmLOV4z/IzTaDEZKAyNL5+DctpQU32h9ynSw1XojBBGBceB6hMcc6ykziKc4ZN4kcYfYvKVm0tAQlnFEfu8xImHbb05EOcjK2kBFwcYA2yTCVZcxzsaVnnbPAelGI4C1jghnb2dn6ZQwGpgt59L7xyqBL8UYo4xgSZ7AjEQwnO0prFnKU48xZ29QjgLEqklizFrpVwepPOWqa5lCfIWNWn/4qVUN8FG920ko1Y0gyeXznCc+AFbrjNRblnD3CsYiTFOcuptlBqElRLhpNbIPYGyPxR0WLTG0LOBjh5ENYehzoboFoTlECPgS3AT8AzQArUP/U9h0tVhJdQtN257aDXmzDkacUYEy6QDpm0zZ5z6+jheGgtTEwBtKDPZFhDY85QnHpeLkOGsrAVQb+3DoG/T6vAjz84XaP5uU6MoFO+5VGEDeZnaaXMStnG8jktFiTX+/6OnPrlEVLSgj794jn4n2p0aq/KVqLIIc58dr1MzMy9cCWOm+Hg8gLg+P8Vo+SpUyr40NeY9DOLpOop0+0Pq9JFGtMoxSliRm1ldmY6nRIDZy691TrV+zinppYaTucb0lJa8gKjSSKDjnimhK3WJ4EymEY7zw2rGVKCeB/u7KEyb8tzUj39aaCCTVA0V9EdsqdEmVTVrOkJMF0PzA5Zz06JC9mUGUeFocDHQG+QIwUsAtpI1jKUWaTRKHq42h8JwFrmUpdBiSPIIIleTKQkp5hx8/1wFioUP2WRtDeOdA8sEAxqNwSAT/e3pz7fM10H+yu5g5g3cvh0PLzXRy0zAkTxq0TW8GSoS3KamXTmAf1kdBer1DnSN+VRFAd2IqWLLpyhLpvZQw3Tn1RdLzuZnaknGtY1Mc+Xia2oX/wRWDO+BfQpmLa4QYM1rlGfAcP6Ps3PIDfcTZJsWaDjX0zIjpomHCJK7AYSYELSoyTp2t1QlKLDCONKRilOA4oeu5SWvCpX8hb12BVxkypYzipBvbzv2PSuGgBKdz3k2dgonwzBVsis9yoAHG1JBLDA7jEoegfZ0WinB+7gwYFvELzTIYC1UzarNNtNDtF29kUkJLGOpbRUpRVH4c4Gn/HlS/fARNjV8SaP8Gk/F4ydBnGWCe36nmMuEeuRsj1CWFk902GQm6MDXYidEEsWJ8qUplSVk1BBBZSUgkRtM3JkK0RFq2VROyCqIyqYPA7mvF9N4BoUPbYETC71CLH8RGyfn/ikz0NAIuLaRBju8ZsUAg81+SMdkI13OVI4zSCrRYqqN73epSYxXlKBwtfj7oJyuvHjFBxz3YWaMAgVMoQMuq19EFsS3/YO13BB51DrA1vgWPvybKc2NdijaMhhXFYI02IvEsbhGXHEQEw/StHZXoPpTz22MLBmKr2PX5B9WffXnpn+G1tgBI8++ytCqmuoCOUcP2eRYPHMdI701zwkONUqWPs0TQtCWeEdxA/Abfo5XiiIa+202AuN83H88iecfSqD0WCfY7Tt2rCiqH+3FxuGGXRRv8II43zDexJSeAWeQkzyEvXxQ+cPkfonKqhX0HZWOm3BXKB89+lFhb3Q8Jt9vUCKoKI/PnoO/sYL8RSINc7rCt0PyzUninleIW3roAZ7KSjaWuGkElug/URwUa/X840rddwMZy4vAGaVSsOFm6w76sA6GJcWnPyeRYLt4dSgvqYxjajEXPZQg7U0M+sejeygYRniTTP0wV/UTJyqwVQZTet+DKisZjNG6P12RJnTP1vsNd1C+WTOz2nHPVEqq2kEjmejytr3pWcDjfXF7jnO8pxUzo3V7bTeiRoY9LIxwCaIScyBv4PsYckcvQ7Z30UR834Ond7XGVXdBnbyzouP8MTXk+EGPHZfAwSVR6vzL+JfgvdcxMRt1Z9/Dp/xVwDqZSoaxkPFBc/d/DIAz84ZzevilNrPHgGMQLQfwRL5OSv/+ILxPE1zVlCbbYCmET8DrINHM1WG8tXE3qxNTKE4Z8gGZm16wDw/Nert4H0e4TH+ZS6bKjeSo/nFb/EUsJHTlOKhcx8C0LOYeiBqxAayv3JRueE+yhU7SvayBLKXJRDTYiuVOcBcdNr1Tnjk+rcB+MD4XiyiFCrQBtkiAdEWxFiQC+0UUAPZQ7X4VAA1WGsGXc4EEeHi8TxFd3m3s56GDtET0wmB/CsNNdhQPS63nagdEo3W8VxkWnxnrevzYH2rOpyhOH/JXO93+zDCCMODo5TnKOXZSS1yqcJymqusZarKUp6MrcTJx8tAdYhpu5Xs+QlKvAU8CtcRXuI2yfHI73wDSyf4ZrJCFPpx6wzOWL2f8Rb6awU8iraANNy9VgNZwY/lL8MlxDT4yiGLuhK4YwdVZCkAcjQlFjQdVuOav/7CNm6kKvtpy0LWpzbky4H3KE/Rq1Dn9TZUxrg88EY6PKP710hn9FIgqUtL1rVvAgvc/E3ToT41I0rNmroZVpBKRQ5R6bdslY38CZWFvEapwV5VHKgJJzOgTjT88SVcVRVIRptgolTN66OYYHqeegXNlZ+m9p+Uv0IU95J7redz85FbUVGv9zpfq13QRGURxUL9/X/oguVulZmdJ2GSoOGN31Ccs3zatz1/i9BfYKDMZbJLXXMOQWUoGctQrjn/10VolG6nrKaVKltY2EqwAn2efvHwpITBmLKwVcihPEd5gVd0oymF71AYFwTh4PIC4EtaMpoX+aFmLWpX3kWfI29RooKHkigt3lPWh/FvdOHDdRy27a8Ge4LWWQaCXKMCyXkDVSB556jPKM/Rgu3sD2j13XyW5txJfTYC8FkQPpJ30GmwZaW+b4j+UKyfkdksq+xIHLw4DWT3UAGmN57gA+g8WQ02G7DVEYL1hqsDg5WCtGZTmbXnARgB0qV61kvblQC8JrMYyiDYA3L3YMTLklbiDDGyBgDDdTYzSa5mHH3ZKyOpNuGg+lx/Ud/5c7zGeJ6mk8wjnfdsferBB+zRA3Ec6kb8FOoOf4biFOcsGywfZDQuRheDWBoS02wr2eMSOIgKGI3JiQZsJHLdMRW874APdjwJzQpGiYX8q8N6492IZ5B5MLGAQWVhj+8Ef0Gl9SHMCa9nDiAmcSsxiVs1vdqD/ow1H3y68AnPMrEounrRcKVKqodx4VGeo5ylODXJYh3JfDu/GUyFWm1/YJeooeiaC2+CvZD9bgLcfQZ+LgGLQKY4iKPs8vwrHcV+/lYk/TatR4aqwDJoe4t9dKGyUndMQ0r1TGDWsIUo3PPbjOuI676L8hxlLSmc21YWDgO3TYKJPcHlVtTdM2DMPUvp8rCdrga6ulmf2g8WALj4RK/69H7gGRfl6h/gWFk3fNyOKl1yKc8xRVteiCop3A/sgQqdMemupeOBTXBVFVRQ+SNQSu/4NtSYvge4AboxnQn0JhdgrBfV9EPgNUC7WzHXDaVdcNINM1zwdoDzroNOagH/drGh7m2U23yUv82fD5NBBpjDl99Z/neoxwwEQw1WiM+9lp9/Cm2osNVXFmh762eJV9d/pItirx2n0rWHqcUuKnOAo5RnDYoe91ihjnhxcKWOm2Fa7EWCk9qruMW5htBQh7XST6fppfnFIIYWSLW0IGjNfDOTCh7lU3+KteDx0PSHUKiGbZmD6K7+BoKh9ipE6BTG/NTbFRShqvAacLyWgvRTjANRRr2CwZ+qqxjq+Y7TmMY4etuNnvOpCCsa+KeGeVOCevGGmmHOhypk+RMHzFdR4M9Gew0FZylR5K8wwriQUBTYwlMM86Py6hToCfFHvo9XGBSlUqyYHnh9XdZTl/VFc7ACQAR+jPC/3SVAO80X/boA17Goq15FAYMaW9jf06Vw3s8nrsRx89Lv4Z8A7dR0HlHkUOINSBuiRFvEW/6FfcCasfQED7MbpyPXwDTmAjBPKAomOhHlRId1UuqaS1cYBYquEmcGnEZQY6XHfk5zkoG7WU57ZlK6WCMm8gTJZFD9u51spi5no8rSQft6DohSFIYMkgOdFovAj76xjFH9+8eYPswiTSnOGlgKMdP1w3ySap+tORvR5NCmhwoku/CJrfi7+8PvMmXq44plux9ip2yhLpvpoudWfyAOQ0U3ttkWsjMTEKQTM2cr6bxHLXaxXHOZvs68C4BhjERE304tGQ1vjwRKsF5zISMYxDL5GS2qruEO1hKzfyvv9H6SJ3ibGLbyvJjAH78J+vAWnSt8zAnKMJeu1Oc7+jGGZqximP4udm6qh6j/EgdlZUaJ4sAnZAOT5XwoBg+Z88MwmhcB6Np3LpHn9nFwQzWyMxN4IdFNI9cWaAV0BPGz9DEZBw9VTC60Z9+CfIUhwTBShsAebIWBNwVWBZDp+VKLNQJV6SfxbjywWNVgYxK3kj1HnzOvLGdFDsNBaLR1C5QCWUlAzYJnjMMI40rBYlpzgjJkkEwsP1H5r4ruv6vjTfAVKoN1EIrde5xz/y6rVGM1lU6kevZjirDlGbWI9uP4M7tXFL4dtjb5UuDUQ5e0BnP/AdnMf1Bp86r05wfouJ0zi0k2AyyfL5AAaElOcYIybKQ+5ZIPQDIca6gUdJNkS9btPgMjS8AglOL3NJfyGTUPnci5v5ZVVNVYy44/B36FYy9Gwpcurm/xE6/xLFXZT/Fe67mu4W9wA5wqC3vKVCfuf3tVFrMm7EyoTklOs5H63LP6S1Y2uZVTlASgNtuJOpHLT2VimEUaDdhIbbaxpbQLtPCviAAqa0rsLvCIN01XWcueLpikz++TlgzmPfrv7RJqDVPU34bAvTugXTxnKcGQ9oP0MUaetzEN8ncd+Nu2QHhb/9W0WMOWRPlaGvvPfz+sn0cIN1Sy9PF+F0yAc9eXhQcOs4GG1GUzpTnJ5N3qS/0gmjAuE4SDywuAD3iYR5jMfqoSWXM7syY/gDjh+8u00ijsWZEk9SfVuk5JfcdITLsSCH5D2YzzlNVIXTgXKGOmAtD6NGI9CwZ2JnlUBslk+NRpGmjLZzxjoQLu8aNWW+yXfub//ycqc6tUPi3enym7sXqI/1WO5drEo8Qkupic2QXowqL3VaC1iI606TGHNlPm0JNJTKInPAGMBKpC1jrlwiySXwJgk1zHJhmHSwe4WSXV+li2cEpzcPagKK/ysLqrTqMT8Ci7xG6kHGSeHYWRtBD3wDr4PUFwdUdJG5arOgL9NV61UyIPCFJbLdfHakh5lH3JQu5h8dZ7AVWDGSPPMA2oIsuoehJggZ5AeEgfsTbb2Eh9TlCGtYwGHqRyw31mPSYPw5nKUOIUyBoC+uprT9damlYkXpALPRlQMQ7z+jNqBuUwwDaZkW7u2ljvNCiKZXp9C68VG422zuqJyaw2rUsm8gwT/QWBC6Fcqu9yf4FmPwzZ/MHm9kaAaZPst8D4fVprLb2D07QTs5Wa4W8wP6UVAPduB3mjc78vdRjCBGGEcb6Rxiw2U5c0ZrGTOA5GVqN43m6qzNkNQG6Eeso8Z9Tmp+rXCuf9Cdvv9w5bUKmEfJQomrUuLNhY6rO+nW970QLkMh3o+Wx/VcD9BzuewQDyN66KCMzzIQOJBVWHjTSgKvv5dnszNde6ExUk/gLrXmkCr6CeFh9BBVuGhllTlC7CY+0wfe4X/MEWUQ81e31Q0VHXKWXwn5Nq0viLNUyiJw1ZT1zKLjJI4hEms4tafH9zfTbfXJdHmEwOUVRlP8/wBjlNRnGUcrRmMQBP8DYNymxkwonejCgziD68QfaSBJgHspX+/FPV4XkWZVHSKF4FTbfdDT1TbOdUNADqu5Dfq++MBGCscT3kwPpMqNTGLK3ZTF1mR6jIWhTLP+3VgHldrvB8F0Y/zDYBAsuCBJBys1cf6nqWB7Mg8Z2caePzeymQbc6Huu3VUKz1cQ7+pxoZ7ZNJZQUHqUz16Czd0Plav5RxpY6b4eDyAmDRlI6kdZ/NAF5mYf1OHLq5NHmUoRIn8rWfmFGegNO7vitU+AsEDcy7Ra+3BLp3o4KgVQylPhsZyTAEMG9gV1u96AkUx7IlS/me+kH7cque1rVmKP0WfZcA4oy6zKNIHfR8GOQYPZkEaRI2ClUz8gbQEOSrL0FfyVAted8YJUAQW095XqYxy6whNc91C2mhIn+r/zrUHaxTgWapMpIH5yhxntYsZpBUQUy2+Ak2wjOb3jWPd5TyZGcmsAkgUUVohpPl+6RTg91Mkj35VGQF+cRwsPgkMuUn1D+i9s0GWNohlVU0BVSc7YOWdmEfAzLRf/AZCvIzwBRmptVbtOfYiki/GUi/x28bmGZrzViayxIhNnEr2d4ZX6DUN7Cyxa2U5yj3bl+cv86EEcYVjA94hOd4jTU0JpYsmAe5/aMhHqo8vpsqebvJXRutvCUBOQbi+EGJ/qzQmcp39brHvYPL8w+5UgcpTuvyEVReEJuKFhDHg6ouLAs1TlYHJgNdgF57YXF1z/wpQFeQAzWbYxvQTwUF5/aVhSTLQwEt4UVgJjTc8Q0b7rqN2Xelw1I3sylPyYNqBB8bMZh6HVETBAtg1xdxTF74hOpfWxDiRljsgtYeVdFFVKOKPMGzoh7wCXC3/kAWX8jDqMBSumH93apW80kdWFrFagJMbMICqKTToVdDTX7iRrYz+2o8IlIXABey3tI7K1mQjGXg/VtsdLqhJgAO7YDR8Zx7riwMUNn0g1TmKOVpZFKsL7/g8kpFuObyEsOFkpv2thABfOxIQq3968AMv3WUxj5G0I8R9HNsI4RbyYf7qTm1ZnHNTNr7gUV+QsKmINN0Gl/ooGyobXT19Ed0dH54sdrN5AdCuBlHb96gl2M9pbWdJ+uWz2NkFk39qFPtr1EjbDueQ/2lY1ZTX/+BfgMGBdbIZF4I5KdWU/QpeM3PpYwrVVI9jEsfZY4csr0Xu8/4aWmHtwWCtx1JQSAcspTB+3HxbCYKagNRlP0tlXfE/3G8xZlCgNXiJWC7gVwU+7PC4lKzJQkVl2u/C4MrddwMZy4vAGR3EFOmsaN7Db65uSG3Td4AQOzDvgqTBryXN+ULAKKUHhqGwqlfI3vr8a2zRI2B/7qVFYkF4hbgLvWyZiMBRtCPwYw1qbODGMrAUR4qrYHN1KUq++mu5aL39o/jvjH24NRKmVhLMz83GhcxcivZwlNTGDNlq0lBrM93gIpCH+IT5tCW2T08aapk1hFNDnMMN+1fBSPczzP45VeVCfOXYGgaDVs9iueavGzWYB7W3J5d1DIpOAA/URV4hG3UZnj/kap/DezfkcgE1g0iJnEr87mXpbSkIRuIIod36MVK7lCfhRxEfcnrshdNdMb0ZV5kbuJ9PJvpqyhq1JAmk0Et2YXZYpZt/cRzvShX7ChPMJEX175GFbmbRv97ieU3/wWA5rf9l6OUpyEb6Mx8VV9piUmdMpag6at/tSw4qb8rfT2FIihlfr9DLHUXLTzLA1FojMAxQztnZ9DEFlj6a18utbZe4r/e0mi7bmETYLCZ5fTOdhp02PKaZGD83qy/zywSiG3rmQDJ/lyvWw2lW5zgAJV56UZVEOby44l5ueBKVL0L48IjmQwm0oueTGIWafC6XrELcq+LVlk1jdKTVWC5a+1N9p3cfQYZrR5x5Bw7pc8z7nQDfV+mUjwcmgRUA/aFbOVgtplvDyzlMvt6Yc7m1df79M1gmoJmG6fpNs71lOIG4Cc9gWfQESNQ2R/zs2TBzSBlrGoWIOity2bOUJwarfbwdf+7VBfvQ9FcKlVXGc0ngXumgXUieYt61ftCUZ02PXYLvOGC2/X6JDd87YLrYUPmbfC0Wnz7F435WqzhdOXXoJ2LkpOP0G2Omi2ePqkHGSTBJDx00UouyAA6aDrz3B2QlkJuZyBN73SWG160qNjeDizQdX2PumCs9o18W/0Vb38OL+rv9UF9DrujsrYV8ZxT9ilrmaXAX2H6/nT4Tykza+lNic0PLVQG1hwMeT/eCNYH87fwJJDqyeIGykr6W1eYjKoQO/S1qv++gvrOM6BRgw2cpbhZf30540ocN8PB5QWC7A4/cZbb1m6AaIhtscW5nZ8f6tfa6beTLCJawH8daCBf2Os+59LVb7bRCoNqm4Jdik6OAeiKmwGcpQQvidI+2zp9XiEgWyT4rtO1fvVBeXAB9ICOeLiMu7UnJOjlv6q76GDGsvvFj4i+JVfRfhaAVS9oEj0ZcWoQR0uVZy73ATCNHjTlC6bo6sbabFM3ibFusseC3PESIn4hQ+TXTOFBM/hVgcc2GuAbgA1mBFNlGgeobB7D+CCvoS0tFr9EFyYTxy5qs03RkJdBmRYqwqku0+nOFFJZzv3nZpjHjKUXMSlbyZpRByKg+br/AlC+zq+UOqf8OTsXMzviR5LfGfIEOFOGIB17sOtkkyO9vDD9CRVYv3OnAHIiz5jLt53QQeQFGHeMes1YgmcvY8brSaPJylKhJlnsp+p57mEYYfx5sKBvZ54fN4IPeITt8xuouVSrDMHdmAI+JypUQswHooBH1TIjqLTCXmfpWWbPjO0rVL/lyuBtHLez1v4VGYz7Z2zgZjXjWbAynnrNvqMnk/j6y7tIGrOadRHah/g2lDDPGeBVfW9/3nLfXupm00qX6e0JUCvxBwBOyO78PAoPY7W9Z6LcxAI3pyvD9PtdcLNatKnBLfa6Qz3h7QnG4pUftkGQ8aan/h34AjXOv6T/ujVd1rXX0+4d4NBkoAk86CzuBMBUYPkOSv4zitM7Kqh5y4r+m59vXEqWJN4INdFhQ3K8xy4oFZinaLGLaU0qy/nZHD/DtNjLBeHg8gJhJSlaFiZ0GDfhDszD4x7sjMa6LvK/mc1NNVUrQr0ZiYHAy57thwfh19dgJ3tvUT/4W78L3LYwsFI46xXAhP590mmZz22eYzSmCk8+IMQi5JdtERUlU4P01U4fzf+xigImXTUV9YCwQr//j3N7I3tn1G62Zybz6Rz8OPnMBBQVvJVki2I7pwymFXNo6y0ee9lD0XvCQ0YYlxYUJbaSz3IjaDwfap5CT5aZvpbG8ncx75tWD8sCH8frnimEHi9qFtzn+mJCRMDtIX4f4i7Lm+Z6mZMK7yWA/IrYKCry9HxtE8rxAlFOrcI9hYFT7aUQi/QyZ+9Lf6I/HgXfPy+u1HHzyvvEFxEVOazUI89AH97USyeEtO2rMpe+TDAzYUIoakqM7BJoMx/INTCIs4Cd1iq/863BBFhFMx97k1HN1HajZx6nqidRyFqa0YEZVGW/XqKCToMK6PIKVIvnKIf5s1Fl9WfSN5z3vG6Y72OqtAFsyryFmF+3spI7EPNzVP91F2O66/dTdONrjYOmM5FejKwzHFoA4+DkSOh27GNAmTAvLHUPr4uzlP4til4VFD3168y7uIH90A8VeG0A3lP9EfGwSSpKTixZPJpkyIDeavY1iQwAdjGG2/iG/Utu4NTtULvMFkpx2hQNAiihv5fdraowhv8jRiifmpG5ApDMW9YV7nTDly5GMYwTLUbzcTGVNY5iGl9wL1XIYVrXTqRPnU3PVv8ElD3Le217K/VX03JEn9sAPpSyBXhrToklEKPV+AYxgga6/134kE9M/VrLPgIMnMmsJkm2NGmv3lgntOu1dF7vrf6qKK6Q1NaYsbevN4JFgAwjYMyn6I8/eAeZxgR7wqJsfmhTi5Z8WTQHCiOMKwCVx+5j0umetC65mO2HG1Br1A/sEjVgYgVoaNRTWh5fHkZlLd9Tb8UuzyprTaGTrYg9+PR/vyqQCqYFUiYiXtH/v2Bf5zvxlu6z3LavHz19VQHnTnNbDz32VsdtAaJQqrs5RENlaNNsDhtpwGs8R73M75TlRyzwT6jSeLepVE6UCyb9AU+74M0dsHSHsvS4Q/Wz5MHnANiTV4NTERUQU1Bj9y9Qve1O9hLH1xFG1LjG8nk9fcp9JVp9lxssHV4OXOt5WyrvCIzNAaYr2mtFfT5e1jTYTUBtoJ9LTZYudcMp43vTF0lzl8p4HsoGspFTnNg0jyqLjM+Br+I5XdkNbhcl/36E06Mr+Dm79iJR63UTVI3VsM4JQJf19lZ1QlDl1id1O4uokX7csAsdBeqrxZ7EXx+D/1amwyEU5dkSaNZpdoyjlGfD9ttod+MCFtM6tE6FcckgHFxeIDRjLRPoya011nKWErRkadBtVnEX4hb4mrtMuuogs1hO1Qga9hz6Pq1sIoL8oL1rJQ1IvQ9Gqe1b4zz1Kld6AsM9xNmUZQ0EU6XNNzaA1AzSQEI0ZlDphR5GhnDKMFgnoAaU7glzW94PH8DMGu3U9tpa5B22UZoTbEmMpU6LLHM/W3rEApBAFkgXW/mAOuIRpsqNmA8m60Ag2ZsYyTO8wftne1CruKdOpdSPcLpeKU5TinGGHwjQAq8iHQfUkmnADwxhGBtoxELuMa1SarCH/VQl/b3ZfP/ojfRhPA3YZgaUgSCHaVXYlsHpstlLEky5d0igS1DNXn0MbSdyvmDUS3pbcxqZR2twGQqcMpYHT6iA9dhWHbj6yUx3+vEz5D3F4Bjc9PMuxElVmCNvyFcXLilcqZLqYVwcnDxWhnURySR11xTNSsB1wIYSKphUdtGKFjsVeADTLgkwvS3PC1z6oX6ofayVj6P6UkTwfjj3V4PpA6OOLRS8B6tONKN1mcXUZhujxDCqyN1UydxN7vxoqpBDrvHg/xzw3FWqZj8yHk7pffR0If/lsO95qODukJu9NVzcmfcZoNTRv11m/2yGvYzM00FWeag3R9dyttVKf9VBTodSeVDyYBSn/+n1rONSx5LzXSpYenstdEiB610q/v7Zck6WrwU+hzpOz0t3q3X/qw43u6G+S1Fon3PBdXB6gz2wtHs5Pu2wPzukjA8aaBYEASmphcxYeo6h/lr77y9j6VnvfR17JRAiUKf84zxK/ybYsjKeqGY5MBeGp440VaEZU7i+XwxcqeNmWC32IiOWrXY11IG+qq3grMAZCP5UubyXh6reNYihDGIoPZlAT4dsq+ivXgZG0A/RX9Vhetdi+uuvz7JE9TKw2idsCN5fbzgpvtrws2CmV6bWCXNoy0pSgra72AhVmc07ABXLMP0o/cHn2nW4loz9COFGXOu9hz8HvM/DnxnnSfXuGiHEu0KIdhf784Vx+cGwHPFZHiCwDDTuWRWrHcclP8I44hXMzCQEViwV4g+E+MN/gyB9BBBr1MuK+k4zvUb7lep1viCEG7EZRD6DmIqnf7H1O5AdVMh9KYjC7J+tjqEIIITnVbj9LDKps/blf5y/CaBLCGG12DDOKz7gESpymPTJszl+v47rS6k/5iDyon1Gp9N305RymgXJrCNZxjGLdDPgtIqoONFbQ4E3laFMAB9Og8rqDX8iQEK4fWarnPYhpcu0Ndn7T1283RC+TlSBZSxbmZ3Yl04ihZgHvSiwIWJo0kDqfraZro/MgwdAloXOqxcwoMlLdGYBAE9mfoD8Q99Rx6qM8DTSuJvPbdnGXKIYIk/zwI8zYZekzQ1zATUzW+3ng8y+KZ3ZpJOdp/jD6a0UJefnzJpqB4kQTQ7j6M0ylDHaLE19flXmspZb6cJkHm0xgZYs5RmdfZtHBwD6MZbbhYtjv9ek7IZznE3J4V+PPgjA46KWOsYiNwPlWQYBcqE9a20I7Yi2wNLgWUtPxtKDT4cadFitADvUvm8r1l3bBPmrV9Drh/bqbxY2gyaIhSDwVXc11hsw7EOOlok0M5FiIbzrsK3xUBPIH9Ok4vrJWBpB5v9uuBd+gl9qXkPkkd/4o2wxSlQoAoOwPyd+k1I+frE7Ecalg4OR1Xg47x0mr1FehxjPpQdRGUqA5VBlzG5y3422Zy1XGFlL/b5DPMydBNzh13pEyp4B+yOEW9EoQQnbNHVBqrrX+dznKlq20x7BhiK3QYcVWozIrCH05+8cClanQxPPxHO9vO/YyC3m549it6K/WmB7/w0cezaSDcsaMntJOnwDueJ6qHQVbfLmsCiiI8a9nVlauOeom1ulKoT89t1mSvTHwcVFqfS6qSXT2NUMvhQ3Uu64ro9Ng2t+sSjxHLKIDPaDNo0VN3QhHRFaffbhge8ATyiVWeECaXyf08Htgg76O1oBzACyk5SH4s9uvS4e5kYAeSiuK3AT6IoUNXZ1cCl1XFKQDUCgabbpwIA8bpfr+PpdTe3t5YbHLWr8+rHOqiBrfLe+VNHynu06AnNX6/XO46F1f/mFd72lE/U1VDqspx+pAdbls4+PoihHT0Rw8u/AS5KVOU3heuC2HTAxgNhSGJckwsHlBUQN9ih7i3gou0DdfbLSFK3VnBj62r5NBknKksNSW+mkxumDv7g8NNcACHQTsFJb/VFpDXgsR7oyWMvGDR4D4pZmkBF80LT2Y+98HVSu96y/PTEDJkFMoiVD9JFvwBqsvx1QwV8jNkO0gDtQNiwJ8OBnH3GmLJygNJMTu/A+3Xg00zdb3ClTRSFSBxjNgG43xFLn/SwWrejI7T2UEJOoulBbqvxkbjs1T8kZrk5sbFqTGJhFGn3XvcX4JEWr8dTlwnviAHVlVV7gFQ5Smc3UNddtks9RdsI5evd+lQmbnmdkPZX6ltJTB+iUxbXCO6iUfgzArTDqDI1r16zzsBzK3I+NEht48Lzc4CTq02DTdjgOZaJPklNBKQNXu9AdK2JcqfSeMC4CHkUFliug+sCdHDyifkMnv66klTuh3pjv2NT/FrM9zfEEngD94s3yOh9U0uPGId+IyKxbxKsec7lnvBHCDaleVL8XPNlKOR9EBWw0XfsxlA2JzbokCHXQZx+WSWTjHrwRj1G0lPFmHSMQ0IqEDbBr+01Ub7WTvc3ioJKivq4jmSp5u8nBpTKtGajA7WoX336CkoxoiaKOjnQplVYgUIXHsXsjYZ36/+wZ9Qi6qest0OEWte973PA/F4ve1anEx0EOhEd4x7KXbkoF1uURxenDGN4UzWB2CqwCDriBQfD6GZjbTdmXMB3SXGBwJB5ye6i9DS27j/Wqe3wdNWnQKIKv9zeDXh5Bnj8zAlmT5G8/zrRZKa9CCP07jUTZvbQEBgvODShLTPetnO5eip+NTPRlOAX5Zxk3hRDXAK8Bd0kpg+qThmmxFxn+qJ5O1NhQ1gVCoFpFK8bR23wZ+I76fEd9OjCDDsxA3KKDMi8YtZgprPSp2fQ3M1s85ziiO4jH1Ksg2E2U+TofaMscn2Wh0GcvBfTxKlQolXeEUnlHqMt66loieJHpuUYiz+0j8lxgWX6xDN6gV9Dji2sJSIkNSv8Ksv2FgFhoPz9XEhU2jDAuNxSEFlnoY1om5AJRYovkWJe4Gb0R1IoiEgMVFjFyJxqlCGEyNN/HFItMkZui33fBv7vCfPeibvD1Qdv4pbh6lvtv4/Z5hXH+IIQYK4T4SQghhRD1LMtvFEL8VwixXf8NmhqWUv4mpewBbAvl2OHM5QXEvN1p9IyeROMma6j0j5Os/j8P3dWcEX0L0DZM6kE2QXtbrqMq+3lzoCpsTGMas0j3ocwCjhlLRTWNg3UuM+Pms510Bc1wBcL8HN+SqVe/UwHq88LZSkW8D8Xu8Vo4S1FdxXtQ7B4VrKZlqoruT0UWnUgxz9cPWpG2vGVzo65ymGmW4UEjLAUhbskhylAp4STUh5sW7IL54PqXm2nGFHhFD9UVUJTYRHykpRPI4sceyoupJvtpyxzaSFgUkQCVPJktV8RAhm0aRXLEOqKX5KqFrWAxranP99RI2sFobeA2lw4cpiLlOcp9sjarqG2aCT/CB3T9dR68Cp+PTuWma3bRmDVMq9dJZcd1rwwcpTz7qartVQo2YopxqJlFL9bz68N68eyyieb7TsOMbK89w27QVcW1QKQL+avebwEGGH/U1WRWm/6XR8tE+ijKAjze1hD2sQv2BKLDBkNvFP/tHuBOlkJl4BQcLl6RZnwFaKbVZQzJlWkGHcZFwD64vvFPHG1Ynr0LNZMlCxis14+GTe/eAvejxGVePwMNvR5n3rNmHnvaVWP1cjHQDx1WUzQ9tgshZhWPAeV0myO+q32okauUWIxogM3X0QmmB+ZGr7r2lcBX6chmKqi+Me97tovSVJGq5sabEuuDXjtgn2qzt3+cur8fALZAbg+1XByGa04d5LdSE5VnJHEwMV1limcAdIPbgduhSjMdWNYFGhoidPDgyn/xkdiPVcvwmK6MueZUL377z3W0mTMH5WQ9h0WjVeZS9HJDmovJLZ6A5fo7vD9e+56Wh+aKc7yTWsrL8mOgKbDCBfVQT7mV4pXQj3QhxJswyyK6Y8zvGwzduW5sqsHfAF/qv/8EnimFPxsRKx3WttzxejlqLpdzQEQEZvQEqhH2dxzr+2BB4/mAEKFkP3coxdgBQNN4aA7FXtOCkTk1OPdbWdO/9nLERR435wFvoHL5VrwDTJBSThVCPABMRPkoIISopd9bsVhKmS85pXBweYFxipIsJ5WOxxfxCYrq6nNLedkN63xvRvNpR8yorSRrTkka08gemA7Vdf1ZkJiheuJOpe7qByojOgw5ynedIQWdQbJNIbYDMzhBGcoA7aMWmFTatTSjBjvZTF32EMfzwHDZz6zHHGxxXD53nR5hIgELe0L2gFj2kJ2ZwKf7HmJ2Ylu6SOi0fSFiO8gbPW2f4G3zf+P8zKEtk+hJF62s+5D+a0UlTsAooX5qM4H9kPVyHbgefnhY1SwuylSD3ObEunyu3aDrZGapHaxQ9TSxbNVOo3CIMizkBHQXjM57FoDoJbk0brWMz8/ezZE6JZm15AHSWilZwcb0ZvHct5jR4T72UIP5tOO0LsYtzllOUIZHUFLw/RnDHDpwy55NsB/oDHXZjPuBF/iAhwFF0zQEoGaRzpvj+tOq73zKcxSApyLe4vUyA9hCI6S1rNZ4cFsIBzdoEmcQn84ynOTxZaroVQg3MbKLOVaLZZjWKYHgNPAaFNoMmpD0a9HSafPrdwlQLtVTuxnLVrIXqsB9RtsOukVTW/t911em2u8H2caNuM0HFd/r7/LClenXFcZFwA44eqI8xwZHwlYoPeMQJ7MqUTrrkKLG6glYUzx9g+W6VM5XBKriDeSBqYLQblrR0wgyfa0VvJdJTQX1zlr69SQc579/gbbzafNV4PtrSH6f1XbDG/HwIFzf4Cd+7l9TmdqPBj3XqQJL8172hwoIrBgOLHWTCx4F1g2w/0hVKlc4yHoaQqXHLLWVmJ9PCDfc72JRxY60aaFZQh+7oZ3az8Mz32Fy+yegXbyizn4BHNAncAVmQG/DobXq75Mp8Lb+Hp8CyDOtOCyPDR48aXxG/TAyDWgJdQauZ4soR7FfqsI/+/nVnQgEIdaan1m9L5y9TX4QklrslhX6n1T/+/FLcW1j+z90ESBtJ/ME8A4Uu/M4554rS7HX1F+eQV2Lly3O27gZKYTIsLx/V0ppkzeTUn4NICxfhhCiCurJzvAE+hj4pxDiWinlr1LKXZBvW3gfhJ8ULiCejx7NR3RnPQ3peOcixv/6f2rFtR6ZVfkUiOUueBliZiranXd2MoMkM4AKFYGCylAwmLGm0I4TTlDGxw8zFMgeUFxnJ8/VKwuLMDNa3uiIr9rMTeyEtYKFdIIUiUiERXREZqrgMiTcLGn09jesX/AXOA68Clsy4KY9u/h4SEcm6Ezfqp2tEPHqaSZGbiV7nL3WrjnLWG+NxqZIY1xGRL4GmfBM4hucoDSf6ECwsfaQie2wBVCBYRw/sEuoTO3fZCyfiiyW0JCf5WvsWRavtAgSgLmwb3RlinMWF6MDOLTBknHtTVEJgNf8azUBOGa3ZV+waBmpgVHCuy2683hnj6pSBklkkYC/scXf9+sP/nww/bYtk7/9hwLvDGhM261kkYBRaTzBcmLc5NKcFXAW4thFK6EKkR4K6/mEEUZoeBqOLY2E145ApQqcfFiJwJz8a3klHLMCSmcdgq7aikQHUKWzDnEyVgvGeAWXhsclhE6b9TV9D2EbZwcv2/5s2OhfO8C2rZnZ9BL/ucOzvcwDsbIBfAU5gNh/yns3Dn2KVxNmjwGpyo/750aoB/p/AtXhL+OXs/1cTw5+ja6rvMrj1fkCQLw6pzNUP0repdK2p1tW4OSmSuxtMl7NB/zvFsClhHK8Mm4iAvgcFh2qD7vioHlHU4NisshBSl3HijGG9FUTAVLVvy5q39Hr3Cs1dxVQ3g18qxbXcamA9BegjQtcO8zzYEBcBdQBNq5QC6amsuWlRnA/nDOILw6T8OcTFyIALUoEylh6flc5wDuqpvVNYCqc+2tZqAPnnitLuUlqUvfY3b4MpDA4IKUM3ULBgxrAPinlWQAp5VkhxH69PODTmRBiApAghHgHeEVK+ZO/tuGay0sA55t3LgZCSz6jJZ/lbztdV1mDnQEDy6D76V407b3r3GLZWqBzN1QblZxPyDyBzNPh1fjzYGjlBX81gKHW2Ra0jvdC41Kq0wj13P5ZYAgTXGmS6mFc2iishYQQRxDiiP7fi3Kaj/uNaG/PWoqhdnEz7/X24wS3Jgl4bAeLEcdld6lXUUDcBUJkUZvv831PtrY31BKCbmOtuxxmf28uL4In2pC/b4eawqJAfuqF/V2v1ox7UdQ3CrECIVYUeHvf/V064/j5xp9p3JRS9pZSVpdSPhEosIRw5vKCYiyDEbvP8HX0bbAbfmhWi3riQXO9WW8ZD3IUpLHOzGz11d6SBvXQyA7FjlIBRfZbCYi37NRYT7Dhq2ZpoAMzyCCZZDK4bxTME9sQLzvPku0hTvlZAgb72qDBGsHrUu6xt7cEigssmc3BnsWcu85DkeVBPaOVCSS5oZ/ux5eA8TD/NcT08ARSu1NUPWc0MDUzTS9Vmb+eTHLMeHrjEBWJbbeFrNV14E6ocwr4J9QYOo9Vz8xTx3m9ClQahFwpmMZ66AsPDJyFGAi1Rikt8xxUXyqSrT5HR5CpmCSDD5Y/CcUhrdVU87sdj6r/6MA8mPEiZ7puIVu+xSqaMoB0/idHEXs2iwqzTns6vAy4H6r97yDcLMEhsJydqWseU50zkeAVIDXEpEQbvpeGiqzheWlVkbVeI4/NhFhN8zbVU+902//mY+Y1gyaIzvpam+m/XaB6E2941Grzlw31hlUldwAvA3D/QlWzOYGeuLa+x08J11Np/0lOxZVki3yBhMu+4jKMMC4g4lC6hP+uAA+jMk2jgWdLqCTUw3ByRSVPnVwqcDcqw5mqFokpYAqMXqf+KGuMHfCcylKp+8dz6n+T8WCogDrfT6z2IeDJplmZhDbFbKOdHv4MWy7FBDEyOL6BpRMF12m9EUQadd+yWV/P8p2l7CqoGqXyjnAqQnVcREC5vZFKNfUB2NKzETzzB3x4Ffyu2v93XHN4XvehnQsWuCHK6/wcclOlyyPkRkRzmgoqizoY9R2N1G2zgHstn+UrF+jA8vq8n4gihw1iMW1uqA/LYJHYSBtZn3Ukk9sZfKylZ8dDT5fKcB42zpVxflDe22WBSimKxvu213lMcMEiB77wGTds1GrukalwYAU88xWMdnHjC9+zXXjE7ozJDZu6LKjaTkBKS30nFWxtbGPWoR2qNtTc3iGjatJq7SfCycqmQFoGAexF1D6Ndvnetd7OmwlwBOimlHjjgXvy4P4IKk/ex8FN1RQtfrLeNhCtO4z8YA9QTQhRXGctiwNV9fIiQzi4vMCQ0SWIZSpZ9etw03u7WCYN8RXfgUxZjvjajlTlZ/2f/6DRdsxRgCXoC7mvpq9xwSm1hgel+CiEttYbz7P+AwXZA8zPLl34ewjwByehHwPPNXmZjCZJDBo9iqvFClKrougxQPTOXORfBe/XUxJ4PTyyPz4QEdLMjk7r04mYPltJZzr8z9NmDY0dBZkM3HdqLukLZsNu2No3hgqTstXHvgNFYLjZc4fvjWFSNR7Q106QekkrnOpsC4vzQeO5lKhBMhGeYx6vLxxgX/EbTKMbgzNeJatZTSDUX+qlj3CmMYwLggOoCcUvoU7eerZENDJtCdmhX7tQgeYKtVjOQflHpoaw/9c8VhJS6iCrDFDaBSd9J6xCelB3OK7VA1PO978fw5qkIDAsRnr7WW61BZFfqMDSG8dGR8JLEpYKVb/4xlVwGGQfXRv6vFsFcQCT9GdoCDwZy/Y5IGUDz86sQUAnt8f2ZbLugz6nqh9HvASHaoK8zXwnRnZkkZHJux0o7XUe1RwybWR9QHtiBiQKeeixUj6N6As8l4K01L+a3/lT+ph/B/hKfY5ZsH3APt3OufYwEKSsE7jBoR22YDL4/lyO/0PR1nMaQWfodZTB9medONkB9+6AD+PhZvVlH4ysBv8B0vhTDJ6X0rgppcwVQmxAyaFN1X/XS5nfgqXACNNiLzDE7jP53iaNafm2PuhgVoPhsQ/xoiJY2zht773euu0ghtKa+T52I4WBSFSvC4G2zHG0F8kXbvd/py2qm3B+MVrPwgOmdUZhbTNEgNJVqz2HbbkXJSwQklltyypeDkhm9RVJiz1D8SJ/hRFGUUD0D94m3/v0GjNt/4+jyC0wRIPQKYMXgloYKkVTCLfqe4MQ2q6ERnzjf/0o/QpOOPLd1pIYDPbdiAp+lj9FSBTQ80WLdT6W73ctmmHzSfW7bQj2Ip7jFA39NdC1eSVRYuHijptCiPFCiL1AdWCpEOIHveoJ4GkhxHbgaf2+SBHOXF4E9OYtqAKcgZ06K9gcO6VVaINkb1uRGNmF/fr/NKaR3TidTmumkT01Af7rRvQG/uLivjXQiA3MpSsd2BCwP3uIYxBT2Exdqst0ksmwrbfVW451M1DVAdtgpcMatZ1fDtXLhrvhPRffvt9MCQSAh+LqAHNWS7pUkNZIvc3OTIDXFR1x9hQV8XRkoSnc05GFpBtTmTgLAPnDGP6Pv2XO53UG0ClxGgepzBT5b1K736sEJACWADugR9R0Nbt7Pfw+WEWRpd4DZoJYLAF14xwuRlJd7mQuSlF0Gt0offMJhmSO5VV68xrPsTdTff//SWzJPUu+ZFrXTmSNq6Oyk5PgwNvliHzvGAmubGgHcb03AbCTm2jPTBYsU4UnssV4M7CMZSvZ4hNHxWGfc20J5k31woZAPzsFlrEeiqyBpnyBIThWVLOkNjpZADqs2Ua6zNobq8KsE5Js13XRKM++njnApNyZOA6DV77Kzr7VL6kZyzDCuGwwA6XkOQm2rG0Ei/AoiVdECewbGI3KZII9ezjPM34KYdzEe0INnRna42XCeNJo080ne2SjHa5yw6uW+1yyRylWHcvts43nvui/uF0IN9R3vn96MqiTQZdc2LedBkQhpWI6WRVizQBRW0jJgV5R1WBUZrKrC24DngeWuuEN3ZdyQJwWwbkeqshHAMgdBsRiiu4YKJV3hNPvqGO0ko1YYhy/noS9nllXo41Yro/X0gVLd8BIy7mfC9RCZakXGHWEKlO5SGyESBe1fv2BRcNu8mzTAXgJeBZYB+bjT3IKZFgyqWcw/aHEtQ5Cc2+vgLe/QqXOEhRt9fp4mNEGunoCS0W1dmvbDWsm8WmCwQwOI4EO8TZqrXMG83PHDKXTuGteM0VoQVJQOqwB54B8OvAEPJQD/45S9Oa7UeYZA3ypwWGEDillH6CPw/Kt+JLMixTh4PICQ0aX4AvWKxuJa7B4EvrHLE2NNQYqZfcQWC12XuOu5sA6Tzh7nh61uUMqJJNBXasXpAXVZTpV2c9SlNVIvvCoGxoFHjj9Yj3wYOAm+cVC7CoQf8tUGViZCI9wlGl0ozhnSJsy1bTw+CDmScVMj0cNuMWhWFW4Kh5z3ke+IuBqmN9nDTeyjbtYSgZJrOZ2tQ8eZktiLAmbsunr0pMMwB8fA3dC+lezVRC7AWgCkbcfg0GQ9qiyLdmJZRD1wn6qMh+L12iSMegUjlfiHVRacXviF6ziLh9lWCsdLBgKU/94sZBBE0c13JKc5lCL0qwlhbv/t4I49troy5czZNiKJIwLhGKvHefck2XVrGsbVNCYqh/ke2DajQCKLtsTzyQgvvVvarZOrzO1Y7wDSLsXploW4KFduMGdv8m0oPTXkJRjY5Dy4XwdNxBkXxAVXXDTJNR5+krRgwegKMJ7XYo4twk4ALktNI31ACrgPIMKijcqPYif86qa+14yp736Xj4G9grKJSv1z1OUUoEsYHp4GRikqKGiI14WFN3w1MPqALMj7Op4k7ouOgOt1aSkqIDSOZhrCfRv0RtarFDkHJfyXbbiX8CLqer/l6tBZDz8GyVrsAHlKNUu/5TYwsDxOlwZGl07JAsS8zip+ehVoP0EuoZv9bP8CKyK8rzNAZIv/6DyPI6b1wgh3gUWSCkXnI8DFAZhWuwlgnE+FRMKBaHE5hcFUQpNYSUprEQ09mRZi7RP3QtGLRUi8HbB6LDpvJ//g15AGP6V+UEo9GWxLOBq0nmfdN7P97XoQytb5jmWEAsQ4pK7J4YEJ0rs+fyNhhFGGPmgaT6lXx3xCRhBZTKF2OG4zmzTrOgofE6U0cJQB/2t97nf5kN59HKG9XOKvoHX25Z7UZ4vZbqmkwpsftb73a/+rVxoiIjQRYfEwoLRpP/k+E1K+filGFhCOHN5UdDsxNccr1eMstHniF6Uy7g26pdtSmvHA6kQM8r+sBoju5j/m9nMaspXMGbNVmrTiCUDlSSdVaDFmEUyaiiNv0u10qsARg0chhyl1m2mrqkC24EZWI105tIV0R9uHeOgc67xZfd7aDVlPrcOU23WDnOZqrGmwI+ujzFUZ9VNRs929fPa4YMgewtEksoA/Vu2BqC9zvpu0LxZay5yDm05TCVACe8Eg0z0BAcdmAvAvR3V/o1ZcJH0BAwGOVuozPPVcNUeoC0wHzWDOxHW/gjtP14CO+Cjg/Gk6hr+Iz/BXb+/SsKrsON5iK+qtwGuehDoCmtvh5SGwI+wcsqt9Oilgl3D97KT9IgrfU99Yloov8U0ppFBO5JZp64Hnd1WgajKUKcxjVmkmzWUnRaq82JcSzT0osIGwSruMoNda7+M85i9LLSMqVAaRMg+zhnPXrwBwESesW+nfy8e+mzgDOg625UceN/gTLMtf+KA/s/ju2W9dh458hElTsHpa0vyxc0qW11Eyv+XBMI03zAuBM49V9ZD2koCjoJJ1jmMylbO1cI02WXg5xKmsA+PovyKvRAatc6/UqxjNkbfv836vjNAUxdUBNFeZ8z64OkbDkFmPuyhjYyl90O5lOn2ZYc89E9DZVMEml981A27hsLnAnpP59YTK/m2bTNFhx0+DSqpMaJK3m5yR0XDIHWsOrI9Wyw7zhafePZZyUXprEPUqLCH7T0bKIGmvXBsRSRbiES21edtg2rLc8Bz8Wq5cNNO1qU4Z5nXsavKQDZ3Qar6TOvYTW5HnUFdoY93HA9Jp4RleQdN6d2lj3PoJPAPEC6Qbqiv2xkZ1DNu5Cj9XY+KR3wCxWod59ymskpA6Gc3nkx4qj7/oWWwhXhbt39SLVj1JnTw0GdFR6esu3/ktwzFCCLlW4HbFRYG/dVb9EjKNmpdJa/Mb6UomByl6PAf56hlo6OUoM8Ay7PhZYorcdwMB5cXAcvLpHL3ryv44trbueubr+kb/xbiRWfqXAZJ1GAPXwvFcbUGmKAerFuyiy8H3kPtUdt9trcOOPfJ2rZ1rZnPkv7tGTjGKHQbZgaV3vDUq3Wl+pid7Kcqe/vHmdTb4jlqND8bVRaAJd3bc98U/4JBZv/8iDDMntIWpkCn6Xq6KkWafH9jKDPEf4ZYskhSYtZgOsGbDuuN1TTmFKWoz0ZemjOAWH5C8WBQA9QKIE91RAg3dHAhqwrkMyBaAzUh5RiK7nocUuvB2q8gCvgDSLkfFj0PbToDy2DBQbXrdr2BSZCSAVWTfqQpKjA3abDSlw6bRYIWXtpAcSCZdcwi3SeDlkwG21DfvQo2fRWIjaBSLAP+qped8KyfRg+1Xp9aoa1VOulZ4lkO+3SC3cpEUXiN4DJUCOEmSbbEGkwGq7cMti4QjIDyaBl7QGlkL9Vf9VQzK/E+arGTyhxkJ7UKdLwwwrji8QCUvO0IpyvnmLYhrANdoaDQoQK8rqO7WaigMg1FozVos5/jU8sGigLrBNvD+qpJQDVgn2Ndm5QuRb8MxcY8FZhsr6v0tigxEEr9urHO8IfMIdquwLkSn1pIGSiIreRSNYolUPoIa8GUQr8uHQYpdd1ckQj1ozEe9mswny1GDeNU6NR2GrNXpsN9atHJ2Epsn1VJ1SsCx8oCldR9VByyn1MjM2VkGRd07AwPQ7mpB4DeHCvrNgPAKkPbklsrWtmnNFTLHl+oJgrfraAmCtscmcMigQpMO+jj1AMoDcPvVoElwPI3MSVuhV0dWEoXZMC5bWUVLfY+1Gc3LG6KAnPfVMq1QfxaAynD+lsfkhbCCvXHmr0saPDped68Vb9fZAsw1fpb4ZBaLm7R/crYAfcCo+OhRJRSin0Ffd10g3v0fi8htfgwAkPIwlboXqZITk6WGRkZwRueB4jdZ5Bnr2J5zb/QfNN/OVSvNACVOGHL6BlBgjW4zPdMlc8Mp8ukwbYapYJL43j52q9X5tEaXHqvc2rvtNwnu9kCVUsDSC/tBfAEl7KIVDuN8234GH6ofRsf4hPH9lbPp4qnleHab6UmIoe+xDj3U/Qd/hbkouKO24BScKROScocP02Jb0Ak/IEsrWpwYq/dAmDW0oYarBlZaH+TAgBjdD13f8ablFp/+/cXXJrrywAjLAsO67b5qLH02adPBjJIezO49ASMoQSXBYVTcGn2RV97v91cCoDiZ86wsNQ9Zj1zKFnz8wkhxDopZSiPv0FxVXIDGZHxn6LYlQ25IqbI+hjG+cWFHDfNGjnN/JBT9OTWN6ga91g89iTGPE4a6qH0alRwpOsw85MN8ulHgAd0Q3FU+rp7OLc9GmBfxpzootDF0azBpW1fFkGfUCCEGz7Tx9sL9HIDA+Gzq1RmcZClTtEIXCKgVZ6a6l0i6sBn8XRq6zu+iDmY4ziHdKA31/czWqmrMs/C5EpGZ69A1V1CPXmYTaKiWpSmJh/kTLQPpLoo2sj6LIroaKuxBHT2cq1+Y7ElsRaJSU//othN7tBo1Y/qqOtKz9/Kc1zSCCW4NAV/Uj3Liiq4VMf2Di676eXx6hmqkm6XB6IqaqLIEH40ft+HikYwMFSEx83CI1xzeQnBCGYCwaAhBqu781cLGch+BJQyrKEOa7UjEQPhHR6hJZ+ZarCggkNr9rGfLfIIjPxIx4vu6GpJ36zkD8Txg5cXp9MyK4YyyPShdMJzFsUI41x/SJcC1TwWFLuJogsf0oUPHddHntvnuLyoIcpgMRh3hr+aTr+1Qfr69BFTuAAw6jfKnzhgBo6Xox3KhYZEcPZc8SJ/hRGGN5xq5ApTP+i/5s5Td5nfmjtTXRtstf5inH1d0P0UQa1foO2j2G0GoWb7OTroK8ix2gevlQtIwbW281MzUJDvujDWYn5rX4OMTyqYDdbmTYR403ldR3wyllZLtlBra4sa4gb1upD4s9YHX6njZpgWexEgo0swjU7qzVmoNOMkH3b9m1pnyexld07g2Zmj2UMNW02bAUMEqC8TbMuFcMNf7NQba1B536gZzBuoMl3Vxxg2I/4DMcfPMEZ5XY7qr1JWZ6PKmoGld3ayNfNpNQaW3NLenI2W31kaNFWDkZwC4hP1kl0w5d2NjKbn80GM3EpMpleNhwOOn1I/wrKlPPYp/oJKj0quylx+QhfKcBKArcTirdwspdrXqROjmFBGfZ8P0AgxbATSPQiGTFADh1JuJ6aezoxWSIBWIIE4lLVICc6SwlpFP90kgAegXhXHfgrh1hTnpqw615QDxaqpDFqSHmy+1N+9pqCutfTbmFE2AlNzWxTVkw3ACKUg6HPctr7ZzFi2UhtfOjbkI3BcAfJX5yy7E6R0XZBAMFDG0uxLIojtML24mo3dU7wGrVnMaUqe9/6FEcafGrswWRLyKf3w+Y3OcLyCsh+phUf0tTrwP5SNwQDgd718hfPulT3JHfp/i7BLdwt7Jljm5yWUdUcIkEdAhKCeFyw7Y+1TDtE+ojSh7MMR97jhaRe8qffX4SqVpRuMsl5JBu6w3KNvhyV3Wbi298BsdsK/vY7dyU3Jg89RN2Iz0JoNc4CeLnjN6/hmhnEgVLoKZum60S+x2LpM99CRJ6rWVR7f7bWju6FDCos6giL0aBqr1OJNqcDcCE9bo87Wain1MxCpPkdui2hVS1sduHcR9GsD9wBjQwks3cDFi5ocM+Q6U2moyHqryQoB1CzgAZvqZw+Lkq0QRnbYheKuv6cbu5ASj6hWpXhVsVMO9dtuCjyMoj0/VMD+hHHREA4uLxKSyGAdyey7uTLV5hwkBecb1eudBwAQM1MFJsmsI46dVCE38AH+6yxpPpeuAdVh91iCTCvVUgkEfWBzWh3JMDaOMbiMnXlVDAZgbKhM69IoalMQ/PaBohxW4JSPEqz6jL6f8yZ28iFddM2kM4Yx0vbeWo/5GgPMgG2L/ACAFNb6pcgGxAiDluUrcGPUVDqp1EaTE/BoTYutCkiHLSrIExbKlgO2caNJJfZBpMvXPwx8/FvzC2/6a37osNL8LJ6gsbB02icz1TWyJLEpzf73LZ/fHCTVezlCwpkzl/6MaRh/AkxWf2x1goeMu2EX5AuWTIfVlqQ6ajgwlq0A7gY5HWdU0pHpIf3+wXwEZc/7CeRe0n8dJuicrEhMmmmAe2xAfBW4z96UWQDplS0zg7c3j2DQFo2g/Pa+X/C1UJORtWQa8AM78fXwjGI39UlmrZfomZQuRDfYEHcbDAdm69XPWe/F1r6ocyS0qI50eF6RR1C1+oehrfaz/oAnEJVQAUlLMIfGY14brwDc8eDSway2UeFnQzl4OlACDuj3FePVNl8A/2sDN+/ACEhDC+Lz/E+UOiRbrWU+gSZYzxeKqlLOu+7Tg0eRsrrnrfEbrAWM3QH74j0U3fpc/jh/42bYiiSMi4cR9GOERX7VH5XCoMIGwgxNlC0KCAfHDx9p7czA4jxOCEZ3tSKYLUkwOBsC63Ud/VtTWOnG3tiNx+cpVAquP0qq9/aire8DjJVaKyx1Fvl90DGO9QjvIDpb6mUuEs4nzdWg1Qb6HsMII4zCQwSxhTyvx3byG3JqF8TyuaAUxsJQH/NlRZEPSwjP/i3jip9jFc85rtYF8Fl0spfwtocRA+2Uyfp8x6UCIVYUfh8Rns/nRJPNf58cbErqWmorvdsHWOe77xUhfWanZyPrMqOPl7L1y2WAsBVJGL6o0z8LANlYwNWQMCsbgHFpvU2aq5zpCVCSWWdaZKQzK+C+/WUsAQRueFn9oJuOOkpTVjJR5yMHMZSRBFdmGUE/BjMWgPl05nOa8zlv8Z40zJPVwCP6o4xwaU+xMcd58Lt/AfCReEw1i4Vi9xw3FWYFQFc3xX7pB5Q1j3fN/06pz5VonVXzZMr8Ce9spzajSg0yP5NRgzkFVVxt0GBbM5/FtNdd2kp2xwTy5pSBpEFACR5D9bsnk1hLF1y4NR33VoaL2xk+RDJk2CCGR4xUxemHPDfRmDlbKcFZMFRf/WA1ngLZcfX8m04NYigDJYzU36cRWLZKnM8SDDXhraQz3VSIBUWHNZK+ymeyGpVTLTWb36g/sq9zYCkdHgJi2WoKEDnByFh6e2g62Z2ICEwaklOms6BIZnWBs5LFS5wJqd0/b+ypTMGBmmTBGThB6QId81KGlIKzZ8JDRhgXAJ18F0npoEmQiqLAAozWFMm2kNtfZ+t+V9khJ7qoXTHWXyaoHVIm+lnlMgXNfPpUABj3WOcaOzWmSpluy1QK7QgmdYArpUvdS71FbLz3p+cjbZnhN/R+n9F81UPANhdf724OmuVz4PTdlg3iwDIpnTs/mi+JRrb3CPyZ+NzTJ9lR93GqWlX+xAGOlolUAjxCpapER2gzZw6LBloirUou6uVZAsvhwNMwedgTyKEoqmxLlIhTlv7fULydCsw1stRu9XUbKrcbtciQmVxyYT5iNUXRgX9RxzK/7+YuWL5C9XUY4FLMMym9S2dCzzAa6vPO65z3XxB402BDgb+A0lguZar6u9I3sFRZccOapLreztLg0A6VvYwH05TgPfv6CyXkcz5wpY6bV94nvkQgx6gs1VZiSFidDbkwroNvUOGhGybwHHsAJyMJ/xgilGrlYB2UDZdKy31I47EMaQz3rZlBMhmWekP/6MpcWxbUG8GUMafldONsVFmm6L54D0BSumzL1Ji+UPmcgSpSDAIr1dVfoGw9p/6yfqHhW6R0MZrnfFfNXUS2gFrSl5Lkj8rahzeZRRrjedqrn4Gx7lwSB4tP8trG97ObDy862DtQrBoAvRLfoHyiofE/2G+NkhMySDKPaQSbnWZOC1nt1oazoasbBu9Xk0JnLg+XvM5xuUHlitUa+OU5SmvtuRr3016+S6pHDfZwCxsLdfxLDWqQDNNiw7gAOPMJtAsscCfzdJYn1bMs991oT7CZ6rtNfuA3qDTWz7ezPfzvp2APxv62i2lmMGICjA+VXKHfSw27h2fcUNMFNV1U/3Ene8URVZJoPCaWd/FbpKak8gfKCmqn2VeriE9klPbYko+QOx+YDLK9JbDPU8FUueO9vTpj9xltM2oOC+mI0B95U4QWbQjw2W49ov21URG3kQlU9X0W1ds83yypuU4/a4hmwFh9ju5ABUEjdZC6PBWKIPFmnAtwpsnme39ONiUFCCid950aQps2QZd591FEoCYgdsUpeuyHqLrXAc7fz+WEK3XcDNNiLxNYKZbP2YpMih7tmWm+/MFpdjXy3D4iz+1DdHeYudQYQx+bsp51f97CBE402RCZSiZ2a608K0KlzYaCaaSF1C6//fZGLFtt10ANdjrPcK/0PU/+6LVBFf8cVGKdlBC9+2bdv2MfLbRZb/pqQei054teY9BfwwgjjIsPIT7RryOFpg8K4fZRVA9FmVO0x8eXElT2SjjMZRrLxSvqBSAaqJe/fhUlrJTLotynzzJLUGSlzDqhueY62bYfFeB4y/yvM+Cthuu4H69xSwSQ9FUKr8EFewqDwtGe14bcv0DHKcz15q30a1BmfZ/lFvmlxIZpsX9ehDOXFxFraEx9NiovxNXw/M+KDtvXDytyLvfRgXmAChiM7JD1wd7IIgmdlbxP1rZlygw662BTUKUrHZjBZuoqsR/hpp20E/CNQPHcX8sCYyFD3RBG0I+N1Ace5hMeAuzWGMX6KcqruAXOXVeWf8g+tvUGHdZpmRF7xrIVKRMUXTXTeabWmw5r1HPKHvZ2N7HTDCyNgHOx4u2ayCKBCXN6Mp1udJPXM4RhwL84rWmPN1+7Te1busx91WAPDwij8lydm6lSZa3SmWUGeiLTyMZ6UFULDqWwgVU0Ze6m++nLW1BPOnpYjmSYrT52iVhPJ3kUZJyivq50PEU2yBb2QeXdTGU6bfTNUIQNZj+SRYJPULkxSAW+bAHibYflVl8zcPS+zK+X5fnwvDRgfO5IDnKAyjzwo+ro0Rt6kGo4ff/ZILkiZ2DDuBhI1FkzECIEE0mAfwF3oQQpAZob208CqnnRYAsHIdwwRGdfvJ6iZGHIMMY+vDI7UnqYIKZCunSZ6q3CoHge8ihvBgoqbXTYfvHQBrjTBQeg3C8H2DsuTqmcG7TSO4FNZ6Ch+rDFfunMuefKwsdxMFv37d4/gE8ByG2gGTvGvO5St9nHVCPr+G8Xx5aCFkxnkdhIQ9kagA0LUR6VBj11rhsauVQ/0AFuQ5TS+DJ1mCpHdpMrfuZboe6/4j3NFS4OGGLxr7pURmzsOPM8gcsnY2hOfh/DQzEe6YJBk1DDvrqW5FDdbmjodFXHbKkfOqyBoqDD+sUWNzwZ7PipzitapiK/KNzhRQTqt343kCyUr+1o0MSgyx9X6LgZDi4vIroyl89pzk1VdkE0yDjjjubL/8wigZZ8xkbqU9Gx0KPg8KZpLhCbA1J5WslG+j97JGPQLJkC1ppJA/8nxiMl9Heitz7oPZi6fAKXmMStIVNFDUR7BY8GbXa34a7tgN5M4guasobGDGcoL/CK37bbqM1wYbWdcPmorcnE4JnL4pwlw+T/+sL6ufcQp4SVpLqGrFlJgzJlbe9EUbXVHvlhf3nbjgCOFiUA2UPV8W4c9r3tGE6zztaA0RoASv+J8oA4X/UYTkqG3jhLcTZTl2U3qM9xhuLUycxS2wdm1YURRhh+IKWiHlqzk0bNpXfGUs7xMAycfrPCd5EzdTAUZc4FvpkWM8Bw2tZhnfzed5kHMYFW+vTHVOT0E0iGRI09DNzpBqrB0X0ce8YFk/Tn/MDatZHQwUWxty2spPvTkR31+f/sKmTbdJXRMkrx66HUcz3l/6yYo2YNZEdLv3UAt57bAIhquxvylNJtKJnX3Iho2uTNYZG7I5ACr3vWyZmerKXsq7UgUGUg/s6PlE+rCYT1n2PY1TAWuL8nfOwGnNX48wPb95hPOmx+gs1A/ZTShfAv8RB4v15BpScATfVa7kuJ9flO44BnUdfBIeBFbR9TKb7ISmXCuLAIB5eXKKwCBI1ZTlX2AxV92qnAIslnO8Pn0h+MrFhjVAqzP+Od+zEQij2Tv74HgqPPpXcb/VAeowOyQ5QBPNHZBD1r2DtAgGgg8tw+niqmCmNq6Yyfd6bTyEAOY6RJc3V2mLT0UbgZ4qcG1FO83gbmCkTHhbDOtw5BfXeNfZY7t8MMPq2Bo8iETg5BjDWzHQpEWzyaFnrG3ym4BHvWM8ZJZAM8djctgxxXe2H6E/HpxRsATKQIL8JCwp8K8J8dUgrO/HHlzcCGcXEgQswAWsVhxLt64d1ajKth4Ecc4yHXWvdmg8625ecB16C8yu9B6OBSBtfJC0on9b+d2yNO47Re0xcLm2EKePzPdJCbj/IPIVZDJTu7pKppHabuM6EElvmh/YqOqJo+430PTJEea4AXSi2th+qpOnBes4v5gK2uVPpaxhT98Vbo/adallmegQq7/4ii02K4GLhSx81wcHmR0ZD1amBsCJzSC1cLPGZZHhgZy8as4XlRBYGbTjKOZNYpSqTlhqh8BH2FY4JZJzjdgM69UZbho/rBd7CC5pyluEn9+0S726aw0qSjSOmikZYfXc9tZiBpBJaOx53iuyyLBFVQv9JDBYqVXeiv1xs2JZ26L7Ttw5sO64SlOurx17RV5iq1r0RPq7bModOv6gFgtoDhoiQxsgsxOsisq21NFgmlFCqEZcBKWoTc2BY2QWy9LYASoTOQQRLJrDPXwVagIVkkONZLDjbcxYHZmSqIjGy4j9RivuqtfRjDfNoBkL1MBaaGYmugepZQlFaNQDdt2DRml0ln+9gGyhdTr49psZXsaxM8k5nGcQPM9oMni9lLvxfCM1PcizdYp8+e0T9/QapVNOJomUj8obz2Zju2QnuzhZC1zM5MYFuCYECZlynJaQCaL1iHREC7IjILu6QgOHc2PGSEcQHxMMg5FWyLjHuq6IiavFoBVNYr9zrso1JPODQJISYVmhprPqy3c8EGnIZpE6IZjqJC3g//dlxlo8CGjEPeAYVBvYz3Pb6XUqz8F4jOLkg4o7KT9wP3u+DOadSRddnymGYqHVZ/IqMOkitWQaV0MDTP7nHDOh2If2H5jNW08uo6F8aQVafjerYINXNZcucRTn9TgXKpvTlWPRJ0cJkrdMpUB83tZF0WCDes93wHVZY9otpGKArudmpTa+gP7BKaS6vp0eJRFzYHNXv1jn9McEHvHUAF4IiiHP8er0SPfip4raCRlb9UIEMIpEOBU5Dp95h5xjb6PE52QTkAl0mH5wlU9vKyx5U5bl55n/gSw2lKqQE0HsQmPHLYf3GZRvNraG7+CDvJONv2s4XKxom/qPehzk7NE6p2cK4cb3phDmYsKdiV1m4dpd5HcpC5dKA4Z1lMe0YUoK7Mmq00spNWw2DfmWOXkrZOPAHrFC0W4O/vq4zl7B72CMAINjviUWFRVN2RsFOHO3HqoX+W1rrvgV1hdj9VKW4WaChsJVb/N44TlFbqdXpJFp7Z2uwkY9b1W8w0YAepVerwwVHKU5+NLKY9NdhJU1aygYbUZyPrLQUHs0g3s2VZJOgJAlXbaFAvrTPGxjURK9U2DvoTgOFxqajMlf+zj4N/1bTmVEhaqOobDVsSqxWJ9zVoLj+hsqWiDGi3F7I768DyrOMmcOAf+p//sy321Fw+E7DoX1wLT/06DkfH8iAoaFbUrGueDAPGv0xzlpNyRAX14qZzqt/57k0YYYRhhbUu0JphtL43H0TboOq0jJjqc/1ok6pps07cWMv+fJYbdNMKzutEe/wGlvJ7TO9LOQxTBChYLaZTUGnNZlrXW7NR1jHfEPCpJw97ttMZy1J5Rzi9yeEDAcwAPpgK37hgm2fxFjEfJjaCM0BvN2xTx6oim5Jr0JN/0cdP0hZk3p7Zh1CBw91ALzdqRFoKLV2Aqqc9dnck1IOfI2qqbYwaxLnqcy4wvr9KxvEfIbeCCirlEXU+dgn93ehCTRWE6u1WGH3RY0lzPTnwgdvcr7DKW0qnukgdFBqBZcs2OpBWwjpCrD0v2cv82pBYs5XGe8++ij6LKWWqg1XJt/p4bXQb+1pz8qOGC/bsUC8qw+gIVXMJ8DCe+ukwnHCNEOJdYMGl6HUZDi4vMqLJ4YeUOG5y7eKAuxyRi44hhvs+mho3g1i2kkESUlqokflQ3DLrK/P59PsEH/CBpcbSEAYy8O37zeC9ZmbW0KidKAo0zFRZ0A2Jap+egNSTsTQCS7+IC+0Dz6cde6jB3xI/1EseMteNoy8JZKljWnc3Ahv83bhFfRVoZun3rdnu2K4pqzhrzjKo79m71rQ+G221sjIRWrOOo5RXs8RJnmtiPu1YqetGolvYa1Arp+7TAXg143Qi2sK6tk2QC53rlZzgQ8H9K3DSM5D51En9P3vnHl5FdfX/zwYKNYpyExSExAJyEVBCFIuKAVEuVl4JWCxRVKR4oaJSrAjCMShUXygqFm9VFBQqNQRfUEBFjahU2hCoIEQJlYuiIDcF8QcC+/fH3nM9M+eSCyRkf57nPDlnZs+ePXNOZs+atdZ3WWFiE7xGZRD+82kZg5a3EpSBOT3AwEx0ErW9mgl4LC0GTZvBCKZxkJr84nnnBxGWl1rpkUAVFCYwVFzkMBC3WoI/pypHnRYidYc5JuOx9OTChWgJxTUUYwirHYuafWuIESZUB7suZhQXvQTP3qTeT1TXdDlMfRQ1IpAPO1q9qIwCHYrM/A1YxoQbKSNcirJsP/pCx+Y+G2F9FtCvI+yDQwNPVcavvu6Keni9vf2U2I7IwlMiazvNLLvU3lfwe1e/AG30uvc3wLMtYXSc76KuNigzG6lw2rNRRvH82JtVVRLxWnrYugFVfuZCJSy1E/gfl2E/peJ5epOm/ObN76W0/jsrHsa4rII0pZgMXa3WbaAoj1jjhProzDIKtqscwCDVVzdCx7HeMdkt/Z3YHbiYAef7YleFyGGtfBlQCrDJMJ6xHuGcz2gRsx8hclifpCHufjro5GXEyD1YK6BdeD2nfrxq54t+QStKe5kS70H/7rPxx2wpTyZY3sxEsbyqGbaKg9fQTERKPgx/uKt1bjvJHpCkGqwV+gqxQ2QhvjJtH/Kom9TeDQZDogjrmv/iXLjHyev259fFyrezcrVEPWyPldeLs0Ev8968Bnl37Ae4V+vQzwVekRi7nVs4ZgxxESIH2uv9rHHPG9bDweAokah+As5LWeWp2Q8Ge8VsVvL+uwPvOx7EqPUXAE3Dt+9DnlKWBdiTnJfvWGN5R+VR3/IkvZOBfZegD6ELA4TVwXR7JMMMx7CoAEPVxhiXFYBD1ISh0CB3P2yDu5dbdSxH223cYZGqbqGTW/eg/AkYTYRH7AntoUmjgGgPYxDPchsAGRTQmG0UuLIBrfDYfrxKY6KVZS0SyXP0sCoH0IWXH0LZOaMiyMnRTVfPuAg5BNIKixwZdh/uUNgtNFIePBI3Pq3z24+VZLCSxwtH296/B5iowj1FDq9IJfiTbWukqyfk2cygjx6DVZbEXdspVYeopoHtRXyL7cqwBLaubckT7YZxJ9N4jHuAaG+lxRGq8yntPcIyKpeyL1d2X8CV6QsYLNcwFuemrCsf6D4dMqvlB4r+1H/ja1v51wqHtUqcyK4wL8UVnjUKUn1iFeccUHKIn+NSi23neCvFDOAWn7e9getp83fA7fpDvtPE76Hv9J2VE6ru7pINcy2pSFAaRbQFrmYhBWTw27cXsGGk++5na4n6rfBIYTyXhuNDpv6b71uWD9Q91ZvDdXsES1oguVqYg/TfOfYSO7+yNCy0Qm2Sl4+2QmGF2I4Q25GykV4eomy72zE03UI+Ks/yVG8JEvd2M0A8oKN0blKvmrcp1+ChoTqU9lvgQ73BfuAaPLme6vzpc1dXK4LeogcxJcfVBqddN99xjALG6getz7ZU5WWAu/Me4XHh3A+JLGiYtwUgUGNd6MhbAIaj8kX768+XArSEd1AhuwCsgKEuo+z5ryEj0y67pkJ4UTmYNVrCYet4IjGNuXBj8km1XN7pW74eKdt4G9s1QTvrNtEPIfwPQsrDO24Zkf4QV0WmZ7kdVu57uC5EDlwacTzCAPkROG8DTNkAv2vprLNyZoW6VwzfdwWnis6bxrisALxPNzquXs9nA5pzbtFGHpt2v1oxYnTsDUvIVlrgfiK61X7fwlNDMRY9WEqdRnv1p7CsPoVjMDqPeJ8isVDezCHWhdX72DRZj6WFlV/ZR4vvtCssZm26cy4eYzQiCzrlfQyyB2kecyycj+nCx3RhE63Jll7DEhxDMUpptJ1ztbRMncd8bYIM+niKpdnk8iJv2p+LXRJ5Sswn2qjctbqJc/OWRGjn5vGtteHY2vMQxNpXPPGeQC4Pzw0p6cQZz1vpJp6Q0VCep/9/3+S+X03ggys70wNHinFTiUZXCZDAYfOY2lD+SF2rmBleNWrbWMwP2c4Kwc/ECafMBJkXdB1pGfg50BNzdUSVIVno3LgHhb+7l8lJesx/Dh6rNV6H4NDORHB7KW1D+4rgtnHZCxTAoYJTVZ1L/azMCTPWhsMy/f4ylWspZUvEFXrc1rPvF9AGqGMciNOBndoQfd93HzAFb/mJYSr//vFrR3tFeVwsIgvs49cGWNAtybv6OLRYnHgQqKsN8r6dnfIrAFwGBfmOATd8AzRtqV5bV0CNks1BlpHp/41J2Rkh1kdvkEzfsnNSKVIQ7rF0+sy034fmLccw+ELVYt0PJV7W53JmS7jR+a3wNbrsSyWnis6b1eI3MRwvxAXKiHAbEiMIcO0l0+eYBMN1XO0eZpSz/F4nzDV02wvUqwWf0YLPAOX5jKdU66ezK8dTzPCuG89YttCILXaV5tjMZCAzCS6bURLSKGI2AxAix3NBF2IiQkyMai8KiRqr8kCriXkLjUijKNS4f4JbecLWTo1PIpPMWMYzFmX5OSGxzniTwX0sdh/vlS4kNhFu1WempIhF2DXyDAZD5UTUU4ak21OZTImKwD5jCYn1DTFgovr4uXSDSAKxzIkwcR+7mJTAth0SaPOoftXDKfmS7BjDS0Yn3seg+G2S6i9OaHXMbWOktiY1hiSNwkT6c7+S3Z9oC6IrtjBVycexOOpBu6FqYDyXFYCRTId6T3Huso1QC+gHW5o2hJedNptobRuW7lBGIXKYx0nO09RJMJocPtThrJaBKH0TjGXoWaqxUkaYQTY5wC1joktfdKKAn0jhdf150gUTkP+G6tt/pDo/xs27dCM+hQtlN2AZK+iKsEJteitjxCqTAXAn0+g/w7n7T5UD2URruzYlYBuNVv3KZmwPNTotj6WVH5iRvpJ2hcqgk+k6B/OBYu7hMQD2UofLWUoRY+kvJzKfFp6cTYtNtOZTWnEeg1Vfsrf9pC9VFrFZG2tWSGwsjlDD9iZvoRHN1u6Adipv2x0G0xOlKvG2WGUruM4rzLbDeTuzgokEF1hrcPRrbvU9Wqp//tfsTG/iMSxFIfAO1L9XGZ+iTxPlCVjkzt2MPhdu7HBYa0IbEFEv65nFb3Q7dxmRAbE9lkEGpT/MtYf23C7lqqi2JcEuV3LyXDbKFsz71VXU4hCXFepcl+Sj3iofh4/3AAxVASE2OOqcGplHVLF5v9CLTb7r757nEfWUoE9J8hAd8Z6Shhq2R8pfxClBEmcMMrGHqB5W4lxj14IMeahs533erSesm3TOqJgNTxerPMg9OdHX5CK0+qtD/be+Zlf15/WYtdfvCbXdnSMm8+Sj98L50PCdWsDNbKeZ57w03L1Fj+lFuw9+1Ct/qV8AzWHHtUotFu2JtOb2RegnDD2Aj4Ar1EteqY2uehFVVoQlUFeFpcoFfs/cB6DThew80K2uYz38kwqbjYPjqfR+91Y4rFMyBuAfIb8Lb9RWmHiRECtUyCnAh8HGo19JNlH8IbHJlB2J4kPH+y/EBliBCoddAjAIW8xgibNJpQyHdVMF501jXFYQllycSa8X8pVxOR+aZexA3ifAJes6jTguwwSwjMyQCBNPGwsrb3Ms4xkzeTyTLgg2WGx0JEQx59qL/KGdVk3McNk6K99TKdB+QkcALmIVoMJbv2V67HG4sAzPuXoCcpcfmRBgFFxfmEtqehH3BuSsbqI1IguuJ9dz0evA54DzlM5Z1xrSIY0POEINPtYhl24jtRnb6egS7oEHPPu8a+1z0E5ytyuc+C0rHFn2dRmdIN6NMKZ7/FjUSYXqe5yYjp1jCcFG0q7JTZD34VWP/Q3MI5v+B2ZHHQ94HxJ4lr+mQp2YokOUvgtukwglzZu095OEOqzFnTKFTdRmE2k8VXiiSsMaDBUfmed40eQwbWwOxTYu5e7kBUcSMfziqcU6ff0iuZ2XAvdU6jaiE/Fc1sz/gcM/V+fobOchsZTZKqrjqtlODct04D5oxBanFqUrl3TXM008ojxWzc1UeUTVWlZ2p1J79Y/fbSi5VyzOsUuTSCcd1qMWXjJ2wyVh67R7dM924BkYFQEicJJeXaDHXEZRL7F+cyUR+YndXwK/7zjhsmWFOzRdiO1wdUtd75LQ0HdD5cCExVZw/OGGVpisP1zW/7msiRcK25MFCYXMJrVPAY35slyOqw959pPO8TpAtCRhVLP1N+RHCP0qTD7ENBEGokqlvMOlvKMUCoDwXMw0ijxhU2GU13g9+0gkLLucQ2r94bQmPDYBJOoJbFm/DIYE8IvziBXqFbYeSpZ36A4lFBPUy7PeFy4oTnVeJdvXbE89y7JA1FNeIa9nTJ2PRM5JWEikqIetMBqP6tt/TFjDwR5bVJqJ18vmnreTRbh/LFHrwtXa4/cbHHpaHiS6r6CQ2PIap7rXybe9mda9T+xtNujX4gTbV+Lcyyo6bxrPZQWhF+8jjsDRPgLRGKeQrEaMgdRJ0YZDqhxIK75w1z3m0S76Kd9yGDvJ8mB5Z8g7mM6VYz6E+4F3rLzKM2Oqy066dwKnTfyW0z7+lr01z6Azy8hoBHXY62knXbuychLkv10NzgOlsqY/T8mBUREWd++mF7wfte+5OvT118LxCJ6h93tjVGvlCQzCCpkJm6Da1Stm0O4ZjGMCDzCRhuxgDR24lgUufVgnPMt/SyBlb/X9fRPYPdU5bI9tk3vFWkHNdi+xibMBd3mPqdDO8goWIWXENiy9+43Y2w3qPoNNpDEbJeGr8iq9iSo7qzVJSLxQpuNp53lS+5H6M2+8CtNOnaDUfAXBT0ftUJgxQMtoD3kYYiTwmFfg57lrlcfSSv0J8nRa4bC38gQrtQJyPKEeN+5yJMrwbEBqnyIWcDVLuYKW4m1ukErOcBa/T7jfSos1SRoM5YyULYONRb3MEx47Gm9Vpau0odBDe0XqJl7j0rMvkQM54V6eRHIu3STiMfLfRAeVQ0k6pDbOw7yau5Q6+dEzpsCbEbgqxwmvvCoH7oyoEEXXrcGOts1oKG/W3kuVBiEWoeTQH4Zq/X90Gv9fSzYvAF4CirXRvsjVf4/4x9M7L4/Fg50fRB/yYOca/Ultb4fDAmQAjwPnA48A71tlOqx9tVThsR8BdwMLcTyueyxDsxH8n+7/f6ZC3ZGq1MnX56vVTR5Jwise/OQ6mRqOfuPY/3uwPJwlybEM69MZJ77PmQn1F0XUOXbxOx36/XctnlSj5GG8FYrymzdPE0I8ByyUUi4slz2UAmNcViDWDmuBmA78CngZPv5TJ+7hMS7pUuCZPDNYSQv9RHCVDhd157nJ5Ynt75pJr/L6BcGlRfw4iq9nRK2zwzNLqDXkXMh8RuUL8E2hMrYeSx9dpvWU3BPRBCYqb91SKyR0CDCES7mVNaiyIteG9JPtMTk13+jQ2N/1VqGfc6KbBDGZPwHKMLaMSyu/1u2RnOsyp/NRBrn1MFr9DoJLmKR2LQotb2JRkrxB62FCWqLtEzEqrVyhEC+qrfgX9sXEwR9Om2x47Ao6c6XcwMsjlFE5a1rJxmEwGELQt0vSpYnjNjhlZ2w1WJv5P8CbLeEVPDlbyeAp8RCUXbBa/9X7lT+UbD/WvsoDFRYbbbjId6IWcbDeqSrE9M0IWJGutYDtqFzMfHefCiFy2DEpAnUjnPLVTvaf5ShxyzsArPDaFtBXC+Nkhgx2aQ4N5c0A7FgQgSfVMrs/GVHG5Ms5LH7Z+dxbtvcalO7jHA9iNTA/B/gtcGpAiQ9X24UtsQOAHgMlP7sC/meJKm3ziU5/2NgZmjwNdW93tk8kzNRXcqTk9IobJltev6nw/YH7y42pHmv/hrQy86CWypis3dL+f5WypSeK7FgfTyXieynlsOM9iDBMWOwJQp1D30Yt84cfTmYEkxmRdN/JKr2KexMLwfCEH80gShHW0zbAyFhFW1ahYnSscJlkQ2b84cRWWKhf+dRN2H7CwjvCFGRLgjVW/7hjjdczlgTCTd2hsWIqdmHwoPWxxin6gAgw2uyQnWsd41C8rXJoSp9HYyg3JPBzObyOMUKIS4QQLwghXhFC/PXYj8CQDGKaDpkspfqrp0/33BNjrkooBLEvMdVjg/oPXhZ9DbdCZksbNismeV8l6qME12ZRr/TfW1RobyIqvQFtnO+70BsuegzSYp0w0KBw1RW2V1KIJ7FqYJb7mO5Qr+NBrBQqj9LxMQw5LjdOkHkzWYznsgIRIYfc+tcrdbQjcPF3K0k7fT39l6uJZZ4oJlUOpIBOzBvjKMae9mC0YWkxacwEzppUzCyKPWblFXzIU7zKNf9+lQOk8CFdbQ+kuwTICqITL5pSTGO28a979brJzjb+9u5wWEe2O/7FIo8+wCKnQvJK9SqNIucM7QUcEmCIXZKuHul+VOgkpNxd+CwAqelFdlCxyILecezX3vInFhdmkZpexOb/toK/q+Vur6Elz23VgRrY7iXmLrtJrezqqNkOZAVzuZFNrhqSytA/H4CJTLANS39Ii2NETrBVXTfH8VwmlW/5ut7mEpAH1PGlyehyL8lODkFFosNIRPjH76W0ci3nHFC/h7D6l+4QWsuzKcRcLpFdGMlUbuVZOk9b4RGHMpQPQogpqDLoaUB7KeVavfwcYCZQH9gFDJZSxkygklJ+hA7qFkL8nxDiFCnl/nIcviFJ7Bv+2r4V+Xp9PZcXRC9TzpPH4CpU+F0mMF8ryJQwNDYI+QN2LuFX3Vs4AiQJ4C9NYhW8d4zHFkjpVoN35qpEPTjt+Tc7aMQOcdBZODE8/FLkweXfKWXtd5doZe1fAw/pa+8NEVunTtQD9swGBsHlwOXq+in25GCLcstItAH7B/13PtwgGwN/42WxzV5tCfx47Kp+2mu6ZwOLr82CAVmgD2nxDOWxFLfkhJ+Xw4DQ66RlWD4J1nV9T45dr9Jzn/J4S7s9DIJ/o14Fn8J/OsB/bofzVsDQsvWoBYWBqzGHhapG79+ZL111RRMNib0DuD2CfCqh5gkRWufSYglKKRaUB5M5duhsScS4DBUHY1xWIHLJhsuvh49gyf2ZdDuQb5eu6MoHpMpOttHhNhJ61NTVgckmDZX3Buri049XKdD5ZvdSstg9v9JrPORkYLK6QFTf/iNHzziZ/5UjwNp/RgQKfPkCQ5SBKrSK7DwJ84b0of8ti0LDLDriSJq5w2M+02U8zo0hJmCVLxms205gImkUkZpexMUsZ06hyldMTVcGneMZdE/82MWiN6Q3ZYOEkUzlMNXtNqm/+hxk/9BxWMzlRqh3EwBTGU4XYAUXetq04gtqs48jVKcjq5nPdTSlmK+E9ziDxjrvvWyu7L6AK7svcMKYEyCwULhl4KeDSHH2mUu2bUCnUUTqIhWGG10wWhu/rifh8krgO3xPdMOl+y0vtxziWqb7c5c0sfroJHsA8XMurXIjlsFpifzIPvr9vAirD+xk/+gG7J1WJ2ZfJxwSOHLc9v468ATwoW/5M8B0KeUrQojrgWeB7gBCiOb6s5u3pFRB/kKIq4D1xrCswGSCHAHirrl6QZF98ymEVhFdqlcVAL10fmC+XnaPNipf8nbrL+cQ6iH5SwQxNfo6uJUWyjuWgGEZln8etD/LsAwzKEqad9lwjCrxsWNSs8AczHf7XgULc+Djq1RY6kM/A2OASchZQd7HOXAR0CNCo3e2uPIvfezRBsNfnfG+LLbBxAhMBMb6ckzfcR1jHlp8qKVSGK8HY3arOOU1tGehCJc09av5qjlIH0RdLSSwJwfG6fU6hk8eRZfGisBCbekW6GOgH5z3KbAPqAHP67H/Lfi7sLyRUnZ25Va6cmitfVq5kllAvzujyu2UNdYDjTAj0hJsKk/FWCvNSryA+n/tBcxuCUQg23oQUH77P6Yc33nzuGGMy4rG6RJqCHptzWdm099yFeru1vF66b+eJ4PZhBHLMHxdfM5NshF12MuLVk0ngr2VblQNxhaeHMt42wBUe0Ml+R89QykDxJog+3ddFH0bWUqCPJZ+2rKOPdThkvR32EpTwPEigiUkoZ+aBmy/0IrzXBmw0kXcJ3pgi9D4qc4RT61Tp091Pi3jsn/32Z52PePuURmOyXgv5YEEQnLfdeXtxKliUxYCFvGwPJnPppRs+/1LGnDWtGIlrQ8lfGRjSAbtbUS47jiEEA1RclNWuMHfgb8KIU6XUn4npdyIqnYXhRDiJiBNSjm6PMdtKCHvogRZLOrqiIg9Odoj6TJKHkZNi2mu9ta2rwPzlSM7GfEU+wHY1DgNk8RdmsRr4IbP4cmytt4FtmCKp9RDSDiszALxoP5wcR48osc12vVwb7fLmJ+vH2bGEOKxDdgxymBwlLi1YNDY8AeHsa73k8QN6k3tllA78XxXz7xSDagTo/HCqYD+nrq1hPetsc7Xx78Cbu8MdC5TL5/zm078fPiJJaQHsb2Y1rEkqgZcFsjdIAYBG4A3gL+HC1oZKhfGuKwkuG+0rZt5v4GhFGWDt+OGCJyl3spJROVQTmU4H+qMdrdB+j5dAOiGUgnyq78GqsEmekxawDSe4I11DFdKJV7k9rzFCnX1E8ujqTyZ8cNHivQdTGuv1mtCiEJtvPlCYsHy/CVOTxZQhz0QxxM3gNm0srWElbGaRnxhn7JCjMcj5BDkXYxqXwFRHs3g8NkqRfmo3jUQQhS4Pj8npXwutLVDU+BrKeURACnlESHENr085BcGQojfoEySN4QQzwDjpAz7RRqON2HhgmXWfyx1zb+4bswfVX/lfTH66op9VyXf8/blEQpK8KbZCZtNpK2OBq+buBGd0BgmAWNLXqoj6f1ZWTmXOUqyYfmelvaBO3JJdEUZj9b3oD2B4l5gY1mP1kugmnFJ+xJKrVzKxBXOw/uqGHmLlgfcLQ5VJaiCKuvGuKyATL36DkYue4rr6v+DWv+AwyGT6ydaKfY6K6mP1hyhBmfJbAYzS5cX8SetgOgC1yyHa2QrGhMd++BOtH4vCQVY+wKW4eQxHGl0MuIF+NOMaU4Io/VUeJBruxciQFekrgQt0oGOIAuTD4+wypZMALag5K39pUluZK793gqRbcs61mmBoCX0ohdL2CzmhYe1dnPCZkfoPD4pXUZskZ7JWstgb+BafWDtdNzv2+rPY+2UNPxW3Y+FZVT3xIn5qcVBmssBdiju3xhMT+D3zIryKK482olTqu0DrPIkKmfTjSXAIxc5Ia9WyZGwfNd+zFfbpuiHHW9A/+6u8O18x2Mpnna2k99ZCfshN1sDYtyA3aJ/a0NcT/8DzIOgft0lRvz48y+tXMvaB4A0SG2rvu/Nha1Llf9bKSk/SfWdUspgN305IKV8A/tRm6EiIruCeByl/trvVBUem4cqH5GvDTX3vFgE/KzC7cRq7Hxwy2tJP29pk1g3/x4jMCAtAECsAx4B2dbl3Xw9uG0YooPe36f6s8vwDM03/yACl+XYRmcgezYEemlr3ma5+QIKcq7RZVf2ATuBKYXqs+cOcQzM/xlGRah5/w8c+gS4KocdAvylrjzH6TIoxCI45cfhAOw/q0GUAq19jBZaSXbH6c1QU5x6Ei1/UG0SEflW84trwTU4XsKFwNU4OZlZEeg3Uq2vG3F5LVGf5+er90/r5U/F8N7GU3U96m9fMi+du0SJf5+xStscTzw509cBD6G8l3W94yuv6KVjSvnNmxUaY1xWQEYynZyutRlALuf+sJH5p17NH6WTx5dLtm1YulElHpRBMlYve0gqY2Kl9lS+3kV5Jf3hsqKL7mO5N9zT8lja+/B5KEvisbS3nUOoYph0GWPuGo5+etgJN/EJy8G8kbl235torQxBbTikyv62l+81+to1L/24n5za+3E5B92GiO2xXOssyyUbRmYnpPrqz5kMOi92n8B4xvLU0fKThRuyO1rmd9572XZdNHdtzHj+oXhhs8eLxinboO02bmAW4wrDa8EajilbgSZCiOraa1kdaKyXG05AQj0ezUvRp/WwM0aON4R7LG0DNMQQ9ed3Jjsu9d7l0QttH9tjebBegFHp2lfKD3v46Za6nuVWrub2MU55CDkZxALdV/uIqiMJyD6JHdv+/AZwfYwGl0Ub1Q2/26Lf3WyL/wCBpUjkMleO6B7Xio1YOnjIPJ8Rkx+JDpV9BLjV6mi3Csm+M6ICWCIfBA69LPMly8Jj6fSVxO+uPHMtdwfk7/5PdCSXEFXNtXniYUqRVDL8JSjcJFMy5GFGMZypDMebVGJ5tCyqb/8xtN+kS5Qk6IEsL/npz2hhexXDCDq3A5idcKkPm7UCIRarl6t0R6zvL4yp+pv6hI6ehwo9WeDxYg5kZnJj9BFUNiSqja+USbLnRXTVYUtB60QOIh37VVZk8LHtrUym/a08YSvLGnCewJb1q6TDkXIHqurg7/Si3wGrTIjriYtd0iFAlCa5fnIQ4nmEeD54/QT1Sqivdfr1qBM6m/R4OpRsu2RoxBYasSVwnbgicLGzPpGSLJNU+oCnzEucNAfr4bJYFLtdafEbNGVRIgVU/qY4BnfR7nIlsdslft90PMp8RJWVqeddp17b8VNRwnpLRAWbN48VxnNZQYl8+SishC9HnMmz3MoIpjGLwfZ6JxQWNou5Whl2tb1sIhMYrXMIHx3jegK6HB0u62XMcmcWqDbqxxKNWcoI1bf/CPzIkUYnOyvWY3uxLCxDU0pgciSwxqXbmwgwixsBWMU+chkAwESiL0Tu0hD7AsKCrTDYG5lrv9/k8l7yMLRhE/v/Xp1razm1LuYykAOcxL26nXyvNULM0wcy1m7Xik/tLMcwlLEp6Z8+G3wGWhfLECrST6Jbx/Y6KkXWHDrLNJZyuVKedTGLG8islg9Aga7tMluHMQUV0bA8jfKAHqv1/CHE4DuznpKUzzigqp7XZh/vkwndfeVXTgd8dazjPVG1JxVXiOyw154IDZlNJIzmHlUlm3j5qnafeXBl1iYAGrCLp9Nv5ja72rjhWCCEmIZS0joDWCqE2CWlPBe4DZgphBiP8lMMjtGNoRKh1EJPhX0oFVF/5KUW8ZHDfBEwT7tyHOvpa2ia7nNq2dyoyiRFT4Jyza1wWJv2kehlbi6L7VmNhduoTPlhDwdOrRvV5qeH6vLr15Ra+z+7d4NIDjvcu9mTwzmyH9AB2VddF3kQlTaxAQKCqRR/gFNGq5x12UcbsntyYJTvGHR+pXxHh89mKuXu7TrXXVhZ2AFl4+3vtG5ElUjR47WXgRKVm78Cujmho+L3wHYcPYpZ1u9sENy6ASVjaolCDIInre+gF0rmtOwI0qBIJN/YVpu163i6VWqjlZATz/e12ifU3LVdvt4u0zMue79abEoIVH5w3d72Mocn7W0qtWFZhTHGZQUl7ez1bFrZhm00ZunK38BhKO7cgi4sZxp3Ao5RYRGmDCt9Ij8P+C09vLl3E1WaojYUw/sNWu4xKt2MwpafDhyjq6TEZi1b1hVvzcSurGAVJZcys8JVC4iuxQguQyhPG7YH4bWD13JyLaUjbYkHJEKrdv9BSvU42sr7hO32PmI5cee+fZN6o6N/RjI9sJ07PDbehFFAJ1rxhTM++71j/MkEnh7L7t7PVuit34PpKOz6hIPWh4fGlldeRbzyI+HtL1Y3UKhzLQphQHoysksnGMcxd0RKOQI8pXqt5UVA7OQmQ6VFvuNb8LuWKp1C5MCz8a8Xcre+Qd/khCyGXWc8YajJCItZNsYvwnM04xHTqLTalMGN9r4foh+2AtR/62t2DWzCP6/tpmoHvxcdaaQMy4BxTdLfRy7sGxOxNRVApTlY5Z3sZe+AXxGVq6IfCu7Pb2DnvAP0HmbNvwkqPLny96xwapGnLxX9g5VZAfgf4O9zQERAbsCTT9ovAu10nxOO3WUnXv6mamOFd8f3cLrbH0us0G3bg7lngzIwf9dS17kEKRsR+L1UVqpozuVxC4sVQqQIIeYKIYqFEEVawS+s7e91u41CiL8KoQIRhBCZQogDQojV+pXYf9UJQtOAHMJjFerwMKMCPaDJ4g6T3EKjmMp9ImQCTiTcdTxjY663CApbbUXwjuOFtwqBrQzrWZ5EqQ9wwnItA24mA+1xNqXY8zvwh8omQ4OjX9Pg6NclXp8oIkWFS8UKmUr0d1waddmwUKygcNg6h74t+Y5OFKpoeE9FoqrNmyJLhzDGsCfcoa3u/+mw/2/r2nJcwgL7qlep+/GN2wopdNOILYgFIOJMB/6QWCFyYuZ3+r8L936d0Eb98oQ9us77JBVGGzRu97E1YkvMENag7UtDsr8H0V2/yjA8VmQFneP4YbHH+vcsRL7tpVT3OsFtopaFaz+p9RVUd6HElN+8eZoQ4jkhxNXH7mAS53h6LkcB+6SULYQQLYEPhRAt/AWthRBnox5jdAR2AYtR6eCzdJN1x1Jp8FixidbMHtCfaw7OVz+kX3rXH9Ff3VkymwwK6MerUZ7ER5J8+mPlT1r9LGnUS68JLzjZj1d5Xagg0KiCzzpUKchjWe1bXfPy2mhPZ6q4Q5VOAXjZe7FM/3QdskN4BA4oY9MK/7yRYo9hqbyPA20hAEvM53msR+RN7bYn1zpiG5ZFpDEVbwmSNIpIlf2ZyAPMZgDZuphIc4pZ7JodUsVLbJY3sYVGjGQqVl1SJfKT7ekvg5XMvlKp09519Ak7nDUejudQPZkey3iePXqrvWxXfhMadN9FA3axUiwFGey99WOFxMqR0ECr27kNOjkBsplBLWA2QxB94M5Fk6nNPqbhVWuS3+Eo0CbMSWrb19zL7uLZ16INyww+ppPsEddTmU1uwjk+p/TaSfUa6kquvq9ZMdsbSsRpQojngIVSyoXHezAVnKo7b/4/l2BKv4jyGP4//fkF4BXXuqHqrfsmPUxhNabyagBWNIO0yk4k4K20ojXchmWsEH4hZoN+aOgXBCqJAWGL4IRo+uyq/jyc5B+HTmy9VNV8/GIB8JIe338lDBVOOGPdiDr/V0UbevvPauApRxXIuABPZt0IuHRdFs/KgtdV1I9fOKe3bK/a1APedXmsdVuRhbqPWmgZaNrCed767ApxvS4HqKe9lsrbZhu3+ThKsxPKxrtmGce2V89tWNq1L3uROEviikgloiIbKxw2ln6GFQ7rcBlSZjrb/A7lqQTbW0kvVJXiHFddVhMOG4/vpZQBQeIVg+NpXA4ElRwmpdyg65z1Bl7ztRsAvG6JNAgh/gbcTBW4y8smlyW1utElYznLq3ehDnv5iZNcIYeKIzhKsscq1MEKrU1G0CcI6XpC2oztShVvBraqG3e7LpIhogdhKrAWEwIzC4PZRGuPR/KLrA6sz0sLbRvmEU2VRWwWrYHeSIlHRqF/+uyoGqVu5tOP/KOZgPPdrrJPiDKIb/RtMwA1SVqhsmMpiOp3qpYz7CqDn4DurNYkdEzu9UlWhvFg5XHGbZfA77g0yrIyhnjRs9zl+XzkcI2oEiVVlvLxNFboSbKCUaXmTbtGYb2Q5X2A3kPtkH77xtxSI81EZek+7dyoJlpUvjyQJQsqid9vgFLsdppBAl5SK0zVeZAYUREqR7xzgliAKpV8ncvqqNvSq+ArWyKmoXIxAf2MV39fvvPeIAINWsLOHPirXjcBGsqbAdiRBTuymilDMexX28067jVqP66xBCq3NugM7zrj4rBrLvxEj+F2/XmVy1D+q+5zkPc4LJkLf1mR0hA07kTDYo/t7znTNkDDDNFoQxP4u+uhyhxtUC9DGZwhwWCVuhQJVMkIneOpFtsM2Oz6vAW32yjxducIIQqFECuEEP577ipFUEhnrFCJMLXXoHDbRAnan7iX0JIj4oLE+vUfVyKhsKA8lcnkSibCeMYynrHRCnRJhruWByVRo02EBke/tlURE0X0KaHSrrW977d0PMLY7H2Xs5qhwZAgVW7eDAuJLUk4ZFDIYWkQHbCVXq0QyUS3C+rjeOHefxfed5ZfoV+TAjay2rjVYUO+Eyv0tiQqrUm3v1a/UvT3HV6BJbH+fA8ixXL9Koe5yPp9JnKerFDZ8gzxFm2dV/AYnNDYpPuOGe5svJaVnXLzXAohCsFVkMhLo5DlyVIINJVSfq/DgJYKIb6WUi4NGdMwtNZYs2ZhQ6tY3MbTrKqeTq9l+dASPjnzfDqxkl93WQVA6nJlSLi9mZZMu1vIZwbZ3CLOUctDngLN5zqPYXkFH/Iwo1jBqEARIGsbJIjB6uXGCocNMiyPnnGyegoagByi+kqdVcTmwtakpqtjdF9w1sYI2TiXYs4lOM/SUxerWPngNrWQuIVnlnKF3t8XpMrN9GKJy6NZ5JGjfKjeRPVk1WVYLiJLh8wW2XUzU9NVXSzlzQRktBpuY75hG41pzDY7HDaXbPs72RpgTKdRxGYxl+ZygGf5JFGdMfJZ+/OB7inMpx+PMdpeZk2asYR8rJCvBq4ns36P4WyUGpMYD2TANO6NaeBa4axBnkdxrX6TGz65+MPJrHI6Ba5Cc0HqjG6s0iQFXGznV7o9lqN4mL+MeAAyG9gh6WIEyGmhwzrxkcDPx3sQJzZm3gwhExWS6GfxBo/Xzg6BzESF2o1G1St8NhIi7ulNAEtEbVpmqbwwsTa0iSedIGqdyFH1IbU6bJBhKWW2PRZL6dUKbQ0bm9VuRz39HT3vjDdwjP7yIy8Ba3JgZFfOqfYF/zy1m1cqay2cOeZLvql3tjq/j4BrOnEMhbu0SAvA0hxbBVaIHI/IDjt9aq6vaCN2ih7/Hld7HYgT5dV735on2gcfpMVSYJ8OP+3QEn62DOF6wBylPj4FNe9oFVtZqL1qfdTYWJyDyO4FH7tOSjvvbtxKrc4yb9irn3j1Sf3Ey71MKOrH5eWMSmfS4vRaVzFke2sssffjVo51vJsRby6u9f4iYPEGGOX6f/bVoK2UHswqOm+Wm3EppbtsfDRCiC1AKmDd/jUD1yMzB6sdrnZb9T5+cO3vSyHE66j6AoGTpJTyOeA5gIyMjCQFlo8PyqgpZNPhNiw480ouZAXX8hqXLH+HRuyw280bo8IsUyd5b+qtf8YZAWGY87nO9l5aeZZBBkwyBMbu+3IuxQzgBWVEVt/+I9W3e1Vmt9CIzbMgtVCVGdlEaw4eEPzSjrWBduKGhC40GaxkAhM9Xk5/GO14xjKBiQgxEeqOZdDu2E/NCujEYrEemIiUY50LbF2dD1KC+r8DmWm7FY74/i0T+U42ilxSdR7lYF7mIWr6+mjqCcVV3sTw0Fw/O6s1Qfy/+O2cvjuRuqgoZvhvYp1FENeqnEsp1fsww9NtMFoEGY6xsASLbnLFdCzu043eI4IuTVUMCRw53oM4sTHzpheRhceotG9ErTIT9LKXufPsAGVMug2gPV8h5Vnevvu1hPk5CBH7xtXyfjlnTn92ab3J90gaIV5Cypv0++g8uLC6lMnQiC1OzmUYf0AZShfDP8UyqO2UBQEQc4G56v2Zu7/kGzEL5ltGCo4RaBmV7lzMlahM4aVw0qY9APx0mmvfe1TOqx0dMgrkGG247MlRY3kJ6Innu7b7Bxah2gtcho9+eGk/cMDJaZR5wMct4TJUyKJ7TpmSAx9EVKjmNcCe7cAzzvqL4xs6JTWG7HBv8WRon1b+pWPAdvatT2wsCRmhOlLJ8l5KX+SSO+zV8WBm6nVBY9kAl7aEdiolSlwAbHQ16N1S3S9O9hqVlZoqOm8ez5zL14BbgQItTHABTjFsN/OAZUL9ynYBv0cXHhJCnAl8K6WUQoh6wJXAA8di8MeSDFZCe3iTPvRd+TYZnVay1RcJ1X/SbI5QnVWcT+qkIk99QYCFXM01JbgtCPNY+vntrJn6XflEWP2ySMIN6r1MMGsobq5lCxno3TxILWqzj5vkTF7kNnu5Kv0ykbayDb1lG68X1MfmQn3+0y0hGMdj6e7PT3Q2ZWw20RpkRAkraS/vLG6gv1zJ5yjPZ1hYav9FsXM/k6X/BGc/GawM7TtWrqQj3KMnl2sDGg2IeAR+pmuPpWVcguOxvJVg3AZokOH5l0UPQC/o3SePu3iCc6Z9yucc59g1g6EqzpuZ4bmXgeQDT6OMoSXa6BRfle2YolPaPcQS+XG8MS/F3U1cozCof9fDzVjGqbvMi11D8i8R+GMO7AsWOerCcgByQwRjxBXAOJTRBrBHeZbFJGBihAOnYr+XY3zj6QO4Q1AnRug/ZjYHyGOxaI9lHIYdaxSJpMkfXgH0ispp9Kjl1m2E3K2/sxTg8fD8R6fm5BLXsuQ8k4oe+u8/Et6irLx6sTyWSfcVlHPppsAVRr24pOfKUBE5njmXk4E6Qohi4A1gmJRyH4AQYoIQ4jYAKeV/gYeAT1Clev+LowvXH1grhFgNLANellL+3zE9igpKrHIUDY5+HZhv2Zll9GSBXWYkVq5kolj5CaA8lmJGCfrwPb2KlUM5k4F2TmSi5UdKOo6yzN0pCVbZkR68mVD7kuY+lpRkcj+tPJmyIF5fGXwcWG7EEAdTiqQiYObNEJLJwfTn0QX3lxOVR+nP3yur0iKlJd6x19r9A7V2/xC4riTeoc4si1oWmj/nCr/1lIqZpGpgWnUwy0MfIYyoudyV/xdYDq1awDblkeN4HHUFkiXZfMtkc6Qry3mISxWdN4WMpTd8ApORkSELCuI8fqxAiKdAdlbxl1s6NeQBHgZgG435nFZU17+2TbS2Dcu36BtVXsQK+7u02of28p4sYC91AKjDXi7VE8e4e5XXMqiUiJ/q21VpkSONTrZzL+Usx7Dk4hx4wfW0c0jAMQqYp2U8s1jk5HC+7IR1WJNPmNdwJgPZ6AojbU4xN1rxPElgGabraMs84Q2j/Uo+yVlZakJkPrwiB3ASB+xxg3Oed/5bh2F1jv9/1oLPADisFWKDPJturO/5CNX5nFZ8VaiO28pR3Szm2k8zLcMyyJtojXXX40oZ0P3U3T6OGEqy7txNv0EZy4Pp2fdAvW+/5qV7PzrsTAaIZngmogERuy/LyHT3m8HHdNKuB7/XchQPsw6lXvDhga7sX6Ief4flLVV0hBAry6rkhDg7Q5JTDtfMG0UxKrTTlCKp4ByPeVOIudBPhfwHKoCiH/TN1zev/Voq72Um9jbushTgvWYJoYwuKR3L0b6edHN5g6zD1s8s5X2OYSkXOLmT7lBZT1+aWPlusYhdusS5ca+5qxEH63mtYMuw9C+3+31T9zkaWLNdr3kman/iDrjwKXV/sIKu3n3rkFi3N9E2LLvpUFfLuFyt/pxyt5pH96U0iDu3ixmoxyl4fwfua7znvZ4n5Huu76BuxBVS7fIiuw1je72VjzsHRARkgLEjIiCj8yxLivNbqQd171T97o5en6yXMshQS6QPK/8Soj2a7nzKxMZg/Ua/hif0Nnc5vx0VYeCuC/4voKF+vyPhMZcFlWTeLLMxlgfHMyzWkATyDviYTlw8fyW5nQbwcuHvAWVIVOewxxB5K4b+uNuoDEJtq7Z/IAGj0sKdM+kZdxf1VwDcklw9MbsP1zaxQlEBjyE5k8RqOQbxUOFE+qdHe/pekWvIJxOZp560LhZZXC9yY9aEKi+shwerOJ/qHOaP6Q/zl8IHVEhuJ+9kUpbhr4mwidZJeUpjGZVJkxv7d6ZCYsNrYf7raGd2vd6EU3rt5JReO00ZEgvrCWzZY0qRGEqFMjZcdQIz8eRq+o3SWCJmnnYBeZTiUdf68AChY4oVThjmnQwyKp1tE89vk0+B+EIZlZzjW5kb0P4dvEqzjznLFc61Nd7cDsBvgh9Mx8M7H0SiIlvkbpeBWTfi5IwCMCjcsCxjnO8iOufSvb5ikJlk+zn672XOoidaOmVrAtlRwY65BJTfvFmhOZ5hsYZyINHSD8OZaqts+lEiOz/a3khAF/74zNMuSCG2pMRSHRML1CsW7lDYZMNrRCG22muRLuYRj6FMj1o2mwGevsL2E1guZG1s2bUWfMZAZjKQmTHbhZFMSRCRonNL3Mv6RIeSxTrW0L5Px1Zy9SwPCkXySNwnFyITL8S2MoUfVRiqaHiPoeIgxPMIscH7qhf7/9kd7ui/ppTXNSCZcNlExiFE9LU7qlSTX/01QcQEYpZCcZ+3RFJDwkpMRG07CUQz9XLabECIH+zv1tqHP6QyXupD0BzjHp/9vgxSf0L3k9RvYEPUMSa2XfBvp9xKk4SEwgrhfZWs7/Djr9TzdRWdN43nshKRzSss6deLfDL5S/pwAGqzD4BX9V9VO9vB76G0PrsNy7cvUFfAC/+9TOdSdLLXjUXXjYjhBezMMjtEBoCX9V8tvCMuADIiyH87TbZoVf1mbHcWvgD9Zyxi85BGtmJsM7aHGpZWHykcAEZErb+RuXY9TL9CLLg9m9rbWSSgtRJYPK3dt463T3sl0yhiLAPZXK81t+yeTmO+QW5TV1IhFvGKy3v5UTXtGess7RImxAlzDcQyOtuF6953RoXlTOEBcnVILHJgYFhtGkVkM4dJj06g/r1OyGtPFsDIlbxFX4SW9lBhqE2Q3bXnOQS3F8AtZFTg+h2FsbNaE+dm722QV0a3sUqXcHm4R9ITuuW66XB7RBPJkf2GxvSs9hafZ7ViZZ7+DitpSKzBcELQb6DyQu55Pm5TUQ9HITYT23sZ68bUHQ5rc7X2ILnCXu3292mDbALI8a7lvnBYp3/XtUnfJFuhsbHG5V3XAmiBlLEjUA4NPBWWbqCR3AskKQj0nwicl6NFfWZDSL3rk85Qiq8iq65a0E+LsPw1Rt9jrTBj9UfUQynIapxj9ZWGcXuYb9ElTlyey0ZsofdrVshhln29F6cDa4BLcdRhcc0N72pv5b3u/et9v9USaAk9HWPHHcoMvWCACoFV+7NEfJILjxXatSNdZb6ssUp5p7MsTimTRCip9y+2uE++/puZ8BjUOfwA6ri28XiJdUpT3Zaw519A8g+WDRUDY1xWIjbRmiJgHBNYo5UrH+U+Pt96Hq82vQaAeV305LM8uA+L6a6agJbRZ10S3SGuliSOqhzpRc4KTuwvSYioexu/vp1M4OnfCKbRwDawk9x3ujY0i6BN1iZOe/XbuNu8UG+4mpzOHQ57VJ7A57SCTothZW/aFG7CX1RApquneptpHX2O2kkOxxDAUec/+jvwPzxw6nF6ySU7pvCPO5RaqvRRO8cRgsPIgoomOKHE2a7aq46RG1R3UsoI4u2Q5eOd94mSSIhtWH9zFqk7l5v6PEP7rDV66W2BbaskleCJqeEEpd9QICzvMhKdN5ev/8cfwZaNliEqp2VNMuGyiY2jOG4pCflObO9P6P7Hg4jkwHl6wR+V8WvNF0u5ym6b8sOe6O3zEnhwVzeibJAC3X4KMMXJJ7S8XVK2dIwJnQcq80DU8xpW8rXYSri8C1wSsu5y1xzxAp4cTMBlVM6B272/F7vGZK5VazKGcmxSv4GWJRIITORha1kSll+Z8D3fnRElK3a3KxQ5KFe3HlA3ovMwK3GNS4sqOG+asNgqRFOKaRryNNIdAlsaevAmPXizRCGzW2hkeyPdiELIZgbZlEBqthRYRlpJQzLCjifeungEjSeNohKP0xLVSWjfAeGxJaWk4w0LrfW0KYH67MPuR+kGg+G4k+j1QYj4Xs2w/svKMyLGg+iqX/4Q3A6xQ08T6j8w/HF2YMhsaRFJ3Bl6cir96wIeGlrqsGHro/pI8Pt5mFEJf58iK7FIFmcMKxzDshT9JLav8HqVnjDogDEFhi4fo1Bw1X94WKzo6m+72Pu5XuzwYOPBrFwYz2UlozWbGMBsW9H1iy1tSWu2nou1q1LG8Vj6cRuVBds7MbrRo4xlKRNRxQitv3468gkAq+gauN6NOxzWIvWC7eHrxHZSpTLsNsXo1xNSG4I7HPYzHXJhLXOL/9zIXGg9l5fyVKhsBHUhc+cpbhZzeUmug92wkL4IobzEqfJsACbQmwkBHlh3eKr1hC/omavl6ctde71acFD9adppAx1Rj1oXinVR2z3CPbgrOoapzDpPoCcw8T6AJqGGpZVzaXkxg7CVXh/USq8TYgsHWcZ64PhCrkSxamICdjmRoFqVUX2FpOHewyMs5XIGAg/1GcW4RVN4aZHyVsoyMqRPCKqoMIHhODM/B4iE/v/a7HkeRg1VhVdejcDtevlolOfMhd8gCFJ09eMJkT0lejnnBw9LiJ+h/S+i+vUL6cTyQiV8Y/2B8gLFnxld47sC6BGBpXofWqjm3TlXwVlwa9cneJ1+7HiqGbSry2kXfcv3td5VbSfq6/3dwGFo2H0LBw6oyWMw84HfQwbKizh/A6f8qENpHwDa6d25HkIrY3MQMAeuyuFxuY27aQx7cjjlx+G0SCmmI7B6W0duaryIl06/jcU046HvfA8GfwKmqDHRCcSWw7C/BuTosWS62loq9rfo438rAnsA9G/I3VZoT6XcFO+0xsUdDgvaQxv0Pfdrqf8Hyh/RVo8l+jYjgMzkd7AauB54UhuWvYC/b7C9lzYeB3kEqMSGZfnNm6cJIZ6jgqqsG+OyEpLGl9x85CUAajU7xD5q86fCaQA0TH+ExxjtaW+Ft2zVxoWVRznpgglU09LeRxqdHBjimiy2ATPLWWZfMN2lSAKMyjBjUabDwQO3qA8pwVJxbgGfhFTnXLiNnid17ubFLOcgtZwx6Il/IDM54vu3aYt1JfYaTf7jcUJ/otcpoR7V74B2qhxd7srrA8d7livvZiAz6QjcxrO8L7uFHqP/BsYymndWi53DI3Rqrl2e5OHwtmkUMVgn3C6niy5uo4w3/2/SPRYrnC1mQWz3djq0VlwLw0JCYD25lgmG1aynLTU5lNggqiLGuDQcQ0QW0C8C80PWu64bUg5V/+dTcoAm8OpQFe7YT//P57u260vcOx9LEVbepz+7HnJZ18QoVgMf5sC4CGRGkBNcc9+an5HyF7F3Svi1Ktzw9F6/ZVern+g8PbEAal7yA4eeUTmmcoyvs/sj8Oe5Knl0ZQeCsjSu6foqr0+6DupmQyacM+ZT2rKO15c56Rn7H2nADRP+BkDmlUvI79MLzgB6tKR+ShHDeYo/MQ0mQ6d3PmblKxczRh5hGZfy0dvAxJb8ecxe7hePAbfCmRFYCrAzekCacadPod13/+Ylqcqn3RTtZPTSC/gWNa438EtVwB+AnethVBuYYn3hvwD5L2A3cKNaNH+FnWMZlGsZlFcZhjsUNAzvw4nOtqfP+p6DfieJhIJbRmXcMbqMz6BQ2FjhsXKZ/l3ubAl1e6uFS3y/UdvruRgpe7u2jhwXRf4yoYqqrBvjshIyhQe4tXptAFL4iV3UpzC9LbNIXrr16G9O9hh6S+nhFecJYRUXJb2vRFEXkRKI38QhSNTHz07q883uxqyhPQfrnRr1JPEf4kZnjPpiV1bOrdy113PwV9AiRd0Y9Ov0dwC68DEAe6nLlbIj/oeKqziflahyR0G5sSXBzrv03Ui58yzt2pf6xistoJ+mbLXfx6vbWRKeu1Z5LJ8tRSmTHizl8bzRrM2/AIBTHnFqrxmOCRX6Cazh+JLsTaWUKjdTXAc0Rxucvhvrw8oD6Tzw83k2HyWafOyyCe5awFZunT1XPOQIjyWSJ3as8+ai9vOOFk17HKg9EPmD8iY2nLWFHXnNeC7vLlXn9w6otbsPl495k6ZjtvJS1m188V4HGnbfzoNd1cPDrTTlheuH8/IgVSrt7jmPUJCbQU5KhD9mTWdzvdYc2V2da0a+yusF17FSLIWVFzOXgRymOmddWczkK+/ld7Pm01/OZhcNeGNbD5pTTC4D+IJWAJzT+HN2UR9mwt19HuHxeqNZ+9wF3LRhLpdPfhMmAr+BK+9YwNvT+kKNGlx5h/qi3i64GjKEysU9A5VrmguMiiAna62BucDeNmpS6zgSspcCtfUZWwLMRFmox4B84hqdZcZ6636nHH976zcQJk6kcm4X+5aV31AM5YfJuTwBCRJ0AZQiaAAlKXHhieO/QL3c7631Yf3Gysf05wWEHU88Zuoj82MpyEbt11dWY7JPgXa8LW/kjMs9NiEmOu/rOeVRyotYZUCSKT2S0L5iWNAlKUkS2M94HGXYWO26qFfS/QfI6YtokWFDGBL4uRxe+gmsMSwNQcTLa4v6nw4pLyF8omRRuWlTyy4nTYifVThsyBjjb59gruky9SpLAsue5BG3HNiJgJh2vEfgxZ1/mMxvSPwieL4rSV+JkkwZErs8kC8XWYSXY628lN+8WaExnstKipVjlkYR43mICDk0ZyPzuRZIzLAAAFzdSURBVAZQhs0ElLGzlKs8hqWdT6k9lpb59a97lcfSfX2Qkx2hk3H3TrGXJcNvZRr/mKE9fkOiDUt3WRK/kaLWfUCrlPVMZWRgwOtntGAyiXkmg7A8agJVSqN3eh7ddBzV3+VqQNWwhFaMk+q8qpqf1QGoy17miG9UH0KpxfoJW24xmT+xpV1DPuRSjlCdmhxkFeezaW0b1aCdpM4hpWK7t+YZtlH7j8Ib7T6C1FtB/Ub6S2VM92QBtdnn8ShauZO2J9KFHBnbsHSTwUrWoeJm6rA3Zv6lZx+WYmAChmXg9nG8llLqotm5zmSaob3BcDFv0ZM7sybzYq+beSRFPYEfTskEQgwGQ9kg83QO3jPe5WIw8LL1/+kqyM4gT+6WHXb/glaotJbbnkannRC6BIfV5j7fYPJRuYP6pk48Gt3GW3LkZ/03B9onoXQdkl+ZtGLmxJZ6O1fIZF+AU0n5g5XQVtdpfwn0nzCbeZYV//Jsdrys59OJETuQ6NBHp5LWdxPL6aJCR/8KH11/BTW2HeEgtfjnfzN5+pybuf3fLwJwKctYk9KefdTmlFd2UuuXB7n/v1M5/1cr4CPgdxEogPrpu+jJWxyiJr8/8DceGjyKcRdMYd4ooD20a/tv1m7pyOPNVAm2fLqx8L8DuKbPqzz+3Ggydy8h/9pe0BLe3daDqxe8xh7qKK/lA8AbsPJoJ3Y90ESF++ZDww5bOHioJt+/dAb8iBMsdRjomQMbx0PzfwFWKCw6d7ezVphd4vm+AkNSEwiHtduGeSb3bHD2H8Uc/Tex30WQ2A8Q9zjsMQbkYoo7Etq1YlRLpdCLNjDPcvVjqT3X7o38IYk+DRUSY1xWco5Qg9Wcz6V8yD5qc4haDA24MXaXmvDTgo3Oh3wSytP2xPG78yfbAI1QOScuI/Qf7m1d+ZiBrIx4DKVlXJZQzUQ/bsEeN2FGqLXP2cwhu2ie+tBaasMyum5jDY7YNSbppz2U83VIx1KYxQ0AIZJI4XTR4ky5ZEO78JiQ1PSi0FDTfnaiUmIGXiyCypB41uvzJlLUvmKJAMXsJ8ETZYlWJaMGqwxQ5zdrGZedpn3MiwduBmD/kgb8gedVCJghGgkcOd6DMFQ5biOw/i1Y+VqWMI728kzRfye7Qu/e139dD8qibqL/GF5HN1mk/IX3Rr59JLQOZvS2SRijIRkscozX6+XmwKl1A5fPezsbKUEsyIYncQR+rBqV/SKQD1t3N6ULy1k/oKMKLe0F+c/1ouaAH7jzV1O4/e0XOeU/KrWg/6eLqNboR949owD+rwFj+97D/aMeIzMvnzpb9pK/rBenXfQt22nIXAbyRb0OUB3G7fwz8DNXDlzMIWpSh73Mb5bFCC3glv9DN8b96gEemjYRHoD8Br0487UvGUAuR6jOrAOD2X99A/gDpP5QxOa3W7NraRO47TBMqQFLYccnzeAiVL3Nx3Hqbu5ZAQyC5u4JqZcS87kbiEz1lCgpT7zho4nVzwSQP0M8gzMpoaiEyNd/MxPf5CufUV0jce2FSkMVnTdNWOwJTElCB5PehyskNmrdDPXyLCvjcIyg8NZkcYe3+sNY3aGuzrIyvCCvFeoVsm4LjRjITPrxatyu/GG6JUWkYKvFut835ksa86XTbmqwuIUVJhs2HvFe8LaJhsVG9VfC7QwGQ8XEHworxPOIZiCa+drFKl1QLzqc1h0qKB5VHshYpRvKEneYbHib8i0VASAm4Skf4glLLIeyJlWFZEuS+H+7QjyJEE/afZV1iZOyQrQlYQEgQ9XFeC4rOVtpQT+a8uS197L+tTQyKOAcPqdNl00AXMo7fMgVALaBMp/rbMXYiUyww2QnAdXe+JGjvznZs48HmEL17T9SbdSPHGnkXeem+vYfqTYZZ/s2zjo5JDGjzO2xFOmQWvgBoEJXrdqQQaqyyYTEWnmYfs+mKIRx6fA5rRAHLI/hRK4XHZFyrO0DFKxho8iFlRE2dnpdL3VCStqyivWFHdlc2Np1PL2hk05OX6aNya6OV3IkytLKJZuxFDnhsC661cxnPkqVrxVf6KXRnktl0D1sezU30doW22nFF+yjNi8fVV7Vx6o54bClrV9piwCF5F9aRipvlG4/9v50OKxlWAqRA7/WT2ubeNv46USB/f5gSk2+OdqY/WUzrBMboxZrOJY8A8z3RuLIWSDeUKI9KmRQL5ctPUXZbTJVKkdpjDb5Xgk20uGw8tPEDEs/YhlwmTPmRD2atoquTzRFxCrlMgpYkwO0gIfgtI+/5fu1ERqmq6JZO8RB6AH0gLev6Eu1V36ERyBz2BLyt3WDT2px6A+n8uTfr4G/wPlXrgLgo8wrOMrJXCk70oKp3C8eo5P8mMe3jOLqZvOhCBp03cXGtufC3XDT7md4acZtvD2kO3PIphHb6U8uF05bQ6cRBdzGswAsXp/Fms7t+fOIe7h/62PwTxicNYtHr41AGlw4eRn/SusKf4fNrc+G/wfcAtxZg7OmFfNV4xaOo+0TqHnGD3ANHMo8FU7prOaoNyIw1lHdFdXQgk6Z8HQhUk/uUkaSrpmpqBeyfoMqP5IAyXra/d7KRJRkwxB3gHxKvQB4KjO8rcjX73Qbl/NcNAO5xZUeowV93GqxSYeFVzSq4LwpZBWVYsrIyJAFBQXxG1YSpjOU7TTioacmknpHESN40s6/jGdcWoh7odoor3EZVDIkDKtmprW9f1vhMhylywBx51wGtbfahrVLliDj0mMQdZpIquwPxFY4tbaR6d6LX1vUxLqOjk5bVxmSIOPSjdvbl4zCqhA5sDJCanp4PUkr9zYoTNoyLuOFwZaUeLUzhb6Bk91L2H8SxiVAEWk8xj0spwubDqhapSeaQqwQYqWUMqNM+mqcIRlaDtfMh8pujIby5VjOm/YN+3ynDp5zAxpdaiPh/iysyhYfem+2PfUsjzGWESrlL0pvXPrCC23jUk8vUaVIyoAgI8C/LBFDIdnyVFHbn663t8pWhZ0TLSAjf/B6cK0QZvd6KBsjxzIug0qXqPXJ/7aPNVaepW1YxmvvMy4ts8OKRJBb3G2Pv3Fp5s3SY8JiqzidWUYOo23DMFl6siBUhdaiLasSVhELwzIsIVwFNln6kOepjxlEkQ7uLCvcxxHFMuEYnpo0ewTRIabxFGFLqhabRpEKXX3PMfjCcIekutvHGlsi/cZrK6715lwmkn85naFMZyi1D+wkJ6DupsFgqBiIQSHL6zmGgvU5aDmUb2ih6KtfIsdWhk3WO1nSENhGOoYnbv8Jpguk/LCHlB/2xG8YQO0DO6l9ILr+pLXcva48Q34TPSeJUt6hyaUhKdVY8TFCfBy4Xax+Et2H+75OtFVGpGNIxtrOHxK8Ifp/uwJ/B4b4mLDYE4ThPK+EZ4bn8Oodr7ODhnzURXksxTMgOyiPZVOKtQezLdto7OnDDntNwlvp5kijkz3eyShWuZ5YacVYOSvcEzmvULnS0piKpbau2noNS7cn0v3eyp+0VHMtbmQuo/FeuHanp1CvULvUVo5lcyH0To82PK0cTCl9JUbecp6opdl5iY7nUkrHsEzruj7KqziA2eTq91YIcFc+iNp/EGkoYZ9Bsikwg9kM8Rh21vtcsnmLvvYDAb/3sv4bX3NKtX2B+yitVxFcYbO6LzEeO0pGdo/ft/9pdBQDIsjX4FYt+mApKofxPEOpzT5G8yikQLb9DRgCKb9i0AZDNL9EhWL+piXckgM0gboqHNYOgUV7euo66qiKOUqAxoV0Xc6FmA39smF+gEdE+wJE35J7L93eT8sjloigj5S/cD5cVjJvjdwdbWgDyCwQV+j370SvF5NK7s10vG1qrB6Dc2JECwwlfjylFXVxzxGhXst6wD7r99LS8Va6w0Z9qqXJeF9DxxbisXRITgG2rLBVX59OIH3pKa9hGdWXNjClzPS9Xwx1e4P1LGM/ntB21a63fX4rfTgsVNl50xiXJxDZ5PKlHMUa2rONxjy4fDQPfvpIaPsVWFJzXYnEcKjFwm+gVFv8I0d7e/My19FR/YMlSRaLbG9dVz6wDbIwFdhkeCSBC/disZ7F/D7akNSElf5YFFgwRRnGsQR3BnR9JbB8R1hobC7Znv5mM8SzriTsP1pb5WAmaER6FF5X67/do5V1PdvovhN4wBlqcPrDXd2fw4zKPyxSuVunZO5kyoFRJ1wIrMFwoiD1/bUQzwNfOyv2WPmXQ1W6+T/1jemeDboAu6uT+eHqr1Kq66PoG1A+YrxjHIaOzzY6S37TG++GOWz9dpoFLo/ir4kZaQdOresR+EmG/ScHezyta6u73/I0EBI+J378+bkcf0OmpL+L4LYXh25X2v34M+pUmZJMgFDvpRXqKi7Q571gMarUC7DHCldoGWXEGyofJiy2ilDWyrHi9+oVs41WkhXp+hWjqC+gg1Qtb2W48ukMsplRAuMpqM/PaFEmirNJE6YQCzEN0KYU0zSGeNGxUBssD6zfSIm3j3PcVjkZNxl8TA6jTYhsopRfMejThBDPCSGuPnYHY6gKCJETHDL7tnp5lukw0igV6+4guupXAtdXd3isPwzRM64krtPxwj6DjtFaHti+lPmk4grHCxq1LuDYwsJnywN3OHTY8cfc/lQn1zJqXch5LguEiO0NLNt9JfI7rnz3ERWS8ps3KzTGc3mCMe7aKdAS7pw0mUX04f869OR/urwFwJv0YCtLIcSYsvIuwxRhrVBW1gPnO8svRcXYfMgVKjR2lb4oZcQoxjvLMSjc4j5ghY+qkNBlukh2Vz6wwz/93LhWV9FsN5c5ttE5kOYk7uWsywElzONyOn4krVqRynM5gNn0l2lRXkFrm/V5yizsxRIg2uM4gNlkALlrr4/av7/PrnxABiv1p3BRn020pgdvIsRcUqUTLuwOhXV7EVVIat9Aj6ClGmu31TdZcmQCIas+MSBr7AP0X/fxeXIo85X3M5E51RZnSDJU5laesH30AK1SPk9oO4OP8qvX9b2Ucli59GyolCiPJSoMdk8OvKCVV4e487XUHCHlUGdDyxOVqa8N86NvkMVzYKfRL8yBnAgipMauEDnQLfo6I06NDpn0sPBnXesSrcJ6bAkLja059wcOfRRtOZU0HNYSnXHCGL3Jsla/tV0ibtZ86Q5TjodlWCfjoVS1jJXnzhaCcoXIlib0Vkr1mxETlKc7GdyCPgmJGx2j0FBH9bVk9S/tsFpUCGzQe3uZnXrVG1Gvt6ozCnCjP0TW8mBGi/xUGqponUtjXJ5gyNdA5MGlfMga2jOCJxi2/An6shAgUCXWqaEY7DjobGc8um7RVxOtBiu8YUjWevfTuEQvkMu4jGZsRxSqfExLARWgB0u9jds58RlWmO5MbVzNZKBtYAYZpu4SJiILeADWpivju12WWpea5zVqs3VeYzyyUUU+/W3T2q3X40mer6w6ZCHnMVUOpAZHcGJUFRms1IZeycJlwxCP+j5rRdj+IYqwYfgfMICTY8ntuk3IDSCE/65qH9jJoBSoySEe73OrvfyBAyp3tiDl4sDtDAZDBUIXuBejgldHKZJaBoyrTIRnftoEcpj3oZac4O3Hfv+eb3mIV8vux5U3mbDCa0h5iLDl9no9NdvGk/ZISl9Yr2U8y/GnEiAUnjT+nE11vrx5l272X6/DZOfn2LmwQj+IlH3cubJWf8FKqf57jMCxaaM1A+i0+2NWPpfcNT7mQ4NyIur3W0q13HgE5Y6638c7x1adSxUKGxv13X6t+84MbnQ3du6lEFaI+2K98l/AhfF3ZKhQGOPyBERmQU+GkkOEF7mZ58RemsrzGddlCmOWhz9mi1XDEpS3MYgPucL7pKtj0IUp+klYkEEBXoGf1PQiNhe29tWMjI9b3CcpHoZ2DxTzSvoA/t8rglYp6+1VuWTbBqOH+ROBsbSptwlwJoRslnuazSvMRtYUtnEZhuX1u9Q26h2DMOiiv5SrECuvYnMhNE//zB5rEMkI8siRSbRd5BiWsfbvGUMJxYGSfYK7lab8VnRimKwDwCFqubyXJu8yKaqgMIHh2OP2RooslxeybgTqttTX2ARLNTx+GFIdUTe3jzzWtSSsBmBZGB/HIq+vvAyT0P3FKJ0h81yGjDb+RBIlr7bTLGlvWkO2s/g5r/5BWZ6TZD2W9nYuQZ9EcguPdw5ooiRaliQKS9CnbssocR8HnZdJJfRcQpWcN03OZRVFXBC97GGiHws3OPp1dENfP0F9lYZjlXeQLG4FVit/M6EyJWtFwscUVKokqKyHKMRbmzNBGhz9Ouo7DS33sSJ6P0Hbg9ewBJjKcKYy3NsmQWn8MKxcGqsMiXiv8uaYGgyG0iHE83H//2OVIhFZsa8fQgv7iL4gOujXVP0qQemRRCnpNc3v/YvbPs95lYZk8xD9JWLsCJUksfJQEy1BYrX1l7GKGp/r/MdrC5RILyDxUh/R7YRYX2nnPX8JEsOJi/FcnqC8RV+a0pYeLOVpuYnqHOHu5Y9wgBQev8ArYPL6vdfx0OSQeCNg49HmUfl4ftxP1y4s9IXRDgWej9okiiwWRRlhmwtbu0JiW2vBn05kEfuxpzKAprKzWhNbsMcKgQ0rUeLNAcllfMpYltBLf94EwJysIfTO887G6+XfmMMBHqqn+isijdZsYvbaW7yD6gRipeQvttE13SPeY4XeTkW5DEcyldWcz/k6xLUpxVTnMEeogZU3a22fmg6bxVw2As3lAFrwGcWcC0BnVI7HXG6kAbEfFoTR4OjXnt+AO8eypAajZZDKkBBaf8mRZGvW9eBNBqUUU5e9dJCwnVoA5B5Q4j5GLTZJqqikuuH4IbJQl95VG4BBoZ4nK6zPH0YqsoDXazihmM+Brd+VGdzPsSYqJDJOKKwb2dW34H82BHoQS+plKyvEJJySJNZ1/Cp9nA0i0KBleIkpTbLfzSKyEEv0hwQyumOFO4vT1XxUHt7gwJzKPT6DsgR5qokQ5p2Pu10C4bBOvy2xogwswzLwYcSeDVhlWIS4EMtbWVk8t6FU0XnTGJcnMFtpgVjWgkFdZ3Ol6AEvjKZwSFty/20pZyoDRU6G4b6alxZxjcqAmpgr8M548m/A3xK7QPjlrYGkQ2LLi1yyIQ/wlBrZhMiCcXljGbd7LINcHkbRfhGsVGEcdn3PQvhj4fTQ47FCgrfQiKmMpNl3O9Tn0xtyMR/b7c5KL2YrLTx+01Q5kE20pgWfhR5D0PcZWu5DR++4PZU7qzWxDUu73QRwpfBqpkf3FyNnMhECJ9Y4E89zYi8AV0pVc/Qt+rImxTqPxrhMCkv1zmA41nRsGZVG4RHyCUHmoVTN6wP+CJvDwNWR0FqWMa9XI38RY2XpKOnNdKyw1MD2ST6sC+0nSYPLuY5HlJFxpGRGWyLiPu45w27/WnBbm9oRO/RZvhbfsxqW3hNzmwS/Y3fOsLOsDce6BmZZYQtA+Y1LLcQld7sFfC6snAI+fspv3jxNCPEcsFBKubBc9lAKjHFZhenHqyzYrkR8btPRmOJeZWz6iacke7yw8iot0R4nz3JqyBaxSaOIiTygP7XyLG/LOtbR1l62+Y7W8DTQr0S7ijmGZfGbJY0VWtsKlW84MdoitHGHwtY/vxwGE7RP/XS9tEYoKCGfzinx2xkMhsqBEDkgtGrsUffy8LAY8VwC/YZ4hSyxMnmfb7n2cJU297IihzVaobJ+A9QR7Yk2ZK1SI/tSGoQLFGUl7n2zwo7dAkklJZbnLygixl5Wjh4nSzU2ZpsyegAQvP9oj6kYof7KaV7RnmQEfOy+tHqsfMr7PvaY8hPfgaFCq6wb4/IER3aFmTTmJbkOGEhfFjCWibxFT0+7AjL4173+GJtoxAXB3krrQnWh7AYo76U/xDXIKxlzX4WwOd3KQVQevSwW0Yc8niePRYRfed0eunN9dSEnMJGZDLSVZK3QUr+arAqbnRiz7iQA8+GhrInwADzkDrVdGf3Uze+xDFKwBVWK5Ag14HTlgW4GbEWd5/7SKiXTInB7KxzWYi43MpbxfO4ylpMhNBxW5x8lI/zjJiwctrTcyhNANu+KAt6QH/I5rficL+31BRiF2BJRRSXVDccHITYoj0YmgSVFoqgb8XjAhMiBZyNq6liYA89rT5AVKmmJjvfw7ddf57IE5SaSwR3SW1KvZUlKdSRDrDy5lB/28NNp0zjlR5XuIQJuIzzhsG5sAZfkvK7lgfthgWesO63fXsR+WBHrll5oJRPPAxBXf34DN+g7d3ss3WJIZeWxDKq/eizDT211Y5EDv9P7/bv7N3YSUmZW7hIkFlV03jTGZRXgRuYyg2y20pQRTKM9a+jJW/yev5HRaKUyKifDhZOXRYW0WhxpdHKZC/ckQmrh9pghsXNtzyX6b2J1LcPYRGtEYS4A2enOMoXPkHsKhI7Mco/R8vqlphexWWijJomLo7W/IKNWyoirdEziTGSC7bn8nFYx1Vwh2gg+FpSFx9LNX+VW1tAegGe5q2w7NxgMx4atQEYk8KEmqPDYUEGZW8NvnON5HuV9BNa/LKtSFRU5lywsZDZW6O3+k6fDpdqzHHS+k8wXLAuPZUL7Xhq9yNu+fL4nt2ps4Poyzq8MHoO3pqWc5lq3Lvh9wn27vZRP678+z6XHa/p3KzczP/mdGSocRi3WkBB94yYqxEfMCVgWoHpmKbGGfQ7CEunxM4NsZsQwpO7hEc9+PGPTE+xsW691gL2v8Yz13NAkqtxq6dsls24J3eKO3SJMzfVYEKZg24X36cL7pev79NKrzRpKiSVMUNYvgyEBLKVJj5pnEqqZUSqlPgEX0TXY62avf5Sour4VlYqkyulXk/V/D+W+/9Oj8yaTVbgta2KqFLt/3yUcp7//pP5P7nDCWD3L2zrhsSXFCrsV9+pX18SOUYjFrrqXlYwqOm8az2UVYSFX87r4HDiJm+RQDnAS7465CoCbJj/Di9xGU4ppihKKCeLqf7/GApQut5gB3JJDtW+VyqznCVQ6CFQYrC3R7ROjDZPuFiKHVKm8kZb3LE2vy6MPWSzyhMMuIivUsIzFjcxFFMLd6Y/Qj9eZzzWAMiTHpbdSYa4BpFHEYPeCHthPPr0FiWEzrZGytS2W7q7fGYupDGcad9KZFdzMS1Hr53OdbVj24E3eLbwKOlnnrXbouDOAAjrF3b/QTxdlwATjDiGLFw5rqcH++oAzhrAw4JJg3TDI79R5aE4xA8hlAHB2yib2UZv7sx4DYPQxeApcJSifSa1CCxMYjg+h4h9BbXdbN9DWtqoQvBXGKOrhVYd1eavs/tvpvw/q5acAZ0SHQIoOID9NPicw0fDDRmxhh3gxobYA4gqAZsh31OeUH/Y460JyJ2P2N0lvM8ZZ5vdYWvmV+0+e7hmnY9AGezjd34OY5O3X+h7KSpE1YQO2R/wm4DOQR4eP0x0Oay8rwfwj7nVtX0Ivd1gJtJKqxHr6TiIPU0qXYSk2wKiA30dd6//4A70gE6UYGwEqqWFpUQmMwbLGGJdVhPlcRz/5Kn/lD1zEJ7xPN/7Bjdw06ZmEtreMyhJzvf47yLs46MK2Wcz1LN9c2Jp56X2i2ln4S4q4GeKrD+nZdzpYuvTzPUI+Sv3V6jcbFSZreTat5RN8k4v7Oh6WX5oqtoeub8Z2T23IzvyLdbRlZIDyqmUMt2CjZ/kp1faFGnAFdCq1cRfToLROn69KzHK6xc9bjbffODL1fpr7zouhQlOhhQkMFQMpW2rxnia2UmyJlVUDQlvlMq9Yj18MSN6nDMvyoKxDZE/6vgEHTq1b6hqWZUGUEdYD2HsMBzAzYNkU1/v5xz7vM9b3LWXENixLamg7xn78/UVtGyK6U5LQWM/2OuRWPAlMUSVz4j04csZdiXMuqyjGuDQA6Dy+jDLtU4gc6Fi6SVMZJeEGUZ1D3wKwt+YZSfapchsTMXq+tktWfATAAnoyS/svrdzFATGM2GONFZp6ZbpbY/8cZ33AE/SeqLZv0Te8Xy10kajHMqyPkgoAhfZ5LVxe+qhtQzxMKRLDMcR/4xl0XQnzBooth+GhGtHlR9x973keJjrlTITIgdqqHzWvnOEsfzNifVTLXHLetgF3E7BPhx7er3MPJwXsezwwAHgIaKMX1gAuAnrm0FDe7LSdod+8BLxyGL6pARcV0kn+xF7qsLH7uVBdNenIJ/qhmnoQK95z7XMq8EenjuDlMsOufwxwkJoATHE9YBWToNOYj+06yy/MGY41XZ7iLZVNGkU0YgdwJoCdBrGJNL759GwoADIl3kewTqrMlYMWgJ57buUJnntU5chXu+lHpjZSE8bdpz8Lb+gNe8NDu0eRgjoPAA8VTuTp9JuJkMMYJgLPevbVlGK+eq4FbYatAlRpKncaR1tWAbB+WUdl/L6Ec0t0ClGIatDuqEoEXisWAbdAzllWSWx40QlDrbnrHgAO1V8KE7Ng7JPQ7U64DvgW+BBYugH4Gupmgi6RLUQOjNK/pcn6gccrwIc5cHsEnnZFS1kPPnTgmdtbKkSOqvWaifoOn84BLgM+gDa6f2082qGvmbqfp7zL4xmZQuRD00znnK3fYJcaYY+rXYBh6Q/lttRjpcyMvdOKTBWdN41xWYWYz3X0JIUB5LKNxnwzqQ77qc29qNojxQf0BSBFudWE9jJKPQG4J3I5BBgSbTgO5m/cUAgvi21qwargyf+0T77V77xGoZQRO8fS8rKlpheR5XKHWXmJQWGm/tIkbpzw2Rui1m3Oak12Xi7iv+rY5a+i40k+5hLOytqpa10qrNCb/nnQX7bgYcZSxIu0ZpOauDotBl4iVZ4NFCHXWHcTkgHMZiqOpdWM7YxkOltoxAByabZ2B2nt1jPNdS7SKKKG0pHVqrCtIR0GyBbMK2xN8/TgGpc5RALPifWdWnUgISQcNgDrZs9WfbXCi/L134+c0OZNtI5SYYzZtyvkNd46cS28e+1VHHjtfdbQgaE8zy2LlLF/LEQRDAZD+WErS6YAJw2Fx4nKHxP1sB9knvLRTvbv1CtGo4y+RwBLEGiebweWPfVHVA5Gmv58aw4wxtv2FdRd007g6l/A+bHH3oX3OZsvmf9DP3ghQpshq1gfJ298x+BmMD0Cn8RuZ9MUbcgoBfhRp06hOkd4kEdomLWFHeIg/CUxr9yr9OP8MffRheU8NSn8KWCLlGIGMpf76z6mIxaV1VlzVyOG1nueVXQM3O6V3QO4fnkup2V8S6OaO/hijssV/ADcPfIRDnBSQmONxx15U3nq9JHq2fQfDnrWnTWkmK+yWsD5UP9ICrv+3QS0cRnK86hw6qefBIaCKJtxBrIJyPX9xsUKeDZYBEikgD+bSbRFPbxYn2CupfU/1cb3oCYszzJTr38bnOID+fF39LuWYCk290IZ8A8De/KBJsCgpGu2GioexrisYrxFX4RYxa1yO2eO2cvdkx7haW4j3yUa09mushi7NImlHhuk4ueEZSReyyvIi7i5UHst0x3j0HrW3Ic8utQksCSJVWYkDMtYe5Pf0C7LKVViGZWWeE82uSynCwC/zVLevb68xf/UA/YstmtcWh7Mojj5n6K9MpLDwmbDxH4g+PxYyzOA/umzmSeKQYfxvo36HiyDe6COD7JyWt1kUKDfRXsuLYEgObJJ1Do3cqR+EvxR+HpI3AMK0Jgv2cbZgesy+Jj2rOGm1+AlsZ1ZqHp3rzKQX/exBISCxZAMJaCKSqobjhO6TIUQc1D5FJYiXEg0zKocqBth/+MN4HK4+m+vsfCCa7lp2DMspQdthymXy+d/O4fNYi6n/Dic/U8Ca1GexCcicFcOXBPh+wZnKC/mvhzYOB4eB+7WF+3HhbqR3w817/6BQ/VVXnf9I0PZ9dVYWFKDYcOesOcNm0tg/YSOnDnhS7459WyqbfiRoxtP5pour/L6tOs468piaskBbBSz+e2sw/yjx43qphuUYVNcAy6H5rIWKwelw99zYNS50ACYGWEbW6jJId460JOMlALYC9cwn5d23cyhS6ChrMV4tjL20MOkMY9O+pr/PENZvCwLLpvN3+VKttGYpwpH0nDMFg6QQns+BaD5oM9ozxo+pxWrJ13E6ksu4r7dOWSSz1v0pB/z2V5PzV9zj6o55oZqL7OxQ3M+79CKDAqYyFjIhe+3nsHEgQ+QNmgT62jLo0fvo+HILeygIdtpxLj7xvLQoImk1P6Ju5c9y2+7zmTDd03tOtP7dtfm+hW5/LHzwwxgHk9xB6+kD2AWg8kji/fJ5E6mcfC7WrywaDjVqh+mEdvZdV19DlGTtqyjVd7npLGJvdSheufDdOZfAGzqmsYu6nNS359owE4efSpC7zvyWCyyuFy+ybsN7lQPUp+BtbMuUA8lbr4AXlwBLc6CSL76ztpEYP0KuLMzh1br7/GFLGU4rrwTfglXtl3A2zP6wp1Aj5bQuyV25bRc/VufAlhe5n+jvJazI3AbMC7ihPiuyYEX9P/GUJyAr/u11/0Z4G7UbxygTSa2hdgEWI9322K9jYXej5iFevBxEX7fgMPWxXC2CmW1w18fAfZDtewfOTpa1Utv3uEzNk44F14Fejax2zv8FLKDSkQVnTeFTLb44AlCRkaGLCgoiN/wBGYG2Qz5zxzEPMlfJgznMVToRmO22W3CSpNAbOOyJITVm7SwjEsr57GPdiH6jctY3kv/Pj6jBc9rc/UxnFgft3EZhNDG5XvyIQC6sTz8wNYqg3VLu4akipeA8LpNlnE5EmWBuUuGhJ2feOfNwjIu59qFWxLDMi7d9S7DsMKMYpUySdS4bKxrU/qNSytUrNMQZVwCvHT6bXY9smGyDmDKjwAIIVZKKcsk3l3UyZB0LYdr5sKyG6OhfDnW86bzcNJtXDaBukNhj17XOwKLrXC6OZx28Fba11zDRyuu4PzOn7B6+UWc3+UT9mmxs5ocYv20jkq4Z/VhSJ0IV0dg4QbYfDbcVAP+oHZD04PwRi0Vjrkwh5q77uFQ/qnwFVw94jUWfnot53RQxtcX21qR2vhLtm5vytEfU+BxQeq0Imqzj2+ONmbXKU3UDfkauOS7d/io8ArYCw27b2HHc83oPSyPNXTgq3Ut4NwcGBqBcVoJ5JYanPJ/O+mW8j7raMtGkcsNsrGKEPpLRHl/1sLiO7rRe8X7MB2unvUa+6hNfr1enL/7Ew5TnYk8wL1MZtPuNA7/rOJpj157MuOWjSWFA9yf9xjkwl/mDOdR7uN/uZcIOWxe11o5J5sAdeHpc27m9kkvcsuY6VTnMDt0tM12GjHyu6d45PS7ATiJA+yjNmtozybO5mZeZBojWL+lPY83G85b9GQrTenKh8w+NIjxNR/ijyOn8+up79OeT/mJFGqzj6cmjITfwDXpqgxXc4r5kK7sozY5RLiUZXxIV1ZwIT+Rwos/3Mybp17FCJ5gFFN4iZvJf7sXfAtnDv6Sb6adzdUjXuPN7X0Y0ehJXjx0E9k11e/rqbdH8tsrZ/L67n40r7eRjqxiBZ3Z+Pa5nJb5Le1rrmH1gY7s/7Y+5/xKzT9fzOmAXSHsN8BvtKf0xlpwPpw2UUVqfT/2DOgNZ3Uv5qs5LeATuHDaMv61pQs1TznAoYJT4W9A7kKYfTX8P+CWHPggApfp0Nb5H0MDXav5TiBixfYqLTQpI0o8pxhYrP9Hbo9Q8+EfOPT4qSocG9dD/5kR1da//P4I/NlVpkRGlBdzNI5X/el8eCJTvbcM0QeAbJdx+V8r99MVHosW73lO97dnA9yg1slZzhCOV51LM2+WHuO5NMREXOAYj3aS+WRfmxgqeE5CeZL71caJLFT5gO78kJKijNFwLe3ZDOByX2XtIh0j1ZpNXi9sXV/oiFAGr5RjPXUuwUnBcBMrtNdNLMOxBVYIbPXQdSp01otbkTdb37BNZEJCeZelRfQhUJ3PKjFS1rUuDQaDwWCoKiSTfpJUv5+WcX8jCZTTENdybAWfDOWCMS6rMNs4k1fPuwZZS7CKNlzKMi5YvBaA13pfzW8vUMaG5aH0SLlbn9+N7lfoWh2eJ1BaeS9IE9JtnDo1LYO9cBOYaHswB9rxI17PZaxw2Las83g6z6WYx/R7y1sJcL1oD8B6GfzESeUB9SbN9qrNc45lpc/Ibqcs62Y4RvaWkPFZxmb1AIm7MM/kkTj/xipsdiX7qMvbIk5eCY4QhOyu/ro9lkKL9spFwV7KWB5Lm6Vq+3iEhcN2GvIxK8VS7hminhqr7yqHTlJZrSuBAi5OYCCGpLDqdRkMxwBPaac+AC5RHZdYCt8CnATvnqXa8C0fbenGrzu/zz/XdYOzDvP5D6346aa6AFyS9w71//A1uw43gT/UgHu05+bjlpCaQ3M5gI0ilzayL+tXdISxwBNq/4ceBgqgzTLlzeIByFig5ohNv0zjm92NOfrmyfAtNJy2hZ9IYXOf1jRctEU9VMuH+nu/pgZHoBPwf7Dj983gBlg8KQu+AlKBnAhEcmC7nkteOMz+sxqwcM86ePVaaHEuL5+uDrvNyFXUZycfPX4Fd93xBH/ufA8vdr6Z9qzhIDXJH9WL1e9dRGb3JRTTgsZs44v66/lAKitk67KmXF+Yy4Ppozmpxx5SrjlACgfYsagZb/fpyWBeZnXb81m4RivG74e7dj9BmzGreGH8cBh6mHOarWMvdWjPp0w//Ra20RiAA6TwQt5wfps1k38t68rVXRfw+fZzmNesL08xnO00ZCBzGSfacaH8gtWczyVT3+GjR6+g7X3rePna3/Pr197n8fG38i86M2fwEAAWz+pGCzZSTHOu/XIh08++hfNZzQ4acQ+P8eWpadzMDAaQy02nz+WUzTvpf+VsjlCdmhxk74g1LFxwLZl9l7BaJ87WZh8A/a+czSbOpn29NXRhOc/uvpWavzwExfD9M2fwUX39I/wavhigc0YXo/IHPwFWAztroTuFKfl8vytTfe6h23QHqqvfyL/mdIX5cOgXWqo4A0i7GrK/As5Syy7Tv//5K+DOi51o0QhYHktAebEBntT/Hx9E4LL18PTTHNp0u+PJdPM4tjYGbSJqjL1wtgcVggsqtHUJyqP6Yo4agBUgdLtru9u119JWnl0MtFAeyt+1dP6Pb82HiZlASxibr5YNyIweY2Wmis6bJiy2ijOW8VzKh/T6Jp9/n9mOhxjPDbwMwKU69/LMC/aqxpnqj99z6cdvXObRh/7P6VzDBIxLiB3iaRmXzbVxGcuYTAZ3KGyiNcn843pI1ExsOx0qaxmeQTzBrQDc5VO9s+iLkkiNVybGMi7BG2Ybht+49KyLY1weCzL4mJncSLsZxfxnSCvO6/o5w5Y9AZhQWD9lGt5zWobk4nK4Zi6u2OE9BodjOW96jEtL3ZWvoxs21dfarRtoKGtx4EAKdVL20pZ1fPjDpbQ/dQ3tWcMKLgRgbd4FnNJrJ/tPns5J34/gpzp1QeZAToT6D6j+d93dBPajbgrPB34JXHMQnq+lFD5/eRj+WANyf8ZWJ3nhanXTvBfIzoHNYyF1osrtXNKAhllbqMNeNu1OU6G1j6Ny4OpAtXN/5OgLKg+NsbPpLU9i8bar4AFtpFiCKdflwNej4bZanLngS74Rs4BB8FZL7rsyh7foyer3LmJQ9xkcIIWmbGUX9TlMdf4hlAwcKyM8lD6Kcf9VE/mvf5XPVpqSzWy20pTDVKcBu1iqQ0xeIZv7eYT67CSDlfyp3jSe3n0ztz/6Ivfdl8OjcyL8Y1BfUjjAcP7KZP5kG2rTuJNWfMEi+pDGJgYzi8e4h86s4E36sHlLC/h/Neh0zsesnHMxtwyaTlO20pVldBc9uU/Co/dG4NfAK/DHPJWE+pd1DzCo7Qz2UZsxTOJ6XiGTfF5YMZxhnZ/gGxqz8NNruanDM2ynIe/u7kGXesvZSX3WjrmAKyctoDqH+YJWnM9qGrGd7Tqa6EMu5cCBFPa/0oAzh33JN+v0Q869qPDo1sCNn8LVHSAbeEt/j08CP6J+Aw1QRuTCDSqP0kodOl1vfz5KTfWXQAs468pivhrZAjLhzL5f8s3cs5WxtidHNbg0W+UFj8YRnFr/JCou9n/1gsvgzs5q3w2A4U/r5Tv03xZwfza8AdXe+ZGjZ8xyrcMR8nkE+B/gL6BlDFQ4t/Xs3ToHZ+Go96qyrKqeZYCqrBij244Cbsyn5i5103Doz6cq3Yrf4JxjcGrOrsnX/WZyLDHzZumpdrwHYKh8qGLVwUI9yQj4iBkuqfWg9YXqlUcf8givcxmLmQzkM1rwmV9Kzb8v4dTKdMuTuxnPWJfibGxKW9cxiHt4hHt4BPCOcTTR53ws411e4Nh9hWHlWlq4pe1Li3ivZP1l8HHZDcKQHJakelm/DIYYxKuFZzAYKgbCpTQvRL56DT5+46kQVNF504TFVnEmopPcfhQcohYzudEW9mnPpxyilvJY5quX/DeIKcF92byc4yviuygq51LlHOp8wxsitpcznigNOII+Fn6hH7cB2DxOX31cdUWuFx2RcizXA+PkodBtpjKcP4qGgOOhTKOIzfUmQl1I3a0MOiEgVSYmthPEXTxLU4rJ10oBab71n9Ke61xe23663Xyu87RrxRf2+wHMjvJeWg8E+ssW5B/NpH5mjEGNAn4TUIbE6ssl1uNvE7ZNoliG5cqsi2l3XzHDhjzBeWIQH8kMLpmhngw+O6RkfRsMhoqF7bEER7yHCLboyA3a0/JyDogI9GvJjhnAKNj/UQMatFX1SJqylR00dDpOg/1nNIB+EX66G+W1/Ez1tWttE7sNm/TrDJQn8o0IfAQn3b2Hn16tS+ZrS1i+uwtt66naSKstcZK/ogSC/gB3yNo8taIBPA8Ns7azdsUFcFGOUqb9cDZ0zoYz4WjxydS87QcOvXIq3JnN4mtRc65VNuUWoFjVwNzxaC1YmMM3AugRodM7HwM7ePSCCBRsJ1UW8T7dGME09lGbFXSmYHsnmHkyzQd/Rh8ms4sGjPmVOuYDpHA2X/LopAiZY5ZQnSNksJIvJnWA1vBy1mDe3aa8mG0bK0nRu3Y/wYX3LWMZXbl60Gsc4CQyKGAyf2IFFzJAD7wLy2nALh4fORqmwhRGsY3G1GEvk/kTU5qpz9tpRM1eP/DCtOG8PeJSHuMe/iMfYBY3wJ2HuaHZixRntaABuwBo1/bf9CeXBuzidp7mf7mX/isWwS/gJ1Jozkbu6DCVpxaM5JQeOxlVbwqTRHWlpqqnxyPUYKOoyTnyJOqw1xZ8ash2MlJW8v6wTDY/11p56J5Hee7OgMzBS8i/sTUsfBKy71Rey/XAZyiP5Euu32ubCCx+Eu6/U30uAuajwkfPABoBH8NXPSXwJKy5k2/apUFd3UfdiPLmfYQax085kKl/9+t36/+Fq9Xnk9KVpz2ia2D+53Y4LwdOiqjtKNYCPRGOnjFFtcnXv6/1OXANav0U3f8fnb5PW92Y72sVwZuZyss4Sv/WrfDWNkSnTbn58wao3VKF/D6SyaGv9PL2aljcDUScOqys0f//4gPdMFbnhoqIMS4NihYSyKDu1p+4r6HyaLWu9TmDmcXP4wS/mOJYh7FCPpMJI908pBGpg8MFbZxwywQS9EJIOGR2pWOQqv1OjGrSis8B7PAZP6m7rXqUypCMrpTpsKWdutlpltjoAHhs7f3qTbvReoytCZPln+gxwdV4BjDb6culjBtEkDKsFSYb67gSJl//DQi9jcVKsRReuJjdnVOoJ+7jabmJNbRXdVcN5U8VlFQ3HD/kbu25rBvRYbHPq5vO01FpXDt9URu36FDathEuZR8/nTaN5XIwe3+ow09pKufypE17+GlgXVL/VsRmoeeHc7WBeZ5WhEXnv43S/b4ZgatU3z/dHYF2kC9W0EY24iC1WEdHGg3bwo7Xm3Fh12XQVSmti/Ej4XXgLNhLXbgoh96yPZDH4ruKYR70/m8eixdkcSjtVFX65NmIMmRyc+AybTjckw4FEXZ0B97XhT/HRWA9rBQXA//Lr+Uh/tm1G5u7NoLrYGz/hzk672ReuCObf302G9JgY+G59En/A1dtf4Mav1D/zIfqL4QPsuES6EQBf1n2AO9yFVePeY1z+Jz2rOGOxtP5nFZ8yKXwPNxQ72Ve3H4TTRttpTb7eJ1+zGUgbVlHDY7YJUPW0J7a7IffwLYDjRmcMounhoxkzQz47X//D14TypDPgbPWFfNVu1N5gIkMIJfzxCDqH7kZUidyh3wdgI36cfFasYjVsiMHOInViy7isT730LCzUt0lHXZRH4AL+y7jX4VdOZCewm/lmbx79Gt2DWgCefD2vX3hLfjwQF1qpRyi2Op76gWs3XkB9R/+mvuG5fDopAjMz1FG4X8i5M/ppUI5774TrgesNKEzUBHSlwNpukSIVWPSpbrKJxElDPAVqgZrgYQ/tgTmwMyD8JtaOkcvXRlde4HHcuDvevunP1brQBmO1l389cDTOUBDtey8TWr5UICIqtG5PkedbC7mzKccNXYhUAZvrwiyq3rg3EYqYb91dNQ1M89A9vHVvfxzPuCErbo9l1JmIqx52VKJLQAOAuep7bgzU4XwYpUg8d7PJHM/WaGpgvOmCYs1JEW88NRYIbOJYIXCHk9Ed/UClYcp6oWHZs1mgEcIyO7DV3rTDmcVi215bUDlXlovTQsdyBt3nPpc9WSBrfRqLXeTRhFjia7cPZkRTGZE3P2UBbcqRQzAydtMlMZ8aavJGo4jljBBWb8MBoPBYDgRqaLzphH0MXjYQiNS71XexA8md1aKeMBe6thFl7NCPImiK6rAL8ALEY83yTJKg7bdQiMK6ARA/0K1fnF6NwB6fZevGp0u7bYAt6GS1f01LsGpc3lT4VzGpXtDZqPGXKhKhthPsKkB3dQ28j1lPF5fT9W6tMJdN9HaztF8RSr1VX89TMu4lPcJ7un8ZwAe3zIKUt9Ry2Vv+I/wVhHR4j7uMiIDmE1TtgLR3kbLiLwyXRmWbxc6JUSsZW/RlzSKyGYOsxlkjx+wDcul9IhZfiSmwE+MmpUDmM28qSoEd9hIZVw+10cJ7rjVYmP1IRZogYPxSlThkgnv0IKNNGK7LThhlGHDKVNhgtoZko7lcM38sGILExgczLxpMBhOdMy8WXpMWKzBQzO28/M45UWr8SVMPHsstThIfXbRQ0t5DWcq8+mnleqs2HjgUt3JCyUPZZDpwd5Ry5DanIAyqRUKe5NrWR/yAg1RK7y0j1SlRxaL9fD+RFJlf6xw0vW70+z2vViCEIujjEpRz2t8SiuVcwU8tuJ+xJkqAzuqGPAR4DzvA55izqUpxVrlFRbqvIfHvFvSPP0zDlLLY1Tena48pFZYUk8W0Ap/mCzak1mHAsKvTY6gj6sUiStvMhkPs6Xi+mwSEc7iWuAG1O/s/gjXTHqV16+9jg6vreHReuo3Jncn3p+hlJSfpPppQojngIVSyoVxWxsMBoPBUBmooqVIjHFpiKLGqcrYefXUfoxgGsU0Z+jB51lTqwNLuTx8ww/DY+TDvJ2gDNpUX1kLyzPJ6brRVqctJJaF2Ts9z/aIxmMqym22SG7yKKz6PZLoddeLW6INxSA6q3MZGB9wXvyogQI6hYoBFXNuQDkQx7vpDpUNw5KMj0WQx9ImM7wUSS7ZEOCNjOo/VpufoJPswcqu8HrX6/j1svdZRUdjVJ5YfC9lUJEig8FgMBgMlQ1jXBpOSHrwJjXLeR+J1OSM11641HGsCHXVzvnX3EIj26gGdWwAS7nK6acQ/jd9BH8S9XVfJfMeW95Kt6BPPJVXd+1LwwmMJaluMBgMBoMhPlV03jTGpSGU65jPZ7QghQP8q1ZnpjGCVZzPbK6nITtAQnUOawEfXXpkMEoanuQMHMv7pTxxi0hN9xli6djVLt3hrVYpkaCQ10PU8hhgsWjNJvv9ZwfPVW9qHWEPyrKqi7KsBvMySBj/wyRasN7eZtxuJ7fTEvjJJtc2HpNNba7OYTqzgsn8KWa7/ulKAXZeYXbUulh5lFYJGsuYTKtmeWtr220sA9OtDus2MGW6Kn/yutZ2d+dmur2/mwtb2+1jIfoAz7jiR3LXwT0dWCmWMka+xQo68+54/X1OiN2XoRyQVEnVO4PBYDAYSkQVnTeNcWmIybkUcy7wsK5PNZTn2UsdJmwfR4dGa+hvhY3ecFyHGUWiRmWyFNCJ23iWRademfS2y7hMv3O8kEFG5yZaw1rBgHavcC//C+ApqOIOZZ0nil39TGO5fDWpMe3KVwbkKd2VMRhYgiRGXcr5XGfHKFvGZZnxcQeVaDog4uSLGqPSYDAYDAaDocJiSpEYyo3SlCSxsEp4+PmUVnxKK/vzeMYGtkuU8YxlPGP5ksahbcIEbNxeupKSRpH9CmIgMwE8ZU9m4PVWPsGt9nt3TUsofYmYeFihs+AWAkpi+zhjK8+xG5KgCkqqGwwGg8FQYqrgvGk8l4aEeIApPAB0pCbjLpgCmdBn8iK68iHvyerAW3QXy3XrCKpQb/LIdMdY68frgFNaZJHHf6cYyFw20iLh/q2+MlgJKM9sGkUMdrXZUasRvxK3kiqLgEJPjuTiwiwWpWcxgNkM5yn+JKaRKouYxQ1sLmzNQ4BMd0SA3J5Jd95kPAa0eyVqWRpFTAQGkEs2ucxgIS/IL5jAONIo4h4gk/f5kK6AY5DGIrV7dB6oZZjmEh1qG5PfAB+pt/uP1iazWr7qI90ynoPzUy3D8cJFy6jPTkCFH//u/PmwB0r6WzIYDAaDwWA4AanQKuvGuDQkxQ28zCn/3kctDrGIPuykPtcxl+4XLPe1jCSdZ2hhGSCrmMVYHuZ8VkW16cDnus1AmlNslx8pKbO4Qe/XCsCMNmiCcgb/V47gXqYB3tzEZIknCjSXG0mjiLE8bLcdoo3ACdoYv4tn9V+F27gsqcBPwizR+0kHMbUJ/UfOjt3ez7gI/7oWUl9Tx/K7a+dz/u5PWH36RUAE+V3ZDtdQAqqopLrBYDAYDCWi/ObNCq2ybsJiDcccMUO9kmUmA23P47HEH5IphH/94qht4oW5lpYBzE4q9LU8w2LdIbGJ1L60xi5SvNsaKjiW6l1ZvwwGg8FgOBGpovOm8VwakmIk03mKz9g4/lz+POEe7r/3MdZM7sCwfz9BB9bwB9FUNXwhgpgBckjJ9jOesczSSrAdWQ3AKs6PapesxzKovaVmKoBz0j8FHE+f2yBzjDkVLrqCzswT9ez1YWqo0bUo45NLNgOYzRFq2OGpgSGlhfDb9BXqOHzjzOVGu90/QkJLrfBgaG0fa4m8nL2wQ2J5AHJHekNq3fvxc6YczDfjgdxP2Szmq4UvRFg94yLjsTQYDAaDwWCoRBjj0pA0xZyL+AnuH/wYV896jTrs5Tmxl/fkOh6UDXiwe7AIj0WiBuf+o6osRodqKgS2Q6lGXTKCDC0V7quNp5DQX08tyxLuO1bOYwGdbKPY4rcyjbncGOXRBOgvg/NSk86rDMCtJhvmiXTvZ97UbPXk7Q3o1P1jVq64WK24tAPQAbms1EMylAdVVFLdYDAYDIYSUUXnTRMWayhXRBf1Kgs+owWfBYj3lCZcthFbYq4XWerlZgCzEYXKa9hK69YK8TNCJBerIMRittCILTSCtcJ5H4emFCe1HzdjbV3c8VHLQ8f5qHqVBUvoVjYdGQwGg8FgMBgqHMZzaSgRcrL625GmLBTrAOhy4J90P3k5fA6/Pud9FtCX059XNRmFyEko3NIKIZ2Xvoo21VS/n9n1IdGflYF5bimMLCcXMoWG6VvYUdiMV9IHcL3oqA8w2k+aSzZ9yCOFn0L7zWYGc4RyzUoJrBDKudk5OXWjfqh6lfO5LnB9R1aTka5CTee6wl/HMp55oro+Bu/YrfV+0igiGxgjjzCRCQmHx1olR3Y9rmpjypHhNTFHMJknC+9lcTrQQy+8HFZyMYwCrlMv2TbmLg3HEyPoYzAYDAZD4lTRedMYl4ZSsYqLQF6ESIdf3haBUfDQOaNYSQanD97HJcvf4SNdosQthONXkv2UVozmz4ByE2axiCy0salzFS1j0u+9dPL5kmc7zTwiNK/IVYzlYUQWLM7rxuI86MX79vpFqDIkdFIiPp/L3gCMl2PZSPPwHU0VMFLanslmbEfK3gxgqlrfLjhE1fLI3shcO9y1s17nbp9GETlEgFa2kRhE2PLZDIrK6bQeCMj7lCEp3tNGZPfww7TDYh92LdwLd+rdruZ80PpHzb/7jO0H1PnYl9IgvFNDxaEKTpIGg8FgMJSYKjhvGuPSUCZIbaDdyhOME7VpLttDL9iFNho+j0Cr+P30Ts/TZqXuNx3weShrs6/U43UbUpekvwNANrmlUndtzkYmMJHZbsO5s1SGZQhhOY+Wx3Im85Mawzl8nrTIkd+olDKSkLLszmrK2GRk/H1cyofsS6/Ni9xM6n1FZLCSee+pY49lrBoMBoPBYDAYKg8m59JwfFghYIUgjz7k0cezajxjS919WH6mH7fXMp5hGVbOw1+axOqrLMuQWGGybsYz1s79jId/7ELk0JMF9GRBmYxP9FEvwwlMFZVUNxgMBoOhRFTRedN4Lg1lyrPcxbMShjKdjWedSys+J0X2YNPRr9n1ufJ0vXFOD1jh3a4Dn7OIxAzLZmz3fD6XYvqQB7p0idvzmQgfFV5BanqRHscaFmZdC0Cvb/JVgzO97edlZUM/kHmOYfmQmMhLcqD9OdXtvRzpfPCPnbVqgy3tGgaud4fDHqG67dH0n6dNtGYsrZhUOIGbgDHp41lHcAKjyrusHrjOwp9vubNaE0jAw/jPAx359dRVANw5cjLVOUIBnZjAONb2uYC/L+rH7z6dz6EOtTiz+5ds4+z4nRoMBoPBYDAYKgXGuDSUCy+kD4e/wuv/HYh8vxpjbxnHpJ0q8a7vaf/H0ftPRj4oyFqoE/CuVn8mMLFM9p+o2I9Te7K18zfPXuttKyNRyrEWr8gBHI5jsJWG6hzxhNDOYZAaU4K1M1OlV01X5WX2LbPxyUVOvuVfRg5nLgNpyHYOUYtu5PM8Qxm3aCzraKu1koxRWemoopLqBoPBYDCUiCo6b5qwWEOFIJEcv0RDXd/hUsAbmhpr20t5h0tReZev0ZfXytDoKm8SCYkNw60ca50rd5kS8Z56haFEfmK3MRgS4DQhxHNCiKuP90AMBoPBYDCUDuO5NJQLlsCPEBN4X/6aSW9PgBbKE3j0rZO58r0FCPEgdIvA+8qw/JY63MgsAN6ir20Y+gVnAB0G64TALlrWH4B3ul5SJuP/jBbM1Uqtbm9q7zy13xR+YgAgZbY2yh6mLevoLS1BIu+Y3SqxHtpJvTyagcxkBZ2BTvY5SEOJ4ZwPzCv0igFNZAJ10kcAcC/TbIP9GtmKjqy2Q2rrs4td1E/ibMTnHh4BRjOEGXz6QzrDazzF8pRfs4/aLKcLMxjC8wwt030ajjHlJ6n+vZRyWLn0bDAYDAbD8cKUIjEYyh6VuxdBXgkNjm4DYGfxWTRiMx/JCVzy+wjsjfB24aVx+4oV6jqz62/t95ZaqtsojbXth1xhv7+WBQl5RysC8UJiw+pUjmR61DLnXDmlSuKpuO6s1gQxRX/oDv0PzGYws6ixBaa3G0Zt9gNQTAuKaRGqjGuoJFTRSdJgMBgMhhJRRedNExZrqJCUJtzTqg0Zts69XqzTL626KuoFjyVRVVaLslaLDWIgM2N+jkcP3qQHb3qWuRVkg0JexZbDiC3qSmnUYQ0Gg8FgMBgMbozn0nDMsOoiip4SXoBLbimAX8P/FfZkEVfRnjU8wQha99iM+F9HTGcyI7iXaZ6+/IqwlrcylmGZKO3qFdN892cAFHMuDwXsN5sZAAxgNput8NQAT6IoBDoBbEfKGOGxAczlRs/nAcwmA1hBZ7rwMaCMWLeHtoBO9nbunMqJLo+kH8uYfCtOrqlIActetg3LoXBD1t8YzCz6kUc/5vNZu+bc/d6z/La7MnaNx/IEwZJUNxgMBoPBEJ8qOm8a49JwzJHabuwy5H0A/ue5tygc1pYj1KDpwa2sWtqGf9GeC25fy+Sn70yqb8vITHS9bAsjmMwCLVe7abdlqJ3rtAkwGmccuIXslFdC92MZfAElMEvNVloALaI8oyo/M3E663owBWTYy9wGZmBYbG4NWKrf50PNV37g5W2DqdN4L21ZRx8WsZrz6d09j33UTrosjMFgMBgMBoOh8mLCYg2VgocZxcOMKvN+w0qLlKrPJEJot9DIfrkZwGy7vqU/xNZa7sa9Pigktx+v0o9XPf0mipiqtjcYOFIOL4PBYDAYTlSq4LxpjEvDcWM53VhON+QwSO+6jiYLd/FQrfGcyTYe5x64C+7d+iSDmONsNF8kXJIkUSPvyXr3srlea5bQC1GINr8GhLa/lScYkfI4uWRTn50MS3+C5umfUYMjbC70qsTKdOWptby1zdieUEhsEP7w0k20piOrPWVc3CGyk0R1+xXERCYwSVTnbbGKgcz1lCEBFWLciC3258wDS8gcuYTMRerFw3Bo7amMazyBbTSmH6+TywC6sZxFZBmv5YmILIeXwWAwGAwnKlVw3jRhsYYKgVwGQjyI3PEgYoNE/k3w8It/5IH3/kKTJ3YxbtAUPkrP8G70rIDL9fsWJftvk3nOe7/8jr/cSRDPchfZzKAzK/iYLqSmF7E5q7WnXw9rdaCsXYIk2NDMXXu9bpfNJlprc3dlaOjrZjHXVoe1DUyfWuxYxtuG5lipjEgpJzDDZ7TeyhM0YgfN9eeGI7fw+sjr+PXU99nHKfTkLb18O//ocyMPPTIRuQgwuZUGg8FgMBgMVRpjXBoqDHbZktOBhtCNfPge+CPIjwXMgS1TGlKbfeyjdvz+4pTq8NOaTXqbXABmE2whPstdns+zGQKocNTNWdE1OcuSrT6P7XyuS+op1hh5hIlM8HgohzCbBke/Zld+E67u/hpnokrG/PHoVPbtrc2oelN4dsqtfHH0HG6o9jIrXTmayqg0GAwGg8FgMBiMcWk4QXCrxMYT9UmE8YxFy7zGxMpfLJEiqs+LGYRTXiT6X7UpxdTiIKBUbS0SVX8FeJV+NKcYWJLYmA0Gg8FgMBgMhhCMcWmomLSQXAxs6deIZt/sgO3w7ZTTaPblDngW8h7pxLn1N/LD2TXZWr2pd9tpAs53fe4a27Vn5WV+ld6AfDL10lZksJICn4FpGZP9mK+X9AOUcZsDLMyzDLoQYzOGIRnVbq2jNbucLjGb+0uSBDGRCbxJD16lH504yEim8gj3A9Cp2kpWZsLCbWr8dzd+jCeq3UVBPeWl3JXfBKbA4/mjAZAHEjsMg8FgMBgMBkPVwRiXhgpNM7bDmbBqeFvSC9exNr0F51bfSAo/MXPAb+lGPrXZR78jr5fpficw0c65jEUu2Umrr9okYGjOXXtTaLuvClWIbGq6N1s0EY+lxUFqso62pPElA6vNZWXjThymOgc4ieGH/grA3ppnMKkO8HDyocYGg8FgMBgMhqqDUYs1nPC4jURLQTaZciGlQYjFCLHY9Vm9SsN0hkaVGvHTgs/4mAz79Rkt2JlAnqrBYDAYDAaDwVBSjOfSUCnoyDrtNSuGidDrVQFtYOZ5vwWgUfUdADRlK3NHqPzLCZFJ9MmZB8BntODc7zbiV7+ZzlD+mg5/EH/kLD4kVSrLLxulEiuyQOCoyuaSTRpF3MozgFVfshMFdGITrbkRp86kO0zVMQbVMiFgs1S1LUNLk8TxbLo9lta+WvAZ9/EocF/gNkeoTn12csWKj/ipcwqdKKCNuJn+sgG3vDeb+plK2Kd+5td8/9EZaqPuxmNpAPW/8/PxHoTBYDAYDJWEqjlvGuPSUDm5ThleN84X8Cv4sbVywp/83lEmfDTJ/mVP5k+evMnF6d3o9Z98+pw3j8WF0SVG4uUtWvyevyXUTsregGNcSll6z+VwnucxPgO8Qj5u7uNRqutKu43ZRlc+4E1+Q9+334b6wK/gcpZypriH/rKFrb6bWS0fumuBou6lG6fBYDAYDAaDoWphwmINBhf/pj1CrIdvRNzQUz9C5JTLmFSNy9lMZbjtFbV4jb7sIQX+Izh4oJRWq8EQEwkcLoeXwWAwGAwnIlVz3jSeS0Plpp/yYD7KWPW5N0xgEp/1bs5cBjLhy0nMPXsgKfxE7dP30bvwfV5KH8gz3M6a9Ke46j/v8tl5zZHbXmDomX+N6j41r4hNi9vAYtjT+yQAMrTXcqAuefLbwgXQyTEsg7yf7mXSjnZ1wmG34AqR/U7Yqwa0eyXwsG/gZfqygGJf3cvi79ox6vSHaMAu0vgSgHfpwaZv2rDlzIYcvASeSRlGCzZyVfG78G6E/KNfq3F1h1CVW4Ohiob3GAwGg8FQMqrmvGmMS8MJwQQmOh96T+RcYAKw6uz5jDoyhfnVr+F9MpHpMJq2rKctTdnK9PNu4THu4YYzX+b5tX+guN1ZyA1tmXrmHRRwAQ3W7g/d5yFqcn1hLgBSRsrt2LqwnE2ksYk0OrKaS/mQfdRmImPJfe963u/+a9qft4YGRfspbn0WddjL6O8e54nThwHwGPcw9syH6cfrPML93L3sWTXmriBbADQpt7EbDAaDwWAwGKoOJizWYCgDRvEwfcjjCW5FiMVMZyiiEHIYTRpFiELlnezBm2yhkcq7fEHAfP2yyDWhrYaKStUM7zEYDAaDoWRUzXnTeC4NJzQdWQfV4UbXsk6s5LdZC0jNK2IqIxnEHCIPPQr9oEXxVxS1SGUpl9OP+VRvd4SD7WoCcMZ730NLyK1xPeyAj8/rhEwRHGwGY3moVONstnYHW9o1VB8+gJ0DTqFB7n5yX7ueH8dUY02tDtRmH2/Rk568BUBnVvBx9050+/KffHn2mRS0zmAbjRlALrNP78/dorHuXYXvPg48JiWya6mGajAYDAaDwWAwBHLcjEshRArwItAJZYaPklK+EdCuCfAKkA5skFJm+Nb/HlV3QQCLgRFSyqPlPHxDJeZaFiDzQNzbmraT19GWdYj+EllN8HWL+rR+YTOLzu9Pcaez2E5DWhdtBuDHi6vxRa1WdFyxnsNtoD47YQv88gWJvE4gMh4kW+7iOfl3htWbxZm7v2QFF7Lp1TZQD4T4EnnT2QDIPwjSblkPwKab2zDgxVfIXXY9nAoshAZ5+5k65w4GDMhlL3UAOPftjXDlWzzAw/yN33Pulxv59Oxz4Et44OyH+TP3s5wu3FI4W+0jdiUTgyFJqmbuSEXCzJsGg8FQmaia86aQx+kOVAgxHmgmpRwqhGgJfAi0kFLu97U7DTgPddv9oHuSFEKcrbfrCOxCTZKzpZSz4u0/IyNDFhQUlNnxGCo/RaTRZksxAB80u5jL+q5A3ifQujgc7gs1boY9806izu6fEBOADBBdf0Ye+QU/Nq7GKZ8d4a/pQ+0+JzOKzRNakzq+iM8PtOGXJy/i5+/7sP3U+lTnMGfc/L0zgJ7wY79q1Dii7vG2pZzJdhpx0Z9Xc/AuqPUjsB0GtnuJ6fwBgAbsOxanxlBJEUKs9BsWJe+rg4RFZdGVj6ZlNsYTHTNvGgwGQ/li5s3SczxzLgeCqkQvpdwAFAC9/Y2klN9LKZcBQcoqA4DXpZTf6aeuf9P9GgwGg8FwomHmTYPBYDBUaI5nzmUzYLPr8xag6XHow2AAoDWbkM2sTyuQCwAkXKyW1ACYBx0p4kvawFgYffqDyBW/gIbwaK3RpKYXMbzoBUa0/l+eLLyXu9Mf4fGlo7l5/EvUehSgN784TSLfEnx75Wlkv/iCvf9OrGTkqKe4Z8qfAejJW+yiPhf1WU2tDcB5Ek63MijdWaQGw7Gi4gsJnOCYedNgMBgqFVVv3iw341IIUYiaxIJoVF77jYUQYhgwDKBZs7ChGQyx2URrqKfCyR8B6KzKkEzQL1pLpgHT0gFG89gy3TLnEaRdDlNyBjDb0/MQmDKdx+zPo9Wf88rlMAwGQwXDzJsGg8FgqOyUm3EppUyPtV4IsQVIBb7Ti5oB7ye5G6sPi2bA1hhjeg54DlTuSJL7MhgMhipMuQkTnCaEeA5YKKVcWB47qCyYedNgMBhOJKqmoM/xzLl8DbgVQAsTXAAsSbKPecA1QojThRDVgN8D/yjTURoMBoOhPPleSjmsqhuWCWLmTYPBYDBUaI5nzuVk4CUhRDFwBBgmpdwHIISYAGyTUj4jhKiOyg+phXrC/RXwvJTyQSnlf4UQDwGf6D7fRsmvGwwGg6FMsYpBG44jZt40GAyGSsOJMW8KIS4BbkbNKXullH+I1f64GZdSyh+Ba0PWjXe9PwKcFaOfZ4Fny3yABoPBYHBRNcN7KhJm3jQYDIbKxPGbN4UQU4D+QBrQXkq5Vi8/B5gJ1EeVoxqs1cdDkVJ+BHykt/8/IcQp/hJYbo5nWKzBYDAYDAaDwWAwGMqW14GueNXBQZWzmi6lPAeYjutBoxCiuRBiqe91r2v9VcD6WIYlHN+wWIPBYDBUGk6M8B6DwWAwGI4N5TZvNhBCFLg+P6fF15w9K28jQgh7mRCiIZAOXKEX/R34qxDidF37eCPQI2iHQoibgDQp5eh4gzPGpcFgMBgMBoPBYDBUDnZKKTNKsF1T4GudOoGU8ogQYpte/l3YRkKI3wAPA28IIZ4BxkkpQ9sb49JgMBgMCWByLg0Gg8FgSJwTY96UUr5BjDx+P8a4NBgMBkMCmLBYg8FgMBgSp8LNm1uBJkKI6tprWR1oTIxaxyXBCPoYDAaDwWAwGAwGwwmMlHIHsBr4nV70O2BVrBDXkmA8lwaDwWBIgBMjvMdgMBgMhmPDcS1FMg3IAs4AlgohdkkpzwVuA2YKIcYDe4DBZb1vY1waDAaDwWAwGAwGwwmClHIEMCJgeRHQuTz3bYxLg8FgMCRAhcsdMRgMBoOhAlNu8+ZpQojngIVSyoXlsYPSYIxLg8FgMCSACYs1GAwGgyFxym3e/F5KOaw8Oi4LjKCPwWAwGAwGg8FgMBhKjfFcGgwGgyFBTFiswWAwGAyJU/XmTeO5NBgMBoPBYDAYDAZDqTGeS4PBYDAkgMm5NBgMBoMhcarmvGmMS4PBYDAkQNWcJA0Gg8FgKBlVc940YbEGg8FgMBgMBoPBUDk4TQjxnBDi6uM9kCCM59JgMBgMCWDqXBoMBoPBkDjlNm+aUiQGg8FgMBgMBoPBYDixMZ5Lg8FgMCRA1cwdMRgMBoOhZFTNedMYlwaDwWBIABMWazAYDAZD4lTNedOExRoMBoPBYDAYDAaDodQYz6XBYDAYEqBqhvcYDAaDwVAyqua8aTyXBoPBYDAYDAaDwWAoNcZzaTAYDIYEqJq5IwaDwWAwlIyqOW8az6XBYDAYDAaDwWAwVA5OE0I8J4S4+ngPJAjjuTQYDAZDAlTN3BGDwWAwGEpGuc2b30sph5VHx2WBMS4NBoPBkABVM7zHYDAYDIaSUTXnTRMWazAYDAaDwWAwGAyGUmM8lwaDwWBIABMWazAYDAZD4lTNedN4Lg0Gg8FgMBgMBoPBUGqM59JgMBgMCVA1c0cMBoPBYCgZVXPeFFLK4z2G44IQ4jtg8zHYVQNg5zHYT0XGnAOFOQ/mHMCxPQepUsrTy6IjIcQS1NjLmp1Syl7l0K+hjDHz5jHFnAOFOQ/mHFgcq/Ng5s1SUmWNy2OFEKJASplxvMdxPDHnQGHOgzkHYM6BwRAP8z9izoGFOQ/mHFiY81B5MDmXBoPBYDAYDAaDwWAoNca4NBgMBoPBYDAYDAZDqTHGZfnz3PEeQAXAnAOFOQ/mHIA5BwZDPMz/iDkHFuY8mHNgYc5DJcHkXBoMBoPBYDAYDAaDodQYz6XBYDAYDAaDwWAwGEqNMS5LiRAiRQgxVwhRLIQoEkL8JqRdEyHE+0KI74UQBQHrf6/72CiE+KsQolJ9N4meB9028FiFEJlCiANCiNX6teLYHUHJEEKcI4T4pxDiC/23ZUCb6kKI6fp4i4UQQxNZV5kog/PwoBBih+u7n35sj6D0JHgOrhRCFAghDgohpvjWnRC/BYMhHmbeVJh508ybZt408+YJiZTSvErxAsYDz+v3LYFvgVMC2p0GdAV+AxT41p0NfAWcjjL43wIGH+9jK6fzEHqsQKb/3FT0F/AecL1+fz3wXkCbwfo4q+nj/gpIi7euMr3K4Dw8CEw53sdxDM5BC6Aj8LD/eE+U34J5mVe8l5k3kz4PZt4086aZN828WWleleopXwVlIPAMgJRyA1AA9PY3klJ+L6VcBuwP6GMA8LqU8jsp5VHgb7rfykRC54ET41gBEEI0BNKBv+tFfwfShRD+4rsD/397dxMqVRnHcfz7S3ftIokSrSDKpEhCF1GLIkpwo5sgg6iFZotwURAtIwiCWkWl0KpFLRShoKTMwEUtJPElCkqsKDMiK9AWhS/9W8wJhrzeO3Nnzp2X+/3Awz13Zp7j/3nOmfvjuWfuEXizqv6pqtPAu8BDPTw3EYY0DxOt1zmoqhNVdQS4MMNupnZ+pP8xNzvMTXPT3DQ3p46Ly8GtBH7o+v5HYMUI9jFqvY5hrtfdnORwkoNJHht+mUO1AjhVVRcBmq8/c+m4ZxvzNBz7YcwDwMNJvkiyL8ldbRbcgl7nYDbTcC5IvTA3O8xNc9PcNDenztJRFzDukhymc/LO5JqFrGWUFmgeDgMrqupMkhuB/UlOVdX+Ie1f42sn8GJVnU/yAPBeklur6vdRFyapP+Zmh7mplpmbGkteuZxDVd1ZVVdfpl2k81uS67u6rARO9vnPDGMfrRriPFz2dVV1tqrONNvf0/l4w90tDGdYTgLLkyyBzh+WA9dx6bhnm5uxP/Y9GHgequqXqjrfbH/cPH5by3UPU69zMJtpOBckc7Nhbs7I3OwwN83NqeXicnC7gW0AzV2u1gEf9rmPPcCmJMuaO8BtBXYNtcr29ToPlx1rkmuTpNm+CngQONp+6fNTVb/SqW9z89Bm4Ejzuf9uu4GtSa5o/pZgE515mOu5iTCMeUiy/L8XJVkD3AB802bdw9THHMxm4s8FqUfmZoe5aW6am+bm9On1zj+2mRtwJZ2T+wSdN/XGrudeAJ5stpfQuYvVaeBcs/1812u3Ad82bQewZNRja2MeZhsr8BTwFZ0fNl8Cz456XD2MexVwEDjefL2leXwvsLbr2O/oGvMTXf0v+9wktSHMw1vNMT8GfA5sGPWYWpqDe5r3/lngz2Z7/TSdCzbbXM3c7G8eZhuruTm5PyvNTXNzWluagyNJkiRJ0rz5sVhJkiRJ0sBcXEqSJEmSBubiUpIkSZI0MBeXkiRJkqSBubiUJEmSJA3MxaUkSZIkaWAuLqUWJDmQ5Lskz82j7/YkJ5IcaqM2SZLGjbkpTQcXl1J7tlfVS/12qqpXgS0t1CNJ0jgzN6UJ5+JS6kOSDUmOdrVzSTb20O/xJPuS7ErydZJPkqxO8kGS40neTpKFGIMkSQvF3JQWFxeXUh+qam9VramqNcDrwCHgox67rwOerqpVwF/AO8AjwGrgduD+4VcsSdLomJvS4uLiUpqHJOuBZ4CNVfV3j90+q6qfmu0jwKdVdaaqLgDHgJtaKFWSpJEzN6XFYemoC5AmTZI7gJ3A+qo63UfX7jC9OMP3vh8lSVPH3JQWD69cSn1IshzYAzxaVcdHXY8kSePM3JQWFxeXUn+2AMuA17puTnDfqIuSJGlMmZvSIpKqGnUN0tRJcgB4paren2f/e5v+a4dYliRJY8nclKaDVy6ldvwBvDzf/wwaeAP4behVSZI0nsxNaQp45VKSJEmSNDCvXEqSJEmSBubiUpIkSZI0MBeXkiRJkqSBubiUJEmSJA3MxaUkSZIkaWD/ArqqqHSlw/zuAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 936x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_1D\n",
+      "Plotting data with name abs_logger_space_zx\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_2DQ\n",
+      "Plotting data with name logger_2D_kf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=3)\n",
     "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=3)\n",
@@ -468,7 +2383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -478,7 +2393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -500,13 +2415,560 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_loggers_122\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:21010:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:21010:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:21253:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:21253:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_1D.comp:479:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:21492:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_1D_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_1D.comp:479:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:21492:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_1D_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
+      "                                                ^~~~~~~~\n",
+      "./python_tutorial.c:21683:18: note: expanded from macro 'filename'\n",
+      "#define filename mccabs_logger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
+      "                                                ^\n",
+      "                                                \"%s\", \n",
+      "./python_tutorial.c:21683:18: note: expanded from macro 'filename'\n",
+      "#define filename mccabs_logger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2DQ.comp:535:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:21907:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_2DQ_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2DQ.comp:535:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:21907:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_2DQ_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_kf.comp:536:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:22135:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_2D_kf_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_kf.comp:536:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:22135:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_2D_kf_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space_time.comp:632:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:22373:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_2D_space_time_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space_time.comp:632:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:22373:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_2D_space_time_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "19 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_loggers_122\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: Sample_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: Sample_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: Sample_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "Allocating 3D arrays \n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 4\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "name of process [2]: Sample_inc \n",
+      "component index [2]: 4 \n",
+      "name of process [3]: Sample_pow \n",
+      "component index [3]: 5 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 2\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 0.347892 \n",
+      "number of processes [0]: 2 \n",
+      "name of material    [1]: Sample \n",
+      "component index     [1]: 6 \n",
+      "my_absoprtion       [1]: 0.273042 \n",
+      "number of processes [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 2\n",
+      "\n",
+      "name of geometry    [0]: sample_geometry \n",
+      "component index     [0]: 8 \n",
+      "Volume.name         [0]: sample_geometry \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.273042 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: sample_container \n",
+      "component index     [1]: 9 \n",
+      "Volume.name         [1]: sample_container \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: sample_container_lid \n",
+      "component index     [2]: 10 \n",
+      "Volume.name         [2]: sample_container_lid \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
+      "Volume.p_physics.number of processes [2]: 2 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: 0.015500 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: cryostat_wall \n",
+      "component index     [3]: 11 \n",
+      "Volume.name         [3]: cryostat_wall \n",
+      "Volume.p_physics.is_vacuum           [3]: 0 \n",
+      "Volume.p_physics.my_absorption       [3]: 0.347892 \n",
+      "Volume.p_physics.number of processes [3]: 2 \n",
+      "Volume.geometry.shape                [3]: cylinder \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: 0.000000 \n",
+      "Volume.geometry.center.z             [3]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
+      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [4]: cryostat_wall_vacuum \n",
+      "component index     [4]: 12 \n",
+      "Volume.name         [4]: cryostat_wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [4]: 1 \n",
+      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
+      "Volume.p_physics.number of processes [4]: 0 \n",
+      "Volume.geometry.shape                [4]: cylinder \n",
+      "Volume.geometry.center.x             [4]: 0.000000 \n",
+      "Volume.geometry.center.y             [4]: 0.000000 \n",
+      "Volume.geometry.center.z             [4]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
+      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [5]: outer_cryostat_wall \n",
+      "component index     [5]: 13 \n",
+      "Volume.name         [5]: outer_cryostat_wall \n",
+      "Volume.p_physics.is_vacuum           [5]: 0 \n",
+      "Volume.p_physics.my_absorption       [5]: 0.347892 \n",
+      "Volume.p_physics.number of processes [5]: 2 \n",
+      "Volume.geometry.shape                [5]: cylinder \n",
+      "Volume.geometry.center.x             [5]: 0.000000 \n",
+      "Volume.geometry.center.y             [5]: 0.000000 \n",
+      "Volume.geometry.center.z             [5]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.100000 \n",
+      "Volume.geometry.geometry_parameters.height [5]: 0.150000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [6]: outer_cryostat_wall_vacuum \n",
+      "component index     [6]: 14 \n",
+      "Volume.name         [6]: outer_cryostat_wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [6]: 1 \n",
+      "Volume.p_physics.my_absorption       [6]: 0.000000 \n",
+      "Volume.p_physics.number of processes [6]: 0 \n",
+      "Volume.geometry.shape                [6]: cylinder \n",
+      "Volume.geometry.center.x             [6]: 0.000000 \n",
+      "Volume.geometry.center.y             [6]: 0.000000 \n",
+      "Volume.geometry.center.z             [6]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.097000 \n",
+      "Volume.geometry.geometry_parameters.height [6]: 0.140000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 8\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7]\n",
+      "LIST: Direct_children for Volume           0 = [6]\n",
+      "LIST: Intersect_check_list for Volume      0 = [6]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [6]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for sample_geometry with cylinder shape made of Sample\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = []\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [2,3]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
+      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for sample_container with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  2 = [1]\n",
+      "LIST: Direct_children for Volume           2 = [1]\n",
+      "LIST: Intersect_check_list for Volume      2 = [1]\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [3,5]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [5]\n",
+      "LIST: Next_volume_list for Volume          2 = [3,5,1]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for sample_container_lid with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  3 = []\n",
+      "LIST: Direct_children for Volume           3 = []\n",
+      "LIST: Intersect_check_list for Volume      3 = [2]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [5]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = [5]\n",
+      "LIST: Next_volume_list for Volume          3 = [5,2]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "List overview for cryostat_wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  4 = [1,2,3,5]\n",
+      "LIST: Direct_children for Volume           4 = [5]\n",
+      "LIST: Intersect_check_list for Volume      4 = [5]\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [7]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = [7]\n",
+      "LIST: Next_volume_list for Volume          4 = [7,5]\n",
+      "      Is_vacuum for Volume                 4 = 0\n",
+      "      is_mask_volume for Volume            4 = 0\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = []\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 0\n",
+      "\n",
+      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  5 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           5 = [2,3]\n",
+      "LIST: Intersect_check_list for Volume      5 = [2,3]\n",
+      "LIST: Mask_intersect_list for Volume       5 = []\n",
+      "LIST: Destinations_list for Volume         5 = [4]\n",
+      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
+      "LIST: Next_volume_list for Volume          5 = [4,2,3]\n",
+      "      Is_vacuum for Volume                 5 = 1\n",
+      "      is_mask_volume for Volume            5 = 0\n",
+      "      is_masked_volume for Volume          5 = 0\n",
+      "      is_exit_volume for Volume            5 = 0\n",
+      "LIST: mask_list for Volume                 5 = []\n",
+      "LIST: masked_by_list for Volume            5 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
+      "      mask_mode for Volume                 5 = 0\n",
+      "\n",
+      "List overview for outer_cryostat_wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  6 = [1,2,3,4,5,7]\n",
+      "LIST: Direct_children for Volume           6 = [7]\n",
+      "LIST: Intersect_check_list for Volume      6 = [7]\n",
+      "LIST: Mask_intersect_list for Volume       6 = []\n",
+      "LIST: Destinations_list for Volume         6 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 6 = []\n",
+      "LIST: Next_volume_list for Volume          6 = [0,7]\n",
+      "      Is_vacuum for Volume                 6 = 0\n",
+      "      is_mask_volume for Volume            6 = 0\n",
+      "      is_masked_volume for Volume          6 = 0\n",
+      "      is_exit_volume for Volume            6 = 0\n",
+      "LIST: mask_list for Volume                 6 = []\n",
+      "LIST: masked_by_list for Volume            6 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
+      "      mask_mode for Volume                 6 = 0\n",
+      "\n",
+      "List overview for outer_cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  7 = [1,2,3,4,5]\n",
+      "LIST: Direct_children for Volume           7 = [4]\n",
+      "LIST: Intersect_check_list for Volume      7 = [4]\n",
+      "LIST: Mask_intersect_list for Volume       7 = []\n",
+      "LIST: Destinations_list for Volume         7 = [6]\n",
+      "LIST: Reduced_destinations_list for Volume 7 = [6]\n",
+      "LIST: Next_volume_list for Volume          7 = [6,4]\n",
+      "      Is_vacuum for Volume                 7 = 1\n",
+      "      is_mask_volume for Volume            7 = 0\n",
+      "      is_masked_volume for Volume          7 = 0\n",
+      "      is_exit_volume for Volume            7 = 0\n",
+      "LIST: mask_list for Volume                 7 = []\n",
+      "LIST: masked_by_list for Volume            7 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
+      "      mask_mode for Volume                 7 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_zx_I=728.27 logger_space_zx_ERR=5.09048 logger_space_zx_N=40035 \"logger_zx.dat\"\n",
+      "Detector: logger_space_zy_I=2799.53 logger_space_zy_ERR=16.4125 logger_space_zy_N=140638 \"logger_zy.dat\"\n",
+      "Detector: logger_1D_I=20463.5 logger_1D_ERR=32.8573 logger_1D_N=1.01146e+06 \"logger_1D_time.dat\"\n",
+      "Detector: abs_logger_space_zx_I=958.74 abs_logger_space_zx_ERR=1.34679 abs_logger_space_zx_N=1.01146e+06 \"abs_logger_zx.dat\"\n",
+      "Detector: logger_2DQ_I=20463.5 logger_2DQ_ERR=32.8573 logger_2DQ_N=1.01146e+06 \"logger_2DQ.dat\"\n",
+      "Detector: logger_2D_kf_I=20463.5 logger_2D_kf_ERR=32.8573 logger_2D_kf_N=1.01146e+06 \"logger_2D_kf.dat\"\n",
+      "Detector: logger_2D_space_time_I=11.8034 logger_2D_space_time_ERR=0.501094 logger_2D_space_time_N=591 \"logger_2D_space_time.dat_0\"\n",
+      "Detector: logger_2D_space_time_I=11.283 logger_2D_space_time_ERR=0.499298 logger_2D_space_time_N=535 \"logger_2D_space_time.dat_1\"\n",
+      "Detector: logger_2D_space_time_I=196.544 logger_2D_space_time_ERR=1.98744 logger_2D_space_time_N=10233 \"logger_2D_space_time.dat_2\"\n",
+      "Detector: logger_2D_space_time_I=521.214 logger_2D_space_time_ERR=3.23585 logger_2D_space_time_N=27167 \"logger_2D_space_time.dat_3\"\n",
+      "Detector: logger_2D_space_time_I=5123.74 logger_2D_space_time_ERR=16.0719 logger_2D_space_time_N=249726 \"logger_2D_space_time.dat_4\"\n",
+      "Detector: logger_2D_space_time_I=9855.13 logger_2D_space_time_ERR=24.225 logger_2D_space_time_N=478183 \"logger_2D_space_time.dat_5\"\n",
+      "Detector: logger_2D_space_time_I=1826.59 logger_2D_space_time_ERR=11.5129 logger_2D_space_time_N=90517 \"logger_2D_space_time.dat_6\"\n",
+      "Detector: logger_2D_space_time_I=482.193 logger_2D_space_time_ERR=4.51241 logger_2D_space_time_N=25689 \"logger_2D_space_time.dat_7\"\n",
+      "Detector: logger_2D_space_time_I=82.0829 logger_2D_space_time_ERR=2.14281 logger_2D_space_time_N=4472 \"logger_2D_space_time.dat_8\"\n",
+      "Detector: logger_2D_space_time_I=68.2735 logger_2D_space_time_ERR=1.87432 logger_2D_space_time_N=3282 \"logger_2D_space_time.dat_9\"\n",
+      "Detector: logger_2D_space_time_I=76.3738 logger_2D_space_time_ERR=1.91524 logger_2D_space_time_N=3652 \"logger_2D_space_time.dat_10\"\n",
+      "Detector: logger_2D_space_time_I=494.956 logger_2D_space_time_ERR=3.61712 logger_2D_space_time_N=26009 \"logger_2D_space_time.dat_11\"\n",
+      "Detector: logger_2D_space_time_I=500.174 logger_2D_space_time_ERR=3.65417 logger_2D_space_time_N=26848 \"logger_2D_space_time.dat_12\"\n",
+      "Detector: logger_2D_space_time_I=80.3675 logger_2D_space_time_ERR=1.9862 logger_2D_space_time_N=4670 \"logger_2D_space_time.dat_13\"\n",
+      "Detector: logger_2D_space_time_I=29.441 logger_2D_space_time_ERR=1.54549 logger_2D_space_time_N=1657 \"logger_2D_space_time.dat_14\"\n",
+      "Detector: logger_2D_space_time_I=8.03876 logger_2D_space_time_ERR=0.421307 logger_2D_space_time_N=585 \"logger_2D_space_time.dat_15\"\n",
+      "Detector: logger_2D_space_time_I=4.29369 logger_2D_space_time_ERR=0.31758 logger_2D_space_time_N=298 \"logger_2D_space_time.dat_16\"\n",
+      "Detector: logger_2D_space_time_I=2.6055 logger_2D_space_time_ERR=0.204429 logger_2D_space_time_N=195 \"logger_2D_space_time.dat_17\"\n",
+      "Detector: logger_2D_space_time_I=1.29316 logger_2D_space_time_ERR=0.169389 logger_2D_space_time_N=95 \"logger_2D_space_time.dat_18\"\n",
+      "Detector: logger_2D_space_time_I=0.543753 logger_2D_space_time_ERR=0.0924595 logger_2D_space_time_N=44 \"logger_2D_space_time.dat_19\"\n",
+      "Detector: logger_2D_space_time_I=0.271376 logger_2D_space_time_ERR=0.087438 logger_2D_space_time_N=17 \"logger_2D_space_time.dat_20\"\n",
+      "Detector: logger_2D_space_time_I=0.407183 logger_2D_space_time_ERR=0.136734 logger_2D_space_time_N=20 \"logger_2D_space_time.dat_21\"\n",
+      "Detector: logger_2D_space_time_I=0.164999 logger_2D_space_time_ERR=0.0513004 logger_2D_space_time_N=13 \"logger_2D_space_time.dat_22\"\n",
+      "Detector: logger_2D_space_time_I=0.0915192 logger_2D_space_time_ERR=0.0371662 logger_2D_space_time_N=7 \"logger_2D_space_time.dat_23\"\n",
+      "Detector: logger_2D_space_time_I=0.0817829 logger_2D_space_time_ERR=0.0336206 logger_2D_space_time_N=11 \"logger_2D_space_time.dat_24\"\n",
+      "Detector: logger_2D_space_time_I=0.11093 logger_2D_space_time_ERR=0.0421695 logger_2D_space_time_N=8 \"logger_2D_space_time.dat_25\"\n",
+      "Detector: logger_2D_space_time_I=0.161131 logger_2D_space_time_ERR=0.0513603 logger_2D_space_time_N=11 \"logger_2D_space_time.dat_26\"\n",
+      "Detector: logger_2D_space_time_I=0.0316249 logger_2D_space_time_ERR=0.023766 logger_2D_space_time_N=2 \"logger_2D_space_time.dat_27\"\n",
+      "Detector: logger_2D_space_time_I=0.0257836 logger_2D_space_time_ERR=0.0210225 logger_2D_space_time_N=2 \"logger_2D_space_time.dat_28\"\n",
+      "Detector: logger_2D_space_time_I=0.0140946 logger_2D_space_time_ERR=0.0140946 logger_2D_space_time_N=1 \"logger_2D_space_time.dat_29\"\n",
+      "Detector: logger_2D_space_time_I=0.0124473 logger_2D_space_time_ERR=0.0124473 logger_2D_space_time_N=1 \"logger_2D_space_time.dat_30\"\n",
+      "Detector: logger_2D_space_time_I=0.0286945 logger_2D_space_time_ERR=0.0230787 logger_2D_space_time_N=2 \"logger_2D_space_time.dat_31\"\n",
+      "Detector: logger_2D_space_time_I=0 logger_2D_space_time_ERR=0 logger_2D_space_time_N=0 \"logger_2D_space_time.dat_32\"\n",
+      "Detector: logger_2D_space_time_I=0 logger_2D_space_time_ERR=0 logger_2D_space_time_N=0 \"logger_2D_space_time.dat_33\"\n",
+      "Detector: logger_2D_space_time_I=0 logger_2D_space_time_ERR=0 logger_2D_space_time_N=0 \"logger_2D_space_time.dat_34\"\n",
+      "Detector: logger_2D_space_time_I=0 logger_2D_space_time_ERR=0 logger_2D_space_time_N=0 \"logger_2D_space_time.dat_35\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "data = instrument.backengine()"
    ]
@@ -521,9 +2983,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_2D_space_time\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-03-21 15:42:16,778 WARNING:MovieWriter imagemagick unavailable; using Pillow instead.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving animation with filename : \"animation_demo.gif\"\n"
+     ]
+    }
+   ],
    "source": [
     "ani_data = functions.name_search(\"logger_2D_space_time\", data)\n",
     "for frame in ani_data:\n",
@@ -552,7 +3048,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {
     "tags": [
      "scroll-output"
@@ -567,7 +3063,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/docs/source/tutorial/Union_tutorial_5_masks.ipynb b/docs/source/tutorial/Union_tutorial_5_masks.ipynb
index 9d9dbc44..41af5db3 100644
--- a/docs/source/tutorial/Union_tutorial_5_masks.ipynb
+++ b/docs/source/tutorial/Union_tutorial_5_masks.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,21 +36,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'instrument' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-2-02960afe2720>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mAl_inc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstrument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_component\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Al_inc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Incoherent_process\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mAl_inc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msigma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0082\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mAl_inc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_cell_volume\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m66.4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mAl_pow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstrument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_component\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Al_pow\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Powder_process\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'instrument' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
     "Al_inc.sigma = 0.0082\n",
@@ -107,25 +95,251 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_67\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:11415:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:11415:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "13 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_67\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 1\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 0.347892 \n",
+      "number of processes [0]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 1\n",
+      "\n",
+      "name of geometry    [0]: wall \n",
+      "component index     [0]: 5 \n",
+      "Volume.name         [0]: wall \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.347892 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.100000 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.150000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: wall_vacuum \n",
+      "component index     [1]: 6 \n",
+      "Volume.name         [1]: wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [1]: 1 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.000000 \n",
+      "Volume.p_physics.number of processes [1]: 0 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.080000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.160000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 3\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2]\n",
+      "LIST: Direct_children for Volume           0 = [1,2]\n",
+      "LIST: Intersect_check_list for Volume      0 = [1,2]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [1,2]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = [2]\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = []\n",
+      "LIST: Next_volume_list for Volume          1 = [0,2]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  2 = []\n",
+      "LIST: Direct_children for Volume           2 = []\n",
+      "LIST: Intersect_check_list for Volume      2 = []\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [0,1]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [1]\n",
+      "LIST: Next_volume_list for Volume          2 = [0,1]\n",
+      "      Is_vacuum for Volume                 2 = 1\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_zx_I=1451.97 logger_space_zx_ERR=4.41664 logger_space_zx_N=112127 \"logger_zx.dat\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.settings(ncount=2E6, output_path=\"data_folder/union_masks\")\n",
     "\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space_zx\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plotter.make_sub_plot(data)"
    ]
@@ -156,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -178,23 +392,312 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_68\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:11875:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:11875:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "13 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_68\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 1\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 0.347892 \n",
+      "number of processes [0]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 1\n",
+      "\n",
+      "name of geometry    [0]: wall \n",
+      "component index     [0]: 5 \n",
+      "Volume.name         [0]: wall \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.347892 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.100000 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.150000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: wall_vacuum \n",
+      "component index     [1]: 6 \n",
+      "Volume.name         [1]: wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [1]: 1 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.000000 \n",
+      "Volume.p_physics.number of processes [1]: 0 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.080000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.160000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: window \n",
+      "component index     [2]: 8 \n",
+      "Volume.name         [2]: window \n",
+      "Volume.p_physics.is_vacuum           [2]: 1 \n",
+      "Volume.p_physics.my_absorption       [2]: 0.000000 \n",
+      "Volume.p_physics.number of processes [2]: 0 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: 0.000000 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.090000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.170000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: mask \n",
+      "component index     [3]: 9 \n",
+      "Volume.name         [3]: mask \n",
+      "Volume.geometry.shape                [3]: box \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: 0.000000 \n",
+      "Volume.geometry.center.z             [3]: 0.900000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 5\n",
+      "number_of_masks = 1\n",
+      "number_of_masked_volumes = 1\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4]\n",
+      "LIST: Direct_children for Volume           0 = [1,3,4]\n",
+      "LIST: Intersect_check_list for Volume      0 = [1,2,4]\n",
+      "LIST: Mask_intersect_list for Volume       0 = [3]\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [3,1,2]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = [2,4]\n",
+      "LIST: Mask_intersect_list for Volume       1 = [3]\n",
+      "LIST: Destinations_list for Volume         1 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = []\n",
+      "LIST: Next_volume_list for Volume          1 = [0,3,2]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  2 = []\n",
+      "LIST: Direct_children for Volume           2 = []\n",
+      "LIST: Intersect_check_list for Volume      2 = [4]\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [0,1,3]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [1,3]\n",
+      "LIST: Next_volume_list for Volume          2 = [0,1,3]\n",
+      "      Is_vacuum for Volume                 2 = 1\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for window with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  3 = [2]\n",
+      "LIST: Direct_children for Volume           3 = [2]\n",
+      "LIST: Intersect_check_list for Volume      3 = [2,4]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [0,1]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = [1]\n",
+      "LIST: Next_volume_list for Volume          3 = [0,1,2]\n",
+      "      Is_vacuum for Volume                 3 = 1\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 1\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = [4]\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = [0]\n",
+      "      mask_mode for Volume                 3 = 1\n",
+      "\n",
+      "List overview for mask with box shape made of Mask\n",
+      "LIST: Children for Volume                  4 = []\n",
+      "LIST: Direct_children for Volume           4 = []\n",
+      "LIST: Intersect_check_list for Volume      4 = []\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [0,1,2]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = [1,2]\n",
+      "LIST: Next_volume_list for Volume          4 = [0,1,2]\n",
+      "      Is_vacuum for Volume                 4 = 0\n",
+      "      is_mask_volume for Volume            4 = 1\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = [3]\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 1\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_zx_I=1302.31 logger_space_zx_ERR=4.18281 logger_space_zx_N=100498 \"logger_zx.dat\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space_zx\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plotter.make_sub_plot(data)"
    ]
@@ -209,7 +712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -238,23 +741,409 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_69\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:12367:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:12367:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "13 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_69\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 1\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 0.347892 \n",
+      "number of processes [0]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 1\n",
+      "\n",
+      "name of geometry    [0]: wall \n",
+      "component index     [0]: 5 \n",
+      "Volume.name         [0]: wall \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.347892 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.100000 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.150000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: wall_vacuum \n",
+      "component index     [1]: 6 \n",
+      "Volume.name         [1]: wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [1]: 1 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.000000 \n",
+      "Volume.p_physics.number of processes [1]: 0 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.080000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.160000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: window \n",
+      "component index     [2]: 8 \n",
+      "Volume.name         [2]: window \n",
+      "Volume.p_physics.is_vacuum           [2]: 1 \n",
+      "Volume.p_physics.my_absorption       [2]: 0.000000 \n",
+      "Volume.p_physics.number of processes [2]: 0 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: 0.000000 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.090000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.170000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: mask \n",
+      "component index     [3]: 9 \n",
+      "Volume.name         [3]: mask \n",
+      "Volume.geometry.shape                [3]: box \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: 0.000000 \n",
+      "Volume.geometry.center.z             [3]: 0.900000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [4]: outer_window \n",
+      "component index     [4]: 10 \n",
+      "Volume.name         [4]: outer_window \n",
+      "Volume.p_physics.is_vacuum           [4]: 1 \n",
+      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
+      "Volume.p_physics.number of processes [4]: 0 \n",
+      "Volume.geometry.shape                [4]: cylinder \n",
+      "Volume.geometry.center.x             [4]: 0.000000 \n",
+      "Volume.geometry.center.y             [4]: 0.000000 \n",
+      "Volume.geometry.center.z             [4]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.110000 \n",
+      "Volume.geometry.geometry_parameters.height [4]: 0.180000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [5]: outer_window_Al \n",
+      "component index     [5]: 11 \n",
+      "Volume.name         [5]: outer_window_Al \n",
+      "Volume.p_physics.is_vacuum           [5]: 0 \n",
+      "Volume.p_physics.my_absorption       [5]: 0.347892 \n",
+      "Volume.p_physics.number of processes [5]: 2 \n",
+      "Volume.geometry.shape                [5]: cylinder \n",
+      "Volume.geometry.center.x             [5]: 0.000000 \n",
+      "Volume.geometry.center.y             [5]: 0.000000 \n",
+      "Volume.geometry.center.z             [5]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.090000 \n",
+      "Volume.geometry.geometry_parameters.height [5]: 0.190000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [6]: mask_outer \n",
+      "component index     [6]: 12 \n",
+      "Volume.name         [6]: mask_outer \n",
+      "Volume.geometry.shape                [6]: box \n",
+      "Volume.geometry.center.x             [6]: 0.000000 \n",
+      "Volume.geometry.center.y             [6]: 0.000000 \n",
+      "Volume.geometry.center.z             [6]: 1.100000 \n",
+      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 8\n",
+      "number_of_masks = 2\n",
+      "number_of_masked_volumes = 3\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7]\n",
+      "LIST: Direct_children for Volume           0 = [4,5,6,7]\n",
+      "LIST: Intersect_check_list for Volume      0 = [1,2,4,7]\n",
+      "LIST: Mask_intersect_list for Volume       0 = [3,5,6]\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [3,5,6,1,2]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = [2,4,7]\n",
+      "LIST: Mask_intersect_list for Volume       1 = [3,6]\n",
+      "LIST: Destinations_list for Volume         1 = [0,5]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = [5]\n",
+      "LIST: Next_volume_list for Volume          1 = [0,5,3,6,2]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  2 = []\n",
+      "LIST: Direct_children for Volume           2 = []\n",
+      "LIST: Intersect_check_list for Volume      2 = [4,7]\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [0,1,3,5,6]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [1,3,5,6]\n",
+      "LIST: Next_volume_list for Volume          2 = [0,1,3,5,6]\n",
+      "      Is_vacuum for Volume                 2 = 1\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for window with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  3 = [2]\n",
+      "LIST: Direct_children for Volume           3 = [2]\n",
+      "LIST: Intersect_check_list for Volume      3 = [2,4]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [0,1]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = [1]\n",
+      "LIST: Next_volume_list for Volume          3 = [0,1,2]\n",
+      "      Is_vacuum for Volume                 3 = 1\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 1\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = [4]\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = [0]\n",
+      "      mask_mode for Volume                 3 = 1\n",
+      "\n",
+      "List overview for mask with box shape made of Mask\n",
+      "LIST: Children for Volume                  4 = []\n",
+      "LIST: Direct_children for Volume           4 = []\n",
+      "LIST: Intersect_check_list for Volume      4 = []\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [0,1,2]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = [1,2]\n",
+      "LIST: Next_volume_list for Volume          4 = [0,1,2]\n",
+      "      Is_vacuum for Volume                 4 = 0\n",
+      "      is_mask_volume for Volume            4 = 1\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = [3]\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 1\n",
+      "\n",
+      "List overview for outer_window with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  5 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           5 = [1,3]\n",
+      "LIST: Intersect_check_list for Volume      5 = [2,7]\n",
+      "LIST: Mask_intersect_list for Volume       5 = [6]\n",
+      "LIST: Destinations_list for Volume         5 = [0,1]\n",
+      "LIST: Reduced_destinations_list for Volume 5 = [1]\n",
+      "LIST: Next_volume_list for Volume          5 = [0,1,6,2]\n",
+      "      Is_vacuum for Volume                 5 = 1\n",
+      "      is_mask_volume for Volume            5 = 0\n",
+      "      is_masked_volume for Volume          5 = 1\n",
+      "      is_exit_volume for Volume            5 = 0\n",
+      "LIST: mask_list for Volume                 5 = []\n",
+      "LIST: masked_by_list for Volume            5 = [7]\n",
+      "LIST: masked_by_mask_index_list for Volume 5 = [1]\n",
+      "      mask_mode for Volume                 5 = 1\n",
+      "\n",
+      "List overview for outer_window_Al with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  6 = [2,3]\n",
+      "LIST: Direct_children for Volume           6 = [3]\n",
+      "LIST: Intersect_check_list for Volume      6 = [2,7]\n",
+      "LIST: Mask_intersect_list for Volume       6 = []\n",
+      "LIST: Destinations_list for Volume         6 = [0,1,5]\n",
+      "LIST: Reduced_destinations_list for Volume 6 = [1,5]\n",
+      "LIST: Next_volume_list for Volume          6 = [0,1,5,2]\n",
+      "      Is_vacuum for Volume                 6 = 0\n",
+      "      is_mask_volume for Volume            6 = 0\n",
+      "      is_masked_volume for Volume          6 = 1\n",
+      "      is_exit_volume for Volume            6 = 0\n",
+      "LIST: mask_list for Volume                 6 = []\n",
+      "LIST: masked_by_list for Volume            6 = [7]\n",
+      "LIST: masked_by_mask_index_list for Volume 6 = [1]\n",
+      "      mask_mode for Volume                 6 = 1\n",
+      "\n",
+      "List overview for mask_outer with box shape made of Mask\n",
+      "LIST: Children for Volume                  7 = []\n",
+      "LIST: Direct_children for Volume           7 = []\n",
+      "LIST: Intersect_check_list for Volume      7 = []\n",
+      "LIST: Mask_intersect_list for Volume       7 = []\n",
+      "LIST: Destinations_list for Volume         7 = [0,1,2]\n",
+      "LIST: Reduced_destinations_list for Volume 7 = [1,2]\n",
+      "LIST: Next_volume_list for Volume          7 = [0,1,2]\n",
+      "      Is_vacuum for Volume                 7 = 0\n",
+      "      is_mask_volume for Volume            7 = 1\n",
+      "      is_masked_volume for Volume          7 = 0\n",
+      "      is_exit_volume for Volume            7 = 0\n",
+      "LIST: mask_list for Volume                 7 = [5,6]\n",
+      "LIST: masked_by_list for Volume            7 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
+      "      mask_mode for Volume                 7 = 1\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_zx_I=1149.44 logger_space_zx_ERR=3.92972 logger_space_zx_N=95537 \"logger_zx.dat\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space_zx\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plotter.make_sub_plot(data)"
    ]
@@ -269,7 +1158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 12,
    "metadata": {
     "tags": [
      "scroll-output"
@@ -277,14 +1166,233 @@
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'instr' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-4-95b89adce35b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minstrument\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMcStas_instr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"python_tutorial\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"run_folder\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mAl_inc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstrument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_component\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Al_inc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Incoherent_process\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mAl_inc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msigma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0082\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mAl_inc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_cell_volume\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m66.4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'instr' is not defined"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/python_tutorial_data_17\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:12065:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:12065:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "13 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/python_tutorial_data_17\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 1\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 0.347892 \n",
+      "number of processes [0]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 1\n",
+      "\n",
+      "name of geometry    [0]: box \n",
+      "component index     [0]: 5 \n",
+      "Volume.name         [0]: box \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.347892 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: box \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: box_mask \n",
+      "component index     [1]: 6 \n",
+      "Volume.name         [1]: box_mask \n",
+      "Volume.geometry.shape                [1]: box \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [0.707107 0.000000 -0.707107] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [-0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.707107 -0.000000 0.707107] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: cylinder_mask \n",
+      "component index     [2]: 7 \n",
+      "Volume.name         [2]: cylinder_mask \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: 0.000000 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.105000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.120000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 4\n",
+      "number_of_masks = 2\n",
+      "number_of_masked_volumes = 1\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           0 = [1,2,3]\n",
+      "LIST: Intersect_check_list for Volume      0 = [2,3]\n",
+      "LIST: Mask_intersect_list for Volume       0 = [1]\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [1]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for box with box shape made of Al\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = [2,3]\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = []\n",
+      "LIST: Next_volume_list for Volume          1 = [0]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 1\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = [2,3]\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = [0,1]\n",
+      "      mask_mode for Volume                 1 = 1\n",
+      "\n",
+      "List overview for box_mask with box shape made of Mask\n",
+      "LIST: Children for Volume                  2 = []\n",
+      "LIST: Direct_children for Volume           2 = []\n",
+      "LIST: Intersect_check_list for Volume      2 = []\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = []\n",
+      "LIST: Next_volume_list for Volume          2 = [0]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 1\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = [1]\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 1\n",
+      "\n",
+      "List overview for cylinder_mask with cylinder shape made of Mask\n",
+      "LIST: Children for Volume                  3 = []\n",
+      "LIST: Direct_children for Volume           3 = []\n",
+      "LIST: Intersect_check_list for Volume      3 = []\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = []\n",
+      "LIST: Next_volume_list for Volume          3 = [0]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 1\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = [1]\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 1\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_zx_I=3973.85 logger_space_zx_ERR=10.331 logger_space_zx_N=158346 \"logger_zx.dat\"\n",
+      "loading system configuration\n",
+      "\n"
      ]
     }
    ],
@@ -355,15 +1463,34 @@
     "master = instrument.add_component(\"master\", \"Union_master\")\n",
     "\n",
     "\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space_zx\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plotter.make_sub_plot(data)"
    ]
diff --git a/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb b/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
index 67fd914e..38db0fd2 100644
--- a/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
+++ b/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,21 +38,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'instrument' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-e9c217e34638>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mAl_inc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstrument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_component\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Al_inc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Incoherent_process\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mAl_inc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msigma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0082\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mAl_inc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_cell_volume\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m66.4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mAl_pow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstrument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_component\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Al_pow\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Powder_process\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'instrument' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
     "Al_inc.sigma = 0.0082\n",
@@ -145,13 +133,343 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_external_91\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:14810:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:14810:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:15053:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:15053:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "14 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_external_91\n",
+      "\n",
+      "          Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 1\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 0.347892 \n",
+      "number of processes [0]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 1\n",
+      "\n",
+      "name of geometry    [0]: sample_volume \n",
+      "component index     [0]: 5 \n",
+      "Volume.name         [0]: sample_volume \n",
+      "Volume.p_physics.is_vacuum           [0]: 1 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.000000 \n",
+      "Volume.p_physics.number of processes [0]: 0 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: sample_container \n",
+      "component index     [1]: 6 \n",
+      "Volume.name         [1]: sample_container \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: sample_container_lid \n",
+      "component index     [2]: 7 \n",
+      "Volume.name         [2]: sample_container_lid \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
+      "Volume.p_physics.number of processes [2]: 2 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: 0.015500 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: cryostat_wall \n",
+      "component index     [3]: 8 \n",
+      "Volume.name         [3]: cryostat_wall \n",
+      "Volume.p_physics.is_vacuum           [3]: 0 \n",
+      "Volume.p_physics.my_absorption       [3]: 0.347892 \n",
+      "Volume.p_physics.number of processes [3]: 2 \n",
+      "Volume.geometry.shape                [3]: cylinder \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: 0.000000 \n",
+      "Volume.geometry.center.z             [3]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
+      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [4]: cryostat_wall_vacuum \n",
+      "component index     [4]: 9 \n",
+      "Volume.name         [4]: cryostat_wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [4]: 1 \n",
+      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
+      "Volume.p_physics.number of processes [4]: 0 \n",
+      "Volume.geometry.shape                [4]: cylinder \n",
+      "Volume.geometry.center.x             [4]: 0.000000 \n",
+      "Volume.geometry.center.y             [4]: 0.000000 \n",
+      "Volume.geometry.center.z             [4]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
+      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 6\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4,5]\n",
+      "LIST: Direct_children for Volume           0 = [4]\n",
+      "LIST: Intersect_check_list for Volume      0 = [4]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [4]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for sample_volume with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = []\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [2,3]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
+      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
+      "      Is_vacuum for Volume                 1 = 1\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for sample_container with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  2 = [1]\n",
+      "LIST: Direct_children for Volume           2 = [1]\n",
+      "LIST: Intersect_check_list for Volume      2 = [1]\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [3,5]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [5]\n",
+      "LIST: Next_volume_list for Volume          2 = [3,5,1]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for sample_container_lid with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  3 = []\n",
+      "LIST: Direct_children for Volume           3 = []\n",
+      "LIST: Intersect_check_list for Volume      3 = [2]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [5]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = [5]\n",
+      "LIST: Next_volume_list for Volume          3 = [5,2]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "List overview for cryostat_wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  4 = [1,2,3,5]\n",
+      "LIST: Direct_children for Volume           4 = [5]\n",
+      "LIST: Intersect_check_list for Volume      4 = [5]\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = []\n",
+      "LIST: Next_volume_list for Volume          4 = [0,5]\n",
+      "      Is_vacuum for Volume                 4 = 0\n",
+      "      is_mask_volume for Volume            4 = 0\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = []\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 0\n",
+      "\n",
+      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  5 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           5 = [2,3]\n",
+      "LIST: Intersect_check_list for Volume      5 = [2,3]\n",
+      "LIST: Mask_intersect_list for Volume       5 = []\n",
+      "LIST: Destinations_list for Volume         5 = [4]\n",
+      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
+      "LIST: Next_volume_list for Volume          5 = [4,2,3]\n",
+      "      Is_vacuum for Volume                 5 = 1\n",
+      "      is_mask_volume for Volume            5 = 0\n",
+      "      is_masked_volume for Volume          5 = 0\n",
+      "      is_exit_volume for Volume            5 = 0\n",
+      "LIST: mask_list for Volume                 5 = []\n",
+      "LIST: masked_by_list for Volume            5 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
+      "      mask_mode for Volume                 5 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_zx_I=3316.64 logger_space_zx_ERR=8.14809 logger_space_zx_N=173630 \"logger_zx.dat\"\n",
+      "Detector: logger_space_zy_I=3316.64 logger_space_zy_ERR=8.14809 logger_space_zy_N=173630 \"logger_zy.dat\"\n",
+      "Detector: banana_I=321.255 banana_ERR=2.53184 banana_N=16907 \"banana_1647874009.th\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.set_parameters(wavelength=3.0)\n",
     "instrument.settings(ncount=3E6, output_path=\"data_folder/union_external\")\n",
@@ -161,9 +479,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space_zx\n",
+      "Plotting data with name logger_space_zy\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name banana\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "functions.name_plot_options(\"logger_space_zx\", data_empty, log=True, orders_of_mag=4)\n",
     "functions.name_plot_options(\"logger_space_zy\", data_empty, log=True, orders_of_mag=4)\n",
@@ -181,7 +539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -190,22 +548,392 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_external_92\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:14810:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:14810:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:15053:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:15053:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "14 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_external_92\n",
+      "\n",
+      "          Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 1\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 0.347892 \n",
+      "number of processes [0]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 1\n",
+      "\n",
+      "name of geometry    [0]: sample_volume \n",
+      "component index     [0]: 5 \n",
+      "Volume.name         [0]: sample_volume \n",
+      "Volume.p_physics.is_vacuum           [0]: 1 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.000000 \n",
+      "Volume.p_physics.number of processes [0]: 0 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: sample_container \n",
+      "component index     [1]: 6 \n",
+      "Volume.name         [1]: sample_container \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: sample_container_lid \n",
+      "component index     [2]: 7 \n",
+      "Volume.name         [2]: sample_container_lid \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
+      "Volume.p_physics.number of processes [2]: 2 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: 0.015500 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: cryostat_wall \n",
+      "component index     [3]: 8 \n",
+      "Volume.name         [3]: cryostat_wall \n",
+      "Volume.p_physics.is_vacuum           [3]: 0 \n",
+      "Volume.p_physics.my_absorption       [3]: 0.347892 \n",
+      "Volume.p_physics.number of processes [3]: 2 \n",
+      "Volume.geometry.shape                [3]: cylinder \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: 0.000000 \n",
+      "Volume.geometry.center.z             [3]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
+      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [4]: cryostat_wall_vacuum \n",
+      "component index     [4]: 9 \n",
+      "Volume.name         [4]: cryostat_wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [4]: 1 \n",
+      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
+      "Volume.p_physics.number of processes [4]: 0 \n",
+      "Volume.geometry.shape                [4]: cylinder \n",
+      "Volume.geometry.center.x             [4]: 0.000000 \n",
+      "Volume.geometry.center.y             [4]: 0.000000 \n",
+      "Volume.geometry.center.z             [4]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
+      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 6\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4,5]\n",
+      "LIST: Direct_children for Volume           0 = [4]\n",
+      "LIST: Intersect_check_list for Volume      0 = [4]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [4]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for sample_volume with shape cylinder\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = []\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [2,3]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
+      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
+      "      Is_vacuum for Volume                 1 = 1\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 1\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for sample_container with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  2 = [1]\n",
+      "LIST: Direct_children for Volume           2 = [1]\n",
+      "LIST: Intersect_check_list for Volume      2 = [1]\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [3,5]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [5]\n",
+      "LIST: Next_volume_list for Volume          2 = [3,5,1]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for sample_container_lid with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  3 = []\n",
+      "LIST: Direct_children for Volume           3 = []\n",
+      "LIST: Intersect_check_list for Volume      3 = [2]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [5]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = [5]\n",
+      "LIST: Next_volume_list for Volume          3 = [5,2]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "List overview for cryostat_wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  4 = [1,2,3,5]\n",
+      "LIST: Direct_children for Volume           4 = [5]\n",
+      "LIST: Intersect_check_list for Volume      4 = [5]\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = []\n",
+      "LIST: Next_volume_list for Volume          4 = [0,5]\n",
+      "      Is_vacuum for Volume                 4 = 0\n",
+      "      is_mask_volume for Volume            4 = 0\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = []\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 0\n",
+      "\n",
+      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  5 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           5 = [2,3]\n",
+      "LIST: Intersect_check_list for Volume      5 = [2,3]\n",
+      "LIST: Mask_intersect_list for Volume       5 = []\n",
+      "LIST: Destinations_list for Volume         5 = [4]\n",
+      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
+      "LIST: Next_volume_list for Volume          5 = [4,2,3]\n",
+      "      Is_vacuum for Volume                 5 = 1\n",
+      "      is_mask_volume for Volume            5 = 0\n",
+      "      is_masked_volume for Volume          5 = 0\n",
+      "      is_exit_volume for Volume            5 = 0\n",
+      "LIST: mask_list for Volume                 5 = []\n",
+      "LIST: masked_by_list for Volume            5 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
+      "      mask_mode for Volume                 5 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_zx_I=2171.73 logger_space_zx_ERR=6.59926 logger_space_zx_N=113410 \"logger_zx.dat\"\n",
+      "Detector: logger_space_zy_I=2171.73 logger_space_zy_ERR=6.59926 logger_space_zy_N=113410 \"logger_zy.dat\"\n",
+      "Detector: banana_I=214.568 banana_ERR=2.07006 banana_N=11274 \"banana_1647874019.th\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space_zx\n",
+      "Plotting data with name logger_space_zy\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name banana\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
     "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
@@ -223,7 +951,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -244,22 +972,401 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_external_93\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:15340:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:15340:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:15583:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:15583:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "14 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_external_93\n",
+      "\n",
+      "          Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 1\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 0.347892 \n",
+      "number of processes [0]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 1\n",
+      "\n",
+      "name of geometry    [0]: sample_volume \n",
+      "component index     [0]: 5 \n",
+      "Volume.name         [0]: sample_volume \n",
+      "Volume.p_physics.is_vacuum           [0]: 1 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.000000 \n",
+      "Volume.p_physics.number of processes [0]: 0 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: sample_container \n",
+      "component index     [1]: 6 \n",
+      "Volume.name         [1]: sample_container \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: sample_container_lid \n",
+      "component index     [2]: 7 \n",
+      "Volume.name         [2]: sample_container_lid \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
+      "Volume.p_physics.number of processes [2]: 2 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: 0.015500 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: cryostat_wall \n",
+      "component index     [3]: 8 \n",
+      "Volume.name         [3]: cryostat_wall \n",
+      "Volume.p_physics.is_vacuum           [3]: 0 \n",
+      "Volume.p_physics.my_absorption       [3]: 0.347892 \n",
+      "Volume.p_physics.number of processes [3]: 2 \n",
+      "Volume.geometry.shape                [3]: cylinder \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: 0.000000 \n",
+      "Volume.geometry.center.z             [3]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
+      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [4]: cryostat_wall_vacuum \n",
+      "component index     [4]: 9 \n",
+      "Volume.name         [4]: cryostat_wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [4]: 1 \n",
+      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
+      "Volume.p_physics.number of processes [4]: 0 \n",
+      "Volume.geometry.shape                [4]: cylinder \n",
+      "Volume.geometry.center.x             [4]: 0.000000 \n",
+      "Volume.geometry.center.y             [4]: 0.000000 \n",
+      "Volume.geometry.center.z             [4]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
+      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 6\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4,5]\n",
+      "LIST: Direct_children for Volume           0 = [4]\n",
+      "LIST: Intersect_check_list for Volume      0 = [4]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [4]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for sample_volume with shape cylinder\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = []\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [2,3]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
+      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
+      "      Is_vacuum for Volume                 1 = 1\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 1\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for sample_container with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  2 = [1]\n",
+      "LIST: Direct_children for Volume           2 = [1]\n",
+      "LIST: Intersect_check_list for Volume      2 = [1]\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [3,5]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [5]\n",
+      "LIST: Next_volume_list for Volume          2 = [3,5,1]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for sample_container_lid with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  3 = []\n",
+      "LIST: Direct_children for Volume           3 = []\n",
+      "LIST: Intersect_check_list for Volume      3 = [2]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [5]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = [5]\n",
+      "LIST: Next_volume_list for Volume          3 = [5,2]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "List overview for cryostat_wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  4 = [1,2,3,5]\n",
+      "LIST: Direct_children for Volume           4 = [5]\n",
+      "LIST: Intersect_check_list for Volume      4 = [5]\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = []\n",
+      "LIST: Next_volume_list for Volume          4 = [0,5]\n",
+      "      Is_vacuum for Volume                 4 = 0\n",
+      "      is_mask_volume for Volume            4 = 0\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = []\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 0\n",
+      "\n",
+      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  5 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           5 = [2,3]\n",
+      "LIST: Intersect_check_list for Volume      5 = [2,3]\n",
+      "LIST: Mask_intersect_list for Volume       5 = []\n",
+      "LIST: Destinations_list for Volume         5 = [4]\n",
+      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
+      "LIST: Next_volume_list for Volume          5 = [4,2,3]\n",
+      "      Is_vacuum for Volume                 5 = 1\n",
+      "      is_mask_volume for Volume            5 = 0\n",
+      "      is_masked_volume for Volume          5 = 0\n",
+      "      is_exit_volume for Volume            5 = 0\n",
+      "LIST: mask_list for Volume                 5 = []\n",
+      "LIST: masked_by_list for Volume            5 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
+      "      mask_mode for Volume                 5 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "Detector: logger_space_zx_I=2189.03 logger_space_zx_ERR=6.62732 logger_space_zx_N=114250 \"logger_zx.dat\"\n",
+      "Detector: logger_space_zy_I=2189.03 logger_space_zy_ERR=6.62732 logger_space_zy_N=114250 \"logger_zy.dat\"\n",
+      "Detector: banana_I=1692 banana_ERR=5.14373 banana_N=323257 \"banana_1647874029.th\"\n",
+      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
+      "    SPLIT 37 COMPONENT sample=PowderN(...)\n",
+      "  in the instrument description python_tutorial.instr.\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "data_wrong = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space_zx\n",
+      "Plotting data with name logger_space_zy\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name banana\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "functions.name_plot_options(\"logger_space_zx\", data_wrong, log=True, orders_of_mag=4)\n",
     "functions.name_plot_options(\"logger_space_zy\", data_wrong, log=True, orders_of_mag=4)\n",
@@ -289,7 +1396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -311,22 +1418,591 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_external_94\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:15972:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:15972:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:16215:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:16215:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "17 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_external_94\n",
+      "\n",
+      "          Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 1\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 0.347892 \n",
+      "number of processes [0]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 1\n",
+      "\n",
+      "name of geometry    [0]: sample_volume \n",
+      "component index     [0]: 5 \n",
+      "Volume.name         [0]: sample_volume \n",
+      "Volume.p_physics.is_vacuum           [0]: 1 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.000000 \n",
+      "Volume.p_physics.number of processes [0]: 0 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: sample_container \n",
+      "component index     [1]: 6 \n",
+      "Volume.name         [1]: sample_container \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: sample_container_lid \n",
+      "component index     [2]: 7 \n",
+      "Volume.name         [2]: sample_container_lid \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
+      "Volume.p_physics.number of processes [2]: 2 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: 0.015500 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: cryostat_wall \n",
+      "component index     [3]: 8 \n",
+      "Volume.name         [3]: cryostat_wall \n",
+      "Volume.p_physics.is_vacuum           [3]: 0 \n",
+      "Volume.p_physics.my_absorption       [3]: 0.347892 \n",
+      "Volume.p_physics.number of processes [3]: 2 \n",
+      "Volume.geometry.shape                [3]: cylinder \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: 0.000000 \n",
+      "Volume.geometry.center.z             [3]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
+      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [4]: cryostat_wall_vacuum \n",
+      "component index     [4]: 9 \n",
+      "Volume.name         [4]: cryostat_wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [4]: 1 \n",
+      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
+      "Volume.p_physics.number of processes [4]: 0 \n",
+      "Volume.geometry.shape                [4]: cylinder \n",
+      "Volume.geometry.center.x             [4]: 0.000000 \n",
+      "Volume.geometry.center.y             [4]: 0.000000 \n",
+      "Volume.geometry.center.z             [4]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
+      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 6\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4,5]\n",
+      "LIST: Direct_children for Volume           0 = [4]\n",
+      "LIST: Intersect_check_list for Volume      0 = [4]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [4]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for sample_volume with shape cylinder\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = []\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [2,3]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
+      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
+      "      Is_vacuum for Volume                 1 = 1\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 1\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for sample_container with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  2 = [1]\n",
+      "LIST: Direct_children for Volume           2 = [1]\n",
+      "LIST: Intersect_check_list for Volume      2 = [1]\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [3,5]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [5]\n",
+      "LIST: Next_volume_list for Volume          2 = [3,5,1]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for sample_container_lid with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  3 = []\n",
+      "LIST: Direct_children for Volume           3 = []\n",
+      "LIST: Intersect_check_list for Volume      3 = [2]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [5]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = [5]\n",
+      "LIST: Next_volume_list for Volume          3 = [5,2]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "List overview for cryostat_wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  4 = [1,2,3,5]\n",
+      "LIST: Direct_children for Volume           4 = [5]\n",
+      "LIST: Intersect_check_list for Volume      4 = [5]\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = []\n",
+      "LIST: Next_volume_list for Volume          4 = [0,5]\n",
+      "      Is_vacuum for Volume                 4 = 0\n",
+      "      is_mask_volume for Volume            4 = 0\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = []\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 0\n",
+      "\n",
+      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  5 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           5 = [2,3]\n",
+      "LIST: Intersect_check_list for Volume      5 = [2,3]\n",
+      "LIST: Mask_intersect_list for Volume       5 = []\n",
+      "LIST: Destinations_list for Volume         5 = [4]\n",
+      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
+      "LIST: Next_volume_list for Volume          5 = [4,2,3]\n",
+      "      Is_vacuum for Volume                 5 = 1\n",
+      "      is_mask_volume for Volume            5 = 0\n",
+      "      is_masked_volume for Volume          5 = 0\n",
+      "      is_exit_volume for Volume            5 = 0\n",
+      "LIST: mask_list for Volume                 5 = []\n",
+      "LIST: masked_by_list for Volume            5 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
+      "      mask_mode for Volume                 5 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 1\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 0.347892 \n",
+      "number of processes [0]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 1\n",
+      "\n",
+      "name of geometry    [1]: sample_container \n",
+      "component index     [1]: 6 \n",
+      "Volume.name         [1]: sample_container \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: sample_container_lid \n",
+      "component index     [2]: 7 \n",
+      "Volume.name         [2]: sample_container_lid \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
+      "Volume.p_physics.number of processes [2]: 2 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: 0.015500 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: cryostat_wall \n",
+      "component index     [3]: 8 \n",
+      "Volume.name         [3]: cryostat_wall \n",
+      "Volume.p_physics.is_vacuum           [3]: 0 \n",
+      "Volume.p_physics.my_absorption       [3]: 0.347892 \n",
+      "Volume.p_physics.number of processes [3]: 2 \n",
+      "Volume.geometry.shape                [3]: cylinder \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: 0.000000 \n",
+      "Volume.geometry.center.z             [3]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
+      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [4]: cryostat_wall_vacuum \n",
+      "component index     [4]: 9 \n",
+      "Volume.name         [4]: cryostat_wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [4]: 1 \n",
+      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
+      "Volume.p_physics.number of processes [4]: 0 \n",
+      "Volume.geometry.shape                [4]: cylinder \n",
+      "Volume.geometry.center.x             [4]: 0.000000 \n",
+      "Volume.geometry.center.y             [4]: 0.000000 \n",
+      "Volume.geometry.center.z             [4]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
+      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 5\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4]\n",
+      "LIST: Direct_children for Volume           0 = [3]\n",
+      "LIST: Intersect_check_list for Volume      0 = [3]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [3]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for sample_container with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = []\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [2,4]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = [4]\n",
+      "LIST: Next_volume_list for Volume          1 = [2,4]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for sample_container_lid with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  2 = []\n",
+      "LIST: Direct_children for Volume           2 = []\n",
+      "LIST: Intersect_check_list for Volume      2 = [1]\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [4]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [4]\n",
+      "LIST: Next_volume_list for Volume          2 = [4,1]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for cryostat_wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  3 = [1,2,4]\n",
+      "LIST: Direct_children for Volume           3 = [4]\n",
+      "LIST: Intersect_check_list for Volume      3 = [4]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = []\n",
+      "LIST: Next_volume_list for Volume          3 = [0,4]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  4 = [1,2]\n",
+      "LIST: Direct_children for Volume           4 = [1,2]\n",
+      "LIST: Intersect_check_list for Volume      4 = [1,2]\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [3]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = [3]\n",
+      "LIST: Next_volume_list for Volume          4 = [3,1,2]\n",
+      "      Is_vacuum for Volume                 4 = 1\n",
+      "      is_mask_volume for Volume            4 = 0\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = []\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 0\n",
+      "\n",
+      "Union_master component master_after_sample initialized sucessfully\n",
+      "WARNING: Ray started in volume ''sample_container'' rather than the surrounding vacuum in component master_after_sample. This warning is only shown once.\n",
+      "Detector: logger_space_zx_I=3807.39 logger_space_zx_ERR=13.328 logger_space_zx_N=301169 \"logger_zx.dat\"\n",
+      "Detector: logger_space_zy_I=3807.39 logger_space_zy_ERR=13.328 logger_space_zy_N=301169 \"logger_zy.dat\"\n",
+      "Detector: banana_I=1664.43 banana_ERR=6.00861 banana_N=306860 \"banana_1647874040.th\"\n",
+      "WARNING: No Union_master component defined after Union volume named sample_volume_replace, this components did not affect the simulation in any way.\n",
+      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
+      "    SPLIT 37 COMPONENT sample=PowderN(...)\n",
+      "  in the instrument description python_tutorial.instr.\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space_zx\n",
+      "Plotting data with name logger_space_zy\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name banana\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
     "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
@@ -352,9 +2028,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f88311129a0>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "banana_empty = functions.name_search(\"banana\", data_empty)\n",
     "banana_wrong = functions.name_search(\"banana\", data_wrong)\n",
diff --git a/docs/source/tutorial/Union_tutorial_7_Tagging_history.ipynb b/docs/source/tutorial/Union_tutorial_7_Tagging_history.ipynb
index 3e6a675e..e98c43da 100644
--- a/docs/source/tutorial/Union_tutorial_7_Tagging_history.ipynb
+++ b/docs/source/tutorial/Union_tutorial_7_Tagging_history.ipynb
@@ -183,7 +183,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "with open(\"run_folder/Union_history.dat\") as file:\n",
+    "with open(\"run_folder/union_history.dat\", \"r\", errors=\"ignore\") as file:\n",
     "    instrument_string = file.read()\n",
     "    print(instrument_string)"
    ]
@@ -273,7 +273,7 @@
    },
    "outputs": [],
    "source": [
-    "with open(\"run_folder/union_history.dat\") as file:\n",
+    "with open(\"run_folder/union_history.dat\", \"r\", errors=\"ignore\") as file:\n",
     "    instrument_string = file.read()\n",
     "    print(instrument_string)"
    ]
diff --git a/docs/source/user_guide/parameters_and_variables.ipynb b/docs/source/user_guide/parameters_and_variables.ipynb
index d1cd5b62..e6e1db67 100644
--- a/docs/source/user_guide/parameters_and_variables.ipynb
+++ b/docs/source/user_guide/parameters_and_variables.ipynb
@@ -20,19 +20,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "acceptable-emperor",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Parameter named: 'first_par' without set value.\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from mcstasscript.interface import instr, functions, plotter\n",
     "instrument = instr.McStas_instr(\"parameters_and_variables\")\n",
@@ -51,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "adverse-default",
    "metadata": {},
    "outputs": [],
@@ -70,21 +61,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "cellular-server",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "       first_par           // My first parameter!\n",
-      "double double_par          // Typed double\n",
-      "int    int_par             // Typed int\n",
-      "string string_par          // Typed string\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "double_par = instrument.add_parameter(\"double\", \"double_par\", comment=\"Typed double\")\n",
     "int_par = instrument.add_parameter(\"int\", \"int_par\", comment=\"Typed int\")\n",
@@ -104,22 +84,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "rental-indication",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Parameter named: 'par_with_value' with value: 5.2\n",
-       " Added value at creation"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.add_parameter(\"par_with_value\", value=5.2, comment=\"Added value at creation\")"
    ]
@@ -134,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "narrow-guard",
    "metadata": {},
    "outputs": [],
@@ -145,22 +113,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "thirty-sunrise",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "       first_par               // My first parameter!\n",
-      "double double_par      = 1.2   // Typed double\n",
-      "int    int_par         = 3     // Typed int\n",
-      "string string_par              // Typed string\n",
-      "       par_with_value  = 5.2   // Added value at creation\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_parameters()"
    ]
@@ -178,23 +134,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "portuguese-circuit",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Parameter named: 'double_par' with value: 1.2\n",
-      " Typed double\n",
-      "  Legal intervals:\n",
-      "    [0,5]\n",
-      "    [7,inf]\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "double_par.add_interval(0, 5, intervals_are_legal=True)\n",
     "double_par.add_interval(7, None, intervals_are_legal=True)\n",
@@ -211,23 +154,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "unknown-edition",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Parameter named: 'double_par' with value: 1.8\n",
-      " Typed double\n",
-      "  Legal intervals:\n",
-      "    [0,5]\n",
-      "    [7,inf]\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "double_par.value = 1.8\n",
     "print(double_par)"
@@ -235,23 +165,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "familiar-church",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Parameter named: 'double_par' with value: 10\n",
-      " Typed double\n",
-      "  Legal intervals:\n",
-      "    [0,5]\n",
-      "    [7,inf]\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "try:\n",
     "    double_par.value = 10\n",
@@ -273,29 +190,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "honey-theta",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failed to set value!\n",
-      "Parameter named: 'int_par' with value: 3\n",
-      " Typed int\n",
-      "  Allowed values:\n",
-      "    1\n",
-      "    2\n",
-      "    3\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "int_par.add_option(1, options_are_legal=True)\n",
-    "int_par.add_option(2, options_are_legal=True)\n",
     "int_par.add_option(3, options_are_legal=True)\n",
+    "int_par.add_option(2, options_are_legal=True)\n",
+    "int_par.add_option(1, options_are_legal=True)\n",
     "\n",
     "try:\n",
     "    int_par.value = 10\n",
@@ -324,18 +226,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "confidential-village",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Declare variable: 'declared_var' of type double\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "declared_var = instrument.add_declare_var(\"double\", \"declared_var\", comment=\"Declared variable\")\n",
     "print(declared_var)"
@@ -359,18 +253,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "valid-offense",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Declare variable: 'integer' of type int with value: 5\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "declared_var = instrument.add_declare_var(\"int\", \"integer\", value=5, comment=\"Declared integer\")\n",
     "print(declared_var)"
@@ -386,18 +272,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "polish-integration",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Declare variable: 'declared_array' of type double with value: [2, 3, 4]. Array with length 3\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "var = instrument.add_declare_var(\"double\", \"declared_array\", value=[2, 3, 4], array=3)\n",
     "print(var)"
@@ -413,23 +291,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "after-reply",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "type    variable name   value      array length  \n",
-      "-----------------------------------------------\n",
-      "double  declared_var                             \n",
-      "int     integer         5                        \n",
-      "double  declared_array  [2, 3, 4]  3             \n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_variables()"
    ]
@@ -445,23 +310,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "dangerous-telling",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT source = Source_simple\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92minteger\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92mdouble_par\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mdist\u001b[0m = \u001b[1m\u001b[92m5.0*first_par\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[92mdeclared_array[1]\u001b[0m\u001b[0m [m]\n",
-      "AT [0, 0, 0] ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "source = instrument.add_component(\"source\", \"Source_simple\")\n",
     "source.xwidth = double_par\n",

From 24467e5ef4ed3d60006a6fb96b6b9de342a9dee3 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 22 Mar 2022 08:29:21 +0100
Subject: [PATCH 228/403] Included link to documentation in warning.

---
 mcstasscript/interface/instr.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 303eb58e..abc177c5 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1819,7 +1819,8 @@ def run_full_instrument(self, **kwargs):
         warnings.warn(
             "run_full_instrument will be removed in future version of McStasScript. \n"
             + "Instead supply parameters with set_parameters, set settings with "
-            + "settings and use backengine() to run. See examples in package.")
+            + "settings and use backengine() to run. See examples in package. "
+            + "Documentation now at https://mads-bertelsen.github.io")
 
         if "foldername" in kwargs:
             kwargs["output_path"] = kwargs["foldername"]

From aa11b564d8be89d43b20b76d9a9edd6ecd7f35b3 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 22 Mar 2022 08:34:20 +0100
Subject: [PATCH 229/403] Changed version system so it can be found by
 mcstasscript.

---
 mcstasscript/_version.py |  1 +
 setup.py                 | 17 ++++++++++++++---
 2 files changed, 15 insertions(+), 3 deletions(-)
 create mode 100644 mcstasscript/_version.py

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
new file mode 100644
index 00000000..2b57de33
--- /dev/null
+++ b/mcstasscript/_version.py
@@ -0,0 +1 @@
+__version__ = "0.0.38"
diff --git a/setup.py b/setup.py
index cb570994..3b8c405b 100644
--- a/setup.py
+++ b/setup.py
@@ -1,11 +1,22 @@
-import setuptools
+import os
+from setuptools import setup, find_packages
+from glob import glob
+
+# Get version number
+here = os.path.abspath(os.path.dirname(__file__))
+version_path = os.path.join(here, "guide_bot", "_version.py")
+version = {}
+with open(version_path) as fp:
+    exec(fp.read(), version)
+found_version = version['__version__']
+print("Version read from file:", found_version)
 
 with open("README.md", "r") as fh:
     long_description = fh.read()
 
 setuptools.setup(
      name='McStasScript',
-     version='0.0.37',
+     version=found_version,
      author="Mads Bertelsen",
      author_email="Mads.Bertelsen@ess.eu",
      description="A python scripting interface for McStas",
@@ -13,7 +24,7 @@
      long_description=long_description,
      long_description_content_type="text/markdown",
      url="https://github.com/PaNOSC-ViNYL/McStasScript",
-     install_requires=['numpy', 'matplotlib', 'PyYAML', "ipywidgets", "libpyvinyl"],
+     install_requires=['numpy', 'matplotlib', 'PyYAML', 'ipywidgets', 'libpyvinyl', 'pickle'],
      packages=setuptools.find_packages(),
      classifiers=[
          "Programming Language :: Python :: 3",

From 257d557e02af269525a573ca0679f8552c3347fa Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 22 Mar 2022 08:39:19 +0100
Subject: [PATCH 230/403] First version of mcstasscript init.

---
 mcstasscript/__init__.py | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/mcstasscript/__init__.py b/mcstasscript/__init__.py
index e69de29b..bb892736 100644
--- a/mcstasscript/__init__.py
+++ b/mcstasscript/__init__.py
@@ -0,0 +1,19 @@
+# Version number
+from ._version import __version__
+
+from .interface.instr import McStas_instr
+from .interface.instr import McXtrace_instr
+
+from .interface.functions import load_data
+from .interface.functions import load_metadata
+from .interface.functions import load_monitor
+from .interface.functions import name_plot_options
+from .interface.functions import name_search
+
+from .interface.plotter import interface
+from .interface.plotter import make_animation
+from .interface.plotter import make_plot
+from .interface.plotter import make_sub_plot
+
+from .interface.reader import McStas_file
+

From 867c232f5c754f2f83a5df4d0f3fc2bd81a713fb Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 22 Mar 2022 08:41:13 +0100
Subject: [PATCH 231/403] Added configurator to __init__

---
 mcstasscript/__init__.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/mcstasscript/__init__.py b/mcstasscript/__init__.py
index bb892736..0cecb747 100644
--- a/mcstasscript/__init__.py
+++ b/mcstasscript/__init__.py
@@ -9,6 +9,7 @@
 from .interface.functions import load_monitor
 from .interface.functions import name_plot_options
 from .interface.functions import name_search
+from .interface.functions import Configurator
 
 from .interface.plotter import interface
 from .interface.plotter import make_animation

From 64c36ae6176cead347088535c8b3d714f9852707 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 22 Mar 2022 10:07:05 +0100
Subject: [PATCH 232/403] Update to documentation, now uses simple import.

---
 docs/source/getting_started/quick_start.ipynb |   10 +-
 .../McStasScript_tutorial_1_the_basics.ipynb  |  511 +---
 .../McStasScript_tutorial_2_SPLIT.ipynb       |   20 +-
 ...tasScript_tutorial_3_EXTEND_and_WHEN.ipynb |    8 +-
 .../McStasScript_tutorial_4_JUMP.ipynb        |    6 +-
 ...n_tutorial_1_processes_and_materials.ipynb |  768 +----
 .../tutorial/Union_tutorial_2_geometry.ipynb  |  916 +-----
 .../tutorial/Union_tutorial_3_loggers.ipynb   | 2625 +----------------
 .../Union_tutorial_4_conditionals.ipynb       |   75 +-
 .../tutorial/Union_tutorial_5_masks.ipynb     | 1211 +-------
 ...ial_6_Exit_and_number_of_activations.ipynb | 1802 +----------
 .../Union_tutorial_7_Tagging_history.ipynb    |   16 +-
 docs/source/user_guide/component_object.ipynb |  382 +--
 docs/source/user_guide/data.ipynb             |  308 +-
 docs/source/user_guide/functions.ipynb        |  209 ++
 .../source/user_guide/instrument_object.ipynb |  511 +---
 .../source/user_guide/instrument_reader.ipynb |   82 +-
 docs/source/user_guide/laue_example.py        |    8 +-
 .../user_guide/parameters_and_variables.ipynb |    4 +-
 docs/source/user_guide/plotting.ipynb         |  151 +-
 20 files changed, 701 insertions(+), 8922 deletions(-)
 create mode 100644 docs/source/user_guide/functions.ipynb

diff --git a/docs/source/getting_started/quick_start.ipynb b/docs/source/getting_started/quick_start.ipynb
index ff1df342..9c2318bb 100644
--- a/docs/source/getting_started/quick_start.ipynb
+++ b/docs/source/getting_started/quick_start.ipynb
@@ -9,7 +9,7 @@
     "This section is a quick start guide that will show the basic functionality of McStasScript. It assumes the user is already familiar with McStas itself, if this is not the case, it is recommended to start with the tutorial which can serve as an introduction to both McStas and McStasScript.\n",
     "\n",
     "## Importing the package\n",
-    "The package includes an interface folder that contains the modules which the  user is meant to use directly."
+    "McStasScript needs to be imported into the users python environment."
    ]
   },
   {
@@ -19,7 +19,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
@@ -38,7 +38,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"first_instrument\")"
+    "instrument = ms.McStas_instr(\"first_instrument\")"
    ]
   },
   {
@@ -256,7 +256,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data, log=True)"
+    "ms.make_sub_plot(data, log=True)"
    ]
   },
   {
@@ -293,7 +293,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "PSD_data = functions.name_search(\"PSD\", data)"
+    "PSD_data = ms.name_search(\"PSD\", data)"
    ]
   },
   {
diff --git a/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb b/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
index 5aeb1faa..282cbda0 100644
--- a/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
+++ b/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
@@ -48,16 +48,16 @@
    "metadata": {},
    "source": [
     "### Import the McStasScript package\n",
-    "The McStasScript modules intended for normal use is located in the interface submodule, and one usually imports the necessary modules from there."
+    "McStasScript needs to be imported into the users python environment, you can abbreviate the name for easier access."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
@@ -74,11 +74,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "configurator = functions.Configurator()\n",
+    "configurator = ms.Configurator()\n",
     "configurator.set_mcrun_path(\"/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\")\n",
     "configurator.set_mcstas_path(\"/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\")"
    ]
@@ -95,11 +95,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
    ]
   },
   {
@@ -112,75 +112,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are the available component categories:\n",
-      " contrib\n",
-      " misc\n",
-      " monitors\n",
-      " obsolete\n",
-      " optics\n",
-      " samples\n",
-      " sources\n",
-      " union\n",
-      "Call show_components(category_name) to display\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_components()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are all components in the sources category.\n",
-      " Adapt_check     Moderator           Source_Optimizer   Source_gen\n",
-      " ESS_butterfly   Monitor_Optimizer   Source_adapt       Source_simple\n",
-      " ESS_moderator   Source_Maxwell_3    Source_div         \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_components(\"sources\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Source_div ________________________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[4m\u001b[1mxwidth\u001b[0m\u001b[0m [m] // Width of source\n",
-      "\u001b[4m\u001b[1myheight\u001b[0m\u001b[0m [m] // Height of source\n",
-      "\u001b[4m\u001b[1mfocus_aw\u001b[0m\u001b[0m [deg] // FWHM (Gaussian) or maximal (uniform) horz. width divergence\n",
-      "\u001b[4m\u001b[1mfocus_ah\u001b[0m\u001b[0m [deg] // FWHM (Gaussian) or maximal (uniform) vert. height divergence\n",
-      "\u001b[1mE0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Mean energy of neutrons.\n",
-      "\u001b[1mdE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Energy half spread of neutrons.\n",
-      "\u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Ang] // Mean wavelength of neutrons (only relevant for E0=0)\n",
-      "\u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Ang] // Wavelength half spread of neutrons.\n",
-      "\u001b[1mgauss\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [0|1] // Criterion\n",
-      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1/(s cm 2 st energy_unit)] // flux per energy unit, Angs or meV\n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.component_help(\"Source_div\")"
    ]
@@ -202,19 +154,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "source Source_div AT (0, 0, 0) ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "src = instrument.add_component(\"source\", \"Source_div\")\n",
     "instrument.print_components()"
@@ -230,23 +174,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT source = Source_div\n",
-      "  \u001b[1mxwidth\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1myheight\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1mfocus_aw\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1mfocus_ah\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "AT [0, 0, 0] ABSOLUTE\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(src)"
    ]
@@ -261,23 +191,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT source = Source_div\n",
-      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mfocus_aw\u001b[0m = \u001b[1m\u001b[92m1.2\u001b[0m\u001b[0m [deg]\n",
-      "  \u001b[1mfocus_ah\u001b[0m = \u001b[1m\u001b[92m2.3\u001b[0m\u001b[0m [deg]\n",
-      "AT [0, 0, 0] ABSOLUTE\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "src.xwidth = 0.1\n",
     "src.yheight = 0.05\n",
@@ -297,31 +213,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Source_div ________________________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[4m\u001b[1mxwidth\u001b[0m\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m] // Width of source\n",
-      "\u001b[4m\u001b[1myheight\u001b[0m\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m] // Height of source\n",
-      "\u001b[4m\u001b[1mfocus_aw\u001b[0m\u001b[0m = \u001b[1m\u001b[92m1.2\u001b[0m\u001b[0m [deg] // FWHM (Gaussian) or maximal (uniform) horz. width \n",
-      "                        divergence \n",
-      "\u001b[4m\u001b[1mfocus_ah\u001b[0m\u001b[0m = \u001b[1m\u001b[92m2.3\u001b[0m\u001b[0m [deg] // FWHM (Gaussian) or maximal (uniform) vert. height \n",
-      "                        divergence \n",
-      "\u001b[1mE0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Mean energy of neutrons.\n",
-      "\u001b[1mdE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Energy half spread of neutrons.\n",
-      "\u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Ang] // Mean wavelength of neutrons (only relevant for E0=0)\n",
-      "\u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Ang] // Wavelength half spread of neutrons.\n",
-      "\u001b[1mgauss\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [0|1] // Criterion\n",
-      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1/(s cm 2 st energy_unit)] // flux per energy unit, Angs or meV\n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "src.show_parameters()"
    ]
@@ -338,18 +232,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    wavelength  = 5.0  // Wavelength in [Ang]\n",
-      "int order       = 1    // Monochromator order, integer\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "wavelength = instrument.add_parameter(\"wavelength\", value=5.0,\n",
     "                                      comment=\"Wavelength in [Ang]\")\n",
@@ -368,25 +253,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT source = Source_div\n",
-      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mfocus_aw\u001b[0m = \u001b[1m\u001b[92m1.2\u001b[0m\u001b[0m [deg]\n",
-      "  \u001b[1mfocus_ah\u001b[0m = \u001b[1m\u001b[92m2.3\u001b[0m\u001b[0m [deg]\n",
-      "  \u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[92mwavelength\u001b[0m\u001b[0m [Ang]\n",
-      "  \u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[92m0.01*wavelength\u001b[0m\u001b[0m [Ang]\n",
-      "AT [0, 0, 0] ABSOLUTE\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "src.lambda0 = wavelength\n",
     "src.dlambda = \"0.01*wavelength\" # When performing math use a string and the parameter name\n",
@@ -411,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -427,26 +296,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT guide = Guide_gravity\n",
-      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mw2\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mh2\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92m8.0\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92m3.5\u001b[0m\u001b[0m [1]\n",
-      "  \u001b[1mG\u001b[0m = \u001b[1m\u001b[92m-9.82\u001b[0m\u001b[0m [m/s2]\n",
-      "AT [0, 0, 2] RELATIVE source\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "guide.w1 = 0.05\n",
     "guide.w2 = 0.05\n",
@@ -473,7 +325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -498,7 +350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -507,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -520,23 +372,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT mono = Monochromator_flat\n",
-      "  \u001b[1mzwidth\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.08\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mQ\u001b[0m = \u001b[1m\u001b[92mmono_Q\u001b[0m\u001b[0m [1/angstrom]\n",
-      "AT [0, 0, 8.5] RELATIVE guide\n",
-      "ROTATED [0, 'mono_rotation', 0] RELATIVE guide\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(mono)"
    ]
@@ -551,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -569,7 +407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -579,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -590,22 +428,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT sample = PowderN\n",
-      "  \u001b[1mreflections\u001b[0m = \u001b[1m\u001b[92m\"Na2Ca3Al2F14.laz\"\u001b[0m\u001b[0m []\n",
-      "  \u001b[1mradius\u001b[0m = \u001b[1m\u001b[92m0.015\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
-      "AT [0, 0, 1.1] RELATIVE beam_dir\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "sample.print_long()"
    ]
@@ -622,7 +447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -644,7 +469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -653,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -676,25 +501,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "source   Source_div         AT      (0, 0, 0)             ABSOLUTE         \n",
-      "guide    Guide_gravity      AT      (0, 0, 2)             RELATIVE source  \n",
-      "mono     Monochromator_flat AT      (0, 0, 8.5)           RELATIVE guide    \n",
-      "                            ROTATED (0, mono_rotation, 0) RELATIVE guide\n",
-      "beam_dir Arm                AT      (0, 0, 0)             RELATIVE mono     \n",
-      "                            ROTATED (0, mono_rotation, 0) RELATIVE mono\n",
-      "sample   PowderN            AT      (0, 0, 1.1)           RELATIVE beam_dir\n",
-      "banana   Monitor_nD         AT      (0, 0, 0)             RELATIVE sample  \n",
-      "monitor  PSD_monitor        AT      (0, 0, 0.1)           RELATIVE sample  \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.print_components()"
    ]
@@ -724,18 +533,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    wavelength  = 2.8  // Wavelength in [Ang]\n",
-      "int order       = 1    // Monochromator order, integer\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=2.8)\n",
     "instrument.show_parameters()"
@@ -743,24 +543,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Instrument settings:\n",
-      "  ncount:           5.00e+06\n",
-      "  output_path:      data_folder/mcstas_basics\n",
-      "  run_path:         run_folder\n",
-      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
-      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
-      "  executable:       mcrun\n",
-      "  force_compile:    True\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.settings(ncount=5E6, output_path=\"data_folder/mcstas_basics\")\n",
     "instrument.show_settings()"
@@ -768,47 +553,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_basics_7\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS=\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "1 warning generated.\n",
-      "INFO: ===\n",
-      "Warning: 64538 events were removed in Component[7] monitor=PSD_monitor()\n",
-      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/mcstas_basics_7\n",
-      "\n",
-      "      monochromator rotation = 22.4519 deg\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "Detector: banana_I=1.35081e-06 banana_ERR=2.24941e-08 banana_N=10752 \"banana.dat\"\n",
-      "Detector: monitor_I=4.07521e-05 monitor_ERR=2.69874e-07 monitor_N=49651 \"psd.dat\"\n",
-      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
-      "    SPLIT 47 COMPONENT sample=PowderN(...)\n",
-      "  in the instrument description python_tutorial.instr.\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = instrument.backengine() # Perform simulation"
    ]
@@ -823,32 +574,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name banana\n",
-      "Plotting data with name monitor\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data)"
+    "ms.make_sub_plot(data)"
    ]
   },
   {
@@ -870,34 +600,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name banana\n",
-      "Plotting data with name monitor\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "functions.name_plot_options(\"monitor\", data, log=True)\n",
-    "functions.name_plot_options(\"banana\", data, left_lim=90, right_lim=150)\n",
-    "plotter.make_sub_plot(data, fontsize=16)"
+    "ms.name_plot_options(\"monitor\", data, log=True)\n",
+    "ms.name_plot_options(\"banana\", data, left_lim=90, right_lim=150)\n",
+    "ms.make_sub_plot(data, fontsize=16)"
    ]
   },
   {
@@ -910,111 +619,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/********************************************************************************\n",
-      "* \n",
-      "* McStas, neutron ray-tracing package\n",
-      "*         Copyright (C) 1997-2008, All rights reserved\n",
-      "*         Risoe National Laboratory, Roskilde, Denmark\n",
-      "*         Institut Laue Langevin, Grenoble, France\n",
-      "* \n",
-      "* This file was written by McStasScript, which is a \n",
-      "* python based McStas instrument generator written by \n",
-      "* Mads Bertelsen in 2019 while employed at the \n",
-      "* European Spallation Source Data Management and \n",
-      "* Software Center\n",
-      "* \n",
-      "* Instrument python_tutorial\n",
-      "* \n",
-      "* %Identification\n",
-      "* Written by: Python McStas Instrument Generator\n",
-      "* Date: 11:20:52 on January 07, 2022\n",
-      "* Origin: ESS DMSC\n",
-      "* %INSTRUMENT_SITE: Generated_instruments\n",
-      "* \n",
-      "* \n",
-      "* %Parameters\n",
-      "* \n",
-      "* %End \n",
-      "********************************************************************************/\n",
-      "\n",
-      "DEFINE INSTRUMENT python_tutorial (\n",
-      "wavelength = 2.8, // Wavelength in [Ang]\n",
-      "int order = 1 // Monochromator order, integer\n",
-      ")\n",
-      "\n",
-      "DECLARE \n",
-      "%{\n",
-      "double mono_Q = 1.714;\n",
-      "double wavevector;\n",
-      "double mono_rotation;\n",
-      "%}\n",
-      "\n",
-      "INITIALIZE \n",
-      "%{\n",
-      "// Start of initialize for generated python_tutorial\n",
-      "wavevector = 2.0*PI/wavelength;\n",
-      "mono_rotation = asin(mono_Q/(2.0*wavevector))*RAD2DEG;\n",
-      "printf(\"monochromator rotation = %g deg\\n\", mono_rotation);\n",
-      "%}\n",
-      "\n",
-      "TRACE \n",
-      "COMPONENT source = Source_div(\n",
-      " xwidth = 0.1, yheight = 0.05,\n",
-      " focus_aw = 1.2, focus_ah = 2.3,\n",
-      " lambda0 = wavelength, dlambda = 0.01*wavelength)\n",
-      "AT (0,0,0) ABSOLUTE\n",
-      "\n",
-      "COMPONENT guide = Guide_gravity(\n",
-      " w1 = 0.05, h1 = 0.05,\n",
-      " w2 = 0.05, h2 = 0.05,\n",
-      " l = 8, m = 3.5,\n",
-      " G = -9.82)\n",
-      "AT (0,0,2) RELATIVE source\n",
-      "\n",
-      "COMPONENT mono = Monochromator_flat(\n",
-      " zwidth = 0.05, yheight = 0.08,\n",
-      " Q = mono_Q)\n",
-      "AT (0,0,8.5) RELATIVE guide\n",
-      "ROTATED (0,mono_rotation,0) RELATIVE guide\n",
-      "\n",
-      "COMPONENT beam_dir = Arm()\n",
-      "AT (0,0,0) RELATIVE mono\n",
-      "ROTATED (0,mono_rotation,0) RELATIVE mono\n",
-      "\n",
-      "COMPONENT sample = PowderN(\n",
-      " reflections = \"Na2Ca3Al2F14.laz\", radius = 0.015,\n",
-      " yheight = 0.05)\n",
-      "AT (0,0,1.1) RELATIVE beam_dir\n",
-      "\n",
-      "COMPONENT banana = Monitor_nD(\n",
-      " xwidth = 2, yheight = 0.3,\n",
-      " restore_neutron = 1, options = \"theta limits=[5 175] bins=150, banana\",\n",
-      " filename = \"banana.dat\")\n",
-      "AT (0,0,0) RELATIVE sample\n",
-      "\n",
-      "COMPONENT monitor = PSD_monitor(\n",
-      " nx = 100, ny = 100,\n",
-      " filename = \"psd.dat\", xwidth = 0.05,\n",
-      " yheight = 0.08, restore_neutron = 1)\n",
-      "AT (0,0,0.1) RELATIVE sample\n",
-      "\n",
-      "FINALLY \n",
-      "%{\n",
-      "// Start of finally for generated python_tutorial\n",
-      "%}\n",
-      "\n",
-      "END\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "with open(\"run_folder/python_tutorial.instr\") as file:\n",
     "    data = file.read()\n",
diff --git a/docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb b/docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
index b756fa92..670ebaa5 100644
--- a/docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
+++ b/docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
@@ -22,7 +22,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
@@ -31,7 +31,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\",\n",
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\",\n",
     "                                output_path=\"data_folder/mcstas_SPLIT\")"
    ]
   },
@@ -124,7 +124,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data_low)"
+    "ms.make_sub_plot(data_low)"
    ]
   },
   {
@@ -164,7 +164,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data_reasonable)"
+    "ms.make_sub_plot(data_reasonable)"
    ]
   },
   {
@@ -198,7 +198,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data_unreasonable)"
+    "ms.make_sub_plot(data_unreasonable)"
    ]
   },
   {
@@ -231,7 +231,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data_ref)"
+    "ms.make_sub_plot(data_ref)"
    ]
   },
   {
@@ -251,10 +251,10 @@
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "banana_low = functions.name_search(\"banana\", data_low)\n",
-    "banana_reasonable = functions.name_search(\"banana\", data_reasonable)\n",
-    "banana_unreasonable = functions.name_search(\"banana\", data_unreasonable)\n",
-    "banana_ref = functions.name_search(\"banana\", data_ref)\n",
+    "banana_low = ms.name_search(\"banana\", data_low)\n",
+    "banana_reasonable = ms.name_search(\"banana\", data_reasonable)\n",
+    "banana_unreasonable = ms.name_search(\"banana\", data_unreasonable)\n",
+    "banana_ref = ms.name_search(\"banana\", data_ref)\n",
     "\n",
     "plt.figure(figsize=(14,6))\n",
     "plt.errorbar(banana_low.xaxis, banana_low.Intensity, yerr=banana_low.Error, fmt=\"r\")\n",
diff --git a/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb b/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
index 3d2ee193..83254c6b 100644
--- a/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
+++ b/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
@@ -14,7 +14,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
@@ -23,7 +23,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\",\n",
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\",\n",
     "                                output_path=\"data_folder/mcstas_EXTEND_WHEN\")"
    ]
   },
@@ -113,7 +113,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data)"
+    "ms.make_sub_plot(data)"
    ]
   },
   {
@@ -207,7 +207,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter.make_sub_plot(new_data)"
+    "ms.make_sub_plot(new_data)"
    ]
   },
   {
diff --git a/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb b/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb
index 012472e2..a72aafe0 100644
--- a/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb
+++ b/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb
@@ -38,7 +38,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
@@ -47,7 +47,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
    ]
   },
   {
@@ -217,7 +217,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data)"
+    "ms.make_sub_plot(data)"
    ]
   },
   {
diff --git a/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb b/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
index ef9c2b69..ff3e45db 100644
--- a/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
+++ b/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
@@ -21,20 +21,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
    ]
   },
   {
@@ -47,53 +47,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are all components in the Work directory category.\n",
-      "No components found in this category! Available categories:\n",
-      " sources\n",
-      " optics\n",
-      " samples\n",
-      " monitors\n",
-      " misc\n",
-      " contrib\n",
-      " union\n",
-      " obsolete\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_components(\"Work directory\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Incoherent_process ________________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1msigma\u001b[0m = \u001b[1m\u001b[94m5.08\u001b[0m\u001b[0m [barns] // Incoherent scattering cross section\n",
-      "\u001b[1mf_QE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Fraction of quasielastic scattering (rest is elastic)\n",
-      "\u001b[1mgamma\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Lorentzian width of quasielastic broadening (HWHM)\n",
-      "\u001b[1mpacking_factor\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // How dense is the material compared to optimal 0-1\n",
-      "\u001b[1munit_cell_volume\u001b[0m = \u001b[1m\u001b[94m13.8\u001b[0m\u001b[0m [AA^3] // Unit_cell_volume\n",
-      "\u001b[1minteract_fraction\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // How large a part of the scattering events \n",
-      "                                should use this process 0-1 (sum of all processes \n",
-      "                                in material = 1) \n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.component_help(\"Incoherent_process\")"
    ]
@@ -109,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,24 +93,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Union_make_material _______________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1mprocess_string\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated names of physical processes\n",
-      "\u001b[4m\u001b[1mmy_absorption\u001b[0m\u001b[0m [1/m] // Inverse penetration depth from absorption at standard \n",
-      "                       energy \n",
-      "\u001b[1mabsorber\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [0/1] // Control parameter, if set to 1 the material will have \n",
-      "                        no scattering processes \n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.component_help(\"Union_make_material\")"
    ]
@@ -161,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -179,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -197,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -226,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -256,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -292,49 +242,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Union_logger_2D_space _____________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1mtarget_geometry\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated list of geometry names \n",
-      "                                     that will be logged, leave empty for all \n",
-      "                                     volumes (even not defined yet) \n",
-      "\u001b[1mtarget_process\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated names of physical \n",
-      "                                    processes, if volumes are selected, one can \n",
-      "                                    select Union_process names \n",
-      "\u001b[1mD_direction_1\u001b[0m = \u001b[1m\u001b[94m\"x\"\u001b[0m\u001b[0m [string] // Direction for first axis (\"x\", \"y\" or \"z\")\n",
-      "\u001b[1mD1_min\u001b[0m = \u001b[1m\u001b[94m-5.0\u001b[0m\u001b[0m [1] // histogram boundery, min position value for first axis\n",
-      "\u001b[1mD1_max\u001b[0m = \u001b[1m\u001b[94m5.0\u001b[0m\u001b[0m [1] // histogram boundery, max position value for first axis\n",
-      "\u001b[1mn1\u001b[0m = \u001b[1m\u001b[94m90.0\u001b[0m\u001b[0m [1] // number of bins for first axis\n",
-      "\u001b[1mD_direction_2\u001b[0m = \u001b[1m\u001b[94m\"z\"\u001b[0m\u001b[0m [string] // Direction for second axis (\"x\", \"y\" or \"z\")\n",
-      "\u001b[1mD2_min\u001b[0m = \u001b[1m\u001b[94m-5.0\u001b[0m\u001b[0m [1] // histogram boundery, min position value for second axis\n",
-      "\u001b[1mD2_max\u001b[0m = \u001b[1m\u001b[94m5.0\u001b[0m\u001b[0m [1] // histogram boundery, max position value for second axis\n",
-      "\u001b[1mn2\u001b[0m = \u001b[1m\u001b[94m90.0\u001b[0m\u001b[0m [1] // number of bins for second axis\n",
-      "\u001b[1mfilename\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Filename for logging output\n",
-      "\u001b[1morder_total\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time, 0 for \n",
-      "                         all orders \n",
-      "\u001b[1morder_volume\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time in the \n",
-      "                          same geometry \n",
-      "\u001b[1morder_volume_process\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time \n",
-      "                                  in the same geometry, using the same process \n",
-      "\u001b[1mlogger_conditional_extend_index\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // If a conditional is used with \n",
-      "                                              this logger, the result of each \n",
-      "                                              conditional calculation can be made \n",
-      "                                              available in extend as a array called \n",
-      "                                              \"logger_conditional_extend\", and one \n",
-      "                                              would then acces \n",
-      "                                              logger_conditional_extend[n] if \n",
-      "                                              logger_conditional_extend_index is \n",
-      "                                              set to n \n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.component_help(\"Union_logger_2D_space\")"
    ]
@@ -348,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -387,42 +297,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Union_master ______________________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1mverbal\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [bool] // Toogles terminal output describing the defined simulation\n",
-      "\u001b[1mlist_verbal\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Toogles information of all internal lists in \n",
-      "                            intersection network \n",
-      "\u001b[1mfinally_verbal\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Toogles information about cleanup performed in \n",
-      "                               finally section \n",
-      "\u001b[1mallow_inside_start\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Set to 1 to allow rays to start inside the \n",
-      "                                   defined geometry \n",
-      "\u001b[1menable_tagging\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Enable tagging of ray history (geometry, \n",
-      "                               scattering process) \n",
-      "\u001b[1mhistory_limit\u001b[0m = \u001b[1m\u001b[94m300000.0\u001b[0m\u001b[0m [bool] // Limit the number of unique histories that \n",
-      "                                   are saved \n",
-      "\u001b[1menable_conditionals\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [bool] // Use conditionals with this master\n",
-      "\u001b[1minherit_number_of_scattering_events\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Inherit the number of \n",
-      "                                                    scattering events from last \n",
-      "                                                    master \n",
-      "\u001b[1mrecord_absorption\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Toggles logging of absorption\n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.component_help(\"Union_master\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -439,284 +323,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Instrument settings:\n",
-      "  ncount:           5.00e+06\n",
-      "  output_path:      data_folder/union_materials\n",
-      "  run_path:         run_folder\n",
-      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
-      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
-      "  executable:       mcrun\n",
-      "  force_compile:    True\n",
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/tutorial/data_folder/union_materials_5\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
-      "                                                ^~~~~~~~\n",
-      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
-      "#define filename mccabs_logger_space_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
-      "                                                ^\n",
-      "                                                \"%s\", \n",
-      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
-      "#define filename mccabs_logger_space_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "14 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/tutorial/data_folder/union_materials_5\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Cu.laz' (Table_Read_Offset)\n",
-      "Table from file 'Cu.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
-      "  '# TITLE *-Cu-[FM3-M] Otte, H.M.[1961];# CELL 3.615050 3.615050 3.615050 90. ...'\n",
-      "PowderN: powder: Reading 19 rows from Cu.laz\n",
-      "PowderN: powder: Read 19 reflections from file 'Cu.laz'\n",
-      "PowderN: powder: Vc=47.24 [Angs] sigma_abs=15.12 [barn] sigma_inc=2.2 [barn] reflections=Cu.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: incoherent \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: powder \n",
-      "component index [1]: 4 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 3\n",
-      "name of material    [0]: inc_material \n",
-      "component index     [0]: 2 \n",
-      "my_absoprtion       [0]: 0.000000 \n",
-      "number of processes [0]: 1 \n",
-      "name of material    [1]: abs_material \n",
-      "component index     [1]: 3 \n",
-      "my_absoprtion       [1]: 3.000000 \n",
-      "number of processes [1]: 0 \n",
-      "name of material    [2]: powder_material \n",
-      "component index     [2]: 5 \n",
-      "my_absoprtion       [2]: 1.200000 \n",
-      "number of processes [2]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 3\n",
-      "\n",
-      "name of geometry    [0]: box_inc \n",
-      "component index     [0]: 7 \n",
-      "Volume.name         [0]: box_inc \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.000000 \n",
-      "Volume.p_physics.number of processes [0]: 1 \n",
-      "Volume.geometry.shape                [0]: box \n",
-      "Volume.geometry.center.x             [0]: 0.040000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: box_powder \n",
-      "component index     [1]: 8 \n",
-      "Volume.name         [1]: box_powder \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absorption       [1]: 1.200000 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: box \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: box_abs \n",
-      "component index     [2]: 9 \n",
-      "Volume.name         [2]: box_abs \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absorption       [2]: 3.000000 \n",
-      "Volume.p_physics.number of processes [2]: 0 \n",
-      "Volume.geometry.shape                [2]: box \n",
-      "Volume.geometry.center.x             [2]: -0.040000 \n",
-      "Volume.geometry.center.y             [2]: 0.000000 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 4\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           0 = [1,2,3]\n",
-      "LIST: Intersect_check_list for Volume      0 = [1,2,3]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [1,2,3]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for box_inc with box shape made of inc_material\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = []\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = []\n",
-      "LIST: Next_volume_list for Volume          1 = [0]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for box_powder with box shape made of powder_material\n",
-      "LIST: Children for Volume                  2 = []\n",
-      "LIST: Direct_children for Volume           2 = []\n",
-      "LIST: Intersect_check_list for Volume      2 = []\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = []\n",
-      "LIST: Next_volume_list for Volume          2 = [0]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for box_abs with box shape made of abs_material\n",
-      "LIST: Children for Volume                  3 = []\n",
-      "LIST: Direct_children for Volume           3 = []\n",
-      "LIST: Intersect_check_list for Volume      3 = []\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = []\n",
-      "LIST: Next_volume_list for Volume          3 = [0]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_I=3.1635e-09 logger_space_ERR=3.59609e-12 logger_space_N=790141 \"logger.dat\"\n",
-      "Detector: abs_logger_space_I=1.70717e-09 abs_logger_space_ERR=1.53146e-12 abs_logger_space_N=1.81278e+06 \"abs_logger.dat\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=8.0)\n",
     "instrument.settings(ncount=3E6, output_path=\"data_folder/union_materials\")\n",
@@ -737,32 +350,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space\n",
-      "Plotting data with name abs_logger_space\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data)"
+    "ms.make_sub_plot(data)"
    ]
   },
   {
@@ -777,284 +369,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Instrument settings:\n",
-      "  ncount:           5.00e+06\n",
-      "  output_path:      data_folder/union_materials\n",
-      "  run_path:         run_folder\n",
-      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
-      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
-      "  executable:       mcrun\n",
-      "  force_compile:    True\n",
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/tutorial/data_folder/union_materials_6\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
-      "                                                ^~~~~~~~\n",
-      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
-      "#define filename mccabs_logger_space_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
-      "                                                ^\n",
-      "                                                \"%s\", \n",
-      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
-      "#define filename mccabs_logger_space_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "14 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/tutorial/data_folder/union_materials_6\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Cu.laz' (Table_Read_Offset)\n",
-      "Table from file 'Cu.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
-      "  '# TITLE *-Cu-[FM3-M] Otte, H.M.[1961];# CELL 3.615050 3.615050 3.615050 90. ...'\n",
-      "PowderN: powder: Reading 19 rows from Cu.laz\n",
-      "PowderN: powder: Read 19 reflections from file 'Cu.laz'\n",
-      "PowderN: powder: Vc=47.24 [Angs] sigma_abs=15.12 [barn] sigma_inc=2.2 [barn] reflections=Cu.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: incoherent \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: powder \n",
-      "component index [1]: 4 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 3\n",
-      "name of material    [0]: inc_material \n",
-      "component index     [0]: 2 \n",
-      "my_absoprtion       [0]: 0.000000 \n",
-      "number of processes [0]: 1 \n",
-      "name of material    [1]: abs_material \n",
-      "component index     [1]: 3 \n",
-      "my_absoprtion       [1]: 3.000000 \n",
-      "number of processes [1]: 0 \n",
-      "name of material    [2]: powder_material \n",
-      "component index     [2]: 5 \n",
-      "my_absoprtion       [2]: 1.200000 \n",
-      "number of processes [2]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 3\n",
-      "\n",
-      "name of geometry    [0]: box_inc \n",
-      "component index     [0]: 7 \n",
-      "Volume.name         [0]: box_inc \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.000000 \n",
-      "Volume.p_physics.number of processes [0]: 1 \n",
-      "Volume.geometry.shape                [0]: box \n",
-      "Volume.geometry.center.x             [0]: 0.040000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: box_powder \n",
-      "component index     [1]: 8 \n",
-      "Volume.name         [1]: box_powder \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absorption       [1]: 1.200000 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: box \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: box_abs \n",
-      "component index     [2]: 9 \n",
-      "Volume.name         [2]: box_abs \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absorption       [2]: 3.000000 \n",
-      "Volume.p_physics.number of processes [2]: 0 \n",
-      "Volume.geometry.shape                [2]: box \n",
-      "Volume.geometry.center.x             [2]: -0.040000 \n",
-      "Volume.geometry.center.y             [2]: 0.000000 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 4\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           0 = [1,2,3]\n",
-      "LIST: Intersect_check_list for Volume      0 = [1,2,3]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [1,2,3]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for box_inc with box shape made of inc_material\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = []\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = []\n",
-      "LIST: Next_volume_list for Volume          1 = [0]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for box_powder with box shape made of powder_material\n",
-      "LIST: Children for Volume                  2 = []\n",
-      "LIST: Direct_children for Volume           2 = []\n",
-      "LIST: Intersect_check_list for Volume      2 = []\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = []\n",
-      "LIST: Next_volume_list for Volume          2 = [0]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for box_abs with box shape made of abs_material\n",
-      "LIST: Children for Volume                  3 = []\n",
-      "LIST: Direct_children for Volume           3 = []\n",
-      "LIST: Intersect_check_list for Volume      3 = []\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = []\n",
-      "LIST: Next_volume_list for Volume          3 = [0]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_I=1.13342e-09 logger_space_ERR=5.90802e-13 logger_space_N=3.86971e+06 \"logger.dat\"\n",
-      "Detector: abs_logger_space_I=4.16101e-11 abs_logger_space_ERR=2.36615e-14 abs_logger_space_N=4.12627e+06 \"abs_logger.dat\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=2.8, source_width=0.03)\n",
     "instrument.settings(ncount=5E6, output_path=\"data_folder/union_materials\")\n",
@@ -1065,34 +386,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space\n",
-      "Plotting data with name abs_logger_space\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "functions.name_plot_options(\"logger_space\", data, log=True)\n",
-    "functions.name_plot_options(\"abs_logger_space\", data, log=True)\n",
-    "plotter.make_sub_plot(data)"
+    "ms.name_plot_options(\"logger_space\", data, log=True)\n",
+    "ms.name_plot_options(\"abs_logger_space\", data, log=True)\n",
+    "ms.make_sub_plot(data)"
    ]
   },
   {
diff --git a/docs/source/tutorial/Union_tutorial_2_geometry.ipynb b/docs/source/tutorial/Union_tutorial_2_geometry.ipynb
index 5e47afe2..62c6751f 100644
--- a/docs/source/tutorial/Union_tutorial_2_geometry.ipynb
+++ b/docs/source/tutorial/Union_tutorial_2_geometry.ipynb
@@ -16,20 +16,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
    ]
   },
   {
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,42 +109,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Union_cylinder ____________________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1mmaterial_string\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [] // material name of this volume, defined using \n",
-      "                          Union_make_material \n",
-      "\u001b[4m\u001b[1mpriority\u001b[0m\u001b[0m [1] // priotiry of the volume (can not be the same as another volume) \n",
-      "                A high priority is on top of low. \n",
-      "\u001b[4m\u001b[1mradius\u001b[0m\u001b[0m [m] // Outer radius volume in (x,z) plane\n",
-      "\u001b[4m\u001b[1myheight\u001b[0m\u001b[0m [m] // Cylinder height in (y) direction\n",
-      "\u001b[1mvisualize\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // set to 0 if you wish to hide this geometry in mcdisplay\n",
-      "\u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [1] // Relative index of component to focus at, e.g. next is +1\n",
-      "\u001b[1mtarget_x\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m]\n",
-      "\u001b[1mtarget_y\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Position of target to focus at\n",
-      "\u001b[1mtarget_z\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m]\n",
-      "\u001b[1mfocus_aw\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // horiz. angular dimension of a rectangular area\n",
-      "\u001b[1mfocus_ah\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // vert. angular dimension of a rectangular area\n",
-      "\u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // horiz. dimension of a rectangular area\n",
-      "\u001b[1mfocus_xh\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // vert. dimension of a rectangular area\n",
-      "\u001b[1mfocus_r\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // focusing on circle with this radius\n",
-      "\u001b[1mp_interact\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // probability to interact with this geometry [0-1]\n",
-      "\u001b[1mmask_string\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [] // Comma seperated list of geometry names which this \n",
-      "                      geometry should mask \n",
-      "\u001b[1mmask_setting\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [] // \"All\" or \"Any\", should the masked volume be simulated \n",
-      "                       when the ray is in just one mask, or all. \n",
-      "\u001b[1mnumber_of_activations\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // Number of subsequent Union_master components \n",
-      "                                   that will simulate this geometry \n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.component_help(\"Union_cylinder\")"
    ]
@@ -189,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -225,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -276,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -293,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -314,317 +281,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Instrument settings:\n",
-      "  ncount:           3.00e+06\n",
-      "  output_path:      data_folder/union_geometry\n",
-      "  run_path:         run_folder\n",
-      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
-      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
-      "  executable:       mcrun\n",
-      "  force_compile:    True\n",
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_geometry_49\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:14851:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:14851:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:15094:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:15094:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:15337:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_xy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:15337:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_xy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "15 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_geometry_49\n",
-      "\n",
-      "          Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: Sample_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: Sample_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: Sample_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 4\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "name of process [2]: Sample_inc \n",
-      "component index [2]: 4 \n",
-      "name of process [3]: Sample_pow \n",
-      "component index [3]: 5 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 2\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 1.391570 \n",
-      "number of processes [0]: 2 \n",
-      "name of material    [1]: Sample \n",
-      "component index     [1]: 6 \n",
-      "my_absoprtion       [1]: 1.092170 \n",
-      "number of processes [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 2\n",
-      "\n",
-      "name of geometry    [0]: sample_geometry \n",
-      "component index     [0]: 8 \n",
-      "Volume.name         [0]: sample_geometry \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 1.092170 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: sample_container \n",
-      "component index     [1]: 9 \n",
-      "Volume.name         [1]: sample_container \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absorption       [1]: 1.391570 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: sample_container_lid \n",
-      "component index     [2]: 10 \n",
-      "Volume.name         [2]: sample_container_lid \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absorption       [2]: 1.391570 \n",
-      "Volume.p_physics.number of processes [2]: 2 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: 0.015500 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 4\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           0 = [2,3]\n",
-      "LIST: Intersect_check_list for Volume      0 = [2,3]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [2,3]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for sample_geometry with cylinder shape made of Sample\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = []\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [2,3]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
-      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for sample_container with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  2 = [1]\n",
-      "LIST: Direct_children for Volume           2 = [1]\n",
-      "LIST: Intersect_check_list for Volume      2 = [1]\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [0,3]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [3]\n",
-      "LIST: Next_volume_list for Volume          2 = [0,3,1]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for sample_container_lid with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  3 = []\n",
-      "LIST: Direct_children for Volume           3 = []\n",
-      "LIST: Intersect_check_list for Volume      3 = [2]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = []\n",
-      "LIST: Next_volume_list for Volume          3 = [0,2]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_zx_I=17768.4 logger_space_zx_ERR=30.9103 logger_space_zx_N=950589 \"logger_zx.dat\"\n",
-      "Detector: logger_space_zy_I=17768.4 logger_space_zy_ERR=30.9103 logger_space_zy_N=950589 \"logger_zy.dat\"\n",
-      "Detector: logger_space_xy_I=17768.4 logger_space_xy_ERR=30.9103 logger_space_xy_N=950589 \"logger_xy.dat\"\n",
-      "Detector: banana_I=1937.6 banana_ERR=10.3152 banana_N=108657 \"banana_1643033933.th\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=3.0)\n",
     "instrument.settings(ncount=3E6, output_path=\"data_folder/union_geometry\")\n",
@@ -643,37 +306,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space_zx\n",
-      "Plotting data with name logger_space_zy\n",
-      "Plotting data with name logger_space_xy\n",
-      "Plotting data with name banana\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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Us7CwsLA4jVBAkxMXq9Ba33zaB3Puw+6VFhYWFuchTnKvPO9xxkmp1nojshk2Pj/M730dcNsx6v/kOG0fs56FhYWFxemF8/TX4vvD7pUWFhYW5yfsXik446TUwsLCwuL8hMJuMhYWFhYWFseD3SsFZzoljIWFhYXFeQrHJel4h4WFhYWFxY8ZJ7lXnvf6C5aYW1hYWFicFliXJAsLCwsLi+PjJPfK815/wVpKLSwsLCxOC+zTXwsLCwsLi+PDehUJrKXUwsLCwuK04SQ2mfP+6a+FhYWFhcXxYAmZXQMLCwsLi9ME675rcS4jhgIASkgAYAz/BCCVXD5mEACXsZgiYhnPDBYyhB6s4f+V/y8vR97E7/k7Y3iHscwEYCsduLP+WQID6gD4kMtZygBiKKGQeNaRwRjeYQbj6c5apnM7HdlEN7J9bayiV4Mxbqc9AOms5w2uJ7c+lU8DevMQU+jB577rHdjCVpKOOdd1ZPAcd3IHz3IZiykjimEs4BHuJ5Uc3qu/kmcD7gTgRW7hVl7iRW5hAq8CEEQdJcQAsJ/mlBHla/cyFpPOV2wn0TeemfVjCQyoYwoPMZXJXMm7pLMegAd5GICObAKgFWWsohe3M53p3O4b8xje8fXRl0/YT3OGsYAFDKMHa9hCEmVEkc56PmYQqeT66j7EY+QTxw38k6k8xGSmMJL3AZjENK7kXwRSSzeyuZRPWEMP5nIFE5lONhl0YCsA60lnO+2ZwKt0Zy2vMoF0vuI2XuQpJvEQU/gDj/PH+r8yNmAmUZTxMYN8fc3lCrqRzTuMYQzv8A5juIK5jGA+APMYTg6p7KMFd/A/ANzEG/yLK/mYywA4SAid2MxfXnsEgOgb5XtbVx/IFQHvs4mOAHQjG4B3GMM2byKXez70rYdzzwGKiAOghGg208lXpiObGMn7vntYSDx7TJ3c+lQA9gS0PeZ3zOLYsHulwLrvWlhYWFicFliXJItzEZfyEa9IGlhBoZLD4vzBomPfzz00/wEH8h1wnLGfacxhGDEU+B7oWJwc7F4pUFrrMz2Gsw6ZmZk6KyvrTA/DwsLC4geFUmqt1jrzVLWXqpR+6wRlusMp7dPih8H5vk9uIZ6vSGdU8QdQZk4WQtXAAMLerod+QCnoZLeOmgdV1wQQllcPYYBXyuzp2wyAVsUHqIwOJryoBgrk2oqRl9B31udQBQwGioAoYDns+XUzWhUeYE98M9aTDsCARZ8BUNsTgpYDaTIuyoCesCe2Ga1WHoBypI9RQB7SfpoZqBd2JkXRdksZRMDO1lGUEUWng5vxZEm9qtEBhC0382iGvB4y9TcAKabPaDm1J60ZgdTRcm011Jo5FEDBwGhqCKZD+Q5qmoInT8rXJkJQFpAALAAyoDYDguYBvYE6sxYR4E0Gz2LYOTyKthvK3DEcAroBceANA0+ulN+VFIGHGlrmV0sbXjlPqRl/vLnHae0IpI7EJcUUDIwmYWUptekQWAuFkdEkPFUKw8w6pkif/xx6Fb9a9G/p12Bv6xBazq2GpqB7wMGwAMKK6qmKCyBsbj2Vo4MJz6qBONNWR6DE3IoLYUtoB9p78wkrrYdsv7XNh4KrZYHjy0tR+VCZEUx4QQ1USV0Az5vgvV7GHZQLlZnBhC+vgWSgKXI/gsx6RkJtBAQtdu8dCfBw/D08uOhJ1g1OodtzuXgnwKbQjgTjpfOKbyjoGy1jyJPvvCqS9QuhGoBl9Kc/yygknt/xNwA+4Wecr7B75emBdd+1sLCwsDgtsLnXLM5VzGMEE+peZUtsO+IjdgDgyYf9nuaE9amA5UAxqCCEHEUBCVAXFERlCtQEBtNq4wGogFZrDgCwskd3eu9eizcKgsNAeaFv8edCFg1Z29PDkMph0GrJAWozodXKAwyoEzLqkIugMqgaHEDYknrojJDGWmi15YAQkSpYNzGFbvNzhfhFA4ukiZ2/jqJtfpkQ1TJoW1RGW8ogEr7u24EuxVsJm2uIdzbSngcqE4MBCI+oEdKbIv1QAa1WHBDStRr2TGxGi8oD7EtqRkJhKQTBf2IH8fMVi9nbV5wUQw9WE5SMEMVE6Te+rpDwuBrIN/MMlLX1FMhc2xaXYTxQhbgCBYnRJGwoxRMjY+QQtJlVwd6rQ6iKC2BzYifSK3NZH55CtxW5EOfOI6l4h++BQzBeiAKvJ4Cw7HoSqkqhNxR0jiYqcQ9ha+ohGm4o/7fMu9aMIwtaJlTLeD8FFQhhF9ZDHvJwwmPKRZu5xkGtB/anyTq0XFtNlwRxBaYpeAeBZznU9oagCDmdkF8KebB3cAgtF1VL/03B86lc16MNIQ8CLoTw7BoKBgrjLCKO5uyXtV1YA1EQVAtkQGWCrMMTgfcypfxRvH2gQ91WIeJA1y8386+Lfklp3xh6HPwcVSftl0U2o9WGA3Qo38GmyAsAKCGGtrvLKGwdz2UsNpM+f0npqYbdKwV2DSwsLCwsTgsclyQLi3MJr3ADQ1jFO4FjSGc9LUL3AeCJOECb3RUwG+gBXAgcgqpMYx3Nh+YZNah8IK6GyuRgIXDGwphKDiwETzTy6ysaITcrEWJabghsIlRGB1MTG0yrwgNiqXSstXEIEcyDsA31kCFtrO6eQWpdjljkooFk6FaYK+1WmrrGMtb2qTI5XwX0Qax2XqiMC6bLNENK4mQM4YE1UCFlw/NqpIFyoDPsio2gzdwK3y/JLX3bQRIkbdzBV5070r5uu9TNh2G9F1ObAS03imXNZ+lMkLXosnGrWDMLYMu4duTTnp+t+FTWuALC5gtJLrjOz3KYBwlBpRAFBa2jiQrfQ9jGeqpGBtByTTUFPaLp9mUuHIJu5PJh3/5c/sEyPHFmHmWwOi2DnkuyCa2rhmwIq61ndd8MVtGLSSumk7ChVOZbJmuiskBngjIPEb4e3oEo9tAqsYKgOMjvHkt8ZTFBgTAncShDvAsJz6qhNgWCFgBXiUWzZbFZhwRgm9xPog2HzYTt4e1IKt1BwpelEG7uFeY7lw30RB4WAFsj25FUsUNIbwXggSjvHjzeelqE7SN8Qw2shp23RBFILWW0Io4iWu6WMUwteBTCwFMKnrAaWA+eOvlOXlv4HpTC3u4heEqEfDc/eABvJuwPbcZ2EpEhZ3Fp60WM5H1f3O9DWJws7F4psO67R8H57pZkYWFhcTScapekrkrpeScok/gjcEk6H3E+75OvcAPprGcrHYiijDiKAAikTsgTNHykfwghA1HwdecOdFm0VcjGcoRMiCYSVZ2NZbMK6AdV4QHM9FzLTRvepCo5gLAF9eJyWYQQyXDTfjouKa1CSJLBzqFR7KMFXYq3CrmJQ9x5qxDCm+KWrYoXGZH9nua02VDhI4GAkJ1SYDWQaV6NeyhxCHF1+o2Uc1XJYlWkKbDerEEy7BocQZsZFVLPa+pHmvqO1lAU7BnajFYfHJBrxmJKlBl3rVmDClM+Qt7XZpjlL4AVaZfQt/hzasMgKN/0scgdH3GmfgRC+irMepgxVA2TNd87MkTIcgWQIGR7Hy3oPPcbWfta5AFDfg0FSWKZxSttbOnejqRZO8RyW45YrM266kSoC4KgCmANYtGukvur+0l9VWTuVyawzow5wpRJhvmRgxmRb0zcXoS1bjSvzr2thdnxw+nEJrpu2CzXD8DqcRm0J582SypkPcMgv3csAM3ZT3CdkPOywCiCqaHtl2VSrgoK+kYTV1nKvvBmhHgPiit3HXw9tAOdKrdSFwSeKvf7M6f7UDqxmWzkBt1Z/+x5LXpk98rTAyt0ZGFhYWFxWmDzlFqciwjlIM3ZT4bxFd1CB7bQgS5LtkIYfNS5j5AL415KBb54xS5rtvL14A5CIDKB3rDioktYcdElhC2oR/dAyEkZhJXXc9PuN8XqWVovRLYCiVeMF4vcrsERsF4IYFVyAMTBrqERQn4Soe2aMrrs3gpZ4O2Ga4GMRMpU4LN0hmXVE5ZVj4ca2AhV0QFCpIKALGA17L0lhNpkYIhZjHDYeVGUkLoIObYMbsfKtO6UemLAC+suShHinQykQ5sPKmTu6VDbD5lXPmKpzXCPVvMP8N+haS4RzUOsgEHAXKAEvM5P8ECZT1CFIXlh0HfL51AG68NTIA/y42OpHY2Q+gsRslwKOkzKV44Qd1XS5Qgrr4c4caGlAPb2CIFaaFNcQeeN3/is0t4EJI6zFmIOlrI6LUPWNxKSCnfI3CsQYl4ALJYxqGy4NnwGz8bfzNIrfypxskFyqEI58Ao5B3m/NO2nck8SYH9EMMPLF4mbrdesUSlCSD1iya6MljmNrJxP11mb0XFQNTyA2lHQ7WA2bWZVsGtgBHsGN8PbDRI3FJO4sZj9NCe0qobQqhpxvc0vkzmEicU7Ib+UuiCJgw7Lkocltb0hhhKCisBTJg9VKjOCqcwIZgDLKCOKDmzlzvpnT/AXZtEYVuhIYC2lR8H5/ATYwsLC4lg41U9/L1JKLzpBmTY/gqe/5yPO530yhgJ2lV9AUaS4O7ZZacx1CbA3PoSWW6qF+IUhBKgK1602F7FgxZlrFbhW1TjY2zmElmuqJXb0gwPsGhpBm/wKPkzsz+VfLoMDUJsOQXkIyRli2nXcN42YDxWI9TQDKBfyF5QrfWyJb0fSlztkDNsQQmNEbgCIEnfMlouqXWElpNyuKyNo82VFQ0IbZNox7qI7r46i7awyqkYGSKxlNuLOnA+rr86g57RsIWBxZnwlQCF8fXUHusw3luZSqB0thLJD3VbC59bI+KpwRaLC/NbWxMz61rIKKtOCCV9gBH1qEcv0IGmbQ0A6eCPAs1DGXjVQBIiYK018PakDXdZuRSeKW65DuikCb0+J7QQgUWJLE5aU+sSKdg6WRWu7sgyaQm0y7AmPoOXBCjwFZr2jZd67hkbIdyhO7pVONIQU0PGgSs09OGTm65F6RJt2gsz8nfjUIiABtnRuB0Cst4iwwnpqoyHIK2QxrLKeWo98pkzKJq3cISJLudKWI9JVEhkhbumIG3TCBnGJ3hkbRauDZWIRPWQEkgqQhwcJMo+rI2cA8K/KcewLb8ZUJrPOWEut0NHJ40zulUqppsCLiG9Cvdb6rlPdx8nCWkotLCwsLE4LAhSEND3+YWFxtqEjm1gY2Z9PuFRyezZDjuXQsrDaJU1FYp3cNTgCbdxFd42LoCojQCyDVUi848Bon/BMy/nVUAWt5h6ANGizpQIqYUj5Mp8ib1AFQlCSzWuW9M1yIFFITuXoYLFGeoEECMqG2hTYFR9B0geGkG5GCGMEQviK5NjbPYSWX4paLIMkLpIwIB7aTKsQMrTA1EmGdX1TJPY0UY6YSrGqhW2oh1LYdUeEkKgg6Lk7W0jvHSFi3Vwo/XtHQJcvt/raqLougCAvdFuSS3h+DXSX+jhqxp0RQpwpxM1H/gvMUQThuTV4B8HkzvezKy0CfY3bxt7BIZArFj3vEDlX7QnFGwNcI0eXNVuhANQG4ELwisAxOgM8xmq4c2gU1EKLun2sG5gi5LEI2haXifCSUSYOKoM2uyvwmHHqDHNvkqG5d7+QwXLY1T0ClWvu6yGYGzmUdZ1ToBa8UQjxzBf3WUCsutFyj5kNX6fJvaqNgw7lO+hQvoOwonp0pMSqesPEIs5m+R5VhQdAGSSt3QFRsKt1hBDMFFB1cgRRx4rWl8BmyaVLU1n7akLweoL5unUHVsRfQmAt8rAhXb7rqgJeqbuZV+puZlL432hReYAMshnPjAb5TS1OjFO1VyqlnlBK5SultFIqze98R6XUZ0qpzebVTzecUcByrfUdwEGl1Bl7SHzGSekJFsopE6iUmqaU2qqU2qKUmuB3bbBSKksp5VVKPdGo3p+VUqVKqWxzTPsh5mRhYWFhAUpBk8DjHxYnB7tX/nDoRjYt2Mc+WtCrfC35F8WSf1EsZMLX8R3E0pQrhNSTBW02VIilrQDazK2Q+DvE2kYCJHxQSsIHpUJUHevjhYirbS1QAmo27Bpu3HIdy1kE6AjQg6HqlgCqbgmAptBmSQXhi2qERIUZ0lYrJKRNYYVYJitgT2/jFpol/RWMi6ZgXLTETy5GUqOUQpfCrdRmmn57AouhcmIwexKb4Y1CxIJK8MV6loRHUTkhWMYaCW1mVFA1PAA6w+rWGQA0r6yWtpKBcvAsoIH1NWxDPVXhAULeApE4yEjY1TlC3FerTN0yY0k8JOtLghy7hkawJa0dB0NDmDrrUdqsqUAtkblWRQbQ8t1qaTvXxD7WQqvdB8RqutocTWHvyBCqegSwJakdnhJZA5UL5Ekqn7ZzJZg3tKqGbnNz4YCs487YKHbGRok1u0rupQ4Ebay9qkrI/4dJ/QkrrBerdgG0ya8QgmrWYYh3IR29myAXCkPbsStR5p+wtpSlnX8qYkydM6AWqu4PoMvcrezqHYHXE4DaIIS6MiEYVSXW3htD/yGk0ljuw9bUQ5QIMOloaLOoQh5ilAKfyhFcV0PfNZ+jU2DA7s9kDqWwn+aEl9bQZclW+i75HFUEuy6KoCo+gJ2xUexJbMbHgYP4OHAQz335e0rCo+jLCv7IX7/jX96PF6dwr3wf6At80+j8i8A0rXVHYBrwkt+1C4Dt5n0+0P67zeL744y77yqllgCvaa3fUkpdB9yotR7YqMwNwLXAUORf9Tqgj9Z6u1IqCWgO/BJoqrW+16/en4Fm/udOBuezW5KFhYXFsXCqXZIyg5TOanGCPsus++7J4GzbK8/nfTKGAmZxNS3YRw6pXJv/nlzYiBC5OuN2mS+qtz03ZuNNQKxkRYh7oyFUROMTxSEB1/W2AiFGGVDZ0+SxrDBpP56qht5iufMsBsKgqrfYEMIW1IvVsgwhxwkIYSuVmMnweTWuwJAjjpSBEL1889ko7+Z3j6WOQJLe2iHpX7Jg9cgMWrCXzmu/ERLquNFG4qZj6Ym488ZITtAaPER59xBUV0/wIbG+8SluKpps8xqGWJSBfZEhov66GSFRhwzJXwDEgk4xIkDOtQuhJDSahDUSvFuVEUBYaT0F8dEkzC8VQj1Y5rWnczNJx2Ncfr9OFDddonGFlsAnKFU7Tj4GzcON6w1CrLplMq7AWrFGU2vKGPVd3UPGuTcthO20p9uSXPAaV+GN9XDI5PUslDXcE9+M4LoawsuNAvA6WUfCEbfiEYiY0krjKmweUOhIN/8ppeZ7ZFyZKxNFhAnAGwd7QqNo+2UZKy/qTgv2EUcR+2lOQn4pWxLb0b5yB0HrTRtAQVo0zdlPy0XVbBksrt+rL8qgZ36277u8MzGKtkvKfBb/uMpS3gn/JWMq5W9jXvhQtpOIx3zZg6nhJt7gfMXZvlcqpbYDv9Bab1BKRSN/aVFa6zqlVCDy3yFZa71bKXUt0ERrPUMp9TDwv1rr/36P6XxnnFFLqVmoi4G3zam3gYuVUq0bFR0DvKK1rtda70aeBIwG0Fpv0Vqvw80aZWFhYWFxNkAhVpDjHRYnhN0rf1ikkkMRccxjhPzoNuI0VQON2NB6IQgfdu9Pz5XZ6GjwrEdIjgchgSlQMDJaLKJOTOg25Keho5Kb6UdITZk19JD4zEJj4YsDoiTnZVieWL1qPaYvowxLgpQJX1DDf0emyedS2DOymdTfiJCx7ubIBW8KRNWVkbRxhxBoryjC9lybTed8Q0ibAhESL8kixHWzTtRpaYbkBF1TQQ3BhOXXsz+0GWqunPcp985DyKBRDVbZcrScVS3zuFDa39U7As8chPSXg3KUfkskbYsnDxKeKvVZSsMKJQVPwkul0BN2/TqCnfFRFHSOplWx5IXFC8zFp5hcG2Hm4Dw86AYMhKAqUe/953VXyXol4CPulT2D8aw2Mb7xuCTdiA2pOiGkRcTRbVGuENk0Ea7aeVGUWF4L4auLOsIGyRsaPq/GtdZ2k3bmJA6laqK42iZ8UIpOMX2ZhwtqOYRV1rv9O4JWhyB8eQ1V8QHsSWpGYWg7qglh40UX0HvNWrqs3UpgXR0JS0rZk9iMQOrYF97MFZ/Kh4QvS2m5oppdgyPw4sGbDD2/zPalElqXmCJCSOWQsKSUhFmleD0BDK+bj9cTgNcTQCLbuYE3CKbmvCekpwUnt1e2Mt4uznHzSbYeD+zUWtcBmNcicx5gDtBfKfUU0PxMEVI48+67J1ooBwk0NEUXHKXMsXC1UuorpdQipdRPv++ALSwsLCxOEk5G8OMdFicDu1f+gFjK5cxjOCOZK8Iuxm21zNMKj1fyZYb9rV6EiapAzTOiMSmQ3yNWrIrbIGFFqWt1C0IISBCQgE81Nbygxmexqh0El69cJjbuZMTtNh8Igz1pzdiT1swXv7hnYDP5odrUxCIGSts/WbuB2jDgELR64IDrCpuBxH0Wyjj3hkb4yHBtCpAn6UsoRb5ZxqKJx5wfAauvzGD1lRm0WWFSjFTIvJP+ITkyWy05IKlR8qA2EXFtvVDGTz4QAUsH/pSlA38KkRA0TfraHt5OFHszZG0cC2dVcgBEQM9Z2eCBPZOaueliymRelROCYZu4xdYRKJZUR6CoCvZOCvE9LAjymrX4uTnKoDbBtFUCv/rHv8V11bFQJkB4aQ2kQ0H3aF+c8J7YZj5S6qSs6VK4lT2DmwlZLJWcr/toIeQ2D7oWb4Zo6LpiM/SEPcObsWd4M3F3roJRiz5gmWcAu3pEsGdoM7GsGtVgJ8bT64g9gcTcGtK6emAGYVn1tCo8QNLaHYRQLQ8WAKIhfF4Nlf0k523cwWJaVAppXz08g9XDM2QeydBmbQWhHKQwtJ2r3txN4n4pRR5oJMCeq8UtPLSqhrDKesIq6+m2O5dNdGIASxnA0iP/qCyOj5PbK/dorTP9jpdPRdda62qt9Tit9SSt9Z2nos3vivP9J8GLwCNa68NKqZ8B/6uUStFalzUuaJ443AyQkJDwAw/TwsLC4jyEwqfYaXFW46T2yh/TPplKLnEUUZAWLfGgQEK5sdRVwZ4/NaPVlwfE8pZi4h690D6wmKo4k7/Ti1gJHYe7EvNaCuHUUJsCQYuQH//JsC+8GTm9U+m78XO2XNSOJM8OIVFl0MpjrH/LpWyr0gNiNU0ETx5CPsuAbRD0JkKWpiKCRXly3j9HaJuKCiEaoyHobaAnBHUDfo+QlBVmXGngGQQ8Aj3XZEv9ZHxCSCoMscROQEi0BxgFQY8DVyHfrGTT1j0w4JrPpI1PzbjCIGnRDrEgO9bVIOBtCOtsXJXDgWehVdwBN29pAdAHwj01sFLaScgzDwGikXP9oOWCailfZOoNM2My54LyzZqOkPGol5DHOL2BkfgsmQkFpSIHUwat3joAM0wbr0PLudWwAFolH5D7FQHhCTV0Wb1VvgMFslaslHtPPLSKlvvZ6sJscW8+BD/3LhZL61wZO4sRl+RyqLpG3JWpA28y5ISm0K1YbmgSW8T1eZ7EH7ctLqMgMZr9ic0lZ26GiELtSouQmONymVPPRXI/vX0kNpoIiK8sJqgA5qQNZdSWD9jY+gI6e75hTo+hDPEupNoTSqviA2yJbYfX46FLvlih7038C+OZQZF5mvEv7uMhHsPiJHFye2WEUuplYL7Wev63aL0QaKuUCvRz33UyGp9VONOk9GQXqgAJxHVMyo2fBh8VWutdfu8/UkoVIs+tlh+l7MvAyyCxMt9hLhYWFhYW/nBckiy+L86KvfLHtE9eybt48VBDsOQFRQSNyAIyodXKA0KinDQrRoxH5UJYTL241m5DSJpD7etMLOTKemrjJE5x15URtJlbIYqoBNJ35edQCUn5O1zF3CiEBAKMQFJ/lCLiRqWmnyrkR20hbszh3xCS5lgWTS5VmiJEMQ4hSnnm6Au8CYc3QJMoIB6q74eQTGAW7t/yIoQo+qcumWza6y39VD8LIYYssljmThiixgtCxIyFFkdteB4wzsylsxnns6b9CNOOI+9Vh8RjVsDhJ6FJb2TNnbyxzRBS7igY15kxv4nPMk0aQlDDoHochKRD9Xp55W+4ccGlCFl8E/nV7DFjN/eDnuYeZeGmtFmJq5rruHZnm3bA/fU9FVEDTkMeGmSb8b2JEP01wDDY72lOmFcs1J4yaB+63UfQvbEeAkPr+OrqDsRRTNsVZWTHZjCieBF0EwtrYWg7knbvoDIumJ/H/x+zGc2Bzs0BWEc3hvReSHh2DUFVsCstgsvqFvNU0u1MKp5Oq5/uYM/GduztHMIKLqVr7HpCqOYr0ukUJaQ0k7UMZAkd2UQqOVh8S5zcXlmhtT5Zl10ftNalSqls5Jv2lnldZ0I8ziqcUfddrXUp8id4jTl1rIWaDdyklAowMTRXAO+dqH2lVFu/9xmIotSm7ztuCwsLC4uTgHXfPSWwe+UPi0v5iJe4hat5m1oCWUUvVtELgmDv0BCxeEaY2MpSScPCIYSgJSCkxYjokI9L+sIhtKoeimB/eAiqCHJJFWtgoSG9dWYQTh7UQvBmItbWTMQSWiaCNqxGfsheiHw7HGtggrm2DSG2QYjiqzOOLITsFAGlUL7clDHjb3INQngSISQFcas94LdAGe54Vq6WclyDG+v4PIRcj5DhNQiBGy3zP7xIDnqasS1HHqVUIlbFtxDSG4Sbs9Nj1iMOXxwk0eZ1ATRJNGuwAFm/UoRMxuMjiYdXm3WZiBvP+TYwDMpXQsgIKM+CkAsRQhxt5mlydBKEEM8EGsalOu7Rq5H7WI5LnKMRIh1m5pnpt0aOK/VEc59eNe04FtIU019nWB2bwT5asCspgl3dI6AWsk0uUAAPXlqUVxNDKdlkUNA3mhGLFrE09qe82Hoce0Kj6FC+g8rIYNYGduc+HieIOl/8ZxxFhFZJnthdsWJNDc+qYQzvUBUZwPKAfhABgXV1dGIz0XWltC0uoxOb2RfejH3hzYinkId5kG5kk0MqOaRi8S1wivZKpdRzSqkdQDvgY6XU1+bSrcBvlVKbgd+az2cdznRMKRxjoZRSC/xy5byJ/NnmIX/6U7XW20y5PuYGTAJuUUrtUEoNMfUeVUptUEp9CbwCXO//RNjCwsLC4jRC4f6oPNZhcbKwe+UPhDiK6M9SllYOphVljNrwAaM2fAAeaLmxmr1pIVAFLQ9WsKL7JbT50rhE1uIjh95MhJwkgu4tB9kiWFM7AlpuqIYoSCEHiqBycLCQHWP9KhgZLYRusCjw1iYbwaFyYKOIIHmvAuYjd9ohbI5V7lbzOcF8XoiQnChzPgMhf2VQXWs+rzcLkIuQo3Lkx3A2bjxsEEL2NgAzoLdDRgvgcDbu37Sj7ppp5jQPuB6a9JODUtNWIFSuQb61h8w4IsxamjypxOGu72BzLESIrYmlJQ4RiMoyn8tNO+HAGmgyyKzDXxCLajpCZNdA5GSgCiIT/ebmQYhklenHIaeB5lqJOQb6zWW56XMlPkVkDuFahQ+Ztc/Fl6+VGeB9QNaPOuQvOM30sVjK91ySTafyb2hTXEFoXTXeGLGq18ZJvtKDhHIwLICEL0vpwRoSNpZCDERRRio57KMFKg/Cy2sYsPszfr5hMVk+n3IIxktQmaQfalVZwUfxfSAf4srFxN9ly1Z2xkZxMDCEGEoIraphdWyGpBIikFoCGcUc6gikO1lM4FU+4WdYfAuc3F4ZoZR6WSk1/FjNaK3v0Fq301oHaa3baK27mPMbtdY9tNYdzetZ+dDxjKeEORtxPkvdW1hYWBwLp1zmPkzprBM8MFdZpycljFKqKeKcVwnUa63vOtV9/JhxPu+TU7iPHqzh8t3LoEREhkDyXLIO9gxuRqsVB0Rht3U0CWtLXcXZAtzYsGjz2UFTk0omz69MGVAEVaPFRrDZ04n0ylyCqhDy4gGiJM0JQKstB9iZFEXbtWVCRtPhq74d6Tprs5SvQCyOHoTkLEceQ8z2G0emtMk2hDA5KU4KEBIYgRAjJ7eoEWfyxaQaCy7dEeKVjpDQQsTK6JTfhsSbZuC6tvr/nM5GCJ9Td6LpsxQhmGsR4paHxLouwbUGx5mx5iHkdY3px1mDOxHCfsisR5A576TkAXG9DTTtZpjzBch964a41o5DCPpyhCQ6btnppo3VCOGsMuMeZtrZYNapTPqvzIbwW3At4cl+9aNwCXUFcn+CzNrEwc7WUWymI9GUklq+lcLIaKK8eyQ9DPBhYn8GHFxG8CHIjkwhijISlpTKPGJkDbbEtyNp2g4YISJM4eU1PtGknbFRVBNCMDUk7C5lRetLiKaErSTRi1W0KK+mKNJ85+Lgw9j+XL5xGbs6RzCfEQB0Mm67E3keEJXh85mYnk975dmEs8FSamFhYWFxPuIUWUqVUk8opfKVUlopleZ3vqNS6jOl1GbzmuxXbRSwXGt9B3DQz5poYXFcfMwgagjmX61/SUFaNIXEU0g8HICqfgG0qDyAToOq8AASnivFmwI0Ne68DmnKErfLnT2i3NQeh4wgUgRCGIrwpV4Jy64nbKOomG4Jv4C9sSFSrhjIh1azDtBq1gEog+Z1+4XwpACJ0HXDZiE3QQjpOYSQtR4IYZuPECZnHDOQxzUOOXZccAeZsa2G7Y+aecQg5CgMsT461tMMhKiZXJ4UmTIVpt1PzblA85oI9IPq2XKwBCFf6xECOxghzukIOc03dROlr7zxyP8LJ72Okzc1Gp/oElX40tuwXMZ12LFaBiJW0ETcPLFvIm6zBaZuiul3A2LZzUBcfJ81fTskc5DpLxIhrl5zHx0BqGzjLlyGxIUmQvhI0264WQ/HhRgkDnexWet+uO7Bi0EHQtsvyxhQ/Bn7aS65W4Gw/Hrf/cxgHZ4i+DiyD/EUcpAQeWCSFg3r4JX460kq3kHVhABoCoWB8TJek1ImlIOSx/TLUja2voB9tGAtmXQni2CvlwWRg2hbXEZtshDSXnWr2Ns5hDqCyCSLTLJozn7e4Ab68olP7MjiW8B6FQGWlFpYWFhYnC6cupjS9xEZlsaiPS8C07TWHYFpwEt+1y4Atpv3+UicpIXFCRFHEf3rltGBrQRSR7cluZIWoxkE1dUTlAf7IkMIW14v7psAHkmpAQjxyIAe5dm0nV8mRC8Cl7BuMK+JiB3fSUNSImVmMJ6WS6pdYhlmXnuANx3C365BR4O3J+gwpFwf0/4BxMJYjqv+OwyfarAvPcwgcy0PsfBuQIiasfy1T8BVjl2AkMdYcxxCLJJXISRyOUKmHOIaZubnpINxcp7WQshoORiHqMymmH4dK2iBea1CSOtyOLwAkgch1lBnLYPMmEpN346r7BKznnlSv0k/xAqbYeqVIRbSEaZeMmIVNiSQCHNf8hESO9q8LkJIY5x57xD02bhWT49pMxqaZJp1X47rQl1l5hBu5j/OrE+2ueZYcI0FnGjJ17rroghYKSq7xEBCcSnkw5bO7djSuR01eCAQfrb2U+YyklaUsSL2ElrU7cPbBzLJYlbsFWz2dMIbBs3ZD9vAGyNH88pqmrMfbzJ03vANl7CG/iyjVWUFYaX1tGc7rJbUQKnkEL6hhk10Yj3ptGc77dnOPEZwR+V0NtGJbmSzmU5YfAuc3F55Qvfdcx3WffcoOJ/dkiwsLCyOhVPukhSudNYlJ+hzMd8Ae/xOvXys/GtKqe3AL7TWG5RS0UgiiSg/RdoyIFlrvVspdS3QRGs9Qyn1MPC/ZzIp+PkGu0+ePlzKR2ymEzsrL2BV+CV092ax3tMVgJ4bstmbFkLL/Gpx+80MoMbjoeVz1UKKFiDEqAjXaumQRSfHZQVC0iqROtnmfIapl4HERXoRC2MEHM6DJoNNOUfEycllWQUEiWBS5Ag4vBiajEYImRe4DlHvddoHDudCkwhcUhqGEMVPEQLruOHWmTE6JNoxwj0pfVJrzsUDxVC5HMLvR1xhN0BlEYR4oEkargKw41IdZebQ06xbYqM+y5B8po4lNdK0m44QUxCC2ceM20FP3LjcKEQluM60UWvm4RDQa8z5cuShRCVCZv8t7ey9JYSl9GfUkg+YN3AwI4oXURsGB8OC+ThwEACjdn/A1607AIggF3ATb2Bx+nCG9srz3n3Xah9aWFhYWJweOC5Jx8ee77jRxgM7tdZ1AIaYFpnzu4E5wAtKqa5AoCWkFucSxvAOr4dfTzrrqQsKomdhtu9ayw+qxSqYCWGl9YTVmlycDkEzOSdJQMjPGnM+2jQQZa5XmrIpCIkKwk0bMwghU+Zck2hcC280QlbzESthFeAVQsohaOLEeoL8/W/ATYVirKi5FdB1sLR/eDU0iQMeR4jpIlwhoDiEpGYhllEnrrUON37TERfqDeHDEOulyf0aPhixPCYgxG897i/fSFwRpgjEKrrSnFtkzgWauUSYMXhx3a+d+awzYwyTe0K2WT8nJU48rpt0CkJ2nf948xASe8i029OcHyHr1nJtNaM8H7BnYDNG5C+CZpLPdgtJxFEsTbQeTDA1BFLLfiTNSwwFlHB+5xI+r3Bye+V5D+u+a2FxFiGwpIrAkipUQS2qoPaY5dRYOSwszmqcQZckrXW11nqc1nqS1vrOU9m2hcXpRDeyuZcn2E9z9tECj9eI0tRKnODXQzsIuXG2iJUIeVqOxGZegxA6Jz9oJkKiepqjCCFXwxGylo/8LWYBI+CwI4oUJOlSqDX1epsDXKGh9dJ2ZZ7ps9Scb4orAARCzkrgcJYcXTMRsgw0CTJjzEBIWiIQAdULgA1w+EWEtDnj6Imbf7QMER5KQUSVSnDFmirM+Jw5O2q+TnqdPogLcpRp+1WEjA4z8wtECGaZuT4MsYw6c8L0EYdYU2vNPehu5pGG69o7x4zRUQqu8zvycUntatNOihnzbCAGssjkq8SO/Ld1GsF1NbRgL6l1OaTW5VBLIAcJoUfd5+ynuY+YWpxDsO67gLWUWlicEigFKXodADl0YxhzAFjAqO/VbmjlXg6GtzR9zAdA66P/P1IXy6v+4nt1aWFx6uBstMfHd0oIjkSKtVVKBfq578aZ8xYW5yzWkcFNvMJTTKLLkq3sGhjhszTGHCwlIavUZ0GsDYOgPIRcOnk+03BjO416LyWIwBG41s25kpszMhGfW2n5ixB5DUJovRDpuM0uwI2f3QCMNO+LpO/w4QiBqzDtvwqHq6BJiqmfC9XZEBJj6iXI+CrnQrhDInNx08EAIU/KmJtMkLH6xIXAVQUGifUsMfPONe+LcdPFFAi5jvy9ue6QSsevIhIhhumm3bfMGJx42CEI2XSsysm4FuUiU9ch6YVmrRL8yjhr4rzOQFx7MWvq/McqxE05M8OMKwW+bt2ByyqXcTAsmJrAYGYGjiWHVIYELgRgVOEHzIsfzL7AFtQRyEvcgsU5htO7V54zsKTUwsJAqRcA0Po2Rhj9/HmMdq9f7JaNWC0p/PYFt/lOffVgBQBr6NvgfH2bJwBYomWzGaiGoACtH2pQLmXmOjNmjdYysO9CRpWaInUbtW9hcUoQwGlzSdJalyqlshG70FvmdZ3Wevfp6dHC4ofDAJbiwcs/B15FKjl0PSi+s7WBAZBZjycXquIDCFtSz84pUbS9rExITAJCDr2I6FEdQkqdNDGY80ZJNrIHLqH91JDQbQiR9CIED4QsOiQrGSFp6VCSK5yQKoSYVSAWxyDjxluGzyU2xLFYAiRC5QLjXhstfVbnQkgf02dThNx1NuMIM+1v9BtPhalbi5DwDeb9INycoMadNtIhidfji6GtnO3n7utBrJ1OvGmGzIENuLG30cgal+FaQANNmRSEtDrqyh7T99u4aXLizFpHm/IOnDjeMnO+0q/9CGjvzSeoCvaHN6ftljLGJs6kMDCeGoIBmBL/B5qznxJiWEp/OrLpvE7Hcl7iNO6V5xIsKbX40UOpKXTQV/p9fofh2vVsH+FL8DaaY0H0wrr5PvtbSJX6wpRxWW1jMur0kaaHATCAh/zadfpwLaQ5dPO122Aum03Zjscc6nGh5kFwn0oAvJHh360RCwt/nHiXiVBKvQzM11rPP1oBpdRzSIqXNsDHSqkykxT8VuCfSqnJwF7ghlM2bguLM4RPZ/6Mu8c+zTxGMISFNGc/gcZ6WBLaiijvHjyl9YRW1UMytJ1bJha/PoibaB+EnGXjuqFm4BLCOoR4Zoqrbm02hPwBWG/iOzMRUheGbHsLEJJ0wNRPNtfyIMaxEEbIZ2IQErbE1J0HhzcYgloA5fnSRORKCO8t55yUKiEZCCG9CngecRV2YlOjEWux12+hynCtmdG4pDkLWY/eZuwZyA9+57p5DXfUcdMQApuLuOhmSdqakFEICd0ga0UEQr4d0SJMPSfPa28zjpUIqa5ARJ6cdXdcocchj9FAVJWdmNUK933VLQGEldbDSiQfaR1cWL6N8viWhJfW0Dx2Pwm7ZSL7WzfnAR7mRW4jlIOmYUtKzzlYRmaXwMLCQQfdl2BqyFWbma/q0XpMg+uOJdJTXkn1gVBuj5yOGnafXFsAKscte3PqswC8xJGhbEp9IHX00COurecnUuYr02dXv3rzzLkRR7dwql647lm+vo5iCd2t3DkdR31bqSnWgmrx/XByT39P6JJkco3ecZTzG5GfdRYW5w36jP2IUKoZwTyiKKNFeTVbI9sBEOstYp+nJcVDQ0j6YAe6B9QNgqA1iMUvHliIa7lMw03t4ljnHEtnKTTJgCZRSAykF5qMQAhrHUKQ3sQVTHJcb534yQzEzde4yPryilYi7qlLjMtuCmLJHAiR/r86HUuqI1ZUibgFr8e1zFYhbrXrEbLp1G+Km5PUEXiKRiypkeZ8nTnnpMcpxafIC4aU9kMSS/UzY2kKNIOQC01/g81YnH6amnk7qDJ99EPI+AZc8roNIffZCHl2Us0MAp4x9RcB95v1y0PI6isQ9rd6+d/ZE9YlphBPId7iCPZENuPV2LHUEcSA1ksBCKaGy1jMu1zJ/9T/FoA1AX/nOX6HxTmCk9srT/gA91yHJaUWP3o0Jl5xukWDz3fztHknllLHgqhW3QcPQnSvAmIAneoq3flHdPhbSI+FeY2ssP5k1EHGCCfTds9jk8VxoFf59/0QS+nFUhYyAHOh9ZFE1CHUegRAuI/MOnjWzOhOvzSQrep3ArAnoO3Rx2JhcXJxMuf9Rmth8W2QW59KYUA8ndhEy+Jq9sQ2I5gaAGZ6rmVM3Tu0zS2jcrDEGLYqPCBE7ABilYtGXHbDEJIUDyVPQozjElqLz8pYnQUhmbhup9sQAhaESwwTTHvbTH0nFUsVQtqKEULliPM4gkQFJidpDEKYC3FzmJYihCwNXxxp3kJIdtxkq+CF5XDbkwiBLjL9Ouq0kaafPHzqvxQh1kpH+CnK1K1DRJ3Cpe3wQ6aNQzJGRiGEIBkhkPEIUR6JuN+OMmU3mH7fxo1tDTJl15lxO2u2DcnTWmnGl2DqBJl5OylhHPfmSISsZpq5DDR1AyGjPJetke3Iis0klIOkkkMquVxYLjckLzKZ8bxOFt1ZHtDPxpSei7AxpYAlpRY/AhzN6uiPp5gIwCSmHXGtVf1OZjfSqHZcbXUvIZLOw+MkvmYLXQzBuxOdavpXItevdYh5PdJCCvjUdnVCkFhTY6WcNk9119ETdbv87wLQ0xvW9yejAGomcO0XLPkOqYi1foi/cwd/5w5+x3O+8534is1TZSGjHvTry8amWhwLdqO1sPhW2BPQlg8pZD/NqYosodXKA1RlilvmEM9CwvNq+DqtA11WbmVv70CxBF6IRFXnI+6hzyLWQy8wG2IycAldGEKe5kFIP4REOcq0G8z1UsSyV4ZPzKfakLmQxxFiVoIQrvXAaDg8A5r0Q4jmEqheb2JE6xCyuA33/0EGQiodstkZkp0UL02BaLjNURAOQsSGtuGmhLkedt4Pba83/TtKv9nABMTCm4VYMFeaMWSbeTq5TjfCzgXQdgRCSsOASiifAZG9cS24zyJk0bGEJoMv20otLjFdiKuUGmXGE48Q30MIQW5q6jgpYXqbPjDXg8x8nHQ420AlQFRkGSBW0VAOUksgh0zFlxhHKjksYwDvIB5eqb6FsjhnYBmZXQKL8wdxSLBKkS8hmkGLWp5MuJOnzMdJawyb66ENmYomVt/APT5S6NYvm9CWgayCe+WzTj3Sqlmak8ALqeMB6MYtZKQKgTzVcK2XRpN/+mVHL+dLJSN/3gPV8ye01jruxkpFoPU4ltILd9eEu2aKhVSPBSabfuaJhVS96dd3Dj4ybmFhxRssLL49pnAfSUTxCPfzSeVgdvaOou0GISVlafB15w40Zz9USR5Ln0DPAoR0PYuQtOUIAapAiGsf08Fi3DhHx+qZacpkIKlaEhFLagWENAWug5AFps5Lps3Rpg+TW7RJhjk/C+gLIaWmnyJpr2QlxAwxbWxDCFoQQtxmmL5jECtnJkImExEimIeQ3WK3ftsMYD2szIbeUbjqvE+atqJlfNUVEFKGWHV7AveYNg5B254IeTXWWdIhMgWIgMPTjPLvCES8yWPWKgIh65hxRZlxNgNizbijEFJZhBDjKHxkmxG4oTZR5n44RDkDebBQZNrOhC3x7UjasoOWRTvY07cZId6DrPVkUmaIanu2s4lOACxjAAD/HvgrHlqCxbkCu1cClpRanONQORCbegwy6od7Cp5FJzhfdyGlceSTIv6q7ANiEwopIrGB8q5+ze3nWBAS9jogqiuNsUQPMu9WHeWqXzsJfn+Oa42V9GJ/S+tDKPXxcdtw2vHlMP2mK1xwGKWqfZZaf9dcrR9C3QhwJ7w+Q65PhSWTIZMsn8uv9suJ6luLJDB6TNLOTORJtCWlFg6s+66FxbdGHYFcyieApAOJrytkXZoEhHbbkguUQgTU9oSgClyL4yAkz2UEQrTCEILjqL06ESDpULnRCA1lIYQqz7QRgZDCAmAChCQj1klHYRaEvEUiJBh8aWEql5s4Ta/0e7gMvtgGPdKl3Rh/9V0nv2ck4tY7GiFtbyLuslnmWjFuvKhj4QUIgu3Z0H4I9L4eDs8zYkqJCKF1CGEYhDhb8AwzT8f19kIzl0zTdrmZa5q032SiKfcsvjjRw9nGGrzSXAvDTSlzNULYM4C1wC1w+C5oMsqsp7POv8HNMetYox3F3t5mDPlmTYogqXCHz+251e4DrG6dwbtcSXu2A7Cd9qSznjt4zndu0pIjPb8szmLYvRKwpNTiHMSxLKLHOt+A7AFX9niLVfRqcM6po76C4Udx8z2a9a8TX7F5RFeGzpOcpJex+KguwANYxYuMZxPjuZXXuQWxSi5GLJ1b6EIqbo7TGAqIvhhKfP5BfuPQR7eOHlFupr/F9CQxYBz0d8fsj6X04ueV/zGfWroXRmJhcXwEnrCEdd+1sPDDPlpQRBzXFr7HunhDRguNO6YHlsb/lEu8awhbWS9EsSnibhuE/E9eiZAth2B2Q0igQwiLILwHroCPIwQ0GCFI5cgDxhnyOa8U2oUZV1ygejUcroNwRxHXKM6GR8r4SJTXJkXQYyQSG5mLkL/lZgwZZsxNEcK1EXFxzTRjjcQVLcKMZyQuKc2G9imGCN9iCGmyyXvaA1esyagEU2H6LEDIKGZteiCWYa+ZRwbwKWLBfAsht6MRkjzIKBPn4Vq1RiMEMtusW6SZb2/gWWM9fh6xli439+UZ8MkzeICOwGbcfKWV5l5kIw8D+sm8n+p7O71YxX6acS9PsIBhZimz2U57Yr1FPO75AwBPs4VCkrA4h2D3SktKLc49+JNOIYvu5yS+9sV2+kMNc9/rBdcet/35X40+Iv40ia8BjmgX4IOLR/HkFxOPOO+PW40lFVxFXqdNgHRM4CvdjiCjHbSjLnFk3/5QkvqUjn2lLZ1gJqEb/Zl/80CDj441GI5tER7AKg46GWL8CfpRyLpPNMlc85TbFDM/WgQgPzotLCxOGuvI4OG6B8mPjyWjPJeyyGYUxLcAICG/lAErP3PzjpbhpnyZjRCfDIQgpSFk8JAp5zynrEK2zVyEdHU29cpoGPNZAGRCciEiEmSIWMiFEIJpw7HSViDkqUjq+MjtCkS0J1vOVZs8oCFBiPjQcoTUpZg28hHiGW7G4IyzyLRhUsqQKeMLd2IyTS7W8IHmc7SZS655PxJJU5PuzoMFiEWzMxIjOhhx/e1jrg1EiLGzTv8xc+yNS/DjzLjScJWEqxCyewjXPbrC9JGICB05YktV8N8/pvGTf2xw87yCEGWT6kangfLCpA3T0XHwSeQlJBSXcmmsWNNv5QX+zu+Z6bmWf/IrbuIVLM4x2L0SkGU4o1BKdVRKfaaU2mxek49SJlApNU0ptVUptUUpNcHv2mClVJZSyquUeuJk61mcuxjBbN9xPCg15QgVWQfDmMMw5hxxXnc9tiCSr90b5dhEV3i+Ft6vZRLT2ERHnxXUwS08e8Q5cAlpfkl7Akuq+PdTv+LfT/3Kl/YFQC2R4/sgia+Puw7AcddyPC8ynheP24eaKseJ4D+OyTzAZB5AzTSCTBbnJxyXpOMdFicFu1f+ePAJPyPiP168BFPTFNaTzkFCOEgIWxLbCQED9vRuxtdpHVyiFo2IHcUhlr04hOw5rqlx5ihDiNOFiGgPCGFaY440hLj1Qyx1PRDrXZZfu+FABeQtQIjbMHM+F3ETXgTlFQjxdMSTCkVYKaQfQsrWIP8D0pE5xJnPVUhsaZYZq+NinAzlRXJUL8S3DhQgokt5uCq8Rbg5TZ1UK9GmvUJz3GrODTPnnwRfivE085ppynolJpYLZW4sMMeriDBRjJmDIzqFWfta4OemjXhz3smXirTxk99uEDK6HMkzu9zclwxgEKg8qA2DLWntUHngJRiAKPYQxR5u9VPFX8gQagimxpSxOEdg90rg7Jjmi8A0rfVbSqnrEKeGgY3KXItEsCUj/4bWKaU+1lpvR5w0bgJ+yZHPGY5Xz+Icxfyc0QxPFRJ1N4/xjDI7T/OHYLVYE1WXRjk2dx2/zaXGnffvRtHogwSzMzlPlovfJbjsbjxUIruxwf/Jn5DqDzcfxWp4tDyl4FpcA00Gbj2JBoS0cdk48hvkKQVxHwYhx8OYw9C+sIBRxNG8YT/atbA2VslVT8HwSW5fjd2UXz9KlKwyS9NhztfEUkSfyfIjapbx5b2auQ3KeyPDUauAlQ9BbyGmf2qkCHxMkSqLcxsKm3vt1MHulT8ifDaiG6FUkxOaQq+Dn+ExFkYiQEcD0dDqHwdo1fuAkLl5co0ChIA5QkErERfTDFxxniEIkXLyezqCQmEIIcxDyNVG5BuxBCGRTjoXhyQCybea8nPNuXR8CrqRTt1oUz8Nn4WxshDCB5m2yhHC5pDmNbixmhvEfTh5CJBr2gQhfFlmnk1x41BXI4JDExHCOAwhi2HmWj9cEr/ArFmR9OOzygbhChJdg1hOq0xM7ONmLR0twzzEPXq96SsX+Z+33JSJwhfb6nPLfdusE2Zd5pt5jzNjfBS4ByoTgwmfWwPdpWj7yh3oZNhOIl/HFrPf7PWTeIpXuIl01lNEHB3ZZBo/MgTI4izFye2V5z3OKClVSkUDFwM/M6feBp5XSrXWWu/2KzoGeEVrXQ/sVkq9j/yb/bvWeotp6/8dpYtj1jsd87E4tXgRUbS9ldePEOeB0dzNY77Paiby1LMn6ErAOYcR6Wnkq7+AUSgFSVoslkc4u3wIdJkCsS6xrYl6Wvr6hVtM3y6vKgdezrkTunzBS36Ey180yR9ODGldTDe3rRENh6AHOpZWaelYEKGiUT43XIfYBZZUmT7Cjl4RIcMwGpUj/xO/rXJuMXFHdWk+oh8nhLdRypipPgElS0bPS9jca6cEdq/88WEBw5ha+CgJQaVQCl5DxjzbQNXBrIuu4Ooe7wuZckSDihASNBIhN4cQEuTkDs0zjdcBTU08ZjOgH+S9Dcl34lobHZfgbFN3EEK8QIhqKUImFyPEsIc5518nAldoqSlunk8gPMO05SjaJiKEOAvhUoH43HKTe+PGni4y9QIRN1qHrDsiQg7hXmT6mg08gvzFxJkyDrHNQwjfBty8qw6JrEViansiBPdtRFm3GLF6OrGxkxFyG2bm3dOse1PkMVBPcy+c2Ni3EfLqOFCNNmu33vQ7AbgemAPh5TUsfeynDNj9GU+E30Vz9jNx7T8YELmUpQxgAEsB2OZN5H3PSDLIJpsMWlHGHp8Z2eKcwMntlec9zrT7bjywU2tdB2Bei3CdHBwkAN/4fS44Spmj4aTrKaVuNq5NWbt37z5aEYszhGO5nuaYFNJgiGczOUIr9xJaubdBWf1f9zga5jGCgbevwkMNHpOknK8fguJq+hR9RB/di4Bd9xJYUkVA7yr0qIb1dSrQ5QtArH5xvkexZg4zxarrEOkG13rJcTwUkShPU/3sSJvoymblWiWP56LrG6d+qIEF+Vpe41peO2Z5tUiOBm3MkWMLXdhCF2IoIIYCrmYuVzP3qHVOBmqmkPvjKR1bnIMIPMFhcTI4K/ZKu0/+MIihgDt4jqroAP4bm8bei0KoDQygNjAAmsLGiy7g6jXvC4EKg1rnmWc0kiszCiFPyYgVNBDXXfdC+Vy+GMKTEfKVC8kZiGV0A0IkNyLfsO4I0VwpfRGGEFp/IrfBlE1BSKoTc5mBEORhCGGtMm0726NDgOMQgrsIIb8Oed6AWFeXyxjJQuI/O8sYyMbNEbrIzDfb1E1ESK2TIqYpQhBLzbhrzfu1yLd/vBl/Hq4bsRNjew9i2exm2otGyOMEhGQWmrZMftfq2bguz/MQkhmHkNUos5b9zOHE0Uabz2EIyY8CBsOA+Z+R3zqWHqyhA1vZ070Z7St3kME6WrCPFuwjqK4egCe4l3gKLSE9V2H3SsvLHWitXwZeBsjMzNQnKG5xGqDMxqpng1LLeKHRXfC5nBryNVh3a1jAZEs5GN4SdZO8j30lnzgg2BdcAtvpbNoDpd4F4Gn9EM/cV8v7BVfCBV8BX6D1xSigJfsAmBbzG7/OXkeZ1J/6C1DqC9Nmw3yg8xiNKqil/9gPySaDpVwOQBJbTAl3Dko588SU+Rq4zGeJ1EfRLtD6IQJLqqhvIyFi/iJDP49x9PpdK60yyVqXTHJY8ARfeZVqrKWmjcbuwsfC0VSC/cdCl4Yuw4Bv7UDWz4dsIMPmOz1vYMUbzivYffKHQUc2UUg87T3bSWQ7+2lOqEd+lW5OiqfbxlwhUhXg7Qf7Q5vRKvuAkLA+iJJrPPILrx+iJpuM+7dYAZEZuHlJnZjPMIT8BSIkyRHdaRQLuXMltI1DyOZ8hAA6pKvIlIvCdQNegJBLx5oLQj4LoPpFI5oUZsaSixDGYoR8vo2QyWyE6Dlz2IYQ2g2mXB1CGMORB9RVZg5FCKHMRiyeq3FznWaavkrMXMMQy+0CM5YLgXXmfIHMZ/s8aD8IN57VyXPa1Iy9D4RMAp4y8+8BTDVr5TXnBuGm04k254Yh5PtVhMjnmnUvg/jKYvaHN6eIOAqJxxteSkv2UWSSmwaG1nGFdy5rPD0IpI5M1tKefFzHCouzHie3V573oS5nmpQWAm2VUoFa6zqlVCDyb6uwUbkC4ALAsXM1fqp7LHzXehanCSOZBcBcrvadcwik9tPaidUXMJXJFJHIrdpV1QXjrltQy6KCI9O9OHj5lRsAuIk3AGjPRt+1IcxjoXGH9SdKOiFI+tAelwjqEGA0Sk1hmN9PMKW2Q7f2bt1GZNQfMsbLj339+OlLT0gO62LCfG6x/lbGxi7DIO66S/3S4fyLG91r39FC2ThONW3wf9mQ8xM5t0e5LPsE0GNBtcZ1i7I496H40TzhPc2we+WPCJvpRBaZlBBDOutJ2FAqQjpAq0O5VCYHE76yBkrBsxI8iQeEdJbh5vLsh9zVSnPO33h2CCGOPYBaWLYQ+icgJMix8G0z7URA5XwId+I9gbY9cXNp9gCy4PByaGKeT+58Edqm4Ior5ZmyZbhhji8CQRCSATSDrxZC1yEIiWyKkMOrgWlmrN1NXUefMNn0vcHMzbHApphxL4fDBdCkpxlDAWIxzUcsueCLWSUDsWjeatrPwFUr7olYYb1Sr30kQngdV+ZsUx6EGJcgisPJSDzq82Yui5F7mILkYnVyp/ZAYkmnIRbZbFwXZhN3GjQPuvbeTNfKzey6KII2xRX8N9ZRYpIwmlWeXhwklDiKiGIPFucYTm6vPO9DXc4oKdValyqlspE/3bfM67pGMTIgUQE3KaXmIP9+rgD6nkQX37WexWlAJ77yZRB5gMk8mtNQslVtBt1R3k9GrqVyh7nqKsmpr+DJro6AkOQFVcrEfxZPQQEvN+JBjnV0CA3VhBx32qe5r8H59mz01QFDhA35StMmv8y67cA/gYZxkupl4EXo+MVXbH5OpHz1HQ2KUHgSHnUOMTYGVLqxmktYA8DLStajg+7LvYiV9FZeF4tnnLF4FjVuUbCJTr73A45yXakX5M29t8nrCP9r8wFj2uzWcN7+SEv9L+tXXHLEeSfOVX8hca6qoBZVALEJ8tta77axpecVbELwUwK7V/74ML7yTbaEX0A1IexJa0aLygMA7GvdjFZr5D3Oo4koXFdaDyIU1A8hc461M9qcByFUUfhUaPtn4MZSppvzQxCCFmfcfJNx4zcdwaDlCPHsAU2izPVm0NZYcck05T2INdMR/AEhhM3M+23QdRAusTZxpiXjITICmjiOThthe7a8bZ8hn+nh164HIcD9YPt6aH+N+ezkP3XS5WSZ9u5BSOpiZJ9z3G4X+K1XD8QSXGDKOa69laaNKFy35FFmbTfj5l11yD8I0SxHHJScyJsChKDejxD1UabvQNNWmOmzKaxLTGEVvciIzSbDuw4H2Z5uNGc/N/AGWWSy1eYnPfdgY0qBs2MJbgX+qZSaDOwFbgBQSi0AJmuts5DnSj1wIw2mai3JG5VSfYBZiNOGUkpdDfxaa73wePUszgxySGUTXXmAyaSlykP59a/8BLW5YbnbZkpeTz0WVChQbeIlvxQiNMmQUQeOlS6JKwG4OUcI3U2NXEAXNhIMeiZHyOj8VLHCbg2VessPOhm1j8QGtaABSf22UO8A9KT7mJVkspIJvMp60pmu9tNRi4LtJty8NH8YMYVNdGI77X3nbtYS+/ocXcAvBypA4wwuSlWbd+Lf3Ngt2i33N/hmEs4OqhtJnIjw1Chk9wTWHUbrJjQm5RtmipWUsVpIaEmVT2zpeKJLICT1WNZvi3MQipNxSTrvn/6eIti98keCMbzDwvBBbKITE+peJby8hp2txdTZdkmZTzG2ckQw4S/WCGlxRHqyEUKzXMqQaA7nM6ac427bFCFOQbA9F9onQ2U2hDtquysRshiGq0bvuJyCkMs8xNU1Clfhtw44gBDcKISk+auLFsLhCmgShjxiyUYIWjQ+EaWYWxGLrONSXATtHSun14yn3MyrDkiD6rchZBu0vxA3PrQQl7jG+Y1hBkKuaxHCORX5K+uNkM4XkdyklWatUsz8MnB/PTtj9iJkHTOmQHPOad/rt5bZNCTsVQiJ72bK1Jpys5F41DSZS/PY/Uz84B/sGdoMj7ee18OvB6ATm3iAh3mEB+nFKnqxip64pNXiHMDJ7ZXnPc74rz+ttfOsq/H5YX7v64DbjlH/U6DdMa4ds57FDwdX9Ke5Txn20YLJcIVJp/KWXD1WDKE+CI2JjwNlvE8d5VlfHdNWJisByPL567i5P/VAeDJ1IgDTMTK61VNYrhf4yh5LwTawpIoleqG0lyNj+3WqEOWhN8cy5OaFlBJD0R2rmPHOrUwwJLqaEPBzmT0W4tlCe/LZSpL88gSu4H3fWMfwDgCvmKsLGcJ60tk8VsjsT2eKk+6qo9hCb/Ujsf5xsY3xFBN5E9n01hn9+xf0HG6LG37McQ9jDkPHirpxYyhHafdtv3Q978p3oGhSIqpAfvEo487sU+x16pvrlrSeQ7Duu6cMdq/88WAYC9hPc0YwjwWBw+jVehVRXuOSaYhR7SAI3yAuvJQhxGg+QmiiELLjuPIW0zBGNMJcy0OSCtXKtfbXAGsg3BEScso6RNFRrXWsgAmIhTDWfA43r9Fyvnq2cc8NRFxXixCCiSGkvYFCqJxm3IMH4hNkOvwmNMG0F4P8Wo3BjQeNQMh2gmnXWFdDRpvxlSHKw0vMe4eI5+H+8u2MkMp8hCTmIpbiBIRgvg4MgsoHjLU42qyVo86LrBcpZixODtcDiKW51Mx9NPAbfO7SvtyxIKTemdNmfOQaL+L6u0DO1WZA0oYdEA9ePOwPryPDEM/NdOJ1bvSliLmDZ4GP+MTGlJ47sHslcBaQUovzF2oY3LzgWRqnM5FULUENiJDEjLouq3qsEE718ZGEU5mfYH7c0YfG6UmizGNb5c8Drzuy3tbJpt74LvS78SEueG0jsBFPiAnkIYzbtfzDz6c9H6SGMTdnJP+jmgEv8KV+hud8rsZiEX75pjthLbzwxXie4w66mQ3kf8cMAWApA2hPPrfd+Lr8AFgL47mb5uwnlRymMZEYSnl87EPoPyhSL/qCJLbQgzXEUcRw5nNx+Rc8G+nmQg1+vhJ/qCWQob8E4H5eoYwoXmSOS0zfd3bWILT+PWo6MO1i9O1wN4vw+j3avtURd2pzJIntZh4Rr2tERv3JvJ55pEKwnuSoFYNOMK67Ca4Kr/+DCn8y2qp+JwCxAfLofz0/weIshH36a2HxrfE64/g7vycYL9O4XZR2HZfTPrDzoijqCCTBUyqWtENITOQwhOS8iFgHr0GIaARidXP+nTvupnFIvGSEacNRpS1ECFQhQo7KTB0HyYh4UhkuqQ1CrHwXmjqDIcRYZqvzxPoKEGOeDzcZadqPhPCJSGznIkTkpxCapCHkzVHVdfofaV4X4wodjTPzyUbiYp3yjoJwNG7qmWjcHKNZCHEsNX29ZdavwKyJiVcNH4xrme1prjkEPw5XMCnbrMkg3DjWMES8qAeuUvE1iIATZnzJuOl8vAgxftWcL5e2g9ZDZc9gAmtrqSOQTXTifa4AIJ5Cn+jRNG4njiLfZ4tzBHavBCwptTiNENJ4Jy8dp4wjXrSFLqgRJhZy3nEq0JCMNiasjeFz1z1GOccN+B7z+ZLXVvC5WgqvjQHgrfBrWcDPeZEs3GzXsCjnUvbRkjrdn+kvT+JpE/lZRyAfzBzF8LGzMUZaNtGJ3OndmHO77KYpKpcM/SWxFFFKNGNfe40JvArAAzzCZ3ED+GXRv3hv7LX8cua/WDKzF0v5KbkrutG+r1idn6i/lwd2P0xo82p+d/Dv1NXKI7bbI6eTSg43F70Cszz8adIDADxX81sIFpJ+1eR5YhLJxBVoOArmIxbR4+UhvZSPAFhnnsj6q/9Cw1yz0FBYCuQBRexYjsB3Ud6NMQFPx1IDtjgDsE9/LSy+NYqIo4g4WrGH/9T9gqqMAN//6rCsetZwCaO2fCCWvQQk9jADsQIuQMhMink/DiFR4IoMrYfyUoh0LHcmlQlLECtdBS7pclxPvYglFMidDSmOy3AWbjoYx2LrxF2mSzshRRASRsNfnJWmXg8kvtKJCV2Jz7pbshJi7keskcbFNc9IUUQFQaTTz0uI1XMEsiYLcYnzEIQcZprXXDhsfkM0uUbGXb1aXH7pgavCu9G8VuGS+Im46sKO+3I0QmYDceNus82cwvHF3FKOuAVH4MaOYtpaj7j55poyHoR8OzlVw2BX9wjabKyACCiODaaIWJ/HVEfvJp7w/I4iYlnGADqyCYtzDHavBM58nlKL8wxqhBzfFg45bYDXp8DrU1C3u7k/48jnKSbyFBPFkmosoB9xKR9xKQCprCOVdagbpV4nvqITXx051q5yANw19TEumboCgBlaMqB28yU8E0zPmcT0nEn8jicYfOMn7KMF0ZTCLVMoI4obeIMerIFrpzBv41XoOYqhOXNE1CgNUgq2yKE3kn17T8bwDjN/ciO9WMV+mjOwaCk5NSkwF1bRi7EzX+O9p65lKQPIIhP6TWErSWwliV8GvEt9WhjtQ/M5cG8rqu9qSXXPliSxhebsh4893DXpMf7y+CNksI7Hgv/Ic/yWq0Y1YvzZQUS1K0GpKbKWWcDDIpa0Na4LW+MaElL9xdFdfdXUI88dUUZNOWocbvHMRJ9bt3rZCEU1rjtdDocE7wloy56AtqznJ9ZKejbDEW843mGEjpRSx/YLt7D4EWGzEaNbQV8OBoaQ40klLKuesKx6aAajVn4Ah2DdyBQqM4JFmX0t4vqZCBjBQLojYkVOShgnvjQZIjNNHQ9uDtF4xJLXD3Ep7YdLSCPxKdymZCAkFIRsXYiQtWGI9TMIIVRFCMmLMucy/Ca5Hr7KRchfpPR9uBQhbaZcTCZCUjPMuO+B5Ew5goIQwpyBENIIxOXWjKM62/TpkO7Z5nMVNBksB4eABcbFuMqMPU7O4UEIYoXpu7NZy7lImhhnLZ2yEXB4nrSxcp45F4PrMhyBkP4is16zzVFl1tKx7DoqwlVmbpFyf9qsqWBj5wv4T+wgklbuoBVldPRuoqN3E594+pJJFtMq7+J5fsMd/A+59Tan2jmFk9srz3v8SKZp8UMh4t1dAKiZbdDGAuYjIv8SK5n2s4xtoYtLSI0nqRoGHRb4kdRWUDw5ET1VCO/WeR2O6PdnfOJ7n/uym/uz+MZEivyspI6bafbmnvCuqP0q9TF/0F7SWc8/1B5u15DEVtaRwSp6UUcgt738OlwmCkGb6ATZMJXJAAzVX9GercxlJBlkw78eYlhnsaqGUs17r10LO2DJZBGz3EsL1k7PZNwX78BLEg8KMDxuHvMnj4b/g+K0RPLf2A73TGH9pHTe73U1/6uHcJ+x/C5jAG/vHsk0bueu6aIgvJUOtGc7o8rnEPyLSp5JFRGnB//wCIHUMp4ZTJ3zELcaJaTNS7rCoGpu19P5C8HyEADgemPNfPjEokOOyp+WpWB4qpPXR9LR3NpYhMnAsaC+MLZhGX+5G/9UM/p2l5AeC9ZCehZC0VDc5OiwQkcWFo2w3njmzORaJhVOd4nmNvB2g72hEXRbmyukxSGNKQjRmm3eZyMKt3VI3Kfz77wCIWCBCPnpjRDTSMSC51gLCxArX0/gTSjJluox/cz1TKAIDmcbkleJxFPmImSsDCGtVUB3I0LkxKUmQ9cohMjOk3aaBOEK/0QjJHml33jz8AkEheQhlt88qVO5xMSlxgIbIWQEIu7kiCElmnVyYktBCKYjhFRp1mENvvQ71Jo1GIyQyjxT7m2zxiBKugtlHk2Ma3LvWITQbzD1UpBUMd2RuN8ofFZnKsy6xyHENwh5OJyJq1jcVCylAD9fsxidAqnksN8jIUWZZJFDKi2DdvMUk/hj/V+xOMdwcnvlea9Ub0mpxSlDDAV4gqF05vHJgX+soLofhj8qPp8hCw6yYZif1cuQWPrUwmNBKKNFNH3EJGbMG0P8a3dLGy8/3YDM6JtN3OplLgF23EozGj08LCAG+Jfv89s6mwd5mHG8zhAW0ptVrKEHf735bnohCjz5tIe3YMcbSdx8w7MUEk8wXh7gUSKXHIQ28MG8UQwdMYe5JVcw/MbZzM8Z7SOfwdQQSB39L/6QISwkn/bcyktc/FUOv546DaaKINLMnBvh6wG04EUqPvEQoe6nvy4BJKXME9xLd7L4hEtZO7o3/AzmtxkNs+D6ma+QkZNNJlk8xx28p7YwqWA6t8Q/Q3+W8rK6lBf0eN7RY3id8QzWOawnnWL1Bj99YymfFYjVeQjzWJQzArqIgq/kbXVRHGfiQBuln5G4YXf9hzCPwbrbEerHYHK+ft1eyjvfi6NYVD/hZz5ruWNZ9Xf/hmMLX1mcIViZewuL74Q4iujFKpbSX/6/mr+j2hQIPgRtKirY0r0dSYt2iMXTiVPMRuJMFyKWNifWMRmx6oEQtDqEEGUgRMwovLIEIWfhuClglkv5GCNfUP0qhAyB8tkQORqaDEFUar1QXQUhcabdDQhxLAWKICQGNz+nozK70MwtHxiOxNUZcaWS2RBzjXzOzYaU0UiMKdAkDvkRvw3oB+E9cXNcH6Kh8u41CPF13GGd2L1oM+8qhACW4VP5JRLJNxpt6uJXb7KZm9NGPzOvJYhmxWxZCzBt5Zl+UkyZCrMmmLlHmjqD/e7FYuBt2PtxCMFeL22WVFA6MIY9PZpRQgw5pPpSy8WZDfip0EnMZzi/Dfgf0/hjWJwjOLm98rx/gKv0SSa2/zEhMzNTZ2VlnbighY88XKHF3WguVx9ZZqbfh0zApBrRrwgpBUh71KSHMa6Yk5FYyL8USPs3J7gpYF7iTtqzkVCEKOXGiWX0+qJXAHiDm3zxhaW3G4L8G78xdJkCVz5E2uz/cisvUUQcj74zldvHPMXtTDdpazpSSDwvF0wkNqGQ4tBE3j4oMaHN2c9ChvBGzfVUPNwGdsB7rw1jM534o2rBT3VfhrCQP49+jA9mi/rtdG5n/gixHvILOHSdoulFGq6AiEd2sTq4Jwv4OdO5nSUMZB8tuOjiTVzxxSzez7kausznT3o1qxBJ2hbs4zI+pgX7eMMo5AZRB0AsRQxgGQ/yMFsLOvFMwkTuinuJ4UXyaNfJU7q9vD3BTWtID13PZ1MH0H3ySi7jY9bRjUV3CHns89xHfJoj8aL/Th3BVStkd9Z9YSJPATCNSce853qsvB88Vup9Unkp1RE1ck3HCCEFHynlQxE/Aj9i+pg8nIj9g6PiDAdrhBy3ChYhq8Zxr77vXB+bYubbQCm1VmudeeKSJ4fMVKWz3jxBn5mc0j4tfhjYffL04RVu4EEe5mEeZD3pPLf790K0AMph70UhHCSUtl+WCXGsRfbWFxEyVIUQoTLE6pdmymw0bTixnyCkqR/kPgsp6Qgpy8BNtRJh2ovE/dHshcOrDTFMQKyj5eb6QNN3nrjjNhmMELgqJObTEfjpZ9p00rn0wI3NTEAssUXmfS5CQNOheo5U91lcgxDLYwlueptEM+5Ic20b7JwNbXubsTn5UTubdUgxYyzDtSA7YkjLTZlyhDSuNn0662diY6u3mTGF4VpkHSu2x8xhAkI6HQVekPvnuGc6isalwNX4SHStkbPYHx5CEXGUEUU66+WBBZDJWiToKJXttGed8X+26runD3avPD2wv9Ysvhcc4ZqRzALc9C9FJBJDAaU/E1KoP8LNRWrSf6qZwOXADsn9KQUbxQeOM1/RJe4ppb7gAh1K7sxuDYq+qbqTputI57+UOHGG0yVNCUiako20x6Nr2MQyrqt/i4eYQtnf28I1tazgUrLIJJ31rKU72Rf35M9f3Ec1IXx88DLiKQTErWpaye3UTwsjYGIVoc2r+dXBNxgQupQUnUQi65jINNrPzuchppBV0p1uMdk8P28CANGU4lkEv837O/9TcDf7FscyYejzxFPI9bzJG1zPg1ue5N9fjCCUg/w19T5S/rGdVQQwy5D+QuIJpI4cUvk7vyftxi3w+t+4S9dzN08DsK++BSkJ67lr24v8peheBrCMNfQQSy9Q0yecGuCzpgPo8MXXrH2tN2t//TFX6b30ee4jktjKO5VjmJE6hkfNQ4KjIYmvfaQw1SgM67EN782imSPoM/ZI91ut2zf4HBhVRWCJqPZq/ZBYuI2TShGJjOGfgOvyvKcmin3BbeR78TspF/v3fGLHQnGBPEVuLL5k8QPi5FySLCws/LCedGYzmlRyuJQVUAFzkoYC8POoD6gjkLZrysTiZuIZWYCQGS9CDAsQoSEPYinNwE1DUouQsEiEbK2ElOuh5E2ISUYsr8MRcvcWQsAcggXiauvkLY2XfivXQogHmizBlzqmSQJsnw3tf4MQ1yLYbvKYto+Tzz5V3QrTXm/ESphsjnyEwG0DyiAkypR3Yk8LTRsRCCnNQMicI9BkhJ/a9kZck/NxY1unumtRbVLVhDhtZpsyvc381yAEtaeZi0PwByJKw3OAPkj8rmPBdeJ1R+C6GicjsamObqJD+gPNuRLEwrtS5lPZM5jw8hpqPVBCNFvpwLDKRTwXfjtDkLR0Ud49tPfk04J9xFDis6BanEOweyVghY4svgOcdB0OAkuqmFcy/KhWUn/ojnB7x6dO3L6awl9UMFN5xHfuJe70HQDfqKXy9DcNdBFQfKT6zn1MQfnpG/ViKevpyoXDiighmrIdMdwf8ChRv9tJn4SlbFAJFBJPIfFkq2+4/4vJrKU7gdSxNqc3fXKyKCGa29RveSPmV3wxNZX6Nk9wIGwaU0If4hLW8Ao3EU8h8xhBDKU8xSTqAppxN0/zMZfxMZcxeuN8xg9+gVuMLrH6q2YKD/HQA49TRyDrSWdF0iWsohfprKc5B/j8xnTu5mlarThAqxUHWEc3iojj6hXv06V4K2TC83ozz8y7jwtuLCGHVPoHLCOVHOhQyp9GP0GfO7K4545pNOcAzTlAWs5/uSJnFtd/8Qot2EefGz8iRY8giDpaso+FDGF4+DwWMIx7eYKrmsxjeN/ZDO87m058xXR1m48cqkcbrn1gSZUvxysZ+H4EdA/PQusYtJagHf/vklrk1m8QPzocuE9Ekv5dcC1ryaRVcBn7gtuwL7gNamYja7zvCxAkh8WZgxVvsLD41lhHBsHU0Gr3AWYwni1J7Ri18gNGrfwATxWEeA8KcalCiIyjghuEkJzlCJHLRciVk5vTX0k2ESFx0QhhWggx6bg5TucjRCwaIXf5pr1cqMwz5SKgfIb0Hd7PpHEJN3UuBCKhfTKiLjsICIT26XJQAuWOW2wtYi09hBDnOChZjhC6MHMkIMT1HnNUIDGh6837FDPuIMhbDIeXm2sOEfeaNToAh++Rw0c404SMhlxv+onHJfFLzNwP4bpAlyJW1s4IOV2OayVuas5lmD4jEJK90NyHUrMWkebIxn2wEG36CzPvP4XwGTX8q/UvCfJC5y+/oQX72BMewRAWEkMJMZTwiacvcXXF/KL+/0glh8lMPeHvMYuzDHavBKz77lFh3ZKOj8aWJ4d8+Oek9I8J9E8D4u/y6RMdUvK0j69NuS6m7ktGGMlf/GYyBEysov7BMIy3aoP4QXWheXMI/lA0hf8wjH20ZMd0EeR56/YrWcgQ3iwYz08TPiG7MoPZ4eJW24lNJH9VyN+63kEL9jHX5AD7vL4HZR+2hZ9Xu3P6NFT6i63nTxc+SCgHiaeQ7mSxlkyunfUelIFK1Dw/bAKX8gkHkTqTeIrxvM5BQphY+TILwwexjxa0Zzv7aU42GawnnXTWM4FXmccIiohlPDMoNT5D7zCGx/72ZxgEw7q/Ry9WcV/lkxSFR/MuVzKe16kjUIhl3Xwi/uWlww1fs3VOF/jlCzKJ2NtgAnSY+jXprAdE9XcCr7KPFnRnLX1ZQXKBWL+fSZjIE/wOj3lcvvXCLoRk76X6+ZayJve79+Fo34mjwf+75JBSPbhhmaN+vwpq3QKfyn/rDmMbxpdafHuccpekdKWzTpDiSV14/rsknY+w++Tpw6V8xARe5TIWE1dexsGwAIo9os6TtGEHxEBVeAChVfUURUaJ1dS4zPpcSx230w00FPsBsaKWIsTyEEKkKhAylodYA51Y1BRcFV1HIGgU4sZagJDcKMT9diXkZUFyP9PmIYTQeaAkD8LD/Nxuy834nLQrTiqZIji8EpokA4Og5HETV5pl3IGNmJAvXnS9mVe6GVNThCyWmjE3k/59+UCrcC2+YYhlshiIFGtpyHhzLcuMvwgOe6FJijs+kgFnv5tszmebOcWbcQ3ETavjrNl4c34Nbpqe0YhL8yAz7mSEbCeYsQ2BjUMvIPHgN+wPbUarLw9ANHwd24EyxGxcQgyt2MM6usmDaGBo66Xo3VicJti98vTAktKjwG623x4NyEUOPtcXf6Vdh1yMjHmf+eXDqRkn8nMnykvqT0rUc6bdO+BuE8T/NPcxDXGNncirTOQpNtGJTXTiAR7hae7mdpOPtAX7eJUJNGc/yyoH8Nfw+7hLxXGzbsHLS+6EQVMYqsUq+Yvy/zAu8nVe+uAu2XRMDMm9sX9hAq/S+a1vKLgumhqCSZq1g/yrY9lDFKFU06lyK0HOjwTzlHfFRZcA0Hfj59JQLrw2ciy9WEnnf3wDabCyR3ei2ENcXTGbAjtym1HKfYFbuZHXmGaCY/u+9bkvVkb95s98qWcSRxHBdTWEv1iDvgYOhgVQ6omhfXkxqyK7s48WTON2rkXMite++x7xV+axY1sHOlyYwzAWsJ50XuEmRjCPTibXWR2BzFe/gOuNyJHDB9+eT3cdSQkxxFNIonHdziKTTXRFvQP8RP6/xF64HcAnUHQyOJrbrcqB2FTXRbxBecdamnFkPYuTwynfaLsqnfWfE/SZcP5vtOcj7D55+nApH/F3fk9z9lNIPJdvXEZVoji2hW2shwOgU0BlI+TlU4RIvo0QtPmIO2gYQpBuRdKYOEJHjshRCq5LaTRCwhxxokO4saXZyB7oCB9F48sb6rRzOBuajDb9ecRS2WQYQg6NBZMNiHUSxD3VUd51vE2dXJ4OgV6AxHn2gZI5RmjJhP/Q2YxpkRm/k0c1yNRzlHzNGlQfMq65+bjxoJjrdUhM7r+hshLCO5uxpSDkvsiMqTNCYDsDjhvxYr82Bps6ExCSHA+HZ4sbs4+AByHxtA5hdayvA4GfI7lQu8n51Vdm0PPebAqeML8zinewMzaKtvPLmDN8qG8KQ7wLKfO0AmA77XmAhwEbU3o6YffK04MfiUHY4lTDcYvVJs/nsQjALTwLiPuta+m6FrWrYTn/WNTj4Yo7ZnGQUIYATpfqRnjeWEuT+JrXeId+k9ewZKqIAv2V+2jJPgBG1sxhVvA1TOd2ngqfxG1zXidFr2MVtbw9cCQz9VjmTx9Ft9uzuTvyaeIp5NKhi/hk42BmxV4BwBNr/wSRsPS6n1JCDJfyCZOvvp8Sonmp8C4oh2kX/ZoJKf/Akw/Dhr/H3IO/9Cnk3dD5ZZ6v+w0LOg8DoJpQqq4L4FHPAwxhIZ2Lv4HlkDoyh/s9j7CQISzmMtbfcAkb37gAgG7XfcZTTOJ1xqNjbuafXMVMxrI3sAWPTHyQO3mWl703kbi7mBdbj2MM7zCa2Xz811/4NuSq6wL4kMt59UIh9J3YxBAWMp7XyC1IZ0LCq7zDGF7kVgp1PG9xHdtpzy/GfgzAcH2IImBHQXtiEkqoJZBsJJZUrOCOUgQULzJ5SNsd+V1R5um7Lmqophubmk+x2oACtHZTWBbnJPraaKy+C0f/LiolqsUiruSmmrE4zbDquxYW3wlFxJKIl8u3LIMoqAsyf0jRNXAIVC7yv3w9QpBWI0QnzzRQiWtNXI64j15vrmUjpPVC5CGjE4PpWBfDzPWNSIqVHuKmG+mfm9QR9LlK2q6thSaLECLYFJpEI4QxAiGLF5q2HNXZvriuuU1xyfEGM4Yqc60C2AYxcaZcoN/8HDXcfNz42hGmzx5+a3EdhMw34x2BT8GXQ2YNg8z6jIBwrzm/0pQLk/byVkPyOLNO+YjrLaZuESIotQghmC8icaPl0ORWGrro5iIPEZwkBctNf+Xm/QjYc10zWv31AD3Ks2EkfMKlXJv/HusSU4iliP8OTyOUaoaULwOgJDKCGoI5SChRlLk5Sm2A3rkDu1cCdgksTjHUZIidmg+pDsG80712I3CvvNepoOaLG6nKuY1YJxXICNdy6ogn1d9kYiMaWVSfUV6i9XhSXltHLEX8cvoC0m7/L3MZyZ+mPsDrjOfNG2/intce9in1Vmxsw/6uzQnhIEvpz9hRr/EHHqeEaApJYDjzmXfgKvKJZR7Dmfj//sEtb/6D2jgYVmceMz8LjIT+EZ+xNbIddQQydfejqI80146dSXr8eiau/Ie4DSXCgvt+CffArNAxALzx7s2suPISMlhHDR6C8ZLt6ca9PEEZUfIk1FPGQs8QRn35AX+5aDIvrbwLekOOoeLr/vpTuA4GTPuMeY8NZgTz6c0qkhbtYPbg4eSQys88HzOk9UIe+vJxCIdXEm9ixB//7RNsGsM7jKj5X/ZtjkWVal4eeANRlPHp6J/x5OyJvMuV1BDMxXE57ChqxRouYQbj4UFZhvkFIxmUsJCodiXsowX/XvErZvQdw6vGak06cEgJSXTcqht/X5Yc/TyY748WMnsDoqzMhzfJq/m+NCCjY4WkJnGkC68TwyrvLRn9waBA29xrFhbfCnEU8TR38zo3+lxIw+NEtXxPUjNaFRyQvKWfgu4HyrEeehEimomQHxAyNA8hUdvMuSppkzUIeStCLJL9EGtoONJvCULCiiAyEtdLphax7C2ScuX5EDkSDi+CJhGI1bAzQsY6I4TxEEKQ/YltPq5armP/WS/vq5dDSAKyj4SZciWmHggpzcLNseoxc3fyouabcTiW2UhEh6LUvIIQw8VmXD0QQuw8O+8p60tTqZucbtaxCngSeN6Uc1SCa80YDkH1NRDyOEKKI83c+5l7UmvW3ZlHvGkj14y5M7Qaf6BB6pzhPefzdWIHoigjiDriKeRzerDQEONUcmhfuYOPw/uzhh6kBDiiH22xOEdwcnvleQ9LSi2+NWIoILorlJhHfa3qdwKQHrAekdMVNM5V6Q+lphCtJXijNEesX9xHg7pXmHp6npRXCqL1eF7gVgAW0QOA3IJ06hKC6H/7h9zPI8xgPM+8fB9kQ8gzeykkniS2AvBW1yu5qmg2POaBFsBftrNfNyeVHB6u/DNBq0Ht0OhCxZ3el7nlf5/hbp6mc/E3vBR+CwCpb+TQ37uUdz1X8qvCf1MQH82s1ldQf7mSHwdVsHHgBQRRR3RdKeFv17CndTPuq3xSJtQZioijjkAurfyMVeGXkEMqN/EKs7ia6LpSSIZObGbORUN5ikns6d2Mp3vfzSOFfwFgyx/bkawmcIXuxNz7ruGWx57hpafuYs6koWSSxf/xC1odLOOx0D/44omiEsoYGziTq9e8L230aMe+f8ey8rruXMEserGK/TTngtkbqSGYVuxhvvoFf9H30u6LPQS0raK+LIz+qR8CMIFXAegQsJWfNlkHo+HRvqLSu3lJV/428A4AZlFIB7bwE9YfXQn3LdAD5a0/mVTzwDxL4PoxR36H4Nsp63rKKwGo2RUOF5l6h09cz+K7Qyuos7nXLCy+NR7hQYKp4ZW067mUFdSZn2tdirdSmRmMx1uDp1Ae7NUOhqAyhCQuQgjQIUQNdgkwErH8Ge258sUQmYnrUhqNkLAic3jx5e/MWwzJ1+BaHUGIXTYcLgJmm7Y2mPQvhQg5zMUlx3WIayq47rfJCGELNP2VI5bEGBlTyHhE5bYY18pYCmbbl/OJuGQwWq5vfxvaT5DxkGHqLIfqMggpN+ecvNrJpr2m5lxnc34Brsuxx8yps5lHOaLa6+QpHYYrUNRbyoRc43d9JULEV5uxxOHmTQUqZ0H4/WZci007o2V9VaK8D8+qIT6zkLrAQFqUV7M1sh1DWMgWRCujhGhywlMJ5SBDWMij5Sbg1bHmWpz1OMm98ryHjSk9CmysjAv/dCoOnBygDiltHMvnxPwVzxQr15NjJwKwiY4APrnyBYxCGdHcDhd/zdacRtYt/1hC43LpI7Lqda7QnQikjjt4jiLiuKbXXP626g6foNAAllJEHCVEc1eRBKNGtdnDCwG3UUYUt+6eQUHraOIqS3k6/Lf8buP/QDasvjqDhQzhobmP4x0CnjKYFX8FV+9+H4CvWndkYP0SvjmcQFhWPcRAflIsif8ohmjIHx7LDfyT+3gcL8HsoyV9WcGLCKl94oE/oe+BqZF/YD3p/NN7A2F/q4fRsLNzFGVE0XXLZv6e9Fv6s5QaPPR+ai2EQf4tsWYtO7Gf5oxeNB+awn/7pjGfEQxhoY/wLqU/L224iy1p7UheUkhI5l4+DL+cjLpsAMJza8hPiyXxpWIG3PIBr3ATN/AGdQTyv4zg/zGPz7/qy/1dJ/MOY7id6dwzcxpcK0wxRW/k7/yOd7mSfbTg/UVX87fBd/AuV/K5qoJNEvPyeUfRvr9ELUfrhrukEy9c3+YJuef6IZR6lVg9iOKnEuGeKQ3KN7ZyhlbuBaD6/4zY0tEegBjiGtzmSFLKl0d+137MONVxMt0vVnrVp8cv0zTs/I+TOR9h98nTh0v5iNe5ka9IJ5RqkthC0todcjEPsa55gUSoigsgbFG9kKrlCDHKN9fjgRkI8arCFSpKRzx+4s1558dwAUIkjTgReUg86jzYngXtHStnEEIGk5F+4xDCVWb6rUWIWoQ58k0/A3FTwDhxmt2hZAbEDEIsqo4l04srghQEOxdC20waigeF+ZVz6jlxphMQN9rBuKrDQWZOzn8bj1kzR9m31JTL8HstwI0FdVLTrMSNS3UUkOPM/GsRz6A44HFTrpvpdxzi1huE68bcAyHi63FFpdJwc6caEv7PtKuowUMnNpFKDltI8okOfkW66GQwgFt4iYEmh16Jz0fY4lTD7pWnB2eclCqlOgL/RKIiyoAbtNZ5jcoEAs8hZjgNPKa1fvUkrv0ZuB33udhKrfXEE43JbraCFxnPbTNfBxr+2PelgzF7pKOWeixhGoAnU2XZJzGtAdH1J6UgljL/dhzrVofIrb42S+uj+UPA4+ynOftowXrSiaeQ4cznTa5nHK+zjAHczdP8Cxn45+Y/+wvcBsBNvMzf+T1dCreyMz6K7bSn99q16ER4JPIeHpzxpGwkcbCrdQSvMoFP6AtAN9bRnu3kkMpfvfdxkecr8srTUEVAtsSDrKIXI15axM5bomjh3cs+T0va7pYEbStbd6f3yrUATOj9PK+U/4afR77Hf8p/yZrIDHrOyqZqpPzQuGP431jIEL6uvIigRVA1XIJE/uh5jMv4mPZsp2vhZmbFX+HLUdahbisHA0MoJo5aAnmTG+jAFjqxmcs/WMa/hv4SgO5kMYPxXMondGITK+nFZSym1cEy7gv9K1vpwO1MZxn96c5aRn85n1sueoaXc1yXbPbDH3pM4bnKOxgWvoBl9f0pe78tfxr1AKEc5FUmsK++Bc0C9gPwzaOdWXT/pQD8jE98pDQmRnboIhIbklKAJDBG2SNEsY54QLLN/D/boKT8CL/volHs1Qn2ceSxcKo32m6ZAXr5mibHLRMRVHPeb7SnAmfbXmn3ydOHVvU7eTbgTnqwhq9IZ4h3IWHZ9QAU9IgmYUsplYnGWrocIUKO8NBsxPVzM5AOH98Dl12DGwsJrgrvSlzC5Sj1OqlmuiNWTcda6JBMcEmrF3Gp7YbPzRejlOvLQToRIcBOHw6pDEOss44YYC5ikYyAw2XQJE7KlhRBzDCEOJb5zSEa+UtwxJe8ps8UhOD1Nv3XmTKO8i5+r2GmvXgzriLEaupPqh3CGybr6YsHdcYRaOqMQ0iw89eZYPrtacbjqBo76WBmmPqRZjwJZk1TzFFgXqtgS992RFFGy8Jqnoq/nQEs5Wf1HzEoQPQdpvEb3uAGxvAOW+kAwAM8bIWOTiPsXnl6cDb8OnsRmKa1fkspdR3wEvI8zR/XIj9Nk5E/+XVKqY+11ttPcA3gDa31vad/Gj9eqMmQNvW/kP0TOeFHSoemzuEyFrtlc4Au/QG4TP8HVotPz5aLu9CLpfSiFBhw3P6CA2pYRS/ef+5qBt3xH6IoYxW9SCWHKTxEIfGkksNDTOHdyivpH76UEUio2juMoQX7uJL3SC3fCnnwZvz1jOEddnWPoM2aCib2mE7BuGimMplr+Rcp5HBf5ZPcEP4mAKEcpNUHB9g7VNRot6xNY0T3fzNv2lXc96c/89iWPzNi2yLa35LLIzxIC88+fr5kMSsGGvXdf3zOnF8PJZUcXv3bb1jx+0tYsPGXXNn5LSbwKnuubsYKLqV4eBxDWMhw5rEq/BJSr8zx5QX9A49TSDzRlMA9EP/vQnrPWkvB1dHUBAbT8mAFhEKbFRVs7buKTXSiE5uoHBxMCAcB+IS+/Mk7lds8L3IvT7CVJBLZztpQUQCOoQQPXv7FtdzF07x40TheVnfygrFWFxHHUvoTSB3x4YW8N/Va3p48kiGjFhJ6sJoBoUvYWtAJPg1i3NiHefLRBxvcxxcZz7QYuC3ndXlSjEkLs3ACxTeaQq//j7yO/22D1D+OMFYgrjiWysEVvnC+byZmVTf+jwIo5YYv+gspWZxa1AUGnriQxcnA7pU/MjzHHTxX+Ht2xkcRmiwPNRPyS2E9hEbXELQaKgcHsy+wBQlrS+Xh3a3AmwjBGgKXDcFV5B1tGl6NS4ScOEsnb6kTI5mN/EJMRgjqq/jEefLWQ3IcPhEgwhB3YS/yLTV5SsvnQeSrCDF01GqdlDA9gWkIecw2Y5sNhwtMvlNjgYyJk7Z85NMhtYkI2YvAzQdaBbkLIWU07uOVOHwqv3l5xhXZSQkTjZDELDP3IYgltJupH4+48ibiuuMasuyLry36/+y9f3xU1bX+/x4zYTIJmcQZmikJSRNIxKSEBsjHeIkgFioWr9xisVitVntp9WqLpbettrZ6tbW1P620em3lWy3WH1QqvXCljQVBEGpsgJTYjJBAchMzdKbMmB9MJiET5/vHWnt27L1VarGKzHq99iuZc/bZZ58zyVnn2etZzwLuwAoz1SER4CZEBKpMW5teR5veK/NdmL55uu9xPWez7q+F8u6X2FtcSWOxnytZw4Q/H+WJd11MKZ0AFEV7GI7mcVv5jZzH1pT6btpOLnu7+UqHw5EH3AV8IJlMFr9e/xNyzrcyUupwOAqQ9TxfMpkc1ZXcCFCRTNoKSw6H40nggWQyuU4//wj4n2Qy+Z3X2fcfwPi/1dGmV4CtFdJBvirXArSquiqIqMzBW4Rym7z9fx8rAFTplxcp7VKquDCxqiNVT2tL64Ukq2A2WwHYxXk2GmuO54sATEr28H0+Bwgd+Kt7vsu3Z65gmHFM5QD5vMwBptJKFetZwm+4gKr+AzgFT5K4Alo9ZzC9+wCJPOj2TKTsa4fp//I4PJuOsfSin3Mbt/LejoPcXfYplg+vFgl+U8sNwAPby85iLcs4i0Y+3vgLKIGfTfwIH//mL9j7pUpmvBBg+7SzmMJBir4dgavh/neJ9OFS1tFMDZe88jgLT2vgfBoo5yD1f9gtzmkR4pCmwbAPMhJwoee/aPjav6Qcuv/S/2EeW1n71FXcff6naNb6ZAvYzIxfB3jxg+8RFd8gtvg58HK5m3u4DoCv7PweT9bPx0cEF8OU0kn2YJxPZv+ENX/4FEve9yhTaOe7v/4qd37ws9x0+Af8ceIUvqEF2joo43ruYRgX//rJh/nX++8hgo9r+DEAd7GSHf1zuMVzOyH8LGUd+fRyO1/lF60ft38o7cC/6PfcoLVpx9Qq/csIvMMhChF/SQUea+Zv6XdPywKHAaWFdHDYsUZ7zUz1T4NSsRO9+vu+WmeyoWn8a/aZ6Oh7x6/+/r32dvSVaT/55tot3Mx13EMzM8jWhUSAue3Piz8yUT4nsjr3AgIui7F1MDch/sSLgB0j8DOKRCb79HgvxJvBXY38VdXI8ftWw/RzsXmmRojlXMQX7eTVtVHBgrMg7AnAzHNlvNDd4D8XC+Zq9fg++X1kk0ZHgf4O8NToXAoQFeAyXl1j1DPmWupkDv0P6i6vjm/KxPhJUYvjDWNqkYKUyqm08yAA7NbrM5HO5/QeXILkfNZgo81GfbcQ8d9t2HqmTv39It1nti9EqL3otkuAx/Q68pDv6hFEzOkh+P53r+NzL9zLb6bN44I/b+O5d9VQSDD1d7GeJWQzyH6m8rVVd3DOit8C6ZIwb6a9XX2lw+H4LvBhoBSoTiaTL+j212XavMaYm5PJ5ILj6fv32lsdKS0GepLJ5CiAOluzPjW27G8J8D9jPndhK1u91j6ASx0Ox/nAn4Bbk8nk7/6viTgcjk8BnwIoKTk1efi3cRPZDNLELACu5kHgp6/qczO3UEiQiQS5gukEb59IHc9TxeepZh8LeYpuirk99FUgB/5466sEjE7zxfi+/3P8kBVsab0wtf16vs8SOrkp9E1u89+EQa+n/enzLPWv42ZNWL2cn9NMDZ2UAvDNmStZSAP59PKeQ3/ivyd/gE8/vZq8c/7E58d9l+roARwBiC3XGm/RV8j2DPJk8Xwu3LhFnMVHwTV8jOcuqmHdrz/Gig9+m1WNX2RF3k9whOHI+8YTpJAMRgF4b/tBSunknu3/zs65s2A9fP/O61jAZn7/pWkU082V037CUtZR1BFh6xf/iVu4jR2/FqR15IPjOe9rv+PIxybxm7J5XPD/beO5f60BF3z/X69jCespGzrMY8UfIkgh1wzfx9U8YFd1QWqJvvAUOOFKHuKG7/8ELoGO4ols+OD5soIahK2z/onzGn/H1rp/4rx7fsdw+biU4NJN9f/BnYf/gyMTx7OXGjIYZXrwAJ8pXwVHoYZmammCGhggl99OPIcP3PosP/myKODm9L/CY+/6EBF8fOb+7/BDxyA/SO7jES6jiVqWsZZyTzu5DJDBKPXf3M01X/oBy1hLbdXu1Hf/xfZV9g/ks/Ljmta7+UnrDXyq6m6MgvMSHmM/UwEpi3MNd9NLPrkcZYDxrAst5Rb/17g99FXu96+mgfNpfG+MFf4fUsVCfsonQKnjAJXJSanff8rlADQi0ewfj1GNTtvfZ6NvuZt5R9jbwlem/eQ/xvx08VvWcZhCghRyZf8jdHtET4A2BFxqvc1hVTZ3dSPfqMlP9CHg8VmEUluLjb5lAItg5G4LBN2LEeCUgwCvHJheg1BqL0GA7XrsHGLIq20MW+N0vP4MAxUw01B0HwJ/NQKcDXh9HKiBkTbILNMSMjUytuf92PIvqn6borcaUNoE/WHwzCJFx/Wcr3NdDf1PgecaveYOiAfAfa7WKu2zY6TyTNv03o2SUtFNlaQpQSKaJsoJ8HP9uUSv19BtXbx6seDMMffrRf3u7saq65rzH0XowU8At+i9LpAxP7f+XjhH1PcvCG6j/F3tNGGxyUVs4DCFbOAiPrLiZ1SlpJfToPRkshPkK3+F/IXt+Ivtf5Vp43A4pujnsdaQTCa/cyIm9LfYWx0pnYVQht47Zlsr8LFkMrlnzLYW4BPJZPL3+vmLwKRkMrnidfa9G4gkk8kRh8PxAeBhoDKZTEZea16n4grw5/k6uRxlLtvpoIxGzuJafswuZnOWyuWtZRl+wgySzTbmMY9tlNLBYQVsPiL4CeEjwg7msJR1bGIRK0JSq/Q5/9lsZDG1NFFMN+2U00oVV7KGDVzEMVwMkk0x3bRSyXfv+aqspI7NBTHUohrEEYSxUUwX4lSCEPqR+OVyYAS7OOsEMoGiPGhXx9Sj/XoQJzoyCoEhqHBCZ0IEYL1AkUZKo0NQWgg9QVlULQCmF8LOIFRnyX4AjxPcLuiMgRtJvTHmx25LIMLt/cCkHIgPw0gC3FkwMGSPGRxzHXGg1AmhhBxX54MnInId033QH5Nz9/TJebx50NInx3g1L2hPBGaWQGeXXHt9AfSEoUjryYV22jkYkVr32GvwyY2JdoDXRMorkJVnVVA05QzIQlaEE9r6tb8XrrnmB/SSTxUBbg99lVv8ojA8wHjK6OQerudqHqCAEA9yNddxLy2IeFIx3TzCZcxjG9kMksEoU9nPGq5gLjtYEbqbNf6Ps4GLmMoB/ISoopUOyhjVgnfFdHOMcQzjYrO+KZyqoPREr/5Or81M/nfTa0tAvscRTkdKX8fejr7yVPST/ygzQkfl3SLcsLe4kjOG9wOQHXsFR7cslmYwyun/X1z84U6EempqaDoRB2VAXTfWgfiwNUON4u4o8vzOQZxnCZZym8CCTRBA1QKMh/izCmj7EOAWFJ9S6kMA4gYsoPTpceh5PgI8ClyDfSW+AQGSh3gVhTUlZGRqjC7X/TEgA9o2QcWn9XzFiL+pRKLGFyGKuXnA1QgYBQGDZyJ03S2IMy/ROVYgIkhG9bdWr+dRvVZD532/3tsy7Z+l53di68ZegtRHfVz71CIRbPS8OTqeFxv5nSz3cng57MueRpxsRsngvPbf8Vx5DbkM0KyL9XupYQm/opd87uRGnLqAvnVMNYS0nVh7i3zl/wBHxmz6STKZ/MlfmV8n8M/JZPKF42Xa/DU7lSKl3UCRw+HIGHOjTLnnsdaFhEd+r5/Hrvj+1X3JZPJPZoBkMvlbh8PRjTzWnnkTruWktwGEOhDBx1x20Es+A+TyXS0uupzVuDhGCD9BJtJKJeW0M4WD9JJPJ6VUs49mZhCigE5KWcdS1vtFufe7fJ5FbGIr5zFALtdzDwA38U1W8EONgkEtTXzi3x6xRa11RRiQbzsLWckt49UiDW3AzwVsGaE8txP2JExsLVVhhERC+nh0W1SHeSkmnyuzpI9bt/cDXgWI/QggfQkZ1wPsCUKlEzw5kJkhwNLtgn0x8TNRLKAz3IkRYHoBtIUF/HUOQSIm5+sB/EPgd0K/ziMbC2z9CCBFz98TETKqub74kIBjf46M3R+DXATsxvU6ZhZCqEv6EINoVO6XKaaeADweuaZQRMZJJLReHdLfmydjdn4ZSqsRUYssbFHysUqEzciLiVE11O/zx+s/S88SH2EKWOhvYDezhIocEgrSff5/SwlaLWUdA+SmqN8D5DKLJkrpYBQnnwz9hJX+u7iaB1PAdRgXu6kln15CFLCdOdSymzm6kPgJfspt3MpHvrCBed+RcjcPs5TLWUfa/j5L4uAY497qabwTLO0rTyE7wFSOMIHu4mKmIMwc56gIHXV7CzjmHUf5HwSw9vyrj6KnI7amZh62Vue5CKAywkQmLt6MBUxmm5ZG6XkCipYjQLUWAU2Lkee4Cs4R0c8F4M4gVY4l3gJOJ5Qu0jlEdE4VyF/rQ9ic0iyk3udHkaigiYp+GXHeUSRyaMBpDEhAVHNFvQ8iua4Zci0VlXo9/47cC0MvLkYA6feAWxGgbvJNMxCHewgLPtv0epuRKK8T+c/pQsSJEgiovXnMvehH/hMrxlyPWVA+F4nuPqQ/r0DeXYzURoyUoBEJbK3VWmAiuK6FsjWdDJJNSWOYnjofpXTw7vY+GspFY+K24VvpdJVRTDff0VSnL/Bt0nby2HH6yiNvEAgfL9Pmf5nD4bgHONPhcNwHfCuZTHa8Vv+/195SUJpMJsMOh6MZ+Tf+uf7c+38g98eBTzocjieQd/oPgUqhvsY+h8NRlEwme/T3GoRjvf9Nu6CT2EyUdBzHKCRIPr3k08sUDrKQBgDu5EaW8stUtGmuvtRnMMp+pjKbXfyYaxgkmzlsp5UqlrOavZqHejUP8i1u5D+5lhamk8sAM9hLN8UcYxwRfDQxiy+s+mFKtOBVRcDBRtyM0qCh1mjx7LaIBX8vASTg3DwIj5GRjwIDCj4rfNAekUhlD+BTELhzCKYjvmamRlV79Pgi9FgnbEsI6KxEfq+PQCdQVyCgbWaeRCuLeHW0ljGfPYCnANxdAkLbEjJeHHhJO3mzJE+mLWjnUOkTsJjpBG8BxPsEDIc0tlGUB5194NfI7yTAX2JPPBJTsO2Ue5abo/dJgX5RFuzpsnP3eCAegxFDn9KLCAHTJ0P8ELhNbTpDJTOS/THEEfchjrtZj/cCQSj6dYTdH5xFN8VsZw759PKk/5/ppphe8hnHMMV0s505zGYXi7XI23qWcFdoJav8N7AidDcr/XeRy1FyGaCTUlb67yKOmw+xnmpa+ERI6OjrWJqKyN7MHdzLdfzXdxayj2rKVDwibX+/JXGQ4O0l3nAyWtpXnlo2+koGnaeVUksTvuEj5ARfSdUJzTh/lPIXXgIvtBdPkmiqD/GLRhToSf19FFsKpRLUlduaoO/X30c10ngDFNUj0cgy2HM7zKyF/lvBYwR8QABuGMm1zAJ8ENoC/skIGBsbQF+kn5/Dgkx0Xtcgf83TkJqqbYg/T6g/MYEjL6kcTu+/6zaTy5oD8WfAfY6OvUG3N2JL51yN/PV/EfgGttbp6Jh+Tv1pFrhdCKhdhIDTR7GMrUXYFeLgmPlVIuJNRgRqMiLktAl5j7lRv5uHsEJHBuT26XzO0LEDwHw4smY8g2TTSSkl0TAHmcI4juEsb6dUBf+6XcXsYjZBCqmjkTtU9yFtJ48dp6/MczgcPwE2JpPJja/X+USYqrC/btWSE2Vvh5IwZyLJt6cDLyPJt/sdDscm4JZkMtmkq8I/QipOgaD1n+jxr7XvZ8ha2ihwDMmT2fR6czrVaEnL+BlL+SW1NLGOpQDUsJdeTqeBhZynojGtVHFn6EZ2+s8hjpsamtnBHJ5kEaM4Wco6XAyzIPRbtvvPJYNRWqkiohwgU0ZlLcuoZh+L2cheaqimhVp2Uzzajeezx2RSGViq7mSsFP0QliZkKEphCNwH5XkQ7ROQticBM52wL2GjoehPJxaEGgCb0OHKFUSi+9xZEBpSEJoj2ztjltLbDwzoeGZ8v9J+3Qiwy8yQSKUBoW6nrCZ3DsncQgjAjSQkGtqvY4eQiGgcoRyPXUHyFwoI7Y+J75pXC3uaoDwLeoZszWx/JXQGhEocT0h01lxzptJ/K2uAGITahJLbH7MANBQTQBwfkqhofNhSir1KdXa7BOSPJJSKXI+8YFTId8NRxFkHkBXoTdh6ecNjvussePjSDwMwn81sZDHXhv6TNf6Pa1cXhQRpYCELECn8L/AdHuZyghSyhiv4xaKP8+tN57GZ+UxA0PnNoa9zoX8TEwnSSz4L2EI1LYQ1/L6dOSlaOkArUowvn95TjsZ7oilJ763NSv6iadJr9pnmOPgPpe++FYqCJ8Lebr7yVPOT/0ibw295mI+RwShx3GzlPJZHRa3PYUqc7ISuFVJne8Dj5vTdcYm+RRHnMay/u7D1Q40f9SJg0oAvs88AzyACImuQKGsJ8ox+To+v1DFHsc7TiPq0IcDO+O5i6P8xeM6VY/qV9uqpIUW9NUyaniAULcJGJ0cReu1uxO/X86qcUoqRBVAXQik+pJ9rEN+j19v/jOaeGvVb818/in3PQH/W67ZN2Jqt5p6NzS+t0e0mKtuILYczVp23FgH55+vcO3T/Et3frPfrZgSs7tRjDgE3ijhhfjTOsSxoza5kHMfIZYBWqjhvcBsAC7KfYhU30EhdSvsC4JOsIW1vjr3dfeWJpO/+I+0tB6VvRzvVnK0BpRF8rGIFV/MA7Uzheu6ll3y69Qk+jmNkM8garuA2bmUtlwIwm500sJDLeYQ7uZE7+Aqf5H62h+aw3L86FVFtp5xsBmmhmmv4MZtZwFy2M0g2R/CxmI2c/tm4PLBNjugo8vBv1skaZ5cgtUrKMxBtVoBVCPuC4o+MnzSLuwDzfLAvIiAvioQDvDlC230R8X8eYA9Qp0AxjgDPXB1jug86IxY0ApRWQDwoIM7kku6LSaS0RUGuycucpPPy+ySymw0UjAHUbpeAvzYF1B4EqJbnCfgDCCj9uK4QOoNCwd2jFF03EgEeSQjA7td5mlvmVyr0yLCMl5sDmTm2QyCogByh7nbG5NyJhIDpgF5PZZ7Md18ESrMsbdlbgNRs8yMvHGcjTnkIeUnwYnOTzOKCT7b1fMlHAwvJIMEnQj9lp/8cHuAqFrORbAaZSJAtLEgByCpaWcMVLGYj89mcqiV7jHG0U85ctjORILdyW4qyexefJYyfD6lqRxWtHMPFGq5gIU/RwPkpxele8lnLGKXgd7idaEdbVetOPtxU9pp9ZjoC73hFwXeinWp+8h9pc/gti9nIHHZQqIipsF8ems4YDOfBQPZ4JvzhKP3ThPLn+cYxAXnPIc/ZPiyrqAO7+AfyTB4GAhB/ThYXPV9GgJhJu/DJ/lfV6TSAsBCJDJpooqZp9G8CzzTtV4hdRO7HAkijAPwCAgCNbsSZ2DI1j+v4zYj/jyB5n7/A0n87tM8o9DeDpwIBfj4kIlyj45h51+o9aB4zBhplHVvHdRES7ZyGBa2xMfPPwL6XoOcqwYom1WDzZw19OaJ9mrHA33wXTQi9uEv3nz/mvA1AG/zxySm8988HOfKu8UzoPspviucxiDsljFNMN35CjJJBkEIu4XHA1u9O24m3t6uvNDYWlOrnbcDqMUJH/5pMJl+7/uJbYKe91RNI21try/gZ9/BpGjmLalq4jVvJZpDFbGQ9S9jKecxmF7PZRS/5jJLB5TxChAmMkkE2gzSwkDI6GcbFCn7IINmE8LPdfy617KabYropZgZ7yWaQOexgK/OYy3aaqCWEn493/4LTPx8Xek8J4sg6EEQ2NqvJrEAaStIm2NwkAG4EC0jj2IhgKTDdKW1PRABmaY74yT3YKOZZCDCN6zEvaeTPA5yVJ9vjCJA0QNev43S2CXgbQei/INHPAQWKXv1c4YSiAk3bjIiwEQjQS+g5+2PQmBDwOqKX6UYoxG6XtEodqy0oALs/JiJHAwgg7YzYiG9pltCq3E45RzQqLTMPvGUa5YzBvrAWKneKkJN/skSKi7IEMIMc40NaplOowh4kkurOEhDfE0Ryb9qQleknkJcA8yKEfn8x7Iq9rtsVfS3CeWzlE6Gf0u6voJd8ptPCGq5gGBcHKWcf1bQzhXamsIGLOMBUesnnIa6klSrW8WE2cBEA25nLv3EfVQRYyjoaOYul/JKV3MUxXBzDRTZxOimlhem0Ukktu1OiEWn7+ywJJMh4zXac9iuEavo/f7HdKAqegbxKphQEHQ7HFIfDsfkv2hf+/qtKW9refBtmHKNkUNgfppNSnGFwhuH3E6fh6gNf9Ch4pLahp+mY+EaTr1+LANNmbIRxMvL8HUaA0C+AIXBPBk8Z9H8DAUJGPbYFG/ksIVU6JkVVDSKLjQn9PUOjkS7Y2YKALEMbTuh5DfDr0N8jWLrQRsSxBhCAauiwHfrzWWAhRBulUYao/dYrEE7osRv0PgS1zxUI0DYA+lw9TxToBvciPa4bW6ZGc2Tpklqr1GEBeo3cN57QdrHepxrgY3qv/UjU2kRZr4W4yaeNIgsHOVhg+gQCoJ0ITfgBnf/FwO0wtf8gJISt9mTxfGaP7mLhcIMmWeVz9uFmGlhI8eBL3MzXOYP9LGMtaTt57Dh9ZZ7D4fiJw+G46K+N43A4VjkcjpeQV8jNDofjj7rrWuAzDofjAPAZ/fy2s7da6Chtb7EtZiO7mE03xQzi5jy2spZl+IgwgSMcYUKqruXlPMJmFtDOFFaHlnO//1PEcVNFgE5KmcAROijlGONYxJMcYxxBCqWsCBCkkGK6GccxMhilgzLmsZWj5MqrpQuhrDgRcGooNYVYPqrJJa2G/tsl99ONCv8goPE9WAZTqdlu8igRkBgfFjDnQcBVpVcEh8qRSGcgIcf2INTc9j47hSgKYjUK6keUceMIQPUgIHFA+1dnSU6nX2u8BcIyRhHw65jMoxzxz+aYD9ZAW7OCUb2OKODW0Oy+QwJKE0CuKuRuA+YhokdFSltKqIpv5yEonSzHJcy96JLjR/S+luo4/jLo71aVYZ/t/1IMKkqgqFInGZZIql9ViXsi8ntmBjAsCsh+Q9k1eTdG8dEIV5ncpsP6PQ9D2brDdC19D7/iQylRosVsTCnjLmBLCjD6CeEnzHw2c5ByvsvnuZFv8QBXATCDvaxlGeW000kpZXRyaf9jXOTZwFQOALCfqQySzW3cyq3cxhy2A6YkEiyDUypaeiItyWkcSxU3/DvGSSafBXA4HKltSkmaia178CjwI4fD8a5kMvnnZDJ5EEhHQtN2UpqfMGtZxjHPOGaN7ublcllmPZ1eiEBk2ngm/Pwop8+KW0XaAPJQf0h/r0eAaRMCLM/Wfk2Ij/Uiz95pGmnUUi6piKWpY9okfVKrsc3YhWGzStms+8/VUjD1iOBPPQLiDFVHo5ShJ8DvhVCzpJmk8jQTyELlJiRiaGizWTIP71VyfPRB8C5CQOSwzq8fAXp5COX3EqALQk3KalJGTipa+xSy40UkEtumLYwo9naAt0/Gb9sJFYt0/zAS3QR56iT0ugzd6ihWuViVet2mVE8JApS7dP807ftRbNTYhYDUJXKfS2Yd4gnPxcw9/DxdEwvwBI/RXziOT3YLpfu54hpC+Lkz+0Zu0gKoF2qKS9pODjtOX9mXTCY/9ZrjJJMrgBX/x/YXsdnUb1tLR0pPYbuTldzBzeQywHf4IlM5wCDZDJLNQaZQTQsL2EwZnZQh6m/VtFDIYZb61/FdPs9eZtBNMQtp4GEup4GF5NPLVA7gJk4x3RQQooAQGYzyMJcxQC6rWc4oGXRTzHlPaTm8fsQZRBFwaniriTGTbkMcTpPkepYiPisOTM+RQxJY+mmPfi7NkWaYo20JAYJFSFSyLQylCuS8eSJy1IMAXBOprPBJM7mkUaXGhnSqbmQ+ccTHVGcJAB7QnNSQ5miGkAht6WR5QlTrpbXrtbiBeJvMJxOo90mUswgIHZJWofmgcQQ89iOANIRQgUEA84iml5QWwrZDML1CxjHiRW6nzMPtEvA5gtz/niEoLRGgqZvk/vRJYfP+Drn+aEwAeH9MKchDsm1E50YAW0zdvAhNxr7IOBGA6sGKQ3RB0R8iXMYjrAstpYlZrGY5VQRYSAOXhh7lES7jES7jLlaSwSiPcDnZDHIFD7GOpVQRYC3LOMIEqtnHBi6igzK2M4fbPLeyjLUsZR1LWccW5nNT6JuEKWA2u8hlgGpa6CU/BW7T9sZtlIzXbMAEh8PRNKa9ptMdY/9LURBZ/njdPNGxioIOh+O1OVNpS9s/2BawhansZza7mDW6m0iGL7Wv/A8vQRtM6DgqAKYEyafsQkBYAlnkNer11yJgNIaU69qL+FATEfXKMaENSGTxKd12CNpMSZRp2NxNFwKW6nXMsP4cBRYJhdd9CQIqP6rnKNS5JJAo4XOaRtIP/kv0GhpkjP6Ncm58wOew4K5Cr+kpad56UvmZI13Yxes8xI+cj0RXA6LBwLk6d6UaG/VgvHpsQMbiB9hc2ko97xaoOFvHna9j3K2tA1sRACR6exh5V1mkYz+BZGxX633YghVqnKzn/jYCSj16Tyfqd+qCx7iUsw83gxP8g2GeKP4ggxluflM8j98UzyOXARbSQCFB9jOV/UxlDr8lbSeXHYevfMdbOlJ6iprDcRvfTEI1+3AT5yBTyKeXJi2dMUg2WzmPIBNTESVTE6uKVgoJUkWAuWznCBPYxWwGGE8uR1O1RhtYyFy2c68Kd1XTwlJ+qb/vYz9n8N0/PCTOywgtGL5sDULbNY7VqOcuAp6CPU/IAudLCHDzAY0x2TaCAEoPAgxDQ5LfCVaUKKDH+RG1XD8CsjxIjqQbUe1t77NlWdoUoE3KgeyYALXpmnfqzgLnkK09mgu0DcFMjSJOT4joEnqJ9cg19WNZUfOcAub8mg+a6VR6cOzVwkkg0Ug/cu5+vZaiAugMC23Xn6MqwUMSpfXq/Yh2WNGnUlUIjnZJrqihBndGoHKygN+iAgiF5R5kuqQWa7ke780D57BES8mDwCE5R1EFMAql5gXge0hNuWpkVdmHpS1V8GoBjj5kVb0BBt+XzS3+r5FBgkKeIo6bb3Azt/i/llLf7SWfNVzJbHaxlfPYzxks5ZdkM8gO5gDyghekkAg+uilmExeyjLWsZRnGvuP/Ivn0cjUPkMtAqvZpC9NJ2xu3JI7jcaZvVOb+Dds/WlEwbWn7W2wz81nAZm7gbhZkbGaUDBo1yHFBwTYBbVlALQzngCuG+MZuROE1gORsfk8/DwG3AZ/WE8Tk2FSNUpcCtz75nTxobIa6egRsBvR86kf7bwXPJdh6ZyYPNQGeixBAVoikZcxCnvFdOo+LdQ4BrPCPEVvK0OP9CHXoae1Xi0RiZ9k5UEaKZptZh42QdmEjkn49rhJZFDUCe0Y5twIBuUsQ2u9k4PMICDf+KyHnoEz334yNQIMsgYV0bs2w7wmY/mW9VkMZvgpJaYno9+TCRkpLdB4GpHbo+T5Kqo7s3MPPwyG4rf5Gbol+i7rs58kdHqDc1Q7AGq5kKvvJZpBiutPquyehHaev/Ier7/6jLR0pPYXtCD6WsZZe8rmee/gc36eYbkrpYAGbaaWSFfwQF8O4GMZNnHu4ngVsJkghQSYyjmMMkEs1LcygmW6KWc+HNPf0YYZxMY5hxjFMLgPcym1kM6g5qqcLzciP5cYa6s4hrJCCC+s8H4dtD1nw5wUqc2y0sh8rYFSdBe1DIiQ0SVsBEjEtR4CjE4lWmmhrkTqtKErNdUqeZyZQUSgtapwiAlYzkchitk6/XC8pE4kctg9JVHOeT5oBddsi1jeCnCeEgNB2Lc3izlIlXL2+gSFpTo2UDui5irIEkHoRtdyXNCLrLxCxpYoCBfEJG0UmC7zTROjIo0q6Uc2L7Twk95JR8FfIufr75bhMp7REQq4NrwDb8jwt2dYtcx8ZlhsciiAOvwsBpC5e/b0OIc53J+Lg84AKKNoYYTY7NSO5i3EcYz9TGWA8q1nOapZzO7dwC7fTwEI2chGX8wgTOMLi0H9Rw15cDNPELFqppIlZgJSAuT30VSYQYQKRFIjdSw2DuLmDL/My+UTwsYgnWcSTZITGfOlpO25L4tCnx19vHEeezF+xbrR2J6TUZQv537U705a2k87Wsoyf8fGU0KCkKoRIuoAa+NPEPDgErk0ICDSMlADyXxBBopIZiKO7GrhBWwkC+LoR0Jml27IQwFUCdVdBz06sQF2OjuNUJd0urGBdGAF+w1gKcA1S7qUZ4huQ6GEFNhezGnneF2Lrcj6N+IGjcs6RLYifMKJJYR23Zsw5YwgA34ktE2NAHqTKjuHU/V4sTfcpHfspPX8bsoCqasCp/NN6HfM5BNyb0mYV2seA3UqYvkiPvQorWPSoXofJke3CRms36XflJFV+5k//mSdqvo1yrb+fOI0j9eO5tftbNHprGCWDTlcZWzmPrZxHPr1U0cpCGljQv42jWhgtbSePHaev7Esmk596pwJSeJ1IqcPh+PZxjNGfTCa/foLmk7Z/kH0kWcpybuUg5bRQzW3cip8Q3RSzlxpG2cKdfIk1XEk2gwAsYy2NnEU3xZQjK3SdlJLLADXspYVqlrKOFqrppJRSOsllgNnsAiQX5vt8jkbqCDKR7/AFcXYRrGz9ZASQ1mLFEQxlF6Db5l0aKu7/xGzt0ChCve1EAKEXyfc0vtONgD60r18jqdWqJNsYtFTcAQTEDSD5lqZG6ADi92ci0cZtYZu76kZBKvLPNYLMLYpEP9F5hhBwmq3zSyCgsR+JbFZmweYuC5bdus8o+I70QaVGOuMJiZxmMiaXdEjUcRkPmaNS3iWClLVxa7R1XxeUhm3JGz9CwQ33QVEJBLpkzp2qZerxgHfICjnFE1KChjzZlosFzqEWqJgsG/zDEO8C94NIbCqAVS50yXdKAlmdfgFx8C/Kvg/8+Vl+9q6PcAwXm5nP41ySimIC3Be6hnv811FKB0tUTfc+rmGN/+OqnLuM67iXB7gqFdnvoIxV/hs4wgQAfsWHqNFl725K2MFcnuRC7uMaCjnM7aGvkrY3Zse5+vu6eTL/59jHX7vzTbW0n0zbm2GL2EQ2g1QNBnAF4Y/lUwBwhIE+eHesz0Y2C7CRuyASFayFwBVQ+VHdXotIgYE8h10w8hRklsBIk/g/ymDbapj3aWALFF2MrpQiEU6ja23UZJ36uxEWQvsbUHemHOu+GqENh4GF2i9DxzClV96vxxiF3H7IPFuvz9Bc8xCgZubQjSXrtyGgeho2CtlMSuSoZwsULUQAqGHqnEuKvswzEGiGygo9/hyseq9RIK7RbRdja7G+qHPpQxZdY3r9q/WzUZw3tdcLsJFjkNzUHwAXIk+xS+Ddq/pkUT4M+OH/vfgCz51ZQ0bxfkDU5auiB5nqPAjAZs88BsilgYXc57mGOVrxwCYRp+3tbsfpK9/x9nr03SuQ7ITXsiuBtLM9CW0ji5nDDuaxlU7K6CWfQbLxEyaCjyZqWUgDEzgCwANczUruYgsLUqCgjka2M5dOygjjZwMXcRPfYgOLWcdSBhifUoHbyGL2cwYr+CED5HL6TXErpJCDPMANLclQe4aQ1UUVcujZIs/3XKwarhsBa3Ht2oYt1RLFqvCivycQwLanD1qGBBwa9Vs3ku9Zj2UmmfXGChXn6QzL/h5gc9gKK3mxwkgm8BtHAHIp0Nlnt1UiINKYAaZGAKl9COqVuutVwEyBgDsQpd/OsIzhdgo92YtEL/fFFBz7ZNB9EckljbfJvrrJMkZmRCKkA0MyjlcBqdsJPV0SJe5RSrNfAXBRnhU+cuvqeTwg7yxF5h47FRwPjbnvWfo5ijhs8wKTh81v6kIccQ7ysuEBHgL/56Rg+FQO8AW+Qy/5+LQG6WP+jxKmgO3MoY7neZjLWMovaWAhfkI8GVrEEf8EesmnmG7GM0CYAqZwkL3MkDFCywj6C1nEJuK4Wco6HuYyptNCAWHu9H9JJ7uKtP1tdqIoSQ6HYxXyKvhuRFEwkkwm34tkzP3M4XDcgtbuPHGzP25L+8m0nXD7OD/jv/gX1mV/mNnlu1IR0+KKbjxdx8RP9gF+6HpfASXfCAvIMiCoDSrrseVcGhHRORBgFYDMq4AXxRdQIcfOuwQBkGUIiJqMRPyewa7uliE5oH4EeI4i+ZuVWMX8S/ScJpe1Q8YfkcwLMmuRZ34tAvAykEjhXiz4bYN4CNw1yHvCC1h60diIqKEFP6N9DKCrQQBjAoou0W3zsYr+bQioHQU+ApVR/fycju3FAn6Tw1qG/LdfomM8pX1zsHTf3Xpcgd4To2K8RT+7sGA6gIgsmbSlh5Bo7TNyzr3vq2RGd4BcBhhmHGcfbubxiRcxMSdIp0vS4QdxM6hvOoFXqtIcyJPQ0vRdsdcDpU8lk8nbXquDw+GYfALnk7Z/gFWxl2UEKKWTDBK0MJ1CgjzAVSxgCxmMcmXoZ9zs/4YUaGYrAJfxCI9wGaV00kkp7ZQzhYPsYA7dFNNKJSv5AZtZwFqWsYgnuZKH6KQUEPpRiALy6aWuf7c4IQNejKiAF3lIj2IpMUYNT82LAKaAgqN9iMhPpRN26rYiRDgoEyv0ChJd3BOGl/osoC3Ng5190tdE+0xk0olERnuGJKIIEt00+2Y64bcJoQCbqGgA8dWlTsnTCWjEM1vnkInkfIaUKptA6Lc9QyJq1BmRsePDukjdp6JHEQGjIPVY+6Pg8UoZHK+Ouy0mEdx2YOchVRMG6JOapv1RiB+SMZxAICJ05pFRKQ1TVAjxPqH0RvtEIIlhAaRGDMlEmvtjkv8aMiA4R2jH9S7JZY1GJL90JAGZZdDZDKUbEAEKVWBkFHHyfYhAxItIbo552aiBctq5MPTffMf/RZayDh9HyNbM2HV8mDB+CghxL9elyr5U0cooGdzi/xqLeJLnqaOUTsL42c9UhnFRzT4AdvrP4S4+yyYWcTc3kMEo4zjGJ0M/4X7/3xzAS9sYM5Sk17GTXVEw7SfTdkJtBx9gL1UM48LNIIWDhylOHAag1zOerrJ8Sp4Ky/NzPPiGj8hztAUBgi4k0gcCXtsQR2iie30Infd7SGTRCM49of0rEZ97NgKOTDQxMeb4xQggG4bABgXAi2Hkdsish9Dd4F+IXZwsk76m1rZXn++UwcgmyBwGmiH6giyQcqbMw/2MlIDxehHKr4lymv/6PuAjEP8yuI3oU4vMOd6gqrfv17lehSgCG2mzGhh5EDIvAQ7odT+NVf83dUmHsbL7Mb2/m3QMFeejEAHYd+s4BjwXIC8FRmm+CeF03K77P4qNLCd0fs/o9zAMM1YF6FgxkQwSHKaQwER5J4u7snnvdomUDszNpZUqFrOBr5/2FQC+8srX0+D0JLIT5StPdnvNP9lkMvm6dRCOp0/a3n7WTTEuhnmeOiL4eICrqGU3m5mPi2F2+sWjFdOdqjNqLJ+XaaKWKlppYCFz2EEVrVQRoIGFDJLN9dzDbcO3spV57GUGe5lBB6WUc5AmZuG8HXm4ZyAPXxfivJz6GawiUAL627TprlBCupYjC6ROhEI6E6h3ij8tR0BfO1ZxNhqVbZkIkPIg4K8CS9k1Ec//AaZX2iiszyktgszDKOD6kGhoO5LPORMBpfsSEnEsQsSRigqkeZyav6nnc2LFiEIRG9kdSQht2IM48nhMfvcgtUA9HujUiGNU5zgdiX7WT5br70cAcFtYapFGhyRq6c4SoOtBQGUiIaJJgaB87uwTwNkZFAXeQEKceygG/hJtFRKFjQIVtUAeLDAgVV/Be9okN5csAbg9ZsGhWpsLcdIBrAriMFY4oxmmRF/iFv/X+ELo28xgL0fJ5RjjOMY4FrMxJUj0Ze5gPpup43n8hMhmkG3MYy3LGMcxAAoJMoibctpTf9cmCnsFD7GGK1jFCvyEWOzfSCuVLGAzC9jM59OBrr/ZzOrvO1lRMO0n03aibQ6/5QGuJpcBLu74Na4AqTqlE3YfpaQ7DBWQrAM8kPPMKxJlzAJuQZ6tzyCAKQJ8GSiBeIs0KpGo3XJEcC9MyveOqJ4BFUj006u/92O1AGJAI4S6gCBUXqvn3gKZFTKW/99h5Dk7LoeBevB+URpR3f6Csm+aZY7ec1XcbyeiRTAfvIU6BwOuC7CU2jqZp7tEryuAgL8CrUE6Cyu8tFOvp0ZbE2TWkKI+9z+j19cm+8hBxKFKECBrFs/B1tqu13MaPPF+nd8iPSaK1VKYL/eLH+j+RUj+7JOI3xuP5Pwe0n3DMu+ynYc5s/1/mHE4gJs47mGpD/+bufP4zdx5tFDNDPYynX1kMMq/fesBIt8x3KW0nQx2KvjK4zFHMpk8vo4Oh9GLSUVXk8lk65s0r7fUamtrk01NTa/f8SS1e1hOFa00UZvK+7ww9N/8xn8BnZQxh+1sYQH3cD1X8wBLWQfAVs6jlUqqCDCV/XRSSj69rOEKVvBDdjGbbAYZxzHqaCSCj+u5h4U0AEIxWcAWchnggs9uk8mEsSIKRkvG5JnGEOf6PQE/IGBxOqK6m0CAmCnZEkXAYGWOCP0kEEBpxfTlszdLKKsRBJSNYMWRjFBShSrumkgm2IXPIp22EQzqR/4xBrGXVKmAtTRHopv9vFrLKa7HGbXg6QXQE5acznalzBrrRyLD+xJozFnugxF7+mCJgN8QMFOjy6GgUn7VnE7oHJJx23VbOVL2ZV+X1mvF5qyWFohQUbhP7q9H76k/S6Kgxvw+EWyap/ReE2FNAN4cAfxul1J9axBHPwr8uw5QQ0r9kW6kNtxO/dyPrOJvgowvHOV+/6eYzU66KaYztdQNy0bXcvqRKBv8/8IX+A43cwerWc4smsjlKA0sTB1XTDd3hVYCsNQvf9dVBMhmkIvYwF2sZCnryOUon+Cn7A+dwWb/B7hek7FalfL7TjWHw7H7RCrhltV6k//R9IHX7HOV4xftwFbeAZSktJ9M24my+7mSboq5kjW8TD5+pQv5ho+Q88Ir4idN/qXRXHgQ8aUmD7MaAXZZiIMwztBQShtQh4cA0CzdngfsFR+QWaN9arB5qxmI86pDwK9RkI0gDtCFAKwGBFghYDfTzHOsuWReIwGtq71Yr8FQXtExgrrdPPq7kQhlDKHSdug1zgB+AXyEFAWWFh1jEYzcrcAZhO78EBLhNGklRvBoTN5n/wvgORP7rmIYPWAjoF36swVR6H1iTF8PErmOaH8zFsj3k4NEnsN6/ghCQ66Fff96BgPk0kkpg2STTy9L+jeSkdD8YmDvmZVsYDHFdKeYabdzx/9xs9N2ouwt8pUn9JxvR3s9+i4ADodjBXAH8u/0im5OItkGaTvJrJROBsiliVmEKKCO57nT/yVWs5yb+QYbWcwS1lNAmDAFqYdcMV3EcTOX7YTwU0Urd3Ijs9mVemCW0sm9XEcEH8V0s4y1DDAegOm0AHDBzm3i0PoQZ2ZWPA2FyAgGaB5iICIgDMTnjCARxgJEYTc6ZEvAuBEK63QkN7QIm68JAu72DAm4m6TAMYpVxAUBj50JCxhTFFvdH0ZovgmEOlyKREqLEGD3EgJo/QggNQBzktKOQjGZh9slarcR4Imw+Pew5p2a6KwRS3K7IDMhIBKgVClEvw5qNNYHv43AzGEpXeNBAGJnn5bBSUgebWYOFCn47+yTY6cXShT1mT6hI/cnJLJaUQ3ugNCm9oSFjgzgH/Nf3x+UsjehsObV5kFmTIqh72yRIurxYQWlL+pLzjQsp9q805YhjvoF7OKEyU0qgP3+M7mZr1NIkEbqaNXl6it5iG0Z81jlv4FBsnmMS7mDL3MLt7Oduamc6Nnsojb0e672P8ga/8e5g5tT5V5amJ6ilrcwnVp2c23oP1nlv4Ep/oM8xjJWcpdOag1pO36T1d/XdTPvCEpS2k+m7USZREq30k45pf0vUcpL7PD8EwC5rgEOzipmP1O5pFvXcIYRUGkEh4yYTp9sG2kWQaOUuE4f7GyC+vnIQmAQOFeFj0z0T+tkG3DEFuxzexQBk8+M2f8EspB4FKEv1WBLswTEf7EYyRkF+a8wC5wxVXU/H3GsAYRG+37kv6kLcYZ5vFpQqYBUtBUXxJtVvyAP+leD51sIwJwM/Y/L4mrmJdg33/U69w4E5J6Lrae9AVkU7QDPx7Ca3oV6/QbgjyK+yyj/ViPU3izkZWGu9hvC5oo2YyOut+v1diNAt0Wvfbn8Pv2bB+ASqC3cjSso92e4BB7wXIHPK3ofUznAclbzLW6kTpWg/HQRQl8Y0va2t+P0lad8TqmxzwJTk8lk8PU6pu3tb61UUUVrSpBoBntZx4e5g69wM1/nWn7MzXydxWwUqojm7+1mFtuZw1XDD9DgWsgZ7OdyHmEYV6p26fXcw2/5AM3U0EQt5bSnqJNBJsoEnsIK3oQQJ+RCeazIwzoHcaobBPCZ53cmAtYMKDTKsQY49iMg0YnQaHM1V9PQft0Jq4bbk9ASMdhapdmIb+kEPoAAyUDMquCC/acxlOB+rKBRXM/rQaKI8SFoi6niroItNwLe9kTknAPYnNRMBLACePVndEjAa71PaVZI1DMKfNAn4wxGJK+2UQH8B3NEvKlUQbtXI7CVhZq7g5S3GYlJDinAgmpoa1GqMrCtBeZVws4A1CuttjMCA5qTWjRZVH+dTqES+4E9XaJkTEwWDEZGVXU4IXVPvXkw8gJkPqE3cSGWimUipgms8NUoMATlO1/izvovsZXz2MXslHjWBi4ilwH8hLk09CgA7f4KWqhmLctSIl27mE2tfzeL2cDDXMZK7mIz8wG4nEd4kkVs4kJu5M5XRWGzGWQuO1LCSmn72+1UoR2R9pNpO4G2k9lMp4Wgp4CS7WHOmHsAgNPXxzm95gCFZUF+UzyPC9q22VJq9Qh4NDUvAXwqemdyIwHyxJ+kchjnw8h6iVRmGrXALASwPoM4qBA2umfEf8x4z2BLo3wP2ASh+8C/CKsQnIOtd6pzSJUBG5Y0kaIoVqF2Mow8Ll0zlyCgrQmrR2DOPaT7ysB9I+JHwuCpk3lQBjwHnkUIuH0KqUsKAvwOYLUN0OObEfmyRxEaba3OtVbnV4FQbE3/QmyuK/q7Kg/ThgD0Bh3rBb2+Fu3bpvfIqM4XAtfIrj/9II93P9hHrPg0cqKv0F4+iX1UU85BPvnnhzjyLlnwX8UKFrCZUjoZJJtxDPNtvgDqJ9N2ctibpVR/MtnxpkG/lHa07xyropXFof9iF7Mpp537uIYwfhJkcCUPcR/XcC0/poZmyuggn5fJ52UKCHM5j/Cg62oKCNPAQqppoZGz6CUfgKt5gFEy2MVs5rKdB7g6VWVpKgck6pqFFQcIY6XlM7ClYcwqb5eAxh6ssm07MD1HgKKJjnq1+XVbAKXlDsnvA9pCKPDTsQa0X4XTgs7pTtlv6LjGzDlm5kl00e+Ths6hCJlrFFmRDURE7Tas45cWSAshYNUNFNXKcW6n0IpBorfeEq0BqvMNIYDT1Cmd7oN51XZeHiR3dHqOvJdENUIbH4K6ConAVhZCKCD5Ov0xKX8TH5bfozEY0VV0v56vTiPYlU7NXc2RvNCiydJ61CkPxEgh9coczQ/KEODsdgkQDSkNK5HQaPCwNpOf9Jze5FFtxlqQl5fD0EQtxXSxnNXkMkAuA/zEcQXdFPMAV7PUv46p/gOsYyk3cWeKcjuFg2xgMV/n5tSw3RSzlF+ylF8yQC695LOE9exiNgOM50n/P1NAmEGy8RHhc3yfz/H9dL3Sv9Fe4bRU/u9fa7zxOqVvN0v7ybSdMNtNLVWDAUr+HOa3c8+hkbNo5CyogWQeTHjxKBe8uE0icx3Is9LQPwuw9N4K/RlDANwQAnyaIGoUdQMCXN1XYGuMFiMAKg8pVWIWjQMICL0WEekx0dMgAgJLZA7+aiyYq9RxFmMFk55BStMUCnU3d6wgk+Z/ZtYofXi3zmfamFYDRCUKTD3QB0/coucx9Ng25D2jAAvKP4atU/oQ9D+m423RPmcjYDWERGrbEHBaq/fOgE8D/JXqTLE2M/+n9dxeXl3+xQgfmdzYIeSd56NYwaRuOd+7f97HkavGk9P4CjihYDTMbHZRSJCud4nq/HbmsIJV1A0+D8AAuUSYwCYWkbaTx47TV77j7Xgjpbc6HI7VyCMnlVGWTCY3/fVD0vZ2tPP4Dd8nyBr/xwlSyGx24SNCGZ1sYQEFhFnBD1nPh5jLDropZlB1Yzsp5SI2sJ+pHGMcuRxlK+cxlQP4CbGdufSSz+3cwigZdFDGNfyY/UwF4KbQN2ny/z+7wpijLYQ83Kfp9jF0nVDQln5BNnEmEr3MxUZO3fp7ACsGFNXtFxdAv4ZKe5TqOzPHjjGCrb3Zr7+XI0JHL8bEb2Rio62ZMavea85fpJcxM09AWmMM5mkkEiRqOaJRzgSSn9kDeJvkXeKshJw3qufoD0p+6EhMosEG9Jo57IsIhTczQ/b5tW+mE9w5sE/P5XHKec0/ukfptCBA2e0SkOtE6LxRRMypOkfyf+IRqUfq0QH6o+DRl5CiyfK9udVJx2Oq4jsERTlahiYo2/zaN9qneasmV6gLeQk6G1kFNxRus2hRTYrO28BCiukmyETKEdXBp5P/zPXcQzntXMlDhPDTTTHLWJtaMV7Ph7iee3Ayyl5mEMbPXHYwoMV+Phn6Cb/xX0ADC6njeVwMc5ApnMF+Lgj9hjX+j7OMtel6pW/QEqfO6m/aT6bthFgv+dTRyL7saWRnxynkMOWDz8rObgiV5fHuZ/qgBJIF4DgbcSxNSMSyA3l2NiCMkyhEm8B7iZ5gL1Ahef80I4DSw6sil4AALl14jT4FXgNAC5H8VZPnmcDmpjq1DSFRyWKsloB5xoOAtaeALshchPzXVOr+NoQGnENKhZazkVI2L+rxQwjt9xIEWA/DxYsQ2m0JAjC9cvzITsisRgDp41g1/3rwDCPR4qv0mgrQot4Q2ASV55ISHGILIlb0KJa+Va/XNUOuJVXKzpQ7M0+CCr0HXfodPa3blyPvRAakP6339Rm5FxMeOwoF0P6uSZT/+SUi7/JR9ofDuIp7iOacDsCo00lT9iwWs4GzX3mO+adtJm0nnx2Hr3zH2/FGSi/S9lngC9o+fyIm4HA4znA4HL9zOBwH9GfF/9Enw+Fw3ONwOA46HI52h8Ox/O/dd6qaeWlfSAOFmpxRw17GcYxqWghTQDM1LGALR5hAAWF8RPARYTa7aKWKJmYxjmMspIGp7CefXhqpo4wOhhnHndxELU24GCZMAYvYxCI2cYv/a8zYGRAnZ5I8jQPrQxxFeMy+iAA+P3ZRMY5V030Ryek0tNsRBEy6sVRaN9AYhp1D0kb02DYFOga89mDzOEtzRNCnEgHAJrfTgONOzc+cpMcUIKAtEwFdbpcA1J6g1Pns7LOgGiz1uFrPW47UAe1Hy8ggUdJQUJRrKwqgUrPSSvOkgUQdAzFRwt0XhMxCOb+5Lg/grRDabqZTaLruPHgpIc1bKGN4cyQPZwCoq5Ec0kRChCk6YyK+FFIlYbfLfhmdh0QtGCfsCQo49RRIf7qBmADgniEYicjccs0qs1FxzNIvMowtNJ6HOGQXlhqVB3cPr2CA8cxlB2u4gjVcwYLQb1nEk7gZZA1XsJAGiulmLcsAWTW+mgd5mMsIUsgM9jKLJqZwMBXBv8X/NXo5nV5OJ46bYVyM4xi7qAfAzSDZDHKn/0us9Jvc0rQdj5k8mddq7yB70/wkpH3lqWZz2MHn+D7lgwepih4kIwEZCRiuhXe/0JcS5mn1ThHgYxb6OpB9BpwFgBh45yOgags2J7QNKyoURhyeU8cwNFYVL/JWIsCqQvt7kGd1MQKyKhBnGkRAltJoCeo8XAjoNX7eWAnQKGXD6MPSWWfIeCNbtP9Tei2Jv2jo9mI9bhgLjvuAFtFSIICAycVjWp/UQWW53q8zESB7hQxbebHOv1jnZcDiNO17JgL8CxCg34SAzm7gUuQlwrB/8hDa8MV6Pedra8bSgl/Un1chLwLDct4V7/82xYMvEfOcxiDZPPG+DxLNOZ1HXJfziOtyvpLx9dQia8Zpo1QRoIoAaTt57BTzlX/VjvcqlwClyWQy/ro9/3a7D7gnmUz+3OFwfAz4MUKaGGuXI+/uFcir616Hw7E5mUx2/h37Tkmbw3amcJD1LGEBm1nHUkrppJd8xnGM8Qwwg2Y6KU2BSr/qzt7DdeRylJv5BqtYwVLW4WKYYroJa/3RFfyQAXJZSAMh/GxmAS0Iz3Qu28UB5SHOJA9xGkZRz4mVTu+Tz6bUiVmgDWMjhrqYKXRYxFdcgAC7XHVWHhXuMcq3biQyOROrtDs9R/I9ozr2vpjQcA1I9WCVctFjevqEbps5JGA0kpCx9wD1o3YR1dQjHaumm9AxO7Fquigo3JeA+jxwxyRCObMGQs0QD8t8PHpdPiSX061gcQDYE4DyLGAUSnV1O94F5XkCjH3AtqDkoQK0dUlU1O0Sqm1lDox0CLh1F8qFVupKdaVLIqHxYchUgmJpDcT1RagI/T6jY2qSTpaxS1Uq31siLwBxcxPR7958OUPmwhAnDbZeXgQ6LyrDT5jVLE8pOt/n/zd2MZupHCCDUUpC/8Nm/wf4Y3Qmnqw/U5vdRC/5FNNNggxu4k6q2UcL1dwU+iYAt/i/RielHGQK+bzMVA6wmfncxq2U+9uJk80jXMZ80qvPf6slcZwytCPeXD8JaV95ylgYP/+P3/Nf/Avd2ZOYmBEk7BIPlMEo46ZJKPPdG/t471MHiZ17Gjltr8i393PEwahYER2If23GPled+tmLfNt9yrRpRnz0LOTZ3A3Rx8E7DVtPHGHQuC9GqUU6nnFsm0SJ3VuGzOdMrKDdRGwUNoEAvTIsa2o8Nr8SYJoKJM3HUlvNUkwTUj7lGYTK26ZzORMBxSbTIo9UOlBbM1QMY31OHrgXIiVZavSenI8A94RuO1fHO1vvX5feBwOsxyNgtQ3JGe3TeWzS8+Zo/xhChy4bcw/AgvVC5OXkRb2Gp5Hc0jxYtf2L9Mz10U0xhQSZTgstrulcOfgQAKFsEaRspQqAe7kuLXJ0ktlx+sp3vNDR8UZKD2GrRZwwczgcBQg+eFQ3PQrMdDgc7/qLrsuA+5PJ5CvJZPLPwK8QEfC/Z98paTuYyyo+Qyel7GI2V7CGVqrwEWEHc8hlgAg+7mIl7ZSzjqXcxUruYiX59LKUdSlQ4GKYJmpZzXI6KKWXfJqp4Qg+1rMktXJXTQvVtLCdufIgP4pd4SzArq6a6FkH4niGBHC6x8zfvO21I0JEuWP2nYvW3UxIBDIM7FEVXFO+ZQBYkCM5nH6nlpKJyc9cHT+M0FxB/LUXiU6ahVkDUtuHBIy1YEu8fLACnhmS40Z07BYdw/jv8jzZ50cilHGkLEu9TwDpz/o0rzRHAGmm096DQEzaS0g0s9InuTj1JrfVBWSIAu9IQvJF2/tESTeSEFXixoi0ihLpszmm8x3VHNMOcd7RoLyodB6Slwx3no5v8kGdqnTolOvqMavuMcmdNUjcnSVAl6MSTS3N+Yvv2lgWNsfGRFQzECdfAO9dLzVugdRK8C5mA3CMcTRwPrf4v0YXxaz2XsGK7FVsD83hQjaxm1pGcbKSu1jAFgoJcr//U9zv/xT5vEw2gyxiE5u4kF7yqSLADuaygYtop5xlrGUTF7KJC0nb8VsSBwkyXrPxzskpfVP8JKR95alm1/BjbuRbVA/vYzwDAJpFn0sGo2xhAe/e3Uf/onEQg5zmV+T52Y1E8Z7SgUx6TAmywNeoLcKrFwP7tExKDGiBzscRoBQBbx2WyaLMJncNEiFM6FimLEwjUKh1Rft07KexavvddozATj0+jDz3nYjPuEHnNqTjJoAtMLIJeVcw+aDozw4ETJ6NVe83etcmulsM1ELFp0VXAb+2ZrleJmMFjJwIeOzQ61mt8wgg7y7munzaJmLrlQb12KCOnzdmDocQhWSjpRHT1qXtEJaeBQJwRUiXP83NY8JghLpoM/mjvYzjGLkM0J09ie7sSQDkMsDDXMZnWMVnWIU/lciatpPBjtNX9iWTyU+9UwEpHD8obQOedjgcn3M4HNeZdgLOXwz0JJPJUQD9acgSY60ESfEz1jWmzxvdd0raIp5kMRvJp5dBstnCAhazgUbOopROyujETZwCQiziSVZyF0tZx1LWsUO1zVewigFyGSSbQoJkkKCbYvLpZYBcRnHiI8IDXEUx3bRSRStVLGE9w5XIgzgLcWYmJ8WIHRlBgn4gT97ColihomrkeV6KBazGv0SRN0Kn9vViRe4ytYGAq5GEgDQQgBpC/lBGEJ8QjckUQjqV9j5bHsatFFsDMuudli7b2CZ9IkiU9iUsBdhQbwN9NrrbMmRrjpIjdN0PAkVZCuacEjH15kGFz4LryiwLOgeGRIF3Zg083yf1TgeQaG4cAcE9LUJxdo65XyN9Mq5RKe4cskJKmeiLBVCUJ1HZNiPhkqGtSyi5oS5RKXY7Zcxoh5SIoRvcfpnniFK4+vtlzilgexSr6GgErsxLh6F2FyD0pwpYwQ+Zx7aU0NEw47gp9E22M4e57GAZj9FCNY9wGbuYzXP+s3mYy1nEJho4n05KyWCUYVwp+m4rVeyjmpCyAjaxiAHGcxcrCeNnAZuZSJAbuTO10JK24zNZ/XW9ZuOd42jfLD8JaV95Slkv+WQzSKerjANMpcU1nek7DzB95wGK1kW4fPcvoQQ8zx2T+peGJvsLBExdgtVmuBABVVFETOejyDPXgNB+oBriASSKNx5Ka7HiRKMQDY85pgI4jKWX5iFRvseRvyIfAtpKkGd3DbaMy5gFycoa/f1MnUdCxzN03UKEYhuDkTYtJebBUm9zsDmwFfpZ00kI6Xm1JA7dpGqJ+kt0fwgLtGuw0cydCCCsRl4iCpH3lQ16D3fqccZPGcXcMFYDoQw4BKGHdHsGAm4rtH/9mC/bRFBV3Cn1LmToVjvh3R19DLvG4RiGjESCTkp5b/dByttforz9JS7n5wzjYioH+MErK/khK0jbyWXH6Svf8Xa89F0XcBCUhymWPPHTeevM4XB8CvgUQEnJO5f2cFdoJUv9v6SUTgAWDT/Jg66rAQhTwGbmc4CpnMF+1rKMGTSnlHWv5x6aqUlFU+eynYe5nLnsoI5GdjKbTsrYzAIaqeM2bmUq+1mNpCddGnqU0f8eb2k1IL9r1I0OUlLwHAVeEGBWMGQDq+4sGBl6dfQ0gVBhM5Fc0MCQLeGSi83dBJhZIcDRjZRLCQ3BTi0TU659Ssf0Mcq+CVASM7Qk4CzsomokIWCzpc+WdzF9K7UkTSYCGI2ZcUFBL+DXWqC5OZBZK8qInQmtVdon8zKVSUJDci635sm6gT3NFjhX5ojY0swsUbst8kn01OeUUjgAHv0eAkC5zrGoADrDMl6bLrQaMOtBIqkDelxbQoSdon0C4osmQ9shqKiBfiNGgQBrckhRdjOdmosK+A9jSxeYm1xCirKbKkgeA0bhAa5mgPGpUi+jOFnvv5hcBriPaxjPALXsZiENdFPCd/k8YfzMZhdTOUCQQgoJsiJ0N8v9qwF5AaxlN6V0spiNhPDTSSlXsoY1XMkuZjNINq1UMpudvLqqbdpeyyRP5pQRb0j7ybSdEFvLMn7Gx3k/T/M07+e9LxzkT/WyeueP9uEIIorl05DnZ61+LsFG3yLIQ/sQlkaqWkmEEXAaRADXIa0lDRL5K4T+jSpqVwveJbItFaEEAW4PYeXu67VPIzbtohmJYB7SuY3xgZyjx2nZlPgQuCcjjtUE+VxyXGYF8n7gReqhouMl9HprdKyL9T6cC9HPgrcWofkqWI7fpwrDW2SIaIeKN5moLVgAbXJhDeBt0/lM0+sxNOIElpJ7t+7XEjH+jwINIvjn9mPfcYLYF4D52NqyZcgCbEDvXY3cv4QPPF3HeLHsPazlUlawiq7iAiKqQvUdvkgDC7mGH9N7Wj4Aq/gCaTt57BTzlX/VjguUJpPJq9+k83cDRQ6HIyOZTI46HI4MrCD2WOsC3gP8Xj+PXdV9o/teZclk8ifATwBqa2vfUS8SY22l/y52Mws3ceK4OegqZw47qKGZIIV8ge9yhAkcZApTOEgrVcxiNyBlNDooZQdzKCDMepZQSJB2yslXqZhjBFUJdQ2ldDJALveEJFiw2f8BeZA3YWuRgaw6GupKBJtTkpDo3XQfbFYQ87wCzkwkpzMwJIDOg2wfGLJRU6PI24PmRgLb2rQfcqwfWahNIBHBPX3Q3ybH+IGzFGyWOm3+p1Od1yBangbp40VyOtuHBAx7SyDapdHcHFundBJCzY3GhMrq9km0cWBIwLW3D+JbYGaJlGnx5Khab5ulL5sIcW6WvDi0dck5zbk6Y3Kr241ybx54Ihr9VKfbmZDrKtf9mU7YFhYKcaYP/EclsunxWhVkf6HQeUEAabhPhZLy5ForCqGnWe6NJwdZMTcLfBkyFnnYfJwaxNmXaMtBnkr6YkSb/q245Euayv6xf86pWqQvk8/VPEgpHWxkMRmM0kol3ZRwE3eyn6lcxsPcym0Mks1U/wHmsgOA1SynmhaCFHJp6FFW+W/AR4RWKrmee6iilVaqKP5fj6W0HY+dKo72TfST8DbxlaeKn3yr7WoeAODrfIV8elk27UFu4WsAZOfFyXYew5mFOLoIAnZmAD9CvrkOJJpoqKpGNM7It1cigDILyUo2+ZrVCEAzb4bTdPwC5JmdZefYfzd4apBndB4S7XtGf69FwOgzEH8C3OfAngaYebEKFwGZhg1zPpADbiMk5NLtJoI4Tcq2xIcU5Bnf0ac1r5frdZZgxZ2aVNhJ/X7bc1DRBu5aXiWy5K1AgKBLfwZBy1dbKvATOscubMkavx0DZQWxCfii3r8XkOhoWK7FXYIV78tBgPNH9fidep6z9X7fjER/S3RfG6y77UNUe1ooHe5goauBAXJpYhbbOA+Apazjeu6hw6pUpO0ktFPFV76WvSZ91+Fw/KWIwhvq89csmUyatTTz7/lRYK/mtIy1x4FPOhyO0zSH5kPAL//OfaesXTH6EK1Usp05dFNMN8VsZgGXhh6lgYXsZhZVtLJb8/d2M4vdzKKQYErEKEwB7Uwhn15qaSKfXoZxUU0LGYxyhAnk08t6lrDUv46l/nV0UCYPdC/idPL0ZxdwBnalMocUrTcTyX/MxYIuc/gejRbWZ0ktTUPfRfu4x3zeGZNm1HbLkSimySF1IhRdExUtAuorbVRwJCGRwbaELQ+TrecZwVJ5BxSQDgCBQ5LT6nFKvmZdiTQnAhpHEKAa6JI5eXPkvGbOnV0SOOyMaY1WH8zU1q+fo0PSr6ICKiZDZbUA2KIsiXq6EeXewCEFiQlZGfbqPm+hAOHOPrnPdTmQWSAAk/FCL2ZYji1Vxd2iPGmdfVBUoqJICMV3JCZAuUIFq+J9QJaA7hHNS9oTwFKAM7CUpwI5Fy368wWsamNQ5h7BRw176aCUDkopp50Vobs5nV5aqKabYkrppJUqdlPLEtZzE3e+qmbbVPZzJzdxZehnXBn6GT/i0wD8q+MMQOqaFhJkBs0AkgsNlNFJLkdJ2/HbK5ymJOm/3jjJc0rfbD8JaV95qtkDXE0x3ZTSQTaDrN19FaXDHZQOdzCY4cYZAyLwp1l5/On8PAFUNVgl3XN1oDJkYTCK+NuPactDGEk1CNDKQp6zz5BaEPaY6F8Cq8Q7RvXWU4xVlK1HnOG1cvzIJlJA160Mn5k3yDkyy1Rp14AwA+AykJXUPiQKmye0XWLgqRDcSZRXKfZnGqV2HykWEV1ItLYFyU8thgqTb5qHDFSIpdyOkiopw4sICM2A/vU6l2o9XyEiANWHAPlubTV6f67S8w/Dvga9PpNPWoutYTper2+nNq+2DUgd0zN13GY95yVw6fpfMYibsMvP2X9upuTFMBOIsKr7i6zq/iLdFLOV8/hnnmQr89jKPE5Y4kDa/iF2nL7yHW+vFyn9vsPh+CfA8Rp9voes0b1Ruxb4mcPhuAV4GbgSwOFwbAJuSSaTTciaXh2WPHJ7MpnUeM0b3ndKWjfFPJQheueL2UgnpZzBfpawnip/K7kMcJAp5DKAmzgRfIQ0pJlPLzuYy0IaWMQmmqilmZrU2EtZRzfFtFLFSu5iMwuYy3ayGQSgilZxfMYBNGOFjsbmoZhc0wrY8wwswEYIvVkSbduskbsFOVLeJYoFiHHEbxr6bS6WKdOifaIIoCsC6mvgP5vlD8WLbOsB9gWs+q8XUekFCxITut2o8IYQINjfD4khESEiIvTeijwBn2CjuEb8yItEIEmIwq0bAZltbakScaIZlCNKuwB1PujRkjmVhRIBnu4UYDgpR2i2AV0R7jwk53HnCGhsUQpVqRNwwbyzpYZdZkKOGzGxlz6ozpIc0ERCxi4tlKgp2NI0oTaJsubmyM9EQii9cRVrIkPuVaa+5JioNSAULvN3YFawK/UmVWJX9wvht3XnsIy1NFHLeWwDwE8Yt3+QUjrZyjwuCP2G9f6LqaORbcyjjkZ8RGjgfA5Szlx2sCT0BDv956Tou5fwONXs41PJXmA13+JGCgkyny1U0UqQQu7g5tSUbyVtf4sdx+rvyV6n9B/hJyHtK08ZC1HCCq7hm8M30es6ncZZdal95bRDQuTUR3EyYTDCy+e7OX1V3IrjJEhRSBnFChs9roO4ECBqUiOyoG0LVJyrfRPIX0Mj8pZospcN8PPqthaE1RJEntmrVcXXh/j4iG53IYDvEBLBBQFkwEhAan9TIeP0tEFRBDgKmfXIX2QC5t2NRBiNUzyKvC88h7Cv8rBMG6+O9w0E5BkFYqN+a6jKBTqnAiwFuUDGy8zQsQ5hVYOzSCn5pijGGcB4iN4N3vNlvtMXIjVPK5AFAiPYlKPzhlfTfzXflRl6z3KQJ8ZzcGTaeCYEjzJ98AVcXbD9zLMoeFeIbop5olhuxn6msoDNXMe9bGY+gVeq8P2oB9XFT9tJYsfhK9/x6ruvB0qnI/9C/5ezTer2P/09E0gmky8ij7+/3L5ozO+jwL/9lePf0L5T2T69djX/sewmAGaxm1EyGCCXVqrIZpBWqriH67mZO5jH1lR9pHV8mEU8yWEKOY9tNLCQXcxmP1O5mTu4kxu5nEfIZYBdzGYdH+ZOvpQ6byelTCg7QrnvJXmQZyEOR+tQkoFdxawG+sRHuLOkxiiAfwjcQeGZ+fSvNxOrbN+mn92oEq+eu1qdUH8fTHJK5LMHja42w+VKux1B/Kxbp1CaJbShOKJ6a2ye5l6OIDmjcQTk7QuLjynPksjjJLQWeOTVUdxKHwQimqeJ1PnMRei8gRjsa5No61l5Ilg0gkQbi4xDdllRJ5ySOxofkkiuEWHyIaAwEFNQPiqAsVwpWAND0G9We1EKb4G8FLidAkbjQ3L/3bpINxLTHFG09tsQ+H3y3fWE5X7PrAZ3n6j3uvWlqBMoiso2bwn0K7D1GFEIs0pfi5XQb8PmTJWI+uQFod8ww9/MOI4B0Bg6C4Al/l+xnNV0+4vZywxaqaSAEB2Uav6z1DZdyi9Z77/4VWVlLmUtQQrZzhx6yU8trkTwEdfs5Z/yCR5ILYX/vdji1LFTJE/mTfeTkPaVp5Ldxk2EqORLrju5hdupo5GM0VFAcgv/WDaF9248SJErAl0wrjJuQVIlAtQMANO8TIaxJWEOKfXVqN8WCrsl3gTucxFgZsCtib4a1gow8hRkXqLbYxB6EPyaU5oJ8uxWABuPgLsaAWEBUmCUeuBByLwBob4qIb2ohler6xpBpmeR+qFmfbCMVOk4TFqIAdCTkcVOH5ZynKfzL8RKeCX0Xi3Cqgd7gRpwGykxkzptFs1jur1yzBgh8NYgCwBfRNSPz9fzZWCBcIve7+3YYk5GfTCg15On5/kicDtMaD8KFeCKwJEzxzP3z8+TzIDD3kKqaQGgXMv8zWU7c9nO9tPm6uB3kraTw47TV57sC7iva68JSpPJ5PGq86btJLEWplOwrIttzKOQIBF81NFIAwtT4jGDuLmbFUSYQETzS0GiUvuZyix+SSeldFPMjXyLGvbSSRlz2EEL1RQQYivzqCLAXaxkjubu+ThCI3WU571khQ9MHscQgqSysAW0gdJK6AlYYSMvAiRzkQhkj4Kw0iyh82bqvgCW5ZMJbNbooPEjzyNj9mj/sIJeNyJi1K/n8ScEUE0acw/dCPgEAY7lOo5R8zVAtRSbmzqAXbNMIEI/HgQ0tsUE0IYSAiBn+iTS6M2TaGgIqHBKDmhUHX2lz4JcE4Ht137eMhFw6NfIpx8oqoZ9LXIvyhWgewpFmGhSjoDOnj653qIS6A9LJDkToXE1BoTu7PEIqATobBFq8r4EzPNJHunMAogf0jI7Tv0+j0J9oar9lsmX4qnXybv0ux9CXiSi+v3XIQ5+C/JS0gwHLprKyxO8fFJS2gCY69/OLHbjYph/4z4KCJHNIIvZSJBCjqparo8ILUwnjJ8Qfm7mDhoRQLtb39TOYD+5HGWQbM7gAMV0cQOruJk7qA+JQsiof2yYN22vZ0kc73jaUdpPpu3NsMVspIZmEmRwBB+5GRJi8wQjvDfroNBGu4AwOPzYqNwGLDU1AiyCkQch05ThUss0yrNXIVG9en1mG+EfowB7SMcz+ZBA5tlAEDpXQ+nF4K/A5pXGEIpwjozjnqxz2YLNlQSJ2uYhQDDMq8GzieL69RqypF//RhH5A6jow1KWmxGRoyGdQwuyqvyM7jfgukL3NescDDj9ue5bhDjcLOTdpAtRMr5H78GT2OiyWSCuxaYebUIAaQBx8DFk0bdF72WznqcOW7v1I3pOs0AfQnzhhdIntug0crpf4eUyNxMOH2XvxEpm/DnAGRxI5ZB+l8+zjLXsU421/3DIG9Ot6azvk8ZOBV95PHa86rtpe4dYKzN4jhn0kk9I80Kn0M525lDH83yXz3M1DzCKk2wGOcgU1rEUgFk0sZgNNLCQWw9/i+KJtzOMi1+ylCkcJIMEveTzIX7FJ0I/5U7/lxhgvNCNgEGyrVjMZKwQgKH6gDgVJ9ZxRWFbwILJfgREvghcoFHEANCv0cp5qjJbgfiJHiSqWqd/6Z0JaExY5dwoNtLqRaJ+PQr8nNp/EgK4zD+LyUnt1M9xbVGgPkcijH4VccqMCjjMRWt6IuJNJpLbFhNA7amAuCx60qbndw/J9U5CIrtRYIGOuzkMCyqlpihIzuc+PS7aISCzPwGBBNSXWEDqRPNwEEAaR+jIToQybCT0PV7wlIhARE9ARI0S6ngDOs8RhKJbCVAM2REpBePJESXeHiX/hcIqkpQDmaN602r05wtjvvMEtlZpP/IyYlaNK+AIPu7KWEkVgdQCShWtTCRIL6cziyZyOUo2gwQpZDa7WMVnCOOngBA3cwdXhn7Gdv+5PMDVzGYXANnEmcr+lENo4Hxq2U0EH/5UrD1tb8ROkUhp2tJ2Qu1W7sThaOOp5CfYxWzmjEmDYRSG88BlIqM+JNoXQ3ISgwiQehwBZo9B5vnIM1UBHTEktu9FAJnJ528TIaLMPGy96FGEmfOU1ptGPve/CKVXIODKaEQUILmUT+nYYaAEbtsCt85H/IsKHTFD53oUiZrGEB/QR4rKC4hPaJFzeDzguUS3m/k5EX/yon6+CnpugaIKhLproqlhuT6WIH4HvW95co6RgAJ3AwyNCNMm3Qa25I0HKxr1DKkoL7UIkDX3s1avV9NzqEWElIwAFXptecBC7VMt95eJQAd0usp4b+IgAP0F45jRHuBI+XhcDHOAqQBcz70Mkk05BwlSyE+SB/kka0jbyWNpXymWXuE9Be0uPssAuVTRyjFcbGQxl7KWMjqoZh/59JLLADdxJwWE+T6f4/t8Dj9hfESYyn6umfgDnmQRzdTQQSnf4kaOkksx3XyFrwMwj63MoJna0O+pDf2eVqqEdlkHHEAe2iZKZhT++rDiDMi2eqzQUYUiw2okN9KD7E8gz3gDzPwIaCpA6LqdCY00Iqd3Y6OXpahwEgIII4iKrD9Lx8gTUOXTZgqCVGbJ7yZ3tRLJuwwgVNaRYQGTmTq/6JA0owrsL7FlaQItAnzNvooC2BaTqOYgAq6rkQjtvrDMtycg4HFgSKjApVlyPm+hRCpDev37umB6pVCD/Vn2nlcUyvVUlki0tL8b2sIKyp2w7zmoKLFpRE6nUJb9TmkVOZLP6i8Rxd2iQq0dWwgclXl3dknE15+joNylgkcF2nK0md89+rcQwhYYD5JaEbg99FWyGWQ7c9jOHJqoZRf13MVnuSu0kjoaGSSbQbJppoZiulnJXSxmI6tZzir/DbRSRT4v08hZNHIWO5jDINk8xBXEcVNMN9kMEqKAWTTRSz53+r/EnX5LRU/b8ZnUXhv3mi1taUvbq81PF79LfoS60efJp5cfsoLT/xzn9D/HoRhC2QX0V4yzJUsqsSp9wwiQWg5tP0KAmVmlDWubjDiUPAT8mHx+l+ZxLsICw0LAALYKbZNVSb1Zj/Miz+o+6YtX53E1EIFba5BUjC7RTYibVBiTi7oFaILoep1XROcUgKgBdWAjqHnAizCyHguKc/T3B4UZxFM6vzy9HlOGpY1UrVR8elyflGGjACswlACeRoCryQPVlBU6sKJPXmyN0wa9bh8277QEAaNm8X2LjmnefS7Wuc3Cypg9h0RYn4Kq6EH+eOYUTt8Zx9NxjFjxaazhSo7go4ZmavRLmDO8nf1MZQ1XsIYrmMNvSdvJY2lfKZYGpaegrQstpZhuOiljGWsp1CXJBBks5Zfk08s4hvkZV3KMcbRSRStVALRSRTfF5DLAVA6wOPRfVBFgEZsIUoibQRFB8v8/nIySTy9z/TuY69/BMcZRH3qW/nPHCc2oD0VyWMXdav2peYoUqJAQ0iIJAYiZ2NqjCWxaSWmWAK9cVdadhNBi9yGtaMxxuUhJlwCipGtKq01yqmqsjvm81h8Nact0ynFRBc4tY+5tZ5+A3DakvEwAWVD1IFRXj1MApxuhs7pdVlCwwifXFUdEiioRRWCTo+oZwxwtQkDf5iC8BFQWCGXYq9SiEaCuWu6FB4h3CZW3Z0gAc08Y+qMCJE3uZ0IBdG6W0HcnOQVUTi+UY6N9UFoi0dF4QmvGxuR4kALr030Sqe0JC9D1OG0d02ifAOnMEuyLhZHCL0BW5H2kCrYzjZQ4xeN1F5HLUVb5b0gpU5qWywD3j36KO/xf4V6u07zoSqpoxU+YKlpZzXKu416OMIFmaljAFkZxMoqTn/MxshlkFk1sZw5ldDKL3VTTwm5qWcdSNrGIB3gzK368My2JgwQZr9k4ydV305a2E21nsB8XwxzLGMclL27k28NfIOY5jZjnNJ4rq6Hk6TCe6DFxNC3YMmrnIpTUQqAZKq7SfeMRv7pEWwgBUcZ51SKOolL7P6PHTENU1IOyyEqTNhMRVeaOCBggz+42BPSGta+hEm8EFsG+mDQDNkfM2BWaAjNRfqdGzu01GdPXIu8Jv9BWAJlGZXgLhB7CqtxO0/6T9ZhNem3D+tn4n3qd+7lAF/Q8jtU3GJX58lm9V8VjrmsYC2zDSPRzGBFxSiCguhEbvX0GKzbFmHmZfZVI9PYWYK/e251yfscoFI9282L9exguhJwXX+Fzh++llSoGyGWAXGYNN9Hims5sdhF4pSrV0nby2HH6yne8vWFQ6nA40oD2JLW5/h0cZAr7OYP1fIhCgjzJIropoZhujjGOtSzji3yHdsoJUkiQQgoJUkUrN4W+yQyaqQ89y/1+ybmuo5F8emlmBq1UcSc3ciu30UlpKtI6SDaP+T/KwYwpgsIMfT6GOAiz6liCpdq4wLPEzj2BgLZBYCY2b3RmnooUDQnw6tR8yAiy3aPN77TH9CAKJe/BBuecKOgalgikmVI/WjoFUZmtdKqibZYAxLiOMVZkKRMRIDLAdF9CWk9YorLhPgGx03NEybZRQ5L9SEmXTGwJm8wMKR8zvUBatt4PozTcGJbj2vugM6LqwgHp43Gqqm5CIq/mOjIzoC0oQDYalZ9epRh7CqVsTKlPwGbPkIDx/rC9f20RGddrHC2SK+stFEEmd57cK79PqFTeaaJOzDVjvjgvsnJsqFptWHqUcf51cCu3UUiQcRzjblaMgaSSFLQx4yKmcJCJBJnCQfLp5UkW4SPCepYQwp9aWFlIA6NkMJX9TGU/n+N7JMhgN7Us5ZeE8LODOXRSxnJWM4smQq8qTJe24zWhJDlfs6HiDSe7mqDD4ch6/V5pS9vr2wya8REhd/AovzlzHq7hV4i4JhBxTaBqtNVSTE3aiw/ZZkBQDjY1xoc8X+sR4PMCdiH4HMTftuj2pxBfPA1GntPxAuCerxTgLG2bEMDZIaCSDmA3FhjHEMA6hIC78xGg+SLUnS3Nq0r7mYslvzXeBBULgcPA07Bzg45hor+/0Gut1bYIAW7DMmf/v8t48SY95lEE4AaQxc1ndDwdn6d1vD5SNViL/l3vjU/HKEAElkoQp2rG6tLxnpH+/au1v6EmL0Yo1Bl6L87VccsQAGpyWZE5U6jf0e26rRKJzuYBT4LnoWPkcpSt2fP40/vyoAsu3v1rLYzWwVrXpQpPB7jttFu57bS0RvzJZsfpK9/xdlzA0uFwPOJwOHLGfC4Etr5ps0rbm2pbuYBhXNSzi6X8kn1UUyVVwIjgI59epnKAW7idYroJUUCIAvZSQ5BCVvlvYBgXz/nPJoKPMjq0n7y4T+AItezmy3yDFaG7eZjLeJjLmM0u1vFh6VeDOAIjQGDySNuQB38BknMySqpeaSYq+oMo7/ajAkdIbqlHr8+NVH0vRXxLGAGwMxFQWOGUyOe8HAG3BkiWImDNAMgRbBQzU/eXIrThUEKmPjAkfYq0T6dOv1TnFx0S/1I05nV1n557xFxTTOqhTkKEj/xIVDSCtFJs2ZtQWFqRTwDpRT6or5BjpufIeUoLYGaFgGsQ2q23UAB0Zo70r6+Q3NdSFVNyu+Szp0YA/Z5DwLDsiyS0tE6OFjAvkOZzgkcjnEU+ecnwm+RfVVcO9wlQbWtD6Eh5iAM21KVC6Zcq8F6mxw9jxSNq4PN8lwxGqaORXdSzgh+ygh+yiE0pWstm5lPOQQbIZTot+AlzkClcxAaq2ccEjrCNeeQyQC/5SvIdpIoAnwj9lOVIiZhB3DQxi9tDX2Ucxyimm2u4j9a06u7fbKcYJanT4XB8z+FwlL9+17Sl7a+bjwgRfNyZfSNVtHLEk8c4hhnHMPszzhDH1o08Z3MgWYyARONL+7DCQQWI+NEmxL+2Ic6kBnqeQOiqtdicyGEgDJkmB/RsxBE1IyBvIgJwywCP9suRPvEtyHM9pufOQwBps15YH+x5Thrv13NuAaaB+xIEmB0CFkF9vcwltEmvZbH2j2p7VK9/WD9HgApw1+p56xCQfS7yHlGDBezmOjbp8Rch9NkNeg+Mku9YED+kc4tpH7+2o+C5Qce5AToNPbdAv6MmrD7CC1hxxyu0deh1+ZAaqVlyT557oEYiviXQftUkig5HuOCFbby7o4/tdWexd1Ylj3A5j3A5C9gMwHsPH2Q7c7j1ldtI28llp5iv/Kt2vNHOA0CTw+GocTgcHwR+h+i1pe0ktn1UKxX3KFPZz1y2s5V5DJLNXaxkP1Opo5EZNDNDvcqloUe5h+spJMgoTpayDjdx1rGUOG58RHATZza72MQiHvN/FBfHcHEsVZoD4JppP5CHr4mShpHVxyxsIeyjyAM8C4oWSnMjPtXpFD+0Z0iCbZl6TbkIWHsPQms1AkUj2qY7BYDFgQ0G6I05/oNZ8tmDrSX6P0juqMlrNbVFq/NkLoYG3I/gKA9yjukGOOtyjvFhlcDmmEQwO5FSLKGEjPVSTD63RYQ+W5lnqcilTo1m5knUc5JTQOOICglFTZ6OU2qHqiwGPSY/V/fFg9IAAkpTdhdKlJmwvfaRhKgxVlRKTminKgIHwtK8eSL3P2LqrB2V7y3QJcDZlK3xF9iUpVe9GJgXqApszk4bQl8q05uVA3eXfYp7uY5WKlnDFWQzyCo+k2pBCukln3IOMkg2xxjHPVxPE7NoZwq7qCeXo+TTSwEhnmQRe6lJ5aUW081c/w56yaeRs9jEhalFGkNX737V0nbajtdOMUrSdKR+6BaHw/Frh8Pxzw6H47Vql6YtbX/VtnIe57GVbop5984+3v2CtEIOw1FI1iHP0MngiCLAy4C0MAIEv4eAIhOtK8TWL+2T9A0KsCCsi1S+ZM8TgFdpsYex4oNIn8AzCGDtIrWg6P4oth5pGwLCOiDeBoEGoAJm1kuLPiifOVvnexgBksMIONTopP9GBJxtQd4LyrQZEaYCHWe7ntOpY8e0j8ktjel5glhnbO6JAYYX631YrvcSLIurD3k/Gauu60HeV5rlXrFeKgbwos63HvF5Rjiqa8y8tmir0fHMOTSqevYnm2VeJVDe8RIAz02rgSyY++LzzPhDgM913MvnOu5lK/OI4OM3E+dxPffyxGkXU3laK2k7eSyd6iJ2XKA0mUz+B7ACIUvcD1yQTCYfeBPnlbY32b7L5zlIORF8PMQV+DjCESZQTDeFBPk+n6OdKTRTQzUtVNPCUn7JXP8OfsaVfIsb+QQ/pTzURj4vU0sT1bQwSgaNnMUqPsN13EMx3ZqR2sowLuazmRaVLacYQYUgD2kfsgLpQlYYncjDPoGINZwpz/0iJEKZi/ULL2GVdE3ENDzmd9MvkhDgBwJAB5AGAkY7h4Ry60aApqlXGkdyPit8Ern05knOqPFt79FpvkePcWdJzVAPUualZcgyVv05CnI1AjwQk3MZFmx8WKKQnQmJNAaG5BxjabKBPrkWb4787ndqxHhIKLlxNAqqoB2X1HqNRzS/M6agEgG9PW1aE/WQ0JrdaFmaAggF5Hp6kEiruZcjCRt97Q+KeEU0qqA2S5SBi1TZsL4Qm+sUHdMMTTuKFT8ar9sygPmQy1GqaaGKAMV0c8UYVcHDFLKfqUxRQJrNIGtZxvXcA0gJpAHGM4O9ANSym93U4mSUK3mIK3mIMAX8J9fq32mAm7mDUTL4jf8CAO4KreSu0ErS9sbsVKEkJZPJcDKZ/DqSMXY/cC9wyOFw/Hua2pu2v8Xu5ToGyGU/U6nfuZsj9eMFoHRAUUck5Te76gsk2teC+NBuhNYK4k8XQXwT8O/YfMocoA36fw7cgDxzmxAgCbJA+KLWC42B/1qtKz1NPptWacBYn9T6JoqAUBcC3jzye/wBcC+ESo18muadBtHHEaA2hK1Tbtg2HYjzCiCA1YjemVzOPMSnBCH+BALuYnJt3mu1r+H3XaXHJPRnlzYTxQxggWQxEoXN0n2XYMGkU+9ZTPu/qPcgR68riNQXzcCWitF7JDXmdKxzEDB+ts7PRFYL9bus0eMD+rlfFZcZ5sjE8fRXjOO89/2al8vcvFzmxskoNaPNVNHKVs7jZhWbTNvJZadKqstr2fHSd4sRtvsvkH+7zzgcjlMjlvwOtf2hM/ARIYNR7ueTrOVSiulmAhGCFDJALovZSDvl7GAOO5hDAwvZHpqDi2N8ky8xh+0AHNPk0EbqCFLIAaYylx1sYQEPcBUbuIgNXMQAueyinmWsJcIE9n3xDHkIH8LW/noWebibFV2zeqiOrOLLAo5G0Hqh2EVLp/58FgVbWKBqhPIGsMe3qxKuia6aPNCdQwJQK52yPYTQYgMRaSB04UoVEerUY3ORXE83QnP16rniQL3mgWYjoNi8ihfpz3qnUJErKvT8CajM0bxV5GdjWKKVnX2yzZ8FI6Oq4JsQQNyp54giwkUGXI+EbVmcogLN7USuAQTcRvskMloAlE7WcjEKcPdFYF4B9AQFxOZmaY5pDEJBof2+FBMKrzdHclI9HsisECGk/igiBuHDKhKWIE+TMlIr3KlV5CxgFLZPPIty2mmkjoNMoZtibuBuVvIDVvIDlrGW2eziwtB/M8B4SlWkaEXobqoIsJCGVJRzHMcYJJsQftzEadL6pHP0b/UxltHA+eQywDqWso6lqXJGaXtj9gqnnVKUJIfDkY3EWW5F/vVuRpbUNr2V80rbyWVnsJ9uimUB1w++6FGOXDSeIxeNhzBsn3UWjmEo2R2W6GIBovxah60vej7QAe5rkcjjs1g66vngWY44T6PIW4P43DwEuA3rczsCnjMR4R6To+rF+ucsyKzRbcaXm5zWPnCfTYr5MvKcbKMPKAFvvfSLt8lcaSOVyxlvwfoGM24eEhrZqePnAEvArayaVL7mIWw0VUWfiCJgcKxIkUaMGdXz1iOR0CEZJ95k50oJAmIfxdKdC/U8BvBWIoJOhdgobiXi10zaymS97w9pm48sJiQQOu8MBKSaexIFMsDVBDN2B5jQfZRIho8pHEyxeAoIsTtjFjuYQyHB1IJr2k4eO9V85V+z46Xv7gR+lEwmr0b+bUeQR1TaTlIb9ecwwHh8HGGQbJawns/xvVSyfDaDXMt91NKUUngrp501/o9zD9fxCX5KGZ3s9J9DC9Xk08tS1gGwnNUcYQK5DLCQhlT+X67GJA8yhWWsZYBceX0z+RpHx/wO4jwnIw91E0XrhnmLBTxmAxdnSeTSjfioTgQgepFFSaceZvIz3cizvwgBegUIUO1HIo0gUcxSJNLZr/vbhywFOISCzSGhyE7C0naN2FGnodLq2J1hC+b8WaJM+3yfRE1zcyQK2jIkyrVG3X+fquhOcsp7gMmFHdDzuV1KuUUEjOLABwskv3PmZAGM6JzDfTbH1KjvBvrkGqJDWlu1RHJPs4GRiFKPddV6klMVEpF8V0+OCCp5ClQV+JBEd0kIUAaV/e+QiKznY4gjL8BGx0FeMoLIC1EB8sKgX9aRpeNpoZp7uI6reUDAJ5uoZTc7mMM6PkyQQnYxm1X+G1jAFho5i2u4j5X+uxhgPEEmkstRzmA/xxjHDPayjLVU0ZqiA6/hCtzEuZoHmcsOuilmDttZwBbGcYzH/B9l1J9D2t6YnSr0XYfD8UMEiM4ALksmk+9PJpOPJJPJT2KLWqQtba9rB5jKDPby9dGvcKR8PC3eMxjGxTAuYjWnMWu4iaQLW4rE1MRsQ8BpGaQwyXrEYSwec4IAkiZxBuI4C7AgzwC5PPAsQoR8XkT8cos2U84tqMeaOZyDpchWSJkxwsAzEG8QUaMUmOvTeeUpqFyk12ByQy/WcedjAakPSznK031PI/4jQ+fUJp/jj8PICzqeC4n0mshotzaf3g8DYsFSmGPgvgKr2usFPqb9wlhdBJPD26w/jbpvBfYt2USQX9A+EaQu6UK9n4b2vAkJ/Rhf6SUVpX15rpv2WZPgBSjrPsyd3EQv+fSSz9zBZ7mUx8hglAFyU34tbSeXnSq+8rXseEHpB5LJ5MMAyWRyJJlM3gD8x5s2q7T9QyyXozxPHY3UkcsAK/kBCTLIZYBOSrmTm8hlQB97ko+3l5oU+GynnLv4LOM4xuU8nBKPGccx5rKdEH6+wh18kvvJZjBVF/J2bmEdH2Y/U9n3rjNkBVIjY6mimCAP5aPIkogRaADIE+A1iNBTTW3PIiylNz7mZxRLOTURTSNcFMbSZv36u6lPamqiuv+Pe1eOgNMoQh12O+UYkw6LnsMA3Eyda8+QUI/NGG0xEU6Kx6A6CwIJO2eQWqARFV2KJ2xN1KIsiYSWYkvTTILUKnZ/UPx9abX4xly9pmjMguPpJTYS6/dBqAt2dkkUNdMnQ8W7JPfV7RKacZGp65aj1FyvREh7NGk01CVAtefQGGBehnCgzYp8BfLSoi8uqZeMRv3O64Fz5e9rLzOoZTdNzGKQbBaEfstNoW8S0loEIQp4IHQVs9jNZuZTx/PcwN3UIfX95rKDIBPppoTVLGccx6hmH42clVosOY9txHFzjHHk08s+qhnFSZiCV5VDStvfbrL663rN9g6yLqAqmUxem0wm//gX+857KyaUtpPXFtJAd0YxE3YeZfofDqQASM6Lr9DpKsPRhk118WHTIRQEUo04vOsRHxtABH0uQh78TuAh2BPAqvEatgr6U5/VnvfrvlFpUZOnmocAwmHkOd+MRCqHgCZh0Jioqrsa8eEGYBqfn4NwCZ5CQKZftx8COmDPjxC/YESbvNpy9DpzkMgietw5Mmd3rbCKUtdTgYDKSqyCbwABiTV6/0wJm5je1xxgtf6M6vmMQGOWtrDMM1XjNIFlgBkBqhKsoFIXAj73ajO/1yIA1YDZWp2XE7rqCtjEIvLpJfb+03ii+IO0UpUKIoSyC/gNF7CeD+EnxLX8mIOpiuppOxnsFPOVf9UcyWTyrZ7D285qa2uTTU1Nb/U0/iH2OIspJMhmFlBHIw9wFetCS9ns/wAD5GpESV7KH+Yy7uRL3MmN5DLAMtbiJ8wdfJlyDgIwQC7FdDOH7eymlmFczFWabyN1bGcO1/JjesnnDA6wm1kCHv7teSso4EL5p9hQp5G+NxLrTuBR2BZU8aACAXYBE11EqL11TuhXoAeyrwfx1aUIODPlYypLBFSFsAq4bTHZN6LbQIR7QmGZnomemtIsuUgk09CLPYj/n+4T8SKQvNS2iC33YkC1U8cpLZAP/VEBsG6n5HaSUAEhhDrrVNCcmSEAsChLIp/uLInsVubI9kqfKut6pa5oi4ob5SIAPDPDRjczM6SUCwmJjA6oQw8lZK711dCpte2K8uRnpyofe/MgswACbVBZCdE2pWddxavUEVPA1JipV9pBSja/66ICbuU2SulkLttTVNubQt/kJv+3UrV1Z7Gb1SznPLbSSz7bmcPlPMLDXEYtuykkyGx2sYvZDJKNnxDbmYOfMNkqBTWbXWxmATeFvsl9/n+jlSoaWIifECBq1aeKORyO3clksvZEjZdTe2aysun/e80+ux3ntCNq7hvfybky7zQ7lfzkW2Hf53ou42GamcEFh7dZtbgC4JDkk5b8OCwR0GZszuWzWCqrAZyaTzryqAwxEJNnc88zUJAHmUZh14gd5SGCO03IszpHxx6z0Bh/HNw1CBCMIc9wECA2Xn+v0PmcCzwN/W3guUT3BbECPz5s3dAHVc/A5KCa8jdtCLgz0c02JPrZhfUrUQSwVur1mFXijjHzMykkINHZDizd2NRaNeM9p5+NVSBAdO+YexHVeSeQSOiiMXNs0X6Lof/Tyhh6BmGA1WifcxFBqgKdR4W9D9RC7KrTyGl+heK6NtoHKxjIHk875Zzd3UysQOJKDa6FxMkmg1Hu4Mvk0wvADj5A2t4ce4t85Qk959vR3jkqE2l7Q7aZ+azkLhbSQH3oWdb4P84i/yY6KKOQINuZm8rJu5lvsIvZ5DLAXHZwlFw6KWMuO8inl63Mo47nARQobCNIYSrp3uSsbmYB7UxhNrv4ROinAIwuGi9y6PMRsQGTlwI2zxDEsfgQZ1kNXlWRbQuLSi5AfQ40xgREBjSa2K/73Eg5lkBMtrWpsFFoCNq6BLCWovTcmM1F7ceKJnWGBUS+pJ8nIcHcDyD5lCDU24ocES2KJwQUGkElA07jiEBRJGEjuyEgMyw05FI9RwUWoKYCyQk5rynlMoJETuOA1wVFQxIVLc+DPREBqGDzUUFFobykqFjxYckF5ahEPiNYqnOl5qBGA1apOD4s569QkD4Qg5E2OScRAamci32Z8GLpVybqXY28FGhpA5zANDjGOHIZoI5GghTyAFdzPfew1L+ODEbZp2JZbuI8ELqKhf4G3MRTznghT/EQV1BAiGFcHGQKd4Zu5Gr/g8xgLw9zOdXsA6CUTuawg/3+M5nLdm7jVhpYSIGC0rS9cZPaa69LO+pLJpOf+kfMJ21pOxlsDr/lDpo4Si4XHN5Gf8E4snOOAeDsgP6zx+EfDEOJCOAMftDN6Z+MC7CpRvykEZCLIMAuApla8zt3PcJ2MRFDkz5j1O9DiI8NIwApjIBP88b4lNQupUv33YA4wSDiT4xE/ZCO95Qc66nBpud0IYBaS8KwExEVyhMRJOp1nC6EEO+DkWcg04DLS7BR034B2EUL9doLsDTZfuQdYrKeOwsrprQBAdA1WIJ9NQJGTcT0owh4juq1ZOh9MP31mtuegIpztW8M2p6Bisl6bZuUCh3Vc30aG929z143TuQ9KAcBt8OQc+srxG47jce5BFcbBN+XSxw3sYLTOOCaCsBUDlA63MFul9TaBhHLStvJY8fpK9/xlgalp7j9mBu4BricR3jS/8/kMsB+plJGB9uZSx2NxBXG7NDyGQPk4iNCL/kpyuQg2dwVWskt/q8BEn3ayjw2cSHLWAtAOe2M4xjr+DBL+SWNnMVg7nhWZX+GjosmUhY8bIthD2PzVEzeIYjDCJAqFTMdiG4RYGfyRLfFhBpb4YOdEQvwQCi/e2Liswy9NzQkPw1t1oBUNwJsTVkZj/63vJSwJTSNCNE8/bwzJoJDJq+0KAtIaBkadcYzfVK7M45EIEHKxrwUE+Gh0JBcu5aufgABAABJREFUizcH3MMSER3QnFYDjPsRsBtJSC5nqVOowZU+KRNTkCcU3M5DEhENxaDIaZVxQebUE5S+I6OS29ofFHBb5AO35qH6fQLQX4rJuXL1+AGlIhcVynyy9TgypGRM5Wqs0qBB0116k83qs1F89CEvNxfDhrLzGSCXqewngo9hXPgJcYQJXM2DHGQKpXQC0EkpV/sf5NLQo6zxfxyAu1jJclYzj22pv7lxHON+v+CeUTKoZh+rQ8sByPXLcoEpG5PNIPtDZ7CfM9K5pH+nJXEwfIrQjtKWthNlO/gAv+Eb7KOa4MRCakab2eo5B4CB9+XiJ0x9w26oBtcT4MqL2/qazdjnaxRZ1WxGFgZ1dTazBlt+ZBPia+uwtN8aLDW2Ccl3NJFTsBHVhUju6oMI+DJALqI/n0IAoKHeRrC5KcrOQZlBlOkxNYi/342ttaqW6cJGITchAPFsoA2KFmHLvuwkla9KF/QEoKgCSxM2b75nI6C2Uebf8zgUuRCQ/ajeu29jS8fEdJ5GaRekjEwlVEQQsPks0A0VN2Brj1bo9T6r97cFtOqYzVFNIPmxFdhyOlruZtTpFNG9Qsinl/P+8Duee19N6r4M4+JXriU0UUuQiWxLZwucdJb2lWJpUJo2fswNfJ4BcjnKXLaTywCPsYzlrGYTF6ZWb0rpZAOLGWA8X+Hr3MLtLGALfkKMksH9/k8RwUcjdWQzyEp+wHJWs4UFADRRmwKoEzhCFQHWZX8YHxFWs5xl16xl+mMHxPElkFVNQ8Op18l26/YxNFDvLIh8Q3abPNBQAhojEmksxdbs9GdBeMjSeZ1IBLMIUSipRCisXmzZF0PDHVEAmSoz4xSAaii6PQgY9ucIkIwPC8AMAZ4+LYsCMCznMsC4qFBoyPV50K7n82YJCJyk0daXkO2T9PpGEnJbpmuOZ39Yoq5tEbnGaB8k+pQOrDRkT0yox3GlHQX6RLk3kRBw6SmAgaCWmtEFu9wsOVd82Cr6VuoLjydHorTRsMxzZFRpwvVQeT425+g5xPkPYXOPOvRemIRep3yX95dfQZBCshmklE4GyKWVSn7Ep9nBHH7MNWwIXcRy/2oAptPCZTzMDL+UfFnCr+iklHEcYwJHGMZFggwWs4G9zKCbYsppp5DDrPLfAEABYcIUqNjRA+ylhpX+u3RiXyFtb9zSq79pS9vfbhtYSC7DfJyf8Ufey2CGmw/8+VnZaaijZ8PLE91klI3iaT5m01pi2gqwtNfzEZBp3vhMtPI5bJpMFIkaap1SwEbvpulYypjBiUTyHsUKBBkQlQUjHaK8ngKiTmzdTxOlBPHh47Wfqu7iQgCfRndTEUWTt2n86LN67AZ4OAiXnytque7bEeBp6LtlUGQix149j5lzAps76oQiM7dvIKDdidCREwi4/AgCeL1Y+m0bVqHXlJapQUB7NxZkfgRL8W3EgtJFWPXjCv38HDYy+yx4Ko6xb24pve8apGA0TKJMmGdh/UI2sJhSOniEy/g6X2HLK/LOddyqMWl7yy3tK8Xesj9Zh8OR7XA41jocjnaHw/Giw+H459fo+0ntd9DhcPzI4XCc9nr7HA7HPIfDMehwOJq1pdWCX8NyOcoEjjCOY2xmPnPZwTbOo5x2shlM5d+tYBV38BW+yZcYIJd1fJhbuY0CwkTwMUg2hQSppYkWqtnCglSd0kKCrOPDXMlDBCkkgo9iugHIYJT1LBH6bgJ5SJcABxAH+wx25bYQQYGGWzsKFZ8WgLcnIfU93Qggm4n4DxMJdbvEr2VilWyjWMGfHiTCWVErgBQ0/9Qn+3pQhV8n7EvYOp/9SAmZEQQADsSkvmgCrXvqhD1BadE+G7l1AvuCMsdon1JfEfBnLJ6AmXnin9wuaZlIFLctAj1dUn7F7RJAirkeE9mNCZh1uwSQ7tFWmSdgdE9MGbVdkrs6KQf6++W88SGZS1sC6ny25qlZ0PN4dU55MoeBIWTFfWyOUQ2iKGiUB7Ow4kb9pPJ+ti75Jx7hMrYxj9ns0r+aSqoIcDtfZS811NDMKv8Nqb8pgFu5jYm6bL2Z+SxlHQ0sZEXobropxskoGzR3ei3LCFJIN8V0UEoHpTRyFvdwPZ8M/YRe8vETxk+Y76YB6QmxUTJes6XttS3tK09NGyCXPzCd27mFd7/QZ3fkQNeSAhI5cPqv43ieOiZ+sQFhnhxFHtQPav+PkXpep3xmDPGvqrLbb/I7PQgI+yiyEFyDzfkcxT77P4ZlLDmxwFLzNRPmPAXapwxogv6ndZ4Nct4U0O1DANwt2PqiLiyITiBRzjJSCsBxI8R0C1y+XI53VyAR1KPYqHFMr7MOiURWY+tk1yKAOwerYNyMLV+mpWwY1p9Gcdjk6wb0fnXotma9N806dxdWMMrksWbouOdq69D53a33YZNuu0r7jkKiBqYOHqC84yVGMzI44smjqD2SKgnTTTHHcHHj/8/euYdVVeV9/LMEJUDBgCAwGFHwQmiozOBkeW10tNFJq6nsZr2WdhmbblNvzWg21VvTvelmOWVWds/GJk3zXjZSmJQGXlAYUAwSEhQRBNf7x2/tsw8IiAoCtj7Psx84e6+99trrwFnnu383HmEDfZjYbh6723XB0rawa2XLWkrvAPZqreOUUvHA50qpOK31Pu9GSqlYpOZbP+TZ1yLk335uQ8fM6Rkne1BwUzGDh7mE1+jONs5nIeMLPmRBxO/pQAVZJotbAhmkkUwYu9lMT6LJ42y+JIWvWMlQsuhONb4MYwX5RBkRehFTmAVAOAV8ydkAnMcyEsjgTSYSSw4+VONHBWtOG0DlUx0YNuc/skg5tSwdL8oiZHE8gPtkuAqIhi5TocsaiXssMhbMKkTo5Zg1vdD8PAj0MZZJJ6eSU0KmHMhMk5jNLiZJ0JoiPLnsDiLWw76+4vK6swT6eiVFKkcEXd9AiS0tOCDfETqZ8/3NF4QcIzzLEcG3s0iEaZCvxMIOihL3WqekS5dwcYsF1zobgKmjWi2Wyr0HxKraP9jUUo2Crfky3oPG7di5j/Z+4iLcJVisnVVVMs8F+WKpdWqZ4gv9fd3ztxq3pTMCobxE5iAnH7qOgS7X4C6+hWZC85AvOenIwp1GzQIZhfDctP9hWsHTzI24mnyiPNbSfqw3CbcyiSaPxYxkA33ZXNADgHsjHiKBTE+mwb10YjXnkk8kAHlEE00e03iGeUykD9/RlRyiyGcBY83lI9hc0IO3Iy4jgP2MK/gXAHdGYDlODtHOuiQdP3at/JkxjsVM41G6s03Wz0DYfZpkD/IP2k/E/kJygs4grmiHfJYWIg/7FiDCyqn/HG2OpeEm0QH5TPY1x2IhKBhK15icAqcglr103NqbjlXPSbb0hOk7ELeOeDyyLg8B/yWmXSCwETIzTWb7i03/mLbV0vdBk2OgvSNgi834ipDvAI5ATQOukbb+G0y7BYgnjo8Zg3fJsWhz/lZzz1PNeJ10Afm4iYecWqMmJwIHZOw4Ft9AxMLZy2tcmOtFIZbbeCRGNhL5frIJybjrWKknAX8y45lgzn8V9/0KN/s/RNbRZCAUNgd1J7o6D79TKnmfi7gu63XWxiUxtHolAENZSaFPOPfygCevRwS5FHj7PltaNXatFFrSuH8JEuKN1nor8nEzuo52FwEfaa1/1FofAl425x7pmOUoeb/gIjbQhzSSeSbiFrLoTh4xZNCbDHpTRCjl+FOBH1HkG+FQTpEJ8riGOXTmJwBWMIzubOMB/kJXcjwxgJOZzfksJI9o3uciIihkNefSmZ8YykryiOYTxrBg0kgZlCM8g722IOQDOwpMDib50C8BRkLIZRAfLyIwE1hT4paDOYisq+WI+6pjIQURc0Ferw8CmUUSq+l4mYJrMd1aJUKuGLE2hiIxpBGYEjHVJhmR6feMQGOFLBP33jhExBYi+0qBCOOmPChUrKr+vuZhcrAIUievQ4G5hyIknhQfGUunU0TgOm7HpWaBrkJcd51MweVIMiP/QDlYZKzLVEDXKHHN3VkoCYwOlklW3ZwS6DJI4kpDjXtySLhkAu76CCI8c3EzJCWaCTuALM5lyBcdcEvCJMKj0/4o8aIRc6jAjzQGmLjj93mTiQDcX/BXFjOSy5nHTTzHyxHX83LE9XzOuQxjBVl0B2Awn7OQ85nGPxgc8Tln8yXV+DCVF8imK4P5nBy6kk+U5xlzOAWkRfySDBKYy5VcFPG+jSVtQuzT3+PGrpU/M6bxKFOYhZ9JO58bG07YrH2EzdpH4MJD+FRBR/ay5ooB/DA8WNbCQOBKxMrnuODuQv5anFCK5WYrgpwPcWMjiyBoALLWbjf7QhCrovMQsQjX6pmCR7wezAaiYevrpu8lyGf+QNOPH/QeBRE3I4udsXR63Ix9Jca1fW/TZ6rpv9BcdyOQB6XzEcttgdkc4RsFPItYdvuZ+yzEzUvheO+EIFlune8UvqbNPebYRtwaq1vNsQFe7Qtw65J6l7YJwRWuabi1Y1fhxpKWmv7+DgxHvjQ8ZbZrzDXzkPUxzcz7x2Y8KXDmt9sIKKukIhiu2v86b8ZdSGd+IsenKzk+XXnH5xI6s4eVDPN4EVlB2vawa2XLWkpjcBOmgnydjT7Kdkfqo4dS6hvku/jzWuvX6huMUup64HqAmJif5z9zdUQgU+hOmMlKs4dT+Yjx3MjzANzJo4zhE6YVPM3giM8pMEVS+vAdCWRyLa9wLw8SShHV+LCU81jMSBLMY9YEMpjPeNM+gxy6EkU+hYxhD6cyqOALpkf8jU7s40lupc9fNxCbuksWKecJr8lkRzJuoe88ZLEKxi0dMx7iH4eANSbDoKMxiiXG0vOENx7Pou2IL88CnogsFsG4NeCACcYyS7H00z8XTDJYCBFBRzYQKnGcXXKRRXGjNPH3k3EGBcu1upp4lqANwCluKvz+ZqFLyQdGQm8neQMQ0dFcLwhPKvyQIciiFggpY5AvFzEQVIx8CfCFrpNwE0xk4nFxih+IW0DdR+6nC8iCv0HOiS+Tewq5zJxvinrzuNw3hXI8d3Q4MavNZA004+gGFVHg9wXG71gOZ6dEspdO7Mefu3mYN7mcCAqJJo9reYUxLORVruGiiPcBWE8/8onkcwbjsICxJLOOeUbAruC3XEJHVhecS4+IzQBsoC+jWMJiRvJ+wUWe/t7hanwKytgQ0dfTXwb9sDQNh2hH5aEOLT2Mtk6rWSvtOnniWMwoAK5iLmE/7iN7inh/xO7ahU8VnJ5Vwulb18nndShi0VyMfL4nIZ/vSbhWuiLcdbAKupp18WCasVCegpupNwoRfaG44rIf7tPZWDxZ8tvHS//xE3DdW+OheDaExCMuqk5MaQmul0wyFCyUh6U7C6HLBHP9YGTNB89faPkGCEox95Znjg1C1lQfJA5zl9m24iYP8gHSoXg+hNyECMJM3NhYgPfwJEsCasa8Vpsx7TN9DzKvO+J6BF1m5rgU1yU628zXAbMNx31Q4LxXjvV5mWnjB4zHtTp7hbzsvqIjYXn72B0dTDr9uKT0Ayr82rHOT5wbrtv0Oi/3upIXmMoj3AXAg3xmS8K0IVrjWqmUOgd5bOIH7NFa39zc12w2UWoWuPpWrRPhGPcNEK21LjGuS0uVUju11kvraqy1fgl4CaT+2gkYX6tkFrcQZbLQ5BPLDABTp/FiAPrxWASoDNl3V8JMQNx/pe37XmdM5VpAfVPHhYzjmR4MV+RWsZLz0DG+wMOmP1BqCqyaQeK0r7mceYC4Y9445Xmiq/N43edKRrCU3/Ip4/iYJNKppAPdyeK3BZ/yxPjbWMwoPim+EGUS6wTEF7O/KIQxsR+wMOtCdsd1JI9oXmQKL22fxvZuXZjPeC7ifaKLC6n2BV8nnMf5WQGfDBhBCqmELd/nebL72fBzSCWFOLaRQ1fuXvcUVfGwJ6gjYbv28WjkHwGYtv8fvB9wIZdnfQCz4aeH/dlMTwbOT4cSeG3SHxjM5+wngDO/3cbbZ13AKBaTxgCSWQfAqVnlIvZMIorVA35FFPlEVuQTmHdIMiJORxba7cgTWT/Iij2DfPOtIJuuXD3jXVlUk6TtZ5eewzbimLpmDrwBWX89g47sZR3JnJ+6jJdTruS67NcBmBz7LDkmOnbZd+dDGHTv+z3bcntyzuAVgBSAj4rbZf4yLmTR7glEjsyGs+TvKxa430yryjg8hnOlV41QnXD4n1Ec3/MR49mWcWaNNu9wNURA2KGLAEx8zZtcCxABSsm3q3c0xipqhWizoKGq6ufxhPdYaUtrpV0nTwwFhHMHj5FDV/YTAAf24WOeJu6MDKXLriJKYzvQKaQSlYZ8k0tChJiTMdcXEULjcLPgOgmOMs2xQdB+DDAbyteaMi8V5thIoARyFkDXqVA6B4Imm/OX4JZYAdeq6YM8qFwPIY7b7QZEiG00fa6RU0qXQ0Rv4CboMgu33JsTZxoC5QvBfyD4p+Bm1DUPqL+bA30vM2PdiDxEPsWMaZBpfwCIgJAKM+ZxuLXPQdbHbkjCJmf+vHImcMD0uwsYDjtnQ5d7TF+OuL7XtClBHpYXmvMCEXEcjIhWJ2mjY4l1/uO3AjORGGAnKdJ6MxfmwXTY6n1MH3wP9+96iJzIruwOCqYSP0+5vrLYNPbSibP5krt4BIDfsgJLG6KJ1kql1GPAhUjqkj5a641mfw/gNdzqvVcZz5v6h6T1F4hjOkqpfymlOtYOG2lqms19V2vdX2sdVs9WjTxn+oXXKTG4z8C8aahdvce01qVa6xLzezbwEW4OV0sD5BPLrutiUdc1/hyVW4WajmzzzPahbLq/bJyCuHHeAU8NnsJTg6egXgGW+qJjfFHqG1QGqAyIJgu+nwFhsIFf8g6X8A6XsJpzSVTfMMjnCz5hDF8yiEr8GMVirt01j6WcRxFhHDq9mnIC8KWaQSHLZREKh+lB98NCWEcyFEDY+/t4mLuYtfpPHAxrR+yiXdy27nk20AeVCb5b4bXoP8gi7lhY/WA9STzJrbw3fCzEQlUy/Cb7C/6S+jiX7vqIESwFX8gI6kHYH/eBL9z5xD+484l/4Pc0jK3+mM/izoHL4NRN5fSp+I6fxvvDKLg6711i/7mLpYyAA3BJ8UfspRPJrGM2k5nNZBbEjWTYyEWwUQTpkP6ppJKCX8Uh+Qi5EnQIkAa7b+rI3b3u49PYoVzOmwx+/ysGv/8VF1XIA4RN//MLdg4IhUS4jSdkoTMBtpvpyRW8yfl5y6jqDSmk8lnsOXwWew7//PAmVhQMpQMVjO77IU/1nUIFfoyOWUAn9rKNOPyoZB0D+J95b0pioqEV7Hoill1PxNbxVyToBCMuO1ZBxyr3tfO3Zv6+anCRtFH3y+awu10Xit7uIueYvy2ASJ1IpE5ELQC1oPF/55ajQ2tFdZVvg9vPHbtWWmrz7ryreYw7GPztV3zOuVABMXmFxOQV0mV5EZRBUHYlqhByR4aLiHIEYgki3hzRk46Im0xEMDnxp73N79lAsgnlyKVmjdJs6JoELIGgRCQr7Fpzjh+uiAtHLH4gom8CbvIgJ2lDkBmDEX1BKVCeCzyHp2xN6ULceNZi8L8M11spHrFoZsvW93VgIyxdK8n+yDfXMeP11GrdZ8YSLmNf+hZu4sRQ8zMKCt4yfcTgWjXTTZsxMvYug4C3TRtnG2nGN9DMTW+zxcr4yMTNpTDEjC8G1405F3mw7cTzbkKsrn82xwvl/Bt5jt2RHZn64xyKCCOPaJJIJ4l0HvG7m3Es4B0uMXuSPMYFS9ugCdfKj4DB1PSMAQkBeU5r3QP5r5vlHFBKdVdKLa213el1/Hwgs7kFKYDSumUediql7gO6aK2vM8kbvgDitNZ7a7XrBqymZoKGeVrr145wLBL4QWutlZhFVgF/0Vr/60hjS05O1mlpaU12rycTKlf8R8WqKTzBTQDcnvFcjbaTEl4EXDckgGRT6OzjjIvpcLoUTav8NIjIifIBumueiBU9se5r3crD7MefcgJYTxIpfMU/1U3M0ZfwCHeTuT0J1irmTLyEp7gVgAf4i8c6mEECT677X94ccCEppNKRvZy+uoSZg+9iMrNZyVD6sIEo8glbsw9OgZ8G+PMId/Hwt/cBUBULpaHw1MF7+NudD/KvR0cRTiEDv02XJ8XB8H10dyrpQL9NmRAKn512Dr/J+wKAu6Pv4+FZ9/HDlGCe4lb+r/g+qn3h8qA5vLN8EoTD5MRnGcMnbKEnHagkgP1M3TWHNZEDABhcsIr9nTri9yBkPxjJbkKJYxun/ljOmtMGkEECXcnmN098wc7bQglgP6dmlaND4OOQkZ65uDv7KV6L/QOFRNCJvYxiMYsZRTU+3LTrn1QEwylzNL1vXM9E3mQDfUTQA9vuFOtk90e/pzvbWJI7Bjb5Qhj8qf/DfMnZfJV7NpkxcfT+MsdN7e98rM0QK/sLOoeeiJvtML5s8G+tLpQjWJOQJBSAnm6OqUXyWo9GXWuOvWKOeYlRPa7BS/xsUEqta8qEN+2S+un2y1c12KYyNLhJr3my0VrXSrtONh9qHvxh4ms8yp+lXjK+pBif1lOXl7N7eEfCFu3zlEf7KdGfU5eXixBbg3zWjkPElRPHGYzHujdnAUxy3GoP4NaOdgRgIiKmnCRKzjHHtdWEdHgse47oMxbTg2tNKEwoJrW8aedkZge37AywdQ3EjzD9jsONyXSsmlW4SQ2dUJ5U03cEIqSd8WQij1SKkfXgPTOOaDM3TtI9EOG4Co81+WAutHfKl5Uhls9bEGuo4ybtZJZ3xr/VzNPF0gdjzDVNjVT8kPAeX0SYO+FBjmYcb/p91cxfNyQ5klOnNVHOfzPyQi7P+4Anom9kDJ/QiX10yZJQq5/i/PGpribPJ5p3TKj4CoZa991mpIXWyv8Cu712vWS8V+oaXw7wO631RqVUOFLLIlRrXa2U8kHWgXit9Y8NXVMpNQnoqrW+r7H3cjy0pCgNRBwW+iEfJ392FkGl1P1Avtb6RfN6CvLcCOTf/mbzBLneY0qpm4EbkBgZX2Cu1vrvjRmbXWyPnvG8DcB8LkXdKPv08/W3jyaLHf1NHtg78IhSp/SM447pLB5awgVR5t9v0vUieC/hHarxoSebeZ+LSCWFAiKIJpd3X7mat64djw/VvM+FgGRnXaQ2ADBWJ/BWxaUEFh+CrfDJ4BGc/+MyWUg2wIeTRpNABr3e/68scovN4Echae97w91Bf+M8lnFuxWoClxzivbFj2UAf8oliGs/QgQomMJ+HuZsRFeINV+3ry2afHvzy/za6GQz7QGlsB4KyKymLbseTfrfSlw2M27iErMQziKzIx7f6EH4mDkV9q7nn2ulM3/83UgN+RQ828xHjOZfPSWMAAZSTRzTzmMgUZpFCKp3ZQyUd6Fq6A4Avg37Fw9zFwkUXcvnof5JFHGuLh/BlyACq8WE159KTLTxpxP1/1EAAXtDyBr/KNRQRyrl8bqZlMQ9yD9P4B1MLXiAqYhfDWMHr88Tk3n3i9wBkcWaNv4UXuaZOUeqNMkJSX3v4sTi+Fxc3xMoPrkW0LpdfS/009UKrzuqv2y35vME2h07veEJFaUvEyRwPrXWttOtk8/IyV5FMGlX48MsfN/LdaZJxvBofelRsJrD0kJs9tgQ3z8ESRMjEI381fqZ+5zm4DwSLcWt9O7kVAk1fI5HHF8jvO1+ELqOAA9IPgP84c24ZIsqSEUtgMhQshoghiNUvEkl+5CQEykashUDpe1LT2hPTGoOIxkDTZoPpdw1uRl4nqZBMhFzbEZ7JyNodgxuvibl2kunHV+bDk6E3yPQThVu+xXGzLUSSRr2FCM6tZitChKcp3+ZJ2ueMP8SMP938fB35tHGSM/khlmQTk1rwNEQ8ggjj90yfjhv2ZJmH3eM74l+xX76rlEFuXDj78cePSgCWch6fcy4TmccGk+BiAWOtKG1GWvtaWUuUDkA+18/0Op4BXKG1rivAzmnzO8TC+m+z669HErHHS4v5Tmmty3DCFA8/Nr3W61l4mZobc0xr/SySk83SRKjl8lMPl3TjAIXqVQAu0D0B8CsupcMDEB2SB7XEhzc71Jvyy6wZYhXdYtw5dyDxjw/Xfd7j14tV9jaeYwpPU0A4W+hJZ/YQTiE5dKUKH/KI4cJr3ySFVDbTk5UMA2BPcWeYNYEvrk9mM1v4nd8n/Cv89zwfeSN3r3uKBQNGMjRkJTkDunLhIwtZftfZ9D57MyMil3LlJKme0JMtpPilMzhoOXfzML/dtZLvI7tzxdg3eI+LuTjvY1gDOy8NpcumIh7tdSehFBG49RAAbyaOpRN7Kbstg4V+53Nx6seQD35RlayJG8CgTeuI7ZVDDzZDNryaeA0PfXQ/+gxFwiD5/Ng6QGJJsgK6M/iNryAdplbMgSFwZuw2PhtwDnlEk7bkHNRazYDpa0h77hyqrgRf86S6e9A2+pFO1SB4ofoG5vtcwIMht/OXbx/n8rP+STaxVOPLYFbziBoLZPJ/+nVuSJX3/MaUJ6gyGeFmF0xmVMRi/Kjk+nlzWTUxhSELUnm9Ko7REz8EYNG8CZ6HC1fxsuc9ncur9DNm1HQnTf4D7ntew3X3CZyA5ToFp8qoZ7+ayUQtczYv49p6z7c0NYpD1ce/zLT1OJnjwa6VPz8iyGUK0eylE1cxl9Wn/YrB2VLqg0IoTe7A16f1ICEog8DwQyKAMnFddYsQ11FTJ3THKohPRAQSiOXOSXrkCLENkq09qBS2bpdM8f7p0GUgsA9y1kBXxyU4EwrSIeIhYBWUfwH+3eR6Ecnm2iGIWCtDHuqmI+Jwu3QRdAuwFdo/YsZ1pds3JYgFNA0RgMsQ4Vhs+gRxPj+AiDuTW4FixI02ClgLxRshJBkRjI6rchlisUXGe3AOtB+Jayl1SrE485OECP58uY/S5yAoz2sc/XAtzE7pmPmIRdaJk12FazWdgCtogYi7EDfnNLONx83Q+6y8N2F54rVVEQx+VXiSRHYworQnm5nLlaxkKL8i1fMg2dKWaJq1sinRWv8bKSJxwmgxS2lrxj4BPjJOvMIuNdezT2tJdRRH3Rax2qgAYLY5d6IrensM/w6APXQGoIAYxnkeIcICLuZFU6xsBjOZwize4RKSWM+7Koc39AauuPF9Lnj+bc5jqUc4/Wn6LP59/3ksZhT/yJ/Gr6PEKjeT6Qwr/QK/8n0cOv0xtutZRO3fhd9i+H58d858fxtfX5ToyUpcgWRIS6cfyaQRt2kH5MOLwyeRRDoDF4nQ2xPUkbA5+3hg0u3cWvEkgRtFlHIAOAW+G9CDvmu28OGg0Ux4ZhFfT0vkY8aRTBo+VJNABrF5u/guugfvcInHfQvgSW7lLS6lU8VedvlFEZe3g/eix7Iff+LYxgb6MIrF5NCV4d99yVd9+7CFnoxgKafvkoxNayIHsIE+pJBKv//LhC9A5Wn+9t0d/HX6Y/C3d+iuExnFYuYzng+ZQCHhnG2smffyAL5Ucw8Pccb2H0nqlspuwgDYERMHXzTO/fZcPgOo8VS3IUtn7WOe+NKkWvvr6cNaUeunyZ/+9knW/Ovrhht1b3fEaxrr5n+BzzFPf83+5cArWus3lFJXANdqrYebY905XIQt1lo/ao6fD5yrtb776O/MYtfJ5mMmd1OND9N4hip8PJ/ZAJsif0EYRYS9vw8ioSy5nSS324rUtyxEhKmTHTYeWWfjcbPTx0j8ZlAU4oZaJmXJgkYi/TiJi2LNcScyWaJHKH0bgnrhCrdsRBTG4rFkHnwP2scgQs1xcfXDrVPqg7iqViG1RmPN+CqgfLskOCIT6APFyyBkkhmHt9tsNCL+5uNm2/U1WyAijLsh7syhiEgMxk1aOASxJu9DRHE6nqR/VHB4CZhe5joToOAW6SLiIdyyOU5ISLS5bhk1XZsd5/hA3ERJFYhlOxhP3VZAxHiRma9e8MSgG4mggKGspHPFT1T6uYmOAtjPINbwL37PxbzHjuXihSafhJbmoIXWyixgBfCx1vrjI4wvhyZw3z3RtC5Zbmkz7BpnYj/1DI+YdNh2oxGjxn3XOymNJ3b0uzoS3Zhcj5uH90VdB+Evy6qqEmBsRt3jKJwXw994ED0REqjmRl3EDGbCCzOZ/vw79N/+PTu6ncYZ08UNv4IOhFPAU1HTyCGWdJIYGfM5rK1gUtSr7NdduZNHGRuwgLDxRZy/ZBlTLnqKFL7ifz4U666uVFx16Uvcw4PsJoy0Xsns6dUZgIGb0nl69PXc8txLhMXvI3dSOPcWP84zIdfjN0Ceam6gD8+tuZ2+a7ZADKxkGBN8FnE9L/M0tzB43VdkDTiD2I27eC9xLB8zlrnZ1/Na7B+IMo9nV6pUqrUvc/yuoQOV+EcvJIp8LucNcvJ6kxY9gN2EMmzjfzh0huKU4hLGhnyMD9VsiBT3Hh+quf/th6AK7v3fv5JABv6lP5FOP0Lv28m/7n+UczLSyEvIYNfyWHyGV/Eg9/LVRCnHcsG8t/lo4aU8z22wB9J3iHsvV8iP2mK0LhdcpRZxjpZ2artGd1PSplZiI+fvJp/Y+sVkxypGxCzmPGAp59fTyIrRE4oGqtSRWoUppbzVzWFxMsa6iVJuX2ah7Q+epxlvAc8qpU4z9Ti3AefVdUGvOBkrSC2tjiJC6UoOm+nJY9zB05G3eOoUOg9Hf7gomNN3lbjZ1vMRsZOIuIHGIALOEVRRSBIdgO0QNARxKTV1PYOKcEukOZY6x9JZAsWFUCqRL3QdhxvrmQo8jTj5bTXtB5q6o459/2m5BtHmmuDGmcYjgvQc5D84FvyjTT995HhIGm6ZGK+yNp4aoLFmyzf3uRyxzi5DrLYx5rpOWyeudTFiNXVqjjouutGmbRFwM/JNuQSJ8/QBXoUIJw+BqbdNsJk3X0Q8O/4aTojOdtwHBsWmf0x/eF1jDeK2G2h+Dwc9HiZXzybDJ4Eu2UXQEXJOi2Kp+Xg7l9UMZQUfM5ZbeRKGw3wuAOu+23Zo3FpZorW+/qi71rpQKZWOPMJ5w/xc39oEKVhLaZ3YJ8ANo8bgeZyh68hc6rGizosVC2gDolT3Pez0GiVpVAZS9h3QGW5femLdFtmbeIJo8viSs8kijpnMYB4TWVAwFoBDp+fzuH6KbXQnnyiy6I4flXQlh1B240s1z394G1w4kyQ9ij/yDzbQh3NZTRRS2qQzP9GJfQSwnwLC6ZX9Xyoi4P2ACwmliAD2k7L/K3YHhOJHBXO5itvynmdndCgAXbKLyIo9gzyiGZb9H9bEDuBLzqYDFVzEB3TZVcQnkSOIIp9pPM29PARIkqgFyEq4kqH4UM2cBVP5dlxPqvEhizj+8MoCnr12MpfwDnfzMGfzJekkcQ8P8hUpbKYn1yDut6fdvxf84MAfFRcHvEtXcjzW2CvufJ93H5Vr/WHiArhP5vf2Hg/wvnlD/psbR/DpIvandhCD1CPzZrjxv17vFdQvSgVxTdN6Bqq/afdNzX7YA/rGWn8fJvZJD5bESCNiJPDXEaXefVkL6ZFp8qe/ZyZr3jrCZ+lZjb9mW42TORmx62TzcQmvcQnvEMUu4sgCIGyT8TAvQgRNImKhPIBbT/MN3DjGB83xhYiAOwVXAOUhwmwAItoKEbEWiAijIXhKtxCIrPeO1Q/EopgC5BsXWSeGNQoRlsFI7oWFcs3vtkLfUdKeWK8+/HDFs5MZuJcZq3O9QLM/CbF4Olbby3Ddep062Y6orkZEaYm5ZgUioh2LplMSJl7us3QjBE1CLJ0DESHpWHeDzXmhiLA055cb5y3/R3Cz9PZCyse8bsZ0ivm5CbdETDJu4iZM3xuBF8Ck5pD3pRvivns/7O4lpesS9meSFdCddJJIIp2epdsAyQ8BcG7xVywNOYf7Ea9+G1PafLTQWnlES6lS6hnESfx0JClSkdb6TKVULyTU5VTgJyTUZXNTjb+psJZSy7FRBXrh4bsTWM+ujH5udjtcUSLEiqjw+stTJiqqx/3fmT2d3HMTgHqspNvU+6bRmfTkO3NmCs+rG/irfoAH+AuPcQf5RHHoTmc1XUocWdz+4XP8acLDfDTxUq6f9zSXM4/LeZN7eRAuLOctnY4P6/jDPQu4/aEHiGMbX3I2APMZTwD7ySCB8cynd2wGERRyedYHfBI3giHb13JBt3d4gL9QRCgJZFAVDF12yRPumbF3sZeOJLOOT2JHUEAEKxjKM9zCFnrgE5lBPlF0Yi938wjJpJFFHKepOyBOXKQ7pJZyV8gj3Dfubp5hGrNTb8Y/ZT/6O4W6X3PzpbPRlYpPE4fyD/7IaD7Fn3L+038Y878ZL1MxClgM5wfId/PPOZcsusuxdOhKNo9xB0/Nm8Jf9j/IvsDn+EfRrSZeGPjFIqaa7/5OldqHa7zXNelxrbxHansf1yKqR5uj8jOKbCK/EcEZRTZdyeHXE+E/d0pMsFIzidRX1X2BdF+WpZ9fbyZdK0ZbAE2NLJv1EKyUeolGuCQ1yZBaIE7GYjka8omiiDBiyWE+40kmjf29ZP3II5pBS9bBAiid2oGgZZUivIoQ0bMBEV1/x81WG4Uk2XFiKbciotYRe44grEDcRhcgYnEf4k6aDDnLoKvjcjoSDi4U99xOgYjlbyNkvge9HdGVj4hCP+gbSs0xgoi1ahnHwTnQ3qk5GokI0nw51+P+6ovEvjruv7lmXzQiVIsRa6pzP9nmvBIk0VAacDOUPwj+XjVCSzea+qvLzNiGm7kbhwj+IkQAG8G85iEYdJlJHAVuwqdkxEJ7wIwz0Pw+0IzNybrrWFY3mvOLkYcAz5k2SWbsxcAfpP8w9uEfu5m8gDPozB5SSeGCivlsDpL1einnMYsprAmRSk7TuZ/f0HDSHEsro3Fr5REtpVrracC0OvZvwv0EaLVYS2kd2CfAx0YC6z2/Z3oJU11LqHhbt+BwUboZ13yqlJQNceJVa/TjdcwVpXtZZ8xjWvuzgrM5v/QTEoJE2T7NLVTSget4mW0LzmTEuE+ooAOXI+a4G9Tz/F3fRQIZzGAm674ZxI39nyCVFNa1lw/8AQfXcKPxTR7PfELUXWzUr3Pmj9vIPS2cuVxJEWGkksKXG4ezKfEX9Jr/X6lDihRGX8wobnnjJd6+4gL20Jmz+ZLN9CSKfDqzhyJCSSWFzuxhMz14LPuv3BH7Nx6f9xcAlkw8ly8ZxEdcwPrUX/NpylA204M/rZ6F7q1QZ8v/dejmnVzY7n02mDkNYzdTTJjdX3iA7siT1g9SL4ffwL9KR/H7DxdDkqZHtw1sWdCXEeM+AWCimaP/mWeSVDnPCJPM+7m/nvdXzYQ3zfs3UMbliFKH2jXVduVGExmTR1dyANhyqAe723Wp9+9BqUXwLxG2keOceOdg0zYES+No8qe/vZM1c47wWTrwmC2lbSZO5mTErpPNS1WpoiAoVMp++MHu6I4AhK3eR1WSSVi3CkiEim7g9yIiatIRQeS4pS7AFZyJpvN8xHo33OzPx31QvB4RUlWIMDoAB7OhfShS/RCv/gpNu3BELDqlWYrMviTgY0Q4RkDpKggKMn2MMWNw3GaHIwL4FERYOzGZPrgur44rMnjK0RzMNYmKViHZcv0QN1tjyQWzf4k53w83njPX9OtYeuebcTuZcKMRF+Jor3krRASlE6+5xsxXJq6oiDfj3GjmwrnH8bhxrU7Y0xDccjWTEevyPebe18p8ZF8ayXqSmLBuEbsHdKSACELZzTLjvtuVHPbSife5SNx3gTONhd3SPLT2tbKtYkVpHdjF9vhRGdQpSpWaCatEUDii5bDzEKuWygDOFBESqa/ylPsAUM6i8ob5ebp7Xl19/inBTedbSQfG8jH78Wc2k4klhxcLppAW8UtmM5nn778NgD9Of5R7eJB3uIQ/qSje0BJQ489+5nANH+ePgy4Pw6oZ/H3wNB7jDnqwmS+m/4be968ninyW5v0OgKjo7bzNpYA81RzPfBYzilv3PwXAwwF3UY0Pl/AOz3Mj5/I5l779EW9eeiFDWcnnnEsEBbzJ5YCIb4B8IgGYyQx+xyfMZAazmcytPMmrJhnUeSz1lIxZzblsjPolAN3zv+dGnud29RiP6zsAuD3qOa7Mf5k0kunHelYwjF3qdJjpj/+ffgKgPPgZz1zW9bDA+30E9z3UekYN19vmwrrpHjtNvtD2Sta8dITP0iHHJkrN65XAbK9ER/+jtR52XIO2NAq7TjYf5/IZD/O/DMpbxw/RwXxFCuN2LZGDTo3MCiAKdkd2JCxrn4iiD5FssAuRcidDEPEThJR5cUSTk8BoIGJBrEKsc8lgQlY9LrvF6SbJkEmIBIjockq9HMBTPuXg61BeIaVeyJXxEWP63GTG7vyn+5ktz7RNNq+dfiMRi2O6uV4ZYl11YjVLEQumE0LkWF6d2p6bkBzbyYjQrEZEXhLiagtiHT0FEfHeVswoJAFSvDm3zIzxYkQ89jb3jZnvR3ATNQV6tXeE9xDcOt3x5vrO+dVmbgPhzccu5PJffyDi9RTTZogZWxX8kBhMOQHEZu/i09ihdGaPmcoKXuUahrKCIpNscC5XWvfdZqSF1spGJzpqq7Rr6QFYTj7US5iCC+b1vFoNhsyE10FdV+u82u0Avp9Rv7tmY8ZixOtTGXfzJHfzVNTddGIvozNWcOFpC4liF5OZ7RGkLxZM4dfTVzB2+nv8I38ae+jMR4znDb2BAiIoIIJCIhjDQv4edSeR+ioiB2eTRzQTmccX1/6GxPu/JnNuP8aygDHRHzA++i123R/LkPtTeZOJJJPGHjoTShErAoYyN+BKHiq+h55sJlFdyeziyXzMWLIuPYMrrnufM9QfufnQszzJreTQlRy68tS8u3nqkbuJJYd3+1/NZbzNNrWdKiNsfz9xMSmk8tHESwmliJELPuf5ebcRTZ7ErbwNL3Md/VhPD72VWUyRRBpDYTGjyOzfj3n9r2U88/lGD4AkKF95KmcHfckInYzW8r7UtnDWyfczPOI1gP0EsB91P7It57BEWQ2+nxm1BG9Dbc01LC2I45LU0Gbcd5VSY+vrRin1jFJqB+J2u1Qp9b05NBX4o1JqC/BH89piafMUEcrq6F9RTgDjflwiAuUAbOr1C9di6AsFRIgIWmL2PYuIp2wkvtPJ4BqNCKJ4RHxtNeeUQIFTw9NEuRzciAivZAgZIm08LrXOueGIe2+haWuyxgZF45ZeKYTM13HdUpPMeIoQ8egI4SrzOh0RciORkjALcd2A0xAhGoVr6XwcuAk3eVERHFyFWGcTEcvjQEToOm64qYgldoMZj9OfEy9aiFhJxyJWWicONcaMz/l5Cq5VN9SMN9Hsc2q/OmVi3nPnw1Nn1Tm/lxn/ELj8jx+ICE0y7TPl/N29OpKdGEm5WUErIiCBDHyowocqVjCMoawgh1g20Ie5nvo6ljZD49bKEq319SerIAUbU2ppTpLwWEtVBoxO+NB141zh7veOPyXdtazqBBjP23w0RiyMeMWw9sh3XX3vRixx39EHmOBx5fW4AZ8n7XsCPfIl/vHhBOjz49ek8iv20pHPjV/SExG38aeXxL114vWvsI04upJDByqJMGkDr8h4n78nTKM7WfRhA1cxl/mMZywLYClU4wtJ8A+mse2dM+l+yfeE/mUnf2z3DwB+P3Exd82biQ/VJLGe0U+sIPK2bGYwk+4a/sp1TJr7Dv5XlTPy5QVkvHw5OxK6MCJD0hP/g2nQHUiGR96ZAX+BKcyiQndg0vR3ZJF/6wWq5vnwt3l3cNm18wHo/cp6cojl/wZLDbO3uYSebGE881nNYP6sHgHK6T5vG32+2cCya8/n+Wtvo88rG0AyzLNMRcKb/VFApHmfzjZv5pd4GahO0bBWeVx76+UL0NOhD5IKfQO/rLOZ+tD80su8VjNrWGn9ikupvFn8wrR5uHHEPHaW5sfGyVgsR80gU3ZrPUkMzvrKtToCAZSzfnxv+uVlQj6cuWWbx82WYiTBzyOm/S1IfGQ6InycOEZHOI2UfRFTEQG0Rs5rH4Wst4m48acVuBljS5H/ukJEyK4FhhgX30DEajob2sdDnOMa7Fg4nXIsFYjoy0RiPhcgbrOZiCvuGDP+58z5SaaPQeZ8H7MtM8fHyJjb++EmdNqKuPI62XWzEfHb0Rxfh7gkrzHHnZI2C+DgfGg/GbF05iOfYynm3pNwrcYliNvyEESkFyFpZraaY04CqQrTJhM3VhYz9oV44m/ZjghtEEHsC2HZ+wgr3EdFH9gb0JEqn3ZkkEAAEjMzjgUsYByjWMxw4xdcfcjHmp3aEo1bK096rPtuHVi3pGPDsWCNTXiPj7+72H3kke620RNhDKIw9tKJz/nNYZYvx+0y7NBOAHa360Kn/bs9x/cGhKFy3RqYah68MFFcVRczigykg830bdCVc4VJXDTMfAFQq6HH4O/Y0r8vL31zFUkmRtaXavqrS0jUYwDYmNuPF2Ku400mcitP8hh3Gsvnbu5nBsOv+5K/vnwvf8t4kDcSJFPtFep/uF2vAyCaPP6UMYsXEq4hi+4s5HxeZCpDpqXCP2YyUUezlPMoVKcxWkt22kULJpA47ms2LnBF2xfjkrmOl8kp7cqooMVMQ9xqh+eaoM73feVLSCJwURXLYwZzJ48SbsS1U+Nso0qEO/y569GZfMIYfKkGYISp0fMYf0E5GXNflcRGWvf3jKOuOfbOvOsISj3BPe4IWaglZr2oK340iuwatXGd43WJUsvR0+QuSXHJmieO8Fn6+5PfJelkxK6TzYfHfXfNOnYOCqUcf/aaBID9sjOhGsqiTX3SMsRaV4gIonTEmueHWPBmI66tf8CtgZlo9ieb9sFIP07mW2OZPFhm4jWd0jBOkiEnTN8IvdJ0CBqACOEyJIbzadwyLcFmLGm47rvrEXE40oxrEOIym2T6cGJdHUHsCLx0Of1gIewsga4jTJ9bzbGLzXWyEUvpAqRM2RxcQWoSGROBiMMNiBU1Ta5dsBAirsR1WQ5GysdMwM1+7AiIKsQFeTCu+O6DW54mBIlVHWDG5GQIdlyIHWtqiBmb454dhbhj3wUVI8AvF9b36k1n9hC7SyoBvBb5BwCSWccG+hBFPpvpyf8e+j9AvjtZmge7VjYP1lJqaXI+HmeKkxkDZ+1ERwuZwLl8BoB6Hhgq+9uFOo8eA2kIdRXwgPta+pcyJ4t5mwQy2ExPk3ipX6PHrQeDyugLb8B1zOVFrmE2k1mXm8JyPRhYzDC+ZEzMhzzGHYxiMReetpDbf5TBzONyfqIzH7wswu4PCa9xxQKTIXgWPF8qdTxvDXqS0QkfcsN1r/LCy9eQGdWPIbvG8Fd9L5uf6cG83Mv5Q8ybvPvt1QRQzhgWsqjjBGLJYfK42WYOz+e3+z9lX+B/CNehfKR+YkXFh1Qe8APgg5hx3HbbE0xllifuROYkg9eVk2liOxN1GRtZSLs77uDRgjuojgiskbAKREDqV4xwfKU/ap4Rnc7T5ria83gJrwFXHza/cXzvKd9TnxAFqUtb43Utq2ikvuowYVoREgRWjLZOmsBSarH83OhABT8MCsbH/AMl7DeK8hTYGRnKFnowrOo/7DwrlC5+RTWTAOUjQisCEX1rECHkJP7xQSyLG3HjNPvgJjDykb7ahyMCzImVdETpRijOh5BBck57H8SFt9hsCxEBGG/6DIaDa6V2afEy6SIkGHGR3YSIuuVe18g343vbXBtErAUightoHwxdnRjPtbhW4i8QQVeFCMkhiDtvFCIKI3AzADu1UxciYvQ9iLjFCFLv7L6rka8SvRGhGIor8JOAK+Hg7dD+QURgLjT3vhhJ2BSLW7800Px03HjzzL0mIRbiA2asF5t7iIX5AReQ0iuVFQzjtqzn2RkXyl46ekq4+VJNKLuZy1X8U11JuN6DpQ1i10orSi1Nh2Mpq89t0mMRvQLgN1KH8kb3uE9BzfbeT/n2Bkjw/q08DHPhSe72vH5quvyu74f5XMrlvEI/1jOv27VutkGTDMHb+uZYSD3jM6VEnNqrU3lVAtRiYBzi8rqem1iU+hy8CZ2feQcehBxiGcVi7uVBLlywkAvGvc1HW0SRJ477mle4lquZyyuIubEzPzGKxfR8eYtc6FO4va8P+UQxmM959+6r8ZtXyZV9X2Y69zMXianNIo4s4uhKNmfzJUt+GAsPjmVPcSlQSInfLIicQeiOneQQy/ks5H/VLfCvrvxi3Cb+q7LhstFwWU1r4pu1nCUyPEK+n8ei2Yev2ajMU7znbkDfSL28O08EaY0atZcvAh3jEaYBpT952u8POrXG+ToD1HYNTCeyW477/lwL3GHq29aynkLzJk+yHCOHcBN6WCyWRtGPdLbQk18u38gPw4MpIIKuVTvk4GKonuTDsKz/gB9soztdiork21yKHKcCT3IcKhBLYj6u62saIu5CgFLYWQhd0k27YsQdNR63zEkUItKcL82JxlgaDAwB/41AuimvkmTaxiNjMjGb7QcBqaamKUAZlM8Gf8e1tgo3gVCEaROECOZo3HqpjvtvDGKNHWh+D0dE5iikTmg3s+9D03+U6S8eN+mQ46bsB8yHiG7mGoWmz1xEyCaa67yLWJyNWy0gVuFXzf2tMvfga+a7t5n/3qavEDOWMbikIEK6EHHdrULcmF80fVwMl2Z9RHZcJLd9+zy5Z4XTib1U4+PxCssjmmjySCaNOJ3Fk+b7iqUNYddKwLrv1ol1S2paHNGwK8MIigayoh4WD3oEbkWy6joi1ZtxSHXrTwrGMDlitmf/LG6psy9HlDpWWF1rCE94Aj3gNp5zz8sA3oek6Ws5l89JI5n/XDuMRa8M4/yCf3PouUA6/KmU/wv5XwBeZRJ38Bh5RLOYUfRlA7sJ5V2Vw4VazI576cQStZ7Rug9b6Mm23J5wu1kFo4EnZ5KoxzANiVP9nHN5/drr6PCYBP1UhARRG/W8ua8GBOWR8O7D27263vZGlP56Yh1xpw1dR70DQKSWouD5xKKuhUmvvAjgPC6wmXabmCZ3SYpN1sw8wmfp1Sd/mvuTEbtONh8zuZs4U9Lj8l0fUBEMfhvMQcdqWQQLRo5kXN4SsdrNAR4Efo8b4whu2ZYI3DjIIqBEXGA9pV7WIFZKxzXX1/yeiutC62eOlSHi1XERPoCItxDz2il7UoRbiibb7Cs0fQSa84MREfasOTcKEX1O1lsnTtUHseY6S7kfIhyHIGLQSegUbo6Hm36SkNhRE3NKGZik9cI4PMmPcrZC13GIcN1orjEGEbE9kAJUEabvt7z6iMcVsI4oHW/G5SQ92oQIbJMXAafOab6Zv3DEanoxnlI/jJJ+c1PCySCBIkK5/NsP+Oysc/jNt1+w6axfALCOZEIpIoME5nMBleaNSvXU8LE0NXatbB6spdRywohMyGbXvFgURxYRDdUnddxLM+jnEaN1WcsWcDE+BWWHna9yq9Ax8qfvLYJ7L5B+M7/rV6Pdi6a0yu0Zr3r6uM2MX4TvxXCRCMn5jAeg+yvfM1oNBR7jRt2J57ffyu0HngDg+qgX+QfT8DdJCjpQSaypx/nBd5dzTt/P+CLqN0zUu8kijG3XnYn/4z9RPcuHygeM2PzrDDZugevT5nLlxJdlrl4B1d8cl9BPzmYF5Uh9VHYPrHOOR+s+ACxSXaUfr3hR73Zaz3AXVC+cJEUbr/0l3V+RpKiOm67ruj0Mn4IyfChjWMRKlnK+279MC/q2wzPx1rB+vvoNc9KnOr9a2gKNS94QrJR6iZM4TsZiOVqyiOMq5vJ25AX0YQOdUvZ6jsVsKoQSGLdxiYi6bohAuxcRUZm4bq+OO2w1rudQELAO2g9ErHNrELFmMvPmpEPXUEQomSy/5avA3ywh5Wng38ecE4gIMkewOpl0nTqn4eb6sYhocz4PHLfiXOAeOFhhBLLjppvvJZpzEXH6Im50Twwi5ooQIfguEhe6yuve+yDux04c53Ak47CTsCkaEYIHpL+ujkuzIwrzEEFaiIjKyxBhucbcG4gQd+JHnb5CkQRMgeaeK8z9R5prG7dnQKyk5+DWLl2FG3ebKe1iAgs9793TZ11PCl+x+qxfEWrq9+wngCTS2UNnzkP8o5/nOJ4+W048dq0ErCi1nAAcYaEyOGI2Vo+FtLaZsh7c+MfOdR6vjghEzYOXuEXcSXPd//onuIkpuFbPzDH94DHgRWg3wxGzvtxU8KwMqQ4hnU1XEhO+NlljRYg5Io6/iqB+/h1Yfol3UqVbTLtybtQv8JSSQCCtbzC9/sbE/pjsQi+DypUkFyY8FPaA7oE8vcWrts56c20OF/N6el0zJDG+AOqDuo87KOPWrI1FeXe7LihVThEhaO0Pr4AzB3VRHRHIeXzS8EWcsepLDt95jYhl/YpXO2shbd1oGuOSdNLHyVgsR8s1vEoGCVxU+hEAXwaJ18jgdZKNd+34JAauSRexmY/EZ65GEgh51w+NxXVtTTedJyNCrQgRTtVmKwMioOsg83sfOLhAMtr6j8ITk+ofYfovw1NjkxhcK2csItouw82OW4y4wDpxrWkyzvK14D8G2ufhlouRXHu0d2JGHaEajpsg6ABuXoMDiButk+m32tzrSMTCmo6b5TYK15o6wvxcYMZcgrjTbsJ17S3DLUGzHhGtjsDE9OuLiM0xwAYoX2bmC+BKxKpahSReCkQss44LcSFiHS1BBO8c83so7H64Iz5Uk0oKv01dSW5KOIP5nAr8SCCDKpNmOID9zGIKF/E+t/A0YLPvtjnsWgm0oChVSgUg2WkGIP+ud2it/11P2+uAu5BwxUXANK31IaVUF+ANoD+wtbZZu77zmumWLEfizJl0KHJiHVz3Usct9jae87jcLuDiGqcqL+Od/ubw5EXR5Ek7NRetZ3jcOv8v4VbgSUAS8PSIcc51Re9NPMHzGbfV6O/QzEC0cVWtjghEtZc/In3QrX2q82FjhmTDVV+A81GRpGU1SjciUF8CcYg1Mws88ZRanyp3bTzo1QI8iST0yMNu8TD6sZb0a+XR9Z9eeZgnufswy3JD7rK123af4JR+FGHpxJM67RxRWrMPscI6YtPbAlqbOL5n2zRzXBIFi2U0CfRw099wr/0Ap9fbnaUtYNPcHzd2rfz5kUFvFjOKOLJIDRrAoI3rSOqdDkD2gEhifXcxcHm6iJcoJKlRGiJwpiJlVAYh8ZSnmDZ+uF96g3ET9TgCMhwRTCXIN8NR0qa9k5DHEaAAg6VkSmYJ9I1FLIFbceuP5uJJIlSeCf5D4OAaaF8GTDF9vAesNTGl+eYcx4U10Yxho/kZilhDnXqpyFi+WQwRi6HLIFxrpJ+5n2DcONQkJNnRFCR5Um/TxxxEtE6Cg09De8cKW2Du9TJEmMaa/tbgZvrNNm0ny/gPLjCJobqBv5MQqhTXfRdE/Bbjxqhi3p9lZg6yEYGaBMRA2Jx9kAy/LVnJ14MS6bt/IzEFhayJHUAayXQ1gxjFYrqzjXyjlKsPOTVxLG0Gu1YCLRhTqpSaDsRorScrpeKBz4E4rfW+Wu1izbF+yEfeIuBNrfVcpVQwcBaicO7zXmgbOu9IY7OxMieOgNKfKP+3JLrRE9040I+nX4y+X9p4u+s2hLebaU1RKvXeHIaxEoAbco3bq3HlVS/J8R7X1x3X6omNjTKxseaJr3MeH4E2tVTjqOnGCm58pf/vRJR2Dcphj7Hw7poYK9mKj0KUOvf4p4T642o9beuYGzDlexIuZnmGWHKHL5DkT4411Js665HWGotOkGu1++EOwAh6IzC7Dz98Tmr0UcsS69C58gcA9nSwCrW5afI4mehkza1H+Cy9/eRPc388tNa10q6TzUcEubzG1XRmDynF6VSeAn5mqnWi225/oCkLY6yOVCHCJhY3wU8SIsR8xAUXwNcX2ieb9k5JkypEcGXimitKEKEYY45vMvt74alRWrAKImIQEZyOWAbXmn5OQUTkANyYVfPF25O91yn/UoSI51Az5oWIiNxkjoWb350v7r6I2Ntnzq9AxOQuxNq5CokXDUcsk+Gmn2TcmudV5np+iChMQqyvq4DJsPIWGDrGtMlFhGSu15hBLKa7vPpzBO96095JKhVtxuy8P4719T0zzvWmnXPOJCSR0xCoGgLVvlDh14FKnw74V+xnl1+UJ7N+PlHspROppNCJvTyGrL+Nzc1hOXpaaK20MaXNyCWYuhFa661KqTRgNBhV4nIR8JHW+kcApdTLSKnluVrrEmC1UmpoHf3Xe17T34rlePD/3U+eDKyOO4ojSOtDqRxpp7t69nlb/jyZgF8SKylTvkHr/qgb4d21VxP5TTYXxEi5lnHONa8X66zqLx/k6regHzL9fAiRps5mZH42u76L9QhPfb1x240TC58eLsJLZVBn/OwDQX8BxDLsEXPzxAL7bP5k00qyOTg1XR0X2xqYqitPfl2/GK1NT76DB7wWqlplVOoSow6OGHXGtKi/jEl/c/g9Hjr9Mc/7ETlcxHx9YtSDiVetnbzIitE2TOMyCp70LknHiV0rf2b0YDMLGUMfNjAwLR0/ryRFeSHhxGQVwnYI9DXGbKeOZjIiCBciAiwUsZzOkd/9TTkVhph2Q5A4xkHAEti5ALr0QT6LIxBRmotYHJ1SMSAxrMUypohHgMdxXWRfRaysFUi8aiAiwIx7beka6SJkEmI5dGJiQxDLaD4iaCcBDyFCzXHp9c5p4AelsyHoUtzESsGmfba5tzWIcPVDrJu5iAB3shCX4RHX/AG3lE4KsAyGxpjj8bi1RDPNeByBfhkSq7oVV/A6SZdABKjjYt3RjMWxsmLmJRcRs/GIIAZ5z8z1fbMh76xIOlBJKr8i1i+HDBK4oGI+AOF+BYRTwH78uYI3sbRBbPZdoGVFaQzwX6/XucizpGNtd1znKaWuB64HiImJqa+ZpYnxLgeiMoA3THzjD27soLeF1NuNt8Z5hgsT5AP5fS4H4Prrn+Ylda7nuOOSqzJi+YhYdIJrnfW0+QbUPabdFnf/rg9j0RNAfdfwPSkndtPUaXXKoDhJf55w2qly+F7cX5UJH705aja8CDffJ69HN5DQR3/tdU0jJn+1YDUAX2VI1j1H2D2uJavCrFp9fNz/YjgFht8vFtLrpz9db3ZiJ+GTBC95XdsrI69PQRntfpB6pw5HKtXiloc5tcF2ljaKdUk6XlrNWmnXyRPD5/yGLK7lO/qwdmQSCdUZBJVUAhI/WBEFWXHdOfPjbawZO4BBb6wTYZeHCLRYRHhtBx7BdYt13jLHxTUQN7tsIHRJNucUIiIrHtf62gvXurcA11L5HCK6AnGz1h4w7X0Q8bYKEblREDTWq48+ULAMIpJMHdNxZvxOkiSn1EuK6XcjritsEgQ5Lr5O0qFLzeutUL4B/K8xY0lDkkC9jutqjDl2OyJ+I8w8+EH5W+Dv1AlNRgR3H0RwDjHXcOxVZYiAdOa4BPgz8lw5zYy32JxzEyJYi8HkIxLLaLzpJ83MYzpwC+wcHEqXJUVQIHXGf7tkJf1GprOZngxlJRv85AGzD1Us4zx6sJkebAYginzqqhduacXYREfNJ0qVUt/gfgTWJqKe/S2G1vol4CUQt6QWHs7Pl43yw6kVWh/eFtL6iCKbscD1+nP6kc6LZv9UXq1h2asdvwpeFlJHlN5sXpsnWSIwa1r+koavJX3eQOglxwNKfyIqKN9z3I2XfU4EKYhbFSZz7itAmHntEaMi0l/kGm6Ikuy/2u3yMDwp4OtJALSZvodZRx2mmAQJR8Jjtf3GPBBY6R7zFqONZWiQuAUvDJpga46ebNinv0ekLa2Vdp08cTzDNLqSQyw55PlEc2bmNgCyBscxcGE6ZyZuA18YtGmdWAj7IFbRaFwraBmuhTEPcXUFyUDrfMRuN+2cWNAkYB1kboe4bCivgKDpiMhyLJUXI+Jug5xbXAIhFYgVsQwKZkOEc/3JiNhyYlKdzLemjmhEHyhf6JVIKcSMzSnJ4mfqmSaZ84yV8+DT0D4GEb5BsDMduqSaNkPAfy0ihgPNtTMRcV1CzdI2DyFW2Y14rK3+1yCi+R7EklyBuEPHmjkqwBWlaV6/F5k+VuFaR2PMdXzMvB8w/Tkle2IRgRqBxJo6430WuuQWye/JUI0P34/sTii76cweyvHHz/gQP8xd5BPFUFbSz2SzeoY7sbQhrFcR0IyiVNeuK1ELpVQu8AvgR7MrBkzQWk2cdni1y2vEEI71PEsLoROQhaAWTkxpZn4Cvb/JIPP5fqjnod2FZRz630BM+AQAH2RczqqEFFTG5UQmwMeMY5f6lBeO8euT7iHWP9bDoWWu4JrC0yzjPMB1S13PQDDWUNUe/IsgvzSKzkF7jJXRrZittb+J8xQX136sBQbCbtDXupZT70yzDY7zCCLeu66qN06t0ffaXcylONmBXTzZim8Xvyxdy2FQJ1CnAHbiZ7tP/L5Ot12nDAyAvs11TbZi9CRDAwdbehCtG7tWWmpzCa8RAdy28XmyEs8gLm8H66dIsGIU+Xx9USK/XLcR4mF3XEfCMve5tT2DcZMCVSNWwTJEIDlWxnBEoBkBVLoRgnpB8UYIiQKCoPckOb99OhLv6MRmglyro9k3FkKM2+3OfOgyASL8zDUT5dxSE8MZdCnuF+9AKH4PQhJNeZntiCX2FBnz1mUQP0Huxz8UKIXyIvBPl9PbhyIxp6nASOjyd8TCm4iIzgpzv0G4pWeSzHWcuM9U5K891cxbmfm5HRGLaYiYdeJqe5v5PAdX2MYjDwFCcWNvA812F2KdTTb35rgKByPCFtOfE9N6KSL+RyAPAEyCp7LEdqSQSj5RnLq/hJwA2E0Y2+gOwL08RJ/iLVT7wuagHgCcy2d8zm+wtBHsWgm0rPvue8hHSppJ3vBLxDu/Nh8gsTAzkedQ11GvradJzrO0MjJv7Ad/qfC87n3jejYXyAevt2hTGbAqIcXzeleGuOeibwBu8LS5Adet1XFJnYpbg9TBsRxWR4g7a8+Jrt9ufS6uDvogwKms4GzPvhvmmWsY4ar1DF7kGmaZdIRjr33PY7W9/hXHanmLZ3w3rPXO8HA4Thbj29Vjnqy4jmvz8oSzeZQ7WPSEiQO9TRILhf6u/nuQc30leVGDd2vaeyUq0hPdZE+1Y0Sd61t+Bmg85R0a4KR3STpO7Fr5M2QoKyAYOrOH76O702+1SZcbDzEbCsUyuhXCyvaJ2NqIiJn1QG84+Lhxh41FrI8HEDEEIr7GIe6oIRA0CMiFkKmm7TLEqheMiNtuiEBySqmUIOKxCFgHpEBmGvQeglgJR8rYiMFThiZoOFKyxnm2GwSdAqXf8rXgfw7i1mpKw8Tfg/zlJ0NBLkT0NuLVWf7+ijyQdjLuVsHBfGgfjVtzNR6JtQ0F5iNW5AhcN8lwuZfyxeA/Arc26RDcmqi+5mee2RzB712XNRQRss78rEJE8ePIe1Nljjt+D0VeY5hg5ijczNn5iBCONu9ZtsQOB0bto0NIDn4l0I901gckeeqU/obPeCFkKk9yK1Hkk+/xs7a0GRq3Vp70tKQofRSYo5TKQt6K67XWewGUUvcD+VrrF7XW25VSf8Ot6rQESW2PUsoHiYXxQ77Y7ABma63va+g8S9tC4kBlBVBKTJ4XeL67Xuq2S4Dx3Or5vak5lkx2Nzk1UFUlYFx2J/p7jk/lVR4z4q0uN2KHPnwNR8g+fGfB3zl0uhQm89RK7TeD5d+4wrj7bW7ZF0lq1AWlZjKcUYeViKkhImunVEGSPwHoOnIwgWtBVh0bFtOWkxhbe60psGvlz4xqfBnM53waPZQ8ormk+h33YBWUjuhAvk8kvQ78V6xvbyHix1gmKYP2A3HF03LEWucd+pFp2mebNuHAMijPFfFXXAghoYiFrwg3i6zDViTzbKz00bsPIlSTzPFExPOpG7T3MWPxgVITzxk0HNpHybn+V8rP0nQIGi/3yFbkr90XIgaaGNErYaeJw+lShhvnmgkEGxGeiwj0ROQv2hfXrfYc07bQjLFM7tt/nJmHgTJeZiOC1nEnHmHmL83sqzD3g5m3TNiaCfHB5nrBpr9ERGg7YjoQj7uxZzlMN2MagIjZEeaa3ZCHBhcDBbD+rN4k7M+EMkiN/BWd2OvJvvspv2UvnXiRG8gjmleZhKWN0bi18qSnxb4laq3LoO5v4Vrr6bVez+LwHC1orauBMxq4Rp3nWdoujqfbeLbU2H+TSR80n7pNcI7V7vEEk5XHiMW6LKQS4gVau9ZQx41Vx/i6go8/AO/Cf+/1HKt/3Cn1Hqv7+mcBMEuDuhESn4fEmPVs4Jc125rYU8cBsN0Phzh0+mM12gzv/yX6GxhubB9OwqWXucqMrWaSTe97PRoaytxr+ZliXZKOG7tW/vwoIJw0kkliPZ3ZQ6FPOD4poii/8kthaPF/6JX3X6piJTPrTw/7c+oT5fKlNgo3U64fIhQHIYLHEZUhiFhy6n7mI0Iq3MRuBkKID25Jl2zEyueI2kA8Yq307yZ5kR98sxD6D8JNooRc2/8A8m0zD4IGmP2Fpo8Y5DHIAAiKRURuGW49z0LgAPhPlXF06WPOD4fiZRDiWEQdq2iS/F68BEJGmPNDEQHrBwfToL0TJRKDWwpmJCJoncy4VYiIXY8kMorFzfLrh+sCnCvjPCPXzPN75lgZ4hY8FVdsOKV7UsyYwC2Fs9HMcbbZZ+avNKkDPlVVRJJPhV8H/EoqGbzrK9ZGJtEZSRL4PDdxDa+yh85ccUieJ41otxRLG8KulYA1XVjaKPO9LKQAz9UjRh3cuMbnUGMk4Z5TU9TBcTNN1NUUEUYU2Z4YR2+B1luPI1NJvvfa1sXa9UmPVFv1aabwR+CWw74PBnkEuFiKf0lddO//PfmlUQSUwqFZp9YYk5MF2FNex6kcY0TpdfVUfKgtRvsZA8p6T7Vv064OC2kC68k0frzOOI5W3FpOIqxLksVy1PQjnQgK8KOSPXRmYFY6T8TdCEAyaSgj6PYEdSSszz5OzSsXEZON/L8l4VobHZfREUjpEhCR5YcbN+mL1BedjQivbEQoBSJCb4TsKzWJCIPGmzZLIGgIkryoCLrIAN3stgPxlKY5uMCIQeeYU25lHCLI1iGPXuYjos65Hx+vtpm4cbGZEOLsj8KNmS2U8YbEmn3hZv8S6b/9EFzX2RDze77ZAnFjUs01ABGR2bgxo2XUtGr1Af9TgHQ4WAjtA5HkSZlmPkPwWGWpQNyATYkfTkFce0uQpFB+Zi7i5Zo+VVX4Vh/i9KwSyqLbQTh8GjmUZNII2yiZq6YlPkMayXSggn+3+x1P8icsbQwb6gJYUWo5CVEmn5AjOtU8iJzoXRhMhGYfvmZjxi8bdPV14kpncYuXhVQEV0++oyffsZm+HkHb3asvN9tu/W65njHnVnFfzF9M3w/XPGYy9TpxorJPxtJdX+SOqYbNpP5arz1xY2O3qHhP306ftYV2fZxtcq18yTDP/fduBrdpSxvGuiRZLMfEq1zDRbxPNHlQALeGiJfP3uAOHtfRsOx9buymI5g2IqInDakX+h4idAIRsQOyBBZ67c/HTTKYh4ioIij4ECImmLZFEOQdU5oCB+dAe6dsTKmUdmGjee2UXjHxnu1jkFx/3qEggYiLqhGrB180bsc+SGZfJymQkyTID0+ManE+hNwEfGzaxpt2ycAnyBd8Y6E9+B60H2PO3Yrrd7AQEfDpiCh3aoRuxU0alYy4R0ebduOAL3BdgDPN/JXItdo741hixhRi7nkgIj5DgfG4376dEjjBSIbfc2RuKYTdMzsS9sY+SIbVvX7F4NSvqOgDv921ku8ju5ORKAtuJ/ayl06czQam8gKATXLU1rChLoAVpZaTHNWfGtl5wUusZtS0PuoEGIMESm40x15KFxfeWRPFQurNFjWf0boPY8jCKd2SxZkoNRPF+4w1atenQB6J1lUuxbGQ/okq7st9oIZ7cF1W2G0ZxuL7Lzm2LQpG55vgTlzTpVPP1S0tAzpXfvY8bBR4rqcUnMcnACzlfOBwC6nDlww7bF8G/UA3bB22/IywLkkWy1HzDpfwL35PJR3ozB5KB3Ygx6crAD33b4ESyEo5g7jVO0TM5CJCKhcRcvuQOMZ8JH6xD5KB9i1zgSRELKUiLqfzAV/YuhbiR+ERfxGO22+isQA6bq/dZH/7kYgIdRIf9UbEWpLp+4A5dpkZi3dNUV9EjDqWwsugvbOUFUNOOnS9CxFqThKgKjyW0pDepi8fRCg6JWlKcZMRGYHdvjeYnEAiLh1rrbOkp+EmJQI3e68vbhKiXYioDjZjrvDqIwlxfu9l5hk8wpK/m7kOQVyBfRGR6nz7drL1ViFi9gvEov0chM3fJ25dhTAgNg2ioMMByIo8g1lM4RoTflSBH+eymhnMZBr/AOBcrDBtU9i1ErCi1HIS4u2W64qyw0uN1LaQirVvQo0yJ+pM08HE/sSR5dW6n0c0ns0KTHUYHs2fhhMs4lhI1b/MKbWeb3m7xTourmoB8K8ZteIz/w5AFjPc8ivO+F50W6nl5r6G1xSjtamRsEmLRXecM94HYdlD58P7QAN91KY5EktZTgIO4X55q5+T3iXJYjkaCojhUzqLi2bePiiD7rGy/vgtA3pD3OodZA+OJLS6iKD3KiWpTwwiuipw63yOQITlcCg1USJBjoutr2nnA2yH+N54su/uLIQul0HpOggKNRZAp7amcRM+uMS4qsYgoq8Y18rouMP2RlyAgdJNEOS430aZ66chLrMbkKXTxG52HYcbB7sdEdehiIsriKvxGkQQrgJOgfLtJotuGiJetyKiO1HarlkIg67Edct1kiRNRkR0gZmzMebcQtyY04HmOoNMGyNyDz5kxHky8Gqt++po7megmZtzzHnh5hjm+s69FuI+QACPaC4d1IE9Pp0pj97PCoZRRCjP/PhnKsxz7t0BofhRQQYJ3MwwerAZSxujcWvlSY8VpRZLLfqxlr10AtzESg3xJcMgHx5lGgCL9EoA1ERj6Rxas/0KzmZ4xpckJbivoWaNUO/YVEf8Oq7EL6k9Zmyyf9GYCXABEFfzOh734TV/kB2DNEq9YM69oc570fe4VlaL5bjRuPFb9XPSuyRZLEfDuXzGeBKII4u06GT8qCDJ+K0GhpeTHRdJ7Be7iE7aha8TSzkCsSqmAzchItVJTGSS7gSNN6/XIta+Xoj46oOIJSfO8RzoUgUsMImJ8hEh5kTBfAGEGlfbIMQiWYFk4z2Am6RoE26ioQOmTqnjSLPMjDUQV0RXIaIac81JSB7oKES0+iK1PzH7LqaGS69/ksxF+TLwB3GTzTPCcYgRpIG4TkUfIkJ7DiI8fc1YlphzIxCBOtvMUSIi2vMRcQq0vxJ4GteF2qlzOgLXsptu+nXuIxCx6IJYdFchVtRzpP+KCeBXbN6fQgjKrySouBD8oF+v9XSgkvGnvcV0JEZnBcOIIp9H+TN5ZgLryxlhaaU0bq086bGi1GIxONa+fkiMhoPrflt3bOgUnualBbcAz7B83NkMf0LEpa6n0t8wvjTXGoiaB8P5Ei6fCcyESBGaWePORC039UF7yeMzHVUzrhXEKlw7uVIc37NNue7DDJIyOmo68Ncb6H3/ehJYD4i7be2Y14asrBbLUWFdkiyWo+ZzfsNMltGRvZxd/SX7fdx8ApwCsVm7oBv4OnGWvoh4cqyOTlbdfohAGoQII+cbXxmUppokRUMQd9JAxM02EPgQyreajLe5iFV0GbTvZs5Pln1EIwI0GhgJBfdDRLLX9aNxExY5GWbzTB9Oxl8nrrXMvF6OZKwtRGI+A6kp5hxr7QbkS3w6IhQrzJjmGGtpGZ7Mve1NyZuD70H7ZNMfiFh2ysc44wyUe2GJGVuJnEsQ4sJbYa7nFE3qZa5lytIQYvpId98ywhGB65SjSTL9O2NIQQT2YiAG/C4zr0+R+f9heDCnbyxh5ui76MN3hFHEa9VXM8NHvg9MZjYA6SSxAUlPPJ17uZ8HsbQR7FoJgNJat/QYWh3Jyck6LS2tpYdhaYM4Vs/h6kqPNdItMXO4CVIZ4fr4xJsAuM2UqqkdV+rEui70jhutJ/b0E+NLfD5LG4xntVhqo5Rap7VObrL+OiVr+h3hs/Tzpr2m5cRg18nmZTr3kkIqCWTQib3sJwCAmG8LefOsC7l8yQcS21mECLx0RKStQ8TgKYg7aqk5PgA3OY8jxIzo8ZQ4CQWWQU4adB2IWPkWAmMRi+op5nwns+1GxKK4CTeLbRkifh0X3mREQIKM13GdHWn6jMUVqg6ZZjxbzTgd8eq4G2P2p+IKyjTz06kHWmbGVIgI9vmIGBwDxTdLFyHjzT35IeK6Cjf7cLS5/1DEnTYcccN9ETeLMWZ+nXkeZK5bZsYzFXgWEcG9kQcETkxqkDn/MsQSWwzcYq5dhZTbSexA0PRK6AfrL+pNv+WZVAyEjIDedKCSHLoCsJhRdCWHZNK4mPd4gakATKBWiQFLk2HXyuahXUsPwGI5mRjGlwxXV3peq6i62yUg1ko9UTIDP8Yd3J7xnCeLrcVyUuC4JDW0WSyWGpzLZ0STRyopdGIvHaorCa3YTWjFbsiHBDJcN9Eq3JqZjitvOJLsKA8RV9GIFTHKbJMR8eaHCKiOiIhKByqgaxRuuZResv/gRtyEPZlwcI3pq5u57gZz3QOICHQspeAKuhKvPipw3YsHeY1tq7mHXFyLpoMjlOMRl1cfs9+JxXSSHxWb1/Gw1RHTTqztbAgJlo3l5p7zEMEabM6djJvoqEj2H1wl900sIoYdnPjc4eZYNCI4ByHi2HEZdmq13mzuP9hss8z+x83mZXkNur2SinthxUW/pk9pJsSLIPVnP53YywDSGEAa/1dxN/O5gL108gjSJ7kVSxvCrpWAFaUWS5Oj9Q0eK6nOFwtpbStpBv08NUzziSWfWHSC60Ks9YwaFtCFTKhhJa2rjcP5LOV8pHB2dUSgtZJaWg7HJamhzWKx1KAf6fRhA53Zw34CyPOJZoNfXzb49YVkCKWI0uQObqkUp9boNYjgKUZE1SOI4PJFXEMrzLYdN4Z0HJSukjY7FyNiKhyxSFYApbA13WTedYQj0N5xO30XsfYlm+t0NPvMtQ8uo6Z7q6/ZHAtmKWIpPIAI0UTEOum06WZ+RiBuxj5mq0BiMCdB6RIz7oXmZzAcfB1Ih/gJSAxsR0R45kNxiWxEmXvdYOanSJIlscD0X4zEiQLtB+GpuUoJ4sq7CzfT8XLEJbcQEa1+SOyuU+Yjwhx70bR1aqM6bsZp8l7wFiJAPpT58VsPZ+//D7758Hb0BXRmD53ZA8CXnM2XnE25XwAP879spidFhFlB2haxayVgRanFYrFYmguN+0W4vs1isdRgPUksZhQRFFBJB/KIZmBWOgOz0vn+tO5E7C8kaE2lNA6H1Vf8yhVlIAKwDHgIca8tQURjutk2Ad0gZwEUPw5BV0ibLkOAVXAwGxG324FEiB+IGyeZjoi1wYhIrEDqgqbJWOiFiM+7gN4mGZJTC3U4Na0+TszoINNPuNlM7OXO7UgCICfRUiQisvPMeRuA2RA0EnGxjTXn9jIi2onjTEYE7zJpE/KubESbsd5u5mugiUcdglhjndqm8YhINvGl5U5t0Src5FDx5vplZmx5Zp6yzVyUyFx66q06n3+Rps12XKt1PCL0FyIxpqYczqXZH+FDNTnEErG/kAl5i5iQt4j9BDDox3VEkU8+UUSRT5THDG1pEzRurQxWSr2klBrbQqNsdmyiI4vFYrE0DzajoMVy1PhSzfPcyCW8w9jqj+ngU+kRP53ZwycBoxmfuAiVCXSElP1fuYmDcpFYxmzE6vYWIvSKkdhQENfXKhM3mgvFcyAkBU8sZftg3JjQEsTaF4z7EMkPEVenIJbEMkQAZiOCyskcW4YrzAIRC6JTEiYYicXcgIjAMsTSGIInNrOLE59ZhVtj1XFVjkHcZmPMWMpw3XQ34saKTjbnliGisQJMIns5P9kcG4JYbMcjFs5RiFVzIGJpPQexrBaB/xBc1+NNpp9or5+Oa240Ik4XmLbx5vpjcC2oTpbhcDPXQebcGFgwfiRji5egquDT2KEARFBAIeHkBZxBaIDUpamkA7tP68hz3Eg/0llZRw1xSyvHZqoHrKXUYrFYLM3FIeTLV0Pbz+Dpr8VyNIzlYy7hHbqSwzaf7uylE1lnnUHWWWdQjj/n71/ELSF/hwjIPSscvwWIkMlExM1biIXRidE0br0Hn5PN4y4aDiRBiGOpPGBiRw+YvpwESo4wdFxnge8WIiLrAGIBHGL6dZIXrUHiWJ0ERUmmz264LrmOsFuDW5sz3rRzYjodQd0HEYobzRZp2paZfrJxLY0mk+/KBYi1MQkOlphrFuK6Ifc22ypzvSRzTafuaiEi8vsg7r3LzBjzETfeUETopwD3yxzTzfRzkxnTENzY3TeAEXDwcfnJCDNfzns30IxjsozpXD5HbZR7OrdiNeeVrqQDlYzav4TdhLGZnmymJ9H7dzCXqziPZYxhIT3YbGuVtjUat1ae9FhLqcVisViaB/v012I5auZzAXfzCEMrVvC532CiyaN78Q4A9ge2wy8Xnsn/M4RDTFah+00uBYlXLEOEUQkiiKYCl0H7cNMu1LQpgvJ08O8GxZkiTttHAcVQsBgihkDOKujaBxFbzhfjYIjfioizKkR8hSNCEdyYSsdyGIXEjsYiYhg4mAbtH0EEYSYi6PyQ7MF/RqymQebnZEQQOpl7Qe7Zx4zBsZaOQ2IxfYENMNRJWLQG2vdG3IfX4IkTZaA7DwfTjKuxrxl/CCK2NyLiN9CcVyz3Ur5VuvB3LKNPm/ssMK+fM/Ofi5u8aQiwANpPBf5uxnC+ubfJwKuISP6TjP/Ue8shAtYOTqJPxXfsCepIBAXkBZxBPpGMqlgs0x3wK08m3te5ki30xNLGsF5FgBWlFovFYmlO7EJrsRw1c7kSH79qOrGXzuxBGRFUnhJAUa8AYigUsRMIhEDFQPC7HxF+45A4SaecymWIWHvIdL4dj/Dy7wfkQ0i4aVsifUYkA0nQNRTKF4N/AWKpBCgE/yRE3AYjLrihyHiKEHfUdGlXuhyC4s3+ZETAAu3vR2JSqxBLpGPBTEWsm2WISE1CxOg4cx2nLA1ire07CUk4FGv68kNciR3royMGN+DWY003HeQjIjkJ2leYMb5nrhltxvsiIvbN59jOh6BLMvgPdufCI24zzb2swc2w69QjHWiuF22uGWz2m/nmdUT0ViMCNQ0qhoBfPgzcmA7VUJ0occR7fToBsM0vDoAo8gmlyFPDdAqzGFwjRbClTWDXypZz31VKBSil3lFKZSmlNimlftdA2+tMu21KqWeVUu3M/i5KqRVKqRKlVFqtc4YqpfYrpdLNZv9DLRaL5URiXZKOG7tW/jxJ4SsSyCCCAsrx99S/TCOZmE2FlMW2EzETDgSC3+2I2IwFpiN1QPshQjEdEXOvmu1x5AtwHmKl245r0atArH1V5nUZ+I9CBFqa2XJNu0Cv30HEqK85D+BKCAo3Y7oSEWumpEv5PbiZeEMQK2s67Mw1/TqW2ApErJUggi3dbKug75UyPq4w11iAiGMfc09O1t4U3FhO53qOaByCCOExcHArFOea8/MQ62cpUuN0k/zeZbbp1yk90xtmTsetz1oCXIyI6CRznV5mbEXmXjLN787WGxHkiabtMunHb5m510Ao69WOfJ9IgjIrSSrOpB/pBLCfAPZTgR/nsRQ/KlnBMFbYmNK2h10rgZaNKb0D2Ku1jkO88mcrpTrWbqSUigVmAL/GrVB1hTm8zxy7vJ5rZGitk8yW0tQ3YLFYLJYGsLXXmgK7Vv4M6cReiggFIIs4FgwfyYLhIwllN9/36k5g9iERYr6wPqW3CJpTEDdbJ07SEVgl4p7ryVzrxGqaY4xBxFIQIrhCjXtqGSKStpv2iWbrBuWZiGByEg9tRxIFDUEEXDrwIJQ68av5uC7FJeDfx5SiwYw5D4iHLk7caBIiJKMRQRfvNd4SxGpbZK41G7HA5pt9vnIPgFhdnf5SESHqJFByMvumyHy1HwEhkxFRXGDmYTtifa1CEh6lmXseZ7Y1MGOc2R+PiMtliIW0I26Cp2BEmFeZOepjtkDkAYJxm6YYsdY6ItaU+AlMPUSv1P9CBKSGJBFduovo/TuI3r+D17mSCAo4P2uZJ/PuuXyGpQ1h10qgZUXpJYhjBFrrrci/9Og62l0EfKS1/lFrfQh42ZyL1rpEa70aWXAtFovF0pqwC21TYNfKnxlb6Ek0eWSQwF46kcR6hlavZGj1SpKLN3Jm1jaxlBZCVRT0e98EWybhuqxG4ZYmiYcQJxnREOAUyJyNCKVBwDrIWYYr2ILB/y5ENKWbn7twa2tuBf9gRMwVIYK1NyJY8831k4A/GNddgFWQuQAOrpKNQAhKNGP80JyzCRHG8YjwnIRbe3S+GZvzuGW9uYaPubYv4iLbDRF2sabvIDPGKq9x5ZrtZtyY2FDTtjciYkfIa//bgVtwLaEgSYzWmm2c2f84Iui9g+LWm5+3mHm/GYn59TPvUyZiyU0187bGzPdjiGV6nbnuKpg++B6IgU9PG0oU+VT7wvqAJNYHJNGBSuZyFR/GjWYYK5jLlVjaGHatBFpWlMYA//V6nYs8EzvWdnXRQyn1jVIqVSl1dUMNlVLXK6XSlFJpP/74YyO7t1gsFku9aKxL0vHTatZKu06eOPbjzygW0y8vk9N/LCHDJ4EMnwQAsuLOEEtpJrwTdKFYM0MRC1s6IuiiEMupI/xKcYVQLPQeItcpng+EQNcxiIANQh57zEb+guIRsZSLuM9W4yZSCkYEnZMpd6P0TZEZw2JgPOTku9dsP9AroVC46dcp7+Jk6v3C3M+9wATTV6S5jpMx17EoVptzw824tntNYrWZh02IYPRBvtw7fcw2x4PNfWYjFuYyRBQ67rjFZpxRZowv4royO3Gpq5C41UCvdleaMZSYY5fhWkWd7YA5J8LM3T7cpEm95J5fvu1K7s9+iNzIcFJJoXP1HgoCwunEXjqxl2jyuIR3yCCB7+jDrTzJ5/wGSxvCrpVAMyY6Ukp9g5vjrDYRzXVdL74BorXWJcataalSaqfWemldjbXWLwEvASQnJ+sTMD6LxWI5+bGfpg3SltZKu06eOP7GdP7K/ZwfuojNAT2IMvVT9gZ3IG71Dn4YHMzpRSVctP8DKIGqMZB/aTgB7Cds4z5Ig58m+XPqonIRmlfgxnp2Mz99IORiRGAuh4rLwG87IsLCpX3ZyHYEbj0k7qjjzHmLgRhzrPCQCLJw+GmuP6duKndFcTZQCF0XmNcb8PylVwwEvyLIjo4k9ttdkAtZY88gbuMOEW+JiJgMgare4Gusq5741QGImOwDfGHuNa9chPEBPOVdSqM6EPRcJeun9aZHxWYClx9i/WjxOe63KdMV3oWmb+MumzX4DOLyd0BvyB0cDtNMpuMiXDGOGecYud7aF5JIKU6nICSY01NLpL8XEGFahCuqv0AssSDiOx+xpJbA7gEdCft4n9xrLGSlnMHk4tepiICo0kImB82m2seHSjoQym4AxrKAfXRiWvU/SPdJasyfl6U1Yj9Rm89SqrXur7UOq2erRj4KfuF1SgzynK42jW1X+/qlWusS83s28BHiBGKxWCwWS6vArpWW2tzFI/RgM1HsYm7AlUSTR0xeITF5hSz1GUFFMgRUl0M38CuBsuR2+JZBzLpCQov3iWDqCKeuKWfT6F+I8KlGxFQwbo3QGFwX3xTpi1WQPTiS73t1h0AIXH4IIqBiKqyNS2JtXJIIKz+o9vUVy2yU9H/q++XSt+MSWwUkQsUIROx1RFxVU8GvAPCF6NJdfHrWUPCDyIp8EbRFiAiOBk4B3yW4yYACccWpif0svbIDp35b7tZIdVyG8yHHpyuMhX7zMwlMPwT50O/tTPq9nSnz0tvMjVOHNRV0IsRt3EHFlbBzcCidq/fQuXqPzN1WM3+OG/FWKBsurtSd2Eu1L3Sq2EtVbyhLace02L97XKKJhU9OG0Hu+HDXWhsu9xUR91+ZI8x8xcr7Epe9A1UNVT7t8C2CLruKyCeKuB93MIdrmMM1FBHGUs6j0CecKPJJI5knuOk4/wotlhNPS7rvvgdMAVBKxQO/BD6to90HwAVKqdNMJsHrgHeP1LlSKlIppczvIYjTRHrTDN1isVgslhOCXSt/ZsznAkBKgeQRzanZ5fwQHcwP0cFMyFtEhV8HlvqMkHcpFwKfOCTWyxBQxfD12ERx/SyFXh//V8RbMGIh7YbEgg5BxFI3RGQVIpbMcyB20y7OzNtGhVPqJF8sqAMXpTNwUbpHEAa9WCn+dlHww+hg8BELI2WIoIwHHW6sr/mIABtsto4ydt8y+O2uleALvtWH2DkoFEbA2ilJIlCN1XN3XEdX8FZBxZXItYMhaE2lxHf6IrG2vvBDbDAUQ99NW+TaAxHxPQ7KxrejbHw7SWYEnqRGFJs5LAMqwG89dPl7EUGZle41YqAiGXSIbBRDuV8AVMGZP27DtxAC8w7huwZy/GJ55ok/iyU7Svo8/+/LiFlUyA+RwfwQGczuyI6sOWsABZt+wfex3QlL3SdZd32hYgzyHu+TPktjOsAGODN7G7tP62hSGkXRtSKbJNJ5hmlcwyuevx+L5XhQSiUopWYppWYrpeY460Rz0pKi9FGgs1IqC/g3cL3Wei+AUup+pdRUAK31duBvyMfBViRi4A3TzkcptQNZtPsqpXYope4z/V8IbFRKpQOrgde11v86UTdnsVgsFg0cPMJ2YmmJhfY4sWvlz4wt9OQmnufDxNEksZ6qUDg9u4TTs0tYG51EUG4lfdkgoq0bYteOAgrhpzh/EioyJJ40SPaviP41pYkd3OQ8JXjKrXwWd464oFYgJWRykaRCaeC3ChGXRdQQhPgAPRDXWZOZ9vRvS6AbRO/fAX1EFL4dfQFqITIWJ0lRjGxlQaakzQLE/bdQLLVdsovAFwa+kU72oEgoFvEX9sY+EbWGKp92EAM6ComTTZax+FUcghI4fU4JhENpfAf5byhBxPgnEJh/iMD8QxAMWdFniCAtQYR7sfyuY83cXow40fvB7rEd+XDwaPwWw7aQM9gWcgZlY9sRtnofFEGVn8T7vhl3IcTCmZu2UXZTOygB3yIgHaqmAr3g9CUlnL6khLAf9zHox3W82etCulZkU9XbWEqrwC8N9BAz937gV1HJ6pG/Ijc2nA7VldzIc9zIc+z168TAXelM4xnOQzLwpvKrJvlbtJwommatVEo9ppTKVkpppVSi1/4eSqn/KKW2mJ/xDfUDoLXO0FpP0Vo7OakDj/q2jhKltXVirk1ycrJOS0s7ckOLxWI5iVBKrdNaJzddf/20fONsiOAjXlMp9RginroCfbTWG83+HsBryNfKIuAqk6G2keNT7wHXaK1tVtqjxK6TzUcEudzFI3Qlm7P5El+qqcIHgNP/WULppA5U+/hQQQdO31hCRTd4P+BCLv/2A08sKOGwu1dHwr7dR/ZZkcSu3iUCEkQgFkPWRWcQt2aHWC03IDGYF3cgKK3S00/FOeCXj9TKDBE7RmDqIRHBi0FfBioNEZqxEn+6MzGULmuKoBBKx3UgaGMlOhpUKu4YtiJxn/mI+IsCFkDVxeBrMvwSBZRA6ZAOAAS9V+nJhLL7oo6ELdonfRRBWXw7Ajcdkk+BPnIemYiFNB95RNNb9mvzdXxlyK8ZtuQ/IpTTQY8US7MnznSj3DfRsD62N/3yMvkp2p9Tl5STO1IUcsT+QvyWwQ9jg9lFFP0+zhQ33B5yzZ0poXTJK6IiFHYHhNJlURE6BVS6mQeTaOmnSH8205MwdhP37Q6PdXvaaX/n6eI/U3kKpAb8igQyyCOaPqWZ+DrxtQVQFQNLg4YCcDWvmd31hapbjpdWvFaegyS8+xz4nddauRx4RWv9hlLqCuBarfVwc6w7MKtWV4u11o8qpYYBk5FUaTeZzO7NRktaSi0Wi8VyUtNkee4/Qpz+/ltr/4vAc1rrHsBzeC2sSqnuSqmltbY7zbFhSqk3gd3A/mO8OYulWbiEd+jEXsIoohI/sojj9LwSTs8roeIyCEqrpIIObCMOAsHvWRjFYonNLISqJMjqdQZhT+yDIIhds0uEkrFSkg0MgLjlO+RxznrIvSIcUiAovVIEZlQ7iAC/t4BX4e3IC8jxiyXHL1YEm6m3qTbCTyP93dqpvtBlVpGUkImCoGWVsB6UqVOqzeapb5qNCElT1cY3E3b2CpXx+krboFWVBOVXQgrsvCiUnReFEpa9T2I5twJ5EFh4iB/OChZBmYYI7yjgQ2Q/7j5VJtuw1P9AtbGm+hm3XR+gD+yO7gjxEl9bEQH9VmdCFZy6vJzdIzsSs7GQmI2F+G0XC+rpeSX0Kc2UuSmFikDgFOjydhGUgV8ZdFlXBL1kLrKGn0HW8DPQ4UAJnPrPcgamptO9eAdrz0oSt+UqSCIdtVWsyIMXfUVY6j6iyWNzUHdKQzpQGtKB7xJ7cH/QPUSRz4PcQw82W0Ha5mjUWhnmZD832/WH9aL1F1rrGrkElFLhQH/gLbPrLaC/Uuo0c842rfV5tbZHzbEVWuvLzQCSmvy2a9Fs2XctFovF8nPHcUk6zl60/gLA29PWa6F1ah+8BTyrlDrN1OrcBpxXT38rgBVKqX8gC+03xz1Ii6WJeIY7eZmr6MRe3ucixrFAXGCBjIDeJPTJZB+dJPuqL1TcDM8wjZnFD7H2rCSiyCcuawcEQ2lMB4LKKvk+sTt76AzAoHHrWHtaEgN90yETdk4KpYhQ9sR2pu8pW9gd2ZGwvH1i1RwCO/8nlEt//IgfThNxtynyF/QqNIl5TgGf6mr8fCrExTW2I/6x+9ni15NwCtlAH86u/hK/iko6HIDUkCQA9g7uyF46MT5+EaoQ1vZKImpwPp3YS5esIr4f1J0zN21jQ0gP+vptgWKoiIew/UUyEWUiRImVDLvVPj6cvktciLMGn8FuwujJZvIHRHHmmm1isV1SyeoBv2Jw9lcA7E7pyAxmMpMZ6CGVFIQEk04/erKZTuwFH3iVa7jD7zH8QirZHduRvNho+q3LZPUAcY9NIIOwTfv4oVcwp2eV8HWvRBJiMwgsPSTWzkEyR2yAl4dL0qrf7lrJApPK+JKQd8gJ6cog1vF9r+6cmb2NgdXplMW3Y4NfX8Yzn7dTLiCKfAZnfsX6lN50Zg8Ae3zk/exABT5Uk0V3xvOR+SuyJWHaFo1aK3cfo3U2GthpEuehta5WSuWb/fXW9lJKDUXqXyugPeI70KxY9906sG5JFovl50jTuyT11RI01hCx/wVT20B4yZQeqWt8ORiXJKXUAGCu1vpMr+MZwBVa63pFZh0L7c1a68pG3I7FC7tOWiyWnysttFY2+ppNsVa2BNZSarFYLJZmolmf/h4TWuuVwMoTdT2LxWKxWBqmUWtlsFLqJeBjrfXHR9F5HtBFKeVjrKQ+iHP7EUuGnWisKLVYLBZLM3EIKD9So5N+obVYLBaLpX4atVaWaK0PiyM9ElrrQpNd/TIkI/tlwHqtdb2uuy2FTXRksVgslmaiUckbSrTW1x+lIEVrXYhU8bvM7Gq1C63FYrFYLPXTqLUyWCn1klJqbH29KKWeMeW/zgCWKqW+N4emAn9USm0B/mhetzqspdRisVgszUTTuCQppZ4BJgCnIwttkYmPmQq8ppSaDvwEXNVkQ7dYLBaL5YTQqLXyiJZSrfU0YFod+zchlXpbNVaUWiwWi6WZaBqXpLa+0FosFovFUj+NWitPeqz7rsVisViaiSarU2qxWCwWy0lK07jvtnWspdRisVgszUSzZhS0WCwWi+UkoGncd9s6VpRaLBaLpZnQNFdGQYvFYrFYTg4atVae9Fj3XYvFYrE0E87T34Y2i8VisVh+zjRqrbTuuxaLxWKxHBvNWqfUYrFYLJaTgOarU9qWsKLUYrFYLM2Ek7yhQU76hdZisVgslvpp1Fp50tNi7rtKqQCl1DtKqSyl1Cal1O8aaHudabdNKfWsUqqd2f97pdQ6pdRGpdT3SqnbG3OexWKxWE4E1n33eLFrpcVisZzs2LUSWjam9A5gr9Y6DhgLzFZKdazdSCkVC8wAfg3Em+0Kc/gHYKzWOhE4G7hBKXVuI86zWCwWS7PjuCQ1tFmOgF0rLRaL5aSmUWvlSR9T2pKi9BLgRQCt9VYgDRhdR7uLgI+01j9qrQ8BL5tz0Vqnaq3zze8lQCbwiyOdZ7FYLJYTga291gTYtdJisVhOahq1VpZora8/mXMvtGRMaQzwX6/XuUD0sbZTSvUCBgJTjrJ/i8VisTQLtvZaE2DXSovFYjmpadRaedLTbKJUKfUNstjVRUQTXysS+Bdwk/M0+Bj6uB5wvhhVKKU2NtX4moEwYHdLD6IB7PiOndY8NrDjO15a+/h6Nm13+YvhL2FHaNSa56PZaUtrpV0nmxQ7vuPDju/4sOM7Puxa2Qw0myjVWvdv6LhSKhdxH/rR7IoBVtTR1GmHV7s8r37CgaXAo1rrdxt7Xh3jfQl4yfSZprVObmj8LYkd3/HRmsfXmscGdnzHS1sYX1P2p7X+bVP2dzLSltZKu042HXZ8x4cd3/Fhx3d82LWyeWjJmNL3MO5DSql44JfAp3W0+wC4QCl1mskIeB3wrjkvFPgMeFZrPbux51ksFovF0kawa6XFYrFYTnpaUpQ+CnRWSmUB/wau11rvBVBK3a+Umgqgtd4O/A1YC2wFtgNvmD7uBnoAU5RS6Wa7phHnWSwWi8XSFrBrpcVisVhOelos0ZHWugy4uJ5j02u9ngXMqqPdncCdDVyjzvMawUvHcM6JxI7v+GjN42vNYwM7vuPFjs9yVLTitbK1/63Y8R0fdnzHhx3f8WHH9zNEaa1begwWi8VisVgsFovFYvmZ0pLuuxaLxWKxWCwWi8Vi+ZnzsxGlSqkApdQ7SqkspdQmpdTvGmh7nWm3TSn1rEn+gFLq90qpdUqpjUqp75VStzfmvBM4vi5KqRVKqZLamcGUUkOVUvu94olSW8vYGjrvRI3vCGM/prlTSvVQSv1HKbXF/Iyvo42PUuo5c80spdTk4z3WCsZ2n1Kq0Gu+njuasTXR+EYqpdKUUhVKqccae14rGV9rmL+/KvmM+1bJZ96oxpxnads0xWep+pmukydifA2dd6LGd4SxH/X8NcFnVbOtkydgfK3hs96ulXatbD1orX8WGzAdmG1+jwd+ADrW0S4W2AGchoj2xcBV5lgKEGV+DwaygHOPdN4JHF8wMBj4HZBW67yhtfe1orG1hrlr6NgxzR2wHLjC/H4FsLyONleZa7Uz194BdD2eY61gbPcBjx3n/+vxji8O6Ac8UHssxzt3J2B8rWH+RgEB5vezgD2Af1PNn91a54ZdJ4fW3tcC8/ezWitpxevkCRjffbT8Z71dK+1a2Wq2n42lFLgEeBFAa70VSANG19HuIuAjrfWPWutDwMvmXLTWqdoUHNdalwCZuPXd6j3vBI6vRGu9Gth3FNdtDWNr8blrgjHUQElNwP7AW2bXW0B/pdRpdYz9Za31Ia31j8BHuElNjvVYS4/tuGiK8Wmts7TW64GqOi5xXGM/AeM7LppofIu11vtNu+8ABYQe6TxLm8euk8eHXSuPgta8Tp6g8R0Xdq08Puxa2fr4OYnSGOC/Xq9zgehjbaeU6gUMRJ6yHE3/J2R89dBDKfWNUipVKXV1Kxpba5i7I/VxtHMXDezUWlcDmJ/5dYzrWMd0PHPW3GMDuFQp9Z1SaolS6teNHFdTjq8hjvfvrbnHB61r/q4CtmmtdxzleZa2h10nj32dPBHjaw3z15RrZWteJ0/E+KB1fdbXpjXM35FoTfNn18rjpMVKwjQ1SqlvkD+Auoho4mtFAv8CbnKeCDfinBM2vnr4BojWWpcopWKBpUqpnVrrpa1gbA3SCsZX79ydgGu3RV4EHtRaH1RK/Qb4l1Kqt9a6qKUH1kZoNfOnlBqC1LD8zYm+tqXpsevkEWnws74VjK9BWsH47Fp5dLSaz/o2SquZP7tWNg0njaVUa91fax1Wz1aNPKH4hdcpMUBeHV012M6Y+5cCj2qt323seSdqfPWhtS41rlRorbMRN4JBrWFsRzrvBI2v3mMNzV0D5AFdlFI+IAHvQFQd4zqmMR3h2JFo1rFprX/QWh80v39m9ic2cmxNNb6GOJ65a/bxtZb5M0+d3wAu0Fpvbux5ltaLXScb5kif9S09viOd1wbXyta8Tjb7+FrLZ30DtIb5q5fWMn92rWxCdCsIbD0RGxIQ/bL5PR4oADrV0a4bhwfxX22OhQLfAjcczXknanxebYZyeIKESNy6tCHABuD3rWRsLT53Rzh2THMHrKRmAP2KOtpM4vBA+G7Hc6yRc9acY+vi1UcSUAScfpT/r8c1vlp/G7WTIxzX3J2A8bX4/AG/RBbUlGO5L7u1zQ27Th7zOnmCxtfi83eEY0c9f7TidfIEjK/FP+tr/W3YtdKulS26tfgATtiNQiDwHpIJcDNeH5TA/cBUr9dTgG1mewHwMfsfBcqBdK/tmiOddwLH52P+6H8EKs3v95ljNwPfmzFvBP7cWsbWGubuCGM/prkDegGpwBbzs6fZvxBI9pqXF7yue73X+cd0rBWM7TUzT98CXwNjjuH/9XjHd475GysF9prfRzXF3J2A8bWG+fsa+V9N99r6NNX82a11bth18pjXyRMxvtYwf0cY+1HPH614nTwB42sNn/V2rbRrZavZnCdaFovFYrFYLBaLxWKxnHBOmphSi8VisVgsFovFYrG0PawotVgsFovFYrFYLBZLi2FFqcVisVgsFovFYrFYWgwrSi0Wi8VisVgsFovF0mJYUWqxWCwWi8VisVgslhbDilKLxWKxWCwWi8VisbQYVpRaLC2IUmqlUmq7UuruYzh3mlIqSymV1hxjs1gsFoulNWDXSovl5MeKUoul5ZmmtX74aE/SWj8DTG6G8VgsFovF0tqwa6XFchJjRanF0gwopcYopdK9tkql1O8bcd4kpdQSpdS7SqlNSqllSqkEpdQnSqktSqk3lVLqRNyDxWKxWCzNiV0rLRaLgxWlFkszoLVeqLVO0lonAc8BacDiRp7+S+A2rXUvoByYB0wEEoA+wIimH7HFYrFYLCcWu1ZaLBYHK0otlmZEKTUKuB34vdb6QCNPW6O13mF+Xw98obUu0VpXAd8Ccc0wVIvFYrFYWgS7VlosFt+WHoDFcrKilDoLeBEYpbX+8ShO9V6Qq+t4bf9vLRaLxXJSYNdKi8UC1lJqsTQLSqkuwAfAlVrrLS09HovFYrFYWht2rbRYLA5WlFoszcNk4DTgWa8EDsNaelAWi8VisbQi7FppsVgAUFrrlh6DxfKzRSm1EnhMa/3vYzx/qDk/uQmHZbFYLBZLq8GulRbLyY+1lFosLUsx8OixFgQHngd2N/moLBaLxWJpPdi10mI5ybGWUovFYrFYLBaLxWKxtBjWUmqxWCwWi8VisVgslhbDilKLxWKxWCwWi8VisbQYVpRaLBaLxWKxWCwWi6XFsKLUYrFYLBaLxWKxWCwthhWlFovFYrFYLBaLxWJpMawotVgsFovFYrFYLBZLi2FFqcVisVgsFovFYrFYWgwrSi0Wi8VisVgsFovF0mJYUWqxWCwWi8VisVgslhbDilKLxWKxWCwWi8VisbQYVpRaTghKqfuUUiuPt01rQSm1Uil1XzP231UppZVSXZvrGs2FUmqoUkq39WtYLBaLpWVQSs1RSs1phn4nKaVyjuP8oWZt1kqpxxpo1+RruFLqYa9rD22qfpuC5nq/LD8vrChtYyil7lVKpSulypRSO5RSzyilOnodn+T1oVWllPpRKbVUKXW1Ukq14NAfAyZ4jbMuUVejTXNQ34e5UipHKTXpKLqagIzXcjhfApEtPQiLxWKxHBvN/eC1EdxiNuCY1ujmJhKYeYKv+QCtd21t7e+XpQ3g29IDsBw1ZwN/B9YBEcBLQCfgGq82O4BfIg8dTgN+AzwN/Ba47EQO1kFrva8p2rQWtNbFLT2GlsY85PDVWh/03q+1rgR+aJlRWSwWi6Wto7UuaekxNITW+oSvceY70r6WtS/UTXO8X0opP611RVP3a2m9WEtpG0Nrfb7Wep7WerPWejXwV+D3tZpVa61/0Frna62/1Vo/BvwOuFQpNbq+vo0V8Vql1Bql1AGl1FdKqTNrtblNKZVrjn+plOrvdaybUupTpVSp2VKVUnHmmMc117h4DAFmmGvm1G5jXgcopV5WSv2klNqrlHpXKRXmdXyOUuo1pdRDps1OpdS0Y5jW2vPguOcMU0plmmt/qJTq7NWmxlNkpVR3pdRipVS5UqrAjKmd1/EcpdStSqkPlFL7lVIZSqkhRzmuy5VSW5VSFUqpb5VS59U6fol5b8qUUq8rpR6pNZ+nKqXmmzFuVUqNrm05Vkr1N/dWbsY8Qynl43Xc+RtZCRwARtQ3f16v7zN93qKU+sFY7x8+wr2uNON/w9xPjlLqwgban23O2WP6f6vW38ok08cl5udPSqnZSqkOXm0ClFLPmfP3KKU+VkrFNHDNd5VSn3q97mne25FKqcuUUvm15q69Umq3UuqChu7dYrFYWjNNsTYrpYYrpTaZtWahUuoO5eVWq7zcQc168wvgVbMGrXT2q1rWXFXLQmfWo0xznX8DYdRCKTVNKbXdfH5/rZQafAxzEq2UWqbku1E6MKCONucppdLMWLYopW44mjlp5Dgct+HxSqn15p7eV0qdouQ7SIFZm66qdd5IpdR3Sr5fbFVKXVZHn+PM+MuUUsuVUmd4tWnM++WjxAW5wNzjp0qpbl59ON8V/qyU2gWsPJp7t7R9rCht+4QBe47USGv9BZAOjDtC078BTwL9gf8CHyojrpRSlyDuKncB/YDvgYVKqUBz7rPAbsRKmww8Axyq4xq3AP8BHkdcUX5Zz1geBwYDYxERGwPMqdVmvPmZYsb+lKolpI+DvwBXA8OR+/3fuhqZ+VkAlCP3cg3wP8BttZreBXwEnIXc/5tKqfaNGYhSaiBy708BfYF/AR87i4JSKh54A3kPnPfmhlrdPAV0B4YCVyAPNLyvEQosAf4N9AEmmXZ/qtXPfch72xtIa8z4kb+ns4BhwBTgdtXAAxLDjUCmOfdl4C2lVGw9bTsCzyN/d6OBLua1N+HAROTv6UKz/Y/X8ReR+RmN/D39CCxQXg8X6hhfP6XUdUZ8vga8obVeAswHAqgp2scACljY8G1bLBZLq+a41mYlD3jnA4uAJOAD6llfDRMQD7A/Id8ZGhXmo5QKNtdZiqyL/wburdXmWuCPyHqZCMxFvtdEN+YaXswF2iP3+2egxoNXpVRP5D6fA85Evh/MVOZh6zHMyZG4F7gJWYMGI/few/z+NDDLeZCg5OHrAuBDZO1/GpirlKr93ew+4A5zj0HI30Fd1Pd+3YF8r5iEfFeqBD6qtcYmI98VRlDTA9Dyc0Brbbc2ugHBwDbgL177JgE59bR/C1jUQH8aeKBW/2XAb83r/wAPeR33BfKA683rDcAV9fR9H7DS6/VK4L762iAuyZXASK/jvcwYe5jXc4DvavWxBZh6hHscWsf+HGCS+X2oadff6/g9wNq6xg+MMvPU2ev4VCC/Vv/PeL2OMtfoVc84u5rjXb3eu3m12qx13g/EpXtlreOrveYzCDjofe+I2PbMBzAdeLtWHxOBTbXm794j/F0OlY+WGu/rj0AHr31LgIcb6GMl8EWtfV8459S+Rh3nJ5v79fH6v6gGwrzavOTcr5nvA0CQ1/H25n0d2MB1JgClwD/Me9ypVv9zvV6/Bzzf0NzZzW52s1tr2KhjjTb7j3ttRh7obQeU1/G5eH13MX3M8Xqdg1mjGxojNdfyG5CH6z5ex+fVus52zHccr31LgLvrmZfD1h7kAa0G4rz2XU/NNfyV2mse8r3i08bOidf+Or/HmGNdzfELvPa9ABQC7c3rdsA+YLR5/X/Al7X6eRvzncOrzwlexycCPxzl+7UL833RvA4B9jt/S8h3hRIgoKX//u3WMpu1lLZRlFJ+yJO07dR6ItfQacgHS0N85fyiJUZgM9DT7OqFCCHneBViKetldj0P/FOJG+vt3q4dx0A3RBR4X28TYhXu5dVuQ63zdiEWsabAu++G+u0FbNZa7/Ha9x8gUikV1EB/NNBnXddYW2vff3DnogcSZ+yNtxWzG/IQYV09x0GekE5QSu1zNuCf5lxv1jdyzN5s0RJr6tCY9+mrOl73rKuhUipKKTXXuGDtBVYh93u6V7MftNa76xnDmUAHIN/r3n8C/Dn8/j1orT8EFgM3A5O11nu9Dr8GjFfi6haMuNC/3uAdWywWS+umKdbmHsB6rbX395HGet0cDT2BdVrraq99nnErSRIZC3xQa90bRgOf+/Vc5yetdVZd1zH0Af5U6zrTva7T1HPyvdfvhcBWbfI/aK0PAUVIzhE48vcLh8Z+JzoMswaeTs2/m2LkO6b3dTZrrfc3tl/LyYVNdNQGUUr5Ik+xOgEjjDhsDL2QzKgNcSTRWv+JWr+glFqMuPSMRVxTRmqtj3TNumhsJP/BWq81Dbul70WshrUJRixebkc1E/g01O9Rj1VrrZUkK2jsg6EjXeP/2TvzsKqq9Y9/FoOIJHgPBgVCOKBhamjcnEpzSNOulmbXwvI2ajfL1DJt0rRJG9QsLW0epMzS0p94NYe0nLqopCYpDgSCFwoSFBEZ1u+Pd+1z0JwqDI31eZ7zcM4e1l77cODs737f9/ue6obD8fY/dvvzkBTgZ09xrN/zhfFbf0/ONqfLu8iF0t1AJpJOttgsO505nId8Nn5VBwRkn+igSqlaSKpVGdAISROTwbVerZT6H3A9Im73aq3Xnub5WCwWy9lIZXw3n84N8tOh/Djzqfg//3jHqbi9U3rUH/jhmO0KOH1OdRyQ75gXkJuVFXHep8p6T44dFzPuqX4fv2fMM+G4ZAVpNcZGSs8xTO79+8gFcA99mo61SqkrkTz9+afY9PIK+wQid++2m0XbgTYV1vsgaZLuf+Za691a65e11l2R9NGbTnCcEsD7BOtA0pJLjznexUAdfv3l8VvYidQoujF1inWQ9KLfww9AE1XBCAloC+zTWv+WL7ZTHaPNMcva4nkvtvNrQVXxtfN+Vjz3uGO2/w5oqrXeeezjj039d3P5Ma//juezeCztgJe01svMXfvg33is75CbFb7HOf8DJ9lvIiJm44EX1K970r2P1OXego2SWiyWc5/K+G7ejtTjVxQ1x7shWJHjXTP8RIVsGKXU+RydHbMduOyYmsWK3ys5iFN8xHH+7+ec5rk4x3EppRqe4Dgg3zGNj3OcHyuM8Vvfk8riVNcXv4ejfl8m8y6boz83LiTK/EeOY/kLYUXpucdMpKbhVqCGUuoC86j4z9rbLAtTSrVQSj0ELEDq5/5znDErcodSqq9SKgYxl8kCvjTrXgbuV0rdZL6EXgP8kBoNlFKTlbjLRRkR3IITC70fgdZKqXCl1N+OXWmEwNvAK0qpK5S4/L6L1MT+XvEIUvs3TCl1qxLH3PaIWFihtd76O8dcgpzPu0qpZsbAZxxiLFRZTAVuVEoNUUo1VkqNRyJ0r5n1bwBXKHHra6yUehgxdpACFBHHCcDLSqm/K6UuR3qegefu7DREXL+ulLpUiZtsf6XUo5V4Hr+FWKXUI+Z8HkG+zGaeYNtdwG1KqUZKqWsQk6rTxgjZucAcJS6E9ZVSHZW48dY53j5KXIvvRupmPkH+xt465qLifaArYi7x4W+Zk8VisVQxFyilYis+EEH6R7+bExCTxhfM//c7gGs5eaTwR+Q77gKTCgpy47ufEqf85sCbQMUWIgnA34DJ5vtsEGJkB5jCUMkMekaJQ3tDpVScUupR9Rvc8bXW28xc3jLfnV0RU5+KvABcp5Qap5SKUUpdosTJ/p4/8J5UFq8h4v1Jc+z7ECPAl//AmMf7fb0MjFfi/N8M+dzspkKGkaV6Y0XpucediJvZJiSn33lUdIqrZ5alA8uRf8LDkGjOqRiLOMclI06kNzj1GFrrj4DxwIvIXb9mQM8K0VpfRDT8gDHmwSOajmUSEs3azYlrFB9EzG3+D/mHvxdxw/0jvAuMRL4wNps5bgNu/L0DmvqM65BUoP8i6TnvcmJnut9zjDXI7344sBVJCb1Oa51h1qcCA4GhyPvZwsyh4hf0MMR84GtgVoX5FZsxMhDxFAWsNufyIPI5qgpeQ84jGTGOitda7z7BtncB0ch78zTHOCyeJgOQlN93kM/wO8j/yMPHbqjEcfpt4Bmt9Waz+D6gKRVcj81d8DWIicSJ5m6xWCxnI4OR75OKjyb8we9m47/QFynz+Q7ohwiWk/WkHIfcmMxA3OdBROjniGvtQuS6wx3hNMfpg/Ro/848f+6YubwCjEbc8VOQm4uXITfkfwu3IgLyW6SDwVE3RrXWG4BuiKvsRuR7+F/Id/LvfU8qBfM9dT0iRLci1wq3a63X/4Fhj/f7egG5Ufsecn1RE7mOOV6XBks1RB1dU22pzijpLdlJa/1VVc/F8sdR0kNzl9Z6yAnWt0HMDEJ/Y6rSGUdJX7OvtNZPVvFU/jBKqW3AFK31iaK8FovFUq1RSr2OpNFeW9VzORkmQ2aF1vpM1FMee6zjvif2Ws3yV8UaHVksfxFMys03SBuTfshd2SsqrG+N9O/chBgBTQWWnG2C9K+CqZfpj7znCVU8HYvFYjlrMOmp3yO9zbsibbvOmb6UStxzX9Za/56snBONedL3RCk1DolSWyx/SapclCqlGiOh/GDEonqgSUWsuI03cgF9DZIeMUFr/aZZ9wRiplNqHo9qrRefaj+L5S9IC+AJxJV5B5J6XdH52BtJz45GeoF9iaTpWM4MG5HfxT2na0hmsVgs1YRIpByoLrAHGGZKhM521iPfoSBtwyqTU70nU/EY5mVW8rEtliqnytN3lVLLgbe11h8qpW4B7tBadz5mm4FIvVcPRLxuAq7QWqcppboDX2utDymlLkX6E16otS462X5/1vlZLBaLxWKxWCwWi+XEVKnRkVIqBGlR4dwJ+ghoZWy9K9IfeENrXa61/gkpbL8RQGu9uEKj3c1I36TgU+1nsVgsFovFYrFYLJaqp6rddyOAzArurmWI41nEMdtFIvbSDunH2QbEfXSX1nrvb9zPYrFYLBaLxWKxWCxVQJXXlFYWpqfUU8DVv3P/QcAggICAgMsuvvjiSpydxWKxnP1s2LDhZ631sZkqv5tGSrnTWE7EPlistb6mso5p+XOoW7eujoqKquppWCwWi+Uc4mTXGVUtSjOAcKWUt9a6zBgThZnlFUkHLkL6GsExEVClVFukMf11Wuvtp7tfRUy7hpkAcXFxOikp6Y+cl8VisZxzKKWO+//x91IEHLcfUQUeh4ZKqZnAAq31gso8vuXMERUVhf2etFgsFstv4WTXGVWavmtaUSQDN5tFNwObTP1nReYAdyulvEy96fXAZwBKqb8Ds4F+WuuNp7ufxWKxWM4sXoD/KR5AvtZ6kBWkFovFYrFUX6o6UgpwD/CeUmoMYq89EEAplQiM0VonIRbYrQGnVcx4rfVu83w6cm0zQyl3L+NbtdZbTrGfxWKxWM4gCvCt6klYLBaLxWI566lyUaq1/gERjscu71nheRnw7xPs//eTjH3C/SwWi8VyZnEipRaLxWKxWCwno8pFqcVisVj+mijsl4zFYrFYLJZTU9UtYSwWi8XyF8VJ3z3Zw/LnoZRKU0r9oJRKNo/uZnljpdRapdQO8zO6qudqsVgsluqFvYltsVgsljOCTd89K+mntd56zLLXgWla6w+VUrcAM4DOf/7ULBaLxVJdsZFSi8VisZwRbKT07EcpFQK0Aj4yiz4CWhnHeovF8ifQf8Za+s9YW9XTsFiqFCtKLRaLxXLG8DnFAwhSSs1USvWqoilWN2YppTYrpaYrpeoAEUCmMQZ0DAKzzPKjUEoNUkolKaWSfvrp2M5tFovFYrH8fmz6ruUP05RNAGyj5VHLB/IGAO9z92mNU7c8E4CfvcKPeg4wgLfd283ijt8910Z8z3M8QhR7OEhtGrODXTSkKdsAaMM6GrOd0UykNge4kTk8wXhqUeSZJz/zM3Xxo5gVdKIrS4klGT+KWUhP9hHG8ENTyKp1IWlEUYf9+HOIfYQBsJnmhJJDdxaTQQTF1KAMH5qwnTp5RWS4QojMyKFnhLTUTdx3A6MvfJJRTGQ+vcx57GI/dbj2p2XMOb8XdfmZTnvWMq3+ndxa9gEZ3hGspzXtWM1+/sbjPE0cSdzAp6RRH4A6/EIZPvhRTAg5AOQQwi/UoQk72E5j6pNGw7JdbPduzLe0ZiE9j3ovMsx163o6/Op3+Geh1DgAtB77px3TcnqcZvpuvtZ60BmfjAXgSq11hlLKD5gCvApMPt2dtdYzgZkAcXFx+ozM0GKxWCzVEitKLUeh5gKj5LlOPWbdMRf/e4zIgoVHbbeZJubZQ7LfdND3gtpsxm0hP/9HHVoZQZtlhBKA2gjBsSeeYztWuJ8f4Dwe4kWKqAXA11xJNqEALOVaAPrzHp8oFwANdYMTD2z53QzlBYqpAfQD4D36c9vA2bLyNgi+KpMvva4G4B5ed4vZt7mDieYDt4JrAOjOfPZTh29XidCt0ayAWFcyddgPwGJ6oyb9eg4qHnSC53MaXHYX8V4JAExlJP+hE7eUfwgcXzRPYDiP7DDX502O/qxHsBOADBr95vemOmP7lJ5daK0zzM9ipdR0YD4wAghXSnlrrcuUUt5AGJBRhVO1WKolTgrv7MFtq3gmFsufjxWl1RylxnG9FhE5j5uOXrcDdONf7zOB4QD0N6/rsJ+1rk50z5vPkm29+a6pLN9JI9ZO73Ra8xjNOEK8egOQSzjdvRYDULe8OwA/e91h5ptGW73ntMacwHAeuXcy/5zuWbZzQTNYBwQBwUAKhPvlQiSwGnbWbyaXY6VAFuwsbAY1gWzA0bOHzfNC6O23BFLM61z4V/EnMrYL6pfuo37hPrm084OLfX4EoNPutXI8P/jbeTtk/zh5TSpERufAVkg8fIPMJRgmpD4JHeFfWz6ROUSaOe6BG/MXQAgQBkP83oI9cEnILi4J3SVjp/zI0vb/kL/2FPh7TeNx4g3kyVypKe/JJd67JHGvEC5pYPZ3wd8jt/L39lvZ3qwJibhbCNOTRJKIc79u7LWDwbx+Wr+fY3mbAdwZPYtuqSffrhP/ASNgQYSjij/5Pv15j9srvHY+w6NPESTqw8e/+ruwnD7W6OjsQSkVAPhorfOVUgq4CUjWWucopZKBm4EPzc9NWmubn2ux/EkcKS1n108HaXj+edTwsZV1luqJFaXVlIrRxs/VdrQey1Be4P6+MLXvSNQOWdeHjwFHsD7KMP0Sj6jnYbsvjzCZRY07MYbx9KATS1y9Cf45k2d4FIAi/PniXhGV68iBuuvgKz/UVuBigP1OMBWWw3sM5F1uJ4WW/EIdAHK7SkRLrXjXPd/efMd8RMBuVYnclvk+8WGzSJh6B9QFBkiUaxnPM0xPwI9i2upI2XknkI4IrzxECJaZ5x2BLUAssB4RfWVAAHCe2QdExBWaR4pZf9i8DjHLgsyYTgDY2R6gJZAP5CDCOMy89jNjBZntIoFcM04gsLLCeOnAHiDGbJNvtgsx68uAZWa8SEQYewPF5niY/W8Eks05rQSaIwI52WznnFe6zDWhqai/n73CaccKXkkfTtvIr3ll40h4GkLmplOfNGYwmPj33+ZC86b5cYRP6UcxNehJIr2ZD8AqruQrtR6AWzVwOwxhGkvpSusO60kol+PN4UYu2v0/SFPwNLBiHA51h91FvYQiWrKJ3kA33ZKv8q7iwH6Y6hrp3k6peOrpH6mIMv6iu5eH0R94ZMJk9NvQRzc5ajsbIf192D6lZxWhwGcmEuoNbAPuNevuAd5TSo0BfgEGVs0ULZazmzMVyTx0pJSCw6X8cugIoYE1K3Vsi+VcwV4vVFPWYCKY2hPJjKiQreWJkN6Emi0XlwBT1IPs1hcB0Inl7u2Df84kt2s4uV3DWfC5CMai6//GdSxm7XJPrWl8vNSG1uAIAO8+fQ892s1lM82ZuGosdBwHG2DRxr4Ex2YSvDSTUV4TmUefCrP/jrXbOsElIkzWhrXhH+X/B0/Cj3mhJMZLJO/x8hymuEYDshyA3YgYC0Cim6mI8AIRd1cAic5JIaJvNyLQ/MzyLESkgYhEH0TsBZh1wUAzYJ9ZFgKsBrqZfVYjkcliRJgGIELWEZRZFY63G+iC+GHGmu1BRGiMmUcDcw555ucPwBBETB40xwlBRGccHnEcbeaShUcM55vX+WbslYhwTYXintDca4vZOZwxjGd65L0s2Hij++ZCNpGMoxOHqMUBavM0j9ObL+jEV/RnNlcoEYmvlN2PQze9gTK8eZHexDy6iSzCaMo27uEdpnrB02bwHxtcQEKDeB5ZNJlBy+sAMFPtx9/LU+N6LAN4m8Um2g4h7FUXAXk8wmg+0XfzyfLe/Lv8Nf5e/l9y7/Kk9FaMjjpRWJ1wwsNYToBN3z170FrvhmMK/z3rfgBa/7kzslgsDtpUaBccLiU0sGrnYrFUFVaUVmPmmjTMviQyiSF4Aw+qEBZrqRF1ajLdfOxLvf476cAqisr9Oc/rAPfwGmHsY5TXRB6+aioAUYFpdGIFLIfZ5f1pu0PqRqc0Hsww9SoA9+spAPxfu678Y8dSAGI7rAPdnXGIiLhOzQEm8zDBwCoAGup+HKIWbZuuYO1DY+FmGEEHnvd6mIS8eC6KzwagbYJEgoN/zsTfq4j3uRWAu7q9yQUr80X0rTPnVROJG5QiItUEVamPiEAfRLAWm+WOYExGxFtN87O+2bcYEaTZZl1983A6A3ZBxB5IhDIYT0QyH0/0NRXojgjHGDOXUrNfEPCNOd4OoA0iIv0QsfkmEvUMMuM6UVknOooZt9Ts42wXgESJQ8w+YWYe54HfFlhBD4a0fokhbGMTY1g7ohPUAV6UIYczgTrA5+omntCPMZD32FceRgLxJBBPcBnk7gynv5cUhU5XB1gCxOruLDc3Si4wtaPgGCcNZ4OXpAi3Zj0VGaTr8Dk1AFiQ3oc7I2cA0NC1i06sYL+JuAM8px9mGkPYq2pzPK5/+2Pz7Pjpuo34np1cctx1luNj03ctFovl1DiuYQcOl6AdhWqxVDOsKK3G9HWHBI9mmUrifl2L93gfAN1fDGuG8gLbaMoy17UsyhMB0WP8CsLG7KM5W6AR3BtvxEbWELqELaWL11JoDLP5FzCD1rolL/KQOy3zlWSJnOW1EqMil9rGHWVvU8drPw11BtCP7kh96QFq80HnS3iKZ6jxaQHcDcMaT2Ag79MqaxNM9oNweP6FoQAs9urOskQR1kU9ZfwLduZ7xF9HRHCBRwT6IAI1BhGdTiSyPhKJBBGSyUBP3HWdhAGb8EQcA5Dj+FQYN8bsv8WM59SqFiLRyWJEUFYUkph1zcw8vM2yfHP8MjOGU9fqCOguiGj1QSKsPmbMe8Bd8ulEZuPMMVLM+M3MXFLNvpjzzOcoBvI+a707cd5DP3Pwf8HQcDyTk5/koUufIr/Uj6CJxTQc9T25yRKBjG/1Nt6UQeOKUfk6BJfdRQ12kYIUI184AmInyR2DD70eIZRsd6T8kfOlBnTGw8Pc85j55QPExq8jLDKLNKJYNl1+5yltWsJl44A3uV43YT2t6c18pvcZAcCNJoX47tL/ydtcI8ztIqxUCfSQGJ92/5lYQfpbsZFSi8ViOTWOEC0p0xSVlFfxbCyWqsGK0mqO41S6W88D4EGk5u4V9SzP6bCjtn3nkFjF6DygQk3q2vGduOXxxgTflMl0dT/wLDCBpca5tG55JnXJJLduOGvz4CFexM+rmMXl3bmq1X9+Nadc7xIoqyPPk8M51Opryowau3X5G3zQ+W6O9AsEb8j5MoQXeQh2+qGHSpLxKi7nDt6WNi8XwHOthvNI1gQAnil8SuorHW/JIERU5iORRRABth6p42yACMUkPKIs1+y3GE9NaJJ5HoQIu/MQEViKp0bUSfktM49coABJsQVPqnCo2c8Ruo4gdupCAa5FakYLzXZ5Zp7JeKKcMebclpjx2gDv4KlLLTPrCxGh3ROYZ9aX4qmd3WLmZPyNmiMpvP+e+g7UhYMBB2mrtzBGf0Wtgjxe5T4+9e5H41GbyS0PJjg2k1u9PuArrgJgGJO5ra9x5u0DuR8DN8EzXo/JsusheVUbdAcYag46lZEoF7AUurRaSFf+j64s5RFVBxZCFGlEkEFCeTzB92TyvNfD3LljFmwfS3CjTKL4gCMmosrn8sNx1K1dA8JM7atSe2mo82mosVHRSsJ+yVgsFsvJKa8QHC04XGJdeC3VEnu9UA1xzF20pySU+mRRtzyT4LIccr3fBOCR2RKVeuRvZqMf6gLw9tABJBPLIWpx5xioxSFeGSkRz8v0aqArNTjCywymNgeB593HaevaxBN5j7lfbylvTk+vRFzqHdjeH76X5bFeC0kjilzCeXfiPQBcNOoHflSzgXGwYSyMgoRW4sr71sYBqB0akuCl+CE0ZBfF+LGolUR0Xw0bIgMvRyKkjvjbA1yG1GEax10ikbbx+UhabTLyl+JEFkMRAbgbEaKJ5rVTUxpr9g1GxGEhnvpNzPMs8zMVEcO5ZlkoEvmseD8gyxy/OeCUdGab/Z2UYm9EtDbHY4IUiUcQO5FaPzziuMCMkW7mnoxESZ104mJEoOab9cCVzZYwACmuDL4vk2CvXBgFsxjADAbzaOAzZHEhT7z/IiTBoqny/t9S/iG5/5CIqU9iGY3nSn+gHVNbwIA0cgfA0vR/oL6Wb+bl8e3oymOkGVGaUJ5JXl40AK74QzyXIO65u/UMGoSNJSIrg4bsZK5XX3YaU6K2jVewo9xjH12DI2yjKXeWTwNgMd3d69bTAaX2UpGWrGMTbbD8frwU+PudYqPDp1hvsVgsZxmVLRoHd2zAqM+2oBQUFJVwgTU7slRDrCithgQvzTTPwt19GAFyvwmHJ4FOY3lu+XAeMVFUho9FTwLVXfJXxw6V5W9wNwA9Nq6AmyE4NpPWrGcnjThAbd41TTgckdtYSwrmU6onn+lnuMHrU/qSSFfC6KJhKfAygxk2YgbLuBY9CWgF7VqtIIsLZS4vyXx1K1Cmx0fwTZk8y6Pc2vgN3i8aRASp7L2jEWvfbsmNzKEDq9xuvkOy3pKoYgAi7A7jqdksRIRYLp503jg8EVJHAO5ARGCS2a4XEo3saV6nI1HIdET0YcaIMM+dKOQWs/4gIl6bVdjPB4+wLa6wn6ORHBFdiqQBHzbbpiJCMgCPkE5HRG4zxDDJEaqYc45F6l2N8OQwnjrZrYiQzZHzjWu2gfgyEaXxJDCDwazgKvZTh/7MZjHdxRn5ZyAKHuBlQNx6X0gcysObp3LL5k/lc2Z4SkudsdqseTD+aQA6GSsuNVG2qXF3AX93/ZdYkrkhYVaFVi6T0Vmg1CFzkmFM0RL1XKtWod1GXqPpbtJ132QIdcsz+dDrFvcc4ngardvLPNQBYvXRucpqvPzUY7D8BpQCX+9Tb3dmjq1qIsnqBUC51npY1czEYrFYTk5JmdyQrePvy/6iEo6UltvWMJZqR5V/4pVSjZVSa5VSO8zP6ONs462UmqaU2qWU2qmUuqvCum5KqSSlVLFS6sVj9ntSKZWjlEo2j2l/xjmd7fzsFc7PXhK16slcVCKoRMQcp55EUPdTh1EaGD72V/vv3d2Qvbsb0mPjCnosX3HUuk/pRyN20pJNXMVXZJRHuNftUPOIIIMuej/JtOSG+MSj+kt2Zz4vMpKPJvXho0l9UFnFqCxRZE3YQRN2iBBMgq4s5Pn4oegWisu91rNLrWITsaja5dTmALwzjrYbN7F3YyPiSaAuudQl11MjWYwIQNPfk2I8JkEhFdY7ojAHEauOQ28ZImSd1i8x5mcxEu0MwCN0HYOhmuaxB9x+PYVmv1jz0zEycqKTjrBsbtZtMY8w8wjCE1V1ISnH0WZ/p+1MDnJZ/jIekZqORxyvRqLHWWZbp/VNMSLci825NIBYNtFA7aaB2s0j3s8xsXwUw5lMK9WfAcxiE7E8xjM8N2I4/AP6MI8+zEM9K0M+32Ko3H24AugHU+YOpjYHeCL+Rbh0HC+pq3lJXc2XXEkEO4kZtYmYUZs4EjyZXepT4kgijiSUcsLOQryOIF5H0FZ3YJgKY5jyhJrVCHlsKL+MJmx3C8xbyj/kGlbwOE9zNAUkqzKSVRlDmMQQJmH5fXgp8K958sfpoJR6USm1RymllVLNKiw/2fdHX2Cl1noocEgpFfergS0Wi+U3UHSkjP2HjlT6uKVlUkcaFuSP1rAv36aQWKofZ0Ok9HVgmtb6Q6XULcAMoPMx2wwAGiGX28HAJqXUUq11GpJEeTdwA3LJfyzva60fOs7yas33JsUxkZ306fkxn3cWx9H45W8zmAOkmc4BbSc5orMTXfRalm27lvsbvOA2KmrotYtv13Qgr10tOhhL2ekdxEim26r5XOa1gSWA1mONkNjvdlC9KkHqSa/kawBmMJh9S+pzM/O4qtt/6BYmBkdLdvTGK8j0MflGfnRlKWMLxvFw894El8XQWOfSkvW0bJBMFmEM1lk8nFfAl66r6bhjPdc3Ns6qkXhqLn0QcVmMx7CoDZ5oaQQigsPMeuev5RtEeEYgonCLGXc3UoOajScV1zFVuhn4wOwfw9HRTCcS6ZgJxZjlu5FPdMXjO87A3mZ9ABK5rYknVTi5wnFj8ERAI802Dczr3eZnc8SJuCMe8Z2Op151q3lP+sBBakvNLzC7rD8NvXbxOM8wU7/J4wQzDrmJMZnh9Gg8l020ZAjT+OLR7oxgEj8fCabHiLksUn0BGDZgBk/q0dybMAkSajN9YmvajpLP3NvcTreNX3N/qxfoqmvxirqRR3ZEQRMRpH34mCTi2Ksu4l69n03mM9tFx9GE7QDMoTeYCOnlXuvZSUPqjZFa0r2qEQropnNxELffX2MjpL8ThacO+o/xOXJb5etjlp/s++MiPB7be4Ao5C/aYrFYfhf78ov4ufAIrSLr4ONVeXGdUlNUWrOGF8EBNcg5cJiwOjaF11K9qFJRqpQKAVoBV5tFHwGvKqXO11r/VGHT/sAbWuty4Cel1OdIY4sXtNY7zVjX/Xkz/+uRsjyKmB1pgAiPDeWXcZnXBmpzgMX0dm/XpelCpjKSqeZ/cd1yCG6Tyd84xNYdSFpma3jpBanh/JoruV43oTWrAG+WTbqWZQ+KKtI6BqXG8RX+DNJ12LexPtSFe1tNYro6AMCTuogl9KY8P4D/a9yV3v/9AoBHdkj6ZmMdwiGKaMo2DlCbnwnmLt7kESYQ7MplPa15vvFQsp2C0D2IwMpHxGCYeV6GiLYss7wNYmTkrA/C89fiONU6EcUgJCbzAWJMdC0i5CLN/rlICxgneJeLp9WKHyIaTRtV8irMwUnP9UYimsvwpAMH4alfLTPbxZp5ReNJwQ0wP0vNsgI8tbHO+WSb56vNsYLMHH3M825mjHQYkv4W80wa9lWsII4NAHhTSk58JP9KeJ9vl4uDbfBVmeR+E453Bwn/7lKfwrixLKIvwWUi/nJvCefJ+AlwHXDTOIbpCaynNWMZT20O0K3VfJIQkfmcllRgf32Ir0z7mDiS2IuL6eo2gsukX2nuq+EsW3Atbb9cwSZa0niS1K/uR4qjM8wNGaf37hLVm4oO/MFl9ci9RTIJptEeyx9AUSnfMlrrbwCUUu5lp/H9kY4IU8zP5D8+E4vFUp0p19JTNK+whJDapyqYP32c9F0vFGF1apJbeIRfCis/ImuxnM1UdaQ0AsjUWpcBaK3LlFJZZnlFURoJxhZWSMdToXcqblJKdQP+B4zVWq893kZKqUHAIIDIyMjjbfKXopmSvp3Xa4mSfs5N8GExddjP7PL+R237EE/zIo+znSaA9KIE+KD8Vvc26lHgGvgsQYxpBpW/Qe434dzZYRpbjK3tbi0NMkeMkHRIdWMMU3QW+wjjlwrH68UCGuvGDFNhPKn82a3D2Gbahfzt/P087fU414fOo6PpXTqAWUxUEFwmWd23TZnN3hF1qbf7J3IbBDMxciyvppuM7yQ8/UhzETF4GIkQOiJ1D54IZqF51MQTWXTMiAqAixHBmYpHpDptZhKRiOgeRCiaYC85eBxuc/AYJDmRzHykjf0qJB13CfJpbw44rTQ7IAJ2AyKEg835lJn5FCACs41ZHoyI2pAK5xGJR5zvwyOQc/DUtTp1ts3M+1ThTyOFpiQwgGkModSEw+qwH7qkMUq/B8DET8fSr8Nn3KnEcGjYmAkspjspar5noI8luvqoLiOYDKYoMdTarccwn17M5iYWlPfi3e5ieDXly8FU5H79FlMZSSO+Z9dUccx97kuP43KXsKUsi7/W3bvW4Xit4H72ChcjsAvk9R7CqO/+xVp+M4rj568cTV2lVMUI5kyt9czTGP1U3x9zgdeUUi0Ab631f3/r9C0Wi6UiTuuWnw8WV6ooddJ3lcJdS1pm25VaqhlVLUrPNK8Dz2itS5RSVwNfKKVitK6Qr2cwF0EzAeLi4v7y/wocg6MJDCdiufSMnMpIlDrAa/pxAO7hHbO1REqLyv253Gu9e4xYr2QAyvDmq3XhsA4eWD6VpmzjMq8N0GEDn5df797Hqd37fONNXN/qY66f8zEvMpK9fSVy9dxccVTdTmOGdZ4BnSDoP/+j5ZHBtKuxBhDTpH8TRWsdxo7ZLRjWXyJrsJ5FXtcAMGPEPVzNlzzaYCxhZKHjFe6ywGTz8AZ8oGQd+AabdTl4TIAOI4IsHTE7Sj7mDSxAopchQHvcrVVKzCfLNxiyd4P/VgisD0Xzwf8Ks683pH0kmrJVCJQUg28zRDw6QnWrbFqyBw4UyqFIQUQnwAbI3gKh0WZZjiwjBIq2QEkZBIYgArUU0lIhNAB8csDX+avfA3mp4HJaxOQj4jnSjFmACOya5r3oBW/3ime/MY1aNvdauAt+yavDm9zFvQmTmD5mBFfoLwkml6kMRUcplHqSLjqO1zGicvNo+K4lXVospCnbiED6yu6nDrkEU09fRRO2czdvUJsDrFWrgFXumw7P8hg5O46+cfTKFOCHS7h/+QtsoTmL6c5zYaMBxBQpAaATp6Inc7l/+S736yG8Cswlkb6n3NdyHE4vffdnrXWl13tqrYuA2yp7XIvFUn1xOogeOFxKcWnZSbf9LZRU6AmjTB6PPt6dU4vlL0xVi9IMIFwp5W3ucnvj6SBZEScNy7nTfWzk9Lhorf9X4fmXSqkMJOazsjImfy7jmLdEID0aR787BbVZ2rr8O17E6L8fkp6QNYz9a25yXxbRl5xWq3mUZ9hOY4qoJQM+DjGdNwHQHEmXfLdc7HEXTZIL+i4jFrJMXcIwLRGsLTRnALMonuvHlG2jmYak/A5gFlOWSzQsgwhe2vE4i+hLr8Zz0HOfZH6fbkxjCOf1+plQsvlKredRXcbn9GEW8fyoZvO8foc0onj20fEMiX2LaTfdKee97i0RfhcCxeAbKz85jKcNiuOgGwTZORC6FY9BEIC3CM7QQESMpgA1ocQYtvrGADkQaoyOslOMeJS3h7w8iAqRAjeCwNcxJHLShP0gMwnC20PObqhdEzJ3gysA/B0t5geBTk1qGJSkyzglueDfAPybAUlQlA7+sRCVj/ROdUGBEbxpKdDiZsj+CEJjkWhqBhI1BYqyzfGikcq8CLhjWAILpvQC4P6+/SnqW4sOfE0a9anDft4aP4A7l8ziSDc/VtGBusP2ogvroUZooskQg6P90OWOhQBsoymv1JLPXdtDEsncTAvuQxx5DzmfL+QmyBZ3M1nh+cZDmUcf1hrB+cokGYsHx9HdfJ9/yZV0G/k1eS/IWH/jECtoB4jLL8BdOB5oF7KThjTCI0wtf4DTS98NUkrNBBZorRf8htFP9/vDYrFYKgWtwcdLUVqu+aWwpNLGLS0rx9dboZRCod3HsliqE1UqSrXWOUqpZMQG5kPzc9Mx9aQAc4C7lVJzkUvn65EExpOilArXWmea57GIDtheSdM/p1lKVwAizPXbaKbICqf9iwv4D5AHR/BjKddCK1BqHBuAWrqIMnzw5xAAN3SexWdqJ110HMm0ZAzjOeIlqS2vIEJhO00gLoopfx8NptXHoc5GdFyymr3AW3o6YxnHXtWImXogL6lopmgRqMM2zkC9oGnY53ve4G7eLLyP0bWepK3uwLMTOzFs1AR+nHkxy/ViHuMZ0oii17NziOMbbneivkGIyEqGzFwI745EA0OA+pC3XiKHRYXgj7iDFuWCvw8i6oCUVIiJBDIgLRmiIoEAE4FsjdRmRphxgyC0I6xeCe1jZX9XNKSuhuhIKMoC/wAgX465N0cydsMjoSgZQkzk1t8PSkshM8XzOwwPxm3a5BsmP4uKwbcUcfcNkfnnrZdjOs6+gZJFTYtkIAlCIyUi6xsmwto3QN4jf2RMwkw0ty8M7/Ec3iZc+xn9uJKvieO/XEsi8SRwB28T220dnVhBAzWYWJ3BgCfeIo9adGY51/M5s+nv+X0AyQfljQklm2xCaWO8aXZsbAHAF7o7ddhPx5ljWTuoJeN5giaNpS3NDEwqr8nO7tJqIcvGXwvjxvIm3/Oaic5+9kJPXDvks3pV4//g+Bap9FLuj5zMNiRQV5sDLFE/AKD1w7i9XJ2UbMtvQyFp4ScnX2s96LcO/Ru+PywWi6VS0FpT09ebg8WllJaXn3qH06S0XLuNk5zKeY1VpZbqRVVHSgHuAd5TSo0BfgEGAiilEoExWuskxEKmNZ5Lw/Fa691muyuQSrtAealuAu7UWi8GnlVKXYYkIx4Bbq0YPbXgrhNVXpoukxaapdei80SAZlUwOfoPnVhU4X+kk45Znywe4mmCy+4iuRz+6/V3GoyQOrxuk+bTZcRC9z57k5yPXBT1Ou+kPntI2HwHg/TL+HGEh8ufl9UbJJLK4v7UYiEv8hCNW21mx7st2KU+ZbHuzo3Bc3ieh2nMDtJGRXGA2hAkdY2tWc+O8sasoR0/767Hx42ul3GjkTh5fQjviKTl3odUn6WCKw4oBf98KCgQMegbjadvKBDTHkq2gm8QRLnw1F5GAFuNsCuEgkIIDAIyoH00nn6jPhAdDSU5kFcoApRc8G8DUUkmersV/MOQ1FlvEY3+kRDupBfnmGitqX9N2Q0xYRDoknkW5IF/rghMV5AsKyk0gtPJE3DmGGvOMVnOiY54PEqDgBTwjZOfzXtsobdxs+20YS1qlubBSU/z0tzHeaLveFbSgX/xPhOzRrNIX0MGuwgjC9eYQ6wc35qOqidsaMkzrR4D4AVGcpXXV3x24wBaz1nPIxNN/9GfzfHvL+WO8rcpLfWGweNYM6gdXVnGg6oDWvenJ1cC8O9WrwPiyrxmWDuK7vkb/ZnN3bwBQBoXoxtL39O3R90BQIN0J/QNsxggn+XV+xiun/N8yHemuT+vlt9BJUVKlVJTETuxC4ClSqlcrfUlnOD7w2KxWM4EGml1pRDTo8riSGk5Pt6K2YPbAhD9WCK9Y8Mr7wAWyzlAlYtSrfUPiOA8dnnPCs/LgH+fYP9vgHonWPevSprmX44dKobd+iI6GLMggGXvX4seiLt36Ks6g/uuljYbfOnZt4dKBOB5PQqA5nQCunCb1zvUJZcGq7IwepUl6fJr1JE+qG3A98AWaNtZUjXP4wCDWrzMjIxhPBbxBIO9ZvDsDmkkmUQcuqWCn2BQk/cBGJY3gTF6Iq7lh/i/zl15n1v5ROUyRU9gIB9wZf9VtFL9Kc99hDhXEl1YSnqjEGqZiC7zcTvXFmw1KbDvmIhlA0RcloogLToMgYG4TX/ycmQIVzT4tofURIh2HHWjIW815JZCdIyp1QzDY15UitSMIum8riCJfNauCeRLSq8r2URb90BKrojK0ACZl2+wmZvzF+uSdWlbICrYRG4jIG01RIXJeZU4PU9NSxhfH8hMhnATKWU1BHojNX/JMiZORDUESpLBt4tZZ3qaPnDoZTbXkgjmqssu53CMYhOxvNj4CdTVGmZormiwlCvCVjGV+xnOZNKoT73xO5nMcILLriT3v5CyTT5X/2Apg5q+jJ4DqvNkdi8Po8FyYyzUSE4g3iuBshrePH94POeNLIMXZ8v6SYqrAdVIc3/vF0xtseEWeDZxPF16yg2R7uyQNjGjD1E/fB+jb3kSDsub+cqOkXRtvFQ+c+3jaMca/rljPlOajJPPrv51r17LaVJJkVLTa3TocZYf9/vDYrFYzgRaa7y8FEpVbs1naXk5vt6eFjO+3l5u8yOLpbpQ5aLU8ufTk7nAFhqowbymnwFg3sA+tGY9YwCd8Az9eY9VXEmXLz3R02tYQR8+ppsWhTqSqfRkLs3ZQleWcUv5hwC07SCCcwJiNNNxxHoUUG/STvZubATRkGP6kvTjU7qprsxkDwDP60nuOkEAtVz+6YfkSVQrgXjpc9plL//IXMjKsA6007F8Sj+GRc8A4DPdk3n0oFfZAvp6z2XZ8mt5qbPUq9JgidSARkJgMSLInAhmLuKKWwCBzSBwDyJgTc1pbaffZz6QKum3HEYMk5LB1R5YDakpEBVk0n4BXJCXAi6X7B4aDXl75HVRHlATXJG4hV92MkQFSOotzYAsqRX1jYYSkyvgGwIFGSJAi/IlqlqyFaK6G/MmP/AFMrMgPA7IEZHqA6TOkTGi48z5GXfg7HUQWizPM1dCeN8Kx24mY3SqtYIs09umw/JvyewcTBR7eKzZE7B0HOwfyzdqDe/qbeygCd1UV+BOeLkeHYauYrduAHEQdFMxeg4MRtq8qM7w4/JQRjAJ44fFZcvXE08Ci+kOwGS/4fBiRaFoXKLnwyvzR8Jt8O0oyeqvN0p6kS7lWkYj+0xU0FD3oxH92DVRXHr1KFBT4br/LKbGLQUAHPk5EEslUUktYSwWi+VsoH7d8wip7cfKHT9VaqS0tEzj4+VpeeXjpdxtYiyW6oK9XKjmOA6785h/1PLZnF6QuQNf00Nd5XZGzf0wnLWES8R1x3rpW2raa+wd0Yh7J01idnl/9pfXEYdeN+8RryNYTHe3uU0WF3Jv/0ncz1QeYCoAS+J7czPzYDHoYnF2bas7sFZ1Qr+kGDdiFDfMTIRfIHVUBHEkkdY5iq+4CoARIdMlCXw3EhXMR2ovI4AfRNg5+MYgtilJQDBsNPWcrU3P0o2p0DxIUl59Q4AUcIUYp1wfU+eZDuQYQeuk3uZJWnBenqn1zMcdnQ0shdA4IECE7oHVIl59TcTV19Q4Zpta0IIcCLxY0nsz8036b4gcAxeEm3ReekPRmxKhDXUiV3sAP5PWizFm8pPjhDutZA6bY64DOsJM7uZdxMDqps4f05OF9GYBz04dzyLdiRH0Zo7+gGa1dkJRCjH6MPATWUd8WHikJzfV+IhFN4rxlaoFtx06Oow2iRF8NniA+3UC8STRnqd56NcfPoPubWqggUWjPA671+DcHJFI50TGyQ0NYBeXePZ3x98CUcojenvqo02VLL+DM2t0ZLFYLH8qJWWSZuulVKVGSkvK9K8ipSU2UmqpZihrOf1r4uLidFJS0qk3tPxuIthJU7ax5O7ePPeGtIJ55O3JIC0qeau3CBPpbymi4iUt0c4HlfRhOXi4Ozf7SePOBaqcL/TblOKNH0cYcORDatc4yGgmMIHR1DGdULf8+3JJp01BLpbrIz1CnV6dB4EYxL/T0UulePqXgojZVCQt1s+Ms8dscxiyU0X8FRVDYASkpkoNqVOTynmmHjTajFETEa+mxjUzBcIbIDWqxbjTjcnFnYoL5nipZpv6SEuaLCNKMfuli4GSj4+kC/s3EKdeB1+nN6vTDqdU5llgosWOCzE+QBfY88qF7khpQ3Yyg3tIIJ7nGM10hjCHG6l1qIiMWvW4mzdozhbeLLiLov21uSxyPdmEsld9I/vrvwOwS30KQD09gL1qFl9occQdb+yIkmiP5c9BKbWhMtuzxAUpnXTFybdRiVTqMS1/DvZ70lId6fzSVzS9MJAvt2Vznp8PG564ulLGfeDjTSRn7GflSLmx2ubZZXRsfD4T+7WolPEtlrOFk11n2EippUrIoBG1CoLxf+kX+jObB3iZXnfMYUGCFDxGmP4r1+tefK5egT7382DfaVI99paM0cBvD3O4kY7L11Oeq3iTWxnPGEYzgXTvi9hGU5bShRV0otGCvZ6Dm5JFXEidZhbi6XwhYom1FUmbDcYjBnPM9gCpkJYOUbFIL08/IFr6g/r4QGh33O1fCIYfUyG6DLd7LwclygmIIMzDI3QdR+AtZh4BeERzMBTtlt38mwN7JMrpzMk30ghSH0RYJ8s+/i1lPr4BSOTT+at3RG8hHmGbDARBYLSMn5cHrrsQc6SDMJIXeJkHANhFI8YumciGbpdRRC1CyWYs43iz9C7GMZavZl7DV1yDbiQpSasiL6eYGqCh27av2bUNuGQSXcz/pmU7GtFY92Ekfcg+EsL+Ghe4f2VKzHbR8VjOJWz6rsVi+QtRaiKaXkpRXpk1pcdESn28lY2UWqod9nLB8qejEuDO+GkMDjzAUJOW66DjoTvz6abu43rzD/8KfTELyzxpnkHfFRPSSkTrm9zFrZ3fIJJUxjGWRuzkELW423smAOMYywFq83qv2wC4Z/W7MogfErlsgAi/bCAWaefSGol8+iBiNBq32REAERBVakyDos02heDfHTgMeUskUuobAmRB12ggT4yTHPzLZJ/sRJM2Gyb1ogcKwZUrpkq1AySSWZAhrrobUyHKzMHfmB75tjfnsQdwQepKY76UgkQ9D8p2BQXGtKnYnCeI8A2T6OqBQnD5mHMsNu7CAaZO9l2gDdARPt15C/0aSe2wN6Xc0a0lOwJbsL6gNU/zOAC1zjvEw+mTuHPQNN7MuI9fIvxpz2oARjHhV5+HZWo792pYxrXs2NGCoCgxyFabQdubxOc2Nn3XYrH8hSgx/UQbhgQQUrtmpY5bsabU19uLksosWrVYzgGsKLVUCW8lDEHHw5csZKIxRHqDu9nDAzRhOOgmfK6uBl4DsgjiUQB26xnwKuS8I7WwHyAuwZeRxJ3qTT7RYn7z8Nyp0B/qluQy6dCDbK5l6gNDgG7AEkSYJiM9Qg+D/zrE+CgDiYqGQcFq8M0G/1A8NaHFQAC4nDpR85p8YAu4YpHoZ7Gp+QyRcUukvaek0V4BmYshPBYRxoWS7utqhjtN2DcWipJMm5f60LzQ1KkCmVsgPEyOW2RavPinQHRPc07F0poGZP6BLhG6JaUVakrNW+KbZZyCA5AIqx/45pp5ZEB2FmTPhRbdYFajG/h00S0AqJ5Pcqc+yOsF91CMHz0SpIaTCbB8czt+oQ7qUvlSfTXvLu6LfpPbmA07x9FWm211SzKIYPq2Ru7PRv7OC+Q/U01QS0B3g0XxTq3oCiznEF6csT6lFovF8mdTUqbx8fbCz8eb4tKyShu3tPzYmlJl3Xct1Q4rSi1/OjoehvICQ4GpfM3N5Znker/JfN0LgG00ZZmq6245k0YU09U1ADxAU3q9PYcFH4+FCdBs6H85QAKxJHOF/oZ/jp/PbWNe57W+t/Pv2e+whyg212pGBhFy8FJgvfk5GEgEMqQvqbu2shAIhKK5EFgfadmSDq4KkU6CwL8jbEyEVg1kUYkpr/LNQ1Jj94gYLMkXIRoYUWH/FAhvD0XJ4B+MRD29kfThWHDlyzL/NrItGTKGr9k9vA2UpIDvVnHpLSmV2tHURGjRUY7t67jupsPOHIhpjqToOt1+ndTkMERY55h5L5djEwlcDKHtITQZ8IOeJLrTn6foLIaph3mLO4BUlugr6db0aw5u8mYEk5i5/AH4pYjGWg74WWpPmrKNmIlprJ0ozrdvMJBG7KTzJd1pq8U5d626gh4mYLbooJgiXWPF6LmL/ZaxWCx/EUrKyqnh7UUNby+OlFaeaHQMlBx8vKzRkaX6YS8XLFVKI76njhfkMpZhCqldBLrohSymO29wNy9WcF4NNs0+PzvUk8/oR4K6nkd4ApjNW/pdZo25hVblG3n3vntoO30F9zCDz+iHN+aO5hJEiNUEPkaig+nmtZP2WhMRqj3N+tWmZYvDQSQqmge1wW0O5OtjxkBaqhxC2q745oFvBFIjCmTmiiAF8I9D/gqzRJwWpJo023xEJAaY9YclkupvTIxKkk3v0jAkshkAvvsgJtfs68ItsH19IKaiV5BjuBTimS+Y58lI9PdiRJQuMc8jZB4JxHPfhW/K9u9DSb6iQ+ByVhV05meC4BLY4BfHzNkPSNSV5/mS6eyiIQB1yeWnUbU5f+MB1EYZ5LNWPbleN+FzdQXwLHAFi1Qv2ODrnpraJj91UyznEqcXKbVYLJZzglKTZuvn60VhYempdzjtcTW+XkdHSm1LGEt1w4pSy5+OmgQwEj0CpgJKzQXToqNHh7kA3M67ADRIyIIB48gvlShag/LdNPfawjK6EkIOXfRaAJpQxAYu4wDn8ajXszSavpMswug9bAlrprRztwKhFBFbjpNullnmjYhTJ2roh0RM1yPiLR0xPnLIl32i28j+KUmmt2guEAThkYiQ+wERiHmeXd1lKBmyTdE3RpxmmVTfBoiBUk2JcubkixuvTwHkSWkmrmjISwXXQTwGSqXgW9+cX5Y5j2aIGP4BCgoh8DLcDr6zkmFAsDnXGGAL5G01daSpwAYksroVqatdCZffsp6grlLz+UKNh5nAg6xd34niWC8G8QaXzVlNR9WTVB3BatrxrH6Mq+nDjt0mV/j/FPcOncRnrXoe9Zm4lff5/OabgLE8l2DcmEdOBmBoqxe4vylMZSSWcwxbU2qxWP5ClJRpfH3OTKS0hs/RLWFKy22k1FK9sKLUUqVMYDiDdBQz1Thm6l0Mmv4+AK/f+28u6pwNd/16n+uZx7DeM+TFAtnvK67i42ypJy1/MQA6wb09J6GWawbxMgckpgn3AC8hQvQwIjxbyqqib8A/DE/ENB93m5aSYtxpq6WlxtToG8jMgfBRkt7qHwaUQVG61I8GHja1qkZc+pqIUWCgSduNQdxxw5DoLGZe6ZCXInWevl0gZBkUOILVcdsNAFdPpH/ohYj4rY/8RefKXFOzIDrHbB8Ggc55mV6nA/LNceMQo6c24Ao274sjog8jxkj5QKREOvcvvBCAJn2+Y/tPl5LROoLHeYa5Bf/kX4Fv0URH0PLQJmbVGsCO9KZ0iVzM9p2XAqCQO7+P84z79zmJESygF/cmTGIPUe7lF76wh4G87+5bazlHOfW3jK0ptVgsZz1aa0rKy/H1Uvj5eleuKC3X1DrWfbfURkot1QsrSi1/OnqE/KxbnslD5n/wIF2HQaveh3dgyn8Hc5HaANvNDs3HEtRyrKTbXjKOYYTxk67NCjrRmvU8ztPM+u5OZuXfSZ8OH9HvhU+JIo0rtiVx0eYfaMo2kpC2IwNWf+ZJJywGDkPBSqn39I9GajqjEfFXikQM843L7Q+ym28zKFlmTIdyIHuiafFieor6BxkRGQv+Rsj6+sBSIxC7tgH/3bIveaZnqSNKQ6DABHUJAL6BvHwINVHaTMleJjwESJY6Uv8gPO1tnNrYmhAdi0RcDyKC2vQyNRnQEvkNgYJECOyMpOqGABeLaPbxAd9S8540k3EbtMti5ZrWAGx/SoTmjI7DUB2PsF/XoQZH8KaMubX68CZ3oY/4kk4Imd3kBBp320wsycSSzKCNcgOiR9wKepXPAWBRdF8W0ZcLUyWUPcH0qJ1LT+aygr4kYjmHsOm7FovlL0JZuUZriWLW8PaiuBJFaWmZiF0HX28vDpZWXnqwxXIuYEWppcr42SscmIxS48ySSQA0YQcNdT6Qz0o68u/Nr7FgzY2wHzAipSUDaM5mEohnE7GsuLQtN5bPITerLv3DZlOGN/9s+h6d+IrWrGcbphgxDKlbDQSiIfUjqOe46Dq1loniOBsaabYPMm1eTD0nAabdS5DHrIjDSNptDlAqLWHwhsx0CI8D/KCrqevcvBrq+YCrC7ClQt/QAKBQxCxZJj23PYQad14CJOIKQCyUJIJ/pJmbC7JzIDTWbNsTSTnegwhN8KQpO9HWIBHAgSFQsNy4/BYCW00fVMeYKQtplRMAV6z5ko5KVPNa3ZJcgrl23zJ264uon7GPrhH/xyRGMJrn+Bv7iWiUyiRGkEwsz04fz2v33k5TtnGF6gWM43mdy8NMpT+zAZifeiON+B6AnSalux0rgJGswXHgtZwz2D6lZyVKqbHAk0BzrfVWpVRj4D3kVlUuMFBrnXqSISyWakepadHi4+2Fn29li1J9lNGRr7cXpbam1FLN8Dr1JhZL5aI6ywNAqRKCy+7iC72GeF0HVkoYtRg/GrKL8TxBNqF0abeQLu0Wgj/gD2MYz5oj7bieeWwuu5TOiWvIDQ3nYHAtbs76mKkMpYhabCKWVtu2UZsD1OYAZEP2FiTquUwilP6hiHgrA/JhoyNI24vDLX5GkAabRxZyoZ0OhJoIa01EVDaTZb5BULABwrvjEYHF8mjRXNxy2YQI2DARmwU5ZptCc8xYc6zdYoBEjvv0YbWYG+ECVwwQZOZcZsb4CE8KciEiRpMksurMAz8TIQ0zLsOxZjsfOSZbkfrUDLN9cyjDB/2NQn+jaPNhMv9YvxR8YAivsi4ilvf4F5eqeBaN6Es3FjOHG4kjidt5h9R7I9hELJ/SD3FPupiHl0+FpXBL/KfcEv8pqreIUUeQWv4CeJ/iYWpKlVK9qmqK1QmlVCuk83B6hcWvA9O01o2BacCMqpibxXI2c8S44fp6KxMprbyWMCXl5fhUTN/1UtZ911LtsPewLVVOrncI1z20GICQF9IZzxj2qosoKvNnv1cdAOJJAKDpIbFhHbTtfbjkXW6jB3F6Ayt7tmbpT125hVk0DNvJZyob/c6DDL3teR5tOoZn9jwlB4uE0O6IgNwNgb2RFijGXZZAaHUekuq6B3yjEWHXRtJcAQIDgDDp++nKQoRqDp52MgdlfWC0SQ2OxeOGi4wXGgZF+eB/M7Be0mR9/cwcTMS0KMW0i0HGwgUuJ/U2AImABgE/QHauOS8n5Tga8Ibs1RAagwjLIBHgRbtlCP9YIAmIgZR1EONjal9jgCvkHJhnfkbIuGsWdEZtkru3JcMUTwU+xOvcRm8WsIXm1OVnPtEb+OdEmM4Q2rGGLTRnmdqHfuluGo3YxcMqmHe1GFrVN9HxkZ1f4NveYmbVmlUAjDNR8bXqKpmwtpHScw4vPPXSJ8bWlP5JKKX8ENEZj2n6q5QKAVoBV5vNPgJeVUqdr7X+qUomarGchTiRS19vL/x8KtfoSNx3j46UWlFqqW5UuSg9nbQhpZQ3YtR6DaCBCVrrN826bkgfiebAK1rrh05nP0vVoZfDECYxBAguE3OiWK9ktiAOrWsndoIJsqwpIkLr8jMA7x+5lT01GjCu6VguyP0f/3NdgEt9xkr9JF1ZyvgfnuXpix+kt17AXHrQn9kE8zM4v/UuwHxEaBZD3rtQVArhHZGoZCSQJz1HfaPlOaW4W9UAIjD9JMJJDiJCfcyYTv1cKJBi2rvsFqOkLSY9OMoHagcYo6Nk3CZCZJla1HxIyfW0cclebVJ480wdq0MqEt10QejFSBQ4WYyVAv2AYBM9LZT5FWVJ/am/6avqjvgehpjuch6+sWad01bGhSf9N8DM87JiAMYGPsGz6WPoFpnIUKYygkmspzWN2AlR0JWlPPv+ePjXOLrplvTnXe7iTbbqfe6+sYeoxT28BsDl81dxMpTai9b1TrqN5SzDpu+ebYwHPtRa71HKfQEcAWRqrcsAtNZlSqkss9yKUovFUOKOlIooLS4tR2tNhb+l301pWTm+FSKlvt7KnS5ssVQXzobLBSdt6EOl1C1I2lDnY7YZADRC4j/BwCal1FKtdRqSaHg3cAO/vid/sv0sVYRSaUBfqB0FD0GN+wr4mis5EiwtQPhuLDEtNgGwjabEkeTeN//VC1g/Qox25rr6sp0m3K+30uG7b3n60gfp8Oa3RLyYQYtJO2jRcQc/XHYRW2jBU8+MAWDWU3eKkPQDwsAVgCclNxKK1kl7Ft8UKNgqorKgAHy9IbC1mcRWRKBFIEZCh5HIquNYa/qOEmQe2SImQ40odcUht1+cGtYAM59mEPiDjBfqA5mrIbwvhEYjAvRGxG0XJPIZAimJEGNa3JQUGvEaIedCIp5aWRf4X4Ek7DnCudA8inGbONEeqR9tZuaVZfY3c9hz6YVczzwAXsx7CL7xITc+mLGMY8fIFuygBVNeGIzWiiZ8x6iB4wgZmAMsZsSq6RS0r0GQz6NHfR6W6zsAGMAsALKoj4qHHv9ZAYDWIkgt5yAKa3R0lqCUagv8HRj9B8YYBAwCiIyMPMXWFstfC0eU+ngrd/uWkjJNDZ8/LkpLyvXR6bveXpRUYiTWYjkXqFJR+hvShvoDb2ity4GflFKfI5foL2itd5qxrjvOIU6435k4H8vpoXWUMTcaCy9C8ZhAANQGSddkLxxpUYP91AFgBZ1YTTsArhrxHyYyiqRDcQypNY0VdOJbtYKleiM9WUi7F5ezdnYnbrv+Y/7Z4H2+pgPL6URzNsvYqXhcaEuRvwCTbks2+Acg9aUNJGJJmHgikYIn/TYAEaCmBpVIRJxGm31rmvEPm+2jgWII72Je50NmKoTfiKQOhyBCNwR31NUVKdulzoVoEzFliTj1gjE0qgkx94Cj2X2dCrEMM78IKEo1kdEwc771wWnZWlJq6lJNOrITPXb3Ni01yzGv74LJDGcTsQC87bqDCfGjeIO7aaX685z+lKZs47odi+lz0+cMZzJZhPHgxmlc2GoP8zpcTyQZ6HfuJPS2HwF4iBfprLrLMV6uLz+HmmMmO86DPjZCeq5iI6VnEx2RYm4nSloPWAwMB8KVUt4mSuqN/OVnHDuA1nomMBMgLi7OhnEs1YoSk75bw9sLPx8piC8uLTuqv+jvRSKlx6Tv2kippZpR1UZHv0obQmIzEcdsFwn8WOF1+nG2OR6nvZ9SapBSKkkplfTTTzZj6UzSlE3E6N5wYBwcGIdS8tCt4LNWPSEKdm27hDncyKOmn+UR/HiMZ2nMdtqxhoMB05iooB1reE7vB6A/s1mr2pDSPwreVHwy5l/0ZzYxm9P4gIF8wEARXj8gorEmEkn0QcQa5nkYEn/3w2MWFGJ+mqhhifM8DBGfgUgU0ywvyZV9srOAfVCSiog/I2jDo5HUXW/kUxqNiMAGUJCHu140OhYRukGQthtCm8uDPHOsVJlzShKk5pi5hJifxVI3mrLFbJdqzt3Mw7cPEAvZK808HPOl+ubcTQSWPeZ1fThAbX5Us/lRzSabULaqRMYyjv/TX9OcLQC82vgujlCDYmowg8HsbVWXfbPr803C1SSoDFQ7TVeW0pWlJBNLFx1HFx0HD6TBA2kM5QWCP8wkuF423SIT6c58lAuU6zd+0CxnB6c2OrKcBkqpnNN4bD7R/lrrCVrrMK11lNY6CtgLdNdaf4L8N7rZbHozsMnWk1osR1N6nEhpZdWVlpZpfLyOSd+1NaWWaoa9h22wd4D/fLQee9TrCHYCU2EB7B4lIbr7mAZAH+axgF7M+GEYd138Kl7/u40OoV+TDKz4dw8ufC2LfYTxrr6NmCVp8A+Ib/c2TdnGvhZ1eIQJcpAuiCB1ARvMzwAo+sDUeCKOu75xwBbAx6TxOjWnACnGDGg3nrrMAKkbzcyBqN6S/ksOhLYx49WHElOX6uuDtGxJxpMi640IzQYQeJm0mfE3ab1F+VCUA1EhkCm6j/CeSKR2i4jYmPbAPsjcDeH1ZRzygSCICTHn6YMITydteA+UJJuoK0jq7kpZTjCwzMzPB/HqTIKyW7x5UhcBMGLPdMr0/Yzc90/SL5TC04sSs+FaeFXfxbD0aXwS2Zfw9bl06b+QpZ/+AzVN06vxHBJG3uH+vXd5YaF59h7361oAXOn1NQCfb7wJyznM6RkdBSmlZgILtNYLzviczl1+Qv5znAiFVMz/Hu4B3lNKjQF+AQb+znEslr8sR46pKa24rDLGrhgp9fHyckdmLZbqQlWL0gxOL20oHbgI+K95fWwE9ET83v0sZ5AUZa6bdEv3MqXGUU8PcL9uMDGLhqO+5yFeBOArrpLtBsk/6SdWPcZTqgaX606o7ZrGbGaHyuNV/SFPdhtNH+Yxgkl0ZSmdWcEEp4zKhafesz4SUQwGH0ewhYHvHlNbGinbBYaYdebiuiQfSIHSUijKA5fj1FsfonJEfPqamlUwKbJImxiAolzwXwyc5zk+xRXm5mdcd0OAPGk5k5IsKb3hZgy2Qkq6BGjDG+AWxuExQHMo+Qh8I5HoaIAZ36lddYyL8sG3GyJEnYhxA3OuLmTwAiAX8j4Sp+HEcrkm/tDrFkbXf5IJe56kZ/3P2EUjltOZl3oO4UGeYmz5OL6LlLYuBXE1mFvWl4h+qTzFQ9zDDHjhDi4u/4Hcq8JZNvdauEF61fZELI6ncj+LOveVeT4EfAj6ZJfjlrMTxelEQ6377ukxRmt90u8v5Wn6fEpMtNR5/gPQ+sRbWyyWErf7ridSWlxSSZHS8mP6lPrYljCW6keVilKtdY5SKhlJF/qQE6cNzQHuVkrNRS7hrwc6nMYhfu9+lj+BPYTRYKIJPw4fy5dEMYN7YBTMpj/9+JQHd0iklCaLzF6LYFwPmrAd1Kd8m17KJ8t7U5efmayHcyFZDJn0Fi+PGMSrDGEs48glmN7zlsjuwYhYLMRTQ3mecdoNQaKX7cF/NZ5epI4RkiMyD0NeHrhcJrpaaNbliNmQrw8iYLOAaIlkBgbhNnxJKYRW0UCBWWfSabNTxKioIBXyDoMrxwjiIGjVRRyB80yUswiIcaKhfkh0MwwRk3ONIHVJ+xd/l9nOzMd9y8eJmJr6VdbLe7BxNbRqg1SfLQBiwdUcuAty53/JMP0/FtOd7TTm5fqDaMIOjuBHe76hPmm01d/xNrfTl3mspj1l3t6MZRzelLKLRpz//gGWDLySj7xuglWQRn3QMGjj+/TYOJZ6rXayV82Cj0WUWjF6DmNrSisNrfVnlbGNxWL5fZRWiJTWqMRIqdaasvJj0ne9vKz7rqXacTZcLhw3bUgplYjcGU4CPkDu4jqtYsZrrXeb7a4APkbiOkopdRNwp9Z68cn2s1QdWo9FqXE0UIPR5n/ucCbgxxGmdB7NE8sf40uuptn4nfQaM4fmbGGLlnYxCybeCKPHMaD7ZxwqH8hERvEmdzGLAbyNpISq/9OQAiUvKbIDQ/mOFnTv8wUAi++/TiKBJjWXIOTTEYEIMx/EGbcZbkfbojwIDMPTar4l7F1szIgKISXFCMRSI0iD8ERAyyAwAgr2gL8Rpa2aI31Ot4IrRkRooMs45yLPA4MAF6SthqhSSeHNKwR/8xcb6pIa0yinkVIkbFxnxCRIVDTDRFxrmvO6GXgTMk2v0/AQPLW1hUjk2AdaxZljvwtRfc17BfAsfPLFXHKR5qnB/MwBatOcLTzGM8ymP/4UMZv+TGcId/EmbVjHjh0taNt4BZMYwQ2qNTCObh9Lei73wEe9+/A4T9Ow1fcMZgYPx0+Fm8cyqv84JnB0irflHENxOum7lt+AUqom4izfkArf4Vrrh6tsUhZLNcARoD5eFYyOKiFSWjEC6+DjrSgr15SXa7wq9C+1WP7KVLkoPVHakNae+IgxQPr3Cfb/BnERPN66E+5nqTrUjuMvn8pQiIKnJj7D4lHduXzMKhYk3MiB+Nqs2NdD9o3R8NJYasUMZTiTiSKNJTN74/+vQ5xX8y3ySwdzcJE3L/qN5GeCOIQ/CQzgde6RgziprC0R4ViKdLhNRWo6/cz6AqAjlM6FwGYV9gNIghbBiJDD1GyCO93W7VobAtmJYkx04LDpWQru+lGXSQsODDP9SaPFlCgm1my3x4jOmlJfGh4M2U5LmAYQVQbEAZsgLQlaOQ67cbAxUVqlhgRBaa6kAJMNNINwJ0JaJudQsgeKiiGwJuSlm3nlQFQYkGL6m4YC82FbP0+Lnn/veAeAJxo/RineDHn3LdTtU+GL3vTufRf3qefJ0/W4pPH3TGMIAKl6C+MZA7zBdpoQyyZuVpN5Tr/s/hw8nzDULXyVC77IE3fe3iw+/gfHcvZyeum7lt/GHKAGkttQfIptLRZLJVHquO/6KGqUOZHSsj8+brljoFTR6Mi0nCkvx8/L/hO1VA+qXJRaqh8NG38Puh87uYR2SC/Ktauk5lO/LYZHuQSzK1BqEr8acDmZWkRKr95zWJDehz6B83h283gADv7Lm4CMcm7VJdzB29T2PsCVfM1XdOKbG6/m6+e7oVbIl4lepzxRQaf9aSGS/upC0lsLkVTXlUbMOX8lseZnGGTOhXBTn5mSAzFheKqh/cw+eRDaEUiF8FhITZbdo2tCXpapRfUxabvNZU4xuYhYzIW8HHA1g+xkCI2BlHXg77yJ+UiDh3QxWIqKRSJSJiegldOvdI+YJJFq9s1CRDhIi5sGkurre9hEZSNlTkXZ4B8m8/CPQUR5HAzmdXbRCIBujedT10RLL5iXT//b3oXG/+LCdnu4T0UAr+BSo9C7G6CGaLgLdAtFRqMIbuUDruIrOrGC0XoiYxnHB6opdJX+N/pLmMQQXsobwhBjdtX7JJ8py1mKjZSeCRpprWOqehIWS3Wj5KhIqVxTVGak1KdCRNSJmpaWafzslbqlmmA/6pY/nZ1c4n5eZGRWww7fA6BGXEK9SVCMnyRlA1t7Xs4OGgNwP1OZ/+0/2RzZmITSO1jUqhN9mceS+b35cUQoMxgMQCdWMJv+0BwKImugO5h/9l8gAisFceJdBiSLCNu7G6KLIS8FXKbti28MkAtFW8Dfcd8Fatc049SEmJsRgZcFRYfBPwjyksAVZkyNGoiwdCjJN4K0GCgEf3PRnjlfjIryUsDVXuZQlAShkVCSBY2CwNdJz91iHnngG4unTtQb8pKhdgDk7Ibw5pCdDtFxSPpxKKQuk02jI03NqRk/6gEZjyTw7wiZiRAeZ84tWs6vDevIPVQXgAOJ5zO3Xw/+xn5qd/+J/swmtV0EjX7ai+JJ+Hgsbfuv4HZeg0XjeD4xl6d5kC4spcyEzxq4sqiXt5NNtGSShrockM9BX4Bp6Lnw4FTggTdRfIXWd538w2U5u7CR0jPBbqVUba31gaqeiMVSnfCk2XpRZmqPiiuhprRiraqDU19qzY4s1QkrSi1Vylz60mBSFg1HfO9etndEI5i8kR56LotcfWnGTp7LGw5ANqHc128azzGaR1uNoUfWf3gq7DGWvNubi97Nhq4Q/GIms736A6CLFaRCqTEp8mkPOCZGOUjdZ6EIyehQKEkHlxFivsFQsk5En3+wZ86rU6F9c1i/BVoHA1uR1NjzTFTREZ15sl/mFnHIdURtXj74F5s2M+keE6Tw5vLT5YLNK6FFiMyLAIl2BkZAnhGUriAkMlsmxyvIAt9s2FsIoTUl+hleX86jFrJNWg5E1YRoJ93YZdrO5INvCJIUWBN3enH4jYjxk1Nvmw5pP8XQ5/yPZP9cqM0Bsgij4PD5tKq1lui7M3jpjSHE6N6kKJjbvw8AW/RCZgObsmMp/yyAmfd6Ok7sdTViSN6rABxgLos29j3h50VNBD3qhKstZxvW6OhMkA8kKaUWI0UIgK0ptVjONCVu8ajQVF6fUsfQqKIo9fVxRKk1O7JUH+zlgqVKaM0qAOrwuluQNmQX3SdJ3eD0/41gUedWEAu3Ln+DCBMKfKTnZGYmDuRdbudt7qB7mGxfb/NOBjCLiaoV8ZN+AGDq1od56Jmn6MoyRjAJgG0BrUSMOu67LhMFjZN5+R40fUUjgRARa0VJRhyWyjbt42Tf1nFIBHEdFBkTooKt0me0YAME3gTsMW1cDhuzJCDwMFAsArOoFMKNUy57oKBQtgt1BHO+HDcwEIgBl/MXm4uk4gYDAbI+OweiHwA+QlKI/eQ8w7sAKaZG1H0JCwU/SB0rzZGoa7BETrMLISof6cN6HmLcFGKe74bB588AYMzgR8kijDcz7mNaxJ2MYiKxb2wiRj0JzOdOvYYPTLvDN7ibYHIhFCIvzUFtM1+0Jhr+iZKUYDa0dz4gbvRQYOhdqIlYzjUUbtfpk2D7lP42tpuHxWL5E3FqP329vVAm+aq4EkSpOy24YksYk8rrHNNiqQ5YUWqpchqyiyXxvdn10yUsAThflk9ZLqm4jx96hgO1asvCRePYQnMe4kXO33gALhsHy8byXecmjOY5+L4Xwaxm7PKJzOp8Ay9tfpwhLabT3THJKUPEZTESBcTUjRaa5cGmx2gGEjGMMRFO07IFIDQAaA8l88A3D8iHkjKp2Qy8GCgwbV4SzfGA7FypCwVTrxkJLj9IyTKitYEIWV9vIARCDyKtWjbJXLNzILQQEdRItNUVglzw58hxQtsAS8x+wVCwQJx8yTbz8JPzwfQ6DfRB0ph3e1rT+AdBVJDZrtQI8mhgFXCT7OekSM9bdDMF3WpQGOLFfemvkxcZSHR5Ktfrr/l86FhGE8H7RpS26rxNdn4OXmt/Oysd1dlUfv/1+OKoz8QXo7qbZx5zIxshPQc5vUip7VP6G9Ban3YvUovFUnmUlJraT2+FtzEfqoxI6fHdd72OOqbFUh2wotTyp6PeBvZKu1g9xiwDEaMfrWaYlnYhExHzo/RakSQiZsz36mzW05oRTOKJVo8RoTPwZgBRZWks+rEP2l/xJVdQGgetWU/+JX4E3VhM/Jy35UAu5FMfgEQ5s/E46zqRScewKAMIgoIccc8NjzQnYFx2fSORiKWfEX9meXYKhMbhceINECdcR1BmF0KUCyiEmEhTQ5olLWMOFJqUWsz8koAIaQHjbl1jToNipE60TBakrIOoAE9UN9AlrWRKyoxIbgOshDTTEiaqI+72NYHdoGSlLPeNMXMNMa67KebcCmFF67ZugV/aHiZ7D6er91LKz/PlQFkN2nmvkUFe2UunqSvY264RrB3HMD3BXUd6z6R32TxCaoQvnbSdtiNWAEtlv8uWwvCx0OrXn5v3kJTsfzH71ystZyc2fbfSUErdqLWeo5S693jrtdbT/+w5WSzViRITtazh7YUjFYtLK8F9t4KBkoMjUEtspNRSjbCXC5aqoR40vON7urNLXn+0Ceh61CZN2cayztcyYPmHLEqQOsMv4ru7183jeoYzhTSiGOE9Ca+AQwwIfYv+zGZE4PMMZzJBs4rpNWcOT/O4DDofSXuNAQ6aA+UANaFonRGEexCxFgDUh8BCKE1GRCKyPnu1ccw1UUeKIXM1hLcx9Z7FwGERhf6liIp0xGYuIjDLgM7g2ipGQ75+UNvMJy8PXJ/IMYuSpWWLKxqJgoKk3OYAu6EkB3yR1jQlxca115gn+QeAf3Nz7HVAM4jKqXDehUgE1Rwf47hbkCGNf4nGneaMD/hTxH7qAOAb9CRP6COEks1I11OkUZ9gcnkn49+ozMPszYJv1khedPudGyAV1ADNuDxP79EnRjwGQH3dkOd4BICL1OVcN3kYD+oNrOJpXjS/OytGz0FOL33Xcno0Qyq//36cdTacYrGcYUpKf21IdKYipc4xSm1NqaUa4XXqTSyWSubOcfL4FUuZot9nysTRNGIny1QSlMKirGvhqmJui3+dVVzJi0ceogtL2er7d+5Ur/FQ2YsM5nXKZwUQTC7XqXZM/fhhVtCJfQPrsDivO4eoxSFqeYSlNx6jI8csqBBP/eTFiHj0gcwkcHWrMM360sa0aDci9nJh/W5ZRroYJJEPNJc03aJ8M2akPKJCzDGDgJUyhm+AHKu0VMYtKDXb7Bb3Xh8fM+Y68zCClHxYmm+2bSDC0hUpxkclhSJS2WP2CTb7mXlUpCilwrKaEFjfPI8ACqFgucyzrbqewcxgMDP4Qq8hjiQGMItdNOLTx26hKdtQb2huC3uHLmFLmcxwYos3UTvsJ17vcRsX5u1hBVexk4bspCGxbGIXDQG4SGVzkZpPN13CIv28e25qIKjO8rCcYziR0pM9LKeF1nqs+Xn7cR53VPX8LJa/Oo4hkY+3ws9XLp8ro6bU3ae0QqTUaQ9j3Xct1Ql7SWD585kgkbJd8XBhQhYp5U0JLruMXO83GTZpBvjC6EMTaKyz2LFedmkbtobtNCGDrjSqsYvbGsxmb0ldXuQhltKFODZQb8ROQsgGahNxUyq1OcAdPyVQ/EEQfUaIY+w8n5shDNISIeo2YB8SySmV1ivUBAohNRGio4FEcc4lC3d9KIFQhKkjzQFioFUx+PaGgjkiLF3RUPQN+LcRp93U1RBtjI4IhqJ08A+BtN3gKjBGRmXi1luUC1ENEBF6HuAytao+MjcAUmBzOkQHQI9I3NFOgmU7JxXY1QzSkqX/aF6KOPsWpXt+Ff5hQDH490RU9RazIgZJDV4ChEFgLLAV9unJ/G1PEQDb6zfh4R1Tqdd4J9827UDpuptJJhb/h34hmViS1WKe1EWcV3MMXv/zZ8iBV+kT+jkJDOBlHgDgFe6nv4mAJnAHbGjNko2403cX05sX3weVblym7L+scwsbKa10lFI+wCCgMxIhXQ68obUuPemOFovlD3GkQusWRzRWZqT0KKMjt/uuFaWW6oO9wrP86fQaNQeA+dyIUmuANbDQpHQ+OI7zCocA0ITt/Kf1NWwils/oB0B99jCYGXRsuJ56V//M9V9+TFO2cR+vkvFdNCpXk1/qx1QOUJdcyIL5I7oxb9/NMn460BmivHGn2OLCHRXFGAM5bVNSsyA6BhGrFcRci+ayXUEO+Oeb6GiyCNISc2noXxOJUtaHaG8zBkC+RD5TUiTlFh8kkmrMiPw7irg9cBjCXZC6UsyHfJ3eps4coiE1FWoVQniIjJ+dAqFh4BuECNggI3AvBFchEA3+e8wAkUi01bTFYRPQ0xg45QNxiNlRIvh3kffqDt5mTtiNANxbPI31jVtzF28ye1t/LmYru9ZfAo/Dpqi2vKf/SVO28ZV+gesZwQcM5Ag1fvV5WEM7JiqAcdzbSgytGhtzURXfG53w68+Q5RzB1pSeCaYBUcB7yDt8C3ApcE8Vzsli+ctTWqFPqbeXwttLVWpN6VEtYUzU1InOWizVAXu5YPnTmc+N7udaj6UdK1ibCCwcS6+ec1hTXkzuznB+blyXeUify6Wm3vQ5HuF9BnLnl9PIIIJeLOAdbuPrt7qh/q4Z1XkcdzOTT7P7UVAnkJ8vPY/e05YQN+QbAJLaXAHzECG2GhGDPogTUQiwR9q7lJRBYAxEO661WeJQC0CeRC7z8kw/0jAoSQbfQHDFICm9pVBkakpdpcBhqf0E8G0Gvi4I3QpcgQjDBpA3X9a7csSY6MBhmWd0TTOHADyR0hAgB6KNOKYmrE+H1g0gL93MI0vmCkgUNACpZW1e4ZfRAAqSIbAYiY6uAl8jRkmS4/s3MO/VMxBBBgEPmTu3D0Buo2B6qKsg83bqhWXAYhj15TiKD8ESutOVZXzILdRT99NLZ3Ev0+me95Xs7vKk6LbVcdTmAJvxB6AUb2auegDuAdUO9Br7r+qcxIrSM0EH4BKtdTmAUmo2nhwHi8VyhigpK8dLgbeJktbw9qrUPqVO9BUqGB1VwvgWy7mCvVywVDl9mEefnvN4WAWzAEjRI4kpTaM163lQScjyVi2Or3EkEUcSl6ph8F1v+rSYxzTuY9adN1Ajr4A9RNGONUwKfZD7mMZjPMO8IX3YsN70v0xBxFp95NO/B2gvP/NWSrrr3t0m1TYPSnLB9xZZH7jOTDhYepu6muEWfr71EeHnmAKFSD0p6ZJma7JqAePEW2iEYzoSQV0pKbf+AYAfZKdDSBAex10/PAZMIJHdZuKYm5cPoSHQOloO4gqTedUOgJJ08PVBRKaJlDouwLikJ2tgMyhJBd/OnkmWJJtzyjLbDpa5fFAwkLRXpOB0SWJv9BbFKn05HZY/yaqwy4kak8Z1fMGVtb6mIbvoyEo+5BYANtGSHiNWoP+haNR5KwC7Jl7CoFEvs3Z2J0gDouDb/s1ZQzumdBjMsHbSE7VueSY/e4WfzsfJcjahQNv03cpmL/Ifoci89gXTyNlisZwxSsq0u1ULgJ9v5YhST5/SCjWlTksYGym1VCOsKLVUCUqVAHCvfoV4TBsRegFwMWlwGKZsHE1wWSYAieb//gdT7oZ/wI86lPrZA/l31uuU16zJ3a43eN41klBy2ElDFtOdcYwlcloOjWI+YFCX92WAl4H1iDAz7VUckebqCRRDdCQiWlOl7UvB8xAYBqajiZgP1UQEm6lBzdsKrtbAheKW6w8QJGm6LRpA9m4Ija1wvGI8qbiliIgtlHTgwPPMtnnIpWcYFK00DryHzT6mXYxvG3Ctg8050ML5aw4ADoNviDlGqczF3Qpnt2xWki5RW2oaAQoUrDY1sV0QwVzf7DsHGAUfB97EdTtM79AoUOs0y2nHis5taXdoLcV+NfjC+zoumpQN3wDzxvGpvgG4gij2sDeqET07f+b+HNw/6gVeCRsJ++TzMEy/RAIDqMUhnmE8D6wRQWo5N9EKyuy3TKVQoRXMHmCtiZAC3Ih0ErZYLGeQkrJyalQQjjW8vSrH6MikBVcc24mUltqaUks1wl4uWP50hjAJModwb9g0AK4r/4IorzTcvSoBvgSiIDc1nF7957BAbZPln43l0cZjeJynGRCagD+HmMFt9ONTriWRpXTlCVWbK3R37piagPpAs/u/YWzUTWX/YXgihe2BYokW+kbD5vnQor1ZbtrEECCCtCTX9CUFiTi2RERpAJAuUU4KgQwT7QwDUjy9TEObQ8EPsntgIBBkWrn4IMIvXdKGA0MgczeEx0FelhgTkSxRV/8YSJkrY/ibti+B+eAbBi1CkChtTaQ2NQyKUo14DsJjNpMlaccArlizfQ5wMRR8CIGDgSXGpCkS6IIIeW/ABdc9upinnn3I/Wv6vGkfLiSLmOVpLO/cjmL86DFiBfGT3oYRkBA/lpc2A29BbebSY+hcFk3qK4IVeIVL4FMgyReAKQmjeTRemteq5cD/gDgTIW18nA+T5ezGitLKpGIrmE14/iK+w3RwslgsZ47SsvKjzIhq+FRW+q4TKf11SxhrdGSpTlT55YJSqjFi2BCMJCUO1FqnHrONNzAVuAZxG5ygtX7zNNY9CdyLJwlxtdZ6yJk+J8upuTdsGtPjRxD8oScKdpnuSmvWo8ZD/Ji36c9s3uQuAL5zu90k8CIiij64+27KJypUMXh7HWR+aC++SbyatnoFnVjKgKFvsWhoJ+bRx91bs2VQinhWzkfapNQ00cJCaBGGJMGVIUKtmflZKuKxxBgd+TpRxFIk6lhm6i6zgI7IpzhHlmdvgdAYwE/qRAGycySF17cZEg01Itc/FTgItWtCdpKk5HIeECBtYvwLISbWvA3FuGtMC/ZI/akrwERoa0J2sumjehjwgYKtJtpbKi1jADE6ykJqUvMhsLP8LMkyLWhCgA+QdORbZZwHn32aJ9a/CECz1v/lBR6miFpwGDrvXg2vKgZNepmZqpMc42b4sEU/brm0OYuuGc3usPo0oK/U0j4H/FwCD/lCHDzY7mleUmU8O8Ablnl6mVrOXcq9FMV+vqfY6sifMhcHpVQQMBm4WmsdcartzyKe11qnVPUkLJbqypEyfZQZkZ9P5URKjxy3T6nTEsam71qqD1UuSoHXgWla6w+VUrcAMxDZUJEBQCMkaTEY2KSUWqq1TjvFOoD3tdYPYTkrUJ0heGl/ALolzGcwr/MAU8kmlFoc4tW8B1k15ko205zG7GBB5xsJXprJM16PAvCJSmOKTgLgwBu1CWc3++Lr825CfzYQx7U9E2nNeu7hdQBe5gGuyfgK5S15r+NjnoUPkUhnCiLusvBERw8jYnQ9IjxjzPIg8DWXr0WLJWqZliJFXTHdIW0xRIVJJDPGRGDBCNJ8M6YxKQoNQITeHiBS6lP9i4FAicgGNjMCtQyKsqVNjH8YIkKNsKUmIojPg8Be4L/ERF1DgVhETAJFWZBaaCLAGYhoNum7bDHbZpn5ZcicfO9C0nV7IlFSH8T0qDUMZwqPtX4WgNE8R1O2cZF6hk90b3II4cpJX1ODYjboOJL2XMFD9Z/iHW6nmQ7hMW6iwdtZDBrxMo/xLIyQaUSSTW/m8NLbj9NFLyTpyGXUrrETgLr8THJgG9mw4GSfLMvZSpm396k3OgVKqReBGxDX2eZa661m+Slvah6L1jofuEMptfRk252FTFVKhQGJwBfITVZ7xWqx/EmUlJXj61UxUupdSem7x+tTaiOlluqH16k3OXMopUKQjoQfmUUfAa2UUucfs2l/pA9budb6J+BzcFu4nmyd5SxDL4efvcLdpjUzuIcmbOcFRrLD1QKvRhofyvChjIG8T4/lc1nn1YZc6pJLXVbqRNbQDoDP429in6rHZwk9qUsuUaSRQQSzGMC9TGP7d5fyDrfzesRt6Ldqot+qCcmIACsFeuOu5yQfSVUFSILULUjEdKUIRfwQIVhTajs3J0FUNMREA5sgqg1Q39zl8REHXKfVTFE+rJ9jjhMCBEkNamYWZK8zUUmzn6+Zi2+wLPIPAFojYjQHCjbIg1LkNow3sFxSeAmDglTI/ABCzSW6j49crVMIG9MRAVpc4VFoxsoz840EtkJJPtJ4or1Zng4swP3eA4Sxj+i8VHjvLsrwxp8iJjKKm/mY1YeuQDX4kCJqcRUr2Drp74xlHOyFWhQxmeFEko03ZTRlEztphL4DmrKNfL8D7FWaJmwnmFx0AWgrSM9JyvGimBonfZwmnyOusz8es9y5qdkY+cTOcFYopRoqpZYe8xhZCadVJWitrwbaIam7Q4GdSqm3lVLXKaX8q3Z2Fstfn9Kycnf/UHAipZXREubEfUpLbaTUUo2oUlGKWM1kaq3LAMzPLDwWNA6RHH0xkl5hm5OtA7hJKbVZKbVEKdX2RBNRSg1SSiUppZJ++umn33c2lt9ETxayRPVwv34ubzjP5Q2nIbtIVosZwSQG8gGNVu+lCdtpwnaG8Co7aMIwFcbahJbwo/wT/8eapUSQwfPFIxnALJ7lMdZdGsvDk6bizyGinkgh6okU+XQlIWIsHenF6UQfc8zrYog2Zr15OcYEKAw4KA/XbdAiVraju6TjEgGkSj9RUkyv0ppAJPjHQasgcbQtSQZCxHk3PEQyZ32DEcfdJEQII8cpOmxe/yD1odQEX295UCzHS0k128VIim+gS9J/wWwfA+FG4LbqDpnLkPcgC4kKr5c5ZjrOwpugZJ2pha0PBQuQCGoxEA03/rSAx3max3maA5zH/1wX8NTAhxjJC2wjhoTdt5OsFlMz4Ek+0p8ymBn04XM+HNGPtLwoLhrzA/O4nilrRqOOyYf4D07KbzSQwDKVxDJ1EKXGodS40/hEWc5Gyty3mY7/AOo6/3vNY9CxY2itv9FaH+Uwe6qbmlrrXVrrrsc8XjizZ3tm0Vrna60TtNb/RPI45gA9gK1KqS+qdnYWy7lF/xlr6T9j7WlvX1Kmj2rbUlk1pSXlx+tTqo5aZ7FUB86G9N0zyevAM1rrEqXU1cAXSqkYrXXusRtqrWcCMwHi4uLsrakzyBgeA6Ah++ElX5qzhZs7z4O7oFu8NOv8TK8H1rOPMKa1v5NP6QeAN2V0ZSlRugl+FPNk5OP4cYTr231MHEkEfFRO49t2MJe+FOHPzBEDGVDwCQcDa3sm4LjR7kFMhnKgKNfUhYJEUlfKU1cMkGfcdZ1azB/M/p2BZab2MweIBN88RLgGAbnS2zQwQkSeU5Oa57R/iYEWoaZeNMyk3x5GUmyXiElRdi6EBkkKL4B/qJlDoWwXs9y8Xgn+pv9oYLAZJ1gisaENzPaHITwWT+9Sc+6EQXgXJIp8MfjuQaKzKyHwsgrbJ0L3oV8wp0wSEdZ7X87fvisi4dIBDGUqD++YyheNu3Mdn/OUfoJexfK73ObXFH8O8ZHrJm5Yn8hFrX+Ax2XIGQymH5/SjjX02LZCfvcJPSEBbkhMpEvPhSwz1wBKjUNrW2t6LqFRlHHK9N2ftdZxv2P4X93UVEo5NzVPemdRKTUNuFgp9TowUWu953ccv0rRWh8BFpkHSqnWVTsji+WvTUlZ+a9qSg8Wl/7hcd2R0gqC190SxvYptVQjqlqUZgDhSilvc0HhjcSkju25lg5cBPzXvK4YHT3hOq31/5wBtNZfKqUyOEpyWKqC8TwDgOoLPA7vHLmNoP/8j/wLLqBR/E6mqxEsoTcwjt16Bu9wO+1M25jP1XV8wC3s1g0B2E8ddtKQXixgBoOZ2GEMW4lmC80ZzxjGMZb9gedxoeN1FYFEQ510VdOz1D8a6Ql6GEmJvQIRaYdlH1cIHrusEChKB/+tSBVbKCLucqRnqKsL+GYDDSAwF09U1lBSCq72QCmkJkF0DKSkQMw9wGokKtkZNs6BVpFmPs733kH5UVQIPktMj9LuSMg1F8iXdQWFEBppBLM3UBMyzac+PMaM5USG8yvMzzj34ojdGKT2trm8T7MYQJCPFMzeqyfRbdfXvHXpAO5UjblMryaJOJ7Tj9KONRzwq00KTfmY/vhQRgg50CaFHyfEwAqJfL7D7TRlG+tpTb2mUkf6La3lJsQWWLblWobpLQBMmTgaNRH0qON/rixnHxrFkdNP0f3TMIZ356TpnVLqJWA88p9lBRItHqy1/rBKJ2axnGNorfkt2bHHE6W5B89Mn1J3Sxjbp9RSjajS9F2tdQ5y6X+zWXQzsMnUhlZkDnC3UsrLpGZdD3x2qnVKqXBnAKVULGKSsf0MnIrlN6ISodvc+QQ1k/sG+RdcAMB8ejNMT6CHngvTxtJAzeAp1YYD1OYAtVmpr+InHUIDNZghvEp/ZjNsxwzu3DGLzTTnqQYj8aOYf7d6B2/KuHvqB5yvHmIZXVlGV7crLkHIcx9gj/TnBKC5ONeyErgQslMRkZiD1Fe2F7fbomIoSjHjBJpxvKF2gKS/cjtQCNkrzTbNJU3XqRWlFLJXQ3SYvAz1gYJ3TS1rKrAVWkVDthNdNfGfvDx5+PhIP9HQOGA3lOxBikdDwb8jhIZJRJZIc77eki4cHifzL9iD1LemI7eAgs35hZn5euNxHy7AnfKcRBz6JYV+SRFFGnf2nUYYWbyld/ACI0kjikPU4k3u4oIf8um0fi31SWO6OsABzuNB/Rn/HPUePDIWHhnL3kcbsURtYona5P5sDGYGX3K1pBavFzE6ZeLo3/lJs1QlGkUp3id9AEFKqZlKqV6/cXj3TU1wO7Ef76bmX42uxqypO5CJtIaxZn4Wy2/kl0MlbEr/hfxDJae1fWm5Psoht4aPF0cqwYjIEZ6+x20JU7mi9LemLFssfyZVHSkFuAd4Tyk1BvgFGAiglEoExmitkxAv0dbI5TrAeK214yF6snXPKqUuQy7LjwC3VoyeWqENTCQAANjwSURBVKqWJYm95ZYEoPNATYfezOdT+jGY12l977c8OeRhAJbtcAHwXOPR7CGKKTqLYQqG6/O4rPFqXuMeltGVR+ZO5qa+s7lt4+vspCHqFc2TejQDeFMOdBvwPBJVdBxwwyDQ1GWSb4ReMLDbpL4GI3WnJlLq6y0puYDUje6DgizpP+obLdFP3wcgqguERgNboKAAAi+WXUJDgFT5WZQvabquMBGg0WGQtgWibpS5hRonX5dL9nFFyxjZKSZFON/MyRgkrU+BwC0Q0xtcyWbefuY8gyAzCcJNvSw/SD2qv5OWvNVsnw20wf0XVZQO/l2AZRA2Lov/jLgKgCjSmFg+ilpehwAxKerOYgbEf8bChC6MvvhJFtKTrWP+zkotmYVTGcpnaicwjid1ETmEwLO1mT5iBHvj5Xh///C/5Hq/6b59pDfLF7W6cQ9aR534A2U565D03VN+zeRrrX9VR3rKsbXOUUolIzczP+TENzX/qnQA5mqtM5VSNpxisfxGDhwuoVxD9oHDBNU6VesqOFJaflQ008/Hu5KMjo7nvuu0hLHpu5bqQ5WLUq31D4ioPHZ5zwrPy4B/n2D/k637VyVN01LJ6J7QhM3QEw5RCzWxEZiyz97MJ5Frqc0B4BXu1HXd6buXq5Xcq9+lMds5eHg839KaELKJIIN7y6ZzW993qFuQzzu5/2ZC/WFMTX2AGhwhGynGbDRpr7jWlkLeEkm1BSSquRhC2yDRQdPShQgkhbcYdwptUbG44mYmyx9QaCwEOum1eRAdgsRrQERjTQgM8oxZVCj7FxRAYAgiGpHoJkEQVQwkQ0mOREP9G0BJsXHYDTLb5pvnhYi4DpUIbesGkLIbipaZNjI+Eq11xHW404PV4B+Gu19pQZYYJREsr4kB1oF/d+AboCfMpj9Z5uTe7XkPoxLH0YsFeBuHpgwi2JlQjyZGUS6kJ1wMHdWH/KRb8Tr3kKEX0pNEALbRlK/+fo1MZhbsbVyXS8u/Y4rOAgazi0aoJzV8lHbCz5Ll7OU003eDlFIzgQVa6wXH20ApNRXoC1wALFVK5WqtL+EENzX/4uQopd4AugETlFI+nAXf5RbLucahI/K9lV90+pFSf19PjXwN70oyOjpOn1JvL4VSHsFqsVQH7BeZ5U9H7TBPmsyD2mMxvkcwpITp66R5pR5iomNo6jKOxXSTbe5yMf1G2ab7nMUU40fiDzcA4P23g+wMjeamwGlkBYYxiRG03JlCZqNgmp0v9Yp6poKxQDdwlSHpq/UBJxU2F9LSwVXTRENTkGhofdwuva4YoADCG0DabjzCsMBsl4LbHGlvKUTVhMAwRAwj/U1jmkNgPvIXGIiYJ4UBWZCWC6GHRYz6YoyQasr6EpNm7BsGJVngGwepyyA6GHLyIdwPYuJManEAkCxaeuNuaHXQCGEzDwJkno5ID4wAGhj33Y7mPCJx91flCkikJxEmO/KtxAEU4c9khpNMLLvGXIK+XzHh/GE8om6FuFaUL1YkxTcjMf5a5tEHgG/VCr7Fn9t0KKV489R/H2LiIUnPnU1/6njt5xWGUuyodeBy7RS9Rh3/Q2U5K9HgpOiejFNGSrXWQ5E2KMcuP+5Nzb848Uh/7re01r8opaKAl05nR6XU58h/qXKkQv1+rXXy7+n3arGcy2it3aK04DRFaUlZObVrei6bv9qeQ27hkT88l9Lycny8FEp5RKlSCl8vL0psTamlGmFFqaVq+RR6dJsLwKLRveCDEiAT9cGTsn4hDGMyRdQC4PY33iWUbLIJ5W7e4FoSKbvYm0/pR9lz57HzkXqMYTwATQt2sK5RLK9wP3qb+Wf/CSLGEhHBFQLZiZIK6+uIzpom1bYAqdrKRWoqnTTXw9K71LcNRDkR0gxEmKZASaG0kXF1BtcC5BIwCEmPBWJMZJIARJAawUmY1JRGOy7AmNex0hLG39SyAvjmg68fFK2D0JqAt/wxb8yBVjVNSm5NEbhR7SFkK1AmwrPAXGoGOiZISTK/tFSICpNxWW3mnI4M3BJYB/F3JjBy3isAPN3nQbqzmDW0ozXr2VXzEuae34OF9ERnXQZAV9f/0Z/ZDEBqfqczhC4aalBMNjCQDxikGgLTeEIf4cH4aQB0SVjIdpqwd3wjPkoQMXsT8076UbKcfWi8OFLh5oLl96OU+gTp15qotZ7iLNdapwHvnuYw/zL1qCilrgPeRoySnH6vHyqlbkH6vXY+8TAWy7lNzoFidy1nweHTFaX6KKMjpRTl+o+LxpIyfVSPUgdfb1Xp7rsZeYcqZc4Wy5nAilLLn0+T1fxT7+STj8cS3DWTb8tbk7szHLZDt8bz6clCAPbzNxazggvH7Gfv+LoARLGH+5jGMpXEWv0w07mXFXTiSlZBN2i0YS8chqj2KcQFbuAzNQBmwFWDvgLg7qQPxOAnB7fgCu0CFEJekkRBA7tByUpTp5lq2qpEI061AEuMgN2EuPSug9Rccdp1BZmIZSEUfCx1phQjgrUiJsW3aJlp9+KS8aKCgADTPiYXoqNlrtmFEiMMjDX7m5Rifx8oygGCZY6urXJOBabBRaD5C/cNBvKhIMMTKS1JlxpYZ35Rwea5idhS3xzHiShfBn2YB6IbSe7TkicmvYjuo8isH8ysu+9ELdHM7DaQNy68lbt3fsDCQ/8gspYYZefEi6rfm1CXXYh7ckclWfqP6jKe6v0MLBjH/VpuQCxTSQDcNMaK0XOZ02gJc8r0XQsALyNGfo+b1jdfAPO11j+edK8KOILUEASUV+j3erVZ/hHwqlLq/GpUn2upZqTsK3A/P12jI3Hf9YhHLwWVoe9KysrxrVBP6uDj7VXp7rs/HzxCWbmmrFzj7fVrIWyxVCVWlFr+dLQWG9vZ/SGCIlmYBD3i57JIdWXJyt4APNXhISYxAsZLP0uAZGKpxSH4fizTCeMAtflP3nV49dN8vrwPG6a3Z9G9nUhbFYNKE5OjDqxiFR3kOOcBq5CU1XxEnJqfrmggFigE326ICVOQtFYpygL/5AonESNRSp9lIl6jmyMR1Y7gP0fGcKfJZsP6LdA60rMvuUaQOnWhZm6+AIclLTd7GYQGAD5isRnVDDEsAhGKxTI/l49J5Q0xQtpJF74Mae3yA1J9tkRMmoiVIXZugRinvrS+/A4IkHMtKTPpxXmyjBygC6ymHd5LvwagHWuIGrEHtUqjlygK7qoBUXB3xgfcHvEaPRslspjuxJMAwJSPJMybmNCTRux0v5VBxYN5dsQFsEDaxMynl/mcXMwEhjOB4YxmMpZzj9PsU/q7jI6qG1rr1UgOw0ilVFPgOmC2UqoGMB/4Qmu96WRjACil3kT+IyjgGn5Dv1el1CBgEEBkZCQWy7nKD/878P/snXtcVGX+x98PMwMM6GBDwQpCeMHC1MjccLU0L6srbW5ZrWX3sqvdNFu7atr9Zq1lm2VtV6uttHTT1VVLS5PWilUTFVMXEoNiEpDrzPD8/vg+Z8Dklljoz/N5veYFc855znnOcIaZz/l8v59P6PfSqpZljQZ+EgmjlEJDqwleoCml9CD2lBaVVYXcgjd/V8oJCTEHbd82bBwM2KTUxqGBC6ex+NS7JGH2n9Dz+v9wz8DHuQfQq2CRaTztSAHFHM2LPS6kG9sYpDIJ416YAOv+cCoT/vUQKewIGRR9wcmksIN7b3oYgKmOR4RkOYByqCwE9/GEyNeu1yAxgbp+y2iJYykNQEo9g6Cc5dAtRsp0KQVK4MsC6LNR1NbSXGN+1A+Ig4wi6nJOTS6o02ncd+ORMt5qpMurBCiWXtDVBTAgFvpEAxvFCRfEKMlfbY5fYkyQ8mW+X26APgngm28ceq1y3GhjbGT6UtPSzHkmm2NGyxzco8AdAP9ScPVEyoujgcmQ8J8CuqhX616ItGGQ8y1qtUbPUey9zMFNEY/y8threfnca6Vk+WW4ePsLCEuGhXwTGh6nM3iBS2EGzJxxE8vVOrqZ9RN439hTgRoLem4LriMbhxQ0ap/eYBsHB1rrTcAm4CGlVEeEoD4AZDY5UMaOA1BKXQw8BtzzM477PPA8QN++fe0aQBuHLV5YtZ1wRxguh2qx0ZE/qPdxyLV4aE2gFnd4szffGkWgdl9XXwsuR9hBJaUbvq0rlLj+jS/5jSeSt6/53UHbvw0brUWjpFQp9WgLxpdqre8/iPOxcYRgnvnuFDS1oDfqKJ4eYi7HcyGCGk5etZqubKMHvclRF4XG9tR7TaxIdzhzKkyCUwauQql7eZ11bKA3x7f7H38bfjkXV79Ku85B/JvNp8cchO3tAJINScuW34kwhNQp+aTxaUgcy0XgzaXOtbYc0mKpMz9yAnnQJ07IqMthFEmnjKncYI5j3ZQsgJwi4bJ9YmDXdolp2ZkNKf1kHr7VMpdEZJzbCb4cyUEFIFb6SnOz5XS6lRuV1IsQOStGpgshJ93KTw3ZNMqsy4qUiQB2I5zR9L2Sb0p7LSfeauAF6EEOtcVS5TfdO5nX6MhLXMEOUig/OYxEtYt/MZKZmX9hwuiHeLLiDlS+ZgkjOF1vAiCdbNbQH4AJPMkmenDH9CcZOuVDhuq+LJ97BnoswBmo28x8/gTqbdBjmrysbBxiaKFSapfv/kwopaKATtR9hq/SWj/3c/ahtX7NvO7fYvJejUp6pOS92jgCYWV0VtQEiQp34A53/Cyjo3Bn/fJd+b06EGwVKfUHNa4GlFanQxE4SDmlY2Z/xrc/Snyby6EoqwrwG89B2bUNGwcNTSmlFyPmB03hEsAmpTZ+FtQQeG+F/F5AZ9RMeJrb6LhiB73YQD5JfDF2ALz5NF21hyTyybljVGj8xrcBavinnsqrvMJ4nmXgjZ9DHvyHniSwm/8c05OLq19ldsS1dCzYwTJOB+APyR8LwaoCtiOkMpI646FRwGqItzwonUj5axWhMtucAkizsj6tcYMg9x1I7SeLKzcQyjZ1x0q8i8sq5y2BNMvMKAIqSoAdhpA6ZIw3rW49PvnpdNaZMVXmSelvarrMqzQfKAJXGiRmmjkfj2SNlgN5hhhjSoRBvnbGmXNIlTGVeUJ+E/vKOP9KcA1AyoWfgNf+cTHtvVL2dG/e/TyRfDOD7szi4gdfYG9Ee0q2/oaMo7NJuiiXu3iABRcN588XvcI/VCRv6b8CMGSVxPsMHfghyxjGxbzK0Ckfkl2bLvmkE85ArYOLZ7zAxY/Ba1uvkv5dG4cd7PLdgw+l1HjgYeQ/gyWjaOQWVFPj2gFHaa3zzfMzzT6KkFtzR2req40jCMFaTZ6vgkp/EG90OO0inD/D6Kh2H6XUMsv1t5I4BoKNKKUH2X23vDrIcfHtOe437Vm8cTfaNjyycYihKVK6VGs9ranBSqkmPwRt2PgplBjjcs6QRfjfVwRQ6Js0SWyjrKYdS/8g5POcFW/AXC9OqjmaH3j+QYkfjOUH/sJjuMeEU004b2+8DJWn0ZcplPZzJvNZ8P2f+eMx/+Srqb9j4vhniU8q5A/ffywHrgJ6In2dbxqyaMWeRADvIN1WJUBnyfjcVQB9UglFusQXIWQvASG4PiASUvsas6QY8JWDtwBKy6EScfS1COWudZB4HqGe1dQ4RJHMRYhsjImBsZTVgJQZezyEYmncVgtMJPh3QGEVpEabfVSDLw9Kt8ux04yCSr6ZsyHOu16DxDjkv0CEnJ+7ABIHISQ0waivlrOwA+7Y+iS6Rj6Jx1/7IrM+vRL+ABfzKh3YA2dA2LatgKZQx3OdSqGq/DL+QQ2r+IoUdjB54DTWhVyjWoiTQHf/eUNstD3s8t1fBLcCPX+OwZFBNPCOUioaeYf7gDO11lopdSTmvdo4AlGwp5Kismp+44kkISYSpVpevhv4qfuutby2dSW2/trGekrDDpr7rtaavdUBhnWK4cSkDiz4bwHVB9nZ14aN1qJRUqq1vrS5wS3ZxoaN/XA6XDllFtfyjJgW8RijcEA4nLviXYaoSbyn3EA8Y/UWAD7mdP4WvI7pjnsYxxzuSH2SYK4TFVXLxZkvwEzQSS74LyScuZ3d6lXm6ZGMXrGYC6vfQ9XIHUH9uvnHvxIYAoF3wHU8dQ6z5yGWIelANcR3AW+xkNdKU8zmjQFGw87nTHyMFyF7EVC8DryDIPFTpOy2HHmX9YTKlTI+cRRiQJSE1N4GTQ+qByqLwd0TUXFTgR1Q6hNHYPIIqbU+H3g7y3Of1SZi9Y8GwJsgqqc30mSTYuYIsMHMI0229+8AVzlCkqMJGT/5i4xKmmvmcjzc2v1+fqAdAOM/fYY3Si9j/cBeLGcYjyydCiurOTVhJ5/cMhxH4V6YFc3DUZUwzcVrXMw36l266nP5ZsYJADw18Rr20IFlnEGPsK84XXfjPSX3whY9Pk4m2g+0r8VXl41DCHb57i+C7w6AkKK1LiR0S2q/dUdi3quNIxA1wVrCHWEcGysu7+0jnRTsqWrx2Pruu1auaGtLbAONuu+qVhNeCzXBWgK1mhM7xdD32KMA2FvdMoMnGzZ+LbTI6KiB/hXLaMGGjZ8FPQWUmgZTjm5w/V08wO80TGE6I0d/RCF7WL70DEqGitoyJXgfMbvKYXmAAjoS3qGMOApR2zR8B50e3EbB5i4k6Vw2UcaeIUcxseYJVoRLDyNnAguRstVsyfMsXQieTIR4LUKIWTbghPV50NvUA7jM51apDzxLRWZI6YwQy3WwKw9inVA4D+LTgXQofBniewkhdZuSXP9KKCsHZ7n0npaWSyxN6WoIBCSP1OmE3JWionoyTBltPbPL9pYRUzrE94X4AJRuBk9HWJ8NvftCYrQp801GCvQSRPm1PoYS+yFOv1bfaC+zXam8Fq7yunFskJ8TeIofiAWgMztJ9mxnAk/ySO1kwvuWcoo3i2KOZt5TI6lNiObYgs30Yj1MXU/1lCjalY8nnHzSJn7Fh5zBRGYA8DkvASexiR5gnHnvCZvOLd7ZLbiqbByqEKU0vLnN7PLdn4d/G8+Ht5DaD8D+TLZhoyWwnHItg5+J/8jex4m3Kfj3c9+Vn62NbWnMfdfpCGt1abCFiuogACckxhDviQwd14aNQwnNklKl1E2Iq9/P6l+xYaMxaD2Vs3kLgBfzroEqJxw3DS6YSse5knniIMDSeafxGpegoxXPOS4DoAN7ODX5I5LJ51nGM8H7JCns5PmZl3C16sq5D0Zw7vGv8xi30Z81HDu9kH9OGcaHxlhpcP5n0iuaCaVLwTPEqIjlCPk6G3EKmgM7t0PvZKR0tQGlrk8qokxWQ2kRxMWYvlEfkC8KZHw6UCr9nJU5Ms7dD7w7oNREr8SnA3lQViX78BcIOU1NI2SW5IqD3A0mxxRwOaGwGOJzgM7gywZvBhArhLQyR3pZV5fDsFTknEsk3ib0rrdCH6JlvsSYZV7zMwZIhtx50Cka3OkSyXPGu8sBOOXcLGIp5jQ+oSIsijJvOx7/7z1MOvE+Rr+4mO8L2nOMWsgZ5YvpqaexUUne6Nk6yINXTacLBbATjv33ZgDW8VuOUSuBM/mn/ovM8bIWXVI2DlGIUmqbvB9kWKW159VbZn8m27DRAvTu1IE9FTWh555IV4vKd4O1mlrNPuQxVL7bSodcKd/dXykNP4iRMH6juMZ7IvFEyv/kg52BasNGa9GSbwu3AMdprQua29CGjZZiPuej8kSz69r9a9Dn8s1Y2D22M7y5muGDP+H6FTP4iMHMGnAlAVMCeMFYITb6VsX4k5+gP2tIJ5tC4jhdjyUbeIGryCKDY7d/x5dTetBHTZSDfjiOx036Qc50SEtHynatd8EChGR2AX8xpBgnXnyIaZAp3/UkID2gG0x+aSS4I8DVDyFznSVj1BMNrmrAIW6+Hss5d4Mc15MK/jygH5S+LKsqq4WQer1IGW06+JaC9zxINcoogH+dIbPJQI4p5bV6XYNCIMmFYV0QR929puTXa+aOzGvnakjphRDQXISQliIkPQAUQ+oos3w7nBb8hAnnPgTATcwknyR+d+dXPPrgTQxjGf8+8VSeSL6bhzfewzFqPjx8OpEvayiDN/WDXDBvPrNrdxE7exfFkxOZ/O9pPHLTVB6deRPHqEmIjD2MP6ppwCmcoleZyQ7kYSYA2JmlhxlaUL5r42dAa925redgw8bhioqawD5OuR63i73VAWprNWFNZI1a5PCnOaWyrnXkzh+oJbwhpTQs7KCpmUFDQD2RTpyOMBzq4JUG27BxsNASUvqtTUhtHEyo6XDKlFWckgxvcT4DrODMxHobfbSaZzdNhD1ww8Y5Yr0BGBNddGeYxlSOGS1lN/6XFR/v/AMv9x7DRwzm6hWvcuuQ+wmnBp++Ce8isUK3+jjTUhGTohLAA7uWQGIyUtb7lYlLqUJyQ1OpyxgFIWwmJsU9CFEyTU+pb7nkhA5LlZ5MTE+pO1LKdEFySf3F0svqigYWged4YLMYFnWKRkppAzLe2xMhljH7zsO/Q9x2iQXyjCvwCKQfNWhyTGPNecZKf2tluSioIIQ6JdWc8w55HYiVsZQDg5Ay5o2ImjoF5jrG8uTrdwDQ96JPeYzbOOvBt7iYV/nNihL+OuRquuetx6Uq4A03z4wdxw1qDmfpt7ih9hmoguK3EkNuuo/cNpVTZq7iXc4FBpozk+tBaxfKK8syfKs4GxuHG2oJo6b58l0bLYBSyqO1Lm3tNjZsHMmoqAmGylcBYtwutIayqgAxUa5Gx1mqYng9Ujr5D8cz7tV1IcJ3oAjU7uvqa8HpUFQFgq3ad+gYhty2i5Cv/Q6HavW8bdg42GgJKZ2qlJqDdNvV719Z9IvNysb/b0ydxoZbbgLgSc8EOrCHl7gCHoMCOsJjcI7KgO8G0H3IerZsP5GTrpZssexL+nHxqy+gsuCYfvMZqj9k+ZQz6Of5FNbBpWH/IK9nHP8cMox3OZeZ3Mjzd95M2oOGBZ2BFLnlIiTMCWw0pj/JCAmNN/NMh8rl4K5G4lFMTqkrRtZhZZcWATtgZ7GojsOqYGcupAwANgsZ9XSGQK7ZbwCKSiBxJXWqpRM854NnB5BTp7S60xGCmQeuCLCMTF2dEUVzncTBeDJMduo6IBO+fE226zNI9mdFymwrgTRjfOvLgcRYUV1dQ4FC6hTSJIQAFyCkNQa4E9pfWcbtF90LQCaL+IjBHMcWfss6zh4yn8v5O934BjTsJAWAz/RJLORMCsISKL5wDn/WKbz/B6GYNbEePn98oDl4IZ10GRDPubzLBD6hk+9c6UkFZjGefLq14AKzcSghYBsdHSx8DPQ5CNvYsHHEorJm30xRq5S1tMrfJCm1XHDrl+86zO/+1rrvBjWRrv2V0nBH2EEr3w3WapxhKqTupsa14zf1yLkNG4cCWkJKzzSP7oiGAtK/clBIqVKqO/AK8vW8GLhEa537k20cwEzgD+bYD2ut57RmnY22RWXMTBbrj1lEJsNYRj++QqmXATGVjKm+hpKIb9mKl3f0mWRPNKaRZ8Gr717NpHPvY7c+l4t4A6Lhi00DWHlFBlO4kw7soYZwJvAkvTdv5fmHwrnmwWwZPxT808E1yJgH9QO80sfpciLcKBVoh/SKloMbINqokkDuWkitQmJlTLmuPxtSTMYpcSZztBT8ATEzotq49gJcBImrgRIozJbjVuaBcx3EDwBiID4Ndq6ElN2IGdG1QL0rd3U2DEgH8s3+SxAyGQOshT5dJBaGIpkPPikLTktFiCvSv8p2cKXLT3xmHyZfFZDEwnnIO3MojCl9jwSPrJzOFE5mHW9zPvEUspMUZjGe+7mLp5jAIwp26ycZwkeM5Q3OZAGX62I+IoUbvTMBGKw/BmAiM3iI25nNtftcJ5N4DIDHua2xS8nGIYwW9pTaRkctw3FKqc+bWK+ou81lw4aNBlBREyTKVUdKY9xCREsq/SQ1Mc4invXLdy3H3Fa779bua6BkwelQB618t1/XWL7K2xN6HuN2saeFUTg2bPxaaAkpPRtI0VpX/kJzeA6YpbV+XSl1ETAbGPKTbS4EuiF0IRb4Sim1TGu9sxXrbLQRztQ9zG8fk8kiRo79iKetlR9O5anMa4Cp3LJFnFeLiOOZGePoxQYGZWbx4aKh7CaBDfSigij+PPkVuvENWziO+7Ie4NiMzbzDeVQTTq/jP4fVv6XS9COy2pDLannqXyuqo2uA6d0cipC2vsAGQxK7AGsJmR2lWqEKBchVVW0IbR7gMI63XUSJdEeAOxV5p3llWOkMkyuaCfHlQAmsLzIuv5vNvmMh5TxgoxgieXJk36XiA8WAQUCMOO12coLbZJD6C8DVV+bmD8D6tWa/qeDujBDOjbIPV4J5Xi378u8wDr8Os7wLorJ2lvUMhbs993J/6b0AfFQ8kh87u9lNAtOYSurYfHjzAeasK2HsyXOZE1xBx4FT+WzVSfxu1Fdwlhw37Yqv2EBvABZxRqjncA7jSDKNu0/NvB22wVMzrwHE+MrG4QeNsst3Dx4yW7BNTfOb2LBx5KKiJkDUT3pKAUqbIWhW3+iLn+7gon7HAnWqaWuNjr4pKmfXj/t/xXYeRKW0tNIfIuAgpDS3aO9B2bcNGwcLLSGl24Ff5HaKUioOKTX6vVn0JvCMUuoYrfX39TYdA7ygta4FvldKvY84Dz7WinU22ggLOI95ZDJSnS4LLpAfWl8GwDBG8QJXMar7QjpTgFohEqEeAi8uupA/vr0MToJTumex5oshLDh5OH9KXcKxuZupTVVUVIcRvaMWVaI5K+MtVvUfxJMWKU1Fyna/EKdcXzZ486EyV8yE2AgkQeWnsrk7Hsk0raKuTmC7MQ1KNsudQAz4NoqRkDsZKABvHBCJqJWRhEpvd1ZB79Gwax4kJsDOIuidhhBBEHOjBcJhK/NMDmoO5BRBitkmZ6Ws7x0r8wVRXePTEBfhbHHajQcYgJBqQ8Qx5bvkmrmXyNxcCUj5rsOcawFUbhCHYI85l3ZXllHoETHmbs/9/H3zdaQcv5Nuq76Ff0Ns8DJU9l3wpTnG6wHJor0dGDCNl/Um2lPGcoYBsIkeOMwLG8TJHMYD8GIlkAi3qFt4T9/600vIxmECjWpJ+a6NFkBrvbKt52DDxuGOSn8Qd3jdV19PpCGlVU1/zbWIZ/0iWyuztLUuthodKqutj/CDGAlTWhXA46477xh3y1yHbdj4NdESUpoLrDCErn5P6bMH4fhJwC6tddDsM6iUsoIq6pPSZKy6TkEeoa/iB7zORhvhYSbQvf6Cu4ETpjF4bgYfqyyG6r50WWDqRzvUbabWw2W9T+P6MZJtecurs4m4pIaubGNlbgajaj7gT95/kMJOnu5/G6f6/s38hy5gwR3Dmb70QdlJCUK8vHX79QfA3RdYiqiNOwwR81JHFE8iVPZKDHhjoTAH4i0VNNqU5yYjpkHeum0pR8pf42RR7zTZV2KCbJ8SadRVi7jmgDcTWGtMiaqAVEgL1s0nLY5Qdqh/h/S5xqci5beLEOKZB1/mQJ9cGZebC6kJCMkGcBgzpDTk1pNl9OST45EjrwOY5V7IJ4kN9AKkp/S742NoTxnqX5pO32/jW/UGf9YpvL3qMi4c+CJzB17BnFXjGN5/AUt33c5rLMNBkKXKVBmeNgCA4asWAPBXruFm9s8mncFERh+cjgEbvyJEKY1o62nYsGHDBv5gLf6gJrqeUmr1kZZWNl2NYymW9Q16HVb5bit7SrXel+xacIapVquwFkor/cS1bxd6HhMlpFTrhgmxDRttgZaQ0gjgGzDfRAX/7yy7lFJXA1cDJCcnt/Fs/n/jdp5EFVTDLjgrYT7wFu+bdafrDJYXDIZPgBfhRt9jzLzqL6gb5ZJ7OUt6Do/N2Mydl0whnyTCqeFdzmHP8o7cPvJefuBownLKWVPYn6Q7clnFQF4Z/mcALl3xD3GUBSGXPeUnJUjR+GrpsXQVICRtLUIE8xCiBhSuFQIY/wiwAiF0EUiZaxoUroNAcT0332z26fTyF0BZOXhTwZ8jGaTuaCGInxfDKeXgKhFlNC0O6AKFi+qRTqByrZBY/w4ZX1kA7l6ibAK4vwLaQZ8BCElOgFTrsrYCJeLBlYcQ5lRE0c2TcayVc/FEyOvjWw3ei+FjBvMxgwG4kwf4ze4SxnR8m9Me/ASAAUVfwGbYNrATw1lC9aoIZt7yF9556kzGJrxBPOIWtXT2RwB8cPUIALZwHGvoz8cM5hF28N7k8ZxENkyGzvtYH9s4nCA9pbZSasOGjbZHRY3cZW3I6Kg51dBSLOsTOGfYwYmE0Roa4oUuZxj+g+SQW9JA+W5NoJYqf+0+r4cNG22JZkmp1vryX/D4+UCiUsphVFIHUnyY/5Pt8oBjgf+Y5/UV0ANdtw+01s8DzwP07dv3/x3pPlTxvjof/gr0hQLjssqeCLgcnn9MMuLVBA0dpPa0Y0IBu9Wr/O+BqWy6swdltKc7W1isNqDYSpqWHona3xRwvV7IBJ7kYSaHyBDGbMifI8Y/Tie4eprJ5IO/HMpWG7K6AXCCr0gI5K61slliXyGenumGGAZM9IoXfG9CfBx8WQSJMZC7yJDBYtiVY8YPBW8ASrPAMwBKV9dlmA7oB751orqmJUBhAQSKoH0kQhyzZDt3LBAHLoA82FkOaUXi1lu4GrLK4fQYOScCSOluuYmlMS+FJ0mW+0tMnmoSYt60FuhntosH1oE3Q9a/wFXsMfL1qKyljM94gqEsYzbXMovxzDrmSsKPqSGBAi5TPWhXPgL1oWb7Uwl0K8yl9jePw0lTAdBfAixBDYFbV9zP+4+cT8fJ0jT7MpfzMjCWuSEObePwRAtIqe2+a8OGjV8clYaURtUr340OdxKmmi/ftZTS+uTRMidqrRlRrdaENcBKXWHq4PWUVvlD/bOwr8GTTUptHCpolJQqpYZorVc0Nbgl2zQFrXWRUiob6Sp83fz86if9pADvAFcppeYhmtNZ1IUaHug6G22JnhFg+ja5GWA1WxVcqbPZakxwYvmB+5gieZk/PAzAbvdUcE+l6hbFbTwKwNEUA4O4Wi9iBEuoIYJ1+mSSyKdb/reMS5rDb/9r3H3yEDfbztJTauWNkgZsFILafiNQasyHEuq55hr4c40B0mYgHVzF0pPqjgZvOuSsE/dbgFRDHomDRKv8NwcIGFIYYQhpErhM/unOAHgTgFzTI1oMtIOdiyDFlABj9X8CpaUmDsaB9K5iIl8tt/cAocJ7T0/qeksL5YfLyirNJ2R6RHTdGF8JeHfL+nBqQj2gGRkr+XzFQOakj2OC90mqiWD89y+y/pjujOcZ+PD3PBw1jmW5w+iyvoDPep/E43oSw5D7XBM4jqdUNQwWknrr5Pt5Yuzd8EdYSGfeHFuXTJrENgA7EuYwQy1hVDdfvmu77/4MKKX+BTwDfKi1tm+g2rDRQlTUSKlRfaOjsDBFmFL84z/53Dr8uEbH+hvoKQ0ZHbW2fJeGldIlX3/XrAFTS1AdCFLlrw2pwgAd3GJAV1Lp5zcxdjSMjUMD+3tQ12GGUsqtlIpq7AE8cRDmcC1wo1JqK3CjeY5SapFSyrJkeQ0pksxFdJzpWuvtrVxno42xvUcCN+rHuFE/BisHsFjfzbm8y0M9JsAJ0zhn7CLGMYeu338Nhoy2++EHbql4mB1RxxLEwdPP3kYCBXDB6czecQvnpC7i/FnvM5iP2Ulnfkxys4gz6JC2mw5puyFZCGSIsOUjxNSEEOWuNssD0LuXqKRZxfI8MVYeAKVfQGWVqJzESOls1gZgB6SlIyWyxcDxCHksQsyDglBZIkSSIJAFhcVmPsaht0+axM5gomR8psczPtpsF4lc1QmSX+rxGgW0ROYSnwqpXcz4cqQ+oFz2QTV1+aNdZF3hOqhciRDRCLN9lnleYlTSONnHFbzEpbWvcGntK4zhbSYPmcYE75PcUz2dTfSAYujAHo5jK1Wni8lNPknooxRJ5FNGe+ZzNj8QKzcddBnDVywgiwy+oRv8SeY9dOyHnM98qgnnFcaQTzebkB6mCOJo8mHjZ+N54BbgG6XUZKWsBm0bNmw0hYbKdwEcYapZs6KaQOPlu61VSl0OxZ/SE/dbrpQ6KL1yVr9sQ0rpngrbsNvGoYOmynd7A3tpuP9am+XftXYCWuvNQEYDyzPr/R4Ermtk/AGts9G20D74K2fy9Nu3ce+Y2wG4gWdIYSfhVDNSw+1kMOjtLCquVBAG7r2ad7iCs0sXcrxnI/cwnbLrX6DPgk0snjsYpe7lU92XHXRkFtez6K5z2PZAJ76hK3tKOsqBi8EdgxC2HQhZ64koj1kS91KaDa4IICARMRnbYVeu6RFF1NScRdB7EHy7EjwlQBdIdQpRdZca5TRZSmnjU2HXOin7BXCXgNtpjp1kVNdsU0ZrRKXUWCgsgvhI8HaG0pVGWTXqKO2AHOOYWw2e4cA6KF0kMTSuZFlOX5PHOhQql5h4mnSzjwBQIkqtVfYbnymvS2kBeAaa18giw9HwEleQEiYltrO5lke+nIo+Rv5FdEj6kWuOf4ozWMScVTfw14FXc/N9z3PLlATG6yfYRjeWLP4Tj428keqfxIREUcn7BWeTPmYtsRQD0IOvmHxAV5eNQwV2T+nBh9Z6HjBPKXUcMB74Wim1FPir1vqLtp2dDRuHLipC5bv7/k9ytoSUNmB05HQcHKOjmkAt4Q3klCol/aathVWa/NOeUmi+lxZgzOzPAHj7mt+1fjI2bDSBRkmp1ropFdWGjVZhGrfTAXhqzDVsoDdXDpzFi+oHvgGYIOWci78bDcB3e4VQXsWHfFixEFdPzfC8BXxBX+7kAa4ZNZsljKCjvoQo3mYmN7Ho3XMY98AzAMyvOJtzj4kC4N3Ui0QxLEHUwp6I22wu7CqAxDhT4poMfGpccZMhsUu9yVdD7wHyMzXWRMGUQ2VAYmKytkOfGEIRMJRLH2koarMIIXrRss5v3HBdRo2ks8wnfgDyDq0Gj0VirXesBygFXy60j5be0lKfyT9NkIxUb4zkmlZWSWuoO9Wca6nZR5WM2VUF3TZCfBfEYbgXeCx1dxAwh5D7bgIF7DRdnnvowH19JtGfFawqHUIH9rCHDjzGJP70+BIYNI1P9Osw5UJ6MI4ebGLCyIcYyxum5FpwT/LjnJK3iv8m9ObEsVu4cu4sAEawhCLi+TuXc2mzV5SNQxEa1ZLyXRutQw1SbP+qUupfWtsZSjZsNISGyncBwp1hVPmbJpY1AYuU1rFS10EwOgoEa6nVMoefQhlNKFircYQ1pA+1DFYJsBV/Az+PlNqw8WuhJe67NmwcdExFekTVXGCSLLPuCP6VawBYyCiWrzmDLmsKeLn/GC7mVe6Kug+egmt4jtFZi1FXadyf/khlTDt262O4lRlMYyodRu3mLh7k9sKHuC3+cc6y/H3rOc9STZ2lVoKJaKmC0s3giQH6gTsP8BkVM8FsWwCUGBKbYPJKY4wS6oOMONkfpdITWpkH7mzpzQQxTSJfSm4LqyDV6hutFmMjtw88ZwOmlLgwzxBGU94LQAB2bYe4GKOKboSyKkjtK9sVb5fjBNZB/GgkBsZSSdfWnbMnzpxrkZwDSUjfbYIsK5wO8YOAcvjbHLhuQBFPXiaZr2N4myLiqCSKuz338sidU/nswZPIJp1lbw0nMfJbPq6FG/VjdJXbDdzw5Ryi+lTw4NLpADw1/BqeypMYmNsZC29O48U34VFdzG3MBOC2Zq8mG4cqbKX04EMpNRq4AfkvNgvoobXeq5RyAtsAm5TasNEAGjI6AohwhjUbj9KQ0ZGllAZb4ZBrKbANkVKLh/qDtTjCDvz/aGmVVb5bL6c0yialNg492GqojTbD/RYbfRxOLfg3/2Iw/zJxIxbG9n8JBrwRej6QT8AJ1/Ec6o+aM9e/w3pPb+BBruYF3lh1Jd0e/ZYp4fdx27tPsyp+ENcwm+v4G9fxN1H9TE+mLxchaRGIcuqTh2cQQjydsDMbcbm11NUSIFp6TRN7IcQ2IONy1oG/GCrLCZXmUg6ry2FnsZThejvD+hzZx7YqSDFqJl2AKogfAZ5M2PUmQhKdMi0C1GWmAuRD4gBxEKYnsBcS06AyR2JhUhNke29PoFgcgnEAOdLPWlpq5hhESnSLzf5z5JhkyGsQfxn4s6Xv9LpMYASMYgGjWEB7yiijPa9yCavpT8y937GF7ixjGBHFULwskTvDHuQaZvMxpzOHcXDy38ggiw+Gj+DT4X1ZwgiWMIK+rKaCKG7UUTyqRUVNYhtJbOMmHuMmHvu5l5eNQwCSUxre5MPGz8aVwCNa6xO01s9qLZbjWusA4stgw4aNBtBY+e6407pQq5smaJZSWp+0OkJK6YGX71r7bbh8V/Zf00oHXuu86pfvto9wopRNSm0cWrCVUhttivvGTmIPHcgigwe4k0+9v+ch3wTOZCG3qEuADtyn53PZmrdlwIBpwDSKgNe1hHJ22/YtT+kCXmMYPwxsxyUDX2EmN/PduTGEU814nuEHjpbxmcBUIE76RS1zIGLAvxZcaQgxiwZWQsoIM9G9hEihP9e44xYAx8Ou1ZCYDmldgI7yPCUaYZNVEvDrjQG/iWJJiQTKoU8cEASX0+w7CGyQWBq3pYpWSaUuTvk91FPqBf9GEw2zUoiwOx7cCVCYC5XV0L4cXDEyT1c/QuTZc7LZRxGERCzTo5pbBKmRiPFTGpAl/ajxhcAgUKs0u8d0AOBZxrOEEYxjDgAzw29mPmfzUvAKyIWY07/jtsJHWRPfn/euupDrX5jBmTqJv3M576stADyqPwbgbcawhv44CJJFBlkMZKZx3LVx+EIjZlc2Dire0lovqb9AKXWR1vp1O1LHho3GUeFv2Oio01FuAL79sZIOUQ3fKLPI47MX9gktc4Xcd1uvlLoaKt+1lNJA60hpQ+W7YWEKT6SrWVJaW6tZ/20Jce3tNgwbvzyaJaVKqUitddWvMRkbRxaiqKQA6Re9ltlsoge3+R4H4Lel/wFmcp8uA0Cnyn/n1fpkTlVnwsNTuegReGLyeDK6rWQES1iXdSpEwgMn3s3tPMx4ZvEjHZjJzaSUfisHTTCPYoSQWn2a0aY8twAhfzFIqW+B2TYS6avEZHpGyBj/RkhMBYrEVdcdqBfbUiXOuVb5qxXF4iqWXk5PZ+pIsWW4tFFIqtfqL42RPlciEJJqyo1LC8DjgZw8eZ7WxWwTAfG9zDkYEl2ZC05TsuvKBP8i83tPsz8nouoWQGovhLwmA/PMT1PCjA9e/ssYdmPVMUPWfwcBUJ0Km6LSuKviQb6KSud3Q8/iej2XG+NnsogzeO+bC3l200SJ0QHu0XcB8Bc1kdigfFD+O+z3XMtzAIxgAZP4kFtUAk8DD+k92Dj8IOW7zX7M2DmlPw8TEGf5+piIxKrZsGGjEVSGekr3/Z+U2EE8J779sZKeiTH7jYN6Zbb1FE1nmJVT2nqlNKIBpbSufLd1bkeW0VF9910Q5bQ5UvrN93up9AcpLrddem388mhJ+e5OpdQTSik7j8HGQUECO1BeuNU7iydeupsnRt/NReoPzKq5nktqXuE5ruF0z0doPZV71jxOEvm8csyfUU9rTlUfwuVTuXHyY2yc3I1bVRyfq//yeOkkemV8zq4TYymjPR3Yw+CXPyOfJK7iBZxTwDkFWIEQtr4IaetHiPwBog6mIRmecYTKeCkx46pkub8I/AVGWTUlvO40yDUkkS6yz/bRUJoFX65DVEmH7M8TjZDddNiZY5Yb8yNfCUKCfXJ8vyGzldlQWiQPj0eOkZYuGaW+PDPXWHM+VuRNKTidMk9XHOLYG2FcfosQ4h0BleuQnlMrFibXnMMO6tx6HdCZnaSYx30vPYA6popAZ3gg6k6SyGd+1Fk8ziTgRsYyl7S8beyhA3w0jdd7nIt72I+MHf0S96lw7lPhwCu4wypxh1VyG4/RnjI+V+Lwt5BRDNV9Q4RUbZKHjcMHLSzfLdFaX20T0qahlOqrlBoPHK2Uur7e4w6w66Bt2GgOoUgY175KaaJRSnftqWx0bKjM1lmflB4EpbSB/Vq4YXDqPtscKEorA4Q7w4j8yXl3iHKxp6JpUvrF/34EYG91gHP+trrJbW3YaC1aQkp7Az8Cy5VSi5VSf1SNdYLbsNEC7J7bWaLfLcOdj+F6XUpJxGxKImYzjjks3jqaSdwPKdVcVvAqA1hD7vQk/qsHQicYy1ziKYS1UzlHe6mM0TzA3dI3CtzPXajL76WM9kzicb56Ko2vnkoTtTGIqH/lQDZCxnIQkmZllsZC6Qpk+74IUYs1j2gpiw0ROyfsKpKyXjAkUiqLcUWAJwP6JCC2JPGGuEYgiq0PUrpIyS8b5XherxzDcustK5e+U3eMxL24I4ChZp7GMKkyYOaWIyrqrg1mbmY8Bea8nVLqW1kOuWa+RBhn3hKEHAfNvq15LDJzyYf0YDbVhFNNOH+74nK2J3RmlOc9ionlfN5ijO99OrCHD/QFLGEEOcnduG/KA/xDf8FF896lctlRzH32Ck7V/UOPfLpxDc+xXK1juVrHUL2UpXmZLM8bwfIhZ9CdLXRnC7oH6B4HcsXZaCtY5btNPWy0GInU3U77bb3Hb4DL2m5aNmwcHqisCRLhDNvPyfaoKBdR4Q52/dg4KfU3YEgUFqYIU63LKW3K6Mgq6T0YPaX1S3ct7PyhnHX/8zU5dp0hpVCXd2rDxi+FZkmp1rpIa30/opu8ADwLbFdK3aqUimx6tA0b+0LdVPf7o91vguOBW+DZ6RPReup+ZZq3JjxG14S63sITo7bA43A39/Mhmeh3FFFU8Lq+mgWMYlXNaZTRno7P7uE+XcZxbOWMhct5mMk8zGRREAsRdbMcIV4FZuepZlmsrPd4qCNocVC6Wh74EJJmzIR2bjclvEAlUGSVBZcY06ONQIxkjZaulOxRopHS2XJx0XU7wVcAFAnBxYOosgmidHoGiTNvZbU8MDEyvhIhqYkjEELpFRdeMMdwmP5Xy1nXJwTUnQqpyeY84iXWhmqkt9RSS6sR1TjTjI0Bz9IafpNVwm+ySoilmBNKv2Ygn9CfNSzPOoOw2Ht5MXM8HSlg+uYHuZRX6Tn9P5y3eSFDR3/I6aP/BUi0jPVQE+GerY/DlqkM1X33u2bOGbKI0Sxq6HKycRggiLPJh42WQWv9gdb6cuACrfXl9R43a63XtfX8bNg41FFeE9jP5AjEUCixg5tdeyoaHWupla6flNk6HWH4W5FT2pTRkbWs1UpplX8f510LTkdYk87BY2Z/xj/XF4QMkkoPUVOkMbM/C2Wp2ji80aJvBEqpKOBi4HrEcn4OMBjRUIb8YrOz8f8Ta4FE+IsaALwNX4wBQA0EPukAZDBSz+MJdRGMTIGHIfV9aaYM/7aUCd4n2UQPLjvmbS6b/TYX8wJltGdWxQ28uGw8N4/6K6TDPRMf5/UZ50I2nHvme3LsdcYUyIeQyjzqyGgAIYJe8OeZnkuQEtY48KSb5xaRLRAX25RY2YfLB72NeRHRUuLrHgRkidOvN9mMr0LIcRL4d0D7SGM+ZPotE1OlVNdt3Hc9A2D9EjFI8gyQbfzrTO+pyUNdvwR6p4rJUftI8KQSyjK1TJR2roSUoeZ8QHpYs2S8d6g5pySE3MYg5cXWjdFSee1eePpiMsgC4Lz5C/nk7DlcH3yWLmo792VM4m09hrs4m9/X/Jsdx3fhDS6kq+9bVNq9HKu70psNnHP9G/zDI8mji0sHc/mMlxn59kcALF93BgCdxsqNiB4rrHrdUY1dTTYOYdQSZjvsHiQopQZorVcDTqVU5k/Xa63tOzc2bDSBiprgfv2kFhKPcjddvhusRam6kl0LrjDVKqXU34TRUbiz9e6+AJ/mft9gibGzibmPmf0ZGwtKqPLXcsuw7vzt428oqTo0SanWmpzvyhgz+zPevuZ3bT0dG61AS4yOngbOARYAY7XWX5tVc5VSm3/Jydn4/4eHZkrG5e08ibq9EwDamNmpM4CjpwKweC7wBtAJYnvuYkTvOrPJRxScouPRnyj+ffyptGcv/W7PZtXDpzB51DTW04vL+v+diP41rKE/sfcU8+c1C+RYSQp3MkJESxBrXFO26w+AaxCQC65Ys84QON9GQwBBbsOkAsXgiQRioHIl+MqNYpkn61xxSHlwUl28KIjiGW9+LyoRI6PSL8CTgPSxVoE7GkiA0mzwVUGaKRfOXS7jooBEU0pMFfROAPpCvNWPWkWdQdMQxEk4XZZVFso+3AHqDJ1ML+z6OdA7zcwjDnxLwRtJKOO0AjcnPivOuTpOkUEWUeU1POGZyBWFLzEifgkXPDgfOsHRI77nhfirSf3nG/AK/E89zf8Gm8QK8wJcxQt8u6YbHCPP9RBQj0jsDMA2pJX9r0iWqY3DD3aJ7kHDZUh6cUPRvRrscgIbNppCZU2wQaUUILGDm//m72l0bE2wlnBH2H45ps2pjc2hukmlVObaWlIaqNUhU6b6cISpJudea9b1PfYoYtxO8nx+CkuriPccOkWSu0sq+TJvD0oJObVxeKMlSmkeEs69p4F1gxtYZsNGo7idJ/dbtpaTmMtYhk7uwfIsUcr4Zt9tCg2LSecrTtGDeYzbeOn4sRTQkbt8T6D/AoPUI/h0JskVebwQdTUPcieb6EE4NXzfv73syJS9kmuMihKQUtUYQyJLkLJVD0Ioi2VdcQF4TYku2QjZCyIKa7mQyMRoKc+trJKW0gHl4I5EFEiH2TcQP5RQ7ExisjEuSoLKAnBXQW4BpJqe0UAA4o35UXwvSG1n5hBXNzeqzb5XI0Q6GlgLO4sgMQZcq2FXruSa4jPqLdIz644w510NJEHvEuqMjbLAO8DMf7sco/KeKEZePw8A7YMebGKPpx2jWMiU+PvowI90u/MbtnAcSxeN4kre4J+XDOM2HmPPJX9kt5oG3Cn7v9zFt1d048uXenAVLwCgtooU/OwjExk+eQHPcS0LOHO/a8bG4QFRSu0ogYMBrfVV5qf9uWvDxgGgoilSepSbHyv8nPu3Nbx7Xf/91tcEahskjs4wdXByShvqKTWRM60t3w0GNZHO/a1gwpTczQrW6v36bAO1tdQENQromRgTUpi3Fe09ZEjpmNmfsa1ob0gF3v5DOec9t4YwpWzF9DBFs6RUa91oar3WevfBnY6N/+8YxocALOMM4BQAflcgjkc6IQIyZLsdGRI70mVsAfcMnM4t6h4AMvUijmMLBSSw0JCVzt4cZjCRxXoa3oQK9Cr553p9t2c54bpvuP9vt4YUt6M7ZotJ0HJwjUKMgTZKWax/nSGpHcGXJcppfDqQDK6ceieRDGQbZdUQ2cpy6dP0OMETB/GlQBzkrIW0NIQoWhmjRQgJdCIlxEVSdhufLH2lqXGwczmkpJlomFRDbnMIvWML10J8XyDPHNsotv51oqjuKhIy63ICEUZVzSVUWgzgyUTYcwkQgMKVJr4m1xzH+uw+D+mLLYLb//sUp5/4MQBPeq9nes09nB3+Pn///jqyj0nnJa7gE05jBEtIydzBnMJx/HHmMuJuyuMtzmfIaWvgXtnto0OkwdhNBV+o9QC8px+AyRIXBNBFXQPAUN2XhXxorhsbhxOCtlJ6UKGUGgh8qbXeq5S6EjE7ekRrvaOZcbFIlExX5D/QNuAarfX3SqnuwCtIR30xcInWOrfRndmwcRiisia4X0aphcQO4sBbHQg2uL4mUNsgcXQ6Wle+G4qEabB89+AYHQUaIJ0AYaqO9NZ/Xar8Qb4uKCVYq+l8dDSRLkfIuXfHD+UM6HZ0q+ZzsFBWFaC4vIaEmEh+KK/hh701VPqDpMa1b+up2ThAtMR914aNgw6lpu23LJN5KC/0ZTW/rf0Pv639D7Gv7+K+2il8oK/kA30ln3Aar35xNVFUMJlHeH/m+WSQxTlvL+ITTqNTwTZGdPuAQBxcxOu89bezaE8Z/b7Ipt8X2WLc8w/ItWJY8sREiCJTsmtcZr1pED8OUSLXQUqmEMHCteBbCYXF4OqMmB4NAncvoFRKcyk3ES0lkBhpjlXv9s/ODYhxUhFQJYZK8b1knDcdiBVHXiKQslkrmsaU9lIlpcS+bOAkcMcSinZxDQcShIQ6nQjJLzfru5h9lJiHV/peqQaiDSFNM3OtMttHQs6D8hqQCeXH1/3L6M8axoS/zd93X8eqY05h/ooLmM01xFLM37mc59WJXBL/Gpfd9BxFbycz5O01pK36Cj6uI6QAaZk7YeR1XK8r2cpxOGn4S4GNww+SU+po8mHjZ+MZoFwpdQJwK1LN9GILxmngUa31cVrr3kg9ysNm3XPALK11d2AW2LXyNv7/ocIfaLyn1JDSxlRJf7ARUhrWSqOjptx3D5LRUa3WIQJaH9ayKv++n7k7fiinyl9LhDOMuPZS6RLuEKfhnT+U77eftsLukkqcYYqlEwdxUlIHUuPaUVkTJN/XuGGVjUMbtvWhjV8VyziDESxguD6JJYijm0VQN+gLucd3F/ed9wCci6h6XYF+0+ioxSL3/VHnozgfFk7jTh0kbEw5/xh7KcfO3cxY5rKTFN7YcSXTOk/mMv5OMbH0ZgM/niwfOEctrYQukDqaOsMhCw7zPAZ2rYO4PHAlI++SjaJkAlQWg7cnde69XyAkL8O0SvqMqZEXPDFSCbxrLST2kvEp6UCp6SEtAE5CDJiqwJ2OZKmeDP6l5njV4OkMuRsg1rxjvTEmBuYroB1U5hhiXI24+iaAqwoxMkoiFF2Dl1BUDNngGoqUNA8xx7VQjrgUR0oWKqfK67IwYhQXvDofgNxLknheBTlOX8/Eqc+iMjTf056jN+7l1K0Xcr2eAZi+0PMf4E29jmrCuW7ScyGzpNG184j95y7Cw/YN5q4hnD8tWgIfwvbMBGA2nUM2yTYOF2gU1Xb57sFGQGutlVIjgb9prZ9WSp3X3CCttQ/4uN6itcB1Sqk4oA/we7P8TeAZpdQxWuvvD/LcbdhoM1Q0oZR6jMNsY6JnTaB2P+ddkBLb1vSUhoyOGuopNUTV3wolFkBrKdX9Ka4Z1JU752+g6ifqcEWNOBzWH6OUIsLpYMchQkprazWlVQG8US7aRcgXI290OMXl4eyttqNrDlfYpNTGr44ljEJNBAXkzEghR0PaIzuBbfydy2EP3DLmYUawhEt5he66P6co6TW8Us/iRTWepXoZwxd9Av+C8GdK+d9Lx/Pbc/9DKV66dd7I37iW38/8FIbLMf99/KkA/P6LT0UNLUDKWKOh8lOjNlrELA0S04FS8OWA92yE6Bky5w+C28r9jEDeRVZmqUNyQz3Hm/VJQK70jmIMhmgHVEFOAaQlSx+q53jZjvlSFrzzHSGgpQFjNBQNqVa0Cya31FlXuuu2iu7yILcYUjtD5XZDcp1APPjXmmzVBDOPaOoU06Wi/K5fWc/oCHPeTkQp3QsjWAKXvgFA4SXx6OxbmcH1/Gva6dzL7SxjGBdMm4+eqVBfangc83fLZxnDuIsHaO8p4zzeoUglh3SakZPn0Z4y9tCBUYiplQ75i9pk9HCFpZTaOKhwKqUGILfurjTLftaLrJQKA65DDAyTgF1a6yCA1jqolLK8uL//ybirgasBkpOTsWHjcEJlTZAoV8NvlUinLK9thGDWNKaUOsIOSvluQ/sORcIED7x6qLZWo2E/gyaASJfsv9q/rxJbXi3Hqz/m7Wt+x7WvfcHWorIDnsvBRM53Ul7c3uSvWj2kpz2yAl95DT+W13BUtO38frihzUipiZn5O3AyQhMmaa3/2ci2VwGTER6zGLhJa13b1Dql1OmIG+FWs5tqrXXGL3ZCNlqFrpO/5pvpJ8AndcuWMILT+IT3si7kTX02ABeMnQ+vwHC1gnS9lu6ZW5jBrURdUcFRuyshF77JPoGTbsrmuZsuYxtdeWL03QA8NG8Cv8/5VHa+0RzEA+4E5J1QapYVm59e8A4EvjAEz6iqnnTEDCgXKtcap9x24DcdWJ7LqDNUyoPKEnB3QUpoQYjgXugWAwSkfDc3WxRVdzy4oiHVIXExlIsJEj6ERBqy6ImTHlDvKMABlYukXNcVZ9RUp5nXDqQk9yvTX+ok5LRLHKIMR5peWif0Tjbzs0qHfUh0TLac4838leFa+jX20IG3TjyLXqynMzv5w7sfQ2dIeeckJvAQH3Qcwbtzz+WFiquJjNLcVzGJNxhLLzawXElv6NLJpwHQnr0A9OOr/a6N1Uh26QDsKMbDEYcaKVVKnQpcjlzle7TWN7TxlH4u7kFKbFdorb82/aDbmhnzUzwN7EVKgU9q6SCt9fPA8wB9+/a1rS5tHFZoyugoMlwI2hWndm5wfU1AN6hmHjSjo4ZUWEspDbSC9Jq5XdTv2P3WWX2ijSmlXY+J3scwqPMx0SzfXEggWIuzgfn+mli73QfA6+P2/Vr/yDm9GTsniw27ShjY/Zi2mJqNVqAtldJJQJnWuptSKhX4RCnVTWu9t/5GSqnOwFTkg7MYIZ4XAa82tc4M36S17vurnI2NFmGHken0jALWchJp6nK66nMBWDxlMFvozi2P1LUzvfv9RSRk9KfQsLH/zj2OE5O3APOYzFsAdHrpB/57xXEUdqzmwY538cTA8WyiB/M5mze4kMfT7pGYy/qlrLHIV9KNZpkTMRLyAv0Q4paLZIcmgbuYOlLpRPSFZHAbl1zKxSnXHwTfcxCfBpTAzgJIGUAogxTMMcrBVS778OdAaqrZbzVQCD6flAh7jTq7azvExZi+V+SY8YPkGOQZwpwuv1cGqCtFrkKIqVUqXA6l8r8cTzTszIaUQYRKfgFRY3OgdDN4hpt1qUAmjOFtEoxyGUsxxcTyLufy+//eh0rQjD35JXqxgck8zAZ68dqQq7hmxWw+rejLqcpy0V2HXvlH+XUVqIWapY+dts91ksEqAHqxgcuxcbiilrCDUr6rlHociSZLAXpprTea5T/boEdr/SnwqRn/gVKq3U8/dw5laK0/AD6o93wrMLql481rmQqcaW7g5gOJSimHUUkdyH+D/IM8dRs22hSVNUGiIhr+2hsiaP6GVcnGlVLVYAZoS1HdIqX0wEmvpYI2ZKRkKaVVjSils8aevM/yzrHR+IOac/62hg9uOPWA53Qw8PSKXCKcYSSYXmALJyRKOZlNSg9PtOWtjjGIuQLmS8Q6YGQD250LvK+1/t6ooy+Ysc2ts3EY4DP9Pt+MPYFnELHilqjZ6Mmwhe5soTu8BmUV7ZnNtczmWk58ewuf5vUFNuMgyKW+V+h0xTYWkUmaeoDXPFfxYO2ddGAPS+b/ielMgcvhkoznuSTjeQofJBRvQjpCSMsRF9rOSF7pa4gyGAByJTqFvoiSWoqQvQRZZ7nSUi4qpycB4hMQVbOzRLJQjjjj5smDEol/IZY61TLSHK+jlOR64wCfqKxEQuJoU3prmRT1NMcuEOdeUs05AIldzL4tBbjEzCFTfndHyIMEpKvXhxD0AvOokvl6xiNf863e23nwx5nL6DNjE31mbMJBkA7swUGQx068EXbC3EeuYAvH8TkZDFfD4KNpZJHBqUPWwRNTuU+XwZaprB54MqsHnkzKwPq2xqD6yKM+/s7lnLrVVkkPVxwko6P3gYHA/36yvFGDHqVUV6XUsp88bqu3/gwg53AipABKqUil1JVKqQeVUo9ajxaOfQCpTjpLa10NoLUuQv7jXWA2uwD4yu4ntfH/Cf5gLTXB2kbLd91meWVNw6TUH6glokGlNKxVpNTqF23QffcgGB1Vm9LfhkhvhLNhIm4ppVER+75WKUdHN7j9r41graasKoDHlO7WR4zbReejo1n/7Z5ff2I2Wo22VEqT2fcLRh5CEX7Ods3to7tS6kvADzyrtX6lscnYvTK/DiyzGqWm8ZA+XRb2g5EzP2LoTR/yRMV4ZgCLmMWFvIS69V7SJuYTQbVse/40zjj7n8RUQwYn8rz3Ko5jK79TZ/GpfpIUbubvXMaJl2wh/KlS/skZlCeFEWcaOuNvQEhpObAc2GHKa9MRkpkMrjxgEFKCmwmedYgliJVTarU4esG3w2R5llAXt7IdIXnVJjImGUqXgqenDPPngvtUYK8pC04QsupKgNzVdWW8OKRXNGsD9N5uynPTzLHXiQNwZTGkGPXU5wNvO3H+9XoR4rqdULdZ4csQnyrlwQDkm6gYEAKbjRDzfDP/+eZ8ChBSOg7GjnyJufOuAKDTVT/Q84X/sN53CkO8i7ln7F3c1/sBnrllPNEranldt2cLx7GHDty64n56Waz5Yzj1OEs1fRuA3//FlFV/JaZXWUylL6tZQ39ytkp1oZoum+gp2DhMUEsYNbXN9vUcrZSqf9fheVMmGoJRN/fpcWrOoEdr/Q0wrKEDKqUuA1K01rf/jNM5VPAOEI7UflS3dJBx670TaWlZY17LHVrrs4FrgVeUUlOAH4FLDvakbdhoS1QYstmY0ZHLEYYzTO1XymqhJlgbUhb3HacIHITy3QZNlEy2aGvKg1uilFb/hPSWm9cq+idOxZ1DpLR1bsCtxWbTT+pxN0xhSiv9fLTZvqd2OOIXI6WGDDbG7uJ/qePWw5dAkta6xJT5LlNK7dJaL2toY7tX5tfHHW8/Kb/cC/y4muU3dyA7eCc/hCUyjA+5nEW006fxfH8hJR3X7ABOYX74aIaoi5mrx3JHwcN8n3A0I3UvriWTf/N7JlQ/Sd9Xv+APGz+GBXDNZU8xhfvkWBGEym0xhj7uVER1jJCIFNcAhKClIV3JDvBXg8uUvfoLwNUXKAZvBnVutglICXBfqFwJ7hFIkWAJuByEyKwrASGk2abXtETIqy8XUpOBaCjMkYxU3w7IiBVnXlc6lC4y+3AYN+AqIAiVG8Brym+tOfnng6sfIRIe31eOFYIX0Z5Wm97YUVC5ANx9kZLfAYiKejLiMLwI5u64gjevl/7e4GgHV/heQq2DXsM3MH3xg9wXeIB2XwdZOTKDaiI4jVUsZBQnm37QtxkDJ0JscBwAF4e9xlPqNFTcMIbqPWhdl0P6xdgBDJ37IfmdkugQtQemQL7Jm7VxmEBDINCsGvrDAbZZtNigpz6UUn8E7gf+qZR6DrjnMFMFu2mt05rfbF9orb9GvBcaWreZUEq0DRv//2ApoG+s/R/jTuvS4DaRLgeVNQ0TrppALZ7I/b8yO8JamVMaDOIIUw3miB4MpbSpyJlGldLqAEqxHwk/ul04YWr/HtRfG/8rlsgXdyOqd3SEk+LyGorKqohrH9ngNjYOTfxipFRr3aep9UqpPOBY6r48JAMfNbCptR31tstvbp3W2rKtQWu9Qyn1PvI1u0FSauPXxa3aAdzPE1eICVFX3YE9tR0A+Ij+wF0AHMUebl1zP3O5UAZ+MZIhX45EFygGsxj6RnBXwf0svmo0nA0d2+1BJyhGFnzEtwOPJqvnKTwfdjPjaucAkJhTLESrL4R8c4y7LakmRmUzQvaiCfV/5hQZEyBMX6elNuYBPnHM3ZUDKRcjJBBEaY2DXSshsV+9MdmIEVE8EAG+AomQ8Q6AynWQlQen95V17aOB4fDtm5AabWJkMPsqhKwiKANOiQSnIbeV2WJy5OoHC5fAKUjPqavEKLTW//EdiNYSDe7keq+HDyHYO6gjsR5gAOSen0QH9gBwTN6P7E4+mqOHfUtxciLD8pbBf+DTPtI/+pl+n0VkkkABQfOvZnEfaX0r/mMiAPHTC1ms7+ZxJgGgRsPweQtYWjBCXgBgUpRY+N4792EYi43DCForgoFmP2ZilFLPAwu11gt/+TnpfwKdfunj/ILYrpRqr7U+NGwwbdg4DBCKOWkoG8Ug0uVolHA1llPqcoSxN3DgESQ1gdoGTY5ACK9SB0sp3Z/ANdZHW14TJDrcuZ9jr1JKXqM2Vkq/K6kCGibaQMjM6puicpuUHmZoy/Ldd4BrgHXG6Oi31PW01Md7wColYZbFwFXA3ObWKaU6At+ZPDcvEg5y9y94PjYOAPol4CVQj5wAwH2TJ9GBPTzGbfQZu4mhcz+kkDhuYiYAcX2KuGLhXFb1OYWPM/9Ax4IdbOU47nxhCg9un8brXc4jhRzu6XYX73IORcRzZe0s+vqM3W598pWE9IgWSBmsuycULhcDILcxQqosECW1dxx1RkBOWLYAhvUi1LfpShAHFtYBRVJqSwLgAW80Ql6tzy0HQvZMD6s3VQitc7sc+/RkRMktgn+VwJnrIDUdIYsdZRe7VkvuqbsIMrogJPpaYC24q6EwD+Jz4Mwu4C82RNpr4m+sm8RxUJol/aWudCDXrHNAaTZ4BiHvKo/Zvhy6bf4WlSd3hX3Do7ib+/nhP514KW8sP9KBl6eMYcCLX/BfXcbjTKIr3zCL8cRTyBe/HQDvmxfh2AcAyJ+exB2qA111ilwP8wBGyWs3FybxFWW0YxjLWTx2MA3ft7JxqELXKmqqmi3fLdFaX30Auz9SDXpKkM/NJcg7HwCt9V/abko2bBzasMp3wxqIRrEQ6QqjqpGe0sZySp1hrcsprQk0THZBSGC4I4zq1pQHBxsv37WW/bR8t6Im0GiZszNMNRqb82vhu9Iqwp1hvHdd/wbXO82Nh5LKmgbX2zh00Zak9DHgZaXUNqQb72rrzq9SajpQoLV+Tmu9XSl1H9LVB7AUeB2gqXWIU+N1Sik/cp6vGtdCG22IeUj45OMs4l8M5l8sZ2RWHdFYyCg+ZjDHsQWA5WPPIPb1XTjC5INiLmO58oTXuZOpEA0Fi7swY+T19GI967r0pYz2/J0r+IGjOW/WQraN78TD3M4n3lMAGBj4HHohpkAJSMHaailZ3bkIUrogJCwaKDJxMdZXXKO9+/JgWLpxru0LdBYzJE86UgIcMCXAReDLEoLqdoLPlPl6O0OpyT0NBEQNdTuNuVEkFG6H+IuBbBgWDcTCl2uhT3rdHBJjoTQXeicg0S7RwJtQmSeE2mOpvFb/awCoNqTTa85jNXjTzDpLES0BqiSGZtcSIb6WsosXJhz/EI8efxMAN/AMBSTwVsZZXJE/l/ik/3EnD/DOlWdydulC3g6MoeY5D747o/AOqYAnYHjyIpauGQWrp6L7izLK2fDNEDn8iBULAMmyrQ9LSf1DQxeVjUMWWisC/mbLdw9IKdVaFymlspGbma9z5Bj0bDEPGzZstBClVX6gjrA0BHcTSml1I+TR6QgLmRUdCGqCulFSClLC25pImGqjgjbcU+rYZxsL5dVBohshpWFKUVPb9kppx5jIBrNXQRyRAfZU+H/Nadk4CGgzUqq1LgfOa2TdlJ88n009V8WWrNNaP4NksNk4RPEHPmIMr3BsxmbIgDNYxDqEWM5iIh3nTmIPHTiaYv6yaWZo3J09pvDgvOn4X1T08HxJBlncOn0WnAvP9biW8TxDN74hYXwBCRRQTCwD8z+XwelAtSii8ZlIP2gM4DPRKAUICU03y024RGm5IZ3AvO0wzmecdYuQrNHORl3sCZXV4MpCck6TEeJrFEkLHiteJlrGeyPN+FSI74Uoq1VCaCs3QJ9MM5eOdeM8JYj5Ui6iuhYLId2ZB27AuUM23VkCqTFAIdLNbc7JmwGFqyG+H/jWgXe4OR9jepR4AzAPcgsgtR+wEd49+1zijWnUF2oZ3HEF/759JOOTnmAYy3ASZBjLmO85k5o+Hq78chbn8xbDVyzgGp7jaIq5q/8DjKr5gId5hLPmSRvb+1+eDwgZfZgJqJtGwQ8Ad6OtuggbhyEUtcFmP2aaVUqVUjOR2JPfIP4AxVrrEzgCDXq01tPaeg42bBzqGDP7M4BQzmZZlVTpNNS7aUF6Shsv322I2DnDWm901Fj5LkhWaatyUJvoKW0sEqaiJkBUeMP/t8PCFLWtIMkHA9+VVPEbT+NluXOv6kePKUvYU2mT0sMNbZt+a+OIw2gWMZpFDONDhvEhb3Mp/1PF/E8V8+yXE/k8bCCfhw1EGQ76xKq72UMHJveYxrE9NnNnD7lfcd/oSbge14xnFj3YhK5QcDsUkMDD3MHTa27j1FXr6Hz7bmIp5qakR7kp6VF4E/wLIL4LIXJGOdK+WILcpkk1z5MQgtYTfFViBlS5FsZ1kWWuWPCXQE42UC2EkjzwnAm7ihFSGwGl80X9dPeSx85cKdfFKustgNy14DnezMXEwBAjZbWF5eBbKlP1b5SHpWiSJ/PdtRIhnOXixhufLvNzWeQ3AKSBL8fsOxIoApdTVFjvAPN67JD54EXU5GpI7WuW9YQ8XypfqGVCSNdOhY/hCs/znM7HrKcXARys42TOe2gh9IMRLCGCapZuHcU5CYt4g7H8ncu5PfwRADLI4n11DuwB9sC/GEw62bARHpo7gYfmTmjF1WajzVGroCq86UcLoLW+SWvdSWvt1Fr/xhBStNabtdYZWuvu5uf/ewVRKRWnlHpdKbXKPO+tlLq2redlw8ahhECtRus68rTXkNJZFzZud+Juol+yJthI+a6jdZEwjeWfWgh3hLUuEuZAekqrg0RHNKaUQhsLpewureQ3MY2TUrfLQbgjzFZKD0PYpNRGm2IembynH4DBA/Zbd4/3ca4cOAsHQd6qFz+7hePoyxfkTE9hDf35S9ZMGADPL7iEIA4qiOK9/pnsHtiBCx9+kTmLb+BMFnAmCyAIrp7A2QgJ6wt4wJcNpZuRHso8pGS1CCF+1ZCSXkcq6Yz0ju6FnBJIM1ExlduBePAvhcQ0hODFgOdkyS79cq08UmJNlqhx8yUWUnvBl9lQauWEFprjl0BKL+OsGwuuZHkQATvN+l3LoX2kGeMFUs25xAEF4ujrL4fCleCNkfPFAwTBOwj6JIM/22SnDjHzSpOxWJmrccBqmOh9iOf1Nzyvv+HLjB48teYa3i09l6lMoz17KSKe4Ys+QZ2h4VoooCNd+QaOmwZVEE8Ra+jP37mcTfTgDcbSU2dDNowcMm+fv/8dQ57kjiHi0KzUNKR13MZhBQ0EVNMPU76rlDqzmb3ZELyA+Hp3MM83A9e32WxsHDIYM/uzkEJ4JKPKH+S/+XvYXRJquabMlO+2byDb0kKEK4zKRjI4G1M0XWGKQCtYWk0g2IxSqn4xpdQZpghTDfSU+oONKqUje3akXQMuxL8WtNYUllQ3SUqVUnSIcrGnwu4pPdzQlj2lNo5QqLHAn85g5Jh5zGEci1eNJm3FVwDkfHwSDJoGN8PFOoF8kujGNmZyMwDLGEYE1YzM+zfPJF/LxbzGjRlPo9S9nKWP4wHuZIcvjQ3eCm5lBgAfjhzKGRuXy8FND6l/DrhSgZeBVPBajrPbgUjYuV2Wec4G/yKT7Vn/f2CMbB9bhBC4cvBth8RyKCqBxOh6+4sFekEfS7UsB4KiWvoDEO8EoiEt2vSwRpptooF806OajpBEq/ezC7hyZdvEWCSTNBdJLdxuVNftcm5slpLi+FTZvtIoxO5R4HsHvOmGqK9GyDii7nqSELW4yOy3GJ6cegfjpklV/HFs4RMGcqZnAW+UXkY/z6eMYw4PJN7HJb2fp4AEPieDCqKI0/0pugruu+oBeB9YAlvpTdc+X5PCTuInfkg3vmERmWyjKyNXzGMnnUkinxHURcjYOMygqWfF0ygO1OjoSEWi1vo5pdQ1AFrrGqVUG2sXNtoaWmu2FpZxTPuI5jf+f45VW78nUKtD5kZQV77bvglC5XY5+L6s4ejfxtx3nY5WRsI0YXQEtNroqDrQeE9pnZvu/pEwiR0aJn3u8MZLnH8N+MprqAnWNlm+CxhSaiulhxtsUmrjV4eeCxmsooYIlk8/A06HnFUnoQeCGmTUsC1TgReIp5AdpLCMYQDMYCJzGcvk5Ae4qfCvBL9uh3JqJutpLCOJAazhIu+LrKcXG9f8lg/6j+CPfZbR7tMfAChLPwYWgq8E4ksQBbAYIW9xSJlrAqQ4jHK4FFwxUqbrsj6rUmUb1kFiHJQuBY/XuOwOh8QcRG3sDJQb86Fi6t5tkeKIC9LPyXYgBpxW3mkxVJZIP6mrJ7jyxSXX44HKctnEnQuJfYFqKC2CbSuhTxxi4rQd6YuNMD87GwPdaJmD23LT3Wj6XIvk4Yo2Oa3DhdTuXGQchcvN+Y6AVeefwpyXbwDg8sv+xgZ60Z4yXDmazzJOYgFncst7s6lKVeRHdeLvXE4S+by/fQxMAJbBEy+MJ2hyaYI42GMEHytqZia30QO5SWEZHh3NrpZcWjYONWjqXKdtHCzs84oqpTrQSP6ojSMHpVUBfqzwU+kPUlurm4w++f+MMbM/Y1vRXmDfKJWy6gCRrrAGS3AtuMP3J2gAtbUaf1A3ONYR1jqjI38zRkcuRxj+1uSUBhpXSqHhGJyKmsaV0kiXg0p/EK11o0ZDvyS+K5W7nB2bUEoBOrjD2WO77x52sEmpjV8VygthOeXUfjFQFkzNAUDrNPrzEb/TA2lPGRX8m0wWcX7++6gfNZwoZPVEXuI+PZ8i4qn9OJrbx9yLjyhu5yEyyCKAg+cfuZm9tzi4uv8L0m96pWJblIklDAKpEB8BdAb/aiGdxCFKYwFC5iJNdme5IaSdqcsZzQWKTIzMqeDJk+fuLojSWGDGmNgX917knRZjxpfUue6uXytCamKyUUNjYNcCU96bjvhKB6QE2MofBSAZCtdBfJw47fYZBP61Rj3da4yZYsw+NiAkOUaOjXUjPRXcVXKuldtl/q5Ic34+SMk045KRUuQcWEdfBk3Mkr9ZF8WugbF8Q1fIgPN4hze4kC+n9+AmnjIvt5MHzrmPa3kZVamJW5THGN4GoNMauVHAAHn+nn5FrpGJMHSGZRt8EgA/hCU2dVnZOFShgeZvVv+qOaX/D/CeUmo20F4pdRlSuvtS207JRlvDVy5fwKv8tfzhr6tYOmFQG8+obVCrdUghi21XpxqXVQVoF9F46S5ApNPRYPmuv7ZxYudytLZ8t2mjo/BWGh1ZpbkNKaXW8uqf9NGW1wQadd91W469gdpQT+qvCSujNL4ZpTQmykW+r+LXmJKNgwi7p9RGmyDs5HK+zOwB09LkUQ9BHHzC7zmf+SQl5VLbSVFbfC9wCqwdQAQ1PP3lbVw/ZgYdKSCqopLn1R7+Wnorcysu5KHJEzg+YguxFJOqxnHS+M/IJ4l8koRY7iVEzFw95WfOIkQNjEVKVUvM42xDFksRUheDENsIcMcLEfQXIGWuHkKGQIEA4BSFk3hYXyDmRrtyZB/uNNmmdz+Ii0EU1XXAWlFAKwuBbMgqAJIll9SdBv5qedDZmDUlI7mnwu3JNeXHLgd1RkhW72q62baveWyUOWKVDRea18VZtxxLSc2QY62hP119X9PV9zXqU82f+IBtdGM8z3ATMxm0IouPGMx4nuX5Z2/mxUXjYSKMeO8DWOynSHXkbu6nU9YPMpcBO4EMTtadQiZY783I5HpmsVN0WhuHMzTyfmrqYcp3bULaMmitHwNWAV8AmcBMrfVf23ZWNtoavvK6stP6vZRHGkoq/QS1pltcO74rqQqZHZVV+fE00wspSun+BLCmCWLnDAsj2AqltDpYi6s5o6NWuvtCc0rpT3pKq4NERTT8WrmNY29blfBa13bHGHeT23Vwuyix3XcPO9hKqY1fFdoHSj0O/AU0fDmlB+OZxXO8yiXAu5wLwNm8BcC3Q87nhBVfkqO+A+DMjHf4hNN4vY9s9wOxXBX1PHF6BOt4n9NYxXzOZhjLSCKfz/Qn9Nt4LxN6PgTA4LjPpGczFiFyCUAapHkR86JqhIwNAhYB70i+qDcBIaMgqipAQNxrGYIoiT4zdge4IwGv9Jl6IsQYNz5ZhvkLwNUXcsuhdwm4EqTEt7LaKKFx4DZEMiNN9uuNlp+uBFle+A7ED5Ac1OIApKaBqwpSE6AwD3YB3YLgSZB5kI6ouAkIyURKmL3pSPmy07wmycBX5lyzjBocj/SbJkAPNjGeWQCsvrM/3fiGTfRgDG+ziEz+MWQU5z20kNV3nEzu9Uk8xzW8w5ks7T8K8LFC/5FlDOPOjCmcxieg4W7uB0ApP9v1sZwE/J5/t/SSsnEowy7fPehQSg3RWr8BvPGTZSvacFo22hjFe0UpPSrKxY8Vfor3Vu+jFB4pKK30E6bg7JMSeWzJFkqrAsS4XZRVBZrsJ4XGjY4sYtdQ+a7LoUJK6oGg2UiY1uaUNkNKI5xh+5Qs1wRqqQnWNq6UmuWV/iBHHfCsDhyFpVWEKTi6XdPO7R2iXPxoGx0ddrBJqY02wqP0UWM4Sx9HPIXM5CYAagjnn5zBH/kQgPdWZPIxg3nJhFU+yS10YA+FxHNr2CzoCXeun8Jmjsc7twL2wOnX/4uXt16LXq+YdO59PNvzel591PiolCA9ktmAFYPiRcioyQL1Z8PO18S1tjBP4lXIp46MeoGVQDrsWgSJ1VCZA+6+CNHtYrbLg5Qu1H0pN+NdVcAO4YAUA+1EqXSXA52hdIXJRC0QN97KKinRpURMj8CQ3kjwxpne12Lw+aAyAIkDIL4IOUCujHdHIu/2ckJlxMUB8G5AemRjzPZVMh/igOPBvRtRgY0CnMkiPuE0AOYwjt2+BG70zmT67AcJXuOkgI789Q55rW9R6fxP30t7yshdk8QAVvMYkwC4iadDV8LFvArAzmAKv639D8+HXcWj3EY+SSglLDxOX04hyY1cSzYOWdik9JfA48BPcy0eA05ug7nYOERgle9GRzj5scLP3urAEUlKA7UapyOMZG8UICRGSKm/SeddkNLUmkDtfj25Vs9oQ8TOEdZao6Ngo6W11jFbk7cZIqWNEN+fGh1ZCmhTPaVAoy7FvzTe/DwPR5jC2QSRB+gQFU6Vv5Yqf7BNyoxtHBhsUmrjV4fWU/d5Po9MAM4ZvYiu874mTV0Oq0+APXDOGRks1QvYRA8A7uJBHmEyt345C16HrmPFvdXrreAW38P0YBOx/MDHb/0BNfUJWDmRJwaO56O/SID24Hc/E9UvBiGmVnlqECGaqZLvmZoKlEN8JpAFO4vB/ZrMdxfQpy/glOgX/0pTypstCii5sq6yXEpucUrv5/p1Mr53L6BA3nylpeAqF9JYWSXE1OWQY5MOnh0m/7QAiJb4F4CUQbBzOaT0M+vagTfSnE8pQrLzgDhx23UHEBV0O6LqYsyP2knuqcsQWFIREhoHLESIe455fYIwkRn0Z7WcBxvo5v2Gu3iQo6/6luI1ibzY/0KuVN3ZqF+DLbNZx0LOyVrEExnjSaCAxXNHc+zYzYy86iMAhr7w4X7XxzlDFsEkWJw5mMV6K5fySlOXk41DGbW0xH3X7iltAZRS3YDugEcplVlvVQwQ1TazsnGooNiQUovglFe3nUNqWyJYq3GGqVBkyO6SKrrHt6esKkBc+6b7EEO5nYF9jX6aUkqtnNIDNf75pY2OqgNBwp1hjc4twhm2TyRMeY3cRWwsp9TqKW2r8t3m3IotdIiSGxAllX6blB5GsEmpjV8Vo3gHgArzHWq5OgGtF6GGAJHwzdITYMkJkFINN0fAuVN5kl6U0V62ZyjTmEpenyQ+PXkN08Zu4ArfS/h3KrbQlfaUsYhMnpgynomjn2V1z7nczF+JMM1rg7d/JmrpyQh5s/pBsxGlsAT5Em2V2i4XV9rEGBMhA8SXI0Q2D4iRslvXXnANQEpeq4SkulMlGzS+L6wuggG9zIuQBBRKT2hloennrAY3sl93rKz358kxd86D+GjZp8e8Y32rzb4KTCRMCVKaG0B6Nb1APpTuAG9ncz45SC+p8RCK7yzbuwJm/BDzmuQipNgyf/Ka5+XwAHdRbRpye5DDcoaSTTrFjjno6feiBrwFk8bwLO25vvsM9nAULIFbl8zizilTyBy7iDLaE/vCy6Fr4t6CaQxNWEZ6WDbLC4aFlo9c9BHbMxNYSz+6bC2Qr+M2Dj80r5TakTAtwwDgMqQb4LZ6y0vBlCDYOGJRvLeGqHAH00b15KIXs6ioOTJLFAK1GodSociQQtODuLe6+fJdi3BV+WuJqlcdWhMUAtag0ZFRVIO1Gqfj55PS5sp3I5yt7yltSomNdDn2yfO0rpvGlFKrfLchl+JfA/6gJtLVAlLqlj/gngp/s6ZINg4d2KTUxq+KBZwHwGPcxF/URADUVuB1Y9LweAT/nDGMOYzj/TvOh7MgniIWqw2y3jmVuYHfwXVpcN3vuWgr/Lf7cVzLMwxjGQAXV7/KxIgZ3N7zXm7kafbQgf6skfHliIpYivR/Hm9+t0yO8pDcUWMc5IqT567V4Df5nq5+hPou/UXgiQMiTNltArgdknOaGGPKbqNhQBx1ilG++WliXyoLjHNvlZlTUMyMXLHyPCUNCnMgZ7t8GwVITDV9piDv4o7AbkT+LCXksuuxztkj0TGeHPMcoDPsXA0p6bItmxECYUXleKkzfjKlvelk0760EoB3PWfRna2sYiD36TLUaM17OpM15PI+Z7NtdU9uH3Av5MO3LxxNp2PEbbfd/+RnTVU4NTdIPs3yN0VG/p2OghXw2cDBAAzMXMW3W7th4zBFy5RSGy2A1voV4BWl1GVa65fbej42Di34yquJbRdOlFG4ytswS7ItkeyN4jeeSOI8cvPUihCRntLmy3dh/9LUGtPT2RB5tMpIpWz458+3JliLy9k4mXU5VKvdd5smpWH7mDtZCntUM+67bVW+G6itxRnWPHWxlNI9dl/pYQXbfddGm+FRPQO2pMiTxHUMTRBS+ceJy9hED3QfYByczkehMQ/5J8CWNPY+6YC/LYR21SRQwMPcziyuJ4sMaiIieJg7eMQ1lU7qRqYxlZv4KzfxVyFY5YgDbzn4l5odFyHusyVQuRYhqTGIsrgDiANXhDzIAXpJ7IqvBHBCbi7srELIYYIomxayVpp8UctttAR2Fcn+XdHgD5pjG2ffwu3mOCeL0kqCmCSlOCU6JjEZ6Vu1TJmiZf6VhbArG4gwvafJgNc4+EZKluouy9zJqLMpXYBYIdccL2NDrrsxUppMiTmRcpjF9cz2XMlsz5VcWPg6e+hANumMYiH/mDeKZxlPNiexubQnvAzb6IZOUyTOKIZJsPH7bqyN6kdNVcMmBUfzA2sYjF4FepUs69R9G9pWSQ9PWD2lTT1s/CxorV9WSnVVSg1XSmVaj7ael422RXF5Dd7oCKKNwlVRfWS+uSxDowing9jocHaXVBGs1S1SSiMacZa1lMrwBsij0yilB0ocRSltnM0ejJzSiCbYcqTLQXW9nNLyZpTSyDYu3w3WahwtyOCNcRtSajvwHlawlVIbbYK/rJ8pv5y4GKPnsYXj4JYAfOpk69zeJI3dBt26cdnct7HCDu9Q8JS+hnZjg/AwPJVwDccsKOO9UZkcxR4m8CRTmcbTq24jx5/Cn1jAEhIYxxw5Xgb4ngBvEnVkqxAhaduBEeBehJBUD6H+S4oxzkQGOaKKejpD6WroFA3uEcA62Jkn6mbpDvAcDxmm9DWUc1oE7SMJKUiVVYYw5kJinDEligHyTeyL+W7hdNaNoQByc8AFVALxTvAmQ2KJbB8IgKtYzisjzpxbrBgnxVkqcF/wF4MrSXJYC98Bb4xRaJPl9XE55VgUA6lwNMVcm/8yAOPLX0TdrWE17NyUwjm8y/JHzqDr5K+Z6HmUT14QQySGwgsnXszzXELPrG1wFHzWXfJHmQsfczp39HuSM2+S0m6lptFVi7vytwWiknZL+BqAbZzQ8AVl49BEy4yO7J7SnwGl1IPAVdR1e4O80ovabFI22hy+8hriPZEhhetIVUrrGxrFeyIpLK1iryHoLS/f/alSapkF7U/urJLdAzU7aq5HMryV5bvVzexf3Hfr9l9hlNJGe0rD204prQnUUqvhwozmTQ+PirbKd22l9HCCTUpttA0WA7dDnBaSUUgyGazi28dNqebTOXybkgYpcHr/f/HxDX8IDT2JbLrPXU8H9jCXCyEAiziDh7iDJ5mAgyC1PRUX8SJbH+nNfyb9liEOSUu4tPwfeC8A1sq+XAOQvE4rg3Q5UrZaDJXbwW3URgLgM+W73gxEpawCisSYyN2FUMlvSoLEvnh6Aj4pu+1mkT2ABImJyV0HKTHSc0qJENXKcvi8CoYlIKZDi2S/leVCXHOtftBSSO0rxyfObLuOUM6oO15+UmR+Vslxu8XANkPG08oNMV0HruEQPxTINs/LzXkPR0qa2wFpcBbzcYTvBeDypJd54oXxfMHJzC24kDMTFvL65HNZR1/mcxYAO3xpqPR7Q387vV3xXOfLKApZGcMdE58EpLRbqWmAkM/xzOBZ7NLdwxqalpTv2j2lPw9/BrpqrUvbeiI2Dh34ymtI6+gJkdIjsadUa71P9MtvYiL5rqTlpDSyEVLqDymlTZfvHsh8a4JNk0aXIyxEig8Ezbn7Sk5py5XSxoj7r4GyKhEnmvs7guSUgvSU2jh80GakVCkVBfwdsZwJAJO01v9sZNurgMmAQujMTVrrWqVUIvA6Yo+fq7Xu25Jxv9Ap2TgAFCn5Z6l6AT0HAnDj3Md4+lrj43HCx3y0ZCSP+W4EYA8dqCCK9pTx+fqBxPbcRexZu0gin0t5hS+yBnBnxhTCYjdxmV5J7uQkYtZXs7J3huwvGnHf7YgQrWykh9QojKE+yy7gTof1i6D3ICGZXnNzLms1ZIxAYliywZsGvhwpmY1Php0FYkjk3QGkQ1o6YhhUaE66HZAgpDJ3HaSad6Gns8xjmNU4uhKohl3FkNgPKjdAqmWWVILkolbJ76XzwTNAfq/MMWprNKLQOoBIKM0SdTetp+zCv1GMlFwxiCL8hYyprAaXpfiOoK4P1wHFHE1wWzvZwbdAHlx49ouckpDF2cynkHiCOHiO6xi56iNuHvgof9ZCQP+hdqL+rPnsPyfxIHeGroHFM6R/9CsK+VLDSWwCYARLGJGwhFmM52Pf6bKxt9FLycahCDsS5pfAbpuQ2qgPrTXF5TXERocTHSEfKEei+26lP0igVoeU0o27SvCV19QjM830lIZMfPb9mljnvrt/2ahldBQ4gKxSK2qmKdLYWqOj5pTSn0bCVNQ0o5S2YfluWZV1c6HpvyNIT6zLoezy3cMMbamUTgLKtNbdlFKpwCdKqW5a6731N1JKdQamAichRYSLgYuAV5HOwKlIoeW9P2OcjTbEMD6ElDPgLdBjOqEM/7z6sb8ygiUAPH2CYYC7zkLt0bAJuBY4DphTyfP6Gq4+cRmx+my2qvnc+8HD3DjqMSoyonhwzXTcJT8yicf5gaO5vvcMwjElHE8gX5JLkcI3K27FUhWR0lt6Al9Ab+Na6+pJyGUooyNidJRgnG0LDWF1AIMgZSXiypsjsS1pmbKu1HyN9JwEO5dASiykxgnZdEdCVjFkDID1q6G3FS8XLbmjGDOknRvqzTEGOB78q6VclwAQB+4dZi5AYXFdSa6nJ9AZUYYBV7I5784mziZO9uEZBGw3hLQQKVsuB+ZAz6Xb+HSu3PtJoIAzTv6Qs5lPHknsoQNZZPC0uo2ncfCovoks5GbAcWzhbzqf+SxgKtNYqr4yf4+pLOQ89ES4icd4et5tcAOwNgB8yPBkuyLxsIZNSn8JfKaUehN4h3o6tNbafrMcoSivCVITqMUbHU6EM4wwdWQqpXWkRb7ahpu4luK9NfssbwyRzoZLU0NZn43klMKBle9aZLMhsmvB5QgLkdcDQXPuu1YkjBVpU17dMvfdSv+vr+9Yf1+Pu3lSqpQixh1uK6WHGdqSlI4BLgXQWucqpdYBI8FkhtThXOB9rfX3AEqpF4DLgVe11iXAKqXU6Q3sv9FxB/9UbBwohvEh2x+7ivN5i+e33szz3AzA9VqI5AxfJGGRtXCZ+ae9BZ7QkygkjlP0YPJpz6O6mI+Yx8wVf+GFIRezqX8PnnrkdmIn/0DP9dvQPyh+HOKW8TfAzgsMqYtAeied5lEsfZoejFFRtIllSUaUREvBdAInQeESIXxg4mLyERU2zfwE0rogKmkJeJLM+BxIscqC88EdA3SGjCDgg94DoHC1ZJvSDnYuEOV1ZwD6dDH7CADbwZ8Du0pkvpXrzL6iZd+VudIT6rKyTGMQNdRj9lENu9ZCYhdzjh5CRkwksC8hLQIug6fuuYbpTAHgYW5nGlP5gpP55IvhvHLyn3EQgC3AhRP5i7mvsDTvND5G1NCdpMjC90YBcNbotwDIJLzBQl0HAeO8/BVn6eOA8xvYysYhC9t995fAb83PG+sts3tKj2D4DOnyRoejlCI63HlEKqU/Le+0SOSW78rM8uaUUtm+sfLdhsidlV16IEZHdb2qTZfvBmt1iw1+forqQG2TESqRLgdaC0GOcDpCSmlj7rvWa9AWPaWlP6N8F2BvtZ8lG3fz0OhezW9s45BAW5LSZOB/9Z7nIQmOB7pdq8Yppa4GrgZITm6+idrGgWEEC3AAeoz1PMi1PEcWA1G3mI0WT2OWnsoOEgjb6wdnkDNXzQdgoTqKW9XZsHYYIzPm8blKJqgdLM4aDTHQl3XM5hp6Tv4Pj3A7YfHlfNU7jQ7sAeCo1ypJSaUu03M7opg6gFRTShuBkLgBYiREgaxju0zPtxG8AyA+k5Dq6MsCbyohhZV0o1ha8SoJhCxJduZCSqo45SamyTr/OuO4GwSiId6KqCkWArszD/rEUkcoi2Scq9wQ7AIZR7L53RzL2xlRdUHIgZU9isw1sci8DumEemKJMPvvC/4Fphc2VfZ/847nGZcgplHfRHTjNS7GTQXxJ/+Pf/EHLs3/B3evf4L2K79nVdRA+iRvYg0DeFA5gBTO1FtYU9s/dD28P+98uo7+mu5sYRtd4WPoWfAfADbwW2A06NGcjZDXBHZQQGdsHEY48gSbXxRa68FtPQcbhxaKyyVSLbadmLtERTiOSKW01FLSDPm0VL3Pd/gAaBfRjPtuI0ppXfluQz2ldTmlPxchUtqEO65FrP3BWhxhPz9zpiZQG3KibQgWyazyCyktrwkQ7gxr8FxBFEj3T0p+fy38nJ5SAGdY2AH1+tpoO/xipFQp9SXyFbkhxDeyvM2gtX4eeB6gb9++9lX8K2EJophlMo/ti27gWp5DKq6hMwXoZHiOywFYRCYcNYw031fkFFTzKpdyDGWMZS7jMubgKNxLrXqcNB1FUW0cKWE7CJa1g+8BK/plNPhvBVc1EvfiAVYgpNHEtFAOhQXgXggeQzQLVxqiCJQGwGuph0BlsTjj7sqRbFJAyKhD+kGdxeKi6y+WVR7zrkvsAnihcDnE96ozVirdCIVV0KnEGChFQEqVmW9WvRfPB4V5EMiFxASozAN3EZApc3PmAUEpG/YMkWW+deA15xEishlIb60XiETIqRPIrVNQ/WvFoffDzkMBKce9kNcZwRKe+P3d4ILHF01iU1IPJiU9zl41iz5Hb+L672fQgR95Sv/IKBbSZX0BnChmRrv1k9zKDADmLrrCvCiw8ZHfQhKoDrJIZ8J8WyE9PNEypdR2320BlFI9mlqvtd70a83FxqEFX7mllErfR3S484h03/1p+W6U6X/M2iEfvp7m3HcNia3+OUZHYRZp/PlfG5varwWrtLcmWBsyYvo5qA4Em1RirX1KLIyLiuog0Y2opBbc4Y426Sn96U2H5uB0qFaZRNmAMbM/A+Dta373qxzvFyOlWus+Ta1XSuUBxyKUAYTAftTAptZ21NsuvwVTONBxNn5BLGEU6ktxntJ9CLmtjtS9iKsuZB5nEx0RRKlp/E4P5DO1itP1GNoj5TdkB8jJ6wXHlnLMuDL4HQRxMJ5n+PFoL6P1PJZ3OYnvt7fHXV1BXrc4jn22kFpT/qveAdfFiFNtnplUuZSxeqPB3QtRKq1SVi+sXwm9uxBSKVOiEfUnWsp9U5PB7QRPR+QKizP7dkLiKGA17NpuVFGMejkUeE7MhuKNIuqOkTGeJPBEmmMkgG85eOOgdAV4LKW0nawvRIyYKACnFTuzUsgqQE4J9IlETJHKwes188OYLFnWYEmESDZOyTx1VwFphjRfDJRAHIUcZVTnUSwknBru+/ckljCCuSof/ncJF/V7l1v1/TiZxg5S+IhryVE1PKvHw1NA56ns3t4BgCeYyO/5N5wh1wEPT23wurGuE60bXm/jEIXGSnNqCrb7bsvwYRPrNJJebOMIRLEhpbHR9ZTSIzCn9KeGRmFhour9WNFCoyNXI0qpRR4bUkpbYXTUVK+qBUvJPFByVROoDeWvNrX/atMjunjj7mZvaLhdjrYp3zWmRS0mpWGKilb049r49dGW5bvvANcA64zR0W+BCxrY7j2kb3QaYlh0FTC3Bfs/0HE2fmn8W36M7zMjtGgRo1kVIQxpIPCULgDeYpLOB7K4mwcA+HPyG/xj7qW8rK/jsiFvc8+au+hdsR6AmHXVjBw4j5Hb5+EgSPTUWiLuLOKc699gJx0B6Lx2N1hazPGI4VEGJG5ECF0JMAApcXUCcdDbSi+x3i3lQKT0bKZaPakOYDP4SsDrBBJkvTsHSIfEYnkO4E6A3FshtYs435YWSURMaN+G8FIoP71GrXSVIGQUIAl8q6F3KuxaKVOIt/o/o02+aTR488CVAOvXQienkFu/mUdqgvzuqkZU0ww5B/aaKJy+QB7Ep8pPLoYN9KYG+eKTTjYp7OCUVzdw3yWTOEnHs5P5rC/oRXv2EkE1/1h6KZ2GbyNd/0D21t50fUnyRo+qsEJiYeMffwtfmza5E3bSScuXqV7I33WEnVx1+EJTl6Rpo1XQWtt16zYaxNPL5Z+61yKl4c5QtMeRhJ8qpW9f8zsmvp3NvK924QxTTfZWQp1qWFnTiPtug5EwQkoPRCmt6ylt2uhI9n9gpLQ6UNsipdQqxw3Wahyq6d7VSFdYm5BS6+/b7meV79pKaWtQWRNk83dl7PihnM5HR//ix2vLb3uPAS8rpbYhX1uu1lqXASilpgMFWuvntNbblVL3EUqWZCkSA4NSyoH0jUYgJWDfAnO01vc2Nc5G20JPtn6byCzzf7wbX9PBlHLeSSa3qEu5Re9gJ515kts5xyhlD+s1XDh2Ln86ZgkffD+CWYynf9Qalo6WMuDOA3eyjGGU0Z5lDw8jiIN3v7+I9tEiyJcNPUZKVROAQcAio0BGI860AaR8tQhK82W5PyB5nqHSWeNO6+4i2/sLwJUp6719IWuJVMJ6QMhegcTLuPvJ8Mq1kk+KVyJl3BFAJKzfDr1jESWzCHlXxCImSxEm5sWqqikwimuJqLTeBMAJpQWwsxh6m57ZsnIozDFKrwPoAi4rmiYWXJZanGSO1xHYLXNjreyzsgDcqcAGuHLrG3zWXcqrpzKNpS+Ngvfh7j5P8FHP3/EWY3iGG7iUV2S/4+Dbo7sR9WUF73XPpC9fMIDVdIvKZRjLcBCETsDD8NCrE7jjv0/y7VygCr6lG8OvWADAcH1SqNTbxmGEluWU2viVoJR6HDgHSAF6aa03muXdgVcIdbJzidY6t7H92Di0EKjVKFVnThMd7gipp0cSGuo57JHgYd5Xu2gX6UQ1Q7YcYYpwR9g+uZ3QtFJqkcYD6iltUfnuL6uU1pFS2X+t1oQ1zd1xhzuoaqNImHYRzhYbPjkdilotpckRTfTt2mgcZVV+aoK1/Gvjd1x3etdf/HjNXHq/HLTW5Vrr87TW3bTWx2mtP6i3borW+rl6z2drrbuax3Va66BZHtRad9JaH6O1Dje/39vcOBuHJgqJ506jiHbVPVnImTx10+2oBHhG5/OMzudPo5bwJBMA+NMxS3AQYOn0UfSc9x+4G579ciJbv+zNbTzKYD7i/C/eJ+WYHBZEjWJB1KiQC23hIkI+z56eiJEPQD6UfoG45XqBzuJgC4hy2BcxPCpFWGcJuNLB9w4QB7uWQEYsRAHx/aQk1x8EysXMyL9OCJ6rn8zDGye5oHgg1Tj+Ui3z8QeQ2y3HA8nmebp5IGQRjJIaI/vweM1+OkuvK0BaKnVmRgVI36gpD/btQBRZJ7BOyomJQxTTVPnd6ZTXBR/oZxWPMJlHmMyS//6JL6/owesLzuU/PXvSjjKWMIJ3OZePVRZDWUbXvK9hEmyd2Jv3OZtjryrk26u6MYxlgJRec74Q0n0QSYiQLv3tKCKoZtR+xtw2DnlY5btNPWz8mngfKUb530+WPwfM0lp3B2YBs3/ledloBfzBWlxhYSHSFRXhDEV7HEkoqwqglPTUWjghQYweWmqOE+EK269fsimX3FD57gEomaGeUkfLjI4OBKKUNr5/Sz2uDrRcKW2z8t0qf4v/jgDXDBISVWLHwhwwrL/zJ7nfN7ndmNmfhfpPWwO7Ls5Gm+Do2l1MCnscgNt5kjG8zVek8yzjKaM9sRTTgT10nfkNS5+OZYeJErl1wf08oe4ORa6M4W16T9nAIwW3s7JPBoMLV3B2/Pv846pLGfXCQvac3IH5jOakpcZatgSIhPgBiCawGlFIjyekhHoMCbQUSpIQklbf+bYzUuKbIPurNCW3iQnys325xLWAIbelUqoL1GWkRkCpT1TOylyjhFYRcgN2JZhjeOWnKw38xrDJFWFKbANIyW4J4vxbCu7OMkd3Arh9UJhrnIKtOJhyM49c8Jo+V5KBTHAtkmNjRcukicERm4F0+GpiGtWmfDfzxPd4mDu48N33UBdo/ux/hZPIJoiD2uJ7wQf3xN6HXqdQnTRJ5MOceQCc+cICRpsEi79nQoeayTidQWJ77uKH3onMIxOAc34r2yzgvEauJBuHNOzy3UMKWutPgX0UI6VUHNAH+L1Z9CbwjFLqGCtSzcahDX9Qh8pIQZTSikPU6OiXNE6xlLSwekpajwT54LaySpuD2+UIETQLNYFanGFqn/1aCJXvtsp9tyVK6YH1RjbfU7qvUhoIaqIimlYVI10O9rbBTY+yKn+L+0kBOhjX4T2VfuI8kb/UtP5fwyKl63b+SEVNoNH82oMFm5TaOKQQTjWf33aGPLkA2h3/A2/qqwA4n/mo54HZcH1/KfW9dPE/eHjkLWhXJCrPz+TkB6ggiv+9EE841eyhA3MYx5bhX8g+5r8vJapLEXLWEyF+nZGy3HcQRXEdQtRKZZ1/JVjGd74iMUXCim0pgLgYhMCavk5PJrABMRiKhV0bIDHWnKQpu6UcPKZ/1R0t2/lyjRlRJPhyoH20xL6UFoEnAK6eZh+7gYCUGLsc4I4F8iFnO6TtFcfdyiqI7ys9oaUrjJNwsjk3ECXUC5UrwR2Q/VXmGLMnB+x6BxJvBhZCTjGkTYOOFDCCJQB8zGA20YNN5/aA86bxUvV0iiLiqSAKtQLpzQVU33vhQ3hw0XRgGu9pYf8qTz7UOiXvpH045NMNpaahgPfM52/4klLZ9kv5YtG0fZqNQw52+e7hgCRgV/0KJKVUgVluk9JfEQdK2I6NjeKoqPDQ86jwQ1cp9Qdrf7EM1dIGSEuM20WEM6zFJZ+Rrv2dZf3B2kaJo+W+eyBKaUtIacjo6AD2X1urqQk211O6bzZroFaH1N/G4HY5+L6s+mfPp7UorQz8LKXUek/ssZXSA0Z0uJNIl4PC0mrOnrWaJRMGNbhdnq8iZNzVGrRZ+a6NIxs/hCWyjGEUkEAm8/iKdBYXnMHi/qP582Ov8OfHXkH3gXlRZwvxoQeX8xx8DCVXRlBNBDtIIXPkewxlGSOO+YBPk/vxiPoDTz9yG3vowG9eL2EJI4ighguy5nNB1vyQskkCoRiYUh9S1ppNiICSAFRBaa4hpHFIWWucURcjETUxW6JcikqAzeDPk3F8ihBSZFn7SCgslgclZl0A6d6KQMqCUyX/FACvlM26kuWYHi/sykPI6G7ZdlcueOJMb2sCFG6HtHQgKCXJ3hhzLIxrb5Wcp7/cPHLkuftUc75rTSmx6WVP7AWFf5Xjp2UCq6Fj3g/cVzuF+2qncBbz2UMHLrh+Piv1IgZFrGITPZjPWdx/7q081vFGHtWTYdxUnsgcD3vgZD2MB7mLc7IWwW6nPAzU70O/co7KAOBN7/nUnOvBxmEKu3z3/xWUUlcrpdYppdZ9/73NVw829lYHDojcFO+tCTnvAkRHiFKq9aHnPPpdSRVbCsv4Ye/BJzVlVQ2TlmRvFAkxLVPKJINzf6OjRkmpw3Lf/fmvdXUo//SXMTqyiGxLekqrA7XU1uqWkdLwNsoprf555bsdooxSWnHk9VcfDOytDlBQUsWf+yahFJRUNvyBHazVfF9Wja+8huJWvq9tpdRGm2EZZ6AmGlX0WtDdQX02jZNM5IjyXsoTvt50YA8p7CCdrzh37rv8gX8xnllcpO7jA30Lu0mgB5t4kgnk6DFMZAZ76MBHF/2Omzc+z7yeI3k+4xI5zqMI+QwgvZs9wWOZCgH4DBEsABLAMxwp8Y02vaZAIGDUzJ6Qs1L45YABsg9XlZBcTwaUZhkVM1nUzrK8umMQh5Tl+qSvszIXEgcg6m0vpKd1AHUxLdX7f39PTCdEIAkas6Z8WF0MA5LMeZhYGTojBk6+uvGuWPMaWMqpAwoD4N1hngdMmTMICffBmcnzWfislNJuvf44AO559i6mM4UvsgawJeM41tCfxaqU2ODvKXbMYYXuD8BDYyfwKpcwigV80W9ZaB7j9R6yyODbM7vBmVPhZjG1OmfrIn7X/SN+t+Ij1jAYG4chapFrrGnYOaVti3wgUSnlMCqpA/mvsV+Emp3n/cshEKxl0+5S2kc40Vo3a8pTH77ympDzLohSGjAq2aFm8GIRsdXbfuBP6YktGtNSBbmskZ7D+q9Nc4gM379fsiZYGyKHP4W1PHAg7rsWaWxJTukBqFDVTfTCWogMle8GQ+62V5zatNF3W/WUllUF6HpMu+Y3NIipV75r4+fjm6K9gPRlt49wNvo6ZufvCd2U+WjL95x7cqcDPqatlNpoM6jpQAfgWnk+ggXEBsdxO09yh/dJAOYwjjmM22fcZ2GDGV/zDP/X3pmHR1Wdf/xzZjLZNxJIIEAEZBcUBIuCIotLRaVuqPXnUuuurdate3GtrbW1dUerVq3aKi6oBUVBEFk1LAIS9iWQQELWSSbLbOf3xzl3MkkmySQhZKLn8zzzZObec899782d5H7vu8EKFnEG73Mhf9/6G67mNT5iJh+Li/g1f2baupXMHvVbJrISH3ZVVAdU2Ook1G2XExWuaxUZ6oOqqGvlaaKXO+GrWvVKG4h6nPOp8kxOOhtK16AEZRYkjwGKVPhsXCY496jxfcerFxPUektwpmUpQVqzATxaEHp2ACXKy7o3BxgPabHKG1q4GyUwLUFap/YddyowHCZlaVsK9THmAAXg3KqOz5GhPb8x+lxYjZhGwIifoQSsVfzpIEqw5wHjYCjb+PjWqXx861QeEcep6rnAJ87zKJ0QzyZG8/GTFyFnXUvx5eoP0ztcQhw1TGEpuSvH8qi+1/q5jId9v6OALJVv2hPYC9x5H2xT/UhXrZvKqnVGkHZbJOrBSEsv3afUCNKuQUpZhHpUZ/0l+DGw3uSTHl0KymuREpy1XuZvOhj2djVuHzUeH2mJQZ5SXYW3upPCZDvCAN1WYvmO4rC32V9azZ5iV6vjlKe0ac7hWzedEnZIdGyUrYkX0O2VzQo7ewf6lHoCorH1QkftCd+1hGxLotfyotZ6fZTXKI9icCh4KEKFOB8NnDXGU9pZhCpUtFOL0iGZiaTGR1Pr8ZNXUt1k2yVb1c1slE2wOLewwbq2FkAynlJDl3PK0CUArPWPCyyTAY/eWCayhIs/XxBYd6n/VW7meaZxNhPYxPm+j3ANtDGfGWxgLH+Qv2MGCzhl3E95aMUfKZiYxVrU3DfHvKJySCtQojAGJRLXokReARAFjiEocbYWSFEhulZNILJQRZAyoHCD6gmalqbnzNBzuHThooGQ3IdArimo+fNLoC9qbmqBUog7G3VrmKuLHMWqcNoBJ4NnNSRPg2RdjGlvHgzoj/Jl1Ko5PXngmK7EbZwX1dPUpdfv12I5ivpKwxVQs1iHCQ/R6xYAVcBYffx1ULMc4i4CPoKRP8mlnFQA1skH+CGfcAaLcIxTT8k+3zGRDbePIf/2dPquKCH9v9ezkHLu4a/cxtPICgEL4I1zLgbgqTlRPMW98KcHgAeQUolRcTtQDNwT6ooxdBus8F1DRCCEeBK4COgNLBJClEgpj0M9GnxVCDEbKAOu7kIzv5fklaqbPbtNcOdbGzhjRGYgtLIlSlwqFCE4fDc+Rt3audxeerTBS3g0KCivAeDLHcVhe4RLq93UevxsOlDB6H4pzY6rrPUyOKNjt7Vx0XbKGrXTcbeQU+qwWeG1kdcSxirY1JK3PDao0FGZzr20xFxzqPDdo9v/U0rZ7EOH5kiMiSLKJo5qTmlnFvLqTOq8fuq8Pi6dsxIhBG/ddAo7iqpw2AXHpMXTI95BXiksyi1s4Em/7PlVbMqv4AcD0jg2I5G3vs5j1pyVzL1ZRcj59e/N55dh5XUbUWroMuRs9fN3LGEJUylZrkJ5+k/eCcAlvMM7XEJ/4AfTlgHw1aeTeVv8kLf5IfTI5Eob/NY/m0fWPMhzE68lCh8vcj3HspNj5TSK3Yd46dnbkBfqL0MBSqQNp75qrVXoKAclTIPyRRkDrFaVbPtma8OL9PpYVUQoEPqbhfIongyexSrMlxI93okSwKg+pVGA06XatmSmQ24uDMhT+6mp0IWPEiC5v5pzZwX0/VxX8gUGDNJzWqL2LF05d7VuQ+MCR6Le53R9bHl6uZWnnqhsSE4AR4XqmZo2HiWsS1De2BHK28tq4CK4LvsNfpKnujW9yRVMYA1TWEr1jnjKSWVa3jI2Zw/nQf4Ak+AC5rGUKQzKLoBXQMRIpk+bz7X8C4DXH7mEJ7kdHpnKeHI4g/ksvuhc0t/JB6Dkfzq8yxQ46p5IwgnfNRwlpJS3A7eHWL4V9XjO0EXsK1VPLfukxHKgrIa80mqGZia1ul2pFlBpCTGBZVZLlEirwOvx+Sl01tI7OZZDzlp2Ha5icEbLx+jzSywn5FUvrWFY76Rmb/ibC99tC9/sL28iuDze5osFWTmlvnZ4SttS6KhdOaXhzB/UEsbyKKa24imNc9hx+/x4fX6iWggNDpdwhFytx4/XL9tUfVcIQWq84zsRvtuZYtfvl2wvrKTa7SPWYWOgjmb479d5RNlsRNltxDrsxDpsDUTprOdWsrmgghqPnynDezEsM4n/fJXX4CHAgbIaDlbUcsqfFjMgPb7VYkhGlBq6nG0MI4sC5GT1eSL7mc2DAGxhJJ+K9TwkKwH46uwl/EMW8Ivs52H/A/xFlvDLIU/ywY6zGcBe3uIyZvMgHzGTXROP4/6Vv2b7rUP5HX8A4I//fgh+ofZTuAkyp6O8nj5UsaHN1OdhbqK+r2eF6jEKOvTVat8C1BQqr+je1TBAV7d1jIA0r557oJ5HP1yKGwRReaqtS/I4NdeIEpTItGoxDNLb1KllAxKUJ9WzWNsQVJAot0h7cUeAZ4Oyb28BpLsgrQ9KXPvUPI4hyjsKEDceMkfr/VVA2nR9zKMJ5NQGBHsaEAPf5A0jh/EA7Kc/o9nILgbz/kc/RtglH8w4m0wKeeHJO+COB/it9DGOHNbtOZFJ9uVs/ulJMA3+xbUsPl7lE5+18UM+fXAm42fnsFicBXg4zfYlANUz4/lUZCIAdv0AOSj8PCtDBGCF7xoMhhbJK60m2m4LCMpSV3ghhyUBURrsKVXer0irwFvorMUvYdb4fjz1+U6ueyWHL37ZcnrGIWctbp+fQT0T2F3savaY2uNJC4VNCPyNCkS15CkNtIRph6f0peW7gc4rdFQXTviuXvfG6n3ce/ZwIAxPqfbg13r9JHZQlEopOVBWTWJMy3LEWauETlsfOqTEOY5qn9JSl5v46MjK424OS+heM3EA1W4fGUkxVNR4yD1Yyel/WYKzxhMI5X7rplP408e5vPDFbo677xMyk2LZX1aNx6c8oOeO7kNmciyxDhs7iqo4429LibLbOFhRS0yUjaLKOsqrPa2GoZucUkOXIcQDTGQJ74qdvCt2BpYnUcmV/tc5R0wJLJvKUqaylGPlJfxi3fMwD/wl9/P70ofhWrDjYz/9mc2DDGMbl/AOzIG3uIwLmMdQtjGUbapFPLB3LmSejfKW2lEezg3ANJTYy0GF3FohuSOU59NrFUgqBIogd4eufpsBA8bDmjzwWKHBUaimCnnA3XpfOtfTodvDlK5BCb86VA/REtjoUst2bEKJQS/EjVHLHKN0W5gEbZsdBqcAm2HpF1Cpvb8DUiBtAhSu0DmtY1Be01wloONi1TZAfQVgS4Cfr6sIF+ljrwU+By6CX/Mnrit4mesKXuZC3ucjZvLLG55k7vnn88WMCfwobz7rGQNTgG/u41e+R+lJCclPuNm88ST4GSy2ncti27nIjSA3KnF7zOytzGcGUjqQ0sGtPMO8vEsAOEsWwq4fhH9hGSIHU33X8D1gxhPLmPTnxR2aY39pNf3S4vj7ZWMAmoSQNkep7r/ZoPpuhHpKC8rVk9yTBqQRbbeF1etyn84lveMMlXdiFeNpjOVJ66in1G4T+BpV0nV7/c0KR0cHWsJY+4lrIUw7kFPajvDdcDylQgiEAL+sz71sNadUi64jkVeas6+M/PJathVWccqfFuNvpopxZTtFaWp8dCBXtrOpqPawo6iKfSHyLjvKwYoath6qbHJtQtvzNi97fhUXPbuCfSUu9hS7uOutDcQ6bKz6zXRG9ElCAPtKq4l12OnfIy6w3RkjMpGAq87H7mIXMVF24hw2xvZP5Zj0BGIddkb2ScYmYOdhF1sPVSKAUX2T6ZOiBOsxafEt2mZEqSGimMF7gffpvuv5VMxErr6fZ7mVZ7mVCaxRYi8Krkl7AYDom52cJ05jRulibucJ5nATAPuOzyRXfMis/37E1aVvc3Xp2yqbaogSkOxGCa7Rui1MDEqYxqDEY5raD0UoMXeRetWUoPI1M2DE6XpcEezIgQnZ4EhB5WW6YMcGIBNKf4USnnUoL2iFmjsuBvBCoVW4KAEmjADsMGSILnzkhR0rVHsafOqVH1TJt6YOyIMpGXpZis5JdUHmTN2fNFfvM6v+lV+ijzMdmKmO21kAvK9Fsx0lZvfrnwvhY7EJVsfA6hgqSeJO/s4H/zybYWzjDp7gt9kPspSpcMIDkAs/tb9MIZnk35XOgeN7wlbgdUCqCrviUWVyPDWMZQPD2MgwNrKFkfwj+zYWMpOlpVOITq3khUHX8E+T6ta9sMJ3W3oZDN2cA2U15JfXBnL42sO+kmqy0+IDHs/SMIuzBMJ3gwodWZ6aSPOUWvmkWalxnDSwB/1buUEFdXMMcGJ2D4Sg2fA/S7S8/XWTotFtwmEXeP2ywc3/NwfK2aGLvjTG3oGWMKcN6UVGUkyLIbCBnNJ2eGLrPaUte+6ibAKPrz6nNLkV4RfwlB6BCrzvrj2ATajzfrCilpP+uIhL56xsMs6pH0Ykx7XNE54a56DMdXSefi7boWrDVdR4OtwaJZjyajcHymqoqPHwwYb8Buu2F1aydl9Z4PoPh8paDxsPVFDorKOkyk2t10//HvHYbYJ5t53K8f1SyE6L57g+ycQEPTA5MbsHfVJiibYLhmYksvG+s8h96BzeuWViYMx7t07ixOweDMlIpH+POI7LSubdWyaRnRbPiD7JrUZGmPBdQ5cRKGqzrf4ivZ4XubjgfZgXA7c9wOtyE3vow5tCVSO8WA7g4uPf4EtOYyFn4z4vmd+unA0SXuQq9rv7s3n5SfSftp8fizcBGH35DJahEil7zK2BL1R1W8cYAsWNkjNQYnETSoClo0JvXShvZx2wUNkYpwWbZ48WbxnANBhSh8q9tHqh5sEQHR5c6IW0ErW9p0AL1xEQV1hfLIl0wKnEZ3KWGusYj/J8upTQ3LFaLfei5oubDskb9IIo3Zu0VBdSGq+OrXCT2iZzkpofPXff4P9Tet7kWcBiVDEkr9o3KShhvh8OyKeIRynJNUxgG8O47t43uPSxV/ktj3DpvR9CPkyX81ksHmDlZVczki1cw6ucz4eMuuJrhrEdrhhMJnlk/ApOYxkvvHwHuYxF/lTZMZHLAbijmWvH0E0w4buG7zglVXW4tMeoyFkXltBqjJSSvJJqxh3TIxA6Ga6ntMTlxmEXJAWFPybERKanND8gSmPJTktg4beHWt1mb4kLh12QlRrHwPQERvQJ3bfaCu8Mp5hKS1gisMRVR0aSyqfxS0mUreVCR+0RpYectWQmt9w/NboDhY7C8ZSCKnZU5/VTUeMhOTaq1TxRS5R2tC3MrOdWsi6vnAvH9uNAWTX55TUcKKuhstbLuIc+45j0eN67VfWlc9aEJ5gbkxLvYOuhyg7ZGS5LthYhBEgJ8zcd5L11B3B7JQnRdmw2EVY+aHDuqPX+QFk1fqlCrX/z3ib+81VeoJDQC8t24/VLDjnDE8F+v2RvSTU2IRjRN4k4h/rdBxdVi3XY6ZNib2Kv3SZY9Zvpre7DEqnB3ttwc2GNKDUcdc5gPgCL151rLQDg9tLHeEr8DnrEcGPpE3BrKgVaQV0llRvwWp5h2vaVXDX0nwD8mxt4ZOKD8CLsGNmfjdEnUDgtg5NWbGaNnMDyT89kPGvJ0dV3z1y9HOw6BDYLPAt0P88sVeHWMRAlRqN0zukMYIMuPnSyPoBcIEZ5KB2jUMJtjdqGsSgP7AgozYG0BCjNUyG2Hv03o6gC+qYA64FsyDwZcEHpAkgbAcnJqFDeKL18gxabRdBP55HGpaht2aDGkIASz1uBK6HvClTYcDpkzkJVEbYq9Orqu86tel/Z+pjTUaHEsfXvS+eq4kc1myBuCGxiND/8aCkAW84fyd0iA3ZJaojn31zFpY+9SiVJXMK7LL7lDdz+fEbbNgXO/2axAJ+cSQnpgevhXf8lcJ3ynPJT9aDiMv6r105ldJpS1TfwWqjLyRDJ+AnkXhsM30UWflsY8KoVOmvbJUrLqz1U1nnJTosn1mEnIdpOaZjenVJXHWkJ0Q2q2FotYVzuyHoiVFBeQ494B/HRUQxIj6fU5cZZ62mxeE1eSTX905QXp2+POA5oYduYgxXqD01LPTnD4dfnjODm19dS5KwXpVJCc0WCrZzS9oTvFjrr6JvaiijtQKGj+uq7LZ+TM0dmsnp3CWXV7laLHAHERav5Ohq+W1rtxiclF4/ry8RjewJwxt+WUupyN8mptsK225oznBoXfVRawvj9kqXbDzPzhCw+21LIw//Lxev349fXzojerRctAyX0o+02pJTsKKrCVefF4/MTZRMMSI9nW2EVB3UY/EXPrmB9Xjk2oR5iFVfV0TMxpsX5F2w+SLXbx6CeCXz089NCjjlSxZTaM48RpYajzuJpSozKz0FMA8bAzz9/LLA+t3QAACMe3QvALx99Eluuyiv593k3MOLr9VzFa5wlbuZ8OReAkWzh2NIDJMeq8IkZk+azyT0ahnvJYRzROk7wzNjlSriBav2iQ2XZo6rS1ixWBYDYD5mWWBsDcUXU31zHAqXKm7nmQ5iQAQGNlat+1HyoWq0QC2mW11H//Y5zQWEeZGpPJulADKRlQGmuGl+4QXk2V6yASYNUWG1N0M19XAIB8Wv1KcWne6MugL07lPD15kFcnRLGaRW6SNMGNUfyBG1vtjoXVGg7M/TySZDWH9gBcaNh/bsjmFq9lP+efwEAj/h/yxS5iau4kqudb7Ik+VTOmq0KFG168Hj6PbuTD5nJJOcKAJbvPBPWwjD+yxhtxFP+nze4Nm7nMRYwg10FTwJwR1b9up7+fIpt4TVbN0QIxlNq+I4zf1MBcQ47NR4fhWF6KxpjtYPJ1oI2LTGasjBvpEuq3A0q70J9S5jO7lN64TMr2FPi4uM7TqNPSlyr4wvKa8hKVeOOSVfHmldSzai+zbd52VtSHchDy0qJI/dgUchxB8qUWG1NgLVGryR1Lg/r8EufX1Lr8TXroYuytb/QUZGzlrHZqS2OsXJZO9NT2j8tnvc35FPkrKNHK0WOgIBX7dfvbuTjX0xus10WZdrLf/LA+ofUi+6eAsCkPy8mv7yWH/5jGZ/8YjKPf7oNoE3VdwF6xDtwuX24vc0Xq2orlz2/CrfXz/u3TaKixsO5T35JUWUdbq+facMzWLevjP1lNSTGRNE7OYadh11UhFEBePmOYjYeqMAu4IzHv6DU5SYpNgqH3cbAnvF8+LNTGf/wIg6U1zD5L0uo8fiQwAtXjeeG13K48JkVfPmraSHtBXjpJydxz9vfEOew89ldpzcZFwkYUWo46sjPmy6bwQLu4nH+JF9jHhfya/7O9F8pj+oi/3nwMtz2m7/x4dczyRUfMk9eyK2ykAe4j17iHj4CHl0refrE6ymmJ0uZwrLo0ynXf/CXoZ8I7UHlgGYTqIbLBlTF2VyI+xnwL+qr32agciqzUKINlBezQgm94xNQ3tESKMypr+Yblw1xqGJFmbGQPArQvVfTRsG6DZBp9WJNQAneBEjLVvZlZgE+mJQOTILkBarIUpol0rL0djp3lDxgCMTpHqkDdFNVRwzs3QADsqC0SPdT1W1lKAVORnlHXahc2iH6HI1AFTeapM+DF8b+JZdlv/wBl38+D4DCaRnsYjDXXfQG113/BpxbA/uUAjkg3iC65E5OPLQF6VH/VG8/8S8APMm9CLFNG/EiUt7HTTKVDYxlDTCOHMZl5fAW1wCQwyR6+hvmURi6EUaUGr6jlLrcrNhZQkZSDDUeH4ec7QsLsPImj0lXoTBp8dGBqrqtUeJyNyhyBPXhlZ3tKa2o8VBe7eGxhdt4/NIxrY4vKK8lW4tR61j3tSBKpZTsK3ExYaD6p9W3RxzFVXXUenxNerjml9Vgt4kG+W3tIcMSpZV12j4Xfgnx0aFvl4UQ2G0CbxtbwtR5fZS43PRuJXw3ym7DJjqv+i4oUSolbDno5IT+qa3Oa11fjasUA8yasxIBvH1zy78HKSWVdV6SYx3YQoRcL757Cic88GnggY2VU9taZeDGWOMrajyBBw4dxQozPv7+hbq/Z/3vZvKQXry+ah8xDjsLbj+N6Cgbw//wcSDEvzlmPrWcbw86cdgFHp9k12EX8dF2RvROCkRBCCEY1CuB8hpP4LykJ0Rz5shMkmKiKKqsw++XDc5nrcdHQXkNxVV1nPKnxdR6/QzvndThMPfOostEqRAiHnX7Pw5123KPlPJ/zYy9AfgVIICPgdullH4hRF9U2ZQTgR1SyvFB20wBFgDb9aI6KaXpwxZp2OGKz17m31zFFsYilo0F4NeTlVC9e9kziEmSWyc/Htikn/w/ZnAz48jBi52X5HbuqH6CqfFz+Znor0etYZJrBVXLe3LwrFRVERbgLOBFlAhcQaCtCrtRAnQuqojRDlQupUsJyyG14NT1E5JPh9wNMGI8StitV2Mzs1EexjHg/BySJ0F8LiQP1OOsQkQFMFr/D66pgLjdKC/qEFTrmc06V1VX/vV8qMKK01D2gF5nVfPVRZPI1ZV/AQcoD2wRDDgZPLnwlBfuqyOQU0oOqvJuNoEequSgBHoJqmuh1RomSh3DfTzArdOeBSCLg/yy9DFsz7l4OfOnHJRZgXYx7y6+D3e6B3qAmCdJPzWf6SwCQCxoeAnMZSYwnTGsZ2/gSQGIdSB1f1LjIe2mRGD4rhBiJCpd2Y66sq+VMsTdlcHQCl/vVU8WeyZGU17joaidovQvn6jwnf5pyovYIyGakqrwCx1lNwoZttsEcQ57p+eUnjO6N88s2cX76/O5/tRBjMwKne9psbOoKlAJ1bJ5b4mr2fEXP7eSarcv4FXtq72sBytqA70ULfLLa+idHNvhvpm9GonS3IMqH7GlNh9RNtHmnFJr/szk1oWSw25rtZVGKML1lFq/i4oaT1ieUqt9S/Axz3puJfvLaiiqrMVht3HuE1+SV1bNyD7JIcM495fW4PFJ7jxzaMh9xDrs9EyMJr+8lhq3jxq3l5goW5OHEa2RosORK2rcR0SUnvn4FxwoqyEpJopqtw+bEIzsk8SeYhf90+LpkRDN3EYPRn54XG9W7y5tZkYl0HcXuxDAknumcMlzqyh01vLv6yYw7pgeDcbOvXkis55bibPWQ3SULfCAICM5hl2HXazaXcKTi3cAKnz2peV72K+jCPD4eeLyMfxoTOTeT3Wlp/QeoFJKOVgIMQT4UggxWErZoMSZEGIgcB8BfxQfA1cCr6FqnN6H8l3dH2IfW4KFqiFysKquArz5pKpu80ajlu538Qz/mvw1oHqZAkxmGXn058/8iigdD+vFzpj49YxkC2vktRS9nI08UyB+dB+vf3YJj/A7LuMtNWkuqvLsYug7C/gCFY47CfgUJc4sr+gMYLEuVlShiyEBpMGI0SihWYsSlC5UFd0iyEyH5ARgD/TNBkogvwisPwO5OscUOxS6YMAYlAiuBbJhZy6MSEBd3UPA4QKcUFqhPanqoFXF3xIYcrruT5qgwpE9uaiQXisc1wuOKLhvBOobb/1dzlZjHZawBSXEK/Q5WaE/T9L2lUAdMbzJFYHfkbs2huuyXuRLJlNEBh+JLbwkt3PZtLeYJy/gndJL+EvavWqs3vHb6dfA6vtgIYyYvZ77mKnOy4NjOWv2h5TTg09Fusrj+S3wM/XPO713MWAEarfiCIXvCiH+ClwMDABGSyk36+VDgVdRj2BKgKullDtaNEnKLaBKdAsh5qIeTYUurWkwtEDO3lKio2y8f9skznj8i3Z5Suu8Poqr6oh12ALeuLT4aHYUhndJlrrcpCc2zQNMiLF3evXd3Ydd9EmJpaiyjiteXM2G2Wc1O9ZZ68EnZSDnMyEmip6JMeS10D7Dqu46QHtVrdDfgvKapqK0rCYgWjtCrMNOUmxUkCh1tuqBddhteFsI3w0uXmNhhXpntOIpBZUn257w3fqc0paFnPUwBFS12taw8qZrPfU2HXTWcshZS1pCNFV1XrYcdBJlF81W6LUe6Iwf0CPkeoDfnzuSW95Yx/bCSlITojlpYGKrtjXGOp6yI9CrtNbjY0+xi4RoO1///gyuemkNQKDoUHOM6pvCvA0FHK6sCymMP/ymgKo6L4N6JtCvRzzZaXFkpcY2EaQWc0MUEkqLj2avrZo3v8qjqs7L4co6dhZVMueLXaTGOajz+jgmPSGiBSl0rSi9DFR8npRyhxAiBzgH5asK5hJgnpTyMIAQ4p/AtcBrUsoKYJn2ihq6EfJXyhPGmfXeMIA/Tb5Tv/s7AJs4ia8Zze/5IwDF9MRHFKuydMXenDp4R33J/3v7j3lURyT09B1g3WcjSaWcauI59b0ctV+vgAroexWqmu5oVLjqDpT40i1Y0LmUxKAq8mYSCL+t2aByLPGibmfTtCgcob2lpeB0aQ9pEeDTXzQdNjsCPW8UDBiNErSJuvBQrBa8FXru/frn6ZC2GCUSQYUef676kbJDF2hKAXJ1ISNLWKIrDU/S9ru0Taj3Dv1/3VMBjuuhZk59mxpq9TlZoOe+ElZtnwrDdFEifguvOph35QWU2PvCN/ATOYfrXr6PfT/NZDw5fBg7k4ms5BbmMIMFPJT3AFyor4ECgLGMR+WcytmAFqjo/+/yEetkQb2sN3QbjlxO6TzgCeDLRsvnAM9IKV8XQlwJPI/qNowQ4lj9OZiFUsrHhBBTgeuBYuDIN5UzfC/I2VfGCf1SiImy0zs5lsJ2iNIXvthNrcfPv649KbCsR0J4OaV1Xh9Vdd4m4bugCsN8svkQf7xwdJttCpc9xS6Oy0rGd6CCEpcbKWWDgkvBHChtmvM5ID2+RU9pjRY9A7QA7ad7JuZbnp8g8str+MHAtCbL20OvpJgGovTYXgkteuii7KLNhY6sayUzqXVRWuPxMX/jQe47/7g27aMuTE9pZlKsEr4+f1iFjmIddmKibA2q75a63CTGRDEkIxGvz6+9pnVsOejE4/MHqhpb5OwrIyk2iqEZzRcAsiotf3OgnL3FLs4b3adV2xpjhe+WHwFRuji3CK9fMlgXJGtNjFocl6Vu3L4tqGDKsIwG68pcbu6du5H4aDuLdJ6nEKLZvrjBBD/kmHvLRB78aAv/WrHHun3ijMeXAfDxHac1W7U60uhKUZoN7Av6nIeSAu0dF4qhQoh1qBbtz0opX22PoYbORzwK9AH4O7JRK8ofLNukbj2BjDdVg87dBVmMda9jZHQuq6ZM5e7jH+YGXuDnchdX8CYwjzpi2Mmx3OF8gnMu0v1P56G8iBko33sVSvRpkUgySrRFoUJ664BEXYBI+9yjSlCCdTjsXaHyNR2no3JTdaGgZKvXaa0qPpRpCV5QeZyFqIJLVt/QIkgeoyv2ZlD/zRwCexeDIxf6poPHEpQ7VIivY4g+hj0oj63l6YzSx1WhqwtvQAnZAlTQIsAolDe0RI9ZrSoKx8Wg/E5j9LkYSKCY0u6hWQx6tQCAKVd/wn+4nBJ6goSlTOFn4kXgARb99AymsoSN8cdTTTzFpPMaVyGXOeAvMOPKd5mhzZjBWv1OlX638kctj6jlVZe/wtDdkByR8F0p5XKgwQ2vECIDlbpxpl70H+BpIUQvKeVhKeUuArW9m8y3BFgihHgKdaWv67iVhu8TtR4fm/MruO7UQYDyeG0pcLZpjqLKWh5ftJ20hGimBt2spiVEU+32hcydDMaqUPru2gP8bNqQBuvs7QgpbQt+v2RPsYvThvRk8tBezP7gWwqddfROCS2y9pepZz/BfQ+z0+NZtauk2X1Uu73YBIFCR5nJsQhBkwq8Xp+fQ87aI+IpBeiV2FCUntSK2I2yCTxtPNeWKG3ufAVj131E20q4OaU2m6Bfjzh2F7vCztk8bUhP9usHDbsOV1Ht9jH7vJH89FSVgnPZ86tw1nqo9fhZvbuE04b0arB9zt5Sxh3TI2Q+qUV2Wjzx0Xb+981B/BKGhlnBNpgeWmT/aUEuZ47MDNgWTLhVYt9dd4DeybHMvz101drmsMLavy1wNhClM59azrbCSjx+P4MzkgLnor3Vb6+Y0J+XV+xBCBiakRgottRdBCl0oijVYjC7mdWZnbXfINYB/aWUFToEeJEQIl9KuSjUYCHEjcCNANnZzZltOBIInR561l0f6iUzA4JDCBDXqBLs72nZ8oPJv+ar09WXtOg/fXhFXsmgLQWkDD7EFvcIzj9+Lps4nr0MYBeD+Vf1tQBULe3JVzNGMzF5JZMtB8sMlPYZhbo6k1FXo1XsJxnlnRyDCgbUoiytlIDDzqFzNRkCA6wquE7glyihWYISvQfVmLjVwHjqv20lKHE4Hpiu55qu5k+bifLzzNVzDIEBM4HhwGadK2rNEYUSj1koMWx5QSv0HLEoMWp5Pp3AI9TnpYLKsXWp/bAC0q5HCds9ev8FwI/V57LJcaRSHhAZcVTT58ly8EK/u3ZyNgv5Qqdtny6W8oG8AAA7PvY9OhyA2371N5694i5YAD95cw7xVFNCOm/5L+NZTHXd7ySt36v11JEyFi9IKV8IY+b+QL6U0gcgpfQJIQr08sPNbaQjay5B1ShwAJvD2JfB0IBv9pfj8UlO0uGHvZNjWbK1qEVvYWNW7SpBSshqJEzStOezrNrdYlXb4kolSkPlUcY57IE2Gq0xa85KkDTJhWuJgooa6rx+BvVKDITXbi+sbF6U6sIs/76uvrTHV7tLOVhR26z4zk6Lp1+P+MDNenSUjcykWAoaidJDzlp8fknfHkdIlCbF8G2Bk4ufXUFBRW2rN/VRNhu+NlbfLXTW4bCLsHI4U+McFFXVUVnraVNLlDfXKH9OOG1y+qXFs7vYFRBxrTE4I4kvth/G6/NzzUtfATAjyJP51k2nMOu5lazNK2P+xoMNRGl5tZsdRVWt9jm12QTDeifxlQ71Hd4OUdorKQZB056qtR4f+eU1CCHw+WWrhX8uenYF6/LKuWXKsW0uEpQS5+CY9Hg251cElrm9frYVVuLzS0b0Tmpzq5tQDM5IYlhmEvHRdt6/bVIT8d0d6DRRKmVwUGZThBB5wDHU3zxkA0tCDLXGETRufxj7dwa93yOEmIeSIyFFqb4JegFg/PjxpujFUWAhMzmD+ZzBfBZxbrPjvhoymZ/Lxxose2XkZdztry9+FEMdqZTTn/2Mjt/Euxf9H+nv5PMIv2PR4fP4Xa8/AJB/eTp9N5eo/FFdyIgS1JWxQU82HnXVWb1AdQhu4BHLYpSoXY0Sc5aoszyhY1C3uUNQYvd6PeYVvf4nel9W5VzLBis0d4/eZgXqSrf2MUHPB0pkDtKfC1FeUV0llyEoQVqkbbED7wcdl67MS5HeV4J+jdHHuAOwwpszUKXEkiF1lL4R0DZ8/ORFAKT/Ip8afxwvLbqNl9bfBsBD8h5+dMVCNfB+OOVX6qv9DHfx7O/hJyOV67uaeF65+mbSX1HeUVFQxw+ydvGV+Jb35EcAyF81qoxk+K5RfDRz/6WUS4GlR2t/hu8Wl85ZSZ3Xzym6r6KV95WZHEO12xeoKBoOX+0pJTEmig9+dmqD5ZYwKHW1LEp3FKkiPKEE3c2nH8sfF+RS6nIHRG5z7C2upsRVx6l//pw+qbFhhSXuPqyebg7smcCQDJXrt72wkslDe4Ucf6CshoRoewMRFuNQYmlfSTXDGgkOv1+Se7CSC8c2fFCZlRrbJHzX+nzEPKU6fNfyMLYmSsuq3SzeWtimfRQ5a8lIig3rAcazV47j4udW8snmQ8waH26goGpTYxeiRW+kRbbOK00J01M6JCMRj0+yr7SaEh262/iBxNxbJnLHf9fzybeHeOiCUYEQ3rX7ygBIimldgozok8z6vHKi7bZAxea2YOUIB7dkcdZ42HpIfXckMPHPixmQntCih7JI5wBffGL7HpxX1npZnFsUCGX+z1d51Hn9DMtMZMEd7W+r05iFd9bPdaT6jR5NujJ8dy6q2ESOLnR0Eson05h3UXmjD6Bu3W8A3mxtciFEH+CQlFIKIdJQPqHfHynjDe1H3qV+inUwXT+6UL9eOEuOZaHOK7z4ogVKiF1fX+gI4KkH72X67PlcZlPFi+oCOYfgJppq4pn+3nwWX3EuhW9m8livn5NKOTmMw45XCbAolPdxP/XitJb60N4s6r2MA/U4KwxxNirPVFe8Zbz+mYDyXG7V263X83yByue0qt5+SH1ZliK971htRxHsvLwfg18/oOz7SM/rRYlBaw7t2SQb5QF1oURlHkowF6DCenO0/VbxJiuvFD0+AyW4D+rxC4DTUYGQyXruSerYRQGITyWfPlYfunLWm19SsqEvp574GZy1hV1nDcbtj+YPV/wVgI/fnMqDzCZep+3dxBPw8B28ct7NgTnka9BTRyZdl/UimxjND+RxXCzOUevNI6LvOilCiBeAj6TUTyLCYz/QVwhh115SO+pKb/WhpcHQXsprPGwvrOKbAxXEOeyBHLxMXbCmyFkbtij9em8pJx7To4nnxRKRpa20hck96FSFlm5tKiKtkMHcg04mDe7Z7By1Hh8lrjrsNqHCYsN0Au0pVv9IBvVKID0xhp6J0WzTN/qh2F9aTf+0+AYizKocuq2wsokozSutpqrOy3GNKvruK1HLg8nXntN+R8hTmpEUS1WdF6cWMiNbEaWClv9Pub1+bI2clYectWFV3gU4MTuVmCgbD/1vS5tEabXb12LV4GC+2Kb8Q+F6SodkqgcRH6zPp8ZTXyG5MeeO7sMHGwo478kvWXinypnM2VeGwy54/7ZJre7HeiAwqFdCk7zUcLllymAe/WQrhboQ096SaqKjbIzsk8zBihoOOesQqDBwu000eVBw3pNfcqC8hh7xDga3kAPbEukJ0ZS63Jz658+pcnup8/g5ZVA6b95gmoIE05Wi9DHgFSHETtRt8Y1SykoAIcSDQIGUco6UcrcQ4iGUXwqUj+t1Pc6OyjeNQd3YHABelFLej6rUeIsQwoM6zteklB8cvcMztIY8EXr6xwDwsfwrf+fOhuvfgzOYzz38lQ26pUsSlcyc/SF7GADAYs6ggCyKlmk35lPqx6VzXyXxxWI2VY9mVUI0l8qdAPiIUkJvIEo8WoJ0MfXirQQlTn+MEnlWRd4N2rA5euwMlMAsCpqnAiUYY1BCcwdK/OWgPJugckrt1IcMp6DEYREwAgb/4oAaU4cSpnXUF4uxoj+KqO9TakeJUMvbmosSmbWo4kopqG9Ain5ZKTyfouoKVaj9sl3Pl6vt96EEqfYafzvqWD4ddRpnbVeh0OmD80m/PJ/nbLdw6aM6FPvXaxgho/nbm3dxt/9xLnK+x3vJF3HOFuUpvWfkVC58cx7nXFEfFHHhFf/lHtsafpP8d3DCaDYxz38BbDOhvN0fiUrpb5EKKeWNbZ5ZyiIhxAbUN/V1/XO9VRTPYOgMyqs9Ks8xPYGEoBt+S5Qeqqhr8cbVCqmbc+U4thdWhayGmZagRG1ronTroUqGZiaGDN+1bua/LahoUZQu234Yv4RXrjmJW15fS3kYBZYAdh+uIjEmil6JSlgNzUxie1HzFYP3l1WTndbQ0/XerZM47r5PyD3oZOYJWQ3WbTmogt2sIjEWMQ4bJS4/rjovCdrTZnlKs46gpxTgcFUdCTH2VluJCCFC9uy0yD3kJL6RN7vQWdtEiLc0v9UeJb88vCrDPr/E55dcdlJ4IjYp1oHDXhvI322NY3spUfrPL1UOY89mvPGTh/bCbhMUB7U4ytlbyqi+KWG1dxmhz5GV49seTh/ai0c/2cqy7Yd56vOd1Hh8DM1M5P3bJuH3S07582IOOesYMfsTfH5JSpyDZb+cSmJMFKt3l7LzcBUOu2Dx3VPabcNnd53Onxbk8vyy3YB6kPGbGcPDDvX/vtBlolRK6QJmNbNudqPPz9O0iiI6l6hfM3M8DTzdcUsNRxqxDI6d/C0AxbbjOIP5AHyapiuvltZ7TqfrqL4xbOCHWCGg1zOQvawk6OlwqvpxytwljCeHVMoZFr+dh274I1zfk/G6JUyms0R1xrVEXYZ+DUR5HqP0azRK7LlQomwI9f6Xs1Bhr6/osWnUC7tc6j2qZxNoFcMQlAcSlBi0tuuP8kbuIFCYiMwgO1JQ4jCG+vBcgvaJtqs/SryOQHk5g73AoLynX6C8uiVBc2zS70uC9m0xRp+TcUAsHLd1F8exi/Sh+YEhU2xLubSXFqTFawjmb7a7eCh5Nj/j6cBfmnNsS5B+6PemekhwG8+whgmsYQIvOK/mTa5gk19Vi7xg6H/1TJdj6K74gaaVMhvRqqdUCPEkcBHQG1UfoERKeRxwM/CqEGI2UAZcHWp7w/ePUG04jsTYuGg704Zn8uI1DSPOe2tR2loFXiklbp+fHB3CeNKApkV00hKUCCoLw1M6tVE1z/o5oumT0nrxpY83HyIlzsEpx6Zz/WmDePLzHWGF/O4udjGoV0LgpnpoZhJv5+zH75dNwkWllOwvrWkijqOjbBzbK5Hcg01tfPh/W4B6j5xFcqyDAmr5em9poGjMa6v24bCLNvewbA5LhHp8kt7J4VSjtTXbE7agvIZajx+3198gd3ZvcVOPb4s2JcZQUFHLj55ezrG9Elu9VvcUq5zNUX1TWhxnkRQbxYnZPejRyu/dIiEmir6pceSX13D+CVk89eOxIcfFOuz0TIimqKqO8mo3cdF2vjlQwTWnHBNyfGOG90nGJup7o7aHEX2S6JUUwwvLdrO/tJrUOAcLf6HCXG02wZrfnsH0vy2l1OUmyiY4XOXmxIc+Q0rVi9VuEwzJSGz1O9Eav/zhcD7YUECpq47hvZM5vl9qh+b7LtKVnlLD95hdy45DBoXR/5V7kCF6C9/Ok7iJZiYLA8tu40Wm8gkAlSRRdKLykvZbt5P99GdVgRKrQ7O2MfSfG7mWf7GTYwGIsjyTUSgRtxsl9nJRorGI+tYwa1DCrEIvbxzBk4AKLy7Vc2SgPKJRKOG6GiX8rBYtVs7pKOrbsjj1vAUoMevS8xaihGYp9d7PEdQLSif1HlQvSjxaXt5Rep8+/XO1ntOyc4WeY4ieM0+vs6rsWnmlC/Scy/S6UfDfURconaF5t+ASzj88l48WzFIn49wV5AovP7lQ94X9PfWOsmb+h05gDen6wJbwQ86wzQ890NANCasnTKueUinl7cDtIZZvRX0LDQZ+9PRy7DbBe7dOatFz1V72l1azr6SaaycOaLLO8pT+fdF2Lh4X8lk5ACUuN7sOu7j1jbUI4Ph+TUVDSpwDIaC0hTYWRZW1FFe5W8x3HNknOeBxDEWd18eHGwpIS3DgsNuYMqwXTyzewZc7DrfYzzCvpJqVO0vokVAfpjw0M4lqtyoe07+Rt63E5abG4yM7hBduZJ9kVoaowFvt9hHnsDcRmu/fOokTHviUlbtKmDIsgz3FLpy1nlbbnrQFy/sL8Pr1rf95SYiJoqzaQ0W1p0lOpvXwwS9h9W5lc+OereEQ47DTJzmWgopaMpJaF7PfFlie5vAqr7Yn/9AS1ZeOb/56B/jXtT9gxpNf8t66fE7on4Lb62d8iIcxoUiMieKEfqlhtUhpDiEEUkp2FCnv/rFBD1Msgr2gP/zHMgrKa4iJspEU6yAtIbrNxY1CYbcJVv92eofn+S5jRKnhqCMnqwq8Ikfll97Ok3rNueykP+9zIX+R8EvxN+A8ACawjDVMRlwEP3/vMZQrU/Mi8DBcyPsAPNXo3vVFrmeXeAeAqfICLp87T4m3bNT9ciYqFDcWVaAoESUuh6O8iwNRobtW+kMKSqxZbV3sqOq5C/RYUOHA+1G3ywtQHkpLlGagRGIu9Tmh41FeyxEocRqLEop2vZ9Bent70Bx21Dc4CyUmrbDhPXpZLEpw1lFfzGmH3hfUC26r2FJ/lGgdrueZpT9PINBuppAMSuzqRulW+R7vZ13IrTzLR0tm8cpjl4GEa3iLS3gDgHf4P25DFaR6y3uZ3nFf5lCfU3puUO0x8ThM1znH7xsP6XeAsMJ3DYY2E+zdvHTOSgrKawN5X6ByHitqPGwpcAbyK5vbvszlprCyDq/PHzIU1mL5zmIATh3SNBw2LtpOlE1Q3Yr3q6TKjd2mKn4mxUaF9O7ZbYLUOEeLntKtB1X+5vA+zYeAjsxKZvHWImY9tzJkZd331uXjkzLgATq+XyppCdEs3da8KL3kuZV8W+AkMTaKd4PmHKo9mte9+jWf3nl6g22syrv9ezQVpcP7JPHe+nzKXO6Al87j81NV5w3ZniQu2s7Y7FRW7irmnCeWse1QJUIIBjST09geLE9pfLQ9rOI6f7poNFe99BWbQ4RK5+wtxdIzn28tYsqwDDburwjMHy5v3XQKrjovYx78lN3FVRRV1vLzN9cH1jVmc34F0VE2BmckNll3pEiNc+D1+5l0bPPh4aCuw4QYO39ZuJUbJysHgVUgLBzCyT1tjayUOGKibCy6awpxrZz3T35x5AoPGdqGEaWGLsEqdgQ08IIOZj/36vf3SkCvm2lt9x4QGKE5EQha/mQgNeX4oB0GNZ1u4m8J4gT985f654UtjG1Mc/OGO8clbdhXOPRHHc/57djW+h8wvOHiO4A7Ag6Iu3gGgCWo4shvBca9w/8F3j+D+mU/E3Svd27oItj6umi+ErOhuyE5EuG7hsilLaGvoZj13EqEgLfDbERvUVHjodrtw+Pzs7vYRXGVG4ddUF7tochZS4nLjZRw/tPLOSYtnk/vnEyU3Rbon5hfVhMonFJQUUtVnZecfWWcPCi92X0u31lMZnJMIJ+uMT0Toyl01lFUqSqrWlw6ZyV+CS9fexIVNR4yk2PJSo1tsaZQjcfH/E0HeeiCUSHXWyGvLRXhsda53E2Fcnm1m798spWkmChS4pT4s9sEAvjomwIeu+T4gECf+dRyvH4/C+6YzIGyamo8Pl68ZnwDwTasdxI2oUR3Y/brnM/GHlSoz33NPehkohZ0H24owOuXzYZLTjy2J/9YvJ1ouw2H3cYX904Nq99nuKQlROOwiwYe05YYpfNeNx5oKkq/3ltGYkwU1W4fb329nwdmHkfOvlKEaFsLHlAe2WN7JbK9sJLTHl2CX0qyUuNCtiH6tsDJ8N5J7S4OFA69U2LpnRIbVnXfPsmx7Dzs4snFO4h12OgZ5rk9Unz481NbH2TocjrvajUYDAbD9xzLU9rSS4XvGkHavdhRWMnkv3xOqcvN5oKKZnviXfb8qmbXlVTVsX5/OTn7yjjz8S+QUlJR7WHyX5Yw4g8fc+mclSG3c9Z62FlURV5pNcfdt5DiKjd9U+N44/qTkcAfPtiMlHBsrwRio2zsLnZx/AOf4vb6qajxkHuwkso6LyUuNx9syA+EIX6y+VCz9i/YdJAFmw7i9zffh/Q/N56CBP77lSpAsPFAOeMf/oyv9paxYX85Ly/fg0QVMnrv1km8e2vzHqDYKDvOGg/l1W4ue35Vk3PxwrLdRNttgeq/oTh5UDpRNsHWQ5WqF6lGSsmZj39BWbWHt28+pcEDgfTEaLx+yds5BwLnOveQk9yDlSzbfpjCyjp6JUY3EV9JsQ5umzqYEpebDfvLG6yzPKWhquNaonTB5oOMvn8hU/+6lGeX7iQ+2k5qXOgqxpMGpyMl1Hn9zLlq3BEVpKDE+dj+qWFXx+2REE3/tLgGfShBnbuth5wkxTqw2wR1Xj9bD1Wydl8ZwzKTwq7SHExKnIPhvZOQUuW87iup5uNG162UkjV7SjlU0XJ+c0d566ZTwn4YlZ4Yw8g+SfSId5CZdGR/X4bvDsZTajAYDIZOIqxCR4ZuiNcvySut/91uK6zkzMe/ICXOEci/evTi4/nmQDk94qORUnL5C6qIvnUj+/yy3Xj9EiFgR1EVJz70GTUeH7Uelbi+ucBJVZ2XxJgo/H7J+U8vxy4EpwxOx+uXZKXEctBZi8Mu6Jsay7hjetAzMYaF3xbSKymGT+88nR+/sIpvDlRQ7faxZFsRhytV+5Pj+6awMb+CX76zEYCEGDsLvz3EfeePpM7r59I5qyh21XG4sg6PT93kn5idygtXN99Sd2DPBFLionhy8Q7mfLGLarcPuxBE6fPxj0U7cNgF//v5ac3OYfHmDSdz/tPL+cMH35J70Emtx8+OwkqGZCaxvbCS8hoPybEt38L1SIhmYM8EdhRVsbekmgNl1STFOvj7Z9s5XOWmT0psk5zUhb+YzKw5q/j7ou1cMDaLPy3Yqvpd2gTXvvI1dqFydkNx0+nHMueLXVz54mpW/WY6V764hsOVdfRKiiE9ITpQLTeYnokxOOyC11fnAaqfI8BTPx7L+Y0q8lqc0D8Vu02QHBvVbKGnjtJWz/3xfVPZmF/eYNms51YipSoiVF7jxu2FN9bsY31eOT8aE/rYWsP67lz2/Cq+LajAL+Gh/23hpeV7QErevnkiTyzajs8vQ57vrqI79sw0HH0i54o1GAwGw3eMsAodmfDdbkhMlJ1x2anUePzsKKrEVeelXBfmibbbkEjO+scyPF4/BytqGf/wIqrdPmIdNmo9Ppy1Hl5btZeeidHUenz0iI/mQFkNdptgWGYS+0pcVLt93P32Br4tcFLorMXjU7kDG/Mr6JkYzcrfTGfWcyux2UTgpvfMkZn856s8fnhcb+w2wds3T8Tr83PKnz/njTV51Hp8zBrXjz9ffDyPLdzKM0t2kRwbxX3nH8fdc79h/MOLKHW5sbIUesQ7qKrzkpkUy39uPJmYqJbz0fqkxLG9sBK7EGSnxTH/9tNIinWwdl8Zl8xZSXpCdFjhjiOzkrl24gBeXK6KEUTZBOc88SVCgECotjRhtO/47K7TmfDIIgqddZz6aH0rrt7JsfQP4bkUQvDrc4ZzyZxVHDd7IRK4cfIgxvZP5ZY31tErMSZkGC6oojTZafHsOuzixIc+C/y+Cipqm/V6gvKyVtZ6WHD7aVz7ytdUu33MGN2n2fEOu43RfZNxNG7+2YWM7pfC/E0HuejZFbx36yQqajzklarr+d1bJpIQE8Vdb23gP1/tx+eXjB8Qfk5lKKzrfe2+Ui5+bhUHtVd08G8XIIFeidH0TOxYtViD4WhjRKnBYDAYOonO61Nq6FoG9Urg3Vsncdnzq4h12BnROwlnrZeqWi8ut5eyag92G3z4s1P56StfcbjKTXy0nbJqDz/+52q2Hqyk1uPnkzsmMqCnyk2c+ZSqnmsVNnnxy908PD8XUGGLPXVoqavOGyia0zgvb0OeqnZ6wdh6T1SU3cYFY7L455dK4Fk9Ma+ZOIAXlu0mMzmW6SMyEKhKsekJ0aTGO0iMieKDn7UtF625IinjjunBmH6pRLWhiuidZw5lwaaDpCfG8MwVJ/LDJ5ZR7fbhsMO82yY16eHZHAPSE8hMjqWi2sMhZy3HpMez8BeTmw1DHj8gjazUWIqcdfRJieWuM4cGfseted8W3z2FdXllXPniGhJjbHx8x2RS4x0tVpod1DMBIWBIZhLLfzUtrGOad1tk5QiO6Z8KQHGVmxq3j1Mf/Zxaj4/Xr58QOGdXnXIM761XLdXGHxNe9dnWGHdMGgN7JpBfVk3vlFjcXkl8tJ3Fd59uemAauh1CdkLZ9O7O+PHjZU5OTlebYTAYDEcVIcRaKWXz8Yltnm+YDNFiuhFTj+g+DUeHlv5PWqGFQzOTeO/WSVw6ZyVun2TebZN4Z+0B7p37DUIoMbKohYb0Ukqm/e0L4qPtzL+99ZBXi+BKrha5B52c88SXZCTFsOo300O2eJj216VE2UWT6rEGQ2v4/ZITH/6MimoPx/dL4ZsDFQzu1fD6llIy6r6FuH1+tj98jhGNhu8lLd1nGFEaAiNKDQbD95EjL0qHSni2lVFnGlHaDenI/8k6r6/VMNjOYOyDn5IYE8WXYXrjDIa2UF7t5gePLMbt9fPE5WNCttXZV+LCWeNldIj+tAbD94GW7jNM+K7BYDAYOomwwndNTun3jK4QpADrZ5/VJfs1fD9IjY/m87tPp7jKHQjnbUw4fU8Nhu8rRpQaDAaDoZMIq0+pySmNAIQQQ4FXgXSgBLhaSrmja60yGLoX/XrE069H6wWoDAZDUyKndJnBYDAYvmOE1afUEBnMAZ6RUg4FnqH1ZGCDwWAwGI4YxlNqMBgMhk7C9CntDgghMoATgTP1ov8ATwshekkpD3edZQaDwWD4vmA8pQaDwWDoJKw+pS29VE6pEOL8rrLSQH8gX0rpA9A/C/TyAEKIG4UQOUKInMOHjVY1GAwGw5Gjy0SpECJeCPGWEGKnEGKrEOK8FsbeoMftEkI8LYSw6eU/EkKsFUJsFkJ8K4S4O5ztDAaDwXA0CCt8t0JKeaMpchT5SClfkFKOl1KO79WrV1ebYzAYDIbvEF0p0u4BKqWUg4HzgReFEImNBwkhBgL3AacAQ/TrSr36EHC+lHIUMBG4RQhxWhjbGQwGg6HTscJ3W3oZIoD9QF8hhB1A/8zSyw0Gg8Fg6HS6UpRehiqsgK7wlwOcE2LcJcA8KeVhKaUf+KfeFinlGillgX5fAeQCx7S2ncFgMBiOBmGF7xq6GCllEbAB+LFe9GNgvcknNRgMBsPRoisLHWUD+4I+59Eof6Ut44QQw4GTgZvaOL/BYDAYOoWw+pQaIoObgVeFELOBMuDqLrbHYDAYDN8jOk2UCiHWoYRhKDKP8L76AB8At1me03bMcSNg9cqrE0JsPlL2dQI9geKuNqIFjH3tJ5JtA2NfR4l0+4Yd2ekKFsLve7YyKJLPx/cGKeVWYEK449euXVsshNjX+sijRqR/t1qiu9reXe0GY3tXYWzvGiLJ9mOaW9FpolRKeWJL64UQeSjDrPCgbGBJiKHWOILGBfJcdCn7RcBjUsq3w90uhL0vAC/oOXOklONbsr8rMfZ1jEi2L5JtA2NfR+kO9h3J+aSUPzyS8xkiByllRFU6ivTvVkt0V9u7q91gbO8qjO1dQ3exvStzSueiQ22FEEOAk4BPQox7F7hACNFLV8+9AXhbb5cOfAY8LaV8MdztDAaDwWAwGAwGg8EQGXSlKH0MSBVC7AT+B9wopawEEEI8KIS4GUBKuRt4CFgN7AB2A6/rOX4NDAVuEkJs0K9rw9jOYDAYDAaDwWAwGAwRQJcVOpJSuoBZzayb3ejz88DzIcbdC9zbwj5CbhcGL7Rjm6OJsa9jRLJ9kWwbGPs6irHPYOgcuvO1211t7652g7G9qzC2dw3dwnYhpexqGwwGg8FgMBgMBoPB8D2lK8N3DQaDwWAwGAwGg8HwPed7I0qFEPFCiLeEEDuFEFuFEOe1MPYGPW6XEOJpXSgJIcSPhBBrhRCbhRDfCiHuDme7o2hfXyHEEiFEReMqmkKIKUKI6qDc2zWRYltL2x0t+1qxvV3nTggxVAixSgixXf8cEmKMXQjxjN7nTiHE9R1dFwG23S+EKAo6X8+0xbYjZN9ZQogcIUSdEOKv4W4XIfZFwvn7g1B/474R6m/e2eFsZzB0JkKIdCHEAiHENiHERiHEe0KIXnpdq9d8pCCEuE8IIYUQo/TniLddCBErhHhOCLFDCLFJCGF1K+gOtp8nhFiv/55uFEJcpJdHnO1CiL8KIfYEXx96ebO2RspxhLK9pe9spNveaP19bfmdHE1auGZCfmcjyfYmSCm/Fy9gNvCifj8EOAQkhhg3EDgA9EKJ9oXA1XrdBCBLv08BdgKntbbdUbQvBZgMnAfkNNpuSuNlEWRbJJy7lta169wBnwNX6vdXAp+HGHO13pdN7/sAMKAj6yLAtvuBv3bw+9pR+wYDY4GHG9vS0XN3FOyLhPN3NhCv358AlANxR+r8mZd5tecFpAFTgj4/Bryk37d6zUfCCzgR+BjYB4zqLrYDTwJ/pz7tK7M72A4IoCzoXB8PVOq/XxFnO3Aq0B/Ya9nc2nmOlOMIZXtL39lItz1oXZPvbHewvbnvbCTZ3uRYutqAo/hL+xYYH/T5f8CsEOPuRbWYsT5fAsxvZs6Pgn6pYW/X2fYRQkSFWhZBtnX5uWtlXZvPHZCBupG36892/blXo3HzgUuCPj8N3NuRdRFg2/10QFQdCfuCljWxpSPn7ijZFzHnTy8XQAXQ70icP/MyryP1Ai5G9SkP65rv6hcQA6xCPQTdC4zqDrYDidqmxEbLu4PtAigBJunPk4HtkW47DYVds7ZG4nEQQtgFrbsYWBSp109j20N9Z7uD7c19ZyPVduv1vQnfBbJRTzks8lBPFto1TggxHDgZ9bShLfMfFfuaYagQYp0QYo0Q4poIsi0Szl1rc7T13PUH8qWUPgD9syCEXe21qSPnrLNtA7hch+p8KoQ4JUy7jqR9LdHR662z7YPIOn9XA7uklAfauJ3B0GkIlV5xC/Ah4V/zXc2DwOtSyj1By7qD7ceihN19QqUdLBVCWN6ZiLZdqrvuS4EPhBD7gHnANXQD24NoydZucxyNvrPQPWwP9Z2FyLe9ue8sRLDtXdYS5kgjhFiHulkKReYR3lcf4APgNillQZjbHDX7mmEd0F9KWSGEGAgsEkLkSykXRYBtLRIB9jV77o7Cvrsjc4A/Sik9QogzUTcDI6SUJV1tWDchYs6fEOJ0VL/nM4/2vg2GVngKqEJ56sd2sS2toh8unYTqr97diAIGAeullPcKISagIsVCtvWLJIQQUcBvgB9JKVcIISYBbwFXda1l30uCv7MRz3fxOyuEGNzFdrXId8ZTKqU8UUrZs5mXD/U0/5igTbKB/SGmanGcECIDFS70mJTy7XC3O1r2NYeU0imlrNDv96CeFk6KBNta2+4o2dfsupbOXQvsB/oKIeygisMAWSHsapdNraxrjU61TUp5SErp0e8/08ubFA7oZPtaoiPnrtPti5Tzp/8hvw5cIKXcFu52BkNnI1RxsCHAZVJKP+Ff813J6cBwYI8QYi/QD5WbfSyRb/s+wAv8B0BKuQYoBmqIfNvHoGqBrADQP11ALZFvu0VL13d3uPZDfWch8m0P+Z0VQpxF5Nve3Hd2KJFs+9GOF+6qFypP65/6/RCgEEgKMW4QTQveXKPXpQPfALe0ZbujZV/QmCk0zdvsQ32ycxqwCfXkMBJs6/Jz18q6dp07YCkNE8mXhBjzE5oWjRnUkXVhnrPOtK1v0BxjUCEkvdv4fe2QfY2ujcY5mx06d0fBvi4/f6inw3nAhPYcl3mZV2e9gD8CS9CFuIKWt3rNR9KLhvlfEW878Clwln4/FHWDmxrptgO9AScwTH8egSp8lBbJttM0t7FZWyPtOELYHvI72x1sb+/vJBJsb+47G4m2B2zuagOO4i8rAZiLqpi7jSBRgYoZvzno803ALv16jvpk4MdQTwY3BL2ubW27o2ifHXWDeBhw6/f363U/QxUE2gBsBn4ZKbZFwrlrxfZ2nTvUE7Y1qKIKa6j/p7gAXZhJn5fngvZ7Y9D27VoXAba9qs/TN8DXwIx2fF87at+p+hpzoiotHgDOPhLn7ijYFwnn72vUd3VD0Gv0kTp/5mVe7XkBxwES9Xfeui7f1+tCXvOR+qKhKI1421EPbpeiHsquA87pRrb/n7b7G/26IFJtR1VMPYDych0Cvm3N1kg5jlC2t/SdjXTbQ4wJfGe7g+3NfWcjyfbGL8v7YzAYDAaDwWAwGAwGw1HnO5NTajAYDAaDwWAwGAyG7ocRpQaDwWAwGAwGg8Fg6DKMKDUYDAaDwWAwGAwGQ5dhRKnBYDAYDAaDwWAwGLoMI0oNBoPBYDAYDAaDwdBlGFFqMBgMBoPBYIhohBB7hRCjjsJ+ZgohHtPvBwghbuzAXFIIkdjGbV4RQhwQQjzZzPopQoic9tqk55gohNjQHvsMhs7CiFKDoYvR/xQ2CiHOaMe2rwkhDgkh/toZthkMBoPB8H1CSvmhlPJe/XEA0G5R2gH+LKW8vbMml1KulFKO6az5DYb2YESpwRAZTJRSLmrrRlLKq4E5nWCPwWAwGAwRjxDiaiHEJv1w930hRIZe/hMhxKdCiLeEEN8KIVYIIXrrddFCiBeEENuFEMuFEE8LId4J2u4dPf0zwEjtVbTWN/AuBn8WQlwkhNgqhFgphPh9IzsnCCGWCCHW6te5bTjGh4UQO4UQXwDnNlp3jRBijZ7zcyHEsNaO0WCIRIwoNRg6ASHEcCHEfiHEMfrz/UKI/4a57StCiOf1P5d9Qoi/CyGmCSG+1OFLd3Su9QaDwWAwRD46nPfPwFlSyuOBzcBTQUNOAu6RUh4HbAF+rpffBGQDI4EzgPHN7OI2YIuUcoyU8pJWbMkA/gn8SEo5EXAHrUtFPUC+Qko5DjgPeF4vb+0YzwdmAmOAacDwoHWnAZcCk/W8jwEvt/EYDYaIIKqrDTAYvotIKbcKIX4LvCWEmA38GPXPMVyOA6YDdmAvkAKcDvQBtgkhXpJSVh1Zqw0Gg8Fg6FZMBRZIKQ/qz88D3wStXyGl3K/frwbODNru31JKL+AVQvwHOK2DtpwMrJNSbtOfXwAe1e8nAgOBj4UQ1ngJDAZayw+dCrxl/c8XQrwEWF7Y84ETgDV6XgH0CNruSB+jwdBpGFFqMHQSUsp/CyGmA/OA06SUzjZsPk9KWQcghNiG+qfrB/KFEGVAP2DrkbbZYDAYDIZuhECJu2CCP9cGvfdRf98bartw8aEjDYUQsY1saQ4BbJRSTm7H/lqb92Up5exm1rX3GA2Go44J3zUYOgkhRDTK41kOZLZx88b/SJv7x2owGAwGw/eVxcAMK1cUuAEIpz7DEuBKIUSUFpaXNTPOiYpUCmYX9ZFPVwQtXwWMFUIM0Z+vD1q3EhgihJhqLRBCnCSC3KYtsBi4VAiRIISwA9cGrfsIuFoI0U/PaRdCjGvjMRoMEYG5sTUYOo/HgLXAT1AhOxOllAe61iSDwWAwGLoti4QQ3qDPo4HfAJ8JISSwG5VL2RpzUGGv3wL7Uf+r40OM24hKmdkMbNV5pXei8kEPAv+zBkopi3T7mI+EEKXA20HryoQQM4HHhBD/AKK1refTijdTSvk/IcQpwAagACU2++p1y4QQvwM+1II1GpirjyfcYzQYIgIhpfHsGwxHGiHEBcADwAQpZa0Q4jqUOJ2q8zuCx0ogKShf5BUgR0r5tP68FPirlPJ/+vNe4Dwp5Wb9+X4gUUp5T6cfmMFgMBgM3wGEEElSykohRAzwITBXSvliBNj1CkH3AB2cq8VjbHz/YTB0JUaUGgxdTEf/KRhRajAYDAZD2xBCrAFigFhUyO8vGj807gqEEE8A5wCfdLRXaXPHKISYCDwL9AYGSSmrO2i2wdBhjCg1GLoYIcQhoBC4u629SoUQr6Gq+r0mpXywM+wzGAwGg8FgMBg6EyNKDQaDwWAwGAwGg8HQZZjquwaDwWAwGAwGg8Fg6DKMKDUYDAaDwWAwGAwGQ5dhRKnBYDAYDAaDwWAwGLoMI0oNBoPBYDAYDAaDwdBlGFFqMBgMBoPBYDAYDIYu4/8BWDYxeVPi7CUAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 936x504 with 7 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
-    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
-    "functions.name_plot_options(\"logger_space_xy\", data, log=True, orders_of_mag=4)\n",
-    "plotter.make_sub_plot(data)"
+    "ms.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
+    "ms.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
+    "ms.name_plot_options(\"logger_space_xy\", data, log=True, orders_of_mag=4)\n",
+    "ms.make_sub_plot(data)"
    ]
   },
   {
@@ -696,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -743,7 +383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -762,31 +402,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"800\"\n",
-       "            height=\"450\"\n",
-       "            src=\"run_folder/python_tutorial_mcdisplay_0/index.html\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7fc898bb3a00>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_instrument()"
    ]
@@ -801,445 +419,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_geometry_50\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:15483:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:15483:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:15726:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:15726:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:15969:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_xy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:15969:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_xy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "15 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_geometry_50\n",
-      "\n",
-      "          Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: Sample_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: Sample_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: Sample_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 4\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "name of process [2]: Sample_inc \n",
-      "component index [2]: 4 \n",
-      "name of process [3]: Sample_pow \n",
-      "component index [3]: 5 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 2\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 1.391570 \n",
-      "number of processes [0]: 2 \n",
-      "name of material    [1]: Sample \n",
-      "component index     [1]: 6 \n",
-      "my_absoprtion       [1]: 1.092170 \n",
-      "number of processes [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 2\n",
-      "\n",
-      "name of geometry    [0]: sample_geometry \n",
-      "component index     [0]: 8 \n",
-      "Volume.name         [0]: sample_geometry \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 1.092170 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: sample_container \n",
-      "component index     [1]: 9 \n",
-      "Volume.name         [1]: sample_container \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absorption       [1]: 1.391570 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: sample_container_lid \n",
-      "component index     [2]: 10 \n",
-      "Volume.name         [2]: sample_container_lid \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absorption       [2]: 1.391570 \n",
-      "Volume.p_physics.number of processes [2]: 2 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: 0.015500 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: cryostat_wall \n",
-      "component index     [3]: 14 \n",
-      "Volume.name         [3]: cryostat_wall \n",
-      "Volume.p_physics.is_vacuum           [3]: 0 \n",
-      "Volume.p_physics.my_absorption       [3]: 1.391570 \n",
-      "Volume.p_physics.number of processes [3]: 2 \n",
-      "Volume.geometry.shape                [3]: cylinder \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: 0.000000 \n",
-      "Volume.geometry.center.z             [3]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
-      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [4]: cryostat_wall_vacuum \n",
-      "component index     [4]: 15 \n",
-      "Volume.name         [4]: cryostat_wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [4]: 1 \n",
-      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
-      "Volume.p_physics.number of processes [4]: 0 \n",
-      "Volume.geometry.shape                [4]: cylinder \n",
-      "Volume.geometry.center.x             [4]: 0.000000 \n",
-      "Volume.geometry.center.y             [4]: 0.000000 \n",
-      "Volume.geometry.center.z             [4]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
-      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [5]: outer_cryostat_wall \n",
-      "component index     [5]: 16 \n",
-      "Volume.name         [5]: outer_cryostat_wall \n",
-      "Volume.p_physics.is_vacuum           [5]: 0 \n",
-      "Volume.p_physics.my_absorption       [5]: 1.391570 \n",
-      "Volume.p_physics.number of processes [5]: 2 \n",
-      "Volume.geometry.shape                [5]: cylinder \n",
-      "Volume.geometry.center.x             [5]: 0.000000 \n",
-      "Volume.geometry.center.y             [5]: 0.000000 \n",
-      "Volume.geometry.center.z             [5]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.100000 \n",
-      "Volume.geometry.geometry_parameters.height [5]: 0.150000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [6]: outer_cryostat_wall_vacuum \n",
-      "component index     [6]: 17 \n",
-      "Volume.name         [6]: outer_cryostat_wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [6]: 1 \n",
-      "Volume.p_physics.my_absorption       [6]: 0.000000 \n",
-      "Volume.p_physics.number of processes [6]: 0 \n",
-      "Volume.geometry.shape                [6]: cylinder \n",
-      "Volume.geometry.center.x             [6]: 0.000000 \n",
-      "Volume.geometry.center.y             [6]: 0.000000 \n",
-      "Volume.geometry.center.z             [6]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.097000 \n",
-      "Volume.geometry.geometry_parameters.height [6]: 0.140000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 8\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7]\n",
-      "LIST: Direct_children for Volume           0 = [6]\n",
-      "LIST: Intersect_check_list for Volume      0 = [6]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [6]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for sample_geometry with cylinder shape made of Sample\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = []\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [2,3]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
-      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for sample_container with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  2 = [1]\n",
-      "LIST: Direct_children for Volume           2 = [1]\n",
-      "LIST: Intersect_check_list for Volume      2 = [1]\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [3,5]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [5]\n",
-      "LIST: Next_volume_list for Volume          2 = [3,5,1]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for sample_container_lid with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  3 = []\n",
-      "LIST: Direct_children for Volume           3 = []\n",
-      "LIST: Intersect_check_list for Volume      3 = [2]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [5]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = [5]\n",
-      "LIST: Next_volume_list for Volume          3 = [5,2]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "List overview for cryostat_wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  4 = [1,2,3,5]\n",
-      "LIST: Direct_children for Volume           4 = [5]\n",
-      "LIST: Intersect_check_list for Volume      4 = [5]\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [7]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = [7]\n",
-      "LIST: Next_volume_list for Volume          4 = [7,5]\n",
-      "      Is_vacuum for Volume                 4 = 0\n",
-      "      is_mask_volume for Volume            4 = 0\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = []\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 0\n",
-      "\n",
-      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  5 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           5 = [2,3]\n",
-      "LIST: Intersect_check_list for Volume      5 = [2,3]\n",
-      "LIST: Mask_intersect_list for Volume       5 = []\n",
-      "LIST: Destinations_list for Volume         5 = [4]\n",
-      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
-      "LIST: Next_volume_list for Volume          5 = [4,2,3]\n",
-      "      Is_vacuum for Volume                 5 = 1\n",
-      "      is_mask_volume for Volume            5 = 0\n",
-      "      is_masked_volume for Volume          5 = 0\n",
-      "      is_exit_volume for Volume            5 = 0\n",
-      "LIST: mask_list for Volume                 5 = []\n",
-      "LIST: masked_by_list for Volume            5 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
-      "      mask_mode for Volume                 5 = 0\n",
-      "\n",
-      "List overview for outer_cryostat_wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  6 = [1,2,3,4,5,7]\n",
-      "LIST: Direct_children for Volume           6 = [7]\n",
-      "LIST: Intersect_check_list for Volume      6 = [7]\n",
-      "LIST: Mask_intersect_list for Volume       6 = []\n",
-      "LIST: Destinations_list for Volume         6 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 6 = []\n",
-      "LIST: Next_volume_list for Volume          6 = [0,7]\n",
-      "      Is_vacuum for Volume                 6 = 0\n",
-      "      is_mask_volume for Volume            6 = 0\n",
-      "      is_masked_volume for Volume          6 = 0\n",
-      "      is_exit_volume for Volume            6 = 0\n",
-      "LIST: mask_list for Volume                 6 = []\n",
-      "LIST: masked_by_list for Volume            6 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
-      "      mask_mode for Volume                 6 = 0\n",
-      "\n",
-      "List overview for outer_cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  7 = [1,2,3,4,5]\n",
-      "LIST: Direct_children for Volume           7 = [4]\n",
-      "LIST: Intersect_check_list for Volume      7 = [4]\n",
-      "LIST: Mask_intersect_list for Volume       7 = []\n",
-      "LIST: Destinations_list for Volume         7 = [6]\n",
-      "LIST: Reduced_destinations_list for Volume 7 = [6]\n",
-      "LIST: Next_volume_list for Volume          7 = [6,4]\n",
-      "      Is_vacuum for Volume                 7 = 1\n",
-      "      is_mask_volume for Volume            7 = 0\n",
-      "      is_masked_volume for Volume          7 = 0\n",
-      "      is_exit_volume for Volume            7 = 0\n",
-      "LIST: mask_list for Volume                 7 = []\n",
-      "LIST: masked_by_list for Volume            7 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
-      "      mask_mode for Volume                 7 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_zx_I=20806.8 logger_space_zx_ERR=38.1707 logger_space_zx_N=1.17952e+06 \"logger_zx.dat\"\n",
-      "Detector: logger_space_zy_I=20806.8 logger_space_zy_ERR=38.1707 logger_space_zy_N=1.17952e+06 \"logger_zy.dat\"\n",
-      "Detector: logger_space_xy_I=20806.8 logger_space_xy_ERR=38.1707 logger_space_xy_N=1.17952e+06 \"logger_xy.dat\"\n",
-      "Detector: banana_I=1899.84 banana_ERR=9.43645 banana_N=112573 \"banana_1643033946.th\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data_cryo = instrument.backengine()"
    ]
@@ -1254,34 +440,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space_zx\n",
-      "Plotting data with name logger_space_zy\n",
-      "Plotting data with name logger_space_xy\n",
-      "Plotting data with name banana\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 7 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data_cryo, log=[True, True, True, False], orders_of_mag=5)"
+    "ms.make_sub_plot(data_cryo, log=[True, True, True, False], orders_of_mag=5)"
    ]
   },
   {
@@ -1297,25 +460,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "banana_can = functions.name_search(\"banana\", data)\n",
-    "banana_cryo = functions.name_search(\"banana\", data_cryo)\n",
+    "banana_can = ms.name_search(\"banana\", data)\n",
+    "banana_cryo = ms.name_search(\"banana\", data_cryo)\n",
     "\n",
     "import copy\n",
     "import numpy as np\n",
diff --git a/docs/source/tutorial/Union_tutorial_3_loggers.ipynb b/docs/source/tutorial/Union_tutorial_3_loggers.ipynb
index 1205f357..8481d148 100644
--- a/docs/source/tutorial/Union_tutorial_3_loggers.ipynb
+++ b/docs/source/tutorial/Union_tutorial_3_loggers.ipynb
@@ -18,54 +18,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter\n",
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+    "import mcstasscript as ms\n",
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1mAl_inc\u001b[0m  \u001b[1mIncoherent_process\u001b[0m \n",
-      "  AT      (0, 0, 0) ABSOLUTE\n",
-      "\u001b[1mAl_pow\u001b[0m  \u001b[1mPowder_process\u001b[0m \n",
-      "  AT      (0, 0, 0) ABSOLUTE\n",
-      "\u001b[1mAl\u001b[0m  \u001b[1mUnion_make_material\u001b[0m \n",
-      "  AT      (0, 0, 0) ABSOLUTE\n",
-      "\u001b[1mSample_inc\u001b[0m  \u001b[1mIncoherent_process\u001b[0m \n",
-      "  AT      (0, 0, 0) ABSOLUTE\n",
-      "\u001b[1mSample_pow\u001b[0m  \u001b[1mPowder_process\u001b[0m \n",
-      "  AT      (0, 0, 0) ABSOLUTE\n",
-      "\u001b[1mSample\u001b[0m  \u001b[1mUnion_make_material\u001b[0m \n",
-      "  AT      (0, 0, 0) ABSOLUTE\n",
-      "\u001b[1msource\u001b[0m  \u001b[1mSource_div\u001b[0m \n",
-      "  AT      (0, 0, 0) ABSOLUTE\n",
-      "\u001b[1msample_geometry\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0, 1) RELATIVE source\n",
-      "\u001b[1msample_container\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0, 0) RELATIVE sample_geometry\n",
-      "\u001b[1msample_container_lid\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0.0155, 0) RELATIVE sample_container\n",
-      "\u001b[1mcryostat_wall\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0, 0) RELATIVE sample_geometry\n",
-      "\u001b[1mcryostat_wall_vacuum\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0, 0) RELATIVE sample_geometry\n",
-      "\u001b[1mouter_cryostat_wall\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0, 0) RELATIVE sample_geometry\n",
-      "\u001b[1mouter_cryostat_wall_vacuum\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0, 0) RELATIVE sample_geometry\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
     "Al_inc.sigma = 0.0082\n",
@@ -153,26 +118,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are all components in the Work directory category.\n",
-      "No components found in this category! Available categories:\n",
-      " contrib\n",
-      " misc\n",
-      " monitors\n",
-      " obsolete\n",
-      " optics\n",
-      " samples\n",
-      " sources\n",
-      " union\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_components(\"Work directory\")"
    ]
@@ -201,49 +149,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Union_logger_2D_space _____________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1mtarget_geometry\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated list of geometry names \n",
-      "                                     that will be logged, leave empty for all \n",
-      "                                     volumes (even not defined yet) \n",
-      "\u001b[1mtarget_process\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated names of physical \n",
-      "                                    processes, if volumes are selected, one can \n",
-      "                                    select Union_process names \n",
-      "\u001b[1mD_direction_1\u001b[0m = \u001b[1m\u001b[94m\"x\"\u001b[0m\u001b[0m [string] // Direction for first axis (\"x\", \"y\" or \"z\")\n",
-      "\u001b[1mD1_min\u001b[0m = \u001b[1m\u001b[94m-5.0\u001b[0m\u001b[0m [1] // histogram boundery, min position value for first axis\n",
-      "\u001b[1mD1_max\u001b[0m = \u001b[1m\u001b[94m5.0\u001b[0m\u001b[0m [1] // histogram boundery, max position value for first axis\n",
-      "\u001b[1mn1\u001b[0m = \u001b[1m\u001b[94m90.0\u001b[0m\u001b[0m [1] // number of bins for first axis\n",
-      "\u001b[1mD_direction_2\u001b[0m = \u001b[1m\u001b[94m\"z\"\u001b[0m\u001b[0m [string] // Direction for second axis (\"x\", \"y\" or \"z\")\n",
-      "\u001b[1mD2_min\u001b[0m = \u001b[1m\u001b[94m-5.0\u001b[0m\u001b[0m [1] // histogram boundery, min position value for second axis\n",
-      "\u001b[1mD2_max\u001b[0m = \u001b[1m\u001b[94m5.0\u001b[0m\u001b[0m [1] // histogram boundery, max position value for second axis\n",
-      "\u001b[1mn2\u001b[0m = \u001b[1m\u001b[94m90.0\u001b[0m\u001b[0m [1] // number of bins for second axis\n",
-      "\u001b[1mfilename\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Filename for logging output\n",
-      "\u001b[1morder_total\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time, 0 for \n",
-      "                         all orders \n",
-      "\u001b[1morder_volume\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time in the \n",
-      "                          same geometry \n",
-      "\u001b[1morder_volume_process\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time \n",
-      "                                  in the same geometry, using the same process \n",
-      "\u001b[1mlogger_conditional_extend_index\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // If a conditional is used with \n",
-      "                                              this logger, the result of each \n",
-      "                                              conditional calculation can be made \n",
-      "                                              available in extend as a array called \n",
-      "                                              \"logger_conditional_extend\", and one \n",
-      "                                              would then acces \n",
-      "                                              logger_conditional_extend[n] if \n",
-      "                                              logger_conditional_extend_index is \n",
-      "                                              set to n \n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.component_help(\"Union_logger_2D_space\")"
    ]
@@ -258,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -299,424 +207,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_loggers_118\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:14067:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:14067:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:14310:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:14310:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "14 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_loggers_118\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: Sample_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: Sample_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: Sample_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 4\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "name of process [2]: Sample_inc \n",
-      "component index [2]: 4 \n",
-      "name of process [3]: Sample_pow \n",
-      "component index [3]: 5 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 2\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 0.347892 \n",
-      "number of processes [0]: 2 \n",
-      "name of material    [1]: Sample \n",
-      "component index     [1]: 6 \n",
-      "my_absoprtion       [1]: 0.273042 \n",
-      "number of processes [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 2\n",
-      "\n",
-      "name of geometry    [0]: sample_geometry \n",
-      "component index     [0]: 8 \n",
-      "Volume.name         [0]: sample_geometry \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.273042 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: sample_container \n",
-      "component index     [1]: 9 \n",
-      "Volume.name         [1]: sample_container \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: sample_container_lid \n",
-      "component index     [2]: 10 \n",
-      "Volume.name         [2]: sample_container_lid \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
-      "Volume.p_physics.number of processes [2]: 2 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: 0.015500 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: cryostat_wall \n",
-      "component index     [3]: 11 \n",
-      "Volume.name         [3]: cryostat_wall \n",
-      "Volume.p_physics.is_vacuum           [3]: 0 \n",
-      "Volume.p_physics.my_absorption       [3]: 0.347892 \n",
-      "Volume.p_physics.number of processes [3]: 2 \n",
-      "Volume.geometry.shape                [3]: cylinder \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: 0.000000 \n",
-      "Volume.geometry.center.z             [3]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
-      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [4]: cryostat_wall_vacuum \n",
-      "component index     [4]: 12 \n",
-      "Volume.name         [4]: cryostat_wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [4]: 1 \n",
-      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
-      "Volume.p_physics.number of processes [4]: 0 \n",
-      "Volume.geometry.shape                [4]: cylinder \n",
-      "Volume.geometry.center.x             [4]: 0.000000 \n",
-      "Volume.geometry.center.y             [4]: 0.000000 \n",
-      "Volume.geometry.center.z             [4]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
-      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [5]: outer_cryostat_wall \n",
-      "component index     [5]: 13 \n",
-      "Volume.name         [5]: outer_cryostat_wall \n",
-      "Volume.p_physics.is_vacuum           [5]: 0 \n",
-      "Volume.p_physics.my_absorption       [5]: 0.347892 \n",
-      "Volume.p_physics.number of processes [5]: 2 \n",
-      "Volume.geometry.shape                [5]: cylinder \n",
-      "Volume.geometry.center.x             [5]: 0.000000 \n",
-      "Volume.geometry.center.y             [5]: 0.000000 \n",
-      "Volume.geometry.center.z             [5]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.100000 \n",
-      "Volume.geometry.geometry_parameters.height [5]: 0.150000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [6]: outer_cryostat_wall_vacuum \n",
-      "component index     [6]: 14 \n",
-      "Volume.name         [6]: outer_cryostat_wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [6]: 1 \n",
-      "Volume.p_physics.my_absorption       [6]: 0.000000 \n",
-      "Volume.p_physics.number of processes [6]: 0 \n",
-      "Volume.geometry.shape                [6]: cylinder \n",
-      "Volume.geometry.center.x             [6]: 0.000000 \n",
-      "Volume.geometry.center.y             [6]: 0.000000 \n",
-      "Volume.geometry.center.z             [6]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.097000 \n",
-      "Volume.geometry.geometry_parameters.height [6]: 0.140000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 8\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7]\n",
-      "LIST: Direct_children for Volume           0 = [6]\n",
-      "LIST: Intersect_check_list for Volume      0 = [6]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [6]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for sample_geometry with cylinder shape made of Sample\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = []\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [2,3]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
-      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for sample_container with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  2 = [1]\n",
-      "LIST: Direct_children for Volume           2 = [1]\n",
-      "LIST: Intersect_check_list for Volume      2 = [1]\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [3,5]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [5]\n",
-      "LIST: Next_volume_list for Volume          2 = [3,5,1]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for sample_container_lid with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  3 = []\n",
-      "LIST: Direct_children for Volume           3 = []\n",
-      "LIST: Intersect_check_list for Volume      3 = [2]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [5]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = [5]\n",
-      "LIST: Next_volume_list for Volume          3 = [5,2]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "List overview for cryostat_wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  4 = [1,2,3,5]\n",
-      "LIST: Direct_children for Volume           4 = [5]\n",
-      "LIST: Intersect_check_list for Volume      4 = [5]\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [7]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = [7]\n",
-      "LIST: Next_volume_list for Volume          4 = [7,5]\n",
-      "      Is_vacuum for Volume                 4 = 0\n",
-      "      is_mask_volume for Volume            4 = 0\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = []\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 0\n",
-      "\n",
-      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  5 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           5 = [2,3]\n",
-      "LIST: Intersect_check_list for Volume      5 = [2,3]\n",
-      "LIST: Mask_intersect_list for Volume       5 = []\n",
-      "LIST: Destinations_list for Volume         5 = [4]\n",
-      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
-      "LIST: Next_volume_list for Volume          5 = [4,2,3]\n",
-      "      Is_vacuum for Volume                 5 = 1\n",
-      "      is_mask_volume for Volume            5 = 0\n",
-      "      is_masked_volume for Volume          5 = 0\n",
-      "      is_exit_volume for Volume            5 = 0\n",
-      "LIST: mask_list for Volume                 5 = []\n",
-      "LIST: masked_by_list for Volume            5 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
-      "      mask_mode for Volume                 5 = 0\n",
-      "\n",
-      "List overview for outer_cryostat_wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  6 = [1,2,3,4,5,7]\n",
-      "LIST: Direct_children for Volume           6 = [7]\n",
-      "LIST: Intersect_check_list for Volume      6 = [7]\n",
-      "LIST: Mask_intersect_list for Volume       6 = []\n",
-      "LIST: Destinations_list for Volume         6 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 6 = []\n",
-      "LIST: Next_volume_list for Volume          6 = [0,7]\n",
-      "      Is_vacuum for Volume                 6 = 0\n",
-      "      is_mask_volume for Volume            6 = 0\n",
-      "      is_masked_volume for Volume          6 = 0\n",
-      "      is_exit_volume for Volume            6 = 0\n",
-      "LIST: mask_list for Volume                 6 = []\n",
-      "LIST: masked_by_list for Volume            6 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
-      "      mask_mode for Volume                 6 = 0\n",
-      "\n",
-      "List overview for outer_cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  7 = [1,2,3,4,5]\n",
-      "LIST: Direct_children for Volume           7 = [4]\n",
-      "LIST: Intersect_check_list for Volume      7 = [4]\n",
-      "LIST: Mask_intersect_list for Volume       7 = []\n",
-      "LIST: Destinations_list for Volume         7 = [6]\n",
-      "LIST: Reduced_destinations_list for Volume 7 = [6]\n",
-      "LIST: Next_volume_list for Volume          7 = [6,4]\n",
-      "      Is_vacuum for Volume                 7 = 1\n",
-      "      is_mask_volume for Volume            7 = 0\n",
-      "      is_masked_volume for Volume          7 = 0\n",
-      "      is_exit_volume for Volume            7 = 0\n",
-      "LIST: mask_list for Volume                 7 = []\n",
-      "LIST: masked_by_list for Volume            7 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
-      "      mask_mode for Volume                 7 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_zx_I=20561.2 logger_space_zx_ERR=35.2947 logger_space_zx_N=1.0129e+06 \"logger_zx.dat\"\n",
-      "Detector: logger_space_zy_I=20561.2 logger_space_zy_ERR=35.2947 logger_space_zy_N=1.0129e+06 \"logger_zy.dat\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=3.0)\n",
     "instrument.settings(ncount=3E6, output_path=\"data_folder/union_loggers\")\n",
@@ -726,32 +223,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space_zx\n",
-      "Plotting data with name logger_space_zy\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data, log=True, orders_of_mag=4)"
+    "ms.make_sub_plot(data, log=True, orders_of_mag=4)"
    ]
   },
   {
@@ -774,424 +250,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_loggers_119\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:14067:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:14067:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:14310:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:14310:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "14 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_loggers_119\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: Sample_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: Sample_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: Sample_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 4\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "name of process [2]: Sample_inc \n",
-      "component index [2]: 4 \n",
-      "name of process [3]: Sample_pow \n",
-      "component index [3]: 5 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 2\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 0.347892 \n",
-      "number of processes [0]: 2 \n",
-      "name of material    [1]: Sample \n",
-      "component index     [1]: 6 \n",
-      "my_absoprtion       [1]: 0.273042 \n",
-      "number of processes [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 2\n",
-      "\n",
-      "name of geometry    [0]: sample_geometry \n",
-      "component index     [0]: 8 \n",
-      "Volume.name         [0]: sample_geometry \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.273042 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: sample_container \n",
-      "component index     [1]: 9 \n",
-      "Volume.name         [1]: sample_container \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: sample_container_lid \n",
-      "component index     [2]: 10 \n",
-      "Volume.name         [2]: sample_container_lid \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
-      "Volume.p_physics.number of processes [2]: 2 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: 0.015500 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: cryostat_wall \n",
-      "component index     [3]: 11 \n",
-      "Volume.name         [3]: cryostat_wall \n",
-      "Volume.p_physics.is_vacuum           [3]: 0 \n",
-      "Volume.p_physics.my_absorption       [3]: 0.347892 \n",
-      "Volume.p_physics.number of processes [3]: 2 \n",
-      "Volume.geometry.shape                [3]: cylinder \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: 0.000000 \n",
-      "Volume.geometry.center.z             [3]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
-      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [4]: cryostat_wall_vacuum \n",
-      "component index     [4]: 12 \n",
-      "Volume.name         [4]: cryostat_wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [4]: 1 \n",
-      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
-      "Volume.p_physics.number of processes [4]: 0 \n",
-      "Volume.geometry.shape                [4]: cylinder \n",
-      "Volume.geometry.center.x             [4]: 0.000000 \n",
-      "Volume.geometry.center.y             [4]: 0.000000 \n",
-      "Volume.geometry.center.z             [4]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
-      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [5]: outer_cryostat_wall \n",
-      "component index     [5]: 13 \n",
-      "Volume.name         [5]: outer_cryostat_wall \n",
-      "Volume.p_physics.is_vacuum           [5]: 0 \n",
-      "Volume.p_physics.my_absorption       [5]: 0.347892 \n",
-      "Volume.p_physics.number of processes [5]: 2 \n",
-      "Volume.geometry.shape                [5]: cylinder \n",
-      "Volume.geometry.center.x             [5]: 0.000000 \n",
-      "Volume.geometry.center.y             [5]: 0.000000 \n",
-      "Volume.geometry.center.z             [5]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.100000 \n",
-      "Volume.geometry.geometry_parameters.height [5]: 0.150000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [6]: outer_cryostat_wall_vacuum \n",
-      "component index     [6]: 14 \n",
-      "Volume.name         [6]: outer_cryostat_wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [6]: 1 \n",
-      "Volume.p_physics.my_absorption       [6]: 0.000000 \n",
-      "Volume.p_physics.number of processes [6]: 0 \n",
-      "Volume.geometry.shape                [6]: cylinder \n",
-      "Volume.geometry.center.x             [6]: 0.000000 \n",
-      "Volume.geometry.center.y             [6]: 0.000000 \n",
-      "Volume.geometry.center.z             [6]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.097000 \n",
-      "Volume.geometry.geometry_parameters.height [6]: 0.140000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 8\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7]\n",
-      "LIST: Direct_children for Volume           0 = [6]\n",
-      "LIST: Intersect_check_list for Volume      0 = [6]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [6]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for sample_geometry with cylinder shape made of Sample\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = []\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [2,3]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
-      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for sample_container with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  2 = [1]\n",
-      "LIST: Direct_children for Volume           2 = [1]\n",
-      "LIST: Intersect_check_list for Volume      2 = [1]\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [3,5]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [5]\n",
-      "LIST: Next_volume_list for Volume          2 = [3,5,1]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for sample_container_lid with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  3 = []\n",
-      "LIST: Direct_children for Volume           3 = []\n",
-      "LIST: Intersect_check_list for Volume      3 = [2]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [5]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = [5]\n",
-      "LIST: Next_volume_list for Volume          3 = [5,2]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "List overview for cryostat_wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  4 = [1,2,3,5]\n",
-      "LIST: Direct_children for Volume           4 = [5]\n",
-      "LIST: Intersect_check_list for Volume      4 = [5]\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [7]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = [7]\n",
-      "LIST: Next_volume_list for Volume          4 = [7,5]\n",
-      "      Is_vacuum for Volume                 4 = 0\n",
-      "      is_mask_volume for Volume            4 = 0\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = []\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 0\n",
-      "\n",
-      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  5 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           5 = [2,3]\n",
-      "LIST: Intersect_check_list for Volume      5 = [2,3]\n",
-      "LIST: Mask_intersect_list for Volume       5 = []\n",
-      "LIST: Destinations_list for Volume         5 = [4]\n",
-      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
-      "LIST: Next_volume_list for Volume          5 = [4,2,3]\n",
-      "      Is_vacuum for Volume                 5 = 1\n",
-      "      is_mask_volume for Volume            5 = 0\n",
-      "      is_masked_volume for Volume          5 = 0\n",
-      "      is_exit_volume for Volume            5 = 0\n",
-      "LIST: mask_list for Volume                 5 = []\n",
-      "LIST: masked_by_list for Volume            5 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
-      "      mask_mode for Volume                 5 = 0\n",
-      "\n",
-      "List overview for outer_cryostat_wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  6 = [1,2,3,4,5,7]\n",
-      "LIST: Direct_children for Volume           6 = [7]\n",
-      "LIST: Intersect_check_list for Volume      6 = [7]\n",
-      "LIST: Mask_intersect_list for Volume       6 = []\n",
-      "LIST: Destinations_list for Volume         6 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 6 = []\n",
-      "LIST: Next_volume_list for Volume          6 = [0,7]\n",
-      "      Is_vacuum for Volume                 6 = 0\n",
-      "      is_mask_volume for Volume            6 = 0\n",
-      "      is_masked_volume for Volume          6 = 0\n",
-      "      is_exit_volume for Volume            6 = 0\n",
-      "LIST: mask_list for Volume                 6 = []\n",
-      "LIST: masked_by_list for Volume            6 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
-      "      mask_mode for Volume                 6 = 0\n",
-      "\n",
-      "List overview for outer_cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  7 = [1,2,3,4,5]\n",
-      "LIST: Direct_children for Volume           7 = [4]\n",
-      "LIST: Intersect_check_list for Volume      7 = [4]\n",
-      "LIST: Mask_intersect_list for Volume       7 = []\n",
-      "LIST: Destinations_list for Volume         7 = [6]\n",
-      "LIST: Reduced_destinations_list for Volume 7 = [6]\n",
-      "LIST: Next_volume_list for Volume          7 = [6,4]\n",
-      "      Is_vacuum for Volume                 7 = 1\n",
-      "      is_mask_volume for Volume            7 = 0\n",
-      "      is_masked_volume for Volume          7 = 0\n",
-      "      is_exit_volume for Volume            7 = 0\n",
-      "LIST: mask_list for Volume                 7 = []\n",
-      "LIST: masked_by_list for Volume            7 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
-      "      mask_mode for Volume                 7 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_zx_I=3800.09 logger_space_zx_ERR=10.0702 logger_space_zx_N=200291 \"logger_zx.dat\"\n",
-      "Detector: logger_space_zy_I=14815.6 logger_space_zy_ERR=30.8042 logger_space_zy_N=713524 \"logger_zy.dat\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "logger_zx.target_geometry = '\"outer_cryostat_wall,cryostat_wall\"'\n",
     "logger_zy.target_geometry = '\"sample_geometry\"'\n",
@@ -1201,32 +266,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space_zx\n",
-      "Plotting data with name logger_space_zy\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data, log=[True, False], orders_of_mag=4)"
+    "ms.make_sub_plot(data, log=[True, False], orders_of_mag=4)"
    ]
   },
   {
@@ -1244,424 +288,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_loggers_120\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:14067:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:14067:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:14310:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:14310:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "14 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_loggers_120\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: Sample_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: Sample_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: Sample_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 4\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "name of process [2]: Sample_inc \n",
-      "component index [2]: 4 \n",
-      "name of process [3]: Sample_pow \n",
-      "component index [3]: 5 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 2\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 0.347892 \n",
-      "number of processes [0]: 2 \n",
-      "name of material    [1]: Sample \n",
-      "component index     [1]: 6 \n",
-      "my_absoprtion       [1]: 0.273042 \n",
-      "number of processes [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 2\n",
-      "\n",
-      "name of geometry    [0]: sample_geometry \n",
-      "component index     [0]: 8 \n",
-      "Volume.name         [0]: sample_geometry \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.273042 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: sample_container \n",
-      "component index     [1]: 9 \n",
-      "Volume.name         [1]: sample_container \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: sample_container_lid \n",
-      "component index     [2]: 10 \n",
-      "Volume.name         [2]: sample_container_lid \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
-      "Volume.p_physics.number of processes [2]: 2 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: 0.015500 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: cryostat_wall \n",
-      "component index     [3]: 11 \n",
-      "Volume.name         [3]: cryostat_wall \n",
-      "Volume.p_physics.is_vacuum           [3]: 0 \n",
-      "Volume.p_physics.my_absorption       [3]: 0.347892 \n",
-      "Volume.p_physics.number of processes [3]: 2 \n",
-      "Volume.geometry.shape                [3]: cylinder \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: 0.000000 \n",
-      "Volume.geometry.center.z             [3]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
-      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [4]: cryostat_wall_vacuum \n",
-      "component index     [4]: 12 \n",
-      "Volume.name         [4]: cryostat_wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [4]: 1 \n",
-      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
-      "Volume.p_physics.number of processes [4]: 0 \n",
-      "Volume.geometry.shape                [4]: cylinder \n",
-      "Volume.geometry.center.x             [4]: 0.000000 \n",
-      "Volume.geometry.center.y             [4]: 0.000000 \n",
-      "Volume.geometry.center.z             [4]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
-      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [5]: outer_cryostat_wall \n",
-      "component index     [5]: 13 \n",
-      "Volume.name         [5]: outer_cryostat_wall \n",
-      "Volume.p_physics.is_vacuum           [5]: 0 \n",
-      "Volume.p_physics.my_absorption       [5]: 0.347892 \n",
-      "Volume.p_physics.number of processes [5]: 2 \n",
-      "Volume.geometry.shape                [5]: cylinder \n",
-      "Volume.geometry.center.x             [5]: 0.000000 \n",
-      "Volume.geometry.center.y             [5]: 0.000000 \n",
-      "Volume.geometry.center.z             [5]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.100000 \n",
-      "Volume.geometry.geometry_parameters.height [5]: 0.150000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [6]: outer_cryostat_wall_vacuum \n",
-      "component index     [6]: 14 \n",
-      "Volume.name         [6]: outer_cryostat_wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [6]: 1 \n",
-      "Volume.p_physics.my_absorption       [6]: 0.000000 \n",
-      "Volume.p_physics.number of processes [6]: 0 \n",
-      "Volume.geometry.shape                [6]: cylinder \n",
-      "Volume.geometry.center.x             [6]: 0.000000 \n",
-      "Volume.geometry.center.y             [6]: 0.000000 \n",
-      "Volume.geometry.center.z             [6]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.097000 \n",
-      "Volume.geometry.geometry_parameters.height [6]: 0.140000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 8\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7]\n",
-      "LIST: Direct_children for Volume           0 = [6]\n",
-      "LIST: Intersect_check_list for Volume      0 = [6]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [6]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for sample_geometry with cylinder shape made of Sample\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = []\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [2,3]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
-      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for sample_container with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  2 = [1]\n",
-      "LIST: Direct_children for Volume           2 = [1]\n",
-      "LIST: Intersect_check_list for Volume      2 = [1]\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [3,5]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [5]\n",
-      "LIST: Next_volume_list for Volume          2 = [3,5,1]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for sample_container_lid with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  3 = []\n",
-      "LIST: Direct_children for Volume           3 = []\n",
-      "LIST: Intersect_check_list for Volume      3 = [2]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [5]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = [5]\n",
-      "LIST: Next_volume_list for Volume          3 = [5,2]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "List overview for cryostat_wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  4 = [1,2,3,5]\n",
-      "LIST: Direct_children for Volume           4 = [5]\n",
-      "LIST: Intersect_check_list for Volume      4 = [5]\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [7]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = [7]\n",
-      "LIST: Next_volume_list for Volume          4 = [7,5]\n",
-      "      Is_vacuum for Volume                 4 = 0\n",
-      "      is_mask_volume for Volume            4 = 0\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = []\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 0\n",
-      "\n",
-      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  5 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           5 = [2,3]\n",
-      "LIST: Intersect_check_list for Volume      5 = [2,3]\n",
-      "LIST: Mask_intersect_list for Volume       5 = []\n",
-      "LIST: Destinations_list for Volume         5 = [4]\n",
-      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
-      "LIST: Next_volume_list for Volume          5 = [4,2,3]\n",
-      "      Is_vacuum for Volume                 5 = 1\n",
-      "      is_mask_volume for Volume            5 = 0\n",
-      "      is_masked_volume for Volume          5 = 0\n",
-      "      is_exit_volume for Volume            5 = 0\n",
-      "LIST: mask_list for Volume                 5 = []\n",
-      "LIST: masked_by_list for Volume            5 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
-      "      mask_mode for Volume                 5 = 0\n",
-      "\n",
-      "List overview for outer_cryostat_wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  6 = [1,2,3,4,5,7]\n",
-      "LIST: Direct_children for Volume           6 = [7]\n",
-      "LIST: Intersect_check_list for Volume      6 = [7]\n",
-      "LIST: Mask_intersect_list for Volume       6 = []\n",
-      "LIST: Destinations_list for Volume         6 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 6 = []\n",
-      "LIST: Next_volume_list for Volume          6 = [0,7]\n",
-      "      Is_vacuum for Volume                 6 = 0\n",
-      "      is_mask_volume for Volume            6 = 0\n",
-      "      is_masked_volume for Volume          6 = 0\n",
-      "      is_exit_volume for Volume            6 = 0\n",
-      "LIST: mask_list for Volume                 6 = []\n",
-      "LIST: masked_by_list for Volume            6 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
-      "      mask_mode for Volume                 6 = 0\n",
-      "\n",
-      "List overview for outer_cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  7 = [1,2,3,4,5]\n",
-      "LIST: Direct_children for Volume           7 = [4]\n",
-      "LIST: Intersect_check_list for Volume      7 = [4]\n",
-      "LIST: Mask_intersect_list for Volume       7 = []\n",
-      "LIST: Destinations_list for Volume         7 = [6]\n",
-      "LIST: Reduced_destinations_list for Volume 7 = [6]\n",
-      "LIST: Next_volume_list for Volume          7 = [6,4]\n",
-      "      Is_vacuum for Volume                 7 = 1\n",
-      "      is_mask_volume for Volume            7 = 0\n",
-      "      is_masked_volume for Volume          7 = 0\n",
-      "      is_exit_volume for Volume            7 = 0\n",
-      "LIST: mask_list for Volume                 7 = []\n",
-      "LIST: masked_by_list for Volume            7 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
-      "      mask_mode for Volume                 7 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_zx_I=732.116 logger_space_zx_ERR=5.1384 logger_space_zx_N=39935 \"logger_zx.dat\"\n",
-      "Detector: logger_space_zy_I=2799.48 logger_space_zy_ERR=16.3849 logger_space_zy_N=140144 \"logger_zy.dat\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "logger_zx.order_total = 2\n",
     "\n",
@@ -1673,32 +306,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space_zx\n",
-      "Plotting data with name logger_space_zy\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data, log=True, orders_of_mag=3)"
+    "ms.make_sub_plot(data, log=True, orders_of_mag=3)"
    ]
   },
   {
@@ -1715,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1773,585 +385,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_loggers_121\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:20436:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:20436:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:20679:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:20679:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_1D.comp:479:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:20918:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_1D_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_1D.comp:479:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:20918:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_1D_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
-      "                                                ^~~~~~~~\n",
-      "./python_tutorial.c:21109:18: note: expanded from macro 'filename'\n",
-      "#define filename mccabs_logger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
-      "                                                ^\n",
-      "                                                \"%s\", \n",
-      "./python_tutorial.c:21109:18: note: expanded from macro 'filename'\n",
-      "#define filename mccabs_logger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2DQ.comp:535:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:21333:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_2DQ_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2DQ.comp:535:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:21333:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_2DQ_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_kf.comp:536:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:21561:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_2D_kf_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_kf.comp:536:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:21561:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_2D_kf_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "18 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_loggers_121\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: Sample_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: Sample_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: Sample_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 4\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "name of process [2]: Sample_inc \n",
-      "component index [2]: 4 \n",
-      "name of process [3]: Sample_pow \n",
-      "component index [3]: 5 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 2\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 0.347892 \n",
-      "number of processes [0]: 2 \n",
-      "name of material    [1]: Sample \n",
-      "component index     [1]: 6 \n",
-      "my_absoprtion       [1]: 0.273042 \n",
-      "number of processes [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 2\n",
-      "\n",
-      "name of geometry    [0]: sample_geometry \n",
-      "component index     [0]: 8 \n",
-      "Volume.name         [0]: sample_geometry \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.273042 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: sample_container \n",
-      "component index     [1]: 9 \n",
-      "Volume.name         [1]: sample_container \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: sample_container_lid \n",
-      "component index     [2]: 10 \n",
-      "Volume.name         [2]: sample_container_lid \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
-      "Volume.p_physics.number of processes [2]: 2 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: 0.015500 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: cryostat_wall \n",
-      "component index     [3]: 11 \n",
-      "Volume.name         [3]: cryostat_wall \n",
-      "Volume.p_physics.is_vacuum           [3]: 0 \n",
-      "Volume.p_physics.my_absorption       [3]: 0.347892 \n",
-      "Volume.p_physics.number of processes [3]: 2 \n",
-      "Volume.geometry.shape                [3]: cylinder \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: 0.000000 \n",
-      "Volume.geometry.center.z             [3]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
-      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [4]: cryostat_wall_vacuum \n",
-      "component index     [4]: 12 \n",
-      "Volume.name         [4]: cryostat_wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [4]: 1 \n",
-      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
-      "Volume.p_physics.number of processes [4]: 0 \n",
-      "Volume.geometry.shape                [4]: cylinder \n",
-      "Volume.geometry.center.x             [4]: 0.000000 \n",
-      "Volume.geometry.center.y             [4]: 0.000000 \n",
-      "Volume.geometry.center.z             [4]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
-      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [5]: outer_cryostat_wall \n",
-      "component index     [5]: 13 \n",
-      "Volume.name         [5]: outer_cryostat_wall \n",
-      "Volume.p_physics.is_vacuum           [5]: 0 \n",
-      "Volume.p_physics.my_absorption       [5]: 0.347892 \n",
-      "Volume.p_physics.number of processes [5]: 2 \n",
-      "Volume.geometry.shape                [5]: cylinder \n",
-      "Volume.geometry.center.x             [5]: 0.000000 \n",
-      "Volume.geometry.center.y             [5]: 0.000000 \n",
-      "Volume.geometry.center.z             [5]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.100000 \n",
-      "Volume.geometry.geometry_parameters.height [5]: 0.150000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [6]: outer_cryostat_wall_vacuum \n",
-      "component index     [6]: 14 \n",
-      "Volume.name         [6]: outer_cryostat_wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [6]: 1 \n",
-      "Volume.p_physics.my_absorption       [6]: 0.000000 \n",
-      "Volume.p_physics.number of processes [6]: 0 \n",
-      "Volume.geometry.shape                [6]: cylinder \n",
-      "Volume.geometry.center.x             [6]: 0.000000 \n",
-      "Volume.geometry.center.y             [6]: 0.000000 \n",
-      "Volume.geometry.center.z             [6]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.097000 \n",
-      "Volume.geometry.geometry_parameters.height [6]: 0.140000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 8\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7]\n",
-      "LIST: Direct_children for Volume           0 = [6]\n",
-      "LIST: Intersect_check_list for Volume      0 = [6]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [6]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for sample_geometry with cylinder shape made of Sample\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = []\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [2,3]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
-      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for sample_container with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  2 = [1]\n",
-      "LIST: Direct_children for Volume           2 = [1]\n",
-      "LIST: Intersect_check_list for Volume      2 = [1]\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [3,5]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [5]\n",
-      "LIST: Next_volume_list for Volume          2 = [3,5,1]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for sample_container_lid with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  3 = []\n",
-      "LIST: Direct_children for Volume           3 = []\n",
-      "LIST: Intersect_check_list for Volume      3 = [2]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [5]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = [5]\n",
-      "LIST: Next_volume_list for Volume          3 = [5,2]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "List overview for cryostat_wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  4 = [1,2,3,5]\n",
-      "LIST: Direct_children for Volume           4 = [5]\n",
-      "LIST: Intersect_check_list for Volume      4 = [5]\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [7]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = [7]\n",
-      "LIST: Next_volume_list for Volume          4 = [7,5]\n",
-      "      Is_vacuum for Volume                 4 = 0\n",
-      "      is_mask_volume for Volume            4 = 0\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = []\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 0\n",
-      "\n",
-      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  5 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           5 = [2,3]\n",
-      "LIST: Intersect_check_list for Volume      5 = [2,3]\n",
-      "LIST: Mask_intersect_list for Volume       5 = []\n",
-      "LIST: Destinations_list for Volume         5 = [4]\n",
-      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
-      "LIST: Next_volume_list for Volume          5 = [4,2,3]\n",
-      "      Is_vacuum for Volume                 5 = 1\n",
-      "      is_mask_volume for Volume            5 = 0\n",
-      "      is_masked_volume for Volume          5 = 0\n",
-      "      is_exit_volume for Volume            5 = 0\n",
-      "LIST: mask_list for Volume                 5 = []\n",
-      "LIST: masked_by_list for Volume            5 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
-      "      mask_mode for Volume                 5 = 0\n",
-      "\n",
-      "List overview for outer_cryostat_wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  6 = [1,2,3,4,5,7]\n",
-      "LIST: Direct_children for Volume           6 = [7]\n",
-      "LIST: Intersect_check_list for Volume      6 = [7]\n",
-      "LIST: Mask_intersect_list for Volume       6 = []\n",
-      "LIST: Destinations_list for Volume         6 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 6 = []\n",
-      "LIST: Next_volume_list for Volume          6 = [0,7]\n",
-      "      Is_vacuum for Volume                 6 = 0\n",
-      "      is_mask_volume for Volume            6 = 0\n",
-      "      is_masked_volume for Volume          6 = 0\n",
-      "      is_exit_volume for Volume            6 = 0\n",
-      "LIST: mask_list for Volume                 6 = []\n",
-      "LIST: masked_by_list for Volume            6 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
-      "      mask_mode for Volume                 6 = 0\n",
-      "\n",
-      "List overview for outer_cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  7 = [1,2,3,4,5]\n",
-      "LIST: Direct_children for Volume           7 = [4]\n",
-      "LIST: Intersect_check_list for Volume      7 = [4]\n",
-      "LIST: Mask_intersect_list for Volume       7 = []\n",
-      "LIST: Destinations_list for Volume         7 = [6]\n",
-      "LIST: Reduced_destinations_list for Volume 7 = [6]\n",
-      "LIST: Next_volume_list for Volume          7 = [6,4]\n",
-      "      Is_vacuum for Volume                 7 = 1\n",
-      "      is_mask_volume for Volume            7 = 0\n",
-      "      is_masked_volume for Volume          7 = 0\n",
-      "      is_exit_volume for Volume            7 = 0\n",
-      "LIST: mask_list for Volume                 7 = []\n",
-      "LIST: masked_by_list for Volume            7 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
-      "      mask_mode for Volume                 7 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_zx_I=718.303 logger_space_zx_ERR=5.06453 logger_space_zx_N=39477 \"logger_zx.dat\"\n",
-      "Detector: logger_space_zy_I=2765.06 logger_space_zy_ERR=15.8723 logger_space_zy_N=140097 \"logger_zy.dat\"\n",
-      "Detector: logger_1D_I=20449.7 logger_1D_ERR=32.9156 logger_1D_N=1.01088e+06 \"logger_1D_time.dat\"\n",
-      "Detector: abs_logger_space_zx_I=956.183 abs_logger_space_zx_ERR=1.34122 abs_logger_space_zx_N=1.01088e+06 \"abs_logger_zx.dat\"\n",
-      "Detector: logger_2DQ_I=20449.7 logger_2DQ_ERR=32.9156 logger_2DQ_N=1.01088e+06 \"logger_2DQ.dat\"\n",
-      "Detector: logger_2D_kf_I=20449.7 logger_2D_kf_ERR=32.9156 logger_2D_kf_N=1.01088e+06 \"logger_2D_kf.dat\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space_zx\n",
-      "Plotting data with name logger_space_zy\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_1D\n",
-      "Plotting data with name abs_logger_space_zx\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_2DQ\n",
-      "Plotting data with name logger_2D_kf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=3)\n",
-    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=3)\n",
-    "functions.name_plot_options(\"abs_logger_space_zx\", data, log=True, orders_of_mag=3)\n",
-    "functions.name_plot_options(\"logger_1D\", data, log=True, orders_of_mag=3)\n",
-    "functions.name_plot_options(\"logger_2DQ\", data, log=True, orders_of_mag=3)\n",
-    "functions.name_plot_options(\"logger_2D_kf\", data, log=True, orders_of_mag=3)\n",
+    "ms.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=3)\n",
+    "ms.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=3)\n",
+    "ms.name_plot_options(\"abs_logger_space_zx\", data, log=True, orders_of_mag=3)\n",
+    "ms.name_plot_options(\"logger_1D\", data, log=True, orders_of_mag=3)\n",
+    "ms.name_plot_options(\"logger_2DQ\", data, log=True, orders_of_mag=3)\n",
+    "ms.name_plot_options(\"logger_2D_kf\", data, log=True, orders_of_mag=3)\n",
     "\n",
-    "plotter.make_sub_plot(data[0:2])\n",
-    "plotter.make_sub_plot(data[2:4])\n",
-    "plotter.make_sub_plot(data[4:6])"
+    "ms.make_sub_plot(data[0:2])\n",
+    "ms.make_sub_plot(data[2:4])\n",
+    "ms.make_sub_plot(data[4:6])"
    ]
   },
   {
@@ -2383,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2393,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2415,560 +475,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_loggers_122\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:21010:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:21010:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:21253:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:21253:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_1D.comp:479:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:21492:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_1D_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_1D.comp:479:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:21492:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_1D_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
-      "                                                ^~~~~~~~\n",
-      "./python_tutorial.c:21683:18: note: expanded from macro 'filename'\n",
-      "#define filename mccabs_logger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
-      "                                                ^\n",
-      "                                                \"%s\", \n",
-      "./python_tutorial.c:21683:18: note: expanded from macro 'filename'\n",
-      "#define filename mccabs_logger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2DQ.comp:535:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:21907:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_2DQ_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2DQ.comp:535:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:21907:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_2DQ_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_kf.comp:536:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:22135:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_2D_kf_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_kf.comp:536:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:22135:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_2D_kf_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space_time.comp:632:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:22373:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_2D_space_time_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space_time.comp:632:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:22373:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_2D_space_time_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "19 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_loggers_122\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: Sample_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: Sample_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: Sample_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "Allocating 3D arrays \n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 4\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "name of process [2]: Sample_inc \n",
-      "component index [2]: 4 \n",
-      "name of process [3]: Sample_pow \n",
-      "component index [3]: 5 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 2\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 0.347892 \n",
-      "number of processes [0]: 2 \n",
-      "name of material    [1]: Sample \n",
-      "component index     [1]: 6 \n",
-      "my_absoprtion       [1]: 0.273042 \n",
-      "number of processes [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 2\n",
-      "\n",
-      "name of geometry    [0]: sample_geometry \n",
-      "component index     [0]: 8 \n",
-      "Volume.name         [0]: sample_geometry \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.273042 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: sample_container \n",
-      "component index     [1]: 9 \n",
-      "Volume.name         [1]: sample_container \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: sample_container_lid \n",
-      "component index     [2]: 10 \n",
-      "Volume.name         [2]: sample_container_lid \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
-      "Volume.p_physics.number of processes [2]: 2 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: 0.015500 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: cryostat_wall \n",
-      "component index     [3]: 11 \n",
-      "Volume.name         [3]: cryostat_wall \n",
-      "Volume.p_physics.is_vacuum           [3]: 0 \n",
-      "Volume.p_physics.my_absorption       [3]: 0.347892 \n",
-      "Volume.p_physics.number of processes [3]: 2 \n",
-      "Volume.geometry.shape                [3]: cylinder \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: 0.000000 \n",
-      "Volume.geometry.center.z             [3]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
-      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [4]: cryostat_wall_vacuum \n",
-      "component index     [4]: 12 \n",
-      "Volume.name         [4]: cryostat_wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [4]: 1 \n",
-      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
-      "Volume.p_physics.number of processes [4]: 0 \n",
-      "Volume.geometry.shape                [4]: cylinder \n",
-      "Volume.geometry.center.x             [4]: 0.000000 \n",
-      "Volume.geometry.center.y             [4]: 0.000000 \n",
-      "Volume.geometry.center.z             [4]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
-      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [5]: outer_cryostat_wall \n",
-      "component index     [5]: 13 \n",
-      "Volume.name         [5]: outer_cryostat_wall \n",
-      "Volume.p_physics.is_vacuum           [5]: 0 \n",
-      "Volume.p_physics.my_absorption       [5]: 0.347892 \n",
-      "Volume.p_physics.number of processes [5]: 2 \n",
-      "Volume.geometry.shape                [5]: cylinder \n",
-      "Volume.geometry.center.x             [5]: 0.000000 \n",
-      "Volume.geometry.center.y             [5]: 0.000000 \n",
-      "Volume.geometry.center.z             [5]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.100000 \n",
-      "Volume.geometry.geometry_parameters.height [5]: 0.150000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [6]: outer_cryostat_wall_vacuum \n",
-      "component index     [6]: 14 \n",
-      "Volume.name         [6]: outer_cryostat_wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [6]: 1 \n",
-      "Volume.p_physics.my_absorption       [6]: 0.000000 \n",
-      "Volume.p_physics.number of processes [6]: 0 \n",
-      "Volume.geometry.shape                [6]: cylinder \n",
-      "Volume.geometry.center.x             [6]: 0.000000 \n",
-      "Volume.geometry.center.y             [6]: 0.000000 \n",
-      "Volume.geometry.center.z             [6]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.097000 \n",
-      "Volume.geometry.geometry_parameters.height [6]: 0.140000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 8\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7]\n",
-      "LIST: Direct_children for Volume           0 = [6]\n",
-      "LIST: Intersect_check_list for Volume      0 = [6]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [6]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for sample_geometry with cylinder shape made of Sample\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = []\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [2,3]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
-      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for sample_container with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  2 = [1]\n",
-      "LIST: Direct_children for Volume           2 = [1]\n",
-      "LIST: Intersect_check_list for Volume      2 = [1]\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [3,5]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [5]\n",
-      "LIST: Next_volume_list for Volume          2 = [3,5,1]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for sample_container_lid with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  3 = []\n",
-      "LIST: Direct_children for Volume           3 = []\n",
-      "LIST: Intersect_check_list for Volume      3 = [2]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [5]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = [5]\n",
-      "LIST: Next_volume_list for Volume          3 = [5,2]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "List overview for cryostat_wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  4 = [1,2,3,5]\n",
-      "LIST: Direct_children for Volume           4 = [5]\n",
-      "LIST: Intersect_check_list for Volume      4 = [5]\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [7]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = [7]\n",
-      "LIST: Next_volume_list for Volume          4 = [7,5]\n",
-      "      Is_vacuum for Volume                 4 = 0\n",
-      "      is_mask_volume for Volume            4 = 0\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = []\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 0\n",
-      "\n",
-      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  5 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           5 = [2,3]\n",
-      "LIST: Intersect_check_list for Volume      5 = [2,3]\n",
-      "LIST: Mask_intersect_list for Volume       5 = []\n",
-      "LIST: Destinations_list for Volume         5 = [4]\n",
-      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
-      "LIST: Next_volume_list for Volume          5 = [4,2,3]\n",
-      "      Is_vacuum for Volume                 5 = 1\n",
-      "      is_mask_volume for Volume            5 = 0\n",
-      "      is_masked_volume for Volume          5 = 0\n",
-      "      is_exit_volume for Volume            5 = 0\n",
-      "LIST: mask_list for Volume                 5 = []\n",
-      "LIST: masked_by_list for Volume            5 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
-      "      mask_mode for Volume                 5 = 0\n",
-      "\n",
-      "List overview for outer_cryostat_wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  6 = [1,2,3,4,5,7]\n",
-      "LIST: Direct_children for Volume           6 = [7]\n",
-      "LIST: Intersect_check_list for Volume      6 = [7]\n",
-      "LIST: Mask_intersect_list for Volume       6 = []\n",
-      "LIST: Destinations_list for Volume         6 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 6 = []\n",
-      "LIST: Next_volume_list for Volume          6 = [0,7]\n",
-      "      Is_vacuum for Volume                 6 = 0\n",
-      "      is_mask_volume for Volume            6 = 0\n",
-      "      is_masked_volume for Volume          6 = 0\n",
-      "      is_exit_volume for Volume            6 = 0\n",
-      "LIST: mask_list for Volume                 6 = []\n",
-      "LIST: masked_by_list for Volume            6 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
-      "      mask_mode for Volume                 6 = 0\n",
-      "\n",
-      "List overview for outer_cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  7 = [1,2,3,4,5]\n",
-      "LIST: Direct_children for Volume           7 = [4]\n",
-      "LIST: Intersect_check_list for Volume      7 = [4]\n",
-      "LIST: Mask_intersect_list for Volume       7 = []\n",
-      "LIST: Destinations_list for Volume         7 = [6]\n",
-      "LIST: Reduced_destinations_list for Volume 7 = [6]\n",
-      "LIST: Next_volume_list for Volume          7 = [6,4]\n",
-      "      Is_vacuum for Volume                 7 = 1\n",
-      "      is_mask_volume for Volume            7 = 0\n",
-      "      is_masked_volume for Volume          7 = 0\n",
-      "      is_exit_volume for Volume            7 = 0\n",
-      "LIST: mask_list for Volume                 7 = []\n",
-      "LIST: masked_by_list for Volume            7 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
-      "      mask_mode for Volume                 7 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_zx_I=728.27 logger_space_zx_ERR=5.09048 logger_space_zx_N=40035 \"logger_zx.dat\"\n",
-      "Detector: logger_space_zy_I=2799.53 logger_space_zy_ERR=16.4125 logger_space_zy_N=140638 \"logger_zy.dat\"\n",
-      "Detector: logger_1D_I=20463.5 logger_1D_ERR=32.8573 logger_1D_N=1.01146e+06 \"logger_1D_time.dat\"\n",
-      "Detector: abs_logger_space_zx_I=958.74 abs_logger_space_zx_ERR=1.34679 abs_logger_space_zx_N=1.01146e+06 \"abs_logger_zx.dat\"\n",
-      "Detector: logger_2DQ_I=20463.5 logger_2DQ_ERR=32.8573 logger_2DQ_N=1.01146e+06 \"logger_2DQ.dat\"\n",
-      "Detector: logger_2D_kf_I=20463.5 logger_2D_kf_ERR=32.8573 logger_2D_kf_N=1.01146e+06 \"logger_2D_kf.dat\"\n",
-      "Detector: logger_2D_space_time_I=11.8034 logger_2D_space_time_ERR=0.501094 logger_2D_space_time_N=591 \"logger_2D_space_time.dat_0\"\n",
-      "Detector: logger_2D_space_time_I=11.283 logger_2D_space_time_ERR=0.499298 logger_2D_space_time_N=535 \"logger_2D_space_time.dat_1\"\n",
-      "Detector: logger_2D_space_time_I=196.544 logger_2D_space_time_ERR=1.98744 logger_2D_space_time_N=10233 \"logger_2D_space_time.dat_2\"\n",
-      "Detector: logger_2D_space_time_I=521.214 logger_2D_space_time_ERR=3.23585 logger_2D_space_time_N=27167 \"logger_2D_space_time.dat_3\"\n",
-      "Detector: logger_2D_space_time_I=5123.74 logger_2D_space_time_ERR=16.0719 logger_2D_space_time_N=249726 \"logger_2D_space_time.dat_4\"\n",
-      "Detector: logger_2D_space_time_I=9855.13 logger_2D_space_time_ERR=24.225 logger_2D_space_time_N=478183 \"logger_2D_space_time.dat_5\"\n",
-      "Detector: logger_2D_space_time_I=1826.59 logger_2D_space_time_ERR=11.5129 logger_2D_space_time_N=90517 \"logger_2D_space_time.dat_6\"\n",
-      "Detector: logger_2D_space_time_I=482.193 logger_2D_space_time_ERR=4.51241 logger_2D_space_time_N=25689 \"logger_2D_space_time.dat_7\"\n",
-      "Detector: logger_2D_space_time_I=82.0829 logger_2D_space_time_ERR=2.14281 logger_2D_space_time_N=4472 \"logger_2D_space_time.dat_8\"\n",
-      "Detector: logger_2D_space_time_I=68.2735 logger_2D_space_time_ERR=1.87432 logger_2D_space_time_N=3282 \"logger_2D_space_time.dat_9\"\n",
-      "Detector: logger_2D_space_time_I=76.3738 logger_2D_space_time_ERR=1.91524 logger_2D_space_time_N=3652 \"logger_2D_space_time.dat_10\"\n",
-      "Detector: logger_2D_space_time_I=494.956 logger_2D_space_time_ERR=3.61712 logger_2D_space_time_N=26009 \"logger_2D_space_time.dat_11\"\n",
-      "Detector: logger_2D_space_time_I=500.174 logger_2D_space_time_ERR=3.65417 logger_2D_space_time_N=26848 \"logger_2D_space_time.dat_12\"\n",
-      "Detector: logger_2D_space_time_I=80.3675 logger_2D_space_time_ERR=1.9862 logger_2D_space_time_N=4670 \"logger_2D_space_time.dat_13\"\n",
-      "Detector: logger_2D_space_time_I=29.441 logger_2D_space_time_ERR=1.54549 logger_2D_space_time_N=1657 \"logger_2D_space_time.dat_14\"\n",
-      "Detector: logger_2D_space_time_I=8.03876 logger_2D_space_time_ERR=0.421307 logger_2D_space_time_N=585 \"logger_2D_space_time.dat_15\"\n",
-      "Detector: logger_2D_space_time_I=4.29369 logger_2D_space_time_ERR=0.31758 logger_2D_space_time_N=298 \"logger_2D_space_time.dat_16\"\n",
-      "Detector: logger_2D_space_time_I=2.6055 logger_2D_space_time_ERR=0.204429 logger_2D_space_time_N=195 \"logger_2D_space_time.dat_17\"\n",
-      "Detector: logger_2D_space_time_I=1.29316 logger_2D_space_time_ERR=0.169389 logger_2D_space_time_N=95 \"logger_2D_space_time.dat_18\"\n",
-      "Detector: logger_2D_space_time_I=0.543753 logger_2D_space_time_ERR=0.0924595 logger_2D_space_time_N=44 \"logger_2D_space_time.dat_19\"\n",
-      "Detector: logger_2D_space_time_I=0.271376 logger_2D_space_time_ERR=0.087438 logger_2D_space_time_N=17 \"logger_2D_space_time.dat_20\"\n",
-      "Detector: logger_2D_space_time_I=0.407183 logger_2D_space_time_ERR=0.136734 logger_2D_space_time_N=20 \"logger_2D_space_time.dat_21\"\n",
-      "Detector: logger_2D_space_time_I=0.164999 logger_2D_space_time_ERR=0.0513004 logger_2D_space_time_N=13 \"logger_2D_space_time.dat_22\"\n",
-      "Detector: logger_2D_space_time_I=0.0915192 logger_2D_space_time_ERR=0.0371662 logger_2D_space_time_N=7 \"logger_2D_space_time.dat_23\"\n",
-      "Detector: logger_2D_space_time_I=0.0817829 logger_2D_space_time_ERR=0.0336206 logger_2D_space_time_N=11 \"logger_2D_space_time.dat_24\"\n",
-      "Detector: logger_2D_space_time_I=0.11093 logger_2D_space_time_ERR=0.0421695 logger_2D_space_time_N=8 \"logger_2D_space_time.dat_25\"\n",
-      "Detector: logger_2D_space_time_I=0.161131 logger_2D_space_time_ERR=0.0513603 logger_2D_space_time_N=11 \"logger_2D_space_time.dat_26\"\n",
-      "Detector: logger_2D_space_time_I=0.0316249 logger_2D_space_time_ERR=0.023766 logger_2D_space_time_N=2 \"logger_2D_space_time.dat_27\"\n",
-      "Detector: logger_2D_space_time_I=0.0257836 logger_2D_space_time_ERR=0.0210225 logger_2D_space_time_N=2 \"logger_2D_space_time.dat_28\"\n",
-      "Detector: logger_2D_space_time_I=0.0140946 logger_2D_space_time_ERR=0.0140946 logger_2D_space_time_N=1 \"logger_2D_space_time.dat_29\"\n",
-      "Detector: logger_2D_space_time_I=0.0124473 logger_2D_space_time_ERR=0.0124473 logger_2D_space_time_N=1 \"logger_2D_space_time.dat_30\"\n",
-      "Detector: logger_2D_space_time_I=0.0286945 logger_2D_space_time_ERR=0.0230787 logger_2D_space_time_N=2 \"logger_2D_space_time.dat_31\"\n",
-      "Detector: logger_2D_space_time_I=0 logger_2D_space_time_ERR=0 logger_2D_space_time_N=0 \"logger_2D_space_time.dat_32\"\n",
-      "Detector: logger_2D_space_time_I=0 logger_2D_space_time_ERR=0 logger_2D_space_time_N=0 \"logger_2D_space_time.dat_33\"\n",
-      "Detector: logger_2D_space_time_I=0 logger_2D_space_time_ERR=0 logger_2D_space_time_N=0 \"logger_2D_space_time.dat_34\"\n",
-      "Detector: logger_2D_space_time_I=0 logger_2D_space_time_ERR=0 logger_2D_space_time_N=0 \"logger_2D_space_time.dat_35\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = instrument.backengine()"
    ]
@@ -2983,49 +496,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_2D_space_time\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2022-03-21 15:42:16,778 WARNING:MovieWriter imagemagick unavailable; using Pillow instead.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving animation with filename : \"animation_demo.gif\"\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "ani_data = functions.name_search(\"logger_2D_space_time\", data)\n",
+    "ani_data = ms.name_search(\"logger_2D_space_time\", data)\n",
     "for frame in ani_data:\n",
     "    frame.set_plot_options(log=True, colormap=\"jet\")\n",
     "    \n",
-    "plotter.make_animation(ani_data, filename=\"animation_demo\", fps=2)"
+    "ms.make_animation(ani_data, filename=\"animation_demo\", fps=2)"
    ]
   },
   {
@@ -3048,7 +527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
@@ -3063,15 +542,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#ani_data = functions.name_search(\"logger_2D_space_time\", data)\n",
+    "#ani_data = ms.name_search(\"logger_2D_space_time\", data)\n",
     "#for frame in ani_data:\n",
-    "#    frame.set_plot_options(log=True, colormap=\"jet\", orders_of_mag=6)\n",
+    "#    ms.set_plot_options(log=True, colormap=\"jet\", orders_of_mag=6)\n",
     "#    \n",
-    "#plotter.make_animation(ani_data, filename=\"animation_demo_long\", fps=5)"
+    "#ms.make_animation(ani_data, filename=\"animation_demo_long\", fps=5)"
    ]
   },
   {
diff --git a/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb b/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
index a0fef557..29edda24 100644
--- a/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
+++ b/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
@@ -22,8 +22,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter\n",
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+    "import mcstasscript as ms\n",
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
    ]
   },
   {
@@ -233,27 +233,27 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "logger_zx = functions.name_search(\"logger_space_zx\", data)\n",
+    "logger_zx = ms.name_search(\"logger_space_zx\", data)\n",
     "logger_zx.set_plot_options(log=True, orders_of_mag=9, colormap=\"jet\")\n",
     "\n",
-    "logger_zy = functions.name_search(\"logger_space_zy\", data)\n",
+    "logger_zy = ms.name_search(\"logger_space_zy\", data)\n",
     "logger_zy.set_plot_options(log=True, orders_of_mag=9, colormap=\"jet\")\n",
     "\n",
-    "logger_2DQ_sample = functions.name_search(\"logger_2DQ_sample\", data)\n",
+    "logger_2DQ_sample = ms.name_search(\"logger_2DQ_sample\", data)\n",
     "logger_2DQ_sample.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
-    "logger_2DQ_env = functions.name_search(\"logger_2DQ_environment\", data)\n",
+    "logger_2DQ_env = ms.name_search(\"logger_2DQ_environment\", data)\n",
     "logger_2DQ_env.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
-    "plotter.make_sub_plot([logger_zx, logger_zy, logger_2DQ_sample, logger_2DQ_env], fontsize=10)\n",
+    "ms.make_sub_plot([logger_zx, logger_zy, logger_2DQ_sample, logger_2DQ_env], fontsize=10)\n",
     "\n",
-    "time = functions.name_search(\"logger_time_all\", data)\n",
+    "time = ms.name_search(\"logger_time_all\", data)\n",
     "time.set_plot_options(log=True)\n",
-    "plotter.make_sub_plot([time], fontsize=18)\n",
+    "ms.make_sub_plot([time], fontsize=18)\n",
     "\n",
-    "banana = functions.name_search(\"banana_detector\", data)\n",
+    "banana = ms.name_search(\"banana_detector\", data)\n",
     "banana.set_plot_options(log=True, orders_of_mag=7, cut_max=0.001)\n",
-    "plotter.make_sub_plot([banana], fontsize=18)"
+    "ms.make_sub_plot([banana], fontsize=18)"
    ]
   },
   {
@@ -294,18 +294,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-1-926d3f87d6e5>, line 30)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-1-926d3f87d6e5>\"\u001b[0;36m, line \u001b[0;32m30\u001b[0m\n\u001b[0;31m    banana_detector.xwidth = 1\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Set up instrument parameters describing what spot to investigate\n",
     "instrument.add_parameter(\"tag_angle\", value=-95)\n",
@@ -394,7 +385,7 @@
     "banana_limited = functions.name_search(\"banana_detector_limited\", data_con)\n",
     "banana_limited.set_plot_options(log=False)\n",
     "\n",
-    "plotter.make_sub_plot([banana, banana_limited], fontsize=10)"
+    "ms.make_sub_plot([banana, banana_limited], fontsize=10)"
    ]
   },
   {
@@ -411,24 +402,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "logger_zx = functions.name_search(\"logger_space_zx\", data_con)\n",
+    "logger_zx = ms.name_search(\"logger_space_zx\", data_con)\n",
     "logger_zx.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
-    "logger_zy = functions.name_search(\"logger_space_zy\", data_con)\n",
+    "logger_zy = ms.name_search(\"logger_space_zy\", data_con)\n",
     "logger_zy.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
-    "logger_2DQ_sample = functions.name_search(\"logger_2DQ_sample\", data_con)\n",
+    "logger_2DQ_sample = ms.name_search(\"logger_2DQ_sample\", data_con)\n",
     "logger_2DQ_sample.set_plot_options(log=True, orders_of_mag=7, colormap=\"YlOrRd\")\n",
     "\n",
-    "logger_2DQ_env = functions.name_search(\"logger_2DQ_environment\", data_con)\n",
+    "logger_2DQ_env = ms.name_search(\"logger_2DQ_environment\", data_con)\n",
     "logger_2DQ_env.set_plot_options(log=True, orders_of_mag=7, colormap=\"YlOrRd\")\n",
     "\n",
-    "plotter.make_sub_plot([logger_zx, logger_zy, logger_2DQ_sample, logger_2DQ_env], fontsize=10)\n",
+    "ms.make_sub_plot([logger_zx, logger_zy, logger_2DQ_sample, logger_2DQ_env], fontsize=10)\n",
     "\n",
-    "time = functions.name_search(\"logger_time_all\", data_con)\n",
+    "time = ms.name_search(\"logger_time_all\", data_con)\n",
     "time.set_plot_options(log=True)\n",
     "\n",
-    "plotter.make_sub_plot([time], fontsize=18)"
+    "ms.make_sub_plot([time], fontsize=18)"
    ]
   },
   {
@@ -472,24 +463,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "logger_zx = functions.name_search(\"logger_space_zx\", data_con_f)\n",
+    "logger_zx = ms.name_search(\"logger_space_zx\", data_con_f)\n",
     "logger_zx.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
-    "logger_zy = functions.name_search(\"logger_space_zy\", data_con_f)\n",
+    "logger_zy = ms.name_search(\"logger_space_zy\", data_con_f)\n",
     "logger_zy.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
-    "logger_2DQ_sample = functions.name_search(\"logger_2DQ_sample\", data_con_f)\n",
+    "logger_2DQ_sample = ms.name_search(\"logger_2DQ_sample\", data_con_f)\n",
     "logger_2DQ_sample.set_plot_options(log=True, orders_of_mag=7, colormap=\"YlOrRd\")\n",
     "\n",
-    "logger_2DQ_env = functions.name_search(\"logger_2DQ_environment\", data_con_f)\n",
+    "logger_2DQ_env = ms.name_search(\"logger_2DQ_environment\", data_con_f)\n",
     "logger_2DQ_env.set_plot_options(log=True, orders_of_mag=7, colormap=\"YlOrRd\")\n",
     "\n",
-    "plotter.make_sub_plot([logger_zx, logger_zy, logger_2DQ_sample, logger_2DQ_env], fontsize=10)\n",
+    "ms.make_sub_plot([logger_zx, logger_zy, logger_2DQ_sample, logger_2DQ_env], fontsize=10)\n",
     "\n",
-    "time = functions.name_search(\"logger_time_all\", data_con_f)\n",
+    "time = ms.name_search(\"logger_time_all\", data_con_f)\n",
     "time.set_plot_options(log=True)\n",
     "\n",
-    "plotter.make_sub_plot([time], fontsize=18)"
+    "ms.make_sub_plot([time], fontsize=18)"
    ]
   },
   {
@@ -506,19 +497,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "logger_zx = functions.name_search(\"logger_space_zx\", data_con)\n",
+    "logger_zx = ms.name_search(\"logger_space_zx\", data_con)\n",
     "logger_zx.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
-    "logger_zy = functions.name_search(\"logger_space_zy\", data_con)\n",
+    "logger_zy = ms.name_search(\"logger_space_zy\", data_con)\n",
     "logger_zy.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
-    "logger_zx_f = functions.name_search(\"logger_space_zx\", data_con_f)\n",
+    "logger_zx_f = ms.name_search(\"logger_space_zx\", data_con_f)\n",
     "logger_zx_f.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
-    "logger_zy_f = functions.name_search(\"logger_space_zy\", data_con_f)\n",
+    "logger_zy_f = ms.name_search(\"logger_space_zy\", data_con_f)\n",
     "logger_zy_f.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
-    "plotter.make_sub_plot([logger_zx, logger_zy, logger_zx_f, logger_zy_f], fontsize=10)"
+    "ms.make_sub_plot([logger_zx, logger_zy, logger_zx_f, logger_zy_f], fontsize=10)"
    ]
   },
   {
diff --git a/docs/source/tutorial/Union_tutorial_5_masks.ipynb b/docs/source/tutorial/Union_tutorial_5_masks.ipynb
index 41af5db3..90830e9f 100644
--- a/docs/source/tutorial/Union_tutorial_5_masks.ipynb
+++ b/docs/source/tutorial/Union_tutorial_5_masks.ipynb
@@ -10,20 +10,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
    ]
   },
   {
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,220 +95,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_67\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:11415:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:11415:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "13 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_67\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 1\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 0.347892 \n",
-      "number of processes [0]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 1\n",
-      "\n",
-      "name of geometry    [0]: wall \n",
-      "component index     [0]: 5 \n",
-      "Volume.name         [0]: wall \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.347892 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.100000 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.150000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: wall_vacuum \n",
-      "component index     [1]: 6 \n",
-      "Volume.name         [1]: wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [1]: 1 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.000000 \n",
-      "Volume.p_physics.number of processes [1]: 0 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.080000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.160000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 3\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2]\n",
-      "LIST: Direct_children for Volume           0 = [1,2]\n",
-      "LIST: Intersect_check_list for Volume      0 = [1,2]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [1,2]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = [2]\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = []\n",
-      "LIST: Next_volume_list for Volume          1 = [0,2]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  2 = []\n",
-      "LIST: Direct_children for Volume           2 = []\n",
-      "LIST: Intersect_check_list for Volume      2 = []\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [0,1]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [1]\n",
-      "LIST: Next_volume_list for Volume          2 = [0,1]\n",
-      "      Is_vacuum for Volume                 2 = 1\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_zx_I=1451.97 logger_space_zx_ERR=4.41664 logger_space_zx_N=112127 \"logger_zx.dat\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.settings(ncount=2E6, output_path=\"data_folder/union_masks\")\n",
     "\n",
@@ -317,31 +110,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space_zx\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data)"
+    "ms.make_sub_plot(data)"
    ]
   },
   {
@@ -370,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -392,314 +165,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_68\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:11875:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:11875:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "13 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_68\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 1\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 0.347892 \n",
-      "number of processes [0]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 1\n",
-      "\n",
-      "name of geometry    [0]: wall \n",
-      "component index     [0]: 5 \n",
-      "Volume.name         [0]: wall \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.347892 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.100000 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.150000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: wall_vacuum \n",
-      "component index     [1]: 6 \n",
-      "Volume.name         [1]: wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [1]: 1 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.000000 \n",
-      "Volume.p_physics.number of processes [1]: 0 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.080000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.160000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: window \n",
-      "component index     [2]: 8 \n",
-      "Volume.name         [2]: window \n",
-      "Volume.p_physics.is_vacuum           [2]: 1 \n",
-      "Volume.p_physics.my_absorption       [2]: 0.000000 \n",
-      "Volume.p_physics.number of processes [2]: 0 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: 0.000000 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.090000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.170000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: mask \n",
-      "component index     [3]: 9 \n",
-      "Volume.name         [3]: mask \n",
-      "Volume.geometry.shape                [3]: box \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: 0.000000 \n",
-      "Volume.geometry.center.z             [3]: 0.900000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 5\n",
-      "number_of_masks = 1\n",
-      "number_of_masked_volumes = 1\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4]\n",
-      "LIST: Direct_children for Volume           0 = [1,3,4]\n",
-      "LIST: Intersect_check_list for Volume      0 = [1,2,4]\n",
-      "LIST: Mask_intersect_list for Volume       0 = [3]\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [3,1,2]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = [2,4]\n",
-      "LIST: Mask_intersect_list for Volume       1 = [3]\n",
-      "LIST: Destinations_list for Volume         1 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = []\n",
-      "LIST: Next_volume_list for Volume          1 = [0,3,2]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  2 = []\n",
-      "LIST: Direct_children for Volume           2 = []\n",
-      "LIST: Intersect_check_list for Volume      2 = [4]\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [0,1,3]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [1,3]\n",
-      "LIST: Next_volume_list for Volume          2 = [0,1,3]\n",
-      "      Is_vacuum for Volume                 2 = 1\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for window with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  3 = [2]\n",
-      "LIST: Direct_children for Volume           3 = [2]\n",
-      "LIST: Intersect_check_list for Volume      3 = [2,4]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [0,1]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = [1]\n",
-      "LIST: Next_volume_list for Volume          3 = [0,1,2]\n",
-      "      Is_vacuum for Volume                 3 = 1\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 1\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = [4]\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = [0]\n",
-      "      mask_mode for Volume                 3 = 1\n",
-      "\n",
-      "List overview for mask with box shape made of Mask\n",
-      "LIST: Children for Volume                  4 = []\n",
-      "LIST: Direct_children for Volume           4 = []\n",
-      "LIST: Intersect_check_list for Volume      4 = []\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [0,1,2]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = [1,2]\n",
-      "LIST: Next_volume_list for Volume          4 = [0,1,2]\n",
-      "      Is_vacuum for Volume                 4 = 0\n",
-      "      is_mask_volume for Volume            4 = 1\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = [3]\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 1\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_zx_I=1302.31 logger_space_zx_ERR=4.18281 logger_space_zx_N=100498 \"logger_zx.dat\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space_zx\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data)"
+    "ms.make_sub_plot(data)"
    ]
   },
   {
@@ -712,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -741,411 +224,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_69\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:12367:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:12367:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "13 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_69\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 1\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 0.347892 \n",
-      "number of processes [0]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 1\n",
-      "\n",
-      "name of geometry    [0]: wall \n",
-      "component index     [0]: 5 \n",
-      "Volume.name         [0]: wall \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.347892 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.100000 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.150000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: wall_vacuum \n",
-      "component index     [1]: 6 \n",
-      "Volume.name         [1]: wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [1]: 1 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.000000 \n",
-      "Volume.p_physics.number of processes [1]: 0 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.080000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.160000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: window \n",
-      "component index     [2]: 8 \n",
-      "Volume.name         [2]: window \n",
-      "Volume.p_physics.is_vacuum           [2]: 1 \n",
-      "Volume.p_physics.my_absorption       [2]: 0.000000 \n",
-      "Volume.p_physics.number of processes [2]: 0 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: 0.000000 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.090000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.170000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: mask \n",
-      "component index     [3]: 9 \n",
-      "Volume.name         [3]: mask \n",
-      "Volume.geometry.shape                [3]: box \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: 0.000000 \n",
-      "Volume.geometry.center.z             [3]: 0.900000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [4]: outer_window \n",
-      "component index     [4]: 10 \n",
-      "Volume.name         [4]: outer_window \n",
-      "Volume.p_physics.is_vacuum           [4]: 1 \n",
-      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
-      "Volume.p_physics.number of processes [4]: 0 \n",
-      "Volume.geometry.shape                [4]: cylinder \n",
-      "Volume.geometry.center.x             [4]: 0.000000 \n",
-      "Volume.geometry.center.y             [4]: 0.000000 \n",
-      "Volume.geometry.center.z             [4]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.110000 \n",
-      "Volume.geometry.geometry_parameters.height [4]: 0.180000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [5]: outer_window_Al \n",
-      "component index     [5]: 11 \n",
-      "Volume.name         [5]: outer_window_Al \n",
-      "Volume.p_physics.is_vacuum           [5]: 0 \n",
-      "Volume.p_physics.my_absorption       [5]: 0.347892 \n",
-      "Volume.p_physics.number of processes [5]: 2 \n",
-      "Volume.geometry.shape                [5]: cylinder \n",
-      "Volume.geometry.center.x             [5]: 0.000000 \n",
-      "Volume.geometry.center.y             [5]: 0.000000 \n",
-      "Volume.geometry.center.z             [5]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.090000 \n",
-      "Volume.geometry.geometry_parameters.height [5]: 0.190000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [6]: mask_outer \n",
-      "component index     [6]: 12 \n",
-      "Volume.name         [6]: mask_outer \n",
-      "Volume.geometry.shape                [6]: box \n",
-      "Volume.geometry.center.x             [6]: 0.000000 \n",
-      "Volume.geometry.center.y             [6]: 0.000000 \n",
-      "Volume.geometry.center.z             [6]: 1.100000 \n",
-      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 8\n",
-      "number_of_masks = 2\n",
-      "number_of_masked_volumes = 3\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7]\n",
-      "LIST: Direct_children for Volume           0 = [4,5,6,7]\n",
-      "LIST: Intersect_check_list for Volume      0 = [1,2,4,7]\n",
-      "LIST: Mask_intersect_list for Volume       0 = [3,5,6]\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [3,5,6,1,2]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = [2,4,7]\n",
-      "LIST: Mask_intersect_list for Volume       1 = [3,6]\n",
-      "LIST: Destinations_list for Volume         1 = [0,5]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = [5]\n",
-      "LIST: Next_volume_list for Volume          1 = [0,5,3,6,2]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  2 = []\n",
-      "LIST: Direct_children for Volume           2 = []\n",
-      "LIST: Intersect_check_list for Volume      2 = [4,7]\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [0,1,3,5,6]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [1,3,5,6]\n",
-      "LIST: Next_volume_list for Volume          2 = [0,1,3,5,6]\n",
-      "      Is_vacuum for Volume                 2 = 1\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for window with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  3 = [2]\n",
-      "LIST: Direct_children for Volume           3 = [2]\n",
-      "LIST: Intersect_check_list for Volume      3 = [2,4]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [0,1]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = [1]\n",
-      "LIST: Next_volume_list for Volume          3 = [0,1,2]\n",
-      "      Is_vacuum for Volume                 3 = 1\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 1\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = [4]\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = [0]\n",
-      "      mask_mode for Volume                 3 = 1\n",
-      "\n",
-      "List overview for mask with box shape made of Mask\n",
-      "LIST: Children for Volume                  4 = []\n",
-      "LIST: Direct_children for Volume           4 = []\n",
-      "LIST: Intersect_check_list for Volume      4 = []\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [0,1,2]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = [1,2]\n",
-      "LIST: Next_volume_list for Volume          4 = [0,1,2]\n",
-      "      Is_vacuum for Volume                 4 = 0\n",
-      "      is_mask_volume for Volume            4 = 1\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = [3]\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 1\n",
-      "\n",
-      "List overview for outer_window with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  5 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           5 = [1,3]\n",
-      "LIST: Intersect_check_list for Volume      5 = [2,7]\n",
-      "LIST: Mask_intersect_list for Volume       5 = [6]\n",
-      "LIST: Destinations_list for Volume         5 = [0,1]\n",
-      "LIST: Reduced_destinations_list for Volume 5 = [1]\n",
-      "LIST: Next_volume_list for Volume          5 = [0,1,6,2]\n",
-      "      Is_vacuum for Volume                 5 = 1\n",
-      "      is_mask_volume for Volume            5 = 0\n",
-      "      is_masked_volume for Volume          5 = 1\n",
-      "      is_exit_volume for Volume            5 = 0\n",
-      "LIST: mask_list for Volume                 5 = []\n",
-      "LIST: masked_by_list for Volume            5 = [7]\n",
-      "LIST: masked_by_mask_index_list for Volume 5 = [1]\n",
-      "      mask_mode for Volume                 5 = 1\n",
-      "\n",
-      "List overview for outer_window_Al with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  6 = [2,3]\n",
-      "LIST: Direct_children for Volume           6 = [3]\n",
-      "LIST: Intersect_check_list for Volume      6 = [2,7]\n",
-      "LIST: Mask_intersect_list for Volume       6 = []\n",
-      "LIST: Destinations_list for Volume         6 = [0,1,5]\n",
-      "LIST: Reduced_destinations_list for Volume 6 = [1,5]\n",
-      "LIST: Next_volume_list for Volume          6 = [0,1,5,2]\n",
-      "      Is_vacuum for Volume                 6 = 0\n",
-      "      is_mask_volume for Volume            6 = 0\n",
-      "      is_masked_volume for Volume          6 = 1\n",
-      "      is_exit_volume for Volume            6 = 0\n",
-      "LIST: mask_list for Volume                 6 = []\n",
-      "LIST: masked_by_list for Volume            6 = [7]\n",
-      "LIST: masked_by_mask_index_list for Volume 6 = [1]\n",
-      "      mask_mode for Volume                 6 = 1\n",
-      "\n",
-      "List overview for mask_outer with box shape made of Mask\n",
-      "LIST: Children for Volume                  7 = []\n",
-      "LIST: Direct_children for Volume           7 = []\n",
-      "LIST: Intersect_check_list for Volume      7 = []\n",
-      "LIST: Mask_intersect_list for Volume       7 = []\n",
-      "LIST: Destinations_list for Volume         7 = [0,1,2]\n",
-      "LIST: Reduced_destinations_list for Volume 7 = [1,2]\n",
-      "LIST: Next_volume_list for Volume          7 = [0,1,2]\n",
-      "      Is_vacuum for Volume                 7 = 0\n",
-      "      is_mask_volume for Volume            7 = 1\n",
-      "      is_masked_volume for Volume          7 = 0\n",
-      "      is_exit_volume for Volume            7 = 0\n",
-      "LIST: mask_list for Volume                 7 = [5,6]\n",
-      "LIST: masked_by_list for Volume            7 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
-      "      mask_mode for Volume                 7 = 1\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_zx_I=1149.44 logger_space_zx_ERR=3.92972 logger_space_zx_N=95537 \"logger_zx.dat\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space_zx\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data)"
+    "ms.make_sub_plot(data)"
    ]
   },
   {
@@ -1158,246 +254,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/python_tutorial_data_17\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:12065:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:12065:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "13 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/python_tutorial_data_17\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 1\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 0.347892 \n",
-      "number of processes [0]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 1\n",
-      "\n",
-      "name of geometry    [0]: box \n",
-      "component index     [0]: 5 \n",
-      "Volume.name         [0]: box \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.347892 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: box \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: box_mask \n",
-      "component index     [1]: 6 \n",
-      "Volume.name         [1]: box_mask \n",
-      "Volume.geometry.shape                [1]: box \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [0.707107 0.000000 -0.707107] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [-0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.707107 -0.000000 0.707107] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: cylinder_mask \n",
-      "component index     [2]: 7 \n",
-      "Volume.name         [2]: cylinder_mask \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: 0.000000 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.105000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.120000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 4\n",
-      "number_of_masks = 2\n",
-      "number_of_masked_volumes = 1\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           0 = [1,2,3]\n",
-      "LIST: Intersect_check_list for Volume      0 = [2,3]\n",
-      "LIST: Mask_intersect_list for Volume       0 = [1]\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [1]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for box with box shape made of Al\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = [2,3]\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = []\n",
-      "LIST: Next_volume_list for Volume          1 = [0]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 1\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = [2,3]\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = [0,1]\n",
-      "      mask_mode for Volume                 1 = 1\n",
-      "\n",
-      "List overview for box_mask with box shape made of Mask\n",
-      "LIST: Children for Volume                  2 = []\n",
-      "LIST: Direct_children for Volume           2 = []\n",
-      "LIST: Intersect_check_list for Volume      2 = []\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = []\n",
-      "LIST: Next_volume_list for Volume          2 = [0]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 1\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = [1]\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 1\n",
-      "\n",
-      "List overview for cylinder_mask with cylinder shape made of Mask\n",
-      "LIST: Children for Volume                  3 = []\n",
-      "LIST: Direct_children for Volume           3 = []\n",
-      "LIST: Intersect_check_list for Volume      3 = []\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = []\n",
-      "LIST: Next_volume_list for Volume          3 = [0]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 1\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = [1]\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 1\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_zx_I=3973.85 logger_space_zx_ERR=10.331 logger_space_zx_N=158346 \"logger_zx.dat\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")\n",
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")\n",
     "\n",
     "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
     "Al_inc.sigma = 0.0082\n",
@@ -1468,31 +333,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space_zx\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data)"
+    "ms.make_sub_plot(data)"
    ]
   },
   {
diff --git a/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb b/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
index 38db0fd2..a72f052b 100644
--- a/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
+++ b/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
@@ -12,11 +12,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
@@ -29,16 +29,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,343 +133,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_external_91\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:14810:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:14810:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:15053:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:15053:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "14 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_external_91\n",
-      "\n",
-      "          Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 1\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 0.347892 \n",
-      "number of processes [0]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 1\n",
-      "\n",
-      "name of geometry    [0]: sample_volume \n",
-      "component index     [0]: 5 \n",
-      "Volume.name         [0]: sample_volume \n",
-      "Volume.p_physics.is_vacuum           [0]: 1 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.000000 \n",
-      "Volume.p_physics.number of processes [0]: 0 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: sample_container \n",
-      "component index     [1]: 6 \n",
-      "Volume.name         [1]: sample_container \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: sample_container_lid \n",
-      "component index     [2]: 7 \n",
-      "Volume.name         [2]: sample_container_lid \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
-      "Volume.p_physics.number of processes [2]: 2 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: 0.015500 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: cryostat_wall \n",
-      "component index     [3]: 8 \n",
-      "Volume.name         [3]: cryostat_wall \n",
-      "Volume.p_physics.is_vacuum           [3]: 0 \n",
-      "Volume.p_physics.my_absorption       [3]: 0.347892 \n",
-      "Volume.p_physics.number of processes [3]: 2 \n",
-      "Volume.geometry.shape                [3]: cylinder \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: 0.000000 \n",
-      "Volume.geometry.center.z             [3]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
-      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [4]: cryostat_wall_vacuum \n",
-      "component index     [4]: 9 \n",
-      "Volume.name         [4]: cryostat_wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [4]: 1 \n",
-      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
-      "Volume.p_physics.number of processes [4]: 0 \n",
-      "Volume.geometry.shape                [4]: cylinder \n",
-      "Volume.geometry.center.x             [4]: 0.000000 \n",
-      "Volume.geometry.center.y             [4]: 0.000000 \n",
-      "Volume.geometry.center.z             [4]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
-      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 6\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4,5]\n",
-      "LIST: Direct_children for Volume           0 = [4]\n",
-      "LIST: Intersect_check_list for Volume      0 = [4]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [4]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for sample_volume with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = []\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [2,3]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
-      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
-      "      Is_vacuum for Volume                 1 = 1\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for sample_container with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  2 = [1]\n",
-      "LIST: Direct_children for Volume           2 = [1]\n",
-      "LIST: Intersect_check_list for Volume      2 = [1]\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [3,5]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [5]\n",
-      "LIST: Next_volume_list for Volume          2 = [3,5,1]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for sample_container_lid with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  3 = []\n",
-      "LIST: Direct_children for Volume           3 = []\n",
-      "LIST: Intersect_check_list for Volume      3 = [2]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [5]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = [5]\n",
-      "LIST: Next_volume_list for Volume          3 = [5,2]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "List overview for cryostat_wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  4 = [1,2,3,5]\n",
-      "LIST: Direct_children for Volume           4 = [5]\n",
-      "LIST: Intersect_check_list for Volume      4 = [5]\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = []\n",
-      "LIST: Next_volume_list for Volume          4 = [0,5]\n",
-      "      Is_vacuum for Volume                 4 = 0\n",
-      "      is_mask_volume for Volume            4 = 0\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = []\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 0\n",
-      "\n",
-      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  5 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           5 = [2,3]\n",
-      "LIST: Intersect_check_list for Volume      5 = [2,3]\n",
-      "LIST: Mask_intersect_list for Volume       5 = []\n",
-      "LIST: Destinations_list for Volume         5 = [4]\n",
-      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
-      "LIST: Next_volume_list for Volume          5 = [4,2,3]\n",
-      "      Is_vacuum for Volume                 5 = 1\n",
-      "      is_mask_volume for Volume            5 = 0\n",
-      "      is_masked_volume for Volume          5 = 0\n",
-      "      is_exit_volume for Volume            5 = 0\n",
-      "LIST: mask_list for Volume                 5 = []\n",
-      "LIST: masked_by_list for Volume            5 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
-      "      mask_mode for Volume                 5 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_zx_I=3316.64 logger_space_zx_ERR=8.14809 logger_space_zx_N=173630 \"logger_zx.dat\"\n",
-      "Detector: logger_space_zy_I=3316.64 logger_space_zy_ERR=8.14809 logger_space_zy_N=173630 \"logger_zy.dat\"\n",
-      "Detector: banana_I=321.255 banana_ERR=2.53184 banana_N=16907 \"banana_1647874009.th\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=3.0)\n",
     "instrument.settings(ncount=3E6, output_path=\"data_folder/union_external\")\n",
@@ -479,54 +149,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space_zx\n",
-      "Plotting data with name logger_space_zy\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name banana\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "functions.name_plot_options(\"logger_space_zx\", data_empty, log=True, orders_of_mag=4)\n",
-    "functions.name_plot_options(\"logger_space_zy\", data_empty, log=True, orders_of_mag=4)\n",
-    "plotter.make_sub_plot(data_empty[0:2])\n",
-    "plotter.make_sub_plot(data_empty[2])"
+    "ms.name_plot_options(\"logger_space_zx\", data_empty, log=True, orders_of_mag=4)\n",
+    "ms.name_plot_options(\"logger_space_zy\", data_empty, log=True, orders_of_mag=4)\n",
+    "ms.make_sub_plot(data_empty[0:2])\n",
+    "ms.make_sub_plot(data_empty[2])"
    ]
   },
   {
@@ -539,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -548,397 +178,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_external_92\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:14810:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:14810:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:15053:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:15053:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "14 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_external_92\n",
-      "\n",
-      "          Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 1\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 0.347892 \n",
-      "number of processes [0]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 1\n",
-      "\n",
-      "name of geometry    [0]: sample_volume \n",
-      "component index     [0]: 5 \n",
-      "Volume.name         [0]: sample_volume \n",
-      "Volume.p_physics.is_vacuum           [0]: 1 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.000000 \n",
-      "Volume.p_physics.number of processes [0]: 0 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: sample_container \n",
-      "component index     [1]: 6 \n",
-      "Volume.name         [1]: sample_container \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: sample_container_lid \n",
-      "component index     [2]: 7 \n",
-      "Volume.name         [2]: sample_container_lid \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
-      "Volume.p_physics.number of processes [2]: 2 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: 0.015500 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: cryostat_wall \n",
-      "component index     [3]: 8 \n",
-      "Volume.name         [3]: cryostat_wall \n",
-      "Volume.p_physics.is_vacuum           [3]: 0 \n",
-      "Volume.p_physics.my_absorption       [3]: 0.347892 \n",
-      "Volume.p_physics.number of processes [3]: 2 \n",
-      "Volume.geometry.shape                [3]: cylinder \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: 0.000000 \n",
-      "Volume.geometry.center.z             [3]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
-      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [4]: cryostat_wall_vacuum \n",
-      "component index     [4]: 9 \n",
-      "Volume.name         [4]: cryostat_wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [4]: 1 \n",
-      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
-      "Volume.p_physics.number of processes [4]: 0 \n",
-      "Volume.geometry.shape                [4]: cylinder \n",
-      "Volume.geometry.center.x             [4]: 0.000000 \n",
-      "Volume.geometry.center.y             [4]: 0.000000 \n",
-      "Volume.geometry.center.z             [4]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
-      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 6\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4,5]\n",
-      "LIST: Direct_children for Volume           0 = [4]\n",
-      "LIST: Intersect_check_list for Volume      0 = [4]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [4]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for sample_volume with shape cylinder\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = []\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [2,3]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
-      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
-      "      Is_vacuum for Volume                 1 = 1\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 1\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for sample_container with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  2 = [1]\n",
-      "LIST: Direct_children for Volume           2 = [1]\n",
-      "LIST: Intersect_check_list for Volume      2 = [1]\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [3,5]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [5]\n",
-      "LIST: Next_volume_list for Volume          2 = [3,5,1]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for sample_container_lid with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  3 = []\n",
-      "LIST: Direct_children for Volume           3 = []\n",
-      "LIST: Intersect_check_list for Volume      3 = [2]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [5]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = [5]\n",
-      "LIST: Next_volume_list for Volume          3 = [5,2]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "List overview for cryostat_wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  4 = [1,2,3,5]\n",
-      "LIST: Direct_children for Volume           4 = [5]\n",
-      "LIST: Intersect_check_list for Volume      4 = [5]\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = []\n",
-      "LIST: Next_volume_list for Volume          4 = [0,5]\n",
-      "      Is_vacuum for Volume                 4 = 0\n",
-      "      is_mask_volume for Volume            4 = 0\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = []\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 0\n",
-      "\n",
-      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  5 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           5 = [2,3]\n",
-      "LIST: Intersect_check_list for Volume      5 = [2,3]\n",
-      "LIST: Mask_intersect_list for Volume       5 = []\n",
-      "LIST: Destinations_list for Volume         5 = [4]\n",
-      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
-      "LIST: Next_volume_list for Volume          5 = [4,2,3]\n",
-      "      Is_vacuum for Volume                 5 = 1\n",
-      "      is_mask_volume for Volume            5 = 0\n",
-      "      is_masked_volume for Volume          5 = 0\n",
-      "      is_exit_volume for Volume            5 = 0\n",
-      "LIST: mask_list for Volume                 5 = []\n",
-      "LIST: masked_by_list for Volume            5 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
-      "      mask_mode for Volume                 5 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_zx_I=2171.73 logger_space_zx_ERR=6.59926 logger_space_zx_N=113410 \"logger_zx.dat\"\n",
-      "Detector: logger_space_zy_I=2171.73 logger_space_zy_ERR=6.59926 logger_space_zy_N=113410 \"logger_zy.dat\"\n",
-      "Detector: banana_I=214.568 banana_ERR=2.07006 banana_N=11274 \"banana_1647874019.th\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space_zx\n",
-      "Plotting data with name logger_space_zy\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name banana\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
-    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
-    "plotter.make_sub_plot(data[0:2])\n",
-    "plotter.make_sub_plot(data[2])"
+    "ms.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
+    "ms.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
+    "ms.make_sub_plot(data[0:2])\n",
+    "ms.make_sub_plot(data[2])"
    ]
   },
   {
@@ -951,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -972,406 +232,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
+    "scrolled": true,
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_external_93\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:15340:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:15340:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:15583:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:15583:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "14 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_external_93\n",
-      "\n",
-      "          Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 1\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 0.347892 \n",
-      "number of processes [0]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 1\n",
-      "\n",
-      "name of geometry    [0]: sample_volume \n",
-      "component index     [0]: 5 \n",
-      "Volume.name         [0]: sample_volume \n",
-      "Volume.p_physics.is_vacuum           [0]: 1 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.000000 \n",
-      "Volume.p_physics.number of processes [0]: 0 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: sample_container \n",
-      "component index     [1]: 6 \n",
-      "Volume.name         [1]: sample_container \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: sample_container_lid \n",
-      "component index     [2]: 7 \n",
-      "Volume.name         [2]: sample_container_lid \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
-      "Volume.p_physics.number of processes [2]: 2 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: 0.015500 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: cryostat_wall \n",
-      "component index     [3]: 8 \n",
-      "Volume.name         [3]: cryostat_wall \n",
-      "Volume.p_physics.is_vacuum           [3]: 0 \n",
-      "Volume.p_physics.my_absorption       [3]: 0.347892 \n",
-      "Volume.p_physics.number of processes [3]: 2 \n",
-      "Volume.geometry.shape                [3]: cylinder \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: 0.000000 \n",
-      "Volume.geometry.center.z             [3]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
-      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [4]: cryostat_wall_vacuum \n",
-      "component index     [4]: 9 \n",
-      "Volume.name         [4]: cryostat_wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [4]: 1 \n",
-      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
-      "Volume.p_physics.number of processes [4]: 0 \n",
-      "Volume.geometry.shape                [4]: cylinder \n",
-      "Volume.geometry.center.x             [4]: 0.000000 \n",
-      "Volume.geometry.center.y             [4]: 0.000000 \n",
-      "Volume.geometry.center.z             [4]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
-      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 6\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4,5]\n",
-      "LIST: Direct_children for Volume           0 = [4]\n",
-      "LIST: Intersect_check_list for Volume      0 = [4]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [4]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for sample_volume with shape cylinder\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = []\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [2,3]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
-      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
-      "      Is_vacuum for Volume                 1 = 1\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 1\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for sample_container with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  2 = [1]\n",
-      "LIST: Direct_children for Volume           2 = [1]\n",
-      "LIST: Intersect_check_list for Volume      2 = [1]\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [3,5]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [5]\n",
-      "LIST: Next_volume_list for Volume          2 = [3,5,1]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for sample_container_lid with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  3 = []\n",
-      "LIST: Direct_children for Volume           3 = []\n",
-      "LIST: Intersect_check_list for Volume      3 = [2]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [5]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = [5]\n",
-      "LIST: Next_volume_list for Volume          3 = [5,2]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "List overview for cryostat_wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  4 = [1,2,3,5]\n",
-      "LIST: Direct_children for Volume           4 = [5]\n",
-      "LIST: Intersect_check_list for Volume      4 = [5]\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = []\n",
-      "LIST: Next_volume_list for Volume          4 = [0,5]\n",
-      "      Is_vacuum for Volume                 4 = 0\n",
-      "      is_mask_volume for Volume            4 = 0\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = []\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 0\n",
-      "\n",
-      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  5 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           5 = [2,3]\n",
-      "LIST: Intersect_check_list for Volume      5 = [2,3]\n",
-      "LIST: Mask_intersect_list for Volume       5 = []\n",
-      "LIST: Destinations_list for Volume         5 = [4]\n",
-      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
-      "LIST: Next_volume_list for Volume          5 = [4,2,3]\n",
-      "      Is_vacuum for Volume                 5 = 1\n",
-      "      is_mask_volume for Volume            5 = 0\n",
-      "      is_masked_volume for Volume          5 = 0\n",
-      "      is_exit_volume for Volume            5 = 0\n",
-      "LIST: mask_list for Volume                 5 = []\n",
-      "LIST: masked_by_list for Volume            5 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
-      "      mask_mode for Volume                 5 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "Detector: logger_space_zx_I=2189.03 logger_space_zx_ERR=6.62732 logger_space_zx_N=114250 \"logger_zx.dat\"\n",
-      "Detector: logger_space_zy_I=2189.03 logger_space_zy_ERR=6.62732 logger_space_zy_N=114250 \"logger_zy.dat\"\n",
-      "Detector: banana_I=1692 banana_ERR=5.14373 banana_N=323257 \"banana_1647874029.th\"\n",
-      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
-      "    SPLIT 37 COMPONENT sample=PowderN(...)\n",
-      "  in the instrument description python_tutorial.instr.\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data_wrong = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space_zx\n",
-      "Plotting data with name logger_space_zy\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name banana\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "functions.name_plot_options(\"logger_space_zx\", data_wrong, log=True, orders_of_mag=4)\n",
-    "functions.name_plot_options(\"logger_space_zy\", data_wrong, log=True, orders_of_mag=4)\n",
-    "plotter.make_sub_plot(data_wrong[0:2])\n",
-    "plotter.make_sub_plot(data_wrong[2])"
+    "ms.name_plot_options(\"logger_space_zx\", data_wrong, log=True, orders_of_mag=4)\n",
+    "ms.name_plot_options(\"logger_space_zy\", data_wrong, log=True, orders_of_mag=4)\n",
+    "ms.make_sub_plot(data_wrong[0:2])\n",
+    "ms.make_sub_plot(data_wrong[2])"
    ]
   },
   {
@@ -1396,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1418,596 +300,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_external_94\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:15972:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:15972:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:16215:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:16215:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "17 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_external_94\n",
-      "\n",
-      "          Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 1\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 0.347892 \n",
-      "number of processes [0]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 1\n",
-      "\n",
-      "name of geometry    [0]: sample_volume \n",
-      "component index     [0]: 5 \n",
-      "Volume.name         [0]: sample_volume \n",
-      "Volume.p_physics.is_vacuum           [0]: 1 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.000000 \n",
-      "Volume.p_physics.number of processes [0]: 0 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.007500 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.030000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: sample_container \n",
-      "component index     [1]: 6 \n",
-      "Volume.name         [1]: sample_container \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: sample_container_lid \n",
-      "component index     [2]: 7 \n",
-      "Volume.name         [2]: sample_container_lid \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
-      "Volume.p_physics.number of processes [2]: 2 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: 0.015500 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: cryostat_wall \n",
-      "component index     [3]: 8 \n",
-      "Volume.name         [3]: cryostat_wall \n",
-      "Volume.p_physics.is_vacuum           [3]: 0 \n",
-      "Volume.p_physics.my_absorption       [3]: 0.347892 \n",
-      "Volume.p_physics.number of processes [3]: 2 \n",
-      "Volume.geometry.shape                [3]: cylinder \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: 0.000000 \n",
-      "Volume.geometry.center.z             [3]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
-      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [4]: cryostat_wall_vacuum \n",
-      "component index     [4]: 9 \n",
-      "Volume.name         [4]: cryostat_wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [4]: 1 \n",
-      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
-      "Volume.p_physics.number of processes [4]: 0 \n",
-      "Volume.geometry.shape                [4]: cylinder \n",
-      "Volume.geometry.center.x             [4]: 0.000000 \n",
-      "Volume.geometry.center.y             [4]: 0.000000 \n",
-      "Volume.geometry.center.z             [4]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
-      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 6\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4,5]\n",
-      "LIST: Direct_children for Volume           0 = [4]\n",
-      "LIST: Intersect_check_list for Volume      0 = [4]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [4]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for sample_volume with shape cylinder\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = []\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [2,3]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = [2,3]\n",
-      "LIST: Next_volume_list for Volume          1 = [2,3]\n",
-      "      Is_vacuum for Volume                 1 = 1\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 1\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for sample_container with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  2 = [1]\n",
-      "LIST: Direct_children for Volume           2 = [1]\n",
-      "LIST: Intersect_check_list for Volume      2 = [1]\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [3,5]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [5]\n",
-      "LIST: Next_volume_list for Volume          2 = [3,5,1]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for sample_container_lid with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  3 = []\n",
-      "LIST: Direct_children for Volume           3 = []\n",
-      "LIST: Intersect_check_list for Volume      3 = [2]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [5]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = [5]\n",
-      "LIST: Next_volume_list for Volume          3 = [5,2]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "List overview for cryostat_wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  4 = [1,2,3,5]\n",
-      "LIST: Direct_children for Volume           4 = [5]\n",
-      "LIST: Intersect_check_list for Volume      4 = [5]\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = []\n",
-      "LIST: Next_volume_list for Volume          4 = [0,5]\n",
-      "      Is_vacuum for Volume                 4 = 0\n",
-      "      is_mask_volume for Volume            4 = 0\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = []\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 0\n",
-      "\n",
-      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  5 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           5 = [2,3]\n",
-      "LIST: Intersect_check_list for Volume      5 = [2,3]\n",
-      "LIST: Mask_intersect_list for Volume       5 = []\n",
-      "LIST: Destinations_list for Volume         5 = [4]\n",
-      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
-      "LIST: Next_volume_list for Volume          5 = [4,2,3]\n",
-      "      Is_vacuum for Volume                 5 = 1\n",
-      "      is_mask_volume for Volume            5 = 0\n",
-      "      is_masked_volume for Volume          5 = 0\n",
-      "      is_exit_volume for Volume            5 = 0\n",
-      "LIST: mask_list for Volume                 5 = []\n",
-      "LIST: masked_by_list for Volume            5 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
-      "      mask_mode for Volume                 5 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 1\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 0.347892 \n",
-      "number of processes [0]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 1\n",
-      "\n",
-      "name of geometry    [1]: sample_container \n",
-      "component index     [1]: 6 \n",
-      "Volume.name         [1]: sample_container \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.347892 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.009000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.033000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: sample_container_lid \n",
-      "component index     [2]: 7 \n",
-      "Volume.name         [2]: sample_container_lid \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absorption       [2]: 0.347892 \n",
-      "Volume.p_physics.number of processes [2]: 2 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: 0.015500 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.013000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.004000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: cryostat_wall \n",
-      "component index     [3]: 8 \n",
-      "Volume.name         [3]: cryostat_wall \n",
-      "Volume.p_physics.is_vacuum           [3]: 0 \n",
-      "Volume.p_physics.my_absorption       [3]: 0.347892 \n",
-      "Volume.p_physics.number of processes [3]: 2 \n",
-      "Volume.geometry.shape                [3]: cylinder \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: 0.000000 \n",
-      "Volume.geometry.center.z             [3]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.030000 \n",
-      "Volume.geometry.geometry_parameters.height [3]: 0.120000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [4]: cryostat_wall_vacuum \n",
-      "component index     [4]: 9 \n",
-      "Volume.name         [4]: cryostat_wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [4]: 1 \n",
-      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
-      "Volume.p_physics.number of processes [4]: 0 \n",
-      "Volume.geometry.shape                [4]: cylinder \n",
-      "Volume.geometry.center.x             [4]: 0.000000 \n",
-      "Volume.geometry.center.y             [4]: 0.000000 \n",
-      "Volume.geometry.center.z             [4]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.028000 \n",
-      "Volume.geometry.geometry_parameters.height [4]: 0.112000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 5\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4]\n",
-      "LIST: Direct_children for Volume           0 = [3]\n",
-      "LIST: Intersect_check_list for Volume      0 = [3]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [3]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for sample_container with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = []\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [2,4]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = [4]\n",
-      "LIST: Next_volume_list for Volume          1 = [2,4]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for sample_container_lid with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  2 = []\n",
-      "LIST: Direct_children for Volume           2 = []\n",
-      "LIST: Intersect_check_list for Volume      2 = [1]\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [4]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [4]\n",
-      "LIST: Next_volume_list for Volume          2 = [4,1]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for cryostat_wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  3 = [1,2,4]\n",
-      "LIST: Direct_children for Volume           3 = [4]\n",
-      "LIST: Intersect_check_list for Volume      3 = [4]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = []\n",
-      "LIST: Next_volume_list for Volume          3 = [0,4]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "List overview for cryostat_wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  4 = [1,2]\n",
-      "LIST: Direct_children for Volume           4 = [1,2]\n",
-      "LIST: Intersect_check_list for Volume      4 = [1,2]\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [3]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = [3]\n",
-      "LIST: Next_volume_list for Volume          4 = [3,1,2]\n",
-      "      Is_vacuum for Volume                 4 = 1\n",
-      "      is_mask_volume for Volume            4 = 0\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = []\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 0\n",
-      "\n",
-      "Union_master component master_after_sample initialized sucessfully\n",
-      "WARNING: Ray started in volume ''sample_container'' rather than the surrounding vacuum in component master_after_sample. This warning is only shown once.\n",
-      "Detector: logger_space_zx_I=3807.39 logger_space_zx_ERR=13.328 logger_space_zx_N=301169 \"logger_zx.dat\"\n",
-      "Detector: logger_space_zy_I=3807.39 logger_space_zy_ERR=13.328 logger_space_zy_N=301169 \"logger_zy.dat\"\n",
-      "Detector: banana_I=1664.43 banana_ERR=6.00861 banana_N=306860 \"banana_1647874040.th\"\n",
-      "WARNING: No Union_master component defined after Union volume named sample_volume_replace, this components did not affect the simulation in any way.\n",
-      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
-      "    SPLIT 37 COMPONENT sample=PowderN(...)\n",
-      "  in the instrument description python_tutorial.instr.\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space_zx\n",
-      "Plotting data with name logger_space_zy\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name banana\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
-    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
-    "plotter.make_sub_plot(data[0:2])\n",
-    "plotter.make_sub_plot(data[2])"
+    "ms.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
+    "ms.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
+    "ms.make_sub_plot(data[0:2])\n",
+    "ms.make_sub_plot(data[2])"
    ]
   },
   {
@@ -2028,36 +341,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f88311129a0>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "banana_empty = functions.name_search(\"banana\", data_empty)\n",
-    "banana_wrong = functions.name_search(\"banana\", data_wrong)\n",
-    "banana_sample = functions.name_search(\"banana\", data)\n",
+    "banana_empty = ms.name_search(\"banana\", data_empty)\n",
+    "banana_wrong = ms.name_search(\"banana\", data_wrong)\n",
+    "banana_sample = ms.name_search(\"banana\", data)\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
     "plt.figure(figsize=(14,6))\n",
diff --git a/docs/source/tutorial/Union_tutorial_7_Tagging_history.ipynb b/docs/source/tutorial/Union_tutorial_7_Tagging_history.ipynb
index e98c43da..da79974c 100644
--- a/docs/source/tutorial/Union_tutorial_7_Tagging_history.ipynb
+++ b/docs/source/tutorial/Union_tutorial_7_Tagging_history.ipynb
@@ -16,7 +16,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
@@ -25,7 +25,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
    ]
   },
   {
@@ -164,9 +164,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
-    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
-    "plotter.make_sub_plot(data)"
+    "ms.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
+    "ms.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
+    "ms.make_sub_plot(data)"
    ]
   },
   {
@@ -180,7 +180,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
     "with open(\"run_folder/union_history.dat\", \"r\", errors=\"ignore\") as file:\n",
diff --git a/docs/source/user_guide/component_object.ipynb b/docs/source/user_guide/component_object.ipynb
index d64b33c3..ef979a7f 100644
--- a/docs/source/user_guide/component_object.ipynb
+++ b/docs/source/user_guide/component_object.ipynb
@@ -13,14 +13,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "widespread-maple",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr\n",
+    "import mcstasscript as ms\n",
     "\n",
-    "instrument = instr.McStas_instr(\"component_examples\")"
+    "instrument = ms.McStas_instr(\"component_examples\")"
    ]
   },
   {
@@ -34,23 +34,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "documentary-import",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT source = Source_div\n",
-      "  \u001b[1mxwidth\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1myheight\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1mfocus_aw\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1mfocus_ah\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "AT [0, 0, 0] ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "source = instrument.add_component(\"source\", \"Source_div\")\n",
     "print(source)"
@@ -67,23 +54,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "dynamic-holmes",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT source = Source_div\n",
-      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mfocus_aw\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1mfocus_ah\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "AT [0, 0, 0] ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "source.xwidth = 0.1\n",
     "source.set_parameters(yheight=0.03)\n",
@@ -100,30 +74,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "respective-spectrum",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Source_div ________________________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[4m\u001b[1mxwidth\u001b[0m\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m] // Width of source\n",
-      "\u001b[4m\u001b[1myheight\u001b[0m\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m] // Height of source\n",
-      "\u001b[4m\u001b[1mfocus_aw\u001b[0m\u001b[0m [deg] // FWHM (Gaussian) or maximal (uniform) horz. width divergence\n",
-      "\u001b[4m\u001b[1mfocus_ah\u001b[0m\u001b[0m [deg] // FWHM (Gaussian) or maximal (uniform) vert. height divergence\n",
-      "\u001b[1mE0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Mean energy of neutrons.\n",
-      "\u001b[1mdE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Energy half spread of neutrons.\n",
-      "\u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Ang] // Mean wavelength of neutrons (only relevant for E0=0)\n",
-      "\u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Ang] // Wavelength half spread of neutrons.\n",
-      "\u001b[1mgauss\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [0|1] // Criterion\n",
-      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1/(s cm 2 st energy_unit)] // flux per energy unit, Angs or meV\n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "source.show_parameters()"
    ]
@@ -141,23 +95,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "respiratory-springer",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT source = Source_div\n",
-      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mfocus_aw\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1mfocus_ah\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "AT [0.02, 0, 0] ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "source.set_AT([0.02, 0, 0])\n",
     "print(source)"
@@ -173,23 +114,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "compatible-romance",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT source = Source_div\n",
-      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mfocus_aw\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1mfocus_ah\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "AT [0, 0, 0.01] ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "source.set_AT(0.01)\n",
     "print(source)"
@@ -205,19 +133,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "familiar-joshua",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT PSD = PSD_monitor\n",
-      "AT [0, 0, 2] RELATIVE source\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "monitor = instrument.add_component(\"PSD\", \"PSD_monitor\", AT=[0, 0, 2], RELATIVE=source)\n",
     "print(monitor)"
@@ -233,20 +152,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "recreational-overhead",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT PSD = PSD_monitor\n",
-      "AT [0, 0, 2] RELATIVE source\n",
-      "ROTATED [0, 4, 0] RELATIVE source\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "monitor.set_ROTATED([0, 4, 0])\n",
     "print(monitor)"
@@ -262,20 +171,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "automotive-racing",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT PSD = PSD_monitor\n",
-      "AT [0, 0, 2] RELATIVE source\n",
-      "ROTATED [0, 4, 0] ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "monitor.set_ROTATED([0, 4, 0], RELATIVE=\"ABSOLUTE\")\n",
     "print(monitor)"
@@ -291,23 +190,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "beautiful-marking",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT guide = Guide\n",
-      "  \u001b[1mw1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1mh1\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "  \u001b[1ml\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "AT [0, 0, 2] RELATIVE source\n",
-      "ROTATED [0, 0, 0] RELATIVE PSD\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "guide = instrument.add_component(\"guide\", \"Guide\",\n",
     "                                 AT=2, AT_RELATIVE=source, \n",
@@ -328,23 +214,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "better-massachusetts",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT guide = Guide\n",
-      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92mguide_width\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m1.5*guide_width\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1ml\u001b[0m\u001b[91m : Required parameter not yet specified\u001b[0m\n",
-      "AT [0, 0, 2] RELATIVE source\n",
-      "ROTATED [0, 0, 0] RELATIVE PSD\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.add_parameter(\"guide_width\")\n",
     "guide.w1 = \"guide_width\"\n",
@@ -362,24 +235,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "accomplished-currency",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT guide = Guide\n",
-      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92mguide_width\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m1.5*guide_width\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92mguide_length\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92mguide_m_value\u001b[0m\u001b[0m [1]\n",
-      "AT [0, 0, 2] RELATIVE source\n",
-      "ROTATED [0, 0, 0] RELATIVE PSD\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "guide_length = instrument.add_declare_var(\"double\", \"guide_length\")\n",
     "guide.l = guide_length\n",
@@ -399,25 +258,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "discrete-celebrity",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "// This guide points in the same direction as the PSD monitor\n",
-      "COMPONENT guide = Guide\n",
-      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92mguide_width\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m1.5*guide_width\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92mguide_length\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92mguide_m_value\u001b[0m\u001b[0m [1]\n",
-      "AT [0, 0, 2] RELATIVE source\n",
-      "ROTATED [0, 0, 0] RELATIVE PSD\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "guide.set_comment(\"This guide points in the same direction as the PSD monitor\")\n",
     "print(guide)"
@@ -443,25 +287,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "equal-tactics",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "// This guide points in the same direction as the PSD monitor\n",
-      "SPLIT 150 COMPONENT guide = Guide\n",
-      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92mguide_width\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m1.5*guide_width\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92mguide_length\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92mguide_m_value\u001b[0m\u001b[0m [1]\n",
-      "AT [0, 0, 2] RELATIVE source\n",
-      "ROTATED [0, 0, 0] RELATIVE PSD\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "guide.set_SPLIT(150)\n",
     "print(guide)"
@@ -469,19 +298,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "adequate-small",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SPLIT 180 COMPONENT Splitter = Arm\n",
-      "AT [0, 0, 0] ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(instrument.add_component(\"Splitter\", \"Arm\", SPLIT=180))"
    ]
@@ -496,25 +316,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "french-bangladesh",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "// This guide points in the same direction as the PSD monitor\n",
-      "COMPONENT guide = Guide\n",
-      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92mguide_width\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m1.5*guide_width\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92mguide_length\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92mguide_m_value\u001b[0m\u001b[0m [1]\n",
-      "AT [0, 0, 2] RELATIVE source\n",
-      "ROTATED [0, 0, 0] RELATIVE PSD\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "guide.set_SPLIT(0)\n",
     "print(guide)"
@@ -531,31 +336,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "academic-outside",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "// This guide points in the same direction as the PSD monitor\n",
-      "COMPONENT guide = Guide\n",
-      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92mguide_width\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m1.5*guide_width\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92mguide_length\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92mguide_m_value\u001b[0m\u001b[0m [1]\n",
-      "AT [0, 0, 2] RELATIVE source\n",
-      "ROTATED [0, 0, 0] RELATIVE PSD\n",
-      "EXTEND %{\n",
-      "  if (x>0) flag = 1;\n",
-      "  else flag = 0;\n",
-      "%}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.add_declare_var(\"int\", \"flag\")\n",
     "guide.append_EXTEND(\"  if (x>0) flag = 1;\")\n",
@@ -576,20 +362,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "flying-olympus",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT Left_side_PSD = PSD_monitor\n",
-      "WHEN (flag == 1)\n",
-      "AT [0, 0, 0] ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "left_PSD = instrument.add_component(\"Left_side_PSD\", \"PSD_monitor\")\n",
     "left_PSD.set_WHEN(\"flag == 1\")\n",
@@ -606,20 +382,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "immediate-ivory",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT Right_side_PSD = PSD_monitor\n",
-      "WHEN (flag == 0)\n",
-      "AT [0, 0, 0] ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "right_PSD = instrument.add_component(\"Right_side_PSD\", \"PSD_monitor\", WHEN=\"flag == 0\")\n",
     "print(right_PSD)"
@@ -638,30 +404,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "seven-kingdom",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "// This guide points in the same direction as the PSD monitor\n",
-      "COMPONENT guide = Guide\n",
-      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92mguide_width\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m1.5*guide_width\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92mguide_length\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92mguide_m_value\u001b[0m\u001b[0m [1]\n",
-      "AT [0, 0, 2] RELATIVE source\n",
-      "ROTATED [0, 0, 0] RELATIVE PSD\n",
-      "EXTEND %{\n",
-      "  if (x>0) flag = 1;\n",
-      "  else flag = 0;\n",
-      "%}\n",
-      "JUMP target_arm WHEN (vy>0)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.add_component(\"target_arm\", \"Arm\", RELATIVE=guide)\n",
     "guide.set_JUMP(\"target_arm WHEN (vy>0)\")\n",
@@ -679,23 +425,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "consecutive-request",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT mono_1 = Monochromator_flat\n",
-      "AT [0.01, 0, 5] ABSOLUTE\n",
-      "GROUP monochromator\n",
-      "COMPONENT mono_2 = Monochromator_flat\n",
-      "AT [-0.01, 0, 5] ABSOLUTE\n",
-      "GROUP monochromator\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "crystal_1 = instrument.add_component(\"mono_1\", \"Monochromator_flat\")\n",
     "crystal_1.set_GROUP(\"monochromator\")\n",
@@ -721,23 +454,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "assigned-content",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "// code before\n",
-      "COMPONENT code_arm = Arm\n",
-      "AT [0, 0, 0] ABSOLUTE\n",
-      "// code after\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "code_arm = instrument.add_component(\"code_arm\", \"Arm\", c_code_before=\"// code before\")\n",
     "code_arm.set_c_code_after(\"// code after\")\n",
@@ -754,22 +476,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "nuclear-bottle",
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "// This is a comment!\n",
-      "COMPONENT commented_component = Arm\n",
-      "AT [0, 0, 0] ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "commented_component = instrument.add_component(\"commented_component\", \"Arm\", comment=\"This is a comment!\")\n",
     "print(commented_component)"
diff --git a/docs/source/user_guide/data.ipynb b/docs/source/user_guide/data.ipynb
index daef4c70..707642e6 100644
--- a/docs/source/user_guide/data.ipynb
+++ b/docs/source/user_guide/data.ipynb
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "substantial-weekly",
    "metadata": {
     "tags": [
@@ -23,9 +23,9 @@
    },
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter\n",
+    "import mcstasscript as ms\n",
     "\n",
-    "instrument = instr.McStas_instr(\"data_example\")\n",
+    "instrument = ms.McStas_instr(\"data_example\")\n",
     "\n",
     "source = instrument.add_component(\"source\", \"Source_simple\")\n",
     "source.set_parameters(xwidth=0.05, yheight=0.03, dlambda=0.1,\n",
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "apart-duration",
    "metadata": {},
    "outputs": [],
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "collaborative-police",
    "metadata": {},
    "outputs": [],
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "urban-traffic",
    "metadata": {},
    "outputs": [],
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "finished-employment",
    "metadata": {
     "scrolled": true,
@@ -103,43 +103,7 @@
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/user_guide/data_example_21\"\n",
-      "INFO: Regenerating c-file: data_example.c\n",
-      "CFLAGS=\n",
-      "INFO: Recompiling: ./data_example.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "1 warning generated.\n",
-      "INFO: ===\n",
-      "Warning: 54849 events were removed in Component[5] PSD=PSD_monitor()\n",
-      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "INFO: Placing instr file copy data_example.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/user_guide/data_example_21\n",
-      "\n",
-      "      Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: sample: Powder file probably of type Lazy Pulver (laz)\n",
-      "WARNING: but F2 unit is set to barns=0 (fm^2). Intensity might be 100 times too low.\n",
-      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "Detector: banana_I=3.09053e-07 banana_ERR=7.2496e-09 banana_N=13127 \"banana.dat\"\n",
-      "Events:   \"events_dat_list.p.x.y.z.vx.vy.vz.t\"\n",
-      "Detector: PSD_I=5.12526e-05 PSD_ERR=5.1007e-07 PSD_N=10736 \"psd.dat\"\n",
-      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
-      "    SPLIT 504 COMPONENT sample=PowderN(...)\n",
-      "  in the instrument description data_example.instr.\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.settings(ncount=1E5, output_path=\"data_example\")\n",
     "data = instrument.backengine()"
@@ -147,21 +111,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "charged-recovery",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[\n",
-      "McStasData: banana type: 1D  I:3.09053e-07 E:7.2496e-09 N:13127, \n",
-      "McStasDataEvent: events with 43619 events. Variables: p x y z vx vy vz t, \n",
-      "McStasData: PSD type: 2D  I:5.12526e-05 E:5.1007e-07 N:10736]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(data)"
    ]
@@ -177,14 +130,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "knowing-schedule",
    "metadata": {},
    "outputs": [],
    "source": [
-    "PSD = functions.name_search(\"PSD\", data)\n",
-    "banana = functions.name_search(\"banana\", data)\n",
-    "events = functions.name_search(\"events\", data)"
+    "PSD = ms.name_search(\"PSD\", data)\n",
+    "banana = ms.name_search(\"banana\", data)\n",
+    "events = ms.name_search(\"events\", data)"
    ]
   },
   {
@@ -198,44 +151,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "official-major",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "metadata object\n",
-      "component_name: PSD\n",
-      "filename: psd.dat\n",
-      "2D data of dimension (100, 100)\n",
-      "  [-2.25: 2.25] X position [cm]\n",
-      "  [-3.0: 3.0] Y position [cm]\n",
-      "Instrument parameters: \n",
-      " wavelength = 1.2\n",
-      "\n",
-      "metadata object\n",
-      "component_name: banana\n",
-      "filename: banana.dat\n",
-      "1D data of length 250\n",
-      "  [5.0: 175.0] Longitude [deg]\n",
-      " Intensity [n/s/bin]\n",
-      "Instrument parameters: \n",
-      " wavelength = 1.2\n",
-      "\n",
-      "metadata object\n",
-      "component_name: events\n",
-      "filename: events_dat_list.p.x.y.z.vx.vy.vz.t\n",
-      "2D data of dimension (8, 43619)\n",
-      "  [1.0: 43619.0] List of neutron events\n",
-      "  [1.0: 8.0] p x y z vx vy vz t\n",
-      "Instrument parameters: \n",
-      " wavelength = 1.2\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(PSD.metadata)\n",
     "print(banana.metadata)\n",
@@ -259,42 +178,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "previous-theme",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Date': 'Wed Jan 19 16:25:40 2022 (1642605940)',\n",
-       " 'type': 'array_2d(100, 100)',\n",
-       " 'Source': 'data_example (data_example.instr)',\n",
-       " 'component': 'PSD',\n",
-       " 'position': '0 0 5.1',\n",
-       " 'title': 'PSD monitor',\n",
-       " 'Ncount': '100000',\n",
-       " 'filename': 'psd.dat',\n",
-       " 'statistics': 'X0=0.00170905; dX=0.444962; Y0=-0.0029441; dY=0.887821;',\n",
-       " 'signal': 'Min=0; Max=8.13188e-08; Mean=5.12526e-09;',\n",
-       " 'values': '5.12526e-05 5.1007e-07 10736',\n",
-       " 'xvar': 'X',\n",
-       " 'yvar': 'Y',\n",
-       " 'xlabel': 'X position [cm]',\n",
-       " 'ylabel': 'Y position [cm]',\n",
-       " 'zvar': 'I',\n",
-       " 'zlabel': 'Signal per bin',\n",
-       " 'xylimits': '-2.25 2.25 -3 3',\n",
-       " 'variables': 'I I_err N',\n",
-       " 'Parameters': {'wavelength': 1.2}}"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "PSD.metadata.info"
    ]
@@ -314,53 +203,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "composite-property",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Intensity\n",
-      "[[0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
-      "  0.00000000e+00 0.00000000e+00]\n",
-      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
-      "  0.00000000e+00 0.00000000e+00]\n",
-      " [0.00000000e+00 3.16421435e-13 0.00000000e+00 ... 0.00000000e+00\n",
-      "  0.00000000e+00 0.00000000e+00]\n",
-      " ...\n",
-      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
-      "  0.00000000e+00 0.00000000e+00]\n",
-      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
-      "  0.00000000e+00 0.00000000e+00]\n",
-      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
-      "  0.00000000e+00 1.83098259e-10]]\n",
-      "Error\n",
-      "[[0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
-      "  0.00000000e+00 0.00000000e+00]\n",
-      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
-      "  0.00000000e+00 0.00000000e+00]\n",
-      " [0.00000000e+00 3.16421435e-13 0.00000000e+00 ... 0.00000000e+00\n",
-      "  0.00000000e+00 0.00000000e+00]\n",
-      " ...\n",
-      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
-      "  0.00000000e+00 0.00000000e+00]\n",
-      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
-      "  0.00000000e+00 0.00000000e+00]\n",
-      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
-      "  0.00000000e+00 1.83098259e-10]]\n",
-      "Ncount\n",
-      "[[0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 1. 0. ... 0. 0. 0.]\n",
-      " ...\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 1.]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"Intensity\")\n",
     "print(PSD.Intensity)\n",
@@ -382,30 +228,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "closed-tumor",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Events [[ 3.19443415e-13 -1.72869932e-02 -4.51773809e-03 ... -2.41580555e+03\n",
-      "  -3.05806649e+02  1.40746641e-03]\n",
-      " [ 2.13584425e-12  1.71666512e-02 -8.63270069e-03 ... -3.19302620e+01\n",
-      "  -8.06158169e+02  1.61182199e-03]\n",
-      " [ 1.19125080e-12  4.48926741e-02  3.57122451e-03 ... -1.91672521e+02\n",
-      "   2.31988770e+02  1.58734495e-03]\n",
-      " ...\n",
-      " [ 1.32055701e-12 -1.69617301e-03  3.22148392e-03 ... -7.69868816e+02\n",
-      "  -1.41856315e+03  1.53521303e-03]\n",
-      " [ 1.24293709e-12  1.20333693e-01  8.91293709e-02 ...  1.99940422e+03\n",
-      "   3.26712779e+02  1.54102054e-03]\n",
-      " [ 1.51518794e-12  2.36891432e-02 -5.80047664e-03 ... -1.15832262e+03\n",
-      "  -1.66223218e+03  1.39916492e-03]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"Events\", events.Events)"
    ]
@@ -420,21 +246,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "adverse-demonstration",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/user_guide/data_example_21'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "PSD.get_data_location()"
    ]
@@ -450,27 +265,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "furnished-juice",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "plot_options log: False\n",
-       " colormap: jet\n",
-       " show_colorbar: True\n",
-       " cut_min: 0\n",
-       " cut_max: 1\n",
-       " x_limit_multiplier: 1\n",
-       " y_limit_multiplier: 1"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "PSD.plot_options"
    ]
@@ -480,37 +278,17 @@
    "id": "educated-template",
    "metadata": {},
    "source": [
-    "The plotter module of McStasScript can plot a McStasData object directly."
+    "McStasScript can plot a McStasData object directly using for example *make_plot*."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "religious-chambers",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name PSD\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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HWDcqdgtwS6X9e4D3VJ67DTh4vGNOlJN7SZIkTdzkVsvRFjItR5IkSRoQrtxLkiRp4gJnllOAK/eSJEnSgHByL0mSJA0IvzyRJEnSxAVuqJ0CXLmXJEmSBoSTe0mSJGlAmJYjSZKkidsBeEq/ByFX7iVJkqQB4eRekiRJGhCm5UiSJGnirJYzJbhyL0mSJA0IJ/eSJEnSgDAtR5IkSRNntZwpwZV7SZIkaUC4ci9JkqSJc0PtlODKvSRJkjQgXLmXpo3Dy+FDX10M77bye8X4+vhkuZ8PnN0ZW97NuNoc0mP70ikddkOlcfk8ObTS/Poe+5kM15bDMw/5YTF+8Iyvd8SGY5dK57U3o/K5qDm9cP6rKm0n6xrW3qOejtmrXj+8kjQYnNxLkiRp4kzLmRJMy5EkSZIGhJN7SZIkaUCYliNNF5Xc+pr1i/YuP3F6IbceuOqs13XEFixfVmw7f+X/KcZXbzyoGN8wtEcxfuq8D3TElj5+crEtlPPWF864uBg/77tDxXicmMX4zOWd/W9YVR53LZ17/rzydRmO3yvGr/nOGzrHd2h5fBxSef97TLkv5f+Xcv8B5rCmGF+68XnFeO197mmMQ5XzHKq0r/Wd5txL25x17qcEV+4lSZKkAeHkXpIkSRoQpuVIkiRp4nbAajlTgJN7aSq6tjMv/qp5nTnxAHNYW45vvLkY/8kdexbjMbeS610wvKKcQ16r537q4s7ceoClGzvz62t52+sWzyrGL6aco/+MvX9QjPeimkO/vHz+tRz14WvL7b/w/N/pDA6Vx9JLrXyov0cbzuu8vtcc3Zn7D3Dkfl8qD6ZHpb0VS6KcLH9mlvd5fGjuOeXOc7Lq4kvSYDAtR5IkSRoQrtxLkiRpcjiz7DvfAkmSJGkKiog9gaXAA8DTgRMy89GxXuPkXpqKVnWG1s6bU2y6YG45R7mWux2Uc+tPXdl9XvTMQ8p1zmv532soj71Uo37J4eU6/LXc+iUryu1Led4AS5eX+9mwU2cuei1XvvT+AOxf2f9w5bw3FeMXs7AjVs3zr+TQD1OO511RjMchne9/Lbe+dt+CmqsWl/eFLIgzOmLz8+Fi25Mp37fgQ0OVnHsqdfEPs869tM1t4w21EXE38FjzA3BqZl5TaPd64ENA0BrlUGZ+YbzntnBM5wMLgH2Al2bmrW3PzQYuA3YBHgbenpnfGafLlwLXZeZfRMQFwHOBb4/1Aif3kiRJmq6OaZ9AjxYRAXwKODQzb42IlwE3RMTVQNaey8zNbX3sBOyRmfe0xXYGnp6Z94865NXAXwHXF4azFLgoMy+PiOOAi4F5EfG85s/trsnMjwBfB94bEa8ENgG3jXdB3FArSZKkQbYZmNn8+RnAD9om72M9N2I/4P9FxAsBImImMAx0fF2Zmasy897R8YjYDTgAuLIJXQkcEBG7ZuadmXn4qJ+PNO1OBC7JzD8AbgZeO97JunIvTUVnd6aUfIhyWsJuK79XjD9w63OK8VoKxkGs7oidWqmOuWRRORVi9VA5jWPDqnJ5y1pKSfmY5fQbyplD1VSgpTPeXYwfe+0XO2K18pNUMj4+yQnF+PDcynkOdYZqaTlVlRSh084qdA4wtzNUfR8OK4evykr6TSVF7NQspHzNLb+fq1eWP0O7zSt/ztdHx/9DJfVLAE+ZtN6eGRE3tj2+JDMvKbS7olmdXwUsysyftD+ZmRkRfwB8KSJ+Bvwq8LvjPTeqj5si4l3Asog4HvgocHlmfqKH83k28P3M3NT0uSki7m/iD47xumXA4og4AphFK61nTE7uJUmSNNU8lJkHjtPm0My8t0mb+UvgQuC49gYR8STgdOANmXlDRLwa+GxE/DqtXP3ic5n5SHs/mXldRLwfWAl8JDMvmIyTHE9m3gYc3ctrTMuRJEnStDOS/pKZjwN/Q3mH/f7ArMzWHe+a3z8DXjzOc08QEbvT+r51MXBMRPRWcQDuBZ4VETs2/e1IayV+0r9+dHIvSZKkiRupljMZP+OIiKc1ue8jm2bfAsWSZfcBe7Xly78Y2AO4c5zn2o+1J62EzCWZeQZwDPCZiHjV+CNtycz1zfiObULHAmsyc6yUnC1iWo40JRUSyc++odjygaMPKcbjC+WE+XX7zSrG943PdQYPLefWD11fLrP46OKhYnwJ5fzqUk57LT+/lltfy4s/maXF+IIVlbzwQunMWl4415fLbA4fWmlffouKY6+VvKyWyKzky9f2HBRLpFZy6zm9HF6wqFJ+tXKeJfNXls+nVB4UYH3sXenpk90fVNIg2R24qlkB35FWecg/AoiIZcBZmXljZv4wIt4NfD4iRjbKnpCZP2raVp9r81TgzMy8GiAzb46IN9L6h8ATNOUqj26eWx4RD2fmS5qnFwKXRcRZwI+Bt0/4KhQ4uZckSdK0kpnroLyKkfnEXf+ZeQVwRaVt9blRx1o3KnYLcEuh7XuA91T6uQ04eKxjTQYn95IkSZq4YJvexEpl5txLkiRJA8KVe2kqKuW6V3LOa3XrZ84u56Lvu2L0zfSa9o93tl84o5xbPrSonM9/Vef9PABYN6+S5z+3MJahYtOqao7+vHK4lFtfU8sLH47ecuvnLy73s3pjZ7GF2vjO++5QMX7avHJ8yYryGIt7C04vt62Nu7YvoLb/oXSPglMXl8+zus+hvM0DsvIfRu1mBJK2nh2YzDr32kKu3EuSJEkDwsm9JEmSNCBMy5EkSdLEuaF2SojMcu7sVBUxK+Hkfg9D2roKOd27bf5esen6FZX630Pl8FUry3nxC+Z21i6v55yXc67XZSW3flElz3+oUOd+qLc697XU6qsWV86zUqO91L7WtpqLvqiSi144T4ANOxXO9dpi0/pehEmIXzWvfK1qNedLewWgcj4Ah1bGUjLUY3xVJZ7d762Qpq+hmzLzwH6PYsSBL4m88bOT01e8lCl1btOJaTmSJEnSgDAtR5IkSRNntZwpwZV7SZIkaUC4ci9NE+vj3mJ85uMzivGDV369GF+wopJzXsjFn8PaYtuLc005XtsPU8mXP3hGYYyLy22red7Ly3neC+KMckeVnPbiuVbGPTy3nFufLywXYz9txlAxvuTaQv33Wh3+St3+pRvL17y6d6GwR2EBlb0F88p7CxbOuLgYX3JouUZ96bNV3c9QOebw4eVrTt5QjkvSdqrvK/cRcX5E3BURGRH79Xs8kiRJ2gIj1XIm40dbrO+Te+BqYC5wT5/HIUmSJE1rU6YUZkTcDbw+M28du52lMLUdqKQ39OSQSryHkpLVEo61lI+Kaj+rOvuppmVUUmGq51krkVgz1H3TmYd0fz5AtVxnqaRmLf2oZsPh5WPOXF4eYykVqudrW/sMVZSuV+1anTmvnE71oTin3Hk5E8pSmNpOTLFSmC+LvPGfJqev2NtSmFtqWuTcR8RJwEmtRzP7OhZJkiQVBNNkZjnYpkJazrgy85LMPLD1L7hf6fdwJEmSpCnJf19JkiRp4gLSzbB95+RemoquL5T3i1cXm161ubPMIMDXObgYr5ZOrJSULKnl0NdKJK5hTjE+vLwz13v1IZWc86FyuJajf8273lCMP+OiH5Q7KqidT+0a1spYrplXOf+o5LqXnN59U4ANO5Xfz2F2KUTL5SRPXVneLHAQq4vxo2/9cjFeKgV68rzytX0l/1qM75bfK8bXx97FuCRtr/qelhMRF0TEfcBewPKI+Fa/xyRJkiRNR31fuc/M9wDv6fc4JEmStOVyB9j4lH6PQn1fuZckSZI0Ofq+ci+pS0Pl8LtZWoyv36Gci7xu86xifM7QzR2xWj37qxaX8/wXLFpWjJfquQOcurgzR72Wz16rLV+rC/+Fj/9OMc7GcrhU/722V+Ann9qzGD/tnUPF+BzWlA9auM1I7XxqdeFL1xBgyaLyvRJmDj2vs++d7iz3Mbfcx1Ury+//kft9qRgvnf+cjZ2ft7GU3h+AYe7tqR9JGnRO7iVJkjRhm3cIHt/pyZPUW2UlRuMyLUeSJEkaEE7uJUmSpAFhWo40JRXqi59drjm+nkpu9WPlHPV94/5y+8cL7VeVR3fixkuL8d0Wl2uRX/O35ZrzcWdn0nmthn5NLRe7NsYNh9fqv3fWnF+3srw/4RlvK9fK3xCVewVcWw6X9hHU9jnUrkut5jznlsMb6Oz/1CzXnD+Z3y/GL6a8L2J4Rbluf+l+BhxeHt+Z884oxj8095zyC6IcJsv/vUjaepJg444zJqk303K2lCv3kiRJ0oBwci9JkiQNCNNyJEmSNGFJ8Dg7TVJvj0xSP9ufyCwUWp7CImYllXxPaWBEOY++aLJSi4c6QzOXl/O8azXX588r17Ov1nkvqNVnr+X/1+r/Vx12Q+WJVxdivbQFDi2Ha3XhS/sCansIapaysBiv1ZGv5fSX1O5PMBzl3Pr5WW7fi1refvV9ruTuc3a5/r80WIZuyswD+z2KEb9x4JNy2Y3PmJS+9oqHp9S5TSeu3EuSJGnCNrMDG5msDbUCiIg9gaXAA8DTgRMy89GxXmPOvSRJkqadiLg7Im6LiLXNz5GVdrMj4msRcUfz+wWFNmdHREbEfhMc0/kRcVepr27GUfBS4LrMPAlYDzx3vBe4ci9NF1lOM5g/b05P3VTTHgrWzDigGJ9zSDnlY/XGg8pxyvGiSgnHWjnJU+eVr8uSubXUpnJKTakU6IadKuk3p5fDtXKVCxYtK8ZLaS/Di7p/fwD2XVVun39TrhEZ53amYuYLKm0pp21eleU0owVzy+dZSqnJ3SrHPLySKlroY8y4pO3FMZl56zhtlgIXZeblEXEccDEwb+TJiDgAeCVQrOccETsBe2TmPW2xnYGnZ+bo+tJXA38FXN/LOCLiec3jdtcAHwfeGxGvBDYBt41zrk7uJUmSNHGtDbWTlpbzzIi4se3xJZl5Sa+dRMRuwAHAEU3oSuDCiNg1Mx9sJu4XAX8IfKXSzX7AlRFxVGbeHhEzgS8DlwKfaG+Ymaua4/Y0jsy8k8Iuooh4H61zvzoi3g+8FqispLQ4uZckSdJU81CXG2qviNZMehWwKDN/Mur5ZwPfz8xNAJm5KSLub+IPAh8ELs/Mu0ZPyEdk5k0R8S5gWUQcD3y0ec0nii8oG28cNcuAxRFxBDALuGy8Azm5lyRJ0nR0aGbe26y+/yVwIXBcty+OiN8EXgGcNl7bzLyuWTlfCXwkMy/YsiH3JjNvA47u5TVO7qWpKAslGK8t55APLyp3Ucv/nnlIOb5mZWd+/b7xuWLbU3N0WmDLQawuxms558UxVnLrayU/z7trqPzEynL45I6UxpZ9545Om6yPpVby8wQ+WYx/cvEJxXixROji8jFr5URrpUOfMfsHxXgpXz4WlfPcq3sI4oxivJf37q7Fe5bbDpXD88+qlOVcXtmjUMp2lbRVJcHGSatz38XxMu9tfj8eEX8DlP6iuBd4VkTs2KyW70hrBfxe4J3Ai4CRVfu9gGsi4oTMHG7vJCJ2p/U31GLg2Ij4fGb2Urt4rHFMKqvlSJIkaVqJiKc1ue80aTlvAdaObpeZ65v4sU3oWGBNk+d+XmbOysx9MnMf4D7gyMLEfk9ayxRLMvMM4BjgMxHxqm7HO9Y4uu2jW9WV+4gol0N4oscyc8UkjkeSJEkaz+7AVc0K+I7At4E/AoiIZcBZmTmyIXchcFlEnAX8GHh7j8d6KnBmZl4NkJk3R8QbgY67AkbEBbTSaPYAlkfEw5n5kkkaR1fGSsv5LPBvQHl3QctzgX0mc0CSJEmafpJgEztum2NlrgOKtaAzn5h/2OStH9xFn/uMcax1o2K3ALcU2r4HeE+ln67GMVFjTe6/m5nzxnieiOj+nvKSuheV+uolq8rhS2ecWIzP6fzWshXfWKpdf2exba2G/FUry1/4nbq4XIu+5Ly9h4rxOLd8onF6pS565TvFNT3cF2DdvFnF+L4rCvn5wDCV/O/KfoETFnfm6C/deHKxbfVeAZX3/yd3lHPa49Od16uWW79wRnl/wpLTa/cQqCiM8WLK5znztPJY1rJ/ue/ru/9sSdL2YKyc+27uOtPDnWkkSZIkbU3VlfvM/M/xXtxNG0mSJA2+1k2stl21HJWNWy0nIl4dEddHxP0RsT4iHoyI9dticJIkSZK6F5mVfNWRBhF3AGcANwGbRuKZec/WHVptPLOSSq6mNDg67kDNbvnsYsv1K/Yuxk+dV85FruV0l6yZ0Vn7HmDfKOecz3y8nC+9YVVHQYGWQi56Lf97w+HlPuavLNc/X72xnDW4YajSz+JyP70YjkrO/enlcOlca+Pj3HJ4flbqv68oj6X0uah9JmrvW63Of+2YHFa4b8OhlX0lQ+VwVed/Ki1pLr62B0M3dXkX123iRQc+LS+5cb9J6eu34utT6tymk25uYvXjzCzfyUaSJEnSlNHNTaw+HRELI+LXIuJXRn62+sgkSZIk9aSblfv1wMeBi5rHASRso0KmkiRJmvI2swOPM6Pfw9judTO5Pxf4beDmzNy8dYcjCSjWuV//wXLT3c76XjG+5IPlWuR5dPm+dM+Y/YOOWK2ee02ttv6C5cuK8VLO+cEzvl5su3p5OYd+KQuL8TmU6vbXc+tPoLPm/Cc5odi2mlt/aDlcq0Vf3YtQUsnbr42ltv9hyYrOz0Vtf0btngAns7QYv+auNxTjce3Ye7ueoHKtqsytl6Qn6GZyf3/b7XslSZIkTVHdTO6vjYglwGeBx0aCmfntrTYqSZIkTStJsNE6933XzeT+uOb3H7TFEth38ocjCYAslA5cXi4duP6xcinMWknJ02YMFeMH05kOs/qQSjnJ08vpJAsWldNvqmkppfKWKyttKyUi96WSOlQrkTivHD52xRc7g4VSnUA1RaZWrvLUSurIGjrTXoaXV9JsaiVCKV+XDTuV47V0nZLhReWxrB6q3Jz8neW0LEqZYJVrmM8qp43FZ2upPbU3uvbmSdJgG3dyn5nP3RYDkSRJkjQx407uI+Jw4N8yc0Pz+BnAAZm5YiuPTZIkSdNEElbLmQK6qXP/EeCnbY9/Cpy/dYYjSZIkaUt1k3MfmflfyY6ZuTkirHEvbU0fKOTX95hCXMtRZ3E5XCqpOD/LZSOHz62Ugry2m5H90qmLO3PRlywql/C8avHrivETN17a0zFreeSlEpnDh/VWZnLDueVrfhCruxwd9fenMu5qLn5pPwOwZsYBHbF9F5X3LeQfVvLfh8r57wuo7LkofS6Gyk2PXPmlYny3Py6XfF0f5tZLUrtuJvf/EREHZ+ZqgIg4GPjZ1h2WJEmSppPN7GC1nCmgm8n9/wSujohvNY9/HTh66w1JkiRJ0pboplrO1yLi14HfpFXQ7F8y88dbfWSSJEmaNtxQOzV0s3JPM5mvJFNKmnRnF+rcl/LwgTPPOqMY/9Dcc4rxUm11oJgXPYc1xabDp1dy7itq+fLVuvg9tF23eFYxvm9U6t/XFHLd12W574UsLcaHry1flwUrymM/dV7nnoNabn2eWMl/X1XOf183rzz2izm5I1babwAQi8p919rXDK/oPKerVpY/E2srn8/hD1Y+cx8o79Hg7PK9BSRp0FWr5URE5bYzvbWRJEmStG2MtXL/koj4h3FeX14akiRJ0nYlCTfUTgFjTe7f28XrPz9J45AkSZI0QdFWwn5aiJiVFHJGpcFyeGcoyjn3921+ZjH+ko23TngUG1aVa6XPPKRcW/3gGV8vxnup0V6rz84h5fBk5H9D+ZxqY7ly5ZuK8WPnfrEYn7+yPMbSnoalG8t/v9Wuba9KxzyZi4ttS/n5UB/jTz61ZzH+jLf9oCNWO5+TK/sZFuzQ47avNOde24OhmzLzwH6PYsQ+B+6SZ9z4u5PS10nxqSl1btNJVxtqJUmSpLEkwSa8z2m/VTfUSpIkSZpenNxLkiRJA6KrtJyI+BVgr/b2mfntrTUoabtXqGm/21nf66mLWr48yysvKKT519TypVdvPKj7ToANQ51jnLm8nM9fy3+v5dAzVA7nO7qvF1/LlV+48WM9HXM4ymOs1cUvtqXStsf3c/Uhne/R0kpufXX/w1DlmAdX4gXV3PoPVnLra8cs3RNCUl94E6upYdzJfUT8MXAe8CNgcxNOYN+tOC5JkiRJPepm5f5/APtl5j1bezCSJEmSICL2BJYCDwBPB07IzEfHe103k/sfOrGXtrFCqsX6x/YuNt3rKQ8V46eeVS4FeN5uQ8X4M2Z3liuspfaUyikCDK+qpI7U7mV9fWdoA+Vj1lJkaqlAB68spw49Y2PneQLlNJazyk1raiVCF2a51OSSOLszeGi57+r5F9JsoP7eXTrjxI7YghXlVJjaMWupULG+XFp55uzO6zKHteVjnlU55txaClMtL0nStraZHfpyE6uIOJtW8t5LM/PWtvguwKeA5wGPA98FTs7MB5vnZwOXAbsADwNvz8zvTHAs5wMLgH0K4+n1eC8FrsvMv4iIC4DnAuOmxXezofb/RcSHI+KAiPj1kZ8uXidJkiRtNRFxAPBKoLQxLYEPZ+YLM/NlwJ20Us1HLAUuyszZwEVQvulHROwUEc8ZFds5ImYVml8NzAVKC+PF40XE8yJi+aif9wNfBw6PiH8AdgVuK1+FJ+pm5f7tze/fb4uZcy9JkqT/Mskbap8ZETe2Pb4kMy9pbxARO9GaJP8h8JWO8WT+CPhqW+hfgXc3r90NOAA4onnuSuDCiNh1ZGW/zX7AlRFxVGbeHhEzgS8DlwKfGHXMVU3/T+hgnOPdSeH744h4X3PeVzeT/dcC497Rb9zJfWY+d7w2kiRJ0iR6qIs71H4QuDwz7xo9mR4tInagNbEfyf17NvD9zNwEkJmbIuL+Jv6EyX1m3hQR7wKWRcTxwEeb4z5hYj+Oro/XZhmwOCKOAGbRSukZV7elMH8deA2tFfsVmdnV1wJd9j3p+U7StFfK/x4qN535WDnPe8mKQj43sGSoHC+VoKzlkC/dWC6dmHeV/3I9cuWXivGSWj7/kkXlceeJldKWl5bzvzm3HL4y39QRO3buF8uNDymHn/Blb3v4jqFifMnpnec0f3E557y6z2GonIs+c6j83i2IcRd9fqlyCa+a97pi/MSNlxbjpZKn+577uXLnh3aWgYUx8v93KH8uyPKeE0mDISJ+E3gFcFqXL/lr4BHgwi05XmZe16yerwQ+kpkXbEk/PR7zNuDoXl83bs59RLyN1o6l/YE5wPKIeGuvBxpDV/lOkiRJmrqSYCM7TcpPF34LeBFwV0TcTet+TNdExPzRDZtNri8A3pyZI2Xd7wWeFRE7Nm12pLU6fm/pYBGxO61ltsXAMRHR241dejzeRHSzofbPgAMy812Z+S7gQOB/TsbB2/KPrmxCVwIHRMSuk9G/JEmSBk9mnpeZszJzn8zcB7gPODIzh9vbRcQ5wMuBN2bm422vXw+sBY5tQscCawr59iMlKZcDSzLzDOAY4DMR8aoextv18Saqm8k9mfnD0p8nQUf+ETCSfyRJkiT1JCKWRcSBEfESYBGtFfJ/iYi1EdGeb7kQOCUi7gBOaR6XPBU4MzOvAMjMm4E3AjsXjn1BRNxH65uE5RHxrS043oREZiWp8peD/ALwTVrpMgmcBOyfWUhQ7fXgES8H/j4zX9IW+zZwXHPhRmInNccFZr4c/nSih5amtmsLecRD5aa1XOS17F+Mr59brpdfyrmvKeVQb5FS/fuhyem6WnN+Rjnzbw1zOmK1Gvo1G3YqX5eZj0/82l61uJznviAOLsbnZ+f5ACwt/L9k3w/eXx7MY+VwbV/A8AcrtegLH+faNSnV4QdYMLeHvQIA15tzr+3B0E1dbDrdZvY8cFa+48bynqxeLYmpdW7TSTcr9wuBFwK3ND8vAibnnesy/ygzL8nMA1tv8q9M0qElSZKkwdJNKcz1wFu2xsEzc31ErKWVd3Q5WzH/SJIkSRp01cl9RLw6M2+IiOJ3wZnZ43ekVQuByyLiLODH/PKmWZIkSZomNrMDj3dX6UZb0Vgr98cDNwDvLzyXdHGHrG40NTzLSaPS9uqwG7puOrxDJc95eeUFQ+XwwTO+3v0xD68cs2K3eaW7gsPHCvnfC1ZU/moZqnReiW9YVc5dr9X5P3VlZ4720hnlvU4LWVo+aGULU61GfSnPf5jytV0QZ5Q7r9SFv+Zvy/X/v/DO3+kMVnLri3sigOEV5TFedVZ5X8DXz+r8K75234J3L65c26FyuJf/ViRpe1Cd3DdlL8nM12y74UiSJEnaUuPm3EfEqsw8ZLyYJEmStl+tm1jN6PcwtnvdVMt5QnmapqLNr22d4UiSJEnaUmNtqH0/rTvRzoyI9W1P/QpwxdYemKQnOrOyh301D/fUT612+3AU8qivrXRSyeev1T+vHXPBqh627tS+K6ztLTi3Ej+9HN6ftR2xfVdU6r9XjlmtRb+ocp7nduaL1+q/H7y4/D4Pzy13Hc8tbwA4lR7qvw/1Fv/6vPL2qSVzu79vQ838eZXa+uzSW0eStpok3FA7BYyVlnMJ8DngQuCP2+I/zcwfb9VRSZIkSerZWBtqNwAbgNdvu+FIkiRJ2lJjpeV8KjPfFhH/RqHAW2b2dl92Sd37QGd5ww99sFzysKZWlvCaO95QjN+Ve3bELq7cjHrNvM4SjlBPv9lweLks5fyVnakWtTKL6xbPKsZrY1yyqlxqsZQKA3Dsqi92NT4A5pXD1TKeNad3vqdrZpTPs+bileUymwexuhg/ceOlncHDK50PlcOlsqFQLu0J1FOqCkrlUQEursSJyn8XWcvXkrS1JMEmduz3MLZ7Y6Xl/GXz+8+2wTgkSZIkTdBYaTk3Nb+vG4lFxAzg1zKzvONLkiRJUt90U+f+M8DJwEbgG8AzI2JxZp6/tQcnSZKk6cE691PDuJN74IWZuSEijgFWAO8D/hVwci9tLWcX8sILefgAu531vWJ8wdxy/ne+I4rxObNv7oj9ZL/OPHyA+M6qYvyqPLE8lqFKGc9Sjv5h5Zz4Nbl/Mb50YznnvpovXzG8qDNW20OwdMa7y30MlfcL5KXla37k87/UEZuzsfN92BLVPQelVPRazn0lV752zasKH5eZQ+UvgGu59bW9GGT58yJJ26tubmL15Ob3bwHLMvPnwOatNyRJkiRJW6KblftvR8Qw8CLgtIh46lYekyRJkqaZzezgTaymgG4m9+8AjgS+kZk/i4hnAadt3WFJkiRJ6tW4k/vMfDQirgdeGREvBlZn5j9v/aFJ27Fa7e6CB259TjH+hZW/U4wfSWeeN8CGFZ216GNpxy0uALhqXrmGfrXO+1A5vHDlxR2xJYeWc8UXfLDcd62e/9c5uBiv5YtfufhNHbGDK7Xi911xfzFey1GPF5Sv46nZWS9+NeU8/4UzOq8VwJIV5eu1bl65Xv6cQzpz+jesKt+HoKbX9sX7GexQzqEfPqQc321leW/J+speFM62zr2k7VM31XKOBC4H1gABvCwijsvM/7e1BydJkqTpwWo5U0M3aTnnAHMz898BIuJFtCb7Tu4lSZKkKaSbyf2TRyb2AJl5W0Q8eawXSJIkafuShBtqp4BuJvcPRsTxmfl3ABHxDuDBrToqaXtXyt0u1b4HOLocrua/V+rIX5Wduesnbry03Hethv4PK/Xc7yjn+RfzxSt567X4xsr/SJbMrdR5HyqHFx7ysY5YNbe80kdtjPOzXHP/vL/t7Oigd5bz/Gvvxfx55b73XVTeF1CqL1/ro1a3f/7iSvtFlTx6CvFKSnxtLGvZv/wCU+sl6Qm6qXN/MrAwIh6LiEeBhcBJW3dYkiRJknrVTbWcO2lVytkZiMz8j60/LEmSJE0nm9nBDbVTQDdpOUTE64B5QEbEisz88tYdliRJkqRedVMK8xzgKOAztEphnhsRr8rMM7f24CS1ObRczzteVq6hXrvV3Px8uBgv5XQfPOPrxba1XOygMpYV5XCpXv6ceWuLbReytBg/du4Xi/FTV3bWkAc4qFK7vrRH4dR55T6YVw4vWVTO81+9sVy7Pu4sXK+55b65vjyW4WvLxzx1cffnX9tDUVM7n5rSdazdb6Bm/Yq9y08cXnnB9T11L0kDo5uV+98H5mTmzwAi4i+BmwEn95IkSQKsljNVdLOh9vvAz9sePwZUbs8oSZIkqV+6WblfC3w5Ii5rHr8NuKHJwycze/s+V9L4SikF1/bYR48lAjcc3ln2sZZ+U+17VTk8f2W5vOHRszu375x2x1Cx7RzWlOMry/FaikypFCRQPKelh5RTRxbOuLjcxySoXavhUtlQYOYh5fOpnX+xjGWlhOe6xbOK8VqK1MmLy/EFi7r/38Tw4eXP3G7zvleMrz/sk133LUnTTUTsCSwFHgCeDpyQmY+O9ZpuJvf7N7/by1++uvlJwMm9JEnSdi6JbVotJyKuBp4LbAYeAU7JzLW9tIuIpwD/m9YOnseAr2XmFpd8j4jzgQXAPsBLM/PWtudmA5cBuwAPA2/PzO+M0+VLgesy8y8i4oLmPL491gu6KYX5mvHaSJIkSdvYOzJzA0BEvAG4FDigx3YfpjWpn52ZGRG7j35xROwE7JGZ97TFdgaenpmjU9WvBv6K8nfwS4GLMvPyiDgOuJimRENEPK953O4a4OPAeyPilcAm4LbShWjXVSlMSZIkaRt6ZkTc2Pb4ksy8pL3ByIS9MZPWynyHWrtmgv52YK/MzKbtA4Uu9gOujIijMvP2iJgJfJnWPxI+MepYq5q+n9BBROxG6x8URzShK4ELI2LXzHywua9UR/2viHhfc+5XR8T7gdcyTtaMk3tpSiqUMRwq51DXSiSeWqlKWS1BOFToo1IKcgm95bMPr6iUztyjc5AzN5b7qJblnFvuOy+NYvzIGV8q97Oqs58NQ537EADWLJ5TjNfKTy6p5Mv3pLLPoTbGdSvL+fL7LirUQ6jsldh3brl2Qq3MaKmcKMBViztLnlbz8CtjWb+qUgpT0pSxmeDxyUvLeSgzDxyvUUR8AphPq1z7a3ts9zxa6TFnR8RraKXs/PnIBH1EZt4UEe8ClkXE8cBHgcsz8wkT+3E8G/h+Zm5q+twUEfc38QfHeN0yYHFEHAHMopXWMyYn95IkSZqWMvP/A4iItwEfATpXE+rtngTsC6zJzPdHxMHAP0bE8zPzp6Nef12zcr4S+EhmXrC1zmnUcW8Dju7lNdVSmBHR2x1GJEmStB0LNvGkSfnpVWZ+CnhNROzSQ7t7gF/QSpEhM1cDDwGzO86slYs/BCwGjomI3u7mB/cCz4qIHZv+dqS1En9vj/2Ma6w698dExDUR8azJPqgkSZK0pSJi54h4dtvjo4AfNT9dtcvMh4Cv0OTBN9VsdgO+O6qPPWklRy7JzDOAY4DPRMSruh1vZq6nVV7+2CZ0LK1vDMZKydki1X8aZeYRzer91yLizMwcN8dH0iSJHnK0K22XzK20r9Q0L+U6HzRvdffjoF7/vVYv/uCVnXn0wzuUc+iHl1dq7lc8Y+8flI9JOXe/tOdg/rxKzflFlTEW8vahnqO+ZHn373Ot5vzFlK9tL/nyve4JqLav7AtYUNj7Vay3DxxM+TP3oR3OKXde+28ly9dc0sB4GvC5iHgarSoyPwKOaireLAPOyswbx2rX9LMQuDQiPgr8J/C2zPzJqGM9FTgzM68GyMybI+KNQMemp6Zc5dHNc8sj4uHMfEnbsS6LiLOAH9PazDvpxvzeIzMvjogVwL81J72Z1kaEzMzdtsaAJEmSNP1syzr3TVWbV1aee1037Zrn1wG/Pc6x1gHrRsVuAW4ptH0P8J5KP7cBB491rMkw5uQ+Il4BfBL4NHA+rX/xSJIkSZqCqpP7iDgP+H3g5Mzs8Ub2kiRJkra1sVbudwX2z8z/2FaDkdS7+ZsreeGV+u88pdJR4V56C+aWa5Hn35RryMeicnH9Wn71HNZ0xIYPKY975iHl+vcbDinXed+wqhyfM6/zmADDh3Ued/j03vL8a/sZavcWKF2X1RvLBRjmcHMxfumME8sHHSqHiw67oRienw8X47X7FpTq2dfUauIvnbewGP/QaZWc+3PLY5e07W1mBx5np34PY7s31obad27LgUiSJEmamLFKYUqSJEmaRrxDrSRJkiZsW1bLUV38sszn9BAxK6nUdZamn8Mr8cIe9mvL9bx3m/e9Ynx/1hbjpTx3gCWleuGnF5sWa+IDrFtZrsU+Z2M5X7yklitfy7nvtZ9e5N6VvQUnVv7erOTczxyq7Bc4vDDGoS4G1u6wyjEfn/gxq/scKtf2qnnlnPtifn1l3FTK088/q7K3JMqfZ2n7MHRTZh7Y71GMePKBL8tfu/H/Tkpf6+M5U+rcphPTciRJkqQBYVqOJEmSJiwzeHyjaTn95sq9JEmSNCBcuZf6qnJ/uEML+e+VPHfmlcPDi8q1yIcPr9SRL+RoF/OzoZpbfnFlP8yGoXI/py7uTLBeMlTeW1CrZ79ucTnPf9+h+4vxUj1/gPnZmdMdKyq59ZU+qvGhyjFXdh5zOCo15LOcz37i45cW47X69wvoPv99w7WV97/ysS32XVHdE1DJ51/L/pWezLmXporNm3dg42PWue83V+4lSZKkAeHkXpIkSRoQpuVIU1EhBaeUwgEwvKKcxlF12A3F8IZrX90Rm7m8kjpRSbNZsqKcUlOr+HkQqztieWml/OT3yikytVSgq1ZWyjIuKqeOXPPdN3TEjpz3pWLb4UMr13yoHF46493F+LGLvtgZrJQfXTC3PO7aezSnUgq1OMZKmk21LGclfuq8Sh3LgjXMKcZXH3JQMb7+g3t33bek/sjNweOPuqG231y5lyRJkgaEk3tJkiRpQJiWI0mSpInLYPPjVsvpNyf30lSUnbnLwx+s5LPXnFsOn5nl3O3VPNx5zNil2HZdHlCM1/Lfl24sx0/c2FnGccGJvZXfXHpIue8lO1Wu17XlcCmnf/7zy/scrlz5pmK8mEMPfHLeCeWDFvZW5DvKew6+sPh3ivEFK8rv577LK6VAS/sfKnsi5i+u7POIcvnJpY+X34uSapnVmsoY60/UNhJI0mAzLUeSJEkaEK7cS5IkaeI2Bzzm1LLf+rZyHxHHRcQtEfGLiPiTfo1DkiRJGhT9/OfVWuAtwGl9HIM0NR3afX59freco/2Mx39QjK/m4GJ8eFGhdnul5vq+8bnyE4Va+UC1LnqpFv2Jyzvz8AE2rCrnaB884+vlzstl8TmBTxbjpXz5OfPKueULN36sGJ85VK45X3Pqys69FUdSrq1fM/OQyr0IKF+vUvvafQtOnre0GD84yzXqPzS33E/pPNesLPdRvW9DYX8CAFH5zKU595K2T32b3GfmrQARsblfY5AkSdIk+kW/B6BpkRgVEScBJ7UezezrWCRJkqSpaqtN7iPiZqB2v/DdM3NTt31l5iXAJa1+Z1W+bJckSVLfbAYe6/cgtNUm95mVQtiSxnf9DZ2xVeXc4i9sLtc/Z2M5XKtdPz87a5qv3nhQse2Gcyt5zhXrVs4qxhfSmdO9Yady3vZV2ZmfD7BgbrnO+8zl5Vz04cMrOd1DnaFaff6lM95djNdy8a/52zcU43Fn51pFLW+/Vhe+lM8OsGR5ed/GTxbu2TmO75QT2hdQvra1+ve1exEsWVEYSy0lvlZz/6xKzf2zy/siJGl7ZZ17SZIkaUD0Lec+Io4FPgL8N+ANEXEaMD8zv92vMUmSJGkLmZYzJfSzWs6VwJX9Or4kSZI0aKZFtRxpu1Oq3T1UbvruQt461HO052e5LnwpL/wZbyvXyp/5eG/13Pede3/5iaHu+9gnvlyMr8tyPv8a9i/Gv76yXOe/lBe+ZkZ569DFlHPxa/sFjsxy7fqZGws15yv1/GvX6mQuLsbXLC7XkY9zu69JcOriSj5/KYd+rH7mdfaz5LByH1ctruytiDPKnUdlLFkeuyRNJxGxJ7AUeAB4OnBCZj461mvMuZckSdLEJa20nMn4GUdE7BIRyyLi9oi4JSK+EBG7VtreHRG3RcTa5ufIXvvoVkScHxF3RURGxH6jnpsdEV+LiDua3y/oosuXAtdl5knAeuC5473Ayb0kSZKmmwQ+nJkvzMyXAXcC543R/pjM3L/5uaaXPiJip4h4zqjYzhFR+ur4amAucE/huaXARZk5G7gIfvnVa0Q8LyKWj/p5P/B14PCI+AdgV+C2Mc4RMC1Hmj7OLpTHBNbTW1nKkytpPHFZZ7rGqe+spGUsKqdCnLm4nDrxoUPOKQ/msM5zmp8PF5u+fFElnWRuOVwry1grNTl/XmepxX2jnH5zamUo1XKdUS4pyemFcVTKTM6hXPKxVE50rPbXfLMz/So+XT6hJZWUl9p5XjxvYTFeUkvtqqWZwb1d991SqqlZq78paQp6ZkTc2Pb4kua+RwBk5o+Ar7Y9/69AuU5xRQ997AdcGRFHZebtETET+DJwKfCJUX2uAoiIJ3QQEbsBBwBHNKErgQsjYtfMfDAz76TwF1dEvI/WuV/dTPZfC5U6xQ0n95IkSZq4ya2W81BmHthNw4jYgdakvHITDgCuiNaMexWwKDN/0m0fmXlTRLwLWBYRxwMfBS7PzE+MbjuGZwPfH7mJa2Zuioj7m/iDY7xuGbA4Io4AZgGXjXcgJ/eSJEmazv4aeAS4sPL8oZl5b0TsBPxl0+64XvrIzOualfOVwEcy84LJGPh4MvM24OheXmPOvSRJkqaliDgfeAHw5szcXGqTmfc2vx8H/gaemM/aTR8RsTutumWLgWMionwL97p7gWdFxI5NfzvSWonvNedwXK7cS1NRFvLrD63k1ldyy9edVS4ROWfjzV0Po5ZbX8sLv2DjKcV4fj6K8Ti9lOtd7rtWlrGmVq6xVmryhHmf7Ihd852ri22jkv+/bnH5mlet6gzVcuVrJS9rOfdL5pbPfwmd8fkrK99kLy6He7WGzrKcB8+olGR9e+eeAIDds7Q3DdZXl6jMr5e2uZFqOdtIRJwDvBz43WbiXmrzNOBJmbmhSct5C7C2xz72BIaB8zLzioi4Cvh8RByXmf/SzVgzc31ErAWOBS5vfq/JzLFScraIK/eSJEmaViLiJcAiWqvf/9KUuPxi89yyiBjJ198d+GpE3ALcCswG/mi8PkZ5KnBmZl4BkJk3A28Edi6M64KIuA/YC1geEd9qe3ohcEpE3AGc0jyedK7cS5IkaeImd0PtmDLzW0Dxa+HMX5b0ysx1UPj6cJw+RrVbB6wbFbsFuKXQ9j3Aeyr93AaU76Q4iVy5lyRJkgaEK/fSdFHIzwY4c165tvxzb/1BMb5wv3Lu9pKhco52yeqNve0jekZlLDPprHU+PPf3im3nrCznotdy60t166E+9k9yQkfs2EtL385SLqEO7Lvo/vITh5bDXN8ZOu+7Q8WmRz7/S8X48KLy9artxSjtlxheUemjYs688ntR2y9QsuSD5fdt97+v5NZ/cO9yR6X9KZK0HXNyL0mSpInbhmk5qjMtR5IkSRoQTu4lSZKkAWFajjRN3Lf5mcX4XoseKsYvGCrXnN+wqFznvVRHvlYrfcNQpY955Vr0tbz4Uvta7n+t5n6t/n2ptjrAhp3KYx8+vTPvvNe+h5eXc9fXrSzXv3/udzv3ItRy62uuWvy6YnzOL8s4P8G+czv3BZy6ssd7CFQ+F7V6+cV9AU8p930zBxTje1H+nEuaQrZxnXuVuXIvSZIkDQgn95IkSdKAMC1HkiRJE5fAL/o9CDm5l6aieHVHaK+55Zzj3VZ+rxhfH/eW+762nHNeymnvNRe7lotezcWPQu726ZXOK7Xli30A67Kc574wy/0MR2de+JrF5fOpKdWQB1jI0vILnt8ZqtWKr+1bWH1IuW5/bV9Erf59Se39nLm88/4EUK+5P3Oos/2Gw8vju/isk8uDqY278N8KALm88gJJGmym5UiSJEkDwpV7SZIkTdxm4NF+D0JO7qWpKG/ojA2V0w/Wr9i70kk5PvOQckpFKe1hBhuLTT+06JxyH5XUGeaVw6dmoRRmVPqgnJZSS+O5mEp6R8XMxzuvSy3NpFZ+8sSNl/Z0zJLhVeVj5t7lC3PajKFifMnhletValtJ+Zk/r5xmVC2FWikdWuy/kmZTfd+GymEKnyFJ2p45uZckSdLEJfB4vwchc+4lSZKkAeHkXpIkSRoQpuVIU1KhjN9hlaYfqJQCpJC3D2xYVWlf6P9DVHLrD60cclU5vHbe/sX4cBTKPp5eyRU/t3LMiiXnVvq5thwu7kWonM+CucvKT9TKNZ5bfi/K7ixG49pKDc8TK91c38MhK+/nMOX8/9p1Wbqxki9fqkpZeT/Xn1fbQyJpytsMPNbvQciVe0mSJGlAOLmXJEmSBoRpOZIkSZo403KmhMis5HFOURGzkh7rV0uSJA2eoZsy88B+j2JE7H5g8tYbJ6ez/x1T6tymE9NyJEmSpAFhWo4kSZImLjEtZwpw5V6SJEkaEE7uJUmSpAFhWo4kSZImzmo5U4Ir95IkSdKAcOVekiRJE+eG2inBlXtJkiRpQLhyL0mSJE1BEbEnsBR4AHg6cEJmPjrWa1y5lyRJ0sSNbKidjJ8uRMT5EXFXRGRE7DdGu6dExMci4jsR8c2IuKTtuddHxJqIWBsRt0TE0b2edrdjiojZEfG1iLij+f2CLrp8KXBdZp4ErAeeO94LXLmXJEnSdHQ18FfA9eO0+zCtfzLMzsyMiN0BIiKATwGHZuatEfEy4IaIuDozN4+8OCJ2AvbIzHvaYjsDT8/M+3sY01Lgosy8PCKOAy4G5jX9Pa953O4a4OPAeyPilcAm4LZxztXJvSRJkqacZ0bEjW2PL8nMS9obZOYqgNYcvayZhL8d2Cszs3ndA21NNgMzmz8/A/hB+8S+sR9wZUQclZm3R8RM4MvApcAnuhlTROwGHAAc0YSuBC6MiF0z88HMvBM4vDD+9zXnfnVEvB94LbCsesI4uZckSdJkSOAXk9bbQ5l54CT08zzgYeDsiHgN8Ajw55m5qlnF/wPgSxHxM+BXgd8d3UFm3hQR7wKWRcTxwEeByzPzE6PbjuHZwPczc1PT56aIuL+JPzjG65YBiyPiCGAWcNl4B3JyL0mSpEH1JGBfYE1mvj8iDgb+MSKeD/wcOB14Q2beEBGvBj4bEb+emY+0d5KZ1zUr5yuBj2TmBdti8Jl5G9DTPgA31EqSJGlQ3UPr+4QrATJzNfAQMBvYH5iVmTc0z90A/Ax48ehOmjz9IWAxcExEHNTjOO4FnhUROzb97UhrJf7ens9oHE7uJUmSNHGbgUcn6WeSZOZDwFdoct0jYjawG/Bd4D5gr4h4YfPci4E9gDvb+2jKUS4HlmTmGcAxwGci4lU9jGM9sBY4tgkdS+vbhLFScraIk3tJkiRNOxFxQUTcB+wFLI+IbzXxZRHRnq+/EFgUEd8EPgO8LTN/kpk/BN4NfD4ivtE8d0Jm/mjUoZ4KnJmZVwBk5s3AG4Gdux1T2zhOiYg7gFOax5Mumo3D00bErIST+z0MSZKkPhu6aZI2nU6KePqBycE3jt+wG8tjSp3bdOKGWkmSJE1cAo/3exAyLUeSJEkaEK7cS5IkaeJGNtSqr1y5lyRJkgaEk3tJkiRpQPQtLSciLgIOo7X14hHgv2fmJG2xliRJ0jblhtopoZ8r918GXpqZvwGcC3y2j2ORJEmSpr2+rdxn5v9te/g1WncI2yEzN/drTJIkSdJ0NlWq5fwJ8E+1iX1EnASc1Ho0c9uNSpIkSd2xWs6UsNUm9xFxM7B35endM3NT0+4twB8Cc2t9ZeYlwCWt9rOm1y11JUmSpG1kq03uM/OA8dpExJuAc4DDMvOBrTUWSZIkaXvQz2o5rwf+AjgiM+/u1zgkSZI0CTZjtZwpoJ85958ENgKfj4iR2GGZ+XD/hiRJkiRNX/2slrNrv44tSZIkDaKpUi1HkiRJ01litZwpoJ83sZIkSZI0iVy5lyRJ0sS5oXZKcOVekiRJGhBO7iVJkqQBYVqOJEmSJi6BX/R7EHLlXpIkSRoQTu4lSZKkAWFajiRJkiYugcf6PQi5ci9JkiQNCCf3kiRJ0oAwLUeSJEmTI/s9ALlyL0mSJA0IJ/eSJEnSgHByL0mSJA0Ic+4lSZKkKSgiZgPnAT8EfpSZfz7ea1y5lyRJ0iTYDDw6ST/ji4jXR8SaiFgbEbdExNHjtD87IjIi9muLzY6Ir0XEHc3vF/R0yp3HOD8i7hp9nAkc63eByzLzj4CdI+Ll473Ayb0kSZKmlYgI4FPA2zJzf+A44LKIKM5tI+IA4JXA90Y9tRS4KDNnAxcBFxdeu1NEPGdUbOeImFU41NXAXOCewnPVY0XE8yJi+aif9wN/DxweER8FngXsUzq/dqblSJIkaTraDMxs/vwM4AeZuXl0o4jYidZk+g+Br7TFdwMOAI5oQlcCF0bErpn5YFsX+wFXRsRRmXl7RMwEvgxcCnyi/ViZuarpe/QYxjxWZt4JHF45z1OaPj4JfLvS5r84uZckSdIkGEnLmRTPjIgb2x5fkpmXjDzIzIyIPwC+FBE/A36VVgpLyQeByzPzrlGT7mcD38/MTU2fmyLi/ib+X5P7zLwpIt4FLIuI44GPNv09YWI/jq6ONVpEPKsZP8Atmfnv4x3Iyb0kSZKmmocy88DakxHxJOB04A2ZeUNEvBr4bET8emY+0tbuN4FXAKdNZDCZeV2TJrMS+EhmXjCR/no47veBd/byGnPuJUmSNN3sD8zKzBsAmt8/A148qt1vAS8C7oqIu4G9gGsiYj5wL/CsiNgRoPk9q4k/QUTsDgwBi4FjIuKgHsfb9bEmysm9JEmSJsFm4OeT9DOu+4C9IuKFABHxYmAP4M72Rpl5XmbOysx9MnOf5nVHZuZwZq4H1gLHNs2PBdaMyrcnIvYElgNLMvMM4BjgMxHxqq4uS2scXR1rMji5lyRJ0rSSmT8E3g18PiK+AXwGOCEzfxQRyyKimtIzykLglIi4g9bG1YWFNk8FzszMK5pj3wy8Edh5dMOIuCAi7qP1DcHyiPhWj8easMjMrdHvVhMxK+Hkfg9DkiSpz4ZuGisvfVuLeFnCsknq7dlT6tymEzfUSpIkaRIkk1gtR1vItBxJkiRpQDi5lyRJkgaEaTmSJEmaBJN6EyttIVfuJUmSpAHhyr0kSZImQQK/6Pcgtnuu3EuSJEkDwsm9JEmSNCBMy5EkSdIkcEPtVODKvSRJkjQgnNxLkiRJA8K0HEmSJE0C03KmAlfuJUmSpAHh5F6SJEkaEKblSJIkaRIkpuX0nyv3kiRJ0oBwci9JkiQNCNNyJEmSNAk2Az/v9yC2e67cS5IkSQPClXtJkiRNAuvcTwWu3EuSJEkDwsm9JEmSNCBMy5EkSdIkMC1nKnDlXpIkSRoQTu4lSZKkAWFajiRJkiaBaTlTgSv3kiRJ0oBwci9JkiQNCNNyJEmSNAlMy5kKXLmXJEmSBkTfVu4j4gzgzcAmIIBzM/Oz/RqPJEmS1A8R8RRgKfBTYHNmvndL++rnyv2FmfmyzJwDvA74eET8tz6OR5IkSVssgV9M0s/4ImJ2RHwtIu5ofr+g13bd9tGtiDg/Iu6KiIyI/XoY79HAdZn5HuDnEXHglo6hb5P7zNzQ9nBnWp8I04QkSZLUjaXARZk5G7gIuHgL2o3bR0TsFBHPGRXbOSJmFY51NTAXuKfHcTwHuLv5813APpVzGVdk5pa+dsIiYiHwXuDZwIm1tJyIOAk4qXn4QuD2bTLAumcCD/V5DP20vZ8/eA229/MHr8H2fv7gNdjezx/6fw2ek5m79vH4TxAR/0zrmkyGpwCPtT2+JDMvaTvWbsAdwC6ZuSkidgQeBl6QmQ92045WWng3fbwcuBI4KjNvj4iZwJeBSzPzE6XBR8TdwOsz89ZuxhsRbwWenJl/FxH/C/hSZv7blly4rZZzHxE3A3tXnt49Mzdl5lJgaUS8FLgiIpZn5sOjGzdv5iUdvfRJRNyYmVv8dcl0t72fP3gNtvfzB6/B9n7+4DXY3s8fvAajZeZrt+Hhng18PzM3NcfeFBH3N/EHu2wX3fSRmTdFxLuAZRFxPPBR4PLaxH4Lx/sF4GMR8TJgxy2d2MNWnNxn5gE9tP1mc4K/DVy1tcYkSZIk9Sozr4uI9wMrgY9k5gWT3P+jwPGT0Vffctwj4sVtf34uMAf4dr/GI0mSpGnjXuBZTXoLze9ZTbzbdt32QUTsDgwBi4FjIuKgrTTeCevnBtYPRMS3ImIt8HngPZn5730cTy+mTIpQn2zv5w9eg+39/MFrsL2fP3gNtvfzB69B32TmemAtcGwTOhZY054rP167bvuIiD2B5cCSzDwDOAb4TES8arLHOxn6uqFWkiRJ2hIR8SLgMuC/AT8G3t5seF0GnJWZN47Vbrzn2o6zL/CyzLy6LfYyYI/MHB7V9gJaZS33oLXZ+uHMfEm3x5oMTu4lSZKkAWFdeUmSJGlAOLnvQkRcFBG3RcQ3IuKGse4aFhHviojvRsSdEXFhREz7axwRx0XELRHxi4j4kzHa/XZE/Dwi1jY/q7flOLeWbs+/aTtw7z9ARPxKRHy2ObfbIuL1lXYD9Rno5s6FEbFj83fEnc31+f/6MdatocvzH4qI9W3v+UX9GOvWEGPcabKtzcC+/9D1NRjkz8AuEbEsIm5v/j/whYjoqCs/6J8DTS8DMfHYBr4MvDQzfwM4F6jdbOu5wNnAb9K6OcILgOO21SC3orXAW4BPd9H225m5f/Nz8NYd1jazli7Of4Dff4A/A/4jM58PHAV8IiJ2rrQdpM9AN3c/fCvwfFrv928CQxGxzzYb4dbV7d0f/77tPf/jbTe8re5q6neaHDHI7z90dw1gcD8DCXw4M1+YmS8D7gTOK7Qb9M+BphEn913IzP+bmf/ZPPwasFdlRfYY4OpmB/Zm4OPAm7fVOLeWzLw1M78NbO73WPqhh/MfyPe/8WZaEz0y8zvAjcDv9HVEW1m07iZ4AK27EtL8PqCwavdm4OOZubmpenA18PvbbKBbSQ/nP7Ayc1VmjlembiDf/xFdXoOBlZk/ysyvtoX+FXhOoelAfw40vTi5792fAP/UTN5G25snrm58j9adx7YnsyPi5ohYHRHv6PdgtrFBfv97ObdB+Qx03E0QGLmbYLtBfd+7PX+AtzQpC8MR8ZvbcpBTwKC+/70a+M9As6j3buD/FJ72c6ApY6vdoXY6iYibaf2HWbL7yP/cIuItwB/S+opyYHR7/l24GXh2Zm5oUlSWR8T3M3P5pAx0K5nE85+2xrsGPXQ1LT8DmpClwDmZ+Z8RcQTwpYh4cWY+3O+BaZvZXj4Dfw08AlzY74FIY3FyD2TmAeO1iYg3AecAh2XmA5Vm3+OJX9ftzVa489hk6+b8u+znp21/visirgZeTevGD1PWZJ0/0/T9h/GvQUSMnNvIzTb2Br5S6GdafgYq/utugpm5Kep3Exy5Nv/WPB69gjdddXX+mfnDtj//v4i4F9gPuG6bjrZ/BvX979r28BmIiPNp5dMfVfnmfrv/HGjqMC2nC01lkL8AjszMu8doehXwxojYtfn67l3AP2yDIU4JEbFnRETz518D5tPajLq9GOT3/3PAyQBNxZRXAP88utEgfQZ6uJvg54B3RcQOTT76G2l9Fqa1Hu7c+Ky2P+8P7ANM+k1ZprCBfP97MeifgYg4B3g58MbMfLzSbLv/HGgKyUx/xvmhtVr5fVr/oxv52aV57oPAwra2J9PaTX8n8DFgx36PfxLO/1jgPuBntO6odh/w66PPn9Z+hG811+dW4H/2e+zb8vwH9f1vzutptP7n9V1a/9N+Q9tzA/sZAF4ErAbuaH6/sIkvAw5s/rxj816PvO8n9Xvc2/j8L2ve62/QWrV8Xb/HPYnnf0Hz3/svgB8C39qe3v8ersEgfwZeQqtizu1t////4vb2OfBnev14h1pJkiRpQJiWI0mSJA0IJ/eSJEnSgHByL0mSJA0IJ/eSJEnSgHByL0mSJA0IJ/eSppSI+LWIuDciXtEWOyMitnrN6IhYGBF/2vx5/4j4g1HPr42Ip07i8f4uIu6LiAsmsc9XNePMiNh5svqVJE0PlsKUNOVExBuAc4E5wGxaN8yak60bK22rMRwPvD4zj9mKx/g74MbMnPTb2UdEAr+amY9Mdt+SpKnLlXtJU05mfgm4GTiP1g1y/rQ2sW9WqIci4l8i4vaIWND23GsjYk1E3BIR10bE85v4CyPiaxHxjYi4NSL+rIkPRcT5EbELrZtzHd6sgl/Qdqydmz+/ounjlub3K5r4PhHxUESc0xz79og4pNtzj4gTm3F9IyL+LSJ2b+vz3KbP2yLi5RHx8eb4qyNijy251pKkweLkXtJUdQrwTuCezPyHcdpuzsxXAb8HXBIRu0XEbsCngLdm5suATwNXNO3/CFiWmb+RmfsBf9veWWY+DJwFLM/M/TPzPe3PR8QMWreWP7Pp+8+Bq5o4wC7A1zJzDq1/JCzp5oQj4reBRcCRmfkbwGuADW19rmr6/FvgWuCi5vg30bo7sCRpO+fkXtJUNQ/4KfDCiNhpnLZ/C5CZt9Na8X8lcDDwjcz8dtPmk8D+EfGrwErgxIj4UETMA37S49heCGzMzOXNca8FNjZxgEcy8/82f/5X4Hld9vu7wN9n5g+bfh/JzMfa+vyn5s83A/dl5trm8U3A83s8B0nSAHJyL2nKiYhnAn9Fa7J7I/CBXl4OZNvvDpl5FfBq4E7gNFor/D0NsdL3SOzxttgm4Ek99Fszus/HRj3u9hiSpAHm5F7SVPQ3wMcz8xvAfwf+sL16TsEJABHxAmB/YDXwNVor9S9q2rwDWJOZ/9Hk3v8wM/+O1j8cDir0+VNgZuV4twE7RcRrmuO+BngycEfXZ1j2j8DbI2L3pt+du/jWQpKk/+JKj6QppSk/ORt4K0Bm/jgi/hi4NCJenpkbCy97PCJuAJ4JnDyy+TYi3gZ8OiKeBDwIHNe0/wPgrRGxkdZq+38v9Hkt8GcR8Q3guva8+8zc2GzcvSAingb8DDimiW/xuWfmdRFxLrA8IjbTWq0/aos7lCRtdyyFKWlam84lHy2FKUmabKblSFL/bADeszVuYgU8AGyerH4lSdODK/eSJEnSgHDlXpIkSRoQTu4lSZKkAeHkXpIkSRoQTu4lSZKkAeHkXpIkSRoQ/z/Isu6Hh32IcgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 936x504 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plotter.make_plot(PSD)"
+    "ms.make_plot(PSD)"
    ]
   },
   {
@@ -523,33 +301,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "blessed-sandwich",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name PSD\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "PSD.set_plot_options(log=True, top_lim=1.5, bottom_lim=-1.5, colormap=\"hot\", orders_of_mag=2)\n",
-    "plotter.make_plot(PSD)"
+    "ms.make_plot(PSD)"
    ]
   },
   {
diff --git a/docs/source/user_guide/functions.ipynb b/docs/source/user_guide/functions.ipynb
new file mode 100644
index 00000000..fd8f5c8d
--- /dev/null
+++ b/docs/source/user_guide/functions.ipynb
@@ -0,0 +1,209 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "utility-python",
+   "metadata": {},
+   "source": [
+    "# Functions\n",
+    "McStasScritpt includes some freestanding functions located in the functions module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "naughty-layout",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import mcstasscript as ms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "unauthorized-billion",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import laue_example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "royal-camel",
+   "metadata": {
+    "scrolled": true,
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "data = laue_example.create_data(\"data_for_loading\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "negative-captain",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_path = data[0].get_data_location()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "satellite-dealing",
+   "metadata": {},
+   "source": [
+    "## load_data function\n",
+    "The [*load_data*](../_autosummary/mcstasscript.interface.functions.load_data.rst) function reads a McStas dataset from disk and returns it in the form of [McStasData](../_autosummary/mcstasscript.data.data.McStasData.rst) and [McStasDataEvent](../_autosummary/mcstasscript.data.data.McStasDataEvent.rst) objects in a list. The input is the path to the folder containing the McStas data. The path can be absolute or relative. Here the path from the generated example data is used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "plain-growth",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded_data = ms.load_data(data_path)\n",
+    "print(loaded_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "explicit-holocaust",
+   "metadata": {},
+   "source": [
+    "The *load_data* function provides a way to work with data not created in the current session or created without McStasScript."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "presidential-tuesday",
+   "metadata": {},
+   "source": [
+    "## name_search\n",
+    "The [*name_search*](../_autosummary/mcstasscript.interface.functions.name_search.rst) function searches a list of data for instances that match a monitor name or a filename. If a monitor creates more than one dataset, [*name_search*](../_autosummary/mcstasscript.interface.functions.name_search.rst) may return multiple data objects.\n",
+    "\n",
+    "Here a search for the component name is performed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "sticky-circus",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "div_lambda_h = ms.name_search(\"div_lambda_h\", data)\n",
+    "print(div_lambda_h)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "facial-guidance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ms.make_plot(div_lambda_h, fontsize=14, figsize=(10, 6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "opposite-advice",
+   "metadata": {},
+   "source": [
+    "It is also possible to search for the name of the datafile."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "egyptian-spencer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transmission = ms.name_search(\"lambda_transmission.dat\", data)\n",
+    "ms.make_plot(transmission, fontsize=14, figsize=(10, 6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "synthetic-transformation",
+   "metadata": {},
+   "source": [
+    "## name_plot_options\n",
+    "The [*name_plot_options*](../_autosummary/mcstasscript.interface.functions.name_plot_options.rst) function combines a search with *name_search* and a call to its *set_plot_options*. Setting plot options changes how the data will be plotted, and this provides a nice way to control one plot per line in a neat manner."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "reduced-airline",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ms.name_plot_options(\"PSD\", data, log=True, orders_of_mag=7)\n",
+    "ms.name_plot_options(\"div_lambda_h\", data, log=True, orders_of_mag=5)\n",
+    "ms.name_plot_options(\"div_lambda_v\", data, log=False, colormap=\"hot\")\n",
+    "ms.name_plot_options(\"lambda\", data, log=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "satellite-majority",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "ms.make_plot(data, figsize=(8, 6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "brown-membership",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "technical-kennedy",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/user_guide/instrument_object.ipynb b/docs/source/user_guide/instrument_object.ipynb
index 6746a189..b7e60a0e 100644
--- a/docs/source/user_guide/instrument_object.ipynb
+++ b/docs/source/user_guide/instrument_object.ipynb
@@ -15,7 +15,7 @@
    "metadata": {},
    "source": [
     "## Initialization\n",
-    "An instrument object is created with the [McStas_instr](../_autosummary/mcstasscript.interface.instr.McStas_instr.rst) or [McXtrace_instr](../_autosummary/mcstasscript.interface.instr.McXtrace_instr.rst) class in the instr module. When an instrument object is created the only required argument is the name of the instrument which will be used for the instrument filename. There are however a number of keyword arguments that can be used to provide more information and alter the behavior.\n",
+    "An instrument object is created with the [McStas_instr](../_autosummary/mcstasscript.interface.instr.McStas_instr.rst) or [McXtrace_instr](../_autosummary/mcstasscript.interface.instr.McXtrace_instr.rst) class. When an instrument object is created the only required argument is the name of the instrument which will be used for the instrument filename. There are however a number of keyword arguments that can be used to provide more information and alter the behavior.\n",
     "\n",
     "| Keyword argument | Type | Default | Description |\n",
     "| --- | --- | --- | --- |\n",
@@ -33,23 +33,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "liable-newton",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "experienced-locator",
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"instr_name\", author=\"Mads Bertelsen\", origin=\"DMSC\")\n",
-    "instrument_w_settings = instr.McStas_instr(\"instr_name\", ncount=3E6, output_path=\"new_folder\")"
+    "instrument = ms.McStas_instr(\"instr_name\", author=\"Mads Bertelsen\", origin=\"DMSC\")\n",
+    "instrument_w_settings = ms.McStas_instr(\"instr_name\", ncount=3E6, output_path=\"new_folder\")"
    ]
   },
   {
@@ -74,32 +74,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "sapphire-pasta",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Instrument settings:\n",
-      "  output_path:      instr_name_data\n",
-      "  run_path:         .\n",
-      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
-      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
-      "  executable:       mcrun\n",
-      "  force_compile:    True\n",
-      "Instrument settings:\n",
-      "  ncount:           3.00e+06\n",
-      "  output_path:      new_folder\n",
-      "  run_path:         .\n",
-      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
-      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
-      "  executable:       mcrun\n",
-      "  force_compile:    True\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_settings()\n",
     "instrument_w_settings.show_settings()"
@@ -107,26 +85,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "assumed-boundary",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Instrument settings:\n",
-      "  mpi:              4\n",
-      "  seed:             300\n",
-      "  output_path:      instr_name_data\n",
-      "  run_path:         .\n",
-      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
-      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
-      "  executable:       mcrun\n",
-      "  force_compile:    True\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.settings(mpi=4, seed=300)\n",
     "instrument.show_settings()"
@@ -143,23 +105,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "signed-distributor",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Parameter named: 'wavelength' without set value.\n",
-      " Wavelength in AA\n",
-      "\n",
-      "Parameter named: 'wavelength' with value: 5\n",
-      " Wavelength in AA\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "wavelength = instrument.add_parameter(\"wavelength\", comment=\"Wavelength in AA\")\n",
     "print(wavelength)\n",
@@ -182,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "spare-button",
    "metadata": {},
    "outputs": [],
@@ -193,20 +142,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "twelve-tuition",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "// Start of initialize for generated instr_name\n",
-      "wavenumber = 2*PI/wavelength;\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(instrument.initialize_section)"
    ]
@@ -222,20 +161,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "cultural-workplace",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "// Start of finally for generated instr_name\n",
-      "printf(\"Thanks for using McStasScript!\\n\");\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.append_finally('printf(\\\"Thanks for using McStasScript!\\\\n\\\");')\n",
     "print(instrument.finally_section)"
@@ -258,60 +187,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "foreign-chosen",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are the available component categories:\n",
-      " contrib\n",
-      " misc\n",
-      " monitors\n",
-      " obsolete\n",
-      " optics\n",
-      " samples\n",
-      " sources\n",
-      " union\n",
-      "Call show_components(category_name) to display\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_components()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "regulated-hollywood",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are all components in the optics category.\n",
-      " Absorber                 Guide_gravity          Pol_bender\n",
-      " Arm                      Guide_simple           Pol_constBfield\n",
-      " Beamstop                 Guide_tapering         Pol_guide_mirror\n",
-      " Bender                   Guide_wavy             Pol_guide_vmirror\n",
-      " Collimator_linear        He3_cell               Pol_mirror\n",
-      " Collimator_radial        Mask                   Refractor\n",
-      " Derotator                Mirror                 Rotator\n",
-      " Diaphragm                Monochromator_curved   Selector\n",
-      " DiskChopper              Monochromator_flat     Set_pol\n",
-      " Elliptic_guide_gravity   Monochromator_pol      Slit\n",
-      " FermiChopper             PolAnalyser_ideal      V_selector\n",
-      " Filter_gen               Pol_Bfield             Virtual_mcnp_ss_Guide\n",
-      " Guide                    Pol_Bfield_stop        Vitess_ChopperFermi\n",
-      " Guide_anyshape           Pol_FieldBox           \n",
-      " Guide_channeled          Pol_SF_ideal           \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_components(\"optics\")"
    ]
@@ -327,32 +216,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "dominican-dubai",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Guide _____________________________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1mreflect\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [str] // Reflectivity file name. Format <q(Angs-1) R(0-1)>\n",
-      "\u001b[4m\u001b[1mw1\u001b[0m\u001b[0m [m] // Width at the guide entry\n",
-      "\u001b[4m\u001b[1mh1\u001b[0m\u001b[0m [m] // Height at the guide entry\n",
-      "\u001b[1mw2\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Width at the guide exit\n",
-      "\u001b[1mh2\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Height at the guide exit\n",
-      "\u001b[4m\u001b[1ml\u001b[0m\u001b[0m [m] // length of guide\n",
-      "\u001b[1mR0\u001b[0m = \u001b[1m\u001b[94m0.99\u001b[0m\u001b[0m [1] // Low-angle reflectivity\n",
-      "\u001b[1mQc\u001b[0m = \u001b[1m\u001b[94m0.0219\u001b[0m\u001b[0m [AA-1] // Critical scattering vector\n",
-      "\u001b[1malpha\u001b[0m = \u001b[1m\u001b[94m6.07\u001b[0m\u001b[0m [AA] // Slope of reflectivity\n",
-      "\u001b[1mm\u001b[0m = \u001b[1m\u001b[94m2.0\u001b[0m\u001b[0m [1] // m-value of material. Zero means completely absorbing. glass/SiO2 \n",
-      "               Si Ni Ni58 supermirror Be Diamond m=  0.65 0.47 1 1.18 2-6 1.01 1.12 \n",
-      "\u001b[1mW\u001b[0m = \u001b[1m\u001b[94m0.003\u001b[0m\u001b[0m [AA-1] // Width of supermirror cut-off\n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.component_help(\"Guide\")"
    ]
@@ -368,7 +235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "literary-nirvana",
    "metadata": {},
    "outputs": [],
@@ -380,43 +247,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "loving-raleigh",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT source = Source_div\n",
-      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mfocus_aw\u001b[0m = \u001b[1m\u001b[92m3.0\u001b[0m\u001b[0m [deg]\n",
-      "  \u001b[1mfocus_ah\u001b[0m = \u001b[1m\u001b[92m2.0\u001b[0m\u001b[0m [deg]\n",
-      "  \u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[92mwavelength\u001b[0m\u001b[0m [Ang]\n",
-      "  \u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[92m0.1*wavelength\u001b[0m\u001b[0m [Ang]\n",
-      "AT [0, 0, 0] ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(source)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "unnecessary-helen",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "source Source_div AT (0, 0, 0) ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.print_components()"
    ]
@@ -431,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "informative-council",
    "metadata": {},
    "outputs": [],
@@ -445,20 +289,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "moral-apartment",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "source      Source_div AT      (0, 0, 0)   ABSOLUTE            \n",
-      "source_slit Slit       AT      (0, 0, 2)   RELATIVE source     \n",
-      "guide       Guide      AT      (0, 0, 0.1) RELATIVE source_slit\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.print_components()"
    ]
@@ -473,21 +307,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "absent-pizza",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "source      Source_div  AT      (0, 0, 0)   ABSOLUTE            \n",
-      "PSD         PSD_monitor AT      (0, 0, 1.9) RELATIVE source     \n",
-      "source_slit Slit        AT      (0, 0, 2)   RELATIVE source     \n",
-      "guide       Guide       AT      (0, 0, 0.1) RELATIVE source_slit\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "monitor = instrument.add_component(\"PSD\", \"PSD_monitor\", after=\"source\")\n",
     "monitor.set_AT(1.9, RELATIVE=source)\n",
@@ -519,23 +342,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "figured-electronics",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT guide_2 = Guide\n",
-      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92m10.0\u001b[0m\u001b[0m [m]\n",
-      "AT [0, 0, 10.01] RELATIVE guide\n",
-      "ROTATED [0, 0.5, 0] RELATIVE guide\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "guide2 = instrument.copy_component(\"guide_2\", \"guide\")\n",
     "guide2.set_AT(guide.l + 0.01, RELATIVE=guide)\n",
@@ -561,25 +371,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "military-biotechnology",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT source = Source_div\n",
-      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mfocus_aw\u001b[0m = \u001b[1m\u001b[92m3.0\u001b[0m\u001b[0m [deg]\n",
-      "  \u001b[1mfocus_ah\u001b[0m = \u001b[1m\u001b[92m2.0\u001b[0m\u001b[0m [deg]\n",
-      "  \u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[92mwavelength\u001b[0m\u001b[0m [Ang]\n",
-      "  \u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[92m0.1*wavelength\u001b[0m\u001b[0m [Ang]\n",
-      "AT [0, 0, 0] ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "my_source = instrument.get_component(\"source\")\n",
     "print(my_source)"
@@ -587,23 +382,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "ambient-naples",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "COMPONENT guide_2 = Guide\n",
-      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m0.03\u001b[0m\u001b[0m [m]\n",
-      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92m10.0\u001b[0m\u001b[0m [m]\n",
-      "AT [0, 0, 10.01] RELATIVE guide\n",
-      "ROTATED [0, 0.5, 0] RELATIVE guide\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "last_component = instrument.get_last_component()\n",
     "print(last_component)"
@@ -620,58 +402,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "intense-chapter",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/user_guide/instr_name_data_23\"\n",
-      "INFO: Regenerating c-file: instr_name.c\n",
-      "CFLAGS=\n",
-      "INFO: Recompiling: ./instr_name.out\n",
-      "mccode-r.c:1880:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "} /* mcsiminfo_init */\n",
-      "^\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "2 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy instr_name.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/user_guide/instr_name_data_23\n",
-      "\n",
-      "Simulation 'instr_name' (instr_name.instr): running on 4 nodes (master is 'CI0021617', MPI version 3.1).\n",
-      "Detector: PSD_I=0.114429 PSD_ERR=0.000144614 PSD_N=626113 \"PSD.dat\"\n",
-      "Thanks for using McStasScript!\n",
-      "Thanks for using McStasScript!\n",
-      "Thanks for using McStasScript!\n",
-      "Thanks for using McStasScript!\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "honey-conference",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[\n",
-      "McStasData: PSD type: 2D  I:0.114429 E:0.000144614 N:626113]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(data)"
    ]
@@ -687,132 +431,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "headed-simulation",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "loading system configuration\n",
-      "\n",
-      "INFO: Using directory: \"instr_name_mcdisplay\"\n",
-      "INFO: Regenerating c-file: instr_name.c\n",
-      "CFLAGS=\n",
-      "INFO: Recompiling: ./instr_name.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "*t0;\n",
-      "^~~\n",
-      "1 warning generated.\n",
-      "INFO: ===\n",
-      "instrument definition parsed\n",
-      "reading particle data...\n",
-      "\n",
-      "Component                         source AT (0,0,0)    0 m from origin\n",
-      "Component                            PSD AT (0,0,1.9)    1.9 m from origin\n",
-      "Component                    source_slit AT (0,0,2)    2 m from origin\n",
-      "Component                          guide AT (0,0,2.1)    2.1 m from origin\n",
-      "Component                        guide_2 AT (0,0,12.11)    12.11 m from origin\n",
-      "Detector: PSD_I=0.116358 PSD_ERR=0.00841937 PSD_N=191 \"PSD.dat\"\n",
-      "Thanks for using McStasScript!\n",
-      "loading system configuration\n",
-      "\n",
-      "starting particle parsing\n",
-      "ended particle parsing\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"800\"\n",
-       "            height=\"450\"\n",
-       "            src=\"./instr_name_mcdisplay/index.html\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7f91502c15b0>"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_instrument()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "italic-madagascar",
    "metadata": {
     "tags": [
      "hide-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/../tools/Python/mcdisplay/pyqtgraph/mcdisplay.py:292: DeprecationWarning: GraphicsWindow is deprecated, use GraphicsLayoutWidget instead,will be removed in 0.13\n",
-      "  window = pg.GraphicsWindow()\n",
-      "\n",
-      "loading system configuration\n",
-      "\n",
-      "INFO: Using directory: \"instr_name_mcdisplay_0\"\n",
-      "INFO: Regenerating c-file: instr_name.c\n",
-      "CFLAGS=\n",
-      "INFO: Recompiling: ./instr_name.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "*t0;\n",
-      "^~~\n",
-      "1 warning generated.\n",
-      "INFO: ===\n",
-      "instrument definition parsed\n",
-      "reading particle data...\n",
-      "\n",
-      "Component                         source AT (0,0,0)    0 m from origin\n",
-      "Component                            PSD AT (0,0,1.9)    1.9 m from origin\n",
-      "Component                    source_slit AT (0,0,2)    2 m from origin\n",
-      "Component                          guide AT (0,0,2.1)    2.1 m from origin\n",
-      "Component                        guide_2 AT (0,0,12.11)    12.11 m from origin\n",
-      "Detector: PSD_I=0.127933 PSD_ERR=0.0152909 PSD_N=70 \"PSD.dat\"\n",
-      "Thanks for using McStasScript!\n",
-      "loading system configuration\n",
-      "\n",
-      "starting particle parsing\n",
-      "ended particle parsing\n",
-      "\n",
-      "q            - quit\n",
-      "p            - save png\n",
-      "s            - save svg\n",
-      "space        - next ray\n",
-      "click        - enter subplot\n",
-      "right-click  - exit subplot\n",
-      "h/F1         - show component list\n",
-      "\n",
-      "q            - quit\n",
-      "p            - save png\n",
-      "s            - save svg\n",
-      "space        - next ray\n",
-      "click        - enter subplot\n",
-      "right-click  - exit subplot\n",
-      "h/F1         - show component list\n",
-      "wrapped C/C++ object of type ScatterPlotItem has been deleted\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_instrument(format=\"window\")"
    ]
@@ -837,66 +473,43 @@
    "metadata": {},
    "source": [
     "## Dump and load an instrument object\n",
-    "It is possible to save an instrument object to disk and load it later. For now the name is still a required parameter, but it is overwritten by the loading process."
+    "It is possible to save an instrument object to disk and load it later."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "casual-blame",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'dump_file_name.dmp'"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.dump(\"dump_file_name.dmp\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "distant-arthritis",
+   "metadata": {},
+   "source": [
+    "To load an instrument object from a file, use the *from_dump* method that takes the filename."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "advance-soldier",
    "metadata": {},
    "outputs": [],
    "source": [
-    "loaded_instrument = instr.McStas_instr(\"\", dumpfile=\"dump_file_name.dmp\")"
+    "loaded_instrument = ms.McStas_instr.from_dump(\"dump_file_name.dmp\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "id": "european-pepper",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "source      Source_div  AT      (0, 0, 0)     ABSOLUTE            \n",
-      "PSD         PSD_monitor AT      (0, 0, 1.9)   RELATIVE source     \n",
-      "source_slit Slit        AT      (0, 0, 2)     RELATIVE source     \n",
-      "guide       Guide       AT      (0, 0, 0.1)   RELATIVE source_slit\n",
-      "guide_2     Guide       AT      (0, 0, 10.01) RELATIVE guide       \n",
-      "                        ROTATED (0, 0.5, 0)   RELATIVE guide\n",
-      "Instrument settings:\n",
-      "  output_path:      instr_name_data\n",
-      "  run_path:         .\n",
-      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
-      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
-      "  executable:       mcrun\n",
-      "  force_compile:    True\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "loaded_instrument.print_components()\n",
     "loaded_instrument.show_settings()"
diff --git a/docs/source/user_guide/instrument_reader.ipynb b/docs/source/user_guide/instrument_reader.ipynb
index 76f7f155..36ad5a69 100644
--- a/docs/source/user_guide/instrument_reader.ipynb
+++ b/docs/source/user_guide/instrument_reader.ipynb
@@ -22,22 +22,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "alpine-yeast",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter, reader"
+    "import mcstasscript as ms"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "burning-romance",
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument_reader = reader.McStas_file(\"data_example.instr\")"
+    "instrument_reader = ms.McStas_file(\"data_example.instr\")"
    ]
   },
   {
@@ -51,24 +51,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "living-inflation",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "double wavelength  = 1.5  \n",
-      "source Source_simple AT (0, 0, 0)   ABSOLUTE       \n",
-      "sample PowderN       AT (0, 0, 5)   RELATIVE source\n",
-      "banana Monitor_nD    AT (0, 0, 0)   RELATIVE sample\n",
-      "PSD    PSD_monitor   AT (0, 0, 0.1) RELATIVE sample\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"reader_test\")\n",
+    "instrument = ms.McStas_instr(\"reader_test\")\n",
     "instrument_reader.add_to_instr(instrument)\n",
     "\n",
     "instrument.show_parameters()\n",
@@ -86,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "australian-possession",
    "metadata": {},
    "outputs": [],
@@ -104,60 +92,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "operational-earthquake",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\"\"\"\n",
-      "This McStasScript file was generated from a\n",
-      "McStas instrument file. It is advised to check\n",
-      "the content to ensure it is as expected.\n",
-      "\"\"\"\n",
-      "from mcstasscript.interface import instr, plotter, functions\n",
-      "\n",
-      "data_example = instr.McStas_instr(\"data_example_generated\")\n",
-      "data_example.add_parameter(\"double\", \"wavelength\", value=1.5)\n",
-      "\n",
-      "source = data_example.add_component(\"source\", \"Source_simple\")\n",
-      "source.yheight = 0.03\n",
-      "source.xwidth = 0.05\n",
-      "source.dist = 5\n",
-      "source.focus_xw = 0.015\n",
-      "source.focus_yh = 0.03\n",
-      "source.lambda0 = \"wavelength\"\n",
-      "source.dlambda = 0.02\n",
-      "source.set_AT(['0', '0', '0'], RELATIVE=\"ABSOLUTE\")\n",
-      "\n",
-      "sample = data_example.add_component(\"sample\", \"PowderN\")\n",
-      "sample.reflections = \"\\\"Na2Ca3Al2F14.laz\\\"\"\n",
-      "sample.radius = 0.015\n",
-      "sample.yheight = 0.024\n",
-      "sample.set_AT(['0', '0', '5'], RELATIVE=\"source\")\n",
-      "\n",
-      "banana = data_example.add_component(\"banana\", \"Monitor_nD\")\n",
-      "banana.xwidth = 1.5\n",
-      "banana.yheight = 0.4\n",
-      "banana.restore_neutron = 1\n",
-      "banana.options = \"\\\"theta limits=[5 175] bins=250, banana\\\"\"\n",
-      "banana.filename = \"\\\"banana.dat\\\"\"\n",
-      "banana.set_AT(['0', '0', '0'], RELATIVE=\"sample\")\n",
-      "\n",
-      "PSD = data_example.add_component(\"PSD\", \"PSD_monitor\")\n",
-      "PSD.nx = 100\n",
-      "PSD.ny = 100\n",
-      "PSD.filename = \"\\\"psd.dat\\\"\"\n",
-      "PSD.xwidth = 0.075\n",
-      "PSD.yheight = 0.048\n",
-      "PSD.restore_neutron = 1\n",
-      "PSD.set_AT(['0', '0', '0.1'], RELATIVE=\"sample\")\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "with open(\"generated_python_script.py\") as file:\n",
     "    script = file.read()\n",
diff --git a/docs/source/user_guide/laue_example.py b/docs/source/user_guide/laue_example.py
index b905beeb..abc99140 100644
--- a/docs/source/user_guide/laue_example.py
+++ b/docs/source/user_guide/laue_example.py
@@ -1,7 +1,7 @@
-from mcstasscript.interface import instr, functions, plotter
+import mcstasscript as ms
 
 def create_data(path):
-    instrument = instr.McStas_instr("laue_example", output_path=path)
+    instrument = ms.McStas_instr("laue_example", output_path=path)
     
     source = instrument.add_component("source", "Source_simple")
     source.set_parameters(xwidth=0.01, yheight=0.01,
@@ -44,5 +44,5 @@ def create_data(path):
                         restore_neutron=1)
 
     instrument.settings(ncount=5E6)
-    instrument.backengine()
-    return instrument.data
+    data = instrument.backengine()
+    return data
diff --git a/docs/source/user_guide/parameters_and_variables.ipynb b/docs/source/user_guide/parameters_and_variables.ipynb
index e6e1db67..e6e30bd1 100644
--- a/docs/source/user_guide/parameters_and_variables.ipynb
+++ b/docs/source/user_guide/parameters_and_variables.ipynb
@@ -25,8 +25,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter\n",
-    "instrument = instr.McStas_instr(\"parameters_and_variables\")\n",
+    "import mcstasscript as ms\n",
+    "instrument = ms.McStas_instr(\"parameters_and_variables\")\n",
     "\n",
     "first_par = instrument.add_parameter(\"first_par\")\n",
     "print(first_par)"
diff --git a/docs/source/user_guide/plotting.ipynb b/docs/source/user_guide/plotting.ipynb
index 3c777204..599c5e41 100644
--- a/docs/source/user_guide/plotting.ipynb
+++ b/docs/source/user_guide/plotting.ipynb
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "perfect-hours",
    "metadata": {
     "tags": [
@@ -23,8 +23,9 @@
    },
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter\n",
-    "instrument = instr.McStas_instr(\"data_example\")\n",
+    "import mcstasscript as ms\n",
+    "\n",
+    "instrument = ms.McStas_instr(\"data_example\")\n",
     "\n",
     "source = instrument.add_component(\"source\", \"Source_simple\")\n",
     "source.set_parameters(xwidth=0.05, yheight=0.03, dlambda=0.02,\n",
@@ -57,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "general-chance",
    "metadata": {
     "scrolled": true,
@@ -66,40 +67,7 @@
      "hide-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/user_guide/plotting_example_12\"\n",
-      "INFO: Regenerating c-file: data_example.c\n",
-      "CFLAGS=\n",
-      "INFO: Recompiling: ./data_example.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "1 warning generated.\n",
-      "INFO: ===\n",
-      "Warning: 263901 events were removed in Component[4] PSD=PSD_monitor()\n",
-      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "INFO: Placing instr file copy data_example.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/user_guide/plotting_example_12\n",
-      "\n",
-      "      Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "Detector: banana_I=1.13353e-07 banana_ERR=8.60125e-10 banana_N=61381 \"banana.dat\"\n",
-      "Detector: PSD_I=9.76188e-06 PSD_ERR=2.05626e-08 PSD_N=570953 \"psd.dat\"\n",
-      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
-      "    SPLIT 263 COMPONENT sample=PowderN(...)\n",
-      "  in the instrument description data_example.instr.\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.settings(ncount=1E6, output_path=\"plotting_example\")\n",
     "instrument.set_parameters(wavelength=1.5)\n",
@@ -108,20 +76,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "retired-tiffany",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[\n",
-      "McStasData: banana type: 1D  I:1.13353e-07 E:8.60125e-10 N:61381, \n",
-      "McStasData: PSD type: 2D  I:9.76188e-06 E:2.05626e-08 N:570953]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(data)"
    ]
@@ -137,45 +95,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "literary-costs",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name banana\n",
-      "Plotting data with name PSD\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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KRJRrrmJm9u8t/rsBeD/wTPcdUc9Qnqh7AbC3uy/t5sORGtLdKyXKm4uZnQXsDHzS3Zu7+3iqnZn1MbOtW3w/Hlhd/hJ5U1OXnipmZj8F3gf8HVgLfN/df9K9R1X9zOwlYA3/nKneU1d4cmZ2EKWPvt9B6b77KzCuPJFXWujOlRJ7qrZWnpSYmb0PeApYAvytXG509/2776iqm5ltBfwMeBul/2euBr7h7o9264GJVAGd8IuIiIiI1DBFekREREREaphO+EVEREREaphO+EVEREREaphO+EVEREREaphO+EVEREREaphO+EWkqphZfzN73sx2aVE7xcw2+oJzZjbZzL5W/u8dzezTrX7+uJlt3oX7m2lmfyy3reyqbe5aPk43sz5dtV0REem51JZTRKqOmf0ncA6lxeaGA78E3l9e+GpTHcMk4BPuPnEj7mMmMN/dL9kI23agr7u/2tXbFhGRnkVX+EWk6rj7z4BHgWmUFrn6WnayX76SPdXMHjSzxWZ2YIuf7WNmj5nZQjO728z+rVwfYWYPmdkTZvaUmX2jXJ9qZueZ2QDgTGCv8tXy6S321af837uUt7Gw/O8u5fowM1tpZmeV973YzD7c3ttuZp8vH9cTZvaImW3VYpvnlLe5yMx2NrMflff/sJm9syP3tYiI1D6d8ItItToOOBJ41t1/uoGx69x9V2ACcLmZDTKzQcC1wCHuPgq4Abi+PP5LwBx338HdtwOubLmx8qrMpwFz3X1Hdz++5c/NrDdwC/Dt8rZPBW4p1wEGAA+5+/sp/eHwvfbcYDPbHTgZ2NvddwD2AF5usc37y9u8ErgbuLS8/wXAl9uzDxERefPRCb+IVKuPAU3ACDOr28DYKwHcfTGlTwY+CIwBnnD3Z8pjrgZ2NLO+wDzg82b2HTP7GPCXCo9tBLDG3eeW93s3sKZcB3jV3f+7/N+/Ad7dzu3uB/zY3f9U3u6r7v56i23+vPzfjwJ/dPfHy98vAP6twtsgIiJvEjrhF5GqY2YDgYsonQDPB86o5NcBb/FvgbvfAuwG/B44idInARUdYrLt9bXmFrW1wL9UsN1M622+3ur79u5DRETeZHTCLyLV6L+AH7n7E8BXgINbdu0JHAFgZu8BdgQeBh6idEV/ZHnM54DH3P2Vcpb/T+4+k9IfEx8IttkE9Ev2twioM7M9yvvdA3gLsKTdtzA2GzjczLYqb7dPOz7dEBERaZOuCIlIVSm3whwOHALg7n82s2OBq8xsZ3dfE/xas5k9AAwEjlk/wdfMDgNuMLN/AV4CDi2P/zRwiJmtoXRV/ivBNu8GvmFmTwD3tczxu/ua8uTg6Wb2NuCvwMRyvcO33d3vM7NzgLlmto7SVf3xHd6giIgIasspIj1cT24/qbacIiKyKSjSIyLSfV4Gjt8YC28BLwLrumq7IiLSc+kKv4iIiIhIDdMVfhERERGRGqYTfhERERGRGqYTfhERERGRGqYTfhERERGRGqYTfhERERGRGvb/AbEw/JjuGcXVAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 936x504 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plotter.make_plot(data)"
+    "ms.make_plot(data)"
    ]
   },
   {
@@ -188,33 +113,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "pretty-transcription",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name banana\n",
-      "Plotting data with name PSD\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data)"
+    "ms.make_sub_plot(data)"
    ]
   },
   {
@@ -228,33 +132,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "natural-transition",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name banana\n",
-      "Plotting data with name PSD\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data, log=[False, True], fontsize=15)"
+    "ms.make_sub_plot(data, log=[False, True], fontsize=15)"
    ]
   },
   {

From 12ffb8388cf3f9f4655fc9a566ea6530dbb23ae1 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 22 Mar 2022 10:18:08 +0100
Subject: [PATCH 233/403] Updating tests to run python 3.7 to 3.9

---
 .github/workflows/unit_tests_mac.yaml     | 2 +-
 .github/workflows/unit_tests_ubuntu.yaml  | 2 +-
 .github/workflows/unit_tests_windows.yaml | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index c83c1793..4ac3b052 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -4,7 +4,7 @@ jobs:
   build:
     strategy:
       matrix:
-        python-version: [3.6, 3.7, 3.8]
+        python-version: [3.7, 3.8, 3.9]
     runs-on: macos-latest
       
     steps:
diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index 0324a91d..09972fc3 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -4,7 +4,7 @@ jobs:
   build:
     strategy:
       matrix:
-        python-version: [3.6, 3.7, 3.8]
+        python-version: [3.7, 3.8, 3.9]
     runs-on: ubuntu-latest
       
     steps:
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
index 6c49e111..be5426f8 100644
--- a/.github/workflows/unit_tests_windows.yaml
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -4,7 +4,7 @@ jobs:
   build:
     strategy:
       matrix:
-        python-version: [3.6, 3.7, 3.8]
+        python-version: [3.7, 3.8, 3.9]
     runs-on: windows-latest
       
     steps:

From a447c26e467f1b0e5e3dc64836eda723fa32107c Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 22 Mar 2022 10:21:06 +0100
Subject: [PATCH 234/403] Fixed bug in version pickup in setup.py

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 3b8c405b..fb25a369 100644
--- a/setup.py
+++ b/setup.py
@@ -4,7 +4,7 @@
 
 # Get version number
 here = os.path.abspath(os.path.dirname(__file__))
-version_path = os.path.join(here, "guide_bot", "_version.py")
+version_path = os.path.join(here, "mcstasscript", "_version.py")
 version = {}
 with open(version_path) as fp:
     exec(fp.read(), version)

From 5589a734dd51cc65b92e23d7b0e7995627996e32 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 22 Mar 2022 10:24:35 +0100
Subject: [PATCH 235/403] removed pickle from requirements (its built in, so
 can't be found)

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index fb25a369..8e150f0e 100644
--- a/setup.py
+++ b/setup.py
@@ -24,7 +24,7 @@
      long_description=long_description,
      long_description_content_type="text/markdown",
      url="https://github.com/PaNOSC-ViNYL/McStasScript",
-     install_requires=['numpy', 'matplotlib', 'PyYAML', 'ipywidgets', 'libpyvinyl', 'pickle'],
+     install_requires=['numpy', 'matplotlib', 'PyYAML', 'ipywidgets', 'libpyvinyl'],
      packages=setuptools.find_packages(),
      classifiers=[
          "Programming Language :: Python :: 3",

From 325ca5149cb58909899f7c5fa8fe2472fa0e22d2 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 22 Mar 2022 10:32:28 +0100
Subject: [PATCH 236/403] Update of readme

---
 README.md | 27 ++++++++++++++-------------
 1 file changed, 14 insertions(+), 13 deletions(-)

diff --git a/README.md b/README.md
index 6c2678da..007ed23d 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,10 @@
 # McStasScript
-[McStas](http://www.mcstas.org) API for creating and running McStas/McXtrace instruments from python scripting
+[McStas](http://www.mcstas.org) API for creating and running McStas/McXtrace instruments from python scripting.
 
 Prototype for an API that allow interaction with McStas through an interface like Jupyter Notebooks created under WP5 of PaNOSC.
 
+Full documentation can be found [here](https://mads-bertelsen.github.io)!
+
 ## Installation
 McStasScript does not include the McStas installation, so McStas/McXtrace should be installed separately, link to instructions [here](https://github.com/McStasMcXtrace/McCode/tree/master/INSTALL-McStas).
 McStasScript can be installed using pip from a terminal,
@@ -11,8 +13,8 @@ McStasScript can be installed using pip from a terminal,
 
 After installation it is necessary to configure the package so the McStas/McXtrace installation can be found, here we show how the appropriate code for an Ubuntu system as an example. The configuration is saved permanently, and only needs to be updated when McStas or McStasScript is updated. This has to be done from a python terminal or from within a python environment.
 
-    from mcstasscript.interface import functions
-    my_configurator = functions.Configurator()
+    import mcstasscript as ms
+    my_configurator = ms.Configurator()
     my_configurator.set_mcrun_path("/usr/bin/")
     my_configurator.set_mcstas_path("/usr/share/mcstas/2.5/")
     my_configurator.set_mxrun_path("/usr/bin/")
@@ -48,8 +50,8 @@ Using the McStas-shell one can start a jupyter notebook server with this command
 
 For a standard McStas installation on Windows, the appropriate configuration can be set with these commands in a notebook:
 
-    from mcstasscript.interface import functions
-    my_configurator = functions.Configurator()
+    import mcstasscript as ms
+    my_configurator = ms.Configurator()
     my_configurator.set_mcrun_path("\\mcstas-2.6\\bin\\")
     my_configurator.set_mcstas_path("\\mcstas-2.6\\lib\\")
     my_configurator.set_mxrun_path("\\mcxtrace-1.5\\bin\\")
@@ -62,11 +64,11 @@ This section provides a quick way to get started, a more in depth tutorial using
 
 Import the interface 
 
-    from mcstasscript.interface import instr, plotter, functions, reader
+    import mcstasscript as ms
 
 Now the package can be used. Start with creating a new instrument, just needs a name. For a McXtrace instrument use McXtrace_instr instead.
 
-    my_instrument = instr.McStas_instr("my_instrument_file")
+    my_instrument = ms.McStas_instr("my_instrument_file")
 
 Then McStas components can be added, here we add a source and ask for help on the parameters.
 
@@ -120,10 +122,9 @@ Settings for the simulation can be adjusted with the *settings* method, an outpu
 
     my_instrument.settings(output_path="first_run", ncount=1E7)
 
-The simulatiuon is performed with the *backengine* method. After this call, the data can be retrieved from the data attribute.
+The simulatiuon is performed with the *backengine* method. This returns the data generated from the simulation.
 
-    my_instrument.backengine()
-    data = my_instrument.data
+    data = my_instrument.backengine()
 
 Results from the monitors would be stored as a list of McStasData objects in the returned data. The counts are stored as numpy arrays. We can read and change the intensity directly and manipulate the data before plotting.
 
@@ -139,12 +140,12 @@ In a python terminal this would display the data directly:
     
 Plotting is usually done in a subplot of all monitors recorded.    
 
-    plot = plotter.make_sub_plot(data)
+    plot = ms.make_sub_plot(data)
     
 ## Widgets in Jupyter Notebooks
 When using McStasScript in a jupyter notebook, it is possible to plot the data with a widget system instead.
 
-    plotter.interface(data)
+    ms.interface(data)
     
 There is also a widget solution for performing the simulation which works as an alternative to *run_full_instrument*, this method is also called *interface* and is available directly on the instrument. This interface includes setting parameters, simulation options and plotting of the resulting data.
 
@@ -157,7 +158,7 @@ The data from the latest run performed in the simulation widget can be retrieved
 ## Use in existing project
 If one wish to work on existing projects using McStasScript, there is a reader included that will read a McStas Instrument file and write the corresponding McStasScript python instrument to disk. Here is an example where the PSI_DMC.instr example is converted:
 
-    Reader = reader.McStas_file("PSI_DMC.instr")
+    Reader = ms.McStas_file("PSI_DMC.instr")
     Reader.write_python_file("PSI_DMC_generated.py")
 
 It is highly advised to run a check between the output of the generated file and the original to ensure the process was sucessful.

From 9587cbaedb974479a42ae401563cb4895c6bb717 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 22 Mar 2022 10:33:07 +0100
Subject: [PATCH 237/403] Removed pdf documentation, outdated and not useful
 anymore.

---
 McStasScript_documentation.pdf | Bin 595674 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 McStasScript_documentation.pdf

diff --git a/McStasScript_documentation.pdf b/McStasScript_documentation.pdf
deleted file mode 100644
index f20483e67d2047373eb89c29664efd8b194731df..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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From aae9cd3abfc9dedbee406110a226a41b077ac7ee Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 22 Mar 2022 11:16:49 +0100
Subject: [PATCH 238/403] Small typo in setup script.

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 8e150f0e..9e5b70fc 100644
--- a/setup.py
+++ b/setup.py
@@ -14,7 +14,7 @@
 with open("README.md", "r") as fh:
     long_description = fh.read()
 
-setuptools.setup(
+setup(
      name='McStasScript',
      version=found_version,
      author="Mads Bertelsen",

From ed60a10840ea37cbed4ac245cf4c92fa48f55ebd Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 22 Mar 2022 11:17:34 +0100
Subject: [PATCH 239/403] Small typo in setup.py

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 9e5b70fc..d9031648 100644
--- a/setup.py
+++ b/setup.py
@@ -25,7 +25,7 @@
      long_description_content_type="text/markdown",
      url="https://github.com/PaNOSC-ViNYL/McStasScript",
      install_requires=['numpy', 'matplotlib', 'PyYAML', 'ipywidgets', 'libpyvinyl'],
-     packages=setuptools.find_packages(),
+     packages=find_packages(),
      classifiers=[
          "Programming Language :: Python :: 3",
          "License :: OSI Approved :: GNU General Public License (GPL)",

From 9f8121908ba5be037610d53db54c9eb3cf950cc8 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 22 Mar 2022 11:51:16 +0100
Subject: [PATCH 240/403] Update of examples and tutorial. Fix to simulation
 interface to account for libpyvinyl types.

---
 examples/calibration_sample.ipynb             |  13 +-
 examples/random_demonstration.py              |   3 +-
 .../jb_interface/simulation_interface.py      |   2 +
 .../McStasScript_tutorial_1_the_basics.ipynb  |  58 +-
 tutorial/McStasScript_tutorial_2_SPLIT.ipynb  | 113 ++-
 ...tasScript_tutorial_3_EXTEND_and_WHEN.ipynb |  52 +-
 tutorial/McStasScript_tutorial_4_JUMP.ipynb   |  22 +-
 ...n_tutorial_1_processes_and_materials.ipynb | 805 ++----------------
 tutorial/Union_tutorial_2_geometry.ipynb      |  99 ++-
 tutorial/Union_tutorial_3_loggers.ipynb       | 123 ++-
 tutorial/Union_tutorial_4_conditionals.ipynb  | 124 +--
 tutorial/Union_tutorial_5_masks.ipynb         |  64 +-
 ...ial_6_Exit_and_number_of_activations.ipynb | 103 ++-
 .../Union_tutorial_7_Tagging_history.ipynb    |  69 +-
 14 files changed, 595 insertions(+), 1055 deletions(-)

diff --git a/examples/calibration_sample.ipynb b/examples/calibration_sample.ipynb
index c0ca65fd..9106b4a6 100644
--- a/examples/calibration_sample.ipynb
+++ b/examples/calibration_sample.ipynb
@@ -18,10 +18,10 @@
     "%matplotlib widget\n",
     "\n",
     "# Import McstasScript\n",
-    "from mcstasscript.interface import instr, plotter, functions\n",
+    "import mcstasscript as ms\n",
     "\n",
     "# Create a McStas instrument\n",
-    "Instr = instr.McStas_instr(\"calibration_sample\", author = \"Mads Bertelsen\", origin = \"ESS DMSC\")"
+    "Instr = ms.McStas_instr(\"calibration_sample\", author = \"Mads Bertelsen\", origin = \"ESS DMSC\")"
    ]
   },
   {
@@ -383,8 +383,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = Instr.get_interface_data()\n",
-    "print(data)"
+    "data = Instr.get_interface_data()"
    ]
   },
   {
@@ -394,8 +393,8 @@
    "outputs": [],
    "source": [
     "if len(data) != 0:\n",
-    "    EPSD_data = functions.name_search(\"EPSD\", data)\n",
-    "    plotter.make_plot(EPSD_data, figsize=(9,6))"
+    "    EPSD_data = ms.name_search(\"EPSD\", data)\n",
+    "    ms.make_plot(EPSD_data, figsize=(9,6))"
    ]
   },
   {
@@ -412,7 +411,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter.interface(data)"
+    "ms.interface(data)"
    ]
   },
   {
diff --git a/examples/random_demonstration.py b/examples/random_demonstration.py
index 9a838282..60ecd37b 100644
--- a/examples/random_demonstration.py
+++ b/examples/random_demonstration.py
@@ -124,8 +124,7 @@
 Instr.settings(output_path="demonstration", mpi=2, ncount=5E7)
 Instr.set_parameters(energy=600)
 
-Instr.backengine()
-data = Instr.data
+data = Instr.backengine()
 
 # Set plotting options for the data (optional)
 functions.name_plot_options("logger_space_zx_all", data, log=1, orders_of_mag=3)
diff --git a/mcstasscript/jb_interface/simulation_interface.py b/mcstasscript/jb_interface/simulation_interface.py
index ee0a5ced..8437aec0 100644
--- a/mcstasscript/jb_interface/simulation_interface.py
+++ b/mcstasscript/jb_interface/simulation_interface.py
@@ -382,6 +382,8 @@ def update(self, change):
             if type(new_value) is str:
                 if not (new_value[0] == '"' or new_value[0] == "'"):
                     new_value = '"' + new_value + '"'
+        else:
+            new_value = float(new_value)
 
         self.parameters[self.name] = new_value
 
diff --git a/tutorial/McStasScript_tutorial_1_the_basics.ipynb b/tutorial/McStasScript_tutorial_1_the_basics.ipynb
index b6716104..282cbda0 100644
--- a/tutorial/McStasScript_tutorial_1_the_basics.ipynb
+++ b/tutorial/McStasScript_tutorial_1_the_basics.ipynb
@@ -48,7 +48,7 @@
    "metadata": {},
    "source": [
     "### Import the McStasScript package\n",
-    "The McStasScript modules intended for normal use is located in the interface submodule, and one usually imports the necessary modules from there."
+    "McStasScript needs to be imported into the users python environment, you can abbreviate the name for easier access."
    ]
   },
   {
@@ -57,7 +57,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
@@ -78,7 +78,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "configurator = functions.Configurator()\n",
+    "configurator = ms.Configurator()\n",
     "configurator.set_mcrun_path(\"/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\")\n",
     "configurator.set_mcstas_path(\"/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\")"
    ]
@@ -99,7 +99,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
    ]
   },
   {
@@ -143,6 +143,7 @@
    "source": [
     "## Adding a component\n",
     "Now we are ready to add a component to our simulation which is done with the *add_component* method on our instrument. This method requires two inputs:\n",
+    "\n",
     "- Nickname for the component used to refer to this component instance\n",
     "- Name of the component type to be used\n",
     "\n",
@@ -154,7 +155,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "src = instrument.add_component(\"source\", \"Source_div\")\n",
@@ -233,8 +236,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "wavelength = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
-    "order = instrument.add_parameter(\"int\", \"order\", value=1, comment=\"Monochromator order, integer\")\n",
+    "wavelength = instrument.add_parameter(\"wavelength\", value=5.0,\n",
+    "                                      comment=\"Wavelength in [Ang]\")\n",
+    "\n",
+    "order = instrument.add_parameter(\"int\", \"order\", value=1,\n",
+    "                                 comment=\"Monochromator order, integer\")\n",
     "instrument.show_parameters()"
    ]
   },
@@ -262,6 +268,7 @@
    "source": [
     "### Using keyword arguments when adding a component\n",
     "When adding a component, several keyword arguments are available, for example for setting the position of the component.\n",
+    "\n",
     "- AT set position with list of x,y,z coordinates\n",
     "- AT_RELATIVE set reference point for position (name of component instance or object)\n",
     "- ROTATED set rotation around x,y,z axis\n",
@@ -404,7 +411,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sample = instrument.add_component(\"sample\", \"PowderN\", AT=[0, 0, 1.1], RELATIVE=beam_direction)"
+    "sample = instrument.add_component(\"sample\", \"PowderN\",\n",
+    "                                  AT=[0, 0, 1.1], RELATIVE=beam_direction)"
    ]
   },
   {
@@ -506,6 +514,7 @@
    "source": [
     "## Running the simulation\n",
     "Running the simulation is done in three steps\n",
+    "\n",
     "- Setting the parameters with *set_parameters*\n",
     "- Setting the settings with *settings*\n",
     "- Running the McStas simulation with *backengine*\n",
@@ -513,12 +522,13 @@
     "The *set_parameters* method takes a value for each of the parameters defined in the instrument, here wavelength.\n",
     "\n",
     "Settings adjust settings for the simulations, a few examples can be seen here\n",
+    "\n",
     "- ncount sets the number of rays\n",
     "- mpi sets the number of CPU cores used for execution (requires mpi installed)\n",
     "- output_path sets the name of the output folder\n",
     "- increment_folder_name if set to True, automatically changes the foldername if it already exists (default).\n",
     "\n",
-    "The *backengine* method takes no parameters and just performs the simulation."
+    "The *backengine* method takes no parameters and just performs the simulation and returns the generated data."
    ]
   },
   {
@@ -527,7 +537,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument.set_parameters(wavelength=2.8) # Set parameters\n",
+    "instrument.set_parameters(wavelength=2.8)\n",
     "instrument.show_parameters()"
    ]
   },
@@ -537,26 +547,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument.settings(ncount=5E6, output_path=\"data_folder/mcstas_basics\") # Settings\n",
+    "instrument.settings(ncount=5E6, output_path=\"data_folder/mcstas_basics\")\n",
     "instrument.show_settings()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "instrument.backengine() # Perform simulation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
-    "data = instrument.data # The data is available in the data attribute"
+    "data = instrument.backengine() # Perform simulation"
    ]
   },
   {
@@ -573,7 +578,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data)"
+    "ms.make_sub_plot(data)"
    ]
   },
   {
@@ -599,9 +604,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "functions.name_plot_options(\"monitor\", data, log=True)\n",
-    "functions.name_plot_options(\"banana\", data, left_lim=90, right_lim=150)\n",
-    "plotter.make_sub_plot(data, fontsize=16)"
+    "ms.name_plot_options(\"monitor\", data, log=True)\n",
+    "ms.name_plot_options(\"banana\", data, left_lim=90, right_lim=150)\n",
+    "ms.make_sub_plot(data, fontsize=16)"
    ]
   },
   {
@@ -632,6 +637,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
diff --git a/tutorial/McStasScript_tutorial_2_SPLIT.ipynb b/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
index ae9308d2..670ebaa5 100644
--- a/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
+++ b/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Advanced McStas features:  SPLIT\n",
+    "# Advanced McStas features:  SPLIT\n",
     "McStas uses the Monte Carlo ray-tracing technique, which allows some tricks in how the physics is sampled as long as the resulting probability distributions matches the physics. This is possible as each ray has a weight, corresponding to how much intensity this ray represent. The SPLIT keyword can be used to split a ray into many equal parts, which can be useful if the remaining instrument has many different simulated and random outcomes. In this tutorial we will use the SPLIT keyword on a powder sample, as there are many powder Bragg peaks each ray could select, and splitting the ray samples this more efficiently."
    ]
   },
@@ -22,7 +22,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
@@ -31,7 +31,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\", output_path=\"data_folder/mcstas_SPLIT\")"
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\",\n",
+    "                                output_path=\"data_folder/mcstas_SPLIT\")"
    ]
   },
   {
@@ -103,17 +104,27 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
     "instrument.settings(ncount=1E6)\n",
     "\n",
     "instrument.set_parameters(wavelength=2.8)\n",
     "                          \n",
-    "instrument.backengine()\n",
-    "data_low = instrument.data\n",
-    "\n",
-    "plotter.make_sub_plot(data_low)"
+    "data_low = instrument.backengine()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ms.make_sub_plot(data_low)"
    ]
   },
   {
@@ -136,38 +147,58 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
     "# No need to set settings or parameters as these have not changed\n",
-    "instrument.backengine()\n",
-    "data_reasonable = instrument.data\n",
-    "\n",
-    "plotter.make_sub_plot(data_reasonable)"
+    "data_reasonable = instrument.backengine()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ms.make_sub_plot(data_reasonable)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Adding the SPLIT keyword\n",
+    "### Caution on split value\n",
     "It is however possible to mismanage splitting, mainly by simulating a too few rays and splitting too much. Here we do this on purpose to see how such data would look. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
     "sample.set_SPLIT(10000)\n",
     "\n",
     "instrument.settings(ncount=1E3) # Change settings to lower ncount, but keep parameters\n",
     "                          \n",
-    "instrument.backengine()\n",
-    "data_unreasonable = instrument.data\n",
-    "\n",
-    "plotter.make_sub_plot(data_unreasonable)"
+    "data_unreasonable = instrument.backengine()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ms.make_sub_plot(data_unreasonable)"
    ]
   },
   {
@@ -181,16 +212,26 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
     "sample.set_SPLIT(1)\n",
-    "instrument.settings(ncount=5E7)\n",
+    "instrument.settings(ncount=2E7)\n",
     "                          \n",
-    "instrument.backengine()\n",
-    "data_ref = instrument.data\n",
-    "\n",
-    "plotter.make_sub_plot(data_ref)"
+    "data_ref = instrument.backengine()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ms.make_sub_plot(data_ref)"
    ]
   },
   {
@@ -210,10 +251,10 @@
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "banana_low = functions.name_search(\"banana\", data_low)\n",
-    "banana_reasonable = functions.name_search(\"banana\", data_reasonable)\n",
-    "banana_unreasonable = functions.name_search(\"banana\", data_unreasonable)\n",
-    "banana_ref = functions.name_search(\"banana\", data_ref)\n",
+    "banana_low = ms.name_search(\"banana\", data_low)\n",
+    "banana_reasonable = ms.name_search(\"banana\", data_reasonable)\n",
+    "banana_unreasonable = ms.name_search(\"banana\", data_unreasonable)\n",
+    "banana_ref = ms.name_search(\"banana\", data_ref)\n",
     "\n",
     "plt.figure(figsize=(14,6))\n",
     "plt.errorbar(banana_low.xaxis, banana_low.Intensity, yerr=banana_low.Error, fmt=\"r\")\n",
@@ -223,18 +264,20 @@
     "plt.legend([\"Low statistics\", \"High statistics reference\"])\n",
     "\n",
     "plt.figure(figsize=(14,6))\n",
-    "plt.errorbar(banana_reasonable.xaxis, banana_reasonable.Intensity, yerr=banana_reasonable.Error, fmt=\"r\")\n",
+    "plt.errorbar(banana_reasonable.xaxis, banana_reasonable.Intensity,\n",
+    "             yerr=banana_reasonable.Error, fmt=\"r\")\n",
     "plt.errorbar(banana_ref.xaxis, banana_ref.Intensity, yerr=banana_ref.Error, fmt=\"b\")\n",
     "plt.xlabel(\"2Theta [deg]\")\n",
     "plt.ylabel(\"Intensity [n/s]\")\n",
     "plt.legend([\"Low statistics with SPLIT\", \"High statistics reference\"])\n",
     "\n",
     "plt.figure(figsize=(14,6))\n",
-    "plt.errorbar(banana_unreasonable.xaxis, banana_unreasonable.Intensity, yerr=banana_unreasonable.Error, fmt=\"r\")\n",
+    "plt.errorbar(banana_unreasonable.xaxis, banana_unreasonable.Intensity,\n",
+    "             yerr=banana_unreasonable.Error, fmt=\"r\")\n",
     "plt.errorbar(banana_ref.xaxis, banana_ref.Intensity, yerr=banana_ref.Error, fmt=\"b\")\n",
     "plt.xlabel(\"2Theta [deg]\")\n",
     "plt.ylabel(\"Intensity [n/s]\")\n",
-    "plt.legend([\"Very low statistics with unreasonable SPLIT\", \"High statistics reference\"])"
+    "l = plt.legend([\"Very low statistics with unreasonable SPLIT\", \"High statistics reference\"])"
    ]
   },
   {
@@ -254,6 +297,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -270,6 +314,11 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.8.8"
+  },
+  "metadata": {
+   "execution": {
+    "timeout": 100
+   }
   }
  },
  "nbformat": 4,
diff --git a/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb b/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
index 99101f13..83254c6b 100644
--- a/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
+++ b/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Advanced McStas features: EXTEND and WHEN\n",
+    "# Advanced McStas features: EXTEND and WHEN\n",
     "In this tutorial we will look at two advanced features in McStas, the EXTEND block and WHEN condition. Here we will use them to flag certain neutrons with EXTEND, and only record them in monitors when the flag is set using a WHEN condition."
    ]
   },
@@ -14,7 +14,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
@@ -23,8 +23,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\",\n",
-    "                                input_path=\"run_folder\",\n",
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\",\n",
     "                                output_path=\"data_folder/mcstas_EXTEND_WHEN\")"
    ]
   },
@@ -51,7 +50,8 @@
     "src.dist = 1.5\n",
     "src.flux = 1E13\n",
     "\n",
-    "wavelength = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "wavelength = instrument.add_parameter(\"wavelength\", value=5.0,\n",
+    "                                      comment=\"Wavelength in [Ang]\")\n",
     "src.lambda0 = wavelength\n",
     "src.dlambda = \"0.001*wavelength\""
    ]
@@ -95,15 +95,25 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=2.8)\n",
     "instrument.settings(ncount=5E6)\n",
-    "instrument.backengine()\n",
-    "data = instrument.data\n",
-    "\n",
-    "plotter.make_sub_plot(data)"
+    "data = instrument.backengine()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ms.make_sub_plot(data)"
    ]
   },
   {
@@ -149,7 +159,8 @@
     "for reflections in reflection_numbers:\n",
     "    reflections_string = str(reflections)\n",
     "    \n",
-    "    acceptance = instrument.add_component(\"acceptance_\" + reflections_string, \"DivPos_monitor\")\n",
+    "    component_name = \"acceptance_\" + reflections_string\n",
+    "    acceptance = instrument.add_component(component_name, \"DivPos_monitor\")\n",
     "    acceptance.filename = '\"acceptance_' + reflections_string + '.dat\"'\n",
     "    acceptance.set_WHEN(\"n_reflections == \" + reflections_string)\n",
     "    \n",
@@ -176,15 +187,27 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=2.8)\n",
     "instrument.settings(ncount=5E6)\n",
     "instrument.show_settings()\n",
     "\n",
-    "instrument.backengine()\n",
-    "plotter.make_sub_plot(instrument.data)"
+    "new_data = instrument.backengine()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ms.make_sub_plot(new_data)"
    ]
   },
   {
@@ -216,6 +239,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
diff --git a/tutorial/McStasScript_tutorial_4_JUMP.ipynb b/tutorial/McStasScript_tutorial_4_JUMP.ipynb
index 74e18eff..a72aafe0 100644
--- a/tutorial/McStasScript_tutorial_4_JUMP.ipynb
+++ b/tutorial/McStasScript_tutorial_4_JUMP.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Advanced McStas features: JUMP\n",
+    "# Advanced McStas features: JUMP\n",
     "In this notebook we will look at JUMP and how it can be used to control the sequence of execution of components. One instance where this is useful is if a guide splits into two. Consider an instrument with the following components:\n",
     "\n",
     "- source\n",
@@ -38,7 +38,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
@@ -47,7 +47,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
    ]
   },
   {
@@ -65,7 +65,8 @@
     "src.dist = 1.5\n",
     "src.flux = 1E13\n",
     "\n",
-    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0,\n",
+    "                                       comment=\"Wavelength in [Ang]\")\n",
     "src.dlambda = \"0.001*wavelength\"\n",
     "\n",
     "guide = instrument.add_component(\"guide\", \"Guide_gravity\", AT=[0,0,1.5], RELATIVE=src)\n",
@@ -198,14 +199,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=2.8)\n",
     "instrument.settings(ncount=5E6, output_path=\"data_folder/mcstas_JUMP\")\n",
-    "instrument.backengine()\n",
-    "\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -214,7 +217,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data)"
+    "ms.make_sub_plot(data)"
    ]
   },
   {
@@ -253,6 +256,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
diff --git a/tutorial/Union_tutorial_1_processes_and_materials.ipynb b/tutorial/Union_tutorial_1_processes_and_materials.ipynb
index b49e9067..ff3e45db 100644
--- a/tutorial/Union_tutorial_1_processes_and_materials.ipynb
+++ b/tutorial/Union_tutorial_1_processes_and_materials.ipynb
@@ -21,20 +21,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
    ]
   },
   {
@@ -47,53 +47,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are all components in the Work directory category.\n",
-      "No components found in this category! Available categories:\n",
-      " sources\n",
-      " optics\n",
-      " samples\n",
-      " monitors\n",
-      " misc\n",
-      " contrib\n",
-      " union\n",
-      " obsolete\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_components(\"Work directory\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Incoherent_process ________________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1msigma\u001b[0m = \u001b[1m\u001b[94m5.08\u001b[0m\u001b[0m [barns] // Incoherent scattering cross section\n",
-      "\u001b[1mf_QE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Fraction of quasielastic scattering (rest is elastic)\n",
-      "\u001b[1mgamma\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Lorentzian width of quasielastic broadening (HWHM)\n",
-      "\u001b[1mpacking_factor\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // How dense is the material compared to optimal 0-1\n",
-      "\u001b[1munit_cell_volume\u001b[0m = \u001b[1m\u001b[94m13.8\u001b[0m\u001b[0m [AA^3] // Unit_cell_volume\n",
-      "\u001b[1minteract_fraction\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // How large a part of the scattering events \n",
-      "                                should use this process 0-1 (sum of all processes \n",
-      "                                in material = 1) \n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.component_help(\"Incoherent_process\")"
    ]
@@ -109,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,24 +93,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Union_make_material _______________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1mprocess_string\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated names of physical processes\n",
-      "\u001b[4m\u001b[1mmy_absorption\u001b[0m\u001b[0m [1/m] // Inverse penetration depth from absorption at standard \n",
-      "                       energy \n",
-      "\u001b[1mabsorber\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [0/1] // Control parameter, if set to 1 the material will have \n",
-      "                        no scattering processes \n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.component_help(\"Union_make_material\")"
    ]
@@ -161,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -179,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -197,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -226,19 +176,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "src = instrument.add_component(\"source\", \"Source_div\")\n",
     "\n",
-    "source_width = instrument.add_parameter(\"source_width\", value=0.15, comment=\"Width of source in [m]\")\n",
+    "source_width = instrument.add_parameter(\"source_width\", value=0.15,\n",
+    "                                        comment=\"Width of source in [m]\")\n",
     "src.xwidth = source_width\n",
     "src.yheight = 0.03\n",
     "src.focus_aw = 0.01\n",
     "src.focus_ah = 0.01\n",
     "\n",
-    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0,\n",
+    "                                       comment=\"Wavelength in [Ang]\")\n",
     "src.dlambda = \"0.001*wavelength\""
    ]
   },
@@ -247,14 +199,14 @@
    "metadata": {},
    "source": [
     "## Adding geometries that use the material definitions\n",
-    "Here we add three boxes, each using a different material definition and placed next to one another. The *material_string* parameter is used to specify the material name. The *priority* parameter will be explained later, as it is only important when geometries overlap, here they are spatially separated, yet the priorties must still be unique.\n",
+    "Here we add three boxes, each using a different material definition and placed next to one another. The *material_string* parameter is used to specify the material name. The *priority* parameter will be explained later, as it is only important when geometries overlap, here they are spatially separated, yet the priorities must still be unique.\n",
     "\n",
     "It is important to note that these three boxes will be simulated simultaneously in the McStas simulation flow, so no need for GROUP statements to have these in parallel. Because they are simulated simultaneously, a ray can go from one to another, which would not be possible with a standard GROUP."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -290,49 +242,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Union_logger_2D_space _____________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1mtarget_geometry\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated list of geometry names \n",
-      "                                     that will be logged, leave empty for all \n",
-      "                                     volumes (even not defined yet) \n",
-      "\u001b[1mtarget_process\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated names of physical \n",
-      "                                    processes, if volumes are selected, one can \n",
-      "                                    select Union_process names \n",
-      "\u001b[1mD_direction_1\u001b[0m = \u001b[1m\u001b[94m\"x\"\u001b[0m\u001b[0m [string] // Direction for first axis (\"x\", \"y\" or \"z\")\n",
-      "\u001b[1mD1_min\u001b[0m = \u001b[1m\u001b[94m-5.0\u001b[0m\u001b[0m [1] // histogram boundery, min position value for first axis\n",
-      "\u001b[1mD1_max\u001b[0m = \u001b[1m\u001b[94m5.0\u001b[0m\u001b[0m [1] // histogram boundery, max position value for first axis\n",
-      "\u001b[1mn1\u001b[0m = \u001b[1m\u001b[94m90.0\u001b[0m\u001b[0m [1] // number of bins for first axis\n",
-      "\u001b[1mD_direction_2\u001b[0m = \u001b[1m\u001b[94m\"z\"\u001b[0m\u001b[0m [string] // Direction for second axis (\"x\", \"y\" or \"z\")\n",
-      "\u001b[1mD2_min\u001b[0m = \u001b[1m\u001b[94m-5.0\u001b[0m\u001b[0m [1] // histogram boundery, min position value for second axis\n",
-      "\u001b[1mD2_max\u001b[0m = \u001b[1m\u001b[94m5.0\u001b[0m\u001b[0m [1] // histogram boundery, max position value for second axis\n",
-      "\u001b[1mn2\u001b[0m = \u001b[1m\u001b[94m90.0\u001b[0m\u001b[0m [1] // number of bins for second axis\n",
-      "\u001b[1mfilename\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Filename for logging output\n",
-      "\u001b[1morder_total\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time, 0 for \n",
-      "                         all orders \n",
-      "\u001b[1morder_volume\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time in the \n",
-      "                          same geometry \n",
-      "\u001b[1morder_volume_process\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time \n",
-      "                                  in the same geometry, using the same process \n",
-      "\u001b[1mlogger_conditional_extend_index\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // If a conditional is used with \n",
-      "                                              this logger, the result of each \n",
-      "                                              conditional calculation can be made \n",
-      "                                              available in extend as a array called \n",
-      "                                              \"logger_conditional_extend\", and one \n",
-      "                                              would then acces \n",
-      "                                              logger_conditional_extend[n] if \n",
-      "                                              logger_conditional_extend_index is \n",
-      "                                              set to n \n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.component_help(\"Union_logger_2D_space\")"
    ]
@@ -346,11 +258,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "logger = instrument.add_component(\"logger_space\", \"Union_logger_2D_space\", RELATIVE=\"box_powder\")\n",
+    "logger = instrument.add_component(\"logger_space\", \"Union_logger_2D_space\")\n",
+    "logger.set_RELATIVE(\"box_powder\")\n",
     "logger.D_direction_1 = '\"z\"'\n",
     "logger.D1_min = -0.04\n",
     "logger.D1_max = 0.04\n",
@@ -361,7 +274,8 @@
     "logger.n2 = 400\n",
     "logger.filename = '\"logger.dat\"'\n",
     "\n",
-    "logger = instrument.add_component(\"abs_logger_space\", \"Union_abs_logger_2D_space\", RELATIVE=\"box_powder\")\n",
+    "logger = instrument.add_component(\"abs_logger_space\", \"Union_abs_logger_2D_space\")\n",
+    "logger.set_RELATIVE(\"box_powder\")\n",
     "logger.D_direction_1 = '\"z\"'\n",
     "logger.D1_min = -0.04\n",
     "logger.D1_max = 0.04\n",
@@ -383,42 +297,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Union_master ______________________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1mverbal\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [bool] // Toogles terminal output describing the defined simulation\n",
-      "\u001b[1mlist_verbal\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Toogles information of all internal lists in \n",
-      "                            intersection network \n",
-      "\u001b[1mfinally_verbal\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Toogles information about cleanup performed in \n",
-      "                               finally section \n",
-      "\u001b[1mallow_inside_start\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Set to 1 to allow rays to start inside the \n",
-      "                                   defined geometry \n",
-      "\u001b[1menable_tagging\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Enable tagging of ray history (geometry, \n",
-      "                               scattering process) \n",
-      "\u001b[1mhistory_limit\u001b[0m = \u001b[1m\u001b[94m300000.0\u001b[0m\u001b[0m [bool] // Limit the number of unique histories that \n",
-      "                                   are saved \n",
-      "\u001b[1menable_conditionals\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [bool] // Use conditionals with this master\n",
-      "\u001b[1minherit_number_of_scattering_events\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Inherit the number of \n",
-      "                                                    scattering events from last \n",
-      "                                                    master \n",
-      "\u001b[1mrecord_absorption\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Toggles logging of absorption\n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.component_help(\"Union_master\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -435,287 +323,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Instrument settings:\n",
-      "  ncount:           5.00e+06\n",
-      "  output_path:      data_folder/union_materials\n",
-      "  run_path:         run_folder\n",
-      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
-      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
-      "  executable:       mcrun\n",
-      "  force_compile:    True\n",
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/tutorial/data_folder/union_materials_5\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
-      "                                                ^~~~~~~~\n",
-      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
-      "#define filename mccabs_logger_space_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
-      "                                                ^\n",
-      "                                                \"%s\", \n",
-      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
-      "#define filename mccabs_logger_space_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "14 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/tutorial/data_folder/union_materials_5\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Cu.laz' (Table_Read_Offset)\n",
-      "Table from file 'Cu.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
-      "  '# TITLE *-Cu-[FM3-M] Otte, H.M.[1961];# CELL 3.615050 3.615050 3.615050 90. ...'\n",
-      "PowderN: powder: Reading 19 rows from Cu.laz\n",
-      "PowderN: powder: Read 19 reflections from file 'Cu.laz'\n",
-      "PowderN: powder: Vc=47.24 [Angs] sigma_abs=15.12 [barn] sigma_inc=2.2 [barn] reflections=Cu.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: incoherent \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: powder \n",
-      "component index [1]: 4 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 3\n",
-      "name of material    [0]: inc_material \n",
-      "component index     [0]: 2 \n",
-      "my_absoprtion       [0]: 0.000000 \n",
-      "number of processes [0]: 1 \n",
-      "name of material    [1]: abs_material \n",
-      "component index     [1]: 3 \n",
-      "my_absoprtion       [1]: 3.000000 \n",
-      "number of processes [1]: 0 \n",
-      "name of material    [2]: powder_material \n",
-      "component index     [2]: 5 \n",
-      "my_absoprtion       [2]: 1.200000 \n",
-      "number of processes [2]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 3\n",
-      "\n",
-      "name of geometry    [0]: box_inc \n",
-      "component index     [0]: 7 \n",
-      "Volume.name         [0]: box_inc \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.000000 \n",
-      "Volume.p_physics.number of processes [0]: 1 \n",
-      "Volume.geometry.shape                [0]: box \n",
-      "Volume.geometry.center.x             [0]: 0.040000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: box_powder \n",
-      "component index     [1]: 8 \n",
-      "Volume.name         [1]: box_powder \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absorption       [1]: 1.200000 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: box \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: box_abs \n",
-      "component index     [2]: 9 \n",
-      "Volume.name         [2]: box_abs \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absorption       [2]: 3.000000 \n",
-      "Volume.p_physics.number of processes [2]: 0 \n",
-      "Volume.geometry.shape                [2]: box \n",
-      "Volume.geometry.center.x             [2]: -0.040000 \n",
-      "Volume.geometry.center.y             [2]: 0.000000 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 4\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           0 = [1,2,3]\n",
-      "LIST: Intersect_check_list for Volume      0 = [1,2,3]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [1,2,3]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for box_inc with box shape made of inc_material\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = []\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = []\n",
-      "LIST: Next_volume_list for Volume          1 = [0]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for box_powder with box shape made of powder_material\n",
-      "LIST: Children for Volume                  2 = []\n",
-      "LIST: Direct_children for Volume           2 = []\n",
-      "LIST: Intersect_check_list for Volume      2 = []\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = []\n",
-      "LIST: Next_volume_list for Volume          2 = [0]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for box_abs with box shape made of abs_material\n",
-      "LIST: Children for Volume                  3 = []\n",
-      "LIST: Direct_children for Volume           3 = []\n",
-      "LIST: Intersect_check_list for Volume      3 = []\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = []\n",
-      "LIST: Next_volume_list for Volume          3 = [0]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_I=3.1635e-09 logger_space_ERR=3.59609e-12 logger_space_N=790141 \"logger.dat\"\n",
-      "Detector: abs_logger_space_I=1.70717e-09 abs_logger_space_ERR=1.53146e-12 abs_logger_space_N=1.81278e+06 \"abs_logger.dat\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=8.0)\n",
-    "instrument.settings(ncount=5E6, output_path=\"data_folder/union_materials\")\n",
+    "instrument.settings(ncount=3E6, output_path=\"data_folder/union_materials\")\n",
     "instrument.show_settings()\n",
     "\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -730,32 +350,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space\n",
-      "Plotting data with name abs_logger_space\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data)"
+    "ms.make_sub_plot(data)"
    ]
   },
   {
@@ -770,319 +369,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Instrument settings:\n",
-      "  ncount:           5.00e+06\n",
-      "  output_path:      data_folder/union_materials\n",
-      "  run_path:         run_folder\n",
-      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
-      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
-      "  executable:       mcrun\n",
-      "  force_compile:    True\n",
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/tutorial/data_folder/union_materials_6\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
-      "                                                ^~~~~~~~\n",
-      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
-      "#define filename mccabs_logger_space_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
-      "                                                ^\n",
-      "                                                \"%s\", \n",
-      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
-      "#define filename mccabs_logger_space_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "14 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/tutorial/data_folder/union_materials_6\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Cu.laz' (Table_Read_Offset)\n",
-      "Table from file 'Cu.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
-      "  '# TITLE *-Cu-[FM3-M] Otte, H.M.[1961];# CELL 3.615050 3.615050 3.615050 90. ...'\n",
-      "PowderN: powder: Reading 19 rows from Cu.laz\n",
-      "PowderN: powder: Read 19 reflections from file 'Cu.laz'\n",
-      "PowderN: powder: Vc=47.24 [Angs] sigma_abs=15.12 [barn] sigma_inc=2.2 [barn] reflections=Cu.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: incoherent \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: powder \n",
-      "component index [1]: 4 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 3\n",
-      "name of material    [0]: inc_material \n",
-      "component index     [0]: 2 \n",
-      "my_absoprtion       [0]: 0.000000 \n",
-      "number of processes [0]: 1 \n",
-      "name of material    [1]: abs_material \n",
-      "component index     [1]: 3 \n",
-      "my_absoprtion       [1]: 3.000000 \n",
-      "number of processes [1]: 0 \n",
-      "name of material    [2]: powder_material \n",
-      "component index     [2]: 5 \n",
-      "my_absoprtion       [2]: 1.200000 \n",
-      "number of processes [2]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 3\n",
-      "\n",
-      "name of geometry    [0]: box_inc \n",
-      "component index     [0]: 7 \n",
-      "Volume.name         [0]: box_inc \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.000000 \n",
-      "Volume.p_physics.number of processes [0]: 1 \n",
-      "Volume.geometry.shape                [0]: box \n",
-      "Volume.geometry.center.x             [0]: 0.040000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: box_powder \n",
-      "component index     [1]: 8 \n",
-      "Volume.name         [1]: box_powder \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absorption       [1]: 1.200000 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: box \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: box_abs \n",
-      "component index     [2]: 9 \n",
-      "Volume.name         [2]: box_abs \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absorption       [2]: 3.000000 \n",
-      "Volume.p_physics.number of processes [2]: 0 \n",
-      "Volume.geometry.shape                [2]: box \n",
-      "Volume.geometry.center.x             [2]: -0.040000 \n",
-      "Volume.geometry.center.y             [2]: 0.000000 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 4\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           0 = [1,2,3]\n",
-      "LIST: Intersect_check_list for Volume      0 = [1,2,3]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [1,2,3]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for box_inc with box shape made of inc_material\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = []\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = []\n",
-      "LIST: Next_volume_list for Volume          1 = [0]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for box_powder with box shape made of powder_material\n",
-      "LIST: Children for Volume                  2 = []\n",
-      "LIST: Direct_children for Volume           2 = []\n",
-      "LIST: Intersect_check_list for Volume      2 = []\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = []\n",
-      "LIST: Next_volume_list for Volume          2 = [0]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for box_abs with box shape made of abs_material\n",
-      "LIST: Children for Volume                  3 = []\n",
-      "LIST: Direct_children for Volume           3 = []\n",
-      "LIST: Intersect_check_list for Volume      3 = []\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = []\n",
-      "LIST: Next_volume_list for Volume          3 = [0]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_I=1.13342e-09 logger_space_ERR=5.90802e-13 logger_space_N=3.86971e+06 \"logger.dat\"\n",
-      "Detector: abs_logger_space_I=4.16101e-11 abs_logger_space_ERR=2.36615e-14 abs_logger_space_N=4.12627e+06 \"abs_logger.dat\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=2.8, source_width=0.03)\n",
     "instrument.settings(ncount=5E6, output_path=\"data_folder/union_materials\")\n",
     "instrument.show_settings()\n",
     "\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space\n",
-      "Plotting data with name abs_logger_space\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "functions.name_plot_options(\"logger_space\", data, log=True)\n",
-    "functions.name_plot_options(\"abs_logger_space\", data, log=True)\n",
-    "plotter.make_sub_plot(data)"
+    "ms.name_plot_options(\"logger_space\", data, log=True)\n",
+    "ms.name_plot_options(\"abs_logger_space\", data, log=True)\n",
+    "ms.make_sub_plot(data)"
    ]
   },
   {
@@ -1103,7 +413,7 @@
     "- Get newest version of Union components (Both library files and components themselves)\n",
     "\n",
     "Since the Union components need to collaborate, it is important to have the same version of the libraries and components. The newest version of the components can be found here: https://github.com/McStasMcXtrace/McCode/tree/master/mcstas-comps/contrib/union\n",
-    "All libraries for McStas are found here: https://github.com/McStasMcXtrace/McCode/tree/master/mcstas-comps/share but only three are needed for the Union components:\n",
+    "All libraries for McStas are found here: https://github.com/McStasMcXtrace/McCode/tree/master/mcstas-comps/share but only a few are needed for the Union components:\n",
     "- Union_initialization.c\n",
     "- Union_functions.c\n",
     "- Geometry_functions.c\n",
@@ -1119,6 +429,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
diff --git a/tutorial/Union_tutorial_2_geometry.ipynb b/tutorial/Union_tutorial_2_geometry.ipynb
index 5a07c758..62c6751f 100644
--- a/tutorial/Union_tutorial_2_geometry.ipynb
+++ b/tutorial/Union_tutorial_2_geometry.ipynb
@@ -20,7 +20,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
@@ -29,7 +29,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
    ]
   },
   {
@@ -55,7 +55,7 @@
     "\n",
     "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
     "Al.process_string = '\"Al_inc,Al_pow\"'\n",
-    "Al.my_absorption = 100*4*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "Al.my_absorption = 100*4*0.231/66.4 # barns [m^2 E-28]*Å^3 [m^3 E-30]=[m E-2]\n",
     "\n",
     "Sample_inc = instrument.add_component(\"Sample_inc\", \"Incoherent_process\")\n",
     "Sample_inc.sigma = 4*3.4176\n",
@@ -89,7 +89,8 @@
     "src.yheight = 0.035\n",
     "src.focus_aw = 0.01\n",
     "src.focus_ah = 0.01\n",
-    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0,\n",
+    "                                       comment=\"Wavelength in [Ang]\")\n",
     "src.dlambda = \"0.01*wavelength\"\n",
     "src.flux = 1E13"
    ]
@@ -166,7 +167,8 @@
     "sample_geometry.priority = 100\n",
     "sample_geometry.set_AT([0,0,1], RELATIVE=src)\n",
     "\n",
-    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\", RELATIVE=sample_geometry)\n",
+    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\")\n",
+    "container.set_RELATIVE(sample_geometry)\n",
     "container.yheight = 0.03+0.003 # 1.5 mm top and button\n",
     "container.radius = 0.0075 + 0.0015 # 1.5 mm sides of container\n",
     "container.material_string='\"Al\"' \n",
@@ -194,7 +196,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\")\n",
+    "logger_zx.set_RELATIVE(sample_geometry)\n",
     "logger_zx.D_direction_1 = '\"z\"'\n",
     "logger_zx.D1_min = -0.02\n",
     "logger_zx.D1_max = 0.02\n",
@@ -205,7 +208,8 @@
     "logger_zx.n2 = 300\n",
     "logger_zx.filename = '\"logger_zx.dat\"'\n",
     "\n",
-    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\")\n",
+    "logger_zy.set_RELATIVE(sample_geometry)\n",
     "logger_zy.D_direction_1 = '\"z\"'\n",
     "logger_zy.D1_min = -0.02\n",
     "logger_zy.D1_max = 0.02\n",
@@ -216,7 +220,8 @@
     "logger_zy.n2 = 300\n",
     "logger_zy.filename = '\"logger_zy.dat\"'\n",
     "\n",
-    "logger_xy = instrument.add_component(\"logger_space_xy\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_xy = instrument.add_component(\"logger_space_xy\", \"Union_logger_2D_space\")\n",
+    "logger_xy.set_RELATIVE(sample_geometry)\n",
     "logger_xy.D_direction_1 = '\"x\"'\n",
     "logger_xy.D1_min = -0.02\n",
     "logger_xy.D1_max = 0.02\n",
@@ -277,15 +282,18 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=3.0)\n",
-    "instrument.settings(ncount=1E7, output_path=\"data_folder/union_geometry\")\n",
+    "instrument.settings(ncount=3E6, output_path=\"data_folder/union_geometry\")\n",
     "instrument.show_settings()\n",
     "\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -302,10 +310,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
-    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
-    "functions.name_plot_options(\"logger_space_xy\", data, log=True, orders_of_mag=4)\n",
-    "plotter.make_sub_plot(data)"
+    "ms.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
+    "ms.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
+    "ms.name_plot_options(\"logger_space_xy\", data, log=True, orders_of_mag=4)\n",
+    "ms.make_sub_plot(data)"
    ]
   },
   {
@@ -332,28 +340,32 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "inner_wall = instrument.add_component(\"cryostat_wall\", \"Union_cylinder\", before=\"master\")\n",
+    "inner_wall = instrument.add_component(\"cryostat_wall\", \"Union_cylinder\",\n",
+    "                                      before=\"master\")\n",
     "inner_wall.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
     "inner_wall.yheight = 0.12\n",
     "inner_wall.radius = 0.03\n",
     "inner_wall.material_string='\"Al\"' \n",
     "inner_wall.priority = 80\n",
     "\n",
-    "inner_wall_vac = instrument.add_component(\"cryostat_wall_vacuum\", \"Union_cylinder\", before=\"master\")\n",
+    "inner_wall_vac = instrument.add_component(\"cryostat_wall_vacuum\", \"Union_cylinder\",\n",
+    "                                          before=\"master\")\n",
     "inner_wall_vac.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
     "inner_wall_vac.yheight = 0.12 - 0.008\n",
     "inner_wall_vac.radius = 0.03 - 0.002\n",
     "inner_wall_vac.material_string='\"Vacuum\"' \n",
     "inner_wall_vac.priority = 81\n",
     "\n",
-    "outer_wall = instrument.add_component(\"outer_cryostat_wall\", \"Union_cylinder\", before=\"master\")\n",
+    "outer_wall = instrument.add_component(\"outer_cryostat_wall\", \"Union_cylinder\",\n",
+    "                                      before=\"master\")\n",
     "outer_wall.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
     "outer_wall.yheight = 0.15\n",
     "outer_wall.radius = 0.1\n",
     "outer_wall.material_string='\"Al\"' \n",
     "outer_wall.priority = 60\n",
     "\n",
-    "outer_wall_vac = instrument.add_component(\"outer_cryostat_wall_vacuum\", \"Union_cylinder\", before=\"master\")\n",
+    "outer_wall_vac = instrument.add_component(\"outer_cryostat_wall_vacuum\", \"Union_cylinder\",\n",
+    "                                          before=\"master\")\n",
     "outer_wall_vac.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
     "outer_wall_vac.yheight = 0.15 - 0.01\n",
     "outer_wall_vac.radius = 0.1 - 0.003\n",
@@ -380,6 +392,23 @@
     "logger_xy.set_parameters(D1_min=-0.12, D1_max=0.12, D2_min=-0.12, D2_max=0.12)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Viewing the instrument\n",
+    "The instrument can be viewed with the *show_instrument* method. The mock cryostat and detector can be seen in a 3D view."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.show_instrument()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -391,11 +420,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
-    "instrument.backengine()\n",
-    "data_cryo = instrument.data"
+    "data_cryo = instrument.backengine()"
    ]
   },
   {
@@ -412,7 +444,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data_cryo, log=[True, True, True, False], orders_of_mag=5)"
+    "ms.make_sub_plot(data_cryo, log=[True, True, True, False], orders_of_mag=5)"
    ]
   },
   {
@@ -432,8 +464,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "banana_can = functions.name_search(\"banana\", data)\n",
-    "banana_cryo = functions.name_search(\"banana\", data_cryo)\n",
+    "banana_can = ms.name_search(\"banana\", data)\n",
+    "banana_cryo = ms.name_search(\"banana\", data_cryo)\n",
     "\n",
     "import copy\n",
     "import numpy as np\n",
@@ -448,18 +480,12 @@
     "         banana_diff.xaxis, banana_diff.Intensity-10.0, \"k\")\n",
     "plt.xlabel(\"2Theta [deg]\")\n",
     "plt.ylabel(\"Intensity [n/s]\")\n",
-    "plt.legend([\"Sample in can\", \"Sample in can in cryostat\", \"Difference displaced to -10\"])"
+    "l = plt.legend([\"Sample in can\", \"Sample in can in cryostat\", \"Difference displaced to -10\"])"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -476,6 +502,11 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.8.8"
+  },
+  "metadata": {
+   "execution": {
+    "timeout": 100
+   }
   }
  },
  "nbformat": 4,
diff --git a/tutorial/Union_tutorial_3_loggers.ipynb b/tutorial/Union_tutorial_3_loggers.ipynb
index 253246e0..8481d148 100644
--- a/tutorial/Union_tutorial_3_loggers.ipynb
+++ b/tutorial/Union_tutorial_3_loggers.ipynb
@@ -22,8 +22,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter\n",
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+    "import mcstasscript as ms\n",
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
    ]
   },
   {
@@ -41,7 +41,7 @@
     "\n",
     "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
     "Al.process_string = '\"Al_inc,Al_pow\"'\n",
-    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28]*Å^3[m^3 E-30]=[m E-2], factor 100\n",
     "\n",
     "Sample_inc = instrument.add_component(\"Sample_inc\", \"Incoherent_process\")\n",
     "Sample_inc.sigma = 3.4176\n",
@@ -59,7 +59,8 @@
     "src.yheight = 0.035\n",
     "src.focus_aw = 0.01\n",
     "src.focus_ah = 0.01\n",
-    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0,\n",
+    "                                       comment=\"Wavelength in [Ang]\")\n",
     "src.dlambda = \"0.01*wavelength\"\n",
     "src.flux = 1E13\n",
     "\n",
@@ -70,7 +71,8 @@
     "sample_geometry.priority = 100\n",
     "sample_geometry.set_AT([0,0,1], RELATIVE=src)\n",
     "\n",
-    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\", RELATIVE=sample_geometry)\n",
+    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\")\n",
+    "container.set_RELATIVE(sample_geometry)\n",
     "container.yheight = 0.03+0.003 # 1.5 mm top and button\n",
     "container.radius = 0.0075 + 0.0015 # 1.5 mm sides of container\n",
     "container.material_string='\"Al\"' \n",
@@ -168,7 +170,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\")\n",
+    "logger_zx.set_RELATIVE(sample_geometry)\n",
     "logger_zx.D_direction_1 = '\"z\"'\n",
     "logger_zx.D1_min = -0.12\n",
     "logger_zx.D1_max = 0.12\n",
@@ -179,7 +182,8 @@
     "logger_zx.n2 = 300\n",
     "logger_zx.filename = '\"logger_zx.dat\"'\n",
     "\n",
-    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\")\n",
+    "logger_zy.set_RELATIVE(sample_geometry)\n",
     "logger_zy.D_direction_1 = '\"z\"'\n",
     "logger_zy.D1_min = -0.12\n",
     "logger_zy.D1_max = 0.12\n",
@@ -204,14 +208,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=3.0)\n",
-    "instrument.settings(ncount=1E7, output_path=\"data_folder/union_loggers\")\n",
+    "instrument.settings(ncount=3E6, output_path=\"data_folder/union_loggers\")\n",
     "\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -220,7 +227,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data, log=True, orders_of_mag=4)"
+    "ms.make_sub_plot(data, log=True, orders_of_mag=4)"
    ]
   },
   {
@@ -244,14 +251,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
     "logger_zx.target_geometry = '\"outer_cryostat_wall,cryostat_wall\"'\n",
     "logger_zy.target_geometry = '\"sample_geometry\"'\n",
     "\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -260,7 +270,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data, log=[True, False], orders_of_mag=4)"
+    "ms.make_sub_plot(data, log=[True, False], orders_of_mag=4)"
    ]
   },
   {
@@ -279,7 +289,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
     "logger_zx.order_total = 2\n",
@@ -287,8 +301,7 @@
     "logger_zy.target_geometry = '\"NULL\"'\n",
     "logger_zy.order_total = 2\n",
     "\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -297,7 +310,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data, log=True, orders_of_mag=3)"
+    "ms.make_sub_plot(data, log=True, orders_of_mag=3)"
    ]
   },
   {
@@ -325,7 +338,8 @@
     "logger_1D.n1 = 300\n",
     "logger_1D.filename = '\"logger_1D_time.dat\"'\n",
     "\n",
-    "abs_logger_zx = instrument.add_component(\"abs_logger_space_zx\", \"Union_abs_logger_2D_space\",before=\"master\")\n",
+    "abs_logger_zx = instrument.add_component(\"abs_logger_space_zx\", \"Union_abs_logger_2D_space\",\n",
+    "                                         before=\"master\")\n",
     "abs_logger_zx.set_AT([0,0,0], RELATIVE=sample_geometry)\n",
     "abs_logger_zx.D_direction_1 = '\"z\"'\n",
     "abs_logger_zx.D1_min = -0.12\n",
@@ -348,7 +362,8 @@
     "logger_2DQ.n2 = 200\n",
     "logger_2DQ.filename = '\"logger_2DQ.dat\"'\n",
     "\n",
-    "logger_2D_kf = instrument.add_component(\"logger_2D_kf\", \"Union_logger_2D_kf\", before=\"master\")\n",
+    "logger_2D_kf = instrument.add_component(\"logger_2D_kf\", \"Union_logger_2D_kf\",\n",
+    "                                        before=\"master\")\n",
     "logger_2D_kf.Q_direction_1 = '\"z\"'\n",
     "logger_2D_kf.Q1_min = -2.5\n",
     "logger_2D_kf.Q1_max = 2.5\n",
@@ -371,11 +386,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -384,16 +402,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=3)\n",
-    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=3)\n",
-    "functions.name_plot_options(\"abs_logger_space_zx\", data, log=True, orders_of_mag=3)\n",
-    "functions.name_plot_options(\"logger_1D\", data, log=True, orders_of_mag=3)\n",
-    "functions.name_plot_options(\"logger_2DQ\", data, log=True, orders_of_mag=3)\n",
-    "functions.name_plot_options(\"logger_2D_kf\", data, log=True, orders_of_mag=3)\n",
+    "ms.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=3)\n",
+    "ms.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=3)\n",
+    "ms.name_plot_options(\"abs_logger_space_zx\", data, log=True, orders_of_mag=3)\n",
+    "ms.name_plot_options(\"logger_1D\", data, log=True, orders_of_mag=3)\n",
+    "ms.name_plot_options(\"logger_2DQ\", data, log=True, orders_of_mag=3)\n",
+    "ms.name_plot_options(\"logger_2D_kf\", data, log=True, orders_of_mag=3)\n",
     "\n",
-    "plotter.make_sub_plot(data[0:2])\n",
-    "plotter.make_sub_plot(data[2:4])\n",
-    "plotter.make_sub_plot(data[4:6])"
+    "ms.make_sub_plot(data[0:2])\n",
+    "ms.make_sub_plot(data[2:4])\n",
+    "ms.make_sub_plot(data[4:6])"
    ]
   },
   {
@@ -429,7 +447,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "log_2D_st = instrument.add_component(\"logger_2D_space_time\", \"Union_logger_2D_space_time\", before=\"master\")"
+    "log_2D_st = instrument.add_component(\"logger_2D_space_time\", \"Union_logger_2D_space_time\",\n",
+    "                                     before=\"master\")"
    ]
   },
   {
@@ -457,11 +476,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -478,11 +500,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ani_data = functions.name_search(\"logger_2D_space_time\", data)\n",
+    "ani_data = ms.name_search(\"logger_2D_space_time\", data)\n",
     "for frame in ani_data:\n",
     "    frame.set_plot_options(log=True, colormap=\"jet\")\n",
     "    \n",
-    "plotter.make_animation(ani_data, filename=\"animation_demo\", fps=2)"
+    "ms.make_animation(ani_data, filename=\"animation_demo\", fps=2)"
    ]
   },
   {
@@ -506,13 +528,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
     "log_2D_st.time_bins = 128\n",
     "instrument.settings(ncount=2E8, mpi=4)\n",
-    "#instrument.backengine()\n",
-    "#data = instrument.data"
+    "#data = instrument.backengine()"
    ]
   },
   {
@@ -521,11 +546,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#ani_data = functions.name_search(\"logger_2D_space_time\", data)\n",
+    "#ani_data = ms.name_search(\"logger_2D_space_time\", data)\n",
     "#for frame in ani_data:\n",
-    "#    frame.set_plot_options(log=True, colormap=\"jet\", orders_of_mag=6)\n",
+    "#    ms.set_plot_options(log=True, colormap=\"jet\", orders_of_mag=6)\n",
     "#    \n",
-    "#plotter.make_animation(ani_data, filename=\"animation_demo_long\", fps=5)"
+    "#ms.make_animation(ani_data, filename=\"animation_demo_long\", fps=5)"
    ]
   },
   {
@@ -547,6 +572,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -563,6 +589,11 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.8.8"
+  },
+  "metadata": {
+   "execution": {
+    "timeout": 100
+   }
   }
  },
  "nbformat": 4,
diff --git a/tutorial/Union_tutorial_4_conditionals.ipynb b/tutorial/Union_tutorial_4_conditionals.ipynb
index bdef0734..29edda24 100644
--- a/tutorial/Union_tutorial_4_conditionals.ipynb
+++ b/tutorial/Union_tutorial_4_conditionals.ipynb
@@ -22,8 +22,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter\n",
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+    "import mcstasscript as ms\n",
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
    ]
   },
   {
@@ -69,7 +69,8 @@
     "src.focus_ah = 0.01\n",
     "\n",
     "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
-    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0,\n",
+    "                                       comment=\"Wavelength in [Ang]\")\n",
     "src.dlambda = \"0.01*wavelength\"\n",
     "src.flux = 1E13\n",
     "\n",
@@ -180,11 +181,13 @@
     "master = instrument.add_component(\"master\", \"Union_master\")\n",
     "\n",
     "# Adding a banana - tof detector\n",
-    "banana_detector = instrument.add_component(\"banana_detector\", \"Monitor_nD\", RELATIVE=cryostat_center)\n",
+    "banana_detector = instrument.add_component(\"banana_detector\", \"Monitor_nD\",\n",
+    "banana_detector.set_RELATIVE(cryostat_center)\n",
     "banana_detector.xwidth = 1\n",
     "banana_detector.yheight = 0.2\n",
     "banana_detector.restore_neutron = 1\n",
-    "banana_detector.options = '\"banana, theta limits=[-180,180] bins=361, t limits=[0.0011 0.0025] bins=500\"'\n",
+    "options = '\"banana, theta limits=[-180,180] bins=361, t limits=[0.0011 0.0025] bins=500\"'\n",
+    "banana_detector.options = options\n",
     "banana_detector.filename = '\"tof_b.dat\"'"
    ]
   },
@@ -211,14 +214,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=wavelength, A3_angle=theta)\n",
-    "instrument.settings(ncount=1E7, output_path=\"data_folder/union_conditionals\")\n",
+    "instrument.settings(ncount=3E6, output_path=\"data_folder/union_conditionals\")\n",
     "\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -227,27 +233,27 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "logger_zx = functions.name_search(\"logger_space_zx\", data)\n",
+    "logger_zx = ms.name_search(\"logger_space_zx\", data)\n",
     "logger_zx.set_plot_options(log=True, orders_of_mag=9, colormap=\"jet\")\n",
     "\n",
-    "logger_zy = functions.name_search(\"logger_space_zy\", data)\n",
+    "logger_zy = ms.name_search(\"logger_space_zy\", data)\n",
     "logger_zy.set_plot_options(log=True, orders_of_mag=9, colormap=\"jet\")\n",
     "\n",
-    "logger_2DQ_sample = functions.name_search(\"logger_2DQ_sample\", data)\n",
+    "logger_2DQ_sample = ms.name_search(\"logger_2DQ_sample\", data)\n",
     "logger_2DQ_sample.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
-    "logger_2DQ_env = functions.name_search(\"logger_2DQ_environment\", data)\n",
+    "logger_2DQ_env = ms.name_search(\"logger_2DQ_environment\", data)\n",
     "logger_2DQ_env.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
-    "plotter.make_sub_plot([logger_zx, logger_zy, logger_2DQ_sample, logger_2DQ_env], fontsize=10)\n",
+    "ms.make_sub_plot([logger_zx, logger_zy, logger_2DQ_sample, logger_2DQ_env], fontsize=10)\n",
     "\n",
-    "time = functions.name_search(\"logger_time_all\", data)\n",
+    "time = ms.name_search(\"logger_time_all\", data)\n",
     "time.set_plot_options(log=True)\n",
-    "plotter.make_sub_plot([time], fontsize=18)\n",
+    "ms.make_sub_plot([time], fontsize=18)\n",
     "\n",
-    "banana = functions.name_search(\"banana_detector\", data)\n",
+    "banana = ms.name_search(\"banana_detector\", data)\n",
     "banana.set_plot_options(log=True, orders_of_mag=7, cut_max=0.001)\n",
-    "plotter.make_sub_plot([banana], fontsize=18)"
+    "ms.make_sub_plot([banana], fontsize=18)"
    ]
   },
   {
@@ -298,12 +304,16 @@
     "instrument.add_parameter(\"tag_interval\", value=9E-5)\n",
     "\n",
     "# Set up an arm pointing to the relevant spot\n",
-    "spot_dir = instrument.add_component(\"spot_dir\", \"Arm\", RELATIVE=cryostat_center, before=\"master\")\n",
+    "spot_dir = instrument.add_component(\"spot_dir\", \"Arm\",\n",
+    "                                    RELATIVE=cryostat_center, before=\"master\")\n",
     "spot_dir.set_ROTATED([0, \"tag_angle\", 0], RELATIVE=cryostat_center)\n",
     "\n",
     "# Set up a conditional component targeting all our loggers\n",
-    "PSD_conditional = instrument.add_component(\"space_all_PSD_conditional\", \"Union_conditional_PSD\", before=\"master\")\n",
-    "PSD_conditional.target_loggers = '\"logger_space_zx,logger_space_zy,logger_time_all,logger_2DQ_sample,logger_2DQ_environment\"'\n",
+    "PSD_conditional = instrument.add_component(\"space_all_PSD_conditional\", \"Union_conditional_PSD\",\n",
+    "                                           before=\"master\")\n",
+    "\n",
+    "loggers = '\"logger_space_zx,logger_space_zy,logger_time_all,logger_2DQ_sample,logger_2DQ_environment\"'\n",
+    "PSD_conditional.target_loggers = loggers\n",
     "PSD_conditional.xwidth = 0.2\n",
     "PSD_conditional.yheight = 0.2\n",
     "PSD_conditional.time_min = \"tag_time-0.5*tag_interval\"\n",
@@ -317,11 +327,13 @@
     "master.append_EXTEND(\"flag1 = logger_conditional_extend_array[1];\")\n",
     "\n",
     "# Copy of our banana detector, but with WHEN condition to verify we are investigating the right peak\n",
-    "banana_detector = instrument.add_component(\"banana_detector_limited\", \"Monitor_nD\", RELATIVE=cryostat_center)\n",
+    "banana_detector = instrument.add_component(\"banana_detector_limited\", \"Monitor_nD\")\n",
+    "banana_detector.set_RELATIVE(cryostat_center)\n",
     "banana_detector.xwidth = 1\n",
     "banana_detector.yheight = 0.2\n",
     "banana_detector.restore_neutron = 1\n",
-    "banana_detector.options = '\"banana, theta limits=[-180,180] bins=361, t limits=[0.0011 0.0025] bins=500\"'\n",
+    "options = '\"banana, theta limits=[-180,180] bins=361, t limits=[0.0011 0.0025] bins=500\"'\n",
+    "banana_detector.options = options\n",
     "banana_detector.filename = '\"tof_b_limited.dat\"'\n",
     "banana_detector.set_WHEN(\"flag1 > 0\")"
    ]
@@ -339,15 +351,18 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=wavelength, A3_angle=theta, \n",
     "                          tag_angle=-95, tag_time=0.00188, tag_interval=9E-5)\n",
-    "instrument.settings(ncount=3E7) # Can add mpi to improve speed for this longer simulation\n",
+    "instrument.settings(ncount=3E6) # Can add mpi to improve speed for this longer simulation\n",
     "\n",
-    "instrument.backengine()\n",
-    "data_con = instrument.data"
+    "data_con = instrument.backengine()"
    ]
   },
   {
@@ -370,7 +385,7 @@
     "banana_limited = functions.name_search(\"banana_detector_limited\", data_con)\n",
     "banana_limited.set_plot_options(log=False)\n",
     "\n",
-    "plotter.make_sub_plot([banana, banana_limited], fontsize=10)"
+    "ms.make_sub_plot([banana, banana_limited], fontsize=10)"
    ]
   },
   {
@@ -387,24 +402,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "logger_zx = functions.name_search(\"logger_space_zx\", data_con)\n",
+    "logger_zx = ms.name_search(\"logger_space_zx\", data_con)\n",
     "logger_zx.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
-    "logger_zy = functions.name_search(\"logger_space_zy\", data_con)\n",
+    "logger_zy = ms.name_search(\"logger_space_zy\", data_con)\n",
     "logger_zy.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
-    "logger_2DQ_sample = functions.name_search(\"logger_2DQ_sample\", data_con)\n",
+    "logger_2DQ_sample = ms.name_search(\"logger_2DQ_sample\", data_con)\n",
     "logger_2DQ_sample.set_plot_options(log=True, orders_of_mag=7, colormap=\"YlOrRd\")\n",
     "\n",
-    "logger_2DQ_env = functions.name_search(\"logger_2DQ_environment\", data_con)\n",
+    "logger_2DQ_env = ms.name_search(\"logger_2DQ_environment\", data_con)\n",
     "logger_2DQ_env.set_plot_options(log=True, orders_of_mag=7, colormap=\"YlOrRd\")\n",
     "\n",
-    "plotter.make_sub_plot([logger_zx, logger_zy, logger_2DQ_sample, logger_2DQ_env], fontsize=10)\n",
+    "ms.make_sub_plot([logger_zx, logger_zy, logger_2DQ_sample, logger_2DQ_env], fontsize=10)\n",
     "\n",
-    "time = functions.name_search(\"logger_time_all\", data_con)\n",
+    "time = ms.name_search(\"logger_time_all\", data_con)\n",
     "time.set_plot_options(log=True)\n",
     "\n",
-    "plotter.make_sub_plot([time], fontsize=18)"
+    "ms.make_sub_plot([time], fontsize=18)"
    ]
   },
   {
@@ -430,13 +445,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
     "PSD_conditional.overwrite_logger_weight = 1\n",
     "\n",
-    "instrument.backengine()\n",
-    "data_con_f = instrument.data"
+    "data_con_f = instrument.backengine()"
    ]
   },
   {
@@ -445,24 +463,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "logger_zx = functions.name_search(\"logger_space_zx\", data_con_f)\n",
+    "logger_zx = ms.name_search(\"logger_space_zx\", data_con_f)\n",
     "logger_zx.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
-    "logger_zy = functions.name_search(\"logger_space_zy\", data_con_f)\n",
+    "logger_zy = ms.name_search(\"logger_space_zy\", data_con_f)\n",
     "logger_zy.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
-    "logger_2DQ_sample = functions.name_search(\"logger_2DQ_sample\", data_con_f)\n",
+    "logger_2DQ_sample = ms.name_search(\"logger_2DQ_sample\", data_con_f)\n",
     "logger_2DQ_sample.set_plot_options(log=True, orders_of_mag=7, colormap=\"YlOrRd\")\n",
     "\n",
-    "logger_2DQ_env = functions.name_search(\"logger_2DQ_environment\", data_con_f)\n",
+    "logger_2DQ_env = ms.name_search(\"logger_2DQ_environment\", data_con_f)\n",
     "logger_2DQ_env.set_plot_options(log=True, orders_of_mag=7, colormap=\"YlOrRd\")\n",
     "\n",
-    "plotter.make_sub_plot([logger_zx, logger_zy, logger_2DQ_sample, logger_2DQ_env], fontsize=10)\n",
+    "ms.make_sub_plot([logger_zx, logger_zy, logger_2DQ_sample, logger_2DQ_env], fontsize=10)\n",
     "\n",
-    "time = functions.name_search(\"logger_time_all\", data_con_f)\n",
+    "time = ms.name_search(\"logger_time_all\", data_con_f)\n",
     "time.set_plot_options(log=True)\n",
     "\n",
-    "plotter.make_sub_plot([time], fontsize=18)"
+    "ms.make_sub_plot([time], fontsize=18)"
    ]
   },
   {
@@ -479,19 +497,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "logger_zx = functions.name_search(\"logger_space_zx\", data_con)\n",
+    "logger_zx = ms.name_search(\"logger_space_zx\", data_con)\n",
     "logger_zx.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
-    "logger_zy = functions.name_search(\"logger_space_zy\", data_con)\n",
+    "logger_zy = ms.name_search(\"logger_space_zy\", data_con)\n",
     "logger_zy.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
-    "logger_zx_f = functions.name_search(\"logger_space_zx\", data_con_f)\n",
+    "logger_zx_f = ms.name_search(\"logger_space_zx\", data_con_f)\n",
     "logger_zx_f.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
-    "logger_zy_f = functions.name_search(\"logger_space_zy\", data_con_f)\n",
+    "logger_zy_f = ms.name_search(\"logger_space_zy\", data_con_f)\n",
     "logger_zy_f.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
-    "plotter.make_sub_plot([logger_zx, logger_zy, logger_zx_f, logger_zy_f], fontsize=10)"
+    "ms.make_sub_plot([logger_zx, logger_zy, logger_zx_f, logger_zy_f], fontsize=10)"
    ]
   },
   {
@@ -503,6 +521,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -519,6 +538,11 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.8.8"
+  },
+  "metadata": {
+   "execution": {
+    "timeout": 100
+   }
   }
  },
  "nbformat": 4,
diff --git a/tutorial/Union_tutorial_5_masks.ipynb b/tutorial/Union_tutorial_5_masks.ipynb
index e8daeb60..90830e9f 100644
--- a/tutorial/Union_tutorial_5_masks.ipynb
+++ b/tutorial/Union_tutorial_5_masks.ipynb
@@ -14,7 +14,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
@@ -23,7 +23,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
    ]
   },
   {
@@ -49,7 +49,7 @@
     "\n",
     "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
     "Al.process_string = '\"Al_inc,Al_pow\"'\n",
-    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28]*Å^3 [m^3 E-30]=[m E-2], factor 100\n",
     "\n",
     "src = instrument.add_component(\"source\", \"Source_div\")\n",
     "\n",
@@ -59,7 +59,8 @@
     "src.focus_ah = 0.01\n",
     "\n",
     "\n",
-    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0,\n",
+    "                                       comment=\"Wavelength in [Ang]\")\n",
     "src.dlambda = \"0.01*wavelength\"\n",
     "src.flux = 1E13\n",
     "\n",
@@ -77,7 +78,8 @@
     "wall_vac.material_string='\"Vacuum\"' \n",
     "wall_vac.priority = 50\n",
     "\n",
-    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=wall)\n",
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\")\n",
+    "logger_zx.set_RELATIVE(wall)\n",
     "logger_zx.D_direction_1 = '\"z\"'\n",
     "logger_zx.D1_min = -0.12\n",
     "logger_zx.D1_max = 0.12\n",
@@ -94,13 +96,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
     "instrument.settings(ncount=2E6, output_path=\"data_folder/union_masks\")\n",
     "\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -109,7 +114,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data)"
+    "ms.make_sub_plot(data)"
    ]
   },
   {
@@ -161,11 +166,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -174,7 +182,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data)"
+    "ms.make_sub_plot(data)"
    ]
   },
   {
@@ -217,11 +225,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -230,7 +241,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data)"
+    "ms.make_sub_plot(data)"
    ]
   },
   {
@@ -244,10 +255,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")\n",
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")\n",
     "\n",
     "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
     "Al_inc.sigma = 0.0082\n",
@@ -258,7 +273,7 @@
     "\n",
     "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
     "Al.process_string = '\"Al_inc,Al_pow\"'\n",
-    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28]*Å^3 [m^3 E-30]=[m E-2], factor 100\n",
     "\n",
     "src = instrument.add_component(\"source\", \"Source_div\")\n",
     "\n",
@@ -298,7 +313,8 @@
     "cyl_mask.mask_string='\"box\"' \n",
     "cyl_mask.priority = 51\n",
     "\n",
-    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=box)\n",
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\")\n",
+    "logger_zx.set_RELATIVE(box)\n",
     "logger_zx.D_direction_1 = '\"z\"'\n",
     "logger_zx.D1_min = -0.12\n",
     "logger_zx.D1_max = 0.12\n",
@@ -312,8 +328,7 @@
     "master = instrument.add_component(\"master\", \"Union_master\")\n",
     "\n",
     "\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -322,7 +337,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotter.make_sub_plot(data)"
+    "ms.make_sub_plot(data)"
    ]
   },
   {
@@ -334,6 +349,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
diff --git a/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb b/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
index aa08a55b..a72f052b 100644
--- a/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
+++ b/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Advanced geometry component concepts: Exit geometry and number of activations\n",
+    "# Advanced geometry component concepts: Exit geometry and number of activations\n",
     "This notebook explains the concept of exit geometry and the activation counter both of which are tied to the geometry components and how they are treated by the *Union_master*.\n",
     "\n",
     "An exit geometry is created by setting the *material_string* of a geometry to \"Exit\", and if a ray enters such a geometry, it is immediately released from the master component. Normally this only happens when the ray does not intersect any geometries. There are several uses for this, for example inserting a monitor within a Union geometry ensemble."
@@ -16,7 +16,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
@@ -33,7 +33,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
    ]
   },
   {
@@ -51,14 +51,15 @@
     "\n",
     "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
     "Al.process_string = '\"Al_inc,Al_pow\"'\n",
-    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28]*Å^3 [m^3 E-30]=[m E-2], factor 100\n",
     "\n",
     "src = instrument.add_component(\"source\", \"Source_div\")\n",
     "src.xwidth = 0.01\n",
     "src.yheight = 0.035\n",
     "src.focus_aw = 0.01\n",
     "src.focus_ah = 0.01\n",
-    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0,\n",
+    "                                       comment=\"Wavelength in [Ang]\")\n",
     "src.dlambda = \"0.01*wavelength\"\n",
     "src.flux = 1E13\n",
     "\n",
@@ -69,7 +70,8 @@
     "sample_volume.priority = 100\n",
     "sample_volume.set_AT([0,0,1], RELATIVE=src)\n",
     "\n",
-    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\", RELATIVE=sample_volume)\n",
+    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\")\n",
+    "container.set_RELATIVE(sample_volume)\n",
     "container.yheight = 0.03+0.003 # 1.5 mm top and button\n",
     "container.radius = 0.0075 + 0.0015 # 1.5 mm sides of container\n",
     "container.material_string='\"Al\"' \n",
@@ -96,7 +98,8 @@
     "inner_wall_vac.material_string='\"Vacuum\"' \n",
     "inner_wall_vac.priority = 81\n",
     "\n",
-    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=sample_volume)\n",
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\")\n",
+    "logger_zx.set_RELATIVE(sample_volume)\n",
     "logger_zx.D_direction_1 = '\"z\"'\n",
     "logger_zx.D1_min = -0.04\n",
     "logger_zx.D1_max = 0.04\n",
@@ -107,7 +110,8 @@
     "logger_zx.n2 = 300\n",
     "logger_zx.filename = '\"logger_zx.dat\"'\n",
     "\n",
-    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\", RELATIVE=sample_volume)\n",
+    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\")\n",
+    "logger_zy.set_RELATIVE(sample_volume)\n",
     "logger_zy.D_direction_1 = '\"z\"'\n",
     "logger_zy.D1_min = -0.04\n",
     "logger_zy.D1_max = 0.04\n",
@@ -130,14 +134,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=3.0)\n",
-    "instrument.settings(ncount=1E7, output_path=\"data_folder/union_external\")\n",
+    "instrument.settings(ncount=3E6, output_path=\"data_folder/union_external\")\n",
     "\n",
-    "instrument.backengine()\n",
-    "data_empty = instrument.data"
+    "data_empty = instrument.backengine()"
    ]
   },
   {
@@ -146,10 +153,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "functions.name_plot_options(\"logger_space_zx\", data_empty, log=True, orders_of_mag=4)\n",
-    "functions.name_plot_options(\"logger_space_zy\", data_empty, log=True, orders_of_mag=4)\n",
-    "plotter.make_sub_plot(data_empty[0:2])\n",
-    "plotter.make_sub_plot(data_empty[2])"
+    "ms.name_plot_options(\"logger_space_zx\", data_empty, log=True, orders_of_mag=4)\n",
+    "ms.name_plot_options(\"logger_space_zy\", data_empty, log=True, orders_of_mag=4)\n",
+    "ms.make_sub_plot(data_empty[0:2])\n",
+    "ms.make_sub_plot(data_empty[2])"
    ]
   },
   {
@@ -172,11 +179,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -185,10 +195,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
-    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
-    "plotter.make_sub_plot(data[0:2])\n",
-    "plotter.make_sub_plot(data[2])"
+    "ms.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
+    "ms.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
+    "ms.make_sub_plot(data[0:2])\n",
+    "ms.make_sub_plot(data[2])"
    ]
   },
   {
@@ -223,11 +233,15 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true,
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
-    "instrument.backengine()\n",
-    "data_wrong = instrument.data"
+    "data_wrong = instrument.backengine()"
    ]
   },
   {
@@ -236,10 +250,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "functions.name_plot_options(\"logger_space_zx\", data_wrong, log=True, orders_of_mag=4)\n",
-    "functions.name_plot_options(\"logger_space_zy\", data_wrong, log=True, orders_of_mag=4)\n",
-    "plotter.make_sub_plot(data_wrong[0:2])\n",
-    "plotter.make_sub_plot(data_wrong[2])"
+    "ms.name_plot_options(\"logger_space_zx\", data_wrong, log=True, orders_of_mag=4)\n",
+    "ms.name_plot_options(\"logger_space_zy\", data_wrong, log=True, orders_of_mag=4)\n",
+    "ms.make_sub_plot(data_wrong[0:2])\n",
+    "ms.make_sub_plot(data_wrong[2])"
    ]
   },
   {
@@ -287,11 +301,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -300,10 +317,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
-    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
-    "plotter.make_sub_plot(data[0:2])\n",
-    "plotter.make_sub_plot(data[2])"
+    "ms.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
+    "ms.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
+    "ms.make_sub_plot(data[0:2])\n",
+    "ms.make_sub_plot(data[2])"
    ]
   },
   {
@@ -328,9 +345,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "banana_empty = functions.name_search(\"banana\", data_empty)\n",
-    "banana_wrong = functions.name_search(\"banana\", data_wrong)\n",
-    "banana_sample = functions.name_search(\"banana\", data)\n",
+    "banana_empty = ms.name_search(\"banana\", data_empty)\n",
+    "banana_wrong = ms.name_search(\"banana\", data_wrong)\n",
+    "banana_sample = ms.name_search(\"banana\", data)\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
     "plt.figure(figsize=(14,6))\n",
@@ -359,6 +376,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -375,6 +393,11 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.8.8"
+  },
+  "metadata": {
+   "execution": {
+    "timeout": 100
+   }
   }
  },
  "nbformat": 4,
diff --git a/tutorial/Union_tutorial_7_Tagging_history.ipynb b/tutorial/Union_tutorial_7_Tagging_history.ipynb
index b0479bd9..da79974c 100644
--- a/tutorial/Union_tutorial_7_Tagging_history.ipynb
+++ b/tutorial/Union_tutorial_7_Tagging_history.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Union tagging system\n",
+    "# Union tagging system\n",
     "The *Union_master* is capable of recording histories for each neutron in a tree like fashion and add the total intensities for each unique history together. At the end of the simulation these are sorted by intensity and written to file, and the top 20 are shown in the terminal. This system does not work with MPI, if MPI is used only part of the data is written to disk. The system can take up a large amount of memory when used, so it is disabled per default.\n",
     "\n",
     "First we set up a simple instrument with sample, container and a layer of cryostat."
@@ -16,7 +16,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
@@ -25,7 +25,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument = instr.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
+    "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
    ]
   },
   {
@@ -43,7 +43,7 @@
     "\n",
     "Al = instrument.add_component(\"Al\", \"Union_make_material\")\n",
     "Al.process_string = '\"Al_inc,Al_pow\"'\n",
-    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28] * Å^3 [m^3 E-30] = [m E-2], correct with factor 100.\n",
+    "Al.my_absorption = 100*0.231/66.4 # barns [m^2 E-28]*Å^3 [m^3 E-30]=[m E-2], factor 100\n",
     "\n",
     "Sample_inc = instrument.add_component(\"Sample_inc\", \"Incoherent_process\")\n",
     "Sample_inc.sigma = 3.4176\n",
@@ -61,7 +61,8 @@
     "src.yheight = 0.035\n",
     "src.focus_aw = 0.01\n",
     "src.focus_ah = 0.01\n",
-    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0,\n",
+    "                                       comment=\"Wavelength in [Ang]\")\n",
     "src.dlambda = \"0.01*wavelength\"\n",
     "src.flux = 1E13\n",
     "\n",
@@ -72,7 +73,8 @@
     "sample_geometry.priority = 100\n",
     "sample_geometry.set_AT([0,0,1], RELATIVE=src)\n",
     "\n",
-    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\", RELATIVE=sample_geometry)\n",
+    "container = instrument.add_component(\"sample_container\", \"Union_cylinder\")\n",
+    "container.set_RELATIVE(sample_geometry)\n",
     "container.yheight = 0.03+0.003 # 1.5 mm top and button\n",
     "container.radius = 0.0075 + 0.0015 # 1.5 mm sides of container\n",
     "container.material_string='\"Al\"' \n",
@@ -92,7 +94,8 @@
     "inner_wall_vac.material_string='\"Vacuum\"' \n",
     "inner_wall_vac.priority = 81\n",
     "\n",
-    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zx = instrument.add_component(\"logger_space_zx\", \"Union_logger_2D_space\")\n",
+    "logger_zx.set_RELATIVE(sample_geometry)\n",
     "logger_zx.D_direction_1 = '\"z\"'\n",
     "logger_zx.D1_min = -0.04\n",
     "logger_zx.D1_max = 0.04\n",
@@ -103,7 +106,8 @@
     "logger_zx.n2 = 300\n",
     "logger_zx.filename = '\"logger_zx.dat\"'\n",
     "\n",
-    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\", RELATIVE=sample_geometry)\n",
+    "logger_zy = instrument.add_component(\"logger_space_zy\", \"Union_logger_2D_space\")\n",
+    "logger_zy.set_RELATIVE(sample_geometry)\n",
     "logger_zy.D_direction_1 = '\"z\"'\n",
     "logger_zy.D1_min = -0.04\n",
     "logger_zy.D1_max = 0.04\n",
@@ -141,14 +145,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=3.0)\n",
-    "instrument.settings(ncount=1E7, output_path=\"data_folder/union_tagging\")\n",
+    "instrument.settings(ncount=3E5, output_path=\"data_folder/union_tagging\")\n",
     "\n",
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
@@ -157,9 +164,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "functions.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
-    "functions.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
-    "plotter.make_sub_plot(data)"
+    "ms.name_plot_options(\"logger_space_zx\", data, log=True, orders_of_mag=4)\n",
+    "ms.name_plot_options(\"logger_space_zy\", data, log=True, orders_of_mag=4)\n",
+    "ms.make_sub_plot(data)"
    ]
   },
   {
@@ -173,10 +180,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
-    "with open(\"run_folder/Union_history.dat\") as file:\n",
+    "with open(\"run_folder/union_history.dat\", \"r\", errors=\"ignore\") as file:\n",
     "    instrument_string = file.read()\n",
     "    print(instrument_string)"
    ]
@@ -230,11 +241,13 @@
    "outputs": [],
    "source": [
     "# Set up an arm pointing to the relevant spot\n",
-    "spot_dir = instrument.add_component(\"spot_dir\", \"Arm\", RELATIVE=sample_geometry, before=\"master\")\n",
+    "spot_dir = instrument.add_component(\"spot_dir\", \"Arm\", RELATIVE=sample_geometry,\n",
+    "                                    before=\"master\")\n",
     "spot_dir.set_ROTATED([0, 60, 0], RELATIVE=sample_geometry)\n",
     "\n",
     "# Set up a conditional component targeting all our loggers\n",
-    "PSD_conditional = instrument.add_component(\"space_all_PSD_conditional\", \"Union_conditional_PSD\", before=\"master\")\n",
+    "PSD_conditional = instrument.add_component(\"space_all_PSD_conditional\", \"Union_conditional_PSD\",\n",
+    "                                           before=\"master\")\n",
     "PSD_conditional.xwidth = 0.2\n",
     "PSD_conditional.yheight = 0.2\n",
     "PSD_conditional.master_tagging = 1\n",
@@ -244,20 +257,27 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
-    "instrument.backengine()\n",
-    "data = instrument.data"
+    "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
    "outputs": [],
    "source": [
-    "with open(\"run_folder/union_history.dat\") as file:\n",
+    "with open(\"run_folder/union_history.dat\", \"r\", errors=\"ignore\") as file:\n",
     "    instrument_string = file.read()\n",
     "    print(instrument_string)"
    ]
@@ -279,6 +299,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",

From e1d82b62ae3ff0f0ad67c3e5d9e9838fe493d36e Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 22 Mar 2022 16:34:41 +0100
Subject: [PATCH 241/403] Bump of version number.

---
 mcstasscript/_version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 2b57de33..db8ba92d 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.38"
+__version__ = "0.0.39"

From 1a12a3fbe56321e2f2caaa3f8fffac7d3009c384 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 22 Mar 2022 18:55:42 +0100
Subject: [PATCH 242/403] Cryostat builder and example notebook added.

---
 examples/cryostat_demo.ipynb           | 2359 ++++++++++++++++++++++++
 examples/cryostat_image.png            |  Bin 0 -> 95191 bytes
 mcstasscript/__init__.py               |    1 +
 mcstasscript/tools/__init__.py         |    0
 mcstasscript/tools/cryostat_builder.py |  698 +++++++
 5 files changed, 3058 insertions(+)
 create mode 100644 examples/cryostat_demo.ipynb
 create mode 100644 examples/cryostat_image.png
 create mode 100644 mcstasscript/tools/__init__.py
 create mode 100644 mcstasscript/tools/cryostat_builder.py

diff --git a/examples/cryostat_demo.ipynb b/examples/cryostat_demo.ipynb
new file mode 100644
index 00000000..cf3b2119
--- /dev/null
+++ b/examples/cryostat_demo.ipynb
@@ -0,0 +1,2359 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "approved-parade",
+   "metadata": {},
+   "source": [
+    "## Demo of cryostat builder\n",
+    "This notebook contains a quick demo of the Cryostat class that makes the task of adding a cryostat model much less daunting. The system uses McStas Union components, and for the moment only supports McStas 2.X.\n",
+    "\n",
+    "We start the demo by creating an instrument object with just a source."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "composed-custody",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following components are found in the work_directory / input_path:\n",
+      "     Union_sphere.comp\n",
+      "     Texture_process.comp\n",
+      "     Union_cone.comp\n",
+      "     Union_box.comp\n",
+      "     Single_crystal_process.comp\n",
+      "     Union_abs_logger_2D_space.comp\n",
+      "     Union_logger_2D_kf.comp\n",
+      "     Template_process.comp\n",
+      "     PhononSimple_process.comp\n",
+      "     Union_conditional_standard.comp\n",
+      "     Union_abs_logger_1D_space.comp\n",
+      "     Union_abs_logger_event.comp\n",
+      "     NCrystal_process.comp\n",
+      "     Union_abs_logger_1D_space_event.comp\n",
+      "     Union_abs_logger_1D_space_tof.comp\n",
+      "     Union_logger_2D_space.comp\n",
+      "     Union_conditional_PSD.comp\n",
+      "     Union_master.comp\n",
+      "     AF_HB_1D_process.comp\n",
+      "     Union_logger_2D_kf_time.comp\n",
+      "     Union_cylinder.comp\n",
+      "     Union_abs_logger_1D_space_tof_to_lambda.comp\n",
+      "     Powder_process.comp\n",
+      "     Union_make_material.comp\n",
+      "     Incoherent_process.comp\n",
+      "     Union_logger_1D.comp\n",
+      "     Union_logger_3D_space.comp\n",
+      "     IncoherentPhonon_process.comp\n",
+      "     Union_logger_2DQ.comp\n",
+      "     Union_mesh.comp\n",
+      "     Union_logger_2D_space_time.comp\n",
+      "These definitions will be used instead of the installed versions.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mcstasscript as ms\n",
+    "instrument = ms.McStas_instr(\"cryostat_demo\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "secret-convert",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source = instrument.add_component(\"source\", \"Source_simple\")\n",
+    "source.xwidth = 0.01\n",
+    "source.yheight = 0.01\n",
+    "source.focus_xw = 0.01\n",
+    "source.focus_yh = 0.01\n",
+    "source.dist = 2\n",
+    "source.E0 = 5\n",
+    "source.dE = 0.1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eastern-journal",
+   "metadata": {},
+   "source": [
+    "## Creating a cryostat\n",
+    "One use the Cryostat class to make a cryostat object. This object can be placed in the instrument file much like a component with *set_AT* and *set_ROTATED*. Here we place it 2 m after the source."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "restricted-queensland",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orange_cryostat = ms.Cryostat(\"orange\", instrument)\n",
+    "orange_cryostat.set_AT([0,0,2], source)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "premier-greenhouse",
+   "metadata": {},
+   "source": [
+    "The cryostat model is constructed in layers, each of which are added with *add_layer*. The geometry is shown in the left side of the figure below. To define the walls the user specifies two of *inner_radius*, *outer_radius* and *thickness*, where the last one will be calculated. The distance to the top is controlled with *sample_to_top*, ands its thickness with *top_thickness*. A small negative *top_thickness* will remove the top. The bottom is controlled with *sample_to_bottom* and *bottom_thickness*.\n",
+    "\n",
+    "One can set *p_interact* which controls the fraction of the rays that scatters in this layer. This value should be kept below 0.5, but since cryostats are supposed to be transparent even setting 0.2 greatly increases the statistics gain from the simulation.\n",
+    "\n",
+    "![title](cryostat_image.png)\n",
+    "\n",
+    "Here the first of 4 layers are added, the layers have to be added from smallest to largest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "continent-hunter",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orange_cryostat.add_layer(inner_radius=70E-3/2, outer_radius=75E-3/2,\n",
+    "                          sample_to_top=200E-3, top_thickness=-1E-3,\n",
+    "                          sample_to_bottom=83E-3, bottom_thickness=5E-3,\n",
+    "                          p_interact=0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "vietnamese-ribbon",
+   "metadata": {},
+   "source": [
+    "After adding a layer, it can be accessed with the attribute *last_layer*, which allows adding a window. The geometry of a window is shown to the right in the figure above. To control the height of the window, the user provides two of *height*, *sample_to_top* and *sample_to_bottom*. If the window only changes the inner radius, only specify the *inner_radius* parameter, and vice versa with the *outer_radius*. If the window narrows the cryostat from both sides, the user should specify two of *inner_radius*, *outer_radius* and *thickness*. It is not allowed to specify *inner_radius* or *outer_radius* that equals the corresponding value for the layer.\n",
+    "\n",
+    "Here a simple window is added to the last layer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "elegant-delay",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orange_cryostat.last_layer.add_window(outer_radius=73E-3/2, sample_to_top=44.42E-3, sample_to_bottom=88.2E-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adjacent-investing",
+   "metadata": {},
+   "source": [
+    "It is allowed to add multiple windows to a layer, this could for example be needed when describing a window where the outside and inside have different heights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "coated-edwards",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orange_cryostat.last_layer.add_window(inner_radius=71E-3/2, height=40E-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "round-myanmar",
+   "metadata": {},
+   "source": [
+    "If \"Al\" is selected for material, a simple Al model is created unless an Al model already exists in the instrument. When using the material keyword to add other materials, it is up to the user to create the material definition using Union components. The inside is always Vacuum. Here the second layer is added, specifying \"Al\" as the material."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "welcome-cathedral",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orange_cryostat.add_layer(inner_radius=80E-3/2, outer_radius=81E-3/2,\n",
+    "                          sample_to_bottom=90E-3, bottom_thickness=2E-3, \n",
+    "                          sample_to_top=240E-3, top_thickness=-1E-3,\n",
+    "                          material=\"Al\", p_interact=0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "romance-purse",
+   "metadata": {},
+   "source": [
+    "The remaining 2 of the 4 layers are added here, both with windows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "latest-welding",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orange_cryostat.add_layer(inner_radius=95E-3/2, outer_radius=99.5E-3/2,\n",
+    "                          sample_to_bottom=93E-3, bottom_thickness=6E-3,\n",
+    "                          sample_to_top=225E-3, top_thickness=9E-3, p_interact=0.2)\n",
+    "orange_cryostat.last_layer.add_window(outer_radius=97E-3/2, sample_to_top=52E-3, sample_to_bottom=100E-3)\n",
+    "\n",
+    "orange_cryostat.add_layer(inner_radius=120E-3/2, outer_radius=127E-3/2,\n",
+    "                          sample_to_bottom=109E-3, bottom_thickness=11E-3,\n",
+    "                          sample_to_top=205E-3, top_thickness=22E-3, p_interact=0.2)\n",
+    "orange_cryostat.last_layer.add_window(outer_radius=125E-3/2, inner_radius=122E-3/2,\n",
+    "                                      sample_to_top=55.7E-3, sample_to_bottom=93.54E-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "coral-retention",
+   "metadata": {},
+   "source": [
+    "After the cryostat description is done one can optionally add Union loggers that show scattering intensity in space using the *add_spatial_loggers* method. Shows the cryostat from the 3 directions along axis, and a zoom in on a cut in zy that clearly shows all windows added. In addition its possible to record the scattering as a function of time, this is done with the *add_time_histogram* and *add_animation* methods, the first of which adds a simple histogram and the latter of which records spatial scattering in a number of time frames."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "dutch-prevention",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orange_cryostat.add_spatial_loggers()\n",
+    "orange_cryostat.add_time_histogram(t_min=0.00195, t_max=0.0024)\n",
+    "orange_cryostat.add_animation(t_min=0.00195, t_max=0.0024, n_frames=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "coated-assist",
+   "metadata": {},
+   "source": [
+    "At the end it is necessary to run the *build* method, this assigns the appropriate priorities to each Union component used, and adds a Union_master component to the end. Per default the build method adds a *Union_master* component, if one wants to for example add a sample afterwards, this is turned of by setting include_master to False."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "dynamic-origin",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orange_cryostat.build(include_master=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pregnant-rwanda",
+   "metadata": {},
+   "source": [
+    "For further information on these methods, the built in help can be used shown below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "noticed-third",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on method add_animation in module mcstasscript.tools.cryostat_builder:\n",
+      "\n",
+      "add_animation(t_min=0, t_max=0.1, n_frames=10, d1='z', n1=300, d2='y', n2=300) method of mcstasscript.tools.cryostat_builder.Cryostat instance\n",
+      "    Adds 2D_space_time logger that records the information necessary for animation\n",
+      "    \n",
+      "    Adds a 2D_space_time logger that records the requested number of frames, n_frames.\n",
+      "    The time span of both is from t_min to t_max. The default orientation is in the zy\n",
+      "    plane, but this can be chosen along with the desired resolution.\n",
+      "    Errors can happen with too many empty frames, so it is recommended to set\n",
+      "    t_min close to the first scattering time.\n",
+      "    Select the appropriate animation data from simulation output and use\n",
+      "    plotter.make_animation with a filename to save the animation as a gif.\n",
+      "    \n",
+      "    Parameters\n",
+      "    ----------\n",
+      "    \n",
+      "    t_min : float\n",
+      "        Lowest time recorded in [s]\n",
+      "    \n",
+      "    t_max : float\n",
+      "        Highest time recorded in [s]\n",
+      "    \n",
+      "    n_frames : int\n",
+      "        Number of frames in space 2D time logger\n",
+      "    \n",
+      "    d1 : str\n",
+      "        First direction of space 2D time logger, \"x\", \"y\" or \"z\"\n",
+      "    \n",
+      "    n1 : int\n",
+      "        Number of bins in first direction\n",
+      "    \n",
+      "    d2 : str\n",
+      "        Second direction of space 2D time logger, \"x\", \"y\" or \"z\"\n",
+      "    \n",
+      "    n2 : int\n",
+      "        Number of bins in second direction\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(orange_cryostat.add_animation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "signed-shield",
+   "metadata": {},
+   "source": [
+    "The build method adds a large number of components to describe the cryostat. Lets see what this looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "aging-sailing",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1msource\u001b[0m  \u001b[1mSource_simple\u001b[0m \n",
+      "  AT      (0, 0, 0) ABSOLUTE\n",
+      "\u001b[1morange\u001b[0m  \u001b[1mArm\u001b[0m \n",
+      "  AT      (0, 0, 2) RELATIVE source\n",
+      "\u001b[1mAl_inc\u001b[0m  \u001b[1mIncoherent_process\u001b[0m \n",
+      "  AT      (0, 0, 0) ABSOLUTE\n",
+      "\u001b[1mAl_pow\u001b[0m  \u001b[1mPowder_process\u001b[0m \n",
+      "  AT      (0, 0, 0) ABSOLUTE\n",
+      "\u001b[1mAl\u001b[0m  \u001b[1mUnion_make_material\u001b[0m \n",
+      "  AT      (0, 0, 0) ABSOLUTE\n",
+      "\u001b[1morange_layer_0_layer\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0.0555, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_0_layer_vac\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0.0585, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_0_outer_cut_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, -0.02189, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_0_outer_cut_replace_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, -0.02189, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_0_inner_cut_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0.0, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_1_layer\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0.0735, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_1_layer_vac\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0.075, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_2_layer\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0.0675, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_2_layer_vac\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0.066, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_2_outer_cut_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, -0.024000000000000004, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_2_outer_cut_replace_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, -0.024000000000000004, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_3_layer\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0.05349999999999999, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_3_layer_vac\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0.047999999999999994, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_3_outer_cut_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, -0.01892, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_3_outer_cut_replace_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, -0.01892, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_3_inner_cut_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, -0.01892, 0) RELATIVE orange\n",
+      "\u001b[1morange_logger_space_zx\u001b[0m  \u001b[1mUnion_logger_2D_space\u001b[0m \n",
+      "  AT      (0, 0, 0) RELATIVE orange\n",
+      "\u001b[1morange_logger_space_zy\u001b[0m  \u001b[1mUnion_logger_2D_space\u001b[0m \n",
+      "  AT      (0, 0, 0) RELATIVE orange\n",
+      "\u001b[1morange_logger_space_xy\u001b[0m  \u001b[1mUnion_logger_2D_space\u001b[0m \n",
+      "  AT      (0, 0, 0) RELATIVE orange\n",
+      "\u001b[1morange_logger_space_zy_close\u001b[0m  \u001b[1mUnion_logger_3D_space\u001b[0m \n",
+      "  AT      (0, 0, 0) RELATIVE orange\n",
+      "\u001b[1morange_logger_time\u001b[0m  \u001b[1mUnion_logger_1D\u001b[0m \n",
+      "  AT      (0, 0, 0) ABSOLUTE\n",
+      "\u001b[1morange_logger_space_zy_time\u001b[0m  \u001b[1mUnion_logger_2D_space_time\u001b[0m \n",
+      "  AT      (0, 0, 0) RELATIVE orange\n",
+      "\u001b[1morange_master\u001b[0m  \u001b[1mUnion_master\u001b[0m \n",
+      "  AT      (0, 0, 0) ABSOLUTE\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.print_components()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cutting-works",
+   "metadata": {},
+   "source": [
+    "## Displaying the geometry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "turkish-parade",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"800\"\n",
+       "            height=\"450\"\n",
+       "            src=\"./cryostat_demo_mcdisplay_0/index.html\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x7ff29970c880>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "instrument.show_instrument()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "armed-graph",
+   "metadata": {},
+   "source": [
+    "### Running the simulation\n",
+    "This is sufficient to run the simulation and see the resulting plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "paperback-palmer",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/cryostat_demo_data_7\"\n",
+      "INFO: Regenerating c-file: cryostat_demo.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./cryostat_demo.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:18644:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:18644:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:18887:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:18887:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:19130:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_xy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:19130:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_xy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_logger_3D_space.comp:668:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:19379:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_close_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_3D_space.comp:668:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:19379:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_close_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_logger_1D.comp:479:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:19686:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_time_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_1D.comp:479:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:19686:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_time_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_logger_2D_space_time.comp:632:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:19883:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_time_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_2D_space_time.comp:632:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:19883:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_time_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "18 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy cryostat_demo.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/cryostat_demo_data_7\n",
+      "\n",
+      "    Instrument parameters for cryostat_demo (cryostat_demo.instr)\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "Allocating 3D arrays \n",
+      "Allocating 3D arrays \n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 3 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 4 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 1\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 5 \n",
+      "my_absoprtion       [0]: 1.391566 \n",
+      "number of processes [0]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 1\n",
+      "\n",
+      "name of geometry    [0]: orange_layer_0_layer \n",
+      "component index     [0]: 6 \n",
+      "Volume.name         [0]: orange_layer_0_layer \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 1.391566 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.055500 \n",
+      "Volume.geometry.center.z             [0]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.037500 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.287000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: orange_layer_0_layer_vac \n",
+      "component index     [1]: 7 \n",
+      "Volume.name         [1]: orange_layer_0_layer_vac \n",
+      "Volume.p_physics.is_vacuum           [1]: 1 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.000000 \n",
+      "Volume.p_physics.number of processes [1]: 0 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.058500 \n",
+      "Volume.geometry.center.z             [1]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.035000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.283000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: orange_layer_0_outer_cut_0 \n",
+      "component index     [2]: 8 \n",
+      "Volume.name         [2]: orange_layer_0_outer_cut_0 \n",
+      "Volume.p_physics.is_vacuum           [2]: 1 \n",
+      "Volume.p_physics.my_absorption       [2]: 0.000000 \n",
+      "Volume.p_physics.number of processes [2]: 0 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: -0.021890 \n",
+      "Volume.geometry.center.z             [2]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.037501 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.132621 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: orange_layer_0_outer_cut_replace_0 \n",
+      "component index     [3]: 9 \n",
+      "Volume.name         [3]: orange_layer_0_outer_cut_replace_0 \n",
+      "Volume.p_physics.is_vacuum           [3]: 0 \n",
+      "Volume.p_physics.my_absorption       [3]: 1.391566 \n",
+      "Volume.p_physics.number of processes [3]: 2 \n",
+      "Volume.geometry.shape                [3]: cylinder \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: -0.021890 \n",
+      "Volume.geometry.center.z             [3]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.036500 \n",
+      "Volume.geometry.geometry_parameters.height [3]: 0.132625 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [4]: orange_layer_0_inner_cut_0 \n",
+      "component index     [4]: 10 \n",
+      "Volume.name         [4]: orange_layer_0_inner_cut_0 \n",
+      "Volume.p_physics.is_vacuum           [4]: 1 \n",
+      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
+      "Volume.p_physics.number of processes [4]: 0 \n",
+      "Volume.geometry.shape                [4]: cylinder \n",
+      "Volume.geometry.center.x             [4]: 0.000000 \n",
+      "Volume.geometry.center.y             [4]: 0.000000 \n",
+      "Volume.geometry.center.z             [4]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.035500 \n",
+      "Volume.geometry.geometry_parameters.height [4]: 0.040000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [5]: orange_layer_1_layer \n",
+      "component index     [5]: 11 \n",
+      "Volume.name         [5]: orange_layer_1_layer \n",
+      "Volume.p_physics.is_vacuum           [5]: 0 \n",
+      "Volume.p_physics.my_absorption       [5]: 1.391566 \n",
+      "Volume.p_physics.number of processes [5]: 2 \n",
+      "Volume.geometry.shape                [5]: cylinder \n",
+      "Volume.geometry.center.x             [5]: 0.000000 \n",
+      "Volume.geometry.center.y             [5]: 0.073500 \n",
+      "Volume.geometry.center.z             [5]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.040500 \n",
+      "Volume.geometry.geometry_parameters.height [5]: 0.331000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [6]: orange_layer_1_layer_vac \n",
+      "component index     [6]: 12 \n",
+      "Volume.name         [6]: orange_layer_1_layer_vac \n",
+      "Volume.p_physics.is_vacuum           [6]: 1 \n",
+      "Volume.p_physics.my_absorption       [6]: 0.000000 \n",
+      "Volume.p_physics.number of processes [6]: 0 \n",
+      "Volume.geometry.shape                [6]: cylinder \n",
+      "Volume.geometry.center.x             [6]: 0.000000 \n",
+      "Volume.geometry.center.y             [6]: 0.075000 \n",
+      "Volume.geometry.center.z             [6]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.040000 \n",
+      "Volume.geometry.geometry_parameters.height [6]: 0.330000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [7]: orange_layer_2_layer \n",
+      "component index     [7]: 13 \n",
+      "Volume.name         [7]: orange_layer_2_layer \n",
+      "Volume.p_physics.is_vacuum           [7]: 0 \n",
+      "Volume.p_physics.my_absorption       [7]: 1.391566 \n",
+      "Volume.p_physics.number of processes [7]: 2 \n",
+      "Volume.geometry.shape                [7]: cylinder \n",
+      "Volume.geometry.center.x             [7]: 0.000000 \n",
+      "Volume.geometry.center.y             [7]: 0.067500 \n",
+      "Volume.geometry.center.z             [7]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [7]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [7]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [7]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [7]: 0.049750 \n",
+      "Volume.geometry.geometry_parameters.height [7]: 0.333000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [7]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [8]: orange_layer_2_layer_vac \n",
+      "component index     [8]: 14 \n",
+      "Volume.name         [8]: orange_layer_2_layer_vac \n",
+      "Volume.p_physics.is_vacuum           [8]: 1 \n",
+      "Volume.p_physics.my_absorption       [8]: 0.000000 \n",
+      "Volume.p_physics.number of processes [8]: 0 \n",
+      "Volume.geometry.shape                [8]: cylinder \n",
+      "Volume.geometry.center.x             [8]: 0.000000 \n",
+      "Volume.geometry.center.y             [8]: 0.066000 \n",
+      "Volume.geometry.center.z             [8]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [8]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [8]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [8]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [8]: 0.047500 \n",
+      "Volume.geometry.geometry_parameters.height [8]: 0.318000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [8]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [9]: orange_layer_2_outer_cut_0 \n",
+      "component index     [9]: 15 \n",
+      "Volume.name         [9]: orange_layer_2_outer_cut_0 \n",
+      "Volume.p_physics.is_vacuum           [9]: 1 \n",
+      "Volume.p_physics.my_absorption       [9]: 0.000000 \n",
+      "Volume.p_physics.number of processes [9]: 0 \n",
+      "Volume.geometry.shape                [9]: cylinder \n",
+      "Volume.geometry.center.x             [9]: 0.000000 \n",
+      "Volume.geometry.center.y             [9]: -0.024000 \n",
+      "Volume.geometry.center.z             [9]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [9]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [9]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [9]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [9]: 0.049751 \n",
+      "Volume.geometry.geometry_parameters.height [9]: 0.152001 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [9]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [10]: orange_layer_2_outer_cut_replace_0 \n",
+      "component index     [10]: 16 \n",
+      "Volume.name         [10]: orange_layer_2_outer_cut_replace_0 \n",
+      "Volume.p_physics.is_vacuum           [10]: 0 \n",
+      "Volume.p_physics.my_absorption       [10]: 1.391566 \n",
+      "Volume.p_physics.number of processes [10]: 2 \n",
+      "Volume.geometry.shape                [10]: cylinder \n",
+      "Volume.geometry.center.x             [10]: 0.000000 \n",
+      "Volume.geometry.center.y             [10]: -0.024000 \n",
+      "Volume.geometry.center.z             [10]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [10]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [10]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [10]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [10]: 0.048500 \n",
+      "Volume.geometry.geometry_parameters.height [10]: 0.152005 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [10]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [11]: orange_layer_3_layer \n",
+      "component index     [11]: 17 \n",
+      "Volume.name         [11]: orange_layer_3_layer \n",
+      "Volume.p_physics.is_vacuum           [11]: 0 \n",
+      "Volume.p_physics.my_absorption       [11]: 1.391566 \n",
+      "Volume.p_physics.number of processes [11]: 2 \n",
+      "Volume.geometry.shape                [11]: cylinder \n",
+      "Volume.geometry.center.x             [11]: 0.000000 \n",
+      "Volume.geometry.center.y             [11]: 0.053500 \n",
+      "Volume.geometry.center.z             [11]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [11]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [11]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [11]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [11]: 0.063500 \n",
+      "Volume.geometry.geometry_parameters.height [11]: 0.347000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [11]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [12]: orange_layer_3_layer_vac \n",
+      "component index     [12]: 18 \n",
+      "Volume.name         [12]: orange_layer_3_layer_vac \n",
+      "Volume.p_physics.is_vacuum           [12]: 1 \n",
+      "Volume.p_physics.my_absorption       [12]: 0.000000 \n",
+      "Volume.p_physics.number of processes [12]: 0 \n",
+      "Volume.geometry.shape                [12]: cylinder \n",
+      "Volume.geometry.center.x             [12]: 0.000000 \n",
+      "Volume.geometry.center.y             [12]: 0.048000 \n",
+      "Volume.geometry.center.z             [12]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [12]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [12]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [12]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [12]: 0.060000 \n",
+      "Volume.geometry.geometry_parameters.height [12]: 0.314000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [12]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [13]: orange_layer_3_outer_cut_0 \n",
+      "component index     [13]: 19 \n",
+      "Volume.name         [13]: orange_layer_3_outer_cut_0 \n",
+      "Volume.p_physics.is_vacuum           [13]: 1 \n",
+      "Volume.p_physics.my_absorption       [13]: 0.000000 \n",
+      "Volume.p_physics.number of processes [13]: 0 \n",
+      "Volume.geometry.shape                [13]: cylinder \n",
+      "Volume.geometry.center.x             [13]: 0.000000 \n",
+      "Volume.geometry.center.y             [13]: -0.018920 \n",
+      "Volume.geometry.center.z             [13]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [13]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [13]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [13]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [13]: 0.063501 \n",
+      "Volume.geometry.geometry_parameters.height [13]: 0.149241 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [13]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [14]: orange_layer_3_outer_cut_replace_0 \n",
+      "component index     [14]: 20 \n",
+      "Volume.name         [14]: orange_layer_3_outer_cut_replace_0 \n",
+      "Volume.p_physics.is_vacuum           [14]: 0 \n",
+      "Volume.p_physics.my_absorption       [14]: 1.391566 \n",
+      "Volume.p_physics.number of processes [14]: 2 \n",
+      "Volume.geometry.shape                [14]: cylinder \n",
+      "Volume.geometry.center.x             [14]: 0.000000 \n",
+      "Volume.geometry.center.y             [14]: -0.018920 \n",
+      "Volume.geometry.center.z             [14]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [14]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [14]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [14]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [14]: 0.062500 \n",
+      "Volume.geometry.geometry_parameters.height [14]: 0.149245 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [14]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [15]: orange_layer_3_inner_cut_0 \n",
+      "component index     [15]: 21 \n",
+      "Volume.name         [15]: orange_layer_3_inner_cut_0 \n",
+      "Volume.p_physics.is_vacuum           [15]: 1 \n",
+      "Volume.p_physics.my_absorption       [15]: 0.000000 \n",
+      "Volume.p_physics.number of processes [15]: 0 \n",
+      "Volume.geometry.shape                [15]: cylinder \n",
+      "Volume.geometry.center.x             [15]: 0.000000 \n",
+      "Volume.geometry.center.y             [15]: -0.018920 \n",
+      "Volume.geometry.center.z             [15]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [15]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [15]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [15]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [15]: 0.061000 \n",
+      "Volume.geometry.geometry_parameters.height [15]: 0.149240 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [15]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 17\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]\n",
+      "LIST: Direct_children for Volume           0 = [6,7,8,12,14]\n",
+      "LIST: Intersect_check_list for Volume      0 = [6,7,8,12,14]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [6,7,8,12,14]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for orange_layer_0_layer with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  1 = [5]\n",
+      "LIST: Direct_children for Volume           1 = [5]\n",
+      "LIST: Intersect_check_list for Volume      1 = [2,3,4]\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [7,10,11,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = [7,10,11,14,15]\n",
+      "LIST: Next_volume_list for Volume          1 = [7,10,11,14,15,16,2,3,4]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for orange_layer_0_layer_vac with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  2 = []\n",
+      "LIST: Direct_children for Volume           2 = []\n",
+      "LIST: Intersect_check_list for Volume      2 = []\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [1,3,4,5,7,10,11,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [7,10,11,14,15]\n",
+      "LIST: Next_volume_list for Volume          2 = [1,3,4,5,7,10,11,14,15,16]\n",
+      "      Is_vacuum for Volume                 2 = 1\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for orange_layer_0_outer_cut_0 with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  3 = [5]\n",
+      "LIST: Direct_children for Volume           3 = [5]\n",
+      "LIST: Intersect_check_list for Volume      3 = [2,4]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [1,7]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = [7]\n",
+      "LIST: Next_volume_list for Volume          3 = [1,7,2,4]\n",
+      "      Is_vacuum for Volume                 3 = 1\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "List overview for orange_layer_0_outer_cut_replace_0 with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  4 = [5]\n",
+      "LIST: Direct_children for Volume           4 = [5]\n",
+      "LIST: Intersect_check_list for Volume      4 = [2,5]\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [1,3,7]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = [7]\n",
+      "LIST: Next_volume_list for Volume          4 = [1,3,7,2,5]\n",
+      "      Is_vacuum for Volume                 4 = 0\n",
+      "      is_mask_volume for Volume            4 = 0\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = []\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 0\n",
+      "\n",
+      "List overview for orange_layer_0_inner_cut_0 with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  5 = []\n",
+      "LIST: Direct_children for Volume           5 = []\n",
+      "LIST: Intersect_check_list for Volume      5 = [2]\n",
+      "LIST: Mask_intersect_list for Volume       5 = []\n",
+      "LIST: Destinations_list for Volume         5 = [4]\n",
+      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
+      "LIST: Next_volume_list for Volume          5 = [4,2]\n",
+      "      Is_vacuum for Volume                 5 = 1\n",
+      "      is_mask_volume for Volume            5 = 0\n",
+      "      is_masked_volume for Volume          5 = 0\n",
+      "      is_exit_volume for Volume            5 = 0\n",
+      "LIST: mask_list for Volume                 5 = []\n",
+      "LIST: masked_by_list for Volume            5 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
+      "      mask_mode for Volume                 5 = 0\n",
+      "\n",
+      "List overview for orange_layer_1_layer with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  6 = [1,2,3,4,5]\n",
+      "LIST: Direct_children for Volume           6 = [1,2,3,4]\n",
+      "LIST: Intersect_check_list for Volume      6 = [7]\n",
+      "LIST: Mask_intersect_list for Volume       6 = []\n",
+      "LIST: Destinations_list for Volume         6 = [0,8,9,10,11,12,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 6 = [8,12,14]\n",
+      "LIST: Next_volume_list for Volume          6 = [0,8,9,10,11,12,13,14,15,16,7]\n",
+      "      Is_vacuum for Volume                 6 = 0\n",
+      "      is_mask_volume for Volume            6 = 0\n",
+      "      is_masked_volume for Volume          6 = 0\n",
+      "      is_exit_volume for Volume            6 = 0\n",
+      "LIST: mask_list for Volume                 6 = []\n",
+      "LIST: masked_by_list for Volume            6 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
+      "      mask_mode for Volume                 6 = 0\n",
+      "\n",
+      "List overview for orange_layer_1_layer_vac with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  7 = [1,2,3,4,5]\n",
+      "LIST: Direct_children for Volume           7 = [1,2,3,4]\n",
+      "LIST: Intersect_check_list for Volume      7 = [1,2,3,4]\n",
+      "LIST: Mask_intersect_list for Volume       7 = []\n",
+      "LIST: Destinations_list for Volume         7 = [0,6,8,9,10,11,12,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 7 = [6,8,12,14]\n",
+      "LIST: Next_volume_list for Volume          7 = [0,6,8,9,10,11,12,13,14,15,16,1,2,3,4]\n",
+      "      Is_vacuum for Volume                 7 = 1\n",
+      "      is_mask_volume for Volume            7 = 0\n",
+      "      is_masked_volume for Volume          7 = 0\n",
+      "      is_exit_volume for Volume            7 = 0\n",
+      "LIST: mask_list for Volume                 7 = []\n",
+      "LIST: masked_by_list for Volume            7 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
+      "      mask_mode for Volume                 7 = 0\n",
+      "\n",
+      "List overview for orange_layer_2_layer with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  8 = [1,2,3,4,5,9]\n",
+      "LIST: Direct_children for Volume           8 = [9]\n",
+      "LIST: Intersect_check_list for Volume      8 = [6,7,9,10,11]\n",
+      "LIST: Mask_intersect_list for Volume       8 = []\n",
+      "LIST: Destinations_list for Volume         8 = [0,12,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 8 = [12,14]\n",
+      "LIST: Next_volume_list for Volume          8 = [0,12,13,14,15,16,6,7,9,10,11]\n",
+      "      Is_vacuum for Volume                 8 = 0\n",
+      "      is_mask_volume for Volume            8 = 0\n",
+      "      is_masked_volume for Volume          8 = 0\n",
+      "      is_exit_volume for Volume            8 = 0\n",
+      "LIST: mask_list for Volume                 8 = []\n",
+      "LIST: masked_by_list for Volume            8 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 8 = []\n",
+      "      mask_mode for Volume                 8 = 0\n",
+      "\n",
+      "List overview for orange_layer_2_layer_vac with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  9 = [1,2,3,4,5]\n",
+      "LIST: Direct_children for Volume           9 = [1,2,3,4]\n",
+      "LIST: Intersect_check_list for Volume      9 = [6,7]\n",
+      "LIST: Mask_intersect_list for Volume       9 = []\n",
+      "LIST: Destinations_list for Volume         9 = [8,10,11,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 9 = [8,13,14,15]\n",
+      "LIST: Next_volume_list for Volume          9 = [8,10,11,13,14,15,16,6,7]\n",
+      "      Is_vacuum for Volume                 9 = 1\n",
+      "      is_mask_volume for Volume            9 = 0\n",
+      "      is_masked_volume for Volume          9 = 0\n",
+      "      is_exit_volume for Volume            9 = 0\n",
+      "LIST: mask_list for Volume                 9 = []\n",
+      "LIST: masked_by_list for Volume            9 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 9 = []\n",
+      "      mask_mode for Volume                 9 = 0\n",
+      "\n",
+      "List overview for orange_layer_2_outer_cut_0 with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  10 = [3,4,5]\n",
+      "LIST: Direct_children for Volume           10 = [3,4]\n",
+      "LIST: Intersect_check_list for Volume      10 = [6,7,9,11]\n",
+      "LIST: Mask_intersect_list for Volume       10 = []\n",
+      "LIST: Destinations_list for Volume         10 = [8,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 10 = [8,13,14,15]\n",
+      "LIST: Next_volume_list for Volume          10 = [8,13,14,15,16,6,7,9,11]\n",
+      "      Is_vacuum for Volume                 10 = 1\n",
+      "      is_mask_volume for Volume            10 = 0\n",
+      "      is_masked_volume for Volume          10 = 0\n",
+      "      is_exit_volume for Volume            10 = 0\n",
+      "LIST: mask_list for Volume                 10 = []\n",
+      "LIST: masked_by_list for Volume            10 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 10 = []\n",
+      "      mask_mode for Volume                 10 = 0\n",
+      "\n",
+      "List overview for orange_layer_2_outer_cut_replace_0 with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  11 = [3,4,5]\n",
+      "LIST: Direct_children for Volume           11 = [3,4]\n",
+      "LIST: Intersect_check_list for Volume      11 = [6,7,9]\n",
+      "LIST: Mask_intersect_list for Volume       11 = []\n",
+      "LIST: Destinations_list for Volume         11 = [8,10,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 11 = [8,13,14,15]\n",
+      "LIST: Next_volume_list for Volume          11 = [8,10,13,14,15,16,6,7,9]\n",
+      "      Is_vacuum for Volume                 11 = 0\n",
+      "      is_mask_volume for Volume            11 = 0\n",
+      "      is_masked_volume for Volume          11 = 0\n",
+      "      is_exit_volume for Volume            11 = 0\n",
+      "LIST: mask_list for Volume                 11 = []\n",
+      "LIST: masked_by_list for Volume            11 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 11 = []\n",
+      "      mask_mode for Volume                 11 = 0\n",
+      "\n",
+      "List overview for orange_layer_3_layer with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  12 = [1,2,3,4,5,9,10,11,13,15,16]\n",
+      "LIST: Direct_children for Volume           12 = [9,13,15]\n",
+      "LIST: Intersect_check_list for Volume      12 = [6,7,8,13,14,15]\n",
+      "LIST: Mask_intersect_list for Volume       12 = []\n",
+      "LIST: Destinations_list for Volume         12 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 12 = []\n",
+      "LIST: Next_volume_list for Volume          12 = [0,6,7,8,13,14,15]\n",
+      "      Is_vacuum for Volume                 12 = 0\n",
+      "      is_mask_volume for Volume            12 = 0\n",
+      "      is_masked_volume for Volume          12 = 0\n",
+      "      is_exit_volume for Volume            12 = 0\n",
+      "LIST: mask_list for Volume                 12 = []\n",
+      "LIST: masked_by_list for Volume            12 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 12 = []\n",
+      "      mask_mode for Volume                 12 = 0\n",
+      "\n",
+      "List overview for orange_layer_3_layer_vac with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  13 = [1,2,3,4,5,10,11]\n",
+      "LIST: Direct_children for Volume           13 = [1,2,10,11]\n",
+      "LIST: Intersect_check_list for Volume      13 = [6,7,8,10,11]\n",
+      "LIST: Mask_intersect_list for Volume       13 = []\n",
+      "LIST: Destinations_list for Volume         13 = [12,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 13 = [12,14]\n",
+      "LIST: Next_volume_list for Volume          13 = [12,14,15,16,6,7,8,10,11]\n",
+      "      Is_vacuum for Volume                 13 = 1\n",
+      "      is_mask_volume for Volume            13 = 0\n",
+      "      is_masked_volume for Volume          13 = 0\n",
+      "      is_exit_volume for Volume            13 = 0\n",
+      "LIST: mask_list for Volume                 13 = []\n",
+      "LIST: masked_by_list for Volume            13 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 13 = []\n",
+      "      mask_mode for Volume                 13 = 0\n",
+      "\n",
+      "List overview for orange_layer_3_outer_cut_0 with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  14 = [3,4,5,16]\n",
+      "LIST: Direct_children for Volume           14 = [16]\n",
+      "LIST: Intersect_check_list for Volume      14 = [6,7,8,13,15]\n",
+      "LIST: Mask_intersect_list for Volume       14 = []\n",
+      "LIST: Destinations_list for Volume         14 = [0,12]\n",
+      "LIST: Reduced_destinations_list for Volume 14 = [12]\n",
+      "LIST: Next_volume_list for Volume          14 = [0,12,6,7,8,13,15]\n",
+      "      Is_vacuum for Volume                 14 = 1\n",
+      "      is_mask_volume for Volume            14 = 0\n",
+      "      is_masked_volume for Volume          14 = 0\n",
+      "      is_exit_volume for Volume            14 = 0\n",
+      "LIST: mask_list for Volume                 14 = []\n",
+      "LIST: masked_by_list for Volume            14 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 14 = []\n",
+      "      mask_mode for Volume                 14 = 0\n",
+      "\n",
+      "List overview for orange_layer_3_outer_cut_replace_0 with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  15 = [3,4,5,16]\n",
+      "LIST: Direct_children for Volume           15 = [16]\n",
+      "LIST: Intersect_check_list for Volume      15 = [6,7,8,13,16]\n",
+      "LIST: Mask_intersect_list for Volume       15 = []\n",
+      "LIST: Destinations_list for Volume         15 = [12,14]\n",
+      "LIST: Reduced_destinations_list for Volume 15 = [12,14]\n",
+      "LIST: Next_volume_list for Volume          15 = [12,14,6,7,8,13,16]\n",
+      "      Is_vacuum for Volume                 15 = 0\n",
+      "      is_mask_volume for Volume            15 = 0\n",
+      "      is_masked_volume for Volume          15 = 0\n",
+      "      is_exit_volume for Volume            15 = 0\n",
+      "LIST: mask_list for Volume                 15 = []\n",
+      "LIST: masked_by_list for Volume            15 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 15 = []\n",
+      "      mask_mode for Volume                 15 = 0\n",
+      "\n",
+      "List overview for orange_layer_3_inner_cut_0 with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  16 = [3,4,5]\n",
+      "LIST: Direct_children for Volume           16 = [3,4]\n",
+      "LIST: Intersect_check_list for Volume      16 = [6,7,8,13]\n",
+      "LIST: Mask_intersect_list for Volume       16 = []\n",
+      "LIST: Destinations_list for Volume         16 = [15]\n",
+      "LIST: Reduced_destinations_list for Volume 16 = [15]\n",
+      "LIST: Next_volume_list for Volume          16 = [15,6,7,8,13]\n",
+      "      Is_vacuum for Volume                 16 = 1\n",
+      "      is_mask_volume for Volume            16 = 0\n",
+      "      is_masked_volume for Volume          16 = 0\n",
+      "      is_exit_volume for Volume            16 = 0\n",
+      "LIST: mask_list for Volume                 16 = []\n",
+      "LIST: masked_by_list for Volume            16 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 16 = []\n",
+      "      mask_mode for Volume                 16 = 0\n",
+      "\n",
+      "Union_master component orange_master initialized sucessfully\n",
+      "Detector: orange_logger_space_zx_I=3.27832e-07 orange_logger_space_zx_ERR=1.38369e-10 orange_logger_space_zx_N=1.74613e+07 \"orange_space_zx.dat\"\n",
+      "Detector: orange_logger_space_zy_I=3.27832e-07 orange_logger_space_zy_ERR=1.38369e-10 orange_logger_space_zy_N=1.74613e+07 \"orange_space_zy.dat\"\n",
+      "Detector: orange_logger_space_xy_I=3.27832e-07 orange_logger_space_xy_ERR=1.38369e-10 orange_logger_space_xy_N=1.74613e+07 \"orange_space_xy.dat\"\n",
+      "Detector: orange_logger_space_zy_close_I=1.59651e-07 orange_logger_space_zy_close_ERR=7.01031e-11 orange_logger_space_zy_close_N=8.35287e+06 \"orange_space_zy_close.dat_0\"\n",
+      "Detector: orange_logger_time_I=3.27723e-07 orange_logger_time_ERR=1.38327e-10 orange_logger_time_N=1.72568e+07 \"NULL.dat\"\n",
+      "Detector: orange_logger_space_zy_time_I=1.60364e-07 orange_logger_space_zy_time_ERR=6.99308e-11 orange_logger_space_zy_time_N=8.91229e+06 \"orange_logger_space_zy_time.dat_0\"\n",
+      "Detector: orange_logger_space_zy_time_I=1.53909e-07 orange_logger_space_zy_time_ERR=1.15565e-10 orange_logger_space_zy_time_N=4.99431e+06 \"orange_logger_space_zy_time.dat_1\"\n",
+      "Detector: orange_logger_space_zy_time_I=8.68838e-09 orange_logger_space_zy_time_ERR=1.9819e-11 orange_logger_space_zy_time_N=2.08652e+06 \"orange_logger_space_zy_time.dat_2\"\n",
+      "Detector: orange_logger_space_zy_time_I=4.0846e-09 orange_logger_space_zy_time_ERR=1.94728e-11 orange_logger_space_zy_time_N=929554 \"orange_logger_space_zy_time.dat_3\"\n",
+      "Detector: orange_logger_space_zy_time_I=6.77188e-10 orange_logger_space_zy_time_ERR=1.08073e-11 orange_logger_space_zy_time_N=334166 \"orange_logger_space_zy_time.dat_4\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.settings(ncount=1E7)\n",
+    "data = instrument.backengine()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "solar-checkout",
+   "metadata": {},
+   "source": [
+    "### Plotting with interface\n",
+    "Recommend using log plot and orders of magnitude = 5 to see details. It may be necessary to refresh this cell after the simulation has been performed. Use \"zy_close\" to get the clearest view of the the added layers and windows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "mobile-strike",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "621d564560654be190ea2c9eabcaba47",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(Output(layout=Layout(width='75%')), VBox(children=(Label(value='Choose monitor'), Dropdown(layo…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib widget\n",
+    "ms.interface(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "wound-scoop",
+   "metadata": {},
+   "source": [
+    "The \"logger_space_zy_time\" monitor can be animated with "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "inclusive-ireland",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f27279a9d93947ab81ce317e50671659",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-03-22 18:53:45,585 WARNING:MovieWriter imagemagick unavailable; using Pillow instead.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name orange_logger_space_zy_time\n",
+      "Saving animation with filename : \"cryostat_animation.gif\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "ani_data = data[5:10]\n",
+    "for frame in ani_data:\n",
+    "    frame.set_plot_options(log=True, colormap=\"hot\", orders_of_mag=6)\n",
+    "    \n",
+    "ms.make_animation(ani_data, filename=\"cryostat_animation\", fps=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "collective-award",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "McStasData: orange_logger_space_zy_time type: 2D  I:1.60364e-07 E:6.99308e-11 N:8.91229e+06, \n",
+      "McStasData: orange_logger_space_zy_time_1 type: 2D  I:1.53909e-07 E:1.15565e-10 N:4.99431e+06, \n",
+      "McStasData: orange_logger_space_zy_time_2 type: 2D  I:8.68838e-09 E:1.9819e-11 N:2.08652e+06, \n",
+      "McStasData: orange_logger_space_zy_time_3 type: 2D  I:4.0846e-09 E:1.94728e-11 N:929554, \n",
+      "McStasData: orange_logger_space_zy_time_4 type: 2D  I:6.77188e-10 E:1.08073e-11 N:334166]\n",
+      "metadata object\n",
+      "component_name: orange_logger_space_zy_time\n",
+      "filename: orange_logger_space_zy_time.dat_1\n",
+      "2D data of dimension (300, 300)\n",
+      "  [-0.06985: 0.06985] z [m]\n",
+      "  [-0.132: 0.2497] y [m]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "ani_data = ms.name_search(\"orange_logger_space_zy_time\", data)\n",
+    "print(ani_data)\n",
+    "print(data[6].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "trained-harvey",
+   "metadata": {},
+   "source": [
+    "## Adding a sample\n",
+    "It is most natural to add a Union sample to the cryostat, and this could be done by not adding a Union_master component with the cryostat *build* method, and then adding the Union_master after the sample. It can however also be done by inserting the necessary components at the right place in the instrument using the *before* keyword.\n",
+    "\n",
+    "Here a powder sample is added and the simulation is executed again. Notice the sample location is set to the same as the cryostat name, this refers to the arm used as a reference for the cryostat sample position."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "intimate-joint",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nascalf_inc = instrument.add_component(\"nascalf_inc\", \"Incoherent_process\", before=\"Al_inc\")\n",
+    "nascalf_inc.sigma = 3.4176\n",
+    "nascalf_inc.unit_cell_volume = 1079.1\n",
+    "\n",
+    "nascalf_pow = instrument.add_component(\"nascalf_pow\", \"Powder_process\", before=\"Al_inc\")\n",
+    "nascalf_pow.reflections = '\"Na2Ca3Al2F14.laz\"'\n",
+    "\n",
+    "nascalf = instrument.add_component(\"nascalf\", \"Union_make_material\", before=\"Al_inc\")\n",
+    "nascalf.process_string = '\"nascalf_inc,nascalf_pow\"'\n",
+    "nascalf.my_absorption = 100*2.9464/1079.1\n",
+    "\n",
+    "sample = instrument.add_component(\"sample\", \"Union_cylinder\", before=\"orange_master\")\n",
+    "sample.radius = 6E-3\n",
+    "sample.yheight = 30E-3\n",
+    "sample.priority = 1000\n",
+    "sample.material_string = '\"nascalf\"'\n",
+    "sample.p_interact = 0.3\n",
+    "sample.set_AT([0,0,0], RELATIVE=\"orange\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "subjective-continuity",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/cryostat_demo_data_8\"\n",
+      "INFO: Regenerating c-file: cryostat_demo.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./cryostat_demo.out\n",
+      "mccode-r.c:1880:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "} /* mcsiminfo_init */\n",
+      "^\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:201:5: note: expanded from macro 'MPI_MASTER'\n",
+      "  { statement; } \\\n",
+      "    ^~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:19430:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:19430:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:19673:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:19673:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:19916:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_xy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:19916:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_xy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_logger_3D_space.comp:668:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:20165:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_close_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_3D_space.comp:668:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:20165:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_close_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_logger_1D.comp:479:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:20472:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_time_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_1D.comp:479:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:20472:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_time_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_logger_2D_space_time.comp:632:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:20669:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_time_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_2D_space_time.comp:632:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:20669:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_time_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:201:5: note: expanded from macro 'MPI_MASTER'\n",
+      "  { statement; } \\\n",
+      "    ^~~~~~~~~\n",
+      "Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:201:5: note: expanded from macro 'MPI_MASTER'\n",
+      "  { statement; } \\\n",
+      "    ^~~~~~~~~\n",
+      "Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:201:5: note: expanded from macro 'MPI_MASTER'\n",
+      "  { statement; } \\\n",
+      "    ^~~~~~~~~\n",
+      "19 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy cryostat_demo.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/cryostat_demo_data_8\n",
+      "\n",
+      "    Simulation 'cryostat_demo' (cryostat_demo.instr): running on 4 nodes (master is 'CI0021617', MPI version 3.1).\n",
+      "Instrument parameters for cryostat_demo (cryostat_demo.instr)\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: nascalf_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: nascalf_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: nascalf_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: nascalf_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: nascalf_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: nascalf_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "PowderN: nascalf_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: nascalf_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "PowderN: nascalf_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: nascalf_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: nascalf_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "PowderN: nascalf_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "Allocating 3D arrays \n",
+      "Allocating 3D arrays \n",
+      "Allocating 3D arrays \n",
+      "Allocating 3D arrays \n",
+      "Allocating 3D arrays \n",
+      "Allocating 3D arrays \n",
+      "Allocating 3D arrays \n",
+      "Allocating 3D arrays \n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 4\n",
+      "name of process [0]: nascalf_inc \n",
+      "component index [0]: 3 \n",
+      "name of process [1]: nascalf_pow \n",
+      "component index [1]: 4 \n",
+      "name of process [2]: Al_inc \n",
+      "component index [2]: 6 \n",
+      "name of process [3]: Al_pow \n",
+      "component index [3]: 7 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 2\n",
+      "name of material    [0]: nascalf \n",
+      "component index     [0]: 5 \n",
+      "my_absoprtion       [0]: 0.273042 \n",
+      "number of processes [0]: 2 \n",
+      "name of material    [1]: Al \n",
+      "component index     [1]: 8 \n",
+      "my_absoprtion       [1]: 1.391566 \n",
+      "number of processes [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 2\n",
+      "\n",
+      "name of geometry    [0]: orange_layer_0_layer \n",
+      "component index     [0]: 9 \n",
+      "Volume.name         [0]: orange_layer_0_layer \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 1.391566 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.055500 \n",
+      "Volume.geometry.center.z             [0]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.037500 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.287000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: orange_layer_0_layer_vac \n",
+      "component index     [1]: 10 \n",
+      "Volume.name         [1]: orange_layer_0_layer_vac \n",
+      "Volume.p_physics.is_vacuum           [1]: 1 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.000000 \n",
+      "Volume.p_physics.number of processes [1]: 0 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.058500 \n",
+      "Volume.geometry.center.z             [1]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.035000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.283000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: orange_layer_0_outer_cut_0 \n",
+      "component index     [2]: 11 \n",
+      "Volume.name         [2]: orange_layer_0_outer_cut_0 \n",
+      "Volume.p_physics.is_vacuum           [2]: 1 \n",
+      "Volume.p_physics.my_absorption       [2]: 0.000000 \n",
+      "Volume.p_physics.number of processes [2]: 0 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: -0.021890 \n",
+      "Volume.geometry.center.z             [2]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.037501 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.132621 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: orange_layer_0_outer_cut_replace_0 \n",
+      "component index     [3]: 12 \n",
+      "Volume.name         [3]: orange_layer_0_outer_cut_replace_0 \n",
+      "Volume.p_physics.is_vacuum           [3]: 0 \n",
+      "Volume.p_physics.my_absorption       [3]: 1.391566 \n",
+      "Volume.p_physics.number of processes [3]: 2 \n",
+      "Volume.geometry.shape                [3]: cylinder \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: -0.021890 \n",
+      "Volume.geometry.center.z             [3]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.036500 \n",
+      "Volume.geometry.geometry_parameters.height [3]: 0.132625 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [4]: orange_layer_0_inner_cut_0 \n",
+      "component index     [4]: 13 \n",
+      "Volume.name         [4]: orange_layer_0_inner_cut_0 \n",
+      "Volume.p_physics.is_vacuum           [4]: 1 \n",
+      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
+      "Volume.p_physics.number of processes [4]: 0 \n",
+      "Volume.geometry.shape                [4]: cylinder \n",
+      "Volume.geometry.center.x             [4]: 0.000000 \n",
+      "Volume.geometry.center.y             [4]: 0.000000 \n",
+      "Volume.geometry.center.z             [4]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.035500 \n",
+      "Volume.geometry.geometry_parameters.height [4]: 0.040000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [5]: orange_layer_1_layer \n",
+      "component index     [5]: 14 \n",
+      "Volume.name         [5]: orange_layer_1_layer \n",
+      "Volume.p_physics.is_vacuum           [5]: 0 \n",
+      "Volume.p_physics.my_absorption       [5]: 1.391566 \n",
+      "Volume.p_physics.number of processes [5]: 2 \n",
+      "Volume.geometry.shape                [5]: cylinder \n",
+      "Volume.geometry.center.x             [5]: 0.000000 \n",
+      "Volume.geometry.center.y             [5]: 0.073500 \n",
+      "Volume.geometry.center.z             [5]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.040500 \n",
+      "Volume.geometry.geometry_parameters.height [5]: 0.331000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [6]: orange_layer_1_layer_vac \n",
+      "component index     [6]: 15 \n",
+      "Volume.name         [6]: orange_layer_1_layer_vac \n",
+      "Volume.p_physics.is_vacuum           [6]: 1 \n",
+      "Volume.p_physics.my_absorption       [6]: 0.000000 \n",
+      "Volume.p_physics.number of processes [6]: 0 \n",
+      "Volume.geometry.shape                [6]: cylinder \n",
+      "Volume.geometry.center.x             [6]: 0.000000 \n",
+      "Volume.geometry.center.y             [6]: 0.075000 \n",
+      "Volume.geometry.center.z             [6]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.040000 \n",
+      "Volume.geometry.geometry_parameters.height [6]: 0.330000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [7]: orange_layer_2_layer \n",
+      "component index     [7]: 16 \n",
+      "Volume.name         [7]: orange_layer_2_layer \n",
+      "Volume.p_physics.is_vacuum           [7]: 0 \n",
+      "Volume.p_physics.my_absorption       [7]: 1.391566 \n",
+      "Volume.p_physics.number of processes [7]: 2 \n",
+      "Volume.geometry.shape                [7]: cylinder \n",
+      "Volume.geometry.center.x             [7]: 0.000000 \n",
+      "Volume.geometry.center.y             [7]: 0.067500 \n",
+      "Volume.geometry.center.z             [7]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [7]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [7]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [7]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [7]: 0.049750 \n",
+      "Volume.geometry.geometry_parameters.height [7]: 0.333000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [7]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [8]: orange_layer_2_layer_vac \n",
+      "component index     [8]: 17 \n",
+      "Volume.name         [8]: orange_layer_2_layer_vac \n",
+      "Volume.p_physics.is_vacuum           [8]: 1 \n",
+      "Volume.p_physics.my_absorption       [8]: 0.000000 \n",
+      "Volume.p_physics.number of processes [8]: 0 \n",
+      "Volume.geometry.shape                [8]: cylinder \n",
+      "Volume.geometry.center.x             [8]: 0.000000 \n",
+      "Volume.geometry.center.y             [8]: 0.066000 \n",
+      "Volume.geometry.center.z             [8]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [8]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [8]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [8]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [8]: 0.047500 \n",
+      "Volume.geometry.geometry_parameters.height [8]: 0.318000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [8]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [9]: orange_layer_2_outer_cut_0 \n",
+      "component index     [9]: 18 \n",
+      "Volume.name         [9]: orange_layer_2_outer_cut_0 \n",
+      "Volume.p_physics.is_vacuum           [9]: 1 \n",
+      "Volume.p_physics.my_absorption       [9]: 0.000000 \n",
+      "Volume.p_physics.number of processes [9]: 0 \n",
+      "Volume.geometry.shape                [9]: cylinder \n",
+      "Volume.geometry.center.x             [9]: 0.000000 \n",
+      "Volume.geometry.center.y             [9]: -0.024000 \n",
+      "Volume.geometry.center.z             [9]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [9]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [9]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [9]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [9]: 0.049751 \n",
+      "Volume.geometry.geometry_parameters.height [9]: 0.152001 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [9]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [10]: orange_layer_2_outer_cut_replace_0 \n",
+      "component index     [10]: 19 \n",
+      "Volume.name         [10]: orange_layer_2_outer_cut_replace_0 \n",
+      "Volume.p_physics.is_vacuum           [10]: 0 \n",
+      "Volume.p_physics.my_absorption       [10]: 1.391566 \n",
+      "Volume.p_physics.number of processes [10]: 2 \n",
+      "Volume.geometry.shape                [10]: cylinder \n",
+      "Volume.geometry.center.x             [10]: 0.000000 \n",
+      "Volume.geometry.center.y             [10]: -0.024000 \n",
+      "Volume.geometry.center.z             [10]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [10]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [10]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [10]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [10]: 0.048500 \n",
+      "Volume.geometry.geometry_parameters.height [10]: 0.152005 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [10]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [11]: orange_layer_3_layer \n",
+      "component index     [11]: 20 \n",
+      "Volume.name         [11]: orange_layer_3_layer \n",
+      "Volume.p_physics.is_vacuum           [11]: 0 \n",
+      "Volume.p_physics.my_absorption       [11]: 1.391566 \n",
+      "Volume.p_physics.number of processes [11]: 2 \n",
+      "Volume.geometry.shape                [11]: cylinder \n",
+      "Volume.geometry.center.x             [11]: 0.000000 \n",
+      "Volume.geometry.center.y             [11]: 0.053500 \n",
+      "Volume.geometry.center.z             [11]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [11]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [11]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [11]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [11]: 0.063500 \n",
+      "Volume.geometry.geometry_parameters.height [11]: 0.347000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [11]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [12]: orange_layer_3_layer_vac \n",
+      "component index     [12]: 21 \n",
+      "Volume.name         [12]: orange_layer_3_layer_vac \n",
+      "Volume.p_physics.is_vacuum           [12]: 1 \n",
+      "Volume.p_physics.my_absorption       [12]: 0.000000 \n",
+      "Volume.p_physics.number of processes [12]: 0 \n",
+      "Volume.geometry.shape                [12]: cylinder \n",
+      "Volume.geometry.center.x             [12]: 0.000000 \n",
+      "Volume.geometry.center.y             [12]: 0.048000 \n",
+      "Volume.geometry.center.z             [12]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [12]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [12]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [12]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [12]: 0.060000 \n",
+      "Volume.geometry.geometry_parameters.height [12]: 0.314000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [12]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [13]: orange_layer_3_outer_cut_0 \n",
+      "component index     [13]: 22 \n",
+      "Volume.name         [13]: orange_layer_3_outer_cut_0 \n",
+      "Volume.p_physics.is_vacuum           [13]: 1 \n",
+      "Volume.p_physics.my_absorption       [13]: 0.000000 \n",
+      "Volume.p_physics.number of processes [13]: 0 \n",
+      "Volume.geometry.shape                [13]: cylinder \n",
+      "Volume.geometry.center.x             [13]: 0.000000 \n",
+      "Volume.geometry.center.y             [13]: -0.018920 \n",
+      "Volume.geometry.center.z             [13]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [13]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [13]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [13]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [13]: 0.063501 \n",
+      "Volume.geometry.geometry_parameters.height [13]: 0.149241 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [13]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [14]: orange_layer_3_outer_cut_replace_0 \n",
+      "component index     [14]: 23 \n",
+      "Volume.name         [14]: orange_layer_3_outer_cut_replace_0 \n",
+      "Volume.p_physics.is_vacuum           [14]: 0 \n",
+      "Volume.p_physics.my_absorption       [14]: 1.391566 \n",
+      "Volume.p_physics.number of processes [14]: 2 \n",
+      "Volume.geometry.shape                [14]: cylinder \n",
+      "Volume.geometry.center.x             [14]: 0.000000 \n",
+      "Volume.geometry.center.y             [14]: -0.018920 \n",
+      "Volume.geometry.center.z             [14]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [14]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [14]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [14]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [14]: 0.062500 \n",
+      "Volume.geometry.geometry_parameters.height [14]: 0.149245 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [14]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [15]: orange_layer_3_inner_cut_0 \n",
+      "component index     [15]: 24 \n",
+      "Volume.name         [15]: orange_layer_3_inner_cut_0 \n",
+      "Volume.p_physics.is_vacuum           [15]: 1 \n",
+      "Volume.p_physics.my_absorption       [15]: 0.000000 \n",
+      "Volume.p_physics.number of processes [15]: 0 \n",
+      "Volume.geometry.shape                [15]: cylinder \n",
+      "Volume.geometry.center.x             [15]: 0.000000 \n",
+      "Volume.geometry.center.y             [15]: -0.018920 \n",
+      "Volume.geometry.center.z             [15]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [15]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [15]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [15]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [15]: 0.061000 \n",
+      "Volume.geometry.geometry_parameters.height [15]: 0.149240 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [15]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [16]: sample \n",
+      "component index     [16]: 31 \n",
+      "Volume.name         [16]: sample \n",
+      "Volume.p_physics.is_vacuum           [16]: 0 \n",
+      "Volume.p_physics.my_absorption       [16]: 0.273042 \n",
+      "Volume.p_physics.number of processes [16]: 2 \n",
+      "Volume.geometry.shape                [16]: cylinder \n",
+      "Volume.geometry.center.x             [16]: 0.000000 \n",
+      "Volume.geometry.center.y             [16]: 0.000000 \n",
+      "Volume.geometry.center.z             [16]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [16]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [16]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [16]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [16]: 0.006000 \n",
+      "Volume.geometry.geometry_parameters.height [16]: 0.030000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [16]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 18\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]\n",
+      "LIST: Direct_children for Volume           0 = [6,7,8,12,14]\n",
+      "LIST: Intersect_check_list for Volume      0 = [6,7,8,12,14]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [6,7,8,12,14]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for orange_layer_0_layer with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  1 = [5,17]\n",
+      "LIST: Direct_children for Volume           1 = [5]\n",
+      "LIST: Intersect_check_list for Volume      1 = [2,3,4]\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [7,10,11,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = [7,10,11,14,15]\n",
+      "LIST: Next_volume_list for Volume          1 = [7,10,11,14,15,16,2,3,4]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for orange_layer_0_layer_vac with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  2 = [17]\n",
+      "LIST: Direct_children for Volume           2 = [17]\n",
+      "LIST: Intersect_check_list for Volume      2 = [17]\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [1,3,4,5,7,10,11,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [7,10,11,14,15]\n",
+      "LIST: Next_volume_list for Volume          2 = [1,3,4,5,7,10,11,14,15,16,17]\n",
+      "      Is_vacuum for Volume                 2 = 1\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for orange_layer_0_outer_cut_0 with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  3 = [5,17]\n",
+      "LIST: Direct_children for Volume           3 = [5]\n",
+      "LIST: Intersect_check_list for Volume      3 = [2,4]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [1,7]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = [7]\n",
+      "LIST: Next_volume_list for Volume          3 = [1,7,2,4]\n",
+      "      Is_vacuum for Volume                 3 = 1\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "List overview for orange_layer_0_outer_cut_replace_0 with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  4 = [5,17]\n",
+      "LIST: Direct_children for Volume           4 = [5]\n",
+      "LIST: Intersect_check_list for Volume      4 = [2,5]\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [1,3,7]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = [7]\n",
+      "LIST: Next_volume_list for Volume          4 = [1,3,7,2,5]\n",
+      "      Is_vacuum for Volume                 4 = 0\n",
+      "      is_mask_volume for Volume            4 = 0\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = []\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 0\n",
+      "\n",
+      "List overview for orange_layer_0_inner_cut_0 with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  5 = [17]\n",
+      "LIST: Direct_children for Volume           5 = [17]\n",
+      "LIST: Intersect_check_list for Volume      5 = [2]\n",
+      "LIST: Mask_intersect_list for Volume       5 = []\n",
+      "LIST: Destinations_list for Volume         5 = [4]\n",
+      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
+      "LIST: Next_volume_list for Volume          5 = [4,2]\n",
+      "      Is_vacuum for Volume                 5 = 1\n",
+      "      is_mask_volume for Volume            5 = 0\n",
+      "      is_masked_volume for Volume          5 = 0\n",
+      "      is_exit_volume for Volume            5 = 0\n",
+      "LIST: mask_list for Volume                 5 = []\n",
+      "LIST: masked_by_list for Volume            5 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
+      "      mask_mode for Volume                 5 = 0\n",
+      "\n",
+      "List overview for orange_layer_1_layer with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  6 = [1,2,3,4,5,17]\n",
+      "LIST: Direct_children for Volume           6 = [1,2,3,4]\n",
+      "LIST: Intersect_check_list for Volume      6 = [7]\n",
+      "LIST: Mask_intersect_list for Volume       6 = []\n",
+      "LIST: Destinations_list for Volume         6 = [0,8,9,10,11,12,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 6 = [8,12,14]\n",
+      "LIST: Next_volume_list for Volume          6 = [0,8,9,10,11,12,13,14,15,16,7]\n",
+      "      Is_vacuum for Volume                 6 = 0\n",
+      "      is_mask_volume for Volume            6 = 0\n",
+      "      is_masked_volume for Volume          6 = 0\n",
+      "      is_exit_volume for Volume            6 = 0\n",
+      "LIST: mask_list for Volume                 6 = []\n",
+      "LIST: masked_by_list for Volume            6 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
+      "      mask_mode for Volume                 6 = 0\n",
+      "\n",
+      "List overview for orange_layer_1_layer_vac with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  7 = [1,2,3,4,5,17]\n",
+      "LIST: Direct_children for Volume           7 = [1,2,3,4]\n",
+      "LIST: Intersect_check_list for Volume      7 = [1,2,3,4]\n",
+      "LIST: Mask_intersect_list for Volume       7 = []\n",
+      "LIST: Destinations_list for Volume         7 = [0,6,8,9,10,11,12,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 7 = [6,8,12,14]\n",
+      "LIST: Next_volume_list for Volume          7 = [0,6,8,9,10,11,12,13,14,15,16,1,2,3,4]\n",
+      "      Is_vacuum for Volume                 7 = 1\n",
+      "      is_mask_volume for Volume            7 = 0\n",
+      "      is_masked_volume for Volume          7 = 0\n",
+      "      is_exit_volume for Volume            7 = 0\n",
+      "LIST: mask_list for Volume                 7 = []\n",
+      "LIST: masked_by_list for Volume            7 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
+      "      mask_mode for Volume                 7 = 0\n",
+      "\n",
+      "List overview for orange_layer_2_layer with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  8 = [1,2,3,4,5,9,17]\n",
+      "LIST: Direct_children for Volume           8 = [9]\n",
+      "LIST: Intersect_check_list for Volume      8 = [6,7,9,10,11]\n",
+      "LIST: Mask_intersect_list for Volume       8 = []\n",
+      "LIST: Destinations_list for Volume         8 = [0,12,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 8 = [12,14]\n",
+      "LIST: Next_volume_list for Volume          8 = [0,12,13,14,15,16,6,7,9,10,11]\n",
+      "      Is_vacuum for Volume                 8 = 0\n",
+      "      is_mask_volume for Volume            8 = 0\n",
+      "      is_masked_volume for Volume          8 = 0\n",
+      "      is_exit_volume for Volume            8 = 0\n",
+      "LIST: mask_list for Volume                 8 = []\n",
+      "LIST: masked_by_list for Volume            8 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 8 = []\n",
+      "      mask_mode for Volume                 8 = 0\n",
+      "\n",
+      "List overview for orange_layer_2_layer_vac with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  9 = [1,2,3,4,5,17]\n",
+      "LIST: Direct_children for Volume           9 = [1,2,3,4]\n",
+      "LIST: Intersect_check_list for Volume      9 = [6,7]\n",
+      "LIST: Mask_intersect_list for Volume       9 = []\n",
+      "LIST: Destinations_list for Volume         9 = [8,10,11,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 9 = [8,13,14,15]\n",
+      "LIST: Next_volume_list for Volume          9 = [8,10,11,13,14,15,16,6,7]\n",
+      "      Is_vacuum for Volume                 9 = 1\n",
+      "      is_mask_volume for Volume            9 = 0\n",
+      "      is_masked_volume for Volume          9 = 0\n",
+      "      is_exit_volume for Volume            9 = 0\n",
+      "LIST: mask_list for Volume                 9 = []\n",
+      "LIST: masked_by_list for Volume            9 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 9 = []\n",
+      "      mask_mode for Volume                 9 = 0\n",
+      "\n",
+      "List overview for orange_layer_2_outer_cut_0 with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  10 = [3,4,5,17]\n",
+      "LIST: Direct_children for Volume           10 = [3,4]\n",
+      "LIST: Intersect_check_list for Volume      10 = [6,7,9,11]\n",
+      "LIST: Mask_intersect_list for Volume       10 = []\n",
+      "LIST: Destinations_list for Volume         10 = [8,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 10 = [8,13,14,15]\n",
+      "LIST: Next_volume_list for Volume          10 = [8,13,14,15,16,6,7,9,11]\n",
+      "      Is_vacuum for Volume                 10 = 1\n",
+      "      is_mask_volume for Volume            10 = 0\n",
+      "      is_masked_volume for Volume          10 = 0\n",
+      "      is_exit_volume for Volume            10 = 0\n",
+      "LIST: mask_list for Volume                 10 = []\n",
+      "LIST: masked_by_list for Volume            10 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 10 = []\n",
+      "      mask_mode for Volume                 10 = 0\n",
+      "\n",
+      "List overview for orange_layer_2_outer_cut_replace_0 with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  11 = [3,4,5,17]\n",
+      "LIST: Direct_children for Volume           11 = [3,4]\n",
+      "LIST: Intersect_check_list for Volume      11 = [6,7,9]\n",
+      "LIST: Mask_intersect_list for Volume       11 = []\n",
+      "LIST: Destinations_list for Volume         11 = [8,10,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 11 = [8,13,14,15]\n",
+      "LIST: Next_volume_list for Volume          11 = [8,10,13,14,15,16,6,7,9]\n",
+      "      Is_vacuum for Volume                 11 = 0\n",
+      "      is_mask_volume for Volume            11 = 0\n",
+      "      is_masked_volume for Volume          11 = 0\n",
+      "      is_exit_volume for Volume            11 = 0\n",
+      "LIST: mask_list for Volume                 11 = []\n",
+      "LIST: masked_by_list for Volume            11 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 11 = []\n",
+      "      mask_mode for Volume                 11 = 0\n",
+      "\n",
+      "List overview for orange_layer_3_layer with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  12 = [1,2,3,4,5,9,10,11,13,15,16,17]\n",
+      "LIST: Direct_children for Volume           12 = [9,13,15]\n",
+      "LIST: Intersect_check_list for Volume      12 = [6,7,8,13,14,15]\n",
+      "LIST: Mask_intersect_list for Volume       12 = []\n",
+      "LIST: Destinations_list for Volume         12 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 12 = []\n",
+      "LIST: Next_volume_list for Volume          12 = [0,6,7,8,13,14,15]\n",
+      "      Is_vacuum for Volume                 12 = 0\n",
+      "      is_mask_volume for Volume            12 = 0\n",
+      "      is_masked_volume for Volume          12 = 0\n",
+      "      is_exit_volume for Volume            12 = 0\n",
+      "LIST: mask_list for Volume                 12 = []\n",
+      "LIST: masked_by_list for Volume            12 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 12 = []\n",
+      "      mask_mode for Volume                 12 = 0\n",
+      "\n",
+      "List overview for orange_layer_3_layer_vac with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  13 = [1,2,3,4,5,10,11,17]\n",
+      "LIST: Direct_children for Volume           13 = [1,2,10,11]\n",
+      "LIST: Intersect_check_list for Volume      13 = [6,7,8,10,11]\n",
+      "LIST: Mask_intersect_list for Volume       13 = []\n",
+      "LIST: Destinations_list for Volume         13 = [12,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 13 = [12,14]\n",
+      "LIST: Next_volume_list for Volume          13 = [12,14,15,16,6,7,8,10,11]\n",
+      "      Is_vacuum for Volume                 13 = 1\n",
+      "      is_mask_volume for Volume            13 = 0\n",
+      "      is_masked_volume for Volume          13 = 0\n",
+      "      is_exit_volume for Volume            13 = 0\n",
+      "LIST: mask_list for Volume                 13 = []\n",
+      "LIST: masked_by_list for Volume            13 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 13 = []\n",
+      "      mask_mode for Volume                 13 = 0\n",
+      "\n",
+      "List overview for orange_layer_3_outer_cut_0 with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  14 = [3,4,5,16,17]\n",
+      "LIST: Direct_children for Volume           14 = [16]\n",
+      "LIST: Intersect_check_list for Volume      14 = [6,7,8,13,15]\n",
+      "LIST: Mask_intersect_list for Volume       14 = []\n",
+      "LIST: Destinations_list for Volume         14 = [0,12]\n",
+      "LIST: Reduced_destinations_list for Volume 14 = [12]\n",
+      "LIST: Next_volume_list for Volume          14 = [0,12,6,7,8,13,15]\n",
+      "      Is_vacuum for Volume                 14 = 1\n",
+      "      is_mask_volume for Volume            14 = 0\n",
+      "      is_masked_volume for Volume          14 = 0\n",
+      "      is_exit_volume for Volume            14 = 0\n",
+      "LIST: mask_list for Volume                 14 = []\n",
+      "LIST: masked_by_list for Volume            14 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 14 = []\n",
+      "      mask_mode for Volume                 14 = 0\n",
+      "\n",
+      "List overview for orange_layer_3_outer_cut_replace_0 with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  15 = [3,4,5,16,17]\n",
+      "LIST: Direct_children for Volume           15 = [16]\n",
+      "LIST: Intersect_check_list for Volume      15 = [6,7,8,13,16]\n",
+      "LIST: Mask_intersect_list for Volume       15 = []\n",
+      "LIST: Destinations_list for Volume         15 = [12,14]\n",
+      "LIST: Reduced_destinations_list for Volume 15 = [12,14]\n",
+      "LIST: Next_volume_list for Volume          15 = [12,14,6,7,8,13,16]\n",
+      "      Is_vacuum for Volume                 15 = 0\n",
+      "      is_mask_volume for Volume            15 = 0\n",
+      "      is_masked_volume for Volume          15 = 0\n",
+      "      is_exit_volume for Volume            15 = 0\n",
+      "LIST: mask_list for Volume                 15 = []\n",
+      "LIST: masked_by_list for Volume            15 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 15 = []\n",
+      "      mask_mode for Volume                 15 = 0\n",
+      "\n",
+      "List overview for orange_layer_3_inner_cut_0 with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  16 = [3,4,5,17]\n",
+      "LIST: Direct_children for Volume           16 = [3,4]\n",
+      "LIST: Intersect_check_list for Volume      16 = [6,7,8,13]\n",
+      "LIST: Mask_intersect_list for Volume       16 = []\n",
+      "LIST: Destinations_list for Volume         16 = [15]\n",
+      "LIST: Reduced_destinations_list for Volume 16 = [15]\n",
+      "LIST: Next_volume_list for Volume          16 = [15,6,7,8,13]\n",
+      "      Is_vacuum for Volume                 16 = 1\n",
+      "      is_mask_volume for Volume            16 = 0\n",
+      "      is_masked_volume for Volume          16 = 0\n",
+      "      is_exit_volume for Volume            16 = 0\n",
+      "LIST: mask_list for Volume                 16 = []\n",
+      "LIST: masked_by_list for Volume            16 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 16 = []\n",
+      "      mask_mode for Volume                 16 = 0\n",
+      "\n",
+      "List overview for sample with cylinder shape made of nascalf\n",
+      "LIST: Children for Volume                  17 = []\n",
+      "LIST: Direct_children for Volume           17 = []\n",
+      "LIST: Intersect_check_list for Volume      17 = []\n",
+      "LIST: Mask_intersect_list for Volume       17 = []\n",
+      "LIST: Destinations_list for Volume         17 = [2]\n",
+      "LIST: Reduced_destinations_list for Volume 17 = [2]\n",
+      "LIST: Next_volume_list for Volume          17 = [2]\n",
+      "      Is_vacuum for Volume                 17 = 0\n",
+      "      is_mask_volume for Volume            17 = 0\n",
+      "      is_masked_volume for Volume          17 = 0\n",
+      "      is_exit_volume for Volume            17 = 0\n",
+      "LIST: mask_list for Volume                 17 = []\n",
+      "LIST: masked_by_list for Volume            17 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 17 = []\n",
+      "      mask_mode for Volume                 17 = 0\n",
+      "\n",
+      "Union_master component orange_master initialized sucessfully\n",
+      "Detector: orange_logger_space_zx_I=1.09312e-06 orange_logger_space_zx_ERR=8.40756e-10 orange_logger_space_zx_N=3.86041e+07 \"orange_space_zx.dat\"\n",
+      "Detector: orange_logger_space_zy_I=1.09312e-06 orange_logger_space_zy_ERR=8.40756e-10 orange_logger_space_zy_N=3.86041e+07 \"orange_space_zy.dat\"\n",
+      "Detector: orange_logger_space_xy_I=1.09312e-06 orange_logger_space_xy_ERR=8.40756e-10 orange_logger_space_xy_N=3.86041e+07 \"orange_space_xy.dat\"\n",
+      "Detector: orange_logger_space_zy_close_I=1.62573e-07 orange_logger_space_zy_close_ERR=5.53765e-11 orange_logger_space_zy_close_N=1.67282e+07 \"orange_space_zy_close.dat_0\"\n",
+      "Detector: orange_logger_time_I=1.09281e-06 orange_logger_time_ERR=8.4072e-10 orange_logger_time_N=3.81586e+07 \"NULL.dat\"\n",
+      "Detector: orange_logger_space_zy_time_I=4.65892e-07 orange_logger_space_zy_time_ERR=5.42713e-10 orange_logger_space_zy_time_N=1.90655e+07 \"orange_logger_space_zy_time.dat_0\"\n",
+      "Detector: orange_logger_space_zy_time_I=5.97383e-07 orange_logger_space_zy_time_ERR=6.38733e-10 orange_logger_space_zy_time_N=1.19708e+07 \"orange_logger_space_zy_time.dat_1\"\n",
+      "Detector: orange_logger_space_zy_time_I=2.28684e-08 orange_logger_space_zy_time_ERR=5.87969e-11 orange_logger_space_zy_time_N=4.51722e+06 \"orange_logger_space_zy_time.dat_2\"\n",
+      "Detector: orange_logger_space_zy_time_I=5.66116e-09 orange_logger_space_zy_time_ERR=2.65715e-11 orange_logger_space_zy_time_N=1.90526e+06 \"orange_logger_space_zy_time.dat_3\"\n",
+      "Detector: orange_logger_space_zy_time_I=1.00584e-09 orange_logger_space_zy_time_ERR=1.13856e-11 orange_logger_space_zy_time_N=699871 \"orange_logger_space_zy_time.dat_4\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.settings(ncount=2E7, mpi=4)\n",
+    "data = instrument.backengine()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "hired-berry",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "373dea732426454194c618971bfcac10",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(Output(layout=Layout(width='75%')), VBox(children=(Label(value='Choose monitor'), Dropdown(layo…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ms.interface(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "generic-ability",
+   "metadata": {},
+   "source": [
+    "## Future expansions\n",
+    "Some additional features are expected to be added to this system at a later point.\n",
+    "\n",
+    "### Entry windows\n",
+    "Create a layer method to make entry windows, square and circular.\n",
+    "\n",
+    "### Mounting plate\n",
+    "A cryostat usually has a mounting plate with a larger radius than the widest layer, could easily add such a feature.\n",
+    "\n",
+    "### External sample\n",
+    "Create a cryostat method that takes an external sample component that is not in Union but incorperates this into the cryostat using the best practice method."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/cryostat_image.png b/examples/cryostat_image.png
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HcmV?d00001

diff --git a/mcstasscript/__init__.py b/mcstasscript/__init__.py
index 0cecb747..ffd9e4b5 100644
--- a/mcstasscript/__init__.py
+++ b/mcstasscript/__init__.py
@@ -18,3 +18,4 @@
 
 from .interface.reader import McStas_file
 
+from .tools.cryostat_builder import Cryostat
diff --git a/mcstasscript/tools/__init__.py b/mcstasscript/tools/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/mcstasscript/tools/cryostat_builder.py b/mcstasscript/tools/cryostat_builder.py
new file mode 100644
index 00000000..34d42ab0
--- /dev/null
+++ b/mcstasscript/tools/cryostat_builder.py
@@ -0,0 +1,698 @@
+import numpy as np
+
+class Layer:
+    def __init__(self, name, cryostat,
+                 sample_to_bottom, bottom_thickness, sample_to_top, top_thickness=0.01,
+                 inner_radius=None, outer_radius=None, thickness=None,
+                 material="Al", p_interact=0):
+        """
+        Layer of cryostat with a shell made of given material and hollowed by vacuum
+
+        Describes a layer of a cryostat with given geometry and materials. The geometry
+        is specified with distance from sample to top and bottom, along with a
+        thickness of these. In addition an outer and inner radius is needed, here the
+        user can specify two of inner_radius, outer_radius and thickness.
+        The Layer is given the cryostat object of which is it a part to access methods
+        and attributes in a simple way.
+        The layer is built from Union components, and so are not allowed to perfectly
+        intersect the planes from other layers. These are registered and check with the
+        cryostat class. If a custom material is given instead of Al, that material has
+        to exist as a Union_make_material component.
+
+        It is possible to add windows to a layer, these are added with the add_window
+        method, windows can be a reduction of radius from the inside, outside or both.
+
+        Parameters
+        ----------
+
+        sample_to_bottom: float
+            Distance from sample to bottom of layer [m] (bottom thickness added)
+
+        bottom_thickness: float
+            Thickness of layer bottom [m]
+
+        sample_to_top: float
+            Distance from sample to top of layer [m] (top thickness added)
+
+        top_thickness: float
+            Thickness of layer top [m]
+
+        inner_radius: float
+            Inner radius of the cryostat layer [m]
+
+        outer_radius: float
+            Outer radius of the cryostat layer [m]
+
+        thickness: float
+            Thickness of the shell, can be provided instead of either inner or outer radius [m]
+
+        material: string
+            Material of which the cryostat layer should be made
+
+        p_interact: float
+            p_interact set for all Union geometry components
+        """
+
+        self.name = name
+        self.material = material
+        self.p_interact = p_interact
+
+        self.cryostat = cryostat
+        self.instr = self.cryostat.instr
+        self.reference = self.cryostat.origin
+
+        try:
+            self.instr.get_component(self.material)
+        except:
+            raise RuntimeError("No material named '" + str(self.material)
+                               + "' found in given instrument file. \n"
+                               + "Construct with Union_make_material.")
+
+        self.shell_lowest_point = -sample_to_bottom - bottom_thickness
+        self.shell_highest_point = sample_to_top + top_thickness
+        self.shell_center = 0.5 * (self.shell_lowest_point + self.shell_highest_point)
+        self.shell_height = self.shell_highest_point - self.shell_lowest_point
+
+        # Check with the database these wont cause a collision
+        self.cryostat.add_y_plane(self.shell_lowest_point)
+        self.cryostat.add_y_plane(self.shell_highest_point)
+
+        self.vac_lowest_point = -sample_to_bottom
+        self.vac_highest_point = sample_to_top
+        self.vac_center = 0.5 * (self.vac_highest_point + self.vac_lowest_point)
+        self.vac_height = self.vac_highest_point - self.vac_lowest_point
+
+        # Check with the database these wont cause a collision
+        self.cryostat.add_y_plane(self.vac_lowest_point)
+        self.cryostat.add_y_plane(self.vac_highest_point)
+
+        self.inner_radius, self.outer_radius = self.check_radius_input(inner_radius, outer_radius, thickness)
+
+        # Check with the database these wont cause a collision
+        self.cryostat.add_radius(self.inner_radius)
+        self.cryostat.add_radius(self.outer_radius)
+
+        # Set up shell
+        layer = self.instr.add_component(self.name + "_layer", "Union_cylinder")
+        layer.material_string = '"' + self.material + '"'
+        layer.p_interact = self.p_interact
+
+        layer.radius = self.outer_radius
+        layer.yheight = self.shell_height
+
+        layer.set_AT([0, self.shell_center, 0], RELATIVE=self.reference)
+
+        self.layer = layer
+
+        # Set up vacuum
+        layer_vac = self.instr.add_component(self.name + "_layer_vac", "Union_cylinder")
+        layer_vac.material_string = '"Vacuum"'
+
+        layer_vac.radius = self.inner_radius
+        layer_vac.yheight = self.vac_height
+
+        layer_vac.set_AT([0, self.vac_center, 0], RELATIVE=self.reference)
+
+        self.layer_vac = layer_vac
+
+        # List of union components, highest priority first
+        self.outer_cuts = []
+        self.inner_cuts = []
+        # self.main_union_components = [self.layer_vac, self.layer]
+        self.union_components = [self.layer_vac, self.layer]
+
+        # Prepare for windows
+        self.inner_window_index = 0
+        self.outer_window_index = 0
+
+    def count_inputs(self, *args):
+        """
+        Counts how many of given inputs are not None
+        """
+        n_inputs = 0
+        for arg in args:
+            if arg is not None:
+                n_inputs += 1
+        return n_inputs
+
+    def check_radius_input(self, inner_radius, outer_radius, thickness):
+        """
+        Returns inner and outer radius from user input that may include thickness
+
+        The inner_radius, outer_radius and thickness can have one not specified,
+        and this method will calculate the inner and out radius from the two given.
+        A input not given is specified as None.
+        """
+
+        n_inputs = self.count_inputs(inner_radius, outer_radius, thickness)
+
+        if n_inputs != 2:
+            raise RuntimeError("Set two of inner_radius, outer_radius, and thickness.")
+
+        if inner_radius is None:
+            inner_radius = outer_radius - thickness
+        else:
+            inner_radius = inner_radius
+
+        if outer_radius is None:
+            outer_radius = inner_radius + thickness
+        else:
+            outer_radius = outer_radius
+
+        return inner_radius, outer_radius
+
+    def add_window(self, inner_radius=None, outer_radius=None, thickness=None,
+                   height=None, sample_to_top=None, sample_to_bottom=None):
+        """
+        Adds 360 deg window in given height interval
+
+        Adds window as a reduction in thickness of the layer in a certain height
+        interval. The reduction can be both from the outside, inside or both.
+        If only outer_radius is given, a reduction of the outside is assumed.
+        If only inner_radius is given, the window will be on the inside.
+        If two of the three parameters inner_radius, outer_radius and thickness
+        is given, material will be removed from both the outside and inside, only
+        use this if necessary, never specify inner / outer radius identical to the
+        main layer.
+        Any two of height, sample_to_top and sample_to_bottom can be given, or if
+        the window is symmetrical in height around the sample position, the height
+        alone is sufficient.
+        It is possible to add multiple multiple windows by calling this method
+        multiple times, add the tallest first.
+
+        Keyword arguments
+        -----------------
+
+        inner_radius : float
+            inner radius of window (should be larger than main inner_radius)
+
+        outer_radius : float
+            outer radius of window (should be less than main outer_radius)
+
+        thickness : float
+            thickness of the window
+
+        height : float
+            height of window
+
+        sample_to_top : float
+            distance from sample center to top of window
+
+        sample_to_bototm : float
+            distance from sample center to bottom of window
+        """
+
+        # Assume no cuts are made
+        outer_cut = False
+        inner_cut = False
+
+        # Check if cuts are made to inner, outer or both
+        if self.count_inputs(inner_radius, outer_radius, thickness) == 1:
+            if inner_radius is not None:
+                inner_cut = True
+                outer_radius = self.outer_radius
+            elif outer_radius is not None:
+                outer_cut = True
+                inner_radius = self.inner_radius
+            else:
+                # could center this window
+                raise RuntimeError("Cant tell if window is on inside or outside.")
+
+        inner_radius, outer_radius = self.check_radius_input(inner_radius, outer_radius, thickness)
+
+        if inner_radius < self.inner_radius:
+            raise RuntimeError("Window has smaller inner radius than main layer, needs to be larger.")
+
+        if outer_radius > self.outer_radius:
+            raise RuntimeError("Window has larger outer radius than main layer, needs to be smaller.")
+
+        if abs(inner_radius - self.inner_radius) > 1E-5:
+            inner_cut = True
+
+        if abs(outer_radius - self.outer_radius) > 1E-5:
+            outer_cut = True
+
+        if self.count_inputs(height, sample_to_top, sample_to_bottom) == 3:
+            raise RuntimeError("Ambigious definition of window height.")
+
+        if height is not None:
+            window_top = height / 2
+            window_bottom = -height / 2
+            if sample_to_top is not None:
+                window_top = sample_to_top
+                window_bottom = sample_to_top - height
+            if sample_to_bottom is not None:
+                window_top = -sample_to_bottom + height
+                window_bottom = -sample_to_bottom
+        else:
+            window_top = sample_to_top
+            window_bottom = -sample_to_bottom
+
+        window_height = window_top - window_bottom
+        window_position = 0.5 * (window_top + window_bottom)
+
+        if outer_cut:
+            name = self.name + "_outer_cut_" + str(self.outer_window_index)
+            o_cut = self.instr.add_component(name, "Union_cylinder")
+            o_cut.set_AT([0, window_position, 0], RELATIVE=self.reference)
+
+            o_cut.material_string = '"Vacuum"'
+            o_cut.radius = self.outer_radius + 1E-6
+            o_cut.yheight = window_height + 1E-6
+
+            # Check these radius and y_planes do not collide with others
+            self.cryostat.add_radius(o_cut.radius)
+            self.cryostat.add_y_plane(window_top + 0.5E-6)
+            self.cryostat.add_y_plane(window_bottom - 0.5E-6)
+
+            name = self.name + "_outer_cut_replace_" + str(self.outer_window_index)
+            o_cut_m = self.instr.add_component(name, "Union_cylinder")
+            o_cut_m.set_AT([0, window_position, 0], RELATIVE=self.reference)
+
+            o_cut_m.material_string = '"' + self.material + '"'
+            o_cut_m.p_interact = self.p_interact
+            o_cut_m.radius = outer_radius
+            o_cut_m.yheight = window_height + 5E-6
+
+            # Check these radius and y_planes do not collide with others
+            self.cryostat.add_radius(o_cut_m.radius)
+            self.cryostat.add_y_plane(window_top + 2.5E-6)
+            self.cryostat.add_y_plane(window_bottom - 2.5E-6)
+
+            # Update list of outer cut components, order is important
+            self.outer_cuts = [o_cut_m, o_cut] + self.outer_cuts
+
+            self.outer_window_index += 1
+
+        if inner_cut:
+            name = self.name + "_inner_cut_" + str(self.inner_window_index)
+            i_cut = self.instr.add_component(name, "Union_cylinder")
+            i_cut.set_AT([0, window_position, 0], RELATIVE=self.reference)
+
+            i_cut.material_string = '"Vacuum"'
+            i_cut.radius = inner_radius
+            i_cut.yheight = window_height
+
+            # Check these radius and y_planes do not collide with others
+            self.cryostat.add_radius(i_cut.radius)
+            self.cryostat.add_y_plane(window_top)
+            self.cryostat.add_y_plane(window_bottom)
+
+            # Update list of inner cut components, order is important
+            self.inner_cuts = [i_cut] + self.inner_cuts
+
+            self.inner_window_index += 1
+
+        # Create full list of Union components used in order from highest to lowest priority
+        self.union_components = [self.layer_vac] + self.inner_cuts + self.outer_cuts + [self.layer]
+
+
+class Cryostat:
+    def __init__(self, name, instr, reference="PREVIOUS",
+                 min_priority=20, max_priority=100):
+        """
+        Handles addition of a cryostat with multiple layers and windows
+
+        This class can add a description of a cryostat to a McStasScript
+        instrument object. The cryostat is made of several layers consisting
+        of a shell and vacuum, these are added with the add_layer method and
+        should be added in the order from smallest (inside) to largest. Each
+        layer can be accessed with the last_layer attribute, and one can add
+        windows to each layer. Consult the Layer class for details. When all
+        layers are added, it is necessary to build the cryostat, this step is
+        necessary to adjust the priority of each Union component.
+        The position of the cryostat can be adjusted with the set_AT and
+        set_ROTATED methods that works as the standard McStasScript versions.
+        It is possible to add logger components that show scattering in the
+        system with the add_spatial_loggers method.
+
+        Parameters
+        ----------
+
+        name : str
+            Name of the cryostat, will be used in naming of all added components
+
+        instr : instrument object inherited from McCode_instr
+            Instrument object where the cryostat should be added
+
+        Keyword arguments
+        -----------------
+
+        reference : str
+            Name of the component which the cryostat should be located relative to
+
+        min_priority : float
+            Minimum Union priority used (default 20)
+
+        max_priority : float
+            Maximum Union priority used, add a sample with higher priority (default 100)
+        """
+        self.name = str(name)
+        self.instr = instr
+        self.reference = reference
+        self.min_priority = min_priority
+        self.max_priority = max_priority
+
+        self.layers = []
+        self.last_layer = None
+
+        self.origin = self.instr.add_component(self.name, "Arm")
+        self.origin.set_AT([0, 0, 0], RELATIVE=reference)
+
+        # Check if Al exists, if not add it!
+        try:
+            Al_component = None
+            Al_component = instr.get_component("Al")
+        except:
+            Al_inc = self.instr.add_component("Al_inc", "Incoherent_process")
+            Al_inc.sigma = 4 * 0.0082  # Incoherent cross section in Barns
+            Al_inc.unit_cell_volume = 66.4  # Unit cell volume in AA^3
+
+            Al_pow = self.instr.add_component("Al_pow", "Powder_process")
+            Al_pow.reflections = "\"Al.laz\""  # Data file with powder lines
+
+            Al = self.instr.add_component("Al", "Union_make_material")
+            Al.my_absorption = "100*4*0.231/66.4"  # Inverse penetration depth in 1/m
+            Al.process_string = '"Al_inc,Al_pow"'  # Make a material with aluminium incoherent and aluminium powder`
+
+        # Set intial state of loggers, not having been added yet
+        # Important to avoid multiple sets of the same kind added
+        self.spatial_loggers_set = False
+        self.time_logger_set = False
+        self.animation_loggers = []
+
+        # Height database
+        self.used_y_planes = []
+        self.used_radius_values = []
+
+    def set_AT(self, *args, **kwargs):
+        """
+        Sets position of cryostat, sample position used as reference
+        """
+        self.origin.set_AT(*args, **kwargs)
+
+    def set_ROTATED(self, *args, **kwargs):
+        """
+        Sets rotation of cryostat, sample position used as reference
+        """
+        self.origin.set_AT(*args, **kwargs)
+
+    def add_layer(self, *args, **kwargs):
+        """
+        Adds layer to cryostat, all arguments passed to Layer. Consult Layer
+        class for additional help on adding a layer.
+        """
+
+        if "name" not in kwargs:
+            kwargs["name"] = self.name + "_layer_" + str(len(self.layers))
+
+        if "cryostat" not in kwargs:
+            kwargs["cryostat"] = self
+
+        layer = Layer(*args, **kwargs)
+        self.last_layer = layer
+        self.layers.append(layer)
+
+    def find_cryostat_dimensions(self):
+        """
+        Returns spatial extend of cryostat with padding for plotting
+
+        Returns tuple with x, lowest y, highest y and z
+        """
+        # find outer dimensions:
+        max_radius = self.last_layer.outer_radius
+        highest_point = self.last_layer.shell_highest_point
+        lowest_point = self.last_layer.shell_lowest_point
+
+        if highest_point > abs(lowest_point):
+            height = 2 * highest_point
+        else:
+            height = 2 * abs(lowest_point)
+
+        mon_z = 1.1 * max_radius
+        mon_x = 1.1 * max_radius
+        if lowest_point < 0:
+            mon_y_low = 1.1 * lowest_point
+        else:
+            mon_y_low = 0.9 * lowest_point
+
+        if highest_point > 0:
+            mon_y_high = 1.1 * highest_point
+        else:
+            mon_y_high = 0.9 * highest_point
+
+        return mon_x, mon_y_low, mon_y_high, mon_z
+
+    def add_spatial_loggers(self, n_x=500, n_y=500, n_z=500):
+        """
+        Adds spatial Union loggers to the code
+
+        The spatial loggers will be set so they cover the entire cryostat and
+        include views from top, side and front along with a close up of the
+        windows as a slice in zy with limited z of +/- 5 mm.
+
+        Keyword arguments
+        -----------------
+
+        n_x : int
+            Number of bins in x direction
+
+        n_y : int
+            Number of bins in y direction
+
+        n_z : int
+            Number of bins in z direction
+        """
+
+        if self.spatial_loggers_set:
+            raise RuntimeError("Can not add two sets of spatial loggers.")
+
+        self.spatial_loggers_set = True
+
+        # find outer dimensions:
+        mon_x, mon_y_low, mon_y_high, mon_z = self.find_cryostat_dimensions()
+
+        space_2D_zx = self.instr.add_component(self.name + "_logger_space_zx", "Union_logger_2D_space")
+        space_2D_zx.set_AT([0, 0, 0], RELATIVE=self.origin)
+        space_2D_zx.filename = '"' + self.name + '_space_zx.dat"'
+        space_2D_zx.D_direction_1 = '"z"';
+        space_2D_zx.n1 = n_z
+        space_2D_zx.D1_min = -mon_z;
+        space_2D_zx.D1_max = mon_z
+        space_2D_zx.D_direction_2 = '"x"';
+        space_2D_zx.n2 = n_x
+        space_2D_zx.D2_min = -mon_x;
+        space_2D_zx.D2_max = mon_x
+
+        space_2D_zy = self.instr.add_component(self.name + "_logger_space_zy", "Union_logger_2D_space")
+        space_2D_zy.set_AT([0, 0, 0], RELATIVE=self.origin)
+        space_2D_zy.filename = '"' + self.name + '_space_zy.dat"'
+        space_2D_zy.D_direction_1 = '"z"';
+        space_2D_zy.n1 = n_z
+        space_2D_zy.D1_min = -mon_z;
+        space_2D_zy.D1_max = mon_z
+        space_2D_zy.D_direction_2 = '"y"';
+        space_2D_zy.n2 = n_y
+        space_2D_zy.D2_min = mon_y_low;
+        space_2D_zy.D2_max = mon_y_high
+
+        space_2D_xy = self.instr.add_component(self.name + "_logger_space_xy", "Union_logger_2D_space")
+        space_2D_xy.set_AT([0, 0, 0], RELATIVE=self.origin)
+        space_2D_xy.filename = '"' + self.name + '_space_xy.dat"'
+        space_2D_xy.D_direction_1 = '"x"';
+        space_2D_xy.n1 = n_x
+        space_2D_xy.D1_min = -mon_x;
+        space_2D_xy.D1_max = mon_x
+        space_2D_xy.D_direction_2 = '"y"';
+        space_2D_xy.n2 = n_y
+        space_2D_xy.D2_min = mon_y_low;
+        space_2D_xy.D2_max = mon_y_high
+
+        # Adding monitor that shows windows better
+        lowest_inner_radius = self.layers[0].inner_radius
+        largest_outer_radius = self.layers[-1].outer_radius
+
+        space_3D_zy = self.instr.add_component(self.name + "_logger_space_zy_close", "Union_logger_3D_space")
+        space_3D_zy.set_AT([0, 0, 0], RELATIVE=self.origin)
+        space_3D_zy.filename = '"' + self.name + '_space_zy_close.dat"'
+        space_3D_zy.D_direction_1 = '"z"';
+        space_3D_zy.n1 = n_z
+        space_3D_zy.D1_min = -1.02 * largest_outer_radius;
+        space_3D_zy.D1_max = -0.9 * lowest_inner_radius
+        space_3D_zy.D_direction_2 = '"y"';
+        space_3D_zy.n2 = n_y
+        space_3D_zy.D2_min = mon_y_low;
+        space_3D_zy.D2_max = mon_y_high
+        space_3D_zy.D_direction_3 = '"x"';
+        space_3D_zy.n3 = 1
+        space_3D_zy.D3_min = -0.005;
+        space_3D_zy.D3_max = 0.005
+
+    def add_time_histogram(self, t_min=0, t_max=0.1):
+        """
+        Adds histogram of scattering intensity as function of time
+
+        Very useful when setting time range for animation, only one can be added.
+
+        Parameters
+        ----------
+
+        t_min : float
+            Lowest time recorded in [s]
+
+        t_max : float
+            Highest time recorded in [s]
+        """
+
+        if self.time_logger_set:
+            raise RuntimeError("Can not add two sets of time_histogram loggers.")
+
+        self.time_logger_set = True
+
+        time_mon = self.instr.add_component(self.name + "_logger_time", "Union_logger_1D")
+        time_mon.variable = '"time"'
+        time_mon.min_value = t_min
+        time_mon.max_value = t_max
+        time_mon.n1 = 1000
+
+    def add_animation(self, t_min=0, t_max=0.1, n_frames=10,
+                      d1="z", n1=300, d2="y", n2=300):
+        """
+        Adds 2D_space_time logger that records the information necessary for animation
+
+        Adds a 2D_space_time logger that records the requested number of frames, n_frames.
+        The time span of both is from t_min to t_max. The default orientation is in the zy
+        plane, but this can be chosen along with the desired resolution.
+        Errors can happen with too many empty frames, so it is recommended to set
+        t_min close to the first scattering time.
+        Select the appropriate animation data from simulation output and use
+        plotter.make_animation with a filename to save the animation as a gif.
+
+        Parameters
+        ----------
+
+        t_min : float
+            Lowest time recorded in [s]
+
+        t_max : float
+            Highest time recorded in [s]
+
+        n_frames : int
+            Number of frames in space 2D time logger
+
+        d1 : str
+            First direction of space 2D time logger, "x", "y" or "z"
+
+        n1 : int
+            Number of bins in first direction
+
+        d2 : str
+            Second direction of space 2D time logger, "x", "y" or "z"
+
+        n2 : int
+            Number of bins in second direction
+        """
+
+        if d1 == d2:
+            raise RuntimeError("Cant have both d1 and d2 along the same axis.")
+
+        if d1 + d2 in self.animation_loggers:
+            raise RuntimeError("Can only add one animation logger with the same axis.")
+
+        self.animation_loggers.append(d1 + d2)
+
+        # find outer dimensions:
+        mon_x, mon_y_low, mon_y_high, mon_z = self.find_cryostat_dimensions()
+
+        name = self.name + "_logger_space_" + d1 + d2 + "_time"
+        ani_logger = self.instr.add_component(name, "Union_logger_2D_space_time")
+        ani_logger.set_AT([0, 0, 0], RELATIVE=self.origin)
+        ani_logger.filename = '"' + name + '.dat"'
+        ani_logger.time_bins = int(n_frames)
+        ani_logger.time_min = t_min
+        ani_logger.time_max = t_max
+
+        ani_logger.D_direction_1 = '"' + d1 + '"';
+        ani_logger.n1 = int(n1)
+        if d1 == "x":
+            ani_logger.D1_min = -mon_x;
+            ani_logger.D1_max = mon_x
+        elif d1 == "y":
+            ani_logger.D1_min = mon_y_low;
+            ani_logger.D1_max = mon_y_high
+        elif d1 == "z":
+            ani_logger.D1_min = -mon_z;
+            ani_logger.D1_max = mon_z
+        else:
+            raise RuntimeError("Dimension: '" + d1 + "' not recoignized, must be x, y or z.")
+
+        ani_logger.D_direction_2 = '"' + d2 + '"';
+        ani_logger.n2 = int(n2)
+        if d2 == "x":
+            ani_logger.D2_min = -mon_x;
+            ani_logger.D2_max = mon_x
+        elif d2 == "y":
+            ani_logger.D2_min = mon_y_low;
+            ani_logger.D2_max = mon_y_high
+        elif d2 == "z":
+            ani_logger.D2_min = -mon_z;
+            ani_logger.D2_max = mon_z
+        else:
+            raise RuntimeError("Dimension: '" + d2 + "' not recoignized, must be x, y or z.")
+
+    def add_y_plane(self, value):
+        """
+        Adds a y plane to database, checking if it is already present
+
+        If duplicates occur, errors would happen in the Union algorithm
+        """
+
+        for y_plane in self.used_y_planes:
+            if abs(y_plane - value) < 1E-7:
+                raise RuntimeError("Two planes overlap with the same y value.")
+
+        self.used_y_planes.append(value)
+
+    def add_radius(self, value):
+        """
+        Adds a radius to databse, checking if it is already present
+
+        If duplicates occur, errors would happen in the Union algorithm
+        """
+
+        for radius in self.used_radius_values:
+            if abs(radius - value) < 1E-7:
+                raise RuntimeError("The radius of two cylinders are almost equal.")
+
+        self.used_radius_values.append(value)
+
+    def build(self, include_master=True):
+        """
+        Assigns priorities to the internal components of the cryostat
+
+        The build method must be called after all layers and windows are added
+        in order for priorities to be assigned according to the priority window
+        given at class initialization. It is optional to include the Union_master
+        with the build method, if it is not included it must be manually provided
+        later.
+
+        Keyword arguments
+        -----------------
+
+        inclde_master : bool
+            If True a Union_master component is added to the instrument file
+        """
+
+        union_component_list = []
+
+        for layer in self.layers:
+            union_component_list += layer.union_components
+
+        priorities = np.linspace(self.max_priority, self.min_priority, len(union_component_list))
+
+        for component, priority in zip(union_component_list, priorities):
+            component.priority = priority
+
+        if include_master:
+            master = self.instr.add_component(self.name + "_master", "Union_master")
+

From 58598be5fb3a24493d4b447f87804d43870b8720 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 22 Mar 2022 18:59:24 +0100
Subject: [PATCH 243/403] cleared cryostat notebook.

---
 examples/cryostat_demo.ipynb | 2031 +---------------------------------
 1 file changed, 51 insertions(+), 1980 deletions(-)

diff --git a/examples/cryostat_demo.ipynb b/examples/cryostat_demo.ipynb
index cf3b2119..7beef31e 100644
--- a/examples/cryostat_demo.ipynb
+++ b/examples/cryostat_demo.ipynb
@@ -13,50 +13,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "composed-custody",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The following components are found in the work_directory / input_path:\n",
-      "     Union_sphere.comp\n",
-      "     Texture_process.comp\n",
-      "     Union_cone.comp\n",
-      "     Union_box.comp\n",
-      "     Single_crystal_process.comp\n",
-      "     Union_abs_logger_2D_space.comp\n",
-      "     Union_logger_2D_kf.comp\n",
-      "     Template_process.comp\n",
-      "     PhononSimple_process.comp\n",
-      "     Union_conditional_standard.comp\n",
-      "     Union_abs_logger_1D_space.comp\n",
-      "     Union_abs_logger_event.comp\n",
-      "     NCrystal_process.comp\n",
-      "     Union_abs_logger_1D_space_event.comp\n",
-      "     Union_abs_logger_1D_space_tof.comp\n",
-      "     Union_logger_2D_space.comp\n",
-      "     Union_conditional_PSD.comp\n",
-      "     Union_master.comp\n",
-      "     AF_HB_1D_process.comp\n",
-      "     Union_logger_2D_kf_time.comp\n",
-      "     Union_cylinder.comp\n",
-      "     Union_abs_logger_1D_space_tof_to_lambda.comp\n",
-      "     Powder_process.comp\n",
-      "     Union_make_material.comp\n",
-      "     Incoherent_process.comp\n",
-      "     Union_logger_1D.comp\n",
-      "     Union_logger_3D_space.comp\n",
-      "     IncoherentPhonon_process.comp\n",
-      "     Union_logger_2DQ.comp\n",
-      "     Union_mesh.comp\n",
-      "     Union_logger_2D_space_time.comp\n",
-      "These definitions will be used instead of the installed versions.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import mcstasscript as ms\n",
     "instrument = ms.McStas_instr(\"cryostat_demo\")"
@@ -64,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "secret-convert",
    "metadata": {},
    "outputs": [],
@@ -90,8 +50,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "restricted-queensland",
+   "execution_count": null,
+   "id": "silver-narrative",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,7 +61,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "premier-greenhouse",
+   "id": "single-millennium",
    "metadata": {},
    "source": [
     "The cryostat model is constructed in layers, each of which are added with *add_layer*. The geometry is shown in the left side of the figure below. To define the walls the user specifies two of *inner_radius*, *outer_radius* and *thickness*, where the last one will be calculated. The distance to the top is controlled with *sample_to_top*, ands its thickness with *top_thickness*. A small negative *top_thickness* will remove the top. The bottom is controlled with *sample_to_bottom* and *bottom_thickness*.\n",
@@ -115,8 +75,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "continent-hunter",
+   "execution_count": null,
+   "id": "typical-harvest",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,7 +88,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "vietnamese-ribbon",
+   "id": "bound-colon",
    "metadata": {},
    "source": [
     "After adding a layer, it can be accessed with the attribute *last_layer*, which allows adding a window. The geometry of a window is shown to the right in the figure above. To control the height of the window, the user provides two of *height*, *sample_to_top* and *sample_to_bottom*. If the window only changes the inner radius, only specify the *inner_radius* parameter, and vice versa with the *outer_radius*. If the window narrows the cryostat from both sides, the user should specify two of *inner_radius*, *outer_radius* and *thickness*. It is not allowed to specify *inner_radius* or *outer_radius* that equals the corresponding value for the layer.\n",
@@ -138,8 +98,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "elegant-delay",
+   "execution_count": null,
+   "id": "advisory-index",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -148,7 +108,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "adjacent-investing",
+   "id": "thick-transcript",
    "metadata": {},
    "source": [
     "It is allowed to add multiple windows to a layer, this could for example be needed when describing a window where the outside and inside have different heights."
@@ -156,8 +116,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "coated-edwards",
+   "execution_count": null,
+   "id": "unexpected-charleston",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,7 +126,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "round-myanmar",
+   "id": "minimal-folks",
    "metadata": {},
    "source": [
     "If \"Al\" is selected for material, a simple Al model is created unless an Al model already exists in the instrument. When using the material keyword to add other materials, it is up to the user to create the material definition using Union components. The inside is always Vacuum. Here the second layer is added, specifying \"Al\" as the material."
@@ -174,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "welcome-cathedral",
    "metadata": {},
    "outputs": [],
@@ -187,7 +147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "romance-purse",
+   "id": "portuguese-transformation",
    "metadata": {},
    "source": [
     "The remaining 2 of the 4 layers are added here, both with windows."
@@ -195,8 +155,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "latest-welding",
+   "execution_count": null,
+   "id": "settled-intro",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -222,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "dutch-prevention",
    "metadata": {},
    "outputs": [],
@@ -234,7 +194,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "coated-assist",
+   "id": "blessed-protest",
    "metadata": {},
    "source": [
     "At the end it is necessary to run the *build* method, this assigns the appropriate priorities to each Union component used, and adds a Union_master component to the end. Per default the build method adds a *Union_master* component, if one wants to for example add a sample afterwards, this is turned of by setting include_master to False."
@@ -242,8 +202,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "dynamic-origin",
+   "execution_count": null,
+   "id": "sound-alignment",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -260,61 +220,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "noticed-third",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on method add_animation in module mcstasscript.tools.cryostat_builder:\n",
-      "\n",
-      "add_animation(t_min=0, t_max=0.1, n_frames=10, d1='z', n1=300, d2='y', n2=300) method of mcstasscript.tools.cryostat_builder.Cryostat instance\n",
-      "    Adds 2D_space_time logger that records the information necessary for animation\n",
-      "    \n",
-      "    Adds a 2D_space_time logger that records the requested number of frames, n_frames.\n",
-      "    The time span of both is from t_min to t_max. The default orientation is in the zy\n",
-      "    plane, but this can be chosen along with the desired resolution.\n",
-      "    Errors can happen with too many empty frames, so it is recommended to set\n",
-      "    t_min close to the first scattering time.\n",
-      "    Select the appropriate animation data from simulation output and use\n",
-      "    plotter.make_animation with a filename to save the animation as a gif.\n",
-      "    \n",
-      "    Parameters\n",
-      "    ----------\n",
-      "    \n",
-      "    t_min : float\n",
-      "        Lowest time recorded in [s]\n",
-      "    \n",
-      "    t_max : float\n",
-      "        Highest time recorded in [s]\n",
-      "    \n",
-      "    n_frames : int\n",
-      "        Number of frames in space 2D time logger\n",
-      "    \n",
-      "    d1 : str\n",
-      "        First direction of space 2D time logger, \"x\", \"y\" or \"z\"\n",
-      "    \n",
-      "    n1 : int\n",
-      "        Number of bins in first direction\n",
-      "    \n",
-      "    d2 : str\n",
-      "        Second direction of space 2D time logger, \"x\", \"y\" or \"z\"\n",
-      "    \n",
-      "    n2 : int\n",
-      "        Number of bins in second direction\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "help(orange_cryostat.add_animation)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "signed-shield",
+   "id": "smoking-background",
    "metadata": {},
    "source": [
     "The build method adds a large number of components to describe the cryostat. Lets see what this looks like."
@@ -322,80 +238,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "aging-sailing",
+   "execution_count": null,
+   "id": "dimensional-berkeley",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1msource\u001b[0m  \u001b[1mSource_simple\u001b[0m \n",
-      "  AT      (0, 0, 0) ABSOLUTE\n",
-      "\u001b[1morange\u001b[0m  \u001b[1mArm\u001b[0m \n",
-      "  AT      (0, 0, 2) RELATIVE source\n",
-      "\u001b[1mAl_inc\u001b[0m  \u001b[1mIncoherent_process\u001b[0m \n",
-      "  AT      (0, 0, 0) ABSOLUTE\n",
-      "\u001b[1mAl_pow\u001b[0m  \u001b[1mPowder_process\u001b[0m \n",
-      "  AT      (0, 0, 0) ABSOLUTE\n",
-      "\u001b[1mAl\u001b[0m  \u001b[1mUnion_make_material\u001b[0m \n",
-      "  AT      (0, 0, 0) ABSOLUTE\n",
-      "\u001b[1morange_layer_0_layer\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0.0555, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_0_layer_vac\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0.0585, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_0_outer_cut_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, -0.02189, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_0_outer_cut_replace_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, -0.02189, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_0_inner_cut_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0.0, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_1_layer\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0.0735, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_1_layer_vac\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0.075, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_2_layer\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0.0675, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_2_layer_vac\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0.066, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_2_outer_cut_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, -0.024000000000000004, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_2_outer_cut_replace_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, -0.024000000000000004, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_3_layer\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0.05349999999999999, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_3_layer_vac\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0.047999999999999994, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_3_outer_cut_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, -0.01892, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_3_outer_cut_replace_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, -0.01892, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_3_inner_cut_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, -0.01892, 0) RELATIVE orange\n",
-      "\u001b[1morange_logger_space_zx\u001b[0m  \u001b[1mUnion_logger_2D_space\u001b[0m \n",
-      "  AT      (0, 0, 0) RELATIVE orange\n",
-      "\u001b[1morange_logger_space_zy\u001b[0m  \u001b[1mUnion_logger_2D_space\u001b[0m \n",
-      "  AT      (0, 0, 0) RELATIVE orange\n",
-      "\u001b[1morange_logger_space_xy\u001b[0m  \u001b[1mUnion_logger_2D_space\u001b[0m \n",
-      "  AT      (0, 0, 0) RELATIVE orange\n",
-      "\u001b[1morange_logger_space_zy_close\u001b[0m  \u001b[1mUnion_logger_3D_space\u001b[0m \n",
-      "  AT      (0, 0, 0) RELATIVE orange\n",
-      "\u001b[1morange_logger_time\u001b[0m  \u001b[1mUnion_logger_1D\u001b[0m \n",
-      "  AT      (0, 0, 0) ABSOLUTE\n",
-      "\u001b[1morange_logger_space_zy_time\u001b[0m  \u001b[1mUnion_logger_2D_space_time\u001b[0m \n",
-      "  AT      (0, 0, 0) RELATIVE orange\n",
-      "\u001b[1morange_master\u001b[0m  \u001b[1mUnion_master\u001b[0m \n",
-      "  AT      (0, 0, 0) ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.print_components()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cutting-works",
+   "id": "clean-baptist",
    "metadata": {},
    "source": [
     "## Displaying the geometry"
@@ -403,32 +256,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "turkish-parade",
+   "execution_count": null,
+   "id": "exceptional-profit",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"800\"\n",
-       "            height=\"450\"\n",
-       "            src=\"./cryostat_demo_mcdisplay_0/index.html\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7ff29970c880>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_instrument()"
    ]
@@ -444,802 +275,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "paperback-palmer",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/cryostat_demo_data_7\"\n",
-      "INFO: Regenerating c-file: cryostat_demo.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./cryostat_demo.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:18644:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:18644:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:18887:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:18887:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:19130:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_xy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:19130:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_xy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_logger_3D_space.comp:668:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:19379:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_close_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_3D_space.comp:668:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:19379:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_close_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_logger_1D.comp:479:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:19686:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_time_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_1D.comp:479:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:19686:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_time_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_logger_2D_space_time.comp:632:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:19883:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_time_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_2D_space_time.comp:632:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:19883:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_time_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "18 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy cryostat_demo.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/cryostat_demo_data_7\n",
-      "\n",
-      "    Instrument parameters for cryostat_demo (cryostat_demo.instr)\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "Allocating 3D arrays \n",
-      "Allocating 3D arrays \n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 3 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 4 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 1\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 5 \n",
-      "my_absoprtion       [0]: 1.391566 \n",
-      "number of processes [0]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 1\n",
-      "\n",
-      "name of geometry    [0]: orange_layer_0_layer \n",
-      "component index     [0]: 6 \n",
-      "Volume.name         [0]: orange_layer_0_layer \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 1.391566 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.055500 \n",
-      "Volume.geometry.center.z             [0]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.037500 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.287000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: orange_layer_0_layer_vac \n",
-      "component index     [1]: 7 \n",
-      "Volume.name         [1]: orange_layer_0_layer_vac \n",
-      "Volume.p_physics.is_vacuum           [1]: 1 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.000000 \n",
-      "Volume.p_physics.number of processes [1]: 0 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.058500 \n",
-      "Volume.geometry.center.z             [1]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.035000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.283000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: orange_layer_0_outer_cut_0 \n",
-      "component index     [2]: 8 \n",
-      "Volume.name         [2]: orange_layer_0_outer_cut_0 \n",
-      "Volume.p_physics.is_vacuum           [2]: 1 \n",
-      "Volume.p_physics.my_absorption       [2]: 0.000000 \n",
-      "Volume.p_physics.number of processes [2]: 0 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: -0.021890 \n",
-      "Volume.geometry.center.z             [2]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.037501 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.132621 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: orange_layer_0_outer_cut_replace_0 \n",
-      "component index     [3]: 9 \n",
-      "Volume.name         [3]: orange_layer_0_outer_cut_replace_0 \n",
-      "Volume.p_physics.is_vacuum           [3]: 0 \n",
-      "Volume.p_physics.my_absorption       [3]: 1.391566 \n",
-      "Volume.p_physics.number of processes [3]: 2 \n",
-      "Volume.geometry.shape                [3]: cylinder \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: -0.021890 \n",
-      "Volume.geometry.center.z             [3]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.036500 \n",
-      "Volume.geometry.geometry_parameters.height [3]: 0.132625 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [4]: orange_layer_0_inner_cut_0 \n",
-      "component index     [4]: 10 \n",
-      "Volume.name         [4]: orange_layer_0_inner_cut_0 \n",
-      "Volume.p_physics.is_vacuum           [4]: 1 \n",
-      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
-      "Volume.p_physics.number of processes [4]: 0 \n",
-      "Volume.geometry.shape                [4]: cylinder \n",
-      "Volume.geometry.center.x             [4]: 0.000000 \n",
-      "Volume.geometry.center.y             [4]: 0.000000 \n",
-      "Volume.geometry.center.z             [4]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.035500 \n",
-      "Volume.geometry.geometry_parameters.height [4]: 0.040000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [5]: orange_layer_1_layer \n",
-      "component index     [5]: 11 \n",
-      "Volume.name         [5]: orange_layer_1_layer \n",
-      "Volume.p_physics.is_vacuum           [5]: 0 \n",
-      "Volume.p_physics.my_absorption       [5]: 1.391566 \n",
-      "Volume.p_physics.number of processes [5]: 2 \n",
-      "Volume.geometry.shape                [5]: cylinder \n",
-      "Volume.geometry.center.x             [5]: 0.000000 \n",
-      "Volume.geometry.center.y             [5]: 0.073500 \n",
-      "Volume.geometry.center.z             [5]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.040500 \n",
-      "Volume.geometry.geometry_parameters.height [5]: 0.331000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [6]: orange_layer_1_layer_vac \n",
-      "component index     [6]: 12 \n",
-      "Volume.name         [6]: orange_layer_1_layer_vac \n",
-      "Volume.p_physics.is_vacuum           [6]: 1 \n",
-      "Volume.p_physics.my_absorption       [6]: 0.000000 \n",
-      "Volume.p_physics.number of processes [6]: 0 \n",
-      "Volume.geometry.shape                [6]: cylinder \n",
-      "Volume.geometry.center.x             [6]: 0.000000 \n",
-      "Volume.geometry.center.y             [6]: 0.075000 \n",
-      "Volume.geometry.center.z             [6]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.040000 \n",
-      "Volume.geometry.geometry_parameters.height [6]: 0.330000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [7]: orange_layer_2_layer \n",
-      "component index     [7]: 13 \n",
-      "Volume.name         [7]: orange_layer_2_layer \n",
-      "Volume.p_physics.is_vacuum           [7]: 0 \n",
-      "Volume.p_physics.my_absorption       [7]: 1.391566 \n",
-      "Volume.p_physics.number of processes [7]: 2 \n",
-      "Volume.geometry.shape                [7]: cylinder \n",
-      "Volume.geometry.center.x             [7]: 0.000000 \n",
-      "Volume.geometry.center.y             [7]: 0.067500 \n",
-      "Volume.geometry.center.z             [7]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [7]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [7]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [7]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [7]: 0.049750 \n",
-      "Volume.geometry.geometry_parameters.height [7]: 0.333000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [7]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [8]: orange_layer_2_layer_vac \n",
-      "component index     [8]: 14 \n",
-      "Volume.name         [8]: orange_layer_2_layer_vac \n",
-      "Volume.p_physics.is_vacuum           [8]: 1 \n",
-      "Volume.p_physics.my_absorption       [8]: 0.000000 \n",
-      "Volume.p_physics.number of processes [8]: 0 \n",
-      "Volume.geometry.shape                [8]: cylinder \n",
-      "Volume.geometry.center.x             [8]: 0.000000 \n",
-      "Volume.geometry.center.y             [8]: 0.066000 \n",
-      "Volume.geometry.center.z             [8]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [8]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [8]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [8]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [8]: 0.047500 \n",
-      "Volume.geometry.geometry_parameters.height [8]: 0.318000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [8]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [9]: orange_layer_2_outer_cut_0 \n",
-      "component index     [9]: 15 \n",
-      "Volume.name         [9]: orange_layer_2_outer_cut_0 \n",
-      "Volume.p_physics.is_vacuum           [9]: 1 \n",
-      "Volume.p_physics.my_absorption       [9]: 0.000000 \n",
-      "Volume.p_physics.number of processes [9]: 0 \n",
-      "Volume.geometry.shape                [9]: cylinder \n",
-      "Volume.geometry.center.x             [9]: 0.000000 \n",
-      "Volume.geometry.center.y             [9]: -0.024000 \n",
-      "Volume.geometry.center.z             [9]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [9]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [9]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [9]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [9]: 0.049751 \n",
-      "Volume.geometry.geometry_parameters.height [9]: 0.152001 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [9]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [10]: orange_layer_2_outer_cut_replace_0 \n",
-      "component index     [10]: 16 \n",
-      "Volume.name         [10]: orange_layer_2_outer_cut_replace_0 \n",
-      "Volume.p_physics.is_vacuum           [10]: 0 \n",
-      "Volume.p_physics.my_absorption       [10]: 1.391566 \n",
-      "Volume.p_physics.number of processes [10]: 2 \n",
-      "Volume.geometry.shape                [10]: cylinder \n",
-      "Volume.geometry.center.x             [10]: 0.000000 \n",
-      "Volume.geometry.center.y             [10]: -0.024000 \n",
-      "Volume.geometry.center.z             [10]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [10]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [10]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [10]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [10]: 0.048500 \n",
-      "Volume.geometry.geometry_parameters.height [10]: 0.152005 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [10]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [11]: orange_layer_3_layer \n",
-      "component index     [11]: 17 \n",
-      "Volume.name         [11]: orange_layer_3_layer \n",
-      "Volume.p_physics.is_vacuum           [11]: 0 \n",
-      "Volume.p_physics.my_absorption       [11]: 1.391566 \n",
-      "Volume.p_physics.number of processes [11]: 2 \n",
-      "Volume.geometry.shape                [11]: cylinder \n",
-      "Volume.geometry.center.x             [11]: 0.000000 \n",
-      "Volume.geometry.center.y             [11]: 0.053500 \n",
-      "Volume.geometry.center.z             [11]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [11]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [11]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [11]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [11]: 0.063500 \n",
-      "Volume.geometry.geometry_parameters.height [11]: 0.347000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [11]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [12]: orange_layer_3_layer_vac \n",
-      "component index     [12]: 18 \n",
-      "Volume.name         [12]: orange_layer_3_layer_vac \n",
-      "Volume.p_physics.is_vacuum           [12]: 1 \n",
-      "Volume.p_physics.my_absorption       [12]: 0.000000 \n",
-      "Volume.p_physics.number of processes [12]: 0 \n",
-      "Volume.geometry.shape                [12]: cylinder \n",
-      "Volume.geometry.center.x             [12]: 0.000000 \n",
-      "Volume.geometry.center.y             [12]: 0.048000 \n",
-      "Volume.geometry.center.z             [12]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [12]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [12]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [12]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [12]: 0.060000 \n",
-      "Volume.geometry.geometry_parameters.height [12]: 0.314000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [12]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [13]: orange_layer_3_outer_cut_0 \n",
-      "component index     [13]: 19 \n",
-      "Volume.name         [13]: orange_layer_3_outer_cut_0 \n",
-      "Volume.p_physics.is_vacuum           [13]: 1 \n",
-      "Volume.p_physics.my_absorption       [13]: 0.000000 \n",
-      "Volume.p_physics.number of processes [13]: 0 \n",
-      "Volume.geometry.shape                [13]: cylinder \n",
-      "Volume.geometry.center.x             [13]: 0.000000 \n",
-      "Volume.geometry.center.y             [13]: -0.018920 \n",
-      "Volume.geometry.center.z             [13]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [13]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [13]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [13]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [13]: 0.063501 \n",
-      "Volume.geometry.geometry_parameters.height [13]: 0.149241 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [13]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [14]: orange_layer_3_outer_cut_replace_0 \n",
-      "component index     [14]: 20 \n",
-      "Volume.name         [14]: orange_layer_3_outer_cut_replace_0 \n",
-      "Volume.p_physics.is_vacuum           [14]: 0 \n",
-      "Volume.p_physics.my_absorption       [14]: 1.391566 \n",
-      "Volume.p_physics.number of processes [14]: 2 \n",
-      "Volume.geometry.shape                [14]: cylinder \n",
-      "Volume.geometry.center.x             [14]: 0.000000 \n",
-      "Volume.geometry.center.y             [14]: -0.018920 \n",
-      "Volume.geometry.center.z             [14]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [14]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [14]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [14]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [14]: 0.062500 \n",
-      "Volume.geometry.geometry_parameters.height [14]: 0.149245 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [14]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [15]: orange_layer_3_inner_cut_0 \n",
-      "component index     [15]: 21 \n",
-      "Volume.name         [15]: orange_layer_3_inner_cut_0 \n",
-      "Volume.p_physics.is_vacuum           [15]: 1 \n",
-      "Volume.p_physics.my_absorption       [15]: 0.000000 \n",
-      "Volume.p_physics.number of processes [15]: 0 \n",
-      "Volume.geometry.shape                [15]: cylinder \n",
-      "Volume.geometry.center.x             [15]: 0.000000 \n",
-      "Volume.geometry.center.y             [15]: -0.018920 \n",
-      "Volume.geometry.center.z             [15]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [15]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [15]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [15]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [15]: 0.061000 \n",
-      "Volume.geometry.geometry_parameters.height [15]: 0.149240 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [15]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 17\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]\n",
-      "LIST: Direct_children for Volume           0 = [6,7,8,12,14]\n",
-      "LIST: Intersect_check_list for Volume      0 = [6,7,8,12,14]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [6,7,8,12,14]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for orange_layer_0_layer with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  1 = [5]\n",
-      "LIST: Direct_children for Volume           1 = [5]\n",
-      "LIST: Intersect_check_list for Volume      1 = [2,3,4]\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [7,10,11,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = [7,10,11,14,15]\n",
-      "LIST: Next_volume_list for Volume          1 = [7,10,11,14,15,16,2,3,4]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for orange_layer_0_layer_vac with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  2 = []\n",
-      "LIST: Direct_children for Volume           2 = []\n",
-      "LIST: Intersect_check_list for Volume      2 = []\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [1,3,4,5,7,10,11,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [7,10,11,14,15]\n",
-      "LIST: Next_volume_list for Volume          2 = [1,3,4,5,7,10,11,14,15,16]\n",
-      "      Is_vacuum for Volume                 2 = 1\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for orange_layer_0_outer_cut_0 with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  3 = [5]\n",
-      "LIST: Direct_children for Volume           3 = [5]\n",
-      "LIST: Intersect_check_list for Volume      3 = [2,4]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [1,7]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = [7]\n",
-      "LIST: Next_volume_list for Volume          3 = [1,7,2,4]\n",
-      "      Is_vacuum for Volume                 3 = 1\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "List overview for orange_layer_0_outer_cut_replace_0 with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  4 = [5]\n",
-      "LIST: Direct_children for Volume           4 = [5]\n",
-      "LIST: Intersect_check_list for Volume      4 = [2,5]\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [1,3,7]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = [7]\n",
-      "LIST: Next_volume_list for Volume          4 = [1,3,7,2,5]\n",
-      "      Is_vacuum for Volume                 4 = 0\n",
-      "      is_mask_volume for Volume            4 = 0\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = []\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 0\n",
-      "\n",
-      "List overview for orange_layer_0_inner_cut_0 with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  5 = []\n",
-      "LIST: Direct_children for Volume           5 = []\n",
-      "LIST: Intersect_check_list for Volume      5 = [2]\n",
-      "LIST: Mask_intersect_list for Volume       5 = []\n",
-      "LIST: Destinations_list for Volume         5 = [4]\n",
-      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
-      "LIST: Next_volume_list for Volume          5 = [4,2]\n",
-      "      Is_vacuum for Volume                 5 = 1\n",
-      "      is_mask_volume for Volume            5 = 0\n",
-      "      is_masked_volume for Volume          5 = 0\n",
-      "      is_exit_volume for Volume            5 = 0\n",
-      "LIST: mask_list for Volume                 5 = []\n",
-      "LIST: masked_by_list for Volume            5 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
-      "      mask_mode for Volume                 5 = 0\n",
-      "\n",
-      "List overview for orange_layer_1_layer with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  6 = [1,2,3,4,5]\n",
-      "LIST: Direct_children for Volume           6 = [1,2,3,4]\n",
-      "LIST: Intersect_check_list for Volume      6 = [7]\n",
-      "LIST: Mask_intersect_list for Volume       6 = []\n",
-      "LIST: Destinations_list for Volume         6 = [0,8,9,10,11,12,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 6 = [8,12,14]\n",
-      "LIST: Next_volume_list for Volume          6 = [0,8,9,10,11,12,13,14,15,16,7]\n",
-      "      Is_vacuum for Volume                 6 = 0\n",
-      "      is_mask_volume for Volume            6 = 0\n",
-      "      is_masked_volume for Volume          6 = 0\n",
-      "      is_exit_volume for Volume            6 = 0\n",
-      "LIST: mask_list for Volume                 6 = []\n",
-      "LIST: masked_by_list for Volume            6 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
-      "      mask_mode for Volume                 6 = 0\n",
-      "\n",
-      "List overview for orange_layer_1_layer_vac with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  7 = [1,2,3,4,5]\n",
-      "LIST: Direct_children for Volume           7 = [1,2,3,4]\n",
-      "LIST: Intersect_check_list for Volume      7 = [1,2,3,4]\n",
-      "LIST: Mask_intersect_list for Volume       7 = []\n",
-      "LIST: Destinations_list for Volume         7 = [0,6,8,9,10,11,12,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 7 = [6,8,12,14]\n",
-      "LIST: Next_volume_list for Volume          7 = [0,6,8,9,10,11,12,13,14,15,16,1,2,3,4]\n",
-      "      Is_vacuum for Volume                 7 = 1\n",
-      "      is_mask_volume for Volume            7 = 0\n",
-      "      is_masked_volume for Volume          7 = 0\n",
-      "      is_exit_volume for Volume            7 = 0\n",
-      "LIST: mask_list for Volume                 7 = []\n",
-      "LIST: masked_by_list for Volume            7 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
-      "      mask_mode for Volume                 7 = 0\n",
-      "\n",
-      "List overview for orange_layer_2_layer with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  8 = [1,2,3,4,5,9]\n",
-      "LIST: Direct_children for Volume           8 = [9]\n",
-      "LIST: Intersect_check_list for Volume      8 = [6,7,9,10,11]\n",
-      "LIST: Mask_intersect_list for Volume       8 = []\n",
-      "LIST: Destinations_list for Volume         8 = [0,12,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 8 = [12,14]\n",
-      "LIST: Next_volume_list for Volume          8 = [0,12,13,14,15,16,6,7,9,10,11]\n",
-      "      Is_vacuum for Volume                 8 = 0\n",
-      "      is_mask_volume for Volume            8 = 0\n",
-      "      is_masked_volume for Volume          8 = 0\n",
-      "      is_exit_volume for Volume            8 = 0\n",
-      "LIST: mask_list for Volume                 8 = []\n",
-      "LIST: masked_by_list for Volume            8 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 8 = []\n",
-      "      mask_mode for Volume                 8 = 0\n",
-      "\n",
-      "List overview for orange_layer_2_layer_vac with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  9 = [1,2,3,4,5]\n",
-      "LIST: Direct_children for Volume           9 = [1,2,3,4]\n",
-      "LIST: Intersect_check_list for Volume      9 = [6,7]\n",
-      "LIST: Mask_intersect_list for Volume       9 = []\n",
-      "LIST: Destinations_list for Volume         9 = [8,10,11,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 9 = [8,13,14,15]\n",
-      "LIST: Next_volume_list for Volume          9 = [8,10,11,13,14,15,16,6,7]\n",
-      "      Is_vacuum for Volume                 9 = 1\n",
-      "      is_mask_volume for Volume            9 = 0\n",
-      "      is_masked_volume for Volume          9 = 0\n",
-      "      is_exit_volume for Volume            9 = 0\n",
-      "LIST: mask_list for Volume                 9 = []\n",
-      "LIST: masked_by_list for Volume            9 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 9 = []\n",
-      "      mask_mode for Volume                 9 = 0\n",
-      "\n",
-      "List overview for orange_layer_2_outer_cut_0 with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  10 = [3,4,5]\n",
-      "LIST: Direct_children for Volume           10 = [3,4]\n",
-      "LIST: Intersect_check_list for Volume      10 = [6,7,9,11]\n",
-      "LIST: Mask_intersect_list for Volume       10 = []\n",
-      "LIST: Destinations_list for Volume         10 = [8,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 10 = [8,13,14,15]\n",
-      "LIST: Next_volume_list for Volume          10 = [8,13,14,15,16,6,7,9,11]\n",
-      "      Is_vacuum for Volume                 10 = 1\n",
-      "      is_mask_volume for Volume            10 = 0\n",
-      "      is_masked_volume for Volume          10 = 0\n",
-      "      is_exit_volume for Volume            10 = 0\n",
-      "LIST: mask_list for Volume                 10 = []\n",
-      "LIST: masked_by_list for Volume            10 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 10 = []\n",
-      "      mask_mode for Volume                 10 = 0\n",
-      "\n",
-      "List overview for orange_layer_2_outer_cut_replace_0 with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  11 = [3,4,5]\n",
-      "LIST: Direct_children for Volume           11 = [3,4]\n",
-      "LIST: Intersect_check_list for Volume      11 = [6,7,9]\n",
-      "LIST: Mask_intersect_list for Volume       11 = []\n",
-      "LIST: Destinations_list for Volume         11 = [8,10,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 11 = [8,13,14,15]\n",
-      "LIST: Next_volume_list for Volume          11 = [8,10,13,14,15,16,6,7,9]\n",
-      "      Is_vacuum for Volume                 11 = 0\n",
-      "      is_mask_volume for Volume            11 = 0\n",
-      "      is_masked_volume for Volume          11 = 0\n",
-      "      is_exit_volume for Volume            11 = 0\n",
-      "LIST: mask_list for Volume                 11 = []\n",
-      "LIST: masked_by_list for Volume            11 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 11 = []\n",
-      "      mask_mode for Volume                 11 = 0\n",
-      "\n",
-      "List overview for orange_layer_3_layer with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  12 = [1,2,3,4,5,9,10,11,13,15,16]\n",
-      "LIST: Direct_children for Volume           12 = [9,13,15]\n",
-      "LIST: Intersect_check_list for Volume      12 = [6,7,8,13,14,15]\n",
-      "LIST: Mask_intersect_list for Volume       12 = []\n",
-      "LIST: Destinations_list for Volume         12 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 12 = []\n",
-      "LIST: Next_volume_list for Volume          12 = [0,6,7,8,13,14,15]\n",
-      "      Is_vacuum for Volume                 12 = 0\n",
-      "      is_mask_volume for Volume            12 = 0\n",
-      "      is_masked_volume for Volume          12 = 0\n",
-      "      is_exit_volume for Volume            12 = 0\n",
-      "LIST: mask_list for Volume                 12 = []\n",
-      "LIST: masked_by_list for Volume            12 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 12 = []\n",
-      "      mask_mode for Volume                 12 = 0\n",
-      "\n",
-      "List overview for orange_layer_3_layer_vac with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  13 = [1,2,3,4,5,10,11]\n",
-      "LIST: Direct_children for Volume           13 = [1,2,10,11]\n",
-      "LIST: Intersect_check_list for Volume      13 = [6,7,8,10,11]\n",
-      "LIST: Mask_intersect_list for Volume       13 = []\n",
-      "LIST: Destinations_list for Volume         13 = [12,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 13 = [12,14]\n",
-      "LIST: Next_volume_list for Volume          13 = [12,14,15,16,6,7,8,10,11]\n",
-      "      Is_vacuum for Volume                 13 = 1\n",
-      "      is_mask_volume for Volume            13 = 0\n",
-      "      is_masked_volume for Volume          13 = 0\n",
-      "      is_exit_volume for Volume            13 = 0\n",
-      "LIST: mask_list for Volume                 13 = []\n",
-      "LIST: masked_by_list for Volume            13 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 13 = []\n",
-      "      mask_mode for Volume                 13 = 0\n",
-      "\n",
-      "List overview for orange_layer_3_outer_cut_0 with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  14 = [3,4,5,16]\n",
-      "LIST: Direct_children for Volume           14 = [16]\n",
-      "LIST: Intersect_check_list for Volume      14 = [6,7,8,13,15]\n",
-      "LIST: Mask_intersect_list for Volume       14 = []\n",
-      "LIST: Destinations_list for Volume         14 = [0,12]\n",
-      "LIST: Reduced_destinations_list for Volume 14 = [12]\n",
-      "LIST: Next_volume_list for Volume          14 = [0,12,6,7,8,13,15]\n",
-      "      Is_vacuum for Volume                 14 = 1\n",
-      "      is_mask_volume for Volume            14 = 0\n",
-      "      is_masked_volume for Volume          14 = 0\n",
-      "      is_exit_volume for Volume            14 = 0\n",
-      "LIST: mask_list for Volume                 14 = []\n",
-      "LIST: masked_by_list for Volume            14 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 14 = []\n",
-      "      mask_mode for Volume                 14 = 0\n",
-      "\n",
-      "List overview for orange_layer_3_outer_cut_replace_0 with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  15 = [3,4,5,16]\n",
-      "LIST: Direct_children for Volume           15 = [16]\n",
-      "LIST: Intersect_check_list for Volume      15 = [6,7,8,13,16]\n",
-      "LIST: Mask_intersect_list for Volume       15 = []\n",
-      "LIST: Destinations_list for Volume         15 = [12,14]\n",
-      "LIST: Reduced_destinations_list for Volume 15 = [12,14]\n",
-      "LIST: Next_volume_list for Volume          15 = [12,14,6,7,8,13,16]\n",
-      "      Is_vacuum for Volume                 15 = 0\n",
-      "      is_mask_volume for Volume            15 = 0\n",
-      "      is_masked_volume for Volume          15 = 0\n",
-      "      is_exit_volume for Volume            15 = 0\n",
-      "LIST: mask_list for Volume                 15 = []\n",
-      "LIST: masked_by_list for Volume            15 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 15 = []\n",
-      "      mask_mode for Volume                 15 = 0\n",
-      "\n",
-      "List overview for orange_layer_3_inner_cut_0 with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  16 = [3,4,5]\n",
-      "LIST: Direct_children for Volume           16 = [3,4]\n",
-      "LIST: Intersect_check_list for Volume      16 = [6,7,8,13]\n",
-      "LIST: Mask_intersect_list for Volume       16 = []\n",
-      "LIST: Destinations_list for Volume         16 = [15]\n",
-      "LIST: Reduced_destinations_list for Volume 16 = [15]\n",
-      "LIST: Next_volume_list for Volume          16 = [15,6,7,8,13]\n",
-      "      Is_vacuum for Volume                 16 = 1\n",
-      "      is_mask_volume for Volume            16 = 0\n",
-      "      is_masked_volume for Volume          16 = 0\n",
-      "      is_exit_volume for Volume            16 = 0\n",
-      "LIST: mask_list for Volume                 16 = []\n",
-      "LIST: masked_by_list for Volume            16 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 16 = []\n",
-      "      mask_mode for Volume                 16 = 0\n",
-      "\n",
-      "Union_master component orange_master initialized sucessfully\n",
-      "Detector: orange_logger_space_zx_I=3.27832e-07 orange_logger_space_zx_ERR=1.38369e-10 orange_logger_space_zx_N=1.74613e+07 \"orange_space_zx.dat\"\n",
-      "Detector: orange_logger_space_zy_I=3.27832e-07 orange_logger_space_zy_ERR=1.38369e-10 orange_logger_space_zy_N=1.74613e+07 \"orange_space_zy.dat\"\n",
-      "Detector: orange_logger_space_xy_I=3.27832e-07 orange_logger_space_xy_ERR=1.38369e-10 orange_logger_space_xy_N=1.74613e+07 \"orange_space_xy.dat\"\n",
-      "Detector: orange_logger_space_zy_close_I=1.59651e-07 orange_logger_space_zy_close_ERR=7.01031e-11 orange_logger_space_zy_close_N=8.35287e+06 \"orange_space_zy_close.dat_0\"\n",
-      "Detector: orange_logger_time_I=3.27723e-07 orange_logger_time_ERR=1.38327e-10 orange_logger_time_N=1.72568e+07 \"NULL.dat\"\n",
-      "Detector: orange_logger_space_zy_time_I=1.60364e-07 orange_logger_space_zy_time_ERR=6.99308e-11 orange_logger_space_zy_time_N=8.91229e+06 \"orange_logger_space_zy_time.dat_0\"\n",
-      "Detector: orange_logger_space_zy_time_I=1.53909e-07 orange_logger_space_zy_time_ERR=1.15565e-10 orange_logger_space_zy_time_N=4.99431e+06 \"orange_logger_space_zy_time.dat_1\"\n",
-      "Detector: orange_logger_space_zy_time_I=8.68838e-09 orange_logger_space_zy_time_ERR=1.9819e-11 orange_logger_space_zy_time_N=2.08652e+06 \"orange_logger_space_zy_time.dat_2\"\n",
-      "Detector: orange_logger_space_zy_time_I=4.0846e-09 orange_logger_space_zy_time_ERR=1.94728e-11 orange_logger_space_zy_time_N=929554 \"orange_logger_space_zy_time.dat_3\"\n",
-      "Detector: orange_logger_space_zy_time_I=6.77188e-10 orange_logger_space_zy_time_ERR=1.08073e-11 orange_logger_space_zy_time_N=334166 \"orange_logger_space_zy_time.dat_4\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.settings(ncount=1E7)\n",
     "data = instrument.backengine()"
@@ -1256,25 +297,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "mobile-strike",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "621d564560654be190ea2c9eabcaba47",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(Output(layout=Layout(width='75%')), VBox(children=(Label(value='Choose monitor'), Dropdown(layo…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%matplotlib widget\n",
     "ms.interface(data)"
@@ -1282,91 +308,29 @@
   },
   {
    "cell_type": "markdown",
-   "id": "wound-scoop",
+   "id": "floral-injury",
    "metadata": {},
    "source": [
-    "The \"logger_space_zy_time\" monitor can be animated with "
+    "The \"logger_space_zy_time\" monitor can be animated with the built in *make_animation* function."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "inclusive-ireland",
+   "id": "rural-lingerie",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f27279a9d93947ab81ce317e50671659",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2022-03-22 18:53:45,585 WARNING:MovieWriter imagemagick unavailable; using Pillow instead.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name orange_logger_space_zy_time\n",
-      "Saving animation with filename : \"cryostat_animation.gif\"\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "ani_data = data[5:10]\n",
     "for frame in ani_data:\n",
     "    frame.set_plot_options(log=True, colormap=\"hot\", orders_of_mag=6)\n",
     "    \n",
-    "ms.make_animation(ani_data, filename=\"cryostat_animation\", fps=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "collective-award",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[\n",
-      "McStasData: orange_logger_space_zy_time type: 2D  I:1.60364e-07 E:6.99308e-11 N:8.91229e+06, \n",
-      "McStasData: orange_logger_space_zy_time_1 type: 2D  I:1.53909e-07 E:1.15565e-10 N:4.99431e+06, \n",
-      "McStasData: orange_logger_space_zy_time_2 type: 2D  I:8.68838e-09 E:1.9819e-11 N:2.08652e+06, \n",
-      "McStasData: orange_logger_space_zy_time_3 type: 2D  I:4.0846e-09 E:1.94728e-11 N:929554, \n",
-      "McStasData: orange_logger_space_zy_time_4 type: 2D  I:6.77188e-10 E:1.08073e-11 N:334166]\n",
-      "metadata object\n",
-      "component_name: orange_logger_space_zy_time\n",
-      "filename: orange_logger_space_zy_time.dat_1\n",
-      "2D data of dimension (300, 300)\n",
-      "  [-0.06985: 0.06985] z [m]\n",
-      "  [-0.132: 0.2497] y [m]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "ani_data = ms.name_search(\"orange_logger_space_zy_time\", data)\n",
-    "print(ani_data)\n",
-    "print(data[6].metadata)"
+    "ms.make_animation(ani_data, filename=\"cryostat_animation\", fps=2, figsize=(10,6))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "trained-harvey",
+   "id": "announced-prevention",
    "metadata": {},
    "source": [
     "## Adding a sample\n",
@@ -1377,8 +341,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "intimate-joint",
+   "execution_count": null,
+   "id": "frequent-asbestos",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1404,888 +368,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "subjective-continuity",
+   "execution_count": null,
+   "id": "liquid-connectivity",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/cryostat_demo_data_8\"\n",
-      "INFO: Regenerating c-file: cryostat_demo.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./cryostat_demo.out\n",
-      "mccode-r.c:1880:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "} /* mcsiminfo_init */\n",
-      "^\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:201:5: note: expanded from macro 'MPI_MASTER'\n",
-      "  { statement; } \\\n",
-      "    ^~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:19430:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:19430:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:19673:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:19673:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:19916:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_xy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:19916:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_xy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_logger_3D_space.comp:668:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:20165:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_close_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_3D_space.comp:668:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:20165:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_close_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_logger_1D.comp:479:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:20472:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_time_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_1D.comp:479:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:20472:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_time_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_logger_2D_space_time.comp:632:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:20669:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_time_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_2D_space_time.comp:632:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:20669:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_time_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:201:5: note: expanded from macro 'MPI_MASTER'\n",
-      "  { statement; } \\\n",
-      "    ^~~~~~~~~\n",
-      "Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:201:5: note: expanded from macro 'MPI_MASTER'\n",
-      "  { statement; } \\\n",
-      "    ^~~~~~~~~\n",
-      "Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:201:5: note: expanded from macro 'MPI_MASTER'\n",
-      "  { statement; } \\\n",
-      "    ^~~~~~~~~\n",
-      "19 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy cryostat_demo.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/cryostat_demo_data_8\n",
-      "\n",
-      "    Simulation 'cryostat_demo' (cryostat_demo.instr): running on 4 nodes (master is 'CI0021617', MPI version 3.1).\n",
-      "Instrument parameters for cryostat_demo (cryostat_demo.instr)\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: nascalf_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: nascalf_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: nascalf_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: nascalf_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: nascalf_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: nascalf_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "PowderN: nascalf_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: nascalf_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "PowderN: nascalf_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: nascalf_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: nascalf_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "PowderN: nascalf_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "Allocating 3D arrays \n",
-      "Allocating 3D arrays \n",
-      "Allocating 3D arrays \n",
-      "Allocating 3D arrays \n",
-      "Allocating 3D arrays \n",
-      "Allocating 3D arrays \n",
-      "Allocating 3D arrays \n",
-      "Allocating 3D arrays \n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 4\n",
-      "name of process [0]: nascalf_inc \n",
-      "component index [0]: 3 \n",
-      "name of process [1]: nascalf_pow \n",
-      "component index [1]: 4 \n",
-      "name of process [2]: Al_inc \n",
-      "component index [2]: 6 \n",
-      "name of process [3]: Al_pow \n",
-      "component index [3]: 7 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 2\n",
-      "name of material    [0]: nascalf \n",
-      "component index     [0]: 5 \n",
-      "my_absoprtion       [0]: 0.273042 \n",
-      "number of processes [0]: 2 \n",
-      "name of material    [1]: Al \n",
-      "component index     [1]: 8 \n",
-      "my_absoprtion       [1]: 1.391566 \n",
-      "number of processes [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 2\n",
-      "\n",
-      "name of geometry    [0]: orange_layer_0_layer \n",
-      "component index     [0]: 9 \n",
-      "Volume.name         [0]: orange_layer_0_layer \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 1.391566 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.055500 \n",
-      "Volume.geometry.center.z             [0]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.037500 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.287000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: orange_layer_0_layer_vac \n",
-      "component index     [1]: 10 \n",
-      "Volume.name         [1]: orange_layer_0_layer_vac \n",
-      "Volume.p_physics.is_vacuum           [1]: 1 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.000000 \n",
-      "Volume.p_physics.number of processes [1]: 0 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.058500 \n",
-      "Volume.geometry.center.z             [1]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.035000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.283000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: orange_layer_0_outer_cut_0 \n",
-      "component index     [2]: 11 \n",
-      "Volume.name         [2]: orange_layer_0_outer_cut_0 \n",
-      "Volume.p_physics.is_vacuum           [2]: 1 \n",
-      "Volume.p_physics.my_absorption       [2]: 0.000000 \n",
-      "Volume.p_physics.number of processes [2]: 0 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: -0.021890 \n",
-      "Volume.geometry.center.z             [2]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.037501 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.132621 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: orange_layer_0_outer_cut_replace_0 \n",
-      "component index     [3]: 12 \n",
-      "Volume.name         [3]: orange_layer_0_outer_cut_replace_0 \n",
-      "Volume.p_physics.is_vacuum           [3]: 0 \n",
-      "Volume.p_physics.my_absorption       [3]: 1.391566 \n",
-      "Volume.p_physics.number of processes [3]: 2 \n",
-      "Volume.geometry.shape                [3]: cylinder \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: -0.021890 \n",
-      "Volume.geometry.center.z             [3]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.036500 \n",
-      "Volume.geometry.geometry_parameters.height [3]: 0.132625 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [4]: orange_layer_0_inner_cut_0 \n",
-      "component index     [4]: 13 \n",
-      "Volume.name         [4]: orange_layer_0_inner_cut_0 \n",
-      "Volume.p_physics.is_vacuum           [4]: 1 \n",
-      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
-      "Volume.p_physics.number of processes [4]: 0 \n",
-      "Volume.geometry.shape                [4]: cylinder \n",
-      "Volume.geometry.center.x             [4]: 0.000000 \n",
-      "Volume.geometry.center.y             [4]: 0.000000 \n",
-      "Volume.geometry.center.z             [4]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.035500 \n",
-      "Volume.geometry.geometry_parameters.height [4]: 0.040000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [5]: orange_layer_1_layer \n",
-      "component index     [5]: 14 \n",
-      "Volume.name         [5]: orange_layer_1_layer \n",
-      "Volume.p_physics.is_vacuum           [5]: 0 \n",
-      "Volume.p_physics.my_absorption       [5]: 1.391566 \n",
-      "Volume.p_physics.number of processes [5]: 2 \n",
-      "Volume.geometry.shape                [5]: cylinder \n",
-      "Volume.geometry.center.x             [5]: 0.000000 \n",
-      "Volume.geometry.center.y             [5]: 0.073500 \n",
-      "Volume.geometry.center.z             [5]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.040500 \n",
-      "Volume.geometry.geometry_parameters.height [5]: 0.331000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [6]: orange_layer_1_layer_vac \n",
-      "component index     [6]: 15 \n",
-      "Volume.name         [6]: orange_layer_1_layer_vac \n",
-      "Volume.p_physics.is_vacuum           [6]: 1 \n",
-      "Volume.p_physics.my_absorption       [6]: 0.000000 \n",
-      "Volume.p_physics.number of processes [6]: 0 \n",
-      "Volume.geometry.shape                [6]: cylinder \n",
-      "Volume.geometry.center.x             [6]: 0.000000 \n",
-      "Volume.geometry.center.y             [6]: 0.075000 \n",
-      "Volume.geometry.center.z             [6]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.040000 \n",
-      "Volume.geometry.geometry_parameters.height [6]: 0.330000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [7]: orange_layer_2_layer \n",
-      "component index     [7]: 16 \n",
-      "Volume.name         [7]: orange_layer_2_layer \n",
-      "Volume.p_physics.is_vacuum           [7]: 0 \n",
-      "Volume.p_physics.my_absorption       [7]: 1.391566 \n",
-      "Volume.p_physics.number of processes [7]: 2 \n",
-      "Volume.geometry.shape                [7]: cylinder \n",
-      "Volume.geometry.center.x             [7]: 0.000000 \n",
-      "Volume.geometry.center.y             [7]: 0.067500 \n",
-      "Volume.geometry.center.z             [7]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [7]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [7]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [7]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [7]: 0.049750 \n",
-      "Volume.geometry.geometry_parameters.height [7]: 0.333000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [7]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [8]: orange_layer_2_layer_vac \n",
-      "component index     [8]: 17 \n",
-      "Volume.name         [8]: orange_layer_2_layer_vac \n",
-      "Volume.p_physics.is_vacuum           [8]: 1 \n",
-      "Volume.p_physics.my_absorption       [8]: 0.000000 \n",
-      "Volume.p_physics.number of processes [8]: 0 \n",
-      "Volume.geometry.shape                [8]: cylinder \n",
-      "Volume.geometry.center.x             [8]: 0.000000 \n",
-      "Volume.geometry.center.y             [8]: 0.066000 \n",
-      "Volume.geometry.center.z             [8]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [8]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [8]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [8]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [8]: 0.047500 \n",
-      "Volume.geometry.geometry_parameters.height [8]: 0.318000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [8]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [9]: orange_layer_2_outer_cut_0 \n",
-      "component index     [9]: 18 \n",
-      "Volume.name         [9]: orange_layer_2_outer_cut_0 \n",
-      "Volume.p_physics.is_vacuum           [9]: 1 \n",
-      "Volume.p_physics.my_absorption       [9]: 0.000000 \n",
-      "Volume.p_physics.number of processes [9]: 0 \n",
-      "Volume.geometry.shape                [9]: cylinder \n",
-      "Volume.geometry.center.x             [9]: 0.000000 \n",
-      "Volume.geometry.center.y             [9]: -0.024000 \n",
-      "Volume.geometry.center.z             [9]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [9]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [9]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [9]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [9]: 0.049751 \n",
-      "Volume.geometry.geometry_parameters.height [9]: 0.152001 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [9]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [10]: orange_layer_2_outer_cut_replace_0 \n",
-      "component index     [10]: 19 \n",
-      "Volume.name         [10]: orange_layer_2_outer_cut_replace_0 \n",
-      "Volume.p_physics.is_vacuum           [10]: 0 \n",
-      "Volume.p_physics.my_absorption       [10]: 1.391566 \n",
-      "Volume.p_physics.number of processes [10]: 2 \n",
-      "Volume.geometry.shape                [10]: cylinder \n",
-      "Volume.geometry.center.x             [10]: 0.000000 \n",
-      "Volume.geometry.center.y             [10]: -0.024000 \n",
-      "Volume.geometry.center.z             [10]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [10]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [10]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [10]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [10]: 0.048500 \n",
-      "Volume.geometry.geometry_parameters.height [10]: 0.152005 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [10]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [11]: orange_layer_3_layer \n",
-      "component index     [11]: 20 \n",
-      "Volume.name         [11]: orange_layer_3_layer \n",
-      "Volume.p_physics.is_vacuum           [11]: 0 \n",
-      "Volume.p_physics.my_absorption       [11]: 1.391566 \n",
-      "Volume.p_physics.number of processes [11]: 2 \n",
-      "Volume.geometry.shape                [11]: cylinder \n",
-      "Volume.geometry.center.x             [11]: 0.000000 \n",
-      "Volume.geometry.center.y             [11]: 0.053500 \n",
-      "Volume.geometry.center.z             [11]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [11]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [11]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [11]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [11]: 0.063500 \n",
-      "Volume.geometry.geometry_parameters.height [11]: 0.347000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [11]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [12]: orange_layer_3_layer_vac \n",
-      "component index     [12]: 21 \n",
-      "Volume.name         [12]: orange_layer_3_layer_vac \n",
-      "Volume.p_physics.is_vacuum           [12]: 1 \n",
-      "Volume.p_physics.my_absorption       [12]: 0.000000 \n",
-      "Volume.p_physics.number of processes [12]: 0 \n",
-      "Volume.geometry.shape                [12]: cylinder \n",
-      "Volume.geometry.center.x             [12]: 0.000000 \n",
-      "Volume.geometry.center.y             [12]: 0.048000 \n",
-      "Volume.geometry.center.z             [12]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [12]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [12]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [12]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [12]: 0.060000 \n",
-      "Volume.geometry.geometry_parameters.height [12]: 0.314000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [12]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [13]: orange_layer_3_outer_cut_0 \n",
-      "component index     [13]: 22 \n",
-      "Volume.name         [13]: orange_layer_3_outer_cut_0 \n",
-      "Volume.p_physics.is_vacuum           [13]: 1 \n",
-      "Volume.p_physics.my_absorption       [13]: 0.000000 \n",
-      "Volume.p_physics.number of processes [13]: 0 \n",
-      "Volume.geometry.shape                [13]: cylinder \n",
-      "Volume.geometry.center.x             [13]: 0.000000 \n",
-      "Volume.geometry.center.y             [13]: -0.018920 \n",
-      "Volume.geometry.center.z             [13]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [13]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [13]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [13]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [13]: 0.063501 \n",
-      "Volume.geometry.geometry_parameters.height [13]: 0.149241 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [13]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [14]: orange_layer_3_outer_cut_replace_0 \n",
-      "component index     [14]: 23 \n",
-      "Volume.name         [14]: orange_layer_3_outer_cut_replace_0 \n",
-      "Volume.p_physics.is_vacuum           [14]: 0 \n",
-      "Volume.p_physics.my_absorption       [14]: 1.391566 \n",
-      "Volume.p_physics.number of processes [14]: 2 \n",
-      "Volume.geometry.shape                [14]: cylinder \n",
-      "Volume.geometry.center.x             [14]: 0.000000 \n",
-      "Volume.geometry.center.y             [14]: -0.018920 \n",
-      "Volume.geometry.center.z             [14]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [14]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [14]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [14]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [14]: 0.062500 \n",
-      "Volume.geometry.geometry_parameters.height [14]: 0.149245 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [14]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [15]: orange_layer_3_inner_cut_0 \n",
-      "component index     [15]: 24 \n",
-      "Volume.name         [15]: orange_layer_3_inner_cut_0 \n",
-      "Volume.p_physics.is_vacuum           [15]: 1 \n",
-      "Volume.p_physics.my_absorption       [15]: 0.000000 \n",
-      "Volume.p_physics.number of processes [15]: 0 \n",
-      "Volume.geometry.shape                [15]: cylinder \n",
-      "Volume.geometry.center.x             [15]: 0.000000 \n",
-      "Volume.geometry.center.y             [15]: -0.018920 \n",
-      "Volume.geometry.center.z             [15]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [15]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [15]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [15]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [15]: 0.061000 \n",
-      "Volume.geometry.geometry_parameters.height [15]: 0.149240 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [15]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [16]: sample \n",
-      "component index     [16]: 31 \n",
-      "Volume.name         [16]: sample \n",
-      "Volume.p_physics.is_vacuum           [16]: 0 \n",
-      "Volume.p_physics.my_absorption       [16]: 0.273042 \n",
-      "Volume.p_physics.number of processes [16]: 2 \n",
-      "Volume.geometry.shape                [16]: cylinder \n",
-      "Volume.geometry.center.x             [16]: 0.000000 \n",
-      "Volume.geometry.center.y             [16]: 0.000000 \n",
-      "Volume.geometry.center.z             [16]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [16]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [16]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [16]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [16]: 0.006000 \n",
-      "Volume.geometry.geometry_parameters.height [16]: 0.030000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [16]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 18\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]\n",
-      "LIST: Direct_children for Volume           0 = [6,7,8,12,14]\n",
-      "LIST: Intersect_check_list for Volume      0 = [6,7,8,12,14]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [6,7,8,12,14]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for orange_layer_0_layer with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  1 = [5,17]\n",
-      "LIST: Direct_children for Volume           1 = [5]\n",
-      "LIST: Intersect_check_list for Volume      1 = [2,3,4]\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [7,10,11,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = [7,10,11,14,15]\n",
-      "LIST: Next_volume_list for Volume          1 = [7,10,11,14,15,16,2,3,4]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for orange_layer_0_layer_vac with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  2 = [17]\n",
-      "LIST: Direct_children for Volume           2 = [17]\n",
-      "LIST: Intersect_check_list for Volume      2 = [17]\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [1,3,4,5,7,10,11,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [7,10,11,14,15]\n",
-      "LIST: Next_volume_list for Volume          2 = [1,3,4,5,7,10,11,14,15,16,17]\n",
-      "      Is_vacuum for Volume                 2 = 1\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for orange_layer_0_outer_cut_0 with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  3 = [5,17]\n",
-      "LIST: Direct_children for Volume           3 = [5]\n",
-      "LIST: Intersect_check_list for Volume      3 = [2,4]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [1,7]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = [7]\n",
-      "LIST: Next_volume_list for Volume          3 = [1,7,2,4]\n",
-      "      Is_vacuum for Volume                 3 = 1\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "List overview for orange_layer_0_outer_cut_replace_0 with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  4 = [5,17]\n",
-      "LIST: Direct_children for Volume           4 = [5]\n",
-      "LIST: Intersect_check_list for Volume      4 = [2,5]\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [1,3,7]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = [7]\n",
-      "LIST: Next_volume_list for Volume          4 = [1,3,7,2,5]\n",
-      "      Is_vacuum for Volume                 4 = 0\n",
-      "      is_mask_volume for Volume            4 = 0\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = []\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 0\n",
-      "\n",
-      "List overview for orange_layer_0_inner_cut_0 with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  5 = [17]\n",
-      "LIST: Direct_children for Volume           5 = [17]\n",
-      "LIST: Intersect_check_list for Volume      5 = [2]\n",
-      "LIST: Mask_intersect_list for Volume       5 = []\n",
-      "LIST: Destinations_list for Volume         5 = [4]\n",
-      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
-      "LIST: Next_volume_list for Volume          5 = [4,2]\n",
-      "      Is_vacuum for Volume                 5 = 1\n",
-      "      is_mask_volume for Volume            5 = 0\n",
-      "      is_masked_volume for Volume          5 = 0\n",
-      "      is_exit_volume for Volume            5 = 0\n",
-      "LIST: mask_list for Volume                 5 = []\n",
-      "LIST: masked_by_list for Volume            5 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
-      "      mask_mode for Volume                 5 = 0\n",
-      "\n",
-      "List overview for orange_layer_1_layer with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  6 = [1,2,3,4,5,17]\n",
-      "LIST: Direct_children for Volume           6 = [1,2,3,4]\n",
-      "LIST: Intersect_check_list for Volume      6 = [7]\n",
-      "LIST: Mask_intersect_list for Volume       6 = []\n",
-      "LIST: Destinations_list for Volume         6 = [0,8,9,10,11,12,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 6 = [8,12,14]\n",
-      "LIST: Next_volume_list for Volume          6 = [0,8,9,10,11,12,13,14,15,16,7]\n",
-      "      Is_vacuum for Volume                 6 = 0\n",
-      "      is_mask_volume for Volume            6 = 0\n",
-      "      is_masked_volume for Volume          6 = 0\n",
-      "      is_exit_volume for Volume            6 = 0\n",
-      "LIST: mask_list for Volume                 6 = []\n",
-      "LIST: masked_by_list for Volume            6 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
-      "      mask_mode for Volume                 6 = 0\n",
-      "\n",
-      "List overview for orange_layer_1_layer_vac with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  7 = [1,2,3,4,5,17]\n",
-      "LIST: Direct_children for Volume           7 = [1,2,3,4]\n",
-      "LIST: Intersect_check_list for Volume      7 = [1,2,3,4]\n",
-      "LIST: Mask_intersect_list for Volume       7 = []\n",
-      "LIST: Destinations_list for Volume         7 = [0,6,8,9,10,11,12,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 7 = [6,8,12,14]\n",
-      "LIST: Next_volume_list for Volume          7 = [0,6,8,9,10,11,12,13,14,15,16,1,2,3,4]\n",
-      "      Is_vacuum for Volume                 7 = 1\n",
-      "      is_mask_volume for Volume            7 = 0\n",
-      "      is_masked_volume for Volume          7 = 0\n",
-      "      is_exit_volume for Volume            7 = 0\n",
-      "LIST: mask_list for Volume                 7 = []\n",
-      "LIST: masked_by_list for Volume            7 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
-      "      mask_mode for Volume                 7 = 0\n",
-      "\n",
-      "List overview for orange_layer_2_layer with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  8 = [1,2,3,4,5,9,17]\n",
-      "LIST: Direct_children for Volume           8 = [9]\n",
-      "LIST: Intersect_check_list for Volume      8 = [6,7,9,10,11]\n",
-      "LIST: Mask_intersect_list for Volume       8 = []\n",
-      "LIST: Destinations_list for Volume         8 = [0,12,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 8 = [12,14]\n",
-      "LIST: Next_volume_list for Volume          8 = [0,12,13,14,15,16,6,7,9,10,11]\n",
-      "      Is_vacuum for Volume                 8 = 0\n",
-      "      is_mask_volume for Volume            8 = 0\n",
-      "      is_masked_volume for Volume          8 = 0\n",
-      "      is_exit_volume for Volume            8 = 0\n",
-      "LIST: mask_list for Volume                 8 = []\n",
-      "LIST: masked_by_list for Volume            8 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 8 = []\n",
-      "      mask_mode for Volume                 8 = 0\n",
-      "\n",
-      "List overview for orange_layer_2_layer_vac with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  9 = [1,2,3,4,5,17]\n",
-      "LIST: Direct_children for Volume           9 = [1,2,3,4]\n",
-      "LIST: Intersect_check_list for Volume      9 = [6,7]\n",
-      "LIST: Mask_intersect_list for Volume       9 = []\n",
-      "LIST: Destinations_list for Volume         9 = [8,10,11,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 9 = [8,13,14,15]\n",
-      "LIST: Next_volume_list for Volume          9 = [8,10,11,13,14,15,16,6,7]\n",
-      "      Is_vacuum for Volume                 9 = 1\n",
-      "      is_mask_volume for Volume            9 = 0\n",
-      "      is_masked_volume for Volume          9 = 0\n",
-      "      is_exit_volume for Volume            9 = 0\n",
-      "LIST: mask_list for Volume                 9 = []\n",
-      "LIST: masked_by_list for Volume            9 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 9 = []\n",
-      "      mask_mode for Volume                 9 = 0\n",
-      "\n",
-      "List overview for orange_layer_2_outer_cut_0 with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  10 = [3,4,5,17]\n",
-      "LIST: Direct_children for Volume           10 = [3,4]\n",
-      "LIST: Intersect_check_list for Volume      10 = [6,7,9,11]\n",
-      "LIST: Mask_intersect_list for Volume       10 = []\n",
-      "LIST: Destinations_list for Volume         10 = [8,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 10 = [8,13,14,15]\n",
-      "LIST: Next_volume_list for Volume          10 = [8,13,14,15,16,6,7,9,11]\n",
-      "      Is_vacuum for Volume                 10 = 1\n",
-      "      is_mask_volume for Volume            10 = 0\n",
-      "      is_masked_volume for Volume          10 = 0\n",
-      "      is_exit_volume for Volume            10 = 0\n",
-      "LIST: mask_list for Volume                 10 = []\n",
-      "LIST: masked_by_list for Volume            10 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 10 = []\n",
-      "      mask_mode for Volume                 10 = 0\n",
-      "\n",
-      "List overview for orange_layer_2_outer_cut_replace_0 with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  11 = [3,4,5,17]\n",
-      "LIST: Direct_children for Volume           11 = [3,4]\n",
-      "LIST: Intersect_check_list for Volume      11 = [6,7,9]\n",
-      "LIST: Mask_intersect_list for Volume       11 = []\n",
-      "LIST: Destinations_list for Volume         11 = [8,10,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 11 = [8,13,14,15]\n",
-      "LIST: Next_volume_list for Volume          11 = [8,10,13,14,15,16,6,7,9]\n",
-      "      Is_vacuum for Volume                 11 = 0\n",
-      "      is_mask_volume for Volume            11 = 0\n",
-      "      is_masked_volume for Volume          11 = 0\n",
-      "      is_exit_volume for Volume            11 = 0\n",
-      "LIST: mask_list for Volume                 11 = []\n",
-      "LIST: masked_by_list for Volume            11 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 11 = []\n",
-      "      mask_mode for Volume                 11 = 0\n",
-      "\n",
-      "List overview for orange_layer_3_layer with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  12 = [1,2,3,4,5,9,10,11,13,15,16,17]\n",
-      "LIST: Direct_children for Volume           12 = [9,13,15]\n",
-      "LIST: Intersect_check_list for Volume      12 = [6,7,8,13,14,15]\n",
-      "LIST: Mask_intersect_list for Volume       12 = []\n",
-      "LIST: Destinations_list for Volume         12 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 12 = []\n",
-      "LIST: Next_volume_list for Volume          12 = [0,6,7,8,13,14,15]\n",
-      "      Is_vacuum for Volume                 12 = 0\n",
-      "      is_mask_volume for Volume            12 = 0\n",
-      "      is_masked_volume for Volume          12 = 0\n",
-      "      is_exit_volume for Volume            12 = 0\n",
-      "LIST: mask_list for Volume                 12 = []\n",
-      "LIST: masked_by_list for Volume            12 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 12 = []\n",
-      "      mask_mode for Volume                 12 = 0\n",
-      "\n",
-      "List overview for orange_layer_3_layer_vac with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  13 = [1,2,3,4,5,10,11,17]\n",
-      "LIST: Direct_children for Volume           13 = [1,2,10,11]\n",
-      "LIST: Intersect_check_list for Volume      13 = [6,7,8,10,11]\n",
-      "LIST: Mask_intersect_list for Volume       13 = []\n",
-      "LIST: Destinations_list for Volume         13 = [12,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 13 = [12,14]\n",
-      "LIST: Next_volume_list for Volume          13 = [12,14,15,16,6,7,8,10,11]\n",
-      "      Is_vacuum for Volume                 13 = 1\n",
-      "      is_mask_volume for Volume            13 = 0\n",
-      "      is_masked_volume for Volume          13 = 0\n",
-      "      is_exit_volume for Volume            13 = 0\n",
-      "LIST: mask_list for Volume                 13 = []\n",
-      "LIST: masked_by_list for Volume            13 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 13 = []\n",
-      "      mask_mode for Volume                 13 = 0\n",
-      "\n",
-      "List overview for orange_layer_3_outer_cut_0 with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  14 = [3,4,5,16,17]\n",
-      "LIST: Direct_children for Volume           14 = [16]\n",
-      "LIST: Intersect_check_list for Volume      14 = [6,7,8,13,15]\n",
-      "LIST: Mask_intersect_list for Volume       14 = []\n",
-      "LIST: Destinations_list for Volume         14 = [0,12]\n",
-      "LIST: Reduced_destinations_list for Volume 14 = [12]\n",
-      "LIST: Next_volume_list for Volume          14 = [0,12,6,7,8,13,15]\n",
-      "      Is_vacuum for Volume                 14 = 1\n",
-      "      is_mask_volume for Volume            14 = 0\n",
-      "      is_masked_volume for Volume          14 = 0\n",
-      "      is_exit_volume for Volume            14 = 0\n",
-      "LIST: mask_list for Volume                 14 = []\n",
-      "LIST: masked_by_list for Volume            14 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 14 = []\n",
-      "      mask_mode for Volume                 14 = 0\n",
-      "\n",
-      "List overview for orange_layer_3_outer_cut_replace_0 with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  15 = [3,4,5,16,17]\n",
-      "LIST: Direct_children for Volume           15 = [16]\n",
-      "LIST: Intersect_check_list for Volume      15 = [6,7,8,13,16]\n",
-      "LIST: Mask_intersect_list for Volume       15 = []\n",
-      "LIST: Destinations_list for Volume         15 = [12,14]\n",
-      "LIST: Reduced_destinations_list for Volume 15 = [12,14]\n",
-      "LIST: Next_volume_list for Volume          15 = [12,14,6,7,8,13,16]\n",
-      "      Is_vacuum for Volume                 15 = 0\n",
-      "      is_mask_volume for Volume            15 = 0\n",
-      "      is_masked_volume for Volume          15 = 0\n",
-      "      is_exit_volume for Volume            15 = 0\n",
-      "LIST: mask_list for Volume                 15 = []\n",
-      "LIST: masked_by_list for Volume            15 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 15 = []\n",
-      "      mask_mode for Volume                 15 = 0\n",
-      "\n",
-      "List overview for orange_layer_3_inner_cut_0 with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  16 = [3,4,5,17]\n",
-      "LIST: Direct_children for Volume           16 = [3,4]\n",
-      "LIST: Intersect_check_list for Volume      16 = [6,7,8,13]\n",
-      "LIST: Mask_intersect_list for Volume       16 = []\n",
-      "LIST: Destinations_list for Volume         16 = [15]\n",
-      "LIST: Reduced_destinations_list for Volume 16 = [15]\n",
-      "LIST: Next_volume_list for Volume          16 = [15,6,7,8,13]\n",
-      "      Is_vacuum for Volume                 16 = 1\n",
-      "      is_mask_volume for Volume            16 = 0\n",
-      "      is_masked_volume for Volume          16 = 0\n",
-      "      is_exit_volume for Volume            16 = 0\n",
-      "LIST: mask_list for Volume                 16 = []\n",
-      "LIST: masked_by_list for Volume            16 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 16 = []\n",
-      "      mask_mode for Volume                 16 = 0\n",
-      "\n",
-      "List overview for sample with cylinder shape made of nascalf\n",
-      "LIST: Children for Volume                  17 = []\n",
-      "LIST: Direct_children for Volume           17 = []\n",
-      "LIST: Intersect_check_list for Volume      17 = []\n",
-      "LIST: Mask_intersect_list for Volume       17 = []\n",
-      "LIST: Destinations_list for Volume         17 = [2]\n",
-      "LIST: Reduced_destinations_list for Volume 17 = [2]\n",
-      "LIST: Next_volume_list for Volume          17 = [2]\n",
-      "      Is_vacuum for Volume                 17 = 0\n",
-      "      is_mask_volume for Volume            17 = 0\n",
-      "      is_masked_volume for Volume          17 = 0\n",
-      "      is_exit_volume for Volume            17 = 0\n",
-      "LIST: mask_list for Volume                 17 = []\n",
-      "LIST: masked_by_list for Volume            17 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 17 = []\n",
-      "      mask_mode for Volume                 17 = 0\n",
-      "\n",
-      "Union_master component orange_master initialized sucessfully\n",
-      "Detector: orange_logger_space_zx_I=1.09312e-06 orange_logger_space_zx_ERR=8.40756e-10 orange_logger_space_zx_N=3.86041e+07 \"orange_space_zx.dat\"\n",
-      "Detector: orange_logger_space_zy_I=1.09312e-06 orange_logger_space_zy_ERR=8.40756e-10 orange_logger_space_zy_N=3.86041e+07 \"orange_space_zy.dat\"\n",
-      "Detector: orange_logger_space_xy_I=1.09312e-06 orange_logger_space_xy_ERR=8.40756e-10 orange_logger_space_xy_N=3.86041e+07 \"orange_space_xy.dat\"\n",
-      "Detector: orange_logger_space_zy_close_I=1.62573e-07 orange_logger_space_zy_close_ERR=5.53765e-11 orange_logger_space_zy_close_N=1.67282e+07 \"orange_space_zy_close.dat_0\"\n",
-      "Detector: orange_logger_time_I=1.09281e-06 orange_logger_time_ERR=8.4072e-10 orange_logger_time_N=3.81586e+07 \"NULL.dat\"\n",
-      "Detector: orange_logger_space_zy_time_I=4.65892e-07 orange_logger_space_zy_time_ERR=5.42713e-10 orange_logger_space_zy_time_N=1.90655e+07 \"orange_logger_space_zy_time.dat_0\"\n",
-      "Detector: orange_logger_space_zy_time_I=5.97383e-07 orange_logger_space_zy_time_ERR=6.38733e-10 orange_logger_space_zy_time_N=1.19708e+07 \"orange_logger_space_zy_time.dat_1\"\n",
-      "Detector: orange_logger_space_zy_time_I=2.28684e-08 orange_logger_space_zy_time_ERR=5.87969e-11 orange_logger_space_zy_time_N=4.51722e+06 \"orange_logger_space_zy_time.dat_2\"\n",
-      "Detector: orange_logger_space_zy_time_I=5.66116e-09 orange_logger_space_zy_time_ERR=2.65715e-11 orange_logger_space_zy_time_N=1.90526e+06 \"orange_logger_space_zy_time.dat_3\"\n",
-      "Detector: orange_logger_space_zy_time_I=1.00584e-09 orange_logger_space_zy_time_ERR=1.13856e-11 orange_logger_space_zy_time_N=699871 \"orange_logger_space_zy_time.dat_4\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.settings(ncount=2E7, mpi=4)\n",
     "data = instrument.backengine()"
@@ -2293,25 +379,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "hired-berry",
+   "execution_count": null,
+   "id": "smooth-identity",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "373dea732426454194c618971bfcac10",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(Output(layout=Layout(width='75%')), VBox(children=(Label(value='Choose monitor'), Dropdown(layo…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ms.interface(data)"
    ]

From e21b7a921e2e0e94e2521aeeaf6a1e3788fc8601 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 23 Mar 2022 13:48:26 +0100
Subject: [PATCH 244/403] Small fixes from Shervin review.

---
 mcstasscript/_version.py              | 2 +-
 mcstasscript/data/McStasDataFormat.py | 2 +-
 mcstasscript/interface/instr.py       | 4 ++--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index db8ba92d..26cbb133 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.39"
+__version__ = "0.0.40"
diff --git a/mcstasscript/data/McStasDataFormat.py b/mcstasscript/data/McStasDataFormat.py
index 296f8da7..bb197b27 100644
--- a/mcstasscript/data/McStasDataFormat.py
+++ b/mcstasscript/data/McStasDataFormat.py
@@ -48,4 +48,4 @@ def write(cls, object, filename: str, key: str = None):
             original_key = object.key
             key = original_key + "_to_TXTFormat"
         return object.from_file(filename, cls, key)
-    """
\ No newline at end of file
+    """
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index abc177c5..a821bb97 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2131,7 +2131,7 @@ class McStas_instr(McCode_instr):
         Performs simulation, saves in data attribute
 
     run_full_instrument(**kwargs)
-        Depricated method for performing the simulation
+        Deprecated method for performing the simulation
 
     interface()
         Shows interface with jupyter notebook widgets
@@ -2393,7 +2393,7 @@ class McXtrace_instr(McCode_instr):
         Performs simulation, saves in data attribute
 
     run_full_instrument(**kwargs)
-        Depricated method for performing the simulation
+        Deprecated method for performing the simulation
 
     interface()
         Shows interface with jupyter notebook widgets

From d2468734cf492e39524ef18d271d90a868b0913a Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 23 Mar 2022 13:54:18 +0100
Subject: [PATCH 245/403] Small documentation fixes from Celine. Thanks!

---
 docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb b/docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
index 670ebaa5..2004b575 100644
--- a/docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
+++ b/docs/source/tutorial/McStasScript_tutorial_2_SPLIT.ipynb
@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "# Advanced McStas features:  SPLIT\n",
-    "McStas uses the Monte Carlo ray-tracing technique, which allows some tricks in how the physics is sampled as long as the resulting probability distributions matches the physics. This is possible as each ray has a weight, corresponding to how much intensity this ray represent. The SPLIT keyword can be used to split a ray into many equal parts, which can be useful if the remaining instrument has many different simulated and random outcomes. In this tutorial we will use the SPLIT keyword on a powder sample, as there are many powder Bragg peaks each ray could select, and splitting the ray samples this more efficiently."
+    "McStas uses the Monte Carlo ray-tracing technique, which allows some tricks on how the physics is sampled as long as the resulting probability distributions match the physics. This is possible as each ray has a weight, corresponding to how much intensity this ray represents. The SPLIT keyword can be used to split a ray into many equal parts, which can be useful if the remaining instrument has many different simulated and random outcomes. In this tutorial we will use the SPLIT keyword on a powder sample, as there are many powder Bragg peaks each ray could select, and splitting the ray samples this more efficiently."
    ]
   },
   {
@@ -13,7 +13,7 @@
    "metadata": {},
    "source": [
     "### Setting up an example instrument\n",
-    "First we set up an example instrument, this is taken from the basic tutorial and correspond of source, guide, monochromator, sample and banana detector."
+    "First we set up an example instrument, this is taken from the basic tutorial and consists of source, guide, monochromator, sample and banana detector."
    ]
   },
   {

From 00263cade0d778c9ab94068dfc71f3eb1d3455cd Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 23 Mar 2022 14:41:40 +0100
Subject: [PATCH 246/403] Update of cryostat_builder: sample -> origin to
 clarify that it is a point.

---
 examples/cryostat_demo.ipynb           | 2007 +++++++++++++++++++++++-
 examples/cryostat_image.png            |  Bin 95191 -> 94144 bytes
 mcstasscript/tools/cryostat_builder.py |   55 +-
 3 files changed, 1993 insertions(+), 69 deletions(-)

diff --git a/examples/cryostat_demo.ipynb b/examples/cryostat_demo.ipynb
index 7beef31e..46a6c005 100644
--- a/examples/cryostat_demo.ipynb
+++ b/examples/cryostat_demo.ipynb
@@ -13,10 +13,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "composed-custody",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following components are found in the work_directory / input_path:\n",
+      "     Union_sphere.comp\n",
+      "     Texture_process.comp\n",
+      "     Union_cone.comp\n",
+      "     Union_box.comp\n",
+      "     Single_crystal_process.comp\n",
+      "     Union_abs_logger_2D_space.comp\n",
+      "     Union_logger_2D_kf.comp\n",
+      "     Template_process.comp\n",
+      "     PhononSimple_process.comp\n",
+      "     Union_conditional_standard.comp\n",
+      "     Union_abs_logger_1D_space.comp\n",
+      "     Union_abs_logger_event.comp\n",
+      "     NCrystal_process.comp\n",
+      "     Union_abs_logger_1D_space_event.comp\n",
+      "     Union_abs_logger_1D_space_tof.comp\n",
+      "     Union_logger_2D_space.comp\n",
+      "     Union_conditional_PSD.comp\n",
+      "     Union_master.comp\n",
+      "     AF_HB_1D_process.comp\n",
+      "     Union_logger_2D_kf_time.comp\n",
+      "     Union_cylinder.comp\n",
+      "     Union_abs_logger_1D_space_tof_to_lambda.comp\n",
+      "     Powder_process.comp\n",
+      "     Union_make_material.comp\n",
+      "     Incoherent_process.comp\n",
+      "     Union_logger_1D.comp\n",
+      "     Union_logger_3D_space.comp\n",
+      "     IncoherentPhonon_process.comp\n",
+      "     Union_logger_2DQ.comp\n",
+      "     Union_mesh.comp\n",
+      "     Union_logger_2D_space_time.comp\n",
+      "These definitions will be used instead of the installed versions.\n"
+     ]
+    }
+   ],
    "source": [
     "import mcstasscript as ms\n",
     "instrument = ms.McStas_instr(\"cryostat_demo\")"
@@ -24,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "secret-convert",
    "metadata": {},
    "outputs": [],
@@ -45,12 +85,12 @@
    "metadata": {},
    "source": [
     "## Creating a cryostat\n",
-    "One use the Cryostat class to make a cryostat object. This object can be placed in the instrument file much like a component with *set_AT* and *set_ROTATED*. Here we place it 2 m after the source."
+    "One use the Cryostat class to make a cryostat object. This object can be placed in the instrument file much like a component with *set_AT* and *set_ROTATED*. Here we place the cryostat origin 2 m after the source."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "silver-narrative",
    "metadata": {},
    "outputs": [],
@@ -64,7 +104,7 @@
    "id": "single-millennium",
    "metadata": {},
    "source": [
-    "The cryostat model is constructed in layers, each of which are added with *add_layer*. The geometry is shown in the left side of the figure below. To define the walls the user specifies two of *inner_radius*, *outer_radius* and *thickness*, where the last one will be calculated. The distance to the top is controlled with *sample_to_top*, ands its thickness with *top_thickness*. A small negative *top_thickness* will remove the top. The bottom is controlled with *sample_to_bottom* and *bottom_thickness*.\n",
+    "The cryostat model is constructed in layers, each of which are added with *add_layer*. The geometry is shown in the left side of the figure below. To define the walls the user specifies two of *inner_radius*, *outer_radius* and *thickness*, where the last one will be calculated. The distance to the top is controlled with *origin_to_top*, ands its thickness with *top_thickness*. A small negative *top_thickness* will remove the top. The bottom is controlled with *origin_to_bottom* and *bottom_thickness*.\n",
     "\n",
     "One can set *p_interact* which controls the fraction of the rays that scatters in this layer. This value should be kept below 0.5, but since cryostats are supposed to be transparent even setting 0.2 greatly increases the statistics gain from the simulation.\n",
     "\n",
@@ -75,14 +115,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "typical-harvest",
    "metadata": {},
    "outputs": [],
    "source": [
     "orange_cryostat.add_layer(inner_radius=70E-3/2, outer_radius=75E-3/2,\n",
-    "                          sample_to_top=200E-3, top_thickness=-1E-3,\n",
-    "                          sample_to_bottom=83E-3, bottom_thickness=5E-3,\n",
+    "                          origin_to_top=200E-3, top_thickness=-1E-3,\n",
+    "                          origin_to_bottom=83E-3, bottom_thickness=5E-3,\n",
     "                          p_interact=0.2)"
    ]
   },
@@ -91,19 +131,19 @@
    "id": "bound-colon",
    "metadata": {},
    "source": [
-    "After adding a layer, it can be accessed with the attribute *last_layer*, which allows adding a window. The geometry of a window is shown to the right in the figure above. To control the height of the window, the user provides two of *height*, *sample_to_top* and *sample_to_bottom*. If the window only changes the inner radius, only specify the *inner_radius* parameter, and vice versa with the *outer_radius*. If the window narrows the cryostat from both sides, the user should specify two of *inner_radius*, *outer_radius* and *thickness*. It is not allowed to specify *inner_radius* or *outer_radius* that equals the corresponding value for the layer.\n",
+    "After adding a layer, it can be accessed with the attribute *last_layer*, which allows adding a window. The geometry of a window is shown to the right in the figure above. To control the height of the window, the user provides two of *height*, *origin_to_top* and *origin_to_bottom*. If the window only changes the inner radius, only specify the *inner_radius* parameter, and vice versa with the *outer_radius*. If the window narrows the cryostat from both sides, the user should specify two of *inner_radius*, *outer_radius* and *thickness*. It is not allowed to specify *inner_radius* or *outer_radius* that equals the corresponding value for the layer.\n",
     "\n",
     "Here a simple window is added to the last layer."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "advisory-index",
    "metadata": {},
    "outputs": [],
    "source": [
-    "orange_cryostat.last_layer.add_window(outer_radius=73E-3/2, sample_to_top=44.42E-3, sample_to_bottom=88.2E-3)"
+    "orange_cryostat.last_layer.add_window(outer_radius=73E-3/2, origin_to_top=44.42E-3, origin_to_bottom=88.2E-3)"
    ]
   },
   {
@@ -116,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "unexpected-charleston",
    "metadata": {},
    "outputs": [],
@@ -134,14 +174,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "welcome-cathedral",
    "metadata": {},
    "outputs": [],
    "source": [
     "orange_cryostat.add_layer(inner_radius=80E-3/2, outer_radius=81E-3/2,\n",
-    "                          sample_to_bottom=90E-3, bottom_thickness=2E-3, \n",
-    "                          sample_to_top=240E-3, top_thickness=-1E-3,\n",
+    "                          origin_to_bottom=90E-3, bottom_thickness=2E-3, \n",
+    "                          origin_to_top=240E-3, top_thickness=-1E-3,\n",
     "                          material=\"Al\", p_interact=0.2)"
    ]
   },
@@ -155,21 +195,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "settled-intro",
    "metadata": {},
    "outputs": [],
    "source": [
     "orange_cryostat.add_layer(inner_radius=95E-3/2, outer_radius=99.5E-3/2,\n",
-    "                          sample_to_bottom=93E-3, bottom_thickness=6E-3,\n",
-    "                          sample_to_top=225E-3, top_thickness=9E-3, p_interact=0.2)\n",
-    "orange_cryostat.last_layer.add_window(outer_radius=97E-3/2, sample_to_top=52E-3, sample_to_bottom=100E-3)\n",
+    "                          origin_to_bottom=93E-3, bottom_thickness=6E-3,\n",
+    "                          origin_to_top=225E-3, top_thickness=9E-3, p_interact=0.2)\n",
+    "orange_cryostat.last_layer.add_window(outer_radius=97E-3/2, origin_to_top=52E-3, origin_to_bottom=100E-3)\n",
     "\n",
     "orange_cryostat.add_layer(inner_radius=120E-3/2, outer_radius=127E-3/2,\n",
-    "                          sample_to_bottom=109E-3, bottom_thickness=11E-3,\n",
-    "                          sample_to_top=205E-3, top_thickness=22E-3, p_interact=0.2)\n",
+    "                          origin_to_bottom=109E-3, bottom_thickness=11E-3,\n",
+    "                          origin_to_top=205E-3, top_thickness=22E-3, p_interact=0.2)\n",
     "orange_cryostat.last_layer.add_window(outer_radius=125E-3/2, inner_radius=122E-3/2,\n",
-    "                                      sample_to_top=55.7E-3, sample_to_bottom=93.54E-3)"
+    "                                      origin_to_top=55.7E-3, origin_to_bottom=93.54E-3)"
    ]
   },
   {
@@ -182,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "dutch-prevention",
    "metadata": {},
    "outputs": [],
@@ -202,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "sound-alignment",
    "metadata": {},
    "outputs": [],
@@ -220,10 +260,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "noticed-third",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on method add_animation in module mcstasscript.tools.cryostat_builder:\n",
+      "\n",
+      "add_animation(t_min=0, t_max=0.1, n_frames=10, d1='z', n1=300, d2='y', n2=300) method of mcstasscript.tools.cryostat_builder.Cryostat instance\n",
+      "    Adds 2D_space_time logger that records the information necessary for animation\n",
+      "    \n",
+      "    Adds a 2D_space_time logger that records the requested number of frames, n_frames.\n",
+      "    The time span of both is from t_min to t_max. The default orientation is in the zy\n",
+      "    plane, but this can be chosen along with the desired resolution.\n",
+      "    Errors can happen with too many empty frames, so it is recommended to set\n",
+      "    t_min close to the first scattering time.\n",
+      "    Select the appropriate animation data from simulation output and use\n",
+      "    plotter.make_animation with a filename to save the animation as a gif.\n",
+      "    \n",
+      "    Parameters\n",
+      "    ----------\n",
+      "    \n",
+      "    t_min : float\n",
+      "        Lowest time recorded in [s]\n",
+      "    \n",
+      "    t_max : float\n",
+      "        Highest time recorded in [s]\n",
+      "    \n",
+      "    n_frames : int\n",
+      "        Number of frames in space 2D time logger\n",
+      "    \n",
+      "    d1 : str\n",
+      "        First direction of space 2D time logger, \"x\", \"y\" or \"z\"\n",
+      "    \n",
+      "    n1 : int\n",
+      "        Number of bins in first direction\n",
+      "    \n",
+      "    d2 : str\n",
+      "        Second direction of space 2D time logger, \"x\", \"y\" or \"z\"\n",
+      "    \n",
+      "    n2 : int\n",
+      "        Number of bins in second direction\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "help(orange_cryostat.add_animation)"
    ]
@@ -238,10 +322,73 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "dimensional-berkeley",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1msource\u001b[0m  \u001b[1mSource_simple\u001b[0m \n",
+      "  AT      (0, 0, 0) ABSOLUTE\n",
+      "\u001b[1morange\u001b[0m  \u001b[1mArm\u001b[0m \n",
+      "  AT      (0, 0, 2) RELATIVE source\n",
+      "\u001b[1mAl_inc\u001b[0m  \u001b[1mIncoherent_process\u001b[0m \n",
+      "  AT      (0, 0, 0) ABSOLUTE\n",
+      "\u001b[1mAl_pow\u001b[0m  \u001b[1mPowder_process\u001b[0m \n",
+      "  AT      (0, 0, 0) ABSOLUTE\n",
+      "\u001b[1mAl\u001b[0m  \u001b[1mUnion_make_material\u001b[0m \n",
+      "  AT      (0, 0, 0) ABSOLUTE\n",
+      "\u001b[1morange_layer_0_layer\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0.0555, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_0_layer_vac\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0.0585, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_0_outer_cut_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, -0.02189, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_0_outer_cut_replace_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, -0.02189, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_0_inner_cut_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0.0, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_1_layer\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0.0735, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_1_layer_vac\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0.075, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_2_layer\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0.0675, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_2_layer_vac\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0.066, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_2_outer_cut_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, -0.024000000000000004, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_2_outer_cut_replace_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, -0.024000000000000004, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_3_layer\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0.05349999999999999, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_3_layer_vac\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, 0.047999999999999994, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_3_outer_cut_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, -0.01892, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_3_outer_cut_replace_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, -0.01892, 0) RELATIVE orange\n",
+      "\u001b[1morange_layer_3_inner_cut_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
+      "  AT      (0, -0.01892, 0) RELATIVE orange\n",
+      "\u001b[1morange_logger_space_zx\u001b[0m  \u001b[1mUnion_logger_2D_space\u001b[0m \n",
+      "  AT      (0, 0, 0) RELATIVE orange\n",
+      "\u001b[1morange_logger_space_zy\u001b[0m  \u001b[1mUnion_logger_2D_space\u001b[0m \n",
+      "  AT      (0, 0, 0) RELATIVE orange\n",
+      "\u001b[1morange_logger_space_xy\u001b[0m  \u001b[1mUnion_logger_2D_space\u001b[0m \n",
+      "  AT      (0, 0, 0) RELATIVE orange\n",
+      "\u001b[1morange_logger_space_zy_close\u001b[0m  \u001b[1mUnion_logger_3D_space\u001b[0m \n",
+      "  AT      (0, 0, 0) RELATIVE orange\n",
+      "\u001b[1morange_logger_time\u001b[0m  \u001b[1mUnion_logger_1D\u001b[0m \n",
+      "  AT      (0, 0, 0) ABSOLUTE\n",
+      "\u001b[1morange_logger_space_zy_time\u001b[0m  \u001b[1mUnion_logger_2D_space_time\u001b[0m \n",
+      "  AT      (0, 0, 0) RELATIVE orange\n",
+      "\u001b[1morange_master\u001b[0m  \u001b[1mUnion_master\u001b[0m \n",
+      "  AT      (0, 0, 0) ABSOLUTE\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.print_components()"
    ]
@@ -256,10 +403,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "exceptional-profit",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"800\"\n",
+       "            height=\"450\"\n",
+       "            src=\"./cryostat_demo_mcdisplay/index.html\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x7fa94163e670>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "instrument.show_instrument()"
    ]
@@ -275,12 +444,802 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "paperback-palmer",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/cryostat_demo_data\"\n",
+      "INFO: Regenerating c-file: cryostat_demo.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./cryostat_demo.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:18644:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:18644:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:18887:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:18887:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:19130:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_xy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:19130:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_xy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_logger_3D_space.comp:668:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:19379:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_close_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_3D_space.comp:668:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:19379:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_close_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_logger_1D.comp:479:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:19686:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_time_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_1D.comp:479:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:19686:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_time_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_logger_2D_space_time.comp:632:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:19883:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_time_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_2D_space_time.comp:632:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:19883:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_time_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "18 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy cryostat_demo.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/cryostat_demo_data\n",
+      "\n",
+      "    Instrument parameters for cryostat_demo (cryostat_demo.instr)\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "Allocating 3D arrays \n",
+      "Allocating 3D arrays \n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 3 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 4 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 1\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 5 \n",
+      "my_absoprtion       [0]: 1.391566 \n",
+      "number of processes [0]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 1\n",
+      "\n",
+      "name of geometry    [0]: orange_layer_0_layer \n",
+      "component index     [0]: 6 \n",
+      "Volume.name         [0]: orange_layer_0_layer \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 1.391566 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.055500 \n",
+      "Volume.geometry.center.z             [0]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.037500 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.287000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: orange_layer_0_layer_vac \n",
+      "component index     [1]: 7 \n",
+      "Volume.name         [1]: orange_layer_0_layer_vac \n",
+      "Volume.p_physics.is_vacuum           [1]: 1 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.000000 \n",
+      "Volume.p_physics.number of processes [1]: 0 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.058500 \n",
+      "Volume.geometry.center.z             [1]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.035000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.283000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: orange_layer_0_outer_cut_0 \n",
+      "component index     [2]: 8 \n",
+      "Volume.name         [2]: orange_layer_0_outer_cut_0 \n",
+      "Volume.p_physics.is_vacuum           [2]: 1 \n",
+      "Volume.p_physics.my_absorption       [2]: 0.000000 \n",
+      "Volume.p_physics.number of processes [2]: 0 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: -0.021890 \n",
+      "Volume.geometry.center.z             [2]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.037501 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.132621 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: orange_layer_0_outer_cut_replace_0 \n",
+      "component index     [3]: 9 \n",
+      "Volume.name         [3]: orange_layer_0_outer_cut_replace_0 \n",
+      "Volume.p_physics.is_vacuum           [3]: 0 \n",
+      "Volume.p_physics.my_absorption       [3]: 1.391566 \n",
+      "Volume.p_physics.number of processes [3]: 2 \n",
+      "Volume.geometry.shape                [3]: cylinder \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: -0.021890 \n",
+      "Volume.geometry.center.z             [3]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.036500 \n",
+      "Volume.geometry.geometry_parameters.height [3]: 0.132625 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [4]: orange_layer_0_inner_cut_0 \n",
+      "component index     [4]: 10 \n",
+      "Volume.name         [4]: orange_layer_0_inner_cut_0 \n",
+      "Volume.p_physics.is_vacuum           [4]: 1 \n",
+      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
+      "Volume.p_physics.number of processes [4]: 0 \n",
+      "Volume.geometry.shape                [4]: cylinder \n",
+      "Volume.geometry.center.x             [4]: 0.000000 \n",
+      "Volume.geometry.center.y             [4]: 0.000000 \n",
+      "Volume.geometry.center.z             [4]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.035500 \n",
+      "Volume.geometry.geometry_parameters.height [4]: 0.040000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [5]: orange_layer_1_layer \n",
+      "component index     [5]: 11 \n",
+      "Volume.name         [5]: orange_layer_1_layer \n",
+      "Volume.p_physics.is_vacuum           [5]: 0 \n",
+      "Volume.p_physics.my_absorption       [5]: 1.391566 \n",
+      "Volume.p_physics.number of processes [5]: 2 \n",
+      "Volume.geometry.shape                [5]: cylinder \n",
+      "Volume.geometry.center.x             [5]: 0.000000 \n",
+      "Volume.geometry.center.y             [5]: 0.073500 \n",
+      "Volume.geometry.center.z             [5]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.040500 \n",
+      "Volume.geometry.geometry_parameters.height [5]: 0.331000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [6]: orange_layer_1_layer_vac \n",
+      "component index     [6]: 12 \n",
+      "Volume.name         [6]: orange_layer_1_layer_vac \n",
+      "Volume.p_physics.is_vacuum           [6]: 1 \n",
+      "Volume.p_physics.my_absorption       [6]: 0.000000 \n",
+      "Volume.p_physics.number of processes [6]: 0 \n",
+      "Volume.geometry.shape                [6]: cylinder \n",
+      "Volume.geometry.center.x             [6]: 0.000000 \n",
+      "Volume.geometry.center.y             [6]: 0.075000 \n",
+      "Volume.geometry.center.z             [6]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.040000 \n",
+      "Volume.geometry.geometry_parameters.height [6]: 0.330000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [7]: orange_layer_2_layer \n",
+      "component index     [7]: 13 \n",
+      "Volume.name         [7]: orange_layer_2_layer \n",
+      "Volume.p_physics.is_vacuum           [7]: 0 \n",
+      "Volume.p_physics.my_absorption       [7]: 1.391566 \n",
+      "Volume.p_physics.number of processes [7]: 2 \n",
+      "Volume.geometry.shape                [7]: cylinder \n",
+      "Volume.geometry.center.x             [7]: 0.000000 \n",
+      "Volume.geometry.center.y             [7]: 0.067500 \n",
+      "Volume.geometry.center.z             [7]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [7]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [7]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [7]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [7]: 0.049750 \n",
+      "Volume.geometry.geometry_parameters.height [7]: 0.333000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [7]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [8]: orange_layer_2_layer_vac \n",
+      "component index     [8]: 14 \n",
+      "Volume.name         [8]: orange_layer_2_layer_vac \n",
+      "Volume.p_physics.is_vacuum           [8]: 1 \n",
+      "Volume.p_physics.my_absorption       [8]: 0.000000 \n",
+      "Volume.p_physics.number of processes [8]: 0 \n",
+      "Volume.geometry.shape                [8]: cylinder \n",
+      "Volume.geometry.center.x             [8]: 0.000000 \n",
+      "Volume.geometry.center.y             [8]: 0.066000 \n",
+      "Volume.geometry.center.z             [8]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [8]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [8]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [8]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [8]: 0.047500 \n",
+      "Volume.geometry.geometry_parameters.height [8]: 0.318000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [8]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [9]: orange_layer_2_outer_cut_0 \n",
+      "component index     [9]: 15 \n",
+      "Volume.name         [9]: orange_layer_2_outer_cut_0 \n",
+      "Volume.p_physics.is_vacuum           [9]: 1 \n",
+      "Volume.p_physics.my_absorption       [9]: 0.000000 \n",
+      "Volume.p_physics.number of processes [9]: 0 \n",
+      "Volume.geometry.shape                [9]: cylinder \n",
+      "Volume.geometry.center.x             [9]: 0.000000 \n",
+      "Volume.geometry.center.y             [9]: -0.024000 \n",
+      "Volume.geometry.center.z             [9]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [9]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [9]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [9]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [9]: 0.049751 \n",
+      "Volume.geometry.geometry_parameters.height [9]: 0.152001 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [9]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [10]: orange_layer_2_outer_cut_replace_0 \n",
+      "component index     [10]: 16 \n",
+      "Volume.name         [10]: orange_layer_2_outer_cut_replace_0 \n",
+      "Volume.p_physics.is_vacuum           [10]: 0 \n",
+      "Volume.p_physics.my_absorption       [10]: 1.391566 \n",
+      "Volume.p_physics.number of processes [10]: 2 \n",
+      "Volume.geometry.shape                [10]: cylinder \n",
+      "Volume.geometry.center.x             [10]: 0.000000 \n",
+      "Volume.geometry.center.y             [10]: -0.024000 \n",
+      "Volume.geometry.center.z             [10]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [10]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [10]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [10]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [10]: 0.048500 \n",
+      "Volume.geometry.geometry_parameters.height [10]: 0.152005 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [10]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [11]: orange_layer_3_layer \n",
+      "component index     [11]: 17 \n",
+      "Volume.name         [11]: orange_layer_3_layer \n",
+      "Volume.p_physics.is_vacuum           [11]: 0 \n",
+      "Volume.p_physics.my_absorption       [11]: 1.391566 \n",
+      "Volume.p_physics.number of processes [11]: 2 \n",
+      "Volume.geometry.shape                [11]: cylinder \n",
+      "Volume.geometry.center.x             [11]: 0.000000 \n",
+      "Volume.geometry.center.y             [11]: 0.053500 \n",
+      "Volume.geometry.center.z             [11]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [11]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [11]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [11]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [11]: 0.063500 \n",
+      "Volume.geometry.geometry_parameters.height [11]: 0.347000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [11]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [12]: orange_layer_3_layer_vac \n",
+      "component index     [12]: 18 \n",
+      "Volume.name         [12]: orange_layer_3_layer_vac \n",
+      "Volume.p_physics.is_vacuum           [12]: 1 \n",
+      "Volume.p_physics.my_absorption       [12]: 0.000000 \n",
+      "Volume.p_physics.number of processes [12]: 0 \n",
+      "Volume.geometry.shape                [12]: cylinder \n",
+      "Volume.geometry.center.x             [12]: 0.000000 \n",
+      "Volume.geometry.center.y             [12]: 0.048000 \n",
+      "Volume.geometry.center.z             [12]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [12]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [12]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [12]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [12]: 0.060000 \n",
+      "Volume.geometry.geometry_parameters.height [12]: 0.314000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [12]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [13]: orange_layer_3_outer_cut_0 \n",
+      "component index     [13]: 19 \n",
+      "Volume.name         [13]: orange_layer_3_outer_cut_0 \n",
+      "Volume.p_physics.is_vacuum           [13]: 1 \n",
+      "Volume.p_physics.my_absorption       [13]: 0.000000 \n",
+      "Volume.p_physics.number of processes [13]: 0 \n",
+      "Volume.geometry.shape                [13]: cylinder \n",
+      "Volume.geometry.center.x             [13]: 0.000000 \n",
+      "Volume.geometry.center.y             [13]: -0.018920 \n",
+      "Volume.geometry.center.z             [13]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [13]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [13]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [13]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [13]: 0.063501 \n",
+      "Volume.geometry.geometry_parameters.height [13]: 0.149241 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [13]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [14]: orange_layer_3_outer_cut_replace_0 \n",
+      "component index     [14]: 20 \n",
+      "Volume.name         [14]: orange_layer_3_outer_cut_replace_0 \n",
+      "Volume.p_physics.is_vacuum           [14]: 0 \n",
+      "Volume.p_physics.my_absorption       [14]: 1.391566 \n",
+      "Volume.p_physics.number of processes [14]: 2 \n",
+      "Volume.geometry.shape                [14]: cylinder \n",
+      "Volume.geometry.center.x             [14]: 0.000000 \n",
+      "Volume.geometry.center.y             [14]: -0.018920 \n",
+      "Volume.geometry.center.z             [14]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [14]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [14]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [14]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [14]: 0.062500 \n",
+      "Volume.geometry.geometry_parameters.height [14]: 0.149245 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [14]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [15]: orange_layer_3_inner_cut_0 \n",
+      "component index     [15]: 21 \n",
+      "Volume.name         [15]: orange_layer_3_inner_cut_0 \n",
+      "Volume.p_physics.is_vacuum           [15]: 1 \n",
+      "Volume.p_physics.my_absorption       [15]: 0.000000 \n",
+      "Volume.p_physics.number of processes [15]: 0 \n",
+      "Volume.geometry.shape                [15]: cylinder \n",
+      "Volume.geometry.center.x             [15]: 0.000000 \n",
+      "Volume.geometry.center.y             [15]: -0.018920 \n",
+      "Volume.geometry.center.z             [15]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [15]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [15]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [15]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [15]: 0.061000 \n",
+      "Volume.geometry.geometry_parameters.height [15]: 0.149240 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [15]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 17\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]\n",
+      "LIST: Direct_children for Volume           0 = [6,7,8,12,14]\n",
+      "LIST: Intersect_check_list for Volume      0 = [6,7,8,12,14]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [6,7,8,12,14]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for orange_layer_0_layer with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  1 = [5]\n",
+      "LIST: Direct_children for Volume           1 = [5]\n",
+      "LIST: Intersect_check_list for Volume      1 = [2,3,4]\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [7,10,11,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = [7,10,11,14,15]\n",
+      "LIST: Next_volume_list for Volume          1 = [7,10,11,14,15,16,2,3,4]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for orange_layer_0_layer_vac with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  2 = []\n",
+      "LIST: Direct_children for Volume           2 = []\n",
+      "LIST: Intersect_check_list for Volume      2 = []\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [1,3,4,5,7,10,11,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [7,10,11,14,15]\n",
+      "LIST: Next_volume_list for Volume          2 = [1,3,4,5,7,10,11,14,15,16]\n",
+      "      Is_vacuum for Volume                 2 = 1\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for orange_layer_0_outer_cut_0 with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  3 = [5]\n",
+      "LIST: Direct_children for Volume           3 = [5]\n",
+      "LIST: Intersect_check_list for Volume      3 = [2,4]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [1,7]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = [7]\n",
+      "LIST: Next_volume_list for Volume          3 = [1,7,2,4]\n",
+      "      Is_vacuum for Volume                 3 = 1\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "List overview for orange_layer_0_outer_cut_replace_0 with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  4 = [5]\n",
+      "LIST: Direct_children for Volume           4 = [5]\n",
+      "LIST: Intersect_check_list for Volume      4 = [2,5]\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [1,3,7]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = [7]\n",
+      "LIST: Next_volume_list for Volume          4 = [1,3,7,2,5]\n",
+      "      Is_vacuum for Volume                 4 = 0\n",
+      "      is_mask_volume for Volume            4 = 0\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = []\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 0\n",
+      "\n",
+      "List overview for orange_layer_0_inner_cut_0 with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  5 = []\n",
+      "LIST: Direct_children for Volume           5 = []\n",
+      "LIST: Intersect_check_list for Volume      5 = [2]\n",
+      "LIST: Mask_intersect_list for Volume       5 = []\n",
+      "LIST: Destinations_list for Volume         5 = [4]\n",
+      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
+      "LIST: Next_volume_list for Volume          5 = [4,2]\n",
+      "      Is_vacuum for Volume                 5 = 1\n",
+      "      is_mask_volume for Volume            5 = 0\n",
+      "      is_masked_volume for Volume          5 = 0\n",
+      "      is_exit_volume for Volume            5 = 0\n",
+      "LIST: mask_list for Volume                 5 = []\n",
+      "LIST: masked_by_list for Volume            5 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
+      "      mask_mode for Volume                 5 = 0\n",
+      "\n",
+      "List overview for orange_layer_1_layer with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  6 = [1,2,3,4,5]\n",
+      "LIST: Direct_children for Volume           6 = [1,2,3,4]\n",
+      "LIST: Intersect_check_list for Volume      6 = [7]\n",
+      "LIST: Mask_intersect_list for Volume       6 = []\n",
+      "LIST: Destinations_list for Volume         6 = [0,8,9,10,11,12,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 6 = [8,12,14]\n",
+      "LIST: Next_volume_list for Volume          6 = [0,8,9,10,11,12,13,14,15,16,7]\n",
+      "      Is_vacuum for Volume                 6 = 0\n",
+      "      is_mask_volume for Volume            6 = 0\n",
+      "      is_masked_volume for Volume          6 = 0\n",
+      "      is_exit_volume for Volume            6 = 0\n",
+      "LIST: mask_list for Volume                 6 = []\n",
+      "LIST: masked_by_list for Volume            6 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
+      "      mask_mode for Volume                 6 = 0\n",
+      "\n",
+      "List overview for orange_layer_1_layer_vac with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  7 = [1,2,3,4,5]\n",
+      "LIST: Direct_children for Volume           7 = [1,2,3,4]\n",
+      "LIST: Intersect_check_list for Volume      7 = [1,2,3,4]\n",
+      "LIST: Mask_intersect_list for Volume       7 = []\n",
+      "LIST: Destinations_list for Volume         7 = [0,6,8,9,10,11,12,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 7 = [6,8,12,14]\n",
+      "LIST: Next_volume_list for Volume          7 = [0,6,8,9,10,11,12,13,14,15,16,1,2,3,4]\n",
+      "      Is_vacuum for Volume                 7 = 1\n",
+      "      is_mask_volume for Volume            7 = 0\n",
+      "      is_masked_volume for Volume          7 = 0\n",
+      "      is_exit_volume for Volume            7 = 0\n",
+      "LIST: mask_list for Volume                 7 = []\n",
+      "LIST: masked_by_list for Volume            7 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
+      "      mask_mode for Volume                 7 = 0\n",
+      "\n",
+      "List overview for orange_layer_2_layer with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  8 = [1,2,3,4,5,9]\n",
+      "LIST: Direct_children for Volume           8 = [9]\n",
+      "LIST: Intersect_check_list for Volume      8 = [6,7,9,10,11]\n",
+      "LIST: Mask_intersect_list for Volume       8 = []\n",
+      "LIST: Destinations_list for Volume         8 = [0,12,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 8 = [12,14]\n",
+      "LIST: Next_volume_list for Volume          8 = [0,12,13,14,15,16,6,7,9,10,11]\n",
+      "      Is_vacuum for Volume                 8 = 0\n",
+      "      is_mask_volume for Volume            8 = 0\n",
+      "      is_masked_volume for Volume          8 = 0\n",
+      "      is_exit_volume for Volume            8 = 0\n",
+      "LIST: mask_list for Volume                 8 = []\n",
+      "LIST: masked_by_list for Volume            8 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 8 = []\n",
+      "      mask_mode for Volume                 8 = 0\n",
+      "\n",
+      "List overview for orange_layer_2_layer_vac with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  9 = [1,2,3,4,5]\n",
+      "LIST: Direct_children for Volume           9 = [1,2,3,4]\n",
+      "LIST: Intersect_check_list for Volume      9 = [6,7]\n",
+      "LIST: Mask_intersect_list for Volume       9 = []\n",
+      "LIST: Destinations_list for Volume         9 = [8,10,11,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 9 = [8,13,14,15]\n",
+      "LIST: Next_volume_list for Volume          9 = [8,10,11,13,14,15,16,6,7]\n",
+      "      Is_vacuum for Volume                 9 = 1\n",
+      "      is_mask_volume for Volume            9 = 0\n",
+      "      is_masked_volume for Volume          9 = 0\n",
+      "      is_exit_volume for Volume            9 = 0\n",
+      "LIST: mask_list for Volume                 9 = []\n",
+      "LIST: masked_by_list for Volume            9 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 9 = []\n",
+      "      mask_mode for Volume                 9 = 0\n",
+      "\n",
+      "List overview for orange_layer_2_outer_cut_0 with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  10 = [3,4,5]\n",
+      "LIST: Direct_children for Volume           10 = [3,4]\n",
+      "LIST: Intersect_check_list for Volume      10 = [6,7,9,11]\n",
+      "LIST: Mask_intersect_list for Volume       10 = []\n",
+      "LIST: Destinations_list for Volume         10 = [8,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 10 = [8,13,14,15]\n",
+      "LIST: Next_volume_list for Volume          10 = [8,13,14,15,16,6,7,9,11]\n",
+      "      Is_vacuum for Volume                 10 = 1\n",
+      "      is_mask_volume for Volume            10 = 0\n",
+      "      is_masked_volume for Volume          10 = 0\n",
+      "      is_exit_volume for Volume            10 = 0\n",
+      "LIST: mask_list for Volume                 10 = []\n",
+      "LIST: masked_by_list for Volume            10 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 10 = []\n",
+      "      mask_mode for Volume                 10 = 0\n",
+      "\n",
+      "List overview for orange_layer_2_outer_cut_replace_0 with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  11 = [3,4,5]\n",
+      "LIST: Direct_children for Volume           11 = [3,4]\n",
+      "LIST: Intersect_check_list for Volume      11 = [6,7,9]\n",
+      "LIST: Mask_intersect_list for Volume       11 = []\n",
+      "LIST: Destinations_list for Volume         11 = [8,10,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 11 = [8,13,14,15]\n",
+      "LIST: Next_volume_list for Volume          11 = [8,10,13,14,15,16,6,7,9]\n",
+      "      Is_vacuum for Volume                 11 = 0\n",
+      "      is_mask_volume for Volume            11 = 0\n",
+      "      is_masked_volume for Volume          11 = 0\n",
+      "      is_exit_volume for Volume            11 = 0\n",
+      "LIST: mask_list for Volume                 11 = []\n",
+      "LIST: masked_by_list for Volume            11 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 11 = []\n",
+      "      mask_mode for Volume                 11 = 0\n",
+      "\n",
+      "List overview for orange_layer_3_layer with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  12 = [1,2,3,4,5,9,10,11,13,15,16]\n",
+      "LIST: Direct_children for Volume           12 = [9,13,15]\n",
+      "LIST: Intersect_check_list for Volume      12 = [6,7,8,13,14,15]\n",
+      "LIST: Mask_intersect_list for Volume       12 = []\n",
+      "LIST: Destinations_list for Volume         12 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 12 = []\n",
+      "LIST: Next_volume_list for Volume          12 = [0,6,7,8,13,14,15]\n",
+      "      Is_vacuum for Volume                 12 = 0\n",
+      "      is_mask_volume for Volume            12 = 0\n",
+      "      is_masked_volume for Volume          12 = 0\n",
+      "      is_exit_volume for Volume            12 = 0\n",
+      "LIST: mask_list for Volume                 12 = []\n",
+      "LIST: masked_by_list for Volume            12 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 12 = []\n",
+      "      mask_mode for Volume                 12 = 0\n",
+      "\n",
+      "List overview for orange_layer_3_layer_vac with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  13 = [1,2,3,4,5,10,11]\n",
+      "LIST: Direct_children for Volume           13 = [1,2,10,11]\n",
+      "LIST: Intersect_check_list for Volume      13 = [6,7,8,10,11]\n",
+      "LIST: Mask_intersect_list for Volume       13 = []\n",
+      "LIST: Destinations_list for Volume         13 = [12,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 13 = [12,14]\n",
+      "LIST: Next_volume_list for Volume          13 = [12,14,15,16,6,7,8,10,11]\n",
+      "      Is_vacuum for Volume                 13 = 1\n",
+      "      is_mask_volume for Volume            13 = 0\n",
+      "      is_masked_volume for Volume          13 = 0\n",
+      "      is_exit_volume for Volume            13 = 0\n",
+      "LIST: mask_list for Volume                 13 = []\n",
+      "LIST: masked_by_list for Volume            13 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 13 = []\n",
+      "      mask_mode for Volume                 13 = 0\n",
+      "\n",
+      "List overview for orange_layer_3_outer_cut_0 with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  14 = [3,4,5,16]\n",
+      "LIST: Direct_children for Volume           14 = [16]\n",
+      "LIST: Intersect_check_list for Volume      14 = [6,7,8,13,15]\n",
+      "LIST: Mask_intersect_list for Volume       14 = []\n",
+      "LIST: Destinations_list for Volume         14 = [0,12]\n",
+      "LIST: Reduced_destinations_list for Volume 14 = [12]\n",
+      "LIST: Next_volume_list for Volume          14 = [0,12,6,7,8,13,15]\n",
+      "      Is_vacuum for Volume                 14 = 1\n",
+      "      is_mask_volume for Volume            14 = 0\n",
+      "      is_masked_volume for Volume          14 = 0\n",
+      "      is_exit_volume for Volume            14 = 0\n",
+      "LIST: mask_list for Volume                 14 = []\n",
+      "LIST: masked_by_list for Volume            14 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 14 = []\n",
+      "      mask_mode for Volume                 14 = 0\n",
+      "\n",
+      "List overview for orange_layer_3_outer_cut_replace_0 with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  15 = [3,4,5,16]\n",
+      "LIST: Direct_children for Volume           15 = [16]\n",
+      "LIST: Intersect_check_list for Volume      15 = [6,7,8,13,16]\n",
+      "LIST: Mask_intersect_list for Volume       15 = []\n",
+      "LIST: Destinations_list for Volume         15 = [12,14]\n",
+      "LIST: Reduced_destinations_list for Volume 15 = [12,14]\n",
+      "LIST: Next_volume_list for Volume          15 = [12,14,6,7,8,13,16]\n",
+      "      Is_vacuum for Volume                 15 = 0\n",
+      "      is_mask_volume for Volume            15 = 0\n",
+      "      is_masked_volume for Volume          15 = 0\n",
+      "      is_exit_volume for Volume            15 = 0\n",
+      "LIST: mask_list for Volume                 15 = []\n",
+      "LIST: masked_by_list for Volume            15 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 15 = []\n",
+      "      mask_mode for Volume                 15 = 0\n",
+      "\n",
+      "List overview for orange_layer_3_inner_cut_0 with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  16 = [3,4,5]\n",
+      "LIST: Direct_children for Volume           16 = [3,4]\n",
+      "LIST: Intersect_check_list for Volume      16 = [6,7,8,13]\n",
+      "LIST: Mask_intersect_list for Volume       16 = []\n",
+      "LIST: Destinations_list for Volume         16 = [15]\n",
+      "LIST: Reduced_destinations_list for Volume 16 = [15]\n",
+      "LIST: Next_volume_list for Volume          16 = [15,6,7,8,13]\n",
+      "      Is_vacuum for Volume                 16 = 1\n",
+      "      is_mask_volume for Volume            16 = 0\n",
+      "      is_masked_volume for Volume          16 = 0\n",
+      "      is_exit_volume for Volume            16 = 0\n",
+      "LIST: mask_list for Volume                 16 = []\n",
+      "LIST: masked_by_list for Volume            16 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 16 = []\n",
+      "      mask_mode for Volume                 16 = 0\n",
+      "\n",
+      "Union_master component orange_master initialized sucessfully\n",
+      "Detector: orange_logger_space_zx_I=3.28105e-07 orange_logger_space_zx_ERR=1.38489e-10 orange_logger_space_zx_N=1.74618e+07 \"orange_space_zx.dat\"\n",
+      "Detector: orange_logger_space_zy_I=3.28105e-07 orange_logger_space_zy_ERR=1.38489e-10 orange_logger_space_zy_N=1.74618e+07 \"orange_space_zy.dat\"\n",
+      "Detector: orange_logger_space_xy_I=3.28105e-07 orange_logger_space_xy_ERR=1.38489e-10 orange_logger_space_xy_N=1.74618e+07 \"orange_space_xy.dat\"\n",
+      "Detector: orange_logger_space_zy_close_I=1.59649e-07 orange_logger_space_zy_close_ERR=7.0123e-11 orange_logger_space_zy_close_N=8.35294e+06 \"orange_space_zy_close.dat_0\"\n",
+      "Detector: orange_logger_time_I=3.27996e-07 orange_logger_time_ERR=1.38461e-10 orange_logger_time_N=1.72562e+07 \"NULL.dat\"\n",
+      "Detector: orange_logger_space_zy_time_I=1.60352e-07 orange_logger_space_zy_time_ERR=6.99253e-11 orange_logger_space_zy_time_N=8.91413e+06 \"orange_logger_space_zy_time.dat_0\"\n",
+      "Detector: orange_logger_space_zy_time_I=1.54125e-07 orange_logger_space_zy_time_ERR=1.1568e-10 orange_logger_space_zy_time_N=4.99388e+06 \"orange_logger_space_zy_time.dat_1\"\n",
+      "Detector: orange_logger_space_zy_time_I=8.70858e-09 orange_logger_space_zy_time_ERR=1.99575e-11 orange_logger_space_zy_time_N=2.08251e+06 \"orange_logger_space_zy_time.dat_2\"\n",
+      "Detector: orange_logger_space_zy_time_I=4.10802e-09 orange_logger_space_zy_time_ERR=1.94203e-11 orange_logger_space_zy_time_N=930389 \"orange_logger_space_zy_time.dat_3\"\n",
+      "Detector: orange_logger_space_zy_time_I=7.02331e-10 orange_logger_space_zy_time_ERR=1.11529e-11 orange_logger_space_zy_time_N=335323 \"orange_logger_space_zy_time.dat_4\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.settings(ncount=1E7)\n",
     "data = instrument.backengine()"
@@ -297,10 +1256,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "mobile-strike",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60a8109a821d4d299c242cfb61150408",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(Output(layout=Layout(width='75%')), VBox(children=(Label(value='Choose monitor'), Dropdown(layo…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%matplotlib widget\n",
     "ms.interface(data)"
@@ -316,10 +1290,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "rural-lingerie",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74b357a47348442e957dda8c27a1c472",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-03-23 14:18:08,938 WARNING:MovieWriter imagemagick unavailable; using Pillow instead.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name orange_logger_space_zy_time\n",
+      "Saving animation with filename : \"cryostat_animation.gif\"\n"
+     ]
+    }
+   ],
    "source": [
     "ani_data = data[5:10]\n",
     "for frame in ani_data:\n",
@@ -341,7 +1345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "frequent-asbestos",
    "metadata": {},
    "outputs": [],
@@ -368,21 +1372,938 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "liquid-connectivity",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/cryostat_demo_data_1\"\n",
+      "INFO: Regenerating c-file: cryostat_demo.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./cryostat_demo.out\n",
+      "mccode-r.c:1880:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "} /* mcsiminfo_init */\n",
+      "^\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:201:5: note: expanded from macro 'MPI_MASTER'\n",
+      "  { statement; } \\\n",
+      "    ^~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:19430:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:19430:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:19673:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:19673:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:19916:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_xy_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:19916:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_xy_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_logger_3D_space.comp:668:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:20165:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_close_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_3D_space.comp:668:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:20165:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_close_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_logger_1D.comp:479:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:20472:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_time_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_1D.comp:479:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:20472:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_time_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_logger_2D_space_time.comp:632:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./cryostat_demo.c:20669:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_time_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "Union_logger_2D_space_time.comp:632:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./cryostat_demo.c:20669:18: note: expanded from macro 'filename'\n",
+      "#define filename mccorange_logger_space_zy_time_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:201:5: note: expanded from macro 'MPI_MASTER'\n",
+      "  { statement; } \\\n",
+      "    ^~~~~~~~~\n",
+      "Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:201:5: note: expanded from macro 'MPI_MASTER'\n",
+      "  { statement; } \\\n",
+      "    ^~~~~~~~~\n",
+      "Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:201:5: note: expanded from macro 'MPI_MASTER'\n",
+      "  { statement; } \\\n",
+      "    ^~~~~~~~~\n",
+      "19 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy cryostat_demo.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/cryostat_demo_data_1\n",
+      "\n",
+      "    Simulation 'cryostat_demo' (cryostat_demo.instr): running on 6 nodes (master is 'CI0021617', MPI version 3.1).\n",
+      "Instrument parameters for cryostat_demo (cryostat_demo.instr)\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: nascalf_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: nascalf_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: nascalf_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: nascalf_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: nascalf_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: nascalf_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: nascalf_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: nascalf_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: nascalf_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "PowderN: nascalf_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "PowderN: nascalf_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: nascalf_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "PowderN: nascalf_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: nascalf_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "PowderN: nascalf_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: nascalf_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: nascalf_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "PowderN: nascalf_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "Allocating 3D arrays \n",
+      "Allocating 3D arrays \n",
+      "Allocating 3D arrays \n",
+      "Allocating 3D arrays \n",
+      "Allocating 3D arrays \n",
+      "Allocating 3D arrays \n",
+      "Allocating 3D arrays \n",
+      "Allocating 3D arrays \n",
+      "Allocating 3D arrays \n",
+      "Allocating 3D arrays \n",
+      "Allocating 3D arrays \n",
+      "Allocating 3D arrays \n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 4\n",
+      "name of process [0]: nascalf_inc \n",
+      "component index [0]: 3 \n",
+      "name of process [1]: nascalf_pow \n",
+      "component index [1]: 4 \n",
+      "name of process [2]: Al_inc \n",
+      "component index [2]: 6 \n",
+      "name of process [3]: Al_pow \n",
+      "component index [3]: 7 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 2\n",
+      "name of material    [0]: nascalf \n",
+      "component index     [0]: 5 \n",
+      "my_absoprtion       [0]: 0.273042 \n",
+      "number of processes [0]: 2 \n",
+      "name of material    [1]: Al \n",
+      "component index     [1]: 8 \n",
+      "my_absoprtion       [1]: 1.391566 \n",
+      "number of processes [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 2\n",
+      "\n",
+      "name of geometry    [0]: orange_layer_0_layer \n",
+      "component index     [0]: 9 \n",
+      "Volume.name         [0]: orange_layer_0_layer \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 1.391566 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.055500 \n",
+      "Volume.geometry.center.z             [0]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.037500 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.287000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: orange_layer_0_layer_vac \n",
+      "component index     [1]: 10 \n",
+      "Volume.name         [1]: orange_layer_0_layer_vac \n",
+      "Volume.p_physics.is_vacuum           [1]: 1 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.000000 \n",
+      "Volume.p_physics.number of processes [1]: 0 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.058500 \n",
+      "Volume.geometry.center.z             [1]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.035000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.283000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: orange_layer_0_outer_cut_0 \n",
+      "component index     [2]: 11 \n",
+      "Volume.name         [2]: orange_layer_0_outer_cut_0 \n",
+      "Volume.p_physics.is_vacuum           [2]: 1 \n",
+      "Volume.p_physics.my_absorption       [2]: 0.000000 \n",
+      "Volume.p_physics.number of processes [2]: 0 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: -0.021890 \n",
+      "Volume.geometry.center.z             [2]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.037501 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.132621 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: orange_layer_0_outer_cut_replace_0 \n",
+      "component index     [3]: 12 \n",
+      "Volume.name         [3]: orange_layer_0_outer_cut_replace_0 \n",
+      "Volume.p_physics.is_vacuum           [3]: 0 \n",
+      "Volume.p_physics.my_absorption       [3]: 1.391566 \n",
+      "Volume.p_physics.number of processes [3]: 2 \n",
+      "Volume.geometry.shape                [3]: cylinder \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: -0.021890 \n",
+      "Volume.geometry.center.z             [3]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.036500 \n",
+      "Volume.geometry.geometry_parameters.height [3]: 0.132625 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [4]: orange_layer_0_inner_cut_0 \n",
+      "component index     [4]: 13 \n",
+      "Volume.name         [4]: orange_layer_0_inner_cut_0 \n",
+      "Volume.p_physics.is_vacuum           [4]: 1 \n",
+      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
+      "Volume.p_physics.number of processes [4]: 0 \n",
+      "Volume.geometry.shape                [4]: cylinder \n",
+      "Volume.geometry.center.x             [4]: 0.000000 \n",
+      "Volume.geometry.center.y             [4]: 0.000000 \n",
+      "Volume.geometry.center.z             [4]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.035500 \n",
+      "Volume.geometry.geometry_parameters.height [4]: 0.040000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [5]: orange_layer_1_layer \n",
+      "component index     [5]: 14 \n",
+      "Volume.name         [5]: orange_layer_1_layer \n",
+      "Volume.p_physics.is_vacuum           [5]: 0 \n",
+      "Volume.p_physics.my_absorption       [5]: 1.391566 \n",
+      "Volume.p_physics.number of processes [5]: 2 \n",
+      "Volume.geometry.shape                [5]: cylinder \n",
+      "Volume.geometry.center.x             [5]: 0.000000 \n",
+      "Volume.geometry.center.y             [5]: 0.073500 \n",
+      "Volume.geometry.center.z             [5]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.040500 \n",
+      "Volume.geometry.geometry_parameters.height [5]: 0.331000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [6]: orange_layer_1_layer_vac \n",
+      "component index     [6]: 15 \n",
+      "Volume.name         [6]: orange_layer_1_layer_vac \n",
+      "Volume.p_physics.is_vacuum           [6]: 1 \n",
+      "Volume.p_physics.my_absorption       [6]: 0.000000 \n",
+      "Volume.p_physics.number of processes [6]: 0 \n",
+      "Volume.geometry.shape                [6]: cylinder \n",
+      "Volume.geometry.center.x             [6]: 0.000000 \n",
+      "Volume.geometry.center.y             [6]: 0.075000 \n",
+      "Volume.geometry.center.z             [6]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.040000 \n",
+      "Volume.geometry.geometry_parameters.height [6]: 0.330000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [7]: orange_layer_2_layer \n",
+      "component index     [7]: 16 \n",
+      "Volume.name         [7]: orange_layer_2_layer \n",
+      "Volume.p_physics.is_vacuum           [7]: 0 \n",
+      "Volume.p_physics.my_absorption       [7]: 1.391566 \n",
+      "Volume.p_physics.number of processes [7]: 2 \n",
+      "Volume.geometry.shape                [7]: cylinder \n",
+      "Volume.geometry.center.x             [7]: 0.000000 \n",
+      "Volume.geometry.center.y             [7]: 0.067500 \n",
+      "Volume.geometry.center.z             [7]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [7]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [7]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [7]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [7]: 0.049750 \n",
+      "Volume.geometry.geometry_parameters.height [7]: 0.333000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [7]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [8]: orange_layer_2_layer_vac \n",
+      "component index     [8]: 17 \n",
+      "Volume.name         [8]: orange_layer_2_layer_vac \n",
+      "Volume.p_physics.is_vacuum           [8]: 1 \n",
+      "Volume.p_physics.my_absorption       [8]: 0.000000 \n",
+      "Volume.p_physics.number of processes [8]: 0 \n",
+      "Volume.geometry.shape                [8]: cylinder \n",
+      "Volume.geometry.center.x             [8]: 0.000000 \n",
+      "Volume.geometry.center.y             [8]: 0.066000 \n",
+      "Volume.geometry.center.z             [8]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [8]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [8]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [8]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [8]: 0.047500 \n",
+      "Volume.geometry.geometry_parameters.height [8]: 0.318000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [8]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [9]: orange_layer_2_outer_cut_0 \n",
+      "component index     [9]: 18 \n",
+      "Volume.name         [9]: orange_layer_2_outer_cut_0 \n",
+      "Volume.p_physics.is_vacuum           [9]: 1 \n",
+      "Volume.p_physics.my_absorption       [9]: 0.000000 \n",
+      "Volume.p_physics.number of processes [9]: 0 \n",
+      "Volume.geometry.shape                [9]: cylinder \n",
+      "Volume.geometry.center.x             [9]: 0.000000 \n",
+      "Volume.geometry.center.y             [9]: -0.024000 \n",
+      "Volume.geometry.center.z             [9]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [9]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [9]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [9]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [9]: 0.049751 \n",
+      "Volume.geometry.geometry_parameters.height [9]: 0.152001 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [9]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [10]: orange_layer_2_outer_cut_replace_0 \n",
+      "component index     [10]: 19 \n",
+      "Volume.name         [10]: orange_layer_2_outer_cut_replace_0 \n",
+      "Volume.p_physics.is_vacuum           [10]: 0 \n",
+      "Volume.p_physics.my_absorption       [10]: 1.391566 \n",
+      "Volume.p_physics.number of processes [10]: 2 \n",
+      "Volume.geometry.shape                [10]: cylinder \n",
+      "Volume.geometry.center.x             [10]: 0.000000 \n",
+      "Volume.geometry.center.y             [10]: -0.024000 \n",
+      "Volume.geometry.center.z             [10]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [10]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [10]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [10]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [10]: 0.048500 \n",
+      "Volume.geometry.geometry_parameters.height [10]: 0.152005 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [10]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [11]: orange_layer_3_layer \n",
+      "component index     [11]: 20 \n",
+      "Volume.name         [11]: orange_layer_3_layer \n",
+      "Volume.p_physics.is_vacuum           [11]: 0 \n",
+      "Volume.p_physics.my_absorption       [11]: 1.391566 \n",
+      "Volume.p_physics.number of processes [11]: 2 \n",
+      "Volume.geometry.shape                [11]: cylinder \n",
+      "Volume.geometry.center.x             [11]: 0.000000 \n",
+      "Volume.geometry.center.y             [11]: 0.053500 \n",
+      "Volume.geometry.center.z             [11]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [11]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [11]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [11]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [11]: 0.063500 \n",
+      "Volume.geometry.geometry_parameters.height [11]: 0.347000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [11]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [12]: orange_layer_3_layer_vac \n",
+      "component index     [12]: 21 \n",
+      "Volume.name         [12]: orange_layer_3_layer_vac \n",
+      "Volume.p_physics.is_vacuum           [12]: 1 \n",
+      "Volume.p_physics.my_absorption       [12]: 0.000000 \n",
+      "Volume.p_physics.number of processes [12]: 0 \n",
+      "Volume.geometry.shape                [12]: cylinder \n",
+      "Volume.geometry.center.x             [12]: 0.000000 \n",
+      "Volume.geometry.center.y             [12]: 0.048000 \n",
+      "Volume.geometry.center.z             [12]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [12]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [12]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [12]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [12]: 0.060000 \n",
+      "Volume.geometry.geometry_parameters.height [12]: 0.314000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [12]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [13]: orange_layer_3_outer_cut_0 \n",
+      "component index     [13]: 22 \n",
+      "Volume.name         [13]: orange_layer_3_outer_cut_0 \n",
+      "Volume.p_physics.is_vacuum           [13]: 1 \n",
+      "Volume.p_physics.my_absorption       [13]: 0.000000 \n",
+      "Volume.p_physics.number of processes [13]: 0 \n",
+      "Volume.geometry.shape                [13]: cylinder \n",
+      "Volume.geometry.center.x             [13]: 0.000000 \n",
+      "Volume.geometry.center.y             [13]: -0.018920 \n",
+      "Volume.geometry.center.z             [13]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [13]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [13]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [13]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [13]: 0.063501 \n",
+      "Volume.geometry.geometry_parameters.height [13]: 0.149241 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [13]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [14]: orange_layer_3_outer_cut_replace_0 \n",
+      "component index     [14]: 23 \n",
+      "Volume.name         [14]: orange_layer_3_outer_cut_replace_0 \n",
+      "Volume.p_physics.is_vacuum           [14]: 0 \n",
+      "Volume.p_physics.my_absorption       [14]: 1.391566 \n",
+      "Volume.p_physics.number of processes [14]: 2 \n",
+      "Volume.geometry.shape                [14]: cylinder \n",
+      "Volume.geometry.center.x             [14]: 0.000000 \n",
+      "Volume.geometry.center.y             [14]: -0.018920 \n",
+      "Volume.geometry.center.z             [14]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [14]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [14]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [14]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [14]: 0.062500 \n",
+      "Volume.geometry.geometry_parameters.height [14]: 0.149245 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [14]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [15]: orange_layer_3_inner_cut_0 \n",
+      "component index     [15]: 24 \n",
+      "Volume.name         [15]: orange_layer_3_inner_cut_0 \n",
+      "Volume.p_physics.is_vacuum           [15]: 1 \n",
+      "Volume.p_physics.my_absorption       [15]: 0.000000 \n",
+      "Volume.p_physics.number of processes [15]: 0 \n",
+      "Volume.geometry.shape                [15]: cylinder \n",
+      "Volume.geometry.center.x             [15]: 0.000000 \n",
+      "Volume.geometry.center.y             [15]: -0.018920 \n",
+      "Volume.geometry.center.z             [15]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [15]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [15]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [15]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [15]: 0.061000 \n",
+      "Volume.geometry.geometry_parameters.height [15]: 0.149240 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [15]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [16]: sample \n",
+      "component index     [16]: 31 \n",
+      "Volume.name         [16]: sample \n",
+      "Volume.p_physics.is_vacuum           [16]: 0 \n",
+      "Volume.p_physics.my_absorption       [16]: 0.273042 \n",
+      "Volume.p_physics.number of processes [16]: 2 \n",
+      "Volume.geometry.shape                [16]: cylinder \n",
+      "Volume.geometry.center.x             [16]: 0.000000 \n",
+      "Volume.geometry.center.y             [16]: 0.000000 \n",
+      "Volume.geometry.center.z             [16]: 2.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [16]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [16]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [16]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [16]: 0.006000 \n",
+      "Volume.geometry.geometry_parameters.height [16]: 0.030000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [16]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 18\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]\n",
+      "LIST: Direct_children for Volume           0 = [6,7,8,12,14]\n",
+      "LIST: Intersect_check_list for Volume      0 = [6,7,8,12,14]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [6,7,8,12,14]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for orange_layer_0_layer with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  1 = [5,17]\n",
+      "LIST: Direct_children for Volume           1 = [5]\n",
+      "LIST: Intersect_check_list for Volume      1 = [2,3,4]\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [7,10,11,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = [7,10,11,14,15]\n",
+      "LIST: Next_volume_list for Volume          1 = [7,10,11,14,15,16,2,3,4]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for orange_layer_0_layer_vac with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  2 = [17]\n",
+      "LIST: Direct_children for Volume           2 = [17]\n",
+      "LIST: Intersect_check_list for Volume      2 = [17]\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [1,3,4,5,7,10,11,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [7,10,11,14,15]\n",
+      "LIST: Next_volume_list for Volume          2 = [1,3,4,5,7,10,11,14,15,16,17]\n",
+      "      Is_vacuum for Volume                 2 = 1\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for orange_layer_0_outer_cut_0 with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  3 = [5,17]\n",
+      "LIST: Direct_children for Volume           3 = [5]\n",
+      "LIST: Intersect_check_list for Volume      3 = [2,4]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [1,7]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = [7]\n",
+      "LIST: Next_volume_list for Volume          3 = [1,7,2,4]\n",
+      "      Is_vacuum for Volume                 3 = 1\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "List overview for orange_layer_0_outer_cut_replace_0 with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  4 = [5,17]\n",
+      "LIST: Direct_children for Volume           4 = [5]\n",
+      "LIST: Intersect_check_list for Volume      4 = [2,5]\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [1,3,7]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = [7]\n",
+      "LIST: Next_volume_list for Volume          4 = [1,3,7,2,5]\n",
+      "      Is_vacuum for Volume                 4 = 0\n",
+      "      is_mask_volume for Volume            4 = 0\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = []\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 0\n",
+      "\n",
+      "List overview for orange_layer_0_inner_cut_0 with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  5 = [17]\n",
+      "LIST: Direct_children for Volume           5 = [17]\n",
+      "LIST: Intersect_check_list for Volume      5 = [2]\n",
+      "LIST: Mask_intersect_list for Volume       5 = []\n",
+      "LIST: Destinations_list for Volume         5 = [4]\n",
+      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
+      "LIST: Next_volume_list for Volume          5 = [4,2]\n",
+      "      Is_vacuum for Volume                 5 = 1\n",
+      "      is_mask_volume for Volume            5 = 0\n",
+      "      is_masked_volume for Volume          5 = 0\n",
+      "      is_exit_volume for Volume            5 = 0\n",
+      "LIST: mask_list for Volume                 5 = []\n",
+      "LIST: masked_by_list for Volume            5 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
+      "      mask_mode for Volume                 5 = 0\n",
+      "\n",
+      "List overview for orange_layer_1_layer with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  6 = [1,2,3,4,5,17]\n",
+      "LIST: Direct_children for Volume           6 = [1,2,3,4]\n",
+      "LIST: Intersect_check_list for Volume      6 = [7]\n",
+      "LIST: Mask_intersect_list for Volume       6 = []\n",
+      "LIST: Destinations_list for Volume         6 = [0,8,9,10,11,12,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 6 = [8,12,14]\n",
+      "LIST: Next_volume_list for Volume          6 = [0,8,9,10,11,12,13,14,15,16,7]\n",
+      "      Is_vacuum for Volume                 6 = 0\n",
+      "      is_mask_volume for Volume            6 = 0\n",
+      "      is_masked_volume for Volume          6 = 0\n",
+      "      is_exit_volume for Volume            6 = 0\n",
+      "LIST: mask_list for Volume                 6 = []\n",
+      "LIST: masked_by_list for Volume            6 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
+      "      mask_mode for Volume                 6 = 0\n",
+      "\n",
+      "List overview for orange_layer_1_layer_vac with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  7 = [1,2,3,4,5,17]\n",
+      "LIST: Direct_children for Volume           7 = [1,2,3,4]\n",
+      "LIST: Intersect_check_list for Volume      7 = [1,2,3,4]\n",
+      "LIST: Mask_intersect_list for Volume       7 = []\n",
+      "LIST: Destinations_list for Volume         7 = [0,6,8,9,10,11,12,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 7 = [6,8,12,14]\n",
+      "LIST: Next_volume_list for Volume          7 = [0,6,8,9,10,11,12,13,14,15,16,1,2,3,4]\n",
+      "      Is_vacuum for Volume                 7 = 1\n",
+      "      is_mask_volume for Volume            7 = 0\n",
+      "      is_masked_volume for Volume          7 = 0\n",
+      "      is_exit_volume for Volume            7 = 0\n",
+      "LIST: mask_list for Volume                 7 = []\n",
+      "LIST: masked_by_list for Volume            7 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
+      "      mask_mode for Volume                 7 = 0\n",
+      "\n",
+      "List overview for orange_layer_2_layer with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  8 = [1,2,3,4,5,9,17]\n",
+      "LIST: Direct_children for Volume           8 = [9]\n",
+      "LIST: Intersect_check_list for Volume      8 = [6,7,9,10,11]\n",
+      "LIST: Mask_intersect_list for Volume       8 = []\n",
+      "LIST: Destinations_list for Volume         8 = [0,12,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 8 = [12,14]\n",
+      "LIST: Next_volume_list for Volume          8 = [0,12,13,14,15,16,6,7,9,10,11]\n",
+      "      Is_vacuum for Volume                 8 = 0\n",
+      "      is_mask_volume for Volume            8 = 0\n",
+      "      is_masked_volume for Volume          8 = 0\n",
+      "      is_exit_volume for Volume            8 = 0\n",
+      "LIST: mask_list for Volume                 8 = []\n",
+      "LIST: masked_by_list for Volume            8 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 8 = []\n",
+      "      mask_mode for Volume                 8 = 0\n",
+      "\n",
+      "List overview for orange_layer_2_layer_vac with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  9 = [1,2,3,4,5,17]\n",
+      "LIST: Direct_children for Volume           9 = [1,2,3,4]\n",
+      "LIST: Intersect_check_list for Volume      9 = [6,7]\n",
+      "LIST: Mask_intersect_list for Volume       9 = []\n",
+      "LIST: Destinations_list for Volume         9 = [8,10,11,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 9 = [8,13,14,15]\n",
+      "LIST: Next_volume_list for Volume          9 = [8,10,11,13,14,15,16,6,7]\n",
+      "      Is_vacuum for Volume                 9 = 1\n",
+      "      is_mask_volume for Volume            9 = 0\n",
+      "      is_masked_volume for Volume          9 = 0\n",
+      "      is_exit_volume for Volume            9 = 0\n",
+      "LIST: mask_list for Volume                 9 = []\n",
+      "LIST: masked_by_list for Volume            9 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 9 = []\n",
+      "      mask_mode for Volume                 9 = 0\n",
+      "\n",
+      "List overview for orange_layer_2_outer_cut_0 with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  10 = [3,4,5,17]\n",
+      "LIST: Direct_children for Volume           10 = [3,4]\n",
+      "LIST: Intersect_check_list for Volume      10 = [6,7,9,11]\n",
+      "LIST: Mask_intersect_list for Volume       10 = []\n",
+      "LIST: Destinations_list for Volume         10 = [8,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 10 = [8,13,14,15]\n",
+      "LIST: Next_volume_list for Volume          10 = [8,13,14,15,16,6,7,9,11]\n",
+      "      Is_vacuum for Volume                 10 = 1\n",
+      "      is_mask_volume for Volume            10 = 0\n",
+      "      is_masked_volume for Volume          10 = 0\n",
+      "      is_exit_volume for Volume            10 = 0\n",
+      "LIST: mask_list for Volume                 10 = []\n",
+      "LIST: masked_by_list for Volume            10 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 10 = []\n",
+      "      mask_mode for Volume                 10 = 0\n",
+      "\n",
+      "List overview for orange_layer_2_outer_cut_replace_0 with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  11 = [3,4,5,17]\n",
+      "LIST: Direct_children for Volume           11 = [3,4]\n",
+      "LIST: Intersect_check_list for Volume      11 = [6,7,9]\n",
+      "LIST: Mask_intersect_list for Volume       11 = []\n",
+      "LIST: Destinations_list for Volume         11 = [8,10,13,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 11 = [8,13,14,15]\n",
+      "LIST: Next_volume_list for Volume          11 = [8,10,13,14,15,16,6,7,9]\n",
+      "      Is_vacuum for Volume                 11 = 0\n",
+      "      is_mask_volume for Volume            11 = 0\n",
+      "      is_masked_volume for Volume          11 = 0\n",
+      "      is_exit_volume for Volume            11 = 0\n",
+      "LIST: mask_list for Volume                 11 = []\n",
+      "LIST: masked_by_list for Volume            11 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 11 = []\n",
+      "      mask_mode for Volume                 11 = 0\n",
+      "\n",
+      "List overview for orange_layer_3_layer with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  12 = [1,2,3,4,5,9,10,11,13,15,16,17]\n",
+      "LIST: Direct_children for Volume           12 = [9,13,15]\n",
+      "LIST: Intersect_check_list for Volume      12 = [6,7,8,13,14,15]\n",
+      "LIST: Mask_intersect_list for Volume       12 = []\n",
+      "LIST: Destinations_list for Volume         12 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 12 = []\n",
+      "LIST: Next_volume_list for Volume          12 = [0,6,7,8,13,14,15]\n",
+      "      Is_vacuum for Volume                 12 = 0\n",
+      "      is_mask_volume for Volume            12 = 0\n",
+      "      is_masked_volume for Volume          12 = 0\n",
+      "      is_exit_volume for Volume            12 = 0\n",
+      "LIST: mask_list for Volume                 12 = []\n",
+      "LIST: masked_by_list for Volume            12 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 12 = []\n",
+      "      mask_mode for Volume                 12 = 0\n",
+      "\n",
+      "List overview for orange_layer_3_layer_vac with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  13 = [1,2,3,4,5,10,11,17]\n",
+      "LIST: Direct_children for Volume           13 = [1,2,10,11]\n",
+      "LIST: Intersect_check_list for Volume      13 = [6,7,8,10,11]\n",
+      "LIST: Mask_intersect_list for Volume       13 = []\n",
+      "LIST: Destinations_list for Volume         13 = [12,14,15,16]\n",
+      "LIST: Reduced_destinations_list for Volume 13 = [12,14]\n",
+      "LIST: Next_volume_list for Volume          13 = [12,14,15,16,6,7,8,10,11]\n",
+      "      Is_vacuum for Volume                 13 = 1\n",
+      "      is_mask_volume for Volume            13 = 0\n",
+      "      is_masked_volume for Volume          13 = 0\n",
+      "      is_exit_volume for Volume            13 = 0\n",
+      "LIST: mask_list for Volume                 13 = []\n",
+      "LIST: masked_by_list for Volume            13 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 13 = []\n",
+      "      mask_mode for Volume                 13 = 0\n",
+      "\n",
+      "List overview for orange_layer_3_outer_cut_0 with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  14 = [3,4,5,16,17]\n",
+      "LIST: Direct_children for Volume           14 = [16]\n",
+      "LIST: Intersect_check_list for Volume      14 = [6,7,8,13,15]\n",
+      "LIST: Mask_intersect_list for Volume       14 = []\n",
+      "LIST: Destinations_list for Volume         14 = [0,12]\n",
+      "LIST: Reduced_destinations_list for Volume 14 = [12]\n",
+      "LIST: Next_volume_list for Volume          14 = [0,12,6,7,8,13,15]\n",
+      "      Is_vacuum for Volume                 14 = 1\n",
+      "      is_mask_volume for Volume            14 = 0\n",
+      "      is_masked_volume for Volume          14 = 0\n",
+      "      is_exit_volume for Volume            14 = 0\n",
+      "LIST: mask_list for Volume                 14 = []\n",
+      "LIST: masked_by_list for Volume            14 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 14 = []\n",
+      "      mask_mode for Volume                 14 = 0\n",
+      "\n",
+      "List overview for orange_layer_3_outer_cut_replace_0 with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  15 = [3,4,5,16,17]\n",
+      "LIST: Direct_children for Volume           15 = [16]\n",
+      "LIST: Intersect_check_list for Volume      15 = [6,7,8,13,16]\n",
+      "LIST: Mask_intersect_list for Volume       15 = []\n",
+      "LIST: Destinations_list for Volume         15 = [12,14]\n",
+      "LIST: Reduced_destinations_list for Volume 15 = [12,14]\n",
+      "LIST: Next_volume_list for Volume          15 = [12,14,6,7,8,13,16]\n",
+      "      Is_vacuum for Volume                 15 = 0\n",
+      "      is_mask_volume for Volume            15 = 0\n",
+      "      is_masked_volume for Volume          15 = 0\n",
+      "      is_exit_volume for Volume            15 = 0\n",
+      "LIST: mask_list for Volume                 15 = []\n",
+      "LIST: masked_by_list for Volume            15 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 15 = []\n",
+      "      mask_mode for Volume                 15 = 0\n",
+      "\n",
+      "List overview for orange_layer_3_inner_cut_0 with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  16 = [3,4,5,17]\n",
+      "LIST: Direct_children for Volume           16 = [3,4]\n",
+      "LIST: Intersect_check_list for Volume      16 = [6,7,8,13]\n",
+      "LIST: Mask_intersect_list for Volume       16 = []\n",
+      "LIST: Destinations_list for Volume         16 = [15]\n",
+      "LIST: Reduced_destinations_list for Volume 16 = [15]\n",
+      "LIST: Next_volume_list for Volume          16 = [15,6,7,8,13]\n",
+      "      Is_vacuum for Volume                 16 = 1\n",
+      "      is_mask_volume for Volume            16 = 0\n",
+      "      is_masked_volume for Volume          16 = 0\n",
+      "      is_exit_volume for Volume            16 = 0\n",
+      "LIST: mask_list for Volume                 16 = []\n",
+      "LIST: masked_by_list for Volume            16 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 16 = []\n",
+      "      mask_mode for Volume                 16 = 0\n",
+      "\n",
+      "List overview for sample with cylinder shape made of nascalf\n",
+      "LIST: Children for Volume                  17 = []\n",
+      "LIST: Direct_children for Volume           17 = []\n",
+      "LIST: Intersect_check_list for Volume      17 = []\n",
+      "LIST: Mask_intersect_list for Volume       17 = []\n",
+      "LIST: Destinations_list for Volume         17 = [2]\n",
+      "LIST: Reduced_destinations_list for Volume 17 = [2]\n",
+      "LIST: Next_volume_list for Volume          17 = [2]\n",
+      "      Is_vacuum for Volume                 17 = 0\n",
+      "      is_mask_volume for Volume            17 = 0\n",
+      "      is_masked_volume for Volume          17 = 0\n",
+      "      is_exit_volume for Volume            17 = 0\n",
+      "LIST: mask_list for Volume                 17 = []\n",
+      "LIST: masked_by_list for Volume            17 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 17 = []\n",
+      "      mask_mode for Volume                 17 = 0\n",
+      "\n",
+      "Union_master component orange_master initialized sucessfully\n",
+      "Detector: orange_logger_space_zx_I=1.09061e-06 orange_logger_space_zx_ERR=2.64358e-10 orange_logger_space_zx_N=3.86065e+08 \"orange_space_zx.dat\"\n",
+      "Detector: orange_logger_space_zy_I=1.09061e-06 orange_logger_space_zy_ERR=2.64358e-10 orange_logger_space_zy_N=3.86065e+08 \"orange_space_zy.dat\"\n",
+      "Detector: orange_logger_space_xy_I=1.09061e-06 orange_logger_space_xy_ERR=2.64358e-10 orange_logger_space_xy_N=3.86065e+08 \"orange_space_xy.dat\"\n",
+      "Detector: orange_logger_space_zy_close_I=1.62557e-07 orange_logger_space_zy_close_ERR=1.74868e-11 orange_logger_space_zy_close_N=1.67293e+08 \"orange_space_zy_close.dat_0\"\n",
+      "Detector: orange_logger_time_I=1.0903e-06 orange_logger_time_ERR=2.64349e-10 orange_logger_time_N=3.81625e+08 \"NULL.dat\"\n",
+      "Detector: orange_logger_space_zy_time_I=4.64807e-07 orange_logger_space_zy_time_ERR=1.70882e-10 orange_logger_space_zy_time_N=1.90626e+08 \"orange_logger_space_zy_time.dat_0\"\n",
+      "Detector: orange_logger_space_zy_time_I=5.9603e-07 orange_logger_space_zy_time_ERR=2.00616e-10 orange_logger_space_zy_time_N=1.19734e+08 \"orange_logger_space_zy_time.dat_1\"\n",
+      "Detector: orange_logger_space_zy_time_I=2.28432e-08 orange_logger_space_zy_time_ERR=1.85654e-11 orange_logger_space_zy_time_N=4.52069e+07 \"orange_logger_space_zy_time.dat_2\"\n",
+      "Detector: orange_logger_space_zy_time_I=5.62548e-09 orange_logger_space_zy_time_ERR=8.64179e-12 orange_logger_space_zy_time_N=1.9049e+07 \"orange_logger_space_zy_time.dat_3\"\n",
+      "Detector: orange_logger_space_zy_time_I=9.97296e-10 orange_logger_space_zy_time_ERR=3.65841e-12 orange_logger_space_zy_time_N=7.01011e+06 \"orange_logger_space_zy_time.dat_4\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "instrument.settings(ncount=2E7, mpi=4)\n",
+    "instrument.settings(ncount=2E7, mpi=2)\n",
     "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "smooth-identity",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "530f07ae5726474880f72e22c984053f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(Output(layout=Layout(width='75%')), VBox(children=(Label(value='Choose monitor'), Dropdown(layo…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ms.interface(data)"
    ]
diff --git a/examples/cryostat_image.png b/examples/cryostat_image.png
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zxv_7~%OtW&$B{X!$mBRE)r-3GRxIc{cD1AwS5V%6(6t#ak?}S1*(zt@jBg-+KbS##
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Y@pvaH=5pO4FyJ5AD@vEsF6uw|A9#u=od5s;

diff --git a/mcstasscript/tools/cryostat_builder.py b/mcstasscript/tools/cryostat_builder.py
index 34d42ab0..94aa8d99 100644
--- a/mcstasscript/tools/cryostat_builder.py
+++ b/mcstasscript/tools/cryostat_builder.py
@@ -2,14 +2,14 @@
 
 class Layer:
     def __init__(self, name, cryostat,
-                 sample_to_bottom, bottom_thickness, sample_to_top, top_thickness=0.01,
+                 origin_to_bottom, bottom_thickness, origin_to_top, top_thickness=0.01,
                  inner_radius=None, outer_radius=None, thickness=None,
                  material="Al", p_interact=0):
         """
         Layer of cryostat with a shell made of given material and hollowed by vacuum
 
         Describes a layer of a cryostat with given geometry and materials. The geometry
-        is specified with distance from sample to top and bottom, along with a
+        is specified with distance from origin to top and bottom, along with a
         thickness of these. In addition an outer and inner radius is needed, here the
         user can specify two of inner_radius, outer_radius and thickness.
         The Layer is given the cryostat object of which is it a part to access methods
@@ -25,14 +25,14 @@ def __init__(self, name, cryostat,
         Parameters
         ----------
 
-        sample_to_bottom: float
-            Distance from sample to bottom of layer [m] (bottom thickness added)
+        origin_to_bottom: float
+            Distance from origin to bottom of layer [m] (bottom thickness added)
 
         bottom_thickness: float
             Thickness of layer bottom [m]
 
-        sample_to_top: float
-            Distance from sample to top of layer [m] (top thickness added)
+        origin_to_top: float
+            Distance from origin to top of layer [m] (top thickness added)
 
         top_thickness: float
             Thickness of layer top [m]
@@ -68,8 +68,8 @@ def __init__(self, name, cryostat,
                                + "' found in given instrument file. \n"
                                + "Construct with Union_make_material.")
 
-        self.shell_lowest_point = -sample_to_bottom - bottom_thickness
-        self.shell_highest_point = sample_to_top + top_thickness
+        self.shell_lowest_point = -origin_to_bottom - bottom_thickness
+        self.shell_highest_point = origin_to_top + top_thickness
         self.shell_center = 0.5 * (self.shell_lowest_point + self.shell_highest_point)
         self.shell_height = self.shell_highest_point - self.shell_lowest_point
 
@@ -77,8 +77,8 @@ def __init__(self, name, cryostat,
         self.cryostat.add_y_plane(self.shell_lowest_point)
         self.cryostat.add_y_plane(self.shell_highest_point)
 
-        self.vac_lowest_point = -sample_to_bottom
-        self.vac_highest_point = sample_to_top
+        self.vac_lowest_point = -origin_to_bottom
+        self.vac_highest_point = origin_to_top
         self.vac_center = 0.5 * (self.vac_highest_point + self.vac_lowest_point)
         self.vac_height = self.vac_highest_point - self.vac_lowest_point
 
@@ -162,7 +162,7 @@ def check_radius_input(self, inner_radius, outer_radius, thickness):
         return inner_radius, outer_radius
 
     def add_window(self, inner_radius=None, outer_radius=None, thickness=None,
-                   height=None, sample_to_top=None, sample_to_bottom=None):
+                   height=None, origin_to_top=None, origin_to_bottom=None):
         """
         Adds 360 deg window in given height interval
 
@@ -174,8 +174,8 @@ def add_window(self, inner_radius=None, outer_radius=None, thickness=None,
         is given, material will be removed from both the outside and inside, only
         use this if necessary, never specify inner / outer radius identical to the
         main layer.
-        Any two of height, sample_to_top and sample_to_bottom can be given, or if
-        the window is symmetrical in height around the sample position, the height
+        Any two of height, origin_to_top and origin_to_bottom can be given, or if
+        the window is symmetrical in height around the origin position, the height
         alone is sufficient.
         It is possible to add multiple multiple windows by calling this method
         multiple times, add the tallest first.
@@ -195,11 +195,11 @@ def add_window(self, inner_radius=None, outer_radius=None, thickness=None,
         height : float
             height of window
 
-        sample_to_top : float
-            distance from sample center to top of window
+        origin_to_top : float
+            distance from origin to top of window
 
-        sample_to_bototm : float
-            distance from sample center to bottom of window
+        origin_to_bototm : float
+            distance from origin to bottom of window
         """
 
         # Assume no cuts are made
@@ -232,21 +232,21 @@ def add_window(self, inner_radius=None, outer_radius=None, thickness=None,
         if abs(outer_radius - self.outer_radius) > 1E-5:
             outer_cut = True
 
-        if self.count_inputs(height, sample_to_top, sample_to_bottom) == 3:
+        if self.count_inputs(height, origin_to_top, origin_to_bottom) == 3:
             raise RuntimeError("Ambigious definition of window height.")
 
         if height is not None:
             window_top = height / 2
             window_bottom = -height / 2
-            if sample_to_top is not None:
-                window_top = sample_to_top
-                window_bottom = sample_to_top - height
-            if sample_to_bottom is not None:
-                window_top = -sample_to_bottom + height
-                window_bottom = -sample_to_bottom
+            if origin_to_top is not None:
+                window_top = origin_to_top
+                window_bottom = origin_to_top - height
+            if origin_to_bottom is not None:
+                window_top = -origin_to_bottom + height
+                window_bottom = -origin_to_bottom
         else:
-            window_top = sample_to_top
-            window_bottom = -sample_to_bottom
+            window_top = origin_to_top
+            window_bottom = -origin_to_bottom
 
         window_height = window_top - window_bottom
         window_position = 0.5 * (window_top + window_bottom)
@@ -325,6 +325,9 @@ def __init__(self, name, instr, reference="PREVIOUS",
         set_ROTATED methods that works as the standard McStasScript versions.
         It is possible to add logger components that show scattering in the
         system with the add_spatial_loggers method.
+        The position of the cryostat refers to the sample position and is
+        called the origin. It can be placed in McStas with set_AT and
+        set_ROTATED methods on the cryostat object.
 
         Parameters
         ----------

From 39a444493a69545f6b101147f9e491cd1d7b6464 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 22 Apr 2022 14:27:41 +0200
Subject: [PATCH 247/403] Fix of issue where libpyvinyl parameters provided to
 McStasScript were overwritten by similar parameters, which broke the link,
 preventing the libpyvinyl parameters to control the McStasScript parameters
 as intended. This was fixed by removing the McStasScript classes
 ParameterVariable and ParameterContainer that derived from the libpyvinyl
 version, and replacing with use of the libpyvinyl versions and functions for
 the missing methods. The only concession is the addition of a type attribute
 on the libpyvinyl parameter which is needed to keep track of what type to
 write in McStas.

Tests up to date. Need to add new tests for transfering parameters.
---
 mcstasscript/helper/mcstas_objects.py         | 418 ++++++------------
 mcstasscript/interface/instr.py               | 147 ++++--
 mcstasscript/tests/test_dump_and_load.py      |   2 -
 mcstasscript/tests/test_parameter_variable.py |  32 +-
 mcstasscript/tests/test_widget_helpers.py     |  14 +-
 5 files changed, 275 insertions(+), 338 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index ce6d72d5..3b192e37 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -5,292 +5,151 @@
 from libpyvinyl.Parameters.Collections import CalculatorParameters
 
 
-class ParameterVariable(Parameter):
-    """
-    Class describing an input parameter in McStas instrument
-
-    McStas input parameters are of default type double, but can be
-    cast.  If two positional arguments are given, the first is the
-    type, and the second is the parameter name.  With one input, only
-    the parameter name is read.  It is also possible to assign a
-    default value and a comment through keyword arguments. Inherits from the
-    libpyvinyl Parameter.
+def provide_parameter(*args, **kwargs):
+    """Makes a libpyvinyl parameter object
 
-    Attributes
-    ----------
-    type : str
-        McStas type of input: Double, Int, String
+    Examples
+    --------
 
-    name : str
-        Name of input parameter
+    Creates a parameter with name wavelength and associated comment
+    A = provide_parameter("wavelength", comment="wavelength in [AA]")
 
-    value : any
-        Default value/string of parameter, converted to string
-
-    unit : str
-        String descrbing the unit for this variable
-
-    comment : str
-        Comment displayed next to the parameter, could contain units
-
-    Methods
-    -------
-    write_parameter(fo,stop_character)
-        writes the parameter to file fo, uses given stop character
-    """
+    Creates a parameter with name A3 and default value
+    A = provide_parameter("A3", value=30, comment="A3 angle in [deg]")
 
-    def __init__(self, *args, **kwargs):
-        """Initializing mcstas parameter object
+    Creates a parameter with type string and name sample_name
+    A = provide_parameter("string", "sample_name")
 
-        Examples
-        --------
-
-        Creates a parameter with name wavelength and associated comment
-        A = ParameterVariable("wavelength", comment="wavelength in [AA]")
-
-        Creates a parameter with name A3 and default value
-        A = ParameterVariable("A3", value=30, comment="A3 angle in [deg]")
-
-        Creates a parameter with type string and name sample_name
-        A = ParameterVariable("string", "sample_name")
-
-        Parameters
-        ----------
-        If giving a type:
-        Positional argument 1: type: str
-            Type of the parameter, double, int or string
-        Positional argument 2: name: str
-            Name of input parameter
-
-        If not giving type
-        Positional argument 1: name : str
-            Name of input parameter
-
-        Keyword arguments
-            value : any
-                sets default value of parameter
-            comment : str
-                sets comment displayed next to declaration
-
-        """
-
-        if len(args) == 1:
-            self.type = ""
-            name = str(args[0])
-        if len(args) == 2:
-            specified_type = args[0]
-            allowed_types = {"double", "int", "string"}
-            if specified_type not in allowed_types:
-                raise RuntimeError("Tried to create parameter of type \""
-                                   + str(specified_type)
-                                   + "\" which is not among the allowed types "
-                                   + str(allowed_types) + ".")
-
-            self.type = specified_type
-            name = str(args[1])
-
-        if not is_legal_parameter(name):
-            raise NameError("The given parameter name: \""
-                            + name
-                            + "\" is not a legal c variable name, "
-                            + " and cannot be used in McStas.")
-
-        comment = None
-        if "comment" in kwargs:
-            comment = kwargs["comment"]
-            if not isinstance(comment, str):
-                raise RuntimeError("Tried to create a parameter with a "
-                                   + "comment that was not a string.")
-
-        unit = None
-        if "unit" in kwargs:
-            unit = kwargs["unit"]
-            if not isinstance(unit, str):
-                raise RuntimeError("Unit has to be a string")
-
-        super().__init__(name=name, unit=unit, comment=comment)
-
-        if "options" in kwargs:
-            options = kwargs["options"]
-
-            self.add_option(options, True)
-
-        if "value" in kwargs:
-            if not isinstance(kwargs["value"], (str, int, float)):
-                raise RuntimeError("Given value for parameter has to be of "
-                                   + "type str, int or float.")
-
-            self.value = kwargs["value"]
+    Parameters
+    ----------
+    If giving a type:
+    Positional argument 1: type: str
+        Type of the parameter, double, int or string
+    Positional argument 2: name: str
+        Name of input parameter
 
-    def write_parameter(self, fo, stop_character):
-        """Writes input parameter to file"""
+    If not giving type
+    Positional argument 1: name : str
+        Name of input parameter
 
-        if not isinstance(stop_character, str):
-            raise RuntimeError("stop_character in write_parameter should be "
-                               + "a string.")
+    Keyword arguments
+        value : any
+            sets default value of parameter
+        comment : str
+            sets comment displayed next to declaration
 
-        if not self.type == "":
-            fo.write("%s %s" % (self.type, self.name))
+    """
+    if len(args) == 0:
+        # Check all required keyword arguments present
+        if "name" not in kwargs["name"]:
+            raise RuntimeError("Need to provide name, either as first argument"
+                               + " or keyword argument")
+        provided_name = kwargs["name"]
+
+        provided_type = ""
+        if "type" in kwargs:
+            provided_type = kwargs["type"]
+
+    elif len(args) == 1:
+        if "name" in kwargs:
+            raise RuntimeError("Only specify name with argument or keyword argument")
+        provided_name = args[0]
+
+        # Assume default type if not given
+        provided_type = ""
+        if "type" in kwargs:
+            provided_type = kwargs["type"]
+
+    elif len(args) == 2:
+        if "type" in kwargs:
+            raise RuntimeError("Only specify type with argument or keyword argument")
+        provided_type = args[0]
+
+        if "name" in kwargs:
+            raise RuntimeError("Only specify name with argument or keyword argument")
+        provided_name = args[1]
+
+    else:
+        raise RuntimeError("Too many arguments given to parameter")
+
+    if not is_legal_parameter(provided_name):
+        raise NameError("The given parameter name: \""
+                        + provided_name
+                        + "\" is not a legal c variable name, "
+                        + " and cannot be used in McStas.")
+
+    allowed_types = {"", "double", "int", "string"}
+    if provided_type not in allowed_types:
+        raise RuntimeError("Tried to create parameter of type \""
+                           + str(provided_type)
+                           + "\" which is not among the allowed types "
+                           + str(allowed_types) + ".")
+
+    comment = ""
+    if "comment" in kwargs:
+        if not isinstance(comment, str):
+            raise RuntimeError("Tried to create a parameter with a "
+                               + "comment that was not a string.")
+        comment = kwargs["comment"]
+
+    unit = ""
+    if "unit" in kwargs:
+        if not isinstance(unit, str):
+            raise RuntimeError("Unit has to be a string")
+        unit = kwargs["unit"]
+
+    parameter = Parameter(name=provided_name, unit=unit, comment=comment)
+    parameter.type = provided_type
+
+    if "options" in kwargs:
+        parameter.add_option(kwargs["options"], True)
+
+    if "value" in kwargs:
+        parameter.value = kwargs["value"]
+
+    return parameter
+
+
+def write_parameter(fo, parameter, stop_character):
+    if not isinstance(stop_character, str):
+        raise RuntimeError("stop_character in write_parameter should be "
+                           + "a string.")
+
+    if parameter.type is None:
+        # This can happen if parameter given by libpyvinyl directly, infer type
+        if parameter.value is None:
+            raise RuntimeError("Need to set parameter '" + parameter.name
+                               + "' before writing instrument.")
+
+        if isinstance(parameter.value, (float, int)):
+            parameter.type = "double"
+        elif isinstance(parameter.value, str):
+            parameter.type = "string"
         else:
-            fo.write(self.name)
-
-        if self.value is not None:
-            if isinstance(self.value, int):
-                fo.write(" = %d" % self.value)
-            elif isinstance(self.value, float):
-                fo.write(" = %G" % self.value)
-            else:
-                fo.write(" = %s" % str(self.value))
-        fo.write(stop_character)
-
-        if self.comment is None:
-            c_comment = ""
+            raise RuntimeError("Parameter '" + parameter.name + "' has value "
+                               + "of type not recognized by McStasScript.")
+
+    if not parameter.type == "":
+        fo.write("%s %s" % (parameter.type, parameter.name))
+    else:
+        fo.write(parameter.name)
+
+    if parameter.value is not None:
+        if isinstance(parameter.value, int):
+            fo.write(" = %d" % parameter.value)
+        elif isinstance(parameter.value, float):
+            fo.write(" = %G" % parameter.value)
         else:
-            c_comment = "// " + self.comment
-
-        fo.write(c_comment)
-        fo.write("\n")
+            fo.write(" = %s" % str(parameter.value))
+    fo.write(stop_character)
 
+    if parameter.comment is None or parameter.comment == "":
+        c_comment = ""
+    else:
+        c_comment = "// " + parameter.comment
 
-class ParameterContainer(CalculatorParameters):
-    def __init__(self, parameters=None):
-        """
-        McStasScript version of libpyvinyls CalculatorParameters
-
-        Expanded with ability to import standard libpyvinyl parameters to
-        McStasScript and show parameter method.
-        """
-        super().__init__(parameters)
-
-    def import_parameters(self, parameters):
-        """
-        Imports libpyvinyl parameters to this ParameterContainer
-
-        There are further requirements for parameters in McStasScript which
-        need to be checked on import, and a subclass of Parameter is used
-        to store these with additional functionality.
-
-        Parameters:
-            parameters: ParameterContainer
-                libpyvinyl ParameterContainer for conversion
-        """
-        if isinstance(parameters, ParameterContainer):
-            for parameter in parameters:
-                self.add(parameter)
-            return
-
-        if not isinstance(parameters, CalculatorParameters):
-            raise RuntimeError("Uknown parameter class given.")
-
-        # Code for loading from CalculatorParameters
-        for parameter in parameters:
-            try:
-                mcstas_par = ParameterVariable(parameter.name,
-                                               unit=parameter.unit,
-                                               comment=parameter.comment)
-            except:
-                raise NameError("Imported parameter did not have McStas "
-                                + "legal name")
-
-            # Ensure strings get appropriate McStas type.
-            if isinstance(parameter.value, str):
-                mcstas_par.type = "string"
-
-            mcstas_par.__dict__.update(parameter.__dict__)
-
-            self.add(mcstas_par)
-
-    def show_parameters(self, line_limit=100):
-
-        """
-        Method for displaying current instrument parameters
-
-        line_limit : int
-            Maximum line length for terminal output
-        """
-
-        if len(self.parameters) == 0:
-            print("No instrument parameters available")
-            return
-
-        # Find longest fields
-        types = []
-        names = []
-        values = []
-        comments = []
-        for parameter in self.parameters.values():
-            types.append(str(parameter.type))
-            names.append(str(parameter.name))
-            values.append(str(parameter.value))
-            if parameter.comment is None:
-                comments.append("")
-            else:
-                comments.append(str(parameter.comment))
-
-        longest_type = len(max(types, key=len))
-        longest_name = len(max(names, key=len))
-        longest_value = len(max(values, key=len))
-        # In addition to the data 11 characters are added before the comment
-        comment_start_point = longest_type + longest_name + longest_value + 11
-        longest_comment = len(max(comments, key=len))
-        length_for_comment = line_limit - comment_start_point
-
-        # Print to console
-        for parameter in self.parameters.values():
-            print(str(parameter.type).ljust(longest_type), end=' ')
-            print(str(parameter.name).ljust(longest_name), end=' ')
-            if parameter.value is None:
-                print("  ", end=' ')
-                print(" ".ljust(longest_value + 1), end=' ')
-            else:
-                print(" =", end=' ')
-                print(str(parameter.value).ljust(longest_value + 1), end=' ')
-
-            if parameter.comment is None:
-                c_comment = ""
-            else:
-                c_comment = "// " + str(parameter.comment)
-
-            if (length_for_comment < 5
-                    or length_for_comment > len(c_comment)):
-                print(c_comment)
-            else:
-                # Split comment into several lines
-                comment = c_comment
-                words = comment.split(" ")
-                words_left = len(words)
-                last_index = 0
-                current_index = 0
-                comment = ""
-                iterations = 0
-                max_iterations = 50
-                while words_left > 0:
-                    iterations += 1
-                    if iterations > max_iterations:
-                        #  Something went long, print on one line
-                        break
-
-                    line_left = length_for_comment
-
-                    while line_left > 0:
-                        if current_index >= len(words):
-                            current_index = len(words) + 1
-                            break
-                        line_left -= len(str(words[current_index])) + 1
-                        current_index += 1
-
-                    current_index -= 1
-                    for word in words[last_index:current_index]:
-                        comment += word + " "
-                    words_left = len(words) - current_index
-                    if words_left > 0:
-                        comment += "\n" + " " * comment_start_point
-                        last_index = current_index
-
-                if not iterations == max_iterations + 1:
-                    print(comment)
-                else:
-                    print(c_comment.ljust(longest_comment))
+    fo.write(c_comment)
+    fo.write("\n")
 
 
 class DeclareVariable:
@@ -450,6 +309,7 @@ def __repr__(self):
 
         return string
 
+
 class Component:
     """
     A class describing a McStas component to be written to an instrument
@@ -749,7 +609,7 @@ def set_AT(self, at_list, RELATIVE=None):
 
         # If parameter objects given, take their name instead
         for index, element in enumerate(at_list):
-            if isinstance(element, (ParameterVariable, DeclareVariable)):
+            if isinstance(element, (Parameter, DeclareVariable)):
                 at_list[index] = element.name
 
         self.AT_data = at_list
@@ -785,7 +645,7 @@ def set_ROTATED(self, rotated_list, RELATIVE=None):
 
         # If parameter objects given, take their name instead
         for index, element in enumerate(rotated_list):
-            if isinstance(element, (ParameterVariable, DeclareVariable)):
+            if isinstance(element, (Parameter, DeclareVariable)):
                 rotated_list[index] = element.name
 
         self.ROTATED_data = rotated_list
@@ -955,7 +815,7 @@ def write_component(self, fo):
                                     + " not set.")
                 else:
                     continue
-            elif isinstance(val, (ParameterVariable, DeclareVariable)):
+            elif isinstance(val, (Parameter, DeclareVariable)):
                 # Extract the parameter name
                 val = val.name
 
@@ -1097,7 +957,7 @@ class is used as a superclass for classes describing each
                 unit = ""
                 if key in self.parameter_units:
                     unit = "[" + self.parameter_units[key] + "]"
-                if isinstance(val, ParameterVariable):
+                if isinstance(val, Parameter):
                     #val_string = val.print_line() # too long
                     val_string = val.name
                 elif isinstance(val, DeclareVariable):
@@ -1237,7 +1097,7 @@ def show_parameters(self, **kwargs):
             if getattr(self, parameter) is not None:
                 parameter_input = getattr(self, parameter)
                 # Use name when an par/var object is found
-                if isinstance(parameter_input, (ParameterVariable, DeclareVariable)):
+                if isinstance(parameter_input, (Parameter, DeclareVariable)):
                     parameter_input = parameter_input.name
 
                 this_set_value = str(parameter_input)
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index a821bb97..d51b97e3 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -15,9 +15,10 @@
 from mcstasscript.data.pyvinylData import pyvinylMcStasData
 
 from mcstasscript.helper.mcstas_objects import DeclareVariable
-from mcstasscript.helper.mcstas_objects import ParameterVariable
+from mcstasscript.helper.mcstas_objects import provide_parameter
+from mcstasscript.helper.mcstas_objects import write_parameter
 from mcstasscript.helper.mcstas_objects import Component
-from mcstasscript.helper.mcstas_objects import ParameterContainer
+
 from mcstasscript.helper.component_reader import ComponentReader
 from mcstasscript.helper.managed_mcrun import ManagedMcrun
 from mcstasscript.helper.formatting import is_legal_filename
@@ -225,7 +226,7 @@ class McCode_instr(BaseCalculator):
         Returns data set from latest simulation in widget
     """
 
-    def __init__(self, name, parameters=None, dumpfile=None, executable=None,
+    def __init__(self, name, parameters=None, executable=None,
                  author=None, origin=None,  **kwargs):
         """
         Initialization of McStas Instrument
@@ -239,9 +240,6 @@ def __init__(self, name, parameters=None, dumpfile=None, executable=None,
             parameters : ParameterContainer or CalculatorParameters
                 Parameters for this instrument
 
-            dumpfile: str
-                File path to dump file to be loaded
-
             author : str
                 Name of author, written in instrument file
 
@@ -269,20 +267,6 @@ def __init__(self, name, parameters=None, dumpfile=None, executable=None,
                          output_data_types=[pyvinylMcStasData],
                          parameters=parameters)
 
-        # Libpyvinyl uses CalculatorParameters, replace with ParameterContainer
-        if not isinstance(self.parameters, ParameterContainer):
-            # Need to convert McStasScript parameters
-            if isinstance(self.parameters, CalculatorParameters):
-                mcstasscript_parameters = ParameterContainer()
-                mcstasscript_parameters.import_parameters(self.parameters)
-                self.parameters = mcstasscript_parameters
-
-            else:
-                raise TypeError("Input parameter 'parameters' must be of "
-                                + "type CalculatorParameters or "
-                                + "ParameterContainer, not "
-                                + "%s", type(self.parameters))
-
         # Check required attributes has been set by class that inherits
         if not (hasattr(self, "particle") or
                 hasattr(self, "package_name")):
@@ -307,6 +291,15 @@ def __init__(self, name, parameters=None, dumpfile=None, executable=None,
         else:
             self.origin = str(origin)
 
+        # Attempt to classify given parameters in McStas context
+        for parameter in self.parameters.parameters.values():
+            if parameter.value is None or isinstance(parameter.value, (float, int)):
+                parameter.type = "double"
+            elif isinstance(parameter.value, (str)):
+                parameter.type = "string"
+            else:
+                parameter.type = None
+
         self._run_settings = {}  # Settings for running simulation
 
         # Sets max_line_length and adds paths to run_settings
@@ -385,7 +378,7 @@ def init_parameters(self):
         """
         Create empty ParameterContainer for new instrument
         """
-        self.parameters = ParameterContainer()
+        self.parameters = CalculatorParameters()
 
     def _read_calibration(self):
         """
@@ -399,7 +392,7 @@ def add_parameter(self, *args, **kwargs):
         Method for adding input parameter to instrument
 
         Type does not need to be specified, McStas considers that a floating
-        point value with the type double.
+        point value with the type double. Uses libpyvinyl Parameter object.
 
         Examples
         --------
@@ -422,29 +415,114 @@ def add_parameter(self, *args, **kwargs):
             name of parameter
 
         keyword arguments
-            value : any
+            value : float, int or str
                 Default value of parameter
 
+            unit : str
+                Unit to be displayed
+
             comment : str
                 Comment displayed next to declaration of parameter
         """
-        # ParameterVariable class documented independently
-        par = ParameterVariable(*args, ** kwargs)
+        par = provide_parameter(*args, **kwargs)
         self.parameters.add(par)
+
         return par
 
     def show_parameters(self, line_length=None):
+
         """
         Method for displaying current instrument parameters
 
-        keyword arguments
-            line_length : int
-                Maximum line length for terminal output
+        line_limit : int
+            Maximum line length for terminal output
         """
+
+        if len(self.parameters.parameters) == 0:
+            print("No instrument parameters available")
+            return
+
         if line_length is None:
             line_length = self.line_limit
 
-        self.parameters.show_parameters(line_length)
+        # Find longest fields
+        types = []
+        names = []
+        values = []
+        comments = []
+        for parameter in self.parameters.parameters.values():
+            types.append(str(parameter.type))
+            names.append(str(parameter.name))
+            values.append(str(parameter.value))
+            if parameter.comment is None:
+                comments.append("")
+            else:
+                comments.append(str(parameter.comment))
+
+        longest_type = len(max(types, key=len))
+        longest_name = len(max(names, key=len))
+        longest_value = len(max(values, key=len))
+        # In addition to the data 11 characters are added before the comment
+        comment_start_point = longest_type + longest_name + longest_value + 11
+        longest_comment = len(max(comments, key=len))
+        length_for_comment = line_length - comment_start_point
+
+        # Print to console
+        for parameter in self.parameters.parameters.values():
+            print(str(parameter.type).ljust(longest_type), end=' ')
+            print(str(parameter.name).ljust(longest_name), end=' ')
+            if parameter.value is None:
+                print("  ", end=' ')
+                print(" ".ljust(longest_value + 1), end=' ')
+            else:
+                print(" =", end=' ')
+                print(str(parameter.value).ljust(longest_value + 1), end=' ')
+
+            if parameter.comment is None:
+                c_comment = ""
+            else:
+                c_comment = "// " + str(parameter.comment)
+
+            if (length_for_comment < 5
+                    or length_for_comment > len(c_comment)):
+                print(c_comment)
+            else:
+                # Split comment into several lines
+                comment = c_comment
+                words = comment.split(" ")
+                words_left = len(words)
+                last_index = 0
+                current_index = 0
+                comment = ""
+                iterations = 0
+                max_iterations = 50
+                while words_left > 0:
+                    iterations += 1
+                    if iterations > max_iterations:
+                        #  Something went long, print on one line
+                        break
+
+                    line_left = length_for_comment
+
+                    while line_left > 0:
+                        if current_index >= len(words):
+                            current_index = len(words) + 1
+                            break
+                        line_left -= len(str(words[current_index])) + 1
+                        current_index += 1
+
+                    current_index -= 1
+                    for word in words[last_index:current_index]:
+                        comment += word + " "
+                    words_left = len(words) - current_index
+                    if words_left > 0:
+                        comment += "\n" + " " * comment_start_point
+                        last_index = current_index
+
+                if not iterations == max_iterations + 1:
+                    print(comment)
+                else:
+                    print(c_comment.ljust(longest_comment))
 
     def show_variables(self):
         """
@@ -1548,12 +1626,13 @@ def write_full_instrument(self):
         fo.write("\n")
         fo.write("DEFINE INSTRUMENT %s (" % self.name)
         fo.write("\n")
-        # Add loop that inserts parameters here
+        # Insert parameters
         parameter_list = list(self.parameters)
-        for variable in parameter_list[0:-1]:
-            variable.write_parameter(fo, ", ")
-        if len(parameter_list) > 0:
-            parameter_list[-1].write_parameter(fo, " ")
+        end_chars = [", "]*len(parameter_list)
+        if len(end_chars) >= 1:
+            end_chars[-1] = " "
+        for variable, end_char in zip(parameter_list, end_chars):
+            write_parameter(fo, variable, end_char)
         fo.write(")\n")
         fo.write("\n")
 
diff --git a/mcstasscript/tests/test_dump_and_load.py b/mcstasscript/tests/test_dump_and_load.py
index c0998526..7025671a 100644
--- a/mcstasscript/tests/test_dump_and_load.py
+++ b/mcstasscript/tests/test_dump_and_load.py
@@ -7,8 +7,6 @@
 
 from mcstasscript.interface.instr import McStas_instr
 from mcstasscript.interface.instr import McXtrace_instr
-from mcstasscript.helper.mcstas_objects import ParameterContainer
-from mcstasscript.helper.mcstas_objects import ParameterVariable
 
 from .helpers_for_tests import WorkInTestDir
 
diff --git a/mcstasscript/tests/test_parameter_variable.py b/mcstasscript/tests/test_parameter_variable.py
index 53c7c7e9..047bad6f 100644
--- a/mcstasscript/tests/test_parameter_variable.py
+++ b/mcstasscript/tests/test_parameter_variable.py
@@ -1,8 +1,8 @@
 import unittest
 import unittest.mock
 
-from mcstasscript.helper.mcstas_objects import ParameterVariable
-
+from mcstasscript.helper.mcstas_objects import provide_parameter
+from mcstasscript.helper.mcstas_objects import write_parameter
 
 class Test_ParameterVariable(unittest.TestCase):
     """
@@ -16,7 +16,7 @@ def test_ParameterVariable_init_basic(self):
         Smallest possible initialization
         """
 
-        par = ParameterVariable("test")
+        par = provide_parameter("test")
         self.assertEqual(par.name, "test")
 
     def test_ParameterVariable_init_basic_type(self):
@@ -24,7 +24,7 @@ def test_ParameterVariable_init_basic_type(self):
         Initialization with a type
         """
 
-        par = ParameterVariable("double", "test")
+        par = provide_parameter("double", "test")
 
         self.assertEqual(par.name, "test")
         self.assertEqual(par.type, "double")
@@ -34,7 +34,7 @@ def test_ParameterVariable_init_basic_type_value(self):
         Initialization with type and value
         """
 
-        par = ParameterVariable("double", "test", value=518)
+        par = provide_parameter("double", "test", value=518)
 
         self.assertEqual(par.name, "test")
         self.assertEqual(par.type, "double")
@@ -45,7 +45,7 @@ def test_ParameterVariable_init_basic_type_value_comment(self):
         Initialization with type, value and comment
         """
 
-        par = ParameterVariable("double", "test", value=518,
+        par = provide_parameter("double", "test", value=518,
                                 comment="test comment /")
 
         self.assertEqual(par.name, "test")
@@ -58,7 +58,7 @@ def test_ParameterVariable_init_basic_value_comment(self):
         Initialization with value and comment
         """
 
-        par = ParameterVariable("test", value=518,
+        par = provide_parameter("test", value=518,
                                 comment="test comment /")
 
         self.assertEqual(par.name, "test")
@@ -71,7 +71,7 @@ def test_ParameterVariable_init_options_initialize(self):
         Initialization with value and comment
         """
 
-        par = ParameterVariable("test", value=2,
+        par = provide_parameter("test", value=2,
                                 options=[1, 2, 3.1])
 
         self.assertEqual(par.name, "test")
@@ -89,9 +89,9 @@ def test_ParameterVariable_write_basic(self, mock_f):
         used.
         """
 
-        par = ParameterVariable("double", "test")
+        par = provide_parameter("double", "test")
         with mock_f('test.txt', 'w') as m_fo:
-            par.write_parameter(m_fo, "")
+            write_parameter(m_fo, parameter=par, stop_character="")
 
         expected_writes = [unittest.mock.call("double test"),
                            unittest.mock.call(""),
@@ -112,11 +112,11 @@ def test_ParameterVariable_write_complex_float(self, mock_f):
         is used. (float value)
         """
 
-        par = ParameterVariable("double", "test", value=5.4,
+        par = provide_parameter("double", "test", value=5.4,
                                 comment="test comment")
 
         with mock_f('test.txt', 'w') as m_fo:
-            par.write_parameter(m_fo, ")")
+            write_parameter(m_fo, parameter=par, stop_character=")")
 
         expected_writes = [unittest.mock.call("double test"),
                            unittest.mock.call(" = 5.4"),
@@ -138,11 +138,11 @@ def test_ParameterVariable_write_complex_int(self, mock_f):
         is used. (integer value)
         """
 
-        par = ParameterVariable("double", "test", value=5,
+        par = provide_parameter("double", "test", value=5,
                                 comment="test comment")
 
         with mock_f('test.txt', 'w') as m_fo:
-            par.write_parameter(m_fo, ")")
+            write_parameter(m_fo, parameter=par, stop_character=")")
 
         expected_writes = [unittest.mock.call("double test"),
                            unittest.mock.call(" = 5"),
@@ -164,11 +164,11 @@ def test_ParameterVariable_write_complex_string(self, mock_f):
         is used. (string value)
         """
 
-        par = ParameterVariable("double", "test", value="\"Al\"",
+        par = provide_parameter("double", "test", value="\"Al\"",
                                 comment="test comment")
 
         with mock_f('test.txt', 'w') as m_fo:
-            par.write_parameter(m_fo, ",")
+            write_parameter(m_fo, parameter=par, stop_character=",")
 
         expected_writes = [unittest.mock.call("double test"),
                            unittest.mock.call(" = \"Al\""),
diff --git a/mcstasscript/tests/test_widget_helpers.py b/mcstasscript/tests/test_widget_helpers.py
index 5f7a7ece..7fdc6701 100644
--- a/mcstasscript/tests/test_widget_helpers.py
+++ b/mcstasscript/tests/test_widget_helpers.py
@@ -5,7 +5,7 @@
 from mcstasscript.jb_interface.widget_helpers import HiddenPrints
 from mcstasscript.jb_interface.widget_helpers import parameter_has_default
 from mcstasscript.jb_interface.widget_helpers import get_parameter_default
-from mcstasscript.helper.mcstas_objects import ParameterVariable
+from mcstasscript.helper.mcstas_objects import provide_parameter
 
 
 class TestWidgetHelpers(unittest.TestCase):
@@ -35,7 +35,7 @@ def test_parameter_has_default_false(self):
         Check for parameter that does not have default, should be false
         """
 
-        par = ParameterVariable("test")
+        par = provide_parameter("test")
         self.assertFalse(parameter_has_default(par))
 
     def test_parameter_has_default_true(self):
@@ -43,7 +43,7 @@ def test_parameter_has_default_true(self):
         Check for parameter that has default, should be true
         """
 
-        par = ParameterVariable("test", value=8)
+        par = provide_parameter("test", value=8)
         self.assertTrue(parameter_has_default(par))
 
     def test_get_parameter_default_string(self):
@@ -51,7 +51,7 @@ def test_get_parameter_default_string(self):
         Get the default for string parameter
         """
 
-        par = ParameterVariable("string", "test", value="variable")
+        par = provide_parameter("string", "test", value="variable")
         self.assertEqual(get_parameter_default(par), "variable")
 
     def test_get_parameter_default_double_specified(self):
@@ -59,7 +59,7 @@ def test_get_parameter_default_double_specified(self):
         Get the default for specified double parameter
         """
 
-        par = ParameterVariable("double", "test", value=5.5)
+        par = provide_parameter("double", "test", value=5.5)
         self.assertEqual(get_parameter_default(par), 5.5)
 
     def test_get_parameter_default_double(self):
@@ -67,7 +67,7 @@ def test_get_parameter_default_double(self):
         Get the default for parameter that was double by default
         """
 
-        par = ParameterVariable("test", value=5.7)
+        par = provide_parameter("test", value=5.7)
         self.assertEqual(get_parameter_default(par), 5.7)
 
     def test_get_parameter_default_int(self):
@@ -75,7 +75,7 @@ def test_get_parameter_default_int(self):
         Get default value from integer value, should be rounded
         """
 
-        par = ParameterVariable("int", "test", value=5.7)
+        par = provide_parameter("int", "test", value=5.7)
         self.assertEqual(get_parameter_default(par), 5)
 
 

From 2e78111602ffa7fea174508e527dc8e700370dec Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 22 Apr 2022 15:06:17 +0200
Subject: [PATCH 248/403] Added unit test on case where libpyvinyl parameters
 given at instrument creation, ensuring these are detected correctly and can
 be changed both from mcstasscript and libpyvinyl.

---
 mcstasscript/interface/instr.py  |  2 +-
 mcstasscript/tests/test_Instr.py | 30 +++++++++++++++++++++++++++++-
 2 files changed, 30 insertions(+), 2 deletions(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index d51b97e3..5549b8e2 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -293,7 +293,7 @@ def __init__(self, name, parameters=None, executable=None,
 
         # Attempt to classify given parameters in McStas context
         for parameter in self.parameters.parameters.values():
-            if parameter.value is None or isinstance(parameter.value, (float, int)):
+            if isinstance(parameter.value, (float, int)):
                 parameter.type = "double"
             elif isinstance(parameter.value, (str)):
                 parameter.type = "string"
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 77e0e891..50c3d25c 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -5,10 +5,12 @@
 import unittest.mock
 import datetime
 
+from libpyvinyl.Parameters.Collections import CalculatorParameters
+
 from mcstasscript.interface.instr import McStas_instr
 from mcstasscript.interface.instr import McXtrace_instr
 from mcstasscript.helper.formatting import bcolors
-from .helpers_for_tests import WorkInTestDir
+from mcstasscript.tests.helpers_for_tests import WorkInTestDir
 
 run_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '.')
 
@@ -348,6 +350,32 @@ def test_load_config_file_x_ray(self):
         self.assertEqual(my_instrument._run_settings["package_path"], correct_mcxtrace_path)
         self.assertEqual(my_instrument.line_limit, correct_n_of_characters)
 
+    def test_load_libpyvinyl_parameters(self):
+        parameters = CalculatorParameters()
+        int_par = parameters.new_parameter("int_parameter", comment="integer parameter")
+        int_par.value = 3
+
+        double_par = parameters.new_parameter("double_parameter", unit="meV")
+        double_par.value = 3.0
+
+        string_par = parameters.new_parameter("string_parameter")
+        string_par.value = "hello world"
+
+        secret_par = parameters.new_parameter("no_value_par")
+
+        instr = McStas_instr("test_instr", parameters=parameters)
+
+        self.assertEqual(int_par.type, "double")
+        self.assertEqual(double_par.type, "double")
+        self.assertEqual(string_par.type, "string")
+        self.assertEqual(secret_par.type, None)
+
+        instr.set_parameters(int_parameter=4)
+        self.assertEqual(int_par.value, 4)
+
+        int_par.value = 5
+        self.assertEqual(instr.parameters["int_parameter"].value, 5)
+
     def test_simple_add_parameter(self):
         """
         This is just an interface to a function that is tested

From 52e4130bd1f4703f05d5b561f40b8f84fa739fe4 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 22 Apr 2022 15:23:00 +0200
Subject: [PATCH 249/403] Updated doc strings for new functions

---
 mcstasscript/helper/mcstas_objects.py | 23 ++++++++++++++++++++---
 mcstasscript/interface/instr.py       |  6 +++++-
 2 files changed, 25 insertions(+), 4 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 3b192e37..1a177563 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -2,7 +2,6 @@
 from mcstasscript.helper.formatting import is_legal_parameter
 
 from libpyvinyl.Parameters.Parameter import Parameter
-from libpyvinyl.Parameters.Collections import CalculatorParameters
 
 
 def provide_parameter(*args, **kwargs):
@@ -33,11 +32,14 @@ def provide_parameter(*args, **kwargs):
         Name of input parameter
 
     Keyword arguments
-        value : any
+        value : float, int or str
             sets default value of parameter
+        unit : str
+            string that describes the unit
         comment : str
             sets comment displayed next to declaration
-
+        options : list or value
+            list or value of allowed values for this parameter
     """
     if len(args) == 0:
         # Check all required keyword arguments present
@@ -111,6 +113,21 @@ def provide_parameter(*args, **kwargs):
 
 
 def write_parameter(fo, parameter, stop_character):
+    """
+    Writes a parameter object to McStas define section
+
+    Parameters
+    ----------
+
+    fo : file object
+        Open file object to write parameter string to
+
+    parameter : Parameter
+        Parameter object to be written
+
+    stop_character : str
+        Character inserted after parameter, usually comma and space
+    """
     if not isinstance(stop_character, str):
         raise RuntimeError("stop_character in write_parameter should be "
                            + "a string.")
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 5549b8e2..c5a66513 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -298,6 +298,7 @@ def __init__(self, name, parameters=None, executable=None,
             elif isinstance(parameter.value, (str)):
                 parameter.type = "string"
             else:
+                # They will be typed when the instrument is written
                 parameter.type = None
 
         self._run_settings = {}  # Settings for running simulation
@@ -392,7 +393,7 @@ def add_parameter(self, *args, **kwargs):
         Method for adding input parameter to instrument
 
         Type does not need to be specified, McStas considers that a floating
-        point value with the type double. Uses libpyvinyl Parameter object.
+        point value with the type 'double'. Uses libpyvinyl Parameter object.
 
         Examples
         --------
@@ -423,6 +424,9 @@ def add_parameter(self, *args, **kwargs):
 
             comment : str
                 Comment displayed next to declaration of parameter
+
+            options : list or value
+                list or value of allowed values for this parameter
         """
         par = provide_parameter(*args, **kwargs)
         self.parameters.add(par)

From 1f070ca7635b1a599facf66600f7ffc8d95e8add Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Mon, 25 Apr 2022 13:29:04 +0200
Subject: [PATCH 250/403] Fixed a bug where components with definition
 parameters that have double specified is not recoignized correctly.

---
 mcstasscript/_version.py                | 2 +-
 mcstasscript/helper/component_reader.py | 4 ++++
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 26cbb133..205c9068 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.40"
+__version__ = "0.0.41"
diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index aad30062..f74b196d 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -350,6 +350,10 @@ def read_component_file(self, absolute_path):
                             temp_par_type = "string"
                             # remove string from part
                             part = "".join(possible_declare[1:])
+                        if "double" == possible_type:
+                            temp_par_type = "double"
+                            # remove double from part
+                            part = "".join(possible_declare[1:])
 
                         part = part.replace(" ", "")
                         if part == "":

From 01904a1c080cbdbb73d9838733dc2b4a98fb87b0 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Mon, 25 Apr 2022 15:48:59 +0200
Subject: [PATCH 251/403] Added simple tool for generating NCrystal Union
 materials.

---
 mcstasscript/tools/ncrystal_union.py | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)
 create mode 100644 mcstasscript/tools/ncrystal_union.py

diff --git a/mcstasscript/tools/ncrystal_union.py b/mcstasscript/tools/ncrystal_union.py
new file mode 100644
index 00000000..2f4f3691
--- /dev/null
+++ b/mcstasscript/tools/ncrystal_union.py
@@ -0,0 +1,18 @@
+def add_ncrystal_union_material(instrument, name, cfgstr, split=False):
+    try:
+        import NCrystal.mcstasutils as NCms
+    except ImportError or ModuleNotFoundError:
+        raise RuntimeError("Install NCrystal to use this feature in McStasScript.")
+
+    abs_info, ncrystal_processes = NCms.cfgstr_2_unioncfg(cfgstr=cfgstr, split_by_physics=split)
+
+    process_names = []
+    for process in ncrystal_processes:
+        process_name = name + "_" + process[1]
+        process_names.append(process_name)
+        comp = instrument.add_component(process_name, "NCrystal_process")
+        comp.cfg = '"' + process[0] + '"'
+
+    comp = instrument.add_component(name, "Union_make_material")
+    comp.my_absorption = abs_info
+    comp.process_string = '"' + ",".join(process_names) + '"'

From d5277251a7f378bcbe228df48a639e531269a4c6 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 5 May 2022 22:03:22 +0200
Subject: [PATCH 252/403] Fixed lack of suppress_output in settings.

---
 mcstasscript/interface/instr.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index c5a66513..0849bacb 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1680,7 +1680,8 @@ def write_full_instrument(self):
     def settings(self, ncount=None, mpi="not_set", seed=None,
                  force_compile=None, output_path=None,
                  increment_folder_name=None, custom_flags=None,
-                 executable=None, executable_path=None):
+                 executable=None, executable_path=None,
+                 suppress_output=None):
         """
         Sets settings for McStas run performed with backengine
 
@@ -1709,6 +1710,8 @@ def settings(self, ncount=None, mpi="not_set", seed=None,
                 Name of the executable
             executable_path : str
                 Path to mcrun command, "" if already in path
+            suppress_output : bool
+                Set to True to surpress output
         """
 
         settings = {}
@@ -1752,6 +1755,9 @@ def settings(self, ncount=None, mpi="not_set", seed=None,
         if seed is not None:
             settings["seed"] = seed
 
+        if suppress_output is not None:
+            settings["suppress_output"] = suppress_output
+
         if output_path is not None:
             self.output_path = output_path
 

From 2d5bf4deb3b43b871c2e2515a6788cd89270c4ad Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 5 May 2022 22:05:02 +0200
Subject: [PATCH 253/403] Added suppress_output to instrument initialization.

---
 mcstasscript/interface/instr.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 0849bacb..93e39706 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -346,6 +346,9 @@ def __init__(self, name, parameters=None, executable=None,
         if "custom_flags" in kwargs:
             provided_run_settings["custom_flags"] = kwargs["custom_flags"]
 
+        if "suppress_output" in kwargs:
+            provided_run_settings["suppress_output"] = kwargs["suppress_output"]
+
         # Set run_settings, perform input sanitation
         self.settings(**provided_run_settings)
 

From db0535a1198f6ee5433e996714220022df305d24 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 5 May 2022 22:08:12 +0200
Subject: [PATCH 254/403] Version bump

---
 mcstasscript/_version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 205c9068..92ea29de 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.41"
+__version__ = "0.0.42"

From ad79d658342c2fb40b224f41863161fca30358ce Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 6 May 2022 14:13:13 +0200
Subject: [PATCH 255/403] Update including fix of setting mpi to None and
 including gravity in settings.

---
 mcstasscript/helper/managed_mcrun.py    | 13 +++++++--
 mcstasscript/interface/instr.py         | 20 +++++++++++--
 mcstasscript/tests/test_Instr.py        |  3 +-
 mcstasscript/tests/test_ManagedMcrun.py | 37 +++++++++++++++++++++++++
 4 files changed, 67 insertions(+), 6 deletions(-)

diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 603ef6a1..d952af21 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -68,6 +68,8 @@ def __init__(self, instr_name, **kwargs):
                 Sets ncount
             mpi : int, default None
                 Sets thread count, None to disable mpi
+            gravity : bool, default False
+                Enables gravity if True
             parameters : dict
                 Sets parameters
             custom_flags : str, default ""
@@ -88,6 +90,7 @@ def __init__(self, instr_name, **kwargs):
         self.data_folder_name = ""
         self.ncount = int(1E6)
         self.mpi = None
+        self.gravity = False
         self.parameters = {}
         self.custom_flags = ""
         self.executable_path = ""
@@ -121,7 +124,7 @@ def __init__(self, instr_name, **kwargs):
             self.mpi = kwargs["mpi"]
             try:
                 self.mpi = int(self.mpi)
-            except ValueError:
+            except (TypeError, ValueError) as e:
                 if self.mpi is not None:
                     raise RuntimeError("MPI should be an integer, was "
                                        + str(self.mpi))
@@ -131,6 +134,9 @@ def __init__(self, instr_name, **kwargs):
                     raise ValueError("MPI should be an integer larger than"
                                      + " 0, was " + str(self.mpi))
 
+        if "gravity" in kwargs:
+            self.gravity = kwargs["gravity"]
+
         if "seed" in kwargs:
             self.seed = kwargs["seed"]
 
@@ -192,7 +198,10 @@ def run_simulation(self, **kwargs):
         # construct command to run
         option_string = ""
         if self.compile:
-            option_string = "-c "
+            option_string += "-c "
+
+        if self.gravity:
+            option_string += "-g "
 
         if self.mpi is not None:
             mpi_string = "--mpi=" + str(self.mpi) + " "  # Set mpi
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 93e39706..11ed2816 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -340,6 +340,9 @@ def __init__(self, name, parameters=None, executable=None,
         if "mpi" in kwargs:
             provided_run_settings["mpi"] = kwargs["mpi"]
 
+        if "gravity" in kwargs:
+            provided_run_settings["gravity"] = kwargs["gravity"]
+
         if "seed" in kwargs:
             provided_run_settings["seed"] = str(kwargs["seed"])
 
@@ -1684,7 +1687,7 @@ def settings(self, ncount=None, mpi="not_set", seed=None,
                  force_compile=None, output_path=None,
                  increment_folder_name=None, custom_flags=None,
                  executable=None, executable_path=None,
-                 suppress_output=None):
+                 suppress_output=None, gravity=None):
         """
         Sets settings for McStas run performed with backengine
 
@@ -1715,6 +1718,8 @@ def settings(self, ncount=None, mpi="not_set", seed=None,
                 Path to mcrun command, "" if already in path
             suppress_output : bool
                 Set to True to surpress output
+            gravity : bool
+                If True, gravity will be simulated
         """
 
         settings = {}
@@ -1751,6 +1756,9 @@ def settings(self, ncount=None, mpi="not_set", seed=None,
                 raise TypeError("mpi must be an integer or None.")
             settings["mpi"] = mpi
 
+        if gravity is not None:
+            settings["gravity"] = bool(gravity)
+
         if custom_flags is not None:
             str(custom_flags)  # Check a string is given
             settings["custom_flags"] = custom_flags
@@ -1781,8 +1789,14 @@ def settings_string(self):
 
         if "mpi" in self._run_settings:
             value = self._run_settings["mpi"]
-            description += "  mpi:".ljust(variable_space)
-            description += str(int(value)) + "\n"
+            if value is not None:
+                description += "  mpi:".ljust(variable_space)
+                description += str(int(value)) + "\n"
+
+        if "gravity" in self._run_settings:
+            value = self._run_settings["gravity"]
+            description += "  gravity:".ljust(variable_space)
+            description += str(value) + "\n"
 
         if "seed" in self._run_settings:
             value = self._run_settings["seed"]
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 50c3d25c..cc142f95 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1898,6 +1898,7 @@ def test_run_full_instrument_complex(self, mock_sub, mock_stdout):
                                       mpi=7,
                                       seed=300,
                                       ncount=48.4,
+                                      gravity=True,
                                       custom_flags="-fo",
                                       parameters={"A": 2,
                                                   "BC": "car",
@@ -1907,7 +1908,7 @@ def test_run_full_instrument_complex(self, mock_sub, mock_stdout):
         expected_folder_path = os.path.join(THIS_DIR, new_folder_name)
 
         # a double space because of a missing option
-        expected_call = (expected_path + " -c -n 48 --mpi=7 --seed=300 "
+        expected_call = (expected_path + " -c -g -n 48 --mpi=7 --seed=300 "
                          + "-d " + expected_folder_path
                          + " -fo test_instrument.instr "
                          + "theta=\"toy\" has_default=37 A=2 BC=car")
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index c102cf11..c19a100a 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -257,6 +257,43 @@ def test_ManagedMcrun_run_simulation_no_standard(self, mock_sub):
                                          universal_newlines=True,
                                          cwd=mcrun_obj.run_path)
 
+    @unittest.mock.patch("subprocess.run")
+    def test_ManagedMcrun_run_simulation_with_gravity(self, mock_sub):
+        """
+        Check a non standard system call is correct when including gravity
+
+        Here multiple options are used and ncount is a float that should
+        be rounded by the class.
+        """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        with WorkInTestDir() as handler:
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     output_path="test_folder",
+                                     executable_path=executable_path,
+                                     executable="mcrun",
+                                     gravity=True,
+                                     mpi=7, seed=300,
+                                     ncount=48.4,
+                                     custom_flags="-fo")
+
+        mcrun_obj.run_simulation()
+
+        expected_folder_path = os.path.join(THIS_DIR, "test_folder")
+
+        # a double space because of a missing option
+        executable = os.path.join(executable_path, "mcrun")
+        expected_call = (executable + " -c -g -n 48 --mpi=7 --seed=300 "
+                         + "-d " + expected_folder_path + " -fo test.instr")
+
+        mock_sub.assert_called_once_with(expected_call,
+                                         shell=True,
+                                         stderr=-1, stdout=-1,
+                                         universal_newlines=True,
+                                         cwd=mcrun_obj.run_path)
+
     @unittest.mock.patch("subprocess.run")
     def test_ManagedMcrun_run_simulation_parameters(self, mock_sub):
         """

From 86e9b4fc4b5c06c89d06257002b646ee919b28dc Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 6 May 2022 14:14:26 +0200
Subject: [PATCH 256/403] Incremented version number, now have mpi and gravity
 fix.

---
 mcstasscript/_version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 92ea29de..59120223 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.42"
+__version__ = "0.0.43"

From 76a12cac85f3cfb5ff02391cf415b667e221bde1 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 10 May 2022 11:20:18 +0200
Subject: [PATCH 257/403] Renamed show_components to available_components as it
 displays what components are available to the instrument. This made the
 show_components name free, so it is now used for showing components contained
 in the instrument, in line with show_parameters, show_variables and so forth.

Updated components to use explicit keyword arguments instead of
**kwargs in order to prevent misspelled keyword names, as they
would not be applied and the user would not be aware.
---
 mcstasscript/helper/mcstas_objects.py |  76 ++++++----
 mcstasscript/interface/instr.py       | 197 +++++++++++++++-----------
 mcstasscript/tests/test_Instr.py      |  44 +++---
 3 files changed, 180 insertions(+), 137 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 1a177563..c0847c20 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -469,7 +469,11 @@ class contains both methods to write the component to an instrument
 
     __isfrozen = False  # When frozen, no new attributes allowed
 
-    def __init__(self, instance_name, component_name, **kwargs):
+    def __init__(self, instance_name, component_name, AT=None,
+                 AT_RELATIVE=None, ROTATED=None, ROTATED_RELATIVE=None,
+                 RELATIVE=None, WHEN=None, EXTEND=None, GROUP=None,
+                 JUMP=None, SPLIT=None, comment=None, c_code_before=None,
+                 c_code_after=None):
         """
         Initializes McStas component with specified name and component
 
@@ -514,6 +518,12 @@ def __init__(self, instance_name, component_name, **kwargs):
 
             comment: str, default ""
                 Sets comment string
+
+            c_code_before: str, default ""
+                Sets c code before component
+
+            c_code_after: str, default ""
+                Sets c code after component
         """
 
         # Allow addition of attributes in init
@@ -538,7 +548,12 @@ def __init__(self, instance_name, component_name, **kwargs):
         self.c_code_after = ""
 
         # If any keywords are set in kwargs, update these
-        self.set_keyword_input(**kwargs)
+        self.set_keyword_input(AT=AT, AT_RELATIVE=AT_RELATIVE, ROTATED=ROTATED,
+                               ROTATED_RELATIVE=ROTATED_RELATIVE,
+                               RELATIVE=RELATIVE, WHEN=WHEN, EXTEND=EXTEND,
+                               GROUP=GROUP, JUMP=JUMP, SPLIT=SPLIT,
+                               comment=comment, c_code_before=c_code_before,
+                               c_code_after=c_code_after)
 
         """
         Could store an option for whether this component should be
@@ -549,49 +564,52 @@ def __init__(self, instance_name, component_name, **kwargs):
         # Do not allow addition of attributes after init
         self._freeze()
 
-    def set_keyword_input(self, **kwargs):
+    def set_keyword_input(self, AT=None, AT_RELATIVE=None, ROTATED=None,
+                          ROTATED_RELATIVE=None, RELATIVE=None, WHEN=None,
+                          EXTEND=None, GROUP=None, JUMP=None, SPLIT=None,
+                          comment=None, c_code_before=None, c_code_after=None):
         # Allow addition of attributes in init
         self._unfreeze()
 
-        if "AT" in kwargs:
-            self.set_AT(kwargs["AT"])
+        if AT is not None:
+            self.set_AT(AT)
 
-        if "AT_RELATIVE" in kwargs:
-            self.set_AT_RELATIVE(kwargs["AT_RELATIVE"])
+        if AT_RELATIVE is not None:
+            self.set_AT_RELATIVE(AT_RELATIVE)
 
         self.ROTATED_specified = False
-        if "ROTATED" in kwargs:
-            self.set_ROTATED(kwargs["ROTATED"])
+        if ROTATED is not None:
+            self.set_ROTATED(ROTATED)
 
-        if "ROTATED_RELATIVE" in kwargs:
-            self.set_ROTATED_RELATIVE(kwargs["ROTATED_RELATIVE"])
+        if ROTATED_RELATIVE is not None:
+            self.set_ROTATED_RELATIVE(ROTATED_RELATIVE)
 
-        if "RELATIVE" in kwargs:
-            self.set_RELATIVE(kwargs["RELATIVE"])
+        if RELATIVE is not None:
+            self.set_RELATIVE(RELATIVE)
 
-        if "WHEN" in kwargs:
-            self.set_WHEN(kwargs["WHEN"])
+        if WHEN is not None:
+            self.set_WHEN(WHEN)
 
-        if "EXTEND" in kwargs:
-            self.append_EXTEND(kwargs["EXTEND"])
+        if EXTEND is not None:
+            self.append_EXTEND(EXTEND)
 
-        if "GROUP" in kwargs:
-            self.set_GROUP(kwargs["GROUP"])
+        if GROUP is not None:
+            self.set_GROUP(GROUP)
 
-        if "JUMP" in kwargs:
-            self.set_JUMP(kwargs["JUMP"])
+        if JUMP is not None:
+            self.set_JUMP(JUMP)
 
-        if "SPLIT" in kwargs:
-            self.set_SPLIT(kwargs["SPLIT"])
+        if SPLIT is not None:
+            self.set_SPLIT(SPLIT)
 
-        if "comment" in kwargs:
-            self.set_comment(kwargs["comment"])
+        if comment is not None:
+            self.set_comment(comment)
 
-        if "c_code_before" in kwargs:
-            self.set_c_code_before(kwargs["c_code_before"])
+        if c_code_before is not None:
+            self.set_c_code_before(c_code_before)
 
-        if "c_code_after" in kwargs:
-            self.set_c_code_after(kwargs["c_code_after"])
+        if c_code_after is not None:
+            self.set_c_code_after(c_code_after)
 
     def __setattr__(self, key, value):
         if self.__isfrozen and not hasattr(self, key):
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 11ed2816..85c813e0 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -135,7 +135,7 @@ class McCode_instr(BaseCalculator):
     append_trace_no_new_line(string)
         Obsolete method, add components instead (used in write_c_files)
 
-    show_components(string)
+    available_components(string)
         Shows available components in given category
 
     component_help(name)
@@ -195,7 +195,7 @@ class McCode_instr(BaseCalculator):
     print_component_short(instance_name)
         Prints short overview of current state of named component
 
-    print_components()
+    show_components()
         Prints overview of postion / rotation of all components
 
     write_c_files()
@@ -752,7 +752,7 @@ def append_trace_no_new_line(self, string):
 
         self.trace_section = self.trace_section + string
 
-    def show_components(self, *args):
+    def available_components(self, *args):
         """
         Helper method that shows available components to the user
 
@@ -769,7 +769,7 @@ def show_components(self, *args):
         if len(args) == 0:
             print("Here are the available component categories:")
             self.component_reader.show_categories()
-            print("Call show_components(category_name) to display")
+            print("Call available_components(category_name) to display")
 
         else:
             category = args[0]
@@ -827,7 +827,11 @@ def _create_component_instance(self, name, component_name, **kwargs):
         return self.component_class_lib[component_name](name, component_name,
                                                         **kwargs)
 
-    def add_component(self, name, component_name, **kwargs):
+    def add_component(self, name, component_name, before=None, after=None,
+                      AT=None, AT_RELATIVE=None, ROTATED=None,
+                      ROTATED_RELATIVE=None, RELATIVE=None, WHEN=None,
+                      EXTEND=None, GROUP=None, JUMP=None, SPLIT=None,
+                      comment=None, c_code_before=None, c_code_after=None):
         """
         Method for adding a new Component instance to the instrument
 
@@ -892,52 +896,84 @@ def add_component(self, name, component_name, **kwargs):
                              + " Rename or remove one instance of this"
                              + " name."))
 
-        # Insert component after component with this name
-        if "after" in kwargs:
-            if kwargs["after"] not in self.component_name_list:
+        # Condense keyword input relating to component to a dict
+        component_input = {"AT": AT, "AT_RELATIVE": AT_RELATIVE,
+                           "ROTATED": ROTATED,
+                           "ROTATED_RELATIVE": ROTATED_RELATIVE,
+                           "RELATIVE": RELATIVE, "WHEN": WHEN,
+                           "EXTEND": EXTEND, "GROUP": GROUP, "JUMP": JUMP,
+                           "SPLIT": SPLIT, "comment": comment,
+                           "c_code_before": c_code_before,
+                           "c_code_after": c_code_after}
+
+        new_component = self._create_component_instance(name, component_name,
+                                                        **component_input)
+
+        self._insert_component(name, new_component, before=before, after=after)
+        return new_component
+
+    def _insert_component(self, name, component, before=None, after=None):
+        """
+        Inserts component into sequence of components held by instrument
+
+        Internal method to handle placement of a new component in the
+        list of components held by this instrument.
+
+        name : str
+            Instance name of component
+
+        component : Component object
+            Component object to be inserted
+
+        before : str or Component object
+            Reference to component to place this one before
+
+        after : str or Component object
+            Reference to coponent to place this one after
+        """
+
+        if before is not None and after is not None:
+            raise RuntimeError("Only specify either 'before' or 'after'.")
+
+        if after is not None:
+            if isinstance(after, Component):
+                after = after.name
+            # Insert component after component with this name
+            if after not in self.component_name_list:
                 raise NameError(("Trying to add a component after a component"
-                                 + " named \"" + str(kwargs["after"])
+                                 + " named \"" + str(after)
                                  + "\", but a component with that name was"
                                  + " not found."))
 
-            new_index = self.component_name_list.index(kwargs["after"])
-
-            new_component = self._create_component_instance(name,
-                                                            component_name,
-                                                            **kwargs)
-            self.component_list.insert(new_index + 1, new_component)
-
+            new_index = self.component_name_list.index(after)
+            self.component_list.insert(new_index + 1, component)
             self.component_name_list.insert(new_index+1, name)
 
-        # Insert component after component with this name
-        elif "before" in kwargs:
-            if kwargs["before"] not in self.component_name_list:
+        elif before is not None:
+            if isinstance(before, Component):
+                before = before.name
+            # Insert component before component with this name
+            if before not in self.component_name_list:
                 raise NameError(("Trying to add a component before a "
                                  + "component named \""
-                                 + str(kwargs["before"])
+                                 + str(before)
                                  + "\", but a component with that "
                                  + "name was not found."))
 
-            new_index = self.component_name_list.index(kwargs["before"])
-
-            new_component = self._create_component_instance(name,
-                                                            component_name,
-                                                            **kwargs)
-            self.component_list.insert(new_index, new_component)
-
+            new_index = self.component_name_list.index(before)
+            self.component_list.insert(new_index, component)
             self.component_name_list.insert(new_index, name)
 
-        # If after or before keywords absent, place component at the end
         else:
-            new_component = self._create_component_instance(name,
-                                                            component_name,
-                                                            **kwargs)
-            self.component_list.append(new_component)
+            # If after and before keywords absent, place component at the end
+            self.component_list.append(component)
             self.component_name_list.append(name)
 
-        return new_component
-
-    def copy_component(self, name, original_component, **kwargs):
+    def copy_component(self, name, original_component, before=None, after=None,
+                      AT=None, AT_RELATIVE=None, ROTATED=None,
+                      ROTATED_RELATIVE=None, RELATIVE=None, WHEN=None,
+                      EXTEND=None, GROUP=None, JUMP=None, SPLIT=None,
+                      comment=None, c_code_before=None, c_code_after=None):
         """
         Method for adding a copy of a Component instance to the instrument
 
@@ -996,6 +1032,17 @@ def copy_component(self, name, original_component, **kwargs):
         """
         if isinstance(original_component, Component):
             original_component = original_component.name
+
+        # Condense keyword input relating to component to a dict
+        component_input = {"AT": AT, "AT_RELATIVE": AT_RELATIVE,
+                           "ROTATED": ROTATED,
+                           "ROTATED_RELATIVE": ROTATED_RELATIVE,
+                           "RELATIVE": RELATIVE, "WHEN": WHEN,
+                           "EXTEND": EXTEND, "GROUP": GROUP, "JUMP": JUMP,
+                           "SPLIT": SPLIT, "comment": comment,
+                           "c_code_before": c_code_before,
+                           "c_code_after": c_code_after}
+
         """
         If the name starts with COPY, use unique naming as described in the
         McStas manual.
@@ -1025,50 +1072,13 @@ def copy_component(self, name, original_component, **kwargs):
         else:
             component_to_copy = self.get_component(original_component)
 
-        # Insert component after component with this name
-        if "after" in kwargs:
-            if kwargs["after"] not in self.component_name_list:
-                raise NameError("Trying to add a component after a component"
-                                + " named \"" + str(kwargs["after"])
-                                + "\", but a component with that name was"
-                                + " not found.")
-
-            new_index = self.component_name_list.index(kwargs["after"])
+        new_component = copy.deepcopy(component_to_copy)
+        new_component.name = name  # Set new name, duplicate names not allowed
 
-            new_component = copy.deepcopy(component_to_copy)
-            new_component.name = name
-            self.component_list.insert(new_index+1, new_component)
+        self._insert_component(name, new_component, before=before, after=after)
 
-            self.component_name_list.insert(new_index+1, name)
-
-        # Insert component after component with this name
-        elif "before" in kwargs:
-            if kwargs["before"] not in self.component_name_list:
-                raise NameError(("Trying to add a component before a "
-                                 + "component named \""
-                                 + str(kwargs["before"])
-                                 + "\", but a component with that "
-                                 + "name was not found."))
-
-            new_index = self.component_name_list.index(kwargs["before"])
-
-            new_component = copy.deepcopy(component_to_copy)
-            new_component.name = name
-            self.component_list.insert(new_index, new_component)
-
-            self.component_name_list.insert(new_index, name)
-
-        # If after or before keywords absent, place component at the end
-        else:
-            new_component = copy.deepcopy(component_to_copy)
-            new_component.name = name
-            self.component_list.append(new_component)
-            self.component_name_list.append(name)
-
-        # Set the new name of the instance
-        new_component.name = name
-        # Run set_keyword_input again for keyword arguments to take effect
-        new_component.set_keyword_input(**kwargs)
+        # Run set_keyword_input for keyword arguments to take effect
+        new_component.set_keyword_input(**component_input)
 
         return new_component
 
@@ -1332,11 +1342,26 @@ def print_component_short(self, name):
         name : str
             Unique name of component to print
         """
-
         component = self.get_component(name)
         component.print_short()
 
-    def print_components(self, **kwargs):
+    def print_components(self, line_length=None):
+        """
+        Obsolete method, use show_components instead
+
+        Method for printing overview of all components in instrument
+
+        Provides overview of component names, what McStas component is
+        used for each and their position and rotation in space.
+
+        keyword arguments:
+        line_length : int
+            Maximum line length in console
+        """
+        warnings.warn("Print components is changing name to show_components for consistency.")
+        self.show_components(line_length)
+
+    def show_components(self, line_length=None):
         """
         Method for printing overview of all components in instrument
 
@@ -1352,10 +1377,10 @@ def print_components(self, **kwargs):
             print("No components added to instrument object yet.")
             return
 
-        if "line_length" in kwargs:
-            line_limit = kwargs["line_length"]
-        else:
+        if line_length is None:
             line_limit = self.line_limit
+        else:
+            line_limit = line_length
 
         longest_name = len(max(self.component_name_list, key=len))
 
@@ -2155,7 +2180,7 @@ class McStas_instr(McCode_instr):
     append_trace_no_new_line(string)
         Obsolete method, add components instead (used in write_c_files)
 
-    show_components(string)
+    available_components(string)
         Shows available components in given category
 
     component_help(name)
@@ -2215,7 +2240,7 @@ class McStas_instr(McCode_instr):
     print_component_short(instance_name)
         Prints short overview of current state of named component
 
-    print_components()
+    show_components()
         Prints overview of postion / rotation of all components
 
     write_c_files()
@@ -2417,7 +2442,7 @@ class McXtrace_instr(McCode_instr):
     append_trace_no_new_line(string)
         Obsolete method, add components instead (used in write_c_files)
 
-    show_components(string)
+    available_components(string)
         Shows available components in given category
 
     component_help(name)
@@ -2477,7 +2502,7 @@ class McXtrace_instr(McCode_instr):
     print_component_short(instance_name)
         Prints short overview of current state of named component
 
-    print_components()
+    show_components()
         Prints overview of postion / rotation of all components
 
     write_c_files()
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index cc142f95..e9be3f09 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -641,7 +641,7 @@ def test_simple_append_trace_no_new_line(self):
                          + "ABCD")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_show_components_simple(self, mock_stdout):
+    def test_available_components_simple(self, mock_stdout):
         """
         Simple test of show components to show component categories
         """
@@ -653,7 +653,7 @@ def test_show_components_simple(self, mock_stdout):
 
         instr = setup_instr_with_path()
 
-        instr.show_components()
+        instr.available_components()
 
         os.chdir(current_work_dir)
 
@@ -673,7 +673,7 @@ def test_show_components_simple(self, mock_stdout):
         self.assertEqual(output[6], " work directory")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_show_components_folder(self, mock_stdout):
+    def test_available_components_folder(self, mock_stdout):
         """
         Simple test of show components to show components in current work
         directory.
@@ -685,7 +685,7 @@ def test_show_components_folder(self, mock_stdout):
         current_work_dir = os.getcwd()
         os.chdir(THIS_DIR)  # Set work directory to test folder
 
-        instr.show_components("work directory")
+        instr.available_components("work directory")
 
         os.chdir(current_work_dir)
 
@@ -705,14 +705,14 @@ def test_show_components_folder(self, mock_stdout):
         self.assertEqual(output[5], "")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_show_components_input_path_simple(self, mock_stdout):
+    def test_available_components_input_path_simple(self, mock_stdout):
         """
         Simple test of input_path being recognized and passed
         to component_reader so PSDlin_monitor is overwritten
         """
         instr = setup_instr_with_input_path()
 
-        instr.show_components()
+        instr.available_components()
 
         output = mock_stdout.getvalue()
         output = output.split("\n")
@@ -730,7 +730,7 @@ def test_show_components_input_path_simple(self, mock_stdout):
         self.assertEqual(output[6], " work directory")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_show_components_input_path_custom(self, mock_stdout):
+    def test_available_components_input_path_custom(self, mock_stdout):
         """
         Simple test of input_path being recognized and passed
         to component_reader so PSDlin_monitor is overwritten
@@ -739,7 +739,7 @@ def test_show_components_input_path_custom(self, mock_stdout):
         """
         instr = setup_instr_with_input_path_relative()
 
-        instr.show_components()
+        instr.available_components()
 
         output = mock_stdout.getvalue()
         output = output.split("\n")
@@ -1388,17 +1388,17 @@ def test_print_component_short(self, mock_stdout):
         self.assertEqual(output[0], expected)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_print_components_simple(self, mock_stdout):
+    def test_show_components_simple(self, mock_stdout):
         """
-        Tests print_components for simple case
+        Tests show_components for simple case
 
-        print_components calls the print_short method in the component
+        show_components calls the print_short method in the component
         class for each component and aligns the data for display
         """
 
         instr = setup_populated_instr()
 
-        instr.print_components(line_length=300)
+        instr.show_components(line_length=300)
 
         output = mock_stdout.getvalue().split("\n")
 
@@ -1415,11 +1415,11 @@ class for each component and aligns the data for display
         self.assertEqual(output[2], expected)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_print_components_complex(self, mock_stdout):
+    def test_show_components_complex(self, mock_stdout):
         """
-        Tests print_components for complex case
+        Tests show_components for complex case
 
-        print_components calls the print_short method in the component
+        show_components calls the print_short method in the component
         class for each component and aligns the data for display
         """
 
@@ -1434,7 +1434,7 @@ class for each component and aligns the data for display
         comp = instr.get_last_component()
         comp.component_name = "test_name"
 
-        instr.print_components(line_length=300)
+        instr.show_components(line_length=300)
 
         output = mock_stdout.getvalue().split("\n")
 
@@ -1452,9 +1452,9 @@ class for each component and aligns the data for display
         self.assertEqual(output[2], expected)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_print_components_complex_2lines(self, mock_stdout):
+    def test_show_components_complex_2lines(self, mock_stdout):
         """
-        print_components calls the print_short method in the component
+        show_components calls the print_short method in the component
         class for each component and aligns the data for display
 
         This version of the tests forces two lines of output.
@@ -1471,7 +1471,7 @@ class for each component and aligns the data for display
         comp = instr.get_last_component()
         comp.component_name = "test_name"
 
-        instr.print_components(line_length=80)
+        instr.show_components(line_length=80)
 
         output = mock_stdout.getvalue().split("\n")
 
@@ -1492,9 +1492,9 @@ class for each component and aligns the data for display
         self.assertEqual(output[3], expected)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
-    def test_print_components_complex_3lines(self, mock_stdout):
+    def test_show_components_complex_3lines(self, mock_stdout):
         """
-        print_components calls the print_short method in the component
+        show_components calls the print_short method in the component
         class for each component and aligns the data for display
 
         This version of the tests forces three lines of output.
@@ -1511,7 +1511,7 @@ class for each component and aligns the data for display
         comp = instr.get_last_component()
         comp.component_name = "test_name"
 
-        instr.print_components(line_length=1)  # Three lines maximum
+        instr.show_components(line_length=1)  # Three lines maximum
 
         output = mock_stdout.getvalue().split("\n")
 

From 25c5d3f6124fbc3c9e56bccd2f0b5568a5d5cfb3 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 10 May 2022 20:45:53 +0200
Subject: [PATCH 258/403] Removed kwargs in instrument initialization.

Kept for parameters and variables, too deeply tied.
---
 mcstasscript/interface/instr.py | 96 ++++++++++++++++++++++-----------
 1 file changed, 64 insertions(+), 32 deletions(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 85c813e0..3a1ed81f 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -226,8 +226,13 @@ class McCode_instr(BaseCalculator):
         Returns data set from latest simulation in widget
     """
 
-    def __init__(self, name, parameters=None, executable=None,
-                 author=None, origin=None,  **kwargs):
+    def __init__(self, name, parameters=None, author=None,
+                 origin=None, ncount=None, mpi="not_set", seed=None,
+                 force_compile=None, output_path=None,
+                 increment_folder_name=None, custom_flags=None,
+                 executable_path=None, executable=None,
+                 suppress_output=None, gravity=None, input_path=None,
+                 package_path=None):
         """
         Initialization of McStas Instrument
 
@@ -246,20 +251,41 @@ def __init__(self, name, parameters=None, executable=None,
             origin : str
                 Affiliation of author, written in instrument file
 
-            executable : str
-                Name of simulation executable (mcrun or mxrun)
-
-            executable_path : str
-                Absolute path of mcrun or empty if already in path
-
             input_path : str
                 Work directory, will load components from this folder
 
+            mpi : int
+                Number of mpi threads to use in simulation
+
+            output_path : str
+                Sets data_folder_name
+
+            increment_folder_name : bool
+                Will update output_path if folder already exists, default True
+
             ncount : int
-                Number of rays to simulate
+                Sets ncount
 
             mpi : int
-                Number of mpi threads to use in simulation
+                Sets thread count
+
+            force_compile : bool
+                If True (default) new instrument file is written, otherwise not
+
+            custom_flags : str
+                Sets custom_flags passed to mcrun
+
+            executable : str
+                Name of the executable
+
+            executable_path : str
+                Path to mcrun command, "" if already in path
+
+            suppress_output : bool
+                Set to True to surpress output
+
+            gravity : bool
+                If True, gravity will be simulated
         """
 
         super().__init__(name, input=[],
@@ -307,8 +333,8 @@ def __init__(self, name, parameters=None, executable=None,
         self._read_calibration()
 
         # Settings that can't be changed later
-        if "input_path" in kwargs:
-            self.input_path = str(kwargs["input_path"])
+        if input_path is not None:
+            self.input_path = str(input_path)
             if not os.path.isdir(self.input_path):
                 raise RuntimeError("Given input_path does not point to a "
                                    + "folder:\"" + self.input_path + '"')
@@ -316,41 +342,47 @@ def __init__(self, name, parameters=None, executable=None,
             self.input_path = "."
         self._run_settings["run_path"] = self.input_path
 
-        if "package_path" in kwargs:
-            if not os.path.isdir(str(kwargs["package_path"])):
+        if package_path is not None:
+            if not os.path.isdir(str(package_path)):
                 raise RuntimeError("The package_path provided to mccode_instr "
                                    + " does not point to a + directory: \""
-                                   + str(kwargs["package_path"]) + "\"")
-            self._run_settings["package_path"] = kwargs["package_path"]
+                                   + str(package_path) + "\"")
+            self._run_settings["package_path"] = package_path
 
         # Settings for run that can be adjusted by user
         provided_run_settings = {"executable": executable}
 
-        if "executable_path" in kwargs:
-            provided_run_settings["executable_path"] = str(kwargs["executable_path"])
+        if executable_path is not None:
+            provided_run_settings["executable_path"] = str(executable_path)
 
-        if "force_compile" in kwargs:
-            provided_run_settings["force_compile"] = kwargs["force_compile"]
+        if force_compile is not None:
+            provided_run_settings["force_compile"] = force_compile
         else:
             provided_run_settings["force_compile"] = True
 
-        if "ncount" in kwargs:
-            provided_run_settings["ncount"] = kwargs["ncount"]
+        if ncount is not None:
+            provided_run_settings["ncount"] = ncount
+
+        if mpi != "not_set":
+            provided_run_settings["mpi"] = mpi
+
+        if gravity is not None:
+            provided_run_settings["gravity"] = gravity
 
-        if "mpi" in kwargs:
-            provided_run_settings["mpi"] = kwargs["mpi"]
+        if seed is not None:
+            provided_run_settings["seed"] = str(seed)
 
-        if "gravity" in kwargs:
-            provided_run_settings["gravity"] = kwargs["gravity"]
+        if custom_flags is not None:
+            provided_run_settings["custom_flags"] = custom_flags
 
-        if "seed" in kwargs:
-            provided_run_settings["seed"] = str(kwargs["seed"])
+        if suppress_output is not None:
+            provided_run_settings["suppress_output"] = suppress_output
 
-        if "custom_flags" in kwargs:
-            provided_run_settings["custom_flags"] = kwargs["custom_flags"]
+        if output_path is not None:
+            provided_run_settings["output_path"] = output_path
 
-        if "suppress_output" in kwargs:
-            provided_run_settings["suppress_output"] = kwargs["suppress_output"]
+        if increment_folder_name is not None:
+            provided_run_settings["increment_folder_name"] = increment_folder_name
 
         # Set run_settings, perform input sanitation
         self.settings(**provided_run_settings)

From b1de666d0a7dd91f4dffaf46b2051cb92c27d7cf Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 11 May 2022 09:38:30 +0200
Subject: [PATCH 259/403] Removed kwargs in show_parameters and print_short on
 Component.

---
 mcstasscript/helper/mcstas_objects.py | 27 +++++++++++++--------------
 1 file changed, 13 insertions(+), 14 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index c0847c20..75d9fcc3 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -1047,15 +1047,15 @@ class is used as a superclass for classes describing each
     def __repr__(self):
         return self.__str__()
 
-    def print_short(self, **kwargs):
+    def print_short(self, longest_name=None):
         """Prints short description of component to list print"""
         if self.ROTATED_specified:
             print_rotate_rel = self.ROTATED_relative
         else:
             print_rotate_rel = self.AT_relative
 
-        if "longest_name" in kwargs:
-            number_of_spaces = 3+kwargs["longest_name"]-len(self.name)
+        if longest_name is not None:
+            number_of_spaces = 3+longest_name-len(self.name)
             print(str(self.name) + " "*number_of_spaces, end='')
             print(str(self.component_name),
                   "\tAT", self.AT_data, self.AT_relative,
@@ -1065,7 +1065,7 @@ def print_short(self, **kwargs):
                   "\tAT", self.AT_data, self.AT_relative,
                   "ROTATED", self.ROTATED_data, print_rotate_rel)
 
-    def show_parameters(self, **kwargs):
+    def show_parameters(self, line_length=None):
         """
         Shows available parameters and their defaults for the component
 
@@ -1075,18 +1075,17 @@ def show_parameters(self, **kwargs):
         this method.
         """
 
-        if "line_length" in kwargs:
-            line_limit = kwargs["line_length"]
-        else:
-            line_limit = self.line_limit
+        # line_limit created in _create_component_instance on instr
+        if line_length is None:
+            line_length = self.line_limit
 
         # Minimum reasonable line length
-        if line_limit < 74:
-            line_limit = 74
+        if line_length < 74:
+            line_length = 74
 
         print(" ___ Help "
               + self.component_name + " "
-              + (line_limit - 11 - len(self.component_name))*"_")
+              + (line_length - 11 - len(self.component_name))*"_")
         print("|"
               + bcolors.BOLD + "optional parameter" + bcolors.ENDC + "|"
               + bcolors.BOLD
@@ -1147,10 +1146,10 @@ def show_parameters(self, **kwargs):
 
             print(parameter_name + value + unit, end="")
 
-            if characters_before_comment + len(comment) < line_limit:
+            if characters_before_comment + len(comment) < line_length:
                 print(comment)
             else:
-                length_for_comment = line_limit - characters_before_comment
+                length_for_comment = line_length - characters_before_comment
                 # Split comment into several lines
                 original_comment = comment
                 words = comment.split(" ")
@@ -1188,7 +1187,7 @@ def show_parameters(self, **kwargs):
                 else:
                     print(str(original_comment))
 
-        print(line_limit*"-")
+        print(line_length*"-")
 
     def show_parameters_simple(self):
         """

From 6d81b9cb3429c853b6a775e48648c882fb930f13 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 11 May 2022 10:08:22 +0200
Subject: [PATCH 260/403] Removed methods on instrument for manipulating
 components. They duplicated functionality in a worse way and thus just were a
 source of confusion. They are not used in the online documentation.

Improved doc strings on corresponding component methods as they are
now the main interface for changing a component.

Removed all tests pertaining to the instrument methods and checked
that all this functionality is checked directly on the components.
---
 mcstasscript/helper/mcstas_objects.py | 206 ++++++++++++-------
 mcstasscript/interface/instr.py       | 276 --------------------------
 mcstasscript/tests/test_Instr.py      | 242 ++--------------------
 3 files changed, 154 insertions(+), 570 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 75d9fcc3..2c596101 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -629,7 +629,24 @@ def _unfreeze(self):
         self.__isfrozen = False
 
     def set_AT(self, at_list, RELATIVE=None):
-        """Sets AT data, List of 3 floats or single float for z only"""
+        """
+        Method for setting position of component
+
+        Sets the position of the component using provided length 3 list as a
+        vector. If only a float is given, it is considered along the beam
+        direction, [0, 0, z]. The RELATIVE keyword is used to specify in
+        relation to what the position is given, and can take either a string
+        with a component name or a Component object.
+
+        Parameters
+        ----------
+        at_list : List of 3 floats or float
+            Position of component relative to reference component
+
+        keyword arguments:
+            RELATIVE : str
+                Sets reference component for position
+        """
         if isinstance(at_list, (int, float)):
             at_list = [0, 0, at_list]
 
@@ -652,7 +669,19 @@ def set_AT(self, at_list, RELATIVE=None):
             self.set_AT_RELATIVE(RELATIVE)
 
     def set_AT_RELATIVE(self, relative):
-        """Sets AT RELATIVE with string or component instance"""
+        """
+        Sets AT RELATIVE with string or Component instance
+
+        Set relative which becomes the reference for this components position,
+        it is possible to use a string that match another component name or
+        use a Component object where the name is used as the reference.
+
+        Parameters
+        ----------
+
+        relative : str or Component object
+            Used as reference for component position
+        """
 
         # Extract name if Component instance is given
         if isinstance(relative, Component):
@@ -668,7 +697,18 @@ def set_AT_RELATIVE(self, relative):
             self.AT_relative = "RELATIVE " + relative
 
     def set_ROTATED(self, rotated_list, RELATIVE=None):
-        """Sets ROTATED data, List of 3 floats"""
+        """
+        Method for setting rotation of component
+
+        Parameters
+        ----------
+        rotated_list : List of 3 floats
+            Rotation of component relative to reference component
+
+        keyword arguments:
+            RELATIVE : str
+                Sets reference component for rotation
+        """
         if not isinstance(rotated_list, list):
             raise RuntimeError("set_ROTATED should be given a list "
                                + " but received "
@@ -689,7 +729,19 @@ def set_ROTATED(self, rotated_list, RELATIVE=None):
             self.set_ROTATED_RELATIVE(RELATIVE)
 
     def set_ROTATED_RELATIVE(self, relative):
-        """Sets ROTATED RELATIVE with string or Component instance"""
+        """
+        Sets ROTATED RELATIVE with string or Component instance
+
+        Set relative which becomes the reference for this components rotation,
+        it is possible to use a string that match another component name or
+        use a Component object where the name is used as the reference.
+
+        Parameters
+        ----------
+
+        relative : str or Component object
+            Used as reference for component rotation
+        """
 
         self.ROTATED_specified = True
         # Extract name if a Component instance is given
@@ -706,7 +758,17 @@ def set_ROTATED_RELATIVE(self, relative):
             self.ROTATED_relative = "RELATIVE " + relative
 
     def set_RELATIVE(self, relative):
-        """Sets both AT_relative and ROTATED_relative"""
+        """
+        Method for setting reference of component position and rotation
+
+        Input can be either a string matching an earlier component or a
+        Component object.
+
+        Parameters
+        ----------
+        relative : str
+            Reference component for position and rotation
+        """
         # Extract name if a Component instance is given
         if isinstance(relative, Component):
             relative = relative.name
@@ -725,11 +787,16 @@ def set_parameters(self, args_as_dict=None, **kwargs):
         """
         Set Component parameters from dictionary input or keyword arguments
 
-        Relies on attributes added when McStas_Instr creates a subclass from
+        Relies on attributes added when McCode_Instr creates a subclass from
         the Component class where each component parameter is added as an
         attribute.
 
         An error is raised if trying to set a parameter that does not exist
+
+        Parameters
+        ----------
+        args_as_dict : dict
+            Parameters names and their values as dictionary
         """
         if args_as_dict is not None:
             parameter_dict = args_as_dict
@@ -749,28 +816,58 @@ def set_parameters(self, args_as_dict=None, **kwargs):
                 setattr(self, key, val)
 
     def set_WHEN(self, string):
-        """Sets WHEN string, should be a c logical expression"""
+        """
+        Method for setting WHEN c expression of this component
+
+        Parameters
+        ----------
+        WHEN : str
+            Sets WHEN c expression for named McStas component
+        """
         if not isinstance(string, str):
             raise RuntimeError("set_WHEN expect a string, but was "
                                + "given " + str(type(string)))
         self.WHEN = "WHEN (" + string + ")"
 
     def set_GROUP(self, string):
-        """Sets GROUP name"""
+        """
+        Method for setting GROUP keyword of this component
+
+        Parameters
+        ----------
+        GROUP : str
+            Sets GROUP name for named McStas component
+        """
         if not isinstance(string, str):
             raise RuntimeError("set_GROUP expect a string, but was "
                                + "given " + str(type(string)))
         self.GROUP = string
 
     def set_JUMP(self, string):
-        """Sets JUMP string, should contain all text after JUMP"""
+        """
+        Method for setting JUMP expression of this component
+
+        Should contain all code needed after JUMP
+
+        Parameters
+        ----------
+        JUMP : str
+            Sets JUMP expression for named McStas component
+        """
         if not isinstance(string, str):
             raise RuntimeError("set_JUMP expect a string, but was "
                                + "given " + str(type(string)))
         self.JUMP = string
 
     def set_SPLIT(self, value):
-        """Sets SPLIT value, needs to be an integer"""
+        """
+        Method for setting SPLIT value of this component
+
+        Parameters
+        ----------
+        SPLIT : int or str
+            Sets SPLIT value for named McStas component
+        """
         if not isinstance(value, (int, str)):
             raise RuntimeError("set_SPLIT expect a integer or string, but "
                                + "was given " + str(type(value)))
@@ -784,28 +881,56 @@ def set_SPLIT(self, value):
         self.SPLIT = value
 
     def append_EXTEND(self, string):
-        """Appends a line of code to EXTEND block of component"""
+        """
+        Method for adding line of c to EXTEND section of this component
+
+        Parameters
+        ----------
+        EXTEND : str
+            Line of c code added to EXTEND section of named component
+        """
         if not isinstance(string, str):
             raise RuntimeError("append_EXTEND expect a string, but was "
                                + "given " + str(type(string)))
         self.EXTEND = self.EXTEND + string + "\n"
 
     def set_comment(self, string):
-        """Method that sets a comment to be written to instrument file"""
+        """
+        Sets a comment displayed before the component in written files
+
+        Parameters
+        ----------
+        string : str
+            Comment string
+        """
         if not isinstance(string, str):
             raise RuntimeError("set_comment expect a string, but was "
                                + "given " + str(type(string)))
         self.comment = string
 
     def set_c_code_before(self, string):
-        """Method that sets c code to be written before the component"""
+        """
+        Method for setting c code before this component
+
+        Parameters
+        ----------
+        code : str
+            Code to be pasted before component
+        """
         if not isinstance(string, str):
             raise RuntimeError("set_c_code_before expect a string, but was "
                                + "given " + str(type(string)))
         self.c_code_before = string
 
     def set_c_code_after(self, string):
-        """Method that sets c code to be written after the component"""
+        """
+        Method for setting c code after this component
+
+        Parameters
+        ----------
+        code : str
+            Code to be pasted after component
+        """
         if not isinstance(string, str):
             raise RuntimeError("set_c_code_after expect a string, but was "
                                + "given " + str(type(string)))
@@ -913,59 +1038,6 @@ def write_component(self, fo):
         # Leave a new line between components for readability
         fo.write("\n")
 
-    def print_long_deprecated(self):
-        """
-        Prints contained information to Python terminal
-
-        Includes information on required parameters if they are not yet
-        specified. Information on the components are added when the
-        class is used as a superclass for classes describing each
-        McStas component.
-        """
-        if len(self.c_code_before) > 1:
-            print(self.c_code_before)
-        if len(self.comment) > 1:
-            print("// " + self.comment)
-        if self.SPLIT != 0:
-            print("SPLIT " + str(self.SPLIT) + " ", end="")
-        print("COMPONENT", str(self.name),
-              "=", str(self.component_name))
-        for key in self.parameter_names:
-            val = getattr(self, key)
-            parameter_name = bcolors.BOLD + key + bcolors.ENDC
-            if val is not None:
-                unit = ""
-                if key in self.parameter_units:
-                    unit = "[" + self.parameter_units[key] + "]"
-                value = (bcolors.BOLD
-                         + bcolors.OKGREEN
-                         + str(val)
-                         + bcolors.ENDC
-                         + bcolors.ENDC)
-                print(" ", parameter_name, "=", value, unit)
-            else:
-                if self.parameter_defaults[key] is None:
-                    print("  "
-                          + parameter_name
-                          + bcolors.FAIL
-                          + " : Required parameter not yet specified"
-                          + bcolors.ENDC)
-
-        if not self.WHEN == "":
-            print(self.WHEN)
-        print("AT", self.AT_data, self.AT_relative)
-        if self.ROTATED_specified:
-            print("ROTATED", self.ROTATED_data, self.ROTATED_relative)
-        if not self.GROUP == "":
-            print("GROUP " + self.GROUP)
-        if not self.EXTEND == "":
-            print("EXTEND %{")
-            print(self.EXTEND + "%}")
-        if not self.JUMP == "":
-            print("JUMP " + self.JUMP)
-        if len(self.c_code_after) > 1:
-            print(self.c_code_after)
-
     def __str__(self):
         """
         Returns string of information about the component
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 3a1ed81f..f0e376c2 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1148,210 +1148,6 @@ def get_last_component(self):
 
         return self.component_list[-1]
 
-    def set_component_parameter(self, name, input_dict):
-        """
-        Add parameters and their values as dictionary to component
-
-        This method is the primary way of specifying parameters in a
-        component.  Parameters are added to a dictionary specifying
-        parameter name and value pairs.
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        input_dict : dict
-            Set of new parameter name and value pairs to add
-        """
-
-        component = self.get_component(name)
-        component.set_parameters(input_dict)
-
-    def set_component_AT(self, name, at_list, **kwargs):
-        """
-        Method for setting position of component
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        at_list : List of 3 floats
-            Position of component relative to reference component
-
-        keyword arguments:
-            RELATIVE : str
-                Sets reference component for position
-        """
-
-        component = self.get_component(name)
-        component.set_AT(at_list, **kwargs)
-
-    def set_component_ROTATED(self, name, rotated_list, **kwargs):
-        """
-        Method for setting rotiation of component
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        rotated_list : List of 3 floats
-            Rotation of component relative to reference component
-
-        keyword arguments:
-            RELATIVE : str
-                Sets reference component for rotation
-        """
-
-        component = self.get_component(name)
-        component.set_ROTATED(rotated_list, **kwargs)
-
-    def set_component_RELATIVE(self, name, relative):
-        """
-        Method for setting reference of component position and rotation
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        relative : str
-            Reference component for position and rotation
-        """
-
-        component = self.get_component(name)
-        component.set_RELATIVE(relative)
-
-    def set_component_WHEN(self, name, WHEN):
-        """
-        Method for setting WHEN c expression to named component
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        WHEN : str
-            Sets WHEN c expression for named McStas component
-        """
-        component = self.get_component(name)
-        component.set_WHEN(WHEN)
-
-    def append_component_EXTEND(self, name, EXTEND):
-        """
-        Method for adding line of c to EXTEND section of named component
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        EXTEND : str
-            Line of c code added to EXTEND section of named component
-        """
-
-        component = self.get_component(name)
-        component.append_EXTEND(EXTEND)
-
-    def set_component_GROUP(self, name, GROUP):
-        """
-        Method for setting GROUP name of named component
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        GROUP : str
-            Sets GROUP name for named McStas component
-        """
-
-        component = self.get_component(name)
-        component.set_GROUP(GROUP)
-
-    def set_component_JUMP(self, name, JUMP):
-        """
-        Method for setting JUMP expression of named component
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        JUMP : str
-            Sets JUMP expression for named McStas component
-        """
-
-        component = self.get_component(name)
-        component.set_JUMP(JUMP)
-
-    def set_component_SPLIT(self, name, SPLIT):
-        """
-        Method for setting SPLIT value of named component
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        SPLIT : int
-            Sets SPLIT value for named McStas component
-        """
-
-        component = self.get_component(name)
-        component.set_SPLIT(SPLIT)
-
-    def set_component_c_code_before(self, name, code):
-        """
-        Method for setting c code before component
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        code : str
-            Code to be pasted before component
-        """
-
-        component = self.get_component(name)
-        component.set_c_code_before(code)
-
-    def set_component_c_code_after(self, name, code):
-        """
-        Method for setting c code before component
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        code : str
-            Code to be pasted after component
-        """
-
-        component = self.get_component(name)
-        component.set_c_code_after(code)
-
-    def set_component_comment(self, name, string):
-        """
-        Sets a comment displayed before the component in written files
-
-        Parameters
-        ----------
-        name : str
-            Unique name of component to modify
-
-        string : str
-            Comment string
-
-        """
-
-        component = self.get_component(name)
-        component.set_comment(string)
-
     def print_component(self, name):
         """
         Method for printing summary of contents in named component
@@ -2230,42 +2026,6 @@ class McStas_instr(McCode_instr):
     get_last_component()
         Returns component instance of last component
 
-    set_component_parameter(instance_name, dict)
-        Adds parameters as dict to component with instance_name
-
-    set_component_AT(instance_name, AT_data, **kwargs)
-        Sets position of component named instance_name
-
-    set_component_ROTATED(instance_name, ROTATED_data, **kwargs)
-        Sets rotation of component named instance_name
-
-    set_component_RELATIVE(instane_name, string)
-        Sets position and rotation reference for named component
-
-    set_component_WHEN(instance_name, string)
-        Sets WHEN condition of named component, is logical c expression
-
-    set_component_GROUP(instance_name, string)
-        Sets GROUP name of component named instance_name
-
-    append_component_EXTEND(instance_name, string)
-        Appends a line to EXTEND section of named component
-
-    set_component_JUMP(instance_name, string)
-        Sets JUMP code for named component
-
-    set_component_SPLIT(instance_name, string)
-        Sets SPLIT value for named component
-
-    set_component_c_code_before(instance_name, string)
-        Sets c code before the component
-
-    set_component_c_code_after(instance_name, string)
-        Sets c code after the component
-
-    set_component_comment(instance_name, string)
-        Sets comment to be written before named component
-
     print_component(instance_name)
         Prints an overview of current state of named component
 
@@ -2492,42 +2252,6 @@ class McXtrace_instr(McCode_instr):
     get_last_component()
         Returns component instance of last component
 
-    set_component_parameter(instance_name, dict)
-        Adds parameters as dict to component with instance_name
-
-    set_component_AT(instance_name, AT_data, **kwargs)
-        Sets position of component named instance_name
-
-    set_component_ROTATED(instance_name, ROTATED_data, **kwargs)
-        Sets rotation of component named instance_name
-
-    set_component_RELATIVE(instane_name, string)
-        Sets position and rotation reference for named component
-
-    set_component_WHEN(instance_name, string)
-        Sets WHEN condition of named component, is logical c expression
-
-    set_component_GROUP(instance_name, string)
-        Sets GROUP name of component named instance_name
-
-    append_component_EXTEND(instance_name, string)
-        Appends a line to EXTEND section of named component
-
-    set_component_JUMP(instance_name, string)
-        Sets JUMP code for named component
-
-    set_component_SPLIT(instance_name, string)
-        Sets SPLIT value for named component
-
-    set_component_c_code_before(instance_name, string)
-        Sets c code before the component
-
-    set_component_c_code_after(instance_name, string)
-        Sets c code after the component
-
-    set_component_comment(instance_name, string)
-        Sets comment to be written before named component
-
     print_component(instance_name)
         Prints an overview of current state of named component
 
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index e9be3f09..807faa7b 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1124,211 +1124,6 @@ def test_get_last_component_simple(self):
 
         self.assertEqual(comp.name, "third_component")
 
-    def test_set_component_parameter(self):
-        """
-        Tests simple case of set_component_parameter
-
-        set_component_parameter passes a dict from instrument level
-        to a contained component with the given name. It uses the
-        get_component method.
-        """
-
-        instr = setup_populated_instr()
-
-        instr.set_component_parameter("second_component",
-                                      {"radius": 5.8,
-                                       "dist": "text"})
-
-        comp = instr.get_component("second_component")
-
-        self.assertEqual(comp.radius, 5.8)
-        self.assertEqual(comp.dist, "text")
-
-    def test_set_component_parameter_error(self):
-        """
-        Tests set_component_parameter fails when trying to set a parameter
-        that does not exist
-
-        set_component_parameter passes a dict from instrument level
-        to a contained component with the given name. It uses the
-        get_component method.
-        """
-
-        instr = setup_populated_instr()
-
-        with self.assertRaises(NameError):
-            instr.set_component_parameter("second_component",
-                                          {"non_existent_par": 5.8,
-                                           "dist": "text"})
-
-    def test_set_component_AT(self):
-        """
-        set_component_AT passes the argument to the similar method
-        in the component class.
-        """
-
-        instr = setup_populated_instr()
-
-        instr.set_component_AT("second_component",
-                               [1, 2, 3.2], RELATIVE="home")
-
-        comp = instr.get_component("second_component")
-
-        self.assertEqual(comp.AT_data, [1, 2, 3.2])
-        self.assertEqual(comp.AT_relative, "RELATIVE home")
-        self.assertEqual(comp.ROTATED_relative, "ABSOLUTE")
-
-    def test_set_component_ROTATED(self):
-        """
-        set_component_ROTATED passes the argument to the similar
-        method in the component class.
-        """
-
-        instr = setup_populated_instr()
-
-        instr.set_component_ROTATED("second_component",
-                                    [4, 1, -29], RELATIVE="home")
-
-        comp = instr.get_component("second_component")
-
-        self.assertEqual(comp.ROTATED_data, [4, 1, -29])
-        self.assertEqual(comp.ROTATED_relative, "RELATIVE home")
-        self.assertEqual(comp.AT_relative, "ABSOLUTE")
-
-    def test_set_component_RELATIVE(self):
-        """
-        set_component_RELATIVE passes the argument to the similar
-        method in the component class.
-        """
-
-        instr = setup_populated_instr()
-
-        instr.set_component_RELATIVE("second_component", "home")
-
-        comp = instr.get_component("second_component")
-
-        self.assertEqual(comp.ROTATED_data, [0, 0, 0])
-        self.assertEqual(comp.ROTATED_relative, "RELATIVE home")
-        self.assertEqual(comp.AT_relative, "RELATIVE home")
-
-    def test_set_component_WHEN(self):
-        """
-        set_component_WHEN passes the argument to the similar method
-        in the component class.
-        """
-
-        instr = setup_populated_instr()
-
-        instr.set_component_WHEN("second_component", "2>1")
-
-        comp = instr.get_component("second_component")
-
-        self.assertEqual(comp.WHEN, "WHEN (2>1)")
-
-    def test_append_component_EXTEND(self):
-        """
-        append_component_EXTEND passes the argument to the similar
-        method in the component class.
-        """
-
-        instr = setup_populated_instr()
-
-        instr.append_component_EXTEND("second_component", "line1")
-        instr.append_component_EXTEND("second_component", "line2")
-
-        comp = instr.get_component("second_component")
-
-        output = comp.EXTEND.split("\n")
-
-        self.assertEqual(output[0], "line1")
-        self.assertEqual(output[1], "line2")
-
-    def test_set_component_GROUP(self):
-        """
-        set_component_GROUP passes the argument to the similar method
-        in the component class.
-        """
-
-        instr = setup_populated_instr()
-
-        instr.set_component_GROUP("second_component", "developers")
-
-        comp = instr.get_component("second_component")
-
-        self.assertEqual(comp.GROUP, "developers")
-
-    def test_set_component_JUMP(self):
-        """
-        set_component_JUMP passes the argument to the similar method
-        in the component class.
-        """
-
-        instr = setup_populated_instr()
-
-        instr.set_component_JUMP("second_component", "myself 8")
-
-        comp = instr.get_component("second_component")
-
-        self.assertEqual(comp.JUMP, "myself 8")
-
-    def test_set_component_SPLIT(self):
-        """
-        set_component_SPLIT passes the argument to the similar method
-        in the component class.
-        """
-
-        instr = setup_populated_instr()
-
-        instr.set_component_SPLIT("second_component", 3)
-
-        comp = instr.get_component("second_component")
-
-        self.assertEqual(comp.SPLIT, 3)
-
-    def test_set_component_comment(self):
-        """
-        set_component_comment passes the argument to the similar
-        method in the component class.
-        """
-
-        instr = setup_populated_instr()
-
-        instr.set_component_comment("second_component", "test comment")
-
-        comp = instr.get_component("second_component")
-
-        self.assertEqual(comp.comment, "test comment")
-
-    def test_set_c_code_before(self):
-        """
-        set_component_c_code_before passes the argument to the similar
-        method in the component class.
-        """
-
-        instr = setup_populated_instr()
-
-        instr.set_component_c_code_before("second_component",
-                                          "%include before.instr")
-
-        comp = instr.get_component("second_component")
-
-        self.assertEqual(comp.c_code_before, "%include before.instr")
-
-    def test_set_c_code_after(self):
-        """
-        set_component_c_code_after passes the argument to the similar
-        method in the component class.
-        """
-
-        instr = setup_populated_instr()
-
-        instr.set_component_c_code_after("second_component",
-                                         "%include after.instr")
-
-        comp = instr.get_component("second_component")
-
-        self.assertEqual(comp.c_code_after, "%include after.instr")
-
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_print_component(self, mock_stdout):
         """
@@ -1337,8 +1132,7 @@ def test_print_component(self, mock_stdout):
         """
 
         instr = setup_populated_instr()
-        instr.set_component_parameter("second_component",
-                                      {"dist": 5})
+        instr.get_component("second_component").set_parameters(dist=5)
 
         instr.print_component("second_component")
 
@@ -1374,8 +1168,8 @@ def test_print_component_short(self, mock_stdout):
         """
 
         instr = setup_populated_instr()
-        instr.set_component_AT("second_component",
-                               [-1, 2, 3.4], RELATIVE="home")
+        instr.get_component("second_component").set_AT([-1, 2, 3.4],
+                                                       RELATIVE="home")
 
         instr.print_component_short("second_component")
 
@@ -1425,12 +1219,10 @@ class for each component and aligns the data for display
 
         instr = setup_populated_instr()
 
-        instr.set_component_AT("first_component",
-                               [-0.1, 12, "dist"],
-                               RELATIVE="home")
-        instr.set_component_ROTATED("second_component",
-                                    [-4, 0.001, "theta"],
-                                    RELATIVE="etc")
+        instr.get_component("first_component").set_AT([-0.1, 12, "dist"],
+                                                      RELATIVE="home")
+        instr.get_component("second_component").set_ROTATED([-4, 0.001, "theta"],
+                                                            RELATIVE="etc")
         comp = instr.get_last_component()
         comp.component_name = "test_name"
 
@@ -1462,12 +1254,10 @@ class for each component and aligns the data for display
 
         instr = setup_populated_instr()
 
-        instr.set_component_AT("first_component",
-                               [-0.1, 12, "dist"],
-                               RELATIVE="home")
-        instr.set_component_ROTATED("second_component",
-                                    [-4, 0.001, "theta"],
-                                    RELATIVE="etc")
+        instr.get_component("first_component").set_AT([-0.1, 12, "dist"],
+                                                      RELATIVE="home")
+        instr.get_component("second_component").set_ROTATED([-4, 0.001, "theta"],
+                                                            RELATIVE="etc")
         comp = instr.get_last_component()
         comp.component_name = "test_name"
 
@@ -1502,12 +1292,10 @@ class for each component and aligns the data for display
 
         instr = setup_populated_instr()
 
-        instr.set_component_AT("first_component",
-                               [-0.1, 12, "dist"],
-                               RELATIVE="home")
-        instr.set_component_ROTATED("second_component",
-                                    [-4, 0.001, "theta"],
-                                    RELATIVE="etc")
+        instr.get_component("first_component").set_AT([-0.1, 12, "dist"],
+                                                      RELATIVE="home")
+        instr.get_component("second_component").set_ROTATED([-4, 0.001, "theta"],
+                                                            RELATIVE="etc")
         comp = instr.get_last_component()
         comp.component_name = "test_name"
 

From 142f31df8a45c388f7e7b6c5407ac1c7b789bb0e Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 11 May 2022 11:04:59 +0200
Subject: [PATCH 261/403] Removed widget interface from base McStasScript
 import to avoid long import time for the base module.

Now widgets are imported as such:
import mcstasscript.jb_interface as ms_widgets

This imports SimInterface, PlotInterface and show.

ms_widgets.show(data) # shows plotter interface for data
ms.widgets.show(instrument) # shows interface for simulation
---
 mcstasscript/__init__.py                      |  1 -
 mcstasscript/interface/instr.py               | 26 ---------
 mcstasscript/interface/plotter.py             | 15 +----
 mcstasscript/jb_interface/__init__.py         |  3 +
 mcstasscript/jb_interface/show_interface.py   | 55 +++++++++++++++++++
 .../jb_interface/simulation_interface.py      | 14 +++++
 6 files changed, 73 insertions(+), 41 deletions(-)
 create mode 100644 mcstasscript/jb_interface/show_interface.py

diff --git a/mcstasscript/__init__.py b/mcstasscript/__init__.py
index ffd9e4b5..e1c6529e 100644
--- a/mcstasscript/__init__.py
+++ b/mcstasscript/__init__.py
@@ -11,7 +11,6 @@
 from .interface.functions import name_search
 from .interface.functions import Configurator
 
-from .interface.plotter import interface
 from .interface.plotter import make_animation
 from .interface.plotter import make_plot
 from .interface.plotter import make_sub_plot
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index f0e376c2..5f5bda7f 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -23,7 +23,6 @@
 from mcstasscript.helper.managed_mcrun import ManagedMcrun
 from mcstasscript.helper.formatting import is_legal_filename
 from mcstasscript.helper.formatting import bcolors
-from mcstasscript.jb_interface.simulation_interface import SimInterface
 from mcstasscript.helper.unpickler import CustomMcStasUnpickler, CustomMcXtraceUnpickler
 
 
@@ -1882,31 +1881,6 @@ def show_instrument(self, format="webgl", width=800, height=450):
 
         return IFrame(src=html_path, width=width, height=height)
 
-    def interface(self):
-        """
-        Shows simulation interface in jupyter notebook interface
-
-        Needs "%matplotlib widget" in notebook to work correctly
-        """
-        self.write_full_instrument()
-        self.widget_interface = SimInterface(self)
-        return self.widget_interface.show_interface()
-
-    def get_interface_data(self):
-        """
-        Returns data from last run performed with the widget interface
-        """
-
-        if self.widget_interface is None:
-            print("No widget interface initialized, use interface method.")
-            return []
-
-        if self.widget_interface.plot_interface.data is None:
-            print("No run has been performed with the interface widget yet")
-            return []
-
-        return self.widget_interface.plot_interface.data
-
     def saveH5(self, filename: str, openpmd: bool = True):
         """
         Not relevant, but required from BaseCalculator, will be removed
diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index 4c6bae29..bd4e811d 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -8,7 +8,6 @@
 from mcstasscript.helper.plot_helper import _find_min_max_I
 from mcstasscript.helper.plot_helper import _plot_fig_ax
 from mcstasscript.helper.plot_helper import _handle_kwargs
-from mcstasscript.jb_interface.plot_interface import PlotInterface
 
 
 def make_plot(data_list, **kwargs):
@@ -160,16 +159,4 @@ def animate_2D(index):
 
         # check if imagemagick available?
         print("Saving animation with filename : \"" + filename + "\"")
-        anim.save(filename, writer="imagemagick")
-
-def interface(data):
-    """
-    Shows plot interface to explore data
-
-    Parameters
-    ----------
-    data : List of McStasData objects
-    """
-
-    interface = PlotInterface(data)
-    return interface.show_interface()
+        anim.save(filename, writer="imagemagick")
\ No newline at end of file
diff --git a/mcstasscript/jb_interface/__init__.py b/mcstasscript/jb_interface/__init__.py
index e69de29b..40f10dfc 100644
--- a/mcstasscript/jb_interface/__init__.py
+++ b/mcstasscript/jb_interface/__init__.py
@@ -0,0 +1,3 @@
+from .simulation_interface import SimInterface
+from .plot_interface import PlotInterface
+from .show_interface import show
diff --git a/mcstasscript/jb_interface/show_interface.py b/mcstasscript/jb_interface/show_interface.py
new file mode 100644
index 00000000..1d25349a
--- /dev/null
+++ b/mcstasscript/jb_interface/show_interface.py
@@ -0,0 +1,55 @@
+from .simulation_interface import SimInterface
+from .plot_interface import PlotInterface
+
+from mcstasscript.interface.instr import McCode_instr
+from mcstasscript.data.data import McStasData
+
+
+def show(function_input):
+    """
+    Shortcut to showing an interface appropriate for given object
+
+    Can show widget interface for instrument object or list of McStasData
+    objects. Needs "%matplotlib widget" in notebook to work correctly.
+
+    Parameters
+    ----------
+
+    function_input : McCode_instr, list or McStasData
+
+    """
+
+    if isinstance(function_input, McCode_instr):
+        return show_instrunent(function_input)
+
+    elif isinstance(function_input, (list, McStasData)):
+        return show_plot(function_input)
+
+    else:
+        raise RuntimeError("Show did not recoignize object of type"
+                           + str(type(function_input)) + ".")
+
+
+def show_instrunent(instrument):
+    """
+    Shows simulation interface for instrument
+    """
+    simulation_interface = SimInterface(instrument)
+    return simulation_interface.show_interface()
+
+
+def show_plot(data):
+    """
+    Shows plot interface for given data
+    """
+    if isinstance(data, list):
+        for element in data:
+            if not isinstance(element, McStasData):
+                raise RuntimeError("Given list contains elements that "
+                                   + "are not McStasData objects.")
+    else:
+        if not isinstance(data, McStasData):
+            raise RuntimeError("Given data is not McStasData.")
+
+    plot_interface = PlotInterface(data)
+    return plot_interface.show_interface()
diff --git a/mcstasscript/jb_interface/simulation_interface.py b/mcstasscript/jb_interface/simulation_interface.py
index 8437aec0..08da25f7 100644
--- a/mcstasscript/jb_interface/simulation_interface.py
+++ b/mcstasscript/jb_interface/simulation_interface.py
@@ -272,6 +272,20 @@ def make_progress_bar(self):
 
         return widget
 
+    def get_data(self):
+        """
+        Returns last data set from this interface
+        """
+        if self.plot_interface is None:
+            print("No widget interface initialized, use show_interface method.")
+            return []
+
+        if self.plot_interface.data is None:
+            print("No run has been performed with the interface widget yet")
+            return []
+
+        return self.plot_interface.data
+
     def show_interface(self):
         """
         Builds and shows widget interface

From 0dedcb5333b3efd28019ae7f6d3f93c8a9bdac3a Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 11 May 2022 11:32:08 +0200
Subject: [PATCH 262/403] Removed component_name_list attribute in instr. It
 introduced a second way to store the same information as in the component
 list, and could potentially get out of sync introducing bugs.

Now the component_list is a single source of truth, so introducing
remove component and move component will be easier.
---
 mcstasscript/interface/instr.py             | 40 ++++++++++-----------
 mcstasscript/jb_interface/plot_interface.py |  2 ++
 2 files changed, 20 insertions(+), 22 deletions(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 5f5bda7f..afe65eaf 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -410,7 +410,6 @@ def __init__(self, name, parameters=None, author=None,
                                     + name + "\n")
             # Handle components
             self.component_list = []  # List of components (have to be ordered)
-            self.component_name_list = []  # List of component names
 
     def init_parameters(self):
         """
@@ -920,7 +919,7 @@ def add_component(self, name, component_name, before=None, after=None,
                 Comment that will be displayed before the component
         """
 
-        if name in self.component_name_list:
+        if name in [x.name for x in self.component_list]:
             raise NameError(("Component name \"" + str(name)
                              + "\" used twice, " + self.package_name
                              + " does not allow this."
@@ -970,35 +969,34 @@ def _insert_component(self, name, component, before=None, after=None):
             if isinstance(after, Component):
                 after = after.name
             # Insert component after component with this name
-            if after not in self.component_name_list:
+            if after not in [x.name for x in self.component_list]:
                 raise NameError(("Trying to add a component after a component"
                                  + " named \"" + str(after)
                                  + "\", but a component with that name was"
                                  + " not found."))
 
-            new_index = self.component_name_list.index(after)
+            component_names = [x.name for x in self.component_list]
+            new_index = component_names.index(after)
             self.component_list.insert(new_index + 1, component)
-            self.component_name_list.insert(new_index+1, name)
 
         elif before is not None:
             if isinstance(before, Component):
                 before = before.name
             # Insert component before component with this name
-            if before not in self.component_name_list:
+            if before not in [x.name for x in self.component_list]:
                 raise NameError(("Trying to add a component before a "
                                  + "component named \""
                                  + str(before)
                                  + "\", but a component with that "
                                  + "name was not found."))
 
-            new_index = self.component_name_list.index(before)
+            component_names = [x.name for x in self.component_list]
+            new_index = component_names.index(before)
             self.component_list.insert(new_index, component)
-            self.component_name_list.insert(new_index, name)
 
         else:
             # If after and before keywords absent, place component at the end
             self.component_list.append(component)
-            self.component_name_list.append(name)
 
     def copy_component(self, name, original_component, before=None, after=None,
                       AT=None, AT_RELATIVE=None, ROTATED=None,
@@ -1078,6 +1076,8 @@ def copy_component(self, name, original_component, before=None, after=None,
         If the name starts with COPY, use unique naming as described in the
         McStas manual.
         """
+        component_names = [x.name for x in self.component_list]
+
         if name.startswith("COPY("):
             target_name = name.split("(", 1)[1]
             target_name = target_name.split(")", 1)[0]
@@ -1085,18 +1085,18 @@ def copy_component(self, name, original_component, before=None, after=None,
 
             label = 0
             instance_name = target_name + "_" + str(label)
-            while instance_name in self.component_name_list:
+            while instance_name in component_names:
                 instance_name = target_name + "_" + str(label)
                 label += 1
 
-        if name in self.component_name_list:
+        if name in component_names:
             raise NameError(("Component name \"" + str(name)
                              + "\" used twice, " + self.package_name
                              + " does not allow this."
                              + " Rename or remove one instance of this"
                              + " name."))
 
-        if original_component not in self.component_name_list:
+        if original_component not in component_names:
             raise NameError("Component name \"" + str(original_component)
                             + "\" was not found in the " + self.package_name
                             + " instrument. and thus can not be copied.")
@@ -1128,8 +1128,9 @@ def get_component(self, name):
             Unique name of component whose instance should be returned
         """
 
-        if name in self.component_name_list:
-            index = self.component_name_list.index(name)
+        component_names = [x.name for x in self.component_list]
+        if name in component_names:
+            index = component_names.index(name)
             return self.component_list[index]
         else:
             raise NameError(("No component was found with name \""
@@ -1200,7 +1201,7 @@ def show_components(self, line_length=None):
             Maximum line length in console
         """
 
-        if len(self.component_name_list) == 0:
+        if len(self.component_list) == 0:
             print("No components added to instrument object yet.")
             return
 
@@ -1209,7 +1210,8 @@ def show_components(self, line_length=None):
         else:
             line_limit = line_length
 
-        longest_name = len(max(self.component_name_list, key=len))
+        component_names = [x.name for x in self.component_list]
+        longest_name = len(max(component_names, key=len))
 
         # todo Investigate how this could have been done in a better way
         # Find longest field for each type of data printed
@@ -1935,9 +1937,6 @@ class McStas_instr(McCode_instr):
     component_list : list of component instances
         list of components in the instrument
 
-    component_name_list : list of strings
-        list of names of the components in the instrument
-
     component_class_lib : dict
         dict of custom Component classes made at run time
 
@@ -2161,9 +2160,6 @@ class McXtrace_instr(McCode_instr):
     component_list : list of component instances
         list of components in the instrument
 
-    component_name_list : list of strings
-        list of names of the components in the instrument
-
     component_class_lib : dict
         dict of custom Component classes made at run time
 
diff --git a/mcstasscript/jb_interface/plot_interface.py b/mcstasscript/jb_interface/plot_interface.py
index 1254ba3f..5b1c119d 100644
--- a/mcstasscript/jb_interface/plot_interface.py
+++ b/mcstasscript/jb_interface/plot_interface.py
@@ -14,6 +14,7 @@
 
 from mcstasscript.jb_interface.widget_helpers import HiddenPrints
 
+
 class PlotInterface:
     """
     Class for providing plotting interface given McStasScript data
@@ -297,6 +298,7 @@ def update_cmap(self, change):
         """
         self.set_colormap(change.new)
 
+
 class MonitorDropdown:
     """
     Class for creating monitor dropdown menu

From 2397c5106fedb7d26054728a49263051fe71df71 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 11 May 2022 11:59:20 +0200
Subject: [PATCH 263/403] Work in progress on changing component to not have
 RELATIVE in AT_RELATIVE and ROTATED_RELATIVE data.

---
 mcstasscript/helper/mcstas_objects.py | 22 ++++++++++++++++------
 mcstasscript/interface/instr.py       | 15 +++++++++++++++
 2 files changed, 31 insertions(+), 6 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 2c596101..eeca8458 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -694,7 +694,7 @@ def set_AT_RELATIVE(self, relative):
         if relative == "ABSOLUTE":
             self.AT_relative = "ABSOLUTE"
         else:
-            self.AT_relative = "RELATIVE " + relative
+            self.AT_relative = relative
 
     def set_ROTATED(self, rotated_list, RELATIVE=None):
         """
@@ -755,7 +755,7 @@ def set_ROTATED_RELATIVE(self, relative):
         if relative == "ABSOLUTE":
             self.ROTATED_relative = "ABSOLUTE"
         else:
-            self.ROTATED_relative = "RELATIVE " + relative
+            self.ROTATED_relative = relative
 
     def set_RELATIVE(self, relative):
         """
@@ -780,8 +780,8 @@ def set_RELATIVE(self, relative):
             self.AT_relative = "ABSOLUTE"
             self.ROTATED_relative = "ABSOLUTE"
         else:
-            self.AT_relative = "RELATIVE " + relative
-            self.ROTATED_relative = "RELATIVE " + relative
+            self.AT_relative = relative
+            self.ROTATED_relative = relative
 
     def set_parameters(self, args_as_dict=None, **kwargs):
         """
@@ -1010,13 +1010,23 @@ def write_component(self, fo):
         fo.write("AT (%s,%s,%s)" % (str(self.AT_data[0]),
                                     str(self.AT_data[1]),
                                     str(self.AT_data[2])))
-        fo.write(" %s\n" % self.AT_relative)
+        if self.AT_relative == "ABSOLUTE":
+            relative = self.AT_relative
+        else:
+            relative = "RELATIVE " + self.AT_relative
+
+        fo.write(" %s\n" % relative)
 
         if self.ROTATED_specified:
+            if self.ROTATED_relative == "ABSOLUTE":
+                relative = self.ROTATED_relative
+            else:
+                relative = "RELATIVE " + self.ROTATED_relative
+
             fo.write("ROTATED (%s,%s,%s)" % (str(self.ROTATED_data[0]),
                                              str(self.ROTATED_data[1]),
                                              str(self.ROTATED_data[2])))
-            fo.write(" %s\n" % self.ROTATED_relative)
+            fo.write(" %s\n" % relative)
 
         if not self.GROUP == "":
             fo.write("GROUP %s\n" % self.GROUP)
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index afe65eaf..25b4a6b7 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1446,6 +1446,21 @@ def write_c_files(self):
             for component in self.component_list:
                 component.write_component(fo)
 
+    def check_for_errors(self):
+        """
+        Methods that checks for common McStas errors
+        """
+
+        # Check RELATIVE exists
+        seen_instrument_names = ["ABSOLUTE"]
+        for component in self.component_list:
+            seen_instrument_names.append(component.name)
+            component.AT_relative
+            component.ROTATED_relative
+
+
+
+
     def write_full_instrument(self):
         """
         Method for writing full instrument file to disk

From 1408e3cb6012c38a550653d3ba762c5e619e9f6a Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 11 May 2022 13:06:26 +0200
Subject: [PATCH 264/403] Enabled an error check for references to other
 components in AT RELATIVE and ROTATED RELATIVE to inform the user when obvous
 errors are performed.

Check is made when writing the instrument, and aborts if found.
Need to integrate this with interface.
---
 mcstasscript/helper/mcstas_objects.py | 33 +++++++++++++++------------
 mcstasscript/interface/instr.py       | 24 +++++++++++++++----
 2 files changed, 37 insertions(+), 20 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index eeca8458..a3b9487b 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -547,6 +547,10 @@ def __init__(self, instance_name, component_name, AT=None,
         self.c_code_before = ""
         self.c_code_after = ""
 
+        # references to component names
+        self.AT_reference = None
+        self.ROTATED_reference = None
+
         # If any keywords are set in kwargs, update these
         self.set_keyword_input(AT=AT, AT_RELATIVE=AT_RELATIVE, ROTATED=ROTATED,
                                ROTATED_RELATIVE=ROTATED_RELATIVE,
@@ -693,8 +697,10 @@ def set_AT_RELATIVE(self, relative):
         # Set AT relative
         if relative == "ABSOLUTE":
             self.AT_relative = "ABSOLUTE"
+            self.AT_reference = None
         else:
-            self.AT_relative = relative
+            self.AT_relative = "RELATIVE " + relative
+            self.AT_reference = relative
 
     def set_ROTATED(self, rotated_list, RELATIVE=None):
         """
@@ -754,8 +760,10 @@ def set_ROTATED_RELATIVE(self, relative):
         # Set ROTATED relative
         if relative == "ABSOLUTE":
             self.ROTATED_relative = "ABSOLUTE"
+            self.ROTATED_reference = None
         else:
-            self.ROTATED_relative = relative
+            self.ROTATED_relative = "RELATIVE " + relative
+            self.ROTATED_reference = relative
 
     def set_RELATIVE(self, relative):
         """
@@ -778,10 +786,14 @@ def set_RELATIVE(self, relative):
 
         if relative == "ABSOLUTE":
             self.AT_relative = "ABSOLUTE"
+            self.AT_reference = None
             self.ROTATED_relative = "ABSOLUTE"
+            self.ROTATED_reference = None
         else:
-            self.AT_relative = relative
-            self.ROTATED_relative = relative
+            self.AT_relative = "RELATIVE " + relative
+            self.AT_reference = relative
+            self.ROTATED_relative = "RELATIVE " + relative
+            self.ROTATED_reference = relative
 
     def set_parameters(self, args_as_dict=None, **kwargs):
         """
@@ -1010,23 +1022,14 @@ def write_component(self, fo):
         fo.write("AT (%s,%s,%s)" % (str(self.AT_data[0]),
                                     str(self.AT_data[1]),
                                     str(self.AT_data[2])))
-        if self.AT_relative == "ABSOLUTE":
-            relative = self.AT_relative
-        else:
-            relative = "RELATIVE " + self.AT_relative
 
-        fo.write(" %s\n" % relative)
+        fo.write(" %s\n" % self.AT_relative)
 
         if self.ROTATED_specified:
-            if self.ROTATED_relative == "ABSOLUTE":
-                relative = self.ROTATED_relative
-            else:
-                relative = "RELATIVE " + self.ROTATED_relative
-
             fo.write("ROTATED (%s,%s,%s)" % (str(self.ROTATED_data[0]),
                                              str(self.ROTATED_data[1]),
                                              str(self.ROTATED_data[2])))
-            fo.write(" %s\n" % relative)
+            fo.write(" %s\n" % self.ROTATED_relative)
 
         if not self.GROUP == "":
             fo.write("GROUP %s\n" % self.GROUP)
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 25b4a6b7..af7664da 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1452,14 +1452,25 @@ def check_for_errors(self):
         """
 
         # Check RELATIVE exists
-        seen_instrument_names = ["ABSOLUTE"]
+        seen_instrument_names = []
         for component in self.component_list:
             seen_instrument_names.append(component.name)
-            component.AT_relative
-            component.ROTATED_relative
-
-
 
+            if component.AT_reference is not None:
+                if component.AT_reference not in seen_instrument_names:
+                    raise RuntimeError("Component '" + str(component.name)
+                                       + "' referenced unknown component"
+                                       + " named '"
+                                       + str(component.AT_reference)
+                                       + "'.")
+
+            if component.ROTATED_reference is not None:
+                if component.ROTATED_reference not in seen_instrument_names:
+                    raise RuntimeError("Component '" + str(component.name)
+                                       + "' referenced unknown component"
+                                       + " named '"
+                                       + str(component.ROTATED_reference)
+                                       + "'.")
 
     def write_full_instrument(self):
         """
@@ -1470,6 +1481,9 @@ def write_full_instrument(self):
         the object.
         """
 
+        # Catch common errors before writing the instrument
+        self.check_for_errors()
+
         # Create file identifier
         fo = open(os.path.join(self.input_path, self.name + ".instr"), "w")
 

From 4bb0ce0d7a9071abd868141524a930e55dd30301 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 11 May 2022 17:10:01 +0200
Subject: [PATCH 265/403] Checks for RELATIVE errors and allows PREVIOUS
 keyword.

---
 mcstasscript/interface/instr.py | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index af7664da..09a5c3ef 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1456,20 +1456,19 @@ def check_for_errors(self):
         for component in self.component_list:
             seen_instrument_names.append(component.name)
 
-            if component.AT_reference is not None:
-                if component.AT_reference not in seen_instrument_names:
-                    raise RuntimeError("Component '" + str(component.name)
-                                       + "' referenced unknown component"
-                                       + " named '"
-                                       + str(component.AT_reference)
-                                       + "'.")
+            references = []
+            if component.AT_reference not in (None, "PREVIOUS"):
+                references.append(component.AT_reference)
+
+            if component.ROTATED_reference not in (None, "PREVIOUS"):
+                references.append(component.ROTATED_reference)
 
-            if component.ROTATED_reference is not None:
-                if component.ROTATED_reference not in seen_instrument_names:
+            for ref in references:
+                if ref not in seen_instrument_names:
                     raise RuntimeError("Component '" + str(component.name)
                                        + "' referenced unknown component"
                                        + " named '"
-                                       + str(component.ROTATED_reference)
+                                       + str(component.AT_reference)
                                        + "'.")
 
     def write_full_instrument(self):

From 1d56660da417e1cc0686836c0342925aeedb3696 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 12 May 2022 15:01:09 +0200
Subject: [PATCH 266/403] Code for error checking and remove/move components.

Now components checks parameters that are asigned a string containing
only letters against the instrument parameters and variables, if no
match is found, it raises a McStasError. Probably a misspelling, or a
forgotten set of " / '.

Introduced instrument.has_error() that returns True if an error is
found with instrument.check_for_errors. has_error does not raise
any exceptions.

Added methods for moving and removing components in the instrument.
They both check if the instrument had errors before and after the
operations, if the operation introduced an error it warns the user.

Added a setting called checks that can be set to False if these
kinds of checks should not prevent writing the instrument file.
---
 mcstasscript/helper/exceptions.py     |   3 +
 mcstasscript/helper/mcstas_objects.py |  45 +++++
 mcstasscript/interface/instr.py       | 228 ++++++++++++++++++--------
 mcstasscript/tests/test_Instr.py      |  61 +++++++
 mcstasscript/tests/test_component.py  |  16 ++
 5 files changed, 281 insertions(+), 72 deletions(-)
 create mode 100644 mcstasscript/helper/exceptions.py

diff --git a/mcstasscript/helper/exceptions.py b/mcstasscript/helper/exceptions.py
new file mode 100644
index 00000000..580651c5
--- /dev/null
+++ b/mcstasscript/helper/exceptions.py
@@ -0,0 +1,3 @@
+class McStasError(Exception):
+    # Error raised when McStas simulation can't run
+    pass
\ No newline at end of file
diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index a3b9487b..b9eed6d4 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -1,5 +1,6 @@
 from mcstasscript.helper.formatting import bcolors
 from mcstasscript.helper.formatting import is_legal_parameter
+from mcstasscript.helper.exceptions import McStasError
 
 from libpyvinyl.Parameters.Parameter import Parameter
 
@@ -1303,3 +1304,47 @@ def show_parameters_simple(self):
             print(parameter + value + unit + comment)
 
         print("----------" + "-"*len(self.component_name) + "------")
+
+    def check_parameters(self, instrument_variables):
+        """
+        Checks the current component parameters are in given name list
+
+        The name list will typically be a list of instrument parameters and
+        declared variables.
+
+        Parameters
+        ----------
+
+        instrument_variables : list of str
+            List of instrument variable names
+        """
+        McStas_constants = ["DEG2RAD", "RAD2DEG", "MIN2RAD", "RAD2MIN", "V2K",
+                            "K2V", "VS2E", "SE2V", "FWHM2RMS", "RMS2FWHM",
+                            "MNEUTRON" "HBAR", "PI", "FLT_MAX"]
+
+        # parameter_names attribute added in _create_component_instance
+        for par_name in self.parameter_names:
+            par = getattr(self, par_name)
+            if isinstance(par, str):
+                if not par.isalpha():
+                    # Allow numbers and strings with math symbols
+                    # They may have errors, but can't be easily checked
+                    continue
+
+                if par in McStas_constants:
+                    # Allow McStas defined constants
+                    continue
+
+                if par in instrument_variables:
+                    # Allow variables on given whitelist
+                    continue
+
+                # If none of the above approves the variable, raise error
+                raise McStasError("Variable not recognized.\n"
+                                  f"Unrecognized variable '{par}' "
+                                  "assigned to component parameter "
+                                  f"'{par_name}' in component "
+                                  f"'{self.name}' of type "
+                                  f"'{self.component_name}'.\n"
+                                  "This check can be skipped with "
+                                  "settings(checks=False)")
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 09a5c3ef..3e693724 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -24,6 +24,7 @@
 from mcstasscript.helper.formatting import is_legal_filename
 from mcstasscript.helper.formatting import bcolors
 from mcstasscript.helper.unpickler import CustomMcStasUnpickler, CustomMcXtraceUnpickler
+from mcstasscript.helper.exceptions import McStasError
 
 
 class McCode_instr(BaseCalculator):
@@ -231,7 +232,7 @@ def __init__(self, name, parameters=None, author=None,
                  increment_folder_name=None, custom_flags=None,
                  executable_path=None, executable=None,
                  suppress_output=None, gravity=None, input_path=None,
-                 package_path=None):
+                 package_path=None, checks=None):
         """
         Initialization of McStas Instrument
 
@@ -349,7 +350,7 @@ def __init__(self, name, parameters=None, author=None,
             self._run_settings["package_path"] = package_path
 
         # Settings for run that can be adjusted by user
-        provided_run_settings = {"executable": executable}
+        provided_run_settings = {"executable": executable, "checks": True}
 
         if executable_path is not None:
             provided_run_settings["executable_path"] = str(executable_path)
@@ -377,6 +378,9 @@ def __init__(self, name, parameters=None, author=None,
         if suppress_output is not None:
             provided_run_settings["suppress_output"] = suppress_output
 
+        if checks is not None:
+            provided_run_settings["checks"] = checks
+
         if output_path is not None:
             provided_run_settings["output_path"] = output_path
 
@@ -470,7 +474,6 @@ def add_parameter(self, *args, **kwargs):
         return par
 
     def show_parameters(self, line_length=None):
-
         """
         Method for displaying current instrument parameters
 
@@ -939,70 +942,14 @@ def add_component(self, name, component_name, before=None, after=None,
         new_component = self._create_component_instance(name, component_name,
                                                         **component_input)
 
-        self._insert_component(name, new_component, before=before, after=after)
+        self._insert_component(new_component, before=before, after=after)
         return new_component
 
-    def _insert_component(self, name, component, before=None, after=None):
-        """
-        Inserts component into sequence of components held by instrument
-
-        Internal method to handle placement of a new component in the
-        list of components held by this instrument.
-
-        name : str
-            Instance name of component
-
-        component : Component object
-            Component object to be inserted
-
-        before : str or Component object
-            Reference to component to place this one before
-
-        after : str or Component object
-            Reference to coponent to place this one after
-        """
-
-        if before is not None and after is not None:
-            raise RuntimeError("Only specify either 'before' or 'after'.")
-
-        if after is not None:
-            if isinstance(after, Component):
-                after = after.name
-            # Insert component after component with this name
-            if after not in [x.name for x in self.component_list]:
-                raise NameError(("Trying to add a component after a component"
-                                 + " named \"" + str(after)
-                                 + "\", but a component with that name was"
-                                 + " not found."))
-
-            component_names = [x.name for x in self.component_list]
-            new_index = component_names.index(after)
-            self.component_list.insert(new_index + 1, component)
-
-        elif before is not None:
-            if isinstance(before, Component):
-                before = before.name
-            # Insert component before component with this name
-            if before not in [x.name for x in self.component_list]:
-                raise NameError(("Trying to add a component before a "
-                                 + "component named \""
-                                 + str(before)
-                                 + "\", but a component with that "
-                                 + "name was not found."))
-
-            component_names = [x.name for x in self.component_list]
-            new_index = component_names.index(before)
-            self.component_list.insert(new_index, component)
-
-        else:
-            # If after and before keywords absent, place component at the end
-            self.component_list.append(component)
-
     def copy_component(self, name, original_component, before=None, after=None,
-                      AT=None, AT_RELATIVE=None, ROTATED=None,
-                      ROTATED_RELATIVE=None, RELATIVE=None, WHEN=None,
-                      EXTEND=None, GROUP=None, JUMP=None, SPLIT=None,
-                      comment=None, c_code_before=None, c_code_after=None):
+                       AT=None, AT_RELATIVE=None, ROTATED=None,
+                       ROTATED_RELATIVE=None, RELATIVE=None, WHEN=None,
+                       EXTEND=None, GROUP=None, JUMP=None, SPLIT=None,
+                       comment=None, c_code_before=None, c_code_after=None):
         """
         Method for adding a copy of a Component instance to the instrument
 
@@ -1106,13 +1053,116 @@ def copy_component(self, name, original_component, before=None, after=None,
         new_component = copy.deepcopy(component_to_copy)
         new_component.name = name  # Set new name, duplicate names not allowed
 
-        self._insert_component(name, new_component, before=before, after=after)
+        self._insert_component(new_component, before=before, after=after)
 
         # Run set_keyword_input for keyword arguments to take effect
         new_component.set_keyword_input(**component_input)
 
         return new_component
 
+    def remove_component(self, name):
+        """
+        Removes component with given name from instrument
+        """
+
+        # Check for errors before
+        errors_before = self.has_errors()
+
+        if isinstance(name, Component):
+            name = name.name
+
+        component_names = [x.name for x in self.component_list]
+        index_to_remove = component_names.index(name)
+        self.component_list.pop(index_to_remove)
+
+        # Check for errors after removing
+        errors_after = self.has_errors()
+
+        if not errors_before and errors_after:
+            print("Removing the component '" + name + "' introduced errors in "
+                  "the instrument, run check_for_errors() for more "
+                  "information.")
+
+    def move_component(self, name, before=None, after=None):
+        """
+        Moves component with given name to before or after
+        """
+        if isinstance(name, Component):
+            name = name.name
+
+        if before is None and after is None:
+            raise RuntimeError("Must specify 'before' or 'after' when moving "
+                               "a component.")
+
+        # Check for errors before
+        errors_before = self.has_errors()
+
+        if isinstance(name, Component):
+            name = name.name
+
+        component_names = [x.name for x in self.component_list]
+        index_to_remove = component_names.index(name)
+        moved_component = self.component_list.pop(index_to_remove)
+        self._insert_component(moved_component, before=before, after=after)
+
+        # Check for errors after moving
+        errors_after = self.has_errors()
+
+        if not errors_before and errors_after:
+            print("Moving the component '" + name + "' introduced errors in "
+                  "the instrument, run check_for_errors() for more "
+                  "information.")
+
+    def _insert_component(self, component, before=None, after=None):
+        """
+        Inserts component into sequence of components held by instrument
+
+        Internal method to handle placement of a new component in the
+        list of components held by this instrument.
+
+        name : str
+            Instance name of component
+
+        component : Component object
+            Component object to be inserted
+
+        before : str or Component object
+            Reference to component to place this one before
+
+        after : str or Component object
+            Reference to coponent to place this one after
+        """
+
+        if before is not None and after is not None:
+            raise RuntimeError("Only specify either 'before' or 'after'.")
+
+        if before is None and after is None:
+            # If after and before keywords absent, place component at the end
+            self.component_list.append(component)
+            return
+
+        if after is not None:
+            index_addition = 1
+            reference = after
+            description = "after"
+        if before is not None:
+            index_addition = 0
+            reference = before
+            description = "before"
+
+        if isinstance(reference, Component):
+            reference = reference.name
+
+        component_names = [x.name for x in self.component_list]
+        if reference not in component_names:
+            raise NameError("Trying to add a component " + description
+                            + " a component named '" + str(after)
+                            + "', but a component with that name was"
+                            + " not found.")
+
+        new_index = component_names.index(reference) + index_addition
+        self.component_list.insert(new_index, component)
+
     def get_component(self, name):
         """
         Get the component instance of component with specified name
@@ -1446,9 +1496,29 @@ def write_c_files(self):
             for component in self.component_list:
                 component.write_component(fo)
 
+    def has_errors(self):
+        """
+        Method that returns true if errors are found in instrument
+        """
+
+        has_errors = True
+        try:
+            self.check_for_errors()
+            has_errors = False
+        except:
+            pass
+
+        return has_errors
+
     def check_for_errors(self):
         """
         Methods that checks for common McStas errors
+
+        Currently checks for:
+        RELATIVE for AT and ROTATED reference a component that have not yet
+        been defined.
+
+        Using variables in components that have not been defined.
         """
 
         # Check RELATIVE exists
@@ -1465,11 +1535,21 @@ def check_for_errors(self):
 
             for ref in references:
                 if ref not in seen_instrument_names:
-                    raise RuntimeError("Component '" + str(component.name)
-                                       + "' referenced unknown component"
-                                       + " named '"
-                                       + str(component.AT_reference)
-                                       + "'.")
+                    raise McStasError("Component '" + str(component.name) +
+                                      "' referenced unknown component"
+                                      " named '"
+                                      + str(component.AT_reference) + "'.\n"
+                                      "This check can be skipped with"
+                                      " settings(checks=False)")
+
+        # Check variables used have been declared
+        parameters = [x.name for x in self.parameters]
+        variables = [x.name for x in self.declare_list]
+        pars_and_vars = parameters + variables
+
+        # Check component parameters
+        for component in self.component_list:
+            component.check_parameters(pars_and_vars)
 
     def write_full_instrument(self):
         """
@@ -1481,7 +1561,8 @@ def write_full_instrument(self):
         """
 
         # Catch common errors before writing the instrument
-        self.check_for_errors()
+        if self._run_settings["checks"]:
+            self.check_for_errors()
 
         # Create file identifier
         fo = open(os.path.join(self.input_path, self.name + ".instr"), "w")
@@ -1569,7 +1650,7 @@ def settings(self, ncount=None, mpi="not_set", seed=None,
                  force_compile=None, output_path=None,
                  increment_folder_name=None, custom_flags=None,
                  executable=None, executable_path=None,
-                 suppress_output=None, gravity=None):
+                 suppress_output=None, gravity=None, checks=None):
         """
         Sets settings for McStas run performed with backengine
 
@@ -1651,6 +1732,9 @@ def settings(self, ncount=None, mpi="not_set", seed=None,
         if suppress_output is not None:
             settings["suppress_output"] = suppress_output
 
+        if checks is not None:
+            settings["checks"] = checks
+
         if output_path is not None:
             self.output_path = output_path
 
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 807faa7b..1a9493d9 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -11,6 +11,7 @@
 from mcstasscript.interface.instr import McXtrace_instr
 from mcstasscript.helper.formatting import bcolors
 from mcstasscript.tests.helpers_for_tests import WorkInTestDir
+from mcstasscript.helper.exceptions import McStasError
 
 run_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '.')
 
@@ -1089,6 +1090,66 @@ def test_copy_component_keywords(self):
         self.assertEqual(original.AT_data[2], 2)
         self.assertEqual(original.SPLIT, 0)
 
+    def test_remove_component(self):
+        """
+        Ensure a component can be removed
+        """
+        instr = setup_populated_instr()
+
+        instr.remove_component("second_component")
+
+        self.assertEqual(len(instr.component_list), 2)
+        self.assertEqual(instr.component_list[0].name, "first_component")
+        self.assertEqual(instr.component_list[1].name, "third_component")
+
+    def test_move_component(self):
+        """
+        Ensure a component can be moved
+        """
+        instr = setup_populated_instr()
+
+        instr.move_component("second_component", before="first_component")
+
+        self.assertEqual(len(instr.component_list), 3)
+        self.assertEqual(instr.component_list[0].name, "second_component")
+        self.assertEqual(instr.component_list[1].name, "first_component")
+        self.assertEqual(instr.component_list[2].name, "third_component")
+
+    def test_RELATIVE_error(self):
+        """
+        Ensure check_for_errors finds impossible relative statement
+        """
+
+        instr = setup_populated_instr()
+
+        second_component = instr.get_component("second_component")
+        second_component.set_AT([0, 0, 0], RELATIVE="third_component")
+
+        self.assertTrue(instr.has_errors())
+
+        with self.assertRaises(McStasError):
+            instr.check_for_errors()
+
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable=unittest.mock.mock_open)
+    def test_RELATIVE_error_and_checks_false(self, mock_f):
+        """
+        Ensure check_for_errors finds impossible relative statement
+        """
+
+        instr = setup_populated_instr()
+
+        second_component = instr.get_component("second_component")
+        second_component.set_AT([0, 0, 0], RELATIVE="third_component")
+
+        self.assertTrue(instr.has_errors())
+
+        with self.assertRaises(McStasError):
+            instr.write_full_instrument()
+
+        instr.settings(checks=False)
+        instr.write_full_instrument()
+
     def test_get_component_simple(self):
         """
         get_component retrieves a component with a given name for
diff --git a/mcstasscript/tests/test_component.py b/mcstasscript/tests/test_component.py
index 776811b4..fc86ad84 100644
--- a/mcstasscript/tests/test_component.py
+++ b/mcstasscript/tests/test_component.py
@@ -4,6 +4,7 @@
 
 from mcstasscript.helper.mcstas_objects import Component
 from mcstasscript.helper.formatting import bcolors
+from mcstasscript.helper.exceptions import McStasError
 
 
 def setup_Component_all_keywords():
@@ -835,6 +836,21 @@ def test_Component_show_parameters_simple(self, mock_stdout):
         self.assertEqual(output[5],
                          par_name + " = " + value + " [1]" + comment)
 
+    def test_component_error_check(self):
+
+        comp = setup_Component_with_parameters()
+
+        # Currently no ilegal parameters
+        comp.check_parameters([])
+
+        # Introduce illegal parameter
+        comp.new_par1 = "wrong"
+        with self.assertRaises(McStasError):
+            comp.check_parameters([])
+
+        # Check no error is raised when on whitelist
+        comp.check_parameters(["wrong"])
+
 
 if __name__ == '__main__':
     unittest.main()

From 37a6146d7b9c168f87d006b54d48fd5469a54c42 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 12 May 2022 16:17:49 +0200
Subject: [PATCH 267/403] Clean up of examples, correcting bugs and improving
 use of syntax.

---
 examples/McStasScript_demo.ipynb  |  524 +++++++-
 examples/calibration_sample.ipynb |  154 +--
 examples/cryostat_demo.ipynb      | 1991 +----------------------------
 examples/libpyvinyl_example.ipynb |  309 +----
 examples/manual_code.ipynb        |  235 ----
 examples/random_demonstration.py  |   66 +-
 6 files changed, 634 insertions(+), 2645 deletions(-)
 delete mode 100644 examples/manual_code.ipynb

diff --git a/examples/McStasScript_demo.ipynb b/examples/McStasScript_demo.ipynb
index 76a9b5db..d2c7c71a 100644
--- a/examples/McStasScript_demo.ipynb
+++ b/examples/McStasScript_demo.ipynb
@@ -10,102 +10,309 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following components are found in the work_directory / input_path:\n",
+      "     Union_sphere.comp\n",
+      "     Texture_process.comp\n",
+      "     Union_cone.comp\n",
+      "     Union_box.comp\n",
+      "     Single_crystal_process.comp\n",
+      "     Union_abs_logger_2D_space.comp\n",
+      "     Union_logger_2D_kf.comp\n",
+      "     Template_process.comp\n",
+      "     PhononSimple_process.comp\n",
+      "     Union_conditional_standard.comp\n",
+      "     Union_abs_logger_1D_space.comp\n",
+      "     Union_abs_logger_event.comp\n",
+      "     NCrystal_process.comp\n",
+      "     Union_abs_logger_1D_space_event.comp\n",
+      "     Union_abs_logger_1D_space_tof.comp\n",
+      "     Union_logger_2D_space.comp\n",
+      "     Union_conditional_PSD.comp\n",
+      "     Union_master.comp\n",
+      "     AF_HB_1D_process.comp\n",
+      "     Union_logger_2D_kf_time.comp\n",
+      "     Union_cylinder.comp\n",
+      "     Union_abs_logger_1D_space_tof_to_lambda.comp\n",
+      "     Powder_process.comp\n",
+      "     Union_make_material.comp\n",
+      "     Incoherent_process.comp\n",
+      "     Union_logger_1D.comp\n",
+      "     Union_logger_3D_space.comp\n",
+      "     IncoherentPhonon_process.comp\n",
+      "     Union_logger_2DQ.comp\n",
+      "     Union_mesh.comp\n",
+      "     Union_logger_2D_space_time.comp\n",
+      "These definitions will be used instead of the installed versions.\n"
+     ]
+    }
+   ],
    "source": [
-    "from mcstasscript.interface import instr, plotter, functions\n",
+    "import mcstasscript as ms\n",
     "\n",
     "# Creating the instance of the class, insert path to mcrun and to mcstas root directory\n",
-    "Instr = instr.McStas_instr(\"jupyter_demo\")"
+    "instr = ms.McStas_instr(\"jupyter_demo\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The instrument object knows what components are available in this McStas installation and can provide some help."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are the available component categories:\n",
+      " contrib\n",
+      " misc\n",
+      " monitors\n",
+      " obsolete\n",
+      " optics\n",
+      " samples\n",
+      " sources\n",
+      " work directory\n",
+      "Call available_components(category_name) to display\n"
+     ]
+    }
+   ],
    "source": [
-    "Instr.show_components() # Shows available McStas component categories in current installation"
+    "instr.available_components() # Shows available McStas component categories in current installation"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are all components in the sources category.\n",
+      " Adapt_check     Moderator           Source_Optimizer   Source_gen\n",
+      " ESS_butterfly   Monitor_Optimizer   Source_adapt       Source_simple\n",
+      " ESS_moderator   Source_Maxwell_3    Source_div         \n"
+     ]
+    }
+   ],
    "source": [
-    "Instr.show_components(\"sources\") # Display all McStas source components "
+    "instr.available_components(\"sources\") # Display all McStas source components "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Source_simple _____________________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1mradius\u001b[0m = \u001b[1m\u001b[94m0.1\u001b[0m\u001b[0m [m] // Radius of circle in (x,y,0) plane where neutrons are \n",
+      "                    generated. \n",
+      "\u001b[1myheight\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Height of rectangle in (x,y,0) plane where neutrons are \n",
+      "                     generated. \n",
+      "\u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Width of rectangle in (x,y,0) plane where neutrons are \n",
+      "                    generated. \n",
+      "\u001b[1mdist\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Distance to target along z axis.\n",
+      "\u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[94m0.045\u001b[0m\u001b[0m [m] // Width of target\n",
+      "\u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[94m0.12\u001b[0m\u001b[0m [m] // Height of target\n",
+      "\u001b[1mE0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Mean energy of neutrons.\n",
+      "\u001b[1mdE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [meV] // Energy half spread of neutrons (flat or gaussian sigma).\n",
+      "\u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [AA] // Mean wavelength of neutrons.\n",
+      "\u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [AA] // Wavelength half spread of neutrons.\n",
+      "\u001b[1mflux\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1/(s*cm**2*st*energy unit)] // flux per energy unit, Angs or meV if \n",
+      "                                           flux=0, the source emits 1 in 4*PI whole \n",
+      "                                           space. \n",
+      "\u001b[1mgauss\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Gaussian (1) or Flat (0) energy/wavelength distribution\n",
+      "\u001b[1mtarget_index\u001b[0m = \u001b[1m\u001b[94m1\u001b[0m\u001b[0m [1] // relative index of component to focus at, e.g. next is \n",
+      "                        +1 this is used to compute 'dist' automatically. \n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "instr.component_help(\"Source_simple\") # Displays help on the Source_simple component"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "Instr.component_help(\"Source_simple\") # Displays help on the Source_simple component"
+    "First a *Source_simple* component called *Source* is added."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "source = Instr.add_component(\"Source\", \"Source_simple\") # Adds an instance of Source_simple"
+    "source = instr.add_component(\"Source\", \"Source_simple\") # Adds an instance of Source_simple"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One way to adjust the parameters of a component is to access them directly as attributes."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Lets add a parameter to the instrument to control the wavelength of the source\n",
-    "wavelength = Instr.add_parameter(\"double\", \"wavelength\", value=3,\n",
-    "                                 comment=\"[AA] Wavelength emmited from source\")\n",
     "source.xwidth = 0.06\n",
     "source.yheight = 0.08;\n",
     "source.dist = 2\n",
     "source.focus_xw = 0.05\n",
     "source.focus_yh = 0.05\n",
-    "source.lambda0 = wavelength # Can provide a parameter object, in this way spelling is checked\n",
-    "source.dlambda = 0.05\n",
     "source.flux = 1E8"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is possible to create parameter objects and use these directly as input to component parameters."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "wavelength = instr.add_parameter(\"double\", \"wavelength\", value=3, comment=\"[AA] Wavelength emmited from source\")\n",
+    "source.lambda0 = wavelength\n",
+    "source.dlambda = 0.05"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT Source = Source_simple\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.08\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.06\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mdist\u001b[0m = \u001b[1m\u001b[92m2\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_yh\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mlambda0\u001b[0m = \u001b[1m\u001b[92mwavelength\u001b[0m\u001b[0m [AA]\n",
+      "  \u001b[1mdlambda\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [AA]\n",
+      "  \u001b[1mflux\u001b[0m = \u001b[1m\u001b[92m100000000.0\u001b[0m\u001b[0m [1/(s*cm**2*st*energy unit)]\n",
+      "AT [0, 0, 0] ABSOLUTE\n"
+     ]
+    }
+   ],
    "source": [
     "print(source) # Verify that the information is correct"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next a guide is added and a comment is included."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
-    "guide = Instr.add_component(\"Guide\", \"Guide_gravity\", AT=[0,0,2], RELATIVE=\"Source\")\n",
-    "guide.set_comment=\"Beam extraction and first guide piece\""
+    "guide = instr.add_component(\"Guide\", \"Guide_gravity\", AT=[0,0,2], RELATIVE=\"Source\")\n",
+    "guide.set_comment = \"Beam extraction and first guide piece\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Guide_gravity _____________________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[4m\u001b[1mw1\u001b[0m\u001b[0m [m] // Width at the guide entry\n",
+      "\u001b[4m\u001b[1mh1\u001b[0m\u001b[0m [m] // Height at the guide entry\n",
+      "\u001b[1mw2\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Width at the guide exit. If 0, use w1.\n",
+      "\u001b[1mh2\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Height at the guide exit. If 0, use h1.\n",
+      "\u001b[4m\u001b[1ml\u001b[0m\u001b[0m [m] // length of guide\n",
+      "\u001b[1mR0\u001b[0m = \u001b[1m\u001b[94m0.995\u001b[0m\u001b[0m [1] // Low-angle reflectivity\n",
+      "\u001b[1mQc\u001b[0m = \u001b[1m\u001b[94m0.0218\u001b[0m\u001b[0m [AA-1] // Critical scattering vector\n",
+      "\u001b[1malpha\u001b[0m = \u001b[1m\u001b[94m4.38\u001b[0m\u001b[0m [AA] // Slope of reflectivity\n",
+      "\u001b[1mm\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // m-value of material. Zero means completely absorbing. m=0.65  \n",
+      "               glass/SiO2 Si   Ni  Ni58  supermirror Be    Diamond m=  0.65       \n",
+      "               0.47 1   1.18  2-6         1.01  1.12 for glass/SiO2, m=1 for Ni, \n",
+      "               1.2 for Ni58, m=2-6 for supermirror. m=0.47 for Si \n",
+      "\u001b[1mW\u001b[0m = \u001b[1m\u001b[94m0.003\u001b[0m\u001b[0m [AA-1] // Width of supermirror cut-off\n",
+      "\u001b[1mnslit\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // Number of vertical channels in the guide (>= 1) (nslit-1 \n",
+      "                   vertical dividing walls). \n",
+      "\u001b[1md\u001b[0m = \u001b[1m\u001b[94m0.0005\u001b[0m\u001b[0m [m] // Thickness of subdividing walls\n",
+      "\u001b[1mmleft\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // m-value of material for left.   vert. mirror\n",
+      "\u001b[1mmright\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // m-value of material for right.  vert. mirror\n",
+      "\u001b[1mmtop\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // m-value of material for top.    horz. mirror\n",
+      "\u001b[1mmbottom\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // m-value of material for bottom. horz. mirror\n",
+      "\u001b[1mnhslit\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // Number of horizontal channels in the guide (>= 1). \n",
+      "                    (nhslit-1 horizontal dividing walls). this enables to have \n",
+      "                    nslit*nhslit rectangular channels \n",
+      "\u001b[1mG\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m/s2] // Gravitation norm. 0 value disables G effects.\n",
+      "\u001b[1maleft\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // alpha-value of left  vert. mirror\n",
+      "\u001b[1maright\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // alpha-value of right vert. mirror\n",
+      "\u001b[1matop\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // alpha-value of top   horz. mirror\n",
+      "\u001b[1mabottom\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // alpha-value of left  horz. mirror\n",
+      "\u001b[1mwavy\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // Global guide waviness\n",
+      "\u001b[1mwavy_z\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // Partial waviness along propagation axis\n",
+      "\u001b[1mwavy_tb\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // Partial waviness in transverse direction for top/bottom \n",
+      "                       mirrors \n",
+      "\u001b[1mwavy_lr\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // Partial waviness in transverse direction for left/right \n",
+      "                       mirrors \n",
+      "\u001b[1mchamfers\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Global chamfers specifications (in/out/mirror sides).\n",
+      "\u001b[1mchamfers_z\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Input and output chamfers\n",
+      "\u001b[1mchamfers_lr\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Chamfers on left/right mirror sides\n",
+      "\u001b[1mchamfers_tb\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [m] // Chamfers on top/bottom mirror sides\n",
+      "\u001b[1mnelements\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // Number of sections in the guide (length l/nelements).\n",
+      "\u001b[1mnu\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [Hz] // Rotation frequency (round/s) for Fermi Chopper approximation\n",
+      "\u001b[1mphase\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [deg] // Phase shift for the Fermi Chopper approximation\n",
+      "\u001b[1mreflect\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [str] // Reflectivity file name. Format <q(Angs-1) R(0-1)>\n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
    "source": [
     "guide.show_parameters() # Lets view the parameters available in our guide component"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -114,26 +321,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT Guide = Guide_gravity\n",
+      "  \u001b[1mw1\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mh1\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mw2\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mh2\u001b[0m = \u001b[1m\u001b[92m0.05\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1ml\u001b[0m = \u001b[1m\u001b[92m8\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mm\u001b[0m = \u001b[1m\u001b[92m3.5\u001b[0m\u001b[0m [1]\n",
+      "  \u001b[1mG\u001b[0m = \u001b[1m\u001b[92m-9.2\u001b[0m\u001b[0m [m/s2]\n",
+      "AT [0, 0, 2] RELATIVE Source\n"
+     ]
+    }
+   ],
    "source": [
     "print(guide) # Verify the information on this component is correct"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Keyword arguments can be used to specify properties like location when a component is added."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Add a sample to the instrument\n",
-    "sample = Instr.add_component(\"sample\", \"PowderN\", AT=[0, 0, 9], RELATIVE=\"Guide\") "
+    "sample = instr.add_component(\"sample\", \"PowderN\", AT=[0, 0, 9], RELATIVE=\"Guide\") "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -145,28 +375,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are all components in the monitors category.\n",
+      " Brilliance_monitor      Monitor_Sqw           Pol_monitor\n",
+      " Cyl_monitor             Monitor_nD            PreMonitor_nD\n",
+      " DivLambda_monitor       PSD_TOF_monitor       Res_monitor\n",
+      " DivPos_monitor          PSD_monitor           Sqq_w_monitor\n",
+      " Divergence_monitor      PSD_monitor_4PI       Sqw_monitor\n",
+      " EPSD_monitor            PSD_monitor_TOF       TOF2E_monitor\n",
+      " E_monitor               PSD_monitor_psf       TOF2Q_cylPSD_monitor\n",
+      " Event_monitor_simple    PSD_monitor_psf_eff   TOFLambda_monitor\n",
+      " Hdiv_monitor            PSDcyl_monitor        TOF_PSD_monitor_rad\n",
+      " L_monitor               PSDlin_diff_monitor   TOF_cylPSD_monitor\n",
+      " MeanPolLambda_monitor   PSDlin_monitor        TOF_monitor\n",
+      " Monitor                 PolLambda_monitor     TOFlog_monitor\n",
+      " Monitor_4PI             PolTOF_monitor        \n"
+     ]
+    }
+   ],
    "source": [
-    "Instr.show_components(\"monitors\") # Monitors are needed to record information"
+    "instr.available_components(\"monitors\") # Monitors are needed to record information"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Add 4PI detector to detect all neutrons\n",
-    "sphere = Instr.add_component(\"PSD_4PI\", \"PSD_monitor_4PI\", RELATIVE=\"sample\")"
+    "sphere = instr.add_component(\"PSD_4PI\", \"PSD_monitor_4PI\", RELATIVE=\"sample\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT PSD_4PI = PSD_monitor_4PI\n",
+      "  \u001b[1mnx\u001b[0m = \u001b[1m\u001b[92m300\u001b[0m\u001b[0m [1]\n",
+      "  \u001b[1mny\u001b[0m = \u001b[1m\u001b[92m300\u001b[0m\u001b[0m [1]\n",
+      "  \u001b[1mfilename\u001b[0m = \u001b[1m\u001b[92m\"PSD_4PI.dat\"\u001b[0m\u001b[0m [string]\n",
+      "  \u001b[1mradius\u001b[0m = \u001b[1m\u001b[92m1\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mrestore_neutron\u001b[0m = \u001b[1m\u001b[92m1\u001b[0m\u001b[0m [1]\n",
+      "AT [0, 0, 0] RELATIVE sample\n"
+     ]
+    }
+   ],
    "source": [
     "sphere.nx = 300\n",
     "sphere.ny = 300\n",
@@ -178,12 +443,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Add PSD monitor to see the direct beam after the sample\n",
-    "PSD = Instr.add_component(\"PSD\", \"PSD_monitor\", AT=[0,0,1], RELATIVE=\"sample\") \n",
+    "PSD = instr.add_component(\"PSD\", \"PSD_monitor\", AT=[0,0,1], RELATIVE=\"sample\") \n",
     "PSD.xwidth = 0.1\n",
     "PSD.yheight = 0.1\n",
     "PSD.nx = 200\n",
@@ -194,16 +459,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
-    "L_mon = Instr.add_component(\"L_mon\", \"L_monitor\", RELATIVE=\"PSD\")"
+    "L_mon = instr.add_component(\"L_mon\", \"L_monitor\", RELATIVE=\"PSD\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -219,29 +484,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "// Wavelength monitor for narrow range\n",
+      "COMPONENT L_mon = L_monitor\n",
+      "  \u001b[1mnL\u001b[0m = \u001b[1m\u001b[92m150\u001b[0m\u001b[0m [1]\n",
+      "  \u001b[1mfilename\u001b[0m = \u001b[1m\u001b[92m\"wave.dat\"\u001b[0m\u001b[0m [string]\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92m0.1\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mLmin\u001b[0m = \u001b[1m\u001b[92mwavelength - 0.1\u001b[0m\u001b[0m [AA]\n",
+      "  \u001b[1mLmax\u001b[0m = \u001b[1m\u001b[92mwavelength + 0.1\u001b[0m\u001b[0m [AA]\n",
+      "  \u001b[1mrestore_neutron\u001b[0m = \u001b[1m\u001b[92m1\u001b[0m\u001b[0m [1]\n",
+      "AT [0, 0, 0] RELATIVE PSD\n"
+     ]
+    }
+   ],
    "source": [
     "print(L_mon)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Source  Source_simple   AT      (0, 0, 0) ABSOLUTE       \n",
+      "Guide   Guide_gravity   AT      (0, 0, 2) RELATIVE Source\n",
+      "sample  PowderN         AT      (0, 0, 9) RELATIVE Guide \n",
+      "PSD_4PI PSD_monitor_4PI AT      (0, 0, 0) RELATIVE sample\n",
+      "PSD     PSD_monitor     AT      (0, 0, 1) RELATIVE sample\n",
+      "L_mon   L_monitor       AT      (0, 0, 0) RELATIVE PSD   \n"
+     ]
+    }
+   ],
    "source": [
-    "Instr.print_components() # Lets get an overview of the instrument so far"
+    "instr.show_components() # Lets get an overview of the instrument so far"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "double wavelength  = 3  // [AA] Wavelength emmited from source\n"
+     ]
+    }
+   ],
    "source": [
-    "Instr.show_parameters()"
+    "instr.show_parameters()"
    ]
   },
   {
@@ -254,37 +557,106 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Instrument settings:\n",
+      "  ncount:           2.00e+07\n",
+      "  mpi:              4\n",
+      "  gravity:          True\n",
+      "  output_path:      jupyter_demo\n",
+      "  run_path:         .\n",
+      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
+      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
+      "  executable:       mcrun\n",
+      "  force_compile:    True\n"
+     ]
+    }
+   ],
    "source": [
     "# output_path specifies the foldername, if it already exists an index is added\n",
-    "Instr.settings(output_path=\"jupyter_demo\", mpi=4, ncount=2E7)\n",
-    "Instr.show_settings() # Check settings are correct"
+    "instr.settings(output_path=\"jupyter_demo\", mpi=4, ncount=2E7, gravity=True)\n",
+    "instr.show_settings() # Check settings are correct"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "double wavelength  = 1.5  // [AA] Wavelength emmited from source\n"
+     ]
+    }
+   ],
    "source": [
     "# Input parameters are set with set_parameters\n",
-    "Instr.set_parameters(wavelength=1.5)\n",
-    "Instr.show_parameters()"
+    "instr.set_parameters(wavelength=1.5)\n",
+    "instr.show_parameters()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/jupyter_demo\"\n",
+      "INFO: Regenerating c-file: jupyter_demo.c\n",
+      "CFLAGS=\n",
+      "INFO: Recompiling: ./jupyter_demo.out\n",
+      "mccode-r.c:1880:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "} /* mcsiminfo_init */\n",
+      "^\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "2 warnings generated.\n",
+      "INFO: ===\n",
+      "Warning: 510482 events were removed in Component[5] PSD=PSD_monitor()\n",
+      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
+      "Warning: 509825 events were removed in Component[5] PSD=PSD_monitor()\n",
+      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
+      "Warning: 510622 events were removed in Component[5] PSD=PSD_monitor()\n",
+      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
+      "Warning: 511299 events were removed in Component[5] PSD=PSD_monitor()\n",
+      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
+      "INFO: Placing instr file copy jupyter_demo.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/jupyter_demo\n",
+      "\n",
+      "Simulation 'jupyter_demo' (jupyter_demo.instr): running on 4 nodes (master is 'CI0021617', MPI version 3.1).\n",
+      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Cu.laz' (Table_Read_Offset)\n",
+      "Table from file 'Cu.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
+      "  '# TITLE *-Cu-[FM3-M] Otte, H.M.[1961];# CELL 3.615050 3.615050 3.615050 90. ...'\n",
+      "PowderN: sample: Reading 19 rows from Cu.laz\n",
+      "PowderN: sample: Read 19 reflections from file 'Cu.laz'\n",
+      "PowderN: sample: Vc=47.24 [Angs] sigma_abs=15.12 [barn] sigma_inc=2.2 [barn] reflections=Cu.laz\n",
+      "PowderN: sample: WARNING: Did not hit sample from inside (inc). ABSORB.\n",
+      "PowderN: sample: WARNING: Did not hit sample from inside (coh). ABSORB.\n",
+      "PowderN: sample: WARNING: Did not hit sample from inside (coh). ABSORB.\n",
+      "Detector: PSD_4PI_I=42446.8 PSD_4PI_ERR=25.7775 PSD_4PI_N=8.59756e+06 \"PSD_4PI.dat\"\n",
+      "Detector: PSD_I=35457.5 PSD_ERR=24.9492 PSD_N=4.49112e+06 \"PSD.dat\"\n",
+      "Detector: L_mon_I=35457.5 L_mon_ERR=24.9492 L_mon_N=4.49112e+06 \"wave.dat\"\n",
+      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
+      "    SPLIT 8 COMPONENT sample=PowderN(...)\n",
+      "  in the instrument description jupyter_demo.instr.\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# The simulation is performed by calling backengine()\n",
-    "Instr.backengine()\n",
-    "\n",
-    "# The data is retrieved from the data attribute\n",
-    "data = Instr.data"
+    "data = instr.backengine()"
    ]
   },
   {
@@ -298,11 +670,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.494, 475.1862504]\n",
+      "[1.495333333, 474.9090911]\n",
+      "[1.496666667, 473.1532749]\n",
+      "[1.498, 472.6454829]\n",
+      "[1.499333333, 475.884394]\n"
+     ]
+    }
+   ],
    "source": [
-    "wavelength_data = functions.name_search(\"L_mon\", data)\n",
+    "wavelength_data = ms.name_search(\"L_mon\", data)\n",
     "wavelength_intensity = wavelength_data.Intensity\n",
     "wavelength_xaxis = wavelength_data.xaxis\n",
     "\n",
diff --git a/examples/calibration_sample.ipynb b/examples/calibration_sample.ipynb
index 9106b4a6..0e1af75a 100644
--- a/examples/calibration_sample.ipynb
+++ b/examples/calibration_sample.ipynb
@@ -19,9 +19,10 @@
     "\n",
     "# Import McstasScript\n",
     "import mcstasscript as ms\n",
+    "import mcstasscript.jb_interface as ms_widget\n",
     "\n",
     "# Create a McStas instrument\n",
-    "Instr = ms.McStas_instr(\"calibration_sample\", author = \"Mads Bertelsen\", origin = \"ESS DMSC\")"
+    "instr = ms.McStas_instr(\"calibration_sample\", author = \"Mads Bertelsen\", origin = \"ESS DMSC\")"
    ]
   },
   {
@@ -32,13 +33,13 @@
     "Here we will be adding a few powder material definitions, and it is thus easier to create a small python function for the task. Normally the syntax would be like this for each material:\n",
     "\n",
     "`\n",
-    "Al_inc = Instr.add_component(\"Al_inc\",\"Incoherent_process\")\n",
-    "Al_pow = Instr.add_component(\"Al_pow\",\"Powder_process\")\n",
+    "Al_inc = instr.add_component(\"Al_inc\",\"Incoherent_process\")\n",
+    "Al_pow = instr.add_component(\"Al_pow\",\"Powder_process\")\n",
     "Al_inc.sigma = 4*0.0082 # Incoherent cross section in Barns\n",
     "Al_inc.unit_cell_volume = 66.4 # Unit cell volume in AA^3\n",
-    "Al_pow.reflections = \"\\\"Al.laz\\\"\" # Data file with powder lines\n",
+    "Al_pow.reflections = '\"Al.laz\"' # Data file with powder lines\n",
     "Al.my_absorption = \"100*4*0.231/66.4\" # Inverse penetration depth in 1/m\n",
-    "Al.process_string = \"\\\"Al_inc,Al_pow\\\"\" # Make a material with aluminium incoherent and aluminium powder` "
+    "Al.process_string = '\"Al_inc,Al_pow\"' # Make a material with aluminium incoherent and aluminium powder` "
    ]
   },
   {
@@ -47,26 +48,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def add_union_powder(name, data_name, sigma_inc, sigma_abs, unit_V, Instr):\n",
+    "def add_union_powder(name, data_name, sigma_inc, sigma_abs, unit_V, instr):\n",
     "    \"\"\"\n",
     "    This function adds a Union material with incoherent scattering and powder lines\n",
     "    \"\"\"\n",
-    "    material_incoherent = Instr.add_component(name + \"_inc\",\"Incoherent_process\")\n",
+    "    material_incoherent = instr.add_component(name + \"_inc\", \"Incoherent_process\")\n",
     "    material_incoherent.sigma = sigma_inc\n",
     "    material_incoherent.unit_cell_volume = unit_V\n",
-    "    material_powder = Instr.add_component(name + \"_pow\",\"Powder_process\")\n",
-    "    material_powder.reflections = \"\\\"\" + data_name + \"\\\"\"  # Need quotes when describing a filename\n",
-    "    material = Instr.add_component(name,\"Union_make_material\")\n",
+    "    material_powder = instr.add_component(name + \"_pow\", \"Powder_process\")\n",
+    "    material_powder.reflections = '\"' + data_name + '\"'  # Need quotes when describing a filename\n",
+    "    material = instr.add_component(name, \"Union_make_material\")\n",
     "    material.my_absorption = 100*sigma_abs/unit_V\n",
-    "    material.process_string = \"\\\"\" + name + \"_inc,\" + name + \"_pow\" + \"\\\"\"\n",
+    "    material.process_string = '\"' + name + \"_inc,\" + name + \"_pow\" + '\"'\n",
     "    \n",
     "# Add a number of standard powders to our instrument (datafiles included with McStas)\n",
-    "add_union_powder(\"Al\", \"Al.laz\", 4*0.0082, 4*0.231, 66.4, Instr)\n",
-    "add_union_powder(\"Cu\", \"Cu.laz\", 4*0.55, 4*3.78, 47.24, Instr)\n",
-    "add_union_powder(\"Ni\", \"Ni.laz\", 4*5.2, 4*4.49, 43.76, Instr)\n",
-    "add_union_powder(\"Ti\", \"Ti.laz\", 2*2.87, 2*6.09, 35.33, Instr)\n",
-    "add_union_powder(\"Pb\", \"Pb.laz\", 4*0.003, 4*0.17, 121.29, Instr)\n",
-    "add_union_powder(\"Fe\", \"Fe.laz\", 2*0.4, 2*2.56, 24.04, Instr)"
+    "add_union_powder(\"Al\", \"Al.laz\", 4*0.0082, 4*0.231, 66.4, instr)\n",
+    "add_union_powder(\"Cu\", \"Cu.laz\", 4*0.55, 4*3.78, 47.24, instr)\n",
+    "add_union_powder(\"Ni\", \"Ni.laz\", 4*5.2, 4*4.49, 43.76, instr)\n",
+    "add_union_powder(\"Ti\", \"Ti.laz\", 2*2.87, 2*6.09, 35.33, instr)\n",
+    "add_union_powder(\"Pb\", \"Pb.laz\", 4*0.003, 4*0.17, 121.29, instr)\n",
+    "add_union_powder(\"Fe\", \"Fe.laz\", 2*0.4, 2*2.56, 24.04, instr)"
    ]
   },
   {
@@ -84,18 +85,12 @@
    "outputs": [],
    "source": [
     "# Since we want to change the energy and energy range at run time, we add these as instrument parameters\n",
-    "energy = Instr.add_parameter(\"energy\", value=10, comment=\"[meV] Energy of source\")\n",
-    "delta_energy = Instr.add_parameter(\"delta_energy\", value=8, comment=\"[meV] Energy spread of source\")\n",
+    "energy = instr.add_parameter(\"energy\", value=10, comment=\"[meV] Energy of source\")\n",
+    "delta_energy = instr.add_parameter(\"delta_energy\", value=8, comment=\"[meV] Energy spread of source\")\n",
     "\n",
     "# Add a source to the McStas instrument\n",
-    "src = Instr.add_component(\"source\", \"Source_div\")\n",
-    "src.xwidth = 0.11\n",
-    "src.yheight = 0.11\n",
-    "src.focus_aw = 0.1\n",
-    "src.focus_ah = 0.1\n",
-    "src.E0 = energy\n",
-    "src.dE = delta_energy\n",
-    "src.flux = 1E13;"
+    "src = instr.add_component(\"source\", \"Source_div\")\n",
+    "src.set_parameters(xwidth=0.11, yheight=0.11, focus_aw=0.1, focus_ah=0.1, flux=1E13, E0=energy, dE=delta_energy)"
    ]
   },
   {
@@ -113,8 +108,8 @@
    "source": [
     "# Now we want to set a position and rotation of our sample\n",
     "# The rotation should be adjustable, so we add instrument parameters for controling the rotation\n",
-    "rot_y = Instr.add_parameter(\"rotation_y\", value=180, comment=\"[deg] Rotation around vertical\")\n",
-    "rot_x = Instr.add_parameter(\"rotation_x\", value=0, comment=\"[deg] Rotation around horizontal\")"
+    "rot_y = instr.add_parameter(\"rotation_y\", value=180, comment=\"[deg] Rotation around vertical\")\n",
+    "rot_x = instr.add_parameter(\"rotation_x\", value=0, comment=\"[deg] Rotation around horizontal\")"
    ]
   },
   {
@@ -124,9 +119,9 @@
    "outputs": [],
    "source": [
     "# We add arms at the sample position, and a second arm with the correct rotation\n",
-    "sample_pos = Instr.add_component(\"sample_pos\", \"Arm\", AT=[0,0,1], AT_RELATIVE=\"source\")\n",
-    "sample_arm = Instr.add_component(\"sample_arm\", \"Arm\", AT=[0,0,0], AT_RELATIVE=\"sample_pos\")\n",
-    "sample_arm.set_ROTATED([rot_x, rot_y, 0], RELATIVE=\"sample_pos\")"
+    "sample_pos = instr.add_component(\"sample_pos\", \"Arm\", AT=[0, 0, 1], AT_RELATIVE=src)\n",
+    "sample_arm = instr.add_component(\"sample_arm\", \"Arm\", AT=[0, 0, 0], AT_RELATIVE=sample_pos)\n",
+    "sample_arm.set_ROTATED([rot_x, rot_y, 0], RELATIVE=sample_pos)"
    ]
   },
   {
@@ -143,7 +138,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "Instr.component_help(\"Union_cylinder\")"
+    "instr.component_help(\"Union_cylinder\")"
    ]
   },
   {
@@ -153,20 +148,8 @@
    "outputs": [],
    "source": [
     "# We add the large aluminium base that contains samples of the remaining materials\n",
-    "base_cyl = Instr.add_component(\"base_cyl\",\"Union_cylinder\", RELATIVE=\"sample_arm\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We set the parameters and confirm this looks appropriate with the print function\n",
-    "base_cyl.radius = 0.04\n",
-    "base_cyl.yheight = 0.02\n",
-    "base_cyl.priority = 100\n",
-    "base_cyl.material_string = \"\\\"Al\\\"\" # Select our Al material defined earlier\n",
+    "base_cyl = instr.add_component(\"base_cyl\",\"Union_cylinder\", RELATIVE=sample_arm)\n",
+    "base_cyl.set_parameters(radius=0.04, yheight=0.02, priority=100, material_string='\"Al\"')\n",
     "print(base_cyl)"
    ]
   },
@@ -184,7 +167,7 @@
    "outputs": [],
    "source": [
     "# We define a string containing the names of the materials we wish to add\n",
-    "Instr.add_parameter(\"string\", \"material\", value='\"Pb\"',\n",
+    "instr.add_parameter(\"string\", \"material\", value='\"Pb\"',\n",
     "                    comment=\"Material choice for extra material sample\",\n",
     "                    options=['\"Cu\"', '\"Ni\"', '\"Ti\"', '\"Pb\"', '\"Fe\"', '\"Al\"'])\n",
     "\n",
@@ -207,19 +190,17 @@
     "    x_position = round(x_position, 5) # round to 4 digits for easier printing\n",
     "    z_position = distance_from_center * math.sin(counter*angle_seperation*3.14159/180)\n",
     "    z_position = round(z_position, 5) # round to 4 digits for easier printing\n",
-    "    this_sample = Instr.add_component(sample_string + \"_cyl\",\"Union_cylinder\",\n",
+    "    this_sample = instr.add_component(sample_string + \"_cyl\", \"Union_cylinder\",\n",
     "                                      AT=[x_position, 0, z_position], RELATIVE=\"base_cyl\")\n",
     "    this_sample.radius = sample_radius;\n",
     "    this_sample.yheight = 0.019; # yheight must be different from base_cyl\n",
     "    this_sample.priority = 150 + counter; # ensure higher priority than base\n",
     "    if sample_string != \"material\":\n",
-    "        this_sample.material_string = \"\\\"\" + sample_string + \"\\\"\"\n",
+    "        this_sample.material_string = '\"' + sample_string + '\"'\n",
     "    else:\n",
     "        # The user selectable material is a parameter, where the remaining are treated as strings.\n",
     "        this_sample.material_string = sample_string\n",
-    "    counter = counter + 1\n",
-    "    \n",
-    "    "
+    "    counter = counter + 1"
    ]
   },
   {
@@ -237,26 +218,20 @@
    "outputs": [],
    "source": [
     "# A few Union loggers are set up for display of the scattering locations\n",
-    "space_2D_zx = Instr.add_component(\"logger_space_zx_all\", \"Union_logger_2D_space\", AT_RELATIVE=sample_pos)\n",
-    "space_2D_zx.filename = \"\\\"space_zx.dat\\\"\"\n",
-    "space_2D_zx.D_direction_1 = \"\\\"z\\\"\"; space_2D_zx.n1 = 1000\n",
-    "space_2D_zx.D1_min = -0.05; space_2D_zx.D1_max = 0.05\n",
-    "space_2D_zx.D_direction_2 = \"\\\"x\\\"\"; space_2D_zx.n2 = 1000\n",
-    "space_2D_zx.D2_min = -0.05; space_2D_zx.D2_max = 0.05\n",
+    "space_2D_zx = instr.add_component(\"logger_space_zx_all\", \"Union_logger_2D_space\", AT_RELATIVE=sample_pos)\n",
+    "space_2D_zx.set_parameters(filename='\"space_zx.dat\"',\n",
+    "                           D_direction_1='\"z\"', n1=1000, D1_min=-0.05, D1_max=0.05,\n",
+    "                           D_direction_2='\"x\"', n2=1000, D2_min=-0.05, D2_max=0.05)\n",
     "\n",
-    "space_2D_zy = Instr.add_component(\"logger_space_zy_all\", \"Union_logger_2D_space\", AT_RELATIVE=sample_pos)\n",
-    "space_2D_zy.filename = \"\\\"space_zy.dat\\\"\"\n",
-    "space_2D_zy.D_direction_1 = \"\\\"z\\\"\"; space_2D_zy.n1 = 1000\n",
-    "space_2D_zy.D1_min = -0.05; space_2D_zy.D1_max = 0.05\n",
-    "space_2D_zy.D_direction_2 = \"\\\"y\\\"\"; space_2D_zy.n2 = 1000\n",
-    "space_2D_zy.D2_min = -0.05; space_2D_zy.D2_max = 0.05\n",
+    "space_2D_zy = instr.add_component(\"logger_space_zy_all\", \"Union_logger_2D_space\", AT_RELATIVE=sample_pos)\n",
+    "space_2D_zy.set_parameters(filename='\"space_zy.dat\"',\n",
+    "                           D_direction_1='\"z\"', n1=1000, D1_min=-0.05, D1_max=0.05,\n",
+    "                           D_direction_2='\"y\"', n2=1000, D2_min=-0.05, D2_max=0.05)\n",
     "\n",
-    "space_2D_zy = Instr.add_component(\"logger_space_xy_all\", \"Union_logger_2D_space\", AT_RELATIVE=sample_pos)\n",
-    "space_2D_zy.filename = \"\\\"space_xy.dat\\\"\"\n",
-    "space_2D_zy.D_direction_1 = \"\\\"x\\\"\"; space_2D_zy.n1 = 1000\n",
-    "space_2D_zy.D1_min = -0.05; space_2D_zy.D1_max = 0.05\n",
-    "space_2D_zy.D_direction_2 = \"\\\"y\\\"\"; space_2D_zy.n2 = 1000\n",
-    "space_2D_zy.D2_min = -0.05; space_2D_zy.D2_max = 0.05"
+    "space_2D_zy = instr.add_component(\"logger_space_xy_all\", \"Union_logger_2D_space\", AT_RELATIVE=sample_pos)\n",
+    "space_2D_zy.set_parameters(filename='\"space_xy.dat\"',\n",
+    "                           D_direction_1='\"x\"', n1=1000, D1_min=-0.05, D1_max=0.05,\n",
+    "                           D_direction_2='\"y\"', n2=1000, D2_min=-0.05, D2_max=0.05)"
    ]
   },
   {
@@ -273,7 +248,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "master = Instr.add_component(\"calibration_sample\", \"Union_master\", AT_relative=sample_pos)"
+    "master = instr.add_component(\"calibration_sample\", \"Union_master\", AT_RELATIVE=sample_pos)"
    ]
   },
   {
@@ -291,9 +266,8 @@
    "outputs": [],
    "source": [
     "# Add position sensitive detector for transmission measurement\n",
-    "PSD = Instr.add_component(\"PSD\", \"PSD_monitor\", AT=[0,0,1], RELATIVE=sample_pos) \n",
-    "PSD.xwidth = 0.1; PSD.yheight = 0.1; PSD.nx = 200; PSD.ny = 200;\n",
-    "PSD.filename = \"\\\"PSD.dat\\\"\"; PSD.restore_neutron = 1"
+    "PSD = instr.add_component(\"PSD\", \"PSD_monitor\", AT=[0,0,1], RELATIVE=sample_pos) \n",
+    "PSD.set_parameters(filename='\"PSD.dat\"', xwidth=0.1, yheight=0.1, nx=200, ny=200, restore_neutron=1)"
    ]
   },
   {
@@ -303,9 +277,8 @@
    "outputs": [],
    "source": [
     "# Adds 1D position sensitive detector for transmission measurement\n",
-    "PSD = Instr.add_component(\"PSDlin\", \"PSDlin_monitor\", AT=[0,0,1], RELATIVE=sample_pos) \n",
-    "PSD.xwidth = 0.1; PSD.yheight = 0.1; PSD.nx = 200;\n",
-    "PSD.filename = \"\\\"PSDlin.dat\\\"\"; PSD.restore_neutron = 1"
+    "PSD = instr.add_component(\"PSDlin\", \"PSDlin_monitor\", AT=[0,0,1], RELATIVE=sample_pos) \n",
+    "PSD.set_parameters(filename='\"PSDlin.dat\"', xwidth=0.1, yheight=0.1, nx=200, restore_neutron=1)"
    ]
   },
   {
@@ -315,11 +288,9 @@
    "outputs": [],
    "source": [
     "# Add energy position monitor to see Bragg edges\n",
-    "EPSD = Instr.add_component(\"EPSD\", \"EPSD_monitor\", RELATIVE=PSD)\n",
-    "EPSD.xwidth = 0.1; EPSD.yheight = 0.02; EPSD.nE = 300; EPSD.nx = 200;\n",
-    "EPSD.filename = \"\\\"EPSD.dat\\\"\"; EPSD.restore_neutron = 1;\n",
-    "EPSD.Emin = \"energy - delta_energy\"\n",
-    "EPSD.Emax = \"energy + delta_energy\""
+    "EPSD = instr.add_component(\"EPSD\", \"EPSD_monitor\", RELATIVE=PSD)\n",
+    "EPSD.set_parameters(filename='\"EPSD.dat\"', xwidth=0.1, yheight=0.02, nE=300, nx=200, restore_neutron=1,\n",
+    "                    Emin=\"energy - delta_energy\", Emax=\"energy + delta_energy\")"
    ]
   },
   {
@@ -338,7 +309,7 @@
    },
    "outputs": [],
    "source": [
-    "Instr.print_components(line_length=117) # Show nice overview"
+    "instr.show_components(line_length=117) # Show nice overview"
    ]
   },
   {
@@ -346,7 +317,7 @@
    "metadata": {},
    "source": [
     "### Running the simulation\n",
-    "The simulation can now be performed from the Jupyter Notebook using the widget interface."
+    "The simulation can now be performed from the Jupyter Notebook using the widget interface. If one just wants to use the interface, ms_widget.show is sufficient, but we would like to load the data afterwards, and for that purpose one needs to create an interface object with SimInterface like below."
    ]
   },
   {
@@ -355,18 +326,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "Instr.interface()"
+    "#ms_widget.show(instr) # Shortcut, but one can not get sim_interface object\n",
+    "\n",
+    "sim_interface = ms_widget.SimInterface(instr)\n",
+    "sim_interface.show_interface()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [],
    "source": [
-    "Instr.show_instrument()"
+    "instr.show_instrument()"
    ]
   },
   {
@@ -383,7 +357,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = Instr.get_interface_data()"
+    "data = sim_interface.get_data()"
    ]
   },
   {
@@ -411,7 +385,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ms.interface(data)"
+    "ms_widget.show(data)"
    ]
   },
   {
diff --git a/examples/cryostat_demo.ipynb b/examples/cryostat_demo.ipynb
index 46a6c005..95663dac 100644
--- a/examples/cryostat_demo.ipynb
+++ b/examples/cryostat_demo.ipynb
@@ -13,58 +13,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "composed-custody",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The following components are found in the work_directory / input_path:\n",
-      "     Union_sphere.comp\n",
-      "     Texture_process.comp\n",
-      "     Union_cone.comp\n",
-      "     Union_box.comp\n",
-      "     Single_crystal_process.comp\n",
-      "     Union_abs_logger_2D_space.comp\n",
-      "     Union_logger_2D_kf.comp\n",
-      "     Template_process.comp\n",
-      "     PhononSimple_process.comp\n",
-      "     Union_conditional_standard.comp\n",
-      "     Union_abs_logger_1D_space.comp\n",
-      "     Union_abs_logger_event.comp\n",
-      "     NCrystal_process.comp\n",
-      "     Union_abs_logger_1D_space_event.comp\n",
-      "     Union_abs_logger_1D_space_tof.comp\n",
-      "     Union_logger_2D_space.comp\n",
-      "     Union_conditional_PSD.comp\n",
-      "     Union_master.comp\n",
-      "     AF_HB_1D_process.comp\n",
-      "     Union_logger_2D_kf_time.comp\n",
-      "     Union_cylinder.comp\n",
-      "     Union_abs_logger_1D_space_tof_to_lambda.comp\n",
-      "     Powder_process.comp\n",
-      "     Union_make_material.comp\n",
-      "     Incoherent_process.comp\n",
-      "     Union_logger_1D.comp\n",
-      "     Union_logger_3D_space.comp\n",
-      "     IncoherentPhonon_process.comp\n",
-      "     Union_logger_2DQ.comp\n",
-      "     Union_mesh.comp\n",
-      "     Union_logger_2D_space_time.comp\n",
-      "These definitions will be used instead of the installed versions.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import mcstasscript as ms\n",
+    "import mcstasscript.jb_interface as ms_widget\n",
+    "\n",
     "instrument = ms.McStas_instr(\"cryostat_demo\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "secret-convert",
    "metadata": {},
    "outputs": [],
@@ -90,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "silver-narrative",
    "metadata": {},
    "outputs": [],
@@ -115,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "typical-harvest",
    "metadata": {},
    "outputs": [],
@@ -138,7 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "advisory-index",
    "metadata": {},
    "outputs": [],
@@ -156,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "unexpected-charleston",
    "metadata": {},
    "outputs": [],
@@ -174,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "welcome-cathedral",
    "metadata": {},
    "outputs": [],
@@ -195,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "settled-intro",
    "metadata": {},
    "outputs": [],
@@ -222,7 +184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "dutch-prevention",
    "metadata": {},
    "outputs": [],
@@ -242,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "sound-alignment",
    "metadata": {},
    "outputs": [],
@@ -260,54 +222,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "noticed-third",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on method add_animation in module mcstasscript.tools.cryostat_builder:\n",
-      "\n",
-      "add_animation(t_min=0, t_max=0.1, n_frames=10, d1='z', n1=300, d2='y', n2=300) method of mcstasscript.tools.cryostat_builder.Cryostat instance\n",
-      "    Adds 2D_space_time logger that records the information necessary for animation\n",
-      "    \n",
-      "    Adds a 2D_space_time logger that records the requested number of frames, n_frames.\n",
-      "    The time span of both is from t_min to t_max. The default orientation is in the zy\n",
-      "    plane, but this can be chosen along with the desired resolution.\n",
-      "    Errors can happen with too many empty frames, so it is recommended to set\n",
-      "    t_min close to the first scattering time.\n",
-      "    Select the appropriate animation data from simulation output and use\n",
-      "    plotter.make_animation with a filename to save the animation as a gif.\n",
-      "    \n",
-      "    Parameters\n",
-      "    ----------\n",
-      "    \n",
-      "    t_min : float\n",
-      "        Lowest time recorded in [s]\n",
-      "    \n",
-      "    t_max : float\n",
-      "        Highest time recorded in [s]\n",
-      "    \n",
-      "    n_frames : int\n",
-      "        Number of frames in space 2D time logger\n",
-      "    \n",
-      "    d1 : str\n",
-      "        First direction of space 2D time logger, \"x\", \"y\" or \"z\"\n",
-      "    \n",
-      "    n1 : int\n",
-      "        Number of bins in first direction\n",
-      "    \n",
-      "    d2 : str\n",
-      "        Second direction of space 2D time logger, \"x\", \"y\" or \"z\"\n",
-      "    \n",
-      "    n2 : int\n",
-      "        Number of bins in second direction\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "help(orange_cryostat.add_animation)"
    ]
@@ -322,75 +240,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "dimensional-berkeley",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1msource\u001b[0m  \u001b[1mSource_simple\u001b[0m \n",
-      "  AT      (0, 0, 0) ABSOLUTE\n",
-      "\u001b[1morange\u001b[0m  \u001b[1mArm\u001b[0m \n",
-      "  AT      (0, 0, 2) RELATIVE source\n",
-      "\u001b[1mAl_inc\u001b[0m  \u001b[1mIncoherent_process\u001b[0m \n",
-      "  AT      (0, 0, 0) ABSOLUTE\n",
-      "\u001b[1mAl_pow\u001b[0m  \u001b[1mPowder_process\u001b[0m \n",
-      "  AT      (0, 0, 0) ABSOLUTE\n",
-      "\u001b[1mAl\u001b[0m  \u001b[1mUnion_make_material\u001b[0m \n",
-      "  AT      (0, 0, 0) ABSOLUTE\n",
-      "\u001b[1morange_layer_0_layer\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0.0555, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_0_layer_vac\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0.0585, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_0_outer_cut_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, -0.02189, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_0_outer_cut_replace_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, -0.02189, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_0_inner_cut_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0.0, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_1_layer\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0.0735, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_1_layer_vac\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0.075, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_2_layer\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0.0675, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_2_layer_vac\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0.066, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_2_outer_cut_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, -0.024000000000000004, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_2_outer_cut_replace_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, -0.024000000000000004, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_3_layer\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0.05349999999999999, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_3_layer_vac\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, 0.047999999999999994, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_3_outer_cut_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, -0.01892, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_3_outer_cut_replace_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, -0.01892, 0) RELATIVE orange\n",
-      "\u001b[1morange_layer_3_inner_cut_0\u001b[0m  \u001b[1mUnion_cylinder\u001b[0m \n",
-      "  AT      (0, -0.01892, 0) RELATIVE orange\n",
-      "\u001b[1morange_logger_space_zx\u001b[0m  \u001b[1mUnion_logger_2D_space\u001b[0m \n",
-      "  AT      (0, 0, 0) RELATIVE orange\n",
-      "\u001b[1morange_logger_space_zy\u001b[0m  \u001b[1mUnion_logger_2D_space\u001b[0m \n",
-      "  AT      (0, 0, 0) RELATIVE orange\n",
-      "\u001b[1morange_logger_space_xy\u001b[0m  \u001b[1mUnion_logger_2D_space\u001b[0m \n",
-      "  AT      (0, 0, 0) RELATIVE orange\n",
-      "\u001b[1morange_logger_space_zy_close\u001b[0m  \u001b[1mUnion_logger_3D_space\u001b[0m \n",
-      "  AT      (0, 0, 0) RELATIVE orange\n",
-      "\u001b[1morange_logger_time\u001b[0m  \u001b[1mUnion_logger_1D\u001b[0m \n",
-      "  AT      (0, 0, 0) ABSOLUTE\n",
-      "\u001b[1morange_logger_space_zy_time\u001b[0m  \u001b[1mUnion_logger_2D_space_time\u001b[0m \n",
-      "  AT      (0, 0, 0) RELATIVE orange\n",
-      "\u001b[1morange_master\u001b[0m  \u001b[1mUnion_master\u001b[0m \n",
-      "  AT      (0, 0, 0) ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "instrument.print_components()"
+    "instrument.show_components()"
    ]
   },
   {
@@ -403,32 +258,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "exceptional-profit",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"800\"\n",
-       "            height=\"450\"\n",
-       "            src=\"./cryostat_demo_mcdisplay/index.html\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7fa94163e670>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_instrument()"
    ]
@@ -444,805 +277,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "paperback-palmer",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/cryostat_demo_data\"\n",
-      "INFO: Regenerating c-file: cryostat_demo.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./cryostat_demo.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:18644:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:18644:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:18887:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:18887:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:19130:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_xy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:19130:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_xy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_logger_3D_space.comp:668:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:19379:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_close_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_3D_space.comp:668:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:19379:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_close_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_logger_1D.comp:479:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:19686:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_time_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_1D.comp:479:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:19686:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_time_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_logger_2D_space_time.comp:632:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:19883:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_time_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_2D_space_time.comp:632:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:19883:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_time_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "18 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy cryostat_demo.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/cryostat_demo_data\n",
-      "\n",
-      "    Instrument parameters for cryostat_demo (cryostat_demo.instr)\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "Allocating 3D arrays \n",
-      "Allocating 3D arrays \n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 3 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 4 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 1\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 5 \n",
-      "my_absoprtion       [0]: 1.391566 \n",
-      "number of processes [0]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 1\n",
-      "\n",
-      "name of geometry    [0]: orange_layer_0_layer \n",
-      "component index     [0]: 6 \n",
-      "Volume.name         [0]: orange_layer_0_layer \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 1.391566 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.055500 \n",
-      "Volume.geometry.center.z             [0]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.037500 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.287000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: orange_layer_0_layer_vac \n",
-      "component index     [1]: 7 \n",
-      "Volume.name         [1]: orange_layer_0_layer_vac \n",
-      "Volume.p_physics.is_vacuum           [1]: 1 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.000000 \n",
-      "Volume.p_physics.number of processes [1]: 0 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.058500 \n",
-      "Volume.geometry.center.z             [1]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.035000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.283000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: orange_layer_0_outer_cut_0 \n",
-      "component index     [2]: 8 \n",
-      "Volume.name         [2]: orange_layer_0_outer_cut_0 \n",
-      "Volume.p_physics.is_vacuum           [2]: 1 \n",
-      "Volume.p_physics.my_absorption       [2]: 0.000000 \n",
-      "Volume.p_physics.number of processes [2]: 0 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: -0.021890 \n",
-      "Volume.geometry.center.z             [2]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.037501 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.132621 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: orange_layer_0_outer_cut_replace_0 \n",
-      "component index     [3]: 9 \n",
-      "Volume.name         [3]: orange_layer_0_outer_cut_replace_0 \n",
-      "Volume.p_physics.is_vacuum           [3]: 0 \n",
-      "Volume.p_physics.my_absorption       [3]: 1.391566 \n",
-      "Volume.p_physics.number of processes [3]: 2 \n",
-      "Volume.geometry.shape                [3]: cylinder \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: -0.021890 \n",
-      "Volume.geometry.center.z             [3]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.036500 \n",
-      "Volume.geometry.geometry_parameters.height [3]: 0.132625 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [4]: orange_layer_0_inner_cut_0 \n",
-      "component index     [4]: 10 \n",
-      "Volume.name         [4]: orange_layer_0_inner_cut_0 \n",
-      "Volume.p_physics.is_vacuum           [4]: 1 \n",
-      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
-      "Volume.p_physics.number of processes [4]: 0 \n",
-      "Volume.geometry.shape                [4]: cylinder \n",
-      "Volume.geometry.center.x             [4]: 0.000000 \n",
-      "Volume.geometry.center.y             [4]: 0.000000 \n",
-      "Volume.geometry.center.z             [4]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.035500 \n",
-      "Volume.geometry.geometry_parameters.height [4]: 0.040000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [5]: orange_layer_1_layer \n",
-      "component index     [5]: 11 \n",
-      "Volume.name         [5]: orange_layer_1_layer \n",
-      "Volume.p_physics.is_vacuum           [5]: 0 \n",
-      "Volume.p_physics.my_absorption       [5]: 1.391566 \n",
-      "Volume.p_physics.number of processes [5]: 2 \n",
-      "Volume.geometry.shape                [5]: cylinder \n",
-      "Volume.geometry.center.x             [5]: 0.000000 \n",
-      "Volume.geometry.center.y             [5]: 0.073500 \n",
-      "Volume.geometry.center.z             [5]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.040500 \n",
-      "Volume.geometry.geometry_parameters.height [5]: 0.331000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [6]: orange_layer_1_layer_vac \n",
-      "component index     [6]: 12 \n",
-      "Volume.name         [6]: orange_layer_1_layer_vac \n",
-      "Volume.p_physics.is_vacuum           [6]: 1 \n",
-      "Volume.p_physics.my_absorption       [6]: 0.000000 \n",
-      "Volume.p_physics.number of processes [6]: 0 \n",
-      "Volume.geometry.shape                [6]: cylinder \n",
-      "Volume.geometry.center.x             [6]: 0.000000 \n",
-      "Volume.geometry.center.y             [6]: 0.075000 \n",
-      "Volume.geometry.center.z             [6]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.040000 \n",
-      "Volume.geometry.geometry_parameters.height [6]: 0.330000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [7]: orange_layer_2_layer \n",
-      "component index     [7]: 13 \n",
-      "Volume.name         [7]: orange_layer_2_layer \n",
-      "Volume.p_physics.is_vacuum           [7]: 0 \n",
-      "Volume.p_physics.my_absorption       [7]: 1.391566 \n",
-      "Volume.p_physics.number of processes [7]: 2 \n",
-      "Volume.geometry.shape                [7]: cylinder \n",
-      "Volume.geometry.center.x             [7]: 0.000000 \n",
-      "Volume.geometry.center.y             [7]: 0.067500 \n",
-      "Volume.geometry.center.z             [7]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [7]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [7]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [7]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [7]: 0.049750 \n",
-      "Volume.geometry.geometry_parameters.height [7]: 0.333000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [7]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [8]: orange_layer_2_layer_vac \n",
-      "component index     [8]: 14 \n",
-      "Volume.name         [8]: orange_layer_2_layer_vac \n",
-      "Volume.p_physics.is_vacuum           [8]: 1 \n",
-      "Volume.p_physics.my_absorption       [8]: 0.000000 \n",
-      "Volume.p_physics.number of processes [8]: 0 \n",
-      "Volume.geometry.shape                [8]: cylinder \n",
-      "Volume.geometry.center.x             [8]: 0.000000 \n",
-      "Volume.geometry.center.y             [8]: 0.066000 \n",
-      "Volume.geometry.center.z             [8]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [8]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [8]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [8]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [8]: 0.047500 \n",
-      "Volume.geometry.geometry_parameters.height [8]: 0.318000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [8]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [9]: orange_layer_2_outer_cut_0 \n",
-      "component index     [9]: 15 \n",
-      "Volume.name         [9]: orange_layer_2_outer_cut_0 \n",
-      "Volume.p_physics.is_vacuum           [9]: 1 \n",
-      "Volume.p_physics.my_absorption       [9]: 0.000000 \n",
-      "Volume.p_physics.number of processes [9]: 0 \n",
-      "Volume.geometry.shape                [9]: cylinder \n",
-      "Volume.geometry.center.x             [9]: 0.000000 \n",
-      "Volume.geometry.center.y             [9]: -0.024000 \n",
-      "Volume.geometry.center.z             [9]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [9]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [9]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [9]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [9]: 0.049751 \n",
-      "Volume.geometry.geometry_parameters.height [9]: 0.152001 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [9]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [10]: orange_layer_2_outer_cut_replace_0 \n",
-      "component index     [10]: 16 \n",
-      "Volume.name         [10]: orange_layer_2_outer_cut_replace_0 \n",
-      "Volume.p_physics.is_vacuum           [10]: 0 \n",
-      "Volume.p_physics.my_absorption       [10]: 1.391566 \n",
-      "Volume.p_physics.number of processes [10]: 2 \n",
-      "Volume.geometry.shape                [10]: cylinder \n",
-      "Volume.geometry.center.x             [10]: 0.000000 \n",
-      "Volume.geometry.center.y             [10]: -0.024000 \n",
-      "Volume.geometry.center.z             [10]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [10]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [10]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [10]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [10]: 0.048500 \n",
-      "Volume.geometry.geometry_parameters.height [10]: 0.152005 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [10]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [11]: orange_layer_3_layer \n",
-      "component index     [11]: 17 \n",
-      "Volume.name         [11]: orange_layer_3_layer \n",
-      "Volume.p_physics.is_vacuum           [11]: 0 \n",
-      "Volume.p_physics.my_absorption       [11]: 1.391566 \n",
-      "Volume.p_physics.number of processes [11]: 2 \n",
-      "Volume.geometry.shape                [11]: cylinder \n",
-      "Volume.geometry.center.x             [11]: 0.000000 \n",
-      "Volume.geometry.center.y             [11]: 0.053500 \n",
-      "Volume.geometry.center.z             [11]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [11]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [11]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [11]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [11]: 0.063500 \n",
-      "Volume.geometry.geometry_parameters.height [11]: 0.347000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [11]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [12]: orange_layer_3_layer_vac \n",
-      "component index     [12]: 18 \n",
-      "Volume.name         [12]: orange_layer_3_layer_vac \n",
-      "Volume.p_physics.is_vacuum           [12]: 1 \n",
-      "Volume.p_physics.my_absorption       [12]: 0.000000 \n",
-      "Volume.p_physics.number of processes [12]: 0 \n",
-      "Volume.geometry.shape                [12]: cylinder \n",
-      "Volume.geometry.center.x             [12]: 0.000000 \n",
-      "Volume.geometry.center.y             [12]: 0.048000 \n",
-      "Volume.geometry.center.z             [12]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [12]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [12]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [12]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [12]: 0.060000 \n",
-      "Volume.geometry.geometry_parameters.height [12]: 0.314000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [12]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [13]: orange_layer_3_outer_cut_0 \n",
-      "component index     [13]: 19 \n",
-      "Volume.name         [13]: orange_layer_3_outer_cut_0 \n",
-      "Volume.p_physics.is_vacuum           [13]: 1 \n",
-      "Volume.p_physics.my_absorption       [13]: 0.000000 \n",
-      "Volume.p_physics.number of processes [13]: 0 \n",
-      "Volume.geometry.shape                [13]: cylinder \n",
-      "Volume.geometry.center.x             [13]: 0.000000 \n",
-      "Volume.geometry.center.y             [13]: -0.018920 \n",
-      "Volume.geometry.center.z             [13]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [13]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [13]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [13]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [13]: 0.063501 \n",
-      "Volume.geometry.geometry_parameters.height [13]: 0.149241 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [13]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [14]: orange_layer_3_outer_cut_replace_0 \n",
-      "component index     [14]: 20 \n",
-      "Volume.name         [14]: orange_layer_3_outer_cut_replace_0 \n",
-      "Volume.p_physics.is_vacuum           [14]: 0 \n",
-      "Volume.p_physics.my_absorption       [14]: 1.391566 \n",
-      "Volume.p_physics.number of processes [14]: 2 \n",
-      "Volume.geometry.shape                [14]: cylinder \n",
-      "Volume.geometry.center.x             [14]: 0.000000 \n",
-      "Volume.geometry.center.y             [14]: -0.018920 \n",
-      "Volume.geometry.center.z             [14]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [14]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [14]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [14]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [14]: 0.062500 \n",
-      "Volume.geometry.geometry_parameters.height [14]: 0.149245 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [14]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [15]: orange_layer_3_inner_cut_0 \n",
-      "component index     [15]: 21 \n",
-      "Volume.name         [15]: orange_layer_3_inner_cut_0 \n",
-      "Volume.p_physics.is_vacuum           [15]: 1 \n",
-      "Volume.p_physics.my_absorption       [15]: 0.000000 \n",
-      "Volume.p_physics.number of processes [15]: 0 \n",
-      "Volume.geometry.shape                [15]: cylinder \n",
-      "Volume.geometry.center.x             [15]: 0.000000 \n",
-      "Volume.geometry.center.y             [15]: -0.018920 \n",
-      "Volume.geometry.center.z             [15]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [15]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [15]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [15]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [15]: 0.061000 \n",
-      "Volume.geometry.geometry_parameters.height [15]: 0.149240 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [15]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 17\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]\n",
-      "LIST: Direct_children for Volume           0 = [6,7,8,12,14]\n",
-      "LIST: Intersect_check_list for Volume      0 = [6,7,8,12,14]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [6,7,8,12,14]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for orange_layer_0_layer with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  1 = [5]\n",
-      "LIST: Direct_children for Volume           1 = [5]\n",
-      "LIST: Intersect_check_list for Volume      1 = [2,3,4]\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [7,10,11,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = [7,10,11,14,15]\n",
-      "LIST: Next_volume_list for Volume          1 = [7,10,11,14,15,16,2,3,4]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for orange_layer_0_layer_vac with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  2 = []\n",
-      "LIST: Direct_children for Volume           2 = []\n",
-      "LIST: Intersect_check_list for Volume      2 = []\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [1,3,4,5,7,10,11,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [7,10,11,14,15]\n",
-      "LIST: Next_volume_list for Volume          2 = [1,3,4,5,7,10,11,14,15,16]\n",
-      "      Is_vacuum for Volume                 2 = 1\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for orange_layer_0_outer_cut_0 with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  3 = [5]\n",
-      "LIST: Direct_children for Volume           3 = [5]\n",
-      "LIST: Intersect_check_list for Volume      3 = [2,4]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [1,7]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = [7]\n",
-      "LIST: Next_volume_list for Volume          3 = [1,7,2,4]\n",
-      "      Is_vacuum for Volume                 3 = 1\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "List overview for orange_layer_0_outer_cut_replace_0 with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  4 = [5]\n",
-      "LIST: Direct_children for Volume           4 = [5]\n",
-      "LIST: Intersect_check_list for Volume      4 = [2,5]\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [1,3,7]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = [7]\n",
-      "LIST: Next_volume_list for Volume          4 = [1,3,7,2,5]\n",
-      "      Is_vacuum for Volume                 4 = 0\n",
-      "      is_mask_volume for Volume            4 = 0\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = []\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 0\n",
-      "\n",
-      "List overview for orange_layer_0_inner_cut_0 with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  5 = []\n",
-      "LIST: Direct_children for Volume           5 = []\n",
-      "LIST: Intersect_check_list for Volume      5 = [2]\n",
-      "LIST: Mask_intersect_list for Volume       5 = []\n",
-      "LIST: Destinations_list for Volume         5 = [4]\n",
-      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
-      "LIST: Next_volume_list for Volume          5 = [4,2]\n",
-      "      Is_vacuum for Volume                 5 = 1\n",
-      "      is_mask_volume for Volume            5 = 0\n",
-      "      is_masked_volume for Volume          5 = 0\n",
-      "      is_exit_volume for Volume            5 = 0\n",
-      "LIST: mask_list for Volume                 5 = []\n",
-      "LIST: masked_by_list for Volume            5 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
-      "      mask_mode for Volume                 5 = 0\n",
-      "\n",
-      "List overview for orange_layer_1_layer with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  6 = [1,2,3,4,5]\n",
-      "LIST: Direct_children for Volume           6 = [1,2,3,4]\n",
-      "LIST: Intersect_check_list for Volume      6 = [7]\n",
-      "LIST: Mask_intersect_list for Volume       6 = []\n",
-      "LIST: Destinations_list for Volume         6 = [0,8,9,10,11,12,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 6 = [8,12,14]\n",
-      "LIST: Next_volume_list for Volume          6 = [0,8,9,10,11,12,13,14,15,16,7]\n",
-      "      Is_vacuum for Volume                 6 = 0\n",
-      "      is_mask_volume for Volume            6 = 0\n",
-      "      is_masked_volume for Volume          6 = 0\n",
-      "      is_exit_volume for Volume            6 = 0\n",
-      "LIST: mask_list for Volume                 6 = []\n",
-      "LIST: masked_by_list for Volume            6 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
-      "      mask_mode for Volume                 6 = 0\n",
-      "\n",
-      "List overview for orange_layer_1_layer_vac with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  7 = [1,2,3,4,5]\n",
-      "LIST: Direct_children for Volume           7 = [1,2,3,4]\n",
-      "LIST: Intersect_check_list for Volume      7 = [1,2,3,4]\n",
-      "LIST: Mask_intersect_list for Volume       7 = []\n",
-      "LIST: Destinations_list for Volume         7 = [0,6,8,9,10,11,12,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 7 = [6,8,12,14]\n",
-      "LIST: Next_volume_list for Volume          7 = [0,6,8,9,10,11,12,13,14,15,16,1,2,3,4]\n",
-      "      Is_vacuum for Volume                 7 = 1\n",
-      "      is_mask_volume for Volume            7 = 0\n",
-      "      is_masked_volume for Volume          7 = 0\n",
-      "      is_exit_volume for Volume            7 = 0\n",
-      "LIST: mask_list for Volume                 7 = []\n",
-      "LIST: masked_by_list for Volume            7 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
-      "      mask_mode for Volume                 7 = 0\n",
-      "\n",
-      "List overview for orange_layer_2_layer with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  8 = [1,2,3,4,5,9]\n",
-      "LIST: Direct_children for Volume           8 = [9]\n",
-      "LIST: Intersect_check_list for Volume      8 = [6,7,9,10,11]\n",
-      "LIST: Mask_intersect_list for Volume       8 = []\n",
-      "LIST: Destinations_list for Volume         8 = [0,12,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 8 = [12,14]\n",
-      "LIST: Next_volume_list for Volume          8 = [0,12,13,14,15,16,6,7,9,10,11]\n",
-      "      Is_vacuum for Volume                 8 = 0\n",
-      "      is_mask_volume for Volume            8 = 0\n",
-      "      is_masked_volume for Volume          8 = 0\n",
-      "      is_exit_volume for Volume            8 = 0\n",
-      "LIST: mask_list for Volume                 8 = []\n",
-      "LIST: masked_by_list for Volume            8 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 8 = []\n",
-      "      mask_mode for Volume                 8 = 0\n",
-      "\n",
-      "List overview for orange_layer_2_layer_vac with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  9 = [1,2,3,4,5]\n",
-      "LIST: Direct_children for Volume           9 = [1,2,3,4]\n",
-      "LIST: Intersect_check_list for Volume      9 = [6,7]\n",
-      "LIST: Mask_intersect_list for Volume       9 = []\n",
-      "LIST: Destinations_list for Volume         9 = [8,10,11,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 9 = [8,13,14,15]\n",
-      "LIST: Next_volume_list for Volume          9 = [8,10,11,13,14,15,16,6,7]\n",
-      "      Is_vacuum for Volume                 9 = 1\n",
-      "      is_mask_volume for Volume            9 = 0\n",
-      "      is_masked_volume for Volume          9 = 0\n",
-      "      is_exit_volume for Volume            9 = 0\n",
-      "LIST: mask_list for Volume                 9 = []\n",
-      "LIST: masked_by_list for Volume            9 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 9 = []\n",
-      "      mask_mode for Volume                 9 = 0\n",
-      "\n",
-      "List overview for orange_layer_2_outer_cut_0 with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  10 = [3,4,5]\n",
-      "LIST: Direct_children for Volume           10 = [3,4]\n",
-      "LIST: Intersect_check_list for Volume      10 = [6,7,9,11]\n",
-      "LIST: Mask_intersect_list for Volume       10 = []\n",
-      "LIST: Destinations_list for Volume         10 = [8,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 10 = [8,13,14,15]\n",
-      "LIST: Next_volume_list for Volume          10 = [8,13,14,15,16,6,7,9,11]\n",
-      "      Is_vacuum for Volume                 10 = 1\n",
-      "      is_mask_volume for Volume            10 = 0\n",
-      "      is_masked_volume for Volume          10 = 0\n",
-      "      is_exit_volume for Volume            10 = 0\n",
-      "LIST: mask_list for Volume                 10 = []\n",
-      "LIST: masked_by_list for Volume            10 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 10 = []\n",
-      "      mask_mode for Volume                 10 = 0\n",
-      "\n",
-      "List overview for orange_layer_2_outer_cut_replace_0 with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  11 = [3,4,5]\n",
-      "LIST: Direct_children for Volume           11 = [3,4]\n",
-      "LIST: Intersect_check_list for Volume      11 = [6,7,9]\n",
-      "LIST: Mask_intersect_list for Volume       11 = []\n",
-      "LIST: Destinations_list for Volume         11 = [8,10,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 11 = [8,13,14,15]\n",
-      "LIST: Next_volume_list for Volume          11 = [8,10,13,14,15,16,6,7,9]\n",
-      "      Is_vacuum for Volume                 11 = 0\n",
-      "      is_mask_volume for Volume            11 = 0\n",
-      "      is_masked_volume for Volume          11 = 0\n",
-      "      is_exit_volume for Volume            11 = 0\n",
-      "LIST: mask_list for Volume                 11 = []\n",
-      "LIST: masked_by_list for Volume            11 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 11 = []\n",
-      "      mask_mode for Volume                 11 = 0\n",
-      "\n",
-      "List overview for orange_layer_3_layer with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  12 = [1,2,3,4,5,9,10,11,13,15,16]\n",
-      "LIST: Direct_children for Volume           12 = [9,13,15]\n",
-      "LIST: Intersect_check_list for Volume      12 = [6,7,8,13,14,15]\n",
-      "LIST: Mask_intersect_list for Volume       12 = []\n",
-      "LIST: Destinations_list for Volume         12 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 12 = []\n",
-      "LIST: Next_volume_list for Volume          12 = [0,6,7,8,13,14,15]\n",
-      "      Is_vacuum for Volume                 12 = 0\n",
-      "      is_mask_volume for Volume            12 = 0\n",
-      "      is_masked_volume for Volume          12 = 0\n",
-      "      is_exit_volume for Volume            12 = 0\n",
-      "LIST: mask_list for Volume                 12 = []\n",
-      "LIST: masked_by_list for Volume            12 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 12 = []\n",
-      "      mask_mode for Volume                 12 = 0\n",
-      "\n",
-      "List overview for orange_layer_3_layer_vac with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  13 = [1,2,3,4,5,10,11]\n",
-      "LIST: Direct_children for Volume           13 = [1,2,10,11]\n",
-      "LIST: Intersect_check_list for Volume      13 = [6,7,8,10,11]\n",
-      "LIST: Mask_intersect_list for Volume       13 = []\n",
-      "LIST: Destinations_list for Volume         13 = [12,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 13 = [12,14]\n",
-      "LIST: Next_volume_list for Volume          13 = [12,14,15,16,6,7,8,10,11]\n",
-      "      Is_vacuum for Volume                 13 = 1\n",
-      "      is_mask_volume for Volume            13 = 0\n",
-      "      is_masked_volume for Volume          13 = 0\n",
-      "      is_exit_volume for Volume            13 = 0\n",
-      "LIST: mask_list for Volume                 13 = []\n",
-      "LIST: masked_by_list for Volume            13 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 13 = []\n",
-      "      mask_mode for Volume                 13 = 0\n",
-      "\n",
-      "List overview for orange_layer_3_outer_cut_0 with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  14 = [3,4,5,16]\n",
-      "LIST: Direct_children for Volume           14 = [16]\n",
-      "LIST: Intersect_check_list for Volume      14 = [6,7,8,13,15]\n",
-      "LIST: Mask_intersect_list for Volume       14 = []\n",
-      "LIST: Destinations_list for Volume         14 = [0,12]\n",
-      "LIST: Reduced_destinations_list for Volume 14 = [12]\n",
-      "LIST: Next_volume_list for Volume          14 = [0,12,6,7,8,13,15]\n",
-      "      Is_vacuum for Volume                 14 = 1\n",
-      "      is_mask_volume for Volume            14 = 0\n",
-      "      is_masked_volume for Volume          14 = 0\n",
-      "      is_exit_volume for Volume            14 = 0\n",
-      "LIST: mask_list for Volume                 14 = []\n",
-      "LIST: masked_by_list for Volume            14 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 14 = []\n",
-      "      mask_mode for Volume                 14 = 0\n",
-      "\n",
-      "List overview for orange_layer_3_outer_cut_replace_0 with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  15 = [3,4,5,16]\n",
-      "LIST: Direct_children for Volume           15 = [16]\n",
-      "LIST: Intersect_check_list for Volume      15 = [6,7,8,13,16]\n",
-      "LIST: Mask_intersect_list for Volume       15 = []\n",
-      "LIST: Destinations_list for Volume         15 = [12,14]\n",
-      "LIST: Reduced_destinations_list for Volume 15 = [12,14]\n",
-      "LIST: Next_volume_list for Volume          15 = [12,14,6,7,8,13,16]\n",
-      "      Is_vacuum for Volume                 15 = 0\n",
-      "      is_mask_volume for Volume            15 = 0\n",
-      "      is_masked_volume for Volume          15 = 0\n",
-      "      is_exit_volume for Volume            15 = 0\n",
-      "LIST: mask_list for Volume                 15 = []\n",
-      "LIST: masked_by_list for Volume            15 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 15 = []\n",
-      "      mask_mode for Volume                 15 = 0\n",
-      "\n",
-      "List overview for orange_layer_3_inner_cut_0 with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  16 = [3,4,5]\n",
-      "LIST: Direct_children for Volume           16 = [3,4]\n",
-      "LIST: Intersect_check_list for Volume      16 = [6,7,8,13]\n",
-      "LIST: Mask_intersect_list for Volume       16 = []\n",
-      "LIST: Destinations_list for Volume         16 = [15]\n",
-      "LIST: Reduced_destinations_list for Volume 16 = [15]\n",
-      "LIST: Next_volume_list for Volume          16 = [15,6,7,8,13]\n",
-      "      Is_vacuum for Volume                 16 = 1\n",
-      "      is_mask_volume for Volume            16 = 0\n",
-      "      is_masked_volume for Volume          16 = 0\n",
-      "      is_exit_volume for Volume            16 = 0\n",
-      "LIST: mask_list for Volume                 16 = []\n",
-      "LIST: masked_by_list for Volume            16 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 16 = []\n",
-      "      mask_mode for Volume                 16 = 0\n",
-      "\n",
-      "Union_master component orange_master initialized sucessfully\n",
-      "Detector: orange_logger_space_zx_I=3.28105e-07 orange_logger_space_zx_ERR=1.38489e-10 orange_logger_space_zx_N=1.74618e+07 \"orange_space_zx.dat\"\n",
-      "Detector: orange_logger_space_zy_I=3.28105e-07 orange_logger_space_zy_ERR=1.38489e-10 orange_logger_space_zy_N=1.74618e+07 \"orange_space_zy.dat\"\n",
-      "Detector: orange_logger_space_xy_I=3.28105e-07 orange_logger_space_xy_ERR=1.38489e-10 orange_logger_space_xy_N=1.74618e+07 \"orange_space_xy.dat\"\n",
-      "Detector: orange_logger_space_zy_close_I=1.59649e-07 orange_logger_space_zy_close_ERR=7.0123e-11 orange_logger_space_zy_close_N=8.35294e+06 \"orange_space_zy_close.dat_0\"\n",
-      "Detector: orange_logger_time_I=3.27996e-07 orange_logger_time_ERR=1.38461e-10 orange_logger_time_N=1.72562e+07 \"NULL.dat\"\n",
-      "Detector: orange_logger_space_zy_time_I=1.60352e-07 orange_logger_space_zy_time_ERR=6.99253e-11 orange_logger_space_zy_time_N=8.91413e+06 \"orange_logger_space_zy_time.dat_0\"\n",
-      "Detector: orange_logger_space_zy_time_I=1.54125e-07 orange_logger_space_zy_time_ERR=1.1568e-10 orange_logger_space_zy_time_N=4.99388e+06 \"orange_logger_space_zy_time.dat_1\"\n",
-      "Detector: orange_logger_space_zy_time_I=8.70858e-09 orange_logger_space_zy_time_ERR=1.99575e-11 orange_logger_space_zy_time_N=2.08251e+06 \"orange_logger_space_zy_time.dat_2\"\n",
-      "Detector: orange_logger_space_zy_time_I=4.10802e-09 orange_logger_space_zy_time_ERR=1.94203e-11 orange_logger_space_zy_time_N=930389 \"orange_logger_space_zy_time.dat_3\"\n",
-      "Detector: orange_logger_space_zy_time_I=7.02331e-10 orange_logger_space_zy_time_ERR=1.11529e-11 orange_logger_space_zy_time_N=335323 \"orange_logger_space_zy_time.dat_4\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.settings(ncount=1E7)\n",
-    "data = instrument.backengine()"
+    "empty_data = instrument.backengine()"
    ]
   },
   {
@@ -1256,28 +299,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "mobile-strike",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "60a8109a821d4d299c242cfb61150408",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(Output(layout=Layout(width='75%')), VBox(children=(Label(value='Choose monitor'), Dropdown(layo…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%matplotlib widget\n",
-    "ms.interface(data)"
+    "ms_widget.show(empty_data)"
    ]
   },
   {
@@ -1290,42 +318,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "rural-lingerie",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "74b357a47348442e957dda8c27a1c472",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2022-03-23 14:18:08,938 WARNING:MovieWriter imagemagick unavailable; using Pillow instead.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name orange_logger_space_zy_time\n",
-      "Saving animation with filename : \"cryostat_animation.gif\"\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "ani_data = data[5:10]\n",
+    "ani_data = empty_data[5:10]\n",
     "for frame in ani_data:\n",
     "    frame.set_plot_options(log=True, colormap=\"hot\", orders_of_mag=6)\n",
     "    \n",
@@ -1345,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "frequent-asbestos",
    "metadata": {},
    "outputs": [],
@@ -1372,940 +370,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "liquid-connectivity",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/cryostat_demo_data_1\"\n",
-      "INFO: Regenerating c-file: cryostat_demo.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./cryostat_demo.out\n",
-      "mccode-r.c:1880:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "} /* mcsiminfo_init */\n",
-      "^\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:201:5: note: expanded from macro 'MPI_MASTER'\n",
-      "  { statement; } \\\n",
-      "    ^~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:19430:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:19430:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:19673:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:19673:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:19916:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_xy_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:19916:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_xy_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_logger_3D_space.comp:668:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:20165:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_close_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_3D_space.comp:668:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:20165:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_close_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_logger_1D.comp:479:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:20472:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_time_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_1D.comp:479:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_1D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:20472:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_time_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_logger_2D_space_time.comp:632:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./cryostat_demo.c:20669:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_time_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "Union_logger_2D_space_time.comp:632:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_3D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./cryostat_demo.c:20669:18: note: expanded from macro 'filename'\n",
-      "#define filename mccorange_logger_space_zy_time_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:201:5: note: expanded from macro 'MPI_MASTER'\n",
-      "  { statement; } \\\n",
-      "    ^~~~~~~~~\n",
-      "Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:201:5: note: expanded from macro 'MPI_MASTER'\n",
-      "  { statement; } \\\n",
-      "    ^~~~~~~~~\n",
-      "Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:201:5: note: expanded from macro 'MPI_MASTER'\n",
-      "  { statement; } \\\n",
-      "    ^~~~~~~~~\n",
-      "19 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy cryostat_demo.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/cryostat_demo_data_1\n",
-      "\n",
-      "    Simulation 'cryostat_demo' (cryostat_demo.instr): running on 6 nodes (master is 'CI0021617', MPI version 3.1).\n",
-      "Instrument parameters for cryostat_demo (cryostat_demo.instr)\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: nascalf_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: nascalf_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: nascalf_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: nascalf_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
-      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
-      "PowderN: nascalf_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: nascalf_pow: Reading 841 rows from Na2Ca3Al2F14.laz\n",
-      "PowderN: nascalf_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: nascalf_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: nascalf_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "PowderN: nascalf_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "PowderN: nascalf_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: nascalf_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "PowderN: nascalf_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: nascalf_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "PowderN: nascalf_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: nascalf_pow: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
-      "PowderN: nascalf_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "PowderN: nascalf_pow: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "Allocating 3D arrays \n",
-      "Allocating 3D arrays \n",
-      "Allocating 3D arrays \n",
-      "Allocating 3D arrays \n",
-      "Allocating 3D arrays \n",
-      "Allocating 3D arrays \n",
-      "Allocating 3D arrays \n",
-      "Allocating 3D arrays \n",
-      "Allocating 3D arrays \n",
-      "Allocating 3D arrays \n",
-      "Allocating 3D arrays \n",
-      "Allocating 3D arrays \n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 4\n",
-      "name of process [0]: nascalf_inc \n",
-      "component index [0]: 3 \n",
-      "name of process [1]: nascalf_pow \n",
-      "component index [1]: 4 \n",
-      "name of process [2]: Al_inc \n",
-      "component index [2]: 6 \n",
-      "name of process [3]: Al_pow \n",
-      "component index [3]: 7 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 2\n",
-      "name of material    [0]: nascalf \n",
-      "component index     [0]: 5 \n",
-      "my_absoprtion       [0]: 0.273042 \n",
-      "number of processes [0]: 2 \n",
-      "name of material    [1]: Al \n",
-      "component index     [1]: 8 \n",
-      "my_absoprtion       [1]: 1.391566 \n",
-      "number of processes [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 2\n",
-      "\n",
-      "name of geometry    [0]: orange_layer_0_layer \n",
-      "component index     [0]: 9 \n",
-      "Volume.name         [0]: orange_layer_0_layer \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 1.391566 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.055500 \n",
-      "Volume.geometry.center.z             [0]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.037500 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.287000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: orange_layer_0_layer_vac \n",
-      "component index     [1]: 10 \n",
-      "Volume.name         [1]: orange_layer_0_layer_vac \n",
-      "Volume.p_physics.is_vacuum           [1]: 1 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.000000 \n",
-      "Volume.p_physics.number of processes [1]: 0 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.058500 \n",
-      "Volume.geometry.center.z             [1]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.035000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.283000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: orange_layer_0_outer_cut_0 \n",
-      "component index     [2]: 11 \n",
-      "Volume.name         [2]: orange_layer_0_outer_cut_0 \n",
-      "Volume.p_physics.is_vacuum           [2]: 1 \n",
-      "Volume.p_physics.my_absorption       [2]: 0.000000 \n",
-      "Volume.p_physics.number of processes [2]: 0 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: -0.021890 \n",
-      "Volume.geometry.center.z             [2]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.037501 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.132621 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: orange_layer_0_outer_cut_replace_0 \n",
-      "component index     [3]: 12 \n",
-      "Volume.name         [3]: orange_layer_0_outer_cut_replace_0 \n",
-      "Volume.p_physics.is_vacuum           [3]: 0 \n",
-      "Volume.p_physics.my_absorption       [3]: 1.391566 \n",
-      "Volume.p_physics.number of processes [3]: 2 \n",
-      "Volume.geometry.shape                [3]: cylinder \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: -0.021890 \n",
-      "Volume.geometry.center.z             [3]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [3]: 0.036500 \n",
-      "Volume.geometry.geometry_parameters.height [3]: 0.132625 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [4]: orange_layer_0_inner_cut_0 \n",
-      "component index     [4]: 13 \n",
-      "Volume.name         [4]: orange_layer_0_inner_cut_0 \n",
-      "Volume.p_physics.is_vacuum           [4]: 1 \n",
-      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
-      "Volume.p_physics.number of processes [4]: 0 \n",
-      "Volume.geometry.shape                [4]: cylinder \n",
-      "Volume.geometry.center.x             [4]: 0.000000 \n",
-      "Volume.geometry.center.y             [4]: 0.000000 \n",
-      "Volume.geometry.center.z             [4]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.035500 \n",
-      "Volume.geometry.geometry_parameters.height [4]: 0.040000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [5]: orange_layer_1_layer \n",
-      "component index     [5]: 14 \n",
-      "Volume.name         [5]: orange_layer_1_layer \n",
-      "Volume.p_physics.is_vacuum           [5]: 0 \n",
-      "Volume.p_physics.my_absorption       [5]: 1.391566 \n",
-      "Volume.p_physics.number of processes [5]: 2 \n",
-      "Volume.geometry.shape                [5]: cylinder \n",
-      "Volume.geometry.center.x             [5]: 0.000000 \n",
-      "Volume.geometry.center.y             [5]: 0.073500 \n",
-      "Volume.geometry.center.z             [5]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.040500 \n",
-      "Volume.geometry.geometry_parameters.height [5]: 0.331000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [6]: orange_layer_1_layer_vac \n",
-      "component index     [6]: 15 \n",
-      "Volume.name         [6]: orange_layer_1_layer_vac \n",
-      "Volume.p_physics.is_vacuum           [6]: 1 \n",
-      "Volume.p_physics.my_absorption       [6]: 0.000000 \n",
-      "Volume.p_physics.number of processes [6]: 0 \n",
-      "Volume.geometry.shape                [6]: cylinder \n",
-      "Volume.geometry.center.x             [6]: 0.000000 \n",
-      "Volume.geometry.center.y             [6]: 0.075000 \n",
-      "Volume.geometry.center.z             [6]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [6]: 0.040000 \n",
-      "Volume.geometry.geometry_parameters.height [6]: 0.330000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [7]: orange_layer_2_layer \n",
-      "component index     [7]: 16 \n",
-      "Volume.name         [7]: orange_layer_2_layer \n",
-      "Volume.p_physics.is_vacuum           [7]: 0 \n",
-      "Volume.p_physics.my_absorption       [7]: 1.391566 \n",
-      "Volume.p_physics.number of processes [7]: 2 \n",
-      "Volume.geometry.shape                [7]: cylinder \n",
-      "Volume.geometry.center.x             [7]: 0.000000 \n",
-      "Volume.geometry.center.y             [7]: 0.067500 \n",
-      "Volume.geometry.center.z             [7]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [7]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [7]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [7]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [7]: 0.049750 \n",
-      "Volume.geometry.geometry_parameters.height [7]: 0.333000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [7]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [8]: orange_layer_2_layer_vac \n",
-      "component index     [8]: 17 \n",
-      "Volume.name         [8]: orange_layer_2_layer_vac \n",
-      "Volume.p_physics.is_vacuum           [8]: 1 \n",
-      "Volume.p_physics.my_absorption       [8]: 0.000000 \n",
-      "Volume.p_physics.number of processes [8]: 0 \n",
-      "Volume.geometry.shape                [8]: cylinder \n",
-      "Volume.geometry.center.x             [8]: 0.000000 \n",
-      "Volume.geometry.center.y             [8]: 0.066000 \n",
-      "Volume.geometry.center.z             [8]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [8]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [8]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [8]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [8]: 0.047500 \n",
-      "Volume.geometry.geometry_parameters.height [8]: 0.318000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [8]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [9]: orange_layer_2_outer_cut_0 \n",
-      "component index     [9]: 18 \n",
-      "Volume.name         [9]: orange_layer_2_outer_cut_0 \n",
-      "Volume.p_physics.is_vacuum           [9]: 1 \n",
-      "Volume.p_physics.my_absorption       [9]: 0.000000 \n",
-      "Volume.p_physics.number of processes [9]: 0 \n",
-      "Volume.geometry.shape                [9]: cylinder \n",
-      "Volume.geometry.center.x             [9]: 0.000000 \n",
-      "Volume.geometry.center.y             [9]: -0.024000 \n",
-      "Volume.geometry.center.z             [9]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [9]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [9]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [9]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [9]: 0.049751 \n",
-      "Volume.geometry.geometry_parameters.height [9]: 0.152001 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [9]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [10]: orange_layer_2_outer_cut_replace_0 \n",
-      "component index     [10]: 19 \n",
-      "Volume.name         [10]: orange_layer_2_outer_cut_replace_0 \n",
-      "Volume.p_physics.is_vacuum           [10]: 0 \n",
-      "Volume.p_physics.my_absorption       [10]: 1.391566 \n",
-      "Volume.p_physics.number of processes [10]: 2 \n",
-      "Volume.geometry.shape                [10]: cylinder \n",
-      "Volume.geometry.center.x             [10]: 0.000000 \n",
-      "Volume.geometry.center.y             [10]: -0.024000 \n",
-      "Volume.geometry.center.z             [10]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [10]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [10]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [10]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [10]: 0.048500 \n",
-      "Volume.geometry.geometry_parameters.height [10]: 0.152005 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [10]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [11]: orange_layer_3_layer \n",
-      "component index     [11]: 20 \n",
-      "Volume.name         [11]: orange_layer_3_layer \n",
-      "Volume.p_physics.is_vacuum           [11]: 0 \n",
-      "Volume.p_physics.my_absorption       [11]: 1.391566 \n",
-      "Volume.p_physics.number of processes [11]: 2 \n",
-      "Volume.geometry.shape                [11]: cylinder \n",
-      "Volume.geometry.center.x             [11]: 0.000000 \n",
-      "Volume.geometry.center.y             [11]: 0.053500 \n",
-      "Volume.geometry.center.z             [11]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [11]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [11]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [11]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [11]: 0.063500 \n",
-      "Volume.geometry.geometry_parameters.height [11]: 0.347000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [11]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [12]: orange_layer_3_layer_vac \n",
-      "component index     [12]: 21 \n",
-      "Volume.name         [12]: orange_layer_3_layer_vac \n",
-      "Volume.p_physics.is_vacuum           [12]: 1 \n",
-      "Volume.p_physics.my_absorption       [12]: 0.000000 \n",
-      "Volume.p_physics.number of processes [12]: 0 \n",
-      "Volume.geometry.shape                [12]: cylinder \n",
-      "Volume.geometry.center.x             [12]: 0.000000 \n",
-      "Volume.geometry.center.y             [12]: 0.048000 \n",
-      "Volume.geometry.center.z             [12]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [12]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [12]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [12]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [12]: 0.060000 \n",
-      "Volume.geometry.geometry_parameters.height [12]: 0.314000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [12]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [13]: orange_layer_3_outer_cut_0 \n",
-      "component index     [13]: 22 \n",
-      "Volume.name         [13]: orange_layer_3_outer_cut_0 \n",
-      "Volume.p_physics.is_vacuum           [13]: 1 \n",
-      "Volume.p_physics.my_absorption       [13]: 0.000000 \n",
-      "Volume.p_physics.number of processes [13]: 0 \n",
-      "Volume.geometry.shape                [13]: cylinder \n",
-      "Volume.geometry.center.x             [13]: 0.000000 \n",
-      "Volume.geometry.center.y             [13]: -0.018920 \n",
-      "Volume.geometry.center.z             [13]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [13]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [13]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [13]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [13]: 0.063501 \n",
-      "Volume.geometry.geometry_parameters.height [13]: 0.149241 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [13]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [14]: orange_layer_3_outer_cut_replace_0 \n",
-      "component index     [14]: 23 \n",
-      "Volume.name         [14]: orange_layer_3_outer_cut_replace_0 \n",
-      "Volume.p_physics.is_vacuum           [14]: 0 \n",
-      "Volume.p_physics.my_absorption       [14]: 1.391566 \n",
-      "Volume.p_physics.number of processes [14]: 2 \n",
-      "Volume.geometry.shape                [14]: cylinder \n",
-      "Volume.geometry.center.x             [14]: 0.000000 \n",
-      "Volume.geometry.center.y             [14]: -0.018920 \n",
-      "Volume.geometry.center.z             [14]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [14]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [14]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [14]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [14]: 0.062500 \n",
-      "Volume.geometry.geometry_parameters.height [14]: 0.149245 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [14]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [15]: orange_layer_3_inner_cut_0 \n",
-      "component index     [15]: 24 \n",
-      "Volume.name         [15]: orange_layer_3_inner_cut_0 \n",
-      "Volume.p_physics.is_vacuum           [15]: 1 \n",
-      "Volume.p_physics.my_absorption       [15]: 0.000000 \n",
-      "Volume.p_physics.number of processes [15]: 0 \n",
-      "Volume.geometry.shape                [15]: cylinder \n",
-      "Volume.geometry.center.x             [15]: 0.000000 \n",
-      "Volume.geometry.center.y             [15]: -0.018920 \n",
-      "Volume.geometry.center.z             [15]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [15]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [15]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [15]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [15]: 0.061000 \n",
-      "Volume.geometry.geometry_parameters.height [15]: 0.149240 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [15]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [16]: sample \n",
-      "component index     [16]: 31 \n",
-      "Volume.name         [16]: sample \n",
-      "Volume.p_physics.is_vacuum           [16]: 0 \n",
-      "Volume.p_physics.my_absorption       [16]: 0.273042 \n",
-      "Volume.p_physics.number of processes [16]: 2 \n",
-      "Volume.geometry.shape                [16]: cylinder \n",
-      "Volume.geometry.center.x             [16]: 0.000000 \n",
-      "Volume.geometry.center.y             [16]: 0.000000 \n",
-      "Volume.geometry.center.z             [16]: 2.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [16]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [16]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [16]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [16]: 0.006000 \n",
-      "Volume.geometry.geometry_parameters.height [16]: 0.030000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [16]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 18\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]\n",
-      "LIST: Direct_children for Volume           0 = [6,7,8,12,14]\n",
-      "LIST: Intersect_check_list for Volume      0 = [6,7,8,12,14]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [6,7,8,12,14]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for orange_layer_0_layer with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  1 = [5,17]\n",
-      "LIST: Direct_children for Volume           1 = [5]\n",
-      "LIST: Intersect_check_list for Volume      1 = [2,3,4]\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [7,10,11,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = [7,10,11,14,15]\n",
-      "LIST: Next_volume_list for Volume          1 = [7,10,11,14,15,16,2,3,4]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for orange_layer_0_layer_vac with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  2 = [17]\n",
-      "LIST: Direct_children for Volume           2 = [17]\n",
-      "LIST: Intersect_check_list for Volume      2 = [17]\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [1,3,4,5,7,10,11,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [7,10,11,14,15]\n",
-      "LIST: Next_volume_list for Volume          2 = [1,3,4,5,7,10,11,14,15,16,17]\n",
-      "      Is_vacuum for Volume                 2 = 1\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for orange_layer_0_outer_cut_0 with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  3 = [5,17]\n",
-      "LIST: Direct_children for Volume           3 = [5]\n",
-      "LIST: Intersect_check_list for Volume      3 = [2,4]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [1,7]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = [7]\n",
-      "LIST: Next_volume_list for Volume          3 = [1,7,2,4]\n",
-      "      Is_vacuum for Volume                 3 = 1\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "List overview for orange_layer_0_outer_cut_replace_0 with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  4 = [5,17]\n",
-      "LIST: Direct_children for Volume           4 = [5]\n",
-      "LIST: Intersect_check_list for Volume      4 = [2,5]\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [1,3,7]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = [7]\n",
-      "LIST: Next_volume_list for Volume          4 = [1,3,7,2,5]\n",
-      "      Is_vacuum for Volume                 4 = 0\n",
-      "      is_mask_volume for Volume            4 = 0\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = []\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 0\n",
-      "\n",
-      "List overview for orange_layer_0_inner_cut_0 with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  5 = [17]\n",
-      "LIST: Direct_children for Volume           5 = [17]\n",
-      "LIST: Intersect_check_list for Volume      5 = [2]\n",
-      "LIST: Mask_intersect_list for Volume       5 = []\n",
-      "LIST: Destinations_list for Volume         5 = [4]\n",
-      "LIST: Reduced_destinations_list for Volume 5 = [4]\n",
-      "LIST: Next_volume_list for Volume          5 = [4,2]\n",
-      "      Is_vacuum for Volume                 5 = 1\n",
-      "      is_mask_volume for Volume            5 = 0\n",
-      "      is_masked_volume for Volume          5 = 0\n",
-      "      is_exit_volume for Volume            5 = 0\n",
-      "LIST: mask_list for Volume                 5 = []\n",
-      "LIST: masked_by_list for Volume            5 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 5 = []\n",
-      "      mask_mode for Volume                 5 = 0\n",
-      "\n",
-      "List overview for orange_layer_1_layer with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  6 = [1,2,3,4,5,17]\n",
-      "LIST: Direct_children for Volume           6 = [1,2,3,4]\n",
-      "LIST: Intersect_check_list for Volume      6 = [7]\n",
-      "LIST: Mask_intersect_list for Volume       6 = []\n",
-      "LIST: Destinations_list for Volume         6 = [0,8,9,10,11,12,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 6 = [8,12,14]\n",
-      "LIST: Next_volume_list for Volume          6 = [0,8,9,10,11,12,13,14,15,16,7]\n",
-      "      Is_vacuum for Volume                 6 = 0\n",
-      "      is_mask_volume for Volume            6 = 0\n",
-      "      is_masked_volume for Volume          6 = 0\n",
-      "      is_exit_volume for Volume            6 = 0\n",
-      "LIST: mask_list for Volume                 6 = []\n",
-      "LIST: masked_by_list for Volume            6 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 6 = []\n",
-      "      mask_mode for Volume                 6 = 0\n",
-      "\n",
-      "List overview for orange_layer_1_layer_vac with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  7 = [1,2,3,4,5,17]\n",
-      "LIST: Direct_children for Volume           7 = [1,2,3,4]\n",
-      "LIST: Intersect_check_list for Volume      7 = [1,2,3,4]\n",
-      "LIST: Mask_intersect_list for Volume       7 = []\n",
-      "LIST: Destinations_list for Volume         7 = [0,6,8,9,10,11,12,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 7 = [6,8,12,14]\n",
-      "LIST: Next_volume_list for Volume          7 = [0,6,8,9,10,11,12,13,14,15,16,1,2,3,4]\n",
-      "      Is_vacuum for Volume                 7 = 1\n",
-      "      is_mask_volume for Volume            7 = 0\n",
-      "      is_masked_volume for Volume          7 = 0\n",
-      "      is_exit_volume for Volume            7 = 0\n",
-      "LIST: mask_list for Volume                 7 = []\n",
-      "LIST: masked_by_list for Volume            7 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
-      "      mask_mode for Volume                 7 = 0\n",
-      "\n",
-      "List overview for orange_layer_2_layer with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  8 = [1,2,3,4,5,9,17]\n",
-      "LIST: Direct_children for Volume           8 = [9]\n",
-      "LIST: Intersect_check_list for Volume      8 = [6,7,9,10,11]\n",
-      "LIST: Mask_intersect_list for Volume       8 = []\n",
-      "LIST: Destinations_list for Volume         8 = [0,12,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 8 = [12,14]\n",
-      "LIST: Next_volume_list for Volume          8 = [0,12,13,14,15,16,6,7,9,10,11]\n",
-      "      Is_vacuum for Volume                 8 = 0\n",
-      "      is_mask_volume for Volume            8 = 0\n",
-      "      is_masked_volume for Volume          8 = 0\n",
-      "      is_exit_volume for Volume            8 = 0\n",
-      "LIST: mask_list for Volume                 8 = []\n",
-      "LIST: masked_by_list for Volume            8 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 8 = []\n",
-      "      mask_mode for Volume                 8 = 0\n",
-      "\n",
-      "List overview for orange_layer_2_layer_vac with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  9 = [1,2,3,4,5,17]\n",
-      "LIST: Direct_children for Volume           9 = [1,2,3,4]\n",
-      "LIST: Intersect_check_list for Volume      9 = [6,7]\n",
-      "LIST: Mask_intersect_list for Volume       9 = []\n",
-      "LIST: Destinations_list for Volume         9 = [8,10,11,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 9 = [8,13,14,15]\n",
-      "LIST: Next_volume_list for Volume          9 = [8,10,11,13,14,15,16,6,7]\n",
-      "      Is_vacuum for Volume                 9 = 1\n",
-      "      is_mask_volume for Volume            9 = 0\n",
-      "      is_masked_volume for Volume          9 = 0\n",
-      "      is_exit_volume for Volume            9 = 0\n",
-      "LIST: mask_list for Volume                 9 = []\n",
-      "LIST: masked_by_list for Volume            9 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 9 = []\n",
-      "      mask_mode for Volume                 9 = 0\n",
-      "\n",
-      "List overview for orange_layer_2_outer_cut_0 with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  10 = [3,4,5,17]\n",
-      "LIST: Direct_children for Volume           10 = [3,4]\n",
-      "LIST: Intersect_check_list for Volume      10 = [6,7,9,11]\n",
-      "LIST: Mask_intersect_list for Volume       10 = []\n",
-      "LIST: Destinations_list for Volume         10 = [8,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 10 = [8,13,14,15]\n",
-      "LIST: Next_volume_list for Volume          10 = [8,13,14,15,16,6,7,9,11]\n",
-      "      Is_vacuum for Volume                 10 = 1\n",
-      "      is_mask_volume for Volume            10 = 0\n",
-      "      is_masked_volume for Volume          10 = 0\n",
-      "      is_exit_volume for Volume            10 = 0\n",
-      "LIST: mask_list for Volume                 10 = []\n",
-      "LIST: masked_by_list for Volume            10 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 10 = []\n",
-      "      mask_mode for Volume                 10 = 0\n",
-      "\n",
-      "List overview for orange_layer_2_outer_cut_replace_0 with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  11 = [3,4,5,17]\n",
-      "LIST: Direct_children for Volume           11 = [3,4]\n",
-      "LIST: Intersect_check_list for Volume      11 = [6,7,9]\n",
-      "LIST: Mask_intersect_list for Volume       11 = []\n",
-      "LIST: Destinations_list for Volume         11 = [8,10,13,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 11 = [8,13,14,15]\n",
-      "LIST: Next_volume_list for Volume          11 = [8,10,13,14,15,16,6,7,9]\n",
-      "      Is_vacuum for Volume                 11 = 0\n",
-      "      is_mask_volume for Volume            11 = 0\n",
-      "      is_masked_volume for Volume          11 = 0\n",
-      "      is_exit_volume for Volume            11 = 0\n",
-      "LIST: mask_list for Volume                 11 = []\n",
-      "LIST: masked_by_list for Volume            11 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 11 = []\n",
-      "      mask_mode for Volume                 11 = 0\n",
-      "\n",
-      "List overview for orange_layer_3_layer with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  12 = [1,2,3,4,5,9,10,11,13,15,16,17]\n",
-      "LIST: Direct_children for Volume           12 = [9,13,15]\n",
-      "LIST: Intersect_check_list for Volume      12 = [6,7,8,13,14,15]\n",
-      "LIST: Mask_intersect_list for Volume       12 = []\n",
-      "LIST: Destinations_list for Volume         12 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 12 = []\n",
-      "LIST: Next_volume_list for Volume          12 = [0,6,7,8,13,14,15]\n",
-      "      Is_vacuum for Volume                 12 = 0\n",
-      "      is_mask_volume for Volume            12 = 0\n",
-      "      is_masked_volume for Volume          12 = 0\n",
-      "      is_exit_volume for Volume            12 = 0\n",
-      "LIST: mask_list for Volume                 12 = []\n",
-      "LIST: masked_by_list for Volume            12 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 12 = []\n",
-      "      mask_mode for Volume                 12 = 0\n",
-      "\n",
-      "List overview for orange_layer_3_layer_vac with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  13 = [1,2,3,4,5,10,11,17]\n",
-      "LIST: Direct_children for Volume           13 = [1,2,10,11]\n",
-      "LIST: Intersect_check_list for Volume      13 = [6,7,8,10,11]\n",
-      "LIST: Mask_intersect_list for Volume       13 = []\n",
-      "LIST: Destinations_list for Volume         13 = [12,14,15,16]\n",
-      "LIST: Reduced_destinations_list for Volume 13 = [12,14]\n",
-      "LIST: Next_volume_list for Volume          13 = [12,14,15,16,6,7,8,10,11]\n",
-      "      Is_vacuum for Volume                 13 = 1\n",
-      "      is_mask_volume for Volume            13 = 0\n",
-      "      is_masked_volume for Volume          13 = 0\n",
-      "      is_exit_volume for Volume            13 = 0\n",
-      "LIST: mask_list for Volume                 13 = []\n",
-      "LIST: masked_by_list for Volume            13 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 13 = []\n",
-      "      mask_mode for Volume                 13 = 0\n",
-      "\n",
-      "List overview for orange_layer_3_outer_cut_0 with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  14 = [3,4,5,16,17]\n",
-      "LIST: Direct_children for Volume           14 = [16]\n",
-      "LIST: Intersect_check_list for Volume      14 = [6,7,8,13,15]\n",
-      "LIST: Mask_intersect_list for Volume       14 = []\n",
-      "LIST: Destinations_list for Volume         14 = [0,12]\n",
-      "LIST: Reduced_destinations_list for Volume 14 = [12]\n",
-      "LIST: Next_volume_list for Volume          14 = [0,12,6,7,8,13,15]\n",
-      "      Is_vacuum for Volume                 14 = 1\n",
-      "      is_mask_volume for Volume            14 = 0\n",
-      "      is_masked_volume for Volume          14 = 0\n",
-      "      is_exit_volume for Volume            14 = 0\n",
-      "LIST: mask_list for Volume                 14 = []\n",
-      "LIST: masked_by_list for Volume            14 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 14 = []\n",
-      "      mask_mode for Volume                 14 = 0\n",
-      "\n",
-      "List overview for orange_layer_3_outer_cut_replace_0 with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  15 = [3,4,5,16,17]\n",
-      "LIST: Direct_children for Volume           15 = [16]\n",
-      "LIST: Intersect_check_list for Volume      15 = [6,7,8,13,16]\n",
-      "LIST: Mask_intersect_list for Volume       15 = []\n",
-      "LIST: Destinations_list for Volume         15 = [12,14]\n",
-      "LIST: Reduced_destinations_list for Volume 15 = [12,14]\n",
-      "LIST: Next_volume_list for Volume          15 = [12,14,6,7,8,13,16]\n",
-      "      Is_vacuum for Volume                 15 = 0\n",
-      "      is_mask_volume for Volume            15 = 0\n",
-      "      is_masked_volume for Volume          15 = 0\n",
-      "      is_exit_volume for Volume            15 = 0\n",
-      "LIST: mask_list for Volume                 15 = []\n",
-      "LIST: masked_by_list for Volume            15 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 15 = []\n",
-      "      mask_mode for Volume                 15 = 0\n",
-      "\n",
-      "List overview for orange_layer_3_inner_cut_0 with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  16 = [3,4,5,17]\n",
-      "LIST: Direct_children for Volume           16 = [3,4]\n",
-      "LIST: Intersect_check_list for Volume      16 = [6,7,8,13]\n",
-      "LIST: Mask_intersect_list for Volume       16 = []\n",
-      "LIST: Destinations_list for Volume         16 = [15]\n",
-      "LIST: Reduced_destinations_list for Volume 16 = [15]\n",
-      "LIST: Next_volume_list for Volume          16 = [15,6,7,8,13]\n",
-      "      Is_vacuum for Volume                 16 = 1\n",
-      "      is_mask_volume for Volume            16 = 0\n",
-      "      is_masked_volume for Volume          16 = 0\n",
-      "      is_exit_volume for Volume            16 = 0\n",
-      "LIST: mask_list for Volume                 16 = []\n",
-      "LIST: masked_by_list for Volume            16 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 16 = []\n",
-      "      mask_mode for Volume                 16 = 0\n",
-      "\n",
-      "List overview for sample with cylinder shape made of nascalf\n",
-      "LIST: Children for Volume                  17 = []\n",
-      "LIST: Direct_children for Volume           17 = []\n",
-      "LIST: Intersect_check_list for Volume      17 = []\n",
-      "LIST: Mask_intersect_list for Volume       17 = []\n",
-      "LIST: Destinations_list for Volume         17 = [2]\n",
-      "LIST: Reduced_destinations_list for Volume 17 = [2]\n",
-      "LIST: Next_volume_list for Volume          17 = [2]\n",
-      "      Is_vacuum for Volume                 17 = 0\n",
-      "      is_mask_volume for Volume            17 = 0\n",
-      "      is_masked_volume for Volume          17 = 0\n",
-      "      is_exit_volume for Volume            17 = 0\n",
-      "LIST: mask_list for Volume                 17 = []\n",
-      "LIST: masked_by_list for Volume            17 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 17 = []\n",
-      "      mask_mode for Volume                 17 = 0\n",
-      "\n",
-      "Union_master component orange_master initialized sucessfully\n",
-      "Detector: orange_logger_space_zx_I=1.09061e-06 orange_logger_space_zx_ERR=2.64358e-10 orange_logger_space_zx_N=3.86065e+08 \"orange_space_zx.dat\"\n",
-      "Detector: orange_logger_space_zy_I=1.09061e-06 orange_logger_space_zy_ERR=2.64358e-10 orange_logger_space_zy_N=3.86065e+08 \"orange_space_zy.dat\"\n",
-      "Detector: orange_logger_space_xy_I=1.09061e-06 orange_logger_space_xy_ERR=2.64358e-10 orange_logger_space_xy_N=3.86065e+08 \"orange_space_xy.dat\"\n",
-      "Detector: orange_logger_space_zy_close_I=1.62557e-07 orange_logger_space_zy_close_ERR=1.74868e-11 orange_logger_space_zy_close_N=1.67293e+08 \"orange_space_zy_close.dat_0\"\n",
-      "Detector: orange_logger_time_I=1.0903e-06 orange_logger_time_ERR=2.64349e-10 orange_logger_time_N=3.81625e+08 \"NULL.dat\"\n",
-      "Detector: orange_logger_space_zy_time_I=4.64807e-07 orange_logger_space_zy_time_ERR=1.70882e-10 orange_logger_space_zy_time_N=1.90626e+08 \"orange_logger_space_zy_time.dat_0\"\n",
-      "Detector: orange_logger_space_zy_time_I=5.9603e-07 orange_logger_space_zy_time_ERR=2.00616e-10 orange_logger_space_zy_time_N=1.19734e+08 \"orange_logger_space_zy_time.dat_1\"\n",
-      "Detector: orange_logger_space_zy_time_I=2.28432e-08 orange_logger_space_zy_time_ERR=1.85654e-11 orange_logger_space_zy_time_N=4.52069e+07 \"orange_logger_space_zy_time.dat_2\"\n",
-      "Detector: orange_logger_space_zy_time_I=5.62548e-09 orange_logger_space_zy_time_ERR=8.64179e-12 orange_logger_space_zy_time_N=1.9049e+07 \"orange_logger_space_zy_time.dat_3\"\n",
-      "Detector: orange_logger_space_zy_time_I=9.97296e-10 orange_logger_space_zy_time_ERR=3.65841e-12 orange_logger_space_zy_time_N=7.01011e+06 \"orange_logger_space_zy_time.dat_4\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.settings(ncount=2E7, mpi=2)\n",
-    "data = instrument.backengine()"
+    "data_sample = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "smooth-identity",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "530f07ae5726474880f72e22c984053f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(Output(layout=Layout(width='75%')), VBox(children=(Label(value='Choose monitor'), Dropdown(layo…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "ms.interface(data)"
+    "ms_widget.show(data_sample)"
    ]
   },
   {
diff --git a/examples/libpyvinyl_example.ipynb b/examples/libpyvinyl_example.ipynb
index 5f6c4f54..6c7c8994 100644
--- a/examples/libpyvinyl_example.ipynb
+++ b/examples/libpyvinyl_example.ipynb
@@ -2,12 +2,12 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "thirty-runner",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mcstasscript.interface import instr, functions, plotter"
+    "import mcstasscript as ms"
    ]
   },
   {
@@ -20,52 +20,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "earned-ribbon",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The following components are found in the work_directory / input_path:\n",
-      "     Union_sphere.comp\n",
-      "     Texture_process.comp\n",
-      "     Union_cone.comp\n",
-      "     Union_box.comp\n",
-      "     Single_crystal_process.comp\n",
-      "     Union_abs_logger_2D_space.comp\n",
-      "     Union_logger_2D_kf.comp\n",
-      "     Template_process.comp\n",
-      "     PhononSimple_process.comp\n",
-      "     Union_conditional_standard.comp\n",
-      "     Union_abs_logger_1D_space.comp\n",
-      "     Union_abs_logger_event.comp\n",
-      "     NCrystal_process.comp\n",
-      "     Union_abs_logger_1D_space_event.comp\n",
-      "     Union_abs_logger_1D_space_tof.comp\n",
-      "     Union_logger_2D_space.comp\n",
-      "     Union_conditional_PSD.comp\n",
-      "     Union_master.comp\n",
-      "     AF_HB_1D_process.comp\n",
-      "     Union_logger_2D_kf_time.comp\n",
-      "     Union_cylinder.comp\n",
-      "     Union_abs_logger_1D_space_tof_to_lambda.comp\n",
-      "     Powder_process.comp\n",
-      "     Union_make_material.comp\n",
-      "     Incoherent_process.comp\n",
-      "     Union_logger_1D.comp\n",
-      "     Union_logger_3D_space.comp\n",
-      "     IncoherentPhonon_process.comp\n",
-      "     Union_logger_2DQ.comp\n",
-      "     Union_mesh.comp\n",
-      "     Union_logger_2D_space_time.comp\n",
-      "These definitions will be used instead of the installed versions.\n"
-     ]
-    }
-   ],
-   "source": [
-    "calculator = instr.McStas_instr(name=\"demo_instrument\")"
+   "outputs": [],
+   "source": [
+    "calculator = ms.McStas_instr(name=\"demo_instrument\")"
    ]
   },
   {
@@ -78,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "parental-albert",
    "metadata": {},
    "outputs": [],
@@ -98,42 +58,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "incident-nitrogen",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "double source_energy          // Source mean energy\n",
-      "double source_height          // Height of source\n",
-      "       sample_height          // Height of sample\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "calculator.show_parameters()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "subjective-battle",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " - Parameters object -\n",
-      "source_energy       [meV]     Source mean energy   L[3.1, inf]\n",
-      "source_height       [cm]      Height of source   L[0.01, 0.2]\n",
-      "sample_height       [cm]      Height of sample   L[0.0, 0.05]\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(calculator.parameters)"
    ]
@@ -148,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "accomplished-twelve",
    "metadata": {},
    "outputs": [],
@@ -158,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "foreign-developer",
    "metadata": {},
    "outputs": [],
@@ -173,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "champion-knife",
    "metadata": {},
    "outputs": [],
@@ -183,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "wrong-retailer",
    "metadata": {},
    "outputs": [],
@@ -196,67 +134,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "anticipated-tampa",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are all components in the monitors category.\n",
-      " Brilliance_monitor      Monitor_Sqw           Pol_monitor\n",
-      " Cyl_monitor             Monitor_nD            PreMonitor_nD\n",
-      " DivLambda_monitor       PSD_TOF_monitor       Res_monitor\n",
-      " DivPos_monitor          PSD_monitor           Sqq_w_monitor\n",
-      " Divergence_monitor      PSD_monitor_4PI       Sqw_monitor\n",
-      " EPSD_monitor            PSD_monitor_TOF       TOF2E_monitor\n",
-      " E_monitor               PSD_monitor_psf       TOF2Q_cylPSD_monitor\n",
-      " Event_monitor_simple    PSD_monitor_psf_eff   TOFLambda_monitor\n",
-      " Hdiv_monitor            PSDcyl_monitor        TOF_PSD_monitor_rad\n",
-      " L_monitor               PSDlin_diff_monitor   TOF_cylPSD_monitor\n",
-      " MeanPolLambda_monitor   PSDlin_monitor        TOF_monitor\n",
-      " Monitor                 PolLambda_monitor     TOFlog_monitor\n",
-      " Monitor_4PI             PolTOF_monitor        \n"
-     ]
-    }
-   ],
-   "source": [
-    "calculator.show_components(\"monitors\")"
+   "outputs": [],
+   "source": [
+    "calculator.available_components(\"monitors\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "experienced-arizona",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Cyl_monitor _______________________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1mnr\u001b[0m = \u001b[1m\u001b[94m20.0\u001b[0m\u001b[0m [1] // Number of pixel (radial) columns\n",
-      "\u001b[1mfilename\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [string] // Name of file in which to store the detector image\n",
-      "\u001b[1myheight\u001b[0m = \u001b[1m\u001b[94m10.0\u001b[0m\u001b[0m [m] // Height of detector\n",
-      "\u001b[1mradius\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [m] // Radius of detector\n",
-      "\u001b[1mrestore_neutron\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // If set, the monitor does not influence the neutron \n",
-      "                             state \n",
-      "\u001b[1mthmin\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m\n",
-      "\u001b[1mthmax\u001b[0m = \u001b[1m\u001b[94m360.0\u001b[0m\u001b[0m\n",
-      "\u001b[1mnowritefile\u001b[0m = \u001b[1m\u001b[94m0\u001b[0m\u001b[0m [1] // If set, monitor will skip writing to disk\n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "calculator.component_help(\"Cyl_monitor\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "wooden-helicopter",
    "metadata": {},
    "outputs": [],
@@ -266,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "preliminary-training",
    "metadata": {},
    "outputs": [],
@@ -281,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "official-withdrawal",
    "metadata": {},
    "outputs": [],
@@ -291,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "colored-policy",
    "metadata": {},
    "outputs": [],
@@ -308,23 +206,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "based-torture",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "source                Source_div     AT      (0, 0, 0)   ABSOLUTE       \n",
-      "powder                PowderN        AT      (0, 0, 1)   RELATIVE source\n",
-      "cyl_monitor           Cyl_monitor    AT      (0, 0, 0)   RELATIVE powder\n",
-      "acceptance_horizontal DivPos_monitor AT      (0, 0, 0.1) RELATIVE powder\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "calculator.print_components()"
+    "calculator.show_components()"
    ]
   },
   {
@@ -337,52 +224,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "prescription-whole",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/mcstasscript/interface/instr.py:1696: UserWarning: run_full_instrument will be removed in future version of McStasScript. \n",
-      "Instead supply parameters with set_parameters, set settings with settings and use backengine() to run. See examples in package.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/run_full_instrument_path_0\"\n",
-      "INFO: Regenerating c-file: demo_instrument.c\n",
-      "CFLAGS=\n",
-      "INFO: Recompiling: ./demo_instrument.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "1 warning generated.\n",
-      "INFO: ===\n",
-      "Warning: 9658 events were removed in Component[4] acceptance_horizontal=DivPos_monitor()\n",
-      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "INFO: Placing instr file copy demo_instrument.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/run_full_instrument_path_0\n",
-      "\n",
-      "  Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Ni.laz' (Table_Read_Offset)\n",
-      "Table from file 'Ni.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
-      "  '# TITLE *Nickel-Ni-[FM3-M] Swanson, H.E.;Tatge, E.[1954] [carcinogen];# CEL ...'\n",
-      "PowderN: powder: Reading 19 rows from Ni.laz\n",
-      "PowderN: powder: Read 19 reflections from file 'Ni.laz'\n",
-      "PowderN: powder: Vc=43.76 [Angs] sigma_abs=17.96 [barn] sigma_inc=20.8 [barn] reflections=Ni.laz\n",
-      "Detector: cyl_monitor_I=0.0559433 cyl_monitor_ERR=0.00038608 cyl_monitor_N=55188 \"cylinder.dat\"\n",
-      "Detector: acceptance_horizontal_I=1.32435 acceptance_horizontal_ERR=0.00101504 acceptance_horizontal_N=1.89432e+06 \"acceptance_h.dat\"\n",
-      "PowderN: powder: Info: you may highly improve the computation efficiency by using\n",
-      "    SPLIT 19 COMPONENT powder=PowderN(...)\n",
-      "  in the instrument description demo_instrument.instr.\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "old_data = calculator.run_full_instrument(parameters={\"source_energy\": 320, \"source_height\":0.025, \"sample_height\":0.04},\n",
     "                               ncount=2E6, foldername=\"run_full_instrument_path\")"
@@ -398,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "independent-millennium",
    "metadata": {},
    "outputs": [],
@@ -408,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "smooth-cholesterol",
    "metadata": {},
    "outputs": [],
@@ -426,69 +271,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "daily-louisiana",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/path_to_output_0\"\n",
-      "INFO: Regenerating c-file: demo_instrument.c\n",
-      "CFLAGS=\n",
-      "INFO: Recompiling: ./demo_instrument.out\n",
-      "mccode-r.c:1880:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "} /* mcsiminfo_init */\n",
-      "^\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "2 warnings generated.\n",
-      "INFO: ===\n",
-      "Warning: 11913 events were removed in Component[4] acceptance_horizontal=DivPos_monitor()\n",
-      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "Warning: 11974 events were removed in Component[4] acceptance_horizontal=DivPos_monitor()\n",
-      "         (negative time, miss next components, rounding errors, Nan, Inf).\n",
-      "INFO: Placing instr file copy demo_instrument.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/examples/path_to_output_0\n",
-      "\n",
-      "  Simulation 'demo_instrument' (demo_instrument.instr): running on 2 nodes (master is 'CI0021617', MPI version 3.1).\n",
-      "Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Ni.laz' (Table_Read_Offset)\n",
-      "Table from file 'Ni.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
-      "  '# TITLE *Nickel-Ni-[FM3-M] Swanson, H.E.;Tatge, E.[1954] [carcinogen];# CEL ...'\n",
-      "PowderN: powder: Reading 19 rows from Ni.laz\n",
-      "PowderN: powder: Read 19 reflections from file 'Ni.laz'\n",
-      "PowderN: powder: Vc=43.76 [Angs] sigma_abs=17.96 [barn] sigma_inc=20.8 [barn] reflections=Ni.laz\n",
-      "Detector: cyl_monitor_I=0.0551286 cyl_monitor_ERR=0.000242487 cyl_monitor_N=136616 \"cylinder.dat\"\n",
-      "Detector: acceptance_horizontal_I=1.32419 acceptance_horizontal_ERR=0.000641536 acceptance_horizontal_N=4.7368e+06 \"acceptance_h.dat\"\n",
-      "PowderN: powder: Info: you may highly improve the computation efficiency by using\n",
-      "    SPLIT 19 COMPONENT powder=PowderN(...)\n",
-      "  in the instrument description demo_instrument.instr.\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "calculator.backengine()"
+   "outputs": [],
+   "source": [
+    "data = calculator.backengine()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "talented-poison",
+   "id": "under-walnut",
    "metadata": {},
    "source": [
-    "### Get data"
+    "### Get data later"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "global-loading",
+   "execution_count": null,
+   "id": "committed-hardwood",
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = calculator.data"
+    "calculator.output[\"simulation_data\"].get_data()[\"data\"]"
    ]
   },
   {
@@ -501,33 +307,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "retired-brick",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name cyl_monitor\n",
-      "Plotting data with name acceptance_horizontal\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plotter.make_sub_plot(data, log=True, cut_max=0.011, orders_of_mag=1.5)"
+   "outputs": [],
+   "source": [
+    "ms.make_sub_plot(data, log=True, cut_max=0.011, orders_of_mag=1.5)"
    ]
   },
   {
@@ -563,7 +348,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from_dump = instr.McStas_instr(\"\", dumpfile='dump_file.dmp')"
+    "from_dump = ms.McStas_instr.from_dump('dump_file.dmp')"
    ]
   },
   {
@@ -575,7 +360,7 @@
    "source": [
     "print(from_dump.name)\n",
     "from_dump.show_parameters()\n",
-    "from_dump.print_components()\n",
+    "from_dump.show_components()\n",
     "from_dump.write_full_instrument()"
    ]
   },
diff --git a/examples/manual_code.ipynb b/examples/manual_code.ipynb
deleted file mode 100644
index 078483d3..00000000
--- a/examples/manual_code.ipynb
+++ /dev/null
@@ -1,235 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from mcstasscript.interface import instr, plotter, functions\n",
-    "\n",
-    "# Creating the instance of the class, insert path to mcrun and to mcstas root directory\n",
-    "detector = instr.McStas_instr(\"LOKI_detector\",\n",
-    "                              mcrun_path = \"/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5/bin\",\n",
-    "                              mcstas_path = \"/Applications/McStas-2.5.app/Contents/Resources/mcstas/2.5\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detector.add_parameter(\"wavelength\")\n",
-    "detector.add_parameter(\"height\",value=1.0,comment=\"Height in [m]\")\n",
-    "detector.add_parameter(\"string\",\"reflection_filename\")\n",
-    "detector.add_parameter(\"string\",\"data_filename\",value=\"\\\"data.dat\\\"\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detector.add_declare_var(\"double\",\"energy\")\n",
-    "detector.add_declare_var(\"int\",\"flag\")\n",
-    "detector.add_declare_var(\"double\",\"tube_radius\",value=0.013)\n",
-    "detector.add_declare_var(\"double\",\"displacements\",array=7)\n",
-    "detector.add_declare_var(\"double\",\"t_array\",array=4,value=[0.65E-6,0.65E-6,1E-6,1E-6])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detector.append_initialize(\"energy=pow(2*PI/wavelength*K2V,2)*VS2E;\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detector.add_component(\"Origin\",\"Arm\")\n",
-    "src = detector.add_component(\"source\",\"Source_simple\",RELATIVE=\"Origin\")\n",
-    "detector.add_component(\"beam_extraction\",\"Guide_gravity\",AT=[0,0,2],RELATIVE=\"source\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detector.add_component(\"pre_guide_slit\",\"Slit\",before=\"beam_extraction\",\n",
-    "                       AT=[0,0,1],RELATIVE=\"source\",comment=\"Slit before the guide\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detector.print_components()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detector.set_component_parameter(\"source\",{\"xwidth\" : 0.12, \"E0\" : \"energy\"})\n",
-    "detector.set_component_parameter(\"source\",{\"yheight\" : 0.12})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detector.print_component(\"source\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detector.set_component_AT(\"source\",[0.01,0,0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detector.set_component_ROTATED(\"beam_extraction\",[0,2.0,0],RELATIVE=\"Origin\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detector.set_component_RELATIVE(\"beam_extraction\",\"pre_guide_slit\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detector.print_components()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detector.set_component_WHEN(\"beam_extraction\",\"vx > 0\")\n",
-    "detector.print_component(\"beam_extraction\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detector.append_component_EXTEND(\"beam_extraction\",\"n_scattering = SCATTERED - 2\")\n",
-    "detector.print_component(\"beam_extraction\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detector.set_component_GROUP(\"beam_extraction\",\"guides\")\n",
-    "detector.print_component(\"beam_extraction\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detector.set_component_JUMP(\"beam_extraction\",\"myself iterate 3\")\n",
-    "detector.print_component(\"beam_extraction\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detector.set_component_comment(\"beam_extraction\",\"Simulating severe misalignment\")\n",
-    "detector.print_component(\"beam_extraction\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detector.show_components()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detector.show_components(\"samples\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detector.component_help(\"Phonon_simple\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/random_demonstration.py b/examples/random_demonstration.py
index 60ecd37b..29005923 100644
--- a/examples/random_demonstration.py
+++ b/examples/random_demonstration.py
@@ -2,31 +2,31 @@
 # Written by Mads Bertelsen, ESS DMSC
 import random
 
-from mcstasscript.interface import instr, plotter, functions
+import mcstasscript as ms
 
 # Create a McStas instrument
-Instr = instr.McStas_instr("random_demo",
-                           author = "Mads Bertelsen",
-                           origin = "ESS DMSC")
+instr = ms.McStas_instr("random_demo",
+                        author = "Mads Bertelsen",
+                        origin = "ESS DMSC")
 
 # Set up a material called Cu with approrpiate properties
 # (uses McStas Union components, here the processes)
-Cu_inc = Instr.add_component("Cu_incoherent", "Incoherent_process")
+Cu_inc = instr.add_component("Cu_incoherent", "Incoherent_process")
 Cu_inc.sigma = 4*0.55
 Cu_inc.packing_factor = 1
 Cu_inc.unit_cell_volume = 55.4
 
-Cu_powder = Instr.add_component("Cu_powder", "Powder_process")
+Cu_powder = instr.add_component("Cu_powder", "Powder_process")
 Cu_powder.reflections = "\"Cu.laz\""
 
-Cu = Instr.add_component("Cu", "Union_make_material")
+Cu = instr.add_component("Cu", "Union_make_material")
 Cu.my_absorption = "100*4*3.78/55.4"
 Cu.process_string = "\"Cu_incoherent,Cu_powder\""
 
 # Add neutron source
-Source = Instr.add_component("source", "Source_div")
+Source = instr.add_component("source", "Source_div")
 # Add parameter to select energy at run time
-energy = Instr.add_parameter("double", "energy", value=10, comment="[meV] source energy")
+energy = instr.add_parameter("double", "energy", value=10, comment="[meV] source energy")
 
 Source.xwidth = 0.12
 Source.yheight = 0.12
@@ -62,11 +62,11 @@
     volume_material = random.choice(material_name_list)
 
     # Add a McStas Union geometry with unique name
-    this_geometry = Instr.add_component("volume_" + str(volume), "Union_box")
+    this_geometry = instr.add_component("volume_" + str(volume), "Union_box")
     this_geometry.xwidth = random.uniform(0.01, max_side_length)
     this_geometry.yheight = random.uniform(0.01, max_side_length)
     this_geometry.zdepth = random.uniform(0.01, max_side_length)
-    this_geometry.material_string = "\"" + volume_material + "\""
+    this_geometry.material_string = '"' + volume_material + '"'
     this_geometry.priority = current_priority
     this_geometry.p_interact = 0.3
     
@@ -74,63 +74,63 @@
     this_geometry.set_ROTATED(rotation, RELATIVE="ABSOLUTE")
 
 # A few Union loggers are set up for display of the scattering locations
-space_2D_zx = Instr.add_component("logger_space_zx_all", "Union_logger_2D_space")
-space_2D_zx.filename = "\"space_zx.dat\""
+space_2D_zx = instr.add_component("logger_space_zx_all", "Union_logger_2D_space")
+space_2D_zx.filename = '"space_zx.dat"'
 space_2D_zx.n1 = 1000
-space_2D_zx.D_direction_1 = "\"z\""
+space_2D_zx.D_direction_1 = '"z"'
 space_2D_zx.D1_min = -0.05
 space_2D_zx.D1_max = 0.05
 space_2D_zx.n2 = 1000
-space_2D_zx.D_direction_2 = "\"x\""
+space_2D_zx.D_direction_2 = '"x"'
 space_2D_zx.D2_min = -0.05
 space_2D_zx.D2_max = 0.05
 space_2D_zx.set_AT([0,0,1])
 
-space_2D_zy = Instr.add_component("logger_space_zy_all", "Union_logger_2D_space")
-space_2D_zy.filename = "\"space_zy.dat\""
+space_2D_zy = instr.add_component("logger_space_zy_all", "Union_logger_2D_space")
+space_2D_zy.filename = '"space_zy.dat"'
 space_2D_zy.n1 = 1000
-space_2D_zy.D_direction_1 = "\"z\""
+space_2D_zy.D_direction_1 = '"z"'
 space_2D_zy.D1_min = -0.05
 space_2D_zy.D1_max = 0.05
 space_2D_zy.n2 = 1000
-space_2D_zy.D_direction_2 = "\"y\""
+space_2D_zy.D_direction_2 = '"y"'
 space_2D_zy.D2_min = -0.05
 space_2D_zy.D2_max = 0.05
 space_2D_zy.set_AT([0,0,1])
 
 # Union master component that executes the simulation of the random boxes
-Instr.add_component("random_boxes", "Union_master")
+instr.add_component("random_boxes", "Union_master")
 
 # McStas monitors for viewing the beam after the random boxes
-PSD = Instr.add_component("detector", "PSD_monitor", AT=[0,0,2])
+PSD = instr.add_component("detector", "PSD_monitor", AT=[0,0,2])
 PSD.xwidth = 0.1
 PSD.yheight = 0.1
 PSD.nx = 500
 PSD.ny = 500
-PSD.filename = "\"PSD.dat\""
+PSD.filename = '"PSD.dat"'
 PSD.restore_neutron = 1
 
-big_PSD = Instr.add_component("large_detector", "PSD_monitor", AT=[0,0,2])
+big_PSD = instr.add_component("large_detector", "PSD_monitor", AT=[0,0,2])
 big_PSD.xwidth = 1.0
 big_PSD.yheight = 1.0
 big_PSD.nx = 500
 big_PSD.ny = 500
-big_PSD.filename = "\"big_PSD.dat\""
+big_PSD.filename = '"big_PSD.dat"'
 big_PSD.restore_neutron = 1
 
-Instr.print_components()
+instr.show_components()
 
 # Run the McStas simulation, a unique foldername is required for each run
-Instr.settings(output_path="demonstration", mpi=2, ncount=5E7)
-Instr.set_parameters(energy=600)
+instr.settings(output_path="demonstration", mpi=2, ncount=5E7)
+instr.set_parameters(energy=600)
 
-data = Instr.backengine()
+data = instr.backengine()
 
 # Set plotting options for the data (optional)
-functions.name_plot_options("logger_space_zx_all", data, log=1, orders_of_mag=3)
-functions.name_plot_options("logger_space_zy_all", data, log=1, orders_of_mag=3)
-functions.name_plot_options("detector", data, log=1, colormap="hot", orders_of_mag=0.5)
-functions.name_plot_options("large_detector", data, log=1, orders_of_mag=8)
+ms.name_plot_options("logger_space_zx_all", data, log=1, orders_of_mag=3)
+ms.name_plot_options("logger_space_zy_all", data, log=1, orders_of_mag=3)
+ms.name_plot_options("detector", data, log=1, colormap="hot", orders_of_mag=0.5)
+ms.name_plot_options("large_detector", data, log=1, orders_of_mag=8)
 
 # Plot the resulting data on a logarithmic scale
-plot = plotter.make_sub_plot(data)
+plot = ms.make_sub_plot(data)

From 897bb65e789970378ecd35aa97ff686ef32864d0 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 12 May 2022 17:17:06 +0200
Subject: [PATCH 268/403] Check of all doc notebooks for consitensy.

Added new sections on move and remove component in instrument.
Changed show_components to available_components
and print_components to show_components
---
 .../McStasScript_tutorial_1_the_basics.ipynb  |  8 +-
 .../tutorial/Union_tutorial_3_loggers.ipynb   | 11 +--
 .../Union_tutorial_4_conditionals.ipynb       | 10 +-
 .../source/user_guide/instrument_object.ipynb | 96 ++++++++++++++++---
 .../source/user_guide/instrument_reader.ipynb |  2 +-
 5 files changed, 98 insertions(+), 29 deletions(-)

diff --git a/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb b/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
index 282cbda0..a44d9c42 100644
--- a/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
+++ b/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
@@ -116,7 +116,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument.show_components()"
+    "instrument.available_components()"
    ]
   },
   {
@@ -125,7 +125,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument.show_components(\"sources\")"
+    "instrument.available_components(\"sources\")"
    ]
   },
   {
@@ -161,7 +161,7 @@
    "outputs": [],
    "source": [
     "src = instrument.add_component(\"source\", \"Source_div\")\n",
-    "instrument.print_components()"
+    "instrument.show_components()"
    ]
   },
   {
@@ -505,7 +505,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument.print_components()"
+    "instrument.show_components()"
    ]
   },
   {
diff --git a/docs/source/tutorial/Union_tutorial_3_loggers.ipynb b/docs/source/tutorial/Union_tutorial_3_loggers.ipynb
index 8481d148..f30d2924 100644
--- a/docs/source/tutorial/Union_tutorial_3_loggers.ipynb
+++ b/docs/source/tutorial/Union_tutorial_3_loggers.ipynb
@@ -113,7 +113,7 @@
     "outer_wall_vac.material_string='\"Vacuum\"' \n",
     "outer_wall_vac.priority = 61\n",
     "\n",
-    "instrument.print_components()"
+    "instrument.show_components()"
    ]
   },
   {
@@ -122,7 +122,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument.show_components(\"Work directory\")"
+    "instrument.available_components(\"union\")"
    ]
   },
   {
@@ -562,13 +562,6 @@
     "\n",
     "![SegmentLocal](animation_demo_long.gif \"Animation\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb b/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
index 29edda24..c90b5012 100644
--- a/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
+++ b/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
@@ -181,7 +181,7 @@
     "master = instrument.add_component(\"master\", \"Union_master\")\n",
     "\n",
     "# Adding a banana - tof detector\n",
-    "banana_detector = instrument.add_component(\"banana_detector\", \"Monitor_nD\",\n",
+    "banana_detector = instrument.add_component(\"banana_detector\", \"Monitor_nD\")\n",
     "banana_detector.set_RELATIVE(cryostat_center)\n",
     "banana_detector.xwidth = 1\n",
     "banana_detector.yheight = 0.2\n",
@@ -379,10 +379,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "banana = functions.name_search(\"banana_detector\", data_con)\n",
+    "banana = ms.name_search(\"banana_detector\", data_con)\n",
     "banana.set_plot_options(log=True, orders_of_mag=7, cut_max=0.001)\n",
     "\n",
-    "banana_limited = functions.name_search(\"banana_detector_limited\", data_con)\n",
+    "banana_limited = ms.name_search(\"banana_detector_limited\", data_con)\n",
     "banana_limited.set_plot_options(log=False)\n",
     "\n",
     "ms.make_sub_plot([banana, banana_limited], fontsize=10)"
@@ -498,15 +498,19 @@
    "outputs": [],
    "source": [
     "logger_zx = ms.name_search(\"logger_space_zx\", data_con)\n",
+    "logger_zx.set_title(\"Conditional\")\n",
     "logger_zx.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
     "logger_zy = ms.name_search(\"logger_space_zy\", data_con)\n",
+    "logger_zy.set_title(\"Conditional\")\n",
     "logger_zy.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
     "logger_zx_f = ms.name_search(\"logger_space_zx\", data_con_f)\n",
+    "logger_zx_f.set_title(\"Conditional final weight\")\n",
     "logger_zx_f.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
     "logger_zy_f = ms.name_search(\"logger_space_zy\", data_con_f)\n",
+    "logger_zy_f.set_title(\"Conditional final weight\")\n",
     "logger_zy_f.set_plot_options(log=True, orders_of_mag=9, colormap=\"YlOrRd\")\n",
     "\n",
     "ms.make_sub_plot([logger_zx, logger_zy, logger_zx_f, logger_zy_f], fontsize=10)"
diff --git a/docs/source/user_guide/instrument_object.ipynb b/docs/source/user_guide/instrument_object.ipynb
index b7e60a0e..7ef18e12 100644
--- a/docs/source/user_guide/instrument_object.ipynb
+++ b/docs/source/user_guide/instrument_object.ipynb
@@ -178,11 +178,11 @@
     "## Help features\n",
     "There are a few methods built into the instrument class that helps the user, these are:\n",
     "\n",
-    "- *show_components*\n",
+    "- *available_components*\n",
     "- *component_help*\n",
     "\n",
-    "### show_components\n",
-    "The *show_components* method shows the component categories, and if called with the name of a category, will show all components in the specified category. The categories can include the work directory if any components are located there."
+    "### available_components\n",
+    "The *available_components* method shows the component categories, and if called with the name of a category, will show all available components in the specified category. The categories can include the work directory if any components are located there."
    ]
   },
   {
@@ -192,7 +192,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument.show_components()"
+    "instrument.available_components()"
    ]
   },
   {
@@ -202,7 +202,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument.show_components(\"optics\")"
+    "instrument.available_components(\"optics\")"
    ]
   },
   {
@@ -262,7 +262,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument.print_components()"
+    "instrument.show_components()"
    ]
   },
   {
@@ -270,7 +270,7 @@
    "id": "outdoor-motor",
    "metadata": {},
    "source": [
-    "There are a number of keyword arguments allowed when adding a component. These will mainly be discussed on the [component object page](component_object.ipynb), but a few are relevant for the instrument, because the handle in what order components are sequenced in the instrument. To illustrate this we add a slit and a guide to the instrument at reasonable positions. Notice these new components are added at the end of the instrument."
+    "There are a number of keyword arguments allowed when adding a component. These will mainly be discussed on the [component object page](component_object.ipynb), but a few are relevant for the instrument, because they handle in what order components are sequenced in the instrument. To illustrate this we add a slit and a guide to the instrument at reasonable positions. Notice these new components are added at the end of the instrument."
    ]
   },
   {
@@ -294,7 +294,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument.print_components()"
+    "instrument.show_components()"
    ]
   },
   {
@@ -316,7 +316,7 @@
     "monitor.set_AT(1.9, RELATIVE=source)\n",
     "monitor.set_parameters(xwidth=0.1, yheight=0.1, filename='\"PSD.dat\"')\n",
     "\n",
-    "instrument.print_components()"
+    "instrument.show_components()"
    ]
   },
   {
@@ -331,6 +331,78 @@
     "It is important to note that the McStas instrument file is read sequentially, so the position of the PSD monitor can not be relative to a later component, but must only refer to earlier components. At this point in development it is not possible to reorder components in the instrument object."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "after-setup",
+   "metadata": {},
+   "source": [
+    "## Moving a component\n",
+    "It is also possible to move a component in the component sequence of the instrument. Lets add a *Lmonitor* at the end at move it to the PSD. The *move_component* command takes a name or component object to be moved, and then the same before and after keywords as *add_component*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "loose-motivation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Lmon = instrument.add_component(\"Lmon\", \"L_monitor\", RELATIVE=\"guide\")\n",
+    "instrument.show_components()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "constant-footwear",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.move_component(Lmon, after=source)\n",
+    "instrument.show_components()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "enabling-business",
+   "metadata": {},
+   "source": [
+    "It is easy to introduce a mistake in an instrument by moving a component, as components uses each other as references for position and rotation. In the above example the *Lmon* was placed relative to the *guide* component, and thus McStasScript warns that an error was introduced when the component was moved, as *guide* has not been defined at the new position of *Lmon*. The method *check_for_errors* can be called for further information, here in a try block to catch the exception."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "assured-paint",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    instrument.check_for_errors()\n",
+    "except Exception as e:\n",
+    "    print(str(e))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "superb-source",
+   "metadata": {},
+   "source": [
+    "## Removing a component\n",
+    "Components can be removed with the *remove_component* method. The input can be either a component name or a component object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "severe-luther",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.remove_component(Lmon)\n",
+    "instrument.show_components()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "distinct-thousand",
@@ -397,7 +469,7 @@
    "metadata": {},
    "source": [
     "## Run the simulation\n",
-    "The simulation is executed with a call to the *backengine* method, which will return the generated data. If the simulation fails, the method returns None."
+    "The simulation is executed with a call to the *backengine* method, which will return the generated data. If the simulation fails, the method returns None. McStasScript does check for some common mistakes before attempting to run the McStas simulation, if any problem is found a useful error message will be shown."
    ]
   },
   {
@@ -426,7 +498,7 @@
    "metadata": {},
    "source": [
     "## Visualizing the instrument\n",
-    "It is possible to visualize the instrument using the visualization features in McStas / McXtrace. If done in a Jupyter Notebook, the visualization can be viewed in the notebook. The *show_instrument* method is used, and one can choose the format with the keyword *format* which can be either \"window\" or \"webgl\". When using window format a new window is opened with 2D visualization, and with the webgl format a 3D viewer is opened in a new browser tab. Be aware that the parameters set for the instrument is used for the visualization."
+    "It is possible to visualize the instrument using the visualization features in McStas / McXtrace. If done in a Jupyter Notebook, the visualization can be viewed in the notebook. The *show_instrument* method is used, and one can choose the format with the keyword *format* which can be either \"window\" or \"webgl\". When using window format a new window is opened with 2D visualization, and with the webgl format a 3D viewer is opened inside the notebook. Be aware that the parameters set for the instrument is used for the visualization."
    ]
   },
   {
@@ -511,7 +583,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loaded_instrument.print_components()\n",
+    "loaded_instrument.show_components()\n",
     "loaded_instrument.show_settings()"
    ]
   },
diff --git a/docs/source/user_guide/instrument_reader.ipynb b/docs/source/user_guide/instrument_reader.ipynb
index 36ad5a69..3ca98702 100644
--- a/docs/source/user_guide/instrument_reader.ipynb
+++ b/docs/source/user_guide/instrument_reader.ipynb
@@ -60,7 +60,7 @@
     "instrument_reader.add_to_instr(instrument)\n",
     "\n",
     "instrument.show_parameters()\n",
-    "instrument.print_components()"
+    "instrument.show_components()"
    ]
   },
   {

From 675e06f4eee3448cffb2072b80802de014c6cd5c Mon Sep 17 00:00:00 2001
From: Shervin Nourbakhsh <shervin.nourbakhsh@ill.fr>
Date: Fri, 13 May 2022 08:30:19 +0200
Subject: [PATCH 269/403] making output_path a method of the class and not a
 member. It is now using the base_dir from the BaseCalculator

---
 mcstasscript/interface/instr.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 11ed2816..d4530b8c 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -364,9 +364,6 @@ def __init__(self, name, parameters=None, executable=None,
         self.component_class_lib = {}
         self.widget_interface = None
 
-        # Ensure output_path field exist (not ensured by BaseCalculator)
-        if not hasattr(self, "output_path"):
-            self.output_path = self.name + "_data"
 
         # Avoid initializing if loading from dump
         if not hasattr(self, "declare_list"):
@@ -381,6 +378,14 @@ def __init__(self, name, parameters=None, executable=None,
             self.component_list = []  # List of components (have to be ordered)
             self.component_name_list = []  # List of component names
 
+    @property
+    def output_path(self) -> str:
+        return self.base_dir
+
+    @output_path.setter
+    def output_path(self, value: str) -> None:
+        self.calculator_base_dir = value
+
     def init_parameters(self):
         """
         Create empty ParameterContainer for new instrument

From 822fc592e0530019964497187e08cc4abae2d4cc Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Mon, 16 May 2022 12:23:11 +0200
Subject: [PATCH 270/403] Added feature that allows hiding the new tab from
 show_instrument when using a notebook. Also added the keyword argument
 new_tab to force opeing a new tab in case that is desired.

Documentation updated.
---
 docs/source/getting_started/quick_start.ipynb |  4 +-
 .../source/user_guide/instrument_object.ipynb | 40 ++++++++++++-------
 mcstasscript/interface/instr.py               | 21 ++++++----
 3 files changed, 41 insertions(+), 24 deletions(-)

diff --git a/docs/source/getting_started/quick_start.ipynb b/docs/source/getting_started/quick_start.ipynb
index 9c2318bb..27d22930 100644
--- a/docs/source/getting_started/quick_start.ipynb
+++ b/docs/source/getting_started/quick_start.ipynb
@@ -57,7 +57,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument.show_components()"
+    "instrument.available_components()"
    ]
   },
   {
@@ -67,7 +67,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument.show_components(\"sources\")"
+    "instrument.available_components(\"sources\")"
    ]
   },
   {
diff --git a/docs/source/user_guide/instrument_object.ipynb b/docs/source/user_guide/instrument_object.ipynb
index 7ef18e12..f75a8a9b 100644
--- a/docs/source/user_guide/instrument_object.ipynb
+++ b/docs/source/user_guide/instrument_object.ipynb
@@ -498,7 +498,23 @@
    "metadata": {},
    "source": [
     "## Visualizing the instrument\n",
-    "It is possible to visualize the instrument using the visualization features in McStas / McXtrace. If done in a Jupyter Notebook, the visualization can be viewed in the notebook. The *show_instrument* method is used, and one can choose the format with the keyword *format* which can be either \"window\" or \"webgl\". When using window format a new window is opened with 2D visualization, and with the webgl format a 3D viewer is opened inside the notebook. Be aware that the parameters set for the instrument is used for the visualization."
+    "It is possible to visualize the instrument using the visualization features in McStas / McXtrace. This is done using the *show_instrument* method that show the instrument with the currently set parameters. The method takes a format keyword, of which there are two allowed:\n",
+    "\n",
+    "| Format | Description | |\n",
+    "| :-- | :-- | --- |\n",
+    "| webgl | 3D view in notebook or browser tab | (default) |\n",
+    "| window | 2D view in window | |\n",
+    "\n",
+    "\n",
+    "The default format webgl behaves differently whether in a notebook or from a script. In a notebook, the output will be shown directly in the cell as shown in the example below, but in a script it will open a new browser tab. If a new browser tab is desired even when running from a notebook, set the keyword argument *new_tab* to True.\n",
+    "\n",
+    "When using the 3D view in webgl, use these controls to manipulate the view:\n",
+    "\n",
+    "| Action | Effect on view |\n",
+    "| :-- | :-: |\n",
+    "| Hold left click and drag | Rotate |\n",
+    "| Hold right click and drag | Move |\n",
+    "| Hold mouse wheel and drag up/down | Zoom in/out |"
    ]
   },
   {
@@ -511,6 +527,14 @@
     "instrument.show_instrument()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "disciplinary-tamil",
+   "metadata": {},
+   "source": [
+    "The window format is a 2D view which is opened in a new window, and may not always work if one use McStasScript through the cloud or a docker container. It is better suited for getting measurements and ensuring the geometry is exactly as desired."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -525,20 +549,6 @@
     "instrument.show_instrument(format=\"window\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "common-clause",
-   "metadata": {},
-   "source": [
-    "When using the 3D view, use these controls to manipulate the view:\n",
-    "\n",
-    "| Action | Effect on view |\n",
-    "| --- | --- |\n",
-    "| Hold left click and drag | Rotate |\n",
-    "| Hold right click and drag | Move |\n",
-    "| Hold mouse wheel and drag up/down | Zoom in/out |\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "dressed-branch",
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 3e693724..feb71673 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1906,7 +1906,7 @@ def run_full_instrument(self, **kwargs):
 
         return self.backengine()
 
-    def show_instrument(self, format="webgl", width=800, height=450):
+    def show_instrument(self, format="webgl", width=800, height=450, new_tab=False):
         """
         Uses mcdisplay to show the instrument in web browser
 
@@ -1921,6 +1921,8 @@ def show_instrument(self, format="webgl", width=800, height=450):
                 width of IFrame if used in notebook
             height : int
                 height of IFrame if used in notebook
+            new_tab : bool
+                Open webgl in new browser tab
         """
 
         parameters = {}
@@ -1961,8 +1963,19 @@ def show_instrument(self, format="webgl", width=800, height=450):
         instr_path = os.path.join(self.input_path, self.name + ".instr")
         instr_path = os.path.abspath(instr_path)
 
+        try:
+            shell = get_ipython().__class__.__name__
+            is_notebook = shell == "ZMQInteractiveShell"
+        except:
+            is_notebook = False
+
+        options = ""
+        if is_notebook and executable == "mcdisplay-webgl" and not new_tab:
+            options += "--nobrowse "
+
         full_command = (bin_path + executable + " "
                         + dir_control
+                        + options
                         + instr_path
                         + " " + parameter_string)
 
@@ -1972,12 +1985,6 @@ def show_instrument(self, format="webgl", width=800, height=450):
                                  universal_newlines=True,
                                  cwd=self.input_path)
 
-        try:
-            shell = get_ipython().__class__.__name__
-            is_notebook = shell == "ZMQInteractiveShell"
-        except:
-            is_notebook = False
-
         if not is_notebook or executable != "mcdisplay-webgl":
             print(process.stderr)
             print(process.stdout)

From 834800992db89559347cd717380f0721bd85c91b Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Mon, 16 May 2022 12:30:40 +0200
Subject: [PATCH 271/403] Version bump to 0.0.44

---
 mcstasscript/_version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 59120223..fa571767 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.43"
+__version__ = "0.0.44"

From 9971107c274b7904206330db1be04d77a3a443af Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Mon, 16 May 2022 18:00:20 +0200
Subject: [PATCH 272/403] Final documentation pass before update 0.0.44.

Found fix for timeout problem of building documentation.
---
 docs/source/conf.py                           |  2 ++
 docs/source/index.rst                         |  1 +
 .../Union_tutorial_4_conditionals.ipynb       |  4 ++--
 .../user_guide/parameters_and_variables.ipynb |  4 ++--
 .../test_complex_instrument.py                | 19 +++++++++----------
 mcstasscript/jb_interface/show_interface.py   |  2 +-
 6 files changed, 17 insertions(+), 15 deletions(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 72362af7..ffe20d6c 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -50,6 +50,8 @@
 # This pattern also affects html_static_path and html_extra_path.
 exclude_patterns = []
 
+execution_timeout = 200
+
 
 # -- Options for HTML output -------------------------------------------------
 
diff --git a/docs/source/index.rst b/docs/source/index.rst
index bbe83c6f..94b069fd 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -13,6 +13,7 @@ Documentation
    
    getting_started/overview
    getting_started/installation
+   getting_started/version_history
    getting_started/quick_start
    
 .. toctree::
diff --git a/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb b/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
index c90b5012..444bd146 100644
--- a/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
+++ b/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "# Using conditional component to modify loggers\n",
-    "Even with the results from the loggers, it can still be difficult to explain all the features in the resulting scattering pattern. The conditional components can modify a logger so that it only records events when the final state of the neutron satisfy some condition. The condition could be leaving with a certain energy or in a specified direction. Before demonstrating these conditional components, we will set up an interesting sample and sample environment, including a few loggers and a time of flight 2theta detector. "
+    "Even with the results from the loggers, it can still be difficult to explain all the features in the resulting scattering pattern. The conditional components can modify a logger so that it only records events when the final state of the neutron satisfy some condition. The condition could be leaving with a certain energy or in a specified direction. Before demonstrating these conditional components, we will set up an interesting sample and sample environment, including a few loggers and a time of flight 2theta detector."
    ]
   },
   {
@@ -545,7 +545,7 @@
   },
   "metadata": {
    "execution": {
-    "timeout": 100
+    "timeout": 200
    }
   }
  },
diff --git a/docs/source/user_guide/parameters_and_variables.ipynb b/docs/source/user_guide/parameters_and_variables.ipynb
index e6e30bd1..94c16972 100644
--- a/docs/source/user_guide/parameters_and_variables.ipynb
+++ b/docs/source/user_guide/parameters_and_variables.ipynb
@@ -15,7 +15,7 @@
    "metadata": {},
    "source": [
     "## Parameters\n",
-    "Instrument parameters are built on the libpyvinyl parameters, and have some interesting features. First a simple [parameter](../_autosummary/mcstasscript.helper.mcstas_objects.ParameterVariable.rst) is added."
+    "Instrument parameters are built on the libpyvinyl parameters, and have some interesting features. First a simple parameter is added."
    ]
   },
   {
@@ -37,7 +37,7 @@
    "id": "alive-cholesterol",
    "metadata": {},
    "source": [
-    "It is a good habit to add a comment to each [parameter](../_autosummary/mcstasscript.helper.mcstas_objects.ParameterVariable.rst) when creating it. "
+    "It is a good habit to add a comment to each parameter when creating it. "
    ]
   },
   {
diff --git a/mcstasscript/integration_tests/test_complex_instrument.py b/mcstasscript/integration_tests/test_complex_instrument.py
index d9f5cdad..380e749c 100644
--- a/mcstasscript/integration_tests/test_complex_instrument.py
+++ b/mcstasscript/integration_tests/test_complex_instrument.py
@@ -57,16 +57,15 @@ def setup_complex_instrument():
     slit1.append_EXTEND("}")
     slit1.set_GROUP("entrance_slits")
 
-    # Add second slit with instr methods
-    Instr.add_component("slit2", "Slit")
-    Instr.set_component_AT("slit2", ["-1.3*guide_width", 0, 1.5])
-    Instr.set_component_RELATIVE("slit2", "source")
-    Instr.set_component_parameter("slit2", {"xwidth": "guide_width",
-                                            "yheight": 0.05})
-    Instr.append_component_EXTEND("slit2", "if (SCATTERED) {")
-    Instr.append_component_EXTEND("slit2", "  guide_choice = 2;")
-    Instr.append_component_EXTEND("slit2", "}")
-    Instr.set_component_GROUP("slit2", "entrance_slits")
+    # Add second slit with set_parameters
+    slit2 = Instr.add_component("slit2", "Slit")
+    slit2.set_AT(["-1.3*guide_width", 0, 1.5])
+    slit2.set_RELATIVE("source")
+    slit2.set_parameters(xwidth="guide_width", yheight=0.05)
+    slit2.append_EXTEND("if (SCATTERED) {")
+    slit2.append_EXTEND("  guide_choice = 2;")
+    slit2.append_EXTEND("}")
+    slit2.set_GROUP("entrance_slits")
 
     select1 = Instr.add_component("select1", "Arm", RELATIVE="after_guide")
     select1.set_JUMP("select2 WHEN guide_choice == 2")
diff --git a/mcstasscript/jb_interface/show_interface.py b/mcstasscript/jb_interface/show_interface.py
index 1d25349a..61820498 100644
--- a/mcstasscript/jb_interface/show_interface.py
+++ b/mcstasscript/jb_interface/show_interface.py
@@ -15,7 +15,7 @@ def show(function_input):
     Parameters
     ----------
 
-    function_input : McCode_instr, list or McStasData
+    function_input : McCode_instr, list of McStasData
 
     """
 

From 2aa392ef7ed59d106bfe698a01beaac25b74e9a8 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 18 May 2022 11:28:20 +0200
Subject: [PATCH 273/403] Ensure default name for data depend on instrument
 name.

Version bump.
---
 mcstasscript/_version.py        |  2 +-
 mcstasscript/interface/instr.py | 11 ++++++++++-
 2 files changed, 11 insertions(+), 2 deletions(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index fa571767..0c016c9e 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.44"
+__version__ = "0.0.45"
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 9e56a0f4..a94d8d15 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -306,6 +306,10 @@ def __init__(self, name, parameters=None, author=None,
                             + self.name + ".instr"
                             + "\" which is not a legal filename")
 
+        if self.calculator_base_dir == "BaseCalculator":
+            # If the base_dir was not set, set default to depend on instrument name
+            self.calculator_base_dir = self.name + "_data"
+
         if author is None:
             self.author = "Python " + self.package_name
             self.author += " Instrument Generator"
@@ -1255,7 +1259,6 @@ def show_components(self, line_length=None):
         line_length : int
             Maximum line length in console
         """
-
         if len(self.component_list) == 0:
             print("No components added to instrument object yet.")
             return
@@ -1263,6 +1266,12 @@ def show_components(self, line_length=None):
         if line_length is None:
             line_limit = self.line_limit
         else:
+            if not isinstance(line_length, int):
+                raise ValueError("Show components now shows components in"
+                                 " instrument instead of help. For help,"
+                                 " use available_components instead. \n"
+                                 "The argument for show_components is"
+                                 " line_length and has to be an integer.")
             line_limit = line_length
 
         component_names = [x.name for x in self.component_list]

From d81af52cc97f18f52cf520a52db0e0c912a565f4 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 18 May 2022 15:10:17 +0200
Subject: [PATCH 274/403] First functional version of instrument diagram.

Call with instrument.show_diagram()
---
 mcstasscript/helper/instrument_diagram.py | 221 ++++++++++++++++++++++
 mcstasscript/interface/instr.py           |   4 +
 2 files changed, 225 insertions(+)
 create mode 100644 mcstasscript/helper/instrument_diagram.py

diff --git a/mcstasscript/helper/instrument_diagram.py b/mcstasscript/helper/instrument_diagram.py
new file mode 100644
index 00000000..e62c7197
--- /dev/null
+++ b/mcstasscript/helper/instrument_diagram.py
@@ -0,0 +1,221 @@
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+import graphviz
+
+def instrument_diagram(instrument):
+    components = instrument.component_list
+    n_components = len(components) + 1
+    graph_height = n_components / 2.5
+
+    absolute_box = Component_box(name="ABSOLUTE")
+    component_boxes = [absolute_box]
+    component_box_dict = {"ABSOLUTE": absolute_box}
+    for component in components:
+        box = Component_box(component)
+        component_boxes.append(box)
+        component_box_dict[component.name] = box
+
+    margin = 0.001
+    box_height_centers = np.linspace(1-margin, margin, n_components)
+    box_height = (1 - 2*margin)/n_components
+    for box, y_pos in zip(component_boxes, box_height_centers):
+        box.set_y(y_pos)
+        box.set_box_height(box_height)
+
+    for box in component_boxes:
+        box.set_x(0.2)
+
+    lane_width = 0.025
+
+    arrows = []
+    destinations_AT = set()
+    destinations_ROTATED = set()
+    box_names = [x.name for x in component_boxes]
+    for component in components:
+        if component.AT_reference is None:
+            origin = component_box_dict[component.name]
+            target = absolute_box
+        else:
+            origin = component_box_dict[component.name]
+            target = component_box_dict[component.AT_reference]
+
+        #print(origin.name, "to ", target.name)
+        destinations_AT.add(target.name)
+
+        origin_index = box_names.index(origin.name)
+        target_index = box_names.index(target.name)
+        names_between = box_names[target_index:origin_index]
+        #print(origin_index, target_index)
+        #print("between: ", names_between)
+        unique_destinations_between = destinations_AT.intersection(names_between)
+        lane_number = len(unique_destinations_between)
+        #print("destinations between: ", unique_destinations_between)
+
+        # Different technique for lane numbers:
+
+
+        arrow = Arrow(origin, target)
+        arrow.set_lane_width(lane_width)
+        arrow.set_box_offset_origin(0.24*box_height)
+        arrow.set_box_offset_target(-0.2 * box_height)
+        arrow.set_lane(lane_number)
+
+        arrows.append(arrow)
+        #print()
+
+        if not component.ROTATED_specified:
+            continue
+
+        if component.ROTATED_reference is None:
+            origin = component_box_dict[component.name]
+            target = absolute_box
+        else:
+            origin = component_box_dict[component.name]
+            target = component_box_dict[component.ROTATED_reference]
+
+        destinations_ROTATED.add(target.name)
+
+        origin_index = box_names.index(origin.name)
+        target_index = box_names.index(target.name)
+        names_between = box_names[target_index:origin_index]
+        unique_destinations_between = destinations_ROTATED.intersection(names_between)
+
+        arrow = Arrow(origin, target)
+        arrow.set_lane_width(lane_width)
+        arrow.set_lane_offset(0.5)
+        arrow.set_box_offset_origin(0.16 * box_height)
+        arrow.set_box_offset_target(-0.05 * box_height)
+        arrow.color = "red"
+        arrow.set_lane(len(unique_destinations_between))
+        arrows.append(arrow)
+
+    fig, ax = plt.subplots(figsize=(5, graph_height))
+    ax.set(xlim=(0, 1), ylim=(0, 1))
+    ax.axis("off")
+    for box in component_boxes:
+        box.plot_box(ax)
+
+    for arrow in arrows:
+        arrow.set_arrow_width(0.03/graph_height)
+        arrow.plot(ax)
+
+    plt.show()
+
+
+class Component_box:
+    def __init__(self, component_object=None, name=None):
+        self.component_object = component_object
+        if component_object is None:
+            self.name = name
+        else:
+            self.name = self.component_object.name
+        self.position_x = None
+        self.position_y = None
+        self.box_height = None
+
+    def set_box_height(self, box_height):
+        self.box_height = box_height
+
+    def set_x(self, x):
+        self.position_x = x
+
+    def set_y(self, y):
+        self.position_y = y
+
+    def get_origin_x(self):
+        return self.position_x
+
+    def get_origin_y(self):
+        return self.position_y + 0.2 * self.box_height
+
+    def get_target_x(self):
+        return self.position_x
+
+    def get_target_y(self):
+        return self.position_y - 0.15 * self.box_height
+
+    def plot_box(self, ax):
+        bbox = dict(boxstyle="round", facecolor="white", edgecolor="black")
+
+        ax.text(self.position_x + 0.03, self.position_y, self.name,
+                va="center", fontweight="bold", color="black", bbox=bbox)
+
+
+class Arrow:
+    def __init__(self, origin, target):
+        self.origin = origin
+        self.target = target
+        self.lane = None
+        self.lane_width = None
+        self.color = "blue"
+        self.lane_offset = 0
+
+        self.box_offset_origin = 0
+        self.box_offset_target = 0
+        self.arrow_width = 0.003
+
+    def set_lane(self, lane):
+        self.lane = lane
+
+    def set_lane_width(self, lane_width):
+        self.lane_width = lane_width
+
+    def set_lane_offset(self, offset):
+        self.lane_offset = offset*self.lane_width
+
+    def get_lane_value(self):
+        return self.lane*self.lane_width + self.lane_offset
+
+    def set_box_offset_origin(self, value):
+        self.box_offset_origin = value
+
+    def set_box_offset_target(self, value):
+        self.box_offset_target = value
+
+    def set_arrow_width(self, value):
+        self.arrow_width = value
+
+    def plot(self, ax):
+        origin_x = self.origin.position_x
+        origin_y = self.origin.position_y + self.box_offset_origin
+
+        origin_lane_x = origin_x - self.get_lane_value()
+        origin_lane_y = origin_y
+
+        ax.plot([origin_x + 0.05, origin_lane_x], [origin_y, origin_lane_y], color=self.color)
+
+        target_x = self.target.position_x
+        target_y = self.target.position_y + self.box_offset_target
+
+        target_lane_x = target_x - self.get_lane_value()
+        target_lane_y = target_y
+
+        ax.plot([origin_lane_x, target_lane_x], [origin_lane_y, target_lane_y], color=self.color)
+
+        ax.arrow(x=target_lane_x, y=target_lane_y,
+                 dx=self.get_lane_value() + 0.01, dy=0,
+                 color=self.color, length_includes_head=True,
+                 width=self.arrow_width)
+
+
+def instrument_diagram_gv(instrument):
+    dot = graphviz.Digraph('instrument_layout', comment='Connections in instrument', filename="test.gv")
+
+    components = instrument.component_list
+
+    dot.node("ABSOLUTE")
+    for component in components:
+        dot.node(component.name)
+
+    print(dot.source)
+
+    for component in components:
+        if component.AT_reference is None:
+            dot.edge(component.name, "ABSOLUTE", constraint=False)
+        else:
+            dot.edge(component.name, component.AT_reference, constraint=False)
+
+    print(dot.source)
+
+    dot.view()
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index a94d8d15..b37c7246 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -25,6 +25,7 @@
 from mcstasscript.helper.formatting import bcolors
 from mcstasscript.helper.unpickler import CustomMcStasUnpickler, CustomMcXtraceUnpickler
 from mcstasscript.helper.exceptions import McStasError
+from mcstasscript.helper.instrument_diagram import instrument_diagram
 
 
 class McCode_instr(BaseCalculator):
@@ -2016,6 +2017,9 @@ def show_instrument(self, format="webgl", width=800, height=450, new_tab=False):
 
         return IFrame(src=html_path, width=width, height=height)
 
+    def show_diagram(self):
+        instrument_diagram(self)
+
     def saveH5(self, filename: str, openpmd: bool = True):
         """
         Not relevant, but required from BaseCalculator, will be removed

From 75cd9674eaf9dc0716907d67d537569262625ad0 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 18 May 2022 16:08:37 +0200
Subject: [PATCH 275/403] Update of instrument diagram, now makes it as wide as
 it needs to based on internal connections.

---
 mcstasscript/helper/instrument_diagram.py | 130 ++++++++++++----------
 1 file changed, 71 insertions(+), 59 deletions(-)

diff --git a/mcstasscript/helper/instrument_diagram.py b/mcstasscript/helper/instrument_diagram.py
index e62c7197..635cb838 100644
--- a/mcstasscript/helper/instrument_diagram.py
+++ b/mcstasscript/helper/instrument_diagram.py
@@ -1,9 +1,11 @@
 import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
-import graphviz
 
 def instrument_diagram(instrument):
+    lane_width = 0.033 # spacing between lanes
+    margin = 0.01 # Margin on figure at top and bottom
+
     components = instrument.component_list
     n_components = len(components) + 1
     graph_height = n_components / 2.5
@@ -16,22 +18,22 @@ def instrument_diagram(instrument):
         component_boxes.append(box)
         component_box_dict[component.name] = box
 
-    margin = 0.001
+
     box_height_centers = np.linspace(1-margin, margin, n_components)
     box_height = (1 - 2*margin)/n_components
     for box, y_pos in zip(component_boxes, box_height_centers):
         box.set_y(y_pos)
         box.set_box_height(box_height)
 
-    for box in component_boxes:
-        box.set_x(0.2)
 
-    lane_width = 0.025
 
     arrows = []
-    destinations_AT = set()
-    destinations_ROTATED = set()
     box_names = [x.name for x in component_boxes]
+
+    AT_connections = {}
+    AT_lane_numbers = {}
+    ROTATED_connections = {}
+    ROTATED_lane_numbers = {}
     for component in components:
         if component.AT_reference is None:
             origin = component_box_dict[component.name]
@@ -40,29 +42,7 @@ def instrument_diagram(instrument):
             origin = component_box_dict[component.name]
             target = component_box_dict[component.AT_reference]
 
-        #print(origin.name, "to ", target.name)
-        destinations_AT.add(target.name)
-
-        origin_index = box_names.index(origin.name)
-        target_index = box_names.index(target.name)
-        names_between = box_names[target_index:origin_index]
-        #print(origin_index, target_index)
-        #print("between: ", names_between)
-        unique_destinations_between = destinations_AT.intersection(names_between)
-        lane_number = len(unique_destinations_between)
-        #print("destinations between: ", unique_destinations_between)
-
-        # Different technique for lane numbers:
-
-
-        arrow = Arrow(origin, target)
-        arrow.set_lane_width(lane_width)
-        arrow.set_box_offset_origin(0.24*box_height)
-        arrow.set_box_offset_target(-0.2 * box_height)
-        arrow.set_lane(lane_number)
-
-        arrows.append(arrow)
-        #print()
+        AT_connections[origin] = target
 
         if not component.ROTATED_specified:
             continue
@@ -74,23 +54,52 @@ def instrument_diagram(instrument):
             origin = component_box_dict[component.name]
             target = component_box_dict[component.ROTATED_reference]
 
-        destinations_ROTATED.add(target.name)
+        ROTATED_connections[origin] = target
+
 
-        origin_index = box_names.index(origin.name)
-        target_index = box_names.index(target.name)
-        names_between = box_names[target_index:origin_index]
-        unique_destinations_between = destinations_ROTATED.intersection(names_between)
+    for origin, target in AT_connections.items():
+        lane_number = find_lane_number(origin=origin, target=target, box_names=box_names,
+                                       connections=AT_connections,
+                                       lane_numbers=AT_lane_numbers,
+                                       component_box_dict=component_box_dict)
 
         arrow = Arrow(origin, target)
         arrow.set_lane_width(lane_width)
-        arrow.set_lane_offset(0.5)
+        arrow.set_box_offset_origin(0.24 * box_height)
+        arrow.set_box_offset_target(-0.2 * box_height)
+        arrow.set_lane(lane_number)
+
+        arrows.append(arrow)
+
+    for origin, target in ROTATED_connections.items():
+        lane_number = find_lane_number(origin=origin, target=target, box_names=box_names,
+                                       connections=ROTATED_connections,
+                                       lane_numbers=ROTATED_lane_numbers,
+                                       component_box_dict=component_box_dict)
+
+        arrow = Arrow(origin, target)
+        arrow.set_lane_width(lane_width)
+        arrow.set_lane_offset(0.4)
         arrow.set_box_offset_origin(0.16 * box_height)
         arrow.set_box_offset_target(-0.05 * box_height)
         arrow.color = "red"
-        arrow.set_lane(len(unique_destinations_between))
+
+        arrow.set_lane(lane_number)
         arrows.append(arrow)
 
-    fig, ax = plt.subplots(figsize=(5, graph_height))
+    # Infer how wide the figure should be based on number of lanes used
+    highest_lane = 0
+    for arrow in arrows:
+        highest_lane = max(arrow.lane, highest_lane)
+
+    lane_width = highest_lane/3
+    name_width = 3  # Reserve space of 3 for component names
+    graph_width = lane_width + name_width
+
+    for box in component_boxes:
+        box.set_x(lane_width / graph_width) # Places boxes so they get name_width space
+
+    fig, ax = plt.subplots(figsize=(graph_width, graph_height))
     ax.set(xlim=(0, 1), ylim=(0, 1))
     ax.axis("off")
     for box in component_boxes:
@@ -103,6 +112,30 @@ def instrument_diagram(instrument):
     plt.show()
 
 
+def find_lane_number(origin, target, box_names, connections, lane_numbers, component_box_dict):
+    origin_index = box_names.index(origin.name)
+    target_index = box_names.index(target.name)
+    names_between = box_names[target_index + 1:origin_index]
+
+    max_lane_number = 0
+    for name_between in names_between:
+        between_object = component_box_dict[name_between]
+        if name_between == "ABSOLUTE":
+            continue
+
+        if between_object not in connections:
+            continue
+
+        if connections[between_object] is target:
+            continue
+
+        if lane_numbers[between_object] > max_lane_number:
+            max_lane_number = lane_numbers[between_object]
+
+    lane_number = max_lane_number + 1
+    lane_numbers[origin] = lane_number
+    return lane_number
+
 class Component_box:
     def __init__(self, component_object=None, name=None):
         self.component_object = component_object
@@ -198,24 +231,3 @@ def plot(self, ax):
                  color=self.color, length_includes_head=True,
                  width=self.arrow_width)
 
-
-def instrument_diagram_gv(instrument):
-    dot = graphviz.Digraph('instrument_layout', comment='Connections in instrument', filename="test.gv")
-
-    components = instrument.component_list
-
-    dot.node("ABSOLUTE")
-    for component in components:
-        dot.node(component.name)
-
-    print(dot.source)
-
-    for component in components:
-        if component.AT_reference is None:
-            dot.edge(component.name, "ABSOLUTE", constraint=False)
-        else:
-            dot.edge(component.name, component.AT_reference, constraint=False)
-
-    print(dot.source)
-
-    dot.view()

From 8aea22fff659131e4c342ac91cb713cdc5369f5d Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 18 May 2022 16:44:30 +0200
Subject: [PATCH 276/403] Finishing touches on instrument diagram.

---
 mcstasscript/helper/instrument_diagram.py | 62 +++++++++++++++--------
 mcstasscript/interface/instr.py           | 13 +++++
 2 files changed, 53 insertions(+), 22 deletions(-)

diff --git a/mcstasscript/helper/instrument_diagram.py b/mcstasscript/helper/instrument_diagram.py
index 635cb838..156d3c84 100644
--- a/mcstasscript/helper/instrument_diagram.py
+++ b/mcstasscript/helper/instrument_diagram.py
@@ -1,10 +1,17 @@
-import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
 
+
 def instrument_diagram(instrument):
-    lane_width = 0.033 # spacing between lanes
-    margin = 0.01 # Margin on figure at top and bottom
+    """
+    Plots diagram of components in instrument with RELATIVE connections
+
+    All components in the instrument are shown as text fields and arrows are
+    drawn showing the AT RELATIVE and ROTATED RELATIVE connections between
+    components.
+    """
+    lane_width = 0.033  # spacing between lanes
+    margin = 0.01  # Margin on figure at top and bottom
 
     components = instrument.component_list
     n_components = len(components) + 1
@@ -18,15 +25,12 @@ def instrument_diagram(instrument):
         component_boxes.append(box)
         component_box_dict[component.name] = box
 
-
     box_height_centers = np.linspace(1-margin, margin, n_components)
     box_height = (1 - 2*margin)/n_components
     for box, y_pos in zip(component_boxes, box_height_centers):
         box.set_y(y_pos)
         box.set_box_height(box_height)
 
-
-
     arrows = []
     box_names = [x.name for x in component_boxes]
 
@@ -56,7 +60,7 @@ def instrument_diagram(instrument):
 
         ROTATED_connections[origin] = target
 
-
+    # Make arrows for AT connections
     for origin, target in AT_connections.items():
         lane_number = find_lane_number(origin=origin, target=target, box_names=box_names,
                                        connections=AT_connections,
@@ -68,9 +72,11 @@ def instrument_diagram(instrument):
         arrow.set_box_offset_origin(0.24 * box_height)
         arrow.set_box_offset_target(-0.2 * box_height)
         arrow.set_lane(lane_number)
+        #arrow.set_linestyle("--")
 
         arrows.append(arrow)
 
+    # Make arrows for ROTATED connections
     for origin, target in ROTATED_connections.items():
         lane_number = find_lane_number(origin=origin, target=target, box_names=box_names,
                                        connections=ROTATED_connections,
@@ -83,6 +89,7 @@ def instrument_diagram(instrument):
         arrow.set_box_offset_origin(0.16 * box_height)
         arrow.set_box_offset_target(-0.05 * box_height)
         arrow.color = "red"
+        #arrow.set_linestyle("--")
 
         arrow.set_lane(lane_number)
         arrows.append(arrow)
@@ -93,11 +100,12 @@ def instrument_diagram(instrument):
         highest_lane = max(arrow.lane, highest_lane)
 
     lane_width = highest_lane/3
+    lane_width = max(0.6, lane_width)
     name_width = 3  # Reserve space of 3 for component names
     graph_width = lane_width + name_width
 
     for box in component_boxes:
-        box.set_x(lane_width / graph_width) # Places boxes so they get name_width space
+        box.set_x(lane_width / graph_width)  # Places boxes so they get name_width space
 
     fig, ax = plt.subplots(figsize=(graph_width, graph_height))
     ax.set(xlim=(0, 1), ylim=(0, 1))
@@ -113,6 +121,9 @@ def instrument_diagram(instrument):
 
 
 def find_lane_number(origin, target, box_names, connections, lane_numbers, component_box_dict):
+    """
+    Helper function for finding how many lanes the current connection should go
+    """
     origin_index = box_names.index(origin.name)
     target_index = box_names.index(target.name)
     names_between = box_names[target_index + 1:origin_index]
@@ -136,8 +147,15 @@ def find_lane_number(origin, target, box_names, connections, lane_numbers, compo
     lane_numbers[origin] = lane_number
     return lane_number
 
+
 class Component_box:
+    """
+    Helper class for creating text boxes
+    """
     def __init__(self, component_object=None, name=None):
+        """
+        Text box object
+        """
         self.component_object = component_object
         if component_object is None:
             self.name = name
@@ -156,18 +174,6 @@ def set_x(self, x):
     def set_y(self, y):
         self.position_y = y
 
-    def get_origin_x(self):
-        return self.position_x
-
-    def get_origin_y(self):
-        return self.position_y + 0.2 * self.box_height
-
-    def get_target_x(self):
-        return self.position_x
-
-    def get_target_y(self):
-        return self.position_y - 0.15 * self.box_height
-
     def plot_box(self, ax):
         bbox = dict(boxstyle="round", facecolor="white", edgecolor="black")
 
@@ -176,16 +182,24 @@ def plot_box(self, ax):
 
 
 class Arrow:
+    """
+    Helper class for creating arrows with connections
+    """
     def __init__(self, origin, target):
+        """
+        Arrow object with origin Component_box and target component_box
+        """
         self.origin = origin
         self.target = target
         self.lane = None
         self.lane_width = None
-        self.color = "blue"
         self.lane_offset = 0
 
         self.box_offset_origin = 0
         self.box_offset_target = 0
+
+        self.origin_linestyle = "-"
+        self.color = "blue"
         self.arrow_width = 0.003
 
     def set_lane(self, lane):
@@ -209,6 +223,9 @@ def set_box_offset_target(self, value):
     def set_arrow_width(self, value):
         self.arrow_width = value
 
+    def set_linestyle(self, value):
+        self.origin_linestyle = value
+
     def plot(self, ax):
         origin_x = self.origin.position_x
         origin_y = self.origin.position_y + self.box_offset_origin
@@ -216,7 +233,8 @@ def plot(self, ax):
         origin_lane_x = origin_x - self.get_lane_value()
         origin_lane_y = origin_y
 
-        ax.plot([origin_x + 0.05, origin_lane_x], [origin_y, origin_lane_y], color=self.color)
+        ax.plot([origin_x + 0.05, origin_lane_x], [origin_y, origin_lane_y],
+                color=self.color, linestyle=self.origin_linestyle)
 
         target_x = self.target.position_x
         target_y = self.target.position_y + self.box_offset_target
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index b37c7246..7dbe1ade 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2018,7 +2018,20 @@ def show_instrument(self, format="webgl", width=800, height=450, new_tab=False):
         return IFrame(src=html_path, width=width, height=height)
 
     def show_diagram(self):
+        """
+        Shows diagram of component connections in insrument
+
+        Shows a diagram with all components as text fields and arrows between
+        them showing the AT RELATIVE and ROTATED RELATIVE connections. Spatial
+        connections are shown in blue, and rotational in red. ROTATED
+        connections are only shown when they are specified.
+        """
+        if self.has_errors():
+            print("The instrument has some error, this diagram is still"
+                  "shown as it may help find the bug.")
+
         instrument_diagram(self)
+        self.check_for_errors()
 
     def saveH5(self, filename: str, openpmd: bool = True):
         """

From ccc8ed426bc2d385ffaae4cc47a3c6b335b2a337 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 18 May 2022 16:45:10 +0200
Subject: [PATCH 277/403] version bump.

---
 mcstasscript/_version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 0c016c9e..68baa3ed 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.45"
+__version__ = "0.0.46"

From 5b63b7407ebf092f3ab21bccd17c492922e168e3 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 18 May 2022 16:49:53 +0200
Subject: [PATCH 278/403] Missing line in doc string.

---
 mcstasscript/helper/instrument_diagram.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/mcstasscript/helper/instrument_diagram.py b/mcstasscript/helper/instrument_diagram.py
index 156d3c84..1fef3921 100644
--- a/mcstasscript/helper/instrument_diagram.py
+++ b/mcstasscript/helper/instrument_diagram.py
@@ -9,6 +9,12 @@ def instrument_diagram(instrument):
     All components in the instrument are shown as text fields and arrows are
     drawn showing the AT RELATIVE and ROTATED RELATIVE connections between
     components.
+
+    Parameters
+    ----------
+
+    instrument : McCode_instr
+        Instrument object from which the component list is taken
     """
     lane_width = 0.033  # spacing between lanes
     margin = 0.01  # Margin on figure at top and bottom

From 8076695c77c9c94b8ff92f1fdb44afed0f096776 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 31 May 2022 13:06:11 +0200
Subject: [PATCH 279/403] New version of instrument diagram. Now takes advanced
 keywords like GROUP and JUMP into account. It also shows some connections
 used within the union package.

Use with instrument.show_diagram()
---
 mcstasscript/_version.py                  |   2 +-
 mcstasscript/helper/instrument_diagram.py | 917 +++++++++++++++++++---
 mcstasscript/interface/plotter.py         |   1 +
 3 files changed, 828 insertions(+), 92 deletions(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 68baa3ed..a15559fa 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.46"
+__version__ = "0.0.47"
diff --git a/mcstasscript/helper/instrument_diagram.py b/mcstasscript/helper/instrument_diagram.py
index 1fef3921..6096c9c2 100644
--- a/mcstasscript/helper/instrument_diagram.py
+++ b/mcstasscript/helper/instrument_diagram.py
@@ -1,5 +1,9 @@
 import matplotlib.pyplot as plt
 import numpy as np
+import copy
+
+from libpyvinyl.Parameters.Parameter import Parameter
+from mcstasscript.helper.mcstas_objects import DeclareVariable
 
 
 def instrument_diagram(instrument):
@@ -8,7 +12,9 @@ def instrument_diagram(instrument):
 
     All components in the instrument are shown as text fields and arrows are
     drawn showing the AT RELATIVE and ROTATED RELATIVE connections between
-    components.
+    components. When more advanced features are used, these are displayed
+    with arrows on the right side of the diagram, currently JUMP, GROUP and
+    use of Union components are visualized.
 
     Parameters
     ----------
@@ -16,123 +22,354 @@ def instrument_diagram(instrument):
     instrument : McCode_instr
         Instrument object from which the component list is taken
     """
-    lane_width = 0.033  # spacing between lanes
-    margin = 0.01  # Margin on figure at top and bottom
 
+    # Grab components from instrument file and make text box objects
     components = instrument.component_list
-    n_components = len(components) + 1
-    graph_height = n_components / 2.5
 
-    absolute_box = Component_box(name="ABSOLUTE")
+    # Prepare legend
+    component_reader = instrument.component_reader
+    component_categories = copy.deepcopy(component_reader.component_category)
+
+    absolute_box = ComponentBox(name="ABSOLUTE")
     component_boxes = [absolute_box]
     component_box_dict = {"ABSOLUTE": absolute_box}
     for component in components:
-        box = Component_box(component)
+        box = ComponentBox(component)
         component_boxes.append(box)
         component_box_dict[component.name] = box
 
-    box_height_centers = np.linspace(1-margin, margin, n_components)
-    box_height = (1 - 2*margin)/n_components
-    for box, y_pos in zip(component_boxes, box_height_centers):
-        box.set_y(y_pos)
-        box.set_box_height(box_height)
-
-    arrows = []
     box_names = [x.name for x in component_boxes]
 
-    AT_connections = {}
-    AT_lane_numbers = {}
-    ROTATED_connections = {}
-    ROTATED_lane_numbers = {}
+    color_choices = {"AT": "blue", "ROTATED": "red", "JUMP": "black", "GROUP": [0.4, 0.4, 0.4], "Union": "green"}
+
+    # Arrows for the left side of the diagram
+    AT_arrows = generate_AT_arrows(components, component_box_dict, box_names, color=color_choices["AT"])
+    ROTATED_arrows = generate_ROTATED_arrows(components, component_box_dict, box_names, color=color_choices["ROTATED"])
+
+    # Arrow for the right side of the diagram
+    JUMP_arrows = generate_JUMP_arrows(components, component_box_dict, box_names, color=color_choices["JUMP"])
+    GROUP_arrows = generate_GROUP_arrows(components, component_box_dict, box_names, color=color_choices["GROUP"])
+    Union_arrows = generate_Union_arrows(components, component_box_dict, box_names,
+                                         component_categories, color=color_choices["Union"])
+
+    # Create canvas
+    canvas = DiagramCanvas(AT_arrows + ROTATED_arrows, component_boxes, JUMP_arrows + GROUP_arrows + Union_arrows,
+                           component_categories=component_categories, colors=color_choices)
+
+    # Plot diagram
+    canvas.plot()
+
+
+def generate_GROUP_arrows(components, component_box_dict, box_names, existing_lane_numbers=None, color=None):
+    """
+    Generate Arrow objects related to the GROUP keyword
+    """
+    connections = {}
+    if existing_lane_numbers is None:
+        lane_numbers = {}
+    else:
+        lane_numbers = existing_lane_numbers
+
+    groups = {}
     for component in components:
-        if component.AT_reference is None:
-            origin = component_box_dict[component.name]
-            target = absolute_box
+        if component.GROUP != "":
+            group_reference = component.GROUP
+            if group_reference not in groups:
+                groups[group_reference] = [component_box_dict[component.name]]
+            else:
+                groups[group_reference].append(component_box_dict[component.name])
+
+    for group_name, group_members in groups.items():
+        if len(group_members) == 1:
+            # Meaningless group
+            continue
+        base = group_members[0]
+        connected_list = group_members[1:]
+
+        for connected in connected_list:
+            connections[connected] = base
+
+    arrows = []
+    """
+    Reverse connections because group arrows are ordered from last elements to first, as the first encountered
+    element is considered the base for the group that all the others point to.
+    """
+    for origin, target in reversed(connections.items()):
+        lane_number = find_lane_number(origin=origin, target=target, box_names=box_names,
+                                       connections=connections, lane_numbers=lane_numbers,
+                                       component_box_dict=component_box_dict)
+
+        group_name = origin.component_object.GROUP
+
+        arrow = Arrow(origin, target, kind="GROUP", description=group_name)
+        arrow.set_connection(True)
+        if color is None:
+            arrow.color = [0.4, 0.4, 0.4]
         else:
-            origin = component_box_dict[component.name]
-            target = component_box_dict[component.AT_reference]
+            arrow.color = color
+        arrow.set_lane(lane_number + 2) # Leave name for JUMP description
 
-        AT_connections[origin] = target
+        arrows.append(arrow)
 
-        if not component.ROTATED_specified:
-            continue
+        if existing_lane_numbers is not None:
+            existing_lane_numbers = lane_numbers
+
+    return arrows
+
+
+def generate_JUMP_arrows(components, component_box_dict, box_names, existing_lane_numbers=None, color=None):
+    """
+    Generate Arrow objects related to the JUMP keyword
+    """
+    connections = {}
+    if existing_lane_numbers is None:
+        lane_numbers = {}
+    else:
+        lane_numbers = existing_lane_numbers
+
+    for component in components:
+        if component.JUMP != "":
+            jump_reference = component.JUMP.split(" ")[0]
+            if jump_reference not in component_box_dict:
+                raise ValueError("JUMP reference: " + str(jump_reference)
+                                 + " not found.")
 
-        if component.ROTATED_reference is None:
             origin = component_box_dict[component.name]
-            target = absolute_box
+            connections[origin] = component_box_dict[jump_reference]
+
+    arrows = []
+    for origin, target in connections.items():
+        lane_number = find_lane_number(origin=origin, target=target, box_names=box_names,
+                                       connections=connections, lane_numbers=lane_numbers,
+                                       component_box_dict=component_box_dict)
+
+        arrow = Arrow(origin, target, kind="JUMP", description="JUMP")
+        arrow.set_sub_lane(1)
+        if color is None:
+            arrow.color = "black"
         else:
-            origin = component_box_dict[component.name]
-            target = component_box_dict[component.ROTATED_reference]
+            arrow.color = color
+        arrow.set_lane(lane_number + 2)  # Leave name for JUMP description
 
-        ROTATED_connections[origin] = target
+        arrows.append(arrow)
 
-    # Make arrows for AT connections
-    for origin, target in AT_connections.items():
+        if existing_lane_numbers is not None:
+            existing_lane_numbers = lane_numbers
+
+    return arrows
+
+
+def generate_Union_arrows(components, component_box_dict, box_names, component_categories,
+                          existing_lane_numbers=None, color=None):
+    """
+    Generate Arrow objects related to use of Union components
+
+    Currently supports processes, materials, geometries and master. Can be
+    expanded to also support loggers, abs_loggers and conditionals.
+    """
+    connections = {}
+    if existing_lane_numbers is None:
+        lane_numbers = {}
+    else:
+        lane_numbers = existing_lane_numbers
+
+    process_names = []
+    material_names = []
+    geometry_names = []
+    master_names = []
+    geometry_activation_counters = {}
+    for component in components:
+        category = component_categories[component.component_name]
+        if category == "union" or True:
+            if "_process" in component.component_name:
+                # Process component
+                process_names.append(component.name)
+
+            elif component.component_name == "Union_make_material":
+                # Make material component
+                material_names.append(component.name)
+
+                process_string = component.process_string
+                processes = process_string.strip('"').split(",")
+                for process in processes:
+                    if process not in process_names:
+                        print("Didn't find process of name '" + process + "'")
+                        print(process_names)
+                    else:
+                        origin = component_box_dict[process]
+                        connections[origin] = component_box_dict[component.name]
+
+            elif "material_string" in component.parameter_names:
+                # Geometry
+                geometry_names.append(component.name)
+
+                material = component.material_string.strip('"')
+                if material not in material_names:
+                    print("Didn't find material of name '" + material + "'")
+                    print(material_names)
+                else:
+                    origin = component_box_dict[material]
+                    connections[origin] = component_box_dict[component.name]
+
+                if component.number_of_activations is not None:
+                    number_of_activations = component.number_of_activations
+                else:
+                    number_of_activations = component.parameter_defaults["number_of_activations"]
+
+                geometry_activation_counters[component.name] = number_of_activations
+
+            elif "_abs_logger" in component.parameter_names:
+                # Abs logger
+                pass
+
+            elif "_logger" in component.parameter_names:
+                # Logger
+                pass
+
+            elif "target_loggers" in component.parameter_names:
+                # Conditional
+                pass
+
+            elif component.component_name == "Union_master":
+                # Master
+                master_names.append(component.name)
+
+                for geometry in geometry_names:
+                    if geometry_activation_counters[geometry] > 0:  # May need to account for floating point precision
+                        # Only include if activation counter for this geometry is still positive
+                        geometry_activation_counters[geometry] -= 1
+
+                        origin = component_box_dict[geometry]
+                        connections[origin] = component_box_dict[component.name]
+
+    arrows = []
+    for origin, target in connections.items():
         lane_number = find_lane_number(origin=origin, target=target, box_names=box_names,
-                                       connections=AT_connections,
-                                       lane_numbers=AT_lane_numbers,
+                                       connections=connections, lane_numbers=lane_numbers,
                                        component_box_dict=component_box_dict)
 
-        arrow = Arrow(origin, target)
-        arrow.set_lane_width(lane_width)
-        arrow.set_box_offset_origin(0.24 * box_height)
-        arrow.set_box_offset_target(-0.2 * box_height)
+        arrow = Arrow(origin, target, kind="Union")
+        arrow.set_sub_lane(2)
         arrow.set_lane(lane_number)
-        #arrow.set_linestyle("--")
+
+        if color is None:
+            arrow.color = "green"
+        else:
+            arrow.color = color
+
+        if target.name in master_names:
+            arrow.set_linestyle("--")
 
         arrows.append(arrow)
 
-    # Make arrows for ROTATED connections
-    for origin, target in ROTATED_connections.items():
+        if existing_lane_numbers is not None:
+            existing_lane_numbers = lane_numbers
+
+    return arrows
+
+
+def generate_AT_arrows(components, component_box_dict, box_names, color=None):
+    """
+    Generate Arrow objects related to the AT relationship of components
+    """
+    connections = {}
+    lane_numbers = {}
+    for component in components:
+        origin = component_box_dict[component.name]
+        if component.AT_reference is None:
+            target = component_box_dict["ABSOLUTE"]
+        elif component.AT_reference == "PREVIOUS":
+            origin_index = box_names.index(component.name)
+            target_index = origin_index - 1
+            target = component_box_dict[box_names[target_index]]
+        else:
+            target = component_box_dict[component.AT_reference]
+
+        connections[origin] = target
+
+    arrows = []
+    # Make arrows for AT connections
+    for origin, target in connections.items():
         lane_number = find_lane_number(origin=origin, target=target, box_names=box_names,
-                                       connections=ROTATED_connections,
-                                       lane_numbers=ROTATED_lane_numbers,
+                                       connections=connections, lane_numbers=lane_numbers,
                                        component_box_dict=component_box_dict)
 
-        arrow = Arrow(origin, target)
-        arrow.set_lane_width(lane_width)
-        arrow.set_lane_offset(0.4)
-        arrow.set_box_offset_origin(0.16 * box_height)
-        arrow.set_box_offset_target(-0.05 * box_height)
-        arrow.color = "red"
-        #arrow.set_linestyle("--")
-
+        arrow = Arrow(origin, target, kind="AT")
+        arrow.set_box_offset_origin(0.24)
+        arrow.set_box_offset_target(-0.2)
+        if color is None:
+            arrow.color = "blue"
+        else:
+            arrow.color = color
         arrow.set_lane(lane_number)
+        # arrow.set_linestyle("--")
+
         arrows.append(arrow)
 
-    # Infer how wide the figure should be based on number of lanes used
-    highest_lane = 0
-    for arrow in arrows:
-        highest_lane = max(arrow.lane, highest_lane)
+    return arrows
 
-    lane_width = highest_lane/3
-    lane_width = max(0.6, lane_width)
-    name_width = 3  # Reserve space of 3 for component names
-    graph_width = lane_width + name_width
 
-    for box in component_boxes:
-        box.set_x(lane_width / graph_width)  # Places boxes so they get name_width space
+def generate_ROTATED_arrows(components, component_box_dict, box_names, color=None):
+    """
+    Generate Arrow objects related to the ROTATED relationship of components
+    """
+    connections = {}
+    lane_numbers = {}
+    for component in components:
+        if not component.ROTATED_specified:
+            continue
+
+        if component.ROTATED_reference is None:
+            origin = component_box_dict[component.name]
+            target = component_box_dict["ABSOLUTE"]
+        elif component.ROTATED_reference == "PREVIOUS":
+            origin_index = box_names.index(component.name)
+            target_index = origin_index - 1
+            target = component_box_dict[box_names[target_index]]
+        else:
+            origin = component_box_dict[component.name]
+            target = component_box_dict[component.ROTATED_reference]
 
-    fig, ax = plt.subplots(figsize=(graph_width, graph_height))
-    ax.set(xlim=(0, 1), ylim=(0, 1))
-    ax.axis("off")
-    for box in component_boxes:
-        box.plot_box(ax)
+        connections[origin] = target
 
-    for arrow in arrows:
-        arrow.set_arrow_width(0.03/graph_height)
-        arrow.plot(ax)
+    arrows = []
+    # Make arrows for ROTATED connections
+    for origin, target in connections.items():
+        lane_number = find_lane_number(origin=origin, target=target, box_names=box_names,
+                                       connections=connections, lane_numbers=lane_numbers,
+                                       component_box_dict=component_box_dict)
+
+        arrow = Arrow(origin, target, kind="ROTATED")
+        arrow.set_lane_offset(0.2)
+        arrow.set_box_offset_origin(0.16)
+        arrow.set_box_offset_target(-0.05)
+        if color is None:
+            arrow.color = "red"
+        else:
+            arrow.color = color
+
+        arrow.set_sub_lane(1)
+        arrow.set_lane(lane_number)
+        arrows.append(arrow)
 
-    plt.show()
+    return arrows
 
 
 def find_lane_number(origin, target, box_names, connections, lane_numbers, component_box_dict):
     """
-    Helper function for finding how many lanes the current connection should go
+    Helper function for finding how many lanes the current connection should go out
     """
     origin_index = box_names.index(origin.name)
     target_index = box_names.index(target.name)
-    names_between = box_names[target_index + 1:origin_index]
+    if origin_index < target_index + 1:
+        names_between = box_names[origin_index + 1: target_index]
+    else:
+        names_between = box_names[target_index + 1:origin_index]
+
+    if origin.name == "Cu":
+        debug_mode = True
+    else:
+        debug_mode = False
 
     max_lane_number = 0
     for name_between in names_between:
@@ -140,10 +377,14 @@ def find_lane_number(origin, target, box_names, connections, lane_numbers, compo
         if name_between == "ABSOLUTE":
             continue
 
-        if between_object not in connections:
+        if between_object not in connections and between_object not in connections.values():
             continue
 
-        if connections[between_object] is target:
+        if between_object in connections:
+            if connections[between_object] is target:
+                continue
+
+        if between_object not in lane_numbers:
             continue
 
         if lane_numbers[between_object] > max_lane_number:
@@ -151,12 +392,324 @@ def find_lane_number(origin, target, box_names, connections, lane_numbers, compo
 
     lane_number = max_lane_number + 1
     lane_numbers[origin] = lane_number
+    lane_numbers[target] = lane_number
+
     return lane_number
 
 
-class Component_box:
+class DiagramCanvas:
+    def __init__(self, left_side_arrows, component_boxes, right_side_arrows, component_categories, colors):
+        """
+        Creates diagram of instrument file with given boxes and arrows
+
+        Makes diagram with column of boxes with arrows between them. The boxes
+        are described by the ComponentBox class and arrows by Arrow class. Some
+        arrows are drawn on the left side, AT and ROTATED relationships, while
+        more advanced connections are on the right side. A legend is provided
+        where the colors corresponding to component categories are shown along
+        with the arrow colors used for different features. The right side
+        arrows only show up in the legend when in use to reduce clutter. When
+        using this diagram in matplotlib widget mode, it is possible get more
+        information on each component by hovering the mouse of the beginning
+        the component box.
+        """
+        self.left_side_arrows = left_side_arrows
+        self.component_boxes = component_boxes
+        self.right_side_arrows = right_side_arrows
+        self.all_arrows = left_side_arrows + right_side_arrows
+        self.component_categories = component_categories
+        self.colors = colors
+
+        # Identify cases where multiple written input goes to or from same box
+        arrow_connections = {}
+        for arrow in self.right_side_arrows:
+            if arrow.origin not in arrow_connections:
+                arrow_connections[arrow.origin] = 1
+            else:
+                arrow_connections[arrow.origin] += 1
+
+            if arrow.target not in arrow_connections:
+                arrow_connections[arrow.target] = 1
+            else:
+                arrow_connections[arrow.target] += 1
+
+        for key in arrow_connections:
+            if arrow_connections[key] > 1:
+                displacements = list(np.linspace(-0.16, 0.16, arrow_connections[key]))
+                over_populated_box = key
+                for arrow in self.right_side_arrows:
+                    if arrow.origin is over_populated_box:
+                        arrow.set_origin_congested(True)
+                        if not arrow.connection:
+                            arrow.set_box_offset_origin(displacements.pop())
+
+                    if arrow.target is over_populated_box:
+                        arrow.set_target_congested(True)
+                        if not arrow.connection:
+                            arrow.set_box_offset_target(displacements.pop())
+
+        # Style constants
+        self.FIG_HEIGHT_PER_BOX = 0.4
+        self.FIG_WIDTH_PER_LANE = 0.33
+        self.FIG_WIDTH_PER_WEIGHTED_CHARACTER = 0.122
+        self.FIG_EXTRA_WIDTH_FOR_TEXT = 0.15
+        self.FIG_LEFT_MARGIN = self.FIG_WIDTH_PER_LANE * 0.5
+        self.FIG_RIGHT_MARGIN = self.FIG_WIDTH_PER_LANE * 0.5
+        self.FIG_LEGEND_HEADLINE = 0.2
+        self.FIG_RIGHT_SIDE_MINIMUM_SPACE = 3
+
+        self.graph_height = self.FIG_HEIGHT_PER_BOX * len(component_boxes)
+
+        # Distribute boxes over graph height
+        margin = 0.5/len(component_boxes)
+        box_height_centers = np.linspace(1 - margin, margin, len(component_boxes))
+        box_height = (1 - 2 * margin) / len(component_boxes)
+        for box, y_pos in zip(component_boxes, box_height_centers):
+            box.set_y(y_pos)
+            box.set_box_height(box_height)
+
+        lane_numbers = [x.lane for x in left_side_arrows] + [0]
+        self.highest_lane_left = max(lane_numbers)
+
+        lane_numbers = [x.lane for x in right_side_arrows] + [0]
+        self.highest_lane_right = max(lane_numbers)
+
+        weighted_box_names = [x.weighted_name_length for x in component_boxes]
+        self.characters_in_longest_name = max(weighted_box_names)
+
+        self.left_space = self.highest_lane_left * self.FIG_WIDTH_PER_LANE
+        self.box_space = self.characters_in_longest_name * self.FIG_WIDTH_PER_WEIGHTED_CHARACTER + self.FIG_EXTRA_WIDTH_FOR_TEXT
+        self.right_space = self.highest_lane_right * self.FIG_WIDTH_PER_LANE
+
+        self.right_space = max(self.right_space, self.FIG_RIGHT_SIDE_MINIMUM_SPACE)
+
+        self.graph_width = self.FIG_LEFT_MARGIN + self.left_space + self.box_space + self.right_space + self.FIG_RIGHT_MARGIN
+
+        self.text_start_graph = (self.FIG_LEFT_MARGIN + self.left_space) / self.graph_width
+        self.text_end_graph = (self.FIG_LEFT_MARGIN + self.left_space + self.box_space) / self.graph_width
+
+        lane_width = self.left_space/self.graph_width/self.highest_lane_left
+
+        # Rescale all
+        for arrow in self.all_arrows:
+            arrow.set_lane_width(lane_width)
+            arrow.set_lane_offset(0.18)
+            arrow.set_arrow_width(0.02 / self.graph_height)
+
+        for box in self.component_boxes:
+            box.set_box_indent(self.FIG_EXTRA_WIDTH_FOR_TEXT/self.graph_width)
+            box.set_x(self.text_start_graph)  # Places boxes so they get name_width space
+
+        # Prepare legend
+        all_categories = list(set(component_categories.values()))
+        all_categories.sort()
+
+        # Colors from http://vrl.cs.brown.edu/color
+        category_colors = ["#7487fb", "#81cc4c", "#e586fe", "#fe707d",
+                           "#00d618", "#ffa300", "#cc9966", "#53c6ef",
+                           "#baa3c6", "#aebf8a"]
+
+        needed_colors = category_colors[:len(all_categories)]
+        self.category_color_dict = dict(zip(all_categories, needed_colors))
+
+        active_categories = []
+        for box in self.component_boxes:
+            if box.component_object is None:
+                continue
+
+            category = component_categories[box.component_object.component_name]
+            if category not in active_categories:
+                active_categories.append(category)
+
+        self.legend_height = self.FIG_LEGEND_HEADLINE + self.FIG_HEIGHT_PER_BOX * (len(all_categories) // 2)
+
+        self.make_legend()
+
+    def make_legend(self):
+
+        box_top = 1 - self.FIG_LEGEND_HEADLINE / self.legend_height
+
+        fig, ax = plt.subplots(figsize=(7.0, self.legend_height))
+        ax.set(xlim=(0, 1), ylim=(0, 1))
+        ax.axis("off")
+
+        bbox = dict(boxstyle="round", facecolor="white", edgecolor="white")
+
+        ax.text(0.37, 1.05, "Legend", va="center", ha="center", fontweight="semibold", bbox=bbox, fontsize="large")
+
+        legend_boxes = [ComponentBox(name="Arm")]
+        for category, color in self.category_color_dict.items():
+            box = ComponentBox(name=category)
+            box.set_background_color(color)
+            legend_boxes.append(box)
+
+        # Color components boxes after same color scheme
+        for box in self.component_boxes:
+            if box.component_object is None:
+                continue
+
+            component_type = box.component_object.component_name
+            if component_type == "Arm":
+                box.set_background_color("white")
+            else:
+                component_category = self.component_categories[component_type]
+                box.set_background_color(self.category_color_dict[component_category])
+
+        batch_cut = len(legend_boxes) // 2
+        batches = [legend_boxes[:batch_cut], legend_boxes[batch_cut:]]
+
+        batch_end = 0.0
+        for batch in batches:
+            margin = 0.5 / len(batch)
+            box_height_centers = np.linspace(box_top, margin, len(batch))
+            box_height = (1 - 2 * margin) / len(batch)
+            for box, y_pos in zip(batch, box_height_centers):
+                box.set_x(batch_end)
+                box.set_y(y_pos)
+                box.set_box_height(box_height)
+                box.set_box_indent(self.FIG_EXTRA_WIDTH_FOR_TEXT / self.graph_width)
+
+                box.plot_box(ax)
+
+            fig.canvas.draw()
+            for box in batch:
+                box.calculate_bbox_dimensions(ax, self.graph_width)
+
+            box_ends = [x.graph_box_end for x in batch]
+            batch_end = max(box_ends)
+
+        # Always show AT and RELATIVE, but the others only when present
+        show_GROUP = False
+        show_JUMP = False
+        show_Union = False
+        for arrow in self.all_arrows:
+            if arrow.kind == "GROUP":
+                show_GROUP = True
+            if arrow.kind == "JUMP":
+                show_JUMP = True
+            if arrow.kind == "Union":
+                show_Union = True
+
+        arrow_width = 0.012
+        AT_HEIGHT = 0.83
+        START_WIDTH = 0.45
+        TEXT_WIDTH_INDENT = 0.01
+        DISPLACEMENT = 0.2
+        TEXT_DISPLACEMENT = 0.08
+        LINE_LENGTH = 0.33
+
+        current_displacement = 0
+        ax.arrow(x=START_WIDTH, y=AT_HEIGHT + current_displacement, dx=LINE_LENGTH, dy=0, color=self.colors["AT"],
+                 length_includes_head=True, width=arrow_width,
+                 head_width=5.0 * arrow_width, head_length=2.5 * arrow_width)
+        ax.text(START_WIDTH+TEXT_WIDTH_INDENT, AT_HEIGHT + TEXT_DISPLACEMENT + current_displacement,
+                "RELATIVE AT", va="center", weight="semibold")
+
+        current_displacement -= DISPLACEMENT
+        ax.arrow(x=START_WIDTH, y=AT_HEIGHT+current_displacement, dx=LINE_LENGTH, dy=0, color=self.colors["ROTATED"],
+                 length_includes_head=True, width=arrow_width,
+                 head_width=5.0 * arrow_width, head_length=2.5 * arrow_width)
+        ax.text(START_WIDTH + TEXT_WIDTH_INDENT, AT_HEIGHT + current_displacement + TEXT_DISPLACEMENT,
+                "RELATIVE ROTATED", va="center", weight="semibold")
+
+        if show_Union:
+            current_displacement -= DISPLACEMENT
+            ax.arrow(x=START_WIDTH, y=AT_HEIGHT + current_displacement, dx=LINE_LENGTH, dy=0,
+                     color=self.colors["Union"], length_includes_head=True, width=arrow_width,
+                     head_width=5.0 * arrow_width, head_length=2.5 * arrow_width)
+            ax.text(START_WIDTH + TEXT_WIDTH_INDENT, AT_HEIGHT + current_displacement + TEXT_DISPLACEMENT,
+                    "Union", va="center", weight="semibold")
+
+        if show_JUMP:
+            current_displacement -= DISPLACEMENT
+            ax.arrow(x=START_WIDTH, y=AT_HEIGHT+current_displacement, dx=LINE_LENGTH, dy=0,
+                     color=self.colors["JUMP"], length_includes_head=True, width=arrow_width,
+                     head_width=5.0 * arrow_width, head_length=2.5 * arrow_width)
+            ax.text(START_WIDTH + TEXT_WIDTH_INDENT, AT_HEIGHT+current_displacement+TEXT_DISPLACEMENT,
+                    "JUMP", va="center", weight="semibold")
+
+        if show_GROUP:
+            current_displacement -= DISPLACEMENT
+            ax.plot([START_WIDTH, START_WIDTH+LINE_LENGTH], 2*[AT_HEIGHT + current_displacement],
+                    color=self.colors["GROUP"])
+            ax.text(START_WIDTH + TEXT_WIDTH_INDENT, AT_HEIGHT + current_displacement + TEXT_DISPLACEMENT,
+                    "GROUP", va="center", weight="semibold")
+
+    def plot(self):
+        fig, ax = plt.subplots(figsize=(self.graph_width, self.graph_height))
+        ax.set(xlim=(0, 1), ylim=(0, 1))
+        ax.axis("off")
+
+        # Start by placing scatter points corresponding to the start of each box
+        box_x = []
+        box_y = []
+        box_info = []
+        for box in self.component_boxes:
+            box_x.append(box.position_x + box.box_indent)
+            box_y.append(box.position_y)
+            if box.component_object is None:
+                info = box.name
+            else:
+                info = component_description(box.component_object)
+            box_info.append(info)
+
+        # These scatter points will be the basis for mouse hovering showing annotations
+        sc = ax.scatter(box_x, box_y, color="white")
+
+        # Plot all the boxes
+        for box in self.component_boxes:
+            box.plot_box(ax)
+
+        fig.canvas.draw()
+        for box in self.component_boxes:
+            # Calculate the box end position, requires they are already drawn
+            box.calculate_bbox_dimensions(ax, self.graph_width)
+
+        for arrow in self.left_side_arrows:
+            # Plot arrows on left side
+            arrow.plot_left_side(ax)
+
+        for arrow in self.right_side_arrows:
+            # Plot arrows on right side
+            arrow.plot_right_side(ax, self.text_end_graph)
+
+        # Create anotation box that will be shown when hovering the mouse over a box
+        annot = ax.annotate("", xy=(0, 0), xytext=(20, 10), textcoords="offset points",
+                            bbox=dict(boxstyle="round", fc="w"), annotation_clip=False)
+        annot.set_visible(False)
+
+        # Helper function to update the anotation box with correct text
+        def update_annot(ind):
+            pos = sc.get_offsets()[ind["ind"][0]]
+            annot.xy = pos
+            text = "{}".format(" ".join([box_info[n] for n in ind["ind"]]))
+            annot.set_text(text)
+            annot.get_bbox_patch().set_facecolor([0.95, 0.95, 0.95])
+
+        # Helper function to detect hovering and update annotation accordingly
+        def hover(event):
+            vis = annot.get_visible()
+            if event.inaxes == ax:
+                cont, ind = sc.contains(event)
+                if cont:
+                    update_annot(ind)
+                    annot.set_visible(True)
+                    fig.canvas.draw_idle()
+                else:
+                    if vis:
+                        annot.set_visible(False)
+                        fig.canvas.draw_idle()
+
+        # Connect the hover function to the canvas event signal
+        fig.canvas.mpl_connect("motion_notify_event", hover)
+
+        # Show the figure
+        plt.show()
+
+
+class ComponentBox:
     """
-    Helper class for creating text boxes
+    Helper class for creating text boxes describing components
     """
     def __init__(self, component_object=None, name=None):
         """
@@ -167,9 +720,19 @@ def __init__(self, component_object=None, name=None):
             self.name = name
         else:
             self.name = self.component_object.name
+
+        # Produced weighted length of name, capital letters count for 1.2
+        self.weighted_name_length = len(self.name) + 0.2*sum(1 for c in self.name if c.isupper())
+
         self.position_x = None
         self.position_y = None
         self.box_height = None
+        self.box_indent = None
+        self.background_color = "white"
+
+        self.t = None # text object
+        self.graph_box_end = None # graph position where text ends
+        self.graph_box_start = None  # graph position where text ends
 
     def set_box_height(self, box_height):
         self.box_height = box_height
@@ -180,24 +743,54 @@ def set_x(self, x):
     def set_y(self, y):
         self.position_y = y
 
+    def set_box_indent(self, value):
+        self.box_indent = value
+
+    def set_background_color(self, value):
+        self.background_color = value
+
     def plot_box(self, ax):
-        bbox = dict(boxstyle="round", facecolor="white", edgecolor="black")
+        bbox = dict(boxstyle="round", facecolor=self.background_color, edgecolor="black")
 
-        ax.text(self.position_x + 0.03, self.position_y, self.name,
-                va="center", fontweight="bold", color="black", bbox=bbox)
+        self.t = ax.text(self.position_x + self.box_indent, self.position_y, self.name,
+                         va="center", fontweight="semibold", color="black", #font="monospace",
+                         bbox=bbox)
+
+    def get_text_start(self):
+        return self.graph_box_start
+
+    def get_text_end(self):
+        return self.graph_box_end
+
+    def calculate_bbox_dimensions(self, ax, graph_width):
+        """
+        Method that gets dimensions of plotted text box
+        """
+        transf = ax.transAxes.inverted()
+        bb = self.t.get_window_extent(renderer=plt.gcf().canvas.get_renderer())
+        bbox = bb.transformed(transf)
+
+        TEXT_PADDING_IN_FIG_UNITS = 0.08
+        box_padding = TEXT_PADDING_IN_FIG_UNITS / graph_width
+        self.graph_box_start = bbox.x0 - box_padding
+        self.graph_box_end = bbox.x1 + box_padding
 
 
 class Arrow:
     """
     Helper class for creating arrows with connections
     """
-    def __init__(self, origin, target):
+    def __init__(self, origin, target, kind=None, description=None):
         """
         Arrow object with origin Component_box and target component_box
         """
         self.origin = origin
         self.target = target
+        self.kind = kind
+        self.description=description
+
         self.lane = None
+        self.sub_lane = 0
         self.lane_width = None
         self.lane_offset = 0
 
@@ -207,51 +800,193 @@ def __init__(self, origin, target):
         self.origin_linestyle = "-"
         self.color = "blue"
         self.arrow_width = 0.003
+        self.connection = False
+
+        self.origin_congested = False
+        self.target_congested = False
 
     def set_lane(self, lane):
         self.lane = lane
 
+    def set_sub_lane(self, lane):
+        self.sub_lane = lane
+
     def set_lane_width(self, lane_width):
         self.lane_width = lane_width
 
     def set_lane_offset(self, offset):
-        self.lane_offset = offset*self.lane_width
+        self.lane_offset = offset
 
     def get_lane_value(self):
-        return self.lane*self.lane_width + self.lane_offset
+        return self.lane_width * (self.lane + self.sub_lane * self.lane_offset)
+
+    def set_connection(self, value):
+        self.connection = value
+
+    def set_origin_congested(self, value):
+        self.origin_congested = value
+
+    def set_target_congested(self, value):
+        self.target_congested = value
 
     def set_box_offset_origin(self, value):
+        """
+        In units of box height
+        """
         self.box_offset_origin = value
 
+    def get_box_offset_origin(self):
+        return self.origin.box_height * self.box_offset_origin
+
     def set_box_offset_target(self, value):
+        """
+        In units of box height
+        """
         self.box_offset_target = value
 
+    def get_box_offset_target(self):
+        return self.target.box_height * self.box_offset_target
+
     def set_arrow_width(self, value):
         self.arrow_width = value
 
     def set_linestyle(self, value):
         self.origin_linestyle = value
 
-    def plot(self, ax):
-        origin_x = self.origin.position_x
-        origin_y = self.origin.position_y + self.box_offset_origin
+    def plot_left_side(self, ax):
+        origin_x = self.origin.get_text_start()
+        origin_y = self.origin.position_y + self.get_box_offset_origin()
 
         origin_lane_x = origin_x - self.get_lane_value()
         origin_lane_y = origin_y
 
-        ax.plot([origin_x + 0.05, origin_lane_x], [origin_y, origin_lane_y],
+        ax.plot([origin_x, origin_lane_x], [origin_y, origin_lane_y],
                 color=self.color, linestyle=self.origin_linestyle)
 
-        target_x = self.target.position_x
-        target_y = self.target.position_y + self.box_offset_target
+        target_x = self.target.get_text_start()
+        target_y = self.target.position_y + self.get_box_offset_target()
 
         target_lane_x = target_x - self.get_lane_value()
         target_lane_y = target_y
 
         ax.plot([origin_lane_x, target_lane_x], [origin_lane_y, target_lane_y], color=self.color)
 
+        arrow_length = self.target.get_text_start() - target_lane_x
         ax.arrow(x=target_lane_x, y=target_lane_y,
-                 dx=self.get_lane_value() + 0.01, dy=0,
+                 dx=arrow_length, dy=0,
                  color=self.color, length_includes_head=True,
-                 width=self.arrow_width)
+                 width=self.arrow_width, head_width=5.0*self.arrow_width,
+                 head_length=7.0*self.arrow_width)
+
+    def plot_right_side(self, ax, text_end):
+        origin_x = self.origin.get_text_end()
+        origin_y = self.origin.position_y + self.get_box_offset_origin()
+
+        origin_lane_x = text_end + self.get_lane_value()
+        origin_lane_y = origin_y
+
+        ax.plot([origin_x, origin_lane_x], [origin_y, origin_lane_y],
+                color=self.color, linestyle=self.origin_linestyle)
+
+        if self.description is not None and not self.origin_congested:
+            bbox = dict(boxstyle="round", facecolor="white", edgecolor="white")
+            ax.text(0.5*(origin_x + origin_lane_x), origin_lane_y,
+                    self.description, ha="center", va="center", bbox=bbox)
+
+        target_y = self.target.position_y + self.get_box_offset_target()
+
+        target_lane_x = text_end + self.get_lane_value()
+        target_lane_y = target_y
+
+        ax.plot([origin_lane_x, target_lane_x], [origin_lane_y, target_lane_y], color=self.color)
+
+        if self.connection:
+            mid_point = 0.5*(target_lane_x + self.target.get_text_end())
+            ax.plot([target_lane_x, self.target.get_text_end()],
+                    [target_lane_y, target_lane_y], color=self.color)
+        else:
+            arrow_length = target_lane_x - self.target.get_text_end()
+            mid_point = target_lane_x - 0.5*arrow_length
+            ax.arrow(x=target_lane_x, y=target_lane_y,
+                     dx=-arrow_length, dy=0,
+                     color=self.color, length_includes_head=True,
+                     width=self.arrow_width, head_width=5.0*self.arrow_width,
+                     head_length=7.0*self.arrow_width)
+
+        if self.description is not None and not self.target_congested:
+            bbox = dict(boxstyle="round", facecolor="white", edgecolor="white")
+            ax.text(mid_point, target_lane_y,
+                    self.description, ha="center", va="center", bbox=bbox)
+
+
+def component_description(component):
+    """
+    Returns string of information about the component
+
+    Includes information on required parameters if they are not yet
+    specified. Information on the components are added when the
+    class is used as a superclass for classes describing each
+    McStas component. Uses mathtext for bold and italics.
+    """
+    string = ""
+
+    if len(component.c_code_before) > 1:
+        string += component.c_code_before + "\n"
+    if len(component.comment) > 1:
+        string += "// " + component.comment + "\n"
+    if component.SPLIT != 0:
+        string += "SPLIT " + str(component.SPLIT) + " "
+    string += "COMPONENT " + str(component.name)
+    string += " = $\\bf{" + str(component.component_name).replace("_", "\_") + "}$\n"
+    for key in component.parameter_names:
+        val = getattr(component, key)
+        parameter_name = key
+        if val is not None:
+            unit = ""
+            if key in component.parameter_units:
+                unit = "[" + component.parameter_units[key] + "]"
+            if isinstance(val, Parameter):
+                val_string = val.name
+            elif isinstance(val, DeclareVariable):
+                val_string = val.name
+            else:
+                val_string = str(val)
+
+            value = "$\\bf{" + val_string.replace("_", "\_").replace('\"', "''").replace('"', "\''") + "}$"
+            string += "  $\\bf{" + parameter_name.replace("_", "\_") + "}$"
+            string += " = " + value + " " + unit + "\n"
+        else:
+            if component.parameter_defaults[key] is None:
+                string += "  $\\bf{" + parameter_name.replace("_", "\_") + "}$"
+                string += " : $\\bf{Required\ parameter\ not\ yet\ specified}$\n"
+
+    if not component.WHEN == "":
+        string += component.WHEN + "\n"
+
+    string += "AT " + str(component.AT_data)
+    if component.AT_reference is None:
+        string += " $\\it{ABSOLUTE}$\n"
+    else:
+        string += " RELATIVE $\\it{" + component.AT_reference.replace("_", "\_") + "}$\n"
+
+    if component.ROTATED_specified:
+        string += "ROTATED " + str(component.ROTATED_data)
+        if component.ROTATED_reference is None:
+            string += " $\\it{ABSOLUTE}$\n"
+        else:
+            string += " $\\it{" + component.ROTATED_reference.replace("_", "\_") + "}$\n"
+
+    if not component.GROUP == "":
+        string += "GROUP " + component.GROUP + "\n"
+    if not component.EXTEND == "":
+        string += "EXTEND %{" + "\n"
+        string += component.EXTEND + "%}" + "\n"
+    if not component.JUMP == "":
+        string += "JUMP " + component.JUMP + "\n"
+    if len(component.c_code_after) > 1:
+        string += component.c_code_after + "\n"
+
+    return string.strip()
+
+
 
diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index bd4e811d..290c6cb6 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -62,6 +62,7 @@ def make_sub_plot(data_list, **kwargs):
 
     if "filename" in kwargs:
         fig.savefig(kwargs["filename"])
+        plt.close(fig)
     else:
         plt.show()
 

From 93704645e40e3a9ed46f57edec17a56982e6245a Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 7 Jun 2022 16:13:21 +0200
Subject: [PATCH 280/403] Refactor and fix of instrument diagram:   Now in own
 folder and split over 11 files instead of 1   Improved routing, both simpler
 and avoids some bugs   Improved handling of congestion

---
 mcstasscript/helper/instrument_diagram.py     | 992 ------------------
 mcstasscript/instrument_diagram/arrow.py      | 149 +++
 mcstasscript/instrument_diagram/box.py        |  70 ++
 mcstasscript/instrument_diagram/canvas.py     | 344 ++++++
 .../component_description.py                  |  71 ++
 .../instrument_diagram/connections.py         | 109 ++
 .../instrument_diagram/generate_AT.py         |  43 +
 .../instrument_diagram/generate_GROUP.py      |  47 +
 .../instrument_diagram/generate_JUMP.py       |  38 +
 .../instrument_diagram/generate_ROTATED.py    |  48 +
 .../instrument_diagram/generate_Union.py      | 119 +++
 .../instrument_diagram/make_diagram.py        |  63 ++
 mcstasscript/interface/instr.py               |   2 +-
 13 files changed, 1102 insertions(+), 993 deletions(-)
 delete mode 100644 mcstasscript/helper/instrument_diagram.py
 create mode 100644 mcstasscript/instrument_diagram/arrow.py
 create mode 100644 mcstasscript/instrument_diagram/box.py
 create mode 100644 mcstasscript/instrument_diagram/canvas.py
 create mode 100644 mcstasscript/instrument_diagram/component_description.py
 create mode 100644 mcstasscript/instrument_diagram/connections.py
 create mode 100644 mcstasscript/instrument_diagram/generate_AT.py
 create mode 100644 mcstasscript/instrument_diagram/generate_GROUP.py
 create mode 100644 mcstasscript/instrument_diagram/generate_JUMP.py
 create mode 100644 mcstasscript/instrument_diagram/generate_ROTATED.py
 create mode 100644 mcstasscript/instrument_diagram/generate_Union.py
 create mode 100644 mcstasscript/instrument_diagram/make_diagram.py

diff --git a/mcstasscript/helper/instrument_diagram.py b/mcstasscript/helper/instrument_diagram.py
deleted file mode 100644
index 6096c9c2..00000000
--- a/mcstasscript/helper/instrument_diagram.py
+++ /dev/null
@@ -1,992 +0,0 @@
-import matplotlib.pyplot as plt
-import numpy as np
-import copy
-
-from libpyvinyl.Parameters.Parameter import Parameter
-from mcstasscript.helper.mcstas_objects import DeclareVariable
-
-
-def instrument_diagram(instrument):
-    """
-    Plots diagram of components in instrument with RELATIVE connections
-
-    All components in the instrument are shown as text fields and arrows are
-    drawn showing the AT RELATIVE and ROTATED RELATIVE connections between
-    components. When more advanced features are used, these are displayed
-    with arrows on the right side of the diagram, currently JUMP, GROUP and
-    use of Union components are visualized.
-
-    Parameters
-    ----------
-
-    instrument : McCode_instr
-        Instrument object from which the component list is taken
-    """
-
-    # Grab components from instrument file and make text box objects
-    components = instrument.component_list
-
-    # Prepare legend
-    component_reader = instrument.component_reader
-    component_categories = copy.deepcopy(component_reader.component_category)
-
-    absolute_box = ComponentBox(name="ABSOLUTE")
-    component_boxes = [absolute_box]
-    component_box_dict = {"ABSOLUTE": absolute_box}
-    for component in components:
-        box = ComponentBox(component)
-        component_boxes.append(box)
-        component_box_dict[component.name] = box
-
-    box_names = [x.name for x in component_boxes]
-
-    color_choices = {"AT": "blue", "ROTATED": "red", "JUMP": "black", "GROUP": [0.4, 0.4, 0.4], "Union": "green"}
-
-    # Arrows for the left side of the diagram
-    AT_arrows = generate_AT_arrows(components, component_box_dict, box_names, color=color_choices["AT"])
-    ROTATED_arrows = generate_ROTATED_arrows(components, component_box_dict, box_names, color=color_choices["ROTATED"])
-
-    # Arrow for the right side of the diagram
-    JUMP_arrows = generate_JUMP_arrows(components, component_box_dict, box_names, color=color_choices["JUMP"])
-    GROUP_arrows = generate_GROUP_arrows(components, component_box_dict, box_names, color=color_choices["GROUP"])
-    Union_arrows = generate_Union_arrows(components, component_box_dict, box_names,
-                                         component_categories, color=color_choices["Union"])
-
-    # Create canvas
-    canvas = DiagramCanvas(AT_arrows + ROTATED_arrows, component_boxes, JUMP_arrows + GROUP_arrows + Union_arrows,
-                           component_categories=component_categories, colors=color_choices)
-
-    # Plot diagram
-    canvas.plot()
-
-
-def generate_GROUP_arrows(components, component_box_dict, box_names, existing_lane_numbers=None, color=None):
-    """
-    Generate Arrow objects related to the GROUP keyword
-    """
-    connections = {}
-    if existing_lane_numbers is None:
-        lane_numbers = {}
-    else:
-        lane_numbers = existing_lane_numbers
-
-    groups = {}
-    for component in components:
-        if component.GROUP != "":
-            group_reference = component.GROUP
-            if group_reference not in groups:
-                groups[group_reference] = [component_box_dict[component.name]]
-            else:
-                groups[group_reference].append(component_box_dict[component.name])
-
-    for group_name, group_members in groups.items():
-        if len(group_members) == 1:
-            # Meaningless group
-            continue
-        base = group_members[0]
-        connected_list = group_members[1:]
-
-        for connected in connected_list:
-            connections[connected] = base
-
-    arrows = []
-    """
-    Reverse connections because group arrows are ordered from last elements to first, as the first encountered
-    element is considered the base for the group that all the others point to.
-    """
-    for origin, target in reversed(connections.items()):
-        lane_number = find_lane_number(origin=origin, target=target, box_names=box_names,
-                                       connections=connections, lane_numbers=lane_numbers,
-                                       component_box_dict=component_box_dict)
-
-        group_name = origin.component_object.GROUP
-
-        arrow = Arrow(origin, target, kind="GROUP", description=group_name)
-        arrow.set_connection(True)
-        if color is None:
-            arrow.color = [0.4, 0.4, 0.4]
-        else:
-            arrow.color = color
-        arrow.set_lane(lane_number + 2) # Leave name for JUMP description
-
-        arrows.append(arrow)
-
-        if existing_lane_numbers is not None:
-            existing_lane_numbers = lane_numbers
-
-    return arrows
-
-
-def generate_JUMP_arrows(components, component_box_dict, box_names, existing_lane_numbers=None, color=None):
-    """
-    Generate Arrow objects related to the JUMP keyword
-    """
-    connections = {}
-    if existing_lane_numbers is None:
-        lane_numbers = {}
-    else:
-        lane_numbers = existing_lane_numbers
-
-    for component in components:
-        if component.JUMP != "":
-            jump_reference = component.JUMP.split(" ")[0]
-            if jump_reference not in component_box_dict:
-                raise ValueError("JUMP reference: " + str(jump_reference)
-                                 + " not found.")
-
-            origin = component_box_dict[component.name]
-            connections[origin] = component_box_dict[jump_reference]
-
-    arrows = []
-    for origin, target in connections.items():
-        lane_number = find_lane_number(origin=origin, target=target, box_names=box_names,
-                                       connections=connections, lane_numbers=lane_numbers,
-                                       component_box_dict=component_box_dict)
-
-        arrow = Arrow(origin, target, kind="JUMP", description="JUMP")
-        arrow.set_sub_lane(1)
-        if color is None:
-            arrow.color = "black"
-        else:
-            arrow.color = color
-        arrow.set_lane(lane_number + 2)  # Leave name for JUMP description
-
-        arrows.append(arrow)
-
-        if existing_lane_numbers is not None:
-            existing_lane_numbers = lane_numbers
-
-    return arrows
-
-
-def generate_Union_arrows(components, component_box_dict, box_names, component_categories,
-                          existing_lane_numbers=None, color=None):
-    """
-    Generate Arrow objects related to use of Union components
-
-    Currently supports processes, materials, geometries and master. Can be
-    expanded to also support loggers, abs_loggers and conditionals.
-    """
-    connections = {}
-    if existing_lane_numbers is None:
-        lane_numbers = {}
-    else:
-        lane_numbers = existing_lane_numbers
-
-    process_names = []
-    material_names = []
-    geometry_names = []
-    master_names = []
-    geometry_activation_counters = {}
-    for component in components:
-        category = component_categories[component.component_name]
-        if category == "union" or True:
-            if "_process" in component.component_name:
-                # Process component
-                process_names.append(component.name)
-
-            elif component.component_name == "Union_make_material":
-                # Make material component
-                material_names.append(component.name)
-
-                process_string = component.process_string
-                processes = process_string.strip('"').split(",")
-                for process in processes:
-                    if process not in process_names:
-                        print("Didn't find process of name '" + process + "'")
-                        print(process_names)
-                    else:
-                        origin = component_box_dict[process]
-                        connections[origin] = component_box_dict[component.name]
-
-            elif "material_string" in component.parameter_names:
-                # Geometry
-                geometry_names.append(component.name)
-
-                material = component.material_string.strip('"')
-                if material not in material_names:
-                    print("Didn't find material of name '" + material + "'")
-                    print(material_names)
-                else:
-                    origin = component_box_dict[material]
-                    connections[origin] = component_box_dict[component.name]
-
-                if component.number_of_activations is not None:
-                    number_of_activations = component.number_of_activations
-                else:
-                    number_of_activations = component.parameter_defaults["number_of_activations"]
-
-                geometry_activation_counters[component.name] = number_of_activations
-
-            elif "_abs_logger" in component.parameter_names:
-                # Abs logger
-                pass
-
-            elif "_logger" in component.parameter_names:
-                # Logger
-                pass
-
-            elif "target_loggers" in component.parameter_names:
-                # Conditional
-                pass
-
-            elif component.component_name == "Union_master":
-                # Master
-                master_names.append(component.name)
-
-                for geometry in geometry_names:
-                    if geometry_activation_counters[geometry] > 0:  # May need to account for floating point precision
-                        # Only include if activation counter for this geometry is still positive
-                        geometry_activation_counters[geometry] -= 1
-
-                        origin = component_box_dict[geometry]
-                        connections[origin] = component_box_dict[component.name]
-
-    arrows = []
-    for origin, target in connections.items():
-        lane_number = find_lane_number(origin=origin, target=target, box_names=box_names,
-                                       connections=connections, lane_numbers=lane_numbers,
-                                       component_box_dict=component_box_dict)
-
-        arrow = Arrow(origin, target, kind="Union")
-        arrow.set_sub_lane(2)
-        arrow.set_lane(lane_number)
-
-        if color is None:
-            arrow.color = "green"
-        else:
-            arrow.color = color
-
-        if target.name in master_names:
-            arrow.set_linestyle("--")
-
-        arrows.append(arrow)
-
-        if existing_lane_numbers is not None:
-            existing_lane_numbers = lane_numbers
-
-    return arrows
-
-
-def generate_AT_arrows(components, component_box_dict, box_names, color=None):
-    """
-    Generate Arrow objects related to the AT relationship of components
-    """
-    connections = {}
-    lane_numbers = {}
-    for component in components:
-        origin = component_box_dict[component.name]
-        if component.AT_reference is None:
-            target = component_box_dict["ABSOLUTE"]
-        elif component.AT_reference == "PREVIOUS":
-            origin_index = box_names.index(component.name)
-            target_index = origin_index - 1
-            target = component_box_dict[box_names[target_index]]
-        else:
-            target = component_box_dict[component.AT_reference]
-
-        connections[origin] = target
-
-    arrows = []
-    # Make arrows for AT connections
-    for origin, target in connections.items():
-        lane_number = find_lane_number(origin=origin, target=target, box_names=box_names,
-                                       connections=connections, lane_numbers=lane_numbers,
-                                       component_box_dict=component_box_dict)
-
-        arrow = Arrow(origin, target, kind="AT")
-        arrow.set_box_offset_origin(0.24)
-        arrow.set_box_offset_target(-0.2)
-        if color is None:
-            arrow.color = "blue"
-        else:
-            arrow.color = color
-        arrow.set_lane(lane_number)
-        # arrow.set_linestyle("--")
-
-        arrows.append(arrow)
-
-    return arrows
-
-
-def generate_ROTATED_arrows(components, component_box_dict, box_names, color=None):
-    """
-    Generate Arrow objects related to the ROTATED relationship of components
-    """
-    connections = {}
-    lane_numbers = {}
-    for component in components:
-        if not component.ROTATED_specified:
-            continue
-
-        if component.ROTATED_reference is None:
-            origin = component_box_dict[component.name]
-            target = component_box_dict["ABSOLUTE"]
-        elif component.ROTATED_reference == "PREVIOUS":
-            origin_index = box_names.index(component.name)
-            target_index = origin_index - 1
-            target = component_box_dict[box_names[target_index]]
-        else:
-            origin = component_box_dict[component.name]
-            target = component_box_dict[component.ROTATED_reference]
-
-        connections[origin] = target
-
-    arrows = []
-    # Make arrows for ROTATED connections
-    for origin, target in connections.items():
-        lane_number = find_lane_number(origin=origin, target=target, box_names=box_names,
-                                       connections=connections, lane_numbers=lane_numbers,
-                                       component_box_dict=component_box_dict)
-
-        arrow = Arrow(origin, target, kind="ROTATED")
-        arrow.set_lane_offset(0.2)
-        arrow.set_box_offset_origin(0.16)
-        arrow.set_box_offset_target(-0.05)
-        if color is None:
-            arrow.color = "red"
-        else:
-            arrow.color = color
-
-        arrow.set_sub_lane(1)
-        arrow.set_lane(lane_number)
-        arrows.append(arrow)
-
-    return arrows
-
-
-def find_lane_number(origin, target, box_names, connections, lane_numbers, component_box_dict):
-    """
-    Helper function for finding how many lanes the current connection should go out
-    """
-    origin_index = box_names.index(origin.name)
-    target_index = box_names.index(target.name)
-    if origin_index < target_index + 1:
-        names_between = box_names[origin_index + 1: target_index]
-    else:
-        names_between = box_names[target_index + 1:origin_index]
-
-    if origin.name == "Cu":
-        debug_mode = True
-    else:
-        debug_mode = False
-
-    max_lane_number = 0
-    for name_between in names_between:
-        between_object = component_box_dict[name_between]
-        if name_between == "ABSOLUTE":
-            continue
-
-        if between_object not in connections and between_object not in connections.values():
-            continue
-
-        if between_object in connections:
-            if connections[between_object] is target:
-                continue
-
-        if between_object not in lane_numbers:
-            continue
-
-        if lane_numbers[between_object] > max_lane_number:
-            max_lane_number = lane_numbers[between_object]
-
-    lane_number = max_lane_number + 1
-    lane_numbers[origin] = lane_number
-    lane_numbers[target] = lane_number
-
-    return lane_number
-
-
-class DiagramCanvas:
-    def __init__(self, left_side_arrows, component_boxes, right_side_arrows, component_categories, colors):
-        """
-        Creates diagram of instrument file with given boxes and arrows
-
-        Makes diagram with column of boxes with arrows between them. The boxes
-        are described by the ComponentBox class and arrows by Arrow class. Some
-        arrows are drawn on the left side, AT and ROTATED relationships, while
-        more advanced connections are on the right side. A legend is provided
-        where the colors corresponding to component categories are shown along
-        with the arrow colors used for different features. The right side
-        arrows only show up in the legend when in use to reduce clutter. When
-        using this diagram in matplotlib widget mode, it is possible get more
-        information on each component by hovering the mouse of the beginning
-        the component box.
-        """
-        self.left_side_arrows = left_side_arrows
-        self.component_boxes = component_boxes
-        self.right_side_arrows = right_side_arrows
-        self.all_arrows = left_side_arrows + right_side_arrows
-        self.component_categories = component_categories
-        self.colors = colors
-
-        # Identify cases where multiple written input goes to or from same box
-        arrow_connections = {}
-        for arrow in self.right_side_arrows:
-            if arrow.origin not in arrow_connections:
-                arrow_connections[arrow.origin] = 1
-            else:
-                arrow_connections[arrow.origin] += 1
-
-            if arrow.target not in arrow_connections:
-                arrow_connections[arrow.target] = 1
-            else:
-                arrow_connections[arrow.target] += 1
-
-        for key in arrow_connections:
-            if arrow_connections[key] > 1:
-                displacements = list(np.linspace(-0.16, 0.16, arrow_connections[key]))
-                over_populated_box = key
-                for arrow in self.right_side_arrows:
-                    if arrow.origin is over_populated_box:
-                        arrow.set_origin_congested(True)
-                        if not arrow.connection:
-                            arrow.set_box_offset_origin(displacements.pop())
-
-                    if arrow.target is over_populated_box:
-                        arrow.set_target_congested(True)
-                        if not arrow.connection:
-                            arrow.set_box_offset_target(displacements.pop())
-
-        # Style constants
-        self.FIG_HEIGHT_PER_BOX = 0.4
-        self.FIG_WIDTH_PER_LANE = 0.33
-        self.FIG_WIDTH_PER_WEIGHTED_CHARACTER = 0.122
-        self.FIG_EXTRA_WIDTH_FOR_TEXT = 0.15
-        self.FIG_LEFT_MARGIN = self.FIG_WIDTH_PER_LANE * 0.5
-        self.FIG_RIGHT_MARGIN = self.FIG_WIDTH_PER_LANE * 0.5
-        self.FIG_LEGEND_HEADLINE = 0.2
-        self.FIG_RIGHT_SIDE_MINIMUM_SPACE = 3
-
-        self.graph_height = self.FIG_HEIGHT_PER_BOX * len(component_boxes)
-
-        # Distribute boxes over graph height
-        margin = 0.5/len(component_boxes)
-        box_height_centers = np.linspace(1 - margin, margin, len(component_boxes))
-        box_height = (1 - 2 * margin) / len(component_boxes)
-        for box, y_pos in zip(component_boxes, box_height_centers):
-            box.set_y(y_pos)
-            box.set_box_height(box_height)
-
-        lane_numbers = [x.lane for x in left_side_arrows] + [0]
-        self.highest_lane_left = max(lane_numbers)
-
-        lane_numbers = [x.lane for x in right_side_arrows] + [0]
-        self.highest_lane_right = max(lane_numbers)
-
-        weighted_box_names = [x.weighted_name_length for x in component_boxes]
-        self.characters_in_longest_name = max(weighted_box_names)
-
-        self.left_space = self.highest_lane_left * self.FIG_WIDTH_PER_LANE
-        self.box_space = self.characters_in_longest_name * self.FIG_WIDTH_PER_WEIGHTED_CHARACTER + self.FIG_EXTRA_WIDTH_FOR_TEXT
-        self.right_space = self.highest_lane_right * self.FIG_WIDTH_PER_LANE
-
-        self.right_space = max(self.right_space, self.FIG_RIGHT_SIDE_MINIMUM_SPACE)
-
-        self.graph_width = self.FIG_LEFT_MARGIN + self.left_space + self.box_space + self.right_space + self.FIG_RIGHT_MARGIN
-
-        self.text_start_graph = (self.FIG_LEFT_MARGIN + self.left_space) / self.graph_width
-        self.text_end_graph = (self.FIG_LEFT_MARGIN + self.left_space + self.box_space) / self.graph_width
-
-        lane_width = self.left_space/self.graph_width/self.highest_lane_left
-
-        # Rescale all
-        for arrow in self.all_arrows:
-            arrow.set_lane_width(lane_width)
-            arrow.set_lane_offset(0.18)
-            arrow.set_arrow_width(0.02 / self.graph_height)
-
-        for box in self.component_boxes:
-            box.set_box_indent(self.FIG_EXTRA_WIDTH_FOR_TEXT/self.graph_width)
-            box.set_x(self.text_start_graph)  # Places boxes so they get name_width space
-
-        # Prepare legend
-        all_categories = list(set(component_categories.values()))
-        all_categories.sort()
-
-        # Colors from http://vrl.cs.brown.edu/color
-        category_colors = ["#7487fb", "#81cc4c", "#e586fe", "#fe707d",
-                           "#00d618", "#ffa300", "#cc9966", "#53c6ef",
-                           "#baa3c6", "#aebf8a"]
-
-        needed_colors = category_colors[:len(all_categories)]
-        self.category_color_dict = dict(zip(all_categories, needed_colors))
-
-        active_categories = []
-        for box in self.component_boxes:
-            if box.component_object is None:
-                continue
-
-            category = component_categories[box.component_object.component_name]
-            if category not in active_categories:
-                active_categories.append(category)
-
-        self.legend_height = self.FIG_LEGEND_HEADLINE + self.FIG_HEIGHT_PER_BOX * (len(all_categories) // 2)
-
-        self.make_legend()
-
-    def make_legend(self):
-
-        box_top = 1 - self.FIG_LEGEND_HEADLINE / self.legend_height
-
-        fig, ax = plt.subplots(figsize=(7.0, self.legend_height))
-        ax.set(xlim=(0, 1), ylim=(0, 1))
-        ax.axis("off")
-
-        bbox = dict(boxstyle="round", facecolor="white", edgecolor="white")
-
-        ax.text(0.37, 1.05, "Legend", va="center", ha="center", fontweight="semibold", bbox=bbox, fontsize="large")
-
-        legend_boxes = [ComponentBox(name="Arm")]
-        for category, color in self.category_color_dict.items():
-            box = ComponentBox(name=category)
-            box.set_background_color(color)
-            legend_boxes.append(box)
-
-        # Color components boxes after same color scheme
-        for box in self.component_boxes:
-            if box.component_object is None:
-                continue
-
-            component_type = box.component_object.component_name
-            if component_type == "Arm":
-                box.set_background_color("white")
-            else:
-                component_category = self.component_categories[component_type]
-                box.set_background_color(self.category_color_dict[component_category])
-
-        batch_cut = len(legend_boxes) // 2
-        batches = [legend_boxes[:batch_cut], legend_boxes[batch_cut:]]
-
-        batch_end = 0.0
-        for batch in batches:
-            margin = 0.5 / len(batch)
-            box_height_centers = np.linspace(box_top, margin, len(batch))
-            box_height = (1 - 2 * margin) / len(batch)
-            for box, y_pos in zip(batch, box_height_centers):
-                box.set_x(batch_end)
-                box.set_y(y_pos)
-                box.set_box_height(box_height)
-                box.set_box_indent(self.FIG_EXTRA_WIDTH_FOR_TEXT / self.graph_width)
-
-                box.plot_box(ax)
-
-            fig.canvas.draw()
-            for box in batch:
-                box.calculate_bbox_dimensions(ax, self.graph_width)
-
-            box_ends = [x.graph_box_end for x in batch]
-            batch_end = max(box_ends)
-
-        # Always show AT and RELATIVE, but the others only when present
-        show_GROUP = False
-        show_JUMP = False
-        show_Union = False
-        for arrow in self.all_arrows:
-            if arrow.kind == "GROUP":
-                show_GROUP = True
-            if arrow.kind == "JUMP":
-                show_JUMP = True
-            if arrow.kind == "Union":
-                show_Union = True
-
-        arrow_width = 0.012
-        AT_HEIGHT = 0.83
-        START_WIDTH = 0.45
-        TEXT_WIDTH_INDENT = 0.01
-        DISPLACEMENT = 0.2
-        TEXT_DISPLACEMENT = 0.08
-        LINE_LENGTH = 0.33
-
-        current_displacement = 0
-        ax.arrow(x=START_WIDTH, y=AT_HEIGHT + current_displacement, dx=LINE_LENGTH, dy=0, color=self.colors["AT"],
-                 length_includes_head=True, width=arrow_width,
-                 head_width=5.0 * arrow_width, head_length=2.5 * arrow_width)
-        ax.text(START_WIDTH+TEXT_WIDTH_INDENT, AT_HEIGHT + TEXT_DISPLACEMENT + current_displacement,
-                "RELATIVE AT", va="center", weight="semibold")
-
-        current_displacement -= DISPLACEMENT
-        ax.arrow(x=START_WIDTH, y=AT_HEIGHT+current_displacement, dx=LINE_LENGTH, dy=0, color=self.colors["ROTATED"],
-                 length_includes_head=True, width=arrow_width,
-                 head_width=5.0 * arrow_width, head_length=2.5 * arrow_width)
-        ax.text(START_WIDTH + TEXT_WIDTH_INDENT, AT_HEIGHT + current_displacement + TEXT_DISPLACEMENT,
-                "RELATIVE ROTATED", va="center", weight="semibold")
-
-        if show_Union:
-            current_displacement -= DISPLACEMENT
-            ax.arrow(x=START_WIDTH, y=AT_HEIGHT + current_displacement, dx=LINE_LENGTH, dy=0,
-                     color=self.colors["Union"], length_includes_head=True, width=arrow_width,
-                     head_width=5.0 * arrow_width, head_length=2.5 * arrow_width)
-            ax.text(START_WIDTH + TEXT_WIDTH_INDENT, AT_HEIGHT + current_displacement + TEXT_DISPLACEMENT,
-                    "Union", va="center", weight="semibold")
-
-        if show_JUMP:
-            current_displacement -= DISPLACEMENT
-            ax.arrow(x=START_WIDTH, y=AT_HEIGHT+current_displacement, dx=LINE_LENGTH, dy=0,
-                     color=self.colors["JUMP"], length_includes_head=True, width=arrow_width,
-                     head_width=5.0 * arrow_width, head_length=2.5 * arrow_width)
-            ax.text(START_WIDTH + TEXT_WIDTH_INDENT, AT_HEIGHT+current_displacement+TEXT_DISPLACEMENT,
-                    "JUMP", va="center", weight="semibold")
-
-        if show_GROUP:
-            current_displacement -= DISPLACEMENT
-            ax.plot([START_WIDTH, START_WIDTH+LINE_LENGTH], 2*[AT_HEIGHT + current_displacement],
-                    color=self.colors["GROUP"])
-            ax.text(START_WIDTH + TEXT_WIDTH_INDENT, AT_HEIGHT + current_displacement + TEXT_DISPLACEMENT,
-                    "GROUP", va="center", weight="semibold")
-
-    def plot(self):
-        fig, ax = plt.subplots(figsize=(self.graph_width, self.graph_height))
-        ax.set(xlim=(0, 1), ylim=(0, 1))
-        ax.axis("off")
-
-        # Start by placing scatter points corresponding to the start of each box
-        box_x = []
-        box_y = []
-        box_info = []
-        for box in self.component_boxes:
-            box_x.append(box.position_x + box.box_indent)
-            box_y.append(box.position_y)
-            if box.component_object is None:
-                info = box.name
-            else:
-                info = component_description(box.component_object)
-            box_info.append(info)
-
-        # These scatter points will be the basis for mouse hovering showing annotations
-        sc = ax.scatter(box_x, box_y, color="white")
-
-        # Plot all the boxes
-        for box in self.component_boxes:
-            box.plot_box(ax)
-
-        fig.canvas.draw()
-        for box in self.component_boxes:
-            # Calculate the box end position, requires they are already drawn
-            box.calculate_bbox_dimensions(ax, self.graph_width)
-
-        for arrow in self.left_side_arrows:
-            # Plot arrows on left side
-            arrow.plot_left_side(ax)
-
-        for arrow in self.right_side_arrows:
-            # Plot arrows on right side
-            arrow.plot_right_side(ax, self.text_end_graph)
-
-        # Create anotation box that will be shown when hovering the mouse over a box
-        annot = ax.annotate("", xy=(0, 0), xytext=(20, 10), textcoords="offset points",
-                            bbox=dict(boxstyle="round", fc="w"), annotation_clip=False)
-        annot.set_visible(False)
-
-        # Helper function to update the anotation box with correct text
-        def update_annot(ind):
-            pos = sc.get_offsets()[ind["ind"][0]]
-            annot.xy = pos
-            text = "{}".format(" ".join([box_info[n] for n in ind["ind"]]))
-            annot.set_text(text)
-            annot.get_bbox_patch().set_facecolor([0.95, 0.95, 0.95])
-
-        # Helper function to detect hovering and update annotation accordingly
-        def hover(event):
-            vis = annot.get_visible()
-            if event.inaxes == ax:
-                cont, ind = sc.contains(event)
-                if cont:
-                    update_annot(ind)
-                    annot.set_visible(True)
-                    fig.canvas.draw_idle()
-                else:
-                    if vis:
-                        annot.set_visible(False)
-                        fig.canvas.draw_idle()
-
-        # Connect the hover function to the canvas event signal
-        fig.canvas.mpl_connect("motion_notify_event", hover)
-
-        # Show the figure
-        plt.show()
-
-
-class ComponentBox:
-    """
-    Helper class for creating text boxes describing components
-    """
-    def __init__(self, component_object=None, name=None):
-        """
-        Text box object
-        """
-        self.component_object = component_object
-        if component_object is None:
-            self.name = name
-        else:
-            self.name = self.component_object.name
-
-        # Produced weighted length of name, capital letters count for 1.2
-        self.weighted_name_length = len(self.name) + 0.2*sum(1 for c in self.name if c.isupper())
-
-        self.position_x = None
-        self.position_y = None
-        self.box_height = None
-        self.box_indent = None
-        self.background_color = "white"
-
-        self.t = None # text object
-        self.graph_box_end = None # graph position where text ends
-        self.graph_box_start = None  # graph position where text ends
-
-    def set_box_height(self, box_height):
-        self.box_height = box_height
-
-    def set_x(self, x):
-        self.position_x = x
-
-    def set_y(self, y):
-        self.position_y = y
-
-    def set_box_indent(self, value):
-        self.box_indent = value
-
-    def set_background_color(self, value):
-        self.background_color = value
-
-    def plot_box(self, ax):
-        bbox = dict(boxstyle="round", facecolor=self.background_color, edgecolor="black")
-
-        self.t = ax.text(self.position_x + self.box_indent, self.position_y, self.name,
-                         va="center", fontweight="semibold", color="black", #font="monospace",
-                         bbox=bbox)
-
-    def get_text_start(self):
-        return self.graph_box_start
-
-    def get_text_end(self):
-        return self.graph_box_end
-
-    def calculate_bbox_dimensions(self, ax, graph_width):
-        """
-        Method that gets dimensions of plotted text box
-        """
-        transf = ax.transAxes.inverted()
-        bb = self.t.get_window_extent(renderer=plt.gcf().canvas.get_renderer())
-        bbox = bb.transformed(transf)
-
-        TEXT_PADDING_IN_FIG_UNITS = 0.08
-        box_padding = TEXT_PADDING_IN_FIG_UNITS / graph_width
-        self.graph_box_start = bbox.x0 - box_padding
-        self.graph_box_end = bbox.x1 + box_padding
-
-
-class Arrow:
-    """
-    Helper class for creating arrows with connections
-    """
-    def __init__(self, origin, target, kind=None, description=None):
-        """
-        Arrow object with origin Component_box and target component_box
-        """
-        self.origin = origin
-        self.target = target
-        self.kind = kind
-        self.description=description
-
-        self.lane = None
-        self.sub_lane = 0
-        self.lane_width = None
-        self.lane_offset = 0
-
-        self.box_offset_origin = 0
-        self.box_offset_target = 0
-
-        self.origin_linestyle = "-"
-        self.color = "blue"
-        self.arrow_width = 0.003
-        self.connection = False
-
-        self.origin_congested = False
-        self.target_congested = False
-
-    def set_lane(self, lane):
-        self.lane = lane
-
-    def set_sub_lane(self, lane):
-        self.sub_lane = lane
-
-    def set_lane_width(self, lane_width):
-        self.lane_width = lane_width
-
-    def set_lane_offset(self, offset):
-        self.lane_offset = offset
-
-    def get_lane_value(self):
-        return self.lane_width * (self.lane + self.sub_lane * self.lane_offset)
-
-    def set_connection(self, value):
-        self.connection = value
-
-    def set_origin_congested(self, value):
-        self.origin_congested = value
-
-    def set_target_congested(self, value):
-        self.target_congested = value
-
-    def set_box_offset_origin(self, value):
-        """
-        In units of box height
-        """
-        self.box_offset_origin = value
-
-    def get_box_offset_origin(self):
-        return self.origin.box_height * self.box_offset_origin
-
-    def set_box_offset_target(self, value):
-        """
-        In units of box height
-        """
-        self.box_offset_target = value
-
-    def get_box_offset_target(self):
-        return self.target.box_height * self.box_offset_target
-
-    def set_arrow_width(self, value):
-        self.arrow_width = value
-
-    def set_linestyle(self, value):
-        self.origin_linestyle = value
-
-    def plot_left_side(self, ax):
-        origin_x = self.origin.get_text_start()
-        origin_y = self.origin.position_y + self.get_box_offset_origin()
-
-        origin_lane_x = origin_x - self.get_lane_value()
-        origin_lane_y = origin_y
-
-        ax.plot([origin_x, origin_lane_x], [origin_y, origin_lane_y],
-                color=self.color, linestyle=self.origin_linestyle)
-
-        target_x = self.target.get_text_start()
-        target_y = self.target.position_y + self.get_box_offset_target()
-
-        target_lane_x = target_x - self.get_lane_value()
-        target_lane_y = target_y
-
-        ax.plot([origin_lane_x, target_lane_x], [origin_lane_y, target_lane_y], color=self.color)
-
-        arrow_length = self.target.get_text_start() - target_lane_x
-        ax.arrow(x=target_lane_x, y=target_lane_y,
-                 dx=arrow_length, dy=0,
-                 color=self.color, length_includes_head=True,
-                 width=self.arrow_width, head_width=5.0*self.arrow_width,
-                 head_length=7.0*self.arrow_width)
-
-    def plot_right_side(self, ax, text_end):
-        origin_x = self.origin.get_text_end()
-        origin_y = self.origin.position_y + self.get_box_offset_origin()
-
-        origin_lane_x = text_end + self.get_lane_value()
-        origin_lane_y = origin_y
-
-        ax.plot([origin_x, origin_lane_x], [origin_y, origin_lane_y],
-                color=self.color, linestyle=self.origin_linestyle)
-
-        if self.description is not None and not self.origin_congested:
-            bbox = dict(boxstyle="round", facecolor="white", edgecolor="white")
-            ax.text(0.5*(origin_x + origin_lane_x), origin_lane_y,
-                    self.description, ha="center", va="center", bbox=bbox)
-
-        target_y = self.target.position_y + self.get_box_offset_target()
-
-        target_lane_x = text_end + self.get_lane_value()
-        target_lane_y = target_y
-
-        ax.plot([origin_lane_x, target_lane_x], [origin_lane_y, target_lane_y], color=self.color)
-
-        if self.connection:
-            mid_point = 0.5*(target_lane_x + self.target.get_text_end())
-            ax.plot([target_lane_x, self.target.get_text_end()],
-                    [target_lane_y, target_lane_y], color=self.color)
-        else:
-            arrow_length = target_lane_x - self.target.get_text_end()
-            mid_point = target_lane_x - 0.5*arrow_length
-            ax.arrow(x=target_lane_x, y=target_lane_y,
-                     dx=-arrow_length, dy=0,
-                     color=self.color, length_includes_head=True,
-                     width=self.arrow_width, head_width=5.0*self.arrow_width,
-                     head_length=7.0*self.arrow_width)
-
-        if self.description is not None and not self.target_congested:
-            bbox = dict(boxstyle="round", facecolor="white", edgecolor="white")
-            ax.text(mid_point, target_lane_y,
-                    self.description, ha="center", va="center", bbox=bbox)
-
-
-def component_description(component):
-    """
-    Returns string of information about the component
-
-    Includes information on required parameters if they are not yet
-    specified. Information on the components are added when the
-    class is used as a superclass for classes describing each
-    McStas component. Uses mathtext for bold and italics.
-    """
-    string = ""
-
-    if len(component.c_code_before) > 1:
-        string += component.c_code_before + "\n"
-    if len(component.comment) > 1:
-        string += "// " + component.comment + "\n"
-    if component.SPLIT != 0:
-        string += "SPLIT " + str(component.SPLIT) + " "
-    string += "COMPONENT " + str(component.name)
-    string += " = $\\bf{" + str(component.component_name).replace("_", "\_") + "}$\n"
-    for key in component.parameter_names:
-        val = getattr(component, key)
-        parameter_name = key
-        if val is not None:
-            unit = ""
-            if key in component.parameter_units:
-                unit = "[" + component.parameter_units[key] + "]"
-            if isinstance(val, Parameter):
-                val_string = val.name
-            elif isinstance(val, DeclareVariable):
-                val_string = val.name
-            else:
-                val_string = str(val)
-
-            value = "$\\bf{" + val_string.replace("_", "\_").replace('\"', "''").replace('"', "\''") + "}$"
-            string += "  $\\bf{" + parameter_name.replace("_", "\_") + "}$"
-            string += " = " + value + " " + unit + "\n"
-        else:
-            if component.parameter_defaults[key] is None:
-                string += "  $\\bf{" + parameter_name.replace("_", "\_") + "}$"
-                string += " : $\\bf{Required\ parameter\ not\ yet\ specified}$\n"
-
-    if not component.WHEN == "":
-        string += component.WHEN + "\n"
-
-    string += "AT " + str(component.AT_data)
-    if component.AT_reference is None:
-        string += " $\\it{ABSOLUTE}$\n"
-    else:
-        string += " RELATIVE $\\it{" + component.AT_reference.replace("_", "\_") + "}$\n"
-
-    if component.ROTATED_specified:
-        string += "ROTATED " + str(component.ROTATED_data)
-        if component.ROTATED_reference is None:
-            string += " $\\it{ABSOLUTE}$\n"
-        else:
-            string += " $\\it{" + component.ROTATED_reference.replace("_", "\_") + "}$\n"
-
-    if not component.GROUP == "":
-        string += "GROUP " + component.GROUP + "\n"
-    if not component.EXTEND == "":
-        string += "EXTEND %{" + "\n"
-        string += component.EXTEND + "%}" + "\n"
-    if not component.JUMP == "":
-        string += "JUMP " + component.JUMP + "\n"
-    if len(component.c_code_after) > 1:
-        string += component.c_code_after + "\n"
-
-    return string.strip()
-
-
-
diff --git a/mcstasscript/instrument_diagram/arrow.py b/mcstasscript/instrument_diagram/arrow.py
new file mode 100644
index 00000000..68e132c1
--- /dev/null
+++ b/mcstasscript/instrument_diagram/arrow.py
@@ -0,0 +1,149 @@
+class Arrow:
+    """
+    Helper class for creating arrows with connections
+    """
+    def __init__(self, origin, target, lane=None, kind=None, description=None):
+        """
+        Arrow object with origin Component_box and target component_box
+        """
+        self.origin = origin
+        self.target = target
+        self.kind = kind
+        self.description=description
+
+        self.lane = lane
+        self.sub_lane = 0
+        self.lane_width = None
+        self.lane_offset = 0
+
+        self.box_offset_origin = 0
+        self.box_offset_target = 0
+
+        self.origin_linestyle = "-"
+        self.color = "blue"
+        self.alpha = 1.0
+        self.arrow_width = 0.003
+        self.arrow_length = 0.01
+        self.connection = False
+
+        self.origin_congested = False
+        self.target_congested = False
+
+    def set_lane(self, lane):
+        self.lane = lane
+
+    def set_sub_lane(self, lane):
+        self.sub_lane = lane
+
+    def set_lane_width(self, lane_width):
+        self.lane_width = lane_width
+
+    def set_lane_offset(self, offset):
+        self.lane_offset = offset
+
+    def get_lane_value(self):
+        return self.lane_width * (self.lane + self.sub_lane * self.lane_offset)
+
+    def set_connection(self, value):
+        self.connection = value
+
+    def set_origin_congested(self, value):
+        self.origin_congested = value
+
+    def set_target_congested(self, value):
+        self.target_congested = value
+
+    def set_box_offset_origin(self, value):
+        """
+        In units of box height
+        """
+        self.box_offset_origin = value
+
+    def get_box_offset_origin(self):
+        return self.origin.box_height * self.box_offset_origin
+
+    def set_box_offset_target(self, value):
+        """
+        In units of box height
+        """
+        self.box_offset_target = value
+
+    def get_box_offset_target(self):
+        return self.target.box_height * self.box_offset_target
+
+    def set_arrow_width(self, value):
+        self.arrow_width = value
+
+    def set_arrow_length(self, value):
+        self.arrow_length = value
+
+    def set_linestyle(self, value):
+        self.origin_linestyle = value
+
+    def set_alpha(self, value):
+        self.alpha = value
+
+    def plot_left_side(self, ax):
+        origin_x = self.origin.get_text_start()
+        origin_y = self.origin.position_y + self.get_box_offset_origin()
+
+        origin_lane_x = origin_x - self.get_lane_value()
+        origin_lane_y = origin_y
+
+        ax.plot([origin_x, origin_lane_x], [origin_y, origin_lane_y],
+                color=self.color, linestyle=self.origin_linestyle)
+
+        target_x = self.target.get_text_start()
+        target_y = self.target.position_y + self.get_box_offset_target()
+
+        target_lane_x = target_x - self.get_lane_value()
+        target_lane_y = target_y
+
+        ax.plot([origin_lane_x, target_lane_x], [origin_lane_y, target_lane_y], color=self.color)
+
+        arrow_length = self.target.get_text_start() - target_lane_x
+        ax.arrow(x=target_lane_x, y=target_lane_y,
+                 dx=arrow_length, dy=0,
+                 color=self.color, length_includes_head=True,
+                 width=self.arrow_width, head_width=5.0*self.arrow_width,
+                 head_length=self.arrow_length)
+
+    def plot_right_side(self, ax, text_end):
+        origin_x = self.origin.get_text_end()
+        origin_y = self.origin.position_y + self.get_box_offset_origin()
+
+        origin_lane_x = text_end + self.get_lane_value()
+        origin_lane_y = origin_y
+
+        ax.plot([origin_x, origin_lane_x], [origin_y, origin_lane_y],
+                color=self.color, linestyle=self.origin_linestyle, alpha=self.alpha)
+
+        if self.description is not None and not self.origin_congested:
+            bbox = dict(boxstyle="round", facecolor="white", edgecolor="white", alpha=0.85)
+            ax.text(0.5*(origin_x + origin_lane_x), origin_lane_y,
+                    self.description, ha="center", va="center", bbox=bbox)
+
+        target_y = self.target.position_y + self.get_box_offset_target()
+
+        target_lane_x = text_end + self.get_lane_value()
+        target_lane_y = target_y
+
+        ax.plot([origin_lane_x, target_lane_x], [origin_lane_y, target_lane_y], color=self.color, alpha=self.alpha)
+
+        if self.connection:
+            mid_point = 0.5*(target_lane_x + self.target.get_text_end())
+            ax.plot([target_lane_x, self.target.get_text_end()],
+                    [target_lane_y, target_lane_y], color=self.color, alpha=self.alpha)
+        else:
+            arrow_length = target_lane_x - self.target.get_text_end()
+            mid_point = target_lane_x - 0.5*arrow_length
+            ax.arrow(x=target_lane_x, y=target_lane_y,
+                     dx=-arrow_length, dy=0,
+                     color=self.color, length_includes_head=True,
+                     width=self.arrow_width, head_width=5.0*self.arrow_width,
+                     head_length=self.arrow_length, alpha=self.alpha)
+
+        if self.description is not None and not self.target_congested:
+            bbox = dict(boxstyle="round", facecolor="white", edgecolor="white", alpha=0.85)
+            ax.text(mid_point, target_lane_y,
+                    self.description, ha="center", va="center", bbox=bbox)
\ No newline at end of file
diff --git a/mcstasscript/instrument_diagram/box.py b/mcstasscript/instrument_diagram/box.py
new file mode 100644
index 00000000..2bad6d89
--- /dev/null
+++ b/mcstasscript/instrument_diagram/box.py
@@ -0,0 +1,70 @@
+import matplotlib.pyplot as plt
+
+
+class ComponentBox:
+    """
+    Helper class for creating text boxes describing components
+    """
+    def __init__(self, component_object=None, name=None):
+        """
+        Text box object
+        """
+        self.component_object = component_object
+        if component_object is None:
+            self.name = name
+        else:
+            self.name = self.component_object.name
+
+        # Produced weighted length of name, capital letters count for 1.2
+        self.weighted_name_length = len(self.name) + 0.2*sum(1 for c in self.name if c.isupper())
+
+        self.position_x = None
+        self.position_y = None
+        self.box_height = None
+        self.box_indent = None
+        self.background_color = "white"
+
+        self.t = None # text object
+        self.graph_box_end = None # graph position where text ends
+        self.graph_box_start = None  # graph position where text ends
+
+    def set_box_height(self, box_height):
+        self.box_height = box_height
+
+    def set_x(self, x):
+        self.position_x = x
+
+    def set_y(self, y):
+        self.position_y = y
+
+    def set_box_indent(self, value):
+        self.box_indent = value
+
+    def set_background_color(self, value):
+        self.background_color = value
+
+    def plot_box(self, ax):
+        bbox = dict(boxstyle="round", facecolor=self.background_color, edgecolor="black")
+
+        self.t = ax.text(self.position_x + self.box_indent, self.position_y, self.name,
+                         va="center", fontweight="semibold", color="black", #font="monospace",
+                         bbox=bbox)
+
+    def get_text_start(self):
+        return self.graph_box_start
+
+    def get_text_end(self):
+        return self.graph_box_end
+
+    def calculate_bbox_dimensions(self, ax, graph_width):
+        """
+        Method that gets dimensions of plotted text box
+        """
+        transf = ax.transAxes.inverted()
+        bb = self.t.get_window_extent(renderer=plt.gcf().canvas.get_renderer())
+        bbox = bb.transformed(transf)
+
+        TEXT_PADDING_IN_FIG_UNITS = 0.08
+        box_padding = TEXT_PADDING_IN_FIG_UNITS / graph_width
+        self.graph_box_start = bbox.x0 - box_padding
+        self.graph_box_end = bbox.x1 + box_padding
\ No newline at end of file
diff --git a/mcstasscript/instrument_diagram/canvas.py b/mcstasscript/instrument_diagram/canvas.py
new file mode 100644
index 00000000..d09adcd5
--- /dev/null
+++ b/mcstasscript/instrument_diagram/canvas.py
@@ -0,0 +1,344 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+from mcstasscript.instrument_diagram.box import ComponentBox
+from mcstasscript.instrument_diagram.component_description import component_description
+
+class DiagramCanvas:
+    def __init__(self, left_side_arrows, component_boxes, right_side_arrows, component_categories, colors):
+        """
+        Creates diagram of instrument file with given boxes and arrows
+
+        Makes diagram with column of boxes with arrows between them. The boxes
+        are described by the ComponentBox class and arrows by Arrow class. Some
+        arrows are drawn on the left side, AT and ROTATED relationships, while
+        more advanced connections are on the right side. A legend is provided
+        where the colors corresponding to component categories are shown along
+        with the arrow colors used for different features. The right side
+        arrows only show up in the legend when in use to reduce clutter. When
+        using this diagram in matplotlib widget mode, it is possible get more
+        information on each component by hovering the mouse of the beginning
+        the component box.
+        """
+        self.left_side_arrows = left_side_arrows
+        self.component_boxes = component_boxes
+        self.right_side_arrows = right_side_arrows
+        self.all_arrows = left_side_arrows + right_side_arrows
+        self.component_categories = component_categories
+        self.colors = colors
+
+        # Identify cases where multiple written input goes to or from same box
+        arrow_connections = {x: [] for x in self.component_boxes}
+        for arrow in self.right_side_arrows:
+            arrow_connections[arrow.origin].append(arrow)
+            arrow_connections[arrow.target].append(arrow)
+
+        for box in arrow_connections:
+            arrows = arrow_connections[box]
+            if len(arrows) > 1:  # Only look at cases with more than one arrow to / from box
+                # Find unique kinds
+                kinds_entry = set()
+                kinds_exit = set()
+                for arrow in arrows:
+                    if arrow.target is box:
+                        kinds_entry.add(arrow.kind)
+
+                    if arrow.origin is box:
+                        kinds_exit.add(arrow.kind)
+
+                n_collected_arrows = len(kinds_entry) + len(kinds_exit)
+                if n_collected_arrows == 1:
+                    # If there is only one kind and direction, we are done
+                    continue
+
+                # Distribute placements on the box equally for the different kinds / with entry/exit kept apart
+                displacements = list(np.linspace(-0.16, 0.16, n_collected_arrows))
+
+                entry_displacements = {}
+                exit_displacements = {}
+                for arrow in arrows:
+
+                    if arrow.target is box:
+                        arrow.set_target_congested(True)  # Mark congested, avoids writing description on the line
+
+                        if arrow.kind in entry_displacements:
+                            arrow.set_box_offset_target(entry_displacements[arrow.kind])
+                        else:
+                            displacement = displacements.pop()
+                            entry_displacements[arrow.kind] = displacement
+                            arrow.set_box_offset_target(displacement)
+
+                    if arrow.origin is box:
+                        arrow.set_origin_congested(True) # Mark congested, avoids writing description on the line
+
+                        if arrow.kind in exit_displacements:
+                            arrow.set_box_offset_origin(exit_displacements[arrow.kind])
+                        else:
+                            displacement = displacements.pop()
+                            exit_displacements[arrow.kind] = displacement
+                            arrow.set_box_offset_origin(displacement)
+
+        # Style constants
+        self.FIG_HEIGHT_PER_BOX = 0.4
+        self.FIG_WIDTH_PER_LANE = 0.33
+        self.FIG_WIDTH_PER_WEIGHTED_CHARACTER = 0.122
+        self.FIG_EXTRA_WIDTH_FOR_TEXT = 0.15
+        self.FIG_LEFT_MARGIN = self.FIG_WIDTH_PER_LANE * 0.5
+        self.FIG_RIGHT_MARGIN = self.FIG_WIDTH_PER_LANE * 0.5
+        self.FIG_LEGEND_HEADLINE = 0.2
+        self.FIG_RIGHT_SIDE_MINIMUM_SPACE = 3
+
+        self.graph_height = self.FIG_HEIGHT_PER_BOX * len(component_boxes)
+
+        # Distribute boxes over graph height
+        margin = 0.5/len(component_boxes)
+        box_height_centers = np.linspace(1 - margin, margin, len(component_boxes))
+        box_height = (1 - 2 * margin) / len(component_boxes)
+        for box, y_pos in zip(component_boxes, box_height_centers):
+            box.set_y(y_pos)
+            box.set_box_height(box_height)
+
+        lane_numbers = [x.lane for x in left_side_arrows] + [0]
+        self.highest_lane_left = max(lane_numbers)
+
+        lane_numbers = [x.lane for x in right_side_arrows] + [0]
+        self.highest_lane_right = max(lane_numbers)
+
+        weighted_box_names = [x.weighted_name_length for x in component_boxes]
+        self.characters_in_longest_name = max(weighted_box_names)
+
+        self.left_space = self.highest_lane_left * self.FIG_WIDTH_PER_LANE
+        self.box_space = self.characters_in_longest_name * self.FIG_WIDTH_PER_WEIGHTED_CHARACTER + self.FIG_EXTRA_WIDTH_FOR_TEXT
+        self.right_space = self.highest_lane_right * self.FIG_WIDTH_PER_LANE
+
+        self.right_space = max(self.right_space, self.FIG_RIGHT_SIDE_MINIMUM_SPACE)
+
+        self.graph_width = self.FIG_LEFT_MARGIN + self.left_space + self.box_space + self.right_space + self.FIG_RIGHT_MARGIN
+
+        self.text_start_graph = (self.FIG_LEFT_MARGIN + self.left_space) / self.graph_width
+        self.text_end_graph = (self.FIG_LEFT_MARGIN + self.left_space + self.box_space) / self.graph_width
+
+        lane_width = self.left_space/self.graph_width/self.highest_lane_left
+
+        # Arrow width and height in graph units, compensate for graph size to roughly equalize
+        arrow_width = 0.02 / (self.graph_height + 0.5) # added constant to avoid asymptote near 0
+        arrow_length = 0.2 / (self.graph_width + 0.05)
+
+        # Rescale all
+        for arrow in self.all_arrows:
+            arrow.set_lane_width(lane_width)
+            arrow.set_lane_offset(0.18)
+            arrow.set_arrow_width(arrow_width)
+            arrow.set_arrow_length(arrow_length)
+
+        for box in self.component_boxes:
+            box.set_box_indent(self.FIG_EXTRA_WIDTH_FOR_TEXT/self.graph_width)
+            box.set_x(self.text_start_graph)  # Places boxes so they get name_width space
+
+        # Prepare legend
+        all_categories = list(set(component_categories.values()))
+        if "work directory" not in all_categories:
+            all_categories.append("work directory")
+        all_categories.sort()
+
+        # Colors from http://vrl.cs.brown.edu/color
+        category_colors = ["#7487fb", "#81cc4c", "#e586fe", "#fe707d",
+                           "#00d618", "#ffa300", "#cc9966", "#53c6ef",
+                           "#baa3c6", "#aebf8a"]
+
+        needed_colors = category_colors[:len(all_categories)]
+        self.category_color_dict = dict(zip(all_categories, needed_colors))
+
+        active_categories = []
+        for box in self.component_boxes:
+            if box.component_object is None:
+                continue
+
+            category = component_categories[box.component_object.component_name]
+            if category not in active_categories:
+                active_categories.append(category)
+
+        self.legend_height = self.FIG_LEGEND_HEADLINE + self.FIG_HEIGHT_PER_BOX * (len(all_categories) // 2)
+
+        self.make_legend()
+
+    def make_legend(self):
+
+        box_top = 1 - self.FIG_LEGEND_HEADLINE / self.legend_height
+
+        fig, ax = plt.subplots(figsize=(7.0, self.legend_height))
+        ax.set(xlim=(0, 1), ylim=(0, 1))
+        ax.axis("off")
+
+        bbox = dict(boxstyle="round", facecolor="white", edgecolor="white")
+
+        ax.text(0.37, 1.05, "Legend", va="center", ha="center", fontweight="semibold", bbox=bbox, fontsize="large")
+
+        legend_boxes = [ComponentBox(name="Arm")]
+        for category, color in self.category_color_dict.items():
+            box = ComponentBox(name=category)
+            box.set_background_color(color)
+            legend_boxes.append(box)
+
+        # Color components boxes after same color scheme
+        for box in self.component_boxes:
+            if box.component_object is None:
+                continue
+
+            component_type = box.component_object.component_name
+            if component_type == "Arm":
+                box.set_background_color("white")
+            else:
+                component_category = self.component_categories[component_type]
+                box.set_background_color(self.category_color_dict[component_category])
+
+        batch_cut = len(legend_boxes) // 2
+        batches = [legend_boxes[:batch_cut], legend_boxes[batch_cut:]]
+
+        batch_end = 0.0
+        for batch in batches:
+            margin = 0.5 / len(batch)
+            box_height_centers = np.linspace(box_top, margin, len(batch))
+            box_height = (1 - 2 * margin) / len(batch)
+            for box, y_pos in zip(batch, box_height_centers):
+                box.set_x(batch_end)
+                box.set_y(y_pos)
+                box.set_box_height(box_height)
+                box.set_box_indent(self.FIG_EXTRA_WIDTH_FOR_TEXT / self.graph_width)
+
+                box.plot_box(ax)
+
+            fig.canvas.draw()
+            for box in batch:
+                box.calculate_bbox_dimensions(ax, self.graph_width)
+
+            box_ends = [x.graph_box_end for x in batch]
+            batch_end = max(box_ends)
+
+        # Always show AT and RELATIVE, but the others only when present
+        show_GROUP = False
+        show_JUMP = False
+        show_Union = False
+        for arrow in self.all_arrows:
+            if arrow.kind == "GROUP":
+                show_GROUP = True
+            if arrow.kind == "JUMP":
+                show_JUMP = True
+            if arrow.kind == "Union":
+                show_Union = True
+
+        arrow_width = 0.012
+        AT_HEIGHT = 0.83
+        START_WIDTH = 0.45
+        TEXT_WIDTH_INDENT = 0.01
+        DISPLACEMENT = 0.2
+        TEXT_DISPLACEMENT = 0.08
+        LINE_LENGTH = 0.33
+
+        current_displacement = 0
+        ax.arrow(x=START_WIDTH, y=AT_HEIGHT + current_displacement, dx=LINE_LENGTH, dy=0, color=self.colors["AT"],
+                 length_includes_head=True, width=arrow_width,
+                 head_width=5.0 * arrow_width, head_length=2.5 * arrow_width)
+        ax.text(START_WIDTH+TEXT_WIDTH_INDENT, AT_HEIGHT + TEXT_DISPLACEMENT + current_displacement,
+                "RELATIVE AT", va="center", weight="semibold")
+
+        current_displacement -= DISPLACEMENT
+        ax.arrow(x=START_WIDTH, y=AT_HEIGHT+current_displacement, dx=LINE_LENGTH, dy=0, color=self.colors["ROTATED"],
+                 length_includes_head=True, width=arrow_width,
+                 head_width=5.0 * arrow_width, head_length=2.5 * arrow_width)
+        ax.text(START_WIDTH + TEXT_WIDTH_INDENT, AT_HEIGHT + current_displacement + TEXT_DISPLACEMENT,
+                "RELATIVE ROTATED", va="center", weight="semibold")
+
+        if show_Union:
+            current_displacement -= DISPLACEMENT
+            ax.arrow(x=START_WIDTH, y=AT_HEIGHT + current_displacement, dx=LINE_LENGTH, dy=0,
+                     color=self.colors["Union"], length_includes_head=True, width=arrow_width,
+                     head_width=5.0 * arrow_width, head_length=2.5 * arrow_width)
+            ax.text(START_WIDTH + TEXT_WIDTH_INDENT, AT_HEIGHT + current_displacement + TEXT_DISPLACEMENT,
+                    "Union", va="center", weight="semibold")
+
+        if show_JUMP:
+            current_displacement -= DISPLACEMENT
+            ax.arrow(x=START_WIDTH, y=AT_HEIGHT+current_displacement, dx=LINE_LENGTH, dy=0,
+                     color=self.colors["JUMP"], length_includes_head=True, width=arrow_width,
+                     head_width=5.0 * arrow_width, head_length=2.5 * arrow_width)
+            ax.text(START_WIDTH + TEXT_WIDTH_INDENT, AT_HEIGHT+current_displacement+TEXT_DISPLACEMENT,
+                    "JUMP", va="center", weight="semibold")
+
+        if show_GROUP:
+            current_displacement -= DISPLACEMENT
+            ax.plot([START_WIDTH, START_WIDTH+LINE_LENGTH], 2*[AT_HEIGHT + current_displacement],
+                    color=self.colors["GROUP"])
+            ax.text(START_WIDTH + TEXT_WIDTH_INDENT, AT_HEIGHT + current_displacement + TEXT_DISPLACEMENT,
+                    "GROUP", va="center", weight="semibold")
+
+    def plot(self):
+        fig, ax = plt.subplots(figsize=(self.graph_width, self.graph_height))
+        ax.set(xlim=(0, 1), ylim=(0, 1))
+        ax.axis("off")
+
+        # Start by placing scatter points corresponding to the start of each box
+        box_x = []
+        box_y = []
+        box_info = []
+        for box in self.component_boxes:
+            box_x.append(box.position_x + box.box_indent)
+            box_y.append(box.position_y)
+            if box.component_object is None:
+                info = box.name
+            else:
+                info = component_description(box.component_object)
+            box_info.append(info)
+
+        # These scatter points will be the basis for mouse hovering showing annotations
+        sc = ax.scatter(box_x, box_y, color="white")
+
+        # Plot all the boxes
+        for box in self.component_boxes:
+            box.plot_box(ax)
+
+        fig.canvas.draw()
+        for box in self.component_boxes:
+            # Calculate the box end position, requires they are already drawn
+            box.calculate_bbox_dimensions(ax, self.graph_width)
+
+        for arrow in self.left_side_arrows:
+            # Plot arrows on left side
+            arrow.plot_left_side(ax)
+
+        for arrow in self.right_side_arrows:
+            # Plot arrows on right side
+            arrow.plot_right_side(ax, self.text_end_graph)
+
+        # Create anotation box that will be shown when hovering the mouse over a box
+        annot = ax.annotate("", xy=(0, 0), xytext=(20, 10), textcoords="offset points",
+                            bbox=dict(boxstyle="round", fc="w"), annotation_clip=False)
+        annot.set_visible(False)
+
+        # Helper function to update the anotation box with correct text
+        def update_annot(ind):
+            pos = sc.get_offsets()[ind["ind"][0]]
+            annot.xy = pos
+            text = "{}".format(" ".join([box_info[n] for n in ind["ind"]]))
+            annot.set_text(text)
+            annot.get_bbox_patch().set_facecolor([0.95, 0.95, 0.95])
+
+        # Helper function to detect hovering and update annotation accordingly
+        def hover(event):
+            vis = annot.get_visible()
+            if event.inaxes == ax:
+                cont, ind = sc.contains(event)
+                if cont:
+                    update_annot(ind)
+                    annot.set_visible(True)
+                    fig.canvas.draw_idle()
+                else:
+                    if vis:
+                        annot.set_visible(False)
+                        fig.canvas.draw_idle()
+
+        # Connect the hover function to the canvas event signal
+        fig.canvas.mpl_connect("motion_notify_event", hover)
+
+        # Show the figure
+        plt.show()
\ No newline at end of file
diff --git a/mcstasscript/instrument_diagram/component_description.py b/mcstasscript/instrument_diagram/component_description.py
new file mode 100644
index 00000000..c186c9f2
--- /dev/null
+++ b/mcstasscript/instrument_diagram/component_description.py
@@ -0,0 +1,71 @@
+from libpyvinyl.Parameters.Parameter import Parameter
+from mcstasscript.helper.mcstas_objects import DeclareVariable
+
+def component_description(component):
+    """
+    Returns string of information about the component
+
+    Includes information on required parameters if they are not yet
+    specified. Information on the components are added when the
+    class is used as a superclass for classes describing each
+    McStas component. Uses mathtext for bold and italics.
+    """
+    string = ""
+
+    if len(component.c_code_before) > 1:
+        string += component.c_code_before + "\n"
+    if len(component.comment) > 1:
+        string += "// " + component.comment + "\n"
+    if component.SPLIT != 0:
+        string += "SPLIT " + str(component.SPLIT) + " "
+    string += "COMPONENT " + str(component.name)
+    string += " = $\\bf{" + str(component.component_name).replace("_", "\_") + "}$\n"
+    for key in component.parameter_names:
+        val = getattr(component, key)
+        parameter_name = key
+        if val is not None:
+            unit = ""
+            if key in component.parameter_units:
+                unit = "[" + component.parameter_units[key] + "]"
+            if isinstance(val, Parameter):
+                val_string = val.name
+            elif isinstance(val, DeclareVariable):
+                val_string = val.name
+            else:
+                val_string = str(val)
+
+            value = "$\\bf{" + val_string.replace("_", "\_").replace('\"', "''").replace('"', "\''") + "}$"
+            string += "  $\\bf{" + parameter_name.replace("_", "\_") + "}$"
+            string += " = " + value + " " + unit + "\n"
+        else:
+            if component.parameter_defaults[key] is None:
+                string += "  $\\bf{" + parameter_name.replace("_", "\_") + "}$"
+                string += " : $\\bf{Required\ parameter\ not\ yet\ specified}$\n"
+
+    if not component.WHEN == "":
+        string += component.WHEN + "\n"
+
+    string += "AT " + str(component.AT_data)
+    if component.AT_reference is None:
+        string += " $\\it{ABSOLUTE}$\n"
+    else:
+        string += " RELATIVE $\\it{" + component.AT_reference.replace("_", "\_") + "}$\n"
+
+    if component.ROTATED_specified:
+        string += "ROTATED " + str(component.ROTATED_data)
+        if component.ROTATED_reference is None:
+            string += " $\\it{ABSOLUTE}$\n"
+        else:
+            string += " $\\it{" + component.ROTATED_reference.replace("_", "\_") + "}$\n"
+
+    if not component.GROUP == "":
+        string += "GROUP " + component.GROUP + "\n"
+    if not component.EXTEND == "":
+        string += "EXTEND %{" + "\n"
+        string += component.EXTEND + "%}" + "\n"
+    if not component.JUMP == "":
+        string += "JUMP " + component.JUMP + "\n"
+    if len(component.c_code_after) > 1:
+        string += component.c_code_after + "\n"
+
+    return string.strip()
\ No newline at end of file
diff --git a/mcstasscript/instrument_diagram/connections.py b/mcstasscript/instrument_diagram/connections.py
new file mode 100644
index 00000000..8174285c
--- /dev/null
+++ b/mcstasscript/instrument_diagram/connections.py
@@ -0,0 +1,109 @@
+class IndexConnection:
+    def __init__(self, start_index, end_index):
+        self.start_index = start_index
+        self.end_index = end_index
+
+        if start_index <= end_index:
+            self.interval_start = start_index
+            self.interval_end = end_index
+        else:
+            self.interval_start = end_index
+            self.interval_end = start_index
+
+    def compatible_with(self, new):
+        # Three ways the two intervals can overlap:
+
+        if self.interval_start < new.interval_start < self.interval_end:
+            # Start point included in existing interval
+            return False
+
+        if self.interval_start < new.interval_end < self.interval_end:
+            # End point included in existing interval
+            return False
+
+        if self.interval_start > new.interval_start and self.interval_end < new.interval_end:
+            # New interval includes existing interval
+            return False
+
+        return True
+
+
+class Lane:
+    def __init__(self):
+        self.connections = []
+
+    def add_connection(self, start_index, end_index):
+        """
+        If possible adds connection and returns True, otherwise returns False
+        """
+        new_connection = IndexConnection(start_index, end_index)
+
+        # Check if there is room for this lane
+        for connection in self.connections:
+            if not connection.end_index == new_connection.end_index:
+                # Allow connections to collide when the end index matches
+                if not connection.compatible_with(new_connection):
+                    # If connection incompatible, return false
+                    return False
+
+        # No problems, this connection can be included in this lane
+        self.connections.append(new_connection)
+        return True
+
+
+class Connection:
+    def __init__(self, origin, target):
+        self.origin = origin
+        self.target = target
+        self.lane_number = None
+
+    def set_lane_number(self, value):
+        self.lane_number = value
+
+
+class ConnectionList:
+    def __init__(self):
+        self.connections = []
+
+    def add(self, origin, target):
+        self.connections.append(Connection(origin, target))
+
+    def get_connections(self):
+        return self.connections
+
+    def get_origins(self):
+        return [x.origin for x in self.connections]
+
+    def get_targets(self):
+        return [x.target for x in self.connections]
+
+    def get_pairs(self):
+        return zip(self.get_origins(), self.get_targets())
+
+    def get_targets_for_origin(self, given_origin):
+
+        return_targets = []
+        for origin, target in self.get_pairs():
+            if origin == given_origin:
+                return_targets.append(target)
+
+        return return_targets
+
+    def distribute_lane_numbers(self, box_names):
+        lanes = []
+
+        for connection in self.connections:
+            start_index = box_names.index(connection.origin.name)
+            end_index = box_names.index(connection.target.name)
+
+            proposed_lane = 0
+            while True:
+                if proposed_lane >= len(lanes):
+                    lanes.append(Lane())
+
+                if lanes[proposed_lane].add_connection(start_index=start_index, end_index=end_index):
+                    connection.set_lane_number(proposed_lane + 1)
+                    break
+
+                proposed_lane += 1
+
diff --git a/mcstasscript/instrument_diagram/generate_AT.py b/mcstasscript/instrument_diagram/generate_AT.py
new file mode 100644
index 00000000..99cd66ff
--- /dev/null
+++ b/mcstasscript/instrument_diagram/generate_AT.py
@@ -0,0 +1,43 @@
+from mcstasscript.instrument_diagram.connections import ConnectionList
+from mcstasscript.instrument_diagram.arrow import Arrow
+
+
+def generate_AT_arrows(components, component_box_dict, box_names, color=None):
+    """
+    Generate Arrow objects related to the AT relationship of components
+    """
+    connections = ConnectionList()
+    for component in components:
+        origin = component_box_dict[component.name]
+        if component.AT_reference is None:
+            target = component_box_dict["ABSOLUTE"]
+        elif component.AT_reference == "PREVIOUS":
+            origin_index = box_names.index(component.name)
+            target_index = origin_index - 1
+            target = component_box_dict[box_names[target_index]]
+        else:
+            target = component_box_dict[component.AT_reference]
+
+        connections.add(origin, target)
+
+    connections.distribute_lane_numbers(box_names=box_names)
+
+    arrows = []
+    for connection in connections.get_connections():
+        origin = connection.origin
+        target = connection.target
+        lane = connection.lane_number
+
+        arrow = Arrow(origin, target, lane=lane, kind="AT")
+
+        arrow.set_box_offset_origin(0.24)
+        arrow.set_box_offset_target(-0.2)
+        if color is None:
+            arrow.color = "blue"
+        else:
+            arrow.color = color
+        # arrow.set_linestyle("--")
+
+        arrows.append(arrow)
+
+    return arrows
diff --git a/mcstasscript/instrument_diagram/generate_GROUP.py b/mcstasscript/instrument_diagram/generate_GROUP.py
new file mode 100644
index 00000000..b09364eb
--- /dev/null
+++ b/mcstasscript/instrument_diagram/generate_GROUP.py
@@ -0,0 +1,47 @@
+from mcstasscript.instrument_diagram.connections import ConnectionList
+from mcstasscript.instrument_diagram.arrow import Arrow
+
+
+def generate_GROUP_arrows(components, component_box_dict, box_names, color=None):
+    """
+    Generate Arrow objects related to the GROUP keyword
+    """
+    connections = ConnectionList()
+
+    groups = {}
+    for component in components:
+        if component.GROUP != "":
+            group_reference = component.GROUP
+            if group_reference not in groups:
+                groups[group_reference] = [component_box_dict[component.name]]
+            else:
+                groups[group_reference].append(component_box_dict[component.name])
+
+    for group_name, group_members in groups.items():
+        if len(group_members) == 1:
+            # Meaningless group
+            continue
+        base = group_members[0]
+        connected_list = group_members[1:]
+
+        for connected in connected_list:
+            connections.add(connected, base)
+
+    connections.distribute_lane_numbers(box_names=box_names)
+
+    arrows = []
+    for connection in connections.get_connections():
+        origin = connection.origin
+        target = connection.target
+        lane = connection.lane_number + 2  # Make room for group name
+
+        arrow = Arrow(origin, target, lane=lane, kind="GROUP", description=group_name)
+        arrow.set_connection(True)
+        if color is None:
+            arrow.color = [0.4, 0.4, 0.4]
+        else:
+            arrow.color = color
+
+        arrows.append(arrow)
+
+    return arrows
diff --git a/mcstasscript/instrument_diagram/generate_JUMP.py b/mcstasscript/instrument_diagram/generate_JUMP.py
new file mode 100644
index 00000000..f245ef63
--- /dev/null
+++ b/mcstasscript/instrument_diagram/generate_JUMP.py
@@ -0,0 +1,38 @@
+from mcstasscript.instrument_diagram.connections import ConnectionList
+from mcstasscript.instrument_diagram.arrow import Arrow
+
+
+def generate_JUMP_arrows(components, component_box_dict, box_names, color=None):
+    """
+    Generate Arrow objects related to the JUMP keyword
+    """
+    connections = ConnectionList()
+
+    for component in components:
+        if component.JUMP != "":
+            jump_reference = component.JUMP.split(" ")[0]
+            if jump_reference not in component_box_dict:
+                raise ValueError("JUMP reference: " + str(jump_reference)
+                                 + " not found.")
+
+            origin = component_box_dict[component.name]
+            connections.add(origin, component_box_dict[jump_reference])
+
+    connections.distribute_lane_numbers(box_names=box_names)
+
+    arrows = []
+    for connection in connections.get_connections():
+        origin = connection.origin
+        target = connection.target
+        lane = connection.lane_number + 2  # Make room for group name
+
+        arrow = Arrow(origin, target, lane=lane, kind="JUMP", description="JUMP")
+        arrow.set_sub_lane(1)
+        if color is None:
+            arrow.color = "black"
+        else:
+            arrow.color = color
+
+        arrows.append(arrow)
+
+    return arrows
diff --git a/mcstasscript/instrument_diagram/generate_ROTATED.py b/mcstasscript/instrument_diagram/generate_ROTATED.py
new file mode 100644
index 00000000..a50e7db7
--- /dev/null
+++ b/mcstasscript/instrument_diagram/generate_ROTATED.py
@@ -0,0 +1,48 @@
+from mcstasscript.instrument_diagram.connections import ConnectionList
+from mcstasscript.instrument_diagram.arrow import Arrow
+
+
+def generate_ROTATED_arrows(components, component_box_dict, box_names, color=None):
+    """
+    Generate Arrow objects related to the ROTATED relationship of components
+    """
+    connections = ConnectionList()
+    lane_numbers = {}
+    for component in components:
+        if not component.ROTATED_specified:
+            continue
+
+        if component.ROTATED_reference is None:
+            origin = component_box_dict[component.name]
+            target = component_box_dict["ABSOLUTE"]
+        elif component.ROTATED_reference == "PREVIOUS":
+            origin_index = box_names.index(component.name)
+            target_index = origin_index - 1
+            target = component_box_dict[box_names[target_index]]
+        else:
+            origin = component_box_dict[component.name]
+            target = component_box_dict[component.ROTATED_reference]
+
+        connections.add(origin, target)
+
+    connections.distribute_lane_numbers(box_names=box_names)
+
+    arrows = []
+    for connection in connections.get_connections():
+        origin = connection.origin
+        target = connection.target
+        lane = connection.lane_number
+
+        arrow = Arrow(origin, target, lane=lane, kind="ROTATED")
+
+        arrow.set_sub_lane(1)
+        arrow.set_box_offset_origin(0.16)
+        arrow.set_box_offset_target(-0.05)
+        if color is None:
+            arrow.color = "red"
+        else:
+            arrow.color = color
+
+        arrows.append(arrow)
+
+    return arrows
diff --git a/mcstasscript/instrument_diagram/generate_Union.py b/mcstasscript/instrument_diagram/generate_Union.py
new file mode 100644
index 00000000..b07fd0d8
--- /dev/null
+++ b/mcstasscript/instrument_diagram/generate_Union.py
@@ -0,0 +1,119 @@
+from mcstasscript.instrument_diagram.connections import ConnectionList
+from mcstasscript.instrument_diagram.arrow import Arrow
+
+
+def generate_Union_arrows(components, component_box_dict, box_names, component_categories, color=None):
+    """
+    Generate Arrow objects related to use of Union components
+
+    Currently supports processes, materials, geometries and master. Can be
+    expanded to also support loggers, abs_loggers and conditionals.
+    """
+    connections = ConnectionList()
+
+    process_names = []
+    material_names = []
+    geometry_names = []
+    master_names = []
+    geometry_activation_counters = {}
+    for component in components:
+        category = component_categories[component.component_name]
+        if category == "union" or True:
+            if "_process" in component.component_name:
+                # Process component
+                process_names.append(component.name)
+
+            elif component.component_name == "Union_make_material":
+                # Make material component
+                material_names.append(component.name)
+
+                process_string = component.process_string
+                if not isinstance(process_string, str):
+                    continue
+
+                processes = process_string.strip('"').split(",")
+                for process in processes:
+                    if process not in process_names:
+                        print("Didn't find process of name '" + process + "'")
+                        print(process_names)
+                    else:
+                        origin = component_box_dict[process]
+                        connections.add(origin, component_box_dict[component.name])
+
+            elif "material_string" in component.parameter_names:
+                # Geometry
+                geometry_names.append(component.name)
+
+                if component.number_of_activations is not None:
+                    number_of_activations = component.number_of_activations
+                else:
+                    number_of_activations = component.parameter_defaults["number_of_activations"]
+
+                geometry_activation_counters[component.name] = number_of_activations
+
+                if isinstance(component.material_string, str):
+                    material = component.material_string.strip('"')
+                    if material not in material_names:
+                        if material not in ["Vacuum", "vacuum"]:
+                            print("Didn't find material of name '" + material + "'")
+                            print(material_names)
+                    else:
+                        origin = component_box_dict[material]
+                        connections.add(origin, component_box_dict[component.name])
+
+                if isinstance(component.mask_string, str):
+                    masks = component.mask_string.strip('"').split(",")
+                    for mask in masks:
+                        if mask not in geometry_names:
+                            print("Didn't find geometry target of name '" + mask + "'")
+                            print(geometry_names)
+                        else:
+                            target = component_box_dict[mask]
+                            connections.add(component_box_dict[component.name], target)
+
+            elif "_abs_logger" in component.parameter_names:
+                # Abs logger
+                pass
+
+            elif "_logger" in component.parameter_names:
+                # Logger
+                pass
+
+            elif "target_loggers" in component.parameter_names:
+                # Conditional
+                pass
+
+            elif component.component_name == "Union_master":
+                # Master
+                master_names.append(component.name)
+
+                for geometry in geometry_names:
+                    if geometry_activation_counters[geometry] > 0:  # May need to account for floating point precision
+                        # Only include if activation counter for this geometry is still positive
+                        geometry_activation_counters[geometry] -= 1
+
+                        origin = component_box_dict[geometry]
+                        connections.add(origin, component_box_dict[component.name])
+
+    connections.distribute_lane_numbers(box_names=box_names)
+
+    arrows = []
+    for connection in connections.get_connections():
+        origin = connection.origin
+        target = connection.target
+        lane = connection.lane_number
+
+        arrow = Arrow(origin, target, lane=lane, kind="Union")
+        arrow.set_sub_lane(2)
+
+        if color is None:
+            arrow.color = "green"
+        else:
+            arrow.color = color
+
+        if target.name in master_names:
+            arrow.set_linestyle("--")
+
+        arrows.append(arrow)
+
+    return arrows
diff --git a/mcstasscript/instrument_diagram/make_diagram.py b/mcstasscript/instrument_diagram/make_diagram.py
new file mode 100644
index 00000000..8604e44f
--- /dev/null
+++ b/mcstasscript/instrument_diagram/make_diagram.py
@@ -0,0 +1,63 @@
+import copy
+
+from mcstasscript.instrument_diagram.box import ComponentBox
+from mcstasscript.instrument_diagram.generate_AT import generate_AT_arrows
+from mcstasscript.instrument_diagram.generate_ROTATED import generate_ROTATED_arrows
+from mcstasscript.instrument_diagram.generate_JUMP import generate_JUMP_arrows
+from mcstasscript.instrument_diagram.generate_GROUP import generate_GROUP_arrows
+from mcstasscript.instrument_diagram.generate_Union import generate_Union_arrows
+from mcstasscript.instrument_diagram.canvas import DiagramCanvas
+
+
+def instrument_diagram(instrument):
+    """
+    Plots diagram of components in instrument with RELATIVE connections
+
+    All components in the instrument are shown as text fields and arrows are
+    drawn showing the AT RELATIVE and ROTATED RELATIVE connections between
+    components. When more advanced features are used, these are displayed
+    with arrows on the right side of the diagram, currently JUMP, GROUP and
+    use of Union components are visualized.
+
+    Parameters
+    ----------
+
+    instrument : McCode_instr
+        Instrument object from which the component list is taken
+    """
+
+    # Grab components from instrument file and make text box objects
+    components = instrument.component_list
+
+    # Prepare legend
+    component_reader = instrument.component_reader
+    component_categories = copy.deepcopy(component_reader.component_category)
+
+    absolute_box = ComponentBox(name="ABSOLUTE")
+    component_boxes = [absolute_box]
+    component_box_dict = {"ABSOLUTE": absolute_box}
+    for component in components:
+        box = ComponentBox(component)
+        component_boxes.append(box)
+        component_box_dict[component.name] = box
+
+    box_names = [x.name for x in component_boxes]
+
+    color_choices = {"AT": "blue", "ROTATED": "red", "JUMP": "black", "GROUP": [0.4, 0.4, 0.4], "Union": "green"}
+
+    # Arrows for the left side of the diagram
+    AT_arrows = generate_AT_arrows(components, component_box_dict, box_names, color=color_choices["AT"])
+    ROTATED_arrows = generate_ROTATED_arrows(components, component_box_dict, box_names, color=color_choices["ROTATED"])
+
+    # Arrow for the right side of the diagram
+    JUMP_arrows = generate_JUMP_arrows(components, component_box_dict, box_names, color=color_choices["JUMP"])
+    GROUP_arrows = generate_GROUP_arrows(components, component_box_dict, box_names, color=color_choices["GROUP"])
+    Union_arrows = generate_Union_arrows(components, component_box_dict, box_names,
+                                         component_categories, color=color_choices["Union"])
+
+    # Create canvas
+    canvas = DiagramCanvas(AT_arrows + ROTATED_arrows, component_boxes, JUMP_arrows + GROUP_arrows + Union_arrows,
+                           component_categories=component_categories, colors=color_choices)
+
+    # Plot diagram
+    canvas.plot()
\ No newline at end of file
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 7dbe1ade..2d54271f 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -25,7 +25,7 @@
 from mcstasscript.helper.formatting import bcolors
 from mcstasscript.helper.unpickler import CustomMcStasUnpickler, CustomMcXtraceUnpickler
 from mcstasscript.helper.exceptions import McStasError
-from mcstasscript.helper.instrument_diagram import instrument_diagram
+from mcstasscript.instrument_diagram.make_diagram import instrument_diagram
 
 
 class McCode_instr(BaseCalculator):

From 07b8a20c4bf45ae751c8961e220fd9bc70d625e9 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 8 Jun 2022 15:23:26 +0200
Subject: [PATCH 281/403] Introduced changes to box outline in diagram to show
 EXTEND and WHEN keywords being used. Added exception of Exit as Union
 material, this now does not show an error message.

---
 mcstasscript/instrument_diagram/box.py        | 35 +++++++++++++------
 .../instrument_diagram/connections.py         | 16 +++------
 .../instrument_diagram/generate_Union.py      |  8 +++--
 3 files changed, 34 insertions(+), 25 deletions(-)

diff --git a/mcstasscript/instrument_diagram/box.py b/mcstasscript/instrument_diagram/box.py
index 2bad6d89..1c4a401b 100644
--- a/mcstasscript/instrument_diagram/box.py
+++ b/mcstasscript/instrument_diagram/box.py
@@ -9,24 +9,35 @@ def __init__(self, component_object=None, name=None):
         """
         Text box object
         """
+        # set defaults
+        self.position_x = None
+        self.position_y = None
+        self.box_height = None
+        self.box_indent = None
+        self.background_color = "white"
+        self.outline_style = "-"
+        self.outline_width = 1
+
+        self.t = None  # text object
+        self.graph_box_end = None  # graph position where text ends
+        self.graph_box_start = None  # graph position where text ends
+
+        # Load component
         self.component_object = component_object
         if component_object is None:
             self.name = name
         else:
             self.name = self.component_object.name
 
-        # Produced weighted length of name, capital letters count for 1.2
-        self.weighted_name_length = len(self.name) + 0.2*sum(1 for c in self.name if c.isupper())
+            # Decorate the box depending on the McStas features used
+            if self.component_object.WHEN != "":
+                self.outline_style = "--"
 
-        self.position_x = None
-        self.position_y = None
-        self.box_height = None
-        self.box_indent = None
-        self.background_color = "white"
+            if self.component_object.EXTEND != "":
+                self.outline_width = 2.5
 
-        self.t = None # text object
-        self.graph_box_end = None # graph position where text ends
-        self.graph_box_start = None  # graph position where text ends
+        # Produced weighted length of name, capital letters count for 1.2
+        self.weighted_name_length = len(self.name) + 0.2*sum(1 for c in self.name if c.isupper())
 
     def set_box_height(self, box_height):
         self.box_height = box_height
@@ -44,7 +55,9 @@ def set_background_color(self, value):
         self.background_color = value
 
     def plot_box(self, ax):
-        bbox = dict(boxstyle="round", facecolor=self.background_color, edgecolor="black")
+        bbox = dict(boxstyle="round", facecolor=self.background_color,
+                    edgecolor="black", linestyle=self.outline_style,
+                    linewidth=self.outline_width)
 
         self.t = ax.text(self.position_x + self.box_indent, self.position_y, self.name,
                          va="center", fontweight="semibold", color="black", #font="monospace",
diff --git a/mcstasscript/instrument_diagram/connections.py b/mcstasscript/instrument_diagram/connections.py
index 8174285c..c37af54f 100644
--- a/mcstasscript/instrument_diagram/connections.py
+++ b/mcstasscript/instrument_diagram/connections.py
@@ -11,18 +11,10 @@ def __init__(self, start_index, end_index):
             self.interval_end = start_index
 
     def compatible_with(self, new):
-        # Three ways the two intervals can overlap:
-
-        if self.interval_start < new.interval_start < self.interval_end:
-            # Start point included in existing interval
-            return False
-
-        if self.interval_start < new.interval_end < self.interval_end:
-            # End point included in existing interval
-            return False
-
-        if self.interval_start > new.interval_start and self.interval_end < new.interval_end:
-            # New interval includes existing interval
+        """
+        Check if the new interval can coexist with the existing
+        """
+        if new.interval_end > self.interval_start and new.interval_start < self.interval_end:
             return False
 
         return True
diff --git a/mcstasscript/instrument_diagram/generate_Union.py b/mcstasscript/instrument_diagram/generate_Union.py
index b07fd0d8..eed03a6c 100644
--- a/mcstasscript/instrument_diagram/generate_Union.py
+++ b/mcstasscript/instrument_diagram/generate_Union.py
@@ -14,6 +14,7 @@ def generate_Union_arrows(components, component_box_dict, box_names, component_c
     process_names = []
     material_names = []
     geometry_names = []
+    simulated_geometry_names = []
     master_names = []
     geometry_activation_counters = {}
     for component in components:
@@ -51,10 +52,13 @@ def generate_Union_arrows(components, component_box_dict, box_names, component_c
 
                 geometry_activation_counters[component.name] = number_of_activations
 
+                if component.material_string is not None:
+                    simulated_geometry_names.append(component.name)
+
                 if isinstance(component.material_string, str):
                     material = component.material_string.strip('"')
                     if material not in material_names:
-                        if material not in ["Vacuum", "vacuum"]:
+                        if material not in ["Vacuum", "vacuum", "Exit", "exit"]:
                             print("Didn't find material of name '" + material + "'")
                             print(material_names)
                     else:
@@ -87,7 +91,7 @@ def generate_Union_arrows(components, component_box_dict, box_names, component_c
                 # Master
                 master_names.append(component.name)
 
-                for geometry in geometry_names:
+                for geometry in simulated_geometry_names:
                     if geometry_activation_counters[geometry] > 0:  # May need to account for floating point precision
                         # Only include if activation counter for this geometry is still positive
                         geometry_activation_counters[geometry] -= 1

From 40452a6267f5345c5c782440a0537d5f1cc494c3 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 8 Jun 2022 15:28:01 +0200
Subject: [PATCH 282/403] Updates to documentation, including instrument
 diagram in tutorial.

---
 .../McStasScript_tutorial_1_the_basics.ipynb  |  17 +
 ...tasScript_tutorial_3_EXTEND_and_WHEN.ipynb |  72 +-
 .../McStasScript_tutorial_4_JUMP.ipynb        |  17 +
 ...n_tutorial_1_processes_and_materials.ipynb | 897 +++++++++++++++++-
 .../tutorial/Union_tutorial_5_masks.ipynb     |  42 +-
 ...ial_6_Exit_and_number_of_activations.ipynb |  60 +-
 6 files changed, 1025 insertions(+), 80 deletions(-)

diff --git a/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb b/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
index a44d9c42..3eee3824 100644
--- a/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
+++ b/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
@@ -508,6 +508,23 @@
     "instrument.show_components()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Show instrument diagram\n",
+    "While show components provides a text based overview of the instrument file, the instrument diagram can provide a visual overview. The diagram shows the sequence of components and color them based on their category. Arrows show the AT and ROTATED references within the instrument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.show_diagram()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb b/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
index 83254c6b..55b507ff 100644
--- a/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
+++ b/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
@@ -24,7 +24,7 @@
    "outputs": [],
    "source": [
     "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\",\n",
-    "                                output_path=\"data_folder/mcstas_EXTEND_WHEN\")"
+    "                             output_path=\"data_folder/mcstas_EXTEND_WHEN\")"
    ]
   },
   {
@@ -42,18 +42,12 @@
    "outputs": [],
    "source": [
     "src = instrument.add_component(\"source\", \"Source_simple\")\n",
-    "\n",
-    "src.xwidth = 0.02\n",
-    "src.yheight = 0.02\n",
     "src.focus_xw = guide_opening_w = 0.05\n",
     "src.focus_yh = guide_opening_h = 0.06\n",
-    "src.dist = 1.5\n",
-    "src.flux = 1E13\n",
+    "src.dist = source_guide_distance = 1.5\n",
     "\n",
-    "wavelength = instrument.add_parameter(\"wavelength\", value=5.0,\n",
-    "                                      comment=\"Wavelength in [Ang]\")\n",
-    "src.lambda0 = wavelength\n",
-    "src.dlambda = \"0.001*wavelength\""
+    "wavelength = instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
+    "src.set_parameters(xwidth=0.02, yheight=0.02, flux=1E13, lambda0=wavelength, dlambda=\"0.001*wavelength\")"
    ]
   },
   {
@@ -62,17 +56,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "guide = instrument.add_component(\"guide\", \"Guide_gravity\", AT=[0,0,1.5], RELATIVE=src)\n",
-    "guide.w1 = guide_opening_w\n",
-    "guide.h1 = guide_opening_h\n",
-    "guide.w2 = guide_opening_w\n",
-    "guide.h2 = guide_opening_h\n",
-    "guide.l = guide_length = 15\n",
-    "guide.mleft = 4.0\n",
-    "guide.mright = 4.0\n",
-    "guide.mtop = 0.0\n",
-    "guide.mbottom = 0.0\n",
-    "guide.G = -9.82"
+    "guide = instrument.add_component(\"guide\", \"Guide_gravity\")\n",
+    "guide.set_AT([0, 0, source_guide_distance], RELATIVE=src)\n",
+    "guide.set_parameters(w1=guide_opening_w, w2=guide_opening_w,\n",
+    "                     h1=guide_opening_h, h2=guide_opening_h,\n",
+    "                     l=15, G=-9.82, mleft=4.0, mright=4.0, mtop=0.0, mbottom=0.0)"
    ]
   },
   {
@@ -82,14 +70,9 @@
    "outputs": [],
    "source": [
     "acceptance = instrument.add_component(\"acceptance\", \"DivPos_monitor\")\n",
-    "acceptance.set_AT([0,0, guide_length + 0.1], RELATIVE=guide)\n",
-    "acceptance.nh = 200\n",
-    "acceptance.ndiv = 200\n",
-    "acceptance.filename = '\"acceptance.dat\"'\n",
-    "acceptance.xwidth = 0.08\n",
-    "acceptance.yheight = 0.05\n",
-    "acceptance.maxdiv_h = 1.5\n",
-    "acceptance.restore_neutron = 1"
+    "acceptance.set_AT([0,0, guide.l + 0.1], RELATIVE=guide)\n",
+    "acceptance.set_parameters(xwidth=0.08, yheight=0.05, nh=200, maxdiv_h=1.5, ndiv=200,\n",
+    "                          filename='\"acceptance.dat\"', restore_neutron=1)"
    ]
   },
   {
@@ -137,7 +120,7 @@
    "source": [
     "instrument.add_declare_var(\"int\", \"n_reflections\")\n",
     "guide.append_EXTEND(\"n_reflections = SCATTERED - 2;\")\n",
-    "guide.print_long()"
+    "print(guide)"
    ]
   },
   {
@@ -164,18 +147,29 @@
     "    acceptance.filename = '\"acceptance_' + reflections_string + '.dat\"'\n",
     "    acceptance.set_WHEN(\"n_reflections == \" + reflections_string)\n",
     "    \n",
-    "    acceptance.set_AT([0,0, guide_length + 0.1], RELATIVE=guide)\n",
-    "    acceptance.nh = 200\n",
-    "    acceptance.ndiv = 200\n",
-    "    acceptance.xwidth = 0.08\n",
-    "    acceptance.yheight = 0.05\n",
-    "    acceptance.maxdiv_h = 1.5\n",
-    "    acceptance.restore_neutron = 1\n",
+    "    acceptance.set_AT([0,0, guide.l + 0.1], RELATIVE=guide)\n",
+    "    acceptance.set_parameters(xwidth=0.08, yheight=0.05, nh=200, maxdiv_h=1.5, ndiv=200, restore_neutron=1)\n",
     "    \n",
-    "    acceptance.print_long()\n",
+    "    print(acceptance)\n",
     "    print(\"\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The instrument diagram shows the use of *EXTEND* and *WHEN* keywords by decorating the corresponding component boxes with a heavier outline or dashed outline respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.show_diagram()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -215,7 +209,7 @@
    "metadata": {},
    "source": [
     "### Interpretation of the data\n",
-    "The original monitor is unchanged as it was not modified. On the monitors with different numbers of reflections, we see the middle line correspond to zero reflections, the two lines around those are for one reflection and so forth. This explains why the lines further from the center has lower intensity, as they underwent more reflections while also having a larger angle of incidence."
+    "The original monitor is unchanged as it was not modified. On the monitors with different numbers of reflections, we see the middle line correspond to zero reflections, the two lines around those are for one reflection and so forth. This explains why the lines further from the center has lower intensity, as they underwent more reflections while also having a larger angle of incidence on the mirrors."
    ]
   },
   {
diff --git a/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb b/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb
index a72aafe0..6ad01bfe 100644
--- a/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb
+++ b/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb
@@ -189,6 +189,23 @@
     "banana2.options = '\"theta limits=[5 175] bins=150, banana\"'"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Instrument diagram showing JUMP\n",
+    "The instrument diagram helps visualize what happens with a JUMP statement, as JUMPS are visualized. From the diagram, it can be seen that the two daughter instruments exist in parallel after the \"split_arm\" component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.show_diagram()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb b/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
index ff3e45db..837c7fc6 100644
--- a/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
+++ b/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,18 +47,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No components added to instrument object yet.\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.show_components(\"Work directory\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Incoherent_process ________________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1msigma\u001b[0m = \u001b[1m\u001b[94m5.08\u001b[0m\u001b[0m [barns] // Incoherent scattering cross section\n",
+      "\u001b[1mf_QE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Fraction of quasielastic scattering (rest is elastic)\n",
+      "\u001b[1mgamma\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Lorentzian width of quasielastic broadening (HWHM)\n",
+      "\u001b[1mpacking_factor\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // How dense is the material compared to optimal 0-1\n",
+      "\u001b[1munit_cell_volume\u001b[0m = \u001b[1m\u001b[94m13.8\u001b[0m\u001b[0m [AA^3] // Unit_cell_volume\n",
+      "\u001b[1minteract_fraction\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // How large a part of the scattering events \n",
+      "                                should use this process 0-1 (sum of all processes \n",
+      "                                in material = 1) \n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.component_help(\"Incoherent_process\")"
    ]
@@ -74,7 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,9 +119,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Union_make_material _______________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1mprocess_string\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated names of physical processes\n",
+      "\u001b[4m\u001b[1mmy_absorption\u001b[0m\u001b[0m [1/m] // Inverse penetration depth from absorption at standard \n",
+      "                       energy \n",
+      "\u001b[1mabsorber\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [0/1] // Control parameter, if set to 1 the material will have \n",
+      "                        no scattering processes \n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.component_help(\"Union_make_material\")"
    ]
@@ -111,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -171,12 +212,53 @@
     "| abs_material    | Only has absorption                                              |\n",
     "| powder_material | Has both incoherent and powder process in addition to absorption |\n",
     "\n",
+    "The instrument diagram can show the components and their underlying connections. Connections between Union components are shown on the right side. From the diagram it is clear the same incoherent process is used in both inc_material and powder_material. In most cases a user would make individual incoherent processes for each material to set independent incoherent cross sections. It is also visible that powder_material contains two processes and abs_material contains none."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x129.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 406.08x172.8 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "instrument.show_diagram()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "Let us defined a quick test instrument to see these materials are behaving as expected. First we add a source."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -206,9 +288,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x129.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 406.08x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "box_inc = instrument.add_component(\"box_inc\", \"Union_box\", AT=[0.04,0,1], RELATIVE=src)\n",
     "box_inc.xwidth = 0.03\n",
@@ -232,6 +339,47 @@
     "box_inc.priority = 12"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us take another look at how this changed the instrument diagram. The three geometries have been added, and each have a line to their material component. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x129.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 462.384x374.4 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "instrument.show_diagram()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -242,9 +390,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Union_logger_2D_space _____________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1mtarget_geometry\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated list of geometry names \n",
+      "                                     that will be logged, leave empty for all \n",
+      "                                     volumes (even not defined yet) \n",
+      "\u001b[1mtarget_process\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated names of physical \n",
+      "                                    processes, if volumes are selected, one can \n",
+      "                                    select Union_process names \n",
+      "\u001b[1mD_direction_1\u001b[0m = \u001b[1m\u001b[94m\"x\"\u001b[0m\u001b[0m [string] // Direction for first axis (\"x\", \"y\" or \"z\")\n",
+      "\u001b[1mD1_min\u001b[0m = \u001b[1m\u001b[94m-5.0\u001b[0m\u001b[0m [1] // histogram boundery, min position value for first axis\n",
+      "\u001b[1mD1_max\u001b[0m = \u001b[1m\u001b[94m5.0\u001b[0m\u001b[0m [1] // histogram boundery, max position value for first axis\n",
+      "\u001b[1mn1\u001b[0m = \u001b[1m\u001b[94m90.0\u001b[0m\u001b[0m [1] // number of bins for first axis\n",
+      "\u001b[1mD_direction_2\u001b[0m = \u001b[1m\u001b[94m\"z\"\u001b[0m\u001b[0m [string] // Direction for second axis (\"x\", \"y\" or \"z\")\n",
+      "\u001b[1mD2_min\u001b[0m = \u001b[1m\u001b[94m-5.0\u001b[0m\u001b[0m [1] // histogram boundery, min position value for second axis\n",
+      "\u001b[1mD2_max\u001b[0m = \u001b[1m\u001b[94m5.0\u001b[0m\u001b[0m [1] // histogram boundery, max position value for second axis\n",
+      "\u001b[1mn2\u001b[0m = \u001b[1m\u001b[94m90.0\u001b[0m\u001b[0m [1] // number of bins for second axis\n",
+      "\u001b[1mfilename\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Filename for logging output\n",
+      "\u001b[1morder_total\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time, 0 for \n",
+      "                         all orders \n",
+      "\u001b[1morder_volume\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time in the \n",
+      "                          same geometry \n",
+      "\u001b[1morder_volume_process\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time \n",
+      "                                  in the same geometry, using the same process \n",
+      "\u001b[1mlogger_conditional_extend_index\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // If a conditional is used with \n",
+      "                                              this logger, the result of each \n",
+      "                                              conditional calculation can be made \n",
+      "                                              available in extend as a array called \n",
+      "                                              \"logger_conditional_extend\", and one \n",
+      "                                              would then acces \n",
+      "                                              logger_conditional_extend[n] if \n",
+      "                                              logger_conditional_extend_index is \n",
+      "                                              set to n \n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.component_help(\"Union_logger_2D_space\")"
    ]
@@ -258,7 +446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -297,22 +485,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ___ Help Union_master ______________________________________________________________\n",
+      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
+      "\u001b[1mverbal\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [bool] // Toogles terminal output describing the defined simulation\n",
+      "\u001b[1mlist_verbal\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Toogles information of all internal lists in \n",
+      "                            intersection network \n",
+      "\u001b[1mfinally_verbal\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Toogles information about cleanup performed in \n",
+      "                               finally section \n",
+      "\u001b[1mallow_inside_start\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Set to 1 to allow rays to start inside the \n",
+      "                                   defined geometry \n",
+      "\u001b[1menable_tagging\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Enable tagging of ray history (geometry, \n",
+      "                               scattering process) \n",
+      "\u001b[1mhistory_limit\u001b[0m = \u001b[1m\u001b[94m300000.0\u001b[0m\u001b[0m [bool] // Limit the number of unique histories that \n",
+      "                                   are saved \n",
+      "\u001b[1menable_conditionals\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [bool] // Use conditionals with this master\n",
+      "\u001b[1minherit_number_of_scattering_events\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Inherit the number of \n",
+      "                                                    scattering events from last \n",
+      "                                                    master \n",
+      "\u001b[1mrecord_absorption\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Toggles logging of absorption\n",
+      "-------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.component_help(\"Union_master\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
     "master = instrument.add_component(\"master\", \"Union_master\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Although the Union_master component is not told specifically what geometry components to simulate, it picks up the relevant information from the components added before it. The instrument diagram shows these hidden connections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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VLbqAooiwxqBs+Ouvv3JdCX8/iYmJYcmSJSa0VcjFC2exmRVJj/CMe/F/89+QAs7irEwxFz/DbDtWAE3trYTCplRCC56ZUwbSFDQD4gDsAlaRd9BLxX3Aqnwo7u7u//Xy8hr9/PPPu/j5+enud8RaKSWXL182z5kzJ+n27du/xsXFvZD/VUWLEKI+BnbQ0eBMGyfXYhhtTFv4eDgtmTVJRpLpKaUMsbdKCkVBsfhQ5qAZlMwGZA+wEtgEHJESNa3RTlibYEsAHVxcXPoZDIbK3P/MjzI5OflqUlLScmBLDmEyigVCiPIIBuEu2iGKYVdbkkqCPISZ36WUF+2tjkJxLwhBMNqaoUNoPdDNwGFlQIoPVhkUhUKhsAdCIKQsUdNfHiis7aHogD5Ozh4DdXqnysXfaXAHCWZjWuJJY1rSfCnlXluWLYSo5qBjhKcL7YQoloNeVmGWxEYn8Dfwu5SyNIW7VygUNsSadSjC0cl9rptHhf7N2r7s5uFVVRQsrkjxQEoTNyKPmQ6E/DclLTVhjMmUlmsmvXtBCNHUxYltTz2EU+cAnJ1LSYR+CcQkwsKdJO4+x6W4ZIKklKXZma9QKKzEGoPSyODiu/v5N8+5Ojm73ye17j83Io8x/6s2CSZjso+UstBx8n3cxPr3B/Dwq91toV3xQ0p47DOSVh/iLbOUX9pbH2t5UFMA59dui0w/YBJ3ZscdBv5PSrmsoKmChRC7gCA0p3klKWV0prblxDS0UP+5tiXT9WaL7uFoTvgZUkqVm6EYYY0zvUvtho/pSrIxAShboSFuHhVNQBNblJeYSvCQNvnLlVSEgOHBuPi609feutiIBzEFMOTSbiHESGApUAd417LVBZYKIUZQgFTBltwqQZa2O3Mn8derFpmxlv0bma7LHJAzv7aMs+hlQks6tlEI4ZhPexVFiDUGxd3VtWyRfIn7t39ByIb385UL2fA+s9525Oh+7e9wzse1mfV2/ioaXHwkYJMVhmlGnH1ymh1vRzrNADEcwm2UFdxXa5+nbUqzL1LK36WU7wKRgDda5sbMpKcA3oCWzjdf7koBPB9t3UQ3KWWYpb7fsaQCxpICOJdUu1lSAFuOpacAXialzLwgNT3p1gZgQ24pgNPJo93TLZ9PSylnSSlnofWEAKZLKa9n0veo5fjR9GNSygTuGNMP0UZLR1rqXGm5bqXlfEKm69LLKkhbVkopZ6OlCjhn+RyUV3sVRYt1032FrkicJvtD/svOjdPzlDGbjNRt1J8+QxZQtWaHPGXvJntOpcJh4+KKHaWpfUKIspZhnLxSAEdZtr8KWGzmFMDzLcfuHj7LF8u0+PTr08PH5JUCOCrTlic5tVsIUR7ws4hsySSeHtm1mhCiXD7lpuesTwY+Bbaj5ZSpn59OmShQW6SUKWjhVeBOUjNFMcBmruOrF3ayff17RF45iNmcRp2GfXm479eErHuP08eWkZRwA9+ydWjTZTJ1G/UHYNbbjrh7ViEgcCjHDsxDCB0dH/mYBs2GMefj2sTevpAhBzBxZlrG8bZdpxC690fqNR6Ik8GLnRun03PgD3j71szQac/WWRzc+Q1mcxot20+gVfvxtmpuviSnwgs/w6pDEJcEFbzg2Y7wcCN49ke4eEOTq18ZZj4B3RtrPYmaE6CqL/RvCfO2Q81y8PNz8MbvsP00tKsDi14FHzcY9R38sh2m9IUle+FKtHbd1yPBJYc5ZltOwDuLIPQSuBtgQCv46AlwM8Dfh+GNhXD2OjjqoV4lWPwq1MjzMVKiSX9gRZN7CuB1mWQKQq4pgK2YyPALWs6VYUKIb8k7BfAn91Butnbb6MWqC5pRWo+WO2YD0B7NFzS5gGXcS1vSX4bVFOJihE0WJMZEh7Pop15cCQ+hWbtX6Nx7Fh5eVdmyeiL7Q76gfKWmdO79CYkJUaz8bShXLuzIuDY+9goJcRG0CB5HQlwE65e/gtlkpOtjs3Fx03rjfYYsoM+QBVnqvHhuEw91m0rNeo+QG1cv7qJN57fQ6RzYumYSF88VXSSGtUc0g9C1AfzwLDzfGRz04OoEo9vDl8PhP49rRuCJryA1Uy6xy7e0z55N4OAFaPEutK+nGZONx+C7uzICLN0L43tCUz/4+V+YuZJsnL8OvWfBrQR4sw/0DoSv1muGCuDtP+HiTfjyKfh4MDSrDmmle7nYA5MC+C6ytVtKeR1tmA607JXpdLZ8XpRS5tf7STem3dCG9aZZ9ofn4J/KjQK1RQjhDKT/4e/ITU5R9NikhxJ2ai1pqfE0afUMwd2mZhz/+oPKAPQc8D0ubmVITYln699vcvb4SqpU13L7ODl70HPA9+j0DhwI+ZKEuAgS4iOoFdAHR0c3krhB/aaDs9XZe/A8PL21LJCZDVRmevT/Tqs3NYGtayZx/tTfVKvVOUdZW1O7gmZA9oeDtys0rQZPBMGZCJgfAseuZE28GBYF6VONvVxh9lOw4Sj8vgsaVtF6IZW3aAblbGTWuqYN0HobzapDy3dhzWF4f0BWmbVHIDEVTl6F/2Ryg64+pH0GVIYjl2D1YWhcFYa0gToVbXtPihMPWArgDPJo9xS0hF0/CSE+soi/bfmckpeSQgh3oD9a0NZRmU5NQOulFDQjZH5tedRinEYB/mgxuzJPUFDYmaJdLZFD19rg4otOr6mh12vRSsxmY67y6aQbk3tUwIprrKORHxz/SHu4H78Cry+ET/+Gil5w9DK88xh0rA+T/oBDFyAp9Y5B8XbVmu5o2U939ust73nGfHoOebVyYCt4ocud/XRv2LwXNIO3Pwz+CYUZK+CXF2BE8D03vcTwAKUAzkJO7ZZS/iKEiEOL0pvuuDwCPC+lXJqPnuk56/+UUi5PP2jxzbRHMwAFMSj5tWU2kIg2YWEm8IEtpvwrbIdNDErNej1xdHLn2IH5uHlUwsO7KjG3wqnbsC+H93zPP0tfoGa9HhwI+RIhdNRu8GiBynVx8SE2OpwDO77Cp0wdatbrcU96Za4XwL9ez3tum7X8e1LzazSqCq38tf9fva35UgCiE2DPec24FJapS7Xy5m/X9nvlEGO3ZxNtuG39MQiqBZ4uWv1ODtClIUxcCH6+mu/kwk3YF3bHz1OaeBBTAFvk82s3FsORn/HIlipYSvkzOawjkVLOQQvmmL4ffnedluNzyaMtua35URQ/bGJQvHxqMHD0Kravf48DO/6L2WzMcMo7OLpy+uhSws+sw6dsHTr1npUx3JUfrTq+weZVE9i0cjxlKjS8Z4NSzb8jOzfOQEoTnXr9X5ENdwG4OUPIGc2nkWrSnOtfPKY5uZ/+Hn7bqT34O9bXhrEKw8BW8PlazR8zugO8lYO99i8PqyfClMUw3TJnKaCy5nsBbfjti3UQGXPHYf/yw4XTS6GwKZqfqyFwEimN+Ykrih6rUgC37jhpeoeeM+ySAtiWzP8qKCbyyoG+UsothS1LJ4Q57ReEvgjvSvosr82ToVPA/a9v4zEY/BX7b8TJlve/NoXiLoQYgDYMmAbs507E4YPKwBQPSknEKYVC8QDgjOZD8UCbUdcSLSeKI0Ls405OlEPKwNgHawxKalpqgon7nwPlvmNMSwLtB1lo9DpMSak4uBtsUVrBmPuCthUVSdqdssn9uu8IMRr4yt5qKGyKA2TJfeLMnRS/wUAr4D9oBmYvsAJtxb+iiLDGKOwPO/1PcknPo5KcFM3tW+cN3AkjUSg8XDi+Oac5OKWIDUdJjU/OspK6OOMPOKItfFNb6djSyPsl2BlttpkZaEfOcdIU9xFrfCh6Ryf3w3UaPF6nRfBYJ3evqhR83ZL9kWYTN6+fYOvfbyZG3zi7ICU5xibv+Hqd6OftyoJvRuEaXBecS0nIOikhJgmW7kNOW0pcYiqBUsowe+uVL0K4og2JKEoP3dBm02WOv2cC4tFmw51CyyW/EdiJikRc5FibYMvHwdF1qk7nMMBkSvUuUSGehJB6B+craSnxP5jNxs9yWHlciKJFHx833k5Jo6FZlh7/lIOOZL2OLTFJTL0rmJ9CUXQIMQxtenIyORuQBDtqp0ClAFYoFCUFLUDlS2ghaXYoA1L8sNqgWMItdEGLV1SUSCAC2CKlTM5PuCixxINqjJZjpaQPesWi3eOb9lZEoVCUDKwyKDqdGKRz0M2rVN8r1buSi74ow5qbzVLeuphgjgqL1xlTzY/aYg2JLRBCeLoZHDc4OegbNK1d0ezsWJQrUmyLlHAzNtEUev66M4L/pKQa1UwZhUKRL9Y45cs7OOkuPDc32FCxrv1yLYXtu8GCsXvijanmspb8CHbFzeA0p0PT6iPeH93ZWVc06WLuO5HR8Qx5f3FiXGJqh4JE3FUoFA821rxF967VppzRnsYEoGbLspSt4W4ma7htu2GWcuBzfZqXGmMCUMHHnX7BAU6ODroB+UuXDIQQMnN+dSFEDcux8AJeP8oiP/V+6ahQlFSsMSgVy9ZwzzFgXVFTppqbDi3Nqt1JSTN6VSlbKrLjZsGvvKeDs6NDDXvrUYxIzw+/OD9BheJBwxqDIoTO+jjwy6Ye4r2WqwjbV/hQtkInBEUZkz4vpM0zChcLhBAIUfKjIhQUIUQnSw9ktRBiiRAiRghxVAgRaBHpiJY/ZKBFvpwQ4ichxBUhRJwQYoclxW7m3s9eIcQPQohbQojzQggVdlNRKinyB0WrgdUZOKMZ5fw98hdW5IvRZLNlNIqs9AAOAsvQItzOzEVuATAaLanWOxbZ1UII/0wyLdGST/0I1ETLV69QlDpssvgu+moisx/bhGcFAwGdK3J49RV8KrvQ971A1n1xnIuHbuHX1JcnPmrO3sUXOLTqMqO+bYObjxP/zD5B6NorJMWk4uLlRL0OFXjsnSYAHFp1mR2/nuPWpUQcDXo6PV+XoCdq2ELl+05KmpEPF2xje+hFEpLTKOPpwuMP1eeZ3s34+e9DrNp5mqjbCVQq48GQLo0Y2LEBAI9O/o1rN+NZMWMolct68PynKzlw+horZmhRJB57ZyEB1cpStbwnO49dZuqoTjSvU4n//bWXbaEXuRWbRMUy7vx37CNUKevJxgPn+WnNQS5ExuDtbuDRtnV5tk9z9Dodq3ae5ofVB4iMjsfZ0QH/yj58M643BqdSsyYzJyTaDG9hySOf3q+82zKvk1J+IISoh5Zxsc7dBQkh3NBWb8egJaIyCSHqAq+gGaS/LaInpJSvW1LXTsypLIWiNGDTJ0dspLYspHbbchxdd5Xvhm+j43N1kBLO77nBvqUXs8hHnI5l56/nqdmyDIF9/Ii7kUzCLW3C1vGN11g29RCe5Q08PKY+Ukr0jiVnTGnnscus3nWGh1v4E9y4GhG34hEC5v1zmG9X7COwdkVG9Qzktw2hfPTbdjxcnejRqnaByj5x8Qa1q/oyflAbqpX34r2fN7Mt9CLBjavRuU8NwiNuYzJJDp+L4O05GwmoXpZnezfn+IUovl99AIOzAyN7BDLrjx24GRx5e1h7ElPSOBZ2HbO51C90vQz4AVUy/R/u5FRP57rlM30GYX5/K/Kuz2xlSSlTLOOi+nvQV6EoMdjUoBjcHXjk9Yac332Do+uuUs7fnU7P1mX/8ouc33ODW5eyLmz1LG/A2c2BqLB4wvbfoEItT9oMrQnA0Q1XAeg2NoAmPatkq6u4U7WcJ3qd4MSFKDxcnKhTtQzdWvrz2ldauvHXn2hLQPVyuLs48dacDWw5GF5gg1KlrAfvjuiIEILkVCMhRy/h6uzIJy92w9HhzrNq2fYTmKXkWHgUx8KjMo6HhF5iZI9Aalb05tSlm+w4dolalX0Y3KURroaSvh4zX/4CxgCLhRAb0XKhpx+/J6SUCZb87D2A74QQR4DhaCHW/7GRvgpFicG2BsXDESEEOgetJ+HioT2cdHpt32zK+vLm5uPMmEUdOflvJFHn4/n3pzNs/eE041d1taVadqF2FV8WTX2CkKMXOX8tmtmLd/HrhiN4WeLbp3vw73bk63WaW8tk1kZgYhOyL7Gp4OuecX1B1hE90akhHQOrZ+y7OGnfy/8m9GHLoXBOX7rJP3vP8d3K/Xz3eh9a1K18j60tUbyNFrW2PzABrZfyHlpeeGsYDnwM9AGGAKHAZCnleSFEjUJrq1CUIOw6WH7jQjwh889ROcCbygFenN15nVuXEklJMNKwayWOrb/G+i9PkHhbS8HhaNDTom81e6pcYA6cvsamg2HUquxDwxrl2XQgjKjbiTzarh7Hw6P49I8d9G5bl4UbQwHo3EzrmVUt58nlqFgWbTlOOW9Xzl65lWc9Ls6OtGvkx/bQi7zx7Xq6NKvJhcjbPPZQPToF1uDX9aFsPRxOjYreABw8e43aVXxpUqsCH/66jYBqZaldxZezV24RHnGbyFulOzySlDIezZBMyOX8FjLNHLw7D/rd+c+llFHA07mUleVay7GSM26rUNwjdjUoDs56boTFc3xjBGlJRjwruNBjfAO8KrjgVcGFx6cY2bUwjA3/PZHhlC8puDg7cPhsBCt3nCLNaKZyWQ8mDm5G91a1AFi54xSf/B5CpTIevDUsOOP4mH6tibgVz18hJwluXI26fmU4fSnvcFrTRnfim+V72R56kd0nLlPJ14P+HQJoWqsiHz3/MD/9fZD/LtuNk4OeWlV86RdcAYDkVCNz1x4iJiEFD1cn+gbXp2uLmvf1vigU1iKmiRrAdLS1QFuAc/I9Fd22OGFVTvngUbWmdxsTYPe1CYsmH0g4uu7qGMtbo13RCWHe9b9nRfqQVWlh+faTfLFk1x+xCSlD7K2L4sFGTMsIX5+G1vNLRjMuq9EMzHllYOyLNT0Us9kkzRSDFMAmo1mSNSWo/RBIs1mKkhsSMmdMZjNSUuLyc4tpoizwiL31UNiUdmiz7tIXsbkC/YDuaM+jJDFNbOGOgQlTBqZoscaghF09HpNE1qxpdiHydKwEikX2QFdnx8hzV6Mr1a9W1t6q2JTTl26mJKWknbC3HlbwBvA6xeWFQ2Ercppy7Wb5dEGbbPEoWvqIq2jTwxVFhDUG5e/LodEc33iNgC4V7RJvxGyS7F9+0Rx3IyURLdmO3TGazD/N+mPH+M9e7uHq6eZsb3VswqGzEazedcZkMss/7a2LFewi+9oSRcnGzbK55nI+FjAAkcA61NTtIsfaFMBBTq76xXoHna+br3NaUaaUl2aIi0p2Ai6mJBgfl1KeKrrac0cI4eBmcPw+1WgaWsHHPcXJQV9iu9oSSUx8ikNiSlpKcqpxtJRyhb11UigsPpTvAHfLoVjAGW3h6Hq08Df/yvdkpH00VBQmY6MAagFFPcYjgWtSyov5StoBIYQHUJfSkbHxpJRSBQtTFAvENNETLZzNZe4YkK3KgBQfCpVT3hKbqPTFbM9KgpQysaDCFkPrRfExKBKIkVKm2VsRhaKwiGnCS74nY+ythyJnrB3y8vc0eM1NTE1o4+TgbBLFJIK8rZFIUo0pDq5O7sdik2+/IKXcnZusEELoDS6TELqJ0mT01OkdisWbvZQSszFNOBhcNqTFx462LMRTKBQKm2PNOhSDi6PrhfFdppQd2eZlnbuz3Sd73VdSjCmsDP2TN5e/GJ+clhQgpbyck5zeyflV14p+M9vMmOvmWathsUqOkhJzi5NzZ6WFr5x/xpgY30gWpluqUCgUuWCNO7173fINDK90fLPUGxMAZwdnBjYbzqONBul1Qpfr4j69s2Fci7e/dPOq3ahYGRMAZy9fmoyd4ag3uFQHmtpbH4VCUTqxxqAEBNVoXyxSABclrWo85OLm7NE8t/PGxITqPgG5nrY7Qgh8G7Q0AwH21kWhUJROrDEoekcHJ7vkc7gUHU6VyToGft85Y//TDVNZe3z5fa/bQeeITuiccjsvpVmnc7CdH/74Dx+xpJ0P4at/s1mZOkdHgZ3jtykUitJLiXq4lHErxzeDf6OMe3lAMyifbXqfQc1H0rNBX6vKNJqMOOiL322o0vkxPKrXwbdhS3urolAoFAXCJk/SS9HhtPnEn0peVenVoB+LD87Hz7cmnw34iQ/+nsSeC9tpWa0d3w37Eye9E/+3fgprji3lVsIN/MvW5bXO79C70QAAqkzWUdGzCv0Dh/Hn/l/Q6XRMeeQT+gc+yc2EKF7+Yxhta3ZEJ3QM+qELAIsO/MKiA78wqPlIZg/8mdVHl/Dllg85F3UKH9cy9GrUnze7fYCrkxvjFo9m0YFfeKbdWLaeWYfB0YWFT69j/OLR7AnfRnJaEhU9q/BKxzd5qvXzhbovCdcusnZAU1wr+vHI0iMALGnng2tFPwKeeYv9M16hWo8nSLoZSfTx/XjWDKD1+z/gVqkaVzav4MRPH9Pina9xq1ydhKsXCP3qXW4c2oHZlIZP/eY0HjMN7zqNiTqwnX/HPErFtt3QORu4vncLruWr0Oq9OXjXbVy4L1ehUCgKiE3XuF+L0SZAdarbk6NXD/LI1y1pXSOYltXasf3cRhbs+Y6pqyfwfchsGlYK5L1en3Iz4TovLhzM3gs7MsqJiL3C9bhIng8ez/W4CN5a/hJGU9b4hHXLN2Bc5/8A0KZGB74Z/Bsjgl5k74UQXlw4mBvxkbzbaxaNKzfnh5AvmLbm9SzXrzm2lKfbvsqz7V5jycH5bDi5ir5Nh/JR32/p23RogRJX2YKr29ZQqV13yga249axvZxaMDubjDSZCHljCFe2rKB672HUe/I1og5sY/v4gaTG3ZmSH7lnE951mlClYx9iw05y9NtpRdIGhUKhABsbFE+DF9P6zGZIi9EA1C3fkPFdptC3qTY5Kvzm2Qx/x6cDfmR40As899A4zNLMuhN3MrC6O3vwaf8feKXjm5T3qEhCajzX4yOy1FXWvTwP1dJ6KH6+NXm86RCa+wXxz/G/MEszzz00nhFBLzKr//cArD22PMv1b3b7gJFtXmJQ8xHUKaf5qfeEb+fIlX1U861Jv8Bhtrw1uVKtxxPUGfIytQZqvaH4S+ezycRdPENc2Ek8/QNo/Mo06o0YT4WgLqTcus6to3sy5Mq37kzA6InUGz4+17IUCoXifmFjg+KNEAIHveac9nbx0SoRmg/faM4p8Gv2KbbeLr4Zfg0nvRZo0WTOHkE9rwWVGVN3c5nCW8X7TubHTnV78M+YAwxsNpz4lHgmLn2O0fP75lp2QRF6rd3SpLU7NTY6m4yzTzkA9E7OFtk8IsVnbksO7TJYytIVpCyFQqGwMUWeveORhv0AeGPpcyzYM4cfdnyBTujoHvD4PZfl7eoLwJHL+1h26DfORp2iR4O+6ISO70NmM3/3d0xa+nyWenNiVehi/jqyEB/XMgRWbYmT3omrtwsfKszgWwG9ixtJN64RvmoBh7+YbFU5HtXq4FmzPrHnjnP0f9M4NX82kbs3YShTAd9GrQutp0KhUNiCIjco7/X6lOceGseRK/t5b/V4fFzL8L8hv9Oqert7Lqt+hUb0bTKUi9FhjPnzKTacXEmr6u34dugf+LqWZdqa1zl0ZS/PPvQa7/aalWs5rk5ubD69limrXuODtW9Sp3wDZj7+TWGaCYDOwYFmr3+Cs3dZjn47HbdK1fK/KAeEXk+7T36ncqdHCVs5n1PzP6dcs4d46NNFOHl4FVpPhUKhsAVWpQB+peOb0yf3mFnKchPmzR/75zJtzetLbifeGpjTeaHTmfv/GyXSh7mKI7veGRl/ZfOKl6WU8+2ti0KhKH1YZRQezFhQMuOfXCWK+W0p7vopFIqSjTUG5dbl2xdSbK5JMSci9oo5JS35Wm7n9U7O8ck3cj1dLEiKvGIGbtpbD4VCUTqxxqCsXX9ipe5qTI5Bd0slccmx/Lrnh8RkY9JfuckIB6e155b+WGzzl8ecO07M2aNOwFZ766JQKEonVuVDMTi6THLQOU7t03ig8POubhBFmQO4CJFIbsZfT10R+mdaclrSnwmp8c/kNtwnhKiiN7ju9vQP8KjQupOHztFQPEIOSzOx4acTr21bI8xpqc+aTUbbBQdTKBSKTBQmBXAdoLdO6MqV5gRbZmmOQUs3eig/35EQwgD0BJoKnT7XQJJFiZRmM1JeA1bklstFoVAobEGhUgArFAqFQpGOVcEhLbnkn/Y1uAwDWS6n1e4lHQGJcampa1LNpm+klFcLdI0QzQzOLq84OjoGIYtNTvnSjslkNoUmJiX8T0qp/EMKhR2xZh2K8HByWte4TPl245oHuVZ198wzBEpJRCKJTU1h8ZkTqQtPHo1OMKYFSikj8rpGCNHe4GxYO+TREYZG9ZrqHB2KxYhXqcdkMnI6/KRcsPSnpMTkxKdNJuMf9tZJoXhQscagtKrs5r75/NNj3ZyK8SI+WzF63YqU306GfpBqMn2Ql5yHu2fIS0+Na9ct+JGiUk2RiSMnD/LuZ5MuJyYlVHsw10kpFPbHmulZ7R/1r+fwIBgTgH616jl7OjnnayWSkpNatm0WXBQqKXKgcb1AjEZjeaCMvXVRKB5UrDEoBm9n5wL7B6bu3Ipu9nTmHjtsRVW5M/fYYXSzpzN1Z8GGzafu3MrsA7vvuR4PLXKva35yZpPJ0cUlXzGb0X14O4aP72/VtRNnvEL34e04fOJAocvKj3X/rmbe0h+IT4i7L+WnI4TAycnJCLjc14oUCkWuFL/ct/eJ93f/S3UPL8Y1D7qn60SWD/tjNpvvhOa3EW+/PA2Dc87P4fT6rK1z3bY1HDl5kO7te+Pu5mFVGSaTEX0B0jQLhBrqUijsiM0MypZL4fxnx2ZCb17H3dGJHtVr8XFw14zz+69f49sj+zgRfYMuVWvwQ7dHKePiyt9hZ5m0fQNnb9/CUaennk8ZFvUeSA0vbw5HRfDmto3sibyKg05H+8p+zOrQjZpePtnqj0pM4M3tG1l34TxxaSm0KF+JTzt0o1n5SuhmTwfgQlwMutnTqe7hRdgzY7OV37N6LT7t0I1yrm6Fvh+//TWXuYvn8MYLU+gW/AhPTxpCZNQ1ln63jvMXz/DatOfp3LY7b788lXMXzvDDH19z6twJ9Ho9jeo15fmhr1KpfGXW/buaWd/PoGNQV25ER3H6/An++GpVlrpWbFjC1/M+o0n9Zrw/4f9wMWTtKZ06f5zPf/yYqJsRtGnWnpTUrJFzZn7zHhXKVqRt8+Bc64u8EZFFx5ZNgnhh2Fi8PX2QUrJk7e+s2fwX129E4O7myZiRr7P8nz85cvIgACMmaCme5322BIOzgR//+IZ9obtJTEqkZlV/Rj/xIk0DmhMRdY0REwZQp2Z9Kpevwr7Q3QzoOYR5S3+gY1BX3hmjfZdvffwaB47u5bsP51PTr1ahvy+FQlF4bGJQzsdE0/uvhTjq9Exv25kjNyL56dghriXEE1SxCgB/nTvFO62D+efCOZafO4Xnv+v4pUdfJods4mJcDF906onRbOZgVARpZhO3k5Ppuew3biUnMbVNR2JSU/i/fTs4ffsWh57Mnut9xD9/selSGK80bYWPwcA3h/fRa/lCTo18hd8e6cewv5dR1sWVLzv1wM3RidvJyTyybCE6IRjXLIjY1BRmH9zNzeQkVvcdWuh70qxhK+YunsOREwdp2TiIy9e0/Conzh7l5LljFpmWxCfEMfmT8cTFxzK8/7MkJsXzx6oFXL52kW8/nJdR3o79/zL08VF0btMNR8c7M8hWrF/CV/M+pW3zYN55ZTpOluRa6RiNRqZ/+Q7Xb0byZN/RSClZv31Nvvpnri8lLYV3PpmA0An69xxMYlICS9f+QWxcLDPe+JQlfy9kzsKvqFa5Bi89NY74RG1468m+o7k9L5qLV8N5efh4vDy88fLw5v0v32Z/6B56dOiDf7Xa/LLke/4zayJzZs4nPerCmbCT+PvV4oVhYwmo3ZDdh3YQsm8rN2/fQCd0HDp+gIZ1myhjolAUI2xiUNaGnyPJaOTpps0Y26w1JrOZJWdOsO7CORqVLQ/Aa81a80KTFgysE8Dyc6dYE3YWgADfshy5EcmasDM0KlueIXUbUsenDGvCzhCZmECfmnV4u7Xm7F4ddoZjN6M4fTtrfMOEtFTWXTiHBGYfzOon2X/9GkPqNWLY38twc3BkSL1GAKwJO0NEYjwAU3fd8cOsv3ieVJOJwk46qOtfHzdXd0JPHiT05CG8PLypUqEqR04e5OS54wA0a9iC42dCiY65RVDgQwx9bAQAuw6GcOFKGFeuXcoor0u77jzVd3SWOm5ER/HVvE8JCmzHu2M/zHFY6NK1C1y/GYm/X21GDngOgJ0HthF26Vye+meub8+hHdyK0e75vKU/ZMgcOLaHNGMaW3dvAmD8M2/TsG7jLOV4e/pw8Wo4bZoFU7FcJZKSkzhwdC9uru6Me+ZN9Do9VyIusWLDEvYd2U2rpm0BqFiuMhOenZwx1Daw11BmfDWF1ZuW4+Hmgdlsonfne0/KplAo7h829aHcybqb/3h7uswvPR5nUN0G7L9+jXUXzvHhnu3M7f4YZS0O7ixZb/Mp08vJmUV97qQrMUtJozLl8rymfZVqTAlqn+UanQ18FHqdnib1A9l5YDsbd/xDw7pNqFa5BoeO7+f8hTNUrlCVCmUrceFyGJD1nuV0/8qVqZDtmLOjM8JZx/EzoYRfDqNW9TrZZHKaQVuQWbU51deoXlOe6vt0xr5ZmvO9V3n9FtLXL+UkU75MhSzHg1t1omK5yqzZvAJf7zJ4uHnQoXWXfNuhUCiKDptEdexZoxauDo7MP36E/x7awwsbVxOTmkKP6rVwc9AmhH1xcA9zQg/w/AZt/L9XjdoATNy2gXMx0dTzKUN9n7IAXIyLpV0lPyq4urE2/Bwf7Q1hcsgmjt6MomGZctT1zjoz1M3Rie7VaxGTmsKvJ0K5HBfH9iuXeHnT3xn+EF+DC9eTEvj52CG2XbmYUf6ua5fZcfUyF2JjWHH+NN8e2Y+DzjbBLps1bAXAroPbaVwvkCb1Azl66jCJyYk0a9gSgAZ1GuPj5cu+I7v4feU8fvrzW8Ivn6d6lZpUqeSXZ/ke7p588PosUtPSeOvj1wi/fD6bTLXKNahQtiLnL51l3pIfmLt4To5yeZGu48mzxzh+JpTIGxHsPLCNVRuXodc7ZDzYP/9xJqs3LefP1b+yfe8WTUc3TwD++XcV2/ZuxsXgQotGrUlIjOeLnz9m+bpFbAhZi7OTgZZNcp8wodfp6dfjCW7dvsHZ8FM8HNwr2/CeQqGwLzZ5cvp7+bDq8SE0LFOOd0I2s/r8GUY1aMovPe4MSTxRtwE/Hj3IhkthPO5fl087dAO0VelfHtzDs+tXsjrsDANq1+flpi3xNhhY228YnarW4P/27eD70AP0q1WPFY8NRp/DA39ej8d5umEg6y+G8dKm1fx6MpTOfjUyzv+ndXtcHBx5Zv1K/m/fjozyu/rV5MtDexi39R+2Xb5IF7+atrglABlGQ0pJ4/qBNKzbBL1lKK1ZA+2cu5sHH77xOU0CmvHnql9Zs/kvHmrZkfcnfIJel/+wW8O6TXjvtQ9JTErgzY/GcunahSznHRwc+M+rH+DvV5tl6/7k+o0I6vkH3FM70nUMbNiSZev+5H8LZhN66nBG+wY8MoRnB7+M2WzmmwWzWfr37xnX9u0+iAplK/HbX3P55DttbeikF9+lR4fe7D64g58XfUf1yjX4YOIsKpWvkqcePTs+iodlppga7lIoih9WpQB+s2W76TODu5bOmPV3seVSOINWLz58IykxMC85ndCZ1/zyryiIEVDcO9dvRHA6/BQfffMejesHMnPS7GwyA17smRCXEBsgpbyUvQSFQnG/eWDWoShKNmv/XcVvy+dSw68Wr46caG91FApFDlhjUJJjU1OMwAMR/TAuLRUgMT85nV6Xlpyc5OTm6n7/lXoAGdH/WUb0fzbX81JKUtNSHICkotNKoVBkxpphqx0rzp9ONZrNNlemOLLi/OnUuNTUf/KTczG4HthzeEdRqKTIgeNnjqLX6W8CN/MVVigU9wWrwtd7OjltblG+cutxzYNcqrp72GSabXFCSohJTWHp2RPGH48eup1oTGuaX04UIUQXg7PLyhH9n3VpVK+pcHRQ6VCKAi18/Sl+XvRtYmJS4gtGY9oCe+ukUDyoWJWxUQhh0AnxkrezYaiUslxpDKCkEyIxyZi2NslonF1QJ68QorWrwXWsTq8PQir/VJEgMEkpjyUkxn8lpVxvb3UUigcZlQJYoVAoFDbhgZj6q1AoFIr7jzIoCoVCobAJyqAoFAqFwiYog6JQKBQKm6AMikKhUChsgjIoCoVCobAJyqAoFAqFwib8P+gXxAEvZ75OAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 504x129.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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gBOapYvMA1yua0Ihw8S/VS2vgtK2DUGqfWpPQrTWlr717YiTgWWKZhzddwuddVVezqknat5XI+dNwqtew3AndZDDQZPhI6nQdgGtgUEVDGg5MAoyYfxftSusS6FbRjmwg68abKIp11ZqEXpnyUi+yK2wUPu17knxgO5Hzp9H03me4dHQv6WeP4R3Sla6T52Pn6o4xL5djC2dwft0ycpLjcPDyo0v4PLxDupTadtEZbtN7nyZp31ay42NoNXICdm4eHJlrHokIGRtOo8EPAvDPi8NJOXYAQ04WDl6+NLjtf7QZHUbkt9OInD8NgL1Tx7J36lg6TvyCRoPu59j/zSRqzWJyk+NxDgik1ZOvUX/AcACW9fDE0defer0GEbPpV1o+Pp60k4eLz7odvHzZM2UM8dvXkZ+Zhr2HN3W730boK9O40XUGGSaniXDxNTAAc3K/A3NS12EebgEwAEMq8vOxkX9sHYBS+6iEXkliNq6k1cgJRP+xlMRdGzm7cgFBj77E4S/e4fQv3+DVpjNBj79CTkIs0nTjmRvjt6+jxSPjOPx5GIc/fxvP1h0JeuwVDs2eyP6PX6PBbf9Do9PhHdKV+v2HYczPI27rWk58PwuPFm0J6HcX6Wciid28isbDn8Q3tCdewZ048cNnHP1mKgH9h+E59FFiNq9iZ9goHP0Cij9kcpIukJMUR8iYcFwDW5B28nCJ2NyaBuPVpjPSaCT5wHbO/fZ/uNRvTNCjL93wfckwmQL8AvxSeDE0GPPF0P9hLvCQLsPkXxYdfEWppVRCryTNHxhD03ueQmg0XIrYTWbMGQBiNqwEoNvURTj61itze03vfZomw54k+o8lXDy8i6BHXyKgzxBO/jibnMQL5F5KwM7Nk4yoExz/biamgvzifdNOHqbBwHtwa9KK2M2r8GrdiQa3jgAgdot5zD9240piN64s3idh54bihK61d6Tr5PnFZ9xnVy4o3k6aTGTFniXqjyUYc/4bZUi9IumXhQyTEjhS+PpIhAsXwMXihhSlllIJvZIUjadr9OaRA2k0VKg9vasHAEKnB8Cu6HuNtrB9I9FrfyJmwwq8Q7oQ9Ph4kvdv48QPn2HMyzU3cp1JITu8MQuneg2Lv3f09b/svfhcc/gkcfdmziz/FpeGzQkZ+y5Zsec49NnE//qsABkmM4HMCjekKLWEmsvlJgvofzcAOyY+ztnfviNy/kckH9hupdbNpfGMebnkJsURt3VtibV2buZrufHb/uT8umXkp6cS0Mc8PB21ZjE5CTGknTxM5Pxp5KUkla3HwnJ8pvxc8i4lEXPZWb6iKDeXOkO/ydo+/x56JxfOb1jBgRmv4uBdB7/Ofa3SdsNB9xO/fR2Je//mzMoF1Os1iIyoE8Xr6w8YRvTapVz4Zw2xm1fR/9uNtHh4HFKaOLd6MftnvIqdmydewZ1wqtugTH3W6dKPwKGPErNhBSeXzqXBwHu4dGR3mfYV4eJT/iuorFwtFKxWW7QZkCTDZHsrtKVUUUJaseBx0cM71rpF0BqEKK7rbJUiFPbuXpE9Zixt6d2mszWaK5aXevGqZVoHR3QOTlbtpyooyErn9zua55km5X+JSujXE1r47wErtZUpw2R9K7SlVFHqDN1ykkqYKPD3wc2uWtZq5Ou0HvWG1fuyNWmSIJAyTL5k61iqsqIzcxkm+1qrLaVmUwndQhLis+POt/IO6WrVdnvNWnHVMmf/QKv2UVVkx0ej0dun2DoORalpVEK3UEF6yvcnFs/u4t/nTmetvfWmQq9jpXH0qk6aTBz/7tM8aTL8ZOtYFKWmUQndcv+XGXN20OqhLYf4delv0ru4aYVQNwuVhTEvx5S4Z4vJkJ113Jib87at41GUmkYldAsVFoi+TwjRInbjyl5AzbtqWXnygX3AXmnNq/GKogAqoZeblPIEcOKGGyqKotwkaqxAURSlhlAJXVEUpYZQQy7lIITwQohH9K4eg4UQagy9vKTMzs9IW4c0/Z+UsmxzDSiKck0qoVtICOGrdXTeW6dLf5+AfkMddU5qMsDyMmZncWHrH73jtq0dL4ToKKWMs3VMilKdqYRuIaHTjQ7oM6RO53e+LK26jmKhBrfd67jvo5d1UWt+fAl43dbxKEp1VmsSuvWKRAe3anDrCJXMrajBwBH6mI2/DkEldEWpEHVR1GK5er2Le5m33jJ2CMt6eJIVF12JMZVP7JbVHJ33YYVjs+Q9lrat3tUdTCbnCgWhKEqtOEM3gpWLRAvheeMtq764f1YTteZHfDv0wvmy4haWMBkMtHpyAnnDkrD38C5XG+I6hTcURSm72pDQb4prFWcuEr12KVF/LKEgI5XAIQ/TZvS7CI2G49/P4vSyb8i9mIje2RWv4E70nLH0mv1sGTuE5P3baPHIi8Ru/JWCzDRCX/mIrLgoTvzwGXpnN9q//il1uw2gICuDf8YNIyP6JKb8PBx9/Wl679M0f2BMcfFpgL+fHwpA789/w7NVeyK+fp8Lm1eRl3YJt8YtaTP6Hfw69SErLpq1I9rh0TIUl4DGJOzcQMeJczj101yS929j0LKD6Bydr3ksVOJWlMqlhlysxDukKyFj3qXtC1NwbRTEie9nEbPhvxkUE3dtIuiRF3HwrsOJH2YTtWYx+RlpHJnzLnauHnR8YxZBj71S5rPcxD1baHb/aPIzUtn13rMk7d9Os/vHkJ0Qw8FP3wTMZ751uw+k7QtTaDPmXezcvTj02UQuReyhyfCR+IT2AKDlk6/RJXweroFBHJo9iVNL5+DXdQAtH3+Fgsw0tk94qMQQSeqxA2gdnGg7biquDZtafCwURakc6gzdCgy52dcszlyk7Yvv49kyFDtXD3ZMfJz47etoOOgBHOsEkB1/noRdm3Bv0ooWD71Qpj5bPj6egD5DOP7dp+QmxxEyOgz3ZsFEfvshWbFnCuPK4eLh3STt+xhpNBbvm3ryME2GPYmzfyOSD2zHr2NvfDv0AuDCZnPR6HOrFpXoL2nvP/h2vAUAJ/9GdHxrdqln3Nc7Fg0G3nPN9yMdpZeas/sqoWC1ucyt2Za1qCpKVqYSuhXcsDhzaYRAo9MxcNE/XPh7NWmnIzm9bB4R8z7gtsU7cKnf5Lp9FhWJ1ujMP0K9mwdC+1/BaIBTP80lcfcm6t0ymCbDRxKzfhlRa368LK5ShkCEACHo/uH3aC+rlnR5PE516l9z+KRcx0KprdRDHFamErpVXL84M8ChzybS8I4HOLV0LgB1u99KQVYGBz6ZgHdIFzxahHDx0A5yki6QezHhhgm9bGGZ4zLkZJF14RwJuzaVWF1UNPr8huXkXkwgoO9Q/PvcydmVCzn323f49xlK7sV4YtYvp/u0xWXtFLj+sSiNyBGXrFGZpyapjIpFVekYV7G/FmoEldCt4EbFmQHq9riN44s+oSArneYPPU+jwQ9iKsgj92IiR7+dRkFmOvYePgQ9+hLebbtZJa5m94/m4uFdXIrYgyzIp94td3B2xYLi9YFDHyF+x3rO/rqIsysWMGTNKdq+MAWdowuxm34lYdcm7D198GnXAztXd/Iz0qxyLBRFqRy1oUi0AUBK63x4VVaR6MtJk4n89KsrtOmcXNDa2Vdav7aSduoIW8YMicrPSA20dSxVSW05Q69KMVV36gy9CspOiGHtiHZXLe848QsC73zIBhEpilIdqIRuOaM0Giq1Awcvv1KLRrs1blmp/dqKyWgEIYw33lJRlOtRCd1CJqPxWOrJw6192nWvtKdktPYOtaZoNBTe3inlcVvHoSjVnXqwyEKGrPQvI7+dlpN64vCNN1ZuKO30UY58OTm7IDNtjq1jUZTqTp2hW0hKuV6rtxu3+blBH2n0dg46RycjCFXwuByMeTlaY15ugTQZJ0kpf7d1PIpS3amEXg7GgvxvhRALjLnZjQoyUh1tHU81lguck1KabB2IotQEKqGXU2ESOmvrOBRFUYqoMXRFUZQaQiV0RVGUGkINuZSDEKKJ1sHpFY3e7lakdLB1POVkQoiTBRmpX0opl9s6GEVRKk4ldAsJIQK1Do57mo4Y5Vbvlju0emdXW4dULtJgJO3M0cCILyf30Dk4NTHkZs+wdUyKolSMSugW0tg5jG181+OuIWPDtbaOpaI8gtriHdLFed0jPcOEEJ9KKSv3EVhFUSpVbUjoGszFezZbozG9S/PWdXveXmOOm0v9Jti5ecjc5PgWwFFbx6MoSvnVhouiJoom6bYGka/TOdq+QP251YtZ1sOTPVPGVLgtrYOTCaiu1wIURSlUY840ryMTrDelr717YiTgaY22bMlkMBRXO1IUpWaoDWfolS4rLpplPTzZOGogez8Yx6rbG/PXg11J3LMFgNhNq9jwRB9W9g9gzfA2HJz1FoacLEwF+awcUJ8/7g0F4Py6ZSzr4UnEN+8DsPXle1nWw5OM86cx5GSxO/xZVt3WiI2jBpJ6/ECJGAw5WRycNZE/hoewsn8AG58aUNx/UXwbRvZj59sjWXVbI+K2/XnTjo+iKDeHSuhWlHJ0L3buXgQ98iKZsWfZMfFxkg/tYMfbT5J7KYm2L0zGo0U7Ti2dy6HZk9Do7fBp143sC1FkJ8SQfGA7AMn7t2EyGLh4eCeOdQJwbdCUYwtnEP3nT3i0bE/gkEeI++ePEn0fmj2JU0vn4Nd1AC0ff4WCzDS2T3iIrLjo4m1Sjx1A6+BE23FTcW3Y9KYeG0VRKp/6m9uK3Jq0ImTMuwAkH9pB/LY/OfjJ62Ay0fyBMTQZPpKAfnfz+z9ruLBlNR0mzMSvUx8Sdmwgef82kg5sp16vQSTs2sTFwzsxZGcS0PcugOJ6oG3HvodHUFsMOVkcnj2puO8Lm81zW51btahETEl7/8G34y0AOPk3ouNbs0sv8Pwg40W4CLD2MamlVDV7xSZUQq9kQlN4d2NREr0imfp17ANAzKZVZJw7Tsc3PiV+xwZOfD/LvL6zeT1Xlgq88nshQAi6f/g9Wgen4sWXF5t2qlO/9GSuWJuqZq/YhEroVpR+JpIjc8PRu7iTsHMjeld32r38AZtHD+bU0jnoHJ1I2LkRAP++QwBwbxGCnYc3cVv/QO/ihldwZzyDQon/dx0Avh17A1Cn2wBSTxzi0Bfv0GDAcE7//FWJvv373MnZlQs599t3+PcZSu7FeGLWL6f7tMVlC/5HPpZS7rPSoajVVDV7xVZUQrcir+DO5KVe4szKhTj7B9L+1el4h3Sl2+QFRC6cwaHPJmHn5kGz+0cT/MxEwHyDvG+HW4jduBLvtt0QGg0+7XtwKWI3ro1b4uhTF4CWj48nOz6GuG1ryU+7SL1b7uD0L98U9932hSnoHF2I3fQrCbs2Ye/pg0+7Hti5upOfkWaT46Eoys2lEroVaezs6PjmLDq+OavE8oB+dxHQ765r7tdtygJgQfH3IWPeLR6LL6JzdKbLu1+XWBb6ykcl1rd9YTJtX5h8Vft6F3dGbE+x4J0oilIdqbtclKvH4xVFqZbUGbrlcgxZGSUWONdrWK3PgA05WVog29ZxKIpSMeoM3UKGnKy1F/5eXWDrOKwl/ewxDFkZJuCkrWNRFKVi1Bm6hUwF+XOi/lgySkrp4d/7Tr3e2QWofrcCSqOBtFMRMnLBRzlSyjellEZbx6QoSsWohG4hKWWMEKJ91Oofnovd+OsdIJ1uvFeVZDQZjccNWelfSyn/snUwiqJUnEro5SCljAXeLnwpiqJUCWoMXVEUpYZQCV1RFKWGUEMu5SSE0AOtUIUhqjIJXCgcIlOUGk8l9HLQOji+rLFzmGzv4W3SOTqbbB2PUjopTeQkxdnrXdxPGLLS75JSRtk6JkWpTCqhW0gIMdjey29Kn7lrnFwbqDnFqzqTwcDJJV8EH1swfYMQormU6rFYpeZSY+gW0ru4PdN61BsqmVcTGp2OFg+P02odnesAIdfbVoSLJiJcPCTChd1NCk9RrKo2nKE7Aloh2GyNxvQuASFujVtaoynlJhFC4Na4pTHpUmJj4FDx8nDhBPQBhgFDAQ9AC6QAf1zdkqJUbbUhoecD1jvjEiYhtLY/bOdWL2bv1LE0GvwgnSbNsXU4pcrPSOPU0rk41WtI4J0PlbudpH1b+fv5oWV6r9faVqPTCQRaES5aAXcA/wM6ALmAK/897psGPCTCRadyBwyNAES4qOhzCo2ArAq2odQits9MlW8vgJT0tUZj9u6JkYCnNdqyJZPBgEZXuT/+gsw0IudPw6d9z3IndJPBgGtgEF3C5+Hs36hiATXEH1h2xdIrP+zdgUcq1lGx96zQhpqSQSmz2pDQK11WXDRrR7TDs3VH3Ju2Jnbzbzh4+RE6/iP8OvUhdtMqji36mIzoU9i5exLQ9y6Cn5mIRqdn1aAm2Hv6cMcvBzi/bhm7wkbR8snXCH76Lba+fC8JOzdw29I9OPrUZf9HrxC3bS0uDZvj1bpDiRgMOVlEfP0+FzavIi/tEm6NW9Jm9Dv4depTHJ9Hy1BcAhqTsHMDHSfOIaDPnVe9l6IzXM9WHXBp0IS4rX/i2boDbce+x77pr5B+9hh1uw2kS/g3aHR6Tv3yNSd++Iy8S0noHJ3xbN2B0FemI7Ra1o5oB5iLXi/r4YlP+570+eJ3kvZtJeKryaSdjkTn5ExA36G0GR2GztGZPVPGELXmR5rd9xwJOzeitXeg7bip7AobRaPBD+IV3InYTas4POddcpLi0Oj1uDcLpt249/FsdYMSntFcwDyOXnSG3g7Iw1wy7vIJeTYA28r7+wA8XvjvoutudWO3A8kVbEOpRVRCt6KUo3vx7dCLoEdeJOKb99kx8XF6TF/CjrefxMGrDm1fmEz8jg2cWjoXY14OHSbMxKddNxJ2bCA7IYbkA9sBcwI0GQxcPLwTxzoBuDZoypG54UT/+RO+nfpQv/8wji/6uETfh2ZP4uzKhQTe9TjO9RoQteZHtk94iFt/+Ld4m9RjB3BvGkzbcVNxbXj9i7opkfuo12sQeSnJJO7ezObRdxD87CSi1iwmdtOvxPYdSoNbR+DoXZcWD41Do7cjOy6K49/P4sAnr9Ft6iLavTyNgzNfxzUwiFZPvoa9py+ZsefY9ur9ONWpT9Aj48iKO8/pX75BGo20f+2/9xS7eRVBj72CztH5qtj0ru40HTEKnZMLOUlxnFg8m93vPcdtP+68/g/IfH/LMcxn4Z6Fr0DAAOgv2zIW2H79xq5rGJAmw2RYBdoAqOj+Si2jEroVuTVpVVxpKPnQDuK3/cnBT14Hk4nmD4yhyfCRBPS7m9//WcOFLavpMGEmfp36kLBjA8n7t5F0YDv1eg0iYdcmLh7eiSE7k4C+5kpHCbs2AdB27Ht4BLXFkJPF4dmTivu+sPl3AM6tKnlSmLT3H3w73gKAk38jOr41u0yFot2bBdPqydc4++siEndvxq9TH5rfP5r8tEuknTxCZsxZAPLSLnH8u5nkJscX75t28gg6R2fq9RrEwZmvY+/pQ4NbRwBwetk8jLnZZESdIOLrqcX7xG8vOT9Y8LNv0+iOB8zvYd/WEusM2Vmc/uUbsi6cK16WEX0SY17u9d+UP75AKuCMeShDW8pWBuCxwldFnKng/opiMZXQK5nQFOaMoiR6RTL169gHgJhNq8g4d5yOb3xK/I4NnPjeXMbOr7N5/VVVha78XggQgu4ffo/W4b8JIF3qNyn+2qlO/TIlcwC9q4e5WZ35xNXOzaPE+5FGA4bcbA58/CpCq6fT23Nx8PJj26v3YczLuWH7Af3upvGwJy4Lv+QdtE516l9z3/0zxpObHE+7l6fhGtiCPe89R+7FBIz5edfv9BJpwIvAvZjvbikAnCj5/yAH8xl6RSqWBAK7K7C/opSLSuhWlH4mkiNzw9G7uJOwcyN6V3favfwBm0cP5tTSOegcnUjYuREA/75DAHBvEYKdhzdxW/9A7+KGV3BnPINCif93HQC+HXsDUKfbAFJPHOLQF+/QYMBwTv/8VYm+/fvcydmVCzn323f49xlK7sV4YtYvp/u0xZX3hqUEBNJYQH56Kkl7/0Ya/7uGV/QhkHn+NFF/LMGtSSvqdBuI1sGJxN2b8AruiM7JlZTIfWh0evw69bao+/y0SyTs2EDuxYSy7ZBLvgyTS4BvRbjQAV2BIcA9mO8oKboj6hUZJtVti0q1oxK6FXkFdyYv9RJnVi7E2T+Q9q9OxzukK90mLyBy4QwOfTYJOzcPmt0/muBnJgLme6R9O9xC7MaVeLfthtBo8Gnfg0sRu3Ft3BJHn7oAtHx8PNnxMcRtW0t+2kXq3XIHp3/5prjvti9MQefoQuymX0nYtQl7Tx982vXAztWd/Iy0Snm/OkdnQl+ZRsQ373Pih89oOuIptJeNeeud3Wj+0POcXbmIPZNH02TEKNqPn07PGUuJ+GYqkQumA+AWGESz+0eXud/Q8TM4OHMCZ5Z/S/2B9+AaGETGueM33vFWnhThYlwpa+IwX3z0xHwv+iQRLl4vc0DVQyiACBebK9hOMyBJhskbXIFWbEFY80noood3rHWLoDVYOyZ7d6/IHjOWtvRu07l4WdFdJEV3cVQHhuzMq4YohEZbfFZd02x9eUR6gtvGv+mJq61jsZHQwn8PWKGdTBkmrz0mVkZFHy4yTPataFuKmTpDt5CEq8evq6EDn0wgas2PJZY51W3AHcsPXWOP6k1KCetYIP+Sy20diy1YK3la4QxfqUQqoVtIQEZ+esnrZc71GjJie0Wuod18LR4eR4Pb7yuxTGtfc2cCzk+9KIF0W8ehKJVJJXQLFWRlrIz6Y0nbuj1usy/rHSNVkVvjltSWOWmyLkSRfvaYA/DvDTdWlGpMJXQLSaPhy4R/1z35z4vD69Xvf7ezzskFQfVN7DWZNBnJiD5lPL18Xh5CvCWlVPOiKDWaSugWklJeEkJ0SNqz5d7UY/tvFxqti61jUkonkSZTXt5pY17OEinlDR4jVZTqTyX0cpBSZgALCl+KoihVgipwoSiKUkOohK4oilJDqCGXchJC+ALdMFdEqupMwGnggKqpqSg1l0roFhJCCCd7/Wd2Ou0zrQN9c10cq375SYPRxKnYS5qs3PwkIUQ/KWWUrWNSFMX6VEK33AM+7k5PLnxjmJ2bs33Vz+aFpJQs+vOg48K1B1ZgLr2mKEoNo8bQLeTmZP/4yMHtnd2c7W0dikWEEDw8MERrMJpaCyHq2DoeRVGsrzacoTcDXIom6aooV8e6IQ383KzR1E2n12nxdnPMi03OqAeUcc5ZRVGqi9qQ0JOs25wUmmr8yL9GIyTqLzNFqZFqfEKXEqvO2+zukhCJed7sm85gNKHTqlysKErpanxCvxnyCgy8//0/bD0cTVZuAd5ujtzdsyVP3dmeBX8c4Pd/T5CUmkU9b1ce6N+Ge/u0BmDoW4uJu5jJqqkP4u/jyjMf/8a+E3GsmvogAHdN/JFWDX2o7+fGvxExvPtEXzo0r8fcX3fzz+FoLqXnUNfbhdnj7iDAx40N+84wf81+ohLS8HBxYGj3Fowa0gGtRn0IKEptoP6nW8G/ETGs3nGSzi0DmPRob4bf0gqtVvB/fx7ky1V78HF34rUHeqIRgg8Xb+XP3afK3HZkdDIOdjpe/l83Gvq5E7ZgEz9vOUqzAC9ef6gnfdo1wmiUHDwdz5tfb0Cn1TDqzg60DvTlm9X7+H5dzZzfXFGUq6kzdCuo7+uGViOIjErC1dGO5vW9ubVTE178fC0A4+/rTqtGvrg42vHG1+vZvP8ct3duVqa2A3xceeexPgghyM03sO3IeZzs9Ux/7lb0uv+K1q/YGolJSiLOJRFx7r/LBtsOn+fx20Ot+n4VRamaanVCF+Z5b1cBqcAnUrK/PO00C/Di53fvY9uRaM7EpfDpLzv4Yf0h3F0cCvsRRf2VUDQUYjSZAEjPurpqfR0vl+L9y/KQ5319g+kT2qj4e0c7veVvSFGUaqm2D7m4YK76/giwTwikEGQJwTYhGCtE2T7w9p2I46fNETjY6QgO9MPRXkdSaja925oT68dLt7Ny6zG+/m0vAP3aNwbMZ/YAP28+ynd/HeRU7KXr9uNor6dHmwZk5xXw2pfrWLXtOLOX7yQqIZW+oYFohGDLwXNExacRFZ/Gyq3H2H08tnxHRlGUaqfGn6ELwSXAHXNl9yuV9oHmBPQofH0uBEYgBtgAfOzmfPUOjvY6Dp6K57ftxykwmPD3ceXV+9tzW+emAPy2/TjTl2yjnrcrbzzUq3j588O7EH8pk1+3HaNXSENaNPDmxPmL130/4U/2Zc7K3Ww9HM3OyBjqeblyT+9WtGtalw+fGcj8P/Yze8VO7HRamgZ4MbyXeoZIUWoLYc25mooe3pGSvlZrtIKEwARWKymU6+bscG7W84NahjSpnoly+NtL0s4npveXUu6zdSzKzWPtItEVbcfabSlmNf4MHfNMg1rgZCnrNEDTG+xfULjvKmAmsMloqr4TFppMUmA+Joqi1DC1IaEDGKWkxZULhcCVkpXgJeYLpDuBr4GVUlIie7s5y7Pn4lNbhzarW4nhVo68AgPJadn2mIeQFEWpYWr1RVEpyQC+x3zm3UBKNFLiJSV3SMmKK5M5QEZ2/rxvft+blZhSveoNG4wmvvptb4Fep90rpUy2dTyKolhfbTlDvyYpedSy7eVKR3v9R8MmLXnT38c1z9lBX3xbYVVlMJqITkyzF3A0K7dguK3jURSlctT6hF4eOXkF7wkhZpyLTw3FfFdMVWcEzqjCFopSs6mEXk5Symxgu63jUBRFKVKrx9AVRVFqEpXQFUVRagg15FIOQghfodE+oXdxG4wQ1WEMvTT5huysv00Fed9KKc/YOhhFUSpOJXQLCSHqaB2c9vnfcoeXf5+hDnpnV1uHVC7G/FyS9v7T5eyvi14QQvSUUh62dUyKolSMSugWEjr9mPr9h/l0mvSFna1jqSj/WwbbOdVtYBc5f9pU4C5bx6MoSsXUhoSuwTyD7WZrNKZ3adWq/sDh1T6ZFwnoO5SIr6f0Lm2dCBcCaAcMBu4FsmWY7HUz41MUpexqQ0I3YdWLv3l6vbNbiSVZcdGsHdEOn/Y96fPF79br6hqW9fDEqW4D7lhe8WpEehc3pMHgUPS9CBdewK3APcDtmOfBsSt8ZVS4Q0VRKk1tSOiZYL0ZIO3dEyMRwiZFoot0CZ+H1tFK12KFAAQiXAwDpgLNgDygtIsDDiJcVN+ZyRQ1w2YNVxsS+k1jzMtlz5QxxG1di72nL6HjP8KvUx9iN63i2KKPyYg+hZ27JwF97yL4mYkIjZZNTw8kI+ok/b/dgM7ZjfWP9ULv4sbARf9g51b658ausFE41W2Af687OLd6MXunjqXh7feRczGBlKN7cWvcii7vzcO5XkOklJxcMoezvy4iO/48dm4ehI6fTkCfIVc2KzE/UVr09bWohF59Hbd1AErlUgndilKO7sW3Qy9aPDyOiG/eZ8fEx+kxfQk73n4SB686tH1hMvE7NnBq6VyMeTl0mDCTLu/NZ+NT/dkV/ix2rh4Yc3Po9cnP10zm13LhnzW0HvUmWjt74rf/xfHvP6XDa59w8scvOPz527gGBtHupQ8oyEgtdX8ZJn8FfhXhwhMYAAwH7gD0hS97IFeGSbdSG1AUxeZUQrcityatCBnzLgDJh3YQv+1PDn7yOphMNH9gDE2GjySg3938/s8aLmxZTYcJM3ELbEHoy9PY+/7zAAQ/9zbeIV0t7rvh7ffR/IExuAYGEb/9LzLPm28tj9m4EoCOb84qU7syTKYAvwC/FF4UbYM5sf8PyLE4MEVRbhqV0CuZ0GgLvxAl/71MTtKF/75OvHDV+rKw9/QFQGtnD4A0GsrVzuVkmJTA4cLXRxVuUFGUSqUSuhWln4nkyNxw9C7uJOzciN7VnXYvf8Dm0YM5tXQOOkcnEnZuBMC/r3kMO/ngv0TOn4ZPaA/sPLw5s/xb/Dr2JqCfdW4Lr9//blKO7mXvBy/S7L7nKMhKxyWgMQF9h1qlfUVRqg6V0K3IK7gzeamXOLNyIc7+gbR/dTreIV3pNnkBkQtncOizSdi5edDs/tEEPzOR/PQUdoWNQufoTOd3vkTn5EJKxF72fvACHkHtcPZvVOGYmj8wFmkyce737zn46RvYuZovipamqMbjFX6SYXKOCBdOwJpS1i+UYXKhCBc+mIdqrjRXhsmlIlw0AL4rZf3HMkz+JsJFEPBVKeunyDC5XoSLUODTUta/JcPkdhEuegDvl7L+JRkmD4hwMRCYVMr6Z2WYPC7CxVBgfCnrH5Vh8rwIF/cDo0tZf68Mk8kiXDwBPFG4rBnwiwyTL5WyvaJUGpXQrcC5XkNGbE8p/r7jm7NKrA/od1fpZ9yOzgxeGVFi0eBfzd8b8/PIS7141S52bp4l+gq88yEC73yo+HvfDr1KrBcaDUGPvEjQIy9a9qaUinABQm0dhFL7CCmtdxda0dOY1rrn2xqEIBVASjys0Z69u1dkjxlLW3q36WyN5q6p6HbEKw1adhDneg2t1k9BVjq/39E8z1iQ53DjrZWyqMnV7K353mrycbIVdYZuOaM1LjjeSJ2u/ek1a8VVyx28/Kzaj8lgAI0w3nhLRVGqOpXQLWQyGo+mHj/Y2qdd90otJOroUxdHn7qV2QUAqScOobV3iK70jhRFqXSqwIWFDFnpXx6d/1HOpYg9WHO4yhYyY85w4OPXsgzZWZ/ZOhZFUSpOnaFbSEq5UaPXP/fPuGEzEMJN6+BogEo9Wa8UpoJ8jSk/1yA02pnSaPjS1vEoilJxKqGXg6mg4DshxPeAvyE709HW8ZRTARAjpVTj54pSQ6iEXk7SPN4Sa+s4FEVRiqgxdEVRlBpCJXRFUZQaQg25lIMQopfe1eMDY05WZ5PRqLd1PNWCQOrsHS9JKRcbc7PflFKqmRsVxcpUQreQECJU6+D0Z9txU53q9RqE3rm0wj7KlaTJROb5074RX01+Nnn/9laYy9spimJFKqFbSOvoPDbo4XH2l8+fopSNe9PWdJu6yGHVoCa9hRANpZTXfKCpcC72BjLs2tsoilJSbUjojoC2aJ6ZitK7Bgb7tO+ptUZbtZFGb4dnUGhe8oFt7YASybqwQPVAzNWSBgHuIlz0lWHybxuEqijVTm1I6PmYK9ZbiUGr0ZdsLisumrUj2uHTvid9vvjdel1VIUn7tvL380Ot8h619vYC0ItwoQU6AncCI4DmlCxQnQb0KNyuOvEAEOGin43jqAweQJatg1BKVxsS+l6w3gyQ9u6JkYBlBT8VTAYDGt1lv2718AEuAW6Agf9+Fy//tHQEPrhJIVaGjbYOoJKk2ToApXS1IaHfNMa8XPZMGUPc1rXYe/oSOv4j/Dr1IXbTKo4t+piM6FPYuXsS0Pcugp+ZiNBo2fT0QDKiTtL/2w3onN1Y/1gv9C5uDFz0zzULRS/r4Ymjnz8NbvsfUb//gNbBkTbPvUOD2+4FzGfTEV9NJu10JDonZ+p06U/I2HDsPX1Ye19HchIvcNdf50g9cYjNz9xGw0H30/mdL9n30cucXbmQXp8ux69jbw7NnkTUH0tw9KlLvV6DSsQgjUaO/d9MotYsJjc5HueAQFo9+Rr1Bwz/L0Zff+r1GkTMpl9p+fh4mt9/WX2IVNKB1zHXKu0BZAPOwOVn4/nAeTBPgVyNBBX+e9ymUVSOBsAxWwehlE4ldCtKOboX3w69aPHwOCK+eZ8dEx+nx/Ql7Hj7SRy86tD2hcnE79jAqaVzMebl0GHCTLq8N5+NT/VnV/iz2Ll6YMzNodcnP18zmRfJSbxAfloKLZ98jaPfTGX35NF4BLVD6PRsG38fQqcj+JmJpJ2O4Nxv35F7MYFeM3/Br3Mfzq5YQMrRvVw8shuA5P3bAEjatw2NnT0+7bpx9rf/49RPX+LePISm9zzF6eXzSvR/4ofPOPrNVAL6D8Nz6KPEbF7FzrBROPoF4B3SxRxj0gVykuIIGROOa2CLK94A+TJMfgl8KcKFHeakPhTz+Lk/5qkJ9MALMkz+UcEfzU2l5vlWbEUldCtya9KKkDHvApB8aAfx2/7k4Cevg8lE8wfG0GT4SAL63c3v/6zhwpbVdJgwE7fAFoS+PI297z8PQPBzb+Md0vWGfeld3Ojw+kyERkPm+VOc/uUbEnZvQggNxrwcmg59mmb3PYs0Gond9CsJuzZiyMnCr6M5oSft386lI7uo12sQcVvXciliD5nRJ/Ht0AutvSMJuzYB0GrkBAL6DMHO3Ysdbz1W3H/sFvM4euzGlcRuXFm8PGHnhuKErrV3pOvk+Wjtr187Q4bJfGBz4Wu8CBf+mG9r7Avsv+HBUBQFUAm90glN4QiCECX/vUxO0oX/vk68cNX6MvdVou3S+/PteAtoNCTt/ZuU4wfo/PaXpBw7SOT8jwDw69THvOGVUwNfY6rgDm/MwumyCkqOvv7FX9t7+twwmZdGhskLwILCl6IoZVSrE7oQ6IEcwAScBjYAS4GtUmLxZOfpZyI5MjccvYs7CTs3ond1p93LH7B59GBOLZ2DztGJhJ3m62T+fYcAkHzwXyLnT8MntAd2Ht6cWf4tfh17l16D9DIFmensm/Yy7s2CiV67FKHV4de5H0KrQ+vgRPTaJbg0aEraqQgKMtOp020AOkdndI7OeDQPIWn/VgB8QrvjE9qdmPXLAfAtTOh1ug7gwpbfiZz/Eflplzi97JsS/Qf0GUJK5D6i1iwmcMgj5KencOGfNQQ/MwmuHF5RFOWmqLIJvTDZOlmhKW1he+6lrHMqXK8FWha+xhZufwnYAfwOLJGSlFL2L8EruDN5qZc4s3Ihzv6BtH91Ot4hXek2eQGRC2dw6LNJ2Ll50Oz+0QQ/M5H89BR2hY1C5+hM53e+ROfkQkrEXvZ+8AIeQe1w9m90zb4c6wRg7+nNsQXT0Tu7EfrqDFwbNgOg54ylRHw1mYivJqNzdKbR4IcIGRtevK9fp96kHj+IW5NW2Ll54tu+JzHrl6NzdsWrVQcAGg99lPSzx4heu5QTP3xGQN+hpJ08UtxGi4fHIaWJc6sXs3/Gq9i5eeIV3Amnug1udJiKFY01X+EnGSbniHDhBKwpZf1CGSYXinDhA/xSyvq5MkwuFeGiAfBdKes/lmHyNxEugoCvSlk/RYbJ9SJchAKflrL+LRkmt4tw0QN4v5T1LwHNSlmuKJWuyiZ0zHc9WDO+VAu39wIGA3cAc4QgH/jNzu3qDZ3rNWTE9v/yfcc3Z5VYH9DvrtLPuB2dGbwyosSiwb+avzfm55GXevGqXYoulgqhoc1z79DmuXeu2sa3Qy/6fvXnNd9YyNj3CBn7XvH3TYaPpMnwkSW2EVotoS9/SOjLHxYvazM6rMT6lo+Pp+Xj40vt4/LjUQu52DoApXaqygm9qjxMUjQInQck36xOz69bxt6pY69aPmjZwZsVQqW73l0gMkxmw7WfHZBhMvkG68/fYP3xG6w/cIP126+1XoRXvwpWSs1QlRN6OoCU5qfuyqvokf/SHiwSAkfMfwkUkZgTuBHz/c+bgGXAWikxAti7iwPG3MqfKLBO1/70mrXiquUOXn7V/uzXkJMtMX9AKopiRVU5od8MBiAXcwLfD6wFfpCSc9fawZiXvSVh18Zgv069K/XYOfrUxdGnbmV2YROGnCxSjx90oPAJXkVRrKdWJ3QpKcD8eHmZGfNyvzj989dP6l3cXfx7DRI6J5dSb0VUSiqcPpej8z7IFjr9SpmbHW/rmBSlpqnVCb08pJQnhBDdj383863j383sJ00my2+0roUEmIROH2fIylggTcbPbB2PotREKqGXg5QyAnjY1nEoiqJcTtUUVRRFqSFUQlcURakh1JBLOQkh7IEQLLyoWoXlAEeklLm2DkRRlPJRCb0cdI7OEzR29mGOPvUMOmcXi+d8qYoM2VkiJ+mCXufoPM2Ym/2elNeYjUtRlCpLJXQLCSGGOvjUe6fvV2udnC+bZbAmyE6MZctzg1/Ljo+OBH6ydTyKolhGjaFbSO/iPqrVyAnONS2ZAzj5BRD8zFvOelePp6+3nQgXziJcDBHhotvNik1RlBurDWfozQCXoikAKkrv4h/i1qSlNZqqklwbtwRk08uXiXAhgNaYJyq7D2iH+Snb85hnqFQUpQqoDQk9yaqtCZMoLlpRKCsumrUj2uHTvid9vvjdqt1Zas+UMUSt+ZHen/+Gb4deFu+v0WpBohHhwgMYgLkk3B2APeYJ04oepLIDfEW4mGKdyGsUBwB1bG6oAeaTAsVKanxCl5L21mzP3j0xErh+wc+a4Vvgnhts4wVMvAmxVFfq2NzY37YOoCap8Qn9ZjLm5bJnyhjitq7F3tOX0PEf4depD7GbVnFs0cdkRJ/Czt2TgL53EfzMRIRGy6anB5IRdZL+325A5+zG+sd6oXdxY+Cif65ZKHrPlDHEb19HfmYa9h7e1O1+G6GvTCtR7i1h5wYOzHydnIQY/PsMpf2r09HaOxK15kciF0wnJzEWrb0Dbo1b0mvWCnQOV9USeQxzUr8bc/FmD8yzUV6+YTKlF4Go7SYV/qvO0K8vFZhr6yBqEpXQrSjl6F58O/SixcPjiPjmfXZMfJwe05ew4+0ncfCqQ9sXJhO/YwOnls7FmJdDhwkz6fLefDY+1Z9d4c9i5+qBMTeHXp/8fM1kDuDWNBivNp2RRiPJB7Zz7rf/w6V+Y4Iefal4m7ita2l2/2ii1y4lavUPOPr5E/z0Wxz89A10zq60f+0TDDlZXIrYAybTVX3IMJmFuWLQGuBZES6aYi7cfB/QDXPZvmQZJqda8xjWBCJcvAagjo1ys6mEbkVuTVoRMuZdAJIP7SB+258c/OR1MJlo/sAYmgwfSUC/u/n9nzVc2LKaDhNm4hbYgtCXp7H3/ecBCH7ubbxDul6zD2kykRV7lqg/lmDMySpennrycIntWo96k4B+d+HRoi0bR/Yj/t91BD/9Fq6Ngkg9eYj4Hetxb9KKZv97Bp3TjQvsyDB5GpgDzBHhwh7oBTcuy6coys2jEnolK76AWjTFbilT7eYkXfjv68QLV62/XOLuzZxZ/i0uDZsTMvZdsmLPceiziRjzrv+ApygsvHTL7JVc+Hs1aSePcH7dMo7O+4DeX/yOb/ueZX5PMkzmYS6orShKFaISuhWln4nkyNxw9C7uJOzciN7VnXYvf8Dm0YM5tXQOOkcnEnZuBMC/7xAAkg/+S+T8afiE9sDOw5szy7/Fr2Pv0muQAkUPcJryc8m7lETMxpWlbnf02w/Jz0gleu1SAOr2uBWA/R+9gkdQKO5NW5N2OoKMqBPkJMRY8zAoimIjKqFbkVdwZ/JSL3Fm5UKc/QNp/+p0vEO60m3yAiIXzuDQZ5Owc/Og2f2jCX5mIvnpKewKG4XO0ZnO73yJzsmFlIi97P3gBTyC2uHs3+iqPup06Ufg0EeJ2bCCk0vn0mDgPVw6svuq7QL63c3JpXPJTbpAozsfLh5fN+bmcPy7meSnp2Dn4k7g0McI6Hf3VfuLcLG5lLf4kwyTc0S4cMI8tn6lhTJMLhThwgf4pZT1c2WYXCrCRQPgu1LWfyzD5G8iXAQBX5WyfooMk+tFuAil9Iuxb8kwuV2Eix7A+6Wsf0mGyQMiXAzkvwuXl3tWhsnjIlwMBUqrfv2oDJPnRbi4Hxhdyvp7C2udOmO+xqAoN5VK6FbgXK9hiTqfHd+cVWJ9QL+7Sj/jdnRm8MqIEosG/2r+3pifR17qxat2sXPzpOObn9Hxzf9qRLQaOaH4606T5tBp0hwAWj/1+lX7d3t/URnekVJBGv4rLq4oN42w5hxM1yvIXI62Ugvb8qhoW9Zk5+4V2XP6kpbeIV0qtZ9zqxezd+rYq5YPWnaQypx2IPXkYf4eOzQqPyM1sNI6qeFEuDAAyDCpTpiUm0r9wllIQHpe2tVnztZWp2t/es1acdVyBy+/Su03P+0SQqNJr9ROFEWpFCqhW6ggM3151O+L29brOchBVGJxaEefujj61K209ksjpSRq9eL8guzMlTe1Y0VRrEIldAtJk/GrxD2bn9gyenD9+gOGu+icXKjMxH6zGHIyid30W2ZK5L4EaSiYdeM9FEWpalRCt5CUMlUI0enioR3D0k4duVVodS7UgAtg0mTMMmRlrAdWSCkzbB2PoiiWUwm9HKSUWcAPhS9FUZQqQRW4UBRFqSFUQlcURakh1JBLOQghNEBXzBNUOdo4nJrACBwF1kopc2wdjKJUVyqhW0gIYa9zclmjd3Hv4t97sL3e1UNv65iqO1NBvjF5/7astNMRuUKI7lLKM7aOSVGqI5XQLSXEU+7N2nTr/fkqJ41O5XIr0QJux/9vpsvx72d9C/SzdUCKUh2pMXQL6V09Hmx233MqmVeCJiOe0hhysnoKIdTBVZRyqMpn6I6Atmh+mKpC7+IX7Ohbz9Zh1Eh6Zzc0Or3JaDS4ApdsHY+iVDdV+Qw9H/PFsqpFcFWRiqy4aJb18GTL2CG2iakmqfaPaCmK7VTlM/S9YJ2ZG63J3j0xErh2wc8qxGQwoNFV5R+xoijWpP63W5k0Gjn2fzOJWrOY3OR4nOo2oOn/nqHpPU8BkHL8IHvff4Hs+PP433IHGVEnuRSxh96f/4Zvh14k7NzIgU8mUJCRRsM7HiBm4wpyEmKLp81N2reViK8mk3Y6Ep2TMwF9h9JmdBg6R2f2TBlD1JofaXbfcyTs3IjW3oEBC7eUGmfUmh+JXDCdnMRYtPYOuDVuSa9ZK8hLSWbtiHZ4tu6Ie9PWxG7+DQcvP0LHf4Rfpz4UZGXwz7hhZESfxJSfh6OvP03vfZrmD4wBIDsxliNzwkna9w/56Sm4NGhKv2/WoXNw4syK+Zz6+Wuy46Jx8K1Hs/uepdm9z9y0n42i1HRVecilWjr+wyyOfjMVB586tHv5Q9BoODDjVc6vW4bJUMCOtx4j7eRhmgwfiYNPPS5F7CneNy/tEjsmPUFWXDTNH3oeQ3YGOQmxxeszY8+x7dX7yU9PJeiRcdTtfhunf/mGw5+/UyKG2M2raPq/Z2h2f2lFdcwOfvoGJkM+7V/7hNZPT8SpXiMw/VdkJ+XoXuzcvQh65EUyY8+yY+Lj5KenIISgbveBtH1hCm3GvIuduxeHPpvIpYg9SKOR7a89wPm/fsavc1/avzoDn3bdkUYj59ctY//08Tj6+tNq5ASc6jbg4CevE7Ph6imCFUUpH3WGbmUXtqwGoN2LH+DZMhS9izs7Jz3Bhb9X49akJdlx0bg1aUWb594GIG7rH6SfiQTg4uGdGLIyqNvzdoIeeRFpMhGzYQUFmebpyRN2rMeYm01G1Akivp5a3Gf89r9KxBD87Ns0uuOB68bp2iiI1JOHiN+xHvcmrWj2v2fQObmQl2a+FunWpBUhY94FIPnQDuK3/cnFwzvxbNWRi4d3k7TvY6Txv0scqScPo3N2Je3kEVwaNqPz23MBCBzyCACxW34HIHH3JhJ3byreL277X9QfMNyCI6woyrWohF5ZCi+clja1rtBc/w8j84Oo1xbQ724aD3vimts71al/w/Bumb2SC3+vJu3kEc6vW8bReR/Q+4vfcarb4HqRceqnuSTu3kS9WwbTZPhIYtYvI2rNjxjzcm/YJ0Drpyfi1aZT8ff2bl5l2k9RlBtTCd3KAvoMISVyH4c+fZOGgx/k1FLzmap/nyG4NmqBU72GpJ2K4Oi8DzAZDMVn5wDeIV3RObuSsGsjJxbPJvP86eKzc4A63QaidXAicfcmvII7onNyJSVyHxqdHr9OvS2Kc/9Hr+ARFIp709aknY4gI+oEOQkxxQk9/UwkR+aGo3dxJ2HnRvSu7niHdOHioR0AGHKyyLpwjoRd/51tuzZohnuzYNJORbB78mh82/ci5fgB2jz3DgF9hhC7cSXn1y/DwcsXQ24Oibs2Un/gPXgEtS338VYU5T9qDN3KWjw8jtZPTyQnKY6Dn7yONBoJfXUGDQbeg0anp9vURbg3b8Opn78i71Ii7s2CAbBz88De3YuukxfgVKc+x7+fhd7VAzsPb/N6V3dcAgLpOWMpbk1bE7lgOoe/eIf0M5H4tO9pcZzG3ByOfzeTPe+/QOqxgwQOfYyAfncXr/cK7kxe6iWOfz8LZ/9Auk1dhJ2bJ83uH41PaA8uRewhZt0y6t1yR/E+Qqulx0c/0uDWESTu2sT+GeNJPrAdodXS4NYRtJ/wCQI4+OmbHF/0MVKacG8aXLEDrihKsapcJNpqbVmTvbtXZI8ZS1t6t+lcrv3j/11HQUYa9l5+pBw/wJG54TjXbchtS3ah0ek5v24ZGp0evav5zPjED5/h0647feausbgvaTKRn55y1XKdkwtaO/tS98mKi2btiHb4tO9Jny9+t7jPilrZ3z/PmJvjL6Wstg8WqSLRiq2oXzjLSaTpxltdQ15KMhFfTSE3JQl7dy8C+gwl+NlJFE0lkB0Xzcmlc8nPSMXB05fAIY8Q/OykcvWVnRDD2hHtrlreceIXBN75ULnfQ6WSUgDWO8tQlFpEnaFbyM7Nc13oyx8ObDjofluHckPGvFySC8e8L+fWuOVNL0BdFnkpyay+u3WeNBQ4Symr3lPCZaTO0BVbUb9wFirISP2/Y9/N7F7vlsHOemdXW4dzXVp7B+p07mvrMMpESknkwhkFOgfHtfkZ+dU2mSuKLamEbrkfchJi+64e2vIhn3bd8nXObprSbk1Uys5UkC8vRewRhpys84bszFG2jkdRqiuV0C0kpTQBTwkhwhJ2buyBqlhkDQYgEtgvrTkGqCi1jEro5SSljAF+snUciqIoRdR96IqiKDWESuiKoig1hBpyKQchRF2h04/SO7ncgRDOto5HsYhJmkzxBRmpi4Efq/PtkYpyJZXQLSSEqKd1cNpfv//dHv59htjrnd1sHZJiAWkykZMYy8klX/TOuhA1FKj6DxQoShmphG4hjd5uTIPb7vXs+MYsO1vHopRfQP+7ndcMazNECNFKShl54z0Upeqrygm9GeBS9YpEt2wV0PculcyrOZ2DE3W738r5P3/qjfmWSUWp9qryRdEkINPWQVxF5OkteUJ0y9ghLOvhSVZctFW6t3Z7tZmdq7sOUNdAlBqjyp6hS0l7W8dQGnv3xEiEqBZFom82aTQitFpbh1F2NygkoijVTZVN6NXNPy8OJ+XYAQw5WTh4+dLgtv/RZnRY8frotUuJ+mMJBRmpBA55mDaj30VoNBz/fhanl31D7sVE9M6ueAV3oueMpWXu98qi0XW69CdkbDj2nj4YcrPZ/9ErxG39A5cGzfAK7sTpX76m0eAH6TRpDnmpF9kzdSzJB7bjGdQOxzr1if5jCa1Gvk7rUW+Ql5LM4TnvkrBzI4bsTDxbtqPtC1PxCGpL0r6t/P38UOp0HQACkg/8S98v1+LRIuSqGDNjz7H3/edJPX4Qk8GAU936hIwNx/+WwWwZO4Tk/dsIfu5tzq36joKs9BLH58iXk4las5i81IvYubjj06EX7cdPx97TByklJ5fM4eyvi8iOP4+dmweh46cT0GcIqScPc/iLd0mJ3IvQ6qjbdQBtx03F3tPHKj9vRamKVEK3Eu+QrtTvPwxjfh5xW9dy4vtZeLT4rxJP4q5NBD3yIqd++pITP8zGtVEL/PsM5cicd3FvFkzw0xPJS7tE+umIMveZGXuObePvQ+h0BD8zkbTTEZz77TtyLybQa+YvHF/0CdFrl+LTvicNbr2XYwunl9j/4KdvEr/tT/z7DsW3Qy+OXlanFGD3e8+RuPdvmo54Gjs3D04vm8fW8f/j9qW7i7dJ2LmB5g89j3/vIddMlqd//ork/dto+eRrONdrSPq54yXqkQIk/LueoMdeLnF8Aoc8gnNAIC2feBUhBKmnjnB2xQLs3D3p8NonnPzxCw5//jaugUG0e+kDCjJSAcjPSGPbK/8DoaH5/WMoyMrg5NI55KVfotfHP5f5+CpKdaMSuhUYCgs3H/9uJqaC/OLlaScPF3/d9sX38WwZip2rBzsmPk789nU0HPQAjnUCyI4/T8KuTbg3aUWLh14oc78JO9ZjzMuh6dCnaXbfs0ijkdhNv5KwayOGnCzid6w39/3CFDxbhmLIzuTw52+X2B+gw+ufYu/uRWb0KU7/8o35PeVkkbBrI0jJqaVzSvSbcuxgca1U3w630Pb5ydeN0zWwBQCJuzbj1aYTXq06ULfHbSW2Ke34BA55hNykOE4unVucrAHSThwBIGbjSgA6vjkL75Cuxevjtv9F7sUEAI7O+6B4eeKuzZgK8tHo1TVtpWZSCd0Kotf+RMyGFXiHdCHo8fEk79/GiR8+u37hZCHQ6HQMXPSPuVjz6UhOL5tHxLwPuG3xDlzqN7EgAlHcZuldlb8otd7Fja5TF/23wGTCvUkr0s8eA8Cp7o0LUjcZ9iRujVuSfGA7KZH7ObV0Lo3ufJhOEz+/XlBkRJ/i6LwPsPf0peuUhZgM+ex+9xmM+Tk37BPAp113Wo6cUCJ2NW6u1GQqoVuFeYJAY14uuUlxxG1de9UWhz6bSMM7HiguGl23+60UZGVw4JMJeId0waNFCBcP7SAn6QK5FxPKlNCLikZHr12CS4OmpJ2KoCAznTrdBqBzdKZut4GkHj/IoS/eoX7/YZz6ae5V+5//62f2T38F73bdiF7739i9ztE8Hp+wcwPn//wJn/a9yI6LJnrtUgb9sh/Olv3onF42j7yUJJzqNcKzFVz4ezXZCTE3PD4UTrxoKsgjP+0icdv/KrFP/f53k3J0L3s/eJFm9z1HQVY6LgGN8e3YG3svPy5G7OHS4V04+NYj7eQRchJjqdO1f9kDV5RqRiV0K2g46H7it68jce/fnFm5gHq9BpERdaLENnV73MbxRZ9QkJVO84eep9HgBzEV5JF7MZGj306jIDMdew8fgh59Ce+23crUb1HR6IivJhPx1WR0js40GvwQIWPDAQh6/BWyE2K48M8aCjJSqddzEGdWzEfvar5Jp92L71OQmUb8v+vJS0ky35e9bhl2bub1nd/5kiNfvkf8v+s5v345jr7++HbsbfHx0do7cn79CnISYkCjwTukS3GM1zs+QqOh5ZOvcfrnrzj+w2c0GT6S+G1/Fu/T/IGxSJOJc79/z8FP38DO1XxR1M7VnV4zf+HI3HBO/fQlxoJ8XOo3JnDooxbHrijVSZUtQVdVVbRIdFmUp7hzaXIvJXF+3S+4NWlFQUYaEV9PJfP8KXp9upw6nfuSfu4EyQe24VK/CTlJcRz+IoyCzDQGLNyCW2CQxXEXZKZhMhhKLNPodOhd3K+5T9FdLoOWHcS5XkOL+6yIAzPfyD/981dvSik/sWa7qgSdYivqF64KslpxZ2kias1iMqJOodHpcWnYlC7vflNclk4a8jm19Euy4qLQ2jvg3qQ1LZ98rVzJHGD76w+TvH9biWU+7XvS54vfy9WeoiiWUQndckZpKKjUDhy8/Og1a8VVy90at7SsHe86DFz0zzXXuzdrw20/7rQ4vmtp+8IU8i+7GwXAztXjuvvYMtmbCvJNmKslKUqNoBK6hUxGw+GU4wdb+4T2qLRCotWpuPPlPFuG2joEi1w8vCsfOG7rOBTFWtQ9XBYyZGXMjZw/LSf5wHZU+cvqyZCbzYnFs01ZsWezgY22jkdRrEWdoVtISvm3Rqt7aturD8yQRoOP1t7BUHwfuFLlSWkSxpwsvdbBaY8xN/sRKWXljp8pyk2kEno5mIyGJUKIpYCPMS/H0dbxKBa7ZMxIrXozeSpKBamEXk7SPN6SZOs4FEVRiqgxdEVRlBpCJXRFUZQaQg25lIMQopXOyeVNITT9pDQ52DoeSwitLtmYk7XEZCiYLqXMtnU8iqJYj0roFhJCNNfaO+5o8ciLLvV63q7RO7vZOqQyK6x473Pih8/eSD74761CiFukuvdSUWoMldAtpLV3HNv03qedWj3xarUcrnKp3xifdt0d1gxv086QndkR2HOtbUW4EJiLdZ+SYSrxK0pVZ+2E3gxwKZqkqybSuzQNrtN1QLX+IBRaLXW6DtBGrf6hK1ckdBEuPIABwD3AHYAHMBJYeHOjVBTFUtZOTDX/Nj5RoNU63Pxbz1NPHObC36vx7dAL3w69KtyezslFC9iLcKEB2gODgXuBlkAuUDSWlAN0EuEivsKd1h6CoknyFeUmsmpCl5L21myvKrJ3T4wEPG92v6knDxM5fxpAuRO6yWBAo7vsR65FCyRjfj8G/vt9uLxGmx0wtvCllJ3J1gEotU+1HjqoKrLiolk7oh2Ofv749xlK9NolONdrRMeJn3P48zAuHtqBV0gXuk1ZSNLevzk8511ykuLQ6PW4Nwum3bj38WzVnry0S+yZMoaLB//FmJeLg289gh55CY3ejr1Tzfk0cv40IudPo9XI12k96g3OrJjPqZ+/JjsuGgffejS771ma3fsMAH/c05bs+PO0Gvk6Z1f9H/UHDKPdi+//F7gJE+ZEfQ9wG+bbWO0omdC1QDxw5iYcypqiO2C84VaKYmUqoVtRTuIFAOp0HUDM+uVsHNmPViNfR0oTSXu2cHblAjxbd6DpiFHonFzISYrjxOLZ7H7vOW77cSfRa5cSv+1PmgwfiWfrjmTFnAEkvu170nj4k5xdsYCAvncR0O8u3Jq04vy6ZeyfPh6/zv1odMcDJOzezMFPXsfB05f6A4YXx5W4dwvBz7yFo19AyYAlUobJH0W4WAH0Bp7EPG6up+QENduBUZV57GqYc6ghF8UGVEK3Ir2LG+1e+oDE3ZuJWb8ct8YtafXkazj41CVpzxYyY8/iGhjE6V++IevCueL9MqJPYszLxbVRCwCSD/4LGg0eLdpSv/8w9M6ueLXuxNkVC3Br0ooGt44AIHLBdAASd28icfem4vbitv9VIqF3Cfsap7oNrg5Yg0aEiy2YzyhzABeuftjMhPkM/p6KHp9apmyVrBXFilRCtyK9iztCCDQ6vfn7wuIOQqMFQBoN7J8xntzkeNq9PA3XwBbsee85ci8mYMzPo263AQxY+DcJuzeRfiaSfR+M4/yfP9P781UIce0ZHVs/PRGvNp2Kv7d38yqxvtRkDiCRwBGgKeCN+WKo0xVbCaCg8KWUjSMqoSs2oBK6jeSnXSJhxwZyLyYUL4vZ+CspkftwbdQcz1btiVm/nOyEGAD0bh4AJO3fyvm/fsE7tDsBfYYQu3El59cvw8HLF0NuDom7NlJ/4D14BLW9cRDmIZexwFgRLtphHk+/C6hz2VYC+EWGSQtq39VuIlxstnUMSu2kEvpNFjp+BgdnTuDM8m+pP/AeXAODyDhnLpqjc3Qifsd6Ti//FkwmXAODCBkbDkCdzv3w7dSHi4d2kLx/G12nLKTBrSMoyErn9M9fc/DTN9E5OuPRsh3uTYPLFoxAiHDxHjAC8zMEeYDrFVtJ4G5126JFvIBjtg5CqX2EevLbMvbuXpE9Zixt6d2ms61DqZADM9/IP738q4m8zSNAWyAL8xj6lS6/nVEpu80yTPazdRBK7aL+o1pIAtSED0FpAiMmGSZDRbjwBAZivvA5CPPvhR6wx5zQJwM/2CzW6umsrQNQah+V0C0kILsgK93WYVRYfnqqAfNZOTJMpgA/Az8Xzt8Sgvn2xf8BocARGSZP2yhURVHKSCV0CxVkZ66O2bCyTd1uA+1uvHXVZMjJIn77XwKunnOncBKuQ4WvaSJc2MswmXeTQ1QUpRzUGLqFhBB1tQ5O+/x7D/YM6DPUQevofN1bCqsSKU3kJF7g5JI5WTmJsSsLsjIesXVMiqJYj0ro5SCE8BUa7RN6F7fBCHHlfdtVmjQaLxRkpi0BfpZSqvlGFKUGUQldURSlhqiWRRoURVGUq6mEriiKUkOohK4oilJDqISuKIpSQ6iEriiKUkOohK4oilJDqISuKIpSQ6iEriiKUkP8P09/bFWqYv90AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 462.384x374.4 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "instrument.show_diagram()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -323,13 +578,284 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Instrument settings:\n",
+      "  ncount:           3.00e+06\n",
+      "  output_path:      data_folder/union_materials\n",
+      "  run_path:         run_folder\n",
+      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
+      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
+      "  executable:       mcrun\n",
+      "  force_compile:    True\n",
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_materials_45\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
+      "                                                ^~~~~~~~\n",
+      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
+      "#define filename mccabs_logger_space_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
+      "                                                ^\n",
+      "                                                \"%s\", \n",
+      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
+      "#define filename mccabs_logger_space_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "14 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_materials_45\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Cu.laz' (Table_Read_Offset)\n",
+      "Table from file 'Cu.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
+      "  '# TITLE *-Cu-[FM3-M] Otte, H.M.[1961];# CELL 3.615050 3.615050 3.615050 90. ...'\n",
+      "PowderN: powder: Reading 19 rows from Cu.laz\n",
+      "PowderN: powder: Read 19 reflections from file 'Cu.laz'\n",
+      "PowderN: powder: Vc=47.24 [Angs] sigma_abs=15.12 [barn] sigma_inc=2.2 [barn] reflections=Cu.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: incoherent \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: powder \n",
+      "component index [1]: 4 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 3\n",
+      "name of material    [0]: inc_material \n",
+      "component index     [0]: 2 \n",
+      "my_absoprtion       [0]: 0.000000 \n",
+      "number of processes [0]: 1 \n",
+      "name of material    [1]: abs_material \n",
+      "component index     [1]: 3 \n",
+      "my_absoprtion       [1]: 3.000000 \n",
+      "number of processes [1]: 0 \n",
+      "name of material    [2]: powder_material \n",
+      "component index     [2]: 5 \n",
+      "my_absoprtion       [2]: 1.200000 \n",
+      "number of processes [2]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 3\n",
+      "\n",
+      "name of geometry    [0]: box_inc \n",
+      "component index     [0]: 7 \n",
+      "Volume.name         [0]: box_inc \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.000000 \n",
+      "Volume.p_physics.number of processes [0]: 1 \n",
+      "Volume.geometry.shape                [0]: box \n",
+      "Volume.geometry.center.x             [0]: 0.040000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: box_powder \n",
+      "component index     [1]: 8 \n",
+      "Volume.name         [1]: box_powder \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absorption       [1]: 1.200000 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: box \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: box_abs \n",
+      "component index     [2]: 9 \n",
+      "Volume.name         [2]: box_abs \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absorption       [2]: 3.000000 \n",
+      "Volume.p_physics.number of processes [2]: 0 \n",
+      "Volume.geometry.shape                [2]: box \n",
+      "Volume.geometry.center.x             [2]: -0.040000 \n",
+      "Volume.geometry.center.y             [2]: 0.000000 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 4\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           0 = [1,2,3]\n",
+      "LIST: Intersect_check_list for Volume      0 = [1,2,3]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [1,2,3]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for box_inc with box shape made of inc_material\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = []\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = []\n",
+      "LIST: Next_volume_list for Volume          1 = [0]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for box_powder with box shape made of powder_material\n",
+      "LIST: Children for Volume                  2 = []\n",
+      "LIST: Direct_children for Volume           2 = []\n",
+      "LIST: Intersect_check_list for Volume      2 = []\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = []\n",
+      "LIST: Next_volume_list for Volume          2 = [0]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for box_abs with box shape made of abs_material\n",
+      "LIST: Children for Volume                  3 = []\n",
+      "LIST: Direct_children for Volume           3 = []\n",
+      "LIST: Intersect_check_list for Volume      3 = []\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = []\n",
+      "LIST: Next_volume_list for Volume          3 = [0]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_I=3.16864e-09 logger_space_ERR=4.64761e-12 logger_space_N=474493 \"logger.dat\"\n",
+      "Detector: abs_logger_space_I=1.70535e-09 abs_logger_space_ERR=1.97625e-12 abs_logger_space_N=1.08778e+06 \"abs_logger.dat\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.set_parameters(wavelength=8.0)\n",
     "instrument.settings(ncount=3E6, output_path=\"data_folder/union_materials\")\n",
@@ -350,9 +876,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space\n",
+      "Plotting data with name abs_logger_space\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ms.make_sub_plot(data)"
    ]
@@ -369,13 +916,284 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Instrument settings:\n",
+      "  ncount:           5.00e+06\n",
+      "  output_path:      data_folder/union_materials\n",
+      "  run_path:         run_folder\n",
+      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
+      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
+      "  executable:       mcrun\n",
+      "  force_compile:    True\n",
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_materials_46\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
+      "                                                ^~~~~~~~\n",
+      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
+      "#define filename mccabs_logger_space_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
+      "                                                ^\n",
+      "                                                \"%s\", \n",
+      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
+      "#define filename mccabs_logger_space_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "14 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_materials_46\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Cu.laz' (Table_Read_Offset)\n",
+      "Table from file 'Cu.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
+      "  '# TITLE *-Cu-[FM3-M] Otte, H.M.[1961];# CELL 3.615050 3.615050 3.615050 90. ...'\n",
+      "PowderN: powder: Reading 19 rows from Cu.laz\n",
+      "PowderN: powder: Read 19 reflections from file 'Cu.laz'\n",
+      "PowderN: powder: Vc=47.24 [Angs] sigma_abs=15.12 [barn] sigma_inc=2.2 [barn] reflections=Cu.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: incoherent \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: powder \n",
+      "component index [1]: 4 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 3\n",
+      "name of material    [0]: inc_material \n",
+      "component index     [0]: 2 \n",
+      "my_absoprtion       [0]: 0.000000 \n",
+      "number of processes [0]: 1 \n",
+      "name of material    [1]: abs_material \n",
+      "component index     [1]: 3 \n",
+      "my_absoprtion       [1]: 3.000000 \n",
+      "number of processes [1]: 0 \n",
+      "name of material    [2]: powder_material \n",
+      "component index     [2]: 5 \n",
+      "my_absoprtion       [2]: 1.200000 \n",
+      "number of processes [2]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 3\n",
+      "\n",
+      "name of geometry    [0]: box_inc \n",
+      "component index     [0]: 7 \n",
+      "Volume.name         [0]: box_inc \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.000000 \n",
+      "Volume.p_physics.number of processes [0]: 1 \n",
+      "Volume.geometry.shape                [0]: box \n",
+      "Volume.geometry.center.x             [0]: 0.040000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: box_powder \n",
+      "component index     [1]: 8 \n",
+      "Volume.name         [1]: box_powder \n",
+      "Volume.p_physics.is_vacuum           [1]: 0 \n",
+      "Volume.p_physics.my_absorption       [1]: 1.200000 \n",
+      "Volume.p_physics.number of processes [1]: 2 \n",
+      "Volume.geometry.shape                [1]: box \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: box_abs \n",
+      "component index     [2]: 9 \n",
+      "Volume.name         [2]: box_abs \n",
+      "Volume.p_physics.is_vacuum           [2]: 0 \n",
+      "Volume.p_physics.my_absorption       [2]: 3.000000 \n",
+      "Volume.p_physics.number of processes [2]: 0 \n",
+      "Volume.geometry.shape                [2]: box \n",
+      "Volume.geometry.center.x             [2]: -0.040000 \n",
+      "Volume.geometry.center.y             [2]: 0.000000 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 4\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           0 = [1,2,3]\n",
+      "LIST: Intersect_check_list for Volume      0 = [1,2,3]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [1,2,3]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for box_inc with box shape made of inc_material\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = []\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = []\n",
+      "LIST: Next_volume_list for Volume          1 = [0]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for box_powder with box shape made of powder_material\n",
+      "LIST: Children for Volume                  2 = []\n",
+      "LIST: Direct_children for Volume           2 = []\n",
+      "LIST: Intersect_check_list for Volume      2 = []\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = []\n",
+      "LIST: Next_volume_list for Volume          2 = [0]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for box_abs with box shape made of abs_material\n",
+      "LIST: Children for Volume                  3 = []\n",
+      "LIST: Direct_children for Volume           3 = []\n",
+      "LIST: Intersect_check_list for Volume      3 = []\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = []\n",
+      "LIST: Next_volume_list for Volume          3 = [0]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_I=1.13125e-09 logger_space_ERR=5.9031e-13 logger_space_N=3.86142e+06 \"logger.dat\"\n",
+      "Detector: abs_logger_space_I=4.15533e-11 abs_logger_space_ERR=2.36464e-14 abs_logger_space_N=4.11813e+06 \"abs_logger.dat\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.set_parameters(wavelength=2.8, source_width=0.03)\n",
     "instrument.settings(ncount=5E6, output_path=\"data_folder/union_materials\")\n",
@@ -386,9 +1204,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space\n",
+      "Plotting data with name abs_logger_space\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ms.name_plot_options(\"logger_space\", data, log=True)\n",
     "ms.name_plot_options(\"abs_logger_space\", data, log=True)\n",
diff --git a/docs/source/tutorial/Union_tutorial_5_masks.ipynb b/docs/source/tutorial/Union_tutorial_5_masks.ipynb
index 90830e9f..bebfa3b1 100644
--- a/docs/source/tutorial/Union_tutorial_5_masks.ipynb
+++ b/docs/source/tutorial/Union_tutorial_5_masks.ipynb
@@ -90,7 +90,16 @@
     "logger_zx.n2 = 300\n",
     "logger_zx.filename = '\"logger_zx.dat\"'\n",
     "\n",
-    "master = instrument.add_component(\"master\", \"Union_master\")"
+    "master = instrument.add_component(\"master\", \"Union_master\")\n",
+    "\n",
+    "instrument.show_diagram()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the above diagram it is clear that the wall is made of Al and the master simulates the wall and the wall_vacuum."
    ]
   },
   {
@@ -160,7 +169,16 @@
     "mask.zdepth = 0.09\n",
     "mask.priority = 1\n",
     "mask.mask_string='\"window\"'\n",
-    "mask.set_AT([0,0,-0.1], RELATIVE=wall)"
+    "mask.set_AT([0,0,-0.1], RELATIVE=wall)\n",
+    "\n",
+    "instrument.show_diagram()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The windows was added to the diagram and is connected to the master as expected. The mask shows up as a component that only acts on the window geometry, as the mask itself is not simulated, it just modifies the window."
    ]
   },
   {
@@ -219,7 +237,16 @@
     "mask.zdepth = 0.09\n",
     "mask.priority = 2\n",
     "mask.mask_string='\"outer_window,outer_window_Al\"'\n",
-    "mask.set_AT([0,0,0.1], RELATIVE=wall)"
+    "mask.set_AT([0,0,0.1], RELATIVE=wall)\n",
+    "\n",
+    "instrument.show_diagram()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the outer window the mask_outer acts on two geometries, outer_window and outer_window_Al. Notice that both the arrow for Al and mask_outer go to outer_window_Al as they both impact that component, one as a material and the other as a mask."
    ]
   },
   {
@@ -328,6 +355,15 @@
     "master = instrument.add_component(\"master\", \"Union_master\")\n",
     "\n",
     "\n",
+    "instrument.show_diagram()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "data = instrument.backengine()"
    ]
   },
diff --git a/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb b/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
index a72f052b..d39a9b2a 100644
--- a/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
+++ b/docs/source/tutorial/Union_tutorial_6_Exit_and_number_of_activations.ipynb
@@ -128,7 +128,16 @@
     "banana.xwidth = 1.5\n",
     "banana.yheight = 0.4\n",
     "banana.restore_neutron = 1\n",
-    "banana.options = '\"theta limits=[5 175] bins=250, banana\"'"
+    "banana.options = '\"theta limits=[5 175] bins=250, banana\"'\n",
+    "\n",
+    "instrument.show_diagram()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see there is one material defined, *Al*, and 3 of the 5 geometries use this as their material. The 5 geometries are all picked up by the master component."
    ]
   },
   {
@@ -159,6 +168,13 @@
     "ms.make_sub_plot(data_empty[2])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The loggers show our empty cryostat and sample container illuminated by a beam, and the banana monitor contains some powder scattering from the aluminium. "
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -173,7 +189,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sample_volume.material_string='\"Exit\"' "
+    "sample_volume.material_string='\"Exit\"'\n",
+    "instrument.show_diagram()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The diagram does not change between *Vacuum* and *Exit* being used for the *sample_volume*. Let us run the simulation and see the difference."
    ]
   },
   {
@@ -201,12 +225,19 @@
     "ms.make_sub_plot(data[2])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the logger data one can see the back part of the sample container and exit of the cryostat is no longer illuminated, as the rays are removed from the Union simulation as soon as the touch the volume inside the sample container. The removed rays do not disappear completely, they are delivered to the next McStas component in the instrument."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Adding an external component in the gap\n",
-    "We can now see any part of the beam that intersected the exit volume is basically removed from the simulation. It is now however possible to insert another component within that exit volume, for example a sample not available as a Union process. Here we just use a PowderN sample in order to demonstrate. We select the same dimensions as the exit volume, but subtract 10 micrometer to avoid a perfect overlap."
+    "We can now see any part of the beam that intersected the exit volume is basically removed from the Union simulation. It is now however possible to insert another component within that exit volume, for example a sample not available as a Union process. Here we just use a PowderN sample in order to demonstrate. We select the same dimensions as the exit volume, but subtract 10 micrometer to avoid a perfect overlap."
    ]
   },
   {
@@ -219,7 +250,9 @@
     "sample.set_AT([0,0,0], RELATIVE=sample_volume)\n",
     "sample.radius = sample_volume.radius - 1E-5\n",
     "sample.yheight = sample_volume.yheight - 2E-5\n",
-    "sample.reflections = '\"Na2Ca3Al2F14.laz\"'"
+    "sample.reflections = '\"Na2Ca3Al2F14.laz\"'\n",
+    "\n",
+    "instrument.show_diagram()"
    ]
   },
   {
@@ -261,7 +294,7 @@
    "metadata": {},
    "source": [
     "### Interpretation of the data\n",
-    "Now we have added a sample inside the Union geometry, but when the neutron reaches that sample, it is ignored by the sample environment leading to unphysical behavior. Here the beam does not illuminate the sample environment on the way out, and all rays scattered by the PowderN sample are not attenuated by the "
+    "Now we have added a sample inside the Union geometry, but when the neutron reaches that sample, it is ignored by the sample environment leading to unphysical behavior. Here the beam does not illuminate the sample environment on the way out, and all rays scattered by the PowderN sample are not attenuated by the walls of the cryostat when leaving."
    ]
   },
   {
@@ -287,15 +320,24 @@
     "inner_wall.number_of_activations = 2\n",
     "inner_wall_vac.number_of_activations = 2\n",
     "\n",
-    "sample_replacement = instrument.add_component(\"sample_volume_replace\", \"Union_cylinder\")\n",
+    "sample_replacement = instrument.add_component(\"sample_volume_replace\", \"Union_cylinder\", after=sample)\n",
     "sample_replacement.yheight = sample_volume.yheight\n",
     "sample_replacement.radius = sample_volume.radius\n",
     "sample_replacement.material_string='\"Vacuum\"' \n",
     "sample_replacement.priority = 101\n",
     "sample_replacement.set_AT([0,0,0], RELATIVE=sample_volume)\n",
     "\n",
-    "master_2 = instrument.add_component(\"master_after_sample\", \"Union_master\", after=\"sample\")\n",
-    "master_2.allow_inside_start=1"
+    "master_2 = instrument.add_component(\"master_after_sample\", \"Union_master\", after=sample_replacement)\n",
+    "master_2.allow_inside_start=1\n",
+    "\n",
+    "instrument.show_diagram()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that *sample_volume* is only simulated in the first master, there is no arrow to the second master as with all the other geometries."
    ]
   },
   {
@@ -364,7 +406,7 @@
    "metadata": {},
    "source": [
     "### Interpretation of the data\n",
-    "We see that the wrong simulation have slightly lower background, and more peak intensity. We also see the peak shape is different near the aluminium Bragg peaks. Since the Union components contain a powder process, one can also recreate this example without using an external PowderN component to check the accuracy of the approach. It is however still not perfect, as the Union powder process will perform multiple scattering where PowderN will not. "
+    "We see that the wrong simulation have slightly lower background, and more peak intensity. We also see the peak shape is different near the aluminium Bragg peaks. Since the Union components contain a powder process, one can also recreate this example without using an external PowderN component to check the accuracy of the approach. It is however not a fair comparison, as the Union powder process will perform multiple scattering where PowderN will not. "
    ]
   },
   {

From 45b01070572baac64e064f978644288ed6caea04 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 10 Jun 2022 13:24:11 +0200
Subject: [PATCH 283/403] Improvements to diagram.   Now supports all types of
 Union components in connections.   Now have small curves of the arrow lines
 entering and exiting lanes, removing risks of ambiguity   Fixed small problem
 in interface where an error would occur if input could not be converted to
 float

---
 mcstasscript/instrument_diagram/arrow.py      |  62 +++++-
 mcstasscript/instrument_diagram/box.py        |  16 +-
 mcstasscript/instrument_diagram/canvas.py     |  56 ++++-
 .../instrument_diagram/generate_Union.py      |  60 +++++-
 .../instrument_diagram/make_diagram.py        |   2 +-
 .../jb_interface/simulation_interface.py      |  10 +-
 mcstasscript/tests/test_instrument_diagram.py | 200 ++++++++++++++++++
 7 files changed, 370 insertions(+), 36 deletions(-)
 create mode 100644 mcstasscript/tests/test_instrument_diagram.py

diff --git a/mcstasscript/instrument_diagram/arrow.py b/mcstasscript/instrument_diagram/arrow.py
index 68e132c1..735274fc 100644
--- a/mcstasscript/instrument_diagram/arrow.py
+++ b/mcstasscript/instrument_diagram/arrow.py
@@ -84,60 +84,102 @@ def set_alpha(self, value):
         self.alpha = value
 
     def plot_left_side(self, ax):
+        # stop a bit before the lane and make a little triangle
+        triangle_leg_x = 0.25 * self.arrow_length
+        triangle_leg_y = 2.0 * self.arrow_width
+
+        # box to lane
         origin_x = self.origin.get_text_start()
         origin_y = self.origin.position_y + self.get_box_offset_origin()
 
         origin_lane_x = origin_x - self.get_lane_value()
         origin_lane_y = origin_y
 
-        ax.plot([origin_x, origin_lane_x], [origin_y, origin_lane_y],
-                color=self.color, linestyle=self.origin_linestyle)
+        entry_point_x = origin_lane_x + triangle_leg_x
+
+        x_points = [origin_x, entry_point_x]
+        y_points = [origin_y, origin_lane_y]
 
+        # lane
         target_x = self.target.get_text_start()
         target_y = self.target.position_y + self.get_box_offset_target()
 
         target_lane_x = target_x - self.get_lane_value()
         target_lane_y = target_y
 
-        ax.plot([origin_lane_x, target_lane_x], [origin_lane_y, target_lane_y], color=self.color)
+        exit_point_x = target_lane_x + triangle_leg_x
+
+        # Plot lane but ease in and out of the lane with a small triangle
+        x_points += [entry_point_x, origin_lane_x, origin_lane_x, target_lane_x, origin_lane_x, exit_point_x]
+        y_points += [origin_lane_y, origin_lane_y + triangle_leg_y, origin_lane_y + triangle_leg_y,
+                     target_lane_y - triangle_leg_y, target_lane_y - triangle_leg_y, target_lane_y]
+        ax.plot(x_points, y_points, color=self.color, linestyle=self.origin_linestyle)
 
-        arrow_length = self.target.get_text_start() - target_lane_x
-        ax.arrow(x=target_lane_x, y=target_lane_y,
+        # lane to box
+        arrow_length = self.target.get_text_start() - target_lane_x - triangle_leg_x
+        ax.arrow(x=target_lane_x + triangle_leg_x, y=target_lane_y,
                  dx=arrow_length, dy=0,
                  color=self.color, length_includes_head=True,
                  width=self.arrow_width, head_width=5.0*self.arrow_width,
                  head_length=self.arrow_length)
 
     def plot_right_side(self, ax, text_end):
+        # stop a bit before the lane and make a little triangle
+        triangle_leg_x = 0.25 * self.arrow_length
+        triangle_leg_y = 2.0 * self.arrow_width
+
+        # origin to lane
         origin_x = self.origin.get_text_end()
         origin_y = self.origin.position_y + self.get_box_offset_origin()
 
         origin_lane_x = text_end + self.get_lane_value()
         origin_lane_y = origin_y
 
-        ax.plot([origin_x, origin_lane_x], [origin_y, origin_lane_y],
+        entry_point_x = origin_lane_x - triangle_leg_x
+
+        x_points = [origin_x, entry_point_x]
+        y_points = [origin_y, origin_lane_y]
+
+        """
+        ax.plot([origin_x, entry_point_x], [origin_y, origin_lane_y],
                 color=self.color, linestyle=self.origin_linestyle, alpha=self.alpha)
+        """
 
         if self.description is not None and not self.origin_congested:
             bbox = dict(boxstyle="round", facecolor="white", edgecolor="white", alpha=0.85)
             ax.text(0.5*(origin_x + origin_lane_x), origin_lane_y,
                     self.description, ha="center", va="center", bbox=bbox)
 
+        # lane
         target_y = self.target.position_y + self.get_box_offset_target()
 
         target_lane_x = text_end + self.get_lane_value()
         target_lane_y = target_y
 
-        ax.plot([origin_lane_x, target_lane_x], [origin_lane_y, target_lane_y], color=self.color, alpha=self.alpha)
+        exit_point_x = target_lane_x - triangle_leg_x
+
+        if target_lane_y < origin_lane_y:
+            leg_dir = 1
+        else:
+            leg_dir = -1
+
+        triangle_leg_y_dir = leg_dir * triangle_leg_y
+
+        # Plot lane but ease in and out of it with a small triangle
+        x_points += [entry_point_x, target_lane_x, origin_lane_x, target_lane_x, origin_lane_x, exit_point_x]
+        y_points += [origin_lane_y, origin_lane_y - triangle_leg_y_dir, origin_lane_y - triangle_leg_y_dir,
+                     target_lane_y + triangle_leg_y_dir, target_lane_y + triangle_leg_y_dir, target_lane_y]
+        ax.plot(x_points, y_points, color=self.color, alpha=self.alpha, linestyle=self.origin_linestyle)
 
+        # target
         if self.connection:
             mid_point = 0.5*(target_lane_x + self.target.get_text_end())
-            ax.plot([target_lane_x, self.target.get_text_end()],
+            ax.plot([target_lane_x - triangle_leg_x, self.target.get_text_end()],
                     [target_lane_y, target_lane_y], color=self.color, alpha=self.alpha)
         else:
-            arrow_length = target_lane_x - self.target.get_text_end()
+            arrow_length = target_lane_x - self.target.get_text_end() - triangle_leg_x
             mid_point = target_lane_x - 0.5*arrow_length
-            ax.arrow(x=target_lane_x, y=target_lane_y,
+            ax.arrow(x=target_lane_x - triangle_leg_x, y=target_lane_y,
                      dx=-arrow_length, dy=0,
                      color=self.color, length_includes_head=True,
                      width=self.arrow_width, head_width=5.0*self.arrow_width,
diff --git a/mcstasscript/instrument_diagram/box.py b/mcstasscript/instrument_diagram/box.py
index 1c4a401b..2f95010e 100644
--- a/mcstasscript/instrument_diagram/box.py
+++ b/mcstasscript/instrument_diagram/box.py
@@ -1,11 +1,11 @@
 import matplotlib.pyplot as plt
-
+from mcstasscript.helper.mcstas_objects import Component
 
 class ComponentBox:
     """
     Helper class for creating text boxes describing components
     """
-    def __init__(self, component_object=None, name=None):
+    def __init__(self, box_input):
         """
         Text box object
         """
@@ -23,10 +23,11 @@ def __init__(self, component_object=None, name=None):
         self.graph_box_start = None  # graph position where text ends
 
         # Load component
-        self.component_object = component_object
-        if component_object is None:
-            self.name = name
-        else:
+        if isinstance(box_input, str):
+            self.component_object = None
+            self.name = box_input
+        elif isinstance(box_input, Component):
+            self.component_object = box_input
             self.name = self.component_object.name
 
             # Decorate the box depending on the McStas features used
@@ -35,6 +36,9 @@ def __init__(self, component_object=None, name=None):
 
             if self.component_object.EXTEND != "":
                 self.outline_width = 2.5
+        else:
+            raise ValueError("Input for box needs to be of type Component or str, not "
+                             + str(type(box_input)))
 
         # Produced weighted length of name, capital letters count for 1.2
         self.weighted_name_length = len(self.name) + 0.2*sum(1 for c in self.name if c.isupper())
diff --git a/mcstasscript/instrument_diagram/canvas.py b/mcstasscript/instrument_diagram/canvas.py
index d09adcd5..f8b9f3fe 100644
--- a/mcstasscript/instrument_diagram/canvas.py
+++ b/mcstasscript/instrument_diagram/canvas.py
@@ -1,3 +1,5 @@
+import copy
+
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -160,23 +162,22 @@ def __init__(self, left_side_arrows, component_boxes, right_side_arrows, compone
 
         self.legend_height = self.FIG_LEGEND_HEADLINE + self.FIG_HEIGHT_PER_BOX * (len(all_categories) // 2)
 
-        self.make_legend()
-
     def make_legend(self):
 
         box_top = 1 - self.FIG_LEGEND_HEADLINE / self.legend_height
 
-        fig, ax = plt.subplots(figsize=(7.0, self.legend_height))
+        fig, ax = plt.subplots(figsize=(6.2, self.legend_height))
         ax.set(xlim=(0, 1), ylim=(0, 1))
-        ax.axis("off")
+        ax.get_xaxis().set_ticks([])
+        ax.get_yaxis().set_ticks([])
 
         bbox = dict(boxstyle="round", facecolor="white", edgecolor="white")
 
-        ax.text(0.37, 1.05, "Legend", va="center", ha="center", fontweight="semibold", bbox=bbox, fontsize="large")
+        ax.text(0.385, 1.03, "Legend", va="center", ha="center", fontweight="semibold", bbox=bbox, fontsize="large")
 
-        legend_boxes = [ComponentBox(name="Arm")]
+        legend_boxes = [ComponentBox("Arm")]
         for category, color in self.category_color_dict.items():
-            box = ComponentBox(name=category)
+            box = ComponentBox(category)
             box.set_background_color(color)
             legend_boxes.append(box)
 
@@ -229,7 +230,7 @@ def make_legend(self):
 
         arrow_width = 0.012
         AT_HEIGHT = 0.83
-        START_WIDTH = 0.45
+        START_WIDTH = 0.47
         TEXT_WIDTH_INDENT = 0.01
         DISPLACEMENT = 0.2
         TEXT_DISPLACEMENT = 0.08
@@ -272,7 +273,36 @@ def make_legend(self):
             ax.text(START_WIDTH + TEXT_WIDTH_INDENT, AT_HEIGHT + current_displacement + TEXT_DISPLACEMENT,
                     "GROUP", va="center", weight="semibold")
 
+        box_y_coordinates = list(np.linspace(margin, box_top, 5))
+        box_x_coordinate = 0.83
+
+        # Check if any boxes are decorated for EXTEND or WHEN
+        for box in self.component_boxes:
+            if box.component_object is not None:
+                if box.component_object.EXTEND != "":
+                    EXTEND_box = copy.deepcopy(box)
+                    EXTEND_box.name = "EXTEND"
+                    EXTEND_box.background_color = "white"
+                    EXTEND_box.set_x(box_x_coordinate)
+                    EXTEND_box.set_y(box_y_coordinates.pop())
+                    EXTEND_box.plot_box(ax)
+                    break
+
+        for box in self.component_boxes:
+            if box.component_object is not None:
+                if box.component_object.WHEN != "":
+                    WHEN_box = copy.deepcopy(box)
+                    WHEN_box.name = "WHEN"
+                    WHEN_box.background_color = "white"
+                    WHEN_box.set_x(box_x_coordinate)
+                    WHEN_box.set_y(box_y_coordinates.pop())
+                    WHEN_box.plot_box(ax)
+                    break
+
     def plot(self):
+
+        self.make_legend()
+
         fig, ax = plt.subplots(figsize=(self.graph_width, self.graph_height))
         ax.set(xlim=(0, 1), ylim=(0, 1))
         ax.axis("off")
@@ -312,13 +342,21 @@ def plot(self):
 
         # Create anotation box that will be shown when hovering the mouse over a box
         annot = ax.annotate("", xy=(0, 0), xytext=(20, 10), textcoords="offset points",
-                            bbox=dict(boxstyle="round", fc="w"), annotation_clip=False)
+                            va="center", annotation_clip=False,
+                            bbox=dict(boxstyle="round", fc="w"))
         annot.set_visible(False)
 
         # Helper function to update the anotation box with correct text
         def update_annot(ind):
             pos = sc.get_offsets()[ind["ind"][0]]
             annot.xy = pos
+
+            n_lines_in_info = len(box_info[ind["ind"][0]].split("\n"))
+            if n_lines_in_info > 4:
+                annot.set_position((20, n_lines_in_info*(5 - pos[1]*10)))
+            else:
+                annot.set_position((20, 0))
+
             text = "{}".format(" ".join([box_info[n] for n in ind["ind"]]))
             annot.set_text(text)
             annot.get_bbox_patch().set_facecolor([0.95, 0.95, 0.95])
diff --git a/mcstasscript/instrument_diagram/generate_Union.py b/mcstasscript/instrument_diagram/generate_Union.py
index eed03a6c..73617d6e 100644
--- a/mcstasscript/instrument_diagram/generate_Union.py
+++ b/mcstasscript/instrument_diagram/generate_Union.py
@@ -15,6 +15,9 @@ def generate_Union_arrows(components, component_box_dict, box_names, component_c
     material_names = []
     geometry_names = []
     simulated_geometry_names = []
+    abs_loggers = []
+    loggers = []
+    conditionals = []
     master_names = []
     geometry_activation_counters = {}
     for component in components:
@@ -75,17 +78,60 @@ def generate_Union_arrows(components, component_box_dict, box_names, component_c
                             target = component_box_dict[mask]
                             connections.add(component_box_dict[component.name], target)
 
-            elif "_abs_logger" in component.parameter_names:
-                # Abs logger
-                pass
+            elif "_logger" in component.component_name:
 
-            elif "_logger" in component.parameter_names:
-                # Logger
-                pass
+                if "_abs_logger" in component.component_name:
+                    # Absoption logger
+                    abs_loggers.append(component.name)
+                    abs_logger = True
+                else:
+                    # Scattering logger
+                    loggers.append(component.name)
+                    abs_logger = False
+
+                target_geometry = component.target_geometry
+                if isinstance(target_geometry, str):
+                    geometries = target_geometry.strip('"').split(",")
+                    for geometry in geometries:
+                        if geometry not in simulated_geometry_names:
+                            print(component.name)
+                            print("Didn't find geometry of name '" + geometry + "'")
+                            print(simulated_geometry_names)
+                        else:
+                            origin = component_box_dict[geometry]
+                            connections.add(origin, component_box_dict[component.name])
+
+                if abs_logger:
+                    # Abs loggers do not have target_process, as they target absorption
+                    continue
+
+                target_process = component.target_process
+                if isinstance(target_process, str):
+                    processes = target_process.strip('"').split(",")
+                    for process in processes:
+                        if process not in process_names:
+                            print(component.name)
+                            print("Didn't find process of name '" + process + "'")
+                            print(process_names)
+                        else:
+                            origin = component_box_dict[process]
+                            connections.add(origin, component_box_dict[component.name])
 
             elif "target_loggers" in component.parameter_names:
                 # Conditional
-                pass
+                conditionals.append(component.name)
+
+                target_loggers = component.target_loggers
+                if isinstance(target_loggers, str):
+                    loggers = target_loggers.strip('"').split(",")
+                    for logger in loggers:
+                        if logger not in loggers + abs_loggers:
+                            print(component.name)
+                            print("Didn't find logger with name '" + logger + "'")
+                            print(loggers, abs_loggers)
+                        else:
+                            target = component_box_dict[logger]
+                            connections.add(component_box_dict[component.name], target)
 
             elif component.component_name == "Union_master":
                 # Master
diff --git a/mcstasscript/instrument_diagram/make_diagram.py b/mcstasscript/instrument_diagram/make_diagram.py
index 8604e44f..2d430504 100644
--- a/mcstasscript/instrument_diagram/make_diagram.py
+++ b/mcstasscript/instrument_diagram/make_diagram.py
@@ -33,7 +33,7 @@ def instrument_diagram(instrument):
     component_reader = instrument.component_reader
     component_categories = copy.deepcopy(component_reader.component_category)
 
-    absolute_box = ComponentBox(name="ABSOLUTE")
+    absolute_box = ComponentBox("ABSOLUTE")
     component_boxes = [absolute_box]
     component_box_dict = {"ABSOLUTE": absolute_box}
     for component in components:
diff --git a/mcstasscript/jb_interface/simulation_interface.py b/mcstasscript/jb_interface/simulation_interface.py
index 08da25f7..6556f74b 100644
--- a/mcstasscript/jb_interface/simulation_interface.py
+++ b/mcstasscript/jb_interface/simulation_interface.py
@@ -148,10 +148,10 @@ def run_simulation_live(self, change):
         self.progress_bar.value = 0
         plot_data = None
         for index in range(sim_parts):
+            self.instrument.settings(**run_arguments)
+            self.instrument.set_parameters(self.parameters)
             try:
                 with HiddenPrints():
-                    self.instrument.settings(**run_arguments)
-                    self.instrument.set_parameters(self.parameters)
                     self.instrument.backengine()
             except NameError:
                 print("McStas run failed.")
@@ -397,7 +397,11 @@ def update(self, change):
                 if not (new_value[0] == '"' or new_value[0] == "'"):
                     new_value = '"' + new_value + '"'
         else:
-            new_value = float(new_value)
+
+            try:
+                new_value = float(new_value)
+            except:
+                return
 
         self.parameters[self.name] = new_value
 
diff --git a/mcstasscript/tests/test_instrument_diagram.py b/mcstasscript/tests/test_instrument_diagram.py
new file mode 100644
index 00000000..a3ce7cd3
--- /dev/null
+++ b/mcstasscript/tests/test_instrument_diagram.py
@@ -0,0 +1,200 @@
+import os
+import unittest
+import unittest.mock
+import copy
+
+from mcstasscript.instrument_diagram.arrow import Arrow
+from mcstasscript.instrument_diagram.box import ComponentBox
+from mcstasscript.instrument_diagram.connections import IndexConnection
+from mcstasscript.instrument_diagram.connections import Lane
+from mcstasscript.instrument_diagram.connections import ConnectionList
+from mcstasscript.instrument_diagram.canvas import DiagramCanvas
+from mcstasscript.tests.helpers_for_tests import WorkInTestDir
+from mcstasscript.interface.instr import McStas_instr
+
+
+def setup_instr_no_path():
+    """
+    Sets up a neutron instrument without a package_path
+    """
+
+    with WorkInTestDir() as handler:
+        instrument = McStas_instr("test_instrument")
+
+    return instrument
+
+def setup_populated_instr():
+    """
+    Sets up a neutron instrument with some features used and three components
+    """
+    instr = setup_instr_no_path()
+
+    instr.add_parameter("double", "theta")
+    instr.add_parameter("double", "has_default", value=37)
+    instr.add_declare_var("double", "two_theta")
+    instr.append_initialize("two_theta = 2.0*theta;")
+
+    A = instr.add_component("first_component", "test_for_reading")
+    B = instr.add_component("second_component", "test_for_reading")
+    C = instr.add_component("third_component", "test_for_reading")
+    D = instr.add_component("fourth_component", "test_for_reading")
+
+    B.set_AT(0, RELATIVE=A)
+    C.set_AT(0, RELATIVE=A)
+    D.set_AT(0, RELATIVE=B)
+
+    return instr
+
+def setup_boxes():
+
+    instr = setup_populated_instr()
+
+    comp1 = instr.get_component("first_component")
+    comp2 = instr.get_component("third_component")
+
+    return ComponentBox(comp1), ComponentBox(comp2)
+
+
+class TestInstrumentDiagram(unittest.TestCase):
+    """
+    Instrument diagram is not critical for operation, tests just to ensure it runs
+    """
+
+    def test_Box_initialize_name(self):
+        """
+        Check that a box can be initialized with a string
+        """
+        name = "ABSOLUTE"
+        box = ComponentBox(name)
+
+        self.assertEqual(box.name, name)
+
+    def test_Box_initialize_comp(self):
+        """
+        Check that a box can be initialized with a component object
+        """
+        instr = setup_populated_instr()
+        comp = instr.get_last_component()
+
+        box = ComponentBox(comp)
+
+        self.assertEqual(box.name, "fourth_component")
+
+    def test_Arrow_initialize(self):
+        """
+        Check that an arrow can be initialized with two Box instances
+        """
+
+        box1, box2 = setup_boxes()
+
+        arrow = Arrow(origin=box1, target=box2, kind="KIND")
+
+        self.assertEqual(arrow.origin, box1)
+        self.assertEqual(arrow.target, box2)
+        self.assertEqual(arrow.kind, "KIND")
+
+        string_box = ComponentBox("string")
+        arrow = Arrow(origin=box1, target=string_box, kind="KIND_2")
+
+        self.assertEqual(arrow.origin, box1)
+        self.assertEqual(arrow.target, string_box)
+        self.assertEqual(arrow.kind, "KIND_2")
+
+    def test_IndexConnection(self):
+        """
+        Ensure IndexConnections find overlap
+        """
+
+        A = IndexConnection(2, 8)
+        B = IndexConnection(9, 10)
+        C = IndexConnection(5, 7)
+
+        self.assertTrue(A.compatible_with(B))
+        self.assertTrue(B.compatible_with(C))
+        self.assertFalse(A.compatible_with(C))
+
+    def test_Lane(self):
+        """
+        Ensure a lane reports when a connection can be added and saves allowed
+        """
+
+        lane = Lane()
+
+        self.assertTrue(lane.add_connection(2, 8))
+        self.assertTrue(lane.add_connection(9, 12))
+        self.assertTrue(lane.add_connection(7, 8))  # Same destination allowed
+        self.assertFalse(lane.add_connection(3, 9))
+
+        # ensure only allowed connections added
+        self.assertEqual(len(lane.connections), 3)
+
+    def test_connection(self):
+        """
+        Ensure connection lists can return included connections and set lanes
+        """
+        c_list = ConnectionList()
+
+        box_list = []
+        name_list = []
+        for index in range(0,10):
+            name = "box_" + str(index)
+            name_list.append(name)
+            box_list.append(ComponentBox(name))
+
+        # Add some connections to list
+        c_list.add(box_list[0], box_list[5])  # Lane 1 # Connection 0
+        c_list.add(box_list[6], box_list[8])  # Lane 1 # Connection 1
+        c_list.add(box_list[1], box_list[7])  # Lane 2 # Connection 2
+        c_list.add(box_list[3], box_list[6])  # Lane 3 # Connection 3
+        c_list.add(box_list[0], box_list[8])  # Lane 4 # Connection 4
+
+        targets_for_0 = c_list.get_targets_for_origin(box_list[0])
+        self.assertEqual(targets_for_0, [box_list[5], box_list[8]])
+
+        c_list.distribute_lane_numbers(name_list)
+
+        connections = c_list.get_connections()
+        self.assertEqual(connections[0].lane_number, 1)
+        self.assertEqual(connections[1].lane_number, 1)
+        self.assertEqual(connections[2].lane_number, 2)
+        self.assertEqual(connections[3].lane_number, 3)
+        self.assertEqual(connections[4].lane_number, 4)
+
+    def test_DiagramCanvas_initialize(self):
+        """
+        Ensure the DiagramCanvas can be initialized
+        """
+        string_box_0 = ComponentBox("string_0")
+        string_box_1 = ComponentBox("string_1")
+        string_box_2 = ComponentBox("string_2")
+        arrow_0 = Arrow(origin=string_box_1, target=string_box_0)
+        arrow_1 = Arrow(origin=string_box_2, target=string_box_0)
+        arrow_0.set_lane(1)
+        arrow_1.set_lane(1)
+
+        left_side_arrows = [arrow_0, arrow_1]
+
+        boxes = [string_box_0, string_box_1, string_box_2]
+
+        arrow_0 = Arrow(origin=string_box_0, target=string_box_1)
+        arrow_1 = Arrow(origin=string_box_1, target=string_box_0, kind="JUMP")
+        arrow_2 = Arrow(origin=string_box_0, target=string_box_2, kind="Union")
+        arrow_0.set_lane(1)
+        arrow_1.set_lane(1)
+        arrow_2.set_lane(2)
+        right_side_arrows = [arrow_0, arrow_1, arrow_2]
+
+        # Get component_categories
+        with WorkInTestDir() as handler:
+            instrument = McStas_instr("test_instrument")
+
+        component_reader = instrument.component_reader
+        component_categories = copy.deepcopy(component_reader.component_category)
+
+        color_choices = {"AT": "blue", "ROTATED": "red", "JUMP": "black", "GROUP": [0.4, 0.4, 0.4], "Union": "green"}
+
+        canvas = DiagramCanvas(left_side_arrows=left_side_arrows, component_boxes=boxes,
+                               right_side_arrows=right_side_arrows,
+                               component_categories=component_categories,
+                               colors=color_choices)
+

From aa1224205d402b77a74fa7d3b719e48fa4ea30eb Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 10 Jun 2022 13:48:20 +0200
Subject: [PATCH 284/403] Updates to documentation.

Widgets now included in their own file.
---
 docs/source/conf.py                           |    2 +-
 docs/source/index.rst                         |    1 +
 ...n_tutorial_1_processes_and_materials.ipynb |  861 +----------
 .../tutorial/Union_tutorial_5_masks.ipynb     | 1311 ++++++++++++++++-
 .../source/user_guide/instrument_object.ipynb |   32 +-
 docs/source/user_guide/widgets.ipynb          |  441 ++++++
 mcstasscript/_version.py                      |    2 +-
 7 files changed, 1782 insertions(+), 868 deletions(-)
 create mode 100644 docs/source/user_guide/widgets.ipynb

diff --git a/docs/source/conf.py b/docs/source/conf.py
index ffe20d6c..42a8929e 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -67,7 +67,7 @@
 html_theme_options = {
     #"logo_only": True,
     "repository_url": "https://github.com/PaNOSC-ViNYL/McStasScript",
-    "repository_branch": "mcstasscript_on_libpyvinyl",
+    "repository_branch": "master",
     "path_to_docs": "docs",
     "use_repository_button": True,
     "use_issues_button": True,
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 94b069fd..041ec16f 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -26,6 +26,7 @@ Documentation
    user_guide/data
    user_guide/plotting
    user_guide/functions
+   user_guide/widgets
    user_guide/instrument_reader
 
 .. toctree::
diff --git a/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb b/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
index 837c7fc6..a1b2cd10 100644
--- a/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
+++ b/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,44 +47,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No components added to instrument object yet.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_components(\"Work directory\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Incoherent_process ________________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1msigma\u001b[0m = \u001b[1m\u001b[94m5.08\u001b[0m\u001b[0m [barns] // Incoherent scattering cross section\n",
-      "\u001b[1mf_QE\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Fraction of quasielastic scattering (rest is elastic)\n",
-      "\u001b[1mgamma\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Lorentzian width of quasielastic broadening (HWHM)\n",
-      "\u001b[1mpacking_factor\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [1] // How dense is the material compared to optimal 0-1\n",
-      "\u001b[1munit_cell_volume\u001b[0m = \u001b[1m\u001b[94m13.8\u001b[0m\u001b[0m [AA^3] // Unit_cell_volume\n",
-      "\u001b[1minteract_fraction\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // How large a part of the scattering events \n",
-      "                                should use this process 0-1 (sum of all processes \n",
-      "                                in material = 1) \n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.component_help(\"Incoherent_process\")"
    ]
@@ -100,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -119,24 +93,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Union_make_material _______________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1mprocess_string\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated names of physical processes\n",
-      "\u001b[4m\u001b[1mmy_absorption\u001b[0m\u001b[0m [1/m] // Inverse penetration depth from absorption at standard \n",
-      "                       energy \n",
-      "\u001b[1mabsorber\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [0/1] // Control parameter, if set to 1 the material will have \n",
-      "                        no scattering processes \n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.component_help(\"Union_make_material\")"
    ]
@@ -152,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -170,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -188,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -217,34 +176,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x129.6 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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On4jQ6ay9xrwcGvZ7hGMrF9P9/34kacYrlOXnEt7zfk5vW8vOt16iJCsdvcGLkPbdaTfuPevq5VcgEoUAgjifCFtgXd07Buv338srNnfA44JTzwK9KxKplvgDJY4OQnFOmk+SV1Mtcd8nr7NvzjS6vf8dSe+OJ+3HBTTo85CtfMOV5P+5h4jegzi08EPWjbyXlsMncnjxR6TMnUbUA0NBpyO8x314BNWlNPs0BxfMIum98XR58wuaPDyCQws/JLzH/YT3vB+fyKZsnfgMZ/YnEf3ISEpzT3Pwi/cwhNSjyV+fB8BcWkxJ9ilaj5qCe0BQpVj0Bi+iHhiK3uBFwZF9HP7mE/wat6DZ0/+s9j2IRPE48CHna1WbuPTn7HqZ0z2Af1VsWlPq6AAU56T5JGnPaolX0vSpl/CsG8GhhR8S0KIdMY++QP7B3RxbsYiSUydwDwzm9OZfyEneBhUruucf3ofe4EWdVp04tPBDfKOaE9F7EKbis2Tt3ABS2m7DATK3/mpLkgAdJvyHqmbvzWWlHPl2jq3sxLlr1cAvwNNAUyAOaAk0BNyw9sZ0WBNoVQ+clgG7Aa09a9kS7cWkOAnNJ0l7Vku8EjdvP3R6ayfL1cu3ok0XwFo18dDij8jZs5XGf32OsK592TF1NKbis9bjLvOQu190LK1GTbH9/Vy7AC4GryoTJMDe2ZMpSj9K27HTcfMNYMuEZzAby674HmSCPAUsuXi/SBSBQDTWW+5mQFusibQ+1gk3I9ae5DsyQX51xQspipPQfJK0d7XE61LRezQVF5GdtImSzHRcva1Jz9XHH4DspE0cX7WEet3uIbhtV7J2bSQnaRMeQWHk7N6MT8NoAlu0q8GlJEhJeVEhmdvXX1/cgEyQucCWis2mYvwyDGvyjASWXvfFFOUWovkkWVEt8aqTZNyLb6DT68n4fTkn1/5Ave73XncsTR4aRtYfv5P+24+E9xiAb1RzSjKtd4FBcZ0Jbt+d7F0bydqxnn5Lk+mQ8BFJ747n8DefYDGZ8GvcnAb9HqnRtWKHT2T76yM5tOgjGg8ayslfv7vu+KsiE6QE0is2RVEuYu9qiSYAKe2TfO1dLdFUWoy5tPIkqYuHAb2Hpz2ad6iT637kjzfHrC3Ly+np6Fi0TFVLVK6W1nuSmSVZ6XZb0eLgF7MqTaIANH/mFVo8O85el3CY0ux0LKby6p9CVxTlqmk9Se4uyUwvOrhglmfM4DHXHWuDfo9QJ65zpX1e9SKvt1mHKzh6gL0fv1FiKirUzleQFOUWoekkKaUsEkJ02jdn2pajP37p6RkaLnCmaok1UF5YIPMP7XG3GI3DpZTLHB2PotxqNJ0kwVoETAjRtPDogbjCowfqOjoeDSoG9kop0xwdiKLcijSfJAGklIXA746OQ1EU56PuXRVFUaqhkqSiKEo1asXtthC2b4WEOjoWjbMAx6SUZxwdiKLcKjSfJIUQfnpP73VAU/eAICMI+z39fouRZrMozTllcPX0/txUUjRMSmlxdEyKUttpOkkKIdz0nt6/Rdz915i2Y2e4CyE8rnyWcysvKmDdyP6PFp1MLQdGOjoeRanttD4m2drNNyCyIkE6OpZawdXLlzve/dbLbCwbKtSHpijXzd49SR3WVcPW2qe5mQWe9ZZY1L/1q+MeEITQ6ZDgB+Q5Oh5Fqc3s3ZO0AHYcM5TodC41OvLoj1+yJD6Ag1++b7/LX4f9n0/nz4UfXvP5+z59kyXxAZxYU/3qP1l//M6S+AB2Th9bab8QOknVC+sqinIV7N2TPAs3tlpibbF//kzc/QKJfnjEVZ9rMZmsJSAaRhMY2/4GRKcoSk1peuIGqigEdpliXOfkH97H6iE9KD51nKZPvEjMY6Mpzc1ia8Kz5O7bgRA6fCJj6Pr215fUlQFrz2z9qPuo1+1eSnNOU5h2kLi/TyP/z2SOfDePgJjWdHnrK9x8/Eh691WOrVyMqagQj+Awmj7+N6IGDmHdC/0xlxRRXFLEkvgAGt7zKO3//R/2z59B6nefY8zPJTC2I23HTsc7PJJ9n75JytxpNLx3MFk7f6feHffg6uVLytxpdHptHp53hvPrc70pSD2ANJvwiYwh7m9vENQm/sZ++IqiaH7i5tJCYBXFuOJenErTx18k/8+9JL13fsXxzG3raDTgKTwCQ9jzwUTy/tzD8ZWLydqxniYPDqP16Cn4R8descxD5o71NOj7EFJKtr/+AsWZJ6l3xz1kJ20i7acvAfCJjKHl8/+m1agpeASEsGv6WIoyjtF8yMvo3Nxxqyh/G/WXZ0j76Sv2zp5CYIvbaPr43yg4vJctE4ZUvubWX2n6+N8I69rnknhCOvSg9ZjXaD70FUpzMtn+xuhr/UgVRbkKmu9JXlwIzGIsq7IYV0TF65H9BxM1cAjCRc8fU8eQvXMj3hFRAGTv2oC0mKnfaxAedap/Lj2sa18aD3qO4yu/IWfPVmKHT6T41DGOr1xMUbp1LYmik0c5vORTzKXFtvMK0w5St3MvhIsevYcnEb0HAXDwyw8AOLH6f5xY/T8ASnNOYyw4/9x3zOAxRA0cUhHrJtt+U/FZ8g4kceC/M5Hm88ndXKaqrCrKjab5nmRFITAbWzGuQc9y+8wlGELqYTFWUW30ghXXw7r2pecnqwjt1Iuc3Zv5bbS1rnV1XH2si6GLc4XBvH0RuvNFwQqOHuTggll4RzSm89QviOz/OADmMmuxrktm5Cvi6TDpY25/73/WbeYSXNzPV6bwCKp6kaNjKxZxatMqwnvcT9fpi/Bv1sZ6LaOx2vegKMr103ySrCgEdl4VxbgudHTZAo4sncehhR+CEAS1jefEmu/I2LACz9BwfBs1A6A0+9R1RmaNw1xWStmZTDIvSrquPn6U5eWQ9tNXFKTuJ+z2vgCk/fQ1JadPkr1zAynz3sbF/crPx58rsWEqLaYgdT8FNSstqyiKHWg+SVYUArNp8tAwApq3I/23HynNycQ3qnml4+t26U3q0s8ozc2k1QuJ+Ee3wsXDwMlfv+ePt17ixJql1L/rL4T3vP+64vKNbEqTh0dSmn2Ko9//l9AuvSq9HjN4DDpXV7a/NpKTa5fR8J5HiR2RwNkTh9n5zj84vmoJQXE1m3hp0OchQjr0IHvnRnJ2b1ETNopyE9m7EFgegJT426c9MSCkfffP75i11C6FwC4kLZZK44EAQueCm6+/vS/lEEt71iszl5WEqcUuKlOFwJSrpfWJG4vFZLohDRefPsHyQXGV9nnWjaDft7tvyPVuJikl0mLWYX24X1GU66D1JHms8Nifeku5EZ2r25WPvgoegSHc/t7/Ku2ryfhgbVCccQzADBQ4OBRFqfW0niSTzaXFmzeNe7xLp9c+89QbvOzWsIu7B6EdetitPa0oyjjGupH3Fgudy2vSnmMpiuKkNJ0kpZRmIcQ9OXu2LP7+7sh7dHpXM0KtJ3k50mLWYbFI4aJ/zVRaPNXR8SjKrUDTSRJASmkEBgghXMxmk7+j49E4M5Avy43qPxJFsRPNJ8lzpJRmIMfRcSiK4lw0/5ykoiiKI6kkqSiKUo1acbsthPAC4qg91RJNwBFgn5phVpTaTfNJUggR7mLw3GIIDvfxDA2HWlDKwWIskwVHUlzN5cavhRDPqkSpKLWXppOkEMLLxeC5pfnT/wxt+sTfNR3rxSqqFj5cdDI1D/iHo+NRFOXaaH1MspUhKMyntiVIsFYt7DBxthdCPOroWBRFuXb2Tj4GwMV+1RJnFBhCltbaW1VDSDgWY1mAo+NQFOXa2bsnacT6QLPdOKqc7JmUnSyJD2D7ayOvuY1aMHyqKMoV2LsnuQNUtURpNiNcalYKV1EUbdP6mOQl1RKLMo6xJD6AdSPvZdO4x/n+7oZsSxyG2VhGdtIm1jzbi6V31Wf5g+04snQeAMmzJ7MkPoDCtD/J2bOVJfEBHPjvuwD80K8xa4dbVw3P3L6OnwfF8fNfWnH8l28rxVGSlc6m8U/yfZ9Ifry/OXv+MwlZUVni5wdas/TOcHa+/Q++692AfLVyuKLcMjQ/IXJJtcQKOXu20HL4RBCCYysW4VU/ikOLZqNzdaP1qMmk/fw1O9/6O971owhqE8+B+TPJSd5GeWGe9fzkbRSm/YkxP5eguC6YjWVsSxyGqaSY2BETOLF6aaXrbUscxpn9SUQ/MpLS3NMc/OI9DCH1aPLX5wEwlxZTkn2K1qOmXFKqViSKd4E2dv5onEkTIEsmyLaODkRxPppPkhdXSzwnMLYDTQeP4eyJVNLXLePQotmUF+bR9MmXiPrLM3iFN+L3Fx/g1OZVNB8yFuHiQm7yVoyF+YR2uovc5G3WiotAnbguFKYdpDTnNA36PETjQc/h0zCG38YMBKzVCrN2bgApSZk7zRZD5tZfbUkSoMOE/+DqbfdF1BXwdnQAivPSfJKsqJZYzbBA5cnvqiZ6XL188Wvc0taTbDt2BptefYJjKxaBTkdQ604UWReqPd9qFc9/+0XH0mrUlErtnuNi8LpsgpQJ8sXLx69cybmSC4riCJpPkhXVEg0X789N3saBBbPIregNNnloOIcWzebosi8whIZzbPlCAOp2uRuAoDbxHFr8EUKnI7htV/yjW5G1fR1+0a1w9fbDp2EMHnVCSf/tZw4v+aTS7bbe05vgtl3J2rWRnKRNeASFkbN7Mz4Nowls0e7GfwiKojiM5iduLq6WeE6d1p3J3bOVzO3riLj7QZo+8SLxb32JIbQ+u2f9m9KcTNq+PJOQ2+4AICiuC0iJb1Rz9J7eBMZ2OL8fcHFzp0PCR7j5BbD/85kEXJT8OiR8RHj3+zj8zSfs+WAiRSdTCWhx241864qiaIC9qyWuBXs+AnRptcSijGMsHxRH3fi76frOQntc5oYpP5vPsntjSs3Gskt6wkrN2bPCoaqWqFwtrfcki8vP1t5aVuVFhQgXfZmj41AU5dppfUxyX/6RFPf8w3vxa9wSAK+wBgzaWDtKSR9dtsCk0+v3OjoORVGunaaTpJTypNDphqwbcc/cFs+ONxiCw6DqJ4I0xWJ9sN2c9tNXmebS4occHY+iKNdO00kSQFosXwsh8vZ+/NrDOle3Bo6Op0akLDeVFO+1lJdNl1KmOzocRVGuneaTJICUcjmw3NFxKIrifLR/76ooiuJAKkkqiqJUQyVJRVGUatSKMUkhRH9Xb9/BwkVf39GxOCtpsWSaigq/t5hNnzs6FkW5mTSfJHUu+idcffxnxw6f4OkRHOawlcqdmZQSY14u++a82VfvYYg0lZYkOjomRblZNJ0khRD1XdwNH/eY/bOHb6Nmjg7H6YV2vsvzlydvf1kI8YuUcoOj41GUm0HTSRJo7hvVvMy3UTMPRweigCGoLnU79+LY8oWxQI2SpEgUOiACiKnYYgEfYKhMkOorm4rm2TtJNgG87Vkt0dXrO/s0pdiFq7evHvC8cJ9IFAII5nwibI51JfYYIAwor9jcsC57ZwTGAhk3K25FuVb2TpJZdm7PYc6k7GTN0DtpeM+jtP/3fxwdjk3GxpWc2fcHDe99DK+wq/8C0tWsovTzA60py89l4OoTl7wmEsVg4EGsCTEC65MSpVh/pzyBCwePXS863QSkikRR0yWo3CquWVLD46ujB/6wQzuKk7BrkpQSu9YgUdUSL3Vq0yqOLPmU4Ha3X3WStJhMuPvXoWPipxiCw643FB/AH2tpBTesCRKsv1NXml0TQJW1i67AHsMuFmC/HdpRnITWxySrrJa4fFAcQW3icfMNIOuP3wjr2pd2r87iTMof7H5/AgWp+/EIDCFm8BiiBj5N8uzJHJg/k7u/2oqx4Axrh/UhdkQCTZ94kR/6NcanYTQ9Zi8nc/s6dkwdAxYL4XcOrBRHSVY6u2aOI2vHelzcDTTo+zCxwycidDprjysvh4b9HuHYysV0/78f8Y9pdcl7OdczixrwNKk/zCekfXeiBg5hx9TRSLOZ2/71AXU79+L0trXsfOslSrLS0Ru8CGnfnXbj3uPk2h84suRTANaPug+AQRvPkLNnK7tn/Zv8I/swBNWlxdBxRNz9V9tnFRjbARcPTwoO76Pnp7+wNeFZ6sbfbS2Q9sV7HFo0m7K8HDwCgom8/0laDH3lij8XmSBnA7MBRKLQA5FYb6+jgdZAKyAKayItxlpnwxPr75wLUE8myBrdbqs1IBVH0nySvBWqJV7IXFKE2VhKndiOpK9bRt6BJGIeG0PSrPEkf5hI3c690Bu8iHpgKHqDFwVH9nH4m0/wa9yCiLsfJKTjnWRuXUOzIWPxjWyKseAMG8c+gntAMM2eeomsHb+zbcpwfCKb4upjXas4N3kbMYPHUP+ixA/gGRJOs6f/CVKSsXEFKXPeJKRDd4Jad67xz0gmSBNwqGKrRCQKT6Ax1gTaFIgDvIDasd6d4vQ0nyRvuWqJOh1xf3uDtJ++4vSW1TTo+zBNHhrGwS9nUZSeBoC5rJQj386h6GSq7bT8w/toVq8h3hFRZG5dQ8ht3QhudzsZG1ZgLDiDseAMe2efL1KWuWM94T2svU3/mNa0esH6aOPFBc9Kz2STMnea7T8PgILD+64qSV6OSBQGLk2QLbGOUWr+d09RoBb8ot4K1RIv5OJuQOfqhtBb5zJcva1tCJ0L0mIBYO/syRSlH6Xt2Om4+QawZcIzmI1ll3l/1jgb9HuEBn0ftu29cLzSI6hulbGYSorY/f6/MASH0e7lGeT9mcyB+TNs16qJi261Y7DeZld3qw3W8cvmIlFk1vAy7lgnexTlptN8knTGaolSSpCS8qJCMrevr/Saq48/ACd+/Q5TSRF1WnXCzTeA05tXE9C8LdJsJmPDCpoPGYtn3YgrXQghBBajEWNhHqc2rqhxjCJRTAaewfqIjw4owvr7dPHwiM9lmtha44tZFV3l8YpiF5pf4MIZqyXGDp+IITScQ4s+umQCqMHdD+LTMIYj384h6d1XcfMNIP7tr/Gu34jkDyez//Pp6D0MV06QWJN/7MhELOVlHFr8MSEde15NmGuAT4EfgIOc/13K58oJTWIdv0yp4ZZLFeOdinIz2LVaor3V1mqJ5WfzsZgq3x26evui01/8uGDts2vGy8bD33wyTko588L9Fz1QHg204PwD5fWwPkBejrWnqQca1HR2W1EcSeu32wVl+bmOjuGqbXxlMNk7K39rr9sHPxDc7nYHRWQ/ZWeyy4FLSljKBCmBzIrt9wtfq+Krid4VxymK5mm9Jxmsc/M42vWdhZ4h7WvPM+Vn9u/CeMFsMUBA0za4+fo7JB57yT+8j7XD+pSYis92kVImOToeRbkZNJ0kAYQQPVw8DD82uv8pN0Nofb2oBdUSbzlSUpqbaU5dOq/MVHz2OYvZ9KWjQ1KUm0XzSRJACBEHoq+Lh6GhWlDSAaSU5rKSDKRcK6X8zdHhKMrNVCuSpKIoiqOoe1dFUZRqqCSpKIpSDZUkFUVRqqGSpKIoSjVUklQURamGSpKKoijVUElSURSlGipJKoqiVOP/AQU5ZGzzHcdAAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 406.08x172.8 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_diagram()"
    ]
@@ -258,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -288,34 +222,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x129.6 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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nb3asaA4Exbamw8h3MXv78vd/7gNg+5SxbJ8ylh4TfsGvXgwb33uB1HXLMHt6U3/gbbQa/jLKZHL0GrPSaXDN7RxaMIOe//uVTe8+jyU7g6jeN3J8zRI2vPUsBalJuHn7EtGxJx1e+MCxenkFdLzOAmaVvFAJKhi4EugHDAQaAoWAu47Xwy7styJEzWH4m8mdXS2xItm7t1Cv/xBy9+9g6WPXUbffzXj4B7F9ylgs2RlgMhHV6wbaPv0GTe96muzdW9n0wUt4BoXS+LZHAYjqdSOdEybhH92UNQnDSFm9hMa3DKf2lQPZ9fUH7P1xUun5bIX5FKQl0+aJV/EMDisTi5u3LzE3P0jbp9+kXv8hHFn0E3tnfHI+Xx0AOl5n6nj9i47XT+l43RTHqty3A7edd2NC1ECG70k6s1riuTS991l8IuuxZ9rHBLfoQOwdj5O9azOH5k+nIPkIniHhHF/1O+mJa6BkRffsvdtw8/YltHUX9kz7mICY5tTrP4Ti/BOkblgOWpdehgOkrP6Dxv96pPTnTqM/wt3vzLIONksh+36cXFp24uS5LpaO19nA3ItuSIgawvBJ0pnVEs/Fwy8Qk5s7AO6+ASVtmgFH1cQ9Mz4hfctqGv3rYWp3H8i6N56kOP+EY7+zVDoMbNKK1k+8WvrzyXYBzN6+5SZIgK0Tx5CXdID2I8fhERDM36MfwGa1XPBnE0JcGMMnSWdXS7woJb3H4vw80jatpCAlCXc/R9Jz9w8CIG3TSg4vnEmdHtcS3r47qRtXkL5pJV5htUnfvAr/Bk0IadGhEqfSoDVFebmkrF1W6RBVgnqfkuqColztwCmVF6XqYg1h+DFJZ1dLvBiNbx1GcPMOJP35K4XpKQTENC99L6xtV8I79iRt00pWxz+EJTuDTvGfENXzBvb+8BlbJrxM3tH9BLe4olLnajX8ZbxrRbFn+icExba+6NiF00nVxRqiOlRL/KLHh7MrrsVaScWF+dgKC8psM3t54+bl44zmXero0l9Z/+aIJZas9N6ujsXInFUtUaou1hxGv9xOLUxz3sI2u74eX2YSBaD5A8/T4qEXnHYOVylMT8ZeXHzU1XEIcbkxepLcnH/8SOGeGZ/4Nb5lmPliG6t/ze2Etu1aZptvneiLbdblcg/tYdtnr+cX5+XMcHUsQlxuDJ0ktdYnlFJdEz8es+rgr996eUdEmSp7O09NYT2Rbc/cvsHDbrWM0FrPdnU8QlxuDJ0kAbTWB5RSzbJ2be6QtWtzTayWeC75wFat9W5XByLE5cjwSRJAa50FLHZ1HEKImkeuXYUQogKSJIUQogLV4nJbOZ75CwVquToWg7MDh7XWJ1wdiBCXC8MnSaWUv5uP32JtK27jERhayFmekRagbTZlyUrzdvPx+9RWkPeEduaTAkLUUIZOkkopDzcfv6VRvQc1v+LF8R7KZPI491E1mzU3m6WPXnNvfvLhYuApV8cjRHVn9DHJ1u5+gU2ueHG8l9wfWTke/oFcNX6Wr81qGabOtjSREKLSDF4t8b0c37ozbZIgz49XSATKZFLaURUxy9XxCFGdGTz7aEymyj2NeODXb5kZF8yubz+s4pgqZ8cX49g97eMLPn7bpDeZGRfMkcUVP0STuv4vZsYFs2HcyDLblTJpcM5CI0LUZM7uSdqgaqslVhc7vnwPz8AQmpSUdTgf9uJionrf6Fh7slXHKohOCFFZhp64gXIKgZ2lGNdJ2Xu3sej+XuQnH6bp3U8Te+eTFGaksjr+ITK2rUMpE/7RsXR/+/sz6sqAo2e27IkbqNPjOgrTj5N7cBdtnxlL9u5E9s2eSnBsG7q99R0e/oFsev9FDi2YQXFeLl7htWl611PEDL6fpY9fj60gj/yCPGbGBdPg2jvo+J+P2PHlu+yf/QXW7AxCWnWm/chx+EVFs23Sm2yfMpYG1w0ldcNf1LnqWtx9A9g+ZSxdXpuKT58o/ni4Pzn7d6JtxfhHx9L2qdcJaxdXtV++EML4SfKMQmAlxbi8wiIpTDvOrm/Gs+mDl6g34BYAUtYspfkDI9k741O2THiZiE69SF33J6nrltH0nmfxrV2fzB0bzlnmIWXdMloNH03ixFdZ+9/Hiep1I3WuupbDC2ZwcO63NLntUfyjY2n5yH+wF1k5vOAHNo4bSa0ufWl+/yiWb70VNx8/2j0zFt86DTg49zu2TnyVun1vIrBRC/bOnMTfo++n75Q/Tp1z9R80u///8KsbQ9rGlWXiiejUi+gb7saak8me6Z+w9vUnGTh9XcVf3jO8rhJU84p3qnHagVNWJndWO84kq6VXAcMnyX8WArNbLeUW46pX8n709UOJGXw/yuzG+jdGkLZhBX71YgBI27gcbbdRt98QvEIrvi+9dveBNBryMIcX/ED6ltW0Gv4y+cmHOLxgBnlJBwHIO3qAvTMnYSvMLz0u9+AuIrv2Q5ndcPPyoV7/IQDs+nYCAEcW/cSRRT8BUJh+HGtOZumxsUNHEDP4/pJYTyXJ4vwTZO3cxM6v3kPbTiV3m6XsAsKixvNzdQCXI8MnyX8WAquoGFcZp91HXbv7QHp/tpDjq5dw/O/f2fX1B1z5wU/U6tTrrOd193cshq5OFgbzC0CZThUFyzmwi13fjCewSWuaP/A8ycvncWDO19gsjmJdZ9x9UxJPp1c+xTM4/OSHw+x5qnyPV1j5ixwdmj+d5JULqdv3JhpcewdbP3udrB0bsVnPUdniPV7SWmdWvFPNcjmvTG6wXu1lw+Cz26WFwE4ppxjX6Q7M+YZ9s6ayZ9rHoBRh7eM4sng2x5bPx6dWFAENmwFQmJZ8kZE54rBZCrFkppCyZkmZd939A7FkpXNw7nfk7N9B7SsHAnBw7vcUHD9K2oblbJ/6NmZPr3Of6eRnLswnZ/8OcpxQWlYIUTmGT5L/LARWUTEugMhu/dk/63MKM1Jo/XgCQU1aY/by5ugfP7P+rWc5sngWdfveRFTvGy8qroDopjS+7TEK05I58PNX1OrWr8z7sUNHYHJ3Z+1rj3F0yRwaXHsHrR6N58SRvWx45zkOL5xJWNvKTbzUv/pWIjr1Im3DCtI3/y0TNkJcQs4uBFYMoLVzLuOVUoMiOvb84qrxs5xSCOx02m4vMx4IoExmPAKCnH0ql5jVu47FZimoLZfbZdWEy20jxXQ5MPqYpN1uK66ShvOPH2HekLZltvlE1uOaHzdXyfkuNW23mXCsCiSEuAhGT5KHTxza42YvLsJUMoHiLF4hEVz5wU9ltlVmfLA6yE8+DI4b+3NcHIoQ1Z7Rk2RicUHe2lX/vq9zl1eneJs9PM99RCWZPb0qnN2urgpSj7H08evzldn8lrbKUmlCXCxDJ0mtdbFSamDq+r9mze4b1dfs6VOkTEr+xz8Lu81mslstZpO7+9vFBfkJro5HiMuBoZMkgNa6EBiolHIvzs898zlCcTobkFZcXCRjkUI4ieGT5Ela6yLgmKvjEELULIa/T1IIIVxJkqQQQlSgWlxuK6W8gJZA+Q83G08RsA/YK8W4hKjeDJ8klVK1zd6+qzyDw4J8IqJ0dVhs215kIffgLg+7zfaFUuoxSZRCVF+GTpJKKR+zt++q2KFP1m7xwPPOvZu8illzs1k6fOA9+ceP5ADPuzoeIcSFMfqYZGvvsMig6pYgwVG1sPOYST4odberYxFCXDhn9yRNOJZSXOKc5t7N8Y6YVW0vVb3Do7BbLcGujkMIceGc3ZO0c3KhRSdxVenozO0bmBkXzNrXHrvgNpTJ+OOnQoiKObsneQKkWqK22VDmypXCFUIYm9HHJM+olph37BAz44JZ+th1rHzhLn4e0IA1CcOwWS2kbVrJ4of6MatvXebd0oF9s6YCkDhxDDPjgsk9uJv0LauZGRfMzq/eB+CXaxqxZLhj1fCUtUv5bUhbfrupNYd//7FMHAWpSax86R5+vjqaX29szpaPXkHbHU///XZzG2b1iWLD288xu399smXlcCEuG4ae3YZyqiWWSN/yNy2HvwxKcWj+dHzrxrBn+kRM7h60eWIMB3/7ng1vPYNf3RjC2sWx88v3SE9cQ1FuluP4xDXkHtyNNTuDsLbdsFktrEkYRnFBPq0eHc2RRbPKnG9NwjAyd2yiye2PUZhxnF1ff4B3RB0a/+sRAGyF+RSkJdPmiVfPKFWrEtT7lFTXExdEqgAKlzF8kvxntcSTQlp1ounQEZw4sp+kpXPYM30iRblZNL3nWWJuegDfqIb89fTNJK9aSPP7R6LMZjISV2PNzaZWl75kJK5xVFwEQtt2I/fgLgrTj1P/6ltpNORh/BvE8ueIwYCjWmHqhuWgNdunjC2NIWX1H6VJEqDT6I9w93P6IupCqgAKFzJ8kvxntcQzlZ0nKm+ix903gMBGLUt7ku1HvsvKF+/m0PzpYDIR1qYLeccOlW21nPu/A5u0ovUTr5Zp9ySzt+9ZE6SO10+fPX5xLlIFULiS4ZNkSbVE739uz0hcw85vxpNR0htsfOtw9kyfyIE5X+NdK4pD86YBENltAABh7eLYM+MTlMlEePvuBDVpTerapQQ2aY27XyD+DWLxCq1F0p+/sXfmZ2Uut918/Ahv353UjStI37QSr7DapG9ehX+DJoS06FD1X4IQwmUMP3Hzz2qJJ4W26UrGltWkrF1KvQG30PTup4l761u8a9Vl8/j/UJieQvtR7xFxxVUAhLXtBloTENMcNx8/Qlp1OrUdMHt40in+EzwCg9nxxXsE/yP5dYr/hKieN7D3h8/YMuFl8o7uJ7jFFVX50YUQBmD4nuTZuHn70u3Nr8tsC2vbjT6Tfi93/6jeNzJkxanCge2eeZN2z7xZZp+Ijj25Zuam0p/bnHZp7R1eh67/nVpu25dL8TAhxJmM3pPML8rLdXUMF6wo/wTKbLa4Og4hxIUzek9ye86+7Z45B3YSEN0UAN/a9cv0CI3s0G/TbCY39+2ujkMIceEMnSS11kdMZrdhS4YN/LjVoy/7eIVFopTRO79gL7KStmmlbf/sL1Jthfm3uToeIcSFM3SSBLDbir9USmUmfvTKncrsVs/V8VSKpshmKdhqsxSM1VofdnU4QogLZ/gkCaC1/gX4xdVxCCFqHuNfuwohhAtJkhRCiApIkhRCiApUizFJpdTVbj5+t5vcParHxM1lSNuKU4pO5Pyitf7O1bEIcSkZPkkqk+k2d7+Az1s89JK3d3htcNFK5TWa1liy09n++duDzJ5eDWyWwjfPfZAQlwdDJ0mlVJTZ02tqz4/negU2aunqcGq8yLgBPovu7TFaKbVUa73yXPurBGUG0PHaVvXRCVE1nJ0kvQGz8wqBfZIR0PBjS2Cjll7OaU9cDJ+IKGp17cvh+TNaA2ckSZWg3HAsLtwLuB7oDMwEpGKkqLacnSStgMc596q0PDdZxNZYPPyD3ABfAJWg3IEOnEqKHYEiHP8NnFxRvplKUC0u8rQ+Jee72HbA8Q95gRPaETWEs5PkOrg8CoFlbt/A4gf70ODaO+j4n49cEUK5jq1YQOa29TS47k58a9c/7+Pzjh1i3pC2RMYNoPs70yrc97eb22DJzmDwoiNnvKcS1ATgYRx3SNg59Y/jP3v9HYGt5x1o+ZzVznontSNqAEOPSVZXVVktMXnlQvbNnER4hyvPO0nai4vxDAqlc8IkvMNrX2woc4AgoA8QAuQC/uXsVwwcKmf7+ahT8mfSRbYDEAhsdEI7ooYwfJIsr1rivCFtCWsXh0dAMKnr/6R294F0eHE8mdvXs/nD0eTs34FXSASxQ0cQM/g+EieOYeeX7zHgu9VYczJZMuxqWj0aT9O7n+aXaxrh36AJvSbOI2XtUta9MQLsdqL6DC4TR0FqEhvfe4HUdcswe3pTf+BttBr+MspkcvS4stJpcM3tHFowg57/+5Wg2NZnfJaTPbOYQfex/5cviejYk5jB97PujSfRNhtX/HsCkV37cXzNEja89SwFqUm4efsS0bEnHV74gKNLfmHfzEkALHviBgCGrMgkfctqNo//D9n7tuEdFkmLB1+g3oB/lX5XIa06YfbyIWfvNnpP+p3V8Q8RGTfAUSDt6w/YM30ilqx0vILDib7xHlo8+Pw5fy86Xs8D5qkEVQsYBNyOYwzS9x+75gMXO+u2ALDreN3rItsR4rwZPkleDtUST2cryMNmLSS0VWeSls4ha+cmYu8cwabxL5H4cQKRXfvh5u1LzM0P4ubtS86+bez94TMCG7Wg3oBbiOjch5TVi2l2/0gCoptizclkxcjb8QwOp9m9z5K67i/WvDoc/+imuPs7xnMzEtcQO3QEdf+R+MExGdPsvv8DrTm2Yj7bJ79JRKeehLXpWuHvRSWo/wDDgXDAgqNYV3n3ZwXgnDHA6ruwqKjWDJ8kL7tqiSYTbZ96nYNzv+P434uoP/A2Gt86jF3fjicv6SAANksh+36cTN7R/aWHZe/dRrM6DfCrF0PK6sVEXNGD8A5Xcmz5fKw5mVhzMtk68dRK6inrlhHVy9HbDIptQ+vHEwDOKHhWmJnG9iljS//xAMjZu+2cSRJIBo7hSJIax2W1ezn72XBM6F0MdyDjItsQ4oIYPkleDtUST2f29Mbk7oFyc+QTdz9HG8pkRtvtAGydOIa8pAO0HzkOj4Bg/h79ADar5SyfzxFn/Wtup/7AU0tXnj5e6RUWWW4sxQV5bP7w33iH16bDqHfJ2p3Izi/fLT1XRXS8ngRMUgnKA+gE9AauA9rjSIqeOCZzlut43fOcDQphUIZPkjWxWqLWGrSmKC+XlLXLyrzn7h8EwJE/ZlNckEdo6y54BARzfNUigpu3R9tsHFs+n+b3j8Qn8hxPcWqNUgq71Yo1N4vkFfMrF2AnBqsENaicdyzA3zguvYOAMKDeZVoSth04rdxtYyBVx+v2TmhLOJnhF7ioidUSWw1/Ge9aUeyZ/skZE0D1B9yCf4NY9v04mU3vv4hHQDBxb3+PX92GJH48hh1fjMPNy/vcCRJH8m/1WAL2Igt7ZnxKROfezghf4xg/PAxs4OJntmsCPxzDFsKAVHmXlRfcWMmTNs67T1INiujY84urxs8qvY49n/v8XKXoRDb24uIy29z9AjC5lTdkV71sfHeUde8Pn72gtX7P1bG40skepDNm3J3VljNjEqcY/XI715JTPYp+nW7F80NJ27C8zLYeE34hvMOVLorIeSyZaUXITLOoQYyeJBNzD+xyT9u4grB2cUD1qJbY5snXsJ42WwwQ2LiVa4JxopwDu0he9bui5MkqIWoCQydJrXWKUmrQX8/dMrvRkIc9vSPqmKtDtcTynDi0x9UhXARNYUaq3vvDZwW2gvzHtNYbXB2REJeKoZMkgNb6d6VUz93ffnit2cs3GlXuDcuiKmm0zVKQpG3Fi7XWi10djhCXkuGTJIDWei2w1tVxCCFqnup57SqEEJeIJEkhhKiAJEkhhKhAtRiT9HR3e8Tb0+0+u9a1jDZvo9F2hTqQk29J0Fr/5ep4hBDOZfgk6enuNirQ1zP++Tuv9KkV7FvuAhauVGyzs+doRuNx01Z0U0pdp7Ve6uqYhBDOY+gkqZSK8fZ0S/j8hcFekSF+rg7nrFo1jKBWsK/vyIkLZ+JY1EEIcZlwdpJsDPg5r1riR5mN67xiiQzxM3y1xC7N61JUbAtUSvlorfNdHY8QwjmcPXGTCpxwXnOFZm/P6rEohMmkcDObbJyqEiiEuAw4tSepNU5dD89RLTHKJdUShRAC5BagMoptdleHIIQwGENP3MCZ1RIzcgr496RFJB5IwaQU0ZFBvP/EQH78czs//bmDnDwLzRuEMeqO7jSqE8IrU5cwZ+UuvnzxJlpEh3PViCkE+Xnxy+t38suKnSR8sZT+V8SwNymTBrUCef3hvnzyyzrmrd5DZm4BLRqE89nIGzlRYGXc9BX8teUQdrumT/uGPHdbHF4ehv8KhRAXwfA9SQ9PzzJjfL+t3s2anUnc3qcVT/+rK7H1Qvnpzx18PHstTaJCeGxwJ7YdTOW5jxZUume4ctsRbu7RnOu6xTJ13kamzttITJ1gRt7enWb1HZPV46atYO6q3VzfLZbBVzZj9vKdTPxZHicX4nJ3SbtBSjEIx1L132pNpZZEV6pstcT6EY5FytfvOoa9sebqTo2ZvmQrAM/c0o36tQJJ3J/CvNV7OHg8q1Jx3RjXlNv7ONZ7nDJ3A0rBGw/3xdfrVCf2zy2HsNk1Xy3YXLpt1bYjlWpfCFF9XeprxVklf36tFDbgILAQeE9rdpZ3gNZlqyVe1aYBU18YzKptR1i59TBfzN9EoK+js1nefeYmk2OjzW7HWmTDYrWdsU94kE+Zn8/2VE9ogDdjHjhVB8bDzVz+pxRCXDacermtFEopflaKpeW9/rG7GYgBhgE7lEIrxQmlWK4UI5Ry3EpjtVjK1Df9fd0+/tx8kMgQP2LqBANwW29HL/DdGSv5fnEiyzYdpG54AA1qBVEn1B+Auat28+FPf2M/R02fq9rUx641L362iNnLdzBu2grH9tb1Sc8pYNmmgxxLP8EfGw6wYO3ei/zGhBBG5+yeZA6Oy+kL5QvElbzeB24qqZZYWlLWy8ONRev3cyw9Fw93M/07xnD3gDaYTIpZf+1g7c4kWjQIZ9Qd3XEzmxh8ZTOWbT7Ib6v3cF3XJni6V9z7u29gOyxFNn77ezdrdyTRMtpRxO652+Iwm038vm4fP6/YSf2IQO4e0PYiPqoQojpwdrXEYhw9xLMVivKvRDMngE3AF6COXxFb+8tPnrsh8FwHGUHc45Ot1mJbLa11lqtjEVVLqiXWHFUxJmnTmoDy3lDqjMmaCscllaJjUlquSWttuIUt/inrROHJS/lsV8cihHCeS30L0FTgd+AuwKQ1blrTSGuGn2XiZnN2nuXgG9/8ZXFmj9fZThRYefz9X/M83c2fayMHKoQ4b5d0dltr7j+//bVVKXXlvNV7ls5bs6dpiL+3tZzeqEsV2+wqLTvf291s/j7fUvSYq+MRQjiX4R8X0VpnK6XaA7XzC4siXR1POezAIWuRLcPVgQghnM/wSRKg5BI2qeQlhBCXjOEfSxRCCFeSJCmEEBWoFpfbynH/T22glqtjqaR84IDW2nLOPYUQhmb4JKmUCnTz8VsKNPUMDrOCMtTsdnmKC/LMRblZZqXU9Vrrxa6ORwhx4QydJJVSHm4+fn/WG/Cv2PYj3/VUShm+1s1JqRuWs/y5W39RSvXTWq90dTxCiAtj9DHJNh4BwdElCdLVsZyX8Pbdaf7g8z5uPn4PujoWIcSFc3ZP0gQo51VLfC/Hp85Me3VLkCf5RTXE5O4R4+o4hBAXztk9STs484kYjclUdtWevGOHmBkXzPL/u815p/mHta89xsy4YDK3b7iodpQjdqP31oUQFXB2T/IEgNb0ckZjjmqJPS95tcSYmx6gVpe++EY1vCTnUwnKB+gGuOl4Pf+SnFQIUSmGnriBMwuBnVScl8uKUXeQtnEFtbsPpMOL48ncvp7NH44mZ/8OvEIiiB06gpjB97F35iQ2jhtJ+5HjqNd/CAvu7Iq7fxB9P1+C2ePMMtn7fprCwbnf0WfyYoryQpk3pC2hbbrgERBC6vq/qN19AJ1e+QylFMmrfmfrJ6+Re2AXHgHBdHntc0Jbd67wM6kE5Ydjzcx+wDVAU6AQcOe0tTOFEK5n+CTp6eF+ZhYD0hNX03L4yyizG4fmT8e3bgx7pk/E5O5BmyfGcPC379nw1jP41Y0h5uYHObp0DokfJ5CydhmWzFS6vflNuQnybNIT19DykX9TmJbM4YUzibnpATxDa7Hyhbvw8A+i9RMJFOVmo+1nlodQCcofuApHUhwINAYKcCwyfHI8wR2wqQT1/vl8P8JlGgOprg5CVD3DJ0mTyVTumF5Iq040HTqCE0f2k7R0DnumT6QoN4um9zxLzE0P4BvVkL+evpnkVQuJ6NiDK14cz+93d+foH7Npes+zhLTocF5xhLS4gmb3PItCkbljA3nHDpG9dyt2q4Vm9z1HoyEPV3T4E8Dr/9jmXt7HBZ46r8CEK535L6K47Bg+SdrtZQuBnansPNHZZsKLcrOxWR0PwBSmHz/vODwCHPV0lJvjK3PUJ6u0N4HZQE/gOuBKHElS4+hNnmQDnjnv4ISryIMCNYDhk6TFWmShnHG6jMQ17PxmPBmJawBofOtw9kyfyIE5X+NdK4pD86YBENltAPbiItb+9zE8g0Kp0+M69v04majeN1I7bsBFxRbRuQ8mD092TB0HQFFeLmFtuxHWtluZ/XS81sC2ktfHKkEpoAmnkmYPwAvQOl5PuKighBBOZfjbU0oKgZ0htE1XMrasJmXtUuoNuIWmdz9N3Fvf4l2rLpvH/4fC9BTaj3qPiCuuYvvnb5O9O5F2z75Fmydfw79BLOvffBpr7sVVWvCv14hub3yFV1gkWybEs2/m5JO3/VRIx2ut4/UuHa8/0/F6MBAKtAa6XFRAQginc3YhsCwArQlyTntqUETHnl9cNX5WlRQCKy7Mx1ZYUGab2csbNy+fsxxxfpKWzWXdG08utWSl93JKg+KyJIXAjM3ol9t2e3FxlTW+6+vxbJ8ytsy25g88T4uHXnBK+/biItBaBveFqMaMniQP5R7a7WYvsmJyL/d2yYtS/5rbCW3btcw23zrRTms/Z+82bFbLLqc1KIS45IyeJBNthfmrVr5wV7cur33u4+bte+4jzoNfVDR+UdFObRNA22wcXvSj3vXdhzm2woIPnX4CIcQlY+gkqbW2KaWuTd/y94yfB0Rfa3Jzt6GMv56kvcjqZvLwSLEVFlyjtd7m6niEEBfO0EkSHGVlgUFKKbPNVhzk6ngqKb84v6jg3LsJIYzO8EnyJO2YAEl3dRxCiJrF8PdJCiGEK0mSFEKIClSLy22lVD0cj/BVl2qJl0oGsEJrvdPVgQhxuTJ8klRKtTZ7ev8Z3rGH2bdOtIeqrrUcnExrrQvTkouSVy5EKTVIa73I1TEJcTkydJJUSgWZPb2WXfHvCQH1+t0syfFMHiVVGWcrpTporeXGdSGczNlJ0hswO68Q2FcpvlFvKEmQZxfevju1uvTRSUvndAMkSQrhZM6euLHi1IVIU708g8Oc19xlyjss0gMIcXUcQlyOnN2TXAfOLQSmVNlCYHnHDjFvSFsi4wbQ/Z1pzjiNSy3/v9tIXrGAgTM34Vu7/oU1IsO0QlQZQ49JirOzFxdjcqv8r08lqFo4FvcdAKzQ8frzqopNiMuJ4e+TPFe1xJ8HNGBNwjBsVgtpm1ay+KF+zOpbl3m3dGDfrKkA7J05iZlxwez7aQpFJ7L59cbmLBjarbScwz+drLu95aNXmHtTK34b0paUtUsBsFktbPrgJX69sTk/D2jAilF3kn/8CMWF+fzYI4K/X34QgD+fHsKsvnXRNht7ZnzKzLhgjq9Zgs1qYU3CsJJj76A4L7fMuQ/M+Zr5t3diVp8o/nhkAJk7Nzm2//otM+OC+Xv0AywY2o2/R99f4femElQdlaDuUAlqqkpQR4CDwCTgIeC+Sn35Qgjj9yRdWS0xY8tqmt71FIkfv8qahGEMnLmJHV+MY8+0j2lw7R341W/M1k//S1FuFj0/nktw07ZkJK5Ba03m9nXYCvLI3ruN9C1/o8xuhLbsyL6fPufQ/OnU6Xk9Ia06sXXimNLzpa7/i3WvP0lE5z40uPYODs79jpWj7uTq6etK9zn+9yJaPPQS3rXqnhGvSlADgbuAPjjGKK2A/+lfZ8mfnVWC2lOpX4C4FMKBTa4OQpTP8EnSldUSmz/4PBEde5KRuJZD86dz4tBuklcuBJOJ9qPew+zhSfLy+aRtWklx/glC23Zl97cTSFm7FLvVSli7ONITV5ORuIag2Da4+fiRtuEvAFo/Pga/ug059tdvpG9aBcCxFfMBSFm9mJTVp2pM5Rw4da94g+vuovGtw84W8g3ALTiuEOyUTZCn8wIanfMLEJeKBra6OghRPsMnSSNUS6xsiYuwtt3Y/e0Edn//P4KbtSOsXRxJS+eQn3yYqN6Dztb4aX93/NH6ydcIbNzS8YPdjm/tBmTvcfw/5B0eefY44/XjKkE9DXQAegHXAx2BIsCDUz3JdcC9lfpQ4lLI0fH6sKuDEOUzfJJ0ZbXE7VPeIvfgLo79NQ+vsEj86jehdtwAsnZsZMPbz+FfvzHpW9cS1i4ONx8/wtrGgVIcX7WI2DufJKRVJ3ZMfQegtIJieIerSFr2K1v+9zIhrTqRsXVt6flqd7+a3d9N4MjCmXj4B1KYlszBedO5+vvVlf6+dLwuAv4ueY1VCcoNaMeppNkZ2KHjtfRchKgEwyfJkmqJZyTJ0mqJ65aVVkuM6NiDzR+OZvP4/+AVWqu0WuLWz14ne3ciXV//kshu/Uld9yfr33ya/t+sxMP/7DXGQtt0ZefXH+AREMQVL32I2cOTpvc8S9GJHI4s+omkpb9QO+5q2j33FgAeAUEENGxGzr7thLTqTGirTqW355wsE9Fw8H1kbl9P0l+/YS+yEtKqU+nldniHK7ni3/9j19cfsOGdkXgFhxHesedFfX86XhcDa0te76gEde5yjkKIUs6ulrgEnHmfpGuqJW585/84OPc7+kxeTHDz9lVxaqfa+O4o694fPntBa/2eq2MRriPVEquG0XuSluKC/Cpr/GzVEqub4oI8G1D+/UxCiIti9CS5K2ffNs+C1CS8w+s4vfGzVUv0i4qm438+cvr5qkJRXg4pa5fZAVkuTYgqYOgkqbXeZ/b0fm3xQ/1e6jDqXR/vsDpgcu4jeJ5BoWV+Ls7PJWv3Fqeeo0posGSmsuWjV/KsOZnTgD9cHZIQlyNDJ0kAm6Xgv2ZP74y1rz1+r7bbZNHd0yhlyrBZLb/YCvPHaK3tro5HiMuR4ZMkgM1S8DHwsavjEELUPIZ/dlsIIVxJkqQQQlRAkqQQQlSgWoxJKqWud/cLGKrMbmcufWN8NrvVsru4IO99reVRQCGqG8MnSZPZ7W53/6CJrYaP9vEKr33WBSyMyl5sI2fv1h47v3r/VqVUD621LIklRDVi6CSplKpr9vT+tNfE37wCGjZzdTgXLKrndcq3boz/+rFPzQWiXB2PEKLynJ0kGwN+zquW+ElGQMwnloCGzbyc057r1Ot7k1r76vBaSilvrXXBuY8QQhiBsyduUoETzmsuz83d92zrxlYvymzG5OZRjGPBWyFENeHUnqTWOHXJHKWeHQTGqpb4281tsGRnMHjRkUt+biHEpWfoMcmaRCUoE9AG6Iljcdy2wDU6Xq+r8EAhRJUyfJI8V7XEtI0rqN19IB1eHE/m9vVs/nA0Oft34BUSQezQEcQMvo+9MyexcdxI2o8cR73+Q1hwZ1fc/YPo+/mScouB7fz6A/ZMn4glKx2v4HCib7yHFg+WXUJty//iOTDna/zqNaLLmMn4RNZj36zP2TH1HSyZaXiGhNP4luHE3vlEuZ+rZPHbdkBvHEmxE2AD3HFckucBzVWC2n3BX56oacw4/hsSTmT4JOmKaok+EVE0u+//QGuOrZjP9slvEtGpJ2FtHMuq2QrysOZm03DQfez88l02ffAS3d74ii3/i8cnsh4tHnoJS0YKylzO19sFf5WgZuNIisU4kmJ5gZiAry7kOxM1mvyj6mSGT5KuqJZYmJnG9iljKcrNKt2Ws3dbaZLEZKL9c29hcvfg0LzvSV3vqIDoV68ReUcPkLZxOUFN21H/6lvObDyfYmALUAeoDxTgSJT//JwaKMRRxEuIyvBEkqTTGT5JXupqicUFeWz+8N94h9emw6h3ydqdyM4v3y099oxznHb6Hh/O5ugfv5C1axOJE8dwZNGP9Jo4r+wJtlCgN+vHAVSCCgauBPoBA4FoHInRD1DAUB2vfzz7ZxdCVDXDJ8lLXi1Ra5RS2K1WrLlZJJfUwi6zi83GhnGj8AwKpSA1iTq9bgBg0/svEdysLUGxbXFfMoeCtOQKP5uO15nALyUvVIIKALrjSJpXIrWYhXA5wyfJS10t0c3Hj1aPJbDj87fYM+NTIrv1K615fZLZ2xev4DD2/TSFkJadaDvivwAUnchm26Q3Kc4/gU/tBrR+7JXz+qw6XucAv5W8hBAG4NRqic7mqmqJbl4+VXE6ZvWuY7FZCmprrTOr5ARCCKczek8yx5KdUWWNn61aYouHXnD6uezFRdiLrG44bu0RQlQTRk+SibkHd7unrF1GRMce5977PJ2tWmJV2PXNh8VmL5/dRXk51io5gRCiShj6chtAKdXL7OX9a8Mb7/XwrlXXTZV/R5Bh2YuLyNq12XLsz9/SbYX5nbXWR10dkxCi8gyfJAGUUm1BDTR7eTeofgtK2opslsI9wHStdcXT3UIIw6kWSVIIIVylel27CiHEJSZJUgghKiBJUgghKiBJUgghKiBJUgghKiBJUgghKiBJUgghKiBJUgghKvD/b3mEIcBBP3AAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 406.08x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "box_inc = instrument.add_component(\"box_inc\", \"Union_box\", AT=[0.04,0,1], RELATIVE=src)\n",
     "box_inc.xwidth = 0.03\n",
@@ -348,34 +257,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x129.6 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 462.384x374.4 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_diagram()"
    ]
@@ -390,49 +274,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Union_logger_2D_space _____________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1mtarget_geometry\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated list of geometry names \n",
-      "                                     that will be logged, leave empty for all \n",
-      "                                     volumes (even not defined yet) \n",
-      "\u001b[1mtarget_process\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Comma seperated names of physical \n",
-      "                                    processes, if volumes are selected, one can \n",
-      "                                    select Union_process names \n",
-      "\u001b[1mD_direction_1\u001b[0m = \u001b[1m\u001b[94m\"x\"\u001b[0m\u001b[0m [string] // Direction for first axis (\"x\", \"y\" or \"z\")\n",
-      "\u001b[1mD1_min\u001b[0m = \u001b[1m\u001b[94m-5.0\u001b[0m\u001b[0m [1] // histogram boundery, min position value for first axis\n",
-      "\u001b[1mD1_max\u001b[0m = \u001b[1m\u001b[94m5.0\u001b[0m\u001b[0m [1] // histogram boundery, max position value for first axis\n",
-      "\u001b[1mn1\u001b[0m = \u001b[1m\u001b[94m90.0\u001b[0m\u001b[0m [1] // number of bins for first axis\n",
-      "\u001b[1mD_direction_2\u001b[0m = \u001b[1m\u001b[94m\"z\"\u001b[0m\u001b[0m [string] // Direction for second axis (\"x\", \"y\" or \"z\")\n",
-      "\u001b[1mD2_min\u001b[0m = \u001b[1m\u001b[94m-5.0\u001b[0m\u001b[0m [1] // histogram boundery, min position value for second axis\n",
-      "\u001b[1mD2_max\u001b[0m = \u001b[1m\u001b[94m5.0\u001b[0m\u001b[0m [1] // histogram boundery, max position value for second axis\n",
-      "\u001b[1mn2\u001b[0m = \u001b[1m\u001b[94m90.0\u001b[0m\u001b[0m [1] // number of bins for second axis\n",
-      "\u001b[1mfilename\u001b[0m = \u001b[1m\u001b[94m\"NULL\"\u001b[0m\u001b[0m [string] // Filename for logging output\n",
-      "\u001b[1morder_total\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time, 0 for \n",
-      "                         all orders \n",
-      "\u001b[1morder_volume\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time in the \n",
-      "                          same geometry \n",
-      "\u001b[1morder_volume_process\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [1] // Only log rays that scatter for the n'th time \n",
-      "                                  in the same geometry, using the same process \n",
-      "\u001b[1mlogger_conditional_extend_index\u001b[0m = \u001b[1m\u001b[94m-1.0\u001b[0m\u001b[0m [1] // If a conditional is used with \n",
-      "                                              this logger, the result of each \n",
-      "                                              conditional calculation can be made \n",
-      "                                              available in extend as a array called \n",
-      "                                              \"logger_conditional_extend\", and one \n",
-      "                                              would then acces \n",
-      "                                              logger_conditional_extend[n] if \n",
-      "                                              logger_conditional_extend_index is \n",
-      "                                              set to n \n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.component_help(\"Union_logger_2D_space\")"
    ]
@@ -446,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -485,42 +329,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ___ Help Union_master ______________________________________________________________\n",
-      "|\u001b[1moptional parameter\u001b[0m|\u001b[1m\u001b[4mrequired parameter\u001b[0m\u001b[0m|\u001b[1m\u001b[94mdefault value\u001b[0m\u001b[0m|\u001b[1m\u001b[92muser specified value\u001b[0m\u001b[0m|\n",
-      "\u001b[1mverbal\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [bool] // Toogles terminal output describing the defined simulation\n",
-      "\u001b[1mlist_verbal\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Toogles information of all internal lists in \n",
-      "                            intersection network \n",
-      "\u001b[1mfinally_verbal\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Toogles information about cleanup performed in \n",
-      "                               finally section \n",
-      "\u001b[1mallow_inside_start\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Set to 1 to allow rays to start inside the \n",
-      "                                   defined geometry \n",
-      "\u001b[1menable_tagging\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Enable tagging of ray history (geometry, \n",
-      "                               scattering process) \n",
-      "\u001b[1mhistory_limit\u001b[0m = \u001b[1m\u001b[94m300000.0\u001b[0m\u001b[0m [bool] // Limit the number of unique histories that \n",
-      "                                   are saved \n",
-      "\u001b[1menable_conditionals\u001b[0m = \u001b[1m\u001b[94m1.0\u001b[0m\u001b[0m [bool] // Use conditionals with this master\n",
-      "\u001b[1minherit_number_of_scattering_events\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Inherit the number of \n",
-      "                                                    scattering events from last \n",
-      "                                                    master \n",
-      "\u001b[1mrecord_absorption\u001b[0m = \u001b[1m\u001b[94m0.0\u001b[0m\u001b[0m [bool] // Toggles logging of absorption\n",
-      "-------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.component_help(\"Union_master\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -536,34 +354,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x129.6 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 462.384x374.4 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.show_diagram()"
    ]
@@ -578,284 +371,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Instrument settings:\n",
-      "  ncount:           3.00e+06\n",
-      "  output_path:      data_folder/union_materials\n",
-      "  run_path:         run_folder\n",
-      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
-      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
-      "  executable:       mcrun\n",
-      "  force_compile:    True\n",
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_materials_45\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
-      "                                                ^~~~~~~~\n",
-      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
-      "#define filename mccabs_logger_space_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
-      "                                                ^\n",
-      "                                                \"%s\", \n",
-      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
-      "#define filename mccabs_logger_space_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "14 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_materials_45\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Cu.laz' (Table_Read_Offset)\n",
-      "Table from file 'Cu.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
-      "  '# TITLE *-Cu-[FM3-M] Otte, H.M.[1961];# CELL 3.615050 3.615050 3.615050 90. ...'\n",
-      "PowderN: powder: Reading 19 rows from Cu.laz\n",
-      "PowderN: powder: Read 19 reflections from file 'Cu.laz'\n",
-      "PowderN: powder: Vc=47.24 [Angs] sigma_abs=15.12 [barn] sigma_inc=2.2 [barn] reflections=Cu.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: incoherent \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: powder \n",
-      "component index [1]: 4 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 3\n",
-      "name of material    [0]: inc_material \n",
-      "component index     [0]: 2 \n",
-      "my_absoprtion       [0]: 0.000000 \n",
-      "number of processes [0]: 1 \n",
-      "name of material    [1]: abs_material \n",
-      "component index     [1]: 3 \n",
-      "my_absoprtion       [1]: 3.000000 \n",
-      "number of processes [1]: 0 \n",
-      "name of material    [2]: powder_material \n",
-      "component index     [2]: 5 \n",
-      "my_absoprtion       [2]: 1.200000 \n",
-      "number of processes [2]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 3\n",
-      "\n",
-      "name of geometry    [0]: box_inc \n",
-      "component index     [0]: 7 \n",
-      "Volume.name         [0]: box_inc \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.000000 \n",
-      "Volume.p_physics.number of processes [0]: 1 \n",
-      "Volume.geometry.shape                [0]: box \n",
-      "Volume.geometry.center.x             [0]: 0.040000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: box_powder \n",
-      "component index     [1]: 8 \n",
-      "Volume.name         [1]: box_powder \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absorption       [1]: 1.200000 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: box \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: box_abs \n",
-      "component index     [2]: 9 \n",
-      "Volume.name         [2]: box_abs \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absorption       [2]: 3.000000 \n",
-      "Volume.p_physics.number of processes [2]: 0 \n",
-      "Volume.geometry.shape                [2]: box \n",
-      "Volume.geometry.center.x             [2]: -0.040000 \n",
-      "Volume.geometry.center.y             [2]: 0.000000 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 4\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           0 = [1,2,3]\n",
-      "LIST: Intersect_check_list for Volume      0 = [1,2,3]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [1,2,3]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for box_inc with box shape made of inc_material\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = []\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = []\n",
-      "LIST: Next_volume_list for Volume          1 = [0]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for box_powder with box shape made of powder_material\n",
-      "LIST: Children for Volume                  2 = []\n",
-      "LIST: Direct_children for Volume           2 = []\n",
-      "LIST: Intersect_check_list for Volume      2 = []\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = []\n",
-      "LIST: Next_volume_list for Volume          2 = [0]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for box_abs with box shape made of abs_material\n",
-      "LIST: Children for Volume                  3 = []\n",
-      "LIST: Direct_children for Volume           3 = []\n",
-      "LIST: Intersect_check_list for Volume      3 = []\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = []\n",
-      "LIST: Next_volume_list for Volume          3 = [0]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_I=3.16864e-09 logger_space_ERR=4.64761e-12 logger_space_N=474493 \"logger.dat\"\n",
-      "Detector: abs_logger_space_I=1.70535e-09 abs_logger_space_ERR=1.97625e-12 abs_logger_space_N=1.08778e+06 \"abs_logger.dat\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=8.0)\n",
     "instrument.settings(ncount=3E6, output_path=\"data_folder/union_materials\")\n",
@@ -876,30 +398,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space\n",
-      "Plotting data with name abs_logger_space\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ms.make_sub_plot(data)"
    ]
@@ -916,284 +417,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Instrument settings:\n",
-      "  ncount:           5.00e+06\n",
-      "  output_path:      data_folder/union_materials\n",
-      "  run_path:         run_folder\n",
-      "  package_path:     /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1\n",
-      "  executable_path:  /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\n",
-      "  executable:       mcrun\n",
-      "  force_compile:    True\n",
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_materials_46\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:17318:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
-      "                                                ^~~~~~~~\n",
-      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
-      "#define filename mccabs_logger_space_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_abs_logger_2D_space.comp:543:49: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_abs_storage.Detector_2D.Filename,filename);\n",
-      "                                                ^\n",
-      "                                                \"%s\", \n",
-      "./python_tutorial.c:17560:18: note: expanded from macro 'filename'\n",
-      "#define filename mccabs_logger_space_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "14 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_materials_46\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Cu.laz' (Table_Read_Offset)\n",
-      "Table from file 'Cu.laz' (block 1) is 19 x 18 (x=1:6), constant step. interpolation: linear\n",
-      "  '# TITLE *-Cu-[FM3-M] Otte, H.M.[1961];# CELL 3.615050 3.615050 3.615050 90. ...'\n",
-      "PowderN: powder: Reading 19 rows from Cu.laz\n",
-      "PowderN: powder: Read 19 reflections from file 'Cu.laz'\n",
-      "PowderN: powder: Vc=47.24 [Angs] sigma_abs=15.12 [barn] sigma_inc=2.2 [barn] reflections=Cu.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: incoherent \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: powder \n",
-      "component index [1]: 4 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 3\n",
-      "name of material    [0]: inc_material \n",
-      "component index     [0]: 2 \n",
-      "my_absoprtion       [0]: 0.000000 \n",
-      "number of processes [0]: 1 \n",
-      "name of material    [1]: abs_material \n",
-      "component index     [1]: 3 \n",
-      "my_absoprtion       [1]: 3.000000 \n",
-      "number of processes [1]: 0 \n",
-      "name of material    [2]: powder_material \n",
-      "component index     [2]: 5 \n",
-      "my_absoprtion       [2]: 1.200000 \n",
-      "number of processes [2]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 3\n",
-      "\n",
-      "name of geometry    [0]: box_inc \n",
-      "component index     [0]: 7 \n",
-      "Volume.name         [0]: box_inc \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.000000 \n",
-      "Volume.p_physics.number of processes [0]: 1 \n",
-      "Volume.geometry.shape                [0]: box \n",
-      "Volume.geometry.center.x             [0]: 0.040000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: box_powder \n",
-      "component index     [1]: 8 \n",
-      "Volume.name         [1]: box_powder \n",
-      "Volume.p_physics.is_vacuum           [1]: 0 \n",
-      "Volume.p_physics.my_absorption       [1]: 1.200000 \n",
-      "Volume.p_physics.number of processes [1]: 2 \n",
-      "Volume.geometry.shape                [1]: box \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: box_abs \n",
-      "component index     [2]: 9 \n",
-      "Volume.name         [2]: box_abs \n",
-      "Volume.p_physics.is_vacuum           [2]: 0 \n",
-      "Volume.p_physics.my_absorption       [2]: 3.000000 \n",
-      "Volume.p_physics.number of processes [2]: 0 \n",
-      "Volume.geometry.shape                [2]: box \n",
-      "Volume.geometry.center.x             [2]: -0.040000 \n",
-      "Volume.geometry.center.y             [2]: 0.000000 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 4\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           0 = [1,2,3]\n",
-      "LIST: Intersect_check_list for Volume      0 = [1,2,3]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [1,2,3]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for box_inc with box shape made of inc_material\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = []\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = []\n",
-      "LIST: Next_volume_list for Volume          1 = [0]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for box_powder with box shape made of powder_material\n",
-      "LIST: Children for Volume                  2 = []\n",
-      "LIST: Direct_children for Volume           2 = []\n",
-      "LIST: Intersect_check_list for Volume      2 = []\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = []\n",
-      "LIST: Next_volume_list for Volume          2 = [0]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for box_abs with box shape made of abs_material\n",
-      "LIST: Children for Volume                  3 = []\n",
-      "LIST: Direct_children for Volume           3 = []\n",
-      "LIST: Intersect_check_list for Volume      3 = []\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = []\n",
-      "LIST: Next_volume_list for Volume          3 = [0]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_I=1.13125e-09 logger_space_ERR=5.9031e-13 logger_space_N=3.86142e+06 \"logger.dat\"\n",
-      "Detector: abs_logger_space_I=4.15533e-11 abs_logger_space_ERR=2.36464e-14 abs_logger_space_N=4.11813e+06 \"abs_logger.dat\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.set_parameters(wavelength=2.8, source_width=0.03)\n",
     "instrument.settings(ncount=5E6, output_path=\"data_folder/union_materials\")\n",
@@ -1204,30 +434,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space\n",
-      "Plotting data with name abs_logger_space\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ms.name_plot_options(\"logger_space\", data, log=True)\n",
     "ms.name_plot_options(\"abs_logger_space\", data, log=True)\n",
diff --git a/docs/source/tutorial/Union_tutorial_5_masks.ipynb b/docs/source/tutorial/Union_tutorial_5_masks.ipynb
index bebfa3b1..8137d1e0 100644
--- a/docs/source/tutorial/Union_tutorial_5_masks.ipynb
+++ b/docs/source/tutorial/Union_tutorial_5_masks.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,9 +36,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 446.4x129.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 429.84x259.2 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
     "Al_inc.sigma = 0.0082\n",
@@ -104,13 +127,220 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_98\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:11415:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:11415:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "13 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_98\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 1\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 0.347892 \n",
+      "number of processes [0]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 1\n",
+      "\n",
+      "name of geometry    [0]: wall \n",
+      "component index     [0]: 5 \n",
+      "Volume.name         [0]: wall \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.347892 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.100000 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.150000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: wall_vacuum \n",
+      "component index     [1]: 6 \n",
+      "Volume.name         [1]: wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [1]: 1 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.000000 \n",
+      "Volume.p_physics.number of processes [1]: 0 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.080000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.160000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 3\n",
+      "number_of_masks = 0\n",
+      "number_of_masked_volumes = 0\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2]\n",
+      "LIST: Direct_children for Volume           0 = [1,2]\n",
+      "LIST: Intersect_check_list for Volume      0 = [1,2]\n",
+      "LIST: Mask_intersect_list for Volume       0 = []\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [1,2]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = [2]\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = []\n",
+      "LIST: Next_volume_list for Volume          1 = [0,2]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  2 = []\n",
+      "LIST: Direct_children for Volume           2 = []\n",
+      "LIST: Intersect_check_list for Volume      2 = []\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [0,1]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [1]\n",
+      "LIST: Next_volume_list for Volume          2 = [0,1]\n",
+      "      Is_vacuum for Volume                 2 = 1\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_zx_I=1449.21 logger_space_zx_ERR=4.41242 logger_space_zx_N=111903 \"logger_zx.dat\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.settings(ncount=2E6, output_path=\"data_folder/union_masks\")\n",
     "\n",
@@ -119,9 +349,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space_zx\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ms.make_sub_plot(data)"
    ]
@@ -152,9 +402,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 446.4x129.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 429.84x316.8 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "window = instrument.add_component(\"window\", \"Union_cylinder\", before=\"master\")\n",
     "window.set_AT([0,0,0], RELATIVE=wall)\n",
@@ -183,22 +456,312 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_99\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:11875:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:11875:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "13 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_99\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 1\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 0.347892 \n",
+      "number of processes [0]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 1\n",
+      "\n",
+      "name of geometry    [0]: wall \n",
+      "component index     [0]: 5 \n",
+      "Volume.name         [0]: wall \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.347892 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.100000 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.150000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: wall_vacuum \n",
+      "component index     [1]: 6 \n",
+      "Volume.name         [1]: wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [1]: 1 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.000000 \n",
+      "Volume.p_physics.number of processes [1]: 0 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.080000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.160000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: window \n",
+      "component index     [2]: 8 \n",
+      "Volume.name         [2]: window \n",
+      "Volume.p_physics.is_vacuum           [2]: 1 \n",
+      "Volume.p_physics.my_absorption       [2]: 0.000000 \n",
+      "Volume.p_physics.number of processes [2]: 0 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: 0.000000 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.090000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.170000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: mask \n",
+      "component index     [3]: 9 \n",
+      "Volume.name         [3]: mask \n",
+      "Volume.geometry.shape                [3]: box \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: 0.000000 \n",
+      "Volume.geometry.center.z             [3]: 0.900000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 5\n",
+      "number_of_masks = 1\n",
+      "number_of_masked_volumes = 1\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4]\n",
+      "LIST: Direct_children for Volume           0 = [1,3,4]\n",
+      "LIST: Intersect_check_list for Volume      0 = [1,2,4]\n",
+      "LIST: Mask_intersect_list for Volume       0 = [3]\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [3,1,2]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = [2,4]\n",
+      "LIST: Mask_intersect_list for Volume       1 = [3]\n",
+      "LIST: Destinations_list for Volume         1 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = []\n",
+      "LIST: Next_volume_list for Volume          1 = [0,3,2]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  2 = []\n",
+      "LIST: Direct_children for Volume           2 = []\n",
+      "LIST: Intersect_check_list for Volume      2 = [4]\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [0,1,3]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [1,3]\n",
+      "LIST: Next_volume_list for Volume          2 = [0,1,3]\n",
+      "      Is_vacuum for Volume                 2 = 1\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for window with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  3 = [2]\n",
+      "LIST: Direct_children for Volume           3 = [2]\n",
+      "LIST: Intersect_check_list for Volume      3 = [2,4]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [0,1]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = [1]\n",
+      "LIST: Next_volume_list for Volume          3 = [0,1,2]\n",
+      "      Is_vacuum for Volume                 3 = 1\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 1\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = [4]\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = [0]\n",
+      "      mask_mode for Volume                 3 = 1\n",
+      "\n",
+      "List overview for mask with box shape made of Mask\n",
+      "LIST: Children for Volume                  4 = []\n",
+      "LIST: Direct_children for Volume           4 = []\n",
+      "LIST: Intersect_check_list for Volume      4 = []\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [0,1,2]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = [1,2]\n",
+      "LIST: Next_volume_list for Volume          4 = [0,1,2]\n",
+      "      Is_vacuum for Volume                 4 = 0\n",
+      "      is_mask_volume for Volume            4 = 1\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = [3]\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 1\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_zx_I=1310.62 logger_space_zx_ERR=4.19615 logger_space_zx_N=101182 \"logger_zx.dat\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space_zx\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ms.make_sub_plot(data)"
    ]
@@ -213,9 +776,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 446.4x129.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 431.597x403.2 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "o_window = instrument.add_component(\"outer_window\", \"Union_cylinder\", before=\"master\")\n",
     "o_window.set_AT([0,0,0], RELATIVE=wall)\n",
@@ -251,22 +837,409 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_100\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:12367:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:12367:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "13 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_100\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 1\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 0.347892 \n",
+      "number of processes [0]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 1\n",
+      "\n",
+      "name of geometry    [0]: wall \n",
+      "component index     [0]: 5 \n",
+      "Volume.name         [0]: wall \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.347892 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: cylinder \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.100000 \n",
+      "Volume.geometry.geometry_parameters.height [0]: 0.150000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: wall_vacuum \n",
+      "component index     [1]: 6 \n",
+      "Volume.name         [1]: wall_vacuum \n",
+      "Volume.p_physics.is_vacuum           [1]: 1 \n",
+      "Volume.p_physics.my_absorption       [1]: 0.000000 \n",
+      "Volume.p_physics.number of processes [1]: 0 \n",
+      "Volume.geometry.shape                [1]: cylinder \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.080000 \n",
+      "Volume.geometry.geometry_parameters.height [1]: 0.160000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: window \n",
+      "component index     [2]: 8 \n",
+      "Volume.name         [2]: window \n",
+      "Volume.p_physics.is_vacuum           [2]: 1 \n",
+      "Volume.p_physics.my_absorption       [2]: 0.000000 \n",
+      "Volume.p_physics.number of processes [2]: 0 \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: 0.000000 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.090000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.170000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [3]: mask \n",
+      "component index     [3]: 9 \n",
+      "Volume.name         [3]: mask \n",
+      "Volume.geometry.shape                [3]: box \n",
+      "Volume.geometry.center.x             [3]: 0.000000 \n",
+      "Volume.geometry.center.y             [3]: 0.000000 \n",
+      "Volume.geometry.center.z             [3]: 0.900000 \n",
+      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [4]: outer_window \n",
+      "component index     [4]: 10 \n",
+      "Volume.name         [4]: outer_window \n",
+      "Volume.p_physics.is_vacuum           [4]: 1 \n",
+      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
+      "Volume.p_physics.number of processes [4]: 0 \n",
+      "Volume.geometry.shape                [4]: cylinder \n",
+      "Volume.geometry.center.x             [4]: 0.000000 \n",
+      "Volume.geometry.center.y             [4]: 0.000000 \n",
+      "Volume.geometry.center.z             [4]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.110000 \n",
+      "Volume.geometry.geometry_parameters.height [4]: 0.180000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [5]: outer_window_Al \n",
+      "component index     [5]: 11 \n",
+      "Volume.name         [5]: outer_window_Al \n",
+      "Volume.p_physics.is_vacuum           [5]: 0 \n",
+      "Volume.p_physics.my_absorption       [5]: 0.347892 \n",
+      "Volume.p_physics.number of processes [5]: 2 \n",
+      "Volume.geometry.shape                [5]: cylinder \n",
+      "Volume.geometry.center.x             [5]: 0.000000 \n",
+      "Volume.geometry.center.y             [5]: 0.000000 \n",
+      "Volume.geometry.center.z             [5]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.090000 \n",
+      "Volume.geometry.geometry_parameters.height [5]: 0.190000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [6]: mask_outer \n",
+      "component index     [6]: 12 \n",
+      "Volume.name         [6]: mask_outer \n",
+      "Volume.geometry.shape                [6]: box \n",
+      "Volume.geometry.center.x             [6]: 0.000000 \n",
+      "Volume.geometry.center.y             [6]: 0.000000 \n",
+      "Volume.geometry.center.z             [6]: 1.100000 \n",
+      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 8\n",
+      "number_of_masks = 2\n",
+      "number_of_masked_volumes = 3\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7]\n",
+      "LIST: Direct_children for Volume           0 = [4,5,6,7]\n",
+      "LIST: Intersect_check_list for Volume      0 = [1,2,4,7]\n",
+      "LIST: Mask_intersect_list for Volume       0 = [3,5,6]\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [3,5,6,1,2]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for wall with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = [2,4,7]\n",
+      "LIST: Mask_intersect_list for Volume       1 = [3,6]\n",
+      "LIST: Destinations_list for Volume         1 = [0,5]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = [5]\n",
+      "LIST: Next_volume_list for Volume          1 = [0,5,3,6,2]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 0\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
+      "      mask_mode for Volume                 1 = 0\n",
+      "\n",
+      "List overview for wall_vacuum with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  2 = []\n",
+      "LIST: Direct_children for Volume           2 = []\n",
+      "LIST: Intersect_check_list for Volume      2 = [4,7]\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [0,1,3,5,6]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = [1,3,5,6]\n",
+      "LIST: Next_volume_list for Volume          2 = [0,1,3,5,6]\n",
+      "      Is_vacuum for Volume                 2 = 1\n",
+      "      is_mask_volume for Volume            2 = 0\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = []\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 0\n",
+      "\n",
+      "List overview for window with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  3 = [2]\n",
+      "LIST: Direct_children for Volume           3 = [2]\n",
+      "LIST: Intersect_check_list for Volume      3 = [2,4]\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [0,1]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = [1]\n",
+      "LIST: Next_volume_list for Volume          3 = [0,1,2]\n",
+      "      Is_vacuum for Volume                 3 = 1\n",
+      "      is_mask_volume for Volume            3 = 0\n",
+      "      is_masked_volume for Volume          3 = 1\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = []\n",
+      "LIST: masked_by_list for Volume            3 = [4]\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = [0]\n",
+      "      mask_mode for Volume                 3 = 1\n",
+      "\n",
+      "List overview for mask with box shape made of Mask\n",
+      "LIST: Children for Volume                  4 = []\n",
+      "LIST: Direct_children for Volume           4 = []\n",
+      "LIST: Intersect_check_list for Volume      4 = []\n",
+      "LIST: Mask_intersect_list for Volume       4 = []\n",
+      "LIST: Destinations_list for Volume         4 = [0,1,2]\n",
+      "LIST: Reduced_destinations_list for Volume 4 = [1,2]\n",
+      "LIST: Next_volume_list for Volume          4 = [0,1,2]\n",
+      "      Is_vacuum for Volume                 4 = 0\n",
+      "      is_mask_volume for Volume            4 = 1\n",
+      "      is_masked_volume for Volume          4 = 0\n",
+      "      is_exit_volume for Volume            4 = 0\n",
+      "LIST: mask_list for Volume                 4 = [3]\n",
+      "LIST: masked_by_list for Volume            4 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
+      "      mask_mode for Volume                 4 = 1\n",
+      "\n",
+      "List overview for outer_window with cylinder shape made of Vacuum\n",
+      "LIST: Children for Volume                  5 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           5 = [1,3]\n",
+      "LIST: Intersect_check_list for Volume      5 = [2,7]\n",
+      "LIST: Mask_intersect_list for Volume       5 = [6]\n",
+      "LIST: Destinations_list for Volume         5 = [0,1]\n",
+      "LIST: Reduced_destinations_list for Volume 5 = [1]\n",
+      "LIST: Next_volume_list for Volume          5 = [0,1,6,2]\n",
+      "      Is_vacuum for Volume                 5 = 1\n",
+      "      is_mask_volume for Volume            5 = 0\n",
+      "      is_masked_volume for Volume          5 = 1\n",
+      "      is_exit_volume for Volume            5 = 0\n",
+      "LIST: mask_list for Volume                 5 = []\n",
+      "LIST: masked_by_list for Volume            5 = [7]\n",
+      "LIST: masked_by_mask_index_list for Volume 5 = [1]\n",
+      "      mask_mode for Volume                 5 = 1\n",
+      "\n",
+      "List overview for outer_window_Al with cylinder shape made of Al\n",
+      "LIST: Children for Volume                  6 = [2,3]\n",
+      "LIST: Direct_children for Volume           6 = [3]\n",
+      "LIST: Intersect_check_list for Volume      6 = [2,7]\n",
+      "LIST: Mask_intersect_list for Volume       6 = []\n",
+      "LIST: Destinations_list for Volume         6 = [0,1,5]\n",
+      "LIST: Reduced_destinations_list for Volume 6 = [1,5]\n",
+      "LIST: Next_volume_list for Volume          6 = [0,1,5,2]\n",
+      "      Is_vacuum for Volume                 6 = 0\n",
+      "      is_mask_volume for Volume            6 = 0\n",
+      "      is_masked_volume for Volume          6 = 1\n",
+      "      is_exit_volume for Volume            6 = 0\n",
+      "LIST: mask_list for Volume                 6 = []\n",
+      "LIST: masked_by_list for Volume            6 = [7]\n",
+      "LIST: masked_by_mask_index_list for Volume 6 = [1]\n",
+      "      mask_mode for Volume                 6 = 1\n",
+      "\n",
+      "List overview for mask_outer with box shape made of Mask\n",
+      "LIST: Children for Volume                  7 = []\n",
+      "LIST: Direct_children for Volume           7 = []\n",
+      "LIST: Intersect_check_list for Volume      7 = []\n",
+      "LIST: Mask_intersect_list for Volume       7 = []\n",
+      "LIST: Destinations_list for Volume         7 = [0,1,2]\n",
+      "LIST: Reduced_destinations_list for Volume 7 = [1,2]\n",
+      "LIST: Next_volume_list for Volume          7 = [0,1,2]\n",
+      "      Is_vacuum for Volume                 7 = 0\n",
+      "      is_mask_volume for Volume            7 = 1\n",
+      "      is_masked_volume for Volume          7 = 0\n",
+      "      is_exit_volume for Volume            7 = 0\n",
+      "LIST: mask_list for Volume                 7 = [5,6]\n",
+      "LIST: masked_by_list for Volume            7 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
+      "      mask_mode for Volume                 7 = 1\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_zx_I=1135.73 logger_space_zx_ERR=3.90617 logger_space_zx_N=93991 \"logger_zx.dat\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space_zx\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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fgJ8GXh1CeGSKPk80s2cX7y8GHgIeCiFsCyGcFkI4I4RwBvCPwEtDCKONczwDuIsoofwd4HXATjP72RkOlRDCQWAM6E27eoG9IYRpJZxHg2L2hBBCCCGEEAsWM3suUZJ5D/DXKd7u/hDCJWZ2O/COEMJu4MeBj5vZjwOPEg29i0MIUyVjaePxwH8KIdwCEELYY2avBp7eMq4PAK9JbXeZ2YMhhOem5l8HPmJm7wC+DbzpCD/2jLAj+2yLB7PTAvzaXA9DCCGEEEKIw7D1CzNNUDLXnGMWDhv0doS8BBbM559vSMYphBBCCCGEEIsQyTiFEEIIIYQQs8IyZh5nJ44/8uwJIYQQQgghxCJExp4QQgghhBBCLEIk4xRCCCGEEELMCpJxzi/k2RNCCCGEEEKIRYiMPSGEEEIIIYRYhMjYE0IIIYQQQohFyJwbe2Z2jpn9jZndk7bPaemzzsx2m9kjZvbeRttJZjZoZveZ2b1m9qsnbvRCCCGEEEIIx2P2ZvNHHD1zbuwBHwQGQwjnAIPAH7X0+TLwZuA9LW2/BJwNPAf418BWMzvj+AxVCCGEEEIIIRYGc2rsmdlyYA0wknaNAGvM7KllvxDCvSGEvcD/aznNpcB/CyEcCiF8E7gFeP3xG7UQQgghhBBCzH/muvTCs4EHQgiPAoQQHjWzr6f935zhOU4HJov3X03HCyGEEEIIIU4gJwFPmutBiMxcG3snFDN7C/CW+O7JczoWIYQ4dp6ftn+Xtm8v2t5V7zN4UefhG3+U2h4bt9uLtk1xs67/0wAc4JzcNGn3xxf74jmXd38VgMfz/dznYZ4AwMGu0+OO/T+qzt372Po41nYOzVneH899cPz0zsbVd0x9YP5OhBBCiKXLXMfsfQ14ppmdBDHZCnBa2j9TvgqsKN6fPtXxIYTrQwgXhBAuID2ICCGEEEIIIcRiZE49eyGEg2Y2BvQCN6Xt3hR7N1M+DrzZzD4JnAq8Gvi5WR6qEEIcR5oeOsheun11L9i67k/n16NDyVu39qLOtvFr6pdYX7xOXrZ1IXrGRvteCcDyia/mLj2MpWvEts3978xtAz7eXXFzkBavm7Mzbka635B39Y5/Kr5Inrl1wz9KY35lx+E+jgPdlddwPTfHcexrfMZd1csV/WcCMDl0XtyxsfAsuidzY9MzKG+gEEKIxYWFEOZ2AGbnAR8BfhL4NvCmEMIBM7sdeEcIYbeZrSU+MjwJMOA7wK+EED6bvIHbgXXplAMhhOsPf93TAvzacfhEQghRMJgMkmRYLA/P7ehycGhqY8lllKNd0RC6e+Ls3HYjlwGVQXQl13Wcc0X/F4FCegmsCMkQGk+G0LWp4arquiu643Hnck+8fmmIrY6G3+Y0fQwkw3Jzd2UQOgN96fOPVMbWSIiG3xg99T5jxYENGWl5/T3dXQCs6ZuoHbd5ojBIG2MaKIzfjs+WDFr/rkqyYdlVGJb7ZSQKIU40W78QlWnznx6z8Gez7E469f+xYD7/fGPOjb25QsaeEOKYcUNud3r/+ZY+PWk7Fjel9yzHsyXDps3YaBoke/q7ctsWtsU2Sx6+IhZu+fBX0+Xjhd17B7BiOF6nI65uU+fws7G1oTp+3Q2fTh8pfrhsrJbes5WPrZ2z9Azu5NLaNbJBVXojtzUGUsb17arv83MPDDU8fVTfqX+PAFewA4DevuhhbH5XUBiX7hHdWRtwfZ+Pdazok2MU34UQQhw7MvYWyuefb8x1zJ4QQgghhBBCiOPAksrGKYQQ0/P86uUVyVt2VXq/q9m38Ci5R6oWM9aQCibPWplVcsVE3ZNXSi090+Xkrii1dA/bmqGJqo9nz9yXtoU3zK8zui1uR4Yv6Rj/Dq4A4IqJK4HK0wXAlo7uGfcyZm+bf+7ByrPY8d0UZPloYmB97FN6Pa8Y3lE7PjzVcputrStS9nJ+fTzFmCa74rUme6pr3rn9VQD0Nj5j/lxQeSk9ZrL43fo4D9ozANgTVgOFrBRYN5HiIZO3sRbz6N+JewizLFRyUCGEELOLZJxCiKWLyzCbRgtkQ8pj5Loeug+AM0/Zn7tko8WPazEIMw3JIVRGisshXV5Ytm1LVpdLNseyLhQO9p1eH39p7CRDwg1KN3qgim3LsXKNMdZeu0HSU7SNNEo2+HlLg8bP/bh0yA2VsdnbFY3K8L5owG25aGs8pjQMr01bl8YWMkpPRJPlmEPxfCP9xTXSvg6jDTpkoNMa8i4N3d7ZJ/edaJGRTneNhpHpn6cWF9mUsRYxj5KGCrEUWTgyzjUnWfirx8/uOZ/wPck4jxbJOIUQQgghhBBiESLPnhBikZJKFwx2yvCczcPRI+NetJzohKIsQcqC6Z6lWnkDT3riXpciQYonQbn/oZUALPutdK9tS+KSqCVvcbln8qztmUgZKMcLqWB3veD5pJ1VfbYQvT85i+T+qZOndHjxKDyCqXRB6bVr0paNs02+mc+dsmF6YhaXk9YSpPjvwn9/LYXXs0cs/Y7WTVS/m/PZWx9jS/IWT6yz+YZOyelI9yW1sX32jlflNrs//S4bf1Prhqvr++8ky3i7Cq9d43v3ovQl/l24Z/cSPpnbJvuSl3Zk6t+JEGKxIc/eQvn88w0Ze0KIBUyKsetNdeaGOx/2L00GhT8sewZKqMfPAfVadC4bXN0iA0zkEgAeR1caJKk8AV4LLhkGZcbNbAh4psvSEGyUHnD55gv429zltvHXA5WR5YYJdNbJK6+b5acNqWDb99dqtDVlq2P1MZfjztLElnhGN/Z8rLn+Xtm/xchzY9GN2xUhSmzLWEDv45TZODsMMR/jtcUBV6Vt+psoDcmOeoA+1iIb6aEHfwyAZf/YOcf678v/NstYv9xnuP47/ec7VuQ2uzGdc6zjsAplAxVikbFwjL0LHmNh90/M7jntQRl7R4tknEIIIYQQQgixCJFnTwixQHh75y6X+LlHZWXhfWtI5VqTYDS8bdu4Oje5R6XDi1N4w9wT5p4i91RBS/KTabJbtiV48XO7/K81CUjz+ILQE5Of7PwPrwbqXrOm/LGWfMUZixuXc5bfTS6GniSi7vXq+K6g8pYWXr9mPcG2wu0ubcw1/AoPXx5/MytoQXigkfylJXnKxcMfB2Cc56WhVr8/rwXoGVI3hypTZpb9Nq9feIY7vpPSi5pkv1lquyGO7dB7q4yjTz9lEqiS0NSKuleD7Dx3vsYdtXEPWJFplhenrbx+Qiwc5NlbKJ9/viFjTwgxj6gMuuXhG0CRcbLFWMoyyvFOGZw/ALscLksF7WNVn2Qs3t0fM26+jffnpmwINePaygfrtiyYTjOLpfdpyerYLMEALZk+m9ekkgNmqWU5trG0bTEIPJ6wGU9WjsOvf+hZ0QBpkyN6XJsbkrWYvYYhWJINoNU/qjcUUtn82dxIGbyIqfAsnLXSEYlD29P4d04917nktDbGZqH6lt+xxzF2GH/FmK7kOgCu48rcNtawrrMh2pJN9dCuNP5Tf1i19TYkxWNxU/v9WTJc93UaiVkG6xlaVfpBiAWAjL2F8vnnGzL2hBBzSDLu0sNrGTPWfNjOnq6yFlrDo9IaV5WMpT39jfg6OmvBlclXTuYRAB7h5NqIy+s349Lu7j47t63qu7f+UZOxWkvw0vSEtXmG0vX8WmXikRzz5cZuGXPoRqobi6WxPFVZhp6iz1j9PMv7i+QxFr1NK0LyfqUkLtMZsrkPlWfPr+vlLV7OZ3Kf3H+a2L0Oipi5ZoxlzRBtM66oDFuoPGteS6/OXQCEd7wMAHttmkfbYj6d1XdUr6cyXEvDfixte+gYq39/Hn/qCyJuxEP1/fnv7fF8v2rzBC+NBZTad2S+8CLvnxDzgwVk7P2Yhd1Pnd1z2tdl7B0titkTQgghhBBCiEWIPHtCiOOMxwqlOKGW4tbutSqLiueYueTZcg/Fwa4ig2ZP3LhU0+OsoO5JAiqvTymBG0vbTXSSvFQeK+e0xk6lcZSeFaeMA4NGLF8qOI6vVbZ5dvan6/em8gbDLd6XlkyhWQbZFuPnHrj0ubOnrchm2ZTBVp4e2BNWA1WG07aYO5d4buHd8dxFUfeRiSS73JBklzumjrnMY+srxpa8XO6tvbn4vTv+t5C9X0PV303+Wyr2QV3O2YxnrHmd/XOWnjwaMsr095e9v0Mt8YyN65byzoP2D/GFewFLr2nOFJv+Flam33H5d9yMVe0r4iGH0+dPHsHsNW+JZ83X3a+i7kLMLfLsLZTPP9+QsSeEOK4sD9Fo+6eHYur4ZWuLe05L7TrHH7ZzzFI6rnygzkZGepCtydhSYg0vy+CUD+1uXJ7LAQDW2L7c5skzmvFYbmBAlTK/9WG5GbM3lrbTPZC3PGx3GCZtckaXCBafdWS4YWyVxlKS73kSFP8eytINOVbSDdKyLET6TJ4Exi5Mv9O2eEanTWKZYva8pmFJNo78uMHCEPRztySmcbJstyWesxmzliWzRS08r3nosZ51QyxJO9PCQetiQ/r7Wx6eG4/pqwzL8m+wpDVBjR83Un1/uQZk04CcrhZhUUNyJLwhDn+8EePYJgPeGK91d/j3uWmV7Y8vfOFkRGUehDj+LCBj72QLu581u+e0L8vYO1ok4xRCCCGEEEKIRchj5noAQojFQIuc8Nq4OZgcYTdPvDq+6CkOc09Ccr7Ukp8kb8uy8VB772UOoEpWMjoSr3vucOX9mOxN50peB/dmlB6apmdjc+jMJtksS7BmV6enyD0yLl0E6N0Uz51lfH31RCtQfd7RbUmqOlwkQdkWPTo9/WOxz9okuWtL8LLvoo62ZsbH0pvkbdlrNJyu2SKR9eLi7mkqj9vyH7bGHUOpofQs+Wv3OpZJSa6NGy88fibRU1RKb/P3tit9t/3FdzuUsn/21yWmbR5C/07Kv5tcYN3/7lziWCRV8c84uqUu9QRgX/1tq1R2Zfws/vdfenSb3797Yemu+uQEQcNjaRzV/0Yeb/qOp8tGOvo4P676bvLfvXtiPVFLM6kMwMr4e1s1XiQc6qV2fbak//v1xfewvy5/FkIIMTdIximEOEKKel3JyMgPjWNFN39wbNSy8xgqgIPjKYugy+rKOLuNdSPt0oduAdploH58fogH7nzoVQBceMqtQPWAXIt5a9TCq7V5OYM0fi/PUD70+nU9Lq986J8qLqxGeljO8XXFeHKs3FB9HDWDqhF7V2tLv5MsMS1kfFl+5w/5yej2WDqoSlR4fF5NDtmUT/o4eop9PpbdabujkHEmSWYzm2kNXwjY2XhPJeW9/6GVAJx5Sqex2Pxu3DCEzuyrbTJg/1tyI/HNtjK3/UduT5/jovpnLej4my5i7nKG0iSt9etO2lm5T7g9GsJnXDT1Z8u1F9tKbzSyv5Yxe1MtgOTzQMffm38eqP+fQbXoUju+Ec8o40+IY0UyzoXy+ecb8uwJIYQQQgghZodl0KhaJOYQefaEENPjmf5emN5fVbT56n3ynpQSxW3JtbBmQ0P2WBzfTExSq/PlXq6GVM2LVENVO6wtG2RHNsE0/j03FAlWkrcqe2HKbJSJUpoJ9SQmrR6pKY7340o5oTO5IV33qmJnI1Np9swNdmYzZaRTvtj0OrV9t+718zGVSVyabaU3p6N/w0MIVYITp1avLsl9mwli2jxLbXXifCzZo9T4W4Hqe8tj9YQ90OGRbqvhOK3X0WnIIGtSV/+8Lm0uvaHpb9K/I/dwt507F4Uvi9o3/7Zb/v+a3sra3/a1advIAlvKiKf73/K/6ZzYyGtXlt9/dz3DaM3DvVFePiGOnAXk2Xu8hd1nzu45bb88e0eLjD0hREVv8bDtD/Cr69kQa7FTKUbIH5bbMhbmzIGNIufQGc9WYyxtGw+ypbGUH2D9nLup8Cmhkbmx9kCcxpSNvb5OY8+/By+YnjMRAsvDN4DqYb0WTzdU/7ytReGbBc9bsiGW3zdU8WqtbOyUSrbSGFPrA3lTmlk8yJcF1gGu48raeaCSn2Y5a2HQuOHpmU6dsmB8Po+XfOidQTbOtkyljYynUMRPNn8n5fmaMsji99YsA9KUZZbXbRaQByrZbfqO8/Etiw3+N+Exq+X4O76Hoki6F4hftillsa1lqk1y0Yact5Rq+ljcgPXfMRSS6p2hPo7y73d//b5Ry2LbNA5zwfm/QwgxFTL2Fsrnn29IximEEEIIIYSYHQzJOI8TZvZk4A+Al4QQnj2TY2TsCSGyR2/a1f+0Cl/KGN37NroheRquqppWhHiu7IVwD1lKdALwcj5TO3erZyq15cybXS1ewB46xp8/R0Nq2FbLLCe/KGqZ5VpkyZP2w+4fS5/rvtwnJ5tJnhXPqllez8njLrIy7hlOteBSvb6ygLtnA+3dGBOleN2/GsmTufmGlKhjV0uCjYaHCmCUlAW04dksk4hkiaFn7CzaDjYyg/Z2farjs3n/O/svjJ9xdZXCcmBfPTGO/x5qXk8vnB7Sd9TVmQU1/920JBFxsgyx/LtJSV+aderKBD1eX8/rFJaf//H9SVKaPm/+XymydGaPoF93fym1vcgvEo9vFGCH4u+0Vsw8kWofetKZvf2pFmSRhGfZvrrXrUz+AncBMDJ8PVD9T5ffUa49mMaxpfvdua13bcO73JagZ/9daYzRW1cm+Gl66R/2WoRWnlRePiHEzDGzU4EbgbOAR4B7gV8LIXyzpe85wEeAU4EHgTeFEL6U2l4B/GeiyboM2BpC+OQxjOu9wGuBM4DnhRDunsk4piKE8B1gg5ndNeMxSMYpxFLj7dXLhiHXls3Rccmbx1dB8ZDYlJVRyNfSg2xOk1/EZXXIJx9XHT+VDNPPC4XsrRE72HY9jzPyLJMAd4eYYdGNzj/lFblt1VDKuukGaEvsVZahNotblzTiqsrxZ0NkqPP7a8pO3ditFe72z+9S25WF1LEnbsJz4hP00945mZtyjJyXl9jf+IzAnu5kZLnkrpQ4NrI55sLtLdlU27J5No2ytoyVh3bFcedMm6WxksY5XaxlBy0xc9k4aRjmUBQ193IUPUVjQy7aFt/XLPnQWlZimr+bbMg1pK5QGHXp9+2LBJemrLAAN6e/E/97+codVTbRp10U/xaybLdNFtvM+Flmrm3cL1yWW/5tOrW/iSaN/41WqedGFWwXIrKAZJxPsLD73Nk9p41NL+M0s1OA7hDC59L79wCnhBB+paXvnwE3hBBuMrM3AhtCCC8yMwMeAv5tCOFuM+sG/gp4cgjhUHH8ycDTQwiTxb4nAk8KIXy9ca21wCTwl8ArGsZe6zhS21nAHzWG/tkQwntS+10hhBdP/62l68jYE2KJsa/F2PKH5cLAcy9ZMwV968NbW1mAqYykMvbLH6g3pNinGyoj4FO8pnaJLWlw24onczcgmskgoEow4WURWh+ah+q170pDNtciayQKKWPPmt9facRs42oAnsffA4XxWJK+o7b6cG44dBgdZS205nfaUp4gG5RWGXLrwo/SvkbSl9Kz2kiiUsZsNeu5hcuiYbb3osqguIS4EOrfjcc8Avw9z6udp5kMBooYt9Jb6GxM31eqAZe9ZmUtv4a3r2YQNr+3XY0tldf1APGJpVaTcXXj9+XXbfFM5xp4XcXxPXGTYwZbksD4uP3/wL9PqP62eu0tsW+IwTFlXGD2zLm3ru3vZnu9b/n/47/vvKDTlrwm7Wsu7ECx2LIh3W9+UFx/rP5ZL574OAC3Db0+78vnTL+3WoKhmgdQiKXCkjf2JoFvFbuuDyFcP2V/s9cCv9E0iMxsOXAPcGoI4VEzO4noVXtOOv+3gFeGEP7KzH4O+FAI4ZzGOX4aGAEuDiEcSNLKO4iG24emGM9XKIy96cbR5o1sOd+MjT3JOIUQQgghhBCzwzLqSp3Z4VszNXbNbBnwG8CnW5qfDTwQQngUIBlaXweeHUL4ppm9AbjVzL4H/Cvg5c0ThBC+YGZvBm43s8uB9wE3TWXoTcGU4wCmNfbMbBA4z8w+CAyEEO6frr+MPSEWKStClFTl1f6euGnLSpidZYWczaWVDw/HuLQs/aMebwWVF7CUitlgUg000tPXVui9YHi6fZfp/d2T594e99CtsSIwajBtu+Om9Lr553SpYvZ0tMSFuYdrdLCzzb0gB3edXvscUH23YWV0Ndj+SinR2/Op2nnc01GTYRKPb8tU6p4N/06umIieltLD5F6j7H3pqcbvssf1IUn7Qqdn0+MP/XddxgW6JzSPrbs4MGdqjR4uG/lhx/FND/CqXZVnM8emeRxl8++woLU4vXv03Fu1raW4uX+X3Y33UMkHm4XfCzyOspkVFgpPWkM+WsZz+nc61t/TcbxfP3+36X+kFjOb/rbW7EzjuLY6fMcNV9THkX7X60L1v529pP4dtfxtTe5q/P0V9K7+WP0zFceP7qrLjv1/s7w35P/tq+Kmdt9J3kb/n7z1oSgHXra2+v/p8OyWf3+D6dySeAoh2vlD4LvUggMOj5k9BrgaeFXy7P0b4GYz6wohfLfsG0L4czP7beAvgPeEED4wS2M/LCGEjcDGmfaXjFOIxYCXTCjrrfW2PABDXWqWYvZcTlbickmP/ckp8Ac7U+BnqVzxgH9ofb0+WLOmXm1fS+mFi4ejtOuRlNLLH15LeWdzbB5DBkUcWcPYnUlcYDn+q0/ZGq/hD68tcY1en6ysBZe/i8b3f3H3x/Pr27qSbC090JYlDVprz1HF0kEhlUyGRZux4A/7tbT6ze9kjNo4oPP39+Huyki9fOjm+mdL38PIcFGWw43SNkOmWTKjpXRAc5GgrSxAk+nKK9Qkui6pbJQHCP9c6QNDUq3mmMENxTU/n7bpu/UyBxeecmvu4n+vWXJZlMzoiPFLn9+/c6gMHzeIyrIUvmCQjaxp/jabcmwo/t9GGlLrQgbcvG7+/4fqHtD8va3ulLFm3lu8fiGtlHX+cmIgP3cZQzwWNx0lL2r1JpXgRSw2FpCM84kWdj9vds9pn59Z6YWUEKWbKLF8pKV9OhnnCuCPQwhdRf/9xMQp/7txnqcB/wO4DegF1ocQprzxzLaM80hYNpsnE0IIIYQQQixhlgE/Pss/M8DM/gvw08Cr2ww9gBDCQeKSUW/a1QvsTQbWPwLPMrNz0/lWAk8H7ivPYWbPIKY1Hggh/A7wOmCnmf3szEZ62HHMKvLsCbGgeX7ctHjxOhKKpFX7sjize+880UmbxLKj8HmL1K7Ne+MeDaeW4KJxnKe8r3mx0njz6n2Sqnkhc4B/emgFUHlU2gqWu7fJr19+RpcqvsNeBcATQiVDdRlp9r4kyeLmYuEue1Qa14TOsgrZe1YkynBvlZdwqEkVHRehpOM8YQq0eP8K70fHd9ritclJdxrlKWr9PflJb5H8xL2j7uFyj2Dx9+fZTz3pR+27amRhbEtU0yEtLGWYDY+ivy8L0Oesq8l7XS8rkjyTg/XfX+34ZvH6loLr0/39+t+Zf+5aFtnk9fV9/vtv9Qyn78pLYUBVFqJMaANTJKFpftdQyVavStv0t+2yUCi8hpbuMVR5APz31MzCW35/WVqcfo/+9wBFgp9pSmdkaXH6bi/jxty2qitKgjsyhRZ/v1X23bekPfL0iYXOAvLsPcnC7ucfvt+RYP/zsNk4nwvcTfSWPZx23x9CuMTMbgfeEULYnfqeRyx58JPAt4meuwOp7ZeIdxPPvnlNCOGWxrV+ipj585ZiXzcxQ+doo+8HgNcQjcZvAQ+GEOvNTDeO2UTGnhALjcYDKtCZ+RLqD6clpdTLswruu6jjmJxFMD305ux6hZzLY7TaDAo3Tjqkmm0PZP5gXarre7xTvaxAaay6IeFGUtvDejZ67R/i+1TTCypDuC2bp7flbJjNNPXQUR6hzdisPmz8HG1xXR1SW4rvOxkLnpa+LGGQv+/Gdw2FIZcMgLLNjUT/vO9+aCsAy3a2zAfNzJflR5qoG9KtMr42Qy59Tx0xh2UtvJ7GxYq/m/w3lc7T/K6gJUNkKdHdniSqm0L93G1lNdoWOVY3ykp4DcByzP47bTG28v9W2wKIX78he65JVBv7WsfYkMGWn635P+FG3vqidENTGjrd35bHE959Q5VxddV4Msjc6O2qfjc5fraZDbb8/ofrn62sgdhh3DX+D8vj8u/61K0IsbCRsbdQPv98QzJOIYQQQgghhFiEyLMnxLwnLY95La8ic+DyG6bxOjWSZmTvWelxGkvnmfCV/mdUbe4ldG9No24YFHKsFu9Bfp2u0Sb1a9aw++fXrshNL/1Ekmb66r8XgC+9F80aeKUMsin1a+4v2Ri9Bp7BFDqLwTeTYkAlp/Pvwb0IUCT2mC5RSrMofAt7JhoeveLzuDSxrK/mZO/JNF6fDw/Hz3S53RR3lEXZXW7a8B6WZM9sU5YHnTLass5c41xtyXuav9s2vPZgx2el5bspZYwNb2M+bqzo06zv11JDL//dpTqRWRYJLTLkKjO21zl0mlk5a+Nv8f7m63oSk5FOGWbzc5deL/fsNusclklgnA6pMoUn1T36LTU0/e8813mcxuvdWm+w0efu/pY6jUONv7vyf70htS2TUFXS7HcixMJhAXn2nmxhd9tcewzY7fLsHS0qvSDEfGWwId/rS0bPWNXFH/Y8i11P0XhgOEqt/IEoxwz1V8e7QePHjfa2xIy5xCrduD3LJVTFtO3auGgUXl8ZO0aoHVfGLOXrp9ID/jnsmmrxaZ2Xx3EDZIxO0gPh4/tTrFMhA/WHw1VbWoqZOx5r6GUqimyiPt78sJuutXO4Mghdfuif8emnTOa2bHj2xI0bRldyXef1m1kFqR5O19j74vuUVv/O7a/KfW7m1UBR5qEtntL3lQbl/viQe/mYG65JYrm/qM+6upHOfnWpyYnxT71Zt5gemkc6L9870ujTcS4YXfnK2rgAWBm/94Mbo9SwtWB6+t5c/unvoTIytk14AfKPVcenv+ksh5yBsVHGiv4ZLwJg1Yb0t3UVnSSDNmdKra2r3lMbdzYsx6oePv7JtZ0y1Ov6UxmOLW5Ax+9mskrUWmUa3RU/64H+Kr4vy0fT9UYtfR+lRLzx+cuYx44+LcaeG5uTPS3xoI2FoLbSI01p7qq+4v84natDajvWOUSnNGRH8d+zS9JVukEIsXiRjFMIIYQQQgghFiGScQoxn9hXrKwn+WROguIJDtpqoZ3aWdTaZYdNGVgpi3PvU/aCjRer542kB60Sr5QooW3V3+vhHeBcYPpkFG11+nISC5f/NWV1VN43v9aWQsd150PRA+Z18rb9ftzaE6t7XvYepM9RSwKRvu+c8c89fIWcz2WAOVFMV+UZvXsifqcv5zOdA0/kGmz+Gcv6Yz9IW/eW+HV7C6llriumTIORwmPoCWU8m2jhGcweIfesuaz02urwQ++t1xmsZapt1D6sMj8W3sOG7LjMRune3exRb2S1BDq8Xy7nhUaSHor/n7b6gy0yRk+wkrPANmva0fk/2Zb8xPFz9/HRvO/yruQ1dm9tcW/rkHg2fw9U37dnzP3sHZVH+6UX1bPv+n2gNcFM/n+9qzp5+tvI/9s56VTRR/9TYt6xgGScP2lh94tm95z2Sck4jxYZe0LMKenhdF9npsP8sJVKDngWxzapnz8I2f2d/8/N+B4vCQDQ81CMK8ulC4qHuJxOforMd0BHpsHaA+FQQ5o1Vu8LLbE/1xbndmOncVybHLR8yHOa8kvPhllmhWwWai+NxVyWIpVOaJX6NcsTlBkrG99JLk493mnQV0hONjc04mKho2B4zdjrqht7bTJcJ//dFDFrOVNoMs5yxsqWvw3/e22WWYDKWMrZMYvFEh9/eE6SWj+tkEg3jd2ejlNXpUPcsGwrfTFNNtqOfcXxOR7Rx+sZX8vyHi7VLLKAOqMb0jm99IePf6zotL+exbc1U2qTWqZixfOJ+YaMvYXy+ecbMvaEOOEU3odGyQP31EGVFr75sFgaVNmAaDx8ApWX6Cpq12il8WAIxGo1gJ0cx+FxgZ6uHag/nEHhaYK8Mt7bGFtLCn1PGNHGudRLzuQ099TLMEC799Hj6nIMXVu9tJaYo2btwPxgWvRp1lJrPffqpmdBHoMFz8qGl93/Jgrj3b3s2aO8+YcdbbmGXboPtBk2rYZcMyHJDBIktZYsSZ5oT9BUq/PXSCxUY6pYvfL/xxdC0nf04f4q1vXyobQ40/RWjxXHxyFmo/NC7sxN15FiFhuJgdqS+bR5LR3//+1QQZT98+9UCzFirpGxt1A+/3xDMXtCCCGEEEIIsQiRZ0+IE0XyBvgqOlQr6x1xYVCteicZUu7TV/RprKi3pfVvFgUvZZC+ep9LL4xXMWfNwtV+nMf5QBVzlCWPQ5XXLZwZvYRnXJRKEKRV8zIuzsmfaazY6dLIhtejFi/ULFy9v/Isenr77P3sa/F+NtKz87iqad0N7Z+/rcxBloxp9X+J0+K1bytd0fTIXZu25Zr1xvrff5lNtXm/8L/Nr9yxMvfJku5GCQyoPGLZa90W65eOy3/3RemIkXB9Gn7MzJs9YiOFLNnlk005ZUHznlbG/k5uSOdsyrkL8n2r7d7W+Ew1GW7DE59LkPQVcvC2LLbOfkk8xVywgDx7p1rY/dLZPaeNyLN3tMjYE+J445KvnvR+rLPLyESjFh5FrJ0bYB1yQNgcoiQwlwAocGlkNnJa4lSa6c1LcsxNSsvu8UyljLQZR1QzhPY3apm11BLLNdg8scVg9UDon9+lZX7uMr2+x+q1ylh70rYp31pdGYT+0NpRrws6pWojesATs0VKCOJ/7y0S6xwz63F8ZQ25xv+yJzMq7wO+KNJxHjoXcvJiSU9x0rHGNSYqYzEbeSme2MtS3MmFuY/LLt0Q83sNwLsf2grAxCnx+LfxfqB+P/H/bU/QUlKr2UfLvYqq5mA2RMvP44b36s443o5rtCRGykZyNoAlzRYnAhl7C+Xzzzck4xRCCCGEEEKIRYg8e0IcL5qJSZqSQwoZk3um9hcyqLTqnz181lJcOp0rZwNskWOFB1I2voasqxxTueru5KQF7q1LiRbKJAi+ku8ZK2syrjR+P7ev0Jer9z7e7GloK2qdPqN/V+4VgCqJTfYQ9lWeUV99z+nlk2S2TFQxaimhi/+u5L0Tc8LbO/a4t6xKiFQUvE//W+7R81IOnrgECu9bI+MndMo/832jlCw2ks+UyWOapRv+6aEVAJx5yv7cJ59zdWfpiywRbSY/Ku6NU3rmi7HkRDP2jHi+lLEYivtWSlS1/IZO+XzzvlmWx3AvaYf3E7JnsMqY3JkNWIjZR569hfL55xsy9oSYTQoDr8PYcDnmto6j2tO7N+JY/IGojEvx1OtOm7HkEqe21O0ucfKMd16KATrri7nx11rmINWp8gdUKD5veoB0Y62n0FPl+nJOUZahlItCEXtXSk4bKeBLQ7bDAGzLWLhRxp2Y73Qagi4pLOPwoFHnr7HY4v+/UMTDeekClzWW96b0/5INmkKi3VFqouV/yxeFXE5eXt/pqPNXMpbO04jLK6/j98bWe1KjZMyhZ3VmOs6MNOIjy8/SWGyC4h7UzIpaLnYpflfMOgvI2HuKhd0Xz+457cMy9o4WyTiFEEIIIYQQYhEiz54Qx4InX/GadjuKld19jeQLLXKonFAgFeOuZeocqq92N4tzQ6dHzeVJQJZB5Tp9XkC5yAromTpzMhfrzCaYV/ank1p5NtDC6/invAKAVX331j5HLVFCsxZgCx1ZOMvkFJ484Yr0PX6+OG7CkzZ01t4TYmFTr8/n8sMywVPO5unyyZbkQ1kinu41nkEXKtm31/4svWF+T3HaaoDm+oDJI/jhic46ezmxSiqSPnJDkTE0fZbmGIF8D8pevxbPYHOMNfl48kh2FLwvvf7JS+fS0FbvYfLw5c9RKitcEqvMnWLWkGdvoXz++YaMPSGOmGkMojJmrq1QN1UMHYD9Zvr/S/LF1tThSSKUZYnFA11T6ljixlHHg1lX8UDiMYLJMK2VbmjE6k1X3sEpZag55i8ZiS4nu7R4aCpLNQDVAypVplGP9XMjtyZHa45RskyxlFlZyMiT1NKl3rX/+564ycbOeD27JZDvX24A5nsVLdLKlky/zftfTaKeF5VSHKIvjF1bHe6lT5wyA2czVtGpGYS742bFDY3FqqLNS11U5SXOqq7fyNDZ9v35Z8sG8c7iecq/Exl9YtZYQMbecgu7Xzu757QPytg7WiTjFEIIIYQQQohFiDx7QsyYliQkzZXtUgbkCVKaMsqShtfMZZXQktBlrHFMed2G9w+mroXVtvrt8qne8UIG1kjw4lLPWi2v5BHwVfuyllfHvkbCAyillklqVhRe9oQI7glc05W8gPs7a2KpzpUQUxHvW6WnKkuik1RxJLwBqO4ZUHjNG5knofDotyRGyjS9fW3ev8Se7pggKmf1LcaWKercNWXjbRlH/T7l99GafL4p23ycH1Rc0683Ulc/QPVd5DqfxX0v4x69nY33IC+fOErk2Vson3++IWNPiMOSsuH5ZF9O2j1p25KNbk9/V2qKlliOYSmlj22ZOZ2xuHE51jauBppxOemhIcm3amUF+hrGZUsK8w75aUs2vsz6+nigkkY1M+9B9QDkD1l3PvQqoMry2Xr98rt19jeLycuwE+LYSPe00oCCukHmizIeV/uDqskXsHKG3PS/XpYuyPcpP0+5SNV2PZj23tpqrG1sLPysLLJppuu5fDxn54Rq4Wx/3dhtW+xqjRl0pomL9IU7LwtRGouSdoqjQ8beQvn8843HzPUAhJi/1D15j+f7AExuqoy1ZmC+x5IArBlPq9Sr64H+5Qr5ucP31I6vefbSw4XH3OQHkTJByr5ratcojS2GqZ3br/spXlON0fbFFyvTjrL0QXcjZqYnnqctLvHx/d+niV/Pjc4zh2NZhxWndBqL2Uguksfk+lwy7oSYZVJZgJF6opdysSrXAG2UMJiOsWyhUSVPctZ2esY8idPL+QwAkztbYp7TAtTolmrxKhtw29PY0mXL+GRXUuzsjzG+m4c744ndM5fvreXn76+XiumlMASTIsLjige6Or8/v/+G238MANtVLKz7ApbHWMroE4sNA06e60EIRzF7QgghhBBCCLEIkYxTiJIim11efXWpk5dOKDJgZhlTVyqAXkocU3rvZuHfWnyHyyZd/lN67TY19rXFzrhnbLpMmY1rlOPPsXKDDTkXVKvUY2m7vzN2xWVbOT6wr1iZb0hbc3xN2cc/t7JoCjEPKDIN99alidNlCt4zXJesQ3VvcUWDy9ChiqfLcsy2sjQe4+z3nZZ7lCspXDLadm/2bL5lNs9Muq7fx9rikduyKmeJ5nQx2zT2lZ5Rl3HmLMJSMYiZsIBknE+zsLt3ds9p10nGebTI2BMCOuvlQS1GBSqJUE0qmejtShKfMi6l+ZCwsfOhxeP6LuGTQCXZhCrGL8fq9X2qNg4oHkjSM1YpVWo+5PjD2nT1olqNzQY+ZiikqomyrMRLL7o1Xt8NSsXcCbEAiQbgulDJMptGWhtuuPn9shZrnO4zLhXN9yorEjRliWRdqgnUa3VCXggrYwZ9AephngA0apA2St3k8hSlQZjG6At45X2zKW3tqAVa0FHLr+W67+dtAKyyyzqOF6JiARl7z7Cw+5dn95w2IGPvaJGMUwghhBBCCCEWIfLsCQHk7HSF180lQZ5F8sxTYoKR0vuWZZxDhTTT8Wxuw1HycycXAkVSFOjMhteWBKEp+SkovWwAF3Jnfu0r6nnVPMuiigx2KTGDpz53DyN0pl5vJpqBQpI5XVFl/0xKuCLEIiHdL1c27l9FgiX3duUsxG3y7UZx81KG2aRW+D0pGbLXbENqu6o4wO83KUOnexGh8sx1lMVpK2vTloXYP8sYteO8uDrAzae8GoBe+1jc0ZKNM7wvFay/MT6HueQfYNI6FRhiqSPP3kL5/PMNZeMUS5AiLqXxIOBZ2qB6AHjVRJysvV7d5L7qoeX87r31U5cxG+lhwbO5ZUNqmvi4Mhvnn/EiAFb13Bt3NFOhU9WeG5mI8iXPGAowYHUD1ssy+MMXwGRf3F4yHMdWkyptSnGIXh5hMH7ubAQCh3bFh5VcTqFWSyo9bI3IuBNicZGyee5vlHAoSrfk7JmNeqNQyDXTYpPfY8psns06ozVDaCzeg7IBmOrjbb7hXbnP3uCLXPF9GSvt53LZpd8bffGu1j8NKcdHF8dn+Wcax4Wn3Jr75HOl76aWaXlT/Gx2f7pvprmiZhDnTJ0un60+mxBCHAky9oQQQgghhBCzg0ovzCsk4xRLiEZNKag8cc3MkzCzIP6mfLEsSp7ack0ol3oWx+TA/uRFLKVC+Xoe9D9yR8f4PSFBWzY6XyXPGeNasso1s9nVaHy27OErV5/H0raj8DlIrinEEqPIZuz3suwRKwqge7KXZoZMv1dCpYjwDJl1RcJ5teM65JhU3jq/D17HlbntSq6rXaPjXgn5fulJr0qpvo/TlSAd9UJLXMZezA2ll7P8bLUapqsbCb2UsVgsJBnnaRZ2v3l2z2m/Kxnn0SJjTywdBjuNLTdgyrTgTvOBYNSiLGddkgeVuEFWkxo10pO3ZWXrMLZaHhaakqGa1KivESs41nm8G2LrQnzYqsW+JA49qyHHhCzJbBZX9jTrAGuskr0KIURFWlzrvaij5e7hswFY1Rcl6uGyFLv2zOr+kxfCZpD5s20hyu+bHfF1VIZgvheu7iwrk8+Z7uNlpk/PjFzFI9fvsfFyPQActGcAsDx8I7d5ZtBm+EA5/g4js1akXpLOpYmMvYXy+ecbMvbE4qc3TpYjw3GyXn/HLbnJHy7yg0VpCCXDy4Pul21K/ysjxaTrsSojRdITZzA+5Li3rS2ZSzMhQRmz4vF3f8orALiRzrTcfq48xp3V/3Ozrp0bm7W4mL7Ta+P3dOe1cboBOqIEK0KIo6WIlR6sG4Ad6oeC5f1f7dj3z3esAIryLum+PW2d0ccVJ7iKeptT3tuTRy18NxmiY8W91Q1Jj1/uSQ2lQZpee/KrNUNFmZoUf71nIrWl2Ou2JDKtRu5GxfEtTRaQsfcsC7vfOrvntM0y9o4WlV4QQgghhBBCiEWIErSIRUzKFJdWRt2jteWid+ceI0RvnxfgrWVMI3q9skcvr7AW2TQ9Lm+409u1kzOBatV5dFfyGhZynAP9MVbDJZou+YFKErTK/jDu8Fi9FqmnZ4Fzbx4UcSRj9b61uJQ0/nXDcUzbfn9rbhq4sCkfkkdPCHG0FPePjS+O2ySbLDNlOlmZ4Pex7VXbGROxDE6WPSY55f0Prcx9lq2P923PVFzGJWeJ5kjMsOwSy4Nri3tjwj6cyiIU8dSZHmrXr40/XSN79EoPXbqH57YUH54LyBf78nGlF9JLXux3b6nuzUKIqZGMUywuBovJslnnLb1vNYg2dsZsbO6Okppcp84TpRQTu/fxmIuHeUJu89ThHRLRtiB+f5Ap6lQ1x+tlEXw8tTEl6nWa7o/7QjQ6s6ypvH56ffHwxwG4re/1VduIpEJCiBPAviS1L+Pi2pJGNXCJpydf6e0qjvGkUc1apgUu7feFwMu4Mbc1ZfMDG4q5ZUe9hp8bfV5TFYrSEV56oqWGoEv7m8looLi3u7FXGLsfnojzzeV9qVSOJPZLBMk4F8rnn2/IsyeEEEIIIYSYHZZRj5MVc4o8e2JRMRLG8uucMS2tjLqHrPS+5Uyb7nUrioIvn4irtHdyIQBrxpPkpvCMHVFZBh9jW1Y3P1/pmWtkkWsriu6ypaefMgkUq8nQUYzYPXvujYSGbAhgv9J7CyHmiN4iedVwI7FVea9KCohm6Zm2e255v3NKDxpU2TFLqWYzsVXt3r6x7jU8tL2RxIvO+26NRmIY71tThiS5vXsEa9dvqEPytezmzmuJRcQC8uydbmH3b83uOe1KefaOFnn2xAInxeUl+WVvX9GUZIh3D0eD6EX8GdAwiLrH4tZrKpXZ0FJmuC3922p9SjnN5No0kXfHTSnV8cnZ97mRVsqTmvWW7nzoVfn1srX17J8dsh7g6v6t8TN5HF7Lw07zYaOW8a61Pp4QQswBI9X9d5R4nxrd0lmD1A2xXhpGXjO7JrB3OMnwi0zLLq2/gh3xPJs+VTsvVBLPSx+6BYBl7y3KQoR69uVlXbHNs2sCrOlLi4Mtdfaa2TzzYuGGaowrbmjECJYG3lgax0SjvuC+4t6+Wgt3QoiIjD0hhBBCCCHE7GDAyXM9iMWJmT0Z+APgJSGEZ8/oGMk4xYKmtyFDLAum99dlNG31jpoyzLJguSdd8cQoHXXnIHvdfKXXV4qhkgrV5DfUE8R85Y7odczFhFf/iA5yLb/OJDKtyV4SzdpVuYC7vaXopYB+IcR8JmWcLGrz5XtbyuLpnrpS4j5d8q3sZfN7aqqpV6o2XCrqMtCaIsLnmWYx9haJ/zauBhrJV9Kc4J+jTLrluMrDMy2XZI9kSkjjXs9c96/cJ2nnImIByThXWNj99tk9p/364WWcZvZe4LXAGcDzQgh3T9HvFcB/Jpqly4CtIYRPmtmpwI3AWcAjwL3Ar4UQvnnU455mTGZ2DvAR4FTgQeBNIYQvzfC8d4UQXjyjvjL2xMKjKs47Eq4HKqnN1adszW1elDbHXLhE5gfVmTYP17NpumFXHu94pjUvdg5VjEUz8yZUDx7N1OFtKbxbY/4a/bMcszQIWx4yMl4Wwh9WrKWPEEIsBFaW8vO4uLY5xMUqj+erST3T/TKXWSgMoVzWoGkIFlJLnxt8HnBZJxSx1kmimTMuW7VYKMTsI2NvBsbeWmAS+EvgFW3GnpkZ8BDwb0MId5tZN/BXwJOBnwC6QwifS33fA5wSQviVxjlOBp4eQpgs9j0ReFII4eszHZOZ/RlwQwjhJjN7I7AhhPAiMzsL+KPG0D8bQnhPceyMjT3JOIUQQgghhBDzmaeY2e7i/fUhpBX/RAhhF0C056blENG4g2jgfSOEcIhoBH6u6Pd54Ddajl8FjJjZxSGEA0laeQdwA/ChmYzJzJYDa4CXpF0jwHYze2oI4T5gRobcTJCxJxYQ0aPnq7kAveNxhXX9O+M/0d5PVHKYPcN12ebmG+JKbSmnyd67sbSjUnh2ZFPLBc/Luk0efJ+yeI72FEkAkkfRk7hk6ZFVy10rwn1AJcvJhdepVqmzjHRXGmtLLUBva6vlVF1P9fKEEAuUWqbgOBdkiboXTh8vshsntcPkruTR21Q15Xvx9qnDAFzt4YqOLby7ahyLmzKzMsDoYHH9japTKpYwyzgeMXvfmg3PZgghmNkbgFvN7HvAvwJe3uxnZsuIhl7Hg1UI4Qtm9mbgdjO7HHgfcFMI4UPNvtPwbOCBEMKj6ZyPmtnX0/5pZaNmNgicZ2YfBAZCCPdP218yTjH/iRN7R3HwkmRseXYyqGdfA4o4jSqTmhuOuXB6V3FMitlwGZBLLcu4kFL2CS2lDKBTMlQYi25QforXAEV5h3K8DdyIBVhj++J5ktFYolgNIcTiJkn696V4vlLOngw3l1i+n7flplW2P7alWOtRS8eXC3lu+KVz1mScU8TK1WSkLhvdL6NPzBYLSMZ5poXdLY9Dx4JdMfPSC2b2FaaWcT4GuBO4JoTwV2b2b4heta4QwneLfoPAM4HXJK9f23VeA3wCeE8I4T8cyZjM7KeBPw4hPLfoMwG8MYSwZyafc6bI2BPzn8FpkrB0171vy7u/mtua5Qi8LXvoIHvJmqm4Ac7lAFAkdJmibh6QDbOLhz+ed902/nqg8r5lg9KqRAOsjNdvi+Pzz+SrxznhS2nENg3CkXcihBBLi84kLn6/PvSszhp4zcQsy/vT3NBVlOVJcYHrQip9M95S5645J7TF/Hlil426N4tjRcbeLBl7FxCNrK5i335icpT/nd6/l1hU6+IQwiNTXONpwP8AbgN6gfUhhCmz3rUYe8uBe4BTk1fvJGKSluccS0KYNpbN5smEEEIIIYQQSxgDHjfLP7PHPwLPMrNzAcxsJfB04L70/r8APw28ehpD7xnEAsUDIYTfAV4H7DSzn53pIEIIB4mi8N60qxfYO9uGHsizN9fDEDNgT4hyxDVdycO2s2hM8s0yHiOTwnjD6+PKrt0f/9bL0gcuw+xJQRguz6mdM2XF9ILrlxbSzaaMssz4luNIiiLqQBHLAcvDN4D2eMCcRbRRTN3jTQAu40YAVtllCCHE0qbK1Jy9fOn+XUr8vcSOx+Xl0IDCM5djvtO8s3yiUo34fNFxnqIoe44Db8wjQCP+UIiZsoA8ez9lYfcsK5etd0bZOD8AvIZovH0LeDCE8Fwzux14Rwhhd+r3S8SsCy7PvCaEcIuZPRe4m+hxezi13R9CuKRxnZ8iZu28pdjXTczQOTqTMaW284ilF34S+DbRu3jgCL6WGSFjT8xfmvLNjY2aSFDJaNIkXcZVNOvcZanOeCHVaVLEfOzpT5O9x9H5g0Ah3fGEKDnWrzREr42bFTdE4zKXaWiRkebzFaUbspHXSO9d1gIcMH+4Ub08IYSoiPfG5SkcpiyZk5NejU+jM2vEgef7MXTINw+tjwuKZU28MrYbqphvKKX8iuMTR4KMvYXy+ecbknEKIYQQQgghxCJEpRfEPKOowumyl+QtGwlvAOqlE5rer1zstsAzXvpq7t7uqjyDHx96ktRz8w9z25q1yaPn3r5GmQWA0e3p+j3U+1KVenCJj3v0ysyZXnC9lJYejnpxdHn0hBCik3hvPGipVFWhorisO8rfmyVrXAUC5Ht6TsyypWrKc0Cam5bt7FRIjfQ3EmuV5x5M241SZohFyvEpvSCOEhl7Yp6QJr0y9fVY2qaJtZdoyO2ZqEoPjK6NE6hLHP09FBN4mqx9YvdJGOD84SiJtL40WVd2ZM70ua37agAufegWoJ7Vzc/Vax+LO9ZW43/3Q1sB2HlKNPY2h6hpyNnZqKSiW5JGtJbxLUmF9nQnOWmKD6SQcQohhJiOpCVbXe1ZNXgv0DlH1OLqeuLGsyHnezzt93Kox1O7kdeUc0IVUtDTnzJ9Hrb+sxBCHD2ScQohhBBCCCHEIkQJWsT8YGVaIe0p9o3FTS5cmzKdeZZKqOri9dpb4o59RZ2l1Smhi8t3PMFKIcfxVdvqkj008dVbl1rWA+0fW79uIfHM424UgXePYfmZcsB/Gfi/+g7qSOojhBDHzMpGYhZPrFXcv3MWzY3pPryymlvyvX2ofm+vkST9HkYwbd+UOZodytIppmMBJWg528Lu98zuOe01StBytEjGKeaWNOmGlSlmjmrxIWdBa2S6LGUxHqO3IrhBWJ06l0PwguvDUTpzJxfmPp5ps8yC6ZzfkEu6kVcWRV8X7kj7GoYlVRmGZgmGSYpJ3w3PXY0tVKnDVYxXCCFmj1z6wGPm0r22yKac5fNrO+cInxsGtiejsa28QiJn/txVyPeHG5mefZHv84URqvIMYiHjdfbEvGDOZZxmdo6Z/Y2Z3ZO2z2npc5KZDZrZfWZ2r5n9atG21cwOmtlY+hlsHi+EEEIIIYQQS4354Nn7IDAYQrjJzN4I/BHwokafXwLOBp4DnArsNbO7QghfSe1/HEL4rRM1YDF7uBzGLGXB7K3aOmrXJYnN5KbKM+beum2kJCrbb8ltV7M1Ht6dCt4mT9vNE5dWF0metCu6kxx0vMrmObr6ovqY+tN2ZeW9y16+lFhmc3e1GjuwKa7S5kLtafW3lI5u4d1xbLvSZ1LhXSGEOEEkafy2dB8vJP6eNMtl93c+9KrclpN0lXVVm6R7eU7iUkj0m7VbNw/He/3A/vIEytQphJgd5jRmz8yWE6vUnxpCeNTMTgIeBJ4TQvhm0e9PgR0hhD9J77cDkyGE95jZVuCJR2rsKWZvDinjJZIB5FnMyuxmOdNlYYABdanj2sa+MubNY/RcSrAjSi2Xh2/kLgf7Tq8d59kxAdb0TdSvm/p4AV2Ap58yCVQFe8v4vJwh1OP6kiyzLLNQxv/V+gKa5IUQ4gQyWM0/zTmpzOLsc1LO1Jnel1LPWmZlqMUD5hCFVI7B54Qyri9f32T0CWcBxeyda2H3f53dc9q/U8ze0TLXnr1nAw+EEB4FSAbf19P+bxb9Tgcmi/dfTX2c9Wa2Dvgn4JoQwt8c32GLY6JYDfXJcaCrEftQ4N6yS1Psw5rV76sae+srsrXkJyk2zuvdDfwgXuPg0Om5j3sGD26I+y7hk9W5RzxBSqrTRDTElm28veqzMp1rf4rZ40e5adRj/nrTZ0uB/pO7qpi/ycelyV2B+UIIMbdsrJJiNePxyhquniQsL0QmQ250Z1H6p7tRu29/NTc08UW/ybWVsVctfPpxMvaEEEfHnMfszQIfBM4MIXQD7wFuNbNT2zqa2VvMbLeZ7SZ5YoQQQgghhBBiMTLXnr2vAc80s5MKGedpaX/JV4EVwP9O77OnL4TwT94phPA/zOxrwCrgz5sXCyFcD1wPLuMUJ5R9qYRB4X3Lq54vTDsKiWbvxljEdiS8AYA1XSkrWiikMkPpxbVxc+4NlSxycktcJR3YEK+bvXi2I/c5uLE+xMkdtJBWVEdamvbPYLV1pOG1G9EKrRBCzD+Ke3OOn4tKjLuHz85NL+LP4os0X7kss6RDollkYc7x6D1xc35/VIGMUkg/fS4cTNL+7crUKYQ4OubU2AshHDSzMWIKjJvSdm8Zr5f4OPBmM/skMUHLq4GfAzCzZ4YQHkive4AzgAMnYPhixry99i7XlqOqQeR8itfk157yOktlkpymLL3gHHpvjKO7mVfnfaPr6/KZgztaLTkhhBCiQTL8NkYZf9f6+3LLnafE8j0966NFuGw8rh2XCbpGt8f55+H+J3SeuiducmIWl2yWCbqaIQ1lMpjVM/sEQswZBpw814MQzlx79gB+HfiImb0D+DbwJgAzux14RwhhN3Aj8ALgS+mY3w0hfDm9fpeZ/TTwKPBD4LLS2yeEEEIIIYQQS5E5N/ZCCF8kGnLN/S8rXj8K/MYUx//y8RudmBVSWYIcsM4rO7p44dmckrogPBC9dmf0x1XUh6lWStf1x3MuGwq190BRsiHJYGpprYUQQojD8S4Alq0tIj98bvGMzylB2F7Or/okz9zB8ZTEa2ORoCWV79lJKgPkWaSLbNKe6XMsuQFzEjOoMlpLzimEmAFzWnphLlHphROBp4xO2Sz3JaNrdTXpeRmEnCGzLJ2QcCPR5Zs1qWcyDpd3p3i88SrTZp6ImzFzQgghxFET57bNIUo9B8aT8VWWBXLjLi12Mla0Nerz3d0d4wHfxvs7ruTzni+IxsNT7Vi7uaO/WMwsoNILXRZ23zi757QLVHrhaFkM2TiFEEIIIYQQQjSYcxmnWMSsTPXk0iqmZ+E8N1QZM89PtegGiCujbUVpc3Ha5BG8JBS18FJ9o4ObCo+eM3JH5z4hhBDimIhqlb2pBl4urr6rqsUXbv8xAOzGpJ4qE654gfW0b9Wue+OLMkFLT9zkJC4bKhnnnhu6AFgzmMIeNkq9IoSYGhl74rgxMtEeczC5qUhBnTKW+aSXDTvomBA3hxg7kSUzRduujTGuby0/LEagEgdCCCFmmzgXjfbFuWh0bZq3CmPNdtZDZEb6L8mve0lGYUvYgrOnPxl0qeSQz6cAl5AWPHN5hjQnyugTQrQgY0/MLvsqQ6x3qN60eaKRZho6YhfKeD6vL+R1ii5NMQsDu4rj02S5djBNrBvlzRNCCHECSPHgI8NjQN2z5yqV84ejeqW3r2gbTsnKvM5smsfu7q9q+a0aT96+tKDpi6YAk+Pnpesmj+JQdW4h5gXLgB+f60EIRzF7QgghhBBCCLEIkWdPzA4rp8lGlrJwDiTJi69GQrVa6SmrRwcrGeeh9VGa6QVr12yb6Dh+C+8GYHK9S0Ml3RRCCHHi6LWe+GJltS/HnK+OsevrQhWP7hk2m6zquze/XjEcFS0+t7UpWq7svq72vsqADZoLhRCOjD0xK7RKNJMMMxuAaULq7SokJ0nGmWsK9ffkpmV90chbPpzKKhCTsNTkLEPJyNtfyD+FEEKIE02RhOXQs+Ji5ViIFuCaoc4asl4XdrSrHrsOsI2rAdgykRY0x6tY91yzti8dl+r80XtRdYIRGXtiDjHg5LkehHAk4xRCCCGEEEKIRYg8e+IYeTsAA0PJi1dkF8veOs/G6Vk0N1ZeuHXdMaFKDl5vyU7Wk6rRjm45vfYeKLKfvetoBi+EEELMDkU2zGXUs3Fu7q/aBizKLUd3Jc/cCztP5UlX3Ps3SZHFOiV2cannucRyRqNbimzWI29PLzQ3CrHUkbEnjokV4T4AJvvSRFQYa73jyYDzEgpJsrk8fCP3ybELflxp7KXXWeLi593/seKd5JtCCCHmKb4g2V/sG0xyyzTHZVnmUH2ug2qO3NPdlfetIUpCPYwhG4JlzLyHUWw86pELIRYJMvaEEEIIIYQQs8My4HFzPQjhyNgTx4QHjW8ejhKVnVza0ZbzqaRVx57usdynWWeotno5ngLam7X41j+2er1fRWSFEELMM7Kk00MdquRly/tT0rGhlHSsuyc2lMqWbXEzuTbOoxd231m1lR48yN7DFRNfzLsm7f74wjNla64UYskiY08cOUXh9M3dcQK5jBsB2MEVVb80Wbkh6EXR12yospKtuyHKV/r4KABb/CBgRXecuO5/KGYzW7Y2xUBo0hJCCLEgiDFzy/urufEKdgCwtz+WHPK4dpdzAoyuTQuhKQzi4KbTO0+djMMV/alMw1AV13d3eAUAL+czsc2O4SMIIRY0MvaEEEIIIYQQs4NBUOmFeYOMPXEEpIKthYRkR3dcrXSPnstSANYNx1VKL6Y+sCUVVb+hs6j6ML/UcTWXgS7blTx6Palh/1F/ACGEEOKEc9CekV/vDdGjN7ohee+uipvR1TuKI2ISl0MP/hgAF55ya27J9fWSfHNyZ5wr3cMHcCOXxTZPnrayqIErdYwQSwoZe2LmrIyTTzmhTHalicTj6gpDcJQ4IbnRNzoe35dF0b0cg0s28/mAoltkRJk3hRBCLESqEgijad67+4azgUpqeW4yAiOxLNGyvrjY6fMoQK4+1BM3OZtnkbl6oCfNrcOdEk9l6BRiaSFjT8ycZNCVBpkHhPtEUpuQEr4KuXw4BqXv5fyOPn58LcC8NPwA1QsSQgix4ElxeC+fSEae18lrKb0QHonBdjZU1O3zxdUU4p5LGPUUB47FTZ5bi0Xac1Os4KjtParhCyEWFjL2hBBCCCGEELNCWAY/VOmFeYOMPXF4BqMcxMsirNlUZdOc3JC8bxfEjUs1oZJmejbOgQ3xPKOfL7KKbYqbQ+vj6uWFVHEJqW5si4dPCCGEWKDsjxLNbr4LwG3jr+/o4p442x062twzmNU2XmZhsJgrt6RtCq0o59HJTf5anj0hlgIy9sThSemd13RF62vPRFELbyjuc2Nt2c5qYjq3O0pTXLbpZRZqUpV07qtP2drZloLPFUwuhBBi8fB3ANyWkpe5Ybauvyi9YDFGnsG0Y3tuYvNEWkC1WMPP4+lrx6eF1/Bf4ty884FX5zaPmx/YmI5XiIQQixoZe0IIIYQQQohZ4dAy45GTHzvLZ/3hLJ9vYWJmTwb+AHhJCOHZMzlGxp6YhnqpBV9NdG9eydgpsfD55v7KCzfQlVYtk1STjTGb5t3h7NxnlcU6CgNJKlpm85RHTwghxKJlJM1xa+P8d0V3VXphdF89WUuZYMXxsgzL1kZFTZnpenl3TIh2xgOdtYq8rNG6kNQ2KrguFglm9l7gtcAZwPNCCHdP0e8rwA/SD8DmEMJnU9vjiMbUi1P734QQ3nI8xmRm5wAfAU4FHgTeFEL40nTnCyF8B9hgZnfNdAwy9sTUNEoteJmEkf6qTl7v+KcAWNMXDUBP8wxURl6Sam4OUSqyqu/e3CVPNklyUjv3SM8sfAghhBBiHpMWQnf0X1HtS3F56ybiHHnnQ6/KTR72UIZNABzsK+Lhx9K2pSySz8m5Xp9i98Ti4RbgOuAvZ9D3dVMYg79PNPLOCSEEM3tas4OZnQw8PYQwWex7IvCkEMLXj2BMHwQGQwg3mdkbgT8CXpTOd1Z6X/LZEMJ7ZvDZasjYE0IIIYQQQsxnnmJmu4v314cQri87hBB2AZgdnbs6GWxvAp4VQgjpnP/c0nUVMGJmF4cQDiRp5R3ADcCHZjImM1sOrAFeknaNANvN7KkhhG+GEO4jehePGRl7YmqSZ84zfS0PTwCgd+hTucue/pisZcvwto7DJ7enjF9p1XIipMQuW6o+56cVxbHunnjururcIBmnEEKIxU5UvVyR3XEw2hO9bq56Wbat8uKNDEcFjIdNDJDCIIokLl6zNtfwo5KFbu5Ox42l4zyMAmCj5l1x7ASMH570Y7N81h9+K4RwwSye8KMWLbBdwNtDCP8CnEWUU15jZr8IfBf4j26wOSGEL5jZm4Hbzexy4H3ATSGEmqF3GJ4NPBBCeDSd81Ez+3ra/83pDjSzQeA8M/sgMBBCuH+6/jL2RIO3d+7aF+WcVyTja2f/pbnJ5ZulAefkgut90ej7W17Q0WdgKE0yySCUgSeEEGIp0mtFWNBgo3Gs6JfCJ0a6U9hDkmXmLVUx9bzo+sKqbccNSS6aFnRrsfYbj3zcQixA/m0I4WtJjnktcankjUS76KeAvSGE3zazFwC3mdnZIYT/U54ghPDnZvbbwF8A7wkhfOBEDT6EsBGY8X/rsuM4FiGEEEIIIYSYN4QQvpa2jwBDwL9JTZPA/yNKKgkh/C3wLeCc5jlSLN9Womv+dWb2/CMcxteAZ5rZSel8JwGnpf2zijx7os7KKlWuJ0vp7YuriANrr+nsPxKLw24ejnWDtt2xNTed0Z2ygKXVxiuImcb2dp+f+xzojv8/efWxM3GYEEIIsaTwxGgPE8MnDu4skq8kQVnv6o8BsDkkWWZXMUenwu0M1hOtQeX1c7LCBiqPouSc4hgIGI9w8iyf9buzchYz+3HgMSGE7yQZ53qS7zyE8C0z+1/EOLrRlC1zOXBv4xzPAEaBbSGEj5rZJ4A/MbM3hhD+eibjCCEcNLMxoBe4KW33hhCmlXAeDZbiD5ccZqcF+LW5Hsb8o9TuexisK6Q9q2Z3IfkYasQKbKITVzonqeeK7mrSyROZ/UPa83dHMWghhBBiEdHbWFwtJJq1zJoF64aLoupDMUavaTQCHByPhqPPxe+kutblXTfHFyp9NA/Z+oVZjlk7bqy+4DHh9t0/MavnfJY9eNjPb2YfAF4DPJ3okXswhPBcM7sdeEcIYbeZ/RTwCeCk9DMBvDWE8I10jp8iJlo5FfgR8DshhDsa1/kpoDuEcEuxr5uYoXN0JmNKbecRSy/8JPBtYumFA0f85RwGefZEIsbqreuvJguv2eMTQyvJyMvxeY0VQ6gmIJ98zu2+J7f18VEALqfr6IYthBBCLDbG4ubiiY8DcFvf63OTJ2jxZGmuwtnCu6vjk3HoNfVqrI4x8pO9se3ytTdXbbkurivStAArFg4hhLcCb23Z/7Li9ZeB85t9Gu2/cJjrfBn4cmPfODA+0zGlti9CS0KLWUYxe0IIIYQQQgixCJFnT0QGY6xeVWSVKsNmkows7/8qADupsnGOTDRWFAupyaFnxZoiZ6ZAPPcaeippgNFt/lqSESGEEALIMsrxNN+WEk2Po18xHBU1np2zlHf6fP0C/hagHj+VondGu+L8e2h7Vf+rKtTu5b3k2RNHziGW8UNmu/SCOFpk7InI7s5dHpu3sztONrlej12U+2wJ0cib7IpykM0TldG2bChOGofWx4lk2dqW+ND9P+rcJ4QQQog8t05uquSYzdAIl3Fe2X1d7nNwKIZfPNI/VusL1Zx84cStccutuc3PtaM/lmcYPbra1EKIeYRknEIIIYQQQgixCJFnb8kTg7A335BSN49XWbn2pvjV9cTg7R3Elb5DD1au+WXjdW9dLYWz90mykHUTcTXyQFGuZNJu7ugvhBBCCKoSClSKmuylSwnSevlU53Frp36/bFOo7SsTs/k8Xyl4vByT5JxCLFRk7C15oi6/h+s7Wjx+7/zhvQAc7IuykGVbKgNveXeMCzi4/xlxx9qqTh/r48alndkQnCJttBBCCCFKkpG1qzL2PFOnx8z3Wqy3x75q/vW5edTivnB7pcc8YzjG0bdlz/bF2M3hXQAM2Nvr4xBiBhxiGd8vyn2IuUUyTiGEEEIIIYRYhMizt8RZHmtIZumGrwYCPH74+wBcmmScAyOpFt9wVVuyYxWwr5Bx9lDbl4PKNxYrlEIIIYSYnpFq3l0XYmIzr7PHYGf3nuT+Gx2MCh27sQi5uH/qy+S6fN1pR8rUzcYjHbAQYr4gY2+Jc9D+Ib5I84BPEADv520ArBq/N+5YGTdl6QRngGTklXECG+OEtCLcF48bUpkFIYQQ4ljIce8pZs/lnL5oC3A+Mfziiv4dQD2ub0V/LNnwKV4DwCV8sjp5CrPY9sBWAHb2x2zckzL2xBEQMJVemEdIximEEEIIIYQQixB59pYkz69e9kZJ5ailencrqwDvGyfiymBOqNKTtquL2niDRUIWqHn23KPn9fkmd3UGgwshhBDicFQJUh7muQCsmEhF1Yc6s3GOkpQ0aU722npQ1dVbMz4Rz9P9xerANN+b18XtO+aBCyHmGBl7S5AV4cziXbzJT25JhliRKXMnUb7hUhEvnTA23JP7HByPGTrZlnYUxp6XbMjnGVEBdSGEEOJYyOEX++rx78v7q5h7D8lwyWetTNI/1s+X4/SAFcPpmaAv7ts8nLJpP66Ix9+hUAwhFhIy9pYg7mmDKv7OV/buf9bK3JYnh02pbyrFUIT1wc64yclXhqp4vr3dsU7fw0q/K4QQQswSsWRSXpxN28f3fz/3cCPPjbblw5UheLArLdKmuX1zf2W85Vq7Y+l9Kpl08Q0fz31u23GMwxeLnoDxCCfP9TBEQjF7QgghhBBCCLEIkWdvCZIzeQHruqNHbhtbALjwlFurjslr56uG2XvXVWTjTPLNA8PpnGU2zsRBSwXXedexDFsIIYQQeS69prbXQyeg8si5h+7xVF6/8L4Yv7f3oqjk2ZLjMODD3THs4vKe6lwAt42/vnjnZSBUaF2IhYCMvSVFTMwyOVTp8yfXxtc5mLu65zMynNI598d0zqMbYh9P8wxw6UO3ALDMbgdgRREW4KmfR3vTuUdm4SMIIYQQIsfTsz/F1a0tjD9feE2LtWX4Rq6595tpx87cxOi2FNrhsXvpeaFM4jJ5zAMXix2VXphfSMYphBBCCCGEEIsQefaWIF5QFarVvlFLWb16q1IKvfax+GJf2ndB2l+kee711cPBuJm0O3LbwGBdRiKEEEKIWWK/z7dvj5sim3b2+iWvXZ7jgc0heQI9GUt5XIzoyIldfP6e3HhW0en+ox+zEOKEI2NvKZHSNJ/Lp/OuHL/n9fKKm/5IeAMAO0gyTpd6lhODv05GX7j9ZbnJnpmkIhtVckEIIYQ4HjRr2kIVRuGxe+tCsRCb9vnCb2sN3Gb8/c6ipu62ZDiOKGZPiIWAjD0hhBBCCCHErHCIZTyimL15g4y9JYjX1oMqG+ckcWXPk7IA9PYluaav8LV46DaHmBXM5SA58Ls4Vy89szJuIYQQQjjRs7aN64F6iMVnz3wVUCVtKed9V+Rkj96W4pSepM337Y6bke7q2YDhuOkd6TmGsQshThQy9pYQnk2rTM/sEk036LKBV+La/32P7Wga6IoTyfKJWLD18cNVeufeLj/XO5uHCSGEEGIWyEZeEWKxpX9rfGFxkdalngDrh+MzQI7ZW10s5KaQDl8IHr0gGom949WzweZun9Ofn7aScwoxn5GxtySol1zY9s9bc8ul74w3/TW7JuKOLXSSJ4LOuL4VE9GA3MbVHYf17v/YUY9YCCGEEDOgRXUzsMVj9aLRVtbXzXF8/cmgG3wlTbz27uiuzraB1W9v7JGxJ+rE0gsnz/UwREKlF4QQQgghhBBiESLP3lJgZcyctbk/Si/2sjI3relKHr2UwnldqFYIXePv8o/J8RTX119p98dSPJ5LNjdPlJLNu2bpAwghhBCinTjXrghn5j2utsnhFJuq3u7Ru4IdcUd/1ebz/pqhhtqnUPQsD98A4GDX6XHH/mMcvhDiuCJjbymwPxpwHl83sOmaqs0ngI0vBmC0q4rL2zPRBcAlfDK+747v14xPdF4jnWfASnmHjD0hhBDi+BJllJNDVS293u3RyPNQCw/jKPFaup5oDaoSS76o6wu6dFfH+bNEzr0mY0+IeY2MPSGEEEIIIcSsEDCVXphHyNhbCvQ2smjuaumTMm1WWbZgS8rB7Nk7b+bSjj4e6O0S0R39V+S2g3ZswxZCCCHEDCkSteTwi75Oj975/bHg+ui+6MXbmeZ2qLJw9m6InsHwnTiR27+ryiqxM10jZfieHJmNwQshjhcy9pYA64bjzft84g1+oK+Sca4YTjdrOyu2DRZt/bFtdOiVtffnck/us7w/llzwyaKHsdw2mq4nhBBCiBOHG3nLh+McnePzqBZpc709CoMw1dBbcUOc71/KrXHHeHHyVIvv4eEnzOqYhRDHBxl7QgghhBBCiFnhEMtUemEeIWNvCeCeudG1yUOXvHlQSDxSgk733gFMdsW2uyfOBuBtvH/Ka3imzm3dVb290Ry9LYQQQojjysoiZGMkZthm+LlHdAqfy/P79SkL90SVhXvHcAzXyLX7eoukbyNlRm4hxHxAxt5SYGO66ffGTF2l9MIlnm4QenwewADxBr5q6N7a6UqD0GWb53dHiUjv0KeKnrrpCyGEECeEVEIJYHOIGTp3EI29nEETWD4RpZ1ecuHg+OnVOZJEM5dcSNm8e/uKuX0kxQYOPrb+XggxL1FRdSGEEEIIIYRYhMiztxQYjB69cGbMqnVGURTnzodeBcCyjbcDlTcPqGrorE1bD+YusntNjsXXozujZ5CN8uYJIYQQJ54X51cDqeaeF1AfpfLeuSJntC/N22tzU07o8ni+D8DkynqiF4DHD6e2HAZSyEdVc0+g0gvzDRl7SwAvi2B9MXXy5osqg2zZzpROuTduvJAqQO+uKNvwVMyju9LEsCV3qdrGU9vKwljcL8NPCCGEODG8q3gd52IP0cjSzYLNw3GOLrNo10MxgE1xc7Dr9I59Wb65slHeSQgxr5Cxt5hJhtcO0k0+GWkDGwqD7IK4GRmORl6py/eJYC/nA1WZhVp5hb6GAbhf2n0hhBBiPnB3f0ywtqqvir0fHUuG20TclOWYfN53tt2xFQCjqLOXPIHLwzeAygsIMKn6ukLMO2TsCSGEEEIIIWaFQyzjYVSH8XhgZk8G/gB4SQjh2TM5RsbeYiZl5jo4HrX7OctWC71d0aO3YqIz02YuwLo9bg5MfJ+pedc0bUIIIYQ47qQY+1Vrk0eviMvz1zn8Yqxqas737v3jZYVqZ1eUbR7cdXrtWkLMNWb2XuC1wBnA80IId0/R7xbgTOAQ8F3g34cQxlLbOcBHgFOBB4E3hRC+dDzGdDTXCiF8B9hgZnfNdAwy9hYxy0NMuXxwKO1wqWV5Y26UZZi0s3LTjhBr6eSb/s6Wi6xt2SeEEEKIOacj5p6qfFKe73s7Y+584TdLPFd2nvvQ+qjZHOuvGteMXHrMYxbiGLgFuA74y8P0++VkNGFmrwJuANaktg8CgyGEm8zsjcAfAS8qDzazk4GnhxAmi31PBJ4UQvj6EYxpymuZ2VnpfclnQwjvOcxn60DGnhBCCCGEEGJBE0LYBWA2ffCoG3qJJxM9fJjZcqLR95LUNgJsN7OnhhC+WRyzChgxs4tDCAeStPIOotH4oZmM6XDXCiHcR5li9xiQsbeIOWj/AMDmEAuee1HVdROfzn1GSat9ydu3ItyX2w5wTjx+IgZsu7xjcldVesEzfXoSl9FZ/QRCCCGEOGLG4mbUUhjHvqrpXO6JL0LjPVX2zkxSBLmHsOTpRKdGmbQN9h7deMWi4jiVXniKme0u3l8fQrj+aE9mZh8C1gEGXJh2Pxt4IITwKEAI4VEz+3ran429EMIXzOzNwO1mdjnwPuCmEELN0DsMM7rWFGMfBM4zsw8CAyGE+6frP62xZ2a/P4PB/p8Qwu/NoJ84wawIZwKwk7j11Ms5gyawYjhKNc7tv4cmruff231+vaGQbm575lYA7Os/THt0oxdCCCHmFC99NHhNR9NoV3oGSKEZ5QKuSzOXpVJN2WjcVBiBHgrisX9FDT9wSejfHdWwhZiGb4UQLpitk4UQfhXAzC4D3gO87AiP/3Mz+23gL4D3hBA+MFtjm8G1NwIbZ9p/2WHaLwO+d5ifNx3VSIUQQgghhBBijggh3Aj8opmdCnwNeKaZnQSQtqel/TXM7GnAVmJmwteZ2fOP8NIzvtaxcjgZ52gIYdrK2Gb2U7M4HjGLTI6f17p/z3BXfr1mKKba8pW9ssaOB3SPXptW9K7qPNc/PJACvLvSat7+YxiwEEIIIWaBtwOdoRZQeelcmnmg+5zcdmZjEvewjyvYkfeN9fcAVWiIh3oADMzY1yDE3JASqfxkCOFr6f3FwEPAQyGEYGZjQC9wU9rubcTrYWbPIEYubQshfNTMPgH8iZm9MYTw1zMZRwjh4EyuNRtMa+yFEH75cCeYSR8xRzSkFpu74w15S1sNBi+4PtQp+Vh+Q5R/HhzvTLP8993Piy9UTF0IIYSYJ8QySAPjcU7/cHeVJXN0bTT2PC5/cqhYGPYwjZGYqXt0JC7kjg5+qvMSad6vx+x9rHZ9sTQ5xDJ+yMkn/Lpm9gHgNcDTgbvM7MEQwnPN7HbgHSGE3cCPAx83sx8HHiUaeheHEJJ2mV8HPmJm7wC+TbuC8fHAfwoh3AIQQthjZq9O153RmI7gWsfMjBO0mNkTgGeVx4QQJqY+QgghhBBCCCGOPyGEtwJvbdn/suL1PwMvnOYcXwRecJjrfBn4cmPfODA+0zHN9FqzwYyMPTN7K/BfiNbvobQ7AJJwzmNyLZ20ajdA8tq1FUC9Nm7W3VBk6kwJWg52nV7vW3jxdvSnWnyDScYpCYcQQggxx0QZJ6ujh+7y3ps7ekxuSR69tnq5gxfV3vrzBMBkVzxuJLwBgDF6jm2oQojjykw9e1cB57YUChTzmMm+dEMevgSA3r4ow1g3XBh0npkzyTjLm/aK7mQs7kzxfEkGWko21t9xCwDmOZyFEEIIMcdEGeXmNDUPDFXG24f7o6Tz8vFoAPrcDpXs0427bVwNQO94p4yztyvuK2P2hIDjVnpBHCUzNfb+UYbewmUH0fvmRl5ZesGNvBzGN1w1ee2dSZJnMAVje7pmgB0XJcOxbzZHLIQQQohjZaCvMw7/cqKRl8ss2A9z27oQ5/Rt6eFgTVeM1vHSTQBXTOyondufMSJ3zdLIhRCzxUyNvWtS8cHbgR/4zhDC7cdlVEIIIYQQQgghjomZGnsXp59ziJlrIMbsHbOxZ2bnAB8BTgUeBN4UQvhSo89JwAeIFe4DMdXphw7XttTxguk541bS2bNp6mMez/fza/cAZo/gC1/Z0T8XZ+05xsEKIYQQYlbJ8/d4MX+vjnH3yzYmj57H3BesGa/n3ytLL7jU05VB5XNDjvXbqKLqQswXZmrsXQKcEUJ4+DiM4YPAYAjhJjN7I/BHwIsafX4JOBt4DtEo3Gtmd4UQvnKYtiVNs86e6+rf/dDWvG/ZpiToTzft9VRB3ANb4g3db/KjO9JN/KrOiYERlV4QQggh5hPZyFvf0nhFmsuLBC15AXdnvWutLNPGON97gpZSxjm5vb2+r1haBGxOSi+IdpbNsN+XgVl/mjez5cAaYCTtGgHWmNlTG10vBf5bCOFQKjZ4C/D6GbQJIYQQQgghxJJkpp69LwF/Zma3UI/ZGzrG6z8beCCE8Gg636Nm9vW0v6wgfzowWbz/aupzuDYBudTCwLa4MjcwVq3Q7ZnoAmBNX5Rs9AyPVcelpC07huvlFZZ3V4HaJKXHwb5UnmEEIYQQQswD1nVHGacnVYEqiyZXxc2e7q7ctmb/PgBWdN8HwOSm5KnbXpwzxHIO7tEbtULtM5i2KsMkxLxhpsbeycB9wPOKfQsu176ZvQV4S3z35Dkdy4ng7u6zAXhR958B0NM/BtSzcd5MTMGcyzNsKNIrJ2mHl2PY3N9ZesHLOUjGKYQQQsxPeoeKud3LSaeF4DXXFvF5vXEzaWcBVXbOz575qtzFhuLjX1V776zq+I0qwyBcxqnSC/OFGRl7IYQrDt/rqPga8EwzOyl59U4CTkv7S74KrAD+d3pfevOma6sRQrgeuB7A7LQFZ6wKIYQQQgghxEyZ1tgzsxeFEP7sWPtMRQjhoJmNEdeSbkrbvSn2ruTjwJvN7JPEJCyvBn5uBm1LmlUb7o0vPp92TIwB9aLqOauW19nbkptyUfWv3LESgJdedGs8pqzbM5a2K5OMY/+xj1sIIYQQx06trm7CM3W3MTmUZJtb6onYdl706upNCuDJGb5XFh170vPBiDx8QswXDufZe7+Z/WvApunzPuD8YxjDrwMfMbN3AN8G3gRgZrcD7wgh7AZuBF5AjB0E+N0QwpfT6+naljYXpO1VcZNLMBRZOl3P78XUaxND2me/mZygz0z7C4OQ1XfM1miFEEIIMYu4YZeNOIpnAF/kLbJxujRzG1cDVajG6Nrq2WCkP4V9UEhDne2du4QQc8vhjL1u4Lu0G3sh7f+nYxlACOGLRGOtuf9lxetHgd+Y4vgp24QQQgghhBAnjkMs4xGVXpg3TGvshRBmWppBLAC8ht7ArkqGOUqnxMM5l3sAmNzvwddJ1rGt6NSbau/lBC0qpCqEEELMLc+vvauSqVRMru3MtLm+Pz4nbOHd8bgWyWfvePToretPBdvLbJy8+GgHLIQ4Tsw0G6dYiKRMWxf3fxyo4vM8qyZUqZMPjqfSCaVE00klF3IK5+EihbP1pFdvn50xCyGEEOIYiQuvk0NxQXZ5f1Uy6Z8eWgHA1f1bARjYXi0A5zj+a+Nm3Q3JoOsqFoYbBddXhDPz68kuxe8LMd+QsbeYSYbbbX2xxnwur9BV6Oz3J4/cvs7DPcbPjcbR7fFmP7r/oqrTvnhj92Quk9NFdwohhBDiBBA9e764O2DVguzTQ0xYfnAoLvKum6iStuXY/h9Er5+XXqqxPvWdSH2LeED2K45fqPTCfEMyTSGEEEIIIYRYhBy1Z8/MloUQDs3mYMRxYixuzuUAAId2Ve63ZZtSps3kvSuzcuW0ypviJmf16qtW8fZ0dwFVcfaB2Ru1EEIIIY4BD9UYCW/I+67kuvgizfel9+5gXz2kw71/NQdfM9xjbflGcftCzDdmZOyZ2TDw5hDC99L704AR4OeP49jEsZIMuM0T9Xo3F55ya/Um3bRdhulJWQBGNyWNfrqRZ+Ov0OtvSdlaRs2lne865mELIYQQ4liIRtfB8Tg318okJBmmz+U5Zh8qQ84TseXkaxWbh+M874ZkeKBaQDZ+mF7pWUCI+cJMPXv3ALvNrBd4BvBBYOvxGpQQQgghhBBi4REwlV6YR8zI2AshbDWzvwL+ilj4/CUhBOVaWiDsTTXve5Kec3SoyqqVg7dTBq713TdXB/bHjQdsnztxT+197VyDacfG2Ry5EEIIIY6cmKBleXfMwnmwq/DepdCMzd1p/h+qsnG6Imj5cDpuy+m1/VBk7EzY/aF4V1cSCSHmnpnKOJ8N/C7wMeC5wL83s6tCCD+c/kgxHxgdjwbZge5kpBU1dXb2X1rru5PqvcfmHdoeJRrL1qYbepFta0Xa9TBPAOCgjD0hhBBiXpAlmpuKnWsbnYr3XqLhOq4ECvnnrs7+Xo5prLunup6eAYSYd8xUxvlXwNUhhI+a2WOB9wJ/C8llJIQQQgghhFjyHGKZSi/MI2Zq7L0khHAAIITwI+BKM3vV8RuWmBV8tS4FY7sMc9vE1blL73hctXOpR07CApUHzz2BPXGzbqIK2B5N3r91w6nw6uyMXAghhBBHy8qYmMWTr23rLub9oTjvD+yKcszwp1WClS1/ujX28Xq8Pamh9AYmL5979HLGTiHEvGSmMXsHWvbd2tZXzCNSNq0VE/Fm73LO0dVFUXSi4XaQZ8S+4Yu5ZTJl8br6lK21057P3vx69HHpnH0eB/jYoqdSMAshhBAnGp/3nd6+IhvnWNr2xM0Zf1qlYPAC6WWhdajH+q/rj23v520ArNp+77EPWAhx3DjqOntiATASPXOTW5Jxd23av7IwyDbVjsg3eoAV/XGyaAZjD3QV7937ty9dY+RYBiyEEEKIYyXXwx3xGPuzctvmEMsieGKWSava1oWk0kmLwzlWr4jZGyW2rVobt6VhOVk5CYUQ8wQZe0IIIYQQQohZ4RDL+H5K3CfmHhl7i5krkrdt/dRd3HvXhnv5vM+5wzHmL6/4AZu7o1RzoO8ihBBCCDEP8Bi7pOzxuHzoVOuwr1L7uFzTpZqj29J8Xyuu/tjaNWqx/kKIeYeMvcXMjijfWBfiTbpDllEwOd5ys15bb5vcFbeemhmqUg2bh1O9npG3FydQzJ4QQghxoukooTBUxOxtjM8EI+ENAOzgitw0uqtazIVibqcwELc0LlaUYxJCzD9k7AkhhBBCCCFmhYCp9MI8QsbeoiZ61kZTELbLMsoCqJkWb5+zp78LgDW7JgDoyam8YLQrrgJeNnEjAAM8/1gGLIQQQoijolLWHByPUsve9Y0SCgCDsc09emVoxkj/JfG45Akc9SQsw1XIx5/yCgBWjacsnL1FGMeIFD1CzDdk7C0Fkhwzp07eXrSlbJyH1scUWhee0llRY83QRP08XYXMIx3/cj4TXwwWctCNuukLIYQQJ4a78qt13Sl8Y+crO3qNdNcNujJ23+vrNUs3bKOq07eqLxl5LuesxfMJIeYby+Z6AEIIIYQQQgghZh959pYAm7tTgPX6FGD9wqrNV/SWDYW4o/D6bZ6Ix53fH4uob3vtVgBs/w9zn3X99cDsc/vvya9HNx7z0IUQQggxAzwZG8BY0m2u6I5zfK67B/SORe/dyETy8I0XyVuSWscTs/nxtQQva6lT1u7djxAEjEc4ea6HIRIy9pYAnmZ53URKpdxXyTrOJRpnnmmzLLLumTa/csdKAF76iSjxXEdl4J1PNAS90LpfQwghhBAnjlErYud6kwHmEst9nf2zkVfG7PvrtPA7uSk+G2zuf2fukks3eFknZeMUYl4jGacQQgghhBBCLELk2VsKrE6B2qRVv8GqybNweaatUurxcP8TALBnRonnCFHycS4Hcp9L+GR8kTyCteQtXnhV9faEEEKI40spp3Tcw7e6SKKSiqjv6Y6Ztm/uvjQ3DeyKXrvlE406ffax4pxxc/HExwG4zSaOeehCiJlhZk8G/gB4SQjh2TM5RsbekiBm6Dr04MsAuPmUV+eWnHo5GWmu4a+1rX4GAL2DUfJRyjkm7f64L9wMwMDaovDq6henFzL2hBBCiONDLHlUhlF4zN7BodMB2Dz8rtw2kMox0Z3eb6jm7bzw2xUXfrdMvBuAPWF17rOmLxp3t3W9Pu2pngmEADjEsjmps2dm7wVeC5wBPC+EcPcU/c4BPgKcCjwIvCmE8KVGn2uArdOd51jHNJNxNAkhfAfYYGZ3TdevRMbekiAaW8t2piQsZXD1trjZM5Fq6XVVK3Ru+G0LMb+y3+BLg25diJPLXs6vnQ9gc4iTy4Ad+ycQQgghRAuDUbUz2lft2jycErOl+TrH2VHV0ltjMZBvefhqbpscqsfve6KWNeurZ4O7J84GYFXXvbP1CYSYLW4BrgP+8jD9PggMhhBuMrM3An8EvMgbzWwNMZ3hV9sONrOTgaeHECaLfU8EnhRC+PoRjGnKcZjZWel9yWdDCO85zGfrQDF7QgghhBBCiAVNCGFXCOFr0/Uxs+XAGmAk7RoB1pjZU1P7ycSAp34gTHGaVcD/MLNz0zFPBkaBl810TIcbRwjhvhDCixs/R2zogTx7S4vk0fNSDAADW+Jq380p8yY7q+6u0R8Jb4jHpZXCHsaqPp6O2Us27K/iAioPoCQeQgghxPHASyhNrj2vs3FX567e1XFuXxHui8d1FcelZ4Dl3dGhcQU7ABjoqTyDb+P98cV+ze2ineNUeuEpZra7eH99COH6ozjPs4EHQgiPAoQQHjWzr6f93wR+F7gphHC/Wbs0LYTwBTN7M3C7mV0OvC8d86FZHMeUmNkgcJ6ZfRAYCCHcP11/GXtLiXTT9wBsgD39Sb45niQaLTLMHLuX0jq78QdkA9Inm27+PjfdNv56hBBCCHH8cGOtjNkrZZtALXxjXYilEnIZpp6iX3pOOLg+xvqVRp5TlW/ae7RDFuJo+FYI4YLjeQEz+9fAzwBbDtc3hPDnZvbbwF8A7wkhfOB4jq1x7Y3AjKtZS8YphBBCCCGEWAp8DXimmZ0EkLanpf0/D5wH3G9mXwGeBXzWzNY1T2JmTyMmcHkX8Doze/4sjmNWkWdvKbExSS5WVit1a7Ynj57LL/dVqZsH7O1A5eG788FXAbBsvJIwu9Rj0s6K25WdchDPFKasnEIIIcTs4mUSakXVV6ZtSrRSll4YXfnKelsbPn+7DLQlsZsQC5EQwkEzGyMWEbkpbfeGEL5J/OvOf+HJ4HtFMxunmT2DGKO3LYTwUTP7BPAnZvbGEMJfz8I4ZhUZe0uKaLyVko09w6nOTorZ20lVb2e9l1NIcpAs/ywU01fcEPX8HsKaUzpT1fBZU5xTCCGEELNBXEg9uCFKLlPERaQpduutFnJzpk6Xeq4v+rmRl/atmOiswVuE7QvRSsDmqvTCB4DXAE8H7jKzB0MIzzWz24F3hBD8CfbXgY+Y2TuAbwNvOsJLPR74TyGEWwBCCHvM7NXpujMa0yyNY0bI2BNCCCGEEEIsaEIIbwXe2rL/ZY33XwReMIPznTHF/i8DX27sGwfGZzqmIxnHsSJjb0mR6i+OVVKPnJglreId2lVlHsrF15ND24utlgy4RHllPGcZIO51+cDlI1VRVyGEEEIcA71pLr8qbtZ1V/PvAc6pdZ2k8sy9+6GtAAyQPHs9Vb+R7liDb8dESsw2FCWf7g0EGOhLx+0/hrELIU4YMvaWOOGBaNztnHg1UI/HGzkl3vTvHo4FVF/OZ4C6nGNduAeAK0gTRKEjceOwezhm6LxNxdWFEEKIWcHn2PXc3NE2Oh6NNDcAH+5/Qm57OqkO9D+mHUXewVxOKZEzdndVRdVVckEcjkMYj8yBjFO0o2ycQgghhBBCCLEIkWdvSZGyYfZUMk67MXnynhk3ZcH1HbVo76qWjwdsQ1VvZ3TtK2myub+++nebZwHVqqAQQghxTExuiHPywAVxbj20vpLPTKQEabd1pXq3ReZNr4vruHQTYEd3nPddBrpmQ/TolSEao1LpCLGgkLG3FBkrXqcJIGfOHC+kGp6hK5VluDh8HCgmj+J4N+wGNlTZOPdyPlBp/nNcgHT+QgghxNGxr55Fc8UN0XhbZj+s+gym7JvNEgrAucTwC7rjprevkm6uG45GXQ7XGIubegygiqkLsZCQsbcU2X9Hfrmi/0wA1gwlI6+spdOTtpvipHHbUDLydhZ90gTiKZyX3/DV3OReP48ryAHiI8c0eiGEEGLp4oZbT9x4zF5PGMtderuSAVfO6Qmvx3fowRhTtWxL6GhbEdK8nRQ9k3b/LAxcLBUCy/ghJ8/1MERCMXtCCCGEEEIIsQiRZ29J8nf51eRQit/bPk13X0Xc0nhfHLeuP0o/PAMYVHKQfK3t5yGEEEKII+X51cu19e1OLgWKIunA8omosjk4FAuu1+L0+uNmWV/l0cvsi0qeyaE0X3us38a/6+wrhFgQyNhb6iTDzZOu5Bs8VMbdtvo2/GoVne0lG5z3d78tv/57nldrG92UDMGNxzJgIYQQYglzbdr+IG4m16Z5u5Bs9qRgu9G10djbxtW5rXc8STzTHL+iuzIEPZ7viu4dsW9fvRSDEDMhqPTCvEIyTiGEEEIIIYRYhMizJ4D2sgru5ds8nDJtDkWJyJYXbc19fF+mkHi6jDNLO72ttzhmRGUYhBBCiOlYF36UX4/G5NmVVHP89I7+OQt28vZt4d1VY5qLcxbtrmJOTrnaruS6+GKkSugmhFiYyNhb6iRja0WImv9JO6tq2xc3HgfgcXmlgTfSH+vzjKW0YDv7L81tPtks708TEnFCKmP5RpWZUwghhGjH69NSxMCnOLqmkVfWyR3YFY8rJZqOz9u9Q0miWWTYzqEceeF2x9GMWggxj5CxJ4QQQgghhJgVDrGMh3nCXA9DJGTsCQAmx5OMM3SuArrEc3RnXRYClTTE++TMXZAzdR7cFVcfR4bjauIOrqj6+Krlfsk5hRBCiBo7W/a5162RRXtgZyHHTG05eUsRYtG7tp50ZaT7kvz6yu4o33QljuriCrHwkbEnIqtjPMDkYEt5hP0pVuDamJI5vL7KxvnS7lsB2DYRM31lWQhkwy98N/a38ZjmeV13IUfp8Wscw9iFEEKIRcjy7hgGMdpXlTVaMdzInp0Mu1KyOUlq82zaZXH1tK9ZHgmqUg1s1AKsEIsFGXsicVfclGURrog1+DaHdwFVLR8ra/P8Ztxkr19JWkm0Lan/+rg5MHFO1cfLO4x4DSHV8hFCCLG0WRfOB2DUja/CWHMljsfRj/X31PYDHNoVF1mX/WOaf7dVx6eqDDl5WlkyqfIAak4WR08IxiM/VOmF+YJKLwghhBBCCCHEIkSePZGIq3e+mhiJq4bu0fOirM8b/vvco+uh+4Bq9TD8z0ri+bRPTAKVLGTdxKfTebbkPpd0fxKASV5cG4cQQgixVBntSmqZnrRjS9GYVDKjm2KfXEJhVxWzd3P/qwEYOSVl3hyrQix8LvZrrNp5b3Vu9yCOaC4WYrEgY0/UKOMC8uSSZB05qHt1Ve+HwRjH5zEDtrOSeC4nxhq41CSfe7g6PMtOetMOBYMLIYRY6nhiFpdVrq7q3XnNPZ9TL+2/OR5SlD7yRGhu0G2eKMoyePmknripxfqtvn82Ri+EmEfI2BNCCCGEEELMCocOLeOHPzh5rochEjL2RJ2Rymu3fPgbQJWC+dCzUsD3YJGgJXn09kx0AbCmbyI3XTEci7HmVcQkD/GgcCAHjefsYvLsCSGEWIqsrGSYXiB9YFvc59486JR4rumK867LMwFGh1Kf5CHM8zDkeTucGef0vazMTWuovINCiMWBjD3R4F351T9fsxUAe2007p5+SozBK7OCjfTHeIA1Q5WR59Tq6QHsTtvtHV2LLGLPL/YqZkAIIcQSoaip58aZl0coF0lHJuo1a7NhV5DDJ7ytnHd74uaM4VjzaD03H+vIhRDzGBl7QgghhBBCiFkhHDIeeVilF+YLMvbElNjv3h5fPC1uruiOsswd3ZXH7kquiy/c25dr9MDj+T5QZAobjyuVtWDwvlQvKBVaH91XrFCulmdPCCHEIqc3SSzLWnjNsIdibu1d/ylq9KS+aztDJGrF1BPuLRxLBw7Y24vWd3UeIIRY0MjYE4dleX/MqtmMvQNyCmg2xc3I8CW5qdc+Fl+Eet/JnVXh1xwjmDJ0utEHMOrxC/urLGJCCCHEosLn1I3FXDeSQhoGLwJgRX+1SHpu/z1AlY3Tjbfz2Zv7DGwpYvSoZJ1QGJDZuNQcK8RiRkXVhRBCCCGEEGIRYiGEw/dahJidFuDX5noYC4N99RXC5d1fza9dqlklWCnYVX97d//ZAKzqKwq4bmFqfNVxRKuOQgghFhmuXknKmFbVjCdtKSWead70ufig/UPHqTeHGAYx0HdNZ9vwO+ttmmMXCFu/EEK4YK5HMROs+6cDn/nb2T3piscumM8/35CMUxye1XEiWBFiSuZzuSc3XUGM41v/iVuAKnNnGy/nM/HFWLUvp5fuakx6UE18LmdRdk4hhBCLBQ9/6K9n1wQgJbh2aebA2sJoS4aflze6LNwIwNt4f+7SEXZRLL7KyBNiaSFjT8yYyaHovVvfX6Vp7h2PgeK9X4rvwwOW2ywF6zVTQJe1gPZyfnyxP8Xu7Xpsbsurj7t8YpKxJ4QQYoEzWDfEertSwpVisdPnzWyYFSqYnPQsGXQD21OfnuJ4L9mQ4vpqXsOG6kYIsbiRsSeEEEIIIYSYHQ4Z/EAmxnxBvwkxc1JR1ryKCHkl0lcRt7C1ahuKmwOcE1+k1cTR7VV66BUTKcPYYPTolRnHdhJlozljJ54eWqmhhRBCLCSe37FnpDvKN3t7omevnP+2JVfe6FhnwXT39h3aHpU0yzzldVE4vSzCDo1snBv3IoRYOsjYEzNn/x1xm1JBQ6dE80D/OVX/jdFI+8qZKwF46fCtQD09dDboUt/J7VWil+UTMfh8XYjXHe1Kk9f+Y/4kQgghxNzQrH2XJJrrqUIk1gzFoD2fBw+On57bVgxHo/DMxmTofQGu40oAxoZ7gCIuHgAZe0IsJVR6QQghhBBCCCEWIfLsiSMgJkhZ3v/cvMe9dKMbo7dvW//VuW0s9ABg4ylRC9ELWGYcOzgUVyvXhdjm2T0BejdEacvoVdWKphBCCLFwiOEHK8J9ec9kV9z2bopznGfjrJHULge3d85/k5uiAiYnakmhFT39Y7mPJ0/LJRz2K/OmOIEE4AdzPQjhyNgTR0xZ02fAa/Dti5veoU9VHRtSFc8Ktnz4qx19PL5gdHUhEQ11iejmiTSxVQk/hRBCiPlLikcvJZo7JuKCp0sze+1jsWFflY16JKR4vr5iTnXSvOnZOPdMROvRpZ9AFb/Xk7YKfxBiySIZpxBCCCGEEEIsQuTZE0dBUe/u2ovqTVe1dN9Wf/t4vp9fH3pWdNNdfcpWAHamwu1QefRc4pJrEfUWJxu5o3NMQgghxJwSs29e3P9xALZdszW3DHw8KWJ2xo0nIStxGaYrYTzkAWBdd1K9EOfIC7kzNhRqmvA/49xqI7cfw2cQQsw3zGwtcAVwMvAvIYRNhzlExp44RnZ0ZujMpFIL4bI06Xw8xu5N2lm5y7IrUsroq+LGU1ED9G5PBds3viVdI25qKaTTZKeC60IIIeYL60KMuftbXgCAvTZUjV9K27QQOro2zWMbC6MvzXcH1ycjr6dq8vJFHtqQ6a5e2sl+PcXqiTngEPDwib2kmZ0B3FLs+gngSSGEU1r6vgL4z4ARVY5bQwifTG3nAB8BTgUeBN4UQvhS8xxHMK73Aq8FzgCeF0K4u2g74muFEHaRnrDN7FYze2II4bvTHSMZpxBCCCGEEGLBEkL4Sgihx3+Iht9ws5+ZGXAjcFnq90bgI2bmNtEHgcEQwjnEZZc/ajnHyWa2orHviWZ2WsvQbgF+DphsaWu9lpmdZWZ3NX5+u3G9lwP7D2fogTx74phJHrWN6e3KwsOXHMtnXJQiw29M+3urIPTsGfxBPK53rAhGT8eP9F8f2/pin7Ioe65BNOIFa+XhE0IIMQcMlrXsogLl4IbomRu5oVKtrL/sFgDs/uR9SyqY2vyZsnF6gpey4Pq53APAwHi63urYd3n4RnX8SKc0VIilgpn9GPBLwEun6HIIeHJ6/RPAN0IIh8xsObAGeElqGwG2m9lTQwjfLI5fBYyY2cUhhANm9mTgDuAG4EPlhZInjmhj1sY43bXuA148zee7HDgjhLBlqj4lMvbE7HBmnKRGJqoJbQvvBirZZrg9/qG/9KJbcx8v+HpFkppcli1CeDmfAYpSDWOdl+1JO0cHXQYjY08IIcSJ5O0de0YtGW4pxjyXQgB6n5le3J+2KdauzFTtWa/zYmdx/LbuWOJodFuc9zzmb9SKhVQtfIq5JACPzPpZn2Jmu4v314cQrp+i7yuBB0IIezqGFkIwszcAt5rZ94B/Bbw8NT87Hfdo6vuomX097f9mcY4vmNmbgduT4fU+4KYQQs3QOwwzulaTJEH9PeAzZvZB4D81DNEOZOyJ2eH+aKyVNfR89XFbiBPTGSn3c5mC2uv0+QrlANXKqMfvncsBAA5MnAPAZN95uc+dD70KgIn+aFCu2viHxaA02QkhhDhePL/+tiw35AqWsbgp49F9njzQnea0rjin1WrqrUxlGTYko/Gq4tQpWdmKiejtG+1ytYu8eWJR860QwgUz7LuB6GXrwMweA1wNvCqE8Fdm9m+Am82s60gGE0L48ySt/AvgPSGEDxzJ8UdLCOEzwLOO5BjF7AkhhBBCCCEWPClu7ueBj07RpQc4LYTwVwBp+z1gJfA14JlmdlI610nAaWl/8zpPA7YC7wJeZ2bPb/Y5DDO+1rEiz56YVbJ0BaoCsSlD2Kd4DQBr+ibo4HGdu8Zu6Km9zx69kR/lfWcOR2/h5HhsWxGquIZJk2dPCCHE8SKF1HhcXXcx/2xJ81WKx6sVR0/xdMvDE+JxyUPn8xjA5u6olnHVS3nucyeiasbLE1VozhMCuBz40xDCg1O0/yPwLDM7N8XbrQSeDtwXQnjIzMaIAuyb0nZvUyZpZs8ARoFtIYSPmtkngD8xszeGEP56JoMMIRycybVmAwshHL7XIsTstAC/NtfDWNz0JkmmxyP0x3iE67iy6jKUJsBddOKSGA9U39cyoSb5i0+WpUR0wDyO4l1HN34hhBBiKlamOa6npW2kPm8t7y7i8bqiRNNLJ+REKyWpLMO64VRTr1xITcZlVapBRt7SYOsXjkDGOKfYcy4IXLf78B2PhJfbjD6/md0DvDWEcGex73bgHSGE3en9LwFbiIlaAK4JIdyS2s4jlkP4SeDbxHIIBxrX+Cmg249J+7qBp4cQRht9PwC8hmhQfgt4MITw3JleazaQZ08IIYQQQgix4EllDJr7XtZ4/1GmkHmGEL4IqUDm1Nf4MvDlxr5xYLyl71uBtx7ttWYDefbECSB62Dxj2FixDHoFO4AqYN3fAwz0pdXOsbRjfz0VNQDbG5fqKV57QtrVKiorhBBidlgXzgcqGeXm/hYP3eqpSyc4rkxhZ9yUSVx8nmz1+uVraG5bWsizt1A+/3xDnj1x/FkZJ7sDxMWWmkHnE1mSrFw2XJVe2DEcDcCD4ylD2bZ4nnX9n67O3R83no1sZLiaLHvtY+n66Rr7NTEKIYQ4ClZWRtcBouHmRt5eovFXGmu9Kz9V6zMw1GK09dTflguhXlYoPBBLFtnHi4X5zx/x6IU4sQTgB3M9COHMWTZOM3uCmd1sZvea2RdT3Yip+r459bvPzLZ7lXsz+wUz+76ZjaWfvz1xn0AIIYQQQggh5i9z6dn7LeD/hhDONrPnAH9pZmeHEL5bdjKzM4FrgPOBB4mFZN4I/HHqMiG37jwnyS8901hbLb3etXEVdNXQvR2H391/NgBvG34/UNXmAxjYEM9VFnN3XDbqHsVJO4bPIIQQYgkSs6l7EjCoMkNn9clQUp/0F4claaZ7/UpFSq6Ll/rkzJtWFGdPiV12XJSO+83i3FKpCCGOgLk09i4FfhkghPAlM9sNXAR8vNHvdcAtnorUzP4bcAWVsSfmPSkb5vpomK2bqCY9l62EM5NU5cZKqrJ8OGYvywZgys7phWiBXGi2d7wzq2dHjITknEIIIWZEKpnVG7NgThZpF1YMp1IJQ+fVjigXIkd3JYOuu+XUycjz+WpgV5yb7g5n5y5/z/Pi5e0taU9VckgIIY6EuTT2Tgcmi/dfBZ59FP3OMbM9xDvhUAjhI7M9UCGEEEIIIcQMOIRi9uYRx83YSwbY6VM0P22WLrMHeHYI4TtJ7nmXmT0QQrhrijG9BUjLZE+epSGIGZM8aqNdRaB6qq/+DxedFV+8rFq9PLgl/vl4fb6DffH9ucP3VMc3C7Zvqwq2T66Nq67ruqMncRJfhS2kMrkGX1rF9SK5AL0p6+dYer8pbTdWY1wR7ovn9oLvY1TsTzWQVl5UP77EPZEjqpckhBDzhT0h6iYPcD0AV3JdbvP7vdfAc4/eTi7NfTwxi+NyTqiSrnBm3Ngzo6Jl1XgRxrA+bQfTdqMUKUKIo+O4GXshhDXTtZvZV4EVgFeKPx34Xy1dvR9Fv6+la/yf4nr3m9ktwL8BWo29EML1EO/csfSCmBMKo2e0L0pdVm1JkpfBqs2Lpz/ME+KOVErhs3e8KvfZe9FKAA5wbsdl/PgcH5Guu67/jtxnNGVIqybmytjybGgdEtF9VemH+x+K1182Fv+cytjB3q50XEOyU3sISDEeO4fjQ8KkydgTQog5obdaiFzTyJ5Z3rc9Vs/DEHJceFcl6xzYGY/3eWhyQ9VmV8X5wgutr+PTtfMATPak/hur+UoIIY6GuZRxfpxY6G53StDyM0BvS79PAH9hZu8kJmh5MzAMYGbPAP4phBDM7BRgHfAfT8TgxTFQrlB6HJ0bUmurphwP4W1jcWM7Kzt9hJTgZehTncf7xNvmUZuCWk2jbX6RNN7BepkIgGVb0liSQZfHQRXQ74lp/HPs7a9WeH1FuIr9aPM6CiGEOH7E++7m4Wpuck9ca727Rg09UjmxcrHPa8eOjqfFxlLSluaCg+ujWuWKiStr1wIYGPG5QAuAYgEiGee8Yi6NvfcAHzaze4FHgbeEEP4vgJn9LvD1EMIHQwhfNrP/TFVZZhS4Kb1+LfAbZvYj4mf54xDCrSf0UwghhBBCCCHEPMRCWJpqxijj/LW5HoZIsplwWYxhcFkmwJq+GH/ncREu+Sy9d45n3syST4pi7GkV1VNfj9pF1XEeczdej+8DGB16Ze3ckxbjCjeHyuM24PGHqbyEl3soj8+ZRlNchst6yut6fEZNBmo9nR9UCCHELOHesxT50VvNDbX4a6gVQPc5Kcs3033cZZlQxZj7fLWnvyu3rbH3AbA5RK9dLri+scy4KWWHaLL1Cwul1JidcUHgHbtn96S/Ygvm8883ZOyJecHm9Gc4UCZvSQZUjpFLRls5ad6cAuJzfF1XJaN0w2n9HbcAsOWirbW+AJc+FNvOPGU/UI+58BIR2ch0tlQv3Th0w65miKbxZmMxSTVH+guDri+ON6fy7quun6/jgfoqGSGEELNAMvLS3OL38VJGeQU7ANh2zVYAtrxza25ziWeeG8Y6r5Dnj2nmhrzPZaEy8MS0yNhbKJ9/vjGXMk4hhBBCCCHEYiKgmL15hIw9MS8YsLjSuicUUpcNUcZ56FlRBrlsbXT/ubwTqFZUPeNlkYzFvWa97iEriuI6VybZzQv4WwA+NfGa3LYlZWJxr9s2rq6dF+CK4bj6O7o9rt7WpDq74jizVDNJdHb0X5H77BmO/S/hk37RTtzDmctDKGBfCCGOjCr51Uh4A1BlWs7et11V757hMQDstUl20le1+ZywfDjOH+4FHGhk8IRC2TFeqDbco5eTfcmjJ4Q4fsjYE/OEONmt6SoMuVSnzr4T3+75RDSM1mwp+vjknCZNN56gMrY2d0f5o2dHy7EUVMaay3LWDFXn9vp+1xEzpfmDwd3DZ+c+q4ZiXSTPvFmWgPAYwW3JglszGM+dS0EAoz3xtceAbOu+OrflzJ4uY92W4klGZOwJIcTMqEs2oZL7b56Ic8PO7lT6ZuNZVZ8tSWLvMdbD1Rmr7MmJVELHDTvolHj6HAGFXH9EZRWEEMcfGXtCCCGEEEKI2UGlF+YVMvbE/KJIQuJJW3byaqBIvrK/yFjWG1drXVazZrzyzPm+gb5UOD3VUNr2nK25j9ntAKwL8Zzlyuy53BMv4bLNsbhZtene3Cdn+Ey1lHZ0VxJNz9TmslMfz8P9RcbQ5GX0VeADw1VR3ey19Axt6bOyr6Xek2RAQghRkDx6vfUEX1AlT3FFR87iXHj/cmKsNKVMJ8McWJvmmO5q/vJ91elurtpGJMkXQpw4ZOyJecuApRe9ydjyeLy11YScs2G6NLKnOn5yJEpy1oWqnALA0945Wb15bSpqm4qzu9QTijgOj6O71q9fHZ4L5qYHiVGKzJ3+cNGIw3s838+v3QD0B4Fa4V6/jn9ef188tOTYE/tY2iOjTwixVKni8txw83IILseHSpIfHqiXxRnpLoqiTzSKohf3XcfDBjy+22PPARiMm7zoWGaaRpmVhRAnDhl7Yv7zuLjxGLqDG6qYu9EfpJi3ibqHDchGknvNRrfEbVkLyVdmtwy/G6ivvp7fvxcogu6v6hyar+QO7Ip9yjp9dMeNp/N2T2EZs+eJZfLx/UWdv12p3/a48ZINpfdwC3HcDCaDcOPzqdCqsRBiKZDue4OP7Whx9cTYcE/e5/dto27klaV7lk/EecJj9triqcd5Xv1iLZ7ByV3JI6jSOUKIOULGnhBCCCGEEGJ2UMzevEJF1cWCYV1oFLKlpRj5SBXP10yvndNct/UZqlZ0M0m2MzIcV33di9ZWeL0sxuvkrJ9Jxpklp2WR9mbh9KJ0RCZ59twLWPMeJt7P2wD4+2Kludfekl7JwyeEWIy4kuHFcbOy8Kx5bLd7264tDvOH0LG07Ykbj+uGQtHRLIAO+Z7sGTZzds6yj+KpxayzgIqqP/OCwMZZLqr+OyqqfrTI2BMLj5YEJXeHlQCssv25aUW4D4A/5RWxrS8mVvEyB9AwvKA2WbtsMht5FmMA3UCEKsbP4/vKBC85oL/tYcFxIy8ZcqXE1Gs3+TX8/WXcmPusGr+3do3y+t38PQC32RPTHhl9QohFxGCaC9K91WPwAOw307NNT9zU7vvj9fu+yzjbFu081s4X9gDOJ0r8d5JKNpTJW5zVXlZB910xW8jYWyiff74hGacQQgghhBBidghIxjmPkLEnFh7bqpfuZXs5nwFgc6gkM5emZCtZ2phWf3OWzZJGEhiogvVdquOewprkM5VF2BNSwfeiKHtTfumU6bk945t77Xqyrqi6vq8o+yrypUUSmfxdJDloWex3W39MKHDbFWm8O7xFK81CiAXGYKHocBllUjJ48ivbVSiVcvbmuBm1i4pz1dtc8r45VPfGfC9O9++aNzAl38r3W7/XKwmLEGIeImNPLDxGqgm1d6xuEHndpBKPvXBZZo7hK9jc/85aXyDLf7YRjaa2uL51IUp1bk6GWBvNWL29w9UYvQxDTsvdUxyYXnu9vncS+3jdPqCShrbE/OVMnTuiQbo8PBeAg/bi4iKKJxFCzGN661JNqIw8z57sC2F+H4finpqOKxcCfZ7IC3+D0RDs4frc50qui8d1d84N53dHGefo9nR8NiyL+aMh0a/V4BtqfKa0aOcx6AD3PxRDE5ad+sO4o8z0eW3aXpW2ScZf+/xexme1DFAhljrL5noAQgghhBBCCCFmHyVoEYuDwc46dR5EXytUTmOFdUO9hl5bgpSat6+Jy3fSym4tQUtK6OIryl5w172BUEmDvJZTKcPMdQVTVs/lw/X3AIykc/UmiVLh9Qy3bwXAbkz/42Op4YXV4StuSNe1QhoqhBBzTv1+6RmHAVYNxcRU4bsxIcvT/sMkAAe7qnvj5onkkUsevpGJqmB6TrrVzOLcW3nPOjI9F57Fi/s/DsBtQ6+vD7no43PJwfE0pvVVWzOLp3vkdk6jEGnLAp1rtrpnsSz8PiKP3uJjASVoefoFgctmOUHLe5Wg5WiRsScWCfHBoJZ6u0U+Aw3jL8lnPFPbFVVgW8YztA30dcqJskSou/5gUV7f2dOd4vo2VDLMPTekfeNxn2eFq103jTdfo6+4Roo13HxDbPPMnQAHh9JDRjN2sIh59Aeah3lCPMY6P78QQpwQVk59/6wZMilW2uOos0G2pejj97l0j64txCUjyxcH2+K4D62PhuSFp9wa+3RVfdxwdGm/n8cXGKExF1AZn1Dcw9N4fbGvDQ8jyLJ8qs/r81bOKj1yR3GkYrMXHzL2Fsrnn29IximEEEIIIYQQixAlaBGLhBR8/8JqNTV70soMmdDqmXOPXpm8JXvSGjLQMmOnS3Rcosm+lqGlFeY1xHGUQfhrutLYeuKmd0uRBMYD62OcPgM99dVgIK92N71/AAO7p5CfFp/fpaZV8P/bi45K3iKEOJ6kouj7kgy9UB3UPHlN0v3Ks3BOEj1dfs8HWLM23VvT/W59kcV4YGO87oH+c2qnLcMArmYr0CKVLEnn9iRa24qb88DOFFqQEnS1qj6ujZvJq1rq9CXZZ+/OOCfU7u0pEYzPW6Mjfv+WN08I0YlknGIREw2XHDPnk+3+SuqyIpwJdMp6oJjkG3LQMuNn7tOTdnjsR8t1PU6jfOhw2gr3+oNDNgibsiaKTJ8uQyoekHLMSSr425ZVrinx9PNB9QCTDUIZf0KIWaEuu8/xdfb2jp4dUk1g87D3TzsGOyX2Lol3aXsp1fT7vBtL3sfveVAZki6nL+MBl0/U4/FchlkzKNN91uPxtt2xNbfZM+vPXa0Li34vT5JVBot4wiRJzXF8KvmwRFhAMs6nXRC4dJZlnH8oGefRIs+eWMTUjS1/oCjj2s71rCX9cVPWUnLjzOPZ3MgbK+sjpNXe8D/jU8fesDI3rbH3xRf7/Frx4aGspZfjMLyWU/FAMkrd2Gym8oYiVsNPWRiykyOxxMLd4WygSmqQHx4oDNL0YHKgu3rY8Yer5SE92FhahdfqsRDiSCnj8fJ9Knr0cmKSIkGKG27ZoGnxrK0I8bhuUsKU8SphyqUP3QLAllPiPbZcyJtyIa3wLJ47HO/X13ElUNVEhc7FNe/blszL7/c5URbU1Rk04rAdjzVMxq7PH1Dc92XkCSFmgGL2hBBCCCGEEGIRIs+eWPzsb4+9A6B7rPa2jIvIhXeTt290V5LxlLEk6XDbmVZt+4q25ORrSi3LOJHJDWnVuk2Y4JIkv17TwwfVCnkqCry8/7m56eCGuEq+avze2vlGwhtyn94NKUbw82k8aztjR1yqtC6Mxc9RrkJn2aoknkKIAvfkuUeuUBTkGL3EpN0PwIrC+ZXl5/2d8vPmvXxyV+d9a9nGWIx8efgGUM+0fLN7En1sfk/tqY4vM2tC4U0rSR66trg+H7crSVx6CmWMd/JklnHkjns2U4iBxyUCKqsg5j+PAt+d60EIRzF7Yumxr3pQaI1jS3gilsfz/dp+T4UNnam3S4mny3dGN8QHgXU3NNJkU8XVeaxHKefJcSnN0gvFWPODUJsh2/iM2XgtUoh3xAGuLh7IUoxIvoZ1lrfI9Z5MCQKEEM/PrzaHeC9oqx1X1oyDou5csd/3uXyxVh6hmbylpbxCvlYylsq2fM6uhgHX03nOVkOsOY7t9TFD52csJfbZ2PX7bSN2Eaq5IJdXsLrxKZYiCyhm7ykXBF45yzF7OxSzd7RIximEEEIIIYQQixB59sSSZl2I3q6ceXK8WI1N0p6cKS55z8oCuDlD3FjcHNplue3qU7bG4zwLaE/LAJIMKJeJGK/KRDSLwXs2N/t48T97VX2srTRlVEVWt7wy3bKKvbw7JWbpS/LVsbjxgsIAV3Jd7JMKuNfKUtgz0itJPIVYnERv/7oQvVauYgDyvcll7KWMsrevKDFTUiQu8fusZwP2RCUlnaVjWmgUVweyJ27PRLzvXsidQF3F4Ym5XMZe3o/dW5ll+FelhjIbsmfM9DmiyNScM4zmbJot92Zno3sEpZoQ8uwtlM9/vDCztcAVwMnAv4QQ2orCdKCYPbGkqeSHMWYk3F5l09wysbX1mNIgzOUNklRo2am357ZDD74MgIFN0dhzQ8gNIyA/HFzS/UmgXifKOf+a/QC89J23xmte1CJHemF6yNoxdaZNz3Q30l8Za72kGk5JqllKrjyOhn31WMXeruJBzWWg6eHpIMVn869yv6dTvytt9dAixKIgGSe5ZMHni7Z0vxhdn2KeKQzBTfU+Ze1RJ99n032kJotM952REO9lbjyW58nldFwGWWRa9vvVJcT7rt+T7+4/O3dZ1ZdincfiJtc5hWyUrrihIT8tZPG5BuBIuo+urOIUc/+euNk8ke7VtXhoxeWJBUwAfnDiL2tmX0lX9qtvDiF8tmg/FbgROAt4BLgX+LUQwjdT+znAR4BTgQeBN4UQvnSMY3ov8FrgDOB5IYS7i7Yjul4IYRfpzmlmt5rZE0MIh42OlIxTCCGEEEIIsRh4XQihJ/18ttEWgN8PIZwbQugG7qNWdIUPAoMhhHOAQeCP2i5gZieb2YrGviea2Wkt3W8Bfg6YbGlrvZ6ZnWVmdzV+fru41suB/TMx9ECePbHkcS9TTCxQq4WUFmRdjlPKNx1foc1FdqlWby88JXriwplR2nkG+2vng4ZslLqMM0szXeLztE6Jj2doy8lfrqpWr3PSlrR67ivcpYRqZLi+Ml6v/3RW7fhcgL0UDexq2ZfISRdcxrQ2fTcby17y8gkxv0me+UJi6AmpRpMQYXKjyymLwzz5SG9nxskscUz3L79H1Dx8/vjl3rJSIpnuxfm+lc5d1qLzzMK1xC5OD7XrLh+O9++cubgcb6MmXol7D/185RxRXTfKMGvJVzake3MSpGVFRSH1FELMPiGEh4DPFbs+D/wGgJktB9YAL0ltI8B2M3uqe/4KVgEjZnZxCOGAmT2Z+M9+A/ChxjXdE1c7wWGudx/w4rbPYGaXA2eEEKa5O9WRsScEkI2Okep/a8VwZzFbaGRwS0ZOjmsrHmh8sre10YA89FD8R1+2qTNONqfVLv91U/a2deFHtfOVxYE9PXiWajaz1BXnzA8fY1WTZxNtfaDxhzs/Z09nF5d/OjUZaDJks0FpH4vHhL/r6D9pN7cMQAgxZ6TSCTlmucgC7LLNnI3XJZLl+vjKqePoPB7O7y2Htsd744Xcmvt4ofJ8vy0kkh4rlzMWJ/ljWdYml7xZ33m8S9l7x+P9zzMnl6V4xrp7am2l0ZizL6c+B1NZnmz8Ffj9zrMhA1lglo1el8yj+6BYJBzieMg4n2JmZSDg9SGE61v6fdSiZbULeHsI4V/aTmZmy4iGnj9UPRt4IITwKEAI4VEz+3raXzP2QghfMLM3A7cn4+t9wE0hhJqhdxhmfL1izK8Afg/4jJl9EPhPLYZoBzL2hBBCCCGEEPOZb80gQcu/DSF8zcxOBq4lapLeOEXfPyRWA9w+Rfu0hBD+PEkr/wJ4TwjhA0dzniO85meAZx3pcTL2hKhRZY6ctFQzKqRMnR7wP/HpjqNG13ZKhUa60+px8p49vb9Frj1dDafENne79Xe2ee27dSGtYhdJEHzVONfw45qOazYzfvYUbr/eXfWMeb6KXnrvLuNGAFYNJflTWTh5MJ1n26dq78sVbpfBrgju4bufCkk8hTi+pHtcb5RY1wp/D8WtFwWvyc/T/+1kT11+OTnWmUSlI+MvVYZLly0uS+qHWvKolAjKs//WMnhu8UvE+4ZL1EvcE9fmmXSPnGccdspC6n786No41lJR0fRkugy0LflW2307F0zPSWfk0RNiNgghfC1tHzGzISqvXY2UNOU5wMUhhENp99eAZ5rZScnLdhJwWtrfdo6nAVuJD469ZvYnIYQjeXA5ousdCyq9IMQM2Zz+VcqHhkPro/zoj095AwCf4HW57bbx18cXjYedMuPmmqEYo+dyyEuLSX9NX4rfSw82LkvKme8o4uGcFjmmG3TvfmgrADef8v+3d+5RdtVVnv/ugOJrSYsagSghyisFVMoYn81qdcYJoLLEB0JFdBAFu1MMsmyVdM80xu7RSUQdpFNpRFYQH1UgoqGxCVYz42MyPmaYUEkziUEkBFBsQBR1xBfZ88f57d/Zv9/51U1V5VbVvTffz1pZVXXPuef8zrm/e3L22d/93ae71f9Lsq6vIYwyqHBjE5u6uwbu+TqJjCsbd7F2ZihtKuylVrn1Ols4ELIv1A3PLbiLQYoFX/77m7djOb1etPzu7Dtt6zgZuTlbvg+fBJAGUnads+tfY58otH7x1zYb5+jE149c0p4Eq1nLnFZtcSbaXpHCg7TYescT2+HQcZNMhS5qvfCMZYpXt7n1wldbt14QkacCOFBVHw0yzv8MoE9V35it9xEArwDwOlX9TbbsmwCuUtUviMjZAN6lqq8u7OswAGMA1qjqF0VkKYAvAzhbVb8zwfjuAfD6zI1zUvvbVxjsETJZBkOfPWciEFsfSLiRGa7/Y4/1GOemJir+psewmhM9ui7gTfrpoe4Jtcrdkd3yyBsAAIceUmUNH+yrnyw36mlywwTUT8Qte3idy9qV+grmWLYzzyIC9U1Ongn1vbgu3pA9dfdiCrtxM6OagcIAaE9OyASE4M4s/0uBkP0MgY3P9PuHOsm6hfcVH+hkhk7FWmMLhPzYjGhQldYuA1kbBSBe26zfX7KPcG312T/r+Wc1e3ZJtfpioM5oxn35XqYD1Y+89U5CCPziufG13jt43SLToYuCvYOXKf60zcHepr0Ge88HcAOAA8K/7QAuVNUHRORmAJcAeAzAHQDuDL8DwC4LCEXkOFStEJ4B4OeoWiHsnGBf/aq60b3WD+BQVR3L1r0cwJsAHArgYQA/U9Xjp7K/fYUyTkIIIYQQQkjXoqp3A94JKVn2WvenlNYJ6/0AwEsnua+7s9e2AdhWWPdCABfuy/72FWb2CJkytRxqoS4CUMsf7SkuUGfLTKoUM31eehme/pqM09ezjUmQWukDyd59XV1cN2TLLFMGuAa9uRzLYVKpnz5StYtJnEJtNwNhjCPN2pdGOwZvr96f1QwWsncx+1iSeGZPxvMn9QBqqZVlLSn1JPszi913I2tcnpBfCwp1dS3fY+sPlR2Di9suyTCzjOJacVLTraem73fH0WiUviS0N3DlMpY1tBYw3lU5jtMSD5YrKCkLDJfZi21l+tKMnlcxxH1QqknaBjN73XL8nQYze4RMmfqGYrdUrRrmaxU0jTut4U4cC6Dus7cKqwEA147UUsk1+CsArgWCv9kYrH5EaWa4edu5spaYRwvzi6ofPhCMEqP8hqRgNPBX/auT9wB1AGs3YjEQLfXLCvtIth1kS2tflvaU8lhA/MKVzUDOAmA7pvGSjnNJVrMz4G52R03SRaMX0muE3ndh3sfgQ5xB0klZ6xQfbIXXYh1bMBHxAZFJHa2u165VgDNvMomoeSAM1TLKGACiUM8bsIdN8Xs/WC/TH1fXTUEwb3ESS2vjEtcPNYhr19d9Tu16aTW/u61vqH/feBjHBuuTWjBYsevdjvrc5v31jEReOsSeeWQ/RjETrRfINJk31wMghBBCCCGEENJ+KOMkpC1YC4T6yfbYisypruBYl2evPGY9Hp8e25P5kixrqGm+ku83uts5E5eGrflo/TR6VN+a7MIya771QjSoCZIl73gXl2XnIXHFsyfktsyZwTScRuuB1JjUKmQNvAlDUVIGUFZFugszfXKZIrvO2Bw3V2BrYQAAd2yv3DBP2Fa1RUnMV0LLFmxNG58n37/cdMRl9BuGLLbMSR3jtUXOr7YdJO8lTKHgWxfkjc+TsZlrcG465d0wV6buwdYKB6ivd0VpumEqC8voDT+huSxkD7esrMyzlkp9bSSk/XSRjPPpyxTL2izj/AZlnNOFMk5C2kJVKzZmN1FA7CtnxBukzXUQkjtWesc2e82CLLNH972oYuuEIMOKsk6HBXcD28er7Tm3c3Oci4HkaL0sOtXlbphPqtfZ/dusz5YP0EaqH3eMVDedr8PXqnWkllNZf0BzFT0FN9bbPimVhkYHPZO8OmINzwoX4BWCSwDYvY5W6KTTsFq118RX7GHLoGxqLIuEBz/zgtTR15l9Hm+vfgkB0e1a1wNbHfCn8N5qHyH4OWukdtO9dmUauPhrS+NBypJmQBSvLUFiudvZFsQHT9byIdTcYWsd7Nn7Tc65KkjNAdczNOyv6AZsbqLhHA2e5a4bF6Tr2DUmaWsDu/6kgbV/v8EgjxDSyTDYI4QQQgghhLSHPWDNXgdBGSchM03oz2fZs8/210+Bd6CS/5j5SbFPVXha32hgDjSeqHuDlJgRDJm9mD102S8zPTjzkY0AgFMOqTNr9pT7q3gTgLoHX+IYapnIQp8s/XD1RP7kG6pt5j0BgfrJfqtefva03p70WzYCqDOb3lgiji3vxZU5APrXinIuk4QtodELaTchkzdcZb1yySEAYEn12kKtvndeVmnf84PwOwDANpzYWCdf19x9q/1W14vNQ9V39Cm6GACwVLbW62TSSJ8htyzfv26qXHyPPHVHY/9egQA4FQJ8f9Jw3QpmLr4HafwulmTrDRfeiou1duONUtVwHF7i/fVNb0jeJ5/XdLtArWiwa5t3A43n1DKZvDaQ2aCLZJxPW6bob7OM87uUcU4XBnuEzBbDBflgoY7PmL8yrbEzxz0vp3oyKmfOvHYOcE6Z27IbwELNoN1Y2d/+Nbtpey8+VY3H1dVYrZAFiWvc3dLSbZVetOHmWaBR++PGGc+D7dfVLpkcLcrB3LHZ+6we0oLcxNXPahVL9ZDhd1vfgs1E6uUkqRVs/UCMKqCzdgBJIJe5YUbH3AH3dvsuDGWOs349a0+ysvkgaEt/qCNbUX0P/bw3TKodWx/01WOMD4csoBmug0X73i86JAR5Jt92wZI9wCk2HLfvmdUIh9pB/9DHrnP59xcA5o+Ea8KK8CBrpCDDtDGNhxcG3P6zB2/n9AX5547CtUUOq17w9dCjlHuTuYDBXrccf6fBYI+QWeevG69EwwV3QxPr0CwQshuk8fp9DYMVV09nPffM1rx4Q5QZpPg+gbGvXXhCvkWXAACWnuuK/uyy28iCAXfofwAAnLDirmT//um9BYA2pqTmz1onhH5bdiOYWMDn9Xs+WBuvfkRbegt6nQW8bzWRrOPXs3P62+Z78m0W7eHtJn0HbxB7Butnl/Wy83MjPoDZln1/gaahkgVJzuDEMko275M+caGvZj4nk8DKvpOXhZ/+Finvj9miF559b61mFmi2QYmtE8br12KwaMfvvncxk571Ak0eNoXgOAaNroVM3WpiV/V3MH/5MOpg9ZxtIYDLM3QOM22xrGOx5s/ez+8vmXO6KNh7yjLFcW0O9m5nsDdd2HqBEEIIIYQQQnoQZvYImUsWZ9JO5/IWJV5Z9s0//W84djqJ5WeDm947HqkyTPMu0HQ7jijndE3N41N+eyI/gbtlNaTrGu+3bINl4kwGmdTl5E/vpX6ybuemUWtYknoGWZfPPsQ6yJBZKNUFxnGvT135AFfjFOqbclktAFy8Pa21SuzhTeKZ2dv7zy/PULD2Z66ZOOvuaciOS5ndFWnWuZj1y1qW+MxynnVKsnZDqfwxfkdd65RGO4KC/DmXgfvvT77fUs2eyShjpu5cd/24KPzMG7ijKVstuvmOZg6f6+pF87enMk57v8/MGdYqxp9bUzDYddOOO6lHZj0e6TiY2euW4+80GOwR0klsdcFSf3oDuOe5oZfWtYXvrF1TL3KvrUlXsYDIY2Ynx2IngLrODqhvNu2mNd6QOTmVBafxZtXdrOU3cCYHW5NoxSrM/GXtBnezeCuS98UbbHfTF2Ws1pNwhzdsqDYwX48H4Or6HHbTZzLSRMYV6pe2bA+1T3Zu/HnNjSKGC30OLwtj3ZCNFWiYPyQythXZgwCTtQ46o43R1KCCNYMFzCBp1AdtoY1BHiw1p2bSny1uUgaqX6wON+/JBlfzZQ8HCssMC7oSGfGSLMh0NXON/eUPFByxzcGK5vzP5Z/2QAOoa+Xs+2q1f4CrmbNjGwgL3DnOTVeSmsWMRt8+T2ZUBaCWnRYeXDWwhysXFJbl204MX/hdIp0Gg71uOf5Og60XCCGEEEIIIe1BwdYLHQQze4R0KkHGqJ+oMnry2t9Xr/un+NlTay9DjKYn4al9lGo6xz17oh2d+9Y785XMRCJm2MTtf7HLNqCWXAKtjWXy9ZNm6LYsb5Qe3m8SLqDO1hWzF+Gpv7mJmizNG7yYtNT2YXI0oCn/io6F4qR+wXwlZhi9YUzIGkSp5/pCZiOM8bT+6wEAN/WdERf5cwnUbS5se0D2WebkLTv63DmOWaaQPi017G6Z2bAm4JORuLVat9lMPBKNbQpZ06HUvMe+B3s2S1zFMuA272OTbziHxyDxLWXvTOJXlPhmn4Vtz7cXiBJhy2j5rJEd28vC3/asutRmIMwRn3WLDpF5ZtJnujKnX581nsgZ17t5xnkTvhteGWCyxzinC67C0SE0nCuTLAP1Ncmf08ZY8rYuPtNpWc7cqdRn77Ksn/+M4/ferlFDNF8h3UAXZfaevEyxqM2ZvR3M7E0XBnuEdDqD6Q1VUeo3XnhfJluyoMff9DZkU/4GaQIXOwt6gObNngVdQFo/CNR27bF20BMcL5dvaN5Qm0SsIecssGVlX/w9l12WZKiNYHPcbSyTWEapqm+3YDeZtq6TeObup1GiV2jvYPtNAjkLErLxe+I2w2eVBMLZ+U+ChaydR6l1R14PWepvaOfSxl2qeTI5n992oy2GfdZuzuZBevFzy2vevMQ4D3bzvovuGEt/l863EWvW8pYfQ4WaucxVFkD8vBoSYR8QhkDOO+QajeM+NwRP/vsT5rQdxz/h9XHZCedWDrmNJ++jhYB0AMl2gFqarb+rvtNH3hBaMPjzH97Xqk9fPH+Z86cff3TDdbd4jVYTJVlnVkeZXPdYj0e6EgZ73XL8nQZlnIQQQgghhJD2sAeUcXYQzOwR0m345uy5QYHPTOTZjizTAdTmCZaR8W581nA5GsWY+UrJFMEe4JWeueUSr4KbZlFiGrI8lhGUE6u/5eD6mjWZzFRuupFkJi0zGjIaC/VHcVmxGTTKjeftXCcGL0HuaduM23PZHz38idUxbaqOyX82jWxXOMdJ9qaF02jMQA4XzDuCDDKa1/Qd0Vgl75OWOI2GjGKUz4YMTTL+8L5SZsr3ekzG7M951rsucWo0VqXr+uyln8tAKqONmSHLHhWMWWqH2OCUuvXU5kr5/PUS33DeS+61MSN7WXgh71fpyM+x32/d37Jg1JJlLYvjyCXezhQoZtKtp99F9fvsOxQdb8251u+/helNbh6V98T0Y4uZQTf/it93oCiDjfMnOt4CzOiR7qSLMnsHLVMsaHNmbxcze9OFwR4hPcB8rSRKSc2a3WRlckyrz/PEWj0XiFysHw1vC7Vr4WY7sZe3GrVwk5XIsDKJlcnyvCV9vKEsNJ7O63lsHS8VjbVDJnks1TzZDflIM6CMMjpfqxjIg+VYe+ft5b8Xfhac/vIAcK2E+rSSq2LAav+AWr4YJW95nZY7tsZNP2rHR5Nf+npCO7cTBV1Afdy5ZBJAw/q/1Pjbtt1oLg5X/xgCsFyyCbjjNjmqP1cTNDM3OSOAxmdj+wCcI2omUfZBSC779TWUsdVJCMCiU+799f+nd/QfBQA4oe+uxvvzQLTkJtt4SFA6fpN29zeDtTjf87o6R+74m5BLUwfdvAsPR0b1rdWi8J32Na/5Z5u0ohgPPwfS7eGdbh/hlq4h2QSaNYqlB1Cxxo+BHekVGOx1y/F3GmyqTgghhBBCCCE9CDN7hPQEQT6WOBZWT8tjv69CZijvM+czM7c88gYAwKJDMvOFAbfbkD0sZQhyN79ccgeg0ZS6aNAwHl4oZGh8JghIZZQxIxnkjDFTUjDxKDalzgwqbKyJK2PIdpUyU/FcZHK2ROpoxzjalOGVsjUNssyGl6iaIUXM9hayHybfW7NpNQBAX1ovm3dS9X9DbGC9viAjzD//gvQ1SgR9RvTq6nitYbllyqzfHNCUDZckmjFr1aoXWyBp6p1JQmPfuNIxGuNuW9vLGbFE6hrmWTx/9v0DmtLakuT4suqHyXaLBjNnZX+7DHOjUXuL713xu5lTkF/HfWSSS79OyQ01z9bG81mQ6sbPxslYbZ4vRpWRP6cvfO93+N6EzOiRXqOLMntPWKb4kzZn9h5mZm+6MNgjpNcJLRzM5l3PqO3pZVeoFTOplNQSQ5NxxhvqklQzBDv2fqsBBCZuouyDnSiHs4DE3+zmEsVwY5u0d8hrhUrW7y0c+xrNnN37t4xUEs834isA6ptmb+Fu0kgL9s58ZGNcNu+ZhVYZSGWo0Y0yD/pQB0L5TX7R8dTG/aR6vYs3hCCrJHEdSmV40fHSBWu5dX4iVc3kk1Gq6Vpo2DyJn3Eh2LFgIb7fzb/8+H0gZe0z3otPVdu2+TPuxmjjDQFFSSLbCFJc8OSdUYFyy5JcjpoElHngO1RouG6f31Aa/AKFdiQ+kPKunw5fc1rLf/9v9YJrkzJRsJpsKw/g3fzZsy5z1g3fW/+wYTuq789N6+t2IhG7B8xl0IVgsRGYojDv6K5J9gsY7HXL8XcaDPYI6XlCrZjd7LnshwUOsa5rm6uryWr94s2fq4sa3VDdQB+LnQCchTzQvFlrFWxZQOMzFTbOy8LP76GB3bTaja1vD2B1UDGzktvkeywzNOBey25gS33y8rrCNS5aXSpbq18yC/2SCU4pkLVt7cSx1bqh5impmQzvzz/HavjNAM7IMzKWfbFsKNAMbpNANtzkNzJipXM7Hn7uKLQlyGz5fVsLC4gs2+czsnW2LAQyg6em+0LTYCYhr0fLW2gAsf7RxuE/t5g1tQDWjsft3x6uxMycr3nMAtDcDAmos1ylrKN9XpbZOgi/D7sfiOtYkG+fX6wLBGJwFbOuYd3kQUKr70tWs1nMzMcedoVANn+4UujTl2d0/fmP9a8M7sh+BYO9bjn+ToM1e4QQQgghhBDSgzCzR8j+xuKCvHKg+pG46YWn5wt1UbJqKVNUknH6BtuAy7B42/Xx8NMyKu7JfumJPpDV/gT5XpSV3d+8nuWtIyxTAjQzUvp2J3H9fCpRM7yM86xNG5N1i06lRqFmzrZVahwez0lwFbTPodiCoORGGLDPxmowAeCUQ24E4DIsltl0Uk3L+pjUdFC+1Nx4yGiZrK7UeiLKgZ0M0rJuVitqmdkBlxqL0rzQ8qBovS9ZOwTvGGnnJM9CodDOYElTFtlwoy1kHe395nD7xJBhA5ounD7rljcTjzWXhbYeEefGedr26wEAN/UFieS16XEBzYyob2sQHWLDZxIzjH7OhjHlcxRoIf8sSIUbNcNAnUnNXGx9ptzmRn3eP1reJyH7DV2U2TtgmeKpbc7s/YqZvenCYI8QEgPAvE4JqG867UbUB3HxhqxQz2Y3t14aCGTBitWomWGFt5e3YCeTk2KJG+PW7IbY3WyetjLcEMvp4ZVbq+PQWvoVb+TD+H3NUVwWgttSu4JcBnmmC3auC+83gxULSM3wBigEbi5os2A33iSXzlFgz+YQ7J7krud5AO0DwnBOTQ753/FvAADHf6yu+brmg1U9n0kEvcTXggoLKKyGzgcLDWnmYheIDWRjyow6qiGGgMRqDp3EN0o0Q0C4XKt55CWbMaAL56+ljNAotSco9VC0wDWrC0zIl7WQsRalivk+SuO0c5K1ogDqhxoNMxugGQDnRi+O0jWhEQDb8biaUZN4R/noy9wGslq9OFeSawNbJxCSwmCvW46/06CMkxBCCCGEEEJ6EGb2CCFlTO6ZtyfwtLJst9fGqx/efMSIjoNZhsevb9kaMzgpyuGCDM+kd0CdGWk0BXdjvWOkanz9LzgRQCZVtGxF1hah2DrBzkPpHGXZIt9CoGE+ckFzPZM2mkS2mKEJWbNS4+5SA3E9tcoEyqbs+t/qc/RtKUKWseHiWpJRFiSi+bE15gFSSWM1/DpDHJ0m+46whRXu/Jubam5wA6D+vCyjFbJu3s0yfibetMUI52TPWdV5jLJY76BZmNPGRKZB3s0zZjaDnNpnpFtm/XLMBMfJYOP5O7fa/8INzcxabCpfysxai4NgjGPGLEXzIXPx9S1LbL5Ytjq6i1KqScjEdFFmT5YpDmxzZu+PzOxNlwPnegCEkA5lR7jZ3pxJJX0NWxbc2A0iAOxGkD+OZAGB7zFmsq9CD7solww3i2NLqmBnuYtPxjYfEV6rbj59QGCSwHw7nhOkklRaC4JSn8JIONaxk9wNvUnNTgrH5s5NdBoNrQu8RNFIavQA6CJXMyg3V+/fWr1/bHMzIIrjDa/5Grax4bB+LocFIK/UZIxRjrumHo/Vb565sgo63jjylbjM92MEEANhL4M1uW88X+78W63k2FBw00So7xp5IK7T6KE34PZn5zkL8nzNaXSGLQWwdi5sm0FZu1t2uZVCkHNWM5AeQ3VMiWw2Xyd8FvYAIq9hBer5W5Ixrh2tah5tbifvz3pORvmsO9YYrAWp8e4lzZrDhsOtm1txLFYie3X9fbDAczE+CwA4Z1s1R17YX9fs5teGsaGsvhIAhpoSUUIIIe2FMk5CCCGEEEII6UGY2SOEtCZ7+r58pM4wjK1KszYlp07LbI1qlYUYT1I0FZbFGbvMZc0skzAc/g5mLGPb3FhWZhJNrxqxrIVlf0qOh+MfSsfk3h+bemeOlYnj5rq0qbo1+a7GmWb0cqMXAHhse5DThUzXyafeWA9gcSETkpNnrXwD97DMnCJP2X5LXGTyR++eCgC7V7nMUnDTXDvcdG99bGUmozRcm0WTNMYM01lubpj8cGt6jCZrBepsl+3LL4syxCCDtOyxd/yM533zcck4AJdRtcyyyU9X1ePZ89xMorm+INEMWEbPyxjHBqv1zaAn6UEXzo299vU3V06pJ99Qf/5jo9VnMzaBKy4ADMpA9cvW5rjMNGd8ZbXOWtTn5l83LQQAyLVVZjKeD2ci05j/F9Vzy44pZlJDpvT27c3sZRzTKCWahBAyF7BmjxCy74T6Pi9VzGueGg3UHXGZr3maqB7QB3RXp/V03g3Ut2hobNuwm/2r0xokAHVbiBCY6K9DndtnC46XJllzMs7Y8iGcB3M1tBtlAPWNcKl1QthmbKod6qtwkVsnq1WMzpWFMVnwAgDzwk1+lEhGp9OCY2TWegJAow6rKNHN1k22kzmE3tGf1k5Wq4d2FOeGeeQkh7lDZEPyCcTPrdQWxD4Da7Wx6tTVyetAoZm5+4xsvjbqKFs0BY/1eUjr5zxeHjt2bpiv4bi3bO+Ly07BLck2TT6bzK1V6b68ZDiXFFt9oJeKWquOQw/Z3RhnDLIzqXKUfhNCZoAuq9lDm2v2wJq96UIZJyGEEEIIIYT0IMzsEUJmhrx5u/XZunqTWyc0zA6ZBp/ZsKxJ3qfPJHwA8NNHKjnavAuq65g36LAsRS4bTZo7h4ygZT+SzEomtYtZHJc9yzMqJaleI0PpHSft/WGbPuNjvf9i9sSyZ06qGd/fl51rFLJffp2C+yQwQWYpoGfUmUGTm9oxmrTVO142zpvff+ZwmZ8HwB23yQh99jNmIG8Nx9GUOBqxAbjPuoXfbb6YK+ix2BlXWRVSo43G80Ccy9HFMkh0vZtmNDgJn7/POlvWsh//AgC4aUVojj7u9pH1SUy2nc0NO8drNq2O68iC8J0oZP3suJNMcIb+OGSyd4XtePmzvW8Je+ERMnsws9ctx99pMNgjhMwSVQ1YYm9vtu4hgLEbTACQv0yvTaWA0LAbcrPCB5xTYit7+syW/qt4U1y0dEVVgGYW/kvXh4I0d9Ofj8lLRa1GzILTW3BKtR3ZWm/AArc16Xs8VntlstRS7VhJqhfrr6K9vQuyh09N3l9svG7kjbuB2qk0aw6e0Gqb4RzGZvAhWPfBcmwdEZwmS4GUBZkWPJWIn41zKo3nfUka9Kbn78NhWWggvq1eFs93CPLMRdXXXEaH0zDHowMmgE/hvQDcZ2rS5svcwH8bfrZob2IS1bgv35Q8EAPxbXUgHj9TewBzUXMfVoca5bSJU6nBII+Q2YPBXrccf6fBYI8QModUASDeGW6+L3KLrOZsvPoxuv2NyImW89a6wffAM4OUcGMe+5atKGTBQqbDZxEbve9KFv5Zfz0LDIE6WIwGJSsKRiFZRsgb3NhrFqzYDX2SfcuzTi5jlgckPujKM5mfxPsAAK/D1xqHGAOIQg88C1aiUYsPCLNgz4IHoBnAWUDrDW4eXJGav/jzFgO48Bm3qlmLuNYTMdlbGFscY9YnLsls2X7yQKxQMxhx8ydm1kKtX/z8fbCZ17q6BwkWwFnWuphZtjq6sB2fNW4EwPZAwI9/B7N2hHQWDPa65fg7DdbsEUIIIYQQQkgPwsweIaRzGHSZmdHUadOyN95lM2aUsiwIUK6xA5A0e46Oi5m7oMdcCefdH66VhdqvmL0712VmLkIy7qJjY3ADjbVfTiqYZ8ZKY4vHGNokeDfMLSurLONOHAsAGOxzMj5zGM3khyWJ6Pz+exv7tTYINl5bx2fjLMuUbDMnZJLMaTJKZdFsq2HHA9RZ06JE1Mg+G8vsAnVtm9W1mVPpokN2xHUso9myvi2rp/Of//INE9d65rLdYl0hsm23kJHaOv4YY9Z1fdq43R9bzEyy9o6QLoCZvW45/plERE4C8E4ABwH4haq2Klap3sNgjxDS2bwk/KxuRJer6/NXMu/IyW6gvWTPZHDecr6x7YBZ6A84F41GIFOw5x+TEICFnnKtpKa+5irWWoUgzWSsSe2VC1zz/ZvssBikZBJZM3Pxx2ZSy1LrgoHESaQpJwQQAwk7Xt+DzrYd2xKEgKSVVNKCZo/Jbu9ZsBgAIK90/59lxii+ri/KGIMJTQy2fc2bBdkrs/YC1Y4TbGyJDLggn41kdZA2J5OAPNTTXbyh+dnEOjoLAEPtofXGA9zctPk/6s9f+p0ihHQD3RTsvUiTfjlt4Yktj19Engng8wBeAOB3AO4C8B5Vfaiw7pMA/FcAr0FVIf1dVT0/LDsGwDUAngngZwDeoao/nO6oReTjAN4M4EgAJ6rqHW7ZPu1LRG4E8DZV/XWr9SjjJIQQQgghhHQzCuBjqnqsqvYD+BGaj0SNj6EK8o5R1RMB/I1bdgWAYVU9BsAwgE/nbxaRg0RkYfba00Tk8MK+NgL4MwDNpqUt9iUiLxCRW7N/H3DLXwdgx94CPYCZvbkeBiFknwgZiq2nTriGZZbWhFTP0m21VNCaeX8ebwfgpJbAhNlCn2GyjGB0U3RZxNxgpei4GJ5RlrJwuenIHSPVWE/Ydlfj2EqN6i2jFdshtHDFjGYi3rHTzmn4r9IyjYDL5IXzFccvL4nrjOqVALyb4wvqbQe5ae4maUYtADAoX0rWTcxDMtMRy8glBiWZ+UhRxphJhZPbghbZYhu3nYeSDDhmAgfCWL0xT8jIxc84nLeLtc605dnmYmbRxhvkwLiaTc0J6V2Y2ZvK8YvImwH8haq+Jnv9aQDuB/DcPFASkfkA7gTwTFV9XEQOQJVxO9pnCEXkRQBGAZymqjtF5GAAmwBsUNWrJhjPPQBeb5m9ye5rgm2dA+BIVV09qXPBYI8Q0ptUN9DLtbqhL9WORflc5rwIlOWLQBYQGlEq94f6NXMYzf5rsu16Yl1XCxlko28f0HxmmTtQlsYI1EGdtWAIy/Ro1/ri+qx1hQ9+8rYKmSwUAJZvn7hPXl5/abV+SUBkAZlt00kn8/5yJp8s9UksBW1RmpkH6S1aSPjPzY4pzp/1X228LW4zjN/adPhx5ucm6bOYt1Hwc2txmFt0zCRkP6Kbgr0XKvCtNm/14N0AHnYvXKkanio29i/zAIwB+EdVvTxbtgTAV8K/VwP4NYD/pKqbQxD3OVU93q2/HcDZqrol284rAWwAcA6ATwD4Qr6vbP17kAZ7k95Xtp3Xo8oImn323+wtODyw1UJCCCGEEEIImWMenkKw+/eogrh1hWUHAng+gNtV9QMi8lIAN4nIUVMZjKp+K8gqvw3g0laBXjtR1a8BeO5U3sNgjxDSo1TZjrGYrHpC8joADK4LGZmB6kdJBji2Nc3+eDlgzNDAsoa1G6Zl9PIeamMn1RlGc7E0GaPPLNkyMzYZG6/e56WC4yPVwB+Uw8L4nQwzY2yz2294kPhgMLi0LNzJqJuC4/rws/RfZZ7RK2TEoowy9JD38te8v1w0o/EZspClzPsk+vfHcxnGcexIndnL+xt6GWqeiTttZXWwN/Wd0TyQAaT7ckSJbGYGAwCDqJbtWVdNwOtwelzmHWUBRMOW3de6bN54+LnDMnofrZfVpqGEEEIcwRDlaFQSyz2FVXYD+CMqGSZU9fsi8jCAYwDcC2CBiBzgpJWHA7ivsJ/nAFiN6uI8KCJfVtWpyCzum+y+9hXKOAkhpMTiIKkcCH/nDbRRu0laK4Kk5ixrHB7dGb2c0uR7habqe86qggRr+XBafwhI1tcBSS41Ldb8hbYMiVNjVuNXqv1ryEdLTp99oZn39ub7t/SHdgqhTYK1MPAUJbGB/Pi9ZDUGcuF82efgHT9N2mlBtpdRtmp1YOT1hJ68CbzJUK2VB1DX8cUxe4mmWOD619mWPwpCCCmz38s493r8IvIRAK8A8DpV/U2L9cYAfFxVx4Ij5ncAHKWqvxCRbwK4SlW/ICJnA3iXqr46e/9hqGSia1T1iyKyFMCXUUkwvzPBPu+Bk3GG1/a6r3bAzB4hhBBCCCGkTewB8Nis7lFEjkf1BO1OAN8REQDYpapvFJGbAVyiqtb8788BbBCRTwD4A4C3q+ov3LJrROQSAD8H8I7C7p6MqlZuIwCo6hYROR3AoYVxXQ7gTWHZrSLyM1enN5l97TPM7BFCyJR4SfOl4YIbaPgvxfqkmbviJ/G+uEp01rQMk88ajqR9/UoGLY2M3Fn1smhoUsperUvXiU3R1x9RWDkwVBuEjOpbAdQyRpO/ejObRhP50ritB+HicP58a9ismXw8ftTnJjZxt6yjP9bMadMapwN1tjCXiHoHTMvoPYanAAA+hfcixzJ7llFMMpVDuaSWJiqEkH2hmzJ7S7RKfLWTQ7vm+DuNOQv2ROQpAK4G8CJU2tn3h6LDfL0FAL4AYCmAH+YftIicB+BiAILK9vTCCTS62XYZ7BFCZhILCs31OUj0hgvtHYJEcctIX1zkW0QAaa1eTu4YCrjAwwVpxnx9AEAdSFpgs1t2xXWsDYCtE+vTgDqQiq6QVUCbNLwPAWix4XhBWgmkMlZDF1VBmixw/1eZpHPVBH+jlpFeF/abOJ2Op/uwcUR5JxAblUcWu3rMHSZJZXNyQshswWCvW46/05hLGef7AfxKVY8SkaMB/A8ROarQHPDXAD4E4OmoCiEjIrIoLHshqt4UmwCcDeBzMzx2QgjZC/8r+xkYmrgX2tJxF+ANpMvMoMW3IIjLllSZsYX6I7efEKwMhp52znwm1vhZQGjClq11sLO2L8tW+qzbeLXNO7aH3n/rqwzlTqTBW7IPl3W7eHua7fRZO8MMVaJpjLjgK/TOs4zkk0eq0gzfesGCvJitG3cbH7CdVNusa+h81jb73IqmKAzyCCGkiWK2ZZxkYubN4b7PRNUnAqr6Q1S3Gw0tlKo+qqrfRhX05bwFwEZVfShk8z4TtksIIYQQQggh+zVzKeP8FYDnWyNAEVkP4C5V/eQE678KlXPOMvfa3wO4V1UvDX+/BJWrTf/e908ZJyGk13CZqcXZs7Pfut9flr1tKo3HUTcTH8/Sj2e69ghL14csZcgwmnQUAB7sC7V2ecN2X3Noy6y+0O9qNNTD2TGW2hNEp0s6XBJCeoFuknH2KzBx6cH0WNQ1x99pzJiMU0S2AJio2v85M7XfVojI+QDOr/46eC6GQAghM4iTFe5oITHclf092lxlbMh+a9YDDg4NFDe7NhFWpHLVBwVNhgqvTbSsJKNsdYwM8gghhJCZC/ZUdWmr5SJyL4CFAB4KLx0B4BtT3I1twzgCLZoRquqVAK6s9n/4/mlDSgghhBBCyIzxOIBfzvUgSGAua/auR9BRBoOWFwO4ZYrbuAHA6SLybBGZB+A8AF9q6ygJIYQQQgghpAuZy2DvUgB/IiJ3AfgagPNV9VcAICJ/KyJ/Hn4/QETuRxUc9ovI/SKyGgBU9W4AfwfgewB+COBuVG0aCCGEEEIIIWS/Zs5aL6jq/wNwxgTLLnG/Pw7guS2282kAn277AAkhhBBCCCFTZA/YeqFzmMvMHiGEEEIIIYSQGYLBHiGEEEIIIYT0IAz2CCGEEEIIIaQHmbOaPUIIIYQQQkivwZq9ToKZPUIIIYQQQgjpQRjsEUIIIYQQQkgPQhknIYQQQgghpE1QxtlJMLNHCCGEEEIIIT0Igz1CCCGEEEII6UEY7BFCCCGEEEJID8KaPUIIIYQQQkibULBmr3NgZo8QQgghhBBCehAGe4QQQgghhBDSgzDYI4QQQgghhJAehDV7hBBCCCGEkDbBPnudBDN7hBBCCCGEENKDMNgjhBBCCCGEkB6EMk5CCCGEEEJIm6CMs5NgZo8QQgghhBBCehAGe4QQQgghhBDSgzDYI4QQQgghhJAehDV7hBBCCCGEkDbBmr1Ogpk9QgghhBBCCOlBGOwRQgghhBBCSA9CGSchhBBCCCGkTVDG2Ukws0cIIYQQQgghPQiDPUIIIYQQQgjpQSjjJIQQQgghhJAOQUSeBOAKAL8EsEdVL5rutpjZI4QQQgghhLQJq9lr57+9IyLHiMh3ReTO8PPoqa432W1MFhH5uIjsEhEVkROmMN43AfiWql4I4Dcismy6Y2CwRwghhBBCCOl2rgAwrKrHABgG8OlprLfXbYjIQSKyMHvtaSJyeGFfGwH8GYDdUxzHQgD3hN93AThygmPZKwz2CCGEEEIIIV2LiMwHsBTAaHhpFMBSEXn2ZNeb7DYAnADgn0Xk2LDNgwGMAXhtPi5V3ayq901jvPeiCvgQfpaCxUmxH9fsPfAwsHraJ65LeBaAh+d6EKQr4dwh04Vzh0wXzh0yXfaHubNw76t0Cg98HVj9rDZv9Ekicpv7+0pVvdL9/TwAP1bVxwFAVR8XkZ+E1x+a5HoymW2o6v8RkfMA3Cwi5wD4BIAvqOpVUzievY33KwD+QUT6ARygqv97CttO2G+DPVXNo/SeQ0RuU9Vpa3zJ/gvnDpkunDtkunDukOnCudNZqOopcz2GmUZVvyUiHwDwbQCXqurlbd7+YwDOace2KOMkhBBCCCGEdDP3AVggIgcAQPh5eHh9sutNdhsQkecAWA3gowDeIiIvmaHx7jMM9gghhBBCCCFdi6o+CGAcwGB4aRDA7ar60GTXm+w2ROQwALcCWKuq/xHAWwBcKyKvaPd424Goaru3SToEETk/0zMTMik4d8h04dwh04Vzh0wXzh0CACJyHIBrADwDwM8BvENVd4rIzQAuUdXbWq23t2VuP88H0K+qG91r/QAOVdWxbN3LUbVROBRVXenPVPX4ye6rHTDYI4QQQgghhJAehDJOQgghhBBCCOlBGOx1MSLyFBG5TkTuEpEfiMjrJ1hvgYh8Q0QezWxrbfl5YRs/EpF1IsJ50eNMdu6EdYvzQ0ReJSK/EZHx8O/7s3cEZDYRkWNE5Lsicmf4eXRhnQNEZDjMk7tE5N2TWUZ6mzbMndUi8qC7zgzP7hGQuWKSc2e5iNwmIr8TkY9ny3jdIQQM9rqd9wP4laoeBeA0AFeJyNMK6/0awIcAvC1fICKLwrKXAzg6/Dt7xkZMOoVJzZ1JzI/tqjoQ/r10FsZN5oYrAAyr6jEAhgF8urDO2wAchWqOvBzAahE5chLLSG+zr3MHAD7nrjNDMz1g0jFMZu7cDeA8AJcWlvG6QwgY7HU7Z6K6GEJVfwjgNgCn5iup6qOq+m1UQV/OWwBsDC5EewB8JmyX9DaTmjvg/NjvEZH5AJYCGA0vjQJYKiJ5r9IzAXxGVfcEN7GNAM6YxDLSo7Rp7pD9kMnOHVW9S1VvB/DHwmY4rwgBg71u5wgAu93f9wJ43hxsg3Qfk/3c97beMSKyRUS+LyL/vv3DJB3A8wD8WFUfB4Dw8ydozpdWc4XXmf2TdswdADhLRLaJyJiIvHwmB0w6hsnOnVbwukMIgAPnegBkYkRkC6qLVYnnzOZYSHcxS3NnC4DnqeqjQe55q4j8WFVvbdP2CSHkCgAfUdU/iMi/A3CjiCxW1Z/N9cAIIaQbYGavg1HVpar6rAn+PY7qKdVC95YjANw3xd20Yxukw2jj3JlwPVX9pao+Gn7fhUoi86czcDhkbrkPwAIROQCoTA8AHI7mfGk1p3id2T/Z57mjqj9V1T+E3/85vH7CDI+bzD2TnTut4HWHEDDY63auB/AeAAguVS8GcMsUt3EDgNNF5NnBZfE8AF9q6yhJJzLZuTPh/BCRw0REwu+HAFgOYHzmh05mE1V9ENXnOhheGgRwe6iB8VwP4DwRmRfqak5HNX/2toz0KO2YOyKywFYSkQEARwJoe9Nh0llMYe60gtcdQsCm6l2NiDwVwGcBvBDA4wA+qKo3hmV/C+AnqnpFeCK2G8BBAA4G8CCAq1R1dVj3PQA+GDY7BuAC08mT3mSycyf8XZwfInIBgL8A8AdUkvDPqerHZvVAyKwgIscBuAbAMwD8HMA7VHWniNwM4BJVvS1cZ9ahCvoBYK2qXhneP+Ey0tu0Ye5cA+BFqK5TvwfwIVW9ebaPg8w+k5w7JwG4FsDTAQiARwG8S1W/zusOIRUM9gghhBBCCCGkB6GMkxBCCCGEEEJ6EAZ7hBBCCCGEENKDMNgjhBBCCCGEkB6EwR4hhBBCCCGE9CAM9gghhBBCCCGkB2GwRwghhBBCCCE9CIM9Qggh00JEvikid4vIqmm890IRuUtEbpuJsRFCCCGEwR4hhJB940JVXTPVN6nq5QDePQPjIYQQQkiAwR4hhJAEEXmtiIy7f78XkTdM4n3niMiYiHxJRH4gIv9NRPpE5J9E5E4R+aKIyGwcAyGEEEIY7BFCCMlQ1ZtVdUBVBwAMA7gNwNcn+fYXA3ifqh4H4DEAIwBWAOgDcCKAf9v+ERNCCCGkBIM9QgghRUTkZAB/CeANqvrbSb7tf6rq/eH32wFsVtVHVfWPALYCOGoGhkoIIYSQAgfO9QAIIYR0HiKyBMAVAE5W1Yem8FYfFD5e+Jv/7xBCCCGzBDN7hBBCEkRkAYAbALxdVe+c6/EQQgghZHow2COEEJLzbgDPBrDOmbS8eq4HRQghhJCpIao612MghBDShYjINwF8XFW/Ns33vyq8f1kbh0UIIYSQADN7hBBCpssjAC6dblN1AOsBPNz2URFCCCEEADN7hBBCCCGEENKTMLNHCCGEEEIIIT0Igz1CCCGEEEII6UEY7BFCCCGEEEJID8JgjxBCCCGEEEJ6EAZ7hBBCCCGEENKD/H+yKwc56H2UZAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 936x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ms.make_sub_plot(data)"
    ]
@@ -281,13 +1254,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "scroll-output"
-    ]
-   },
-   "outputs": [],
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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z9fLuVxfBxnzCFv213vxtIIYf9POWqaXyZoQuIFAOZGeDqiqkpsLp03D5MsyZA8bG0Lcv9OsHrq6gUb1fnZQJpXLWEomkQmPJmZmZiph2ZUFVRUU+E6OqkA2qkuItDqkMhIRAvXqQnl7RlggUxNtrjVJezxQND4f16+G//+T/OxcX+OYb+PjjirGxKlIqZ+3g4MDatWuVZUuJuX79Oh06dKiw9vOjoUNDtE6oUuzZzJ/pln4VYinQeaBOvXH1Kqz9kpKaClIp6OXzECJQOUhOzn+/VAoZGfJ35g8ewNOn5WtXVadUWfeys7NxdHRk/PjxzJgxQyEJVdZkZmayc+dO5syZw927d3MtJ65oIiIisG/kQNJcsXwanqoy154rkQwZHEpD/w8RIQ+D0dfPJ4xSSQkMlDttgcrHzp2wcuWb/49IBLq6kJYGTZvCoEHQuzfUr1/0RKcPlYKy7pU6DOLj48PkyZNZunQphoaG5ZLP+uXLlzg5OeHl5VWpHDVAzZo1OeHpw/jPJhD44xNU9dWVmytEGcggIz6dBo0bsPm4R5Vy1ACVWHPig+fcOcjKAk1N+dNPnz7g7g4dO0I5jeWqLUpRigFISkoiNja23JRiCkpsU5mIj4+vtEoxhoaGVc5JC1R+YmNhzx7o0gXq1q1oa6omZTKyfpvk5GQSExPLxVknJiYSGhpa6nrEYjHm5uYYGxsrwarcyGQyEhMTSUxMVHrdyuB97JJIJFhbW6OrK0z/E8gfY2N4Zz2cgJIotbN+9OgRw4aP5fHjh+gbWCASVZ3lrTKZlPiXz2jazIWdOzYr9AZLy5kzZ5g6cTRxL2Mx1VetNrG5rGwZ4bEZfNyrJ3/9uw0tLa2iTxIQEFAKpQqDZGVlUdumLg1c5tCoxUQkVWgKWA5ZmWn4n19B3PND3Ll9Ld/83CXh2bNnNGlUD49PUujVuPq9RElIgc+2aiCq1YvN26pwci0BgUpKmch6nT17FjWNGjRpPa1KOmoAFVUNWnX8jojIWAICAkpd3949e+jbVEbvJtXPUQPoa8GaEWkcPOxFSkr1TrcqIFCZKJWzDgwMxLhGQ2XZUmGIRCLMLBoRGBhY6roCH9/H2aJ6zysz0gEjXVVevHhR0aYo+FA1GIvqN8iTQLVp0wYtLS20tLRo06aNIuFacbUaW7dujUgkQkNDQ5H8Kqdv+f0sXLiwyL7knC+RSNDT08PZ2ZnvvvtOGAQUQKli1lKpFMRKz7KaL9fOryEtLYGPun5faDm/Ez9y8eRieg76m4bNx7JheV0S40P4clnh4gVisYq8P6VEmp2FSuVaVAlAxyVw9gEErQIbJcjvqUhQyvVSNh+iBmNh/fbw8GDcuHEYGxvz448/ArBs2TIGDBiAh4cHPXv2LFKr8eHDh1y+fBmJREJ6ejo7duxg6tSp/PbbbyQnJxMdHc2MGTMwMTHht99+A6Bhw4bkhEyL6svq1avJzs7Gw8ODpUuXcurUKXx9fYstu/ahUD6eVglc8/uNxPiQQp21NDsLh4YDMDatR02rluVonUBl4UPTYCyq3wsWLABg48aN9O3bFwA7OzsGDBjAggULGDNmTJFajTk3qm+//ZaffvoJDw8Ppk6dSp8+fQD5k0nOori3z8tx1kX1pU+fPtjY2DB16lScnJy4dOkSu3fvZsSIEYX2+UOjTKZuPA+5yK6/u/PbIlPW/GDAkV3jyMhI5rTnl6xfbsfq7/XZ/KsLj+7uU5yzYp4q65bZcPboPP5cYsnapVbcu7ENQDE6zim3Yp6qYv+Kear4nfiRdctqc/bo1zy6uw/PHaN4FuSby6YrZ1ew/mdb1i614uq53I+hZU1aBoxdD8ZTQW0cWM2ERfvA7xE4fg3an8h/mi+A43KhD4KjQTRaXnbmFjCcDM3mw60Q6L4ctD6Brj9D3OulvePWy8t/vxecvgGDyTDhL0jNyN+mM/fhox9BbyJYfA6fb4bk1yphR29Bw29AYwLoTgSX7+X2VAU+NA3GHPLrd1RUFGFhYQC50hN37twZgNDQUKKjC//H5sj0aWho8MUXX9CuXTsuX77MgwcPitfpEvRFXV2dXr16ASjUeQTeoHRnnRAXzO6NvQgP9qNp2+l06r0CXf1anPH6kmt+a6hh3phOvX8hJTmaw9uGEx7yJiNfUmI4ya8iaN5uFsmvIvA5MB1pdhZd+q5GU9sEgI+HbeXjYVtztRkaeIqPui2kTj03CuJ56CVad/oGsViFs0fmEhp4WtldLxDv27D5PHRpAH9/CpM6ycMIWmowvj38Ohrm94PwOBjyO2S8lQjq2Uv5757OcCMEmn8P7etBW3s4GQDr38k6u+8qzO4Jja3gX19YdjivPU+joPcKeJkMX38MvZvA7z7w1Wthknm7IDQWfh0Fy4dC09qQWUXSYH9oGow5FNXv9+XUqVOEhYXRvn174uLiFHaWRDasJH3JCa0py/7qhNLDIEEPvcnMSMK5xSe067ZQsf+Pn+RzmHsO/AtNbWMy0pM4e/Rrntw7jGXttgCoqevSc+BfiCUqXPf7leRXESQnRWDn+DGqqtqkEkP9xkPztNl76Gb0DOTqM287/7fpMWC9vN2MZM4emcvTh0extuuk5N7nT10zuXO+FgwGWtDYGoa0gscRsMUPAsJzC74ERYP66/+MvhasHgUn7sKOS+BkCQvcweKM3Fk/iczd1qKBMLCF3MG6fA9HbsGPA3OX8b4NKRnw4DnM3/Nmv9dN+W9HC7gdBl63oFEtGNYa7Gsq95qUFUeOHGHHjh1s3ryZ6dOnc/r06Vxf/BwNxuLyrgbj2+zatavEsl45GowXL14slgZjccmv3zVq1MDKyoqwsDDOnj2rCFucPi0fqFhbWxc5as+5Ufn4+ChCPgBbtmzhp59+KlZ6ieL2JT09naNHjwJySTWB3FRczDqfO6eGphFiidwkiUSeh1kqzSqwfA45jrqEBrzHOe9HQyu497Pccd4Lhy+2w8qjUFMf7j6D7/qCa32YuxNuhshDFznO2kBL3nXV19uGr/MrSF5/R7KKGPEW1stBLWBy5zfb4teFN0+W30yuBcGxO7DkEHhMhjHtStz1cudD0mB8m4L6vXjxYsaNG8eECRP45ptvAPkLRoDFixcXamdSUhL79u1DX18/lwrOqlWrOHfuXLFlw4rqy+HDh5FKpWzatImnT5/SunVrBg8eXGS9HxpKd9Z16vVEVU2HgOtb0NY1R9egFgkvg3FwcufWlb84tm8yder14Lrfr4hEYuo26FOsejU1DUmMC+b6hd8xNLanTr0eJbLr7XYBbOv1LHHf3hffB7D3KjSsBS1s5X8/jwez16k54pLhylO54y4tC/fJ69tyXr7dq3HeMj2d5SEYnwBoZQd6mvL21VSgsxN8uR2sjKCeOYTEgn8QhMaU3rby4kPRYHyX/Po9duxYdHV1+eWXXxQvG52dndmwYQMDBgwotL4cmb4hQ4bg7u6u2B8VFcW5c+fYtGlTsZx1UX2ZNWsWWlpaWFtbM2/ePObPny/MBMkHpTtrfUMbBo335LzPD1y/8BtSaRb2Tu50df8DFVUtHt3dR/Dj4xia2NOx9wpFCKQoWrh+xWnPOZw6PBtjM6cSO2trW1cunlyCTJZNx17/V24hEABtdfB7LI8hZ2RDHVNY01c+hW7CX7DtotyputaXhzZKw6AW8D9vefx7fAf4Jp97oW0N8PoSFuyBxQfl+xwt5LFukIdk1hyHyATQ0ZCHVaZVct3VD1GDEYruN8jncRflmCGvKtP48eMZP358nnKTJk1i0qRJim0bG5s3bd6+DfPmQZ8+jHNzY1whK6TzuzYCBVOq5ebr1q3j763X6fTxn2VhW7nivWsQPy4YS//+/UtVz2dTPqF+6kY+66Ykw4rJuPXgcR5OfwsdHYsuX1rs5+lw5NT1Eo8qBao5a9fCjBlytVypFExM5Fpe/fpB+/aCnlcxKLuse7LKtzDifZDJpMp5Ay0SUQnXiigdqbSC3thnZ8OmTQXLkQhULOfPy3W9clYhPnsmd+Bbtsj1vFq0gCFD5Cq6Qg7VElEqZ12jRg2SE8OUZUuFkhgXqhQhAzNzK0KvqFDeQoybJst/yoP0TIhOyCiT1LJFcvMmvPUILlAJeXe0IpVCTkre8+fhwgX56HvaNPjjj/K3r4pSKmfdrVs3xo6dQET4NWpaNleWTeVO8OMTpCRF0LJl6Vc99u3njttvy5ndPQvLfETJqwNrT4lp3qQRhoaG5d94nTryx+lKmif8gyc6GqKi5GKLb/O2vleTJnJ9r2LE0QXeUCpnraury9atHowc1QOrOm3Q0atdxfJZZ5MY94SI8JscPrQfFZXSR4UaN27M7C8X4PTdIro2UsVMNwuRqOwFGcqDzGwxN8JUiE7R4sTpnRVjhJERnDlTMW0LFM3atfA67wkaGm+c9Nv6Xjo6FWlhlUUpsl6JiYkcP36cyMjIclGKURZisRhLS0u6deum9ET6ERERnDhxotLKer0PEokEe3t7XF1dhalVAvlz8KA8Jt2iBQweDL16yWPTworEYlPQC0alaTAKCAgIAHLFXCU8pX6olNlskLS0NH5Z8Qt79u8kOjqmXEfWIsDYxJiB7oOZ+9XXlUZmKjo6mp9/Xoq3lxeJr6p2bFVFooJTQyc+mzFbsYJPQKBQBEddJpRqZC2TyXD7uDthSQ9oOaIWBuZalGfIWiaDxMhUrm4PxwhrTp/wrfAEMMnJybRo3hQnc3X6tKmLoY5mlX4CzMyScvtpJGsP3+SP9X8xcOCgok8SEBB4b8pkZB0QEMC1G/5M298WiUrFvFg0tNDCytmI9UMuce3aNVxc8vSxXPH09MRQXco3w6tPIhprM30MdTX4eclPgrMWEKggSuVhr1y5Qh0Xkwpz1DmIJSJsXAy5fPlyhdoBcPGiH83rVsD84zKmZX1Lbty+UynVYeCNvFUOxZHqepscCaq3l1sLCFQmSjWyzsjIQKJeOZ7xJeoiMt6d21kBZKSlo6NW/WJ2qioSRIjIzs4uVlrMqkaORFfDhlVfU1SgelKh37r9C2/yg4snQf5VKKWbQJUkRxi2d+/eDBw4EH19fRo2bKgQbz179izDhw9nzx55gu/o6GgmTJiApaUlurq6tG3bljOv53fnjNpbtGjBp59+ipGREba2tnmEagUElEmFOusWg2ozaElTTG11K9KMakNWduUMUVQmjh07RtOmTenfvz8BAQHMmzcv33KjRo3i33//pWfPnixZsoSAgAB69+7N06dPFWX8/f3R19fnk08+ISgoiM8//7y8uiHwAaL05/W45yms7nsKPTMNHDvV5JZXOIYWmrj/0ITja+4RevMlVo2NGPJzM67uCeGm5zPGrWuNtqEax1bf5453OKkJGWjqq1Gvgxl9v3MG4KbnMy78F8jLsBRUNSR0nORAqyE2yja/TEjPzGLp1nOcvxNKclomxnqa9PuoPp/0bsq/R2/iefER0fHJmBvrMqxzQwa5yvMb9/l2Gy9ikzi0ZDgWJrpMWnmY649ecGiJPJl+3++242htQq0aelwMeMbCcR1pZm/O2oNXOXcnlJeJqdQ01uG3GW5Ymuhx8vpTNh65QUhkAgY6GvRp48CnHzdDIhbjefERf3tdJzIuCXVVFWwtDPlzVm80qkhIRyQSIZPJkMlkir+BPCGb7t27M3/+fB4+fIiHhwePHz/OU1dycjI+Pj7o6+uzYcMGJBIJjx494o8//uDYsWO4ucnl4xwdHVm5ciXp6emsWLEi37oEBJRFmX0TEyPl6qt125hy9/hz1o8+h+tEe2QyeHolBv99obnKRzxK5OJ/T6njYkyTj614FZNG8st0AO6dfMH+hTfRq6FB18/qI5PJkKhWjlh5cbgY8AyvS4/p2tyWdo2siXiZhEgEm4/dYt0hf5rUrcm4nk3YduIOP287j66WGj1aFC8j2f3QGOrWMmL24NZY19Dnh39Pc+5OKO0aWdPpYxuCI+LJzpZxKzCCeRtO4ljbhE97N+NeSDR/eV1HQ12FsT2asGLnBbQ1VJk3oj0p6ZkEBEUhlVad1ai1atUiLCyM8PBwxd8AVlZWucrlJOtSV5crEWVlFZ5wK+elZX5TQt+tKzu7ighVClRJysxZa+io4PaFE08vx3D3+HNMbXXo+KkD1w6E8vRKDC/Dcqe41Kuhgbq2CtFBSQRdi8HMTo/Ww+Wab3dPPAeg2wxHnHsWrpZRGallqodELOJ+SDS6mmrY1zKmm4stM3/3BuCLIW1wrG2KjqYa32w4wZkbwcV21pYmunw/xhWRSERaRhZ+d8PQUlfllyndUFWRKMrtP38fqUxGQHA0AW9JlfvdCWNsjybUqWnAw7BYLgSEYWdhyNDODdHSqDpLyvv168fvv//OoEGD6NKlC/v27VPsLyna2tp0796dY8eOMXnyZJydndmyZQtaWlr06FEy0QsBAWVRds5aVxWRSIRYRT4i0dSVf/HFEvm2NDv3qE3bUJ3PdrvywDeS6KdJ+G58zNm/HzHbs0tZmVhu1LU0YvfCIfjdDeXpizhW77nEfyduo68jT8T+ZvSW+zzJ60f47NfT5RKT0/PUbWakozi/OAuchnR0wrVJbcW2ppr8/7J2zsecuRnMo7BYjl0NZP3ha6z/4mOaO1iUsLcVw7Jly1BVVWXfvn2sWrWKWrVqsWjRImbMmPFe9W3ZsoWvv/4aT09PduzYQaNGjVi6dCm2traCwolAhVBpApIxIUn4bQnEwtEAC0d9nlyM4mVYCunJWTh1MSfA5wU+v94nJV4+PU9VQ0Jz9/cRyi1/rj96wakbQdhZGOJkU4NT14OIjk+hT9t63AuOZuXOC/Ru48D2k3cA6NRU/kRRy1SPZ9GJ7D5zD1MDLZ6Evyy0HU11Vdo2tOL8nVC+WudD56Z1CImMp+9H9ejYxIb/fO5w9lYwNjUNALjx5AV1LY1wtjNj6X/ncLQ2oa6lEU/CXxIcEU/ky6qT4F9HR4dVq1axatWqfI937Ngx180slxQVeeW0TE1N2bhxY751vXsuFO9G+SEgk8mISo7CTMesok2pdlQaZ62iLiEmKIl7JyPITM1Cz0yTHrMboG+mib6ZJv0WZHFpexAnfruveMFYVdBUV+HWkwgOX3hIZpYUCxNdvhzalO4t7AA4fOEhv+zww9xYl29GtFPs/6x/SyJeJnHQ7wHtGlnjYGXMo7DYQttaNL4jfx64yvk7oVy+/wxzI10GdHCksV1Nfp7UlY1Hb/Db/suoqUiwszSifzv5lyotI4tN3jdJSE5HV0sN93b16dK8TpleF4Hqx7Y72xi1fxRWelb0q9ePfvX70d66Peoq6hVtWpWn1BqMHidW4TavXlnYViKOr3rIQJepzM7JpVtBTJsyGe34uwzrXP0WV7Sa9jepqWlCelSBAll7dS1zjs8hLSsNsUiMjpoO6VnptLJsxWCnwbjVdcPOyK6izazUlEluEC0tLTJSKsfc3owkaaXIuqeto0NSRGZFm6F0UtMzEYvEShFoKA0RSRG0/actyRlVJ0TzIZGSlaJISSCVSUlMl2ed9A315erzq8z1mYuBhgF9HPrwabNPaWHZoiLNrVKU6pvn6urK57MjSUuqi4ZOxY220lOyeHIxmo7/17HCbMihU+cuzD24i3E9GqMiqT7Lsk9eD6J9uzYVntUwMimS0IRQVCXC6L6ykiXNfzpkalaq/HhKFv/c+AdTbVPBWZeAUjnr2rVrM2rEaLZM2kPj/mYYmGsiEpffl1kmlZEQmcbtg5H069ufevUqPhzTrVs3zK3rMvPPk7i51MZQVwMRVWdO+LtkZmdzJyiGw5cCOeR5pKLNwdnMmaMjj5KcKYysKyNHHx9ly+0tCscMoKGigVgkRltVmz4OfXCv706nOp3QURPkvUpCqZViZDIZBw4cYM/+nTyPeF7ub8VrmpkzyH0IAwYMqDQJhtLS0ti+fTtHPA8SHxcHVN2ZAioqqjRu2pxx4ydQv379ijZHoJKz9upaZnjPQEtVi7SsNFwsXBjSYAhu9m7YG9lX+JNZVUCQ9RIQEChznsY95dfLv9Kzbk9ca7uiqapZ0SZVOcpM1gvg1q1bHDx4kMjnVUswt6Ro62jTuWtnunXrVuiLNplMhq+vL0ePHSM2Lr78DCwCbU1NOrl2wM3NDTU1tYo2R6AaYmtoy+qeqyvajGpJqUfWK39ZyS/LVuDeaDi19GwQl6euVzkiQ0ZCWjzej/ZTy8GcA57783V4MpmMMRM+wfvMOWp06o+qoUmlefTLSn5F3MVjWGipcuq4Nzo6QsxQQKCyUSZhkKioKBxsHfCZfhtLA6tCzqw+ZGZnMnRzFz7/YQojR47Mc9zX15eBYz/ho79Po6JV+ZyhTCrl2ryRzBrQg1mzZlW0OQICAu9QkLMu1TD45MmTfOTQ+YNx1ACqElUGOI3C2/NYvsePeHtTo6N7pXTUACKxGHO3ERw4mr/9AgIClZNSOeuEhASMNE2UZUuVwUSnBvFxCfkeexmfgKqhaTlbVDLUDU1IiM/ffgEBgcpJqQPMFTWHOCwuGMtvxQz6q5Nie+WJhXjfO1DmbRfZZyVeknt//8zetoYEe21TXqUiURWeTCgg8GFSaRI5lRRjbVP+HLoNYx15AviwuGBWnfqRwc3G0rOB+3vVmZWdhYqkcl0Sy0590a1tj5FTnhCWgIDAB4TSPVNYXDCtf7HFXL8WvRr0Z8+NLVgZ1WHVwI38dHQuV0LO42LdlvUjdqEmUeP/fBZwJGAfL5NjsDVxYGan7+jdcCAAlt+KqalnyYAmI9h1zQOxWMwCt18Y0GQkscnRTNs5gjZ1XBGLxAz+uzMAu697sPu6B4ObjWX1oH/xuruXX88sJTD6IYZaxvRqOICvu/2Elpo2s/aMZ/d1Dz5pO4Ozj4+joarJ9gnHmb1nPFeCz5GWmUpNPUumu37NqJaT3vuaJL8IxXtgY7RqWuG27zYAe9saolXTCsdPvuHakulY9xhCamwkcfeuoVfHkZY//o22uTXhpw9xf+Nymn/3B9oWtUl+HsKd378n5uYFpNmZGNZvRqPPFmFg34jo6+fx/awPNdt0Q6yuQdTVM2jVsKTFDxswcGhU+n+ugIBAhVFm8+xeJDwDoKNDT+4+v4HbHy60tGmHi3VbzgeeZOuV9Sz0msNffqtxMm/CD71WEpscxZTtQ7kackFRT0RiOFGvIpnUbjZRryL45sBUsrJz5x5wqNGAWZ3mA9DapgN/Dt3GmFZTuBrix5TtQ4lJiuT7XitoZNGMv/3WsOjIF7nOPxKwjwltPufTtjPZe2MLJx544t54OD+7r8O98fBymTv+/NwRzNt2x6RJW14GXOXh1tV5ysiys/H7ahjhZw5Ru/cI6o2cSfT1c5yfPYiMV29i0JFXTmFg74yl68ckBj3g7rpFZW6/gIBA2VJmzlpPQ59FH69mWPPxADjUcGJ25wW4Nx4GQHDsE0V8eeXAfxjdajITP5qFVCbl+P2Dinp01HVZOeBvprt+TQ3dmiRnJBGVFJGrLROdGnxkJx9ZWxnVoV/jYTSzasWxeweRyqRM/Gg2Y1pNYcWAvwDwDjiQ6/yvu/3E2NZTGdxsDPamjgBcCT7P7XB/rI3q0L/JCKVfn3ex7jEE+2HTsBskH8EnhT3NU+ZV6GNeBT1Az9aRRtMXUW/MbMxadSb9ZRQv715RlKvRshOO47+k3ujZBdYlICBQtShDZ22ASCRC5XV2NANNQ3mDIrkuYJY0P3HRvG/mDDSNFHFkNclrYdJ8snoV9tJPsSilgMUplgZvFGc6OvTg2GfXGdR0NEnpSXy5byLjt7gXWHdxEEnkfZa9FlTNSIzLU0b99QwSiZr667KFCLm+3Y98+qTxui5xceoSEBCoElTockM3p/4AfLVvIluvbODvC2sQi8R0dyy5yKmBlhEAt5/5s//mNp5EP6RHA3fEIjF/+a1my+X1zN03KVe7+eF5Zw8Hb2/HUMuYJrVcUJOo8Tw+tMDyxUHDyAyJpjapMS8I9tzKrTXfvlc9utb26NWpT2LgPe6uXcTDLauJvHwKDWMzjBq2LJWNAgIClZsKddY/9FrJxI9mcTv8Gj94zcZQy5i1w3bQonbbEtdV36wh7s7DCY0L4rNdozjx4DAtardl3fCdGGmZsOjIF9wMv8qnH83k+14rCqxHS02b04+8WeA5k5+8v8a+RgOW9fuzNN1ErKJC0y9+Qd3AhLvrFqNt/n7akSKJhLa/7MCiYx+CDm/h4Zb/Ydr0Iz5auRs1Xf1S2SggIFC5KbWs14UtN/j543VlYVul5di9g+yL/pfD3gfzHJs0bTpXNGtTd9D7zx4pa2JuXyJ+44/cvHyh6MICAgLlSpksN1dRUSFTmlGaKqokGdkZBWbdU1VRQZpZuWW9pJmZqFawPJeAgEDJKJWzbtSoEdfCLlXrtKj5cS38As7N8hfEbd6kMSn3r5azRSUj/u5lmjUW5l0LCFQlSuWsW7ZsiZ6JDvO9Pic8PqzaO+2k9Fds9/+H/Xe2MWbsmHzL9O/fn8QAfx7/9yvpCS/L2cLCyUpLIcxnLyF7NjBl4qcVbY6AgEAJKNWzsEgk4thJb76a/RU9NzQjPjGuSusNFoYMGaoqqnT8qBNe3p7Y2dnlW87Q0JDzZ04x66uv8RmwnIz0tEqRz1omkyGRSHBp8xEHdu+kadOmFW2SgIBACVCqrFdWVvWezyuRSErseCvTNXkf+wUEBMqXMpX1kkql3Lp1i8jI6inrpaGhQbNmzdDXL/70uMzMTPz9/YmPjy87wwRyYWRkRPPmzQuVXBMQqKqU+lN95coVBru7oyEFGwNDxNVw5JaUmcGt5+FMmz6NZf/3f0WOTnfu3MmUKVMxM6mJob4hSs2ZKlAAMmLjYolLiMVjswe9evWqaIMEBJRKqZx1WloafdzcWP9RV/rZ1VOWTZWSmNQUum3fQcPGjRk1alSB5QIDA5k6ZRpLv1hFXZvqfU0qIwGPbjNyxEjuP7hPzZo1K9ocAQGlUarZIKdOnaK+oUm1d9QAJppazG3Ugp0emwstt3fvXtq36CQ46grCycEZF+fWHDyYd8GSgEBVplTOOiwsDHs9AyWZUvmpZ2TMs2fPCi0TEhyCpVmtcrJIID8sTGsRFhZW0WYICCiVUjlrmUyGpAQx6oUXzyJevZhNAbdK02weNgXcQrx6MQsvni22HauvXy5xOxKRCGm+2QLfIJXKEIslJa77fek+ui2jZw94r3O/XDKd7qPbcuv+9VLXVRTHfb3YvO9vkpJflUn9byMWi5FKpWXejoBAefJBvjb/8bIvtXX1mdWsVUWb8t5IpVKlT8ObN20RGuqahbb3vm0eP3eE2w9u0L19b3S0dd+rjuzsLCSVTHZNQKC8KJNP/pmwYOZfOM2d2Ch0VNXoUduO5e26KI5fi3rButv+3I+LoXMtG/7u1gdjTS2OBj1h7vkTPIl/iapYQj1DY3b3HoSNvgG3oiP4+txJrkQ+R0Uspr2FFSs6dKOOvmGe9qNTkvn6/EmOhzzlVWY6zWuYs7JDN5rWMEe8ejEAIa8SEK9eTG1dfYI+mZGn/p617VjZoRumWtqluhbbDm5i054NfDV5Ad3auTFh7jAio1+wb/1xnoY+ZuaiSXRq05150xYSGPKYv3f+wcPA+0gkEhrWa8yk4Z9jXsOC475erPhrCa6tuhATF82jp/fZ+btnrrYOndjLH5tX4Vy/KT/O+T80NbRyHX/49B7/+2c50bERtG7anvSM9FzHl/35A2YmNWnTrF2B7UXGROSy0cW5FZNHzMBAzxCZTMZe7x0cOX2QqJgIdLT1+GzsFxw4tovbD24AMGaOXLJt86q9aKhr8M/OP/G/c5mU1BTq1LJl/JApNHZsRkT0C8bMGYh9nfpY1LDE/85lBvYcxuZ9f+PaqgvffSb/P36zfCbX715l/dIt1LHKf6GSgEB1QOnO+mlCHL0PbkdVLGFxm07cjolkY8BNXiQn0aqmJQAHAx/yXct2HAsJ5EDgQ/R8j+PRw51v/U4R+iqBNR17kiWVciM6gkxpNvFpafTcv42XaaksbO1KQkY6/+d/gUfxL7k5Mm92uzHHDnIqLIjpjVtgqKHBn7f86XVgOw/HTmebW39GHN2PiaYWv3bsgbaqGvFpabjt345YJGJW01YkZqSz+sZlYtNS8XIfXqrr0dSpBZv2bOD2/Ru4NGrFsxfy3Nj3n9zlQWDA6zIuJCW/4ttfZvMqKZHRAz4lJTWJnZ5befYilHVL37zUvHDNl+H9xtGpdTdUVdUU+w/57OX3zStp06wd301fjNpr4YEcsrKyWPzrd0TFRjLSfTwymQyf80eKtP/t9tIz0/nulzmIxCIG9BxKSmoy+7x3kvgqkSVfrWTv0e1s2P471hY2TB01i6QUechjpPt44jfHEfo8mGmjZ6Ova4C+rgE//jqPa3eu0KPDx9ha18Vj71/MX/ElG5ZtQSSSR+geBz3A1sqOySNm4FjXics3L+Dnf5bY+BjEIjE3713HycFZcNQC1R6lO2vv4EBSs7KY0LgpM5q2JFsqZe/j+xwPCaShiVyJfGbTlkx2bs4ge0cOBD7kSNATAByNTLgdE8mRoMc0NKnBMAcn7A2NORL0mMiUZD6uY8+8lu0A8Ap6TEBsNI/iY3O1n5yZwfGQQGTA6hu549LXol4wrF5DRhzdj7aKKsPqyZMxHQl6TERKEgALL72Je/uEPiUjOxs1yfvHoB1s66OtpcOdBze48+Am+roGWJrV4vaDGzwIvAdAU6fm3Ht8h7iEl7Rq8hHD+8rzjly64UdIeBDhL968LOvctjuj3MfnaiMmLprfN6+kVZO2fD9jab6hgrAXIUTFRmJrVZexAycCcPH6OYLCAgu1/+32rty8wMsE+fXevO9vRZnrAVfIzMrk7OVTAMz+ZB5O7wj0GugZEvo8mNZN21HT1JzUtFSu372KtpYOsz75GolYQnhEGIdO7MX/9mVaNG4DQE1TC+Z8+q0i/DKo13CW/L4Ar1MH0NXWRSrNpnenkotVCAhUNcosAPhGSavoGGdOGY8e/Rjs0IBrUS84HhLI0ivn2dS9LyaaWrnqhKKXmeirqbP740GKbalMRkNj00LPaW9pzYJW7XOdU9pFPhKxBOf6Tbh4/TwnLxzDycEZawsbbt67xtOQx1iY1cLMxJyQZ0FA7uuV37UzNTbLs09dVR2Ruph7j+8Q/CwIu9r2ecrkt7K0OKtN82uvYb3GjHKfoNiWyqRFXqfCPgc5+WTyK1PD2CzX/nYtOlLT1IIjpw9hZGCMrrYuHVp2LrIfAgJVHaUrxfS0sUNLRZUt927z280rTD7pRUJGOj1q26GtItdjXHPjChvuXGfSCXnMtZdNXQC+PHeCwIQ46hkaU9/QBIDQV4m0NbfCTEsb7+BAfr7qx7d+p7gbG42TsSkOBsa52tdWVaN7bTsSMtL57/4dnr16xfnwMKadOqqIPxtpaBKVmsy/ATc5Fx6qqP/Si2dceP6MkMQEDj19xLrb11ARl/4SNXVqAcClG+dpVK8JzvWbcPfhLVLSUmjqJE8B0MC+EYb6RvjfvsSOw5vZuGsdwc+eUtuyDpbmVoXWr6ujx09frCAjM5Nvls8k+FlegVxrCxvMTGryNOwJm/f+zaY9G/ItVxg5Nj54EsC9x3eIjIng4vVzeJ7cj0SionCa//tnGV6nDrDL6z/OXz0jt1FbD4Bjvp6cu3oaTQ1NmjdsSXJKEmv+Xc6B47s54eeNupoGLs4Fv/iViCX07zGEl/ExPAl+SNd2vfKEfAQEqiNKd9a2+oZ49huGk7Ep3/mdxuvpY8Y1aIxHjzePqkMcGvDP3RucCAuin60DKzt0A+SZ7X69cYVPfQ7jFfSYgXXrM62xCwYaGnj3H0HHWjb8n/8F/rpznf529TjUdyiSfJzp5h79mODUBJ/QIKae8uK/B3foZGWjOD6/ZXs0VVT5xOcw/+d/QVF/F6s6/HrzCrPOHuPcs1A6W9VRyjXJccgymYxG9Zvg5OCM5HVopWkD+TEdbV2WfvU/nB2bssvzP46cPshHLq78OOcXJMWYCujk4MwPM5eSkprM1z/PIOxFSK7jKioqzP/8J2yt6rL/+C6iYiKoZ+tYon7k2NjEyYX9x3exdutq7jy8pejfQLdhfDp0GlKplD+3rmbf0R2Kc927D8bMxJxtBzfxy/qfAJg75Xt6dOjN5RsX+Hf3empb2PDTlyswr2FZqB09Xfug+3pGiRACEfhQKLWs142/NrGuQ/eysK3ScSs6grFXz3Lr4cMCy0ydMg1Jqib9ug0qsIzA+xMVE8Gj4If8/OcPNKrfhGVzV+cps+3gJsztjVm6dGn5GyggUErKJOueSCRCSvXLslcQ2TKZYpZCQYjFImFBRhni7evJtgObsLGy4/OxX+ZbRiqTCalgBaodpXLWlpaW7EiMV5IplZ/HcS+pVavwpeRW1lZcOnOtnCz68Bgz4FPGDChc5SYyJpw2XZuVk0UCAuVDqWLWXbp04W5MFN7BT5RlT6UlPi2NFXf8GTRqZKHlBgwYgO/lUyV+eSegHB49vc/lmxfo27dvRZsiIKBUSjWy1tTU5ICnJ4Pd+2Pqfx4bPYNqms86kyvPQpgwYQJjx44ttKyDgwO//raG6dOnU9vSBgN9w2ordVaZkCEjNi6G55Hh/LvpXywsLCraJAEBpaIUWa/s7GyuXr1KREREtVSK0dTUpEWLFhgbGxdd+DVpaWlcunSJuLi4MrRM4G2MjY1p3bo1ampqRRcWEKikFPSCsUTOWiQSRQMhRRYUEBAQEHhfastksjwr+ErkrAUEBAQEKgalL4oREBAQEFA+grMWEBAQqAIIzlpAQECgCiA4awEBAYEqgOCsBQQEBKoAgrMWEBAQqAIIzlpAQECgCiA4awEBAYEqgOCsBQQEBKoA/w9ABM2hE3BXxgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 446.4x129.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 429.84x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")\n",
     "\n",
@@ -360,18 +1352,273 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 13,
+   "metadata": {
+    "tags": [
+     "scroll-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/python_tutorial_data_37\"\n",
+      "INFO: Regenerating c-file: python_tutorial.c\n",
+      "CFLAGS= -I@MCCODE_LIB@/share/\n",
+      "INFO: Recompiling: ./python_tutorial.out\n",
+      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
+      "  *t0;\n",
+      "  ^~~\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
+      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
+      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
+      "            int (* _Nonnull __compar)(const void *, const void *));\n",
+      "                            ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
+      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
+      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
+      "void printf_history(struct dynamic_history_list *history) {\n",
+      "                                                 ^\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
+      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
+      "};\n",
+      "^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
+      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
+      "                                         ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^~~~~~~~\n",
+      "./python_tutorial.c:12065:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^~~~~~~~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
+      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
+      "                                            ^\n",
+      "                                            \"%s\", \n",
+      "./python_tutorial.c:12065:18: note: expanded from macro 'filename'\n",
+      "#define filename mcclogger_space_zx_filename\n",
+      "                 ^\n",
+      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
+      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
+      "                                                       ^\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "              ^~~~~~~~~~~~~~~~~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
+      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
+      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
+      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
+      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
+      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
+      "#define MPI_MASTER(instr) instr\n",
+      "                          ^~~~~\n",
+      "13 warnings generated.\n",
+      "INFO: ===\n",
+      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/python_tutorial_data_37\n",
+      "\n",
+      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
+      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
+      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
+      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
+      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
+      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
+      "---------------------------------------------------------------------\n",
+      "global_process_list.num_elements: 2\n",
+      "name of process [0]: Al_inc \n",
+      "component index [0]: 1 \n",
+      "name of process [1]: Al_pow \n",
+      "component index [1]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_material_list.num_elements: 1\n",
+      "name of material    [0]: Al \n",
+      "component index     [0]: 3 \n",
+      "my_absoprtion       [0]: 0.347892 \n",
+      "number of processes [0]: 2 \n",
+      "---------------------------------------------------------------------\n",
+      "global_geometry_list.num_elements: 1\n",
+      "\n",
+      "name of geometry    [0]: box \n",
+      "component index     [0]: 5 \n",
+      "Volume.name         [0]: box \n",
+      "Volume.p_physics.is_vacuum           [0]: 0 \n",
+      "Volume.p_physics.my_absorption       [0]: 0.347892 \n",
+      "Volume.p_physics.number of processes [0]: 2 \n",
+      "Volume.geometry.shape                [0]: box \n",
+      "Volume.geometry.center.x             [0]: 0.000000 \n",
+      "Volume.geometry.center.y             [0]: 0.000000 \n",
+      "Volume.geometry.center.z             [0]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [1]: box_mask \n",
+      "component index     [1]: 6 \n",
+      "Volume.name         [1]: box_mask \n",
+      "Volume.geometry.shape                [1]: box \n",
+      "Volume.geometry.center.x             [1]: 0.000000 \n",
+      "Volume.geometry.center.y             [1]: 0.000000 \n",
+      "Volume.geometry.center.z             [1]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [1]: [0.707107 0.000000 -0.707107] \n",
+      "Volume.geometry.rotation_matrix[1]           [1]: [-0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [1]: [0.707107 -0.000000 0.707107] \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
+      "\n",
+      "name of geometry    [2]: cylinder_mask \n",
+      "component index     [2]: 7 \n",
+      "Volume.name         [2]: cylinder_mask \n",
+      "Volume.geometry.shape                [2]: cylinder \n",
+      "Volume.geometry.center.x             [2]: 0.000000 \n",
+      "Volume.geometry.center.y             [2]: 0.000000 \n",
+      "Volume.geometry.center.z             [2]: 1.000000 \n",
+      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
+      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
+      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.105000 \n",
+      "Volume.geometry.geometry_parameters.height [2]: 0.120000 \n",
+      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
+      "---------------------------------------------------------------------\n",
+      "number_of_volumes = 4\n",
+      "number_of_masks = 2\n",
+      "number_of_masked_volumes = 1\n",
+      "\n",
+      " ---- Overview of the lists generated for each volume ---- \n",
+      "List overview for surrounding vacuum\n",
+      "LIST: Children for Volume                  0 = [1,2,3]\n",
+      "LIST: Direct_children for Volume           0 = [1,2,3]\n",
+      "LIST: Intersect_check_list for Volume      0 = [2,3]\n",
+      "LIST: Mask_intersect_list for Volume       0 = [1]\n",
+      "LIST: Destinations_list for Volume         0 = []\n",
+      "LIST: Reduced_destinations_list for Volume 0 = []\n",
+      "LIST: Next_volume_list for Volume          0 = [1]\n",
+      "LIST: mask_list for Volume                 0 = []\n",
+      "LIST: masked_by_list for Volume            0 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
+      "      mask_mode for Volume                 0 = 0\n",
+      "\n",
+      "List overview for box with box shape made of Al\n",
+      "LIST: Children for Volume                  1 = []\n",
+      "LIST: Direct_children for Volume           1 = []\n",
+      "LIST: Intersect_check_list for Volume      1 = [2,3]\n",
+      "LIST: Mask_intersect_list for Volume       1 = []\n",
+      "LIST: Destinations_list for Volume         1 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 1 = []\n",
+      "LIST: Next_volume_list for Volume          1 = [0]\n",
+      "      Is_vacuum for Volume                 1 = 0\n",
+      "      is_mask_volume for Volume            1 = 0\n",
+      "      is_masked_volume for Volume          1 = 1\n",
+      "      is_exit_volume for Volume            1 = 0\n",
+      "LIST: mask_list for Volume                 1 = []\n",
+      "LIST: masked_by_list for Volume            1 = [2,3]\n",
+      "LIST: masked_by_mask_index_list for Volume 1 = [0,1]\n",
+      "      mask_mode for Volume                 1 = 1\n",
+      "\n",
+      "List overview for box_mask with box shape made of Mask\n",
+      "LIST: Children for Volume                  2 = []\n",
+      "LIST: Direct_children for Volume           2 = []\n",
+      "LIST: Intersect_check_list for Volume      2 = []\n",
+      "LIST: Mask_intersect_list for Volume       2 = []\n",
+      "LIST: Destinations_list for Volume         2 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 2 = []\n",
+      "LIST: Next_volume_list for Volume          2 = [0]\n",
+      "      Is_vacuum for Volume                 2 = 0\n",
+      "      is_mask_volume for Volume            2 = 1\n",
+      "      is_masked_volume for Volume          2 = 0\n",
+      "      is_exit_volume for Volume            2 = 0\n",
+      "LIST: mask_list for Volume                 2 = [1]\n",
+      "LIST: masked_by_list for Volume            2 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
+      "      mask_mode for Volume                 2 = 1\n",
+      "\n",
+      "List overview for cylinder_mask with cylinder shape made of Mask\n",
+      "LIST: Children for Volume                  3 = []\n",
+      "LIST: Direct_children for Volume           3 = []\n",
+      "LIST: Intersect_check_list for Volume      3 = []\n",
+      "LIST: Mask_intersect_list for Volume       3 = []\n",
+      "LIST: Destinations_list for Volume         3 = [0]\n",
+      "LIST: Reduced_destinations_list for Volume 3 = []\n",
+      "LIST: Next_volume_list for Volume          3 = [0]\n",
+      "      Is_vacuum for Volume                 3 = 0\n",
+      "      is_mask_volume for Volume            3 = 1\n",
+      "      is_masked_volume for Volume          3 = 0\n",
+      "      is_exit_volume for Volume            3 = 0\n",
+      "LIST: mask_list for Volume                 3 = [1]\n",
+      "LIST: masked_by_list for Volume            3 = []\n",
+      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
+      "      mask_mode for Volume                 3 = 1\n",
+      "\n",
+      "Union_master component master initialized sucessfully\n",
+      "Detector: logger_space_zx_I=3995.18 logger_space_zx_ERR=10.3585 logger_space_zx_N=159248 \"logger_zx.dat\"\n",
+      "loading system configuration\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting data with name logger_space_zx\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ms.make_sub_plot(data)"
    ]
diff --git a/docs/source/user_guide/instrument_object.ipynb b/docs/source/user_guide/instrument_object.ipynb
index f75a8a9b..e76fb4c4 100644
--- a/docs/source/user_guide/instrument_object.ipynb
+++ b/docs/source/user_guide/instrument_object.ipynb
@@ -430,14 +430,7 @@
    "id": "parental-theme",
    "metadata": {},
    "source": [
-    "## Getting components"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hidden-shopping",
-   "metadata": {},
-   "source": [
+    "## Getting components\n",
     "It is always possible to retrieve the component objects corresponding to components in the instrument with the *get_component* and *get_last_component* methods."
    ]
   },
@@ -463,6 +456,29 @@
     "print(last_component)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "third-blend",
+   "metadata": {},
+   "source": [
+    "### Instrument diagram\n",
+    "McStasScript can generate a diagram of an instrument file to aid the user in understanding its content. Use the *show_diagram* method to display the figure. The legend shows how the different component categories are represented with different colors, and how AT and ROTATED is represented with arrows. The rest of the figure is the actual diagram of this instrument, showing the sequence of components.\n",
+    "\n",
+    "If in a notebook and using the %matplotlib widget backend, hovering the mouse over the left side of the boxes show further information on the individual components.\n",
+    "\n",
+    "The diagram will also show use of the keywords EXTEND, WHEN, JUMP and GROUP, as well as connections between Union components, though none of these are present in this diagram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "annoying-preservation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.show_diagram()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "cardiovascular-hughes",
diff --git a/docs/source/user_guide/widgets.ipynb b/docs/source/user_guide/widgets.ipynb
new file mode 100644
index 00000000..034f47c5
--- /dev/null
+++ b/docs/source/user_guide/widgets.ipynb
@@ -0,0 +1,441 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "delayed-redhead",
+   "metadata": {},
+   "source": [
+    "# Widgets\n",
+    "McStasScript includes a few widgets designed for use in Jupyter Notebooks. They are not loaded with the standard import package as they rely on additional dependencies and requires more time to import which is not appropriate for scripts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "lasting-donna",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import mcstasscript as ms\n",
+    "import mcstasscript.jb_interface as ms_widget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "silver-portal",
+   "metadata": {},
+   "source": [
+    "## Set up of an example instrument\n",
+    "As an example we set up a simple model of a time of flight powder diffractometer on a long pulsed source."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fleet-genealogy",
+   "metadata": {
+    "tags": [
+     "scroll-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "instrument = ms.McStas_instr(\"POWTOF\")\n",
+    "\n",
+    "instrument.add_parameter(\"double\", \"Lmin\", value=0.5, comment=\"[AA] Lower edge of wavelength band\")\n",
+    "instrument.add_parameter(\"double\", \"Wavelength_band\", value=1.8, comment=\"[AA] Width of wavelength band\")\n",
+    "instrument.add_parameter(\"double\", \"two_theta\", value=90.0, comment=\"[deg] TOF det 2 theta\")\n",
+    "instrument.add_parameter(\"double\", \"npulses\", value=2.0,\n",
+    "                         comment=\"[1] Number of pulses from source\",\n",
+    "                         options=[1, 2, 3, 4, 5, 6])\n",
+    "\n",
+    "instrument.add_declare_var(\"int\", \"npulses_declare\")\n",
+    "instrument.add_declare_var(\"double\", \"Lmax\")\n",
+    "instrument.add_declare_var(\"double\", \"Lcenter\")\n",
+    "\n",
+    "instrument.append_initialize(\"npulses_declare=npulses; \")\n",
+    "instrument.append_initialize(\"Lmax = Lmin + Wavelength_band; \")\n",
+    "\n",
+    "instrument.add_declare_var(\"double\", \"sample_position\", value=160)\n",
+    "\n",
+    "Origin = instrument.add_component(\"Origin\", \"Progress_bar\")\n",
+    "\n",
+    "Source = instrument.add_component(\"Source\", \"Source_simple\")\n",
+    "Source.yheight = 0.03\n",
+    "Source.xwidth = 0.03\n",
+    "Source.dist = \"sample_position\"\n",
+    "Source.focus_xw = 0.03\n",
+    "Source.focus_yh = 0.03\n",
+    "Source.lambda0 = \"Lmin + 0.5*Wavelength_band\"\n",
+    "Source.dlambda = \"0.5*Wavelength_band\"\n",
+    "Source.flux = 1E13\n",
+    "Source.append_EXTEND(\"double t_between_pulses=1.0/14.0;\")\n",
+    "Source.append_EXTEND(\"double pulse_n=(double) floor(rand01()*npulses_declare);\")\n",
+    "Source.append_EXTEND(\"double pulse_delay=t_between_pulses*pulse_n;\")\n",
+    "Source.append_EXTEND(\"t=2860*rand01()*1E-6 + pulse_delay;\")\n",
+    "Source.set_AT(0, RELATIVE=Origin)\n",
+    "\n",
+    "instrument.add_declare_var(\"double\", \"tmax_source\")\n",
+    "instrument.add_declare_var(\"double\", \"last_pulse_t\")\n",
+    "instrument.append_initialize(\"last_pulse_t = 1E6*(npulses-1)*1/14.0;\")\n",
+    "instrument.append_initialize(\"tmax_source = 3000 + 1.1*last_pulse_t;\")\n",
+    "\n",
+    "TOFmon1 = instrument.add_component(\"TOFmon1\", \"TOF_monitor\")\n",
+    "TOFmon1.nt = 200\n",
+    "TOFmon1.filename = '\"TOFmon1\"'\n",
+    "TOFmon1.xwidth = 0.02\n",
+    "TOFmon1.yheight = 0.02\n",
+    "TOFmon1.tmin = 0\n",
+    "TOFmon1.tmax = \"tmax_source\"\n",
+    "TOFmon1.restore_neutron = 1\n",
+    "TOFmon1.set_AT([0, 0, 1e-6], RELATIVE=Source)\n",
+    "\n",
+    "TOFLambda1 = instrument.add_component(\"TOFLambda1\", \"TOFLambda_monitor\")\n",
+    "TOFLambda1.nL = 100\n",
+    "TOFLambda1.nt = 200\n",
+    "TOFLambda1.tmin = 0\n",
+    "TOFLambda1.tmax = \"tmax_source\"\n",
+    "TOFLambda1.filename = '\"TOFLambda1\"'\n",
+    "TOFLambda1.xwidth = 0.02\n",
+    "TOFLambda1.yheight = 0.02\n",
+    "TOFLambda1.Lmin = 0.1\n",
+    "TOFLambda1.Lmax = 5\n",
+    "TOFLambda1.restore_neutron = 1\n",
+    "TOFLambda1.set_AT([0, 0, 1e-6], RELATIVE=Source)\n",
+    "\n",
+    "PSD = instrument.add_component(\"PSD\", \"PSD_monitor\")\n",
+    "PSD.filename = '\"PSD\"'\n",
+    "PSD.xwidth = 0.03\n",
+    "PSD.yheight = 0.03\n",
+    "PSD.set_AT([0, 0, 0.5], RELATIVE=Source)\n",
+    "\n",
+    "\n",
+    "instrument.add_parameter(\"double\", \"enable_chopper_1\", value=0,\n",
+    "                         comment=\"Enable clockwise chopper\", options=[0, 1])\n",
+    "instrument.add_parameter(\"double\", \"enable_chopper_2\", value=0,\n",
+    "                         comment=\"Enable counter clockwise chopper\", options=[0, 1])\n",
+    "\n",
+    "instrument.add_declare_var(\"double\", \"speed\")\n",
+    "instrument.add_declare_var(\"double\", \"delay\")\n",
+    "instrument.add_declare_var(\"double\", \"chopper_position\", value=6.5)\n",
+    "instrument.append_initialize(\"Lcenter = Lmin + 0.5*Wavelength_band; \")\n",
+    "instrument.append_initialize(\"speed = 2.0*PI/Lcenter*K2V; \")\n",
+    "instrument.append_initialize(\"delay = chopper_position/speed; \")\n",
+    "instrument.append_initialize(\"delay = delay + 1.340E-3; \")\n",
+    "instrument.add_parameter(\"frequency_multiplier\", value=1,\n",
+    "                         comment=\"[1] Chopper frequency as multiple of source frequency\")\n",
+    "\n",
+    "chopper = instrument.add_component(\"chopper\", \"DiskChopper\")\n",
+    "chopper.theta_0 = 4.0\n",
+    "chopper.radius = 0.35\n",
+    "chopper.yheight = 0.03\n",
+    "chopper.nu = \"frequency_multiplier*14.0\"\n",
+    "chopper.delay = \"delay\"\n",
+    "chopper.set_AT([0, 0, 'chopper_position - 0.01'], RELATIVE=Source)\n",
+    "chopper.set_WHEN(\"enable_chopper_1 > 0.5\")\n",
+    "\n",
+    "counter_chopper = instrument.add_component(\"counter_chopper\", \"DiskChopper\")\n",
+    "counter_chopper.theta_0 = 4.0\n",
+    "counter_chopper.radius = 0.35\n",
+    "counter_chopper.yheight = 0.03\n",
+    "counter_chopper.nu = \"-frequency_multiplier*14.0\"\n",
+    "counter_chopper.delay = \"delay\"\n",
+    "counter_chopper.set_AT([0, 0, 'chopper_position - 0.005'], RELATIVE=Source)\n",
+    "counter_chopper.set_WHEN(\"enable_chopper_2 > 0.5\")\n",
+    "\n",
+    "instrument.add_declare_var(\"double\", \"speed_max\")\n",
+    "instrument.append_initialize(\"speed_max = 2*PI/Lmin*K2V;\")\n",
+    "\n",
+    "instrument.add_declare_var(\"double\", \"speed_min\")\n",
+    "instrument.append_initialize(\"speed_min = 2*PI/Lmax*K2V;\")\n",
+    "\n",
+    "instrument.add_declare_var(\"double\", \"chopper_tmin\")\n",
+    "instrument.add_declare_var(\"double\", \"chopper_tmax\")\n",
+    "instrument.append_initialize(\"chopper_tmin = 0.9*1E6*chopper_position/speed_max;\")\n",
+    "instrument.append_initialize(\"chopper_tmax = 1.1*1E6*chopper_position/speed_min + last_pulse_t;\")\n",
+    "\n",
+    "TOFmon2 = instrument.add_component(\"TOFmon2\", \"TOF_monitor\")\n",
+    "TOFmon2.nt = 100\n",
+    "TOFmon2.filename = '\"TOFmon2\"'\n",
+    "TOFmon2.xwidth = 0.02\n",
+    "TOFmon2.yheight = 0.02\n",
+    "TOFmon2.tmin = \"chopper_tmin\"\n",
+    "TOFmon2.tmax = \"chopper_tmax\"\n",
+    "TOFmon2.restore_neutron = 1\n",
+    "TOFmon2.set_AT([0, 0, 'chopper_position'], RELATIVE=Source)\n",
+    "\n",
+    "TOFLambda2 = instrument.add_component(\"TOFLambda2\", \"TOFLambda_monitor\")\n",
+    "TOFLambda2.nL = 100\n",
+    "TOFLambda2.nt = 200\n",
+    "TOFLambda2.tmin = \"chopper_tmin\"\n",
+    "TOFLambda2.tmax = \"chopper_tmax\"\n",
+    "TOFLambda2.filename = '\"TOFLambda2\"'\n",
+    "TOFLambda2.xwidth = 0.02\n",
+    "TOFLambda2.yheight = 0.02\n",
+    "TOFLambda2.Lmin = 0.1\n",
+    "TOFLambda2.Lmax = 5\n",
+    "TOFLambda2.restore_neutron = 1\n",
+    "TOFLambda2.set_AT([0, 0, 'chopper_position'], RELATIVE=Source)\n",
+    "\n",
+    "instrument.add_declare_var(\"double\", \"sample_tmin\")\n",
+    "instrument.add_declare_var(\"double\", \"sample_tmax\")\n",
+    "instrument.append_initialize(\"sample_tmin = 0.9*1E6*sample_position/speed_max;\")\n",
+    "instrument.append_initialize(\"sample_tmax = 1.1*1E6*sample_position/speed_min + last_pulse_t;\")\n",
+    "\n",
+    "TOFmon3 = instrument.add_component(\"TOFmon3\", \"TOF_monitor\")\n",
+    "TOFmon3.nt = 200\n",
+    "TOFmon3.filename = '\"TOFmon3\"'\n",
+    "TOFmon3.xwidth = 0.02\n",
+    "TOFmon3.yheight = 0.02\n",
+    "TOFmon3.tmin = \"sample_tmin\"\n",
+    "TOFmon3.tmax = \"sample_tmax\"\n",
+    "TOFmon3.restore_neutron = 1\n",
+    "TOFmon3.set_AT([0, 0, 'sample_position'], RELATIVE=Source)\n",
+    "\n",
+    "TOFLambda3 = instrument.add_component(\"TOFLambda3\", \"TOFLambda_monitor\")\n",
+    "TOFLambda3.nL = 100\n",
+    "TOFLambda3.nt = 200\n",
+    "TOFLambda3.tmin = \"sample_tmin\"\n",
+    "TOFLambda3.tmax = \"sample_tmax\"\n",
+    "TOFLambda3.filename = '\"TOFLambda3\"'\n",
+    "TOFLambda3.xwidth = 0.02\n",
+    "TOFLambda3.yheight = 0.02\n",
+    "TOFLambda3.Lmin = 0.1\n",
+    "TOFLambda3.Lmax = 5\n",
+    "TOFLambda3.restore_neutron = 1\n",
+    "TOFLambda3.set_AT([0, 0, 'sample_position'], RELATIVE=Source)\n",
+    "\n",
+    "Sample = instrument.add_component(\"Sample\", \"Powder1\")\n",
+    "Sample.radius = 0.003\n",
+    "Sample.yheight = 0.02\n",
+    "Sample.q = 5\n",
+    "Sample.d_phi = 12\n",
+    "Sample.set_AT([0, 0, 'sample_position'], RELATIVE=Source)\n",
+    "\n",
+    "beamstop = instrument.add_component(\"beamstop\", \"Beamstop\")\n",
+    "beamstop.radius = 0.2\n",
+    "beamstop.set_AT([0, 0, 0.5], RELATIVE=Sample)\n",
+    "\n",
+    "TOF_cylPSD = instrument.add_component(\"TOF_cylPSD\", \"TOF_cylPSD_monitor\")\n",
+    "TOF_cylPSD.nt = 100\n",
+    "TOF_cylPSD.nphi = 180\n",
+    "TOF_cylPSD.filename = '\"TOF_cylPSD\"'\n",
+    "TOF_cylPSD.radius = 2.0\n",
+    "TOF_cylPSD.yheight = 0.20\n",
+    "TOF_cylPSD.tmin = \"sample_tmin\"\n",
+    "TOF_cylPSD.tmax = \"sample_tmax\"\n",
+    "TOF_cylPSD.restore_neutron = 1\n",
+    "TOF_cylPSD.set_AT([0, 0, 0], RELATIVE=Sample)\n",
+    "\n",
+    "TOFdetArm = instrument.add_component(\"TOFdetArm\", \"Arm\")\n",
+    "TOFdetArm.set_AT([0, 0, 0], RELATIVE=Sample)\n",
+    "TOFdetArm.set_ROTATED([0, 'two_theta', 0], RELATIVE=Sample)\n",
+    "\n",
+    "TOFdet = instrument.add_component(\"TOFdet\", \"TOF_monitor\")\n",
+    "TOFdet.nt = 100\n",
+    "TOFdet.filename = '\"TOFdet\"'\n",
+    "TOFdet.xwidth = 0.01\n",
+    "TOFdet.yheight = 0.2\n",
+    "TOFdet.tmin = 70000\n",
+    "TOFdet.tmax = 80000\n",
+    "TOFdet.restore_neutron = 1\n",
+    "TOFdet.set_AT([0, 0, 2.001], RELATIVE=TOFdetArm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "three-public",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "data = instrument.backengine()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "facial-monkey",
+   "metadata": {},
+   "source": [
+    "## Plotting interface\n",
+    "The data generated by the above instrument can now be shown in a widget interface. Before launching the interface it is important to choose the widget backend for matplotlib, which is done in the following cell. The *show* function in ms_widget is then used to display the interface, it just needs the returned data as an argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "patient-shelter",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib widget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "seven-dairy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#ms_widget.show(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "global-extension",
+   "metadata": {},
+   "source": [
+    "An image of the interface is shown below as the interactive widget is not currently working in the HTML documentation.\n",
+    "\n",
+    "![plotting interface](plotting_widget.png \"Plotting widget\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "thirty-patch",
+   "metadata": {},
+   "source": [
+    "In the above interface the left side shows the current plot, and the right side contains a dropdown box for selecting the a new plot and plot options. The log plot checkbox works for both 1D and 2D data, though the remaining options are only applied for 2D datasets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aware-applicant",
+   "metadata": {},
+   "source": [
+    "## Simulation interface\n",
+    "It is also possible to run the simulation from a widget, simply pass an instrument object to the *show* function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "adolescent-monte",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "#ms_widget.show(instrument)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "supported-contrast",
+   "metadata": {},
+   "source": [
+    "An image of the interface is shown below as the interactive widget is not currently working in the HTML documentation.\n",
+    "\n",
+    "![simulation interface](simulation_widget.png \"Simulation widget\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "announced-passage",
+   "metadata": {},
+   "source": [
+    "The top of the interface contains all input parameters of the instrument. These can be either text fields or dropdown boxes, the latter being the case if options are given to the parameter object. The next elements from the top is the run button, ncount field and mpi field. Pressing the run button starts the simulation, and while it is running, the icon will be an hourglass. Underneath is the plotting interface which will be updated each time a simulation completes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "convenient-honolulu",
+   "metadata": {},
+   "source": [
+    "### Interface object\n",
+    "The *show* function does not allow the user to retrieve the simulated data. If that is desired one instead have to use the *SimInterface* class that creates a SimInterface object. This object can be used to show display the interface with *show_interface*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "entertaining-pantyhose",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim_interface = ms_widget.SimInterface(instrument)\n",
+    "#sim_interface.show_interface()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "swedish-dubai",
+   "metadata": {},
+   "source": [
+    "Through the *SimInterface* object it is possible to access data from the last simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "verbal-concentrate",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = sim_interface.get_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "chronic-theater",
+   "metadata": {},
+   "source": [
+    "## Instrument diagram as a widget\n",
+    "The last widget included in McStasScript is the instrument diagram, though it only behaves in an interactive manner when the matplotlib widget backend is used. As a widget it is possible to get information on each component box by hovering the mouse of the left edge of the box. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "manual-series",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#instrument.show_diagram()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "arabic-adult",
+   "metadata": {},
+   "source": [
+    "An image of the interface is shown below as the interactive widget is not currently working in the HTML documentation.\n",
+    "\n",
+    "![instrument diagram](instrument_diagram_widget.png \"Instrument diagram\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "neutral-azerbaijan",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index a15559fa..4c683e41 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.47"
+__version__ = "0.0.48"

From 0f45056847735de5d3f5f9d9a9a7b406d0ab85fc Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 10 Jun 2022 13:48:59 +0200
Subject: [PATCH 285/403] Images for documentation.

---
 .../user_guide/instrument_diagram_widget.png   | Bin 0 -> 103039 bytes
 docs/source/user_guide/plotting_widget.png     | Bin 0 -> 66326 bytes
 docs/source/user_guide/simulation_widget.png   | Bin 0 -> 99272 bytes
 3 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 docs/source/user_guide/instrument_diagram_widget.png
 create mode 100644 docs/source/user_guide/plotting_widget.png
 create mode 100644 docs/source/user_guide/simulation_widget.png

diff --git a/docs/source/user_guide/instrument_diagram_widget.png b/docs/source/user_guide/instrument_diagram_widget.png
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literal 0
HcmV?d00001


From 29a0086510ad7e387a5e5912d9ea40a84c9a36a1 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 10 Jun 2022 14:26:58 +0200
Subject: [PATCH 286/403] Adds quotes around executables to run even when there
 are spaces in path.

---
 .../getting_started/version_history.ipynb     | 93 +++++++++++++++++++
 mcstasscript/helper/managed_mcrun.py          |  3 +
 mcstasscript/interface/instr.py               |  2 +-
 mcstasscript/tests/test_Instr.py              |  6 ++
 mcstasscript/tests/test_ManagedMcrun.py       |  7 ++
 5 files changed, 110 insertions(+), 1 deletion(-)
 create mode 100644 docs/source/getting_started/version_history.ipynb

diff --git a/docs/source/getting_started/version_history.ipynb b/docs/source/getting_started/version_history.ipynb
new file mode 100644
index 00000000..c102f143
--- /dev/null
+++ b/docs/source/getting_started/version_history.ipynb
@@ -0,0 +1,93 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "armed-language",
+   "metadata": {},
+   "source": [
+    "# Version history\n",
+    "This page shows major changes in recent releases with an emphasis on what the user should be aware of."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "automatic-charleston",
+   "metadata": {},
+   "source": [
+    "## 0.0.48 - 10th of June 2022\n",
+    "- Improvements to instrument diagram\n",
+    "    - Colors component depending on category\n",
+    "    - EXTEND and WHEN shown with decorating component box\n",
+    "    - Hovering mouse over left side of box shows component info\n",
+    "    - All Union connections included\n",
+    "    - Improved line arrows to turn in a few segments, greatly clarifying reading busy diagrams\n",
+    "    - Included in all documentation\n",
+    "- Bug fix in simulator widget that avoids a crash when value in text field could not be converted to float\n",
+    "- Included documentation on widgets in [widget_user_guide](../user_guide/widgets.ipynb)\n",
+    "- Now allows for spaces in McStas path, working on how this could work for data path\n",
+    "\n",
+    "## 0.0.47 - 31th of May 2022\n",
+    "Introducing instrument diagrams, they are really useful for getting a quick overview of an instrument\n",
+    "- Instrument diagrams now available, use the instrument method *show_diagram*\n",
+    "- Instrument output_path now reports to libpyvinyl calculator path system (Thanks Shervin)\n",
+    "- Update of the documentation to include *show_diagram* in [instrument user guide](../user_guide/instrument_object.ipynb), [JUMP tutorial](../tutorial/McStasScript_tutorial_4_JUMP.ipynb)\n",
+    "\n",
+    "\n",
+    "## 0.0.44 - 16th of May 2022\n",
+    "This version is a cleanup of the syntax and streamlining of the package. It improves the consistency of the language use, removes unused methods and is better at recognizing when wrong input is made.\n",
+    "\n",
+    "- Renaming of methods to make the \"show\" verb in method names more consistent.\n",
+    "    - show_components did show all available McStas components, now shows components in current instrument\n",
+    "    - print_components still shows all components in current instrument, but also a warning pointing to show_instrument as best practice\n",
+    "    - available_components now shows all available McStas components for help purposes\n",
+    "- Moved away from allowing arbitrary keyword arguments in some places, so it is now an error to misspell keyword arguments.\n",
+    "    - Instrument initialization\n",
+    "    - Instrument.settings method\n",
+    "    - Instrument.show_components (and print_components) methods\n",
+    "    - Component initialization\n",
+    "- Removed instrument methods for manipulating components like set_component_AT() as they provided an additional way of doing the same thing, but more error prone than working with the component objects directly.\n",
+    "- Moved widgets out of base import of package to speed up import\n",
+    "- Widgets for Jupyter notebooks now loaded with: \n",
+    "    - *import mcstasscript.jb_interface as ms_widget*\n",
+    "    - ms_widget.show(instrument) / ms_widget.show(data) shows widget interface for instrument / data\n",
+    "    - ms_widget.SimInterface(instrument) needed if one wants to retrieve data made in an interface\n",
+    "- Added instrument methods for moving and removing components (both explained in [instrument object page](../user_guide/instrument_object.ipynb))\n",
+    "    - instrument.move_component\n",
+    "    - instrument.remove_comopnent\n",
+    "- Introduced error checking before writing / running McStas file, can be disabled with settings(checks=False)\n",
+    "    - Checks all Component AT / ROTATED RELATIVE statements have valid targets\n",
+    "    - Checks that variables used as component parameters have been declared\n",
+    "- When using show_instrument in a notebook, a new tab was opened and it was shown in the notebook, changed it so no new tab is made when running in a notebook. If a new tab is desired use the keyword argument *new_tab* and set it to True."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "synthetic-multiple",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index d952af21..ef28f0d2 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -250,6 +250,7 @@ def run_simulation(self, **kwargs):
                     or self.executable_path[-1] == "/"):
                 mcrun_full_path = os.path.join(self.executable_path,
                                                self.executable)
+        mcrun_full_path = '"' + mcrun_full_path + '"' # Path in quotes to allow spaces
 
         # Run the mcrun command on the system
         full_command = (mcrun_full_path + " "
@@ -258,6 +259,8 @@ def run_simulation(self, **kwargs):
                         + self.name_of_instrumentfile
                         + parameter_string)
 
+        print(full_command)
+
         process = subprocess.run(full_command, shell=True,
                                  stdout=subprocess.PIPE,
                                  stderr=subprocess.PIPE,
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 2d54271f..c36be5b8 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1988,7 +1988,7 @@ def show_instrument(self, format="webgl", width=800, height=450, new_tab=False):
         if is_notebook and executable == "mcdisplay-webgl" and not new_tab:
             options += "--nobrowse "
 
-        full_command = (bin_path + executable + " "
+        full_command = ('"' + bin_path + executable + '" '
                         + dir_control
                         + options
                         + instr_path
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 1a9493d9..9a65d075 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1631,6 +1631,7 @@ def test_x_ray_run_full_instrument_basic(self, mock_sub, mock_stdout):
                                       parameters={"theta": 1})
 
         expected_path = os.path.join(executable_path, "mxrun")
+        expected_path = '"' + expected_path + '"'
 
         expected_folder_path = os.path.join(THIS_DIR, new_folder_name)
 
@@ -1699,6 +1700,7 @@ def test_run_backengine_basic(self, mock_sub, mock_stdout):
             instr.backengine()
 
         expected_path = os.path.join(executable_path, "mcrun")
+        expected_path = '"' + expected_path + '"'
 
         expected_folder_path = os.path.join(THIS_DIR, new_folder_name)
 
@@ -1754,6 +1756,7 @@ def test_run_full_instrument_complex(self, mock_sub, mock_stdout):
                                                   "theta": "\"toy\""})
 
         expected_path = os.path.join(executable_path, "mcrun")
+        expected_path = '"' + expected_path + '"'
         expected_folder_path = os.path.join(THIS_DIR, new_folder_name)
 
         # a double space because of a missing option
@@ -1805,6 +1808,7 @@ def test_run_full_instrument_overwrite_default(self, mock_sub,
                                                   "has_default": 10})
 
         expected_path = os.path.join(executable_path, "mcrun")
+        expected_path = '"' + expected_path + '"'
         expected_folder_path = os.path.join(THIS_DIR, new_folder_name)
 
         # a double space because of a missing option
@@ -1848,6 +1852,7 @@ def test_run_full_instrument_x_ray_basic(self, mock_sub, mock_stdout):
                                       parameters={"theta": 1})
 
         expected_path = os.path.join(executable_path, "mxrun")
+        expected_path = '"' + expected_path + '"'
         expected_folder_path = os.path.join(THIS_DIR, new_folder_name)
 
         # a double space because of a missing option
@@ -1883,6 +1888,7 @@ def test_show_instrument_basic(self, mock_sub, mock_stdout):
         os.chdir(current_work_dir)
 
         expected_path = os.path.join(executable_path, "bin", "mcdisplay-webgl")
+        expected_path = '"' + expected_path + '"'
         expected_instr_path = os.path.join(THIS_DIR, "test_instrument.instr")
 
         # a double space because of a missing option
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index c19a100a..a8517fe0 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -182,6 +182,8 @@ def test_ManagedMcrun_run_simulation_basic(self, mock_sub):
 
         # a double space because of a missing option
         executable = os.path.join(executable_path, "mcrun")
+        executable = '"' + executable + '"'
+
         expected_call = (executable + " -c -n 1000000 "
                          + "-d " + expected_folder_path + "  test.instr")
 
@@ -211,6 +213,7 @@ def test_ManagedMcrun_run_simulation_basic_path(self, mock_sub):
         expected_folder_path = os.path.join(THIS_DIR, "test_folder")
 
         executable = os.path.join(executable_path, "mcrun")
+        executable = '"' + executable + '"'
         # a double space because of a missing option
         expected_call = (executable + " -c -n 1000000 "
                          + "-d " + expected_folder_path + "  test.instr")
@@ -248,6 +251,7 @@ def test_ManagedMcrun_run_simulation_no_standard(self, mock_sub):
 
         # a double space because of a missing option
         executable = os.path.join(executable_path, "mcrun")
+        executable = '"' + executable + '"'
         expected_call = (executable + " -c -n 48 --mpi=7 --seed=300 "
                          + "-d " + expected_folder_path + " -fo test.instr")
 
@@ -285,6 +289,7 @@ def test_ManagedMcrun_run_simulation_with_gravity(self, mock_sub):
 
         # a double space because of a missing option
         executable = os.path.join(executable_path, "mcrun")
+        executable = '"' + executable + '"'
         expected_call = (executable + " -c -g -n 48 --mpi=7 --seed=300 "
                          + "-d " + expected_folder_path + " -fo test.instr")
 
@@ -320,6 +325,7 @@ def test_ManagedMcrun_run_simulation_parameters(self, mock_sub):
         expected_folder_path = os.path.join(THIS_DIR, "test_folder")
         # a double space because of a missing option
         executable = os.path.join(executable_path, "mcrun")
+        executable = '"' + executable + '"'
 
         expected_call = (executable + " -c -n 48 --mpi=7 "
                          + "-d " + expected_folder_path + " -fo test.instr "
@@ -358,6 +364,7 @@ def test_ManagedMcrun_run_simulation_compile(self, mock_sub):
         expected_folder_path = os.path.join(THIS_DIR, "test_folder")
 
         executable = os.path.join(executable_path, "mcrun")
+        executable = '"' + executable + '"'
         # a double space because of a missing option
         expected_call = (executable + " -n 48 --mpi=7 "
                          + "-d " + expected_folder_path + " -fo test.instr "

From 32cd28c83aae00a283b4005ad294c52bbf7e1c60 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Mon, 13 Jun 2022 14:05:43 +0200
Subject: [PATCH 287/403] forgot __init__.py file for python module
 instrument_diagram.

---
 mcstasscript/instrument_diagram/__init__.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 mcstasscript/instrument_diagram/__init__.py

diff --git a/mcstasscript/instrument_diagram/__init__.py b/mcstasscript/instrument_diagram/__init__.py
new file mode 100644
index 00000000..e69de29b

From b240099881071d35fb00a6307b0da5e2e86e28a6 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Mon, 13 Jun 2022 14:07:00 +0200
Subject: [PATCH 288/403] version bump

---
 mcstasscript/_version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 4c683e41..38c28ffe 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.48"
+__version__ = "0.0.49"

From febca284f8d1fd51ade1e34c07a74d3ccb5bdb77 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 16 Jun 2022 17:00:28 +0200
Subject: [PATCH 289/403] Removed print from managed_mcrun.

---
 mcstasscript/helper/managed_mcrun.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index ef28f0d2..d45ebfc7 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -259,8 +259,6 @@ def run_simulation(self, **kwargs):
                         + self.name_of_instrumentfile
                         + parameter_string)
 
-        print(full_command)
-
         process = subprocess.run(full_command, shell=True,
                                  stdout=subprocess.PIPE,
                                  stderr=subprocess.PIPE,

From 345c06ee13a2e21aa763c918635d451b3fbd499b Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 17 Jun 2022 14:18:57 +0200
Subject: [PATCH 290/403] Adding support for USERVARS which is available in
 McStas 3.X. Includes showing these in show_variables (needs documentation)

For use in McStas 2.X instruments, USERVARS are not allowed. A
method was added which moves all USERVARS to DECLARE, which for
McStas 2.X would have the same function. Needs documentation.

Introduced checks for avoiding having same name in declare, uservars
and instrument parameters, so each of these are now forced to not
have clashes in namespace, which is important.

Also included new method to display the current instrument code.

Added unit tests for clash detection and USERVARS section.
---
 mcstasscript/helper/mcstas_objects.py |   3 +-
 mcstasscript/interface/instr.py       | 176 ++++++++++++++++++++++----
 mcstasscript/tests/test_Instr.py      |  93 +++++++++++++-
 mcstasscript/tests/test_user_var.py   |  68 ++++++++++
 4 files changed, 313 insertions(+), 27 deletions(-)
 create mode 100644 mcstasscript/tests/test_user_var.py

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index b9eed6d4..a23cfa33 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -178,6 +178,7 @@ class DeclareVariable:
     This class is initialized with type, name.  Using keyword
     arguments, the variable can become an array and have its initial
     value set.
+    Can also be used as a user variable in uservars (McStas 3.0)
 
     Attributes
     ----------
@@ -203,7 +204,7 @@ class DeclareVariable:
     """
     def __init__(self, type, name, **kwargs):
         """
-        Initializing mcstas parameter object
+        Initializing mcstas declare variable or user variable object
 
         Examples
         --------
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index c36be5b8..1e116410 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -408,6 +408,7 @@ def __init__(self, name, parameters=None, author=None,
         # Avoid initializing if loading from dump
         if not hasattr(self, "declare_list"):
             self.declare_list = []
+            self.user_var_list = []
             self.initialize_section = ("// Start of initialize for generated "
                                        + name + "\n")
             self.trace_section = ("// Start of trace section for generated "
@@ -478,7 +479,18 @@ def add_parameter(self, *args, **kwargs):
             options : list or value
                 list or value of allowed values for this parameter
         """
+        names = [x.name for x in self.declare_list
+                 if isinstance(x, DeclareVariable)]
+        names += [x.name for x in self.user_var_list
+                  if isinstance(x, DeclareVariable)]
+        names += [x.name for x in self.parameters.parameters.values()]
+
         par = provide_parameter(*args, **kwargs)
+
+        if par.name in names:
+            raise NameError(f"A parameter or variable with name '{par.name}'"
+                            f" already exists!")
+
         self.parameters.add(par)
 
         return par
@@ -582,52 +594,68 @@ def show_variables(self):
         Shows declared variables in instrument
         """
 
+        all_variables = [x for x in self.declare_list + self.user_var_list
+                         if isinstance(x, DeclareVariable)]
+
         type_heading = "type"
-        variable_types = [x.type for x in self.declare_list]
+        variable_types = [x.type for x in all_variables]
         variable_types.append(type_heading)
         max_type_length = len(max(variable_types, key=len))
 
         name_heading = "variable name"
-        variable_names = [x.name for x in self.declare_list]
+        variable_names = [x.name for x in all_variables]
         variable_names.append(name_heading)
         max_name_length = len(max(variable_names, key=len))
 
-        value_heading = "value"
-        variable_values = [str(x.value) for x in self.declare_list]
-        variable_values.append(value_heading)
-        max_value_length = len(max(variable_values, key=len))
-
         vector_heading = "array length"
-        variable_vector = [str(x.vector) for x in self.declare_list]
+        variable_vector = [str(x.vector) for x in all_variables]
         variable_vector.append(vector_heading)
         max_vector_length = len(max(variable_vector, key=len))
 
+        value_heading = "value"
+        variable_values = [str(x.value) for x in all_variables]
+        variable_values.append(value_heading)
+        max_value_length = len(max(variable_values, key=len))
+
         padding = 2
-        string = ""
-        string += type_heading.ljust(max_type_length + padding)
-        string += name_heading.ljust(max_name_length + padding)
-        string += value_heading.ljust(max_value_length + padding)
-        string += vector_heading.ljust(max_vector_length + padding)
-        string += "\n"
-        string += "-"*(max_type_length + max_name_length + max_value_length
+        header = ""
+        header += type_heading.ljust(max_type_length + padding)
+        header += name_heading.ljust(max_name_length + padding)
+        header += vector_heading.ljust(max_vector_length + padding)
+        header += value_heading.ljust(max_value_length + padding)
+        header += "\n"
+        header += "-"*(max_type_length + max_name_length + max_value_length
                        + max_vector_length + 3*padding)
-        string += "\n"
+        header += "\n"
+
+        string = "DECLARE VARIABLES \n"
+        string += header
+
+        if len(self.user_var_list) > 0:
+            first_user_var = self.user_var_list[0]
+        else:
+            first_user_var = None
+
+        for variable in all_variables:
+            if variable is first_user_var:
+                string += "\n"
+                string += "USER VARIABLES (per neutron, only use in EXTEND)\n"
+                string += header
 
-        for variable in self.declare_list:
             string += str(variable.type).ljust(max_type_length + padding)
             string += str(variable.name).ljust(max_name_length + padding)
 
-            if variable.value != "":
-                value_string = str(variable.value)
-            else:
-                value_string = ""
-            string += value_string.ljust(max_value_length + padding)
-
             if variable.vector != 0:
                 vector_string = str(variable.vector)
             else:
                 vector_string = ""
             string += vector_string.ljust(max_vector_length + padding)
+
+            if variable.value != "":
+                value_string = str(variable.value)
+            else:
+                value_string = ""
+            string += value_string.ljust(max_value_length + padding)
             string += "\n"
 
         print(string)
@@ -662,6 +690,10 @@ def add_declare_var(self, *args, **kwargs):
 
         names = [x.name for x in self.declare_list
                  if isinstance(x, DeclareVariable)]
+        names += [x.name for x in self.user_var_list
+                  if isinstance(x, DeclareVariable)]
+        names += [x.name for x in self.parameters.parameters.values()]
+
         if declare_par.name in names:
             raise NameError("Variable with name '" + declare_par.name
                             + "' already present in instrument!")
@@ -685,6 +717,57 @@ def append_declare(self, string):
 
         self.declare_list.append(string)
 
+    def add_user_var(self, *args, **kwargs):
+        """
+        Method for adding user variable to instrument
+
+        Parameters
+        ----------
+
+        parameter type : str
+            type of input parameter
+
+        parameter name : str
+            name of parameter
+
+        keyword arguments
+            array : int
+                default 0 for scalar, if specified length of array
+
+            comment : str
+                Comment displayed next to declaration of parameter
+
+        """
+
+        if "value" in kwargs:
+            raise ValueError("Value not allowed for UserVars.")
+
+        # DeclareVariable class documented independently
+        user_par = DeclareVariable(*args, **kwargs)
+
+        names = [x.name for x in self.declare_list
+                 if isinstance(x, DeclareVariable)]
+        names += [x.name for x in self.user_var_list
+                  if isinstance(x, DeclareVariable)]
+        names += [x.name for x in self.parameters.parameters.values()]
+
+        if user_par.name in names:
+            raise NameError("Variable with name '" + user_par.name
+                            + "' already present in instrument!")
+
+        self.user_var_list.append(user_par)
+        return user_par
+
+    def move_user_vars_to_declare(self):
+        """
+        Moves all uservars to declare for compatibility with McStas 2.X
+        """
+
+        for var in self.user_var_list:
+            self.declare_list.append(var)
+
+        self.user_var_list = []
+
     def append_initialize(self, string):
         """
         Method for appending code to the initialize section
@@ -1566,6 +1649,44 @@ def check_for_errors(self):
         for component in self.component_list:
             component.check_parameters(pars_and_vars)
 
+    def read_instrument_file(self):
+        """
+        Reads current instrument file if it exists, otherwise creates one first
+        """
+
+        instrument_path = os.path.join(self.input_path, self.name + ".instr")
+        if not os.path.exists(instrument_path):
+            self.write_full_instrument()
+            if not os.path.exists(instrument_path):
+                raise RuntimeError("Failing to write instrument file.")
+
+        with open(instrument_path, "r") as fo:
+            return fo.read()
+
+    def show_instrument_file(self, line_numbers=False):
+        """
+        Displays the current instrument file
+
+        Parameters
+        ----------
+        line_numbers : bool
+            Select whether line numbers should be displayed
+        """
+
+        instrument_code = self.read_instrument_file()
+
+        if not line_numbers:
+            print(instrument_code)
+            return
+        else:
+            lines = instrument_code.split("\n")
+            number_of_lines = len(lines)
+            line_space = len(str(number_of_lines))
+            for index, line in enumerate(lines):
+                line_number = str(index + 1).ljust(line_space) + " | "
+                full_line = line_number + line
+                print(full_line.replace("\n", ""))
+
     def write_full_instrument(self):
         """
         Method for writing full instrument file to disk
@@ -1635,6 +1756,15 @@ def write_full_instrument(self):
             fo.write("\n")
         fo.write("%}\n\n")
 
+        # Write uservars
+        if len(self.user_var_list) > 0:
+            fo.write("USERVARS \n%{\n")
+            for user_var in self.user_var_list:
+                user_var.value = ""  # Ensure no value
+                user_var.write_line(fo)
+                fo.write("\n")
+            fo.write("%}\n\n")
+
         # Write initialize
         fo.write("INITIALIZE \n%{\n")
         fo.write(self.initialize_section)
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 9a65d075..45a773bf 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -386,12 +386,36 @@ def test_simple_add_parameter(self):
         """
         instr = setup_instr_root_path()
 
-        instr.add_parameter("double", "theta", comment="test par")
+        parameter = instr.add_parameter("double", "theta", comment="test par")
 
-        parameter = instr.parameters["theta"]
         self.assertEqual(parameter.name, "theta")
         self.assertEqual(parameter.comment, "test par")
 
+    def test_user_var_block_add_parameter(self):
+        """
+        Checks that adding a parameter with a name already used for a
+        user variable fails with NameError
+        """
+        instr = setup_instr_root_path()
+
+        instr.add_user_var("double", "theta")
+
+        with self.assertRaises(NameError):
+            instr.add_parameter("double", "theta", comment="test par")
+
+
+    def test_declare_var_block_add_parameter(self):
+        """
+        Checks that adding a parameter with a name already used for a
+        declared variable fails with NameError
+        """
+        instr = setup_instr_root_path()
+
+        instr.add_declare_var("double", "theta")
+
+        with self.assertRaises(NameError):
+            instr.add_parameter("double", "theta", comment="test par")
+
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_show_parameters(self, mock_stdout):
         """
@@ -461,7 +485,7 @@ def test_show_parameters_line_break(self, mock_stdout):
         comment_line = "lines in order to ensure it really works. "
         self.assertEqual(output[8], " "*33 + comment_line)
 
-    def test_simple_add_declare_parameter(self):
+    def test_simple_add_declare_variable(self):
         """
         This is just an interface to a function that is tested
         elsewhere, so only a basic test is performed here.
@@ -475,6 +499,69 @@ def test_simple_add_declare_parameter(self):
         self.assertEqual(instr.declare_list[0].name, "two_theta")
         self.assertEqual(instr.declare_list[0].comment, " // test par")
 
+    def test_parameter_block_add_declare_variable(self):
+        """
+        Checks a NameError is raised when using declare variable of same
+        name as instrument parameter.
+        """
+        instr = setup_instr_root_path()
+
+        instr.add_parameter("two_theta")
+        with self.assertRaises(NameError):
+            instr.add_declare_var("double", "two_theta")
+
+    def test_user_var_block_add_declare_variable(self):
+        """
+        Checks a NameError is raised when using declare variable of same
+        name as declared variable.
+        """
+        instr = setup_instr_root_path()
+
+        instr.add_user_var("double", "two_theta")
+        with self.assertRaises(NameError):
+            instr.add_declare_var("double", "two_theta")
+
+    def test_simple_add_user_variable(self):
+        """
+        This is just an interface to a function that is tested
+        elsewhere, so only a basic test is performed here.
+
+        DeclareVariable is tested in test_declare_variable.
+        """
+        instr = setup_instr_root_path()
+
+        user_var = instr.add_user_var("double", "two_theta_user", comment="test par")
+
+        self.assertEqual(user_var.name, "two_theta_user")
+        self.assertEqual(user_var.comment, " // test par")
+        self.assertEqual(instr.user_var_list[0].name, "two_theta_user")
+        self.assertEqual(instr.user_var_list[0].comment, " // test par")
+
+        with self.assertRaises(ValueError):
+            instr.add_user_var("double", "illegal", value=8)
+
+    def test_declare_block_add_user_variable(self):
+        """
+        Checks a NameError is raised when using user variable of same
+        name as declare variable already defined.
+        """
+        instr = setup_instr_root_path()
+
+        instr.add_declare_var("double", "two_theta")
+        with self.assertRaises(NameError):
+            instr.add_user_var("double", "two_theta")
+
+    def test_parameter_block_add_user_variable(self):
+        """
+        Checks a NameError is raised when using user variable of same
+        name as parameter already defined.
+        """
+        instr = setup_instr_root_path()
+
+        instr.add_parameter("two_theta")
+        with self.assertRaises(NameError):
+            instr.add_user_var("double", "two_theta")
+
     def test_simple_append_declare(self):
         """
         Appending to declare adds an object to the declare list, and the
diff --git a/mcstasscript/tests/test_user_var.py b/mcstasscript/tests/test_user_var.py
new file mode 100644
index 00000000..43526005
--- /dev/null
+++ b/mcstasscript/tests/test_user_var.py
@@ -0,0 +1,68 @@
+import os
+import os.path
+import io
+import unittest
+import unittest.mock
+
+from mcstasscript.interface.instr import McStas_instr
+from mcstasscript.tests.helpers_for_tests import WorkInTestDir
+from mcstasscript.helper.mcstas_objects import DeclareVariable
+
+run_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '.')
+
+def setup_instr_no_path():
+    """
+    Sets up a neutron instrument without a package_path
+    """
+
+    with WorkInTestDir() as handler:
+        instrument = McStas_instr("test_instrument")
+
+    return instrument
+
+
+class Test_user_vars(unittest.TestCase):
+    """
+    Tests of user var behavior, collisions tested in test_Instr
+    """
+
+    def test_simple_initialize(self):
+        """
+        Test adding basic flag
+        """
+        my_instrument = setup_instr_no_path()
+
+        flag = my_instrument.add_user_var("int", "flag")
+
+        self.assertIsInstance(flag, DeclareVariable)
+        self.assertEqual(len(my_instrument.user_var_list), 1)
+        self.assertIs(my_instrument.user_var_list[0], flag)
+
+    def test_move_user_var_to_declare(self):
+        """
+        Test ability to move user vars to declare for compatibility
+        """
+        my_instrument = setup_instr_no_path()
+
+        declare_var1 = my_instrument.add_declare_var("int", "declare_var_1")
+        declare_var2 = my_instrument.add_declare_var("double", "declare_var_2", value=4)
+        flag = my_instrument.add_user_var("int", "flag")
+        another_user = my_instrument.add_user_var("double", "lifetime")
+
+        self.assertEqual(len(my_instrument.declare_list), 2)
+        self.assertEqual(len(my_instrument.user_var_list), 2)
+        self.assertIs(my_instrument.declare_list[0], declare_var1)
+        self.assertIs(my_instrument.declare_list[1], declare_var2)
+        self.assertIs(my_instrument.user_var_list[0], flag)
+        self.assertIs(my_instrument.user_var_list[1], another_user)
+
+        # Moves all user vars to declare
+        my_instrument.move_user_vars_to_declare()
+
+        self.assertEqual(len(my_instrument.declare_list), 4)
+        self.assertEqual(len(my_instrument.user_var_list), 0)
+        self.assertIs(my_instrument.declare_list[0], declare_var1)
+        self.assertIs(my_instrument.declare_list[1], declare_var2)
+        self.assertIs(my_instrument.declare_list[2], flag)
+        self.assertIs(my_instrument.declare_list[3], another_user)
+

From 2fe9730438c0b78e92e5628ff0ce65307793e054 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Mon, 20 Jun 2022 12:18:21 +0200
Subject: [PATCH 291/403] Makes instrument reader handle USERVARS in 3.0
 instruments.

Collects lines with same start point as lines with same end point are in diagrams.

Corrected error in check_for_errors when complex declare statements used.
---
 mcstasscript/instr_reader/control.py          |  18 +-
 mcstasscript/instr_reader/read_declare.py     |   6 +-
 mcstasscript/instr_reader/read_uservars.py    | 284 ++++++++++++++++++
 .../instrument_diagram/connections.py         |  18 +-
 mcstasscript/interface/instr.py               |   5 +-
 5 files changed, 320 insertions(+), 11 deletions(-)
 create mode 100644 mcstasscript/instr_reader/read_uservars.py

diff --git a/mcstasscript/instr_reader/control.py b/mcstasscript/instr_reader/control.py
index 208bbda7..bcb19b3a 100644
--- a/mcstasscript/instr_reader/control.py
+++ b/mcstasscript/instr_reader/control.py
@@ -3,6 +3,7 @@
 from mcstasscript.interface.instr import McStas_instr
 from mcstasscript.instr_reader.read_definition import DefinitionReader
 from mcstasscript.instr_reader.read_declare import DeclareReader
+from mcstasscript.instr_reader.read_uservars import UservarsReader
 from mcstasscript.instr_reader.read_initialize import InitializeReader
 from mcstasscript.instr_reader.read_trace import TraceReader
 from mcstasscript.instr_reader.read_finally import FinallyReader
@@ -121,6 +122,7 @@ def _read_file(self):
                 self._get_next_line, self._return_line]
         self.Definition_reader = DefinitionReader(*args)
         self.Declare_reader = DeclareReader(*args)
+        self.Uservars_reader = UservarsReader(*args)
         self.Initialize_reader = InitializeReader(*args)
         self.Trace_reader = TraceReader(*args)
         self.Finally_reader = FinallyReader(*args)
@@ -128,13 +130,14 @@ def _read_file(self):
         # A mode for each type that activates the correct reader function
         definition_mode = False
         declare_mode = False
+        uservars_mode = False
         initialize_mode = False
         trace_mode = False
         finally_mode = False
         comment_mode = False
         any_mode = False
 
-        # check if insturment name has be read from file yet
+        # check if instrument name has be read from file yet
         instr_name_read = False
 
         self._open_file()
@@ -153,6 +156,10 @@ def _read_file(self):
                 declare_mode = True
                 any_mode = True
 
+            if line.strip().startswith("USERVARS") and not any_mode:
+                uservars_mode = True
+                any_mode = True
+
             if (line.strip().startswith("INITIALIZE") or
                line.strip().startswith("INITIALISE")) and not any_mode:
                 initialize_mode = True
@@ -183,11 +190,17 @@ def _read_file(self):
                 any_mode = definition_mode
 
             if declare_mode and not comment_mode:
-                # Read line from definition
+                # Read line from declare
                 declare_mode = self.Declare_reader.read_declare_line(line)
                 # When read_declare finds the end, it will return False
                 any_mode = declare_mode
 
+            if uservars_mode and not comment_mode:
+                # Read line from uservars
+                uservars_mode = self.Uservars_reader.read_uservars_line(line)
+                # When read_uservars finds the end, it will return False
+                any_mode = uservars_mode
+
             if initialize_mode and not comment_mode:
                 # Read line from initialize
                 initialize_mode = self.Initialize_reader.read_initialize_line(line)
@@ -217,6 +230,7 @@ def update_file_name(self):
 
         self.Definition_reader.set_instr_name(self.instr_name)
         self.Declare_reader.set_instr_name(self.instr_name)
+        self.Uservars_reader.set_instr_name(self.instr_name)
         self.Initialize_reader.set_instr_name(self.instr_name)
         self.Trace_reader.set_instr_name(self.instr_name)
         self.Finally_reader.set_instr_name(self.instr_name)
diff --git a/mcstasscript/instr_reader/read_declare.py b/mcstasscript/instr_reader/read_declare.py
index 28cf4559..305b26e3 100644
--- a/mcstasscript/instr_reader/read_declare.py
+++ b/mcstasscript/instr_reader/read_declare.py
@@ -57,7 +57,7 @@ def read_declare_line(self, line):
 
         # Check for functions
         if ("(" in line and ";" not in line and " " in line.strip()
-                and not self.in_declare_function):
+                and not self.in_declare_function and "#pragma" not in line):
 
             # If in function, it will define a block
             n_curly_brackets = line.count("{")
@@ -129,8 +129,8 @@ def read_declare_line(self, line):
             # if in function, stop now
             self.in_declare_function = False
 
-        # Grab defines
-        if line.strip().startswith("#define"):
+        # Grab defines and pragmas
+        if line.strip().startswith("#define") or line.strip().startswith("#pragma"):
             # Include define statements as declare append
             line = line.rstrip()
             line = line.replace('\\n', "\\\\n")
diff --git a/mcstasscript/instr_reader/read_uservars.py b/mcstasscript/instr_reader/read_uservars.py
new file mode 100644
index 00000000..c9d18f0c
--- /dev/null
+++ b/mcstasscript/instr_reader/read_uservars.py
@@ -0,0 +1,284 @@
+from mcstasscript.instr_reader.util import SectionReader
+
+
+class UservarsReader(SectionReader):
+    """
+    Reads the uservars section of a McStas instrument file and adds
+    the found parameters / functions / structs to the McStasScript
+    Instr instance. The information can also be written to a python
+    file for reproduction of a McStas instrument.
+    """
+
+    def __init__(self, Instr, write_file, product_filename,
+                 get_next_line, return_line):
+
+        super().__init__(Instr, write_file, product_filename,
+                         get_next_line, return_line)
+
+        self.in_uservars_function = False
+        self.in_struct_definition = False
+        self.bracket_counter = 0
+
+    def read_uservars_line(self, line):
+        """
+        Reads line of instrument uservars, returns bolean.  If it encounters
+        the end of the uservars section, it returns False, otherwise True.
+
+        The contents of the uservars section is written to the McStasScript
+        Instr object.
+        """
+
+        continue_uservars = True
+
+        # Remove comments
+        if "//" in line:
+            line = line.split("//", 1)[0]
+
+        # Remove %} and signify end if this is found
+        if "%}" in line:
+            continue_uservars = False
+            line = line.split("%}", 1)[0]
+
+        if "/*" in line:
+            line = line.split("/*", 1)[0].strip()
+
+        if self.in_uservars_function:
+            if "{" in line:
+                self.bracket_counter += 1
+
+            if "}" in line:
+                self.bracket_counter -= 1
+
+            if self.bracket_counter == 0:
+                self.in_uservars_function = False
+
+            self.Instr.append_uservars(line)
+            self._write_uservars_line(line)
+
+        # Check for functions
+        if ("(" in line and ";" not in line and " " in line.strip()
+                and not self.in_uservars_function):
+
+            # If in function, it will define a block
+            n_curly_brackets = line.count("{")
+            n_curly_brackets -= line.count("}")
+
+            while n_curly_brackets != 0 or ("{" not in line):
+                next_line = self.get_next_line()
+                line += next_line
+
+                n_curly_brackets = line.count("{")
+                n_curly_brackets -= line.count("}")
+
+            after_curly_bracket = line.split("}")[-1]
+
+            uservars_lines = line.split("\n")
+            for uservars_line in uservars_lines:
+                uservars_line = uservars_line.rstrip()
+                uservars_line = uservars_line.replace('\\n', "\\\\n")
+                uservars_line = uservars_line.replace('"', "\\\"")
+                self.Instr.append_uservars(uservars_line)
+                self._write_uservars_line(uservars_line)
+
+            line = after_curly_bracket
+
+        # Check for struct / function that returns struct
+        if line.strip().startswith("struct "):
+            # Can be a function returning struct or struct definition
+
+            # If struct definition, no parenthesis and ; after )
+            n_curly_brackets = line.count("{")
+            n_curly_brackets -= line.count("}")
+
+            # Add lines until end of block found
+            while n_curly_brackets != 0 or ("{" not in line):
+
+                next_line = self.get_next_line()
+                line += next_line
+
+                n_curly_brackets = line.count("{")
+                n_curly_brackets -= line.count("}")
+
+                if "{" in line:
+                    before_curly_bracket = line.split("{", 1)[0]
+                    if "(" in before_curly_bracket and ")" in before_curly_bracket:
+                        # This is a function that returns a struct!
+                        self.in_uservars_function = True
+
+            after_curly_bracket = line.split("}")[-1]
+
+            # if not in function, add until ; is found
+            while ";" not in after_curly_bracket and not self.in_uservars_function:
+                # It is surely a struct, find ;
+                line += self.get_next_line()
+                after_curly_bracket = line.split("}")[-1]
+
+            uservars_lines = line.split("\n")
+            for uservars_line in uservars_lines:
+                uservars_line = uservars_line.rstrip()
+                uservars_line = uservars_line.replace('\\n', "\\\\n")
+                uservars_line = uservars_line.replace('"', "\\\"")
+                self.Instr.append_uservars(uservars_line)
+                self._write_uservars_line(uservars_line)
+
+            if self.in_uservars_function:
+                line = line.split("}")[-1].strip()
+            else:
+                line = line.split(";")[-1].strip()
+
+            # if in function, stop now
+            self.in_uservars_function = False
+
+        # Grab defines
+        if line.strip().startswith("#define"):
+            # Include define statements as uservars append
+            line = line.rstrip()
+            line = line.replace('\\n', "\\\\n")
+            line = line.replace('"', "\\\"")
+            self.Instr.append_uservars(line)
+            self._write_uservars_line(line)
+
+        if "\n" in line:
+            line = line.strip("\n")
+
+        # Read single line parameter definitions
+        if ";" in line and not self.in_uservars_function:
+            # This line contains c statements
+            statements = line.split(";")
+
+            for statement in statements:
+                statement = statement.strip()
+                if (statement != "\n" and statement != " "
+                        and len(statement) > 1):
+                    self._read_uservars_statement(statement)
+
+        return continue_uservars
+
+    def _read_uservars_statement(self, statement):
+        """
+        Reads single uservars statements, which can have multiple
+        variables.
+        """
+
+        statement = statement.strip()
+
+        # Find type (same for all parameters in one statement)
+        this_type = statement.split(" ", 1)[0]
+        statement = statement.split(" ", 1)[1].strip()
+
+        if this_type == "const":  # other c keywords to consider?
+            this_type += " " + statement.split(" ", 1)[0]
+            statement = statement.split(" ", 1)[1].strip()
+
+        # Check for bracket initialization of arrays
+        if "," in statement:
+            variables = statement.split(",")
+            fixed_variables = []
+            array_mode = False
+            for variable in variables:
+
+                if "{" not in variable and array_mode is False:
+                    fixed_variables.append(variable)
+                elif "{" in variable:
+                    temp_variable = variable + ","
+                    array_mode = True
+                elif "}" not in variable:
+                    temp_variable += variable + ","
+                else:
+                    temp_variable += variable
+                    fixed_variables.append(temp_variable)
+                    array_mode = False
+
+            variables = fixed_variables
+
+        else:
+            # No commas means just one parameter
+            variables = [statement]
+
+        # Treat each variable independently
+        for variable in variables:
+            variable = variable.strip()
+
+            dynamic_size = False
+            kw_args = {}
+
+            if "=" in variable:
+                value = variable.split("=")[1].strip()
+                # remove the value part before proceeding
+                variable = variable.split("=")[0].strip()
+
+                if "{" in value:
+                    # handle array as value
+                    value = value.split("{")[1]
+                    if "{" in value:
+                        raise ValueError("Can not load arrays with larger"
+                                         + "than 1 dimension yet.")
+                    value = value.split("}")[0]
+                    values = value.split(",")
+                    return_value = []
+                    for val in values:
+                        return_value.append(float(val))
+                else:
+                    try:
+                        return_value = float(value)
+                    except:
+                        value = value.replace('"', "\\\"")
+                        #return_value = '"' + value + '"'
+                        return_value = value
+
+                kw_args["value"] = return_value
+
+            # Handle array
+            if "[" in variable:
+                array_sizes = []
+                array_size_strings = variable.split("[")
+                # remove the array size part before proceeding
+                variable = variable.split("[", 1)[0].strip()
+                for array_size_string in array_size_strings:
+                    if "]" in array_size_string:
+                        this_size = array_size_string.split("]")[0]
+                        try:
+                            # Size uservarsd normally
+                            array_sizes.append(int(this_size))
+                        except:
+                            # No size uservarsd means the size is automatic
+                            dynamic_size = True
+
+                if len(array_sizes) > 1:
+                    raise ValueError("Can not handle arrays with larger"
+                                     + " than 1 dimension yet")
+                if not dynamic_size:
+                    kw_args["array"] = array_sizes[0]
+
+            if dynamic_size:
+                # McStasScript needs size of array, so it is found manually
+                kw_args["array"] = len(kw_args["value"])
+
+            # value, array and typeremoved, all that remians is the name
+            variable_name = variable
+            self.Instr.add_user_var(this_type, variable_name, **kw_args)
+
+            # Also write it to a file?
+            write_string = []
+            write_string.append(self.instr_name)
+            write_string.append(".add_user_var(")
+            write_string.append("\"" + this_type + "\"")
+            write_string.append(", ")
+            write_string.append("\"" + variable_name + "\"")
+            write_string.append(self._kw_to_string(kw_args))
+            write_string.append(")\n")
+
+            # Write uservars parameter to python file
+            self._write_to_file(write_string)
+
+    def _write_uservars_line(self, string):
+
+        string = string.rstrip()
+
+        write_string = []
+        write_string.append(self.instr_name)
+        write_string.append(".append_uservars(")
+        write_string.append("\"" + string + "\"")
+        write_string.append(")\n")
+
+        self._write_to_file(write_string)
diff --git a/mcstasscript/instrument_diagram/connections.py b/mcstasscript/instrument_diagram/connections.py
index c37af54f..777269db 100644
--- a/mcstasscript/instrument_diagram/connections.py
+++ b/mcstasscript/instrument_diagram/connections.py
@@ -32,11 +32,21 @@ def add_connection(self, start_index, end_index):
 
         # Check if there is room for this lane
         for connection in self.connections:
-            if not connection.end_index == new_connection.end_index:
+            # Check for number of reasons for connection being allowed
+            if connection.end_index == new_connection.end_index:
                 # Allow connections to collide when the end index matches
-                if not connection.compatible_with(new_connection):
-                    # If connection incompatible, return false
-                    return False
+                continue
+
+            if connection.start_index == new_connection.start_index:
+                # Allow connections to collide when the start index matches
+                continue
+
+            if connection.compatible_with(new_connection):
+                # Allow if there are no index overlap in lane
+                continue
+
+            # If connection incompatible, return false
+            return False
 
         # No problems, this connection can be included in this lane
         self.connections.append(new_connection)
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 1e116410..7b3cf699 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1642,7 +1642,8 @@ def check_for_errors(self):
 
         # Check variables used have been declared
         parameters = [x.name for x in self.parameters]
-        variables = [x.name for x in self.declare_list]
+        variables = [x.name for x in self.declare_list
+                     if isinstance(x, DeclareVariable)]
         pars_and_vars = parameters + variables
 
         # Check component parameters
@@ -2157,7 +2158,7 @@ def show_diagram(self):
         connections are only shown when they are specified.
         """
         if self.has_errors():
-            print("The instrument has some error, this diagram is still"
+            print("The instrument has some error, this diagram is still "
                   "shown as it may help find the bug.")
 
         instrument_diagram(self)

From c426b9098d7a0e1bc7a7cdf10a2e5cb0c759d670 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Mon, 20 Jun 2022 12:57:47 +0200
Subject: [PATCH 292/403] Fixed problem in connections allowing skipping both
 origin and destination collisions, allowing problems in the diagram.

Made logic so a single arrow can only skip either start or end
checks.
---
 mcstasscript/instrument_diagram/connections.py | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/mcstasscript/instrument_diagram/connections.py b/mcstasscript/instrument_diagram/connections.py
index 777269db..5b4f52e5 100644
--- a/mcstasscript/instrument_diagram/connections.py
+++ b/mcstasscript/instrument_diagram/connections.py
@@ -30,15 +30,21 @@ def add_connection(self, start_index, end_index):
         """
         new_connection = IndexConnection(start_index, end_index)
 
+        # A line can skip on either start or end, not both
+        skipped_end = False
+        skipped_start = False
+
         # Check if there is room for this lane
         for connection in self.connections:
             # Check for number of reasons for connection being allowed
-            if connection.end_index == new_connection.end_index:
-                # Allow connections to collide when the end index matches
+            if connection.start_index == new_connection.start_index and not skipped_end:
+                # Allow connections to collide when the start index matches
+                skipped_start = True
                 continue
 
-            if connection.start_index == new_connection.start_index:
-                # Allow connections to collide when the start index matches
+            if connection.end_index == new_connection.end_index and not skipped_start:
+                # Allow connections to collide when the end index matches
+                skipped_end = True
                 continue
 
             if connection.compatible_with(new_connection):

From 17d9a373a033ae226ec4ccda5d92397b158c61d4 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Mon, 20 Jun 2022 13:52:10 +0200
Subject: [PATCH 293/403] Version bump

---
 mcstasscript/_version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 38c28ffe..bf41adb9 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.49"
+__version__ = "0.0.50"

From d8ec33c7e3bfa2d40deeb870960d2e43d7a9c89b Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Mon, 20 Jun 2022 14:34:14 +0200
Subject: [PATCH 294/403] Updates to documentation for v 0.0.50

---
 .../source/getting_started/installation.ipynb |   12 +-
 .../getting_started/version_history.ipynb     |    8 +
 .../McStasScript_tutorial_1_the_basics.ipynb  |    4 +-
 ...tasScript_tutorial_3_EXTEND_and_WHEN.ipynb |    4 +-
 .../McStasScript_tutorial_4_JUMP.ipynb        |    4 +-
 .../tutorial/Union_tutorial_5_masks.ipynb     | 1299 +----------------
 .../source/user_guide/instrument_object.ipynb |   19 +
 .../source/user_guide/instrument_reader.ipynb |   19 +
 .../user_guide/parameters_and_variables.ipynb |  275 +++-
 9 files changed, 326 insertions(+), 1318 deletions(-)

diff --git a/docs/source/getting_started/installation.ipynb b/docs/source/getting_started/installation.ipynb
index a887698b..26087596 100644
--- a/docs/source/getting_started/installation.ipynb
+++ b/docs/source/getting_started/installation.ipynb
@@ -65,8 +65,8 @@
     "### Typical Mac OS configuration\n",
     "\n",
     "```\n",
-    "from mcstasscript.interface import functions\n",
-    "my_configurator = functions.Configurator()\n",
+    "import mcstasscript as ms\n",
+    "my_configurator = ms.Configurator()\n",
     "my_configurator.set_mcrun_path(\"/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/bin/\")\n",
     "my_configurator.set_mcstas_path(\"/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1/\")\n",
     "my_configurator.set_mxrun_path(\"/Applications/McXtrace-1.5.app/Contents/Resources/mcxtrace/1.5/bin/\")\n",
@@ -77,8 +77,8 @@
     "### Typical Unix configuration\n",
     "\n",
     "```\n",
-    "from mcstasscript.interface import functions\n",
-    "my_configurator = functions.Configurator()\n",
+    "import mcstasscript as ms\n",
+    "my_configurator = ms.Configurator()\n",
     "my_configurator.set_mcrun_path(\"/usr/bin/\")\n",
     "my_configurator.set_mcstas_path(\"/usr/share/mcstas/2.7.1/\")\n",
     "my_configurator.set_mxrun_path(\"/usr/bin/\")\n",
@@ -89,8 +89,8 @@
     "### Typical Windows configuration\n",
     "\n",
     "```\n",
-    "from mcstasscript.interface import functions\n",
-    "my_configurator = functions.Configurator()\n",
+    "import mcstasscript as ms\n",
+    "my_configurator = ms.Configurator()\n",
     "my_configurator.set_mcrun_path(\"\\\\mcstas-2.7.1\\\\bin\\\\\")\n",
     "my_configurator.set_mcstas_path(\"\\\\mcstas-2.7.1\\\\lib\\\\\")\n",
     "my_configurator.set_mxrun_path(\"\\\\mcxtrace-1.5\\\\bin\\\\\")\n",
diff --git a/docs/source/getting_started/version_history.ipynb b/docs/source/getting_started/version_history.ipynb
index c102f143..3f44b3f1 100644
--- a/docs/source/getting_started/version_history.ipynb
+++ b/docs/source/getting_started/version_history.ipynb
@@ -14,6 +14,14 @@
    "id": "automatic-charleston",
    "metadata": {},
    "source": [
+    "## 0.0.50 - 20th of June 2022\n",
+    "- Support for McStas 3.X\n",
+    "    - USERVARS can now be used with *add_user_var* [parameters_and_variables](../user_guide/parameters_and_variables.ipynb)\n",
+    "    - instrument_reader can read McStas 3.X files with simple USERVARS\n",
+    "- Improvements to instrument diagrams\n",
+    "- Introduced checks for using same name for parameters, declare variables and user variables\n",
+    "- Now possible to easily show instrument file for debugging with *show_instrument_file*\n",
+    "\n",
     "## 0.0.48 - 10th of June 2022\n",
     "- Improvements to instrument diagram\n",
     "    - Colors component depending on category\n",
diff --git a/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb b/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
index 3eee3824..57059a4e 100644
--- a/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
+++ b/docs/source/tutorial/McStasScript_tutorial_1_the_basics.ipynb
@@ -640,9 +640,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "with open(\"run_folder/python_tutorial.instr\") as file:\n",
-    "    data = file.read()\n",
-    "    print(data)"
+    "instrument.show_instrument_file()"
    ]
   },
   {
diff --git a/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb b/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
index 55b507ff..99b2a000 100644
--- a/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
+++ b/docs/source/tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
@@ -226,9 +226,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "with open(\"run_folder/python_tutorial.instr\") as file:\n",
-    "    instrument_string = file.read()\n",
-    "    print(instrument_string)"
+    "instrument.show_instrument_file()"
    ]
   }
  ],
diff --git a/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb b/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb
index 6ad01bfe..9c347936 100644
--- a/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb
+++ b/docs/source/tutorial/McStasScript_tutorial_4_JUMP.ipynb
@@ -259,9 +259,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "with open(\"run_folder/python_tutorial.instr\") as file:\n",
-    "    instrument_string = file.read()\n",
-    "    print(instrument_string)"
+    "instrument.show_instrument_file()"
    ]
   },
   {
diff --git a/docs/source/tutorial/Union_tutorial_5_masks.ipynb b/docs/source/tutorial/Union_tutorial_5_masks.ipynb
index 8137d1e0..353736c1 100644
--- a/docs/source/tutorial/Union_tutorial_5_masks.ipynb
+++ b/docs/source/tutorial/Union_tutorial_5_masks.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,32 +36,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 446.4x129.6 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 429.84x259.2 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Al_inc = instrument.add_component(\"Al_inc\", \"Incoherent_process\")\n",
     "Al_inc.sigma = 0.0082\n",
@@ -127,220 +104,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_98\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:11415:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:11415:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "13 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_98\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 1\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 0.347892 \n",
-      "number of processes [0]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 1\n",
-      "\n",
-      "name of geometry    [0]: wall \n",
-      "component index     [0]: 5 \n",
-      "Volume.name         [0]: wall \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.347892 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.100000 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.150000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: wall_vacuum \n",
-      "component index     [1]: 6 \n",
-      "Volume.name         [1]: wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [1]: 1 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.000000 \n",
-      "Volume.p_physics.number of processes [1]: 0 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.080000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.160000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 3\n",
-      "number_of_masks = 0\n",
-      "number_of_masked_volumes = 0\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2]\n",
-      "LIST: Direct_children for Volume           0 = [1,2]\n",
-      "LIST: Intersect_check_list for Volume      0 = [1,2]\n",
-      "LIST: Mask_intersect_list for Volume       0 = []\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [1,2]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = [2]\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = []\n",
-      "LIST: Next_volume_list for Volume          1 = [0,2]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  2 = []\n",
-      "LIST: Direct_children for Volume           2 = []\n",
-      "LIST: Intersect_check_list for Volume      2 = []\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [0,1]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [1]\n",
-      "LIST: Next_volume_list for Volume          2 = [0,1]\n",
-      "      Is_vacuum for Volume                 2 = 1\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_zx_I=1449.21 logger_space_zx_ERR=4.41242 logger_space_zx_N=111903 \"logger_zx.dat\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument.settings(ncount=2E6, output_path=\"data_folder/union_masks\")\n",
     "\n",
@@ -349,29 +119,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space_zx\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ms.make_sub_plot(data)"
    ]
@@ -402,32 +152,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 446.4x129.6 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 429.84x316.8 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "window = instrument.add_component(\"window\", \"Union_cylinder\", before=\"master\")\n",
     "window.set_AT([0,0,0], RELATIVE=wall)\n",
@@ -456,312 +183,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_99\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:11875:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:11875:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "13 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_99\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 1\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 0.347892 \n",
-      "number of processes [0]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 1\n",
-      "\n",
-      "name of geometry    [0]: wall \n",
-      "component index     [0]: 5 \n",
-      "Volume.name         [0]: wall \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.347892 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.100000 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.150000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: wall_vacuum \n",
-      "component index     [1]: 6 \n",
-      "Volume.name         [1]: wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [1]: 1 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.000000 \n",
-      "Volume.p_physics.number of processes [1]: 0 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.080000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.160000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: window \n",
-      "component index     [2]: 8 \n",
-      "Volume.name         [2]: window \n",
-      "Volume.p_physics.is_vacuum           [2]: 1 \n",
-      "Volume.p_physics.my_absorption       [2]: 0.000000 \n",
-      "Volume.p_physics.number of processes [2]: 0 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: 0.000000 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.090000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.170000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: mask \n",
-      "component index     [3]: 9 \n",
-      "Volume.name         [3]: mask \n",
-      "Volume.geometry.shape                [3]: box \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: 0.000000 \n",
-      "Volume.geometry.center.z             [3]: 0.900000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 5\n",
-      "number_of_masks = 1\n",
-      "number_of_masked_volumes = 1\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4]\n",
-      "LIST: Direct_children for Volume           0 = [1,3,4]\n",
-      "LIST: Intersect_check_list for Volume      0 = [1,2,4]\n",
-      "LIST: Mask_intersect_list for Volume       0 = [3]\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [3,1,2]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = [2,4]\n",
-      "LIST: Mask_intersect_list for Volume       1 = [3]\n",
-      "LIST: Destinations_list for Volume         1 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = []\n",
-      "LIST: Next_volume_list for Volume          1 = [0,3,2]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  2 = []\n",
-      "LIST: Direct_children for Volume           2 = []\n",
-      "LIST: Intersect_check_list for Volume      2 = [4]\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [0,1,3]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [1,3]\n",
-      "LIST: Next_volume_list for Volume          2 = [0,1,3]\n",
-      "      Is_vacuum for Volume                 2 = 1\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for window with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  3 = [2]\n",
-      "LIST: Direct_children for Volume           3 = [2]\n",
-      "LIST: Intersect_check_list for Volume      3 = [2,4]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [0,1]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = [1]\n",
-      "LIST: Next_volume_list for Volume          3 = [0,1,2]\n",
-      "      Is_vacuum for Volume                 3 = 1\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 1\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = [4]\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = [0]\n",
-      "      mask_mode for Volume                 3 = 1\n",
-      "\n",
-      "List overview for mask with box shape made of Mask\n",
-      "LIST: Children for Volume                  4 = []\n",
-      "LIST: Direct_children for Volume           4 = []\n",
-      "LIST: Intersect_check_list for Volume      4 = []\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [0,1,2]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = [1,2]\n",
-      "LIST: Next_volume_list for Volume          4 = [0,1,2]\n",
-      "      Is_vacuum for Volume                 4 = 0\n",
-      "      is_mask_volume for Volume            4 = 1\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = [3]\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 1\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_zx_I=1310.62 logger_space_zx_ERR=4.19615 logger_space_zx_N=101182 \"logger_zx.dat\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space_zx\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ms.make_sub_plot(data)"
    ]
@@ -776,32 +213,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 446.4x129.6 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 431.597x403.2 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "o_window = instrument.add_component(\"outer_window\", \"Union_cylinder\", before=\"master\")\n",
     "o_window.set_AT([0,0,0], RELATIVE=wall)\n",
@@ -837,409 +251,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_100\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:12367:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:12367:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "13 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/data_folder/union_masks_100\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 1\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 0.347892 \n",
-      "number of processes [0]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 1\n",
-      "\n",
-      "name of geometry    [0]: wall \n",
-      "component index     [0]: 5 \n",
-      "Volume.name         [0]: wall \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.347892 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: cylinder \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [0]: 0.100000 \n",
-      "Volume.geometry.geometry_parameters.height [0]: 0.150000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: wall_vacuum \n",
-      "component index     [1]: 6 \n",
-      "Volume.name         [1]: wall_vacuum \n",
-      "Volume.p_physics.is_vacuum           [1]: 1 \n",
-      "Volume.p_physics.my_absorption       [1]: 0.000000 \n",
-      "Volume.p_physics.number of processes [1]: 0 \n",
-      "Volume.geometry.shape                [1]: cylinder \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [1]: 0.080000 \n",
-      "Volume.geometry.geometry_parameters.height [1]: 0.160000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: window \n",
-      "component index     [2]: 8 \n",
-      "Volume.name         [2]: window \n",
-      "Volume.p_physics.is_vacuum           [2]: 1 \n",
-      "Volume.p_physics.my_absorption       [2]: 0.000000 \n",
-      "Volume.p_physics.number of processes [2]: 0 \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: 0.000000 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.090000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.170000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [3]: mask \n",
-      "component index     [3]: 9 \n",
-      "Volume.name         [3]: mask \n",
-      "Volume.geometry.shape                [3]: box \n",
-      "Volume.geometry.center.x             [3]: 0.000000 \n",
-      "Volume.geometry.center.y             [3]: 0.000000 \n",
-      "Volume.geometry.center.z             [3]: 0.900000 \n",
-      "Volume.geometry.rotation_matrix[0]           [3]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [3]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [3]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [3]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [4]: outer_window \n",
-      "component index     [4]: 10 \n",
-      "Volume.name         [4]: outer_window \n",
-      "Volume.p_physics.is_vacuum           [4]: 1 \n",
-      "Volume.p_physics.my_absorption       [4]: 0.000000 \n",
-      "Volume.p_physics.number of processes [4]: 0 \n",
-      "Volume.geometry.shape                [4]: cylinder \n",
-      "Volume.geometry.center.x             [4]: 0.000000 \n",
-      "Volume.geometry.center.y             [4]: 0.000000 \n",
-      "Volume.geometry.center.z             [4]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [4]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [4]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [4]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [4]: 0.110000 \n",
-      "Volume.geometry.geometry_parameters.height [4]: 0.180000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [4]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [5]: outer_window_Al \n",
-      "component index     [5]: 11 \n",
-      "Volume.name         [5]: outer_window_Al \n",
-      "Volume.p_physics.is_vacuum           [5]: 0 \n",
-      "Volume.p_physics.my_absorption       [5]: 0.347892 \n",
-      "Volume.p_physics.number of processes [5]: 2 \n",
-      "Volume.geometry.shape                [5]: cylinder \n",
-      "Volume.geometry.center.x             [5]: 0.000000 \n",
-      "Volume.geometry.center.y             [5]: 0.000000 \n",
-      "Volume.geometry.center.z             [5]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [5]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [5]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [5]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [5]: 0.090000 \n",
-      "Volume.geometry.geometry_parameters.height [5]: 0.190000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [5]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [6]: mask_outer \n",
-      "component index     [6]: 12 \n",
-      "Volume.name         [6]: mask_outer \n",
-      "Volume.geometry.shape                [6]: box \n",
-      "Volume.geometry.center.x             [6]: 0.000000 \n",
-      "Volume.geometry.center.y             [6]: 0.000000 \n",
-      "Volume.geometry.center.z             [6]: 1.100000 \n",
-      "Volume.geometry.rotation_matrix[0]           [6]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [6]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [6]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [6]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 8\n",
-      "number_of_masks = 2\n",
-      "number_of_masked_volumes = 3\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3,4,5,6,7]\n",
-      "LIST: Direct_children for Volume           0 = [4,5,6,7]\n",
-      "LIST: Intersect_check_list for Volume      0 = [1,2,4,7]\n",
-      "LIST: Mask_intersect_list for Volume       0 = [3,5,6]\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [3,5,6,1,2]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for wall with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = [2,4,7]\n",
-      "LIST: Mask_intersect_list for Volume       1 = [3,6]\n",
-      "LIST: Destinations_list for Volume         1 = [0,5]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = [5]\n",
-      "LIST: Next_volume_list for Volume          1 = [0,5,3,6,2]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 0\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = []\n",
-      "      mask_mode for Volume                 1 = 0\n",
-      "\n",
-      "List overview for wall_vacuum with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  2 = []\n",
-      "LIST: Direct_children for Volume           2 = []\n",
-      "LIST: Intersect_check_list for Volume      2 = [4,7]\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [0,1,3,5,6]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = [1,3,5,6]\n",
-      "LIST: Next_volume_list for Volume          2 = [0,1,3,5,6]\n",
-      "      Is_vacuum for Volume                 2 = 1\n",
-      "      is_mask_volume for Volume            2 = 0\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = []\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 0\n",
-      "\n",
-      "List overview for window with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  3 = [2]\n",
-      "LIST: Direct_children for Volume           3 = [2]\n",
-      "LIST: Intersect_check_list for Volume      3 = [2,4]\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [0,1]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = [1]\n",
-      "LIST: Next_volume_list for Volume          3 = [0,1,2]\n",
-      "      Is_vacuum for Volume                 3 = 1\n",
-      "      is_mask_volume for Volume            3 = 0\n",
-      "      is_masked_volume for Volume          3 = 1\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = []\n",
-      "LIST: masked_by_list for Volume            3 = [4]\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = [0]\n",
-      "      mask_mode for Volume                 3 = 1\n",
-      "\n",
-      "List overview for mask with box shape made of Mask\n",
-      "LIST: Children for Volume                  4 = []\n",
-      "LIST: Direct_children for Volume           4 = []\n",
-      "LIST: Intersect_check_list for Volume      4 = []\n",
-      "LIST: Mask_intersect_list for Volume       4 = []\n",
-      "LIST: Destinations_list for Volume         4 = [0,1,2]\n",
-      "LIST: Reduced_destinations_list for Volume 4 = [1,2]\n",
-      "LIST: Next_volume_list for Volume          4 = [0,1,2]\n",
-      "      Is_vacuum for Volume                 4 = 0\n",
-      "      is_mask_volume for Volume            4 = 1\n",
-      "      is_masked_volume for Volume          4 = 0\n",
-      "      is_exit_volume for Volume            4 = 0\n",
-      "LIST: mask_list for Volume                 4 = [3]\n",
-      "LIST: masked_by_list for Volume            4 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 4 = []\n",
-      "      mask_mode for Volume                 4 = 1\n",
-      "\n",
-      "List overview for outer_window with cylinder shape made of Vacuum\n",
-      "LIST: Children for Volume                  5 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           5 = [1,3]\n",
-      "LIST: Intersect_check_list for Volume      5 = [2,7]\n",
-      "LIST: Mask_intersect_list for Volume       5 = [6]\n",
-      "LIST: Destinations_list for Volume         5 = [0,1]\n",
-      "LIST: Reduced_destinations_list for Volume 5 = [1]\n",
-      "LIST: Next_volume_list for Volume          5 = [0,1,6,2]\n",
-      "      Is_vacuum for Volume                 5 = 1\n",
-      "      is_mask_volume for Volume            5 = 0\n",
-      "      is_masked_volume for Volume          5 = 1\n",
-      "      is_exit_volume for Volume            5 = 0\n",
-      "LIST: mask_list for Volume                 5 = []\n",
-      "LIST: masked_by_list for Volume            5 = [7]\n",
-      "LIST: masked_by_mask_index_list for Volume 5 = [1]\n",
-      "      mask_mode for Volume                 5 = 1\n",
-      "\n",
-      "List overview for outer_window_Al with cylinder shape made of Al\n",
-      "LIST: Children for Volume                  6 = [2,3]\n",
-      "LIST: Direct_children for Volume           6 = [3]\n",
-      "LIST: Intersect_check_list for Volume      6 = [2,7]\n",
-      "LIST: Mask_intersect_list for Volume       6 = []\n",
-      "LIST: Destinations_list for Volume         6 = [0,1,5]\n",
-      "LIST: Reduced_destinations_list for Volume 6 = [1,5]\n",
-      "LIST: Next_volume_list for Volume          6 = [0,1,5,2]\n",
-      "      Is_vacuum for Volume                 6 = 0\n",
-      "      is_mask_volume for Volume            6 = 0\n",
-      "      is_masked_volume for Volume          6 = 1\n",
-      "      is_exit_volume for Volume            6 = 0\n",
-      "LIST: mask_list for Volume                 6 = []\n",
-      "LIST: masked_by_list for Volume            6 = [7]\n",
-      "LIST: masked_by_mask_index_list for Volume 6 = [1]\n",
-      "      mask_mode for Volume                 6 = 1\n",
-      "\n",
-      "List overview for mask_outer with box shape made of Mask\n",
-      "LIST: Children for Volume                  7 = []\n",
-      "LIST: Direct_children for Volume           7 = []\n",
-      "LIST: Intersect_check_list for Volume      7 = []\n",
-      "LIST: Mask_intersect_list for Volume       7 = []\n",
-      "LIST: Destinations_list for Volume         7 = [0,1,2]\n",
-      "LIST: Reduced_destinations_list for Volume 7 = [1,2]\n",
-      "LIST: Next_volume_list for Volume          7 = [0,1,2]\n",
-      "      Is_vacuum for Volume                 7 = 0\n",
-      "      is_mask_volume for Volume            7 = 1\n",
-      "      is_masked_volume for Volume          7 = 0\n",
-      "      is_exit_volume for Volume            7 = 0\n",
-      "LIST: mask_list for Volume                 7 = [5,6]\n",
-      "LIST: masked_by_list for Volume            7 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 7 = []\n",
-      "      mask_mode for Volume                 7 = 1\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_zx_I=1135.73 logger_space_zx_ERR=3.90617 logger_space_zx_N=93991 \"logger_zx.dat\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space_zx\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ms.make_sub_plot(data)"
    ]
@@ -1254,32 +281,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 446.4x129.6 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 429.84x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")\n",
     "\n",
@@ -1352,273 +356,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "scroll-output"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/python_tutorial_data_37\"\n",
-      "INFO: Regenerating c-file: python_tutorial.c\n",
-      "CFLAGS= -I@MCCODE_LIB@/share/\n",
-      "INFO: Recompiling: ./python_tutorial.out\n",
-      "mccode-r.c:2837:3: warning: expression result unused [-Wunused-value]\n",
-      "  *t0;\n",
-      "  ^~~\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1604:105: warning: incompatible pointer types passing 'int (const struct saved_history_struct *, const struct saved_history_struct *)' to parameter of type 'int (* _Nonnull)(const void *, const void *)' [-Wincompatible-pointer-types]\n",
-      "  qsort(total_history.saved_histories,total_history.used_elements,sizeof (struct saved_history_struct), Sample_compare_history_intensities);\n",
-      "                                                                                                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/stdlib.h:161:22: note: passing argument to parameter '__compar' here\n",
-      "            int (* _Nonnull __compar)(const void *, const void *));\n",
-      "                            ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1613:20: warning: incompatible pointer types passing 'struct saved_history_struct *' to parameter of type 'struct dynamic_history_list *' [-Wincompatible-pointer-types]\n",
-      "    printf_history(&total_history.saved_histories[history_iterate]);\n",
-      "                   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:1434:50: note: passing argument to parameter 'history' here\n",
-      "void printf_history(struct dynamic_history_list *history) {\n",
-      "                                                 ^\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Incoherent_process.comp:65:\n",
-      "In file included from /Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Union_functions.c:2030:\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:839:1: warning: non-void function does not return a value in all control paths [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:883:1: warning: non-void function does not return a value [-Wreturn-type]\n",
-      "};\n",
-      "^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3274:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3276:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3278:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: warning: if statement has empty body [-Wempty-body]\n",
-      "    if (dist_to_corner > sphere_2_radius); { sphere_2_radius = dist_to_corner ; }\n",
-      "                                         ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//share/Geometry_functions.c:3280:42: note: put the semicolon on a separate line to silence this warning\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: warning: format string is not a string literal (potentially insecure) [-Wformat-security]\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^~~~~~~~\n",
-      "./python_tutorial.c:12065:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^~~~~~~~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_logger_2D_space.comp:574:45: note: treat the string as an argument to avoid this\n",
-      "  sprintf(this_storage.Detector_2D.Filename,filename);\n",
-      "                                            ^\n",
-      "                                            \"%s\", \n",
-      "./python_tutorial.c:12065:18: note: expanded from macro 'filename'\n",
-      "#define filename mcclogger_space_zx_filename\n",
-      "                 ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include/secure/_stdio.h:47:56: note: expanded from macro 'sprintf'\n",
-      "  __builtin___sprintf_chk (str, 0, __darwin_obsz(str), __VA_ARGS__)\n",
-      "                                                       ^\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:15: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "              ^~~~~~~~~~~~~~~~~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:788:90: warning: expression result unused [-Wunused-value]\n",
-      "          if (volume_index_main,Volumes[volume_index_main]->geometry.is_mask_volume == 0 ||\n",
-      "          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//contrib/union/Union_master.comp:789:92: warning: expression result unused [-Wunused-value]\n",
-      "              volume_index_main,Volumes[volume_index_main]->geometry.is_masked_volume == 0 ||\n",
-      "              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~\n",
-      "mccode-r.h:219:27: note: expanded from macro 'MPI_MASTER'\n",
-      "#define MPI_MASTER(instr) instr\n",
-      "                          ^~~~~\n",
-      "13 warnings generated.\n",
-      "INFO: ===\n",
-      "INFO: Placing instr file copy python_tutorial.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/python_tutorial_data_37\n",
-      "\n",
-      "    Opening input file '/Applications/McStas-2.7.1.app/Contents/Resources/mcstas/2.7.1//data/Al.laz' (Table_Read_Offset)\n",
-      "Table from file 'Al.laz' (block 1) is 26 x 18 (x=1:8), constant step. interpolation: linear\n",
-      "  '# TITLE *Aluminum-Al-[FM3-M] Miller, H.P.jr.;DuMond, J.W.M.[1942] at 298 K; ...'\n",
-      "PowderN: Al_pow: Reading 26 rows from Al.laz\n",
-      "PowderN: Al_pow: Read 26 reflections from file 'Al.laz'\n",
-      "PowderN: Al_pow: Vc=66.4 [Angs] sigma_abs=0.924 [barn] sigma_inc=0.0328 [barn] reflections=Al.laz\n",
-      "---------------------------------------------------------------------\n",
-      "global_process_list.num_elements: 2\n",
-      "name of process [0]: Al_inc \n",
-      "component index [0]: 1 \n",
-      "name of process [1]: Al_pow \n",
-      "component index [1]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_material_list.num_elements: 1\n",
-      "name of material    [0]: Al \n",
-      "component index     [0]: 3 \n",
-      "my_absoprtion       [0]: 0.347892 \n",
-      "number of processes [0]: 2 \n",
-      "---------------------------------------------------------------------\n",
-      "global_geometry_list.num_elements: 1\n",
-      "\n",
-      "name of geometry    [0]: box \n",
-      "component index     [0]: 5 \n",
-      "Volume.name         [0]: box \n",
-      "Volume.p_physics.is_vacuum           [0]: 0 \n",
-      "Volume.p_physics.my_absorption       [0]: 0.347892 \n",
-      "Volume.p_physics.number of processes [0]: 2 \n",
-      "Volume.geometry.shape                [0]: box \n",
-      "Volume.geometry.center.x             [0]: 0.000000 \n",
-      "Volume.geometry.center.y             [0]: 0.000000 \n",
-      "Volume.geometry.center.z             [0]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [0]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [0]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [0]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [0]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [1]: box_mask \n",
-      "component index     [1]: 6 \n",
-      "Volume.name         [1]: box_mask \n",
-      "Volume.geometry.shape                [1]: box \n",
-      "Volume.geometry.center.x             [1]: 0.000000 \n",
-      "Volume.geometry.center.y             [1]: 0.000000 \n",
-      "Volume.geometry.center.z             [1]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [1]: [0.707107 0.000000 -0.707107] \n",
-      "Volume.geometry.rotation_matrix[1]           [1]: [-0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [1]: [0.707107 -0.000000 0.707107] \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [1]: [0.000000 0.000000 1.000000] \n",
-      "\n",
-      "name of geometry    [2]: cylinder_mask \n",
-      "component index     [2]: 7 \n",
-      "Volume.name         [2]: cylinder_mask \n",
-      "Volume.geometry.shape                [2]: cylinder \n",
-      "Volume.geometry.center.x             [2]: 0.000000 \n",
-      "Volume.geometry.center.y             [2]: 0.000000 \n",
-      "Volume.geometry.center.z             [2]: 1.000000 \n",
-      "Volume.geometry.rotation_matrix[0]           [2]: [1.000000 0.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[1]           [2]: [0.000000 1.000000 0.000000] \n",
-      "Volume.geometry.rotation_matrix[2]           [2]: [0.000000 0.000000 1.000000] \n",
-      "Volume.geometry.geometry_parameters.cyl_radius [2]: 0.105000 \n",
-      "Volume.geometry.geometry_parameters.height [2]: 0.120000 \n",
-      "Volume.geometry.focus_data_array.elements[0].Aim             [2]: [0.000000 0.000000 1.000000] \n",
-      "---------------------------------------------------------------------\n",
-      "number_of_volumes = 4\n",
-      "number_of_masks = 2\n",
-      "number_of_masked_volumes = 1\n",
-      "\n",
-      " ---- Overview of the lists generated for each volume ---- \n",
-      "List overview for surrounding vacuum\n",
-      "LIST: Children for Volume                  0 = [1,2,3]\n",
-      "LIST: Direct_children for Volume           0 = [1,2,3]\n",
-      "LIST: Intersect_check_list for Volume      0 = [2,3]\n",
-      "LIST: Mask_intersect_list for Volume       0 = [1]\n",
-      "LIST: Destinations_list for Volume         0 = []\n",
-      "LIST: Reduced_destinations_list for Volume 0 = []\n",
-      "LIST: Next_volume_list for Volume          0 = [1]\n",
-      "LIST: mask_list for Volume                 0 = []\n",
-      "LIST: masked_by_list for Volume            0 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 0 = []\n",
-      "      mask_mode for Volume                 0 = 0\n",
-      "\n",
-      "List overview for box with box shape made of Al\n",
-      "LIST: Children for Volume                  1 = []\n",
-      "LIST: Direct_children for Volume           1 = []\n",
-      "LIST: Intersect_check_list for Volume      1 = [2,3]\n",
-      "LIST: Mask_intersect_list for Volume       1 = []\n",
-      "LIST: Destinations_list for Volume         1 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 1 = []\n",
-      "LIST: Next_volume_list for Volume          1 = [0]\n",
-      "      Is_vacuum for Volume                 1 = 0\n",
-      "      is_mask_volume for Volume            1 = 0\n",
-      "      is_masked_volume for Volume          1 = 1\n",
-      "      is_exit_volume for Volume            1 = 0\n",
-      "LIST: mask_list for Volume                 1 = []\n",
-      "LIST: masked_by_list for Volume            1 = [2,3]\n",
-      "LIST: masked_by_mask_index_list for Volume 1 = [0,1]\n",
-      "      mask_mode for Volume                 1 = 1\n",
-      "\n",
-      "List overview for box_mask with box shape made of Mask\n",
-      "LIST: Children for Volume                  2 = []\n",
-      "LIST: Direct_children for Volume           2 = []\n",
-      "LIST: Intersect_check_list for Volume      2 = []\n",
-      "LIST: Mask_intersect_list for Volume       2 = []\n",
-      "LIST: Destinations_list for Volume         2 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 2 = []\n",
-      "LIST: Next_volume_list for Volume          2 = [0]\n",
-      "      Is_vacuum for Volume                 2 = 0\n",
-      "      is_mask_volume for Volume            2 = 1\n",
-      "      is_masked_volume for Volume          2 = 0\n",
-      "      is_exit_volume for Volume            2 = 0\n",
-      "LIST: mask_list for Volume                 2 = [1]\n",
-      "LIST: masked_by_list for Volume            2 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 2 = []\n",
-      "      mask_mode for Volume                 2 = 1\n",
-      "\n",
-      "List overview for cylinder_mask with cylinder shape made of Mask\n",
-      "LIST: Children for Volume                  3 = []\n",
-      "LIST: Direct_children for Volume           3 = []\n",
-      "LIST: Intersect_check_list for Volume      3 = []\n",
-      "LIST: Mask_intersect_list for Volume       3 = []\n",
-      "LIST: Destinations_list for Volume         3 = [0]\n",
-      "LIST: Reduced_destinations_list for Volume 3 = []\n",
-      "LIST: Next_volume_list for Volume          3 = [0]\n",
-      "      Is_vacuum for Volume                 3 = 0\n",
-      "      is_mask_volume for Volume            3 = 1\n",
-      "      is_masked_volume for Volume          3 = 0\n",
-      "      is_exit_volume for Volume            3 = 0\n",
-      "LIST: mask_list for Volume                 3 = [1]\n",
-      "LIST: masked_by_list for Volume            3 = []\n",
-      "LIST: masked_by_mask_index_list for Volume 3 = []\n",
-      "      mask_mode for Volume                 3 = 1\n",
-      "\n",
-      "Union_master component master initialized sucessfully\n",
-      "Detector: logger_space_zx_I=3995.18 logger_space_zx_ERR=10.3585 logger_space_zx_N=159248 \"logger_zx.dat\"\n",
-      "loading system configuration\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = instrument.backengine()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting data with name logger_space_zx\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x504 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ms.make_sub_plot(data)"
    ]
diff --git a/docs/source/user_guide/instrument_object.ipynb b/docs/source/user_guide/instrument_object.ipynb
index e76fb4c4..e2ad5eec 100644
--- a/docs/source/user_guide/instrument_object.ipynb
+++ b/docs/source/user_guide/instrument_object.ipynb
@@ -565,6 +565,25 @@
     "instrument.show_instrument(format=\"window\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "supreme-stamp",
+   "metadata": {},
+   "source": [
+    "### Showing instrument file\n",
+    "McStasScript writes the instrument file for McStas in the process of running or visualizing the instrument. The file can be shown with the *show_instrument_file* method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "korean-industry",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.show_instrument_file(line_numbers=True)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "dressed-branch",
diff --git a/docs/source/user_guide/instrument_reader.ipynb b/docs/source/user_guide/instrument_reader.ipynb
index 3ca98702..90363d8d 100644
--- a/docs/source/user_guide/instrument_reader.ipynb
+++ b/docs/source/user_guide/instrument_reader.ipynb
@@ -60,9 +60,28 @@
     "instrument_reader.add_to_instr(instrument)\n",
     "\n",
     "instrument.show_parameters()\n",
+    "instrument.show_variables()\n",
     "instrument.show_components()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "owned-particle",
+   "metadata": {},
+   "source": [
+    "If the instrument file used is for McStas 3.X and uses the USERVARS feature, it will fail when being executed with McStas 2.X. McStasScript includes a method to move any USERVARS to declare, which may allow the instrument to run with a McStas 2.X backend. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "divided-restoration",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.move_user_vars_to_declare()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "modern-happiness",
diff --git a/docs/source/user_guide/parameters_and_variables.ipynb b/docs/source/user_guide/parameters_and_variables.ipynb
index 94c16972..0931a730 100644
--- a/docs/source/user_guide/parameters_and_variables.ipynb
+++ b/docs/source/user_guide/parameters_and_variables.ipynb
@@ -20,10 +20,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "acceptable-emperor",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parameter named: 'first_par' without set value.\n",
+      " [dimensionless]\n",
+      " \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "import mcstasscript as ms\n",
     "instrument = ms.McStas_instr(\"parameters_and_variables\")\n",
@@ -42,12 +53,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "adverse-default",
    "metadata": {},
    "outputs": [],
    "source": [
-    "second_parameter = instrument.add_parameter(\"first_par\", comment=\"My first parameter!\")"
+    "second_parameter = instrument.add_parameter(\"second_par\", comment=\"My first parameter!\")"
    ]
   },
   {
@@ -61,10 +72,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "cellular-server",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       first_par           // \n",
+      "       second_par          // My first parameter!\n",
+      "double double_par          // Typed double\n",
+      "int    int_par             // Typed int\n",
+      "string string_par          // Typed string\n"
+     ]
+    }
+   ],
    "source": [
     "double_par = instrument.add_parameter(\"double\", \"double_par\", comment=\"Typed double\")\n",
     "int_par = instrument.add_parameter(\"int\", \"int_par\", comment=\"Typed int\")\n",
@@ -84,10 +107,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "rental-indication",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Parameter named: 'par_with_value' with value: 5.2\n",
+       " [dimensionless]\n",
+       " Added value at creation"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "instrument.add_parameter(\"par_with_value\", value=5.2, comment=\"Added value at creation\")"
    ]
@@ -102,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "narrow-guard",
    "metadata": {},
    "outputs": [],
@@ -113,10 +149,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "thirty-sunrise",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       first_par               // \n",
+      "       second_par              // My first parameter!\n",
+      "double double_par      = 1.2   // Typed double\n",
+      "int    int_par         = 3     // Typed int\n",
+      "string string_par              // Typed string\n",
+      "       par_with_value  = 5.2   // Added value at creation\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.show_parameters()"
    ]
@@ -134,10 +183,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "portuguese-circuit",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parameter named: 'double_par' with value: 1.2\n",
+      " [dimensionless]\n",
+      " Typed double\n",
+      "  Legal intervals:\n",
+      "    [0,5]\n",
+      "    [7,inf]\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "double_par.add_interval(0, 5, intervals_are_legal=True)\n",
     "double_par.add_interval(7, None, intervals_are_legal=True)\n",
@@ -154,10 +217,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "unknown-edition",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parameter named: 'double_par' with value: 1.8\n",
+      " [dimensionless]\n",
+      " Typed double\n",
+      "  Legal intervals:\n",
+      "    [0,5]\n",
+      "    [7,inf]\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "double_par.value = 1.8\n",
     "print(double_par)"
@@ -165,10 +242,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "familiar-church",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parameter named: 'double_par' with value: 10\n",
+      " [dimensionless]\n",
+      " Typed double\n",
+      "  Legal intervals:\n",
+      "    [0,5]\n",
+      "    [7,inf]\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "try:\n",
     "    double_par.value = 10\n",
@@ -190,10 +281,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "honey-theta",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Failed to set value!\n",
+      "Parameter named: 'int_par' with value: 3\n",
+      " [dimensionless]\n",
+      " Typed int\n",
+      "  Allowed values:\n",
+      "    3\n",
+      "    2\n",
+      "    1\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "int_par.add_option(3, options_are_legal=True)\n",
     "int_par.add_option(2, options_are_legal=True)\n",
@@ -226,10 +333,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "confidential-village",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Declare variable: 'declared_var' of type double\n"
+     ]
+    }
+   ],
    "source": [
     "declared_var = instrument.add_declare_var(\"double\", \"declared_var\", comment=\"Declared variable\")\n",
     "print(declared_var)"
@@ -253,10 +368,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "valid-offense",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Declare variable: 'integer' of type int with value: 5\n"
+     ]
+    }
+   ],
    "source": [
     "declared_var = instrument.add_declare_var(\"int\", \"integer\", value=5, comment=\"Declared integer\")\n",
     "print(declared_var)"
@@ -272,10 +395,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "polish-integration",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Declare variable: 'declared_array' of type double with value: [2, 3, 4]. Array with length 3\n"
+     ]
+    }
+   ],
    "source": [
     "var = instrument.add_declare_var(\"double\", \"declared_array\", value=[2, 3, 4], array=3)\n",
     "print(var)"
@@ -291,10 +422,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "after-reply",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DECLARE VARIABLES \n",
+      "type    variable name   array length  value      \n",
+      "-----------------------------------------------\n",
+      "double  declared_var                             \n",
+      "int     integer                       5          \n",
+      "double  declared_array  3             [2, 3, 4]  \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument.show_variables()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "nervous-poverty",
+   "metadata": {},
+   "source": [
+    "## User variables\n",
+    "McStas 3.0 introduced USERVARS next to declare variables, these differ in that they are specific to the neutron and can thus be used in EXTEND blocks on McStas 3.0, which is not allowed for normal declare variables. In McStasScript user variables act like declared variables, the only difference being they do not accept a initial value and will show up in a different section of show variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "electronic-police",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Declare variable: 'flag' of type int\n"
+     ]
+    }
+   ],
+   "source": [
+    "user_var = instrument.add_user_var(\"int\", \"flag\", comment=\"Example of USERVAR\")\n",
+    "print(user_var)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "built-folks",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DECLARE VARIABLES \n",
+      "type    variable name   array length  value      \n",
+      "-----------------------------------------------\n",
+      "double  declared_var                             \n",
+      "int     integer                       5          \n",
+      "double  declared_array  3             [2, 3, 4]  \n",
+      "\n",
+      "USER VARIABLES (per neutron, only use in EXTEND)\n",
+      "type    variable name   array length  value      \n",
+      "-----------------------------------------------\n",
+      "int     flag                                     \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "instrument.show_variables()"
    ]
@@ -305,15 +507,28 @@
    "metadata": {},
    "source": [
     "## Using parameters and variables\n",
-    "Instrument parameters and variables are can both be used when setting component attributes. One can use either the name in a string, or the object directly. When using a string, it is allowed to do basic math and use several parameter / variables. It is also possible to select a certain element from an array."
+    "Instrument parameters and declare variables are can both be used when setting component attributes. One can use either the name in a string, or the object directly. When using a string, it is allowed to do basic math and use several parameter / variables. It is also possible to select a certain element from an array."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "dangerous-telling",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMPONENT source = Source_simple\n",
+      "  \u001b[1myheight\u001b[0m = \u001b[1m\u001b[92minteger\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mxwidth\u001b[0m = \u001b[1m\u001b[92mdouble_par\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mdist\u001b[0m = \u001b[1m\u001b[92m5.0*first_par + 0.1*double_par\u001b[0m\u001b[0m [m]\n",
+      "  \u001b[1mfocus_xw\u001b[0m = \u001b[1m\u001b[92mdeclared_array[0]\u001b[0m\u001b[0m [m]\n",
+      "AT [0, 0, 0] ABSOLUTE\n"
+     ]
+    }
+   ],
    "source": [
     "source = instrument.add_component(\"source\", \"Source_simple\")\n",
     "source.xwidth = double_par\n",

From 9e768dc676e4a5b437c59ec7afb1198f77f91312 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 8 Sep 2022 16:08:48 +0200
Subject: [PATCH 295/403] Update of reader so it understands keywords in
 capital and lowercase letters.

Highlight of errors in output of McStas runs.
Found a way to merge standard error and standard output so it is cronologically ordered.
---
 mcstasscript/helper/managed_mcrun.py     | 88 ++++++++++++++++++++++--
 mcstasscript/instr_reader/read_trace.py  | 21 ++++--
 mcstasscript/tests/test_Instr.py         | 10 +--
 mcstasscript/tests/test_ManagedMcrun.py  | 14 ++--
 mcstasscript/tests/test_dump_and_load.py |  3 +-
 5 files changed, 110 insertions(+), 26 deletions(-)

diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index d45ebfc7..d63b56c1 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -4,6 +4,7 @@
 import mmap
 import warnings
 
+from mcstasscript.helper.formatting import bcolors
 from mcstasscript.data.data import McStasMetaData
 from mcstasscript.data.data import McStasDataBinned
 from mcstasscript.data.data import McStasDataEvent
@@ -261,17 +262,15 @@ def run_simulation(self, **kwargs):
 
         process = subprocess.run(full_command, shell=True,
                                  stdout=subprocess.PIPE,
-                                 stderr=subprocess.PIPE,
+                                 stderr=subprocess.STDOUT,
                                  universal_newlines=True,
                                  cwd=self.run_path)
 
         if "suppress_output" in kwargs:
             if kwargs["suppress_output"] is False:
-                print(process.stderr)
-                print(process.stdout)
+                print_sim_output(process.stdout)
         else:
-            print(process.stderr)
-            print(process.stdout)
+            print_sim_output(process.stdout)
 
         if not os.path.isdir(self.data_folder_name):
             warnings.warn("Simulation did not create data folder, most likely failed.")
@@ -499,3 +498,82 @@ def load_monitor(metadata, data_folder_name):
             + "connected to monitor named "
             + metadata.component_name)
 
+
+def print_sim_output(sim_output):
+    print(highlight(sim_output, "error", return_section=True, after_lines=10, highlight_type="FAIL"))
+    print(highlight(sim_output, "error", return_section=False, highlight_type="FAIL"))
+
+
+def highlight(string, search_term, return_section=False, highlight_type=None, after_lines=5):
+    """
+    Highlights search term in string and returns it, if return_section only sections with term is returned
+    """
+
+    search_term = search_term.lower()
+
+    if not isinstance(string, str):
+        return None
+
+    # Early exit if search term is not in string
+    output = string.lower().find(search_term)
+    if output == -1:
+        if return_section:
+            return ""
+        else:
+            return string
+
+    if return_section:
+        instances = list(findall(string, search_term))
+        n_instances = len(instances)
+        print(f"---- Found {n_instances} places in McStas output with "
+              f"keyword '{search_term}'. \n")
+
+    if highlight_type is None:
+        highlight_start = ""
+        highlight_end = ""
+    else:
+        if not hasattr(bcolors, highlight_type):
+            raise RuntimeError(f"Used highlight_type {highlight_type} "
+                               f"in highlight not found in bcolors.")
+        else:
+            highlight_start = getattr(bcolors, highlight_type)
+            highlight_end = bcolors.ENDC
+
+    return_string = ""
+
+    lines = string.split("\n")
+    total_lines = len(lines)
+    for index, line in enumerate(lines):
+        output = line.lower().find(search_term)
+        if output == -1:
+            if not return_section:
+                return_string += line + "\n"
+        else:
+            replaced_string = line[:output]
+            replaced_string += highlight_start
+            replaced_string += line[output:output + len(search_term)]
+            replaced_string += highlight_end
+            replaced_string += line[output + len(search_term):]
+            replaced_string += "\n"
+
+            return_string += replaced_string
+            if return_section:
+                extra_lines = min(total_lines - index, after_lines)
+                for line_index in range(1, extra_lines):
+                    line_to_include = lines[index + line_index]
+                    if line_to_include.lower().find(search_term) != -1:
+                        break
+                    return_string += line_to_include + "\n"
+                return_string += "-"*70 + "\n"
+
+    return return_string
+
+
+def findall(s, p):
+    """
+    Yields all the positions of the pattern p in the string s.
+    """
+    i = s.lower().find(p)
+    while i != -1:
+        yield i
+        i = s.lower().find(p, i+1)
\ No newline at end of file
diff --git a/mcstasscript/instr_reader/read_trace.py b/mcstasscript/instr_reader/read_trace.py
index f94aff80..a8680dea 100644
--- a/mcstasscript/instr_reader/read_trace.py
+++ b/mcstasscript/instr_reader/read_trace.py
@@ -89,12 +89,13 @@ def read_trace_line(self, line):
             line = line.split("SPLIT", 1)[1].strip()
             if line.startswith("COMPONENT"):
                 # Default split without indicating amount
-                self.SPLIT = "\"\""
+                self.SPLIT = ""
             else:
                 try:
                     self.SPLIT = int(line.split(" ", 1)[0].strip())
                 except:
-                    self.SPLIT = "\"" + line.split(" ", 1)[0].strip() + "\""
+                    #self.SPLIT = "\"" + line.split(" ", 1)[0].strip() + "\""
+                    self.SPLIT = line.split(" ", 1)[0].strip()
 
                 if " " in line:
                     # If the line continues, remove the SPLIT number
@@ -244,7 +245,7 @@ def read_trace_line(self, line):
             self.current_component.set_parameters(par_dict)
 
         # Read keywords given after parameters but before position (WHEN)
-        if line.strip().startswith("WHEN"):
+        if line.strip().upper().startswith("WHEN"):
             if "(" in line:
                 line = line.split("(", 1)[1].strip()
                 # need to find the closing parenthesis
@@ -344,7 +345,7 @@ def read_trace_line(self, line):
                                                RELATIVE=relative_name)
 
         # Read keywords after component position (GROUP, EXTEND, JUMP)
-        if line.strip().startswith("GROUP"):
+        if line.strip().upper().startswith("GROUP"):
             line = line.strip()
 
             group_name = line.split(" ", 1)[1].strip()
@@ -355,7 +356,7 @@ def read_trace_line(self, line):
 
             self.current_component.set_GROUP(group_name)
 
-        if line.strip().startswith("EXTEND"):
+        if line.strip().upper().startswith("EXTEND"):
             line = line.split("EXTEND", 1)[1].strip()
             self.EXTEND_mode = True
 
@@ -372,7 +373,7 @@ def read_trace_line(self, line):
                 line = line.replace('"', "\\\"")
                 self.current_component.append_EXTEND(line)
 
-        if line.strip().startswith("JUMP "):
+        if line.strip().upper().startswith("JUMP "):
             line = line.strip().split(" ", 1)[1]
             self.current_component.set_JUMP(line)
 
@@ -464,7 +465,13 @@ def _write_component_to_py(self):
                 write_string = []
                 write_string.append(self.current_component.name)
                 write_string.append(".set_SPLIT(")
-                write_string.append(str(self.current_component.SPLIT))
+                if self.current_component.SPLIT == "":
+                    write_string.append('""')
+                else:
+                    if isinstance(self.current_component.SPLIT, str) and self.current_component.SPLIT != "":
+                        write_string.append('"' + str(self.current_component.SPLIT) + '"')
+                    else:
+                        write_string.append(str(self.current_component.SPLIT))
                 write_string.append(")\n")
 
                 self._write_to_file(write_string)
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 45a773bf..8309314f 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1733,7 +1733,7 @@ def test_x_ray_run_full_instrument_basic(self, mock_sub, mock_stdout):
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
                                          cwd=expected_run_path,
-                                         stderr=-1, stdout=-1,
+                                         stderr=-2, stdout=-1,
                                          universal_newlines=True)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
@@ -1799,7 +1799,7 @@ def test_run_backengine_basic(self, mock_sub, mock_stdout):
 
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
-                                         stderr=-1, stdout=-1,
+                                         stderr=-2, stdout=-1,
                                          universal_newlines=True,
                                          cwd=run_path)
 
@@ -1854,7 +1854,7 @@ def test_run_full_instrument_complex(self, mock_sub, mock_stdout):
 
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
-                                         stderr=-1, stdout=-1,
+                                         stderr=-2, stdout=-1,
                                          universal_newlines=True,
                                          cwd=run_path)
 
@@ -1906,7 +1906,7 @@ def test_run_full_instrument_overwrite_default(self, mock_sub,
 
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
-                                         stderr=-1, stdout=-1,
+                                         stderr=-2, stdout=-1,
                                          universal_newlines=True,
                                          cwd=run_path)
 
@@ -1950,7 +1950,7 @@ def test_run_full_instrument_x_ray_basic(self, mock_sub, mock_stdout):
 
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
-                                         stderr=-1, stdout=-1,
+                                         stderr=-2, stdout=-1,
                                          universal_newlines=True,
                                          cwd=run_path)
 
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index a8517fe0..70e14255 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -6,7 +6,7 @@
 from mcstasscript.helper.managed_mcrun import load_results
 from mcstasscript.helper.managed_mcrun import load_metadata
 from mcstasscript.helper.managed_mcrun import load_monitor
-from .helpers_for_tests import WorkInTestDir
+from mcstasscript.tests.helpers_for_tests import WorkInTestDir
 
 class TestManagedMcrun(unittest.TestCase):
     """
@@ -189,7 +189,7 @@ def test_ManagedMcrun_run_simulation_basic(self, mock_sub):
 
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
-                                         stderr=-1, stdout=-1,
+                                         stderr=-2, stdout=-1,
                                          universal_newlines=True,
                                          cwd=mcrun_obj.run_path)
 
@@ -220,7 +220,7 @@ def test_ManagedMcrun_run_simulation_basic_path(self, mock_sub):
 
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
-                                         stderr=-1, stdout=-1,
+                                         stderr=-2, stdout=-1,
                                          universal_newlines=True,
                                          cwd=mcrun_obj.run_path)
 
@@ -257,7 +257,7 @@ def test_ManagedMcrun_run_simulation_no_standard(self, mock_sub):
 
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
-                                         stderr=-1, stdout=-1,
+                                         stderr=-2, stdout=-1,
                                          universal_newlines=True,
                                          cwd=mcrun_obj.run_path)
 
@@ -295,7 +295,7 @@ def test_ManagedMcrun_run_simulation_with_gravity(self, mock_sub):
 
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
-                                         stderr=-1, stdout=-1,
+                                         stderr=-2, stdout=-1,
                                          universal_newlines=True,
                                          cwd=mcrun_obj.run_path)
 
@@ -333,7 +333,7 @@ def test_ManagedMcrun_run_simulation_parameters(self, mock_sub):
 
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
-                                         stderr=-1, stdout=-1,
+                                         stderr=-2, stdout=-1,
                                          universal_newlines=True,
                                          cwd=mcrun_obj.run_path)
 
@@ -372,7 +372,7 @@ def test_ManagedMcrun_run_simulation_compile(self, mock_sub):
 
         mock_sub.assert_called_once_with(expected_call,
                                          shell=True,
-                                         stderr=-1, stdout=-1,
+                                         stderr=-2, stdout=-1,
                                          universal_newlines=True,
                                          cwd=mcrun_obj.run_path)
 
diff --git a/mcstasscript/tests/test_dump_and_load.py b/mcstasscript/tests/test_dump_and_load.py
index 7025671a..f667d0b1 100644
--- a/mcstasscript/tests/test_dump_and_load.py
+++ b/mcstasscript/tests/test_dump_and_load.py
@@ -8,8 +8,7 @@
 from mcstasscript.interface.instr import McStas_instr
 from mcstasscript.interface.instr import McXtrace_instr
 
-from .helpers_for_tests import WorkInTestDir
-
+from mcstasscript.tests.helpers_for_tests import WorkInTestDir
 
 run_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '.')
 

From 7befe4e55bfff7053af4253577f6cb1f2261d2be Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 27 Sep 2022 16:21:42 +0200
Subject: [PATCH 296/403] First version of MCPL bridge.

A subsection of the instrument can be executed.
instr.run_from(component_reference) sets start.
instr.run_to(component_reference) sets end.

MCPL input and output components are auto generated.
It is checked if the run_from / run_to points chosen are allowed,
if there are RELATIVE references across the files it wouldn't work,
so that is simply not allowed and an error is shown that tells
the user what needs to be fixed.

Does not work yet, the mcpl files are written in folders and with
a compression. Work in progress.
---
 mcstasscript/helper/mcstas_objects.py |  28 +++--
 mcstasscript/interface/instr.py       | 160 +++++++++++++++++++++++---
 2 files changed, 165 insertions(+), 23 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index a23cfa33..1b4a6763 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -950,7 +950,7 @@ def set_c_code_after(self, string):
                                + "given " + str(type(string)))
         self.c_code_after = string
 
-    def write_component(self, fo):
+    def write_component(self, fo, overwrite_location=False):
         """
         Method that writes component to file
 
@@ -1020,18 +1020,28 @@ def write_component(self, fo):
         if not self.WHEN == "":
             fo.write("%s\n" % self.WHEN)
 
+        if overwrite_location:
+            written_AT_data = [0, 0, 0]
+            written_AT_relative = "ABSOLUTE"
+            written_ROTATED_data = [0, 0, 0]
+            written_ROTATED_relative = "ABSOLUTE"
+        else:
+            written_AT_data = self.AT_data
+            written_AT_relative = self.AT_relative
+            written_ROTATED_data = self.ROTATED_data
+
         # Write AT and ROTATED section
-        fo.write("AT (%s,%s,%s)" % (str(self.AT_data[0]),
-                                    str(self.AT_data[1]),
-                                    str(self.AT_data[2])))
+        fo.write("AT (%s,%s,%s)" % (str(written_AT_data[0]),
+                                    str(written_AT_data[1]),
+                                    str(written_AT_data[2])))
 
-        fo.write(" %s\n" % self.AT_relative)
+        fo.write(" %s\n" % written_AT_relative)
 
         if self.ROTATED_specified:
-            fo.write("ROTATED (%s,%s,%s)" % (str(self.ROTATED_data[0]),
-                                             str(self.ROTATED_data[1]),
-                                             str(self.ROTATED_data[2])))
-            fo.write(" %s\n" % self.ROTATED_relative)
+            fo.write("ROTATED (%s,%s,%s)" % (str(written_ROTATED_data[0]),
+                                             str(written_ROTATED_data[1]),
+                                             str(written_ROTATED_data[2])))
+            fo.write(" %s\n" % written_ROTATED_relative)
 
         if not self.GROUP == "":
             fo.write("GROUP %s\n" % self.GROUP)
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 7b3cf699..e6a0b601 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -418,6 +418,9 @@ def __init__(self, name, parameters=None, author=None,
             # Handle components
             self.component_list = []  # List of components (have to be ordered)
 
+            self.run_from_ref = None
+            self.run_to_ref = None
+
     @property
     def output_path(self) -> str:
         return self.base_dir
@@ -439,6 +442,78 @@ def _read_calibration(self):
         """
         pass
 
+    def run_to(self, component_ref, **kwargs):
+        """
+        Set limit for instrument, only run to given component, save MCPL there
+        """
+
+        if isinstance(component_ref, Component):
+            component_ref = component_ref.name
+
+        # Check references are valid
+        self.subset_check(start_ref=self.run_from_ref, end_ref=component_ref)
+
+        self.run_to_ref = component_ref
+
+        if component_ref is not None:
+            if "filename" not in kwargs:
+                kwargs["filename"] = '"' + self.name + "_" + component_ref + ".mcpl" + '"'
+
+            self.run_to_component_parameters = kwargs
+
+    def run_from(self, component_ref, **kwargs):
+        """
+        Set limit for instrument, only run from given component, loads MCPL there
+        """
+
+        if isinstance(component_ref, Component):
+            component_ref = component_ref.name
+
+        # Check references are valid
+        self.subset_check(start_ref=component_ref, end_ref=self.run_to_ref)
+
+        self.run_from_ref = component_ref
+
+        if component_ref is not None:
+            if "filename" not in kwargs:
+                kwargs["filename"] = '"' + self.name + "_" + component_ref + ".mcpl" + '"'
+
+            self.run_from_component_parameters = kwargs
+
+    def subset_check(self, start_ref, end_ref):
+        """
+        Checks that when the instrument is broken into subsets, it is still valid
+        """
+
+        # Check current subset of instrument is self contained
+        is_self_contained = True
+        try:
+            self.check_for_relative_errors(start_ref=start_ref, end_ref=end_ref)
+        except McStasError:
+            is_self_contained = False
+
+        if not is_self_contained:
+            print("When using a subset of the instrument, component references "
+                  "must be only internal as these sections are split into separate "
+                  "files. When seeing only the specified subset of the instrument, "
+                  "this reference can not be resolved.")
+            self.check_for_relative_errors(start_ref=start_ref, end_ref=end_ref)
+
+        # Check the part after is self consistent
+        is_self_contained = True
+        try:
+            self.check_for_relative_errors(start_ref=end_ref)
+        except McStasError:
+            is_self_contained = False
+
+        if not is_self_contained:
+            print("When using a subset of the instrument, component references "
+                  "must be only internal as these sections are split into separate "
+                  "files. When seeing only the specified subset of the instrument, "
+                  "this reference can not be resolved. \n"
+                  "In this case the remaining instrument would fail.")
+            self.check_for_relative_errors(start_ref=end_ref)
+
     def add_parameter(self, *args, **kwargs):
         """
         Method for adding input parameter to instrument
@@ -1620,10 +1695,46 @@ def check_for_errors(self):
         """
 
         # Check RELATIVE exists
-        seen_instrument_names = []
+        self.check_for_relative_errors()
+
+        # Check variables used have been declared
+        parameters = [x.name for x in self.parameters]
+        variables = [x.name for x in self.declare_list
+                     if isinstance(x, DeclareVariable)]
+        pars_and_vars = parameters + variables
+
+        # Check component parameters
         for component in self.component_list:
+            component.check_parameters(pars_and_vars)
+
+    def check_for_relative_errors(self, start_ref=None, end_ref=None):
+        """
+        Method for checking if RELATIVE locations does not contain unknown references
+
+        Using the start_ref and end_ref keyword arguments, a subset of the
+        instrument can be checked for internal consistency.
+        """
+        if start_ref is not None or end_ref is not None:
+            component_names = [x.name for x in self.component_list]
+
+        if start_ref is not None:
+            start_index = component_names.index(start_ref)
+        else:
+            start_index = 0
+
+        if end_ref is not None:
+            end_index = component_names.index(end_ref)
+        else:
+            end_index = len(self.component_list)
+
+        seen_instrument_names = []
+        for component in self.component_list[start_index:end_index]:
             seen_instrument_names.append(component.name)
 
+            if component.name == start_ref:
+                # Avoid checking first component when start_ref != 0
+                continue
+
             references = []
             if component.AT_reference not in (None, "PREVIOUS"):
                 references.append(component.AT_reference)
@@ -1635,21 +1746,10 @@ def check_for_errors(self):
                 if ref not in seen_instrument_names:
                     raise McStasError("Component '" + str(component.name) +
                                       "' referenced unknown component"
-                                      " named '"
-                                      + str(component.AT_reference) + "'.\n"
+                                      " named '" + str(ref) + "'.\n"
                                       "This check can be skipped with"
                                       " settings(checks=False)")
 
-        # Check variables used have been declared
-        parameters = [x.name for x in self.parameters]
-        variables = [x.name for x in self.declare_list
-                     if isinstance(x, DeclareVariable)]
-        pars_and_vars = parameters + variables
-
-        # Check component parameters
-        for component in self.component_list:
-            component.check_parameters(pars_and_vars)
-
     def read_instrument_file(self):
         """
         Reads current instrument file if it exists, otherwise creates one first
@@ -1778,9 +1878,41 @@ def write_full_instrument(self):
 
         # Write trace
         fo.write("TRACE \n")
-        for component in self.component_list:
+        component_names = [x.name for x in self.component_list]
+
+        start_index = 0
+        end_index = len(self.component_list)
+        if self.run_from_ref is not None:
+            start_index = component_names.index(self.run_from_ref)
+            # Add MCPL input component
+            MCPL_in = self._create_component_instance("MCPL_" + self.run_from_ref, "MCPL_input")
+            MCPL_in.set_comment("Automatically inserted to split instrument into parts")
+            MCPL_in.set_parameters(**self.run_from_component_parameters)
+            MCPL_in.write_component(fo)
+
+            # Ensure from component reset to MCPL position
+            self.component_list[start_index].write_component(fo, overwrite_location=True)
+            start_index += 1 # skip this component in component writing loop
+
+        if self.run_to_ref is not None:
+            end_index = component_names.index(self.run_to_ref)
+
+        # Main component loop
+        for component in self.component_list[start_index:end_index]:
             component.write_component(fo)
 
+        if self.run_to_ref is not None:
+            replaced_component = self.component_list[end_index]
+
+            # Add MCPL output component
+            MCPL_out = self._create_component_instance("MCPL_" + self.run_to_ref, "MCPL_output")
+            MCPL_out.set_comment("Automatically inserted to split instrument into parts")
+            MCPL_out.set_parameters(**self.run_to_component_parameters)
+            MCPL_out.set_AT(replaced_component.AT_data, RELATIVE=replaced_component.AT_reference)
+            if replaced_component.ROTATED_specified:
+                MCPL_out.set_ROTATED(replaced_component.ROTATED_data, RELATIVE=replaced_component.ROTATED_reference)
+            MCPL_out.write_component(fo)
+
         # Write finally
         fo.write("FINALLY \n%{\n")
         fo.write(self.finally_section)

From d244081a1d5a65e97344adfebf97e0432b4d5d3b Mon Sep 17 00:00:00 2001
From: Shervin Nourbakhsh <shervin.nourbakhsh@ill.fr>
Date: Tue, 27 Sep 2022 16:34:26 +0200
Subject: [PATCH 297/403] If instrument file not found, it is generated even if
 force_compile=False

---
 mcstasscript/interface/instr.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 7b3cf699..e8a92be1 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1965,7 +1965,8 @@ def backengine(self):
         method.
         """
 
-        if self._run_settings["force_compile"]:
+        instrument_path = os.path.join(self.input_path, self.name + ".instr")
+        if not os.path.exists(instrument_path) or self._run_settings["force_compile"]:
             self.write_full_instrument()
 
         parameters = {}

From b98e0af2020b34ba9fb35c7267477d7c0c1d144a Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 28 Sep 2022 15:28:00 +0200
Subject: [PATCH 298/403] Fixes to how the MCPL bridge feature is implemented.
 Now syncs well between diagram, component and write_full_instrument as they
 all use the new make_component_subset to generate the component list they
 use. This component list is a copy so local changes can be made to the
 components around the MCPL components.

Fixed small problem that prevented unit tests to run.
---
 mcstasscript/helper/mcstas_objects.py |  1 +
 mcstasscript/interface/instr.py       | 93 ++++++++++++++++++++-------
 2 files changed, 70 insertions(+), 24 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 1b4a6763..9b1e3bcc 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -1029,6 +1029,7 @@ def write_component(self, fo, overwrite_location=False):
             written_AT_data = self.AT_data
             written_AT_relative = self.AT_relative
             written_ROTATED_data = self.ROTATED_data
+            written_ROTATED_relative = self.ROTATED_relative
 
         # Write AT and ROTATED section
         fo.write("AT (%s,%s,%s)" % (str(written_AT_data[0]),
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index e6a0b601..ddaf0c27 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1422,6 +1422,17 @@ def show_components(self, line_length=None):
             print("No components added to instrument object yet.")
             return
 
+        printed_components = self.make_component_subset()
+
+        if len(printed_components) == 0:
+            print("No components in subset.")
+            return
+
+        if self.run_from_ref is not None:
+            print("Showing subset of instrument after cut at '"
+                  + self.run_from_ref
+                  + "' component.")
+
         if line_length is None:
             line_limit = self.line_limit
         else:
@@ -1433,7 +1444,7 @@ def show_components(self, line_length=None):
                                  " line_length and has to be an integer.")
             line_limit = line_length
 
-        component_names = [x.name for x in self.component_list]
+        component_names = [x.name for x in printed_components]
         longest_name = len(max(component_names, key=len))
 
         # todo Investigate how this could have been done in a better way
@@ -1443,7 +1454,7 @@ def show_components(self, line_length=None):
         at_relative_list = []
         rotated_xyz_list = []
         rotated_relative_list = []
-        for component in self.component_list:
+        for component in printed_components:
             component_type_list.append(component.component_name)
             at_xyz_list.append(str(component.AT_data[0])
                                + str(component.AT_data[1])
@@ -1528,7 +1539,7 @@ def coordinates_to_string(data):
                 n_lines = 3
 
         if n_lines == 1:
-            for component in self.component_list:
+            for component in printed_components:
                 p_name = str(component.name)
                 p_name = p_name.ljust(longest_name + name_pad)
 
@@ -1557,7 +1568,7 @@ def coordinates_to_string(data):
                     print(p_name, p_comp_name, "AT", p_AT, p_AT_RELATIVE)
 
         elif n_lines == 2:
-            for component in self.component_list:
+            for component in printed_components:
                 p_name = str(component.name)
                 p_name = p_name.ljust(longest_name + name_pad)
 
@@ -1593,7 +1604,7 @@ def coordinates_to_string(data):
                           "AT     ", p_AT, p_AT_RELATIVE)
 
         elif n_lines == 3:
-            for component in self.component_list:
+            for component in printed_components:
                 p_name = bcolors.BOLD + str(component.name) + bcolors.ENDC
 
                 p_comp_name = (bcolors.BOLD
@@ -1616,6 +1627,11 @@ def coordinates_to_string(data):
                     print(p_name + " ", p_comp_name, "\n",
                           " AT     ", p_AT, p_AT_RELATIVE)
 
+        if self.run_to_ref is not None:
+            print("Showing subset of instrument until cut at '"
+                  + self.run_to_ref
+                  + "' component.")
+
     def write_c_files(self):
         """
         Obsolete method for writing instrument parts to c files
@@ -1878,30 +1894,66 @@ def write_full_instrument(self):
 
         # Write trace
         fo.write("TRACE \n")
-        component_names = [x.name for x in self.component_list]
 
+        for component in self.make_component_subset():
+            component.write_component(fo)
+
+        # Write finally
+        fo.write("FINALLY \n%{\n")
+        fo.write(self.finally_section)
+        # Alternatively hide everything in include
+        fo.write("%}\n")
+
+        # End instrument file
+        fo.write("\nEND\n")
+
+        fo.close()
+
+    def make_component_subset(self):
+        """
+        Uses run_from and run_to specifications to extract subset of components
+
+        Adds MCPL component at start and/or end as needed, and adjusts the
+        surrounding components as necessary.
+        """
+
+        if self.run_from_ref is None and self.run_to_ref is None:
+            # Simple case, just return full component list
+            return self.component_list
+
+        # Need to extract subset
+        # Starting with component named run_from_ref, ending with run_to_ref
+        component_names = [x.name for x in self.component_list]
         start_index = 0
         end_index = len(self.component_list)
         if self.run_from_ref is not None:
             start_index = component_names.index(self.run_from_ref)
+
+        if self.run_to_ref is not None:
+            end_index = component_names.index(self.run_to_ref)
+
+        # Create a copy of the used component instances
+        component_subset = copy.deepcopy(self.component_list[start_index:end_index])
+
+        if self.run_from_ref is not None:
             # Add MCPL input component
             MCPL_in = self._create_component_instance("MCPL_" + self.run_from_ref, "MCPL_input")
             MCPL_in.set_comment("Automatically inserted to split instrument into parts")
             MCPL_in.set_parameters(**self.run_from_component_parameters)
-            MCPL_in.write_component(fo)
+            #MCPL_in.write_component(fo)
 
             # Ensure from component reset to MCPL position
-            self.component_list[start_index].write_component(fo, overwrite_location=True)
-            start_index += 1 # skip this component in component writing loop
+            first_component = component_subset[0]
 
-        if self.run_to_ref is not None:
-            end_index = component_names.index(self.run_to_ref)
+            # Since a copy of the component is used, we can alter some properties safely
+            first_component.set_AT([0, 0, 0], "ABSOLUTE")
+            if first_component.ROTATED_specified:
+                first_component.set_ROTATED([0, 0, 0], "ABSOLUTE")
 
-        # Main component loop
-        for component in self.component_list[start_index:end_index]:
-            component.write_component(fo)
+            component_subset = [MCPL_in] + component_subset
 
         if self.run_to_ref is not None:
+            # MCPL component will replace the component after the last included
             replaced_component = self.component_list[end_index]
 
             # Add MCPL output component
@@ -1911,18 +1963,11 @@ def write_full_instrument(self):
             MCPL_out.set_AT(replaced_component.AT_data, RELATIVE=replaced_component.AT_reference)
             if replaced_component.ROTATED_specified:
                 MCPL_out.set_ROTATED(replaced_component.ROTATED_data, RELATIVE=replaced_component.ROTATED_reference)
-            MCPL_out.write_component(fo)
-
-        # Write finally
-        fo.write("FINALLY \n%{\n")
-        fo.write(self.finally_section)
-        # Alternatively hide everything in include
-        fo.write("%}\n")
+            #MCPL_out.write_component(fo)
 
-        # End instrument file
-        fo.write("\nEND\n")
+            component_subset += [MCPL_out]
 
-        fo.close()
+        return component_subset
 
     def settings(self, ncount=None, mpi="not_set", seed=None,
                  force_compile=None, output_path=None,

From 8e0e44b8e1bcc8dad749bafcc500b25b798f0428 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 29 Sep 2022 18:10:04 +0200
Subject: [PATCH 299/403] Changes to how dumps to and from MCPL work. Now makes
 a database of results to look into when choosing where to find the file.

Looking for newest compatible from the specified point.

instr.show_dumps() shows an overview of the available.

Still uses absolute paths.

Some more work needed, but good progress!
---
 mcstasscript/helper/beam_dump_database.py     | 186 ++++++++++++++++++
 mcstasscript/helper/mcstas_objects.py         |  29 +--
 .../instrument_diagram/make_diagram.py        |   2 +-
 mcstasscript/interface/instr.py               | 119 ++++++++---
 4 files changed, 286 insertions(+), 50 deletions(-)
 create mode 100644 mcstasscript/helper/beam_dump_database.py

diff --git a/mcstasscript/helper/beam_dump_database.py b/mcstasscript/helper/beam_dump_database.py
new file mode 100644
index 00000000..5c5bfbf5
--- /dev/null
+++ b/mcstasscript/helper/beam_dump_database.py
@@ -0,0 +1,186 @@
+import os
+from datetime import datetime
+import time
+import json
+
+TIME_FORMAT = "%d/%m/%Y %H:%M:%S"
+
+class BeamDumpDatabase:
+    def __init__(self, name, path):
+        self.name = name
+        self.path = path
+
+        self.data = {}
+
+        self.database_path = os.path.join(self.path, self.name + "_db")
+        if os.path.isdir(self.database_path):
+            self.load_database(self.database_path)
+        else:
+            self.create_new_database(self.database_path)
+
+    def create_new_database(self, path):
+        os.mkdir(path)
+
+    def load_database(self, path):
+        dump_points = os.listdir(path)
+
+        for dump_point in dump_points:
+            dump_point_path = os.path.join(path, dump_point)
+            if not os.path.isdir(dump_point_path):
+                continue
+
+            if dump_point not in self.data:
+                self.data[dump_point] = {}
+
+            runs = os.listdir(dump_point_path)
+            for run in runs:
+                if run.endswith(".json"):
+                    run_path = os.path.join(dump_point_path, run)
+                    dump = BeamDump.dump_from_JSON(run_path)
+                    self.data[dump_point][dump.data["run_name"]] = dump
+
+    def create_folder_for_dump_point(self, dump_point):
+        dump_point_path = os.path.join(self.database_path, dump_point)
+        if not os.path.isdir(dump_point_path):
+            os.mkdir(dump_point_path)
+
+        return dump_point_path
+
+    def load_data(self, data_path, parameters, run_name, dump_point, comment):
+        files = os.listdir(data_path)
+
+        for file in files:
+            file = os.path.join(data_path, file) # Get full path
+            if os.path.isdir(file):
+                continue
+
+            if file.endswith((".mcpl", ".mcpl.gz")):
+                file_path = os.path.abspath(file)
+                dump = BeamDump(data_path=file_path, parameters=parameters,
+                                dump_point=dump_point, run_name=run_name,
+                                comment=comment)
+                dump_path = self.create_folder_for_dump_point(dump_point)
+                dump.dump_to_JSON(dump_path)
+
+                if dump_point not in self.data:
+                    self.data[dump_point] = {}
+
+                run = dump.data["run_name"]
+                self.data[dump_point][run] = dump
+
+    def newest_at_point(self, point):
+        if point not in self.data:
+            raise KeyError("This dump point wasn't in the database.")
+
+        runs = self.data[point]
+        return self.sort_by_time(runs, return_latest=True)
+
+    def sort_by_time(self, runs, return_latest=False):
+        time_to_run = {}
+        for run in runs:
+            dump = runs[run]
+            time_loaded = dump.data["time_loaded"]
+            time_to_run[time_loaded] = dump
+
+        sorted_times = sorted((time.strptime(d, TIME_FORMAT) for d in time_to_run.keys()), reverse=True)
+
+        if return_latest:
+            latest = time.strftime(TIME_FORMAT, sorted_times[0])
+            return time_to_run[latest]
+
+        return_list = []
+        for sorted_time in sorted_times:
+            return_list.append(time_to_run[time.strftime(TIME_FORMAT, sorted_time)])
+
+        return return_list
+
+    def show_in_order(self, component_names):
+        if len(self.data) == 0:
+            print("No data in dump database yet. Use run_to method to create dump.")
+
+        for name in component_names:
+            if name in self.data:
+                print(name + ":")
+                dumps = self.sort_by_time(self.data[name])
+                for dump in dumps:
+                    if len(dump.data["parameters"]) < 4:
+                        par_string = ": " + str(dump.data["parameters"])
+                    else:
+                        par_string = ""
+
+                    print(" ", dump.data["run_name"].ljust(10), ":",
+                          dump.data["time_loaded"].ljust(16), ":",
+                          dump.data["comment"], par_string)
+
+    def __repr__(self):
+
+        string = ""
+        for point in self.data:
+            string += point + ":\n"
+            for run in self.data[point]:
+                string += "  " + str(self.data[point][run]) + ":\n"
+
+        return string
+
+class BeamDump:
+    def __init__(self, data_path, parameters, dump_point, run_name=None, time_loaded=None, comment=""):
+
+        simple_parameters = {}
+        for parameter in parameters:
+            if hasattr(parameters[parameter], "value"):
+                simple_parameters[parameter] = parameters[parameter].value
+            else:
+                simple_parameters[parameter] = parameters[parameter]
+
+        self.data = {"data_path": data_path,
+                     "dump_point": dump_point,
+                     "parameters": simple_parameters,
+                     "run_name": run_name,
+                     "comment": comment}
+
+        if time_loaded is None:
+            self.data["time_loaded"] = datetime.now().strftime(TIME_FORMAT)
+        else:
+            self.data["time_loaded"] = time_loaded
+
+        """
+        self.data_path = data_path
+        self.dump_point = dump_point
+        self.parameters = parameters
+        self.time_loaded = datetime.now().strftime("%H:%M:%S")
+        """
+
+    @classmethod
+    def dump_from_JSON(cls, filepath):
+        with open(filepath, "r") as f:
+            data = json.loads(f.read())
+
+        return cls(**data)
+
+    def dump_to_JSON(self, folder_path):
+
+        if self.data["run_name"] is None:
+            base_name = "run"
+        else:
+            base_name = self.data["run_name"]
+
+        index = 0
+        proposed_name = base_name
+        while os.path.isfile(os.path.join(folder_path, proposed_name + ".json")):
+            proposed_name = base_name + "_" + str(index)
+            index += 1
+
+        self.data["run_name"] = proposed_name
+
+        filepath = os.path.join(folder_path, self.data["run_name"] + ".json")
+        if os.path.isfile(filepath):
+            raise RuntimeError("Run with this destination and run_name: '"
+                               + self.data["run_name"]
+                               + "' already exists in BeamDumpDatabase.")
+
+        with open(filepath, "w") as outfile:
+            json.dump(self.data, outfile)
+
+    def __repr__(self):
+        return str(self.data)
+
diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 9b1e3bcc..a23cfa33 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -950,7 +950,7 @@ def set_c_code_after(self, string):
                                + "given " + str(type(string)))
         self.c_code_after = string
 
-    def write_component(self, fo, overwrite_location=False):
+    def write_component(self, fo):
         """
         Method that writes component to file
 
@@ -1020,29 +1020,18 @@ def write_component(self, fo, overwrite_location=False):
         if not self.WHEN == "":
             fo.write("%s\n" % self.WHEN)
 
-        if overwrite_location:
-            written_AT_data = [0, 0, 0]
-            written_AT_relative = "ABSOLUTE"
-            written_ROTATED_data = [0, 0, 0]
-            written_ROTATED_relative = "ABSOLUTE"
-        else:
-            written_AT_data = self.AT_data
-            written_AT_relative = self.AT_relative
-            written_ROTATED_data = self.ROTATED_data
-            written_ROTATED_relative = self.ROTATED_relative
-
         # Write AT and ROTATED section
-        fo.write("AT (%s,%s,%s)" % (str(written_AT_data[0]),
-                                    str(written_AT_data[1]),
-                                    str(written_AT_data[2])))
+        fo.write("AT (%s,%s,%s)" % (str(self.AT_data[0]),
+                                    str(self.AT_data[1]),
+                                    str(self.AT_data[2])))
 
-        fo.write(" %s\n" % written_AT_relative)
+        fo.write(" %s\n" % self.AT_relative)
 
         if self.ROTATED_specified:
-            fo.write("ROTATED (%s,%s,%s)" % (str(written_ROTATED_data[0]),
-                                             str(written_ROTATED_data[1]),
-                                             str(written_ROTATED_data[2])))
-            fo.write(" %s\n" % written_ROTATED_relative)
+            fo.write("ROTATED (%s,%s,%s)" % (str(self.ROTATED_data[0]),
+                                             str(self.ROTATED_data[1]),
+                                             str(self.ROTATED_data[2])))
+            fo.write(" %s\n" % self.ROTATED_relative)
 
         if not self.GROUP == "":
             fo.write("GROUP %s\n" % self.GROUP)
diff --git a/mcstasscript/instrument_diagram/make_diagram.py b/mcstasscript/instrument_diagram/make_diagram.py
index 2d430504..712a75a5 100644
--- a/mcstasscript/instrument_diagram/make_diagram.py
+++ b/mcstasscript/instrument_diagram/make_diagram.py
@@ -27,7 +27,7 @@ def instrument_diagram(instrument):
     """
 
     # Grab components from instrument file and make text box objects
-    components = instrument.component_list
+    components = instrument.make_component_subset()
 
     # Prepare legend
     component_reader = instrument.component_reader
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index ddaf0c27..bf81d9f6 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -25,6 +25,7 @@
 from mcstasscript.helper.formatting import bcolors
 from mcstasscript.helper.unpickler import CustomMcStasUnpickler, CustomMcXtraceUnpickler
 from mcstasscript.helper.exceptions import McStasError
+from mcstasscript.helper.beam_dump_database import BeamDumpDatabase
 from mcstasscript.instrument_diagram.make_diagram import instrument_diagram
 
 
@@ -418,8 +419,15 @@ def __init__(self, name, parameters=None, author=None,
             # Handle components
             self.component_list = []  # List of components (have to be ordered)
 
+            # Run subset settings
             self.run_from_ref = None
             self.run_to_ref = None
+            self.run_to_comment = ""
+            self.run_to_name = None
+            self.run_from_component_parameters = None
+
+            # DumpDatabase
+            self.dump_database = BeamDumpDatabase(self.name, self.input_path)
 
     @property
     def output_path(self) -> str:
@@ -442,7 +450,7 @@ def _read_calibration(self):
         """
         pass
 
-    def run_to(self, component_ref, **kwargs):
+    def run_to(self, component_ref, run_name=None, comment=None, **kwargs):
         """
         Set limit for instrument, only run to given component, save MCPL there
         """
@@ -455,13 +463,19 @@ def run_to(self, component_ref, **kwargs):
 
         self.run_to_ref = component_ref
 
+        if run_name is not None:
+            self.run_to_name = run_name
+
+        if comment is not None:
+            self.run_to_comment = str(comment)
+
         if component_ref is not None:
             if "filename" not in kwargs:
                 kwargs["filename"] = '"' + self.name + "_" + component_ref + ".mcpl" + '"'
 
             self.run_to_component_parameters = kwargs
 
-    def run_from(self, component_ref, **kwargs):
+    def run_from(self, component_ref, run_name=None, **kwargs):
         """
         Set limit for instrument, only run from given component, loads MCPL there
         """
@@ -475,11 +489,27 @@ def run_from(self, component_ref, **kwargs):
         self.run_from_ref = component_ref
 
         if component_ref is not None:
+            if run_name is not None:
+                if component_ref not in self.dump_database.data:
+                    raise KeyError("This run_from point doesn't have any runs.")
+
+                if run_name not in self.dump_database.data[component_ref]:
+                    print(self.dump_database.data[component_ref])
+                    raise KeyError("Given run name not in database.")
+
+                dump = self.dump_database.data[component_ref][run_name]
+                kwargs["filename"] = '"' + dump.data["data_path"] + '"'
+
             if "filename" not in kwargs:
-                kwargs["filename"] = '"' + self.name + "_" + component_ref + ".mcpl" + '"'
+                newest_dump = self.dump_database.newest_at_point(component_ref)
+                kwargs["filename"] = '"' + newest_dump.data["data_path"] + '"'
 
             self.run_from_component_parameters = kwargs
 
+    def show_dumps(self):
+        component_names = [x.name for x in self.component_list]
+        self.dump_database.show_in_order(component_names)
+
     def subset_check(self, start_ref, end_ref):
         """
         Checks that when the instrument is broken into subsets, it is still valid
@@ -499,6 +529,10 @@ def subset_check(self, start_ref, end_ref):
                   "this reference can not be resolved.")
             self.check_for_relative_errors(start_ref=start_ref, end_ref=end_ref)
 
+        if end_ref is None:
+            # If there is no end_ref, there are no parts after to check
+            return
+
         # Check the part after is self consistent
         is_self_contained = True
         try:
@@ -1730,21 +1764,17 @@ def check_for_relative_errors(self, start_ref=None, end_ref=None):
         Using the start_ref and end_ref keyword arguments, a subset of the
         instrument can be checked for internal consistency.
         """
-        if start_ref is not None or end_ref is not None:
-            component_names = [x.name for x in self.component_list]
 
-        if start_ref is not None:
-            start_index = component_names.index(start_ref)
-        else:
-            start_index = 0
-
-        if end_ref is not None:
-            end_index = component_names.index(end_ref)
+        if start_ref is None and end_ref is None:
+            component_list = self.make_component_subset()
+        elif start_ref == self.run_from_ref and end_ref == self.run_to_ref:
+            component_list = self.make_component_subset()
         else:
-            end_index = len(self.component_list)
+            start_i, end_i = self.get_component_subset_index_range(start_ref, end_ref)
+            component_list = self.component_list[start_i:end_i]
 
         seen_instrument_names = []
-        for component in self.component_list[start_index:end_index]:
+        for component in component_list:
             seen_instrument_names.append(component.name)
 
             if component.name == start_ref:
@@ -1755,7 +1785,8 @@ def check_for_relative_errors(self, start_ref=None, end_ref=None):
             if component.AT_reference not in (None, "PREVIOUS"):
                 references.append(component.AT_reference)
 
-            if component.ROTATED_reference not in (None, "PREVIOUS"):
+            if ( component.ROTATED_specified and
+                   component.ROTATED_reference not in (None, "PREVIOUS")):
                 references.append(component.ROTATED_reference)
 
             for ref in references:
@@ -1909,6 +1940,32 @@ def write_full_instrument(self):
 
         fo.close()
 
+    def get_component_subset_index_range(self, start_ref=None, end_ref=None):
+        """
+        Provides start and end index for components in run_from to run_to range
+
+        Optionally start_ref and end_ref can be given manually which would
+        overwrite the internal run_from and run_to references.
+        """
+
+        if start_ref is None:
+            start_ref = self.run_from_ref
+
+        if end_ref is None:
+            end_ref = self.run_to_ref
+
+        # Starting with component named run_from_ref, ending with run_to_ref
+        component_names = [x.name for x in self.component_list]
+        start_index = 0
+        end_index = len(self.component_list)
+        if start_ref is not None:
+            start_index = component_names.index(start_ref)
+
+        if end_ref is not None:
+            end_index = component_names.index(end_ref)
+
+        return start_index, end_index
+
     def make_component_subset(self):
         """
         Uses run_from and run_to specifications to extract subset of components
@@ -1921,28 +1978,23 @@ def make_component_subset(self):
             # Simple case, just return full component list
             return self.component_list
 
-        # Need to extract subset
-        # Starting with component named run_from_ref, ending with run_to_ref
-        component_names = [x.name for x in self.component_list]
-        start_index = 0
-        end_index = len(self.component_list)
-        if self.run_from_ref is not None:
-            start_index = component_names.index(self.run_from_ref)
-
-        if self.run_to_ref is not None:
-            end_index = component_names.index(self.run_to_ref)
+        start_index, end_index = self.get_component_subset_index_range()
 
         # Create a copy of the used component instances
-        component_subset = copy.deepcopy(self.component_list[start_index:end_index])
+        if start_index == end_index:
+            component_subset = [copy.deepcopy(self.component_list[start_index])]
+        else:
+            component_subset = copy.deepcopy(self.component_list[start_index:end_index])
 
         if self.run_from_ref is not None:
             # Add MCPL input component
             MCPL_in = self._create_component_instance("MCPL_" + self.run_from_ref, "MCPL_input")
             MCPL_in.set_comment("Automatically inserted to split instrument into parts")
-            MCPL_in.set_parameters(**self.run_from_component_parameters)
+            if self.run_from_component_parameters is not None:
+                MCPL_in.set_parameters(**self.run_from_component_parameters)
             #MCPL_in.write_component(fo)
 
-            # Ensure from component reset to MCPL position
+            # Ensure first component reset to MCPL position
             first_component = component_subset[0]
 
             # Since a copy of the component is used, we can alter some properties safely
@@ -1959,7 +2011,8 @@ def make_component_subset(self):
             # Add MCPL output component
             MCPL_out = self._create_component_instance("MCPL_" + self.run_to_ref, "MCPL_output")
             MCPL_out.set_comment("Automatically inserted to split instrument into parts")
-            MCPL_out.set_parameters(**self.run_to_component_parameters)
+            if self.run_from_component_parameters is not None:
+                MCPL_out.set_parameters(**self.run_to_component_parameters)
             MCPL_out.set_AT(replaced_component.AT_data, RELATIVE=replaced_component.AT_reference)
             if replaced_component.ROTATED_specified:
                 MCPL_out.set_ROTATED(replaced_component.ROTATED_data, RELATIVE=replaced_component.ROTATED_reference)
@@ -2173,6 +2226,14 @@ def backengine(self):
         output_data = self.output[key]
         output_data.set_dict(data_dict)
 
+        # Check for mcpl files and load those to database
+
+        self.dump_database.load_data(data_path=simulation.data_folder_name,
+                                     parameters=self.parameters.parameters,
+                                     dump_point=self.run_to_ref,
+                                     run_name=self.run_to_name,
+                                     comment=self.run_to_comment)
+
         if "data" not in self.output.get_data():
             print("\n\nNo data returned.")
             return None

From 9edcd30b37b214e9495d2ca1a42dea76b836bee0 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 30 Sep 2022 10:00:01 +0200
Subject: [PATCH 300/403] Updates for dump database. Better formatting when
 writing dumps.

Still issues:
  No checks for whether the files still exists when using them
  No ability to clear the database
  Run names used to version gets annoying, too frequent need to update (tags instead)
---
 mcstasscript/helper/beam_dump_database.py | 28 +++++++++++++++--------
 mcstasscript/interface/instr.py           | 14 ++++++------
 2 files changed, 25 insertions(+), 17 deletions(-)

diff --git a/mcstasscript/helper/beam_dump_database.py b/mcstasscript/helper/beam_dump_database.py
index 5c5bfbf5..4270c1aa 100644
--- a/mcstasscript/helper/beam_dump_database.py
+++ b/mcstasscript/helper/beam_dump_database.py
@@ -47,6 +47,13 @@ def create_folder_for_dump_point(self, dump_point):
         return dump_point_path
 
     def load_data(self, data_path, parameters, run_name, dump_point, comment):
+        """
+        Searches folder for MCPL data and attributes it to given input properties
+        """
+        if not os.path.isdir(data_path):
+            # If the folder doesn't exist, skip search
+            return
+
         files = os.listdir(data_path)
 
         for file in files:
@@ -97,6 +104,11 @@ def sort_by_time(self, runs, return_latest=False):
     def show_in_order(self, component_names):
         if len(self.data) == 0:
             print("No data in dump database yet. Use run_to method to create dump.")
+            return
+
+        print("Run point:")
+        print(" ", "run_name".ljust(12), ":", "time".ljust(20), ":", "comment")
+        print("-"*60, "--- -- -")
 
         for name in component_names:
             if name in self.data:
@@ -108,8 +120,8 @@ def show_in_order(self, component_names):
                     else:
                         par_string = ""
 
-                    print(" ", dump.data["run_name"].ljust(10), ":",
-                          dump.data["time_loaded"].ljust(16), ":",
+                    print(" ", dump.data["run_name"].ljust(12), ":",
+                          dump.data["time_loaded"].ljust(20), ":",
                           dump.data["comment"], par_string)
 
     def __repr__(self):
@@ -126,12 +138,15 @@ class BeamDump:
     def __init__(self, data_path, parameters, dump_point, run_name=None, time_loaded=None, comment=""):
 
         simple_parameters = {}
+        # Convert complex parameter objects to simple key - value pairs
         for parameter in parameters:
             if hasattr(parameters[parameter], "value"):
+                # The full parameter objects store their value in value attribute
                 simple_parameters[parameter] = parameters[parameter].value
             else:
                 simple_parameters[parameter] = parameters[parameter]
 
+        # The fields in data must correspond to the input of the class
         self.data = {"data_path": data_path,
                      "dump_point": dump_point,
                      "parameters": simple_parameters,
@@ -143,19 +158,12 @@ def __init__(self, data_path, parameters, dump_point, run_name=None, time_loaded
         else:
             self.data["time_loaded"] = time_loaded
 
-        """
-        self.data_path = data_path
-        self.dump_point = dump_point
-        self.parameters = parameters
-        self.time_loaded = datetime.now().strftime("%H:%M:%S")
-        """
-
     @classmethod
     def dump_from_JSON(cls, filepath):
         with open(filepath, "r") as f:
             data = json.loads(f.read())
 
-        return cls(**data)
+        return cls(**data) # Since the data fields correspond to class input
 
     def dump_to_JSON(self, folder_path):
 
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index bf81d9f6..c0eafa7e 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2226,13 +2226,13 @@ def backengine(self):
         output_data = self.output[key]
         output_data.set_dict(data_dict)
 
-        # Check for mcpl files and load those to database
-
-        self.dump_database.load_data(data_path=simulation.data_folder_name,
-                                     parameters=self.parameters.parameters,
-                                     dump_point=self.run_to_ref,
-                                     run_name=self.run_to_name,
-                                     comment=self.run_to_comment)
+        if self.run_to_ref is not None:
+            # Check for mcpl files and load those to database
+            self.dump_database.load_data(data_path=simulation.data_folder_name,
+                                         parameters=self.parameters.parameters,
+                                         dump_point=self.run_to_ref,
+                                         run_name=self.run_to_name,
+                                         comment=self.run_to_comment)
 
         if "data" not in self.output.get_data():
             print("\n\nNo data returned.")

From ee93f4bf232b5190f10bdbc3001702a47ecafe05 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 30 Sep 2022 11:31:08 +0200
Subject: [PATCH 301/403] Migrated mcpl filenames to parameters to avoid
 recompiling when running from many different mcpl files.

---
 mcstasscript/helper/beam_dump_database.py | 51 +++++++++++++++++++----
 mcstasscript/interface/instr.py           | 51 ++++++++++++++++++-----
 2 files changed, 85 insertions(+), 17 deletions(-)

diff --git a/mcstasscript/helper/beam_dump_database.py b/mcstasscript/helper/beam_dump_database.py
index 4270c1aa..be5683ff 100644
--- a/mcstasscript/helper/beam_dump_database.py
+++ b/mcstasscript/helper/beam_dump_database.py
@@ -46,23 +46,54 @@ def create_folder_for_dump_point(self, dump_point):
 
         return dump_point_path
 
-    def load_data(self, data_path, parameters, run_name, dump_point, comment):
+    def load_data(self, expected_filename, data_folder_path, parameters, run_name, dump_point, comment):
         """
-        Searches folder for MCPL data and attributes it to given input properties
+        Find given MCPL datafile and load it into database
         """
-        if not os.path.isdir(data_path):
+        if not os.path.isdir(data_folder_path):
             # If the folder doesn't exist, skip search
             return
 
-        files = os.listdir(data_path)
+        expected_filename = expected_filename.strip('"')
+        expected_filename = os.path.split(expected_filename)[-1]
+        expected_filename = expected_filename.strip(".gz")
+        expected_filename = expected_filename.strip(".mcpl")
 
+        possible_endings = [".mcpl", ".mcpl.gz"]
+
+        for ending in possible_endings:
+            file_path = os.path.join(data_folder_path, expected_filename + ending)
+            if not os.path.isfile(file_path):
+                continue
+
+            dump = BeamDump(data_path=file_path, parameters=parameters,
+                            dump_point=dump_point, run_name=run_name,
+                            comment=comment)
+            dump_path = self.create_folder_for_dump_point(dump_point)
+            dump.dump_to_JSON(dump_path)
+
+            if dump_point not in self.data:
+                self.data[dump_point] = {}
+
+            run = dump.data["run_name"]
+            self.data[dump_point][run] = dump
+
+        """
+        files = os.listdir(data_folder_path)
         for file in files:
-            file = os.path.join(data_path, file) # Get full path
+            file = os.path.join(data_folder_path, file) # Get full path
             if os.path.isdir(file):
                 continue
 
             if file.endswith((".mcpl", ".mcpl.gz")):
                 file_path = os.path.abspath(file)
+                filename = file.strip(".gz")
+                filename = filename.strip(".mcpl")
+
+                if expected_filename != filename:
+                    # Not the mcpl file for which we are searching
+                    continue
+
                 dump = BeamDump(data_path=file_path, parameters=parameters,
                                 dump_point=dump_point, run_name=run_name,
                                 comment=comment)
@@ -74,6 +105,7 @@ def load_data(self, data_path, parameters, run_name, dump_point, comment):
 
                 run = dump.data["run_name"]
                 self.data[dump_point][run] = dump
+        """
 
     def newest_at_point(self, point):
         if point not in self.data:
@@ -125,15 +157,16 @@ def show_in_order(self, component_names):
                           dump.data["comment"], par_string)
 
     def __repr__(self):
-
         string = ""
         for point in self.data:
             string += point + ":\n"
             for run in self.data[point]:
-                string += "  " + str(self.data[point][run]) + ":\n"
+                string += "  " + self.data[point][run].data["run_name"] + "\n:"
+                string += "    " + str(self.data[point][run]) + ":\n"
 
         return string
 
+
 class BeamDump:
     def __init__(self, data_path, parameters, dump_point, run_name=None, time_loaded=None, comment=""):
 
@@ -189,6 +222,10 @@ def dump_to_JSON(self, folder_path):
         with open(filepath, "w") as outfile:
             json.dump(self.data, outfile)
 
+    def print_all(self):
+        for key in self.data:
+            print(key, self.data[key])
+
     def __repr__(self):
         return str(self.data)
 
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index c0eafa7e..8ea129d9 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -425,6 +425,7 @@ def __init__(self, name, parameters=None, author=None,
             self.run_to_comment = ""
             self.run_to_name = None
             self.run_from_component_parameters = None
+            self.run_to_component_parameters = None
 
             # DumpDatabase
             self.dump_database = BeamDumpDatabase(self.name, self.input_path)
@@ -470,9 +471,20 @@ def run_to(self, component_ref, run_name=None, comment=None, **kwargs):
             self.run_to_comment = str(comment)
 
         if component_ref is not None:
+            mcpl_par_name = "run_to_mcpl"
+
+            if mcpl_par_name not in self.parameters.parameters:
+                self.add_parameter("string", mcpl_par_name)
+
             if "filename" not in kwargs:
-                kwargs["filename"] = '"' + self.name + "_" + component_ref + ".mcpl" + '"'
+                #kwargs["filename"] = '"' + self.name + "_" + component_ref + ".mcpl" + '"'
+                #kwargs["filename"] = mcpl_par_name
+                auto_name = '"' + self.name + "_" + component_ref + ".mcpl" + '"'
+                self.set_parameters({mcpl_par_name: auto_name})
+            else:
+                self.set_parameters({mcpl_par_name: kwargs["filename"]})
 
+            kwargs["filename"] = mcpl_par_name
             self.run_to_component_parameters = kwargs
 
     def run_from(self, component_ref, run_name=None, **kwargs):
@@ -489,21 +501,36 @@ def run_from(self, component_ref, run_name=None, **kwargs):
         self.run_from_ref = component_ref
 
         if component_ref is not None:
+            mcpl_par_name = "run_from_mcpl"
+
+            if mcpl_par_name not in self.parameters.parameters:
+                self.add_parameter("string", mcpl_par_name)
+
+            if "filename" not in kwargs:
+                newest_dump = self.dump_database.newest_at_point(component_ref)
+                #kwargs["filename"] = '"' + newest_dump.data["data_path"] + '"'
+                auto_name = '"' + newest_dump.data["data_path"] + '"'
+                self.set_parameters({mcpl_par_name: auto_name})
+            else:
+                self.set_parameters({mcpl_par_name: kwargs["filename"]})
+
             if run_name is not None:
                 if component_ref not in self.dump_database.data:
                     raise KeyError("This run_from point doesn't have any runs.")
 
                 if run_name not in self.dump_database.data[component_ref]:
-                    print(self.dump_database.data[component_ref])
                     raise KeyError("Given run name not in database.")
 
                 dump = self.dump_database.data[component_ref][run_name]
-                kwargs["filename"] = '"' + dump.data["data_path"] + '"'
+                dump_filename = '"' + dump.data["data_path"] + '"'
+                print(run_name, dump_filename, dump.data["run_name"])
+                self.set_parameters({mcpl_par_name: dump_filename})
+
+                # kwargs["filename"] = '"' + dump.data["data_path"] + '"'
+
 
-            if "filename" not in kwargs:
-                newest_dump = self.dump_database.newest_at_point(component_ref)
-                kwargs["filename"] = '"' + newest_dump.data["data_path"] + '"'
 
+            kwargs["filename"] = mcpl_par_name
             self.run_from_component_parameters = kwargs
 
     def show_dumps(self):
@@ -1992,7 +2019,7 @@ def make_component_subset(self):
             MCPL_in.set_comment("Automatically inserted to split instrument into parts")
             if self.run_from_component_parameters is not None:
                 MCPL_in.set_parameters(**self.run_from_component_parameters)
-            #MCPL_in.write_component(fo)
+
 
             # Ensure first component reset to MCPL position
             first_component = component_subset[0]
@@ -2011,12 +2038,11 @@ def make_component_subset(self):
             # Add MCPL output component
             MCPL_out = self._create_component_instance("MCPL_" + self.run_to_ref, "MCPL_output")
             MCPL_out.set_comment("Automatically inserted to split instrument into parts")
-            if self.run_from_component_parameters is not None:
+            if self.run_to_component_parameters is not None:
                 MCPL_out.set_parameters(**self.run_to_component_parameters)
             MCPL_out.set_AT(replaced_component.AT_data, RELATIVE=replaced_component.AT_reference)
             if replaced_component.ROTATED_specified:
                 MCPL_out.set_ROTATED(replaced_component.ROTATED_data, RELATIVE=replaced_component.ROTATED_reference)
-            #MCPL_out.write_component(fo)
 
             component_subset += [MCPL_out]
 
@@ -2198,6 +2224,8 @@ def backengine(self):
         if self._run_settings["force_compile"]:
             self.write_full_instrument()
 
+        print(self.parameters)
+
         parameters = {}
         for parameter in self.parameters:
             if parameter.value is None:
@@ -2227,8 +2255,11 @@ def backengine(self):
         output_data.set_dict(data_dict)
 
         if self.run_to_ref is not None:
+            filename = self.parameters.parameters["run_to_mcpl"].value
+
             # Check for mcpl files and load those to database
-            self.dump_database.load_data(data_path=simulation.data_folder_name,
+            self.dump_database.load_data(expected_filename=filename,
+                                         data_folder_path=simulation.data_folder_name,
                                          parameters=self.parameters.parameters,
                                          dump_point=self.run_to_ref,
                                          run_name=self.run_to_name,

From 81cdcc0927236bcdf6832888bfba9999931a45c2 Mon Sep 17 00:00:00 2001
From: Peter Willendrup <pkwi@fysik.dtu.dk>
Date: Tue, 4 Oct 2022 15:09:52 +0200
Subject: [PATCH 302/403] Patch with McStas-compatible print (allows direct
 copy-pasting to instr file)

---
 mcstasscript/helper/mcstas_objects.py | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index a23cfa33..015894a4 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -1071,14 +1071,17 @@ class is used as a superclass for classes describing each
         if self.SPLIT != 0:
             string += "SPLIT " + str(self.SPLIT) + " "
         string += "COMPONENT " + str(self.name)
-        string += " = " + str(self.component_name) + "\n"
+        string += " = " + str(self.component_name) + "(\n"
+        lastval = self.parameter_names[-1]
+        previous = None
+        prevunit = ""
         for key in self.parameter_names:
             val = getattr(self, key)
             parameter_name = bcolors.BOLD + key + bcolors.ENDC
             if val is not None:
                 unit = ""
                 if key in self.parameter_units:
-                    unit = "[" + self.parameter_units[key] + "]"
+                    unit += " // [" + self.parameter_units[key] + "] "
                 if isinstance(val, Parameter):
                     #val_string = val.print_line() # too long
                     val_string = val.name
@@ -1092,8 +1095,12 @@ class is used as a superclass for classes describing each
                          + val_string
                          + bcolors.ENDC
                          + bcolors.ENDC)
-                string += "  " + parameter_name
-                string += " = " + value + " " + unit + "\n"
+                if previous is not None:
+                    string += ", " + prevunit + "\n"
+                string += "   " + parameter_name
+                string += " = " + value
+                previous = parameter_name
+                prevunit = unit
             else:
                 if self.parameter_defaults[key] is None:
                     string += "  " + parameter_name
@@ -1101,9 +1108,10 @@ class is used as a superclass for classes describing each
                     string += " : Required parameter not yet specified"
                     string += bcolors.ENDC + "\n"
 
+        string += unit + "\n)\n"
         if not self.WHEN == "":
             string += self.WHEN + "\n"
-        string += "AT " + str(self.AT_data) + " "
+        string += "AT (" + str(self.AT_data[0]) + ", " + str(self.AT_data[1]) + ", " + str(self.AT_data[2]) + ") "
         string += str(self.AT_relative) + "\n"
         if self.ROTATED_specified:
             string += "ROTATED " + str(self.ROTATED_data)

From 562e07165cb81ca91c059fe955149bb0ce2f2c40 Mon Sep 17 00:00:00 2001
From: Peter Willendrup <pkwi@fysik.dtu.dk>
Date: Tue, 4 Oct 2022 20:23:56 +0200
Subject: [PATCH 303/403] Express AT and ROTATED via tuples, thanks @g5t.

---
 mcstasscript/helper/mcstas_objects.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 015894a4..b674d053 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -1111,10 +1111,11 @@ class is used as a superclass for classes describing each
         string += unit + "\n)\n"
         if not self.WHEN == "":
             string += self.WHEN + "\n"
-        string += "AT (" + str(self.AT_data[0]) + ", " + str(self.AT_data[1]) + ", " + str(self.AT_data[2]) + ") "
+        string += f"AT {tuple(self.AT_data)} "
+        #"AT (" + str(self.AT_data[0]) + ", " + str(self.AT_data[1]) + ", " + str(self.AT_data[2]) + ") "
         string += str(self.AT_relative) + "\n"
         if self.ROTATED_specified:
-            string += "ROTATED " + str(self.ROTATED_data)
+            string += f"ROTATED {tuple(self.ROTATED_data)} "
             string += " " + self.ROTATED_relative + "\n"
         if not self.GROUP == "":
             string += "GROUP " + self.GROUP + "\n"

From dda57c456379225cfb3181becc2d039764d7f1d9 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 13 Oct 2022 15:14:25 +0200
Subject: [PATCH 304/403] Cleanup of MCPL bridge system, including more doc
 strigns and checks.

Now it is not allowed to have a "from" after a "to".
---
 mcstasscript/helper/beam_dump_database.py | 298 +++++++++++++++++-----
 mcstasscript/interface/instr.py           | 143 +++++++++--
 2 files changed, 351 insertions(+), 90 deletions(-)

diff --git a/mcstasscript/helper/beam_dump_database.py b/mcstasscript/helper/beam_dump_database.py
index be5683ff..bc526096 100644
--- a/mcstasscript/helper/beam_dump_database.py
+++ b/mcstasscript/helper/beam_dump_database.py
@@ -3,10 +3,19 @@
 import time
 import json
 
-TIME_FORMAT = "%d/%m/%Y %H:%M:%S"
+TIME_FORMAT = "%d/%m/%Y %H:%M:%S" # Time format used for database
 
 class BeamDumpDatabase:
     def __init__(self, name, path):
+        """
+        Database over location and run properties of beam dumps
+
+        name : str
+            Name of instrument for which this database is connected
+
+        path : str
+            input_path for instrument, database is placed there
+        """
         self.name = name
         self.path = path
 
@@ -19,11 +28,21 @@ def __init__(self, name, path):
             self.create_new_database(self.database_path)
 
     def create_new_database(self, path):
+        """
+        Creates directory for database, may be expanded in future
+        """
         os.mkdir(path)
 
     def load_database(self, path):
+        """
+        Loads an existing database from disk
+
+        path : str
+            Path for the database to be loaded
+        """
         dump_points = os.listdir(path)
 
+        # Each dump point is a folder in the database root
         for dump_point in dump_points:
             dump_point_path = os.path.join(path, dump_point)
             if not os.path.isdir(dump_point_path):
@@ -32,14 +51,26 @@ def load_database(self, path):
             if dump_point not in self.data:
                 self.data[dump_point] = {}
 
+            # Each run is a json file in the dump_point folder
             runs = os.listdir(dump_point_path)
             for run in runs:
                 if run.endswith(".json"):
                     run_path = os.path.join(dump_point_path, run)
                     dump = BeamDump.dump_from_JSON(run_path)
-                    self.data[dump_point][dump.data["run_name"]] = dump
+
+                    run_name = dump.data["run_name"]
+                    if run_name not in self.data[dump_point]:
+                        self.data[dump_point][run_name] = {}
+
+                    tag = dump.data["tag"]
+                    self.data[dump_point][run_name][tag] = dump
 
     def create_folder_for_dump_point(self, dump_point):
+        """
+        Adds folder to database for dump_point if it hasn't been made yet
+
+        Returns path to the folder whether it was created here or existed
+        """
         dump_point_path = os.path.join(self.database_path, dump_point)
         if not os.path.isdir(dump_point_path):
             os.mkdir(dump_point_path)
@@ -48,17 +79,40 @@ def create_folder_for_dump_point(self, dump_point):
 
     def load_data(self, expected_filename, data_folder_path, parameters, run_name, dump_point, comment):
         """
-        Find given MCPL datafile and load it into database
+        Include MCPL file into database with given metadata
+
+        Attempts to load MCPL file from McStas output. If the file does not
+        exists, the method will return without adding anything to the database.
+
+        expected_filename : str
+            Filename given as McStas parameter, can include explicit double quotes
+
+        data_folder_path : str
+            Path to the data folder that contains the MCPL file
+
+        parameters: dict
+            dict with parameter names and values for this run
+
+        run_name : str
+            Specified run name for this run
+
+        dump_point : str
+            Name of component where MCPL cut ended, can start from here later
+
+        comment : str
+            Comment on the run that can be included in metadata
         """
         if not os.path.isdir(data_folder_path):
             # If the folder doesn't exist, skip search
             return
 
+        # Sanitize expected_filename
         expected_filename = expected_filename.strip('"')
         expected_filename = os.path.split(expected_filename)[-1]
-        expected_filename = expected_filename.strip(".gz")
-        expected_filename = expected_filename.strip(".mcpl")
+        expected_filename = expected_filename.split(".gz")[0]
+        expected_filename = expected_filename.split(".mcpl")[0]
 
+        # The system might or might not be able to compress the data, search for both
         possible_endings = [".mcpl", ".mcpl.gz"]
 
         for ending in possible_endings:
@@ -66,80 +120,149 @@ def load_data(self, expected_filename, data_folder_path, parameters, run_name, d
             if not os.path.isfile(file_path):
                 continue
 
-            dump = BeamDump(data_path=file_path, parameters=parameters,
+            if dump_point not in self.data:
+                self.data[dump_point] = {}
+
+            if run_name not in self.data[dump_point]:
+                self.data[dump_point][run_name] = {}
+
+            n_tags = len(self.data[dump_point][run_name])
+            # Find relative path
+            db_path = self.path
+            rel_path = os.path.relpath(file_path, start=db_path)
+
+            # Create dump with given metadata
+            dump = BeamDump(data_path=rel_path, parameters=parameters,
                             dump_point=dump_point, run_name=run_name,
-                            comment=comment)
+                            comment=comment, tag=n_tags)
+            # Write to disk
             dump_path = self.create_folder_for_dump_point(dump_point)
             dump.dump_to_JSON(dump_path)
 
-            if dump_point not in self.data:
-                self.data[dump_point] = {}
-
-            run = dump.data["run_name"]
-            self.data[dump_point][run] = dump
+            self.data[dump_point][run_name][n_tags] = dump
+            return dump
 
+    def get_dump(self, point, run_name=None, tag=None):
         """
-        files = os.listdir(data_folder_path)
-        for file in files:
-            file = os.path.join(data_folder_path, file) # Get full path
-            if os.path.isdir(file):
-                continue
+        Getter for dumps with specified point, run_name and tag
+        """
+        if point not in self.data:
+            raise KeyError("The dump point '" + point + "' wasn't in the database.")
 
-            if file.endswith((".mcpl", ".mcpl.gz")):
-                file_path = os.path.abspath(file)
-                filename = file.strip(".gz")
-                filename = filename.strip(".mcpl")
+        if run_name is None and tag is None:
+            return self.newest_at_point(point)
 
-                if expected_filename != filename:
-                    # Not the mcpl file for which we are searching
-                    continue
+        if run_name not in self.data[point]:
+            raise KeyError("The run_name '" + run_name + "' wasn't in the database "
+                           + "at the dump_point '" + point + "'.")
 
-                dump = BeamDump(data_path=file_path, parameters=parameters,
-                                dump_point=dump_point, run_name=run_name,
-                                comment=comment)
-                dump_path = self.create_folder_for_dump_point(dump_point)
-                dump.dump_to_JSON(dump_path)
+        if tag is None:
+            return self.newest_at_point_and_run(point, run_name)
 
-                if dump_point not in self.data:
-                    self.data[dump_point] = {}
+        tag = int(tag) # Ensure tag is an integer for database lookup
 
-                run = dump.data["run_name"]
-                self.data[dump_point][run] = dump
-        """
+        if tag not in self.data[point][run_name]:
+            raise KeyError("The tag '" + str(tag) + "' was not in the database for"
+                           + "run_name '" + run_name
+                           + "at the dump_point '" + point + "'.")
+
+        dump = self.data[point][run_name][tag]
+        if not dump.file_present(self.path):
+            raise RuntimeError("The dump datafile was not found in the expected location.")
+
+        return dump
 
     def newest_at_point(self, point):
+        """
+        Gets newest dump at a given point
+
+        point : str
+            String with component name matching the dump point
+        """
+
         if point not in self.data:
-            raise KeyError("This dump point wasn't in the database.")
+            raise KeyError("The dump point '" + point + "' wasn't in the database.")
 
         runs = self.data[point]
         return self.sort_by_time(runs, return_latest=True)
 
+    def newest_at_point_and_run(self, point, run):
+        """
+        Gets the newest dump at a given point and run name
+
+        point : str
+            String with component name matching the dump point
+
+        run : str
+            String matching the run_name desired
+        """
+        if point not in self.data:
+            raise KeyError("The dump point '" + point + "' wasn't in the database.")
+
+        if run not in self.data[point]:
+            raise KeyError("The run_name '" + run + "' wasn't in the database "
+                           + "at the dump_point '" + point + "'.")
+
+        runs = {run: self.data[point][run]}
+        return self.sort_by_time(runs, return_latest=True)
+
     def sort_by_time(self, runs, return_latest=False):
+        """
+        Sorts a given dict of runs and returns a list or the latest if return_latest is true
+
+        The input data is given as a dictionary of runs matching the database structure
+        with all the individual tags underneath the runs. It is usually used with all runs
+        being from the same point, but not necessary for the method to work.
+
+        runs : dict
+            Dictionary with runs containing dictionary of tags pointing to dumps
+
+        return_latest : bool
+            If True only latest dump is returned, otherwise sorted list
+        """
         time_to_run = {}
         for run in runs:
-            dump = runs[run]
-            time_loaded = dump.data["time_loaded"]
-            time_to_run[time_loaded] = dump
+            for tag in runs[run]:
+                dump = runs[run][tag]
+                time_loaded = dump.data["time_loaded"]
+                time_to_run[time_loaded] = dump
 
         sorted_times = sorted((time.strptime(d, TIME_FORMAT) for d in time_to_run.keys()), reverse=True)
 
-        if return_latest:
-            latest = time.strftime(TIME_FORMAT, sorted_times[0])
-            return time_to_run[latest]
-
         return_list = []
         for sorted_time in sorted_times:
             return_list.append(time_to_run[time.strftime(TIME_FORMAT, sorted_time)])
 
+        if return_latest:
+            for dump in return_list:
+                if dump.file_present(self.path):
+                    return dump
+                else:
+                    point = dump.data["dump_point"]
+                    run_name = dump.data["run_name"]
+                    tag = dump.data["tag"]
+                    data_path = dump.data["data_path"]
+
+                    print(f"Latest file had run_name {run_name} and tag {tag}, but the file was"
+                          f"not found in the expected location, and thus skipped.")
+                    print("Expected path: ", data_path)
+
         return return_list
 
     def show_in_order(self, component_names):
+        """
+        Method to print content of database in order of component_names
+
+        component_names : list
+            List of strings for component names in the instrument
+        """
+
         if len(self.data) == 0:
             print("No data in dump database yet. Use run_to method to create dump.")
             return
 
         print("Run point:")
-        print(" ", "run_name".ljust(12), ":", "time".ljust(20), ":", "comment")
+        print(" ", "run_name".ljust(12), ":", "tag", ":", "time".ljust(20), ":", "comment")
         print("-"*60, "--- -- -")
 
         for name in component_names:
@@ -147,29 +270,65 @@ def show_in_order(self, component_names):
                 print(name + ":")
                 dumps = self.sort_by_time(self.data[name])
                 for dump in dumps:
-                    if len(dump.data["parameters"]) < 4:
+                    if len(dump.data["parameters"]) < 2:
                         par_string = ": " + str(dump.data["parameters"])
                     else:
                         par_string = ""
 
                     print(" ", dump.data["run_name"].ljust(12), ":",
+                          str(dump.data["tag"]).ljust(3), ":",
                           dump.data["time_loaded"].ljust(20), ":",
                           dump.data["comment"], par_string)
 
     def __repr__(self):
+        """
+        Basic repr of the data included in the database.
+        """
         string = ""
         for point in self.data:
             string += point + ":\n"
             for run in self.data[point]:
-                string += "  " + self.data[point][run].data["run_name"] + "\n:"
-                string += "    " + str(self.data[point][run]) + ":\n"
+                for tag in self.data[point][run]:
+                    string += "  " + self.data[point][run][tag].data["run_name"] + "\n:"
+                    string += "    " + str(self.data[point][run][tag]) + ":\n"
 
         return string
 
 
 class BeamDump:
-    def __init__(self, data_path, parameters, dump_point, run_name=None, time_loaded=None, comment=""):
+    def __init__(self, data_path, parameters, dump_point, run_name,
+                 time_loaded=None, comment="", tag=0, record_name=""):
+        """
+        Class describing a beamdump made somewhere in an instrument
+
+        Inputs for class matches what can be read from json file describing
+        dump to facilitate easy recreation from file. Strips complex parameters
+        into simple dict with par name and value for easy storage.
+
+        data_path : str
+            Path of MCPL datafile
 
+        parameters : dict
+            Parameters used to run simulation
+
+        dump_point : str
+            Name of component where beam was dumped
+
+        run_name : str
+            Specified run name for this dump
+
+        time_loaded : str
+            Optional, usually generated at first load, timestamp
+
+        comment : str
+            Comment for this beamdump
+
+        tag : integer
+            Tag for this beamdump to differentiate similar dumps
+
+        record_name : str
+            Unique filename combining run_name and tag
+        """
         simple_parameters = {}
         # Convert complex parameter objects to simple key - value pairs
         for parameter in parameters:
@@ -184,7 +343,9 @@ def __init__(self, data_path, parameters, dump_point, run_name=None, time_loaded
                      "dump_point": dump_point,
                      "parameters": simple_parameters,
                      "run_name": run_name,
-                     "comment": comment}
+                     "comment": comment,
+                     "tag": tag,
+                     "record_name": record_name}
 
         if time_loaded is None:
             self.data["time_loaded"] = datetime.now().strftime(TIME_FORMAT)
@@ -193,39 +354,52 @@ def __init__(self, data_path, parameters, dump_point, run_name=None, time_loaded
 
     @classmethod
     def dump_from_JSON(cls, filepath):
+        """
+        Load dump from json file
+
+        filepath : str
+            Path to json file for load
+        """
         with open(filepath, "r") as f:
             data = json.loads(f.read())
 
         return cls(**data) # Since the data fields correspond to class input
 
     def dump_to_JSON(self, folder_path):
+        """
+        Writes representation of the object to file
 
-        if self.data["run_name"] is None:
-            base_name = "run"
-        else:
-            base_name = self.data["run_name"]
-
-        index = 0
-        proposed_name = base_name
-        while os.path.isfile(os.path.join(folder_path, proposed_name + ".json")):
-            proposed_name = base_name + "_" + str(index)
-            index += 1
+        folder_path : str
+            Folder in which the dump will be placed
+        """
 
-        self.data["run_name"] = proposed_name
+        self.data["record_name"] = self.data["run_name"] + "_" + str(self.data["tag"])
 
-        filepath = os.path.join(folder_path, self.data["run_name"] + ".json")
+        filepath = os.path.join(folder_path, self.data["record_name"] + ".json")
         if os.path.isfile(filepath):
             raise RuntimeError("Run with this destination and run_name: '"
-                               + self.data["run_name"]
+                               + self.data["record_name"]
                                + "' already exists in BeamDumpDatabase.")
 
         with open(filepath, "w") as outfile:
             json.dump(self.data, outfile)
 
+    def file_present(self, origin_path):
+        """
+        Checks whether the file is present and returns bool
+        """
+        return os.path.isfile(os.path.join(origin_path,self.data["data_path"]))
+
     def print_all(self):
+        """
+        Print all entries in data for dump
+        """
         for key in self.data:
-            print(key, self.data[key])
+            print(key, ":",  self.data[key])
 
     def __repr__(self):
+        """
+        Simple representation of data in object
+        """
         return str(self.data)
 
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 8ea129d9..9de46634 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -451,11 +451,29 @@ def _read_calibration(self):
         """
         pass
 
-    def run_to(self, component_ref, run_name=None, comment=None, **kwargs):
+    def reset_run_points(self):
         """
-        Set limit for instrument, only run to given component, save MCPL there
+        Reset run_from and run_to points to the full instrument
+        """
+        self.run_from_ref = None
+        self.run_to_ref = None
+
+    def run_to(self, component_ref, run_name="Run", comment=None, **kwargs):
         """
+        Set limit for instrument, only run to given component, save MCPL there
+
+        The method accepts keywords for the MCPL output component allowing to
+        store for example userflags or setting the filename directly.
 
+        component_ref : str / component object
+            Name of component where the instrument simulation should end
+
+        run_name : str
+            Name associated with the generated beam dump
+
+        comment : str
+            Comment asscoiated with the generated beam dump
+        """
         if isinstance(component_ref, Component):
             component_ref = component_ref.name
 
@@ -463,33 +481,60 @@ def run_to(self, component_ref, run_name=None, comment=None, **kwargs):
         self.subset_check(start_ref=self.run_from_ref, end_ref=component_ref)
 
         self.run_to_ref = component_ref
-
-        if run_name is not None:
-            self.run_to_name = run_name
+        self.run_to_name = run_name
 
         if comment is not None:
             self.run_to_comment = str(comment)
+        else:
+            self.run_to_comment = ""
 
         if component_ref is not None:
             mcpl_par_name = "run_to_mcpl"
 
             if mcpl_par_name not in self.parameters.parameters:
+                # Need to add parameter to instrument for mcpl filename
                 self.add_parameter("string", mcpl_par_name)
 
             if "filename" not in kwargs:
-                #kwargs["filename"] = '"' + self.name + "_" + component_ref + ".mcpl" + '"'
-                #kwargs["filename"] = mcpl_par_name
+                # Generate a reasonable filename
                 auto_name = '"' + self.name + "_" + component_ref + ".mcpl" + '"'
                 self.set_parameters({mcpl_par_name: auto_name})
             else:
+                # Set the instrument parameter to the given filename
                 self.set_parameters({mcpl_par_name: kwargs["filename"]})
 
+            # Set the mcpl component parameter to the filename instrument parameter
             kwargs["filename"] = mcpl_par_name
+
+            # Check the given keywords arguments are legal for the MCPL output component
+            dummy_MCPL = self._create_component_instance("MCPL_output", "MCPL_output")
+            try:
+                dummy_MCPL.set_parameters(kwargs)
+            except:
+                # Provide information on what stage caused the problem
+                print("Problems detected with input arguments for MCPL output component")
+                # Show the exception for the failure to set parameters on the component
+                dummy_MCPL.set_parameters(kwargs)
+
+            # Store parameters for the MCPL output component
             self.run_to_component_parameters = kwargs
 
-    def run_from(self, component_ref, run_name=None, **kwargs):
+    def run_from(self, component_ref, run_name=None, tag=None, **kwargs):
         """
-        Set limit for instrument, only run from given component, loads MCPL there
+        Set limit for instrument, only run from given component, load MCPL ot start
+
+        The method accepts keywords for the MCPL input component allowing to
+        set for example the smear for direction / energy / position and
+        repeat count.
+
+        component_ref : str / component object
+            Name of component where the instrument simulation should start
+
+        run_name : str
+            Run name of dump to use as starting point of simulation
+
+        tag : integer
+            Tag of the desired dump
         """
 
         if isinstance(component_ref, Component):
@@ -504,11 +549,12 @@ def run_from(self, component_ref, run_name=None, **kwargs):
             mcpl_par_name = "run_from_mcpl"
 
             if mcpl_par_name not in self.parameters.parameters:
+                # Need to add parameter to instrument for mcpl filename
                 self.add_parameter("string", mcpl_par_name)
 
             if "filename" not in kwargs:
+                # Find newest dump from database
                 newest_dump = self.dump_database.newest_at_point(component_ref)
-                #kwargs["filename"] = '"' + newest_dump.data["data_path"] + '"'
                 auto_name = '"' + newest_dump.data["data_path"] + '"'
                 self.set_parameters({mcpl_par_name: auto_name})
             else:
@@ -516,32 +562,57 @@ def run_from(self, component_ref, run_name=None, **kwargs):
 
             if run_name is not None:
                 if component_ref not in self.dump_database.data:
-                    raise KeyError("This run_from point doesn't have any runs.")
+                    raise KeyError("The run_from'  point doesn't have any runs.")
 
                 if run_name not in self.dump_database.data[component_ref]:
                     raise KeyError("Given run name not in database.")
 
-                dump = self.dump_database.data[component_ref][run_name]
+                if tag is None:
+                    dump = self.dump_database.newest_at_point_and_run(component_ref, run_name)
+                else:
+                    dump = self.dump_database.data[component_ref][run_name][tag]
+
                 dump_filename = '"' + dump.data["data_path"] + '"'
-                print(run_name, dump_filename, dump.data["run_name"])
                 self.set_parameters({mcpl_par_name: dump_filename})
 
                 # kwargs["filename"] = '"' + dump.data["data_path"] + '"'
 
+            kwargs["filename"] = mcpl_par_name
 
+            # Ensure the kwargs are allowed
+            dummy_MCPL = self._create_component_instance("MCPL_input", "MCPL_input")
+            try:
+                dummy_MCPL.set_parameters(kwargs)
+            except:
+                print("Problems detected with input arguments for MCPL input component")
+                dummy_MCPL.set_parameters(kwargs)
 
-            kwargs["filename"] = mcpl_par_name
             self.run_from_component_parameters = kwargs
 
     def show_dumps(self):
         component_names = [x.name for x in self.component_list]
         self.dump_database.show_in_order(component_names)
 
+    def show_dump(self, point, run_name, tag):
+        self.dump_database.get_dump(point, run_name, tag).print_all()
+
     def subset_check(self, start_ref, end_ref):
         """
         Checks that when the instrument is broken into subsets, it is still valid
+
+        start_ref : str
+            Name of starting component
+
+        end_ref : str
+            Name of end component
         """
 
+        start_i, end_i = self.get_component_subset_index_range(start_ref, end_ref)
+        if start_i > end_i:
+            raise McStasError("Running from '" + start_ref + "' to '" + end_ref
+                              + "' not possible as '" + end_ref + "' is before '"
+                              + start_ref + "' in the component sequence.")
+
         # Check current subset of instrument is self contained
         is_self_contained = True
         try:
@@ -563,7 +634,7 @@ def subset_check(self, start_ref, end_ref):
         # Check the part after is self consistent
         is_self_contained = True
         try:
-            self.check_for_relative_errors(start_ref=end_ref)
+            self.check_for_relative_errors(start_ref=end_ref, allow_absolute=False)
         except McStasError:
             is_self_contained = False
 
@@ -573,7 +644,7 @@ def subset_check(self, start_ref, end_ref):
                   "files. When seeing only the specified subset of the instrument, "
                   "this reference can not be resolved. \n"
                   "In this case the remaining instrument would fail.")
-            self.check_for_relative_errors(start_ref=end_ref)
+            self.check_for_relative_errors(start_ref=end_ref, allow_absolute=False)
 
     def add_parameter(self, *args, **kwargs):
         """
@@ -971,7 +1042,7 @@ def append_finally_no_new_line(self, string):
 
     """
     # Handle trace string differently when components also exists
-    #  A) Coul d have trace string as a component attribute and set
+    #  A) Could have trace string as a component attribute and set
     #     it before / after
     #  B) Could have trace string as a McStas_instr attribute and
     #     still attach placement to components
@@ -1784,7 +1855,7 @@ def check_for_errors(self):
         for component in self.component_list:
             component.check_parameters(pars_and_vars)
 
-    def check_for_relative_errors(self, start_ref=None, end_ref=None):
+    def check_for_relative_errors(self, start_ref=None, end_ref=None, allow_absolute=True):
         """
         Method for checking if RELATIVE locations does not contain unknown references
 
@@ -1812,10 +1883,24 @@ def check_for_relative_errors(self, start_ref=None, end_ref=None):
             if component.AT_reference not in (None, "PREVIOUS"):
                 references.append(component.AT_reference)
 
+            if not allow_absolute:
+                if component.AT_relative == "ABSOLUTE":
+                    raise McStasError("Component '" + component.name
+                                      + "' was set relative to ABSOLUTE"
+                                      + " which is not allowed after an"
+                                      + " instrument split.")
+
             if ( component.ROTATED_specified and
                    component.ROTATED_reference not in (None, "PREVIOUS")):
                 references.append(component.ROTATED_reference)
 
+            if not allow_absolute:
+                if component.ROTATED_relative == "ABSOLUTE" and component.ROTATED_specified:
+                    raise McStasError("Component '" + component.name
+                                      + "' was set relative to ABSOLUTE"
+                                      + " which is not allowed after an"
+                                      + " instrument split.")
+
             for ref in references:
                 if ref not in seen_instrument_names:
                     raise McStasError("Component '" + str(component.name) +
@@ -1824,6 +1909,7 @@ def check_for_relative_errors(self, start_ref=None, end_ref=None):
                                       "This check can be skipped with"
                                       " settings(checks=False)")
 
+
     def read_instrument_file(self):
         """
         Reads current instrument file if it exists, otherwise creates one first
@@ -1890,7 +1976,7 @@ def write_full_instrument(self):
         fo.write("* python based McStas instrument generator written by \n")
         fo.write("* Mads Bertelsen in 2019 while employed at the \n")
         fo.write("* European Spallation Source Data Management and \n")
-        fo.write("* Software Center\n")
+        fo.write("* Software Centre\n")
         fo.write("* \n")
         fo.write("* Instrument %s\n" % self.name)
         fo.write("* \n")
@@ -2020,7 +2106,6 @@ def make_component_subset(self):
             if self.run_from_component_parameters is not None:
                 MCPL_in.set_parameters(**self.run_from_component_parameters)
 
-
             # Ensure first component reset to MCPL position
             first_component = component_subset[0]
 
@@ -2224,8 +2309,6 @@ def backengine(self):
         if self._run_settings["force_compile"]:
             self.write_full_instrument()
 
-        print(self.parameters)
-
         parameters = {}
         for parameter in self.parameters:
             if parameter.value is None:
@@ -2258,12 +2341,16 @@ def backengine(self):
             filename = self.parameters.parameters["run_to_mcpl"].value
 
             # Check for mcpl files and load those to database
-            self.dump_database.load_data(expected_filename=filename,
-                                         data_folder_path=simulation.data_folder_name,
-                                         parameters=self.parameters.parameters,
-                                         dump_point=self.run_to_ref,
-                                         run_name=self.run_to_name,
-                                         comment=self.run_to_comment)
+            db = self.dump_database
+            out = db.load_data(expected_filename=filename,
+                               data_folder_path=simulation.data_folder_name,
+                               parameters=self.parameters.parameters,
+                               dump_point=self.run_to_ref,
+                               run_name=self.run_to_name,
+                               comment=self.run_to_comment)
+
+            if out is None:
+                print("Expected MCPL file was not loaded!")
 
         if "data" not in self.output.get_data():
             print("\n\nNo data returned.")

From 9935121c5b930b44352c57e0d1002cdba46a6d9e Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 14 Oct 2022 14:27:47 +0200
Subject: [PATCH 305/403] Refactor of BeamDumpDatabase newest_at_point and
 newest_at_point_and_run into one method with optional argument.

Added unit tests for instr class beam dump methods.
---
 mcstasscript/helper/beam_dump_database.py |  40 ++--
 mcstasscript/interface/instr.py           |  37 ++--
 mcstasscript/tests/test_Instr.py          | 213 +++++++++++++++++++++-
 3 files changed, 252 insertions(+), 38 deletions(-)

diff --git a/mcstasscript/helper/beam_dump_database.py b/mcstasscript/helper/beam_dump_database.py
index bc526096..941ce14f 100644
--- a/mcstasscript/helper/beam_dump_database.py
+++ b/mcstasscript/helper/beam_dump_database.py
@@ -157,7 +157,7 @@ def get_dump(self, point, run_name=None, tag=None):
                            + "at the dump_point '" + point + "'.")
 
         if tag is None:
-            return self.newest_at_point_and_run(point, run_name)
+            return self.newest_at_point(point, run=run_name)
 
         tag = int(tag) # Ensure tag is an integer for database lookup
 
@@ -172,23 +172,9 @@ def get_dump(self, point, run_name=None, tag=None):
 
         return dump
 
-    def newest_at_point(self, point):
+    def newest_at_point(self, point, run=None):
         """
-        Gets newest dump at a given point
-
-        point : str
-            String with component name matching the dump point
-        """
-
-        if point not in self.data:
-            raise KeyError("The dump point '" + point + "' wasn't in the database.")
-
-        runs = self.data[point]
-        return self.sort_by_time(runs, return_latest=True)
-
-    def newest_at_point_and_run(self, point, run):
-        """
-        Gets the newest dump at a given point and run name
+        Gets newest dump at a given point and optionally a run_name
 
         point : str
             String with component name matching the dump point
@@ -196,14 +182,22 @@ def newest_at_point_and_run(self, point, run):
         run : str
             String matching the run_name desired
         """
+
         if point not in self.data:
             raise KeyError("The dump point '" + point + "' wasn't in the database.")
 
-        if run not in self.data[point]:
-            raise KeyError("The run_name '" + run + "' wasn't in the database "
-                           + "at the dump_point '" + point + "'.")
+        if run is None:
+            # Collect all runs at the point for sort_by_time
+            runs = self.data[point]
+        else:
+            # If a run name is specified, check it exists
+            if run not in self.data[point]:
+                raise KeyError("The run_name '" + run + "' wasn't in the database "
+                               + "at the dump_point '" + point + "'.")
+
+            # Collect all the runs in a dict for sort_by_time
+            runs = {run: self.data[point][run]}
 
-        runs = {run: self.data[point][run]}
         return self.sort_by_time(runs, return_latest=True)
 
     def sort_by_time(self, runs, return_latest=False):
@@ -243,10 +237,12 @@ def sort_by_time(self, runs, return_latest=False):
                     tag = dump.data["tag"]
                     data_path = dump.data["data_path"]
 
-                    print(f"Latest file had run_name {run_name} and tag {tag}, but the file was"
+                    print(f"Latest file had run_name '{run_name}' and tag '{tag}', but the file was "
                           f"not found in the expected location, and thus skipped.")
                     print("Expected path: ", data_path)
 
+            raise RuntimeError("No beam dumps available.")
+
         return return_list
 
     def show_in_order(self, component_names):
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 9de46634..9885adca 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -458,6 +458,24 @@ def reset_run_points(self):
         self.run_from_ref = None
         self.run_to_ref = None
 
+    def show_run_subset(self):
+        """
+        Shows current subset of instrument selected with run_from and run_to methods
+        """
+        if self.run_from_ref is None and self.run_to_ref is None:
+            print("No run_from or run_to point set.")
+            return
+
+        if self.run_from_ref is None:
+            print("Running from start of the instrument", end="")
+        else:
+            print(f"Running from component named '{self.run_from_ref}'", end="")
+
+        if self.run_to_ref is None:
+            print(" to the end of the instrument.")
+        else:
+            print(f" to component named '{self.run_to_ref}'.")
+
     def run_to(self, component_ref, run_name="Run", comment=None, **kwargs):
         """
         Set limit for instrument, only run to given component, save MCPL there
@@ -534,7 +552,7 @@ def run_from(self, component_ref, run_name=None, tag=None, **kwargs):
             Run name of dump to use as starting point of simulation
 
         tag : integer
-            Tag of the desired dump
+            Tag of the desired dump (only allowed if run_name is given)
         """
 
         if isinstance(component_ref, Component):
@@ -554,29 +572,18 @@ def run_from(self, component_ref, run_name=None, tag=None, **kwargs):
 
             if "filename" not in kwargs:
                 # Find newest dump from database
-                newest_dump = self.dump_database.newest_at_point(component_ref)
+                newest_dump = self.dump_database.newest_at_point(component_ref, run=run_name)
                 auto_name = '"' + newest_dump.data["data_path"] + '"'
                 self.set_parameters({mcpl_par_name: auto_name})
             else:
                 self.set_parameters({mcpl_par_name: kwargs["filename"]})
 
-            if run_name is not None:
-                if component_ref not in self.dump_database.data:
-                    raise KeyError("The run_from'  point doesn't have any runs.")
-
-                if run_name not in self.dump_database.data[component_ref]:
-                    raise KeyError("Given run name not in database.")
-
-                if tag is None:
-                    dump = self.dump_database.newest_at_point_and_run(component_ref, run_name)
-                else:
-                    dump = self.dump_database.data[component_ref][run_name][tag]
+            if run_name is not None and tag is not None:
+                dump = self.dump_database.get_dump(component_ref, run_name, tag)
 
                 dump_filename = '"' + dump.data["data_path"] + '"'
                 self.set_parameters({mcpl_par_name: dump_filename})
 
-                # kwargs["filename"] = '"' + dump.data["data_path"] + '"'
-
             kwargs["filename"] = mcpl_par_name
 
             # Ensure the kwargs are allowed
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 8309314f..cbd85e51 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -12,9 +12,18 @@
 from mcstasscript.helper.formatting import bcolors
 from mcstasscript.tests.helpers_for_tests import WorkInTestDir
 from mcstasscript.helper.exceptions import McStasError
+from mcstasscript.helper.mcstas_objects import Component
+from mcstasscript.helper.beam_dump_database import BeamDump
 
 run_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '.')
 
+class DummyComponent(Component):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def set_parameters(self, *args, **kwargs):
+        pass
+
 def setup_instr_no_path():
     """
     Sets up a neutron instrument without a package_path
@@ -138,6 +147,17 @@ def setup_populated_instr():
     return instr
 
 
+def setup_populated_instr_with_dummy_MCPL_comps():
+    """
+    Sets up a neutron instrument with some features used and three components
+    """
+    instr = setup_populated_instr()
+
+    instr.component_class_lib["MCPL_input"] = DummyComponent
+    instr.component_class_lib["MCPL_output"] = DummyComponent
+
+    return instr
+
 def setup_populated_instr_with_dummy_path():
     """
     Sets up a neutron instrument with some features used and three components
@@ -234,6 +254,15 @@ def setup_populated_with_some_options_instr():
     return instr
 
 
+def insert_mock_dump(instr, component_name, run_name="Run", tag=0):
+    dump = BeamDump("", {}, component_name, run_name, tag=tag)
+    dump.file_present = lambda *_: True  # Overwrite file check
+
+    instr.dump_database.data[component_name] = {}
+    instr.dump_database.data[component_name][run_name] = {}
+    instr.dump_database.data[component_name][run_name][tag] = dump
+
+
 class TestMcStas_instr(unittest.TestCase):
     """
     Tests of the main class in McStasScript called McStas_instr.
@@ -390,6 +419,7 @@ def test_simple_add_parameter(self):
 
         self.assertEqual(parameter.name, "theta")
         self.assertEqual(parameter.comment, "test par")
+        self.assertTrue(parameter in instr.parameters.parameters.values())
 
     def test_user_var_block_add_parameter(self):
         """
@@ -1595,7 +1625,7 @@ def test_write_full_instrument_simple(self, mock_f):
          my_call("* python based McStas instrument generator written by \n"),
          my_call("* Mads Bertelsen in 2019 while employed at the \n"),
          my_call("* European Spallation Source Data Management and \n"),
-         my_call("* Software Center\n"),
+         my_call("* Software Centre\n"),
          my_call("* \n"),
          my_call("* Instrument test_instrument\n"),
          my_call("* \n"),
@@ -1990,6 +2020,187 @@ def test_show_instrument_basic(self, mock_sub, mock_stdout):
                                          universal_newlines=True,
                                          cwd=".")
 
+    def test_show_dumps_works(self):
+        """
+        Ensures show_dumps runs
+        """
+
+        instr = setup_populated_instr_with_dummy_MCPL_comps()
+        insert_mock_dump(instr, "second_component")
+
+        instr.show_dumps()
+
+    def test_show_dump_works(self):
+        """
+        Ensures show_dump runs and that db can get the stored dump
+        """
+
+        instr = setup_populated_instr_with_dummy_MCPL_comps()
+        insert_mock_dump(instr, "second_component", run_name="custom_run", tag=31)
+
+        instr.show_dump("second_component", "custom_run", 31)
+
+        dump = instr.dump_database.get_dump("second_component", "custom_run", 31)
+        self.assertEqual(dump.data["dump_point"], "second_component")
+        self.assertEqual(dump.data["run_name"], "custom_run")
+        self.assertEqual(dump.data["tag"], 31)
+
+    def test_set_run_to_component(self):
+        """
+        Testing run_to method updates instr state correctly
+        """
+
+        instr = setup_populated_instr_with_dummy_MCPL_comps()
+
+        second_component = instr.get_component("second_component")
+
+        # Ensure remaining instrument can work
+        third_component = instr.get_component("third_component")
+        third_component.set_RELATIVE("second_component")
+
+        instr.run_to(second_component)
+
+        self.assertEqual(instr.run_from_ref, None)
+        self.assertEqual(instr.run_to_ref, "second_component")
+        self.assertEqual(instr.run_to_name, "Run")
+
+        # Check filename added to parameters
+        self.assertTrue("run_to_mcpl" in instr.parameters.parameters)
+        self.assertEqual(instr.parameters.parameters["run_to_mcpl"].type, "string")
+
+        instr.run_to("third_component", run_name="Test_name")
+        self.assertEqual(instr.run_to_ref, "third_component")
+        self.assertEqual(instr.run_to_name, "Test_name")
+
+    def test_set_run_to_component_keywords(self):
+        """
+        Testing run_to method updates instr state with passed keywords
+        """
+
+        instr = setup_populated_instr_with_dummy_MCPL_comps()
+
+        second_component = instr.get_component("second_component")
+
+        # Ensure remaining instrument can work
+        third_component = instr.get_component("third_component")
+        third_component.set_RELATIVE("second_component")
+
+        instr.run_to(second_component, test_keyword=58)
+
+        self.assertIn("test_keyword", instr.run_to_component_parameters)
+        self.assertEqual(instr.run_to_component_parameters["test_keyword"], 58)
+
+    def test_set_run_to_nonexistant_component_fails(self):
+        """
+        Ensures run_to fails if the component doesn't exist
+        """
+
+        instr = setup_populated_instr_with_dummy_MCPL_comps()
+
+        with self.assertRaises(ValueError):
+            instr.run_to("component_that_does_not_exists")
+
+    def test_set_run_to_component_with_ABSOLUTE_in_remaining_fails(self):
+        """
+        Ensures run_to fails if the remaining instrument refers to absolute
+        """
+        instr = setup_populated_instr_with_dummy_MCPL_comps()
+
+        with self.assertRaises(McStasError):
+            # Fails because the rest of the instrument has reference to ABSOLUTE
+            instr.run_to("second_component")
+
+    def test_set_run_to_component_with_early_ref_in_remaining_fails(self):
+        """
+        Ensures run_to fails if the remaining instrument refers to absolute
+        """
+        instr = setup_populated_instr_with_dummy_MCPL_comps()
+
+        comp = instr.get_component("third_component")
+        comp.set_RELATIVE("first_component")
+
+        with self.assertRaises(McStasError):
+            # Fails because the rest of the instrument has reference to component before split
+            instr.run_to("second_component")
+
+    def test_set_run_from_component(self):
+        """
+        Testing run_from method updates instr state correctly
+        """
+        instr = setup_populated_instr_with_dummy_MCPL_comps()
+
+        insert_mock_dump(instr, "second_component")
+
+        second_component = instr.get_component("second_component")
+
+        # Ensure remaining instrument can work
+        third_component = instr.get_component("third_component")
+        third_component.set_RELATIVE("second_component")
+
+        instr.run_from(second_component)
+
+        self.assertEqual(instr.run_to_ref, None)
+        self.assertEqual(instr.run_from_ref, "second_component")
+
+        # Check filename added to parameters
+        self.assertTrue("run_from_mcpl" in instr.parameters.parameters)
+        self.assertEqual(instr.parameters.parameters["run_from_mcpl"].type, "string")
+
+    def test_set_run_from_component_fails_if_no_dump(self):
+        """
+        Ensure run_from fails if there are no dumps at that location
+        """
+        instr = setup_populated_instr_with_dummy_MCPL_comps()
+
+        with self.assertRaises(KeyError):
+            instr.run_from("second_component")
+
+        # Also fails if database is populated at a different component
+        insert_mock_dump(instr, "second_component")
+
+        with self.assertRaises(KeyError):
+            instr.run_from("first_component")
+
+    def test_set_run_from_component_keywords(self):
+        """
+        Testing run_to method updates instr state with passed keywords
+        """
+
+        instr = setup_populated_instr_with_dummy_MCPL_comps()
+
+        insert_mock_dump(instr, "second_component")
+
+        second_component = instr.get_component("second_component")
+
+        # Ensure remaining instrument can work
+        third_component = instr.get_component("third_component")
+        third_component.set_RELATIVE("second_component")
+
+        instr.run_from(second_component, test_keyword=37)
+
+        self.assertIn("test_keyword", instr.run_from_component_parameters)
+        self.assertEqual(instr.run_from_component_parameters["test_keyword"], 37)
+
+    def test_set_run_from_and_run_to(self):
+        """
+        Ensure it is possible to use run_from and run_to, and that reset work
+        """
+
+        instr = setup_populated_instr_with_dummy_MCPL_comps()
+        insert_mock_dump(instr, "second_component")
+        third_component = instr.get_component("third_component")
+        third_component.set_RELATIVE("second_component")
+
+        instr.run_from("second_component")
+        instr.run_to("third_component")
+
+        self.assertEqual(instr.run_from_ref, "second_component")
+        self.assertEqual(instr.run_to_ref, "third_component")
+
+        instr.reset_run_points()
+
+        self.assertEqual(instr.run_from_ref, None)
+        self.assertEqual(instr.run_to_ref, None)
 
 if __name__ == '__main__':
     unittest.main()

From 2b507871140a6f549225b6d9a895085e4c83389f Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 14 Oct 2022 15:51:11 +0200
Subject: [PATCH 306/403] Fix of component print code and associated unittests.

---
 mcstasscript/helper/mcstas_objects.py | 35 ++++++++++++++++-----------
 mcstasscript/tests/test_Instr.py      |  7 +++---
 mcstasscript/tests/test_component.py  | 17 +++++++------
 3 files changed, 34 insertions(+), 25 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index b674d053..fe0a6083 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -1071,17 +1071,20 @@ class is used as a superclass for classes describing each
         if self.SPLIT != 0:
             string += "SPLIT " + str(self.SPLIT) + " "
         string += "COMPONENT " + str(self.name)
-        string += " = " + str(self.component_name) + "(\n"
-        lastval = self.parameter_names[-1]
-        previous = None
-        prevunit = ""
+        string += " = " + str(self.component_name) + "("
+
+        last_par_name = ""
+        for key in self.parameter_names:
+            if getattr(self, key) is not None:
+                last_par_name = key
+
         for key in self.parameter_names:
             val = getattr(self, key)
             parameter_name = bcolors.BOLD + key + bcolors.ENDC
             if val is not None:
                 unit = ""
                 if key in self.parameter_units:
-                    unit += " // [" + self.parameter_units[key] + "] "
+                    unit += " // [" + self.parameter_units[key] + "]"
                 if isinstance(val, Parameter):
                     #val_string = val.print_line() # too long
                     val_string = val.name
@@ -1095,27 +1098,31 @@ class is used as a superclass for classes describing each
                          + val_string
                          + bcolors.ENDC
                          + bcolors.ENDC)
-                if previous is not None:
-                    string += ", " + prevunit + "\n"
-                string += "   " + parameter_name
+
+                string += "\n"
+                string += "  " + parameter_name
                 string += " = " + value
-                previous = parameter_name
-                prevunit = unit
+                if key != last_par_name:
+                    string += ","
+                string += unit
+
             else:
                 if self.parameter_defaults[key] is None:
+                    string += "\n"
                     string += "  " + parameter_name
                     string += bcolors.FAIL
                     string += " : Required parameter not yet specified"
-                    string += bcolors.ENDC + "\n"
+                    string += bcolors.ENDC
 
-        string += unit + "\n)\n"
+        string += "\n)"
         if not self.WHEN == "":
-            string += self.WHEN + "\n"
+            string += " " + self.WHEN
+        string += "\n"
         string += f"AT {tuple(self.AT_data)} "
         #"AT (" + str(self.AT_data[0]) + ", " + str(self.AT_data[1]) + ", " + str(self.AT_data[2]) + ") "
         string += str(self.AT_relative) + "\n"
         if self.ROTATED_specified:
-            string += f"ROTATED {tuple(self.ROTATED_data)} "
+            string += f"ROTATED {tuple(self.ROTATED_data)}"
             string += " " + self.ROTATED_relative + "\n"
         if not self.GROUP == "":
             string += "GROUP " + self.GROUP + "\n"
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index cbd85e51..c12d9317 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1317,12 +1317,12 @@ def test_print_component(self, mock_stdout):
         output = mock_stdout.getvalue().split("\n")
 
         self.assertEqual(output[0],
-                         "COMPONENT second_component = test_for_reading")
+                         "COMPONENT second_component = test_for_reading(")
 
         par_name = bcolors.BOLD + "dist" + bcolors.ENDC
         value = (bcolors.BOLD + bcolors.OKGREEN
                  + "5" + bcolors.ENDC + bcolors.ENDC)
-        self.assertEqual(output[1], "  " + par_name + " = " + value + " [m]")
+        self.assertEqual(output[1], "  " + par_name + " = " + value + " // [m]")
 
         par_name = bcolors.BOLD + "gauss" + bcolors.ENDC
         warning = (bcolors.FAIL
@@ -1336,7 +1336,8 @@ def test_print_component(self, mock_stdout):
                    + bcolors.ENDC)
         self.assertEqual(output[3], "  " + par_name + warning)
 
-        self.assertEqual(output[4], "AT [0, 0, 0] ABSOLUTE")
+        self.assertEqual(output[4], ")")
+        self.assertEqual(output[5], "AT (0, 0, 0) ABSOLUTE")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_print_component_short(self, mock_stdout):
diff --git a/mcstasscript/tests/test_component.py b/mcstasscript/tests/test_component.py
index fc86ad84..69178362 100644
--- a/mcstasscript/tests/test_component.py
+++ b/mcstasscript/tests/test_component.py
@@ -628,17 +628,17 @@ def test_Component_print_long(self, mock_stdout):
         output = output.split("\n")
 
         self.assertEqual(output[0], "// test comment")
-        self.assertEqual(output[1], "SPLIT 7 COMPONENT test_component = Arm")
+        self.assertEqual(output[1], "SPLIT 7 COMPONENT test_component = Arm(")
 
         par_name = bcolors.BOLD + "new_par1" + bcolors.ENDC
         value = (bcolors.BOLD + bcolors.OKGREEN
                  + "1.5" + bcolors.ENDC + bcolors.ENDC)
-        self.assertEqual(output[2], "  " + par_name + " = " + value + " [m]")
+        self.assertEqual(output[2], "  " + par_name + " = " + value + ", // [m]")
 
         par_name = bcolors.BOLD + "new_par2" + bcolors.ENDC
         value = (bcolors.BOLD + bcolors.OKGREEN
                  + "3" + bcolors.ENDC + bcolors.ENDC)
-        self.assertEqual(output[3], "  " + par_name + " = " + value + " [AA]")
+        self.assertEqual(output[3], "  " + par_name + " = " + value + ", // [AA]")
 
         par_name = bcolors.BOLD + "new_par3" + bcolors.ENDC
         warning = (bcolors.FAIL
@@ -649,18 +649,19 @@ def test_Component_print_long(self, mock_stdout):
         par_name = bcolors.BOLD + "this_par" + bcolors.ENDC
         value = (bcolors.BOLD + bcolors.OKGREEN
                  + "test_val" + bcolors.ENDC + bcolors.ENDC)
-        self.assertEqual(output[5], "  " + par_name + " = " + value + " []")
+        self.assertEqual(output[5], "  " + par_name + " = " + value + ", // []")
 
         par_name = bcolors.BOLD + "that_par" + bcolors.ENDC
         value = (bcolors.BOLD + bcolors.OKGREEN
                  + "\"txt_string\"" + bcolors.ENDC + bcolors.ENDC)
-        self.assertEqual(output[6], "  " + par_name + " = " + value + " [1]")
+        # No comma after last parameter
+        self.assertEqual(output[6], "  " + par_name + " = " + value + " // [1]")
 
-        self.assertEqual(output[7], "WHEN (1==2)")
+        self.assertEqual(output[7], ") WHEN (1==2)")
 
-        self.assertEqual(output[8], "AT [0.124, 183.9, 157] RELATIVE home")
+        self.assertEqual(output[8], "AT (0.124, 183.9, 157) RELATIVE home")
         self.assertEqual(output[9],
-                         "ROTATED [482, 1240.2, 0.185] RELATIVE etc")
+                         "ROTATED (482, 1240.2, 0.185) RELATIVE etc")
         self.assertEqual(output[10], "GROUP developers")
         self.assertEqual(output[11], "EXTEND %{")
         self.assertEqual(output[12], "nscat = 8;")

From 72efbd152b228099e6a0e76570f0d0840599b53f Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Mon, 17 Oct 2022 14:31:52 +0200
Subject: [PATCH 307/403] Version bump, now version 0.0.51, updates to
 documentation for MCPL bridge.

---
 .../getting_started/version_history.ipynb     |   4 +
 docs/source/index.rst                         |   1 +
 ...McStasScript_tutorial_5_MCPL_bridges.ipynb | 541 ++++++++++++++++++
 docs/source/user_guide/widgets.ipynb          |  16 +-
 mcstasscript/_version.py                      |   2 +-
 mcstasscript/interface/instr.py               |   9 +-
 6 files changed, 563 insertions(+), 10 deletions(-)
 create mode 100644 docs/source/tutorial/McStasScript_tutorial_5_MCPL_bridges.ipynb

diff --git a/docs/source/getting_started/version_history.ipynb b/docs/source/getting_started/version_history.ipynb
index 3f44b3f1..5655020c 100644
--- a/docs/source/getting_started/version_history.ipynb
+++ b/docs/source/getting_started/version_history.ipynb
@@ -14,6 +14,10 @@
    "id": "automatic-charleston",
    "metadata": {},
    "source": [
+    "## 0.0.51 - 17th of October 2022\n",
+    "- New feature for segmenting the instrument using MCPL input/output [tutorial here](../tutorial/McStasScript_tutorial_5_MCPL_bridges.ipynb)\n",
+    "- Printed components now conform to McStas syntax (contribution from Peter Willendrup and Erik Knudsen)\n",
+    "\n",
     "## 0.0.50 - 20th of June 2022\n",
     "- Support for McStas 3.X\n",
     "    - USERVARS can now be used with *add_user_var* [parameters_and_variables](../user_guide/parameters_and_variables.ipynb)\n",
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 041ec16f..5224300a 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -37,6 +37,7 @@ Documentation
    tutorial/McStasScript_tutorial_2_SPLIT.ipynb
    tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
    tutorial/McStasScript_tutorial_4_JUMP.ipynb
+   tutorial/McStasScript_tutorial_5_MCPL_bridges.ipynb
    
 .. toctree::
    :caption: McStas Union Tutorial
diff --git a/docs/source/tutorial/McStasScript_tutorial_5_MCPL_bridges.ipynb b/docs/source/tutorial/McStasScript_tutorial_5_MCPL_bridges.ipynb
new file mode 100644
index 00000000..2951b137
--- /dev/null
+++ b/docs/source/tutorial/McStasScript_tutorial_5_MCPL_bridges.ipynb
@@ -0,0 +1,541 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "swedish-cathedral",
+   "metadata": {},
+   "source": [
+    "# Dynamic instrument cuts with MCPL bridges\n",
+    "\n",
+    "There are some instances where it is beneficial to write to McStas instruments to simulate something by transfering the beam from one to the other through an MCPL file. This is most commonly done to save on computational time, imagine for example an instrument with a long complicated guide with high resolution choppers, only allowing few neutrons through. If one wants to simulate a number of different samples on such an instrument, it would be nice to run the simulation up until the end of the guide once, and then once for each sample starting after the guide.\n",
+    "\n",
+    "That can be done in McStas using the MCPL output and MCPL_input component, which saves and loads the beam to a file respectively. Then one would have two instrument files, the first ending with an MCPL_output, and the next starting with an MCPL_input. \n",
+    "\n",
+    "\n",
+    "In McStasScript it is possible to do the same thing dynamically through the instrument object. The instrument can be executed in segments, and the MCPL_output / MCPL_input components are added automatically. The instrument object will also keep track of the generated MCPL files. This tutorial will show an example of how this can be done, and importantly show that there are some limitations on how the instrument is written to allow for this."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "devoted-liberal",
+   "metadata": {},
+   "source": [
+    "## A simple example instrument\n",
+    "The instrument below does not benefit greatly from being segmented, but is sufficient to show how the system is used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "motivated-factory",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import mcstasscript as ms\n",
+    "\n",
+    "instr = ms.McStas_instr(\"bridge_demo\", input_path=\"run_folder\")\n",
+    "\n",
+    "src = instr.add_component(\"Source\", \"Source_simple\")\n",
+    "src.E0 = instr.add_parameter(\"energy\", value=10)\n",
+    "src.set_parameters(xwidth=0.1, yheight=0.1, dE=2,\n",
+    "                   focus_xw=0.03, focus_yh=0.03, dist=2)\n",
+    "\n",
+    "guide_1 = instr.add_component(\"guide_1\", \"Guide_gravity\")\n",
+    "guide_1.set_parameters(w1=0.03, h1=0.03, m=3, l=10)\n",
+    "guide_1.set_AT(2, RELATIVE=src)\n",
+    "\n",
+    "guide_2 = instr.add_component(\"guide_2\", \"Guide_gravity\")\n",
+    "guide_2.set_parameters(w1=0.03, h1=0.03, m=3, l=10)\n",
+    "guide_2.set_AT(guide_1.l + 0.01, RELATIVE=guide_1)\n",
+    "\n",
+    "guide_end = instr.add_component(\"Guide_end\", \"Arm\")\n",
+    "guide_end.set_AT(guide_2.l, RELATIVE=guide_2)\n",
+    "\n",
+    "sample_position = instr.add_component(\"Sample_position\", \"Arm\")\n",
+    "sample_position.set_AT(22.5, RELATIVE=src)\n",
+    "\n",
+    "sample = instr.add_component(\"sample\", \"PowderN\", RELATIVE=sample_position)\n",
+    "sample.reflections = instr.add_parameter(\"string\", \"data\", value='\"Cu.laz\"')\n",
+    "sample.set_parameters(radius=0.005, yheight=0.05)\n",
+    "\n",
+    "banana = instr.add_component(\"banana\", \"Monitor_nD\", RELATIVE=sample)\n",
+    "banana.xwidth = 2.0\n",
+    "banana.yheight = 0.3\n",
+    "banana.restore_neutron = 1\n",
+    "banana.filename = '\"banana.dat\"'\n",
+    "banana.options = '\"theta limits=[5 175] bins=150, energy limits=[5,15] bins=100, banana\"'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "framed-burlington",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instr.show_diagram()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "packed-darkness",
+   "metadata": {},
+   "source": [
+    "## Requirements for the instrument to be segmented\n",
+    "In order to cut the instrument into two sections, the positions and rotations in the latter part are not allowed to refer to any component in the first part. In the above example it would not be possible to make a cut at any component from *guide_1* to *Guide_end*, as the sample position would not be able to be set relative to something in another instrument file. It would however be possible to create a cut at the *Source* (not very useful) or at the *Sample_position* and at any component after.\n",
+    "\n",
+    "In order to set the end of an instrument segment, one use the *run_to* method on the instrument object, providing the name of the component or component object of the component that should act as the transfer point. This will check if its allowed to segment the instrument at that component, so lets try at the Guide_end which wouldn't be possible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "mechanical-sierra",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    instr.run_to(guide_end)\n",
+    "except Exception as e:\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "specific-analyst",
+   "metadata": {},
+   "source": [
+    "## Instruments suitable for being segmented\n",
+    "Instruments can be written in a way where there are more options to segment the instrument, we could just specify the position of the sample relative to the end of the guide, then this instrument could be cut anywhere."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "precise-poster",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_position.set_AT(0.5, RELATIVE=guide_end)\n",
+    "instr.show_diagram()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "super-visibility",
+   "metadata": {},
+   "source": [
+    "There is no need to write all instruments with only references to the previous component in order to allow segmentation of the instrument at every component, but it is worthwhile to keep this in mind for points in your instrument where segmentation is natural, such as the end of a guide.\n",
+    "\n",
+    "## Running the first part of an instrument\n",
+    "To run the instrument up to a certain point and dump the beam to disk, one need to use the *run_to* method to set the end point, and then run the instrument as one normally would with *backengine*. One can always use *show_run_subset* to see what part of the instrument is currently selected for execution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "electoral-zimbabwe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instr.run_to(guide_end)\n",
+    "instr.show_run_subset()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "outside-speed",
+   "metadata": {},
+   "source": [
+    "### Metadata for beamdump\n",
+    "It is possible to set the name of the beam dump and to add a comment, which is good practice so it easier to keep track of the beam dumps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "controversial-ballet",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instr.run_to(guide_end, \"10 meV\", comment=\"Run with spread of +/- 2 meV\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "confused-spain",
+   "metadata": {},
+   "source": [
+    "### Instrument information\n",
+    "This information is also reflected in the instrument diagram and show components. Here it is visible that the *Guide_end* component has been substituted with a *MCPL_Guide_end* component that has been given its exact position."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "intensive-serve",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instr.show_components()\n",
+    "instr.show_diagram()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fuzzy-hydrogen",
+   "metadata": {},
+   "source": [
+    "This part of the instrument can now be executed as normal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "remarkable-issue",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instr.settings(suppress_output=True)\n",
+    "instr.backengine()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fancy-memphis",
+   "metadata": {},
+   "source": [
+    "## The dump database\n",
+    "The instrument object keeps track of the generated MCPL files in a small database written to disk next to the instrument file. These can be shown with the *show_dumps* method, and will persist through sessions as the data is saved on disk. This database doesn't copy the MCPL data, but keeps track on where it is located on the disk, so moving or deleting the generated data folders can cause trouble."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "other-provincial",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instr.show_dumps()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "handled-rwanda",
+   "metadata": {},
+   "source": [
+    "### Information on a specific beam dump\n",
+    "It is possible to get further information on one beam dump with the *show_dump* method. This method require the component name where the dump is located, and the most recent version is then displayed. It is possible to add *run_name* and *tag* to show a specific beam dump."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "young-float",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instr.show_dump(guide_end)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "breeding-chocolate",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instr.show_dump(guide_end, run_name=\"10 meV\", tag=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "involved-morocco",
+   "metadata": {},
+   "source": [
+    "### Clearing the database\n",
+    "Clearing this database can be done by removing the database folder from the disk, it is a folder that has the same name as the instrument and ends with \"_db\". There is no method in McStasScript to do so as there is risk of deleting important data on accident. It is also possible to change the name of the instrument, as the database is tied to the name."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "judicial-alignment",
+   "metadata": {},
+   "source": [
+    "## Running from a beam dump\n",
+    "Now that we have a beam dump in the database, we can use it to run from. It is however important to remember run_to is still set to *guide_end*, so asking the instrument to run_from *guide_end* would not be allowed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "hybrid-classic",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    instr.run_from(guide_end)\n",
+    "except Exception as e:\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "above-westminster",
+   "metadata": {},
+   "source": [
+    "### Clearing run_to or run_from\n",
+    "It is possible to clear run_to or run_from by giving the input *None*, or both can be cleared with the method *reset_run_points*. This allows us to run from the *guide_end* beam dump. The *run_from* method would result in an error if no dumps where found at that point in the beam dump database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "spanish-seattle",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instr.reset_run_points()\n",
+    "instr.run_from(guide_end)\n",
+    "\n",
+    "instr.show_components()\n",
+    "instr.show_diagram()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "accessible-distribution",
+   "metadata": {},
+   "source": [
+    "Now we see the part of the instrument after the *Guide_end* component as expected."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unable-penguin",
+   "metadata": {},
+   "source": [
+    "### Selecting a certain beam dump\n",
+    "Just running *run_from* will select the most recent beam dump at that position, and is often the appropriate choice. It is however possible to select any beam dump in the database, provided it is at the specified component using the *run_name* and *tag* keyword arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "based-brooklyn",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instr.run_from(guide_end, run_name=\"10 meV\", tag=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pending-calcium",
+   "metadata": {},
+   "source": [
+    "### Settings to the MCPL_input and MCPL_output components\n",
+    "Both the *run_from* and *run_to* methods are able to pass parameters to the MCPL components used, so the *run_to* method accepts parameters for the MCPL_output component, while run_from accept parameters from the MCPL_input component. Here we show the help of the MCPL_input component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "strange-estate",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instr.component_help(\"MCPL_input\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "elementary-acting",
+   "metadata": {},
+   "source": [
+    "When loading a beam from disk, it is possible to run each neutron several times with small differences in its position, direction and energy. This can have similar problematic effects on the statistics as SPLIT if used without care, so think about the justification to use repeats and smear for your application. Using smear of direction from a source with an almost isotropic distribution of directions would be sensible, but using it on a tightly collimated beam could introduce inaccuracies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "amazing-bracelet",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instr.run_from(guide_end, repeat_count=3, E_smear=0.01, dir_smear=0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "manual-holder",
+   "metadata": {},
+   "source": [
+    "## Running a segment of the simulation\n",
+    "As a demonstration we choose to run from the guide_end to (but not including) the detector to show that one can use both *run_from* and *run_to* at the same time, and thus segment an instrument into an arbitrary number of segments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "unique-simpson",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instr.run_to(banana, run_name=\"Cu sample\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fitting-decimal",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instr.show_components()\n",
+    "instr.show_diagram()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "creative-dining",
+   "metadata": {},
+   "source": [
+    "### Executing the simulation\n",
+    "Now that we have one run for a 10 meV beam at the sample position, we can quickly simulate a range of different samples using the same beam. We use the following powder descriptions included in McStas to get 4 different powder patterns.\n",
+    "\n",
+    "- Cu.laz\n",
+    "- Al.laz\n",
+    "- Au.laz\n",
+    "- Na2Ca3Al2F14.laz\n",
+    "\n",
+    "Since the underlying instrument doesn't change, we can save some time by disabling *force_compile* when running these simulations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "intensive-webcam",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instr.backengine()\n",
+    "\n",
+    "instr.settings(force_compile=False)\n",
+    "\n",
+    "instr.set_parameters(data='\"Al.laz\"')\n",
+    "instr.run_to(banana, run_name=\"Al sample\")\n",
+    "instr.backengine()\n",
+    "\n",
+    "instr.set_parameters(data='\"Au.laz\"')\n",
+    "instr.run_to(banana, run_name=\"Au sample\")\n",
+    "instr.backengine()\n",
+    "\n",
+    "instr.set_parameters(data='\"Na2Ca3Al2F14.laz\"')\n",
+    "instr.run_to(banana, run_name=\"Calibration\")\n",
+    "instr.backengine()\n",
+    "\n",
+    "instr.settings(force_compile=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "serial-pakistan",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instr.show_dumps()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pressed-fluid",
+   "metadata": {},
+   "source": [
+    "## Running to the end of the instrument\n",
+    "We now wish to just run the detector for the four different beam dumps made just before the detector. To do so we set *run_to* to None, letting the instrument know we intend to run to the end of the instrument, and setting *run_from* to banana with specifications on the dump corresponding to the desired sample. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "developing-fossil",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instr.run_to(None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cognitive-spirituality",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instr.run_from(banana, run_name=\"Cu sample\")\n",
+    "data1 = instr.backengine()\n",
+    "\n",
+    "instr.settings(force_compile=False) # Avoid recompiling\n",
+    "instr.run_from(banana, run_name=\"Al sample\")\n",
+    "data2 = instr.backengine()\n",
+    "\n",
+    "instr.run_from(banana, run_name=\"Au sample\")\n",
+    "data3 = instr.backengine()\n",
+    "\n",
+    "instr.run_from(banana, run_name=\"Calibration\")\n",
+    "data4 = instr.backengine()\n",
+    "instr.settings(force_compile=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "amateur-carroll",
+   "metadata": {},
+   "source": [
+    "### Plotting the resulting data\n",
+    "We can now plot the data and see we indeed get four different sets of powder data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "improved-brisbane",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ms.make_sub_plot(data1 + data2 + data3 + data4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fundamental-albany",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/user_guide/widgets.ipynb b/docs/source/user_guide/widgets.ipynb
index 034f47c5..e0dbae55 100644
--- a/docs/source/user_guide/widgets.ipynb
+++ b/docs/source/user_guide/widgets.ipynb
@@ -91,8 +91,8 @@
     "TOFmon1.set_AT([0, 0, 1e-6], RELATIVE=Source)\n",
     "\n",
     "TOFLambda1 = instrument.add_component(\"TOFLambda1\", \"TOFLambda_monitor\")\n",
-    "TOFLambda1.nL = 100\n",
-    "TOFLambda1.nt = 200\n",
+    "TOFLambda1.nL = 200\n",
+    "TOFLambda1.nt = 300\n",
     "TOFLambda1.tmin = 0\n",
     "TOFLambda1.tmax = \"tmax_source\"\n",
     "TOFLambda1.filename = '\"TOFLambda1\"'\n",
@@ -152,7 +152,7 @@
     "instrument.add_declare_var(\"double\", \"chopper_tmin\")\n",
     "instrument.add_declare_var(\"double\", \"chopper_tmax\")\n",
     "instrument.append_initialize(\"chopper_tmin = 0.9*1E6*chopper_position/speed_max;\")\n",
-    "instrument.append_initialize(\"chopper_tmax = 1.1*1E6*chopper_position/speed_min + last_pulse_t;\")\n",
+    "instrument.append_initialize(\"chopper_tmax = 1.1*1E6*chopper_position/speed_min + tmax_source;\")\n",
     "\n",
     "TOFmon2 = instrument.add_component(\"TOFmon2\", \"TOF_monitor\")\n",
     "TOFmon2.nt = 100\n",
@@ -165,8 +165,8 @@
     "TOFmon2.set_AT([0, 0, 'chopper_position'], RELATIVE=Source)\n",
     "\n",
     "TOFLambda2 = instrument.add_component(\"TOFLambda2\", \"TOFLambda_monitor\")\n",
-    "TOFLambda2.nL = 100\n",
-    "TOFLambda2.nt = 200\n",
+    "TOFLambda2.nL = 200\n",
+    "TOFLambda2.nt = 300\n",
     "TOFLambda2.tmin = \"chopper_tmin\"\n",
     "TOFLambda2.tmax = \"chopper_tmax\"\n",
     "TOFLambda2.filename = '\"TOFLambda2\"'\n",
@@ -193,8 +193,8 @@
     "TOFmon3.set_AT([0, 0, 'sample_position'], RELATIVE=Source)\n",
     "\n",
     "TOFLambda3 = instrument.add_component(\"TOFLambda3\", \"TOFLambda_monitor\")\n",
-    "TOFLambda3.nL = 100\n",
-    "TOFLambda3.nt = 200\n",
+    "TOFLambda3.nL = 200\n",
+    "TOFLambda3.nt = 300\n",
     "TOFLambda3.tmin = \"sample_tmin\"\n",
     "TOFLambda3.tmax = \"sample_tmax\"\n",
     "TOFLambda3.filename = '\"TOFLambda3\"'\n",
@@ -217,7 +217,7 @@
     "beamstop.set_AT([0, 0, 0.5], RELATIVE=Sample)\n",
     "\n",
     "TOF_cylPSD = instrument.add_component(\"TOF_cylPSD\", \"TOF_cylPSD_monitor\")\n",
-    "TOF_cylPSD.nt = 100\n",
+    "TOF_cylPSD.nt = 200\n",
     "TOF_cylPSD.nphi = 180\n",
     "TOF_cylPSD.filename = '\"TOF_cylPSD\"'\n",
     "TOF_cylPSD.radius = 2.0\n",
diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index bf41adb9..a3ebe305 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.50"
+__version__ = "0.0.51"
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index fd5c8ee8..fe01a949 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -600,7 +600,10 @@ def show_dumps(self):
         component_names = [x.name for x in self.component_list]
         self.dump_database.show_in_order(component_names)
 
-    def show_dump(self, point, run_name, tag):
+    def show_dump(self, point, run_name=None, tag=None):
+        if isinstance(point, Component):
+            point = point.name
+
         self.dump_database.get_dump(point, run_name, tag).print_all()
 
     def subset_check(self, start_ref, end_ref):
@@ -619,6 +622,10 @@ def subset_check(self, start_ref, end_ref):
             raise McStasError("Running from '" + start_ref + "' to '" + end_ref
                               + "' not possible as '" + end_ref + "' is before '"
                               + start_ref + "' in the component sequence.")
+        if start_i == end_i:
+            raise McStasError("Running from and to the same component is not supported "
+                              + "here both run_from and run_to are set to "
+                              + "'" + start_ref + "'.")
 
         # Check current subset of instrument is self contained
         is_self_contained = True

From 4b58173d8ba8429f80271e9c5b2fa4ddf8bd8f4b Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 25 Oct 2022 14:35:22 +0200
Subject: [PATCH 308/403] Small fixes:

McStas components can now have strings when only a single z AT coordinate
is given so variables can be used without specifying all three directions.

orders_of_mag now works for 1d plots in log mode

fixed bug in instrument diagram for ROTATED arrows when using PREVIOUS.
---
 mcstasscript/helper/mcstas_objects.py         |  2 +-
 mcstasscript/helper/plot_helper.py            | 20 ++++++++++++++++---
 .../instrument_diagram/generate_ROTATED.py    |  3 +--
 3 files changed, 19 insertions(+), 6 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index fe0a6083..ae320df2 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -653,7 +653,7 @@ def set_AT(self, at_list, RELATIVE=None):
             RELATIVE : str
                 Sets reference component for position
         """
-        if isinstance(at_list, (int, float)):
+        if isinstance(at_list, (int, float, str)):
             at_list = [0, 0, at_list]
 
         if not isinstance(at_list, list):
diff --git a/mcstasscript/helper/plot_helper.py b/mcstasscript/helper/plot_helper.py
index 697f102e..f5fbf90a 100644
--- a/mcstasscript/helper/plot_helper.py
+++ b/mcstasscript/helper/plot_helper.py
@@ -93,9 +93,6 @@ def _plot_fig_ax(data, fig, ax, **kwargs):
 
         ax.errorbar(x, y, yerr=y_err)
 
-        if data.plot_options.log:
-            ax.set_yscale("log", nonpositive='clip')
-
         ax.set_xlim(data.metadata.limits[0] * x_axis_mult,
                     data.metadata.limits[1] * x_axis_mult)
 
@@ -112,6 +109,23 @@ def _plot_fig_ax(data, fig, ax, **kwargs):
         if data.plot_options.custom_xlim_right:
             ax.set_xlim(right=data.plot_options.right_lim)
 
+        if data.plot_options.log:
+            ax.set_yscale("log", nonpositive='clip')
+
+            n_non_zero = np.count_nonzero(data.Intensity)
+            if n_non_zero == 0:
+                # Plot is empty, return
+                return
+
+            non_zero = np.nonzero(data.Intensity)
+            min_value_log = np.log10(min(data.Intensity[non_zero]))
+            max_value_log = np.log10(max(data.Intensity[non_zero]))
+
+            orders_of_mag = data.plot_options.orders_of_mag
+            if max_value_log - min_value_log > orders_of_mag:
+                ax.set_ylim(top=10.0 ** (max_value_log * 1.1))
+                ax.set_ylim(bottom=10.0 ** (max_value_log - orders_of_mag))
+
     elif len(data.metadata.dimension) == 2:
 
         min_value, max_value = _find_min_max_I(data)
diff --git a/mcstasscript/instrument_diagram/generate_ROTATED.py b/mcstasscript/instrument_diagram/generate_ROTATED.py
index a50e7db7..13019b65 100644
--- a/mcstasscript/instrument_diagram/generate_ROTATED.py
+++ b/mcstasscript/instrument_diagram/generate_ROTATED.py
@@ -12,15 +12,14 @@ def generate_ROTATED_arrows(components, component_box_dict, box_names, color=Non
         if not component.ROTATED_specified:
             continue
 
+        origin = component_box_dict[component.name]
         if component.ROTATED_reference is None:
-            origin = component_box_dict[component.name]
             target = component_box_dict["ABSOLUTE"]
         elif component.ROTATED_reference == "PREVIOUS":
             origin_index = box_names.index(component.name)
             target_index = origin_index - 1
             target = component_box_dict[box_names[target_index]]
         else:
-            origin = component_box_dict[component.name]
             target = component_box_dict[component.ROTATED_reference]
 
         connections.add(origin, target)

From f85d687de8d72e3053450abbb27214c51d59a6bd Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 26 Oct 2022 15:06:45 +0200
Subject: [PATCH 309/403] Initial implementation of debugging tool that place
 event monitors at desired positions in the instrument and then produce
 figures as requested using the event data.

The main purpose is to avoid having monitors all over the McStas
instrument for debugging purposes by instead having this available
on demand.

Tests and documentation to follow.
---
 mcstasscript/__init__.py                      |   2 +
 mcstasscript/_version.py                      |   2 +-
 mcstasscript/data/data.py                     | 124 +++++++++++++++++
 mcstasscript/helper/plot_helper.py            |   3 +-
 .../instrument_diagnostics/__init__.py        |   0
 .../instrument_diagnostics/diagnostics.py     | 129 ++++++++++++++++++
 .../instrument_diagnostics/event_plotter.py   |  52 +++++++
 .../instrument_diagnostics/plot_overview.py   |  69 ++++++++++
 mcstasscript/instrument_diagnostics/view.py   |  29 ++++
 9 files changed, 407 insertions(+), 3 deletions(-)
 create mode 100644 mcstasscript/instrument_diagnostics/__init__.py
 create mode 100644 mcstasscript/instrument_diagnostics/diagnostics.py
 create mode 100644 mcstasscript/instrument_diagnostics/event_plotter.py
 create mode 100644 mcstasscript/instrument_diagnostics/plot_overview.py
 create mode 100644 mcstasscript/instrument_diagnostics/view.py

diff --git a/mcstasscript/__init__.py b/mcstasscript/__init__.py
index e1c6529e..289c01b3 100644
--- a/mcstasscript/__init__.py
+++ b/mcstasscript/__init__.py
@@ -18,3 +18,5 @@
 from .interface.reader import McStas_file
 
 from .tools.cryostat_builder import Cryostat
+
+from .instrument_diagnostics.diagnostics import Diagnostics
diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index a3ebe305..478240bb 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.51"
+__version__ = "0.0.52"
diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index 7455662b..d4d1b594 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -1,5 +1,6 @@
 import matplotlib.pyplot
 import numpy as np
+import copy
 
 
 class McStasMetaData:
@@ -664,6 +665,129 @@ def __init__(self, metadata, events, **kwargs):
         data_lines = metadata.dimension[1]
         self.Intensity = self.Events[0:data_lines, :]
 
+        self.variables = self.metadata.info["variables"].strip()
+        self.variables = self.variables.split()
+
+        self.labels = {"t": "t [s]",
+                       "x": "x [m]",
+                       "y": "y [m]",
+                       "z": "z [m]",
+                       "vx": "vx [m/s]",
+                       "vy": "vy [m/s]",
+                       "vz": "vz [m/s]",
+                       "l": "wavelength [AA]",
+                       "e": "energy [meV]",
+                       "speed": "speed [m/s]",
+                       "dx": "divergence x [deg]",
+                       "dy": "divergence y [deg]"}
+
+    def find_variable_index(self, axis):
+        return self.variables.index(axis)
+
+    def scale_weights(self, factor):
+        self.Events[:, self.find_variable_index("p")] *= factor
+
+    def get_label(self, axis):
+        axis = axis.lower()
+
+        if axis in self.labels:
+            return self.labels[axis]
+        else:
+            return ""
+
+    def get_data_column(self, axis):
+
+        m_n_const = 1.674927e-27
+        h_const = 6.626068e-34
+
+        if axis.lower() == "speed":
+            # Convert velocity to speed (must be before l and e)
+            vx = self.Events[:, self.find_variable_index("vx")]
+            vy = self.Events[:, self.find_variable_index("vy")]
+            vz = self.Events[:, self.find_variable_index("vz")]
+            return np.sqrt(vx ** 2 + vy ** 2 + vz ** 2)
+
+        elif axis.lower() == "l":
+            # Convert speed to lambda
+            speed = self.get_data_column("speed")
+            lambda_meter = h_const / (m_n_const*speed)
+            return lambda_meter*1E10
+
+        elif axis.lower() == "e":
+            # Convert speed to energy
+            speed = self.get_data_column("speed")
+            energy_joule = 0.5 * m_n_const * speed ** 2
+            return energy_joule/1.60217663E-19*1E3
+
+        elif axis.lower() == "dx":
+            # Convert velocity to divergence x
+            vx = self.Events[:, self.find_variable_index("vx")]
+            vz = self.Events[:, self.find_variable_index("vz")]
+            return np.arctan(vx/vz) * 180 / np.pi
+
+        elif axis.lower() == "dy":
+            # Convert velocity to divergence y
+            vy = self.Events[:, self.find_variable_index("vy")]
+            vz = self.Events[:, self.find_variable_index("vz")]
+            return np.arctan(vy/vz) * 180 / np.pi
+
+        else:
+            index = self.find_variable_index(axis)
+            return self.Events[:, index]
+
+    def make_1d(self, axis1, n_bins=50):
+        data = self.get_data_column(axis1)
+        label = self.get_label(axis1)
+
+        weights = self.get_data_column("p")
+        intensity, edges = np.histogram(data, bins=n_bins, weights=weights)
+        error_squared, edges = np.histogram(data, bins=n_bins, weights=weights**2)
+        error = np.sqrt(error_squared)
+        ncount, edges = np.histogram(data, bins=n_bins)
+
+        centers = edges[0:-1] + 0.5*(edges[1] - edges[0])
+
+        metadata = copy.deepcopy(self.metadata)
+        metadata.dimension = len(centers)
+        metadata.info["type"] = "array_1d"
+        metadata.limits = [centers[0], centers[-1]]
+
+        binned = McStasDataBinned(metadata, intensity=intensity,
+                                  error=error, ncount=ncount, xaxis=centers)
+        binned.set_title("Binned data generated from events")
+        binned.set_xlabel(label)
+        binned.set_ylabel("Intensity per bin [n/s]")
+
+        return binned
+
+    def make_2d(self, axis1, axis2, n_bins=100):
+        data1 = self.get_data_column(axis1)
+        label1 = self.get_label(axis1)
+        data2 = self.get_data_column(axis2)
+        label2 = self.get_label(axis2)
+
+        weights = self.get_data_column("p")
+        intensity, edges2, edges1 = np.histogram2d(data2, data1, bins=n_bins, weights=weights)
+        error_squared, edges2, edges1 = np.histogram2d(data2, data1, bins=n_bins, weights=weights**2)
+        error = np.sqrt(error_squared)
+        ncount, edges2, edges1 = np.histogram2d(data2, data1, bins=n_bins)
+
+        centers1 = edges1[0:-1] + 0.5*(edges1[1] - edges1[0])
+        centers2 = edges2[0:-1] + 0.5*(edges2[1] - edges2[0])
+
+        metadata = copy.deepcopy(self.metadata)
+        metadata.dimension = [len(centers1), len(centers2)]
+        metadata.info["type"] = "array_2d"
+        metadata.limits = [centers1[0], centers1[-1], centers2[0], centers2[-1]]
+
+        binned = McStasDataBinned(metadata, intensity=intensity,
+                                  error=error, ncount=ncount)
+        binned.set_title("Binned data generated from events")
+        binned.set_xlabel(label1)
+        binned.set_ylabel(label2)
+
+        return binned
+
     def __str__(self):
         """
         Returns string with quick summary of data
diff --git a/mcstasscript/helper/plot_helper.py b/mcstasscript/helper/plot_helper.py
index f5fbf90a..7b77cc69 100644
--- a/mcstasscript/helper/plot_helper.py
+++ b/mcstasscript/helper/plot_helper.py
@@ -82,7 +82,6 @@ def _plot_fig_ax(data, fig, ax, **kwargs):
     When plotting 2D objects, returns the pcolormesh object
     """
 
-    print("Plotting data with name " + data.metadata.component_name)
     if type(data.metadata.dimension) == int:
 
         x_axis_mult = data.plot_options.x_limit_multiplier
@@ -123,7 +122,7 @@ def _plot_fig_ax(data, fig, ax, **kwargs):
 
             orders_of_mag = data.plot_options.orders_of_mag
             if max_value_log - min_value_log > orders_of_mag:
-                ax.set_ylim(top=10.0 ** (max_value_log * 1.1))
+                ax.set_ylim(top=1.1*10.0 ** max_value_log)
                 ax.set_ylim(bottom=10.0 ** (max_value_log - orders_of_mag))
 
     elif len(data.metadata.dimension) == 2:
diff --git a/mcstasscript/instrument_diagnostics/__init__.py b/mcstasscript/instrument_diagnostics/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/mcstasscript/instrument_diagnostics/diagnostics.py b/mcstasscript/instrument_diagnostics/diagnostics.py
new file mode 100644
index 00000000..9993621f
--- /dev/null
+++ b/mcstasscript/instrument_diagnostics/diagnostics.py
@@ -0,0 +1,129 @@
+import copy
+
+from mcstasscript.helper.mcstas_objects import Component
+from mcstasscript.instrument_diagnostics import event_plotter
+from mcstasscript.interface.functions import name_search
+from mcstasscript.instrument_diagnostics.view import View
+from mcstasscript.instrument_diagnostics.plot_overview import PlotOverview
+
+def sanitise_comp_name(comp_name):
+    if isinstance(comp_name, Component):
+        comp_name = Component.name
+
+    if not isinstance(comp_name, str):
+        raise ValueError("comp_name should be a string or component object!")
+
+    return comp_name
+
+class Diagnostics:
+    def __init__(self, instr):
+        self.original_instr = instr
+        self.instr = None
+        self.reset_instr()
+
+        # Set of Component names
+        self.points = set()
+        self.point_collect_rates = {}
+        self.monitor_names = {}
+
+        self.views = []
+
+        self.data = None
+
+        self.event_plotters = []
+
+    def reset_instr(self):
+        self.instr = copy.deepcopy(self.original_instr)
+
+    def add_point(self, comp_name, collect=1):
+        comp_name = sanitise_comp_name(comp_name)
+        try:
+            self.instr.get_component(comp_name)
+        except KeyError:
+            raise KeyError("Component '" + comp_name + "' not found in instrument.")
+
+        self.points.add(comp_name)
+        if collect != 1:
+            self.point_collect_rates[comp_name] = collect
+
+    def remove_point(self, comp_name):
+        comp_name = sanitise_comp_name(comp_name)
+        self.points.remove(comp_name)
+        self.point_collect_rates.remove(comp_name)
+
+    def add_view(self, axis1, axis2=None, bins=100, same_scale=True, **kwargs):
+        view = View(axis1=axis1, axis2=axis2, bins=bins, same_scale=same_scale, **kwargs)
+        self.views.append(view)
+
+    def clear_views(self):
+        self.views = []
+
+    def add_monitors(self):
+        comp_names = [x.name for x in self.instr.make_component_subset()]
+        self.monitor_names = {}
+        ordered_point_list = []
+        for name in comp_names:
+            if name in self.points:
+                ordered_point_list.append(name)
+
+        for name in ordered_point_list:
+            comp = self.instr.get_component(name)
+            file_name = self.add_monitor(comp)
+            self.monitor_names[name] = file_name
+
+    def add_monitor(self, before):
+        name = "Diag_before_" + before.name
+        mon = self.instr.add_component(name, "Monitor_nD", before=before)
+
+        options = '"square boarders n x y z vx vy vz t, list all neutrons"'
+        mon.set_parameters(restore_neutron=1,
+                           xwidth=100, yheight=100,
+                           options=options,
+                           filename='"' + name + ".diag" + '"')
+
+        mon.set_AT(before.AT_data, RELATIVE=before.AT_reference)
+        if before.ROTATED_specified:
+            mon.set_ROTATED(before.ROTATED_data, RELATIVE=before.ROTATED_reference)
+
+        if before.name in self.point_collect_rates:
+            collect_rate = self.point_collect_rates[before.name]
+            mon.set_WHEN("rand01() < " + str(collect_rate))
+
+        return name
+
+    def run(self):
+        self.reset_instr()
+        self.add_monitors()
+
+        self.data = self.instr.backengine()
+
+        self.read_data()
+
+    def read_data(self):
+
+        self.event_plotters = []
+        for comp in self.monitor_names:
+            name = self.monitor_names[comp]
+            event_data = name_search(name, self.data)
+            plotter = event_plotter.EventPlotter(name, event_data)
+            if comp in self.point_collect_rates:
+                rate_correction = 1/self.point_collect_rates[comp]
+                plotter.scale_weights(rate_correction)
+
+            self.event_plotters.append(plotter)
+
+    def plot(self):
+
+        overview = PlotOverview(self.event_plotters, self.views)
+        overview.plot_all()
+
+
+
+
+
+
+
+
+
+
+
diff --git a/mcstasscript/instrument_diagnostics/event_plotter.py b/mcstasscript/instrument_diagnostics/event_plotter.py
new file mode 100644
index 00000000..cbf0e5c4
--- /dev/null
+++ b/mcstasscript/instrument_diagnostics/event_plotter.py
@@ -0,0 +1,52 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+from mcstasscript.helper.plot_helper import _plot_fig_ax
+
+class EventPlotter:
+    def __init__(self, name, data):
+        self.name = name
+        self.data = data
+
+    def scale_weights(self, factor):
+        index = self.data.find_variable_index("p")
+        self.data.Events[:, index] *= factor
+
+    def add_view_limits(self, view):
+        data = self.data.get_data_column(view.axis1)
+        view.set_axis1_limits(np.min(data), np.max(data))
+
+        if view.axis2 is not None:
+            data = self.data.get_data_column(view.axis1)
+            view.set_axis1_limits(np.min(data), np.max(data))
+
+    def get_view_limits_axis1(self, view):
+        data = self.data.get_data_column(view.axis1)
+        return np.min(data), np.max(data)
+
+    def get_view_limits_axis2(self, view):
+        if view.axis2 is None:
+            return np.NaN, np.NaN
+        data = self.data.get_data_column(view.axis2)
+        return np.min(data), np.max(data)
+
+    def plot(self, view, fig, ax):
+
+        if view.axis2 is None:
+            data = self.data.make_1d(axis1=view.axis1, n_bins=view.bins)
+            data.set_title("")
+            if view.axis1_limits is not None:
+                data.set_plot_options(left_lim=view.axis1_limits[0], right_lim=view.axis1_limits[1])
+            data.set_plot_options(**view.plot_options)
+
+        else:
+            data = self.data.make_2d(axis1=view.axis1, axis2=view.axis2, n_bins=view.bins)
+            data.set_plot_options(show_colorbar=False)
+            data.set_title("")
+            if view.axis1_limits is not None and view.axis2_limits is not None:
+                data.set_plot_options(left_lim=view.axis1_limits[0], right_lim=view.axis1_limits[1],
+                                      bottom_lim=view.axis2_limits[0], top_lim=view.axis2_limits[1])
+            data.set_plot_options(**view.plot_options)
+
+        _plot_fig_ax(data, fig, ax)
+
diff --git a/mcstasscript/instrument_diagnostics/plot_overview.py b/mcstasscript/instrument_diagnostics/plot_overview.py
new file mode 100644
index 00000000..67d802be
--- /dev/null
+++ b/mcstasscript/instrument_diagnostics/plot_overview.py
@@ -0,0 +1,69 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+from mcstasscript.instrument_diagnostics.event_plotter import EventPlotter
+
+class PlotOverview:
+    def __init__(self, event_plotter_list, view_list):
+        self.event_plotter_list = event_plotter_list
+        self.n_points = len(event_plotter_list)
+        self.views = view_list
+        self.n_plots = len(view_list)
+
+    def plot_all(self, figsize=None, same_scale=True):
+
+        if same_scale:
+            self.set_same_scale()
+
+        if figsize is None:
+            # Scale size after number of plots
+            figsize = (1 + self.n_plots*3, self.n_points*3)
+
+        fig, axs = plt.subplots(self.n_points, self.n_plots, figsize=figsize)
+
+        # Fix cases where n_points or n_plots is 1 and axs is not a list of lists
+        if self.n_plots == 1:
+            axs = [axs]
+        if self.n_points == 1:
+            axs = [axs]
+
+        for plotter, ax_row in zip(self.event_plotter_list, axs):
+            major_label_set = False
+            for view, ax in zip(self.views, ax_row):
+                plotter.plot(view=view, fig=fig, ax=ax)
+
+                if not major_label_set:
+                    ylabel = ax.get_ylabel()
+                    row_name = plotter.name.replace("_", "\ ")
+                    new_label = r"$\bf{" + row_name + "}$" + "\n" + ylabel
+                    ax.set_ylabel(new_label)
+
+                major_label_set = True
+
+        fig.tight_layout()
+
+    def set_same_scale(self):
+        for view in self.views:
+
+            if not view.same_scale:
+                # View controls same scale or not
+                continue
+
+            axis1_mins = []
+            axis1_maxs = []
+
+            axis2_mins = []
+            axis2_maxs = []
+
+            for plotter in self.event_plotter_list:
+                lim_min, lim_max = plotter.get_view_limits_axis1(view)
+                axis1_mins.append(lim_min)
+                axis1_maxs.append(lim_max)
+
+                lim_min, lim_max = plotter.get_view_limits_axis2(view)
+                axis2_mins.append(lim_min)
+                axis2_maxs.append(lim_max)
+
+            view.set_axis1_limits(np.nanmin(axis1_mins), np.nanmax(axis1_maxs))
+            if view.axis2 is not None:
+                view.set_axis2_limits(np.nanmin(axis2_mins), np.nanmax(axis2_maxs))
\ No newline at end of file
diff --git a/mcstasscript/instrument_diagnostics/view.py b/mcstasscript/instrument_diagnostics/view.py
new file mode 100644
index 00000000..3167150a
--- /dev/null
+++ b/mcstasscript/instrument_diagnostics/view.py
@@ -0,0 +1,29 @@
+class View:
+    def __init__(self, axis1, axis2=None, bins=100, same_scale=True, **kwargs):
+        self.same_scale = same_scale
+
+        self.axis1 = axis1
+        self.axis1_limits = None
+
+        self.axis2 = axis2
+        self.axis2_limits = None
+
+        self.bins = bins
+
+        self.plot_options = kwargs
+
+    def set_axis1_limits(self, start, end):
+        if start > end:
+            raise ValueError("Start point over end for this view.")
+
+        self.axis1_limits = start, end
+
+    def set_axis2_limits(self, start, end):
+        if start > end:
+            raise ValueError("Start point over end for this view.")
+
+        self.axis2_limits = start, end
+
+    def clear_limits(self):
+        self.axis1_limits = None
+        self.axis2_limits = None
\ No newline at end of file

From 786dd52dd101c4aeddf9f3a4acb0ecc7de03ecc4 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 27 Oct 2022 16:04:55 +0200
Subject: [PATCH 310/403] New feature, diagnostics tool can now plot intensity
 and ray evolution throughout the instrument.

Fixed bugs in other diagnostic tool mode resulting from use of PREVIOUS
which the added monitors could distrupt. Now use of PREVIOUS is simply
overwritten with the actual references. There can still be issues with
target_index, this can also be fixed.
---
 .../instrument_diagnostics/diagnostics.py     | 122 +++++++++++++++---
 .../instrument_diagnostics/event_plotter.py   |  58 +++++++++
 .../instrument_diagnostics/plot_overview.py   |  42 ++++--
 mcstasscript/instrument_diagnostics/view.py   |  40 ++++++
 4 files changed, 236 insertions(+), 26 deletions(-)

diff --git a/mcstasscript/instrument_diagnostics/diagnostics.py b/mcstasscript/instrument_diagnostics/diagnostics.py
index 9993621f..437672b8 100644
--- a/mcstasscript/instrument_diagnostics/diagnostics.py
+++ b/mcstasscript/instrument_diagnostics/diagnostics.py
@@ -1,3 +1,5 @@
+import matplotlib.pyplot as plt
+import numpy as np
 import copy
 
 from mcstasscript.helper.mcstas_objects import Component
@@ -23,7 +25,7 @@ def __init__(self, instr):
 
         # Set of Component names
         self.points = set()
-        self.point_collect_rates = {}
+        self.point_collect_rays = {}
         self.monitor_names = {}
 
         self.views = []
@@ -32,10 +34,14 @@ def __init__(self, instr):
 
         self.event_plotters = []
 
+        # Analysis
+        self.I_data = None
+        self.I_monitors = None
+
     def reset_instr(self):
         self.instr = copy.deepcopy(self.original_instr)
 
-    def add_point(self, comp_name, collect=1):
+    def add_point(self, comp_name, rays=50000):
         comp_name = sanitise_comp_name(comp_name)
         try:
             self.instr.get_component(comp_name)
@@ -43,13 +49,17 @@ def add_point(self, comp_name, collect=1):
             raise KeyError("Component '" + comp_name + "' not found in instrument.")
 
         self.points.add(comp_name)
-        if collect != 1:
-            self.point_collect_rates[comp_name] = collect
+
+        if not isinstance(rays, int):
+            if not rays == "all":
+                raise ValueError("The ray keyword for a point should be all or an integer")
+
+        self.point_collect_rays[comp_name] = rays
 
     def remove_point(self, comp_name):
         comp_name = sanitise_comp_name(comp_name)
         self.points.remove(comp_name)
-        self.point_collect_rates.remove(comp_name)
+        self.point_collect_rays.remove(comp_name)
 
     def add_view(self, axis1, axis2=None, bins=100, same_scale=True, **kwargs):
         view = View(axis1=axis1, axis2=axis2, bins=bins, same_scale=same_scale, **kwargs)
@@ -75,7 +85,8 @@ def add_monitor(self, before):
         name = "Diag_before_" + before.name
         mon = self.instr.add_component(name, "Monitor_nD", before=before)
 
-        options = '"square boarders n x y z vx vy vz t, list all neutrons"'
+        rays = self.point_collect_rays[before.name]
+        options = f'"square boarders n x y z vx vy vz t, list {rays} neutrons"'
         mon.set_parameters(restore_neutron=1,
                            xwidth=100, yheight=100,
                            options=options,
@@ -85,14 +96,12 @@ def add_monitor(self, before):
         if before.ROTATED_specified:
             mon.set_ROTATED(before.ROTATED_data, RELATIVE=before.ROTATED_reference)
 
-        if before.name in self.point_collect_rates:
-            collect_rate = self.point_collect_rates[before.name]
-            mon.set_WHEN("rand01() < " + str(collect_rate))
-
         return name
 
     def run(self):
         self.reset_instr()
+        self.remove_previous_use()
+
         self.add_monitors()
 
         self.data = self.instr.backengine()
@@ -106,9 +115,6 @@ def read_data(self):
             name = self.monitor_names[comp]
             event_data = name_search(name, self.data)
             plotter = event_plotter.EventPlotter(name, event_data)
-            if comp in self.point_collect_rates:
-                rate_correction = 1/self.point_collect_rates[comp]
-                plotter.scale_weights(rate_correction)
 
             self.event_plotters.append(plotter)
 
@@ -117,13 +123,95 @@ def plot(self):
         overview = PlotOverview(self.event_plotters, self.views)
         overview.plot_all()
 
+    def add_I_monitor(self, before):
+        name = "I_before_" + before.name
+        mon = self.instr.add_component(name, "Monitor_nD", before=before)
 
+        options = f'"square boarders intensity"'
+        mon.set_parameters(restore_neutron=1,
+                           xwidth=100, yheight=100,
+                           options=options,
+                           filename='"' + name + ".diag" + '"')
 
+        mon.set_AT(before.AT_data, RELATIVE=before.AT_reference)
+        if before.ROTATED_specified:
+            mon.set_ROTATED(before.ROTATED_data, RELATIVE=before.ROTATED_reference)
 
+        return name
 
-
-
-
-
+    def intensity_analysis(self):#, start=None, end=None):
+        self.reset_instr()
+        self.remove_previous_use()
+
+        self.I_monitors = []
+        for comp in self.I_component_list[1:]:
+            mon_name = self.add_I_monitor(comp)
+            self.I_monitors.append((mon_name, comp.name))
+
+        self.I_data = self.instr.backengine()
+
+    def remove_previous_use(self):
+        self.I_component_list = self.instr.make_component_subset()
+        previous_component = None
+        for comp in self.I_component_list:
+            if comp.AT_reference == "PREVIOUS":
+                comp.set_AT_RELATIVE(previous_component)
+
+            if comp.ROTATED_specified:
+                if comp.ROTATED_reference == "PREVIOUS":
+                    comp.set_ROTATED_RELATIVE(previous_component)
+
+            previous_component = comp
+
+    def plot_I_analysis(self, figsize=None):
+
+        if figsize is None:
+            figsize = (8, len(self.I_component_list)/5 + 1)
+
+        intensities = []
+        ray_counts = []
+        component_names = []
+        indicies = []
+
+        index = 0
+        for I_monitor_name, component_name in self.I_monitors:
+            mon_data = name_search(I_monitor_name, self.I_data)
+            values = mon_data.metadata.info["values"].split()
+            # values contain strings of: Intensity, Error, Ncount
+            intensities.append(float(values[0]))
+            ray_counts.append(float(values[2]))
+            component_names.append(component_name)
+            indicies.append(index)
+            index += 1
+
+        # Extend with the last one
+        intensities.append(intensities[-1])
+        ray_counts.append(ray_counts[-1])
+        indicies.append(index)
+        component_names = [self.I_component_list[0].name] + component_names
+
+        fig, ax = plt.subplots(1, 1, figsize=figsize)
+
+        intensities.reverse()
+        ray_counts.reverse()
+        component_names.reverse()
+
+        ax.step(intensities, indicies, where="post", color="k")
+        ax.set_yticks(indicies)
+        ax.set_yticklabels(component_names, fontsize=18)
+        ax.set_xlabel("Intensity [n/s]", fontsize=18, color="k")
+        ax.set_xscale("log", nonpositive='clip')
+        ax.xaxis.set_tick_params(labelsize=16)
+        ax.set_ylim([-0.5, index + 0.5])
+
+        ax.grid(True)
+
+        ax2 = ax.twiny()
+        ax2.step(ray_counts, indicies, where="post", color="g", linestyle="--")
+        ax2.set_xlabel("Ray count", fontsize=18, color="g")
+        ax2.set_xscale("log", nonpositive='clip')
+        ax2.xaxis.set_tick_params(labelsize=16)
+        xlim = ax2.get_xlim()
+        ax2.set_xlim([xlim[0]*0.9, xlim[1]*1.1])
 
 
diff --git a/mcstasscript/instrument_diagnostics/event_plotter.py b/mcstasscript/instrument_diagnostics/event_plotter.py
index cbf0e5c4..5d47f847 100644
--- a/mcstasscript/instrument_diagnostics/event_plotter.py
+++ b/mcstasscript/instrument_diagnostics/event_plotter.py
@@ -4,15 +4,44 @@
 from mcstasscript.helper.plot_helper import _plot_fig_ax
 
 class EventPlotter:
+    """
+    Plots event data onto given views
+    """
     def __init__(self, name, data):
+        """
+        EventPlotter stores name and data, can produce plots given views
+
+        Parameters:
+
+        name : str
+            Name of dataset
+
+        data : McStasEventData
+            Data object with event data
+        """
         self.name = name
         self.data = data
 
     def scale_weights(self, factor):
+        """
+        Scales all weights in contained McStasEventData object
+
+        Parameters:
+
+        factor : float
+            Scale factor to be applied
+        """
         index = self.data.find_variable_index("p")
         self.data.Events[:, index] *= factor
 
     def add_view_limits(self, view):
+        """
+        Sets limits of View object from what is applicable to this data
+
+        Parameters:
+        view : View
+            View for which limits should be set
+        """
         data = self.data.get_data_column(view.axis1)
         view.set_axis1_limits(np.min(data), np.max(data))
 
@@ -21,16 +50,45 @@ def add_view_limits(self, view):
             view.set_axis1_limits(np.min(data), np.max(data))
 
     def get_view_limits_axis1(self, view):
+        """
+        Finds limits for axis1 of given view with the contained data
+
+        Parameters:
+        view : View
+            View for which limits should be retrieved
+        """
         data = self.data.get_data_column(view.axis1)
         return np.min(data), np.max(data)
 
     def get_view_limits_axis2(self, view):
+        """
+        Finds limits for axis1 of given view with the contained data
+
+        Parameters:
+        view : View
+            View for which limits should be retrieved
+        """
         if view.axis2 is None:
             return np.NaN, np.NaN
         data = self.data.get_data_column(view.axis2)
         return np.min(data), np.max(data)
 
     def plot(self, view, fig, ax):
+        """
+        Plots binned data generated from contained data on axis from view
+
+        A view can also contain additional plot options which will be passed
+        to the standard plotting tools.
+
+        view : View
+            View defining onto which axis and what bins EventData should be binned
+
+        fig : Matplotlib fig
+            Figure object for figure
+
+        ax : Matplotlib ax
+            Axes object for figure
+        """
 
         if view.axis2 is None:
             data = self.data.make_1d(axis1=view.axis1, n_bins=view.bins)
diff --git a/mcstasscript/instrument_diagnostics/plot_overview.py b/mcstasscript/instrument_diagnostics/plot_overview.py
index 67d802be..308e394a 100644
--- a/mcstasscript/instrument_diagnostics/plot_overview.py
+++ b/mcstasscript/instrument_diagnostics/plot_overview.py
@@ -1,17 +1,43 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-from mcstasscript.instrument_diagnostics.event_plotter import EventPlotter
-
 class PlotOverview:
+    """
+    Class for plotting list of views at different points in instrument
+    """
     def __init__(self, event_plotter_list, view_list):
+        """
+        Stores list of event plotters and views, able to create figure
+
+        parameters:
+
+        event_plotter_list : list of EventPlotter objects
+            EventPlotter objects for each investigated point in instrument
+
+        view_list : list of View objects
+            View specifications which will all be used for each point
+        """
+
         self.event_plotter_list = event_plotter_list
         self.n_points = len(event_plotter_list)
         self.views = view_list
         self.n_plots = len(view_list)
 
     def plot_all(self, figsize=None, same_scale=True):
+        """
+        Plots all views for all guide points
 
+        The same_scale features is enabled per default, but even then
+        individual Views can opt out of being drawn on the same scale.
+
+        Parameters:
+
+        figsize : tuple of length 2
+            size of figure, default scales according to number of plots
+
+        same_scale : bool
+            Allow the use of the same scale feature (default)
+        """
         if same_scale:
             self.set_same_scale()
 
@@ -19,13 +45,8 @@ def plot_all(self, figsize=None, same_scale=True):
             # Scale size after number of plots
             figsize = (1 + self.n_plots*3, self.n_points*3)
 
-        fig, axs = plt.subplots(self.n_points, self.n_plots, figsize=figsize)
-
-        # Fix cases where n_points or n_plots is 1 and axs is not a list of lists
-        if self.n_plots == 1:
-            axs = [axs]
-        if self.n_points == 1:
-            axs = [axs]
+        fig, axs = plt.subplots(self.n_points, self.n_plots,
+                                figsize=figsize, squeeze=False)
 
         for plotter, ax_row in zip(self.event_plotter_list, axs):
             major_label_set = False
@@ -43,6 +64,9 @@ def plot_all(self, figsize=None, same_scale=True):
         fig.tight_layout()
 
     def set_same_scale(self):
+        """
+        Uses minimum and maximum of each dataset to find global min/max
+        """
         for view in self.views:
 
             if not view.same_scale:
diff --git a/mcstasscript/instrument_diagnostics/view.py b/mcstasscript/instrument_diagnostics/view.py
index 3167150a..ca796731 100644
--- a/mcstasscript/instrument_diagnostics/view.py
+++ b/mcstasscript/instrument_diagnostics/view.py
@@ -1,5 +1,36 @@
 class View:
     def __init__(self, axis1, axis2=None, bins=100, same_scale=True, **kwargs):
+        """
+        Plot view on one or two axis to be generated from event data
+
+        The possible specifiers are defined in McStasEvenData and are
+        x : x position [m]
+        y : x position [m]
+        z : x position [m]
+        vx : x velocity [m/s]
+        vy : x velocity [m/s]
+        vz : x velocity [m/s]
+        speed : [m/s]
+        dx : divergence x [deg]
+        dy : divergence y [deg]
+        t : time [s]
+        l : wavelength [AA]
+        e : energy [meV]
+
+        Parameters:
+
+        axis1 : str
+            Specifier for first axis to be shown
+
+        axis2 : str (optional)
+            Specifier for second axis to be shown
+
+        bins : int or [int, int]
+            Number of bins for generation of histogram
+
+        same_scale : bool
+            Controls whether all displays of this view is on same ranges
+        """
         self.same_scale = same_scale
 
         self.axis1 = axis1
@@ -13,17 +44,26 @@ def __init__(self, axis1, axis2=None, bins=100, same_scale=True, **kwargs):
         self.plot_options = kwargs
 
     def set_axis1_limits(self, start, end):
+        """
+        Sets the axis1 limits
+        """
         if start > end:
             raise ValueError("Start point over end for this view.")
 
         self.axis1_limits = start, end
 
     def set_axis2_limits(self, start, end):
+        """
+        Sets the axis2 limits
+        """
         if start > end:
             raise ValueError("Start point over end for this view.")
 
         self.axis2_limits = start, end
 
     def clear_limits(self):
+        """
+        Clears all limits
+        """
         self.axis1_limits = None
         self.axis2_limits = None
\ No newline at end of file

From 22234acc1500c3c081ff5869de7567acaa203db6 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Mon, 31 Oct 2022 14:41:45 +0100
Subject: [PATCH 311/403] Update of diagnostics, common aspects of a
 diagnostics instrument has been collected in diagnostics_instrument, which
 will now only have code relevant to keeping a seperate copy of an instrument
 with common corrections such as removing use of PREVIOUS and updating
 target_index after insertion of new components.

The event recording system is now called beam_diagnostics and
inherits from diagnostics_instrument.
The intensity analysis is called intensity_diagnostics, and is
now accessed through the instrument diagram that is used to display
the component order. Use show_diagram(analysis=True).

The instrument diagram was updated as well, it now shows connections
for target_index and shares color with JUMP. Legend is kept up to date.
---
 mcstasscript/__init__.py                      |   2 +-
 .../beam_diagnostics.py                       | 116 ++++++++++++++
 .../instrument_diagnostics/diagnostics.py     | 147 +++++++++++++++---
 .../diagnostics_instrument.py                 |  68 ++++++++
 .../intensity_diagnostics.py                  | 107 +++++++++++++
 mcstasscript/instrument_diagram/canvas.py     |  85 ++++++++--
 .../generate_target_index.py                  |  52 +++++++
 .../instrument_diagram/make_diagram.py        |  17 +-
 mcstasscript/interface/instr.py               |  17 +-
 9 files changed, 577 insertions(+), 34 deletions(-)
 create mode 100644 mcstasscript/instrument_diagnostics/beam_diagnostics.py
 create mode 100644 mcstasscript/instrument_diagnostics/diagnostics_instrument.py
 create mode 100644 mcstasscript/instrument_diagnostics/intensity_diagnostics.py
 create mode 100644 mcstasscript/instrument_diagram/generate_target_index.py

diff --git a/mcstasscript/__init__.py b/mcstasscript/__init__.py
index 289c01b3..b49f2a5f 100644
--- a/mcstasscript/__init__.py
+++ b/mcstasscript/__init__.py
@@ -19,4 +19,4 @@
 
 from .tools.cryostat_builder import Cryostat
 
-from .instrument_diagnostics.diagnostics import Diagnostics
+from .instrument_diagnostics.beam_diagnostics import BeamDiagnostics as Diagnostics
diff --git a/mcstasscript/instrument_diagnostics/beam_diagnostics.py b/mcstasscript/instrument_diagnostics/beam_diagnostics.py
new file mode 100644
index 00000000..6ee2fc27
--- /dev/null
+++ b/mcstasscript/instrument_diagnostics/beam_diagnostics.py
@@ -0,0 +1,116 @@
+from mcstasscript.helper.mcstas_objects import Component
+from mcstasscript.instrument_diagnostics.diagnostics_instrument import DiagnosticsInstrument
+from mcstasscript.instrument_diagnostics.view import View
+from mcstasscript.instrument_diagnostics.plot_overview import PlotOverview
+from mcstasscript.instrument_diagnostics import event_plotter
+from mcstasscript.interface.functions import name_search
+
+def sanitise_comp_name(comp_name):
+    if isinstance(comp_name, Component):
+        comp_name = Component.name
+
+    if not isinstance(comp_name, str):
+        raise ValueError("comp_name should be a string or component object!")
+
+    return comp_name
+
+
+class BeamDiagnostics(DiagnosticsInstrument):
+    def __init__(self, instr):
+        super().__init__(instr)
+
+        # Set of Component names
+        self.points = set()
+        self.point_collect_rays = {}
+        self.monitor_names = {}
+
+        self.views = []
+
+        self.data = None
+
+        self.event_plotters = []
+
+    def add_point(self, comp_name, rays=50000):
+        comp_name = sanitise_comp_name(comp_name)
+        try:
+            self.instr.get_component(comp_name)
+        except KeyError:
+            raise KeyError("Component '" + comp_name + "' not found in instrument.")
+
+        self.points.add(comp_name)
+
+        if not isinstance(rays, int):
+            if not rays == "all":
+                raise ValueError("The ray keyword for a point should be all or an integer")
+
+        self.point_collect_rays[comp_name] = rays
+
+    def remove_point(self, comp_name):
+        comp_name = sanitise_comp_name(comp_name)
+        self.points.remove(comp_name)
+        del self.point_collect_rays[comp_name]
+
+    def add_view(self, axis1, axis2=None, bins=100, same_scale=True, **kwargs):
+        view = View(axis1=axis1, axis2=axis2, bins=bins, same_scale=same_scale, **kwargs)
+        self.views.append(view)
+
+    def clear_views(self):
+        self.views = []
+
+    def add_monitors(self):
+        comp_names = [x.name for x in self.instr.make_component_subset()]
+        self.monitor_names = {}
+        ordered_point_list = []
+        for name in comp_names:
+            if name in self.points:
+                ordered_point_list.append(name)
+
+        for name in ordered_point_list:
+            comp = self.instr.get_component(name)
+            file_name = self.add_monitor(comp)
+            self.monitor_names[name] = file_name
+
+    def add_monitor(self, before):
+        name = "Diag_before_" + before.name
+        mon = self.instr.add_component(name, "Monitor_nD", before=before)
+
+        rays = self.point_collect_rays[before.name]
+        options = f'"square boarders n x y z vx vy vz t, list {rays} neutrons"'
+        mon.set_parameters(restore_neutron=1,
+                           xwidth=100, yheight=100,
+                           options=options,
+                           filename='"' + name + ".diag" + '"')
+
+        mon.set_AT(before.AT_data, RELATIVE=before.AT_reference)
+        if before.ROTATED_specified:
+            mon.set_ROTATED(before.ROTATED_data, RELATIVE=before.ROTATED_reference)
+
+        return name
+
+    def run(self):
+        self.reset_instr()
+        self.remove_previous_use()
+
+        self.add_monitors()
+
+        self.correct_target_index()
+
+        self.data = self.instr.backengine()
+
+        self.read_data()
+
+    def read_data(self):
+
+        self.event_plotters = []
+        for comp in self.monitor_names:
+            name = self.monitor_names[comp]
+            event_data = name_search(name, self.data)
+            plotter = event_plotter.EventPlotter(name, event_data)
+
+            self.event_plotters.append(plotter)
+
+    def plot(self):
+
+        overview = PlotOverview(self.event_plotters, self.views)
+        overview.plot_all()
+
diff --git a/mcstasscript/instrument_diagnostics/diagnostics.py b/mcstasscript/instrument_diagnostics/diagnostics.py
index 437672b8..f84d2494 100644
--- a/mcstasscript/instrument_diagnostics/diagnostics.py
+++ b/mcstasscript/instrument_diagnostics/diagnostics.py
@@ -3,10 +3,17 @@
 import copy
 
 from mcstasscript.helper.mcstas_objects import Component
-from mcstasscript.instrument_diagnostics import event_plotter
-from mcstasscript.interface.functions import name_search
-from mcstasscript.instrument_diagnostics.view import View
-from mcstasscript.instrument_diagnostics.plot_overview import PlotOverview
+from mcstasscript.instrument_diagnostics.beam_diagnostics import BeamDiagnostics
+from mcstasscript.instrument_diagnostics.intensity_diagnostics import IntensityDiagnostics
+
+
+class Diagnostics:
+    def __init__(self, instr):
+        self.diag_beam = BeamDiagnostics(instr)
+        self.diag_intensity = IntensityDiagnostics(instr)
+
+
+
 
 def sanitise_comp_name(comp_name):
     if isinstance(comp_name, Component):
@@ -17,7 +24,69 @@ def sanitise_comp_name(comp_name):
 
     return comp_name
 
-class Diagnostics:
+class DiagnosticsInstrument:
+    def __init__(self, instr):
+        self.original_instr = instr
+        self.instr = None
+        self.reset_instr()
+
+        self.component_list = self.instr.make_component_subset()
+
+    def reset_instr(self):
+        self.instr = copy.deepcopy(self.original_instr)
+
+    def remove_previous_use(self):
+        self.component_list = self.instr.make_component_subset()
+        previous_component = None
+        for comp in self.component_list:
+            if comp.AT_reference == "PREVIOUS":
+                comp.set_AT_RELATIVE(previous_component)
+
+            if comp.ROTATED_specified:
+                if comp.ROTATED_reference == "PREVIOUS":
+                    comp.set_ROTATED_RELATIVE(previous_component)
+
+            previous_component = comp
+
+    def correct_target_index(self):
+        """
+        Need to correct target_index based on original instrument
+        """
+        original_component_list = self.original_instr.make_component_subset()
+        original_comp_names = [x.name for x in original_component_list]
+
+        modified_component_list = self.instr.make_component_subset()
+        modified_comp_names = [x.name for x in modified_component_list]
+
+        for comp in original_component_list:
+            # Find components that use target index
+
+            if not hasattr(comp, "target_index"):
+                # Component doesnt have the target_index setting
+                continue
+            if comp.target_index is None:
+                # A value has not been specified for target_index setting
+                continue
+            if comp.target_index == 0:
+                # target_index is disabled
+                continue
+
+            # Only here if target_index is used, correct it in modified instr
+
+            # Find original index and the name of the original target
+            original_comp_index = original_comp_names.index(comp.name)
+            comp_target_name = original_comp_names[original_comp_index + comp.target_index]
+
+            # Find index of the original and target in modified instrument
+            modified_comp_index = modified_comp_names.index(comp.name)
+            index_of_new_target = modified_comp_names.index(comp_target_name)
+            new_target_index = index_of_new_target - modified_comp_index
+
+            # Apply the new target_index to the component in the modified instrument
+            modified_comp = self.instr.get_component(comp.name)
+            modified_comp.target_index = new_target_index
+
+class DiagnosticsOld:
     def __init__(self, instr):
         self.original_instr = instr
         self.instr = None
@@ -104,6 +173,8 @@ def run(self):
 
         self.add_monitors()
 
+        self.correct_target_index()
+
         self.data = self.instr.backengine()
 
         self.read_data()
@@ -123,6 +194,57 @@ def plot(self):
         overview = PlotOverview(self.event_plotters, self.views)
         overview.plot_all()
 
+    def remove_previous_use(self):
+        self.I_component_list = self.instr.make_component_subset()
+        previous_component = None
+        for comp in self.I_component_list:
+            if comp.AT_reference == "PREVIOUS":
+                comp.set_AT_RELATIVE(previous_component)
+
+            if comp.ROTATED_specified:
+                if comp.ROTATED_reference == "PREVIOUS":
+                    comp.set_ROTATED_RELATIVE(previous_component)
+
+            previous_component = comp
+
+    def correct_target_index(self):
+        """
+        Need to correct target_index based on original instrument
+        """
+        original_component_list = self.original_instr.make_component_subset()
+        original_comp_names = [x.name for x in original_component_list]
+
+        modified_component_list = self.instr.make_component_subset()
+        modified_comp_names = [x.name for x in modified_component_list]
+
+        for comp in original_component_list:
+            # Find components that use target index
+
+            if not hasattr(comp, "target_index"):
+                # Component doesnt have the target_index setting
+                continue
+            if comp.target_index is None:
+                # A value has not been specified for target_index setting
+                continue
+            if comp.target_index == 0:
+                # target_index is disabled
+                continue
+
+            # Only here if target_index is used, correct it in modified instr
+
+            # Find original index and the name of the original target
+            original_comp_index = original_comp_names.index(comp.name)
+            comp_target_name = original_comp_names[original_comp_index + comp.target_index]
+
+            # Find index of the original and target in modified instrument
+            modified_comp_index = modified_comp_names.index(comp.name)
+            index_of_new_target = modified_comp_names.index(comp_target_name)
+            new_target_index = index_of_new_target - modified_comp_index
+
+            # Apply the new target_index to the component in the modified instrument
+            modified_comp = self.instr.get_component(comp.name)
+            modified_comp.target_index = new_target_index
+
     def add_I_monitor(self, before):
         name = "I_before_" + before.name
         mon = self.instr.add_component(name, "Monitor_nD", before=before)
@@ -148,20 +270,9 @@ def intensity_analysis(self):#, start=None, end=None):
             mon_name = self.add_I_monitor(comp)
             self.I_monitors.append((mon_name, comp.name))
 
-        self.I_data = self.instr.backengine()
-
-    def remove_previous_use(self):
-        self.I_component_list = self.instr.make_component_subset()
-        previous_component = None
-        for comp in self.I_component_list:
-            if comp.AT_reference == "PREVIOUS":
-                comp.set_AT_RELATIVE(previous_component)
-
-            if comp.ROTATED_specified:
-                if comp.ROTATED_reference == "PREVIOUS":
-                    comp.set_ROTATED_RELATIVE(previous_component)
+        self.correct_target_index()
 
-            previous_component = comp
+        self.I_data = self.instr.backengine()
 
     def plot_I_analysis(self, figsize=None):
 
diff --git a/mcstasscript/instrument_diagnostics/diagnostics_instrument.py b/mcstasscript/instrument_diagnostics/diagnostics_instrument.py
new file mode 100644
index 00000000..e6e4da58
--- /dev/null
+++ b/mcstasscript/instrument_diagnostics/diagnostics_instrument.py
@@ -0,0 +1,68 @@
+import copy
+
+class DiagnosticsInstrument:
+    def __init__(self, instr):
+        self.original_instr = instr
+        self.instr = None
+        self.instr_settings = {}
+        self.reset_instr()
+
+        self.component_list = self.instr.make_component_subset()
+
+    def reset_instr(self):
+        self.instr = copy.deepcopy(self.original_instr)
+        self.instr.settings(**self.instr_settings)
+
+    def settings(self, **kwargs):
+        self.instr_settings.update(kwargs)
+
+    def remove_previous_use(self):
+        self.component_list = self.instr.make_component_subset()
+        previous_component = None
+        for comp in self.component_list:
+            if comp.AT_reference == "PREVIOUS":
+                comp.set_AT_RELATIVE(previous_component)
+
+            if comp.ROTATED_specified:
+                if comp.ROTATED_reference == "PREVIOUS":
+                    comp.set_ROTATED_RELATIVE(previous_component)
+
+            previous_component = comp
+
+    def correct_target_index(self):
+        """
+        Need to correct target_index based on original instrument
+        """
+        original_component_list = self.original_instr.make_component_subset()
+        original_comp_names = [x.name for x in original_component_list]
+
+        modified_component_list = self.instr.make_component_subset()
+        modified_comp_names = [x.name for x in modified_component_list]
+
+        for comp in original_component_list:
+            # Find components that use target index
+
+            if not hasattr(comp, "target_index"):
+                # Component doesnt have the target_index setting
+                continue
+            if comp.target_index is None:
+                # A value has not been specified for target_index setting
+                continue
+            if comp.target_index == 0:
+                # target_index is disabled
+                continue
+
+            # Only here if target_index is used, correct it in modified instr
+
+            # Find original index and the name of the original target
+            original_comp_index = original_comp_names.index(comp.name)
+            comp_target_name = original_comp_names[original_comp_index + comp.target_index]
+
+            # Find index of the original and target in modified instrument
+            modified_comp_index = modified_comp_names.index(comp.name)
+            index_of_new_target = modified_comp_names.index(comp_target_name)
+            new_target_index = index_of_new_target - modified_comp_index
+
+            # Apply the new target_index to the component in the modified instrument
+            modified_comp = self.instr.get_component(comp.name)
+            modified_comp.target_index = new_target_index
\ No newline at end of file
diff --git a/mcstasscript/instrument_diagnostics/intensity_diagnostics.py b/mcstasscript/instrument_diagnostics/intensity_diagnostics.py
new file mode 100644
index 00000000..d3c12547
--- /dev/null
+++ b/mcstasscript/instrument_diagnostics/intensity_diagnostics.py
@@ -0,0 +1,107 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+from mcstasscript.instrument_diagnostics.diagnostics_instrument import DiagnosticsInstrument
+from mcstasscript.interface.functions import name_search
+
+class IntensityDiagnostics(DiagnosticsInstrument):
+    def __init__(self, instr):
+        super().__init__(instr)
+
+        self.data = None
+        self.monitors = None
+
+    def add_monitor(self, before):
+        name = "I_before_" + before.name
+        mon = self.instr.add_component(name, "Monitor_nD", before=before)
+
+        options = f'"square boarders intensity"'
+        mon.set_parameters(restore_neutron=1,
+                           xwidth=100, yheight=100,
+                           options=options,
+                           filename='"' + name + ".diag" + '"')
+
+        mon.set_AT(before.AT_data, RELATIVE=before.AT_reference)
+        if before.ROTATED_specified:
+            mon.set_ROTATED(before.ROTATED_data, RELATIVE=before.ROTATED_reference)
+
+        return name
+
+    def run(self):#, start=None, end=None):
+        self.reset_instr()
+        self.remove_previous_use()
+
+        self.monitors = []
+        for comp in self.component_list[1:]:
+            mon_name = self.add_monitor(comp)
+            self.monitors.append((mon_name, comp.name))
+
+        self.correct_target_index()
+
+        self.data = self.instr.backengine()
+
+    def plot(self, figsize=None, ax=None, show_comp_names=True,
+             y_tick_positions=None, ylimits=None):
+
+        if figsize is None:
+            figsize = (8, len(self.component_list)/5 + 1)
+
+        intensities = []
+        ray_counts = []
+        component_names = []
+        indicies = []
+
+        index = 0
+        for I_monitor_name, component_name in self.monitors:
+            mon_data = name_search(I_monitor_name, self.data)
+            values = mon_data.metadata.info["values"].split()
+            # values contain strings of: Intensity, Error, Ncount
+            intensities.append(float(values[0]))
+            ray_counts.append(float(values[2]))
+            component_names.append(component_name)
+            indicies.append(index)
+            index += 1
+
+        # Extend with the last one
+        intensities.append(intensities[-1])
+        ray_counts.append(ray_counts[-1])
+        indicies.append(index)
+        component_names = [self.component_list[0].name] + component_names
+
+        if not show_comp_names:
+            component_names = [""] * len(component_names)
+
+        if ax is None:
+            fig, ax = plt.subplots(1, 1, figsize=figsize)
+
+        intensities.reverse()
+        ray_counts.reverse()
+        component_names.reverse()
+
+        if y_tick_positions is None:
+            y_positions = indicies
+        else:
+            y_tick_positions.reverse()
+            y_positions = y_tick_positions
+
+        ax.step(intensities, y_positions, where="post", color="k", zorder=3.5)
+        ax.set_yticks(y_positions)
+        ax.set_yticklabels(component_names, fontsize=18)
+        ax.set_xlabel("Intensity [n/s]", fontsize=18, color="k")
+        ax.set_xscale("log", nonpositive='clip')
+        ax.xaxis.set_tick_params(labelsize=16)
+
+        if ylimits is None:
+            ax.set_ylim([-0.5, index + 0.5])
+        else:
+            ax.set_ylim(ylimits)
+
+        ax.grid(True)
+
+        ax2 = ax.twiny()
+        ax2.step(ray_counts, y_positions, where="post", color="g", linestyle="--", zorder=3.6)
+        ax2.set_xlabel("Ray count", fontsize=18, color="g")
+        ax2.set_xscale("log", nonpositive='clip')
+        ax2.xaxis.set_tick_params(labelsize=16)
+        xlim = ax2.get_xlim()
+        ax2.set_xlim([xlim[0]*0.9, xlim[1]*1.1])
diff --git a/mcstasscript/instrument_diagram/canvas.py b/mcstasscript/instrument_diagram/canvas.py
index f8b9f3fe..074d8acc 100644
--- a/mcstasscript/instrument_diagram/canvas.py
+++ b/mcstasscript/instrument_diagram/canvas.py
@@ -7,7 +7,8 @@
 from mcstasscript.instrument_diagram.component_description import component_description
 
 class DiagramCanvas:
-    def __init__(self, left_side_arrows, component_boxes, right_side_arrows, component_categories, colors):
+    def __init__(self, left_side_arrows, component_boxes, right_side_arrows,
+                 component_categories, colors, intensity_diagnostics=None):
         """
         Creates diagram of instrument file with given boxes and arrows
 
@@ -21,6 +22,10 @@ def __init__(self, left_side_arrows, component_boxes, right_side_arrows, compone
         using this diagram in matplotlib widget mode, it is possible get more
         information on each component by hovering the mouse of the beginning
         the component box.
+
+        If intensity_diagnostics is given with an IntensityDiagnostics object,
+        a graph of the intensity and n rays throughout the instrument is
+        generated instead of the right side arrows.
         """
         self.left_side_arrows = left_side_arrows
         self.component_boxes = component_boxes
@@ -29,6 +34,12 @@ def __init__(self, left_side_arrows, component_boxes, right_side_arrows, compone
         self.component_categories = component_categories
         self.colors = colors
 
+        if intensity_diagnostics is None:
+            self.intensity_analysis_mode = False
+        else:
+            self.intensity_analysis_mode = True
+            self.intensity_diagnostics = intensity_diagnostics
+
         # Identify cases where multiple written input goes to or from same box
         arrow_connections = {x: [] for x in self.component_boxes}
         for arrow in self.right_side_arrows:
@@ -88,7 +99,10 @@ def __init__(self, left_side_arrows, component_boxes, right_side_arrows, compone
         self.FIG_LEFT_MARGIN = self.FIG_WIDTH_PER_LANE * 0.5
         self.FIG_RIGHT_MARGIN = self.FIG_WIDTH_PER_LANE * 0.5
         self.FIG_LEGEND_HEADLINE = 0.2
-        self.FIG_RIGHT_SIDE_MINIMUM_SPACE = 3
+        if self.intensity_analysis_mode:
+            self.FIG_RIGHT_SIDE_MINIMUM_SPACE = 5
+        else:
+            self.FIG_RIGHT_SIDE_MINIMUM_SPACE = 3
 
         self.graph_height = self.FIG_HEIGHT_PER_BOX * len(component_boxes)
 
@@ -218,13 +232,19 @@ def make_legend(self):
 
         # Always show AT and RELATIVE, but the others only when present
         show_GROUP = False
-        show_JUMP = False
         show_Union = False
+        show_JUMP_target_index = False
+        any_JUMP = False
+        any_target_index = False
         for arrow in self.all_arrows:
             if arrow.kind == "GROUP":
                 show_GROUP = True
             if arrow.kind == "JUMP":
-                show_JUMP = True
+                show_JUMP_target_index = True
+                any_JUMP = True
+            if arrow.kind == "target_index":
+                show_JUMP_target_index = True
+                any_target_index = True
             if arrow.kind == "Union":
                 show_Union = True
 
@@ -258,13 +278,20 @@ def make_legend(self):
             ax.text(START_WIDTH + TEXT_WIDTH_INDENT, AT_HEIGHT + current_displacement + TEXT_DISPLACEMENT,
                     "Union", va="center", weight="semibold")
 
-        if show_JUMP:
+        if show_JUMP_target_index:
+            if any_JUMP and any_target_index:
+                legend_text = "JUMP / target_index"
+            elif any_JUMP:
+                legend_text = "JUMP"
+            elif any_target_index:
+                legend_text = "target_index"
+
             current_displacement -= DISPLACEMENT
             ax.arrow(x=START_WIDTH, y=AT_HEIGHT+current_displacement, dx=LINE_LENGTH, dy=0,
                      color=self.colors["JUMP"], length_includes_head=True, width=arrow_width,
                      head_width=5.0 * arrow_width, head_length=2.5 * arrow_width)
             ax.text(START_WIDTH + TEXT_WIDTH_INDENT, AT_HEIGHT+current_displacement+TEXT_DISPLACEMENT,
-                    "JUMP", va="center", weight="semibold")
+                    legend_text, va="center", weight="semibold")
 
         if show_GROUP:
             current_displacement -= DISPLACEMENT
@@ -336,9 +363,49 @@ def plot(self):
             # Plot arrows on left side
             arrow.plot_left_side(ax)
 
-        for arrow in self.right_side_arrows:
-            # Plot arrows on right side
-            arrow.plot_right_side(ax, self.text_end_graph)
+        if not self.intensity_analysis_mode:
+            for arrow in self.right_side_arrows:
+                # Plot arrows on right side
+                arrow.plot_right_side(ax, self.text_end_graph)
+        else:
+            # Make insert with intensity and ray count graph
+
+            # Find x coordinate of insert
+            box_ends = [b.get_text_end() for b in self.component_boxes]
+            latest_box_end = max(box_ends)
+            remaining_space = 1.0 - latest_box_end
+            axes_start_x = latest_box_end + 0.05 * remaining_space  # 0.1 works well if no names shown
+
+            # Get y position for all boxes, but skip ABSOLUTE box
+            y_positions = [box.position_y for box in self.component_boxes[1:]]
+            y_spacing = y_positions[0] - y_positions[1]
+            upper_y_lim = y_positions[0] + 0.5 * y_spacing
+            lower_y_lim = y_positions[-1] - 0.5 * y_spacing
+
+            # Insert is done in figure coordinate system, need mother ax dimensions
+            ax_pos = ax.get_position()
+
+            # Find figure coordinates of the corners of the desired inset
+            inset_y_bottom = ax_pos.y0 + lower_y_lim*(ax_pos.y1 - ax_pos.y0)
+            inset_y_top = ax_pos.y0 + upper_y_lim*(ax_pos.y1 - ax_pos.y0)
+
+            inset_x_start = ax_pos.x0 + axes_start_x*(ax_pos.x1 - ax_pos.x0)
+            inset_x_end = ax_pos.x1
+
+            # Create inset
+            inset_ax = fig.add_axes((inset_x_start, inset_y_bottom,
+                                     inset_x_end-inset_x_start,
+                                     inset_y_top-inset_y_bottom))
+
+            # Ensure the new axis is plotted under the old one for annotations to show up
+            ax.set_zorder(4)
+
+            # Plot graph, convey tick positions and ylimits to match main diagram
+            self.intensity_diagnostics.run()
+            self.intensity_diagnostics.plot(ax=inset_ax, y_tick_positions=y_positions,
+                                            ylimits=[lower_y_lim, upper_y_lim],
+                                            show_comp_names=False)
+
 
         # Create anotation box that will be shown when hovering the mouse over a box
         annot = ax.annotate("", xy=(0, 0), xytext=(20, 10), textcoords="offset points",
diff --git a/mcstasscript/instrument_diagram/generate_target_index.py b/mcstasscript/instrument_diagram/generate_target_index.py
new file mode 100644
index 00000000..c7da57b6
--- /dev/null
+++ b/mcstasscript/instrument_diagram/generate_target_index.py
@@ -0,0 +1,52 @@
+from mcstasscript.instrument_diagram.connections import ConnectionList
+from mcstasscript.instrument_diagram.arrow import Arrow
+
+
+def generate_target_index_arrows(components, component_box_dict, box_names, color=None):
+    """
+    Generate Arrow objects related to the target_index
+    """
+    connections = ConnectionList()
+    component_names = [x.name for x in components]
+
+    for component in components:
+        if not hasattr(component, "target_index"):
+            # Component doesnt have the target_index setting
+            continue
+        if component.target_index is None:
+            # A value has not been specified for target_index setting
+            continue
+        if component.target_index == 0:
+            # target_index is disabled
+            continue
+
+        this_component_index = component_names.index(component.name)
+        target_component_index = this_component_index + component.target_index
+        target_component_reference = component_names[target_component_index]
+
+        if target_component_reference not in component_box_dict:
+            raise ValueError("target_index reference: "
+                             + str(target_component_reference)
+                             + " not found.")
+
+        origin = component_box_dict[component.name]
+        connections.add(origin, component_box_dict[target_component_reference])
+
+    connections.distribute_lane_numbers(box_names=box_names)
+
+    arrows = []
+    for connection in connections.get_connections():
+        origin = connection.origin
+        target = connection.target
+        lane = connection.lane_number + 3  # Make room for target_index
+
+        arrow = Arrow(origin, target, lane=lane, kind="target_index", description="target_index")
+        arrow.set_sub_lane(2)
+        if color is None:
+            arrow.color = "black"
+        else:
+            arrow.color = color
+
+        arrows.append(arrow)
+
+    return arrows
diff --git a/mcstasscript/instrument_diagram/make_diagram.py b/mcstasscript/instrument_diagram/make_diagram.py
index 712a75a5..60eac00b 100644
--- a/mcstasscript/instrument_diagram/make_diagram.py
+++ b/mcstasscript/instrument_diagram/make_diagram.py
@@ -4,12 +4,14 @@
 from mcstasscript.instrument_diagram.generate_AT import generate_AT_arrows
 from mcstasscript.instrument_diagram.generate_ROTATED import generate_ROTATED_arrows
 from mcstasscript.instrument_diagram.generate_JUMP import generate_JUMP_arrows
+from mcstasscript.instrument_diagram.generate_target_index import generate_target_index_arrows
 from mcstasscript.instrument_diagram.generate_GROUP import generate_GROUP_arrows
 from mcstasscript.instrument_diagram.generate_Union import generate_Union_arrows
 from mcstasscript.instrument_diagram.canvas import DiagramCanvas
+from mcstasscript.instrument_diagnostics.intensity_diagnostics import IntensityDiagnostics
 
 
-def instrument_diagram(instrument):
+def instrument_diagram(instrument, analysis=False):
     """
     Plots diagram of components in instrument with RELATIVE connections
 
@@ -51,13 +53,22 @@ def instrument_diagram(instrument):
 
     # Arrow for the right side of the diagram
     JUMP_arrows = generate_JUMP_arrows(components, component_box_dict, box_names, color=color_choices["JUMP"])
+    target_index_arrows = generate_target_index_arrows(components, component_box_dict,
+                                                       box_names, color=color_choices["JUMP"])
     GROUP_arrows = generate_GROUP_arrows(components, component_box_dict, box_names, color=color_choices["GROUP"])
     Union_arrows = generate_Union_arrows(components, component_box_dict, box_names,
                                          component_categories, color=color_choices["Union"])
 
+    intensity_diagnostics=None
+    if analysis:
+        intensity_diagnostics = IntensityDiagnostics(instrument)
+        intensity_diagnostics.settings(suppress_output=True)
+
     # Create canvas
-    canvas = DiagramCanvas(AT_arrows + ROTATED_arrows, component_boxes, JUMP_arrows + GROUP_arrows + Union_arrows,
-                           component_categories=component_categories, colors=color_choices)
+    canvas = DiagramCanvas(AT_arrows + ROTATED_arrows, component_boxes,
+                           JUMP_arrows + target_index_arrows + GROUP_arrows + Union_arrows,
+                           component_categories=component_categories, colors=color_choices,
+                           intensity_diagnostics=intensity_diagnostics)
 
     # Plot diagram
     canvas.plot()
\ No newline at end of file
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index fe01a949..78204f61 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -27,6 +27,7 @@
 from mcstasscript.helper.exceptions import McStasError
 from mcstasscript.helper.beam_dump_database import BeamDumpDatabase
 from mcstasscript.instrument_diagram.make_diagram import instrument_diagram
+from mcstasscript.instrument_diagnostics.intensity_diagnostics import IntensityDiagnostics
 
 
 class McCode_instr(BaseCalculator):
@@ -2519,22 +2520,32 @@ def show_instrument(self, format="webgl", width=800, height=450, new_tab=False):
 
         return IFrame(src=html_path, width=width, height=height)
 
-    def show_diagram(self):
+    def show_diagram(self, analysis=False):
         """
         Shows diagram of component connections in insrument
 
         Shows a diagram with all components as text fields and arrows between
         them showing the AT RELATIVE and ROTATED RELATIVE connections. Spatial
         connections are shown in blue, and rotational in red. ROTATED
-        connections are only shown when they are specified.
+        connections are only shown when they are specified. To see the intensity
+        and number of rays over the course of the instrument, use analysis=True.
+
+        parameters:
+        analysis : bool
+            If True, a plot of intensity and ncount over the instrument is included
         """
         if self.has_errors():
             print("The instrument has some error, this diagram is still "
                   "shown as it may help find the bug.")
 
-        instrument_diagram(self)
+        instrument_diagram(self, analysis=analysis)
         self.check_for_errors()
 
+    def show_analysis(self):
+        beam_diag = IntensityDiagnostics(self)
+        beam_diag.run()
+        beam_diag.plot()
+
     def saveH5(self, filename: str, openpmd: bool = True):
         """
         Not relevant, but required from BaseCalculator, will be removed

From d90e55ab1f2ddc803805ad314714dd50b385e7c0 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 1 Nov 2022 16:12:44 +0100
Subject: [PATCH 312/403] Fixed bug with GROUP label in diagrams, the last
 GROUP name was displayed for all groups.

Added the ability for diagrams to show analysis of any parameter
that can be recorded with monitor_nD throughout the instrument,
for example the time or wavelength. This uses the primary plotting
tool, and as such can be controlled, though the keyword arguments
have not been transferred through yet.
---
 .../instrument_diagnostics/diagnostics.py     | 328 ------------------
 .../diagnostics_instrument.py                 |   1 +
 .../intensity_diagnostics.py                  | 186 +++++++++-
 mcstasscript/instrument_diagram/canvas.py     |  19 +-
 .../instrument_diagram/connections.py         |  20 +-
 .../instrument_diagram/generate_GROUP.py      |   3 +-
 .../instrument_diagram/make_diagram.py        |   5 +-
 mcstasscript/interface/instr.py               |   8 +-
 8 files changed, 220 insertions(+), 350 deletions(-)
 delete mode 100644 mcstasscript/instrument_diagnostics/diagnostics.py

diff --git a/mcstasscript/instrument_diagnostics/diagnostics.py b/mcstasscript/instrument_diagnostics/diagnostics.py
deleted file mode 100644
index f84d2494..00000000
--- a/mcstasscript/instrument_diagnostics/diagnostics.py
+++ /dev/null
@@ -1,328 +0,0 @@
-import matplotlib.pyplot as plt
-import numpy as np
-import copy
-
-from mcstasscript.helper.mcstas_objects import Component
-from mcstasscript.instrument_diagnostics.beam_diagnostics import BeamDiagnostics
-from mcstasscript.instrument_diagnostics.intensity_diagnostics import IntensityDiagnostics
-
-
-class Diagnostics:
-    def __init__(self, instr):
-        self.diag_beam = BeamDiagnostics(instr)
-        self.diag_intensity = IntensityDiagnostics(instr)
-
-
-
-
-def sanitise_comp_name(comp_name):
-    if isinstance(comp_name, Component):
-        comp_name = Component.name
-
-    if not isinstance(comp_name, str):
-        raise ValueError("comp_name should be a string or component object!")
-
-    return comp_name
-
-class DiagnosticsInstrument:
-    def __init__(self, instr):
-        self.original_instr = instr
-        self.instr = None
-        self.reset_instr()
-
-        self.component_list = self.instr.make_component_subset()
-
-    def reset_instr(self):
-        self.instr = copy.deepcopy(self.original_instr)
-
-    def remove_previous_use(self):
-        self.component_list = self.instr.make_component_subset()
-        previous_component = None
-        for comp in self.component_list:
-            if comp.AT_reference == "PREVIOUS":
-                comp.set_AT_RELATIVE(previous_component)
-
-            if comp.ROTATED_specified:
-                if comp.ROTATED_reference == "PREVIOUS":
-                    comp.set_ROTATED_RELATIVE(previous_component)
-
-            previous_component = comp
-
-    def correct_target_index(self):
-        """
-        Need to correct target_index based on original instrument
-        """
-        original_component_list = self.original_instr.make_component_subset()
-        original_comp_names = [x.name for x in original_component_list]
-
-        modified_component_list = self.instr.make_component_subset()
-        modified_comp_names = [x.name for x in modified_component_list]
-
-        for comp in original_component_list:
-            # Find components that use target index
-
-            if not hasattr(comp, "target_index"):
-                # Component doesnt have the target_index setting
-                continue
-            if comp.target_index is None:
-                # A value has not been specified for target_index setting
-                continue
-            if comp.target_index == 0:
-                # target_index is disabled
-                continue
-
-            # Only here if target_index is used, correct it in modified instr
-
-            # Find original index and the name of the original target
-            original_comp_index = original_comp_names.index(comp.name)
-            comp_target_name = original_comp_names[original_comp_index + comp.target_index]
-
-            # Find index of the original and target in modified instrument
-            modified_comp_index = modified_comp_names.index(comp.name)
-            index_of_new_target = modified_comp_names.index(comp_target_name)
-            new_target_index = index_of_new_target - modified_comp_index
-
-            # Apply the new target_index to the component in the modified instrument
-            modified_comp = self.instr.get_component(comp.name)
-            modified_comp.target_index = new_target_index
-
-class DiagnosticsOld:
-    def __init__(self, instr):
-        self.original_instr = instr
-        self.instr = None
-        self.reset_instr()
-
-        # Set of Component names
-        self.points = set()
-        self.point_collect_rays = {}
-        self.monitor_names = {}
-
-        self.views = []
-
-        self.data = None
-
-        self.event_plotters = []
-
-        # Analysis
-        self.I_data = None
-        self.I_monitors = None
-
-    def reset_instr(self):
-        self.instr = copy.deepcopy(self.original_instr)
-
-    def add_point(self, comp_name, rays=50000):
-        comp_name = sanitise_comp_name(comp_name)
-        try:
-            self.instr.get_component(comp_name)
-        except KeyError:
-            raise KeyError("Component '" + comp_name + "' not found in instrument.")
-
-        self.points.add(comp_name)
-
-        if not isinstance(rays, int):
-            if not rays == "all":
-                raise ValueError("The ray keyword for a point should be all or an integer")
-
-        self.point_collect_rays[comp_name] = rays
-
-    def remove_point(self, comp_name):
-        comp_name = sanitise_comp_name(comp_name)
-        self.points.remove(comp_name)
-        self.point_collect_rays.remove(comp_name)
-
-    def add_view(self, axis1, axis2=None, bins=100, same_scale=True, **kwargs):
-        view = View(axis1=axis1, axis2=axis2, bins=bins, same_scale=same_scale, **kwargs)
-        self.views.append(view)
-
-    def clear_views(self):
-        self.views = []
-
-    def add_monitors(self):
-        comp_names = [x.name for x in self.instr.make_component_subset()]
-        self.monitor_names = {}
-        ordered_point_list = []
-        for name in comp_names:
-            if name in self.points:
-                ordered_point_list.append(name)
-
-        for name in ordered_point_list:
-            comp = self.instr.get_component(name)
-            file_name = self.add_monitor(comp)
-            self.monitor_names[name] = file_name
-
-    def add_monitor(self, before):
-        name = "Diag_before_" + before.name
-        mon = self.instr.add_component(name, "Monitor_nD", before=before)
-
-        rays = self.point_collect_rays[before.name]
-        options = f'"square boarders n x y z vx vy vz t, list {rays} neutrons"'
-        mon.set_parameters(restore_neutron=1,
-                           xwidth=100, yheight=100,
-                           options=options,
-                           filename='"' + name + ".diag" + '"')
-
-        mon.set_AT(before.AT_data, RELATIVE=before.AT_reference)
-        if before.ROTATED_specified:
-            mon.set_ROTATED(before.ROTATED_data, RELATIVE=before.ROTATED_reference)
-
-        return name
-
-    def run(self):
-        self.reset_instr()
-        self.remove_previous_use()
-
-        self.add_monitors()
-
-        self.correct_target_index()
-
-        self.data = self.instr.backengine()
-
-        self.read_data()
-
-    def read_data(self):
-
-        self.event_plotters = []
-        for comp in self.monitor_names:
-            name = self.monitor_names[comp]
-            event_data = name_search(name, self.data)
-            plotter = event_plotter.EventPlotter(name, event_data)
-
-            self.event_plotters.append(plotter)
-
-    def plot(self):
-
-        overview = PlotOverview(self.event_plotters, self.views)
-        overview.plot_all()
-
-    def remove_previous_use(self):
-        self.I_component_list = self.instr.make_component_subset()
-        previous_component = None
-        for comp in self.I_component_list:
-            if comp.AT_reference == "PREVIOUS":
-                comp.set_AT_RELATIVE(previous_component)
-
-            if comp.ROTATED_specified:
-                if comp.ROTATED_reference == "PREVIOUS":
-                    comp.set_ROTATED_RELATIVE(previous_component)
-
-            previous_component = comp
-
-    def correct_target_index(self):
-        """
-        Need to correct target_index based on original instrument
-        """
-        original_component_list = self.original_instr.make_component_subset()
-        original_comp_names = [x.name for x in original_component_list]
-
-        modified_component_list = self.instr.make_component_subset()
-        modified_comp_names = [x.name for x in modified_component_list]
-
-        for comp in original_component_list:
-            # Find components that use target index
-
-            if not hasattr(comp, "target_index"):
-                # Component doesnt have the target_index setting
-                continue
-            if comp.target_index is None:
-                # A value has not been specified for target_index setting
-                continue
-            if comp.target_index == 0:
-                # target_index is disabled
-                continue
-
-            # Only here if target_index is used, correct it in modified instr
-
-            # Find original index and the name of the original target
-            original_comp_index = original_comp_names.index(comp.name)
-            comp_target_name = original_comp_names[original_comp_index + comp.target_index]
-
-            # Find index of the original and target in modified instrument
-            modified_comp_index = modified_comp_names.index(comp.name)
-            index_of_new_target = modified_comp_names.index(comp_target_name)
-            new_target_index = index_of_new_target - modified_comp_index
-
-            # Apply the new target_index to the component in the modified instrument
-            modified_comp = self.instr.get_component(comp.name)
-            modified_comp.target_index = new_target_index
-
-    def add_I_monitor(self, before):
-        name = "I_before_" + before.name
-        mon = self.instr.add_component(name, "Monitor_nD", before=before)
-
-        options = f'"square boarders intensity"'
-        mon.set_parameters(restore_neutron=1,
-                           xwidth=100, yheight=100,
-                           options=options,
-                           filename='"' + name + ".diag" + '"')
-
-        mon.set_AT(before.AT_data, RELATIVE=before.AT_reference)
-        if before.ROTATED_specified:
-            mon.set_ROTATED(before.ROTATED_data, RELATIVE=before.ROTATED_reference)
-
-        return name
-
-    def intensity_analysis(self):#, start=None, end=None):
-        self.reset_instr()
-        self.remove_previous_use()
-
-        self.I_monitors = []
-        for comp in self.I_component_list[1:]:
-            mon_name = self.add_I_monitor(comp)
-            self.I_monitors.append((mon_name, comp.name))
-
-        self.correct_target_index()
-
-        self.I_data = self.instr.backengine()
-
-    def plot_I_analysis(self, figsize=None):
-
-        if figsize is None:
-            figsize = (8, len(self.I_component_list)/5 + 1)
-
-        intensities = []
-        ray_counts = []
-        component_names = []
-        indicies = []
-
-        index = 0
-        for I_monitor_name, component_name in self.I_monitors:
-            mon_data = name_search(I_monitor_name, self.I_data)
-            values = mon_data.metadata.info["values"].split()
-            # values contain strings of: Intensity, Error, Ncount
-            intensities.append(float(values[0]))
-            ray_counts.append(float(values[2]))
-            component_names.append(component_name)
-            indicies.append(index)
-            index += 1
-
-        # Extend with the last one
-        intensities.append(intensities[-1])
-        ray_counts.append(ray_counts[-1])
-        indicies.append(index)
-        component_names = [self.I_component_list[0].name] + component_names
-
-        fig, ax = plt.subplots(1, 1, figsize=figsize)
-
-        intensities.reverse()
-        ray_counts.reverse()
-        component_names.reverse()
-
-        ax.step(intensities, indicies, where="post", color="k")
-        ax.set_yticks(indicies)
-        ax.set_yticklabels(component_names, fontsize=18)
-        ax.set_xlabel("Intensity [n/s]", fontsize=18, color="k")
-        ax.set_xscale("log", nonpositive='clip')
-        ax.xaxis.set_tick_params(labelsize=16)
-        ax.set_ylim([-0.5, index + 0.5])
-
-        ax.grid(True)
-
-        ax2 = ax.twiny()
-        ax2.step(ray_counts, indicies, where="post", color="g", linestyle="--")
-        ax2.set_xlabel("Ray count", fontsize=18, color="g")
-        ax2.set_xscale("log", nonpositive='clip')
-        ax2.xaxis.set_tick_params(labelsize=16)
-        xlim = ax2.get_xlim()
-        ax2.set_xlim([xlim[0]*0.9, xlim[1]*1.1])
-
-
diff --git a/mcstasscript/instrument_diagnostics/diagnostics_instrument.py b/mcstasscript/instrument_diagnostics/diagnostics_instrument.py
index e6e4da58..c2e9bdfb 100644
--- a/mcstasscript/instrument_diagnostics/diagnostics_instrument.py
+++ b/mcstasscript/instrument_diagnostics/diagnostics_instrument.py
@@ -14,6 +14,7 @@ def reset_instr(self):
         self.instr.settings(**self.instr_settings)
 
     def settings(self, **kwargs):
+        self.instr.settings(**kwargs)
         self.instr_settings.update(kwargs)
 
     def remove_previous_use(self):
diff --git a/mcstasscript/instrument_diagnostics/intensity_diagnostics.py b/mcstasscript/instrument_diagnostics/intensity_diagnostics.py
index d3c12547..ebb07d2f 100644
--- a/mcstasscript/instrument_diagnostics/intensity_diagnostics.py
+++ b/mcstasscript/instrument_diagnostics/intensity_diagnostics.py
@@ -1,21 +1,26 @@
+import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
+from matplotlib.ticker import MaxNLocator
+from matplotlib.colors import BoundaryNorm
 
 from mcstasscript.instrument_diagnostics.diagnostics_instrument import DiagnosticsInstrument
 from mcstasscript.interface.functions import name_search
+from mcstasscript.data.data import McStasDataBinned
+from mcstasscript.helper.plot_helper import _plot_fig_ax
 
 class IntensityDiagnostics(DiagnosticsInstrument):
     def __init__(self, instr):
         super().__init__(instr)
 
         self.data = None
+        self.data_dim = None
         self.monitors = None
 
-    def add_monitor(self, before):
+    def add_monitor(self, before, options):
         name = "I_before_" + before.name
         mon = self.instr.add_component(name, "Monitor_nD", before=before)
 
-        options = f'"square boarders intensity"'
         mon.set_parameters(restore_neutron=1,
                            xwidth=100, yheight=100,
                            options=options,
@@ -27,22 +32,135 @@ def add_monitor(self, before):
 
         return name
 
+    def run_general(self, variable=None, limits=None):#, start=None, end=None):
+        self.reset_instr()
+        self.remove_previous_use()
+
+        if limits is None:
+            limit_string = "auto"
+        else:
+            if not isinstance(limits, list) or len(limits) != 2:
+                raise TypeError("limits has to be a list of length 2.")
+            limit_string = f"limits=[{limits[0]},{limits[1]}]"
+
+        if variable is None:
+            options = f'"square boarders intensity"'
+            self.data_dim = 0
+        else:
+            options = f'"square {variable} {limit_string} bins=300"'
+            self.data_dim = 1
+
+        self.monitors = []
+        for comp in self.component_list[1:]:
+            mon_name = self.add_monitor(before=comp, options=options)
+            self.monitors.append((mon_name, comp.name))
+
+        self.correct_target_index()
+
+        self.data = self.instr.backengine()
+
     def run(self):#, start=None, end=None):
         self.reset_instr()
         self.remove_previous_use()
 
+        options = f'"square boarders intensity"'
         self.monitors = []
         for comp in self.component_list[1:]:
-            mon_name = self.add_monitor(comp)
+            mon_name = self.add_monitor(before=comp, options=options)
             self.monitors.append((mon_name, comp.name))
 
         self.correct_target_index()
 
         self.data = self.instr.backengine()
 
-    def plot(self, figsize=None, ax=None, show_comp_names=True,
+    def plot(self, figsize=None, ax=None, fig=None, show_comp_names=True,
              y_tick_positions=None, ylimits=None):
 
+        if self.data_dim == 0:
+            self.plot_0D(figsize=figsize, ax=ax, fig=fig,
+                         show_comp_names=show_comp_names,
+                         y_tick_positions=y_tick_positions,
+                         ylimits=ylimits)
+        elif self.data_dim == 1:
+            self.plot_1D(figsize=figsize, ax=ax, fig=fig,
+                         show_comp_names=show_comp_names,
+                         y_tick_positions=y_tick_positions,
+                         ylimits=ylimits)
+
+    def plot_1D(self, figsize=None, ax=None, fig=None, show_comp_names=True,
+                y_tick_positions=None, ylimits=None):
+
+        if figsize is None:
+            figsize = (8, len(self.component_list) / 5 + 1)
+
+        data_sets = []
+        for I_monitor_name, component_name in self.monitors:
+            mon_data = name_search(I_monitor_name, self.data)
+            intensity = mon_data.Intensity
+            axis = mon_data.xaxis
+            data_sets.append({"name": component_name, "I": intensity, "axis": axis})
+
+        if y_tick_positions is None:
+            y_positions = np.linspace(1, len(data_sets), len(data_sets))
+            y_sep = y_positions[1] - y_positions[0]
+            y_positions = np.append(y_positions, y_positions[-1] + y_sep)
+        else:
+            #y_tick_positions.reverse()
+            y_positions = y_tick_positions
+
+        # Find min and max for axis
+        for index, data_set in enumerate(data_sets):
+            if index == 0:
+                max_axis = max(data_set["axis"])
+                min_axis = min(data_set["axis"])
+            else:
+                max_axis = max(max(data_set["axis"]), max_axis)
+                min_axis = min(min(data_set["axis"]), min_axis)
+
+        # New axis from min to max
+        n_bins = 300
+        axis = np.linspace(min_axis, max_axis, n_bins)
+        sep = axis[1] - axis[0]
+        bin_axis = np.append(axis - 0.5 * sep, axis[-1] + sep)
+        intensities = np.zeros((len(data_sets), len(axis)))
+
+        for index, data_set in enumerate(data_sets):
+            new_bins = np.digitize(data_set["axis"], bin_axis)
+            boarder_bins = np.where(new_bins == n_bins)
+            new_bins[boarder_bins] -= 1
+            intensities[index, new_bins] += data_set["I"]
+
+        if ax is None:
+            fig, ax = plt.subplots(1, 1, figsize=figsize)
+
+        component_names = [x.name for x in self.original_instr.make_component_subset()]
+        component_names.reverse()
+        if not show_comp_names:
+            component_names = [""] * len(component_names)
+
+        metadata = mon_data.metadata
+        metadata.dimension = [len(axis), len(y_positions) - 1]
+        metadata.limits = [0, 0, 0, 0]
+        metadata.limits[0] = min(axis)
+        metadata.limits[1] = max(axis)
+        metadata.limits[2] = min(y_positions)
+        metadata.limits[3] = max(y_positions)
+
+        display_data = np.flip(intensities, 0)
+        data = McStasDataBinned(metadata, display_data, np.zeros((1, 1)), np.zeros((1, 1)))
+
+        data.set_plot_options(show_colorbar=False, log=True, orders_of_mag=5)
+        data.set_ylabel("")
+
+        _plot_fig_ax(data, fig, ax)
+
+        ax.set_yticks(y_positions)
+        ax.set_yticklabels(component_names, fontsize=18)
+        ax.set_xlabel(mon_data.metadata.xlabel, fontsize=18)
+
+    def plot_0D(self, figsize=None, ax=None, fig=None, show_comp_names=True,
+                y_tick_positions=None, ylimits=None):
+
         if figsize is None:
             figsize = (8, len(self.component_list)/5 + 1)
 
@@ -84,12 +202,16 @@ def plot(self, figsize=None, ax=None, show_comp_names=True,
             y_tick_positions.reverse()
             y_positions = y_tick_positions
 
+        # Ensure x scale for intensity and n count share same tick marks
+        I_limits, N_limits = common_range_limits(intensities, ray_counts)
+
         ax.step(intensities, y_positions, where="post", color="k", zorder=3.5)
         ax.set_yticks(y_positions)
         ax.set_yticklabels(component_names, fontsize=18)
         ax.set_xlabel("Intensity [n/s]", fontsize=18, color="k")
         ax.set_xscale("log", nonpositive='clip')
         ax.xaxis.set_tick_params(labelsize=16)
+        ax.set_xlim(I_limits)
 
         if ylimits is None:
             ax.set_ylim([-0.5, index + 0.5])
@@ -102,6 +224,56 @@ def plot(self, figsize=None, ax=None, show_comp_names=True,
         ax2.step(ray_counts, y_positions, where="post", color="g", linestyle="--", zorder=3.6)
         ax2.set_xlabel("Ray count", fontsize=18, color="g")
         ax2.set_xscale("log", nonpositive='clip')
-        ax2.xaxis.set_tick_params(labelsize=16)
-        xlim = ax2.get_xlim()
-        ax2.set_xlim([xlim[0]*0.9, xlim[1]*1.1])
+        ax2.xaxis.set_tick_params(labelsize=16, colors="g")
+        ax2.set_xlim(N_limits)
+
+def common_range_limits(data_I, data_N):
+    # Convert to numpy
+    data_I = np.array(data_I)
+    data_I_nonzero = data_I[np.nonzero(data_I)]
+    data_N = np.array(data_N)
+    data_N_nonzero = data_N[np.nonzero(data_N)]
+
+    max_I = max(data_I_nonzero)
+    min_I = min(data_I_nonzero)
+    I_orders_of_mag = np.log10(max_I) - np.log10(min_I)
+
+    max_N = max(data_N_nonzero)
+    min_N = min(data_N_nonzero)
+    N_orders_of_mag = np.log10(max_N) - np.log10(min_N)
+
+    I_is_largest = I_orders_of_mag > N_orders_of_mag
+
+    if I_is_largest:
+        # Use intensity scale
+        max_large = max_I
+        min_large = min_I
+        max_small = max_N
+    else:
+        max_large = max_N
+        min_large = min_N
+        max_small = max_I
+
+    log_extra = 0.1  # Extra scale to avoid having data just at the edge
+
+    # Round I scale up
+    log_large_scale_max = np.ceil(np.log10(max_large)) + log_extra
+    log_large_scale_min = np.floor(np.log10(min_large))
+    large_limits = [10 ** log_large_scale_min, 10 ** log_large_scale_max]
+
+    # Find how many orders of mag this cover
+    large_orders_of_mag = log_large_scale_max - log_large_scale_min
+
+    # Apply same to ray count
+    log_small_scale_max = np.ceil(np.log10(max_small)) + log_extra
+    log_small_scale_min = log_small_scale_max - large_orders_of_mag
+    small_limits = [10 ** log_small_scale_min, 10 ** log_small_scale_max]
+
+    if I_is_largest:
+        I_limits = large_limits
+        N_limits = small_limits
+    else:
+        I_limits = small_limits
+        N_limits = large_limits
+
+    return I_limits, N_limits
\ No newline at end of file
diff --git a/mcstasscript/instrument_diagram/canvas.py b/mcstasscript/instrument_diagram/canvas.py
index 074d8acc..a2d5a391 100644
--- a/mcstasscript/instrument_diagram/canvas.py
+++ b/mcstasscript/instrument_diagram/canvas.py
@@ -8,7 +8,8 @@
 
 class DiagramCanvas:
     def __init__(self, left_side_arrows, component_boxes, right_side_arrows,
-                 component_categories, colors, intensity_diagnostics=None):
+                 component_categories, colors, intensity_diagnostics=None,
+                 variable=None, limits=None):
         """
         Creates diagram of instrument file with given boxes and arrows
 
@@ -34,6 +35,8 @@ def __init__(self, left_side_arrows, component_boxes, right_side_arrows,
         self.component_categories = component_categories
         self.colors = colors
 
+        self.variable = variable
+        self.limits = limits
         if intensity_diagnostics is None:
             self.intensity_analysis_mode = False
         else:
@@ -379,8 +382,13 @@ def plot(self):
             # Get y position for all boxes, but skip ABSOLUTE box
             y_positions = [box.position_y for box in self.component_boxes[1:]]
             y_spacing = y_positions[0] - y_positions[1]
-            upper_y_lim = y_positions[0] + 0.5 * y_spacing
-            lower_y_lim = y_positions[-1] - 0.5 * y_spacing
+
+            if self.variable is None:
+                upper_y_lim = y_positions[0] + 0.5 * y_spacing
+                lower_y_lim = y_positions[-1] - 0.5 * y_spacing
+            else:
+                upper_y_lim = y_positions[0]
+                lower_y_lim = y_positions[-1]
 
             # Insert is done in figure coordinate system, need mother ax dimensions
             ax_pos = ax.get_position()
@@ -401,8 +409,9 @@ def plot(self):
             ax.set_zorder(4)
 
             # Plot graph, convey tick positions and ylimits to match main diagram
-            self.intensity_diagnostics.run()
-            self.intensity_diagnostics.plot(ax=inset_ax, y_tick_positions=y_positions,
+            self.intensity_diagnostics.run_general(variable=self.variable, limits=self.limits)
+            self.intensity_diagnostics.plot(ax=inset_ax, fig=fig,
+                                            y_tick_positions=y_positions,
                                             ylimits=[lower_y_lim, upper_y_lim],
                                             show_comp_names=False)
 
diff --git a/mcstasscript/instrument_diagram/connections.py b/mcstasscript/instrument_diagram/connections.py
index 5b4f52e5..65d1fe2d 100644
--- a/mcstasscript/instrument_diagram/connections.py
+++ b/mcstasscript/instrument_diagram/connections.py
@@ -60,10 +60,17 @@ def add_connection(self, start_index, end_index):
 
 
 class Connection:
-    def __init__(self, origin, target):
+    def __init__(self, origin, target, info=None):
+        """
+        Describes a connection between origin and target with lane number
+
+        Can contain info as well which can be used for example to mark
+        if the connection is part of a certain group.
+        """
         self.origin = origin
         self.target = target
         self.lane_number = None
+        self.info = info
 
     def set_lane_number(self, value):
         self.lane_number = value
@@ -71,10 +78,17 @@ def set_lane_number(self, value):
 
 class ConnectionList:
     def __init__(self):
+        """
+        List of connections with utility functions
+
+        Can distribute the connections over a number of lanes to ensure all
+        connections can be seen without crossing that provide ambitious
+        interpretations.
+        """
         self.connections = []
 
-    def add(self, origin, target):
-        self.connections.append(Connection(origin, target))
+    def add(self, origin, target, info=None):
+        self.connections.append(Connection(origin, target, info=info))
 
     def get_connections(self):
         return self.connections
diff --git a/mcstasscript/instrument_diagram/generate_GROUP.py b/mcstasscript/instrument_diagram/generate_GROUP.py
index b09364eb..a4f5f7b6 100644
--- a/mcstasscript/instrument_diagram/generate_GROUP.py
+++ b/mcstasscript/instrument_diagram/generate_GROUP.py
@@ -25,7 +25,7 @@ def generate_GROUP_arrows(components, component_box_dict, box_names, color=None)
         connected_list = group_members[1:]
 
         for connected in connected_list:
-            connections.add(connected, base)
+            connections.add(connected, base, info=group_name)
 
     connections.distribute_lane_numbers(box_names=box_names)
 
@@ -34,6 +34,7 @@ def generate_GROUP_arrows(components, component_box_dict, box_names, color=None)
         origin = connection.origin
         target = connection.target
         lane = connection.lane_number + 2  # Make room for group name
+        group_name = connection.info
 
         arrow = Arrow(origin, target, lane=lane, kind="GROUP", description=group_name)
         arrow.set_connection(True)
diff --git a/mcstasscript/instrument_diagram/make_diagram.py b/mcstasscript/instrument_diagram/make_diagram.py
index 60eac00b..7143befc 100644
--- a/mcstasscript/instrument_diagram/make_diagram.py
+++ b/mcstasscript/instrument_diagram/make_diagram.py
@@ -11,7 +11,7 @@
 from mcstasscript.instrument_diagnostics.intensity_diagnostics import IntensityDiagnostics
 
 
-def instrument_diagram(instrument, analysis=False):
+def instrument_diagram(instrument, analysis=False, variable=None, limits=None):
     """
     Plots diagram of components in instrument with RELATIVE connections
 
@@ -68,7 +68,8 @@ def instrument_diagram(instrument, analysis=False):
     canvas = DiagramCanvas(AT_arrows + ROTATED_arrows, component_boxes,
                            JUMP_arrows + target_index_arrows + GROUP_arrows + Union_arrows,
                            component_categories=component_categories, colors=color_choices,
-                           intensity_diagnostics=intensity_diagnostics)
+                           intensity_diagnostics=intensity_diagnostics, variable=variable,
+                           limits=limits)
 
     # Plot diagram
     canvas.plot()
\ No newline at end of file
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 78204f61..4ba17086 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2520,7 +2520,7 @@ def show_instrument(self, format="webgl", width=800, height=450, new_tab=False):
 
         return IFrame(src=html_path, width=width, height=height)
 
-    def show_diagram(self, analysis=False):
+    def show_diagram(self, analysis=False, variable=None, limits=None):
         """
         Shows diagram of component connections in insrument
 
@@ -2538,12 +2538,12 @@ def show_diagram(self, analysis=False):
             print("The instrument has some error, this diagram is still "
                   "shown as it may help find the bug.")
 
-        instrument_diagram(self, analysis=analysis)
+        instrument_diagram(self, analysis=analysis, variable=variable, limits=limits)
         self.check_for_errors()
 
-    def show_analysis(self):
+    def show_analysis(self, variable=None):
         beam_diag = IntensityDiagnostics(self)
-        beam_diag.run()
+        beam_diag.run_general(variable=variable)
         beam_diag.plot()
 
     def saveH5(self, filename: str, openpmd: bool = True):

From 0cefb157731c96de127306c70052d41053d22da8 Mon Sep 17 00:00:00 2001
From: Shervin Nourbakhsh <shervin.nourbakhsh@ill.fr>
Date: Wed, 12 Oct 2022 10:14:47 +0200
Subject: [PATCH 313/403] mcpl file path added to output collection, mcpl input
 added to MCPL_input component filename added a check if a gzipped file exists
 to be added to the output data collection

---
 mcstasscript/data/MCPLDataFormat.py | 36 +++++++++++++++++++++
 mcstasscript/data/pyvinylData.py    | 25 +++++++++++++++
 mcstasscript/interface/instr.py     | 50 +++++++++++++++++++++++++----
 3 files changed, 105 insertions(+), 6 deletions(-)
 create mode 100644 mcstasscript/data/MCPLDataFormat.py

diff --git a/mcstasscript/data/MCPLDataFormat.py b/mcstasscript/data/MCPLDataFormat.py
new file mode 100644
index 00000000..9ff8f8e7
--- /dev/null
+++ b/mcstasscript/data/MCPLDataFormat.py
@@ -0,0 +1,36 @@
+from libpyvinyl.BaseFormat import BaseFormat
+from mcstasscript.helper.managed_mcrun import load_results
+
+
+class MCPLDataFormat(BaseFormat):
+    def __init__(self) -> None:
+        super().__init__()
+
+    @classmethod
+    def format_register(self):
+        key = "mcpl"
+        desciption = "MCPL file"
+        file_extension = ".mcpl"
+        read_kwargs = [""]
+        write_kwargs = [""]
+        return self._create_format_register(
+            key, desciption, file_extension, read_kwargs, write_kwargs
+        )
+
+    @staticmethod
+    def direct_convert_formats():
+        # Assume the format can be converted directly to the formats supported by these classes:
+        # AFormat, BFormat
+        # Redefine this `direct_convert_formats` for a concrete format class
+        return []
+
+    @classmethod
+    def read(cls, filename: str) -> dict:
+        """Read the data from the file with the `filename` to a dictionary. The dictionary will
+        be used by its corresponding data class."""
+        raise NotImplemented("Read method for MCPL is not implemented nor required")
+
+    @classmethod
+    def write(cls, object, filename: str, key: str = None):
+        """Don't have a way to write McStasData"""
+        raise NotImplemented("Write method for MCPL is not implemented nor required")
diff --git a/mcstasscript/data/pyvinylData.py b/mcstasscript/data/pyvinylData.py
index 1a261fbd..8667648d 100644
--- a/mcstasscript/data/pyvinylData.py
+++ b/mcstasscript/data/pyvinylData.py
@@ -1,5 +1,6 @@
 from libpyvinyl.BaseData import BaseData
 from mcstasscript.data.McStasDataFormat import McStasFormat
+from mcstasscript.data.MCPLDataFormat import MCPLDataFormat
 
 
 class pyvinylMcStasData(BaseData):
@@ -49,3 +50,27 @@ def from_file(cls, filename: str, format_class, key, **kwargs):
     def from_dict(cls, data_dict, key):
         """Create the data class by a python dictionary."""
         return cls(key, data_dict=data_dict)
+
+
+class pyvinylMCPLData(BaseData):
+    def __init__(
+        self,
+        key,
+        data_dict=None,
+        # the filename can be assigned later.
+        # If filename == "" or None it fails the consistency check of BaseData
+        filename="none",
+        file_format_class=None,
+        file_format_kwargs=None,
+    ):
+        expected_data = {}
+        super().__init__(key, expected_data, None, filename, MCPLDataFormat, None)
+
+    def supported_formats(self):
+        format_dict = {}
+        self._add_ioformat(format_dict, MCPLDataFormat)
+        return format_dict
+
+    @classmethod
+    def from_file(cls, filename: str, key="mcpl"):
+        return cls(key, filename=filename)
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index fe01a949..3a696650 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -12,7 +12,8 @@
 from libpyvinyl.BaseCalculator import BaseCalculator
 from libpyvinyl.Parameters.Collections import CalculatorParameters
 
-from mcstasscript.data.pyvinylData import pyvinylMcStasData
+from mcstasscript.data.pyvinylData import pyvinylMcStasData, pyvinylMCPLData
+from mcstasscript.data.MCPLDataFormat import MCPLDataFormat
 
 from mcstasscript.helper.mcstas_objects import DeclareVariable
 from mcstasscript.helper.mcstas_objects import provide_parameter
@@ -2320,6 +2321,8 @@ def backengine(self):
         method.
         """
 
+        self.add_input_to_mcpl()
+
         instrument_path = os.path.join(self.input_path, self.name + ".instr")
         if not os.path.exists(instrument_path) or self._run_settings["force_compile"]:
             self.write_full_instrument()
@@ -2327,7 +2330,7 @@ def backengine(self):
         parameters = {}
         for parameter in self.parameters:
             if parameter.value is None:
-                raise RuntimeError("Unspecified parameter: '" + parameter.name
+                raise RuntimeError("Parameter value not set for parameter: '" + parameter.name
                                    + "' set with set_parameters.")
 
             parameters[parameter.name] = parameter.value
@@ -2345,11 +2348,16 @@ def backengine(self):
         # Load data and store in __data
         #data = simulation.load_results()
         #self._set_data(data)
+        
+        ## look for MCPL_output components and the defined filenames
+        self.add_mcpl_to_output(simulation)
 
+        # simulation results from .dat files loaded as dict
         data = simulation.load_results()
         data_dict = {"data": data}
-        key = self.output_keys[0]
-        output_data = self.output[key]
+        # adding to the libpyvinyl output datacollection with key = sim_data_key
+        sim_data_key = self.output_keys[0]
+        output_data = self.output[sim_data_key] 
         output_data.set_dict(data_dict)
 
         if self.run_to_ref is not None:
@@ -2367,12 +2375,42 @@ def backengine(self):
             if out is None:
                 print("Expected MCPL file was not loaded!")
 
-        if "data" not in self.output.get_data():
+        if "data" not in self.output[sim_data_key].get_data():
             print("\n\nNo data returned.")
             return None
         else:
-            return self.output.get_data()["data"]
+            return self.output[sim_data_key].get_data()["data"]
+
+    def add_input_to_mcpl(self):
+        try:
+            mcpl_file = self.input["mcpl"].filename
+            for comp in self.component_list:
+                if comp.component_name == "MCPL_input":
+                    comp.filename = '"' + mcpl_file + '"'
+                    break
+        except:
+            return
 
+    def add_mcpl_to_output(self, managed_mcrun):
+        MCPL_extension = MCPLDataFormat.format_register()["ext"]
+        num_mcpl_files = 0
+        for comp in self.component_list[::-1]: # starting from the last one!
+            if comp.component_name == "MCPL_output":
+                num_mcpl_files = num_mcpl_files+1
+
+                absfilepath = os.path.join(managed_mcrun.data_folder_name,
+                                           comp.filename.strip('"').strip("'")
+                                           +MCPL_extension)
+                if os.path.exists(absfilepath+".gz"):
+                    absfilepath+=".gz"
+                if os.path.exists(absfilepath) is False:
+                    raise RuntimeError(f"MCPL file: {absfilepath} nor {absfilepath}.gz not found")
+                mcpl_file =  pyvinylMCPLData.from_file(absfilepath)
+                if num_mcpl_files>1:
+                    mcpl_file.key = mcpl_file+str(num_mcpl_files)
+                self.output.add_data(mcpl_file)
+                self.output_keys.append(mcpl_file.key)
+        
     def run_full_instrument(self, **kwargs):
         """
         Runs McStas instrument described by this class, returns list of

From 419ea254a85168bd910e68ae72210303e2a52ce5 Mon Sep 17 00:00:00 2001
From: Shervin Nourbakhsh <shervin.nourbakhsh@ill.fr>
Date: Wed, 12 Oct 2022 10:41:50 +0200
Subject: [PATCH 314/403] make the mcpl methods for internal use only

---
 mcstasscript/interface/instr.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 3a696650..b8a898df 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2321,7 +2321,7 @@ def backengine(self):
         method.
         """
 
-        self.add_input_to_mcpl()
+        self.__add_input_to_mcpl()
 
         instrument_path = os.path.join(self.input_path, self.name + ".instr")
         if not os.path.exists(instrument_path) or self._run_settings["force_compile"]:
@@ -2350,7 +2350,7 @@ def backengine(self):
         #self._set_data(data)
         
         ## look for MCPL_output components and the defined filenames
-        self.add_mcpl_to_output(simulation)
+        self.__add_mcpl_to_output(simulation)
 
         # simulation results from .dat files loaded as dict
         data = simulation.load_results()
@@ -2381,7 +2381,7 @@ def backengine(self):
         else:
             return self.output[sim_data_key].get_data()["data"]
 
-    def add_input_to_mcpl(self):
+    def __add_input_to_mcpl(self):
         try:
             mcpl_file = self.input["mcpl"].filename
             for comp in self.component_list:
@@ -2391,7 +2391,7 @@ def add_input_to_mcpl(self):
         except:
             return
 
-    def add_mcpl_to_output(self, managed_mcrun):
+    def __add_mcpl_to_output(self, managed_mcrun):
         MCPL_extension = MCPLDataFormat.format_register()["ext"]
         num_mcpl_files = 0
         for comp in self.component_list[::-1]: # starting from the last one!

From 5994ba789f681558e90aa88b57f7809c65d7f5ea Mon Sep 17 00:00:00 2001
From: Shervin Nourbakhsh <shervin.nourbakhsh@ill.fr>
Date: Wed, 7 Sep 2022 11:15:53 +0200
Subject: [PATCH 315/403] data collection taking the name from the calculator
 name

---
 mcstasscript/interface/instr.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index b8a898df..acee7526 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -292,7 +292,7 @@ def __init__(self, name, parameters=None, author=None,
         """
 
         super().__init__(name, input=[],
-                         output_keys=["simulation_data"],
+                         output_keys=[name + "_data"],
                          output_data_types=[pyvinylMcStasData],
                          parameters=parameters)
 

From 69966dbd4c77084af0fcf9b50d11cee88bdf2289 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 4 Nov 2022 15:25:39 +0100
Subject: [PATCH 316/403] Update of CI workflows for new version of libpyvinyl

---
 .github/workflows/unit_tests_mac.yaml     | 2 +-
 .github/workflows/unit_tests_ubuntu.yaml  | 2 +-
 .github/workflows/unit_tests_windows.yaml | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index df2e34c8..607a1ce8 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -18,7 +18,7 @@ jobs:
         uses: actions/checkout@v2
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
-          ref: 1.1.0
+          ref: 1.1.2
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}
diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index a95eb464..10f54815 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -18,7 +18,7 @@ jobs:
         uses: actions/checkout@v2
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
-          ref: 1.1.0
+          ref: 1.1.2
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
index 57a7de80..e2257f48 100644
--- a/.github/workflows/unit_tests_windows.yaml
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -18,7 +18,7 @@ jobs:
         uses: actions/checkout@v2
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
-          ref: 1.1.0
+          ref: 1.1.2
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}

From 5acff0f7fe543c14f097fc8c1182d66ee4784c5c Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 4 Nov 2022 15:31:57 +0100
Subject: [PATCH 317/403] Going to libpyvinyl master for CI builds.

---
 .github/workflows/unit_tests_mac.yaml     | 2 +-
 .github/workflows/unit_tests_ubuntu.yaml  | 2 +-
 .github/workflows/unit_tests_windows.yaml | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index 607a1ce8..b801c7b1 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -18,7 +18,7 @@ jobs:
         uses: actions/checkout@v2
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
-          ref: 1.1.2
+          ref: master
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}
diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index 10f54815..ca58bd64 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -18,7 +18,7 @@ jobs:
         uses: actions/checkout@v2
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
-          ref: 1.1.2
+          ref: master
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
index e2257f48..2d5d43a5 100644
--- a/.github/workflows/unit_tests_windows.yaml
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -18,7 +18,7 @@ jobs:
         uses: actions/checkout@v2
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
-          ref: 1.1.2
+          ref: master
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}

From 06207e62b86b32549e344724afcde743d2bcf512 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Mon, 14 Nov 2022 16:34:39 +0100
Subject: [PATCH 318/403] Update of diagnostics tools, they now support:

Diagnostics before or after components.
After is for example useful to see where rays originate in a sample

User flags in diagnostics
Now userflags can be used when running beam diagnostics and can
be chosen as plotting axis using their original names.

Refactor and doc strings added. Added print and show methods.
---
 .../beam_diagnostics.py                       | 342 +++++++++++++++---
 .../instrument_diagnostics/event_plotter.py   |  20 +-
 mcstasscript/instrument_diagnostics/view.py   |  11 +
 3 files changed, 323 insertions(+), 50 deletions(-)

diff --git a/mcstasscript/instrument_diagnostics/beam_diagnostics.py b/mcstasscript/instrument_diagnostics/beam_diagnostics.py
index 6ee2fc27..6a55c782 100644
--- a/mcstasscript/instrument_diagnostics/beam_diagnostics.py
+++ b/mcstasscript/instrument_diagnostics/beam_diagnostics.py
@@ -1,3 +1,4 @@
+from mcstasscript.helper.mcstas_objects import DeclareVariable
 from mcstasscript.helper.mcstas_objects import Component
 from mcstasscript.instrument_diagnostics.diagnostics_instrument import DiagnosticsInstrument
 from mcstasscript.instrument_diagnostics.view import View
@@ -14,15 +15,55 @@ def sanitise_comp_name(comp_name):
 
     return comp_name
 
+class DiagnosticsPoint:
+    def __init__(self, instr, comp_name, before=True, rays=50000):
+        comp_name = sanitise_comp_name(comp_name)
+        try:
+            instr.get_component(comp_name)
+        except KeyError:
+            raise KeyError("Component '" + comp_name + "' not found in instrument.")
+
+        self.component = comp_name
+        self.before = before # False for after
+
+        if not isinstance(rays, int):
+            if not rays == "all":
+                raise ValueError("The ray keyword for a point should be all or an integer.")
+
+        self.rays = rays
+        self.filename = None
+
+    def set_filename(self, filename):
+        self.filename = filename
+
+    def __eq__(self, other):
+        return self.component == other.component and self.before == other.before
+
+    def __repr__(self):
+        string = "Diagnostics point "
+        if self.before:
+            string += "before: "
+        else:
+            string += "after:  "
+
+        string += self.component.ljust(25)
+        string += " - rays: "
+        string += str(self.rays)
+
+        return string
 
 class BeamDiagnostics(DiagnosticsInstrument):
     def __init__(self, instr):
         super().__init__(instr)
 
-        # Set of Component names
-        self.points = set()
-        self.point_collect_rays = {}
-        self.monitor_names = {}
+        # points to investigate with options
+        self.points = []
+        self.ordered_point_list = []
+
+        # flags for monitor_nD user variables
+        self.flags = []
+
+        self.monitor_names = []
 
         self.views = []
 
@@ -30,64 +71,268 @@ def __init__(self, instr):
 
         self.event_plotters = []
 
-    def add_point(self, comp_name, rays=50000):
-        comp_name = sanitise_comp_name(comp_name)
-        try:
-            self.instr.get_component(comp_name)
-        except KeyError:
-            raise KeyError("Component '" + comp_name + "' not found in instrument.")
+    def __repr__(self):
+        string = f"Instrument diagnostics for: {self.instr.name}\n"
+        string += "Diagnostics points: \n"
+
+        if len(self.points) == 0:
+            string += "  No diagnostics points yet\n"
+        for point in self.points:
+            string += "  " + point.__repr__() + "\n"
+
+        string += "Views: \n"
+        if len(self.views) == 0:
+            string += "  No views yet\n"
+
+        for view in self.views:
+            string += "  " + view.__repr__() + "\n"
 
-        self.points.add(comp_name)
+        if self.data is None:
+            string += "Does not yet contain simulated data"
+        else:
+            string += "Does contain simulated data"
 
-        if not isinstance(rays, int):
-            if not rays == "all":
-                raise ValueError("The ray keyword for a point should be all or an integer")
+        return string
+
+    def add_flag(self, flag):
+        """
+        Adds flag to be monitored in event list, maximum of 3 allowed
+
+        The flag must be available in declared variables or user vars.
+
+        Parameters:
+
+        flag: str
+            str with variable name to be recorded to event dataset
+        """
+        if not isinstance(flag, str):
+            raise ValueError("flag has to be a string.")
+
+        # find declare and user_var names in instrument
+        names = [x.name for x in self.instr.declare_list
+                 if isinstance(x, DeclareVariable)]
+        names += [x.name for x in self.instr.user_var_list
+                  if isinstance(x, DeclareVariable)]
+
+        if flag not in names:
+            raise ValueError(f"flag {flag} was not found in declared "
+                             f"variables or uservars of the instrument.")
+
+        if len(self.flags) >= 3:
+            raise ValueError("Can't add more than three flags")
+
+        self.flags.append(flag)
+
+    def clear_flags(self):
+        """
+        Clears flags
+        """
+        self.flags = []
+
+    def show_flags(self):
+        """
+        Shows list of current flags
+        """
+        if self.flags is not None:
+            for index, flag in enumerate(self.flags):
+                print(f" user{index+1}={flag}")
+
+    def add_point(self, before=None, after=None, rays=50000):
+        """
+        Adds point in which the beam is investigated with given number of rays
+
+        Can be before and/or after components, this is chosen by using the
+        appropriate keyword argument.
+
+        Parameters:
+
+        before : str or component instance
+            Reference to component beam should be monitored just before
+
+        after : str or component instance
+            Reference to component beam should be monitored just after
+
+        rays : int or "all"
+            Number of rays to record, either integer or the string "all"
+        """
+        if before is None and after is None:
+            raise ValueError("Specify either before or after with component name.")
+
+        if before is not None:
+            self._add_diagnostics_point(comp_name=before, before=True, rays=rays)
+
+        if after is not None:
+            self._add_diagnostics_point(comp_name=after, before=False, rays=rays)
+
+    def _add_diagnostics_point(self, comp_name, before, rays):
+        """
+        Internal function to add the DiagnosticsPoint to list
+        """
+        point = DiagnosticsPoint(self.instr, comp_name=comp_name, before=before, rays=rays)
+        if point in self.points:
+            # overwrite that point
+            index = self.points.index(point)
+            self.points[index] = point
+        else:
+            # add point to list
+            self.points.append(point)
+
+    def remove_point(self, before=None, after=None):
+        """
+        Removes a point from the list
+
+        Specify whether the point in question is before or after the
+        given component name or instance by using the appropriate
+        keyword argument
+
+        Parameter:
+
+        before : str or component instance
+            Name or component instance for which point before should be removed
+
+        after : str or component instance
+            Name or component instance for which point after should be removed
+        """
+        if before is None and after is None:
+            raise ValueError("Specify either before or after with component name.")
+
+        if before is not None:
+            before = sanitise_comp_name(before)
+            for index, point in enumerate(self.points):
+                if point.component == before and point.before:
+                    del self.points[index]
+
+        if after is not None:
+            after = sanitise_comp_name(after)
+            for index, point in enumerate(self.points):
+                if point.component == after and not point.before:
+                    del self.points[index]
+
+    def show_points(self):
+        """
+        Shows current diagnostics points
+        """
+        for point in self.points:
+            print(point)
 
-        self.point_collect_rays[comp_name] = rays
+    def add_view(self, axis1, axis2=None, bins=100, same_scale=True, **kwargs):
+        """
+        Add a view with one or two axes that will be shown at all diagnostics points
 
-    def remove_point(self, comp_name):
-        comp_name = sanitise_comp_name(comp_name)
-        self.points.remove(comp_name)
-        del self.point_collect_rays[comp_name]
+        Parameters:
+        axis1: str
+            Name of parameter for first axis
 
-    def add_view(self, axis1, axis2=None, bins=100, same_scale=True, **kwargs):
+        axis2: str
+            Name of parameter for second axis
+
+        bins : int or list of length 2
+            Number of bins for histogram (can be list of length 2 for 2D)
+
+        same_scale : bool
+            Select if the same scale should be ensured for all points with this view
+        """
         view = View(axis1=axis1, axis2=axis2, bins=bins, same_scale=same_scale, **kwargs)
         self.views.append(view)
 
     def clear_views(self):
+        """
+        Clear list of views
+        """
         self.views = []
 
+    def show_views(self):
+        """
+        Shows views
+        """
+        for view in self.views:
+            print(view)
+
     def add_monitors(self):
-        comp_names = [x.name for x in self.instr.make_component_subset()]
-        self.monitor_names = {}
-        ordered_point_list = []
-        for name in comp_names:
-            if name in self.points:
-                ordered_point_list.append(name)
+        """
+        Adds monitors as specified by list of diagnostics points
 
-        for name in ordered_point_list:
-            comp = self.instr.get_component(name)
-            file_name = self.add_monitor(comp)
-            self.monitor_names[name] = file_name
+        The method sorts the points with the component sequence so the
+        resulting plots are in the correct order.
+        """
+        comp_names = [x.name for x in self.instr.make_component_subset()]
+        self.monitor_names = []
+        self.ordered_point_list = []
+        point_names = [x.component for x in self.points]
 
-    def add_monitor(self, before):
-        name = "Diag_before_" + before.name
-        mon = self.instr.add_component(name, "Monitor_nD", before=before)
+        for name in comp_names:
+            before_point = None
+            after_point = None
+            if name in point_names:
+                for point in self.points:
+                    if point.component == name:
+                        if point.before:
+                            before_point = point
+                        else:
+                            after_point = point
+
+            if before_point is not None:
+                self.ordered_point_list.append(before_point)
+            if after_point is not None:
+                self.ordered_point_list.append(after_point)
+
+        for point in self.ordered_point_list:
+            comp = self.instr.get_component(point.component)
+            filename = self.add_monitor(point, comp)
+            point.set_filename(filename)
+
+    def add_monitor(self, point, comp_instance):
+        """
+        Adds monitor_nD event monitor to diagnostics instrument for given point
+
+        Needs point and component instance that match
+
+        Parameters:
+
+        point : DiagnosticsPoint
+            Point for which monitor should be added
+
+        comp_instance : Component
+            Component instance relative to which the monitor should be placed
+        """
+
+        if not point.component == comp_instance.name:
+            raise RuntimeError("Given point and component instance should match!")
+
+        user_vars = [None, None, None]
+        flags_str = ""
+        for index, flag in enumerate(self.flags):
+            flags_str += " user" + str(index+1)
+            user_vars[index] = '"' + flag + '"'
+
+        if point.before:
+            name = "Diag_before_" + point.component
+            options = f'"square boarders n x y z vx vy vz t{flags_str}, list {point.rays}"'
+            mon = self.instr.add_component(name, "Monitor_nD", before=point.component)
+        else:
+            name = "Diag_after_" + point.component
+            options = f'"previous n x y z vx vy vz t{flags_str}, list {point.rays}"'
+            mon = self.instr.add_component(name, "Monitor_nD", after=point.component)
 
-        rays = self.point_collect_rays[before.name]
-        options = f'"square boarders n x y z vx vy vz t, list {rays} neutrons"'
         mon.set_parameters(restore_neutron=1,
                            xwidth=100, yheight=100,
                            options=options,
+                           user1=user_vars[0], user2=user_vars[1], user3=user_vars[2],
                            filename='"' + name + ".diag" + '"')
 
-        mon.set_AT(before.AT_data, RELATIVE=before.AT_reference)
-        if before.ROTATED_specified:
-            mon.set_ROTATED(before.ROTATED_data, RELATIVE=before.ROTATED_reference)
+        mon.set_AT(comp_instance.AT_data, RELATIVE=comp_instance.AT_reference)
+        if comp_instance.ROTATED_specified:
+            mon.set_ROTATED(comp_instance.ROTATED_data, RELATIVE=comp_instance.ROTATED_reference)
 
         return name
 
     def run(self):
+        """
+        Runs diagnostics with all included points
+
+        Saves data in data attribute and the resulting instrument can be found
+        in the instr attribute
+        """
         self.reset_instr()
         self.remove_previous_use()
 
@@ -100,16 +345,29 @@ def run(self):
         self.read_data()
 
     def read_data(self):
+        """
+        Reads the generated data and organizes it as event plotter instances
+        """
 
         self.event_plotters = []
-        for comp in self.monitor_names:
-            name = self.monitor_names[comp]
-            event_data = name_search(name, self.data)
-            plotter = event_plotter.EventPlotter(name, event_data)
+
+        for point in self.ordered_point_list:
+            event_data = name_search(point.filename, self.data)
+            plotter = event_plotter.EventPlotter(point.filename, event_data,
+                                                 flag_info=self.flags)
 
             self.event_plotters.append(plotter)
 
     def plot(self):
+        """
+        Plots the generated data for all points with all views
+        """
+
+        if len(self.event_plotters) == 0:
+            if len(self.views) == 0:
+                print("No data to plot yet! Add views and run.")
+            else:
+                print("No data to plot yet! Use the run method to generate data.")
 
         overview = PlotOverview(self.event_plotters, self.views)
         overview.plot_all()
diff --git a/mcstasscript/instrument_diagnostics/event_plotter.py b/mcstasscript/instrument_diagnostics/event_plotter.py
index 5d47f847..9c1b758e 100644
--- a/mcstasscript/instrument_diagnostics/event_plotter.py
+++ b/mcstasscript/instrument_diagnostics/event_plotter.py
@@ -7,7 +7,7 @@ class EventPlotter:
     """
     Plots event data onto given views
     """
-    def __init__(self, name, data):
+    def __init__(self, name, data, flag_info=None):
         """
         EventPlotter stores name and data, can produce plots given views
 
@@ -18,9 +18,13 @@ def __init__(self, name, data):
 
         data : McStasEventData
             Data object with event data
+
+        flag_info : list of str
+            List of flag names in order U1 U2 U3
         """
         self.name = name
         self.data = data
+        self.flag_info = flag_info
 
     def scale_weights(self, factor):
         """
@@ -31,7 +35,7 @@ def scale_weights(self, factor):
         factor : float
             Scale factor to be applied
         """
-        index = self.data.find_variable_index("p")
+        index = self.data.find_variable_index("p", flag_info=self.flag_info)
         self.data.Events[:, index] *= factor
 
     def add_view_limits(self, view):
@@ -42,11 +46,11 @@ def add_view_limits(self, view):
         view : View
             View for which limits should be set
         """
-        data = self.data.get_data_column(view.axis1)
+        data = self.data.get_data_column(view.axis1, flag_info=self.flag_info)
         view.set_axis1_limits(np.min(data), np.max(data))
 
         if view.axis2 is not None:
-            data = self.data.get_data_column(view.axis1)
+            data = self.data.get_data_column(view.axis1, flag_info=self.flag_info)
             view.set_axis1_limits(np.min(data), np.max(data))
 
     def get_view_limits_axis1(self, view):
@@ -57,7 +61,7 @@ def get_view_limits_axis1(self, view):
         view : View
             View for which limits should be retrieved
         """
-        data = self.data.get_data_column(view.axis1)
+        data = self.data.get_data_column(view.axis1, flag_info=self.flag_info)
         return np.min(data), np.max(data)
 
     def get_view_limits_axis2(self, view):
@@ -70,7 +74,7 @@ def get_view_limits_axis2(self, view):
         """
         if view.axis2 is None:
             return np.NaN, np.NaN
-        data = self.data.get_data_column(view.axis2)
+        data = self.data.get_data_column(view.axis2, flag_info=self.flag_info)
         return np.min(data), np.max(data)
 
     def plot(self, view, fig, ax):
@@ -91,14 +95,14 @@ def plot(self, view, fig, ax):
         """
 
         if view.axis2 is None:
-            data = self.data.make_1d(axis1=view.axis1, n_bins=view.bins)
+            data = self.data.make_1d(axis1=view.axis1, n_bins=view.bins, flag_info=self.flag_info)
             data.set_title("")
             if view.axis1_limits is not None:
                 data.set_plot_options(left_lim=view.axis1_limits[0], right_lim=view.axis1_limits[1])
             data.set_plot_options(**view.plot_options)
 
         else:
-            data = self.data.make_2d(axis1=view.axis1, axis2=view.axis2, n_bins=view.bins)
+            data = self.data.make_2d(axis1=view.axis1, axis2=view.axis2, n_bins=view.bins, flag_info=self.flag_info)
             data.set_plot_options(show_colorbar=False)
             data.set_title("")
             if view.axis1_limits is not None and view.axis2_limits is not None:
diff --git a/mcstasscript/instrument_diagnostics/view.py b/mcstasscript/instrument_diagnostics/view.py
index ca796731..3c1b182d 100644
--- a/mcstasscript/instrument_diagnostics/view.py
+++ b/mcstasscript/instrument_diagnostics/view.py
@@ -43,6 +43,17 @@ def __init__(self, axis1, axis2=None, bins=100, same_scale=True, **kwargs):
 
         self.plot_options = kwargs
 
+    def __repr__(self):
+        string = f"View ({self.axis1}"
+        if self.axis2 is not None:
+            string += f", {self.axis2}"
+
+        string += ")"
+        string = string.ljust(25)
+        string += f" bins: {str(self.bins)}"
+
+        return string
+
     def set_axis1_limits(self, start, end):
         """
         Sets the axis1 limits

From 738eb8ed0bb9fd28d9fd5e0a5067c9f9691974ee Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 15 Nov 2022 08:57:58 +0100
Subject: [PATCH 319/403] Update of data with ability to use flag info to set
 axis when working with event data.

---
 mcstasscript/data/data.py | 113 +++++++++++++++++++++++++++++++++-----
 1 file changed, 99 insertions(+), 14 deletions(-)

diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index d4d1b594..90d22e2e 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -681,21 +681,74 @@ def __init__(self, metadata, events, **kwargs):
                        "dx": "divergence x [deg]",
                        "dy": "divergence y [deg]"}
 
-    def find_variable_index(self, axis):
+    def find_variable_index(self, axis, flag_info=None):
+        """
+        Returns variable index for given axis name
+
+        Parameters:
+
+        axis : str
+            Name of desired axis
+
+        flag_info: list
+            List of flag names used for user variables in event data
+        """
+        if flag_info is not None:
+            # If flag info given, use it to find user var string
+            for index, flag in enumerate(flag_info):
+                if axis == flag:
+                    axis = f"U{index + 1}"
+
         return self.variables.index(axis)
 
     def scale_weights(self, factor):
+        """
+        Scales all event weights with given factor
+
+        Parameters:
+
+        factor : float
+            Factor with which all weights are scaled
+        """
         self.Events[:, self.find_variable_index("p")] *= factor
 
-    def get_label(self, axis):
+    def get_label(self, axis, flag_info=None):
+        """
+        Returns data label corresponding to given axis name
+
+        Parameters:
+
+        axis : str
+            Name of parameter
+
+        flag_info : list
+            list of names for user variables in event data set
+        """
         axis = axis.lower()
 
+        if flag_info is not None:
+            # If flag info given, use it to find user var string
+            for index, flag in enumerate(flag_info):
+                if axis == flag:
+                    return f"User{index+1}: {flag}"
+
         if axis in self.labels:
             return self.labels[axis]
         else:
             return ""
 
-    def get_data_column(self, axis):
+    def get_data_column(self, axis, flag_info=None):
+        """
+        Returns data column corresponding to given axis name
+
+        Parameters:
+
+        axis : str
+            Name of parameter
+
+        flag_info : list
+            list of names for user variables in event data set
+        """
 
         m_n_const = 1.674927e-27
         h_const = 6.626068e-34
@@ -732,14 +785,28 @@ def get_data_column(self, axis):
             return np.arctan(vy/vz) * 180 / np.pi
 
         else:
-            index = self.find_variable_index(axis)
+            index = self.find_variable_index(axis, flag_info=flag_info)
             return self.Events[:, index]
 
-    def make_1d(self, axis1, n_bins=50):
-        data = self.get_data_column(axis1)
-        label = self.get_label(axis1)
+    def make_1d(self, axis1, n_bins=50, flag_info=None):
+        """
+        Bin event data along to given axis to create binned dataset
+
+        Parameters:
+
+        axis1 : str
+            Name of parameter for binned axis
+
+        n_bins : integer
+            Number of bins for histogramming
+
+        flag_info : list
+            list of names for user variables in event data set
+        """
+        data = self.get_data_column(axis1, flag_info)
+        label = self.get_label(axis1, flag_info)
 
-        weights = self.get_data_column("p")
+        weights = self.get_data_column("p", flag_info)
         intensity, edges = np.histogram(data, bins=n_bins, weights=weights)
         error_squared, edges = np.histogram(data, bins=n_bins, weights=weights**2)
         error = np.sqrt(error_squared)
@@ -760,13 +827,31 @@ def make_1d(self, axis1, n_bins=50):
 
         return binned
 
-    def make_2d(self, axis1, axis2, n_bins=100):
-        data1 = self.get_data_column(axis1)
-        label1 = self.get_label(axis1)
-        data2 = self.get_data_column(axis2)
-        label2 = self.get_label(axis2)
+    def make_2d(self, axis1, axis2, n_bins=100, flag_info=None):
+        """
+        Bin event data along to given axes to create binned dataset
+
+        Parameters:
+
+        axis1 : str
+            Name of parameter for first axis
+
+        axis2 : str
+            Name of parameter for second axis
+
+        n_bins : integer or list
+            Number of bins for histogramming, can be list with two elements
+
+        flag_info : list
+            list of names for user variables in event data set
+        """
+
+        data1 = self.get_data_column(axis1, flag_info)
+        label1 = self.get_label(axis1, flag_info)
+        data2 = self.get_data_column(axis2, flag_info)
+        label2 = self.get_label(axis2, flag_info)
 
-        weights = self.get_data_column("p")
+        weights = self.get_data_column("p", flag_info)
         intensity, edges2, edges1 = np.histogram2d(data2, data1, bins=n_bins, weights=weights)
         error_squared, edges2, edges1 = np.histogram2d(data2, data1, bins=n_bins, weights=weights**2)
         error = np.sqrt(error_squared)

From 439a8f3d8dbbc38414140172f7db9bded71129fb Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 4 Nov 2022 15:25:39 +0100
Subject: [PATCH 320/403] Update of CI workflows for new version of libpyvinyl

---
 .github/workflows/unit_tests_mac.yaml     | 2 +-
 .github/workflows/unit_tests_ubuntu.yaml  | 2 +-
 .github/workflows/unit_tests_windows.yaml | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index df2e34c8..607a1ce8 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -18,7 +18,7 @@ jobs:
         uses: actions/checkout@v2
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
-          ref: 1.1.0
+          ref: 1.1.2
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}
diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index a95eb464..10f54815 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -18,7 +18,7 @@ jobs:
         uses: actions/checkout@v2
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
-          ref: 1.1.0
+          ref: 1.1.2
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
index 57a7de80..e2257f48 100644
--- a/.github/workflows/unit_tests_windows.yaml
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -18,7 +18,7 @@ jobs:
         uses: actions/checkout@v2
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
-          ref: 1.1.0
+          ref: 1.1.2
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}

From d2f7c7b067ccc349ab97d946923615442a5f3934 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 4 Nov 2022 15:31:57 +0100
Subject: [PATCH 321/403] Going to libpyvinyl master for CI builds.

---
 .github/workflows/unit_tests_mac.yaml     | 2 +-
 .github/workflows/unit_tests_ubuntu.yaml  | 2 +-
 .github/workflows/unit_tests_windows.yaml | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index 607a1ce8..b801c7b1 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -18,7 +18,7 @@ jobs:
         uses: actions/checkout@v2
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
-          ref: 1.1.2
+          ref: master
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}
diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index 10f54815..ca58bd64 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -18,7 +18,7 @@ jobs:
         uses: actions/checkout@v2
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
-          ref: 1.1.2
+          ref: master
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
index e2257f48..2d5d43a5 100644
--- a/.github/workflows/unit_tests_windows.yaml
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -18,7 +18,7 @@ jobs:
         uses: actions/checkout@v2
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
-          ref: 1.1.2
+          ref: master
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}

From 97b65311f5613b1f183d31de8e1ffc1c8b5dc117 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 17 Nov 2022 14:16:30 +0100
Subject: [PATCH 322/403] Final update of diagnostics system. Now checks if
 McStas / McXtrace version is 2 or 3 in order to provide the correct input to
 Monitor_nD.

Fixed bug in instrument reader.

Allow 0D data sets.

Included attributes for total_I / E and N in metadata.

Updateed documentation.
---
 .../getting_started/version_history.ipynb     |  11 +
 docs/source/index.rst                         |   1 +
 .../McStasScript_tutorial_6_Diagnostics.ipynb | 850 ++++++++++++++++++
 mcstasscript/data/data.py                     |  74 +-
 mcstasscript/helper/check_mccode_version.py   |  30 +
 mcstasscript/helper/managed_mcrun.py          |  26 +-
 mcstasscript/helper/plot_helper.py            |  10 +-
 mcstasscript/instr_reader/read_trace.py       |  16 +-
 .../beam_diagnostics.py                       |  45 +-
 .../diagnostics_instrument.py                 |  49 +-
 mcstasscript/instrument_diagnostics/view.py   |   2 +-
 .../instrument_diagram/make_diagram.py        |   2 +-
 mcstasscript/interface/instr.py               |  17 +
 13 files changed, 1110 insertions(+), 23 deletions(-)
 create mode 100644 docs/source/tutorial/McStasScript_tutorial_6_Diagnostics.ipynb
 create mode 100644 mcstasscript/helper/check_mccode_version.py

diff --git a/docs/source/getting_started/version_history.ipynb b/docs/source/getting_started/version_history.ipynb
index 5655020c..a6305294 100644
--- a/docs/source/getting_started/version_history.ipynb
+++ b/docs/source/getting_started/version_history.ipynb
@@ -14,6 +14,17 @@
    "id": "automatic-charleston",
    "metadata": {},
    "source": [
+    "## 0.0.52 - 17th of November 2022\n",
+    "- New features for diagnostics of instruments, [tutorial here](../tutorial/McStasScript_tutorial_6_Diagnostics.ipynb)\n",
+    "    - Diagrams can now plot intensity and ncount along instrument\n",
+    "    - Diagrams can now plot any variable along the instrument\n",
+    "    - Diagnostics object can show beam at a specified number of points in the instrument\n",
+    "- McStasEventData can now generate binned data, described in [data](../user_guide/data.ipynb)\n",
+    "- All datasets now have attributes total I / E / N in metadata, shown in [data](../user_guide/data.ipynb)\n",
+    "- McStasScript can now read 0D monitors\n",
+    "- Improvements to reader, better handling of using COPY for component names\n",
+    "- Instrument objects now have a mccode_version attribute that keeps major version as integer\n",
+    "\n",
     "## 0.0.51 - 17th of October 2022\n",
     "- New feature for segmenting the instrument using MCPL input/output [tutorial here](../tutorial/McStasScript_tutorial_5_MCPL_bridges.ipynb)\n",
     "- Printed components now conform to McStas syntax (contribution from Peter Willendrup and Erik Knudsen)\n",
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 5224300a..d43f39f2 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -38,6 +38,7 @@ Documentation
    tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
    tutorial/McStasScript_tutorial_4_JUMP.ipynb
    tutorial/McStasScript_tutorial_5_MCPL_bridges.ipynb
+   tutorial/McStasScript_tutorial_6_Diagostics.ipynb
    
 .. toctree::
    :caption: McStas Union Tutorial
diff --git a/docs/source/tutorial/McStasScript_tutorial_6_Diagnostics.ipynb b/docs/source/tutorial/McStasScript_tutorial_6_Diagnostics.ipynb
new file mode 100644
index 00000000..69684493
--- /dev/null
+++ b/docs/source/tutorial/McStasScript_tutorial_6_Diagnostics.ipynb
@@ -0,0 +1,850 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "scientific-equity",
+   "metadata": {},
+   "source": [
+    "# Diagnostics\n",
+    "When working with a McStas instrument it is possible to record what is happening not only at the detector, but along the entire instrument. It is tempting to add many monitors along the instrument to investigate all this for every run of the instrument, but this can result in confusing instrument output with many monitors where only few are important.\n",
+    "\n",
+    "McStasScript has a diagnostics tool that allows inserting monitors temporarily to understand the instrument, and in this way they do not pollute the results of every run. For our examples we will need an example instrument, so here we have a source with a guide that contains a velocity selector followed by a monochromator and sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "previous-layer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import mcstasscript as ms\n",
+    "\n",
+    "instr = ms.McStas_instr(\"demo_instr\")\n",
+    "\n",
+    "# McStas 2.X\n",
+    "instr.add_declare_var(\"double\", \"x_at_source\")\n",
+    "instr.add_declare_var(\"double\", \"x_after_guide\")\n",
+    "# McStas 3.X\n",
+    "#instr.add_user_var(\"double\", \"x_at_source\")\n",
+    "#instr.add_user_var(\"double\", \"x_after_guide\")\n",
+    "\n",
+    "wavelength = instr.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength for mono in [AA]\")\n",
+    "w_min = instr.add_parameter(\"w_min\", value=3, comment=\"Minimum wavelength from source[AA]\")\n",
+    "w_max = instr.add_parameter(\"w_max\", value=6, comment=\"Maximum wavelength from source[AA]\")\n",
+    "\n",
+    "src = instr.add_component(\"Source\", \"Source_simple\")\n",
+    "src.set_parameters(xwidth=0.1, yheight=0.1, focus_xw=0.04, focus_yh=0.06, dist=2, flux=1E7)\n",
+    "src.lambda0 = \"(w_max+w_min)/2\"\n",
+    "src.dlambda = \"(w_max-w_min)/2\"\n",
+    "src.append_EXTEND(\"x_at_source=x;\")\n",
+    "src.append_EXTEND(\"x_after_guide=0;\")\n",
+    "\n",
+    "guide1 = instr.add_component(\"guide1\", \"Guide_gravity\")\n",
+    "guide1.set_parameters(w1=src.focus_xw, h1=src.focus_yh,\n",
+    "                     m=2, G=-9.82, l=10)\n",
+    "guide1.set_AT(src.dist, RELATIVE=src)\n",
+    "\n",
+    "v_sel = instr.add_component(\"V_sel\", \"V_selector\")\n",
+    "v_sel.set_parameters(xwidth=guide1.w1, yheight=guide1.h1, zdepth=0.4,\n",
+    "                     radius=0.15, length=0.3, nu=370)\n",
+    "v_sel.set_AT(guide1.l + 0.5*v_sel.zdepth + 1E-6, RELATIVE=guide1)\n",
+    "\n",
+    "guide2 = instr.add_component(\"guide2\", \"Guide_gravity\")\n",
+    "guide2.set_parameters(w1=src.focus_xw, h1=src.focus_yh,\n",
+    "                     m=2, G=-9.82, l=15)\n",
+    "guide2.set_AT(0.5*v_sel.zdepth + 0.01, RELATIVE=v_sel)\n",
+    "guide2.set_ROTATED([0, 0.7, 0], RELATIVE=v_sel)\n",
+    "\n",
+    "guide3 = instr.add_component(\"guide3\", \"Guide_gravity\")\n",
+    "guide3.set_parameters(w1=guide2.w1, h1=guide2.h1,\n",
+    "                     w2=guide2.w1*0.8, h2=guide2.h1*0.8,\n",
+    "                     m=2, G=-9.82, l=10)\n",
+    "guide3.set_AT(guide2.l + 0.01, RELATIVE=guide2)\n",
+    "guide3.set_ROTATED([0, 0.7, 0], RELATIVE=guide2)\n",
+    "guide3.append_EXTEND(\"x_after_guide = x;\")\n",
+    "\n",
+    "mono_Q = instr.add_declare_var(\"double\", \"mono_Q\", value=1.714) # Q for Ge 311\n",
+    "instr.add_declare_var(\"double\", \"wavevector\")\n",
+    "instr.append_initialize(\"wavevector = 2.0*PI/wavelength;\")\n",
+    "\n",
+    "mono_rotation = instr.add_declare_var(\"double\", \"mono_rotation\")\n",
+    "instr.append_initialize(\"mono_rotation = asin(mono_Q/(2.0*wavevector))*RAD2DEG;\")\n",
+    "instr.append_initialize('printf(\"monochromator rotation = %g deg\\\\n\", mono_rotation);')\n",
+    "\n",
+    "mono = instr.add_component(\"mono\", \"Monochromator_flat\")\n",
+    "mono.zwidth = 0.05\n",
+    "mono.yheight = 0.08\n",
+    "mono.Q = mono_Q\n",
+    "mono.set_AT(guide3.l + 0.3, RELATIVE=guide3)\n",
+    "mono.set_ROTATED([0, mono_rotation, 0], RELATIVE=guide3)\n",
+    "\n",
+    "beam_direction = instr.add_component(\"beam_dir\", \"Arm\", AT_RELATIVE=mono)\n",
+    "beam_direction.set_ROTATED([0, mono_rotation, 0], RELATIVE=mono)\n",
+    "\n",
+    "slit = instr.add_component(\"slit\", \"Slit\")\n",
+    "slit.radius = 0.03\n",
+    "slit.set_AT(0.6, RELATIVE=beam_direction)\n",
+    "\n",
+    "sample = instr.add_component(\"sample\", \"PowderN\")\n",
+    "sample.set_AT(0.1, RELATIVE=slit)\n",
+    "sample.radius = 0.015\n",
+    "sample.yheight = 0.05\n",
+    "sample.reflections = '\"Na2Ca3Al2F14.laz\"'\n",
+    "\n",
+    "banana = instr.add_component(\"banana\", \"Monitor_nD\", RELATIVE=sample)\n",
+    "banana.xwidth = 1.2\n",
+    "banana.yheight = 0.3\n",
+    "banana.restore_neutron = 1\n",
+    "banana.filename = '\"banana.dat\"'\n",
+    "banana.options = '\"theta limits=[20 175] bins=150, banana\"'\n",
+    "\n",
+    "Lam = instr.add_component(\"Lam\", \"L_monitor\")\n",
+    "Lam.nL = 160\n",
+    "Lam.filename = '\"Lambda.dat\"'\n",
+    "Lam.xwidth = 0.05\n",
+    "Lam.yheight = 0.05\n",
+    "Lam.Lmin = \"wavelength*0.3\"\n",
+    "Lam.Lmax = \"wavelength*1.7\"\n",
+    "Lam.restore_neutron = 1\n",
+    "Lam.set_AT([0, 0, 0.1], RELATIVE=sample)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acting-expansion",
+   "metadata": {},
+   "source": [
+    "## Diagram in analysis mode\n",
+    "One important aspect of a McStas simulation is how the intensity and number of rays evolves over the course of the instrument. To investigate this, analysis can be enabled in the diagram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "catholic-baseball",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 446.4x129.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 502.646x345.6 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "instr.show_diagram(analysis=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "patent-mountain",
+   "metadata": {},
+   "source": [
+    "Such a graph shows the intensity measured just before each component in the instrument. There are some circumstances that can be difficult to interpret correctly, for example here we see the intensity and number of rays rise after the banana detector, how can this be? This is because the banana detector is placed at the sample position, inside the sample, and some rays are already past that point and are thus not recorded.\n",
+    "\n",
+    "\n",
+    "## Analyzing a variable\n",
+    "Furthermore one can investigate any variable from the table below with the variable keyword.\n",
+    "\n",
+    "| Axis string | Full name | Unit | Description |\n",
+    "| --- | --- | --- | --- |\n",
+    "| t | time | s | Particle time |\n",
+    "| x | x position | m | Coordinate x of particle |\n",
+    "| y | y position | m | Coordinate y of particle |\n",
+    "| z | z position | m | Coordinate z of particle |\n",
+    "| vx | x velocity | m/s | Velocity projected onto x |\n",
+    "| vy | y velocity | m/s | Velocity projected onto y |\n",
+    "| vz | z velocity | m/s | Velocity projected onto z |\n",
+    "| p | weight | intensity | Particle weight McStas n/s |\n",
+    "| l | lambda | AA | Particle wavelength |\n",
+    "| e | energy | meV | Particle energy |\n",
+    "| speed | speed | m/s | Speed (length of velocity vector) |\n",
+    "| dx | x divergence | deg | Divergence from z axis along x axis |\n",
+    "| dy | y divergence | deg | Divergence from z axis along y axis |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "contained-horror",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 446.4x129.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 502.646x345.6 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "instr.show_diagram(variable=\"l\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "improved-navigation",
+   "metadata": {},
+   "source": [
+    "Above we see the wavelength distribution along the instrument.\n",
+    "- Before guide1: Uniform distribution from source\n",
+    "- After guide1: Some transmission losses, especially at lower wavelength\n",
+    "- After V_sel: Chooses a limited wavelength range\n",
+    "- After guide2 and guide3: further transmission losses\n",
+    "- After mono: No change (neutrons are either transmitted or scattered)\n",
+    "- After slit: Only rays scattered by mononchromator continue\n",
+    "\n",
+    "This tool is useful for quickly understanding what is going on in an instrument. Below we set up a situation where the wavelength selected by the monochromator is on the edge of what is transmitted by the velocity selector. The limit keyword can be used to zoom into an important area of the plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "loaded-trailer",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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dGTVqFCkpKRk2FtMJCgris88+U72/du2aKtwsoPqB6N27tyo3ZWpqKrNmzWLXrl0sWbLkk2Up7jRo0IC5c+cCYGZmBkBycjIvXrwAID4+nqioKEqUKPHJfa5evVoVDbJmzZoZ6iZPnszvv/+eKfJkVqTbpXPqlJFn3/SnT59y4cIFJEnK4Imwc+dOpkyZAih3698l3Z9US0sLPT09tLW1VRsH2V3BFGre3j/I6f5kZS1wN1f+QFxKgU2v4Edj6KWfBwIWXWxtbTNt0LwdFqFGjRqqjaB3Sd+MfJeZM2dmKluzZg1r1qxRvc/qyW/w4MEMHjw4Q5mZmRnJyclZjvMpG0ujR49m9OjRGco0NTWJiopSvbewsPjPbiy/jYWFRaYf0nnz5vHgwQN69+7N7t27GTt2LIcOHfrkPuvXr0/duplOfwNgY2PD559/nimG+7vExMTg7e0NKM1xOSHPlPWuXbuQJIlp06ZRv359ANavX4+HhwcxMTF5NUzho5kObIyH1bHwNA32JYCxDNKfivYlgJUGbI1XKul62mAgU66QA9PgYALUfsdnu5Y2mMpgVwKYayg/pTIaYCoHr0Rw1IQqmkqlHZY97wSBID9JSYGLF6FePdBWw1GE4OBgduzYoXpfrVo1Fi1aRM2aNfnrr7/Q0dFh69at7N69O9NG7/vw9PRUxWivVKlSJo+dqVOnsmXLliyvPXfuHCEhIaxfv56IiAiGDBlC+fLlczS3PFXWMpmMr7/+GktLZcCodFeln376Ka+GKXw01IHZxrA+DhbFgIMGTDGGwNfG5GY6sDMeItJgkpFydQxK88a2eJgSBQtNMvZpIodfzZT9rY4FPRnMNAZdGXgnw84EpeJvog199Qp0ugLBh9iwAcaOBU1NaNQIeveG9u2hoIJ1Xrt2LYNJydXVlZSUFH799Vc0NTVZtmwZhw4dYuLEibRt2xZjY+OP9vnDDz+o/p4wYUImZV2lShW6d+/Ovn37Ml37yy+/YGBggJ2dHQsWLOC7777L8dxyHHVv8+bNHDt2jM2bN+d48OwSGBhIkyZNCAwMLLAx36Vh68b49PaHph9xsXOPhx+i4VujjHbnfMZkVCo7pm2hXbt2BTamQJDO2rUwaRKkxzfT1weFAszNoUsX6NoVmjeH3Jxl0dXVJSkpKVOM9qJGaGioyitp7dq1jBo1CsiHqHs6OjoZMmEXBPHx8apYyupCT1dX6SddWEmU1H6P8pOAAKhUCT6y+S5QI28fwExXEc+ewW+/wbZtys+ubl2YOhVenyHJFtra2iQlJREXl8sDZGrmbf9s7U+wGeVYWVesWBEfHx8iIiKyDICTHxw6dEjtOfpqVKrBuTNXSGklwYd2dbvrK18FSXAaiXfjqFChQsGOW4AkJChXap/w9CpQE+/ToQoFJCcrvzb+/vBONIBPxs7ODj8/P7Zv386wYcMyeM4UFVJSUpg+fbrqvb29/UevybGyrlWrFgMHDsTNzY1FixZRunTpfIsTkpCQgJeXF6tXr+b48eP5MsanMvP7GRxpeYTH80JJ6aih3CxUNwogNA39hanMmvEj5cqVU7dE+UblynDnzpvHbEHhYudOWLr0zecjk4GRESQmgrMz9OoFHTsqP8ecqosRI0bw1VdfERkZibOzMyYmJkUu6l5MTAwpKUp3XWdn5/d6m7xNjpW1TCZj7ty5WFhYsGjRIsLCwnLa1UfR1tbGwcGB48ePq31lXaJECby9zjPmm7FcW3aNxIREtcqTjlVpK4ZNH8qIL0eoW5R8R+Q+KLycOQOpqaCnp3z66dwZunUDV1fIozDrjB07luTkZL755huADC6MRQ1nZ2eOHTuGoaHhR9uKtF4CgSDPiIyEPXvAzQ0cHfN3LE9PTy5fvkxISEj+DpQP6Ovr4+joSM+ePTMd0HnfBqNQ1gKBQFCIyJccjJIkMWPGbNz3exAZmX85GPMCLS2lKWXFL4tzfIIondDQUL4aNZibN26QkFg03RJ0dbSpXLkKv6xZj8hYLxAUfnKsrCVJYsyYcRz2vIRL2+UYGFmR83PU+U9aaiKBD7xo0bINJ7w8c6ywQ0NDadG0Ad2rBjPry1SMiuiZlLgkcL/ynBbNGnDyzMWPZqkQCATqJcdR965cucKeff/QZcAhypRviImZLSZmNoX2VcKyErUajca58feMm5DzU0Q/zZ+Dm90z5vdOxckabCyK5qtqWfihi4L+tSOYPX1Kju9HYeDtwPNyuZxSpUoxfvx4VaKA9EDwb78mTpwIKE+4yWQyIiIisuy7V69eyGQyypUrhyRJWQa5T38NHjxYFfp3yZIlrF27FplMxoIFC1T9JSQkYGhoiLW1NZIkZdnPu1H33pZTJpNhZGRE8+bN8ff3V9VHRkYyZMgQLC0t0dHRoVatWqp41+kyvS/Ifm7m+fYrPchUetB+DQ0NTExMqFevHr/99lsuP2VBjlfWDx48oIx1XXR0TT7euBBR3rElnrt/yfH1D+/fZGjV4hNgyq2qgpledz7esAhQ3HMwAvz+++/cu3ePJUuWMGnSJP755x8AevTowenTpxk6dCi1a9dm4cKF9OnTh+PHj9O8eXOVzIMHD8bIyIiVK1cCyih1uZln//79VdlR3nUZnTNnDtra2qxatYpRo0bx/PnzTGFrBZ9OjpV1cnIyGhr5d1Iu4vlt7t7Yg7V9c8rbN39vu8O7h3Lr6ha+GOuNnoEF6xZVwL5SB3oMPpBle00tPVJSso6A9ikkJyWhWwhy5brOg1P+EL4GLD6eTvC96GlDcnLRtLu/S3HPwQjKcKsWFhYsX75cNb+TJ09y+vRpGjduzPr16wFlvtJ27drx448/cvLkSVXQ/C+//FIVZD+d7du353ieFStWVMmur5/xEFibNm2oW7cuvXr1olKlSixcuJApU6YU6xO2+Umep/XKKyLD7uB9fA5PH516bxtFWiq1GoykU7+tmJoL59v/OsU5B2M6L168wNPTk9TUVFV0y/Rg9o0aNVK1a9y4MQA3b97M13nOnDlTJfv7gvrb2tpSuXJlEhISuH///if1K8hMnkeuv3V1KxdPLSbqxSMMjMvQe9gR/H134HvxDxLjX2BVrg5uXX7BolQ1zh37Ee/jc6jVcDQB94+RmPiSVl1XYlWuLgf/Uv6qex+fg/fxOfQZfoxbVzZx6+oWajYYwcM7HtRr+g1hIb4ZVtYAyUnRuG/uTtDjMzhU7kibnr+jqZk/v+ZhUfDZGvB5qMyjW6UMLPkcRv4JTyJAWxMaO8K6YVC2BMzaB7PdYYwbeFxX9rFtNMw5AOfuQf/G8NtQZblsAFS0gqaVYM8lZT/bxoBZFk/JC/6G309ARCw0rgBrB4N9Sdh/Gb7bAYGRYG4InzWCpZ/ny61QO+k5GIH35mBs2rQpwEezv2eVg3H16tWcPn0aV1fXT5apZ8+ejB07lj179lC1alWioqIYMSLjwSVbW1vWrVsHKBMffIh08429vb0qTVhuyO08R4wYoTI1fejIdG4D7wvyeGX99NFpDu8egkKRSsvOP1O5Rh8C7h/lrOcMLK2q07TNj4QGXWb/5p6kpb3JjBL40ItajUaRlBjF6SM/oGdgSR0XZUaZCk496NRvK+Ylq6jaBz05R+NWMyhTvmGWcjwL9KasbRPKO7Tg9vW/8PP5PS+nmYFt58HrNkxoq1SCtWxAkmBQU1gxAL5qBf/egFnuGa87ew++dFUq0ebzoKEDVCqtVLjX30rRdi8USplAn/pw2A/m7M8sw6Yz8P1uaOAAUzuBXyD0WaWsm7kPEpJhzSD4tgMYFOMn0OKcgzGdHTt20K9fPx49eqQKPZzu2XThwgVVu/RA905OTh/sL7fzrFChgkr29ynrgIAA7t69i56eHo75fVKmGJOnK+uH/srNDteOi3GorMwCfGCb8j9mi46LMbOoQMjTi9zx3cHLiHuq6+o0mUDN+sPx9fmdlxH30dY2oIxNY66cW4FFqWpUrpnxP3fTNnNwrNoZgOs+mXeZy5RvSP1mk3gV+ZD7t/bz9PFparuMy8upqqigXMhxyh/SKkK/hsqV71/nwe/pm3Y3nma8blwbGNQEZuyF0qYwuycoJLj6BB6HK5U+QLkSMK83JKfC+lNwMou9QI9ryn93+ihfAKFR8CJWKd99Xzh+G2rbwIgWeTn7wkV6DkZ9fX26du3KjBkzOHDgzd7FuXPnVEmYy5UrlyGt15w5c1Sr2s6dO2fIwZi+4ff111+zd+9eVq1ala1YFH379sXT0/OjORjTadeunSqD0ru4ubnRtWtXTp8+zYoVK5g4cSKurq40a9aM06dPM3z4cJydnVWK/GMbermd5/Xr11Wym5mZZcga7+npyenTp1m1ahXJycnMnj1b2KtzQQEm8Hv/44+unvK4pUyuiSQpbWUfelwyNP7wqiidgkhz1MkZLsyEozfhyA34yUNpboiMhdk9oKEjdFoKie8kITfVBy3NN38DaLx+zknLwlz4oamkV20bDSVfR6NTSKCvrSzbdxkuP4bFh2D5vxC04r1dFQuKWw7Gd9HV1WXq1KmMHz+eJUuWsHTpUvbt28e3336Lu7s7mzdvpnLlyixZsiSTKehtoqKicj3Pbdu2sW3bNkCZn/BtZT19+nQMDQ2pWLEi06ZNY/jw4R/sS/Bh8lRZO1TpxOUzyzj5z3fERQcT/SoQuwqtuX9zHycPfYeNoxsP7nhgWsIBM4sPB2TS1VMmu3z25Cz+vjtxqNrlk+UIDrzAxdNLCQlUPhZaf8CbJLfsuQi+geBYCqqVVdqdI1+HqY1NBPfLkJILT7+gF/DDbgiPVirgFlUzt+nsDHsvwaazypX9ozDlCvzMdPhmm9KvumZ58LyhNKukpoFm0QpS9kGKew7GrOQcN24c48a9eVo0Nzf/pKStb8dQNjExyTIrd07nmc7GjRtV/tuCvCNPbdbWdk1p33sDMrkGxw9O5I7vTso7uuHSejZhIX6c+fd/WJWrQ7eBe9HQeL97EkBZGxfKO7Qk6MlZPHZ8QWJ85CfLUdbGheAAbwIeeFGl1mfUqJ9/v+j62srNv1EbYJcP9G0A1+dC5TKw4YxylW2Si7DWlUpDWDTsugjtasD/umZuM6gpLOwL90Nh9EbY7g3NKivrklNhwUH48g+ISYRVA4uXohYUMvz8lDFQf/1VmSlCkGfkKq3X8tWeuHXfmE+i5Q/RrwI5sMmV0JCcpQZr17IhE+v60K5G3sqVFbIBytX6zXzMXHThAUz8uxIXrvp/vLFA8DHWroXx45XZchUKsLBQ5vLq2hWaNs1dPq//CHkeyEkul6OQit5JPoUiFbk85w8UcrkGqUVv2u8lNU05pyJDWhps3Pj+dCQC9XL2rDKvV3o+r6AgpQLfskWZz6tePejTR5lFV3iGZIscK+ty5crxMvwekkKBLBfKr6B5EXaXsmWtc3x9ufL2+Aefp9OHzy7kCVLW2e3zFP8QsC5fhII4Xb8OI4p/goUizbsHahQKiI5W/n32LJw/r1x9jxkDr10FBR8nx8q6YcOGlLTU5dSh8TTvsKJIKOzw0JucODiStWuX57iPL0eNo1N7d6pbx9G2AEwh+cnZuzBtrx679uU8sFWBY2enfJxO//ILChfh4RAWpky2+DZv5/eqVUuZ36tHD7WIWFTJVfKB6OhoOnTsxqWL3hiZWCIrxCFSU1ISSU6K47fff2XAF1/kqq9z587RvWsHUlOSMNIrQO/HPCQuMY00Sc7O3e60adNG3eIIigtr18LXXytNHrq6b5T02/m9PiGF1X+ZfEk+YGxszNkzXsTFxb03xGRhQVtbm1KlSuXKXp2Oi4sLz8NfER4eTkIRzdyqq6tLyZIlxfFfQd5SpozyUICLC/TuDR06KG3T4v9ZrsmTZaGBgcEHwzoWR2QyGSVLllS3GAJB4aJrV+Xmr2bRfOIszOT6jnp7e/Pvv0cIeR5Mfp8XlAFWJUvTpk3bj8ZQyE/S0tLYunUrN/x8iYuNUZsc2cWshAWNXVxU8YcFgnxBKOp8IVd39e+//2bQsC+o2bU0BiW08v9RR5K4+yyFFd2W88evG+jZs2f+jpcFaWlpDBk0EN+Lp2nmVAZjnQ8f7ilMvAxNYsymP/D/+ju+/W6yusURCATZIMfK+vHjxwwY3J9+v9SibFXTPBTp41RuUYqhwwdTs2bNAo/itXTJEu5cPcfqca3RK0KKOp1OjSoyaukiatZypnXr1uoWRyAQfCI53m3z9fXFpoZFgStqgDKVTbBztuT69esFPrbPhXN0a+xQJBU1gFUJQ1rWKs/FixfVLYpAIMgGOVbW0dHR6BrlbGHuPus6M+t68Oz2q5wOj46RBtFq8LWNjnqFsX7RDvNorK9FdNQrdYshEAiygVp2Aur1ssGxkSUlyuUiwtF/gNQ0BZoahf+wkUAgyH/yVFm/DI5neRcvbJxLoKWnQeD1FzQbWgG5poxT6+5jVlaffkvqcmlPANc9ghixuQkmVhJ7frjKs5uvkMllWNga0n95PQzMdDi39SGX9wQQHZaIWTl9RmxuirZu4Ypj8SI6gR/+OM7NJ2HIZTJsrUxZ/lU79p25g/sZf6LjkqhiY8Hkz1xwKFOCWRtP4uF9j83TulPV1pKm4//E1FCXg/M/5+D5u8zedIrWdex5GPwSm1ImzB/uxm8Hr3Dk4gNexiRQ1caSdd91ITYhmaW7znP2RiAKhURLZzsm9W2MrrYmvx28zN5Td4hNSKakmQGjutSlXX0Rh0EgKMrky8r6qd9LWo+rQsTjWI6v9seungW1OpfDZ8cTLmx/nKGt3+FnPL4USdMhjpiW0SP4dhSKNInrHkF4Lr9DOSdTmgx25PmDaKS0/E8mkF0OX7zPpbvBDGlfizLmRtwOCMf9jD9rD1ymafXyNKhajtX7LzJpjSd7ZmcOPJ8V3reDGNWlLlYlDNl45Dobj1ynsZM1LZ1r8/DZCwCW7jzPIZ/7fN6qOnKZjC2efhjoaTO0vTPrPK5Su2JpOjeqSHBkDApF4btvAoEge+SLsi7nZErjL+wJ9n/Fq5AEmg5WKmKfHU94+SwePeM3m3Pm5ZWHaQKuRiIpSlC9bRmMLHS5e+Y5AF2n16Skg1F+iJknlC9pAsDVeyEoHCXa1nNk18lbAHzduxHlS5lw83EYRy4+IOD5q0/qs0vjSvRrqcyd9+eha8hksGC4Gwa62qo2Z24EkqaQ2OL5JhPKhdtBfNW9PubGejwNi8Lv4XOq2ZWkZW27PJqtQCBQF/mirHWNlMpYQ1Npb9Ux1EQmV/pgS++s8io1LcXwjS48vBDBA+8wzm56yMDVDfJDrHyhaQ0bNk7txoXbQXjfesqmf30xeZ2VNiu3c/nr+5CmUJCckkZScuZ4q5amGW3574u5Ym6sx49D3yRV1NbUQFNDzvbpvfC69pi7gREs2HaGK3eDmTPs/emdBAJB4Uftu1e3jgVz90wYJla6WNorV9Ax4UlUaloKgANzfLmyP5DDS2+RFJeqTlGz5NiVR5zxC8CqhCH2ZZSpyPq2UK6Kl+32ZofXTU77BlDO0hibUqaUMVfO8dCF+6x090HxkUBaTWuURyFJTFt3nAPn/Fm687yyvHp5IqMTOO0bQEhkLCeuPcHz8kPiEpNZsc8HmQyq2FiiraVBeFR8Pt4BgUBQEKj9XKiWrga3j4fwKiQeTW0NnFqXoWqr0mjrahD3MolLewI4tOgmZmX1cRtbWd3iZkJXW5PjVx8TEhmDtpYGrevaM6BNDeRyGfvP+nP5bjBVbSyZ/JkLmhpyujWpzGm/AA5ffEDHhhXQ0frwhungdrVISknjsM99LvsHU83WEoBJfRujoSHn2JVH/H3+LuVLmjCgTU005HKCI2I45fuEpOQ0bEubMrprpgBeAoGgiJGrtF6rds+h0yz1KNB/5vgzsss0hg4dWqDjtm7ZjC5OhjR2ynkCA3Wz8ch19B2b8dOixeoWRSAQvMP7QqTmygzy3/QxkCEV8Zln5wdaIBAUDnKsrM3NzYl/mZKXsmSL+BepWFhYFPi45hYWvIxJLPBx85IXsSlYliylbjEEAkE2yLGyrlOnDs/vRXPnRGheyvNJ+J8MJfjOK+rWLXhbbNt2Hdl07DYRRXTT7vaTcI5eeUzz5s3VLYpAIMgGOd5gtLKy4ui/x2nd1g3/Iy/QK6FBvmf1kiDhZRqB11/w7+GjlClTJp8HzMyQoUMJDn7GkCXLqVOxDPo6hetE5fuQJIhJSOGSfxDrN2ymXr166hZJIBBkg1zlYAQICgrC29ubsLCwvJYtSywtLWnUqBHW1urd4Lt48SI3b94sUmm9TExMqFu3LpUrFz6vGoFAoOR9G4y5VtYCgUAgyDvyJWEuQHx8PHfu3CE8PDy3XRUKtLW1cXBwwMbG5oPtnj9/jr+/v1pX1lpaWjg4OGBra6s2GQQCQcGQK2UdGBiIa5MW6MsNsTSyeu+x6KJEUmoi/sE3+Wr8WGbPmZ1lm63btjFyzFjMHaqgqae+RMGKlGQiH95h1PAvWbRgvshULhAUY3KsrF+9eoVrkxYMrjWOLxtNyEuZ1E54zHP6bnDD2NiYSd9NylB38OBBxn7zLS5rDmNsX0VNEr4h6VUkmyd0xdjYmOnfT1O3OAKBIJ/IsevexYsXKW1gXewUNYClUSkWdFjLjm27MtVt27UHh8FTCoWiBtAxNafa5OVs3bFT3aIIBIJ8JMfKOjQ0lNJG5fJSlo/Sa10Lyn4v50VcBKHRwSw9Nosjt/fny1hlTK0JfR6SqfxZSCh6JfPGZfBwjxrsd8v9PdQrWZbw0IL3dxcIBAVHrmzWMlnBBu2b2HI6A2JHYqhjzJ1QP5Z5/Ujv2oNoV7VbtvtKTUtFU+P905d/YG4yed7Mu+Y3PyGl5P4UaF7JIxAICi95+i1/+vIJZb+X0+P35nyxsQMVZxmz+tRP/HpmKZVnm9J6pTNPXz7h2aunDN3Snao/lqD2grLM8JhIUmoSAA0W2eE405B5R6ZQbY45HVbXJyxGuWpc7jWHMTs/JzYpmg5r6gOw++omyn4vZ+eVjbxKeMnEPUOoPrck1eeWZPzuQbxKeAm8WZXPODgBp7mWHLvrwdJjs6g5zwr7GXq4LKmA+/W/cjTv8Ktn2dvYjGtLvwPg2tLv2NvYTFV+pE8dLs8dy4HW5TkzoQepicrTj77LpnBp7hgA0pKT8P3le/7pUoW/29hwfvLnxD8PAuDy3DHsbWyG36rpHOpajUPdqhFx/XwOPyWBQFAUyZcl2ZVAb5o6tsJM35wFnt9z4t4R+tQexO0QX9adW864XV9w1P8go5t9R/MKbVl/fgUrTsxTXZ+QEk9EbDgtK3XE99lltl1al2mMKW3mAtDQthlr+v5FI7vmzPCYyO6rm+hTZxB96wxm77UtzPCYmOG6myHX+aHdQiwMS7HM60cqlKzCgq5r6VGrPwpJkR+3g7igR+iYmWPuVJ+wSyd4duLvTG38Ny3lwc61lKrfgopfTCDk/L9cmjUiQ5tIPx/sug0hISyY2+t/yhdZBQJB4SRflLWzdQNGNvmGejYuSJLEV82nMrTxeAD8Q2/i8+QMta0bMs51Ggu7rUUuk3Pi3pE3QsnkLOi6hsENlavOoJdPMo3R3LENANYl7Ohasx/lS9jhdfcQVsZlmd5+Mf9rvwgr47KcuHs4w3VzO6/ks7rDqFm2LiWNrHgceZ8rgecpbVKODtV65MftQNfCiupjf8Smw2cAxIc+zdQm1PsoyOU4T/6ZygO/wbxaXSJ8vUmNj1W1qTpsKlWGfItcW4f4kMB8kVUgEBRO8kVZG+uaAqCpofX6vQka8owxND7kE6yrpYeuli6acqVNOS2LFW9OfYqtjJWbg1oaWhwdd53xrj8AMPXAaCbvH5mjPtNtxlKaMpNNSkxUhnptI1Nlu9c2ciktcyqvT0Hb2EzVj6TIWR8CgaBoUuA7U7paejS0bcbVpxdYdWoh3x8Yg0JS0LJS+2z1Y6KnVFw3g6+x33c7L+IicKvUkdDoZ8w7MoV5R6YQGv2MlpU6ZHl9bFIMc49MQS6TU6NsXXQ0dQmNDs7RnPStlHFKwq+eI9BzNyHnjnzkisyUbtwGFAquLZ7E3S3Libx1GYtajdHUN8yRTAKBoHihlrReK/psYfrB8aw+9RO6WnoMazSOca7fZ6sPmxL2dK/5OYdv7WPszv64jzjN7E4/A7D98p8A9HQeoCp7Fw25Jk9fPMbz9gESUxNwtKzC5NZzcjQffStrKvYfxyP3jTzcvQ7z6vV5fuF4tvqoNPAbUmKjCTruTvCpg5Ru3JZakxblSB6BQFD8yFVar4Orj/FLt035JZtaefYqkB6bm/L0Hdtw01ZtkToOx6phKzVJlpnEyOecH9qcF2HC11ogKOrkS1ovgUAgEBQMOVbWenp6xCXH5KUshYrYpBj0dPUylevr6WXw0CgMpMTHoqOnq24xBAJBPpJjZV2tWjUuPznP0yzc6ooD7n7bcHJyylReu4YTz4/vy7FHR37wzHM3TtUyyyoQCIoPuUo+sGrlKhbPWcqEpv+jpFHpYhGiMzElgXOPvTj//DgnznhRunTpDPXx8fG07dSZIAwo5dZDrd4aaUlJvLh2loTLx/A+fZKyZcuqTRaBQJA35Evyga/GfYWFpQX/HDjM8yfPc9NVoUFbR5sKdR04Oe0EVlZWmer19fX51+Mgc+cv4OrFA8TFqS9xroGONnUcHZgmFLVAUOwRab0EAoGgECG8QQQCgaAIkyszSEpKCqO/HM4//3gQ/vJlXsmkFjQ0NLAtU5Yff1pI3759P9j23r17fPH5F9y+c5vEpMQCkrB4o6mpiZ2NHStXr6RVq8Ljwy4QFBZyrKzT0tL4vFdv4v3v4dOtP1b6hkV6gzE5LY0rYSH0HTUaSZLo169flu0ePHhAC9cW9GjzGVOHz0FfV7+AJS2eJKck4+d/jT69+7J7zy7c3NzULZJAUKjIsc36/PnzDOvZm2u9BqGjqZZT6/nC+eCnDDh3lEdBQVnWjxk9hqiQeAb3ylnQJ8GHOX3Ri6M+Hlzw8Va3KAKBWshzm/WTJ0+oaVmqWClqgHqlyhAYEoJCkXVs60cPH1HJvmoBS/XfobJ9VQICAtQthkBQ6Mixsk5NTUX7nbCn72PjLV/ky+ew5ErerJZmeZ9CvnwOe+7f/mC74NgYZnmfYv8D/0/uW0tDA8UHnjZSU1PR1NT65P5yQmh4CG0GNGb60m+zdd29R3doM6Axi39TJmZY/Ntc2gxozL1Hd/JMtlv3brB53x88DLiXZ32+jaamFmmvQ80KBII3FGtvkOC4GH70Oc2Bh3fVLconk5eKqpNbd6aNmU3pUpl9sHM6zu37N9jq/icPA+5n+1qFQkF2zG4CgeANeW7DWHfjKouveBMcG0M1c0uWNmutqrsZEUbdv9YREB3FlHoufFunEWHxcXx+eB8+oc+Qy2RUMbPgYNd+WOjpM//SWdbduMaLxATqlirNyhbtqGZeMtOYHo/u8b/zJ3jw6iWOpmYsadaaVuXtqb99PQCb7vix6Y4ff7buQnfHSnx9ypN/Ht9HIUl0d6zM8uZt0dfK2Wr5z12/suPgZtYv2k5MbDQTfxzJsL6j6dtpAL1Gt8e6jA0/T/+Vm3d9+e2vlQQ8e0wJkxL07tifji27ERoewsBvelLV0QkdHV0eP33Iill/qPpPTU3lf0snceOuL7MnLqRujYYZxr926zLL/liAJCloVr9lxvty3J2jZw+xavZ64oziMo2za/U/HDnlwS6PrYS/CMOhvCNjB02igm0lUlJT2Lz3D054e/Iq+iUV7aswqOdw1u1YBcCSdfNYsm4em5ftJTEpgTVbfubuozsYGRjRzrUL/bsORiaT0WZAY8qUKodD+Qpc9D1Pi0ZtOHLqIGvnbsTBpiJ7Dm/n979WMu+7ZdR7Z24CgeANebqy9nr6mJHH/8FST5+lzVoTGBNF1793Evk6QeyxwMcMd6qNlYEhk88cwzc8lG3+N/B6+oTxteqzpGlralpakSZJbLjty/TzJ6lhUZK5jVtw6Xkw3f7eRco7MTnuvYyk1z970NPU4of6TdDR0KTHwd2ExMUwt3ELAJqVLc9f7bvTvFx5vj7lyVb/GwyqWpNhTs78ees6M7xP5njO1SvXAuDO/ZvceXALgNv3b/I0JIDo2CicKtYkOiaKmT9PJiwylBGffYWpsRm/bFjEtVtvDhjdfnCTCraVGNRzuKpMkiQW/z4HvzvX+N9XczIp6uSUZH5aO5uY2Cj6dPyCu59g7nh7HN87V1n2x3xKWVrxedfBRMdGM3PZZJKTk9h5cAs7PbZgU9aesQOVCrx8GVtaNlamU+vUUrlqNzIwYubPk/F/eJvBvUZgZ+3I5r3r+Pf0P6oxg58HYaBvyIjPxtGjXV9kMpmq/tzlU5QwtaC2U72cfQACwX+EPF1ZH3r8AIBZDZvT2saewJgoFlw6h+7rTcgh1WoyskYdNOVyhh/z4FRQIBVMSwBw+lkgaZJEv0rVsDIw5PBj5WP20matqWBmzsXQZ/x19yb3XkVmGPNo4COS09LwCX2GT+gzVbl3SBBtbOz53/kT2Bmb0q+SMtCRx+P7pCoUGeznRwMf5XjOVStURy7X4PaDm8TGxVC3egPuPLjJnfs3AaheqSa3H9wkJi6Gz7oMpJNbd0qXLMu0RRO55HeBLq16AuBoU5Ev+40FlDZrgCs3L5KWlsa3w3+gUe2mmcZ+GhzAi6hI3Fza0qV1T6zL2DBl4fgPyvv2OL9vV66Sr9y4yJUbF1VtAoKfcOHaWWQyGT989SP6egaqOofyFfE670llh6q0aNSax08fEhIWTItGbejetg/1azbC5/o5Lvl60655JwCMDU2YOHQK8tfpz2o71cPrvCe92n/Gnfs36dGub6a0bwKBICP54srxMXfrt62Wnewr4t13KEcDH/FvwEMWXT6PZ4/+b/X14c7STaDf1WlEaxt7VXmVEhaExsVleY2VviGb23VVvdfRyPltMNAzwN7agTuvFfKEId8xe/k0jp//F7lMTrWKNbhx1zd9Nu/tx9zMIlOZiaEpr6JfcvLCMdxc2qLxETk/xR6cYZzX7Ud8Pg57a0cAFJICK8vSr6XNLO/7Po4PfUxmJiVUihqgs1sPZi2fytI/FqCQFLRqkr2UbgLBf5E8NYN0sFN+4Wd5n+I3vytsuOWLmY4uianKzawNt3z5/cZVfrnmgwxoXq48e+7fxuPxPayNjKlawhJQenF0sKsAwKTTnqy8fpG/H93DwcSMiqbmGcZsY2OPtoYG7g/v8jjqFdfCQvn+nBcpaQrMdJUxnq+Fh7Ld/yYRCfF0sqtAaHwsfz+6R0B0FPse+LPz3q1czbt65Vo8CXrEi1eR1KjsjINNBa7duoxdeQcM9A2p6uiEkYER/57ywMNrP1v3K9OO1a/Z6IP9VrCrxJf9xnD5hg8/r1+Yqd66jA0lTMzxvnKGv4/uZdv+DdmSu4GzCwAnvY8SFvkc/4e3WLPlZ4wMjGno3ASFpGDeqhkcOeXB2q3LATA0MAbgkt8FTvkcx7q0DWVKlsX76ln2e+7m9+0rlXOr9f65NXB2wdK8FNduXcLBpgL25R2zJbdA8F8kT5V1S2s7fnPrSFhCPJNOH6WckREHuvTF/PUpv/Z2jvzmd4XQuFh+atqKmpZW6Gtqsff+HUYfP8Tu+7fpW7EqvSpUZXDVmvzYyBXf8DC+P+dF3VKl2d+lD1oaGR+XK5qZs7dTbwy1tJh46l+WX/PBwcQMM11d7E3M+LySE/devqD/EXf8X0Twc/M2DHNyZve920w89S8+Ic9wKW2dq3k7VaqJJEnYlrNHT1efKo5Kk4tTxZoAGBuZMPvrRVial+K3bSt4+SqSCUMmU6tqnY/23avD53Rs2Q3PM4fYtHddhjptLW2mjJ6JkaEJ2w9uopJD9vy/a1apzbfDfyAhKYFVm5Zw6MQBqlWoDkDfzgPo22kAT549YuWmJdx/ovSoaVS7CRXsKnPm0kkWrJmFpqYms77+iYp2ldmw+zceBNxnYM/htGna8b3jasg1aO/aBYBWLmJVLRB8CrnKwXhs2Uo2tSh+XzaNX+aSmpqa4dE9nVYtW9GybkfhuZAL7jy4xe5D27h4/Txbl7tjamymqnvxKpJxs4cSFh6mRgkFAvWR5/GsNTQ0SJEKT7aUvCIlLe2DdnINDQ1SU8Whjdwwf/V0YuJiGPXFxAyKGiA1NQW5hthsFAjeJcfK2sbGBr/w5ySnpaFdjL5c18JDKW9lleWqGsDW3o77T/xpVLtJAUtWfNjy87731t1/cheb8jYFKI1AUDTIsc26YcOGONasyWfHDhIWH1fkT6alKhRcCg2mx7/7+XHBgve2++abrzl2/hCHTx0U4VHzkJTUFK7duszKzUv4cc5sdYsjEBQ6cpUpJjk5mS8HDuTgP4eIjo/7gGNa0aBsyVLM/WkhAwYO/GC727dv069vP+7euyfiWOQRMpmMcmWtWbN2Ne3bF799EIHgU3mfzTrP0noVdTuuTCZDI5vmHIVC8d7ofILsIZfL32t6Egj+S+SJspbJZOGAiF8pEAgE+YeNJEmW7xZmS1kLBAKBQD2I506BQCAoAghlLRAIBEUAoawFAoGgCCCUtUAgEBQBhLIWCASCIoBQ1gKBQFAEEMpaIBAIigBCWQsEAkERQChrgUAgKAL8H/PLFVjX6XdBAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 446.4x129.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 502.646x345.6 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "instr.set_parameters(wavelength=4.3, w_min=1, w_max=6)\n",
+    "instr.show_diagram(variable=\"l\", limits=[3.5, 6])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "subtle-gossip",
+   "metadata": {},
+   "source": [
+    "## Beam analysis\n",
+    "In addition to an overview of the instrument, McStasScript can view detailed beam characteristics at user selected points along the instrument. In order to access these features, the user has to create a diagnostics object from the instrument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "standard-reverse",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diag = ms.Diagnostics(instr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "presidential-nicaragua",
+   "metadata": {},
+   "source": [
+    "The diagnostics object contains a copy of the instrument as it was when the diagnostics object was created, so if the instrument is updated, a new diagnostics object should be made. The diagnostics object will also take a copy of the parameters and settings of the instrument, but these can be modified with the usual methods, without impacting the original instrument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "analyzed-gasoline",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Instrument settings:\n",
+      "  ncount:           1.00e+07\n",
+      "  output_path:      demo_instr_data\n",
+      "  run_path:         .\n",
+      "  package_path:     /Users/madsbertelsen/McStas/McCode/install/mcstas/3.9999-git\n",
+      "  executable_path:  /Users/madsbertelsen/McStas/McCode/install/mcstas/3.9999-git/bin/\n",
+      "  executable:       mcrun\n",
+      "  force_compile:    True\n"
+     ]
+    }
+   ],
+   "source": [
+    "diag.settings(ncount=1E7, suppress_output=True)\n",
+    "diag.show_settings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "vietnamese-discrimination",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " wavelength  = 5  // Wavelength for mono in [AA]\n",
+      " w_min       = 4  // Minimum wavelength from source[AA]\n",
+      " w_max       = 6  // Maximum wavelength from source[AA]\n"
+     ]
+    }
+   ],
+   "source": [
+    "diag.set_parameters(wavelength=5, w_min=4, w_max=6)\n",
+    "diag.show_parameters()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lesser-midnight",
+   "metadata": {},
+   "source": [
+    "The diagnostics object can hold two additional kinds of information that will help with diagnostics, *points* and *views*. \n",
+    "\n",
+    "### Diagnostics points\n",
+    "The *points* are points in the instrument where the beam is analyzed. Such a point is given as either *before* or *after* a component in the instrument.\n",
+    "\n",
+    "If one inserts a point *before* a component, the beam will be analyzed by a flat monitor at the position of the component, this monitor will be before the chosen component in the instrument component sequence. The particles are restored, so it is as if the monitor was not there at all.\n",
+    "\n",
+    "Selecting *after* would place the monitor after the chosen component in the component sequence, and the monitor will record the particles as they leave the chosen component.\n",
+    "\n",
+    "In both cases the monitor will record events at those points, and as default it will record 50000 rays. This can be changed with the *rays* keyword argument. Notice that the full intensity will not be shown in these diagnostics results as only part of the rays are recorded to save drive space, but it is possible to set rays to \"all\" if full intensities are required, at the cost of some large data files especially at higher ncounts.\n",
+    "\n",
+    "We add some points to the diagnostics object with the *add_point* method. We start by focusing on the guide system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "metallic-avatar",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Diagnostics point before: guide1                    - rays: 50000\n",
+      "Diagnostics point after:  V_sel                     - rays: 50000\n",
+      "Diagnostics point after:  guide3                    - rays: 50000\n"
+     ]
+    }
+   ],
+   "source": [
+    "diag.clear_points()\n",
+    "diag.add_point(before=\"guide1\")\n",
+    "diag.add_point(after=\"V_sel\")\n",
+    "diag.add_point(after=\"guide3\")\n",
+    "\n",
+    "diag.show_points()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "conscious-schedule",
+   "metadata": {},
+   "source": [
+    "It is possible to remove points with the *remove_point* method, it uses the same syntax. It is also possible to clear all points with *clear_points*.\n",
+    "\n",
+    "User variables or flags in the instrument can be included in the event information recorded at each point, to tell the diagnostics object which of these should be recorded, use the *add_flag* method. A maximum of three flags can be investigated and the current flags can be cleared with *clear_flags*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "included-elevation",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Instrument diagnostics for: demo_instr\n",
+      "Diagnostics points: \n",
+      "  Diagnostics point before: guide1                    - rays: 50000\n",
+      "  Diagnostics point after:  V_sel                     - rays: 50000\n",
+      "  Diagnostics point after:  guide3                    - rays: 50000\n",
+      "Views: \n",
+      "  No views yet\n",
+      "Recording following user variables:\n",
+      "  x_at_source\n",
+      "  x_after_guide\n",
+      "Does not yet contain simulated data\n"
+     ]
+    }
+   ],
+   "source": [
+    "diag.clear_flags()\n",
+    "diag.add_flag(\"x_at_source\")\n",
+    "diag.add_flag(\"x_after_guide\")\n",
+    "print(diag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aging-origin",
+   "metadata": {},
+   "source": [
+    "### Running diagnostics\n",
+    "We can now run the diagnostics instrument using the run method. This will insert the monitors at the requested positions within the instrument and perform a simulation with the current settings and parameters. It can now be seen in the print of the diagnostics object that it contains simulated data, and the number of rays recorded at each point. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "structured-canada",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "loading system configuration\n",
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/demo_instr_data_10\"\n",
+      "INFO: Regenerating c-file: demo_instr.c\n",
+      "CFLAGS=\n",
+      "      \n",
+      "WARNING:\n",
+      " The parameter format of sample is initialized \n",
+      " using a static {,,,} vector.\n",
+      "  -> Such static vectors support literal numbers ONLY.\n",
+      "  -> Any vector use of variables or defines must happen via a \n",
+      "     DECLARE/INITIALIZE pointer.\n",
+      "\n",
+      "\n",
+      "-----------------------------------------------------------\n",
+      "\n",
+      "Generating single GPU kernel or single CPU section layout: \n",
+      "\n",
+      "-----------------------------------------------------------\n",
+      "\n",
+      "Generating GPU/CPU -DFUNNEL layout:\n",
+      "\n",
+      "-----------------------------------------------------------\n",
+      "INFO: Recompiling: ./demo_instr.out\n",
+      "./demo_instr.c:7212:123: warning: format specifies type 'long' but the argument has type 'unsigned long long' [-Wformat]\n",
+      "            { printf(\"Monitor_nD: %s cannot reallocate Vars->Mon2D_Buffer[%li] (%li). Skipping.\\n\", Vars->compcurname, i, (Vars->Neutron_Counter+Vars->Buffer_Block)*sizeof(double)); Vars->Flag_List = 1; }\n",
+      "                                                                                ~~~                                       ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "                                                                                %llu\n",
+      "./demo_instr.c:7773:123: warning: format specifies type 'long' but the argument has type 'long long' [-Wformat]\n",
+      "        if (Vars->Flag_Verbose) printf(\"Monitor_nD: %s write monitor file %s List (%lix%li).\\n\", Vars->compcurname, fname,Vars->Neutron_Counter,Vars->Coord_Number);\n",
+      "                                                                                   ~~~                                    ^~~~~~~~~~~~~~~~~~~~~\n",
+      "                                                                                   %lli\n",
+      "2 warnings generated.\n",
+      "INFO: ===\n",
+      "monochromator rotation = 42.9985 deg\n",
+      "Opening input file '/Users/madsbertelsen/McStas/McCode/install/mcstas/3.9999-git//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "*** TRACE end *** \n",
+      "Events:   \"Diag_before_guide1_diag_list.p.n.x.y.z.vx.vy.vz.t.U1.U2\"\n",
+      "Events:   \"Diag_after_V_sel_diag_list.p.n.x.y.z.vx.vy.vz.t.U1.U2\"\n",
+      "Events:   \"Diag_after_guide3_diag_list.p.n.x.y.z.vx.vy.vz.t.U1.U2\"\n",
+      "Detector: banana_I=8.70058 banana_ERR=0.364257 banana_N=2636 \"banana.dat\"\n",
+      "Detector: Lam_I=633.253 Lam_ERR=14.9202 Lam_N=13466 \"Lambda.dat\"\n",
+      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
+      "    SPLIT 9 COMPONENT sample=PowderN(...)\n",
+      "  in the instrument description demo_instr.instr.\n",
+      "INFO: Placing instr file copy demo_instr.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/demo_instr_data_10\n",
+      "\n",
+      "Instrument diagnostics for: demo_instr\n",
+      "Diagnostics points: \n",
+      "  Diagnostics point before: guide1                    - rays: 50000 / 50000\n",
+      "  Diagnostics point after:  V_sel                     - rays: 50000 / 50000\n",
+      "  Diagnostics point after:  guide3                    - rays: 50000 / 50000\n",
+      "Views: \n",
+      "  No views yet\n",
+      "Recording following user variables:\n",
+      "  x_at_source\n",
+      "  x_after_guide\n",
+      "Does contain simulated data\n"
+     ]
+    }
+   ],
+   "source": [
+    "diag.run()\n",
+    "print(diag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sophisticated-parallel",
+   "metadata": {},
+   "source": [
+    "### Views\n",
+    "Since the data recorded at the points is event data, we can choose how to view this data after the simulation. This is done by adding *views*.\n",
+    "\n",
+    "All views need to define at least one axis the events should be binned into, and can optionally choose a second for a 2D histogram. The axis keyword takes all the same inputs as the variable for diagrams and any flags defined.\n",
+    "\n",
+    "Here we define a few views and plot the information. The plot will contain all views at all points, but remember they only record up to the number of specified rays, so the intensities are not accurate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "sustained-creature",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diag.clear_views()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "extended-injection",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x648 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diag.clear_views()\n",
+    "diag.add_view(\"x\")\n",
+    "diag.add_view(\"x_at_source\")\n",
+    "diag.add_view(\"l\", \"dx\")\n",
+    "\n",
+    "diag.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "handmade-proportion",
+   "metadata": {},
+   "source": [
+    "From this data we see the spatial distribution before the first guide element is uniform as expected.\n",
+    "\n",
+    "When we look at the beam after the velocity selector we see a slightly narrower spatial distribution, and can see that rays that make it to this point were most likely to be from the center of the source. We also see the velocity selector limits the wavelength range as expected, but also introduces some correlation between wavelength and divergence due to the direction of rotation.\n",
+    "\n",
+    "After the last guide element we see the final beam, it is homogeneous spatially, but there are issues with the divergence distribution and a surprising correlation with the wavelength. The gaps in divergence arise from the compression by the guide which is done in a problematic way.\n",
+    "\n",
+    "### View keyword arguments\n",
+    "Views have two keyword arguments that can alter how they behave, and in addition to these they can take all keyword arguments *set_plot_options* would allow.\n",
+    "\n",
+    "| Keyword | Default | Description |\n",
+    "| --- | --- | --- |\n",
+    "| same_scale | False | Ensures all views are plotted on the same scale |\n",
+    "| bins | 100 | Number of bins, for 2D plot can be a list of 2, one for each axis |\n",
+    "\n",
+    "Below we demonstrate how the same scale keyword argument changes a view."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "derived-miniature",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x648 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diag.clear_views()\n",
+    "diag.add_view(\"t\")\n",
+    "diag.add_view(\"t\", same_scale=True)\n",
+    "diag.add_view(\"t\", \"l\")\n",
+    "\n",
+    "diag.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sorted-poetry",
+   "metadata": {},
+   "source": [
+    "Without *same_scale* enabled we see the tof distribution at each point, while the one with *same_scale* enabled draws attention to how the distribution widens as the beam propagates.\n",
+    "\n",
+    "We can now analyze the backend of the instrument by defining a new range of points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "representative-machine",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "loading system configuration\n",
+      "INFO: Using directory: \"/Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/demo_instr_data_11\"\n",
+      "INFO: Regenerating c-file: demo_instr.c\n",
+      "CFLAGS=\n",
+      "      \n",
+      "WARNING:\n",
+      " The parameter format of sample is initialized \n",
+      " using a static {,,,} vector.\n",
+      "  -> Such static vectors support literal numbers ONLY.\n",
+      "  -> Any vector use of variables or defines must happen via a \n",
+      "     DECLARE/INITIALIZE pointer.\n",
+      "\n",
+      "\n",
+      "-----------------------------------------------------------\n",
+      "\n",
+      "Generating single GPU kernel or single CPU section layout: \n",
+      "\n",
+      "-----------------------------------------------------------\n",
+      "\n",
+      "Generating GPU/CPU -DFUNNEL layout:\n",
+      "\n",
+      "-----------------------------------------------------------\n",
+      "INFO: Recompiling: ./demo_instr.out\n",
+      "./demo_instr.c:9179:123: warning: format specifies type 'long' but the argument has type 'unsigned long long' [-Wformat]\n",
+      "            { printf(\"Monitor_nD: %s cannot reallocate Vars->Mon2D_Buffer[%li] (%li). Skipping.\\n\", Vars->compcurname, i, (Vars->Neutron_Counter+Vars->Buffer_Block)*sizeof(double)); Vars->Flag_List = 1; }\n",
+      "                                                                                ~~~                                       ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "                                                                                %llu\n",
+      "./demo_instr.c:9740:123: warning: format specifies type 'long' but the argument has type 'long long' [-Wformat]\n",
+      "        if (Vars->Flag_Verbose) printf(\"Monitor_nD: %s write monitor file %s List (%lix%li).\\n\", Vars->compcurname, fname,Vars->Neutron_Counter,Vars->Coord_Number);\n",
+      "                                                                                   ~~~                                    ^~~~~~~~~~~~~~~~~~~~~\n",
+      "                                                                                   %lli\n",
+      "2 warnings generated.\n",
+      "INFO: ===\n",
+      "monochromator rotation = 42.9985 deg\n",
+      "Opening input file '/Users/madsbertelsen/McStas/McCode/install/mcstas/3.9999-git//data/Na2Ca3Al2F14.laz' (Table_Read_Offset)\n",
+      "Table from file 'Na2Ca3Al2F14.laz' (block 1) is 841 x 18 (x=1:20), constant step. interpolation: linear\n",
+      "  '# TITLE *-Na2Ca3Al2F14-[I213] Courbion, G.;Ferey, G.[1988] Standard NAC cal ...'\n",
+      "PowderN: sample: Reading 841 rows from Na2Ca3Al2F14.laz\n",
+      "PowderN: sample: Read 841 reflections from file 'Na2Ca3Al2F14.laz'\n",
+      "PowderN: sample: Vc=1079.1 [Angs] sigma_abs=11.7856 [barn] sigma_inc=13.6704 [barn] reflections=Na2Ca3Al2F14.laz\n",
+      "*** TRACE end *** \n",
+      "Events:   \"Diag_after_guide3_diag_list.p.n.x.y.z.vx.vy.vz.t.U1.U2\"\n",
+      "Events:   \"Diag_after_mono_diag_list.p.n.x.y.z.vx.vy.vz.t.U1.U2\"\n",
+      "Events:   \"Diag_before_sample_diag_list.p.n.x.y.z.vx.vy.vz.t.U1.U2\"\n",
+      "Events:   \"Diag_after_sample_diag_list.p.n.x.y.z.vx.vy.vz.t.U1.U2\"\n",
+      "Detector: banana_I=8.96808 banana_ERR=0.207958 banana_N=8143 \"banana.dat\"\n",
+      "Detector: Lam_I=643.273 Lam_ERR=8.61571 Lam_N=40247 \"Lambda.dat\"\n",
+      "PowderN: sample: Info: you may highly improve the computation efficiency by using\n",
+      "    SPLIT 9 COMPONENT sample=PowderN(...)\n",
+      "  in the instrument description demo_instr.instr.\n",
+      "INFO: Placing instr file copy demo_instr.instr in dataset /Users/madsbertelsen/PaNOSC/McStasScript/github/McStasScript/docs/source/tutorial/demo_instr_data_11\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "diag.clear_points()\n",
+    "diag.add_point(after=\"guide3\")\n",
+    "diag.add_point(after=\"mono\", rays=8E4)\n",
+    "diag.add_point(before=\"sample\", rays=8E4)\n",
+    "diag.add_point(after=\"sample\", rays=8E4)\n",
+    "\n",
+    "diag.settings(ncount=3E7)\n",
+    "diag.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "enhanced-somerset",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Instrument diagnostics for: demo_instr\n",
+      "Diagnostics points: \n",
+      "  Diagnostics point after:  guide3                    - rays: 50000 / 50000\n",
+      "  Diagnostics point after:  mono                      - rays: 80000 / 80000\n",
+      "  Diagnostics point before: sample                    - rays: 80000 / 80000\n",
+      "  Diagnostics point after:  sample                    - rays: 80000 / 80000\n",
+      "Views: \n",
+      "  View (t)                  bins: 100\n",
+      "  View (t)                  bins: 100\n",
+      "  View (t, l)               bins: 100\n",
+      "Recording following user variables:\n",
+      "  x_at_source\n",
+      "  x_after_guide\n",
+      "Does contain simulated data\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(diag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "governing-lease",
+   "metadata": {},
+   "source": [
+    "We set up a new list of views to investigate the backend of the instrument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "optimum-closing",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x864 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diag.clear_views()\n",
+    "\n",
+    "diag.add_view(\"x\")\n",
+    "diag.add_view(\"l\", same_scale=True)\n",
+    "diag.add_view(\"l\", \"dx\", log=True, orders_of_mag=4.5)\n",
+    "diag.add_view(\"dx\", \"dy\", log=True, orders_of_mag=6)\n",
+    "\n",
+    "diag.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "accomplished-belly",
+   "metadata": {},
+   "source": [
+    "From this data we see the expected distributions after the guide which we already considered. The data after the monochromator is however unexpected. This is because it shows both rays that scattered on the monochromator and rays that didn't, those that did are near x=0 while those that did not are near x=0.2, which is the exit of the guide in the rotated coordinate system of the monochromator. The remaining views after the monochromator shows that the total wavelength distribution is unchanged, but now a its divided into the direct beam at -42 deg, and the scattered beam at 42 deg.\n",
+    "Before the sample we see the wavelength distribution reduced to that which made it through the slit, so the transmitted beam is no longer included.\n",
+    "After the sample we see how the center of the beam was attenuated and the Debye-Scherrer rings can be seen in both 2D plots.\n",
+    "\n",
+    "The diagnostics system can be used to investigate many interesting aspects of the instrument, here we will look at one final aspect using the defined user variables that record position at the source and after the guide, as we can look at the correlations between the two. When looking at such data over the different points in the instrument, only events that made it to that point is included, so the correlation may change slightly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "rolled-company",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x864 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diag.clear_views()\n",
+    "diag.add_view(\"x_at_source\")\n",
+    "diag.add_view(\"x_after_guide\")\n",
+    "diag.add_view(\"x_at_source\", \"x_after_guide\", bins=[40, 40])\n",
+    "\n",
+    "diag.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "stunning-niagara",
+   "metadata": {},
+   "source": [
+    "Here we see that if the ray originated at the positive x side of the source, it is also likely to be on the positive x side after the guide. Mainly the center 4 cm of the source is used in this configuration of the instrument, which could change depending on wavelength and coating of the guide."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "crude-intent",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index 90d22e2e..5d260841 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -68,6 +68,10 @@ def __init__(self):
         self.ylabel = None
         self.title = None
 
+        self.total_I = None
+        self.total_E = None
+        self.total_N = None
+
     def add_info(self, key, value):
         """Adding information to info dict"""
         self.info[key] = value
@@ -78,6 +82,8 @@ def extract_info(self):
         # Extract dimension
         if "type" in self.info:
             type_data = self.info["type"]
+            if "array_0d" in type_data:
+                self.dimension = 0
             if "array_1d" in type_data:
                 type_data = type_data.split("(")[1]
                 type_data = type_data.split(")")[0]
@@ -134,6 +140,13 @@ def extract_info(self):
         if "title" in self.info:
             self.title = self.info["title"].rstrip()
 
+        if "values" in self.info:
+            value_list = self.info["values"]
+            value_list = value_list.strip().split(" ")
+            self.total_I = float(value_list[0])
+            self.total_E = float(value_list[1])
+            self.total_N = float(value_list[2])
+
     def set_title(self, string):
         """Sets title for plotting"""
         self.title = string
@@ -155,7 +168,14 @@ def __repr__(self):
             string += "filename: " + str(self.filename) + "\n"
 
         if self.dimension is not None:
-            if isinstance(self.dimension, int):
+            if type(self.dimension) == int and self.dimension == 0:
+                string += "0D data"
+                if self.xlabel is not None:
+                    string += " " + self.xlabel + "\n"
+                if self.ylabel is not None:
+                    string += " " + self.ylabel + "\n"
+
+            elif type(self.dimension) == int and self.dimension != 0:
                 string += "1D data of length " + str(self.dimension) + "\n"
                 if self.limits is not None:
                     string += "  [" + str(self.limits[0]) + ": "
@@ -485,21 +505,21 @@ def __str__(self):
 
         string = "McStasData: "
         string += self.name + " "
-        if type(self.metadata.dimension) == int:
+        if type(self.metadata.dimension) == int and self.metadata.dimension == 0:
+            string += "type: 0D "
+        elif type(self.metadata.dimension) == int and self.metadata.dimension != 0:
             string += "type: 1D "
         elif len(self.metadata.dimension) == 2:
             string += "type: 2D "
         else:
             string += "type: other "
 
-        if "values" in self.metadata.info:
-            values = self.metadata.info["values"]
-            values = values.strip()
-            values = values.split(" ")
-            if len(values) == 3:
-                string += " I:" + str(values[0])
-                string += " E:" + str(values[1])
-                string += " N:" + str(values[2])
+        if self.metadata.total_I is not None:
+            string += " I:" + str(self.metadata.total_I)
+        if self.metadata.total_E is not None:
+            string += " E:" + str(self.metadata.total_E)
+        if self.metadata.total_N is not None:
+            string += " N:" + str(self.metadata.total_N)
 
         return string
 
@@ -590,7 +610,9 @@ def __init__(self, metadata, intensity, error, ncount, **kwargs):
         self.Error = error
         self.Ncount = ncount
 
-        if type(self.metadata.dimension) == int:
+        if type(self.metadata.dimension) == int and self.metadata.dimension == 0:
+            self.data_type = "Binned 0D"
+        elif type(self.metadata.dimension) == int and self.metadata.dimension != 0:
             self.data_type = "Binned 1D"
             if "xaxis" in kwargs:
                 self.xaxis = kwargs["xaxis"]
@@ -668,6 +690,16 @@ def __init__(self, metadata, events, **kwargs):
         self.variables = self.metadata.info["variables"].strip()
         self.variables = self.variables.split()
 
+        # Calculate I, E and N
+        p_array = self.get_data_column("p")
+        total_I = p_array.sum()
+        total_E = np.sqrt((p_array ** 2).sum())
+        total_N = len(p_array)
+
+        self.metadata.total_I = total_I
+        self.metadata.total_E = total_E
+        self.metadata.total_N = total_N
+
         self.labels = {"t": "t [s]",
                        "x": "x [m]",
                        "y": "y [m]",
@@ -819,8 +851,17 @@ def make_1d(self, axis1, n_bins=50, flag_info=None):
         metadata.info["type"] = "array_1d"
         metadata.limits = [centers[0], centers[-1]]
 
+        total_I = np.sum(intensity)
+        total_E = np.sqrt(error_squared.sum())
+        total_N = np.sum(ncount)
+        metadata.info["values"] = "{:2.6E} {:2.6E} {:2.6E}".format(total_I, total_E, total_N)
+        metadata.total_I = total_I
+        metadata.total_E = total_E
+        metadata.total_N = total_N
+
         binned = McStasDataBinned(metadata, intensity=intensity,
                                   error=error, ncount=ncount, xaxis=centers)
+
         binned.set_title("Binned data generated from events")
         binned.set_xlabel(label)
         binned.set_ylabel("Intensity per bin [n/s]")
@@ -851,6 +892,9 @@ def make_2d(self, axis1, axis2, n_bins=100, flag_info=None):
         data2 = self.get_data_column(axis2, flag_info)
         label2 = self.get_label(axis2, flag_info)
 
+        if isinstance(n_bins, list):
+            n_bins.reverse()
+
         weights = self.get_data_column("p", flag_info)
         intensity, edges2, edges1 = np.histogram2d(data2, data1, bins=n_bins, weights=weights)
         error_squared, edges2, edges1 = np.histogram2d(data2, data1, bins=n_bins, weights=weights**2)
@@ -865,6 +909,14 @@ def make_2d(self, axis1, axis2, n_bins=100, flag_info=None):
         metadata.info["type"] = "array_2d"
         metadata.limits = [centers1[0], centers1[-1], centers2[0], centers2[-1]]
 
+        total_I = intensity.sum()
+        total_E = np.sqrt(error_squared.sum())
+        total_N = ncount.sum()
+        metadata.info["values"] = "{:2.6E} {:2.6E} {:2.6E}".format(total_I, total_E, total_N)
+        metadata.total_I = total_I
+        metadata.total_E = total_E
+        metadata.total_N = total_N
+
         binned = McStasDataBinned(metadata, intensity=intensity,
                                   error=error, ncount=ncount)
         binned.set_title("Binned data generated from events")
diff --git a/mcstasscript/helper/check_mccode_version.py b/mcstasscript/helper/check_mccode_version.py
new file mode 100644
index 00000000..4924842b
--- /dev/null
+++ b/mcstasscript/helper/check_mccode_version.py
@@ -0,0 +1,30 @@
+import os
+import subprocess
+
+def check_mcstas_major_version(mcstas_bin_path):
+    """
+    Checks installed McStas version
+    """
+    mcstas_command = os.path.join(mcstas_bin_path, "mcstas")
+    output = subprocess.check_output([mcstas_command, "-v"])
+
+    output = output.decode("utf-8")
+    output = output.split(" (", 1)[0]
+    output = output.split("version", 1)[1]
+    major_version = output.split(".", 1)[0]
+
+    return int(major_version)
+
+def check_mcxtrace_major_version(mcstas_bin_path):
+    """
+    Checks installed McXtrace version
+    """
+    mcstas_command = os.path.join(mcstas_bin_path, "mcxtrace")
+    output = subprocess.check_output([mcstas_command, "-v"])
+
+    output = output.decode("utf-8")
+    output = output.split(" (", 1)[0]
+    output = output.split("version", 1)[1]
+    major_version = output.split(".", 1)[0]
+
+    return int(major_version)
\ No newline at end of file
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index d63b56c1..31e38704 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -411,18 +411,25 @@ def load_metadata(data_folder_name):
         # Close mccode.sim
         f.close()
 
+        """
         # Create a list for McStasData instances to return
         results = []
 
+        
         # Load datasets described in metadata list individually
         for metadata in metadata_list:
+
             # Load data with numpy
             data = np.loadtxt(data_folder_name
                               + "/"
                               + metadata.filename.rstrip())
 
             # Split data into intensity, error and ncount
-            if type(metadata.dimension) == int:
+            if type(metadata.dimension) == int and metadata.dimension == 0:
+                Intensity = data.T
+
+            if type(metadata.dimension) == int and metadata.dimension != 0:
+
                 xaxis = data.T[0, :]
                 Intensity = data.T[1, :]
                 Error = data.T[2, :]
@@ -434,6 +441,7 @@ def load_metadata(data_folder_name):
                 Intensity = data.T[:, 0:data_lines - 1]
                 Error = data.T[:, data_lines:2*data_lines - 1]
                 Ncount = data.T[:, 2*data_lines:3*data_lines - 1]
+        """
 
     return metadata_list
 
@@ -458,7 +466,21 @@ def load_monitor(metadata, data_folder_name):
     data = np.loadtxt(filename)
 
     # Split data into intensity, error and ncount
-    if type(metadata.dimension) == int:
+    if type(metadata.dimension) == int and metadata.dimension == 0:
+        Intensity = data.T
+        if metadata.total_E is None:
+            Error = np.zeros(1)
+        else:
+            Error = np.array(metadata.total_E)
+
+        if metadata.total_N is None:
+            Ncount = np.zeros(1)
+        else:
+            Ncount = np.array(metadata.total_N)
+
+        return McStasDataBinned(metadata, Intensity, Error, Ncount)
+
+    elif type(metadata.dimension) == int and metadata.dimension != 0:
         xaxis = data.T[0, :]
         Intensity = data.T[1, :]
         Error = data.T[2, :]
diff --git a/mcstasscript/helper/plot_helper.py b/mcstasscript/helper/plot_helper.py
index 7b77cc69..4d5dc0d9 100644
--- a/mcstasscript/helper/plot_helper.py
+++ b/mcstasscript/helper/plot_helper.py
@@ -82,7 +82,15 @@ def _plot_fig_ax(data, fig, ax, **kwargs):
     When plotting 2D objects, returns the pcolormesh object
     """
 
-    if type(data.metadata.dimension) == int:
+    if type(data.metadata.dimension) == int and data.metadata.dimension == 0:
+        # Can't plot 0D data, show the info
+        ax.text(0.5, 0.9, data.metadata.title, ha="center")
+        ax.text(0.5, 0.7, "I: " + str(float(data.Intensity)), ha="center")
+        ax.text(0.5, 0.5, "E: " + str(float(data.Error)), ha="center")
+        ax.text(0.5, 0.3, "N: " + str(int(data.Ncount)), ha="center")
+        ax.axis("off")
+
+    elif type(data.metadata.dimension) == int and data.metadata.dimension != 0:
 
         x_axis_mult = data.plot_options.x_limit_multiplier
 
diff --git a/mcstasscript/instr_reader/read_trace.py b/mcstasscript/instr_reader/read_trace.py
index a8680dea..986c9aa6 100644
--- a/mcstasscript/instr_reader/read_trace.py
+++ b/mcstasscript/instr_reader/read_trace.py
@@ -147,13 +147,27 @@ def read_trace_line(self, line):
             if component_name == "COPY":
                 # Copy instance
                 self.component_copy_target = line.split(")", 1)[0].strip()
-                if "(" in line:
+
+                if line.strip().startswith("("):
                     line = line.split("(", 1)[1].strip()
+
                 if self.component_copy_target == "PREVIOUS":
                     # Get the previous component name
                     last_component = self.Instr.get_last_component()
                     self.component_copy_target = last_component.name
 
+                if "(" in instance_name:
+                    # Using the copy notation, replace this with meaningful replacement
+                    base_name = self.component_copy_target + "_copy"
+                    name = base_name
+                    comp_names = [x.name for x in self.Instr.component_list]
+                    index = 0
+                    while name in comp_names:
+                        name = base_name + "_" + str(index)
+                        index += 1
+
+                    instance_name = name
+
                 self.current_component = self.Instr.copy_component(instance_name,
                                                                    self.component_copy_target)
 
diff --git a/mcstasscript/instrument_diagnostics/beam_diagnostics.py b/mcstasscript/instrument_diagnostics/beam_diagnostics.py
index 6a55c782..5f69a5d0 100644
--- a/mcstasscript/instrument_diagnostics/beam_diagnostics.py
+++ b/mcstasscript/instrument_diagnostics/beam_diagnostics.py
@@ -26,16 +26,25 @@ def __init__(self, instr, comp_name, before=True, rays=50000):
         self.component = comp_name
         self.before = before # False for after
 
-        if not isinstance(rays, int):
+        if isinstance(rays, str):
             if not rays == "all":
                 raise ValueError("The ray keyword for a point should be all or an integer.")
+        else:
+            rays = int(rays)
 
         self.rays = rays
+
+        # Attributes set by add_monitors
         self.filename = None
+        # Attributes set by read_data
+        self.recorded_rays = None
 
     def set_filename(self, filename):
         self.filename = filename
 
+    def set_recorded_rays(self, rays):
+        self.recorded_rays = rays
+
     def __eq__(self, other):
         return self.component == other.component and self.before == other.before
 
@@ -48,6 +57,9 @@ def __repr__(self):
 
         string += self.component.ljust(25)
         string += " - rays: "
+        if self.recorded_rays is not None:
+            string += str(self.recorded_rays)
+            string += " / "
         string += str(self.rays)
 
         return string
@@ -87,6 +99,11 @@ def __repr__(self):
         for view in self.views:
             string += "  " + view.__repr__() + "\n"
 
+        if len(self.flags) != 0:
+            string += "Recording following user variables:\n"
+        for flag in self.flags:
+            string += "  " + str(flag) + "\n"
+
         if self.data is None:
             string += "Does not yet contain simulated data"
         else:
@@ -208,6 +225,12 @@ def remove_point(self, before=None, after=None):
                 if point.component == after and not point.before:
                     del self.points[index]
 
+    def clear_points(self):
+        """
+        Remove all points
+        """
+        self.points = []
+
     def show_points(self):
         """
         Shows current diagnostics points
@@ -215,7 +238,7 @@ def show_points(self):
         for point in self.points:
             print(point)
 
-    def add_view(self, axis1, axis2=None, bins=100, same_scale=True, **kwargs):
+    def add_view(self, axis1, axis2=None, bins=100, same_scale=False, **kwargs):
         """
         Add a view with one or two axes that will be shown at all diagnostics points
 
@@ -303,15 +326,25 @@ def add_monitor(self, point, comp_instance):
         flags_str = ""
         for index, flag in enumerate(self.flags):
             flags_str += " user" + str(index+1)
-            user_vars[index] = '"' + flag + '"'
+            if self.instr.mccode_version == 2:
+                # Monitor_nD in McStas 2.X needs to be a raw variable
+                user_vars[index] = flag
+            else:
+                # Monitor_nD in McStas 3.X (and onwards) needs to be a string
+                user_vars[index] = '"' + flag + '"'
+
+        if isinstance(point.rays, (float, int)):
+            ray_str = str(int(point.rays + 1)) # Monitor_nD seems to record one ray less than requested
+        else:
+            ray_str = point.rays # Case of rays set to all
 
         if point.before:
             name = "Diag_before_" + point.component
-            options = f'"square boarders n x y z vx vy vz t{flags_str}, list {point.rays}"'
+            options = f'"square boarders n x y z vx vy vz t{flags_str}, list {ray_str}"'
             mon = self.instr.add_component(name, "Monitor_nD", before=point.component)
         else:
             name = "Diag_after_" + point.component
-            options = f'"previous n x y z vx vy vz t{flags_str}, list {point.rays}"'
+            options = f'"previous n x y z vx vy vz t{flags_str}, list {ray_str}"'
             mon = self.instr.add_component(name, "Monitor_nD", after=point.component)
 
         mon.set_parameters(restore_neutron=1,
@@ -320,6 +353,7 @@ def add_monitor(self, point, comp_instance):
                            user1=user_vars[0], user2=user_vars[1], user3=user_vars[2],
                            filename='"' + name + ".diag" + '"')
 
+
         mon.set_AT(comp_instance.AT_data, RELATIVE=comp_instance.AT_reference)
         if comp_instance.ROTATED_specified:
             mon.set_ROTATED(comp_instance.ROTATED_data, RELATIVE=comp_instance.ROTATED_reference)
@@ -353,6 +387,7 @@ def read_data(self):
 
         for point in self.ordered_point_list:
             event_data = name_search(point.filename, self.data)
+            point.set_recorded_rays(event_data.metadata.total_N)
             plotter = event_plotter.EventPlotter(point.filename, event_data,
                                                  flag_info=self.flags)
 
diff --git a/mcstasscript/instrument_diagnostics/diagnostics_instrument.py b/mcstasscript/instrument_diagnostics/diagnostics_instrument.py
index c2e9bdfb..57ba502e 100644
--- a/mcstasscript/instrument_diagnostics/diagnostics_instrument.py
+++ b/mcstasscript/instrument_diagnostics/diagnostics_instrument.py
@@ -2,22 +2,69 @@
 
 class DiagnosticsInstrument:
     def __init__(self, instr):
+        """
+        Diagnostics instrument object with related capabilities
+
+        The diagnostics instrument takes a copy of an instrument object
+        and can modify this version in ways relevant for diagnostics of
+        the instrument. It can remove use of PREVIOUS and correct the
+        use of target_index to make it safe to add additional components
+        without altering the instrument. It carries it's own settings
+        and parameters that wont impact the original instrument. The
+        diagnostics instrument can be reset to the original state, and
+        will have settings and parameters applied again.
+
+        Parameters:
+
+        instr : McCode_instr object
+            Instrument which needs a diagnostics copy
+        """
         self.original_instr = instr
         self.instr = None
         self.instr_settings = {}
+        self.instr_parameters = {}
         self.reset_instr()
 
         self.component_list = self.instr.make_component_subset()
 
     def reset_instr(self):
+        """
+        Resets instrument to original state, with new pars and settings
+        """
         self.instr = copy.deepcopy(self.original_instr)
         self.instr.settings(**self.instr_settings)
+        self.instr.set_parameters(**self.instr_parameters)
 
     def settings(self, **kwargs):
+        """
+        Apply settings as in an instrument object
+        """
         self.instr.settings(**kwargs)
         self.instr_settings.update(kwargs)
 
+    def show_settings(self):
+        """
+        Show settings of diagnostics instrument
+        """
+        self.instr.show_settings()
+
+    def set_parameters(self, **kwargs):
+        """
+        Set parameters as in instrument object
+        """
+        self.instr.set_parameters(**kwargs)
+        self.instr_parameters.update(kwargs)
+
+    def show_parameters(self):
+        """
+        Show parameters as on instrument object
+        """
+        self.instr.show_parameters()
+
     def remove_previous_use(self):
+        """
+        Replaces use of PREVIOUS with direct references
+        """
         self.component_list = self.instr.make_component_subset()
         previous_component = None
         for comp in self.component_list:
@@ -32,7 +79,7 @@ def remove_previous_use(self):
 
     def correct_target_index(self):
         """
-        Need to correct target_index based on original instrument
+        Corrects target_index based on original instrument
         """
         original_component_list = self.original_instr.make_component_subset()
         original_comp_names = [x.name for x in original_component_list]
diff --git a/mcstasscript/instrument_diagnostics/view.py b/mcstasscript/instrument_diagnostics/view.py
index 3c1b182d..ea089f66 100644
--- a/mcstasscript/instrument_diagnostics/view.py
+++ b/mcstasscript/instrument_diagnostics/view.py
@@ -1,5 +1,5 @@
 class View:
-    def __init__(self, axis1, axis2=None, bins=100, same_scale=True, **kwargs):
+    def __init__(self, axis1, axis2=None, bins=100, same_scale=False, **kwargs):
         """
         Plot view on one or two axis to be generated from event data
 
diff --git a/mcstasscript/instrument_diagram/make_diagram.py b/mcstasscript/instrument_diagram/make_diagram.py
index 7143befc..2d59a5a9 100644
--- a/mcstasscript/instrument_diagram/make_diagram.py
+++ b/mcstasscript/instrument_diagram/make_diagram.py
@@ -60,7 +60,7 @@ def instrument_diagram(instrument, analysis=False, variable=None, limits=None):
                                          component_categories, color=color_choices["Union"])
 
     intensity_diagnostics=None
-    if analysis:
+    if analysis or variable is not None:
         intensity_diagnostics = IntensityDiagnostics(instrument)
         intensity_diagnostics.settings(suppress_output=True)
 
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 4ba17086..998a10e0 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -26,6 +26,8 @@
 from mcstasscript.helper.unpickler import CustomMcStasUnpickler, CustomMcXtraceUnpickler
 from mcstasscript.helper.exceptions import McStasError
 from mcstasscript.helper.beam_dump_database import BeamDumpDatabase
+from mcstasscript.helper.check_mccode_version import check_mcstas_major_version
+from mcstasscript.helper.check_mccode_version import check_mcxtrace_major_version
 from mcstasscript.instrument_diagram.make_diagram import instrument_diagram
 from mcstasscript.instrument_diagnostics.intensity_diagnostics import IntensityDiagnostics
 
@@ -406,6 +408,8 @@ def __init__(self, name, parameters=None, author=None,
         self.component_class_lib = {}
         self.widget_interface = None
 
+        # Holds major version of underlying package
+        self.mccode_version = None
 
         # Avoid initializing if loading from dump
         if not hasattr(self, "declare_list"):
@@ -2538,6 +2542,9 @@ def show_diagram(self, analysis=False, variable=None, limits=None):
             print("The instrument has some error, this diagram is still "
                   "shown as it may help find the bug.")
 
+        if variable is not None:
+            analysis = True
+
         instrument_diagram(self, analysis=analysis, variable=variable, limits=limits)
         self.check_for_errors()
 
@@ -2732,6 +2739,11 @@ def __init__(self, name, **kwargs):
 
         super().__init__(name, executable=executable, **kwargs)
 
+        try:
+            self.mccode_version = check_mcstas_major_version(self._run_settings["executable_path"])
+        except:
+            self.mccode_version = "Unknown"
+
     def _read_calibration(self):
         this_dir = os.path.dirname(os.path.abspath(__file__))
         configuration_file_name = os.path.join(this_dir, "..",
@@ -2955,6 +2967,11 @@ def __init__(self, name, **kwargs):
 
         super().__init__(name, executable=executable, **kwargs)
 
+        try:
+            self.mccode_version = check_mcxtrace_major_version(self._run_settings["executable_path"])
+        except:
+            self.mccode_version = "Unknown"
+
     def _read_calibration(self):
         this_dir = os.path.dirname(os.path.abspath(__file__))
         configuration_file_name = os.path.join(this_dir, "..",

From e3d0099c0051faca46cf43f159613398277a3083 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 17 Nov 2022 14:51:10 +0100
Subject: [PATCH 323/403] Fix of index.rst typo.

---
 docs/source/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/index.rst b/docs/source/index.rst
index d43f39f2..e52321c2 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -38,7 +38,7 @@ Documentation
    tutorial/McStasScript_tutorial_3_EXTEND_and_WHEN.ipynb
    tutorial/McStasScript_tutorial_4_JUMP.ipynb
    tutorial/McStasScript_tutorial_5_MCPL_bridges.ipynb
-   tutorial/McStasScript_tutorial_6_Diagostics.ipynb
+   tutorial/McStasScript_tutorial_6_Diagnostics.ipynb
    
 .. toctree::
    :caption: McStas Union Tutorial

From 9a4d5e1cadd2f321e15bc9769cfa510610464a2b Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 17 Nov 2022 15:18:38 +0100
Subject: [PATCH 324/403] Fixed tests for instrument that now have two
 subprocess runs, and thus shoulnd't use assert_run_once_with but just
 assert_run_with.

---
 mcstasscript/tests/test_Instr.py | 61 ++++++++++++++++----------------
 1 file changed, 31 insertions(+), 30 deletions(-)

diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index c12d9317..b23ed211 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1742,6 +1742,7 @@ def test_x_ray_run_full_instrument_basic(self, mock_sub, mock_stdout):
                 raise RuntimeError("Folder_name was supposed to not "
                                    + "exist before "
                                    + "test_run_backengine_basic")
+
             instr = setup_populated_x_ray_instr_with_dummy_path()
             instr.run_full_instrument(output_path=new_folder_name,
                                       increment_folder_name=False,
@@ -1761,11 +1762,11 @@ def test_x_ray_run_full_instrument_basic(self, mock_sub, mock_stdout):
 
         expected_run_path = os.path.join(THIS_DIR, ".")
 
-        mock_sub.assert_called_once_with(expected_call,
-                                         shell=True,
-                                         cwd=expected_run_path,
-                                         stderr=-2, stdout=-1,
-                                         universal_newlines=True)
+        mock_sub.assert_called_with(expected_call,
+                                    shell=True,
+                                    cwd=expected_run_path,
+                                    stderr=-2, stdout=-1,
+                                    universal_newlines=True)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_run_backengine_existing_folder(self, mock_stdout):
@@ -1828,11 +1829,11 @@ def test_run_backengine_basic(self, mock_sub, mock_stdout):
                          + "  test_instrument.instr"
                          + " theta=1 has_default=37")
 
-        mock_sub.assert_called_once_with(expected_call,
-                                         shell=True,
-                                         stderr=-2, stdout=-1,
-                                         universal_newlines=True,
-                                         cwd=run_path)
+        mock_sub.assert_called_with(expected_call,
+                                    shell=True,
+                                    stderr=-2, stdout=-1,
+                                    universal_newlines=True,
+                                    cwd=run_path)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     @unittest.mock.patch("subprocess.run")
@@ -1883,11 +1884,11 @@ def test_run_full_instrument_complex(self, mock_sub, mock_stdout):
                          + " -fo test_instrument.instr "
                          + "theta=\"toy\" has_default=37 A=2 BC=car")
 
-        mock_sub.assert_called_once_with(expected_call,
-                                         shell=True,
-                                         stderr=-2, stdout=-1,
-                                         universal_newlines=True,
-                                         cwd=run_path)
+        mock_sub.assert_called_with(expected_call,
+                                    shell=True,
+                                    stderr=-2, stdout=-1,
+                                    universal_newlines=True,
+                                    cwd=run_path)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     @unittest.mock.patch("subprocess.run")
@@ -1935,11 +1936,11 @@ def test_run_full_instrument_overwrite_default(self, mock_sub,
                          + " -fo test_instrument.instr "
                          + "theta=\"toy\" has_default=10 A=2 BC=car")
 
-        mock_sub.assert_called_once_with(expected_call,
-                                         shell=True,
-                                         stderr=-2, stdout=-1,
-                                         universal_newlines=True,
-                                         cwd=run_path)
+        mock_sub.assert_called_with(expected_call,
+                                    shell=True,
+                                    stderr=-2, stdout=-1,
+                                    universal_newlines=True,
+                                    cwd=run_path)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     @unittest.mock.patch("subprocess.run")
@@ -1979,11 +1980,11 @@ def test_run_full_instrument_x_ray_basic(self, mock_sub, mock_stdout):
                          + "  test_instrument.instr"
                          + " theta=1 has_default=37")
 
-        mock_sub.assert_called_once_with(expected_call,
-                                         shell=True,
-                                         stderr=-2, stdout=-1,
-                                         universal_newlines=True,
-                                         cwd=run_path)
+        mock_sub.assert_called_with(expected_call,
+                                    shell=True,
+                                    stderr=-2, stdout=-1,
+                                    universal_newlines=True,
+                                    cwd=run_path)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     @unittest.mock.patch("subprocess.run")
@@ -2015,11 +2016,11 @@ def test_show_instrument_basic(self, mock_sub, mock_stdout):
                          + " " + expected_instr_path
                          + "  theta=1.2 has_default=37")
 
-        mock_sub.assert_called_once_with(expected_call,
-                                         shell=True,
-                                         stderr=-1, stdout=-1,
-                                         universal_newlines=True,
-                                         cwd=".")
+        mock_sub.assert_called_with(expected_call,
+                                    shell=True,
+                                    stderr=-1, stdout=-1,
+                                    universal_newlines=True,
+                                    cwd=".")
 
     def test_show_dumps_works(self):
         """

From 30b2e867f863ced9fd958e3cbe2698928c55e947 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 17 Nov 2022 15:24:05 +0100
Subject: [PATCH 325/403] Version bump to get fixed tests to pypi package.

---
 mcstasscript/_version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 478240bb..5062d8b8 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.52"
+__version__ = "0.0.53"

From 48cf5597a8b0b2afc37cde7587a1b3e736006fed Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Fri, 18 Nov 2022 10:46:42 +0100
Subject: [PATCH 326/403] Update README.md

Fixed obsolete documentation, instrument methods and widget explanation.
---
 README.md | 43 +++++++++++++++++++++++++++++--------------
 1 file changed, 29 insertions(+), 14 deletions(-)

diff --git a/README.md b/README.md
index 007ed23d..a084d013 100644
--- a/README.md
+++ b/README.md
@@ -143,17 +143,20 @@ Plotting is usually done in a subplot of all monitors recorded.
     plot = ms.make_sub_plot(data)
     
 ## Widgets in Jupyter Notebooks
-When using McStasScript in a jupyter notebook, it is possible to plot the data with a widget system instead.
+When using McStasScript in a jupyter notebook, it is possible to plot the data with a widget system instead. To do so, import the jupyter notebook widget interface and use show.
 
-    ms.interface(data)
+    import mcstasscript.jb_interface as ms_widget
+    ms_widget.show(data)
     
-There is also a widget solution for performing the simulation which works as an alternative to *run_full_instrument*, this method is also called *interface* and is available directly on the instrument. This interface includes setting parameters, simulation options and plotting of the resulting data.
+There is also a widget solution for performing the simulation which works as an alternative to *backengine*, this method is also included in the jb_interface show command, just provide an instrument object instead of data. This interface includes setting parameters, simulation options and plotting of the resulting data.
 
-    my_instrument.interface()
+    ms_widget.show(instr)
     
-The data from the latest run performed in the simulation widget can be retrieved with *get_interface_data*.
+If one wants to have access to the data generated in the widget, the widget needs to be created as an object with SimInterface. The resulting object will have a *show_interface* method to display the interface, and a *get_data* method to retrieve the latest generated dataset.
 
-    data = my_instrument.get_interface_data()
+    sim_widget = ms_widget.SimInterface(instr)
+    sim_widget.show_interface()
+    data = sim_widget.get_data()
 
 ## Use in existing project
 If one wish to work on existing projects using McStasScript, there is a reader included that will read a McStas Instrument file and write the corresponding McStasScript python instrument to disk. Here is an example where the PSI_DMC.instr example is converted:
@@ -164,23 +167,35 @@ If one wish to work on existing projects using McStasScript, there is a reader i
 It is highly advised to run a check between the output of the generated file and the original to ensure the process was sucessful.
 
 ## Method overview
-Here is a quick overview of the available methods of the main classes in the project. Most have more options from keyword arguments that are explained in the manual, but also in python help. To get more information on for example the show_components method of the McStas_instr class, one can use the python help command help(instr.McStas_instr.show_components).
+Here is a quick overview of the available methods of the main classes in the project. Most have more options from keyword arguments that are explained in the manual, but also in python help. To get more information on for example the show_components method of the McStas_instr class, one can use the python help command help(instr.McStas_instr.show_components). Many methods take a reference to a component, that can either be a string with the component name or a component object, here written as Cref in type hint.
 
     instr
     └── McStas_instr(str instr_name) # Returns McStas instrument object on initialize
-        ├── show_components(str category_name) # Show available components in given category
-        ├── component_help(str component_name) # Prints component parameters for given component name   
+        ├── show_parameters() # Prints list of parameters        
+        ├── show_settings() # Prints current instrument settings
+        ├── show_variables() # Prints list of declare variables and user vars
+        ├── show_components() # Prints list of components and their location
+        ├── show_instrument() # Shows instrument drawing with current parameters
+        ├── show_instr_file() # Prints the current instrument file
+        ├── show_diagram() # Show figure describing the instrument object
+        ├── set_parameters() # Sets instrument parameters as keyword arguments
+        ├── available_components(str category_name) # Show available components in given category        
+        ├── component_help(Cref component_name) # Prints component parameters for given component name   
         ├── add_component(str name, str component_name) # Adds component to instrument and returns object
+        ├── copy_component(str name, Cref original_name) # Copies a component to instrument and returns object        
+        ├── remove_component(Cref name) # Removes component
+        ├── move_component(str name, Cref before / after, ) # Moves component to either before or after another
+        ├── get_component(str name) # Gets component object
+        ├── get_last_component() # Gets last component object        
         ├── add_parameter(str name) # Adds instrument parameter with name
         ├── add_declare_var(str type, str name) # Adds declared variable with type and name
+        ├── add_user_var(str type, str name) # Adds user var with type and name        
+        ├── append_declare(str string) # Appends a line to declare section (c syntax)        
         ├── append_initialize(str string) # Appends a line to initialize (c syntax)
-        ├── print_components() # Prints list of components and their location
+        ├── append_finally(str string) # Appends a line to finally (c syntax)        
         ├── write_full_instrument() # Writes instrument to disk with given name + ".instr"
         ├── settings(kwargs) Settings as keyword arguments.
-        ├── backengine() # Runs simulation.
-        ├── data # Data attribute, contains list of McStasData objects
-        ├── interface() # Shows widget interface in jupyter notebook
-        └── get_interface_data() # Returns data set from latest simulation performed in interface
+        └── backengine() # Runs simulation.
         
     component # returned by add_component
     ├── set_AT(list at_list) # Sets component position (list of x,y,z positions in [m])

From e0cf35d97ed7611f3fb67786636dbb85de7dbce9 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 22 Nov 2022 17:41:59 +0100
Subject: [PATCH 327/403] Adding axis1_values and axis2_values to view which
 plots lines at the indicated value (lists) to make it easy to check things in
 views are as calculated.

Fixed bug in instrument diagram target_index.

Made all right side arrows on instrument diagram in try blocks
as they are not critical. Prints a message if they fail.
---
 mcstasscript/_version.py                      |  2 +-
 .../beam_diagnostics.py                       | 16 +++++++---
 .../instrument_diagnostics/event_plotter.py   | 17 ++++++++++
 mcstasscript/instrument_diagnostics/view.py   | 16 +++++++++-
 .../generate_target_index.py                  |  8 ++++-
 .../instrument_diagram/make_diagram.py        | 31 +++++++++++++++----
 6 files changed, 77 insertions(+), 13 deletions(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 5062d8b8..b504b9f0 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.53"
+__version__ = "0.0.54"
diff --git a/mcstasscript/instrument_diagnostics/beam_diagnostics.py b/mcstasscript/instrument_diagnostics/beam_diagnostics.py
index 5f69a5d0..52cebc21 100644
--- a/mcstasscript/instrument_diagnostics/beam_diagnostics.py
+++ b/mcstasscript/instrument_diagnostics/beam_diagnostics.py
@@ -334,7 +334,10 @@ def add_monitor(self, point, comp_instance):
                 user_vars[index] = '"' + flag + '"'
 
         if isinstance(point.rays, (float, int)):
-            ray_str = str(int(point.rays + 1)) # Monitor_nD seems to record one ray less than requested
+            ray_value = point.rays
+            if self.instr.mccode_version == 3:
+                ray_value += 1 # In McStas 3.0 Monitor_nD grabs one event too few
+            ray_str = str(int(ray_value)) # Monitor_nD seems to record one ray less than requested
         else:
             ray_str = point.rays # Case of rays set to all
 
@@ -386,7 +389,12 @@ def read_data(self):
         self.event_plotters = []
 
         for point in self.ordered_point_list:
-            event_data = name_search(point.filename, self.data)
+            try:
+                event_data = name_search(point.filename, self.data)
+            except NameError:
+                # Data was not generated, no neutrons reached it or simulation failed
+                continue
+
             point.set_recorded_rays(event_data.metadata.total_N)
             plotter = event_plotter.EventPlotter(point.filename, event_data,
                                                  flag_info=self.flags)
@@ -400,9 +408,9 @@ def plot(self):
 
         if len(self.event_plotters) == 0:
             if len(self.views) == 0:
-                print("No data to plot yet! Add views and run.")
+                print("No data to plot! Add views and run.")
             else:
-                print("No data to plot yet! Use the run method to generate data.")
+                print("No data to plot! Use the run method to generate data.")
 
         overview = PlotOverview(self.event_plotters, self.views)
         overview.plot_all()
diff --git a/mcstasscript/instrument_diagnostics/event_plotter.py b/mcstasscript/instrument_diagnostics/event_plotter.py
index 9c1b758e..3cc6ec52 100644
--- a/mcstasscript/instrument_diagnostics/event_plotter.py
+++ b/mcstasscript/instrument_diagnostics/event_plotter.py
@@ -112,3 +112,20 @@ def plot(self, view, fig, ax):
 
         _plot_fig_ax(data, fig, ax)
 
+        x_lims = ax.get_xlim()
+        y_lims = ax.get_ylim()
+
+        if view.axis1_values is not None:
+            for value in view.axis1_values:
+                ax.plot([value, value], y_lims, "k")
+
+            ax.set_xlim(x_lims)
+            ax.set_ylim(y_lims)
+
+        if view.axis2_values is not None:
+            for value in view.axis2_values:
+                ax.plot(x_lims, [value, value], "k")
+
+            ax.set_xlim(x_lims)
+            ax.set_ylim(y_lims)
+
diff --git a/mcstasscript/instrument_diagnostics/view.py b/mcstasscript/instrument_diagnostics/view.py
index ea089f66..ba73b6c5 100644
--- a/mcstasscript/instrument_diagnostics/view.py
+++ b/mcstasscript/instrument_diagnostics/view.py
@@ -1,5 +1,6 @@
 class View:
-    def __init__(self, axis1, axis2=None, bins=100, same_scale=False, **kwargs):
+    def __init__(self, axis1, axis2=None, bins=100, same_scale=False,
+                 axis1_values=None, axis2_values=None, **kwargs):
         """
         Plot view on one or two axis to be generated from event data
 
@@ -28,6 +29,12 @@ def __init__(self, axis1, axis2=None, bins=100, same_scale=False, **kwargs):
         bins : int or [int, int]
             Number of bins for generation of histogram
 
+        axis1_values : float or list of floats
+            Values that will be plotted as lines on plot
+
+        axis2_values : float or list of floats
+            Values that will be plotted as lines on plot
+
         same_scale : bool
             Controls whether all displays of this view is on same ranges
         """
@@ -36,8 +43,15 @@ def __init__(self, axis1, axis2=None, bins=100, same_scale=False, **kwargs):
         self.axis1 = axis1
         self.axis1_limits = None
 
+        if isinstance(axis1_values, (float, int)):
+            axis1_values = [axis1_values]
+        self.axis1_values = axis1_values
+
         self.axis2 = axis2
         self.axis2_limits = None
+        if isinstance(axis2_values, (float, int)):
+            axis2_values = [axis2_values]
+        self.axis2_values = axis2_values
 
         self.bins = bins
 
diff --git a/mcstasscript/instrument_diagram/generate_target_index.py b/mcstasscript/instrument_diagram/generate_target_index.py
index c7da57b6..7bf62fd3 100644
--- a/mcstasscript/instrument_diagram/generate_target_index.py
+++ b/mcstasscript/instrument_diagram/generate_target_index.py
@@ -20,8 +20,14 @@ def generate_target_index_arrows(components, component_box_dict, box_names, colo
             # target_index is disabled
             continue
 
+        try:
+            int(component.target_index)
+        except:
+            # Skip cases where target_index is not an integer
+            continue
+
         this_component_index = component_names.index(component.name)
-        target_component_index = this_component_index + component.target_index
+        target_component_index = this_component_index + int(component.target_index)
         target_component_reference = component_names[target_component_index]
 
         if target_component_reference not in component_box_dict:
diff --git a/mcstasscript/instrument_diagram/make_diagram.py b/mcstasscript/instrument_diagram/make_diagram.py
index 2d59a5a9..4925bb3a 100644
--- a/mcstasscript/instrument_diagram/make_diagram.py
+++ b/mcstasscript/instrument_diagram/make_diagram.py
@@ -52,12 +52,31 @@ def instrument_diagram(instrument, analysis=False, variable=None, limits=None):
     ROTATED_arrows = generate_ROTATED_arrows(components, component_box_dict, box_names, color=color_choices["ROTATED"])
 
     # Arrow for the right side of the diagram
-    JUMP_arrows = generate_JUMP_arrows(components, component_box_dict, box_names, color=color_choices["JUMP"])
-    target_index_arrows = generate_target_index_arrows(components, component_box_dict,
-                                                       box_names, color=color_choices["JUMP"])
-    GROUP_arrows = generate_GROUP_arrows(components, component_box_dict, box_names, color=color_choices["GROUP"])
-    Union_arrows = generate_Union_arrows(components, component_box_dict, box_names,
-                                         component_categories, color=color_choices["Union"])
+    try:
+        JUMP_arrows = generate_JUMP_arrows(components, component_box_dict, box_names, color=color_choices["JUMP"])
+    except:
+        JUMP_arrows = []
+        print("Generation of JUMP arrows failed, please send bug report.")
+
+    try:
+        target_index_arrows = generate_target_index_arrows(components, component_box_dict,
+                                                           box_names, color=color_choices["JUMP"])
+    except:
+        target_index_arrows = []
+        print("Generation of target_index arrows failed, please send bug report.")
+
+    try:
+        GROUP_arrows = generate_GROUP_arrows(components, component_box_dict, box_names, color=color_choices["GROUP"])
+    except:
+        GROUP_arrows = []
+        print("Generation of GROUP arrows failed, please send bug report.")
+
+    try:
+        Union_arrows = generate_Union_arrows(components, component_box_dict, box_names,
+                                             component_categories, color=color_choices["Union"])
+    except:
+        Union_arrows = []
+        print("Generation of Union arrows failed, please send bug report.")
 
     intensity_diagnostics=None
     if analysis or variable is not None:

From ffc9ad1735053760076ea7bb1597d2a1bbb3a656 Mon Sep 17 00:00:00 2001
From: Gregory Tucker <gregory.tucker@ess.eu>
Date: Wed, 15 Feb 2023 14:14:42 +0100
Subject: [PATCH 328/403] [Fix] Determine base for C integer literals

---
 mcstasscript/helper/component_reader.py | 27 ++++++++++++++++++++++++-
 1 file changed, 26 insertions(+), 1 deletion(-)

diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index f74b196d..854befca 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -2,6 +2,31 @@
 import math
 
 
+def c_integer_literal_base(s: str) -> int:
+    """
+    Determine the base of a C style integer literal.
+
+    base    literal
+    ------- --------------------
+    binary      0b### for # in (0,1)
+    octal       0#### for # in (0,7)
+    decimal     N#### for N in (1,9) and # in (0,9)
+    hexadecimal 0x### for # in (0,9)&(A,F)
+
+    The ambiguity between octal and decimal for '0' is not important
+    since both result in the same integer value.
+    """
+    if len(s) == 0:
+        return 0
+    if '0' != s[0]:
+        return 10
+    if 'x' in s:
+        return 16
+    if 'b' in s:
+        return 2
+    return 8
+
+
 class ComponentInfo:
     """
     Internal class used to store information on parameters of components
@@ -385,7 +410,7 @@ def read_component_file(self, absolute_path):
                                     # value could be parameter name
                                     par_value = par_value
                             elif temp_par_type == "int":
-                                par_value = int(par_value)
+                                par_value = int(par_value, c_integer_literal_base(par_value))
 
                             result.parameter_names.append(par_name)
                             result.parameter_defaults[par_name] = par_value

From 5e3c779ca7937f47c2b1685e7504b06b0c5c4773 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 17 Feb 2023 14:49:45 +0100
Subject: [PATCH 329/403] Small fix of component reader when finding vectors

---
 mcstasscript/_version.py                | 2 +-
 mcstasscript/helper/component_reader.py | 4 ++++
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index b504b9f0..c1f9172d 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.54"
+__version__ = "0.0.55"
diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index 854befca..82dfeeb1 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -379,6 +379,10 @@ def read_component_file(self, absolute_path):
                             temp_par_type = "double"
                             # remove double from part
                             part = "".join(possible_declare[1:])
+                        if "vector" == possible_type:
+                            temp_par_type = "double"
+                            # remove double from part
+                            part = "".join(possible_declare[1:])
 
                         part = part.replace(" ", "")
                         if part == "":

From 04d609f896c25673f7c8c419ca8b0b3f4ce302ab Mon Sep 17 00:00:00 2001
From: Peter Willendrup <peter.willendrup@esss.se>
Date: Fri, 14 Apr 2023 11:27:16 +0200
Subject: [PATCH 330/403] Update syntax for plotting

---
 examples/McStasScript_demo.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/McStasScript_demo.ipynb b/examples/McStasScript_demo.ipynb
index d2c7c71a..9c33b981 100644
--- a/examples/McStasScript_demo.ipynb
+++ b/examples/McStasScript_demo.ipynb
@@ -709,9 +709,9 @@
    "outputs": [],
    "source": [
     "# Adjusting PSD_4PI plot\n",
-    "functions.name_plot_options(\"PSD_4PI\", data, log=1, colormap=\"hot\", orders_of_mag=5)\n",
+    "ms.name_plot_options(\"PSD_4PI\", data, log=1, colormap=\"hot\", orders_of_mag=5)\n",
     "\n",
-    "plot = plotter.make_sub_plot(data) # Making subplot of our monitors"
+    "plot = ms.make_sub_plot(data) # Making subplot of our monitors"
    ]
   }
  ],

From 0d3ff4cb7f6817d93af5a0a4f612fa70999bf330 Mon Sep 17 00:00:00 2001
From: Peter Willendrup <peter.willendrup@esss.se>
Date: Fri, 14 Apr 2023 11:39:28 +0200
Subject: [PATCH 331/403] Update with McStas 3.x relevant syntax

---
 examples/calibration_sample.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/calibration_sample.ipynb b/examples/calibration_sample.ipynb
index 0e1af75a..d2938125 100644
--- a/examples/calibration_sample.ipynb
+++ b/examples/calibration_sample.ipynb
@@ -277,8 +277,8 @@
    "outputs": [],
    "source": [
     "# Adds 1D position sensitive detector for transmission measurement\n",
-    "PSD = instr.add_component(\"PSDlin\", \"PSDlin_monitor\", AT=[0,0,1], RELATIVE=sample_pos) \n",
-    "PSD.set_parameters(filename='\"PSDlin.dat\"', xwidth=0.1, yheight=0.1, nx=200, restore_neutron=1)"
+    "PSDlin = instr.add_component(\"PSDlin\", \"PSDlin_monitor\", AT=[0,0,1], RELATIVE=sample_pos) \n",
+    "PSDlin.set_parameters(filename='\"PSDlin.dat\"', xwidth=0.1, yheight=0.1, nbins=200, restore_neutron=1)"
    ]
   },
   {

From 956f926373bb63965643f480e88cee9d25f445b1 Mon Sep 17 00:00:00 2001
From: Peter Willendrup <peter.willendrup@esss.se>
Date: Fri, 14 Apr 2023 11:45:11 +0200
Subject: [PATCH 332/403] Apply McStas 3.x syntax

---
 examples/libpyvinyl_example.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/libpyvinyl_example.ipynb b/examples/libpyvinyl_example.ipynb
index 6c7c8994..643b7527 100644
--- a/examples/libpyvinyl_example.ipynb
+++ b/examples/libpyvinyl_example.ipynb
@@ -196,10 +196,10 @@
    "source": [
     "mon.xwidth = 0.2\n",
     "mon.yheight = 0.2\n",
-    "mon.maxdiv_h = 30.0\n",
+    "mon.maxdiv = 30.0\n",
     "mon.filename = '\"acceptance_h.dat\"'\n",
     "mon.restore_neutron = 1\n",
-    "mon.nh = 300\n",
+    "mon.nb = 300\n",
     "mon.ndiv = 300\n",
     "mon.set_AT(0.1, RELATIVE=sample)"
    ]

From 4c0a42ef54b412debc8b4abf95b1f37d472b5860 Mon Sep 17 00:00:00 2001
From: Peter Willendrup <peter.willendrup@esss.se>
Date: Fri, 14 Apr 2023 12:44:54 +0200
Subject: [PATCH 333/403] Update syntax to get plot outputs

---
 examples/libpyvinyl_example.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/libpyvinyl_example.ipynb b/examples/libpyvinyl_example.ipynb
index 643b7527..ad175ed7 100644
--- a/examples/libpyvinyl_example.ipynb
+++ b/examples/libpyvinyl_example.ipynb
@@ -294,7 +294,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "calculator.output[\"simulation_data\"].get_data()[\"data\"]"
+    "calculator.output.get_data()[\"data\"]"
    ]
   },
   {

From b71e98e1521751d760dbcbffbf39d7d1b080eec2 Mon Sep 17 00:00:00 2001
From: Peter Willendrup <peter.willendrup@esss.se>
Date: Fri, 14 Apr 2023 12:57:27 +0200
Subject: [PATCH 334/403] Add Union_init and  Union_stop for 3.x support

---
 examples/random_demonstration.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/examples/random_demonstration.py b/examples/random_demonstration.py
index 29005923..cbf3fb0c 100644
--- a/examples/random_demonstration.py
+++ b/examples/random_demonstration.py
@@ -11,6 +11,9 @@
 
 # Set up a material called Cu with approrpiate properties
 # (uses McStas Union components, here the processes)
+
+Init = instr.add_component("init", "Union_init")
+
 Cu_inc = instr.add_component("Cu_incoherent", "Incoherent_process")
 Cu_inc.sigma = 4*0.55
 Cu_inc.packing_factor = 1
@@ -118,6 +121,8 @@
 big_PSD.filename = '"big_PSD.dat"'
 big_PSD.restore_neutron = 1
 
+Stop = instr.add_component("stop", "Union_stop")
+
 instr.show_components()
 
 # Run the McStas simulation, a unique foldername is required for each run

From a858ee56157d81186963216f71fe9af1816c07f5 Mon Sep 17 00:00:00 2001
From: Peter Willendrup <peter.willendrup@esss.se>
Date: Fri, 14 Apr 2023 13:02:50 +0200
Subject: [PATCH 335/403] Updates for Union in McStas 3.x

---
 examples/calibration_sample.ipynb | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)

diff --git a/examples/calibration_sample.ipynb b/examples/calibration_sample.ipynb
index d2938125..4830ff54 100644
--- a/examples/calibration_sample.ipynb
+++ b/examples/calibration_sample.ipynb
@@ -25,6 +25,16 @@
     "instr = ms.McStas_instr(\"calibration_sample\", author = \"Mads Bertelsen\", origin = \"ESS DMSC\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add Union_init for McStas 3.x support\n",
+    "Init = instr.add_component(\"init\", \"Union_init\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -293,6 +303,18 @@
     "                    Emin=\"energy - delta_energy\", Emax=\"energy + delta_energy\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Add Union_stop for McStas 3.x support\n",
+    "Stop = instr.add_component(\"stop\", \"Union_stop\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

From 8b2861de6e3ed47e6407915cab6da34a4c243bbe Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 14 Apr 2023 15:06:17 +0200
Subject: [PATCH 336/403] Update that removes the need to provide a name in
 often used methods provided the resulting object is stored in a python
 variable whos name can be reused. For example

wavelength = instrument.add_parameter("wavelength", value=5)

wavelength = instrument.add_parameter(value=5)

Are eqvivalent.

The methods with this feature are:
    add_parameter
    add_declare_var
    add_user_var
    add_component
    copy_component
---
 mcstasscript/_version.py              |  2 +-
 mcstasscript/helper/mcstas_objects.py | 22 ++++++++--
 mcstasscript/helper/name_inspector.py | 27 ++++++++++++
 mcstasscript/interface/instr.py       | 56 +++++++++++++++++++++++--
 mcstasscript/tests/test_Instr.py      | 59 +++++++++++++++++++++++++++
 5 files changed, 157 insertions(+), 9 deletions(-)
 create mode 100644 mcstasscript/helper/name_inspector.py

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index c1f9172d..95248a02 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.55"
+__version__ = "0.0.56"
diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index ae320df2..78345ba7 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -1,6 +1,7 @@
 from mcstasscript.helper.formatting import bcolors
 from mcstasscript.helper.formatting import is_legal_parameter
 from mcstasscript.helper.exceptions import McStasError
+from mcstasscript.helper.name_inspector import find_python_variable_name
 
 from libpyvinyl.Parameters.Parameter import Parameter
 
@@ -44,9 +45,14 @@ def provide_parameter(*args, **kwargs):
     """
     if len(args) == 0:
         # Check all required keyword arguments present
-        if "name" not in kwargs["name"]:
-            raise RuntimeError("Need to provide name, either as first argument"
-                               + " or keyword argument")
+        if "name" not in kwargs:
+            try:
+                name = find_python_variable_name(error_text="", n_levels=3)
+                kwargs["name"] = name
+            except:
+                raise RuntimeError("Need to provide name, either as first argument"
+                                   + ", keyword argument or python variable.")
+
         provided_name = kwargs["name"]
 
         provided_type = ""
@@ -202,7 +208,7 @@ class DeclareVariable:
     write_line(fo)
         Writes a line to text file fo declaring the parameter in c
     """
-    def __init__(self, type, name, **kwargs):
+    def __init__(self, type, name=None, **kwargs):
         """
         Initializing mcstas declare variable or user variable object
 
@@ -212,6 +218,9 @@ def __init__(self, type, name, **kwargs):
         Creates a variable with name A3 and default value
         A = DeclareVariable("double", "A3", value=30)
 
+        Creates a variable with type integer and name A
+        A = DeclareVariable("int")
+
         Creates a variable with type integer and name sample_number
         A = DeclareVariable("int", "sample_number")
 
@@ -244,6 +253,11 @@ def __init__(self, type, name, **kwargs):
             raise RuntimeError("Given type of DeclareVariable should be a "
                                + "string.")
 
+        if name is None:
+            error_text = ("When using automatic assignment of name, the call"
+                          " need to assign it to a variable name")
+            name = find_python_variable_name(error_text=error_text, n_levels=3)
+
         self.name = str(name)
 
         par_name = self.name
diff --git a/mcstasscript/helper/name_inspector.py b/mcstasscript/helper/name_inspector.py
new file mode 100644
index 00000000..ee8778b7
--- /dev/null
+++ b/mcstasscript/helper/name_inspector.py
@@ -0,0 +1,27 @@
+def find_python_variable_name(error_text, n_levels=1):
+    """
+    Finds call in stack n_levels above and gets variable name
+
+    This function is used to avoid having to manually specify a name
+    in the method or function inputs. For example with a parameters:
+
+    wavelength = instrument.add_parameter("wavelength", value=3)
+
+    can be equivalent to:
+
+    wavelength = instrument.add_parameter(value=3)
+
+    When this function is used to deduct the appropriate name.
+    The n_levels is the number of function calls between the user
+    call and this function, for each level of nesting add another
+    level. An error_text should be given for cases where it was not
+    possible to extract the variable name.
+    """
+
+    import inspect
+
+    stack = inspect.stack()
+    calling_string = ' '.join(stack[n_levels][4])
+    if '=' not in calling_string:
+        raise NameError(error_text)
+    return calling_string.split('=')[0].strip()
\ No newline at end of file
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 7fad0c5d..7679d4a6 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -29,10 +29,12 @@
 from mcstasscript.helper.beam_dump_database import BeamDumpDatabase
 from mcstasscript.helper.check_mccode_version import check_mcstas_major_version
 from mcstasscript.helper.check_mccode_version import check_mcxtrace_major_version
+from mcstasscript.helper.name_inspector import find_python_variable_name
 from mcstasscript.instrument_diagram.make_diagram import instrument_diagram
 from mcstasscript.instrument_diagnostics.intensity_diagnostics import IntensityDiagnostics
 
 
+
 class McCode_instr(BaseCalculator):
     """
     Main class for writing a McCode instrument using McStasScript
@@ -1180,8 +1182,8 @@ def _create_component_instance(self, name, component_name, **kwargs):
         return self.component_class_lib[component_name](name, component_name,
                                                         **kwargs)
 
-    def add_component(self, name, component_name, before=None, after=None,
-                      AT=None, AT_RELATIVE=None, ROTATED=None,
+    def add_component(self, name, component_name=None, *, before=None,
+                      after=None, AT=None, AT_RELATIVE=None, ROTATED=None,
                       ROTATED_RELATIVE=None, RELATIVE=None, WHEN=None,
                       EXTEND=None, GROUP=None, JUMP=None, SPLIT=None,
                       comment=None, c_code_before=None, c_code_after=None):
@@ -1242,6 +1244,26 @@ def add_component(self, name, component_name, before=None, after=None,
                 Comment that will be displayed before the component
         """
 
+        if component_name is None:
+            # Try to interpret name as the name of a McStas component
+            #  and the python variable name as the given name to this
+            #  instance of the McStas component.
+            if name in self.component_reader.component_path:
+                # Name is an available McStas component!
+                component_name = name
+
+                # Find name through call
+                text = ("When adding a component without giving both "
+                        "name and type it is necessary to assign the "
+                        "component object a python variable name that "
+                        "can be used for the McStas component.")
+                name = find_python_variable_name(error_text=text, n_levels=2)
+            else:
+                raise NameError("As no name is given, the input is interpreted"
+                                " as a component name, yet no component of"
+                                " type " + str(name) + " is found in McStas"
+                                " installation or work directory.")
+
         if name in [x.name for x in self.component_list]:
             raise NameError(("Component name \"" + str(name)
                              + "\" used twice, " + self.package_name
@@ -1265,8 +1287,8 @@ def add_component(self, name, component_name, before=None, after=None,
         self._insert_component(new_component, before=before, after=after)
         return new_component
 
-    def copy_component(self, name, original_component, before=None, after=None,
-                       AT=None, AT_RELATIVE=None, ROTATED=None,
+    def copy_component(self, name, original_component=None, *, before=None,
+                       after=None, AT=None, AT_RELATIVE=None, ROTATED=None,
                        ROTATED_RELATIVE=None, RELATIVE=None, WHEN=None,
                        EXTEND=None, GROUP=None, JUMP=None, SPLIT=None,
                        comment=None, c_code_before=None, c_code_after=None):
@@ -1326,6 +1348,32 @@ def copy_component(self, name, original_component, before=None, after=None,
             comment : str
                 Comment that will be displayed before the component
         """
+
+        if original_component is None:
+            # Try to interpret name as the name of a McStas component
+            #  to be copied and the python variable name as the given
+            #  name to the new instance of the McStas component.
+            if isinstance(name, Component):
+                name = original_component.name
+
+            component_names = [x.name for x in self.component_list]
+            if name in component_names:
+                # name is an existing component name
+                original_component = name
+
+                # Find new name through call
+                text = ("When making a component copy without providing both "
+                        "name of the new instance and name of the original "
+                        "component it is necessary to assign the new "
+                        "component object to a python variable name that can "
+                        "be used for the new McStas component object.")
+                name = find_python_variable_name(error_text=text, n_levels=2)
+            else:
+                raise NameError("As no name is given, the input is interpreted"
+                                " as a component name, yet no component named"
+                                + str(name) + " is found in the McStas "
+                                "instrument.")
+
         if isinstance(original_component, Component):
             original_component = original_component.name
 
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index b23ed211..5e227103 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -446,6 +446,19 @@ def test_declare_var_block_add_parameter(self):
         with self.assertRaises(NameError):
             instr.add_parameter("double", "theta", comment="test par")
 
+    def test_infer_name_add_parameter(self):
+        """
+        Test that name can be ommited when defining a parameter and that
+        the python variable name is used in its place.
+        """
+        instr = setup_instr_root_path()
+
+        theta = instr.add_parameter(type="double", comment="test par")
+
+        self.assertEqual(theta.name, "theta")
+        self.assertEqual(theta.comment, "test par")
+        self.assertTrue(theta in instr.parameters.parameters.values())
+
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_show_parameters(self, mock_stdout):
         """
@@ -551,6 +564,18 @@ def test_user_var_block_add_declare_variable(self):
         with self.assertRaises(NameError):
             instr.add_declare_var("double", "two_theta")
 
+    def test_infer_add_declare_variable(self):
+        """
+        Check that name can be inferred from python variable when
+        adding a declared variable.
+        """
+        instr = setup_instr_root_path()
+
+        two_theta = instr.add_declare_var("double", comment="test par")
+
+        self.assertEqual(instr.declare_list[0].name, "two_theta")
+        self.assertEqual(instr.declare_list[0].comment, " // test par")
+
     def test_simple_add_user_variable(self):
         """
         This is just an interface to a function that is tested
@@ -1018,6 +1043,24 @@ def test_add_component_simple(self, mock_stdout):
         comp.set_GROUP("developers")
         self.assertEqual(instr.component_list[0].GROUP, "developers")
 
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_add_component_infer_name(self, mock_stdout):
+        """
+        Testing add_component works when name is left out and inferred
+        from the name of the python variable in the call.
+        """
+
+        instr = setup_instr_with_path()
+
+        test_component = instr.add_component("test_for_reading")
+
+        self.assertEqual(len(instr.component_list), 1)
+        self.assertEqual(instr.component_list[0].name, "test_component")
+
+        # Test the resulting object functions as intended
+        test_component.set_GROUP("developers")
+        self.assertEqual(instr.component_list[0].GROUP, "developers")
+
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_add_component_simple_keyword(self, mock_stdout):
         """
@@ -1150,6 +1193,22 @@ def test_copy_component_simple(self):
         self.assertEqual(comp.AT_data[1], 0)
         self.assertEqual(comp.AT_data[2], 2)
 
+    def test_infer_copy_component_simple(self):
+        """
+        Checks that a component can be copied using the name
+        while giving the new instance name as the python variable
+        """
+
+        instr = setup_populated_with_some_options_instr()
+
+        copy_of_second_comp = instr.copy_component("second_component")
+
+        self.assertEqual(copy_of_second_comp.name, "copy_of_second_comp")
+        self.assertEqual(copy_of_second_comp.yheight, 1.23)
+        self.assertEqual(copy_of_second_comp.AT_data[0], 0)
+        self.assertEqual(copy_of_second_comp.AT_data[1], 0)
+        self.assertEqual(copy_of_second_comp.AT_data[2], 2)
+
     def test_copy_component_simple_fail(self):
         """
         Checks a NameError is raised if trying to copy a component that does

From 63d16c5354aa46b52fa6f60b4cf463044840d512 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 18 Apr 2023 16:36:00 +0200
Subject: [PATCH 337/403] Addition of ipympl as dependency to fix issues with
 jupyter notebook widgets not displaying. Avoid running checks for errors when
 settings(checks=False) is used, was used in diagrams.

---
 mcstasscript/_version.py        | 2 +-
 mcstasscript/interface/instr.py | 7 +++++--
 requirements.txt                | 1 +
 3 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index c1f9172d..95248a02 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.55"
+__version__ = "0.0.56"
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 7fad0c5d..d82d3125 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2576,7 +2576,8 @@ def show_diagram(self, analysis=False, variable=None, limits=None):
         analysis : bool
             If True, a plot of intensity and ncount over the instrument is included
         """
-        if self.has_errors():
+
+        if self.has_errors() and self._run_settings["checks"]:
             print("The instrument has some error, this diagram is still "
                   "shown as it may help find the bug.")
 
@@ -2584,7 +2585,9 @@ def show_diagram(self, analysis=False, variable=None, limits=None):
             analysis = True
 
         instrument_diagram(self, analysis=analysis, variable=variable, limits=limits)
-        self.check_for_errors()
+
+        if self._run_settings["checks"]:
+            self.check_for_errors()
 
     def show_analysis(self, variable=None):
         beam_diag = IntensityDiagnostics(self)
diff --git a/requirements.txt b/requirements.txt
index e9e0f793..37c38e28 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,3 +3,4 @@ matplotlib
 PyYAML
 ipywidgets
 libpyvinyl
+ipympl

From 6618d454fbb8423e9c2d58c804b3b61ea930c328 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 18 Apr 2023 16:54:38 +0200
Subject: [PATCH 338/403] Update of actions to new setup-python version and
 enabling manual dispatch.

---
 .github/workflows/unit_tests_mac.yaml     | 6 ++++--
 .github/workflows/unit_tests_ubuntu.yaml  | 6 ++++--
 .github/workflows/unit_tests_windows.yaml | 6 ++++--
 3 files changed, 12 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index b801c7b1..ca7e4d54 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -1,5 +1,7 @@
 name: McStasScript unit tests on OS X
-on: [push]
+on:
+  push:
+  workflow_dispatch:
 jobs:
   build:
     strategy:
@@ -9,7 +11,7 @@ jobs:
       
     steps:
       - name: Temporary developer libpyvinyl checkout
-        uses: actions/checkout@v2
+        uses: actions/checkout@v4
         with:
           ref: ${{ env.BRANCH }}
           path: mcstasscript
diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index ca58bd64..df266362 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -1,5 +1,7 @@
 name: McStasScript unit tests on ubuntu
-on: [push]
+on:
+  push:
+  workflow_dispatch:
 jobs:
   build:
     strategy:
@@ -9,7 +11,7 @@ jobs:
       
     steps:
       - name: Temporary developer libpyvinyl checkout
-        uses: actions/checkout@v2
+        uses: actions/checkout@v4
         with:
           ref: ${{ env.BRANCH }}
           path: mcstasscript
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
index 2d5d43a5..76f0f105 100644
--- a/.github/workflows/unit_tests_windows.yaml
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -1,5 +1,7 @@
 name: McStasScript unit tests on windows
-on: [push]
+on:
+  push:
+  workflow_dispatch:
 jobs:
   build:
     strategy:
@@ -22,7 +24,7 @@ jobs:
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
               

From 0f392745da24a5b6c9de931d2a6a230be8532b97 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 18 Apr 2023 17:03:20 +0200
Subject: [PATCH 339/403] Update of syntax

---
 .github/workflows/unit_tests_mac.yaml     | 2 ++
 .github/workflows/unit_tests_ubuntu.yaml  | 2 ++
 .github/workflows/unit_tests_windows.yaml | 2 ++
 3 files changed, 6 insertions(+)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index ca7e4d54..ad839a3a 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -1,7 +1,9 @@
 name: McStasScript unit tests on OS X
+
 on:
   push:
   workflow_dispatch:
+
 jobs:
   build:
     strategy:
diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index df266362..66ee2e74 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -1,7 +1,9 @@
 name: McStasScript unit tests on ubuntu
+
 on:
   push:
   workflow_dispatch:
+
 jobs:
   build:
     strategy:
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
index 76f0f105..f6c068b9 100644
--- a/.github/workflows/unit_tests_windows.yaml
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -1,7 +1,9 @@
 name: McStasScript unit tests on windows
+
 on:
   push:
   workflow_dispatch:
+
 jobs:
   build:
     strategy:

From 5df9d75a80ba95353bcbc43f2234f3e127dd1a86 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 18 Apr 2023 17:06:27 +0200
Subject: [PATCH 340/403] Issue with syntax in github actions.

---
 .github/workflows/unit_tests_mac.yaml    | 4 ++--
 .github/workflows/unit_tests_ubuntu.yaml | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index ad839a3a..6ff85835 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -13,7 +13,7 @@ jobs:
       
     steps:
       - name: Temporary developer libpyvinyl checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v2
         with:
           ref: ${{ env.BRANCH }}
           path: mcstasscript
@@ -26,7 +26,7 @@ jobs:
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
               
diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index 66ee2e74..8586f0d1 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -13,7 +13,7 @@ jobs:
       
     steps:
       - name: Temporary developer libpyvinyl checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v2
         with:
           ref: ${{ env.BRANCH }}
           path: mcstasscript
@@ -26,7 +26,7 @@ jobs:
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
               

From e7a649d0b26c4d764456ec365954de8d5b316134 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 18 Apr 2023 17:13:17 +0200
Subject: [PATCH 341/403] Updated duplicate name.

---
 .github/workflows/unit_tests_mac.yaml     | 2 +-
 .github/workflows/unit_tests_ubuntu.yaml  | 2 +-
 .github/workflows/unit_tests_windows.yaml | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index 6ff85835..de4b72f9 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -12,7 +12,7 @@ jobs:
     runs-on: macos-latest
       
     steps:
-      - name: Temporary developer libpyvinyl checkout
+      - name: Temporary developer McStasScript checkout
         uses: actions/checkout@v2
         with:
           ref: ${{ env.BRANCH }}
diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index 8586f0d1..69c49fa0 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -12,7 +12,7 @@ jobs:
     runs-on: ubuntu-latest
       
     steps:
-      - name: Temporary developer libpyvinyl checkout
+      - name: Temporary developer McStasScript checkout
         uses: actions/checkout@v2
         with:
           ref: ${{ env.BRANCH }}
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
index f6c068b9..5661724f 100644
--- a/.github/workflows/unit_tests_windows.yaml
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -12,7 +12,7 @@ jobs:
     runs-on: windows-latest
       
     steps:
-      - name: Temporary developer libpyvinyl checkout
+      - name: Temporary developer McStasScript checkout
         uses: actions/checkout@v2
         with:
           ref: ${{ env.BRANCH }}

From 3902405a3b9f6aa484036c812ee7ae1c7a99bbd7 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 20 Apr 2023 13:36:06 +0200
Subject: [PATCH 342/403] Update of integration tests to support McStas 2 and
 3.

Include astrox folder in component_reader so McXtrace can find those components.
---
 mcstasscript/helper/component_reader.py       |   3 +-
 .../test_complex_instrument.py                |  17 +-
 .../test_input_folder/PSDlin_monitor_3.comp   | 151 ++++++++++++++++++
 .../test_simple_instrument.py                 |  20 ++-
 4 files changed, 183 insertions(+), 8 deletions(-)
 create mode 100644 mcstasscript/integration_tests/test_input_folder/PSDlin_monitor_3.comp

diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index 82dfeeb1..d72e64c7 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -81,7 +81,8 @@ def __init__(self, mcstas_path, input_path="."):
                        "misc",
                        "contrib",
                        "obsolete",
-                       "union"]
+                       "union",
+                       "astrox"]
 
         self.component_path = {}
         self.component_category = {}
diff --git a/mcstasscript/integration_tests/test_complex_instrument.py b/mcstasscript/integration_tests/test_complex_instrument.py
index 380e749c..d5a30a5d 100644
--- a/mcstasscript/integration_tests/test_complex_instrument.py
+++ b/mcstasscript/integration_tests/test_complex_instrument.py
@@ -8,12 +8,14 @@
 from mcstasscript.interface import instr, functions
 from mcstasscript.jb_interface.simulation_interface import SimInterface
 
+
 class FakeChange:
     def __init__(self, new=None, old=None, name=None):
         self.new = new
         self.old = old
         self.name = name
 
+
 def setup_complex_instrument():
     """
     Sets up guide system with two guides that are placed next to one
@@ -104,7 +106,10 @@ def setup_complex_instrument():
     PSD1 = Instr.add_component("PSD_1D_1", "PSDlin_monitor")
     PSD1.set_AT([0, 0, 0.2], RELATIVE="after_guide")
     PSD1.xwidth = 0.1
-    PSD1.nx = 100
+    if Instr.mccode_version > 2:
+        PSD1.nbins = 100
+    else:
+        PSD1.nx = 100
     PSD1.yheight = 0.03
     PSD1.filename = "\"PSD1.dat\""
     PSD1.restore_neutron = 1
@@ -113,7 +118,10 @@ def setup_complex_instrument():
     PSD2 = Instr.add_component("PSD_1D_2", "PSDlin_monitor")
     PSD2.set_AT([0, 0, 0.2], RELATIVE="after_guide")
     PSD2.xwidth = 0.1
-    PSD2.nx = 100
+    if Instr.mccode_version > 2:
+        PSD2.nbins = 100
+    else:
+        PSD2.nx = 100
     PSD2.yheight = 0.03
     PSD2.filename = "\"PSD2.dat\""
     PSD2.restore_neutron = 1
@@ -122,7 +130,10 @@ def setup_complex_instrument():
     PSD = Instr.add_component("PSD_1D", "PSDlin_monitor")
     PSD.set_AT([0, 0, 0.2], RELATIVE="after_guide")
     PSD.xwidth = 0.1
-    PSD.nx = 100
+    if Instr.mccode_version > 2:
+        PSD.nbins = 100
+    else:
+        PSD.nx = 100
     PSD.yheight = 0.03
     PSD.filename = "\"PSD_all.dat\""
     PSD.restore_neutron = 1
diff --git a/mcstasscript/integration_tests/test_input_folder/PSDlin_monitor_3.comp b/mcstasscript/integration_tests/test_input_folder/PSDlin_monitor_3.comp
new file mode 100644
index 00000000..f9297440
--- /dev/null
+++ b/mcstasscript/integration_tests/test_input_folder/PSDlin_monitor_3.comp
@@ -0,0 +1,151 @@
+/*******************************************************************************
+*
+* McStas, neutron ray-tracing package
+*         Copyright 1997-2002, All rights reserved
+*         Risoe National Laboratory, Roskilde, Denmark
+*         Institut Laue Langevin, Grenoble, France
+*
+* %I
+* Written by:  Kim Lefmann
+* Date: May 7, 2001
+* Origin: Risoe
+*
+* Rectangular 1D PSD, measuring intensity vs. position along an axis, 
+*
+* %D
+* A 1-dimensional PSD measuring intensity along either the horizontal axis x (default) or
+* the vertical axis y. 
+*
+*
+* Example: PSDlin_monitor(nbins=20, filename="Output.x", xmin=-0.1, xmax=0.1, ymin=-0.1, ymax=0.1)
+*
+* %P
+* INPUT PARAMETERS:
+*
+* xmin: [m]    Lower x bound of detector opening.
+* xmax: [m]    Upper x bound of detector opening.
+* ymin: [m]    Lower y bound of detector opening.
+* ymax: [m]    Upper y bound of detector opening.
+* xwidth: [m]  Width of detector. Overrides xmin, xmax.
+* yheight: [m] Height of detector. Overrides ymin, ymax.
+* nbins: [1]   Number of positional bins.
+* filename: [str] Name of file in which to store the detector image.
+* vertical: [1]   Flag to indicate whether the monitor measures along the horiz. or vert. axis
+* restore_neutron: [1] If set, the monitor does not influence the neutron state.
+* nowritefile: [1]      If set, monitor will skip writing to disk
+*
+* OUTPUT PARAMETERS:
+*
+* PSDlin_N:    Array of neutron counts
+* PSDlin_p:    Array of neutron weight counts
+* PSDlin_p2:   Array of second moments
+*
+* %E
+******************************************************************************/
+
+DEFINE COMPONENT PSDlin_monitor_3
+DEFINITION PARAMETERS ()
+SETTING PARAMETERS (int nbins=20, string filename=0, xmin=-0.05, xmax=0.05, ymin=-0.05, ymax=0.05, int nowritefile=0,
+    xwidth=0, yheight=0, int restore_neutron=0, int vertical=0)
+OUTPUT PARAMETERS ()
+/* Neutron parameters: (x,y,z,vx,vy,vz,t,sx,sy,sz,p) */
+
+DECLARE
+%{
+  DArray1d PSDlin_N;
+  DArray1d PSDlin_p;
+  DArray1d PSDlin_p2;
+%}
+
+INITIALIZE
+%{
+  int i;
+
+  if (xwidth  > 0) { xmax = xwidth/2;  xmin = -xmax; }
+  if (yheight > 0) { ymax = yheight/2; ymin = -ymax; }
+
+  if ((xmin >= xmax) || (ymin >= ymax)) {
+    printf("PSDlin_monitor: %s: Null detection area !\n"
+           "ERROR           (xwidth,yheight,xmin,xmax,ymin,ymax). Exiting",
+           NAME_CURRENT_COMP);
+    exit(0);
+  }
+
+  PSDlin_N = create_darr1d(nbins);
+  PSDlin_p = create_darr1d(nbins);
+  PSDlin_p2 = create_darr1d(nbins);
+
+  for (i=0; i<nbins; i++)
+  {
+    PSDlin_N[i] = 0;
+    PSDlin_p[i] = 0;
+    PSDlin_p2[i] = 0;
+  }
+
+  // Use instance name for monitor output if no input was given
+  if (!strcmp(filename,"\0")) sprintf(filename,NAME_CURRENT_COMP);
+%}
+
+TRACE
+%{
+  int i;
+
+  PROP_Z0;
+  if (x>xmin && x<xmax && y>ymin && y<ymax){ 
+    /* Bin number */
+    if (!vertical){
+      i = floor(nbins*(x-xmin)/(xmax-xmin));
+    }else{
+      i = floor(nbins*(y-ymin)/(ymax-ymin));
+    }
+    if((i >= nbins) || (i<0))
+    {
+      printf("ERROR: (%s) wrong positioning in linear PSD. i= %i \n",NAME_CURRENT_COMP,i);
+      exit(1);
+    }
+    double p2 = p*p;
+    #pragma acc atomic
+    PSDlin_N[i] = PSDlin_N[i] +1;
+    #pragma acc atomic
+    PSDlin_p[i] = PSDlin_p[i] + p;
+    #pragma acc atomic
+    PSDlin_p2[i] = PSDlin_p2[i] + p2;
+    SCATTER;
+  }
+  if (restore_neutron) {
+    RESTORE_NEUTRON(INDEX_CURRENT_COMP, x, y, z, vx, vy, vz, t, sx, sy, sz, p);
+  }
+%}
+
+SAVE
+%{
+if (!nowritefile) {
+  if (!vertical){
+    DETECTOR_OUT_1D(
+        "Linear PSD monitor","Test","Intensity","x", xmin, xmax, nbins,
+        &PSDlin_N[0],&PSDlin_p[0],&PSDlin_p2[0],filename);
+  }else{
+    DETECTOR_OUT_1D(
+        "Linear PSD monitor","Test","Intensity","y", ymin, ymax, nbins,
+        &PSDlin_N[0],&PSDlin_p[0],&PSDlin_p2[0],filename);
+  }
+}
+%}
+
+FINALLY
+%{
+  destroy_darr1d(PSDlin_N);
+  destroy_darr1d(PSDlin_p);
+  destroy_darr1d(PSDlin_p2);
+%}
+
+MCDISPLAY
+%{
+  multiline(5, (double)xmin, (double)ymin, 0.0,
+               (double)xmax, (double)ymin, 0.0,
+               (double)xmax, (double)ymax, 0.0,
+               (double)xmin, (double)ymax, 0.0,
+               (double)xmin, (double)ymin, 0.0);
+%}
+
+END
diff --git a/mcstasscript/integration_tests/test_simple_instrument.py b/mcstasscript/integration_tests/test_simple_instrument.py
index 0e99a421..17325e86 100644
--- a/mcstasscript/integration_tests/test_simple_instrument.py
+++ b/mcstasscript/integration_tests/test_simple_instrument.py
@@ -22,7 +22,10 @@ def setup_simple_instrument():
 
     PSD.set_AT([0, 0, 1], RELATIVE="source")
     PSD.xwidth = 0.1
-    PSD.nx = 100
+    if Instr.mccode_version > 2:
+        PSD.nbins = 100
+    else:
+        PSD.nx = 100
     PSD.yheight = 0.03
     PSD.filename = "\"PSD.dat\""
     PSD.restore_neutron = 1
@@ -47,11 +50,17 @@ def setup_simple_instrument_input_path():
     source.dE = 1.0
     source.flux = 1E10
 
-    PSD = Instr.add_component("PSD_1D", "PSDlin_monitor")
+    if Instr.mccode_version > 2:
+        PSD = Instr.add_component("PSD_1D", "PSDlin_monitor_3")
+    else:
+        PSD = Instr.add_component("PSD_1D", "PSDlin_monitor")
 
     PSD.set_AT([0, 0, 1], RELATIVE="source")
     PSD.xwidth = 0.1
-    PSD.nx = 100
+    if Instr.mccode_version > 2:
+        PSD.nbins = 100
+    else:
+        PSD.nx = 100
     PSD.yheight = 0.03
     PSD.filename = "\"PSD.dat\""
     PSD.restore_neutron = 1
@@ -81,7 +90,10 @@ def setup_simple_slit_instrument():
     PSD = Instr.add_component("PSD_1D", "PSDlin_monitor")
     PSD.set_AT([0, 0, 1], RELATIVE="source")
     PSD.xwidth = 0.1
-    PSD.nx = 100
+    if Instr.mccode_version > 2:
+        PSD.nbins = 100
+    else:
+        PSD.nx = 100
     PSD.yheight = 0.03
     PSD.filename = "\"PSD.dat\""
     PSD.restore_neutron = 1

From 59fc55c058145a2a3da739eb96fb309b1e140cc3 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 20 Apr 2023 17:24:56 +0200
Subject: [PATCH 343/403] Added openacc and NeXus option to settings, including
 unit tests.

Updated version to 0.0.57.

Small corrections to docs, including showing McStas 2 / 3 flexibility in Union intro.
---
 ...n_tutorial_1_processes_and_materials.ipynb |  42 +++++-
 .../Union_tutorial_4_conditionals.ipynb       |   6 +-
 docs/source/user_guide/data.ipynb             | 126 ++++++++++++++++--
 mcstasscript/_version.py                      |   2 +-
 mcstasscript/helper/managed_mcrun.py          |  18 +++
 mcstasscript/interface/instr.py               |  33 ++++-
 mcstasscript/tests/test_Instr.py              |  51 +++++++
 mcstasscript/tests/test_ManagedMcrun.py       |  80 +++++++++++
 8 files changed, 337 insertions(+), 21 deletions(-)

diff --git a/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb b/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
index a1b2cd10..cab2003d 100644
--- a/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
+++ b/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
@@ -37,6 +37,24 @@
     "instrument = ms.McStas_instr(\"python_tutorial\", input_path=\"run_folder\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Differences between McStas versions\n",
+    "The Union components can be used in both McStas 2.X and McStas 3.X, but when used in McStas 3.X it is necessary to add an *Union_init* component named init before any other Union component and a *Union_stop* component after all other Union components. To make the notebooks compatible with both, the init and stop component can be added inside an if-statement that checks what version of McStas is used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if instrument.mccode_version == 3:\n",
+    "    init = instrument.add_component(\"init\", \"Union_init\") # This component has to be called init"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -51,7 +69,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "instrument.show_components(\"Work directory\")"
+    "instrument.available_components(\"union\")"
    ]
   },
   {
@@ -345,6 +363,24 @@
     "master = instrument.add_component(\"master\", \"Union_master\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### McStas 3.X compatability\n",
+    "As mentioned at the start of the notebook it is necessary to add a *Union_stop* component after all Union coponents in the instrument, this is typically after the last master."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if instrument.mccode_version == 3:\n",
+    "    stop = instrument.add_component(\"stop\", \"Union_stop\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -465,7 +501,9 @@
     "- Union_initialization.c\n",
     "- Union_functions.c\n",
     "- Geometry_functions.c\n",
-    "- Union_last_functions.c (if on McStas 3.X)"
+    "- Union_last_functions.c (if on McStas 3.X)\n",
+    "\n",
+    "If using McStas 3.X, it could be that the Union_init component and Union_stop component was not added."
    ]
   },
   {
diff --git a/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb b/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
index 444bd146..425e15e4 100644
--- a/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
+++ b/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
@@ -67,8 +67,6 @@
     "src.yheight = 0.035\n",
     "src.focus_aw = 0.01\n",
     "src.focus_ah = 0.01\n",
-    "\n",
-    "instrument.add_parameter(\"wavelength\", value=5.0, comment=\"Wavelength in [Ang]\")\n",
     "src.lambda0 = instrument.add_parameter(\"wavelength\", value=5.0,\n",
     "                                       comment=\"Wavelength in [Ang]\")\n",
     "src.dlambda = \"0.01*wavelength\"\n",
@@ -417,7 +415,7 @@
     "ms.make_sub_plot([logger_zx, logger_zy, logger_2DQ_sample, logger_2DQ_env], fontsize=10)\n",
     "\n",
     "time = ms.name_search(\"logger_time_all\", data_con)\n",
-    "time.set_plot_options(log=True)\n",
+    "time.set_plot_options(log=True, orders_of_mag=6)\n",
     "\n",
     "ms.make_sub_plot([time], fontsize=18)"
    ]
@@ -478,7 +476,7 @@
     "ms.make_sub_plot([logger_zx, logger_zy, logger_2DQ_sample, logger_2DQ_env], fontsize=10)\n",
     "\n",
     "time = ms.name_search(\"logger_time_all\", data_con_f)\n",
-    "time.set_plot_options(log=True)\n",
+    "time.set_plot_options(log=True, orders_of_mag=6)\n",
     "\n",
     "ms.make_sub_plot([time], fontsize=18)"
    ]
diff --git a/docs/source/user_guide/data.ipynb b/docs/source/user_guide/data.ipynb
index 707642e6..6166fa67 100644
--- a/docs/source/user_guide/data.ipynb
+++ b/docs/source/user_guide/data.ipynb
@@ -66,8 +66,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "event = instrument.add_component(\"events\", \"Monitor_nD\", RELATIVE=sample)\n",
-    "event.set_parameters(xwidth=0.5, yheight=0.5, restore_neutron=1, filename='\"events.dat\"')\n",
+    "event = instrument.add_component(\"events\", \"Monitor_nD\")\n",
+    "event.set_AT(0.1, RELATIVE=sample)\n",
+    "event.set_parameters(xwidth=0.1, yheight=0.1, restore_neutron=1, filename='\"events.dat\"')\n",
     "event.options = '\"list all auto, x y z vx vy vz t\"'"
    ]
   },
@@ -125,7 +126,7 @@
    "metadata": {},
    "source": [
     "## McStasData objects\n",
-    "The data retrieved from the instrument object is in the form of a list that contains [McStasData](../_autosummary/mcstasscript.data.data.McStasData.rst) and [McStasDataEvent](../_autosummary/mcstasscript.data.data.McStasDataEvent.rst) objects. The McStasScript module [*functions*](../_autosummary/mcstasscript.interface.functions.rst) contains a function called [*name_search*](../_autosummary/mcstasscript.interface.functions.name_search.rst) which can be used to select a certain element of such a data list. It will match the component name first and if no match is found it will check for match with the filename. Here [*name_search*](../_autosummary/mcstasscript.interface.functions.name_search.rst) is used to retrieve the PSD [McStasData](../_autosummary/mcstasscript.data.data.McStasData.rst) object."
+    "The data retrieved from the instrument object is in the form of a list that contains [McStasDataBinned](../_autosummary/mcstasscript.data.data.McStasDataBinned.rst) and [McStasDataEvent](../_autosummary/mcstasscript.data.data.McStasDataEvent.rst) objects. McStasScript contains a function called [*name_search*](../_autosummary/mcstasscript.interface.functions.name_search.rst) which can be used to select a certain element of such a data list. It will match the component name first and if no match is found it will check for match with the filename. Here [*name_search*](../_autosummary/mcstasscript.interface.functions.name_search.rst) is used to retrieve the PSD [McStasDataBinned](../_autosummary/mcstasscript.data.data.McStasDataBinned.rst) object."
    ]
   },
   {
@@ -146,7 +147,7 @@
    "metadata": {},
    "source": [
     "### Accessing metadata\n",
-    "The McStasData object carries relevant metadata in a *metadata* attribute. Using the python print function this object can display basic information on the contained data."
+    "Both McStasDataBinned and McStasDataEvent object carries relevant metadata in a *metadata* attribute. Using the python print function this object can display basic information on the contained data."
    ]
   },
   {
@@ -188,6 +189,26 @@
     "PSD.metadata.info"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "balanced-spine",
+   "metadata": {},
+   "source": [
+    "The total intensity, error or ncount is kept in the values are often of interest, so these are given their own attributes: *total_I*, *total_E* and *total_N*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "weighted-pillow",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(PSD.metadata.total_I)\n",
+    "print(PSD.metadata.total_E)\n",
+    "print(PSD.metadata.total_N)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "acknowledged-orlando",
@@ -218,14 +239,43 @@
     "print(PSD.Ncount)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "registered-vocabulary",
+   "metadata": {},
+   "source": [
+    "The original path to the data is also contained within the McStasData object and can be returned with get_data_location."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "adverse-demonstration",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PSD.get_data_location()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "central-democracy",
    "metadata": {},
    "source": [
+    "### Event data\n",
     "McStasDataEvent objecst stores event data, and for this reason does not have *Error* or *Ncount*. The event information is contained in a 2D Numpy array in the *Intensity* and *Events* attributes. "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "north-adjustment",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(events)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -238,20 +288,64 @@
   },
   {
    "cell_type": "markdown",
-   "id": "registered-vocabulary",
+   "id": "fancy-cooling",
    "metadata": {},
    "source": [
-    "The original path to the data is also contained within the McStasData object and can be returned with get_data_location."
+    "The [McStasDataEvent](../_autosummary/mcstasscript.data.data.McStasDataEvent.rst) objects have some help functions to work with the contained event data. The first is *get_data_column* that returns the data for a given axis. The available axis shown in the table below.\n",
+    "\n",
+    "| Axis string | Full name | Unit | Description |\n",
+    "| --- | --- | --- | --- |\n",
+    "| t | time | s | Particle time |\n",
+    "| x | x position | m | Coordinate x of particle |\n",
+    "| y | y position | m | Coordinate y of particle |\n",
+    "| z | z position | m | Coordinate z of particle |\n",
+    "| vx | x velocity | m/s | Velocity projected onto x |\n",
+    "| vy | y velocity | m/s | Velocity projected onto y |\n",
+    "| vz | z velocity | m/s | Velocity projected onto z |\n",
+    "| p | weight | intensity | Particle weight McStas n/s |\n",
+    "| l | lambda | AA | Wavelength |\n",
+    "| e | energy | meV | Particle energy |\n",
+    "| speed | speed | m/s | Speed (length of velocity vector) |\n",
+    "| dx | x divergence | deg | Divergence from z axis along x axis |\n",
+    "| dy | y divergence | deg | Divergence from z axis along y axis |\n",
+    "| U1 | User1 | Userdefined | User defined flag in Monitor_nD |\n",
+    "| U2 | User2 | Userdefined | User defined flag in Monitor_nD |\n",
+    "| U3 | User3 | Userdefined | User defined flag in Monitor_nD |\n",
+    "\n",
+    "\n",
+    "Here we get the wavelength information for each neutron in the event dataset."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "adverse-demonstration",
+   "id": "floating-chancellor",
    "metadata": {},
    "outputs": [],
    "source": [
-    "PSD.get_data_location()"
+    "events.get_data_column(\"l\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ranking-booth",
+   "metadata": {},
+   "source": [
+    "It is also possible to produce a McStasDataBinned dataset from a McStasDataEvent dataset by binning along one or two chosen dimensions using *make_1d* and *make_2d*. It supports all the same variables as above. For each method it is possible to choose how many bins should be used with the n_bins keyword argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "corresponding-baking",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "speed_1d = events.make_1d(\"speed\", n_bins=60)\n",
+    "divergence_2d = events.make_2d(\"dx\", \"dy\", n_bins=[120, 120])\n",
+    "\n",
+    "print(speed_1d)\n",
+    "print(divergence_2d)"
    ]
   },
   {
@@ -333,18 +427,28 @@
     "| top_lim | float | | Upper limit to plot range of y axis|"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "binding-joining",
+   "metadata": {},
+   "source": [
+    "McStasDataBinned generated from Event files can be plotted in the same manner."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "frozen-participation",
+   "id": "banner-fitting",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "ms.make_sub_plot([speed_1d, divergence_2d], log=True)"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "banner-fitting",
+   "id": "greatest-spanking",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 95248a02..71017a60 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.56"
+__version__ = "0.0.57"
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 31e38704..76c146b5 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -83,6 +83,10 @@ def __init__(self, instr_name, **kwargs):
                 If True, forces compile. If False no new instrument is written
             run_folder : str
                 Path to folder in which to run McStas
+            openacc : bool, default False
+                If True, adds the --openacc flag to mcrun call
+            NeXus : bool, default False
+                If True, adds the --format=NeXus to mcrun call
 
         """
 
@@ -92,6 +96,8 @@ def __init__(self, instr_name, **kwargs):
         self.ncount = int(1E6)
         self.mpi = None
         self.gravity = False
+        self.openacc = False
+        self.NeXus = False
         self.parameters = {}
         self.custom_flags = ""
         self.executable_path = ""
@@ -138,6 +144,12 @@ def __init__(self, instr_name, **kwargs):
         if "gravity" in kwargs:
             self.gravity = kwargs["gravity"]
 
+        if "openacc" in kwargs:
+            self.openacc = kwargs["openacc"]
+
+        if "NeXus" in kwargs:
+            self.NeXus = kwargs["NeXus"]
+
         if "seed" in kwargs:
             self.seed = kwargs["seed"]
 
@@ -204,6 +216,12 @@ def run_simulation(self, **kwargs):
         if self.gravity:
             option_string += "-g "
 
+        if self.NeXus:
+            option_string += "--format=NeXus "
+
+        if self.openacc:
+            option_string += "--openacc "
+
         if self.mpi is not None:
             mpi_string = "--mpi=" + str(self.mpi) + " "  # Set mpi
         else:
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 7756162a..0015074c 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -240,7 +240,7 @@ def __init__(self, name, parameters=None, author=None,
                  increment_folder_name=None, custom_flags=None,
                  executable_path=None, executable=None,
                  suppress_output=None, gravity=None, input_path=None,
-                 package_path=None, checks=None):
+                 package_path=None, checks=None, NeXus=None, openacc=None):
         """
         Initialization of McStas Instrument
 
@@ -399,6 +399,12 @@ def __init__(self, name, parameters=None, author=None,
         if increment_folder_name is not None:
             provided_run_settings["increment_folder_name"] = increment_folder_name
 
+        if NeXus is not None:
+            provided_run_settings["NeXus"] = NeXus
+
+        if openacc is not None:
+            provided_run_settings["openacc"] = openacc
+
         # Set run_settings, perform input sanitation
         self.settings(**provided_run_settings)
 
@@ -2205,7 +2211,8 @@ def settings(self, ncount=None, mpi="not_set", seed=None,
                  force_compile=None, output_path=None,
                  increment_folder_name=None, custom_flags=None,
                  executable=None, executable_path=None,
-                 suppress_output=None, gravity=None, checks=None):
+                 suppress_output=None, gravity=None, checks=None,
+                 openacc=None, NeXus=None):
         """
         Sets settings for McStas run performed with backengine
 
@@ -2235,9 +2242,13 @@ def settings(self, ncount=None, mpi="not_set", seed=None,
             executable_path : str
                 Path to mcrun command, "" if already in path
             suppress_output : bool
-                Set to True to surpress output
+                Set to True to suppress output
             gravity : bool
                 If True, gravity will be simulated
+            openacc : bool
+                If True, adds --openacc to mcrun call
+            NeXus : bool
+                If True, adds --format=NeXus to mcrun call
         """
 
         settings = {}
@@ -2293,6 +2304,12 @@ def settings(self, ncount=None, mpi="not_set", seed=None,
         if output_path is not None:
             self.output_path = output_path
 
+        if openacc is not None:
+            settings["openacc"] = bool(openacc)
+
+        if NeXus is not None:
+            settings["NeXus"] = bool(NeXus)
+
         self._run_settings.update(settings)
 
     def settings_string(self):
@@ -2357,6 +2374,16 @@ def settings_string(self):
             description += "  force_compile:".ljust(variable_space)
             description += str(value) + "\n"
 
+        if "NeXus" in self._run_settings:
+            value = self._run_settings["NeXus"]
+            description += "  NeXus:".ljust(variable_space)
+            description += str(value) + "\n"
+
+        if "openacc" in self._run_settings:
+            value = self._run_settings["openacc"]
+            description += "  openacc:".ljust(variable_space)
+            description += str(value) + "\n"
+
         return description.strip()
 
     def show_settings(self):
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 5e227103..e21e0cde 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1894,6 +1894,57 @@ def test_run_backengine_basic(self, mock_sub, mock_stdout):
                                     universal_newlines=True,
                                     cwd=run_path)
 
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    @unittest.mock.patch("subprocess.run")
+    def test_run_backengine_complex_settings(self, mock_sub, mock_stdout):
+        """
+        Test settings are passed to backengine with complex settings
+
+        Check run performs the correct system call with settings. Here
+        the output_path is set to a name that does not correspond to a
+        existing file so no error is thrown.
+        """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        with WorkInTestDir() as handler:
+            new_folder_name = "folder_name_which_is_unused"
+            if os.path.exists(new_folder_name):
+                raise RuntimeError("Folder_name was supposed to not "
+                                   + "exist before "
+                                   + "test_run_backengine_basic")
+
+            instr = setup_populated_instr_with_dummy_path()
+
+            instr.set_parameters({"theta": 1})
+            instr.settings(output_path=new_folder_name,
+                           increment_folder_name=True,
+                           executable_path=executable_path,
+                           mpi=5, ncount=19373, openacc=True,
+                           NeXus=True, force_compile=False,
+                           seed=300, gravity=True, checks=False)
+            instr.backengine()
+
+        expected_path = os.path.join(executable_path, "mcrun")
+        expected_path = '"' + expected_path + '"'
+
+        expected_folder_path = os.path.join(THIS_DIR, new_folder_name)
+
+        # a double space because of a missing option
+        expected_call = (expected_path + " -g --format=NeXus "
+                         + "--openacc "
+                         + "-n 19373 --mpi=5 --seed=300 "
+                         + "-d " + expected_folder_path
+                         + "  test_instrument.instr"
+                         + " theta=1 has_default=37")
+
+        mock_sub.assert_called_with(expected_call,
+                                    shell=True,
+                                    stderr=-2, stdout=-1,
+                                    universal_newlines=True,
+                                    cwd=run_path)
+
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     @unittest.mock.patch("subprocess.run")
     def test_run_full_instrument_complex(self, mock_sub, mock_stdout):
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index 70e14255..52deced1 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -376,6 +376,86 @@ def test_ManagedMcrun_run_simulation_compile(self, mock_sub):
                                          universal_newlines=True,
                                          cwd=mcrun_obj.run_path)
 
+    @unittest.mock.patch("subprocess.run")
+    def test_ManagedMcrun_run_simulation_NeXus(self, mock_sub):
+        """
+        Check run with NeXus works
+        """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        with WorkInTestDir() as handler:
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     output_path="test_folder",
+                                     executable_path=executable_path,
+                                     executable="mcrun",
+                                     mpi=7,
+                                     ncount=57.4,
+                                     force_compile=False,
+                                     NeXus=True,
+                                     custom_flags="-fo",
+                                     parameters={"A": 2,
+                                                 "BC": "car",
+                                                 "th": "\"toy\""})
+
+        mcrun_obj.run_simulation()
+
+        expected_folder_path = os.path.join(THIS_DIR, "test_folder")
+
+        executable = os.path.join(executable_path, "mcrun")
+        executable = '"' + executable + '"'
+        # a double space because of a missing option
+        expected_call = (executable + " --format=NeXus -n 57 --mpi=7 "
+                         + "-d " + expected_folder_path + " -fo test.instr "
+                         + "A=2 BC=car th=\"toy\"")
+
+        mock_sub.assert_called_once_with(expected_call,
+                                         shell=True,
+                                         stderr=-2, stdout=-1,
+                                         universal_newlines=True,
+                                         cwd=mcrun_obj.run_path)
+
+    @unittest.mock.patch("subprocess.run")
+    def test_ManagedMcrun_run_simulation_openacc(self, mock_sub):
+        """
+        Check run with openacc works
+        """
+
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        executable_path = os.path.join(THIS_DIR, "dummy_mcstas")
+
+        with WorkInTestDir() as handler:
+            mcrun_obj = ManagedMcrun("test.instr",
+                                     output_path="test_folder",
+                                     executable_path=executable_path,
+                                     executable="mcrun",
+                                     mpi=7,
+                                     ncount=48.4,
+                                     openacc=True,
+                                     force_compile=False,
+                                     custom_flags="-fo",
+                                     parameters={"A": 2,
+                                                 "BC": "car",
+                                                 "th": "\"toy\""})
+
+        mcrun_obj.run_simulation()
+
+        expected_folder_path = os.path.join(THIS_DIR, "test_folder")
+
+        executable = os.path.join(executable_path, "mcrun")
+        executable = '"' + executable + '"'
+        # a double space because of a missing option
+        expected_call = (executable + " --openacc -n 48 --mpi=7 "
+                         + "-d " + expected_folder_path + " -fo test.instr "
+                         + "A=2 BC=car th=\"toy\"")
+
+        mock_sub.assert_called_once_with(expected_call,
+                                         shell=True,
+                                         stderr=-2, stdout=-1,
+                                         universal_newlines=True,
+                                         cwd=mcrun_obj.run_path)
+
     def test_ManagedMcrun_load_data_PSD4PI(self):
         """
         Use test_data_set to test load_data for PSD_4PI

From ec4a47a673f9880ab70f5f6f63289c00c6fc2c47 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 25 Apr 2023 13:43:36 +0200
Subject: [PATCH 344/403] Added search and dependency keywords.

set_dependency on instrument, reset with set_dependency("").

add_search on instrument (handled with a SearchStatement and SearchStatementList class)
add_search on component

In a search statement it is possible to set SHELL to True as keyword argument to get that feature.

Both component and instrument has:
show_search to display current search statements
clear_search to reset the list to empty
---
 mcstasscript/_version.py                |   2 +-
 mcstasscript/helper/mcstas_objects.py   |  38 +++++
 mcstasscript/helper/search_statement.py |  87 +++++++++++
 mcstasscript/interface/instr.py         |  58 ++++++-
 mcstasscript/tests/test_Instr.py        | 194 ++++++++++++++++++++++++
 mcstasscript/tests/test_component.py    |  37 +++++
 6 files changed, 414 insertions(+), 2 deletions(-)
 create mode 100644 mcstasscript/helper/search_statement.py

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 71017a60..d588f3c6 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.57"
+__version__ = "0.0.58"
diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 78345ba7..850aeb1c 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -2,6 +2,7 @@
 from mcstasscript.helper.formatting import is_legal_parameter
 from mcstasscript.helper.exceptions import McStasError
 from mcstasscript.helper.name_inspector import find_python_variable_name
+from mcstasscript.helper.search_statement import SearchStatement, SearchStatementList
 
 from libpyvinyl.Parameters.Parameter import Parameter
 
@@ -562,6 +563,7 @@ def __init__(self, instance_name, component_name, AT=None,
         self.comment = ""
         self.c_code_before = ""
         self.c_code_after = ""
+        self.search_statement_list = SearchStatementList()
 
         # references to component names
         self.AT_reference = None
@@ -964,6 +966,38 @@ def set_c_code_after(self, string):
                                + "given " + str(type(string)))
         self.c_code_after = string
 
+    def add_search(self, statement, SHELL=False):
+        """
+        Adds a search statement to the component
+
+        The search line can be used to tell McStas to search for files in
+        additional locations. Double quotes are added.
+
+        Parameters
+        ----------
+            statement : str
+                The search statement
+
+            SHELL : bool (default False)
+                if True, shell keyword is added
+        """
+
+        self.search_statement_list.add_statement(SearchStatement(statement, SHELL=SHELL))
+
+    def clear_search(self):
+        """
+        Clears the component of all search statements
+        """
+
+        self.search_statement_list.clear()
+
+    def show_search(self):
+        """
+        Shows all search statements of component
+        """
+
+        print(self.search_statement_list)
+
     def write_component(self, fo):
         """
         Method that writes component to file
@@ -985,6 +1019,9 @@ def write_component(self, fo):
         if len(self.comment) > 1:
             fo.write("// %s\n" % (str(self.comment)))
 
+        # Write search statements
+        self.search_statement_list.write(fo)
+
         if self.SPLIT != 0:
             fo.write("SPLIT " + str(self.SPLIT) + " ")
 
@@ -1080,6 +1117,7 @@ class is used as a superclass for classes describing each
 
         if len(self.c_code_before) > 1:
             string += self.c_code_before + "\n"
+        string += self.search_statement_list.make_string()
         if len(self.comment) > 1:
             string += "// " + self.comment + "\n"
         if self.SPLIT != 0:
diff --git a/mcstasscript/helper/search_statement.py b/mcstasscript/helper/search_statement.py
new file mode 100644
index 00000000..e9fe6381
--- /dev/null
+++ b/mcstasscript/helper/search_statement.py
@@ -0,0 +1,87 @@
+class SearchStatement:
+    def __init__(self, statement, SHELL=False):
+        """
+        A McStas search statement, used either in instrument before component
+        """
+
+        self.statement = str(statement)
+        self.SHELL = SHELL
+
+    def make_string(self):
+        """
+        Generates the search string
+        """
+        if self.SHELL:
+            shell_part = "SHELL "
+        else:
+            shell_part = ""
+
+        if self.statement[0] != '"' and self.statement[-1] != '"':
+            self.statement = '"' + self.statement + '"'
+
+        return f'SEARCH {shell_part}{self.statement}'
+
+    def write(self, fo):
+        """
+        Writes search string to file
+        """
+        fo.write(self.make_string() + "\n")
+
+    def __repr__(self):
+        """
+        Prints search string
+        """
+        return self.make_string()
+
+
+class SearchStatementList:
+    def __init__(self):
+        """
+        Keeps a number of search statements together
+        """
+        self.statements = []
+
+    def add_statement(self, statement):
+        """
+        Add new search statement
+        """
+        if not isinstance(statement, SearchStatement):
+            raise ValueError("SearchStatementList only supports adding SearchStatement objects.")
+
+        self.statements.append(statement)
+
+    def clear(self):
+        """
+        Clear all search statements
+        """
+        self.statements = []
+
+    def write(self, fo):
+        """
+        Write search statements to a file
+        """
+        for statement in self.statements:
+            statement.write(fo)
+
+    def make_string(self):
+        """
+        Makes string with all search statements
+        """
+        string = ""
+        for statement in self.statements:
+            string += statement.make_string() + "\n"
+
+        return string
+
+    def __repr__(self):
+        """
+        Show search statements with print
+        """
+        if len(self.statements) == 0:
+            return "No Search statements yet"
+
+        string = "List of SEARCH statements: \n"
+        for statement in self.statements:
+            string += "  " + statement.make_string() + "\n"
+
+        return string
\ No newline at end of file
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 0015074c..cb832f0a 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -30,11 +30,11 @@
 from mcstasscript.helper.check_mccode_version import check_mcstas_major_version
 from mcstasscript.helper.check_mccode_version import check_mcxtrace_major_version
 from mcstasscript.helper.name_inspector import find_python_variable_name
+from mcstasscript.helper.search_statement import SearchStatement, SearchStatementList
 from mcstasscript.instrument_diagram.make_diagram import instrument_diagram
 from mcstasscript.instrument_diagnostics.intensity_diagnostics import IntensityDiagnostics
 
 
-
 class McCode_instr(BaseCalculator):
     """
     Main class for writing a McCode instrument using McStasScript
@@ -424,12 +424,15 @@ def __init__(self, name, parameters=None, author=None,
         if not hasattr(self, "declare_list"):
             self.declare_list = []
             self.user_var_list = []
+            self.dependency_statement = ""
+            self.search_statement_list = SearchStatementList()
             self.initialize_section = ("// Start of initialize for generated "
                                        + name + "\n")
             self.trace_section = ("// Start of trace section for generated "
                                   + name + "\n")
             self.finally_section = ("// Start of finally for generated "
                                     + name + "\n")
+
             # Handle components
             self.component_list = []  # List of components (have to be ordered)
 
@@ -2078,6 +2081,9 @@ def write_full_instrument(self):
         for variable, end_char in zip(parameter_list, end_chars):
             write_parameter(fo, variable, end_char)
         fo.write(")\n")
+        if self.dependency_statement != "":
+            fo.write("DEPENDENCY " + str(self.dependency_statement) + "\n")
+        self.search_statement_list.write(fo)
         fo.write("\n")
 
         # Write declare
@@ -2207,6 +2213,56 @@ def make_component_subset(self):
 
         return component_subset
 
+    def set_dependency(self, string):
+        """
+        Sets the DEPENDENCY line of instruments, expanding its system search
+
+        The dependency line can be used to tell McStas to search for files in
+        additional locations. Double quotes are added.
+
+        Parameters
+        ----------
+            string : str
+                The dependency string
+        """
+
+        if string[0] != '"' and string[-1] != '"':
+            string = '"' + string + '"'
+
+        self.dependency_statement = string
+
+    def add_search(self, statement, SHELL=False):
+        """
+        Adds a search statement to the instrument
+
+        The dependency line can be used to tell McStas to search for files in
+        additional locations. Double quotes are added.
+
+        Parameters
+        ----------
+            statement : str
+                The search statement
+
+            SHELL : bool (default False)
+                if True, shell keyword is added
+        """
+
+        self.search_statement_list.add_statement(SearchStatement(statement, SHELL=SHELL))
+
+    def clear_search(self):
+        """
+        Clears the instrument of all search statements
+        """
+
+        self.search_statement_list.clear()
+
+    def show_search(self):
+        """
+        Shows all search statements on instrument level
+        """
+
+        print(self.search_statement_list)
+
     def settings(self, ncount=None, mpi="not_set", seed=None,
                  force_compile=None, output_path=None,
                  increment_folder_name=None, custom_flags=None,
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index e21e0cde..7ec6275b 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1748,6 +1748,200 @@ def test_write_full_instrument_simple(self, mock_f):
         handle = mock_f()
         handle.write.assert_has_calls(wrts, any_order=False)
 
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable=unittest.mock.mock_open)
+    def test_write_full_instrument_dependency(self, mock_f):
+        """
+        The write_full_instrument method write the information
+        contained in the instrument instance to a file with McStas
+        syntax. Here tested with the dependency section enabled.
+
+        The test includes a time stamp in the written and expected
+        data that has an accuracy of 1 second.  It is unlikely to fail
+        due to this, but it can happen.
+        """
+
+        instr = setup_populated_instr()
+        instr.set_dependency("-DMCPLPATH=GETPATH(data)")
+        instr.write_full_instrument()
+
+        t_format = "%H:%M:%S on %B %d, %Y"
+
+        my_call = unittest.mock.call
+        wrts = [
+            my_call("/" + 80 * "*" + "\n"),
+            my_call("* \n"),
+            my_call("* McStas, neutron ray-tracing package\n"),
+            my_call("*         Copyright (C) 1997-2008, All rights reserved\n"),
+            my_call("*         Risoe National Laboratory, Roskilde, Denmark\n"),
+            my_call("*         Institut Laue Langevin, Grenoble, France\n"),
+            my_call("* \n"),
+            my_call("* This file was written by McStasScript, which is a \n"),
+            my_call("* python based McStas instrument generator written by \n"),
+            my_call("* Mads Bertelsen in 2019 while employed at the \n"),
+            my_call("* European Spallation Source Data Management and \n"),
+            my_call("* Software Centre\n"),
+            my_call("* \n"),
+            my_call("* Instrument test_instrument\n"),
+            my_call("* \n"),
+            my_call("* %Identification\n"),
+            my_call("* Written by: Python McStas Instrument Generator\n"),
+            my_call("* Date: %s\n" % datetime.datetime.now().strftime(t_format)),
+            my_call("* Origin: ESS DMSC\n"),
+            my_call("* %INSTRUMENT_SITE: Generated_instruments\n"),
+            my_call("* \n"),
+            my_call("* \n"),
+            my_call("* %Parameters\n"),
+            my_call("* \n"),
+            my_call("* %End \n"),
+            my_call("*" * 80 + "/\n"),
+            my_call("\n"),
+            my_call("DEFINE INSTRUMENT test_instrument ("),
+            my_call("\n"),
+            my_call("double theta"),
+            my_call(", "),
+            my_call(""),
+            my_call("\n"),
+            my_call("double has_default"),
+            my_call(" = 37"),
+            my_call(" "),
+            my_call(""),
+            my_call("\n"),
+            my_call(")\n"),
+            my_call('DEPENDENCY "-DMCPLPATH=GETPATH(data)"\n'),
+            my_call("\n"),
+            my_call("DECLARE \n%{\n"),
+            my_call("double two_theta;"),
+            my_call("\n"),
+            my_call("%}\n\n"),
+            my_call("INITIALIZE \n%{\n"),
+            my_call("// Start of initialize for generated test_instrument\n"
+                    + "two_theta = 2.0*theta;\n"),
+            my_call("%}\n\n"),
+            my_call("TRACE \n"),
+            my_call("COMPONENT first_component = test_for_reading("),
+            my_call(")\n"),
+            my_call("AT (0,0,0)"),
+            my_call(" ABSOLUTE\n"),
+            my_call("\n"),
+            my_call("COMPONENT second_component = test_for_reading("),
+            my_call(")\n"),
+            my_call("AT (0,0,0)"),
+            my_call(" ABSOLUTE\n"),
+            my_call("\n"),
+            my_call("COMPONENT third_component = test_for_reading("),
+            my_call(")\n"),
+            my_call("AT (0,0,0)"),
+            my_call(" ABSOLUTE\n"),
+            my_call("\n"),
+            my_call("FINALLY \n%{\n"),
+            my_call("// Start of finally for generated test_instrument\n"),
+            my_call("%}\n"),
+            my_call("\nEND\n")]
+
+        expected_path = os.path.join(".", "test_instrument.instr")
+        mock_f.assert_called_with(expected_path, "w")
+        handle = mock_f()
+        handle.write.assert_has_calls(wrts, any_order=False)
+
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable=unittest.mock.mock_open)
+    def test_write_full_instrument_search(self, mock_f):
+        """
+        The write_full_instrument method write the information
+        contained in the instrument instance to a file with McStas
+        syntax. Here tested with the search section enabled.
+
+        The test includes a time stamp in the written and expected
+        data that has an accuracy of 1 second.  It is unlikely to fail
+        due to this, but it can happen.
+        """
+
+        instr = setup_populated_instr()
+        instr.add_search("first_search")
+        instr.add_search("second search", SHELL=True)
+        instr.write_full_instrument()
+
+        t_format = "%H:%M:%S on %B %d, %Y"
+
+        my_call = unittest.mock.call
+        wrts = [
+            my_call("/" + 80 * "*" + "\n"),
+            my_call("* \n"),
+            my_call("* McStas, neutron ray-tracing package\n"),
+            my_call("*         Copyright (C) 1997-2008, All rights reserved\n"),
+            my_call("*         Risoe National Laboratory, Roskilde, Denmark\n"),
+            my_call("*         Institut Laue Langevin, Grenoble, France\n"),
+            my_call("* \n"),
+            my_call("* This file was written by McStasScript, which is a \n"),
+            my_call("* python based McStas instrument generator written by \n"),
+            my_call("* Mads Bertelsen in 2019 while employed at the \n"),
+            my_call("* European Spallation Source Data Management and \n"),
+            my_call("* Software Centre\n"),
+            my_call("* \n"),
+            my_call("* Instrument test_instrument\n"),
+            my_call("* \n"),
+            my_call("* %Identification\n"),
+            my_call("* Written by: Python McStas Instrument Generator\n"),
+            my_call("* Date: %s\n" % datetime.datetime.now().strftime(t_format)),
+            my_call("* Origin: ESS DMSC\n"),
+            my_call("* %INSTRUMENT_SITE: Generated_instruments\n"),
+            my_call("* \n"),
+            my_call("* \n"),
+            my_call("* %Parameters\n"),
+            my_call("* \n"),
+            my_call("* %End \n"),
+            my_call("*" * 80 + "/\n"),
+            my_call("\n"),
+            my_call("DEFINE INSTRUMENT test_instrument ("),
+            my_call("\n"),
+            my_call("double theta"),
+            my_call(", "),
+            my_call(""),
+            my_call("\n"),
+            my_call("double has_default"),
+            my_call(" = 37"),
+            my_call(" "),
+            my_call(""),
+            my_call("\n"),
+            my_call(")\n"),
+            my_call('SEARCH "first_search"\n'),
+            my_call('SEARCH SHELL "second search"\n'),
+            my_call("\n"),
+            my_call("DECLARE \n%{\n"),
+            my_call("double two_theta;"),
+            my_call("\n"),
+            my_call("%}\n\n"),
+            my_call("INITIALIZE \n%{\n"),
+            my_call("// Start of initialize for generated test_instrument\n"
+                    + "two_theta = 2.0*theta;\n"),
+            my_call("%}\n\n"),
+            my_call("TRACE \n"),
+            my_call("COMPONENT first_component = test_for_reading("),
+            my_call(")\n"),
+            my_call("AT (0,0,0)"),
+            my_call(" ABSOLUTE\n"),
+            my_call("\n"),
+            my_call("COMPONENT second_component = test_for_reading("),
+            my_call(")\n"),
+            my_call("AT (0,0,0)"),
+            my_call(" ABSOLUTE\n"),
+            my_call("\n"),
+            my_call("COMPONENT third_component = test_for_reading("),
+            my_call(")\n"),
+            my_call("AT (0,0,0)"),
+            my_call(" ABSOLUTE\n"),
+            my_call("\n"),
+            my_call("FINALLY \n%{\n"),
+            my_call("// Start of finally for generated test_instrument\n"),
+            my_call("%}\n"),
+            my_call("\nEND\n")]
+
+        expected_path = os.path.join(".", "test_instrument.instr")
+        mock_f.assert_called_with(expected_path, "w")
+        handle = mock_f()
+        handle.write.assert_has_calls(wrts, any_order=False)
+
     # mock sys.stdout to avoid printing to terminal
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_run_full_instrument_required_par_error(self, mock_stdout):
diff --git a/mcstasscript/tests/test_component.py b/mcstasscript/tests/test_component.py
index 69178362..10a97f13 100644
--- a/mcstasscript/tests/test_component.py
+++ b/mcstasscript/tests/test_component.py
@@ -452,6 +452,43 @@ def test_Component_write_to_file_simple(self, mock_f):
         handle = mock_f()
         handle.write.assert_has_calls(expected_writes, any_order=False)
 
+    @unittest.mock.patch('__main__.__builtins__.open',
+                         new_callable=unittest.mock.mock_open)
+    def test_Component_write_to_file_simple_search(self, mock_f):
+        """
+        Testing that a Component can be written to file with the
+        expected output. Here with simple input and search.
+        """
+
+        comp = Component("test_component", "Arm")
+        comp.add_search("A search_statement")
+        comp.add_search("Another search_statement", SHELL=True)
+        comp.add_search('"One with double quotes"')
+
+        comp._unfreeze()
+        # Need to set up attribute parameters
+        # Also need to categorize them as when created
+        comp.parameter_names = []
+        comp.parameter_defaults = {}
+        comp.parameter_types = {}
+        comp._freeze()
+
+        with mock_f('test.txt', 'w') as m_fo:
+            comp.write_component(m_fo)
+
+        my_call = unittest.mock.call
+        expected_writes = [my_call('SEARCH "A search_statement"\n'),
+                           my_call('SEARCH SHELL "Another search_statement"\n'),
+                           my_call('SEARCH "One with double quotes"\n'),
+                           my_call("COMPONENT test_component = Arm("),
+                           my_call(")\n"),
+                           my_call("AT (0,0,0)"),
+                           my_call(" ABSOLUTE\n")]
+
+        mock_f.assert_called_with('test.txt', 'w')
+        handle = mock_f()
+        handle.write.assert_has_calls(expected_writes, any_order=False)
+
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
     def test_Component_write_to_file_include(self, mock_f):

From 1f4d8a4fdd1569d024e8109c76113f84223c15d2 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 25 Apr 2023 14:21:00 +0200
Subject: [PATCH 345/403] Update of documentation and fix of bug in resetting
 dependency.

---
 .../getting_started/version_history.ipynb     | 16 ++++
 docs/source/user_guide/component_object.ipynb | 36 ++++++++-
 .../source/user_guide/instrument_object.ipynb | 81 +++++++++++++++++++
 mcstasscript/interface/instr.py               |  5 ++
 4 files changed, 136 insertions(+), 2 deletions(-)

diff --git a/docs/source/getting_started/version_history.ipynb b/docs/source/getting_started/version_history.ipynb
index a6305294..ecb6f958 100644
--- a/docs/source/getting_started/version_history.ipynb
+++ b/docs/source/getting_started/version_history.ipynb
@@ -14,6 +14,22 @@
    "id": "automatic-charleston",
    "metadata": {},
    "source": [
+    "## 0.0.58 - 25th of April 2023\n",
+    "- Support for new McStas features\n",
+    "    - DEPENDENCY: instrument.set_dependency [info here](../user_guide/instrument_object.ipynb)\n",
+    "    - SEARCH: instrument.add_search / component.add_search [instrument info here](../user_guide/instrument_object.ipynb) [component info here](../user_guide/component_object.ipynb)\n",
+    "    - NeXus: settings(NeXus=True), still can't read data so backengine will fail after writing data\n",
+    "    - openacc: settings(openacc=True), for compiling to GPU\n",
+    "- Infers parameter, variable and component names\n",
+    "    - Each time anything is added to an instrument, one gives it a name and python variable name, now the McStas name can be inferred from the python name so the following statements are equivalent:\n",
+    "        - wavelength = instrument.add_parameter(\"wavelength\", value=5.0)\n",
+    "        - wavelength = instrument.add_parameter(value=5.0)\n",
+    "    - And likewise\n",
+    "        - my_arm = instrument.add_component(\"my_arm\", \"Arm\")\n",
+    "        - my_arm = instrument.add_component(\"Arm)\n",
+    "- Examples are being updated to run McStas 3.X, not all documentation updated yet\n",
+    "- Fixed issue where astrox components where loaded in McXtrace\n",
+    "\n",
     "## 0.0.52 - 17th of November 2022\n",
     "- New features for diagnostics of instruments, [tutorial here](../tutorial/McStasScript_tutorial_6_Diagnostics.ipynb)\n",
     "    - Diagrams can now plot intensity and ncount along instrument\n",
diff --git a/docs/source/user_guide/component_object.ipynb b/docs/source/user_guide/component_object.ipynb
index ef979a7f..3c1a9301 100644
--- a/docs/source/user_guide/component_object.ipynb
+++ b/docs/source/user_guide/component_object.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "widespread-maple",
    "metadata": {},
    "outputs": [],
@@ -487,11 +487,43 @@
     "print(commented_component)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "similar-provision",
+   "metadata": {},
+   "source": [
+    "### SEARCH\n",
+    "It is also possible to set a number of SEARCH statements for a component. McStas will not include the component if it is not found in that search statement. These work just as for instruments and are documented [here](instrument_object.ipynb). Note that McStasScript does not yet search for components in these locations, so for now this syntax can only be used to use different versions of existing components with the same input parameters."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "stable-blond",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SEARCH \"/search/for/component/here\"\n",
+      "COMPONENT code_arm = Arm(\n",
+      ")\n",
+      "AT (0, 0, 0) ABSOLUTE\n"
+     ]
+    }
+   ],
+   "source": [
+    "arm = instrument.add_component(\"code_arm\", \"Arm\")\n",
+    "arm.add_search(\"/search/for/component/here\")\n",
+    "print(arm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "pointed-queens",
+   "metadata": {},
    "outputs": [],
    "source": []
   }
diff --git a/docs/source/user_guide/instrument_object.ipynb b/docs/source/user_guide/instrument_object.ipynb
index e2ad5eec..8cb31dd7 100644
--- a/docs/source/user_guide/instrument_object.ipynb
+++ b/docs/source/user_guide/instrument_object.ipynb
@@ -116,6 +116,87 @@
     "print(wavelength)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "sunset-bishop",
+   "metadata": {},
+   "source": [
+    "### Searching for components and data\n",
+    "McStas have a few keywords for adjusting where to search for data and components. This is typically added right after the parameters, so its natural to include it here in the documentation of the instrument object.\n",
+    "\n",
+    "#### Dependency\n",
+    "The DEPENDENCY keyword allows McStas to search for data and software at runtime. There is just one dependency line for an instrument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "surprised-jurisdiction",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.set_dependency(\"/dependency/example\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "photographic-norwegian",
+   "metadata": {},
+   "source": [
+    "As the instrument won't run unless the dependency is set to a valid path, let's reset it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "sticky-affect",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.set_dependency(\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "applied-gallery",
+   "metadata": {},
+   "source": [
+    "#### Search\n",
+    "The SEARCH keyword allows McStas to search for components before McStas code generation. There can be multiple SEARCH statements in an instrument. It is possible to enable SHELL processing so a system command can be given that should return a path. McStasScript has yet to implement this features fully, so McStasScript won't find components in search paths. For now it can only be used to overwrite existing components with identical input parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dominican-tension",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.add_search(\"/search/example\")\n",
+    "instrument.add_search(\"pwd\", SHELL=True)\n",
+    "\n",
+    "instrument.show_search()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "level-being",
+   "metadata": {},
+   "source": [
+    "It is possible to clear all search statements from an instrument with the *clear_search* method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "middle-conservation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instrument.clear_search()\n",
+    "instrument.show_search()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "fourth-glasgow",
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index cb832f0a..30da65db 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2226,6 +2226,11 @@ def set_dependency(self, string):
                 The dependency string
         """
 
+        # Disable by giving an empty string
+        if len(string) == 0:
+            self.dependency_statement = ""
+            return
+
         if string[0] != '"' and string[-1] != '"':
             string = '"' + string + '"'
 

From 4d5b235aecde1e9215a59cea5f2f43ec4c7d89c3 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 25 Apr 2023 14:22:20 +0200
Subject: [PATCH 346/403] reset notebook.

---
 docs/source/user_guide/component_object.ipynb | 21 +++++--------------
 1 file changed, 5 insertions(+), 16 deletions(-)

diff --git a/docs/source/user_guide/component_object.ipynb b/docs/source/user_guide/component_object.ipynb
index 3c1a9301..bd6cbc5e 100644
--- a/docs/source/user_guide/component_object.ipynb
+++ b/docs/source/user_guide/component_object.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "widespread-maple",
    "metadata": {},
    "outputs": [],
@@ -489,7 +489,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "similar-provision",
+   "id": "better-chance",
    "metadata": {},
    "source": [
     "### SEARCH\n",
@@ -498,21 +498,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "stable-blond",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SEARCH \"/search/for/component/here\"\n",
-      "COMPONENT code_arm = Arm(\n",
-      ")\n",
-      "AT (0, 0, 0) ABSOLUTE\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "arm = instrument.add_component(\"code_arm\", \"Arm\")\n",
     "arm.add_search(\"/search/for/component/here\")\n",
@@ -522,7 +511,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "pointed-queens",
+   "id": "focused-short",
    "metadata": {},
    "outputs": [],
    "source": []

From f51e80cc7353daf4468a2724d8e2033a0f4cb632 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 25 Apr 2023 14:55:15 +0200
Subject: [PATCH 347/403] Small changes to readme.

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index a084d013..0958e78c 100644
--- a/README.md
+++ b/README.md
@@ -205,7 +205,7 @@ Here is a quick overview of the available methods of the main classes in the pro
     ├── set_comment(str string) # Set comment explaining something about the component
     └── print_long() # Prints currently contained information on component
     
-    functions
+    mcstasscript functions
     ├── name_search(str name, list McStasData) # Returns data set with given name from McStasData list
     ├── name_plot_options(str name, list McStasData, kwargs) # Sends kwargs to dataset with given name
     ├── load_data(str foldername) # Loads data from folder with McStas data as McStasData list
@@ -214,12 +214,12 @@ Here is a quick overview of the available methods of the main classes in the pro
         ├── set_mcstas_path(str path) # sets mcstas path
         └── set_line_length(int length) # sets maximum line length
     
-    plotter
+    mcstasscript plotter
     ├── make_plot(list McStasData) # Plots each data set individually
     ├── make_sub_plot(list McStasData) # Plots data as subplot
     └── interface(list McStasData) # Shows plotting interface in jupyter notebook
 
-    reader
+    mcstasscript reader
     └──  McStas_file(str filename) # Returns a reader that can extract information from given instr file
 
     InstrumentReader # returned by McStas_file

From 932c59dd187972ce9b2b7e0a125155f0fbae5189 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 28 Apr 2023 09:51:50 +0200
Subject: [PATCH 348/403] Updating checks of c variable names with regular
 expressions and list of reserved keywords.

---
 mcstasscript/helper/formatting.py | 30 ++++++++++++++++--------------
 1 file changed, 16 insertions(+), 14 deletions(-)

diff --git a/mcstasscript/helper/formatting.py b/mcstasscript/helper/formatting.py
index f41ef1e2..18c8e86a 100644
--- a/mcstasscript/helper/formatting.py
+++ b/mcstasscript/helper/formatting.py
@@ -1,3 +1,5 @@
+import re
+
 class bcolors:
     """
     Helper class that contains formatting classes and functions
@@ -17,20 +19,20 @@ def is_legal_parameter(name):
     Function that returns true if the given name can be used as a
     parameter in the c programming language.
     """
-
-    if name == "":
-        return False
-
-    if " " in name:
-        return False
-
-    if "." in name:
-        return False
-
-    if not name[0].isalpha():
-        return False
-
-    return True
+    # List of C reserved keywords
+    reserved_keywords = [
+        "auto", "break", "case", "char", "const", "continue", "default", "do",
+        "double", "else", "enum", "extern", "float", "for", "goto", "if",
+        "inline", "int", "long", "register", "restrict", "return", "short",
+        "signed", "sizeof", "static", "struct", "switch", "typedef", "union",
+        "unsigned", "void", "volatile", "while", "_Bool", "_Complex", "_Imaginary"
+    ]
+
+    # Regular expression pattern for a legal C variable name
+    pattern = r'^[a-zA-Z_][a-zA-Z0-9_]*$'
+
+    # Check if the input string matches the pattern and is not a reserved keyword
+    return re.match(pattern, name) and name not in reserved_keywords
 
 
 def is_legal_filename(name):

From 9251009b5da0c685c843bb8a685d746d9da148df Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 2 May 2023 11:14:47 +0200
Subject: [PATCH 349/403] Updated version of component reader, now handles
 comments in parameter region.

---
 mcstasscript/helper/component_reader.py | 181 +++++++++++++-----------
 1 file changed, 97 insertions(+), 84 deletions(-)

diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index d72e64c7..b96757a4 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -1,5 +1,22 @@
 import os
 import math
+import re
+
+
+def remove_c_comments(code):
+    """
+    Removes comments from a multiline piece of c code
+    """
+    # Remove single-line comments
+    code = re.sub(r'//.*', '', code)
+
+    # Remove multi-line comments
+    code = re.sub(r'/\*.*?\*/', '', code, flags=re.DOTALL)
+
+    # Remove empty lines
+    code = '\n'.join([line for line in code.split('\n') if line.strip()])
+
+    return code
 
 
 def c_integer_literal_base(s: str) -> int:
@@ -281,7 +298,6 @@ def read_component_file(self, absolute_path):
         Reads a component file and expands component_info_dict
 
         The information is stored as ComponentClass instances.
-
         """
 
         result = ComponentInfo()
@@ -345,97 +361,94 @@ def read_component_file(self, absolute_path):
             if (line.strip().startswith("DEFINITION PARAMETERS")
                     or line.strip().startswith("SETTING PARAMETERS")):
 
-                line = line.split("//")[0]  # Remove comments
-                parts = line.split("(")
-                parameter_parts = parts[1].split(",")
-                parameter_parts = self.correct_for_brackets(parameter_parts)
-                parameter_parts = list(filter(("\n").__ne__, parameter_parts))
-
-                break_now = False
+                define_section = line
                 while True:
-                    # Read all definition parameters
+                    line = file_o.readline()
 
-                    for part in parameter_parts:
+                    end_keywords = ("SHARE", "INITIALIZE", "INITIALISE", "DECLARE", "TRACE")
+                    if line.strip().upper().startswith(end_keywords):
+                        break
 
-                        temp_par_type = "double"
+                    define_section += line
 
-                        part = part.strip()
-
-                        # remove trailing )
-                        if ")" in part:
-                            part = part.replace(")", "")
-                            break_now = True
-
-                        possible_declare = part.split(" ")
-                        possible_type = possible_declare[0].strip()
-                        if "int" == possible_type:
-                            temp_par_type = "int"
-                            # remove int from part
-                            part = "".join(possible_declare[1:])
-                        if "string" == possible_type:
-                            temp_par_type = "string"
-                            # remove string from part
-                            part = "".join(possible_declare[1:])
-                        if "double" == possible_type:
-                            temp_par_type = "double"
-                            # remove double from part
-                            part = "".join(possible_declare[1:])
-                        if "vector" == possible_type:
-                            temp_par_type = "double"
-                            # remove double from part
-                            part = "".join(possible_declare[1:])
-
-                        part = part.replace(" ", "")
-                        if part == "":
-                            continue
-
-                        if part.startswith("//"):
-                            break_now = True
-                            continue
-
-                        if part.startswith("/*"):
-                            break_now = True
-                            continue
-
-                        if "=" not in part:
-                            # no default value, required parameter
-                            result.parameter_names.append(part)
-                            result.parameter_defaults[part] = None
-                            result.parameter_types[part] = temp_par_type
-                        else:
-                            # default value available
-                            name_value = part.split("=")
-                            par_name = name_value[0].strip()
-                            par_value = name_value[1].strip()
-
-                            if temp_par_type == "double":
-                                try:
-                                    par_value = float(par_value)
-                                except ValueError:
-                                    # value could be parameter name
-                                    par_value = par_value
-                            elif temp_par_type == "int":
-                                par_value = int(par_value, c_integer_literal_base(par_value))
-
-                            result.parameter_names.append(par_name)
-                            result.parameter_defaults[par_name] = par_value
-                            result.parameter_types[par_name] = temp_par_type
-
-                    if break_now:
-                        break
+                clean_define_section = remove_c_comments(define_section)
 
-                    new_line = file_o.readline().split("//")[0]
-                    new_line = new_line.split(",")
-                    new_line = self.correct_for_brackets(new_line)
-                    parameter_parts = new_line
+                # Define the delimiters as a list of strings
+                delimiters = ["DEFINITION PARAMETERS", "SETTING PARAMETERS", "OUTPUT PARAMETERS"]
 
-            if line.startswith("DECLARE"):
-                break
+                # Create a regex pattern using alternation and join the delimiters with the '|' symbol
+                delimiter_pattern = r'\s*(' + '|'.join(map(re.escape, delimiters)) + r')\s*'
 
-            if line.startswith("TRACE"):
-                break
+                # Split the text using pattern
+                clean_define_sections = re.split(delimiter_pattern, clean_define_section)
+
+                # Extract parameters from definition and settings part
+                parameter_section = ""
+                for index, section in enumerate(clean_define_sections):
+                    if section in ("DEFINITION PARAMETERS", "SETTING PARAMETERS"):
+                        parameter_section += clean_define_sections[index + 1].strip("(").strip(")")
+
+                # Convert parameter section to single line, then split in parts seperated by comma
+                parameter_section = parameter_section.replace('\n', '')
+                parameter_parts = parameter_section.split(",")
+                # Combine parts that should be together, for example by brackets
+                parameter_parts = self.correct_for_brackets(parameter_parts)
 
-            if line_number == 4000:
+                # Each part now corresponds to a parameter to be read
+                for part in parameter_parts:
+
+                    temp_par_type = "double"
+
+                    part = part.strip()
+
+                    possible_declare = part.split(" ")
+                    possible_type = possible_declare[0].strip()
+                    if "int" == possible_type:
+                        temp_par_type = "int"
+                        # remove int from part
+                        part = "".join(possible_declare[1:])
+                    if "string" == possible_type:
+                        temp_par_type = "string"
+                        # remove string from part
+                        part = "".join(possible_declare[1:])
+                    if "double" == possible_type:
+                        temp_par_type = "double"
+                        # remove double from part
+                        part = "".join(possible_declare[1:])
+                    if "vector" == possible_type:
+                        temp_par_type = "double"
+                        # remove double from part
+                        part = "".join(possible_declare[1:])
+
+                    part = part.replace(" ", "")
+                    if part == "":
+                        continue
+
+                    if "=" not in part:
+                        # no default value, required parameter
+                        result.parameter_names.append(part)
+                        result.parameter_defaults[part] = None
+                        result.parameter_types[part] = temp_par_type
+                    else:
+                        # default value available
+                        name_value = part.split("=")
+                        par_name = name_value[0].strip()
+                        par_value = name_value[1].strip()
+
+                        if temp_par_type == "double":
+                            try:
+                                par_value = float(par_value)
+                            except ValueError:
+                                # value could be parameter name
+                                par_value = par_value
+                        elif temp_par_type == "int":
+                            par_value = int(par_value, c_integer_literal_base(par_value))
+
+                        result.parameter_names.append(par_name)
+                        result.parameter_defaults[par_name] = par_value
+                        result.parameter_types[par_name] = temp_par_type
+
+                # End while loop running through file when parameters are read
                 break
 
         file_o.close()

From 6397fb8b5c0e5b21bf729026aef904768063ad65 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 2 May 2023 12:50:39 +0200
Subject: [PATCH 350/403] Fixed bugs that caused infinite loops.

---
 mcstasscript/helper/component_reader.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index b96757a4..18711a3a 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -309,6 +309,10 @@ def read_component_file(self, absolute_path):
             line_number += 1
             line = file_o.readline()
 
+            if not line:
+                # Exit at end of file
+                break
+
             # find parameter comments
             if line.startswith("* %P"):
 
@@ -366,7 +370,7 @@ def read_component_file(self, absolute_path):
                     line = file_o.readline()
 
                     end_keywords = ("SHARE", "INITIALIZE", "INITIALISE", "DECLARE", "TRACE")
-                    if line.strip().upper().startswith(end_keywords):
+                    if line.strip().upper().startswith(end_keywords) or not line:
                         break
 
                     define_section += line
@@ -386,10 +390,10 @@ def read_component_file(self, absolute_path):
                 parameter_section = ""
                 for index, section in enumerate(clean_define_sections):
                     if section in ("DEFINITION PARAMETERS", "SETTING PARAMETERS"):
-                        parameter_section += clean_define_sections[index + 1].strip("(").strip(")")
+                        parameter_section += clean_define_sections[index + 1].strip("(").strip(")") + ", "
 
                 # Convert parameter section to single line, then split in parts seperated by comma
-                parameter_section = parameter_section.replace('\n', '')
+                parameter_section = parameter_section.replace('\n', ' ')
                 parameter_parts = parameter_section.split(",")
                 # Combine parts that should be together, for example by brackets
                 parameter_parts = self.correct_for_brackets(parameter_parts)

From b6fceeafac5a79e0821969a36a8ce0e92fea43f9 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 2 May 2023 12:58:24 +0200
Subject: [PATCH 351/403] Fixed spelling mistake in comment.

---
 mcstasscript/helper/component_reader.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index 18711a3a..fbe64d62 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -392,7 +392,7 @@ def read_component_file(self, absolute_path):
                     if section in ("DEFINITION PARAMETERS", "SETTING PARAMETERS"):
                         parameter_section += clean_define_sections[index + 1].strip("(").strip(")") + ", "
 
-                # Convert parameter section to single line, then split in parts seperated by comma
+                # Convert parameter section to single line, then split in parts separated by comma
                 parameter_section = parameter_section.replace('\n', ' ')
                 parameter_parts = parameter_section.split(",")
                 # Combine parts that should be together, for example by brackets

From eeb29eb04d30754b735240e9f1c1acae2dad6604 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 2 May 2023 13:02:59 +0200
Subject: [PATCH 352/403] Update version number as behavior changed.

---
 mcstasscript/_version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index d588f3c6..40efd505 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.58"
+__version__ = "0.0.59"

From 2614d29ccf812e3f295595e9d9b083221f9c4cd6 Mon Sep 17 00:00:00 2001
From: Gregory Tucker <gregory.tucker@ess.eu>
Date: Thu, 8 Jun 2023 13:26:20 +0200
Subject: [PATCH 353/403] [Apply][Fix] On top of the up-to-date master

---
 mcstasscript/helper/mcstas_objects.py | 76 ++++++++++-----------------
 1 file changed, 28 insertions(+), 48 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 850aeb1c..c50baf80 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -155,17 +155,20 @@ def write_parameter(fo, parameter, stop_character):
                                + "of type not recognized by McStasScript.")
 
     if not parameter.type == "":
-        fo.write("%s %s" % (parameter.type, parameter.name))
+        fo.write(f"{parameter.type} {parameter.name}")
     else:
         fo.write(parameter.name)
 
+    if parameter.unit != 'dimensionless':
+        fo.write(f'("{parameter.unit}")')
+
     if parameter.value is not None:
         if isinstance(parameter.value, int):
-            fo.write(" = %d" % parameter.value)
-        elif isinstance(parameter.value, float):
-            fo.write(" = %G" % parameter.value)
+            fo.write(f" = {parameter.value:d}")
         else:
-            fo.write(" = %s" % str(parameter.value))
+            # let Python figure out how to best represent a floating point value; %G truncates
+            # and f"{x}" is equivalent to "%s" % str(x)
+            fo.write(f" = {parameter.value}")
     fo.write(stop_character)
 
     if parameter.comment is None or parameter.comment == "":
@@ -298,34 +301,23 @@ def write_line(self, fo):
         """
 
         if self.value == "" and self.vector == 0:
-            fo.write("%s %s;%s" % (self.type, self.name, self.comment))
+            fo.write(f"{self.type} {self.name}; {self.comment}")
         if self.value != "" and self.vector == 0:
             if self.type == "int":
-                fo.write("%s %s = %d;%s" % (self.type, self.name,
-                                            self.value, self.comment))
+                fo.write(f"{self.type} {self.name} = {self.value:d}; {self.comment}")
             else:
-                try:
-                    fo.write("%s %s = %G;%s" % (self.type, self.name,
-                                                self.value, self.comment))
-                except TypeError:
-                    # Value could not be converted to float, write as string
-                    fo.write("%s %s = %s;%s" % (self.type, self.name,
-                                                self.value, self.comment))
+                # let Python handle float->str conversion
+                fo.write(f"{self.type} {self.name} = {self.value}; {self.comment}")
         if self.value == "" and self.vector != 0:
-            fo.write("%s %s[%d];%s" % (self.type, self.name,
-                                       self.vector, self.comment))
+            fo.write(f"{self.type} {self.name}[{self.vector:d}]; {self.comment}")
         if self.value != "" and self.vector != 0:
             if isinstance(self.value, str):
-                # value is a string
-                string = self.value
-                fo.write("%s %s[%d] = %s;" % (self.type, self.name,
-                                              self.vector, string))
+                fo.write(f"{self.type} {self.name}[{self.vector:d}] = {self.value}; {self.comment}")
             else:
                 # list of values
-                fo.write("%s %s[%d] = {" % (self.type, self.name, self.vector))
-                for i in range(0, len(self.value) - 1):
-                    fo.write("%G," % self.value[i])
-                fo.write("%G};%s" % (self.value[-1], self.comment))
+                fo.write(f"{self.type} {self.name}[{self.vector:d}] = {{"
+                         + ",".join([f"{value}" for value in self.value])
+                         + f"}}; {self.comment}")
 
     def __repr__(self):
         string = "Declare variable: '"
@@ -1017,7 +1009,7 @@ def write_component(self, fo):
 
         # Write comment if present
         if len(self.comment) > 1:
-            fo.write("// %s\n" % (str(self.comment)))
+            fo.write(f"// {self.comment}\n")
 
         # Write search statements
         self.search_statement_list.write(fo)
@@ -1054,11 +1046,7 @@ def write_component(self, fo):
             fo.write("\n")  # If there are parameters, start a new line
 
         for key, val in component_parameters.items():
-            if isinstance(val, float):  # CHeck if value is a number
-                # Small or large numbers written in scientific format
-                fo.write(" %s = %G" % (str(key), val))
-            else:
-                fo.write(" %s = %s" % (str(key), str(val)))
+            fo.write(f" {key} = {val}")
             parameters_written = parameters_written + 1
             if parameters_written < number_of_parameters:
                 fo.write(",")  # Comma between parameters
@@ -1069,37 +1057,29 @@ def write_component(self, fo):
 
         # Optional WHEN section
         if not self.WHEN == "":
-            fo.write("%s\n" % self.WHEN)
+            fo.write(f"{self.WHEN}\n")
 
         # Write AT and ROTATED section
-        fo.write("AT (%s,%s,%s)" % (str(self.AT_data[0]),
-                                    str(self.AT_data[1]),
-                                    str(self.AT_data[2])))
+        fo.write(f"AT {tuple(self.AT_data[:3])}") # is AT_data ever more than 3 elements?
 
-        fo.write(" %s\n" % self.AT_relative)
+        fo.write(f" {self.AT_relative}\n")
 
         if self.ROTATED_specified:
-            fo.write("ROTATED (%s,%s,%s)" % (str(self.ROTATED_data[0]),
-                                             str(self.ROTATED_data[1]),
-                                             str(self.ROTATED_data[2])))
-            fo.write(" %s\n" % self.ROTATED_relative)
+            fo.write(f"ROTATED {tuple(self.ROTATED_data[:3])}") # is ROTATED_data ever more than 3 elements?
+            fo.write(f" {self.ROTATED_relative}\n")
 
         if not self.GROUP == "":
-            fo.write("GROUP %s\n" % self.GROUP)
+            fo.write(f"GROUP {self.GROUP}\n")
 
         # Optional EXTEND section
         if not self.EXTEND == "":
-            fo.write("EXTEND %{\n")
-            fo.write("%s" % self.EXTEND)
-            fo.write("%}\n")
+            fo.write(f"EXTEND %{{\n{self.EXTEND}\n%}}\n")  # doubled {{ and }} to print one of each
 
         if not self.JUMP == "":
-            fo.write("JUMP %s\n" % self.JUMP)
+            fo.write(f"JUMP {self.JUMP}\n")
 
         if len(self.c_code_after) > 0:
-            fo.write("\n")
-            explanation = "From component named " + self.name
-            fo.write("%s // %s\n" % (str(self.c_code_after), explanation))
+            fo.write(f"\n{self.c_code_after} // From component named {self.name}\n")
 
         # Leave a new line between components for readability
         fo.write("\n")

From 4366b32aa226b37ec7671c85cbdaed2b0cce14ea Mon Sep 17 00:00:00 2001
From: Gregory Tucker <gregory.tucker@ess.eu>
Date: Fri, 9 Jun 2023 10:25:31 +0200
Subject: [PATCH 354/403] [Fix] white space issues for tests

---
 mcstasscript/helper/mcstas_objects.py       | 14 ++++----
 mcstasscript/tests/test_Instr.py            | 29 +++++++++-------
 mcstasscript/tests/test_component.py        | 37 +++++++++++----------
 mcstasscript/tests/test_declare_variable.py |  7 ++--
 mcstasscript/tests/test_instrument.instr    | 10 +++---
 5 files changed, 52 insertions(+), 45 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index c50baf80..e86ad4d0 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -301,23 +301,23 @@ def write_line(self, fo):
         """
 
         if self.value == "" and self.vector == 0:
-            fo.write(f"{self.type} {self.name}; {self.comment}")
+            fo.write(f"{self.type} {self.name};{self.comment}")
         if self.value != "" and self.vector == 0:
             if self.type == "int":
-                fo.write(f"{self.type} {self.name} = {self.value:d}; {self.comment}")
+                fo.write(f"{self.type} {self.name} = {self.value:d};{self.comment}")
             else:
                 # let Python handle float->str conversion
-                fo.write(f"{self.type} {self.name} = {self.value}; {self.comment}")
+                fo.write(f"{self.type} {self.name} = {self.value};{self.comment}")
         if self.value == "" and self.vector != 0:
-            fo.write(f"{self.type} {self.name}[{self.vector:d}]; {self.comment}")
+            fo.write(f"{self.type} {self.name}[{self.vector:d}];{self.comment}")
         if self.value != "" and self.vector != 0:
             if isinstance(self.value, str):
-                fo.write(f"{self.type} {self.name}[{self.vector:d}] = {self.value}; {self.comment}")
+                fo.write(f"{self.type} {self.name}[{self.vector:d}] = {self.value};{self.comment}")
             else:
                 # list of values
                 fo.write(f"{self.type} {self.name}[{self.vector:d}] = {{"
                          + ",".join([f"{value}" for value in self.value])
-                         + f"}}; {self.comment}")
+                         + f"}};{self.comment}")
 
     def __repr__(self):
         string = "Declare variable: '"
@@ -1073,7 +1073,7 @@ def write_component(self, fo):
 
         # Optional EXTEND section
         if not self.EXTEND == "":
-            fo.write(f"EXTEND %{{\n{self.EXTEND}\n%}}\n")  # doubled {{ and }} to print one of each
+            fo.write(f"EXTEND %{{\n{self.EXTEND}%}}\n")  # EXTEND should have \n, {{ and }} to print one of each
 
         if not self.JUMP == "":
             fo.write(f"JUMP {self.JUMP}\n")
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 7ec6275b..7c76d4ec 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1638,19 +1638,22 @@ def test_write_c_files_simple(self, mock_f):
          call("// Start of trace section for generated test_instrument\n"),
          call("COMPONENT first_component = test_for_reading("),
          call(")\n"),
-         call("AT (0,0,0)"),
+         call("AT (0, 0, 0)"),
          call(" ABSOLUTE\n"),
          call("\n"),
          call("COMPONENT second_component = test_for_reading("),
          call(")\n"),
-         call("AT (0,0,0)"),
+         call("AT (0, 0, 0)"),
          call(" ABSOLUTE\n"),
          call("\n"),
          call("COMPONENT third_component = test_for_reading("),
          call(")\n"),
-         call("AT (0,0,0)"),
+         call("AT (0, 0, 0)"),
          call(" ABSOLUTE\n"),
          call("\n")]
+        
+        for c, w in zip(handle.write.call_args_list, wrts):
+            assert(c == w)
 
         handle.write.assert_has_calls(wrts, any_order=False)
 
@@ -1725,17 +1728,17 @@ def test_write_full_instrument_simple(self, mock_f):
          my_call("TRACE \n"),
          my_call("COMPONENT first_component = test_for_reading("),
          my_call(")\n"),
-         my_call("AT (0,0,0)"),
+         my_call("AT (0, 0, 0)"),
          my_call(" ABSOLUTE\n"),
          my_call("\n"),
          my_call("COMPONENT second_component = test_for_reading("),
          my_call(")\n"),
-         my_call("AT (0,0,0)"),
+         my_call("AT (0, 0, 0)"),
          my_call(" ABSOLUTE\n"),
          my_call("\n"),
          my_call("COMPONENT third_component = test_for_reading("),
          my_call(")\n"),
-         my_call("AT (0,0,0)"),
+         my_call("AT (0, 0, 0)"),
          my_call(" ABSOLUTE\n"),
          my_call("\n"),
          my_call("FINALLY \n%{\n"),
@@ -1821,17 +1824,17 @@ def test_write_full_instrument_dependency(self, mock_f):
             my_call("TRACE \n"),
             my_call("COMPONENT first_component = test_for_reading("),
             my_call(")\n"),
-            my_call("AT (0,0,0)"),
+            my_call("AT (0, 0, 0)"),
             my_call(" ABSOLUTE\n"),
             my_call("\n"),
             my_call("COMPONENT second_component = test_for_reading("),
             my_call(")\n"),
-            my_call("AT (0,0,0)"),
+            my_call("AT (0, 0, 0)"),
             my_call(" ABSOLUTE\n"),
             my_call("\n"),
             my_call("COMPONENT third_component = test_for_reading("),
             my_call(")\n"),
-            my_call("AT (0,0,0)"),
+            my_call("AT (0, 0, 0)"),
             my_call(" ABSOLUTE\n"),
             my_call("\n"),
             my_call("FINALLY \n%{\n"),
@@ -1842,6 +1845,8 @@ def test_write_full_instrument_dependency(self, mock_f):
         expected_path = os.path.join(".", "test_instrument.instr")
         mock_f.assert_called_with(expected_path, "w")
         handle = mock_f()
+        for c, w in zip(handle.write.call_args_list, wrts):
+            assert(c == w)
         handle.write.assert_has_calls(wrts, any_order=False)
 
     @unittest.mock.patch('__main__.__builtins__.open',
@@ -1919,17 +1924,17 @@ def test_write_full_instrument_search(self, mock_f):
             my_call("TRACE \n"),
             my_call("COMPONENT first_component = test_for_reading("),
             my_call(")\n"),
-            my_call("AT (0,0,0)"),
+            my_call("AT (0, 0, 0)"),
             my_call(" ABSOLUTE\n"),
             my_call("\n"),
             my_call("COMPONENT second_component = test_for_reading("),
             my_call(")\n"),
-            my_call("AT (0,0,0)"),
+            my_call("AT (0, 0, 0)"),
             my_call(" ABSOLUTE\n"),
             my_call("\n"),
             my_call("COMPONENT third_component = test_for_reading("),
             my_call(")\n"),
-            my_call("AT (0,0,0)"),
+            my_call("AT (0, 0, 0)"),
             my_call(" ABSOLUTE\n"),
             my_call("\n"),
             my_call("FINALLY \n%{\n"),
diff --git a/mcstasscript/tests/test_component.py b/mcstasscript/tests/test_component.py
index 10a97f13..7615261b 100644
--- a/mcstasscript/tests/test_component.py
+++ b/mcstasscript/tests/test_component.py
@@ -445,7 +445,7 @@ def test_Component_write_to_file_simple(self, mock_f):
         my_call = unittest.mock.call
         expected_writes = [my_call("COMPONENT test_component = Arm("),
                            my_call(")\n"),
-                           my_call("AT (0,0,0)"),
+                           my_call("AT (0, 0, 0)"),
                            my_call(" ABSOLUTE\n")]
 
         mock_f.assert_called_with('test.txt', 'w')
@@ -482,7 +482,7 @@ def test_Component_write_to_file_simple_search(self, mock_f):
                            my_call('SEARCH "One with double quotes"\n'),
                            my_call("COMPONENT test_component = Arm("),
                            my_call(")\n"),
-                           my_call("AT (0,0,0)"),
+                           my_call("AT (0, 0, 0)"),
                            my_call(" ABSOLUTE\n")]
 
         mock_f.assert_called_with('test.txt', 'w')
@@ -518,15 +518,16 @@ def test_Component_write_to_file_include(self, mock_f):
                            my_call("\n"),
                            my_call("COMPONENT test_component = Arm("),
                            my_call(")\n"),
-                           my_call("AT (0,0,0)"),
+                           my_call("AT (0, 0, 0)"),
                            my_call(" ABSOLUTE\n"),
-                           my_call("\n"),
-                           my_call("%include \"after.instr\" // From"
+                           my_call("\n%include \"after.instr\" // From"
                                    + " component named test_component\n"),
                            my_call("\n")]
 
         mock_f.assert_called_with('test.txt', 'w')
         handle = mock_f()
+        for c, w in zip(handle.write.call_args_list, expected_writes):
+            assert(c == w)
         handle.write.assert_has_calls(expected_writes, any_order=False)
 
     @unittest.mock.patch('__main__.__builtins__.open',
@@ -549,7 +550,8 @@ def test_Component_write_to_file_complex(self, mock_f):
             comp.write_component(m_fo)
 
         my_call = unittest.mock.call
-        expected_writes = [my_call("SPLIT 7 "),
+        expected_writes = [my_call("// test comment\n"),
+                           my_call("SPLIT 7 "),
                            my_call("COMPONENT test_component = Arm("),
                            my_call("\n"),
                            my_call(" new_par1 = 1.5"),
@@ -565,19 +567,19 @@ def test_Component_write_to_file_complex(self, mock_f):
                            my_call(" that_par = \"txt_string\""),
                            my_call(")\n"),
                            my_call("WHEN (1==2)\n"),
-                           my_call("AT (0.124,183.9,157)"),
+                           my_call("AT (0.124, 183.9, 157)"),
                            my_call(" RELATIVE home\n"),
-                           my_call("ROTATED (482,1240.2,0.185)"),
+                           my_call("ROTATED (482, 1240.2, 0.185)"),
                            my_call(" RELATIVE etc\n"),
                            my_call("GROUP developers\n"),
-                           my_call("EXTEND %{\n"),
-                           my_call("nscat = 8;\n"),
-                           my_call("%}\n"),
+                           my_call("EXTEND %{\nnscat = 8;\n%}\n"),
                            my_call("JUMP myself 37\n"),
                            my_call("\n")]
 
         mock_f.assert_called_with('test.txt', 'w')
         handle = mock_f()
+        for c, w in zip(handle.write.call_args_list, expected_writes):
+            assert(c == w)
         handle.write.assert_has_calls(expected_writes, any_order=False)
 
     @unittest.mock.patch('__main__.__builtins__.open',
@@ -602,7 +604,8 @@ def test_Component_write_to_file_complex_SPLIT_string(self, mock_f):
             comp.write_component(m_fo)
 
         my_call = unittest.mock.call
-        expected_writes = [my_call("SPLIT VAR "),
+        expected_writes = [my_call("// test comment\n"),
+                           my_call("SPLIT VAR "),
                            my_call("COMPONENT test_component = Arm("),
                            my_call("\n"),
                            my_call(" new_par1 = 1.5"),
@@ -618,19 +621,19 @@ def test_Component_write_to_file_complex_SPLIT_string(self, mock_f):
                            my_call(" that_par = \"txt_string\""),
                            my_call(")\n"),
                            my_call("WHEN (1==2)\n"),
-                           my_call("AT (0.124,183.9,157)"),
+                           my_call("AT (0.124, 183.9, 157)"),
                            my_call(" RELATIVE home\n"),
-                           my_call("ROTATED (482,1240.2,0.185)"),
+                           my_call("ROTATED (482, 1240.2, 0.185)"),
                            my_call(" RELATIVE etc\n"),
                            my_call("GROUP developers\n"),
-                           my_call("EXTEND %{\n"),
-                           my_call("nscat = 8;\n"),
-                           my_call("%}\n"),
+                           my_call("EXTEND %{\nnscat = 8;\n%}\n"),
                            my_call("JUMP myself 37\n"),
                            my_call("\n")]
 
         mock_f.assert_called_with('test.txt', 'w')
         handle = mock_f()
+        for c, w in zip(handle.write.call_args_list, expected_writes):
+            assert(c == w)
         handle.write.assert_has_calls(expected_writes, any_order=False)
 
     @unittest.mock.patch('__main__.__builtins__.open',
diff --git a/mcstasscript/tests/test_declare_variable.py b/mcstasscript/tests/test_declare_variable.py
index 397d6cfc..8e6b1d21 100644
--- a/mcstasscript/tests/test_declare_variable.py
+++ b/mcstasscript/tests/test_declare_variable.py
@@ -166,13 +166,12 @@ def test_DeclareVariable_write_complex_array(self, mock_f):
         with mock_f('test.txt', 'w') as m_fo:
             var.write_line(m_fo)
 
-        expected_writes = [unittest.mock.call("double test[3] = {"),
-                           unittest.mock.call("5,"),
-                           unittest.mock.call("4,"),
-                           unittest.mock.call("3.1}; // test comment")]
+        expected_writes = [unittest.mock.call("double test[3] = {5,4,3.1}; // test comment")]
 
         mock_f.assert_called_with('test.txt', 'w')
         handle = mock_f()
+        for c, w in zip(handle.write.call_args_list, expected_writes):
+            assert(c == w)
         handle.write.assert_has_calls(expected_writes, any_order=False)
 
 
diff --git a/mcstasscript/tests/test_instrument.instr b/mcstasscript/tests/test_instrument.instr
index 145148d7..031b20a0 100644
--- a/mcstasscript/tests/test_instrument.instr
+++ b/mcstasscript/tests/test_instrument.instr
@@ -9,13 +9,13 @@
 * python based McStas instrument generator written by 
 * Mads Bertelsen in 2019 while employed at the 
 * European Spallation Source Data Management and 
-* Software Center
+* Software Centre
 * 
 * Instrument test_instrument
 * 
 * %Identification
 * Written by: Python McXtrace Instrument Generator
-* Date: 10:17:47 on December 14, 2021
+* Date: 10:16:46 on June 09, 2023
 * Origin: ESS DMSC
 * %INSTRUMENT_SITE: Generated_instruments
 * 
@@ -44,15 +44,15 @@ two_theta = 2.0*theta;
 TRACE 
 COMPONENT first_component = test_for_reading(
  gauss = 1.2, test_string = a_string)
-AT (0,0,0) ABSOLUTE
+AT (0, 0, 0) ABSOLUTE
 
 COMPONENT second_component = test_for_reading(
  gauss = 1.4, test_string = b_string)
-AT (0,0,0) ABSOLUTE
+AT (0, 0, 0) ABSOLUTE
 
 COMPONENT third_component = test_for_reading(
  gauss = 1.6, test_string = c_string)
-AT (0,0,0) ABSOLUTE
+AT (0, 0, 0) ABSOLUTE
 
 FINALLY 
 %{

From 5c61a2e022f3d545876266337cd10b9977f8acf3 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 15 Jun 2023 13:23:21 +0200
Subject: [PATCH 355/403] Small bug pointed out by Kristine which prevented
 diagnostic output when the instrument used target_index logic.

---
 mcstasscript/instrument_diagnostics/diagnostics_instrument.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/instrument_diagnostics/diagnostics_instrument.py b/mcstasscript/instrument_diagnostics/diagnostics_instrument.py
index 57ba502e..394922d9 100644
--- a/mcstasscript/instrument_diagnostics/diagnostics_instrument.py
+++ b/mcstasscript/instrument_diagnostics/diagnostics_instrument.py
@@ -104,7 +104,7 @@ def correct_target_index(self):
 
             # Find original index and the name of the original target
             original_comp_index = original_comp_names.index(comp.name)
-            comp_target_name = original_comp_names[original_comp_index + comp.target_index]
+            comp_target_name = original_comp_names[original_comp_index + int(comp.target_index)]
 
             # Find index of the original and target in modified instrument
             modified_comp_index = modified_comp_names.index(comp.name)

From 76feb49a648f4d798bb6d126da464dc31061fe07 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 4 Aug 2023 10:50:28 +0200
Subject: [PATCH 356/403] Small updates:

Added zlabel to colorbar in plotting and metadata
tight_layout for figures written to disk
small syntax fixes.
---
 mcstasscript/_version.py                      |  2 +-
 mcstasscript/data/data.py                     | 23 ++++++++
 mcstasscript/helper/plot_helper.py            | 12 +++--
 .../diagnostics_instrument.py                 |  2 +-
 mcstasscript/jb_interface/plot_interface.py   |  6 ++-
 mcstasscript/tools/cryostat_builder.py        | 52 +++++++++----------
 6 files changed, 63 insertions(+), 34 deletions(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 40efd505..27d5382d 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.59"
+__version__ = "0.0.61"
diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index 5d260841..a8ab6516 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -66,6 +66,7 @@ def __init__(self):
 
         self.xlabel = None
         self.ylabel = None
+        self.zlabel = None
         self.title = None
 
         self.total_I = None
@@ -137,6 +138,8 @@ def extract_info(self):
             self.xlabel = self.info["xlabel"].rstrip()
         if "ylabel" in self.info:
             self.ylabel = self.info["ylabel"].rstrip()
+        if "zlabel" in self.info:
+            self.zlabel = self.info["zlabel"].rstrip()
         if "title" in self.info:
             self.title = self.info["title"].rstrip()
 
@@ -159,6 +162,10 @@ def set_ylabel(self, string):
         """Sets ylabel for plotting"""
         self.ylabel = string
 
+    def set_zlabel(self, string):
+        """Sets zlabel for plotting"""
+        self.zlabel = string
+
     def __repr__(self):
         string = "metadata object\n"
         if self.component_name is not None:
@@ -174,6 +181,8 @@ def __repr__(self):
                     string += " " + self.xlabel + "\n"
                 if self.ylabel is not None:
                     string += " " + self.ylabel + "\n"
+                if self.zlabel is not None:
+                    string += " " + self.zlabel + "\n"
 
             elif type(self.dimension) == int and self.dimension != 0:
                 string += "1D data of length " + str(self.dimension) + "\n"
@@ -184,6 +193,8 @@ def __repr__(self):
                     string += " " + self.xlabel + "\n"
                 if self.ylabel is not None:
                     string += " " + self.ylabel + "\n"
+                if self.zlabel is not None:
+                    string += " " + self.zlabel + "\n"
 
             elif len(self.dimension) == 2:
                 string += "2D data of dimension (" + str(self.dimension[0])
@@ -200,6 +211,9 @@ def __repr__(self):
                         string += str(self.limits[3]) + "]"
                     string += " " + self.ylabel + "\n"
 
+                if self.zlabel is not None:
+                    string += " " + self.zlabel + "\n"
+
         if self.parameters is not None and len(self.parameters)>0:
             string += "Instrument parameters: \n"
             for key in self.parameters:
@@ -486,6 +500,9 @@ def set_xlabel(self, string):
     def set_ylabel(self, string):
         self.metadata.set_ylabel(string)
 
+    def set_zlabel(self, string):
+        self.metadata.set_zlabel(string)
+
     def set_title(self, string):
         self.metadata.set_title(string)
 
@@ -563,6 +580,9 @@ class McStasDataBinned(McStasData):
     set_ylabel : string
         sets ylabel of data for plotting
 
+    set_zlabel : string
+        sets ylabel of data for plotting
+
     set_title : string
         sets title of data for plotting
 
@@ -654,6 +674,9 @@ class McStasDataEvent(McStasData):
     set_ylabel : string
         sets ylabel of data for plotting
 
+    set_zlabel : string
+        sets zlabel of data for plotting
+
     set_title : string
         sets title of data for plotting
 
diff --git a/mcstasscript/helper/plot_helper.py b/mcstasscript/helper/plot_helper.py
index 4d5dc0d9..e7a1c037 100644
--- a/mcstasscript/helper/plot_helper.py
+++ b/mcstasscript/helper/plot_helper.py
@@ -9,6 +9,7 @@
 
 from mcstasscript.data.data import McStasData
 
+
 def _fmt(x, pos):
     """
     Used for nice formatting of powers of 10 when plotting logarithmic
@@ -18,7 +19,7 @@ def _fmt(x, pos):
     if abs(float(a) - 1) < 0.01:
         return r'$10^{{{}}}$'.format(b)
     else:
-        return r'${} \times 10^{{{}}}$'.format(a, b)
+        return r'${}\cdot 10^{{{}}}$'.format(a, b)
 
 
 def _find_min_max_I(data):
@@ -76,7 +77,7 @@ def _plot_fig_ax(data, fig, ax, **kwargs):
     """
     Plots the content of a single McStasData object
 
-    Plotting is controlled through options assosciated with the
+    Plotting is controlled through options associated with the
     McStasData objects.
 
     When plotting 2D objects, returns the pcolormesh object
@@ -188,8 +189,11 @@ def _plot_fig_ax(data, fig, ax, **kwargs):
             if "colorbar_axes" in kwargs:
                 cax = kwargs["colorbar_axes"]
 
-            fig.colorbar(im, ax=ax, cax=cax,
-                         format=matplotlib.ticker.FuncFormatter(_fmt))
+            colorbar = fig.colorbar(im, ax=ax, cax=cax,
+                                    format=matplotlib.ticker.FuncFormatter(_fmt))
+
+            if data.metadata.zlabel is not None:
+                colorbar.set_label(data.metadata.zlabel)
 
             if "colorbar_axes" in kwargs:
                 cax.set_aspect(20)
diff --git a/mcstasscript/instrument_diagnostics/diagnostics_instrument.py b/mcstasscript/instrument_diagnostics/diagnostics_instrument.py
index 394922d9..2117d0b1 100644
--- a/mcstasscript/instrument_diagnostics/diagnostics_instrument.py
+++ b/mcstasscript/instrument_diagnostics/diagnostics_instrument.py
@@ -113,4 +113,4 @@ def correct_target_index(self):
 
             # Apply the new target_index to the component in the modified instrument
             modified_comp = self.instr.get_component(comp.name)
-            modified_comp.target_index = new_target_index
\ No newline at end of file
+            modified_comp.target_index = new_target_index
diff --git a/mcstasscript/jb_interface/plot_interface.py b/mcstasscript/jb_interface/plot_interface.py
index 5b1c119d..acac03c8 100644
--- a/mcstasscript/jb_interface/plot_interface.py
+++ b/mcstasscript/jb_interface/plot_interface.py
@@ -5,6 +5,7 @@
 from collections import OrderedDict
 
 import ipywidgets as widgets
+from ipywidgets import GridBox
 from IPython.display import display, clear_output
 
 import matplotlib.pyplot as plt
@@ -92,7 +93,8 @@ def new_plot(self):
         Sets up original plot with fig, ax and ax for colorbar
         """
         # fig, ax = plt.subplots(constrained_layout=True, figsize=(6, 4))
-        self.fig, (self.ax, self.colorbar_ax) = plt.subplots(ncols=2, gridspec_kw={'width_ratios': [6, 1]})
+        self.fig, (self.ax, self.colorbar_ax) = plt.subplots(ncols=2,
+                                                             gridspec_kw={'width_ratios': [6, 1]})
 
         self.fig.canvas.toolbar_position = 'bottom'
 
@@ -206,7 +208,7 @@ def show_interface(self):
         if self.data is not None:
             self.set_data(self.data)
 
-        return widgets.HBox([output, plot_controls])
+        return widgets.HBox([output, plot_controls], layout=widgets.Layout(width="100%"))
 
 
 class ColormapDropdown:
diff --git a/mcstasscript/tools/cryostat_builder.py b/mcstasscript/tools/cryostat_builder.py
index 94aa8d99..0e585293 100644
--- a/mcstasscript/tools/cryostat_builder.py
+++ b/mcstasscript/tools/cryostat_builder.py
@@ -478,37 +478,37 @@ def add_spatial_loggers(self, n_x=500, n_y=500, n_z=500):
         space_2D_zx = self.instr.add_component(self.name + "_logger_space_zx", "Union_logger_2D_space")
         space_2D_zx.set_AT([0, 0, 0], RELATIVE=self.origin)
         space_2D_zx.filename = '"' + self.name + '_space_zx.dat"'
-        space_2D_zx.D_direction_1 = '"z"';
+        space_2D_zx.D_direction_1 = '"z"'
         space_2D_zx.n1 = n_z
-        space_2D_zx.D1_min = -mon_z;
+        space_2D_zx.D1_min = -mon_z
         space_2D_zx.D1_max = mon_z
-        space_2D_zx.D_direction_2 = '"x"';
+        space_2D_zx.D_direction_2 = '"x"'
         space_2D_zx.n2 = n_x
-        space_2D_zx.D2_min = -mon_x;
+        space_2D_zx.D2_min = -mon_x
         space_2D_zx.D2_max = mon_x
 
         space_2D_zy = self.instr.add_component(self.name + "_logger_space_zy", "Union_logger_2D_space")
         space_2D_zy.set_AT([0, 0, 0], RELATIVE=self.origin)
         space_2D_zy.filename = '"' + self.name + '_space_zy.dat"'
-        space_2D_zy.D_direction_1 = '"z"';
+        space_2D_zy.D_direction_1 = '"z"'
         space_2D_zy.n1 = n_z
-        space_2D_zy.D1_min = -mon_z;
+        space_2D_zy.D1_min = -mon_z
         space_2D_zy.D1_max = mon_z
-        space_2D_zy.D_direction_2 = '"y"';
+        space_2D_zy.D_direction_2 = '"y"'
         space_2D_zy.n2 = n_y
-        space_2D_zy.D2_min = mon_y_low;
+        space_2D_zy.D2_min = mon_y_low
         space_2D_zy.D2_max = mon_y_high
 
         space_2D_xy = self.instr.add_component(self.name + "_logger_space_xy", "Union_logger_2D_space")
         space_2D_xy.set_AT([0, 0, 0], RELATIVE=self.origin)
         space_2D_xy.filename = '"' + self.name + '_space_xy.dat"'
-        space_2D_xy.D_direction_1 = '"x"';
+        space_2D_xy.D_direction_1 = '"x"'
         space_2D_xy.n1 = n_x
-        space_2D_xy.D1_min = -mon_x;
+        space_2D_xy.D1_min = -mon_x
         space_2D_xy.D1_max = mon_x
-        space_2D_xy.D_direction_2 = '"y"';
+        space_2D_xy.D_direction_2 = '"y"'
         space_2D_xy.n2 = n_y
-        space_2D_xy.D2_min = mon_y_low;
+        space_2D_xy.D2_min = mon_y_low
         space_2D_xy.D2_max = mon_y_high
 
         # Adding monitor that shows windows better
@@ -518,17 +518,17 @@ def add_spatial_loggers(self, n_x=500, n_y=500, n_z=500):
         space_3D_zy = self.instr.add_component(self.name + "_logger_space_zy_close", "Union_logger_3D_space")
         space_3D_zy.set_AT([0, 0, 0], RELATIVE=self.origin)
         space_3D_zy.filename = '"' + self.name + '_space_zy_close.dat"'
-        space_3D_zy.D_direction_1 = '"z"';
+        space_3D_zy.D_direction_1 = '"z"'
         space_3D_zy.n1 = n_z
-        space_3D_zy.D1_min = -1.02 * largest_outer_radius;
+        space_3D_zy.D1_min = -1.02 * largest_outer_radius
         space_3D_zy.D1_max = -0.9 * lowest_inner_radius
-        space_3D_zy.D_direction_2 = '"y"';
+        space_3D_zy.D_direction_2 = '"y"'
         space_3D_zy.n2 = n_y
-        space_3D_zy.D2_min = mon_y_low;
+        space_3D_zy.D2_min = mon_y_low
         space_3D_zy.D2_max = mon_y_high
-        space_3D_zy.D_direction_3 = '"x"';
+        space_3D_zy.D_direction_3 = '"x"'
         space_3D_zy.n3 = 1
-        space_3D_zy.D3_min = -0.005;
+        space_3D_zy.D3_min = -0.005
         space_3D_zy.D3_max = 0.005
 
     def add_time_histogram(self, t_min=0, t_max=0.1):
@@ -615,30 +615,30 @@ def add_animation(self, t_min=0, t_max=0.1, n_frames=10,
         ani_logger.time_min = t_min
         ani_logger.time_max = t_max
 
-        ani_logger.D_direction_1 = '"' + d1 + '"';
+        ani_logger.D_direction_1 = '"' + d1 + '"'
         ani_logger.n1 = int(n1)
         if d1 == "x":
-            ani_logger.D1_min = -mon_x;
+            ani_logger.D1_min = -mon_x
             ani_logger.D1_max = mon_x
         elif d1 == "y":
-            ani_logger.D1_min = mon_y_low;
+            ani_logger.D1_min = mon_y_low
             ani_logger.D1_max = mon_y_high
         elif d1 == "z":
-            ani_logger.D1_min = -mon_z;
+            ani_logger.D1_min = -mon_z
             ani_logger.D1_max = mon_z
         else:
             raise RuntimeError("Dimension: '" + d1 + "' not recoignized, must be x, y or z.")
 
-        ani_logger.D_direction_2 = '"' + d2 + '"';
+        ani_logger.D_direction_2 = '"' + d2 + '"'
         ani_logger.n2 = int(n2)
         if d2 == "x":
-            ani_logger.D2_min = -mon_x;
+            ani_logger.D2_min = -mon_x
             ani_logger.D2_max = mon_x
         elif d2 == "y":
-            ani_logger.D2_min = mon_y_low;
+            ani_logger.D2_min = mon_y_low
             ani_logger.D2_max = mon_y_high
         elif d2 == "z":
-            ani_logger.D2_min = -mon_z;
+            ani_logger.D2_min = -mon_z
             ani_logger.D2_max = mon_z
         else:
             raise RuntimeError("Dimension: '" + d2 + "' not recoignized, must be x, y or z.")

From 91124cacd52ef317eb23f4d27ec59a47594c824f Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Tue, 29 Aug 2023 16:19:58 +0200
Subject: [PATCH 357/403] Update that allows reading of nexus files with load
 functions.

backengine will return read nexus data in usual format.
---
 mcstasscript/helper/managed_mcrun.py | 222 +++++++++++++++++++++++++--
 mcstasscript/interface/instr.py      |   6 +-
 mcstasscript/interface/plotter.py    |   2 +
 3 files changed, 215 insertions(+), 15 deletions(-)

diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 76c146b5..3a607f98 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -3,6 +3,7 @@
 import subprocess
 import mmap
 import warnings
+import h5py
 
 from mcstasscript.helper.formatting import bcolors
 from mcstasscript.data.data import McStasMetaData
@@ -106,6 +107,8 @@ def __init__(self, instr_name, **kwargs):
         self.compile = True
         self.run_path = "."
         self.seed = None
+        self.suppress_output = False
+
         # executable_path always in kwargs
         if "executable_path" in kwargs:
             self.executable_path = kwargs["executable_path"]
@@ -175,6 +178,10 @@ def __init__(self, instr_name, **kwargs):
         if "run_path" in kwargs:
             self.run_path = kwargs["run_path"]
 
+        if "suppress_output" in kwargs:
+            self.suppress_output = bool(kwargs["suppress_output"])
+
+
         # get relevant paths and check their validity
         current_directory = os.getcwd()
 
@@ -203,7 +210,7 @@ def __init__(self, instr_name, **kwargs):
             raise RuntimeError("ManagedMcrun found executable_path to "
                                + "be invalid: " + str(self.executable_path))
 
-    def run_simulation(self, **kwargs):
+    def run_simulation(self):
         """
         Runs McStas simulation described by initializing the object
         """
@@ -284,10 +291,7 @@ def run_simulation(self, **kwargs):
                                  universal_newlines=True,
                                  cwd=self.run_path)
 
-        if "suppress_output" in kwargs:
-            if kwargs["suppress_output"] is False:
-                print_sim_output(process.stdout)
-        else:
+        if self.suppress_output is False:
             print_sim_output(process.stdout)
 
         if not os.path.isdir(self.data_folder_name):
@@ -348,7 +352,6 @@ def load_results(data_folder_name):
 
     return results
 
-
 def load_metadata(data_folder_name):
     """
     Function that loads metadata from a mcstas simulation
@@ -370,9 +373,15 @@ def load_metadata(data_folder_name):
     files_in_folder = os.listdir(data_folder_name)
 
     # Raise an error if mccode.sim is not available
-    if "mccode.sim" not in files_in_folder:
-        raise NameError("No mccode.sim in data folder.")
-        
+    if "mccode.sim" in files_in_folder:
+        return load_metadata_text(data_folder_name)
+    elif "mccode.h5" in files_in_folder:
+        return load_metadata_nexus(data_folder_name)
+    else:
+        raise NameError("No mccode.sim or mccode.h5 in data folder.")
+
+
+def load_metadata_text(data_folder_name):
     instrument_parameters = {}
 
     # Open mccode to read metadata for all datasets written to disk
@@ -397,7 +406,7 @@ def load_metadata(data_folder_name):
                     metadata_list.append(current_object)
                 # Stop reading data
                 in_data = False
-                
+
             if in_sim:
                 if "Param" in lines:
                     parm_lst = lines.split(':')[1].split('=')
@@ -407,12 +416,12 @@ def load_metadata(data_folder_name):
                         value = parm_lst[1].strip()
 
                     instrument_parameters[parm_lst[0].strip()] = value
-                    
+
             if in_data:
                 # This line contains info to be added to metadata
                 colon_index = lines.index(":")
                 key = lines[2:colon_index]
-                value = lines[colon_index+2:].strip()
+                value = lines[colon_index + 2:].strip()
                 current_object.add_info(key, value)
 
             if lines == "begin data\n":
@@ -433,7 +442,7 @@ def load_metadata(data_folder_name):
         # Create a list for McStasData instances to return
         results = []
 
-        
+
         # Load datasets described in metadata list individually
         for metadata in metadata_list:
 
@@ -464,9 +473,196 @@ def load_metadata(data_folder_name):
     return metadata_list
 
 
+def load_metadata_nexus(data_folder_name, filename="mccode.h5"):
+    instrument_parameters = {}
+
+    # Open mccode to read metadata for all datasets written to disk
+    with h5py.File(os.path.join(data_folder_name, filename), "r") as f:
+
+        if "entry1" not in list(f.keys()):
+            raise ValueError("h5 file not formatted as expected.")
+
+        if "data" not in list(f["entry1"].keys()):
+            raise ValueError("h5 file not formatted as expected.")
+
+        if "simulation" not in list(f["entry1"].keys()):
+            raise ValueError("h5 file not formatted as expected.")
+
+        if "Param" not in list(f["entry1"]["simulation"].keys()):
+            raise ValueError("h5 file not formatted as expected.")
+
+        # Common information
+
+        # Instrument parameters
+        instrument_parameters = {}
+        loaded_par_dict = f["entry1"]["simulation"]["Param"].attrs
+        for par_name in loaded_par_dict:
+            if par_name == "NX_class":
+                continue
+
+            try:
+                value = float(loaded_par_dict[par_name])
+            except:
+                value = str(loaded_par_dict[par_name])
+
+            instrument_parameters[par_name] = value
+
+        metadata_list = []
+
+        # For each entry in data, make a metadata object
+        for key in f["entry1"]["data"].keys():
+
+            # Make the metadata object and add instrument parameters
+            metadata = McStasMetaData()
+            metadata.add_info("Parameters", instrument_parameters)
+
+            # Add NeXus field name
+            metadata.add_info("NeXus_field", key)
+
+            # Add all the read info from attribute section
+            info = dict(f["entry1"]["data"][key].attrs)
+            info = decode_dict(info)
+            for name, value in info.items():
+                if isinstance(value, bytes):
+                    value = value.decode('utf-8')
+
+                metadata.add_info(name, value)
+
+            metadata_list.append(metadata)
+
+            # Now all info is added, extract info loads it into nice attributes
+            metadata.extract_info()
+
+        return metadata_list
+
+
+def decode_dict(dictionary):
+    for key, value in dictionary.items():
+        if isinstance(value, bytes):
+            dictionary[key] = value.decode('utf-8')
+
+    return dictionary
+
+
 def load_monitor(metadata, data_folder_name):
+    """
+    Switches to appropriate loader function
+    """
+
+    if "NeXus_field" in metadata.info:
+        return load_monitor_nexus(metadata, data_folder_name)
+    else:
+        return load_monitor_text(metadata, data_folder_name)
+
+
+def load_monitor_nexus(metadata, data_folder_name, filename="mccode.h5"):
+    """
+    Function that loads data given metadata and name of data folder
+    This version is for a nexus file
+
+    Loads data for single monitor and returns a McStasData object
+
+    Parameters
+    ----------
+
+    metadata : McStasMetaData object
+        McStasMetaData object corresponding to the monitor to be loaded
+
+    data_folder_name : str
+        path to folder from which metadata should be loaded
+
+    filename : str
+        Name of NeXus file
+    """
+
+    # Open mccode to read metadata for all datasets written to disk
+    with h5py.File(os.path.join(data_folder_name, filename), "r") as f:
+
+        if "entry1" not in list(f.keys()):
+            raise ValueError("h5 file not formatted as expected.")
+
+        if "data" not in list(f["entry1"].keys()):
+            raise ValueError("h5 file not formatted as expected.")
+
+        if "simulation" not in list(f["entry1"].keys()):
+            raise ValueError("h5 file not formatted as expected.")
+
+        if "Param" not in list(f["entry1"]["simulation"].keys()):
+            raise ValueError("h5 file not formatted as expected.")
+
+        NeXus_field = metadata.info["NeXus_field"]
+
+        # Split data into intensity, error and ncount
+        if type(metadata.dimension) == int and metadata.dimension == 0:
+            Intensity = np.array(f["entry1"]["data"][NeXus_field]["data"])
+
+            if metadata.total_E is None:
+                Error = np.zeros(1)
+            else:
+                Error = np.array(metadata.total_E)
+
+            if metadata.total_N is None:
+                Ncount = np.zeros(1)
+            else:
+                Ncount = np.array(metadata.total_N)
+
+            return McStasDataBinned(metadata, Intensity, Error, Ncount)
+
+        elif type(metadata.dimension) == int and metadata.dimension != 0:
+
+            original_xlabel = metadata.info["xlabel"]
+
+            x_field = original_xlabel.replace(" ", "_")
+            x_field = x_field.replace("[", "_")
+            x_field = x_field.replace("]", "_")
+
+            if x_field not in f["entry1"]["data"][NeXus_field].keys():
+                print("Expected this field for x axis: ", str(x_field))
+                print("Existing fields: ", f["entry1"]["data"][NeXus_field].keys())
+                raise ValueError("Didn't find xaxis in NeXus file.")
+
+            xaxis = np.array(f["entry1"]["data"][NeXus_field][x_field])
+            Intensity = np.array(f["entry1"]["data"][NeXus_field]["data"])
+            Error = np.array(f["entry1"]["data"][NeXus_field]["errors"])
+            Ncount = np.array(f["entry1"]["data"][NeXus_field]["ncount"])
+
+            # The data is saved as a McStasDataBinned object
+            return McStasDataBinned(metadata, Intensity, Error, Ncount, xaxis=xaxis)
+
+        elif len(metadata.dimension) == 2:
+            # Need to check if it is binned data or event data
+            if "events" in f["entry1"]["data"][NeXus_field].keys():
+                Events = np.array(f["entry1"]["data"][NeXus_field]["events"])
+                return McStasDataEvent(metadata, Events)
+            else:
+
+                available_fields = f["entry1"]["data"][NeXus_field].keys()
+
+                if "errors" not in available_fields:
+                    raise ValueError("No 'errors' in NeXus data entry called ", NeXus_field)
+
+                if "ncount" not in available_fields:
+                    raise ValueError("No 'ncount' in NeXus data entry called ", NeXus_field)
+
+                # If no events, binned instead
+                xaxis = []  # Assume evenly binned in 2d
+                Intensity = np.array(f["entry1"]["data"][NeXus_field]["data"]).T
+                Error = np.array(f["entry1"]["data"][NeXus_field]["errors"]).T
+                Ncount = np.array(f["entry1"]["data"][NeXus_field]["ncount"]).T
+
+                # The data is saved as a McStasDataBinned object
+                return McStasDataBinned(metadata, Intensity, Error, Ncount, xaxis=xaxis)
+        else:
+            raise NameError(
+                "Dimension not read correctly in data set "
+                + "connected to monitor named "
+                + metadata.component_name)
+
+
+def load_monitor_text(metadata, data_folder_name):
     """
     Function that loads data given metadata and name of data folder
+    This version is for a text file
 
     Loads data for single monitor and returns a McStasData object
 
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 30da65db..5705b78c 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -362,7 +362,9 @@ def __init__(self, name, parameters=None, author=None,
             self._run_settings["package_path"] = package_path
 
         # Settings for run that can be adjusted by user
-        provided_run_settings = {"executable": executable, "checks": True}
+        provided_run_settings = {"executable": executable,
+                                 "checks": True,
+                                 "NeXus": False}
 
         if executable_path is not None:
             provided_run_settings["executable_path"] = str(executable_path)
@@ -2484,7 +2486,7 @@ def backengine(self):
         simulation = ManagedMcrun(self.name + ".instr", **options)
 
         # Run the simulation and return data
-        simulation.run_simulation(**self._run_settings)
+        simulation.run_simulation()
 
         # Load data and store in __data
         #data = simulation.load_results()
diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index 290c6cb6..ce284ce2 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -28,6 +28,7 @@ def make_plot(data_list, **kwargs):
         _plot_fig_ax(data, fig, ax0, **kwargs)
 
     if "filename" in kwargs:
+        fig.tight_layout()
         fig.savefig(kwargs["filename"])
     else:
         plt.show()
@@ -61,6 +62,7 @@ def make_sub_plot(data_list, **kwargs):
     fig.tight_layout()
 
     if "filename" in kwargs:
+        fig.tight_layout()
         fig.savefig(kwargs["filename"])
         plt.close(fig)
     else:

From 6f6075a8cf428dd20edf5fe32f7e7abeb1395d6d Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 30 Aug 2023 13:31:23 +0200
Subject: [PATCH 358/403] Avoid plotting event data as the results are not
 meaningful and often crash the plotter.

Patched issue in instrument diagrams for Union systems that use a variable or parameter
for the number of activations, as this couldn't be directly read. For now this simply
assumes a value of 1.

Bumped version for new release.
---
 mcstasscript/_version.py                      |  2 +-
 mcstasscript/helper/plot_helper.py            | 23 +++++++++++++++++++
 .../instrument_diagram/generate_Union.py      |  7 +++++-
 3 files changed, 30 insertions(+), 2 deletions(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 27d5382d..05f74260 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.61"
+__version__ = "0.0.62"
diff --git a/mcstasscript/helper/plot_helper.py b/mcstasscript/helper/plot_helper.py
index e7a1c037..6a143bf8 100644
--- a/mcstasscript/helper/plot_helper.py
+++ b/mcstasscript/helper/plot_helper.py
@@ -8,6 +8,26 @@
 from matplotlib.colors import BoundaryNorm
 
 from mcstasscript.data.data import McStasData
+from mcstasscript.data.data import McStasDataEvent
+
+
+def remove_eventdata(data_list, verbose=True):
+    """
+    Removes event data from a list, useful as these can't be plotted
+    """
+    reduced_data_list = []
+    skipped_names = []
+    for element in data_list:
+        if not isinstance(element, McStasDataEvent):
+            reduced_data_list.append(element)
+        else:
+            skipped_names.append(element.metadata.component_name)
+
+    if verbose:
+        for name in skipped_names:
+            print(f"Skipped plotting {name} as it contains event data.")
+
+    return reduced_data_list
 
 
 def _fmt(x, pos):
@@ -245,6 +265,9 @@ def _handle_kwargs(data_list, **kwargs):
         # Only a single element, put it in a list for easier syntax later
         data_list = [data_list]
 
+    # Remove event data that can't be plotted in meaningful way
+    data_list = remove_eventdata(data_list)
+
     known_plotting_kwargs = ["log", "orders_of_mag",
                              "top_lim", "bottom_lim",
                              "left_lim", "right_lim",
diff --git a/mcstasscript/instrument_diagram/generate_Union.py b/mcstasscript/instrument_diagram/generate_Union.py
index 73617d6e..1b46a33a 100644
--- a/mcstasscript/instrument_diagram/generate_Union.py
+++ b/mcstasscript/instrument_diagram/generate_Union.py
@@ -49,7 +49,12 @@ def generate_Union_arrows(components, component_box_dict, box_names, component_c
                 geometry_names.append(component.name)
 
                 if component.number_of_activations is not None:
-                    number_of_activations = component.number_of_activations
+                    try:
+                        # If a number is given, it can be used directly
+                        number_of_activations = int(component.number_of_activations)
+                    except:
+                        # If a variable or parameter is used, it can't be known ahead of time, assume 1
+                        number_of_activations = 1
                 else:
                     number_of_activations = component.parameter_defaults["number_of_activations"]
 

From f8e17f34377cecf11914e23cb6fb7231baeae304 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 30 Aug 2023 14:22:50 +0200
Subject: [PATCH 359/403] Added h5py to requirements to ensure users have the
 package for nexus reading.

---
 mcstasscript/_version.py | 2 +-
 requirements.txt         | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 05f74260..2c20ada6 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.62"
+__version__ = "0.0.63"
diff --git a/requirements.txt b/requirements.txt
index 37c38e28..fa30af3c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,3 +4,4 @@ PyYAML
 ipywidgets
 libpyvinyl
 ipympl
+h5py
\ No newline at end of file

From d4dced89e312a9e9f5bb8a99962464e1049b7931 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 1 Dec 2023 11:54:46 +0100
Subject: [PATCH 360/403] Fixed issue with placement of plot in widget on newer
 ipywidget versions.

---
 mcstasscript/jb_interface/plot_interface.py | 17 +++++++++++------
 1 file changed, 11 insertions(+), 6 deletions(-)

diff --git a/mcstasscript/jb_interface/plot_interface.py b/mcstasscript/jb_interface/plot_interface.py
index acac03c8..3f2fb8cc 100644
--- a/mcstasscript/jb_interface/plot_interface.py
+++ b/mcstasscript/jb_interface/plot_interface.py
@@ -93,11 +93,15 @@ def new_plot(self):
         Sets up original plot with fig, ax and ax for colorbar
         """
         # fig, ax = plt.subplots(constrained_layout=True, figsize=(6, 4))
+
         self.fig, (self.ax, self.colorbar_ax) = plt.subplots(ncols=2,
-                                                             gridspec_kw={'width_ratios': [6, 1]})
+                                                             gridspec_kw={'width_ratios': [6, 1]},
+                                                             tight_layout=True)
 
         self.fig.canvas.toolbar_position = 'bottom'
 
+        plt.show()
+
         self.update_plot()
 
     def update_plot(self):
@@ -162,19 +166,21 @@ def update_plot(self):
             else:
                 self.colorbar_ax.set_axis_off()
 
-            #self.ax.set_axis_on()
-
-            plt.tight_layout()
             self.fig.canvas.draw()
 
     def show_interface(self):
         """
         Show the plot interface
         """
+        # turn off automatic plotting
+        plt.ioff()
+
         # Set up plot area
         output = widgets.Output()
+
         with output:
             self.new_plot()
+
         output.layout = widgets.Layout(width="75%")
 
         # could retrieve default plot options from data if given
@@ -201,14 +207,13 @@ def show_interface(self):
         plot_control_list.append(colormap_control.make_widget())
 
         plot_controls = widgets.VBox(plot_control_list,
-                                     #layout=widgets.Layout(width="25%", border="solid"))
                                      layout=widgets.Layout(width="25%"))
 
         # In case data is already supplied, set it
         if self.data is not None:
             self.set_data(self.data)
 
-        return widgets.HBox([output, plot_controls], layout=widgets.Layout(width="100%"))
+        return widgets.HBox([output, plot_controls])
 
 
 class ColormapDropdown:

From 4efe99f84911821f5b396b61476d6dc14ffae0b4 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 1 Dec 2023 14:18:07 +0100
Subject: [PATCH 361/403] mcdisplay is now searched for in both exectuable path
 and package path.

fixed scaling of plotting when there are many subplots.

avoided plots appearing during unittests.
---
 mcstasscript/helper/plot_helper.py            | 13 ++---
 mcstasscript/interface/instr.py               | 12 +++--
 mcstasscript/interface/plotter.py             | 51 ++++++++++++++++---
 .../tests/dummy_mcstas/bin/mcdisplay-webgl    |  0
 mcstasscript/tests/test_Instr.py              |  3 +-
 mcstasscript/tests/test_Plotter.py            | 46 +++++++++++++----
 mcstasscript/tests/test_plot_interface.py     |  3 ++
 .../tests/test_simulation_interface.py        |  3 ++
 8 files changed, 101 insertions(+), 30 deletions(-)
 create mode 100644 mcstasscript/tests/dummy_mcstas/bin/mcdisplay-webgl

diff --git a/mcstasscript/helper/plot_helper.py b/mcstasscript/helper/plot_helper.py
index 6a143bf8..83e98add 100644
--- a/mcstasscript/helper/plot_helper.py
+++ b/mcstasscript/helper/plot_helper.py
@@ -246,10 +246,10 @@ def _handle_kwargs(data_list, **kwargs):
     """
     Handle kwargs when list of McStasData objects given.
 
-    Returns figsize and data_list
+    Returns data_list
 
-    figsize has a default value, but can be changed with keyword argument
     data_list is turned into a list if it isn't already
+    event data is removed as it can't be plotted directly
 
     Any kwargs can be given as a list, in that case apply them to given
     to the corresponding index.
@@ -301,11 +301,4 @@ def _handle_kwargs(data_list, **kwargs):
             # Remove option from kwargs
             del kwargs[option]
 
-    if "figsize" in kwargs:
-        figsize = kwargs["figsize"]
-        if isinstance(figsize, list):
-            figsize = (figsize[0], figsize[1])
-    else:
-        figsize = (13, 7)
-
-    return figsize, data_list
\ No newline at end of file
+    return data_list
\ No newline at end of file
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 5705b78c..9ba8e25c 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2639,14 +2639,20 @@ def show_instrument(self, format="webgl", width=800, height=450, new_tab=False):
                                 + "="
                                 + str(val))  # parameter value
 
-        package_path = self._run_settings["package_path"]
-        bin_path = os.path.join(package_path, "bin", "")
-
         if format == "webgl":
             executable = "mcdisplay-webgl"
         elif format == "window":
             executable = "mcdisplay"
 
+        # Platform dependent, check both package_path and bin
+        executable_path = self._run_settings["executable_path"]
+        bin_path = os.path.join(executable_path, executable)
+
+        if not os.path.isfile(bin_path):
+            # Take bin in package path into account
+            package_path = self._run_settings["package_path"]
+            bin_path = os.path.join(package_path, "bin", "")
+
         dir_name_original = self.name + "_mcdisplay"
         dir_name = dir_name_original
         index = 0
diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index ce284ce2..e73c0096 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -21,7 +21,14 @@ def make_plot(data_list, **kwargs):
     subplot.
     """
 
-    figsize, data_list = _handle_kwargs(data_list, **kwargs)
+    data_list = _handle_kwargs(data_list, **kwargs)
+
+    if "figsize" in kwargs:
+        figsize = kwargs["figsize"]
+        if isinstance(figsize, list):
+            figsize = (figsize[0], figsize[1])
+    else:
+        figsize = (13, 7)
 
     for data in data_list:
         fig, ax0 = plt.subplots(figsize=figsize)
@@ -45,14 +52,39 @@ def make_sub_plot(data_list, **kwargs):
     subplot.
     """
 
-    figsize, data_list = _handle_kwargs(data_list, **kwargs)
+    data_list = _handle_kwargs(data_list, **kwargs)
 
     number_of_plots = len(data_list)
+
     # Find reasonable grid size for the number of plots
-    dim2 = math.ceil(math.sqrt(number_of_plots))
-    dim1 = math.ceil(number_of_plots / dim2)
+    special_cases = {
+        1: (1, 1),
+        4: (2, 2),
+    }
+
+    if number_of_plots in special_cases:
+        dim1 = special_cases[number_of_plots][0]
+        dim2 = special_cases[number_of_plots][0]
+    else:
+        if number_of_plots < 3:
+            dim2 = number_of_plots
+            dim1 = 1
+        else:
+            dim2 = 3
+            dim1 = math.ceil(number_of_plots / dim2)
+
+    if "figsize" in kwargs:
+        figsize = kwargs["figsize"]
+        if isinstance(figsize, list):
+            figsize = (figsize[0], figsize[1])
+    else:
+        # Adjust figure size after number of plots
+        figsize = (1 + dim2*4, 0.5 + 3.0*dim1)
+        if dim1 == 1 and dim2 == 1:
+            # Single plots can be a bit larger
+            figsize = (7, 5)
 
-    fig, axs = plt.subplots(dim1, dim2, figsize=figsize)
+    fig, axs = plt.subplots(dim1, dim2, figsize=figsize, tight_layout=True)
     axs = np.array(axs)
     ax = axs.reshape(-1)
 
@@ -88,7 +120,14 @@ def make_animation(data_list, **kwargs):
 
     """
 
-    figsize, data_list = _handle_kwargs(data_list, **kwargs)
+    data_list = _handle_kwargs(data_list, **kwargs)
+
+    if "figsize" in kwargs:
+        figsize = kwargs["figsize"]
+        if isinstance(figsize, list):
+            figsize = (figsize[0], figsize[1])
+    else:
+        figsize = (13, 7)
 
     if "fps" in kwargs:
         period_in_ms = 1000 / kwargs["fps"]
diff --git a/mcstasscript/tests/dummy_mcstas/bin/mcdisplay-webgl b/mcstasscript/tests/dummy_mcstas/bin/mcdisplay-webgl
new file mode 100644
index 00000000..e69de29b
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 7ec6275b..afbe91ef 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -75,7 +75,8 @@ def setup_instr_with_path():
     with WorkInTestDir() as handler:
         THIS_DIR = os.path.dirname(os.path.abspath(__file__))
         dummy_path = os.path.join(THIS_DIR, "dummy_mcstas")
-        instrument = McStas_instr("test_instrument", package_path=dummy_path)
+        instrument = McStas_instr("test_instrument",
+                                  package_path=dummy_path, executable_path=dummy_path)
 
     return instrument
 
diff --git a/mcstasscript/tests/test_Plotter.py b/mcstasscript/tests/test_Plotter.py
index a0b4fd0d..6096f997 100644
--- a/mcstasscript/tests/test_Plotter.py
+++ b/mcstasscript/tests/test_Plotter.py
@@ -1,4 +1,8 @@
 import unittest
+import unittest.mock
+
+import matplotlib
+matplotlib.use('Agg')
 
 import numpy as np
 import matplotlib.pyplot as plt
@@ -8,6 +12,7 @@
 from mcstasscript.interface.plotter import _find_min_max_I
 from mcstasscript.interface.plotter import _handle_kwargs
 from mcstasscript.interface.plotter import _plot_fig_ax
+from mcstasscript.interface.plotter import make_plot, make_sub_plot, make_animation
 
 
 def get_dummy_MetaDataBinned_1d():
@@ -537,34 +542,55 @@ def test_handle_kwargs_bottom_lim(self):
         self.assertEqual(dummy_data1.plot_options.custom_ylim_bottom, True)
         self.assertEqual(dummy_data2.plot_options.custom_ylim_bottom, True)
 
-    def test_handle_kwargs_figsize_default(self):
+    @unittest.mock.patch("matplotlib.pyplot.subplots")
+    def test_handle_kwargs_figsize_default(self, mock_subplots):
         """
         Tests handle_kwargs delivers default figsize
         """
 
+        # Ensures subplots returns a tuple with two objects
+        mock_fig = unittest.mock.MagicMock()
+        mock_ax = unittest.mock.MagicMock()
+        mock_subplots.return_value = (mock_fig, mock_ax)
+
+        # Actual test
         dummy_data = get_dummy_McStasDataBinned_2d()
-        retrived_figsize, data_list = _handle_kwargs(dummy_data)
-        self.assertEqual(retrived_figsize, (13, 7))
+        make_plot(dummy_data)
+        mock_subplots.assert_called_with(figsize=(13, 7))
 
-    def test_handle_kwargs_figsize_tuple(self):
+    @unittest.mock.patch("matplotlib.pyplot.subplots")
+    def test_handle_kwargs_figsize_tuple(self, mock_subplots):
         """
         Tests handle_kwargs with figsize keyword argument, here
         using tuple as input
         """
 
+        # Ensures subplots returns a tuple with two objects
+        mock_fig = unittest.mock.MagicMock()
+        mock_ax = unittest.mock.MagicMock()
+        mock_subplots.return_value = (mock_fig, mock_ax)
+
+        # Actual test
         dummy_data = get_dummy_McStasDataBinned_2d()
-        found_figsize, data_list = _handle_kwargs(dummy_data, figsize=(5, 9))
-        self.assertEqual(found_figsize, (5, 9))
+        make_plot(dummy_data, figsize=(5, 9))
+        mock_subplots.assert_called_with(figsize=(5, 9))
 
-    def test_handle_kwargs_figsize_list(self):
+    @unittest.mock.patch("matplotlib.pyplot.subplots")
+    def test_handle_kwargs_figsize_list(self, mock_subplots):
         """
         Tests handle_kwargs with figsize keyword argument, here
         using tuple as input
         """
 
+        # Ensures subplots returns a tuple with two objects
+        mock_fig = unittest.mock.MagicMock()
+        mock_ax = unittest.mock.MagicMock()
+        mock_subplots.return_value = (mock_fig, mock_ax)
+
+        # Actual test
         dummy_data = get_dummy_McStasDataBinned_2d()
-        found_figsize, data_list = _handle_kwargs(dummy_data, figsize=[5, 9])
-        self.assertEqual(found_figsize, (5, 9))
+        make_plot(dummy_data, figsize=[5, 9])
+        mock_subplots.assert_called_with(figsize=(5, 9))
 
     def test_handle_kwargs_single_element_to_list(self):
         """
@@ -574,7 +600,7 @@ def test_handle_kwargs_single_element_to_list(self):
 
         dummy_data = get_dummy_McStasDataBinned_2d()
         self.assertFalse(isinstance(dummy_data, list))
-        figsize, data_list = _handle_kwargs(dummy_data)
+        data_list = _handle_kwargs(dummy_data)
         self.assertTrue(isinstance(data_list, list))
 
     def test_plot_function_1D_normal(self):
diff --git a/mcstasscript/tests/test_plot_interface.py b/mcstasscript/tests/test_plot_interface.py
index 94e41ccf..9eb3021d 100644
--- a/mcstasscript/tests/test_plot_interface.py
+++ b/mcstasscript/tests/test_plot_interface.py
@@ -3,6 +3,9 @@
 import io
 import numpy as np
 
+import matplotlib
+matplotlib.use('Agg')  # Set the backend to Agg
+
 from mcstasscript.jb_interface.plot_interface import PlotInterface
 from mcstasscript.jb_interface.plot_interface import LogCheckbox
 from mcstasscript.jb_interface.plot_interface import ColormapDropdown
diff --git a/mcstasscript/tests/test_simulation_interface.py b/mcstasscript/tests/test_simulation_interface.py
index 96983c00..1d52f44b 100644
--- a/mcstasscript/tests/test_simulation_interface.py
+++ b/mcstasscript/tests/test_simulation_interface.py
@@ -4,6 +4,9 @@
 import unittest.mock
 import ipywidgets as widgets
 
+import matplotlib
+matplotlib.use('Agg')  # Set the backend to Agg
+
 from mcstasscript.jb_interface.simulation_interface import SimInterface
 from mcstasscript.jb_interface.simulation_interface import ParameterWidget
 from mcstasscript.jb_interface.widget_helpers import parameter_has_default

From c3cf1b46a63846e5d81c34a48cd8316ed698ac5c Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 1 Dec 2023 14:28:54 +0100
Subject: [PATCH 362/403] Tight layout on all figures

---
 mcstasscript/interface/plotter.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index e73c0096..2111ea3b 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -31,7 +31,7 @@ def make_plot(data_list, **kwargs):
         figsize = (13, 7)
 
     for data in data_list:
-        fig, ax0 = plt.subplots(figsize=figsize)
+        fig, ax0 = plt.subplots(figsize=figsize, tight_layout=True)
         _plot_fig_ax(data, fig, ax0, **kwargs)
 
     if "filename" in kwargs:
@@ -176,7 +176,7 @@ def make_animation(data_list, **kwargs):
     kwargs["fixed_minimum_value"] = minimum_value
     kwargs["fixed_maximum_value"] = maximum_value
 
-    fig, ax0 = plt.subplots(figsize=figsize)
+    fig, ax0 = plt.subplots(figsize=figsize, tight_layout=True)
     im = _plot_fig_ax(data_list[0], fig, ax0, **kwargs)
 
     def animate_2D(index):

From 9a870887ea3a6be272560abc640e289db9fc5db7 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 1 Dec 2023 14:29:52 +0100
Subject: [PATCH 363/403] Bump of version.

---
 mcstasscript/_version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 2c20ada6..93e8a1c3 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.63"
+__version__ = "0.0.64"

From c3485c99d9ad16122db764f4b7d4766f8a6ec6e0 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 1 Dec 2023 14:33:02 +0100
Subject: [PATCH 364/403] Fixed called_with parameters in unittests for
 plotting.

---
 mcstasscript/tests/test_Plotter.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/mcstasscript/tests/test_Plotter.py b/mcstasscript/tests/test_Plotter.py
index 6096f997..341843a4 100644
--- a/mcstasscript/tests/test_Plotter.py
+++ b/mcstasscript/tests/test_Plotter.py
@@ -556,7 +556,7 @@ def test_handle_kwargs_figsize_default(self, mock_subplots):
         # Actual test
         dummy_data = get_dummy_McStasDataBinned_2d()
         make_plot(dummy_data)
-        mock_subplots.assert_called_with(figsize=(13, 7))
+        mock_subplots.assert_called_with(figsize=(13, 7), tight_layout=True)
 
     @unittest.mock.patch("matplotlib.pyplot.subplots")
     def test_handle_kwargs_figsize_tuple(self, mock_subplots):
@@ -573,7 +573,7 @@ def test_handle_kwargs_figsize_tuple(self, mock_subplots):
         # Actual test
         dummy_data = get_dummy_McStasDataBinned_2d()
         make_plot(dummy_data, figsize=(5, 9))
-        mock_subplots.assert_called_with(figsize=(5, 9))
+        mock_subplots.assert_called_with(figsize=(5, 9), tight_layout=True)
 
     @unittest.mock.patch("matplotlib.pyplot.subplots")
     def test_handle_kwargs_figsize_list(self, mock_subplots):
@@ -590,7 +590,7 @@ def test_handle_kwargs_figsize_list(self, mock_subplots):
         # Actual test
         dummy_data = get_dummy_McStasDataBinned_2d()
         make_plot(dummy_data, figsize=[5, 9])
-        mock_subplots.assert_called_with(figsize=(5, 9))
+        mock_subplots.assert_called_with(figsize=(5, 9), tight_layout=True)
 
     def test_handle_kwargs_single_element_to_list(self):
         """

From fd5bac9c3ae04052c81989f2d55376d1fd051839 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 1 Dec 2023 14:52:36 +0100
Subject: [PATCH 365/403] Hotfix of mcdisplay path issue.

---
 mcstasscript/interface/instr.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 9ba8e25c..fd28ca4f 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2651,7 +2651,7 @@ def show_instrument(self, format="webgl", width=800, height=450, new_tab=False):
         if not os.path.isfile(bin_path):
             # Take bin in package path into account
             package_path = self._run_settings["package_path"]
-            bin_path = os.path.join(package_path, "bin", "")
+            bin_path = os.path.join(package_path, "bin", executable)
 
         dir_name_original = self.name + "_mcdisplay"
         dir_name = dir_name_original
@@ -2677,7 +2677,7 @@ def show_instrument(self, format="webgl", width=800, height=450, new_tab=False):
         if is_notebook and executable == "mcdisplay-webgl" and not new_tab:
             options += "--nobrowse "
 
-        full_command = ('"' + bin_path + executable + '" '
+        full_command = ('"' + bin_path + '" '
                         + dir_control
                         + options
                         + instr_path

From 8c5396ab7365a6e3c00e3dd1e4d8d75e0e27a536 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Fri, 1 Dec 2023 14:52:59 +0100
Subject: [PATCH 366/403] Hotfix of mcdisplay path issue.

---
 mcstasscript/_version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 93e8a1c3..65bbbdbc 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.64"
+__version__ = "0.0.65"

From 9bf67c11964ecb2e90759193ad0d4ee065648b9d Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 6 Dec 2023 14:50:48 +0100
Subject: [PATCH 367/403] Improved reliability of reading NeXus data.

Removed intensity from event data, as it was only used in plotting, and now plotting of event data has been removed.
---
 mcstasscript/_version.py                |  2 +-
 mcstasscript/data/data.py               | 27 ++++----
 mcstasscript/helper/managed_mcrun.py    | 90 ++++++++++++++++---------
 mcstasscript/interface/plotter.py       |  3 +
 mcstasscript/tests/test_ManagedMcrun.py |  1 -
 5 files changed, 79 insertions(+), 44 deletions(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 65bbbdbc..c2bff012 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.65"
+__version__ = "0.0.66"
diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index a8ab6516..c4b068ab 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -706,22 +706,25 @@ def __init__(self, metadata, events, **kwargs):
         self.Events = events
         self.data_type = "Events"
 
-        # Intensity for compatibility with plotting routine
-        data_lines = metadata.dimension[1]
-        self.Intensity = self.Events[0:data_lines, :]
-
         self.variables = self.metadata.info["variables"].strip()
         self.variables = self.variables.split()
 
         # Calculate I, E and N
-        p_array = self.get_data_column("p")
-        total_I = p_array.sum()
-        total_E = np.sqrt((p_array ** 2).sum())
-        total_N = len(p_array)
-
-        self.metadata.total_I = total_I
-        self.metadata.total_E = total_E
-        self.metadata.total_N = total_N
+        if "p" in self.variables:
+            p_array = self.get_data_column("p")
+            total_I = p_array.sum()
+            total_E = np.sqrt((p_array ** 2).sum())
+            total_N = len(p_array)
+
+            self.metadata.total_I = total_I
+            self.metadata.total_E = total_E
+            self.metadata.total_N = total_N
+
+        else:
+
+            self.metadata.total_I = None
+            self.metadata.total_E = None
+            self.metadata.total_N = None
 
         self.labels = {"t": "t [s]",
                        "x": "x [m]",
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 3a607f98..112f87fe 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -4,6 +4,7 @@
 import mmap
 import warnings
 import h5py
+import re
 
 from mcstasscript.helper.formatting import bcolors
 from mcstasscript.data.data import McStasMetaData
@@ -592,17 +593,58 @@ def load_monitor_nexus(metadata, data_folder_name, filename="mccode.h5"):
 
         NeXus_field = metadata.info["NeXus_field"]
 
+        available_fields = f["entry1"]["data"][NeXus_field].keys()
+        if not metadata.dimension == 0 and "events" not in available_fields:
+            if "data" not in available_fields:
+                raise ValueError("NeXus reading: data not found! \n"
+                                 + "Monitor metadata:\n" + str(metadata))
+
+            if "errors" not in available_fields:
+                raise ValueError("NeXus reading: errors not found! \n"
+                                 + "Monitor metadata:\n" + str(metadata))
+
+            if "ncount" not in available_fields:
+                raise ValueError("NeXus reading: ncount not found! \n"
+                                 + "Monitor metadata:\n" + str(metadata))
+
+        # Need to check if it is binned data or event data
+        if "events" in available_fields:
+            Events = np.array(f["entry1"]["data"][NeXus_field]["events"])
+            return McStasDataEvent(metadata, Events)
+
         # Split data into intensity, error and ncount
         if type(metadata.dimension) == int and metadata.dimension == 0:
-            Intensity = np.array(f["entry1"]["data"][NeXus_field]["data"])
+
+            if "data" in f["entry1"]["data"][NeXus_field].keys():
+                raise ValueError("Found array data in 0D dataset?")
+
+            values = None
+            if "values" in f["entry1"]["data"][NeXus_field].keys():
+                values = np.array(f["entry1"]["data"][NeXus_field]["values"])
+
+            if metadata.total_I is None:
+                if values is not None:
+                    Intensity = np.array(values[0])
+                else:
+                    raise ValueError("No info on this monitor can be found "
+                                     + "in reading of NeXus file "
+                                     + str(metadata))
+            else:
+                Intensity = np.array(metadata.total_I)
 
             if metadata.total_E is None:
-                Error = np.zeros(1)
+                if values is not None:
+                    Error = np.array(values[2])
+                else:
+                    Error = np.zeros(1)
             else:
                 Error = np.array(metadata.total_E)
 
             if metadata.total_N is None:
-                Ncount = np.zeros(1)
+                if values is not None:
+                    Ncount = np.array(values[3])
+                else:
+                    Ncount = np.zeros(1)
             else:
                 Ncount = np.array(metadata.total_N)
 
@@ -612,14 +654,17 @@ def load_monitor_nexus(metadata, data_folder_name, filename="mccode.h5"):
 
             original_xlabel = metadata.info["xlabel"]
 
-            x_field = original_xlabel.replace(" ", "_")
-            x_field = x_field.replace("[", "_")
-            x_field = x_field.replace("]", "_")
+            # All special characters are substituted with _ in McStas NeXus file
+            x_field = re.sub(r'[^a-zA-Z]', "_", original_xlabel)
 
             if x_field not in f["entry1"]["data"][NeXus_field].keys():
-                print("Expected this field for x axis: ", str(x_field))
-                print("Existing fields: ", f["entry1"]["data"][NeXus_field].keys())
-                raise ValueError("Didn't find xaxis in NeXus file.")
+                error_text = ("Didn't find xaxis in NeXus file. \n"
+                              + "Expected this field for x axis: "
+                              + str(x_field) + "\n"
+                              + "Existing fields: "
+                              + str(f["entry1"]["data"][NeXus_field].keys()))
+
+                raise ValueError(error_text)
 
             xaxis = np.array(f["entry1"]["data"][NeXus_field][x_field])
             Intensity = np.array(f["entry1"]["data"][NeXus_field]["data"])
@@ -630,28 +675,13 @@ def load_monitor_nexus(metadata, data_folder_name, filename="mccode.h5"):
             return McStasDataBinned(metadata, Intensity, Error, Ncount, xaxis=xaxis)
 
         elif len(metadata.dimension) == 2:
-            # Need to check if it is binned data or event data
-            if "events" in f["entry1"]["data"][NeXus_field].keys():
-                Events = np.array(f["entry1"]["data"][NeXus_field]["events"])
-                return McStasDataEvent(metadata, Events)
-            else:
-
-                available_fields = f["entry1"]["data"][NeXus_field].keys()
-
-                if "errors" not in available_fields:
-                    raise ValueError("No 'errors' in NeXus data entry called ", NeXus_field)
-
-                if "ncount" not in available_fields:
-                    raise ValueError("No 'ncount' in NeXus data entry called ", NeXus_field)
-
-                # If no events, binned instead
-                xaxis = []  # Assume evenly binned in 2d
-                Intensity = np.array(f["entry1"]["data"][NeXus_field]["data"]).T
-                Error = np.array(f["entry1"]["data"][NeXus_field]["errors"]).T
-                Ncount = np.array(f["entry1"]["data"][NeXus_field]["ncount"]).T
+            xaxis = []  # Assume evenly binned in 2d
+            Intensity = np.array(f["entry1"]["data"][NeXus_field]["data"]).T
+            Error = np.array(f["entry1"]["data"][NeXus_field]["errors"]).T
+            Ncount = np.array(f["entry1"]["data"][NeXus_field]["ncount"]).T
 
-                # The data is saved as a McStasDataBinned object
-                return McStasDataBinned(metadata, Intensity, Error, Ncount, xaxis=xaxis)
+            # The data is saved as a McStasDataBinned object
+            return McStasDataBinned(metadata, Intensity, Error, Ncount, xaxis=xaxis)
         else:
             raise NameError(
                 "Dimension not read correctly in data set "
diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index 2111ea3b..9552ae63 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -55,6 +55,9 @@ def make_sub_plot(data_list, **kwargs):
     data_list = _handle_kwargs(data_list, **kwargs)
 
     number_of_plots = len(data_list)
+    if number_of_plots == 0:
+        print("No data to plot")
+        return
 
     # Find reasonable grid size for the number of plots
     special_cases = {
diff --git a/mcstasscript/tests/test_ManagedMcrun.py b/mcstasscript/tests/test_ManagedMcrun.py
index 52deced1..804d79f7 100644
--- a/mcstasscript/tests/test_ManagedMcrun.py
+++ b/mcstasscript/tests/test_ManagedMcrun.py
@@ -632,7 +632,6 @@ def test_ManagedMcrun_load_data_Event(self):
         expected_parameters = {"wavelength": 1.0}
         self.assertEqual(mon.metadata.info["Parameters"], expected_parameters)
         self.assertEqual(mon.metadata.parameters, expected_parameters)
-        self.assertEqual(mon.Intensity[12, 1], -0.006163896406)
         self.assertEqual(mon.Events[12, 1], -0.006163896406)
         self.assertEqual(mon.Events[43, 4], 22.06193582)
 

From 811230d76fee11b65e51d1373ac668363b427c9d Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Thu, 14 Dec 2023 14:40:37 +0100
Subject: [PATCH 368/403] Mocked datetime to avoid a flaky test when writing
 instrument code.

---
 mcstasscript/tests/test_Instr.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index afbe91ef..737b1a72 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1657,7 +1657,8 @@ def test_write_c_files_simple(self, mock_f):
 
     @unittest.mock.patch('__main__.__builtins__.open',
                          new_callable=unittest.mock.mock_open)
-    def test_write_full_instrument_simple(self, mock_f):
+    @unittest.mock.patch('datetime.datetime')
+    def test_write_full_instrument_simple(self, mock_datetime, mock_f):
         """
         The write_full_instrument method write the information
         contained in the instrument instance to a file with McStas
@@ -1668,6 +1669,10 @@ def test_write_full_instrument_simple(self, mock_f):
         due to this, but it can happen.
         """
 
+        # Fix datetime for call
+        fixed_datetime = datetime.datetime(2023, 12, 14, 12, 44, 21)
+        mock_datetime.now.return_value = fixed_datetime
+
         instr = setup_populated_instr()
         instr.write_full_instrument()
 
@@ -1692,7 +1697,7 @@ def test_write_full_instrument_simple(self, mock_f):
          my_call("* \n"),
          my_call("* %Identification\n"),
          my_call("* Written by: Python McStas Instrument Generator\n"),
-         my_call("* Date: %s\n" % datetime.datetime.now().strftime(t_format)),
+         my_call("* Date: %s\n" % fixed_datetime.strftime(t_format)),
          my_call("* Origin: ESS DMSC\n"),
          my_call("* %INSTRUMENT_SITE: Generated_instruments\n"),
          my_call("* \n"),

From 2f0a26e85e17eacc4d384f6862d3407819c8d6d5 Mon Sep 17 00:00:00 2001
From: Peter Willendrup <pkwi@fysik.dtu.dk>
Date: Sat, 10 Feb 2024 23:04:05 +0100
Subject: [PATCH 369/403] Implement simple solution to auto-detect McStas and
 McXtrace parameters if run from within a related 'environment'

---
 mcstasscript/interface/instr.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index fd28ca4f..41bba672 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -1,11 +1,13 @@
 from __future__ import print_function
 
 import os
+import shutil
 import datetime
 import yaml
 import subprocess
 import copy
 import warnings
+import re
 
 from IPython.display import IFrame
 
@@ -2938,7 +2940,11 @@ def _read_calibration(self):
         with open(configuration_file_name, 'r') as ymlfile:
             config = yaml.safe_load(ymlfile)
 
-        if type(config) is dict:
+        if os.environ["MCSTAS"] is not None: # We are in a McStas environment, use that
+            self._run_settings["executable_path"] = os.path.dirname(shutil.which("mcrun"))
+            self._run_settings["package_path"] = os.environ["MCSTAS"]
+            self.line_limit = 85
+        elif type(config) is dict:
             self._run_settings["executable_path"] = config["paths"]["mcrun_path"]
             self._run_settings["package_path"] = config["paths"]["mcstas_path"]
             self.line_limit = config["other"]["characters_per_line"]
@@ -3166,7 +3172,11 @@ def _read_calibration(self):
         with open(configuration_file_name, 'r') as ymlfile:
             config = yaml.safe_load(ymlfile)
 
-        if type(config) is dict:
+        if os.environ["MCXTRACE"] is not None: # We are in a McXtrace environment, use that
+            self._run_settings["executable_path"] = os.path.dirname(shutil.which("mxrun"))
+            self._run_settings["package_path"] = os.environ["MCXTRACE"]
+            self.line_limit = 85
+        elif type(config) is dict:
             self._run_settings["executable_path"] = config["paths"]["mxrun_path"]
             self._run_settings["package_path"] = config["paths"]["mcxtrace_path"]
             self.line_limit = config["other"]["characters_per_line"]

From ffbac3798d68fb15c913a4a0bf3e071b3c8347c0 Mon Sep 17 00:00:00 2001
From: Mads Bertelsen <mads.bertelsen@ess.eu>
Date: Wed, 14 Feb 2024 10:36:38 +0100
Subject: [PATCH 370/403] Fix of case where MCSTAS was not found in environ.

---
 mcstasscript/_version.py        |  2 +-
 mcstasscript/interface/instr.py | 14 ++++++++------
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index c2bff012..e89bf933 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.66"
+__version__ = "0.0.67"
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 41bba672..6d266a9e 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2940,14 +2940,15 @@ def _read_calibration(self):
         with open(configuration_file_name, 'r') as ymlfile:
             config = yaml.safe_load(ymlfile)
 
-        if os.environ["MCSTAS"] is not None: # We are in a McStas environment, use that
+        if type(config) is dict:
+            self.line_limit = config["other"]["characters_per_line"]
+
+        if "MCSTAS" in os.environ: # We are in a McStas environment, use that
             self._run_settings["executable_path"] = os.path.dirname(shutil.which("mcrun"))
             self._run_settings["package_path"] = os.environ["MCSTAS"]
-            self.line_limit = 85
         elif type(config) is dict:
             self._run_settings["executable_path"] = config["paths"]["mcrun_path"]
             self._run_settings["package_path"] = config["paths"]["mcstas_path"]
-            self.line_limit = config["other"]["characters_per_line"]
         else:
             # This happens in unit tests that mocks open
             self._run_settings["executable_path"] = ""
@@ -3172,14 +3173,15 @@ def _read_calibration(self):
         with open(configuration_file_name, 'r') as ymlfile:
             config = yaml.safe_load(ymlfile)
 
-        if os.environ["MCXTRACE"] is not None: # We are in a McXtrace environment, use that
+        if type(config) is dict:
+            self.line_limit = config["other"]["characters_per_line"]
+
+        if "MCXTRACE" in os.environ: # We are in a McXtrace environment, use that
             self._run_settings["executable_path"] = os.path.dirname(shutil.which("mxrun"))
             self._run_settings["package_path"] = os.environ["MCXTRACE"]
-            self.line_limit = 85
         elif type(config) is dict:
             self._run_settings["executable_path"] = config["paths"]["mxrun_path"]
             self._run_settings["package_path"] = config["paths"]["mcxtrace_path"]
-            self.line_limit = config["other"]["characters_per_line"]
         else:
             # This happens in unit tests that mocks open
             self._run_settings["executable_path"] = ""

From 3cab00e2afb10866e1e503418a820968ed0f06f7 Mon Sep 17 00:00:00 2001
From: Peter Willendrup <pkwi@fysik.dtu.dk>
Date: Wed, 15 May 2024 21:49:27 +0200
Subject: [PATCH 371/403] Allow McXtrace sessions to visualize using
 mxdisplay(-webgl/-pyqtgraph)

---
 mcstasscript/interface/instr.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 6d266a9e..89f0f802 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2641,10 +2641,15 @@ def show_instrument(self, format="webgl", width=800, height=450, new_tab=False):
                                 + "="
                                 + str(val))  # parameter value
 
+        if self.package_name == "McXtrace":
+            executable = "mxdisplay"
+        else:
+            executable = "mxdisplay"
+
         if format == "webgl":
-            executable = "mcdisplay-webgl"
+            executable = executable+"-webgl"
         elif format == "window":
-            executable = "mcdisplay"
+            executable = executable+"-pyqtgraph"
 
         # Platform dependent, check both package_path and bin
         executable_path = self._run_settings["executable_path"]

From e8adc2939686ad49fca5a6d7b66b200b901cb17e Mon Sep 17 00:00:00 2001
From: Peter Willendrup <pkwi@fysik.dtu.dk>
Date: Wed, 15 May 2024 21:52:55 +0200
Subject: [PATCH 372/403] We should of course still use mcdisplay in the McStas
 case...

---
 mcstasscript/interface/instr.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 89f0f802..aaccac0f 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2644,7 +2644,7 @@ def show_instrument(self, format="webgl", width=800, height=450, new_tab=False):
         if self.package_name == "McXtrace":
             executable = "mxdisplay"
         else:
-            executable = "mxdisplay"
+            executable = "mcdisplay"
 
         if format == "webgl":
             executable = executable+"-webgl"

From 99a5120f455981ac49b06c5a9ab9347a2d16ba83 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Wed, 24 Jul 2024 16:13:31 +0200
Subject: [PATCH 373/403] Increasing python version for workflows

---
 .github/workflows/unit_tests_mac.yaml     | 2 +-
 .github/workflows/unit_tests_ubuntu.yaml  | 2 +-
 .github/workflows/unit_tests_windows.yaml | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index de4b72f9..10e95cc5 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -8,7 +8,7 @@ jobs:
   build:
     strategy:
       matrix:
-        python-version: [3.7, 3.8, 3.9]
+        python-version: [3.8, 3.9, 3.10]
     runs-on: macos-latest
       
     steps:
diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index 69c49fa0..be4ac83f 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -8,7 +8,7 @@ jobs:
   build:
     strategy:
       matrix:
-        python-version: [3.7, 3.8, 3.9]
+        python-version: [3.8, 3.9, 3.10]
     runs-on: ubuntu-latest
       
     steps:
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
index 5661724f..5db98302 100644
--- a/.github/workflows/unit_tests_windows.yaml
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -8,7 +8,7 @@ jobs:
   build:
     strategy:
       matrix:
-        python-version: [3.7, 3.8, 3.9]
+        python-version: [3.8, 3.9, 3.10]
     runs-on: windows-latest
       
     steps:

From 1c79985200226bb055cdb732925f50687a4435eb Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Wed, 24 Jul 2024 16:19:58 +0200
Subject: [PATCH 374/403] Added instrument checker.

---
 mcstasscript/__init__.py                      |  1 +
 mcstasscript/tests/test_instrument_checker.py | 98 +++++++++++++++++++
 mcstasscript/tools/instrument_checker.py      | 21 ++++
 3 files changed, 120 insertions(+)
 create mode 100644 mcstasscript/tests/test_instrument_checker.py
 create mode 100644 mcstasscript/tools/instrument_checker.py

diff --git a/mcstasscript/__init__.py b/mcstasscript/__init__.py
index b49f2a5f..e9946532 100644
--- a/mcstasscript/__init__.py
+++ b/mcstasscript/__init__.py
@@ -18,5 +18,6 @@
 from .interface.reader import McStas_file
 
 from .tools.cryostat_builder import Cryostat
+from .tools.instrument_checker import has_component
 
 from .instrument_diagnostics.beam_diagnostics import BeamDiagnostics as Diagnostics
diff --git a/mcstasscript/tests/test_instrument_checker.py b/mcstasscript/tests/test_instrument_checker.py
new file mode 100644
index 00000000..d0dc8785
--- /dev/null
+++ b/mcstasscript/tests/test_instrument_checker.py
@@ -0,0 +1,98 @@
+import unittest
+import os
+import mcstasscript as ms
+from mcstasscript.tests.helpers_for_tests import WorkInTestDir
+
+from mcstasscript.tools.instrument_checker import has_component
+
+
+def setup_instr_with_path():
+    """
+    Sets up an instrument with a valid package_path, but it points to
+    the dummy installation in the test folder.
+    """
+
+    with WorkInTestDir() as handler:
+        THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+        dummy_path = os.path.join(THIS_DIR, "dummy_mcstas")
+        instrument = ms.McStas_instr("test_instrument",
+                                     package_path=dummy_path,
+                                     executable_path=dummy_path)
+
+    return instrument
+
+
+def setup_populated_instr():
+    """
+    Sets up a neutron instrument with some features used and three components
+    """
+    instr = setup_instr_with_path()
+
+    instr.add_parameter("double", "theta")
+    instr.add_parameter("double", "has_default", value=37)
+    instr.add_declare_var("double", "two_theta")
+    instr.append_initialize("two_theta = 2.0*theta;")
+
+    instr.add_component("first_component", "test_for_reading")
+    instr.add_component("second_component", "test_for_reading")
+    instr.add_component("third_component", "test_for_reading")
+
+    return instr
+
+
+class TestToolInstrumentChecker(unittest.TestCase):
+    """
+    Various test of instrument_checker functions
+    """
+
+    def test_simple_case_name(self):
+        """
+        check component of given name
+        """
+        instr = setup_populated_instr()
+        self.assertTrue(has_component(instr, component_name="second_component"))
+
+    def test_simple_case_name_not_found(self):
+        """
+        check component of given name
+        """
+        instr = setup_populated_instr()
+        self.assertFalse(has_component(instr, component_name="fourth_component"))
+
+    def test_simple_case_type(self):
+        """
+        check component of given name
+        """
+        instr = setup_populated_instr()
+        self.assertTrue(has_component(instr, component_type="test_for_reading"))
+
+    def test_simple_case_type_not_found(self):
+        """
+        check component of given name
+        """
+        instr = setup_populated_instr()
+        self.assertFalse(has_component(instr, component_type="Arm"))
+
+    def test_case_both(self):
+        """
+        check component of given name
+        """
+        instr = setup_populated_instr()
+        self.assertTrue(has_component(instr, component_name="first_component",
+                                      component_type="test_for_reading"))
+
+    def test_case_both_not_found_name(self):
+        """
+        check component of given name
+        """
+        instr = setup_populated_instr()
+        self.assertFalse(has_component(instr, component_name="",
+                                       component_type="test_for_reading"))
+
+    def test_case_both_not_found_type(self):
+        """
+        check component of given name
+        """
+        instr = setup_populated_instr()
+        self.assertFalse(has_component(instr, component_name="first_component",
+                                       component_type="Arm"))
\ No newline at end of file
diff --git a/mcstasscript/tools/instrument_checker.py b/mcstasscript/tools/instrument_checker.py
new file mode 100644
index 00000000..9c23f6f9
--- /dev/null
+++ b/mcstasscript/tools/instrument_checker.py
@@ -0,0 +1,21 @@
+
+
+def has_component(instrument, component_name=None, component_type=None):
+    if component_name is None and component_type is None:
+        raise ValueError("Specify component_name, component_type or both.")
+
+    # Run through components in instrument
+    for comp in instrument.component_list:
+        if component_name is not None:
+            if comp.name != component_name:
+                continue
+
+        if component_type is not None:
+            if comp.component_name != component_type:
+                continue
+
+        # Found case where both component name and type matches
+        return True
+
+    # Did not find any such component
+    return False
\ No newline at end of file

From c765c311ec7d6b0724477ab3673aeac2d6f796d3 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Wed, 24 Jul 2024 16:31:20 +0200
Subject: [PATCH 375/403] Updates to workflows.

---
 .github/workflows/unit_tests_mac.yaml     | 4 ++--
 .github/workflows/unit_tests_ubuntu.yaml  | 6 +++---
 .github/workflows/unit_tests_windows.yaml | 6 +++---
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index 10e95cc5..741d9e4d 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -13,13 +13,13 @@ jobs:
       
     steps:
       - name: Temporary developer McStasScript checkout
-        uses: actions/checkout@v2
+        uses: actions/checkout@v3
         with:
           ref: ${{ env.BRANCH }}
           path: mcstasscript
     
       - name: Temporary developer libpyvinyl checkout
-        uses: actions/checkout@v2
+        uses: actions/checkout@v3
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
           ref: master
diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index be4ac83f..3a2ce712 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -13,13 +13,13 @@ jobs:
       
     steps:
       - name: Temporary developer McStasScript checkout
-        uses: actions/checkout@v2
+        uses: actions/checkout@v3
         with:
-          ref: ${{ env.BRANCH }}
+          ref: ${{ github.ref }}
           path: mcstasscript
     
       - name: Temporary developer libpyvinyl checkout
-        uses: actions/checkout@v2
+        uses: actions/checkout@v3
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
           ref: master
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
index 5db98302..dc471cb7 100644
--- a/.github/workflows/unit_tests_windows.yaml
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -13,13 +13,13 @@ jobs:
       
     steps:
       - name: Temporary developer McStasScript checkout
-        uses: actions/checkout@v2
+        uses: actions/checkout@v3
         with:
-          ref: ${{ env.BRANCH }}
+          ref: ${{ github.ref }}
           path: mcstasscript
     
       - name: Temporary developer libpyvinyl checkout
-        uses: actions/checkout@v2
+        uses: actions/checkout@v3
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
           ref: master

From aa4e2281591ce328a2c3d87ed6d2311dd9f0edde Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Wed, 24 Jul 2024 16:35:45 +0200
Subject: [PATCH 376/403] Changed how python versions are specified

---
 .github/workflows/unit_tests_mac.yaml     | 2 +-
 .github/workflows/unit_tests_ubuntu.yaml  | 2 +-
 .github/workflows/unit_tests_windows.yaml | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index 741d9e4d..e38fbe00 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -8,7 +8,7 @@ jobs:
   build:
     strategy:
       matrix:
-        python-version: [3.8, 3.9, 3.10]
+        python-version: ["3.8", "3.9", "3.10"]
     runs-on: macos-latest
       
     steps:
diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index 3a2ce712..f2aaa6a1 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -8,7 +8,7 @@ jobs:
   build:
     strategy:
       matrix:
-        python-version: [3.8, 3.9, 3.10]
+        python-version: ["3.8", "3.9", "3.10"]
     runs-on: ubuntu-latest
       
     steps:
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
index dc471cb7..6347b9f4 100644
--- a/.github/workflows/unit_tests_windows.yaml
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -8,7 +8,7 @@ jobs:
   build:
     strategy:
       matrix:
-        python-version: [3.8, 3.9, 3.10]
+        python-version: ["3.8", "3.9", "3.10"]
     runs-on: windows-latest
       
     steps:

From 510010bb44f10622de94977566e152d07cc9fe19 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Wed, 24 Jul 2024 16:42:46 +0200
Subject: [PATCH 377/403] Shifted to libpyvinyl branch without Pint.

---
 .github/workflows/unit_tests_mac.yaml     | 2 +-
 .github/workflows/unit_tests_ubuntu.yaml  | 2 +-
 .github/workflows/unit_tests_windows.yaml | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/unit_tests_mac.yaml b/.github/workflows/unit_tests_mac.yaml
index e38fbe00..55332a9a 100644
--- a/.github/workflows/unit_tests_mac.yaml
+++ b/.github/workflows/unit_tests_mac.yaml
@@ -22,7 +22,7 @@ jobs:
         uses: actions/checkout@v3
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
-          ref: master
+          ref: remove_pint_pin
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}
diff --git a/.github/workflows/unit_tests_ubuntu.yaml b/.github/workflows/unit_tests_ubuntu.yaml
index f2aaa6a1..07ecfb82 100644
--- a/.github/workflows/unit_tests_ubuntu.yaml
+++ b/.github/workflows/unit_tests_ubuntu.yaml
@@ -22,7 +22,7 @@ jobs:
         uses: actions/checkout@v3
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
-          ref: master
+          ref: remove_pint_pin
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}
diff --git a/.github/workflows/unit_tests_windows.yaml b/.github/workflows/unit_tests_windows.yaml
index 6347b9f4..333560a3 100644
--- a/.github/workflows/unit_tests_windows.yaml
+++ b/.github/workflows/unit_tests_windows.yaml
@@ -22,7 +22,7 @@ jobs:
         uses: actions/checkout@v3
         with:
           repository: PaNOSC-ViNYL/libpyvinyl
-          ref: master
+          ref: remove_pint_pin
           path: libpyvinyl
     
       - name: Set up Python ${{ matrix.python-version }}

From f23de3b3ebb7f6e85074964e77d506dbc77c0ee2 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Thu, 25 Jul 2024 09:38:13 +0200
Subject: [PATCH 378/403] Further development of instrument_checker and version
 bump.

---
 mcstasscript/__init__.py                      |  2 +-
 mcstasscript/_version.py                      |  2 +-
 mcstasscript/tests/test_instrument_checker.py | 72 ++++++++++++++++---
 mcstasscript/tools/instrument_checker.py      | 34 ++++++++-
 4 files changed, 98 insertions(+), 12 deletions(-)

diff --git a/mcstasscript/__init__.py b/mcstasscript/__init__.py
index e9946532..43970550 100644
--- a/mcstasscript/__init__.py
+++ b/mcstasscript/__init__.py
@@ -18,6 +18,6 @@
 from .interface.reader import McStas_file
 
 from .tools.cryostat_builder import Cryostat
-from .tools.instrument_checker import has_component
+from .tools.instrument_checker import has_component, has_parameter, all_parameters_set
 
 from .instrument_diagnostics.beam_diagnostics import BeamDiagnostics as Diagnostics
diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index e89bf933..2db6156b 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.67"
+__version__ = "0.0.68"
diff --git a/mcstasscript/tests/test_instrument_checker.py b/mcstasscript/tests/test_instrument_checker.py
index d0dc8785..4c3490f3 100644
--- a/mcstasscript/tests/test_instrument_checker.py
+++ b/mcstasscript/tests/test_instrument_checker.py
@@ -3,7 +3,7 @@
 import mcstasscript as ms
 from mcstasscript.tests.helpers_for_tests import WorkInTestDir
 
-from mcstasscript.tools.instrument_checker import has_component
+from mcstasscript.tools.instrument_checker import has_component, has_parameter, all_parameters_set
 
 
 def setup_instr_with_path():
@@ -30,7 +30,10 @@ def setup_populated_instr():
 
     instr.add_parameter("double", "theta")
     instr.add_parameter("double", "has_default", value=37)
+    instr.add_parameter("int", "n_pulses")
+
     instr.add_declare_var("double", "two_theta")
+
     instr.append_initialize("two_theta = 2.0*theta;")
 
     instr.add_component("first_component", "test_for_reading")
@@ -47,35 +50,35 @@ class TestToolInstrumentChecker(unittest.TestCase):
 
     def test_simple_case_name(self):
         """
-        check component of given name
+        check has_component of given name
         """
         instr = setup_populated_instr()
         self.assertTrue(has_component(instr, component_name="second_component"))
 
     def test_simple_case_name_not_found(self):
         """
-        check component of given name
+        check has_component returns false when name is not there
         """
         instr = setup_populated_instr()
         self.assertFalse(has_component(instr, component_name="fourth_component"))
 
     def test_simple_case_type(self):
         """
-        check component of given name
+        check has_component can find component of given type
         """
         instr = setup_populated_instr()
         self.assertTrue(has_component(instr, component_type="test_for_reading"))
 
     def test_simple_case_type_not_found(self):
         """
-        check component of given name
+        check has_component returns false when component type is not found
         """
         instr = setup_populated_instr()
         self.assertFalse(has_component(instr, component_type="Arm"))
 
     def test_case_both(self):
         """
-        check component of given name
+        check has_component when given both name and component type
         """
         instr = setup_populated_instr()
         self.assertTrue(has_component(instr, component_name="first_component",
@@ -83,7 +86,7 @@ def test_case_both(self):
 
     def test_case_both_not_found_name(self):
         """
-        check component of given name
+        check has_component returns false when name not found but type is
         """
         instr = setup_populated_instr()
         self.assertFalse(has_component(instr, component_name="",
@@ -91,8 +94,59 @@ def test_case_both_not_found_name(self):
 
     def test_case_both_not_found_type(self):
         """
-        check component of given name
+        check has_component returns false when type not found but name is
         """
         instr = setup_populated_instr()
         self.assertFalse(has_component(instr, component_name="first_component",
-                                       component_type="Arm"))
\ No newline at end of file
+                                       component_type="Arm"))
+
+    def test_parameter_found(self):
+        """
+        check has_parameter of given name
+        """
+        instr = setup_populated_instr()
+        self.assertTrue(has_parameter(instr, parameter_name="theta"))
+
+    def test_parameter_not_found(self):
+        """
+        check has_parameter returns false when of given name
+        """
+        instr = setup_populated_instr()
+        self.assertFalse(has_parameter(instr, parameter_name="bogus"))
+
+    def test_parameter_found_with_type(self):
+        """
+        check has_parameter of given name and type
+        """
+        instr = setup_populated_instr()
+        self.assertTrue(has_parameter(instr, parameter_name="n_pulses", parameter_type="int"))
+
+    def test_parameter_found_with_type_default(self):
+        """
+        check has_parameter of given name and type (using default double)
+        """
+        instr = setup_populated_instr()
+        self.assertTrue(has_parameter(instr, parameter_name="theta", parameter_type="double"))
+
+    def test_parameter_found_with_wrong_type(self):
+        """
+        check has_parameter of given name
+        """
+        instr = setup_populated_instr()
+        self.assertFalse(has_parameter(instr, parameter_name="theta", parameter_type="int"))
+
+    def test_all_parameters_set_not_set(self):
+        """
+        check all_parameters_set returns false when not all parameters are set of given name
+        """
+        instr = setup_populated_instr()
+        self.assertFalse(all_parameters_set(instr))
+
+    def test_all_parameters_set_actually_set(self):
+        """
+        check all_parameters_set returns false when all parameters are set of given name
+        """
+        instr = setup_populated_instr()
+
+        instr.set_parameters(theta=37, n_pulses=3)
+        self.assertTrue(all_parameters_set(instr))
\ No newline at end of file
diff --git a/mcstasscript/tools/instrument_checker.py b/mcstasscript/tools/instrument_checker.py
index 9c23f6f9..ef174d80 100644
--- a/mcstasscript/tools/instrument_checker.py
+++ b/mcstasscript/tools/instrument_checker.py
@@ -18,4 +18,36 @@ def has_component(instrument, component_name=None, component_type=None):
         return True
 
     # Did not find any such component
-    return False
\ No newline at end of file
+    return False
+
+
+def has_parameter(instrument, parameter_name, parameter_type=None):
+    if parameter_name is None and parameter_type is None:
+        raise ValueError("Specify parameter_name, parameter_type or both.")
+
+    parameter_dict = instrument.parameters.parameters
+
+    if parameter_name not in parameter_dict:
+        return False
+
+    if parameter_type is not None:
+        found_type = parameter_dict[parameter_name].type
+
+        if parameter_type == "double" and found_type == "":
+            # Default McStas type is double
+            return True
+
+        if found_type != parameter_type:
+            return False
+
+    return True
+
+def all_parameters_set(instrument):
+
+    parameter_dict = instrument.parameters.parameters
+
+    for par_object in parameter_dict.values():
+        if par_object.value is None:
+            return False
+
+    return True
\ No newline at end of file

From 2fa3cc560537858c6ed507789ce9071aa67df154 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Mon, 29 Jul 2024 15:14:07 +0200
Subject: [PATCH 379/403] Updated search statements so McStasScript loads the
 appropriate folder of components.

When search statements are cleared, the component database is reset.
---
 mcstasscript/helper/component_reader.py    | 32 +++++++++++++++++-----
 mcstasscript/interface/instr.py            | 13 ++++++++-
 mcstasscript/tests/test_ComponentReader.py |  4 +--
 mcstasscript/tests/test_Instr.py           |  8 +++---
 mcstasscript/tools/instrument_checker.py   | 23 +++++++++++++++-
 5 files changed, 65 insertions(+), 15 deletions(-)

diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index fbe64d62..5aed6fc6 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -126,29 +126,47 @@ def __init__(self, mcstas_path, input_path="."):
             print("input_path: ", input_directory)
             raise ValueError("Can't find given input_path,"
                              + " directory must exist.")
+
         """
         If components are present both in the McStas install and the
         work directory, the version in the work directory is used. The user
         is informed of this behavior when the instrument object is created.
         """
+
+        self.load_components_from_folder(input_directory, "work directory")
+
+    def load_components_from_folder(self, folder, name, verbose=True):
+        """
+        Loads McStas components from given absolute path
+
+        folder : (str) Path for folder to search for components in
+        name : (str) Used for displaying help messages about these components
+        verbose : (bool) If True, help messages are shown about the process
+        """
+
+        if not os.path.isdir(folder):
+            if verbose:
+                print("Did not find specified folder: " + folder)
+            return
+
         overwritten_components = []
-        for file in os.listdir(input_directory):
+        for file in os.listdir(folder):
             if file.endswith(".comp"):
-                abs_path = os.path.join(input_directory, file)
+                abs_path = os.path.join(folder, file)
                 component_name = os.path.split(abs_path)[1].split(".")[-2]
 
                 if component_name in self.component_path:
                     overwritten_components.append(file)
 
                 self.component_path[component_name] = abs_path
-                self.component_category[component_name] = "work directory"
+                self.component_category[component_name] = name
 
         # Report components found in the work directory and install to the user
-        if len(overwritten_components) > 0:
-            print("The following components are found in the work_directory"
+        if len(overwritten_components) > 0 and verbose:
+            print(f"The following components are found in the {name}"
                   + " / input_path:")
-            for name in overwritten_components:
-                print("    ", name)
+            for component_name in overwritten_components:
+                print("    ", component_name)
 
             print("These definitions will be used instead of the installed "
                   + "versions.")
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index aaccac0f..0c325d20 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2240,7 +2240,7 @@ def set_dependency(self, string):
 
         self.dependency_statement = string
 
-    def add_search(self, statement, SHELL=False):
+    def add_search(self, statement, SHELL=False, help_name=""):
         """
         Adds a search statement to the instrument
 
@@ -2254,9 +2254,13 @@ def add_search(self, statement, SHELL=False):
 
             SHELL : bool (default False)
                 if True, shell keyword is added
+
+            help_name : str
+                Name used in help messages regarding the component search
         """
 
         self.search_statement_list.add_statement(SearchStatement(statement, SHELL=SHELL))
+        self.component_reader.load_components_from_folder(statement, name=help_name)
 
     def clear_search(self):
         """
@@ -2265,6 +2269,13 @@ def clear_search(self):
 
         self.search_statement_list.clear()
 
+        # Reset component_reader
+        self.component_class_lib = {}
+        package_path = self._run_settings["package_path"]
+        run_path = self._run_settings["run_path"]
+        self.component_reader = ComponentReader(package_path,
+                                                input_path=run_path)
+
     def show_search(self):
         """
         Shows all search statements on instrument level
diff --git a/mcstasscript/tests/test_ComponentReader.py b/mcstasscript/tests/test_ComponentReader.py
index d9cc7156..aa348a24 100644
--- a/mcstasscript/tests/test_ComponentReader.py
+++ b/mcstasscript/tests/test_ComponentReader.py
@@ -61,7 +61,7 @@ def test_ComponentReader_init_overwrite_message(self, mock_stdout):
 
         setup_component_reader()
 
-        message = ("The following components are found in the work_directory "
+        message = ("The following components are found in the work directory "
                    + "/ input_path:\n     test_for_reading.comp\n"
                    + "These definitions will be used instead of the "
                    + "installed versions.\n")
@@ -77,7 +77,7 @@ def test_ComponentReader_init_overwrite_message_input(self, mock_stdout):
 
         setup_component_reader_input_path()
 
-        message = ("The following components are found in the work_directory "
+        message = ("The following components are found in the work directory "
                    + "/ input_path:\n     test_for_structure.comp\n"
                    + "These definitions will be used instead of the "
                    + "installed versions.\n")
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 737b1a72..371b932c 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -806,7 +806,7 @@ def test_available_components_simple(self, mock_stdout):
 
         self.assertEqual(output[0],
                          "The following components are found in the "
-                         + "work_directory / input_path:")
+                         + "work directory / input_path:")
         self.assertEqual(output[1], "     test_for_reading.comp")
         self.assertEqual(output[2], "These definitions will be used "
                          + "instead of the installed versions.")
@@ -838,7 +838,7 @@ def test_available_components_folder(self, mock_stdout):
 
         self.assertEqual(output[0],
                          "The following components are found in the "
-                         + "work_directory / input_path:")
+                         + "work directory / input_path:")
         self.assertEqual(output[1], "     test_for_reading.comp")
         self.assertEqual(output[2], "These definitions will be used "
                          + "instead of the installed versions.")
@@ -863,7 +863,7 @@ def test_available_components_input_path_simple(self, mock_stdout):
 
         self.assertEqual(output[0],
                          "The following components are found in the "
-                         + "work_directory / input_path:")
+                         + "work directory / input_path:")
         self.assertEqual(output[1], "     test_for_structure.comp")
         self.assertEqual(output[2], "These definitions will be used "
                          + "instead of the installed versions.")
@@ -890,7 +890,7 @@ def test_available_components_input_path_custom(self, mock_stdout):
 
         self.assertEqual(output[0],
                          "The following components are found in the "
-                         + "work_directory / input_path:")
+                         + "work directory / input_path:")
         self.assertEqual(output[1], "     test_for_structure.comp")
         self.assertEqual(output[2], "These definitions will be used "
                          + "instead of the installed versions.")
diff --git a/mcstasscript/tools/instrument_checker.py b/mcstasscript/tools/instrument_checker.py
index ef174d80..3263c161 100644
--- a/mcstasscript/tools/instrument_checker.py
+++ b/mcstasscript/tools/instrument_checker.py
@@ -1,6 +1,13 @@
+def has_component(instrument, component_name=None, component_type=None):
+    """
+    Checks if given instrument has a component with given name and type
 
+    instrument : McStasScript instrument object
 
-def has_component(instrument, component_name=None, component_type=None):
+    component_name : (str) name of component
+
+    component_type : (str) type of component, for example Arm
+    """
     if component_name is None and component_type is None:
         raise ValueError("Specify component_name, component_type or both.")
 
@@ -22,6 +29,15 @@ def has_component(instrument, component_name=None, component_type=None):
 
 
 def has_parameter(instrument, parameter_name, parameter_type=None):
+    """
+    Checks if instrument have parameter with given name and type
+
+    instrument : McStasScript instrument object
+
+    parameter_name : (str) Name of parameter
+
+    parameter_type : (str) Type of parameter, "" treated as double
+    """
     if parameter_name is None and parameter_type is None:
         raise ValueError("Specify parameter_name, parameter_type or both.")
 
@@ -42,8 +58,13 @@ def has_parameter(instrument, parameter_name, parameter_type=None):
 
     return True
 
+
 def all_parameters_set(instrument):
+    """
+    Checks if all parameters of given instrument have default values
 
+    instrument : McStasScript instrument object
+    """
     parameter_dict = instrument.parameters.parameters
 
     for par_object in parameter_dict.values():

From 6ec43bceec303c753b8ece15f46cf0638b0c75b3 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Fri, 9 Aug 2024 07:28:29 +0200
Subject: [PATCH 380/403] Added system that writes down all final values given
 to components for both parameters, AT and ROTATED.

The system can be used for debugging, may be used for some automatic functions in the future.
---
 mcstasscript/_version.py              |  2 +-
 mcstasscript/data/data.py             | 60 ++++++++++++++++++++++
 mcstasscript/helper/mcstas_objects.py | 71 ++++++++++++++++++++++++++-
 mcstasscript/interface/instr.py       | 41 ++++++++++++++--
 mcstasscript/tests/test_Instr.py      |  3 +-
 5 files changed, 170 insertions(+), 7 deletions(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 2db6156b..541f2a98 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.68"
+__version__ = "0.0.70"
diff --git a/mcstasscript/data/data.py b/mcstasscript/data/data.py
index c4b068ab..c0479884 100644
--- a/mcstasscript/data/data.py
+++ b/mcstasscript/data/data.py
@@ -1,6 +1,7 @@
 import matplotlib.pyplot
 import numpy as np
 import copy
+import re
 
 
 class McStasMetaData:
@@ -967,3 +968,62 @@ def __str__(self):
 
     def __repr__(self):
         return "\n" + self.__str__()
+
+def parse_coordinates(line, keyword):
+    # Extract the coordinates from the line
+    match = re.search(r'\(([^)]+)\)', line)
+    if match:
+        coords = match.group(1).split(',')
+        coords = [float(coord.strip()) for coord in coords]
+        return {f'{keyword}_x': coords[0], f'{keyword}_y': coords[1], f'{keyword}_z': coords[2]}
+    return {}
+
+class ComponentData:
+    def __init__(self, file_path):
+        self.file_path = file_path
+        self.data = None
+
+    def read(self):
+
+        components = {}
+
+        current_component = None
+
+        with open(self.file_path, 'r') as file:
+            for line in file:
+                line = line.strip()
+                if line.startswith('COMPONENT'):
+                    match = re.match(r'COMPONENT (\S+) = (\S+)', line)
+                    if match:
+                        component_name = match.group(1)
+                        component_type = match.group(2)
+                        current_component = {'component': component_type}
+                        components[component_name] = current_component
+                        current_component["parameters"] = {}
+                elif '=' in line:
+                    if current_component is not None:
+                        key, value = line.split('=',1)
+                        try:
+                            value = float(value)
+                        except:
+                            pass
+                        current_component["parameters"][key] = value
+                elif line.startswith('AT'):
+                    if current_component is not None:
+                        current_component.update(parse_coordinates(line, 'AT'))
+                        if line.endswith('ABSOLUTE'):
+                            current_component['AT_relative'] = "ABSOLUTE"
+                        else:
+                            current_component['AT_relative'] = line.split('RELATIVE')[1].strip()
+                elif line.startswith('ROTATED'):
+                    if current_component is not None:
+                        current_component.update(parse_coordinates(line, 'ROTATED'))
+                        if line.endswith('ABSOLUTE'):
+                            current_component['ROTATED_relative'] = "ABSOLUTE"
+                        else:
+                            current_component['ROTATED_relative'] = line.split('RELATIVE')[1].strip()
+
+        self.data = components
+
+        return components
+
diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 850aeb1c..5626d4ad 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -490,7 +490,7 @@ def __init__(self, instance_name, component_name, AT=None,
                  AT_RELATIVE=None, ROTATED=None, ROTATED_RELATIVE=None,
                  RELATIVE=None, WHEN=None, EXTEND=None, GROUP=None,
                  JUMP=None, SPLIT=None, comment=None, c_code_before=None,
-                 c_code_after=None):
+                 c_code_after=None, save_parameters=False):
         """
         Initializes McStas component with specified name and component
 
@@ -564,6 +564,7 @@ def __init__(self, instance_name, component_name, AT=None,
         self.c_code_before = ""
         self.c_code_after = ""
         self.search_statement_list = SearchStatementList()
+        self.save_parameters = save_parameters
 
         # references to component names
         self.AT_reference = None
@@ -589,7 +590,8 @@ def __init__(self, instance_name, component_name, AT=None,
     def set_keyword_input(self, AT=None, AT_RELATIVE=None, ROTATED=None,
                           ROTATED_RELATIVE=None, RELATIVE=None, WHEN=None,
                           EXTEND=None, GROUP=None, JUMP=None, SPLIT=None,
-                          comment=None, c_code_before=None, c_code_after=None):
+                          comment=None, c_code_before=None, c_code_after=None,
+                          save_parameters=None):
         # Allow addition of attributes in init
         self._unfreeze()
 
@@ -966,6 +968,12 @@ def set_c_code_after(self, string):
                                + "given " + str(type(string)))
         self.c_code_after = string
 
+    def set_save_parameters(self, value):
+        if value:
+            self.save_parameters = True
+        else:
+            self.save_parameters = False
+
     def add_search(self, statement, SHELL=False):
         """
         Adds a search statement to the component
@@ -1010,6 +1018,8 @@ def write_component(self, fo):
         # Could use character limit on lines instead
         parameters_written = 0  # internal parameter
 
+        save_parameter_string = ""
+
         if len(self.c_code_before) > 0:
             explanation = "From component named " + self.name
             fo.write("%s // %s\n" % (str(self.c_code_before), explanation))
@@ -1078,10 +1088,14 @@ def write_component(self, fo):
 
         fo.write(" %s\n" % self.AT_relative)
 
+        if self.save_parameters:
+            save_parameter_string += " %s\n" % self.AT_relative
+
         if self.ROTATED_specified:
             fo.write("ROTATED (%s,%s,%s)" % (str(self.ROTATED_data[0]),
                                              str(self.ROTATED_data[1]),
                                              str(self.ROTATED_data[2])))
+
             fo.write(" %s\n" % self.ROTATED_relative)
 
         if not self.GROUP == "":
@@ -1104,6 +1118,59 @@ def write_component(self, fo):
         # Leave a new line between components for readability
         fo.write("\n")
 
+    def make_write_string(self):
+        string = ""
+
+        string += f'fprintf(file, "COMPONENT {self.name} = {self.component_name}\\n");\n'
+
+        component_parameters = {}
+        for key in self.parameter_names:
+            val = getattr(self, key)
+            if val is None:
+                if self.parameter_defaults[key] is None:
+                    raise NameError("Required parameter named "
+                                    + key
+                                    + " in component named "
+                                    + self.name
+                                    + " not set.")
+                else:
+                    continue
+            elif isinstance(val, (Parameter, DeclareVariable)):
+                # Extract the parameter name
+                val = val.name
+
+            component_parameters[key] = val
+
+        for key, val in component_parameters.items():
+            par_type = self.parameter_types[key] # from component reader
+
+            cast = ""
+            if par_type == "" or par_type == "double":
+                type_string = "%lf"
+                cast = "(double)"
+            elif par_type == "int":
+                type_string = "%d"
+                cast = "(int)"
+            elif par_type == "string":
+                type_string = "%s"
+            else:
+                raise ValueError("Unknown parameter type: " + par_type)
+
+            string += f'fprintf(file, "{key}={type_string}\\n", {cast} {val});\n'
+
+        string += f'fprintf(file, "AT (%lf,%lf,%lf) {self.AT_relative}\\n",'
+        string += "(double) " + str(self.AT_data[0]) + ","
+        string += "(double) " + str(self.AT_data[1]) + ","
+        string += "(double) " + str(self.AT_data[2]) + ");\n"
+
+        if self.ROTATED_specified:
+            string += f'fprintf(file, "ROTATED (%lf,%lf,%lf) {self.ROTATED_relative}\\n",'
+            string += "(double) " + str(self.ROTATED_data[0]) + ","
+            string += "(double) " + str(self.ROTATED_data[1]) + ","
+            string += "(double) " + str(self.ROTATED_data[2]) + ");\n"
+
+        return string
+
     def __str__(self):
         """
         Returns string of information about the component
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 0c325d20..a0179c97 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -242,7 +242,8 @@ def __init__(self, name, parameters=None, author=None,
                  increment_folder_name=None, custom_flags=None,
                  executable_path=None, executable=None,
                  suppress_output=None, gravity=None, input_path=None,
-                 package_path=None, checks=None, NeXus=None, openacc=None):
+                 package_path=None, checks=None, NeXus=None,
+                 save_comp_pars=None, openacc=None):
         """
         Initialization of McStas Instrument
 
@@ -296,6 +297,9 @@ def __init__(self, name, parameters=None, author=None,
 
             gravity : bool
                 If True, gravity will be simulated
+
+            save_comp_pars : bool
+                If True, McStas run writes all comp pars to disk
         """
 
         super().__init__(name, input=[],
@@ -406,6 +410,11 @@ def __init__(self, name, parameters=None, author=None,
         if NeXus is not None:
             provided_run_settings["NeXus"] = NeXus
 
+        if save_comp_pars is not None:
+            provided_run_settings["save_comp_pars"] = save_comp_pars
+        else:
+            provided_run_settings["save_comp_pars"] = False
+
         if openacc is not None:
             provided_run_settings["openacc"] = openacc
 
@@ -467,7 +476,7 @@ def init_parameters(self):
 
     def _read_calibration(self):
         """
-        Place holder method that should be overwritten by classes
+        Placeholder method that should be overwritten by classes
         that inherit from McCode_instr.
         """
         pass
@@ -2118,6 +2127,22 @@ def write_full_instrument(self):
         fo.write("%include "generated_includes/"
                   + self.name + "_initialize.c")
         """
+
+        save_parameter_code = ""
+        for component in self.make_component_subset():
+            if component.save_parameters or self._run_settings["save_comp_pars"]:
+                save_parameter_code += component.make_write_string()
+
+        if save_parameter_code != "":
+            fo.write('MPI_MASTER(\n')
+            fo.write('FILE *file = fopen("component_parameters.txt", "w");\n')
+            fo.write('if (file) {\n')
+            fo.write(save_parameter_code)
+            fo.write('} else {\n')
+            fo.write('  perror("fopen");\n')
+            fo.write('}\n')
+            fo.write(')\n')
+
         fo.write("%}\n\n")
 
         # Write trace
@@ -2288,7 +2313,7 @@ def settings(self, ncount=None, mpi="not_set", seed=None,
                  increment_folder_name=None, custom_flags=None,
                  executable=None, executable_path=None,
                  suppress_output=None, gravity=None, checks=None,
-                 openacc=None, NeXus=None):
+                 openacc=None, NeXus=None, save_comp_pars=False):
         """
         Sets settings for McStas run performed with backengine
 
@@ -2325,6 +2350,8 @@ def settings(self, ncount=None, mpi="not_set", seed=None,
                 If True, adds --openacc to mcrun call
             NeXus : bool
                 If True, adds --format=NeXus to mcrun call
+            save_comp_pars : bool
+                If True, McStas run writes all comp pars to disk
         """
 
         settings = {}
@@ -2386,6 +2413,9 @@ def settings(self, ncount=None, mpi="not_set", seed=None,
         if NeXus is not None:
             settings["NeXus"] = bool(NeXus)
 
+        if save_comp_pars is not None:
+            settings["save_comp_pars"] = bool(save_comp_pars)
+
         self._run_settings.update(settings)
 
     def settings_string(self):
@@ -2460,6 +2490,11 @@ def settings_string(self):
             description += "  openacc:".ljust(variable_space)
             description += str(value) + "\n"
 
+        if "save_comp_pars" in self._run_settings:
+            value = self._run_settings["save_comp_pars"]
+            description += "  save_comp_pars:".ljust(variable_space)
+            description += str(value) + "\n"
+
         return description.strip()
 
     def show_settings(self):
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 371b932c..50107c91 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -2123,7 +2123,8 @@ def test_run_backengine_complex_settings(self, mock_sub, mock_stdout):
                            executable_path=executable_path,
                            mpi=5, ncount=19373, openacc=True,
                            NeXus=True, force_compile=False,
-                           seed=300, gravity=True, checks=False)
+                           seed=300, gravity=True, checks=False,
+                           save_comp_pars=True)
             instr.backengine()
 
         expected_path = os.path.join(executable_path, "mcrun")

From 076f3bdd8a03ea8ea68f4c4173ec58089554401e Mon Sep 17 00:00:00 2001
From: Neil Vaytet <neil.vaytet@ess.eu>
Date: Wed, 21 Aug 2024 20:33:15 +0200
Subject: [PATCH 381/403] fix bad escape sequence warning in python 3.11

---
 .../instrument_diagram/component_description.py    | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/mcstasscript/instrument_diagram/component_description.py b/mcstasscript/instrument_diagram/component_description.py
index c186c9f2..ed930186 100644
--- a/mcstasscript/instrument_diagram/component_description.py
+++ b/mcstasscript/instrument_diagram/component_description.py
@@ -19,7 +19,7 @@ class is used as a superclass for classes describing each
     if component.SPLIT != 0:
         string += "SPLIT " + str(component.SPLIT) + " "
     string += "COMPONENT " + str(component.name)
-    string += " = $\\bf{" + str(component.component_name).replace("_", "\_") + "}$\n"
+    string += " = $\\bf{" + str(component.component_name).replace("_", "\\_") + "}$\n"
     for key in component.parameter_names:
         val = getattr(component, key)
         parameter_name = key
@@ -34,12 +34,12 @@ class is used as a superclass for classes describing each
             else:
                 val_string = str(val)
 
-            value = "$\\bf{" + val_string.replace("_", "\_").replace('\"', "''").replace('"', "\''") + "}$"
-            string += "  $\\bf{" + parameter_name.replace("_", "\_") + "}$"
+            value = "$\\bf{" + val_string.replace("_", "\\_").replace('\"', "''").replace('"', "\''") + "}$"
+            string += "  $\\bf{" + parameter_name.replace("_", "\\_") + "}$"
             string += " = " + value + " " + unit + "\n"
         else:
             if component.parameter_defaults[key] is None:
-                string += "  $\\bf{" + parameter_name.replace("_", "\_") + "}$"
+                string += "  $\\bf{" + parameter_name.replace("_", "\\_") + "}$"
                 string += " : $\\bf{Required\ parameter\ not\ yet\ specified}$\n"
 
     if not component.WHEN == "":
@@ -49,14 +49,14 @@ class is used as a superclass for classes describing each
     if component.AT_reference is None:
         string += " $\\it{ABSOLUTE}$\n"
     else:
-        string += " RELATIVE $\\it{" + component.AT_reference.replace("_", "\_") + "}$\n"
+        string += " RELATIVE $\\it{" + component.AT_reference.replace("_", "\\_") + "}$\n"
 
     if component.ROTATED_specified:
         string += "ROTATED " + str(component.ROTATED_data)
         if component.ROTATED_reference is None:
             string += " $\\it{ABSOLUTE}$\n"
         else:
-            string += " $\\it{" + component.ROTATED_reference.replace("_", "\_") + "}$\n"
+            string += " $\\it{" + component.ROTATED_reference.replace("_", "\\_") + "}$\n"
 
     if not component.GROUP == "":
         string += "GROUP " + component.GROUP + "\n"
@@ -68,4 +68,4 @@ class is used as a superclass for classes describing each
     if len(component.c_code_after) > 1:
         string += component.c_code_after + "\n"
 
-    return string.strip()
\ No newline at end of file
+    return string.strip()

From c544634fae19ceea882c5f58122874972ed309c4 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Wed, 11 Sep 2024 11:03:29 +0200
Subject: [PATCH 382/403] Version bump after merging pull request on python
 3.11 compatability.

---
 mcstasscript/_version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 541f2a98..c32f0ac7 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.70"
+__version__ = "0.0.71"

From 9bc85b19edf90bbd967c9a26a5ef4e794e99a7b2 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Fri, 20 Sep 2024 11:13:57 +0200
Subject: [PATCH 383/403] Fixed issues with escape characters.

---
 mcstasscript/instrument_diagnostics/plot_overview.py     | 2 +-
 mcstasscript/instrument_diagram/component_description.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/mcstasscript/instrument_diagnostics/plot_overview.py b/mcstasscript/instrument_diagnostics/plot_overview.py
index 308e394a..e35dbd9b 100644
--- a/mcstasscript/instrument_diagnostics/plot_overview.py
+++ b/mcstasscript/instrument_diagnostics/plot_overview.py
@@ -55,7 +55,7 @@ def plot_all(self, figsize=None, same_scale=True):
 
                 if not major_label_set:
                     ylabel = ax.get_ylabel()
-                    row_name = plotter.name.replace("_", "\ ")
+                    row_name = plotter.name.replace("_", "\\ ")
                     new_label = r"$\bf{" + row_name + "}$" + "\n" + ylabel
                     ax.set_ylabel(new_label)
 
diff --git a/mcstasscript/instrument_diagram/component_description.py b/mcstasscript/instrument_diagram/component_description.py
index ed930186..ff3fc89e 100644
--- a/mcstasscript/instrument_diagram/component_description.py
+++ b/mcstasscript/instrument_diagram/component_description.py
@@ -40,7 +40,7 @@ class is used as a superclass for classes describing each
         else:
             if component.parameter_defaults[key] is None:
                 string += "  $\\bf{" + parameter_name.replace("_", "\\_") + "}$"
-                string += " : $\\bf{Required\ parameter\ not\ yet\ specified}$\n"
+                string += " : $\\bf{Required\\ parameter\\ not\\ yet\\ specified}$\n"
 
     if not component.WHEN == "":
         string += component.WHEN + "\n"

From c8b3c780b01322697b048b3c09603b601b6774ad Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Fri, 20 Sep 2024 12:49:22 +0200
Subject: [PATCH 384/403] Get instrument search object to be written before the
 first component instead of near DEFINE and parameters. This syntax works
 where the others have issues.

---
 mcstasscript/helper/mcstas_objects.py | 6 +++++-
 mcstasscript/interface/instr.py       | 6 ++++--
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index 5626d4ad..6462614f 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -1006,7 +1006,7 @@ def show_search(self):
 
         print(self.search_statement_list)
 
-    def write_component(self, fo):
+    def write_component(self, fo, instrument_search=None):
         """
         Method that writes component to file
 
@@ -1032,6 +1032,10 @@ def write_component(self, fo):
         # Write search statements
         self.search_statement_list.write(fo)
 
+        # Add search statement for instrument if supplied
+        if instrument_search is not None:
+            instrument_search.write(fo)
+
         if self.SPLIT != 0:
             fo.write("SPLIT " + str(self.SPLIT) + " ")
 
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index a0179c97..8fa3aaa5 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2096,7 +2096,6 @@ def write_full_instrument(self):
         fo.write(")\n")
         if self.dependency_statement != "":
             fo.write("DEPENDENCY " + str(self.dependency_statement) + "\n")
-        self.search_statement_list.write(fo)
         fo.write("\n")
 
         # Write declare
@@ -2148,8 +2147,11 @@ def write_full_instrument(self):
         # Write trace
         fo.write("TRACE \n")
 
+        # Write all components, the first should get the instrument search list
+        search_object = copy.deepcopy(self.search_statement_list)
         for component in self.make_component_subset():
-            component.write_component(fo)
+            component.write_component(fo, instrument_search=search_object)
+            search_object = None  # Remove for remaining components
 
         # Write finally
         fo.write("FINALLY \n%{\n")

From 6848eba2837f4e108ad56616d9e6d065289d2f1b Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Fri, 20 Sep 2024 12:50:54 +0200
Subject: [PATCH 385/403] Bump version for SEARCH fix

---
 mcstasscript/_version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index c32f0ac7..a18f70f9 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.71"
+__version__ = "0.0.72"

From 721e4215d08369ba4d0b346ee8c40b8dfbe2bcda Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Fri, 20 Sep 2024 14:30:34 +0200
Subject: [PATCH 386/403] Updated test for new SEARCH location.

---
 mcstasscript/tests/test_Instr.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 50107c91..f6c79877 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1911,8 +1911,6 @@ def test_write_full_instrument_search(self, mock_f):
             my_call(""),
             my_call("\n"),
             my_call(")\n"),
-            my_call('SEARCH "first_search"\n'),
-            my_call('SEARCH SHELL "second search"\n'),
             my_call("\n"),
             my_call("DECLARE \n%{\n"),
             my_call("double two_theta;"),
@@ -1923,6 +1921,8 @@ def test_write_full_instrument_search(self, mock_f):
                     + "two_theta = 2.0*theta;\n"),
             my_call("%}\n\n"),
             my_call("TRACE \n"),
+            my_call('SEARCH "first_search"\n'),
+            my_call('SEARCH SHELL "second search"\n'),
             my_call("COMPONENT first_component = test_for_reading("),
             my_call(")\n"),
             my_call("AT (0,0,0)"),

From b4533083feb72059ef17a89d121745b023ae8028 Mon Sep 17 00:00:00 2001
From: MilanKlausz <Milan.Klausz@ess.eu>
Date: Thu, 17 Oct 2024 14:46:20 +0200
Subject: [PATCH 387/403] fixed typos in Union tutorial docs

---
 .../tutorial/Union_tutorial_1_processes_and_materials.ipynb | 6 +++---
 docs/source/tutorial/Union_tutorial_2_geometry.ipynb        | 2 +-
 docs/source/tutorial/Union_tutorial_4_conditionals.ipynb    | 4 ++--
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb b/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
index cab2003d..7ec8b840 100644
--- a/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
+++ b/docs/source/tutorial/Union_tutorial_1_processes_and_materials.ipynb
@@ -205,7 +205,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let us defined a quick test instrument to see these materials are behaving as expected. First we add a source."
+    "Let us define a quick test instrument to see these materials are behaving as expected. First we add a source."
    ]
   },
   {
@@ -287,7 +287,7 @@
    "metadata": {},
    "source": [
     "## Adding loggers that show scattering and absorption\n",
-    "In order to check the three materials behave as expected, we add spatial loggers for scattering and absorption. These are called loggers and abs_loggers, here is the parameters for a logger."
+    "In order to check the three materials behave as expected, we add spatial loggers for scattering and absorption. These are called loggers and abs_loggers, here are the parameters for a logger."
    ]
   },
   {
@@ -342,7 +342,7 @@
    "metadata": {},
    "source": [
     "## Adding the Union master component\n",
-    "The Union master component is what actually executes the simulation, and so it takes information from all Union components defined before and performs the described simulation. This is the component that matters in terms of order of execution within the sequence of McStas components. As all the previous components have described the what the master component should simulate, it has no required parameters. It also does not matter where it is located in space, as it will grab the locations described by all previous Union components that need a spatial location, such as the geometries and loggers."
+    "The Union master component is what actually executes the simulation, and so it takes information from all Union components defined before and performs the described simulation. This is the component that matters in terms of order of execution within the sequence of McStas components. As all the previous components have described what the master component should simulate, it has no required parameters. It also does not matter where it is located in space, as it will grab the locations described by all previous Union components that need a spatial location, such as the geometries and loggers."
    ]
   },
   {
diff --git a/docs/source/tutorial/Union_tutorial_2_geometry.ipynb b/docs/source/tutorial/Union_tutorial_2_geometry.ipynb
index 62c6751f..9dc4bb6b 100644
--- a/docs/source/tutorial/Union_tutorial_2_geometry.ipynb
+++ b/docs/source/tutorial/Union_tutorial_2_geometry.ipynb
@@ -455,7 +455,7 @@
     "The different layers of the cryostat both result in scattering from the aluminium the beam has to move through, but also some increase intensity where it illuminated by scattering from the sample.\n",
     "\n",
     "### Comparing situation with and without cryostat\n",
-    "It could be interesting to see what difference adding the cryostat did to the measured signal in the banana monitor, here we extract the numpy arrays and plot them manually with matplotlib for at direct comparison. Ensure you run the two simulations with the same wavelength in order for a comparison to be meaningful."
+    "It could be interesting to see what difference adding the cryostat did to the measured signal in the banana monitor, here we extract the numpy arrays and plot them manually with matplotlib for a direct comparison. Ensure you run the two simulations with the same wavelength in order for a comparison to be meaningful."
    ]
   },
   {
diff --git a/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb b/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
index 425e15e4..a69077a8 100644
--- a/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
+++ b/docs/source/tutorial/Union_tutorial_4_conditionals.ipynb
@@ -263,7 +263,7 @@
     "\n",
     "We have two 2DQ loggers recording the scattering vector, one just for the sample and one for the sample environment. On the sample monitor we see that many Bragg peaks scatter some intensity, but 010 and 0-10 have the most intensity, they are at [0.9, -1.5] and [1.5, -0.9]. The two circles are incoherent scattering from the two most common wavevectors, the initial beam and the beam scattered from 010. On the 2DQ logger for the sample environment, we mainly see the Debye-Scherrer cones as lines within the circles defined by the two predominant wavevectors. It seems the used wavelength allows two different Bragg conditions to be met in the aluminium.\n",
     "\n",
-    "The time logger show a surprising amount of complexity. The 5 peaks from entering and exiting two layers and intersecting the sample are clear, but all structure after 0.0012 is a surprise. There is also an unexpected peak at 0.00115, this may be the scattered beam intersecting the outer layer of the cryostat, this happens a bit sooner than the direct beam because the path is shorter when scattered from the front of the sample.\n",
+    "The time logger shows a surprising amount of complexity. The 5 peaks from entering and exiting two layers and intersecting the sample are clear, but all structure after 0.0012 is a surprise. There is also an unexpected peak at 0.00115, this may be the scattered beam intersecting the outer layer of the cryostat, this happens a bit sooner than the direct beam because the path is shorter when scattered from the front of the sample.\n",
     "\n",
     "The time of flight vs 2theta monitor also has a large amount of complexity that is not simple to explain. The bright spot at [0, 0.00155] is the direct beam, while the spot at [60, 0.00155] is the scattered beam. The horizontal line at t=0.00155 must be incoherent scattering from the sample, as it must have had the same distance to all points on the detector. The curved lower branch could be incoherent scattering from where the beam enters the cryostat, as that is closest to the 180 deg point on the detector. The remaining hot spots are all some Debye Scherrer cones from a beam entering or exiting the sample environment, and the more blurry spots may be of even higher order."
    ]
@@ -437,7 +437,7 @@
    "metadata": {},
    "source": [
     "## Using the final weight option\n",
-    "Here we rerun the simulation with the overwrite_logger_weight option in the conditional turned on to see the effect. Without it a ray is recorded in the loggers if it satisfy the conditional, but it does not matter how large the final weight is. For this reason, some rays with high sampling probability to reach the condition but low weight are represented more than is appropriate. This is mainly important when shielding is simulated, as ray that pass through the shielding needs can be heavily overrepresented."
+    "Here we rerun the simulation with the overwrite_logger_weight option in the conditional turned on to see the effect. Without it a ray is recorded in the loggers if it satisfy the conditional, but it does not matter how large the final weight is. For this reason, some rays with high sampling probability to reach the condition but low weight are represented more than is appropriate. This is mainly important when shielding is simulated, as rays that pass through the shielding can be heavily overrepresented."
    ]
   },
   {

From 89293de35ae247f51b5201a0382744b594067fbd Mon Sep 17 00:00:00 2001
From: MilanKlausz <Milan.Klausz@ess.eu>
Date: Fri, 18 Oct 2024 08:45:52 +0200
Subject: [PATCH 388/403] fixed typos in Union tutorials

---
 tutorial/Union_tutorial_1_processes_and_materials.ipynb | 6 +++---
 tutorial/Union_tutorial_2_geometry.ipynb                | 2 +-
 tutorial/Union_tutorial_4_conditionals.ipynb            | 4 ++--
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/tutorial/Union_tutorial_1_processes_and_materials.ipynb b/tutorial/Union_tutorial_1_processes_and_materials.ipynb
index ff3e45db..1d680180 100644
--- a/tutorial/Union_tutorial_1_processes_and_materials.ipynb
+++ b/tutorial/Union_tutorial_1_processes_and_materials.ipynb
@@ -171,7 +171,7 @@
     "| abs_material    | Only has absorption                                              |\n",
     "| powder_material | Has both incoherent and powder process in addition to absorption |\n",
     "\n",
-    "Let us defined a quick test instrument to see these materials are behaving as expected. First we add a source."
+    "Let us define a quick test instrument to see these materials are behaving as expected. First we add a source."
    ]
   },
   {
@@ -237,7 +237,7 @@
    "metadata": {},
    "source": [
     "## Adding loggers that show scattering and absorption\n",
-    "In order to check the three materials behave as expected, we add spatial loggers for scattering and absorption. These are called loggers and abs_loggers, here is the parameters for a logger."
+    "In order to check the three materials behave as expected, we add spatial loggers for scattering and absorption. These are called loggers and abs_loggers, here are the parameters for a logger."
    ]
   },
   {
@@ -292,7 +292,7 @@
    "metadata": {},
    "source": [
     "## Adding the Union master component\n",
-    "The Union master component is what actually executes the simulation, and so it takes information from all Union components defined before and performs the described simulation. This is the component that matters in terms of order of execution within the sequence of McStas components. As all the previous components have described the what the master component should simulate, it has no required parameters. It also does not matter where it is located in space, as it will grab the locations described by all previous Union components that need a spatial location, such as the geometries and loggers."
+    "The Union master component is what actually executes the simulation, and so it takes information from all Union components defined before and performs the described simulation. This is the component that matters in terms of order of execution within the sequence of McStas components. As all the previous components have described what the master component should simulate, it has no required parameters. It also does not matter where it is located in space, as it will grab the locations described by all previous Union components that need a spatial location, such as the geometries and loggers."
    ]
   },
   {
diff --git a/tutorial/Union_tutorial_2_geometry.ipynb b/tutorial/Union_tutorial_2_geometry.ipynb
index 62c6751f..9dc4bb6b 100644
--- a/tutorial/Union_tutorial_2_geometry.ipynb
+++ b/tutorial/Union_tutorial_2_geometry.ipynb
@@ -455,7 +455,7 @@
     "The different layers of the cryostat both result in scattering from the aluminium the beam has to move through, but also some increase intensity where it illuminated by scattering from the sample.\n",
     "\n",
     "### Comparing situation with and without cryostat\n",
-    "It could be interesting to see what difference adding the cryostat did to the measured signal in the banana monitor, here we extract the numpy arrays and plot them manually with matplotlib for at direct comparison. Ensure you run the two simulations with the same wavelength in order for a comparison to be meaningful."
+    "It could be interesting to see what difference adding the cryostat did to the measured signal in the banana monitor, here we extract the numpy arrays and plot them manually with matplotlib for a direct comparison. Ensure you run the two simulations with the same wavelength in order for a comparison to be meaningful."
    ]
   },
   {
diff --git a/tutorial/Union_tutorial_4_conditionals.ipynb b/tutorial/Union_tutorial_4_conditionals.ipynb
index 29edda24..65b35145 100644
--- a/tutorial/Union_tutorial_4_conditionals.ipynb
+++ b/tutorial/Union_tutorial_4_conditionals.ipynb
@@ -265,7 +265,7 @@
     "\n",
     "We have two 2DQ loggers recording the scattering vector, one just for the sample and one for the sample environment. On the sample monitor we see that many Bragg peaks scatter some intensity, but 010 and 0-10 have the most intensity, they are at [0.9, -1.5] and [1.5, -0.9]. The two circles are incoherent scattering from the two most common wavevectors, the initial beam and the beam scattered from 010. On the 2DQ logger for the sample environment, we mainly see the Debye-Scherrer cones as lines within the circles defined by the two predominant wavevectors. It seems the used wavelength allows two different Bragg conditions to be met in the aluminium.\n",
     "\n",
-    "The time logger show a surprising amount of complexity. The 5 peaks from entering and exiting two layers and intersecting the sample are clear, but all structure after 0.0012 is a surprise. There is also an unexpected peak at 0.00115, this may be the scattered beam intersecting the outer layer of the cryostat, this happens a bit sooner than the direct beam because the path is shorter when scattered from the front of the sample.\n",
+    "The time logger shows a surprising amount of complexity. The 5 peaks from entering and exiting two layers and intersecting the sample are clear, but all structure after 0.0012 is a surprise. There is also an unexpected peak at 0.00115, this may be the scattered beam intersecting the outer layer of the cryostat, this happens a bit sooner than the direct beam because the path is shorter when scattered from the front of the sample.\n",
     "\n",
     "The time of flight vs 2theta monitor also has a large amount of complexity that is not simple to explain. The bright spot at [0, 0.00155] is the direct beam, while the spot at [60, 0.00155] is the scattered beam. The horizontal line at t=0.00155 must be incoherent scattering from the sample, as it must have had the same distance to all points on the detector. The curved lower branch could be incoherent scattering from where the beam enters the cryostat, as that is closest to the 180 deg point on the detector. The remaining hot spots are all some Debye Scherrer cones from a beam entering or exiting the sample environment, and the more blurry spots may be of even higher order."
    ]
@@ -439,7 +439,7 @@
    "metadata": {},
    "source": [
     "## Using the final weight option\n",
-    "Here we rerun the simulation with the overwrite_logger_weight option in the conditional turned on to see the effect. Without it a ray is recorded in the loggers if it satisfy the conditional, but it does not matter how large the final weight is. For this reason, some rays with high sampling probability to reach the condition but low weight are represented more than is appropriate. This is mainly important when shielding is simulated, as ray that pass through the shielding needs can be heavily overrepresented."
+    "Here we rerun the simulation with the overwrite_logger_weight option in the conditional turned on to see the effect. Without it a ray is recorded in the loggers if it satisfy the conditional, but it does not matter how large the final weight is. For this reason, some rays with high sampling probability to reach the condition but low weight are represented more than is appropriate. This is mainly important when shielding is simulated, as rays that pass through the shielding can be heavily overrepresented."
    ]
   },
   {

From 8d6c829eed5d2831f8765aeb3644ee63195851ba Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Fri, 8 Nov 2024 14:12:09 +0100
Subject: [PATCH 389/403] Fixed bug where dependency line was read as
 additional parameters in components.

---
 mcstasscript/_version.py                   |  2 +-
 mcstasscript/helper/component_reader.py    |  2 +-
 mcstasscript/tests/test_ComponentReader.py | 50 ++++++++++++++++------
 3 files changed, 40 insertions(+), 14 deletions(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index a18f70f9..8a352f99 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.72"
+__version__ = "0.0.73"
diff --git a/mcstasscript/helper/component_reader.py b/mcstasscript/helper/component_reader.py
index 5aed6fc6..a786538b 100644
--- a/mcstasscript/helper/component_reader.py
+++ b/mcstasscript/helper/component_reader.py
@@ -387,7 +387,7 @@ def read_component_file(self, absolute_path):
                 while True:
                     line = file_o.readline()
 
-                    end_keywords = ("SHARE", "INITIALIZE", "INITIALISE", "DECLARE", "TRACE")
+                    end_keywords = ("SHARE", "INITIALIZE", "INITIALISE", "DECLARE", "TRACE", "DEPENDENCY")
                     if line.strip().upper().startswith(end_keywords) or not line:
                         break
 
diff --git a/mcstasscript/tests/test_ComponentReader.py b/mcstasscript/tests/test_ComponentReader.py
index aa348a24..4f9d9d6e 100644
--- a/mcstasscript/tests/test_ComponentReader.py
+++ b/mcstasscript/tests/test_ComponentReader.py
@@ -93,10 +93,11 @@ def test_ComponentReader_init_filenames(self, mock_stdout):
         component_reader = setup_component_reader()
 
         n_components_found = len(component_reader.component_path)
-        self.assertEqual(n_components_found, 3)
+        self.assertEqual(n_components_found, 4)
         self.assertIn("test_for_reading", component_reader.component_path)
         self.assertIn("test_for_structure", component_reader.component_path)
         self.assertIn("test_for_structure2", component_reader.component_path)
+        self.assertIn("test_for_skipped_sections", component_reader.component_path)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_init_component_paths(self, mock_stdout):
@@ -111,7 +112,7 @@ def test_ComponentReader_init_component_paths(self, mock_stdout):
         component_reader = setup_component_reader()
 
         n_components_found = len(component_reader.component_path)
-        self.assertEqual(n_components_found, 3)
+        self.assertEqual(n_components_found, 4)
 
         expected_path = os.path.join(THIS_DIR, "test_for_reading.comp")
         path = component_reader.component_path["test_for_reading"]
@@ -155,7 +156,7 @@ def test_ComponentReader_init_component_paths_input(self, mock_stdout):
         component_reader = setup_component_reader_input_path()
 
         n_components_found = len(component_reader.component_path)
-        self.assertEqual(n_components_found, 3)
+        self.assertEqual(n_components_found, 4)
 
         self.assertIn("test_for_reading", component_reader.component_path)
 
@@ -194,12 +195,11 @@ def test_ComponentReader_init_categories(self, mock_stdout):
 
         component_reader = setup_component_reader()
 
-        n_categories_found = len(component_reader.component_category)
+        n_categories_found = len(set(component_reader.component_category.values()))
         self.assertEqual(n_categories_found, 3)
         """
-        Categories stored in a dict, so n_categories is the same as the
-        number of components read. Here it happens to be the number of
-        categories as well because of the dummy installation.
+        Categories are the values of the category dict, so n_categories 
+        is the same as the set of the values.
         """
         category = component_reader.component_category["test_for_reading"]
         self.assertEqual(category, "work directory")
@@ -266,7 +266,7 @@ def test_ComponentReader_show_components_short(self, mock_stdout):
         output = mock_stdout.getvalue()
         output = output.split("\n")
 
-        self.assertEqual(len(output), 5)
+        self.assertEqual(len(output), 6)
         self.assertIn(" test_for_structure", output)
         # Check overwritten component is not in the output
         self.assertNotIn(" test_for_reading", output)
@@ -384,9 +384,10 @@ def test_ComponentReader_find_components_names(self, mock_stdout):
         os.chdir(current_work_dir)  # Return to original work directory
 
         n_components_found = len(component_reader.component_path)
-        self.assertEqual(n_components_found, 2)
+        self.assertEqual(n_components_found, 3)
         self.assertIn("test_for_reading", component_reader.component_path)
         self.assertIn("test_for_structure", component_reader.component_path)
+        self.assertIn("test_for_skipped_sections", component_reader.component_path)
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_find_components_categories(self, mock_stdout):
@@ -406,13 +407,15 @@ def test_ComponentReader_find_components_categories(self, mock_stdout):
         component_reader._find_components(dummy_path)
         os.chdir(current_work_dir)  # Return to original work directory
 
-        n_categories_found = len(component_reader.component_category)
-        self.assertEqual(n_categories_found, 2)
+        n_components_found = len(component_reader.component_category)
+        self.assertEqual(n_components_found, 3)
 
         category = component_reader.component_category["test_for_reading"]
         self.assertEqual(category, "misc")
         category = component_reader.component_category["test_for_structure"]
         self.assertEqual(category, "misc")
+        category = component_reader.component_category["test_for_skipped_sections"]
+        self.assertEqual(category, "misc")
 
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_read_component_category(self, mock_stdout):
@@ -487,7 +490,7 @@ def test_ComponentReader_read_component_required(self, mock_stdout):
     @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
     def test_ComponentReader_read_component_int(self, mock_stdout):
         """
-        Test that a integer parameter is read correctly when reading an
+        Test that an integer parameter is read correctly when reading a
         component file.
         Has default, is int type (comments and unit checked already)
         """
@@ -538,6 +541,29 @@ def test_ComponentReader_read_component_string(self, mock_stdout):
         self.assertNotIn("test_string", CompInfo.parameter_units)
         # Have already tested units are read
 
+    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
+    def test_ComponentReader_read_comp_with_skipped_sections(self, mock_stdout):
+        """
+        Read component simply calls read_component_file, but here
+        the output is checked against what is in the dummy file.
+
+        """
+
+        component_reader = setup_component_reader()
+
+        CompInfo = component_reader.read_name("test_for_skipped_sections")
+
+        self.assertEqual(CompInfo.name, "test_for_skipped_sections")
+        self.assertEqual(CompInfo.category, "misc")
+        self.assertEqual(len(CompInfo.parameter_names), 4)
+        self.assertIn("packing_factor", CompInfo.parameter_names)
+        self.assertIn("packing_factor", CompInfo.parameter_defaults)
+        self.assertIn("packing_factor", CompInfo.parameter_types)
+        self.assertEqual(CompInfo.parameter_types["packing_factor"], "double")
+        self.assertIn("packing_factor", CompInfo.parameter_comments)
+        self.assertIn("packing_factor", CompInfo.parameter_units)
+        self.assertEqual(CompInfo.parameter_units["packing_factor"], "1")
+
 
 if __name__ == '__main__':
     unittest.main()

From 2755c777a8b065f34d39cc0ed9892e5881157a9e Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Fri, 8 Nov 2024 14:13:03 +0100
Subject: [PATCH 390/403] Included component made for unit test that lacks
 these sections and has dependency.

---
 .../misc/test_for_skipped_sections.comp       | 184 ++++++++++++++++++
 1 file changed, 184 insertions(+)
 create mode 100644 mcstasscript/tests/dummy_mcstas/misc/test_for_skipped_sections.comp

diff --git a/mcstasscript/tests/dummy_mcstas/misc/test_for_skipped_sections.comp b/mcstasscript/tests/dummy_mcstas/misc/test_for_skipped_sections.comp
new file mode 100644
index 00000000..249b8d59
--- /dev/null
+++ b/mcstasscript/tests/dummy_mcstas/misc/test_for_skipped_sections.comp
@@ -0,0 +1,184 @@
+/*******************************************************************************
+*
+* McStas, neutron ray-tracing package
+*         Copyright 1997-2002, All rights reserved
+*         Risoe National Laboratory, Roskilde, Denmark
+*         Institut Laue Langevin, Grenoble, France
+*
+* Component: Source_simple
+*
+* %I
+* Written by: Kim Lefmann
+* Date: October 30, 1997
+* Modified by: KL, October 4, 2001
+* Modified by: Emmanuel Farhi, October 30, 2001. Serious bug corrected.
+* Origin: Risoe
+*
+* A circular neutron source with flat energy spectrum and arbitrary flux
+*
+* %D
+* The routine is a circular neutron source, which aims at a square target
+* centered at the beam (in order to improve MC-acceptance rate).  The angular
+* divergence is then given by the dimensions of the target.
+* The neutron energy is uniformly distributed between lambda0-dlambda and
+* lambda0+dlambda or between E0-dE and E0+dE.
+* The flux unit is specified in n/cm2/s/st/energy unit (meV or Angs).
+*
+* This component replaces Source_flat, Source_flat_lambda,
+* Source_flux and Source_flux_lambda.
+*
+* Example: Source_simple(radius=0.1, dist=2, focus_xw=.1, focus_yh=.1, E0=14, dE=2)
+*
+* %P
+* radius: [m]                         Radius of circle in (x,y,0) plane where neutrons are generated.
+* yheight: [m]                        Height of rectangle in (x,y,0) plane where neutrons are generated.
+* xwidth: [m]                         Width of rectangle in (x,y,0) plane where neutrons are generated.
+* target_index: [1]                   relative index of component to focus at, e.g. next is +1 this is used to compute 'dist' automatically.
+* dist: [m]                           Distance to target along z axis.
+* focus_xw: [m]                       Width of target
+* focus_yh: [m]                       Height of target
+* E0: [meV]                           Mean energy of neutrons.
+* dE: [meV]                           Energy half spread of neutrons (flat or gaussian sigma).
+* lambda0: [AA]                       Mean wavelength of neutrons.
+* dlambda: [AA]                       Wavelength half spread of neutrons.
+* flux: [1/(s*cm**2*st*energy unit)]  flux per energy unit, Angs or meV if flux=0, the source emits 1 in 4*PI whole space.
+* gauss: [1]                          Gaussian (1) or Flat (0) energy/wavelength distribution
+* packing_factor: [1]                 Comment to be found and read
+*
+* %E
+*******************************************************************************/
+
+DEFINE COMPONENT Source_simple
+SETTING PARAMETERS(string cfg = "", packing_factor=1, interact_fraction=-1, string init="init")
+DEPENDENCY "-Wl,-rpath,CMD(ncrystal-config --show libdir) -Wl,CMD(ncrystal-config --show libpath) -ICMD(ncrystal-config --show includedir)"
+
+DECLARE
+%{
+double pmul, srcArea;
+int square;
+double tx,ty,tz;
+%}
+INITIALIZE
+%{
+square = 0;
+/* Determine source area */
+if (radius && !yheight && !xwidth ) {
+    square = 0;
+    srcArea = PI*radius*radius;
+  } else if(yheight && xwidth) {
+    square = 1;
+    srcArea = xwidth * yheight;
+  }
+
+  if (flux) {
+    pmul=flux*1e4*srcArea/mcget_ncount();
+    if (dlambda)
+      pmul *= 2*dlambda;
+    else if (dE)
+      pmul *= 2*dE;
+  } else {
+    gauss = 0;
+    pmul=1.0/(mcget_ncount()*4*PI);
+  }
+
+  if (target_index && !dist)
+  {
+    Coords ToTarget;
+    ToTarget = coords_sub(POS_A_COMP_INDEX(INDEX_CURRENT_COMP+target_index),POS_A_CURRENT_COMP);
+    ToTarget = rot_apply(ROT_A_CURRENT_COMP, ToTarget);
+    coords_get(ToTarget, &tx, &ty, &tz);
+    dist=sqrt(tx*tx+ty*ty+tz*tz);
+  } else if (dist) {
+    tx = 0;
+    ty = 0;
+    tz = dist;
+  }
+
+
+  if (srcArea <= 0) {
+    printf("Source_simple: %s: Source area is <= 0 !\n ERROR - Exiting\n",
+           NAME_CURRENT_COMP);
+    exit(0);
+  }
+  if (dist <= 0 || focus_xw <= 0 || focus_yh <= 0) {
+    printf("Source_simple: %s: Target area unmeaningful! (negative dist / focus_xw / focus_yh)\n ERROR - Exiting\n",
+           NAME_CURRENT_COMP);
+    exit(0);
+  }
+
+  if ((!lambda0 && !E0 && !dE && !dlambda)) {
+    printf("Source_simple: %s: You must specify either a wavelength or energy range!\n ERROR - Exiting\n",
+           NAME_CURRENT_COMP);
+    exit(0);
+  }
+  if ((!lambda0 && !dlambda && (E0 <= 0 || dE < 0 || E0-dE <= 0))
+    || (!E0 && !dE && (lambda0 <= 0 || dlambda < 0 || lambda0-dlambda <= 0))) {
+    printf("Source_simple: %s: Unmeaningful definition of wavelength or energy range!\n ERROR - Exiting\n",
+           NAME_CURRENT_COMP);
+      exit(0);
+  }
+%}
+TRACE
+%{
+ double chi,E,lambda,v,r, xf, yf, rf, dx, dy, pdir;
+
+ t=0;
+ z=0;
+
+ if (square == 1) {
+   x = xwidth * (rand01() - 0.5);
+   y = yheight * (rand01() - 0.5);
+ } else {
+   chi=2*PI*rand01();                          /* Choose point on source */
+   r=sqrt(rand01())*radius;                    /* with uniform distribution. */
+   x=r*cos(chi);
+   y=r*sin(chi);
+ }
+ randvec_target_rect_real(&xf, &yf, &rf, &pdir,
+			  tx, ty, tz, focus_xw, focus_yh, ROT_A_CURRENT_COMP, x, y, z, 2);
+
+ dx = xf-x;
+ dy = yf-y;
+ rf = sqrt(dx*dx+dy*dy+rf*rf);
+
+ p = pdir*pmul;
+
+ if(lambda0==0) {
+   if (!gauss) {
+     E=E0+dE*randpm1();              /*  Choose from uniform distribution */
+   } else {
+     E=E0+randnorm()*dE;
+   }
+   v=sqrt(E)*SE2V;
+ } else {
+   if (!gauss) {
+     lambda=lambda0+dlambda*randpm1();
+   } else {
+     lambda=lambda0+randnorm()*dlambda;
+   }
+   v = K2V*(2*PI/lambda);
+ }
+
+ vz=v*dist/rf;
+ vy=v*dy/rf;
+ vx=v*dx/rf;
+%}
+
+MCDISPLAY
+%{
+  if (square == 1) {
+    
+    rectangle("xy",0,0,0,xwidth,yheight);
+  } else {
+    
+    circle("xy",0,0,0,radius);
+  }
+  if (dist) {
+    dashed_line(0,0,0, -focus_xw/2+tx,-focus_yh/2+ty,tz, 4);
+    dashed_line(0,0,0,  focus_xw/2+tx,-focus_yh/2+ty,tz, 4);
+    dashed_line(0,0,0,  focus_xw/2+tx, focus_yh/2+ty,tz, 4);
+    dashed_line(0,0,0, -focus_xw/2+tx, focus_yh/2+ty,tz, 4);
+  }
+%}
+
+END

From 046ca8170ab3c597834e510c1baed1741780ef5d Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Wed, 2 Apr 2025 12:42:14 +0200
Subject: [PATCH 391/403] Added handling of damaged utf-8 strings and EQ for
 search statements.

---
 mcstasscript/helper/managed_mcrun.py    |  6 +++++-
 mcstasscript/helper/search_statement.py | 15 +++++++++++++++
 2 files changed, 20 insertions(+), 1 deletion(-)

diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 112f87fe..8e951b4d 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -540,7 +540,11 @@ def load_metadata_nexus(data_folder_name, filename="mccode.h5"):
 def decode_dict(dictionary):
     for key, value in dictionary.items():
         if isinstance(value, bytes):
-            dictionary[key] = value.decode('utf-8')
+            try:
+                dictionary[key] = value.decode('utf-8')
+            except:
+                # Investigate cases where this fail when reading from nexus
+                dictionary[key] = value.decode('utf-8', errors='replace')  # Replaces invalid bytes with '?'
 
     return dictionary
 
diff --git a/mcstasscript/helper/search_statement.py b/mcstasscript/helper/search_statement.py
index e9fe6381..155ad6db 100644
--- a/mcstasscript/helper/search_statement.py
+++ b/mcstasscript/helper/search_statement.py
@@ -33,6 +33,10 @@ def __repr__(self):
         """
         return self.make_string()
 
+    def __EQ__(self, other):
+        if self.statement == other.statement and self.SHELL == other.SHELL:
+            return True
+
 
 class SearchStatementList:
     def __init__(self):
@@ -73,6 +77,17 @@ def make_string(self):
 
         return string
 
+    def __EQ__(self, other):
+        print("in list EQ")
+        if len(self.statements) != len(other.statements):
+            return False
+
+        for index, statement in enumerate(self.statements):
+            if statement != other.statements[index]:
+                return False
+
+        return True
+
     def __repr__(self):
         """
         Show search statements with print

From caf273ec67db36e074444744fb85cf69c2d617bb Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Wed, 2 Apr 2025 13:11:55 +0200
Subject: [PATCH 392/403] Change to how NeXus data is read.

Before the nexus file would be opened and closed for each monitor
to allow using the same code when either one or all monitors were
loaded. This may have caused issues on VISA.

Now the file is opened once and all data read when the user wants
all monitors, and it is handdled differently when just one monitor
is loaded, leading to slighlty more complex code, but hopefully
fix the issue with mulitple readers on VISA.
---
 mcstasscript/_version.py             |   2 +-
 mcstasscript/helper/managed_mcrun.py | 303 +++++++++++++++------------
 2 files changed, 165 insertions(+), 140 deletions(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 8a352f99..e1a95f5f 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.73"
+__version__ = "0.0.74"
diff --git a/mcstasscript/helper/managed_mcrun.py b/mcstasscript/helper/managed_mcrun.py
index 8e951b4d..263e4fc4 100644
--- a/mcstasscript/helper/managed_mcrun.py
+++ b/mcstasscript/helper/managed_mcrun.py
@@ -343,13 +343,42 @@ def load_results(data_folder_name):
 
     """
 
-    metadata_list = load_metadata(data_folder_name)
+    if not os.path.isdir(data_folder_name):
+        raise NameError("Given data directory does not exist.")
+
+    # Find all data files in generated folder
+    files_in_folder = os.listdir(data_folder_name)
 
-    results = []
-    for metadata in metadata_list:
-        result = load_monitor(metadata, data_folder_name)
-        result.set_data_location(data_folder_name)
-        results.append(result)
+    # Raise an error if mccode.sim is not available
+    if "mccode.sim" in files_in_folder:
+        NeXus = False
+    elif "mccode.h5" in files_in_folder:
+        NeXus = True
+    else:
+        raise NameError("No mccode.sim or mccode.h5 in data folder.")
+
+    if NeXus:
+        # Open mccode to read metadata for all datasets written to disk
+        with h5py.File(os.path.join(data_folder_name, "mccode.h5"), "r", swmr=True) as f:
+
+            # Pass file object to all functions to avoid multiple open / close
+            metadata_list = load_metadata_nexus(f)
+
+            results = []
+            for metadata in metadata_list:
+                result = load_monitor_nexus(metadata, f)
+                result.set_data_location(data_folder_name)
+                results.append(result)
+
+    else:
+        # Older workflow, still handles both text and NeXus
+        metadata_list = load_metadata(data_folder_name)
+
+        results = []
+        for metadata in metadata_list:
+            result = load_monitor(metadata, data_folder_name)
+            result.set_data_location(data_folder_name)
+            results.append(result)
 
     return results
 
@@ -377,7 +406,8 @@ def load_metadata(data_folder_name):
     if "mccode.sim" in files_in_folder:
         return load_metadata_text(data_folder_name)
     elif "mccode.h5" in files_in_folder:
-        return load_metadata_nexus(data_folder_name)
+        with h5py.File(os.path.join(data_folder_name, "mccode.h5"), "r", swmr=True) as f:
+            return load_metadata_nexus(f)
     else:
         raise NameError("No mccode.sim or mccode.h5 in data folder.")
 
@@ -474,67 +504,66 @@ def load_metadata_text(data_folder_name):
     return metadata_list
 
 
-def load_metadata_nexus(data_folder_name, filename="mccode.h5"):
+def load_metadata_nexus(file_object):
     instrument_parameters = {}
 
-    # Open mccode to read metadata for all datasets written to disk
-    with h5py.File(os.path.join(data_folder_name, filename), "r") as f:
+    f = file_object
 
-        if "entry1" not in list(f.keys()):
-            raise ValueError("h5 file not formatted as expected.")
+    if "entry1" not in list(f.keys()):
+        raise ValueError("h5 file not formatted as expected.")
 
-        if "data" not in list(f["entry1"].keys()):
-            raise ValueError("h5 file not formatted as expected.")
+    if "data" not in list(f["entry1"].keys()):
+        raise ValueError("h5 file not formatted as expected.")
 
-        if "simulation" not in list(f["entry1"].keys()):
-            raise ValueError("h5 file not formatted as expected.")
+    if "simulation" not in list(f["entry1"].keys()):
+        raise ValueError("h5 file not formatted as expected.")
 
-        if "Param" not in list(f["entry1"]["simulation"].keys()):
-            raise ValueError("h5 file not formatted as expected.")
+    if "Param" not in list(f["entry1"]["simulation"].keys()):
+        raise ValueError("h5 file not formatted as expected.")
 
-        # Common information
+    # Common information
 
-        # Instrument parameters
-        instrument_parameters = {}
-        loaded_par_dict = f["entry1"]["simulation"]["Param"].attrs
-        for par_name in loaded_par_dict:
-            if par_name == "NX_class":
-                continue
+    # Instrument parameters
+    instrument_parameters = {}
+    loaded_par_dict = f["entry1"]["simulation"]["Param"].attrs
+    for par_name in loaded_par_dict:
+        if par_name == "NX_class":
+            continue
 
-            try:
-                value = float(loaded_par_dict[par_name])
-            except:
-                value = str(loaded_par_dict[par_name])
+        try:
+            value = float(loaded_par_dict[par_name])
+        except:
+            value = str(loaded_par_dict[par_name])
 
-            instrument_parameters[par_name] = value
+        instrument_parameters[par_name] = value
 
-        metadata_list = []
+    metadata_list = []
 
-        # For each entry in data, make a metadata object
-        for key in f["entry1"]["data"].keys():
+    # For each entry in data, make a metadata object
+    for key in f["entry1"]["data"].keys():
 
-            # Make the metadata object and add instrument parameters
-            metadata = McStasMetaData()
-            metadata.add_info("Parameters", instrument_parameters)
+        # Make the metadata object and add instrument parameters
+        metadata = McStasMetaData()
+        metadata.add_info("Parameters", instrument_parameters)
 
-            # Add NeXus field name
-            metadata.add_info("NeXus_field", key)
+        # Add NeXus field name
+        metadata.add_info("NeXus_field", key)
 
-            # Add all the read info from attribute section
-            info = dict(f["entry1"]["data"][key].attrs)
-            info = decode_dict(info)
-            for name, value in info.items():
-                if isinstance(value, bytes):
-                    value = value.decode('utf-8')
+        # Add all the read info from attribute section
+        info = dict(f["entry1"]["data"][key].attrs)
+        info = decode_dict(info)
+        for name, value in info.items():
+            if isinstance(value, bytes):
+                value = value.decode('utf-8')
 
-                metadata.add_info(name, value)
+            metadata.add_info(name, value)
 
-            metadata_list.append(metadata)
+        metadata_list.append(metadata)
 
-            # Now all info is added, extract info loads it into nice attributes
-            metadata.extract_info()
+        # Now all info is added, extract info loads it into nice attributes
+        metadata.extract_info()
 
-        return metadata_list
+    return metadata_list
 
 
 def decode_dict(dictionary):
@@ -555,12 +584,13 @@ def load_monitor(metadata, data_folder_name):
     """
 
     if "NeXus_field" in metadata.info:
-        return load_monitor_nexus(metadata, data_folder_name)
+        with h5py.File(os.path.join(data_folder_name, "mccode.h5"), "r", swmr=True) as f:
+            return load_monitor_nexus(metadata, f)
     else:
         return load_monitor_text(metadata, data_folder_name)
 
 
-def load_monitor_nexus(metadata, data_folder_name, filename="mccode.h5"):
+def load_monitor_nexus(metadata, file_object):
     """
     Function that loads data given metadata and name of data folder
     This version is for a nexus file
@@ -573,124 +603,119 @@ def load_monitor_nexus(metadata, data_folder_name, filename="mccode.h5"):
     metadata : McStasMetaData object
         McStasMetaData object corresponding to the monitor to be loaded
 
-    data_folder_name : str
-        path to folder from which metadata should be loaded
-
-    filename : str
-        Name of NeXus file
+    file_object : h5py file object in read mode
     """
 
-    # Open mccode to read metadata for all datasets written to disk
-    with h5py.File(os.path.join(data_folder_name, filename), "r") as f:
+    f = file_object
 
-        if "entry1" not in list(f.keys()):
-            raise ValueError("h5 file not formatted as expected.")
+    if "entry1" not in list(f.keys()):
+        raise ValueError("h5 file not formatted as expected.")
 
-        if "data" not in list(f["entry1"].keys()):
-            raise ValueError("h5 file not formatted as expected.")
+    if "data" not in list(f["entry1"].keys()):
+        raise ValueError("h5 file not formatted as expected.")
 
-        if "simulation" not in list(f["entry1"].keys()):
-            raise ValueError("h5 file not formatted as expected.")
+    if "simulation" not in list(f["entry1"].keys()):
+        raise ValueError("h5 file not formatted as expected.")
 
-        if "Param" not in list(f["entry1"]["simulation"].keys()):
-            raise ValueError("h5 file not formatted as expected.")
+    if "Param" not in list(f["entry1"]["simulation"].keys()):
+        raise ValueError("h5 file not formatted as expected.")
 
-        NeXus_field = metadata.info["NeXus_field"]
+    NeXus_field = metadata.info["NeXus_field"]
 
-        available_fields = f["entry1"]["data"][NeXus_field].keys()
-        if not metadata.dimension == 0 and "events" not in available_fields:
-            if "data" not in available_fields:
-                raise ValueError("NeXus reading: data not found! \n"
-                                 + "Monitor metadata:\n" + str(metadata))
+    available_fields = f["entry1"]["data"][NeXus_field].keys()
+    if not metadata.dimension == 0 and "events" not in available_fields:
+        if "data" not in available_fields:
+            raise ValueError("NeXus reading: data not found! \n"
+                             + "Monitor metadata:\n" + str(metadata))
 
-            if "errors" not in available_fields:
-                raise ValueError("NeXus reading: errors not found! \n"
-                                 + "Monitor metadata:\n" + str(metadata))
+        if "errors" not in available_fields:
+            raise ValueError("NeXus reading: errors not found! \n"
+                             + "Monitor metadata:\n" + str(metadata))
 
-            if "ncount" not in available_fields:
-                raise ValueError("NeXus reading: ncount not found! \n"
-                                 + "Monitor metadata:\n" + str(metadata))
+        if "ncount" not in available_fields:
+            raise ValueError("NeXus reading: ncount not found! \n"
+                             + "Monitor metadata:\n" + str(metadata))
 
-        # Need to check if it is binned data or event data
-        if "events" in available_fields:
-            Events = np.array(f["entry1"]["data"][NeXus_field]["events"])
-            return McStasDataEvent(metadata, Events)
+    # Need to check if it is binned data or event data
+    if "events" in available_fields:
+        Events = np.array(f["entry1"]["data"][NeXus_field]["events"])
+        return McStasDataEvent(metadata, Events)
 
-        # Split data into intensity, error and ncount
-        if type(metadata.dimension) == int and metadata.dimension == 0:
+    # Split data into intensity, error and ncount
+    if type(metadata.dimension) == int and metadata.dimension == 0:
 
-            if "data" in f["entry1"]["data"][NeXus_field].keys():
-                raise ValueError("Found array data in 0D dataset?")
+        if "data" in f["entry1"]["data"][NeXus_field].keys():
+            raise ValueError("Found array data in 0D dataset?")
 
-            values = None
-            if "values" in f["entry1"]["data"][NeXus_field].keys():
-                values = np.array(f["entry1"]["data"][NeXus_field]["values"])
+        values = None
+        if "values" in f["entry1"]["data"][NeXus_field].keys():
+            values = np.array(f["entry1"]["data"][NeXus_field]["values"])
 
-            if metadata.total_I is None:
-                if values is not None:
-                    Intensity = np.array(values[0])
-                else:
-                    raise ValueError("No info on this monitor can be found "
-                                     + "in reading of NeXus file "
-                                     + str(metadata))
+        if metadata.total_I is None:
+            if values is not None:
+                Intensity = np.array(values[0])
             else:
-                Intensity = np.array(metadata.total_I)
+                raise ValueError("No info on this monitor can be found "
+                                 + "in reading of NeXus file "
+                                 + str(metadata))
+        else:
+            Intensity = np.array(metadata.total_I)
 
-            if metadata.total_E is None:
-                if values is not None:
-                    Error = np.array(values[2])
-                else:
-                    Error = np.zeros(1)
+        if metadata.total_E is None:
+            if values is not None:
+                Error = np.array(values[2])
             else:
-                Error = np.array(metadata.total_E)
+                Error = np.zeros(1)
+        else:
+            Error = np.array(metadata.total_E)
 
-            if metadata.total_N is None:
-                if values is not None:
-                    Ncount = np.array(values[3])
-                else:
-                    Ncount = np.zeros(1)
+        if metadata.total_N is None:
+            if values is not None:
+                Ncount = np.array(values[3])
             else:
-                Ncount = np.array(metadata.total_N)
+                Ncount = np.zeros(1)
+        else:
+            Ncount = np.array(metadata.total_N)
 
-            return McStasDataBinned(metadata, Intensity, Error, Ncount)
+        return McStasDataBinned(metadata, Intensity, Error, Ncount)
 
-        elif type(metadata.dimension) == int and metadata.dimension != 0:
+    elif type(metadata.dimension) == int and metadata.dimension != 0:
 
-            original_xlabel = metadata.info["xlabel"]
+        original_xlabel = metadata.info["xlabel"]
 
-            # All special characters are substituted with _ in McStas NeXus file
-            x_field = re.sub(r'[^a-zA-Z]', "_", original_xlabel)
+        # All special characters are substituted with _ in McStas NeXus file
+        x_field = re.sub(r'[^a-zA-Z]', "_", original_xlabel)
 
-            if x_field not in f["entry1"]["data"][NeXus_field].keys():
-                error_text = ("Didn't find xaxis in NeXus file. \n"
-                              + "Expected this field for x axis: "
-                              + str(x_field) + "\n"
-                              + "Existing fields: "
-                              + str(f["entry1"]["data"][NeXus_field].keys()))
+        if x_field not in f["entry1"]["data"][NeXus_field].keys():
+            error_text = ("Didn't find xaxis in NeXus file. \n"
+                          + "Expected this field for x axis: "
+                          + str(x_field) + "\n"
+                          + "Existing fields: "
+                          + str(f["entry1"]["data"][NeXus_field].keys()))
 
-                raise ValueError(error_text)
+            raise ValueError(error_text)
 
-            xaxis = np.array(f["entry1"]["data"][NeXus_field][x_field])
-            Intensity = np.array(f["entry1"]["data"][NeXus_field]["data"])
-            Error = np.array(f["entry1"]["data"][NeXus_field]["errors"])
-            Ncount = np.array(f["entry1"]["data"][NeXus_field]["ncount"])
+        xaxis = np.array(f["entry1"]["data"][NeXus_field][x_field])
+        Intensity = np.array(f["entry1"]["data"][NeXus_field]["data"])
+        Error = np.array(f["entry1"]["data"][NeXus_field]["errors"])
+        Ncount = np.array(f["entry1"]["data"][NeXus_field]["ncount"])
 
-            # The data is saved as a McStasDataBinned object
-            return McStasDataBinned(metadata, Intensity, Error, Ncount, xaxis=xaxis)
+        # The data is saved as a McStasDataBinned object
+        return McStasDataBinned(metadata, Intensity, Error, Ncount, xaxis=xaxis)
 
-        elif len(metadata.dimension) == 2:
-            xaxis = []  # Assume evenly binned in 2d
-            Intensity = np.array(f["entry1"]["data"][NeXus_field]["data"]).T
-            Error = np.array(f["entry1"]["data"][NeXus_field]["errors"]).T
-            Ncount = np.array(f["entry1"]["data"][NeXus_field]["ncount"]).T
+    elif len(metadata.dimension) == 2:
+        xaxis = []  # Assume evenly binned in 2d
+        Intensity = np.array(f["entry1"]["data"][NeXus_field]["data"]).T
+        Error = np.array(f["entry1"]["data"][NeXus_field]["errors"]).T
+        Ncount = np.array(f["entry1"]["data"][NeXus_field]["ncount"]).T
 
-            # The data is saved as a McStasDataBinned object
-            return McStasDataBinned(metadata, Intensity, Error, Ncount, xaxis=xaxis)
-        else:
-            raise NameError(
-                "Dimension not read correctly in data set "
-                + "connected to monitor named "
-                + metadata.component_name)
+        # The data is saved as a McStasDataBinned object
+        return McStasDataBinned(metadata, Intensity, Error, Ncount, xaxis=xaxis)
+    else:
+        raise NameError(
+            "Dimension not read correctly in data set "
+            + "connected to monitor named "
+            + metadata.component_name)
 
 
 def load_monitor_text(metadata, data_folder_name):

From 79fa259c4e644f8efb561bfc4e57e52b28c7ab88 Mon Sep 17 00:00:00 2001
From: Peter Willendrup <pkwi@fysik.dtu.dk>
Date: Tue, 12 Aug 2025 12:29:03 +0200
Subject: [PATCH 393/403] Let McStasScript default to use of webgl-classic vis.
 Will function from notebooks, provided McStas 3.5.35 is used.

---
 mcstasscript/interface/instr.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 8fa3aaa5..0cbb2e5d 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2654,7 +2654,7 @@ def run_full_instrument(self, **kwargs):
 
         return self.backengine()
 
-    def show_instrument(self, format="webgl", width=800, height=450, new_tab=False):
+    def show_instrument(self, format="webgl-classic", width=800, height=450, new_tab=False):
         """
         Uses mcdisplay to show the instrument in web browser
 
@@ -2664,13 +2664,13 @@ def show_instrument(self, format="webgl", width=800, height=450, new_tab=False):
         Keyword arguments
         -----------------
             format : str
-                'webgl' or 'window' format for display
+                'webgl' (currently broken), 'webgl-classic' or 'window' format for display
             width : int
                 width of IFrame if used in notebook
             height : int
                 height of IFrame if used in notebook
             new_tab : bool
-                Open webgl in new browser tab
+                Open webgl/webgl-classic in new browser tab
         """
 
         parameters = {}
@@ -2696,6 +2696,8 @@ def show_instrument(self, format="webgl", width=800, height=450, new_tab=False):
 
         if format == "webgl":
             executable = executable+"-webgl"
+        elif format == "webgl-classic":
+            executable = executable+"-webgl-classic"
         elif format == "window":
             executable = executable+"-pyqtgraph"
 
@@ -2729,7 +2731,7 @@ def show_instrument(self, format="webgl", width=800, height=450, new_tab=False):
             is_notebook = False
 
         options = ""
-        if is_notebook and executable == "mcdisplay-webgl" and not new_tab:
+        if is_notebook and "webgl" in executable and not new_tab:
             options += "--nobrowse "
 
         full_command = ('"' + bin_path + '" '
@@ -2744,7 +2746,7 @@ def show_instrument(self, format="webgl", width=800, height=450, new_tab=False):
                                  universal_newlines=True,
                                  cwd=self.input_path)
 
-        if not is_notebook or executable != "mcdisplay-webgl":
+        if not is_notebook or "webgl" not in executable:
             print(process.stderr)
             print(process.stdout)
             return

From 310b6d809e24d29e8509113ca9f221d546faf9de Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Tue, 19 Aug 2025 10:52:54 +0200
Subject: [PATCH 394/403] Update default instrument visualization to
 webgl-classic

---
 mcstasscript/_version.py          | 2 +-
 mcstasscript/interface/instr.py   | 3 +++
 mcstasscript/interface/plotter.py | 2 ++
 mcstasscript/tests/test_Instr.py  | 2 +-
 4 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index e1a95f5f..7715123d 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.74"
+__version__ = "0.0.75"
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 0cbb2e5d..37063625 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2700,6 +2700,9 @@ def show_instrument(self, format="webgl-classic", width=800, height=450, new_tab
             executable = executable+"-webgl-classic"
         elif format == "window":
             executable = executable+"-pyqtgraph"
+        else:
+            raise ValueError(f"Did not recognize given format '{format}', "
+                             f"must be webgl-classic, webgl or window.")
 
         # Platform dependent, check both package_path and bin
         executable_path = self._run_settings["executable_path"]
diff --git a/mcstasscript/interface/plotter.py b/mcstasscript/interface/plotter.py
index 9552ae63..9a614815 100644
--- a/mcstasscript/interface/plotter.py
+++ b/mcstasscript/interface/plotter.py
@@ -186,6 +186,8 @@ def animate_2D(index):
         data = data_list[index]
         intensity = data.Intensity
 
+        ax0.set_title(data.metadata.title)
+
         im.set_array(intensity.ravel())
         return im,
 
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index f6c79877..d3cc240f 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -2317,7 +2317,7 @@ def test_show_instrument_basic(self, mock_sub, mock_stdout):
 
         os.chdir(current_work_dir)
 
-        expected_path = os.path.join(executable_path, "bin", "mcdisplay-webgl")
+        expected_path = os.path.join(executable_path, "bin", "mcdisplay-webgl-classic")
         expected_path = '"' + expected_path + '"'
         expected_instr_path = os.path.join(THIS_DIR, "test_instrument.instr")
 

From 358cba928c251864201ea24de071f6c16882e304 Mon Sep 17 00:00:00 2001
From: Peter Willendrup <pkwi@fysik.dtu.dk>
Date: Wed, 8 Oct 2025 15:40:18 +0200
Subject: [PATCH 395/403] Add ':' consistently in Instrument: name header
 fields, prompt user to fill in meaningful information.

---
 mcstasscript/interface/instr.py                     | 9 ++++++++-
 mcstasscript/tests/test_Instr.py                    | 6 +++---
 mcstasscript/tests/test_data_set/jupyter_demo.instr | 2 +-
 mcstasscript/tests/test_instrument.instr            | 2 +-
 4 files changed, 13 insertions(+), 6 deletions(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 37063625..afa214b4 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2068,7 +2068,7 @@ def write_full_instrument(self):
         fo.write("* European Spallation Source Data Management and \n")
         fo.write("* Software Centre\n")
         fo.write("* \n")
-        fo.write("* Instrument %s\n" % self.name)
+        fo.write("* Instrument: %s\n" % self.name)
         fo.write("* \n")
         fo.write("* %Identification\n")  # Could allow the user to insert this
         fo.write("* Written by: %s\n" % self.author)
@@ -2077,7 +2077,14 @@ def write_full_instrument(self):
         fo.write("* Origin: %s\n" % self.origin)
         fo.write("* %INSTRUMENT_SITE: Generated_instruments\n")
         fo.write("* \n")
+        fo.write("* !!Please write a short instrument description (1 line) here!!\n")
         fo.write("* \n")
+        fo.write("* %Description\n")
+        fo.write("* Please write a longer instrument description here\n.")
+        fo.write("* \n")
+        fo.write("* \n")
+        fo.write("* Please fill in a parameter table below, formatted like this:")
+        fo.write("* parameter: [unit] Descriptive string\n")
         fo.write("* %Parameters\n")
         # Add description of parameters here
         fo.write("* \n")
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index d3cc240f..3d69dea4 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -1693,7 +1693,7 @@ def test_write_full_instrument_simple(self, mock_datetime, mock_f):
          my_call("* European Spallation Source Data Management and \n"),
          my_call("* Software Centre\n"),
          my_call("* \n"),
-         my_call("* Instrument test_instrument\n"),
+         my_call("* Instrument: test_instrument\n"),
          my_call("* \n"),
          my_call("* %Identification\n"),
          my_call("* Written by: Python McStas Instrument Generator\n"),
@@ -1788,7 +1788,7 @@ def test_write_full_instrument_dependency(self, mock_f):
             my_call("* European Spallation Source Data Management and \n"),
             my_call("* Software Centre\n"),
             my_call("* \n"),
-            my_call("* Instrument test_instrument\n"),
+            my_call("* Instrument:test_instrument\n"),
             my_call("* \n"),
             my_call("* %Identification\n"),
             my_call("* Written by: Python McStas Instrument Generator\n"),
@@ -1885,7 +1885,7 @@ def test_write_full_instrument_search(self, mock_f):
             my_call("* European Spallation Source Data Management and \n"),
             my_call("* Software Centre\n"),
             my_call("* \n"),
-            my_call("* Instrument test_instrument\n"),
+            my_call("* Instrument:test_instrument\n"),
             my_call("* \n"),
             my_call("* %Identification\n"),
             my_call("* Written by: Python McStas Instrument Generator\n"),
diff --git a/mcstasscript/tests/test_data_set/jupyter_demo.instr b/mcstasscript/tests/test_data_set/jupyter_demo.instr
index 51849ce0..aecfd979 100644
--- a/mcstasscript/tests/test_data_set/jupyter_demo.instr
+++ b/mcstasscript/tests/test_data_set/jupyter_demo.instr
@@ -11,7 +11,7 @@
 * European Spallation Source Data Management and 
 * Software Center
 * 
-* Instrument jupyter_demo
+* Instrument: jupyter_demo
 * 
 * %Identification
 * Written by: Python McStas Instrument Generator
diff --git a/mcstasscript/tests/test_instrument.instr b/mcstasscript/tests/test_instrument.instr
index 145148d7..206a1e7d 100644
--- a/mcstasscript/tests/test_instrument.instr
+++ b/mcstasscript/tests/test_instrument.instr
@@ -11,7 +11,7 @@
 * European Spallation Source Data Management and 
 * Software Center
 * 
-* Instrument test_instrument
+* Instrument: test_instrument
 * 
 * %Identification
 * Written by: Python McXtrace Instrument Generator

From f6c1cd0206e3555d67b9828981e7962c6cf65c15 Mon Sep 17 00:00:00 2001
From: Peter Willendrup <pkwi@fysik.dtu.dk>
Date: Wed, 8 Oct 2025 15:45:52 +0200
Subject: [PATCH 396/403] Add a missing newline, push parm table below
 %Parameters

---
 mcstasscript/interface/instr.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index afa214b4..6aec575d 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2083,9 +2083,9 @@ def write_full_instrument(self):
         fo.write("* Please write a longer instrument description here\n.")
         fo.write("* \n")
         fo.write("* \n")
-        fo.write("* Please fill in a parameter table below, formatted like this:")
-        fo.write("* parameter: [unit] Descriptive string\n")
+        fo.write("* Please fill in a parameter table below, formatted like this:\n")
         fo.write("* %Parameters\n")
+        fo.write("* parameter: [unit] Descriptive string\n")
         # Add description of parameters here
         fo.write("* \n")
         fo.write("* %End \n")

From 51cdb4e6cb889db7015856f534a9b6af1f89a429 Mon Sep 17 00:00:00 2001
From: Peter Willendrup <pkwi@fysik.dtu.dk>
Date: Wed, 8 Oct 2025 16:35:48 +0200
Subject: [PATCH 397/403] Partial generation of parameter list

---
 mcstasscript/interface/instr.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 6aec575d..4aa90476 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2080,12 +2080,15 @@ def write_full_instrument(self):
         fo.write("* !!Please write a short instrument description (1 line) here!!\n")
         fo.write("* \n")
         fo.write("* %Description\n")
-        fo.write("* Please write a longer instrument description here\n.")
+        fo.write("* Please write a longer instrument description here!\n")
         fo.write("* \n")
         fo.write("* \n")
-        fo.write("* Please fill in a parameter table below, formatted like this:\n")
         fo.write("* %Parameters\n")
-        fo.write("* parameter: [unit] Descriptive string\n")
+        # Insert parameter names and template for filling in mcdoc table
+        for param in list(self.parameters):
+            fo.write("* " + param.name + ": [unit] Parameter type (" + param.type ") Please add a more descriptive text \n")
+        fo.write("* \n")
+        fo.write("* %Link \n")
         # Add description of parameters here
         fo.write("* \n")
         fo.write("* %End \n")

From 1f2d1efbcef15eaa05863852d65383ca9307c61c Mon Sep 17 00:00:00 2001
From: Peter Willendrup <pkwi@fysik.dtu.dk>
Date: Wed, 8 Oct 2025 16:38:32 +0200
Subject: [PATCH 398/403] Add missing +

---
 mcstasscript/interface/instr.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 4aa90476..6806f35c 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2086,7 +2086,7 @@ def write_full_instrument(self):
         fo.write("* %Parameters\n")
         # Insert parameter names and template for filling in mcdoc table
         for param in list(self.parameters):
-            fo.write("* " + param.name + ": [unit] Parameter type (" + param.type ") Please add a more descriptive text \n")
+            fo.write("* " + param.name + ": [unit] Parameter type (" + param.type + ") Please add a more descriptive text \n")
         fo.write("* \n")
         fo.write("* %Link \n")
         # Add description of parameters here

From 989a1a76889133692413565c2fb6e9a960123d10 Mon Sep 17 00:00:00 2001
From: Peter Willendrup <pkwi@fysik.dtu.dk>
Date: Wed, 8 Oct 2025 18:05:53 +0200
Subject: [PATCH 399/403] Add param.comment as parameter description

---
 mcstasscript/interface/instr.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 6806f35c..8c13b8fe 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2086,7 +2086,7 @@ def write_full_instrument(self):
         fo.write("* %Parameters\n")
         # Insert parameter names and template for filling in mcdoc table
         for param in list(self.parameters):
-            fo.write("* " + param.name + ": [unit] Parameter type (" + param.type + ") Please add a more descriptive text \n")
+            fo.write("* " + param.name + ": [unit] Parameter type (" + param.type + ") " + param.comment + "\n")
         fo.write("* \n")
         fo.write("* %Link \n")
         # Add description of parameters here

From af0b0f9925c6a0860d5903a7cc57602144a3db3e Mon Sep 17 00:00:00 2001
From: Peter Willendrup <pkwi@fysik.dtu.dk>
Date: Wed, 8 Oct 2025 21:53:51 +0200
Subject: [PATCH 400/403] Drop (type) since this is part of the comment string
 already.

---
 mcstasscript/interface/instr.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 8c13b8fe..684d9e3e 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -2086,7 +2086,7 @@ def write_full_instrument(self):
         fo.write("* %Parameters\n")
         # Insert parameter names and template for filling in mcdoc table
         for param in list(self.parameters):
-            fo.write("* " + param.name + ": [unit] Parameter type (" + param.type + ") " + param.comment + "\n")
+            fo.write("* " + param.name + ": [unit] " + param.comment + "\n")
         fo.write("* \n")
         fo.write("* %Link \n")
         # Add description of parameters here

From e24634aca8bff8a1832cf1af1345012cd9e8993c Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Wed, 3 Dec 2025 11:27:34 +0100
Subject: [PATCH 401/403] Updated tests after merging recent pull requests.
 Added default value to _read_configuration so a line_length is always
 available.

Added pypi publish ci
---
 .github/workflows/pypi.yml            | 23 ++++++++++++++
 mcstasscript/_version.py              |  2 +-
 mcstasscript/helper/mcstas_objects.py |  5 ++-
 mcstasscript/interface/instr.py       |  2 ++
 mcstasscript/tests/test_Instr.py      | 44 ++++++++++++++++++++++++---
 5 files changed, 68 insertions(+), 8 deletions(-)
 create mode 100644 .github/workflows/pypi.yml

diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml
new file mode 100644
index 00000000..a1ca9c00
--- /dev/null
+++ b/.github/workflows/pypi.yml
@@ -0,0 +1,23 @@
+name: Publish to PyPI
+
+on:
+  push:
+    tags:
+      - 'v[0-9]+.[0-9]+.[0-9]+' #pattern match
+
+jobs:
+  build_and_publish:
+    runs-on: ubuntu-latest
+    permissions:
+      id-token: write # IMPORTANT: this permission is mandatory for trusted publishing
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Build distribution
+        run: |
+          python3 -m pip install build
+          python3 -mbuild
+
+      - name: Upload distribution to PyPI
+        uses: pypa/gh-action-pypi-publish@v1.12.4
diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index 7715123d..a981d111 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.75"
+__version__ = "0.0.76"
diff --git a/mcstasscript/helper/mcstas_objects.py b/mcstasscript/helper/mcstas_objects.py
index eedce6da..20b00aaa 100644
--- a/mcstasscript/helper/mcstas_objects.py
+++ b/mcstasscript/helper/mcstas_objects.py
@@ -1074,15 +1074,14 @@ def write_component(self, fo, instrument_search=None):
             fo.write(f"{self.WHEN}\n")
 
         # Write AT and ROTATED section
-        fo.write(f"AT {tuple(self.AT_data[:3])}") # is AT_data ever more than 3 elements?
-
+        fo.write(f"AT ({self.AT_data[0]}, {self.AT_data[1]}, {self.AT_data[2]})")
         fo.write(f" {self.AT_relative}\n")
 
         if self.save_parameters:
             save_parameter_string += " %s\n" % self.AT_relative
 
         if self.ROTATED_specified:
-            fo.write(f"ROTATED {tuple(self.ROTATED_data[:3])}") # is ROTATED_data ever more than 3 elements?
+            fo.write(f"ROTATED ({self.ROTATED_data[0]}, {self.ROTATED_data[1]}, {self.ROTATED_data[2]})")
             fo.write(f" {self.ROTATED_relative}\n")
 
         if not self.GROUP == "":
diff --git a/mcstasscript/interface/instr.py b/mcstasscript/interface/instr.py
index 684d9e3e..46270bbf 100644
--- a/mcstasscript/interface/instr.py
+++ b/mcstasscript/interface/instr.py
@@ -3010,6 +3010,8 @@ def _read_calibration(self):
 
         if type(config) is dict:
             self.line_limit = config["other"]["characters_per_line"]
+        else:
+            self.line_limit = 85 # default value in case no configuration file is found
 
         if "MCSTAS" in os.environ: # We are in a McStas environment, use that
             self._run_settings["executable_path"] = os.path.dirname(shutil.which("mcrun"))
diff --git a/mcstasscript/tests/test_Instr.py b/mcstasscript/tests/test_Instr.py
index 901a5502..d21c4237 100644
--- a/mcstasscript/tests/test_Instr.py
+++ b/mcstasscript/tests/test_Instr.py
@@ -4,6 +4,7 @@
 import unittest
 import unittest.mock
 import datetime
+import shutil
 
 from libpyvinyl.Parameters.Collections import CalculatorParameters
 
@@ -337,8 +338,16 @@ def test_load_config_file(self):
         # Check the value matches what is loaded by initialization
         my_instrument = setup_instr_no_path()
 
-        self.assertEqual(my_instrument._run_settings["executable_path"], correct_mcrun_path)
-        self.assertEqual(my_instrument._run_settings["package_path"], correct_mcstas_path)
+        if "MCSTAS" in os.environ:
+            # If MCSTAS is in path, we expect the configuration to find that:
+            self.assertEqual(my_instrument._run_settings["executable_path"], os.path.dirname(shutil.which("mcrun")))
+            self.assertEqual(my_instrument._run_settings["package_path"], os.environ["MCSTAS"])
+        else:
+            # Otherweise, the configuration file
+            self.assertEqual(my_instrument._run_settings["executable_path"], correct_mcrun_path)
+            self.assertEqual(my_instrument._run_settings["package_path"], correct_mcstas_path)
+
+        # In both cases it ought to read the number of characters from the file
         self.assertEqual(my_instrument.line_limit, correct_n_of_characters)
 
     def test_load_config_file_x_ray(self):
@@ -1704,8 +1713,17 @@ def test_write_full_instrument_simple(self, mock_datetime, mock_f):
          my_call("* Origin: ESS DMSC\n"),
          my_call("* %INSTRUMENT_SITE: Generated_instruments\n"),
          my_call("* \n"),
+         my_call("* !!Please write a short instrument description (1 line) here!!\n"),
          my_call("* \n"),
+         my_call('* %Description\n'),
+         my_call('* Please write a longer instrument description here!\n'),
+         my_call('* \n'),
+         my_call('* \n'),
          my_call("* %Parameters\n"),
+         my_call('* theta: [unit] \n'),
+         my_call('* has_default: [unit] \n'),
+         my_call('* \n'),
+         my_call('* %Link \n'),
          my_call("* \n"),
          my_call("* %End \n"),
          my_call("*"*80 + "/\n"),
@@ -1791,7 +1809,7 @@ def test_write_full_instrument_dependency(self, mock_f):
             my_call("* European Spallation Source Data Management and \n"),
             my_call("* Software Centre\n"),
             my_call("* \n"),
-            my_call("* Instrument:test_instrument\n"),
+            my_call("* Instrument: test_instrument\n"),
             my_call("* \n"),
             my_call("* %Identification\n"),
             my_call("* Written by: Python McStas Instrument Generator\n"),
@@ -1799,8 +1817,17 @@ def test_write_full_instrument_dependency(self, mock_f):
             my_call("* Origin: ESS DMSC\n"),
             my_call("* %INSTRUMENT_SITE: Generated_instruments\n"),
             my_call("* \n"),
+            my_call("* !!Please write a short instrument description (1 line) here!!\n"),
             my_call("* \n"),
+            my_call('* %Description\n'),
+            my_call('* Please write a longer instrument description here!\n'),
+            my_call('* \n'),
+            my_call('* \n'),
             my_call("* %Parameters\n"),
+            my_call('* theta: [unit] \n'),
+            my_call('* has_default: [unit] \n'),
+            my_call('* \n'),
+            my_call('* %Link \n'),
             my_call("* \n"),
             my_call("* %End \n"),
             my_call("*" * 80 + "/\n"),
@@ -1890,7 +1917,7 @@ def test_write_full_instrument_search(self, mock_f):
             my_call("* European Spallation Source Data Management and \n"),
             my_call("* Software Centre\n"),
             my_call("* \n"),
-            my_call("* Instrument:test_instrument\n"),
+            my_call("* Instrument: test_instrument\n"),
             my_call("* \n"),
             my_call("* %Identification\n"),
             my_call("* Written by: Python McStas Instrument Generator\n"),
@@ -1898,8 +1925,17 @@ def test_write_full_instrument_search(self, mock_f):
             my_call("* Origin: ESS DMSC\n"),
             my_call("* %INSTRUMENT_SITE: Generated_instruments\n"),
             my_call("* \n"),
+            my_call("* !!Please write a short instrument description (1 line) here!!\n"),
             my_call("* \n"),
+            my_call('* %Description\n'),
+            my_call('* Please write a longer instrument description here!\n'),
+            my_call('* \n'),
+            my_call('* \n'),
             my_call("* %Parameters\n"),
+            my_call('* theta: [unit] \n'),
+            my_call('* has_default: [unit] \n'),
+            my_call('* \n'),
+            my_call('* %Link \n'),
             my_call("* \n"),
             my_call("* %End \n"),
             my_call("*" * 80 + "/\n"),

From 202cac60a43136d3acc5d2b656801b27ecd05142 Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Wed, 3 Dec 2025 13:45:22 +0100
Subject: [PATCH 402/403] Updated manifest to include test_input_folder in
 tests

---
 MANIFEST.in | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/MANIFEST.in b/MANIFEST.in
index f9224b57..6c5096a7 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -4,8 +4,10 @@ include mcstasscript/configuration.yaml
 include mcstasscript/tests/test_for_reading.comp
 include mcstasscript/tests/test_instrument.instr
 include mcstasscript/tests/Union_demonstration_test.instr
+include mcstasscript/tests/test_input_folder/test_for_structure.comp
 include mcstasscript/integration_tests/test_input_folder
 include mcstasscript/integration_tests/test_input_folder/PSDlin_monitor.comp
 graft mcstasscript/tests/dummy_mcstas
 graft mcstasscript/tests/dummy_instrument_folder
 graft mcstasscript/tests/test_data_set
+

From 423274eec61838625289810866203da8b2ca767a Mon Sep 17 00:00:00 2001
From: mads-bertelsen <mads.bertelsen@gmail.com>
Date: Wed, 3 Dec 2025 13:46:38 +0100
Subject: [PATCH 403/403] Updated version

---
 mcstasscript/_version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mcstasscript/_version.py b/mcstasscript/_version.py
index a981d111..beb84c07 100644
--- a/mcstasscript/_version.py
+++ b/mcstasscript/_version.py
@@ -1 +1 @@
-__version__ = "0.0.76"
+__version__ = "0.0.77"